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Denise Johnstone PhD (submission copy)

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The relationship between wood decay and
tree vitality
Denise Margaret Johnstone
A thesis submitted in total fulfilment of
the requirement for the degree of
Doctor of Philosophy
March, 2011
Department of Resource Management and Geography
School of Land and Environment
University of Melbourne
Abstract
Understanding tree vitality is essential to the maintenance of healthy trees in both urban
and forested environments. Tree vitality is difficult to quantify, but is usually assessed by
tree growth and physiological measurements. Understanding some of the processes that
result in trunk failure and the decay of wood is crucial in the risk assessment of trees in
urban environments, or anywhere people and trees coexist. The relationship between wood
decay and tree vitality has not been thoroughly investigated, as in the past tree wood
anatomy and tree physiology were regarded as separate processes. Ascertaining a link
between tree vitality and wood decay will allow tree managers to better manage trees in
terms of their longevity and the risk they may pose to people.
In this study, a system developed for quantifying wood decay in trees, the resi system
utilising the IML-Resi drill, was successful in its ability to predict the percentage of wood
decay in Eucalyptus saligna trees. A picus system, using a Picus Sonic Tomograph, had
difficulty predicting small amounts of decay in the E. saligna trees, as did a visual
method. Tree growth measures such as total leaf area and above ground biomass were
good predictors of a visual vitality index which used crown size, crown density, epicormic
growth and dead branches in E. saligna to assess visual vitality. Leaf chlorophyll
fluorescence was able to predict visual vitality in summer, but not in spring or autumn in
E. saligna. On the other hand bark chlorophyll fluorescence was able to predict visual
vitality in spring, summer and autumn.
The visual vitality index was highly successful as a predictor of the percentage of wood
decay in E. saligna trees. Leaf chlorophyll fluorescence was successful as a predictor of
the percentage of wood decay in E. saligna trees in spring and summer, but not in autumn.
Bark chlorophyll fluorescence was successful as a predictor of the percentage of wood
decay in E. saligna trees in spring, summer and autumn. Bark chlorophyll fluorescence
and the visual vitality index could therefore be used by tree managers to better manage
trees in order to maximize their life span and minimize the risk they may pose to people.
This is to certify that
i.
ii.
iii.
the thesis comprises only my original work towards the PhD except where
indicated in the Preface,
due acknowledgement has been made in the text to all other material used,
the thesis is less than 100,000 words in length, exclusive of tables, maps,
bibliographies and appendices
…………………………………………………
Denise Margaret Johnstone
Preface
A substantial part of chapter two has been published as a review paper with my
supervisors as coauthors in which they helped with supervisory advice; Johnstone, D.,
Moore, G., Tausz, M., Nicolas, M., (2010) The measurement of wood decay in landscape
trees, Arboriculture and Urban Forestry, 36: 121-127.
An edited version of chapter three has been published with my supervisors as coauthors
in which they helped with supervisory advice; Johnstone, D., Tausz, M., Moore, G.,
Nicolas, M., (2010) Quantifying wood decay in Sydney Bluegum (Eucalyptus saligna)
trees, Arboriculture and Urban Forestry, 36: 243-252.
Acknowledgements
I would like to acknowledge the help and support of my supervisors; Dr. Greg Moore,
Assoc. Prof. Michael Tausz and Dr. Marc Nicolas.
I gratefully acknowledge the support of the arboricultural consultancy company Tree
Logic for the extended loan of the Hansatech-Handy PEA fluorometer without which I
would not have been able to take chlorophyll fluorescence measurements.
I would also like to acknowledge the help and enthusiasm of my research assistants; Peter
Perry, Matthew Sauvarin, Edward Waters, William Jackson and Hugh Simpson.
I would also like to acknowledge the help, support and inspiration of my family; in
particular my father Rex Johnstone and son Hugh Simpson, without whom this thesis
would never have been completed.
Table of contents
Abstract ........................................................................................................................................... iii
Preface ............................................................................................................................................ vii
Acknowledgements ......................................................................................................................... ix
Table of contents ............................................................................................................................. xi
List of Figures.............................................................................................................................. xv
List of Tables .............................................................................................................................. xxi
Chapter 1 - Introduction .................................................................................................................. 1
1.1 Aim......................................................................................................................................... 2
1.2 Research approach ................................................................................................................ 2
Chapter 2 – Measuring tree vitality and wood decay in trees ........................................................ 6
2.1 Introduction ........................................................................................................................... 6
2.2 Tree growth and vitality ........................................................................................................ 6
2.2.1 Photosynthesis ............................................................................................................... 8
2.2.2 Bark photosynthesis ..................................................................................................... 11
2.2.3 Water and nutrients ..................................................................................................... 12
2.3 The structure of wood ......................................................................................................... 13
2.4 The process of wood decay ................................................................................................. 20
2.5 Measuring tree vitality ........................................................................................................ 24
2.5.1 Tree growth .................................................................................................................. 24
2.5.2 Leaf or needle morphology and biochemistry ............................................................. 27
2.5.3 Electrical admittance/impedance ................................................................................ 28
2.5.4 Chlorophyll fluorescence and gaseous exchange......................................................... 29
2.5.5 Water status ................................................................................................................. 33
2.5.6 Canopy transparency and reflectance.......................................................................... 35
2.6 Measuring wood decay ....................................................................................................... 35
2.6.1 Electrical conductivity meters ...................................................................................... 36
2.6.2 Constant feed drills....................................................................................................... 37
2.6.3 Sonic and ultrasonic techniques ................................................................................... 40
2.6.4 Breaking core samples .................................................................................................. 42
2.6.5 Compression meters..................................................................................................... 44
2.6.6 Computerized tomography .......................................................................................... 45
2.8 Tree vitality and wood decay .............................................................................................. 48
2.8 Discussion and conclusions ................................................................................................. 50
Chapter 3 – Estimating wood decay in Eucalyptus saligna ........................................................... 53
3.1 Introduction ......................................................................................................................... 53
3.2 Materials and methods ....................................................................................................... 56
3.2.1 Materials....................................................................................................................... 56
3.2.2 Methods ....................................................................................................................... 57
3.3 Results ................................................................................................................................. 76
3.3.1 Results for the picus system ......................................................................................... 77
3.3.2 Results for the resi system ........................................................................................... 86
3.3.3 Results for the visual method ....................................................................................... 91
3.4 Discussion and conclusions ................................................................................................. 95
Chapter 4 – Tree growth and wood decay in Eucalyptus saligna ............................................... 100
4.1 Introduction ....................................................................................................................... 100
4.2 Materials and methods ..................................................................................................... 102
4.2.1 Materials..................................................................................................................... 102
4.3.2 Methods ..................................................................................................................... 102
4.3 Results ............................................................................................................................... 110
4.3.1 Results for the visual vitality index ............................................................................. 110
4.3.2 Results for comparing tree growth and wood density ............................................... 117
4.3.3 Results for comparing tree growth and wood decay ................................................. 119
4.4 Discussion and conclusions ............................................................................................... 122
Chapter 5 – Leaf and bark chlorophyll fluorescence and wood decay in Eucalyptus saligna..... 127
5.1 Introduction ....................................................................................................................... 127
5.2 Materials and methods ..................................................................................................... 130
5.2.1 Materials..................................................................................................................... 130
5.2.2 Methods ..................................................................................................................... 131
5.3 Results ............................................................................................................................... 139
5.3.1 Results for comparing leaf fluorescence and the visual vitality index ....................... 140
5.3.2 Results for comparing bark fluorescence and the visual vitality index ...................... 143
5.3.3 Results for comparing leaf fluorescence and basic wood density ............................. 146
5.3.4 Results for comparing bark fluorescence and basic wood density ............................ 149
5.3.5 Results for comparing leaf fluorescence and wood decay......................................... 152
5.3.6 Results for comparing bark fluorescence and wood decay ....................................... 155
5.4 Discussion and conclusions ............................................................................................... 158
Chapter 6 – General discussion and conclusions ........................................................................ 166
6.1 Introduction ....................................................................................................................... 166
6.2 Urban tree management ................................................................................................... 167
6.3 The benefits of urban trees ............................................................................................... 168
6.3 Forest management .......................................................................................................... 169
6.5 Tree structure and function .............................................................................................. 170
6.6 Conclusion ......................................................................................................................... 173
References ................................................................................................................................... 174
Appendices .................................................................................................................................. 202
Appendix 1 Appendix to chapter 3 .......................................................................................... 202
1.1 Appendix to materials used in chapter 3....................................................................... 202
1.2 Appendix to methods used in chapter 3 ....................................................................... 204
1.3 Appendix to results from chapter 3............................................................................... 243
Appendix 2 Appendix to chapter 4 .......................................................................................... 246
2.1 Appendix to methods used in chapter 4 ....................................................................... 246
2.2 Appendix to results from chapter 4............................................................................... 256
Appendix 3 Appendix to chapter 5 .......................................................................................... 258
3.1 Appendix to methods used in chapter 5 ....................................................................... 258
3.2 Appendix to results from chapter 5............................................................................... 263
List of Figures
Figure 2.1 Diagram of photosynthesis showing the light reactions (photosystem I and II), on the
left, and the dark reactions (the Calvin cycle) on the right. ............................................................ 10
Figure 2.2 Diagram of the conventional model of the lignified wood cell with five cell-wall layers.
......................................................................................................................................................... 14
Figure 2.3 Photosynthesis light reactions for oxygen evolving photosynthetic organisms which are
the source of chlorophyll fluorescence from plant chlorophyll. ..................................................... 31
Figure 3.1 Site of the investigations conducted in this study on the Eucalyptus saligna (Bateman’s
Bay) in the Eucalypt plantation at Tostaree, Victoria. ..................................................................... 56
Figure 3.2 (a) At left, this photograph shows the diameter of a Eucalyptus saligna tree being
measured in order to calculate whole tree wood density measurements; (b) at right a Eucalyptus
saligna tree being cut to 1 m lengths prior to being weighed in order to calculate above ground
biomass and whole tree wood density............................................................................................ 59
Figure 3.3 The Argus-Picus Sonic Tomograph pictured with 12 acoustic sensors, interface box
hammer and straps for attaching the sensors to the tree trunk. .................................................... 61
Figure 3.4 Linear distance measurements being taken with large callipers on a Eucalyptus saligna
tree. ................................................................................................................................................. 61
Figure 3.5 A diagram of the trunk perimeter of a Eucalyptus saligna tree stem. (a) Top, a
diagrammatic representation of measured linear distances. Red numbers represent the order and
number of linear measurements. (b) Above, trunk perimeter “tree geometries”, generated by the
Picus sonic tomograph propriety computer software. ................................................................... 62
Figure 3.6 Acoustic stress wave being sent by a hammer tap to the Picus sonic tomograph sensor
array processed by the interface box and sent to a computer. Photograph by Matthew Sauvarin 63
Figure 3.7 A diagrammatic representation of the acoustic pathways travelled by the Picus sonic
tomograph stress waves when 8 sensors are used in the array. .................................................... 64
Figure 3.8 Picus sonic tomograph “false colour” image. Note 8 sensors were used in this array.
The colours in decreasing order of sonic speed are brown, green, violet and blue........................ 64
Figure 3.9 (a) At left the Picus sonic tomograph image generated by the proprietary software, (b)
at right the image after it has been “smoothed” by hand. The tree is Eucalyptus saligna, tree 32.
......................................................................................................................................................... 65
Figure 3.10 From top right (32a) an original Picus image using 8 sensors for tree 32 and top left
(32b) the image after analysis with ImageJ. Middle right (34a) an original Picus image for a smaller
tree using 6 sensors (34b) the image after analysis with ImageJ. Bottom right (24a) the Picus
image for tree 24 and bottom left (24b) the image after analysis with ImageJ. ............................. 68
Figure 3.11 (a) Top, tree 21 with “cogwheel effect” on the Picus image, as described in the Picus
manual (Anon, 2004). (b), Above, tree 16 with no “cogwheel effect” on the Picus image............. 69
Figure 3.12 The IML-Resi F300S pictured with 3 mm wide drill bit at right. ................................... 70
Figure 3.13 The IML-Resi F300S constant feed drill pictured being used on a Eucalyptus saligna
tree from this study. Photograph by Matthew Sauvarin ................................................................. 70
Figure 3.14 Form in which the data are recorded by the IML-Resi F300S. ..................................... 71
Figure 3.15 The data are recorded on graph traces, and then the putative decay is marked on the
graphs as shown. Tree shown is tree 32, as the trees were renumbered after initial data
collection. ........................................................................................................................................ 72
Figure 3.16 A decay diagram that has been drawn after the putative decay from the Resi F300S
graphs has been measured and the decay outside the drilling points has been inferred using the
resi system of wood decay estimation. ........................................................................................... 73
Figure 3.17 (a) At top, a cross section used for estimating the volume of wood decay in a tree
according to the visual method used in this study. (b) Above shows the needle probe used as part
of the visual method. ...................................................................................................................... 74
Figure 3.18 (a) Top, the percentage of decay using the picus system versus whole tree wood
3
density in kg/m . Includes all 36 Eucalyptus saligna trees; (b) Above, the percentage of decay
3
using the picus system versus the whole tree wood density in kg/m , excluding tree 24. Tree 24 is
an outlying data point in the picus system data set, therefore 35 Eucalyptus saligna trees are
included in this data set. ................................................................................................................. 78
Figure 3.19 The percentage of decay using the picus system versus the whole tree wood density
3
in kg/m . These data exclude the trees less than or equal to 200 mm in diameter at 0.3 m in
height, that is trees 17, 19, 24, 25, 31 and 34 are excluded. Therefore 30 Eucalyptus saligna trees
are included in this data set. ........................................................................................................... 80
Figure 3.20 (a) The percentage of decay using the picus system versus basic wood density in
kg/m3. Includes all 36 Eucalyptus saligna trees. (b) These data exclude the trees less than or equal
to 200 mm in diameter at 0.3 m in height that is trees 17, 19, 24, 25, 31 and 34 are excluded.
Therefore 30 Eucalyptus saligna trees are included in this data set. .............................................. 82
3
3
Figure 3.21 (a) Top, whole tree wood density in kg/m versus basic wood density in kg/m .
2
Includes all 36 Eucalyptus saligna trees. Trend line = logarithmic regression, P = 0.0058, r =
3
3
0.2030. (b) Above, whole tree wood density in kg/m versus basic wood density in kg/m . These
data exclude the trees less than or equal to 200 mm in diameter at 0.3 m in height, that is trees
17, 19, 24, 25, 31 and 34 are excluded. Therefore 30 Eucalyptus saligna trees are included in this
2
data set. Trend line = logarithmic regression, P = 0.027, r = 0.1611. Scale begins at 200 kg/m3. . 84
Figure 3.22 (a) Top, the percentage of decay using the picus system versus the percentage of
wood moisture content. Includes all 36 Eucalyptus saligna trees. (b) Above the percentage of
decay using the picus system versus the percentage of wood moisture content. These data
exclude the trees less than or equal to 200 mm in diameter at 0.3 m in height, that is trees 17, 19,
24, 25, 31 and 34 are excluded. Therefore 30 Eucalyptus saligna trees are included in this data set.
......................................................................................................................................................... 85
Figure 3.23 (a) Top, the percentage of decay using the resi system versus whole tree wood density
3
in kg/m . Includes all 36 Eucalyptus saligna trees. Trend line = logarithmic regression, P = 0.0027,
2
r = 0.2354. (b) Above, the percentage of decay using the resi system versus the whole tree wood
3
density in kg/m . These data exclude trees less than or equal to 200 mm in diameter at 0.3 m in
height, that is trees 17, 19, 24, 25, 31 and 34 are excluded. Therefore 30 Eucalyptus saligna trees
2
are included in this data set. Trend line = logarithmic regression, P = 0.0015, r = 0.3061. ........... 87
Figure 3.24 (a) Top, the percentage of decay using the resi system versus basic wood density in
3
2
kg/m . Includes all 36 Eucalyptus saligna trees. Trend line = logarithmic regression, P = 0.0378, r =
0.1208. (b) Above the percentage of decay using the resi system versus basic wood density in
3
kg/m . These data exclude trees less than or equal to 200 mm in diameter at 0.3 m in height, that
is trees 17, 19, 24, 25, 31 and 34 are excluded. Therefore 30 Eucalyptus saligna trees are included
in this data set. ................................................................................................................................ 89
Figure 3.25 (a) Top, the percentage of decay using the resi system versus the percentage of wood
moisture content. Includes all 36 Eucalyptus saligna trees. (b) Above, the percentage of decay
using the resi system versus the percentage of wood moisture content. These data exclude the
trees less than or equal to 200 mm in diameter at 0.3 m in height, that is trees 17, 19, 24, 25, 31
and 34 are excluded. Therefore 30 Eucalyptus saligna trees are included in this data set. ........... 90
Figure 3.26 (a) Top, the percentage of decay using the visual method versus whole tree wood
3
density in kg/m . Includes all 36 Eucalyptus saligna trees. (b) Above, the percentage of decay
3
using the visual method versus whole tree wood density in kg/m . These data exclude trees less
than or equal to 200 mm in diameter at 0.3 m in height, that is trees 17, 19, 24, 25, 31 and 34 are
excluded. Therefore 30 Eucalyptus saligna trees are included in this data set. ............................. 92
Figure 3.27 These photographs show trunk cross sections of Eucalyptus saligna trees at 0.3 m in
height. (a) At left tree 26, as the trees were renumbered after initial data collection. This tree was
calculated as having 13.53% wood decay using the visual method of decay estimation. (b) At right,
tree 21, as the trees were renumbered after initial data collection. This tree was calculated as
having 8.07% wood decay using the visual method of decay estimation. ...................................... 93
Figure 3.28 (a) Top the percentage of decay using the visual system versus the percentage of
wood moisture content. Includes all 36 Eucalyptus saligna trees. (b) Above the percentage of
decay using the visual system versus the percentage of wood moisture content. These data
exclude trees less than or equal to 200 mm in diameter at 0.3 m in height, that is trees 17, 19, 24,
25, 31 and 34 are excluded. Therefore 30 Eucalyptus saligna trees are included in this data set. 94
Figure 3.29 (a) Top, the percentage of decay using the visual system versus basic wood density in
3
kg/m . Includes all 36 Eucalyptus saligna trees. (b) Above, the percentage of decay using the
3
visual system versus basic wood density in kg/m . These data exclude the trees less than or equal
to 200 mm in diameter at 0.3 m in height, that is trees 17, 19, 24, 25, 31 and 34 are excluded.
Therefore 30 Eucalyptus saligna trees are included in this data set. .............................................. 95
Figure 4.1 Photographs of individual leaves from Eucalyptus saligna used to calculate leaf area, far
left, from the upper canopy (sun leaves), middle, from the lower canopy (shade) leaves and at
right epicormic leaves. Leaves pictured are from tree 20 as the trees were renumbered during the
study. ............................................................................................................................................. 103
Figure 4.2 Method used to calculate sapwood area for the Eucalyptus saligna trees in the study.
(a) Left, a cross sectional diagram at 0.3 m showing the bark (outer ring), sapwood, (adjacent to
the outer ring) and heartwood (represented by the inner ring). Red areas on the diagram are
areas of decay. (b) Photograph of the same Eucalyptus saligna tree cross section at 0.3 m showing
the bark (outer ring), sapwood, (adjacent to the outer ring) and heartwood (within the sapwood).
Cross section pictured is from tree 20 as the trees were renumbered during the study. ............ 104
Figure 4.3 Diagrammatic representation of the visual vitality index for plantation eucalypts used
in this study (after Grimes, 1978; Lindenmayer et al., 1990; Martin et al., 2001) ........................ 107
2
Figure 4.4 The autumn visual vitality index versus total leaf area in m . These data include all trees
therefore 36 Eucalyptus saligna trees are included in this data set. Trend line = linear regression, P
2
= <0.0001, r = 0.6041. .................................................................................................................. 112
Figure 4.5 Photographs of 4 trees taken in September, 2007 showing approximate relative leaf
area/canopy density and tree size. Photographs of the canopy taken from approximately 1.6 m
abutting the trunk looking directly up into the canopy. Top left is tree 4 with no leaves, top right
2
2
tree 7 with a total leaf area of 108.61 m , above left is tree 12 with a total leaf area of 43.75 m
2
2
above right tree 31 with a total leaf area of 1.71 m . Average leaf area is 45.78 m . All
photographs taken on the north side of the trees, red shading highlights each canopy.............. 112
2
-1
Figure 4.6 The autumn visual vitality index versus specific leaf area in mm mg . These data
exclude tree 4, as tree 4 is a zero value for leaf area. Therefore 35 Eucalyptus saligna trees are
2
included in this data set. Trend line = linear regression, P = 0.0008, r = 0.2905. ........................ 113
2
-1
Figure 4.7 (a) Top, the autumn visual vitality index versus Huber value in m m . These data
exclude tree 4, as tree 4 is a zero value for leaf area. Therefore 35 Eucalyptus saligna trees are
2
included in this data set. Trend line = logarithmic regression, P = 0.0119, r = 0.1769; (b) above,
2
-1
the autumn visual vitality index versus Huber value in m m . These data exclude trees 4 and 31;
therefore 34 Eucalyptus saligna trees are included in this data set. ............................................ 114
Figure 4.8 The autumn visual vitality index versus above ground biomass in kg. These data include
all trees therefore 36 Eucalyptus saligna trees are included in this data set. Trend line = linear
2
regression, P < 0.0001, r = 0.6450. ............................................................................................... 115
Figure 4.9 The autumn visual vitality index versus tree height in m. These data include all trees
therefore 36 Eucalyptus saligna trees are included in this data set. Trend line = logarithmic
2
regression, P < 0.0001, r = 0.6483. ............................................................................................... 116
Figure 4.10 The autumn visual vitality index versus above diameter at breast height in mm. These
data include all trees therefore 36 Eucalyptus saligna trees are included in this data set. Trend line
2
= linear regression, P = 0.0007, r = 0.3431. .................................................................................. 116
3
Figure 4.11 Basic wood density in kg/m versus autumn visual vitality index. These data include all
trees therefore 36 Eucalyptus saligna trees are included in this data set. ................................... 118
3
2
-1
Figure 4.12 Basic wood density in kg/m versus specific leaf area in mm mg . These data exclude
tree 4, as tree 4 is a zero value for leaf area. Therefore 35 Eucalyptus saligna trees are included in
this data set. .................................................................................................................................. 118
3
Figure 4.13 Basic wood density in k/m versus diameter at breast height in meters at 1.3 m in
2
height. Includes all 36 Eucalyptus saligna trees. Trend line = linear regression, P = 0.0120, r =
0.1715. ........................................................................................................................................... 119
Figure 4.14 The percentage of decay using the resi system versus the visual vitality index. Trend
2
line = logarithmic regression, P <0.0001, r = 0.4849. All 36 trees are included in this data set. . 120
2
Figure 4.15 Percentage of decay measured by the resi system versus specific leaf area in mm mg
1
. These data exclude tree 4, as tree 4 is a zero value for leaf area. Therefore 35 Eucalyptus saligna
2
trees are included in this data set. Trend line = logarithmic regression, P = 0.0111, r = 0.1801. 121
2
-2
Figure 4.16 Percentage of decay measured by the resi system versus Huber value in m m . These
data exclude tree 4, as tree 4 is a zero value for leaf area. Therefore 35 Eucalyptus saligna trees
are included in this data set. ......................................................................................................... 122
Figure 5.1 The Hansatech-Handy Plant Efficiency Analyser for measuring chlorophyll fluorescence,
showing the dark adaption clip at right attached to a leaf. .......................................................... 132
Figure 5.2 Harvesting branches with a 12 gauge shot gun in preparation for leaf chlorophyll
fluorescence testing. ..................................................................................................................... 133
Figure 5.3 (a) Top, chlorophyll fluorescence testing being carried out on the bark of the Eucalyptus
saligna tree 13 at a height of approximately 6 m. Photograph taken by Matthew Sauvarin. (b)
Below, showing the method for darkening the bark prior to chlorophyll fluorescence testing. .. 134
Figure 5.4 The fast fluorescence rise for Eucalyptus saligna leaves in summer over a 1 second time
period showing the O-J-I-P phases. Chlorophyll fluorescence in mV versus logarithmically
transformed time in ms. ................................................................................................................ 136
Figure 5.5 The fast fluorescence rise for Eucalyptus saligna bark in spring over a 1 second time
period showing the O-J-I phases. Chlorophyll fluorescence in mV versus logarithmically
transformed time in ms. ................................................................................................................ 136
Figure 5.6 Spring visual vitality index versus spring leaf Fv/Fm. These data exclude tree 19 and tree
4. Therefore 34 Eucalyptus saligna trees are included in this data set. Fv/Fm ratio data begins at
0.8000. ........................................................................................................................................... 141
Figure 5.7 Summer visual vitality index versus summer leaf chlorophyll fluorescence at the “O”
step in mV. These data exclude trees 19 and 4. Therefore 34 Eucalyptus saligna trees are included
in this data set. Chlorophyll fluorescence data begins at 100 mV. Trend line = linear regression, P =
2
0.0409, r = 0.1243. ....................................................................................................................... 142
Figure 5.8 Autumn visual vitality index versus autumn leaf chlorophyll fluorescence at the “O”
step in mV. These data exclude trees 19 and 4. Therefore 34 Eucalyptus saligna trees are included
in this data set. Chlorophyll fluorescence data begins at 100 mV. ............................................... 143
Figure 5.9 Spring visual vitality index versus spring bark Fv/Fm. These data exclude tree 4.
Therefore 35 Eucalyptus saligna trees are included in this data set. Fv/Fm ratio data begins at
0.7900 ............................................................................................................................................ 144
Figure 5.10 Summer visual vitality index versus summer bark Fv/Fm. 35 .................................... 145
Figure 5.11 Autumn visual vitality index versus autumn bark Fv/Fm. 35 Eucalyptus saligna trees
are included in this data set, as tree 4 had no live bark. Fv/Fm ratio data begins at 0.8000. Trend
2
line = linear regression, P <0.0001, r = 0.3973. ........................................................................... 146
3
Figure 5.12 Basic wood density in kg/m versus spring leaf Fv/Fm. These data exclude trees 19 and
4. Therefore 34 Eucalyptus saligna trees are included in this data set. Fv/Fm ratio data begins at
3
0.8000, basic density data begins at 400 kg/m . ........................................................................... 147
3
Figure 5.13 Basic wood density in kg/m versus summer leaf Fv/Fm. These data exclude trees 19
3
and 4. Fv/Fm ratio data begins at 0.8200, basic density data begins at 400 kg/m . Therefore 34
2
Eucalyptus saligna trees are included in this data set. Trend line = linear regression, P = 0.0010, r
= 0.2910. ........................................................................................................................................ 148
3
Figure 5.14 Basic wood density in kg/m versus autumn leaf Fv/Fm. These data exclude tree 4 as
tree 4 had no leaves. Therefore 35 Eucalyptus saligna trees are included in this data set. Fv/Fm
3
ratio data begins at 0.7200, basic density data begins at 400 kg/m . ........................................... 149
3
Figure 5.15 Basic wood density in kg/m versus spring bark Fv/Fm. These data exclude tree 4.
Therefore 35 Eucalyptus saligna trees are included in this data set. Fv/Fm ratio data begins at
3
2
0.7900, basic density data begins at 400 kg/m Trend line = linear regression, P = 0.0351, r =
0.1277. ........................................................................................................................................... 150
3
Figure 5.16 Basic wood density in kg/m versus summer bark Fv/Fm. 35 Eucalyptus saligna trees
are included in this data set, as tree 4 had no live bark. Fv/Fm ratio data begins at 0.8000, basic
3
density data begins at 400 kg/m . ................................................................................................. 151
3
Figure 5.17 Basic wood density in kg/m versus autumn bark Fv/Fm. 35 Eucalyptus saligna trees are
included in this data set, as tree 4 had no live bark. Fv/Fm ratio data begins at 0.7900, basic density
3
data begins at 400 kg/m . ............................................................................................................. 152
Figure 5.18 Percentage of decay using the resi system versus spring leaf chlorophyll fluorescence
at the “O” step in mV. These data exclude tree 19 and tree 4. Therefore 34 Eucalyptus saligna
trees are included in this data set. Chlorophyll fluorescence data begins at 100 mV. Trend line =
2
linear regression, P = 0.0041, r = 0.2296. ..................................................................................... 153
Figure 5.19 Percentage of decay using the resi system versus summer leaf Fv/Fm. These data
exclude trees 19 and 4. Therefore 34 Eucalyptus saligna trees are included in this data set. Fv/Fm
2
ratio data begins at 0.8200. Trend line = linear regression, P = 0.0248, r = 0.1477. .................... 154
Figure 5.20 The percentage of decay using the resi system versus autumn leaf Fv/Fm. These data
exclude tree 4 as tree 4 had no leaves. Therefore 35 Eucalyptus saligna trees are included in this
data set. Fv/Fm ratio data begins at 0.7000. .................................................................................. 155
Figure 5.21 Percentage of decay using the resi system versus spring bark Fv/Fm. These data
exclude tree 4. Therefore 35 Eucalyptus saligna trees are included in this data set. Fv/Fm ratio data
2
begins at 0.7800. Trend line = linear regression, P = 0.0356, r = 0.1271. .................................... 156
Figure 5.22 The percentage of decay using the resi system versus summer bark Fv/Fm. 35
Eucalyptus saligna trees are included in this data set, as tree 4 had no live bark. Fv/Fm ratio data
2
begins at 0.8000. Trend line = linear regression, P = 0.0205, r = 0.1480. .................................... 157
Figure 5.23 The percentage of decay using the resi system versus autumn bark Fv/Fm. 35
Eucalyptus saligna trees are included in this data set, as tree 4 had no live bark. Fv/Fm ratio data
2
begins at 0.7900. Trend line = linear regression, P = 0.0373, r = 0.1248. .................................... 158
Figure 6.1 A summary of the relationship between tree vitality and wood decay prior to the
current study. ................................................................................................................................ 171
Figure 6.2 A summary of the relationship between tree vitality and wood decay including
information from the current study. ............................................................................................. 171
List of Tables
Table 2.1 The wood densities and natural durability ratings of selected trees native to the
Northern Hemisphere. ....................................................................................................... 19
Table 2.2 The wood densities and natural durability of selected eucalypts. ..................... 19
Table 2.3 The green modulus of elasticity and modulus of rupture for selected tree
species (After Bottle, 2005; Ozarska, 2009). ...................................................................... 19
Table 3.1 Power analysis parameters used to calculate the number of Eucalyptus saligna
trees sampled in the study. ............................................................................................... 75
Table 3.2 The simple linear regression analyses performed in this study in relation to the
decay estimation methods; the picus system, the resi system and the visual method and
whole tree wood density, wood moisture content and basic wood density. .................... 76
Table 3.3 The logarithmic regression analysis performed in this study in relation to the
decay estimation methods; the picus system, the resi system and the visual method and
whole tree wood density, wood moisture content and basic wood density. .................... 76
Table 3.4 The multiple regression analysis performed in this study in relation to the decay
estimation methods; the picus system, the resi system and the visual method and whole
tree wood density and basic wood density. ....................................................................... 76
Table 3.5 Summarised results from linear regression analysis comparing the percentage
wood decay estimated by the picus system with whole tree wood density. .................... 77
Table 3.6 Summarised results from logarithmic regression analysis comparing the whole
tree wood density on the percentage wood decay estimated by the picus system. ......... 77
Table 3.7 Summarised results from linear regression analysis comparing wood decay
estimation or whole tree wood density data with basic density data. .............................. 79
Table 3.8 Summarised results from logarithmic regression analysis comparing with basic
wood density data with wood decay estimation or whole tree wood density data. ......... 81
Table 3.9 Summarised results from multiple regression analysis comparing the
percentage wood decay estimated by picus system with whole tree wood density and
basic wood density. ............................................................................................................ 81
Table 3.10 Summarised results from linear regression analysis comparing wood decay
estimation data with wood moisture content. .................................................................. 83
Table 3.11 Summarised results from logarithmic regression analysis comparing with wood
moisture content data with wood decay estimation data. ................................................ 83
Table 3.12 Summarised results from linear regression analysis comparing the percentage
wood decay estimated by resi system with whole tree wood density. ............................. 86
Table 3.13 Summarised results from logarithmic regression analysis comparing the whole
tree wood density on the percentage wood decay estimated using the resi system. ....... 86
Table 3.14 Summarised results from multiple regression analysis comparing the
percentage wood decay estimated by resi system with whole tree wood density and basic
wood density. ..................................................................................................................... 88
Table 3.15 Summarised results from linear regression analysis comparing the percentage
wood decay estimated by the visual method with whole tree wood density. .................. 91
Table 3.16 Summarised results from logarithmic regression analysis of variance
comparing whole tree wood density on the percentage wood decay estimated by the
visual method. .................................................................................................................... 91
Table 3.17 Summarised results from multiple regression analysis comparing the
percentage wood decay estimated by the visual method with whole tree wood density
and basic wood density. ..................................................................................................... 93
Table 4.1 Simple linear regression analyses performed in this study in relation to tree
growth, wood density and wood decay estimation methods. ......................................... 109
Table 4.2 Logarithmic regression analyses performed in this study in relation to tree
growth, wood density and wood decay estimation methods. ......................................... 109
Table 4.3 Summarised results from simple linear regression analyses comparing the
autumn visual vitality index with objective measures of tree growth. ............................ 110
Table 4.4 Summarised results from logarithmic regression analyses comparing the
autumn visual vitality index with objective measures of tree growth. ............................ 111
Table 4.5 Summarised results from simple linear regression analyses comparing wood
density with measures of tree growth. ............................................................................ 117
Table 4.6 Summarised results from logarithmic regression analyses comparing the log of
the measurements of tree growth data with wood density. ........................................... 117
Table 4.7 Summarised results from simple linear regression analyses comparing
percentage of decay using the resi system with measures of tree growth. .................... 120
Table 4.8 Summarised results from logarithmic regression analyses comparing the log of
the measurements of tree growth data with percentage of decay using the resi system.
.......................................................................................................................................... 121
Table 5.1 Simple linear regression analyses performed in this study in relation to tree
growth, wood density and wood decay estimation methods. ......................................... 138
Table 5.2 Summarised results from simple linear regression analyses comparing spring
leaf fluorescence with the spring visual vitality index. .................................................... 140
Table 5.3 Summarised results from simple linear regression analyses comparing summer
leaf fluorescence with the summer visual vitality index. ................................................. 141
Table 5.4 Summarised results from simple linear regression analyses comparing autumn
leaf fluorescence with the autumn visual vitality index. .................................................. 142
Table 5.5 Summarised results from simple linear regression analyses comparing spring
bark fluorescence with the spring visual vitality index. ................................................... 144
Table 5.6 Summarised results from simple linear regression analyses comparing summer
bark fluorescence with the summer visual vitality index. ................................................ 145
Table 5.7 Summarised results from simple linear regression analyses comparing autumn
bark fluorescence with the autumn visual vitality index.................................................. 146
Table 5.8 Summarised results from simple linear regression analyses comparing spring
leaf fluorescence with basic wood density data. ............................................................. 147
Table 5.9 Summarised results from simple linear regression analyses comparing summer
leaf fluorescence with the basic wood density data. ....................................................... 148
Table 5.10 Summarised results from simple linear regression analyses comparing autumn
leaf fluorescence with the basic wood density data. ....................................................... 149
Table 5.11 Summarised results from simple linear regression analyses comparing spring
bark fluorescence with basic wood density data. ............................................................ 150
Table 5.12 Summarised results from simple linear regression analyses comparing summer
bark fluorescence with the basic wood density data. ...................................................... 151
Table 5.13 Summarised results from simple linear regression analyses comparing autumn
bark fluorescence with the basic wood density data. ...................................................... 152
Table 5.14 Summarised results from simple linear regression analyses comparing spring
leaf fluorescence with wood decay data.......................................................................... 153
Table 5.15 Summarised results from simple linear regression analyses comparing summer
leaf fluorescence with the wood decay data. .................................................................. 154
Table 5.16 Summarised results from simple linear regression analyses comparing autumn
leaf fluorescence with the wood decay data. .................................................................. 155
Table 5.17 Summarised results from simple linear regression analyses comparing spring
bark fluorescence with wood decay data ........................................................................ 156
Table 5.18 Summarised results from simple linear regression analyses comparing summer
bark fluorescence with the wood decay data. ................................................................. 157
Table 5.19 Summarised results from simple linear regression analyses comparing autumn
bark fluorescence with the wood decay data. ................................................................. 158
Chapter 1 - Introduction
Understanding tree vitality is essential to the maintenance of healthy trees in our
landscape and to maximize any environmental benefits trees may deliver. Tree vitality
is difficult to quantify, but it is compromised by ongoing stress to the plant which in
turn affects the plants’ ability to grow and develop (Larcher, 2003; Dobbertin, 2005).
Understanding the processes that result in trunk failure and the decay of wood is
crucial to the risk assessment of trees in urban environments, or anywhere people and
trees coexist (Shigo, 1991; Mattheck & Breloer, 1994; Matheny & Clark, 1994;
Mattheck, 2007; Schwarze, 2008). Wood decay is essentially wood that has decreased
density as a result of degradation by fungi or bacteria, hence trees with wood decay
have an increased likelihood of failure (Harris et al., 2004). Usually tree physiology
and wood decay processes are believed to be independent as wood decay is believed to
occur primarily in what is described as the non-functioning heartwood of the tree
(Zweifel et al., 2006). For this reason the relationship between wood decay and tree
vitality has not been thoroughly investigated.
Nondestructive but invasive methods for assessing wood decay where the decay is not
apparent were evaluated in a previous study (Johnstone et al., 2007). Trees are
regularly removed unnecessarily because they pose an unacceptable risk, or because
they appear to display poor vitality (Mortimer & Kane, 2004). Other trees are retained
where the decay is not apparent even though they may pose an unacceptable risk to
people (Schwarze, 2008). This study may lead to improvements in the non-destructive
evaluation of decay in a tree trunk and more accurate estimations of tree biomass in
both forest and urban trees. High vitality/low decay trees may be shown to convey
more environmental benefits than low vitality/high decay trees. This study may also
establish new links between the physiology of trees, wood anatomy, tree structure and
wood decay in trees.
1
1.1 Aim
The aim of this research is to establish whether there is a relationship between tree
vitality and wood decay in trees.
1.2 Research approach
In order to meet the aim of this thesis it was important to source plant material that was
decayed, and showed a range of decayed wood from extensively decayed stems to little
or no wood decay. In order to establish trends or correlations in the amount of wood
decay and tree vitality another requirement of the plant material was that it should
display a range of tree vitality indices, from poor vitality to very good vitality. Some of
the measurements in this study were made over two or even three seasons on the same
plant material in order to study the effect of seasonality and tree vitality on variously
decayed trees. The requirement for measurable and variable amounts of wood decay in
the trees, a range of tree vitality indices in the one population and measurements on the
same plant material over three seasons precluded the use of manipulative experiments
in this study due to time constraints.
The rate of wood decay, particularly in wood in service, is known to vary a great deal
between tree species and the causal agent of decay (Schwarze et al., 2000). The
development of wood decay may also be influenced by climatic and environmental
conditions that have not been quantified in previous studies (Schwarze, 2008). Thus it
was important in this study to limit sources of uncontrolled variation in the
development of wood decay in the plant material by using one species of tree in one
location. Only one provenance of one tree species was used in this study. Uncontrolled
variation in plant material was further limited by using the same plant material
throughout the study and by using even-aged plant material.
The plant material chosen for this experiment had to be of manageable size (for
sampling) but also mature enough to have wood decay in the stem. The species chosen
for the study had to have some decay in the stem at a reasonably young age. A plant
that was planted in a large range of geographical locations worldwide would increase
the relevance of the study. The plant chosen, Eucalyptus saligna, is planted in many
2
countries including USA, Brazil, India, South Africa, Zimbabwe and Madagascar
(Burgess, 1988). Thirty-six twenty year old Eucalyptus saligna Sm (Bateman’s Bay)
(Sydney Blue Gum) trees were chosen to examine the relationship between tree vitality
and wood decay. Destructive sampling was an important part of this study. Hence the
trees were grown in a plantation so they could be cut down at the end of the field work
phase.
Practical methods for inferring tree vitality often measure tree growth and the visual
signs of decline in a tree (Dobbertin, 2005; Terho et al., 2007; Martinez-Trinidad et
al., 2010). Tree growth is usually assessed by measures such as tree height, diameter at
breast height and biomass (Dobbertin, 2005). Standard visual indices can be used to
convert the visual signs of tree decline into numerical values (Fostad & Pedersen,
1997; Coops et al., 2004; Cunningham et al., 2007). Measuring the efficiency of
photosynthesis is another way of assessing tree vitality. This can be done by
quantifying the excess energy re-emitted during photosystem II via chlorophyll
fluorescence measurements (Percival & Keary, 2008; Martínez-Trinidad et al., 2009;
Martínez-Trinidad et al., 2010). The quantity of wood decay in the trunk of trees can
be assessed using many different methods including stress wave tomography and
constant feed drills (Johnstone, 2005; Johnstone et al., 2007).
A review of previous experimental work on tree growth, the process of photosynthesis,
wood anatomy and structure, and the assessment of wood decay are essential to the
study. Chapter 2 therefore discusses tree vitality, growth, photosynthetic efficiency,
the structure of wood and the process of wood decay. Chapter 2 specifically tree
vitality and reviews the literature in relation to available techniques for inferring the
vitality of trees and the measurement of wood decay in standing trees.
The aim of Chapter 3 is to assess the amount of decay in the wood of the E. saligna as
accurately as possible within the time frame available for this study. Three decay
estimation methods were used - a Picus sonic tomography system, a IML Resi system
and a visual method after the trees were cut down. These three methods were
compared to whole tree wood density. Chapter 3 explains both the IML-Resi F300S
and the Argus-Picus sonic tomograph systems, how the devices function in the field
3
and the data they produce. Chapter 3 compares the IML-Resi F300S, the Argus-Picus
sonic tomograph systems and the visual method with whole tree wood density for
quantifying wood decay in E. saligna.
The aim of chapter 4 is to determine whether there is an inverse relationship between
tree growth and the percentage of wood decay in trees. Chapter 4 examines whether
tree growth parameters such as tree height, diameter at breast height, leaf area, Huber
value (the ratio of sapwood:leaf area) and above ground biomass can predict visual
tree vitality in E. saligna. Chapter 4 assesses whether a visual vitality index can be
correlated with the quantity of wood decay in E. saligna. Wood density was also
compared with the visual vitality index in chapter 4 as wood decay and wood density
are closely related. Chapter 4 explains the methods used to calculate leaf area, Huber
value and above ground biomass for E. saligna. The visual tree vitality index is based
on a system for assessing eucalypts devised by Grimes (1978) and modified by Martin
et.al., (2001). Chapter 4 explains how this visual tree vitality index is used and how it
has been modified to suit plantation trees.
The aim of Chapter 5 is to assess whether photosynthetic efficiency, as measured by
chlorophyll fluorescence, can be correlated with the quantity of wood decay in trees.
The wood density of the E. saligna was also compared with chlorophyll fluorescence
in chapter 5 as wood decay and wood density are closely related. Chlorophyll
fluorescence was measured by a Hansatech-Handy Plant Efficiency Analyser on both
the bark and trunk of the E. saligna in spring, summer and autumn. Chapter 5 explains
how the Hansatech-Handy Plant Efficiency Analyser functions in the field, the data it
produces and the methods used to collect data from both the bark and leaf samples.
Each experimental chapter was written as a stand alone paper for journal submission to
facilitate the production of manuscripts. Thus some repetition of citations and text
from chapters 1, 2 and 6 was inevitable. A large section of chapter 2 and an edited
version of chapter 3 have already been published (see preface).
Chapter 6 discusses the implications of the results with reference to other studies and
states the outcomes of the study. Appendices to the experimental chapters are
4
presented at the end of the thesis. The appendices include detail on methods and raw
data that does not appear in the main text.
5
Chapter 2 – Measuring tree vitality and wood decay in
trees
2.1 Introduction
It is important to review previous studies used to predict tree vitality and wood decay
before embarking on investigations designed to test for a relationship between these
parameters. Prior to understanding how to measure these parameters, however, it is
important to gain a detailed understanding of tree growth and vitality, the woody
structure of trees and the process of wood decay. Tree growth, structure and the
anatomy of wood have been the subject of many previous investigations; key studies
will be reviewed in this chapter. Many different methods have been used to infer the
vitality of trees and to assess the quantity of wood decay in a tree, and these will also
be reviewed. This overview forms the basis on which the methods in this study were
selected.
The aim of this chapter is:
1. To review previous literature on tree growth, wood anatomy and structure.
2. To define tree vitality and the ways in which tree vitality can be inferred by the
measurement of tree growth and physiological parameters, as reported in previous
studies.
3. To understand the process of wood decay and the ways in which decayed wood is
assessed in standing trees as reported by previous studies.
4. To analyse previous investigations into the relationship between tree vitality and
wood decay.
2.2 Tree growth and vitality
The growth of healthy trees involves cell division, enlargement and differentiation into
the various tissues that constitute a plant, as well as the integration of tissues into the
various organs of the plant; the roots stems and leaves and eventually flowers, fruits
and seeds (Kozlowski et al., 1991). Cell differentiation occurs in three regions of the
plant, the apical meristems of stems and roots and the lateral meristem of the cambium
(Kozlowski et al., 1991).
6
Vitality is defined by the Oxford English Dictionary (1989) as the “vital force, power,
or principle as possessed or manifested by living things; the principle of life;
animation… the ability or capacity on the part of something of continuing to exist or to
perform its functions; power of enduring or continuing”. Prior to the 1800’s vitality
was used to describe the “life force” only in humans or animals, rather than plants
(OED, 1989). Plant vitality as a concept probably came in to use in the discipline of
botany in the 1820’s, with plants being described as having a life force or “living
principle” of which plant vigour (how the plant looks) was the expression (Castle,
1829). In these early botany texts vigour is used as a description of the outward
appearance of the plant – used synonymously with health, as in Smith’s 1807
Introduction to Physiological and Systematical Botany, whereas vitality, or for a plant
to be vital, denotes a resilience or life force present but not always visible. How a plant
reacts to heat or light was seen by the botanists in the 19th century as evidence of plant
vitality (Castle, 1829). Plant vitality was linked to plant function, “food and
nourishment” (photosynthesis and transpiration) and plant reproduction (Castle, 1829).
Castles’ reference to plant vitality in his 1829 Treatise “Introductory Botany” with a
section on the “Vitality of Plants”, notes that plant vitality is positively affected by
heat and light, and negatively affected by “irritability” which today we would describe
as a “stress response” in the plant (Castles, 1829; Larcher, 2003).
In modern times plant vitality is often expressed in terms of plant stress (Kozlowski et
al., 1991; Larcher, 2003). Stress is usually defined as a significant divergence from the
ideal conditions of life, and even if the stressful event occurs only temporarily, the
vitality of the plant is more and more compromised the longer the stress is maintained
(Larcher, 2003). If the stress is present for long enough the plant will never completely
recover from the stress and the responses to the stress may become permanent
(Larcher, 2003). The non-specific effects of stress on plants, and hence effects on
vitality, include alterations in membrane properties, increased respiration, inhibition of
photosynthesis, reduced dry matter production and growth disturbances (Larcher,
2003).
Vitality and vigour often seem confused in their use, yet tree vitality is still used and
referred to as a measure of an individual trees’ reaction to environmental stress in the
7
disciplines of both forestry and arboriculture (Dobertin, 2005; Bühler et al., 2007;
Percival & Keary, 2008; Martínez-Trinidad et al., 2009; Martínez-Trinidad et al.,
2010). An individual plant’s tolerance to an environment is said to be a measure of its
vitality, whereas plant vigour refers to the genetic tolerance of a group of plants to
stress (Shigo, 1986). In fact measuring plant vitality has become synonymous with
instruments that are able to assess vitality prior to any visible symptoms (MaldonadoRodriguez et al., 2003; Percival et al., 2008). Tree vitality cannot be measured directly
hence growth and physiological parameters that indicate tree vitality are used
(Dobbertin, 2005). Tree growth, health and vitality are dependent on the physiological
processes of photosynthesis, nutrient assimilation and the maintenance of a consistent
water balance in the plant.
2.2.1 Photosynthesis
The energy to drive tree growth is provided by photosynthesis (Kozlowski et al.,
1991). Photosynthesis in plants is the process that converts solar energy in sunlight to
chemical energy that can be used by the plant. Photosynthesis converts carbon dioxide
(CO2) to organic material by reducing this CO2 to carbohydrates and results in the
production of sugars, lipids and proteins (Ke, 2001; figure 2.1). The electrons required
for this reaction come from water which is converted into oxygen and hydrogen (Stern,
2006; figure 2.1). In green plants sunlight is absorbed by pigments within the plant
called chlorophylls and accessory pigments such as carotenoids in the thylakoid
membrane of the chloroplasts, mainly in the leaves (Ke, 2001).
