ABSTRACT

DAMSCHEN, ELLEN INGMAN. Plant community response to landscape connectivity and patch shape. (Under the direction of Nick Haddad and Jim Gilliam.)

Land transformation is the single most important factor promoting the global loss of

terrestrial biological diversity. Remaining habitat fragments contain more edges, less interior

habitat, and are more isolated from other habitat fragments, all of which decrease rates of

colonization following local extinctions, reduce reproductive rates and gene flow between

populations, and ultimately lead to species extinctions. The best approach to prevent species

loss, therefore, is to preserve greater areas of habitat. In many cases, however, habitat has

already been fragmented and strategies are needed to configure and manage the remaining

land. Land managers often create reserve networks that incorporate the use of landscape

corridors, linear strips of habitat connecting isolated patches, to reduce species loss by

increasing colonizations and decreasing extinctions. Most empirical tests of corridors have

been limited to individuals and populations, leaving corridor effects on diversity largely

unknown, especially at large spatial scales. Additionally, only a handful of studies have

examined corridor effects on plants, which may be especially sensitive to the abiotic changes

resulting from alterations in patch shape due to dispersal limitation.

Using one of the best-replicated, large-scale habitat fragmentation experiments, I tested

explicitly for corridor effects on plant community diversity and composition by examining

the established plant community and the soil seedbank. My experimental design

distinguished among the three possible ways corridors can affect between-patch processes:

by acting as a movement conduit between connected patches (“connectivity effects”), by

increasing area alone (“area effects”), and by intercepting organisms moving across the

landscape and filtering them into connected patches (“drift-fence effects”). Additionally, I

tested for the importance of within-patch edge effects because corridors increase the amount

of edge relative to core habitat in a given patch. I provide evidence that corridors increase

plant diversity through a combination of connectivity, drift-fence, and edge effects that can

be largely predicted from plant dispersal modes. Biotically dispersed plant species (e.g., by

birds and mammals) were most affected by connectivity effects, while passively-dispersed

species (e.g., by wind or gravity) were most affected by drift-fence effects. Resource

managers should consider these differential responses relative to their conservation goals for

particular species or communities.

In a related experiment, I tested for effects of habitat edges on plant performance, which

are known to have impacts on abiotic conditions and biotic interactions. Edge effects have

been especially well documented for forest-dwelling species along edges created by clearing

or disturbing the surrounding habitat, but edge effects for historically open-habitat species

along edges of forests have been virtually ignored. This is the case for many native

herbaceous species in the southeastern United States that once existed in historically open

longleaf pine forests but are now restricted to openings in modern densely planted pine

forests. I tested empirically for edge effects of open habitat species by planting nine species

of native longleaf pine forest herbs (three grasses, two asters, and four legumes) in equal

densities at six distances (0, 6, 12, 25, 50, and 100 m) from an edge of a dense forest into an

adjacent opening. I measured plant growth and flowering as well as available light and soil

conditions. Using multivariate analysis of variance (MANOVA), I determined that plant

growth and flowering were both significantly affected by the distance to the open habitat

edge. Responses for individual species differed, in that some species performed best near the

edge while others performed worst. Incident photosynthetically active radiation, soil

moisture, and competitive interactions with other study species are likely responsible for the

observed tradeoffs in performance. Resource managers should consider thinning the

overstory of longleaf pine forests, establishing uneven age distributions within pine stands,

and initiating regular low-intensity understory burns for the conservation of these understory

species.

PLANT COMMUNITY RESPONSE TO LANDSCAPE CONNECTIVY AND PATCH SHAPE

by ELLEN INGMAN DAMSCHEN

A dissertation submitted to the Graduate Faculty of North Carolina State University in partial fulfillment of the requirements of the

Degree of Doctor of Philosophy

ZOOLOGY

Raleigh

2005

APPROVED BY:

__________________________ _________________________ Dr. George R. Hess Dr. Thomas R. Wentworth

__________________________ _________________________

Dr. Nicholas M. Haddad Dr. James F. Gilliam

Co-chair of Advisory Committee Co-chair of Advisory Committe

ii

DEDICATION

To my grandparents, Dona and Emery Damschen and Eleanor and Boyd Ingman, my parents,

Janie and Dan Damschen, and to John for always believing in me

iii

BIOGRAPHY

I grew-up outside of the Twin Cities in Hopkins, Minnesota. I am the oldest daughter of

Janie and Dan Damschen and have one younger brother, Aren. For as long as I can

remember, my family has gone to lakes in northern Minnesota and to the north shore of Lake

Superior to camp, hike, fish, and find solitude. My parents also took Aren and me on long

roadtrips each summer, allowing us to see much of the United States. These experiences

fostered an appreciation of natural systems and instilled curiosity for how they function.

