The effects of vegetation control on the early growth of Douglas-fir at a high quality
site in coastal Washington
Kyle Petersen
A thesis
submitted in partial fulfillment of the
requirements for the degree of
Master of Science
University of Washington
2005
Program Authorized to Offer Degree:
College of Forest Resources
University of Washington
Graduate School
This is to certify that I have examined this copy of a master's thesis by
Kyle Petersen
and have found that it is complete and satisfactory in all respects,
and that any and all revisions required by the final
examining committee have been made.
Committee Members:
________________________________________________________
Eric Turnblom
________________________________________________________
Thomas A. Terry
________________________________________________________
Robert B. Harrison
Date: ____________________________________
In presenting this thesis in partial fulfillment of the requirements for a master's degree
at the University of Washington, I agree that the Library shall make its copies freely
available for inspection. I further agree that extensive copying of this thesis is
allowable only for scholarly purposes, consistent with "fair use" as prescribed in the
U.S. Copyright Law. Any other reproduction for any purposes or by any means shall
not be allowed without my written permission.
Signature ___________________________
Date _____________________________
TABLE OF CONTENTS
List of Figures ............................................................................................. ii
List of Tables ............................................................................................. iii
Section 1: Introduction .................................................................................1
Section 2: Objectives and Hypotheses .........................................................3
2.1 Objectives .........................................................................3
2.2 Hypotheses ........................................................................4
Section 3: Literature Review .......................................................................6
3.1 Impacts of Competing Vegetation on Tree Growth ..........6
3.2 Control of Competing Vegetation .....................................9
3.3 Mechanisms of Responses to Vegetation Control ..........10
Section 4: Materials and Methods..............................................................22
4.1 Site Specifics ...................................................................22
4.2 Experimental Design .......................................................23
4.3 Sample Tree Selection ....................................................25
4.4 Crown Structure Measurements ......................................27
4.5 Biomass Sub-sampling Procedures .................................28
4.6 Leaf Area Index Processing ............................................31
4.7 Nutrient Content Processing ..........................................33
4.8 Ratio Estimation Procedures ...........................................36
4.9 Statistical Analysis ..........................................................38
Section 5: Results and Discussion ............................................................52
5.1 Biomass Results ..............................................................52
5.2 Nutrient Content Results .................................................55
5.3 Leaf Area Index Results..................................................57
5.4 Crown Structure Results .................................................58
Section 6: Conclusions..............................................................................69
6.1 Key findings and Suggestions for Future Research ........69
List of References ......................................................................................73
Appendix A: Dry Component Biomass ....................................................78
Appendix B: Nutrient Values..................................................................113
Appendix C: Leaf Area Index ................................................................127
Appendix D: Crown Structure ................................................................133
i
LIST OF FIGURES
Figure Number
1.
2.
3.
4.
5.
6.
Page
Example of Treatment Plot ...................................................................43
Scatter Plot of Above-ground Biomass vs. DBH..................................44
Scatter Plot of Stem Biomass vs. DBH .................................................45
Scatter Plot of Branch Biomass vs. DBH .............................................46
Scatter Plot of Needle Biomass vs. DBH..............................................47
Mean DBH by treatment through year 5...............................................68
ii
LIST OF TABLES
Table Number
Page
1. Summary Statistics for measurement and filler trees ...........................48
2. Eight dbh classes for sample tree selection...........................................49
3. Second Flushing Code Definitions .......................................................49
4. Variables and Formulas for Ratio Estimation ......................................50
5. Significance of regression coefficients of biomass equations ..............51
6. Component Biomass Estimates.............................................................61
7. Percent Biomass Discrepancy ...............................................................62
8. Foliar Nutrient Concentration Comparison ..........................................62
9. Branch Nutrient Concentration Comparison ........................................63
10. Stem Nutrient Concentration Comparison ..........................................63
11. Nitrogen Concentration Comparison ..................................................64
12. Nutrient Content Estimates .................................................................64
13. Leaf Area Index Estimates ..................................................................65
14. Mean Comparison of HLL, HLC, and CW ........................................65
15. Mean Comparison of Section One Crown Variables ..........................66
16. Treatment effect on Douglas-fir taper.................................................66
17. Nutrient deficiency levels ...................................................................67
18. Second flushing results .......................................................................67
iii
ACKNOWLEDGEMENTS
The author wishes to express sincere appreciation to the College of Forest Resources
for providing opportunities and support. I'd like to thank Rob Harrison for his high
standards and encouragement throughout the process. I also thank Tom Terry for his
steadfast attention to details and for his guidance through the development and
execution of the workplan and the analysis of the collected data. Connie Harrington
was instrumental in the planning and the initial stages of tree sampling; and the
project would not have gone as smoothly without invaluable help from the USFS
field team: Diana Livada, James Dollins, Bridget Korman, Leslie Brodie, Warren
Devine, and Joe Kraft. Great appreciation goes to Brad Smythe and SAP Forestry
Inc. for the harvest and delivery of the sample trees.
Special recognition goes to Adrian Ares and Steve Duke for statistical guidance and
collaboration. I am grateful for the instruction and ideas shared by Eric Turnblom on
the subject of experimental design. Darlene Zabowski played a central role in
advancing my understanding of soils and forest ecosystems, and I'd like to thank Dan
and Kriistina Vogt for the good example they have set as researchers and educators.
Bob Edmonds provided an excellent insight into Forest Pathology. Also, I thank Phil
Hurvitz for the introduction to GIS. Frank Greulich supplied valuable training in the
basics of quantitative analysis, and John Perez Garcia deserves extra credit for the
provision of regression analysis tools and the lessons in conservation economics
issues and communication skills.
I am grateful for the community of students at CFR for making the journey interesting
and for providing moral and technical support along the way. In particular, I owe
special thanks to Brian Strahm, Eric Sucre, Garret Liles, Jeff Hatten, and Julie
Forcier.
iv
DEDICATION
To my mother, Kristine,
To my brothers Zach, David, and Brian, and my sister Mariel,
To my Grandparents Elsie and Arthur Petersen,
To my uncle Art and my cousin Serene,
To the Bawdens for encouragement in writing and in making the most of life
v
1
Section 1. Introduction
Douglas-fir trees have been planted on many sites in western Washington in the
interest of maximizing the yield of large quantities of high quality timber for future
use. Strategies to enhance Douglas-fir productivity on high quality sites can be aided
by site-specific information on the effects of harvesting, site preparation activities,
and the control of competing vegetation. In 1998, the Fall River Long-term
Productivity Study (LTSP) was initiated to test the effects of various management
treatments on Douglas-fir at a high quality site with soil and climate conditions
representative of similar high quality sites in coastal Washington. The ultimate
purpose of the Fall River LTSP is to understand the implications of organic matter
manipulation, ground-based harvesting, and early vegetation control management
practices on short- and long-term site productivity (Terry et. al. 2001). Research
conducted at the Fall River site in the second and third year of growth have yielded
understanding of treatment effects on Douglas-fir growth as a function of available
moisture and nutrients. Results from the studies at the Fall River site can be used to
manage similar sites and the results can also be compared across other sites within the
Bob Powers National LTSP study matrix (Powers and Fiddler 1997). Responsibility
of research coordination at the Fall River site is shared by Weyerhaueser Company,
the USFS Pacific Northwest Research Station (PNWRS), and the College of Forest
Resources at the University of Washington. The LTSP study is planned to continue
2
to a rotation age of approximately forty years, at which point the effects of
management on yield and wood quality can be assessed.
3
Section2. Study Objectives and Hypotheses
2.1 Objectives
The objective of this investigation was to determine how the control of
competing vegetation affects Douglas-fir above-ground stem and crown parameters in
the early stages of growth before crown closure. The main treatment response
variables assessed included: (1) above-ground dry biomass/ha, (2) above-ground
nutrient content, and (3) leaf area index (LAI). The stem dimension variables
assessed were: (1) diameter at zero height (ground-level), (2) diameter at 15 cm
height, 3) diameter at 130 cm height (DBH), and (4) total tree height. The crown
structure variables assessed were: (1) total number of branches per one-meter stem
section, (2) size of the largest branch in the lowest one-meter stem section, (3) length
and diameter of two sample branches in the largest size class of each one-meter stem
section, (4) length of two sample branches in all other size classes of each one-meter
stem section, (5) crown width, (6) height to live crown, (7) height to lowest live limb,
(8) number of ramicorn branches, and (9) second flushing status. Additionally, the
years of needle retention on the largest branches of the lowest one-meter section was
determined. Relationships between these parameters were investigated and compared
between two treatment populations. One treatment population received intensive
control of competing vegetation via herbicide applications and the other treatment
population received no herbicide applications. Concurrent studies by the USFS
PNWRS-Olympia Lab have investigated treatment effects on plant-soil-water
relations, and the
4
biomass and percent cover of the competing vegetation was estimated at the 5th year
of growth. Knowledge of the treatment effects on biogeochemical cycling is
dependent on our understanding of Douglas-fir and competing vegetation interrelationships.
2.2 Hypotheses
Hypothesis 1: Above-ground dry biomass/ha
Null Hypothesis: The control of competing vegetation had no significant effect on
Douglas-fir above-ground dry biomass per hectare.
Alternative Hypothesis: The control of competing vegetation significantly increased
Douglas-fir above-ground dry biomass per hectare relative to the treatment without
vegetation control.
Hypothesis 2: Nutrient Content
Null Hypothesis: The control of competing vegetation had no significant effect on
Douglas-fir above-ground nutrient content.
Alternative Hypothesis: The control of competing vegetation significantly increased
Douglas-fir above-ground nutrient content relative to the treatment without vegetation
control.
Hypothesis 3: Leaf Area Index
Null Hypothesis: The control of competing vegetation had no significant effect on
Douglas-fir leaf area index.
Alternative Hypothesis: The control of competing vegetation significantly increased
Douglas-fir leaf area index relative to the treatment without vegetation control.
Hypothesis 4: Crown Structure
Null Hypothesis: The control of competing vegetation had no significant effect on
Douglas-fir crown structure.
5
Alternative Hypothesis: The control of competing vegetation significantly altered
Douglas-fir crown structure relative to the treatment without vegetation control.
Hypothesis 5: Stem Dimensions
Null Hypothesis: The control of competing vegetation has no significant effect on
Douglas-fir stem dimensions.
Alternative Hypothesis: The control of competing vegetation significantly increases
Douglas-fir stem dimensions relative to the treatment without vegetation control.
Hypothesis 6:
Null Hypothesis: The control of competing vegetation has no significant effect on
the relationship between Douglas-fir DBH and foliar nitrogen concentration.
Alternative Hypothesis: The control of competing vegetation significantly alters the
relationship between Douglas-fir DBH and foliar nitrogen concentration relative to
the treatment without vegetation control.
Hypothesis 7:
Null Hypothesis: The control of competing vegetation has no significant effect on
the relationship between DBH and wet leaf area.
Alternative Hypothesis: The control of competing vegetation significantly alters the
relationship between DBH and wet leaf area relative to the treatment without
vegetation control.
6
Section 3. Literature Review
3.1 Impacts of Competing Vegetation on Tree Growth
A number of studies address the productivity of conifer species engaged in
competition for limited resources with other tree, shrub, grass and herbaceous species
during the early stages of growth before crown closure (Smith and Scott 1984; White
and Newton 1989; Knowe et al. 1992; Chang et al.1996; Stein 1999; Wagner 2000).
Specifically, studies on Douglas-fir (Psuedotsuga menziesii (Mirb.) Franco) have
generated evidence that Douglas-fir above-ground parameters are significantly
affected by inter-specific competition (Cole and Newton 1986; Brodie and Walstad
1987; Flint and Childs 1987; Piatek et al. 2003; Roberts et al. 2005). Experimental
manipulation of the spatial distribution and density of competing species in relation to
young Douglas-fir has improved our understanding of crop tree growth response to
neighboring plant community influences in various plantations (Chang et al. 1996;
Wagner and Radosevich 1998).
Tree growth is frequently studied in terms of stem dimensions such as
diameter, basal area, and volume. The annual increments of diameter, basal area,
total height and volume are often measured in tree growth assessments. Douglas-fir
stem dimensions are allometrically related to the roots, branches, and foliage (Oliver
and Larson 1996; Satoo and Madgwick 1982; West 2004). Some researchers on early
growth have modeled the partitioning of dry biomass in Douglas-fir components
7
under various growth circumstances (Bartelink 1998). Ratios of above-ground
biomass to root biomass can give an indication of response to resource availability
and competition for resources. Suppressed Douglas-fir tend to invest less dry
biomass into the crown component compared to non-suppressed Douglas-fir
(Bartelink 1998). Besides reducing overall growth and/or altering biomass allocation
patterns, competition can also lead to tree mortality. However, there are low
mortality rates at the Fall River site regardless of the density of plant competitors.
Leaf area is closely related to photosynthetic rates and the rates of evaporation
and transpiration (Borghetti et al. 1986, Turner et. al 2000). Leaf area index (LAI) is
defined in this study as the projected leaf area per unit surface area of ground.
Borghetti et al. (1986) found strong relationships between Douglas-fir leaf biomass,
leaf area, and diameter at breast height in a 25 year-old stand. This suggests that
competition effects on stem dimensions might lead to reduced LAI in other young
stands of Douglas-fir.
Douglas-fir crowns respond to silvicultural practices, and the crown structure
is closely related to stem parameters and hence, timber quality (Maguire et al. 1994).
Strong relationships between stem diameter and stem biomass and between stem
diameter and crown biomass have been documented by researchers (Helgerson et al.
1988). Losses of lower limbs (crown recession or crown loss) before crown closure
is attributed to self-shading and competition for resources with neighboring plants
(Maguire et al. 1994; Oliver and Larson 1996).
8
In order to describe the effects of vegetation control on tree processes, it is
necessary to develop methods to quantify tree variables. Allometric equations have
served to express the relationships between the whole tree and parts of the whole tree
for a number of different tree species and landscape locations (Helgerson et al. 1988;
Satoo and Madgwick 1982; West 2004). The most frequently used allometric
equation to describe such relationships is Y=aXb, where Y is the tree component
response variable, X is the diameter of the tree at a specified stem height, and a and b
are coefficients established with regression analysis techniques (Crow and Schlaegel
1988). Measurements of the variables of interest (e.g. DBH, total above-ground
biomass) are recorded for a number of trees and a relationship (linear or non-linear) is
developed with a response variable and one or more predictor variables. This model
would be used for predicting total biomass of a whole tree from measurements of
stem diameter at 1.3 m height (DBH). There are some drawbacks to regression
analysis. The main problem with building allometric equations for tree parameter
estimation is that the sampling methodologies commonly involved can introduce a
component of unknown statistical bias (Gregoire et al. 1995). In order to reduce bias,
sampling protocols utilizing probabilistic sampling procedures have been tested with
success in making estimates for important tree parameters of interest (Valentine 1984;
West 2004). Random branch sampling (RBS) and Importance sampling (IS), are
mutually non-exclusive techniques for eliminating bias when estimating individual
tree parameters and collective population parameters (Parresol 1999).
9
The above- and below ground components (stem, branches, foliage, and roots)
of a Douglas-fir tree can be studied at any possible level of detail using any valid
methodology as a system of related parts, and the aim of most research is to
determine how the relationship between components is affected by the larger system
of which one tree or one stand of trees is only a fraction. Radosevich and Osteryoung
(1987) make the suggestion that multiplicative, non-linear functions describe the
nature of plant-environment interactions better than simple linear regression models.
3.2 Control of Competing Vegetation
Clearcutting a conifer stand such as the mixed Douglas-fir/western hemlock
stand at the Fall River site creates very favorable conditions for competing species to
establish during the period when nutrients are expected to become more readily
available because of the assart effect of logging disturbance (Kimmins 1987).
Preharvest control of competing vegetation, prompt planting and post-harvest
control of competing vegetation is expected to give young conifers an advantage in
acquiring limited resources (Wagner 2000). Considerable evidence from research in
Pacific Northwest conifer systems at a number of sites suggests that the time between
stand establishment and crown closure can be substantially reduced when herbaceous
and woody competitor species are controlled (Wagner 2000). The effects of
treatments on crown closure is expected to be more and more noticeable in the next
few years; crown closure is already starting to occur on weeded treatments in the fifth
growing season at the Fall River site. For any given Douglas-fir, the immediate
10
proximity to neighboring root systems and sources of shade partially defines the
growth potential of the tree. During the period of time known as crown closure, the
stand dynamics will shift from inter-specific towards intra-specific competition as the
crop trees shade out the competing vegetation.
The main methods of vegetation control range from herbicide applications to
manual removal to burning. Herbicides have undergone an evolution since the onset
of widespread usage in agricultural settings in the 20th century, and herbicide
applications might be the most economic and effective way to increase site
productivity through vegetation management (Wagner 2000). Control of competing
vegetation achieves the greatest positive effects on crop tree stem parameters when
undertaken as early as possible and control should be carried out for a certain number
of years during early stand development (Wagner 2000). It's hypothesized that early
competition has the largest detrimental effects and that once the stand passes a certain
age, the effects of competition are reduced because of extensive tree root systems and
tree height increment leading to shading (Petersen et al. 1988; Oliver and Larson
1996; Wagner 2000). The crop tree age at which this change occurs might
correspond closely with crown closure.
