To the Graduate School:
The members of the Committee approve the thesis of Rick Arcano on April 26, 2005.
Daniel B. Tinker, Co-Chairman
Gregory K. Brown, Co-Chairman
Elise G. Pendall
Peter D. Stahl
APPROVED:
Gregory K. Brown, Head, Department of Botany
Don Roth, Dean, The Graduate School
ALLOMETRIC MODEL DEVELOPMENT, BIOMASS ALLOCATION PATTERNS, AND
NITROGEN USE EFFICIENCY OF LODGEPOLE PINE IN THE
GREATER YELLOWSTONE ECOSYSTEM
By
Rick Arcano
A thesis submitted to the Department of Botany and the Graduate School of the University of
Wyoming in partial fulfillment of the requirements for a degree of
MASTER OF SCIENCE
In
BOTANY
Laramie, Wyoming
May, 2005
Table of Contents
Abstract
………………………………………………………………………………….iii
Acknowledgments ………………………………………………………………………….…iv
Preface
……………………………………………………………………..…………...v
Chapter 1
Allometric Model Development for Mature Lodgepole Pine Forests in the Greater
Yellowstone Ecosystem ……………………………………………...……….1
Chapter 2
Biomass Allocation Patterns and Nitrogen Use Efficiency for Mature Lodgepole
Pine Forests in the Greater Yellowstone Ecosystem ………………………...40
Summary
…………………………………………………………………………….......89
ii
Arcano, Rick, Allometric Model Development, Biomass Allocation, and Nitrogen Use
Efficiency of Lodgepole Pine in the Greater Yellowstone Ecosystem, M.S.,
Department of Botany, May, 2005.
Estimation of Net Primary Production (NPP) in lodgepole pine (Pinus contorta var.
latifolia (Engelm. Ex Wats.) Critchfield) forests is critical for understanding how climateinduced or anthropogenic changes in fire frequency and pattern may affect the carbon balance
and biomass allocation across the Greater Yellowstone landscape. In addition, biomass
allocation patterns are influenced by nitrogen use efficiency (NUE), yet little is known about
their interactions. Allometric equations that quantify biomass production of lodgepole pine in
the Greater Yellowstone Ecosystem (GYE) were developed in the summer of 2004. Statistical
comparisons were used to determine whether allometric models differed between forests of
different ages and densities, and t-tests were used to determine if allometric models and biomass
allocation patterns in the GYE differed from southeastern Wyoming and in British Columbia,
Canada. One-way ANOVA was used to determine whether biomass allocation patterns and
NUE differed between stand densities and ages, and correlation analyses were used to determine
how they were related. Allometric models differed by both density and age, but differed more by
age. Biomass allocation patterns differed between forest density and age, but coarse root
allocation was relatively constant across forest densities and ages. Aboveground biomass
allocation patterns and allometric models developed in this study differed significantly from
allometric models developed elsewhere, independent of stand density and age. NUE was highest
in the oldest stand, and was correlated with site productivity and biomass allocation patterns.
NPP and Net Ecosystem Production (NEP), which differ among lodgepole pine ecosystems, are
impacted by forest structure, and are at least partly impacted by the interrelationships among
allometric model development, biomass allocation patterns, and NUE.
iii
ACKNOWLEDGMENTS
I would like to thank my graduate advisor, Daniel B. Tinker for providing me the
opportunity to be part of the Department of Botany at the University of Wyoming and providing
me all the tools necessary to succeed. I would like to thank my graduate committee -- Drs.
Gregory K. Brown, Elise G. Pendall, and Peter D. Stahl for their guidance and support. I also
received invaluable support from the entire botany department, including my fellow graduate
students, especially Abbie Gongloff, Jennifer Andersen, Mark Andersen, Peter Ebertowski, and
Erin Foley. I had five diligent and dedicated field assistants whom willingly and effectively
participated in sometimes grueling and tedious field and lab work for this study. They are Lance
East, Lance Farman, Christine Regester, Kellen Nelson, Terez Tepe, and Heather Lyons. I have
estimated that I would have needed 6-10 more years to complete this study without them. I am
very appreciative of my access to the experience and expertise of Drs. Michael G. Ryan of the
Rocky Mountain Research Station of the USDA Forest Service, and Daniel M. Kashian and
William H. Romme from the Department of Forest, Rangeland, and Watershed Stewardship at
Colorado State University. Drs. David E. Legg from the Entomology Department at the
University Wyoming and Rudy M. King from the Rocky Mountain Research Station of the
USDA Forest Service provided me with statistical advice. I was permitted to conduct my
research on the Ashton-Island Park Ranger District of the Caribou-Targhee National Forest and
deeply appreciate it. Without the help of everyone involved, completion of this project would
not have been possible. This research was supported by grants from the Plummer Scholarships
for Environment and Natural Resources Conservation and Management, University of Wyoming
/ National Park Service Research Center, and the USDA Joint Fire Sciences Program.
iv
PREFACE
The global climate has been subject to dramatic change over the past half century, and the
climate of the intermountain west is undergoing similar alterations. Both naturally and
anthropogenically-altered climatic patterns in the western U.S. are likely to cause substantial
changes in natural fire regimes that could result in alterations in forest stand structure and
productivity (Amiro et al. 2000; Dale et al. 2001). These changes in forest structure and
productivity may cause changes in Net Ecosystem Production (NEP), which varies widely across
many forested landscapes. The variability in NEP is produced by heterogeneity in stand ages
and densities that are impacted by natural and anthropogenically altered disturbance regimes
(Chapin et al. 2002; Turner et al. 2004). To determine how NEP is affected by altered
disturbance regimes, quantification of forest structure and determination of how lodgepole pine
(Pinus contorta var. latifolia (Engelm. ex Wats.) trees and stands allocate their biomass above
and belowground is needed. Detailed estimates of forest structure and function requires accurate
and easily obtained measurements of aboveground and belowground tree biomass, which are
necessary components for determining Net Primary Production (NPP) of forested ecosystems.
To accurately estimate NPP, it is important to use allometric equations that consider differences
in allocation patterns of lodgepole pine biomass, which may vary with tree density and stand age
(Pearson et al. 1984). In this study, we developed new allometric models for predicting above
and belowground biomass in mature lodgepole pine forests of the GYE. We also harvested
lodgepole pine above and belowground in three stands differing in densities and ages to
determine the allocation patterns of mature lodgepole pine forests in the GYE across varying
forest structures. This study was part of a larger study funded by the USDA Joint Fire Sciences
program to determine the carbon budget of the Greater Yellowstone Ecosystem and how this
v
budget is changed by alterations in climate and fire regime. Development of allometric
equations for lodgepole pine, which comprises over 80% of the overstory vegetation (Despain
1990) in Yellowstone National Park, is critical for estimating carbon pools and Net Ecosystem
Production across the Greater Yellowstone landscape.
Determining the use efficiency for essential nutrients such as nitrogen (N) is also
important for improving our understanding of biomass accumulation of lodgepole pine. Gross
Primary Production (GPP), a major component of NEP, is equal to the product of resource
supply, the proportion of the resource supply that is taken up, and the efficiency of resource use
(Binkley et al. 2004). In this study, we estimated NUE in three sites in the Greater Yellowstone
Ecosystem that varied by stand age and tree density to determine how NUE relates to variability
in forest structure and site index (see Methods), and to identify relationships between NUE and
biomass allocation patterns.
The specific objectives of this study were (1) to develop allometric models for predicting
above and belowground biomass of mature lodgepole pine trees in the GYE; (2) to determine
how allometric model development and application for lodgepole pine differs with stand density
and age; (3) to compare and contrast allometric equations developed in this study to allometric
equations developed in other locations to determine model variability and applicability across
geographic locations independent of forest structure. (4) to determine how patterns of above and
belowground biomass allocation for mature lodgepole pine forest of the GYE vary with stand
age and density; (5) to quantify patterns of nitrogen use efficiency (NUE) for lodgepole pine
forests among these differing forest structures and productivities; and (6) to examine the
relationships between NUE and biomass allocation patterns.
vi
Chapter 1 will address the allometric model development of lodgepole pine in the GYE,
and will also focus on potential differences in allometric models with differing stand density,
stand age, and geographic location. Chapter 2 will focus on biomass allocation patterns and
nitrogen use efficiency. Specifically, it will focus on how biomass allocation and nitrogen use
efficiency differ with stand density, stand age, and site productivity. In addition, the potential
relationships between biomass allocation patterns and nitrogen use efficiency will be examined.
Next, I will summarize the findings and results of both chapters in a final summary section.
vii
LITERATURE CITED
Amiro, B. D., J. M. Chen and J. Liu 2000. Net primary productivity following forest fire for
Canadian ecoregions. Can. J. For. Res. 30: 939-947.
Binkley, D., J. L. Stape and M. G. Ryan 2004. Thinking about efficiency of resource use in
forests. Forest Ecology and Management 193: 5-16.
Chapin, S. F. I., P. Matson and H. A. Mooney (2002). Principles of terrestrial ecosystem
ecology. New York, NY, Springer-Verlag New York, Inc.
Dale, V. H., L. A. Joyce, S. McNulty, R. P. Neilson, M. P. Ayres, M. D. Flannigan, P. J. Hanson,
L. C. Irland, A. E. Lugo, C. J. Peterson, D. Simberloff, F. J. Swanson, B. J. Stocks and B.
M. Wotton 2001. Climate change and forest disturbances. Bioscience 51: 723-734.
Despain, D. G. (1990). Yellowstone vegetation: consequences and environment in a natural
setting. Boulder, CO, Roberts Rinehart, Inc.
Pearson, J. A., T. J. Fahey and D. H. Knight 1984. Biomass and leaf area in contrasting
lodgepole pine forests. Can. J. For. Res. 14: 259-265.
Turner, M. G., D. B. Tinker, W. H. Romme, D. M. Kashian and C. M. Litton 2004. Landscape
patterns of sapling density, leaf area, and aboveground net primary production in postfire
lodgepole pine forests, Yellowstone National Park (USA). Ecosystems 7: 751-775.
viii
Chapter 1
Allometric Model Development for Mature Lodgepole Pine Forests in the
Greater Yellowstone Ecosystem
INTRODUCTION
Natural and anthropogenic effects on climatic patterns in the western U.S. are likely to
cause substantial changes in natural fire regimes that could result in alterations in forest stand
structure and productivity (Amiro et al. 2000; Dale et al. 2001). Quantification of forest
structure and function requires accurate and easily obtained measurements of aboveground and
belowground tree biomass, which are necessary components for determining Net Primary
Production (NPP) of forested ecosystems. In Yellowstone National Park (YNP), lodgepole pine
(Pinus contorta var. latifolia (Engelm. ex Wats.) Critchfield) comprises over 80% of the
overstory vegetation (Despain 1990). Estimating the biomass of these vast forests is a critical
component in determining the NPP of lodgepole forests across the entire Greater Yellowstone
Ecosystem (GYE). To accurately estimate NPP, it is important to use allometric equations that
consider differences in allocation patterns of lodgepole pine biomass, which may vary with tree
density and stand age.
Useful allometric equations for above and belowground biomass of lodgepole pine have
been developed in Alberta, Canada by Johnstone (1971), in southeastern WY by Pearson et al.
(1984), in southeastern British Columbia by Comeau and Kimmins (1989), and in Washington
and Oregon by Gholz et al. (1979). More recently, allometric equations for predicting biomass
in  10 year-old post-fire forests have also been developed in Yellowstone National Park (YNP)
for aboveground biomass by Turner et al. (2004), and for belowground biomass by Litton et al.
(2003). Although Litton et al. (2004) later suggested that allometric models developed by
1
Comeau and Kimmins (1989) and Pearson et al. (1984) could be appropriate for use in the GYE,
the models were tested on only five mature trees in a single stand, and did not account for natural
variability in stand density and age. In addition, allometric models for predicting belowground
biomass were not critically evaluated. Therefore, new allometric models specific to mature
forests of the Greater Yellowstone Ecosystem are needed. In this study, we developed new
allometric models for predicting above and belowground biomass in mature lodgepole pine
forests of the GYE.
Recently, allometric models were developed and applied to predict aboveground NPP of
young post-fire forests across the entire Yellowstone landscape by Turner et al. (2004). Their
landscape scale extrapolation was made possible by the applicability and accuracy of their
allometric models. Allometric equations developed for lodgepole pine and many other species
(Ter-Mikaelian and Korzukhin 1997) have proven to be very good predictors of above and
belowground biomass for individual trees, where coefficients of determination are often above
0.90 for aboveground tree components and for roots >10mm diameter. Despite the high quality
of allometric equations, previous studies have suggested that they may not be universally
applicable in stands of varying densities, ages, and site qualities. Johnstone (1971) identified a
need to consider stand density and age when he developed allometric models for 100-year-old
lodgepole pine in Alberta, Canada. Since that study, Pearson et al. (1984) suggested that
differences in stand density, age, and site quality may have caused considerable variability in tree
crown biomass, and both Pearson et al. (1984), and Comeau and Kimmins (1989) found that the
foliage biomass: sapwood area ratio decreased with increasing stand density. Variability among
sites suggests that allometrics are also likely to differ among geographic locations due to
differences in substrate, topography, and climate. Keyes and Grier (1981) found that Douglas-fir
2
had proportionately more total root biomass on a low productivity site than on a high
productivity site, and Comeau and Kimmins (1989) found that belowground production
represented a greater proportion of total production in two xeric sites compared to two mesic
sites. Therefore, changes in allocation of biomass with differing site productivities may cause
changes in allometric relationships across gradients in site productivity that could be caused by
differences in substrate, topography, and climate.
Objectives of this study were to: (1) develop allometric models for predicting above and
belowground biomass of mature lodgepole pine trees in the GYE; (2) determine how allometric
model development and application for lodgepole pine differs with stand density and age; and (3)
compare and contrast allometric equations developed in this study to allometric equations
developed in other locations to determine model variability and applicability across geographic
locations independent of forest structure.
We hypothesized that (1) allometric models developed for estimating biomass of above
and belowground tree components in the GYE would vary with stand density and age; and (2)
that allometric models developed for lodgepole pine in the GYE will differ from models
developed elsewhere due to differences in substrate, topography, and climatic conditions.
METHODS
Study Area
The study area was located within the GYE on the Caribou-Targhee National Forest
(CTNF) bordering YNP. The GYE encompasses portions of three states in the western US:
Wyoming, Idaho, and Montana, and is centered around YNP and its surrounding mountains,
valleys, and subalpine plateaus. Elevations in YNP range from 1,620 m near Gardiner, Montana
3
to 3,333 m in the Absaroka mountain range of Wyoming. Mean annual precipitation ranges
from less than 28 cm yr-1 at lower elevations to about 180 cm yr-1 on the southwestern plateau
(Knight 1994). Temperatures are cold during the winter, where high temperatures less than 0C
occur for an average of 87.6 days year-1, and temperatures are rarely more than 32C during the
summer (Dirks and Martner 1982).
There are three main substrates for soil development on the subalpine plateaus of the
ecosystem: the least fertile rhyolite; the less infertile rhyolite; and lake bottom sediments
(Despain 1990). The dominant forest type of the ecosystem is lodgepole pine forest, which
occurs at middle elevations. Spruce/ Fir (Picea engelmannii /Abies lasiocarpa) forests occur at
higher elevations and Douglas-fir (Pseudotsuga menziesii) forests occur at lower elevations.
Whitebark pine (Pinus albicaulis) dominates at the upper treeline and sagebrush (Artemesia sp.)
occurs at the lower treeline. Aspen (Populus tremuloides) occurs more commonly within the
ecosystem outside of YNP (Knight 1994).
