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PRODUCTIVITY, PHYSIOLOGY, COMMUNITY DYNAMICS, AND ECOLOGICAL
IMPACTS OF A GRASSLAND AGRO-ECOSYSTEM: INTEGRATING FIELD STUDIES
AND ECOSYSTEM MODELING
BY
XIAOHUI FENG
DISSERTATION
Submitted in partial fulfillment of the requirements
for the degree of Doctor of Philosophy in Plant Biology
in the Graduate College of the
University of Illinois at Urbana-Champaign, 2014
Urbana, Illinois
Doctoral Committee:
Associate Professor Elizabeth Ainsworth, Chair
Assistant Professor Michael C. Dietze, Director of Research, Boston University
Professor Mark B. David
Associate Professor John B. Taft
ii
ABSTRACT
Grasslands are among the largest ecosystems in the world and provide numerous
ecosystem services. These services include the ecosystem benefits important in an agricultural
context such as biomass production and benefits from an ecological perspective such as
mitigation of greenhouse gas emissions. My research has investigated the physiology,
productivity and community dynamics of grassland ecosystems by combining field studies and
modeling techniques. In an agriculture context, primary productivity is especially important.
Therefore, the first part of my research addresses the optimal harvesting time of prairie biomass
to achieve the maximum yield within one production cycle. Due to the biomass quantity-quality
trade-off, the balance among yield, biomass quality, and impacts on environment needs to be
carefully considered when harvesting prairie mixtures. Allowing grasses to completely senesce
and recycle nutrients can reduce fertilizer requirements and improve feedstock quality by
reducing biomass moisture and mineral content. But the trade-off is that a late harvest always
results in less harvestable biomass. Effects of harvest time on biomass nitrogen concentration,
moisture and yield of prairie production systems are rarely investigated. Therefore, I investigated
responses of these factors to harvest time in prairie mixtures by conducting experiments in a
restored tallgrass prairie in Urbana, IL. The results suggest that the optimal harvest time that
maximizes expected net returns and balances feedstock quality and quantity is between
November and January.
Next, relationships between leaf traits and photosynthetic rates are commonly used to
predict primary productivity at scales from the leaf to the globe. Hence, the second part of my
dissertation focuses on investigating the variation in these relationships across taxonomic scales
using a Bayesian parameterization of leaf photosynthesis models. Photosynthetic CO2 and light
iii
response curves and leaf ecophysiological traits of 25 grassland species were measured. The
effects of leaf traits on photosynthetic capacity were quantified at different taxonomic scales
through leaf photosynthesis model parameterization. I found that the effects of plant
physiological traits on photosynthetic capacity and parameters varied among species and plant
functional types. These results suggest that one broad-scale relationship is not sufficient to
characterize ecosystem conditions and changes at multiple scales.
The third part of my research builds on this foundation of leaf ecophysiology and aims to
integrate the interaction between physiology, community and ecosystem functioning into a
unified picture by testing the effects of photosynthesis on community dynamics. To test the
relationship between photosynthesis and community composition, I measured species-level
photosynthetic rate and abundance in a tallgrass prairie monthly across two growing seasons.
Large portions of within-species and across-species variation in percent cover could be explained
by seasonal changes in photosynthetic rate. My results suggest that photosynthesis influences
community composition by affecting dominance of relatively common species in the prairie and
establishes an important linkage between plant physiology and community which has been
overlooked in previous studies.
iv
ACKNOWLEDGEMENTS
I would like to thank my advisor Michael Dietze for advising me in field experiments and
modeling techniques, revising my manuscripts, proposals and presentations, and his patience and
understanding in the whole process of my doctoral study.
I would also like to thank my committee members Lisa Ainsworth, Mark David, Feng
Sheng Hu, and John Taft for their concern about my progress, their helpful advice and feedback
during preliminary exam, committee meetings, and one-on-one meetings.
Many people have contributed to this project over the years in the field and lab. This
project would not have completed successfully without their help. For the help in plant tissue
chemical analysis I would like to thank Michael Masters. For helping me get proficient with LI-
6400 I want to thank Dan Wang for her hands-on demonstration and my advisor for supporting
me to take the LI-6400 training course. I owe additional thanks to Dan Wang for her valuable
advice on experiments, modeling and writing. For providing LI-6400 instruments and
maintenance I would like to thank Stephen Long, Andrew Leakey and Thomas Voigt. For
improving my computer modeling, database techniques and coding skills I would like to thank
David LeBauer. He also spent long hours developing the PEcAn workflow which tremendously
expedited my modeling process. For their invaluable feedbacks in my seemingly endless drafts, I
would like to thank Jackie Getson, Brady Hardiman, Jaclyn Hatala Matthes, David LeBauer,
Joshua Mantooth, Afshin Pourmokhtarian, Dan Wang and Toni Viskari.
The generous funding which made this work possible came from a grant from the Energy
Biosciences Institute to Mike Dietze. I also thank the EBI Energy farm for establishing and
maintaining the prairie restoration experiment. Finally I would like to thank my family especially
v
my husband Glen for his love, help and support in my work and at home through this long but
rewarding process.
vi
TABLE OF CONTENTS
CHAPTER 1: GENERAL INTRODUCTION ............................................................................... 1
CHAPTER 2: PRAIRIE YIELD, MOISTURE AND NITROGEN CONTENT RESPONSE TO
HARVEST TIME ........................................................................................................................... 8
CHAPTER 3: SCALE-DEPENDENCE IN THE EFFECTS OF LEAF ECOPHYSIOLOGICAL
TRAITS ON PHOTOSYNTHESIS .............................................................................................. 22
CHAPTER 4: PHOTOSYNTHESIS PREDICTS COMMUNITY DYNAMICS IN A
TALLGRASS PRAIRIE ............................................................................................................... 72
BIBLIOGRAPHY ......................................................................................................................... 96
1
CHAPTER 1: GENERAL INTRODUCTION
Grasslands cover approximately 3.5 billion hectares worldwide (Carlier et al., 2009), and
provide numerous ecosystem services (Lemaire et al., 2005; Sanderson et al., 2007; Ignatavicius
et al., 2013), including the resource basis for the implementation of grassland biofuel production
at a global scale. These services include ecosystem benefits important in an agricultural context
such as biomass production, soil conservation, resistance to weed invasion, and forage stability
under changing climate (Weigelt et al., 2009). Equally crucial benefits from an ecological
perspective include decomposition, nutrient retention, enhanced carbon sequestration and the
mitigation of greenhouse gas emissions as well as non-market values such as land conservation
and the maintenance of landscape structure and wildlife habitats (Sanderson et al., 2004).
Grasslands, being a mixture of different grasses, legumes, and herbs, have a higher ecological
restoration value than monocultures (Fargione et al., 2008). However, the aforementioned
ecosystem services can largely depend on the community composition of grasslands.
Over the past two decades, the relationship between biodiversity and ecosystem function
have aroused considerable interest and have been investigated by several studies (Risser, 1995;
Tilman et al., 1996; Grime, 1997; Tilman, 1997; Loreau et al., 2001; Tilman et al., 2001; Hooper
et al., 2005; van Ruijven & Berendse, 2005; Reich et al., 2006; Flombaum & Sala, 2008; Isbell et
al., 2009; Hector et al., 2010; Weigelt et al., 2010; Midgley, 2012; Balvanera et al., 2014; Gross
et al., 2014). The high diversity and short development time of grasslands make it a model
ecosystem to research community dynamics and the relationship between community dynamics
and ecosystem services. As a result, biodiversity-ecosystem function (BEF) studies conducted on
perennial plant species in grassland ecosystems are prevalent in the literature (Tilman et al.,
1996; Tilman et al., 2001; van Ruijven & Berendse, 2005; Reich et al., 2006; Flombaum & Sala,
2
2008; Isbell et al., 2009; Hector et al., 2010; Weigelt et al., 2010). A common theme in these
studies is the focus on the relationship between plant taxonomic or functional-group diversity
and ecosystem function (e.g. production, carbon sequestration) and stability (e.g. responses to
disturbance and climate). Species diversity was regarded as a dependent variable under the effect
of abiotic factors or as a biotic factor regulating ecosystem processes. Nevertheless, plant
community dynamics and ecosystem services such as primary productivity ultimately depend on
plant physiological traits. However, recent studies only focus on the interactions between
community and ecosystem ecology. Plant physiology and community development are usually
studied independently. Integrating the interactions between these two disciplines into a single,
unified picture, is a major challenge which may help bring about a synthesis of plant physiology
and community ecology to clarify the BEF debate. Therefore, understanding of BEF is far from
complete (Perrings et al., 2011) and it is important to investigate and elucidate the linkage
between plant physiology and community dynamics. We need to move beyond the traditional
approach in which investigations are limited to community or ecosystem level studies. An
ecophysiological approach can help us understand what individual species are doing and clarify
the detailed underlying mechanisms of BEF. This will largely extend our understanding of BEF
in high diversity grasslands, a globally important ecosystem.
Because of high productivity, large carbon sequestration capacity and other ecological
services, tallgrass prairie has also been considered as a potential biofuel feedstock. With a
rapidly growing reliance on fossil fuels, human activities have increased atmospheric
concentration of CO2 and other greenhouse gases tremendously since the Industrial Revolution
(IPCC, 2007; Rockström et al., 2009). Global climate change and energy security have
accelerated the interest in production and increased availability of alternative energy sources.
3
Native perennial grasslands require relatively low levels of nutrients and show high yields on
marginal lands and therefore have a large potential to perform as a source of biofuel feedstock
(Fargione et al., 2008). Some experiments have suggested that high diversity mixtures of native
grassland perennials can provide more usable energy, greater greenhouse gas reductions, and
higher carbon and nitrogen accumulation than monocultures (Tilman et al., 2006; Adler et al.,
2009; Werling et al., 2014). However, most of these services largely depend on the
photosynthetic rate of each species and the community composition (Symstad et al., 1998;
Johnson et al., 2008; Phoenix et al., 2008). Therefore, the productivity and ecological impacts of
a prairie production system are still under debate (Tilman et al., 2006; Russelle et al., 2007;
Griffith et al., 2011) and need to be further investigated before large-scale implementation of
biofuel production. Simultaneously, community dynamics also need to be monitored within
biofuel systems given that plant community composition is a major driver of prairie ecosystem
function (Cardinale et al., 2007) and is a good indicator of the prairie ecological restoration value.
In addition, the concerns about biofuel sustainability have mostly focused on first generation
biofuel crops such as corn and soybean (Field et al., 2007; Searchinger et al., 2008; Fletcher Jr et
al., 2010). Impacts on ecosystem carbon and nitrogen cycles of a prairie biofuel production
system remain largely unstudied (Robertson et al., 2008).
In order to better understand the ecosystem processes and functioning of a grassland
ecosystem, my dissertation research consists of several different but interconnected studies. As
an agro-ecosystem, primary productivity is especially important in an agriculture context.
Chapter 2 addresses when to harvest prairie biomass to achieve the maximum yield within a
production cycle. Next, photosynthesis directly affects primary productivity and carbon
sequestration of an ecosystem. Therefore, relationships between leaf traits and photosynthetic
4
rates are commonly used to predict primary productivity at scales from the leaf to the globe.
Chapter 3 addresses how these relationships vary across taxonomic scales in grasslands. Chapter
4 builds upon this foundation of leaf ecophysiology and aims to integrate the interaction between
physiology, community and ecosystem functioning into a unified picture by testing the effects of
photosynthesis on community dynamics. To summarize, my dissertation research focuses on the
productivity, physiology and community dynamics of tallgrass prairie ecosystems. In the
following sections I will introduce the background for each of the four chapters.
Productivity
Selecting the harvest time of biofuel crops is critical for ensuring the quality and quantity
of the feedstock for ethanol production. Allowing for a delayed harvest time reduces drying costs
of the harvested biomass and fertilizer requirements for subsequent crops. However, this delay
also leads to dry matter losses in the harvested biomass and thus, reduction in the feedstock
quantity. Therefore, before deciding an appropriate harvest date, the balance among yield,
biomass quality, and impacts on environment needs to be carefully weighed.
Since biomass yield and nutrient concentration change through time within one
production cycle, my first objective is to determine the optimal harvest time that will maximize
net returns with respect to yield, environment, and feedstock quality. I conducted field
experiments to collect monthly biomass and biomass nutrient data of tallgrass prairie.
Physiology
Photosynthesis ultimately drives the primary productivity and carbon sequestration of an
ecosystem. Leaf photosynthesis models use photosynthesis CO2 (A/Ci) and light (A/q) response
curves to quantify the photosynthetic parameters, the underlying biochemical processes limiting
the rate of photosynthesis (von Caemmerer, 2000). Leaf economic traits, such as leaf N, SLA
5
and chlorophyll content affect C assimilation rate by influencing components of biochemical
processes in photosynthesis (Wright et al., 2004). Findings of global plant trait analysis have
suggested that leaf N and SLA are good indicators of photosynthetic assimilation rates measured
under high light and ambient CO2 (Amax) at global scale (Wright et al., 2004; Wright et al., 2005;
Reich et al., 2007). However, the relationships between leaf traits and photosynthetic parameters
may be sensitive to the temporal, spatial, and taxonomic scales examined (e.g. within species,
across species, across plant functional types, and across biomes). This means that these
relationships are not the same at all scales and the scale dependence of the effects of plant
ecophysiological traits on Amax and photosynthetic parameters needs to be further studied.
Failure to account for scales in ecosystem models and remote sensing, which is quite common,
may result in substantial estimation errors.
My objective is to examine the effects of plant physiological traits on photosynthetic rate
and parameters at different scales (leaf, species, plant functional type, and globe). I conducted
field experiments in the same tallgrass prairie restoration project in year 2010 and 2011 to assess
how these relationships vary with scales. Species-level photosynthetic rate and plant
physiological traits, specifically, chlorophyll, leaf nitrogen and specific leaf area were collected
throughout the growing season. Leaf photosynthesis models including leaf trait effects as
covariates were then parameterized.
Community
High biodiversity in grasslands has been linked to provision of ecosystem services such
as primary productivity, carbon sequestration and nutrient retention. Maintenance of species
biodiversity is of particular concern in species rich grasslands given that ecosystem functioning
exhibits a positive relationship with plant species and functional-group diversity. Plant
6
community composition can be affected by abiotic factors such as soil nutrient level and light
availability, and by biotic factors such as phenology and seed dispersal (Hollingsworth et al.
2008, Hooper et al. 2004). Photosynthetic rate affects relative growth rate and thus is another
biotic factor that may play an important role in shaping community composition. The effect of
photosynthetic capacity on community composition has rarely been studied, but it has been
hypothesized that high assimilation rate leads to more biomass production and thus higher
percent cover (Aerts, 1999; Leguizamon et al., 2011). This positive feedback benefits species
with high photosynthetic rates and contributes to high competitive ability and species dominance.
Understanding the relationship between species photosynthetic rate and community structure is
essential to estimate yield and other ecological services. Recent community and ecosystem
function studies are conducted on community or ecosystem level and have mostly focused on the
linkage between community and ecosystem services such as productivity. However, they have
overlooked the linkage between plant photosynthesis and community dynamics.
In addition to a correlation between photosynthesis and composition, many previous
studies (Kindscher & Tieszen, 1998; Sluis, 2002; Baer et al., 2003; Camill et al., 2004) have also
shown a large shift in community composition over years, specifically a large increase in C4
grass abundance and a decline in forbs. Therefore, the role of year-to-year variability in climate
versus succession in affecting the linkage between photosynthesis and community dynamics also
needs to be clarified.
My objective is to test the effect of photosynthetic rate as a biotic factor on species
dominance in the community and if this linkage between photosynthesis and community
dynamics is related with prairie developmental stages. To accomplish this goal, survey plots
7
have been established in a prairie restoration site since 2010. Changes of community
composition and species-level photosynthesis rate were monitored through four growing seasons.
8
CHAPTER 2: PRAIRIE YIELD, MOISTURE AND NITROGEN CONTENT RESPONSE
TO HARVEST TIME1
Abstract
Mixtures of native grassland perennials are characterized by high productivity and large
carbon sequestration capacity, and thus have a potential as biofuel feedstocks. However, the
balance among yield, biomass quality, and impacts on environment needs to be carefully
considered when selecting a harvest time for prairie mixtures because of the biofuel quantity-
quality tradeoff. The effects of harvest time on nitrogen (N) concentration and biomass was
evaluated in a restored tallgrass prairie in Urbana, IL. Prairie biomass yield was highest in
October, and stayed relatively constant during winter. N varied both across and within species
throughout the growing season. Most species achieved lowest N in January. There was little
overall change in N between January and April. Harvesting the prairie in November would
potentially remove 84.6kg N/ha. Our results suggest that the optimal harvest time that balances
feedstock quality and yield is between November and January.