3.3 Mechanisms of Responses to Vegetation Control
The expected reduction in crop tree productivity resulting from plant
competition is expressed by Oliver and Larson (1996) as a function of limited
growing space. The concept of growing space defines a volume in which tree growth
11
is dependent on the following six factors: sunlight, water, nutrients, carbon dioxide,
oxygen, and temperature. The first five factors listed are thought of as critical
resources, and temperature (in the atmosphere and in the soil) is considered to be an
important condition influencing growth. Other conditions can influence tree growth
such as soil penetrability and air pollution, but these are unlikely factors at the Fall
River site.
The order of importance of growth-limiting factors in early stand development
was assessed at the Fall River site. Competing vegetation was the most-limiting
factor to tree growth at the Fall River site during the first three years of seedling
growth (Roberts et al. 2005). Based on the investigations of Roberts et al. (2005),
late growing season available moisture seemed to be the variable most likely reducing
growth where competition was present. Others have noted that limiting factors may
change with season and over the lifespan of the trees (Radosevich and Osteryoung
1987; Oliver and Larson 1996).
A given plant of fixed genetic make-up can perform optimally when growing
space is at a specific level (i.e. the volume available to growth has the ideal quantities
of the six factors per unit time). Clearly, the amount of growth reduction attributed to
deficiencies in growing space depends on the plant species involved, species
densities, and microsite resources and conditions (Oliver and Larson 1996). The
Law of the Minimum and the Law of Compensation (Oliver and Larson 1996) are
essential components to our understanding of tree response to competition. The Law
of the Minimum is described as the most limiting resource at one given time (e.g.
summer drought). The Law of Compensation is described as the supply of one
12
resource that is dependent on the supply of another resource (e.g. ions are moved
towards roots by mass flow of water).
Models are required to shed light on the dynamics of the many interrelated
variables over time for a given system in which inter- and intra-specific competition
is occurring (Bartelink 1998). The growing space to be potentially utilized by an
expanding crop tree is dependent on the density of adjacent crop trees and other
competing species, and it varies with daily and seasonal fluctuations in growthlimiting factors and conditions within or affecting the available space. The spatial
volume occupied by each plant, and the spatial volume occupied by all adjacent
plants should be incorporated into any model aimed at quantifying space utilization
by competing species. Plant individual and plant species utilization of a defined
space and the resources contained by that defined space can be viewed in terms of
total individual plant volume per unit soil volume occupied and total species volume
per unit soil volume. This lends solidity to the concept of growing space without
delving excessively into the question of which factor(s) or condition(s) are most
limiting for a given crop tree(s) during any given interval of time. However, the
above definition requires quantification of root volume, which was not included in
this thesis study.
Radosevich and Osteryoung (1987) observed that the timing of emergence or
the initial occupancy can be more important than the spatial arrangement of an
establishing community in competition for resources. This observation is related to
the possibility that crop trees and competing vegetation can have different time
13
intervals when they are utilizing resources that are replenished in varying amounts
over time. The seasonality of the resource demands by specific species involved and
the relative degree of resource demand per species are important to plant competition
studies. A more in-depth discussion of the main growth limiting factors linked to
competition at the Fall River site is now in order.
Growth Limiting Factors
Sunlight is the first limiting factor we will discuss. Temperate zone
photosynthetic rates are 5 to 10 mg CO2 dm-2hr-1 for conifers and 10 to 20 mg CO2
dm-2hr-1 for deciduous broadleaved trees and shrubs. Radosevich and Osteryoung
(1987) note that photosynthetic capacity in Douglas-fir seedlings will increase with
increasing light intensity, but the allocation of photosynthate to needle surface area
can decrease relative to an increase in needle thickness. Other morphological
changes have been linked by researchers to higher light intensity and availability such
as increased root to shoot ratios, increased root and shoot dry weight, increased leader
elongation, and increased root elongation (Radosevich and Osteryoung 1987).
Cole and Newton (1986) noted a greater production of shade needles on
Douglas-fir trees when growing in competition with faster-growing, taller alder trees.
Shade needles are known to be more efficient at capturing energy at lower light levels
(Oliver and Larson 1996). Shading from herbaceous spp. and deciduous trees and
shrubs might have the effect of reducing crop tree transpiration losses. Reduced
photosynthesis from shading could be a confounding factor in a study of the
14
interaction between temperature and plant species, and experimental designers want
to devise ways to eliminate or account for such confounding factors.
No data was collected to directly discern the effects of competing vegetation
shading of crop trees. Height to live crown measurements might indirectly measure
shading effects in the lower crown of the Douglas-fir. While the effects of shading
from competitors are expected to grow towards a minimum as the Douglas-fir rise
above and shade-out competitors, it is hypothesized that any significant differences in
tree biomass between weeded and non-weeded trees are linked partially to differences
in light availability, especially during the first couple years of stand regeneration.
Water availability is another limiting factor on seedling growth which can be
affected by competing vegetation. Measurements of predawn moisture potential give
accurate indications of conifer water stress (Cole and Newton 1986; Pallardy et al.
1991). Cole and Newton (1986) found that Douglas-fir water stress, indicated by
predawn moisture potential of tree components, generally increased with the presence
of red alder or grasses in study plots along an east-west transect in the Oregon coastal
range. Moisture stress was lower near the coast relative to sites further inland along
the transect. The accepted theory on water stress states that a lack of water
availability causes stomates to close more often in order to reduce transpirational
losses of water. Intake of carbon dioxide is not possible when the stomates are
closed; therefore, photosynthesis is limited. Stomatal regulation is affected by light,
temperature, humidity, and internal CO2 concentration as well as plant water status
15
(Radosevich and Osteryoung 1987). Douglas-fir might be more tolerant of high
water stress than other species (Radosevich and Osteryoung 1987), but stomatal
closure induced by drought or competition-induced water stress is nevertheless a
strong possibility as a limiting factor at many sites. Root elongation is important in
exploiting the soil volume for tree water supply, and root structure can vary within
one species across differing soil moisture regimes (Cole and Newton 1986). Water
use efficiency can be estimated if tree growth and the amount of water available per
unit time are expressed as a ratio.
Water availability has been cited as the most critical limiting factor in Western
forests (Radosevich and Osteryoung 1987), and the duration of the growing season is
often subject to the soil-plant-climate conditions that influence water availability.
These conditions are: soil water holding capacity, seasonal precipitation, transpiration
by the tree crop and the competing species, and total evapotranspiration. At the Fall
River site it is likely that soil moisture is the most important factor in early stand
growth; for instance, in the second and third growing seasons, volumetric soil
moisture at 0-20 cm depth was lowest in the non-weeded (BO-VC) treatment in
which DBH and height increment was also significantly lower than other treatments
(Roberts et al. 2005). Soil moisture late in the growing season is thought to have a
pronounced effect at the Fall River site in terms of tree growth, and it appears that
available N and soil temperature are significant factors related to tree growth when
moisture is not limiting (Roberts et al. 2005). Flint and Childs (1987) noted that an
16
increase in late season water availability delayed budset, increased second flushing
and increased growth of Douglas-fir seedlings at a dry site in southwest Oregon.
The next potentially limiting factor to be discussed is nutrient availability.
Foliar nitrogen and phosphorous concentrations of Douglas-fir can be significantly
lower under conditions of interspecific competition (Cole and Newton 1986).
However, in Cole and Newton's (1986) study in Oregon, foliar nitrogen of Douglas fir
and total and available soil N did not differ significantly for 3 competition levels of
Douglas-fir with Douglas-fir given understory weed control, Douglas-fir with grasses
without weed control, and Douglas-fir with red alder without weed control. Foliar
phosphorous was found to be higher in treatment with grass as the competing species
(Cole and Newton 1986).
At a high fertility site such as the Fall River site with a post-harvest nutrient
release, exploitive species are expected to take hold quickly after the disturbance.
Kimmins (1987) notes fireweed to be one such species; and fireweed is present at the
site. The characteristics of these species are: high absorption capacity (nutrient
absorption rates per unit of root), high photosynthetic rates, high respiration rates,
rapid growth rates, and high annual seed production. Photosynthesis is sensitive to
foliar nitrogen levels in the above-mentioned competitor types, so eventual declines
in soil N availability is expected to signal a downturn in the growth rates of such
species.
Carbon dioxide can be a limiting factor within certain periods during the day.
As mentioned above, stomatal regulation responses to water or light deficiency might
17
lead to less CO2 intake. Less carbon might have been available for dry matter
partitioning in roots, shoots, and foliage of trees in the plots without vegetation
control relative to plots with vegetation control. The dependence of carbon
sequestration on other factors is another example of the Law of Compensation; an
interaction of limiting factors (a combined effect) exists in many plant-soilatmosphere systems.
Pools and Fluxes
Pools and fluxes exist both internally and externally to trees. A pool can be thought
of broadly as a collection of mass or energy per unit space and a flux can be defined
as the transfer across space of mass or energy per unit time. Pools are either stable
with time or in a state of relatively continuous flux. Stand biomass is a collective
pool in flux governed largely by diurnal and seasonal patterns of resource availability
along with the variable condition of temperature. When pools and fluxes external to
trees are altered as a result of herbicide broadcasts, there is an expectation of
corresponding changes in pools and fluxes within the trees. The pools are the most
simple of the two types of variables to measure, because the data is collected at one
time point. While precise data concerning the various pools comprising a stand of
trees and the soil in which the trees are rooted in is valuable for many purposes, pool
data is less informative than flux data which requires pool value estimations at times
ti {1,2,..i}. However, a flux can be defined as the change in a value from time = 0 to
time = X, so a measurement of a pool value at time = X gives us a flux value.
18
Fluxes deemed important to plant competition studies are photon
absorption/(unit leaf area*unit time), nutrient uptake, water uptake, mineralization,
nutrient leaching, soil water movement, transpiration, evapotranspiration, heat
transfer, internal translocations of elements and compounds, conifer-needle
senescence rates, and fine-root senescence rates. These fluxes become hard to track
as the interval of time involved narrows, so many researchers resort to the
measurement of pools at two or more discreet points in time to infer the continuous
nature of the flux of interest. In reality, these fluxes happen over the continuous
temporal scale, but there are some expected points or intervals where fluxes are
known to change (e.g., dormancy induced reductions in photosynthesis, respiration
and dry matter production). This study accounted for above-ground biomass and
nutrient pools at the fifth year of stand growth given vegetation control or no
vegetation control; the 5 year needle biomass increment is considered an example of a
flux as well as the 5-year nitrogen requirement.
Resource Availability and Allocation
Physiological research on interspecific competition is keenly interested in
microsite effects on the internal processes of individual Douglas-firs by temporallyand spatially-variable distributions of competing vegetation. If soil variation is found
to be insignificant across the area of study, then the microsite of each tree is largely
dependent on the surrounding plant neighbors. Other possible clearcut microsite
effects might be attributed to shading from stumps, existence of red-rot patches, and
19
microclimate influences of woody debris. Microsite effects might be attributable to
the specific type of competing vegetation species adjacent to a given Douglas-fir tree.
For example, fireweed (Epilobium angustifolium) might provide more or less
competition for a particular resource than red elderberry (Sambucus racemosa ssp.
pubens), red huckleberry (Vaccinium parvifolium) , vine maple (Acer circinatum),
salmonberry (Rubus spectabilis) or grass species. The presence of fireweed is an
indicator of a post-harvest nutrient flush, and fireweed is thought to have a relatively
high nutrient demand (Kimmins 1987). Grasses have been hypothesized to affect
crop tree water stress levels because of increased temperatures which increase
transpiration losses during times of highly negative soil water potential (Zedaker
1981). Eissenstat and Mitchell (1983) reported grass and shrub competition to be
related to moisture stress and decreased Douglas-fir seedling diameters. The percent
cover and species composition of competing vegetation within a fixed area
surrounding a given crop tree are important factors in the study of vegetation control
on micro-site conditions.
There exists significant variation in nutrient availability over space and time
that can be partially attributed to soil variation (Walker and Gessel 1991). Substantial
variability in nutrient supply might even exist in soils considered to be relatively
homogenous for a defined area and depth. Additionally, wetting fronts and water
availability might vary with time and throughout a given soil profile. It is clear that
the demand of resources by competing plants detracts from the available pools and
fluxes of resources to the crop plants. The question arises as to how much the below-
20
ground variation in availability of water and nutrients is reflected in the variation of
above- and below-ground tree biomass partitioning. In other words, in what
proportions do the crop trees allocate the resources to tree components under various
states of competition? Biomass shoot to root ratios have been observed or modeled
by various researchers (Chang et al. 1996; Bartelink 1998), with the intent of
understanding how the growing space affects biomass partitioning which in turn has
implications for tree survival and growth increment. Temperate coniferous stands
generally have ≤ 20 % of their biomass in roots (Marion 1979). However, there
might be different biomass allocation quantities to roots according to levels of
competitive stress, and the rates of Douglas-fir root senescence might differ with
competition as well. Resource allocation to roots should be minimal at high index
sites (Newton and Cole, 1991). Newton and Cole (1991) tested for effects on root
development for three levels of competition at varying densities using the type 1a
Nelder design: intraspecific (Douglas-fir competing with Douglas-fir), and two
interspecific situations (Douglas-fir with grass spp. and Douglas-fir with red alder).
At five years of growth, Newton and Cole (1991) found that shoot:root allocations for
Douglas-fir were proportional in the situation of moderate competitive stress at their 3
sites. The ratio averaged approximately 4:1 across three sites in Oregon. Very high
stress was related to lower shoot:root ratios; more resources were allocated to the root
growth. Newton and Cole's (1991) results indicate that competitor species can have a
significant impact on Douglas-fir root biomass.
21
Newton and Cole (1991) also found that trees under higher competitive stress
had proportionally higher losses of lower branches and foliage. Higher allocations to
the below-ground tree components (lower shoot:root ratios) require more respiration
and carbohydrate becomes less available for canopy development; furthermore,
side shading from plant competitors can be a factor leading to crown loss (Oliver and
Larson 1996). Given our collective knowledge concerning site resource availability
and allocation within plants, it is plain to see that crop tree growth and development is
a function of the interactions between environmental factors and plant adaptive
responses. Thus, much research is dedicated to describing plant-soil-atmosphere
relations, and the quantification the plant responses is essential to our understanding
of management effects.
22
Section 4. Materials and Methods
4.1 Site Specifics
The 12.24 ha site is located in the Willapa hills of Pacific County, Washington
(46°43’N,123°36’W). The area has a maritime climate with relatively mild, wet
winters and dry summers. During the five-year interval between May 2000 and April
2005, on-site mean annual air temperature (at 25 cm above-ground) was 8.7 °C and
the five-year mean annual precipitation was 1480 mm (Connie Harrington, personal
communication). Site elevation is 300 m, and the slope is ~10% with a west-facing
aspect. The soil is a medial over clayey, ferrihydritic over parasesquic, mesic Typic
Fluvudand classified in the Boistfort series. The soil is well-drained to moderatelydrained, and the texture is medium (silt loam) to moderately fine (clay loam).
Seismic tests have confirmed that the soil is 5-m deep to weathered basalt and 15-m
deep to hard rock basalt (personal communication with Jim Ward, Weyerhaeuser
Geologist). The soil formed in Miocene basalt with volcanic ash influence in the
surface horizon (Steinbrenner and Gehrke, 1966).
The study area is in the western hemlock (Tsuga heterophylla (Raf.) Sarg.)
zone described by Franklin and Dyrness (1973). The site has an important history of
human management. An old-growth stand was harvested in the 1950's, and the area
was broadcast burned. Douglas-fir seedlings (2+0) were grown in beds for two years
and planted at 2188 trees per ha. Natural regeneration of western hemlock occurred.
The stand was thinned in 1971 from 2235 trees per ha to 1220 trees per ha. The stand
received four fertilizations for a total of 1804 kg urea/ha from 1970 to 1995. The
23
approximately 50-year old stand was harvested in 1999, and the biomass and nutrient
pools of all above- and below-ground components were determined except for the
coarse and fine root components. The average height of the dominant and codominant trees at age-class 50 was estimated to be 34 meters.
4.2 Experimental Design
A randomized complete block design with four replications of twelve
treatments was installed after harvest in 1999. A full description of the treatments is
described in Terry et al. 2001. This investigation only dealt with two of the 12
treatments (1) bole-only removal harvesting (cable yarded) without vegetation control
(BO-VC) and (2) bole-only removal harvesting (cable yarded) with vegetation control
( BO+VC). The term "bole-only" refers to one of the four levels of harvest intensity;
it means that only the bole was removed from the plot during harvest (all tree tops,
logging slash, and butt-cuts were left in place). There were 8 replications of the
BO+VC and BO-VC plots in the study. Treatment plots are 0.25 ha (30 m x 85 m) in
area with inner measurement plots of 0.10 ha (15 m x 70 m) surrounded by a buffer
zone. Following harvesting in the spring of 2000, Douglas-fir seedlings (1+1) were
graded for minimum size variation and planted at a spacing of 2.5 m x 2.5 m
(1600/ha). In all plots, an additional 20 Douglas-fir seedlings were systematically
planted between rows per the treatment plot design in figure 4.1. These extra trees
are referred to as "filler trees". The filler trees were intended to be destructively
sampled three years after planting; however it was decided to wait until the 5th year
24
of plantation growth to harvest the filler trees for the testing of vegetation control
hypotheses. Trees inside the measurement plots at 2.5 x 2.5 m spacing are referred to
as measurement trees.
