Intraspecific Variation and Phenotypic Plasticity in the ... · ii Intraspecific Variation and...

Intraspecific Variation and Phenotypic Plasticity in the invasive vine Vincetoxicum rossicum

by

Simone-Louise E. Yasui

A thesis submitted in conformity with the requirements for the Master’s degree of science

Department of Ecology and Evolutionary Biology

University of Toronto

© Copyright by Simone-Louise Yasui 2016

ii

Intraspecific Variation and Phenotypic Plasticity in the invasive

vine Vincetoxicum rossicum

Simone-Louise Yasui

Department of Ecology and Evolutionary Biology

2016

Abstract

The human-mediated movement of species across the globe has led to the growing field of

invasion biology, which is devoted to understanding more about invasive species and the impact

they have on the ecosystems into which they are introduced. One particular invasive species that

is extremely abundant and widespread in Southern Ontario is the vine species Vincetoxicum

rossicum. V. rossicum is found in a variety of environments including open fields and forest

understories, however, little is known about how the traits of this species varies in the different

environments. In a field study in the Rouge Urban National Park and a complementary

greenhouse study, I found that this invasive species optimizes light capture efficiency by

changing its morphological traits. This potentially contributes to its invasion success and

provides further insights and its future spread. Additionally, this work can provide insight on

other invasive species that exhibit similar invasion strategies

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Acknowledgments

I would like to thank the Rouge Urban National Park for allowing me to conduct my experiment.

Many people have assisted with this thesis, and their help has been invaluable, especially my

supervisor Dr. M.W. Cadotte, and my committee members Dr. N. Mandrak and Dr. P. Kotanen. I

would also like to thank the various people who helped me along the way, including the many

volunteers who helped me collect data, Dr. J.S. MacIvor, Dr. M. Isaac, C. Arnillas, A. Choi, S.

Livingstone, S. Gagliardi, K. Carscadden, Dr. R. Marushia and my parents

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Table of Contents

1. Chapter 1: Introduction

1.1 Invasion Biology …………………………………………………………………. 1

1.2 Potential Mechanisms contributing to Invasions ………………………………… 2

1.3 Thesis Overview …………………………………………………………………. 5

1.3.1 Chapter 2: Quantifying intraspecific variation of Vincetoxicum rossicum

1.3.2 Chapter 3: Quantifying phenotypic plasticity and assessing enemy release

References ……………………………………………………………………………. 9

2. Chapter 2: Intraspecific variation in the morphology of the invasive vine, Vincetoxicum

rossicum across two environmental gradients

2.1 Introduction ……………………………………………………………………… 12

2.2 Methods and Materials …………………………………………………………... 15

2.2.1 Study Site

2.2.2 Sampling

2.2.3 Statistical Analysis

2.3 Results ……………………………………………………………………………. 20

2.3.1 Light Availability

2.3.2 Soil Nutrient Availability

2.3.3 Multivariate linear mixed effect models

2.4 Discussion ………………………………………………………………………... 25

2.4.1 Light Availability

2.4.2 Soil Nutrient Availability

2.4.3 Broader Implications

References ……………………………………………………………………………. 33

Tables ………………………………………………………………………………… 38

Figures ………………………………………………………………………………... 44

Appendix ……………………………………………………………………………... 55

3. Chapter 3: Quantifying phenotypic plasticity and assessing enemy release

3.1 Introduction ………………………………………………………………………. 57

3.2 Methods and Materials …………………………………………………………… 60

3.2.1 Study Site and Experimental Design

3.2.2 Plant Sampling

3.2.3 Biological Control


3.3 Results ……………………………………………………………………………. 64

3.4 Discussion ………………………………………………………………………... 68

References ……………………………………………………………………………. 75

Tables ………………………………………………………………………………… 78

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Figures ………………………………………………………………………………... 81

Appendix ……………………………………………………………………………... 92

4. Chapter 4: Thesis summary

4.1 Summary of chapters …………………………………………………………….. 96

4.2 Implications and future directions ……………………………………………….. 97

References …………………………………………………………………………… 99

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List of Tables

2.1: Linear models of the morphological traits regressed by the environmental variables

2.2 Univariate linear mixed effect models

2.3 Multivariate liner mixed effect models

3.1 Results from the ANOVA Tests for each of the morphological traits for each of the

treatments for Day 60 of the experiment

3.2 Results from the ANOVA Tests for each of the morphological traits for each of the

treatments for Day 100 of the experiment

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List of Figures

2.1 The relationship between species richness and the environmental gradients

2.2 The relationship between species richness and the biomass measurements

2.3 The relationship between various plant traits and log transformed photosynthetically active

radiation

2.4 The relationship between various plant traits and photosynthetically active radiation

2.5 The relationship between various plant traits and canopy cover

2.6 The relationship between various plant traits and soil phosphate concentration

2.7 The relationship between various plant traits and log transformed soil nitrogen concentration

2.8 The relationship between various plant traits and soil nitrogen concentration

2.9 The relationship between various plant traits and log transformed soil nitrate concentration

2.10 The relationship between various plant traits and soil nitrate concentration

2.11 The relationship between plant traits and soil ammonia concentration

3.1 The distribution of trait values across the two light conditions, both at the start of the

experiment and at the end of the experiment

3.2 The distribution of trait values across the four different start conditions, both at the start of

the experiment and at the end of the experiment.

3.3 The distribution of trait values across the three different defoliation treatments

3.4 The distribution of trait values in response to the interaction between light availability and

defoliation

3.5 The distribution of trait values in across the three sites where the roots were collected

3.6 Aboveground biomass across the three sites where the roots were collected

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3.7 The distribution of trait values in response to the interaction between which site the roots

were collected from and which light condition the roots were originally found in


were collected from and which light condition the roots were originally found in


were collected from and light availability


were collected from and light availability


were collected from and defoliation

List of Appendices

2.A1: Measured morphological traits

2.A2: Site map of the Rouge National Urban Park

2.A3: a) Residuals vs Fitted for Total biomass and canopy coverage

3.A1 The distribution of trait values across the twelve different treatment types

3.A2 Results of the Tukey’s HSD test for the 12 treatment types

3.A3: Map of the University of Toronto Scarborough Campus. Yellow pin indicates the Science

Building roof where the greenhouse experiment took place.

3.A4: Picture of the experimental set-up for the greenhouse experiment

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Chapter 1 Introduction

1.1. Invasion Biology

The spread and movement of organisms around the globe has drastically changed our

world (Vilà et al. 2005). While some species that are economically beneficial have been

intentionally introduced, other species have been transported by accident. Whether by intention

or by accident, the movement of species across the globe can lead to unforeseen consequences,

especially species invasion. Increasing concern about such consequences has led to the growth of

the field invasion biology (Cadotte 2006). With regards to the work of presented in this thesis, an

invasive species will be defined as a non-native species that has established in a new

geographical range in which it has proliferated, spread, and persists to the detriment of that

particular habitat (Mack et al. 2000). For a non-native species to become invasive, it must

undergo the entire invasion process, where it must pass through multiple abiotic and biotic

barriers (Blackburn et al. 2011). After passing through these barriers this species could then have

a large impact on biodiversity (Gurevitch and Padilla 2004) and may have a negative impact on

ecosystem functioning (Ehrenfeld 2010).

Overall the field of invasion biology has expanded and grown into a vital subfield of

community ecology (Davis et al. 2006). Ecologists in this field are driven by a few general

questions, which include: How and why do some non-native species become invasive within a

new introduced range while others do not?; i.e. what determines how invasive, in terms of the

degree of negative impacts, an introduced species will be (Drake et al. 1989)? Certainly, a major

driving force behind this is trying to understand the effect of invasive species on native

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biodiversity (Elton 1958). Many researchers are concerned with how the introduction of invasive

species can drive the loss of native biodiversity (Richardson and Pysek 2008) and, ultimately,

global biodiversity (Butchart et al. 2010).

The loss of biodiversity is of particular concern as it has been shown that biodiversity is a

vital component to the proper functioning of ecosystems (Hooper et al. 2005) and to ecosystem

stability (Cleland 2012). The reduction of ecosystem functioning has been most apparent, or at

least most visible, when invasive species disrupt the interactions of native plants with their

mutualistic pollinators (Traveset and Richardson 2006). Disruptions to mutualistic interaction

could result in a decrease in pollination rate, which could potentially lead the decreased fitness of

native plants (Brown et al. 2002). Ultimately, this could affect other vital processes such as

nutrient cycling or productivity

1.2 Potential Mechanisms contributing to Invasions

The seminal work of Charles Elton, published fifty-seven years ago in his book “The

ecology of invasions by animals and plants” (Elton 1958) has inspired many researchers to

explore the underlying mechanisms that contribute to invasion success. Overall, this has led to an

overabundance of hypotheses regarding what makes the best invader (Lowry et al. 2012). In

particular, this type of research has split into two different streams of thought, which are not

mutually exclusive. The first is determining what traits contribute to the invasive potential or

invasiveness of a species (Richardson and van Kleunen 2007), and the second examines

invasibility or what makes a particular habitat more or less susceptible to incoming species

(Davis et al. 2005).

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Many proposed mechanisms examine the invasiveness of a species, primarily focusing on

specific traits that aid the invader at different times during the invasion process (Lowry et al.

2012). For instance, at the beginning of the invasion the species needs to maintain a viable

population, thus, being broadly tolerant of different environmental conditions could aid it in its

establishment (Baker 1965). Additionally, having a high reproductive output can protect it from

being excluded by the native community (Lockwood et al. 2005). Other mechanisms that help

with the persistence of an introduced species fall under the general idea of ‘inherent superiority’,

where an introduced species is able to become highly abundant because it has one or more

characteristics that help it out-compete native species. These characteristics include ‘ideal weed

traits’ described by Baker (1965), such as high reproductive output, effective dispersal ability, or

rapid growth. Additional mechanisms primarily look at biotic interactions; these hypotheses

include the enemy release hypothesis (Keane and Crawley 2002), where an invader in the

introduced range is not regulated by the presence of an enemy such as an herbivore or predator.

This can potentially result in the evolution of increased competitive ability (EICA) in the

invader, thereby increasing its invasive potential (Blossey and Notzoid 1995; Callaway and

Ridenour 2004).

Regarding the invasibility of a region, studies often discuss characteristics of the system’s

niche space and describe how disturbances can open up new niche space for potential invaders in

a process described as the novel niche hypothesis (MacDougall et al. 2009). Disturbances can

potentially lead to an influx of resources, which has been hypothesized to increase a habitat’s

susceptibility to invasion because there are more unused resources to utilize (Fluctuating

resource availability - Davis et al. 2000). Others have looked at how the diversity of the system

affects its invasibility (Diversity-resistance hypothesis - Elton 1965; Kennedy and Naeem 2002).

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For instance, in habitats that are not overly diverse, either in the number of species or in

functional diversity, the trait space occupied by this community may have more open or empty

niche space which could be occupied by an invader (MacDougall et al. 2009).

The list of mechanisms can go on and on, yet empirical work actually assessing the

validity of some of these hypotheses is sometimes lacking. For instance, Jeschke et al. (2012)

reviewed six of the leading hypotheses to date that examined the interactions of invaders with

their new environment and found that some of the hypotheses were better supported by empirical

evidence than others. Lowry et al. (2012) also demonstrated that much of the invasion literature

has primarily focused on terrestrial systems, with many of these studies only looking at plant

invasions. In their review of 1537 papers, they note that, of the vast number of hypotheses,

researchers generally focused on the ones that were related to the superior competitive ability of

the invader, environmental disturbance, and invaded community species richness.

There is a plethora of different hypothesized mechanisms that can contribute to either the

invasiveness of a particular non-native species, or the invasibility of the new range in which it is

introduced. Within response to the overabundance of these proposed mechanisms some

researchers have gone back and begun to synthesize the different hypotheses into a single

framework to reduce the redundancy between them (Catford et al. 2012). I believe it is important

to revisit and synthesize but, in addition, I believe we need to strengthen or find more support for

some of these hypotheses. Only recently have researchers considered determining how

intraspecific variation may influence community assembly and, by extension, invasion success

(Laughlin et al. 2012). Intraspecific variation is the degree of phenotypical difference among

individuals of a single species (Laughlin et al. 2012). This variation provides the raw material for

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natural selection to act upon, and therefore, is important in evolutionary theory (Bolnick et al.

2011). Within an ecological context, intraspecific variation is important to study as it has been

shown to structure communities (Siefert 2012) and influence ecosystem functioning (Lecerf and

Chauvet 2008). Within an invasion context, knowing more about intraspecific variation may help

us better understand community assembly processes such as habitat filtering and niche

differentiation (Jung et al. 2010). Understanding more about these community assembly

processes may then allow us to accurately determine what the ecological niche of an introduced

species is. Knowing this could potentially aid researchers in predicting which introduced species

will become invasive, where that invader could potentially spread, and what kind of impacts it

may have in regions where it forms highly abundant populations.

1.3 Thesis Overview

Using the perennial vine Vincetoxicum rossicum (Asclepidaceae) as my study species, I

assess the intraspecific variation of this highly invasive species across different environmental

gradients. I will assess the contribution of two mechanisms, plasticity and enemy release, on the

invasiveness of this widely distributed invasive species. Vincetoxicum rossicum, which is

commonly known as Dog-Strangling Vine (DSV) and Pale Swallow-Wort, was chosen as the

study species because it is a highly invasive species that is well established within southern

Ontario. Since this species is found in a variety of environments, it makes an ideal study

organism to examine intraspecific variability and various plastic traits that may contribute to its

invasiveness. Additionally, within the introduced range DSV does not have any natural or native

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enemies. Therefore, by using different levels of artificial defoliation, I will be able to assess the

plastic response of DSV to defoliation.

Briefly, DSV was introduced to North America from the Ukraine in the late 1800s

(Kricsfalusy and Miller 2008). After this initial introduction, this species experienced a lag or

latency period of about 60-70 years, where there was limited spread and low population density.

This latency period has been hypothesized to be a result of allee effects, where there is a lower

rate of increase of small populations relative to large populations (DiTommaso et al. 2005). After

this lag period, DSV began to spread considerably and form high density monocultures which

can largely impact both native plant diversity (DiTommaso et al. 2005) and arthropod diversity

(Ernst and Cappuccino 2005). Thus, this species has now become a major concern within

Ontario and has been listed on the Noxious Weed List of Ontario.