__________________
1This chapter appeared in its entirety in Aspects of Applied Biology and is referred to later in this
dissertation as “Feng & Dietze, 2011”. Feng, X., & Dietze, M., (2011). Prairie yield, moisture
and nitrogen content response to harvest time. 112 (Biomass and Energy Crops IV) 271-277.
This article is reprinted with the permission of the publisher and is available from
http://www.aab.org.uk
9
Introduction
Perennial grasses have several advantages as potential biofuel crops over conventional
annual crops such as lower establishment costs, and reduced soil erosion (Roth et al., 2005;
McLaughlin et al., 2002). Particularly, perennial grasses can translocate nutrients from
aboveground to belowground biomass for overwinter storage, which may reduce the amount of
nutrients removed during harvest. In addition translocation of nutrients can substantially reduce
the mineral content of feedstock, which allows perennial grasses to generate less pollution during
combustion than annual crops that cannot directly recycle nutrients (Heaton et al., 2009).
Minimizing feedstock nutrient content and understanding what controls nutrient concentrations is
important, as some plant nutrients, such as nitrogen and sulfur, become atmospheric pollutants
upon combustion of the fuel (McKendry, 2002). In addition to impacting feedstock quality, N
translocation to the roots can reduce the need for additional fertilizer, thus saving on both the
financial and carbon costs of production. Therefore, allowing biofuel crops to completely
senesce and recycle nutrients can reduce fertilizer requirements and improve feedstock quality by
reducing biomass moisture and mineral content. A delayed harvest reduces artificial drying costs
while providing acceptable biomass material, which is important in the biofuel production
economy. On the other hand, a delayed harvest leads to biomass losses due to losses of leaves
and breakdown of stem tips, particularly in temperate climate where snow and ice can reduce or
destroy harvestable biomass in the field. In previous studies biomass yield has been shown to
drop by up to 35% over the winter (Adler et al., 2006). Hence, when choosing an appropriate
harvest date, the biomass producer faces a conflict between yield and quality optimization.
Therefore, the balance among yield, biomass quality, and environmental impacts needs to be
carefully considered when selecting a harvest time for biofuel crops. The trade-off between yield
10
and fuel quality has been studied on Miscanthus×giganteus, switchgrass (Panicum virgatum),
reed canary grass (Phalaris arundiacea) and other potential energy crop monocultures (Xiong et
al., 2008; Xiong et al., 2009; Heaton et al., 2009; Adler et al., 2006; Lewandowski et al., 2003).
However, the effects of harvest time on biomass N concentration, moisture and yield of a prairie
production system are not well documented. Mixtures of native grassland perennials are
characterized by high productivity, large carbon sequestration capacity and have been considered
as potential biofuel feedstocks (Tilman et al., 2006). In addition, prairie mixtures have several
advantages over biofuel crop monoculture, such as increased water quality, and enhanced
wildlife habitat. Therefore, it is important to determine the impacts of harvest time on yield,
nutrient and moisture content in prairie production systems. Considering phenological
differences across species in prairie mixtures, determining the water and nutrient status on the
species level is essential to estimating the overall effects of harvest time. Since different species
start senescing at different times, harvest time for prairie mixtures needs to be selected carefully
with a consideration of all major species. Given the biofuel quantity-quality trade-off, this
chapter aims to (1) determine biomass yield, nitrogen and moisture content response to harvest
time of prairie mixtures; (2) determine the species, nutrient level, and harvest time that will
maximize expected net returns.
11
Materials and Methods
Study site
Twenty eight native species (Table 2.1) were planted in the tallgrass prairie treatments
plots at the Energy Biosciences Institute Energy Farm (40.05º N, 88.18º W), Urbana, IL, USA in
2008. For purposes of simplifying our analyses these species were assigned to four plant
functional types (PFT): C4 grasses, C3 grasses, legumes and forbs. Seeds for all species were
planted at 0.5g m-2 evenly. The experiment region had an average elevation of 224 m, a mean
annual temperature of 10.7°C, a mean annual precipitation of 1042 mm, and a growing season
length that averages 172 days. The experiment plot was in maize-soybean rotation before the
prairie was planted. No water, fertilizers, or herbicides were applied after the prairie was planted.
The grasses were mowed in November every year though in 2010 small subsets of prairie were
left unmowed for sampling throughout the winter. The prairie was 3 years old and well
established when this experiment began. Except Veronicastrum virginicum, all the other 27
species originally planted have been found in the prairie during the experiment. Plant density in
2010 was ~40 stems m-2. Stand abundance was recorded as the number of individual stems for
forbs and legumes, and was the number of clumps for grasses and sedges.
Biomass sampling
Plant biomass was measured every one to two months across the annual crop production
cycle from June 2010 to April 2011. Species-level aboveground biomass was sampled at four
times during the growing season on June 7th, August 1st, October 1st and November 2nd, and
four after the growing season on the 15th day every month from January to April. Samples were
not taken from within 10m of the plot border to minimize edge effects. For the sampling during
growing season, four 0.25 m2 quadrats were set up randomly along a 100 meter transect to
12
sample biomass. Although the quadrat size is small by agronomic standards, foundational
rangeland experiments evaluating the relative efficiencies of quadrat size found 0.25 m2 quadrats
approach an unbiased estimate of species frequency and biomass production for herbaceous
plants (Van Dyne et al., 1963; Bonham and Ahmed, 1989). Eight transects were established
across the prairie for a total of 32 plots. Six 2×2 m2 plots were kept from the annual harvest in
November for sampling after growing season. Biomass was sampled in each plot using a 0.25 m2
quadrat from January to April. During the sampling, each species was cut by hand to a 5 cm
stubble height, put in paper bags and weighed fresh. In January and February 2011, when the
bottom part of plant stands was buried by the snow, plants were cut to the surface of snow.
Biomass samples were separated into leaf, stem and flower. All samples were then oven-dried to
a constant mass at 70 °C and species dry matter was calculated by summarizing the dry matter
content of each component. Species-level dry weight based biomass moisture content was
determined based on the difference between fresh and oven-dry biomass ((fresh weight-dry
weight)/dry weight).
Elemental analysis
Species-level biomass carbon (C) and N concentration were measured for all species
during each harvest. Following the dry biomass measurements, 0.1-0.2g material was sampled
randomly from leaves of each species. Leaf samples were then ground to a fine powder using a
stainless steel pulverizer (Kleco Pulverizer, Kinetic Laboratory Equipment Company, Visalia,
CA, USA). A 2-4 mg sample was then weighed on an analytical balance (CPA2P Electronic
Microbalance, Sartorius AG, Goettingen, Germany) and encapsulated in tin foil. C and N
percentage was determined by combustion and thermal conductivity separation using a
combustive elemental analyzer (Costech Analytical Technologies, Valencia, CA, USA),
13
calibrated with an acetanilide standard (C8H9NO, Costech Analytical Technologies). Leaf
samples were not available for most plant species after senescence, especially for forbs and
legumes. Therefore, both leaf and stem N were measured for all species from November to April
following the same procedure provided above. Standing N mass was calculated using
aboveground biomass yield and biomass N concentration.
Data analysis
Data were analyzed using R. Species, PFT and harvesting time were considered as fixed
variables. Significance was determined using the F statistic and α=0.05
14
Results
Biomass yield and moisture content
Dry biomass yield of prairie mixture achieved peak yield in October (17.57 ± 6.98 Mg/ha)
(Fig. 2.1). From October to November, dry biomass declined by 32.0% to 12.03 ± 3.22 Mg/ha
and kept decreasing from November to January (7.74 ± 2.02 Mg/ha). In general, there was little
overall change in dry biomass yield between January and April harvest. Fresh biomass was not
measured in Jun, but changes of fresh yield had the same trend as dry biomass through the rest of
the study period. Highest fresh yield (28.82 ± 9.80 Mg/ha) was observed in October 2010.
Among PFTs grasses contributed the most to the dry biomass yield through most of the study
period (Fig. 2.1). Both C3 and C4 grasses achieved the highest yield in October. Yield of C3
grasses was relatively stable between November and February and started decreasing after
February. A steady reduction for biomass of C4 grasses between October and April was found.
Yield of forbs was highest in August and slightly fluctuated from October to April, which might
be caused by sampling error. Biomass of legumes was consistently lower than other PFTs.
Unidentified biomass, composed of plant litter which was impossible to attribute to species, had
a tremendous increase in the October harvest, largely because of plant senescence. However, the
amount of yield attributing to plant litter dropped from 3.81 ± 1.23 Mg/ ha to 0.21± 0.12 Mg/ha
and stayed low in February. This is because accumulation of snow in the field largely decreased
harvestable biomass in January and February. Dry weight based biomass moisture content
decreased from 140.5% ± 25.2% to 30.5% ± 15.2% from August to January and maintained
relatively constant from January to April (Fig. 2.1).
Nitrogen concentration
15
When averaged across all species, N concentration declined steadily from a high of 1.63%
± 0.41% in young shoot tissue in early summer to 0.60% ± 0.29% in winter for the prairie (Fig.
2.2). Decrease of N concentration was more dramatic from Jun to Nov. N concentration declined
slowly from 0.70% ± 0.39% to 0.60% ± 0.29% from November 2010 to January 2011 and stayed
relative constant (~0.6%) from January to April. The seasonal progression of N reduction was
generally consistent across all species. Most species achieved lowest N in January. Overall,
legumes had significantly higher N concentration in standing biomass than other PFTs (P<0.001)
through the study period. N concentration of C4 grasses was lower than other PFTs from June to
November. Although N of C4 grasses stayed relatively low during winter 2011 compared to
other PFTs, the difference was not significant.
Standing N mass
Although average N concentration in prairie standing biomass declined steadily between
June and October, standing N mass of mixed prairie increased from 107.1 ± 27.1 kg/ha to 182.4
± 59.4 kg/ha (Fig. 2.3), because of an increase in biomass. Standing N had a dramatic decline by
54% from October to November, which was caused by simultaneous significant decline of both
biomass and N concentration. Mixed prairie stands would potentially remove 84.6 ± 46.5 kg
N/ha with a yield of 12.03 ± 3.22 Mg/ha if harvested in November. The amount of N removed
through harvesting would decrease to 46.6 ± 22.5 kg/ha in January. However, the decrease of
amount of N removed in harvest is mainly due to decrease of biomass. There was little change in
stranding N mass from January to April due to the relatively stable levels of yield and N
concentration.
16
Discussion
Previous studies have shown that yield, biomass quality, and environmental impacts can
be strongly affected by harvest time for Miscanthus×giganteus, switchgrass (Panicum virgatum),
reed canary grass (Phalaris arundiacea) and other potential energy crop monocultures (Xiong et
al., 2008; Xiong et al., 2009; Heaton et al., 2009; Adler et al., 2006; Lewandowski et al., 2003).
The results of this study demonstrate that the quantity-quality trade-off also exists in mixed
prairie production systems, despite the differences among species in senescence phenology. Our
findings suggest that harvesting in November allows the prairie to senesce and recycle nutrients
to a great extent and can potentially reduce fertilizer requirements. Although standing N biomass
fell to lower and stable level in January, it is mainly due to biomass reduction instead of changes
in C: N ratio. However, biomass moisture continued to decline between November and January,
which suggests that a harvest after November can potentially improve feedstock quality by
reducing biomass moisture and reduce drying costs. In addition, since N concentration had a
slight decline from November to January, harvest after November may allow the prairie to
senesce more completely. However, considering the dramatic loss of harvestable biomass
between November and January, the prairie mixture might be harvested as soon as possible after
moisture achieves a level low enough to optimize the benefits with respect to both yield and
environment needs.
The average N concentration in standing biomass of prairie mixture was higher than C4
monocultures of Miscanthus×giganteus and switchgrass which generally maintain a N
concentration < 0.5% in late fall (Heaton et al., 2009; Xiong et al., 2008). C4 grasses generally
have lower N concentration than forbs, C3 grasses and legumes because of high photosynthetic
N use efficiency.
17
Findings of this study suggest that harvest time critically influences N dynamics in
mature stands of prairie mixtures in the temperate climate of Illinois. Our results indicate that
delaying harvest can reduce the moisture and N concentration in biomass feedstock, but the
majority of reductions are realized by late fall and the biofuel crops should be harvested before
risk of weather-related losses overwinter. The harvest time that is optimal with respect to
environment, feedstock quality and yield for the prairie studied is between November and
January.
18
Table 2.1
PFT Family Scientific Name Common Name
C3 grass Poaceae Elymus canadensis Canada wild rye
C3 grass Cyperaceae Carex bicknellii Copper-shouldered oval sedge
C4 grass Poaceae Andropogon gerardii Big bluestem
C4 grass Poaceae Schizachyrium scoparium Little bluestem
C4 grass Poaceae Sorghastrum nutans Indian grass
forb Asteraceae Aster novae-angliae New England aster
forb Asteraceae Coreopsis palmata Prairie coreopsis
forb Asteraceae Coreopsis tripteris Tall tickseed
forb Asteraceae Echinacea pallida Pale coneflower
forb Asteraceae Helianthus grosseserratus Sawtooth sunflower
forb Asteraceae Heliopsis helianthoides Early sunflower
forb Asteraceae Parthenium integrifolium Wild quinine
forb Asteraceae Ratibida pinnata Yellow coneflower
forb Asteraceae Rudbeckia subtomentosa Sweet black-eyed susan
forb Asteraceae Silphium integrifolium Wholeleaf rosinweed
forb Asteraceae Silphium laciniatum Compassplant
forb Asteraceae Silphium perfoliatum Cup plant
forb Asteraceae Silphium terebinthinaceum Prairie dock
forb Asteraceae Solidago rigida Stiff goldenrod
forb Lamiaceae Monarda fistulosa Wild bergamot
forb Lamiaceae Pycnanthemum virginianum Common mountain mint
forb Scrophulariaceae Penstemon digitalis Foxglove beardtongue
forb Scrophulariaceae Veronicastrum virginicum Culver's root
legume Fabaceae Astragalus canadensis Canadian milkvetch
legume Fabaceae Baptisia leucantha White wild indigo
legume Fabaceae Desmodium canadense Showy tick trefoil
legume Fabaceae Lespedeza capitata Roundhead lespedeza
legume Fabaceae Dalea purpurea Purple prairie clover
Table 2.1. Twenty-eight species planted in Energy Biosciences Institute Energy Farm
19
Fig. 2.1. Seasonal changes in (a) average fresh biomass and dry biomass, (b) overall dry weight
based moisture content of mixed prairie standing biomass, and (c) average dry standing biomass
of each plant functional type
20
Fig. 2.2. Changes in (a) average nitrogen concentration (%) of mixed prairie standing biomass, (b)
average nitrogen concentration (%) in standing biomass of each plant functional type, (c) average
carbon concentration (%) of standing biomass, and (d) average C:N ratio of each plant functional
type
21
Fig. 2.3. Seasonal changes in overall standing N mass
22
CHAPTER 3: SCALE-DEPENDENCE IN THE EFFECTS OF LEAF
ECOPHYSIOLOGICAL TRAITS ON PHOTOSYNTHESIS2
Abstract
Relationships between leaf traits and carbon assimilation rates are commonly used to
predict primary productivity at scales from the leaf to the globe. We addressed how the shape
and magnitude of these relationships vary across temporal, spatial, and taxonomic scales to
improve estimates of carbon dynamics. Photosynthetic CO2 and light response curves, leaf
nitrogen (N), chlorophyll (Chl) concentration and specific leaf area (SLA) of 25 grassland
species were measured. In addition, C3 and C4 photosynthesis models were parameterized using
a novel hierarchical Bayesian approach to quantify the effects of leaf traits on photosynthetic
capacity and parameters at different scales. The effects of plant physiological traits on
photosynthetic capacity and parameters varied among species, plant functional types, and
taxonomic scales. Relationships in the grassland biome were significantly different from global
average. Within-species variability in photosynthetic parameters through the growing season
could be attributed to the seasonal changes of leaf traits, especially leaf N and Chl, but these
responses followed qualitatively different relationships from the across-species relationship. The
results suggest that one broad-scale relationship is not sufficient to characterize ecosystem
condition and change at multiple scales. Applying trait relationships without articulating the
scales may cause substantial carbon flux estimation errors.