Chlorophylls and carotenoids in the plant are bound to polypeptides, which provide the
pigment molecules with the appropriate orientation and positioning in relation to each
other (Ke, 2001). The photosystems are made up of three components; the reaction
centre, the inner and the outer antenna (Ke, 2001). Light energy is absorbed by
individual reaction centre molecules and transferred to antenna pigments that are in a
special protein environment that forms the reaction centre (Stern, 2006). A large
number of antenna pigments are clustered together and they harvest the light energy
and transfer it to the reaction centre molecules (Stern, 2006).
8
Too much light, particularly in combination with other stresses can result in the
amount of light that is absorbed by the plant exceeding the capacity of electron transfer
by electron centres (Ke, 2001). Plants can convert some of the excess energy into heat
but if the electron transfer rates are too high the photosynthetic electron transport chain
may be shut down – a process known as photoinhibition (Ke, 2001). The
photoinhibition of some reaction centres can be measured by changes in chlorophyll
fluorescence.
Electron transfer reactions set in motion a series of reduction-oxidation (redox)
reactions (Stern, 2006; figure 2.1). There are two types of reactions centres in plants,
photosystem II and photosystem I (PS II and PS I), both of which are in specialized
membranes called thylakoids, located in chloroplasts where they form membrane
stacks called grana (Stern, 2006).
PS II is the series of reactions in which water splitting and oxygen is produced (Stern,
2006; figure 2.1). When light is absorbed by P680 molecules in PS II, excited electrons
are passed to phaeophytin acceptor molecules in the thylakoid membrane (Stern, 2006;
figure 2.1). From the phaeophytin molecules the electrons move to plastoquinone
molecules which then move the electrons to the side of the stroma (Stern, 2006; figure
2.1).
In PS I electrons are transferred eventually to NADP (nicotinamide adenosine
dinucleotide phosphate) (Stern, 2006; figure 2.1). The reduced form of NADP
(NADPH) can be used for carbon fixation. The oxidized reaction centre chlorophyll
P700 eventually receives another electron from the cytochrome b6f complex (Stern,
2006; figure 2.1). As a consequence, electron transfer through PS II and PS I results in
water oxidation and NADP reduction, with the energy for this process provided by
light (Ladiges et al., 2003; figure 2.1).
The electron flow from water to NADP requires light and is linked to the production of
a proton gradient across the thylakoid membrane (Ladiges et al., 2003). This proton
gradient is used for synthesis of the high-energy molecule adenosine triphosphate
(ATP) (Stern, 2006). ATP and reduced NADP that resulted from the light reactions are
9
used for CO2 fixation in a process that is light-independent (Ladiges et al., 2003; figure
2.1). CO2 fixation involves a number of reactions. The initial CO2 fixation reaction
involves the enzyme ribulose-1, 5-bisphosphate carboxylase/oxygenase (RuBisCO),
which can react with either oxygen (leading to photorespiration and not carbon
fixation) or with CO2 (Figure 2.1).
Figure 2.1 Diagram of photosynthesis showing the light reactions (photosystem I and II), on the left,
and the dark reactions (the Calvin cycle) on the right.
From left, on the oxidising side of Photosystem II (to the left of the blue arrow), P680 oxidised by light
is re-reduced by YZ, that has received electrons from the oxidation of water. The blue vertical arrows
represent photon absorption by the reaction centre chlorophylls: P680 for photosystem II (PSII) and
P700 for photosystems I (PS I). The excited PSII reaction centre chlorophyll P680* transfers an electron
to pheophytin (Pheo). Pheophytin then transfers electrons to the plastoquinone acceptors QA and QB.
The cytochrome b6f complex transfers electrons to plastocyanin (PC), which in reduces P700+. The
acceptor of electrons from P700*(A0) is thought to be a chlorophyll, and the next acceptor (A1) is a
quinone. Membrane-bound iron-sulphur proteins(FeSX, FeSA and FeSB) transfer electrons to ferredoxin
(Fd). The flavoprotein ferredoxin-NADP reductase (FNR) reduces NADP+ to NADPH. The red arrow
indicates cyclic electron flow around PSI. The Calvin cycle begins with carbon input – the carboxylation
phase, in which CO2 is linked to carbon; then the reduction phase in which a carbohydrate is formed
using ATP and NADP from the light reactions; and then the regeneration phase, which restores the CO2
acceptor ribulose-1,5-bisphosphate (After Taiz & Zeiger, 2010).
10
The rate of photosynthesis varies among tree species, between sun and shade leaves,
during the course of the day and during the different seasons (Kozlowski et al., 1991).
Sun plants such as eucalypts have a high photosynthetic rate at high irradiation levels,
whereas shade plants can be damaged at the same level (Lüttge et al., 2003). The
innermost leaves in shade crowns can be present at relative irradiance levels as low as
1-3% whereas in sun crowns inner leaves still receive 10-20% of the light available
(Larcher, 2003). Usually the rate of photosynthesis increases until leaves are fully
expanded and then decreases, and the rate of photosynthesis of leaves living more than
one year usually decreases after the first year (Kozlowski et al., 1991). Shade leaves
are usually thinner and larger than the sun leaves on the same plant (Stern, 2006).
There are usually diurnal variations in light saturated net CO2 assimilation in C3 plants,
but this has not always been observed in eucalypts (Küppers et al., 1986). Küppers et
al., (1986) observed a steady decline in CO2 assimilation throughout the day in
Eucalyptus pauciflora and E. delegatensis.
2.2.2 Bark photosynthesis
Many trees have parenchyma tissue with chloroplasts (chlorenchyma) in their stems,
trunks, green flowers, green fruit and even wood and roots as well as leaves, enabling
these plant parts to photosynthesise (Pfanz et al., 2002). Leaves are often shed either
through natural processes or due to attack from insects or diseases, and in that case
cortical photosynthesis may make up some of the shortfall in carbon production for the
plant (Pfanz et al., 2002; Eyles et al., 2009). Stem cortical photosynthesis utilizes
internal CO2 from mitochondrial respiration and gaseous xylem efflux, and the
transpirational xylem stream supplies inorganic nutrients to the chlorenchyma (Pfanz,
2008). Photosynthesis in bark has been shown to be strongly shade adapted in many
trees, because light must pass through the periderm (Pfanz et al. 2002; Damesin, 2003;
Manetas, 2004). One year old Eucalyptus globulus bark behaved as a shade leaf in a
study by Eyles et al., (2009); however, Tausz et al., (2005) found that parts of sun
exposed Eucalyptus nitens bark had a similar pigment to sun leaves. Corticular
photosynthetic activity levels in stems have been found to be lower than in the leaves
of broadleaf trees such as Betula pendula, Quercus robur and Fagus sylvatica in a
study by Wittman & Pfanz (2008). Photosynthesis was also found to be less efficient
11
in stems compared to leaves but cortical photosynthesis could still be a way of
improving the carbon balance of stems, particularly where water is limiting (Wittman
& Pfanz, 2008).
2.2.3 Water and nutrients
Water absorption in a plant occurs mainly in the roots, with a small amount occurring
through leaves and stems (Kramer & Boyer, 1995). The xylem is the main pathway for
the upward movement of water in trees (Kramer & Boyer, 1995). The driving force for
water movement is the transpiration in the leaves through the stomata (Kramer &
Boyer, 1995). Most stomata occur on the abaxial surface of the leaf even in the
isobilateral leaves of eucalypts (Beadle, 2000). The stomata on Eucalyptus saligna
leaves occur primarily on the abaxial surface, except for some adaxial stoma that can
be found near the mid-rib vein (Ridge et al., 1984). When water supply is adequate,
stomatal conductance in eucalypts is high in the morning and declines during the day
(Beadle, 2000). Stomatal closure can also occur during the day in various eucalypts,
including Eucalyptus saligna, in response to water stress (Beadle, 2000). Water is an
essential part of all physiological processes involving plant growth, and water stress is
increased by environmental stresses such as biotic diseases and abiotic stresses, which
affect the quantity of plant growth in all parts of the tree (Kramer & Boyer, 1995).
Water stress can cause the expansion of air bubbles in the xylem due to tension, a
phenomena known as caviation (Hacke et al., 2001; Taiz & Zieger, 2010). Once a
xylem vessel cavitates it fills with water vapour and then forms an embolism, in quick
succession, slowing xylem hydraulic conductivity (Tyree & Sperry, 1989). Air seeding
of the xylem vessels is thought to occur from the intervessel pits at the pit membranes,
therefore species of trees with larger diameter pit membranes are more vulnerable to
cavitation. Cavitation may also be pathogen induced, or assisted, as when a pathogen
might cause increased water stress or a change in sap chemistry and thus reduce the
surface tension of the liquid (Tyree & Sperry, 1989). Zwienieki et al., (2001a), showed
the refilling of water filled xylem vessels that were previously air filled in 1 m
Fraxinus americana L.(American ash) branch samples. The mechanism for refilling
xylem vessels in woody plants is unknown at present, but pressure transmission
12
through pit membranes must be coordinated in order for it to be successful (Holbrook
et al., 2001). Control over the speed of xylem water flow may occur by a plant’s
ability to adjust the ion concentration in intervessel pits through the regulation of
hydrogels in the pit membranes, increasing the size of the microchannels, and thus
flow, through the pits (Zwienieki et al., 2001b). Increasing flow via the regulation of
the ion concentration in this way may compensate for the decrease in flow due to
embolism (Zwienieki et al., 2001b).
The radial growth rate of a tree is greatly affected by its water relations (Zweifel et al.,
2006). The water status of a plant can be determined by the water status of plant cells
and tissues such as leaves, stems and roots (Kozlowski et al., 1991). The water status
of cells depends on their water potential, which depends on the osmotic potential of the
vacuolar sap and the turgor pressure potential (Kozlowski et al., 1991). Water
potential can be expressed as:
Ψw = Ψs + Ψp
Where Ψw is the total water potential of the cell, Ψs the osmotic potential of the cell
sap and Ψp the turgor pressure (Kozlowski et al., 1991).
Tree roots are able to absorb nutrients over a wide range of nutrient concentrations in a
soil, and can adapt their morphology and biochemistry to regulate nutrient uptake
(Leigh, 2003). The 14 essential mineral nutrients required for plant growth were
discovered from the 1800’s to the 1940’s (Ladiges et al., 2003). They include the
macronutrients nitrogen, potassium, calcium, magnesium, phosphorus and sulphur
(Ladiges et al., 2003). They are translocated from the roots via the xylem to other plant
parts (Stern, 2006).
2.3 The structure of wood
The main chemical constituents of wood are cellulose, hemicellulose, lignin and a
heterogeneous group termed extractives (Bucur, 2006a). The proportion of extractives
varies between species, and they include lower molecular weight carbohydrates,
exudates, phenolic and nitrogenous compounds and minerals (Bootle, 2005). One of
the principal exudates in eucalypt wood is kino, which is believed to be a protective
response to injury to the cambium, similar to the flow of resin in pines (Bootle, 2005).
13
The cellulosic microfibrils that are the main constituent of wood are embedded in a
lignin matrix (Bucur, 2006a). In the lignified cell wall of mature wood, the cellulose
microfibrils provide a flexible framework with high tensile strength, surrounded by the
dense, rigid filler of lignin (Schwarze et al., 2000). Most of these cells are orientated
with their long axes along the stem which helps provides the cells with greater tensile
strength (Lonsdale, 1999). Aggregated microfibrils of increasing size and complexity
form macrofibrils, which are the primary components of the lamellae, the cell element
that connects one cell to another (Bucur, 2006a). There are five layers in the lignified
cell wall (Schwarze et al., 2000). These five layers are the middle lamella, the primary
wall and the S1 (outer), S2 (central) and S3 (inner) layers of the secondary wall (Figure
2.2).
Figure 2.2 Diagram of the conventional model of the lignified wood cell with five cell-wall layers.
The diagram shows (a) the middle lamella that connects one cell to another (b) the primary wall of the
cell, the three layers of the secondary wall (c) the S1 layer, (d) the S2 layer and (e) the S3 layer. (f)
Represents the cell lumen and is the space through which water and nutrients are transported in
conducting elements in the sapwood. (After Schwarze et al., 2000; Bucur, 2006a; Johnstone, 2005).
The main anatomical elements of wood are tracheids, fibres, vessels, rays and
parenchyma cells that are produced by the cambium (Bucur, 2006a). Over time, the
living sapwood is converted to heartwood in the inner part of the tree, which provides
structural support for the tree but no longer transports water (Bootle, 2005). During
14
this period of development from sapwood to heartwood, relatively high levels of
cellular activity occur, with the development of tyloses in the vessels of many species,
including Eucalyptus saligna (Bamber, 1976). It is believed that as a tree grows the
whole cross section of the tree is not required for transport of water or the storage of
food reserves, and much of the essential minerals are reabsorbed when heartwood
forms (Bamber, 1976; Bootle, 2005).
In cool temperate climates the growth of wood has a seasonal pattern, which often
results in the formation of annual “growth rings” (Bootle, 2005). Wood produced early
in the growing season is called “earlywood” with lower density and fibres larger in
diameter, shorter in length and with thinner walls than those produced later in the
season - the “latewood” (Bootle, 2005). The visual contrast between earlywood and
latewood gives rise to the appearance of growth rings. In warmer or tropical climates,
annual growth rings are less common (Bootle, 2005). A growth ring that is observed in
cross section is actually a three dimensional growth increment (Shigo, 1991).
The main structural difference between angiosperm (hardwood) wood and
gymnosperm (softwood) wood is that hardwoods have vessels and softwoods do not
(Bootle, 2005). Another difference is the type of reaction wood produced. Reaction
wood is formed when the tree is subjected to structural stress over a long period and is
therefore very common in branches and in leaning trees (Bootle, 2005). The reaction
wood produced primarily in softwoods is called compression wood because more
tissue is produced on the lower side of the leaning stem or branch (Bootle, 2005).
Compression wood is harder, denser and more brittle than normal wood and the
tracheids are shorter and thicker (Bootle, 2005). Occasionally compression wood is
produced in hardwood trees (Clair et al., 2006).
The reaction wood of hardwoods is usually tension wood as more tissue is produced on
the upper side of the stem or branch (Bootle, 2005). Tension wood is not as easy to
identify macroscopically as compression wood but can be identified microscopically as
the fibres have gelatinous inner walls, which are less lignified (Bootle, 2005). Wood
can vary in its visual characteristics such as texture, grain and figure (Bootle, 2005).
Knots are visible in most woods and are remnants of branch tissue in the stem. They
15
are usually harder, and often darker than the surrounding wood (Bootle, 2005). For the
many reasons stated above wood is therefore a heterogeneous material (Socco et al.,
2004; Maurer et al., 2006; Bucur, 2006a).
Wood properties such as density vary greatly between different tree species, for
example, from balsa (Ochroma pyramidale Cav.Urban) with an air-dried density of
170 kg m-3 to Georgina gidgee (Acacia georginae F.M.Bail) with an air-dried density
of 1330 kg m-3 (Bootle, 2005). The measurement of wood density must be at known
water content as changes in the moisture content of wood affect both its mass and
volume (Walker et al., 1993).
There are five standard ways of describing wood density in the timber industry: ovendry density, air-dry density, green density, nominal density and basic density (Walker
et al., 1993). They are derived as follows:
1. Oven-dry density =
Oven-dry mass of the wood
Oven-dry volume of the wood
2. Air-dry density =
Mass of wood in equilibrium with atmospheric conditions
Volume of wood in equilibrium with atmospheric conditions
3. Green density =
Mass of wood before any drying
occurs
Volume of wood before any
drying occurs
16
4. Nominal density at x% moisture content =
Oven-dry mass of the wood
Oven-dry volume of the wood
5. Basic density =
Oven-dry mass of wood
Volume of wood before any
drying occurs
Air-dry density (ADD) as used in Australia, is the mass divided by the volume where
the wood has been dried in air to a moisture content of approximately 12 percent
(Bootle, 2005). Basic wood density, the amount of woody tissue in a given volume of
green timber, is the most important measure for the timber and paper industries
(Walker et al., 1993). Basic wood density can be measured more accurately than ADD,
because the moisture content is not as variable (Walker et al.,1993).
As well as varying between species, wood density varies within a species due to
genetic and environmental influences, from pith to bark, and on the height from which
the sample is taken within a tree (Bootle, 2005). Wood density is usually lowest near
the pith and increases progressively in the outer parts of the tree trunk (Bootle, 2005).
Table 2.1 illustrates the variation in wood densities of some trees that originate in the
Northern Hemisphere in temperate climate zones. Table 2.2, by way of comparison,
illustrates the wood densities of some eucalypts indigenous to temperate areas of
eastern Australia. It can be seen from these tables that eucalypts and related species
sometimes have substantially higher wood densities than many of the species that
originate from the North American or European temperate zones.
Moisture content also varies within individual trees and between species of tree
(Bootle, 2005). Moisture content in wood is usually expressed as a percentage of the
oven dry weight thus;
17
Moisture content =
The original weight – oven dry weight
Oven-dry weight of wood
X 100
(Walker et al., 1993).
Another definition for moisture content determination used in the pulp and paper
industry is;
Moisture content =
The original weight – oven dry weight
Original weight of wood
X 100
(Walker et al., 1993).
The first method using the oven dry weight as a reference point is more reliable if the
original weight cannot be measured before some drying of the wood occurs (Walker et
al., 1993). However the second method shows the “true” percentage of moisture
content by weight in the wood prior to drying as it is possible with the first method to
obtain values greater than 100% (Walker et al., 1993).
Other properties that affect the structural integrity of trees apart from wood density are
the modulus of elasticity (stiffness) and the modulus of rupture (bending strength). The
elastic modulus (Young’s modulus) measures the length by which an object elongates,
and therefore resists failure and has the units of pressure (Giancoli, 2005). The
modulus of rupture measures how much bending force can be applied to an object
before failure, and is also measured in units of pressure (Bootle, 2005). The modulus
of rupture and the modulus of elasticity are dependent on the material of an object
(Giancoli, 2005). The maximum force that can be applied to any given material
without breaking it is the “ultimate strength” of the material (Giancoli, 2005). The
modulus of elasticity and the modulus of rupture vary for different tree species (see
table 2.3).
18
Table 2.1 The wood densities and natural durability ratings of selected trees native to the Northern
Hemisphere.
Botanical name
Common Name
Air-dry Density
(ADD)
Natural
Durability rating1
Acer pseudoplatanus
Sycamore
560 kg m-3
5
-3
5
-3
Fagus sylvatica
European Beech
690 kg m
Pinus radiata
Radiata Pine
500 kg m
4
Pseudotsuga menziesii
Douglas Fir
500 kg m-3
3
-3
Quercus robur
English/European Oak
690 kg m
2
Sequoia sempervirens
Redwood
450 kg m-3
2
1. Natural durability is assessed in the heartwood and in-ground. 1 is the most durable, 5 the least
durable (After Bootle, 2005; AS 5604, 2005; BS 350-1,1994).
Table 2.2 The wood densities and natural durability of selected eucalypts.
Botanical name
Common Name
Air-dry Density
(ADD)
Natural
Durability rating1
Corymbia maculata (syn. E.
maculata)
Spotted Gum
950 kg m-3
2
Eucalyptus camaldulensis
River Red Gum
900 kg m-3
2
E. globulus
Tasmanian Blue Gum
900 kg m-3
3
-3
E. paniculata
Grey Ironbark
1120 kg m
1
E. saligna
Sydney Blue Gum
700 kg m-3
3
-3
4
Northofagus menziesii
Southern Beech
700 kg m
1. Natural durability is assessed in the heartwood and in-ground. 1 is the most durable, 4 the least
durable (After Bootle, 2005; AS 5604-2005).
Table 2.3 The green modulus of elasticity and modulus of rupture for selected tree species (After Bottle,
2005; Ozarska, 2009).
Botanical Name
Green modulus of elasticity
Green modulus of rupture
Corymbia maculata
18 GPa
99 MPa
Eucalyptus saligna
15 GPa
122 MPa
Eucalyptus paniculata
20 GPa
120 MPa
Pinus radiata
8.1 GPa
42 MPa
Quercus robur
8.3 GPa
59 MPa
The modulus of rupture values for timber are an approximation, as they assume wood
behaves elastically to the point of failure, which it does not (Walker et al., 1993).
19
However, they are nevertheless useful in most applications (Walker et al., 1993). In
general, the modulus of elasticity and the modulus of rupture are often higher in trees
from the eucalypt group than in conifers or northern hemisphere hardwoods, which
means the timber of trees from this group requires a greater force before failure (see
table 2.3). It is also important to note that the moisture content of timber affects
strength (Walker et al., 1993). Both the dry modulus of elasticity and the dry modulus
of rupture are higher than for green timber – dry timber is therefore stronger than green
timber (Walker et al., 1993).
2.4 The process of wood decay
Wood decay is the process by which microorganisms break down wood into simpler
forms in order to provide nutrients for their survival (Harris et al., 2004). The
development of measurable amounts of wood decay in trees from the time of artificial
inoculation by wood decay organisms is slow, and difficult to detect consistently. In a
study by Deflorio et al., (2008) after just over 2 years significant amounts of wood
decay were visible only after the inoculation of very virulent wood decay pathogens
(Kretzschmaria deusta and Trametes versicolor) on very low natural wood durability
tree species, Fagus sylvatica L. (Beech) and Acer pseudoplatanus L. (Sycamore) (BS
350-1, 1994).
Wood decay is usually described as occurring in four stages; incipient, early,
intermediate and advanced (Harris et al., 2004). In the incipient stage of decay there is
a thinning of xylem cell walls and the wood may be discoloured (Harris et al., 2004).
The earliest stage incipient decay can be detected by light microscopy is at about 510% mass loss (Beall & Wilcox, 1987). Much of the wood strength is thought be have
been lost at the incipient stage of decay – it is believed that up to 50% of wood
strength is lost by around 1% mass loss (Beall & Wilcox, 1987).
In the second, early stage of decay, there are slight changes in wood colour, texture
and brittleness (Harris et al., 2004). Decay is clearly recognisable in the intermediate,
third stage and there is a change in wood structure, however the wood, though altered,
remains intact (Harris et al., 2004). In the advanced stages of decay the wood becomes
20
fibrous or powdery and the wood structure is drastically altered or non-existent (Harris
et al., 2004).
Most wood decaying organisms are believed to be fungal. However, some heartwood
decay organisms are dependent on bacteria that detoxify the wood (Marks et al., 1982).
Also, bacteria are known to cause what is commonly known as “bacterial wetwood”.
The wood of trees infected by wetwood bacteria becomes dark brown to gray and
waterlogged, and discoloured early wood occurs in the annual rings (Harris, 1992).
Wetwood bacteria secrete enzymes that degrade the middle lamellae of the cells, but
they are unable to degrade lignin (Tainter & Baker, 1996). They therefore leave most
of the cell wall undamaged and the heartwood structurally intact, and so have little
effect on strength (Tainter & Baker, 1996). Bacteria are often responsible for
degrading wood in anaerobic environments, such as those under water. These groups
are usually referred to as “erosion bacteria” (Björdal et al., 2005)
Wood decay fungi are usually Deuteromycota, Basidiomycota or Ascomycota and
have often been grouped into categories relating to the appearance of the decay that
they cause, namely white, brown or soft rots. White rot can be either simultaneous
(degrading lignin and cellulose) or selective (degrading lignin but not cellulose) while
brown rot fungi degrade the cellulose and hemicellulose, leaving the brown-coloured
lignin matrix (Simpson, 1996). Soft rot fungi degrade cellulose and hemicellulose,
with most hemicellulose degraded in the early stages of decay and the breakdown of
cellulose occurring much more slowly. If degradation is restricted to cavities in the S2
layer it is Type 1 soft rot, if it causes grooves in the S3 layer it is Type 2 (Simpson,
1996; figure 2.2).
Marks et al., (1982) divide fungi that decay wood into categories depending on which
tissues they attack; those that invade tissues that are non functioning such as outer bark
and heartwood and those that invade tissues that contain both living and non functional
tissues such as sapwood and living bark. This division is significant because the fungi
involved differ in their physiology (Marks et al., 1982). Sapwood and inner bark is
well supplied with oxygen and also nitrogen and phosphorus. These fungi are living in
a nutrient rich environment (Marks et al., 1982). Those that attack outer bark and
21
heartwood not only have to survive in a low oxygen and/or low nutrient environment
but may have to detoxify chemical components within these tissues (Marks et al.,
1982).
Marks et al., (1982) further divide the fungi into six groups. Group 1 contains fungi
that decay living bark phloem and sapwood and perhaps heartwood. Group 2, the
“true” heartwood rotting fungi enter mainly at the base of the tree and decay that part
of the tree. Group 3 decay the portion of the tree above the base at the stem. Group 4,
the sclerote – forming fungi store food in a subterranean body – a fruit body that forms
on the sclerotium. Group 5 smothers young seedlings. Group 6 contains saprophytic
organisms that can be used for biological control of other fungi (Marks, et al., 1982).
The way in which living trees react to the incursion of decay causing organisms varies
for different species. However, there have been several generalized theories relating to
the development of decay in trees, beginning with the “Heartrot” concept where decay
in trees was thought to be basically a saprotrophic process (Pearce, 2000). Later, Shigo
(1979) and others developed a generalised model for how trees react to “wounding” be
it by pathogen attack or mechanical means. Shigo “dissected” many trees using a
chainsaw, described the decay patterns and proposed a model by which the tree
“resists” the spread of decay. Shigo’s model, the “compartmentalization of decay in
trees” or “CODIT”, model has gained widespread currency, from the 1980’s (Shigo,
1979). Shigo modeled a tree in terms of “compartments” having discrete “walls”. The
weakest of these anatomical walls restricted the spread of decay up or down the
cylinder of the trunk (Shigo, 1979). This was “wall one” of Shigo’s model. This “wall”
was achieved by the closure of vessels and bordered pits by tyloses and by
polyphenolic deposits (Shigo, 1979). Wall two stopped the inward spread of decay
towards the pith and consisted of the thickly walled and lignified cell elements of the
latewood. Wall three resisted the tangential spread of decay and was made up of the
xylem ray cells (Shigo, 1979). These first three walls were believed to be structurally
present before wounding or infection occurred (Shigo, 1979). Boundaries that were
formed between infected xylem and pre-existing sapwood in walls 1-3 are termed
“reaction zones” (Pearce, 2000). Wall four was an active response in living tissue, by
which a biochemical and anatomical wall was formed in the growth increment by the
22
cambium after the tree was wounded (Shigo, 1979). Interestingly Bamber (1976),
refers to this same phenomenon, but calls it “wound heartwood” formed after injury to
the cambium or phloem. Wall four of the CODIT model was called the “barrier zone”
(Shigo, 1979; Shigo & Hillis, 1973).
Authors such as Boddy (1994) and Schwarze et al. (2000) have further refined the
generalised CODIT model of wood degradation in trees. Boddy (1994) hypothesizes
that the tree is reacting to desiccation rather than “wounding” or decay pathogens
themselves and suggests replacing the “D” in the CODIT model for Decay with either
Dysfunction or Desiccation. Furthermore she claims that fungal spores do not
necessarily need an “entry point” for decay but may already be present in the vascular
system of the tree (“latent decay”) (Boddy, 1994). Schwarze et al. (2000) claim that
the enzymatic potential of the fungal organism is important in wood decay as well as
the structure of the wood of the species being invaded. Schwarze et al. (2000)
hypothesize that the fungus-host interaction is much more complex than previously
believed, and that fungal pathogens can even switch modes from, for example, white
rot to soft rot modes within different cell tissues (Schwarze et al., 2000). According to
several authors, eucalypts appear to react to wounding or infection of sapwood by
compartmentalization (Mireku & Wilkes 1988; Wilkes, 1986). According to Wilkes
(1986) the barrier zone (or wall 4) in eucalypts is predominantly of undifferentiated
parenchyma. If this tissue breaks down the spaces often fill with kino (Wilkes, 1986).
Work on fungus-host interactions such as that done by Schwarze et al., (2000) has not
been done on eucalypts. On the other hand, much research has been done on the wood
structure of eucalypts as many are utilised by the timber industry (Bootle, 2005; Evans,
1994).
Defence responses in sapwood can be measured by a nuclear magnetic resonance
imaging and electron microscopy. They have been measured in conifers have been
measured in conifers using an assay for monoterpene cyclase activity (Lewinsohn et
al., 1991). Defence responses by plant cells should affect plant water relations such as
hydraulic conductance, but the link between tree physiological and wood-anatomical
knowledge has been poorly examined at this stage (Zweifel et al., 2006).
23
2.5 Measuring tree vitality
The measurement of tree vitality is usually correlated with the measurement of the
effect of a stress on the plant. These effects can be divided into stress specific effects,
such as measuring transpiration in relation to drought, or non-specific effects such as
photosynthetic efficiency (Larcher, 2003). Measurement of indicators of tree vitality
such as tree growth (twig extension or stem or root growth) or canopy density (crown
transparency) are very common, but measuring photosynthetic efficiency and carbon
allocation is increasingly being used (Dobbertin, 2005). Growth must be compared
with trees that are healthy and not exposed to any stress as a reference point
(Dobbertin, 2005). Field methods used to describe tree vitality include; canopy density
or reflectance (Weng et al., 2006; Hargrave & Johnson, 2006), crown morphology
(Woodcock et al., 1995), shoot, root and stem growth (Mena-Petite et al., 2003; Repo
et al., 2005), nutrient content analysis of needles (Santerre et al., 1990; Hrdlicke &
Kula, 2004) or leaves (Thomas et al., 2006), electrical resistance or impedance (Shigo,
1991; Blazé, 1992; Repo et al., 2005), plant water relations (Repo et al., 2005; PeñaRojas et al., 2005), needle or leaf size or shape (Nakatani et al., 2004; Pena-Rojas et
al., 2005) and chlorophyll fluorescence (Epron & Dreyer, 1992; Mena-Petite et al.,
2003; Pukacki & Kamińska-Rożek, 2005; Valladares et al., 2004; Peña-Rojas et al.,
2005; Repo et al., 2005; Philip & Azlin, 2005; Weng et al., 2006; Thomas et al., 2006;
Percival et al., 2006).
2.5.1 Tree growth
Tree growth was traditionally measured by tree height and diameter at breast height
(Dobbertin, 2005). Tree height and trunk diameter are still used today to compare the
effect of an imposed stress on a tree. For example, tree height and trunk diameter were
used to assess the effect of planting depth on Platanus occidentialis (sycamore) and
Taxodium distichum (bald cypress) and the effect of drought stress on Acer platanoides
(Norway maple) and Tilia spp. (linden) (Bryan et al., 2010; Fini et al., 2009).
Leaf area is another common method used for assessing growth (Hunt, 2003;
Macfarlane et al., 2007; Calvo-Alvarado et al., 2008; Gotsch et al., 2010). Leaf area
index (LAI) is the ratio of leaf area to ground area and is used for analyzing the growth
24
of crops rather than individual plants (Benjamin, 2003). LAI is a measure of the
productivity of the site rather than the plant itself, as it is a measure of leaf area per
ground area (Hunt, 2003). Sometimes LAI is used to measure the canopies of large
trees through the attenuation of light through the canopy (Russell, 2003). Full frame
fish eye photography can be used for estimating LAI in forests (Macfarlane et al.,
2007).
For individual plants that are not large woody perennials, leaf areas are measured
directly and combined with leaf weights to create ratios such as the “specific leaf area”
of a plant (Hunt, 2003). The specific leaf area is a measure of the density of the leaf as
it is ratio of leaf area to leaf weight, hence denser leaves have a lower value (Hunt,
2003). Specific leaf area is a measure of the density and therefore the health of
individual leaves and is often used for assessing tree growth (Calvo-Alvarado et al.,
2008; Gotsch et al., 2010). Measuring the sapwood area:leaf area ratio or Huber value
is another way of assessing tree growth (Zeppel & Eamus, 2008; Calvo-Alvarado et
al., 2008; O’Grady et al., 2009; Gotsch et al., 2010).The advantage of the Huber value
is that measurements taken at different locations in a plant, or from different plants are
directly comparable (Tyree & Zimmermann, 2002).
Above ground biomass or total biomass is a also a common growth measure for crops
and herbaceous plants (Roberts et al., 1993), but is less common for assessing single
mature trees, presumably because of the time and resources required. Live above
ground biomass has been used for monitoring the rate of change of above ground
carbon stocks due to climatic factors (Castilho et al., 2010). Crown volume (Scott et
al., 1999) or crown shape can also be used for comparing the growth of trees
(Woodcock et al., 1995; Hitusma et al., 2006). On the other hand root growth was
measured by Mena-Petite et al., (2003) in a study of Pinus radiata D. Don seedlings
that were stored in cold conditions prior to planting.
Tree vitality has also been measured using the visual tree assessment of crown
condition. Martinez-Trinidad et al., (2010) used a rating from 1-3 - “good”, “fair” and
“poor” to compare visual vitality with various physiological measurements in Quercus
virginiana (live oak) with variable results. A crown status index based on needle loss
25
and necrosis was used to assess Abies alba (sliver fir), with a rating from 1-7 (Torelli
et al., 1999). The crown class index was inversely correlated with the width of the
outermost annual ring of Abies alba (Torelli et al., 1999). Fostad & Pedersen (1997)
used 5 components including general impression, leaf necrosis and insect attack,
dieback and stem injury to get an overall understanding of tree decline in a number of
broadleaf trees in Oslo, Norway. They did not compare the results statistically with
objective measurements such as tree height or stem diameter. Foliage condition (5
classes) and crown density (9 classes) assessment correlated well with spectral
reflectance imagery in mixed Eucalyptus paniculata, E. pilularis and E. saligna forest
(Coops et al., 2004). Cunningham et al., (2007) used a six part visual assessment
technique for Eucalyptus camaldulensis (river red gum) incorporating crown vigour
(the percentage of the potential crown that contained foliage), the percentage of
epicormic growth, percentage of live foliage, crown depth (the proportion of tree
height that contained live foliage), crown size (projected are of the crown per basal
area) and leaf condition (green yellow or damaged). Percentages were estimated within
a 20% range. The percentage live basal area and plant area index measured by
hemispherical photographs were compared with each element of the visual assessment
technique at a whole site level. Only the “crown vigour” category was a consistent
measure of site condition (Cunningham et al., 2007).
A numerical crown assessment technique was developed for living Eucalyptus
(Corymbia) maculata (spotted gum) and Eucalyptus fibrosa (ironbark) and E.
drepanophylla trees by Grimes (1978) incorporating a score for crown position in
relation to other trees, crown size, crown density, the number of dead branches and
epicormic growth. He found that each of the 5 variables contributed significantly to a
prediction equation for diameter at breast height, but that for best results factors should
be weighted differently, for example epicormic growth on a three point scale and
crown density on a nine point scale. Grimes’ (1978) 25 point scale was combined by
Martin et al., (2001) with an assessment technique for assessing hollow-bearing trees
by Lindenmayer et al., (1990) to assess the response of Eucalypts to several
environmental stresses, including an insect pest, elevated nutrients and Phytopthora
cinnamomi. The scores for each attribute were totaled to give an estimate of the health
26
of each tree, ranging between 1 and 25. The tree health index modified by Martin et
al., (2001), was developed for assessing individual tree health within a stand of trees.
Visual assessment or condition/vigour “indices” use a very wide range of parameters –
with some individual components that are clearly not independent of each other. Those
that have fewer individual components appear to more accurately reflect objective
growth measurements such as stem diameter or tree height.
2.5.2 Leaf or needle morphology and biochemistry
Needle or leaf shape and/or size is also used to assess tree vitality. For example, leaves
were found to be smaller in drought stressed Quercus ilex in a study by Peña-Rojas et
al., (2005). In a study by Nakatani et al., (2004) needle morphology (dry mass to
needle area) of Abies firma was found to be smaller in pollution affected trees.
Nutrients are often measured in leaves to assess the health of a tree. For example
Betula pedula were found to be affected by high pollution levels in the Czech Republic
by a decrease in nitrogen and sulphur content in their leaves (Hrdlicke & Kula, 2004).
Low inorganic phosphorus in Eucalyptus grandis leaves was correlated with slower
growth of the trees in south eastern Australia (Thomas et al., 2006). A range of mineral
nutrients were measured to assess the effect of pollution damage in Picea sp. in France
(Santerre et al., 1990). Barium, strontium and magnesium were not present in high
quantities in the leaves of low vitality Picea.
Glutathione, ascorbic acid and other antioxidants can be measured in leaves or in other
plant parts as an indicator of plant stress (Tausz et al., 2003; Šircelj et al., 2005).
Increases in the antioxidants glutathione and ascorbic acid were found in the leaves of
drought affected Malus domestica (Apple trees) by Šircelj et al., (2005). Glutathione
and ascorbate levels also increased in the leaves of 2 year old Liriodendron tulipifera
(Tulip trees) when exposed to elevated ozone in a study by Ryang et al., (2009).
Ascorbate and α-tocopherol increased in the fine roots of Fagus sylvatica (European
beech) in response to the combined stress of drought and elevated ozone (Haberer et
al., 2008).
27
Measuring the chlorophyll content in the leaves of trees is another way of estimating
tree vitality. This is normally done by extracting the leaf photosynthetic pigment
content in aqueous acetone. Chlorophyll content was shown to decrease in Picea abies
(Norway spruce) needles during drought stress (Pukacki & Kamińska-Rożek, 2005).
Chlorophyll content can also be estimated with a chlorophyll content or SPAD meter.
The SPAD meter correctly predicted low vitality in low nitrogen leaves from Acer
pseudoplatanus (sycamore), Quercus robur (English oak) and Fagus sylvatica
(European beech), but did not predict the total chlorophyll in the leaves when
compared to extraction by acetone solution (Percival et al., 2008).
2.5.3 Electrical admittance/impedance
The use of electrical resistance for measuring the vitality of trees is based on the
principle that higher vitality trees have higher moisture content and therefore higher
concentration of mobile cations in their vascular tissues and therefore a lower
concentration of mobile ions (Shigo, 1985). The Shigometer uses twin needle probes
that penetrate the bark of trees to the cambium/early xylem and measure the electrical
resistance between the probes. The lower the resistance, the healthier the tree. At least
20 healthy trees of the same species should be measured to establish a baseline for
“cambial electrical resistance” (CER hereafter) prior to testing. The plant Impedance
Ratio Meter has twin needle probes and measures the impedance of an alternating
current at two frequencies (Harris, et al., 2004).
CER was not able to detect changes in vitality when compared to the diameter growth
of Liquidamber styraciflua (sweet gum) trees by Clark et al., (1992). On the other
hand Martinez-Trinidad et al., (2010), found that the CER could detect tree vitality in
mature Quercus virginiana (live oak) when compared to a visual assessment of the
trees if the symptoms were acute. CER was also correlated with diameter at breast
height in Acer saccharum (sugar maple), but was not consistently correlated with a
visual vitality assessment method (Wargo et al., 2002). CER was able to detect visual
tree vitality with reasonable accuracy in Abies alba (silver fir) in a study by Torelli et
al., (1999).
28
The magnitude of an alternating current passing through a plant tissue is called the
admittance of the tissue (Harris, 1992). Shoot electrical admittance showed a negative
correlation with drought stress in a study of Picea abies seedlings (Pukacki &
Kamińska-Rożek, 2005). Gibert et al., (2006) found evidence of a direct relationship
between sap flow and electrical potential in the trunk of a Populus nigra (poplar) tree
in spring, but not a consistent relationship in summer.
2.5.4 Chlorophyll fluorescence and gaseous exchange
Chlorophyll a molecules consist of a centralized magnesium atom surrounded by
alternating single and double bonds (Ladiges et al., 2005). When a chlorophyll
molecule absorbs light during photosynthesis an electron in the molecule of the
centralized magnesium atom rises to an excited state (Ladiges et al., 2005, figure 2.3).
As the electron returns to its unexcited state a small proportion of the energy is
dissipated as heat and red fluorescence (Govindjee, 2004). Fluorescence emission is
therefore believed to be complementary to photochemistry and heat dissipation
(Govindjee, 2004). Chlorophyll fluorescence is highest when photochemistry and heat
dissipation are lowest, therefore changes in fluorescence are believed to indicate
changes in photochemical efficiency and heat dissipation (Govindjee, 2004).
Chlorophyll fluorescence can be induced in order to measure changes in
photochemical efficiency. Usually this is done with a flash of red light onto the leaf
after a period of darkness, inducing a time dependent fluorescence kinetic known as
the Kautsky effect (Govindjee, 2004; Percival, 2005).
The most commonly used chlorophyll fluorescence measurement is Fv/Fm, where Fv is
the difference between maximum (Fm) and minimum (F0) fluorescence (Maxwell &
Johnson, 2000). Fv/Fm is the theoretical measure of the quantum efficiency of PSII if
all the PSII reaction centres are open (Maxwell & Johnson, 2000; Figure 2.3). Values
for Fv/Fm of between 0.78 and 0.85 for healthy non-stressed plants are common, with
the optimal value around 0.83 for most plants (Björkman & Demmig, 1987; Maxwell
& Johnson, 2000).
The analysis of the intermediate data points of the fast fluorescence rise is often called
the O-J-I-P polyphasic fast fluorescence rise analysis or the O-K-J-I-P polyphasic fast
29
fluorescence rise analysis (Strasser & Stirbert, 2001; Govindjee, 2004; Strasser et al.,
2004; Susplugas et al., 2000; Percival, 2005). The phases are O at origin (0.05 ms) K
at approximately 0.2 ms, J at approximately 2 ms, I at approximately 20 ms and P at
approximately 200 ms, depending on the actual curve (Strasser & Stirbert, 2001). O or
F0 fluorescence is measured when all the plastoquinone QA electron carrier molecules
are in their oxidized state (Krause & Weis, 1984; Percival, 2005; figure 2.3). The K
step, not apparent in all cases, may be the result of an imbalance in electron flow
coming to the reaction centre from PS II in some species of plants (Strasser et al.,
2004). The O-J phase is believed to represent the reduction of the QA molecule from
QA to QA- (Hsu & Leu, 2003; Strasser et al., 2004; Percival, 2005; figure 2.3). J-I may
be fluorescence from the abaxial layer of the sample in some plants (Hsu & Leu,
2003), or both the J-I and I-P phases could reflect the existence of fast and slow
reducing plastoquinone centres (Percival, 2005; figure 2.3). P or Fm occurs when all
the plastoquinone QA electron carrier molecules are in their reduced state (Krause &
Weis, 1984; Percival, 2005; figure 2.3).
PIABS or the performance index, the “driving force” of photosynthesis, is also
sometimes used as a measure for assessing plant vitality via chlorophyll fluorescence
as part of the “JIP test” (Christen et al., 2007; Percival & AlBalushi, 2007; Swoczyna
et al., 2010). The PIABS has three components (i) relating to the density of PSII reaction
centres per total chlorophyll content (RC/ABS), (ii) a component that relates to the
performance of the light reactions (Fv/F0) and (iii) a component that relates to the dark
Red-Ox reactions (FM-FJ)/(FJ-F0) (Strasser et al., 2004; Christen et al., 2007). Apparent
rates of photosynthetic electron transport (ETR), non-photochemical quenching (NPQ)
and a number of other parameters can be also be derived from the fluorescence kinetic
as part of the JIP test (Lüttge et al., 2003).
30
Figure 2.3 Photosynthesis light reactions for oxygen evolving photosynthetic organisms which are the
source of chlorophyll fluorescence from plant chlorophyll.
From left, on the oxidising side of Photosystem II (to the left of the blue arrow), P680 oxidised by light
is re-reduced by YZ, that has received electrons from the oxidation of water. The blue vertical arrows
represent photon absorption by the reaction centre chlorophylls: P680 for photosystem II (PSII) and
P700 for photosystem I (PS I). The chlorophyll fluorescence ratio Fv/Fm is believed to measure the
quantum efficiency of PSII. The excited PSII reaction centre chlorophyll P680* transfers an electron to
phaeophytin (Pheo). Phaeophytin then transfers electrons to the plastoquinone acceptors QA (in red)
which is thought affect the O-J-I-P phases in the polyphasic chlorophyll fluorescence curve. The
electron is then transferred to QB. The cytochrome b6f complex transfers electrons to plastocyanin (PC),
which reduces P700+, and is thought to affect the measured K step in the OKJIP polyphasic chlorophyll
fluorescence curve. The acceptor of electrons from P700*(A0) is thought to be a chlorophyll, and the
next acceptor (A1) is a quinone. Membrane-bound iron-sulphur proteins (FeSX, FeSA and FeSB) transfer
electrons to ferredoxin (Fd). The flavoprotein ferredoxin-NADP reductase (FNR) reduces NADP+ to
NADPH, which is used in the Calvin cycle to reduce CO2. The red arrow indicates cyclic electron flow
around PSI (After Taiz & Zeiger, 2010).
Chlorophyll fluorescence is thought to be one of the most sensitive measures of
environmental stress in plants. Measurement is made on fully expanded leaves as the
rate of photosynthesis is believed to be at its maximum at this point (Kozlowski et al.,
31
1991). Total time lag between sampling and measurement of chlorophyll fluorescence
should not exceed 3 hours (Epron & Dreyer, 1992). The other common way of
measuring photosynthetic efficiency is to measure the gaseous exchange of CO2. CO2
intake and H2O release are via the stomata, therefore these measurements commonly
include the estimation of CO2 assimilation, stomatal conductance and transpiration.
Gaseous exchange measurements can be performed by an infrared gas analyzer. Epron
et al., (1992) found that drought stressed mature Quercus petraea had strong declines
in CO2 assimilation and a decline in the chlorophyll fluorescence parameter Fv/Fm
during the course of a hot summer day. Pukacki & Kamińska-Rożek, (2005) found a
decline in Fv/Fm for drought stressed Picea abies seedlings and the effect of drought
stress on woody saplings was measured using the chlorophyll fluorescence parameter
Fv/Fm and gas exchange by Valladares et al., (2004). CO2 assimilation decreased in
drought stressed Quercus ilex leaves in a study by Peña-Rojas et al., (2005). Percival
et al., (2006) studied the effect of drought stress on various genotypes of 6 year old
containerised Fraxinus, and used Fv/Fm, CO2 assimilation and chlorophyll content to
measure the effects. The effect of reduced temperatures on trees was measured using
Fv/Fm by both Repo et al., (2005) and Weng et al., (2006). Thomas et al., (2006)
assessed the effect of adding phosphorus to low phosphorus soils on Eucalyptus
grandis seedlings by testing CO2 assimilation and chlorophyll fluorescence. Philip &
Azlin, (2005) detected higher Fv/Fm values in a site with an average bulk density of 1.2
g cm-3 than a site with an average bulk density of 1.8 g cm-3 for Lagestromia speciosa
(L.).
Mena-Petite et al., (2003) measured the chlorophyll fluorescence parameters Fv/Fm and
Fv/Fo and CO2 assimilation and found these to be negatively affected by cold storage
stress in Pinus radiata seedlings. CO2 assimilation and stomatal conductance was
measured by Ogaya & Peñuelas, (2003) to compare two different species’ tolerance to
drought stress, whereas Lüttge et al., (2003) measured transpiration (by measuring the
diffusion porosity on both sides of the leaf and air temperature and relative humidity)
and long-term water use efficiency (WUE) by doing stable isotope analysis. Both the
Ogaya & Peñuelas, (2003) and the Lüttge et al., (2003) study measured Fv/Fm in
addition to CO2 assimilation.
32
2.5.5 Water status
Measuring leaf water potential (Ψw) is the most common parameter used to assess the
water status of a plant. When a plant is dehydrated its water potential decreases
(Kramer & Boyer, 1995). Leaf, and sometimes stem, water potentials are measured in
a pressure chamber. The pressure is increased around a leaf until sap appears at the end
of the shoot where the cut end of the shoot is exposed to atmospheric pressure
(Kirkham, 2005). The pressure exerted in order for the sap to come out of the stem
represents the negative pressure existing in the intact stem (Kirkham, 2005). It is
believed that the amount of pressure required to force water out of the leaf cells into
the xylem is a function of the water potential of the leaf cells (Kirkham, 2005).
Predawn water potentials measure the minimum level of stress a plant is experiencing,
while the midday level indicates the maximum level of water stress (Beadle, 2000).
Leaf and sapwood area, and the ratio of sapwood to leaf area or the “Huber value”
(Hv) are other methods used to evaluate plant water relations with regard to tree
growth, as previously mentioned (Zeppel & Eamus, 2008; Calvo-Alvarado et al.,
2008; O’Grady et al., 2009; Gotsch et al., 2010). The difference in tree height and tree
water use and Huber value was examined in Eucalyptus crebra and the cladode
bearing Callitris glaucophylla when both species were exposed to a drought and nondrought season (Zeppel & Eamus, 2008). There was no relationship between tree
height and Hv and there were no differences between Hv in the two species or seasons.