It was not until after college graduation, however, that I decided to pursue a career in

ecology. A passion for music led me to pursue a Bachelor of Arts degree at Luther College

in Decorah, Iowa where I double majored in flute performance and communication. Two

years after graduating, however, I decided to change my vocation and returned to

undergraduate studies at the University of Minnesota-Twin Cities. I also obtained an

ecological research internship at Cedar Creek Natural History Area in East Bethel,

Minnesota. Working at Cedar Creek was one of the most formative experiences in my life

because I was able to interact with prominent ecologists, gain field experience, and it is

where I met my Ph.D. advisor, Dr. Nick Haddad.

I moved to Raleigh, North Carolina in the fall of 2000 to become Dr. Haddad’s first Ph.D.

student at North Carolina State University. I have had the pleasure to interact with an

incredible group of graduate students and faculty members at NC State. In addition, I have

had the opportunity to spend much of my time in New Ellenton, South Carolina and at the

Savannah River Site while completing my field research. The individuals I met and worked

with in South Carolina made my dissertation experience truly memorable.

iv

ACKNOWLEDGEMENTS

Over the past five years, I have had the privilege of meeting and interacting with many

people who have facilitated the completion of my dissertation and given me great happiness

along the way. Thank you to all of you.

My interest in ecology as a career did not begin in earnest until two years after I graduated

from college. I took an introductory biology course at Luther College in 1999, where I met

Dr. Tom Cottrell, who gave me inspiration and confidence to pursue ecology in the first

place. I also received a great deal of support and encouragement from Jyoti Grewal, who

helped me successfully obtain an internship at Cedar Creek Natural History Area. My

experience at Cedar Creek was one of the most formative experiences in my life and has had

a major impact on my research interests and it is where I met my current Ph.D. advisor, Dr.

Nick Haddad. Thanks to Jenny Goth and Wendy Bengston, Nick Haddad, and Cini Brown

for your leadership and to the many Cedar Creek interns for inspiring me to become an

ecologist.

My dissertation research took place near Aiken, South Carolina at the Savannah River

Site. I spent several years living in New Ellenton, South Carolina, a small southern town that

became home because of the hospitality of our landlords, Robert and Thelma Kirby. I will

always have fond memories of times at 307 Smith Avenue. My work at the U.S. Forest

Service could not have taken place without the help of many people. Thanks to John Blake,

Ed Olson, and Kim Hale for your leadership and the support you have given our project.

Don Imm made much of my work possible, both logistically and financially, and always

made life at the Forest Service more enjoyable. Thanks to Pat Wright for making sure we

always had running vehicles; Jamie Scott for expert knowledge on site preparation, herbicide,

v

and cold storage facilities; and to Ron Mosely and Bob Morgan for the use of the large trucks

when we needed them. Tremendous thanks to the fire crew for maintaining our sites,

especially to Paul Linse, Chris Hobson, Jarvis Brown, Jason Demas, Mark Frizell, Iola

Hallock, Tim Kolnik, Bobby Phillips, Travis McCollough, Kaye Mills, Jason Rose, and

Diana Johnston, for their dedication to the corridor project. The SRWC crew, including

Dave Coyle (a.k.a., “Solver of All Problems”), Mark Coleman, Christy Prenger, and Tucker

Slack always made life enjoyable and provided a wealth of mental and physical resources.

While at North Carolina State, I had the great fortune of becoming friends with a

wonderful group of graduate students. Salinda Daley, my six-foot tall kindred spirit, could

not have been a better roommate and friend. Thank you for the support you gave to me over

my entire dissertation experience. Kristen Rosenfeld, the other half of my brain, has made

my graduate school experience so much more meaningful and has provided sanity during the

times I needed it most. Kristen, thank you for your support, laughter, trust, and confidence.

You will make it difficult for future collaborators to meet my expectations and I could not

ask for a better friend. Thank you to both you and David for all your hospitality and

logistical support over the last two years. Thank you to Jessica and Matt Thompson, Mandy

and Dave Hewitt, Larissa Bailey, Kate Semsar, Dave Davenport, Becky Bartel, Sunny

Snider, and Alesia Read for exciting and fun times over the last five years. Thanks to the

Haddad Lab past and present: Becky Bartel, Jory Brinkerhoff, Brian Hudgens, Daniel

Kuefler, Allison Leidner, and Aimee Weldon. Special thanks to Becky Bartel and Kristen

Rosenfeld for research support and camaraderie during my last two years.