__________________
2This chapter appeared in its entirety in New Phytologist and is referred to later in this
dissertation as “Feng & Dietze, 2013”. Feng, X., & Dietze, M. (2013). Scale-dependence in the
effects of leaf ecophysiological traits on photosynthesis: Bayesian parameterization of
photosynthesis models. 200(4) 1132‐1144. This article is reprinted with the permission of the
publisher and is available from http://onlinelibrary.wiley.com and using DOI:
10.1111/nph.12454
23
Introduction
It is well-known that photosynthetic capacity varies widely among plant species
(Wullschleger, 1993). In the global plant trait network (Glopnet), photosynthetic capacity (Amax)
varied 130-fold when expressed on a dry mass basis, and 40-fold when expressed on a leaf area
basis (Wright et al., 2004). This global survey showed that carbon (C) assimilation rate can be
potentially predicted by leaf ecophysiological traits, specifically leaf nitrogen (N) and specific
leaf area (SLA) (Wright et al., 2004). The correlation between photosynthesis and leaf
ecophysiological traits has attracted attention as we aim to improve our understanding of the
inherent variation in photosynthetic capacity and increase our capacity to predict variability in
gross primary production (GPP). However, existing studies have focused on investigating the
correlation across biomes and plant functional types (PFTs) at broad scales or among species at a
specific time during the growing season (Poorter & Evans, 1998; Zheng & Shangguan, 2007;
Hikosaka & Shigeno, 2009; Archontoulis et al., 2012). Seasonal changes of photosynthetic
capacity within a species, leaf-to-leaf variability, and the causes for this variability were not well
accounted for in these studies. However, seasonal changes can contribute significantly to
variability in GPP (Peng et al., 2011; Wang et al., 2012). More importantly, the differences in
trait-photosynthesis relationships at different scales, which drive total variability, have not been
explicitly explored (e.g. in case of taxonomic scales: leaf, species, PFT, and biome level).
Leaf physiological traits are key determinants of biogeochemical cycles (specifically CO2
fluxes) that link vegetation, soil, and atmosphere at every temporal and spatial scale (Reich et al.,
2007). Many studies have used general leaf trait correlations to predict photosynthesis over
scales ranging from the leaf to the globe (Harley & Baldocchi 1995; Larocque, 2002; Müller et
al., 2005; Braune et al., 2009; Kattge et al., 2009; Ziehn et al., 2011). Biophysical characteristics
24
of vegetation related to photosynthesis such as leaf N and Chlorophyll (Chl) have been used as
proxies in the determination of GPP in modeling and remote sensing (Kattge et al., 2009; Peng et
al., 2011; Gitelson et al., 2012). Therefore, the scale-dependence of these correlations needs to be
articulated to make reliable estimations of carbon gain at different scales.
Enzyme kinetic models of leaf photosynthesis can be used to elucidate fundamental
biochemical processes and quantify biochemical parameters (Von Caemmerer, 2000). Leaf traits
play an important role in determining photosynthetic rate and thus should be incorporated in
photosynthesis models for better C flux estimation. Large amounts of total leaf N (15–35%) are
allocated to Rubisco protein in C3 higher plants (Evans, 1989). The fraction of N invested in
carboxylation enzymes depends on total leaf N concentration (Sage et al., 1987). Therefore, leaf
N concentration is directly correlated with Rubisco activity and maximum Rubisco carboxylation
rate (Vcmax) (Cheng & Fuchigami, 2000). Some leaf photosynthesis models account for effects of
leaf N on C assimilation rate (Wohlfahrt et al., 1998; Müller et al., 2005; Braune et al., 2009;
Ziehn et al., 2011) but, such models are parameterized to capture responses at a single scale (e.g.
individual leaf-level, within-species responses to vertical light profiles or fertilization, global
scale). In addition, important leaf characteristics such as Chl and SLA, which may substantially
affect light use efficiency, are rarely considered in these models. Chlorophylls are primarily
responsible for harvesting light energy (Hopkins & Hüner, 2004), while leaf thickness affects
light absorption efficiency (Farquhar et al., 1980; Hopkins & Hüner, 2004). In addition, leaf
thickness affects mesophyll conductance, the conductance of CO2 from the intercellular space to
the site of carboxylation, and hence carboxylation efficiency (Hikosaka 2004).
Currently, terrestrial ecosystem models incorporate effects of leaf traits by using general
global leaf trait relationships (Bonan et al., 2002, 2011, 2012; Kaplan et al., 2003; Thornton et
25
al., 2007; Kattge et al., 2009), yet often apply these relationships at a different scale to predict
within-canopy responses through time or with canopy position.
To assess the scale-dependent effects of leaf ecophysiological traits in enzyme kinetic
models of photosynthesis, we developed mixed-effect versions of the C3 FvCB model (Farquhar
et al., 1980) and C4 ICT model (Collatz et al., 1992) that include: (1) the effects of leaf N
concentration on Vcmax (Vmax for C4); (2) the effects of Chl and SLA (indicator of leaf thickness)
on quantum efficiency; (3) seasonal variability; and (4) leaf-to-leaf variability. Since there is no
explicit investigation at the global scale for the variability in the relationships between leaf traits
and photosynthetic parameters, we also examined leaf trait-Amax relationships and compared our
results to the global analyses (Wright et al., 2004). Leaf trait-Amax relationships at different scales
(within species, across species, across PFTs and global scale) were explored. The objectives of
our study are to: (1) determine the correlations between Amax and leaf traits, both within and
across species, and compare these patterns to the among-biome relationships from global scale
plant traits analyses (Wright et al., 2004); and (2) parameterize C3 and C4 leaf photosynthesis
models to partition model variability and determine the scale-dependence in effects of leaf traits
on biochemical photosynthetic parameters. We hypothesize that a large portion of the variation
in biochemical photosynthetic parameters can be ascribed to changes in leaf traits since there is a
general correlation between photosynthetic capacity and leaf traits. We also hypothesize that the
leaf trait-photosynthesis relationships should vary at different scales due to different
physiological attributes. Specifically, the effects of leaf traits on Amax and photosynthetic
parameters should vary among leaves of the same species, among species within a functional
group, across PFTs within a biome (C3 grass, C4 grass, forb and legume within grasslands) and
between biomes.
26
Materials and Methods
Study site
Polycultures of 28 native grassland perennial species were planted at the Energy
Biosciences Institute’s Energy Farm in 2008. Seeds for all species were planted evenly at 0.5 g
m-2 and 25 species established sufficiently to allow measurements (Table 3.1). The farm is
located in Urbana, IL, USA (40.05º N, 88.18º W) at an elevation of 224 m. The experimental
region has a mean annual temperature of 10.7°C, and a mean annual precipitation of 1042 mm.
The average growing season length is 172 days. The experimental plot was in maize-soybean
rotation before the planting of the prairie. No water, fertilizers, or herbicides were applied after
the prairie was planted. The grasses were mowed in November every year. The prairie was 3
years old and had well established when this experiment began. Plant density in 2010 was ~40
stems m-2.
Leaf gas exchange measurements
The photosynthetic measurements were made on 13 species in 2010 and extended to 25
species in 2011. A portable photosynthesis system (LI-6400, LI-COR, Inc. Lincoln, NE, USA)
with a red/blue light source and 2 cm2 leaf chamber was used to measure CO2 response (A/Ci)
and light response (A/q) curves. The measurements were taken on sun leaves that were newly
formed and mature. Measurement time was between 10:30 and 16:00 (local time) in the middle
of each month, from June to October in 2010 and May to October in 2011. Three to six leaf
replicates were measured for each species in each month. When a leaf did not completely cover
the chamber, a picture was taken of the measured area with a known length as reference and leaf
area was then determined through image analysis (ImageJ [http://rsbweb.nih.gov/ij/]) (Abramoff
et al., 2004).
27
For A/Ci curves, prior to the measurements, the leaf was acclimated to saturating
irradiance (2000 μmol m−2 s−1) for a half hour at a relative humidity of ~70% and leaf
temperature of 25 ºC. Without changing the above settings, photosynthetic rates were measured
at different chamber CO2 concentrations: 400, 300, 200, 125, 75, 50, 25, 400, 600, 900 and 1300
μmol mol-1. Photosynthesis at 400 ppm CO2 in the A/Ci data was treated as net carbon
assimilation rate at ambient CO2 (i.e. Amax). Given that information on high light-level
photosynthesis can be extracted from A/Ci curves, for efficiency the A/q curve was only
measured across a low light range on the same leaf on which the A/Ci curve was taken; quantum
flux densities were set as 200, 150, 100, 50 and 0 µmol m-2 s-1, with the CO2 concentration of
400 μmol mol-1.
Plant traits measurements
Immediately following gas exchange measurements, two 0.5 cm2 leaf discs were cut from
the measured leaf using a hole-punch and kept in 2 ml 95% ethyl alcohol for 10 days at room
temperature in the dark. Absorbance at 470 nm, 649 nm and 664 nm were measured with a
microplate luminometer (Bio-Tek Instruments, Inc. Winooski, VT, USA) to calculate Chl
concentration. Total Chl (Chla+Chlb) concentration was used in our analysis. Another ten 0.5
cm2 leaf discs were sampled on the same leaf or close leaves (when not enough samples could be
collected from the measured leaf). Discs were oven-dried at 75 °C to a constant mass and
weighed to determine SLA. When one leaf was not able to cover the punch hole (0.5 cm2),
several leaves were aligned and sampled for Chl measurements and SLA was not measured.
Species without SLA measurements include Dalea purpurea, Pycnanthemum virginianum,
Schizachyrium scoparium, and Carex bicknellii. After SLA was determined, samples were
ground to a fine powder using a stainless steel pulverizer (Kleco Pulverizer, Kinetic Laboratory
28
Equipment Company, Visalia, CA, USA). A 2-4 mg sample was weighed on an analytical
balance (CPA2P Electronic Microbalance, Sartorius AG, Goettingen, Germany) and
encapsulated in tin foil. C and N percentage was determined by combustion and thermal
conductivity separation using a combustive elemental analyzer (Costech Analytical
Technologies, Valencia, CA, USA), calibrated with an acetanilide standard (C8H9NO, Costech
Analytical Technologies).
Amax and leaf traits relationships
Amax, leaf N, Chl can be expressed on a leaf dry mass (Amass, Nmass, Chlmass) or a leaf area
(Aarea, Narea, Chlarea) basis. During raw data collection, Amax (µmol m-2 s-1) and Chl (µg cm-2)
were area-based measurements; and leaf N (%) was mass-based measurement. Area- and mass-
based traits were interconverted via SLA (m2 kg-1) (e.g. Narea=Nmass/SLA). Both mass-based and
area-based relationships between Amax and leaf traits (leaf N, SLA and Chl) were determined via
standard major axis (SMA) analysis using a linear bivariate regression model. Data were fit by
species to determine the variability within and among species. The 25 species were assigned to
four PFTs: C3 grass, C4 grass, forb, and legume. Data for each PFT were fit simultaneously to
determine the variability among PFTs and at different taxonomic scales. In addition, the
regressions were compared to the Glopnet analyses to examine the variability of Amax-trait
relationships at different taxonomic and spatial scales. The Glopnet analyses were based on a
dataset that represented 175 sites and contained 2,548 species (Wright et al., 2004). The
relationships between Amax and leaf traits were also examined using the subset data of Glopnet
herbaceous species. Considering the wide range of data (leaf traits varied by one to two orders of
magnitude), the regressions were performed on a log scale, as similarly done in Wright et al.
(2004). In the SMA analyses, level of significance of 0.05 was used to determine the statistical
29
significance. If P-value was less than or equal to 0.05 we rejected the null hypothesis that SMA
regression was not significant. Akaike information criterion (AIC) scores of SMA regressions
were used in model selection (Table 3.2).
Model fitting techniques and statistical analysis
Model fitting for A/Ci and A/q curves was done using a Hierarchical Bayes approach
(Clark, 2007). The major advantages of the Bayesian analysis include the following: (1) all A/Ci
and A/q data for each species from the whole growing season can be fit simultaneously, rather
than fitting these models leaf-by-leaf; (2) prior information for parameters can be assimilated
into models, which can improve model performance especially when data is limited; (3)
uncertainty and variability can be partitioned into multiple processes, such as leaf-to-leaf
variability versus observation error, rather than lumping all variability into a single residual error
term; (4) the estimation of posterior probability distributions for parameters, instead of single
values, facilitates the propagation of model uncertainty to other process models (LeBauer et al.,
2012). Traditional approaches, fit on a leaf-by-leaf basis, will overestimate the variability among
leaves while failing to account for leaf-level uncertainty in subsequent analyses. When the 95%
credible interval (CI) for an effect encompassed 0, the corresponding parameter was removed
one at a time from the full model and deviance information criterion (DIC) was used to confirm
that the resulting model was a better fitting model (model with lower DIC is better). We also
tested the default model, which excludes all the fixed and random effects. The posterior
distributions were obtained by fitting all the A/Ci and A/q data for each species from the whole
growing season simultaneously. Priors and likelihoods are described in the model description
section below. Only the effects of leaf N and Chl were estimated for Dalea purpurea,
Pycnanthemum virginianum, Schizachyrium scoparium and Carex bicknellii. Effects of SLA
30
were not modeled for these species since SLA data was not collected.
Model parameterization analyses were implemented in R 2.14.1 ([http://www.r-
project.org/]) (R Development Core Team, 2011) and WinBUGS 1.4 (Lunn et al., 2000). Trace
plots were used to confirm convergence. Chains were run for 100K steps, discarding the first
50K for burn-in, thinning to 1/25 to reduce autocorrelation, resulting in a total number of 6K
samples per species. Statistical significance was determined using 95% CI.
C3 and C4 photosynthesis models
The hierarchical Bayesian parameterization of C3 and C4 photosynthesis models are
depicted in Fig. 3.1. The data model is assumed to be Normal (observed An is normally
distributed around modeled An):
𝐴𝑛(𝑜)~𝑁(𝐴𝑛
(𝑚), 𝜏2) (1)
where An(o) is observed net photosynthesis, An
(m) is modeled net photosynthesis, and τ is the
residual standard deviation. The FvCB model and simplified ICT model were parameterized for
C3 leaves and C4 leaves, respectively, and model details are summarized in the sections below.
Fixed effects of leaf N, Chl, SLA and month on carbon assimilation rate were incorporated in the
process model. A random leaf effect was used to account for the variation among individual
leaves that plant physiological traits (leaf N, Chl, SLA) and month could not explain. Plant trait
data from a plant trait database (Biofuel Ecophysiological Traits and Yields database [http://ebi-
forecast.igb.uiuc.edu]) (LeBauer et al., 2012) were used to provide prior constraints on the model
parameters. Priors (Table 3.3) were derived at a broad taxonomic or functional level. When
insufficient prior information was available, an uninformative prior distribution was assigned to
the parameter to reflect a small contribution of information.
FvCB model for C3 leaves
31
FvCB model of C3 plant (A/Ci and A/q curve analysis) was described as (Farquhar et al.,
1980; Sharkey, 1985; Harley & Sharkey, 1991; Harley et al., 1992):
𝐴𝑛(𝑚) = min{𝐴𝑣, 𝐴𝑗} − 𝑅𝑑 (2)
where An(m) is modeled net photosynthetic rate, Av is the rate when Rubisco carboxylation is
limiting, Aj is electron transport-limited rate of carboxylation, and Rd is the day (non-
photorespiratory) respiration rate. Triose phosphate utilization (TPU) was not incorporated in the
model because signs of TPU limitation were not observed in the data.
Rubisco-limited photosynthesis is expressed as:
𝐴𝑣 = 𝑉𝑐𝑚𝑎𝑥′ 𝐶𝑖−Г∗
𝐶𝑖+𝐾𝑐(1+𝑂
𝐾𝑜) (3)
𝑉𝑐𝑚𝑎𝑥′ = 𝑉𝑐𝑚𝑎𝑥 + 𝛽𝑁(𝑁 − �̅�) + 𝛽𝑚𝑜𝑛 + 𝜐𝑙𝑒𝑎𝑓 (4)
where Ci and O are intercellular partial pressure (Pa) of CO2 and O2, respectively; Kc and Ko are
the Michaelis-Menten coefficients of Rubisco activity for CO2 and O2, respectively (Pa); Γ* is
the CO2 compensation point in the absence of Rd (Pa); Vcmax is the maximum rate of
carboxylation; βN is the slope of the fixed effect of N concentration in each individual leaf on
Vcmax; N is mass-based N concentration in leaves (%); N is the average N concentration for one
species through the growing season. βmon is a fixed effect of month on Vcmax used to estimate the
photosynthetic variation among different months from May to October relative to a reference
month (July). υleaf is a random individual-leaf effect on Vcmax.