DSV has many of the traits that Baker (1965) had indicated make the ideal weed,

especially in regards to its reproductive potential. DSV is self-compatible and either insect

pollinated or self-pollinated. The seeds that it produces are wind dispersed but do not usually

travel far distances since the seeds are generally polyembryonic making them heavier, but more

likely to germinate. DSV is also capable of clonal reproduction via vegetative growth from

rhizomes (DiTommaso et al. 2005). This vegetative growth is particularly useful for DSV within

shaded areas where the plant produces less flowers and fewer and lighter seed (Sheeley 1992).

Vegetative growth may allow the plants to persist for years until they can exploit any canopy

disturbance that may occur (DiTommaso et al. 2005) Within its native range, DSV is

documented as a rare understory species; however, in its introduced range, it is a dominant plant

capable of forming dense monocultures in both the understory and open field.

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Currently, we know about some of the characteristics of DSV growth and reproductive

output in full sun and full shade conditions (Cappuccino and Mackay 2002; Smith et al. 2006;

Milbrath 2008; and Averill et al. 2011). Generally, DSV is less fecund in the understory, where it

is less dense as a result of decreased vegetative growth, produces fewer flowers, follicles

(seedpods), and seeds (Smith et al. 2006; and Milbrath 2008). Despite this, DSV has been found

to be quite persistent within the understory in Southern Ontario, and the mechanisms

contributing to this persistence are poorly understood. It is believed that plasticity and having a

broad niche breadth enables it to survive in these areas (Averill 2011) and, thus, the focus of this

study was on examining the plastic responses that DSV may exhibit in various light conditions.

The overall aim was to determine how phenotypic plasticity potentially contributes to DSV

invasiveness. DSV will have to express different strategies to survive in the different light

environments, and by measuring the differences in functional traits I can quantify the differences

in these strategies.

1.3.1 Chapter 2: Quantifying intraspecific variation of Vincetoxicum rossicum

In a field study conducted at the Rouge Urban National Park, I observed the differences

in a suite of functional traits across various environments to determine how many of these traits

varied within species and inferred how this variation may affect the success of DSV as an

invader. I hypothesized that if DSV has differential adaptations to multiple environments then it

will exhibit a large range of intraspecific variation across the different environments, particularly

with light availability and soil composition.

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1.3.2 Chapter 3: Quantifying phenotypic plasticity and assessing enemy release

Using a common garden method, I directly tested the plastic response of DSV which was

collected from two different light environments (open and closed canopy), by manipulating the

amount of available light (full sun and full shade). Additionally, I determined if DSV exhibits

plasticity in its growth traits in response to artificial defoliaiton. I hypothesized that if DSV

responds to changes in light availability then it will significantly changes in its growth traits from

low to high light.

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of weeds. The Genetics of Colonizing Species. 147–168.

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V.H., Schreiber, S.J., Urban, M.C., and Vasseur, D.A. (2011). Why intraspecific trait variation

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Chapter 2 Intraspecific variation in the morphology of the invasive vine Vincetoxicum rossicum across two environmental gradients

2.1 Introduction

The invasion process follows several stages (Richardson et al. 2000) and depends on

progression past several filters or barriers that limit either the spread or density of an introduced

species (Colautti and MacIsaac 2004). These barriers include abiotic or environmental filters,

which include major geographical barriers, such as oceans or mountain ranges that limit dispersal

(Richardson et al. 2008), regional climate that places physiological limitations on newly

introduced species affecting their ability to establish in a particular habitat (Richardson et al.

2008). Two other abiotic filters on a local scale that could affect invasion success, and will be the

primary focus of this chapter, include light availability, which has been shown to limit that

distribution of species that are shade intolerant (Valladares and Niinemets 2008), and soil

nutrient availability, particularly soil nitrogen and soil phosphorus which are the two main

limiting nutrient resources in terrestrial ecosystems (Vitousek et al. 2010). Generally, higher soil

nutrients have been shown to have positive effects on individual species (Chaplin et al. 1986)

and communities (Bracken et al. 2014), where nutrient additions can increase biomass

production (Chiarucci et al. 1999) and influence diversity (Hobbs and Atkins 1988). However,

an excess of soil nutrients, particularly nitrogen, can result in the homogenization of the

environment causing a decline in the species diversity of a community (Gilliam 2006).

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Other important filters to biological invasions include biotic regulators, such as predators,

strong competitive interactions or a lack of mutualistic interactions (Richardson et al. 2006). If

all of these barriers are passed, the introduced species considered invasive and may become

dominant in terms of overall abundance and distribution, meaning it can exert a strong influence

on the new community (Levine et al. 2003). Invasive species that become dominant can

potentially result in degradation of community structure and biotic interactions between native

species (Traveset and Richardson 2006). Through dominance and disrupting species interactions,

native species may be excluded, which can result in the reduction or loss a particular ecosystem

functions, including productivity (Cadotte 2013) and ecosystem stability (Tilman et al. 2006).

Many mechanisms have been hypothesized to contribute to the invasiveness of a

particular species or the invasibility of a particular region (see Chapter 1). Studies examining the

causes and consequences of invasions have used ecological niche modelling (ENM) using

occurrence data for invaders to map their niche (Peterson 2003). Other types of models, such as

those used to predict community assembly, focus more on species traits (Laughlin et al. 2012).

These models use the dimensionality of a species form and function to determine the trait space

that the species fills, thus, predicting its abundance and distribution within a community

(Laughlin 2014). In this trait-based approach species are characterized by biological attributes,

such as physiological, morphological, or life history traits (Webb et al. 2010).

A trait-based analysis taking into account the intraspecific variation of the invader would

provide an insightful outlook on the invasion success of an invader. This type of approach would

provide information about the specific response that species have to changes in resource

availability. Understanding more about how an invasive species responds to the environment

14

should give an indication about which areas are currently at risk, in terms of species losses, due

to the increasing abundance of the already established invader, and also which areas are

potentially at risk should the invader spread into that region.

Here, I examine several morphological traits of the invasive species Dog-Strangling

Vine, Vincetoxicum rossicum to determine the degree of intraspecific variation this species

expresses. Dog strangling vine (DSV) has severe impacts on many ecosystems (see Chapter 1),

due to its high abundance and widespread geographical distribution (DiTommaso et al 2005).

The goal of this study will be to determine how this species responds to two environmental

gradients, light and nutrient availability. This study was conducted at the Rouge Urban National

Park, which is Canada’s first urban national park and is unique in that it is protecting and

conserving wildlife near Canada’s largest city. DSV is found in high abundance throughout the

Rouge Park and, thus, poses as a large threat to native vegetation, such as common milkweed

which is the preferred host for monarch butterflies, a threatened species in North America

(DiTommaso and Losey 2003).

In this study, I will address two specific hypotheses regarding the variation in

morphology of DSV. First, the suite traits associated with light capture and efficiency will show

variability across the gradient of light availability. Specifically, I expect that DSV in the low

light will have higher specific leaf area than leaves in high light conditions to optimize light

capture. Second, DSV will show variability in traits associated with efficient nutrient uptake and

growth across a gradient of soil nutrient availability. Specifically, I expect that individuals of

DSV will have higher biomass and greater seed output in areas with larger concentrations of soil

nutrients.

15

2.2 Methods and Materials

2.2.1 Study Site

The study was conducted at the Rouge Urban National Park, located in Scarborough,

Ontario. The study started in spring 2014, 12 30x30m field sites were chosen based on the

presence of DSV. These sites varied in forest canopy coverage; refer to Appendix for a map of

the study area. Within each field site, five transects were laid down, and along each transect 5

plots were flagged, resulting in 25 plots per site and 300 plots in total. Some sites were

categorized as “Transitional sites” and followed a gradient of canopy coverage ranging from 0-

20% to 70-90% along the 5 transects. Other sites were categorized as “Understory” sites and had

an average canopy coverage of 75%.

2.2.2 Sampling

In the summer of 2014, from mid-June until mid-October, a suite of morphological plant

traits, including aboveground biomass, leaf, and seed traits were collected from each site (Refer

to Appendix, Table A1). A subsample of the aboveground biomass was collected for all plant

species present in each of the 300 plots. Prior to collecting the biomass, the number of species

and the percent cover for each species was estimated using intervals of 5% coverage for each of

the 300 plots. The samples of biomass were dried in a standing oven for a minimum of 2 days at

60°C before being weighed. Larger samples were measured in grams on standard scales with

accuracy up to two decimal points, while samples smaller than 2g were measured on precision

16

scale (Sartorius Practum Analytical Weighing balance) with accuracy up to four decimal places.

The total biomass for each plot was determined by summing the biomass of all species.

Five leaf traits were assessed in this study, including: specific leaf area (SLA), which is

an positive correlate of relative growth rate (Cornelissen et al 2003); leaf dry matter content

(LDMC), which is a negative correlate of relative growth rate (Cornelissen et al 2003); leaf

nitrogen content (LNC), which correlates with mass-based maximum photosynthetic rate

(Cornelissen et al 2003); leaf carbon content (LCC), which gives an indication of structural

strength of the leaf (Cornelissen et al 2003); and, the carbon-nitrogen ratio, which is another

indicator of growth and quality (Royer et al 2013). Within the plots where DSV was present (284

of the 300), 10 leaf samples of DSV were collected. Samples were collected per plot by choosing

a mature plant at random. The fresh weight of each leaf was measured with a precision scale and

an image of each leaf was made using a scanner and later processed using ImageJ in order to

determine the leaf area. The leaves were than dried for a minimum of 48 hours, in a standing

oven at 60°C and then reweighed in order to determine the dry weight. Leaf area and dry weight

were used to determine SLA, and dry and fresh weight was used to determine LDMC for each

leaf.

LNC and LCC was determined using the Thremo-Fischer EA 2000 elemental analyzer in

the in TRACES (Teaching and Research in Analytical Chemical and Environmental Science) lab

at UTSC. When determining LNC and LCC, a subset of 120 of the 300 plots was assessed. At

each site, leaf samples from two transects consisting of five plots each were used to determine

LNC and LCC. Full transects, where all five plots along the transect had DSV present, were

randomly chosen for this subsample. To run samples through the elemental analyzer several

17

leaves collected from an individual plot were ground together, thus producing a plot-level

measurement of LNC and LCC. The carbon nitrogen ratio (C:N) for each plot was determined by

dividing LCC by LNC.

Three seed and seed pod traits were assessed. These included individual seed weight,

seed pod weight, and seed pod length. Seed weight gives an indication of germination success as

larger seeds are likely to be polyembryonic which has been shown to increase the likelihood of

germination (Ditomasso et al 2005). Seed pod weight and length also give rough estimates of

germination success as larger pods were predicted to contain larger seeds. These two traits were

used as seed weight was highly skewed towards small seeds (<0.0001g). Seed pods were

collected when the majority of the DSV in the open areas with a greater amount of light had

developed fully mature seed pods. At this time, the seed pods for both transitional and understory

sites were collected from all plots where they were present. The weight and width of the most

mature and healthiest seed pods, which were the largest pods present in the plot that had no

visible damage and hadn’t already burst open, were measured (N=5) and, after removing the

coma of long hairs, the seeds from each pod were counted and weighed.

Information on the two environmental gradients was collected at the same time as the

plant trait values. For the light gradient, photosynthetically active radiation (PAR) measurements

were taken for each plot using a LI-COR LI-191R line quantum sensor. Additionally, a canopy

photo per plot was taken using a Hero 3 GoPro fitted with a fisheye lens. These images were

then analyzed using ImageJ to determine the percentage of tree canopy cover. Lastly, soil

measurements were obtained by taking soil samples collected from each plot using a soil auger,

where two cores were taken from each plot. Soil moisture was calculated by measuring 10-15g

18

of wet soil using a precision scale, drying it at 100°C in a standing oven for 48, reweighing to

obtain the dry weight, and then moisture was determined by subtracting the dry weight from the

fresh weight. Soil pH was measured using a pH meter (Mettler Toledo pH meter). Soil nutrient

availability was determined by using a Quikchem 8500 series 2 flow injection analyzer (FIA).

Using the FIA, the concentration of soil phosphorus in the form of orthophosphate, the simplest

form of phosphorus, was determined for each plot. Soil nitrogen concentration, which is the sum

of nitrate and ammonia, was also determined for each plot using the FIA.


Linear models were applied to each of the plant traits to get a general idea of the direction

of the trait response to the two environmental gradients Linear mixed effect regression models

(LMER) were also applied to the plant traits using the R package Lme4 (Bates et al. 2014).

LMER models were applied to the analysis because both the site where the plots were located

and the placement along the five different transects represent independent samples. Therefore,

site and transect were designated as random effects to resolve the non-independence of sampling

(Winter 2013). The models were considered significant if the p-value was <0.05 in comparison

to a null model with no fixed effects. Four different types of univariate models were used, the

model with the lowest Akaike Information criterion (AIC) value was considered the best. Both

the marginal R2, which describes the proportion of variance explained by the fixed effects, and

the conditional R2, which describes the proportion of variance explained by both the fixed and

random effects were determined using the r.squared function developed by Johnson (2014). This

function also provides AIC values for the models. All univariate models were allowed to have

19

random intercepts for the two random effects; however, the random effect for transect was

removed if the intercepts standard deviation was zero. The four model types include: 1) the fixed

effect with the random effects; 2) same as before but both random effects were also allowed to

vary in their slopes; 3) the slopes could vary for site but not for transect; and 4) the slopes could

vary for transect but not for site.

Multivariate linear mixed effect models incorporating multiple environmental variables

were also used to determine if there were interacting effects between the environmental

variables. Multivariate models were chosen using the step function in the R package lmerTest.

This function performs a backward elimination of non-significant effects. The significance of

these models was determined by comparing the model against a null model that did not have any

fixed effects. The individual values of nitrate and ammonia concentration were removed from the

multivariate models do to scaling issues.

For all models, the response variables were log transformed unless stated otherwise. Plant

traits were log transformed r to resolve issues with skewness in the data and heteroscedasticity.

Generally, most of the data was skewed left since most of the data collection occurred in the

understory with few data points coming from open field habitats (refer to Appendix Figure A3).

Homoscedasticity was assessed examining graphs with residuals plotted against the fitted or

predicted values (refer to Appendix). If the distribution of the residuals was not normalized

following by log transforming the data, I applied a quadratic formula to the model. All statistical

analyses were conducted using the R statistical program version 3.1.3 (R Core Team 2014).

20

2.3 Results

2.3.1 Light availability

Before examining the relationship between the morphological traits of DSV and light

availability, I examined the effect of light on a community measure of diversity, species richness.

It was found that there was a positive linear relationship between the number of species in a plot

and photosynthetically active radiation (PAR) (Fig 1i: R2 = 0.0272, F269 = 8.55, p = 0.00375) and

a negative non-linear relationship with canopy cover (Fig 1ii: R2 = 0.0887, F268 = 14.13, p =

1.46e-06). Consistent with this result, there was a positive non-linear relationship between

diversity and total plot biomass plant biomass (Fig. 2i: R2 = 0.408, F204 = 73.1, p = <2.2e-16).

