Shade tree effects on intraspecific leaf trait plasticity and decomposition in a willow agroforestry

89 Pages·2016·6.01 MB·English

Shade tree effects on intraspecific leaf trait plasticity and
decomposition in a willow agroforestry system
by
Brent Coleman
A thesis submitted in conformity with the requirements
for the degree of Master of Science
Department of Geography
University of Toronto
© Copyright by Brent Coleman 2016
Shade tree effects on intraspecific leaf trait plasticity and
decomposition in a willow agroforestry system
Brent Coleman
Master of Science
Department of Geography
University of Toronto
2016
Abstract

Agroforestry systems enhance nutrient cycling, in part, through modifications to leaf quality and
quantity, and subsequently, decomposition rates. However, leaf traits are plastic and thus
variable across both temporal and spatial scales in agroforestry systems. Using a temperate tree-
based intercropping system with willow, this study examined effects of shade tree functional
group (N -fixer, Non N -fixer, or monoculture) and distance from shade tree on i) willow leaf
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traits (leaf area, leaf weight, specific-leaf area (SLA), leaf nitrogen concentration (LNC), and
subsequently ii) trait variability influence on decomposition rates in a lab incubation. Willow
leaves within agroforestry treatments exhibited greater leaf area, SLA, and LNC than within the
monoculture treatment. Willow leaf decomposition followed a two-pooled kinetic model with k1
values ranging from 0.20 to 0.29 d-1 and k2 values ranging from 0.0019 to 0.0023 d-1. Willow
litter in agroforestry systems presumably provides soils with additional N, potentially reducing
required fertilizer inputs.
ii
Acknowledgments
First and foremost, I am grateful to my advisor Dr. Marney Isaac for taking a chance on me as a
graduate student and for the constant support and guidance I received throughout my research.
Thank you to Dr. Adam Martin for your guidance, for your help with my fieldwork, and for
sharing your extensive knowledge of statistics and R. Thank you to Dr. Carl Mitchell for
participating on my defense committee.
Thank you also to Dr. Naresh Thevathasan for being my ‘official unofficial advisor’, both for
introducing me to Marney with such a high recommendation, and for going above and beyond
for me throughout this entire process. Your continued support and confidence in me is a constant
source of motivation. Thank you to Dr. Andy Gordon for your role as the ‘Agroforestry Sage’,
providing invaluable insight into the field of agroforestry based on more than 30 years of
experience in the field, and for the various opportunities you’ve allowed me. Thank you to Dr.
Paul Voroney for sharing your extensive expertise in the area of soil science and soil incubation
studies, as well as your patience and encouragement. Thanks also to Dr. Alexander Woodley for
your help with my incubation experiment.
Thank you to Serra Buchanan and Stephanie Gagliardi for helping with my sample analyses, and
thanks to my entire lab group Jessie Furze, Kira Borden, and Stuart Livingston for providing
constant moral support, especially when I needed it most. Thanks to the entire U of T ‘leaf gang’
and especially to Keane Tirona for your extensive help with all of the leaf prep.
Thanks to my field crew in Guelph including Sean Simpson, Jordan Graham, Floriane Marsal for
all your help with sample collection and processing. Thanks to Deanna Walter for providing me
with field data and for moral support along the way. Thank you to Dr. Waseem Ashiq for your
encouragement and to Idris Mohammed for showing me the ropes in the Agroforestry lab.
Thanks to Austin Gibson, Greg Richmond, Brent Munger, Chris Lambert, Ben Alexis, and Adam
Lambert for being some of the best friends and musicians I’ve ever known. Thank you Enzo for
teaching me patience and for all the licks; You’re the best dog in the entire world. Thanks to
Michelle for being a voice of reason and for picking up the slack at home when I needed it.
Thanks to my family for their undying love and support, without which I would be completely
lost.
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Table of Contents
Abstract ........................................................................................................................................... ii
Acknowledgments .......................................................................................................................... iii
Table of Contents ........................................................................................................................... iv
List of Tables ................................................................................................................................. vi
List of Figures ................................................................................................................................ ix
List of Appendices ......................................................................................................................... xi
Chapter 1 Introduction ................................................................................................................ 1
1.1 Research Context ............................................................................................................... 1
1.2 Research Questions and Hypotheses ................................................................................. 3
Chapter 2 Literature Review ....................................................................................................... 4
2.1 Effects of Light and Nutrient Gradients on Leaf Traits ..................................................... 4
2.2 Resource Heterogeneity in Agroforestry Systems ............................................................. 6
2.2.1 Light Resources .......................................................................................................... 7
2.2.2 Nutrient Dynamics ...................................................................................................... 7
2.3 Plant Trait Plasticity in Willow Agroforestry .................................................................. 11
2.4 Literature Gaps ................................................................................................................ 12
Chapter 3 Study Materials and Methods ................................................................................... 13
3.1 Site Description ................................................................................................................ 13
3.2 2015 Growing Season ...................................................................................................... 13
3.3 Experimental Design ........................................................................................................ 16
3.3.1 Soil Analysis ............................................................................................................. 16
3.3.2 Willow Canopy Exposure ......................................................................................... 19
3.4 Leaf Traits ........................................................................................................................ 19
3.4.1 Leaf Sampling ........................................................................................................... 19
3.4.2 Leaf Analysis ............................................................................................................ 21
3.5 Litter Decomposition ....................................................................................................... 21
3.6 Statistical Analysis ........................................................................................................... 24
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Chapter 4 Results ...................................................................................................................... 26
4.1 Soil Status ........................................................................................................................ 26
4.2 Leaf traits ......................................................................................................................... 28
4.2.1 Leaf Trait Variation .................................................................................................. 28
4.2.2 Leaf Weight .............................................................................................................. 31
4.2.3 Specific Leaf Area .................................................................................................... 34
4.2.4 Leaf Nitrogen ............................................................................................................ 37
4.3 Leaf Trait Correlations ..................................................................................................... 37
4.4 Leaf Trait Variance Component Analysis ....................................................................... 40
4.5 Litter Incubation .............................................................................................................. 40
Chapter 5 Discussion ................................................................................................................ 54
5.1 Intraspecific Leaf Trait Plasticity .................................................................................... 54
5.2 Leaf Traits Among Treatments ........................................................................................ 55
5.3 Links Between Leaf Traits and Ecosystem Function ...................................................... 56
5.4 Within Canopy and Temporal Leaf Trait Variation ........................................................ 58
Chapter 6 Conclusion ................................................................................................................ 60
References ..................................................................................................................................... 62
Appendices .................................................................................................................................... 72