The rate of photosynthesis when electron-transport rate is limiting is expressed as:
𝐴𝑗 =𝐽(𝐶𝑖−Г∗)
4(𝐶𝑖+2Г∗) (5)
where J is the rate of electron transport and can be described as (Harley et al. 1992):
32
𝐽 =4𝛼′𝑞
√(1+(4𝛼′)2
𝑞2
𝐽𝑚𝑎𝑥2 )
(6)
𝛼′ = 𝛼 + 𝛽𝐶ℎ𝑙(𝐶ℎ𝑙 − 𝐶ℎ𝑙̅̅ ̅̅̅) + 𝛽𝑆𝐿𝐴(𝑆𝐿𝐴 − 𝑆𝐿𝐴̅̅ ̅̅ ̅) + 𝛼𝑙𝑒𝑎𝑓 (7)
where Jmax is the maximum rate of electron transport, q is quantum flux density, and α is the
maximum quantum efficiency derived from the initial slope of the light response curve (mol CO2
per mol absorbed photons). The 4 arises by assuming 4 electrons per carboxylation or
oxygenation. βChl is the slope of fixed effect of chlorophyll concentration in leaves (Chl, µg cm-2)
on α, and Chl is the average chlorophyll concentration for one species through the growing
season. βSLA is the slope of fixed effect of specific leaf area (SLA, m2 kg-1) of each individual
leaf on α, and SLA is the average SLA for one species through the growing season; αleaf is the
random individual-leaf effect on α after Chl and SLA are accounted for (Table 3.3). SLA could
affect mesophyll conductance of CO2, and hence Vcmax. However, in the present model discussed
(Farquhar et al. 1980), mesophyll conductance is considered infinite. Therefore the effect of SLA
on Vcmax was not included in the model.
Simplified ICT model for C4 leaves
In the C4 photosynthesis model (Collatz et al., 1992) (Fig. 3.1), net CO2 assimilation rate
(An) can be modeled as the minimum of three limiting rates:
𝐴𝑛 = min{𝐴𝑐, 𝐴𝑙 , 𝐴𝑒} − 𝑅𝑑 (8)
CO2-limited photosynthesis is expressed as:
𝐴𝑐 =(𝑘+𝑘𝑙𝑒𝑎𝑓)𝐶𝑖
𝑃 (9)
where k is initial slope of photosynthetic CO2 response curve; kleaf is random leaf effect on k. P is
atmospheric pressure and was treated as constant (105 Pa).
33
Light-limited photosynthesis is expressed as:
𝐴𝑙 = 𝛼′𝑞 (10)
where α' is the same α' as expressed in equation 8. Rubisco-limited photosynthesis is expressed
as:
𝐴𝑒 = 𝑉𝑚𝑎𝑥 + 𝛽𝑁(𝑁 − �̅�) + 𝛽𝑚𝑜𝑛 + 𝜐𝑙𝑒𝑎𝑓 (11)
where Vmax is maximum Rubisco capacity of C4 species; βN, N, N , βmon, and υleaf are the same
as expressed in equation 4. In this case, maximum Rubisco capacity of C4 species is referred to
as Vmax due to a different biological interpretation from Vcmax of C3 species (Ripley et al., 2010).
Given that uncertainty may be caused during conversion between area- and mass-based units,
units for An, N, Chl and SLA in models were consistent with the units used during data
collection (Table 3.3).
34
Results
Amax and leaf traits relationships
Area-based Amax of the 25 prairie species examined ranged from 1.15 µmol m-2 s-1 to
39.39 µmol m-2 s-1. The within-species seasonal variability of Amax was high for all species (Amax
varied by 5- to 20-fold).
Within species through the growing season, in both mass-based and area-based
relationships, Amax was positively related with leaf N for 19 species. The slopes of Amass-Nmass
relationships ranged from 1.35 (Elymus canadensis) to 3.31 (Lespedeza capitata) while slopes of
Aarea-Narea had a range of 1.25-3.42 with the same two species having lowest and highest value
respectively (Table 3.4). The pattern of Amax-Chl relationships was similar to Amax-leaf N, with
18 and 20 species showing significant positive mass- (slope ranged 0.75-2.15) and area-based
(slope ranged 0.76-2.57) relationships, respectively (Table 3.4). The Aarea-SLA relationships
were positively significant for 6 species and non-significant for 15 species, while for the Amass-
SLA relationship 13 species were positively significant versus 8 non-significant. Most species
that showed non-significant mass-based relationships also had non-significant area-based
relationships (Table 3.4). The within-species Amax-trait relationships varied considerably among
species and most species were significantly different from the global average (Table 3.4).
When the relationships between Amax and leaf traits were tested across species, both 95%
CI of SMA analyses and AIC scores suggested separate regression models for different grassland
PFTs (Fig. 3.2, Table 3.2). For Amass-Nmass relationship (Fig. 3.2a), the regression lines of C4
grasses, legumes and forbs had similar slopes (1.91-2.46) but different intercepts with legumes
lowest (1.24), forbs in the middle (1.81), and C4 grasses highest (2.15) (Table 3.2). Nmass of
legumes were concentrated on the higher end, while Nmass of C4 grasses were mainly distributed
35
at the lower end suggesting a high photosynthetic nitrogen use efficiency for C4 grasses and low
efficiency for legumes. The Amass-SLA slopes of grasses (3.52-3.57) were significantly higher
than forbs and legumes (2.52-2.71); nonetheless all PFTs had higher slope values (2.52-3.57)
than the global average (1.33) (Fig. 3.2b, Table 3.2). In Amass-Chlmass relationships, although
statistical tests suggested separate fit for each PFT, the regressions of C3 grasses, C4 grasses and
forbs were very close. However, the intercept of legumes (1.69) was significantly lower than
these three PFTs (1.83-2.05) (Fig. 3.2c, Table 3.2). In area-based relationships, the Aarea-Narea
and Aarea-Chlarea relationships showed the same pattern as shown in mass-based relationships.
However, Aarea-SLA relationships were only significant for C4 grasses (Fig. 3.3, Table 3.2).
Across all PFTs, both mass- and area-based regressions between Amax and leaf traits for
grassland species were different from the global average (Fig. 3.2, Fig. 3.3). Grassland species
had substantially higher slopes in Amass-Nmass and Aarea-Narea relationships than the global
average, which indicates higher nitrogen use efficiency in grasslands. In addition, the Amass-SLA
relationship across grassland species also had a significantly higher slope (mean=2.65) than the
global mean of 1.33. Such differences also exist in the Glopnet dataset, where the available data
of herbaceous plants similarly showed higher Amax-leaf N and Amass-SLA slopes when compared
to the global average (Fig. 3.4). In the grassland studied, although the Aarea-SLA relationships
were not significant for 3 out of 4 individual PFTs (C3 grasses, forbs and legumes), when data of
all PFTs were fit simultaneously, the correlation between Aarea and SLA was significant across
grassland species. However, global analysis showed a non-significant Aarea-SLA relationship
(Fig. 3.3).
Partitioning variability in enzyme-kinetic models
The default models of C3 and C4 photosynthesis without mixed effects did not capture
36
the fluctuations in assimilation from leaf to leaf through the growing season (Fig. 3.5). Adding a
random leaf effect to the models improved model performance tremendously but left the
variability among individuals unexplained. When the best fit model was parameterized for each
of the 25 species, the effects of leaf N and Chl were significant for most species but SLA was
only significant for four species (Table 3.5). Parameter means and 95% credible intervals were
summarized in table 3.6.
Photosynthetic responses to CO2 and light were dependent on both species and month
(Fig. 3.6). Within species, Vcmax, Vmax and quantum efficiency declined late in the growing
season for most species. However, model results showed that, within a species through the
growing season, month effects were not significant for any species if leaf traits were also
included. When July was set as the reference month, 95% CI of βmon posterior distributions for
all species encompassed 0, i.e. month was not the factor that affects photosynthesis. Instead,
changes in Vcmax, Vmax and quantum efficiency through growing season could be explained by
the seasonal changes of leaf traits, especially leaf N and Chl (Fig. 3.7). Within species, after the
effects of leaf N, Chl and SLA were accounted for, part of the variation in Vcmax, Vmax and
quantum efficiency among different leaf replicates still could not be explained. This part of the
variation was represented by random leaf effects (υleaf for Vcmax and Vmax, αleaf for quantum
efficiency) (Table 3.3). Although the proportion of variation explained by random leaf effects
was generally smaller than the fixed effects, it was substantial and could not be neglected. For 17
out of 25 species, more than half of the within-species variability of Vcmax and Vmax was caused
by variation in leaf N; and for 16 out of 25 species, more than half of the within-species
variability of quantum efficiency was due to changes in Chl and SLA (Fig. 3.7, Table 3.5).
However, the proportions of fixed effects of quantum efficiency were concentrated on the end
37
with higher values (0.6-0.8) compared to Vcmax and Vmax (Fig. 3.7). Model residual errors were
very small compared to fixed effects and random effects (Table 3.5). Low model residuals
indicate low deviation of predictions from their observed values.
Across species, photosynthetic parameters such as Vcmax, Vmax, Jmax, α, and Rd varied
considerably among different species (Table 3.6). Vmax ranged from 15.22 to 25.57 µmol m-2 s-1
for C4 species while the range of Vcmax for C3 species was 43.21-130.48 µmol m-2 s-1. Jmax
ranged from 60.93 to 197.14 µmol m-2 s-1 and had a strong positive relationship with Vcmax (Fig.
3.8). Vcmax and Jmax of legumes were generally high, which were associated with high leaf N, Chl
and SLA. Quantum efficiency (α) of C3 and C4 species ranged from 0.043-0.088 mol CO2 mol-1
photon and 0.040-0.055 mol CO2 mol-1 photon, respectively. In contrast to previous findings by
Skillman (2008), the range of quantum efficiencies of C4 species was narrow and had large
deviations from values of C3 species. These large deviations may be caused by the limited
number of C4 species. Only three C4 species were included in the analyses and one of them
consistently showed up in lower canopy.
In addition to photosynthetic parameters, the effects of leaf traits on parameters also
varied tremendously across species (Fig. 3.7). The proportion of variation in quantum efficiency
that can be explained by fixed Chl and SLA effects ranged from 0.24 to 0.72 (Table 3.5). Chl
effect was not significant for 4 species while SLA effect was only significant for 4 species (Fig.
3.7, Table 3.5). The proportion of variation in Vcmax contributed by fixed leaf N effects was as
high as 0.75 for Lespedeza capitata and as low as 0.20 for Silphium laciniatum. Three species
did not show significant effects of leaf N on Vcmax (Fig. 3.7, 3.9, Table 3.5). The effects of leaf N
on Vcmax and Vmax depended largely on the taxonomic scale (Fig. 3.9). In the case of legumes, the
within-species relationships were consistent with the within-PFT across-species relationship. The
38
slope values of within-species relationships for legumes ranged from 7.22 to 17.72 (Table 3.5)
and the across-species relationship had a slope of 12.07. For forbs, the across-species
relationship was notably steeper (slope was 72.05) than the within-species relationships (slopes
ranged 4.67-18.95). The across-species trends within the C3 grass and C4 grass PFTs were
difficult to ascertain, due to limited number of species available for analyses (Fig. 3.9). The
within-PFT but across-species slope for legumes was lower than the across-PFT slope, while the
within-PFT slope of forbs was higher than the across-PFT slope. In general, effects of leaf traits
on Amax and photosynthetic parameters varied within-species, among species, and across PFTs.
39
Discussion
Amax and leaf traits relationships
In mass- and area-based relationships, Amax was positively related with leaf traits,
especially leaf N and Chl within and across species. However, the variation of within- and
across-species relationships suggests that relationships between photosynthesis and leaf traits are
not consistent. The within-species relationships varied markedly from species to species. Model
selection suggested separate regressions for different PFTs, which indicates that the PFT-to-PFT
variability is also significant. Furthermore, the relationships across grassland species are
different from Glopnet relationships, most notably suggesting higher SLA values, Amax-SLA
slope and photosynthetic nitrogen use efficiency in grasslands. The available herbaceous plant
data of Glopnet also showed higher Amax-leaf N and Aarea-SLA slopes when compared to the
global average, which is consistent with our findings (Fig. 3.4). Moreover, in the study
conducted by Marino et al. (2010), the Amass-Nmass and Amass-SLA relationships displayed by 25
herbaceous species also showed higher slope values than the global average which further
confirms our results. The difference is that, due to controlled growth conditions and indoor
measurements, relationships displayed by Marino et al. (2010) were much tighter than the
relationships shown in our data and the Glopnet herbaceous subset. In addition, we also found a
pattern in the Aarea-Narea relationship similar to that found by Evans (1989) with herbaceous
plants tending to have higher CO2 assimilation rates than other plant groups for a given nitrogen
content. In all the aforementioned studies, the area-based relationships were not as strong as
mass-based relationships. Indeed, some non-significant area-based relationships showed
statistically strong mass-based relationships (Fig. 3.2, 3.3, 3.4). However, recently it has been
argued that the strong correlations between mass-based measures of photosynthesis, nitrogen and
40
other traits may be a statistical artifact and area-based measured are more physiologically
meaningful as photosynthesis occurs as a flux per unit leaf surface area (Lloyd et al., 2013;
Osnas et al., 2013). Nevertheless, grassland species have different relationships from global
average across a range of taxonomic scales regardless of whether the parameters were expressed
on a mass basis or an area basis. Moreover, confirmation from other studies demonstrates that
this discrepancy is not site-specific.
The variation in Amax-leaf traits relationships is related to physiological traits of plants.
Higher Amax-leaf N and Amax-Chl slopes tend to be observed in C4 species as C4 metabolism
involves CO2 concentrating mechanisms (Sage & Pearcy, 1987). Among C3 species, nitrogen
allocation between photosynthetic and non-photosynthetic apparatus, stomatal and mesophyll
conductance, kinetics of photosynthetic enzymes, dark respiration and light absorption are
important contributors to the variations in Amax-leaf traits relationships among leaves, species
and PFTs (Hikosaka, 2004). A large fraction of leaf nitrogen is allocated to the photosynthetic
apparatus in herbaceous plants, which causes grassland species to have higher photosynthetic
nitrogen use efficiency compared to other biomes.
To summarize, the relationships between Amax and leaf traits are not the same at all
scales, and the total variability may be introduced by the variability from each scale (leaf,
species, PFT, and biome). Comparison between our study, global analyses, and other studies
demonstrates that scale is an important factor that affects the relationships. The variation at
different scales needs to be considered when modeling GPP, as simply knowing leaf traits is not
sufficient to constrain photosynthetic rates. Applying trait relationships without articulating the
scales may cause substantial carbon flux estimation errors. This indicates relationships at one
scale cannot be applied to all scales.
41
Bayesian model parameterization
In traditional A/Ci and A/q curve analysis, leaves are fit independently and the number of
data points from one curve is usually limited and model performance is therefore poorly
constrained. A/q data help to inform the biochemical processes regulating photosynthesis and are
often collected in conjunction with A/Ci curves. However, these data are rarely incorporated into
the fitting procedure (Patrick et al., 2009). Although measurement noise is relatively small, it is
inevitable in realistic testing conditions and even small amounts of variability can cause
significant estimation errors when fitting small data sets on a leaf-by-leaf basis with leaf
photosynthesis models. Segmented fitting methods amplify these limitations even more due to
fewer data points being available in each segmented fit (Zhu et al. 2010). This constraint makes
it hard to partition uncertainty and to attribute variability to specific drivers. In analyses across
multiple leaves it is not uncommon to ignore this uncertainty altogether and treat parameter
estimates as ‘data’ in subsequent analyses. Patrick et al. (2009) presented a hierarchical Bayesian
approach to estimate leaf- and species-level photosynthetic parameters simultaneously using both
A/Ci and A/q data of C3 desert shrubs, which minimized the limitation of available data. This
nested sampling design (leaf replicates nested in species) allowed the modeling of photosynthetic
parameters hierarchically. The failure to include this hierarchical within-species constraint would
result in an overestimation of parameter uncertainty and leaf-to-leaf variability. In addition to
fitting A/Ci and A/q data simultaneously using a hierarchical design, our analyses also
incorporated the fixed effects of leaf traits, month effects, and random effects in order to
explicitly partition variability and reduce model uncertainty. This is a novel approach to
assimilate whole A/Ci and A/q data sets into C3 or C4 leaf photosynthesis models while
simultaneously considering fixed effects, such as leaf N, Chl, and SLA and accommodating the
42
unexplained variability among individual leaves. This Bayesian parameterization method
overcomes the data limitation problem of single-curve fitting method. In addition, the parameter
estimates are probability distributions instead of single data point estimates. Therefore, the
uncertainty in parameter estimates can be included appropriately in subsequent analyses
(LeBauer et al., 2013; Dietze et al., 2013). For example, the application of the photosynthetic
model without accounting for leaf-to-leaf variability can introduce a large and persistent bias to
projections that would be missed if this variability were misattributed to measurement error.