Eight BO+VC plots (two treatment plots per block) received intensive vegetation
control for five years after harvest. First-year (2000) treatment involved a broadcast
application of Oust® (0.21 kg/ha) and Accord Concentrate® (4.67 L/ha) applied with
a surfactant in a water mix at 93.5 L/ha using backpack sprayers ca. 2 weeks prior to
planting Douglas-fir. Second-year treatments (2001) included: (1) a March broadcast
application of 9.3 L/ha of Atrazine 4L® in a water mix at 93.5 L/ha, and (2) an
April-May directed spot-spray of 0.75% by volume Accord Concentrate® water mix
on the vegetation between the rows. Third-year (2002) treatments included (1)
broadcast applications of 9.3 L/ha Atrazine 4L® plus 0.17 kg/ha Oust® in a water
mix at 150 L/ha, (2) a June-July directed spot-spray of 0.75% by volume mix of
Accord Concentrate® in water on the vegetation between the rows, and (3) an AprilMay spot-spray of 1% Transline® plus surfactant solution to control persistent shrub
species. Forth-year treatments included: (1) a March directed-band application
(between rows)of VelparL® at 7 L/ha applied in a water mix at 150 L/ha, (2) a May
spot-spray application of 1% by volume Transline® in water mix on persistent
shrubs, and (3) a June spot-spray application of 0.75 % by volume Accord
Concentrate® in a water mix at 150 L/ha. Fifth-year treatments included: (a) April
13-15 application of Velar L at 5 pints / acre (5.85 Liters/ha) in 20 gal solution / acre
(187 liters solution/ha). This vegetation control treatment was implemented to
25
eliminate any confounding effects associated with potential differential development
of understory vegetative communities across treatments, and the treatment does not
represent a typical operational vegetation control treatment.
Annual estimates of percent cover of competing grasses, forbs, shrubs, and
trees in both treatments confirm that the control of competing vegetation was nearly
complete in BO+VC plots over the five year period. In the summer of 2004, percent
cover (the sum of all herbaceous and shrub species covers by species) estimates were
143 % in the BO-VC treatment and 3 % in the BO+VC treatment (personal
communication Dave Peter, USFS). The USFS PNWRS team estimated the biomass
per hectare and nutrient content of competing vegetation in the BO-VC and BO+VC
treatments for growing season 5.
4.3 Sample Tree Selection
Before filler trees were used as representative trees for biomass determination
a test was conducted to determine if a consistent relationship existed between
Douglas-fir DBH and height for both the “measurement tree” and “filler tree”
populations. There was a possibility that the “filler trees” could have been impacted
by growing in closer proximity to “measurement” trees than a majority of the
measurement plot trees. In December of 2004, the DBH and height of each
measurement plot tree and filler tree were measured to the nearest mm and cm
respectively. A small number of outliers were observed, and the DBH and height of
26
the outliers were checked in the field. Data errors where corrected prior to data
analysis.
The simple linear regression model was fitted for both the measurement trees
and the filler trees: Y = a+bX+ε, where Y is the response variable tree height, a is the
Y-intercept, b is the slope of the regression line, X is the predictor variable DBH, and
ε is the error term. A test of the difference in slope between measurement tree and
filler tree regression lines revealed that there was no significant difference (type I
error rate, α =.05) in the slopes of the two fitted lines across both treatments. Thus, it
was verified that the DBH vs. height relationship was consistent between the
measurement and filler trees, and we decided that the filler trees would suffice as
sampling populations for testing of the main hypotheses. Table 4.1 shows summary
statistics for the measurement and filler tree populations in both treatments.
Frequency histograms of measurement plot tree DBH were plotted. The
histograms were used to guide the selection of representative trees with the objective
of matching the DBH distributions of the sample trees to the DBH distributions of the
measurement populations. The sample trees were chosen closely proportional to the
tree frequency in eight DBH classes for each treatment. The sample trees in each
DBH were not exactly proportional to tree frequency because of human error in the
selection process. Table 4.2 shows the eight DBH classes per treatment, the number
of sample trees chosen from each class, the sample size per class required to choose
the trees proportional to tree frequency, and the sample size per class required to
choose the trees proportional to basal area, where basal area = π r2.
27
The sample trees were chosen randomly from within each class. Nearly an
equal amount of trees was sampled from each of the eight treatment plots per
treatment. One tree was taken from each of the three largest classes in each treatment
when a fraction of a tree was called for by the proportional sampling system. To keep
the sample size constant, one tree was subtracted from the three size classes with the
highest frequency of trees. The objective of avoiding removal of any measurement
plot trees required us to draw some trees from the middle row of the buffer zone trees
in order to represent the largest size classes of the measurement plot population
proportional to frequency. The largest three DBH classes did not exist in the filler
tree populations which had a mean DBH significantly (α = .05) smaller than the mean
DBH of the measurement tree population across both treatments.
4.4 Crown Structure Measurements
Once the sample trees were selected in each of the 16 plots, a series of crown
structure measurements of each sample tree were recorded at the site. Height to live
crown (HLC) was measured to the nearest cm with a pole marked with cm
increments. HLC is defined as the distance from the base of the stem on the uphill
side to the first whorl with at least three quarters of its branches alive. A branch was
considered live if it had more than 10 live needles. Whorls were defined as spokelike, dense assemblages of branches at points along the stems. Height to lowest live
limb (HLL) was measured to the nearest cm with a measuring pole. HLL is defined
as the distance from the base of the stem on the uphill side to the first branch with
28
more than 10 live needles. The number of ramicorn branches on each sample tree
was recorded. A branch was visually coded as ramicorn if the angle between the
branch and the main shoot was steeper than 45 degrees and if the diameter was
substantially larger than nearby, normal branches. If the branch had a very steep
angle it was designated to be a ramicorn branch regardless of diameter. Second
flushing was coded visually according to the coding system found in table 4.3.
Crown width was measured in each cardinal direction (within row and across
rows) to the nearest cm with the measuring pole. Tree rows and columns served as
cardinal directions since they are oriented nearly east-west and north-south. On each
side of the tree, the two closest branch tips to the observer were used as reference
points. The centimeter pole was held horizontally from the tree stem to an imaginary
point between and at the approximate height of the two longest branches. A bow
shaped length of logging slash was used to locate the point at which to measure by
forming an arc between the two longest branches and measuring the distance from the
tree stem to the center of the arc at the height of the two longest branches.
Years of foliage retention was measured in the 1st meter section of every
sample tree. A branch was randomly chosen per sample tree and a lateral shoot of the
main branch was arbitrarily chosen for sampling. The number of shoot segments with
full needle coverage was counted. Each segment counted as one year if it had full
coverage of needles. If a segment had less than full coverage of needles then a
fraction based on visual estimation was added to the total number years of retained
foliage.
29
4.5 Biomass sub-sampling procedures
The methods implemented for estimating dry biomass of stems, branches, and
needles can be described as stratified random sampling of sample trees across
diameter classes followed estimation of biomass per strata using the ratio estimation
technique. The use of this method in estimating sugar maple above-ground dry
biomass is detailed by Briggs et al. (1987) and Parresol (1999). We used virtually the
same method as the one used by Briggs, but it was necessary to introduce a few
modifications to account for different tree species and research objectives.
Mathematical symbols similar to those used by Briggs et al. (1987) and Parresol
(1999) were used.
Each sample tree was stratified into one-meter stem sections. All the
branches, with needles intact, were removed from one section at a time with loppers.
The branches were then stratified into four branch basal diameter (bbd) classes using
caliper tools, and the number of branches in each size class (MHz) was recorded. The
bbd classes were (fine < 3.0 mm, small 3.0-6.0 mm, medium 6.1-10.0 mm, large
>10.0 mm).
The total green mass of each branch size class j in each section h (GHz) was
measured using an Intercom CS200 digital hanging scale (Intercom Co., Minneapolis,
MN) to the nearest 0.001 kg. Cardboard boxes with strings were used as the
weighing boats. The box masses were recorded to the nearest 0.001 kg and
subtracted from the total green masses. The branches from each size class were then
laid out in a row, and a sub-sample of one to four branches was chosen from the row
30
using a random number table to select branches in random order. If the total number
of branches in a size class was less than or equal to four then one branch was chosen.
If the total number of branches in a size class was between four and 20, then two
sample branches were chosen. For size classes with a total number of branches
greater than 20, one additional sample branch was chosen per ten additional branches.
The diameter (lbd) and length (lbl) of the largest branch in the large size class of the
lowest section (section 1) was measured and recorded to the nearest .01 mm and 1 cm
respectively. The years of foliage retained (yf) was measured on the largest limb of
section 1. The side shoots were visually observed to estimate the average if for the
limb. Decimals were used if the oldest stem segment didn't have a full set of needles.
The green mass of the sample branches (GHz) was then weighed on a pan
scale and the masses were recorded to the nearest 0.1 grams. Next, the samples were
placed into labeled bags for transport to drying ovens at UW in Seattle, A. All fine
branches (bbd< 3 mm) on each sample tree were collected in one small bag per tree to
be dried; there were so few fine branches per tree that we decided to weigh all of
them dry and simply add their dry weight to the total estimate per tree. All branch
samples were dried for approximately four days at 70°C to constant mass. The
branches were then stripped of needles. The dry branch sample mass (Dhabi) and the
dry needle sample mass (Dhabi) of each branch sample were weighed and recorded
separately to the nearest 0.1 gram.
The stems were cut into one-meter sections with a portable miter saw, and the
total green mass of each stem section (GHz) was measured on the digital hanging scale
and recorded to the nearest .001 kg. Two 5 cm cookies were then sub-sampled from
31
each section. To locate the first disk, a random number between 0 and 1 was
multiplied by 50 cm (half the length of a one-meter stem section) to arrive at a
distance between 0 and 50 cm. The second cookie was located by measuring 50 cm
up the stem from the first sampling point. The top sections of stem required an
adaptation to the method. The top sections were of variable length (1-99 cm) and a
few of them exhibited forking. If the top section was greater than 50 cm then two
cookies were sub sampled by multiplying random numbers between 0 and 1 by the
length of the section. If the top section was less than 50 cm then only one cookie was
subsampled. If forking was present then both forks were sub-sampled according to
the length of each fork. Therefore, two to four cookies were taken from the top
section when the tree had a forked top. The sub-sampled cookies of each section
were weighed green on a pan scale to the nearest 0.1 grams. The cookies were then
dried at 70°C to constant mass, and the dry mass of each section sub-sample of
cookies was recorded to the nearest 0.1 grams.
4.6 Leaf Area Index Processing
A representative sample of green needles from 10 trees per treatment (n=10)
was required to estimate specific leaf area. The 10 trees were chosen so that each of
the 8 DBH classes was represented, and the extra two trees were taken from the DBH
classes with the highest tree frequencies. One additional LAI sample branch per 10
branches was randomly selected from each size class and section in the same way that
the other n sample branches were chosen. All LAI branches were placed in a labeled
bag to form one composite LAI branch sample per tree. As soon as possible (within
32
24 hours) we began the needle removal process. All the LAI branches per tree were
clipped into manageable units and layed out in random order along a tape measure on
a table. For each branch, a random starting point in the first 10 cm at the base of the
branch was selected using a random number chart of numbers between 1 and 10. At
the starting point all the needles within an ~2 cm length of branch (about the width of
a thumb) were plucked and then placed in a plastic container.
Next, we systematically sampled all needles from each 2 cm length for every
20 to 50 cm section of each LAI branch up from the starting points. All parts of each
sample branch including side branches (tributaries) were sampled. At the time of
sampling we decided how much sample was needed dependent on size and number of
branches chosen. Then we prorated the sampling distance depending on sample
branch size and number between 20 and 50 cm. Therefore, if the sample tree had
numerous large branches then we plucked 2 cm of needles per 50 cm. On the other
hand, if the sample tree had fewer and smaller branches then we chose 20 cm as the
interval. The result was that the composite samples of larger trees with more needles
would be larger in terms of mass than the composite samples of smaller trees.
Each 20 cm segment was clipped off and thrown aside after sub-sampling to
avoid confusion. All the sub-sampled needles once composited for one tree were
placed in plastic ziplock bags at 4 °C until further processing was initiated. Each bag
was then shaken and thoroughly mixed, and four to five 100-needle samples were
randomly picked to represent each whole tree. Two composite samples per sample
tree were weighed dry and averaged to contribute to specific leaf area calculation.
The needle samples were oven-dried at 70 oC for 24 hours to constant dry mass.
33
Three 100 needle samples were counted out for each of the first 10 LAI
sample composites for measuring of area on an area meter (LICOR, model 1300).
Before use the area meter was calibrated with a 50 square cm circular metal disk.
The 100-needle samples were attached with invisible, double-sided tape to the inside
of document protectors and then passed through the area meter four times. The
resulting area was recorded to the nearest 0.01 cm2. The area was divided by four to
calculate the wet leaf area (WLA) per 100 needles. Two more sets of ten 100-needle
samples were processed identically to the above-mentioned samples. The average
WLA for two sets of the 100-needle composites was calculated per tree and
multiplied by the corresponding average dry mass of the composite samples to
produce estimates of specific leaf area (SLA) per tree. Note that leaf area estimated
was single-sided projected leaf area without any correction factors applied.
4.7 Nutrient Content Processing
A sub-set of the same needles used to estimate dry biomass was used for
needle nutrient content estimation. Twenty-eight sample trees per treatment were
chosen randomly and in proportion to the frequency of the measurement trees in DBH
classes for each treatment. Three BO+VC samples were overheated during drying, so
the BO+VC needle composite sample size = 25. After dry-weighing, the sample tree
needles (still separated by size class and section) were milled to 20 mesh in a large
Wiley mill. Then, an amount from each needle size class proportional to dry biomass
of needles from the entire section was divided from each milled needle size class.
The proportional sub-samples were used to form one composite sample per nutrient
34
content sample tree. The resulting composites were then mixed thoroughly and
milled to 40 mesh in a small Wiley mill. Each of the 53 composite needle samples
was stored in a whirly-pak plastic bag. Each needle sample was dried at 70 °C in
small paper envelopes until constant mass was observed before C, N and ICP
analyses.
A sub-set of the same branches used to estimate dry biomass was used for
branch nutrient content estimation. A total of 20 sample trees were chosen randomly
and in close proportion to the frequency of the measurement trees in DBH classes for
each treatment. Eleven samples were drawn from the BO-VC treatment and 9
samples were drawn from the BO+VC treatment. After dry-weighing, the sample tree
branches (still separated by size class and section) were chipped with a gas-powered
chipper. The chipper was cleaned out after each sample was chipped. Then, an
amount from each branch size class proportional to dry biomass of branches from the
entire section was divided from each milled branch size class. The proportional subsamples were used to form one composite sample per nutrient content sample tree.
The chipped composites were mixed thoroughly, milled to 20 mesh in a large Wiley
mill and milled to 40 mesh in a small Wiley mill. Each of the resulting 20 composite
branch samples was stored in a whirl-pak plastic bag. Approximately 30 grams of
each stem sample was dried at 70 °C in small paper envelopes until constant mass
was observed before CHN and ICP analyses.
A sub-set of the same cookies used to estimate dry biomass was used for stem
nutrient content estimation. A total of 21 sample trees per treatment were chosen
randomly and in close proportion to the frequency of measurement tree populations in
35
DBH classes for stem nutrient content estimation. Nine samples were drawn from the
BO-VC treatment and 12 samples were drawn from the BO+VC treatment. After
dry-weighing, the sub-set of sample tree cookies (still separated by section) were
chipped with a gas-powered chipper. The chipper was cleaned out after each sample
was chipped. Composite samples were drawn from each stem section in proportion to
the dry biomass of the corresponding stem section. The chipped sample proportions
were then composited, mixed thoroughly, milled to 20 mesh in a large Wiley mill and
milled to 40 mesh in a small Wiley mill. Each of the 21 composite stem samples was
stored in a whirl-pak plastic bag. The stem samples were dried at 75 °C in small paper
envelopes until constant weight was observed before C, N and ICP analysis.
CHN analysis was carried out by dry combustion in a Perkin-Elmer CHN
autoanalyzer (Perkin Elmer, Wellesley, MA). The composite samples were dried in
the oven at 75 °C overnight before analysis. Four key factors were weighed before
each run. Samples were weighed to approximately 5 mg, placed in foil wrappers,
weighed again, and the weights were recorded to the nearest .001 mg. Then the
samples were stored overnight in an airtight container with desiccants inside. Each
set of samples was run within 24 hours of placing in foil wrappers. For every 10
samples, a duplicate and a standard were processed to determine the precision of the
run.
ICP samples were prepared for analysis using a wet acid digestion procedure
(EPA 1986). Each tree tissue sample was weighed to 0.5 grams and then dried at
75°C to constant mass. Then the samples were ground to 40-mesh and placed in 150
mL digestion beakers. Eight mL of concentrated nitric acid (HNO3) were then added
36
before letting the beakers stand over night. The sample beakers were placed onto a
hot plate and heated at 120 °C for one hour. The beakers were removed from the hot
plate and allow to cool. Four mL of 30% hydrogen peroxide (H202) was added to
each beaker and the beakers were placed back onto the hot plate. Additions of 30%
H202 were repeated until the digests were colorless. The beakers were removed from
the hot plate and allowed to cool. Pure water was used to dilute the solutions to 20
mL. The hot plate temperatures were then reduced to 80 °C, and the solutions were
allowed to evaporate until ~ 5 mL of solution remained. 1:10 nitric acid and DI water
were used to dilute the solution to 26.6 mL. The samples were then analyzed by EPA
method 3050 (EPA 1986) using ICEP 61E equipment (Thermo Jarrell Ash, Franklin,
MA).