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List of Tables
Table 3.1 Local monthly climate data for April-October 2015 compared to historical (1981-
2010) normals from Environment Canada’s Shand Dam weather station, located in Wellington
County approximately 22km from the Guelph Agroforestry Research Station (Environment
Canada 2016). ............................................................................................................................... 17
Table 3.2 Chemical characteristics of incubation soil from Guelph Agroforestry Research Site 23
Table 4.1 Analysis of variance of soil nitrate (mg kg-1), soil ammonium (mg kg-1), and available
soil phosphorus (mg kg-1) for samples collected in May 2015 within the non N -fixer, N -fixer,
2 2
and monoculture plots across all sampling positions (X1-X3). Soil nutrient means and standard
errors (in brackets) are presented. Letters (a-b) within columns denote Tukey HSD homogeneous
subsets (p<0.05; n=4) between distances; Letters (x-y) within rows denote Tukey HSD
homogeneous subsets (p<0.05; n=4) between treatments. ........................................................... 27
Table 4.2 Descriptive statistics for five leaf-level physiological and chemical traits measured on
S. dasyclados in Guelph, Ontario, Canada. Physical traits (leaf area, leaf weight, SLA) are based
on n=1800 individual leaves per treatment, with samples collected monthly from June to October
2016. Chemical traits are based on n=72 individual leaves per treatment, with samples collected
in July and September. .................................................................................................................. 29
Table 4.3 Mean leaf area data for July and September across treatments, distances, and canopy
strata. Standard errors are in brackets (n=10). For each month, significant differences as
determined by the non-parametric Kruskal-Wallis test (p<0.05) are indicated between treatment
means but within a sampling distance and canopy strata (a-c), between canopy strata but within a
treatment and sampling distance (f-h), and between distances but within a treatment and canopy
strata (x-y). .................................................................................................................................... 30
Table 4.4 Mean leaf weight data for July and September across treatments, distances, and canopy
strata. Standard errors are in brackets (n=10). For each month, significant differences as
determined by the non-parametric Kruskal-Wallis test (p<0.05) are indicated between treatment
means but within a sampling distance and canopy strata (a-b), between canopy strata but within a
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treatment and sampling distance (f-h), and between distances but within a treatment and canopy
strata (x-y). .................................................................................................................................... 33
Table 4.5 Mean SLA data for July and September across treatments, distances, and canopy strata.
Standard errors are in brackets (n=10). For each month, significant differences as determined by
the non-parametric Kruskal-Wallis test (p<0.05) are indicated between treatment means but
within a sampling distance and canopy strata (a-c), between canopy strata but within a treatment
and sampling distance (f-h), and between distances but within a treatment and canopy strata (x-
y). .................................................................................................................................................. 36
Table 4.6 Mean Leaf N percentage data for July and September across treatments, distances, and
canopy strata. Standard errors are in brackets (n=10). For each month, significant differences as
determined by the Tukey HSD test (p<0.05) are indicated between treatment means but within a
sampling distance and canopy strata (a-b) and between distances but within a treatment and
canopy strata (x-y). ....................................................................................................................... 38
Table 4.7 Bivariate relationships among leaf area (log-LA, cm2), leaf weight (log-LW, g),
specific leaf area (SLA, m2 kg-1), leaf nitrogen concentration (Leaf N, %), and leaf carbon
concentration (Leaf C, %). The upper right section of the matrix represents the slopes and
associated 95% confidence intervals for a given relationship, based on a standardized major axis
regression analysis. Model r2 and one-tailed p-values (in brackets) for each bivariate model are
presented in the lower left section of the matrix, with significant relationships (n = 5400, except
for Leaf N and Leaf C where n = 216; p ≤ 0.05) highlighted in bold. .......................................... 39
Table 4.8 Variance component analysis of four leaf traits based on nested model where
month/treatment/block/distance/stratum/within, where within represents variation within
individual willows. Values are percentages of explained variance with variables accounting for
the greatest variance in each leaf trait highlighted in bold. .......................................................... 41
Table 4.9 Decomposition model selection based on the relative quality assessed by AIC and
residual standard error (RSE) of three competing decomposition models. The selected model is
highlighted in bold. ....................................................................................................................... 46