There was a very distinctive relationship between total plot biomass and light availability (Fig 4).

With PAR, there was a strong positive trend (Fig. 4i: R2 = 0.01, F205 = 23.8, p = 2.15e-06), which

corresponds with the negative trend for canopy cover (Fig. 5i: R2 = 0.124, F205 = 30.1, p = 1.2e-

07). With canopy cover, there was a non-linear response, indicating that once a certain threshold

of cover is achieved, there is a sharp decline in the amount of biomass present in the area (Fig.

5i).

When examining DSV biomass individually, I found that the best fitting model was a

quadratic model, which predicted that there was a negative relationship with diversity, where an

increase in plot diversity resulted in a decrease in DSV biomass (Fig. 2ii: R2 = 0.0766, F258 =

10.7, p = 3.45e-05). DSV biomass followed similar trends as total plot biomass, with a positive

linear relationship for PAR (Fig. 3i: R2 = 0.0478, F259 = 14.0, p = 0.00022) and a negative non-

linear relationship with canopy cover (Fig. 5ii: R2 = 0.169, F259 = 27.41, p = 1.61e-11).

21

Leaf dry matter content (LDMC) and leaf carbon content (LCC) both had positive linear

relationships (Fig. 3ii and v: R2 = 0.136, F2834 = 449, p = <2.2e-16, and R

2 = 0.0688, F118 = 8.78,

p = 0.0038, respectively) with PAR , while specific leaf area (SLA) and leaf nitrogen content

(LNC) had negative linear relationships (Fig. 3iii and iv: R2 = 0.285, F2834 = 1132, p = <2.2e-16,

and R2 = 0.127, F118 = 18.3, p =3.86e-05, respectively). Next, I examined how the traits

responded to canopy cover. As expected, the opposite relationships were found with PAR;

however, for LDMC and SLA, there were not linear relationships (Fig. 5iii and iv). LNC was

positively related to canopy cover (Fig. 5v: R2 = 0.344, F118 = 63.4, p = 1.19e-12), however,

variation in LCC was not significant.

There were no significant relationships between individual seed weight and any of the

environmental variables with the simple linear models, however, the weight of the seed pods

significantly increased with increasing PAR (Fig. 4ii: R2 = 0.016, F995 = 17.2, p = 3.67e-05) with

a corresponding decrease with increasing canopy cover (Fig. 5vi: R2 = 0.0109, F995 = 11.9, p

=0.000577). Seed pod weight was the only plant trait that was not log transformed to fit the data

into a linear model since the best performing model based on AIC was non-transformed. The

same pattern for PAR and canopy cover was seen with seed pod length (Fig. 3vi and 5vii: R2

=0.0167, F995 = 17.9, p = 2.52e-05, and R2 = 8.75e-03, F995 = 9.8, p = 1.8e-03).

The univariate LMER models for which leaf traits was the response variable showed that

either canopy cover or PAR were the best predictors based on the AIC values (Table 3). LNC,

leaf carbon nitrogen ratio (C:N), and LDMC were the response variables that were best

correlated with models using canopy cover as the fixed effect and site and transect as random

effects. LCC was best predicted using the model that had PAR as the fixed effect. Overall, LNC,

22

LCC, SLA were positively associated with the fixed effects, while C:N and LDMC were

negatively associated with the fixed effects (Table 3). The best LMER models for both seed pod

weight and pod length included PAR as a fixed effect (AIC = -3479.8, R2 = 0.432, χ

2 (3) = 39.3,

p =1.68e-07, and AIC = -903.1, R2 =0.477, χ

2 (3) = 30.0, p =1.41e-06).

2.3.2 Soil nutrient availability

Examining the two the soil properties, phosphate and nitrogen concentration, I found that

there was a negative relationship for species richness and soil phosphate (Fig. 1iii: R2 = 0.0424,

F269 = 13.0, p = 0.0038). However, there were no trends for species richness and soil nitrogen.

For the biomass measurement, there was no relationship for total biomass and phosphate

concentration but, there was a negative relationship for nitrogen concentration (Fig. 7i: R2 =

0.0272, F205 = 6.77, p = 0.01). For soil phosphate, DSV had a positive relationship (Fig. 6i: R2 =

0.0486, F259 = 14.3, p = 0.0002). However, DSV biomass differed from total plot biomass with

soil nitrogen concentration since it was not significant. To investigate this further, soil nitrogen

was separated into its component parts, nitrate and ammonia, and it was found that both total

biomass and DSV biomass was negatively correlated with soil nitrate concentration (Fig. 9i and

ii: R2 = 0.086, F254 = 25, p = 1.08e-06, R

2 = 0.0312, F259 = 9.96, p = 0.0018). With soil ammonia

concentration there was a positive non-linear relationship for total biomass (Fig. 11i: R2 = 0.032,

F204 = 4.41, p = 0.0134)

The best LMER model based on AIC values for total plot biomass was the one that used

soil nitrate concentration was the fixed effect and site as the random effect (Table 3: AIC =

745.0). Overall, this model indicates that increases in soil nitrate correspond to a decrease in

23

total plot biomass (R2 = 0.373, χ

2 (1) = 56.2, p =6.52e-14). DSV biomass shows similar results

(Table 3), where there is a negative relationship soil nitrate concentration (AIC = 950.5, R2 =

0.212, χ2 (1) = 63.6, p =1.5e-15).

For the leaf traits, LDMC and SLA had significant non-linear relationships with soil

phosphate concentration (Fig. 6ii and iii). Out of the 5 leaf traits, only SLA was positively

correlated with soil nitrogen concentration (Fig. 8i: R2 = 0.00876, F2834 = 26.0, p = 3.56e-07),

however, after I separated soil nitrogen into its component parts, I found significant trends (Fig.

9, 10, and 11). LDMC has a negative linear relationship with soil nitrate concentration (Fig. 9iii:

R2 = 0.0141, F2694 = 39.6, p = 3.71e-10) and a non-linear relationship with soil ammonia

concentration (Fig. 11ii: R2 = 0.00189, F2833 = 3.69, p = 00.0252). SLA, LCC, and LNC all have

positive relationships with nitrate concentration (R2 = 0.00829, F2694 = 244, p = <2.2e-16; R

2 =

0.0426, F118 = 5.24, p = 0.0238; and R2 =0.0838, F2834 = 10.3, p = 0.00171, for SLA, LCC and

LNC, respectively). SLA also had a positive non-linear relationship with soil ammonia

concentration (Fig. 11iii: R2 = 0.00355, F2833 = 6.05, p = 0.0024).

For the seed traits, seed weight decreased with soil phosphate concentration (Fig. 6iv:

R2 =0.0225, F995 = 23.9, p = 1.17e-06) and increased with soil nitrogen concentration (Fig. 8ii:

R2 = 0.0275, F995 = 3.75, p = 5.33e-02). Seed pod length followed the same pattern for nitrogen

concentration (Fig. 7ii: R2 = 0.007, F995 = 8.02, p = 4.72e-03); however, for soil phosphate, the

data followed a non-linear pattern (Fig. 6v: R2 = 0.0363, F994 = 19.8, p = 3.85e-09). Seed pod

weight was positively related to soil nitrate concentration (Fig. 10ii: R2 = 0.00792, F995 = 9.85, p

= 2.84e-03), and seed pod length showed a positive non-linear relationship for nitrate (Fig. 10iii:

R2 = 0.00461, F994 = 7.49, p = 5.91e-04).

24

Seed weight, which was not log transformed, was significantly correlated with the

various environmental variables when using a univariate LMER model (Table 3), with the fixed

effect soil nitrate concentration being included in the best model (AIC = -135032, R2 = 0.235, χ

2

(5) = 35.4, p =1.23e-06).

2.3.3 Multivariate linear mixed effect models

The best multivariate LMER models for total plot biomass incorporated all four

environmental variables and their interactions and, for DSV biomass included PAR, canopy

cover, and phosphate (Table 4). However, after performing an ANOVA between the multivariate

model and the best performing univariate model, I found no significant difference between the

models for either total plot biomass or DSV biomass.

For both LDMC and SLA, the best fitting multivariate models included all four

environmental variables and the interactions between them (Table 4). For the other leaf traits, I

found that the multivariate model for LCC did not include soil phosphate as a fixed effect (Table

4) and the best model for LNC was the univariate model (Table 3). By examining leaf C:N, I

found that, instead of soil nitrogen, this model used phosphate as a fixed effect (Table 4).

The multivariate models for seed pod weight and length performed better than the

univariate models. However, after comparing the multivariate model to the univariate one using

an ANOVA I did not find a significant difference between the models for seed weight, thus,

overall the univariate model using only soil nitrate was chosen as the best model.

25

2.4 Discussion

Overall, the results of this study demonstrate that this invasive vine exhibits high

intraspecific variation for a suite of traits in response to different environmental conditions. My

first hypothesis, that the morphological traits of DSV exhibit positive growth in response to

greater light availability, is supported. The trait values for DSV change in predictable ways

across the light gradient, demonstrating that DSV responds to light-stress in such a way that it

increases its biomass production to optimize light capture (Valladares and Niinemetes 2008).

Additionally, my second hypothesis, that the morphological traits of DSV exhibit positive

growth in response to greater soil nutrient availability, is supported. Between the two soil

nutrients, it seems that nitrogen, particularly nitrate, appears to play a stronger role in the

distribution of trait values.

2.4.1 Light availability

Biomass production in these forest habitats exhibited strong trends across the light

gradient, where it was positively correlated with photosynthetically active radiation (PAR) (Fig.

4) and negatively correlated with canopy coverage (Fig. 5). The positive relationship with PAR

indicated that, with more light, the plots produced more biomass, which is expected since light

strongly linked to the positive growth of plant species (Kania and Giacomelli 2001). Therefore,

plots with greater light availability had more species that were more densely packed with one

another. As predicted the opposite trend for canopy cover was seen, however, this trend was non-

linear, indicating that there was a sharp decline in biomass production after a certain threshold of

coverage was surpassed. This threshold had been observed in transitional sites. As the distance

26

increased from the forest edge, less plant species were present. In sites that were categorized as

fully understory, much of the canopy cover was greater than 60%; however, there were

differences in the types of trees providing the cover, i.e. mixed deciduous vs pine stands, thus

accounting for the variation shown in the model (Fig. 4).

DSV biomass displayed similar trends as total plot biomass, where it increased with

increasing PAR (Fig. 3) and decreased with increasing canopy cover (Fig. 5). These trends were

expected because, while DSV in lower light conditions grows taller than in full sun conditions, it

decreases in density as canopy coverage increases (DiTommaso et al. 2005) resulting in lower

overall biomass production. Examining SLA and LDMC across the two environmental gradients,

I consistently found that these two traits displayed opposite trends. This pattern may be a result

of the trade-off described by Porter and de Jong (1999), between rapid biomass production (high

SLA and low LDMC) and the efficient conservation of nutrients (low SLA and high LDMC).

Within the understory where PAR is low and canopy cover is high, it appears that DSV exhibits

rapid biomass production as SLA is high and LDMC is low (Fig. 3 and 5). I had predicted this

type of response because DSV expresses many characteristics of a shade-tolerant species, such as

greater stem height and broader leaves to capture limited light (Milbrath 2008). The other leaf

traits did not show as many significant relationships as SLA and LDMC; however, LNC was

negatively correlated with PAR (Fig. 3) and negatively correlated with canopy cover (Fig. 5).

LCC on the other hand, showed the opposite trend for PAR (Fig. 3). The lack of significant

trends in these traits may be due to a sampling issue as only a small subset of plots were examine

for LNC, LCC and C:N.

27

The seed traits of DSV did not respond characteristically to changes in light availability

as seen in other studies (Cappuccino et al. 2002; and DiTomasso et al. 2005). DSV seeds are

polyembryonic, directly affecting its germination and dispersability (Cuppuccino et al. 2002),

which have direct consequences on its spread across a landscape. Previous studies involving light

and seed characteristics demonstrated that DSV seeds in shaded areas weighed more than those

in open area (Ditommaso et al. 2005).The results of this study do not appear to support this

observation as seeds from my study get heavier with greater light availability (Table 3).

However, the trends appear weak enough that they are almost negligible (Table 3). More

apparent trends are exhibit for the seed pod traits, seed pod weight and length, that both show

increases with greater PAR and decreases with canopy cover.

2.4.2 Soil nutrient availability

The response of DSV to a soil nutrient gradient was then examined. This gradient was

particularly focused on phosphorus and nitrogen, because in most terrestrial ecosystems they are

the two main limiting nutrient resources (Vitousek et al. 2010). Phosphorus, measured in the

form of phosphate due to its high reactivity to oxygen, is a vital component for plant metabolism

as it aids with energy transfer within the plant cells (Ticconi and Abel 2004). Overall, this

nutrient influences early plant growth and maturity as it regulates protein synthesis (Rodriguez

and Fraga 1999). Within my study plots, I found that plots with greater soil phosphate tended to

have lower species richness and that there no significant response of plot biomass production to

soil phosphate concentration. The negative relationship for richness may indicate that a dominant

species may be utilizing phosphate more than subdominants, which has been demonstrated in

28

some phosphorus enrichment studies (Elser et al. 2007). However, the lack of a trend for

biomass production makes it difficult to make this conclusion. A possible explanation for this

lack of response is that the nutrient gradient was not large enough; therefore, directly

manipulating this gradient may provide clearer results.

With soil nitrogen, I found a negative trend, where increases in nitrogen resulted in

lower total biomass (Fig. 7). Nitrogen, measured as the sum of both ammonia and nitrate, is also

a vital component for plant growth as it is a major component in the formation of amino acids

and energy transfer compounds such as ATP (Crawford 1995). Therefore, this negative trend

was not expected but may be explained by a trade-off between nitrogen concentration and

optimal ecosystem functioning described by Bai and colleagues (2010). In their study Bai et al

showed that two communities, a mature and degraded community, respond differently to

nitrogen addition. The mature community saw a large reduction in species richness, while the

degraded community there was only a slight reduction in species richness. In my study plots I

did not find a significant relationship for soil nitrogen, in any form, and species richness,

however the negative trend for total biomass is a good indication that plants producing less and

likely functioning to a lesser degree.