4.8 Ratio Estimation Procedures
The ratio estimation technique was used to produce estimates of dry mass for
each sample stem section similar to the methods used by Briggs et al. (1987) as
described by Parresol (1999). For each stem section and branch size class, the green
biomass of branch samples with needles intact (Σghj) was divided by mhj , the number
of branches in size class j. This calculation yields g-barh , which is the average green
mass of one branch in a size class. Similarly, Σdhjb/mh yields the average dry mass of
one branch (d-barhb) in a size class, and Σdhjn/mh yields the average dry mass of the
needles on one branch (d-barhb) in a size class. The ratio estimators of branch
biomass (rhjb) for size class j of section h were calculated with equation 4.1:
37
rhjb = (d-barh / g-barh) ( d-barhjb / (d-barhjb +d-barhjn)
(4.1)
where d-barh is the average dry mass of one branch with it's needles intact and g-barh
is the average green mass of one branch with it's needles intact.
The ratio estimators of needle biomass (rhjn) for size class j of section h were
calculated with equation 4.2:
rhjn = (d-barh / g-barh) *(d-barhjn / (d-barhjb +d-barhjn)
(4.2)
Each ratio estimator was multiplied by the total green mass of all branches with
needles intact (Ghj) for size class j in section h to yield estimates of dry biomass for
branches (Dhjb) and for needles (Dhjn) in each size class. Total estimates of dry
branch biomass (DTb) and dry needle biomass (DTn) for each sample tree were then
obtained by summing the individual estimates from each size class per section and
then summing the estimates from each section. Table 4.4 presents the equations
involved in ratio estimation. The dry branch mass and the dry needle mass of the fine
branches (bbd < 3 mm) was determined by dry weighing the entire fine size class, so
the extra branch and needle dry masses of the fine size classes were added directly to
the total estimates per tree.
For the estimating the dry biomass of a single stem component, the ratio
equation simplifies to:
rh = (d-barh / g-barh)
(4.3)
38
where d-barh is the average dry mass of one cookie, and g-barh is the average green
mass of one cookie. The ratio estimator, rh, is the ratio of dry mass to green mass of
an average cookie for section h. Each rh was multiplied by the green stem mass of
each section h to estimate dry stem mass for every section. Then the estimated dry
stem mass of each section was added to produce an estimate for each whole stem.
Appendix A contains an example of how a single tree dry biomass is estimated.
4.9 Statistical Analysis
Biomass models
The simple linear model 4.4 was fit for all above-ground biomass components
(stems, branches, needles, and total) in each treatment:
Yi = a + bXi+ εi
(4.4)
where Y = dry biomass, a = intercept coefficient, b = slope coefficient, X = DBH,
and i = 1,...,n. The models were tested for goodness of fit, constancy of error terms
and normality of error terms. Graphical analysis revealed a non-linear trend in all
components. The error terms for all components were close to normal distributions;
however, some skewing was evident upon viewing the normal probability plots and
conducting the correlation test for normality. The modified Levene test and residual
plots showed that the error terms are constant about X for all components for the
simple linear model.
39
The best-fit lines through the scatter of Y vs. X for all components match the
line curvature produced by the following equation:
Y = aXb
(4.5)
Equation 4.5 is known as the allometric equation, and it is expressed as:
ln Y = ln a + b lnX
(4.6)
by log transformation of both sides of the equation. Many researchers have used the
log-tranformed, allometric equation 4.6 for estimating young tree biomass
(Baskerville 1971; Virtucio 1981; Sprugel 1983; Crow and Schlaegel 1988).
For each biomass component we tested the null hypothesis that the slope of
the weeded treatment line is equal to the slope of the non-weeded treatment line using
the ANCOVA method in SPSS 12 (SPSS, Inc.). The following model 4.7 was used
to test the null hypothesis of equal slopes:
ln Yij = ai + bi ln Xij + εij
(4.7)
where i = 0 for BO-VC trees and i = 1 for BO+VC trees, and j = 1,2,...,n.
ai are the intercepts of the two treatment lines, and bi are the slopes of the two
treatment lines. εij is the random error term. We failed to reject the null hypothesis of
40
no treatment effect on slope. Table 4.5 shows the P-values for the tests of slope
difference. We concluded that the slopes of each component model were not affected
by treatment, and the two treatment lines are parallel as shown in figures 4.2-4.5.
The next step was to fit model 4.8 with one slope and two intercepts to test the null
hypothesis of equal intercepts:
ln Yij = ai + b ln Xij + εij
(4.8)
where the slope b, is equal between weeded and non-weeded treatments.
SPSS 12.0 was used to test model 4.8 in each component for a treatment effect on
intercept using the ANCOVA technique. We rejected the null hypothesis of equal
intercepts for each component model. Table 4.5 shows the P-values for the tests of
intercept difference. For every component there was a significant (α = .05) treatment
effect on intercept, so we conclude there are two offset parallel lines representing the
relationship between ln Y and ln X for the total above-ground component as depicted
in figures 4.2-4.5.
Nutrient data analysis
Each nutrient sample tree was randomly chosen from each DBH class, and all
8 classes per treatment were represented with approximately one sample tree
composite per class. However, only 8-12 of the sample trees per treatment, and per
component were composited for analysis, except in the case of foliar N analysis
where most of the sample trees were used. The reason for the small sample sizes was
41
based on time and budget restrictions. Needle, branch and stem component
concentrations of N (mg/g), P, K, S, Ca, and Mg (mg/kg) were tested for treatment
mean effect using ANOVA in SPSS. Nitrogen percent concentrations were plotted
vs. DBH for both treatment sample populations. Scatter plots and simple linear
regression of N concentration vs. DBH indicated a weak to non-existent relationship
in all components (foliage, branches, and stems). Therefore, we calculated
component N content by multiplying the treatment component estimates by the
treatment mean fractions of N rather than stratifying the calculation procedure by
DBH class. P, K, S, Ca, and Mg contents (kg element/ha) per component were
calculated by multiplying a unit conversion factor (10-6) by the estimated treatment
mean concentrations. The products were then multiplied by estimated dry biomass
content per component. The elemental content within each above-ground component
(needles, branches, stems) was summed to provide an estimate of total above-ground
elemental content of N, P, K, S, Ca and Mg.
Leaf area data analysis
The two dry mass values for each tree composite sample were averaged, and
the two corresponding wet leaf area values were averaged. The specific leaf area
(SLA) of each sample was then calculated as the ratio of the wet leaf area average to
the dry mass average (cm2/g). SLA of each composite sample was multiplied by the
estimate of needle biomass (g) to produce a wet leaf area (cm2) estimate per sample
tree. Simple linear regression of wet leaf area (WLA) vs. DBH indicated a linear
42
relationship in both treatments. The slopes of the relationships were shown to be
significantly different (P < .05) with treatment using ANCOVA in SPSS. Estimates
of total wet leaf area per treatment were calculated by plugging DBH into the simple
linear equation for every measurement plot tree. The treatment sum WLA values
were divided by treatment area (0.8 ha), and multiplied by an area conversion factor
(1 ha/1x108 cm2) to produce LAI estimates per treatment. The LAI values were
converted to units of m2 leaf area per m2 of ground area. The linear equation
estimates were compared to estimates from the stand-table approach. The stand table
approach was conducted the same way as it was in the biomass estimate comparison
described earlier. The stand-table treatment estimates of WLA were converted to
LAI by the same area conversions mentioned above.
Crown Structure Data Analysis
The treatment means (stratified by DBH class) of the following crown
structure variables were tested for treatment effects via ANOVA in SPSS: height to
lowest live limb (HLL), height to live crown (HLC), crown width (CW). For both
treatments, the first 1-meter section contained the most branches and branch mass
relative to the higher sections of stem, so we also tested the following stem-section 1
crown structure variables for treatment effects via ANOVA in SPSS: branch count
(BC), branch length (BL), branch basal diameter (BBD), largest branch length (LBL),
and largest branch diameter (LBD), and years of foliage retention (YFR).
43
Figure 4.1 Example of treatment plot. Numbered squares represent the locations of
the 20 filler trees. The inner rectangle of 6 x 28 cells is the measurement plot. A
measurement plot tree was planted in each cell at 2.5 x 2.5 m spacing. The buffer
zone surrounds the measurement plot and the buffer is three rows wide.
44
Figure 4.2 Scatter plots of ln above-ground biomass (g) vs. ln DBH (mm) with
parallel ln-transformed equation lines for each treatment. The two lines have the
same slope and different Y-intercepts. DBH = diameter at 130 cm.
45
Figure 4.3 Scatter plots of ln stem biomass (g) vs. ln DBH (mm) with parallel lntransformed equation lines for each treatment. The two lines have the same slope and
different Y-intercepts. DBH = diameter at 130 cm.
46
Figure 4.4 Scatter plots of ln branch biomass (g) vs. ln DBH (mm) with parallel lntransformed equation lines for each treatment. The two lines have the same slope and
different Y-intercepts. DBH = diameter at 130 cm.
47
Figure 4.5 Scatter plots of ln needle biomass (g) vs. ln DBH (mm) with parallel lntransformed equation lines for each treatment. The two lines have the same slope and
different Y-intercepts. DBH = diameter at 130 cm.
48
Table 4.1 Summary statistics for measurement plot tree and filler tree populations in
both treatments (BO+VC and BO-VC).
49
Table 4.2 Eight DBH classes per treatment used for selecting sample trees, and the
number of sample trees selected per class in addition to the number of trees that
would have been selected if the trees had been selected in proportion to the tree
frequency (tf) and basal area (ba).
na
n (tf)b
n (ba)c
1
5
2
9
7
2
1
1
2
4
5
9
5
1
1
1
1
2
4
10
8
1
1
1
0-23
6
5
24-30
3
4
31-37
10
8
38-44
5
8
45-51
4
3
52-58
1
1
59-65
1
1
66+
1
1
a
n = actual sample size chosen per dbh class
1
3
7
11
5
2
1
1
treatment
BO+VC
dbh class
2
mm
0-29
30-37
38-45
46-53
54-61
62-69
70-77
78+
BO-VC
b
n (tf) = sample size proportional to tree frequency per dbh class
c
n (ba) = sample size proportional to basal area per dbh class,
2
where basal area = π r
Table 4.3 Second flushing code definitions.
50
Table 4.4 Variables and formulas for branch and needle ratio estimation of dry
biomass.
Ghj = green mass of all branches and needles in size class j of section h
Σ ghj = green mass of sample branches with needles in size class j of section h
Σ dhjb = oven-dry mass of sample branches in size class j of section h
Σ dhjn = oven-dry mass of sample needles in size class j of section h
mhj = the number of sub-sample branches with needles in size class j of section h
g-barh = Σ ghj / mh
d-barhb = Σ dhjb / mh
d-barhn = Σ dhjn / mh
rhjb = (d-barh / g-barh) · ( d-barhjb / (d-barhjb +d-barhjn ) = ratio estimator of oven-dry
branch mass to green branch mass in size class j of section h
rhjn = (d-barh / g-barh) · ( d-barhjn / (d-barhjb+ d-barhjn ) = ratio estimator of oven-dry
needle mass to green needle mass in size class j of section h
Yhjb = Gh · rhjb = ratio estimator of oven dry branch mass of size class j in section h
Yhjn = Gh · rhjn = ratio estimator of oven dry needle mass of size class j in section h
YTbj = Σ Yhjb , estimate of dry mass of all branches in size class j
YTnj = Σ Yhjn , estimate of dry mass of all needles in size class j
Ybranch = Σ YTbj , total estimate of dry mass of all branches for whole tree
Yneedle = Σ YTnj , total estimate of dry mass of all needles for whole tree
51
Table 4.5 Significance levels (P) for comparison of regression coefficients of
biomass equations for Douglas-fir trees growing in non-weeded and weeded
treatments. The biomass equation is ln Y = a + b ln X, where Y is component or total
above-ground biomass and X is DBH (diameter at 130 cm). Slopes (a) and intercepts
(b) are significantly different if P ≤ 0.05.
52
Section 5. Results and Discussion
5.1 Biomass Results
Estimates of intercept and slope coefficients were generated by the least
squares technique for each biomass component per treatment. The estimated equation
coefficients are given in table 5.1. The equations were used to calculate estimates of
component biomass kg / ha per treatment using the year 5 measurement plot DBH
values. A correction factor (CF), was multiplied by the estimated value for each tree
to account for the slightly negative bias associated with expressing log transformed
data into original units (Sprugel 1983). The correction factor expression is:
CF = exp((SEE2)/2)
(5.1)
where SEE is the standard error of the estimate in logarithmic terms. Table 5.1
contains the correction factors for each equation and the corrected estimates in kg/ha.
The stand table estimation approach was compared with the allometric equation
approach. Component mean dry biomass values were calculated for the set of sample
trees in each DBH class. The class means were then multiplied by the tree frequency
in each DBH class per plot to produce plot total biomass values. The eight plot totals
per treatment were then summed to treatment totals, and the treatment totals were
divided by the area of 8 treatment plots (0.8 ha) to produce component kg biomass/ha
53
estimates. The stand table estimates are found in table 5.1. It is evident that both
estimation methods (stand-table or allometric equation) yield similar estimates for all
components. The estimates from the BO+VC treatment are more than twice the
estimates from the BO-VC treatment in all components.
Biomass additivity is defined as the condition wherein the sum of the
estimates of the component biomass (needles, branches, stems) approximately equals
the estimates of total above-ground biomass (Ares et al. 2002). Virtucio (1981) noted
that biomass additivity was often not the result when the allometric equation was used
to produce estimates. A biomass discrepancy of less than 1 % between the total
above-ground estimate and the sum of the component estimates is considered
acceptable (Virtucio 1981); in other words if the discrepancy is less than 1% then the
models are considered to exhibit biomass additivity. Table 5.2 contains the percent
biomass discrepancy for the models used in addition to two other models tested: the
simple linear model and the non-linear power equation. The biomass discrepancy is
less than 1% for all three of the equation forms, which supports the validity of the
total above-ground estimates in each treatment. Notice that the biomass estimates are
very close between the three equation forms except in the case of the non-weeded
(BO-VC) treatment estimate which was produced with the simple linear equation (the
difference is ~ 1500 kg/ha between the simple linear estimate and the estimates from
the other two equations). Estimates from the weeded (BO+VC) treatment were not
highly disparate between the three forms of equation used for estimation. The
difference in above-ground biomass per hectare between the two treatments was 7032
54
kg ha-1. This value is comparable to a reported mean difference of 9442 kg ha-1 at 5-6
years of growth between biosolids-application treatments and control treatments on
three units planted to Douglas-fir near Snoqualmie, Washington (Harrison et al.
2002).
At the Fall River site, the mean height of BO+VC trees ~5 years after
treatment is 358 ± 1.8 cm, and the mean height of BO-VC trees ~5 years after
treatment is 310 ± 1.7 cm. The addition of height as a second predictor variable did
not improve the 5-year Douglas-fir biomass estimation models.
The mean DBH of BO+VC trees ~5 years after treatment was 45 ± 0.33 mm, and the
mean DBH of BO-VC trees ~5 years after treatment was 34 ± 0.28 mm. The
difference in mean DBH is 9 mm between the two treatments. Figure 5.1 shows the
mean DBH of the BO+VC and B0-VC treatments for three consecutive years.
Harrington and Tappeiner (1997) report a difference of approximately 25 mm
between the two vegetation control treatments (removal of all tanoak sprouts, herbs,
and shrubs vs. no removal of weeds) at two Douglas-fir plantations (Squaw and Fir
Point) in southwest Oregon during the 5th year of growth. The range of DBH for the
5-year old Douglas-fir was ~25-50 mm according to Harrington and Tappeiner
(1997). At another site in western Oregon, the control of competing vegetation in the
fifth growing season of four Douglas-fir stands produced a mean DBH difference of
63 mm between an intensive herbicide treatment and a control (no herbicide
treatment) by the end of the 13th growing season (Petersen et al. 1988).
For a given DBH in the BO+VC and BO-VC treatments at the Fall River site,
the stem, branch, and needle biomass of weeded trees is larger than stem, branch, and
55
needle biomass of non-weeded trees (see figures 4.2-4.5). An explanation for the
stem component estimates can be found in the analysis of stem taper differences.
Stem taper is defined here as diameter at 130 cm (DBH) divided by diameter at zero
height (groundline diameter). The stem taper was calculated for each sample tree,
and plot means were estimated using the stand-table method. The stem taper means
were compared using ANOVA in SPSS. The stem taper ANOVA results are shown
in table 5.11. It is apparent that the stem taper values are significantly lower (P ≤
0.05) in the weeded treatment. Thus, the weeded stems are more cone-shaped than
the non-weeded stems; this partially explains the difference in intercepts for the
biomass equations. For a given DBH, branch and needle component biomass were
larger in the BO+VC treatment due to the differences observed in the crown structure
data that demonstrated higher branch count, and larger (diameter and length) limbs in
the 1st meter of stem in the vegetation control treatment, which is discussed in thesis
Section 5.4. Young Douglas-fir branch basal diameter was noted as a good predictor
of branch mass and needle mass by Helgerson et al. (1988).