I came to North Carolina State because I wanted to work with Nick Haddad, a new faculty

member whose research on corridors not only tested ecological theory but also provided

vi

information for land management and reserve design. Nick, I could not have had a better

dissertation advisor. You have supported me intellectually and financially, helped be to

become a better ecologist and teacher, provided guidance and sound advice, and most

importantly, you have given me confidence to be an ecologist. I have learned from you to

never “discount my future” too much, that I will “always have more good opportunities than

I have the time for”, and to always remain optimistic. Thank you for everything.

I could not have grown-up with a better family and I am so very grateful for the

tremendous encouragement and support I have gotten. To my brother, Aren, thank you for

your continuous support of what I do. My aunt and uncle, Lorie and Tom Stemig, your

constancy and belief in who I am has given me confidence and allowed me to achieve more

than I thought was possible. To my cousin (i.e., “sister”), Holley Gullickson, and to Shawn,

Joshua, and Tyler, I am so glad to have you as a part of my life. You remind me what is truly

important. I am especially thankful for the lessons my grandparents have taught me: my

Grandma Ingman taught me that nature was exciting and interesting; my Grandpa Ingman

taught me that good story telling and a dry sense of humor can make life a lot more

interesting; my Grandpa Damschen taught me that a positive attitude and endearing spirit can

give you the power to change the world; and my Grandma Damschen gave me ultimate

freedom by always believing I was innately perfect. To my parents, Janie and Dan

Damschen, none of this could have been possible without you: the curiosity you instilled in

me about the natural world, the constant belief that I could do anything, the financial support

when I needed it, and the incredible and constant emotional support that I get each day. I am

truly lucky to have parents that are not only mentors, but also my best friends.

vii

And finally, to John; you have been with me for the whole experience and I don’t know

how I would have completed this enormous task without you by my side. Much has changed

since the early days of changing tires, rebar races, and thermarest furniture, but your

inquisitive spirit, your dedication and hard work, your humorous outlook on life, and your

honorable principles have consistently made my life so much more meaningful. You have

given me strength when I needed it most, challenged me when you knew I could do more,

and always loved me nonetheless. For your tireless patience, support, and love - thank you.

viii

TABLE OF CONTENTS

Page

LIST OF FIGURES x LIST OF TABLES xi CHAPTER 1: HOW CORRIDORS INCREASE PLANT DIVERSITY 1 Abstract 1 Introduction 2 Habitat fragmentation, corridors, and biodiversity 2 Corridor effects and plants 4 Predictions 8 Secondary hypotheses 9 Methods 9 Study site 9 Presence/absence surveys 11 Permanent plot surveys 12 Supplemental edge study 13

Plant species identification 13 Soil moisture 15 Data analysis 16 Total species richness 16 Species richness by dispersal mode 17 Within- and between-patch effects on species richness, evenness, and similarity 18 Supplemental edge study 19 Occurances of rare and common species by patch type 20 Results 20 Total species richness 20 Species richness by dispersal mode 21 Within- and between-patch effects on species richness, evenness, and similarity 22 Supplemental edge study 23 Occurances of rare and common species by patch type 23 Discussion 24

Separating drift-fence and edge effects 25 Applicability of island biogeography 26 Generality of observed patterns 27

Conservation implications and recommendations for future work 28 Acknowledgements 29 Literature cited 31

ix

CHAPTER 2: HABITAT FRAGMENTATION AFFECTS A SEEDBANK 59 Abstract 59

Introduction 60 Fragmentation impacts and seedbanks 60 Methods 62 Study site 62 Seedbank sampling 63 Seedling monitoring 65

Standing vegetation survey 67 Data analysis 67 Results 69 Discussion 70

Fragmentation and the seedbank 71 Relationship between the seedbank and the established plant community 73

Conservation implications 73 Acknowledgements 74 Literature cited 76 CHAPTER 3: EDGE EFFECS ON LONGLEAF PINE SAVANNAH HERBS…………..87 Abstract 87 Introduction 88 Fragmentation and edge effects in open habitats 88 Herbaceous plants in longleaf pine forests 89 Study goals 89 Methods 90 Study site 90 Species and planting methods 91 Plot sampling 93 Environmnetal variables 93 Data analysis 94 Results 95 Discussion 97

Ascribed mechanisms 97 Relationship to historical diversity patterns and conservation 99 Future work 100