Field and Lab Methods
Allometric equations were developed in three lodgepole pine stands on the CTNF that
represented two age classes and two density classes (Table 1). In the young (64 years old)
aggrading age class, two stands of different densities were examined; one dense (YD) (2,452
trees/ha) and the other sparse (YS) (725 trees ha-1). The two young stands were located within
the CTNF near Island Park, ID. Because densities of lodgepole pine stands tend to converge as
they get older (Kashian et al. 2005), a single sparse (674 trees ha-1) stand was sampled in the
older age class (OS) (164 years old).
Although sites differed in density and age, they were located on similar soils. The
Koffgo soil series consists of loamy-skeletal, mixed, superactive Vitrandic Cryochrepts. Parent
4
material was local residuum, colluvium, or alluvium developed from volcanic ash, igneous rocks,
and loess (Bowerman et al. 1999). Elevations ranged from 1951m to 2249m, which is within the
elevation range of lodgepole pine found for the GYE. Precipitation was approximately 114.3 cm
yr-1 in the oldest stand and approximately 76.2 cm yr-1 in the younger stands (Dirks and Martner
1982). All sites were located at least 50 m from the road to facilitate equipment hauling and to
avoid road influences. Stand basal area was similar between the OS (16.84 m2 ha-1) and YS
stands (19.71 m2 ha-1), but was quite different for the YD stand (28.32 m2 ha-1) (Table 1).
All aboveground tree biomass for a total of 46 trees was harvested within the three
stands, and 24 root systems were excavated to develop allometric equations where easily
obtained morphological parameters, such as diameter at breast height, were tested as predictors
of above and belowground tree components (foliage biomass, coarse root biomass, etc.).
Fourteen trees were harvested in the YD stand and 15 trees were harvested in the YS stand; 17
were harvested in the OS stand. For belowground components, 5 root systems were excavated in
both the YS and OS stands due to logistical difficulties associated with large root systems, while
14 root systems were harvested in the YD stand. Initially, we decided to harvest 15 trees in each
stand. However, in the OS stand, three extra trees were harvested to ensure that all trees were
within the proper age range, and one tree had to be removed because it was younger than the
acceptable age range (150-165 years) for that stand. In addition, some field data for one tree in
the YD stand was missing, and therefore, that tree was not included in the analyses.
Five trees were harvested along each of three 25m transects in each stand. Trees were
generally selected at 5m intervals along each transect, but more importantly, trees were selected
based on their diameter to represent the range of tree sizes found for trees in their respective
stands. Trees were not chosen if they had any of the following characteristics: unusually poor
5
tree form, such as crook or sweep of the bole; major forking of the bole; excessive mistletoe; any
defect that would alter the biomass of the tree, such as heart rot or insect damage; or any tree
outside of the acceptable age range (15 years of the oldest tree in the stand).
Prior to harvest, DBH (diameter at breast height, 1.37m) was measured and crown width
was estimated using a meter tape. After felling of the tree, total height and height to crown base
were measured. Crown base was defined as the point along the bole at the bottom of roughly
90% of the crown mass, and crown length was calculated as:
CL = H – HCB
(1)
where CL = crown length, H = total tree height, and HCB = height to the base of the live crown.
Aboveground Components
Tree Bole - Each bole was harvested at ground level and all branches were removed. The
bole was cut into three to four sections with 1 to 2 discs cut out as subsamples to determine
moisture content for dry weight. For each bole, two discs were always taken at DBH, and at
90% of crown base (the location on the bole where most of the crown began). Two discs were
taken from each section if the bole forked near the top or if DBH and crown base were one meter
or closer in proximity. Each bole section was weighed separately using a digital hanging scale
and the disc weight of the subsample was added back into the weight for its respective section.
Discs were then dried to a constant weight at 70C in the lab to determine moisture content.
Dry: wet weight ratio for each disc was then applied to determine dry weight of the entire bole
section. For each DBH and crown base subsample, the following measurements were taken for
determining sapwood area: phloem + bark thickness, total diameter, and heartwood diameter.
Two perpendicular measurements were taken and averaged for each of these parameters except
for diameter at breast height, which was measured with a diameter tape prior to harvest.
6
Sapwood diameter can be determined by subtracting the diameter of phloem + bark and
heartwood diameter from total diameter. Therefore, the following equation was used to
determine sapwood area:
SA = a – b
(2)
where SA = Sapwood Area, a = the basal area excluding phloem and bark, and b = heartwood
area.
Branches - Branches were cut flush with the tree bole and were separated from foliage at
6.4 mm in diameter. Thus, branches consisted of all shoots minus the tree bole that were greater
than 6.4 mm in diameter, because biomass smaller than 6.4 mm are likely to be consumed by fire
(Despain 1990), allowing for post-fire estimates of branch biomass, independent of small twigs
that were likely to be consumed the fire. Branches were compiled rather than separated into
three crown sections, because one subsample from all sections was deemed sufficient to
determine the dry weight of the entire component. A subsample of approximately 4.0 L was
taken to determine moisture content for dry weight for each tree, where it was then dried in the
lab.
Fine Fuels - The fine fuels component was considered to be all needles and associated
twigs less than 6.4 mm in diameter. These were combined because needles and twigs smaller
than 6.4 mm diameter are likely to be consumed by fire (Despain 1990). The fine fuels
component was maintained in separate piles of lower, middle, and upper crown sections for each
tree, since foliage moisture contents were likely to vary with crown height (Brown 1978). A
random subsample approximating the size of approximately 4.0 L volume was taken from each
crown section to determine moisture content. The fine fuels subsamples were then weighed to
7
obtain wet weight. If immediate weighing was not possible, subsamples were stored in a cooler
for no more than 5 days in plastic bags, to prevent moisture loss prior to weighing.
The current year’s growth (2004) was removed and discarded, because many of the
needles were not fully expanded, and samples were collected at varying times throughout the
summer of 2004. The subsample was weighed again to determine the proportion of the fine fuels
component from the current year’s growth. This proportion was then subtracted from the entire
foliage component. After weighing, a random sample of 10 needle fascicles was taken from
each fine fuels subsample, and the length and width of each fascicle was measured to determine
leaf area. A tapered, bisected cylinder was used to determine surface area represented by each
fascicle (Pearson et al. 1984 ; Madgwick 1964). This sample was dried and subsequently
weighed so that surface area of this subsample could be extrapolated to the entire tree’s needle
biomass. This sample was also weighed while still wet for subsequent drying and weighing to
determine moisture content for dry weight determination of total needle biomass. The dry
weight proportion of total foliage biomass that was needles was determined from separating
needles from twigs after the foliage subsamples were dried.
Belowground Components
For each tree, the entire coarse root system (>10mm diameter) was excavated with the
encouragement of a backhoe or come-a-long. Prior to excavation, smaller roots (≈ 10-20 cm)
that could potentially be damaged by the backhoe or other excavation techniques were removed
by hand. After excavation, the root system was divided into four size classes: root crown (i.e.
the massive structure directly beneath the tree bole), lateral roots >50mm in diameter, lateral
roots 25-50mm in diameter, and lateral roots 10-25mm in diameter. Total weight of each root
size class was weighed using a digital hanging scale (Salter-Brecknell). Subsamples were taken
8
and weighed to determine moisture content for dry weight of each size class. Subsamples were
then dried at approximately 70°C where the dry: wet weight ratio was applied to its
corresponding size class for determination of total coarse root system dry weight.
Statistical Analyses
Model Development
All allometric models were developed with SPSS 13.0 (SPSS Inc. 2005). Models for
each tree component (Table 2) were developed for the three individual stands, for all sites
combined, and were also pooled by density and age. The following criteria were used to develop
the best models for predicting biomass of lodgepole pine:
A.) The model must be biologically reasonable (Hilborn and Mangel 1997). For
instance, an exponential model may yield the highest coefficient of determination (R2) for
predicting total coarse root biomass with tree basal area, but an exponential increase in
root biomass with basal area is not biologically reasonable.
B.) The model must be of a form (linear or non-linear) that fits the data, although a
model that does not produce a proper fit may achieve a higher R2. The proper model
form was chosen using the following methods:
1. All potential predictors were plotted against each biomass component.
2. For any given plot, if a relationship was observed, data were examined to
determine its linearity of curvilinearity.
3. To detect more subtle patterns of linearity or curvilinearity of data, the predictor
was plotted against its residuals. If the relationship was linear ( y = ax+b ), a
shotgun pattern around the horizontal axis through the origin was apparent. If the
pattern appeared to be curvilinear, a curvilinear analysis was chosen. A nonlinear
9
power function (y = axb, where y was the dependent variable, x was the
independent variable, and a and b were constants) best fit much of our data.
4. Where more than one predictor exhibited a non-linear relationship, coefficients of
determination (R2), mean square errors, and plots of residuals were used to
determine the best model. In addition, multiple predictors were combined into a
multiplicative power model ( y = a xb x2c ) and compared to models with one
predictor.
C. If two predictors were deemed to be relatively equal based on their biological
plausibility, coefficient of determination (r2/R2), standard error / mean square error, and
plot of the residuals, the most easily measured independent variable was chosen.
Model Comparison
Density and Age
To determine whether allometric models differed between stands of varying densities and
ages, the sum of squares of the residuals for each model, pooled by density and age, were
compared using the extra sum of squares analysis for nested models (Bates and Watts 1988).
The null hypothesis was that slopes and intercepts of the models were the same. If either slopes
or intercepts differed significantly ( < 0.05), it was determined that the models would produce
biomass estimates significantly different from one another. In addition, equations for total
aboveground biomass and coarse root biomass were used to predict biomass in a different stand
from where they were developed. For instance, pooled equations from the two young stands
were used to predict above and belowground biomass in the older stand, to determine the degree
of error produced from using an inappropriate model.
10
Geographic Location Comparisons
Models for mature lodgepole pine forests in this study were compared to models
developed by Pearson et al. (1984) in the Medicine Bow Mountains of southeastern Wyoming
and by Comeau and Kimmins (1989) in British Columbia. These models were used to estimate
biomass using the same measured parameters used to develop allometric models for this study.
Selected models were similar in stand density and age to allometric models developed for this
study. Comparison was further facilitated by selecting sites from this study that were similar to
stands used to develop the models for Pearson et al. (1984) and Comeau and Kimmins (1989).
Paired, two-tailed t-tests were used to assess whether biomass estimates from this study differed
statistically from biomass estimates produced from other models (Comeau and Kimmins 1989);
(Pearson et al. 1984) ( = 0.05). In addition, actual biomass values from this study were
compared against values estimated from the application of our allometric models to determine
whether our estimates biomass values were more similar to the actual values than estimated
values calculated from the application of models developed by Pearson et al. (1984) and Comeau
and Kimmins (1989).
RESULTS
Model summary
Forty-eight allometric models for all measured tree components are shown in Table 2. R2
values ranged from 0.54 to 0.99, and 37 out of 48 of the models had R2 values greater than 0.80.
Generally, for any given component, model strength decreased with increasing tree size,
indicating greater variability in the biomass of larger trees than smaller trees. Volume (basal
area (cm2) * total height (cm)) was the best predictor of total aboveground and bole biomass
11
across all stand densities and ages. Basal area (cm2) was the best predictor of coarse root
biomass, root crown biomass, and lateral root biomass. Best predictors of branch biomass were
basal area and crown length, while basal area and sapwood area best predicted foliage and needle
biomass (Table 2). Allometric models for total aboveground biomass, bole biomass, total coarse
root biomass, and lateral root biomass were the most robust models, because only 1 of those 24
models had R2 values below 0.89. In contrast, the other 24 models, which were for root crown,
branches, fine fuels, and needles were less robust, because R2 values were below 0.80 for 9 of
the 24 models. For total aboveground biomass, the best model was for all sites combined (R2 =
0.95). Allometric models indicated that pooling the stands by age (R2(old) = 0.94, R2(young) =
0.95) produced better models than pooling stands by density (R2(dense) = 0.84, R2(sparse) =
0.94). The same pattern was observed for bole biomass, branch biomass, and root biomass
components (Table 2). An exception for this is lateral root biomass where 94% of the variation
in lateral root biomass was explained by basal area after developing separate models stratified by
age, compared to stratification by stand density where 89% and 91% of the variation in lateral
root biomass was explained by basal area for the two sparse stands and the single dense stand,
respectively.
Model Comparisons Between Densities and Ages
Where statistical model comparisons were possible, most allometric models developed
differed significantly between densities and ages (p < 0.05, Table 3.). Although formal statistical
comparisons were not possible if either the model form or the predictor variables between two
models were considered to be different, those models were nevertheless considered to be
qualitatively different, because the allometric relationships were different. However, models for
the tree bole (extra sum of squares, p = 0.622), root crown (extra sum of squares, p = 0.396), and
12
lateral roots (extra sum of squares, p = 0.608) were not significantly different when stands were
pooled by density, and models for root crowns (extra sum of squares, p = 0.036) and lateral roots
(extra sum of squares, p = 0.020) were only modestly different. In addition, equations for total
aboveground biomass were less different when pooled by density (extra sum of squares, p =
.037) than by age (extra sum of squares, p < 0.001).
Model Comparison Between Geographic Locations
Generally, predicted biomass values from equations developed in this study were closer
to actual biomass of lodgepole pine in the Greater Yellowstone Ecosystem than predicted values
from equations developed by Comeau and Kimmins (1989) and Pearson et al. (1984) (paired ttest, p<0.05, Table 4, Figure 2 and 3). Predicted values applying our study’s equations typically
produced biomass values that were not significantly different (paired t-test, p > 0.05) from actual
biomass values. However, estimates of branch biomass applying Pearson et al.’s equations were
slightly more similar to actual values (paired t-test, p = 0.005) than were estimates values
applying our equations (paired t, test, p = 0.003). Although Pearson et al.’s models appeared to
be better at predicting actual branch biomass in the GYE according to paired t-tests, more indepth examination of Table 4 revealed a different pattern, because the mean value for branch
biomass was closer to the actual value when applying this study’s equations. In addition, Figure
3 identified a clear departure of Pearson et al.’s values from the actual values with increasing tree
diameter, while this study’s predicted values followed the actual values more closely with
increasing diameter. An additional caveat is for the comparison between bole biomass for this
study and bole biomass for Pearson et al.’s study, because Pearson et al’s predicted values for
bole biomass were statistically similar (paired t-test, p = 0.063) to actual bole biomass from this
study. However, predicting bole biomass with our study’s equations produced predicted bole
13
biomass values that were more similar to actual bole biomass values from this study (p = 0.514).
The greatest difference in observed versus predicted biomass between studies was for larger trees
(Table 4, Figure 2 and 3), supporting the observation of increased variation in allometric models
with increasing tree size. Biomass estimates produced by models from studies northwest
(Comeau and Kimmins 1989) and southeast (Pearson et al. 1984) of the GYE produced
significantly lower estimates of root biomass, higher estimates of needle and branch biomass,
and relatively comparable estimates of bole biomass (Table 4, Figure 2, 3). The root biomass
estimates generated from models developed in the Medicine Bow Mountains produced slightly
lower biomass estimates than from this study (Table 4, Figure 3). However, a portion of this
difference is probably attributed to differences in the definition of a root crown between studies.
For this study, a root crown was the portion of the coarse root system that is directly below the
basal swell of the tree bole and within the lateral extent of the diameter of the tree at ground
level. In contrast, Pearson et al. (1984) defined the root crown as the structure directly below the
basal swell of the tree bole extending laterally to a distance of 1.5 x DBH.