This is contary to another study by McDowell et al., (2002) who found Hv declined
consistently with height increase when measured in Pseudotsuga menziesii (douglas
fir) and in 13 other species via meta analysis. Calvo-Alvarado et al., (2008) found that
Hv increased with height for the rain forest species Carapa guianensis, Vochysia
ferruginea, Virola koshnii and Tetragastrus panamensis, but decreased with
Pentaclethra macroloba. A higher Huber value suggests a tree or group of trees has a
greater capacity for water transport relative to leaf area and is therefore more efficient
at water transport (Gotsch et al., 2010).
In a study by Zeppel & Eamus (2008), Callitris glaucophylla had lower water
potentials than Eucalyptus crebra and did not show a variation in leaf area across
33
seasons. Eucalyptus crebra on the other hand, did show a variation in leaf area across
the two seasons, and may have used this variation to control tree water use, and
therefore Ψw. Callitris glaucophylla had varied transpiration rate over different
seasons, whereas Eucalyptus crebra did not, resulting in the total water use for both
species being similar, but employing different mechanisms to cope with seasonality.
Stomatal conductance is generally correlated with air temperature and vapour pressure
deficit (Cohen & Cohen, 1983; Augé et al., 2000). Stomatal conductance was tested by
Gotsch et al., (2010) to investigate the differences between the water use of forest,
compared to savanna, trees. In the late dry season stomatal conductance was higher in
savanna trees, compared to forest trees. Stomatal conductance also decreased in
drought stressed Quercus ilex leaves in a study by Peña-Rojas et al., (2005).
Sap flow was measured by Zeppel & Eamus, (2008) to assess the difference between
the water use characteristics of Eucalyptus crebra and Calliitris glaucophylla. The E.
crebra sap velocity was higher than the C. glaucopylla. Sap flow measurements were
also used by Pfautsch et al., (2010) to track the water use of Eucalyptus regnans
through different seasons and understory densities in south-eastern Australia. A similar
study was undertaken in Panama testing a range of tropical species through both dry
and wet seasons (Kunert et al., 2010).
Some studies have included wood density amongst growth parameters as a measure of
tree water use (O’Grady et al., 2009; Gotsch et al., 2010), ecophysiology (Aiba &
Nakashizuke, 2009) or genetic parameters (Stackpole et al., 2010; Weber & Montes,
2010). Low stem wood density can make trees more vulnerable to cavitation,
especially during drought (Hacke et al., 2001; Holste et al., 2006; Bobich et al., 2010)
but in a study of Picea abies (Norway spruce) wood density was found to be unrelated
to cavitation (Rosner, 2007).
Pre-dawn water potential was measured by Epron and Dreyer (1992) to assess the long
term effect of drought on adult Quercus robur L. and Quercus petraea (Matt.).
Relative water content predawn, leaf hydration at midday, Ψleaf predawn and midday,
stomatal conductance and transpiration were measured on Quercus ilex during drought
34
by Peña-Rojas et al., (2005). Predawn leaf water potential and stomatal conductance
was found to be lower in bare root transplants in P. radiata when subjected to cold
storage stress by Mena-Petite et al., (2003).
2.5.6 Canopy transparency and reflectance
The chlorophyll content of leaves can be measured using reflectance imagery at
particular wavelengths to assess tree vitality (Krumov et al., 2008; Barry et al., 2009).
In a study on birch tree leaves Krumov et al., (2008) were able to detect differences in
drought effect on the leaves by spectral analysis via reflectance imagery. Leaf
photochemical reflectance also decreased in Mangifera indica and Podocarpus nagi
when they were exposed to cold temperatures in a study by Weng et al., (2006).
Remote sensing of a chlorophyll fluorescence signal could detect differences in
drought stress in birch leaves, and olive and peach trees (Zarco-Tejada et al., 2009).
Infra-red remote imaging was also found to have a good agreement with in situ
chlorophyll fluorescence measurements by Hermans et al., (2003). Remote sensing
methods are not used for the assessment of individual trees.
2.6 Measuring wood decay
There are many field devices for measuring wood decay in trees and they vary greatly
in the principles on which they function. The devices for measuring wood decay in
trees are often similar to the equipment used for measuring wood density, as decay
results in a decrease in wood density or mass (Beall & Wilcox, 1987). The major
devices used to measure wood decay in trees include; devices measuring electrical
conductivity (Shigo & Shortle, 1985; Larsson et al., 2004), constant feed drills
measuring mechanical resistance (Mattheck et al., 1997; Rinn et al., 1996; Weber &
Mattheck, 2006), single pulse sonic and ultrasonic devices measuring sonic speed
(Nicolotti & Miglietta, 1998; Sandoz, 1999) devices that use core sampling (Lorenz,
1944; Mattheck et al., 1995; Bethge et al.,1996) , compression meters measuring
resistance to impact (Cown, 1978; Seaby, 1991), and computerized tomography
devices (Nicolotti et al., 2003; Catena & Catena, 2008; Wang et al., 2009).
35
2.6.1 Electrical conductivity meters
The best known example of an electrical conductivity meter is the Shigometer, which
consists of a twisted wire probe and resistance meter (Shigo & Shortle, 1985). When
operating the device, an electrical probe is placed into a small, pre-drilled hole
approximately 3 mm in diameter. The pattern of resistance of the wood to a pulsed
direct current is recorded (Seaby, 1991). The main factor determining electrical
resistance is the concentration of mobile cations, which is usually very different
between sound and degraded wood (Mattheck & Breloer, 1994). Shigo (1991) claimed
that in the region adjacent to wood decay, the concentration of cations in the wood
would increase and therefore electrical resistance would decrease. However, electrical
resistance also decreases if wood is healthy but dry (Nicolotti & Migietta, 1998), and
increases in dry decayed wood and when the probe moves from sapwood to heartwood
(Shigo, 1991). Electrical resistance may also decrease when the probe reaches bacterial
wetwood (Nicolotti & Migietta, 1998).
Several researchers have had very inconsistent results with the Shigometer. Results
have been dependent on the tightness of fit of the electrode and the relative moisture
and resin content of the timber (Seaby, 1991). Shigo & Shortle (1985) advise that the
Shigometer will not function in resin soaked, frozen or dead wood and the operator
should carefully control the amount of contact the needle electrodes have with the
wood. Wilkes & Heather (1983) claim that the Shigometer was not able to detect
decay in eucalypts because mineral levels and the pH of the wood did not change in a
consistent pattern. The Shigometer was also not able to detect decay in Nothofagus
fusca (New Zealand red beech) (Wilson et al., 1982). The regulation of ion
concentration in the xylem to compensate for deceased flow due to embolism
(Zwienieki et al., 2001b) may render the electrical resistance methods ineffective for
detecting decay in drought conditions, particularly in eucalypts. Shigo & Shortle
(1985) acknowledged the variability of species when using their equipment and
recommended establishing a base line for healthy sound trees by first measuring
electrical resistance from a large random sample.
36
The Plant Impedance Ratio Meter measures the impedance of an alternating current at
two frequencies, known as the admittance of the tissue (Blazé, 1992). The high
frequency reading (10 kHz) is divided by the low frequency reading (1 kHz), hence the
output is a “ratio” (Harris, 1992). It is claimed that the admittance of the cell walls of
similar tissues of a plant is the same at all frequencies. The ratio of the higher to lower
frequencies will therefore be greater than one in healthy tissue. The ratio measured by
the Plant Impedance Ratio Meter is therefore not affected by moisture content or
temperature, unlike the Shigometer. In practical terms the gathering of impedance data
with a twisted wire probe in a 3 mm diameter hole to any great depth in a tree is more
difficult than using a constant feed drill that is able to gather data as it drills. As with
the Shigometer, results must be dependent on the tightness of fit of the electrode
(Seaby, 1991). The advantage of the Plant Impedance Ratio Meter is in the
measurement of tree vitality via cambial impedance (with double-needle pins, rather
than a long probe) rather than in decay detection in the deeper tissues of the tree. Tree
vascular tissues must be wounded to the depth of desired decay detection.
The four-point resistivity (RISE) method passes a current through an object with one
pair of electrodes, while measuring the voltage difference with another pair of
electrodes (Larsson et al., 2004). The constant current is passed vertically though the
stem rather than horizontally. The resistivity must be measured against other trees of
similar water content and species and at a similar temperature and humidity and must
be normalized for stem cross sectional area. It cannot be used to assess the volume or
location of the decay.
2.6.2 Constant feed drills
Constant feed drills are simple drills that control and measure the rate of feed of a drill
bit and map the results either electronically or onto a graph. The most common devices
for tree inspection are the Resistograph (also known as the IML-Resistograph or the
IML-Resi) and the Sibert DDD 200. A simple mechanical drill has also been used as a
decay-detecting device, where the drill operator senses the changes in drill resistance
and evaluates the wood shavings (Costello & Quarles, 1999).
37
The Resistograph is a portable, constant feed drill that records on a strip chart or
electronic data recorder the drilling resistance as the bit penetrates the tree at a constant
drive (Mattheck et al., 1997). The amplitude of the graph trace indicates resistance,
and decay in the path of the drill is represented by a fall in drilling resistance. A shaft
covers the drill bit to prevent the operator forcing the drill faster than the constant
drive. The drill bit tip (needle) is 3 mm wide (Bethge et al., 1996). The penetration
speed may be set on the device and the depth of drive is 300 – 1500 mm, so it has the
potential to be used on large diameter trees (Rinn et al., 1996).
A study compared the Resistograph to drilling with a portable drill in Eucalyptus
globulus (Victorian blue gum) and Ulmus glabra (golden elm) (Costello & Quarles,
1999). Wood density levels below a critical level (less than 500 kg m-3 in E. globulus
and less than 400 kg m-3 in U. glabra) were considered decayed. The depth to the point
of decay was grouped into three categories 0-5 cm, 6-10 cm and 11-15 cm. For E.
globulus 85.5% of Resistograph and 73% of the portable drill results were in a 0-5 cm
deviation from accuracy category. For U. glabra 100% of Resistograph and 81% of the
portable drill method were in the 0-5 cm deviation from accuracy category. This is a
very accurate result for the Resistograph and a moderately accurate result for the
portable drill.
The IML-Resi was tested in conjunction with an “expert system” to assess the
accuracy of decay detection in Eucalyptus globulus subsp. pseudoglobulus (Victorian
blue gum) (Johnstone et al., 2007). The compartmentalization of decay in trees (or
CODIT) model (Shigo, 1979) was combined with raw data from the IML-Resi to
predict the cross sectional area of decay. A statistically significant relationship was
established between the predicted total area of decay in a wood section and the actual
area of decay. Using a linear regression analysis 76% of the variation in the readings
could be explained by the predicted area of decay.
Correlations between wood density charts and the Resistograph measurements have
been established for air dried wood samples in six tree species, Abies alba (European
silver fir), Larix decidua (European larch), Picea abies (Norway spruce), Pinus
cembra (Swiss pine), Tilia platyphyllos (bigleaf linden) and a Populus sp. (poplar)
38
(Rinn et al., 1996). In a progeny trial Isik & Li (2003) found a weak to moderate
relationship between wood density and the amplitude of drilling resistance in Pinus
taeda L. (loblolly pine). A strong correlation between average wood density and
resistance was also found in Eucalyptus globulus subsp. pseudoglobulus (Victorian
blue gum) (Johnstone, 2005). The sensitivity of the Resistograph (or the IML-Resi) to
wood properties meant that drill resistance can be affected by the moisture content of
the wood (Rinn et al., 1996; Lin et al., 2003), but was not affected in Eucalyptus
globulus subsp. pseudoglobulus (Johnstone, 2005).
An English prototype for a constant feed drill was developed, the “Decay Detecting
Drill” (DDD), with a 2 mm flared drill bit tip (Seaby, 1991). The number of
revolutions per cm of penetration was plotted against wood density (kg m-3) to test the
effectiveness of the device. According to Seaby (1991), there was a direct relationship
between wood density and resistance except when there was variable moisture content
in the sample. Rotational drag did not affect the results but longitudinal drag reduced
drill bit tip pressure by approximately 10% cm-1. This meant that the drill produced
results that indicated wood density increased with the depth of penetration, even when
this was not the case.
Seaby (1991) speculated that better drill bit tip design might improve drag. The DDD
200 is faster than the Resistograph but provides less data per millimeter (Nicolotti &
Miglietta, 1998). Both the Resistograph and another device, the Densitomat-400, have
a probe design that cuts a hole wider than the drill shaft, which may reduce resistance
problems. Probes of constant feed drills are flexible, causing inconstencies in the data
if the probe rubs against the side of the hole, and may cause deviations in the drilling
path (Nicolotti & Miglietta, 1998; Dolwin, 1999). Other possible causes of drill bit
drag may be the sharpness of the drill bit and the heating of the drill bit. Heating as a
cause of drill bit drag however is very unlikely as most drill bits are steel, and the
coefficient of area of expansion for steel is 24 x 10-6 for every 1º C (Giancoli, 2005).
Thus, even if a drill bit heated to 500º C (which is very unlikely) only a 1% increase in
the cross sectional area would occur. Moore (1999) found that drill bit drag (or
friction) was severe enough to prevent the detection of decay by the Resistograph, a
39
result inconsistent with others (Costello & Quarles 1999; Nicolotti et al., 2003;
Johnstone et al., 2007).
Kersten & Schwarze (2005) found that the IML-Resistograph provides a substrate for
decay fungi as shavings from drilling are retained in the hole, and Toussaint et al.
(2004) found decay increased along drill-needle paths in Tilia sp (linden), as a result of
Resistograph drilling. Helliwell (2007) found that both a 6 mm and a 10 mm drill
diameter resulted in wood staining and discoloration two years after they had been
drilled. Weber & Mattheck (2006) argued that constant feed drills did not result in long
term decay. They claimed that negative short term wood decay was counteracted by
the successful formation of compartmentalization reaction zones (Shigo, 1979) after a
longer period of 8-10 years (Weber & Mattheck 2006). No decay extended past the
compartmentalization barrier zone (Shigo, 1979) in any of the trees they tested, and
there was no decay in trees without any preexisting decay prior to drilling.
2.6.3 Sonic and ultrasonic techniques
The velocity of the propagation of sound waves is much faster in wood than in air
(Bootle, 2005). In a solid medium the velocity of sound depends on the type of wave
and the elasticity and density of the material (Pollard, 1968). Acoustic instruments
usually measure the wave velocity (ν) in wood. The wave velocity in m s-1 is given by:
ν=
Ε
ρ
Where Ε is the modulus of elasticity and ρ is the density (Ouis, 2003). Velocity is
therefore dependent on factors such as species, moisture content, temperature and the
anatomical direction in which the sound is transmitted (Mishiro, 1996). It is also
difficult to translate the velocity of sound to physical properties as wood is an
heterogeneous material (Nicolotti et al., 2003; Socco, et al., 2004; Maurer et al., 2006;
Bucur 2006a; Schubert et al., 2009).
Most instruments for ultrasonic analysis operate at frequencies between 50 kHz to 5
MHz (Bucur, 2003). Instruments that operate above 1 MHz can create images of the
40
objects they scan as resolution increases at these frequencies (Bucur, 2003). To
minimize attenuation of the ultrasound signal the frequency of the signal must be low,
but this results in decreased resolution and, in some instances, the wavelength of the
signal may be large enough that regions of different wood densities may be obscured
(Ouis, 2003; Socco et al., 2004).
The Fujikura-Arborsonic Decay Detector is based on a simple pulse-echo method
measuring the transmission time of an ultrasound pulse (Wade, 1975). It delivers an
ultrasound pulse of 77 kHz that passes through the stem. The signal speed is
approximately 2000 m s-1 through undegraded cell walls. In timber the usual mode of
propagation of ultrasound is via the cell walls. When cells are degraded the ultrasound
signal speed is slowed; the more decay the slower the signal. The Fujikura-Arborsonic
Decay Detector operates using a transducer (which sends the signal) and a receiver
(that receives the signal) on opposite sides of the tree. A 45 mm diameter bark plug is
first removed to provide good contact to the wood. The known distance between the
transducers in millimeters is divided by two to obtain the expected ultrasound
propagation time reading in microseconds. The expected times are given in a table
according to the diameter of the tree. It does not produce a tomographic image. The
maximum recommended tree diameter for the Fujikura-Arborsonic Decay Detector is
1.4 m. Larger trees require quadrant testing, with transducers and receivers at 90° to
each other, rather than at opposite sides of the tree. As decay deep in the core of the
tree is not as important a concern in larger trees, testing in this way is quite appropriate
(Smiley, 1992).
Single pulse ultrasonic devices were able to detect various types of defects and
changes in wood quality in some trees (Nicolotti & Miglietta 1998; Sandoz, 1999).
Decayed wood did not significantly reduce the transmission time of the signal
produced by the Fujikura-Arborsonic Decay Detector in Eucalyptus globulus subsp.
pseudoglobulus (Victorian blue gum) a result inconsistent with Quercus phellos L.
(willow oak) and Quercus nuttallii E.J. Palmer (nuttall oak), where decay did reduce
transmission times (Xu et al., 2000; Johnstone, 2005). The Fujikura-Arborsonic Decay
Detector performed slightly better on Corymbia maculata (spotted gum) samples that
were hollow but was not successful at all in hollow Quercus phellos L. or Quercus
41
nuttallii (Xu et al., 2000; Johnstone, 2005). As with the Silvatest that requires stripping
pieces of bark 30 mm (1.18 inch) diameter to provide good contact (Nicolotti &
Miglietta, 1998), another disadvantage of these “non-invasive” test instruments is bark
must be taken from the trunk, thus wounding the tree.
Stress wave assessment is another method of assessing wood using sonics, rather than
ultrasonics. A stress wave is a complex mixture of frequencies, various components of
which travel through solid, liquid and gas with differing velocities (Wade, 1975).
Stress wave assessment of wood has been successfully modeled in utility poles in the
laboratory (Bulleit & Falk, 1985). The Metriguard Stress Wave Timer uses this
approach (Mattheck & Bethge, 1993). A hammer struck against pins inserted into the
xylem sends a signal across the trunk. It detects changes in wood quality but may be
less accurate than ultrasound because of the number of frequencies involved. The
Metriguard requires species specific reference tables. Inconsistencies in readings can
occur because the hammer is not always struck with the same force (Nicolotti &
Miglietta,1998). Interpretation is complicated as the velocity of the sound may be
slowed by bacterial wetwood, decay and in some cases inaccurate measurement due to
excessive wind speed (Mattheck & Bethge, 1993; Yamaguchi et al., 2001). Perhaps the
best use for single path stress wave time-of-flight testing is as, Wang & Allison (2008)
suggest, as an initial screening process that may justify more sophisticated
investigations.
A more advanced analysis of acoustic single path stress waves can be performed by
reworking the data using Fourier transformations (Lawday & Hodges, 2000). Shorttime Fourier transforms of stress waves predict the extent of wood decay, rather than
just the presence of decay. Acoustic techniques that utilize multiple path stress waves
are classed as “tomography” techniques and are discussed in a following section.
2.6.4 Breaking core samples
Fractometers measure decay by assessing the mechanical properties of an extracted
core of wood. The Fractometer I measures the force required to bend a core sample
(radial bending fracture strength) and the radial angle prior to breaking (stiffness)
(Bethge et al., 1996). Fibers and wood rays must be oriented parallel to the front of the
42
Fractometer, which, the developers claim, simulates the fiber loading due to wind
(Mattheck et al., 1995). Measurement is in “fractometer units” (FU), which can be
converted into units of pressure (MPa) (Bethge et al., 1996).
The theoretical basis for the development of the Fractometer is clearly Hooke’s law
which states that a change in form is proportional to the deforming force or F = k ∆L,
where F is the force pulling on an object, ∆L is the increase in length and k is a
proportionality constant (Giancoli, 2005). The change in form on the Fractometer
sample is measured by the angle setting and the deforming force by the pressure
required to break the sample. The modulus of elasticity can be derived from Hooke’s
law and is the constant “k” (Pollard & Harris, 1968).
The size of the sample cores used in the Fractometer (5 mm diameter) largely
precludes the use of the device in trees that have high wood density. Species such as
eucalypts usually require increment coring with a motorized corer (Downes et al.,
1997). Motorized increment corers yield a 12 mm sample that is too large to be tested
by the Fractometer. When intact 5 mm cores were obtained from Eucalyptus globulus
(Victorian blue gum) (Matheny et al., 1999), they broke when the lever arm was
placed against the sample and no results could be recorded.
Bethge et al., (1996) observed that the Fractometer distinguished between types of
decay. Brown rot leads to very small fracture angle whilst advanced white rot results in
much larger angles. Timber in which lignin is degraded may appear to resist bending
but does not have the stiffness of less decayed wood. On the other hand, timber with
cellulose degradation will be low in elastic strength but stiff. Timber that is not
decayed will be very stiff but also very strong.
The Fractometer II determines the longitudinal compression failure strength as well as
the radial fracture bending strength (Fractometer I measures the latter only) (Bethge et
al., 1996). Fractometer III measures all of the above, the tangential bending fracture
strength, and the radial and tangential shear strength. The developers maintain
however, that the only necessary Fractometer for successful field diagnosis is the
Fractometer I, as excessive fiber loading is most common in the radial direction.
43
Methods that require core sampling are some of the most invasive of the decay
detecting devices, causing decay in Betula lutea Michx. (yellow birch) Tilia americana
L. (basswood) and Acer sacchrum Marsh. (sugar maple) (Lorenz, 1944). However core
sampling did not contribute to tree mortality over a 12 year period in Abies concolor
(white fir) which is said to decay rapidly after mechanical wounding and Abies
magnifica (red fir) (van Mantgem & Stephenson 2004). Matsumoto et al., (2010)
found that the fractometer could detect decreases in compressive strength in Magnolia
obovata trees when they showed only minor weight losses in their wood, indicating it
may be a useful instrument for detecting wood decay in some tree species.
2.6.5 Compression meters
Compression meters that are used to test trees or utility poles usually consist of a probe
with an enlarged tip that is either fired at the surface of the wood, as is the Pilodyn
(Cown, 1978), or driven progressively into a pre-drilled hole, as is the portable
compression meter (Seaby, 1991). With the Pilodyn the depth of pin penetration is
only 7-16 mm (Moura et al., 1987) and for this reason the device is not usually used to
assess wood decay in standing trees. In the case of the Portable Compression Meter the
tip is driven forward by an automatic punch, which provides uniform pulses. These
pulses are counted for each increment of penetration (Seaby, 1991). In the case of
devices using a pre-drilled hole, as the probe progresses, the device enlarges the drill
hole by crushing the sidewalls (Seaby, 1991). The Portable Compression Meter
requires the drilling of a 4 mm hole. The number of impacts required to force the
slightly larger probe into the hole measures the compression strength of the wood
(Barrett, 1987). The Portable Compression Meter can detect incipient decay that is not
visually detectable and assesses the density of the wood (Barrett, 1987). It can also
detect harder zones that are part of the tree’s reaction zone (Barrett, 1987). This may
make interpretation of the results difficult, as the reaction zone wood is stronger, but
may be an indication that decay is imminent. Barrett (1987) suggests these readings of
very hard wood be ignored in the assessment of wood strength. It appears that one
could extrapolate these results for the fractometer, but authors who evaluated that
instrument did not raise this issue, so it is unclear whether this may be a problem.
Aspect appeared to account for variability of results using the Portable Compression
44
Meter, with the growth rate of the west and south being greater than east or north (in
the northern hemisphere) (Seaby, 1991). Again, it appears this phenomenon could
occur with the fractometer but no such observations were made with regard to these
devices.
2.6.6 Computerized tomography
Computerized tomography can employ acoustic rays, electrical resistance, thermal or
radar techniques (Nicolotti et al., 2003). For electrical resistance and acoustic
measurements, sensors are usually placed around a tree (usually 8-16 but occasionally
more) and multiple measurements are gained by sending a signal from one sensor to
the others (Nicolotti et al., 2003; Gilbert & Smiley, 2004; Bucur, 2006b). For radar or
thermal imaging techniques the signal is delivered and allowed to bounce off internal,
and in the case of thermal imaging, external structures (Bucur, 2003; Nicolotti et al.,
2003; Catena & Catena 2008). These instruments produce cross-sectional “pictures” of
the stem, via a computer programmed with complex conversion algorithms. X-rays,
microwave technology, nuclear magnetic resonance (NMR) and neutron imaging for
decay detection are all possible, but are currently very expensive and usually used for
more sophisticated scanning of wood properties (Bucur, 2003).
Thermal imaging with an infrared camera scans for wood defects but cannot accurately
quantify the amount of wood decay (Catena & Catena, 2008). Images are species
specific. Thermography cannot assess residual wall thicknesses (Catena, 2003).
Thermal imaging has the advantage of being non-invasive and it can detect wood
decay in large tree roots or the root collar (Catena, 2003; Catena & Catena 2008).
Georadar devices are usually used to locate tree roots (Ouis, 2003; Hagrey, 2007).
Images are generated via the reflection of electromagnetic waves (Nicolotti et al.,
2003). Georadar techniques were successful in detecting wood decay in the study by
Nicolotti et al., (2003), but required considerable processing of the data. Georadar is
non-invasive.
Nicolotti et al., (2003) assessed results from electrical resistance and ultrasonic
tomography and georadar. They reported good results with electric tomography but the
45
number of replicates was two. Electrical tomography was deemed promising by
Hagrey (2007), but results were qualitative rather than quantitative. Problems with
electrical tomography may be similar to those encountered by the Shigometer in
eucalypts, as the raw data is the same (electrical resistance). However the electric
tomography described by Nicolotti et al., (2003) is less invasive than the other
electrical resistance devices as the electrodes are only driven into a depth of 10 mm
rather than placed in a predrilled hole.
The Pundit (Portable Ultrasonic Nondestructive Digital Indicating Tester) uses
ultrasonic tomography. The operating frequency of the Pundit is 33 kHz and it is
possible with the 16 sensors to get 120 travel time measurements for each trunk cross
section (Nicolotti et al., 2003; Socco et al., 2004). Signal processing for the data
collected in the study by Nicolotti et al., (2003) was carried out with Migratom
software. The two samples used in the study were London plane (Platanus hybrida
Brot.), which were decayed rather than hollow in the centre, with strength losses of
between 22.7% and 53.6% (Nicolotti et al., 2003). Moisture content was higher in the
decayed zones than in the surrounding sound wood. The ultrasonic transducers were
used with a coupling gel placed directly on the bark, but without removing a bark plug.
The ultrasonic signal can be processed by a cathode ray oscilloscope to further
manipulate and control the data supplied by the Pundit (Socco et al., 2004).
The Picus Sonic Tomograph uses sonic tomography. Raw data are the time of
transmission of the sound of a hammer tap on one sensor to each other sensor, 8-12 for
each stem cross section (Gilbert & Smiley, 2004). The Picus is self-calibrating in that
the fastest acoustic transmission time relative to distance is deemed “sound” wood
(Rabe et al., 2004; Schwarze, 2008). A sound wave produced manually is called a
stress wave (Wade, 1975; Bulliet & Falk, 1985; Mattheck & Bethge, 1993). A
disadvantage of the Picus over the Pundit is that it does not deliver a sound pulse of
known frequency, which can lead to inaccuracies in recording the speed of propagation
time (Nicolotti et al., 2003). It is possible to deliver sonic waves at predictable and
repeatable frequencies (Schubert et al., 2009) but this is not the mode of operation of
the Picus. The conversion algorithm for both stress and ultrasound waves is complex
46
as the propagation of sound is not always in a straight line (Bucur, 2003; Maurer et al.,
2006).
Gilbert & Smiley, (2004) evaluated the Picus for location and extent of decay. Decay
was defined as both an absence of wood and wood that could be deflected with finger
pressure. There was a high correlation between the amount of decay detected by the
Picus and the extent of decay assessed visually following felling (r2 = 0. 90) (Gilbert &
Smiley, 2004). The Picus slightly underestimated decay in most cases, with an average
discrepancy of 6%. The stem cross sections exhibited decay not detected by the Picus
in 9% of the readings. The range for error was from minus 3% to minus 20%. Tree
diameters ranged from 250 mm to 490 mm (Gilbert & Smiley, 2004). Similar results
were obtained by Rabe et al., (2004). Decay in the sapwood was not accurately
assessed and was dependant on the host/pathogen combination in a study by Deflorio
et al., (2008). The precise location of the decay was also found to be less accurately
represented by the Picus in some studies (Rabe et al., 2004; Wang & Allison, 2008).
The Picus is minimally invasive as 2 mm nails are inserted a few millimeters into the
xylem (Gilbert & Smiley, 2004).
Bacterial wetwood, cavities and cracks produce inaccuracies in Picus data processing
and may be interpreted as areas of decay (Schwarze & Heuser, 2006; Schwarze, 2008;
Wang et al., 2007; Wang & Allison, 2008; Wang et al., 2009). The position of decay
within the trunk reduces the accuracy of decay assessment using sonic or ultrasonic
wave velocity (Deflorio et al., 2008; Lin et al., 2008; Wang et al., 2009), although
recent advances in signal processing and data interpretation may improve this problem
(Socco et al., 2004). Lin et al., (2008) found that ultrasonic velocities increased when
the size of a manually created circular hole in a cross section (simulating decay in a
cross section) in 30 – 35 cm cross sections increased, but not always in a clear linear
relationship. Sections where a predrilled hole was 9 cm - 11 cm in diameter showed a
blue area (denotes slowest sonic velocities) 11 cm – 21 cm diameter predrilled holes
showed a green area (denotes third slowest sonic velocities) and predrilled holes over
21 cm in diameter showed a violet area (denotes second slowest sonic velocities).
Schubert et al., (2009) found that cavities greater than 5% of the cross section of a tree
trunk could be detected under laboratory conditions by sonic tomography, by
47
converting a digital signal to analog, rather than using manually generated stress waves
as with the Picus. Maurer et al., (2006) found that very low velocity areas are difficult
to identify within areas where acoustic velocities are already decreased when using the
Picus.
The sound frequency of the trunk has also been used experimentally to assess Picea
abies (Norway spruce) wood decay (Axmon, 2004). First the surface (circumferential)
wave velocity is measured, and must be above a minimum level in order to allow for
sources of error such as low moisture or decayed outer sapwood. The theoretical modal
frequency is then calculated for a sound tree using the surface wave velocity. A
significant deviation from the modal frequency would indicate decay or a defect in the
stem. Currently this technique requires as many sensors as the Picus or Pundit
instrument and it is not yet as accurate for detecting decay, but eventually only two or
three sensors may be required, greatly reducing the time taken to measure an
individual tree.
2.8 Tree vitality and wood decay
The link between tree physiological parameters and tree growth and the incidence of
decay has not been rigourously measured for individual trees. It is suggested by some
authors that the extent of decay is related to the vitality of an individual tree, but no
thorough studies have been done with objective measurements to confirm or refute the
suggestion (Shigo et al., 1969; Shigo, 1971; Schwarze, 2008).
Decay caused by a known root rotting pathogen Heterobasidion annosum was
compared to CER (cambial electrical resistance) in a preliminary study of Picea abies
(Norway spruce) and Abies alba (silver fir), however only three trees of each species
were used in this comparison (Vujanovic & Karadzic, 2003). The study by Vujanovic
& Karadzic, (2003) of Picea abies and Abies alba found that CER was higher for the
three trees of the same species with low crown density scores and that those trees had
more wood decay, however no statistical correlations were evident as the replicate
number was too small for statistical analysis.
48
Filip et al., (1995) found that a thinned Abies grandis (grand fir) forest stand had less
decay than an unthinned forest. Significantly the thinned site also had higher average
vitality as measured by CER when compared to the unthinned stand. As with many
forestry studies of this type the focus was on whole population differences (the thinned
stand versus the unthinned stand) rather than individual trees. In the same study higher
vitality as measured by CER was also associated with thinned Pinus ponderosa
(ponderosa pine) and Pinus contorta (lodgepole pine), but wood decay was not
significantly different in thinned versus unthinned stands. Filip et al., (1992) also did
not find a correlation between the percentage of decay and tree growth and CER
measurements in Abies grandis when stands were thinned and /or fertilized. Again this
study focused on whole population differences rather than individual trees. Shortle &
Ostrofsky (1983) did not find a correlation with the percentage of decay in sites with
different CER values and levels of Choristaneura fumifcrana (spruce budworm)
infestation.
The effect of time, different planting sites and pruning regimes on the rate of decay in
Eucalyptus nitens has been examined by Barry et al., (2005). Although this is a rather
indirect way of assessing whether growth effects decay, Barry et al., (2005) did find
some differences between sites and pruning regimes and the rate of decay development
in Eucalyptus nitens but they were not as great as the effect of the passage of time on
the trees. Tree age, planting site and pruning regimes also had an effect on heartrot in
Acacia mangium logs in another study by Barry et al., (2004). This is not to imply that
wood decay only occurs in larger trees, as study of small (350 mm - 457 mm diameter
at breast height) Norway silver and sugar maple trees found that over 50% of the trees
in this diameter class were already decayed (Luley et al., 2009). Another indirect way
of assessing the relationship between tree growth and wood decay was to compare the
rate of wood decay in “fast grown” (intensively managed) Picea abies and “slow
grown” Picea abies (in a multi layered forest) (Edman et. al., 2006). In this study
wood discs were inoculated with decay causing organisms after being cut from the
tree. It was found that the fast grown trees decayed more quickly, probably due to their
low density and higher nitrogen content compared to the slow grown trees (Edman et.
49
al., 2006). The reason cited for increased decay rate in fast grown eucalypts is their
impaired rate of branch shedding (Kile & Johnson, 2000).
Terho et al., (2007) included an investigation of crown vitality in their assessment of
wood decay in Helsinki city. However crown vitality was assessed as either present or
absent in this study – no detailed analysis of the relationship between the quality of
decay and the extent of crown decline was undertaken. Christen et al., (2007)
investigated the “esca” disease in Vitis vinifera (grapevines) and the relationship
between the onset of symptoms and chlorophyll fluorescence (CF) parameters. Esca
disease infects the xylem and causes white rot decay and/or necrosis of woody tissues
and, subsequently, wilting of the leaves. Christen et al., (2007) used 4 categories of
white rot decay and 8 categories of necrosis, rather than percentages of decay.
Necrosis and white rot were more widespread in Cabernet Sauvignon plants than in
Merlot. The more decayed Cabernet Sauvignon plants showed decreased efficiency in
PSII according to the CF results but the difference was only significant at a cultivar
level, rather than at an individual plant level.
2.8 Discussion and conclusions
Healthy tree growth requires that the plant has access to adequate water, light and
mineral nutrients. This enables the tree to photosynthesize and produce the healthy
plant tissue, including the woody tissues of the plant. Wood is a heterogeneous
material made up of tracheids, fibres, vessels, rays and parenchyma cells produced by
the cambium. Different genera and species of trees produce wood that has very
different density, modulus of elasticity, modulus of rupture and microfibral angle.
Eucalypts in general have wood that has higher density, modulus of rupture and
modulus of elasticity than species from the temperate zones of the Northern
Hemisphere. The understanding of the progression of wood decay and fungi-host
interactions involved in wood decay in trees have advanced greatly in the last 50 years,
since the macroscopic observations of Shigo (1979) and his theory of
Compartmentalization of Decay in Trees (CODIT).
There are many different methods by which plant vitality can be inferred. Tree vitality
is often defined with reference to the environmental stress to which the plant has been
50
exposed. As tree vitality cannot be measured directly, using more than one method to
assess tree vitality is very common. Tree growth methods such as total biomass, leaf
area or diameter at breast height can be used to assess tree vitality, often in conjunction
with a visual tree assessment or canopy condition index.
CER (cambial electrical resistance) was a popular method for testing tree vitality in the
1990’s but results were somewhat variable when compared with visual or growth
measurements. Measuring leaf or needle biochemistry, including antioxidant levels is
an increasingly popular way of measuring tree vitality, but requires detailed laboratory
assessment rather than operating as a simple screening tool that can be used in the
field, such as CER measurement. SPAD meters, that are theoretically able to assess the
chlorophyll content in leaves, are largely untested as a method for measuring tree
vitality, but may have potential for this use.
Measuring the water status of a tree is another way of measuring its vitality. In this
instance leaf and stem water potentials, stomatal conductance, sap flow or even wood
density (in conjunction with other measures) may be used to assess tree vitality via the
moisture status of the plant. Physiological measurements that attempt to quantify
photosynthetic efficiency to assess tree vitality are also becoming increasingly popular
for vitality assessment. Photosynthetic efficiency is quantified by gas exchange
measurements or chlorophyll fluorescence. Currently, the most commonly cited
method for assessing tree vitality is chlorophyll fluorescence, because the
instrumentation is robust, very portable and results can be easy to interpret particularly
when the ratio Fv/Fm is used.
Individuals have approached the development of field equipment for measuring decay
in trees differently. The variable moisture content of green and decayed wood may
affect acoustic devices, constant feed drills and most conductivity meters. Devices
using electrical conductivity require a high level of specialized knowledge and
experience in their use. Core sampling techniques are the most damaging to the xylem
of the tree and rely heavily on the correct orientation of samples. Constant feed drills
and most conductivity meters are also invasive instruments, though less so than core
sampling techniques. However, core sampling is very portable and inexpensive to
51
purchase. Ultrasound and stress wave techniques can offer detailed information on the
quality of wood tested, but there may be some difficulty in distinguishing between
decayed wood and bacterial wetwood, or between decayed wood and cavities. Single
pulse ultrasound and stress wave equipment is relatively expensive and can necessitate
damaging the bark of the tree.
Tomographic technologies give a relatively accurate view of the quantity of decay in a
tree compared to core sampling, single sample conductivity and single pulse sonic
devices, as they do not operate in a purely linear fashion. They are much less invasive
and decay causing than constant feed drills and core sampling devices, despite being
less able to indicate the location, and in some instances the quantity, of decay. Thermal
Imaging and radar tomography are completely non-invasive, but appear to be less
accurate in calculating the quantity of decayed wood in a tree stem.
Despite the fact that constant feed drills can cause decay in a tree, they are still very
popular in field use for quantifying wood decay and are quite accurate when compared
to many other methods. Sonic or ultrasonic tomography seems to offer a good balance
between accuracy, invasiveness and ease of use and, despite the cost of the equipment,
are becoming increasingly popular for decay detection in landscape trees. As can been
seen from this review, in order to infer the vitality of trees; tree growth, visual and
physiological parameters are assessed and used to verify one another. Therefore when
comparing tree vitality and wood decay in trees it is necessary to estimate the amount
of wood decay in the trees and undertake tree growth, visual and physiological
measurements.
52
Chapter 3 – Estimating wood decay in Eucalyptus
saligna
3.1 Introduction
The investigations in this chapter focus on the quantitative measurement of the extent of
wood decay in individual trees, so that the amount of wood decay can be compared to the
growth, physiology and ultimately the vitality of the tree. The instruments used for
quantifying wood decay vary greatly in the principles on which they function, hence
method comparisons are needed. Three methods were chosen to quantify decay in this
investigation. Most field instruments quantify decay in a cross section only, so the three
methods were also compared to an estimation of the density of the wood in each tree,
which included decayed trunk wood and branch wood, to gauge which method best
reflected wood decay in the entire tree.
The aim of the investigations undertaken in this chapter was to estimate the percentage of
wood decay in E. saligna in order for wood decay to be compared with tree vitality in later
chapters.
The extent of decay in Eucalyptus globulus and Eucalyptus nitens was found to be greater
axially than either radially or tangentially (Deflorio et al., 2007), suggesting a stem
measurement may detect wood decay, if present, in E. saligna. Therefore the capacity of a
single measurement taken in cross section to approximate the percentage of decay in the
wood of a whole tree at the time of testing is the focus of this study. This study does not
attempt to predict the progression of decay within trees over time.
Field devices for measuring decay in the cross sections of trees vary greatly in their
operating principles and are often similar to the equipment used for measuring wood
density, as decay results in a decrease in wood density or mass (Beall & Wilcox, 1987).
Two of the most common devises for quantifying wood decay in urban trees are: the
Argus-Picus Sonic Tomograph (Argus Electronic GmbH, Rostock, Germany), which
records the time of transmission of multiple acoustic stress waves through the stem of the
53
tree in a cross section and the IML-Resi (Instrumenta Mechanik Labor GmbH, Wiesloch,
Germany) which is a constant feed drill, that records “resistance” on a graph trace.
Methods using the Resi and the Picus were used to estimate wood decay in a trunk cross
section of the trees before they were felled, and a visual method was used after felling.
Electrical conductivity meters such as the Shigometer were not used because interpreting
data from these instruments has been shown to be difficult in certain genera – particularly
Eucalyptus (Wilkes & Heather, 1983). The Fractometer was not chosen as the 5 mm core
samples required by the instrument proved too brittle to be assessed successfully in
Eucalyptus globulus in a study by Matheny et al., (1999). Currently available devices are
similar to the ones used in the experiment although they may have more sophisticated data
recording, software and data processing.
Field instruments quantify decay in small sections only, so the methods of wood decay
estimation were compared to an estimation of the density of the wood in each tree or the
“whole tree wood density” to gauge which method best reflected the amount of wood
decay in the entire tree. It must be emphasized that in this study whole tree wood density
includes trunk or branch wood sections that may be hollow or very decayed, rather like a
pipe, and hence have very low density. Thus, whole tree wood density is a measure of
wood decay in the entire tree. Whole tree wood density in this study should not be
confused with “basic wood density”. There are five standard ways of describing wood
density in the timber industry: oven-dry density, air-dry density, green density, nominal
density and basic density (Walker et al., 1993). All of these density measurements are
taken on wood samples that appear completely sound (non decayed) as were the basic
wood density samples in this study. Thus, the measurement of basic wood density in this
study does not relate directly to the decayed wood in the trees, unlike the measure of
whole tree wood density, which includes visibly decayed wood.
Wood decay was measured in plantation trees as this enabled a statistically significant
number of even aged trees to be measured with relative ease, and to reduce the variation in
whole tree wood density data that may be due to the environmental effects on branch
54
growth such as plant–soil–nutrient interactions and climate (Casella & Sinoquet 2003).
Open grown or urban trees would not have been suitable to test the effectiveness of single
location decay methods for describing the percentage of wood decay in a tree as the ratio
of branch wood to trunk wood would vary considerably due to environmental
considerations and tree age. However the success of the methods in the context of a
plantation can be applied to urban trees. The three decay assessment methods were also
compared with the percentage wood moisture content and basic wood density to assess
whether the decay measurement methods were affected by these two properties.
As the trees were all in the same location it is likely that the biological causal agent for
decay was the same or similar for all trees, thus the causal agent did not add to the
uncontrolled variation inherent in the study. In addition, some of biological agents that
cause wood decay in Australia in eucalypts are confused in their taxonomy, making
correct identification problematic (Simpson, 1996). Hence the causal agent for decay was
not identified in this study.
The first method for measuring decay with the Argus-Picus Sonic Tomograph produces a
“false colour” image from acoustic time of flight raw data gathered in cross section.
Because the instrument software does not allow for any other form of data output and
precludes access to raw data, resultant images were analysed using the image processing
and analysis freeware ImageJ version 1.40g (Rasband, 2008). The method relies on the
experienced use of both the Argus-Picus Sonic Tomograph and image analysis software.
The percentage of area of decay estimated in cross section is then assumed to be the
approximate percentage of wood decay in the entire tree.
The second method uses a previously designed, but slightly modified, method developed
by Johnstone et al., (2007) and combines the IML-Resi raw data and Shigo’s (1979)
compartmentalization of decay in trees (or CODIT) model to predict the quantity of wood
decay beyond the linear drill locations of the IML-Resi. This method relies on the
experienced use of the IML-Resi, knowledge of models of decay in trees and image
55
analysis software. Again the percentage area of decay estimated in the cross section is
assumed to be the approximate percentage of wood decay in the entire tree.
The third method visually assesses wood decay in a cut cross section using a needle probe
for verification and again was assumed to be approximately the percentage volume of
decay in the entire tree. Unlike the previous two methods, this is a destructive method.
3.2 Materials and methods
3.2.1 Materials
All the investigations on the trees in this study were conducted on Eucalyptus saligna Sm
(Bateman’s Bay) trees in a eucalypt plantation at Tostaree in country Victoria, Australia
(Figure 3.1, latitude 37˚47’ longitude 148˚11’). The trees were 18 years old in 2006, with
Figure 3.1 Site of the investigations conducted in this study on the Eucalyptus saligna (Bateman’s Bay) in
the Eucalypt plantation at Tostaree, Victoria.
Trees pictured were 18 years old in December 2006, when the photograph was taken.
56
heights between 17 and 27 m, and diameters at 1.3 m in height of between 142 and 318
mm (Appendix 1.1 Appendix to materials used in chapter 3, appendix table A1.1).
Thirty-six sample trees were part of a larger species/provenance study covering a total
area of approximately 10 hectares. The 36 trees were located within a total of 5 plots of 25
E. saligna trees, 2.5 m x 4.0 m apart amongst other eucalypt species/provenance plots.
The 5 plots were randomly located and orientated throughout the plantation. Individual
trees were deliberately chosen to exclude break or edge trees as they would have had more
access to light than the other trees, thus introducing an uncontrolled variable into the study
(Appendix 1.1 Appendix to materials used in chapter 3 appendix figure A1.1). The trees
were also chosen to provide a range of decayed wood from decayed to very little decay, as
judged by the Visual Tree Assessment (VTA) method (Mattheck & Breloer, 1994;
Mattheck, 2007). Features such as bulges in the trunks or poorly occluded branch stubs
were taken as indicators of possible wood decay. This method was used to choose 36 trees
for the study rather than random sampling in order to provide a range of decayed wood
from decayed to very little decay in the sample set as canvassed by Wang et al., (2009).
3.2.2 Methods
In this investigation data from the Argus - Picus Sonic Tomograph system (the picus
system hereafter) and the IML – Resi system (the resi system hereafter) and a visual
method of decay detection were compared to the whole tree wood density of 36 E. saligna
trees. The data gathered for decay estimation using the three decay assessment methods
were presented as the percentage of wood decay in each case, for ease of comparison. The
whole tree wood density data include in the measurement any low density decayed wood,
or any absence of wood due to wood decay in the stem or branches, and as such was the
best method to assess the validity of more direct wood decay assessment techniques,
particularly in relation to the whole tree. The three decay assessment methods were also
compared with the percentage wood moisture content and basic wood density to assess
whether the decay measurement methods were affected by these two wood properties.
57
Wood moisture content and basic density estimation
When the E. saligna trees were cut down in April 2008 a small trunk wood sample
approximately 25 mm in height and 25 mm across was taken from north to south through
the pith at 1.5 m in height. Samples were taken at a consistent aspect and height so that the
variation in sample data could be controlled. No decay was visible in any of these
samples. The samples were weighed immediately in the field using a balance. Each
sample was then wrapped in plastic and the green volume of the sample was calculated
using the water displacement method (Walker et al., 1993). The average volume of wood
samples was 119.1 cm3, and they ranged in volume from 34.7 cm3 (tree 28) to 238.2 cm3
(tree 10).
The samples were oven dried and weighed every 24 hours in a Thermoline 0100FD
laboratory oven of double shell construction (Thermoline Scientific, Lilydale, Victoria,
Australia) at 102˚C until they were no longer losing weight – they were dried for a total of
94 hours. Wood moisture content was then estimated as: [(original weight – oven dry
weight)/original weight] x 100 (Walker et al., 1993). Basic wood density was estimated
as: oven dry mass of wood/volume of wood when green (Walker et al., 1993).
Whole tree wood density estimation
The whole tree wood density data included in the measurement any low density decayed
wood, or any absence of wood due to wood decay in the stem or branches.
The process for calculating whole tree wood density involved 4 steps.
1. All trunk wood sections were weighed immediately after cutting (fresh or ‘green’
weight) and reduced by the percentage of moisture content in the wood. Wood
moisture content was estimated as: [(original (wet) weight – oven dry
weight)/original (wet) weight] x 100 (Walker et al. 1993).
2. The branches and upper canopy of the trees were weighed (fresh weight) and the
leaf fresh weight was subtracted from the branch and upper canopy weight.
58
3. The green branch wood weight was then reduced by the percentage of wood
moisture content in a small branch sample using the same method as for the trunk
sample above.
4. The resultant dry wood weights for trunk and branches were added and divided by
the total (branch and trunk) fresh wood volume (calculated as for a cylinder, but
measured at every 1 m for the trunk, Figure 3.2). The length of canopy and branch
sections were measured and diameters were taken at the base of each branch. Each
major branch was assumed to be roughly cylindrical.
The resultant parameter was thus defined as whole tree wood density.
A weight scale was used for mass measurements taken for whole tree wood density
estimations. This scale was accurate to within ±10 kg, which means the whole tree wood
density is relative and should only be compared with measurements taken outside this
study with caution.
a
b
Figure 3.2 (a) At left, this photograph shows the diameter of a Eucalyptus saligna tree being measured in
order to calculate whole tree wood density measurements; (b) at right a Eucalyptus saligna tree being cut
to 1 m lengths prior to being weighed in order to calculate above ground biomass and whole tree wood
density.