Acknowledgements 101 Literature cited 102 APPENDICES Appendix A: Vascular plant taxa found in established plant community surveys

from 2001-2003 125 Appendix B: Vascular plant taxa found in seedbank study 134

x

LIST OF TABLES

Page Table 1.1 Predictions for plant community response to corridors 42 Table 1.2 Results from repeated measures ANOVA for total species richness in the

central 100 x 100 m of each patch 43 Table 1.3 Results from repeated measures ANOVA for total species richness by

dispersal mode in the central 100 x 100 m of each patch 44 Table 1.4 Results from repeated measures ANOVA for species richness in the whole

patch 45 Table 1.5 Results from repeated measures ANOVA for species richness by dispersal

mode in the whole patch 47 Table 1.6 Results from repeated measures ANOVA for total species richness of all

species in Poaceae, Cyperaceae, Juncaceae, and Asteraceae in the whole patch 49

Table 1.7 Chi-square analyses of species traits by patch type for rare and intermediate

species 50 Table 2.1 Results from ANOVA for total number of individuals and species richness

in seedbank 81 Table 3.1 Names and characteristics for the nine species used in the edge study 118 Table 3.2 Standardized canonical coefficients from MANOVA analyses for plant

growth and flower production 119

xi

LIST OF FIGURES Page Figure 1.1 The proportion of corridor studies that have focused on: a) animals vs.

plants, and b) individual- or population-level processes vs. multiple-species or community processes 51

Figure 1.2 Experimental landscape located at the Savannah River Site near

Aiken, SC 52 Figure 1.3 Patch and plot design 53 Figure 1.4 Species richness measured by 544 permanent plots in edge and center plot

locations only during July-August 2000 54 Figure 1.5 Species richness measured from patch-level presence/absence suveys (a, c, e)

and a cumulative list of species from permanent plots (b, d, f) for the whole patch 55

Figure 1.6 Species richness measured from patch-level presence/absence suveys (a, c, e)

and a cumulative list of species from permanent plots (b, d, f) for the central 100 x 100 m of each patch 56

Figure 1.7 Species richness measured from patch-level presence/absence surveys of

graminoids and asters in the fall of 2002 57 Figure 1.8 The proportion of total patches (40) occupied by each species found in

presence/absence surveys from 2001-2003 58 Figure 2.1 Conceptual diagram showing how the spatial configuration of habitat can

affect processes that determine seedbank composition 82 Figure 2.2 Study design for seedbank study in an experimental landscape at the

Savannah River Site, South Carolina 83 Figure 2.3 The percentage of unique and overlapping species between the seedbank and

standing vegetation 84 Figure 2.4 NMS ordination of the seedbank and established vegetative community in

2001, 2002, and 2003 85 Figure 2.5 Species richness and total number of individuals in the seedbank of patch

types and locations 86 Figure 3.1 Historical extent of longleaf pine forest 120

xii

Figure 3.2 Location of edge study sites at the Savannah River Site near Aiken, SC 121 Figure 3.3 Plot layout at each site 122 Figure 3.4 Mean light availability and temperature for each distance category 123 Figure 3.5 Mean plant growth and flowering for nine study species 124

CHAPTER 1: HOW CORRIDORS INCREASE PLANT DIVERSITY

A paper to be submitted to Ecology

Ellen I. Damschen and Nick M. Haddad

Abstract

Corridors are a popular reserve design tool used extensively to preserve biodiversity.

Land managers often create reserve networks that incorporate the use of landscape corridors,

linear strips of habitat connecting isolated patches, to reduce species loss. However, corridor

use is controversial and most empirical tests of corridors have been limited to individuals and

populations. In addition, only a handful of studies have examined corridor effects on plants,

which may be especially sensitive to the abiotic changes resulting from alterations in patch

shape. Using the best-replicated, large-scale habitat fragmentation experiment, we

distinguished among the three possible ways corridors can affect between-patch processes:

by acting as a movement conduit between connected patches (“connectivity effects”), by

increasing area alone (“area effects”), and by intercepting organisms moving across the

landscape and filtering them into connected patches (“drift-fence effects”). We provide

evidence that corridors increase plant species richness through multiple mechanisms:

biotically dispersed plant species respond to the connectivity effects corridors provide, while

abiotically dispersed plant species respond to drift-fence effects. Within-patch edge effects

alone did not account for the increase in diversity. Additionally, corridors increased the

probability of dispersal and pollination events for rare and intermediately common species.