DISCUSSION
Variation in stand density and age affected which of several measured tree morphometric
parameters would best predict biomass of lodgepole pine tree components. The product of tree
basal area and total height (volume) was the best predictor of total aboveground and bole
biomass (Table 2). Notably, a tree’s ability to produce stemwood biomass, a major component
of tree bole and total aboveground biomass, is strongly influenced by site productivity (Barnes et
al. 1980). Therefore, a model including tree height, a variable relating more strongly to site
productivity than any other measured parameter (Barnes et al. 1980), can explain a large amount
14
of the variability in bole and total aboveground biomass. In contrast, tree height was not found
to be a useful variable for predicting the biomass of other tree components (Table 2). In
addition, the decrease in variability of the dependent variable that can be explained by its
respective predictor with increasing tree size may be due to greater variability in biomass
allocation patterns with increasing tree size.
Total coarse root biomass (>10 mm), root crown biomass, and lateral root biomass are
best explained by tree basal area (Table 2), where tree diameter and basal area are measured
parameters most closely correlating with spacing effects related to tree density (Barnes et al.
1980), suggesting coarse root biomass is at least partly dependent on tree spacing. Further
studies incorporating nearest neighbor methods may be needed to determine effects of spacing
on the coarse root biomass of individual trees, such as the approach by (Mainwaring and
Maguire 2004) where they modeled the relationship between spacing among individual trees and
tree diameter growth for lodgepole and ponderosa pine in central Oregon. They found that
spatially explicit models including distance-dependent measures of competition among trees
explained more of the variability in tree diameter growth than did implicit measures of
competition based on stand-level averages. Their results suggested that allometric models have
the potential to be improved further by including explicit measures of competition.
Most of the variability in needle and foliage biomass is explained by basal area and
sapwood area (Table 2). Sapwood area has been found to be a good predictor of foliage biomass
in other studies (Snell 1978; Grier and Waring 1974; Pearson et al. 1984; Comeau and Kimmins
1989), because it is a good measure of a tree’s ability to conduct water and nutrients (Taiz and
Zeiger 2002). Needle and foliage biomass in the older stand was best explained either by
sapwood area or a combination of sapwood and basal area (Table 2). This indicates foliage
15
biomass may be better explained by a tree’s ability to conduct water and nutrients in older forests
where water and nutrients are more limiting (Smith and Long 2001; Berger et al. 2004; Ryan et
al. 2004). In contrast, basal area was the single best predictor of foliage biomass in younger
stands (Table 2), indicating that foliage biomass in younger forests is better explained by tree
spacing rather than by the tree’s ability to conduct water and nutrients.
Differences between densities and ages
Allometric models were expected to differ between densities and ages due to differences
in biomass allocation patterns. For the most part, this was supported by the data (Table 3).
However, models for tree bole, root crown, and lateral roots did not differ when pooled by
density, and models for aboveground biomass were less different when pooled by density than
by age. One plausible explanation for this pattern is that stand densities may not have been
different enough for some of the models to differ according to stand density or to be less
different than for models pooled by age. Although 2,452 trees ha-1 was considered in this study
to be a dense stand, “dog-hair” stands of lodgepole pine that are of comparable age can
sometimes reach densities higher than 10,000 trees ha-1 (Koch 1996). Therefore, the relatively
small difference in stand densities in this study may be a factor leading to the relative similarity
of models pooled by density compared with models pooled by age. Although allometric
equations appear to be more similar across densities, application of models significantly different
in density from where they were developed should be approached with caution. Furthermore,
inappropriate model use can cause significant errors when extrapolating allometric equations to
the landscape scale.
The estimated errors produced by inappropriate model use appeared to be related to
differences in aboveground: belowground biomass allocation, where an overestimation of
16
aboveground biomass was coupled with an underestimation of belowground biomass. Predicting
biomass in the OS stand using the pooled model from the younger stands (YS and YD) produced
tremendous error, where total aboveground biomass was 127.0 Mg/ha with the proper model for
that stand, while it was nearly double (215.4 Mg ha-1) using the improper model. For total coarse
root biomass, the error was less pronounced, but still evident, where it was 12.1 Mg ha-1 for the
appropriate model and 10.2 Mg ha-1 for the inappropriate model. Predicting biomass in the
sparse stand using the model developed in the dense stand underestimated total aboveground
biomass significantly, where it was 85.0 Mg ha-1 for the correct model, but was 61.5 Mg ha-1 for
the incorrect model. The inappropriate model overestimated for total coarse root biomass, it was
10.5 Mg ha-1 for the appropriate model and 13.6 Mg ha-1 for the inappropriate model.
Differences between Geographic Locations
Differences in allometric equations among geographic locations are likely attributed to
differences in soil, topography, and climate. The soils on which Pearson et al. (1984) developed
allometrics in the Medicine Bow Mountains were generally less infertile and were derived
primarily from glacial till and fluvial conglomerates, while soils in British Columbia were Orthic
Eutric Brunisols for xeric sites and Brunisolic Gray Luvisols for mesic sites (Comeau and
Kimmins 1989). In contrast, soils of this study were more infertile and were developed on
volcanic substrates (Bowerman et al. 1999). Sites where allometrics were developed by
Comeau and Kimmins (1989) were more productive than sites in this study, where higher site
index (SI), which is associated with higher productivity, ranged from 14.3 to 20.5m at 100 years
for Comeau and Kimmins (1989) compared to our study where SI ranged from 14.3 to 16.0 at
100 years.
17
Bole biomass was the only component that did not differ between the GYE and the
Medicine Bow Mountains (Pearson et al. 1984), and was the component most similar between
the GYE and British Columbia (Comeau and Kimmins 1989), indicating that relationships
between bole biomass and its predictor(s) (basal area and total height) are relatively constant
across gradients of elevation, topography, climate, and site productivity. This further indicates
that a tree will allocate a relatively constant proportion of available resources throughout its
lifespan to wood production, indicating a tree may compensate for reductions in root and foliar
biomass through increases in light, nutrient, and overall photosynthetic efficiency. However,
increases in light use efficiency typically coincide with increases in leaf area (Smethhurst et al.
2003). Therefore, the ability of a tree to produce wood across sites may be controlled by
interactions between nutrient and light use efficiency, where a reduction in light use efficiency is
coupled with an increase in nutrient use efficiency (Binkley et al. 2004). This finding is
consistent with Tilman's (1982) resource ratio model of plant competition that implies stability
among competing plants at different ratios of important resources, such as nitrogen and light
(Gurevitch et al. 2002). Despite the relatively constant relationships between bole biomass and
its predictors across geographic locations, predicted bole biomass values when applying our
equations as opposed to those developed by Pearson et al. (1984) were closer to actual bole
biomass (Table 4, Figure 3), indicating the need for using locally developed allometric models.
Needle biomass estimates were lower for less nutrient poor sites (Pearson et al. 1984;
Comeau and Kimmins 1989) than for actual and predicted biomass from sites in the GYE (Table
4, Figure 3), indicating foliage biomass was higher increasing photosynthate to compensate for
more nutrient limiting conditions that might otherwise decrease wood production. This potential
18
balance between light use and nutrient availability may account for some of the homogeneity in
bole biomass across geographic locations.
Root biomass in the GYE was over predicted by models developed in British Columbia
and southeastern Wyoming, indicating root crown and coarse root biomass is dependent upon a
site’s ability to support production of wood biomass. This suggests that coarse root biomass has
little impact on the trees ability to uptake water and nutrients, because less coarse root biomass
should have been needed to uptake water and nutrients in British Columbia and southeastern
Wyoming, which are less productive sites than the GYE.
The greatest difference in biomass of tree components across geographic locations was
for larger trees (Figure 2, 3), indicating site conditions become increasingly important as forests
become older and trees become taller. This is attributed to an increase in nutrient limitation
(Berger et al. 2004; Ryan et al. 1997; Smith and Resh 1999; Smith and Long 2001), hydraulic
limitation (Ryan and Yoder 1997), and maintenance respiration (Ryan et al. 1995) with increases
in stand age and tree height.
SUMMARY AND CONCLUSIONS
Allometric models developed for mature lodgepole pine in the Greater Yellowstone
Ecosystem were very robust for predicting tree biomass and showed promise for estimating
landscape level biomass of lodgepole pine forests, because R2 values were often above 0.80.
Allometric equations were found to differ between stand densities and ages according to
statistical tests and qualitative observations, and they were also identified to be different among
geographic locations independent of stand density and age. In addition, applications of
inappropriate models sometimes produced tremendous errors and have the potential to do so in
19
further applications. Therefore, wise use of these models is critical, as application of the
appropriate model to the appropriate stand is vital due to differences identified by this study
among forest structures and geographic locations. We strongly suggest that models not be
applied to stands differing significantly in density and age from the stands where the models
were developed, and that models not be applied to geographic locations of considerable distance
from where the models were developed. In addition, allometric models developed in this study
have many potential uses from determination of NPP for carbon studies using multi-year
measurements, fire modeling, and for determination of wood biomass by foresters.
20
Table 1. Sites for the development of allometric models in the Greater Yellowstone Ecosystem.
Soils for all three sites were in the Koffgo series (see Methods).
NAD 83, UTM Zone
Stand Density
12
Stand
(trees > 5cm
Northing Easting
Site
Elevation
Age
DBH per
Stand basal area
(m)
(m)
Name
(m)
(years)
hectare)
(m2 per hectare)
Grassy
Lake
2249
4886015
511735
165
674  175
16.84  14.3
Coffee
Pot
1951
4926541
472232
64
725  72
19.71  11.4
US 20
1951
4925932
472657
64
2452  929
28.32  30.4
21
Table 2. Allometric models for predicting biomass of eight different above and belowground components of P. contorta in the
Greater Yellowstone Ecosystem. Non-linear power functions are listed first, and linear equations are at the end of the table. MSE is
the mean square error for non-linear model and SE is the standard error of the estimate for linear the model. X is the morphometric
predictor variable and Y represents the response variable (biomass (Kg)). The subscript 2 is for predictors that are associated with the
coefficient c in non-linear models.
Non Linear Power Functions (y = axb, or y = axbx2c)
(Y)
Total
Aboveground
Biomass
Tree Bole
Total Coarse
Root Biomass
(>10mm)
Root Crown
Biomass
Lateral Root
Biomass
(>10mm)
Branches
(X)
Volume
Volume
Basal Area
Basal Area
Basal Area
Basal Area, Crown Length2
Basal Area
-
Site(s)
All Sites
Old, Sparse
2 Young Stands
2 Sparse Stands
Young, Dense
Young, Sparse
Old, Sparse
All Sites
Old, Sparse
2 Young Stands
2 Sparse Stands
Young, Dense
Young, Sparse
All Sites
Old, Sparse
2 Young Stands
2 Sparse Stands
Young, Dense
Young, Sparse
All Sites
Old, Sparse
2 Young Stands
2 Sparse Stands
Young, Dense
Young, Sparse
All Sites
Old, Sparse
2 Young Stands
2 Sparse Stands
Young, Dense
Young, Sparse
DBH range (cm)
5.4 - 33.3
11.3 - 33.3
5.4 - 25.0
11.3 - 33.3
5.4 - 15.6
11.7 - 25.0
11.3 - 33.3
5.4 - 32.0
11.3 - 32.0
5.4 - 24.3
11.3 - 32.0
5.4 - 15.6
12.7 - 24.3
5.4 - 32.0
11.3 - 32.0
5.4 - 24.3
11.3 - 32.0
5.4 - 15.6
12.7 - 24.3
5.4 - 32.0
11.3 - 32.0
5.4 - 24.3
11.3 - 32.0
5.4 - 15.6
12.7 - 24.3
5.4 - 33.3
11.3 - 33.3
5.4 - 25.0
11.3 - 33.3
5.4 - 15.6
11.7 - 25.0
22
n
46
17
29
32
14
15
17
24
5
19
10
14
5
24
5
19
10
14
5
24
5
19
10
14
5
46
17
29
32
14
15
a
0.005
0.003
0.0001
0.010
0.00004
0.0004
0.002
0.028
0.188
0.006
29.939
0.005
0.002
0.020
0.224
0.006
0.050
0.007
0.007
0.008
0.010
0.001
0.011
0.0004
0.00003
0.022
0.003
0.001
0.042
0.002
0.004
b
0.793
0.817
1.088
0.741
1.155
1.001
0.822
1.139
0.845
1.406
-0.179
1.467
1.579
1.109
0.745
1.304
0.968
1.267
1.284
1.193
1.146
1.564
1.147
1.738
2.153
0.683
1.136
1.855
0.528
1.548
1.497
c
1.276
0.850
-0.291
1.288
-
R2
0.95
0.94
0.95
0.94
0.84
0.92
0.93
0.95
0.93
0.97
0.99
0.98
0.95
0.84
0.71
0.95
0.73
0.93
0.89
0.94
0.94
0.94
0.89
0.91
0.94
0.69
0.86
0.88
0.54
0.91
0.76
MSE
679.6
758.4
289.4
810.0
109.9
445.2
608.5
11.8
36.4
2.0
5.8
0.3
8.3
13.6
65.5
1.1
33.8
0.2
5.3
2.1
4.8
1.0
5.2
0.2
2.6
69.3
21.6
34.6
96.2
0.4
67.9
Fine Fuels
Needles
Basal Area, Sapwood Area2
Basal Area
Sapwood Area
Basal Area
Basal Area, Sapwood Area2
Basal Area
Sapwood Area
Basal Area
-
All Sites
Old, Sparse
2 Young Stands
2 Sparse Stands
Young, Dense
Young, Sparse
All Sites
Old, Sparse
2 Young Stands
2 Sparse Stands
Young, Dense
Young, Sparse
(X)
Volume
-
Site(s)
All Sites
2 Young Stands
2 Sparse Stands
Young, Dense
Young, Sparse
5.4 - 33.3
11.3 - 33.3
5.4 - 25.0
11.3 - 33.3
5.4 - 15.6
11.7 - 25.0
5.4 - 33.3
11.3 - 33.3
5.4 - 25.0
11.3 - 33.3
5.4 - 15.6
11.7 - 25.0
46
17
29
32
14
15
46
17
29
32
14
15
0.418
0.524
0.007
1.204
0.002
0.011
0.192
0.249
0.007
0.563
0.002
0.014
0.466
0.323
1.512
0.638
1.695
1.427
0.525
0.323
1.426
0.701
1.600
1.312
0.418
0.403
0.317
0.471
-
0.68
0.81
0.93
0.52
0.96
0.86
0.73
0.82
0.89
0.61
0.91
0.78
184.8
58.3
48.7
220.9
1.4
97.1
67.0
33.0
30.7
78.5
1.5
60.3
a
0.0002
0.0002
0.0002
0.0002
0.0002
b
3.420
5.482
1.921
-0.327
0.055
c
r2
0.96
0.93
0.95
0.79
0.95
SE
17.9
8.9
20.8
8.9
7.6
( Linear Models, y = ax + b )
(Y)
Tree Bole
DBH range (cm)
5.4 - 33.3
5.4 - 25.0
11.3 - 33.3
5.4 - 15.6
11.7 - 25.0
23
n
46
29
32
14
15
-
Table 3. Allometric model comparison between densities and ages of mature
P.contorta stands in the Greater Yellowstone Ecosystem. Results are from the
extra sum of squares analysis for nested models (Bates and Watts 1988).
All possible statistical comparisons are shown. Models for comparisons not in this
table had different model forms.