Most importantly, this approach allows the partitioning of uncertainty into multiple processes,
and thus clarifies quantitative contributions of each plant physiological attribute to the total
variation, improves mechanistic understanding, and provides guidelines for future data
collection.
Variability partition in photosynthesis models
Within species, photosynthetic parameters varied considerably through the growing
season. This confirmed the non-trivial amount of leaf-level variation reported by Marino et al.
(2010) and Blonder et al. (2013), though as discussed above previous approaches likely
overestimated leaf-level variability. Much of this variability can be ascribed to leaf traits such as
leaf N, Chl and SLA. This suggests that incorporating leaf traits can reduce model uncertainty
caused by the variation in photosynthetic parameters through the growing season. Nonetheless
some traits (leaf N and Chl) are more important than others (SLA). Although leaf traits can
explain a large part of the variability in photosynthetic capacity, there is still a significant part of
the uncertainty that cannot be explained. Further investigation is needed to ascertain other
possible physiological or environmental factors to reduce the uncertainty. For instance, in
addition to leaf N, other nutrients such as phosphorus also limit photosynthetic rates (Reich &
43
Schoettle, 1988; Warren, 2011). Previous studies (Raviv & Blom, 2001; Kitalima 2002) have
also shown that leaf age and environmental factors such as light and water availability could
have significant impact on photosynthetic parameters. During data collection and documentation,
A/Ci, A/q and associated trait and environmental data should be documented with species, leaf
replicate, location and date information if possible, thereby expanding the potential of
quantifying photosynthetic variation and the relative importance of different factors in
contributing to this variation at different scales (Dietze, 2013).
Across species, both photosynthetic parameters and the effects of leaf traits on the
parameters varied substantially from species to species. This indicates that, relationships between
leaf traits and photosynthesis established at broad scales, such as across-biome relationships, do
not capture the patterns observed at finer scales. Therefore the application of across-PFT
relationships to explain species-to-species differences within a PFT is liable to fail. Within a
PFT, the application of across-species relationships to explain within-species responses to trait
variability is also liable to fail. However, this failure to account for scale is quite common, as
current ecosystem models and remote sensing techniques generally employ broad scale
relationships in order to predict how leaves in a single location will change over time, with
canopy position or soil resources in response to changes in leaf traits. Indeed, our analyses
suggest that these models are consistently overestimating plant sensitivities to changes in leaf N.
However, all hope is not lost! The rejection of a month effect, which was found across all
species, suggests that within a species there is some commonality to the response across leaves
and the response through time. In addition, if we look at the within-species responses to leaf
nitrogen, the slopes of these relationships are remarkably conserved among species (Fig. 3.9),
suggesting that it is the intercepts that vary most from species to species. In addition, the across-
44
species but within-PFT relationships also show a degree of predictability in how intercepts vary
as a function of species average nitrogen content. Therefore, over short time scales, modeled
responses to changes in leaf traits should follow this relatively conserved within-species slope.
By contrast, long-term plant responses to N-addition in a mixed grassland ecosystem should
respond along the across-species curve due to shifts in species composition. Interestingly, the
across-species slope found in our study is consistent with the average aboveground net primary
production (ANPP) response ratio (ANPPN/ANPPctrl=1.53) in fertilized treatments to control
treatments of 32 studies reported by LeBauer and Treseder (2008). Finally, the patterns across
PFTs are also sensible and respond to average leaf nitrogen levels, with legumes having lower
nitrogen use efficiency and C4 grasses being higher. All in all, these patterns make sense and are
consistent with our well-established concepts of functional trade-offs, but they demonstrate that
there is not one single, all encompassing, trade-off curve. Instead, these trade-offs vary with
taxonomic scales, which makes sense as these are fundamentally different trade-offs
(physiological plasticity versus successional niches and evolutionary divergence).
In addition to modeling GPP, our results also have implications for attempts to infer
canopy function from remote sensing. Environmental variables of great interest, such as GPP,
cannot be described directly by radiation measurements of optical reflectance (Kerr &
Ostrovsky, 2003). The ability of remotely sensed variables to act as surrogates for important
ecological characteristics (e.g. productivity) is a function of the closeness of the relationship
between the measured radiation and the environmental variable of interest. In other words,
remote sensing is trying to infer physiology from optical traits, which covary with both leaf
ecophysiological traits and photosynthetic capacity. Indeed, the three traits examined here (leaf
N, SLA, Chl) are all the ones that remote sensing is routinely used to infer. Since relationships
45
between biophysical properties (e.g. leaf N, Chl) and GPP are scale-dependent, the relationships
between optical traits and productivity are also likely sensitive to the scales examined. This
implies that one broad-scale relationship is not sufficient to characterize ecosystem condition and
change at multiple scales. Potential biases or errors of the relationships between leaf traits and
photosynthetic parameters may be exacerbated when the estimation is scaled up from a single
leaf to a canopy level, even to an ecosystem level.
46
Table 3.1
N PFT Scientific Name Common Name Years Measured
2010 2011
1 C3 grass Carex bicknellii Britton Bicknell's sedge Y
2 C3 grass Elymus canadensis L. Canada wildrye Y Y
3 C4 grass Andropogon gerardii Vitman Big bluestem Y Y
4 C4 grass
Schizachyrium scoparium
(Michx.) Nash Little bluestem Y
5 C4 grass Sorghastrum nutans (L.) Nash Indian grass Y Y
6 forb Aster novae-angliae L. New England aster Y
NM forb Coreopsis palmate Prairie coreopsis
7 forb Coreopsis tripteris L. Tall tickseed Y Y
8 forb Echinacea pallida (Nutt.) Nutt. Pale purple coneflower Y
9 forb
Helianthus grosseserratus M.
Martens Sawtooth sunflower Y Y
10 forb
Heliopsis helianthoides (L.)
Sweet Smooth oxeye Y Y
11 forb Monarda fistulosa L. Wild bergamot Y Y
12 forb Parthenium integrifolium L. Wild quinine Y
13 forb
Penstemon digitalis Nutt. ex
Sims Foxglove beardtongue Y
14 forb
Pycnanthemum virginianum (L.)
T. Dur. & B.D. Jacks. ex B.L.
Rob. & Fernald Virginia mountainmint Y
15 forb
Ratibida pinnata (Vent.)
Barnhart
Pinnate prairie
coneflower Y Y
16 forb Rudbeckia subtomentosa Pursh Sweet coneflower Y Y
17 forb Silphium integrifolium Michx. Wholeleaf rosinweed Y Y
18 forb Silphium laciniatum L. Compassplant Y
19 forb Silphium perfoliatum L. Cup plant Y Y
20 forb Silphium terebinthinaceum Jacq. Prairie dock Y
21 forb Solidago rigida L. Stiff goldenrod Y Y
NM forb Veronicastrum virginicum Culver's root
NM legume Astragalus canadensis Canadian milkvetch
22 legume
Baptisia leucantha Torr. & A.
Gray Largeleaf wild indigo Y
23 legume Dalea purpurea Vent. Purple prairie clover Y
24 legume Desmodium canadense (L.) DC. Showy ticktrefoil Y Y
25 legume Lespedeza capitata Michx. Roundhead lespedeza Y
47
Table 3.1 Species list of the prairie mixture. Seeds for all species were planted evenly at 0.5g m-2
and 25 species established sufficiently to allow photosynthetic measurements. One forb species
Coreopsis palmata and one legume species Astragalus canadensis established, but the
abundance was too low for measurements. Veronicastrum virginicum was not found in the
prairie during the study period. Photosynthetic and leaf-trait measurements were performed on
13 most abundant species in 2010 and extended to 25 species in 2011. The species measured in
each year were indicated in the “Years Measured” column. “Y” indicates yes and means that
photosynthetic measurements were performed on the given species.
48
Table 3.2
Mean Mean
C3 grass 1.35 1.01 1.81 2.07 1.96 2.17 0.69 <0.01 18 -25.07
C4 grass 1.91 1.67 2.18 2.19 2.15 2.24 0.62 <0.01 86 -10.07
Forb 2.00 1.88 2.13 1.83 1.81 1.86 0.63 <0.01 363 -162.53
Legume 2.46 2.07 2.92 1.41 1.24 1.58 0.60 <0.01 56 -24.64
Grassland 1.81 1.70 1.92 1.90 1.87 1.92 0.50 <0.01 523 -3.88
Global 1.72 1.63 1.81 1.57 1.54 1.59 0.53 <0.01 712 68.85
Combined 1.76 1.68 1.84 1.70 1.68 1.73 0.38 <0.01 1235 554.88
C3 grass 3.57 2.37 5.38 -1.89 -3.69 -0.09 0.37 <0.01 18 -9.93
C4 grass 3.52 2.97 4.18 -1.77 -2.46 -1.07 0.38 <0.01 86 40.85
Forb 2.52 2.30 2.77 -0.65 -0.90 -0.39 0.21 <0.01 363 189.37
Legume 2.71 2.14 3.42 -0.93 -1.70 -0.15 0.25 <0.01 56 19.86
Grassland 2.65 2.47 2.85 -0.79 -1.01 -0.58 0.29 <0.01 523 243.34
Global 1.33 1.26 1.40 0.68 0.61 0.74 0.50 <0.01 764 109.47
Combined 1.55 1.49 1.62 0.45 0.38 0.52 0.45 <0.01 1287 363.91
C3 grass 1.37 0.98 1.93 1.98 1.83 2.14 0.57 <0.01 18 -18.27
C4 grass 1.41 1.23 1.62 2.14 2.09 2.20 0.59 <0.01 86 -2.34
Forb 1.14 1.06 1.23 2.07 2.05 2.10 0.47 <0.01 362 -6.00
Legume 1.37 1.13 1.65 1.81 1.69 1.93 0.50 <0.01 56 -10.37
Grassland 1.15 1.08 1.22 2.07 2.05 2.09 0.51 <0.01 522 -9.88
Global
Combined
C3 grass 1.25 0.89 1.75 1.14 1.08 1.20 0.57 <0.01 18 -23.91
C4 grass 1.96 1.67 2.29 1.34 1.29 1.40 0.47 <0.01 85 -19.95
Forb 1.95 1.81 2.11 0.93 0.91 0.95 0.45 <0.01 361 -107.23
Legume 1.76 1.40 2.21 0.83 0.74 0.92 0.30 <0.01 56 -0.78
Grassland 1.72 1.60 1.85 1.00 0.98 1.02 0.29 <0.01 520 33.99
Global 1.21 1.13 1.30 0.68 0.65 0.71 0.13 <0.01 723 139.57
Combined 1.16 1.10 1.22 0.83 0.81 0.85 0.08 <0.01 1243 476.17
C3 grass 0.15 0.12 18 -7.20
C4 grass 2.73 2.24 3.33 -1.99 -2.62 -1.36 0.15 <0.01 85 36.53
Forb 0.01 0.10 361 263.02
Legume 0.02 0.27 56 34.77
Grassland 2.17 2.00 2.37 -1.37 -1.58 -1.16 0.04 <0.01 520 321.70
Global 0.00 0.15 764 472.85
Combined 0.00 0.11 1284 905.21
C3 grass 1.49 1.00 2.21 -0.44 -1.10 0.22 0.41 <0.01 18 -17.10
C4 grass 1.35 1.15 1.58 -0.14 -0.35 0.07 0.45 <0.01 85 -15.59
Forb 1.13 1.04 1.22 -0.04 -0.13 0.05 0.35 <0.01 365 -28.44
Legume 1.18 0.97 1.44 -0.28 -0.57 0.01 0.38 <0.01 64 -16.60
Grassland 1.11 1.04 1.19 -0.03 -0.11 0.05 0.36 <0.01 532 -46.46
Global
Combined
95% CI 95% CI
Slope Intercept
ns
ns
ns
Amass-Nmass
Amass-SLA
ns
Relationships Data set
Aarea-Chlarea
na
na
R2 P-value
Amass-Chlmass
na
na
Sample
sizeAIC
Aarea-Narea
Aarea-SLA
ns
49
Table 3.2. Standardized major axis slopes and intercepts with 95% confidence intervals,
coefficients of determination (R2), P-value, sample size and AIC scores for grassland PFTs,
Glopnet and combined grassland and Glopnet data. “na”, not applicable: in these cases the global
relationships were not available. “ns”, not significant: in these cases the correlations were not
significant. While standardized major axis can still be fitted in such cases, the slopes are
essentially meaningless. All other relationships were highly significant.
50
Table 3.3
Symbol Biological interpretation Model Attribute Distribution/
Value
Source
Γ* CO2 compensation point
(Pa)
C3 parameter dlnorm(1.4,0.65) Based on
Medlyn et al.
(2002)
Jmax Maximum rate of electron
transport (µmol m-2 s-1)
C3 parameter dlnorm(4.7,0.67) Based on
Wullschleger
(1993), Medlyn
et al. (2002)
α quantum efficiency derived
from the initial slope of the
light response curve (mol
CO2 mol-1 photon)
C3/C4 parameter dnorm(0.06,0.025) Based on
Skillman et al.
(2008)
αleaf Random individual leaf
effect on α
C3/C4 parameter dnorm(0, τ(αleaf)) Broad prior
τ(αleaf) Standard deviation of αleaf C3/C4 parameter dgamma(0.01,0.01) Broad prior
Vcmax,
Vmax
Maximum rubisco capacity
(µmol m-2 s-1)
C3/C4 parameter dlnorm(4.2,0.65)
/dlnorm(3.1,0.59)
Based on
Collatz et al.
(1992), Medlyn
et al. (2002),
Kattge et al.
(2009)
υleaf Random individual leaf
effect on Vcmax and Vmax
C3/C4 parameter dnorm(0, τ(υleaf)) Broad prior
τ(υleaf) Standard deviation of υleaf C3/C4 parameter dgamma(0.01,0.01) Broad prior
Rd Leaf respiration (µmol m-2
s-1)
C3/C4 parameter dlnorm(0.75,0.801)
/dlnorm(-0.1,0.598)
Based on
Farquhar et al.
(1980), Collatz
et al. (1992)
βN Slope of fixed leaf N effect
on Vcmax and Vmax
C3/C4 parameter dnorm(10,10) Broad prior
βChl Slope of fixed chlorophyll
effect on quantum
efficiency
C3/C4 parameter dnorm(0,0.1) Broad prior
βSLA Slope of fixed SLA effect
on quantum efficiency
C3/C4 parameter dnorm(0,0.1) Broad prior
βmon Fixed effect of month on
Vcmax and Vmax
C3/C4 parameter dnorm(0, τ(Vmon)) Broad prior
τ(βmon) Standard deviation of βmon C3/C4 parameter dgamma(0.01,0.01) Broad prior
Τ Model standard deviation C3/C4 parameter dgamma(0.1,0.1) Broad prior
K Initial slope of
photosynthetic-CO2
response curve (µmol m-2
s-1)
C4 parameter dlnorm(11.5,0.598) Based on
Collatz et al.
(1992)
51
Table 3.3. (cont.)
Symbol Biological interpretation Model Attribute Distribution/
Value
Source
klea
f
Random individual leaf
effect on k
C4 parameter dnorm(0, τ(kleaf)) Broad prior
τ(kleaf) standard deviation of kleaf C4 parameter dgamma(0.01,0.01) Broad prior
An(m) Modeled photosynthesis
rate (µmol m-2 s-1)
C3/C4 dependent
variable
prediction
An(o) Observed photosynthesis
rate (µmol m-2 s-1)
C3/C4 dependent
variable
data
Q Quantum flux density
(µmol m-2 s-1)
C3/C4 independent
variable
data
Ci Intercellular partial
pressure of CO2 (Pa)
C3/C4 independent
variable
data
N N concentration (%) C3/C4 covariate data
N Species average N
concentration (%)
C3/C4 covariate data
Chl Chl (µg cm-2) C3/C4 covariate data
Chl Species average Chl (µg
cm-2)
C3/C4 covariate data
SLA SLA (m2 kg-1) C3/C4 covariate data
SLA Species average SLA (m2
kg-1)
C3/C4 covariate data
O Intercellular partial
pressure of O2 (Pa)
C3 constant 21000 Farquhar et al.
(1980)
Kc Michaelis-Menten
coefficient of Rubisco
activity for CO2 (Pa)
C3 constant 40.4 Bernacchi et al.
(2001)
Ko Michaelis-Menten
coefficient of Rubisco
activity for O2 (Pa)
C3 constant 27800 Bernacchi et al.