2

Introduction

Habitat fragmentation, corridors, and biodiversity

Landscape corridors were originally proposed as a reserve design tool to preserve

biodiversity by connecting two isolated habitat patches with a thin linear strip of habitat

(Diamond 1975). Corridors are based on two of the most fundamental theories in

conservation biology, island biogeography and metapopulation theory, which gives them

intuitive appeal and has led to their widespread use in reserve design (Mann and Plummer

1993, Mann and Plummer 1995). Despite the common use of corridors, however, empirical

tests of corridor effects are generally lacking and the efficacy of corridors has remained

controversial (Noss 1987, Simberloff 1987, Hobbs 1992, Simberloff et al. 1992, Rosenberg

et al. 1997, Beier and Noss 1998, Haddad et al. 2000). Empirical evidence for corridor

effects comes mainly from studies of individuals and populations (Figure 1.1) and shows that

corridors can increase movement rates (Haas 1995, Sutcliffe and Thomas 1996, Gonzalez et

al. 1998, Haddad 1999a, Haddad et al. 2003), population sizes (Fahrig and Merriam 1985,

Haddad and Baum 1999), and rates of gene flow (Aars and Ims 1999, Hale et al. 2001)

between the patches they connect. However, studies have also shown that corridors can have

neutral (Rosenberg et al. 1998, Haddad and Baum 1999, Danielson and Hubbard 2000) or

even negative effects for some species, through processes such as disease transmission or

predation (Hess 1994, Burkey 1997, Orrock et al. 2003, Orrock and Damschen 2005, Weldon

and Haddad 2005). These studies show that corridor effects can be species and life-stage

specific, making ultimate outcomes for community composition unclear.

Only a handful of studies have examined corridor effects on biodiversity (MacClintock et

al. 1977, Schmiegelow et al. 1997, Gilbert et al. 1998, Gonzalez et al. 1998, Collinge 2000,

3

Hoyle and Gilbert 2004, Rantalainen et al. 2004a, Rantalainen et al. 2004b, Rantalainen et al.

2005). Studies of this type are difficult to implement because they require specific spatial

configurations of habitat, posing logistical and ethical challenges, especially for

experimentally controlled and replicated designs.

The first evidence of corridor effects on diversity was provided by MacClintock et al.

(1977), who found that bird species richness was higher in a single forest fragment as a result

of supplementation from other forested habitat through a corridor. This study, however, was

observational and unreplicated. Schmeigelow et al. (1997) also examined how patch size

and connectivity affected avian species richness and found weak effects of corridors, but

higher diversity in the smallest of their patches. This was likely because the corridors in their

study were wider than the smallest patches, making it impossible to separate corridor effects

from the increased patch area associated with corridors. In addition, their study was

conducted over a short time span and patches were non-randomly located so that connected

patches were always adjacent to riparian areas. In an experimental grassland system,

Collinge (2000) also found weak corridor effects on insect diversity. While this study was

well controlled and replicated, the authors attributed the weak corridor effects they observed

to a lack of contrast between the patch habitat and surrounding matrix and to the high

dispersal ability of many insects relative to the size and isolation of patches.

Experimental microcosm studies provide the most conclusive support for theoretical

predictions of corridor effects on diversity (Gilbert et al. 1998, Gonzalez et al. 1998,

Gonzalez and Chaneton 2002, Rantalainen et al. 2004a, Rantalainen et al. 2004b, Rantalainen

et al. 2005). Moss patches connected by a corridor had higher levels of microarthropod

diversity and slower extinction rates (Gilbert et al. 1998, Gonzalez et al. 1998, Gonzalez and

4

Chaneton 2002). However, a recent study in this same microecosystem found no evidence

that corridors slowed the rate of extinction or increased species richness (Hoyle and Gilbert

2004), likely because of temporal changes in weather relative to their previous studies. A

recent series of small-scale experiments using soil decomposer organisms as a model system

(Rantalainen et al. 2004a, Rantalainen et al. 2004b, Rantalainen et al. 2005) found that

corridors increased the species richness of soil fungi in the patches they connected and that

that corridors also sometimes increased the abundance of enchytraeid worms, the species

richness of bacteria (dispersed by enchytraeid worms), and the number of microarthropod

taxa. These relatively small-scale studies provide rigorous tests of corridor effects on

diversity, but their applicability to larger spatial and temporal scales is unknown. Here, we

present results from the first large-scale experimental test of corridors on plant community

diversity in a well-replicated landscape that has a high degree of contrast and controls for

area effects.

Corridor effects and plants

Studies of corridor effects have almost exclusively used animal study organisms. Less

than 10% of all corridor studies have explicitly examined effects of corridors on plant species

(Figure 1.1, ISI Web of Science search, March 22, 2005). There are several additional

studies examining the effects of other “corridor-like” habitats, such as hedgerows,

windbreaks, roads, riparian areas, and right-of-ways on plants (Metzger et al. 1997, Corbit et

al. 1999, Harvey 2000, McCollin et al. 2000, Honnay et al. 2001, Tikka et al. 2001, Watkins

et al. 2003), but these studies only consider processes within these linear habitat types and

not within or between two connected habitat patches.