Measurement position at the trunk for decay assessment methods
The original data for each decay assessment method were collected in cross section at a
height of 0.3 m, converted to a percentage area of the cross section and assumed to be the
59
approximate total percentage of wood decay in the tree. This height was chosen as a test
height for the cross sections as the wood volume was greatest at the base of the trees, and
in fact 11-19% of the total wood volume of the trees was found to be at ground level to 1
m in height (Appendix 1.3 Appendix to results from chapter 3, appendix table A1.11).
Therefore measuring a cross section low in the tree would include more wood tested than
at higher points on the tree. Furthermore, in trees without external signs of decay, the
sections with the highest percentage of decay were usually found to be at the base of the
tree (Johnstone, 2005; Johnstone et al., 2007). Estimating the percentage of wood decay
from sections at 0.3 m may therefore slightly overestimate the percentage of wood decay
in the entire tree, but it does avoid the problem of missing existing decayed wood entirely.
Multiple sampling at different heights was not possible in this study due to time
constraints.
Instrument 1 – the Picus Sonic Tomograph
The Argus-Picus Sonic Tomograph (Figure 3.3) takes acoustic time of flight
measurements across a tree in order to evaluate wood density, as a proxy for wood decay,
in a tree cross section. The first step in the measurement technique for the Argus-Picus
Sonic Tomograph (the Picus hereafter) is to select the correct number of sensor
placements on the tree. According to the Picus manual, the minimum distance between
sensors should be 150 mm and the maximum 500 mm (Anon, 2004). Once the number of
sensors is selected (typically 8-12) evenly spaced nails are hammered into the tree through
the bark and just into the wood of the tree (Anon, 2004). Linear measurements are taken
across the tree from one sensor to the other (Figure 3.4).
Eight sensors would result in 13 linear measurements - between sensor 1 and sensors 2-8
and also between sensor 5 and sensors 1-4 and 5-7 (figure 3.5a). The acoustic sensors are
then placed on the nails and connected to a laptop computer via an interface box (Figure
3.6). Each nail on the tree is tapped in turn and an acoustic stress wave is then sent from
the tapped nail to the other sensors in the array (Figure 3.6 and 3.7).
60
Figure 3.3 The Argus-Picus Sonic Tomograph pictured with 12 acoustic sensors, interface box hammer
and straps for attaching the sensors to the tree trunk.
The Argus-Picus Sonic Tomograph was used as part of the picus system of estimating wood decay
throughout this study.
Figure 3.4 Linear distance measurements being taken with large callipers on a Eucalyptus saligna tree.
Linear distance measurements are taken after nails are hammered into the tree to the depth of the xylem.
The Picus sonic tomograph sensors are mounted on the nails when the sonic velocity measurements are
taken. Photograph William Jackson.
61
a
b
Figure 3.5 A diagram of the trunk perimeter of a Eucalyptus saligna tree stem. (a) Top, a diagrammatic
representation of measured linear distances. Red numbers represent the order and number of linear
measurements. (b) Above, trunk perimeter “tree geometries”, generated by the Picus sonic tomograph
propriety computer software.
Diagram (b) is generated by the Picus sonic tomograph proprietary software after the linear distance data is
input into the computer. When 8 sensors are used linear measurements are taken between sensor 1 and
sensors 2-8 and also between sensor 5 and sensors 1-4 and 5-7.
62
The linear measurements are used by the Picus proprietary software package to generate
a perimeter diagram or “tree geometry” of the tree trunk (Figure 3.5b) (Anon, 2004). An
acoustic stress wave is then sent by a hammer tap to the Picus sonic tomograph sensor
array processed by the interface box and on to a computer (Figure 3.6). The minimum
velocity measurement, relative to the distance travelled by the sound wave, is taken by
the instrument as “sound” or non decayed wood. Thus the instrument itself is self
calibrating for each cross section that is measured. The output is a “false colour” image in
cross section that represents the acoustic speed relative to the distance travelled by the
wave (Figure 3.8). According to the Picus manual blue is the slowest sonic velocity, then
violet, green is the next slowest and brown the fastest sonic velocity (Anon 2004). This
image can then be interpreted to represent decayed or non-decayed areas within a cross
section, according to the Picus manual “the colours green, violet and blue indicate
increasing degrades of decay” (Anon, 2004, page 22).
Figure 3.6 Acoustic stress wave being sent by a hammer tap to the Picus sonic tomograph sensor array
processed by the interface box and sent to a computer. Photograph by Matthew Sauvarin
63
Figure 3.7 A diagrammatic representation of the acoustic pathways travelled by the Picus sonic tomograph
stress waves when 8 sensors are used in the array.
Figure 3.8 Picus sonic tomograph “false colour” image. Note 8 sensors were used in this array. The
colours in decreasing order of sonic speed are brown, green, violet and blue.
64
Wood decay estimation method 1 – the picus system
In 2006 and 2007, thirty six E. saligna were tested with the Argus-Picus Sonic Tomograph
at a height of 0.3 m as described previously, except that the sensors were placed closer
together than recommended by the Picus manual (Anon, 2004). Placing the sensors at a
minimum distance of 75 mm rather than 150 mm maximized the accuracy of the Picus
image as more sensors could be used; interferences or other problems with the collection
of acoustic data were not observed. (Appendix 1.2 Appendix to methods used in chapter 3
for linear distance measurements, appendix table A1.2 – A1.7).
The perimeter diagrams generated by the 2004 Picus software program were smoothed
(Figure 3.9 and Appendix 2.1 Appendix to methods used in chapter 3, appendix figures
A1.2, A1.3, A1.6 and A1.7), and the cross sectional area of the section was then calculated
using ImageJ image analysis software (Appendix 1.3 Appendix to results from chapter 3,
appendix table A1.11).
a
b
Figure 3.9 (a) At left the Picus sonic tomograph image generated by the proprietary software, (b) at right
the image after it has been “smoothed” by hand. The tree is Eucalyptus saligna, tree 32.
65
Initially, the colours on the Picus images were interpreted with reference to the
manufacturers’ recommendations – interpreting the image according to the hue of the
image, that is, green, violet and blue colours indicating increasing levels of decay (Anon,
2004). However, all the images except one were brown in colour, indicating the fastest
acoustic transmission times possible which is normally associated with entirely solid
wood. This meant that according to the Picus instrumentation and software, as used
according to the manufacturers recommendations, all the trees except for one tree were
entirely free of wood decay at 0.3 m in height. However Wang et al., (2009) found that the
light brown colour in the Picus image corresponded to incipient decay in wood. Therefore
rather than discount the Picus results it was decided to reexamine the thirty-five solid
brown images for differences in brightness as well as hue.
Each of the Picus images were converted to a monochrome 8-bit image using the ImageJ
program (Rasband, 2008). The darkest areas (areas where acoustic waves travel fastest)
were used as reference, but the threshold for significant variation of acoustic speed was reset to identify potentially decayed areas. In ImageJ, this was achieved by highlighting the
area of interest (lighter coloured areas) in red (Figure 3.10 and Appendix 1.2 Appendix to
methods used in chapter 3, appendix figures A1.8 – A1.11). The threshold numerical
upper and lower setting in ImageJ for highlighting light coloured areas in red on the
images was set identically for images that corresponded to the larger trees (over 200 mm
in diameter at 0.3 m in height) where 8 sensors were used, thus removing any subjectivity
in assessing the brightness of the image (Figure 3.10 and Appendix 1.2 Appendix to
methods used in chapter 3, appendix figures A1.8 – A1.11). In calculating the estimated
decay the “cogwheel” effect – lighter areas that appear at the midpoint between the
location of each sensor (Figure 3.11 and Anon, 2004) was not removed from the images as
it was judged that this would be reintroducing an element of subjectivity back into the
analysis. A different (lower) numerical threshold setting was used for the smaller trees that
required the use of only 6 sensors (trees less than or equal to 200 mm in diameter at 0.3 m)
as all measurements (in this case both acoustic and distance) are more sensitive to error as
66
they get smaller. In this way the false colour output was re-calibrated through the imaging
software.
Tree 24 was also a special case as this tree was both small (198 mm in diameter at 0.3 m
height) and the image, unlike the other images, was coloured so that when the
thresholding was applied to the image the resulting estimated decay appeared to create an
outlying result (Figure 3.10). For this reason all statistical analyses with the picus system
data were undertaken with three separate data sets:
1. With all 36 trees,
2. With just 35 trees excluding tree 24.
3. With only the 30 larger trees (greater than 200 mm in diameter at 0.3 m).
The percentage areas of decay calculated in cross section were assumed to be the
approximate percentage of decay in the entire tree.
67
32a
34a
24a
32b
34b
24b
Figure 3.10 From top right (32a) an original Picus image using 8 sensors for tree 32 and top left (32b) the
image after analysis with ImageJ. Middle right (34a) an original Picus image for a smaller tree using 6
sensors (34b) the image after analysis with ImageJ. Bottom right (24a) the Picus image for tree 24 and
bottom left (24b) the image after analysis with ImageJ.
68
a
b
Figure 3.11 (a) Top, tree 21 with “cogwheel effect” on the Picus image, as described in the Picus manual
(Anon, 2004). (b), Above, tree 16 with no “cogwheel effect” on the Picus image.
69
Instrument 2 – the IML-Resi
The IML-Resi F300S (Figure 3.12, ‘Resi’ hereafter) measures mechanical resistance as a
drill bit moves through the wood of the tree at a constant speed. The drill must be held
firmly onto the tree by hand, in order to get consistent readings (Figure 3.13).
Figure 3.12 The IML-Resi F300S pictured with 3 mm wide drill bit at right.
The IML-Resi F300S was used as part of the resi system of estimating wood decay throughout this study.
Figure 3.13 The IML-Resi F300S constant feed drill pictured being used on a Eucalyptus saligna tree from
this study. Photograph by Matthew Sauvarin
70
Data for the Resi are recorded in the form of graph “traces” (Figure 3.14); these show
resistance from the bark inwards from right to left on the graph. There are no units of
measurement for the mechanical resistance shown by the drill, but the distance shown in
the graph is the actual distance drilled in centimeters. The IML-Resi F300S measures the
resistance to a maximum of 280 mm into the stem cross section (Figure 3.14). If drilling
resistance is very low, then that indicates wood decay or absence of wood at the point of
drilling (Mattheck et al., 1997).
Figure 3.14 Form in which the data are recorded by the IML-Resi F300S.
The trace shows resistance from the bark inwards. Drilling direction is from right to left and the scale
shown on graphs is in centimeters. Trees were renumbered after initial data collection – data shown in this
diagram are from tree 14.
Wood decay estimation method 2 – the resi system
The second method used a previously designed, but slightly modified, method developed
by Johnstone et al., (2007) and combines the IML-Resi raw data (Figure 3.15) and Shigo’s
(1979) compartmentalization of decay in trees (CODIT) model to predict the quantity of
71
wood decay beyond the linear drill locations of the IML-Resi. This method relied on the
experienced use of the IML-Resi, knowledge of models of decay in trees and image
analysis software.
The same E. saligna were tested with the IML-Resi F300S as were tested with the Picus
and at the same height. Sections were drilled twice from north to south and west to east,
unless over 300 mm in diameter at the test point in which case they were drilled three
times. When a graph trace on a tree 300 mm or less was difficult to interpret, the tree was
also drilled three times to verify the decay location, slightly below and from the opposite
direction to the original drilling position. After the sections were drilled the graph traces
were examined for decay according to the resi system as described in previous research
(Johnstone, 2005; Johnstone et al., 2007, Figure 3.15 and Appendix 1.2 Appendix to
methods used in chapter 3, appendix figures A1.12 – A1.24). The signs of putative decay
on the graph trace include yield in the trace and a lack of the symmetrical resistance
pattern that indicates healthy growth increments (Johnstone, 2005; Johnstone et al., 2007,
figure 3.15 and Appendix 1.2 Appendix to methods used in chapter 3, appendix figures
A1.12 – A1.24). Yield on the trace that was less than 10 mm in length was not recorded as
this has been found to lead to errors (Johnstone, 2005; Johnstone et al., 2007, figure 3.15
and Appendix 1.2 Appendix to methods used in chapter 3, appendix figures A1.12 –
A1.24).
Figure 3.15 The data are recorded on graph traces, and then the putative decay is marked on the graphs as
shown. Tree shown is tree 32, as the trees were renumbered after initial data collection.
72
Cross sectional areas were calculated as described previously for the picus system, using
linear distance measurements and the Picus software and quantifying the area with ImageJ
software for perimeter drawings and area calculations (Appendix 1.2 Appendix to
methods used in chapter 3 and Appendix 1.2 Appendix to methods from chapter 3,
appendix figures A1.4 - A1.7). The resi system was further applied with reference to
Shigo’s CODIT model, and cross sectional diagrams showing putative decay were drawn
(Shigo, 1979; Johnstone, 2005; Johnstone et al., 2007, figure 3.16 and Appendix 1.2
Appendix to methods used in chapter 3, appendix figures A1.25 and A1.26). Although
data for the decay assessment were collected from only 2 or 3 drill points, the accuracy of
this method was shown to be around 75% in a previous study, justifying the use of a
similar method in his study (Johnstone, 2005; Johnstone et al., 2007). Each diagram was
then scanned and the shaded putative decayed areas were quantified using ImageJ
software. The percentage of estimated decay in each cross section according to the resi
system was then calculated.
Figure 3.16 A decay diagram that has been drawn after the putative decay from the Resi F300S graphs has
been measured and the decay outside the drilling points has been inferred using the resi system of wood
decay estimation.
On the diagram the red hatched areas represent decay and the blue lines represent the actual drilling
locations of the Resi F300S. The tree shown is a Eucalyptus saligna, tree 32 in this study. This tree was
calculated as having 0.80% wood decay using the resi system of decay estimation.
73
Wood decay estimation method 3 – the visual method
In April 2008 the same thirty six E. saligna trees were cut down and the cross sections at
0.3 m were retained for each tree. These cross sections were sanded with an “Ozito” 730
watt electric belt sander and oiled with “Sweeney’s Weatherproof Oil” (a mix of linseed
oil, turpentine and a binding agent) to highlight possible areas of decay (Figure 3.17a). As
the sections were highly pigmented a needle probe inserted to a depth of 2 mm (Figure
3.17b) was used to verify the estimated visual decayed area in each section. The decay
assessed was therefore advanced and/or intermediate and not assessed to the level of early
decay and/or incipient decay by this method (Harris et al., 2004).
a
b
Figure 3.17 (a) At top, a cross section used for estimating the volume of wood decay in a tree according
to the visual method used in this study. (b) Above shows the needle probe used as part of the visual
method.
Tree shown is tree 32, as the trees were renumbered after initial data collection. This tree was calculated
as having 0.32% decay using the visual method of decay estimation.
74
Statistical analysis of data
Power analysis was done to calculate the number of trees to use in the project to minimize
the resources required and to ensure there was enough statistical power (> 80%) in the
analysis to detect any important statistical relationships (Lenth, 2001). This was
completed using an on-line power analysis tool (Lenth, 2006). Analyses from previous
studies of both acoustic and resi methods were used to estimate the standard deviation and
the standard deviation of errors (standard error) for the power analysis (Johnstone, 2005).
Table 3.1 shows the values used. VIF (variance inflation factor) was estimated as high, as
there may have been some multicollinearity between decay measurement values.
Detectable beta (estimated detectable difference) was estimated as low, as the trees were
small and may not have had a large amount of decay.
Table 3.1 Power analysis parameters used to calculate the number of Eucalyptus saligna trees sampled in
the study.
No. of predictors
2
Error SD
0.2
SD of x (j)
5
Detectable beta
VIF (j)
4
Sample size
Alpha
0.05
Power
0.05%
25
0.84
A comparison was made between the three methods of decay estimation, and whole tree
wood density and also the wood moisture content and basic wood density at 1.5 m. Simple
linear regression analysis (Table 3.2) and logarithmic regression analysis (Table 3.3) were
calculated using the software package SAS (Statistical Analysis System) version 9.1.
Multiple regression analysis comparing the three methods of decay estimation (as
dependant variables) and the whole tree wood density and basic wood density (as
independent variables) was performed to assess the statistical relationship between the
decay measurement estimations and whole tree wood density independently of each trees’
basic wood density (Table 3.4).
75
Table 3.2 The simple linear regression analyses performed in this study in relation to the decay estimation
methods; the picus system, the resi system and the visual method and whole tree wood density, wood
moisture content and basic wood density.
1
2
3
4
5
6
7
8
9
10
Dependent variable
Percentage volume of decay using the picus system
Percentage volume of decay using the resi system
Percentage volume of decay using the visual method
Percentage volume of decay using the picus system
Percentage volume of decay using the resi system
Percentage volume of decay using the visual method
Percentage volume of decay using the picus system
Percentage volume of decay using the resi system
Percentage volume of decay using the visual method
Whole tree wood density
Independent variable
Whole tree wood density
Whole tree wood density
Whole tree wood density
Wood moisture content
Wood moisture content
Wood moisture content
Basic wood density
Basic wood density
Basic wood density
Basic wood density
Table 3.3 The logarithmic regression analysis performed in this study in relation to the decay estimation
methods; the picus system, the resi system and the visual method and whole tree wood density, wood
moisture content and basic wood density.
Dependent variable
The log of the whole tree wood density data
The log of the whole tree wood density data
The log of the whole tree wood density data
The log of the percentage wood moisture content data
The log of the percentage wood moisture content data
The log of the percentage wood moisture content data
The log of the basic wood density data
The log of the basic wood density data
The log of the basic wood density data
The log of the basic wood density data
1
2
3
4
5
6
7
8
9
10
Independent variable
The percentage volume of decay using the picus system
The percentage volume of decay using the resi system
The percentage volume of decay using the visual method
The percentage volume of decay using the picus system
The percentage volume of decay using the resi system
The percentage volume of decay using the visual method
The percentage volume of decay using the picus system
The percentage volume of decay using the resi system
The percentage volume of decay using the visual method
Whole tree wood density
Table 3.4 The multiple regression analysis performed in this study in relation to the decay estimation
methods; the picus system, the resi system and the visual method and whole tree wood density and basic
wood density.
Dependent variable
Percentage volume of decay using the picus system
Percentage volume of decay using the resi system
Percentage volume of decay using the visual method
1
2
3
Independent variable 1
Whole tree wood density
Whole tree wood density
Whole tree wood density
Independent variable 2
Basic wood density
Basic wood density
Basic wood density
3.3 Results
The results illustrate that for E. saligna trees the resi system showed a stronger statistical
correlation with the volume of wood decay in the entire tree than either the picus system
or the visual method. Basic wood density at 1.5 m did not show a statistical correlation
with the picus system or the visual method, but there was a statistical correlation between
basic wood density and the resi system. The results suggest that the percentage wood
moisture content at 1.5 m does not affect the picus system, the resi system or the visual
method of decay estimation in E. saligna trees (complete raw results for chapter 3 appear
76
in Appendix 1.3 Appendix to results from chapter 3, appendix table A1.11). The measured
VIF value was 1.4, therefore there was more than enough power in the analysis to detect
statistical similarities in the data sets.
3.3.1 Results for the picus system
The relationship between the picus system and whole tree wood density was not
statistically significant to a less than 0.05 level in either linear or logarithmic analyses
(Table 3.5, 3.6 and figure 3.18). As previously stated the picus system method for tree 24
was not consistent with the other 35 trees as this tree’s Picus image was not a uniform
colour (Figure 3.10, 24a). Tree 24 therefore became a statistical outlier in any analysis
with the picus system (Figure 3.18a, 3.21a and 3.22a). When tree 24 was removed there
were no statistical outliers in the picus system data set, but there was still no significant
statistical relationship in either linear or logarithmic regression analysis between the picus
system and whole tree wood density in this data set (Table 3.5 and 3.6 and figure 3.18b).
Table 3.5 Summarised results from linear regression analysis comparing the percentage wood decay
estimated by the picus system with whole tree wood density.
N = the number of samples
P = the probability for the t test that the coefficient of the whole tree wood density is equal to zero
r2 = the variation in picus system data that can be explained by whole tree density
Independent variable1
N P
r2
Whole tree wood density (all trees)
36 0.1057
0.0752
Whole tree wood density (without tree 24)
35 0.1199
0.0717
Whole tree wood density (larger trees only)
30 0.0787
0.1063
1. The dependent variable is percentage wood decay estimated by the picus system in all cases
Table 3.6 Summarised results from logarithmic regression analysis comparing the whole tree wood density
on the percentage wood decay estimated by the picus system.
N = the number of samples
P = the probability for the t test that the coefficient of the picus system is equal to zero
r2 = the variation in log whole tree density that can be explained by picus system data
Dependent variable1
N P
r2
Log whole tree wood (all trees)
36 0.1015
0.0769
Log whole tree wood density (without tree 24)
35 0.1259
0.0695
Log whole tree wood density (larger trees only)
30 0.0798
0.1056
1. The dependent variable is percentage wood decay estimated by the picus system in all cases
77
Percentage of decay picus system
60
50
40
30
20
10
0
0
200
400
600
800
1000
Whole tree density in kg/m3
a
Percentage of decay picus system
30
25
20
15
10
5
0
0
200
400
600
800
1000
Whole tree density in kg/m3
b
Figure 3.18 (a) Top, the percentage of decay using the picus system versus whole tree wood density in
kg/m3. Includes all 36 Eucalyptus saligna trees; (b) Above, the percentage of decay using the picus
system versus the whole tree wood density in kg/m3, excluding tree 24. Tree 24 is an outlying data point
in the picus system data set, therefore 35 Eucalyptus saligna trees are included in this data set.
78
All the trees that were larger than 200 mm in diameter at 0.3 m in height were also
analysed separately (N = 30) because, as previously discussed, the picus system for
smaller trees was slightly different because the smaller diameter trees could only accept 6
acoustic sensors. However there was still no statistically significant linear relationship
between whole tree wood density and the picus system (N = 30, P = 0.0787, r2 = 0.1063)
and the data appeared to be randomly distributed, showing no clear patterns or trends
(Figure 3.19). This was consistent with both logarithmic as well as linear statistical
analysis (Table 3.5 and 3.6). Also there was no relationship between the picus system data
and the basic wood density of the trees measured at 1.5 m, even when the smaller trees
were excluded from analysis (Figure 3.20b table 3.7 and 3.8).
Table 3.7 Summarised results from linear regression analysis comparing wood decay estimation or whole
tree wood density data with basic density data.
N = the number of samples
P = the probability for the t test that the coefficient of basic density is equal to zero
r2 = the variation in wood decay estimation or whole tree wood density data that can be explained by basic
density
Dependent variable1
Picus system data (all trees)
Picus system data (without tree 24)
Picus system data (larger trees)
Resi system data (all trees)
Resi system data (larger trees)
Visual method data (all trees)
Visual method data (larger trees)
Whole tree wood density (all trees)
Whole tree wood density (larger trees)
1. The independent variable is basic wood density in all cases
2. Statistical relationship is significant and negative
3. Statistical relationship is significant and positive
N
36
35
30
36
30
36
30
36
30
P
0.1325
0.0757
0.1180
0.04312
0.3503
0.2650
0.2364
0.00643
0.03023
r2
0.0653
0.0925
0.0846
0.1150
0.0312
0.0364
0.0497
0.1988
0.1571
79
20
Percentage of decay picus system
18
16
14
12
10
8
6
4
2
0
0
200
400
600
800
1000
Whole tree density in kg/m3
Figure 3.19 The percentage of decay using the picus system versus the whole tree wood density in kg/m3.
These data exclude the trees less than or equal to 200 mm in diameter at 0.3 m in height, that is trees 17,
19, 24, 25, 31 and 34 are excluded. Therefore 30 Eucalyptus saligna trees are included in this data set.
Whole tree wood density and basic density showed a very significant linear and
logarithmic relationship (Linear regression, N = 36, P = 0.0064, r2 = 0.1988, logarithmic
regression, N = 36, P = 0.0058, r2 = 0.2030, table 3.7, 3.8 and figure 3.21). The
relationship between whole tree wood density and basic density was not as significant
when only the larger trees were included (Table 3.7, 3.8 and figure 3.21b). Multiple
regression analysis was performed comparing the picus system as a dependent variable
and whole tree wood density and basic density as independent variables. There was no
statistical relationship between whole tree wood density and the picus system data, even
when the interaction between the whole tree wood density and basic wood density was
removed via multiple regression analysis (Table 3.9). When the outlying picus system
datum point tree 24 was removed and only larger trees analysed there was still no
statistically significant relationship (Table 3.9).
There was no linear or logarithmic statistical relationship between the picus system data
and the percentage of wood moisture content even when all the smaller trees were
excluded (Table 3.10, 3.11 and figure 3.22).
80
Table 3.8 Summarised results from logarithmic regression analysis comparing with basic wood density data
with wood decay estimation or whole tree wood density data.
N = the number of samples
P = the probability for the t test that the coefficient of basic density is equal to zero
r2 = the variation in wood decay estimation or whole tree wood density data that can be explained by the
logarithmic function of basic wood density
Independent variable1
N
P
r2
Picus system data (all trees)
36
0.1304 0.0660
Picus system data (without tree 24)
35
0.0700 0.0960
Picus system data (larger trees)
30
0.1142 0.0867
Resi system data (all trees)
36
0.03782 0.1208
Resi system data (larger trees)
30
0.3388 0.0327
Visual method data (all trees)
36
0.2768 0.0347
Visual method data (larger trees)
30
0.2416 0.0486
Whole tree wood density (all trees)
36
0.00583 0.2030
Whole tree wood density (larger trees)
30
0.02793 0.1611
1. The dependent variable is the logarithmic function of the basic wood density data in all cases
2. Statistical relationship is significant and negative
3. Statistical relationship is significant and positive
Table 3.9 Summarised results from multiple regression analysis comparing the percentage wood decay
estimated by picus system with whole tree wood density and basic wood density.
N = the number of samples
P = the probability for the t test that the coefficient of whole tree wood density or basic wood density is
equal to zero
r2 = the variation in picus system data that can be explained by whole tree wood density and basic wood
density
Independent variables1
N P (whole tree) P (basic density) r2
Whole tree wood density and basic wood density (all
36 0.2871
0.3743
0.0973
trees)
Whole tree wood density and basic wood density
35 0.3712
0.2185
0.1153
(without tree 24)
Whole tree wood density and basic wood density
30 0.2100
0.3332
0.1373
(larger trees only)
1. The dependent variable is percentage wood decay estimated by the picus system in all cases
81
Percentage of decay picus system
60
50
40
30
20
10
0
0
200
400
600
Basic wood density in
800
1000
kg/m3
a
20
18
Percentage of decay picus system
16
14
12
10
8
6
4
2
0
0
200
400
600
800
1000
Basic wood density in kg/m3
b
Figure 3.20 (a) The percentage of decay using the picus system versus basic wood density in kg/m3.
Includes all 36 Eucalyptus saligna trees. (b) These data exclude the trees less than or equal to 200 mm
in diameter at 0.3 m in height that is trees 17, 19, 24, 25, 31 and 34 are excluded. Therefore 30
Eucalyptus saligna trees are included in this data set.
82
Table 3.10 Summarised results from linear regression analysis comparing wood decay estimation data with
wood moisture content.
N = the number of samples
P = the probability for the t test that the coefficient of wood moisture content is equal to zero
r2 = the variation in wood decay estimation data that can be explained by wood moisture content
Dependent variable1
Picus system data (all trees)
Picus system data (without tree 24)
Picus system data (larger trees)
Resi system data (all trees)
Resi system data (larger trees)
Visual method data (all trees)
Visual method data (larger trees)
1. The independent variable is wood moisture content in all cases
N
36
35
30
36
30
36
30
P
0.6621
0.4843
0.4149
0.5127
0.5682
0.6370
0.6555
r2
0.0057
0.0149
0.0239
0.0127
0.0118
0.0066
0.0072
Table 3.11 Summarised results from logarithmic regression analysis comparing with wood moisture content
data with wood decay estimation data.
N = the number of samples
P = the probability for the t test that the coefficient of wood moisture content is equal to zero
r2 = the variation in wood decay estimation data that can be explained by the logarithmic function of wood
moisture content
Independent variable1
N
P
r2
Picus system data (all trees)
36
0.6254 0.0071
Picus system data (without tree 24)
35
0.4687 0.0160
Picus system data (larger trees)
30
0.4072 0.0247
Resi system data (all trees)
36
0.4494 0.0169
Resi system data (larger trees)
30
0.5286 0.0143
Visual method data (all trees)
36
0.6586 0.0058
Visual method data (larger trees)
30
0.6703 0.0066
1. The dependent variable is the logarithmic function of the wood moisture content data in all cases
83
1000
Whole tree density in kg/m3
900
800
700
600
500
400
300
200
200
400
600
Basic wood density in
800
1000
kg/m3
a
1000
Whole tree density in kg/m3
900
800
700
600
500
400
300
200
200
400
600
800
1000
Basic wood density in kg/m3
b
Figure 3.21 (a) Top, whole tree wood density in kg/m3 versus basic wood density in kg/m3. Includes all
36 Eucalyptus saligna trees. Trend line = logarithmic regression, P = 0.0058, r2 = 0.2030. (b) Above,
whole tree wood density in kg/m3 versus basic wood density in kg/m3. These data exclude the trees less
than or equal to 200 mm in diameter at 0.3 m in height, that is trees 17, 19, 24, 25, 31 and 34 are
excluded. Therefore 30 Eucalyptus saligna trees are included in this data set. Trend line = logarithmic
regression, P = 0.027, r2 = 0.1611. Scale begins at 200 kg/m3.
84
Percentage of decay picus system
60
50
40
30
20
10
0
0
10
20
30
40
50
60
Percentage wood moisture content
a
20
Percentage of decay picus system
18
16
14
12
10
8
6
4
2
0
0
10
20
30
40
50
60
Percentage wood moisture content
b
Figure 3.22 (a) Top, the percentage of decay using the picus system versus the percentage of wood
moisture content. Includes all 36 Eucalyptus saligna trees. (b) Above the percentage of decay using the
picus system versus the percentage of wood moisture content. These data exclude the trees less than or
equal to 200 mm in diameter at 0.3 m in height, that is trees 17, 19, 24, 25, 31 and 34 are excluded.
Therefore 30 Eucalyptus saligna trees are included in this data set.
85
3.3.2 Results for the resi system
The relationship between the resi system and whole tree wood density was statistically
significant (P < 0.05) in both linear and logarithmic analyses (Figure 3.23, table 3.12 and
3.13). The logarithmic statistical relationships between whole tree wood density and the
resi system were more significant and stronger than the linear relationships (Table 3.12
and 3.13). The linear and logarithmic statistical relationship between whole tree wood
density and the resi system was even more significant with a stronger relationship when
the trees less than or equal to 200 mm in diameter at 0.3 m in height were excluded from
the analysis (Figure 3.23b, table 3.12 and 3.13).
Table 3.12 Summarised results from linear regression analysis comparing the percentage wood decay
estimated by resi system with whole tree wood density.
N = the number of samples
P = the probability for the t test that the coefficient of whole tree wood density is equal to zero
r2 = the variation in resi system data that can be explained by whole tree wood density
Independent variable1
N P
r2
2
Whole tree wood density (all trees)
36 0.0030
0.2307
Whole tree wood density (larger trees only)
30 0.00252 0.2835
1. The dependent variable is percentage wood decay estimated by the resi system in all cases
2. Statistical relationship is significant and negative
Table 3.13 Summarised results from logarithmic regression analysis comparing the whole tree wood density
on the percentage wood decay estimated using the resi system.
N = the number of samples
P = the probability for the t test that the coefficient of the resi system is equal to zero
r2 = the variation in log whole tree wood density that can be explained by resi system data
Dependent variable1
N
P
r2
2
Log whole tree wood density (all trees)
36
0.0027 0.2354
Log whole tree wood density (larger trees only)
30
0.00152 0.3061
1. The independent variable is percentage wood decay estimated by the resi system in all cases
2. Statistical relationship is significant and negative
86
30
Percentage of decay resi system
25
20
15
10
5
0
0
200
400
600
800
1000
Whole tree density in kg/m3
a
30
Percentage of decay resi system
25
20
15
10
5
0
0
b
200
400
600
Whole tree density in
800
1000
kg/m3
Figure 3.23 (a) Top, the percentage of decay using the resi system versus whole tree wood density in
kg/m3. Includes all 36 Eucalyptus saligna trees. Trend line = logarithmic regression, P = 0.0027, r2 =
0.2354. (b) Above, the percentage of decay using the resi system versus the whole tree wood density in
kg/m3. These data exclude trees less than or equal to 200 mm in diameter at 0.3 m in height, that is trees
17, 19, 24, 25, 31 and 34 are excluded. Therefore 30 Eucalyptus saligna trees are included in this data set.
Trend line = logarithmic regression, P = 0.0015, r2 = 0.3061.
87
There was a linear and logarithmic relationship between the resi system data and the basic
wood density of the trees measured at 1.5 m, with all the trees, but not when only the
larger trees were included in the analysis (Figure 3.24 table 3.7 and 3.8). Multiple
regression analysis was performed comparing the resi system as a dependent variable and
whole tree wood density and basic density as independent variables, to remove basic
density as a factor influencing the relationship between whole tree wood density and the
resi system. There was a significant statistical relationship between whole tree density and
the resi system data using multiple regression analysis (Table 3.14).
There was no linear or logarithmic statistical relationship between the resi system data and
the percentage of wood moisture content even when all the smaller trees were excluded
(Table 3.10, 3.11 and figure 3.25).
Table 3.14 Summarised results from multiple regression analysis comparing the percentage wood decay
estimated by resi system with whole tree wood density and basic wood density.
N = the number of samples
P = the probability for the t test that the coefficient of whole tree wood density or basic wood density is
equal to zero
r2 = the variation in resi system data that can be explained by whole tree wood density and basic wood
density
Independent variables1
N P (whole tree) P (basic density) r2
Whole tree wood density and basic wood density (all
36 0.02032
0.3613
0.2502
trees)
Whole tree wood density and basic wood density
30 0.00452
0.8200
0.2849
(larger trees only)
1. The dependent variable is percentage wood decay estimated by the resi system in all cases
2. Statistical relationship is significant and negative
88
30
Percentage of decay resi system
25
20
15
10
5
0
0
200
400
600
800
1000
800
1000
Basic wood density kg/m3
a
30
Percentage of decay resi system
25
20
15
10
5
0
0
b
200
400
600
Basic wood density in
kg/m3
Figure 3.24 (a) Top, the percentage of decay using the resi system versus basic wood density in kg/m3.
Includes all 36 Eucalyptus saligna trees. Trend line = logarithmic regression, P = 0.0378, r2 = 0.1208. (b)
Above the percentage of decay using the resi system versus basic wood density in kg/m3. These data
exclude trees less than or equal to 200 mm in diameter at 0.3 m in height, that is trees 17, 19, 24, 25, 31
and 34 are excluded. Therefore 30 Eucalyptus saligna trees are included in this data set.
89
30
Percentage of decay resi system
25
20
15
10
5
0
0
10
20
30
40
50
60
Percentage wood moisture content
a
Percentage of decay resi system
30
25
20
15
10
5
0
0
b
10
20
30
40
50
60
Percentage wood moisture content
Figure 3.25 (a) Top, the percentage of decay using the resi system versus the percentage of wood
moisture content. Includes all 36 Eucalyptus saligna trees. (b) Above, the percentage of decay using the
resi system versus the percentage of wood moisture content. These data exclude the trees less than or
equal to 200 mm in diameter at 0.3 m in height, that is trees 17, 19, 24, 25, 31 and 34 are excluded.
Therefore 30 Eucalyptus saligna trees are included in this data set.
90
3.3.3 Results for the visual method
The relationship between the visual method and whole tree wood density was not
statistically significant (P > 0.05) in linear and logarithmic analysis (Table 3.15, 3.16 and
figure 3.26). Even when only the larger trees were included, there was no significant
statistical relationship between the visual method and whole tree wood density in either
linear or logarithmic analysis (Figure 3.26, table 3.15 and 3.16). Tree 26 (13.53%) and
tree 21 (8.07%) were not deemed outlying data points for statistical analyses in the visual
method of decay estimation, as the decay in this cross section was clearly visible and easy
to verify as correct, as were all cross sections used to estimate decay using the visual
method (Figure 3.27, 3.17 and Appendix 1.2, Appendix to methods used in chapter 3,
appendix figures A1.27 – A1.29).
Table 3.15 Summarised results from linear regression analysis comparing the percentage wood decay
estimated by the visual method with whole tree wood density.
N = the number of samples
P = the probability for the t test that the coefficient of whole tree wood density is equal to zero
r2 = the variation in visual method data that can be explained by whole tree wood density
Independent variable1
N P
r2
Whole tree wood density (all trees)
36 0.5150
0.0126
Whole tree wood density (larger trees only)
30 0.5759
0.0113
1. The dependent variable is percentage wood decay estimated by the visual method in all cases
Table 3.16 Summarised results from logarithmic regression analysis of variance comparing whole tree wood
density on the percentage wood decay estimated by the visual method.
N = the number of samples
P = the probability for the t test that the coefficient of the visual method is equal to zero
r2 = the variation in log whole tree density that can be explained by visual method data
Dependent variable1
N
P
r2
Log whole tree wood density (all trees)
36
0.5530 0.0104
Log whole tree wood density (larger trees only)
30
0.6286 0.0085
1. The dependent variable is percentage wood decay estimated by the visual method in all cases
91
20
Percentage of decay visual method
18
16
14
12
10
8
6
4
2
0
0
200
400
600
Whole tree density in
800
1000
kg/m3
a
20
Percentage of decay visual method
18
16
14
12
10
8
6
4
2
0
0
200
400
600
Whole tree density in
800
1000
kg/m3
b
Figure 3.26 (a) Top, the percentage of decay using the visual method versus whole tree wood density in
kg/m3. Includes all 36 Eucalyptus saligna trees. (b) Above, the percentage of decay using the visual
method versus whole tree wood density in kg/m3. These data exclude trees less than or equal to 200 mm
in diameter at 0.3 m in height, that is trees 17, 19, 24, 25, 31 and 34 are excluded. Therefore 30
Eucalyptus saligna trees are included in this data set.
92
a
b
Figure 3.27 These photographs show trunk cross sections of Eucalyptus saligna trees at 0.3 m in height.
(a) At left tree 26, as the trees were renumbered after initial data collection. This tree was calculated as
having 13.53% wood decay using the visual method of decay estimation. (b) At right, tree 21, as the trees
were renumbered after initial data collection. This tree was calculated as having 8.07% wood decay using
the visual method of decay estimation.
Multiple regression analysis was performed comparing the visual method as a dependent
variable and whole tree wood density and basic density as independent variables. There
was no significant statistical relationship between whole tree wood density and the visual
method data, using multiple regression analysis, even when only larger trees analysed
(Table 3.17).
Table 3.17 Summarised results from multiple regression analysis comparing the percentage wood decay
estimated by the visual method with whole tree wood density and basic wood density.
N = the number of samples
P = the probability for the t test that the coefficient of whole tree wood density is equal to zero
r2 = the variation in visual method data that can be explained by whole tree wood density and basic wood
density
Independent variables1
N P (whole tree) P (basic density) r2
Whole tree wood density and basic wood density (all
36 0.8606
0.3638
0.0373
trees)
Whole tree wood density and basic wood density
30 0.9174
0.3032
0.0501
(larger trees only)
1. The dependent variable is percentage wood decay estimated by the visual method in all cases
There was no linear or logarithmic statistical relationship between the visual method data
and the percentage of wood moisture content even when all the smaller trees were
excluded (Table 3.10, 3.11 and figure 3.28). Also there was no relationship between the
93
visual method data and the basic wood density of the trees measured at 1.5 m, even when
the smaller trees were excluded from analysis (Figure 3.29, table 3.7 and 3.8).
20
Percentage of decay visual method
18
16
14
12
10
8
6
4
2
0
0
10
20
30
40
50
60
Percentage wood moisture content
a
20
Percentage of decay visual method
18
16
14
12
10
8
6
4
2
0
0
b
10
20
30
40
50
60
Percentage wood moisture content
Figure 3.28 (a) Top the percentage of decay using the visual system versus the percentage of wood
moisture content. Includes all 36 Eucalyptus saligna trees. (b) Above the percentage of decay using the
visual system versus the percentage of wood moisture content. These data exclude trees less than or equal
to 200 mm in diameter at 0.3 m in height, that is trees 17, 19, 24, 25, 31 and 34 are excluded. Therefore
30 Eucalyptus saligna trees are included in this data set.
94
20
Percentage of decay visual method
18
16
14
12
10
8
6
4
2
0
0
200
400
600
800
1000
Basic wood density in kg/m3
a
20
Percentage of decay visual method
18
16
14
12
10
8
6
4
2
0
0
b
200
400
600
800
1000
Basic wood density kg/m3
Figure 3.29 (a) Top, the percentage of decay using the visual system versus basic wood density in kg/m3.
Includes all 36 Eucalyptus saligna trees. (b) Above, the percentage of decay using the visual system
versus basic wood density in kg/m3. These data exclude the trees less than or equal to 200 mm in diameter
at 0.3 m in height, that is trees 17, 19, 24, 25, 31 and 34 are excluded. Therefore 30 Eucalyptus saligna
trees are included in this data set.
3.4 Discussion and conclusions
In this study the estimation of wood decay given by the resi system showed a statistical
correlation with the percentage of wood decay in E. saligna, whereas the picus system and
the visual method of wood decay estimation did not show a statistical correlation. The
95
accuracy of the resi system is supported by Costello & Quarles (1999) who showed that
the Resistograph had a very low deviation from accuracy in assessing wood decay in
Eucalyptus globulus (Victorian blue gum).
There was a very small variation in the estimated volume of decay using the visual
method, from approximately 0.03% (tree 19) to 13.53% (tree 26), with 94% (all but two
trees) estimated as having less than 4% wood decay. The small variation in data would
have contributed to the lack of statistical relationships between the visual method and
whole tree wood density. The decay in a wood section must be advanced or at least at an
intermediate level in order to be detected with this method, that is; 1. advanced, the wood
becomes fibrous and the wood structure is altered or non existent (Harris et al., 2004) or 2.
intermediate; the decay is clearly recognizable and there is a change in wood structure but
it remains intact. The other two stages 3. early decay; where there are slight changes in
wood colour, texture and brittleness and 4. the incipient stage of decay; where there is a
thinning of xylem cell walls and wood may be discoloured, are probably not able to be
detected by the visual method. It was observed that pockets of decay in the sapwood of the
trees were often filled with kino, perhaps as a result of the barrier zone breakdown
described by Wilkes (1986) (Figure 3.27b and Appendix 1.2 Appendix to methods from
chapter 3, appendix figures A1.27 – A1.29).
Basic wood density measured at 1.5 m showed a correlation with the percentage of decay
as calculated using the resi system. This is not surprising as decay results in a decrease in
wood density or mass (Beall & Wilcox, 1987). Much of the wood strength in decaying
wood is believed to be lost at incipient decay level, with up to 50% by around 1% mass
loss (Beall & Wilcox, 1987). Five to ten percent mass loss can be only be detected by light
microscopy, and is not yet visible (Beall & Wilcox, 1987). Slight changes in wood density
may not be detected by the visual method if only at the early or incipient level, but may be
measureable using the resi system described in this study. The IML-Resi, and the
Resistograph, showed clear correlations between basic density and raw drill resistance
values in many studies that did not apply the resi system described here (Rinn et al., 1996;
96
Lin et al., 2003; Isik & Li, 2003; Johnstone, 2005). However applying the resi system the
system locates pockets of decay (Figure 3.16 and Appendix 1.2 Appendix to methods
from chapter 3, appendix figure A1.25 and A1.26) not overall reduced density compared
to other methods. The statistical relationship between the resi system and basic wood
density measurement may be because a proportion of basic wood density samples (at 1.5
m) were of reduced density due to decay, even though the decay was not visible. There
was no statistical relationship between basic wood density measured at 1.5 m and the
picus system and the visual wood decay estimation method.
Whole tree wood density and basic density showed a very significant linear and
logarithmic relationship. This is not surprising as whole tree wood density is strongly
influenced by the density of the sound wood, as well as the decayed wood, in a tree
particularly if the estimated volumes of decay are relatively low, as in this study (picus
system [excluding tree 24] between 0% and 23.50%, resi system between 0% and 23.19%,
visual method between 0.03% and 13.53%). Separating a measure of decay and a measure
of wood density is difficult and may require sophisticated measurements of the exact
density across a wood section, such as SilviScanII, which was beyond the scope of this
study (Evans et al., 1995). Multiple regression analysis was performed comparing each
decay estimation method as a dependent variable and whole tree wood density and basic
density as partial regression coefficients, in an attempt to remove the interplay between
these two variables. The resi system still showed a significant correlation with the whole
tree wood density coefficient within the multiple regression.
The resi system and the picus system are both non-destructive methods of wood decay
assessment, however there is considerable debate over whether the Resi drilling causes
further wood decay in trees with some researchers claiming the damage is negligible
(Weber & Mattheck, 2006), while others imply that it may be significant (Kersten &
Schwarze, 2005; Helliwell, 2007; Schwarze, 2008). Most researchers agree that the Picus
Sonic Tomograph is less likely to contribute to further wood decay than a Resi drill, as
97
nails are inserted only a few millimeters into the xylem with the former (Gilbert & Smiley,
2004), rather than holes being drilled right through the xylem with the Resi drill.
However, it is clear from this and other studies that the Picus Sonic Tomograph does have
difficulty with accurate decay estimation, such as when the origin of the decay is in the
sapwood (Deflorio et al., 2008; Wang et al., 2009), when there are cracks and cavities
present (Schwarze & Heuser, 2006; Wang & Allison, 2008; Wang et al., 2009), the
location of the wood decay is not necessarily centrally located in a cross section
(Schwarze, 2008) and as in this study where the trees are small (from 142 to 318 mm in
diameter at 1.3 m in height). Even when wood decay is centrally located Wang et al.,
(2009) report the Picus underestimates the amount of wood decay. As in this study, Wang
et al., (2009) report that the Picus cannot reliably detect small amounts of sapwood decay
and insect holes in mostly sound wood.
The percentage wood moisture content measured at 1.5 m showed no statistical
relationship with the picus, resi or visual wood decay estimation methods in this study.
This is a positive result for these methods, as it is unlikely that wood moisture content is
causing unexplained variation in the wood decay estimation method data. Moisture
content did affect the Resistograph and the Decay Detecting Drill resistance values in
some studies (Seaby, 1991; Rinn et al., 1996; Lin et al., 2003), but moisture content did
not affect average drill resistance in a previous study by Johnstone (2005). It is very
probable that the resi system removes the variable of moisture content, if present, from the
drill resistance raw data. Though the velocity of sound in wood is affected by moisture
content (Mishiro, 1996) the picus system was not affected by moisture content in this
study.
The unexplained residual variation when each wood decay estimation method is compared
to whole tree wood density – the best proxy for “true” wood decay values - is quite high
for both the resi and picus systems. It is greater than 70% in all cases, even when the
statistical relationships are significant. The possible error in the picus and resi systems is
probably high, as for example the “linear distance” measurements used in both methods
98
may be affected by variable bark thickness between trees which in turn affects the cross
sectional (wood) area of the trees at 0.3 m. The most successful method, the resi system, is
a more subjective method than the picus system, because interpreting graphs is subjective,
particularly ascertaining a lack of growth increments, but also the yield in the graph can be
obscured by “noise” from residual resistance evident on the graph (Johnstone, 2005).
The fact that whole tree wood density is related to basic wood density, but basic wood
density is significantly less related to the decay measures, supports using a decay
measurement as the penultimate measure for estimating decay in this study rather than
whole tree wood density itself. Overall the resi system appears to be the most suitable
method for estimating the percentage of wood decay in E. saligna trees and as such will
be the method used for comparison with tree vitality measurements later in this study.
99
Chapter 4 – Tree growth and wood decay in
Eucalyptus saligna
4.1 Introduction
When comparing tree vitality and wood decay in trees it is necessary to measure tree
growth and tree physiology. The previous chapter (chapter 3) was concerned with
quantifying wood decay. As the results from chapter 3 suggest, wood density and
wood decay are closely related. This chapter will focus on measures of tree growth and
biomass allocation that may indicate tree vitality and compare them with wood density
and wood decay. It has been suggested by some authors that the extent of decay is
related to the growth of a tree (Shigo et al., 1969; Shigo, 1971; Schwarze, 2008).
However, no systematic studies have been done with objective measurements to
confirm or refute the claim that the extent or quantity of wood decay is related to tree
growth.
The aim of this chapter is to establish whether there is an inverse relationship between
tree growth and the percentage of wood decay in a tree.