(2001)
P Atmospheric pressure (Pa) C4 constant 105
Table 3.3. Parameters, data and constants used in C3 and C4 photosynthesis model
52
Table 3.4
Mean
Mean
Mean
Mean
Mean
Mean
1C
. b
ick
nell
ii
2E
. ca
na
den
sis
1.3
51.0
11.8
12.0
71.9
62.1
73.5
72.3
75.3
8-1
.89
-3.6
9-0
.09
1.3
70.9
81.9
31.9
81.8
32.1
4
3A
. g
era
rdii
2.1
41.7
42.6
32.1
32.0
52.2
13.1
92.4
64.1
5-1
.46
-2.4
7-0
.46
1.4
21.1
11.8
22.1
52.0
62.2
5
4S
. sc
op
ari
um
5S
. n
uta
ns
1.8
71.5
62.2
42.2
32.1
62.3
04.3
23.4
35.4
4-2
.58
-3.7
0-1
.46
1.3
91.1
81.6
52.1
32.0
72.2
0
6A
. n
ova
e-a
ng
lia
e2.9
12.3
23.6
51.5
71.4
21.7
23.3
32.0
85.3
4-1
.86
-3.8
30.1
02.0
01.4
22.8
21.7
61.5
81.9
5
7C
. tr
ipte
ris
1.7
31.5
02.0
01.8
91.8
41.9
43.1
72.3
14.3
5-1
.29
-2.3
8-0
.20
0.7
50.6
00.9
42.2
22.1
52.2
8
8E
. p
all
ida
2.2
81.5
53.3
51.9
21.7
92.0
4
9H
. g
ross
ese
rra
tus
2.1
41.6
92.7
21.7
81.6
31.9
21.2
10.9
01.6
22.1
62.0
72.2
6
10
H.
heli
an
tho
ides
2.4
01.9
72.9
31.7
31.6
31.8
34.6
93.4
16.4
3-3
.45
-5.2
5-1
.66
1.3
91.1
21.7
32.0
01.9
22.0
8
11
M.
fist
ulo
sa2.3
71.8
63.0
11.7
01.5
71.8
42.5
11.9
73.2
1-0
.93
-1.7
0-0
.15
1.2
60.9
81.6
22.0
41.9
62.1
1
12
P.
inte
gri
foli
um
1.7
41.0
92.7
82.0
61.9
62.1
51.2
30.8
01.8
92.0
21.9
32.1
1
13
P.
dig
ita
lis
2.0
31.4
72.8
01.9
41.8
42.0
51.1
00.7
81.5
42.0
21.9
02.1
4
14
P.
vir
gin
ian
um
15
R.
pin
na
ta1.9
11.6
72.1
91.8
51.7
91.9
02.6
12.1
43.1
9-0
.75
-1.3
4-0
.15
1.3
21.0
21.7
22.0
31.9
52.1
1
16
R.
sub
tom
en
tosa
1.7
81.5
02.1
01.8
61.8
01.9
22.9
32.2
63.7
9-1
.23
-2.0
8-0
.37
1.1
80.9
31.5
02.0
71.9
92.1
5
17
S.
inte
gri
foli
um
1.8
31.4
72.2
81.8
21.7
41.9
12.7
92.1
13.7
0-0
.81
-1.6
10.0
01.2
00.9
41.5
32.0
11.9
32.0
9
18
S.
lacin
iatu
m
19
S.
perf
oli
atu
m2.4
51.8
53.2
41.6
51.5
01.8
04.1
63.0
45.6
8-2
.39
-3.7
9-1
.00
1.2
50.9
41.6
72.1
52.0
32.2
8
20
S.
tere
bin
thin
aceu
m
21
S.
rig
ida
1.9
91.5
72.5
21.8
81.8
01.9
62.8
72.1
73.7
8-0
.90
-1.7
4-0
.05
1.1
50.9
01.4
61.9
51.8
72.0
3
22
B.
leu
ca
nth
a2.9
32.1
83.9
30.9
50.5
31.3
61.4
71.0
32.1
01.6
51.4
01.9
1
23
D.
pu
rpu
reu
m
24
D.
ca
na
den
se2.7
72.3
63.2
61.3
61.1
91.5
33.4
62.5
64.6
8-1
.96
-3.3
0-0
.63
1.2
50.9
51.6
31.8
91.7
42.0
5
25
L.
ca
pit
ata
3.3
12.4
34.4
91.3
21.0
31.6
12.1
51.7
12.7
11.6
61.5
21.8
1
ns
na
NS
pecie
s
95%
CI
na
na
ns
na
ns
ns
Am
ass-
SL
A
Slo
pe
Inte
rcept
Am
ass-
Chl m
ass
95%
CI
ns
na ns
ns
ns
na
95%
CI
na
na ns
ns
ns
na
Am
ass-
Nm
ass
Slo
pe
Inte
rcept
95%
CI
95%
CI
Slo
pe
Inte
rcept
95%
CI
na
na
na
ns
ns
53
Table 3.4 (cont.)
Inte
rcept
Mean
Mean
Mean
Mean
Mean
Mean
1C
. b
ick
nell
ii
2E
. ca
na
den
sis
1.2
50.8
91.7
51.1
41.0
81.2
01.4
91.0
02.2
1-0
.44
-1.1
00.2
2
3A
. g
era
rdii
2.0
51.5
62.6
91.3
31.2
41.4
32.6
91.9
73.6
6-2
.05
-3.0
6-1
.04
1.4
31.0
71.9
0-0
.19
-0.5
90.2
0
4S
. sc
op
ari
um
2.2
71.3
53.8
0-1
.61
-2.9
6-0
.26
5S
. n
uta
ns
1.8
91.5
72.2
91.3
51.2
91.4
23.2
2.4
74.1
5-2
.42
-3.3
6-1
.48
1.3
01.0
81.5
7-0
.11
-0.3
50.1
3
6A
. n
ova
e-a
ng
lia
e2.8
01.8
84.1
70.9
50.8
11.1
02.5
71.7
43.7
8-1
.60
-2.6
1-0
.59
7C
. tr
ipte
ris
1.7
61.4
82.1
00.9
40.8
90.9
80.7
60.5
90.9
70.4
40.2
80.6
0
8E
. p
all
ida
1.9
61.1
73.2
80.9
60.8
21.1
1
9H
. g
ross
ese
rra
tus
1.9
11.4
62.4
90.9
20.8
11.0
31.2
70.9
41.7
3-0
.09
-0.5
10.3
2
10
H.
heli
an
tho
ides
1.8
01.4
72.2
11.0
10.9
71.0
61.0
40.8
11.3
20.0
3-0
.21
0.2
7
11
M.
fist
ulo
sa1.8
21.3
02.5
4-1
.30
-2.0
8-0
.53
1.1
40.8
21.5
8-0
.05
-0.3
90.2
9
12
P.
inte
gri
foli
um
1.4
30.8
12.5
31.1
00.9
71.2
31.2
20.7
52.0
0-0
.18
-0.7
80.4
2
13
P.
dig
ita
lis
1.9
71.2
83.0
30.9
80.8
31.1
31.0
60.7
31.5
3-0
.04
-0.3
80.3
0
14
P.
vir
gin
ian
um
1.2
20.9
21.6
3-0
.51
-0.8
8-0
.15
15
R.
pin
na
ta2.2
21.7
52.8
10.9
40.8
91.0
01.9
31.4
72.5
2-1
.10
-1.7
0-0
.51
16
R.
sub
tom
en
tosa
2.2
71.7
92.8
80.9
60.9
01.0
22.2
71.6
33.1
6-1
.61
-2.4
7-0
.76
1.2
40.9
21.6
7-0
.14
-0.4
80.1
9
17
S.
inte
gri
foli
um
1.9
61.4
82.6
10.8
10.7
20.9
11.1
90.8
91.5
8-0
.18
-0.5
30.1
7
18
S.
lacin
iatu
m3.0
31.7
95.1
20.5
90.1
90.9
8
19
S.
perf
oli
atu
m2.6
71.9
23.7
10.7
10.5
70.8
61.3
71.0
01.8
9-0
.19
-0.5
70.2
0
20
S.
tere
bin
thin
aceu
m
21
S.
rig
ida
2.0
31.4
62.8
10.9
20.8
31.0
12.2
81.6
13.2
2-1
.33
-2.1
8-0
.48
1.0
70.8
01.4
3-0
.11
-0.4
50.2
4
22
B.
leu
ca
nth
a1.6
61.0
52.6
40.6
40.3
60.9
11.4
61.0
02.1
5-0
.74
-1.4
90.0
2
23
D.
pu
rpu
reu
m0.7
30.4
11.2
80.2
0-0
.39
0.7
9
24
D.
ca
na
den
se2.3
31.7
83.0
50.8
60.7
60.9
61.2
40.9
21.6
6-0
.28
-0.7
10.1
5
25
L.
ca
pit
ata
3.4
21.8
56.3
10.5
40.1
20.9
62.2
71.5
33.3
6-1
.50
-2.5
6-0
.45
N
ns
ns
ns
ns
ns
Specie
s
ns
ns
ns
na ns
ns
na
na ns
na ns
ns
ns
ns
ns
ns
Aar
ea-C
hl a
rea
na
na
ns
na
ns
ns
na ns
ns
95%
CI
Slo
pe
95%
CI
95%
CI
Slo
pe
Inte
rcept
Slo
pe
Inte
rcept
95%
CI
95%
CI
Aar
ea-N
area
95%
CI
Aar
ea-S
LA
54
Table 3.4. Within-species Amax-trait relationships. In both mass-based and area-based
relationships, Amax was positively correlated with leaf N and Chl for most species. Many species
showed non-significant relationships between Amax and SLA. “na”, not applicable: in these cases
SLA data were not collected. “ns”, not significant: in these cases the relationships between Amax
and leaf traits were not significant.
55
Table 3.5
N PFT Speciesτ(N
effect)τ(υleaf)
Proportion of
fixed effects on
Vcmax and Vmax
τ(Chl
effect)
×103
τ(SLA
effect)
×103
τ(αleaf)
×103
Proportion
of fixed
effects on α
τ
1 C3 grass C. bicknellii ns 5.03 ns ns na 12.80 ns 0.93
2 C3 grass E. canadensis 4.68 4.61 0.50 15.13 ns 12.03 0.56 0.47
3 C4 grass A. gerardii 5.33 2.56 0.68 6.30 ns 19.56 0.24 0.42
4 C4 grass S. scoparium 2.50 1.68 0.60 6.56 na 6.53 0.50 0.32
5 C4 grass S. nutans 5.90 2.32 0.72 9.97 ns 11.36 0.47 0.50
6 Forb A. novae-angliae 6.67 5.23 0.56 20.59 ns 7.82 0.72 1.10
7 Forb C. tripteris 4.80 2.76 0.63 14.39 ns 8.10 0.64 1.21
8 Forb E. pallida 6.24 2.57 0.71 ns ns 18.03 ns 0.28
9 Forb H. grosseserratus 6.19 6.75 0.48 26.24 ns 10.37 0.72 1.00
10 Forb H. helianthoides 4.60 2.65 0.63 14.99 ns 11.69 0.56 0.97
11 Forb M. fistulosa 4.11 2.04 0.67 9.98 10.67 9.44 0.69 0.90
12 Forb P. integrifolium 4.50 3.02 0.60 20.47 ns 8.70 0.70 1.05
13 Forb P. digitalis 3.89 3.27 0.54 12.83 ns 15.63 0.45 0.83
14 Forb P. virginianum ns 6.31 ns 16.68 na 7.27 0.70 1.00
15 Forb R. pinnata 3.49 3.52 0.50 6.79 13.87 12.52 0.62 0.83
16 Forb R. subtomentosa 4.51 2.16 0.68 7.51 12.57 13.33 0.60 1.70
17 Forb S. integrifolium 4.03 3.82 0.51 16.34 ns 17.30 0.49 1.58
18 Forb S. laciniatum 1.94 7.61 0.20 ns ns 19.23 ns 0.61
19 Forb S. perfoliatum 5.14 4.06 0.56 12.97 ns 14.99 0.46 0.32
20 Forb S. terebinthinaceum ns 8.59 ns ns ns 17.50 ns 0.41
21 Forb S. rigida 3.15 4.57 0.41 10.33 7.25 6.99 0.72 0.85
22 Legume B. leucantha 6.40 2.76 0.70 17.94 ns 7.75 0.70 1.03
23 Legume D. purpurea 3.48 6.97 0.33 17.59 na 7.14 0.71 0.55
24 Legume D. canadense 6.62 4.55 0.59 18.63 ns 10.52 0.64 1.05
25 Legume L. capitata 8.20 2.78 0.75 18.41 ns 12.73 0.59 1.30
56
Table 3.5 (cont.)
Table 3.5 Variability of Vcmax, Vmax and quantum efficiency contributed by fixed and random
effects.
τ(N effect): standard deviation of fixed leaf N effect on Vcmax or Vmax (N effect=βN×(N- N )).
τ(υleaf): standard deviation of random individual leaf effect on Vcmax or Vmax (υleaf).
Proportion of fixed effects on Vcmax or Vmax: proportion of variability in Vcmax or Vmax
contributed by fixed effect.
τ(Chl effect): standard deviation of fixed chlorophyll effect on quantum efficiency (Chl
effect=βChl×(Chl- Chl )).
Mean Mean Mean
1 C. bicknellii 4.73 -2.27 10.02 0.88 1.34 -2.71 5.07 10.74
2 E. canadensis 9.85 7.07 12.03 1.78 4.87 3.91 5.87 12.72 5.66 -5.59 19.87 15.81
3 A. gerardii 11.76 8.71 14.94 1.29 1.54 1.04 1.99 9.31 3.53 -17.56 22.19 15.79
4 S. scoparium 10.44 4.02 15.42 1.08 1.62 1.41 1.84 12.29
5 S. nutans 12.47 9.32 15.57 1.08 2.55 1.95 3.11 9.81 0.88 -0.93 2.89 13.12
6 A. novae-angliae 14.57 13.33 16.08 1.62 8.23 7.21 9.40 10.02 1.16 -3.13 6.06 16.39
7 C. tripteris 10.53 7.85 12.52 1.35 3.53 2.88 4.23 7.24 1.68 -3.91 6.75 11.76
8 E. pallida 18.95 17.67 20.63 1.24 1.00 -9.24 10.33 11.72 0.57 -4.13 5.74 10.47
9 H. grosseserratus 10.95 8.19 13.65 1.69 5.49 4.53 6.43 10.58 3.04 -1.96 7.35 12.39
10 H. helianthoides 8.21 5.61 10.96 1.50 4.21 3.28 5.09 8.56 1.09 -0.87 3.05 15.70
11 M. fistulosa 11.14 8.95 14.18 1.66 3.84 3.36 4.32 8.10 2.56 1.95 3.71 17.85
12 P. integrifolium 13.13 10.75 15.73 0.99 5.70 4.90 6.56 8.53 -1.30 -18.94 18.97 12.63
13 P. digitalis 11.51 10.24 12.96 0.95 3.41 2.60 4.22 7.34 3.15 -36.85 41.24 11.23
14 P. virginianum 4.67 -3.33 9.97 1.19 2.32 1.79 2.84 10.02
15 R. pinnata 6.93 4.70 9.49 1.58 2.13 1.62 2.82 10.28 4.60 3.88 5.31 13.68
16 R. subtomentosa 7.89 5.31 10.35 1.36 2.17 1.22 3.02 7.90 3.51 2.98 4.08 13.42
17 S. integrifolium 6.32 2.81 9.54 1.31 3.20 2.54 4.09 10.33 3.48 -6.32 14.80 10.33
18 S. laciniatum 3.14 1.19 5.15 1.29 3.18 -2.41 8.72 11.62 -2.10 -16.74 15.40 7.91
19 S. perfoliatum 7.62 4.81 10.39 1.44 3.20 2.50 3.97 6.97 5.92 -5.66 17.49 11.55
20 S. terebinthinaceum 12.52 -14.70 38.72 1.26 1.68 -7.50 10.30 13.57 -1.05 -10.85 8.50 10.64
21 S. rigida 7.89 4.90 11.20 1.34 2.03 1.35 2.93 13.13 3.04 2.27 3.80 11.31
22 B. leucantha 12.89 11.30 14.29 2.97 2.94 2.34 3.58 21.04 -0.94 -10.73 7.65 14.41
23 D. purpurea 7.22 5.71 9.10 2.71 1.29 0.79 1.69 25.04
24 D. canadense 12.48 11.03 14.11 2.33 3.52 2.94 4.43 14.44 4.21 -3.44 10.51 18.10
25 L. capitata 17.72 15.24 19.78 1.88 4.19 2.70 5.74 14.56 9.83 -4.84 21.96 12.76
na
na
na
N Species N Chl SLA
na
βN βChl×103
βSLA×103
95% CI 95% CI 95% CI
57
τ(SLA effect): standard deviation of fixed chlorophyll effect on quantum efficiency (SLA
effect=βSLA×(SLA- SLA )).