5

There are six empirical studies that examine corridor effects on plants within and between

patches. Kirchner et al. (2003) found that the presence and number of corridors in a naturally

patchy wetland system could explain, in part, the number of ponds colonized by a rare

wetland plant, Ranunculus nodiflorus, as a result of increased seed dispersal. The remaining

five studies of corridor effects for upland terrestrial plants all come from the experimental

system that is the focus of this study (see Methods for study site description), which have

shown that corridors can increase positive effects such as pollination and dispersal

(Tewksbury et al. 2002, Haddad et al. 2003, Townsend and Levey 2005) and negative effects

such as increased predation rates (Orrock et al. 2003, Orrock and Damschen 2005),

suggesting that corridor responses for plants can be species and life-stage specific.

Past research (Haddad and Baum 1999, Danielson and Hubbard 2000, Tewksbury et al.

2002, Orrock et al. 2003, Fried and Levey, Orrock and Damschen 2005) has led to three main

hypotheses for how corridors affect between-patch processes. First, and most commonly,

corridors are thought of as movement conduits facilitating the dispersal and movement rates

of organisms between the patches they connect (“connectivity effects”). Second, by

increasing the total habitat area of connected patches, corridors could operate through “area

effects”. Third, corridors could operate through “drift-fence effects”, where organisms are

filtered from the surrounding matrix into connected patches (defined by Anderson and

Danielson (1997) and Haddad and Baum (1999)); corridors might increase this probability

because of their expanse across the landscape. Additionally, while corridors have

theoretically been considered as a way to alter between-patch processes, they also have the

potential to change within-patch dynamics for plants and animals. As two long linear edges,

corridors can affect local abiotic conditions at edges, which may have large consequences for

6

relatively sessile organisms like plants. Other studies have shown that edge effects can lead

to changes in plant survivorship, reproduction, and composition (e.g., Murcia 1995, Fox et al.

1997, Didham and Lawton 1999, Restrepo and Vargas 1999, Euskirchen et al. 2001, Bruna

2002, Benitez-Malvido and Martinez-Ramos 2003, Ries et al. 2004). Thus, between-patch

corridor effects must be separated from within-patch edge effects.

Theoretically, corridors are predicted to increase species diversity by reducing between-

patch isolation, increasing colonization rates, and decreasing extinctions (e.g., through rescue

effects, Brown and Kodric-Brown 1977). Colonization rates are tightly linked to dispersal

ability, which has been correlated with seed dispersal mode for plants (Westoby et al. 1996,

Yao et al. 1999, Dupré and Ehrlén 2002). Therefore, testing for effects of corridors on plants

with similar dispersal modes may be especially useful for making predictions for corridor

responses. The theoretical importance of colonization for corridor effects makes this

interaction most important for testing predictions of corridor effects on plant diversity and is

our primary focus for testing how corridors function.

We considered evidence from previous studies on dispersal to generate our primary

predictions for plant community response to corridors. The role that corridors play in

promoting connectivity is clearest for animal-dispersed plants. The dispersal of biotically

dispersed plant species is dependent on the movement behavior and gut retention time of the

animals that disperse their seeds (Murray 1988, Clench and Mathias 1992, Jordano 1995,

Levey et al. 2005). In the same experimental landscape used here, bird-dispersed seeds had

higher deposition rates in connected patches than either of the unconnected patch types

(Tewksbury et al. 2002). Other studies have also found that patch isolation and small habitat

size had a greater impact on species with larger and fewer seeds that are dispersed by animals

7

(Hoppes 1987, 1988, Herlin and Fry 2000, Dupré and Ehrlén 2002, Gardescu and Marks

2004). This evidence leads us to the conceptual prediction that the diversity of biotically

dispersed species will increase in connected patches because of the connectivity corridors

provide by facilitating foraging movement behavior

On the other hand, predicting corridor effects for abiotically dispersed (e.g., wind, gravity)

plants is not as clear. Wind-dispersal has been assumed to be a superior and far-reaching

dispersal agent, a perspective that was supported by Dupré and Ehrlén (2002), who saw a

strong trend (but statistically insignificant) toward wind-dispersed species being least

affected by habitat isolation when compared to other dispersal modes. Under this

assumption, we would generally not expect corridors to increase colonization rates. In

contrast, recent models have shown that the long-distance dispersal of wind-dispersed plants

is most affected by changes in wind speeds and directions (Nathan and Muller-Landau 2000,

Nathan et al. 2003, Tackenberg et al. 2003, Soons et al. 2004a, Soons et al. 2004b).

Alterations in patch shape (i.e., changing the prevalence and arrangement of edges) could

alter wind speeds and directions, an idea supported by previous studies that have found that

edges affected wind patterns and the deposition of seeds carried by wind (Greene and

Johnson 1996, Cadenasso and Pickett 2001, Morse et al. 2002, Brandford et al. 2004).