Dependent
Variable
Model
Comparison
F
p-value
Total Aboveground
Biomass
Old vs. Young
11.726
<.0001*
Total Aboveground
Biomass
Sparse vs. Dense
3.514
0.037*
Branches
Old vs. Young
19.706
<.0001*
Total Coarse
Roots (>10mm)
Old vs. Young
8.128
<.0001*
Total Coarse
Roots (>10mm)
Sparse vs. Dense
57.919
<.0001*
Root Crown
Old vs. Young
3.937
0.036*
Lateral Root
Biomass (>10mm)
Old vs. Young
4.790
0.020*
Bole
Sparse vs. Dense
0.481
0.622
Root Crown
Sparse vs. Dense
0.971
0.396
Lateral Root
Biomass
Sparse vs. Dense
0.508
0.608
* p-values are significantly different ( = 0.05)
Values in bold were not significantly different
24
Table 4. Comparison of actual P. contorta individual tree biomass vs. estimates from this study in the Greater Yellowstone
Ecosystem and from studies in British Columbia by Comeau and Kimmins (1989) and Pearson et al. (1984). All p-values < 0.05 are
in bold. The upper p-value for each component is the comparison between actual values from this study and predicted values from
this study, and the lower p-value is for the comparison between actual values from this study and predicted values applying the
equations from other studies.
Component
Bole
Branches
Needles
Total Root
Bole
Branches
Needles
Root Crown
Minimum
Biomass (Kg)
Maximum
Biomass (Kg)
Mean Biomass
(Kg)
Age Range
Density Range
This Study (Actual)
10.2
136.8
50.7
64
725 - 2452
This Study (Predicted)
3.3
135.5
51.1
-
-
0.804
Comeau and Kimmins
6.7
170.9
67.9
70 - 78
1770 - 3580
<0.001
This Study (Actual)
0.3
55.9
14.0
64
725 - 2452
This Study (Predicted)
0.3
47.6
14.9
-
-
0.401
Comeau and Kimmins
0.7
33.2
9.6
70 - 78
1770 - 3580
0.037
This Study (Actual)
0.2
59.3
16.2
64
725 - 2452
This Study (Predicted)
0.6
48.1
16.5
-
-
0.739
Comeau and Kimmins
0.4
24.5
5.5
70 - 78
1770 - 3580
<0.001
This Study (Actual)
0.7
33.8
8.1
64
725 - 2452
This Study (Predicted)
0.4
32.5
8.3
-
-
0.400
Comeau and Kimmins
5.8
32.1
15.0
70 - 78
1770 - 3580
<0.001
This Study (Actual)
26.0
329.0
137.1
64 - 165
674 - 2452
This Study (Predicted)
7.0
339.8
106.3
-
-
0.514
Pearson et al.
24.7
256.6
121.8
75 - 240
<2500
0.063
This Study (Actual)
1.7
55.9
21.7
64 - 165
674 - 725
This Study (Predicted)
3.1
29.1
16.0
-
-
0.003
Pearson et al.
3.2
32.9
15.6
75 - 240
<2500
0.005
This Study (Actual)
1.6
44.7
25.8
165
674
This Study (Predicted)
8.5
45.4
25.9
-
-
0.928
Pearson et al.
9.3
30.3
17.1
240
420
<0.001
This Study (Actual)
0.6
35.9
7.7
74-165
674 - 2452
This Study (Predicted)
0.5
33.4
7.9
-
-
0.763
Pearson et al.
-0.1
54.1
13.2
75 - 240
<2500
<0.001
Site
25
p-value
Figure 1. Three Stands Differing in Densities and Ages for allometric model development of P.
contorta in the Greater Yellowstone Ecosystem. Individual stands are depicted by a black
square.
26
Figure 1:
YS
YD
OS
27
Figure 2. Comparison of biomass values among those directly measured by this study, those
predicted by this study’s allometric models, and those predicted by Comeau and Kimmins’
allometric models.
28
Figure 2.
Branches
Tree Bole
60
180
This Study (Actual)
This Study (predicted)
Comeau and Kimmins (predicted)
160
This Study (Actual)
This Study (predicted)
Comeau and Kimmins (predicted)
50
140
Biomass (Kg)
120
40
100
30
80
60
20
40
10
20
0
0
0
5
10
15
20
25
30
0
5
diameter at breast height (cm)
15
20
25
30
diameter at breast height (cm)
Total Coarse Roots
Needles
70
40
This Study (Actual)
This Study (predicted)
Comeau and Kimmins (predicted)
60
This Study (Actual)
This Study (predicted)
Comeau and Kimmins (predicted)
50
Biomass (Kg)
10
30
40
20
30
20
10
10
0
0
0
5
10
15
20
25
30
0
diameter at breast height (cm)
5
10
15
20
diameter at breast height (cm)
29
25
30
Figure 3. Comparison of biomass values among those directly measured by this study, those
predicted by this study’s allometric models, and those predicted by Pearson et al.’s allometric
models.
30
Figure 3.
Tree Bole
400
This Study (Actual)
This Study (predicted)
Pearson et al. (predicted)
300
Biomass (Kg)
Branches
60
This Study (Actual)
This Study (predicted)
Pearson et al. (predicted)
50
40
200
30
20
100
10
0
0
0
5
10
15
20
25
30
35
0
diameter at breast height (cm)
10
20
25
30
35
Root Crown
60
This study (Actual)
This study (predicted)
Pearson et al. (predicted)
40
15
diameter at breast height (cm)
Needles
50
Biomass (Kg)
5
This study (Actual)
This study (predicted)
Pearson et al. (predicted)
50
40
30
30
20
20
10
10
0
0
5
10
15
20
25
30
35
0
5
diameter at breast height (cm)
10
15
20
25
diameter at breast height (cm)
31
30
35
LITERATURE CITED
Amiro, B. D., J. M. Chen and J. Liu 2000. Net primary productivity following forest fire for
Canadian ecoregions. Can. J. For. Res. 30: 939-947.
Barnes, B. V., D. R. Zak, S. R. Denton and S. H. Spurr (1980). Forest Ecology. New York, NY,
John Wiley & Sons, Inc.
Bates, D. M. and D. G. Watts (1988). Biometrics: nonlinear regression and its applications. New
York, NY, John Wiley & Sons.
Berger, U., H. Hildenbrandt and V. Grimm 2004. Age-related decline in forest production:
modelling the effects of growth limitation, neighbourhood competition and self-thinning.
Journal of Ecology 92: 846-853.
Binkley, D., J. L. Stape and M. G. Ryan 2004. Thinking about efficiency of resource use in
forests. Forest Ecology and Management 193: 5-16.
Bowerman, T. S., J. Dorr, S. Leahy, K. Varga and J. Warrick (1999). Targhee National Forest
ecological unit inventory, United States Department of Agriculture, Forest Service
United States Department of Agriculture, Natural Resources Conservation Service
University of Idaho, College of Agriculture. 1-2: 787.
Brown, J. K. 1978. Weight and density of crowns of Rocky Mountain conifers. Res. Pap. Int197. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain
Research Station; 56 p.
Comeau, P. G. and J. P. Kimmins 1989. Above- and below-ground biomass and production of
lodgepole pine on sites with differing soil moisture regimes. Can. J. For. Res. 19: 447454.
36
Dale, V. H., L. A. Joyce, S. McNulty, R. P. Neilson, M. P. Ayres, M. D. Flannigan, P. J. Hanson,
L. C. Irland, A. E. Lugo, C. J. Peterson, D. Simberloff, F. J. Swanson, B. J. Stocks and B.
M. Wotton 2001. Climate change and forest disturbances. Bioscience 51: 723-734.
Despain, D. G. (1990). Yellowstone vegetation: consequences and environment in a natural
setting. Boulder, CO, Roberts Rinehart, Inc.
Dirks, R. A. and B. E. Martner 1982. The climate of Yellowstone and Grand Teton National
Parks. National Park Service Occassional Paper 6.
Gholz, H. L., C. C. Grier, A. G. Campbell and A. T. Brown (1979). Equations for estimating
biomass and leaf area of plants in the Pacific Northwest. Research Paper, Forest Research
Laboratory, Oregon State University. 41: 39pp.
Gurevitch, J., S. M. Scheiner and G. A. Fox (2002). The ecology of plants. Sunderland, MA,
Sinauer Associates, Inc.
Hilborn, R. and M. Mangel (1997). The ecological detective: confronting models with data.
Princeton, NJ, Princeton University Press.
Johnstone, W. D. (1971). Total standing crop and tree component distributions in three stands of
100-year-old lodgepole pine. Forest biomass studies. H. E. Young. Orono, ME,
University of Maine Press. 15th IUFRO Congress: 81-89.
Kashian, D. M., M. G. Turner and W. H. Romme 2005. Variability in leaf area and stemwood
increment along a 300-year lodgepole pine chronosequence. Ecosystems 8: 48-61.
Knight, D. H. (1994). Mountains and plains: the ecology of Wyoming landscapes. New Haven,
CT, Yale University Press.
Koch, P. (1996). Lodgepole Pine in North America. Madison, WI, Forest Products Society.
37
Litton, C. M., M. G. Ryan, D. B. Tinker and D. H. Knight 2003. Belowground and aboveground
biomass in young postfire lodgepole pine forests of contrasting tree density. Can. J. For.
Res. 33: 351-363.
Madgwick, H. A. I. 1964. Estimation of surface area of pine needles with special reference to
Pinus resinosa. J. For. 61: 636.
Mainwaring, D. B. and D. A. Maguire 2004. The effect of local stand structure on growth and
growth efficiency in heterogeneous stands of ponderosa pine and lodgepole pine in
central Oregon. Canadian Journal of Forest Research-Revue Canadienne De Recherche
Forestiere 34: 2217-2229.
Pearson, J. A., T. J. Fahey and D. H. Knight 1984. Biomass and leaf area in contrasting
lodgepole pine forests. Can. J. For. Res. 14: 259-265.
Ryan, M. G., D. Binkley and J. H. Fownes 1997. Age-related decline in forest productivity:
pattern and process. Advances in Ecological Research 27: 213-262.
Ryan, M. G., D. Binkley, J. H. Fownes, C. P. Giardina and R. S. Senock 2004. An experimental
test of the causes of forest growth decline with stand age. Ecological Monographs 74:
393-414.
Ryan, M. G., S. T. Gower, R. M. Hubbard, R. H. Waring, H. L. Gholz, W. P. J. Cropper and S.
W. Running 1995. Woody tissue maintenance repiration of four conifers in contrasting
climates. Oecologia 101: 133-140.
Ryan, M. G. and B. J. Yoder 1997. Hydraulic limits to tree height and tree growth. Bioscience
47: 235-241.
Smethhurst, P., C. Baillie, M. Cherry and G. Holz 2003. Fertilizer effects on LAI and growth of
four Eucalyptus nitens plantations. Forest Ecology and Management 176: 531-542.
38
Smith, F. W. and J. N. Long 2001. Age-related decline in forest growth: an emergent property.
Forest Ecology and Management 144: 175-181.
Smith, F. W. and S. C. Resh 1999. Age-related changes in production and below-ground carbon
allocation in Pinus contorta forests. Forest Science 45: 331-341.
Snell, J. A. K. 1978. Comparison of tree biomass estimators - DBH and sapwood area. Forest
Science 24: 455-457.
Taiz, L. and E. Zeiger (2002). Plant Physiology. Sunderland, MA, Sinauer Associates, Inc.
Ter-Mikaelian, M. T. and M. D. Korzukhin 1997. Biomass equations of sixty-five North
American tree species. Forest Ecology and Management 97: 1-24.
Tilman, D. (1982). Resource competition and community structure. Princeton, NJ, Princeton
University Press.
Turner, M. G., D. B. Tinker, W. H. Romme, D. M. Kashian and C. M. Litton 2004. Landscape
patterns of sapling density, leaf area, and aboveground net primary production in postfire
lodgepole pine forests, Yellowstone National Park (USA). Ecosystems 7: 751-775.
39
Chapter 2
Biomass Allocation Patterns and Nitrogen Use Efficiency for Mature Lodgepole Pine
Forests in the Greater Yellowstone Ecosystem
INTRODUCTION
Net Ecosystem Production (NEP) varies widely across many forested landscapes due to
variability produced by heterogeneity in stand ages and densities as well as natural and
anthropogenically altered disturbance regimes (Chapin et al. 2002; Turner et al. 2004). For
example, in the Greater Yellowstone Ecosystem (GYE), recent and historic fires have created a
complex mosaic of varying forest stand structures (Turner et al. 1997). Understanding the
variability in NEP is, at least in part, dependent upon knowledge of how individual trees allocate
biomass in response to such differences in forest structure. Lodgepole pine (Pinus contorta var.
latifolia (Engelm. ex Wats.) Critchfield) dominates the forested plateaus of the GYE (Tinker et
al. 1994), and therefore contributes greatly to the NEP of the ecosystem. The successional
pathways of stand development for lodgepole pine vary widely after disturbances such as fire,
where initial seedling densities may range from < 100 to > 500,000 seedlings/ha (Turner et al.
2004). As the forests mature, this heterogeneous landscape is somewhat homogenized by a
convergence in stand density (Kashian et al. 2005), resulting in considerable variability in spatial
and temporal patterns of biomass accumulation and net primary production (NPP) across the
landscape (Litton et al. 2004) (Turner et al. 2004).
Although previous studies have identified differences in biomass allocation patterns for
forests differing in density (Pearson et al. 1984; Comeau and Kimmins 1989; Litton et al. 2003;
Litton et al. 2004) and age (Smith and Resh 1999; Ryan et al. 1997; Berger et al. 2004), further
understanding of the relationships between stand structural characteristics and allocation patterns
40
of mature lodgepole pine forests of the GYE is critical for our understanding of how natural and
anthropogenic changes in forest structure will alter NEP. In this study we harvested lodgepole
pine above and belowground in three stands differing in densities and ages to determine the
allocation patterns of mature lodgepole pine forests in the GYE across varying forest structures.
Allocation patterns for mature lodgepole pine forests have been described in several
locations throughout the northern and central Rocky Mountains (Pearson et al. 1984; Comeau
and Kimmins 1989; Litton et al. 2004). In British Columbia, Comeau and Kimmins (1989)
observed that allocation to total root biomass was significantly higher in more xeric stands,
indicating that allocation to roots was higher under more resource limited conditions, such as
those present where stand density is high and stands are old. In the Medicine Bow Mountains of
southeastern Wyoming, Pearson et al. (1984) also found that allocation to roots increased with
increasing stand density; in contrast, Smith and Resh (1999) found no significant shift to
belowground allocation with increasing stand age. In addition, Comeau and Kimmins (1989)
observed that allocation to coarse roots >5mm diameter was greater in more mesic stands,
indicating a need for further study of biomass allocation patterns.
The GYE differs appreciably in substrate, topography, and climate from the locations of
other studies, which may cause biomass allocation patterns to differ. Although Litton et al.
(2004) found that the ratio of total belowground carbon allocation as a proportion of total tree
biomass was remarkably constant across extreme gradients of stand density and age in the GYE,
their estimates of allocation were based on 15 year old lodgepole pine forests, and considered
only five trees in a mature 110-year-old lodgepole pine forest. Therefore, more extensive data
for allocation patterns of mature lodgepole pine forests in the GYE are needed. Also, studies of
the biomass allocation patterns of lodgepole pine have focused largely on stand level allocation
41
patterns (Pearson et al. 1984; Comeau and Kimmins 1989; Litton et al. 2004, Smith and Resh
1999); therefore, more in depth analyses of the allocation of biomass in lodgepole pine for
individual trees are needed.