5.2 Nutrient Content Results
Needle, Branch, and Stem nutrient concentration summary statistics are
shown in tables 5.3-5.6. No significant differences were found between treatments
except in the case of branch N concentration (P < 0.05). The higher mean percent N
concentration in BO-VC branches could be related to the smaller size of the branches;
smaller branches typically have larger percent N concentrations because there is
proportionally less woody tissue and more meristem tissue in small vs. large branches
56
(Rob Harrison, personal communication). Generally, we did not detect treatment
effects on nutrient concentrations; therefore, it is likely that the ratio of nutrient
concentrations (i.e. N:P, N:S, P;S) is not affected by treatment. Table 5.12 shows
needle nutrient concentration (%) deficiency levels for Douglas-fir; the Fall River
needle concentrations from both treatments are above deficiency levels. The mean
percentages of N in the foliage of BO+VC and BO-VC trees are 1.55 and 1.50 %
respectively; the mean values of year-3 foliage percent N were greater (2.30 and 1.81
% in BO+VC and BO-VC treatments, respectively). The Fall River year-5 percent N
values are approximately 5 % lower than the reported percent N concentrations (1.902.05 %) under respective low and high densities of grass competitors on a 5-year
Douglas-fir plantation in coastal Oregon (Cole and Newton 1986). The mean foliar N
concentration was 1.41 % for 10 Douglas-fir stands (ages 16-26) in coastal British
Columbia (Marshall and Jahraus 1987). Turner et al. (1988) report mean foliar N
concentrations of 1.40 % and 1.44 % for two 6-year Douglas-fir stands in the Pacific
Northwest. Marshall and Jahraus (1987) cite local resource availability (light, water,
nutrients) as one of the main reasons for foliar element concentration variability;
provenance is also a factor.
Even though there were generally no treatment effects on the concentrations
of the macronutrients, there is clearly a substantial treatment effect on nutrient
content simply because of the large biomass differences between treatments.
Table 5.7 shows the nutrient content estimates for N, P, K, S, Ca, and Mg. These six
elements are considered macronutrients, which means they are needed in higher
quantities than the main micronutrients (Fe, Mn, Zn, Cu, B, Mo). N, P, S, and K are
57
thought to be the most important nutrients to coastal Douglas-fir productivity
(Marshall and Jahraus 1987). The values for the weeded treatment (BO+VC) in
Table 5.7 can be thought of as estimates of the 5-year Douglas-fir nutrient demand for
N, P, K, S, Ca, and Mg at the Fall River site. The estimates of the 5 year nitrogen
leaching rate in the BO+VC treatment at the Fall River site is 150 kg N ha-1 (Brian
Strahm, personal communication). The estimate of nitrogen leached from the system
in five years in weeded plots is greater than the amount of nitrogen acquired by
above-ground Douglas-fir and competing vegetation (106 kg N ha-1) in weeded plots
during 5 years of growth. Moreover, The 5-year nitrogen leaching estimate is also
greater than the amount of nitrogen acquired by the above-ground Douglas-fir and
competing vegetation (76 kg N ha-1) in non-weeded plots during 5 years of growth.
Estimates were not made for the dead vegetation that was part of the forest floor.
5.3 Leaf Area Index Results
The slopes of the simple linear equations (Y = a + bX, where Y = wet leaf
area, X = DBH, a = intercept, and b = slope) were significantly different between
treatments (P < 0.01). Estimates of one-sided, projected leaf area index estimates
(m2 leaf area per m2 ground area) are shown in table 5.8. The estimated LAI of the
weeded treatment is approximately twice that of the non-weeded treatment. The Fall
River LAI estimates fall into a range reported by Borghetti (1986) between 0.97 and
2.74 m2m-2 for 25-year old Douglas-fir with DBH values between 100 mm and 150
mm, respectively. The maximum DBH of the Fall River trees was 76 mm. Turner et
al. (2000) report a range of LAI values (4.6-6.1 m2m-2) for three Douglas-fir stands in
58
the age class 20-80. The LAI estimates for three mature (80-200 years) and three oldgrowth (+200 years) stands range from 8.9 to16.9 m2m-2 (Turner et al. 2000). The
overall mean specific leaf area (SLA) across the two Fall River treatments was 90.6 ±
1.8 cm2g-1. The mean SLA for the BO+VC sample trees was 92.7 ± 3.1 cm2g-1, and
the mean SLA for the BO-VC sample trees was 89.3 ± 2.0 cm2g-1. These mean SLA
values are notably higher than SLA values for young Douglas-fir reported by other
researchers. Borghetti (1986) reported a range of SLA from 65.1- 82.4 cm2g-1, but
the ages of the stands are higher (25 years-old growth) than the 5-year Fall River
stand. Observed differences in SLA are indicative of the adaptive ability of plant
photosynthetic systems as a function of environmental variation (Del Rio and Berg
1979). Douglas-fir specific leaf area generally increases with leaf age (Borghetti et
al. 1986), and leaf area index increases with stand age (Turner et al. 2000).
5.4 Crown Structure Results
The treatment means for height to lowest live limb (HLL) and height to live
crown (HLC) were significantly greater (P < 0.05) in the non-weeded treatment. This
result corresponds with our knowledge about young tree physiology and plant
competition; more shading in the non-weeded plots might be the main contributor to
the observed difference (Oliver and Larson 1996). The crown width (CW) treatment
mean was significantly smaller (P < 0.05) in the non-weeded treatment. There is a
probable relationship between crown width and crown biomass, but the two variables
were not tested via regression analysis. The ANOVA results for the treatment mean
comparison of HLL, HLC, and CW are found in table 5.9.
59
Harrington and Tappeiner (1997) report a similar range of crown width values
with respect to vegetation control vs. no vegetation control. At year 5 of growth in
the Squaw and Fir Point Douglas-fir plantations in southwest Oregon, the crown
width in the treatments without vegetation control was ~ 60-90 cm , and the crown
width in the treatments with vegetation control was ~100-120 cm (Harrington and
Tappeiner 1997). The five-year crown width values of the BO-VC and BO+VC
treatments at Fall River are comparable with the values from the above-mentioned
Oregon study (BO-VC mean crown width = 84 cm, BO+VC mean crown width = 109
cm).
The following variables from the lowest meter of stems (1st section) were
found to be significantly smaller in the non-weeded treatment (P < 0.05): branch
count (BC), branch length (BL), branch basal diameter (BBD), largest branch length
(LBL), and largest branch diameter (LBD). Table 5.10 contains the results for the
crown structure variables of the 1st stem section. The corresponding data collected
for higher stem sections was not tested with ANOVA, however the collected values
for all stem sections are included in Appendix D.
Maguire et al. (1994) reports crown structure data (number, diameter, and
distribution of primary branches) for six young Douglas-fir stands in the Pacific
Northwest prior to crown closure. The mean DBH (84 ± 3 mm) and mean height (6.5
± 2.0 m) across the six stands are higher than Fall River DBH and height values at
age 5, but the mean maximum branch diameter (17.6 ± 6.0 mm) is close to the Fall
River age 5 values of 14.2 ± 0.11 mm for the BO-VC treatment mean and 19.3 ± 0.28
mm for the BO+VC treatment mean.
60
Note the greater incidence of second flushing among sample trees in the
BO+VC treatment relative to the BO-VC treatment (table 5.13). The markedly larger
number of branches in the BO+VC treatment is probably linked to the second
flushing observations. One can argue convincingly that the setting of more lateral
buds each year during wet periods late in the growing season has led to increased
branch counts where water availability was sufficient because competing vegetation
was controlled. However, we don't have second flushing data for each growing
season, and there was no formal statistical tests applied to the second flushing
categorical data.
ANOVA results suggest a significant treatment effect (P= 0.02) on foliage
retention (YFR), but there is concern about the subjectivity of the methods used for
estimating YFR. The BO+VC treatment mean = 2.61 ± 0.02 years, and the BO-VC
treatment mean = 2.57 ± 0.02 years. If there truly is a significant difference in YFR
between treatments, it is probably not a large difference.
61
Table 5.1 Fifth-year Douglas-fir component biomass estimates for non-weeded (BOVC) and weeded (BO+VC) treatments calculated with natural log-transformed
equations. A correction factor (CF) was applied to the estimates to correct negative
bias. Estimates from the stand-table approach are compared with the regressionbased estimated.
Treatment Component
BO+VC
stem
branch
foliage
above-ground total
Equation
ad
1.651
1.727
1.721
2.838
BO-VC
stem
branch
foliage
above-ground total
1.980
0.832
1.469
2.615
r
2
CF
a
e
b
Y
W
c
b
1.620
1.591
1.531
1.574
0.95
0.68
0.76
0.85
1.461
1.624
1.462
1.502
0.94
0.89
0.92
0.94
1.005
1.042
1.025
1.016
kg ha
4216
4141
4210
4121
3253
3183
11657
11445
28
28
28
28
1.021
1.049
1.026
1.021
2113
1242
1279
4625
31
31
31
31
2048
1066
1273
4533
a
Correction factor, CF = exp((SEE2)/2), where Sy.x is standard error of the estimate in the logarithmic scale
b
Y is treatment estimate of component biomass calculated with log-transformed and corrected equation
of the form ln Y = a + b ln X, where Y is component biomass, X is dbh (diameter at 1.3 m), and a and b
are regression coefficients
c
W is treatment estimate of component biomass calculated using stand-table approach
intercept coefficient, a
e
slope coefficient, b
d
n
-1
62
Table 5.2 Percent biomass discrepancy for three of the models tested for estimation of
biomass. The model that was chosen as the best fit is the log-tranformed allometric
equation. The non-linear power equations were fit using the NLIN function of SAS.
a
BO-VC
Simple linear equation b: Y=a+bX
power equation: Y=aX
log transformed allometric equation: ln Y= ln a + b ln X
BO+VC
Simple linear equation : Y=a+bX
b
power equation: Y=aX
log transformed allometric equation: ln Y= ln a + b ln X
a
b
b
Y
Treatment Equation form
–––––kg ha
6141
4664
4625
Y
-1
biomass discrepancy
–––––
6141
4662
4634
%
0.00
0.05
-0.20
11250
11253
-0.03
11384
11657
11361
11678
0.20
-0.18
the total above-ground biomass estimate calculated from one equation
the summed estimate of total above-ground biomass calculated by adding the needle, branch, and stem estimates
Table 5.3 Comparison of needle component elemental (P, K, S, Ca, Mg)
concentrations between treatments. P-values < 0.05 signal significant treatment
effects on concentration. Estimated treatment means, standard errors, and 95%
confidence intervals (CI) are shown for each treatment.
63
Table 5.4 Comparison of branch component elemental (P, K, S, Ca, Mg)
concentrations between treatments. P-values < 0.05 signal significant treatment
effects on concentration. Estimated treatment means, standard errors, and 95%
confidence intervals (CI) are shown for each treatment.
Table 5.5 Comparison of stem component elemental (P, K, S, Ca, Mg)
concentrations between treatments. P-values < 0.05 signal significant treatment
effects on concentration. Estimated treatment means, standard errors, and 95%
confidence intervals (CI) are shown for each treatment.
64
Table 5.6 Comparison of component N concentrations (mg/g) between treatments.
P-values < 0.05 signal significant treatment effects on concentration. Estimated
treatment means, standard errors, and 95% confidence intervals (CI) are shown for
each treatment.
Table 5.7 Elemental content estimates in each above-ground component by treatment
for N, P, K, S, Ca, and Mg. Elemental content of competing shrubs and herbs is
included (unpublished data from Connie Harrington, 2004).
Elemental Content
Element Treatment
Stem
Branch
Needle
Total above-ground Shrubs+Herbs
–––––––––––––––––––––––––––––––––––––––––kg ha-1––––––––––––––––––––––––––––––––––––––––
N
BO-VC
9.64
12.10
19.24
40.98
34.6
BO+VC
17.90
36.94
50.61
105.45
0.9
P
BO-VC
BO+VC
1.13
2.06
1.25
3.49
2.55
5.61
4.92
11.17
4.4
0.1
K
BO-VC
BO+VC
4.71
10.67
3.89
11.55
5.74
15.22
14.35
37.44
37.9
0.9
S
BO-VC
BO+VC
0.55
1.06
0.56
1.76
1.66
3.97
2.77
6.79
2.0
0.1
Ca
BO-VC
BO+VC
2.29
4.12
2.81
8.54
4.84
12.05
9.95
24.72
24.0
0.4
Mg
BO-VC
BO+VC
0.74
1.26
0.87
2.40
1.19
2.47
2.81
6.14
8.8
0.2
65
Table 5.8 Leaf Area Index (LAI) estimates. LAI is projected, one-sided wet leaf area
(m2 leaf area per m2 ground area) for weeded and non-weeded treatments. Simple
linear equation estimates are compared with stand-table estimates.
Treatment
a
equation
LAI estimates
simple linear
2
stand-table
-2
–––––––– m m ––––––––
a
BO-VC
BO+VC
Y = - 46441 + 3182 X
Y = - 190527 + 7701 X
0.994
2.514
2
0.968
2.248
equations are of the simple linear form, Y = wet leaf area (cm ) and X = dbh (mm)
slopes were found to be significantly different (P < 0.01) between treatment lines
Table 5.9 ANOVA results for height to lowest live limb, height to live crown, and
crown width variables. There is a significant treatment effect if P-value < 0.05.
66
Table 5.10 Section 1 crown structure ANOVA table. There is a significant treatment
effect if P-value < 0.05.
Table 5.11 Treatment effect on Douglas-fir taper. P-values < 0.05 indicate
significant differences.
67
Table 5.12 Nutrient deficiency levels for western conifer species established from
seedlings grown in solution cultures (Walker and Gessel 1991)
ELEMENT
DOUGLAS-FIR
HEMLOCK WR CEDAR
SITKA SPRUCE
ABIES
––––––––––––––––––– % IN DRY FOLIAGE –––––––––––––––
NITROGEN
1.25
1.8
1.5
1.8
1.15
PHOSPHORUS
0.16
0.25
0.13
0.09
0.15
POTASSIUM
0.6
1.1
0.6
0.4
0.50
CALCIUM
0.25
0.18
0.20
0.06
0.12
MAGNESIUM
0.17
0.12
0.06
0.07
SULFUR
0.35
0.4
0.15
Table 5.13 Number of sample trees coded in each of the 6 second-flushing states per
BO+VC and BO-VC treatments, and the total number of second flushes (codes 1-6)
in each treatment. This data represents second flushing during the year 2004 growing
season.
mean diameter (mm) at 1.3 m
68
50
40
30
20
10
0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
years after planting
weeded
non-weeded
Figure 5.1 Mean DBH in each treatment measured at the end of growing seasons 2, 3,
and 4. The differences in mean DBH are significant at the .05 level.
69
Section 6. Conclusions
6.1 Key Findings and Suggestions for Future Research
Douglas-fir Biomass
The control of competing vegetation significantly increased Douglas-fir
above-ground dry biomass per hectare relative to no vegetation control. Elimination
of competitor leaf area and root systems enhanced the resource supply to crop trees.
It is possible that the Douglas-fir root:shoot biomass ratios were greater in the nonweeded treatment where soil resources (e.g. water, nutrients) were more scarce. The
root systems of a few trees from each treatment could be excavated and studied to
determine if there is a significant difference in root:shoot ratios. Further biomass
estimation work could be completed after ten years of growth to learn how the
difference between treatments changes. Additionally, it would be interesting to look
for any changes in above-ground biomass allocation (i.e. ratio of crown biomass to
stem biomass) between stand ages 5 and 10. Importance sampling (IS) techniques
should be employed to reduce the sample sizes, the labor costs, and the site impacts
involved in any destructive sampling of 10 year-old Douglas-fir.
Nutrient Concentrations
The control of competing vegetation had no significant effect on Douglas-fir
above-ground macronutrient concentration (N, P, K, S, Ca, and Mg), except for
percent N concentration in the branch component. Nitrogen concentrations are higher
70
in small branches vs. large branches because there is proportionally more meristem
tissue in small branches. The BO+VC treatment significantly increased Douglas-fir
above-ground macronutrient content (N, P, K, S, Ca, and Mg) relative to the BO-VC
treatment. The biomass difference between the treatments is the reason for the
nutrient content difference. Continued monitoring of the foliar nutrient
concentrations is recommended, and the foliar nutrient content should be estimated
along with foliar biomass at stand age 10.
Leaf Area Index
The control of competing vegetation significantly increased Douglas-fir leaf
area index relative to no vegetation control. Wet leaf area followed a linear
relationship with respect to tree DBH in both treatments. The control of competing
vegetation significantly altered the relationship between DBH and leaf area index
relative to no vegetation control. The large difference in photosynthetic surface-area
between the treatments suggests that the treatment difference between tree
productivity will continue to be pronounced for a number of years. LAI should be
estimated again at 5-year intervals.
Crown Structure
The control of competing vegetation significantly altered Douglas-fir crown
structure relative to no vegetation control. Height to lowest live limb and height to
live crown were significantly higher in the non-weeded treatment. Less available
71
light because of shading by weeds and less soil water and nutrient availability are
possible causes. Crown width was greater in the weeded treatment. Lowest stemsection branch count, branch length, branch basal diameter, largest branch length, and
largest branch diameter were all greater in the weeded treatment. Higher availability
of resources in the weeded treatment is the most simple explanation for larger tree
dimensions and higher number of branches. Second flushing appeared to be more
frequent in the weeded treatment where the branch counts were much higher than
branch counts in the non-weeded treatment. More second flushing in the weeded
treatment is the most probable explanation for higher branch counts in the weeded
treatment. Branching structure has implications for wood quality (Maguire et al.
1994), so it is recommended to continue coding second flushing and assessing branch
counts and size of branches.