Therefore, for abiotically dispersed species, we have two alternative conceptual predictions.

First, the diversity of abiotically dispersed species will be the same in all patch types,

regardless of connection or shape. Alternatively, abiotically dispersed species diversity will

increase in connected patches because corridors intercept passive dispersal.

8

Predictions

We use a well-replicated experimental landscape (see Figures 2 and 3 and Methods for

study description) to test several predictions for corridor effects on plant species diversity

(Table 1.1). If corridors function through connectivity effects, diversity should be higher in

connected patches than in unconnected patches of any shape (Table 1.1). If corridors instead

operate by increasing area (“area effects”), then connected and unconnected patches with the

same area and any shape should be equally diverse. If corridors function through “drift-fence

effects”, then diversity in both connected and unconnected patches with the same shape and

landscape expanse (high edge-to-area ratios) should be equal, but greater than in patches of

equal area and less expanse (lower edge-to-area ratios) and within-patch effects should not be

responsible for this difference. Finally, if within-patch edge effects are driving diversity

patterns, two conditions must be met: 1) patches with the highest edge-to-area ratios should

have much higher or lower diversity than patches with the lowest edge-to-area ratios

(connected or unconnected); and 2) within individual patches, diversity should increase or

decrease and/or composition should change with distance from the edge (Table 1.1).

Our predictions for the relative importance of each of these between-patch corridor effects

are dependent on plant dispersal mode. For biotically dispersed species, we predict corridors

will operate via connectivity effects, resulting in higher diversity in connected patches than in

all unconnected patch types (Table 1.1). For abiotically dispersed species, however, we

predict that corridors will either: 1) have no impact on diversity, or 2) will operate by altering

patch shape, with higher diversity in connected and winged patches than in rectangular

patches as a result of between-patch edge effects. Because edges have often been found to

9

alter resources and/or habitat heterogeneity, within-patch edge effects are predicted to affect

all types of plant species (Weathers et al. 2001, Ries et al. 2004).

Secondary hypotheses

While our strongest predictions are based on dispersal mode, corridors may also may have

other effects through alterations of other interactions such as pollination, herbivory, and seed

predation. Furthermore, it is possible that corridors affect common and rare species

differently. We secondarily focus on testing for the strength of corridor effects as a function

of species rarity. Some species will occur nearly everywhere, others will be intermediately

common, and some will be quite rare across the 40 patches that we examine. Because

species that occur ubiquitously across all patches are likely the least dispersal-limited and

therefore the least likely to respond to corridors, we expect corridors to most strongly affect

those species with intermediate or rare frequencies of occurrence. Furthermore, examining

the life history traits of such species might provide additional insight as to what other life

history traits might predict corridor response.

Methods

Study site

In collaboration with the USDA Forest Service at the Savannah River Site (SRS) near

Aiken, South Carolina, we created large-scale experimental landscapes during the winter of

1999-2000. The SRS, a National Environmental Research Park (NERP), is operated by the

U.S. Department of Energy and occupies nearly 78,000 ha of land in west central South

Carolina (Workman and McLeod 1990). Like many other pine forests in the southeastern

10

United States, the SRS is managed mostly for Pinus taeda (loblolly pine) and Pinus palustris

(longleaf pine). The Forest Service has helped to create open habitat patches, which are

either connected or isolated, in a matrix of mature pine forest. While corridors are typically

thought of as forested strips of land connecting two patches of forest, our design is the

inverse of this. The key element is the contrast that exists between habitat types. In our

system, the openings create conditions that permit a lush community of herbs, shrubs, and

trees to germinate and persist, while the densely planted monocultures of pine forests do not.

In fact, many species that occurred historically in native longleaf pine forests occur only

inside forest openings, including our study patches (personal observation, personal

communication Don Imm, Appendix A).

An “experimental unit” contains five patches: one center patch surrounded by four

peripheral patches (Figures 1.2 and 1.3). The center patch is 100 x 100 m (1 ha) and is

connected by a 150 x 25 m corridor to another 100 x 100 m peripheral patch. Three

additional unconnected peripheral patches are also located 150 m from the central patch.

“Rectangular patches” are 137.5 x 100 m and are equal in area to the center patch plus a

corridor to test for “area effects”. “Winged patches”, also equal in area to the center patch

plus a corridor, have two 75 x 25 m projections off each side of a 100 x 100 m patch to test

for “drift-fence effects”. Each of the patch types also differs in the ratio of edge:area habitat.

Winged and connected patches have edge:area ratios (0.050 and 0.049 respectively) that are

50% higher than rectangular patches (0.034).