DSV biomass followed the same positive trend for soil phosphate concentration (Fig. 6);

however, it was not found to be significantly correlated with soil nitrogen. Once soil nitrogen

was separated into its component parts, DSV biomass was found to be negatively associated with

nitrate concentration, but not with ammonia concentration. Nitrate is generally the preferred form

of nitrogen that plants uptake from the soil (Crawford 1995)).and, therefore, may account for this

difference. The preference for nitrate over ammonia by plants is due to the fact that nitrate is less

29

volatile and more mobile within the soil than the ammonium form and, thus, is easier for plants

to uptake (Crawford 1995).

With the leaf traits, it was found that for phosphate SLA had a negative relationship while

LDMC was positive (Fig. 4). For nitrogen, only SLA was significantly related (Fig. 5); however,

when separated into nitrate and ammonia, I found significant trends for both traits, along with

positive trends for LNC and LCC (Fig 6 and 7). Once again nitrate concentration showed

stronger trends than ammonia. For the seed traits, seed pod weight increased predictably with

soil nitrogen but decreased with soil phosphate concentration. This is most likely a result of the

differences in provisioning resources, as phosphorus promotes root growth so more of the plant

resources may have been sent away from the seeds. Seed pod length increased with both nitrogen

and phosphate, although for phosphate it was a non-linear curve which saturated at higher

concentrations.


The results of this study indicate that there is a positive correlation between the number

of species present in the plot and the total amount of biomass produced by that plot. This

diversity-productivity relationship is an important and well-studied topic within community

ecology (eg. Tilman et al 2001; Cardinale et al 2007; Cadotte 2013). Community productivity

has been used by a large amount of studies as a basic measure of ecosystem function (Tilman

1997). Similar to other well-studied systems, such as the grassland ecosystems, the positive

relationship seen in this study provides support to the idea that diversity is an important

component for community productivity. One caveat regarding this result is that the greatest

amount of diversity that I observed was in sites that transitioned from forest understory into open

30

fields. This means that my observations were more skewed towards plots that had less species

since many of my sites were primarily understory and not transitional. Thus, despite being

significant, the variance explained by the linear model is quite low (R2 = 0.0247). This finding is

important to consider as ecosystem functioning in understory communities may differ quite

significantly from other ecosystems (Sala et al. 2000). Community assemblage, both in terms of

the number and composition of plant species, within understory communities will mainly be

dependent on whether or not the species are shade-tolerant (Antos 2004) or if that species is

dispersal limited.

Specifically related to DSV biomass, there was a negative trend for species richness,

where an increase in the number of species resulted in a decrease of DSV biomass (Fig 1). This

relationship was non-linear, where DSV biomass declined more sharply than expected from a

linear model when there were more species present. Once again this is an important finding

regarding to a community context as it may provide some indication of the diversity-resistance

hypothesis (Levine and D’Antonio 1999) that supports that idea that more diverse communities

are more competitive in terms of occupied niche space and, therefore, are more likely to resist

invasion

A major limitation of this study is that many of the observational plot were located in the

understory and not in transitional or open areas, thus, caution should be taken when interpreting

these results as the data is heavily skewed towards the understory plots. This results in a weaker

light gradient and lower concentrations of soil nutrients.

Overall, more research that explicitly manipulates these two gradients will be necessary

to better understand more about the underlying mechanisms directing the trait responses. The

31

effect of light on invasiveness has not been as thoroughly studied as there had been a prevalent

view that forests were more difficult to invade, particularly if they were old-growth forests with

less disturbance (Belote et al. 2008). A large focus on the invasiveness of different species has

been centered on early successional species, whose traits are most related to performance, i.e.

rapid growth, early reproduction, and short life span (Baker 1965). These ‘invasive’ traits are

generally not conducive for shade tolerance and, thus, invader response to light stress has not

been examined thoroughly (Martin et al 2008). Overall, most invasive species studied in forest

ecosystems have been invasive tree species such as Rhamnus cathartica or common buckthorn

(Mascaro and Schnitzer 2007). Therefore, more work should be done in assessing which

herbaceous plants are invasive within these low-light systems and what are their effects on the

functioning of these forest ecosystems. In the long term, tree invaders will no doubt play a strong

role but, in the short term, herbaceous species could have a high impact. Species like garlic

mustard (Alliaria petiolate) have been extensively studied and have been shown to have the

ability to invade higher quality forests that have little disturbance (Nuzzo 1999). Additionally,

this species can cause high impacts within forests but inhibiting seedling growth by releasing

allelopathic chemicals into the soil (Wolfe et al. 2008); thus, it is important to begin

documenting and learning more about other potential invasive species to protect forest

ecosystems.

Understanding more about nutrient availability is also vital as it has been shown to

structure a community by influencing both species abundance and richness (Willem et al. 2009)

and can have varying impacts on ecosystem functioning (Graham and Mendelsshon 2015).

Within the context of invasion biology, nutrient availability can contribute significantly to the

success of the invader since it has been shown that nutrient additions may facilitate the growth

32

and spread of non-native species but hinder the growth of native species (Seabloom et al. 2015).

Invasive species seem more likely to take advantage of fluctuations in resources brought about

by disturbance events than native species (Davis et al. 2000), giving them a competitive edge.

Moreover, some studies have demonstrated that invasive species are directly influencing

resource levels, thereby, negatively affecting the native species with which they are competing

(Ehrenfeld 2003).

In either of these cases, light availability or nutrient availability, it is clear that invasive

species are using particular strategies to establish and persist. Learning more about the

underlying mechanisms driving these strategies will be vital as it will help conservationist to

better manage these invaders and protect these communities from future invasions. Therefore,

the study I have conducted with DSV is a step forward in understanding the different strategies

invasive plants may employ in spreading to areas that were initially considered to have lower

invasibility.

33

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38

Tables

Table 2.1: Linear models

Response Variable Predictor variable Estimate Std Error Multiple R^2 Adjusted R^2 F-stat df p-value

Total Plot biomass (log) Richness 1.68386 0.25642 0.4142 0.4084 72.11 204 < 2.2e-16

Richness (^2) -0.10084 0.02677

Total Plot biomass (log) PAR (log) 0.0041284 0.0008464 0.104 0.09962 23.79 205 2.151e-06

Total Plot biomass (log) Canopy Cover 3.50042 0.65648

0.1281 0.1238 30.11 205 1.197e-07 Canopy Cover (^2) -0.05048 0.00920

Total Plot biomass (log) Nitrate (log) -0.39172 0.15296 0.03286 0.02785 6.558 193 0.0112

Total Plot biomass (log) Ammonia -0.390072 0.163790

Ammonia (^2) 0.013795 0.007244 0.0414 0.032 4.405 204 0.0134

Total Plot biomass (log) Nitrogen (log) -0.8329 0.3202 0.03196 0.02724 6.768 205 0.009958

DSV Biomass (log) Richness -0.62049 0.18481

0.07656 0.0694 10.69 258 3.45E-05 Richness (^2) 0.04815 0.02163

DSV Biomass (log) PAR (log) 0.3945 0.1053 0.05142 0.04776 14.04 259 0.0002206

DSV Biomass (log) Canopy Cover 0.1186935 0.0355156

0.1752 0.1688 27.41 258 1.61E-11 Canopy Cover (^2) -0.0015333 0.0003324

DSV Biomass (log) Phosphate 0.14291 0.03784 0.05221 0.04855 14.27 259 0.0001969

DSV Biomass (log) Nitrate (log) -0.3428 0.1086 0.03907 0.03515 9.962 245 0.001798

Leaf dry matter content (log) PAR (log) 0.048773 0.002303 0.1366 0.1363 448.5 2834 <2.2e-16

Specific leaf area (log) PAR (log) -0.154842 0.004602 0.2454 0.2852 1132 2834 <2.2e-16

Leaf Nitrogen Content (log) PAR (log) -0.07812 0.01826 0.1343 0.1269 18.3 118 3.86E-05

Leaf Carbon Content (log) PAR (log) 0.005281 0.001789 0.0688 0.06091 8.718 118 0.003802

Leaf dry matter content (log) Canopy Cover 3.48E-03 7.94E-04

0.1997 0.1991 353.4 2833 <2.2e-16 Canopy Cover (^2) -7.13E-05 7.35E-03

Specific leaf area (log) Canopy Cover 5.79E-03 1.49E-03

0.413 0.4126 996.6 2833 <2.2e-16 Canopy Cover (^2) 7.44E-05 1.38E-05

Leaf Nitrogen Content (log) Canopy Cover 9.93E-03 1.25E-03 0.3494 0.3438 63.36 118 1.19E-12

39

Table 2.1: cont.

Response Variable Predictor variable Estimate Std Error Multiple R^2 Adjusted R^2 F-stat df p-value

Leaf dry matter content (log) Phosphate 9.51E-03 6.40E-03 0.005703 0.005703 9.13 2833 0.0001115

Phosphate (^2) -8.96E-04 2.10E-04

Specific leaf area (log) Phosphate -5.52E-02 5.32E-03 0.03687 0.03619 54.22 2833 <2.2e-16

Phosphate (^2) 4.23E-03 4.55E-04

Specific leaf area (log) Nitrogen 5.15E-03 1.01E-03 0.009106 0.008756 26.04 2834 3.56E-07

Leaf dry matter content (log) Nitrate (log) -1.52E-02 2.42E-03 0.01447 0.01411 39.56 2694 3.71E-10

Specific leaf area (log) Nitrate (log) 7.91E-02 5.08E-03 0.08292 0.08258 243.6 2694 <2.2e-16

Leaf Carbon Content (log) Nitrate 1.53E-03 6.70E-04 0.04255 0.03443 5.244 118 0.02381

Leaf Nitrogen Content (log) Nitrate (log) 6.26E-02 1.95E-02 0.08376 0.07565 10.33 113 0.001706

Leaf dry matter content (log) Ammonia 5.67E-03 2.50E-03 2.60E-03 0.001891 3.685 2833 0.02522

Ammonia (^2) -2.84E-04 1.10E-04

Specific leaf area (log) Ammonia -9.98E-03 5.48E-03 0.004251 0.003548 6.047 2833 0.002395

Ammonia (^2) 6.10E-04 2.42E-04

Pod weight PAR 3.76E+00 9.06E-06 0.01698 0.01599 17.19 995 3.67E-05

Pod weight Canopy Cover -3.51E-04 1.02E-04 0.01184 0.01085 11.93 995 0.0005771

Pod length (log) PAR (log) 1.77E-02 1.67E-02 0.01769 0.0167 17.92 995 2.52E-05

Pod length (log) Canopy Cover -1.33E-03 4.26E-04 9.75E-03 8.75E-03 9.796 995 1.80E-03

Pod weight phosphate -2.60E-03 5.31E-04 0.02348 0.0225 23.93 995 1.17E-06

Pod weight Nitrogen 5.18E-04 2.68E-04 0.00375 0.002749 3.745 995 5.33E-02

Pod length (log) Phosphate 8.78E-03 5.93E-03 0.03824 0.0363 19.76 994 3.85E-09

Phosphate (^2) -1.83E-03 5.03E-04

Pod length (log) Nitrogen (log) 3.65E-02 1.29E-02 0.007998 0.007001 8.022 995 4.72E-03

Pod weight Nitrate 1.63E-03 5.44E-04 0.008919 0.007923 8.954 995 2.84E-03

Pod length (log) Nitrate 2.37E-02 6.38E-03 0.01485 0.01287 7.49 994 5.91E-04

Nitrate (^2) -1.59E-03 5.14E-04

Pod length (log) Ammonia (log) 3.37E-02 1.42E-02 0.005607 0.004608 5.611 995 1.80E-02

40

Table 2.2: Univariate linear mixed effect models

Random intercept

Random slope

Random effects (Std.Dev) Fixed effects

Marginal R^2

Conditional R^2

Chi square Chi df

Response Variable

Site Transect Residual Predictor variable Estimate Pr(>Chiq) AIC

Plot biomass (log) site 0.3141 0.9138 PAR (log) 0.2785 5.41E-02 4.30E-01 38.378 2 4.64E-09 766.8466

Plot biomass (log)

site 0.175

0.9582 Phosphate 0.03051 4.08E-03 4.91E-01 7.1733 3 0.06657 798.0633

Plot biomass (log) site and transect 0.55754 0.07181 0.92105 Canopy cover -0.03627 1.76E-01 4.00E-01 44.264 1 2.87E-11 758.9611

Plot biomass (log) site 0.7397 0.9676 Nitrate (log) -0.1028 7.15E-03 3.73E-01 56.208 1 6.52E-14 745.0286

Plot biomass (log) site 0.435 0.9754 Ammonia (log) -0.1847 4.20E-03 3.82E-01 9.5525 3 2.28E-02 795.6842

DSV biomass (log) site 0.743 1.583 PAR (log) 0.304 3.03E-02 2.05E-01 7.5356 1 6.05E-03 1006.609

DSV biomass (log) site

0.6227

1.5783 Canopy cover -0.03122 6.13E-02 1.88E-01 12.34 1 4.43E-04 1001.805

DSV biomass (log) site 0.7832 1.5705 Nitrate (log) -0.219 1.57E-02 2.12E-01 63.637 1 1.50E-15 950.508

DSV biomass (log) site 0.8151 1.6017 Ammonia (log) 0.0006676 2.15E-06 2.06E-01 2.00E-04 0 <2.2e-16 1014.145

Leaf Nitrogen content (log)

site 0.1719

0.1557 PAR (log) -0.0505 5.74E-02 4.48E-01 13.104 3 4.42E-03 -67.69441

Leaf Nitrogen content (log)

site 0.26033

0.14349 Phosphate -0.01339 1.79E-02 6.89E-01 15.889 3 1.20E-03 -70.4743

Leaf Nitrogen content (log) site and transect 0.10357 0.02264 0.1382 Canopy cover 0.009508 3.22E-01 5.73E-01 45.703 1 1.38E-11 -102.2927 Leaf Nitrogen content (log) site 0.13647 0.15615 Nitrate (log) 0.0746 1.21E-01 4.93E-01 10.205 1 1.40E-03 -68.7945

Leaf Carbon content (log) site and transect 0.049464 0.021492 0.015552 PAR (log) 0.008254 1.24E-01 5.53E-01 16.768 5 4.96E-03 -611.7087

Leaf Carbon content (log) site and transect 0.0077 0.006887 0.017153 Nitrate 0.001673 4.81E-02 3.01E-01 4.9473 1 2.61E-02 -607.8877

Carbon Nitrogen ratio (log)

site 0.16554

0.1481 PAR (log) 0.05537 7.29E-02 4.72E-01 15.67 3 1.33E-03 -78.05168

Carbon Nitrogen ratio (log)

site 0.26265

0.13565 Phosphate 0.01631 2.62E-02 7.26E-01 17.907 3 0.0004598 -81.17068

Carbon Nitrogen ratio (log) site and transect 0.09979 0.02102 0.13064 Canopy cover -0.009511 3.44E-01 5.92E-01 50.18 1 1.40E-12 -115.4444

Carbon Nitrogen ratio (log) site 0.1315 0.1511 Nitrate (log) -0.07476 1.15E-01 5.08E-01 9.2962 1 2.30E-03 -76.56041

41

Table 2.2: cont.