Determining the use efficiency for essential nutrients such as nitrogen (N) is also
important for improving our understanding of biomass accumulation of lodgepole pine. Gross
Primary Production (GPP), a major component of NEP, is equal to the product of resource
supply, the proportion of the resource supply that is taken up, and the efficiency of resource use
(Binkley et al. 2004). Also, nitrogen is an essential part of plant proteins and amino acids,
including the enzyme rubisco, which is critical to the Calvin cycle of photosynthesis (Taiz and
Zeiger 2002).
Some important factors affecting the N availability in lodgepole pine forests include the
abundance of N-fixers, such as Lupinus argenteus and Shepherdia canadensis (Fahey et al. 1985;
Hendrickson and Burgess 1989), litter quality (Fahey 1983; Stump and Binckley 1993; Thomas
and Prescott 2000), the abundance of coarse dead wood (Fahey et al. 1985), microbial biomass
(Litton et al. 2003), inputs from precipitation (Fahey et al. 1985), and leaching from the soil
(Fahey et al. 1985). In lodgepole pine forests, nitrogen-fixers, such as Lupinus argenteus and
Shepherdia canadensis have been shown to be important sources of nitrogen for lodgepole pine
forests (Fahey et al. 1985; Hendrickson and Burgess 1989). Lignin: N ratios for lodgepole pine
litter were found to be higher than paper birch and douglas-fir, which coincided with lower net
mineralization of N (Thomas and Prescott 2000), indicating that the poor litter quality typical of
lodgepole pine is associated with lower N availability. In southeastern Wyoming, Fahey et al.
(1985) found high N storage in dead lodgepole pine boles lying on the forest floor. In YNP,
Litton et al. (2003) found that microbial biomass was significantly higher in a 110 year old
42
lodgepole stand than younger < 15 year old stands, indicating that both immobilization and net
mineralization of N could be higher in older lodgepole pine stands. Inputs of N in lodgepole
pine forests of the Greater Yellowstone Ecosystem are probably limited, because in lodgepole
pine forests of southeastern Wyoming, Fahey et al. (1985) found that inputs of N were nearly
twice as high in rainfall compared to snowfall. Therefore, in the GYE and other lodgepole pine
forests of the intermountain west, where the majority of annual precipitation comes as snow
(Dirks and Martner 1982; Knight 1994), inputs of N through precipitation are likely to be low.
In contrast, extremely low inputs of N to the groundwater in lodgepole pine forests as found by
Fahey et al. (1985), indicate efficient uptake of N by lodgepole pine. In addition, production of
older lodgepole pine forests in southeastern Wyoming have been shown by (Binkley et al. 1995)
to respond to fertilization, indicating that they are N limited. Therefore, NUE in lodgepole pine
forests is likely to be an important factor affecting biomass production.
Nitrogen use efficiency (NUE) of lodgepole pine is the amount of biomass or C fixed per
unit of nitrogen that is taken up (Vitousek 1982; Aber and Melillo 2001; Chapin et al. 2002).
Vitousek (1982) found that across biomes, coniferous forests were more efficient at fixing
biomass per unit of nutrient uptaken than more productive temperate deciduous forests,
indicating that nitrogen use efficiency (NUE) should be higher where nutrients are more limiting
(Ryan et al. 1997), such as dense and older forests. In addition, (Gower et al. 1989) suggested
that high NUE in lodgepole pine and Larix occidentalis could be attributed to low nutrient
availability of the volcanic soils in the Washington Cascades. In contrast, Olsson et al. (1998)
found an inverse relationship between NUE and stand age in southeastern Wyoming that they
attributed to a decrease in aboveground productivity caused by increased allocation to roots and
mycorrhizal development. Therefore, it would appear that patterns of variability in the
43
efficiency of nutrient use with forest structure are poorly understood, and this may be especially
true for lodgepole pine forests of the Intermountain West. We estimated NUE in three sites in
the Greater Yellowstone Ecosystem that varied by stand age and tree density to determine how
NUE relates to variability in forest structure and site index (see Methods), and to identify
relationships between NUE and biomass allocation patterns.
The specific objectives of this study were: (1) to determine how patterns of above and
belowground biomass allocation for mature lodgepole pine forest of the GYE vary with stand
age and density; (2) to estimate the carbon storage in three mature lodgepole pine stands
differing in density and age of the Greater Yellowstone Ecosystem; (3) to quantify patterns of
nitrogen use efficiency (NUE) for lodgepole pine forests among these differing forest structures
and productivities; and (4) to examine the relationships between NUE and biomass allocation
patterns.
We hypothesized that patterns in biomass allocation and NUE of lodgepole pine forests
in the GYE would vary with forest structure and geographic location where factors affecting
nutrient availability are different, and we expected that biomass allocation would differ at
multiple scales (i.e. stand level vs. individual tree level). Specifically, lodgepole pine should
allocate a greater proportion of total biomass to coarse roots in more dense stands and older
stands, and is likely to allocate a greater proportion of aboveground biomass to the tree bole
rather than foliage with increasing stand age due to increased competition for water, light, and
nutrients (Ryan et al. 1997). We expected that the efficiency of nitrogen use would increase with
increasing stand density and age, because of increased competition for resources in older and
denser stands. (Binkley et al. 1995) found that tree basal area in older lodgepole pine stands in
44
southeastern WY responded to N fertilization, but not fertilization by P and K, indicating that
lodgepole pine forests are N limited.
We also expected that nitrogen use efficiency would be higher where biomass allocation
to roots was higher as found by Vitousek (1982), because NUE has been found to be higher
where nutrients are more limiting (Vitousek 1982; Cote et al. 2002), such as the nutrient poor
sites where allocation to roots has been found to be greater for douglas-fir in the Cascade
Mountains of Washington, US (Keyes and Grier 1981).
METHODS
Study Area
The study area was situated within the GYE on the Caribou-Targhee National Forest
(CTNF) adjacent to YNP. The GYE includes portions of three states in the western US:
Wyoming, Montana and Idaho, and is signified by Yellowstone National Park and its
surrounding subalpine plateaus, mountains, and valleys. There are three main substrates for soil
development on the subalpine plateaus of the ecosystem: the least fertile rhyolite; the less
infertile rhyolite; and the less infertile lake-bottom sediments (Despain 1990). The dominant
forest type of the ecosystem is lodgepole pine forest, which occurs at middle elevations. Aspen
(Populus tremuloides) occurs more commonly within the ecosystem outside of YNP (Knight
1994). Douglas-fir (Pseudotsuga menziesii) forests occur at lower elevations, and Spruce/ Fir
(Picea engelmannii /Abies lasiocarpa) forests occur at higher elevations. Whitebark pine (Pinus
albicaulis) dominates at the upper treeline and sagebrush (Artemesia spp.) occurs at the lower
treeline.
45
Elevations in YNP range from 1,620 m near Gardiner, Montana to 3,333 m in the
Absaroka mountain range of Wyoming. Mean annual precipitation ranges from less than 28
cm/yr at lower elevations to about 180 cm yr-1 on the southwestern plateau (Knight 1994).
Temperatures are cold during the winter, where high temperatures less than 0C occur for an
average of 87.6 days each year, and are rarely more than 32C during the summer (Dirks and
Martner 1982).
Biomass Allocation Patterns
Field and Lab Methods
Research sites were situated within the Greater Yellowstone Ecosystem (GYE) on the
Caribou-Targhee National Forest (CTNF) adjacent to Yellowstone National Park (YNP), where
the topography, vegetative cover, and substrate were comparable. Allocation patterns were
determined in three stands on the CTNF using two age classes and two density classes. Actual
biomass estimates were used to determine allocation patterns of individual lodgepole pine, and
allometric models (Arcano and Tinker Manuscript in preparation) were used to estimate biomass
at the stand level.
Because densities of lodgepole pine forests tend to converge as they get older (Kashian et
al. 2005), a single sparse (674 trees ha-1) stand (OS) was sampled in the older age class (164
years old) (Figure 1). In the young (64 years old) aggrading age class, two stands were studied;
one dense (YG) (2,452 trees ha-1) and the other sparse (YS) (725 trees ha-1). The two young
stands were located within the CTNF proximal to Island Park, ID (Figure 1). Although sites
differed in density and age, they had comparable soils (Table.1). The Koffgo soil series is
composed of loamy-skeletal, mixed, superactive Vitrandic Cryochrepts, which is due to the super
volcano located beneath YNP (Knight 1994). The parent material consisted of local residuum,
46
colluvium, or alluvium developed on volcanic ash, igneous rocks, and loess (Bowerman et al.
1999). Mean annual precipitation was approximately 76.2 cm yr-1 at the younger stands and
114.3 cm yr-1 at the oldest stand. Elevations ranged from 1951m to 2249m, which is within the
range of elevations found for the GYE.
A total of 46 lodgepole pines were harvested aboveground and 24 were excavated
belowground to determine their biomass allocation patterns. All sites were located at least 50m
from the road to enable equipment hauling and to avoid road influences. Fourteen trees were
harvested in the YD stand and 15 trees were harvested in the YS stand; 17 were harvested in the
OS stand. For belowground components, 5 root systems were excavated in both the YS and OS
stands due to logistical difficulties associated with large root systems, while 14 root systems
were excavated in the dense stand for a total of 24 root systems.
Tree Level Biomass Estimations
Biomass of individual trees was measured for above and belowground components in
each stand as a part of a separate study developing lodgepole pine allometrics for the Greater
Yellowstone Ecosystem (Arcano and Tinker Manuscript in preparation). Biomass of the
following tree components was determined: total coarse root (>10mm diameter), total root
crown, total lateral root, total aboveground, tree bole, branches, foliage, and needles.
Aboveground Biomass
Bole - Each bole was harvested at ground level and all branches were removed. The bole
was cut into three to four sections with 1 to 2 discs cut out as subsamples to determine moisture
content for dry weight. For each bole, two discs were always removed for subsampling at DBH
(diameter at breast height, 1.37m), and at 90% of crown base, (the location on the bole where
most of the live crown began). Two discs were cut from each section if the bole forked near the
47
top or if DBH and crown base were one meter or closer to each other. Each of the bole sections
were weighed separately using a digital hanging scale (Salter-Brecknell) and the disc was
summed back into the weight for its respective section. Discs were then dried in the lab to
determine moisture loss where the dry: wet weight ratio for each disc was used to determine the
dry weight of its respective bole section. All subsamples were dried to a constant weight at
70C.
Fine fuels - The fine fuels component was considered to be all needles, associated cones,
and associated twigs 6.4 mm diameter, because those aboveground components are likely to be
combusted in a fire, and post-fire estimates of branch biomass can then be obtained independent
of combustible materials. The fine fuels component was maintained in separate piles of lower,
middle, and upper crown portions for each tree, since foliage moisture contents were likely to
vary with crown height (Brown 1978). A random subsample approximating the size of 4.0 L in
volume was removed from each crown section to determine moisture content. The fine fuels
subsamples were then weighed to obtain wet weight. If immediate weighing was not feasible,
subsamples were stored in a cooler for no more than 5 days in plastic bags, to prevent moisture
loss prior to weighing.
The current year’s growth (2004) was removed and discarded, because many of the
needles were not yet fully expanded, and samples were collected at varying times throughout the
summer of 2004. The subsample was reweighed to determine the proportion of the foliage
component that was from the current year’s growth. This proportion was then subtracted from
the entire foliage component. After weighing, a random sample of 10 fascicles was collected
from each fine fuels subsample, and the length and width of each fascicle was measured to
determine leaf area. Leaf area index (LAI) could then be determined from the product of leaf
48
area determination for individual trees and stand density (see below). A tapered-bisected
cylinder was used to determine the surface area represented by each fascicle (Pearson et al. 1984;
Madgwick 1964). This sample was then dried and subsequently weighed so that the surface area
of this subsample could be extrapolated to the entire tree’s needle biomass. This sample was
also weighed while still wet for subsequent drying and weighing to determine moisture content
for dry weight determination of total needle biomass. The dry weight proportion of total foliage
biomass that was needles was determined from separating needles from twigs after the foliage
subsamples were dried to a constant weight at 70C.
Branches - Branches were cut flush to the tree bole and were separated from foliage at
6.4 mm in diameter. Thus, branches consisted of all shoots excluding the tree bole that were
greater than 6.4 mm in diameter. A subsample of approximately 4.0 L in volume was taken to
determine moisture content for dry weight for the branches. All subsamples were dried to a
constant weight at 70C in the lab. Branches were compiled rather than separated into three
crown sections, because one subsample from all sections was deemed sufficient to provide an
adequate estimate of dry weight for the component.
Belowground Biomass
For each tree, the entire coarse root system (>10mm diameter) was excavated with the aid
of a backhoe or come-a-long. Prior to excavation, smaller roots that could be damaged by burly
excavation techniques were removed by hand. After excavation, the root system was separated
into four size classes: root crown (i.e. the massive structure directly beneath the tree bole and
within the lateral extent of the diameter of the tree at stump height); lateral roots >50mm in
diameter; lateral roots from 25-50mm in diameter; and lateral roots from 10-25mm in diameter.
Each size class was then weighed wet, before any moisture was lost. Subsamples of
49
approximately 4.0 L in volume were collected to determine the moisture content for dry weight
of each size class, and were posthumously dried to a constant weight at 70°C where the dry: wet
ratio was applied to its corresponding size class for determination of total dry weight for each
size class. Dry weights for each size class were then summed.
Stand Level Biomass Estimations
Allometric models derived from three stands varying in densities and ages were used to
determine biomass allocation patterns for above and belowground components of lodgepole pine
in the three study sites. From variable radius point sampling (Avery and Burkhart 1994), tree
diameters were measured at six points within each stand. Basal area could then be calculated. In
order to estimate total height and height to the base of the live crown in each stand, regression
equations between DBH and total height and between total height and crown base from a related
study (Arcano and Tinker Manuscript in preparation) were used. However, if the relationship
from those regressions was not significant (p>0.05), the mean of height or height to crown base
for each of three diameter classes was applied to all trees in their respective diameter classes.
These measured and estimated parameters were used in the application of allometric equations
for various tree components from a related study in the GYE, (Arcano and Tinker Manuscript in
preparation) and were then applied to the trees measured in variable radius point sampling
(Avery and Burkhart 1994). In variable radius point sampling, stand density can be determined,
where each “in” tree using an angle gauge represents a certain amount of basal area per unit area,
depending on the size of the angle gauge that is being used. “In” trees are trees that are fully
within the angle gauge when sighting through the gauge at the tree. The size of the angle gauge
is referred to as its basal area factor (BAF). The equation for determining the density
represented by each “in” tree is as follows:
50
Di = BAF/BAi
(1)
where Di = the density represented by the given tree, BAF = the basal area factor for the angle
gauge in square feet per acre, and BAi = the basal area in for the given tree. For instance, if a tree
is 7.1cm in diameter or 0.003959 m2 in basal area, and the angle gauge being used had a BAF of
2.296 m2/ha, then that tree would be represented by approximately 579 trees/ha. Next, if there
were 10 trees of exactly the same size at a point, that point would have a density of 5,790
trees/ha. Next, the values for all points were averaged to determine a stand density. In addition,
the appropriate allometric equation from a related study (Arcano and Tinker Manuscript in
preparation) was applied to each tree in each stand to determine a biomass value for the
respective component of each tree. Each biomass value was then multiplied by the density
represented by each tree, which became the stand level biomass represented by that tree. For
instance, a 7.1cm tree in the old, sparse stand was represented by 3.14 Mg/ha of total
aboveground biomass. If there were 10 trees at that point of the same size, then the stand level
total aboveground biomass would be 31.4 Mg/ha. The values for all 6 points in each stand were
averaged to determine the average stand level biomass for all components. There were two
sources of error when estimating stand level biomass: (1) the error associated with using
regression equations for biomass determination of the trees measured in variable point sampling;
and (2) the plot level error associated with the variable radius point sampling method itself.