τ(αleaf): standard deviation of random individual leaf effect on quantum efficiency (αleaf).
Proportion of fixed effects on α: proportion of variability in quantum efficiency contributed by
fixed effect.
τ: model residual error.
“na”, not applicable: in these cases SLA data were not collected for the given species.
“ns”, not significant: in these cases the fixed effects of leaf traits on photosynthetic parameters
were not significant.
58
Table 3.6
nle
af
16
18
38
16
50
19
34
16
34
38
27
16
16
15
41
Am
ax SE
0.4
1.2
1.3
1.8
1.3
1.7
1
1.7
1.5
0.8
0.6
1.4
1.4
1.5
0.8
Mea
n
3.3
16.2
15.9
8.5
15.8
10.8
12.1
15.3
17.2
9.8
9.6
9.3
7.6
5.4
12.3
Rd
95%
CI
1.0
1.5
1.2
0.8
1.1
1.3
1
1
1.7
0.8
0.8
1.2
1.1
0.9
1.8
0.6
1
0.7
0.5
0.7
0.9
0.8
0.7
1.3
0.6
0.5
0.8
0.6
0.6
1.4
Mea
n
0.8
1.3
0.9
0.7
0.9
1.1
0.9
0.8
1.5
0.7
0.6
1
0.8
0.8
1.6
α×
10
95%
CI
0.4
8
0.6
8
0.6
3
0.4
8
0.6
5
0.6
3
0.8
0
0.7
5
0.9
0
0.7
0
0.7
3
0.8
0
0.5
3
0.5
8
0.9
8
0.3
8
0.4
8
0.4
8
0.3
5
0.4
8
0.4
0
0.5
8
0.6
3
0.6
3
0.4
5
0.5
5
0.5
8
0.3
8
0.3
8
0.7
3
Mea
n
0.4
3
0.5
8
0.5
5
0.4
0
0.5
5
0.5
3
0.6
8
0.6
8
0.7
8
0.5
8
0.6
3
0.7
0
0.4
5
0.4
8
0.8
5
J max
, k
95%
CI 74.6
153.7
1.9
×10
5
1.4
×10
5
2.0
×10
5
145.5
161.6
125.9
206
118.3
129.2
134.4
82.7
90.4
179.1
45
107.5
1.1
×10
5
0.8
×10
5
1.2
×10
5
98.6
104.1
85.7
130.8
79.6
81.3
91.3
51.7
57
113.7
Mea
n
60.9
130
1.5
×10
5
1.1
×10
5
1.6
×10
5
120.5
131.4
105.1
166.9
98.4
106.6
111.6
68.4
74.9
145.3
Vcm
ax, V
max
95%
CI
52.5
108
34.6
19.7
34.1
104
102
89.9
144
82.9
84.8
98.4
57.1
68.8
105
34.5
71.4
14.6
10.8
15.7
69
71.2
54.5
96.9
54.7
56.9
62.2
32.5
44.3
77.9
Mea
n
43.2
89.8
23.9
15.2
25.6
85.4
86.2
74
117
66.9
71.2
80.5
44.4
56.3
92.6
Spec
ies
C. bic
knel
lii
E. ca
naden
sis
A. ger
ard
ii
S. sc
opari
um
S. nuta
ns
A. nova
e-angli
ae
C. tr
ipte
ris
E. pall
ida
H. gro
sses
erra
tus
H. hel
ianth
oid
es
M. fi
stulo
sa
P. in
tegri
foli
um
P. dig
itali
s
P. vi
rgin
ianum
R. pin
nata
PF
T
C3 g
rass
C3 g
rass
C4 g
rass
C4 g
rass
C4 g
rass
Forb
Forb
Forb
Forb
Forb
Forb
Forb
Forb
Forb
Forb
N
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
59
Table 3.6 (cont.)
nle
af
29
33
16
30
18
29
17
15
35
15
Am
ax SE
0.9
1.2
2.2
1.2
1
0.9
1.7
2.1
1.1
3.2
Mea
n
9.5
11.3
18.9
10.8
14.8
12.3
16.1
16.1
15
15.1
Rd
95%
CI
1.8
1.7
3.2
1.4
1.7
1.2
1.9
3
2.6
2.4
1.3
1.3
2.3
1
1.2
0.9
1.4
2.1
2
1.7
Mea
n
1.5
1.5
2.8
1.2
1.5
1.1
1.7
2.6
2.3
2
α×
10
95%
CI
0.6
0
0.6
8
0.9
5
0.8
5
0.6
0
0.5
8
0.9
0
0.8
0
0.7
8
0.7
8
0.3
8
0.4
8
0.8
0
0.6
0
0.4
5
0.4
8
0.7
0
0.6
0
0.5
5
0.5
5
Mea
n
0.4
8
0.5
8
0.8
8
0.7
3
0.5
3
0.5
3
0.8
0
0.7
0
0.6
5
0.6
5
J max
, k
95%
CI
76.9
182.2
198.6
143.4
156.3
148.8
196.1
231.2
211.2
187.2
49
117.4
133.9
98.9
111.6
95.9
134.6
164
143.2
125.1
Mea
n
62.8
150.7
169.3
122.7
134.1
121.8
168.3
197.1
176.5
157.3
Vcm
ax, V
max
95%
CI
61
123
131
97.3
117
100
137
150
137
134
33
81.7
90.5
65.7
81.1
72.1
99.2
108
94.1
96.5
Mea
n
45.7
103
110
81
99.5
87.3
119
131
115
112
Spec
ies
R. su
bto
men
tosa
S. in
tegri
foli
um
S. la
cinia
tum
S. per
foli
atu
m
S.
tere
bin
thin
ace
um
S. ri
gid
a
B. le
uca
nth
a
D. purp
ure
a
D. ca
naden
se
L. ca
pit
ata
PF
T
Forb
Forb
Forb
Forb
Forb
Forb
Leg
um
e
Leg
um
e
Leg
um
e
Leg
um
e
N
16
17
18
19
20
21
22
23
24
25
60
Table 3.6. Parameter values of C3 and C4 photosynthesis models with 95% credible intervals.
C3 model was applied for C3 grass, forb, and legume species (parameters include Vcmax, Jmax, α
and Rd); C4 model was applied for C4 grass species (parameters include Vmax, k, α and Rd).
Biological interpretations and units of parameters were given in table 3.3.
61
Fig. 3.1 Bayesian parameterization of photosynthesis models. This figure shows the
parameterization processes of C4 photosynthesis model. C3 model has a slightly different set of
parameters with higher number of parameters. However, the method and procedure for C3 model
parameterization are the same as C4 model. For simplicity, only C4 model is shown in the
diagram. Data model defines that observed An is normally distributed around modeled An with a
variance τ2 ( 𝐴𝑛(𝑜)~𝑁(𝐴𝑛
(𝑚), 𝜏2) ). Process model simulates the biochemical processes of
photosynthetic carbon assimilation and predicts the values of modeled An based on parameters
(solid arrows), covariates data (dashed arrows) and light and CO2 data (dotted arrows).
Parameter model assigns a prior distribution for each parameter used in the process model. µ, σ,
s and θ are parameters that define the prior distributions. Distributions with µ and σ indicate that
the prior distribution is a normal or log-normal distribution with a mean µ and a standard
deviation σ (e.g. dlnorm (µ1, σ1)). Distributions with s and θ indicate that the prior distribution is
a gamma distribution with a shape parameter s and a scale parameter θ (e.g. dgamma(s1,θ1)).
62
Fig. 3.2 Relationships between mass-based Amax and leaf traits. In the panels a, b and c, 95% CI
of SMA analyses and AIC scores suggested separate regression models for grassland PFTs.
Panels d and e show that the regressions between Amass and leaf traits for grassland species were
different from global average. Panel f only shows the Amax-Chl relationship in grassland biome
63
since it was not included in the Glopnet analyses. Lines were drawn for significant relationships.
Further details about slope and intercept values, 95% CIs and AICs are given in table 3.2.
64
Fig. 3.3 Relationships between area-based Amax and leaf traits. In panels a, b and c, for Aarea-Narea
and Aarea-Chlarea relationships, 95% CI of SMA analyses and AIC scores suggested separate
regression models for grassland PFTs. The Aarea-SLA relationship was only significant for C4
grasses. Panels d and e show that the regressions between Aarea and leaf traits for grassland
65
species were different from global average. Panel f only shows the Amax-Chl relationship in
grassland biome since it was not included in the Glopnet analyses. Lines were drawn for
significant relationships. The global relationship between Aarea and SLA was not significant.
66
Fig. 3.4 Comparison of Amax-leaf traits relationships between Glopnet herbaceous data and the
global average. The grey dots indicate Glopnet dataset. Red dots indicate herbaceous plant subset
of Glopnet. Regression lines were drawn for significant SMA regressions.
67
Fig. 3.5 Model predicted photosynthetic rates plot against observed values. Figure shows all data
of Andropogon gerardii from year 2010 and 2011 were fit simultaneously. Panel a shows
performance of the default model which excludes all the fixed and random effects. Panel b shows
performance of the best fit model (leaf N, Chl, υleaf and kleaf were included in the process model).
Deviance information criterion (DIC) was used for model selection.
68
Fig. 3.6 Photosynthetic responses to CO2. Black dots indicate observed values. 95% CI means
95% confidence interval. 95% PI means 95% predictive interval. CO2 response was dependent
on both plant species and month in which the measurements were taken. CO2 responses of
Andropogon gerardii through the growing season are shown in the figure. Each panel shows one
A/Ci curve in each month from June to September. Initial slope of A/Ci curve and maximum
photosynthesis rate at saturating CO2 decreased in late growing season.
69
Fig. 3.7 Histograms showing the relative contributions of fixed and random effects to the
variability in photosynthetic parameters (Vcmax, Vmax and quantum efficiency). Fixed effects
include effects of leaf N on Vcmax and Vmax, and effects of Chl and SLA on quantum efficiency.
“ns” indicates that fixed effects were not significant. A value above 0.5 indicates that more than
half of the variability could be explained by fixed effects. Further details about absolute values of
fixed and random effects for each species are given in table 3.5.
Proportion (Fixed effects/Total of fixed and random effects)
Nu
mb
er
of sp
ecie
s
01
23
45
67
8
Vcmax, Vmax
Quantum efficiency
ns below 0.3 0.3-0.4 0.4-0.5 0.5-0.6 0.6-0.7 0.7-0.8
70
Fig. 3.8 Positive regression between Jmax and Vcmax (Jmax=1.504Vcmax-4.023).
71
Fig. 3.9 Effects of leaf N on Vcmax and Vmax depend on the taxonomic scales. Panel a shows 95%
credible intervals of Vcmax and Vmax and the seasonal variability of leaf N for all species. Error
bars for Vcmax and Vmax indicate 95% credible intervals. Error bars for leaf N indicate +/-standard
error. Panel b shows the relationships between Vcmax (Vmax) and leaf N within each species and
the relationship across all C3 PFTs. Effects of leaf N on Vcmax were not significant for one C3
grass species (Carex bicknellii) and two forb species (Pycnanthemum virginianum and
Schizachyrium scoparium) Regression lines were not drawn for these species. Slope values and
95% credible intervals of within-species Vcmax (Vmax)-leaf N relationships for each species are
available in the “βN” column in table 3.5.
72
CHAPTER 4: PHOTOSYNTHESIS PREDICTS COMMUNITY DYNAMICS IN A
TALLGRASS PRAIRIE
Abstract
Both plant community dynamics and ecosystem services, such as primary productivity,
largely depend on plant physiological traits. Carbon assimilation drives growth, reproduction and
survival at an individual plant level, however the importance of photosynthesis in shaping
community composition and biodiversity has rarely been studied. We hypothesize that high
assimilation rate leads to higher abundance and production both within species through the
growing season and across species at a given time. To test this hypothesis, we measured species-
level photosynthetic rate and abundance in a tallgrass prairie monthly across two growing
seasons. Up to 94% of within-species variation in cover could be explained by seasonal changes
in photosynthetic rate. For common species, a positive across-species relationship between
photosynthesis and cover was found and explained 61-82% of community composition mid-
growing season. Rare species could not be predicted by photosynthetic rate. This suggests that
photosynthesis influences community composition by affecting dominance of relatively common
species in the prairie. This establishes an important linkage between plant physiology and
community which has been overlooked in previous studies.
73
Introduction
The relationship between biodiversity and ecosystem function has been studied
extensively for over a decade (Risser, 1995; Grime, 1997; Tilman, 1997; Loreau et al., 2001;
Hooper et al., 2005; Tilman et al., 2006; Midgley, 2012; Balvanera et al., 2014; Gross et al.,
2014). The shift in community composition could lead to alteration in ecosystem properties such
as productivity, decomposition rates, nutrient cycling, and therefore affects the biogeochemical
processes that regulate the Earth system. These studies reveal the potential ecological
consequences of biodiversity loss and changes in community assembly. Because of their high
diversity and comparatively rapid dynamics, many of these biodiversity-ecosystem function
(BEF) studies have been conducted with perennial plant species in grassland ecosystems (Tilman
et al., 1996; Tilman et al., 2001; van Ruijven & Berendse, 2005; Reich et al., 2006; Flombaum &
Sala, 2008; Isbell et al., 2009; Hector et al., 2010; Weigelt et al., 2010). These studies have
commonly focused on the relationship between plant taxonomic or functional-group diversity
and ecosystem function (e.g. productivity, carbon sequestration). Many studies have additionally
sought to understand how diversity affects the resistance, resilience, and stability of ecosystem
function in the face of abiotic drivers (e.g. disturbance, climate). Most studies, although not all,
found that primary production exhibits a positive relationship with plant species and functional-
group diversity (Loreau et al., 2001). These results attracted a great deal of interest because they
were novel and finding high productivity in high-diversity ecosystems had deep philosophical
appeals unto itself. It seemed counter to the observations that many productive ecosystems are
typically characterized by low species diversity. In addition, the results provided useful
information for conservation by affirming that biodiversity does not only have intrinsic values
but also possesses large practical and utilitarian values.
74
However, the underlying mechanisms that are hypothesized to produce these results are
still under debate. The mechanisms discussed so far may be grouped into two main classes: First
are local deterministic processes such as niche differentiation and facilitation. Second are
stochastic processes involved in community assembly which means local dominance of highly
productive species can lead to increased average primary production with increasing diversity.
With more species, the probability of getting highly productive species goes up. It is not yet
clear, however, if the species with higher productivity once recruited begin to dominate the
community and lead to higher yield. Furthermore, it is unknown if dominance is brought about
by species with particular traits such as photosynthesis that can directly affects primary
productivity. The detailed mechanism needs to be resolved.
Approaches in community and ecosystem ecology have considered species diversity as a
dependent variable controlled by abiotic conditions or as a biotic controller of ecosystem
processes (Loreau et al., 2001). However, both plant community dynamics and ecosystem
services, such as primary productivity, largely depend on plant physiological traits. There are
mutual interactions among community, ecosystem functioning, and plant physiology (Fig. 4.1).
In our study we aim to address how plant community composition is affected by photosynthetic
rate, one of the most important plant physiological traits.
There is a growing body of evidence showing that many ecosystem processes depend
directly or indirectly on the photosynthetic rate of each species and the community composition
(Symstad et al., 1998; Johnson et al., 2008; Phoenix et al., 2008). However, the impact of
photosynthetic rate on plant community composition remains unquantified. Considering the
linkage among plant physiological traits, community and ecosystem processes, understanding the
relationship between photosynthetic rate and community structure is essential to estimate
75
community dynamics, yield and other ecological services. For example, if species with higher
photosynthetic rates tend to be dominant in the community, the assemblage of species in the
community is more likely to achieve higher yield (Sanderson, 2010). However, community
structure may change across the growing season because no one species can maintain peak
photosynthetic rate throughout the year. At longer time scales, one issue of particular concern is
the alteration of plant communities due to differential sensitivities of plant species or functional
groups to rising atmospheric CO2, an effect that is ultimately driven by photosynthetic responses.
Free-air CO2 enrichments have shown that long-term responses of photosynthesis to rising
atmospheric carbon dioxide levels are different among species, functional types and ecosystem
types (Ainsworth & Long, 2005; Lee et al., 2011). Morgan et al. (2007) reported that CO2
enrichment caused plant community structure alteration in short grass steppe and this alteration
might be driven by species differences in the photosynthetic pathway.