Random intercept

Random slope


Marginal R^2

Conditional R^2

Chi square Chi df

Response Variable Site Transect Residual

Predictor variable Estimate Pr(>Chiq) AIC

Leaf dry matter content (log)

site and transect 0.13487 0.04261 0.1117 PAR (log) 0.04323 1.04E-01 3.16E-01 378.16 5 <2.2e-16 -4279.666


site and transect 0.150105 0.0166 0.11555 Phosphate (log) 0.02645 2.08E-02 4.95E-01 180.7 5 <2.2e-16 -4082.209


site and transect 0.265729 0.0625332 0.1097485 Canopy cover -0.00724 3.73E-01 5.38E-01 482.79 5 <2.2e-16 -4385.55


site and transect 0.09389 0.06536 0.11861 Nitrogen (log) 0.005596 3.85E-04 2.40E-01 42.725 5 4.20E-08 -3944.231

Leaf dry matter content (log) site transect 0.071702 0.009454 0.1187999 Nitrate -0.01143 4.11E-02 3.30E-01 37.61 3 3.42E-08 -3943.115


site and transect 0.08217 0.09231 0.11791 Ammonia (log) 0.007228 5.48E-04 2.36E-01 75.624 5 6.89E-15 -3977.13

Specific leaf area (log)

site and transect 0.20837 0.08766 0.21073 PAR (log) -0.1034 1.50E-01 3.84E-01 816.72 5 <2.2e-16 -683.339


site and transect 0.49091 0.05138 0.22281 Phosphate -0.01512 9.55E-04 7.37E-01 461.51 5 <2.2e-16 -328.1324

Specific leaf area (log) transect site 1.07278 0.03183 0.19722 Canopy cover 0.01872 4.22E-01 7.48E-01 1181.4 3 <2.2e-16 -1052.028


site and transect 0.3415 0.158 0.2359 Nitrogen (log) -0.02113 1.03E-03 4.35E-01 152.9 5 <2.2e-16 -19.51822


site and transect 0.15046 0.01473 0.02822 Nitrate (log) 0.02822 1.03E-02 3.63E-01 330.5 5 <2.2e-16 -197.1163


site and transect 0.29896 0.15977 0.23526 Ammonia (log) -0.01363 3.76E-04 4.13E-01 170.27 5 <2.2e-16 -36.88827

Seed weight site transect 0.0008587 0.0007008 0.0034183 Ammonia (log) 2.86E-04 6.17E-02 10.254 3 0.01653 -135010.6

Seed weight

site and transect 3.24E-04 2.16E-05 3.41E-03 Nitrate 0.0001412 1.07E-02 2.35E-01 35.443 5 1.23E-06 -135031.8

Seed weight transect site 2.64E-04 8.016-05 3.42E-03 Nitrogen (log) 2.77E-04 6.09E-02 9.0581 3 0.02853 -135009.4

Seed weight

site and transect 0.001726 0.0001635 0.0034156 Phosphate (log) 5.41E-03 1.23E-01 28.315 5 3.16E-05 -135024.7

Seed weight site transect 0.0008814 0.0005541 0.0034182 PAR (log)

4.98E-04 6.40E-02 12.872 3 0.004922 -135013.3

Seed weight

site and transect 1.49E-03 3.44E-04 3.42E-03 Canopy cover 1.07E-03 3.93E-02 22.589 5 4.04E-04 -135018

42

Table 2.2: cont.

Random intercept

Random slope


Marginal R^2

Conditional R^2

Chi square Chi df

Response Variable Site Transect Residual

Predictor variable Estimate Pr(>Chiq) AIC

Seed pod weight site transect 2.99E-02 1.12E-02 4.15E-02 Canopy cover 5.76E-03 3.58E-01 9.346 3 0.02503 -3453.362

Seed pod weight

site and transect 5.72E-02 5.31E-03 4.05E-02 PAR (log) 0.01176 4.25E-02 4.32E-01 39.299 3 1.68E-07 -3479.768

Seed pod weight

site and transect 2.86E-02 4.36E-03 4.05E-02 Nitrate

5.67E-02 5.85E-01 29.565 5 1.80E-05 -3469.581

Seed pod weight

site and transect 5.93E-02 9.50E-03 4.08E-02 Phosphate 4.44E-02 5.55E-01 16.055 5 6.69E-03 -3456.071

Pod length (log) transect site 0.27111 0.01141 0.14741 PAR (log) 0.14741 1.05E-02 4.77E-01 29.952 3 1.41E-06 -903.0646

Pod length (log) transect site 0.06089 0.01342 0.15021

Phosphate (log) -0.0405 3.68E-02 4.11E-01 18.662 3 0.0003211 -891.7743

Pod length (log) transect site 0.301139 0.00807 0.149905 Canopy cover 0.001613 1.26E-02 5.17E-01 17.466 3 0.0005668 -890.5784

Pod length (log) site transect 0.140851 0.019918 0.149274 Nitrate 0.007296 9.69E-03 4.87E-01 16.154 3 0.001054 -889.2663

Pod length (log) site transect 0.14595 0.11111 0.14979 Ammonia (log)

-0.004804 1.05E-04 4.97E-01 8.8613 3 0.03119 -881.9736

43

Table 2.3: Multivariate linear mixed effect models

Response Variable Random effects (Std.Dev)

Fixed effects Marginal

R^2 Conditional

R^2 Chi

square Chi df Site Transect Residual Pr(>Chiq) AIC

Plot biomass (log) 0.4961 0.9127

tree.cov + par + phosphate + N_sum + tree.cov:phosphate + tree.cov:N_sum + par:N_sum + phosphate:N_sum + tree.cov:phosphate:N_sum 2.36E-01 4.10E-01 60.701 9 9.82E-10 756.5359

DSV Biomass (log) 0.4601 1.5721 par + phosphate + tree.cov + par:tree.cov 1.31E-01 2.00E-01 22.3 4 1.75E-04 997.8451

Leaf Carbon content (log) 0.006222 0.018252 par + N_sum + tree.cov + par:N_sum + par:tree.cov 1.11E-01 2.03E-01 14.872 5 1.09E-02 -603.9607

Leaf carbon nitrogen ratio (log)

0.09198 0.12938

par + phosphate + tree.cov + par:phosphate + par:tree.cov + phosphate:tree.cov + par:phosphate:tree.cov 3.75E-01 5.85E-01 61.705 7 6.89E-11 -116.9689


0.08272 0.02027 0.19719

par + phosphate + N_sum + tree.cov + par:phosphate + par:N_sum + phosphate:N_sum + par:tree.cov + phosphate:tree.cov + N_sum:tree.cov + par:phosphate:N_sum + par:phosphate:tree.cov + par:N_sum:tree.cov + phosphate:N_sum:tree.cov + par:phosphate:N_sum:tree.cov 4.59E-01 5.44E-01 1239.4 15 <2.2e-16 -1085.987


0.04495 0.00775 0.10924

par + phosphate + N_sum + tree.cov + par:phosphate + par:N_sum + phosphate:N_sum + par:tree.cov + phosphate:tree.cov + N_sum:tree.cov + par:phosphate:N_sum + par:phosphate:tree.cov + par:N_sum:tree.cov + phosphate:N_sum:tree.cov + par:phosphate:N_sum:tree.cov 2.58E-01 3.68E-01 557.34 15 <2.2e-16 -4438.842

Seed weight

0.0009318 NA 0.0034176

par + phosphate + N_sum + tree.cov + par:phosphate + par:N_sum + par:tree.cov + phosphate:tree.cov + N_sum:tree.cov + par:phosphate:tree.cov + par:N_sum:tree.cov 2.73E-03 7.17E-02 36.465 11 1.42E-04 -135021.1

Seed pod weight

0.26131 0.05577 0.3624

par + phosphate + N_sum + tree.cov + par:phosphate + par:N_sum + phosphate:N_sum + par:tree.cov + phosphate:tree.cov + N_sum:tree.cov + par:phosphate:N_sum + par:phosphate:tree.cov + par:N_sum:tree.cov + phosphate:N_sum:tree.cov + par:phosphate:N_sum:tree.cov 4.60E-02 3.83E-01 61.527 15 1.38E-07 -3481.544

Seed pod length (log)

0.14318 0.01459 0.14879

par + phosphate + N_sum + tree.cov + par:phosphate + par:N_sum + phosphate:N_sum + par:tree.cov + phosphate:tree.cov + N_sum:tree.cov par:phosphate:N_sum + par:phosphate:tree.cov + par:N_sum:tree.cov + + phosphate:N_sum:tree.cov + par:phosphate:N_sum:tree.cov 4.38E-02 5.06E-01 43.906 15 1.14E-04 -893.0184

44

Figures

0 200 400 600 800

0.0

0.5

1.0

1.5

2.0

Photosynthetically Active Radiation (log)

Num

ber

of

specie

s (

log)

20 40 60 80

0.0

0.5

1.0

1.5

2.0

Canopy CoverN

um

ber

of

Specie

s (

log)

0 5 10 15

0.0

0.5

1.0

1.5

2.0

Soil Phosphate Concentration

Num

ber

of

specie

s (

log)

Fig. 2.1: The relationship between species richness and the environmental gradients, and. i)

PAR: R2 = 0.0272, F269 = 8.55, p = 0.00375; ii) Canopy cover: R

2 = 0.0887, F268 = 14.13, p =

1.46e-06; and, iii) Phosphate: R2 = 0.0424, F269 = 13.0, p = 0.0038

i

iii

ii

45

2 4 6 8 10

-6-4

-20

24

Number of species

To

tal p

lot

bio

ma

ss (

log

)

2 4 6 8 10-4

-20

24

Number of species

DS

V B

iom

ass (

log

)

Fig. 2.2: The relationship between species richness and the biomass measurements. i) Total plot

biomass: R2 = 0.408, F204 = 73.1, p = <2.2e-16; and, ii) DSV biomass: R

2 = 0.0766, F258 = 10.7, p

= 3.45e-05.

i ii

46

2 3 4 5 6 7

-40

24

Photosynthetically active radiation (log)

DS

V B

iom

ass (

log)

2 3 4 5 6 7

4.5

5.0

5.5

6.0


Leaf D

ry M

atter

Conte

nt (log)

2 3 4 5 6 7

2.5

3.5

4.5


Specifi

c L

eaf A

rea (

log)

2 3 4 5 6

0.8

1.2

1.6


Leaf N

itrogen C

onte

nt (log)

2 3 4 5 6

3.7

63.8

03.8

4


Leaf C

arb

on C

onte

nt (log)

2 3 4 5 6 7

3.0

3.5

4.0


Seed P

od L

ength

Fig. 2.3: The relationship between various plant traits and log transformed photosynthetically

active radiation. i) DSV Biomass: R2 = 0.0478, F259 = 14.0, p = 0.00022; ii) Leaf dry matter

content: R2 = 0.136, F2834 = 449, p = <2.2e-16; iii) Specific leaf area: R

2 = 0.285, F2834 = 1132, p

= <2.2e-16; iv) Leaf nitrogen content: R2 = 0.127, F118 = 18.3, p =3.86e-05; v) Leaf carbon

content: R2 = 0.0688, F118 = 8.78, p = 0.0038; and, vi) Seed pod length: R

2 =0.0167, F995 = 17.9,

p = 2.52e-05

i

iii

ii

iv

v vi

47

0 200 600 1000

-6-4

-20

24

Photosynthetically Active Radiation

To

tal p

lot

bio

ma

ss (

log

)

0 200 600 1000

0.0

50

.15

0.2

5

Photosynthetically active radiation

Se

ed

Po

d W

eig

ht

Fig. 2.4: The relationship between various plant traits and photosynthetically active radiation. i)

Total plot biomass: R2 = 0.01, F205 = 23.8, p = 2.15e-06; ii) Seed pod weight: R

2 = 0.016, F995 =

17.2, p = 3.67e-05

i ii

48

Fig. 2.5: The relationship between various plant traits and canopy cover. i) Total plot biomass:

R2 = 0.124, F205 = 30.1, p = 1.2e-07; ii) DSV Biomass: R

2 = 0.169, F259 = 27.41, p = 1.61e-11;

iii) LDMC R2 = 0.199, F2833 = 353, p = <2.2e-16; iv) SLA: R

2 = 0.413, F2833 = 997, p = <2.2e-16;

v) LNC: R2 = 0.344, F118 = 63.4, p = 1.19e-12; vi) Seed pod weight: R

2 = 0.0109, F995 = 11.9, p

=0.000577; and, vii) Seed pod length: R2 = 8.75e-03, F995 = 9.8, p = 1.8e-03

i

iii

ii

iv v

vi vii

49

Fig. 2.6: The relationship between various plant traits and soil phosphate concentration. i) DSV

biomass: R2 = 0.0486, F259 = 14.3, p = 0.0002; ii) LDMC: R

2 = 0.0057, F2833 = 9.13, p =

0.000112; iii) SLA: R2 = 0.03619, F2834 = 54.2, p = <2.2e-16; iv) Seed pod weight: R

2 =0.0225,

F995 = 23.9, p = 1.17e-06; and, v) Seed pod length: R2 = 0.0363, F994 = 19.8, p = 3.85e-09

i

iii ii

iv v

50

1.0 1.5 2.0 2.5 3.0 3.5

-6-4

-20

24

Soil Nitrogen Concentration (log)

To

tal p

lot

bio

ma

ss (

log

)

1.0 1.5 2.0 2.5 3.0 3.5

3.0

3.5

4.0

Soil Nitrogen Concentration (log)S

ee

d P

od

Le

ng

th(l

og

)

Fig. 2.7: The relationship between various plant traits and log transformed soil nitrogen

concentration. i) Total plot biomass: R2 = 0.0272, F205 = 6.77, p = 0.01; and, ii) Seed Pod Length:

R2 = 0.007, F995 = 8.02, p = 4.72e-03

i ii

51

5 10 15 20 25 30

2.5

3.5

4.5

Soil Nitrogen Concentration

Sp

ecific

Le

af

Are

a

5 10 15 20 25 30

0.0

50

.15

0.2

5Soil Nitrogen Concentration

Se

ed

Po

d W

eig

ht

Fig. 2.8: The relationship between various plant traits and soil nitrogen concentration. i) SLA: R2