When measuring growth in mature trees the most common methods used are diameter
at breast height and tree height (Dobbertin, 2005). In this study diameter at breast
height (1.3 m) and tree height were used to measure tree growth. Leaf area is another
common method used for assessing growth (Hunt, 2003; Macfarlane et al., 2007;
Calvo-Alvarado et al., 2008; Gotsch et al., 2010). In this study total leaf area and
specific leaf area were measured. Specific leaf area is a measure of the density and
therefore the health of individual leaves and is often used for assessing tree growth
(Calvo-Alvarado et al., 2008; Gotsch et al., 2010). The leaf area index (LAI) was not
used in this study as LAI is mainly used for crops or modeling forest growth (Hunt,
2003; Macfarlane et al., 2007). LAI is a measure of the productivity of the site rather
than the plant as it is a measure of leaf area per ground area (Hunt, 2003), whereas
individual plant vitality is the focus of this study.
100
The ratio of sapwood area over leaf area (Huber value) is another determinant of tree
growth (Zeppel & Eamus, 2008; Calvo-Alvarado et al., 2008; O’Grady et al., 2009;
Gotsch et al., 2010). The percentage sapwood area and Huber value were measured in
this study. Above ground biomass or total biomass is a also a common growth measure
for crops and herbaceous plants (Roberts et al., 1993), but is less common for
assessing single mature trees, presumably because of the time and resources required
for measurement. Live above ground biomass has been used for monitoring the rate of
change of above ground carbon stocks due to climatic factors (Castilho et al., 2010). In
this study above ground biomass was used to measure growth and the data were also
used for calculating whole tree density. Tree root biomass and thus total biomass was
not measured in this study, due to time constraints.
Many visual assessment methods have been used to assess trees using a large number,
but often similar, criteria (Grimes, 1978; Fostad & Pederson, 1997; Martin et al., 2001;
Coops et al., 2004; Cunningham et al., 2007). Visual vitality methods that
incorporated the health of individual leaves such as leaf necrosis and insect attack
(Fostad & Pederson, 1997), foliage condition (Coops et al., 2004) and leaf condition
(Cunningham et al., 2007) were not used in this study as these are not “growth”
criteria, but may be the symptomatic expression of a particular biotic agent, which is
beyond the scope of this study to examine. Some individual components of visual
assessment methods are not independent of each other and are quite subjective. Fostad
& Pederson (1997), for example used both “general impression” and “dieback” as
criteria and Cunningham et al., (2007) “crown vigour” and “percentage of crown
foliage”. Components of the system used in this study from Martin et al., (2001), were
not completely independent of each other – “crown density” and “crown size” for
example and to a lesser extent “crown position”, but Martin et al., (2001), did not use
subjective descriptions such as “general impression” or “crown vigour” focusing
instead on quantitative descriptions.
The visual tree assessment method (the visual vitality index hereafter) adopted in this
study was based on a visual assessment index for live Eucalypts created by Grimes
(1978) and dead and dying trees by Lindenmayer et al., (1990), further developed by
Martin et al., (2001). When using this method the investigator added five different
101
parameters; crown position, crown size, crown density, dead branches and crown
epicormic growth to arrive at a numerical score for each tree. The method was
modified for the plantation trees in this study. The visual vitality index data were
compared with tree growth measurements such as the total leaf area, the sapwood:leaf
area ratio, above ground biomass, tree height and diameter at breast height (1.3 m) in
order to confirm its effectiveness.
Some studies have included wood density amongst growth parameters as a measure of
tree water use (O’Grady et al., 2009; Gotsch et al., 2010), in relation to ecophysiology
(Aiba & Nakashizuke, 2009) or genetic parameters (Stackpole et al., 2010; Weber &
Montes, 2010). Wood density is compared with growth parameters in this study as
wood density and wood decay are closely related. Basic wood density was measured in
this study.
The resi system was used for decay estimation in this chapter, because it was found to
correlate with whole tree wood density as a measure of the percentage of wood decay
in the previous chapter (chapter 3). The resi system also measures wood decay
directly, unlike the measure of whole tree wood density, making it a better choice for
decay estimation than the whole tree wood density method.
4.2 Materials and methods
4.2.1 Materials
All investigations on the trees in this study were conducted on the same 36 Eucalyptus
saligna Sm trees growing in a eucalypt plantation used in chapter 3 (Figure 3.1). In
order to limit the uncontrolled variation in the study the plant material was even-aged
(18 years old in 2006), in the one location and from a single provenance (Bateman’s
Bay).
4.3.2 Methods
In this investigation measurements such as total leaf area, sapwood area, above ground
biomass, height and diameter at breast height (1.3 m) were compared with a visual
102
vitality index. The visual vitality index was then compared with basic wood density
and the resi system for estimating wood decay as described in chapter 3.
Total leaf area and specific leaf area
Total leaf areas for each tree were calculated after the 36 trees were cut down by
sampling 3 leaves of each leaf type where epicormic, shade (lower) and sun (upper)
leaves were present. Not all trees had all leaf types present. The leaves were
photographed and the area of each leaf type for each tree was calculated using ImageJ
software (Figure 4.1).
Figure 4.1 Photographs of individual leaves from Eucalyptus saligna used to calculate leaf area, far left, from the
upper canopy (sun leaves), middle, from the lower canopy (shade) leaves and at right epicormic leaves. Leaves
pictured are from tree 20 as the trees were renumbered during the study.
Branches from each tree were sorted by the type of leaves they had; either sun, shade
or epicormic leaves. Branches were then sorted into groups that contained
approximately the same number of leaves. Group branch samples of each leaf type
were counted by hand on site and then multiplied by the total number of groups (of
branches) for each tree. The approximate average number of leaves counted was 6% of
the total number of leaves on the trees. The minimum estimated number of leaves
counted (for a very dense tree) was 1%, whereas up to 50% of leaves were counted
when a tree had very few leaves (see Appendix 2.2 Appendix to results from chapter 4
appendix table A2.2 for raw data).
The number of leaves of each type was multiplied by the average area calculated for
that leaf type. The leaf areas for the three leaf types were added together for the total
leaf area in meters squared for 35 trees. One tree (tree 4) had no leaves.
103
The specific leaf area is the ratio of leaf area to dry leaf weight. The leaf area was
calculated as above and converted to mm2. Three leaves from each tree were dried at
80˚C for 48 hours in an Thermoline laboratory oven of double shell construction,
weighed, multiplied by the number of leaves on the tree and the weight in mg. The leaf
area in mm2 divided by leaf weight in mg is the specific area of the leaf, a lower value
indicating a denser leaf (Hunt, 2003).
Sapwood area and sapwood area:leaf area ratio
The sapwood area was measured on a cross section taken at 0.3 m in height as it was
done in conjunction with decay measurements (chapter 3). Sapwood area was
calculated in mm2 and then converted to a percentage of the sapwood and heartwood
of the cross section. Bark was excluded from the calculation and any wood decay
present in the sapwood was subtracted from the total sapwood area (Figure 4.2a). The
sapwood heartwood boundary was clearly visible on the samples (Figure 4.2b).
The Huber value (Hv) was calculated by dividing the sapwood area (m2) by leaf area
(m2) (Gotsch et al., 2010).
Heartwood/sapwood
boundary
Sapwood/bark boundary
a
b
Figure 4.2 Method used to calculate sapwood area for the Eucalyptus saligna trees in the study. (a) Left, a cross
sectional diagram at 0.3 m showing the bark (outer ring), sapwood, (adjacent to the outer ring) and heartwood
(represented by the inner ring). Red areas on the diagram are areas of decay. (b) Photograph of the same
Eucalyptus saligna tree cross section at 0.3 m showing the bark (outer ring), sapwood, (adjacent to the outer ring)
and heartwood (within the sapwood). Cross section pictured is from tree 20 as the trees were renumbered during
the study.
104
Above ground biomass
The process for calculating above ground biomass involved 4 steps.
1. a) All trunk wood sections were weighed in 1 m lengths immediately after cutting
(fresh or ‘green’ weight, figure 3.2). Small samples were taken across the trunk from
north to south at 1.5 m in height after the trees were cut down. The average volume of
wood samples was 119.1 cm3, they ranged in volume from 34.7 cm3 (tree 28) to 238.2
cm3 (tree 10). The samples were oven dried and weighed every 24 hours in an
Thermoline laboratory oven of double shell construction at 102˚C until they were no
longer losing weight – they were dried for a total of 94 hours. Percentage wood
moisture content was estimated as:
The original weight – oven dry weight
Original weight
X 100
(Walker et al., 1993).
b) The weight of the trunk wood sections was then reduced by the percentage of
moisture content in the wood.
2. a) The number of leaves on each tree was estimated using the method for total leaf
area described above, and a small sample (three leaves per tree) was measured both
fresh and after being oven-dried (at 80˚C for 48 hours in an Thermoline laboratory
oven of double shell construction).
b) The leaf weight was reduced by the percentage of moisture content in the leaves.
3. a). The branches and upper canopy of the trees were weighed (fresh weight) and the
leaf fresh weight was subtracted from this amount.
b) The green branch wood weight was then reduced by the percentage of wood
moisture content in a small branch sample using the same method as for the trunk
sample above.
105
4. The resultant dry weights for trunk, branch wood and dry leaf weight were added
together to obtain the above ground biomass.
In this study the bark from the trees was weighed with the other woody tissues from
the trunk and branches, and assumed to have similar moisture content. The outer bark
of E. saligna is smooth, with a sock of rough bark usually 0.5 to 2 m in height,
occasionally up to 6 m (Appendix 2.1 Appendix to methods, appendix figure A2.1 –
A2.9). Therefore estimating the bark dry weight separately from other woody tissues
may have overestimated the weight of the rough outer bark in the trees.
Tree height and diameter at breast height
Tree height was recorded after cutting the trees down. Diameter at breast height was
measured at 1.3 m height with a girth tape.
Visual vitality index
The visual vitality index was developed using a method created by Grimes (1978) and
dead and dying trees by Lindenmayer et al., (1990) further developed by Martin et al.,
(2001). The method incorporates 5 individual scores for crown position in relation to
other trees, crown size, crown density the presence of dead branches, crown epicormic
growth and a dead tree classification (Martin et al., 2001).
Some attributes from Martin et al., (2001) were modified for use with the plantation
trees in this study. The “crown position” attribute was modified to include only 3
variations in position rather than 5 as no trees were completely exposed (with a score
of 5) and no trees were completely lacking in side light in this study (previously with a
score of 1) (Figure 4.3). The crown size and crown density attributes remained the
same from the previous study (Martin et al., 2001; Figure 4.3). The “dead branches”
and “crown epicormic growth” attributes were modified in response to the plantation
trees having fewer and less mature branches with attributes such as the severity of
epicormic growth or the retention of dead branches on the stem (rather than branches)
being given more emphasis (Figure 4.3). As with the method used by Martin et al.,
(2001) the scores were totaled to give an estimate of the visual vitality of the tree and
106
the total scores have a nominal range between 1 and 25 (Appendix 2.1 Appendix to
methods, appendix table A2.1).
Figure 4.3 Diagrammatic representation of the visual vitality index for plantation eucalypts used in this
study (after Grimes, 1978; Lindenmayer et al., 1990; Martin et al., 2001)
107
The visual vitality index was assessed in the trees in spring (October, 2007), summer
(January 2008) and autumn (March 2008). The autumn data were used in this chapter
as they were collected in the closest data collection period to the objective tree growth
measurements, which were done in April, 2008.
Wood density measurement and wood decay estimation
The 36 E. saligna were tested for basic wood density from a small sample collected at
1.5 m in height from the trees when they were felled in 2008. The method is described
in chapter 3. The 36 E. saligna were tested in 2006 and 2007 using the resi system
described in chapter 3.
Statistical analysis of data
Power analysis was done as in chapter 3 to calculate the number of trees to use in the
project to minimize the resources required and to ensure there was enough statistical
power in the analysis to detect any important statistical relationships (Lenth, 2001).
Table 3.1 (chapter 3) shows the values used. VIF (variance inflation factor) was
estimated as high, as preliminary VIF tests revealed some multicollinearity between
measurement values. Detectable beta (estimated detectable difference) was estimated
as low, as the trees were small and may not have had a large amount of decay in them.
A comparison was made between the autumn visual vitality index data and the five
growth measurements, and also the autumn visual vitality index and two derived
measures of tree growth (specific leaf area and the Huber value). Simple linear and
logarithmic regression analysis was performed comparing basic wood density and the
7 growth measures and the visual vitality index. A comparison was also made between
the autumn visual vitality index and the resi system. In addition simple linear and
logarithmic regression analysis was performed comparing the resi system and the 7
objective growth measures to confirm the results from the comparison of the visual
vitality index and decay estimation.
108
Simple linear regression analysis (Table 4.1) and logarithmic regression analysis
(Table 4.2) were calculated using the software package SAS (Statistical Analysis
System) version 9.1.
Table 4.1 Simple linear regression analyses performed in this study in relation to tree growth, wood
density and wood decay estimation methods.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
Dependent variable
Autumn visual vitality index data
Autumn visual vitality index data
Autumn visual vitality index data
Autumn visual vitality index data
Autumn visual vitality index data
Autumn visual vitality index data
Autumn visual vitality index data
Basic wood density
Basic wood density
Basic wood density
Basic wood density
Basic wood density
Basic wood density
Basic wood density
Basic wood density
Percentage of decay using the resi system
Percentage of decay using the resi system
Percentage of decay using the resi system
Percentage of decay using the resi system
Percentage of decay using the resi system
Percentage of decay using the resi system
Percentage of decay using the resi system
Percentage of decay using the resi system
Independent variable
Total leaf area data
Specific leaf area
Percentage sapwood area
Sapwood area:leaf area
Above ground biomass data
Tree height data
Diameter at breast height data
Total leaf area data
Specific leaf area
Percentage sapwood area
Sapwood area:leaf area
Above ground biomass data
Tree height data
Diameter at breast height data
Autumn visual vitality index data
Total leaf area data
Specific leaf area
Percentage sapwood area
Sapwood area:leaf area
Above ground biomass data
Tree height data
Diameter at breast height data
Autumn visual vitality index data
Table 4.2 Logarithmic regression analyses performed in this study in relation to tree growth, wood
density and wood decay estimation methods.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
Dependent variable
The log of the autumn visual vitality index data
The log of the autumn visual vitality index data
The log of the autumn visual vitality index data
The log of the autumn visual vitality index data
The log of the autumn visual vitality index data
The log of the autumn visual vitality index data
The log of the autumn visual vitality index data
The log of the autumn visual vitality index data
The log of the total leaf area data
The log of the specific leaf area
The log of the percentage sapwood area
The log of the sapwood area:leaf area
The log of the above ground biomass data
The log of the tree height data
The log of the diameter at breast height data
The log of the autumn visual vitality index data
The log of the total leaf area data
The log of the specific leaf area
The log of the percentage sapwood area
The log of the sapwood area:leaf area
The log of the above ground biomass data
The log of the tree height data
The log of the diameter at breast height data
Independent variable1
Total leaf area data
Specific leaf area
Percentage sapwood area
Sapwood area:leaf area
Above ground biomass data
Tree height data
Diameter at breast height data
The percentage of decay using the resi system
Basic wood density
Basic wood density
Basic wood density
Basic wood density
Basic wood density
Basic wood density
Basic wood density
Basic wood density
Percentage of decay using the resi system
Percentage of decay using the resi system
Percentage of decay using the resi system
Percentage of decay using the resi system
Percentage of decay using the resi system
Percentage of decay using the resi system
Percentage of decay using the resi system
1. Basic wood density and the percentage of decay were called “independent variables” for the sake of
logarithmic analysis as some growth measures included zero values which were excluded from analysis.
109
4.3 Results
The results illustrate that for E. saligna trees growth measures such as total leaf area,
above ground biomass, tree height and diameter at breast height were good predictors
of the autumn visual vitality index (Tables 4.3 and 4.4, figures 4.4, 4.8 – 4.10). The
autumn visual vitality index, total leaf area and specific leaf area could not predict
basic wood density but measures that related to tree size, such as above ground
biomass, tree height and diameter at breast height were positively correlated with basic
wood density (Tables 4.5 and 4.6, figures 4.11 – 4.13). The autumn visual vitality
index and most growth measures could predict the percentage of wood decay in a tree
as measured by the resi system except sapwood area (Tables 4.7 and 4.8 and figures
4.14 – 4.16). Complete raw results for chapter 4 appear in Appendix 2 (Appendix 2.2,
Appendix to results from chapter 4, appendix table A2.2).
4.3.1 Results for the visual vitality index
The relationships between the autumn visual vitality index and total leaf area, above
ground biomass, tree height and diameter at breast height were statistically significant
in both linear and logarithmic analysis (P < 0.0001, Figure 4.4, 4.8, 4.9 and table 4.3
and 4.4). The relationships were strong, with r2 > 0.50 in all cases (Table 4.3 and 4.4).
Table 4.3 Summarised results from simple linear regression analyses comparing the autumn visual
vitality index with objective measures of tree growth.
N = the number of samples
P = the probability for the t test that the coefficient of the independent variable is equal to zero
r2 = the variation in autumn visual vitality index that can be explained by the independent variable
Independent variable1
N
Total leaf area
36
Specific leaf area (excluding tree 4)
35
Percentage sapwood area
36
Sapwood area:leaf area (excluding tree 4)
35
Sapwood area:leaf area (excluding tree 4 and 31)
34
Above ground biomass
36
Tree height
36
Diameter at breast height
36
1. The dependent variable is the autumn visual vitality index in all cases
2. Statistical relationship is significant and positive
3. Statistical relationship is significant and negative
110
P
<0.00012
0.00083
0.1954
0.02043
0.0524
<0.00012
<0.00012
<0.00012
r2
0.6041
0.2905
0.0488
0.1524
0.1126
0.6450
0.5560
0.5136
Table 4.4 Summarised results from logarithmic regression analyses comparing the autumn visual
vitality index with objective measures of tree growth.
N = the number of samples
P = the probability for the t test that the coefficient of the independent variable is equal to zero
r2 = the variation in log of the autumn visual vitality index data that can be explained by the independent
variable
Independent variable1
N P
Total leaf area
36 <0.00012
Specific leaf area (excluding tree 4)
35 0.00113
Percentage sapwood area
36 0.2081
Sapwood area:leaf area (excluding tree 4)
35 0.01191
Sapwood area:leaf area (excluding tree 4 and 31)
34 0.0775
Above ground biomass
36 <0.00012
Tree height
36 <0.00012
Diameter at breast height
36 <0.00012
1. The dependent variable is the log of the autumn visual vitality index data in all cases
2. Statistical relationship is significant and positive
3. Statistical relationship is significant and negative
r2
0.5656
0.2805
0.0462
0.1769
0.0942
0.6166
0.6483
0.5077
The statistical relationship between the autumn visual vitality index and total leaf area
was stronger in linear rather than logarithmic regression (Figure 4.4 and Table 4.3 and
4.4). Photographs of each tree canopy were taken in September 2007, and could be
used to verify total leaf area and autumn visual vitality measurements (Figure 4.5 and
Appendix 2.1, Appendix to methods from chapter 4, appendix figures A2.1 – A2.9).
Total leaf areas were very low for tree 4, 21, 23 and 31 at 0, 3.98 m2, 5.87 m2 and 1.71
m2 respectively (Appendix 2.2, Appendix to results from chapter 4, appendix tables
A2.2). However none of these values appear as outlying values (Figure 4.4) and appear
correct when verified by photographic evidence (Figure 4.5 and Appendix 2.2,
Appendix to results from chapter 4, appendix figures A2.1 – A2.9). The total leaf area
values for tree 21, 23 and 31 also agree with the crown density value (2/9, 2/9 and 1/9
respectively, average 4/9) and crown size (1/5, 2/5 and 1/5 respectively, average 3/5)
assessments done as part of the autumn visual vitality index (Appendix 2.1, Appendix
to methods from chapter 4, appendix table A2.1).
111
25
Visual vitality index
20
15
10
5
0
0
20
40
60
80
Total leaf area
100
120
m2
Figure 4.4 The autumn visual vitality index versus total leaf area in m2. These data include all trees
therefore 36 Eucalyptus saligna trees are included in this data set. Trend line = linear regression, P =
<0.0001, r2 = 0.6041.
4
7
12
31
Figure 4.5 Photographs of 4 trees taken in September, 2007 showing approximate relative leaf
area/canopy density and tree size. Photographs of the canopy taken from approximately 1.6 m abutting
the trunk looking directly up into the canopy. Top left is tree 4 with no leaves, top right tree 7 with a
total leaf area of 108.61 m2, above left is tree 12 with a total leaf area of 43.75 m2 above right tree 31
with a total leaf area of 1.71 m2. Average leaf area is 45.78 m2. All photographs taken on the north side
of the trees, red shading highlights each canopy.
112
There was a statistically significant and negative relationship between the derived
value of specific leaf area and the autumn visual vitality index, that is lower vitality
trees had a higher specific leaf area (less dense leaves) (Figure 4.6, table 4.3 and 4.4).
The linear relationship was stronger when comparing the autumn visual vitality index
and specific leaf area than the logarithmic relationship (Figure 4.6, table 4.3 and 4.4).
Note tree 4 had no leaves therefore specific leaf area could not be calculated for this
tree.
25
Visual vitality index
20
15
10
5
0
0
5
10
Specific leaf area in
15
mm2
20
mg-1
Figure 4.6 The autumn visual vitality index versus specific leaf area in mm2 mg-1. These data exclude
tree 4, as tree 4 is a zero value for leaf area. Therefore 35 Eucalyptus saligna trees are included in this
data set. Trend line = linear regression, P = 0.0008, r2 = 0.2905.
There was no statistical relationship between the percentage of sapwood area measured
at 0.3 m in height and the autumn visual vitality index (Table 4.3 and 4.4). Tree 21, 23
and 31 have high a Huber value compared to the other trees, however they are not
outlying data points as they have very low total leaf areas, that is 3.98, 5.87 and 1.7 m2
respectively (average leaf area = 45.78 m2) but not lower, or significantly lower,
sapwood areas at 44.96%, 37.06% and 31.28% respectively (average sapwood area =
34.83%). Hence the Huber value is relatively high for tree 21, 23 and 31, at 0.00289,
0.00156 and 0.00618 m2 m-2 respectively (average 0.000612 m2 m-2).
The statistical relationship between the autumn visual vitality index and the Huber
value was logarithmic rather than linear (Figure 4.7, Table 4.3 and 4.4). The
113
relationship between the log of the autumn visual vitality index data and the Huber
value was an inverse relationship, lower vitality trees had a higher Hv (Figure 4.7).
25
Visual vitality index
20
15
10
5
0
0
0.002
0.004
0.006
0.008
Huber value m2 m-2
a
25
Visual vitality index
20
15
10
5
0
0
b
0.001
0.002
0.003
0.004
Huber value m2 m-2
Figure 4.7 (a) Top, the autumn visual vitality index versus Huber value in m2 m-1. These data exclude
tree 4, as tree 4 is a zero value for leaf area. Therefore 35 Eucalyptus saligna trees are included in this
data set. Trend line = logarithmic regression, P = 0.0119, r2 = 0.1769; (b) above, the autumn visual
vitality index versus Huber value in m2 m-1. These data exclude trees 4 and 31; therefore 34 Eucalyptus
saligna trees are included in this data set.
114
Note tree 4 had no leaves therefore the Huber value could not be calculated for this
tree. The significance of the statistical relationship between the autumn visual vitality
index and the Huber value was highly dependent on the results from tree 31 (Figure
4.7a, Table 4.3 and 4.4). Without the value for tree 31 the statistical relationship was
no longer significant in either logarithmic or linear regression (Figure 4.7b, Table 4.3
and 4.4).
The relationship between the autumn visual vitality index and above ground biomass
was stronger in linear rather than logarithmic regression (Figure 4.8, Table 4.3 and
4.4). The relationship between the autumn visual vitality index and tree height was
stronger in logarithmic rather than linear regression (Figure 4.9, Table 4.3 and 4.4).
The relationship between the autumn visual vitality index and diameter at breast height
was stronger in linear rather than logarithmic regression (Table 4.3 and 4.4).
25
Visual vitality index
20
15
10
5
0
0
200
400
600
800
Above ground biomass kg
Figure 4.8 The autumn visual vitality index versus above ground biomass in kg. These data include all
trees therefore 36 Eucalyptus saligna trees are included in this data set. Trend line = linear regression, P
< 0.0001, r2 = 0.6450.
115
25
Visual vitality index
20
15
10
5
0
15
20
25
30
Tree height m
Figure 4.9 The autumn visual vitality index versus tree height in m. These data include all trees
therefore 36 Eucalyptus saligna trees are included in this data set. Trend line = logarithmic regression, P
< 0.0001, r2 = 0.6483.
The relationship between the autumn visual vitality index and diameter at breast height
was strongest in linear rather than logarithmic regression (Figure 4.10, Table 4.3 and
4.4).
25
Visual vitality index
20
15
10
5
0
100
150
200
250
300
350
Diameter at breast height mm
Figure 4.10 The autumn visual vitality index versus above diameter at breast height in mm. These data
include all trees therefore 36 Eucalyptus saligna trees are included in this data set. Trend line = linear
regression, P = 0.0007, r2 = 0.3431.
116
4.3.2 Results for comparing tree growth and wood density
The statistical relationship between wood density and the autumn visual vitality index
was not significant in either linear or logarithmic regression (Figure 4.11, tables 4.5,
4.6). The relationship between wood density and total leaf area, specific leaf area,
sapwood area and Huber value was not significant in either linear or logarithmic
regression (Tables 4.5, 4.6, figure 4.12). The statistical relationship between wood
density and above ground biomass, tree height and diameter at breast height was
significant (Figure 4.13, Tables 4.5, 4.6). The statistical relationships between wood
density and above ground biomass, tree height and diameter at breast height were
stronger in linear regression rather than logarithmic regression (Tables 4.5 and 4.6).
Table 4.5 Summarised results from simple linear regression analyses comparing wood density with
measures of tree growth.
N = the number of sample
P = the probability for the t test that the coefficient of the independent variable is equal to zero
r2 = the variation in wood density data that can be explained by the independent variable
Independent variable1
Total leaf area
Specific leaf area (excluding tree 4)
Percentage sapwood area
Sapwood area:leaf area (excluding tree 4)
Above ground biomass
Tree height
Diameter at breast height
Autumn visual vitality index
1. The dependant variable is basic wood density in all cases.
2. Statistical relationship is significant and positive
N
36
35
36
35
36
36
36
36
P
0.2399
0.0908
0.1075
0.7899
0.00472
0.00102
0.01202
0.0720
r2
0.0404
0.0842
0.0744
0.0022
0.2125
0.2779
0.1715
0.0921
Table 4.6 Summarised results from logarithmic regression analyses comparing the log of the
measurements of tree growth data with wood density.
N = the number of samples
P = the probability for the t test that the coefficient of wood density is equal to zero
r2 = the variation in the log of the measurements of tree growth that can be explained by wood density
Dependant variable1
N
Log of the total leaf area (excluding tree 4)
35
Log of the specific leaf area (excluding tree 4)
35
Log of the percentage sapwood area
36
Log of the sapwood area:leaf area (excluding tree 4)
35
Log of the above ground biomass
36
Log of the tree height
36
Log of the diameter at breast height
36
Log of the autumn visual vitality index
36
1. The dependant variable is the basic wood density data in all cases.
2. Statistical relationship is significant and positive
P
0.4989
0.0541
0.0801
0.5475
0.00552
0.00102
0.01302
0.0608
r2
0.0140
0.1079
0.0873
0.0111
0.2055
0.2746
0.1681
0.0996
117
700
Basic wood density kg/m3
650
600
550
500
450
400
0
5
10
15
20
25
Visual vitality index
Figure 4.11 Basic wood density in kg/m3 versus autumn visual vitality index. These data include all
trees therefore 36 Eucalyptus saligna trees are included in this data set.
700
Basic wood density kg/m3
650
600
550
500
450
400
0
5
10
15
20
Specific leaf area mm2 mg-1
Figure 4.12 Basic wood density in kg/m3 versus specific leaf area in mm2 mg-1. These data exclude tree
4, as tree 4 is a zero value for leaf area. Therefore 35 Eucalyptus saligna trees are included in this data
set.
118
700
Basic wood density kg/m3
650
600
550
500
450
400
100
150
200
250
300
350
Diameter at breast height mm
Figure 4.13 Basic wood density in k/m3versus diameter at breast height in meters at 1.3 m in height.
Includes all 36 Eucalyptus saligna trees. Trend line = linear regression, P = 0.0120, r2 = 0.1715.
4.3.3 Results for comparing tree growth and wood decay
The statistical relationship between the resi system of decay estimation and the autumn
visual vitality index was very significant and negative in both linear and logarithmic
regression (Figure 4.14, Table 4.7 and 4.8). The statistical relationship between the resi
system of decay estimation and the autumn visual vitality index was stronger in
logarithmic rather than linear regression (Figure 4.14, Table 4.7 and 4.8). In addition
there was a very significant inverse linear and a significant inverse logarithmic
relationship between the percentage of decay measured by the resi system and total
leaf area, above ground biomass, tree height and diameter at breast height (Tables 4.7
and 4.8).
The relationship between the resi system of decay estimation and specific leaf area was
significant and positive in both linear and logarithmic regression (Figure 4.15 and table
4.7 and 4.8). The statistical relationship between the resi system of decay estimation
and specific leaf area was slightly stronger in logarithmic rather than linear regression
(Figure 4.15 and table 4.7 and 4.8).
119
The statistical relationship between the resi system of decay estimation and the Huber
value was not significant (Figure 4.16 and table 4.7 and 4.8). There was no significant
statistical relationship between the resi system of decay estimation sapwood area
(Table 4.7 and 4.8).
Percentage of decay resi system
25
20
15
10
5
0
0
5
10
15
20
25
Visual vitality index
Figure 4.14 The percentage of decay using the resi system versus the visual vitality index. Trend line =
logarithmic regression, P <0.0001, r2 = 0.4849. All 36 trees are included in this data set.
Table 4.7 Summarised results from simple linear regression analyses comparing percentage of decay
using the resi system with measures of tree growth.
N = the number of samples
P = the probability for the t test that the coefficient of the independent variable is equal to zero
r2 = the variation in resi system data that can be explained by the independent variable
Independent variable1
N P
Total leaf area
36 <0.00012
Specific leaf area (excluding tree 4)
35 0.01463
Percentage sapwood area
36 0.1127
Sapwood area:leaf area (excluding tree 4)
35 0.4316
Above ground biomass
36 <0.00012
Tree height
36 <0.00012
Diameter at breast height
36 <0.00012
Autumn visual vitality index
36 <0.00012
1. The dependant variable is percentage of decay using the resi system in all cases
2. Statistical relationship is significant and negative.
3. Statistical relationship is significant and positive
120
r2
0.3660
0.1675
0.0724
0.0189
0.4233
0.5029
0.3699
0.4585
Table 4.8 Summarised results from logarithmic regression analyses comparing the log of the
measurements of tree growth data with percentage of decay using the resi system.
N = the number of samples
P = the probability for the t test that coefficient of the percentage of wood decay using the resi system is
equal to zero
r2 = the variation in the log of the measurements of tree growth that can be explained by the percentage
of wood decay.
Dependant variable1
N P
Log of the total leaf area (excluding tree 4)
35 0.00262
Log of the specific leaf area (excluding tree 4)
35 0.01113
Log of the percentage sapwood area
36 0.1050
Log of the sapwood area:leaf area (excluding tree 4)
35 0.2337
Log of the above ground biomass
36 <0.00012
Log of the tree height
36 <0.00012
Log of the diameter at breast height
36 <0.00012
Log of the autumn visual vitality index
36 <0.00012
1. The dependant variable is percentage of decay using the resi system in all cases
2. Statistical relationship is significant and negative.
3. Statistical relationship is significant and positive
r2
0.2434
0.1801
0.0754
0.0427
0.5386
0.4997
0.4143
0.4894
25
Percentage of decay resi system
20
15
10
5
0
0
5
10
15
20
Specific leaf area mm2 mg-1
Figure 4.15 Percentage of decay measured by the resi system versus specific leaf area in mm2 mg-1.
These data exclude tree 4, as tree 4 is a zero value for leaf area. Therefore 35 Eucalyptus saligna trees
are included in this data set. Trend line = logarithmic regression, P = 0.0111, r2 = 0.1801.
121
Percentage of decay resi system
25
20
15
10
5
0
0
0.001
0.002
0.003
0.004
0.005
0.006
0.007
Huber value m2 m-2
Figure 4.16 Percentage of decay measured by the resi system versus Huber value in m2 m-2. These data
exclude tree 4, as tree 4 is a zero value for leaf area. Therefore 35 Eucalyptus saligna trees are included
in this data set.
4.4 Discussion and conclusions
Few studies compare tree growth measurements with visual assessment, particularly
with individual trees rather than between forest stands. In a study of Eucalyptus
maculata (syn Corymbia maculata, Spotted Gum), Eucalyptus fibrosa (Ironbark) and
Eucalyptus drepanophylla, Grimes (1978) examined whether a crown assessment
system for individual trees based on crown position, crown size, crown density, crown
epicormic growth and dead branches could predict diameter at breast height. He found
that each of the 5 variables contributed significantly to a prediction equation for
diameter at breast height, but that for best results factors should be weighted
differently, for example epicormic growth on a three point scale and crown density on
a nine point scale. In a study of Eucalyptus camaldulensis trees in forest stands
Cunningham et al., (2007), used a six part visual assessment incorporating crown
vigour, the percentage of epicormic growth, percentage of live foliage, crown depth,
crown size and leaf condition. Only the “crown vigour” category was a consistent
predictor of site condition using the percentage live basal area and plant area index
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when comparing three forest stands (Cunningham et al., 2007). A crown status index
based on needle loss and necrosis was used to assess individual Abies alba (Sliver fir),
with a rating from 1-7 (Torelli et al., 1999). The crown class index was inversely
correlated with the width of the outermost annual ring in Abies alba (Torelli et al.,
1999).
In the current study above ground biomass, tree height and diameter at breast height
were related measures, dependent on the size of the trees. However, above ground
biomass, tree height and diameter at breast height, were highly correlated with the
autumn visual vitality index, supporting the validity of the index. The visual vitality
index used in this study was heavily based on that of Grimes (1978) and Martin et al.,
(2001) and appears to be an effective way of assessing relative tree vitality within E.
saligna in terms of growth; bringing together a number of criteria, such as crown
density, the size and position of the crown, and the number of dead and epicormic
branches, to assess trees. The autumn visual vitality index was also a good predictor of
total leaf area, making it the best surrogate measure for tree vitality without using more
sophisticated physiological measurements.
There was no relationship between sapwood area and the autumn visual vitality index
in E. saligna. On the other hand trees with a lower visual vitality score had a higher
Huber value. Higher Huber values mean greater transport capacity on a leaf area basis
(Gotsch et al., 2010). This is similar to results from some studies that have shown that
smaller trees have a higher Huber value (McDowell et al., 2002; Calvo-Alvarado et
al., 2008). This result could mean there is a change in biomass allocation that is, a
decline in leaf area per unit sapwood area, in the lower vitality E. saligna. A lower leaf
area per unit sapwood area value was also observed in Corymbia opaca, Eucalyptus
victorix, Eucalyptus camaldulensis, and savanna species, when they were suffering
from moisture stress (O’Grady et al., 2009; Gotsch et al., 2010).
The statistical relationships between wood density and the autumn visual vitality
index, total leaf area, sapwood area and Huber value were not significant, indicating
that there may not be a relationship between wood density and tree vitality. There was
a statistical relationship between wood density and measures of tree size such as above
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ground biomass, tree height and diameter at breast height. In previous studies
Eucalyptus saligna and Eucalyptus globulus were found to decrease in density closer
to the pith (DeBell et al., 2001; Johnstone, 2005). Eucalyptus saligna wood density
was also found to increase with age in a previous study (Lima, 1995). Therefore the
increase in wood density due to tree size in the current study may be due to natural
increases in wood density as the tree increases in size, rather than due to a relationship
between wood density and tree vitality.
There was an inverse relationship between specific leaf area and the autumn visual
vitality data in this study. This suggests the high vitality E. saligna may be producing a
greater proportion of denser leaves than those of lower vitality. Wood density was
negatively correlated with specific leaf area in a study on Corymbia opaca, Eucalyptus
victorix, Eucalyptus camaldulensis and Acacia aneura by O’Grady et al., (2009).
Wood density was also found to be negatively correlated with specific leaf area in
tropical savanna trees (Bucci et al., 2004). Both studies measured sapwood density
only, whereas this study measured basic wood density across the whole stem. There
was no correlation with specific leaf area and wood density in this study, however,
wood decay was positively correlated with specific leaf area, suggesting that the
relationship between very low density (decayed) wood and specific leaf area follows
the general trend of low density wood being associated with high specific leaf areas.
In a study by Wright et al., (2006), the Huber value was higher in Eucalyptus regnans
forest with lower wood density. Gotsch et al., (2010) found similar results in
Schefflera morototoni trees, as did Thomas et al., (2004) in Eucalypus camaldulensis,
Ackerly (2004) in shrubs from the California chaparral and Preston et al., (2006) in
California coastal species. Exceptions to this trend include where the Huber value
increased as wood density increased in tropical savanna trees (Bucci et al., 2004) and
in arid zone woodland trees (O’Grady et al., 2009). There was no statistical
relationship between either wood density or wood decay and Huber value in E. saligna
in this study.
The statistical relationships between total leaf area, above ground biomass, tree height,
diameter at breast height and wood decay in E. saligna were significant. These results
124
support some of the links being made between wood decay and tree growth in other
studies, despite the fact that those studies usually compare forest sites rather than
individual trees. The effect of time, different planting sites and pruning regimes on the
rate of decay in Eucalyptus nitens was examined by Barry et al., (2005). This is an
indirect way of assessing whether tree growth affects decay, and Barry et al., (2005)
did find some differences between sites and pruning regimes and the rate of decay
development in Eucalyptus nitens but they were not as great as the effect of the
passage of time on the trees. Tree age, planting site and pruning regimes also had an
effect on the rate of heartrot in Acacia mangium logs in another study by Barry et al.,
(2004).
Another indirect way of assessing the relationship between tree growth and wood
decay was to compare the rate of wood decay in “fast grown” (intensively managed)
Picea abies and “slow grown” Picea abies (in a multi layered forest) (Edman et al.,
2006). In this study wood discs were inoculated with decay causing organisms after
being cut from the tree. In contradiction to the current study it was found that the fast
grown trees decayed more quickly, probably due to their low density and higher
nitrogen content compared to the slow grown trees (Edman et. al., 2006). The reason
cited for increased decay rate in fast grown eucalypts in previous studies is their
impaired rate of branch shedding (Kile & Johnson, 2000). The rate of wood decay was
not assessed in the current study however the faster growing (bigger) trees in this study
had less wood decay. This is probably because the current study was assessing
individual tree differences at the one site rather than site differences, thus eliminating
the any variability between sites, such as air temperature or soil moisture.
There is an inverse relationship between tree growth and the percentage of wood decay
in a tree as measured by the autumn visual vitality index in E. saligna. Terho et al.,
(2007) also included some investigation of crown vitality in their assessment of wood
decay in Helsinki city. However crown vitality was assessed as either present or absent
by Terho et al., (2007) no detailed analysis of the relationship between the quantity of
decay and the extent of crown decline was undertaken. As the scores for vitality using
the visual vitality index in the current study were taken out of 25 a statistical
125
relationship between wood decay and visual vitality could be examined within E.
saligna.
The inverse relationship between tree growth and the percentage of wood decay in a
tree as measured by the autumn visual vitality index and the measurement of total leaf
area, above ground biomass, tree height and diameter at breast height confirms there is
an inverse relationship between tree growth and wood decay in E. saligna. However, it
is not clear whether the relationship between tree vitality and wood decay extends to
the physiological processes that govern tree growth. The extent of the relationship
between tree vitality and wood decay will be further investigated in Chapter 5 by
comparing chlorophyll florescence in both leaf and bark tissues with wood decay.
126
Chapter 5 – Leaf and bark chlorophyll fluorescence
and wood decay in Eucalyptus saligna
5.1 Introduction
Additional information on a trees’ status vis-à-vis vitality can be gained when
physiological parameters, in addition to tree growth measurements, are used in the
assessment of a tree. The previous chapter (chapter 4) was concerned with comparing
wood decay and tree growth parameters. The results from chapter 4 suggest there is an
inverse relationship between tree growth and wood decay.
The aim of this chapter is to establish if there is a relationship between a physiological
measure that can serve as an indicator for tree vitality and the percentage of wood
decay in a tree.
Field methods used to describe tree vitality via tree physiological measurements
include measuring chlorophyll content (Percival et al., 2008; Martinez-Trinidad et al.,
2010), electrical resistance or impedance (Shigo, 1991; Blazé, 1992; Repo et al.,
2005), plant water relations (Repo et al., 2005; Peña-Rojas et al., 2005) carbon dioxide
assimilation (Epron & Dreyer, 1992; Mene-Petite et al., 2003; Valladares, et al., 2004;
Peña-Rojas et al., 2005) and chlorophyll fluorescence (Epron & Dreyer, 1992; MenePetite et al., 2003; Pukacki & Kamińska-Rożek, 2005; Valladares, et al., 2004; PeñaRojas et al., 2005; Repo et al., 2005; Philip & Azlin, 2005; Weng et al., 2006; Thomas
et al., 2006; Percival et al., 2006).
Measuring the chlorophyll content in the leaves of trees is one way of assessing tree
vitality. Measuring the chlorophyll is normally done by extracting the leaf
photosynthetic pigment content in aqueous acetone. The chlorophyll content of leaves
was shown to decrease in Picea abies (Norway spruce) needles during drought stress
(Pukacki & Kamińska-Rożek, 2005). Chlorophyll content can also be estimated with a
chlorophyll content or SPAD meter. The SPAD meter correctly predicted low vitality
in low nitrogen leaves from Acer pseudoplatanus (sycamore), Quercus robur (English
oak) and Fagus sylvatica (European beech), but did not predict the total chlorophyll in
the leaves when compared to extraction by acetone solution (Percival et al., 2008).
127
CER (cambial electrical resistance) is another method used to assess the vitality of
trees. CER was not able to detect changes in vitality when compared to the diameter
growth of Liquidamber styraciflua (sweet gum) trees by Clark et al., (1992). On the
other hand Martinez-Trinidad et al., (2010), found the CER could detect tree vitality in
Quercus virginiana (live oak) when compared to a visual assessment of the trees if the
symptoms were acute. CER was also correlated with diameter at breast height in Acer
saccharum (sugar maple), but was not consistently correlated with a visual vitality
assessment method (Wargo et al., 2002).
Measuring leaf water potential (Ψw) is the most common parameter used to assess
vitality via the water status of a plant. When a plant is dehydrated its water potential
decreases (Kramer & Boyer, 1995). Stomatal conductance, another way of assessing
the water status of a plant, is generally correlated with air temperature and vapour
pressure deficit (Cohen & Cohen, 1983; Augé et al., 2000). Stomatal conductance
decreased in drought stressed Quercus ilex leaves in a study by Peña-Rojas et al.,
(2005), indicating a higher level moisture deficit and lower vitality in the Q. ilex. Sap
flow was measured by Zeppel & Eamus, (2008) to assess the difference between the
water use characteristics of Eucalyptus crebra and Calliitris glaucophylla. The E.
crebra sap velocity was higher than the C. glaucopylla, hence the former uses water at
a higher rate, and is therefore more likely to suffer from moisture deficit and a
corresponding decline in tree vitality. Sap flow measurements were also used by
Pfautsch et al., (2010) to track the water use of Eucalyptus regnans through different
seasons and understory densities in south-eastern Australia.
Assessing plant responses to environmental stresses or their ability to cope with these
stresses is often measured by chlorophyll fluorescence or CO2 assimilation or both.
The most commonly used chlorophyll fluorescence measurement is Fv/Fm, where Fv is
the difference between maximum (Fm) and minimum (F0) fluorescence (Maxwell &
Johnson, 2000). Fv/Fm is the theoretical measure of the quantum efficiency of
photosystem II (PSII) if all the PSII reaction centres are open (Maxwell & Johnson,
2000; Figure 2.3). Values for Fv/Fm of between 0.78 and 0.85 for healthy non-stressed
plants are common, with the optimal value are around 0.83 for most plants (Björkman
& Demmig, 1987; Maxwell & Johnson, 2000).
128
The analysis of the intermediate data points of the fast fluorescence rise is usually
called the analysis of the O-K-J-I-P polyphasic fast fluorescence rise (Strasser &
Stirbert, 2001; Govindjee, 2004; Strasser et al., 2004; Susplugas et al., 2000; Percival,
2005). The phases are O at origin (0.05 ms) K at approximately 0.2 ms, J at
approximately 2 ms, I at approximately 20 ms and P at approximately 200 ms,
depending on the actual curve (Strasser & Stirbert, 2001). O or F0 is measured when
all the plastoquinone QA electron carrier molecules are in their oxidized state between
PSII and PSI (Krause & Weis, 1984; Percival, 2005; figure 2.3). The K step, not
apparent in all cases, may be the result of an imbalance in electron flow coming to the
reaction centre from PS II in some species of plants (Strasser et al., 2004; figure 2.3).
The O-J phase is believed to represent the reduction of the QA molecule from QA to
QA- between PSII and PSI (Hsu & Leu, 2003; Strasser et al., 2004; Percival, 2005;
figure 2.3). J-I may be fluorescence from the abaxial layer of the sample (Hsu & Leu,
2003), or both the J-I and I-P phases could reflect the existence of fast and slow
reducing plastoquinone centres between PSII and PSI (Percival, 2005, figure 2.3). P or
Fm occurs when all the plastoquinone QA electron carrier molecules are in their
reduced state (Krause & Weis, 1984; Percival, 2005; figure 2.3).
Examples of chlorophyll fluorescence or CO2 assimilation being used to assess plant
stress include Epron et al., (1992), who found that drought stressed mature Quercus
petraea had strong declines in CO2 assimilation and a decline in the chlorophyll
fluorescence parameter Fv/Fm during the course of a hot summer day. Pukacki &
Kamińska-Rożek, (2005) found a decline in Fv/Fm for drought stressed Picea abies
seedlings. The effect of drought stress on woody saplings was measured using the
chlorophyll fluorescence parameter Fv/Fm and gas exchange by Valladares et al.,
(2004). CO2 assimilation decreased in drought stressed Quercus ilex leaves in a study
by Peña-Rojas et al., (2005). Percival et al., (2006) studied the effect of drought stress
on various genotypes of 6 year old containerised Fraxinus spp. and used Fv/Fm, CO2
assimilation and chlorophyll content to measure the effects. Percival & Henderson
(2002), used analyses of the OJIP fluorescence curve to monitor the damage of deicing salts to selected urban tree species. The effect of reduced temperatures on trees
was measured using Fv/Fm by both Repo et al., (2005) and Weng et al., (2006).
129
Thomas et al., (2006) assessed the effect of adding phosphorus to low phosphorus soils
on Eucalyptus grandis seedlings by testing CO2 assimilation and chlorophyll
fluorescence. Philip & Azlin, (2005) detected lower Fv/Fm values in a site with an
average bulk density of 1.8 g cm-3 than at site with an average bulk density of 1.2 g cm3
for Lagerstroemia speciosa.
Chlorophyll fluorescence is both highly cited and reliable as a field measure for
assessing tree vitality and hence was the physiological measure used in this study.
Many trees have chlorenchyma in their bark and stems in addition to their leaves
(Pfanz et al., 2002). Leaves are often shed either through natural processes or due to
attack from insects or diseases and in that case cortical photosynthesis may make up
some of the shortfall in carbon production for the plant (Pfanz et al., 2002; Eyles et al.,
2009). Hence bark chlorophyll fluorescence was also used as a measure of tree vitality
in this study.
Predawn leaf and stem water potentials were also measured, but they did not correlate
with any other measurement taken during the study (data not shown). Leaf and stem
water potentials were not measured during the day. The resi system was again used for
decay estimation in this chapter, because it was found to correlate with whole tree
wood density as a measure of the percentage of wood decay in chapter 3. Basic wood
density was compared with chlorophyll fluorescence parameters as wood density and
wood decay are closely related.
This chapter will use the physiological measurements of leaf and bark chlorophyll
fluorescence over three seasons and compare those measurements with the percentage
of wood decay and basic wood density in the trees, as further evidence of a
relationship or otherwise between tree vitality and wood decay in trees.
5.2 Materials and methods
5.2.1 Materials
All investigations on the trees in this study were conducted on the same 36 E. saligna
trees growing in a eucalypt plantation used in Chapter 3 and 4 (Figure 3.1). In order to
130
limit a major source of variation in many studies the plant material was even-aged (18
years old in 2006), in the one location and from a single provenance (Bateman’s Bay).
5.2.2 Methods
In this investigation chlorophyll fluorescence measurements were compared with a
visual vitality index over three seasons. Chlorophyll fluorescence measurements taken
in three seasons were also compared with basic wood density and the resi system for
estimating wood decay as described in chapter 3.