While the conceptual linkage between photosynthesis and productivity is well understood,
the interactions between photosynthetic rate and community composition are rarely investigated
experimentally. BEF studies typically focus on the interactions between community and
ecosystem ecology, while plant physiology and community development are usually studied
independently. Integrating the interactions between the two will not only help clarify the BEF
debate (Perrings et al., 2011), by shedding light on the underlying mechanisms, but may help
make community dynamics more predictive in the face of global change. This ecophysiological
approach can help us understand what individual species are doing and clarify the detailed
underlying mechanism of BEF. The link between physiology and community dynamics may also
bridge to lower scales of organization, where research in genomics is helping explain the
regulation and evolution of plant physiology.
76
The objective of this study was to examine the effects of photosynthetic rate on
community structure and ecosystem function in a diverse tallgrass prairie restoration experiment.
We hypothesize that photosynthetic rate is a key driver of productivity, and thus predict that
species cover is positively correlated with photosynthetic capacity (Amax) across species at a
given time. Furthermore, we hypothesize that changes in species percent cover and biomass have
similar patterns as changes in photosynthetic capacity within species across the growing season.
Based on this we predict that seasonal changes in abundance within species will be positively
correlated with seasonal changes in photosynthetic capacity.
77
Materials and Methods
Site descriptions
The study was conducted in Energy Biosciences Institute’s Energy Farm (40.05º N,
88.18º W) located in Urbana, IL, USA. Seeds for 28 native grassland perennial species were
planted evenly at 0.5g m-2 in one 4 hectare block and four 0.8 hectare blocks in 2008. The prairie
did not receive irrigation, fertilizers, pesticides or herbicides after it was planted, but had been in
conventional corn-soy rotation prior to restoration. Plant matter was mechanically harvested each
year in November. The prairie had fully established at the beginning of our study (2010) and
plant density was ~40 stems m-2. Only one species originally planted in the prairie
(Veronicastrum virginicum) was not found during the experiment. Twenty-five species
established sufficiently to allow measurements (Table 3.1). See Feng and Dietze (2013) for
additional site information.
Prairie community structure survey
Thirty random permanent 1×1 m2 plots were established in May 2010 in the prairie with
fourteen distributed in the large block and four in each small block. Abundance, percent cover
and average height of each species were measured every month from June to October in 2010
and 2011. Percent cover was determined by visually estimating percent cover of each species
rooted within the 1 m2 plot. Percent cover of bare ground was also recorded. Stand abundance
was recorded as the number of individual stems for forbs and legumes, and was the number of
clumps for grasses. Above-ground biomass production for each species was determined every
two months on dried samples (10 days at 65°C) from three harvests from June to October. Thirty
additional random 0.25 m2 plots were established for each biomass harvest, with the only harvest
of the community composition plots occurring during the November mechanical harvest. Plants
78
in the biomass plots were harvested by hand to soil level and standing biomass was sorted to
species.
Leaf gas exchange measurements
A portable photosynthesis system (LI-6400; LI-COR, Inc. Lincoln, NE, USA) was used
to measure photosynthetic rate. All measurements were taken on newly formed mature sun
leaves between 10:30 and 16:00 (local time) at ambient CO2 concentration with saturating light
level (photosynthetic capacity, denoted as Amax). Photosynthetic rate of 13 relatively common
species were measured in the middle of each month from June to October in 2010. The
measurements were extended to 25 species in 2011, including two C3 grass species, three C4
grass species, sixteen forb species and four legume species (Table 3.1). Three to six leaf
replicates were measured for each species in each month. For each measurement, leaf was placed
in the 2 cm2 leaf chamber with a red/blue light source and acclimated to saturating irradiance of
2000 μmol m−2 s−1, relative humidity of ~70% and leaf temperature of 25°C. A picture was taken
when leaf was too small and could not completely cover the chamber. With a known length as
reference in the picture, measured leaf area was then calculated through image analysis (ImageJ
[http://rsbweb.nih.gov/ij/]) (Abramoff et al., 2004). In addition, measurements of light- and CO2-
response curves were made, along with SLA, leaf N, and leaf chlorophyll, and are reported in
Feng and Dietze (2013).
Statistical analysis
Relationships between Amax and percent cover were determined via standard major axis
(SMA) analysis using a linear bivariate regression model. Within species, data were fit by
species to determine the correlation between Amax and percent cover for each species. Across
species, the 25 species were assigned to four plant functional types (PFT): C3 grass, C4 grass,
79
forb, and legume. Species with percent cover higher than 1% accounted for ~95% of the total
plant cover in sampling plots through the growing seasons. The rest was divided among species
with percent cover lower than 1%. These species were considered as rare species. Comparative
across-species regression analyses were done with all dominant species of all PFTs included and
with only the dominant species of forb PFT included. The statistical analyses were implemented
in R 3.0.1 ([http://www.r-project.org/]) (R Development Core Team, 2013) using package
lmodel2 1.7-2 (Legendre, 2014). Statistical significance was determined using 95% confidence
intervals.
80
Results
Community dynamics
The abundance and percent cover varied both across species and within species across the
growing season. Andropogon gerardii (C4 grass), Sorghastrum nutans (C4 grass), Helianthus
grosseserratus (C3 forb), Ratibida pinnata (C3 forb), and Elymus canadensis (C3 grass), were
the most common species during the study period. Total percent cover of these 5 species was
~60% through the growing season. Seasonal variability of percent cover was relatively higher for
dominant species compared to rare species. In general total plant cover peaked in July and
August. There was a shift in community composition from 2010 to 2011, with a large increase in
C4 grasses and decrease in C3 grasses and forbs (Fig. 4.2). In addition, within-season shifts in
community composition were also observed. Cold season grasses (C3 grasses) had a higher
percent cover in early and late growing season. However, this was only statistically significant in
2010 due to the lower total cover of C3 grasses through growing season in 2011 (P<0.01, Fig.
4.2).
Community structure and Amax
Photosynthetic rate for all species stayed relatively constant or slightly increased during
early growing season (June 16.28+/-0.94, July 17.32+/-0.71) and then decreased from mid to late
growing season (October 4.48+/-0.12). For most abundant forb species, changes of percent cover
had similar patterns to photosynthetic rate from June to October (Fig. 4.3ab). The seasonal
synchronization of the changes of these two variables caused a high within-species correlation
for these species (Fig. 4.4). Amax was positively correlated with percent cover within species
across the growing season for 7 forbs and 1 legume with 9 out of 18 forb and 1 out of 5 legume
81
species measured in 2010. In 2011 the positive within-species correlation was significant for 10
forbs and 2 legumes with 16 out of 18 forb and 4 out of 5 legume species measured (Fig 4.4).
However, no significant within-species relationships were found in rare species (percent cover
<1%). This is associated with the low variation in percent cover of rare species through growing
season (Fig. 4.3c). In addition, there was a general trend of decreasing within-species correlation
with decreasing percent cover (Fig. 4.4). A positive relationship between percent cover and the
correlation coefficient of percent cover and photosynthetic rate were statistically significant
(P<0.01) for forbs and legumes in 2011.
There was no clear relationship between grass species dominance and the within-species
correlation coefficient (Fig. 4.4). For C4 grasses high percent cover at the end of the growing
season typically resulted in a negative correlation between photosynthesis and cover (Fig. 4.3d).
Grass species showed a constant or increasing percent cover through growing season, which was
not in synchronization of the changing patterns of photosynthesis (Fig. 4.3d). This generated a
non-significant or significant negative relationships which cannot be predicted by the dominance
of species.
Across species, positive relationships between percent cover and Amax were found mid-
growing season in 2010. However, when more species with low percent cover (< 1%) were
included in the analyses in 2011, no significant across-species relationship was found in any
month (Fig. 4.5, 4.6). When the relationship between percent cover and Amax were tested across
only dominant species, a stronger positive relationship was observed for forb PFT (significant in
August and September) than the overall relationship across all dominant species from all four
PFTs (Fig. 4.5, 4.6). The strength of the relationship between Amax and cover for dominant forbs
increased from June to September and decreased from September to October (Fig. 4.5). For
82
significant relationships, higher slopes were observed in 2011 compared to 2010. Similar
relationships were observed between photosynthesis and species dry biomass due to high
correlation between percent cover and biomass (Fig. 4.7, 4.8). These relationships generally
apply to forb species with relatively high percent cover (>1%). Rare forbs (percent cover ≤1%)
were outliers due to their consistent low abundance through the growing season (i.e. high
photosynthetic rate did not generate higher percent cover or biomass). In the overall relationships
between photosynthesis and percent cover/ biomass across all dominant species, grass species
were mostly above the linear regression line, while legume species were below the line.
83
Discussion
Community dynamics
Large shifts in functional diversity occurred during the study period, which confirmed the
findings of other grassland community studies (Camill et al., 2004). In species-rich restored
prairie, a frequently observed phenomenon is a significant shift to warm-season C4 grasses
during the developmental stage of the prairie (Kindscher & Tieszen, 1998; Sluis, 2002; Baer et
al., 2003). This change might be due to differences in water/nitrogen use efficiency and
competition for other soil resources among plant functional types (PFT). Our study confirmed
this shift to C4 grasses through time, which may be caused by abiotic factors such as lower
precipitation (Growing season in 2011 was notable drier than 2010 with a total precipitation of
448 mm versus 544 mm from May to October. Illinois State Climatologist's Office
[http://www.sws.uiuc.edu/data.asp]) or the developmental stage of the prairie. Our results also
showed a similar pattern for the within-season shift of C3 grasses in grassland community as
found in previous studies. In addition, forbs and legumes declined at the end of growing season.
Standing biomass of C4 grasses did not drop as rapidly as forbs and legumes during the
senescence period causing a higher dominance of C4 grasses at the end of growing season.
Photosynthesis rate and community structure
Overall we found that for most common species there were clear within-species
relationships between photosynthesis and percent cover. Dominant forb and legume species
show the strongest correlations, suggesting that photosynthesis affects community organization
by affecting the relatively abundant species. Photosynthesis directly affects the amount of carbon
assimilated and therefore primary production. Within a species, high photosynthetic rate leads to
84
high biomass production and percent cover. Therefore, an increasing or a relatively constant but
high percent cover were observed at beginning of growing season with increasing or stable but
high photosynthetic rate. However, forbs and legumes shed leaves as soon as leaves stop
functioning. Therefore, total leaf biomass decreases with functioning biomass. In addition,
photosynthetic rates also gradually decline due to less nutrients allocated to the leaves in late
growing season. The similar changing patterns of photosynthesis and percent cover have caused
a strong correlation between these two variables. By contrast, among grasses the amount of
photosynthetically active leaf biomass decreases at end of growing season, but since leaves do
not drop, the total standing biomass keeps accumulating. Percent cover is good indicator of total
biomass, but not functioning biomass, leading to the negative correlation between photosynthesis
and percent cover among grasses. However, the response of functioning biomass of grasses to
photosynthetic rate was not measured and needs further study.
Several rare forb species (e.g. Silphium laciniatum, Silphium terebinthinaceum) with high
photosynthetic rates had consistently low percent cover, causing a non-significant or negative
within-species correlation between photosynthesis and cover. This suggests that there are other
factors that impact the within-season variability in abundance of rare species other that
photosynthetic rate. For example, seed germination/establishment and root structure (Isselstein et
al., 2002; Grubb, 1977) can have large effects on species establishment success and the difficulty
in establishment may cause the consistently low abundance of the aforementioned mentioned
species through several growing seasons. In addition to establishment success, extreme habitat
specialization, outside forces, disease, and allocation allometry could all potentially affect the
variability in within-species relationships. For example, assuming having same photosynthetic
85
rates, plants allocating more photosynthetic products to roots than stems and leaves will generate
less leaf biomass and thus less cover and light capture than grasses that do the opposite.
Across species, the relationships between photosynthesis and dominance were not
significant at the beginning or end of the growing season (Fig 4.5, 4.8). The role of
photosynthesis become less important at the end of growing season (October). This might be
because differences in senescence and phenology among species appear to have stronger effects
on abundance and cover during this time.
Across species, the positive correlation in each month was stronger among the common
forbs than across all species (Fig 4.5). Several forb species with high photosynthetic rate had low
percent cover and do not fall on the across-species regression line. As with the within-species
correlations, this is likely related to the combination of the retention of senescent biomass by
grasses and the inference that rarity is driven by factors other than assimilation. In addition,
within the forb PFT species may have similar physiological trade-offs and phylogenetic
constraints on growth and allocation. Therefore, difference in photosynthetic traits among these
species may become a major driver of their life-history strategies.
The observation that C4 grasses tended to be more dominant than forbs at a given
assimilation rate (Fig 4.5, 4.8) is consistent with C4 grasses having a higher water- and nitrogen-
use efficiency and higher allocation to leaf production than other PFTs. Similarly, that legumes
stay below the across-species regression line is consistent with their low photosynthetic nitrogen
use efficiency (Feng & Dietze, 2013). There is a positive correlation between percent cover
generated by per unit photosynthesis and PNUE (P<0.01).
The significant across-species relationships for forb PFT had lower slopes in 2011
compared to 2010 (Fig 4.5, 4.8) due to an increase in the dominance of C4 grasses in 2011, i.e.
86
although photosynthesis rate did not change from 2010 to 2011, each forb species had lower
percent cover and biomass in 2011 compared to 2010 due to the large shift to C4 grasses.
However, the relative species composition within the forb PFT did not change. There were no
statistical difference among the relative covers of forbs (Cover of each forb species/total cover of
forbs) in 2010 and 2011. These results imply that photosynthetic rate plays an important role in
shaping the community composition within a PFT and the mechanism of species coexistence
across PFTs may be different from that within a PFT.
These results provide new insights in assessing the community dynamics, productivity
and other ecological services of tallgrass prairie. For example, rising atmospheric CO2 and
predicted climate change may have strong impacts on plant composition. Since there is a positive
correlation between photosynthetic rate and community abundance, our results suggest that
within a PFT species with higher photosynthetic rate under projected future climate will tend to
become more dominant. Due to the nonlinear nature of the photosynthetic responses to CO2, this
implies that the shift in composition may not follow the current rank-order. Leaf photosynthesis
models (Farquhar et al., 1980; Sharkey, 1985; Harley & Sharkey, 1991) can be used to predict
whether the shifts in Amax are the same for all forbs or whether they are larger at one end of the
distribution. If intermediate and rare species show a larger relative increase in Amax, then they
might increase more and the community would become more even. However, if dominant
species have high increase in Amax under future climate, then the community will become less
diverse. Finally, it is still not clear if this correlation is a causative relationship and there is
clearly a need for further investigation. Experiments of high-diversity communities with different
treatments of abiotic factors (CO2, light, water, temperature) leading to changes photosynthetic
rate would help to clarify this relationship.
87
Fig. 4.1 Interactions among community, ecosystem functioning, and plant physiology.
88
Fig. 4.2 Community composition through growing season in year 2010 and 2011.
89
Fig. 4.3 Within-species relationships between percent cover and photosynthetic rate. Two
abundant forb species, one rare forb species and one grass species are shown in the figure.
Average percent cover (solid line) and photosynthesis (dashed line) data in year 2010 (mean+/-
SE) were used in the plots.
.
90
Fig. 4.4 Correlation coefficient for all within-species relationships. Species were listed in the
order of their abundance in year 2011. A position correlation between Amax and percent cover
was found for most dominant forb and legume species. Grass species showed non-significant
relationships or negative correlations. “nm” indicates that the species was not measured in year
2010. “ns” indicates that the relationship was not significant. “*” indicates that the relationship
was significant (P<0.05).
91
Fig. 4.5 Relationships between Amax and percent cover across species from June to October.
Statistic values for dominant forbs are shown in each panel. Significance of relationship
increased from June to September and decreased from September to October. All the dots below
the grey dotted line indicate forbs with a percent cover lower than 1% and were not included in
the regression analyses shown in the panels. More grey dots are shown in the 2011 panels
92
because more rare species were included due to the extended photosynthetic measurements in
2011. When these species were included in the analyses in 2011, no significant across-species
relationship was found in any month.
93
Fig. 4.6 Relationships between Amax and percent cover across dominate species in all PFTS from
June to October. Lines were drawn only for significant relationships. All the dots below the grey
dotted line had a percent cover lower than 1%. Dots in red circles indicate the outlier effect of
legume and grass species. The regression analyses in panel c and d were significant when these
data were not included.
94
Fig. 4.7 Strong correlation between dry biomass and percent cover.
95
Fig. 4.8 Relationships between Amax and dry biomass across dominant species from June to
October. Statistic values for dominant forbs are shown in each panel. Relationship was strongest
during mid-growing season. All the dots below the grey dotted line had dry matter lower than 1g
and were not included in the regression analyses shown in the panels.
96
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