= 0.00876, F2834 = 26.0, p = 3.56e-07; and, ii) Seed pod weight: R2 = 0.0275, F995 = 3.75, p =

5.33e-02

i ii

52

-3 -2 -1 0 1 2

-6-2

02

4

Soil Nitrate Concentration (log)

Tota

l plo

t bio

mass (

log)

-3 -2 -1 0 1 2 3

-40

24


DS

V B

iom

ass (

log)

-3 -2 -1 0 1 2 3

4.5

5.0

5.5

6.0


Leaf D

ry M

atter

Conte

nt (log)

-3 -2 -1 0 1 2 3

2.5

3.5

4.5


Specifi

c L

eaf A

rea (

log)

-2 -1 0 1 2

0.8

1.2

1.6


Leaf N

itrogen C

onte

nt (log)

Fig. 2.9: The relationship between various plant traits and log transformed soil nitrate

concentration. i) Total plot biomass: R2 = 0.0279, F193 = 6.56, p = 0.0112; ii) DSV biomass: R

2 =

0.0312, F259 = 9.96, p = 0.0018; iii) LDMC: R2 = 0.0141, F2694 = 39.6, p = 3.71e-10; d) SLA: R

2

= 0.00829, F2694 = 244, p = <2.2e-16; iv) LNC: R2 =0.0838, F2834 = 10.3, p = 0.00171

i

iii

ii

iv

v

53

0 2 4 6 8 10 12 14

3.7

63.8

03.8

4

Soil Nitrate Concentration

Leaf

Carb

on C

onte

nt

(log)

0 5 10 15

0.0

50.1

50.2

5


Seed P

od W

eig

ht

0 5 10 15

3.0

3.5

4.0


Seed P

od L

ength

(lo

g)

Fig. 2.10: The relationship between various plant traits and soil nitrate concentration. i) LCC,

with non- transformed nitrate concentration: R2 = 0.0426, F118 = 5.24, p = 0.0238 ii) Seed pod

weight: R2 = 0.00792, F995 = 9.85, p = 2.84e-03; and, iii) Seed pod length: R

2 = 0.00461, F994 =

7.49, p = 5.91e-04

i

iii

ii

54

Fig. 2.11: The relationship between plant traits and soil ammonia concentration. i) Total plot

biomass: R2 = 0.032, F204 = 4.41, p = 0.0134; ii) LDMC: R

2 = 0.00189, F2833 = 3.69, p = 0.00252;

and, iii) SLA: R2 = 0.00355, F2833 = 6.05, p = 0.0024

i

iii

ii

55

Appendix

Table 2.A1: Measured morphological traits

Measurement Abbreviation Calculation Unit

Specific leaf area SLA

mm2 mg-1

Leaf dry matter content LDMC

mg g-1

Leaf nitrogen content LNC

Leaf carbon content LCC

Leaf carbon-nitrogen ratio C:N

Aboveground biomass

g

Seed weight

g

Seed pod weight

g

Seed pod length

mm

56

Fig. 2.A2: Site map of the Rouge National Urban Park

Fig. 2.A3: i) Residuals vs Fitted for Total biomass and canopy coverage. Cone shape in the

residuals indicates heteroscedasticity; and ii) QQ plot, curve indicates left skewness in data

i ii

57

Chapter 3 Quantifying phenotypic plasticity and assessing the response to

defoliation in the morphological traits of the invasive vine Vincetoxicum rossicum

3.1 Introduction

One prevalent hypothesis regarding the cause of invasive success has been that invasive

species have broader niches than either non-invasive species or native species. Two hypotheses

related to the broadness of an invasive species niche breadth include Baker’s (1965) niche

breadth–invasion success hypothesis and the enemy release hypothesis (Keane and Crawley

2002).

The first hypothesis, niche breadth–invasion success, can be examined the realized niche

of the invasive species in both its native range and in the introduced range (Vazquez 2006).

Some studies have shown that there is a correlation between these two regions (Hierro et al.

2004), where the characteristics of species in the native range can be used to predict the future

geographical distributions within the introduced range (Peterson 2003). In some studies, it has

been shown that an invasive species has a wide geographical range in its native habitat as well as

the introduced range (Goodwin et al. 1999). The wide geographical distributions of these

species may be a result of the underlying genetic variability of the populations (Bossdorf et al.

2005). This genetic variability could be a result of multiple introduction events, which supply the

population with novel genetic material (Dlugosch and Parker 2008), or through years of

adaptation and evolution, given that the species has been present in the introduced range for a

substantial amount of time (Bossdorf et al. 2005).

58

In the latter case, if there had been a lack of introduction events, the species could

potentially evolve a strategy to be more plastic with certain functional traits (Sultan 2000).

Phenotypic plasticity is an organism’s ability to express different phenotypes in response to

different environments or different biotic interactions, given a particular genotype (Davidson et

al. 2011). Plasticity has been proposed to contribute to invasion success because it contributes to

four invasiveness factors: population persistence over time; high local abundance; successful

colonization of new areas; and a wide geographic range (Sakai et al. 2001). These factors

influence how the invasive species responds to, and later influences, other species within its new

introduced range. Plasticity may enhance ecological niche breadth, allowing an invader to

express advantageous phenotypes in a broader range of environments (Richards et al. 2006).

This means that the invader could potentially have a wide geographic distribution because it is

not as limited by environmental conditions such as climate (Hulme 2008). Plasticity could

facilitate the persistence of populations despite environmental fluctuations (Hulme 2008).

Plasticity could allow an invader to quickly respond to environmental fluctuations or variability,

meaning it could potentially facilitate future colonization in novel environments since the

invader would be able to quickly respond to the different environmental conditions (Hulme

2008). For high local abundance of the invader, there may be a trade-off between the degree of

plasticity and average abundance. This is because less plastic species attain greater abundance at

optimum conditions than more plastic species (Hulme 2008).

The second hypothesis proposed to affect the broadness of an invader’s niche is the

enemy release hypothesis, widely cited as contributing largely to invasion success (Colautti et al.

2004, Richardson and Pysek 2008). In this case, during the process of introduction, the co-

evolved enemies of the introduced species, such as specialized herbivores or predators or other

59

competitors, are not transported along with the introduced species of interest. The introduced

species is ‘released’ and no longer regulated as it would normally be, meaning that it could

potentially be able to compete better against native species because less resources need to be

allocated towards defensive structures (Colautti et al. 2004, Richardson and Pysek 2008). The

potential outcome of the lack of competitive interactions lead to the expansion of the invader’s

niche (Niche expansion hypothesis – Gidoin et al. 2015). Another potential consequence of this

‘release’ is the evolution of increased competitive ability (EICA), where the plant reallocates

resources away from defensive structures to competitive ones, thus, increasing the competitive

ability in relation to native species (Blossey and Notzold 1995). Examples of competitive traits

include novel weapons such as allelopathic chemicals (Novel weapons hypothesis – Callaway

and Ridenour 2004), which have been shown to impair native plant growth (Prati and Bossdorf

2004). Additionally, this lack of population regulation can potentially lead to the expansion of

the invasive species’ niche breadth in the introduced range in comparison to the native range

(Lack 1969; and Gidoin et al. 2015).

To examine these two niche-related hypotheses I assessed the response of the invasive

species Vincetoxicum rossicum to two different light conditions and three different levels of

defoliation. Vincetoxicum rossicum, or Dog-Strangling Vine (DSV), is the ideal study species to

use in this type of assessment for two reasons. First, it is a rare understory species in its native

range but, within North America, it is broadly distributed over a variety of environments

(DiTommaso et al. 2005), therefore it either has a great deal of genetic variability or has evolved

to be more plastic to take advantage of the different environments. Second, in the introduced

range, DSV has no naturally occurring enemies. While in its native range there are several

enemies that feed on DSV including a specialist herbivore called Hypena opulenta (Hazlehurst et

60

al. 2012), a moth that has been approved as a biological control agent in Canada (Casagrande et

al. 2012). The aim of this study will be to directly test if DSV exhibits plasticity by quantifying

the morphological trait response of DSV to changes in light availability and defoliation.

Using a common garden experiment I will address two hypotheses. First, the

morphological traits of DSV will exhibit variability when moved from full sun to full shade

conditions. Specifically, I expect the stem height and specific leaf area of the plants grown in the

shade conditions to be greater than those in the sun conditions. Second, DSV will show negative

growth responses when experiencing a greater degree of defoliation, especially when examining

total biomass.

3.3 Methods and Materials

3.31 Study Site and Experimental Design

The greenhouse study was conducted at the University of Toronto Scarborough campus (UTSC),

Scarborough, Ontario (Refer to Appendix for map). The experiment started in spring 2015,

600.3x0.3x1m enclosures were built using wood. Thirty enclosures were covered with a light

blue sheer fabric and the remaining enclosures were covered in white sheer fabric (Appendix

Fig. A2).

All the enclosures were placed on the top of the UTSC Science Building; in groups of 10 the

enclosures set on the roof and were weighed down by rocks to avoid being blown over by the

wind. Within the group of 10, the enclosures were a minimum of 0.3m away from each other,

among the 6 groups of 10 enclosures there was at least 1m of space. The groups were organized

61

into three individual blocks, 10 blue and 10 white enclosures were in each block. The groups of

blue enclosures were additionally covered in green landscaping mesh to create a stronger shading

effect. Thirty planted pots of DSV from the “Open” treatment (see Plant Sampling) were

randomly placed into the thirty white enclosures and thirty planted pots of DSV from the

“Closed” treatment were randomly placed into the thirty blue enclosures.

3.3.2 Plant Sampling

In the summer of 2015, during the week of June 6 – 12, DSV roots were collected from three

of12 field sites in the Rouge Urban National Park. These sites were chosen because they had

very distinct light gradients as defined by forest canopy coverage. At these sites, roots were

collected from “Sun” areas were canopy cover ranged from 5-20% and from “Shade” areas with

canopy coverage ranging from 75-90%. Individual root bulbs from these areas were separated

and planted in 6 inch flower pots. Generally, 3-4 root blubs were planted using standard potting

soil, with a minimum of 10 pots and a maximum of 20 pots being planted from each cover type.

From each field site, a minimum of 10 pots were planted with roots from the sun areas and

another 10 were planted with roots from shade areas. These pots were then placed behind the

UTSC Science building and separated into two different light treatments labelled “Open” or

“Closed”. In the open treatment, the pots were placed in full sun and the closed treatments were

covered with dark green landscaping mesh to simulate full shade. After a two month growing

period, five of the healthiest pots were chosen from each cover and light treatment, resulting in

60 pots in total to be used in the experiment. Pots from the open treatment were labelled from 1-

62

30, and pots from the closed treatment were labelled 31-60. Using these numbers I determined

pot placement using a random number generator.

In addition to the light treatment I used a defoliation treatment. Originally, I planned to cause

natural defoliation using the biological control agent Hypena opulenta, however, due to rearing

issues (see Biological Control), I used artificial defoliation. Prior to the defoliation treatment, for

each pot, I counted the number of stems that were greater than 10cm in height and determined

their height using a meter stick. Additionally, I determined the stem width for each individual

stem and the number of leaves on that stem.

On Aug 6, 2015, which is designated as Day 60 of the experiment, I randomly applied an

artificial defoliation treatments consisting of 0, 25, or 75% removal to 10 pots using a random

number generator, for each light treatment (open/closed). As stem density was not controlled in

this experiment, the proportion of leaves removed was determined by counting the total number

of leaves in each pot and then multiplying that total by 0, 0.25, or 0.75. Therefore, the number of

leaves removed was done at a pot-level, not an individual level. Leaves were removed randomly

using scissors and were collected and placed in the freezer to be analyzed later for specific leaf

area (SLA) and leaf dry matter content (LDMC). See Chapter 2, Appendix Table A2.1 for

information on measurements. After the defoliation treatment all of the pot were placed in their

designated enclosure and placed on the roof of the UTSC Science building. Pots were watered

every second day, however, if the pots appeared to be dry they were watered more frequently.

Overall, there were 12 different treatment types that differed by location of root collection, light

availability, and percentage of defoliation. Each of these treatment types were replicated 5 times.

63

The experiment ended on Sept 15, 2015’ which is designated as Day 100 of the experiment. On

that day, I collected all above ground biomass from each pot. Before placing the biomass in the

standing oven, the height of each stem was determined and five mature leaves were collected.

These leaves were later processed for SLA and LDMC.

3.3.3 Biological Control

A guide that was outlined by Miller, Tewksbury and Casagrande at the University of Rhode

Island Biological Control lab (Feb 2014) was used for rearing Hypena opulenta from larvae to

adults. Briefly, at the beginning of May 2015, 50 pupae that were stored from the previous

summer were removed from the 4°C fridge and placed in a 25’C growth chamber. After 2-3

weeks, 15 adults emerged from pupae and were placed in two oviposition cages with a pot of

DSV that had been grown for two weeks. In the cages, we attempted to have a ratio of 2 females

to 1 male; however the lack of viable males made this difficult so we had a 3:1 ratio. By the

beginning of June, the Hypena had begun to lay eggs and by mid-June the eggs were hatching.

When larvae had reached 3rd

instar, they were transferred to smaller clear plastic containers and

constantly fed stalks of DSV that were collected from behind the UTSC Science Wing for 2-3

weeks. Approximately 175 larvae successfully reached the 5th

instar stage and, of those

successful larvae, 160 successfully pupated by mid-late July. Pupae were then sexed and placed

into smaller containers with sterilized vermiculite.

Despite being a multivoltine species, our first generation population of Hypena did not emerge

from the pupal stage and we believe they actually began to diapause. Little is known about the

ecology of this species, thus, we are currently unsure as to why our population did not proceed

64

into the next developmental stage. We hypothesize that a possible chemical cue caused this

phenomenon; however, more testing is required.


For each of the four plant traits, I ran an ANOVA to determine if there were any overall

differences between the treatment groups. If there was a significant difference I then ran a

Tukey’s HSD (honest significant difference) test to determine if there were differences between

the 12 different treatment types. All statistical analyses were conducted within R statistical

programming (Core Team 2014).

3.4 Results

To determine if there were any significant differences among the treatment types for each

of the plant traits, which include specific leaf area (SLA), leaf dry matter content (LDMC), stem

height, and above ground biomass, an ANOVA was performed (Fig. A1). Referring to Table 1,

it was found that there were differences among the treatments for SLA, LDMC, and stem height,

but not for aboveground biomass. There was substantial variability between treatment types, and

after performing a Tukey’s HSD test I was able to determine the specific differences (Fig. A2).

Treatments were most distinct from one another based on light treatment, closed vs open;

therefore, I examined each of the treatment levels individually to determine if a particular pattern

in the trait response was visible.