However, the accumulated error from error (1) is actually included in error (2). Therefore the
perceived problem of error accumulation does not actually exist (Giardina and Ryan 2002), and
comparisons of stand biomass between densities and ages remain valid (see below).
Carbon Storage
51
Approximately 48% of the dry weight of a lodgepole pine is comprised of carbon (C)
(Koch 1996); therefore, 48% of each biomass component was its carbon storage. The carbon
storage for our study was compared against other lodgepole pine studies and other forested
biomes to determine whether and how carbon storage for our study related to other studies.
Statistical Analyses
To determine whether allocation patterns at the stand and tree level differed between
density and age, one-way ANOVAs were conducted, followed by Tukey’s HSD post-hoc
analyses. The following variables were used for comparisons: total coarse root biomass: total
aboveground biomass; root crown: lateral root biomass; and percent of total biomass for bole,
branches, and foliage. (Note: The total coarse root biomass: total aboveground biomass ratio
will be referred to in the following text as the below: aboveground biomass ratio.)
Nitrogen Use Efficiency
The NUE of a mature forest can be defined as the grams of biomass accumulated per
gram of nutrient taken up (Vitousek 1982; Aber and Melillo 2001). Furthermore, the actual
nutrient requirement for a tissue is not its concentration while alive, but its concentration upon
senescence (Vitousek 1982; Aber and Melillo 2001). Therefore, litterfall and wood increment
are ideal candidates for determining NUE. In a mature forest, however, litterfall is more easily
measured due to the slow accumulation of wood increment in a mature forest. When measuring
NUE with litterfall, NUE is dependent on the ability of the needles to translocate their nutrients
back into living tissues prior to senescence; therefore, the higher the ratio of carbon: nitrogen, the
higher the NUE. Although measuring NUE in litterfall provides a good estimate of NUE,
measuring the entire N taken up for lodgepole pine would provide a more comprehensive
estimate of NUE.
52
Eight litter trays were placed at fixed equally spaced intervals within a 50m x 50m plot
on the first week of July 2004 in the three sites differing in densities and ages where lodgepole
pine allometrics were developed. Litterfall was then collected on August 25th and placed in
plastic bags for storage and transportation to the laboratory. It was then sorted by species and
P.contorta needles were isolated. These needles were then dried to a constant weight at 70C.
We then analyzed for total carbon (C) and nitrogen (N) through a vario Macro C:H:N analyzer
(Elementar Americas, Inc.). NUE can be defined as units of biomass or C fixed per unit of N
uptaken in litterfall (Vitousek 1982), therefore, the total C:N ratio in litterfall was considered to
be the NUE. A one-way ANOVA followed by Tukey’s HSD post-hoc analysis was chosen to
determine whether NUE differed among densities, ages, and site productivities.
In order to determine relationships between site productivity and NUE, site index (SI)
was calculated for each stand using SI curves for lodgepole pine in the Rocky Mountain,
Intermountain, and Pacific Northwest regions of the US (Alexander 1966; Koch 1996). SI is
only one index of site productivity, and we recognize that there are other indicators of site
productivity, such as soil organic matter, which greatly affects nutrient availability (Stevenson
and Cole 1999). However, SI has been shown to be a better index of site productivity than any
other easily obtained parameter, because tree height is less influenced by stand density than other
indices, where stands of higher productivity have the ability to produce taller trees than stands of
lower productivity (Barnes et al. 1980). In addition, correlation analyses were conducted
between NUE and Site Index, below: aboveground biomass, root crown: lateral root biomass,
and allocation to the bole, branches, foliage, and needles.
53
RESULTS
Individual Tree Level Allocation Patterns
Allocation to bole biomass was highest for all three stands, but there was considerable
variability for allocation to other components. The hierarchy of biomass allocation for tree level
biomass allocation patterns was as follows: for the OS and YD stands it was bole > foliage >
total coarse root biomass > branches, and for the YS stand it was bole > foliage > branches >
total coarse root biomass (Table 2, Figure 2). Aboveground biomass was lowest for the YD and
highest for the OS stand (Table 2). A striking result of the ANOVA for biomass allocation
within trees was that the most structurally dissimilar stands (OS and YD) had the most similar
biomass allocation patterns (p > 0.05, Table 2, Figure 2), except for the below: aboveground
biomass ratio, where the OS and YD stands were significantly different from one another (Figure
2). In contrast, neither the OS and YS stands, nor the YS and YD stands were significantly
different from one another (Table 3). Although differences were not significant according to the
one-way ANOVA (p > 0.05) (Table 3), the root crown: lateral root ratio was lowest for the YS
stand and highest for the YD stand (Table 2). The tree bole as a proportion of total tree biomass
was highest for the YD stand and lowest for the YS stand (Table 2, Figure 2) while in contrast,
branches, foliage, and needles as a proportion of total tree biomass were highest in the YS stand.
Stand Level Allocation Patterns
The hierarchy of stand level biomass allocation for lodgepole pine in the GYE was
similar to the pattern observed for the individual tree level, and was as follows: for both the OS
stand and YD stands it was bole > foliage > total coarse root biomass > branches; and for the YS
stand it was bole > foliage > branches > total coarse root biomass (Table 4, Figure 3). This
indicated that bole biomass was the most dominant component of total tree biomass in all three
54
stands. Similar to allocation patterns for individual trees, stand level biomass allocation was
most similar for the most structurally dissimilar stands (OS and YD) (Table 4, Figure 3). The
results of the one-way ANOVA revealed that stand density and age did not affect how stands
allocated their biomass with respect to below: aboveground biomass (p = 0.07) (Table 3),
although not statistically different, the two young stands did have slightly higher below:
aboveground biomass ratios (0.12) than the OS stand (0.10) (Table 4). For the ratio of root
crown: lateral root biomass, post-hoc analyses (Table 3) showed that the ratio of root crown:
lateral root biomass was more affected by stand age than by stand density; there was no
difference between the YS and YD stand (p = 0.719), but there was a significant difference when
comparing the OS stand to both the YS (p = 0.010), and YD (p = 0.001) stands (Table 3). In
addition, the root crown: lateral root ratio for each stand revealed a similar pattern, where it was
2.2 for the OS stand and 1.4 and 1.5 for the YS and YD stands respectively (Table 4), indicating
a stronger effect of stand age than density on belowground allocation. The percentage of total
biomass allocated to the tree bole was strongly affected by stand density and age, and all
allocation comparisons between stands were significantly different (p < 0.05) (Figure 3).
Allocation to the tree bole differed for all three stands, and it was higher for the OS and YD
stands than the YS stand (Table 4, Figure 3). In contrast, allocation to branches, foliage, and
needles was highest in the YS stand, and lower in the OS and YD stands (Table 4, Figure 3).
Leaf Area Index (LAI) was approximately twice as high in the sparse stands compared to the YD
stand, but was quite similar between the OS and YS stands (Table 4).
55
Comparing biomass allocation patterns across geographic locations independent of stand
density and age
When comparing stands from this study to stands in other studies that had similar
densities and ages, the proportion of total biomass allocated to the tree bole was greater in this
study when compared to Pearson et al.’s (1984) older stand, but was smaller for the younger
stands when compared to both Pearson et al. and Comeau and Kimmins (Table 5, Figure 4). The
opposite pattern was shown for branches, although there are slight differences in the definition of
branches among studies, because we excluded twigs < ¼” from our branches component (see
Methods) (Table 5, Figure 4). The percentage of total biomass allocated to branches and needles
was greater for our study than studies developed in southeastern WY and British Columbia for
all stands except the OS stand (Table 5, Figure 4).
Carbon Storage in the Greater Yellowstone Ecosystem compared to other forests
The patterns found for biomass allocation were identical for carbon storage (Table 5),
because biomass of lodgepole pine is approximately 48% C (Koch 1996). Carbon storage for
total aboveground tree biomass was 70.0 Mg ha-1 for the OS stand, 40.8 Mg ha-1 for the YS
stand, and 46.7 Mg ha-1 for the YD stand. For a 110-year-old lodgepole pine forest in another
study from the GYE, total aboveground tree carbon storage was 86.9 Mg ha-1 (Litton et al. 2004).
For other forest types total aboveground tree biomass was 107.9 Mg ha-1 for a 250-year-old
Pinus ponderosa forest in central Oregon by (Law et al. 2001), 93.4 Mg ha-1 for a 50-120-yearold temperate deciduous forest in eastern Tennessee (Curtis et al. 2002), 120.9-153.6 Mg ha-1 for
tropical forests of the southwestern Brazilian Amazon (Cummings et al. 2002), and 49.9 Mg ha-1
and 39.3 Mg ha-1 for 139-year-old and 66-year-old Pinus sylvestris forests respectively in boreal
Euro-Siberia (Schulze et al. 1999).
56
Nitrogen Use Efficiency and its relation to Stand Productivity and Biomass Allocation
Patterns across stand densities and ages
NUE, estimated as the C:N ratio of needle litterfall (see Methods), was more affected by
stand age than by stand density (Figure 5). The C:N ratio in litterfall / NUE of the YS (104:1)
and YD (100:1) stands were remarkably similar (p = 0.826)(Figure 5), but significantly lower
than the OS stand (130:1; p < 0.05). Although correlations between NUE and site index, and
between site index and biomass allocation patterns were not statistically significant (p < 0.05),
several patterns emerged. NUE was inversely correlated to site index (SI) (r = -0.94), the below:
aboveground biomass ratio (r = -0.99), and allocation to branches (r = -0.66), foliage (r = -0.75),
and needles (r = -0.80), but was positively correlated with the root crown: lateral root biomass (r
= 0.97) and allocation to the tree bole (r = 0.71) (Table 6). Correlations were strongest for the
below: aboveground biomass ratio, where the correlation was nearly significant (p = 0.08),
followed by root crown: lateral root biomass (p = 0.16) and site index (p = 0.23) (Table 6).
DISCUSSION
The results indicated (1) biomass allocation patterns differed between stand densities and
ages; (2) biomass allocation patterns were different at the individual tree level than for the stand
level; (3) however, allocation to coarse roots was relatively constant across stand densities and
ages; (4) biomass allocation patterns were different in the GYE compared to southeastern
Wyoming and British Columbia; (5) carbon storage of mature lodgepole in the GYE was
comparable to, but at the lower end of carbon storage published in other forest types and biomes;
(6) NUE was higher in the old stand, which was likely more nutrient limited (Binkley et al.
1995); and (7) NUE was likely related to site productivity and biomass allocation patterns.
57
Aboveground Biomass
Within the aboveground portion of individual lodgepole pine and lodgepole pine stands,
biomass of branches, needles, and foliage were highest in the YS stand (Table 2,4, Figure 2,3).
At the stand level, branches, needles, and foliage as a proportion of total tree biomass was lowest
in the OS stand, indicating that there was ample room for development of a large tree crown in
the YS stand, in contrast to the YD and OS stands where there was more competition. The OS
stand had more crown competition than the YD stand, because tree spacing was more
homogeneous in the OS stand; in contrast, the YS stand had heterogeneous tree spacing resulting
in an assemblage of open grown and clumped trees (Table 1). In addition, the OS stand may
have had the lowest branch, foliage, and needle biomass due to greater self-pruning of the lower
branches in the older stand, a common characteristic of lodgepole pine (Koch 1996). Although
not statistically significant, the YD stand had the lowest branch, needle, and foliage allocation,
indicating that greater self-pruning may occur with increasing stand density, and also indicating
that the impact of self-pruning on biomass allocation is controlled by density in addition to age.
Similarly, Pearson et al. (1984) found that a sparser stand (2217 trees/ha) had a greater amount of
total biomass allocated to branches (8.3%) than for a denser stand on the same site (14,640
trees/ha), where only 5.2% of total tree biomass was allocated to branches.
Ryan et al. (1997) suggested that young trees allocate more resources to foliage rather
than to woody components, and this trend becomes less pronounced with crown closure. This is
supported by the results of this study, as the open-grown YS stand had 26.5% of total biomass
allocated to foliage, while the OS stand with greater observed crown competition had 10.2% of
total biomass allocated to foliage (Table 2, Figure 2). Pearson et al. (1984) found that allocation
to foliage was greater in a young, sparse stand (75 years old), where allocation to foliage was
58
10.0% of total tree biomass than in an older stand (110 years old), where foliage was only 6.2%
of total tree biomass.
Allocation to tree boles was lowest in the YS stand at the individual tree and stand levels,
while bole allocation to the OS and YD stands were surprisingly comparable to each other. The
low proportion of total tree biomass for boles in the YS stand is likely attributed to a high
proportion of branch and foliage biomass created by a combination of its young age and opengrown stand structure. The similarity in allocation to tree boles between the OS and YD stands
may be related to their competitive similarity. Although not quantified, our observations
suggested both of these stands possessed extensive crown competition, while the YS stand had
less crown competition. Increased allocation to boles in dense and older stands with greater
crown competition was consistent with the results of Nilsson and Albrekston (1993) and
Kaufmann and Ryan (1986). They found that allocation to the tree bole compared to foliage was
greater for competitively suppressed trees. Future studies should include more direct
measurements of competition among trees, such as nearest neighbor analyses.
Leaf Area Index
LAI was much greater in the sparse stands than in the YD stand, suggesting that tree
canopies were much more suppressed in that stand, which is contrary to the results of Pearson et
al. (1984). They found that a dense stand with 14,640 trees/ha had only a slightly lower LAI (7.1
m2/ha) than a less dense stand with 2,217 trees/ha (7.3 m2/ha). Overall, my estimates of LAI
were much lower than estimates reported by Pearson et al. (1984), which could be attributed to
lower stand basal areas for this study (Table 1). Basal areas for this study ranged from 16.84 –
28.32 m2/ha, but Pearson et al. (1984) reported basal areas from 26 – 64 m2/ha. Although
sapwood area has been shown to be a better predictor of foliage mass and leaf area than basal
59
area by Pearson et al. (1984) and by Grier and Waring (1974), basal area has also been shown to
predict foliage mass and leaf area well (Pearson et al. 1984). Therefore, leaf area index should
also be affected by basal area.
Belowground Biomass
For allocation to belowground biomass components at the stand level, the root crown:
lateral root biomass ratio was found to be significantly less for younger stands than for the single
old stand, indicating that the root crown becomes a more dominant part of root systems as stand
age increases, suggesting a greater need for structural support as trees become older and larger.
Similarly, the root crown: lateral root biomass ratio for individual trees revealed that allocation
to root crowns was lowest for the YS stand, but similarly higher for the two stands differing most
in density and age (OS and YD). The YS stand was the most open-grown of the three stands and
likely had the least amount of competition among trees, indicating that the root crown becomes a
greater portion of the root system as belowground competition decreases. Interestingly, greater
differences in stand age for the root crown: lateral root biomass ratio were evident when
comparing sites at the stand level, while greater differences in stand density were revealed when
examining the data at the individual tree level, indicating the importance of sampling at multiple
scales for understanding biomass allocation patterns of lodgepole pine. A possible explanation
for variability in allocation at differing scales is that individual tree allocation patterns are likely
a function of individual tree age and tree spacing around individual trees, while in contrast, stand
level allocation patterns are likely a function of the stand age and density.