The Hansatech Handy Plant Efficiency Analyser
The Hansatech-Handy Plant Efficiency Analyser (Hansatech Instruments, King’s
Lynn, Norfolk, United Kingdom, figure 5.1) measures the intensity of a fluorescence
signal given off by chlorenchyma tissue in order to infer the efficiency of
photosynthesis, as a proxy for tree vitality (Maldonado-Rodriguez et al., 2003). The
measurement is normally carried out on leaves. The first step in the measurement
technique for the Hansatech-Handy Plant Efficiency Analyser (Handy PEA hereafter)
is to darken the leaf tissues for 10-30 minutes using a dark adaption clip supplied with
the instrument (Figure 5.1). After dark adapting the leaf surface the Handy PEA
flashes a high intensity pulse of red light (>3000 µmol m-2 s-1) from a focused array of
LEDs (Anon, 2001). This causes all PSII reaction centres to close and causes
maximum re-emission of energy as fluorescence. The intensity of the chlorophyll
fluorescence signal from the chloroplast is measured and digitized by the Handy PEA
in μs over a 1 second time period. The signal is measured in millivolts (mV). These
measurements can be downloaded onto a computer via a serial port connector.
131
Figure 5.1 The Hansatech-Handy Plant Efficiency Analyser for measuring chlorophyll fluorescence,
showing the dark adaption clip at right attached to a leaf.
Leaf measurements
Leaf chlorophyll fluorescence (CF hereafter) measurements were taken on fully
exposed sun leaves from the upper branches of the trees. As the trees were tall (17-27
m), branches approximately 10 mm in diameter were harvested with a 12 gauge
shotgun in the morning, between 6am and 8am depending on the season (Figure 5.2).
10 leaves from each tree were dark adapted for 30 minutes with the leaf clips supplied.
All trees were tested within 2-3 hours of being harvested as recommended by Epron &
Dreyer (1992).
132
Figure 5.2 Harvesting branches with a 12 gauge shot gun in preparation for leaf chlorophyll
fluorescence testing.
Bark measurements
Bark CF testing was done in a 350 mm strip in cross section at the north side of the
trees, 35 mm apart. The test area on the bark was circular and 4.5 mm in diameter
(Figure 3.5). 8-10 tests were done on each tree after being dark adapted for 30 minutes.
The bark was not damaged or removed in any way. Test results were excluded if the
bark was damaged, decorticating or had only recently been exposed to the sunlight.
The height at which the trees were measured was variable as it was necessary to
measure above the sock of rough bark at the base of the trees (see appendix 3.1,
Appendix to methods from chapter 5, appendix table A3.1 for measurement heights).
133
a
b
Figure 5.3 (a) Top, chlorophyll fluorescence testing being carried out on the bark of the Eucalyptus
saligna tree 13 at a height of approximately 6 m. Photograph taken by Matthew Sauvarin. (b) Below,
showing the method for darkening the bark prior to chlorophyll fluorescence testing.
Fv/Fm
The CF data were averaged from approximately 10 measurements from each tree in
each tissue (bark and leaf) and in each season. The ratio Fv/Fm was calculated from the
raw CF data. Fv/Fm is a derived measure Fv = Fm – Fo, where Fv is the difference
between maximum (Fm) and minimum (Fo) chlorophyll fluorescence (Maxwell,
2000).
134
OKJIP points
In addition to calculating the Fv/Fm ratio the time data taken over a 1 second period
were logarithmically transformed and the O-J-I-P CF phases were allocated following
the method devised by Strasser & Stirbert (2001). Each polyphasic increase in
fluorescence was characterized by examining logarithmic graphs for each season and
in both tissue types; leaf and bark. After an exponential rise in the graphed data each
phase was deemed complete, with the next phase being deemed to start at the critical
point (O, J, I or P) labeled on the graphs (Figure 5.4, 5.5 and appendix 3.1, Appendix
to methods from chapter 5, appendix figures A3.1 – A3.4). Every step is followed by a
characteristic temporary decrease or dip (Strasser et al., 2004; Figure 5.4, 5.5 and
appendix 3.1, Appendix to methods from chapter 5, appendix figures A3.1 – A3.4).
There was no “K” step observed on the graphs. “O” was at origin, taken at 0.05 ms, as
in many other studies (Krause & Weis, 1984; Strasser & Stirbert, 2001; Govindjee,
2004; Strasser et al., 2004; Susplugas et al., 2000; Percival, 2005). The O-J phase was
characterized as ending at 4 ms (J step, figure 5.4 and appendix 3.1, Appendix to
methods from chapter 5, appendix figures A3.1 – A3.4), where in other studies it is
usually slightly faster at 2-3 ms. The “I” step in the leaf fluorescence was observed at
60 ms and in bark at 90 ms, later than the previously reported 20-30 ms. The “P” step
was observed at approximately 700 ms on leaf fluorescence graphs, previously
observed at 200-300 ms in other studies. The “P” step was not observed in bark
fluorescence as the last recording point taken by the instrument was at 1000 ms, and
fluorescence was still increasing at this time (Figure 5.5 and appendix 3.1, Appendix to
methods from chapter 5, appendix figures A3.1 – A3.4). The JIP test was not applied
to the data, instead comparisons were made using the raw scores for O (0.05 ms all
data) J (4 ms all data) I (60 ms leaf 90 ms bark data) and P (700 ms leaf data) and the
1000 ms data point on bark.
135
O
1600
J
I
P
1400
Chorophyll fluorescence mV
1200
1000
800
600
400
200
0
0.01
0.1
1
10
100
1000
Log time ms
Figure 5.4 The fast fluorescence rise for Eucalyptus saligna leaves in summer over a 1 second time
period showing the O-J-I-P phases. Chlorophyll fluorescence in mV versus logarithmically transformed
time in ms.
O
J
I
1000
800
Chlorophyll fluorescence mV
700
600
500
400
300
200
100
0
0.01
0.1
1
10
100
1000
Log time ms
Figure 5.5 The fast fluorescence rise for Eucalyptus saligna bark in spring over a 1 second time period
showing the O-J-I phases. Chlorophyll fluorescence in mV versus logarithmically transformed time in
ms.
136
Visual vitality index
The 36 E. saligna were tested in three seasons; spring (October 2007), summer
(January 2008), and autumn (March 2008). The method is described in chapter 4.
Wood density measurement and wood decay estimation
The 36 E. saligna were tested for basic wood density from a small sample collected at
1.5 m in height from the trees when they were felled in 2008. The method is described
in chapter 3. The 36 E. saligna were tested in 2006 and 2007 using the resi system
described in chapter 3.
Statistical analysis of data
Power analysis was done as in chapter 3 to calculate the number of trees to use in the
project to minimize the resources required and to ensure there was enough statistical
power in the analysis to detect any important statistical relationships (Lenth, 2001).
Table 3.1 (chapter 3) shows the values used. VIF (variance inflation factor) was
estimated as high, as preliminary VIF tests revealed some multicollinearity between
measurement values. Detectable beta (estimated detectable difference) was estimated
as low, as the trees were small and may not have had a large amount of decay in them.
A comparison was made between the spring, summer and autumn chlorophyll
fluorescence data and the spring, summer and autumn visual vitality index. Simple
linear regression analysis was performed comparing spring, summer and autumn
chlorophyll fluorescence data and basic wood density measured in autumn. Simple
linear regression analysis was also performed comparing spring, summer and autumn
chlorophyll fluorescence data and wood decay as measured by the resi system.
Simple linear regression analyses (Table 5.1) were calculated using the software
package SAS (Statistical Analysis System) version 9.1. Logarithmic regression
analysis was not used as it could be seen from graphing the data that linear regressions
fitted the data and that the value of increasing the fit using logarithmic regressions
would be negligible.
137
Table 5.1 Simple linear regression analyses performed in this study in relation to tree growth, wood
density and wood decay estimation methods.
Dependent variable
Spring visual vitality index
Spring visual vitality index
Spring visual vitality index
Spring visual vitality index
Spring visual vitality index
Summer visual vitality index
Summer visual vitality index
Summer visual vitality index
Summer visual vitality index
Summer visual vitality index
Autumn visual vitality index
Autumn visual vitality index
Autumn visual vitality index
Autumn visual vitality index
Autumn visual vitality index
Basic wood density
Basic wood density
Basic wood density
Basic wood density
Basic wood density
Basic wood density
Basic wood density
Basic wood density
Basic wood density
Basic wood density
Basic wood density
Basic wood density
Basic wood density
Basic wood density
Basic wood density
Percentage of decay using the resi system
Percentage of decay using the resi system
Percentage of decay using the resi system
Percentage of decay using the resi system
Percentage of decay using the resi system
Percentage of decay using the resi system
Percentage of decay using the resi system
Percentage of decay using the resi system
Percentage of decay using the resi system
Percentage of decay using the resi system
Percentage of decay using the resi system
Percentage of decay using the resi system
Percentage of decay using the resi system
Percentage of decay using the resi system
Percentage of decay using the resi system
138
Independent variable
Spring leaf chlorophyll fluorescence - Fv/Fm
Spring leaf chlorophyll fluorescence – “O” step
Spring leaf chlorophyll fluorescence – “J” step
Spring leaf chlorophyll fluorescence – “I” step
Spring leaf chlorophyll fluorescence – “P” step
Summer leaf chlorophyll fluorescence - Fv/Fm
Summer leaf chlorophyll fluorescence – “O” step
Summer leaf chlorophyll fluorescence – “J” step
Summer leaf chlorophyll fluorescence – “I” step
Summer leaf chlorophyll fluorescence – “P” step
Autumn leaf chlorophyll fluorescence - Fv/Fm
Autumn leaf chlorophyll fluorescence – “O” step
Autumn leaf chlorophyll fluorescence – “J” step
Autumn leaf chlorophyll fluorescence – “I” step
Autumn leaf chlorophyll fluorescence – “P” step
Spring leaf chlorophyll fluorescence - Fv/Fm
Spring leaf chlorophyll fluorescence – “O” step
Spring leaf chlorophyll fluorescence – “J” step
Spring leaf chlorophyll fluorescence – “I” step
Spring leaf chlorophyll fluorescence – “P” step
Summer leaf chlorophyll fluorescence - Fv/Fm
Summer leaf chlorophyll fluorescence – “O” step
Summer leaf chlorophyll fluorescence – “J” step
Summer leaf chlorophyll fluorescence – “I” step
Summer leaf chlorophyll fluorescence – “P” step
Autumn leaf chlorophyll fluorescence - Fv/Fm
Autumn leaf chlorophyll fluorescence – “O” step
Autumn leaf chlorophyll fluorescence – “J” step
Autumn leaf chlorophyll fluorescence – “I” step
Autumn leaf chlorophyll fluorescence – “P” step
Spring leaf chlorophyll fluorescence - Fv/Fm
Spring leaf chlorophyll fluorescence – “O” step
Spring leaf chlorophyll fluorescence – “J” step
Spring leaf chlorophyll fluorescence – “I” step
Spring leaf chlorophyll fluorescence – “P” step
Summer leaf chlorophyll fluorescence - Fv/Fm
Summer leaf chlorophyll fluorescence – “O” step
Summer leaf chlorophyll fluorescence – “J” step
Summer leaf chlorophyll fluorescence – “I” step
Summer leaf chlorophyll fluorescence – “P” step
Autumn leaf chlorophyll fluorescence - Fv/Fm
Autumn leaf chlorophyll fluorescence – “O” step
Autumn leaf chlorophyll fluorescence – “J” step
Autumn leaf chlorophyll fluorescence – “I” step
Autumn leaf chlorophyll fluorescence – “P” step
5.3 Results
The results illustrate that for E. saligna trees leaf chlorophyll fluorescence could
predict visual tree vitality in summer using the “O” step. (Table 5.3 and figure 5.7).
The results also show that for E. saligna trees bark chlorophyll fluorescence could
predict visual tree vitality in autumn and summer using the Fv/Fm ratio and in autumn
using the “O” step (Tables 5.6 and 5.7 and figures 5.10 and 5.11).
The results show that for E. saligna leaf chlorophyll fluorescence could predict basic
wood density in summer using the Fv/Fm ratio and using the “O” step. (Table 5.9 and
figure 5.13). The results also illustrate that bark chlorophyll fluorescence could predict
basic wood density in spring using the Fv/Fm ratio (Table 5.11 and figure 5.15).
The results illustrate that for E. saligna trees leaf chlorophyll fluorescence could
predict wood decay in spring using the “O” step and in summer using the ratio Fv/Fm.
(Tables 5.14 and 5.15 and figures 5.18 and 5.19). The results also show that bark
chlorophyll fluorescence could predict wood decay in spring, summer and autumn
using the Fv/Fm ratio (Tables 5.17, 5.18 and 5.19 and figures 5.21, 5.22 and 5.23).
Complete raw results for chapter 5 appear in Appendix 3 (Appendix 3.2 Appendix to
results from chapter 5, appendix tables A3.4 – A3.7).
The leaf data for tree 19 in spring, summer and autumn was an outlying fluorescence
result, being more that 2 standard deviations away from the next lowest result, with the
exception of the autumn leaf Fv/Fm ratio data (for example leaf spring Fv/Fm ratio tree
19, 0.6793; tree 11, 0.8074, Appendix 3.2 Appendix to results from chapter 5,
appendix tables A3.4 – A3.7). Therefore the leaf chlorophyll fluorescence statistical
analyses in this chapter were presented without tree 19, with the exception of the
autumn leaf Fv/Fm ratio data.
The bark data for tree 4 in spring was also an outlying fluorescence result, being more
that 2 standard deviations away from the next lowest result (for example bark spring
Fv/Fm ratio tree 4, 0.5390; tree 17, 0.8006, Appendix 3.2 Appendix to results from
139
chapter 5, appendix tables A3.4 – A3.7). Tree 4 had no leaves in spring, summer or
autumn, and the bark had also died by the summer sampling period. Therefore the leaf
and bark chlorophyll fluorescence statistical analyses in this chapter were presented
without tree 4.
5.3.1 Results for comparing leaf fluorescence and the visual vitality index
There was no statistically significant relationship between spring leaf chlorophyll
fluorescence and the visual vitality index (P > 0.05, Table 5.2 and figure 5.6). There
was a statistically significant and negative relationship between summer leaf
chlorophyll fluorescence and the visual vitality index at the “O” step (P < 0.05, Table
5.3 and figure 5.7). There was no statistically significant relationship between summer
leaf Fv/Fm and the visual vitality index or between the J, I and P steps and visual
vitality (Table 5.3). There was no statistically significant relationship between autumn
leaf chlorophyll fluorescence and the visual vitality index (P > 0.05, Table 5.4, figure
5.8).
Table 5.2 Summarised results from simple linear regression analyses comparing spring leaf
fluorescence with the spring visual vitality index.
N = the number of samples
P = the probability for the t test that the coefficient of the independent variable is equal to zero
r2 = the variation in the dependent variable that can be explained by the fluorescence data.
Independent variable1
Spring leaf fluorescence – Fv/Fm
Spring leaf fluorescence - “O” step
Spring leaf fluorescence - “J” step
Spring leaf fluorescence - “I” step
Spring leaf fluorescence - “P” step
1. The dependent variable is the spring visual vitality index in all cases
140
N
34
34
34
34
34
P
0.4740
0.4871
0.5966
0.3550
0.6238
r2
0.0161
0.0152
0.0089
0.0268
0.0076
30
Spring visual vitality index
25
20
15
10
5
0
0.8000
0.8100
0.8200
0.8300
0.8400
0.8500
0.8600
Spring leaf FvFm ratio
Figure 5.6 Spring visual vitality index versus spring leaf Fv/Fm. These data exclude tree 19 and tree 4.
Therefore 34 Eucalyptus saligna trees are included in this data set. Fv/Fm ratio data begins at 0.8000.
Table 5.3 Summarised results from simple linear regression analyses comparing summer leaf
fluorescence with the summer visual vitality index.
N = the number of samples
P = the probability for the t test that the coefficient of the independent variable is equal to zero
r2 = the variation in the dependent variable that can be explained by the fluorescence data.
Independent variable1
N
Summer leaf fluorescence – Fv/Fm
34
Summer leaf fluorescence - “O” step
34
Summer leaf fluorescence - “J” step
34
Summer leaf fluorescence - “I” step
34
Summer leaf fluorescence - “P” step
34
1. The dependent variable is the summer visual vitality index in all cases
2. Statistical relationship is significant and positive
3. Statistical relationship is significant and negative
P
0.0600
0.04093
0.1178
0.2271
0.2867
r2
0.1062
0.1243
0.0747
0.0453
0.0354
141
Summer visual vitality index
25
20
15
10
5
0
100
150
200
250
300
Summer leaf CF "O" step mV
Figure 5.7 Summer visual vitality index versus summer leaf chlorophyll fluorescence at the “O” step in
mV. These data exclude trees 19 and 4. Therefore 34 Eucalyptus saligna trees are included in this data
set. Chlorophyll fluorescence data begins at 100 mV. Trend line = linear regression, P = 0.0409, r2 =
0.1243.
Table 5.4 Summarised results from simple linear regression analyses comparing autumn leaf
fluorescence with the autumn visual vitality index.
N = the number of samples
P = the probability for the t test that the coefficient of the independent variable is equal to zero
r2 = the variation in the dependent variable that can be explained by the fluorescence data.
Independent variable1
N
Autumn leaf fluorescence – Fv/Fm
35
Autumn leaf fluorescence - “O” step
34
Autumn leaf fluorescence - “J” step
34
Autumn leaf fluorescence - “I” step
34
Autumn leaf fluorescence - “P” step
34
1. The dependent variable is the autumn visual vitality index in all cases
2. Statistical relationship is significant and negative
142
P
0.4686
0.1365
0.9436
0.4139
0.3171
r2
0.0160
0.0679
0.0002
0.0210
0.0313
25
Autumn visual vitality index
20
15
10
5
0
100
150
200
250
300
Autumn leaf CF "O" step mV
Figure 5.8 Autumn visual vitality index versus autumn leaf chlorophyll fluorescence at the “O” step in
mV. These data exclude trees 19 and 4. Therefore 34 Eucalyptus saligna trees are included in this data
set. Chlorophyll fluorescence data begins at 100 mV.
5.3.2 Results for comparing bark fluorescence and the visual vitality
index
There was no statistically significant relationship between spring bark chlorophyll
fluorescence and the visual vitality index (P > 0.05, Table 5.5, figure 5.9). There was a
statistically significant and positive relationship between summer bark Fv/Fm and the
visual vitality index (P < 0.05, Table 5.6 and figure 5.10). There was no statistically
significant relationship between summer bark chlorophyll fluorescence at the O, J or I
step and the visual vitality index (P > 0.05, Table 5.6). There was no statistically
significant relationship between summer bark chlorophyll fluorescence at 1000 ms and
the visual vitality index (P > 0.05, Table 5.6). There was a statistically significant and
positive relationship between the bark Fv/Fm measured in autumn and the visual vitality
index (P < 0.05), and a statistically significant and negative relationship when
comparing the O step and the visual vitality index (Table 5.7 and figure 5.11). There
was no statistically significant relationship between autumn bark chlorophyll
fluorescence at the J or I step and the visual vitality index (P > 0.05, Table 5.7). There
was no statistically significant relationship between bark CF at 1000 ms and the visual
vitality index (Table 5.7).
143
Table 5.5 Summarised results from simple linear regression analyses comparing spring bark
fluorescence with the spring visual vitality index.
N = the number of samples
P = the probability for the t test that the coefficient of the independent variable is equal to zero
r2 = the variation in the dependent variable that can be explained by the fluorescence data.
Independent variable1
Spring bark fluorescence – Fv/Fm
Spring bark fluorescence - “O” step
Spring bark fluorescence - “J” step
Spring bark fluorescence - “I” step
Spring bark fluorescence – 1000 ms
1. The dependent variable is the spring visual vitality index in all cases
2. Statistical relationship is significant and positive
N
35
35
35
35
35
P
0.1816
0.4907
0.4835
0.6702
0.9634
r2
0.0534
0.0145
0.0150
0.0056
0.0001
30
Spring visual vitality index
25
20
15
10
5
0
0.7900
0.8000
0.8100
0.8200
0.8300
0.8400
0.8500
0.8600
Spring bark FvFm ratio
Figure 5.9 Spring visual vitality index versus spring bark Fv/Fm. These data exclude tree 4. Therefore
35 Eucalyptus saligna trees are included in this data set. Fv/Fm ratio data begins at 0.7900
144
Table 5.6 Summarised results from simple linear regression analyses comparing summer bark
fluorescence with the summer visual vitality index.
N = the number of samples
P = the probability for the t test that the coefficient of the independent variable is equal to zero
r2 = the variation in the dependent variable that can be explained by the fluorescence data.
Independent variable1
N
Summer bark fluorescence – Fv/Fm
35
Summer bark fluorescence - “O” step
35
Summer bark fluorescence - “J” step
35
Summer bark fluorescence - “I” step
35
Summer bark fluorescence - 1000 ms
35
1. The dependent variable is the summer visual vitality index in all cases
2. Statistical relationship is significant and positive
P
0.00032
0.2561
0.3257
0.9480
0.8576
r2
0.3327
0.0389
0.0293
0.0001
0.0010
25
Summer visual vitality index
20
15
10
5
0
0.8000
0.8100
0.8200
0.8300
0.8400
0.8500
0.8600
Summer bark FvFm ratio
Figure 5.10 Summer visual vitality index versus summer bark Fv/Fm. 35 Eucalyptus saligna trees are
included in this data set, as tree 4 had no live bark. Fv/Fm ratio data begins at 0.8000. Trend line =
linear regression, P = 0.0003, r2 = 0.3327.
145
Table 5.7 Summarised results from simple linear regression analyses comparing autumn bark
fluorescence with the autumn visual vitality index.
N = the number of samples
P = the probability for the t test that the coefficient of the independent variable is equal to zero
r2 = the variation in the dependent variable that can be explained by the fluorescence data.
Independent variable1
N
Autumn bark fluorescence – Fv/Fm
35
Autumn bark fluorescence - “O” step
35
Autumn bark fluorescence - “J” step
35
Autumn bark fluorescence - “I” step
35
Autumn bark fluorescence - 1000 ms
35
1. The dependent variable is the autumn visual vitality index in all cases
2. Statistical relationship is significant and positive
3. Statistical relationship is significant and negative
P
<0.00012
0.03043
0.0829
0.4445
0.7120
r2
0.3973
0.1342
0.0883
0.0178
0.0042
Autumn visual vitality index
25
20
15
10
5
0
0.79
0.8
0.81
0.82
0.83
0.84
0.85
Autumn bark FvFm ratio
Figure 5.11 Autumn visual vitality index versus autumn bark Fv/Fm. 35 Eucalyptus saligna trees are
included in this data set, as tree 4 had no live bark. Fv/Fm ratio data begins at 0.8000. Trend line = linear
regression, P <0.0001, r2 = 0.3973.
5.3.3 Results for comparing leaf fluorescence and basic wood density
There was no statistically significant relationship between spring and autumn leaf
chlorophyll fluorescence and basic wood density (P > 0.05, Table 5.8 and 5.10 and
figure 5.12 and 5.14). There was a statistically significant and positive relationship
146
between summer leaf Fv/Fm and basic wood density (Table 5.9 and figure 5.13). There
was no statistical relationship between summer leaf chlorophyll fluorescence at the O,
J, I or P step and basic wood density (Table 5.9).
Table 5.8 Summarised results from simple linear regression analyses comparing spring leaf
fluorescence with basic wood density data.
N = the number of samples
P = the probability for the t test that the coefficient of the independent variable is equal to zero
r2 = the variation in the dependent variable that can be explained by the fluorescence data.
Independent variable1
N
Spring leaf fluorescence – Fv/Fm
34
Spring leaf fluorescence - “O” step
34
Spring leaf fluorescence - “J” step
34
Spring leaf fluorescence - “I” step
34
Spring leaf fluorescence - “P” step
34
1. The dependent variable is the spring basic wood density data in all cases
P
0.5311
0.7408
0.6197
0.4620
0.8914
r2
0.0124
0.0035
0.0078
0.0170
0.0006
700
Basic wood density kg/m3
650
600
550
500
450
400
0.8000
0.8100
0.8200
0.8300
0.8400
0.8500
0.8600
Spring leaf FvFm ratio
Figure 5.12 Basic wood density in kg/m3 versus spring leaf Fv/Fm. These data exclude trees 19 and 4.
Therefore 34 Eucalyptus saligna trees are included in this data set. Fv/Fm ratio data begins at 0.8000,
basic density data begins at 400 kg/m3.
147
Table 5.9 Summarised results from simple linear regression analyses comparing summer leaf
fluorescence with the basic wood density data.
N = the number of samples
P = the probability for the t test that the coefficient of the independent variable is equal to zero
r2 = the variation in the dependent variable that can be explained by the fluorescence data.
Independent variable1
N
Summer leaf fluorescence – Fv/Fm
34
Summer leaf fluorescence - “O” step
34
Summer leaf fluorescence - “J” step
34
Summer leaf fluorescence - “I” step
34
Summer leaf fluorescence - “P” step
34
1. The dependent variable is the summer basic wood density data in all cases
2. Statistical relationship is significant and positive
3. Statistical relationship is significant and negative
P
0.00102
0.0724
0.0845
0.1343
0.9133
r2
0.2910
0.0974
0.0901
0.0687
0.0004
700
Basic Wood density kg/m3
650
600
550
500
450
400
0.8200
0.8300
0.8400
0.8500
0.8600
0.8700
0.8800
Summer leaf FvFm ratio
Figure 5.13 Basic wood density in kg/m3 versus summer leaf Fv/Fm. These data exclude trees 19 and 4.
Fv/Fm ratio data begins at 0.8200, basic density data begins at 400 kg/m3. Therefore 34 Eucalyptus
saligna trees are included in this data set. Trend line = linear regression, P = 0.0010, r2 = 0.2910.
148
Table 5.10 Summarised results from simple linear regression analyses comparing autumn leaf
fluorescence with the basic wood density data.
N = the number of samples
P = the probability for the t test that the coefficient of the independent variable is equal to zero
r2 = the variation in the dependent variable that can be explained by the fluorescence data.
Independent variable1
N
Autumn leaf fluorescence – Fv/Fm
35
Autumn leaf fluorescence - “O” step
34
Autumn leaf fluorescence - “J” step
34
Autumn leaf fluorescence - “I” step
34
Autumn leaf fluorescence - “P” step
34
1. The dependent variable is the autumn basic wood density data in all cases.
P
0.3872
0.8104
0.5575
0.9049
0.7465
r2
0.0227
0.0018
0.0109
0.0005
0.0033
700
Basic wood density kg/m3
650
600
550
500
450
400
0.7200 0.7400 0.7600 0.7800 0.8000 0.8200 0.8400 0.8600 0.8800
Autumn leaf FvFm ratio
Figure 5.14 Basic wood density in kg/m3 versus autumn leaf Fv/Fm. These data exclude tree 4 as tree 4
had no leaves. Therefore 35 Eucalyptus saligna trees are included in this data set. Fv/Fm ratio data begins
at 0.7200, basic density data begins at 400 kg/m3.
5.3.4 Results for comparing bark fluorescence and basic wood density
There was no statistically significant relationship between summer and autumn bark
chlorophyll fluorescence and basic wood density (P > 0.05, table 5.12 and 5.13 and
figure 5.16 and 5.17). There was a statistically significant and positive relationship
between spring bark Fv/Fm and basic wood density (Table 5.11 and figure 5.15). There
149
was no statistically significant relationship between spring bark chlorophyll
fluorescence at the O, J or I step and basic wood density (P > 0.05, Table 5.11). There
was no statistically significant relationship between spring bark chlorophyll
fluorescence at 1000 ms and basic wood density (P > 0.05, Table 5.11).
Table 5.11 Summarised results from simple linear regression analyses comparing spring bark
fluorescence with basic wood density data.
N = the number of samples
P = the probability for the t test that the coefficient of the independent variable is equal to zero
r2 = the variation in the dependent variable that can be explained by the fluorescence data.
Independent variable1
N
Spring bark fluorescence – Fv/Fm
35
Spring bark fluorescence - “O” step
35
Spring bark fluorescence - “J” step
35
Spring bark fluorescence - “I” step
35
Spring bark fluorescence - 1000 ms
35
1. The dependent variable is the spring basic wood density data in all cases
2. Statistical relationship is significant and positive
P
0.03512
0.7017
0.6913
0.2980
0.1726
r2
0.1277
0.0045
0.0048
0.0328
0.0556
700
Basic wood density kg/m3
650
600
550
500
450
400
0.7900
0.8000
0.8100
0.8200
0.8300
0.8400
0.8500
0.8600
Spring bark FvFm ratio
Figure 5.15 Basic wood density in kg/m3 versus spring bark Fv/Fm. These data exclude tree 4. Therefore
35 Eucalyptus saligna trees are included in this data set. Fv/Fm ratio data begins at 0.7900, basic density
data begins at 400 kg/m3 Trend line = linear regression, P = 0.0351, r2 = 0.1277.
150
Table 5.12 Summarised results from simple linear regression analyses comparing summer bark
fluorescence with the basic wood density data.
N = the number of samples
P = the probability for the t test that the coefficient of the independent variable is equal to zero
r2 = the variation in the dependent variable that can be explained by the fluorescence data.
Independent variable1
N
Summer bark fluorescence – Fv/Fm
35
Summer bark fluorescence - “O” step
35
Summer bark fluorescence - “J” step
35
Summer bark fluorescence - “I” step
35
Summer bark fluorescence - 1000 ms
35
1. The dependent variable is the summer visual vitality index in all cases
P
0.5121
0.3090
0.8317
0.2560
0.1910
r2
0.0131
0.0313
0.0014
0.0389
0.0512
700
Basic wood density kg/m3
650
600
550
500
450
400
0.8000
0.8100
0.8200
0.8300
0.8400
0.8500
0.8600
Summer bark FvFm ratio
Figure 5.16 Basic wood density in kg/m3 versus summer bark Fv/Fm. 35 Eucalyptus saligna trees are
included in this data set, as tree 4 had no live bark. Fv/Fm ratio data begins at 0.8000, basic density data
begins at 400 kg/m3.
151
Table 5.13 Summarised results from simple linear regression analyses comparing autumn bark
fluorescence with the basic wood density data.
N = the number of samples
P = the probability for the t test that the coefficient of the independent variable is equal to zero
r2 = the variation in the dependent variable that can be explained by the fluorescence data.
Independent variable1
N
Autumn bark fluorescence – Fv/Fm
35
Autumn bark fluorescence - “O” step
35
Autumn bark fluorescence - “J” step
35
Autumn bark fluorescence - “I” step
35
Autumn bark fluorescence - 1000 ms
35
1. The dependent variable is the autumn basic wood density data in all cases.
P
0.2490
0.4265
0.4608
0.7343
0.8275
r2
0.0401
0.0193
0.0166
0.0035
0.0015
700
Basic wood density kg/m3
650
600
550
500
450
400
0.7900
0.8000
0.8100
0.8200
0.8300
0.8400
0.8500
Autumn bark FvFm ratio
Figure 5.17 Basic wood density in kg/m3 versus autumn bark Fv/Fm. 35 Eucalyptus saligna trees are
included in this data set, as tree 4 had no live bark. Fv/Fm ratio data begins at 0.7900, basic density data
begins at 400 kg/m3.
5.3.5 Results for comparing leaf fluorescence and wood decay
There was a statistically significant and positive relationship between the spring leaf
CF at the O step and wood decay (P < 0.05, Table 5.14 and figure 5.18). There was no
statistically significant relationship between the spring leaf Fv/Fm ratio and wood decay
(P > 0.05, Table 5.14). There was no statistically significant relationship between
152
spring leaf CF at the J, I and P step and wood decay (P > 0.05, Table 5.14). There was
a statistically significant and negative relationship between the summer leaf Fv/Fm ratio
and wood decay (P < 0.05, Table 5.15 and figure 5.19). There was no statistically
significant relationship between summer leaf CF at the O, J, I, and P step and wood
decay (P > 0.05, Table 5.15). There was no statistically significant relationship
between autumn leaf CF and wood decay (P > 0.05, Table 5.16).
Table 5.14 Summarised results from simple linear regression analyses comparing spring leaf
fluorescence with wood decay data.
N = the number of samples
P = the probability for the t test that the coefficient of the independent variable is equal to zero
r2 = the variation in the dependent variable that can be explained by the fluorescence data.
Independent variable1
Spring leaf fluorescence – Fv/Fm
Spring leaf fluorescence - “O” step
Spring leaf fluorescence - “J” step
Spring leaf fluorescence - “I” step
Spring leaf fluorescence - “P” step
1. The dependent variable is wood decay in all cases
2. Statistical relationship is significant and positive
N
34
34
34
34
34
P
0.5051
0.00412
0.0760
0.4555
0.1574
r2
0.0140
0.2296
0.0951
0.0175
0.0615
Percentage of decay resi system
25
20
15
10
5
0
170
190
210
230
250
270
Spring leaf CF "O" step mV
Figure 5.18 Percentage of decay using the resi system versus spring leaf chlorophyll fluorescence at the
“O” step in mV. These data exclude tree 19 and tree 4. Therefore 34 Eucalyptus saligna trees are
included in this data set. Chlorophyll fluorescence data begins at 100 mV. Trend line = linear regression,
P = 0.0041, r2 = 0.2296.
153
Table 5.15 Summarised results from simple linear regression analyses comparing summer leaf
fluorescence with the wood decay data.
N = the number of samples
P = the probability for the t test that the coefficient of the independent variable is equal to zero
r2 = the variation in the dependent variable that can be explained by the fluorescence data.
Independent variable1
Summer leaf fluorescence – Fv/Fm
Summer leaf fluorescence - “O” step
Summer leaf fluorescence - “J” step
Summer leaf fluorescence - “I” step
Summer leaf fluorescence - “P” step
1. The dependent variable is the summer wood decay data in all cases
2. Statistical relationship is significant and negative
3. Statistical relationship is significant and positive
N
34
34
34
34
34
P
0.02482
0.0800
0.1035
0.4524
0.6596
r2
0.1477
0.0927
0.0807
0.0178
0.0061
Percentage of decay resi method
25
20
15
10
5
0
0.8200
0.8300
0.8400
0.8500
0.8600
0.8700
0.8800
Summer leaf FvFm ratio
Figure 5.19 Percentage of decay using the resi system versus summer leaf Fv/Fm. These data exclude
trees 19 and 4. Therefore 34 Eucalyptus saligna trees are included in this data set. Fv/Fm ratio data
begins at 0.8200. Trend line = linear regression, P = 0.0248, r2 = 0.1477.
154
Table 5.16 Summarised results from simple linear regression analyses comparing autumn leaf
fluorescence with the wood decay data.
N = the number of samples
P = the probability for the t test that the coefficient of the independent variable is equal to zero
r2 = the variation in the dependent variable that can be explained by the fluorescence data.
Independent variable1
Autumn leaf fluorescence – Fv/Fm
Autumn leaf fluorescence - “O” step
Autumn leaf fluorescence - “J” step
Autumn leaf fluorescence - “I” step
Autumn leaf fluorescence - “P” step
1. The dependent variable is the wood decay data in all cases.
N
35
34
34
34
34
P
0.8525
0.8703
0.9692
0.3498
0.3190
r2
0.0011
0.0008
0.0000
0.0274
0.0310
Percentage of decay resi method
25
20
15
10
5
0
0.7000
0.7500
0.8000
0.8500
0.9000
Autumn leaf FvFm ratio
Figure 5.20 The percentage of decay using the resi system versus autumn leaf Fv/Fm. These data exclude
tree 4 as tree 4 had no leaves. Therefore 35 Eucalyptus saligna trees are included in this data set. Fv/Fm
ratio data begins at 0.7000.
5.3.6 Results for comparing bark fluorescence and wood decay
There was a statistically significant and negative relationship between spring, summer
and autumn bark Fv/Fm and wood decay (P < 0.05, tables 5.17- 5.19 and figures 5.215.23). There was no statistically significant relationship between bark CF at the O, J, I
points and wood decay, in spring, summer or autumn (P > 0.05, tables 5.17-5.19).
There was no statistically significant relationship between bark CF at 1000 ms and
wood decay, in spring, summer or autumn (P > 0.05, tables 5.17-5.19).
155
Table 5.17 Summarised results from simple linear regression analyses comparing spring bark
fluorescence with wood decay data
N = the number of samples
P = the probability for the t test that the coefficient of the independent variable is equal to zero
r2 = the variation in the dependent variable that can be explained by the fluorescence data.
Independent variable1
Spring bark fluorescence – Fv/Fm
Spring bark fluorescence - “O” step
Spring bark fluorescence - “J” step
Spring bark fluorescence - “I” step
Spring bark fluorescence - 1000 ms
1. The dependent variable is the wood decay data in all cases
2. Statistical relationship is significant and negative
N
35
35
35
35
35
P
0.03562
0.3633
0.2072
0.6172
0.9013
r2
0.1271
0.0251
0.0478
0.0077
0.0005
25
Percentage of decay resi system
20
15
10
5
0
0.7800
0.8000
0.8200
0.8400
0.8600
Spring bark FvFm ratio
Figure 5.21 Percentage of decay using the resi system versus spring bark Fv/Fm. These data exclude tree
4. Therefore 35 Eucalyptus saligna trees are included in this data set. Fv/Fm ratio data begins at 0.7800.
Trend line = linear regression, P = 0.0356, r2 = 0.1271.
156
Table 5.18 Summarised results from simple linear regression analyses comparing summer bark
fluorescence with the wood decay data.
N = the number of samples
P = the probability for the t test that the coefficient of the independent variable is equal to zero
r2 = the variation in the dependent variable that can be explained by the fluorescence data.
Independent variable1
Summer bark fluorescence – Fv/Fm
Summer bark fluorescence - “O” step
Summer bark fluorescence - “J” step
Summer bark fluorescence - “I” step
Summer bark fluorescence - 1000 ms
1. The dependent variable is the summer wood decay data in all cases
2. Statistical relationship is significant and negative
N
35
35
35
35
35
P
0.03732
0.1014
0.0950
0.2945
0.4295
r2
0.1248
0.0792
0.0822
0.0332
0.0190
Percentage of decay resi system
25
20
15
10
5
0
0.8000
0.8100
0.8200
0.8300
0.8400
0.8500
0.8600
Summer bark FvFm ratio
Figure 5.22 The percentage of decay using the resi system versus summer bark Fv/Fm. 35 Eucalyptus
saligna trees are included in this data set, as tree 4 had no live bark. Fv/Fm ratio data begins at 0.8000.
Trend line = linear regression, P = 0.0205, r2 = 0.1480.
157
Table 5.19 Summarised results from simple linear regression analyses comparing autumn bark
fluorescence with the wood decay data.
N = the number of samples
P = the probability for the t test that the coefficient of the independent variable is equal to zero
r2 = the variation in the dependent variable that can be explained by the fluorescence data.
Independent variable1
Autumn bark fluorescence – Fv/Fm
Autumn bark fluorescence - “O” step
Autumn bark fluorescence - “J” step
Autumn bark fluorescence - “I” step
Autumn bark fluorescence - 1000 ms
1. The dependent variable is the autumn wood decay data in all cases
2. Statistical relationship is significant and negative
N
35
35
35
35
35
P
0.03432
0.3632
0.4782
0.6908
0.9874
r2
0.1288
0.0251
0.0154
0.0049
0.0000
Percentage of decay resi system
25
20
15
10
5
0
0.7900
0.8000
0.8100
0.8200
0.8300
0.8400
0.8500
Autumn bark FvFm ratio
Figure 5.23 The percentage of decay using the resi system versus autumn bark Fv/Fm. 35 Eucalyptus
saligna trees are included in this data set, as tree 4 had no live bark. Fv/Fm ratio data begins at 0.7900.
Trend line = linear regression, P = 0.0373, r2 = 0.1248.
5.4 Discussion and conclusions
There was a statistically significant and negative relationship between summer leaf
chlorophyll fluorescence and the visual vitality index at the “O” step, indicating that in
a period of high (water limiting) stress, leaf chlorophyll fluorescence and the visual
vitality index were correlated, but the relationship was relatively weak (r2 = 0.1243,
table 5.3). A negative relationship between net chlorophyll fluorescence (at the O, J, I
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and P step) and visual vitality is expected as greater net chlorophyll fluorescence
indicates lower vitality (Govindjee, 2004). On the other hand a positive correlation
between Fv/Fm and tree vitality is expected because Fv/Fm is a ratio hence high values
indicate higher vitality (Maxwell & Johnson, 2000).
Values for leaf Fv/Fm of between 0.78 and 0.85 are generally considered to be
“normal” for healthy non-stressed plants by most authors (Björkman & Demmig,
1987; Maxwell & Johnson, 2000). However in this study leaf Fv/Fm rarely drops below
0.78, even with trees that look visually stressed according to the visual vitality index
(Appendix 3.2 Appendix to results from chapter 5 appendix tables A3.4 – A3.7). This
may be because much of the initial CF testing to establish Fv/Fm limits was done on
seedlings rather than mature plants (Björkman & Demmig, 1987; Govindjee, 2004)
and values should be adjusted upward when testing mature tree leaves or bark.
The relationship between leaf chlorophyll fluorescence and tree vitality as illustrated
by the visual vitality index was generally weak, even with the parameter (O step) that
did show a correlation. It was noted that some low vitality trees (visual assessment)
tested in spring had only very new growth, and thus photosystem II may not have
experienced higher relative damage in the chlorophyll of the leaves of these trees than
the high vitality trees, making the chlorophyll fluorescence (net) values lower than
expected and Fv/Fm values higher than expected for these trees. This may explain the
relatively weak relationships between leaf CF and tree vitality overall. Similar results
were obtained in a study by Martinez-Trinidad et al. (2010) when measuring mature
Quercus virginiana. Martinez-Trinidad et al. (2010) suggest that the low vitality
Quercus virginiana may be supporting fewer, but more efficiently operating, leaves.
This may also have been the case in the present study, where some trees with a
substantial amount of new epicormic growth, such as tree 5 and 25 for example, also
had low leaf CF values for the I step (both trees) and the P step (tree 5) (appendix 3.2,
Appendix to results from chapter 5, appendix tables A3.4 – A3.6).
Significantly, in the present study the summer period of investigation coincided with
maximum seasonal tree stress in southern Australia, when the mean average maximum
temperature at the test site in January 2008 was 27ºC (minimum average 16 ºC), when
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CF showed a correlation with the visual vitality index (Bureau of Meteorology
Australia, 2008). This may indicate that leaf CF is sensitive to immediate tree stress,
but may not be a good indicator of longer term effects on tree vitality. Interestingly in
this study predawn leaf and stem water potentials were not significantly depressed or
different in any of the three seasons and nor did predawn water potentials correlate
with any other measurement taken during the study (data not shown), suggesting that
predawn water potential was not as sensitive to the moisture stress the trees were
experiencing as leaf CF. Leaf and stem water potentials were not measured during the
day, when water deficit becomes more pronounced.
There was a statistically significant and positive relationship between summer and
autumn bark Fv/Fm and the visual vitality index. There was also a statistically
significant and negative relationship between the visual vitality index and bark CF at
the O step in autumn. In general, the statistical relationships between bark CF and
visual vitality were stronger and more significant than for leaf CF, even in the summer
and when including an extreme value for leaf CF (for example, summer leaf Fv/Fm N =
35, P = 0.0072, r2 = 0.1991; summer bark Fv/Fm N = 35,P = 0.0003 r2 = 0.3327). This
result suggests that bark fluorescence is a better predictor of tree vitality than leaf
fluorescence in E. saligna.
Tausz et al., (2005) showed that in the bark of Eucalyptus nitens there was less
chlorophyll a on the sun exposed north side of the bark than on the south side, and
indeed that Fv/Fm remained consistently below 0.8 on the north facing bark in spring.
However in the current study bark Fv/Fm values consistently averaged 0.8 or higher in
all seasons for all trees except tree 4 (Appendix 3.2 Appendix to results from chapter
5, appendix tables A3.4 – A3.7). Tree 4 had very low vitality, to the extent that it had
no leaves in September 2007 (the spring data collection period) and by January 2008
(the summer data collection period) the bark had also died. There was an evenness of
CF values across all compass points for E. saligna, unlike for the E. nitens in the Tausz
et al., (2005) study. This result is also an indication that the bark CF data in this study
is not confounded with aspect, despite testing occurring around the circumference of
small and slightly larger trees.
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The method used in this study with regard to bark CF is largely untested, as bark CF
has rarely been used to measure stress in plants prior to this study. Fv/Fm ratios for bark
were well within the leaf value norm, usually beginning at around 0.8000 (for
example, figures 5.9-5.11). Also average leaf Fv/Fm and average bark Fv/Fm were very
similar, even with the very low bark value included (average leaf Fv/Fm = 0.8397,
average bark Fv/Fm = 0.8278), suggesting bark Fv/Fm may be a valid way of measuring
stress in trees with bark chlorenchyma. In fact bark Fv/Fm seemed to be more
responsive to the long term stress suffered by E. saligna than leaf Fv/Fm. This is
consistent with a study by Wittman and Pfanz (2008) who found stem CF was more
sensitive to long term drought effects in than leaf CF in Alnus glutinosa, Prunus
avium, Quercus robur, Betula pendula and Fagus sylvatica. In the study by Wittman
and Pfanz (2008) stems took longer to show the effect of drying stress, but once they
did their sensitivity to drought stress, measured by CF, was greater.
Many authors have found that stem photosynthesis was of a much lower intensity than
in the leaves of northern hemisphere broadleaved trees (Pfanz et al. 2002; Damesin,
2003; Manetas, 2004). In the current study net outputs in millivolts for raw bark CF
values were approximately half that of leaf values (Figure 5.4, 5.5 and appendix 3.1,
Appendix to methods from chapter 5, appendix figures A3.1 – A3.4), but that may be
because the CF signal must pass through a periderm layer. Some authors have found
photosynthesis less efficient in stems (Wittman & Pfansz, 2008), however it can be
seen from the fast fluorescence curves for the bark data (Figure 5.5 and appendix 3.1,
Appendix to methods from chapter 5, appendix figures A3.2 and A3.4), that the curves
had not reached maximum after a 1 second time period, unlike the leaf CF curves
(Figure 5.4 and appendix 3.1, Appendix to methods from chapter 5, appendix figures
A3.1 and A 3.3). The time period that CF is recorded may need to be longer for bark,
as opposed to leaf, hence it is difficult to ascertain whether bark CF is less efficient
from the current study. As claimed by Tausz et al., (2005), bark photosynthesis may be
more important in smooth bark eucalypts because of their low leaf area index, hence
more research on bark photosynthesis is necessary.
There was a statistically significant and positive relationship between summer leaf
Fv/Fm and basic wood density, but not in spring or autumn. This suggests that the trees
161
were suffering drought stress in summer and that the leaf Fv/Fm ratio was sensitive to
the stress, as wood density is sometimes measured in conjunction with other
parameters for assessing the water status of trees (O’Grady et al., 2009; Gotch et al.,
2010). Low stem wood density is sometimes thought to make trees more vulnerable to
xylem cavitation during drought stress (Holste et al., 2006; Bobich et al., 2010) but in
a study of Picea abies (Norway Spruce) wood density was found to be unrelated to
cavitation (Rosner, 2007). If cavitation is occurring in the xylem of the E. saligna in
the current study, it could also be a favourable environment for fungal pathogens
(Rayner & Boddy, 1988), or the pathogens may assist with the cavitation process
(Tyree & Sperry, 1989; Tyree & Zimmerman, 2002). This may explain why there is a
relationship between leaf CF and wood decay in two seasons (spring and summer)
rather than just one as was the case with wood density.
There was a statistically significant and positive relationship between leaf CF values at
the “O” step and wood decay in summer and spring and tree vitality and the “O” step
in summer. It appears that in general the leaf CF “O” step was the first affected by
stress in E. saligna, with the other raw values somewhat affected in order of time (J
and I but not P). Oukarraum et al., (2009) found that the “I” step and the I-P phase was
affected by drought stressed Hordeum vulgare (barley) plants and Shao et al., (2010)
found a similar result in drought stressed Zea mays (maize) plants. This suggests that
the result in the current study may be due not only to drought but to a more complex
set of plant-stress interactions or simply that large woody trees may have a different
leaf CF response to drought than maize or barley plants. F0 (fluorescence at minimum)
was found to be an effective CF parameter to detect drought stress in a study of 30
woody shrubs and trees by Percival & Sheriffs (2001) and in Quercus petraea by
Epron et al., (1992). The J, I and P values were not analysed by Percival & Sheriffs
(2001). F0 is sometimes taken at 0 μs, or sometimes at the O step (5 μs), but in essence
the differences between F0 and the O step are minimal. Therefore the O step and F0
may be a more appropriate measure of drought stress in woody plants than the I step or
the I-P phase. The O-J step is believed to represent the reduction of the QA molecule
from QA to QA- between PSII and PSI (Hsu & Leu, 2003; Strasser et al., 2004;
Percival, 2005, figure 2.3), therefore it appears that the reduction of the plastoquinone
162
QA between PSII and PSI during leaf photosynthesis is associated with wood decay in
E. saligna.
Christen et al., (2007) investigated the “esca” disease in Vitis vinifera (grapevines) and
the relationship between the onset of symptoms and CF parameters. Esca disease
infects the xylem and causes the white rot decay and/or necrosis of woody tissues and,
subsequently, wilting of the leaves. Christen et al., (2007) used 4 categories of white
rot decay and 8 categories of necrosis, rather than percentages of decay. Necrosis and
white rot were more widespread in Cabernet Sauvignon than in Merlot plants. The
more decayed Cabernet Sauvignon plants showed decreased efficiency in PSII and the
PIABS value according to the CF results, compared to the Merlot population. However
the statistical relationship between CF and wood decay was only significant on a
cultivar level in Vitis vinifera, rather than at an individual plant level as was the case in
the E. saligna from the current study.