65

Starting with the light treatments, it was found that plants that were grown under the

shade enclosures had greater SLA (F(3,195)=419, p = <2e-16) and stem height (F(3,193)=49.5, p =

1.88e-11) than plants grown outside of the enclosures (Fig. 1). Corresponding with the high SLA

in the closed condition, LDMC was lower in response to decreased light availability in

comparison to plants in the open condition (Fig. 1e: F(3,195)=83.2, p = <2e-16). However, there

was no significant difference for LDMC at the end of the experiment (Fig. 1iv). Examining the

combined effect of root origin and light treatment (Fig. 2), I found that prior to the defoliation

treatment, the plant traits appeared to be more diverged in their trait values based on the light

conditions. From the time that the roots were planted (Day 0) to the time of the first sampling

(Day 60), the trait values for SLA (F(3,193)=149, p = <2e-16), LDMC (F(3,193)=27.9, p = 4.93e-15)

and, stem height (F(3,252)=19.3, p = 2.66e-11), had all segregated based on the light treatment

(Fig. 2i, iii, and iv). Additionally, at Day 60 SLA, it was also found that the Shade-Closed and

the Sun-Closed treatment were significantly different from one another (Fig. 2i) with a percent

difference of 7.29 (p=0.00107), which may indicate a maternal effect of root origin.

Interestingly, the plant traits appear to converge on similar values 40 days after the application of

the defoliation treatments (Figs. 1 and 2). With the light treatment alone, the percent difference

between the closed and open treatments for SLA changes from 40.9 to 32.7 (Fig. 1ii: F(3,287)=108,

p = <2e-16). Similarly, for stem height, the percent difference between the treatments also

decreases, from 31.6 to 27.1 (Fig. 1vi: F(3,251)=10.9, p = 9.64e-07). The convergence of trait

values by Day 100 is most apparent for LDMC as none of the treatments are significantly

different from one another at the end of the experiment (Fig. 2iv). For SLA, the difference

between the Shade-Closed and Sun-Closed treatment is no longer present (Fig. 2ii) while, for

66

stem height, there is a weaker relationship with the light treatment since the Shade-Open and

Sun-Closed are no longer significantly different (Fig. 2vi).

Aboveground biomass did not show any significant difference between any of the

treatments for light availability, however, there a negative response to defoliation (Fig.3i:

F(2,57)=3.66, p =0.0319). A percent difference of 39.2 was found between the control and the 75%

removal treatments, however, no significant difference was found for either of those treatments

and the 25% removal. SLA was significantly lower for the 75% removal pots (Fig. 3ii:

F(2,288)=6.73, p =0.014), both in comparison to the 25% removal, with a percent difference of

8.05 (p= 0.04), and the control with a difference of 11.6 (p= 0.00106). Similar to biomass,

LDMC was only significantly different between the 75% removal and control treatments (Fig.

3iii: F(2,288)=63.16, p =0.044, percent difference = 10.7). Examining the interaction between light

availability and the amount of defoliation, it seems that defoliation does not have as strong of an

impact as light since it was found that, for SLA (F(5,285)=78.5, p =<2e-16) and stem height

(F(5,249)=6.2, p =1.96e-05), the treatments diverged primarily due to the light treatment (Fig. 4).

Similar to earlier results, SLA and stem height were greater in the lower light conditions.

Since aboveground biomass did not respond to light availability, I attempted to determine

where the plant resources were potentially being allocated by examining leaf dry weight and leaf

area and seeing how they related to SLA and stem height. The area of the leaves was not

significantly different for any of the four start conditions; however, leaf dry weight did change in

response to light (F(3,287)=11.4, p = 4.13e-07). Leaf dry weight is greater in the treatments with

more light and, since SLA decreases with greater light availability (Fig. 2ii), this could

potentially mean that the plants are allocating more resources to the leaves. In contrast, in the

67

shaded conditions, leaf dry weight and SLA decrease; therefore, resources are being sent

elsewhere, likely the stems as they are generally taller with decreasing light availability (Fig.

2vi).

Another potential location where resources could be sent to is the roots, which were not

assessed in this study. However, a significant difference was found after examining stem height

in relation to root origin, where stem height was greater in plants whose roots were originally

from shade conditions than those from sun conditions (Day 60: F(1,254)=5.13, p = 0.0243; and

Day 100: F(1,253)=3.86, p = 0.0506). Since the roots were collected from three specific areas in

the field I checked to see if there were any site related effects on the plant traits (Fig. 5). Prior to

the defoliation treatment there were several site-level differences for SLA (F(2,194)=3.08, p =

0.0481), LDMC (F(2,194)=17, p = 1.59e-07), and stem height (F(2,253)=3.79, p = 0.024). The

differences between aboveground biomass were even more compelling since all three sites are

significantly different from one another (F(2,57)=14.5, p = 8.24e-06), where Site 2 had the greatest

amount of growth, Site 1 was intermediate, and Site 3 had the least amount of aboveground

growth (Fig. 6). Looking at the interaction between site and root origin prior to the defoliation

treatment (Fig. 7), it was found that for LDMC differed the most between Site 3 and Site 1

(F(5,191)=7.05, p = 4.55e-06). This pattern was also seen for stem height (Fig. 7ii: F(2,250)=6.77, p

= 6.12e-06). After the defoliation treatment, it is clear that plants from Site 3 generally produce

less above ground biomass compared to plants from Sites 1 and 2 (Fig. 8i: F(5,54)=6.55, p = 7.89e-

05). Although, this discrepancy is not consistent, as LDMC shows only minor differences

between Sites 1 and 2 (Fig. 8ii: F(5,285)=3.69, p = 0.00296), and stem height shows only

differences between Site 1 and 3 (Fig. 8c: F(5,249)=6.1, p = 2.37e-05).

68

Overall, the site-level effects are quite minimal, as there are no large deviations from

what was expected from previous results. For instance, similar to what was seen in Fig. 1, SLA

(F(5,191)=91.7, p = <2e-16), stem height (F(5,250)=12.6, p = 6.87e-11), and LDMC (F(5,191)=28.2, p

= <2e-16) show relatively strong differences between the two different light conditions before

the defoliation treatment was applied (Fig. 9). On the other hand, some these differences get

weaker after the defoliation treatment, like those seen for LDMC and stem height (Fig. 10iii and

iv), or stay the same like SLA (Fig. 10b). Additionally, there was a strong difference between

Site 3-Open and Site 2-Closed for both biomass production (Fig. 10i) and stem height (Fig.

10iv). Finally, looking at the interaction between site location and defoliation, for biomass the

more apparent differences were between Site 1 and 2 for both the 25% removal and control, but

not for the 75% removal (Fig. 11i). While significant, SLA and LDMC showed minimal

differences between sites or defoliation treatments (Fig. 11ii and iii).

3.5 Discussion

Overall, the results of this study provide some support for the plastic potential of this

invasive species. I have shown support for the first hypothesis, as this invasive vine can respond

relatively quickly to changes in light availability optimizing for light capture. Additionally, the

results indicate that there is some support for the second hypothesis, which states that DSV is

responding to defoliation.

69

3.5.1 Plasticity in DSV morphology

Light availability appears to play a strong role in the distribution of the trait values for

specific leaf area (SLA), leaf dry matter content (LDMC), and stem height. Prior to the

defoliation treatment, there was a very distinctive partitioning pattern where pots with low light,

i.e. the Shade-Closed and Sun-Closed treatments had higher SLA and stem height in comparison

to the treatments with more light. These higher SLA and stem height values give support that the

DSV was responding in such a way that it was optimizing light capture (Valladares and

Niinemets 2008; and Xu et al. 2009). This pattern is supported by the negative trend seen with

LDMC, which is generally negatively correlated to SLA (Poorter and De Jong 1999). After the

application of the defoliation treatments, the trait values appear to converge as these distinctive

patterns seem to weaken, most significantly with LDMC.

The defoliation results (Fig. 3) for SLA, LDMC, and aboveground biomass the control

pots were significantly different from the 75% removal treatment but were indistinguishable

from the 25% removal pots. Similar to the light and defoliation experiment conducted by

Milbrath (2008), I found that a greater degree of defoliation resulted in lower biomass, lower

SLA, and higher LDMC. Milbrath also found that higher frequencies of defoliation resulted in a

decrease in seed output from the plants; however, I did not observe any of these trends as only 3

of my 60 pots were able to produce seed pods. The interaction between light and defoliation was

only significant for two of the plant traits (Fig. 4) and, based on this result, light played a

stronger role in partitioning the trait values for SLA and stem height.

Interestingly, for aboveground biomass, there were no statistical differences between

either of the light treatments. I had predicted that biomass would increase in shaded conditions

70

since a strategy used by shade-tolerant species includes allocating more resources to

aboveground structures to maximize light capture (Xu et al. 2009). By reviewing the results of

SLA and stem height for the start conditions (Fig. 2) and additionally looking at the area and dry

weight of the leaves collected from the plants, I found that in addition to forming broader leaves,

DSV was apparently sending more resources to the stems to grow taller in response to the lack of

light. The allocation of plant resources to different organs is variable across species and

environments (Poorter and Nagel 2000; Gommers et al. 2013). However, this strategy has been

documented in a few understory plants (Valladares and Niinemets 2008), including the

herbaceous species Claytonia perfoliata, which responded to lower light conditions by forming

leaves with higher SLA (McIntyre and Strauss 2014). According to the results of this study, in

higher light conditions, DSV appears to be sending more resources to the leaves as they weigh

more than the leaves from the shade conditions. This strategy may be a result of the plants

compensating for the higher temperatures on the roof. In the open treatment, the soil in the pots

dried out faster so the plants may be allocating more resources to make thicker leaves to offset

evapotranspiration (Citation). Additionally, I expect that, in these conditions, some of the plant

resources are also being sent to the roots to optimize moisture and nutrient uptake from the soil

(Lopez-Bucio et al. 2003). While I did not measure DSV root traits in this study, others have

shown that roots do change in response to different environmental conditions (Milbrath 2008).

Additionally, it was apparent at the time that I collected the roots from the field that roots from

open fields are different than roots from the understory in terms of overall density and mass.

Roots found in higher light conditions formed denser mats, where individual root crowns were

more difficult to distinguish in comparison to root crowns from the understory that could be

easily identified.

71

The fact the plants differ so much due to the roots at the beginning of the study gives

some indication that there could potentially have been some maternal effects, which are the

causal influence of the maternal phenotype (or genotype) on the offspring phenotype (Wolf and

Wade 2009). To look for maternal effects, I first determined whether root origin (sun vs shade

conditions) played a significant role in the trait distribution. Overall, the origin of the roots did

not appear to play a strong role except for stem height, which showed a strong difference

between understory and open field roots. Interested in whether the location of where the roots

were collected, i.e. what field site they were from, affected the overall trait response, I examined

the traits to see if there was a site level effect (Fig. 5). In particular, aboveground biomass

showed large differences between Site 3 and the other two sites. By incorporating the interaction

between site and light availability, this result remains consistent as biomass produced from plants

from Site 3 are lower than the other sites (Fig. 6), especially when including the interaction of

root origin (Fig. 7). The interaction between site and the defoliation treatments does not show

very strong differences but there are some interesting trends. For all of the control and 25%

removal treatments, it is very clear that Site 3 is consistently lower than Site 2. This trend is not

apparent for the 75% removal treatments, which may indicate that either the maternal effects are

not as significant with such a high level of defoliation, or that at this level of defoliation the plant

were so damaged that they could not be separated out based on the maternal effects.


Plasticity in the morphological traits of an invasive species is important to consider

because they may reflect the different strategies that invasive plants may employ to be

72

successful. What is apparent from these results is that light is playing a strong role in

determining the trait distribution for these four plants traits despite there being site level effects,

and that DSV does appear to be responding plastically to both light availability and different

levels of defoliation.

Many invasive species, especially plants, can show a wide array of trait characteristics

across different environments (Valladares and Niinemets 2008), Moreover, many of these

species can take advantage of disturbance events that can alter habitats in such a way that they

become more likely to be invaded (Davis et al. 2000). However, very little is known about

understory invaders and, while assessing the response of DSV to light provides some evidence

for its adaptive strategy, more work looking at a variety of different environmental gradients will

be necessary. Within the Rouge Park, where this plant is extremely pervasive, there are several

different types of forest types, such as mixed deciduous stands and pine stands, which both have

different environmental or microclimate conditions, thus, more intensive studies about how

different factors affect the growth of DSV will be necessary. For example, factors such as soil

nutrient availability have been shown to strongly influence the distribution of invasive species,

but other factors like soil moisture or soil pH will also be important (Ehrenfeld et al. 2001).

A caveat regarding the results of this work is that no direct measurements of fitness were

taken. This includes reproductive success through the production of viable seeds since only 3 of

the 60 pots produced seed pods. The lack of overall growth and seed production may be a result

of the harsher conditions experienced by the plants atop the roof. Green roof studies have shown

that the climatic conditions on top of building roofs can fluctuate quite dramatically, thereby,

limiting the type of plant species that can survive there (Nagase and Dunnett 2010). Therefore,

73

while DSV has been shown to tolerate a wide range of climatic conditions (DiTomasso et al

2005), the high temperature and low precipitation on top of the roofs may have been too extreme.

Thus, it may be beneficial to conduct a similar experiment with more stable growing conditions

such as in a growth chamber or within a greenhouse.

Results from this study indicate that biomass only significantly decreases with a 75%

removal treatment. With DSV being present in such high densities, a lot of money and effort will

be needed to either mechanically or chemically remove this plant (McKague and Cappuccino

2005; and Averill et al. 2008). An alternative route is to use a biological control agent, such as

Hypena opulenta which has been approved for use in Canada (Casagrande et al. 2012).

However, higher densities of this control agent, upwards to 4-8 larvae per plant, will be

necessary in order to reach the 75% defoliation threshold (Weed and Casagrande 2010). Vast

numbers of H. opulenta can be reared in laboratory conditions and have been shown to

effectively feed of DSV; however, the efficacy of this control agent in field conditions has yet to

be explored. Moreover, this control agent has only been found in the understory so it is not clear

whether or not it would be effective in open fields (Weed and Casagrande 2010).

Overall, the next big step for understanding the invasion success of this invasive vine will

be assessing the degree of genetic variability its various populations express. This study indicates

that there are some potential maternal effects at work and, thus, disentangling the differences

between genetic, epigenetic, and plastic responses should shed some light on exactly how this

species is responding to its environment in term of resource allocation. This information could

help inform management processes as it could aid in the development of more targeted

programs. For instance, if more resources are being sent to the aboveground structures in lower

74

light environments, then a leaf defoliator like H. opulenta may best be suited for the understory.