Further Recommendations
The results of the year 5 study of stand growth enhances our ability to make
useful recommendations for the management of similar Douglas-fir stands in the
critical interval between planting and crown closure. To understand the long-term
effects of vegetation control on the main variables of interest, it would be practical to
re-evaluate the variables at 5 year intervals if not more frequently. I recommend the
estimation of crown closure rates as a priority in the next few years; as it is a measure
of the change from interspecific to intraspecific competition in the non-weeded
treatment as well as another treatment effect variable. Gelock (1967) developed a
crown closure estimation scale for coastal Douglas-fir which can be used to estimate
72
crown closure at the Fall River site. Another hypothesis to be tested involves the
quantity of nutrients bound in competing vegetation. When the non-weeded
treatment trees out-compete their competitors there will be a release of nutrients into
the soil system. Nearly the same amount of N was estimated to be in Douglas-fir (41
kg ha-1) as the amount of N estimated to be in competing weeds (35 kg ha-1) during
year 5 within the BO-VC treatment. Future research should seek to answer how
much of the released N will be available for tree growth, and how much released N
will be lost from the system via leaching. It would be worthwhile to determine the
point in time when N leaching rates in the BO+VC treatments approximately equal
leaching rates observed in the non-cut reference stands to the south and west of the
installation., i.e., when N leaching rates become negligible. Vegetation control
increased early plantation growth on a high quality site in the Coast Range. Longterm study of the treatment effects at Fall River will be needed to fully examine the
growth, yield and wood quality implications of intensive vegetation control on similar
sites.
73
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78
Appendix A: Dry Component Biomass
79
Table A1. BO-VC treatment raw biomass and stem dimension values. D0 =
diameter at zero height, DBH = diameter at 130 cm height, HT = total tree height,
taper = DBH / D0 , Ybranches = dry branch mass estimate, Yneedles = dry needle
mass estimate, Ystems = dry stem mass estimate, Yag = dry total above-ground mass
estimate
Tree
Id#
9195
9192
9206
9203
9209
22332
9207
9425
9431
9423
9463
9477
9466
9536
9533
9525
26167
9623
9622
9639
147
9872
9873
9866
247
9905
9917
501
601
701
801
mm
mm
cm
g
g
g
g
D0
56
67
46
56.15
61
96
90
50
50
76
54
62
82
53
60
91
106
43
67
68
45
61
56
64
81
65
70
81
96
n/a
n/a
DBH
29
35
21
32.5
33
49
53
26
31.5
38
22
34
46
31
34
37
60
19.5
40
40.5
20
28
33
35
49
36
40
48
65
5
8
HT
260
302
278
370
315
399
366
220
317
359
240
300
418
250
313
366
378
256
340
372
229
309
315
390
400
300
366
418
500
157
165
Ybranches
419
820
324
553
665
1324
1449
299
388
798
229
863
1345
467
518
1729
1663
396
597
1037
197
799
436
1004
1691
829
1190
1340
2559
40
82
Yneedles
500
966
367
629
757
1000
1388
362
514
956
277
930
1400
514
694
1330
1747
367
835
1001
224
870
536
911
1603
894
1129
1426
1841
59
95
Ystems
898
1194
547
2038
1163
2607
2397
696
882
1379
576
1534
2251
638
1182
1678
3193
484
1531
1630
520
982
905
1434
2221
1103
1595
2480
3778
99
175
Yag
1818
2980
1237
3220
2585
4931
5233
1358
1783
3132
1083
3327
4996
1620
2394
4737
6603
1247
2963
3668
940
2652
1877
3349
5515
2825
3914
5246
8178
197
353
80
Table A2. BO+VC treatment raw biomass and stem dimension values. D0 = diameter
at zero height, , DBH = diameter at 130 cm height, HT = total tree height, taper =
DBH / D0 , Ybranches = dry branch mass estimate, Yneedles = dry needle mass
estimate, Ystems = dry stem mass estimate, Yag = dry total above-ground mass
estimate.
Tree
Id#
9014
9017
9016
101
9222
9231
9223
9419
9409
9411
9410
9450
9456
25079
9587
9591
9584
9599
26844
26830
27846
9674
9835
29664
9829
9931
9921
9940
mm
mm
cm
g
g
g
g
D0
97
112
107
116
78
96
102
92
112
104
119
85
95
110
77
85
98
107
126
128
61
102
115
117
113
83.5
110
108
DBH
38
50
51
68
33
45
46
37
52
55
60
36
48
76
35
46
47
58
63
70
24
53
53
56
60
35
49
54
HT
331
357
481
531
282
413
304
327
456
463
409
218
345
460
320
365
407
380
442
358
255
400
380
415
473
321
372
371
Ybranches
2948
3348
3188
4091
1088
1555
2205
3056
3617
2802
4464
1304
2220
4441
1116
1703
1826
3264
4467
6524
1104
3007
5130
4233
3579
1642
3932
2527
Yneedles
1609
3011
2502
2842
933
1288
2150
2221
2239
2285
3588
1085
2281
3544
913
1785
1962
2744
3257
3743
824
1998
3859
2893
2555
1136
3060
2633
Ystems
2135
3318
3070
4865
1712
2324
2650
1932
3223
3349
4354
1884
2676
5179
1407
2145
2721
3479
3729
4983
815
3496
3539
3860
4354
1480
3141
3058
Yag
6693
9677
8761
11798
3733
5167
7006
7210
9079
8436
12406
4273
7176
13164
3437
5634
6509
9486
11453
15250
2744
8501
12529
10985
10489
4258
10133
8217
81
Figure A1. Scatter plots of natural logarithm of total above-ground biomass (y) vs.
natural logarithm of DBH (diameter at 130 cm above groundline) for each treatment.
Best fit lines, equations and coefficients of determination (r2) for each treatment are
depicted.
82
Figure A2. Scatter plots of natural logarithm of stem biomass (y) vs. natural
logarithm of DBH (diameter at 130 cm above groundline)for each treatment. Best fit
lines, equations and coefficients of determination (r2) for each treatment are depicted.
83
Figure A3. Scatter plots of natural logarithm of branch biomass (y) vs. natural
logarithm of DBH (diameter at 130 cm above groundline)for each treatment. Best fit
lines, equations and coefficients of determination (r2) for each treatment are depicted.
84
Figure A4. Scatter plots of natural logarithm of needle biomass (y) vs. natural
logarithm of DBH (diameter at 130 cm above groundline) for each treatment. Best fit
lines, equations and coefficients of determination (r2) for each treatment are depicted.
85
Non-weeded Treatment (BO-VC) Simple Linear Relationship Graphical Analysis
3000
Branch biomass (g)
2500
2000
1500
1000
500
0
0
10
20
30
40
50
60
70
dbh (mm)
Figure A5. BO-VC scatter plot of branch biomass vs. dbh (diameter at 130 cm above
groundline)
86
2000
1800
Needle biomass (g)
1600
1400
1200
1000
800
600
400
200
0
0
10
20
30
40
50
60
70
dbh (mm)
Figure A6. BO-VC scatter plot of needle biomass vs. dbh (diameter at 130 cm above
groundline)
87
4000
Stem biomass (g)
3000
2000
1000
0
0
10
20
30
40
50
60
70
dbh (mm)
Figure A7. BO-VC scatter plot of stem biomass vs. dbh (diameter at 130 cm above
groundline)
88
10000
Above-ground biomass (g)
8000
6000
4000
2000
0
0
10
20
30
40
50
60
70
dbh (mm)
Figure A8. BO-VC scatter plot of above-ground biomass vs. dbh (diameter at 130 cm
above groundline)
89
1500
1000
residual
500
0
-500
-1000
-1500
0
10
20
30
40
50
60
70
dbh (mm)
Figure A9. BO-VC residual plot for simple linear model with Y = above-ground
biomass residual (observed -predicted values) and X = dbh (diameter at 130 cm above
groundline)
90
1000
800
600
residual
400
200
0
-200
-400
-600
-800
0
10
20
30
40
50
60
dbh (mm)
Figure A10. BO-VC residual plot for simple linear model with Y = stem biomass
residual (observed -predicted values) and X = dbh (diameter at 130 cm above
groundline)
70
91
1000
800
600
residual
400
200
0
-200
-400
-600
0
10
20
30
40
50
60
70
dbh (mm)
Figure A11. BO-VC residual plot for simple linear model with Y = branch biomass
residual (observed -predicted values) and X = dbh (diameter at 130 cm above
groundline)
92
600
residual
400
200
0
-200
-400
0
10
20
30
40
50
60
70
dbh (mm)
Figure A12. BO-VC residual plot for simple linear model with Y = needle biomass
residual (observed -predicted values) and X = dbh (diameter at 130 cm above
groundline)
93
Weeded Treatment (BO+VC) Simple Linear Relationship Graphical Analysis
16000
Above-ground biomass (g)
14000
12000
10000
8000
6000
4000
2000
20
30
40
50
60
70
80
dbh (mm)
Figure A13. BO+VC scatter plot of above-ground biomass vs. dbh (diameter at 130
cm above groundline)
94
6000
Stem biomass (g)
5000
4000
3000
2000
1000
0
20
30
40
50
60
70
80
dbh (mm)
Figure A14. BO+VC scatter plot of stem biomass vs. dbh (diameter at 130 cm above
groundline)
95
4500
4000
Needle biomass (g)
3500
3000
2500
2000
1500
1000
500
20
30
40
50
60
70
dbh (mm)
Figure A15. BO+VC scatter plot of needle biomass vs. dbh (diameter at 130 cm
above groundline)
80
96
7000
Branch biomass (g)
6000
5000
4000
3000
2000
1000
0
20
30
40
50
60
70
dbh (mm)
Figure A16. BO+VC scatter plot of branch biomass vs. dbh (diameter at 130 cm
above groundline)
80
97
4000
3000
residual
2000
1000
0
-1000
-2000
-3000
20
30
40
50
60
70
80
dbh (mm)
Figure A17. BO+VC residual plot for simple linear model with Y = above-ground
biomass residual (observed - predicted values) and X = dbh (diameter at 130 cm
above groundline)
98
600
400
residual
200
0
-200
-400
-600
20
30
40
50
60
70
80
dbh (mm)
Figure A18. BO+VC residual plot for simple linear model with Y = stem biomass
residual (observed -predicted values) and X = dbh (diameter at 130 cm above
groundline)
99
2000
1500
residual
1000
500
0
-500
-1000
-1500
20
30
40
50
60
70
80
dbh (mm)
Figure A19. BO+VC residual plot for simple linear model with Y = branch biomass
residual (observed -predicted values) and X = dbh (diameter at 130 cm above
groundline)
100
1500
residual
1000
500
0
-500
-1000
20
30
40
50
60
70
80
dbh (mm)
Figure A20. BO+VC residual plot for simple linear model with Y = needle biomass
residual (observed -predicted values) and X = dbh (diameter at 130 cm above
groundline)
101
Non-weeded Treatment (BO-VC) Natural Log-Transformed Relationship Graphical
Analysis
0.6
0.4
residual
0.2
0.0
-0.2
-0.4
-0.6
1.0
1.5
2.0
3.0
2.5
3.5
4.0
4.5
ln dbh
Figure A21. BO-VC residual plot for natural log-transformed above-ground biomass
model. Residuals are in units of ln biomass (g). dbh = diameter at 130 cm
102
1.0
0.8
0.6
residual
0.4
0.2
0.0
-0.2
-0.4
-0.6
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
ln dbh
Figure A22. BO-VC residual plot for natural log-transformed branch biomass model.
Residuals are in units of ln biomass (g). dbh = diameter at 130 cm
103
0.6
0.4
residual
0.2
0.0
-0.2
-0.4
-0.6
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
ln dbh
Figure A23. BO-VC residual plot for natural log-transformed needle biomass model.
Residuals are in units of ln biomass (g). dbh = diameter at 130 cm
104
0.8
0.6
residual
0.4
0.2
0.0
-0.2
-0.4
-0.6
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
ln dbh
Figure A24. BO-VC residual plot for natural log-transformed stem biomass model.
Residuals are in units of ln biomass (g). dbh = diameter at 130 cm
105
Weeded Treatment (BO+VC) Natural Log-Transformed Relationship Graphical
Analysis
0.4
0.3
0.2
residual
0.1
0.0
-0.1
-0.2
-0.3
-0.4
3.0
3.2
3.4
3.8
3.6
4.0
4.2
4.4
ln dbh
Figure A25. BO+VC residual plot for natural log-transformed above-ground biomass
model. Residuals are in units of ln biomass (g). dbh = diameter at 130 cm
106
0.8
0.6
residual
0.4
0.2
0.0
-0.2
-0.4
-0.6
3.0
3.2
3.4
3.6
3.8
4.0
4.2
ln dbh
Figure A26. BO+VC residual plot for natural log-transformed branch biomass
model. Residuals are in units of ln biomass (g). dbh = diameter at 130 cm
4.4
107
0.6
0.4
residual
0.2
0.0
-0.2
-0.4
-0.6
3.0
3.2
3.4
3.6
3.8
4.0
4.2
4.4
ln dbh
Figure A27. BO+VC residual plot for natural log-transformed needle biomass model.
Residuals are in units of ln biomass (g). dbh = diameter at 130 cm
108
0.15
0.10
0.05
residual
0.00
-0.05
-0.10
-0.15
-0.20
-0.25
3.0
3.2
3.4
3.6
3.8
4.0
4.2
4.4
ln dbh
Figure A28. BO+VC residual plot for natural log-transformed stem biomass model.
Residuals are in units of ln biomass (g). dbh = diameter at 130 cm
109
Table A3. Part one of the stand-table estimation procedure for calculating plot
estimates of dry component biomass for comparison with allometric equation
estimates. The sample trees were sorted into DBH classes and the mean was
calculated for each class by component. Ybranch = branch biomass, Yag = total
above-ground biomass. The values in this table correspond to the non-weeded (BOVC) treatment.
mm
g
g
g
DBH
Ybranch
Yneedle
Ystems
5.0
39.6
58.8
98.7
BB23
8.0
82.4
95.3
175.3
353.0
3
31
19.5
395.9
367.4
484.1
1247.3
Tree Id#
block
plot#
701
2
BB23
801
2
9623
Yag
197.1
147
4
47
20.0
196.9
223.7
519.5
940.2
9206
1
10
21.0
323.5
366.6
546.9
1237.0
9463
2
23
22.0
229.5
276.9
576.1
1082.5
211.3
231.5
400.1
842.8
class mean (0-23 mm)
9425
2
21
26.0
299.2
362.2
696.5
1357.9
9872
4
47
28.0
799.4
870.4
981.8
2651.6
9195
1
9
29.0
419.3
500.5
898.1
1817.8
505.9
577.7
858.8
1942.4
class mean (24-30 mm)
9536
3
26
31.0
467.4
514.4
638.5
1620.3
9431
2
21
31.5
387.7
513.9
881.8
1783.4
9203
1
10
32.5
553.0
629.0
2037.8
3219.7
9209
1
10
33.0
664.7
757.4
1162.5
2584.6
9873
4
47
33.0
436.1
535.6
905.5
1877.3
9477
2
23
34.0
862.8
930.2
1534.3
3327.4
9533
3
26
34.0
517.6
693.5
1182.5
2393.7
9192
1
9
35.0
820.4
965.6
1194.5
2980.5
9866
4
47
35.0
1003.9
911.4
1434.0
3349.2
9905
4
49
36.0
828.5
893.7
1103.1
2825.3
9525
3
26
37.0
1729.0
1329.8
1677.9
4736.7
751.9
788.6
1250.2
2790.7
797.5
955.5
1378.6
3131.7
class mean (31-37 mm)
9423
2
21
38.0
9622
3
31
40.0
597.4
835.4
1530.6
2963.3
9917
4
49
40.0
1189.5
1129.0
1595.3
3913.9
9639
3
31
40.5
1036.8
1001.2
1630.1
3668.1
905.3
980.3
1533.7
3419.2
class mean (38-44 mm)
9466
2
23
46.0
1344.7
1399.6
2251.2
4995.5
501
2
BB23
48.0
1340.5
1425.8
2479.5
5245.8
22332
1
10
49.0
1324.1
1000.2
2606.8
4931.1
247
4
47
49.0
1691.0
1603.3
2220.6
5514.9
1425.1
1357.2
2389.5
5171.8
1448.6
1387.6
2396.7
5232.8
1448.6
1387.6
2396.7
5232.8
class mean (45-51 mm)
9207
1
10
53.0
class mean (52-58 mm)
class mean (59-65 mm)
g
26167
3
26
60.0
1662.6
1747.0
3193.5
6603.1
601
2
BB23
65.0
2558.8
1841.5
3777.8
8178.2
2110.7
1794.3
3485.6
7390.6
110
Table A4. Part two of the stand-table estimation procedure. The trees/class per plot column contains the frequency of trees in
each DBH class for the corresponding plot. The component plot means (ex. Yag) were multiplied by the tree frequency in a
DBH class to produce a total estimate of biomass (ex. Tag) for each class in each plot. Plot totals are calculated by summing
DBH class totals within each plot. The values in this table are from the non-weeded treatment.