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Table 4.10 Willow leaf organic C decomposition parameter estimates for two pooled kinetic
decay model Y = alpha*exp(-k1*t) + beta*exp(-k2*t), where a and b represent leaf mass (%),
and k1 and k2 represent decay rate constants (days-1). Standard errors for each parameter are
presented in brackets. .................................................................................................................... 48

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List of Figures
Figure 3.1 Recent aerial photograph of the Tree-based Intercropping (TBI) site at the Univeristy
of Guelph Agroforestry Research Station in Guelph, Ontario, Canada (Photo by Naresh
Thevathasan). ................................................................................................................................ 14
Figure 3.2 2006 image of newly planted willows in TBI site at the University of Guelph
Agroforestry Research Station in Guelph, Ontario, Canada (Photo by Naresh Thevathasan). X1-
X3 represent sampling points, with each point encompassing six individual willows within a
double row, on a single transect originating at the base of a mature shade tree (or plot edge in the
case of the monoculture treatment). .............................................................................................. 15
Figure 3.3 2015 image of willows in TBI site at the University of Guelph Agroforestry Research
Station in Guelph, Ontario, Canada. Willows are now more than 4 m in height with upwards of
15-20 stems per plant. ................................................................................................................... 15
Figure 3.5 Schematic of leaf sampling setup, delineating the upper, middle, and lower willow
canopy. Numbers depict stems selected based on length, with leaves selected from the five
marked stems for analysis. ............................................................................................................ 20
Figure 3.6 Example of leaf scan completed for each of three canopy strata per sampling point on
a monthly basis from June to October 2015. ................................................................................ 22
Figure 4.1 Net C mineralization rates (mg C kg-1 dry soil d-1) as generated by upper canopy
willow leaf decomposition across treatments and distances over 56-day incubation. .................. 42
Figure 4.2 Net C mineralization rates (mg C kg-1 dry soil d-1) as generated by willow leaf
decomposition for all distances within the Non N -fixer treatment over 56-day incubation. ...... 43
2
Figure 4.3 Net C mineralization rates (mg C kg-1 dry soil d-1) as generated by willow leaf
decomposition for all distances within the N -fixer treatment over 56-day incubation. .............. 44
2
Figure 4.4 Net C mineralization rates (mg C kg-1 dry soil d-1) as generated by willow leaf
decomposition for the monoculture treatment over 56-day incubation. ....................................... 45
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Figure 4.5 Graphical depiction of decomposition model selection with the nonlinear least squares
line of best fit for each contending model overlaying the global organic C mineralization dataset.
The x- and y-axes have been extended for the purpose of visualizing the estimated organic C
remaining during a full year. ......................................................................................................... 47
Figure 4.6 Organic C mineralization (%) of upper stratum willow leaves for all treatments and
sampling distances during a 56-day incubation period. Line represents the two pooled kinetic
decay model equation Y = alpha*exp(-k1*t) + beta*exp(-k2*t) for the global dataset. Each point
represents the mean percentage of organic C remaining at each treatment-distance combination
(n=4). ............................................................................................................................................. 50
Figure 4.7 Organic C mineralization (%) of upper stratum willow leaves from Non N -fixer
2
treatment from three sampling distances during a 56-day incubation period. Lines represent the
two pooled kinetic decay model equations Y = alpha*exp(-k1*t) + beta*exp(-k2*t) for each
sampling distance. Error bars represent standard error of the mean (n=4). .................................. 51
Figure 4.8 Organic C mineralization (%) of upper stratum willow leaves from N -fixer treatment
2
from three sampling distances during a 56-day incubation period. Lines represent the two pooled
kinetic decay model equations Y = alpha*exp(-k1*t) + beta*exp(-k2*t) for each sampling
distance. Error bars represent standard error of the mean (n=4). .................................................. 52
Figure 4.9 Organic C mineralization (%) of upper stratum willow leaves from monoculture
control during a 56-day incubation period. Line represents the two pooled kinetic decay model
equation Y = alpha*exp(-k1*t) + beta*exp(-k2*t). Error bars represent standard error of the
mean (n=4). ................................................................................................................................... 53

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