However, if resources are being sent to belowground structures, a root herbivore such as root

feeding beetle Eumolpus asclepiadeus may be more suitable (Weed et al. 2011). In either case,

more work must be done to understand how plasticity in the traits of this invasive vine is aiding

or providing a fitness advantage in different environments in comparison to other species.

75

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78

Tables

Table 3.1: Results from the ANOVA Tests for each of the morphological traits for each of the treatments for Day 60 of the experiment

Response

Variable

Full Start Condition (4 types)

Light treatment

(Closed/Open) Root Origin (Shade/Sun)

df Residuals F-stat

p-

value df Residuals

F-

stat

p-

value df Residuals F-stat

p-

value

Specific

leaf area 3 193 148.8

<2e-

16 1 195 419

<2e-

16

Leaf dry

matter

content 3 193 27.9

4.93E-

15 1 195 83.2

<2e-

16

Stem

height 3 252 19.31

2.66E-

11 1 254 49.5

1.88E-

11 1 254 5.132 0.0243

Table 3.1: cont.

Response

Variable

Site Site:Origin Site:Light Site:Origin:Light

df Residuals

F-

stat

p-

value df Residuals

F-

stat

p-

value df Residuals

F-

stat

p-

value df Residuals

F-

stat

p-

value

Specific

leaf area 2 194 3.08

4.81E-

02 5 191 91.7

<2e-

16 11 185 48.8

<2e-

16

Leaf dry

matter

content 2 194 17

1.59E-

07 5 191 7.05

4.55E-

06 5 191 28.2

<2e-

16 11 185 13.2

<2e-

16

Stem

height 2 253 3.79 0.024 5 250 6.77

6.12E-

06 5 250 12.6

6.87E-

11 11 244 10.6

7.26E-

16

79

Table 3.2: Results from the ANOVA Tests for each of the morphological traits for each of the treatments for Day 100 of the experiment

Response

Variable

Full treatment (12 types) Full Start Condition (4 types)

Light treatment

(Closed/Open)

df Residuals

F-

stat

p-


p-

value df Residuals

F-

stat

p-

value

Specific

leaf area 11 279 37.2

<2e-

16 3 287 108.2

<2e-

16 1 289 316.1

<2e-

16

Leaf dry

matter

content 11 279 3.43

1.71E-

03

Stem

height 11 243 4.324

6.62E-

06 3 251 10.89

9.64E-

07 1 253 27.06

4.04E-

07

Above

ground

biomass

Table 3.2: cont.

Response

Variable

Defoliation Light:Defoliation Root Origin (Shade/Sun)

df Residuals F-stat

p-

value df Residuals

F-

stat

p-


p-

value

Specific

leaf area 2 288 6.73

1.40E-

02 5 285 78.46

<2e-

16

Leaf dry

matter

content 2 288 3.16

4.40E-

02

Stem

height 5 249 6.196

1.96E-

05 1 253 3.859 0.0506

Above

ground

biomass 2 57 3.66

3.19E-

02

80

Table 3.2:

Response

Variable

Site Site:Origin Site:Light Site:Defoliation

df Residuals

F-

stat

p-

value df Residuals

F-

stat p-value df Residuals

F-

stat

p-

value df Residuals

F-

stat

p-

value

Specific leaf

area 5 285 65.2

<2e-

16 8 282 7.64

2.93E-

09

Leaf dry

matter

content 5 285 3.69 0.00296 5 285 2.78

1.82E-

02 8 282 2.51 0.0121

Stem height 5 249 6.1

2.37E-

05 5 249 7.09

3.26E-

06

Aboveground

Biomass 2 57 14.5

8.24E-

06 5 54 6.55

7.89E-

05 5 54 6.65

6.84E-

05 8 51 5.84

2.69E-

05

Table 3.2: cont.

Response

Variable

Site:Origin:Light Site:Origin:Defoliation

df Residuals

F-

stat p-value df Residuals

F-

stat p-value

Specific leaf

area 11 279 33.6 <2e-16 17 273 5.77 3.23E-11

Leaf dry

matter

content 11 279 3.64

7.83E-

05 17 273 5.63 6.62E-11

Stem height 11 243 6.93

3.63E-

10 17 237 3.13 5.15E-05

Aboveground

Biomass 11 48 3.5 0.00121 17 42 3.47 0.000526

81

Figures

Fig. 3.1: The distribution of trait values across the two light conditions, both at the start of the

experiment and at the end of the experiment. i) SLA, removal day: F(1,196)=424, p = <2e-16; ii)

SLA, collection day: F(1,290)=284, p = <2e-16; iii) LDMC, removal day: F(1,196)=75.1, p = 1.7e-

15; iv) LDMC, collectionl day: no significant difference; v) Stem height, removal day: F-

(1,254)=49.5, p = 1.88e-11; and, vi) stem height, collection day: F(1,253)=27.1, p = 4.07e-07

i

iii

ii

iv

vi v

82

Fig. 3.2: The distribution of trait values across the four different start conditions, both at the start

of the experiment and at the end of the experiment. Letters indicate no significant difference

between treatments. i) SLA, removal day: F(3,193)=149, p = <2e-16; ii) SLA, collection day: F-

(3,287)=108, p = <2e-16; iii) LDMC, removal day: F(3,193)=27.9, p = 4.93e-15; iv) LDMC,

collection day: no significant differences; v) Stem height, removal day: F(3,252)=19.3, p = 2.66e-

11; and vi) stem height, collection day: F(3,251)=10.9, p = 9.64e-07

i

iii

ii

iv

vi v

83

Fig. 3.3: The distribution of trait values across the three different defoliation treatments. Letters

indicate no significant difference between treatments. i) Aboveground biomass: F(2,57)=3.66, p

=0.0319; ii) SLA: F(2,288)=6.73, p =0.014; and iii) LDMC: F(2,288)=63.16, p =0.044

i

iii

ii

84

Fig. 3.4: The distribution of trait values in response to the interaction between light availability

and defoliation. Letters indicate no significant difference between treatments. i) SLA:

F(5,285)=78.5, p =<2e-16; and ii) Stem height: F(5,249)=6.2, p =1.96e-05

i

ii

85

Fig. 3.5: The distribution of trait values in across the three sites where the roots were collected.

Letters indicate no significant difference between treatments. i) SLA: F(2,194)=3.08, p = 0.0481;

ii) LDMC: F(2,194)=17, p = 1.59e-07; and iii) Stem height: F(2,253)=3.79, p = 0.024

i

iii

ii

86

Fig. 3.6: Aboveground biomass across the three sites where the roots were collected. F(2,57)=14.5,

p = 8.24e-06

87

Fig. 3.7: The distribution of trait values in response to the interaction between which site the

roots were collected from and which light condition the roots were originally found in. Letters

indicate no significant difference between treatments. i) LDMC: F(5,191)=7.05, p = 4.55e-06; and

ii) stem height: F(2,250)=6.77, p = 6.12e-06

i

ii

88


roots were collected from and which light condition the roots were originally found in. Letters

indicate no significant difference between treatments. i) Biomass: F(5,54)=6.55, p = 7.89e-05; ii)

F(5,285)=3.69, p = 0.00296; and iii) Stem height: F(5,249)=6.1, p = 2.37e-05

i

iii

ii

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roots were collected from and light availability. i) SLA: F(5,191)=91.7, p = <2e-16; ii) LDMC:

F(5,191)=28.2, p = <2e-16; and iii) Stem height: F(5,250)=12.6, p = 6.87e-11

i

iii

ii

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roots were collected from and light availability. i) Biomass: F(5,54)=6.65, p = 6.84e-05; ii) SLA:

F(5,285)=65.2, p = <2e-16; iii) LDMC: F(5,285)=2.78, p = 0.0182; and iv) Stem height: F(5,249)=7.09,

p = 3.26e-06

i

iii

ii

iv

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roots were collected from and defoliation. i) Biomass: F(8, 51)=5.84, p = 2.69e-05; ii) SLA: F(8,

282)=7.64, p = 2.93e-09; and iii) LDMC: F(8, 282)=2.51, p = 0.0121

i

iii

ii

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Appendix

Fig. 3.A1: The distribution of trait values across the twelve different treatment types. i) Specific

leaf area (SLA): F(11,279)=37.2, p = <2e-16; ii) Leaf dry matter content (LDMC): F(11,279)=3.43, p

= 1.71e-03; iii) stem height: F(11,243)=4.32, p = 6.62e-06; and iv) above ground biomass, which

was not significant

i

iii

ii

iv

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Fig. 3.A2: Results of the Tukey’s HSD test for the three plant traits that were significant. Grey

squares in indicate a significant difference (p <0.05) between treatments, and white squares

indicate no differences. Treatments, indicated by letters a to l, are ordered by increasing mean

values

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Fig. 3.A3: Map of the University of Toronto Scarborough Campus. Yellow pin indicates the

Science Building roof where the greenhouse experiment took place.

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Fig. 3.A4: Picture of the experimental set-up for the greenhouse experiment

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Chapter 4 Thesis Summary

4.1 Summary of thesis chapters

In the introduction of this thesis I briefly discussed the current state of the field of

invasion biology. Since the publication of Charles Elton’s seminal work (Elton 1958), the field

of invasion biology has grown considerably. As a result of that growth, a great abundance of

potential mechanisms contributing to invasion success have been proposed. Many of these

mechanisms primarily focus on either the invasiveness of a species (Richardson and van Kleunen

2007), or on the invasibility of a particular habitat (Davis et al. 2005). In line with this body of

work, this thesis primarily focused on understanding more about the invasiveness of the invasive

vine Vincetoxicum rossicum or Dog-Strangling Vine.

In Chapter 2, I determined how much this invader varies in its trait values across two

environmental gradients. By assessing a variety of morphological traits it was that that DSV

exhibits a high degree of intraspecific variability in response to different environmental

conditions. The results indicate that in response to light availability DSV can maintain positive

growth by increasing its biomass production to optimize light capture (Valladares and

Niinemetes 2008). Additionally, I found that the morphological traits of DSV vary in response to

differences in soil nutrient availability. Though not consistent across all of the traits, it was

generally found that DSV responded favorably, i.e. showing positive growth, in response to

greater levels of soil nutrients

In Chapter 3, I examined two niche-related mechanisms believed to contribute to

invasion success. The first hypothesis, niche breadth-invasion success, is related to the plasticity

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of an invader’s functional traits (Sultan 2000). Phenotypic plasticity is thought to enhance a

species ecological niche breadth (Richards et al. 2006) and, thus, contribute to the four

invasiveness factors: population persistence over time; high local abundance; successful

colonization of new areas; and a wide geographic range (Sakai et al. 2001). To assess the degree

of plasticity DSV exhibits, I conducted a reciprocal transplant experiment and subjected the

plants to two different light environments. Overall, it was found that DSV can respond relatively

quickly, within one growing season, to changes in light availability. Plasticity in certain

morphological traits, particularly the leaf traits and stem height, allows DSV to optimize light

capture in low light environments, therefore, facilitating positive growth in that stressful

environment.

The second hypothesis that was examined in this chapter was the enemy release

hypothesis, which is believed to contribute a great deal to invasion success (Colautti et al. 2004,

Richardson and Pysek 2008). Using artificial defoliation, I found that all of the morphological

traits respond negatively to high levels of defoliation. This negative response was expected as it

was demonstrated in previous studies (Milbrath 2008), and therefore provides further evidence

that an effect way to manage this invasive species may be possible.

4.2 Implications and future directions

This thesis provides valuable information regarding the intraspecific variation of DSV.

Intraspecific trait variation is an important aspect to consider in community ecology because it

provides a more complete estimation of within-species trait distributions (Violle et al. 2010).

Understanding more about the trait distribution of a single species can then allow ecologists to

scale up and determine how a particular community will assembly based off of resource

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partitioning (Laughlin et al. 2012). Knowing which species are present within a community and

where they fall along a resource gradient should provide enough information to estimate the

niche space of a habitat. Understanding more about the niche space, specifically if there are open

areas for a potential invader to take advantage of should allow us to accurately predict the

vulnerability of that particular habitat to invasions.

Additionally, the plasticity assessment of DSV offers us a view on how this invader

became so successful. DSV can rapidly respond to environmental changes which can be a huge

advantage in a heterogeneous landscape. An important next step will be determining if this

plasticity results in a fitness advantage. Previous studies have found that invasive species are

usually more plastic that either native species or non-invasive species (Davidson et al. 2011),

however, being plastic in certain traits does not always confer a fitness advantage. DSV has not

been well-studied in its native range (DiTommaso et al. 2005), and so we have little information

on how much this species has evolve in the 100 years that it’s been present in North America.

Understanding more about the evolutionary history of this plant could potentially help us manage

this species more effectively. In particular, understanding more about the co-evolution of this

species with its native enemies should provide us with an accurate estimation of the efficacy of

certain control methods such as chemical or biological controls.

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References

Colautti, R.I., Ricciardi, A., Grigorovich, I.A., and MacIsaac, H.J. (2004). Is invasion success

explained by the enemy release hypothesis? Ecology Letters 7, 721–733. Davidson, A. M., Jennions, M., & Nicotra, A. B. (2011). Do invasive species show higher phenotypic

plasticity than native species and, if so, is it adaptive? A meta‐analysis. Ecology letters, 14(4), 419-431

Davis, M.A., Thompson, K., and Philip Grime, J. (2005). Invasibility: the local mechanism

driving community assembly and species diversity. Ecography, 28, 696–704.




Canadian Journal of Plant Science, 85, 243–263.

Elton, C.S. (1958). The ecology of invasions by plants and animals. Methuen, London 18.


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1291–1299.


nigrum under artificial defoliation and different light environments. Botany, 86, 1279–1290.

Richards, C.L., Bossdorf, O., Muth, N.Z., Gurevitch, J., and Pigliucci, M. (2006). Jack of all

trades, master of some? On the role of phenotypic plasticity in plant invasions. Ecology Letters

9, 981–993.

Richardson, D.M., and van Kleunen, M. (2007). Invasion biology and conservation biology: time

to join forces to explore the links between species traits and extinction risk and invasiveness.


Elton. Diversity and Distributions, 14, 161–168.

Sakai, A.K., Allendorf, F.W., Holt, J.S., Lodge, D.M., Molofsky, J., With, K.A., Baughman, S.,

Cabin, R.J., Cohen, J.E., Ellstrand, N.C., et al. (2001). The population biology of invasive specie.

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Violle, C., Enquist, B. J., McGill, B. J., Jiang, L., Albert, C. H., Hulshof, C., ... & Messier, J.

(2012). The return of the variance: intraspecific variability in community ecology. Trends in

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