g
g
g
g
g
g
g
g
plot
trees/class
Ybranch
Tbranch
Yneedle
Tneedle
Ystems
Tstems
Yag
Tag
9
19
211
4015
231
4398
400
7602
843
16014
11
506
5565
578
6355
859
9447
1942
21367
55
752
41356
789
43373
1250
68762
2791
153491
64
905
57940
980
62738
1534
98154
3419
218832
11
1425
15676
1357
14930
2390
26285
5172
56890
2
1449
2897
1388
2775
2397
4793
5233
10466
0
2111
0
1794
0
3486
0
7391
0
plot total
26
211
127449
5494
231
134569
6018
400
215042
10402
843
477060
21914
29
506
14673
578
16753
859
24905
1942
56331
35
752
26318
789
27601
1250
43758
2791
97676
52
905
47076
980
50975
1534
79750
3419
177801
16
1425
22801
1357
21716
2390
38232
5172
82749
1
1449
1449
1388
1388
2397
2397
5233
5233
0
2111
0
1794
0
3486
0
7391
10
plot total
21
l
117810
0
441704
211
6339
231
6944
400
12003
843
25285
29
506
14673
578
16753
859
24905
1942
56331
30
752
22558
789
23658
1250
37506
2791
83722
50
905
45265
980
49014
1534
76683
3419
170962
18
1425
25651
1357
24430
2390
43011
5172
93093
3
1449
4346
1388
4163
2397
7190
5233
15698
0
2111
0
1794
0
3486
0
7391
118832
124962
201298
0
445092
29
211
6128
231
6712
400
11603
843
29
506
14673
578
16753
859
24905
1942
56331
53
752
39852
789
41796
1250
66261
2791
147910
37
905
33496
980
36271
1534
56745
3419
126512
7
1425
9976
1357
9501
2390
16727
5172
36203
5
1449
7243
1388
6938
2397
11983
5233
26164
0
2111
0
1794
0
3486
0
7391
plot total
26
199444
30
plot total
23
124451
111367
117971
188224
24443
0
417562
34
211
7184
231
7869
400
13603
843
34
506
17202
578
19642
859
29199
1942
66043
39
752
29325
789
30755
1250
48758
2791
108839
43
905
38928
980
42152
1534
65947
3419
147028
9
1425
12826
1357
12215
2390
21506
5172
46546
0
1449
0
1388
0
2397
0
5233
0
0
2111
0
1794
0
3486
0
7391
plot total
105466
112634
179013
28657
0
397113
111
Table A4 continued.
g
g
g
g
g
g
g
g
plot
trees/class
Ybranch
Tbranch
Yneedle
Tneedle
Ystems
Tstems
Yag
Tag
31
21
211
4437
231
4861
400
8402
843
17700
25
506
12649
578
14443
859
21470
1942
48561
29
752
21806
789
22869
1250
36256
2791
80932
52
905
47076
980
50975
1534
79750
3419
177801
26
1425
37052
1357
35288
2390
62127
5172
134467
3
1449
4346
1388
4163
2397
7190
5233
15698
1
2111
2111
1794
1794
3486
3486
7391
plot total
47
129477
218681
7391
482550
18
211
3803
231
4166
400
7202
843
15171
10
506
5059
578
5777
859
8588
1942
19424
34
752
25566
789
26812
1250
42507
2791
94885
47
905
42550
980
46073
1534
72082
3419
160705
37
1425
52728
1357
50218
2390
88412
5172
191357
9
1449
13037
1388
12488
2397
21570
5233
47095
0
2111
0
1794
0
3486
0
7391
0
2
0
plot total
49
134393
0
142743
0
145535
0
240360
528638
16
211
3381
231
3703
400
6401
843
32
506
16190
578
18487
859
27481
1942
62158
47
752
35341
789
37064
1250
58760
2791
131165
46
905
41644
980
45093
1534
70548
3419
157285
9
1425
12826
1357
12215
2390
21506
5172
46546
5
1449
7243
1388
6938
2397
11983
5233
26164
0
2111
0
1794
0
3486
0
7391
plot total
116625
123500
196680
13486
0
436805
112
113
Appendix B: Nutrient Values
114
Table B1. Sample concentrations of selected macronutrients from ICP analysis.
Sample ID
9192
9419
26844
9872
9411
9409
601
26830
27846
501
26167
9423
9456
9623
9917
26830
9222
9905
9195
601
9419
9921
9829
9872
9872-Dup
RYE GRASS
STANDARD
SAMPLE SET
µg/g
Ca
µg/g
K
µg/g
Mg
µg/g
P
µg/g
S
needle
needle
needle
needle
needle
needle
needle
needle
needle
needle
needle
needle
needle
needle
needle
branch
branch
branch
branch
branch
branch
branch
branch
branch
branch
Blank
Sample-1
Sample-2
Sample-3
Sample-4
Sample-5
Sample-6
Sample-7
Sample-8
Sample-9
Sample-10
Sample-11
Sample-12
Sample-13
Sample-14
Sample-15
Sample-16
Sample-17
Sample-18
Sample-19
Sample-20
Sample-21
Sample-22
Sample-23
Sample-24
Sample-25
ND
3446
2242
3109
3261
4228
4350
5486
3930
5131
2921
2353
3322
1794
6757
3218
2115
2111
2601
2415
1449
1727
1706
2330
1952
2229
ND
2728
4028
3232
5651
7170
5613
7885
4849
5771
4009
2679
5172
3644
4372
6377
2640
2891
3233
2895
2469
2631
2822
3592
2972
3463
ND
586
647
688
991
1303
1172
1290
638
923
981
644
1081
428
1282
1144
437
601
659
827
462
698
641
611
631
725
ND
1992.50
1371.45
1683.67
2470.10
3044.51
2495.98
3260.91
2402.49
2244.80
2582.31
877.08
2151.65
967.37
1904.16
1405.74
807.58
983.06
1052.93
1022.28
797.96
749.87
925.69
954.16
926.01
1049.76
ND
996.68
981.28
1149.45
1611.51
1702.99
1266.29
1803.04
1544.77
1459.26
1523.37
793.70
1541.15
832.25
1470.55
1280.06
407.45
414.22
493.71
437.27
336.03
416.82
479.12
483.96
453.96
516.03
n/a
Sample-26
2774
11128
1088
1700.51
1315.06
component
115
Table B1. continued
Sample ID
component
SAMPLE SET
µg/g
Ca
µg/g
K
µg/g
Mg
µg/g
P
µg/g
S
9835
9446
101
9639
9525
9419
9223
9872
stem
stem
stem
stem
stem
stem
stem
stem
33
34
35
36
37
38
39
40
1138
1499
1051
1029
685
875
939
996
2125
2236
2960
2180
1552
1518
5677
2900
269
323
221
351
226
265
339
349
437.58
492.02
573.02
707.75
368.75
372.17
737.00
613.49
227.39
246.55
229.14
295.29
209.33
223.89
315.00
292.74
9207
9829
9195
9679
9905
9445
9622
9463
9622
26167
9223
9639
9463
9466
101
9931
9931-dup
stem
stem
stem
stem
stem
stem
stem
stem
branch
branch
branch
branch
branch
branch
branch
needle
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57.00
981
1150
1409
800
1044
1070
1172
827
3158
2406
1958
1840
1931
2099
2689
3813
4003
1483
2249
2549
2043
1904
1640
2187
2830
4151
3286
2009
2514
3933
2894
2575
4245
4363
237
337
569
330
360
303
416
314
984
649
459
495
660
660
505
633
657
324.38
503.96
768.47
438.99
491.30
416.54
513.98
515.4
1286.8
1015.5
685.8
846.4
972.1
983.1
797.7
1062.5
1127.9
196.86
274.59
363.61
262.87
248.44
224.07
257.2
230.2
644.3
438.4
338.6
342.4
414.8
456.5
355.6
1047.8
1106.2
STD run
STD true
SPEX QC-1
Spex QC-1 true
2182
2234
48
50
8679
10515
59.7
62.5
824
867
13.3
12.5
1224.2
1691
29.6
25.0
965.2
802.14
1.54
1.00
SPEX QC-2
Spex QC2 true
94
100
120.1
125.0
26.5
25.0
59.9
50.0
3.05
2.00
QC
5.05
49.3
5.0
10.8
5.13
QC true
5.00
50.0
5.00
10.00
5.00
RYE GRASS STANDARD
116
Table B2. Branch percent nitrogen concentrations from CHN analysis.
%C
%H
%N
Sample id
50.615
51.268
51.126
52.132
51.098
50.902
51.296
50.477
51.2
50.752
50.758
51.274
51.371
51.375
51.875
53.885
51.976
51.922
51.497
51.8
6.693
6.646
6.702
6.702
6.773
6.693
6.765
6.595
6.769
6.738
6.71
6.681
6.721
6.746
6.794
6.953
6.721
6.852
6.72
6.669
0.913
1.117
0.895
0.86
1.179
0.94
1.043
0.902
1.035
0.985
0.848
0.694
0.732
0.886
0.91
0.935
0.961
0.947
0.955
0.842
9195
9203
9466
9463
9525
26167
9622
9639
9872
9905
601
101
9223
9222
9419
9445
9599
26830
9829
9921
treatment
0=BO-VC
1=BO+VC
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
mass
mg
5.329
4.172
4.808
4.592
5.691
5.154
4.752
4.11
5.4
5.224
5.185
4.376
4.505
5.279
4.466
4.144
4.227
5.47
4.116
5.025
117
Table B3. Stem percent nitrogen concentrations from CHN analysis.
%C
%H
%N
Sample id
51.029
50.083
50.333
50.347
50.291
49.988
48.816
49.515
50.611
51.754
49.304
50.666
50.217
50.262
50.2
49.242
50.973
49.729
51.324
50.607
50.397
6.471
6.417
6.557
6.472
6.462
6.602
6.489
6.35
6.71
6.634
6.403
6.662
6.273
6.578
6.409
6.447
6.497
6.344
6.644
6.505
6.366
0.594
0.4
0.418
0.386
0.508
0.401
0.438
0.515
0.526
0.535
0.366
0.476
0.393
0.342
0.418
0.458
0.495
0.442
0.425
0.287
0.458
9195
9207
9463
9446
9525
9622
9639
9872
9905
9014
101
9223
9419
9445
26830
9599
9591
9679
9829
9835
9921
treatment
0=BO-VC
1=BO+VC
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
mass
mg
4.736
5.504
5.718
5.991
5.209
4.743
4.223
4.126
5.82
5.096
5.442
5.414
4.31
4.145
4.019
4.037
5.5
4.846
4.129
4.877
5.129
118
Table B4. Needle percent nitrogen concentrations from CHN analysis.
%C
%H
%N
Sample id
51.213
51.055
51.994
53.72
51.847
52.128
51.64
51.337
51.662
52.304
51.993
52.021
52.227
51.966
51.975
51.192
51.61
51.906
51.42
51.257
50.183
52.16
51.987
51.939
51.466
52.432
4.462
4.105
3.3
4.607
4.092
4.346
3.665
3.637
4.324
3.602
4.197
4.735
4.089
3.78
3.529
4.547
3.766
3.17
3.524
4.378
3.454
3.813
4.457
4.255
3.513
4.316
1.529
1.435
1.712
1.377
1.391
1.393
1.712
1.531
1.272
1.313
1.489
1.245
1.84
1.453
1.539
1.355
1.366
1.558
1.823
1.407
1.365
1.39
1.451
1.731
1.616
1.384
9192
9195
9203
9206
9209
22332
9423
9425
9431
9463
9466
9477
9525
9533
9536
26167
9622
9623
9639
147
247
9866
9872
9873
9905
9917
treatment
0=BO-VC
1=BO+VC
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
mass
mg
11.319
11.862
14.305
10.424
11.971
11.211
13.579
13.734
11.316
13.748
11.639
10.272
11.852
12.47
13.775
10.33
12.788
14.9
13.765
11.168
14.081
12.879
10.45
11.548
13.44
11.097
119
Table B4. continued
%C
%H
%N
Sample id
51.515
50.859
51.75
51.003
51.008
52.343
50.982
51.698
52.081
48.497
52.042
51.782
52.276
52.003
52.121
52.039
52.209
52.302
51.648
51.466
52.438
53.217
51.694
52.066
51.246
3.659
4.381
3.81
3.746
4.886
3.739
3.299
4.089
3.573
4.093
3.775
3.458
3.697
4.032
3.712
4.017
4.001
3.731
3.698
4.1
4.439
4.39
3.782
3.886
3.779
1.402
1.602
1.277
1.826
1.377
1.398
1.41
1.577
1.718
1.421
1.497
1.562
1.678
1.424
1.615
1.863
1.535
1.643
1.783
1.866
1.549
1.553
1.409
1.402
1.51
101
9016
9017
9222
9223
9409
9411
9419
9445
9450
9456
9584
9587
9591
9599
26830
26844
9674
27846
9829
9835
29664
9921
9931
9940
treatment
0=BO-VC
1=BO+VC
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
mass (mg)
mg
13.532
11.378
12.31
12.48
10.201
13.186
14.182
12.299
13.604
11.867
12.69
14.12
13.22
12.022
13.081
12.047
12.225
12.855
13.259
12.016
11.271
11.148
12.829
12.619
13.153
120
1.9
N concentration (%)
1.8
1.7
1.6
1.5
1.4
1.3
1.2
10
20
30
40
50
60
70
dbh (mm)
Figure B1. BO-VC scatter plot of needle component % N vs. dbh (diameter at 130
cm). r2 = .028
121
0.60
N concentration (%)
0.55
0.50
0.45
0.40
0.35
20
25
30
35
40
45
50
dbh (mm)
Figure B2. BO-VC scatter plot of stem component % N vs. dbh (diameter at 130
cm). r2 = .269
55
122
0.60
N concentration (%)
0.55
0.50
0.45
0.40
0.35
0.30
0.25
30
40
50
60
70
dbh (mm)
Figure B3. BO+VC scatter plot of stem component % N vs. dbh (diameter at 130
cm). r2 = .010
80
123
1.20
1.15
N concentration (%)
1.10
1.05
1.00
0.95
0.90
0.85
0.80
10
20
30
40
50
60
70
dbh (mm)
Figure B4. BO-VC scatter plot of branch component % N vs. dbh (diameter at 130
cm). r2 = .083
124
1.00
N concentration (%)
0.95
0.90
0.85
0.80
0.75
0.70
0.65
30
40
50
60
70
80
dbh (mm)
Figure B5. BO+VC scatter plot of branch component % N vs. dbh (diameter at 130
cm). r2 = .015
125
1.9
N concentration (%)
1.8
1.7
1.6
1.5
1.4
1.3
1.2
20
30
40
50
60
70
80
dbh (mm)
Figure B6. BO+VC scatter plot of needle component % N vs. dbh (diameter at 130
cm). r2 = .008
126
127
Appendix C: Leaf Area Index
128
Table C1. Leaf area index raw data for BO-VC and BO+VC treatments. DM = dry
mass of 100-needle sample (g), WLA = wet leaf area of 100-needle sample (cm2),
SLA = specific leaf area (cm2/g), Yneedle = dry mass needle estimate for sample tree,
and WLA/tree = total estimate of wet leaf area per sample tree (cm2).
g
g
g
cm2
cm2
cm2
cm2/g
g
cm2
dbh
DM1
DM2
meanDM
WLAI1(1)
WLA2(1)
meanWLA
SLA
Yneedle
WLA/tree
501
48
0.58
0.55
0.565
50.28
50.35
50.32
89.05
1425.78
126969.78
601
65
0.59
0.58
0.585
48.69
50.68
49.68
84.93
1841.50
156393.35
9203
33
0.37
0.37
0.37
35.70
34.91
35.30
95.41
628.97
60009.01
9207
53
0.43
0.42
0.425
36.49
36.69
36.59
86.09
1387.59
119450.73
9425
26
0.44
0.44
0.44
38.79
38.71
38.75
88.07
362.23
31899.97
9463
22
0.56
0.6
0.58
45.27
44.21
44.74
77.14
276.92
21362.18
9622
40
0.35
0.31
0.33
26.78
28.62
27.70
83.93
835.39
70112.35
9873
33
0.41
0.42
0.415
41.17
40.57
40.87
98.48
535.63
52751.44
9905
36
0.34
0.37
0.355
35.78
33.69
34.73
97.84
893.73
87443.64
22332
49
0.53
0.53
0.53
49.68
47.87
48.77
92.02
1000.24
92040.93
26167
60
0.54
0.55
0.545
47.97
44.85
46.41
85.15
1747.01
148756.01
g
cm^2
BO-VC
Tree#
average
0.45
40.74
89.29
stdev
0.09
7.33
6.52
stderr
0.02
2.21
1.96
cm^2/g
BO+VC
g
g
g
cm^2
cm^2
cm^2
Tree#
dbh
DM1
DM2
meanDM
WLAI1(1)
WLA2(1)
meanWLA
SLA
Yneedle
WLA/tree
9231
45
0.49
0.46
0.475
45.955
41.245
43.60
91.789
1287.7637
118203.15
9445
33
0.44
0.47
0.455
38.495
40.7975
39.64625
87.135
801.04228
69798.511
9450
36
0.46
0.46
0.46
33.4425
33.4325
33.4375
72.690
1084.918
78862.928
9584
47
0.41
0.41
0.41
41.915
40.0175
40.96625
99.918
1961.6735
196005.87
9674
53
0.41
0.37
0.39
36.52
40.700
38.61
99.000
1998.2722
197828.95
9829
60
0.43
0.42
0.425
40.33
42.87
41.60
97.882
2554.7048
250060.52
26830
70
0.48
0.54
0.51
45.7975
43.9775
44.8875
88.015
3743.1228
329449.85
26844
63
0.43
0.39
0.41
42.5175
41.330
41.92375
102.250
3257.4207
333081.2
average
0.44
40.58
92.3
stdev
0.04
3.51
9.7
stderr
0.01
1.17
3.2
129
1.8e+5
1.6e+5
wet leaf area (cm^2)
1.4e+5
1.2e+5
1.0e+5
8.0e+4
6.0e+4
4.0e+4
2.0e+4
0.0
10
20
30
40
50
60
dbh (mm)
F
igure C1. BO-VC scatter plot of wet leaf area vs dbh (diameter at 130 cm).