Bark Fv/Fm ratios were negatively correlated with wood density in E. saligna in spring
only, and in this instance the bark CF statistical relationships were weaker and not as
significant as those for leaf CF (for example, summer leaf Fv/Fm N = 34, P = 0.0010, r2
= 0.2910; spring bark Fv/Fm N = 35, P = 0.0351 r2 = 0.1277). The statistical
relationships between bark Fv/Fm ratios and wood decay were weak but more
consistent over three seasons than leaf correlations (spring bark, N = 35, P = 0.0356, r2
= 0.1271; summer bark, N = 35, P = 0.0373, r2 = 0.1248; autumn bark, N = 35, P =
0.0343, r2 = 0.1288). Therefore PSII in leaves may be more sensitive to the immediate
effects of water flow disruption than bark photosynthesis, but the longer term sustained
effects of moisture stress, such as cavitation and the subsequent entry of wood decay
pathogens, affects PSII in bark in a more consistent pattern than in the leaves. Stem
photosynthesis is believed to use gaseous xylem efflux as a source of CO2 (Pfanz,
2008) therefore if the xylem is not fully functioning it may affect the health of bark
chlorenchyma, and thus PSII. In addition the transpirational xylem stream supplies
inorganic nutrients (and water) to bark chlorenchyma (Pfanz, 2008) so if the xylem
stream is disrupted that may also affect stem photosynthesis. Eucalyptus sp. may be
sensitive to factors that affect stem photosynthesis as stem photosynthesis may be a
more important source of photosynthates for them than for other broad leaf trees,
163
because they have a low leaf area index and are prone to defoliation by insects,
diseases or drought (Tausz et al., 2005; Eyles et al., 2009). Interestingly, unlike with
leaf CF measurements, only the quantum efficiency (Fv/Fm,) of PSII within bark
chlorenchyma was associated with wood decay, the reduction of the plastoquinone QA
molecule between PSII and PSI (O-J step) was not affected.
Cambial electrical resistance (CER) has been used as a method for comparing wood
decay and tree vitality, but most studies compare site treatments rather than individual
trees. The results of these studies are variable and do not always support the results in
the current study using E. saligna and CF measurements – that there is an inverse
relationship between the physiological functioning of trees and wood decay. Filip et
al., (1995) found that a thinned Abies grandis (grand fir) forest stand had less decay
than an unthinned forest. Significantly the thinned site also had higher average vitality
as measured by CER when compared to the unthinned stand. As with many forestry
studies of this type the focus was on whole population differences (the thinned stand
versus the unthinned stand) rather than individual trees. In the same study higher
vitality as measured by CER was also associated with thinned Pinus ponderosa
(ponderosa pine) and Pinis contorta (lodgepole pine), but wood decay was not
significantly different in thinned versus unthinned stands in these two species. Filip et
al., (1992) also did not find a correlation between the percentage of decay and tree
growth and CER measurements in Abies grandis when stands were thinned and /or
fertilized. Again this study focused on whole population differences rather than
individual trees. Shortle & Ostrofsky (1983) did not find a correlation with the
percentage of decay in sites with different CER values and levels of Choristaneura
fumifcrana (spruce budworm) infestation. Decay caused by a known root rotting
pathogen Heterobasidion annosum was compared to CER in a preliminary study of
Picea abies (Norway spruce) and Abies alba (silver fir), however only three trees of
each species were used in this comparison (Vujanovic & Karadzic, 2003). The study
by Vujanovic & Karadzic, (2003) of Picea abies and Abies alba found a that CER was
higher for the three trees of the same species with low crown density scores and that
those trees had more wood decay, however no statistical correlations were made as the
replicate number was too small for statistical analysis.
164
The physiological (chlorophyll fluorescence) measurements in this chapter clearly
support the hypothesis that there is a positive relationship between chlorophyll
fluorescence and wood decay. The results suggest that the functioning of photosystem
II during photosynthesis is impaired when trees are decayed, particularly in the bark
chlorenchyma. The results from this chapter support the inverse relationship
discovered between tree growth and wood decay in the previous chapter (chapter 4),
and suggest that the relationship between tree vitality and wood decay does indeed
extend to the physiological processes that govern tree growth. The implications of the
relationships discovered in chapter 3, 4 and 5 will be discussed in the following
chapter in relation to urban tree management, the benefits of urban trees, forest
management and tree structure and function.
165
Chapter 6 – General discussion and conclusions
6.1 Introduction
The aim of this research was to establish whether there is an inverse relationship
between tree vitality and the amount of wood decay present in a tree. The resi system
was used to quantify the percentage of wood decay in the trees. As Dobbertin (2005)
states, tree vitality cannot be measured, but it can be inferred by growth and
physiological measurements. The vitality of trees in this study was inferred through
growth, visual and a physiological parameter (chlorophyll fluorescence). According to
the results of this study, there is an inverse relationship between tree vitality and wood
decay as consistently illustrated by comparisons between growth, visual and
physiological measurements and the percentage of wood decay in the trees.
The study is limited by the fact that only one species of tree was examined. However,
the species chosen is from the most widely planted genus of tree in the world
(Campinhos, 1999), thus maximizing the relevance of the study. There were also a
number of reasons why it was necessary to use only one species of tree;
1. Work with mature trees is very time consuming, however only mature trees
have wood decay - the study would not have been possible with smaller trees,
hence time constraints precluded the study of more than one tree species.
2. If the same number of trees had been used in the study, but two or three species
had been used the natural variation in decay development, growth and
physiology of the different species may have clouded, or lead to a null, result.
3. The study was testing several new methods developed during the study, and
may require further modification for other species – again time constraints
precluded this development.
4. No previous detailed studies have compared wood decay and mature tree
vitality. Correlations were likely to be neither obvious nor self explanatory,
hence testing several species with fewer trees may have lead to a null result.
The study also did not attempt to indentify the causal agents for wood decay in E.
saligna. The host-pathogen relationships between the casual agents of wood decay and
166
tree species are acknowledged as important (Schwarze et al., 2000). However, fungi
that invade the heartwood of trees need to be tolerant of extreme conditions (Rayner &
Boddy, 1988). Therefore heartwood rotting fungi have attributes that reflect this stress
tolerance, such as slow growth rates, host selectivity and longevity (Rayner & Boddy,
1988). For the reasons stated above, fungi that invade the wood of living Eucalyptus
sp. trees are still relatively poorly described (Simpson, 1996). Therefore this study did
not attempt to identify the causal agents for the decay in E. saligna.
6.2 Urban tree management
Understanding the processes that result in trunk failure is crucial for the risk
assessment of trees in urban environments (Shigo, 1991; Mattheck & Breloer, 1994;
Matheny & Clark, 1994; Mattheck, 2007; Schwarze, 2008). The decay of wood within
a tree trunk is often the cause of tree failure (Lonsdale, 1999; Schwarze et al., 2000). A
method for quantifying wood decay in trees was further developed in this study from a
previous method devised by the author – the resi system (Johnstone, 2005; Johnstone
et al., 2007). The method can be used by tree managers for the assessment of stem and
branch wood decay in trees. The picus method developed in this study, while not
successful in its application in the current study, may be effective when applied to
larger trees or large tree branches, with slightly higher percentages of decay than in the
current study. More research is required on species other than E. saligna to confirm the
usefulness of both methods. This study could also be used as a basis for modeling the
amount of decay in whole trees and thus the risk of trunk/branch/whole tree failure.
Further research is required on various tree species and their associated wood decay
pathogens to confirm the usefulness of the techniques for estimating wood decay
presented here.
A method was developed in this study for the visual assessment of individual trees
based on methods developed by Grimes (1978), Lindenmayer et al., (1990) and Martin
et al., (2001). Potentially the method has a very broad application for assessing urban
trees and for evaluating treatments for urban trees, such as assessing the effect of soil
amelioration techniques and pest and disease treatments versus tree growth and/or
vitality. The method turns visual observations of trees into numerical values, and the
167
components “crown density”, “dead branches” and “crown epicormic growth” have
particular relevance as an assessment method for urban trees. Further research is
required using the method in urban trees and in other species of tree, to confirm the
usefulness of the method.
A new method for assessing the vitality of urban trees was developed in this study
using the physiological measurement of bark chlorophyll fluorescence. The method
has potential for use on secondary branches hence a large number of angiosperm tree
species and a selection of southern conifers could be tested for tree vitality in urban
areas using bark fluorescence. Again, more research is required on species other than
E. saligna to confirm the usefulness of this method.
Leaf fluorescence was found to be successful for the immediate detection of stress
experienced by mature trees in this study, but leaf chlorophyll fluorescence was not
able to detect the longer term effects of tree decline. This is useful information for tree
managers, who will now be able to put leaf chlorophyll fluorescence measurements in
context, and understand that the longer term effects of tree stress may not be apparent
when using leaf chlorophyll fluorescence measurements.
6.3 The benefits of urban trees
The benefits of urban trees have been studied extensively (Nowak ,1993; McPherson
et al., 1997; Akbari, 2002; Jim & Chen, 2008). Benefits include atmospheric carbon
reduction (Nowak, 1993; McPherson et al., 1997; Akbari, 2002), pollution reduction
(McPherson et al., 1997; Jim & Chen, 2008) and a reduction in the heat island effect
(Akbari, 2002). The current study reinforces the importance of tree vitality in relation
to these benefits, rather than merely increasing the number of trees. From this study it
can be seen that;
1. High vitality trees have a greater leaf area, have greater photosynthetic
efficiency and hence can absorb more CO2 from the atmosphere than low
vitality trees.
168
2. High vitality trees have increased biomass and more intact (undecayed) wood
providing a higher volume of timber for carbon sequestration than low vitality
trees.
3. High vitality trees have a greater leaf area and biomass and therefore contribute
more to the reduction of pollution and the heat island effect than low vitality
trees.
Trees will need to be kept healthy in the future so that we can reap the benefits that
spring from them, as increasing world temperatures create a more favourable climate
for many wood decay and other pathogenic fungal organisms (Chen et al., 2000).
6.3 Forest management
Eucalyptus saligna, the species chosen for this project, is planted in many countries
and is a particularly important commercial timber species in Australia, Brazil and
Hawaii (Burgess, 1988). Eucalyptus is the most planted genus throughout the world
with an estimated 6 million hectares of planted forest in over 100 nations (Campinhos,
1999). The results from this study show that healthy (high vitality) E. saligna trees will
not only produce wood for timber producers at a fast rate, but that the timber is more
likely to remain undamaged by decay if trees are keep healthy.
Estimates of both biomass and carbon sequestration using allometric techniques
(measuring diameter at breast height and tree height) in forests are quicker, easier and
less expensive than methods that involve destructively sampling whole trees (Specht &
West, 2003). However, estimates of both biomass and carbon sequestration using
allometric techniques both in plantation and natural forests do not take into account
decayed wood in tree stems (Specht & West 2003; Castilho et al., 2010). The simple
nondestructive and effective method for assessing the percentage of wood decay in a
tree developed in this study could be used to improve the accuracy of biomass, carbon
sequestration and wood quality estimations in forests. This study could be used as a
basis for future research to accurately model the amount of carbon (wood, leaves,
roots) stored in mature trees with respect to species, tree vitality and CO2 absorption.
169
6.5 Tree structure and function
Prior to this study environmental stress had been known to reduce the vitality of plants
(Kozlowski et al., 1991; Larcher, 2003; figure 6.1). Lower vitality leads to decreased
growth and vulnerability to pathogens (Dobbertin, 2005; Rayner & Boddy, 1988;
figure 6.1). Wood decay was therefore assumed to be more prevalent in lower vitality
trees, leading to lower density wood in a more-or-less linear relationship (Rayner &
Boddy, 1988; Beall & Wilcox, 1987; figure 6.1).
In this study a somewhat weak relationship was discovered between tree vitality and
wood density and a relationship was discovered between tree vitality and wood decay
(Figure 6.2). This suggests that decreased tree vitality may cause lower density wood,
which is then more vulnerable to decay. There was also a weak link discovered
between tree growth and wood density (Figure 6.2). The improved understanding of
tree structure and function as a result of this project is summarized in the comparison
of figure 6.1 (before the study) and 6.2 (after the study). The links discovered suggest
complex interactions between wood decay, wood density, tree growth and vitality that
are not linear in nature (Figure 6.2).
The question as to why lower vitality trees are more decayed is not easy to answer.
Lorio (1986) suggested that the production of oleoresin, a protective agent against
Dendroctonus frontalis (southern pine beetle) in Pinus taeda (loblolly pine) is lower in
suppressed or low vitality trees when the production of wood is depressed. Hence, one
reason why low vitality E. saligna trees may be more decayed may be because when
the growth of wood is depressed the synthesis of protective chemical compounds
produced in the wood is also decreased.
The moisture stress the E. saligna were experiencing may also have contributed
directly to the relationship between tree vitality and wood decay discovered in this
study. The water saturation of wood has long been known to prevent the development
of wood decay and air is necessary for the development of decay in wood (Rayner &
Boddy, 1988). The barrier zones in Shigo’s CODIT model (Shigo, 1979) are said to be
a response to xylem embolism by Rayner and Boddy (1988), rather than the incursion
170
Environmental stress
Lower vitality
Decreased resistance to pathogens
Less growth
More wood decay
Lower density wood
Key:
stronger relationships
weaker relationships
Figure 6.1 A summary of the relationship between tree vitality and wood decay prior to the current
study.
Environmental stress
Lower vitality
Decreased resistance to pathogens
Less growth
Lower density wood
More wood decay
Key:
stronger relationships
weaker relationships
Figure 6.2 A summary of the relationship between tree vitality and wood decay including information
from the current study.
171
of decay organisms per se. Cavitation during moisture stress is one way a xylem vessel
can form an embolism (Tyree & Sperry, 1989). The direct relationship between xylem
cavitation and wood decay has not been evaluated, but the introduction of a gaseous
phase during the compartmentalization process, according to Rayner and Boddy
(1988), is a primary component in the development of wood decay in trees. It is when
tree wood dries out, that compartmentalization barriers are often breached (Rayner &
Boddy, 1988).
Lower density trees have also been associated with an increased risk of cavitation
(Holste et al., 2006; Bobich et al., 2010). Therefore it is not surprising that in the
current study, in the hot Australian summer, E. saligna showed a relationship between
tree vitality (as measured by tree growth, visual and chlorophyll fluorescence methods)
and wood density, and an even stronger (inverse) relationship between tree vitality and
wood decay. Low vitality trees are more vulnerable to drought stress and are more
vulnerable to wood decay, for multiple reasons. Unlike many other studies the link
between moisture stress cavitation, embolism and wood density/decay described here
is a within-species effect, rather than the ecological inter-species effect of low wood
density and water relations/growth discussed in other studies (Bucci et al., 2004;
O’Grady et al., 2009). The lower density wood is produced due to stressful
environmental conditions in the E. saligna, there are no genetic differences or
predispositions at play. The link between wood density, tree vitality and wood decay
within species established in this study has not been previously reported.
This study further emphasizes the link between the operation of photosynthesis in
leaves and environmental stress, particularly moisture stress. The O step in the OJIP
fluorescence transient in leaves, that relates to the part of the photosynthetic light
reaction where plastoquinone QA electron carrier molecules are in their oxidized state
between PSII and PSI, is particularly affected by moisture stress in this and other
studies of trees (Epron et al., 1992; Percival & Sheriffs, 2001). This study establishes a
new link between the quantum efficiency of PSII (Fv/Fm) in leaves, wood density and
wood decay. The study also establishes a new and consistent pattern of correlation
between the quantum efficiency of PSII (Fv/Fm) in bark and environmental stress
(moisture stress), wood decay and to a lesser extent wood density. Further research
172
could examine the link between the quantum efficiency of PSII in bark in relation to
other tree species, and other environmental stressors.
The PIABS (Performance Index) chlorophyll fluorescence value has also been used to
successfully quantify drought stress in trees (Percival & AlBalushi, 2007; Swoczyna et
al., 2010). The PIABS value was not calculated in the current study, as it is not as
widely used as the Fv/Fm value. Future studies could examine the effect of wood decay
and wood density in trees on the PIABS value and other derived measures that form part
of the “JIP test”, such as the apparent rates of photosynthetic electron transport (ETR)
and non-photochemical quenching (NPQ) (Lüttge et al., 2003).
6.6 Conclusion
An inverse relationship between tree vitality and the amount of wood decay present in
a tree was well established during the course of this study. The study has lead to
improvements in the non-destructive evaluation of decay in a tree trunk and more
accurate estimations of tree biomass in both forest and urban trees. A method was
developed for assessing the vitality of individual trees using visual indicators and using
bark chlorophyll fluorescence. High vitality/low decay trees clearly convey more
environmental benefits than low vitality/high decay trees. This study establishes new
links between the physiology of trees and wood anatomy, structure and wood decay.
173
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Appendices
Appendix 1 Appendix to chapter 3
1.1 Appendix to materials used in chapter 3
Appendix table A1.1 Summary tree heights, diameters at 1.3 m and 0.3 m in height for the 36 Eucalyptus
saligna trees at Tostaree, Victoria. Data collected April 2008.
Tree
Number
Tree height
in m
Diameter at
1.3 m in mm
Diameter at
0.3 m in mm
Tree
Number
Tree height
in m
Diameter at
1.3 m in mm
Diameter at
0.3 m in mm
1
23.7
258
333
19
17.3
145
160
2
23.4
250
301
20
24.8
222
279
3
20.8
187
237
21
22.7
216
255
4
18.5
186
227
22
24.4
187
219
5
17.4
189
212
23
22.2
182
218
6
25.2
247
320
24
19.1
142
198
7
25.6
275
335
25
18.7
149
200
8
25.5
287
328
26
22.2
203
237
9
25.8
250
321
27
24.9
205
238
10
26
241
285
28
25.8
227
271
11
25.6
265
315
29
24.3
217
265
12
25.4
259
307
30
25.7
316
336
13
26.7
318
226
31
23
168
181
14
24.4
265
318
32
25
229
298
15
23.0
250
300
33
24.7
210
251
16
22.5
238
292
34
23
161
187
17
21.6
147
165
35
24.5
210
253
18
20.4
190
263
36
25.6
283
329
202
Tree location 1
o
o
o
o
o
o
18
2
x
o
o
o
o
o
o
15
o
17
o
1
3
16
x
o
o
Tree location 2
19
o
20
o
o
x
o
o
x
o
x
o
o
24
25
o
x
21
o
o
o
o
22
25
x
Tree location 3
26
o
o
x
29
o
x
x
o
o
o
28
7
o
x
27
6
o
o
x
o
5
4
x
o
Tree location 4
o
36
30
8
32
o
o
o
x
x
x
11
31
o
o
o
x
9
10
x
o
x
x
o
o
Tree location 5
o
o
o
x
x
o
33
13
x
o
o
x
o
12
o
o
o
o
35
o
o
o
34
x
14
Appendix figure A1.1 Tree locations within plots for test trees 1-36 at test site in Tostaree, Victoria
September 2007. Numbers on the plan represent the tree number tested, o = a live untested tree and x = a
dead untested tree. North is at the top of the figure. All plots were adjacent to other Eucalyptus species plots.
203
1.2 Appendix to methods used in chapter 3
Appendix to the picus expert system and the resi expert system of decay
estimation.
Appendix table A1.2 Linear distance measurements of the trunks of Eucalyptus saligna trees for the
generation of a perimeter diagram used in the picus expert system. Cross sections measured at 0.3 m, trees
1-12. The following sensor locations correspond to the following aspects; 1 = north, 3 = west, 5 = south and
7 = east.
Linear distances in mm Tree 1, at 0.3 m in height.
Location
Location
Dist.
Linear distances in mm Tree 7, at 0.3 m in height.
Location
Dist.
Location
Dist.
Location
Dist.
Location
Dist.
Dist.
1-5
320
1-4
299
1-7
220
1-5
322
1-4
300
1-7
217
1-2
115
4-5
123
7-5
247
1-2
155
4-5
105
7-5
238
2-5
286
1-6
310
1-8
120
2-5
189
1-6
295
1-8
106
1-3
213
6-5
134
8-5
292
1-3
246
6-5
138
8-5
308
3-5
207
3-5
203
Linear distances in mm, Tree 2, at 0.3 m in height.
Location
Location
Dist.
Linear distances in mm Tree 8, at 0.3 m in height.
Location
Dist.
Location
Dist.
Location
Dist.
Location
Dist.
Dist.
1-5
297
1-4
263
1-7
210
1-5
335
1-4
309
1-7
223
1-2
117
4-5
135
7-5
215
1-2
133
4-5
100
7-5
228
2-5
271
1-6
276
1-8
118
2-5
290
1-6
310
1-8
111
1-3
203
6-5
122
8-5
278
1-3
230
6-5
123
8-5
307
3-5
200
3-5
216
Linear distances in mm Tree 3, at 0.3 m in height.
Location
Location
Dist.
Linear distances in mm, Tree 9, at 0.3 m in height.
Location
Dist.
Location
Dist.
Location
Dist.
Location
Dist.
Dist.
1-5
300
1-4
272
1-7
209
1-5
296
1-4
273
7-5
225
1-2
117
4-5
112
7-5
228
1-2
110
4-5
106
1-8
119
2-5
267
1-6
278
1-8
113
2-5
288
1-6
267
8-5
283
1-3
202
6-5
142
8-5
282
1-3
221
6-5
212
3-5
194
3-5
205
1-7
217
Linear distances in mm Tree 4, at 0.3 m in height.
Location
Location
Dist.
Linear distances in mm Tree 10, at 0.3 m in height.
Location
Dist.
Location
Dist.
Location
Dist.
Location
Dist.
Dist.
1-5
210
1-4
197
1-7
146
1-5
253
1-4
245
1-7
180
1-2
85
4-5
90
7-5
160
1-2
108
4-5
115
7-5
183
204
2-5
203
1-6
194
1-8
86
2-5
232
1-6
245
1-8
106
1-3
148
6-5
76
8-5
202
1-3
193
6-5
107
8-5
232
3-5
159
3-5
193
Linear distances in mm Tree 5, at 0.3 m in height.
Location
Location
Dist.
Linear distances in mm Tree 11, at 0.3 m in mm
Location
Dist.
Location
Dist.
Location
Dist.
Location
Dist.
Dist.
1-5
214
1-4
195
1-7
130
1-5
305
1-4
280
1-7
222
1-2
95
4-5
90
7-5
152
1-2
121
4-5
123
7-5
208
2-5
203
1-6
190
1-8
69
2-5
281
1-6
272
1-8
118
1-3
146
6-5
79
8-5
197
1-3
208
6-5
108
8-5
283
3-5
160
3-5
221
Linear distances in mm Tree 6, at 0.3 m in height.
Location
Location
Dist.
Linear distances in mm Tree 12, at 0.3 m in height.
Location
Dist.
Location
Dist.
Location
Dist.
Location
Dist.
Dist.
1-5
293
1-4
280
1-7
197
1-5
297
1-4
267
1-7
216
1-2
120
4-5
98
7-5
222
1-2
110
4-5
119
7-5
210
2-5
284
1-6
254
1-8
107
2-5
265
1-6
276
1-8
103
1-3
220
6-5
130
8-5
263
1-3
176
6-5
115
8-5
278
3-5
219
3-5
208
Appendix table A1.3 Linear distance measurements of the trunks of Eucalyptus saligna trees for the
generation of a perimeter diagram used in the picus expert system. Cross sections measured at 0.3 m, trees
13 and 14. The following sensor locations correspond to the following aspects; 1 = north, 3 = west, 5 = south
and 7 = east.
Linear distances in mm Tree 13, at 0.3 m in height.
Location
Location
Dist.
Linear distances in mm Tree 14, at 0.3 m in height.
Location
Dist.
Location
Dist.
Location
Dist.
Location
Dist.
Dist.
1-5
317
1-4
287
1-7
208
1-5
308
1-4
267
1-7
192
1-2
108
4-5
117
7-5
218
1-2
100
4-5
115
7-5
260
2-5
296
1-6
278
1-8
105
2-5
278
1-6
268
1-8
109
1-3
197
6-5
130
8-5
294
1-3
184
6-5
162
8-5
298
3-5
234
3-5
214
205
Appendix table A1.4 Linear distance measurements of the trunks of Eucalyptus saligna trees for the
generation of a perimeter diagram used in the resi expert system. Cross sections measured at 0.3 m, trees 112. The following drilling locations correspond to the following aspects; 1 = north, 3 = west, 5 = south and 7
= east.
Linear distances in mm Tree 1, at 0.3 m in height.
Location
Location
Dist.
Linear distances in mm Tree 7, at 0.3 m in height.
Location
Dist.
Location
Dist.
Location
Dist.
Location
Dist.
Dist.
1-5
310
1-4
293
1-7
241
1-5
324
1-4
291
1-7
246
1-2
118
4-5
104
7-5
233
1-2
132
4-5
105
7-5
216
2-5
272
1-6
300
1-8
134
2-5
287
1-6
305
1-8
134
1-3
213
6-5
125
8-5
289
1-3
208
6-5
127
8-5
299
3-5
194
3-5
189
Linear distances in mm Tree 2, at 0.3 m in height.
Location
Location
Dist.
Linear distances in mm Tree 8, at 0.3 m in height.
Location
Dist.
Location
Dist.
Location
Dist.
Location
Dist.
Dist.
1-5
300
1-4
272
1-7
209
1-5
341
1-4
309
1-7
212
1-2
117
4-5
112
7-5
228
1-2
129
4-5
107
7-5
243
2-5
267
1-6
278
1-8
113
2-5
300
1-6
306
1-8
114
1-3
202
6-5
142
8-5
282
1-3
238
6-5
136
8-5
311
3-5
194
3-5
224
Linear distances in mm Tree 3, at 0.3 m in height.
Location
Location
Dist.
Linear distances in mm Tree 9, at 0.3 m in height.
Location
Dist.
Location
Dist.
Location
Dist.
Location
Dist.
Dist.
1-5
221
1-4
217
1-7
145
1-5
293
1-4
275
1-7
221
1-2
102
4-5
85
7-5
155
1-2
98
4-5
102
7-5
222
2-5
201
1-6
194
1-8
85
2-5
283
1-6
278
1-8
134
1-3
168
6-5
85
8-5
206
1-3
216
6-5
103
8-5
285
3-5
153
3-5
212
Linear distances in mm Tree 4, at 0.3 m in height.
Location
Location
Dist.
1-5
206
207
Location
Dist.
1-4
Linear distances in mm Tree 10, at 0.3 m in height.
192
Location
Dist.
Location
Dist.
Location
Dist.
1-7
136
Dist.
1-5
254
1-4
234
1-7
204
1-2
88
4-5
82
7-5
170
1-2
103
4-5
100
7-5
193
2-5
184
1-6
183
1-8
80
2-5
236
1-6
250
1-8
112
1-3
152
6-5
97
8-5
202
1-3
192
6-5
116
8-5
245
3-5
139
3-5
178
Linear distances in mm Tree 5, at 0.3 m in height.
Location
Location
Dist.
Linear distances in mm Tree 11, at 0.3 m in height.
Location
Dist.
Location
Dist.
Location
Dist.
Location
Dist.
Dist.
1-5
207
1-4
182
1-7
156
1-5
301
1-4
279
1-7
206
1-2
65
4-5
78
7-5
154
1-2
118
4-5
121
7-5
220
2-5
197
1-6
201
1-8
78
2-5
283
1-6
268
1-8
124
1-3
132
6-5
81
8-5
197
1-3
218
6-5
122
8-5
277
3-5
142
3-5
215
Linear distances in mm Tree 6, at 0.3 m in height.
Location
Location
Dist.
Linear distances in mm Tree 12, at 0.3 m in height.
Location
Dist.
Location
Dist.
Location
Dist.
Location
Dist.
Dist.
1-5
297
1-4
267
1-7
195
1-5
288
1-4
273
1-7
211
1-2
135
4-5
91
7-5
257
1-2
110
4-5
109
7-5
214
2-5
267
1-6
275
1-8
98
2-5
258
1-6
267
1-8
116
1-3
222
6-5
155
8-5
286
1-3
193
6-5
113
8-5
270
3-5
174
3-5
188
Appendix table A1.5 Linear distance measurements of the trunks of Eucalyptus saligna trees for the
generation of a perimeter diagram used in the resi expert system. Cross sections measured at 0.3 m, trees 13
and 14. The following drilling locations correspond to the following aspects; 1 = north, 3 = west, 5 = south
and 7 = east.
Linear distances in mm Tree 13, at 0.3 m in height.
Location
Location
Dist.
Linear distances in mm Tree 14, at 0.3 m in height.
Location
Dist.
Location
Dist.
Location
Dist.
Location
Dist.
Dist.
1-5
324
1-4
283
1-7
214
1-5
310
1-4
297
1-7
184
1-2
124
4-5
123
7-5
239
1-2
155
4-5
105
7-5
237
2-5
291
1-6
286
1-8
118
2-5
273
1-6
260
1-8
90
1-3
220
6-5
134
8-5
298
1-3
241
6-5
133
8-5
278
3-5
213
3-5
196
207
Appendix table A1.6 Linear distance measurements of the trunks of Eucalyptus saligna trees for the
generation of a perimeter diagram used in the picus expert system and the resi expert system. Cross sections
measured at 0.3 m, 15 - 26. The following drilling/sensor locations correspond to the following aspects; 1 =
north, 3 = west, 5 = south and 7 = east.
Linear distances in mm, Tree 15, at 0.3 m in height.
Location
Location
Dist.
Linear distances in mm, Tree 21, at 0.3 m in height.
Location
Dist.
Location
Dist.
Location
Dist.
Location
Dist.
Dist.
1-5
296
1-4
272
7-5
219
1-5
235
1-4
225
7-5
202
1-2
118
4-5
118
1-8
104
1-2
89
4-5
86
1-8
90
2-5
278
1-6
255
8-5
265
2-5
196
1-6
215
8-5
234
1-3
223
6-5
122
1-3
158
6-5
122
3-5
202
1-7
181
3-5
144
1-7
180
Linear distances in mm, Tree 16, at 0.3 m in height.
Location
Location
Dist.
Linear distances in mm, Tree 22, at 0.3 m in height.
Location
Dist.
Location
Dist.
Location
Dist.
Location
Dist.
Dist.
1-5
293
1-4
272
7-5
219
1-5
215
1-4
194
7-5
170
1-2
129
4-5
95
1-8
105
1-2
77
4-5
77
1-8
81
2-5
258
1-6
254
8-5
266
2-5
185
1-6
205
8-5
210
1-3
206
6-5
120
1-3
151
6-5
95
3-5
193
1-7
200
3-5
141
1-7
153
Linear distances in mm, Tree 17, at 0.3 m in height.
Location
Location
Dist.
Linear distances in mm, Tree 23, at 0.3 m in height.
Location
Dist.
Location
Dist.
Location
Dist.
Location
Dist.
Dist.
1-4
160
1-3
147
5-4
98
1-5
223
1-4
200
7-5
162
1-2
83
3-4
76
1-6
75
1-2
80
4-5
90
1-8
83
2-4
131
1-5
136
6-4
150
2-5
200
1-6
196
8-5
211
1-3
153
6-5
87
3-5
152
1-7
136
Linear distances in mm, Tree 18, at 0.3 m in height.
Location
Location
Dist.
1-5
255
208
Location
Dist.
1-4
Linear distances in mm, Tree 24, at 0.3 m in height.
224
Location
Dist.
Location
Dist.
Location
Dist.
7-5
205
Dist.
1-4
196
1-3
161
5-4
92
1-2
91
4-5
91
1-8
100
1-2
86
3-4
88
1-6
97
2-5
230
1-6
236
8-5
250
2-4
159
1-5
188
6-4
176
1-3
185
6-5
102
3-5
163
1-7
175
Linear distances in mm, Tree 19, at 0.3 m in height.
Location
Location
Dist.
Linear distances in mm, Tree 25, at 0.3 m in height.
Location
Dist.
Location
Dist.
Location
Dist.
Location
Dist.
Dist.
1-4
160
1-3
152
5-4
73
1-4
197
1-3
179
5-4
86
1-2
97
3-4
74
1-6
58
1-2
103
3-4
90
1-6
91
2-4
130
1-5
115
6-4
141
2-4
171
1-5
158
6-4
159
Linear distances in mm, Tree 20, at 0.3 m in height.
Location
Location
Dist.
Linear distances in mm, Tree 26, at 0.3 m in height.
Location
Dist.
Location
Dist.
Location
Dist.
Location
Dist.
Dist.
1-5
264
1-4
244
7-5
204
1-5
243
1-4
223
7-5
175
1-2
99
4-5
91
1-8
105
1-2
91
4-5
88
1-8
85
2-5
228
1-6
248
8-5
253
2-5
220
1-6
222
8-5
222
1-3
176
6-5
109
1-3
165
6-5
96
3-5
180
1-7
195
3-5
163
1-7
163
Appendix table A1.7 Linear distance measurements of the trunks of Eucalyptus saligna trees for the
generation of a perimeter diagram used in the picus expert system and the resi expert system. Cross sections
measured at 0.3 m, trees 27-36. The following drilling/sensor locations correspond to the following aspects;
1 = north, 3 = west, 5 = south and 7 = east.
Linear distances in mm, Tree 27, at 0.3 m in height.
Location
Location
Dist.
Linear distances in mm, Tree 32, at 0.3 m in height.
Location
Dist.
Location
Dist.
Location
Dist.
Location
Dist.
Dist.
1-5
240
1-4
227
7-5
190
1-5
285
1-4
262
7-5
201
1-2
104
4-5
72
1-8
87
1-2
120
4-5
109
1-8
109
2-5
196
1-6
212
8-5
230
2-5
262
1-6
250
8-5
271
1-3
187
6-5
106
1-3
217
6-5
109
3-5
131
1-7
165
3-5
196
1-7
184
209
Linear distances in mm, Tree 28, at 0.3 m in height.
Location
Location
Dist.
Linear distances in mm, Tree 33, at 0.3 m in height.
Location
Dist.
Location
Dist.
Location
Dist.
Location
Dist.
Dist.
1-5
246
1-4
238
7-5
208
1-5
260
1-4
248
7-5
180
1-2
112
4-5
89
1-8
94
1-2
118
4-5
91
1-8
80
2-5
220
1-6
228
8-5
248
2-5
217
1-6
233
8-5
231
1-3
212
6-5
116
1-3
193
6-5
105
3-5
172
1-7
163
3-5
172
1-7
168
Linear distances in mm, Tree 29, at 0.3 m in height.
Location
Location
Dist.
Linear distances in mm, Tree 34, at 0.3 m in height.
Location
Dist.
Location
Dist.
Location
Dist.
Location
Dist.
Dist.
1-5
257
1-4
246
7-5
190
1-4
196
1-3
166
5-4
93
1-2
106
4-5
90
1-8
101
1-2
109
3-4
81
1-6
92
2-5
235
1-6
233
8-5
238
2-4
156
1-5
164
6-4
170
1-3
198
6-5
103
3-5
174
1-7
177
Linear distances in mm, Tree 30, at 0.3 m in height.
Location
Location
Dist.
Linear distances in mm, Tree 35, at 0.3 m in height.
Location
Dist.
Location
Dist.
Location
Dist.
Location
Dist.
Dist.
1-5
362
1-4
313
7-5
259
1-5
256
1-4
232
7-5
194
1-2
132
4-5
116
1-8
130
1-2
96
4-5
84
1-8
95
2-5
295
1-6
308
8-5
325
2-5
223
1-6
229
8-5
249
1-3
234
6-5
135
1-3
171
6-5
112
3-5
218
1-7
238
3-5
19
1-7
179
Linear distances in mm, Tree 31, at 0.3 m in height.
Location
Location
Dist.
Linear distances in mm, Tree 36, at 0.3 m in height.
Location
Dist.
Location
Dist.
Location
Dist.
Location
Dist.
Dist.
1-4
182
1-3
159
5-4
86
1-5
336
1-4
305
7-5
265
1-2
95
3-4
85
1-6
85
1-2
147
4-5
92
1-8
121
2-4
156
1-5
153
6-4
159
2-5
262
1-6
288
8-5
327
1-3
242
6-5
142
3-5
186
1-7
206
210
a
b
a
b
a
b
a
b
a
1
b
3
5
7
b
9
2
a
b
a
b
a
b
a
4
6
8
b
10
Appendix Figure A1.2 Trunk perimeter cross section outlines for the picus expert system at 0.3 m from the
Eucalyptus saligna trees used in the study. Left at (a), diagram generated from the data on previous pages
and the picus software and right at (b) diagram smoothed by hand for a more accurate representation of cross
sectional perimeter. Trees 1-10. Diagrams on right (b) were used to calculate the area of the cross sections in
mm2. North is at the top of the diagrams at 1.
211
a
b
11
a
b
13
a
b
a
12
b
14
Appendix Figure A1.3 Trunk perimeter cross section outlines for the picus expert system at 0.3 m from the
Eucalyptus saligna trees used in the study. Left at (a), diagram generated from the data on previous pages
and the picus software and right at (b) diagram smoothed by hand for a more accurate representation of cross
sectional perimeter. Trees 11-14. Diagrams on right (b) were used to calculate the area of the cross sections
in mm2. North is at the top of the diagrams at 1.
a
b
1
a
b
3
a
b
a
b
2
4
Appendix Figure A1.4 Trunk perimeter cross section outlines for the resi expert system at 0.3 m from the
Eucalyptus saligna trees used in the study. Left at (a), diagram generated from the data on previous pages
and the picus software and right at (b) diagram smoothed by hand for a more accurate representation of cross
sectional perimeter. Trees 1-4. Diagrams on right (b) were used to calculate the area of the cross sections in
mm2. North is at the top of the diagrams at 1.
212
a
a
b
a
a
a
b
a
a
b
7
a
9
11
b
b
6
5
13
a
b
b
8
10
a
b
12
a
b
14
Appendix Figure A1.5 Trunk perimeter cross section outlines for the resi expert system at 0.3 m from the
Eucalyptus saligna trees used in the study. Left at (a), diagram generated from the data on previous pages
and the picus software and right at (b) diagram smoothed by hand for a more accurate representation of cross
sectional perimeter. Trees 7-14 Diagrams on right (b) were used to calculate the area of the cross sections in
mm2. North is at the top of the diagrams at 1.
213
a
b
a
b
a
15
17
b
a
b
16
a
b
18
a
b
20
a
b
22
b
24
19
a
b
21
a
b
23
a
Appendix Figure A1.6 Trunk perimeter cross section outlines for the picus and resi expert systems at 0.3 m
from the Eucalyptus saligna trees used in the study. Left at (a), diagram generated from the data on previous
pages and the picus software and right at (b) diagram smoothed by hand for a more accurate representation
of cross sectional perimeter. Trees 15-24. Diagrams on right (b) were used to calculate the area of the cross
sections in mm2. North is at the top of the diagrams at 1.
214
a
b
25
a
b
a
b
a
a
a
a
a
27
29
b
31
b
33
b
b
35
26
b
a
b
a
b
28
30
32
a
b
a
b
34
36
Appendix Figure A1.7 Trunk perimeter cross section outlines for the picus and resi expert systems at 0.3 m
from the Eucalyptus saligna trees used in the study. Left at (a), diagram generated from the data on previous
pages and the picus software and right at (b) diagram smoothed by hand for a more accurate representation
of cross sectional perimeter. Trees 27-36. Diagrams on right (b) were used to calculate the area of the cross
sections in mm2. North is at the top of the diagrams at 1.
215
a
b
1
a
a
b
3
a
b
4
a
b
5
a
b
6
a
b
7
a
b
b
Appendix Figure A1.8 Picus images after the acoustic time of flight measurements have been processed by
the picus software, using the perimeter cross section outlines shown in the previous pages. These images are
used as part of the picus expert system. Left shows the original image, right shows the images with the
brightest sections (slowest acoustic velocities) in red after being analysed by the image analysis software
imageJ. Trees 1-8. Scale shown on diagrams is in centimeters. North is at the top of the diagrams at 1.
216
2
8
a
b
9
a
b
10
a
b
11
a
b
12
a
b
13
a
b
14
a
b
a
b
16
15
Appendix Figure A1.9 Picus images after the acoustic time of flight measurements have been processed by
the picus software, using the perimeter cross section outlines shown in the previous pages. These images are
used as part of the picus expert system. Left shows the original image, right shows the images with the
brightest sections (slowest acoustic velocities) in red after being analysed by the image analysis software
imageJ. Trees 9-16. Scale shown on diagrams is in centimeters. North is at the top of the diagrams at 1.
217
a
b
17
a
b
19
a
b
a
b
a
b
a
b
18
a
b
20
a
b
22
23
a
b
24
25
a
21
b
26
Appendix Figure A1.10 Picus images after the acoustic time of flight measurements have been processed
by the picus software, using the perimeter cross section outlines shown in the previous pages. These images
are used as part of the picus expert system. Left shows the original image, right shows the images with the
brightest sections (slowest acoustic velocities) in red after being analysed by the image analysis software
imageJ. Trees 17-26. Scale shown on diagrams is in centimeters. North is at the top of the diagrams at 1.
218
a
a
b
b
a
b
a
b
a
b
27
a
b
28
29
a
b
30
31
a
b
32
a
b
34
33
35
a
b
36
Appendix Figure A1.11 Picus images after the acoustic time of flight measurements have been processed
by the picus software, using the perimeter cross section outlines shown in the previous pages. These images
are used as part of the picus expert system. Left shows the original image, right shows the images with the
brightest sections (slowest acoustic velocities) in red after being analysed by the image analysis software
imageJ. Trees 27-36. Scale shown on diagrams is in centimeters. North is at the top of the diagrams at 1.
219
1
2
3
Appendix figure A1.12 Graph traces from the IML-Resi showing putative decay on the graphs interpreted
according to the resi expert system. These graphs are used as part of the resi expert system. Drilling
direction is from left to right and the scale shown on the graphs is in centimeters. Trees were renumbered
after initial data collection – trees shown are, from top, trees 1-3. The following drilling aspects correspond
to the following drill positions; north = 1 west = 3 south = 5 and east = 7.
220
4
5
6
Appendix figure A1.13 Graph traces from the IML-Resi showing putative decay on the graphs interpreted
according to the resi expert system. These graphs are used as part of the resi expert system. Drilling
direction is from left to right and the scale shown on graphs is in centimeters. Trees were renumbered after
initial data collection – trees shown are, from top, trees 4-6. The following drilling aspects correspond to the
following drill positions; north = 1 west = 3 south = 5 and east = 7.
221
7
8
Appendix figure A1.14 Graph traces from the IML-Resi showing putative decay on the graphs interpreted
according to the resi expert system. These graphs are used as part of the resi expert system. Drilling
direction is from left to right and the scale shown on graphs is in centimeters. Trees were renumbered after
initial data collection – trees shown are, from top, trees 7 and 8. The following drilling aspects correspond to
the following drill positions; north = 1 west = 3 south = 5 and east = 7.
222
9
10
Appendix figure A1.15 Graph traces from the IML-Resi showing putative decay on the graphs interpreted
according to the resi expert system. These graphs are used as part of the resi expert system. Drilling
direction is from left to right and the scale shown on graphs is in centimeters. Trees were renumbered after
initial data collection – trees shown are, from top, trees 9 and 10. The following drilling aspects correspond
to the following drill positions; north = 1 west = 3 south = 5 and east = 7.
223
11
12
Appendix figure A1.16 Graph traces from the IML-Resi showing putative decay on the graphs interpreted
according to the resi expert system. These graphs are used as part of the resi expert system. Drilling
direction is from left to right and the scale shown on graphs is in centimeters. Trees were renumbered after
initial data collection – trees shown are, from top, trees 11 and 12. The following drilling aspects correspond
to the following drill positions; north = 1 west = 3 south = 5 and east = 7.
224
13
14
15
Appendix figure A1.17 Graph traces from the IML-Resi showing putative decay on the graphs interpreted
according to the resi expert system. These graphs are used as part of the resi expert system. Drilling
direction is from left to right and the scale shown on graphs is in centimeters. Trees were renumbered after
initial data collection – trees shown are, from top, trees 13-15. The following drilling aspects correspond to
the following drill positions; north = 1 west = 3 south = 5 and east = 7.
225
16
17
18
Appendix figure A1.18 Graph traces from the IML-Resi showing putative decay on the graphs interpreted
according to the resi expert system. These graphs are used as part of the resi expert system. Drilling
direction is from left to right and the scale shown on graphs is in centimeters. Trees were renumbered after
initial data collection – trees shown are, from top, trees 16-18. The following drilling aspects correspond to
the following drill positions; north = 1 west = 3 south = 5 and east = 7.
226
19
20
21
Appendix figure A1.19 Graph traces from the IML-Resi showing putative decay on the graphs interpreted
according to the resi expert system. These graphs are used as part of the resi expert system. Drilling
direction is from left to right and the scale shown on graphs is in centimeters. Trees were renumbered after
initial data collection – trees shown are, from top, trees 19-21. The following drilling aspects correspond to
the following drill positions; north = 1 west = 3 south = 5 and east = 7.
227
22
23
24
Appendix figure A1.20 Graph traces from the IML-Resi showing putative decay on the graphs interpreted
according to the resi expert system. These graphs are used as part of the resi expert system. Drilling
direction is from left to right and the scale shown on graphs is in centimeters. Trees were renumbered after
initial data collection – trees shown are, from top, trees 22-24. The following drilling aspects correspond to
the following drill positions; north = 1 west = 3 south = 5 and east = 7.
228
25
26
27
Appendix figure A1.21 Graph traces from the IML-Resi showing putative decay on the graphs interpreted
according to the resi expert system. These graphs are used as part of the resi expert system. Drilling
direction is from left to right and the scale shown on graphs is in centimeters. Trees were renumbered after
initial data collection – trees shown are, from top, trees 25-27. The following drilling aspects correspond to
the following drill positions; north = 1 west = 3 south = 5 and east = 7.
229
28
29
30
Appendix figure A1.22 Graph traces from the IML-Resi showing putative decay on the graphs interpreted
according to the resi expert system. These graphs are used as part of the resi expert system. Drilling
direction is from left to right and the scale shown on graphs is in centimeters. Trees were renumbered after
initial data collection – trees shown are, from top, trees 28-30. The following drilling aspects correspond to
the following drill positions; north = 1 west = 3 south = 5 and east = 7.
230
31
32
33
Appendix figure A1.23 Graph traces from the IML-Resi showing putative decay on the graphs interpreted
according to the resi expert system. These graphs are used as part of the resi expert system. Drilling
direction is from left to right and the scale shown on graphs is in centimeters. Trees were renumbered after
initial data collection – trees shown are, from top, trees 31-33. The following drilling aspects correspond to
the following drill positions; north = 1 west = 3 south = 5 and east = 7.
231
34
35
36
Appendix figure A1.24 Graph traces from the IML-Resi showing putative decay on the graphs interpreted
according to the resi expert system. These graphs are used as part of the resi expert system. Drilling
direction is from left to right and the scale shown on graphs is in centimeters. Trees were renumbered after
initial data collection – trees shown are, from top, trees 34-36. The following drilling aspects correspond to
the following drill positions; north = 1 west = 3 south = 5 and east = 7.
232
Appendix table A1.8 Distances of putative decay in mm measured on the IML-Resi graph traces on
previous pages trees 1-12. Used as part of the resi expert system. Left is the beginning of the graph trace.
The following drilling positions correspond to the following aspects; 1 = north, 3 = west, 5 = south and 7 =
east.
Putative Decay – Tree 1 at 0.3 m in height.
Location
From 1 at 0.3 m height
Distance from left of graph trace
59 mm – 71 mm
75 mm – 90 mm
From 3 at 0.3 m height
71 mm – 86 mm
From 5 at 0.3 m height
93 mm – 108 mm
102 mm – 120 mm
Putative Decay – Tree 2 at 0.3 m in height.
Location
Distance from left of graph trace
From 1 at 0.3 m height
55 mm – 100 mm
From 3 at 0.3 m height
From 7 at 0.3 m height
54 mm – 74 mm
77 mm – 88 mm
74 mm – 98 mm
94 mm – 107 mm
102 mm – 118 mm
Putative Decay – Tree 3 at 0.3 m in height.