Below: Aboveground Biomass Ratio
At the stand level, stand density and age had surprisingly little effect on the shifting of
resources to above and belowground biomass, in contrast to a study by Pearson et al. (1984) for
60
lodgepole pine in southeastern Wyoming. Differences in below: aboveground biomass
partitioning among densities ranging from 420 to 14640 trees ha-1 found by Pearson et al. may be
at least partly attributed to the inclusion of roots smaller than 10mm diameter in their study. For
this study, below: aboveground biomass was found to be constant across stand ages, which is
comparable to estimates of total root biomass including fine roots across stand ages for other
studies (Litton et al. 2004; Pearson et al. 1984). Despite the constant relative biomass
partitioning of resources above and belowground at the stand level, below: aboveground biomass
was considerably more variable at the scale of individual trees (Table 2, 4), because below:
aboveground allocation was lowest in the YD stand, the site that likely had the highest
belowground allocation, indicating that both stand density and age had an important effect on
allocation to coarse root biomass. Although there are some similarities in the ratio of below:
aboveground biomass among sites, especially at the stand level, biomass partitioning within
above and belowground components was found to be highly variable with stand density and age.
Biomass Allocation Comparisons Among Geographic Locations
Although fine roots (<10mm diameter) were not considered in this study, direct
comparisons for aboveground biomass components in different geographic regions could be
made. The proportion of aboveground biomass allocated to the tree bole was significantly less
for this study except for the comparison between older stands, and total bole biomass was less in
this study for all stands (Figure 3). This suggests that allocation to the tree bole is in a large part
driven by site productivity, because lodgepole pine forests in the GYE are less productive than
lodgepole pine forests in southeastern Wyoming and in British Columbia due to a combination of
poorer soils developed on volcanic rhyolite, more extreme climatic conditions, and differences in
topography. In addition, site indices for this study ranged from 14.3 – 16.0m at 100 years, and
61
site indices for Comeau and Kimmins (1989) ranged from 14.3 – 20.5 m at 100 years.
Furthermore, estimates of total root biomass were much lower for this study, although fine root
biomass was excluded from this study (Figure 3), suggesting that total coarse root biomass is not
driven by site productivity. Bole biomass allocation was found to be more constant across
geographic locations for comparisons between older stands (Figure 3a), suggesting that during its
lifespan, lodgepole pine may gradually compensate for lower site productivities through
increases in resource use efficiency in order to attain relatively constant wood production despite
differences in site productivity (Binkley et al. 2004; Aber and Melillo 2001; Vitousek 1982).
Interestingly, allocation to branches and needles was higher in the GYE than in British Columbia
and southeastern Wyoming, suggesting that lodgepole pine in the GYE are compensating for
poor site conditions by allocating more biomass to photosynthetic tissues and to branches for
support of photosynthetic tissues, although nitrogen and other soil nutrients would be needed to
produce these tissues. Therefore, lodgepole pine in the GYE may have greater nutrient use
efficiency than lodgepole pine in British Columbia and southeastern Wyoming, although there
are no data to support this, and future research is needed on the variability of nutrient use
efficiency of lodgepole pine across a gradient of site productivities in the intermountain west.
Carbon Storage in the Greater Yellowstone Ecosystem compared to other forests
Carbon storage for our study was comparable to another study in the GYE, and it was
also comparable to carbon storage for other forest types and biomes. However, carbon storage in
mature lodgepole pine trees was at the lower end of values reported for other forest types and
biomes, and the results suggest that only boreal forests have lower carbon stored in aboveground
tree biomass than subalpine lodgepole pine forests (Schulze et al. 1999).
62
Nitrogen Use Efficiency and its relation to Stand Productivity and Biomass Allocation
Patterns across stand densities and ages
NUE was significantly higher in the old, sparse stand than in the two younger stands and
was more affected by stand age than by stand density, suggesting that older forests are more
nutrient limited than younger forests as found by Ryan et al. (1997) and (Binkley et al. 1995).
This also suggested that there was little difference in site productivity among densities. This is
supported by the result that SI was lowest for the OS stand where NUE was highest, indicating
that NUE was highest where site productivity was lowest, because SI is a better index of site
productivity than any other easily obtained index (Barnes et al. 1980). This is further supported
by the research of (Binkley et al. 1995) in southeastern Wyoming, where their results from a
fertilization experiment suggested nitrogen to be more limiting in older forests. An inverse
relationship between NUE and site productivity is consistent with the findings of Vitousek
(1982) and Miller et al. (1979), but has recently been disputed by Binkley et al. (2004), where
they suggested that for NUE to be equivalent to the grams of carbon fixed per unit of nutrient in
litterfall, there would have to be a constant relationship between litter mass and ANPP.
Unfortunately, the time span of this study did not allow for determination of litter mass or ANPP,
and this should be pursued in the future.
NUE was inversely related to the ratio of below: aboveground biomass, because
allocation to coarse roots was greater on more productive sites, which had lower NUE. These
results suggested that allocation to coarse roots >10mm in diameter were more similar to other
woody components such as the tree bole and branches than to fine roots, whereby coarse roots
are present mostly for structural support and are dependent, like the tree bole, on the ability of
fine roots to absorb water and nutrients from the soil.
63
NUE was positively related to the ratio of root crown: lateral root biomass, indicating that
the root crown is more similar in function to the tree bole than to lateral roots, because the root
crown: lateral root biomass ratio and allocation to the tree bole are similarly correlated with
NUE. If the opposite were true, the correlation between NUE and the root crown: lateral root
ratio would be negative. NUE is higher where allocation to the tree bole is higher, suggesting
that NUE is increasing the ability of lodgepole pine to produce woody biomass. In contrast,
NUE was lower where allocation to branch, foliage, and needle biomass was higher. This
indicated that increasing NUE may not be necessary to increase production of woody biomass,
where there was more biomass allocated to photosynthetic tissues and branches tissues in support
of those tissues. Future studies should include estimates of photosynthetic efficiency to test this
hypothesis.
SUMMARY AND CONCLUSIONS
The allocation of resources to above and belowground lodgepole pine biomass was
relatively similar between stand densities and ages as shown by the relatively constant ratios of
total coarse root: total aboveground biomass. Therefore, allocation to coarse root biomass is
relatively constant across forest structures. However, there was considerable variability in
aboveground allocation and belowground allocation when considered independently, because the
ratio of root crown: lateral root biomass differed significantly with stand density and age, and
allocation to the tree bole, branches, fine fuels, and needles differed with stand density and age.
In addition above and belowground allocation patterns were affected differently and the
individual tree and stand levels.
64
There were considerable differences in the biomass allocation patterns of lodgepole pine
among geographic locations. Despite some similarities, generalizing patterns of biomass
allocation across forest structures and among geographic locations should proceed with caution,
and further research into the biomass allocation patterns of lodgepole pine in the Greater
Yellowstone Ecosystem and elsewhere in the Intermountain West is needed. However, if further
research indicates that the ratio of coarse root: total aboveground biomass for lodgepole pine
stands is approximately 0.10 to 0.12 as identified by this study, determination of belowground
lodgepole pine biomass could become considerably easier, because only aboveground
measurements would be needed and the appropriate ratio could be applied to determine coarse
root biomass. Additionally, NUE appears to be related to site productivity and biomass
allocation patterns, although further study of these relationships is needed, and additional study
is needed to determine the relationships of NUE across the intermountain west.
65
Table 1. Sites for the determination of biomass allocation patterns of P. contorta in the
Greater Yellowstone Ecosystem. Soils for all three sites were in the Koffgo series (see
Methods).
NAD 83, UTM
Stand Density
Zone 12
Stand
(trees > 5cm
Northing
Easting
Site
Elevation
Age
DBH per
Stand basal area
(m)
(m)
Name
(m)
(years)
hectare)
(m2 per hectare)
Grassy
Lake
2249
4886015 511735
165
674  175
16.84  14.3
Coffee
Pot
1951
4926541 472232
64
725  72
19.71  11.4
US 20
1951
4925932 472657
64
2452  929
28.32  30.4
Table 2. Mean individual tree level biomass, total coarse root: total aboveground biomass
and root crown: lateral root biomass ratios of three Pinus contorta stands in the GYE.
Mass is in Mg/ha and percent of total tree biomass for each component is in brackets.
STANDS
Component
Young,
Old, Sparse
Sparse
Young, Dense
Biomass
Mass (Kg)
[%]
Mass (Kg)
[%]
Mass (Kg)
[%]
187.9 [86.3]
128.9 [87.6]
41.0 [90.1]
141.2 [64.8]
59.7 [40.6]
31.2 [68.7]
Branches
14.9 [6.9]
24.3 [16.5]
2.7 [5.9]
Fine Fuels
31.8 [14.6]
44.9 [30.6]
7.1 [15.6]
Needles
25.8 [11.8]
27.0 [18.4]
4.6 [10.1]
30.0 [13.8]
18.2 [12.4]
4.5 [9.9]
Root Crown
19.0 [8.8]
10.4 [7.1]
2.6 [5.7]
Lateral Roots
(>10mm)
11.0 [5.0]
7.8 [5.3]
1.9 [4.2]
Total Tree
Biomass
217.9
147.1
45.5
Total
Aboveground
Bole
Coarse Roots
(>10mm)
- Ratios Total Coarse Root:
Total Aboveground
0.16
0.14
0.11
Root Crown: Lateral
Root
1.8
1.4
2.2
Table 3. Tukey’s post-hoc analysis for the total coarse root: total aboveground and root
crown: lateral root biomass ratios at the stand and individual tree levels for three P. contorta
stands in the Greater Yellowstone Ecosystem. Asterisks (*) signify that the one way
ANOVA was not significant (p>0.05) and therefore, no post-hoc analyses were necessary.
P-values in bold are significant at an  of 0.05.
- Tree Level Site Comparisons
p-values
total coarse root: total
Root crown: lateral
(1)
(2)
aboveground
root
Old, Sparse
Young, Sparse
0.234
*
Old, Sparse
Young, Dense
*
0.004
Young, Sparse
Young, Dense
Site Comparisons
(1)
Old, Sparse
(2)
Young, Sparse
Old, Sparse
Young, Sparse
0.282
*
- Stand Level p-values
total coarse root: total
Root crown: lateral
aboveground
root
*
0.010
Young, Dense
*
0.001
Young, Dense
*
0.719
Table 4. Stand level biomass, total coarse root: total aboveground biomass ratio; root crown:
lateral root biomass ratio, and leaf area index of three Pinus contorta stands in the GYE. Mass is
in Mg/ha and percent of total tree biomass for each component is in brackets. Ratios were based
on values derived from individual models for the components involved in the ratios. Percentages
for bole, branches, foliage, needles, root crown, and lateral roots were calculated from the sum of
the bole, branches, foliage, and total roots. Percentages for total aboveground and total root
biomass were calculated from total tree biomass as shown below. All components except for
total tree biomass were derived from individual allometric models by Arcano and Tinker
(Manuscript In Preparation); therefore discrepancies exist between total aboveground biomass
and its components, as well as between total root biomass and its components.
STANDS
Old,
Young,
Young,
Sparse
Sparse
Dense
Component
Mass
(Mg/ha)
[%]
Mass
(Mg/ha)
[%]
Mass
(Mg/ha)
[%]
Total
Aboveground
127.0
[91.3]
84.9
[89.0]
97.3
[89.5]
91.6 [75.1]
42.2 [47.3]
70.7 [64.3]
Branches
5.9 [4.8]
12.9 [14.4]
8.7 [7.9]
Fine Fuels
12.4 [10.2]
23.7 [26.5]
19.0 [17.3]
8.3 [6.8]
15.4 [17.2]
11.5 [10.5]
12.1
[8.7]
10.5
[11.0]
11.5
[10.5]
Root Crown
8.2 [6.7]
6.6 [7.4]
7.1 [6.5]
Lateral Roots
(>10mm)
3.7 [3.0]
4.5 [5.0]
4.8 [4.4]
Total Tree
Biomass
139.1
95.4
108.8
Total Coarse
Root: Total
Aboveground
0.10
0.12
0.12
Root Crown:
Lateral Root
2.2
1.4
1.5
2.5
- Leaf Area Index 2.1
1.2
Bole
Needles
Coarse Roots
(>10mm)
Table 5. Carbon (C) allocation in three stands differing in density and age in the Greater
Yellowstone Ecosystem.
Old, Sparse
STANDS
Young, Sparse
Young, Dense
C (Mg/ha)
C (Mg/ha)
C (Mg/ha)
Total
Aboveground
70.0
40.8
46.7
Bole
44.0
20.3
33.9
Branches
2.8
6.2
4.2
Fine Fuels
6.0
11.4
9.1
Needles
4.0
7.4
5.5
Coarse Roots
(>10mm)
5.8
5.0
5.5
Root Crown
4.0
3.2
3.4
Lateral Roots
(>10mm)
1.8
2.2
2.3
Total Tree
Biomass
66.8
45.8
52.2
Biomass
Component
70
Table 5: Comparison of biomass and percent of total biomass for three Pinus contorta
stands in the GYE to stands in British Columbia by Comeau and Kimmins (1989) and in
southeast Wyoming by Pearson et al. (1989). Percentages are in brackets.
Comparisons
Study
This
Study
Biomass (Mg/ha) [%]
Age
Density
Total
Aboveground
64
2452
97.3 [89.5]
70.7 [64.3]
8.7 [8.0]
11.5 [8.4]
Bole
Branches
Needles
Comeau
and
Kimmins
This
Study
70
1900
119.4 [76.5]
107.4 [68.8]
7.1 [4.6]
4.9 [3.1]
165
674
127.0 [91.3]
91.6 [75.1]
5.9 [4.8]
8.3 [5.4]
Pearson
et al.
240
420
131.9 [77.5]
110.8 [65.1]
14.2 [8.3]
6.9 [4.1]
This
Study
64
725
84.9 [89.0]
42.2 [47.3]
12.9 [14.5]
15.4 [11.3]
Pearson
et al.
75
1280
96.3 [78.4]
74.4 [60.6]
9.6 [7.8]
12.3 [10.0]
71
Table 6. Correlation coefficients (r) and p-values between NUE and site index (SI), biomass
allocation ratios, and proportions of total lodgepole pine biomass for three stands differing in
densities and ages in the Greater Yellowstone Ecosystem
Total Coarse
Root: Total
Root crown:
Fine
SI
aboveground
lateral root
Bole
Branches
Fuels
Needles
r
-0.94
-0.99
0.97
0.71
-0.66
-0.75
-0.80
p
0.230
0.082
0.155
0.497
0.593
0.463
0.409
Figure 1: Three Stands Differing in Densities and Ages for the determination of biomass
allocation patterns of P. contorta in the Greater Yellowstone Ecosystem. Individual stands are
depicted by a black square.
73
Figure 1:
YS
YD
OS
Figure 2: Biomass allocation patterns at the individual tree level for three P. contorta stands
differing in densities and ages in the Greater Yellowstone Ecosystem. Different letters denote
significance according to Tukey’s HSD post-hoc analyses.
Figure 2:
Proportion of Total Tree Biomass
Tree Level
1.0
Old, Sparse
Young, Sparse
Young, Dense
0.8
0.6
0.4
0.2
0.0
B
ee
Tr
s
ole
Br
c
an
he
ls
ue
F
e
Fin
Component
76
s
le
ed
e
N
Figure 3: Biomass allocation patterns at stand level for three P. contorta stands differing in
densities and ages in the Greater Yellowstone Ecosystem. Different letters denote significance
according to Tukey’s HSD post-hoc analyses.