70
130
3.5e+5
wet leaf area (cm^2)
3.0e+5
2.5e+5
2.0e+5
1.5e+5
1.0e+5
5.0e+4
30
40
50
60
70
dbh (mm)
Figure C2. BO+VC scatter plot of wet leaf area vs. dbh (diameter at 130 cm).
80
131
30000
residual
20000
10000
0
-10000
-20000
10
20
30
40
50
60
70
dbh (mm)
Figure C3. BO-VC residual plot for wet leaf area vs. dbh (diameter at 130 cm). Residuals are in
units of wet leaf area (cm2/g).
132
60000
40000
residual
20000
0
-20000
-40000
-60000
30
40
50
60
70
80
dbh (mm)
Figure C4. BO+VC residual plot for wet leaf area vs. dbh (diameter at 130 cm). Residuals are in
units of wet leaf area (cm2/g).
133
Appendix D: Crown Structure
134
Table D1. Data set for crown structure variables: branch count (branch ct.),
mean branch length (mbl)
tree id#
9014
9016
9017
9192
9222
9223
9231
9409
9410
9411
9419
9445
9450
9456
9584
9587
9591
9599
9674
9829
9835
9921
9931
9940
25079
26830
26844
27846
29664
plot#
1
1
1
11
11
11
11
20
20
20
20
22
22
22
29
29
29
29
33
45
45
50
50
50
22
29
29
33
45
stem
sect.
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
size
class
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
branch
ct.
6
8
10
12
11
6
5
6
9
7
16
19
15
16
11
17
16
10
6
8
11
9
7
17
11
8
13
10
9
mbl
(cm)
37
48
75
51.25
43.25
55
46
68.5
54.5
61.5
56
60
35
46.5
43
48
45.5
63.5
52.5
46
69
65.5
48.5
44
40
66.5
42
49
46.5
135
Table D2. Data set for crown structure variables: branch count (branch ct.),
mean branch length (mbl)
tree id#
9014
9016
9017
9192
9222
9223
9231
9409
9410
9411
9419
9445
9450
9456
9584
9587
9591
9599
9674
9829
9835
9921
9931
9940
25079
26830
26844
27846
29664
plot#
1
1
1
11
11
11
11
20
20
20
20
22
22
22
29
29
29
29
33
45
45
50
50
50
22
29
29
33
45
stem
sect.
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
size
class
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
branch
ct.
5
9
18
19
14
11
12
15
8
16
15
7
12
10
12
13
11
12
6
7
13
3
6
12
12
10
6
8
4
mbl
(cm)
89.5
86
117.5
85
89.5
68.5
90.5
126.5
93
89.5
87
79
56
76.5
82
91
108.5
77
88
83.5
98
101
72.5
87
86.5
94.5
82.5
84.5
69
136
Table D3. Data set for crown structure variables: branch count (branch ct.), mean branch length
(mbl), mean basal branch diameter (mbbd), largest branch diameter (lbd), largest branch length
(lbl) ,and year of foliage retained (yf). The variables lbd, lbl, and yf were only recorded in the
lowest 1-meter section. The variable mbbd was recorded in the largest size class of all 1-meter
stem sections.
tree
id#
9014
9016
9017
9192
9222
9223
9231
9409
9410
9411
9419
9445
9450
9456
9584
9587
9591
9599
9674
9829
9835
9921
9931
9940
25079
26830
26844
27846
29664
plot#
1
1
1
11
11
11
11
20
20
20
20
22
22
22
29
29
29
29
33
45
45
50
50
50
22
29
29
33
45
stem
sect.
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
size
class
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
branch
ct.
31
25
23
5
17
24
17
33
27
19
29
16
25
27
23
9
17
26
31
29
26
28
20
26
25
25
24
19
29
cm
mm
mm
cm
years
mbl
145.0
154.0
135.8
99.0
107.5
121.0
97.5
148.3
141.0
134.5
117.5
100.5
83.3
98.0
94.0
99.5
120.0
134.3
138.8
140.0
151.0
147.7
125.5
119.3
146.7
156.3
168.3
114.5
147.0
mbbd
15.3
15.8
12.2
11.0
11.9
15.0
11.3
14.8
16.9
12.5
15.2
12.4
15.4
11.9
10.9
12.7
11.5
15.3
14.4
13.8
16.9
15.5
13.3
13.7
18.3
13.9
21.0
13.2
17.1
lbd
25
18
17
12
14
20
14
18
21
17
21
15
20
19
16
20
17
27
20
19
24
22
15
18
160
25
24
15
21
lbl
182
172
55
102
116
145
110
171
187
152
142
101
95
120
125
130
152
152
159
166
163
176
135
137
20
190
170
140
160
yf
3
3
3
3
2
3
3
2
2.2
2.1
3
3
3
3.1
2
3
2.1
2.1
2.5
3
3
3
2
3
3
2.5
2
2.1
137
Table D4. Data set for crown structure variables: branch count (branch ct.),
mean branch length (mbl
tree id#
147
247
501
601
9190
9195
9203
9206
9207
9209
9423
9425
9431
9463
9466
9477
9525
9533
9536
9622
9623
9639
9866
9872
9873
9905
9917
22332
26167
plot#
47
47
BB23
BB23
9
9
10
10
10
10
21
21
21
23
23
23
26
26
26
31
31
31
47
47
47
49
49
10
26
stem
sect.
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
size
class
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
branch
ct.
8
12
10
11
10
9
13
14
19
12
11
14
15
13
11
10
5
12
12
12
13
9
10
11
17
2
12
18
mbl
(cm)
38.5
71
61.5
45
47.5
23.5
60.25
43
38.5
68.5
24.5
33
40.5
29.5
34
33
57.5
41.5
49
47
49
62
62
50.5
44
44
55
56.5
47.5
138
Table D5. Data set for crown structure variables: branch count (branch ct.),
mean branch length (mbl)
tree id#
147
247
501
601
9190
9195
9203
9206
9207
9209
9423
9425
9431
9463
9466
9477
9525
9533
9536
9622
9623
9639
9866
9872
9873
9905
9917
22332
26167
plot#
47
47
BB23
BB23
9
9
10
10
10
10
21
21
21
23
23
23
26
26
26
31
31
31
47
47
47
49
49
10
26
stem
sect.
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
size
class
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
branch
ct.
5
15
16
16
9
7
2
14
7
17
9
10
13
6
11
10
6
14
11
13
15
5
15
23
13
13
9
4
9
mbl
(cm)
61.5
83.0
59.0
65.0
93.0
80.0
81.0
65.0
50.5
72.5
53.5
72.5
78.5
51.5
85.0
67.5
78.5
76.5
66.5
70.5
89.5
78.0
90.0
103.3
70.5
78.5
85.5
80.0
88.5
139
Table D6. Data set for crown structure variables: branch count (branch ct.), mean branch length
(mbl), mean basal branch diameter (mbbd), largest branch diameter (lbd), largest branch length
(lbl) ,and year of foliage retained (yf). The variables lbd, lbl, and yf were only recorded in the
lowest 1-meter section. The variable mbbd was recorded in the largest size class of all 1-meter
stem sections.
tree
id#
147
247
501
601
9190
9195
9206
9207
9209
9423
9425
9431
9463
9466
9477
9525
9533
9536
9622
9623
9639
9866
9872
9873
9905
9917
22332
26167
plot#
47
47
BB23
BB23
9
9
10
10
10
21
21
21
23
23
23
26
26
26
31
31
31
47
47
47
49
49
10
26
stem
sect.
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
S1
size
class
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
branch
ct.
15
16
11
6
6
4
11
8
16
4
4
4
14
11
19
3
8
5
2
14
11
8
4
11
15
9
11
cm
mm
mm
cm
years
mbl
78
122.5
98
142
134.5
97
82.5
130.5
95.5
95.5
86
90
68.5
101
97.5
146.5
85
82.5
81
96.7
122
133.5
106
102
90.5
115.5
116
132.5
mbbd
10.5
14.7
12.15
14.4
11.5
12.4
10.03
14.9
10.1
12.2
11.1
10.75
10.65
11.5
14.55
17.35
11
11.35
10.85
12.3
11.5
11.9
10.95
11.4
11.8
14.1
12.65
14.9
lbd
12.5
16.4
15.5
16.6
14
lbl
85
127
148
162
131
yf
3
3
3
2.2
2.1
3
12.8
18
13.5
15.5
11.3
11.1
11
15.1
20.3
22.2
11.6
13.9
11.6
12.3
15.2
15.4
12.4
12.1
13.9
17.6
18.5
19.1
104
137
110
109
83
85
73
116
131
173
89
93
94
96.7
130
120
115
103
99
135
137
133
2
3
2.4
2
2.3
2
3
2.1
2
3
2.1
3
2
2.5
2.5
2.5
3
3.5
3
3.1
2.3
140
Table D7. Data set for crown structure variables: branch count (branch ct.),
mean branch length (mbl).
tree id#
9014
9016
9017
9192
9222
9223
9231
9409
9410
9411
9419
9445
9450
9456
9584
9587
9591
9599
9674
9829
9835
9931
9940
25079
26830
26844
27846
29664
stem
sect.
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
size
class
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
branch
ct.
14
4
6
20
5
6
5
4
4
5
14
12
10
13
6
5
15
8
5
8
7
5
8
6
5
4
9
6
mbl
(cm)
10.5
44.0
59.5
27.3
30.7
39.0
40.0
31.5
26.5
41.5
16.0
25.5
22.0
20.0
33.0
40.0
19.5
31.5
37.0
42.5
37.5
21.0
24.0
36.0
50.5
34.5
26.5
23.0
141
Table D8. Data set for crown structure variables: branch count (branch ct.),
mean branch length (mbl).
tree
id#
9014
9016
9017
9192
9222
9223
9231
9409
9410
9411
9419
9445
9450
9456
9584
9587
9591
9599
9674
9829
9835
9921
9931
9940
25079
26830
26844
27846
29664
stem
sect.
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
size
class
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
branch
ct.
5
4
13
11
10
11
8
7
7
5
4
5
10
9
9
15
6
5
4
7
6
4
11
2
5
4
7
10
7
mbl
(cm)
82.5
82.5
81.5
70.5
58
80
70.5
75
78.5
83
73.5
60
51
77.5
71.5
60
69.5
71.5
74.5
68
85
51.5
59
79.5
64.5
101
57
54
72
142
Table D9. Data set for crown structure variables: branch count (branch ct.),
mean branch length (mbl), mean basal branch diameter (mbbd)
tree
id#
9014
9016
9017
9222
9223
9231
9409
9410
9411
9419
9445
9450
9456
9584
9587
9591
9599
9674
9829
9835
9921
9931
9940
25079
26830
26844
29664
stem
sect.
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
size
class
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
branch
ct.
5
12
20
7
10
15
15
11
13
10
4
9
9
17
4
7
13
13
17
3
17
6
8
20
16
15
24
mbbd
(mm)
12.0
13.1
10.0
9.8
12.0
14.9
15.4
12.7
12.4
11.2
12.1
11.9
11.4
12.3
12.0
11.5
11.3
14.4
15.3
13.0
12.9
15.3
11.0
13.3
14.1
16.3
mbl
(cm)
99.3
122.5
135.7
86.0
88.0
114.0
136.5
142.0
108.0
91.0
82.5
57.0
76.5
94.5
76.5
115.0
92.0
97.5
125.0
125.0
111.0
90.5
117.5
84.0
122.0
114.0
132.7
143
Table D10. Data set for crown structure variables: branch count (branch ct.),
mean branch length (mbl).
tree
id#
147
247
501
601
9190
9195
9203
9206
9207
9209
9423
9425
9431
9463
9466
9477
9525
9533
9536
9622
9623
9639
9866
9872
9873
9905
9917
22332
stem
sect.
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
size
class
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
branch
ct.
3
7
6
11
11
14
6
13
2
4
9
10
9
5
9
10
7
3
5
7
5
14
2
9
22
10
10
5
mbl
(cm)
19.5
32
24.5
26
37.5
29
21.55
26.5
34
32
18.5
24.5
16.5
20.5
32
17.5
29
11.5
14.5
34.5
36.5
18.5
31
33
20.67
28.5
19
28.5
26167
S2
s
10
39
144
Table D11. Data set for crown structure variables: branch count (branch ct.),
mean branch length (mbl).
tree
id#
147
247
501
601
9190
9195
9203
stem
sect.
S2
S2
S2
S2
S2
S2
S2
size
class
m
m
m
m
m
m
md
branch
ct.
8
6
4
3
3
8
3
mbl
(cm)
52.5
76.5
69
46
84.5
52
54.5
9206
9207
9209
9423
9425
9431
9463
9466
9477
9525
9533
9536
9622
9623
9639
9866
9872
9873
9905
9917
22332
26167
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
md
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
4
4
8
8
18
7
7
5
12
3
16
10
16
7
6
6
8
6
16
2
9
5
57
52
74
56.5
33
70
43.5
73
51
66.5
71
55
68
65.5
60.5
72.5
64
49
64
60
89
66.5
145
Table D12. Data set for crown structure variables: branch count (branch ct.),
mean branch length (mbl), mean basal branch diameter (mbbd)
tree
id#
247
501
601
9190
9203
9207
9209
9423
9431
9466
9477
9525
9533
9536
9622
9639
9866
9873
9905
9917
22332
26167
stem
sect.
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
S2
size
class
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
branch
ct.
12
10
12
4
10
15
3
6
3
14
6
9
4
4
2
6
4
5
5
9
22
8
mbbd
(mm)
15.4
12.9
17.3
11.0
10.1
11.0
11.1
12.5
10.4
13.4
13.9
13.1
10.5
12.0
11.3
11.0
11.6
10.9
10.6
11.5
12.7
13.5
mbl
(cm)
111.0
99.5
134.0
101.0
76.0
88.0
75.0
93.0
79.5
105.5
90.5
105.0
79.5
73.0
78.0
99.5
100.5
90.5
72.5
95.0
109.3
114.5
146
Table D13. Data set for crown structure variables: branch count (branch ct.),
mean branch length (mbl)
tree id#
stem
sect.
size
class
147
247
501
601
9190
9203
9423
9431
9466
9477
9525
9533
9622
9639
9866
9872
9873
9917
22332
26167
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
branch
ct.
12
18
1
6
13
4
9
11
10
4
10
4
7
3
1
2
12
11
mbl
(cm)
15.0
12.0
17.0
26.0
14.0
16.0
13.0
22.0
19.0
24.0
13.0
25.0
35.0
19.0
32.0
18.0
30.0
27.0
11.0
16.0
147
Table D14. Data set for crown structure variables: branch count (branch ct.),
mean branch length (mbl).
tree
id#
247
501
601
9190
9203
9207
9209
9423
9431
9466
9477
9525
9533
9622
9639
9866
9873
9917
22332
26167
stem
sect.
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
size
class
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
branch
ct.
8
8
1
8
1
9
4
9
9
3
6
5
6
9
7
7
6
8
3
mbl
(cm)
72.0
81.0
62.0
37.0
45.0
65.0
46.0
60.0
32.0
64.0
24.0
64.0
47.0
50.0
83.0
67.0
45.0
42.0
54.0
43.0
148
Table D15. Data set for crown structure variables: branch count (branch ct.),
mean branch length (mbl), mean basal branch diameter (mbbd)
tree id#
601
9525
9639
26167
stem
sect.
S3
S3
S3
S3
size
class
l
l
l
l
branch
ct.
13
1
1
8
mbbd
(mm)
11.5
10.3
10.6
11
mbl
(cm)
128.0
63.0
84.0
91.0
149
Table D16. Data set for crown structure variables: branch count (branch ct.),
mean branch length (mbl)
tree id#
9016
9017
9223
9231
9409
9410
9411
9450
9456
9584
9587
9591
9599
9674
9829
9835
9921
9940
25079
26830
26844
29664
stem
sect.
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
size
class
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
branch
ct.
12
8
9
2
7
11
6
2
16
4
6
10
11
3
13
10
2
5
7
12
14
mbl
(cm)
26.0
34.0
33.0
34.0
24.0
16.0
23.0
9.0
20.0
34.0
22.0
20.0
40.0
35.0
15.0
66.0
25.0
35.0
20.0
30.0
16.0
8.0
150
Table D17. Data set for crown structure variables: branch count (branch ct.),
mean branch length (mbl)
tree id#
9014
9016
9017
9192
9231
9409
9410
9411
9419
9456
9584
9591
9599
9674
9829
9835
9921
9931
9940
25079
26830
26844
29664
stem
sect.
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
size
class
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
branch
ct.
7
11
11
7
7
1
12
12
14
13
3
17
1
15
7
8
20
3
7
3
2
mbl
(cm)
49.0
80.0
63.0
41.0
74.0
78.0
38.0
58.0
62.0
39.0
44.0
69.0
48.0
61.0
60.0
57.0
78.0
66.0
59.0
36.0
70.0
44.0
48.0
151
Table D18. Data set for crown structure variables: branch count (branch ct.),
mean branch length (mbl), mean basal branch diameter (mbbd)
tree
id#
9014
9410
9409
9411
9456
9599
9674
26844
26830
9829
29664
9835
25079
stem
sect.
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
S3
size
class
l
l
l
l
l
l
l
l
l
l
l
l
l
branch
ct.
1
8
9
1
3
1
8
5
10
3
2
9
mbbd
(mm)
13.0
14.4
14.3
12.0
10.8
16.2
11.6
13.5
20.7
14.6
12.0
11.9
10.0
mbl
(cm)
105.0
127.0
119.0
107.0
62.0
125.0
115.0
96.0
137.0
125.0
79.0
99.0
109.0
152