Location
From 1 at 0.3 m height
From 3 at 0.3 m height
Distance from left of graph trace
45 mm – 77 mm
84 mm – 111 mm
59 mm – 83 mm
90 mm – 103 mm
106 mm – 118 mm
Putative Decay – Tree 4 at 0.3 m in height.
Location
Distance from left of graph trace
From 1 at 0.3 m height
33 mm – 142 mm
From 3 at 0.3 m height
49 mm – 117 mm
126 mm – 145 mm
Putative Decay – Tree 5 at 0.3 m in height.
Location
Distance from left of graph trace
From 1 at 0.3 m height
35 mm – 72 mm
75 mm – 135 mm
From 3 at 0.3 m height
64 mm – 75 mm
102 mm – 136 mm
Putative Decay – Tree 6 at 0.3 m in height.
Location
Distance from left of graph trace
From 1 at 0.3 m height
167 mm – 184 mm
From 3 at 0.3 m height
0
233
From 5 at 0.3 m height
214 mm – 232 mm
Putative Decay – Tree 7 at 0.3 m in height.
Location
Distance from left of graph trace
From 1 at 0.3 m height
89 mm – 107 mm
From 3 at 0.3 m height
69 mm – 87 mm
127 mm – 148 mm
From 5 at 0.3 m height
0
Putative Decay – Tree 8 at 0.3 m in height.
Location
Distance from left of graph trace
From 1 at 0.3 m height
96 mm - 113 mm
From 3 at 0.3 m height
0
Putative Decay – Tree 9 at 0.3 m in height.
Location
From 1 at 0.3 m height
Distance from left of graph trace
48 mm – 97 mm
From 3 at 0.3 m height
From 5 at 0.3 m height
111 mm – 154 mm
45 mm – 62 mm
42 mm – 59 mm
108 mm - 141 mm
84 mm – 95 mm
99 mm – 116 mm
Putative Decay – Tree 10 at 0.3 m in height.
Location
Distance from left of graph trace
From 1 at 0.3 m height
137 mm – 153 mm
From 3 at 0.3 m height
0
From 7 at 0.3 m height
57 mm – 164 mm
Putative Decay – Tree 11 at 0.3 m in height.
Location
Distance from left of graph trace
From 1 at 0.3 m height
0
From 3 at 0.3 m height
127 mm – 150 mm
From 7 at 0.3 m height
106 mm – 164 mm
Putative Decay – Tree 12 at 0.3 m in height.
Location
Distance from left of graph trace
From 1 at 0.3 m height
0
From 3 at 0.3 m height
76 mm – 98 mm
From 7 at 0.3 m height
119 mm – 130 mm
234
168 mm – 183 mm
124 mm – 138 mm
Appendix table A1.9 Distances of putative decay in mm measured on the IML-Resi graph traces trees 1324. Used as part of the resi expert system. Left is the beginning of the graph trace. The following drilling
positions correspond to the following aspects; 1 = north, 3 = west, 5 = south and 7 = east.
Putative Decay – Tree 13 at 0.3 m in height.
Location
Distance from left of graph trace
From 1 at 0.3 m height
156 mm – 194 mm
From 3 at 0.3 m height
0
From 7 at 0.3 m height
63 mm – 78 mm
81 mm – 100 mm
Putative Decay – Tree 14 at 0.3 m in height.
Location
Distance from left of graph trace
From 1 at 0.3 m height
0
From 3 at 0.3 m height
0
From 5 at 0.3 m height
0
Putative Decay – Tree 15 at 0.3 m in height.
Location
From 1 at 0.3 m height
From 3 at 0.3 m height
Distance from left of graph trace
72 mm – 180 mm
56 mm – 68 mm
193 mm – 208 mm
88 mm - 111 mm
124mm - 201 mm
Putative Decay – Tree 16 at 0.3 m in height.
Location
Distance from left of graph trace
From 1 at 0.3 m height
64 mm – 86 mm
95 mm – 127 mm
155 mm – 177 mm
From 3 at 0.3 m height
85mm – 136 mm
148 mm - 193 mm
211 mm – 222 mm
Putative Decay – Tree 17 at 0.3 m in height.
Location
Distance from left of graph trace
From 1 at 0.3 m height
30 mm – 84 mm
Between 2 & 3 at 0.3 m
height
49 mm – 70 mm
76 mm - 112 mm
124 mm - 149 mm
Putative Decay – Tree 18 at 0.3 m in height.
Location
Distance from left of graph trace
From 1 at 0.3 m height
79 mm – 103 mm
116 mm – 163 mm
From 3 at 0.3 m height
80 mm – 129 mm
147 mm - 193 mm
235
Putative Decay – Tree 19 at 0.3 m in height.
Location
From 1 at 0.3 m height
Between 2 & 3 at 0.3 m
height
Distance from left of graph trace
27 mm – 54 mm
59 mm – 111 mm
120 mm – 135 mm
43 mm – 61 mm
68 mm - 102 mm
Putative Decay – Tree 20 at 0.3 m in height.
Location
Distance from left of graph trace
From 1 at 0.3 m height
62 mm – 91 mm
111 mm – 168 mm
From 3 at 0.3 m height
49 mm – 69 mm
133 mm – 169 mm
From 5 at 0.3 m height
75 mm – 99 mm
114 mm – 176 mm
Putative Decay – Tree 21 at 0.3 m in height.
Location
From 1 at 0.3 m height
Distance from left of graph trace
67 mm – 99 mm
107 mm – 159 mm
41 mm – 59 mm
From 3 at 0.3 m height
Putative Decay – Tree 22 at 0.3 m in height.
Location
Distance from left of graph trace
From 1 at 0.3 m height
73 mm – 104 mm
124 mm – 136 mm
From 3 at 0.3 m height
73 mm – 94 mm
169 mm - 181 mm
Putative Decay – Tree 23 at 0.3 m in height.
Location
From 1 at 0.3 m height
Distance from left of graph trace
57 mm – 109 mm
180 mm – 210 mm
59 mm – 73 mm
From 3 at 0.3 m height
Putative Decay – Tree 24 at 0.3 m in height.
Location
Distance from left of graph trace
From 1 at 0.3 m height
33 mm – 124 mm
From 3 at 0.3 m height
236
41 mm – 58 mm
100 mm - 124 mm
Appendix table A1.10 Distances of putative decay in mm measured on the IML-Resi graph traces trees 2536. Used as part of the resi expert system. Left is the beginning of the graph trace. The following drilling
positions correspond to the following aspects; 1 = north, 3 = west, 5 = south and 7 = east.
Putative Decay – Tree 25 at 0.3 m in height.
Location
From 1 at 0.3 m height
Distance from left of graph trace
32 mm – 118 mm
148 mm – 161 mm
20 mm – 129 mm
From 3 at 0.3 m height
Putative Decay – Tree 26 at 0.3 m in height.
Location
Distance from left of graph trace
From 1 at 0.3 m height
78 mm – 92 mm
103 mm – 187 mm
From 3 at 0.3 m height
71 mm – 142 mm
165 mm - 182 mm
Putative Decay – Tree 27 at 0.3 m in height.
Location
From 1 at 0.3 m height
Distance from left of graph trace
47 mm – 81 mm
92 mm – 104 mm
0
From 3 at 0.3 m height
Putative Decay – Tree 28 at 0.3 m in height.
Location
From 1 at 0.3 m height
Distance from left of graph trace
67 mm – 96 mm
141 mm – 155 mm
99 mm – 129 mm
From 3 at 0.3 m height
Putative Decay – Tree 29 at 0.3 m in height.
Location
Distance from left of graph trace
From 1 at 0.3 m height
62 mm – 74 mm
From 3 at 0.3 m height
78 mm – 88 mm
109 mm - 121 mm
Putative Decay – Tree 30 at 0.3 m in height.
Location
Distance from left of graph trace
From 1 at 0.3 m height
143 mm – 172 mm
From 3 at 0.3 m height
135 mm – 145 mm
151 mm - 210 mm
From 5 at 0.3 m height
179 mm – 223 mm
239 mm – 251 mm
Putative Decay – Tree 31 at 0.3 m in height.
237
Location
Distance from left of graph trace
From 1 at 0.3 m height
45 mm – 116 mm
Between 2 & 3 at 0.3 m
height
58 mm – 88 mm
109 mm – 124 mm
128 mm – 149 mm
Putative Decay – Tree 32 at 0.3 m in height.
Location
Distance from left of graph trace
From 1 at 0.3 m height
114 mm – 125 mm
Between 2 & 3 at 0.3 m
height
66 mm – 84 mm
192 mm - 203 mm
Putative Decay – Tree 33 at 0.3 m in height.
Location
From 1 at 0.3 m height
Distance from left of graph trace
113 mm – 127 mm
146 mm – 158 mm
204 mm – 226 mm
199 mm – 210 mm
From 3 at 0.3 m height
Putative Decay – Tree 34 at 0.3 m in height.
Location
Distance from left of graph trace
From 1 at 0.3 m height
33 mm – 48 mm
74 mm – 110 mm
From 2 at 0.3 m height
48 mm – 63 mm
84 mm - 107 mm
Putative Decay – Tree 35 at 0.3 m in height.
Location
Distance from left of graph trace
From 1 at 0.3 m height
134 mm – 146 mm
158 mm – 175 mm
From 3 at 0.3 m height
93 mm – 104 mm
115 mm - 136 mm
Putative Decay – Tree 36 at 0.3 m in height.
Location
From 1 at 0.3 m height
Distance from left of graph trace
89 mm – 107 mm
118 mm – 136 mm
From 3 at 0.3 m height
103 mm – 124 mm
From 5 at 0.3 m height
92 mm – 117 mm
238
1
2
3
5
6
7
8
9
10
11
12
13
14
17
18
19
22
23
21
15
4
16
20
24
Appendix Figure A1.25 Resi expert system diagrams using the perimeter cross section outlines and
putative decay in graph traces shown in previous pages. Blue lines represent drilling locations, red shaded
areas represent putative decay according to the resi expert system. Trees 1-24. North is at top of diagrams at
1.
239
25
29
33
26
27
28
30
31
32
35
34
36
Appendix Figure A1.26 Resi expert system diagrams using the perimeter cross section outlines and
putative decay in graph traces shown in previous pages. Blue lines represent drilling locations, red shaded
areas represent putative decay according to the resi expert system. Trees 25-36. North is at top of diagrams
at 1.
Appendix to the visual method of decay estimation.
1
4
2
3
5
6
Appendix Figure A1.27 Photographs of the cross sections. These cross sections were used as part of the
visual method of decay estimation. North is at the top of the photograph. Trees were renumbered after initial
data collection – trees shown are, from top left, trees 1-6.
240
7
8
9
10
11
12
13
14
16
17
19
20
15
18
21
Appendix Figure A1.28 Photographs of the cross sections. These cross sections were used as part of the
visual method of decay estimation. North is at the top of the photograph. Trees were renumbered after initial
data collection – trees shown are, from top left, trees 7-21.
241
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
Appendix Figure A1.29 Photographs of the cross sections. These cross sections were used as part of the
visual method of decay estimation. North is at the top of the photograph. Trees were renumbered after initial
data collection – trees shown are, from top left, trees 22-36.
242
1.3 Appendix to results from chapter 3
Tree number
Appendix table A1.11 Raw results data from chapter 3 including the percentage of decay using the picus and resi expert systems as well as the visual method.
Also included is the percentage wood volume to 1 m, whole tree dry densities the percentage wood moisture content and basic wood density. Data is for all 36
trees.
Picus
expert
system
section
area
mm2
Resi
expert
system
section
area
mm2
Visual
method
section
area
mm2
Picus
expert
system
decay
area
mm2
Resi
expert
system
decay
area
mm2
Visual
method
decay
area
mm2
%
decay
picus
expert
system
%
decay
resi
expert
system
%
decay
visual
method
%
wood
volume
to 1 m
Whole
tree
wood
dry
density
kg/m3
Trunk
wood
dry
density
kg/m3
% wood
moisture
content
Basic
wood
density
kg/m3
1
69920
79291
80119
4590
1430
429
6.56
1.80
0.53
15.8
474
461
52.76
515.196
2
69373
69920
72074
6426
3138
350
9.26
4.49
0.49
12.77
482
471
51.78
488.8
3
37951
38711
40460
4280
2645
489
11.28
6.83
1.21
18.7
496
447
53.48
516.144
4
34607
37501
35115
2868
6001
69
8.29
16.00
0.20
15.18
359
358
40.33
546.984
5
34148
35060
34980
3149
2654
303
9.22
7.57
0.87
19.01
397
410
54.52
476.312
6
72669
70859
73097
1169
666
548
1.61
0.94
0.75
13.19
584
524
47.28
565.993
7
75049
82565
81597
1079
1065
73
1.44
1.29
0.09
11.65
551
519
48.69
558.01
8
81349
78011
81549
708
275
815
0.87
0.35
1.00
12.74
739
709
32.66
577.708
9
73448
73660
79558
3561
5786
917
4.85
7.86
1.15
14.28
574
564
45.81
536.569
10
58572
56644
60348
3980
4713
249
6.80
8.32
0.41
13.06
551
585
43.85
620.196
11
70901
72163
76596
2600
2224
565
3.67
3.08
0.74
12.25
648
615
45.25
613.396
12
64757
65286
66392
2388
614
387
3.69
0.94
0.58
14.47
457
434
51.16
552.595
Tree number
Picus
expert
system
section
area
mm2
Resi
expert
system
section
area
mm2
Visual
method
section
area
mm2
Picus
expert
system
decay
area
mm2
Resi
expert
system
decay
area
mm2
Visual
method
decay
area
mm2
%
decay
picus
expert
system
%
decay
resi
expert
system
%
decay
visual
method
%
wood
volume
to 1 m
Whole
tree
wood
dry
density
kg/m3
Trunk
wood
dry
density
kg/m3
% wood
moisture
content
Basic
wood
density
kg/m3
13
75846
72576
81888
7871
2070
467
10.38
2.85
0.57
11.81
487
446
54.38
539.189
14
70089
71856
73226
2764
0
236
3.94
0.00
0.32
12.54
712
647
38.84
613.36
15
66260
66260
70253
2542
6744
254
3.84
10.18
0.36
12.44
535
504
50.24
530.938
16
63541
63541
66875
1243
4243
1466
1.96
6.68
2.19
11.64
501
490
52.34
485.256
17
21419
21419
22513
1134
3759
26
5.30
17.55
0.11
15.93
505
478
49.79
541.93
18
50441
50441
51482
2862
3533
818
5.67
7.00
1.59
14.73
538
553
44.36
504.74
19
17702
17702
21280
0
2469
7
0.00
13.95
0.03
18.3
459
993
46.13
469.033
20
54717
54717
57041
0
2950
629
0.00
5.39
1.10
13.52
502
487
48.77
534.442
21
45444
45444
47197
5759
3209
3810
12.67
7.06
8.07
14.24
503
534
42.02
493.256
22
36913
36913
41255
1477
1530
248
4.00
4.14
0.60
12.67
482
485
51.98
449.613
23
35303
35303
35581
3021
2965
19
8.56
8.40
0.05
15.47
525
541
44.32
652.461
24
28749
28749
27509
17343
2798
1086
60.33
9.73
3.95
15.62
447
476
47.59
505.187
25
27939
27939
29464
6567
6478
109
23.50
23.19
0.37
19.48
485
509
47.05
473.416
26
43596
43596
44606
5069
5070
6034
11.63
11.63
13.53
14.41
478
473
52.34
526.208
27
43076
43076
48049
2177
1344
42
5.05
3.12
0.09
13.06
495
503
49.73
561.29
28
55696
55696
55459
1858
1809
187
3.34
3.25
0.34
10.5
562
544
48.67
586.197
29
53608
53608
55275
3689
540
259
6.88
1.01
0.47
12.57
505
502
49.5
555.977
Tree number
Picus
expert
system
section
area
mm2
Resi
expert
system
section
area
mm2
Visual
method
section
area
mm2
Picus
expert
system
decay
area
mm2
Resi
expert
system
decay
area
mm2
Visual
method
decay
area
mm2
%
decay
picus
expert
system
%
decay
resi
expert
system
%
decay
visual
method
%
wood
volume
to 1 m
Whole
tree
wood
dry
density
kg/m3
Trunk
wood
dry
density
kg/m3
% wood
moisture
content
Basic
wood
density
kg/m3
30
80725
80725
93988
714
5347
2221
0.88
6.62
2.36
12.68
598
576
42.68
586.406
31
24674
24674
29608
1192
2046
61
4.83
8.29
0.21
18.6
513
522
48.1
556.073
32
76421
76421
63407
4835
609
204
6.33
0.80
0.32
10.8
625
609
39.12
549.079
33
49106
49106
52838
7500
1088
604
15.27
2.22
1.14
11.48
610
600
43.73
514.555
34
27199
27199
28658
1232
1057
130
4.53
3.89
0.45
14.1
601
589
44.67
571.507
35
49383
49383
50193
4142
899
45
8.39
1.82
0.09
10.52
632
606
43.55
529.732
36
76737
76737
77192
3330
1381
589
4.34
1.80
0.76
12.41
573
583
43.9
660.347
Appendix 2 Appendix to chapter 4
2.1 Appendix to methods used in chapter 4
Appendix table A2.1 Visual vitality indices results for test trees 1-36 in March 2008, refined for young
plantation trees based on a method developed by Grimes, 1978, Lindenmayer, et al. 1990 and Martin et al.
2001.
Tree
Number
Crown
position
Crown
size
Crown
density
Dead
branches
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
3
3
2
4
2
1
6
3
2
5
3
3
Crown
epicormic
growth
2
2.5
2.5
1
3
3
3
3
3
3
3
3
3
3
3
2
2
1
3
2
3
2
1
1
2
3
3
3
3
1
3
2
2
3
3
1
5
5
5
2
2
3
3
4
5
5
4
1
1
1
3
1
2
2
1
1
3
3
3
3
3
1
5
2
2
3
3
2
8
9
8
3
3
4
5
6
7
9
6
2
2
2
4
2
3
2
2
2
4
5
5
5
4
1
7
4
3
5
4
1
3
3
3
3
3
3
3
3
3
3
3
3
2
1
5
2
3
2
1
2
3
3
5
3
3
1
5
3
3
3
3
1.5
2.5
2.5
2.5
2
2.5
2
3
3
2.5
2.5
2
1.5
1.5
1.5
3
2
2.5
2
1.5
1.5
2
3
2.5
3
2
2.5
3
1.5
2
2
2
246
Dead tree
classification
4
Total
20
13.5
10.5
4
6.5
21.5
22.5
21.5
13
13.5
15
17
19
20.5
22.5
18
9.5
8.5
6.5
18
9
13.5
10
6.5
7.5
14
17
18.5
17
15
6.5
23
12.5
12
16
15
1
2
3
4
Appendix figure A2.1 Photographs of individual trees taken in September, 2007 showing rough bark
“sock” on each tree, approximate relative leaf area/canopy density and tree size. On the left, the base of the
tree to approximately 2.5 m in height, at right, photographs of the canopy taken from approximately 1.6 m
abutting the trunk looking directly up into the canopy. All photographs taken on the north side of the trees.
Trees 1-4 shown above, as the trees were renumbered during the project.
247
5
6
7
8
Appendix figure A2.2 Photographs of individual trees taken in September, 2007 showing rough bark
“sock” on each tree, approximate relative leaf area/canopy density and tree size. On the left, the base of the
tree to approximately 2.5 m in height, at right, photographs of the canopy taken from approximately 1.6 m
abutting the trunk looking directly up into the canopy. All photographs taken on the north side of the trees.
Trees 5-8 shown above, trees were renumbered during the project.
248
9
10
11
12
Appendix figure A2.3 Photographs of individual trees taken in September, 2007 showing rough bark
“sock” on each tree, approximate relative leaf area/canopy density and tree size. On the left, the base of the
tree to approximately 2.5 m in height, at right, photographs of the canopy taken from approximately 1.6 m
abutting the trunk looking directly up into the canopy. All photographs taken on the north side of the trees.
Trees 9-12 shown above, trees were renumbered during the project
249
13
14
15
16
Appendix figure A2.4 Photographs of individual trees taken in September, 2007 showing rough bark
“sock” on each tree, approximate relative leaf area/canopy density and tree size. On the left, the base of the
tree to approximately 2.5 m in height, at right, photographs of the canopy taken from approximately 1.6 m
abutting the trunk looking directly up into the canopy. All photographs taken on the north side of the trees..
Trees 13-16 shown above, trees were renumbered during the project
250
17
18
19
20
Appendix figure A2.5 Photographs of individual trees taken in September, 2007 showing rough bark
“sock” on each tree, approximate relative leaf area/canopy density and tree size. On the left, the base of the
tree to approximately 2.5 m in height, at right, photographs of the canopy taken from approximately 1.6 m
abutting the trunk looking directly up into the canopy. All photographs taken on the north side of the trees.
Trees 17-20 shown above, trees were renumbered during the project
251
21
22
23
24
Appendix figure A2.6 Photographs of individual trees taken in September, 2007 showing rough bark
“sock” on each tree, approximate relative leaf area/canopy density and tree size. On the left, the base of the
tree to approximately 2.5 m in height, at right, photographs of the canopy taken from approximately 1.6 m
abutting the trunk looking directly up into the canopy. All photographs taken on the north side of the trees.
Trees 21-24 shown above, trees were renumbered during the project.
252
25
26
27
28
Appendix figure A2.7 Photographs of individual trees taken in September, 2007 showing rough bark
“sock” on each tree, approximate relative leaf area/canopy density and tree size. On the left, the base of the
tree to approximately 2.5 m in height, at right, photographs of the canopy taken from approximately 1.6 m
abutting the trunk looking directly up into the canopy. All photographs taken on the north side of the trees.
Trees 25-28 shown above, trees were renumbered during the project.
253
29
30
31
32
Appendix figure A2.8 Photographs of individual trees taken in September, 2007 showing rough bark
“sock” on each tree, approximate relative leaf area/canopy density and tree size. On the left, the base of the
tree to approximately 2.5 m in height, at right, photographs of the canopy taken from approximately 1.6 m
abutting the trunk looking directly up into the canopy. All photographs taken on the north side of the trees.
Trees 29-32 shown above, trees were renumbered during the project.
254
33
34
35
36
Appendix figure A2.9 Photographs of individual trees taken in September, 2007 showing rough bark
“sock” on each tree, approximate relative leaf area/canopy density and tree size. On the left, the base of the
tree to approximately 2.5 m in height, at right, photographs of the canopy taken from approximately 1.6 m
abutting the trunk looking directly up into the canopy. All photographs taken on the north side of the trees.
Trees 33-36 shown above, trees were renumbered during the project.
255
2.2 Appendix to results from chapter 4
Tree number
Appendix table A2.2 Raw results data from chapter 4 including total leaf area, specific leaf area, percentage
sapwood area, the Huber value, above ground biomass, tree height, diameter at breast height (1.3 m) and the
visual vitality index measured in autumn. Data is for all 36 trees.
Leaf
area
m2
Percentage
Leaves
Counted
Specific
leaf
area
mm2
mg-1
%
Sapwood
area at
0.3 m
height
Huber
value m2
m-2
Above
ground
biomass
kg
Tree
height
m
DBH
mm at
1.3 m
height
Autumn
visual
vitality
index
1
71.2
2
12.015
35.12
0.000255
293
23.7
258
20.0
2
35.53
3
7.024
26.59
0.000399
295
23.4
250
13.5
3
13.75
5
7.357
34.30
0.000641
111
20.8
187
10.5
4
0
0
91
18.5
186
4.0
5
43.08
2
13.685
37.13
0.000218
90
17.4
189
6.5
6
65.54
1
5.355
51.47
0.000401
415
25.2
247
21.5
7
108.61
2
6.594
32.31
0.000184
498
25.6
275
22.5
8
92.86
1
7.175
33.77
0.000223
615
25.5
287
21.5
9
29.35
3
7.163
33.01
0.000601
383
25.8
250
13.0
10
25.2
8
7.509
35.11
0.00061
311
26
241
13.5
11
71.69
4
11.126
40.31
0.000312
414
25.6
265
15.0
12
43.75
9
6.091
31.97
0.000321
258
25.4
259
17.0
13
61.92
7
6.691
32.74
0.000302
390
26.7
318
19.0
14
84.71
4
6.322
38.52
0.000218
473
24.4
265
20.5
15
74.65
1
6.547
31.37
0.000216
329
23
250
22.5
16
93.63
1
9.745
32.33
0.000168
325
22.5
238
18.0
17
17.37
5
16.33
34.52
0.000364
84
21.6
147
9.5
18
18.38
4
11.622
30.32
0.000587
170
20.4
190
8.5
19
25.85
16
9.241
19.12
0.00013
68
17.3
145
6.5
20
35.13
3
6.289
41
0.000469
245
24.8
222
18.0
21
3.98
3
11.346
31.28
0.002895
153
22.7
216
9.0
256
36.04
Tree number
Leaf
area
m2
Percentage
Leaves
Counted
Specific
leaf
area
mm2
mg-1
%
Sapwood
area at
0.3 m
height
Huber
value m2
m-2
Above
ground
biomass
kg
Tree
height
m
DBH
mm at
1.3 m
height
Autumn
visual
vitality
index
22
31.51
2
9.692
43.58
0.000391
163
24.4
187
13.5
23
5.87
3
8.674
37.06
0.001564
126
22.2
182
10.0
24
13.32
25
13.678
22.13
0.000355
78
19.1
142
6.5
25
16.35
7
11.582
32.64
0.000418
74
18.7
149
7.5
26
29.5
3
10.463
31.31
0.000351
173
22.2
203
14.0
27
42.66
3
7.286
28.51
0.000249
206
24.9
205
17.0
28
73.13
3
7.124
37.93
0.000201
338
25.8
227
18.5
29
55.26
2
7.37
41.66
0.000289
260
24.3
217
17.0
30
67.32
2
6.03
37.83
0.000406
487
25.7
316
15.0
31
1.71
50
6.83
44.96
0.00618
122
23
168
6.5
32
47.65
3
8.072
37.46
0.000351
370
25
229
23.0
33
87.95
8
10.429
30.21
0.000147
296
24.7
210
12.5
34
17.48
9
17.691
47.87
0.000579
138
23
161
12.0
35
75.26
5
8.309
34.58
0.000183
299
24.5
210
16.0
36
66.83
3
8.05
27.69
0.000243
388
25.6
283
15.0
257
Appendix 3 Appendix to chapter 5
3.1 Appendix to methods used in chapter 5
Appendix table A3.1 Summary tree dimensions for test trees 1-38 at cortical fluorometry test height at the test
site in Tostaree, Victoria. Fluorescence testing was done in a 350 mm strip around the circumference of the
trees. Testing was done at the north side of the trees and every 35 mm. The test area on the bark was circular
and 4.5 mm in diameter.
Tree
Number
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
258
Tree diameter at
test heights of
cortical fluorometry
in mm, September,
2007
244 (at 1.5 m)
244 (at 1.6 m)
167 (at 3.6 m)
108 (at 7.3 m)
128 (at 6.0 m)
232 (at 3.7 m)
269 (at 1.6 m)
295 (at 1.8 m)
231 (at 3.2 m)
215 (at 2.3 m)
235 (at 2.2 m)
137 (at 14.2 m)
237 (at 5.8 m)
192 (at 5.6 m)
247 (at 1.5 m)
240 (at 1.1 m)
145 (at 1.5 m)
189 (at 1.6 m)
143 (at 1.2 m)
185 (at 4.0 m)
168 (at 2.2 m)
191 (at 1.0 m)
155 (at 2.7 m)
145 (at 1.0 m)
116 (at 3.2 m)
200 (at 1.7 m)
192 (at 1.8 m)
228 (at 1.5 m)
218 (at 1.2 m)
245 (at 9.2 m)
156 (at 1.4 m)
219 (at 3.8 m)
220 (at 1.5 m)
156 (at 1.8 m)
210 (at 1.4 m)
263 (at 1.0 m)
Tree diameter at test
heights of cortical
fluorometry in mm,
January, 2008
Tree diameter at
test heights of
cortical fluorometry
in mm, April, 2008
237 (at 1.8 m)
247 (at 1.4 m)
165 (at 3.7 m)
N/A
128 (at 6.0 m)
233 (at 3.7 m)
268 (at 1.7 m)
295 (at 1.8 m)
228 (at 3.5 m)
215 (at 2.4 m)
237 (at 2.2 m)
133 (at 14.9 m)
221 (at 6.2 m)
192 (at 5.8 m)
240 (at 2.7 m)
241 (at 1.2 m)
145 (at 1.6 m)
181 (at 2.2 m)
134 (at 1.9 m)
186 (at 3.9 m)
168 (at 1.9 m)
187 (at 1.4 m)
154 (at 2.3 m)
144 (at 1.1 m)
116 (at 3.2 m)
200 (at 1.6 m)
207 (at 0.7 m)
227 (at 1.5 m)
218 (at 1.3 m)
246 (at 2.6 m)
155 (at 1.4 m)
212 (at 3.7 m)
227 (at 0.8 m)
158 (at 1.6 m)
210 (at 0.7 m)
242 (at 3.5 m)
242 (at 1.8 m)
245 (at 1.4 m)
160 (at 3.7 m)
N/A
128 (at 6.2 m)
232 (at 3.7 m)
269 (at 1.7 m)
296 (at 0.7 m)
230 (at 3.2 m)
216 (at 2.3 m)
235 (at 2.2 m)
132 (at 14.9)
221 (at 6.3 m)
192 (at 5.9 m)
250 (at 1.3 m)
238 (at 1.6 m)
250 (at 1.3 m)
186 (at 1.6 m)
133 (at 1.9 m)
185 (at 4.0 m)
167 (at 2.2 m)
185 (at 1.6 m)
155 (at 2.3 m)
151 (at 0.7 m)
115 (at 3.3 m )
200 (at 1.6 m)
185 (at 1.6 m)
226 (at 1.2 m)
218 (at 1.4 m)
248 (at 2.8 m)
180 (at 0.6 m)
221 (at 2.3 m)
216 (at 1.4 m)
158 (at 1.9 m)
220 (at 0.7 m)
261 (at 1.2 m)
1600
1400
Fluorescence mV
1200
1000
800
600
400
200
0
0.01
0.1
1
10
100
1000
Log time in ms
Appendix figure A3.1 Graph of the fast fluorescence rise for Eucalyptus saligna leaf in spring over a 1
second time period showing the O-J-I-P phases with the OJIP steps shown in red. Time on the graph has
been logarithmically transformed.
800
700
600
Fluorescence mV
500
400
300
200
100
0
0.01
0.1
1
10
100
1000
Log time in ms
Appendix figure A3.2 Graph of the fast fluorescence rise for Eucalyptus saligna bark in summer over a
1 second time period showing the O-J-I-P phases with the OJIP steps shown in red. Time on the graph
has been logarithmically transformed.
259
1600
1400
Fluorescence mV
1200
1000
800
600
400
200
0
0.01
0.1
1
10
100
1000
Log time in ms
Appendix figure A3.3 Graph of the fast fluorescence rise for Eucalyptus saligna leaf in autumn over a
1 second time period showing the O-J-I-P phases with the OJIP steps shown in red. Time on the graph
has been logarithmically transformed.
800
700
Fluorescence mV
600
500
400
300
200
100
0
0.01
0.1
1
10
100
1000
Log time in ms
Appendix figure A3.4 Graph of the fast fluorescence rise for Eucalyptus saligna bark in autumn over a
1 second time period showing the O-J-I-P phases with the OJIP steps shown in red. Time on the graph
has been logarithmically transformed.
260
Appendix table A3.2 Visual vitality indices results for test trees 1-36 in October 2007, refined for
young plantation trees based on a method developed by Martin et al. 2001, Grimes, 1978 and
Lindenmayer, et al. 1990.
Tree
Number
Crown
position
Crown
size
Crown
density
Dead
branches
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
3
3
2
3
3
3
5
4
4
5
5
3
Crown
epicormic
growth
2
2.5
3
1
3
3
3
3
3
3
3
3
3
3
3
2
1
3
2
3
2
1
1
2
3
3
3
3
3
2
3
2
2
2
3
2
4
5
5
3
4
4
4
4
4
5
4
2
2
2
4
3
3
2
1
2
2
3
3
3
4
1
4
2
1
2
3
2
6
8
9
5
5
5
6
6
6
7
6
2
2
2
6
2
2
2
1
2
3
5
3
5
5
1
6
3
2
3
3
1
3
3
5
3
5
3
3
3
3
3
3
3
2
1
5
3
3
2
1
1
3
3
3
3
3
1
3
3
3
3
3
1.5
2.5
2.5
3
2.5
2.5
2
3
3
2.5
2.5
2
2
2
1.5
3
3
3
2
1.5
1.5
2
3
2.5
3
2.5
2.5
3
2
2.5
2
2
Dead tree
classification
Total
18
17.5
15
5
7.5
18.5
21.5
25
16.5
19.5
17
19
19
18.5
20.5
18
11
10
7.5
21
13
14
10
5.5
7.5
12
17
14.5
17
17.5
7.5
19
12
10.5
12
14
5
261
Appendix table A3.3 Visual vitality indices indices results for test trees 1-36 in January 2008, refined
for young plantation trees based on a method developed by Martin et al. 2001, Grimes, 1978 and
Lindenmayer, et al. 1990.
Tree
Number
Crown
position
Crown
size
Crown
density
Dead
branches
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
3
3
2
5
4
3
7
5
4
5
5
3
Crown
epicormic
growth
2.5
2
2.5
1
3
3
3
3
3
3
3
3
3
3
3
2
2
1
3
3
3
3
1
1
2
3
3
3
3
2
3
2
3
3
3
2
5
5
5
5
4
5
5
5
5
5
4
2
2
1
4
3
4
2
1
1
3
4
5
4
3
1
5
4
3
4
3
2
8
8
9
8
7
8
8
9
7
6
5
3
3
2
7
5
6
4
1
2
4
7
7
7
6
1
7
6
4
6
6
2
3
3
3
3
3
3
3
3
3
4
3
3
3
2
4
3
3
3
2
2
3
3
5
3
3
2
4
3
3
3
3
1
2.5
2.5
2.5
2
2.5
2
3
2.5
2
2.5
2
2
1.5
1
3
2.5
2.5
1.5
1
1
2
3
2.5
3
2
2.5
2
2
2
2
2
262
Dead tree
classification
5
Total
22.5
19
14.5
5
8
21.5
21.5
22.5
21
19.5
21
22
22.5
20
20.5
17
12
11.5
7
21
16.5
18.5
13.5
6
7
14
20
22.5
20
17
8.5
21
17
15
18
17
3.2 Appendix to results from chapter 5
Tree #
Appendix table A3.4 Raw results data from chapter 5 including wood decay according to the resi method,
the spring, summer and autumn visual vitality results and spring leaf FvFm, and spring leaf OJIP steps.
Spring
visual
vitality
index
18
Summer
visual
vitality
index
22.5
Autumn
visual
vitality
index
20.0
FvFmleafs
1
%
decay
resi
method
1.8
Oleafs
0.8446
228.0
2
4.49
17.5
19
13.5
0.8494
222.4
3
6.83
15
14.5
10.5
0.8521
222.2
4
16
5
5
4.0
5
7.57
7.5
8
6.5
0.8375
6
0.94
18.5
21.5
21.5
7
1.29
21.5
21.5
22.5
8
0.35
25
22.5
9
7.86
16.5
10
8.32
11
3.08
12
0.94
13
14
15
Jleafs
Ileafs
Pleafs
226.8
1096.3
1412.2
592.2
1063.7
1423.8
584
1078.8
1445.6
182.9
487.8
487.8
827.7
0.8307
226.2
611.8
1020.3
1275.5
0.8399
209.0
566.5
991.5
1255.5
21.5
0.8474
210.4
531.8
1001.5
1328.2
21
13.0
0.8435
224.2
582.9
1014.4
1377.4
19.5
19.5
13.5
0.8384
253.1
720.6
1247
1498.1
17
21
15.0
0.8074
204.0
498.5
889.3
1139.2
19
22
17.0
0.8415
215.2
563.1
967.8
1303.6
2.85
19
22.5
19.0
0.8434
199.4
534.3
937.5
1224
0
18.5
20
20.5
0.8565
215.9
586.4
1094.4
1448.5
10.18
20.5
20.5
22.5
0.8314
247.7
688.7
1085.1
1407
16
6.68
18
17
18.0
0.8501
219.2
573.1
1059.5
1407.3
17
17.55
11
12
9.5
0.8376
247.5
641.3
1111
1455.2
18
7
10
11.5
8.5
0.8422
236.6
651.5
1082.4
1442.4
19
13.95
7.5
7
6.5
0.6793
483.8
1009.5
1286.2
1421.7
20
5.39
21
21
18.0
0.8436
236.7
662.7
1116.2
1453.1
21
7.06
13
16.5
9.0
0.8271
243.5
630.8
1090.3
1345.7
22
4.14
14
18.5
13.5
0.8272
222.1
579.3
929.3
1238.7
23
8.4
10
13.5
10.0
0.8353
230.4
610.7
1055.8
1340.2
24
9.73
5.5
6
6.5
0.8402
250.5
664.5
1166.6
1507.7
25
23.19
7.5
7
7.5
0.8309
230.6
556.8
927.8
1312.3
26
11.63
12
14
14.0
0.8549
205.0
613.2
1045.2
1343.1
27
3.12
17
20
17.0
0.8450
213.2
616.4
969.5
1312.4
28
3.25
14.5
22.5
18.5
0.8417
186.8
527.8
906.1
1132.9
29
1.01
17
20
17.0
0.8405
210.1
585.4
972.9
1250.1
30
6.62
17.5
17
15.0
0.8404
224.8
606.5
937.7
1355.8
31
8.29
7.5
8.5
6.5
0.8429
245.8
635.3
1128.1
1503
32
0.8
19
21
23.0
0.8475
207.3
593.3
1020
1315.5
33
2.22
12
17
12.5
0.8355
209.3
548.8
933
1221.9
34
3.89
10.5
15
12.0
0.8497
215.3
612.0
1067.0
1381.9
35
1.82
12
18
16.0
0.8426
200.4
551.2
927.9
1219.7
36
1.8
14
17
15.0
0.8278
213.8
540.1
890.5
1202.8
263
Tree #
Appendix table A3.5 Raw results data from chapter 5 including spring bark FvFm, and spring bark OJI
steps and spring bark chlorophyll fluorescence at 1000 ms and summer leaf FvFm, and summer leaf OJIP
steps.
FvFmbarks
Obarks
Jbarks
Ibarks
1000msbarks
FvFmleafj
Oleafj
Jleafj
Ileafj
Pleafj
1
0.8376
105.5
263.3
534.9
632.9
0.8553
193.1
543.0
999.0
1286.6
2
0.8200
123.9
325.3
558.5
666.2
0.8511
208.2
575.7
1068
1340.9
3
0.8392
157.0
453.3
815.4
936.0
0.8516
199.6
503.6
970.0
1291.2
4
0.5390
202.0
338.4
420.0
437.9
5
0.8130
124.7
262.0
567.8
642.1
0.8536
159.0
403.2
773.8
1050.5
6
0.8369
108.2
266.6
564.6
645.0
0.8467
175.1
468.0
795.7
1094.4
7
0.8308
130.1
287.9
645.8
749.5
0.8464
175.4
459.6
884.9
1099.0
8
0.8335
141.9
338.0
700.7
827.1
0.8584
163.0
429.9
766.8
1114.5
9
0.8432
124.4
309.5
647.0
781.3
0.8588
186.8
512.3
933.0
1267.9
10
0.8414
117.5
286.9
612.0
718.0
0.8597
180.2
482.9
878.5
1233.6
1305.4
11
0.8420
133.9
319.0
669.4
825.2
0.8594
190.1
481.4
938.1
12
0.8287
138.6
305.1
676.0
785.6
0.8523
210.8
534.7
996.5
1375.7
13
0.8466
128.1
324.0
693.4
808.4
0.8607
196.2
544.8
1015
1351.3
14
0.8434
140.6
365.0
762.1
871.3
0.8611
201.5
520.0
1012
1400.5
15
0.8229
130.5
356.9
650.4
710.9
0.8458
197.7
552.1
956.7
1224.3
16
0.8338
128.0
339.0
654.8
747.7
0.8340
201.8
560.6
961.2
1178.6
17
0.8006
154.4
414.2
679.8
749.7
0.8340
220.8
629.7
1038
1277.4
18
0.8122
103.5
271.5
484.3
545.2
0.8453
192.9
562.4
948.8
1188.2
19
0.8331
117.8
282.7
595.9
685.2
0.7431
418.8
966.1
1343
1517.7
20
0.8282
87.4
197.7
432.6
498.7
0.8487
206.2
544.9
949.6
1311.2
21
0.8310
130.5
325.0
668.4
746.1
0.8298
230.4
572.1
1035
1295.3
22
0.8296
105.6
236.0
524.9
607.3
0.8458
218.4
558.4
1034
1359.0
23
0.8297
126.7
319.3
658.5
722.9
0.8582
200.6
510.4
922.3
1362.4
24
0.8319
156.1
393.1
798.1
892.4
0.8419
257.5
695.7
1216
1544.2
25
0.8298
121.8
287.0
613.7
698.2
0.8360
224.8
574.5
964.0
1301.6
26
0.8517
122.9
310.1
701.7
809.8
0.8658
144.2
380.2
755.4
1037.3
27
0.8446
132.2
345.8
704.6
826.4
0.8540
171.9
474.8
865.7
1136.7
28
0.8441
110.2
277.6
583.7
687.9
0.8572
158.9
408.7
788.9
1072.1
29
0.8472
88.4
189.9
455.5
565.8
0.8575
166.5
436.8
893.8
1126.6
30
0.8415
104.4
250.1
556.2
644.9
0.8722
176.3
451.8
886.8
1234.3
31
0.8318
129.3
334.3
642.0
748.5
0.8499
193.9
485.9
879.4
1251.7
32
0.8245
135.4
310.5
644.0
753.7
0.8450
187.1
562.5
840.5
1152.4
33
0.8305
120.2
289.8
586.5
686.5
0.8554
195.8
498.2
971.7
1304.9
34
0.8372
137.9
349.5
700.3
822.1
0.8557
210.4
550.5
1037
1400.7
35
0.8476
115.4
274.7
616.9
742.4
0.8499
211.9
544.0
1011
1360.8
36
0.8412
120.8
301.5
620.5
739.2
0.8554
191.9
539.7
967.0
1277.9
264
Appendix table A3.6 Raw results data from chapter 5 including summer bark FvFm, and summer bark OJI
steps and summer bark chlorophyll fluorescence at 1000 ms and autumn leaf FvFm, and autumn leaf OJIP
steps.
Tree #
FvFmbarkj
Obarkj
Jbarkj
1
0.8405
126.5
328.7
2
0.8443
119.0
313.6
3
0.8206
134.6
Ibarkj
1000barkj
FvFmleafm
Oleafm
Jleafm
Ileafm
Pleafm
661.4
766.1
0.8488
169.6
460.5
844.7
1053.5
641.4
731.3
0.8561
178
500.8
947.5
1186.8
384.6
717.8
779.1
0.8481
181.7
449.5
847.6
1060.2
4
5
0.8200
99.8
238.4
481.1
534.0
0.8364
212.1
541.7
994.2
1248.3
6
0.8322
104.2
248.5
539.2
602.1
0.8515
222.2
609.8
1149.9
1438.6
7
0.8344
132.8
338.9
699.2
777.3
0.8243
196.4
541.7
994.2
1248.3
8
0.8359
137.3
339.5
713.5
803.8
0.8366
172.7
548.6
924.7
1072.4
9
0.8310
140.2
322.2
692.6
799.0
0.8509
206.3
462.3
806.6
1015.1
10
0.8350
137.6
335.9
711.7
800.1
0.8477
206.3
559.5
1046.2
1333.5
11
0.8338
152.3
366.4
765.3
881.5
0.8446
219.8
636.2
1125.7
1386.7
12
0.8166
123.7
276.8
581.6
650.0
0.8362
207.1
535.6
1014.9
1286
13
0.8532
126.6
332.7
740.9
834.0
0.8343
223.9
546.4
1009.8
1323.4
14
0.8451
137.5
326.0
747.6
855.0
0.8469
203.2
582.4
1044.3
1277.8
15
0.8403
115.0
306.2
632.1
697.2
0.8517
183.5
557.3
978.5
1176.3
16
0.8465
102.8
273.9
580.6
649.5
0.8388
181.6
514.2
917.4
1080.7
17
0.8239
128.5
348.0
633.9
700.4
0.8514
187.4
535.3
970.1
1212
18
0.8289
107.9
285.1
539.6
609.6
0.8466
182.6
505.5
929.3
1140.1
19
0.8092
145.6
352.0
654.6
740.0
0.7436
445.0
995.6
1435.8
1588.8
20
0.8280
121.3
319.8
603.3
672.8
0.8424
207.5
551.0
993.3
1270.6
21
0.8252
164.2
476.8
819.5
879.5
0.8412
220.2
560.9
1089.6
1325.9
22
0.8339
74.2
223.3
390.1
425.6
0.8530
211.5
608.8
1121.5
1377
23
0.8268
128.2
329.7
623.9
660.3
0.8489
211.5
593.7
1112.4
1343.4
24
0.8116
172.9
450.9
796.2
860.3
0.8419
251.4
710.0
1264.8
1502.1
25
0.8206
150.6
344.4
722.0
799.1
0.8386
229.8
576.1
1091.4
1359.8
26
0.8437
147.0
435.6
805.6
899.8
0.8534
196.4
534.2
1044.3
1291.0
27
0.8255
145.0
407.7
716.3
795.2
0.8482
210.9
548.5
1038.7
1335.9
28
0.8296
106.9
270.8
534.2
601.5
0.8278
201.1
536.3
892.8
1122.7
29
0.8421
81.0
182.3
424.1
498.8
0.8490
211.6
608.0
1139.0
1344.4
30
0.8456
109.6
265.6
596.9
687.0
0.8422
181.4
478.5
834.5
1110.3
31
0.8235
117.0
267.6
554.9
639.9
0.8469
212.7
518.7
1025.1
1338.9
32
0.8288
144.0
356.1
728.8
812.4
0.8354
209.8
579.2
1011.2
1219.4
33
0.8414
115.9
276.9
614.3
710.9
0.8006
216.9
480.5
795.2
1059.4
34
0.8227
126.6
292.8
612.7
703.8
0.7847
223.4
573.1
943.4
1097.2
35
0.8307
85.6
213.7
425.3
482.8
0.7340
267.3
603.6
805.4
998.4
36
0.8226
145.9
357.4
708.4
803.9
0.8396
195.7
519.7
903.9
1171.6
265
Appendix table A3.7 Raw results data from chapter 5 including autumn bark FvFm, and autumn bark OJI
steps and autumn bark chlorophyll fluorescence at 1000 ms
Tree #
FvFmbarkm
Obarkm
Jbarkm
Ibarkm
1000barkm
1
0.8337
141.9
356.5
698.1
824.7
2
0.8145
120.6
313.0
563.3
621.9
3
0.8243
124.7
308.6
620.5
690.4
5
0.7969
123.0
284.2
540.7
599.3
6
0.8335
98.8
259.5
518.9
573.3
7
0.8289
132.1
329.5
685.5
751.9
8
0.8335
97.6
226.0
486.3
568.4
4
9
0.8325
134.5
361.9
690.6
777.1
10
0.8316
130.1
341.3
669.9
744.4
11
0.8253
158.8
385.8
765.5
879.8
12
0.8270
123.4
295.9
609.1
702.9
13
0.8432
124.9
325.9
683.5
768.5
14
0.8300
136.2
350.4
703.1
771.9
15
0.8308
123.6
326.3
639.0
709.9
16
0.8362
113.6
288.9
595.9
670.3
17
0.8189
120.6
303.4
578.2
643.1
18
0.8188
158.4
422.6
747.3
840.0
19
0.8006
130.8
326.3
589.3
637.3
20
0.8285
99.7
243.0
502.0
565.7
21
0.8438
174.7
518.8
901.9
968.9
22
0.8316
143.9
399.8
741.5
820.2
23
0.8074
121.5
312.6
575.9
603.6
24
0.7951
138.8
328.0
595.6
650.9
25
0.8259
140.4
346.3
710.4
785.6
26
0.8392
142.8
369.1
744.9
860.4
27
0.8386
152.5
406.0
780.8
914.1
28
0.8356
107.2
273.9
553.6
631.0
29
0.8452
91.8
216.4
489.8
577.1
30
0.8327
99.5
246.1
512.8
579.9
31
0.8115
143.6
363.3
671.0
738.6
32
0.8288
136.6
352.3
693.9
770.5
33
0.8147
146.9
376.8
674.0
762.8
34
0.8304
128.0
311.4
640.9
736.4
35
0.8362
107.5
257.5
545.3
638.9
36
0.8309
146.9
399.1
740.0
839.1
266
267
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