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 122 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 123 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 158 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 159 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. 160 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. 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"Intra-annual radial growth and water relations of trees: implications towards a growth mechanism." Journal of Experimental Botany 57: 1454-1459. Zwieniecki, M.A., Melcher, P.J., Holbrook, N.M. (2001a). "Hydraulic properties of individual xylem vessels of Fraxinus americana." Journal of Experimental Botany 52: 257-264. Zwieniecki, M.A., Melcher, P.J., Holbrook, N.M. (2001b). "Hydrogel control of xylem hydraulic resistance in plants." Science 291: 1059-1062. 201 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