77
Figure 3:
Stand Level
Proportion of Total Tree Biomass
1.0
old, sparse
young, sparse
young, dense
0.8
0.6
0.4
0.2
0.0
s
le
bo
e
ch
an
br
Fin
u
eF
els
Component
78
n
d
ee
les
Figure 4: Biomass allocation patterns at the stand level for P. contorta in the Greater
Yellowstone Ecosystem compared to studies in British Columbia and southeastern Wyoming.
Comparisons are made using data other studies from stands of comparable density and age to
stands from this study.
79
Figure 4:
Young, Dense Stand
Young, Sparse Stand
Old, Sparse Stand
100
% of Total Tree Biomass
This Study
Pearson et al.
This Study
Pearson et al.
This Study
Comeau and Kimmins
80
60
40
20
0
al A
Tot
und
gro
e
v
bo
le
Bo
s
es
dle
nch
nee
bra
v
bo
T
d
un
ro
g
e
le
Bo
s
he
nc
a
br
lA
ota
les
ed
e
n
ou
b
ta
To
Component
lA
r
eg
ov
nd
le
Bo
an
br
ch
es
e
ne
d le
s
Figure 5: Carbon: Nitrogen ratio as an estimate of nitrogen use efficiency in three stands
differing in densities and ages in the Greater Yellowstone Ecosystem. Letters denote
significance according to Tukey’s HSD post-hoc analysis.
Figure 5:
Nitrogen Use Efficiency
160
140
a
120
b
C:N ratio
b
100
80
60
40
20
0
Old, Sparse
Young, Sparse Young, Dense
STAND
82
LITERATURE CITED
Aber, J. D. and J. M. Melillo (2001). Terrestrial Ecosystems. San Diego, CA, Harcourt
Academic Press.
Alexander, R. R. 1966. Site indexes for lodgepole pine, with corrections for stand density:
instructions for field use. Res. Pap. RM-24. Fort Collins, CO: U.S. Department of
Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station; 7 p.
Arcano, R. and D. B. Tinker Manuscript in preparation. Allometric model development for
mature lodgepole pine forests in the Greater Yellowstone Ecosystem.
Avery, T. E. and H. E. Burkhart (1994). Forest Measurements. Boston, MA, McGraw-Hill Inc.
Barnes, B. V., D. R. Zak, S. R. Denton and S. H. Spurr (1980). Forest Ecology. New York, NY,
John Wiley & Sons, Inc.
Berger, U., H. Hildenbrandt and V. Grimm 2004. Age-related decline in forest production:
modelling the effects of growth limitation, neighbourhood competition and self-thinning.
Journal of Ecology 92: 846-853.
Binkley, D., F. W. Smith and Y. Son 1995. Nutrient Supply and Declines in Leaf-Area and
Production in Lodgepole Pine. Canadian Journal of Forest Research-Revue Canadienne
De Recherche Forestiere 25: 621-628.
Binkley, D., J. L. Stape and M. G. Ryan 2004. Thinking about efficiency of resource use in
forests. Forest Ecology and Management 193: 5-16.
Bowerman, T. S., J. Dorr, S. Leahy, K. Varga and J. Warrick (1999). Targhee National Forest
ecological unit inventory, United States Department of Agriculture, Forest Service
United States Department of Agriculture, Natural Resources Conservation Service
University of Idaho, College of Agriculture. 1-2: 787.
83
Brown, J. K. 1978. Weight and density of crowns of Rocky Mountain conifers. Res. Pap. Int197. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain
Research Station; 56 p.
Chapin, S. F. I., P. Matson and H. A. Mooney (2002). Principles of terrestrial ecosystem
ecology. New York, NY, Springer-Verlag New York, Inc.
Cote, B., J. W. Fyles and H. Djalilvand 2002. Increasing N and P resorption efficiency and
proficiency in northern deciduous hardwoods with decreasing foliar N and P
concentrations. Annals of Forest Science 59: 275-281.
Cummings, D. L., J. B. Kauffman, D. A. Perry and R. F. Hughes 2002. Aboveground biomass
and structure of rainforests in the southwestern Brazilian Amazon. Forest Ecology and
Management 163: 293-307.
Curtis, P. S., P. J. Hanson, P. Bolstad, C. Barford, J. C. Randolph, H. P. Schmid and K. B.
Wilson 2002. Biometric and eddy-covariance based estimates of annual carbon storage in
five eastern North American deciduous forests. Agricultural and Forest Meteorology 113:
3-19.
Despain, D. G. (1990). Yellowstone vegetation: consequences and environment in a natural
setting. Boulder, CO, Roberts Rinehart, Inc.
Dirks, R. A. and B. E. Martner 1982. The climate of Yellowstone and Grand Teton National
Parks. National Park Service Occassional Paper 6.
Fahey, T. J. 1983. Nutrient dynamics of aboveground detritus in lodgepole pine (Pinus contorta
ssp. latifolia) ecosystems, southeastern Wyoming. Ecological Monographs 53: 51-72.
Fahey, T. J., J. B. Yavitt, J. A. Pearson and D. H. Knight 1985. The nitrogen cycle in lodgepole
pine forest, southeastern Wyoming. Biogeochemistry 1: 257-275.
84
Giardina, C. P. and M. G. Ryan 2002. Total belowground carbon allocation in a fast-growing
Eucalyptus plantation estimated using a carbon balance approach. Ecosystems 5: 487499.
Gower, S. T., C. C. Grier and K. A. Vogt 1989. Aboveground production and N and P use by
Larix occidentalis and Pinus contorta in the Washington Cascades, USA. Tree
Physiology 5: 1-11.
Grier, C. C. and R. H. Waring 1974. Conifer foliage mass related to sapwood area. Forest
Science 20: 205-206.
Hendrickson, O. Q. and D. Burgess 1989. Nitrogen-fixing plants in a cut-over lodgepole pine
stand of southern British Columbia. Can. J. For. Res. 19: 936-939.
Kashian, D. M., M. G. Turner and W. H. Romme 2005. Variability in leaf area and stemwood
increment along a 300-year lodgepole pine chronosequence. Ecosystems 8: 48-61.
Kashian, D. M., M. G. Turner, W. H. Romme and C. G. Lorimer 2005. Variability and
convergence in stand structural development on a fire-dominated subalpine landscape.
Ecology 86: 643-654.
Kaufmann, M. R. and M. G. Ryan 1986. Physiographic, stand, and environmental effects on
individual tree growth and growth efficiency in subalpine forests. Tree Physiology 2: 4759.
Keyes, M. R. and C. C. Grier 1981. Above- and below-ground net production in 40-year-old
douglas-fir stands on low and high productivity sites. Can. J. For. Res. 11: 599-605.
Knight, D. H. (1994). Mountains and plains: the ecology of Wyoming landscapes. New Haven,
CT, Yale University Press.
Koch, P. (1996). Lodgepole Pine in North America. Madison, WI, Forest Products Society.
85
Law, B. E., P. E. Thornton, J. Irvine, P. M. Anthoni and S. V. Tuyl 2001. Carbon storage and
fluxes in ponderosa pine forests at different developmental stages. Global Change
Biology 7: 755-777.
Litton, C. M., M. G. Ryan and D. H. Knight 2004. Effects of tree density and stand age on
carbon allocation patterns in postfire lodgepole pine. Ecological Applications 14: 460475.
Litton, C. M., M. G. Ryan, D. H. Knight and P. D. Stahl 2003. Soil-surface carbon dioxide efflux
and microbial biomass in relation to tree density 13 years after a stand replacing fire in a
lodgepole pine ecosystem. Global Change Biology 9: 680-696.
Madgwick, H. A. I. 1964. Estimation of surface area of pine needles with special reference to
Pinus resinosa. J. For. 61: 636.
Miller, H. G., J. M. Cooper, J. D. Miller and O. J. L. Pauline 1979. Nutrient cycles in pine and
their adaptation to poor soils. Can. J. For. Res. 9: 19-26.
Nilsson, U. and A. Albrekston 1993. Productivity of needles and allocation of growth in young
Scots pine trees of different competitive status. Forest Ecology and Management 62: 173187.
Olsson, U., D. Binkley and F. W. Smith 1998. Nitrogen supply, nitrogen use, and production in
an age sequence of lodgepole pine. Forest Science 44: 454-457.
Pearson, J. A., T. J. Fahey and D. H. Knight 1984. Biomass and leaf area in contrasting
lodgepole pine forests. Can. J. For. Res. 14: 259-265.
Ryan, M. G., D. Binkley and J. H. Fownes 1997. Age-related decline in forest productivity:
pattern and process. Advances in Ecological Research 27: 213-262.
86
Schulze, E. D., J. Lloyd, F. M. Kelliher, C. Wirth, C. Rebmann, B. Luhker, M. Mund, A. Knohl,
M. Milyukova, W. Schulze, W. Ziegler, A. B. Varlagin, A. F. Sogachev, R. Valentini, S.
Dore, S. Grigoriev, O. Kolle, M. I. Panfyorov, N. Tchebakova and N. N. Vygodskaya
1999. Productivity of forests in the Eurosiberian boreal region and their potential to act as
a carbon sink - a synthesis. Global Change Biology 5: 703-722.
Smith, F. W. and J. N. Long 2001. Age-related decline in forest growth: an emergent property.
Forest Ecology and Management 144: 175-181.
Smith, F. W. and S. C. Resh 1999. Age-related changes in production and below-ground carbon
allocation in Pinus contorta forests. Forest Science 45: 331-341.
Stevenson, F. J. and M. A. Cole (1999). Cycles of Soil: Carbon, Nitrogen, Phosphorous, Sulfur,
Micronutrients. New York, NY, John Wiley and Sons, Inc.
Stump, L. M. and D. Binckley 1993. Relationships between Litter Quality and Nitrogen
Availability in Rocky-Mountain Forests. Canadian Journal of Forest Research-Revue
Canadienne De Recherche Forestiere 23: 492-502.
Taiz, L. and E. Zeiger (2002). Plant Physiology. Sunderland, MA, Sinauer Associates, Inc.
Thomas, K. D. and C. E. Prescott 2000. Nitrogen availability in forest floors of three tree species
on the same site: the role of litter quality. Canadian Journal of Forest Research-Revue
Canadienne De Recherche Forestiere 30: 1698-1706.
Tinker, D. B., W. H. Romme, W. W. Hargrove, R. H. Gardner and M. G. Turner 1994.
Landscape-scale heterogeneity in lodgepole pine serotiny. Can. J. For. Res. 24: 897-903.
Turner, M. G., W. H. Romme, R. H. Gardner and W. W. Hargrove 1997. Effects of fire size and
pattern on early succession in yellowstone national park. Ecological Monographs 67:
411-433.
87
Turner, M. G., D. B. Tinker, W. H. Romme, D. M. Kashian and C. M. Litton 2004. Landscape
patterns of sapling density, leaf area, and aboveground net primary production in postfire
lodgepole pine forests, Yellowstone National Park (USA). Ecosystems 7: 751-775.
Vitousek, P. 1982. Nutrient cycling and nutrient use efficiency. The American Naturalist 119:
553-572.
88
SUMMARY
Biomass was estimated in three lodgepole pine forests differing in stand densities and
ages in the Greater Yellowstone Ecosystem. Forty-six trees were harvested, and 24 root systems
were excavated. The number of trees harvested was relatively equal among stands, but only five
root systems were excavated in sparse stands due to logistical constraints, while all 14 of the
harvested trees in the dense stand were excavated. Allometric equations for above and
belowground tree components were developed to estimate biomass in all three stands.
Additional pooled equations were developed to compare allometric relationships between
differing stand densities and ages. Actual biomass estimates from this study’s allometric models
were compared against estimated biomass values from this study’s equations and equations
developed in southeastern Wyoming and British Columbia. Biomass allocation patterns were
determined from this study’s actual biomass estimates, and biomass allocation patterns were
compared across differing stand densities and ages and among geographic locations independent
of stand densities and ages. Nitrogen use efficiency (NUE) was determined from the C:N ratio
in litterfall in each of the three stands and was compared across stands with different densities
and ages. To determine if NUE was related to site productivity and biomass allocation patterns,
correlation analyses were conducted.
Variation in stand density and age affected measured tree morphometric parameters that
best predicted biomass of lodgepole pine tree components. Allometric models developed for
mature lodgepole pine in the Greater Yellowstone Ecosystem were very robust for predicting
various tree biomass components, and showed potential for estimating landscape level biomass
of lodgepole pine forests, because R2 values were often above 0.90. Allometric equations were
found to differ between stand densities and ages according to qualitative observations and
89
statistical tests, and they were also found to be different among geographic locations independent
of stand density and age. In addition, applications of inappropriate models sometimes produced
tremendous errors and have the potential to do so in future applications.
The allocation of resources to above and belowground lodgepole pine biomass was
relatively similar between stand densities and ages as shown by the relatively constant ratios of
total coarse root: total aboveground biomass, where the total coarse root: total aboveground
biomass ratio at the stand level ranged from 0.10 to 0.12. Therefore, allocation to coarse root
biomass is relatively constant across forest structures. However, allocation within above and
belowground sections of the tree was highly variable across stand densities and ages, and
appeared to be greatly affected by stand level and individual tree level competition. Although
total aboveground biomass was relatively constant across densities and ages, bole biomass was
lowest in the young, sparse stand, but branch, foliage, and needle biomass was highest in the
young, sparse stand. Also, biomass allocation patterns were sometimes different at the stand and
individual tree levels, indicating the need for additional study at multiple scales. In addition,
there were considerable differences in the biomass allocation patterns of lodgepole pine among
geographic locations. Allocation to bole biomass was lower in the Greater Yellowstone
Ecosystem, but allocation to branch and needle biomass was higher, which is likely a product of
the lower site productivity in the GYE than in southeastern Wyoming and British Columbia. In
addition, estimates of lodgepole pine biomass fit well within the residuals of global allometric
models developed by Enquist et al. (1998).
NUE was higher with increasing stand age, indicating higher NUE with increasing
nutrient limitation. Additionally, NUE was negatively correlated with site productivity (site
index), indicating higher NUE with decreasing site productivity. NUE was also negatively
90
correlated with the ratio of total coarse root: total aboveground biomass and allocation to branch,
foliage, and needle biomass. It was determined that NUE is higher when allocation to coarse
root, branch, needle, and foliage biomass is lower. However, NUE was positively correlated
with the ratio of root crown: lateral root biomass and the tree bole, indicating that higher NUE
may increase root crown and bole production.
Differences in allometric equations with different forest structures and geographic
locations indicates the need for careful application of allometric models, supported by the result
that inappropriate model use often produced tremendous errors. Errors for individual trees
produced by inappropriate model application have the potential to propagate when extrapolated
to the stand and ultimately the landscape level. In addition the allometric models developed in
this study showed great promise for a variety of applications including ecosystem carbon studies,
fire modeling, and determination of wood biomass by foresters. Biomass allocation patterns
differed among forest structures and geographic locations, indicating that understanding these
differences is vital to understanding differences in NPP and NEP. In addition, NUE was shown
to differ across forest structures, it was related to site productivity and biomass allocation
patterns, indicating that increasing our understanding of NUE can further our understanding of
how NPP and NEP vary across the landscape.
91
LITERATURE CITED
Enquist, B. J., J. H. Brown and G. B. West 1998. Allometric scaling of plant energetics and
population density. Nature 395: 163-165.
92