There are eight experimental units with five patches each. Four experimental units have

two rectangular patches and one winged patch, while the remaining four have two winged

patches and one rectangular patch. The direction of the corridor was always assigned a

11

random cardinal direction and the position of each patch type was assigned randomly. Patch

and corridor areas were chosen because the dimensions fall within the range of typical Forest

Service management units and because the entire patch and corridor areas are not shaded by

trees along patch edges, which alone could impact plant community composition.

Presence/absence surveys

We recorded the presence or absence of all plant species in each patch, which included the

central 100 x 100m patch area plus the area of a corridor, two wings, or the additional area in

the rectangle. Surveys took place during 15 May – 30 June in 2001, 2002, and 2003. An

established grid system of 10’ tall PVC poles allowed us to determine our location within

12.5m at any point in the patch. We started in a random corner of the patch and walked back

and forth within 12.5m wide sections, visually covering the entire area of the patch. All new

species were recorded, effectively censusing the presence of all plant species in the patch.

Separate surveys were conducted for the central 100 x 100 m area of each patch and for the

“other” areas (e.g., the corridor, wings, or extra area in the rectangle; see Figure 1.3) so that

species richness could be quantified for both this central area or for the whole patch.

From 17 September – 29 October 2002, we also conducted the survey described above for

only species in Poaceae, Cyperaceae, Juncaceae, and Asteraceae. We did this because

species in these families comprise a large proportion of the total species pool in the

Southeastern United States as well as in our experimental sites (Appendix A). Most of these

species, however, flower and fruit during the late summer and early fall (Radford et al. 1964,

Weakley 2005). Detectability of these species may be higher when they are in bloom or

going to seed, so we conducted a separate survey to account for this possible bias.

12

Permanent plot surveys

Plant species richness and abundance were measured within permanently established

nested plots in each patch (Figure 1.3). The size and number of plots was modeled after

another study on successional dynamics in a system similar to ours (Phillips and Shure

1990). Each center patch contained 24 plots and each peripheral patch contained 34 plots,

for a total of 1280 plots. We distributed plots in three locations within each patch: 1) 12

plots within the center 50 x 50 m of each patch (center plots), 2) 12 plots within 12.5 m from

the edge of a patch (edge plots), and 3) 10 plots in each peripheral patch located in other

areas (corridor, wings, or rectangles, depending on patch type). Plots were apportioned this

way to determine how responses to connectivity and patch shape are affected by habitat

edges.

Surveys took place during the height of plant production from 15 July – 20 August 2000-

2003. Nested plots were used to sample in a way that accommodated plant species with

different vegetative habits. Three plots of different sizes were layered concentrically around

a permanent stake: Herbs (herbaceous species, 1 – 30 cm tall) were surveyed in 1 x 1 m

plots, shrubs (woody species > 30 cm tall and < 2.5 cm dbh) in 3 x 3 m plots, and trees

(woody species > 2.5 cm dbh) in 10 x 10 m plots (Figure 1.3). In 2000, plots were

permanently established and 544 plots were sampled in a pilot survey by counting the

number of stems of each species. From 2001-2003, all 1280 plots were surveyed and species

were assigned to one of ten classes of percent cover after Peet et al. (1998): 0-0.01%, 0.01-

1%, 1-2%, 2-5%, 5-10%, 10-25%, 25-50%, 50-75%, 75-95%, >95%, which allowed for

13

consistency among multiple observers. We used the median value of each cover class in

abundance analyses as recommended by Peet et al. (1998).

Supplemental edge survey

Other studies of edge effects have shown varying distances of edge responses into a

clearing from a surrounding pine forest. In addition to shading, root competition is thought

to be one of the most important for limiting herbaceous species abundances and diversity

(Brockway and Outcalt 1998, Harrington et al. 2003, see Chapter 3). These studies have

observed areas where the herbaceous understory is severely reduced or eliminated up to 18 m

away from a mature pine. While the comparisons of our permanent edge and center plots are

well within this observed 18 m edge effect (and should therefore capture any edge response),

we supplemented these analyses with another edge study in 2005. Using only rectangular

patches in six experimental units, four identical transects were used to record the presence or

absence of all species. Transects were 100 m long and 2 m wide. Two were located directly

against the forest-clearing border and two were evenly spaced along the central 12.5 m of the

patch. A list of all species was created for each transect.

Plant species identification

All plants were identified using the nomenclature of Radford et al. (1964). For the genera

Chamaecrista, Dichanthelium, and Panicum, however, it was necessary to use updated

nomenclature (Weakley 2005). A list of all plant species identified in our study is included

in Appendix A. All unknown species were identified from samples and photographs taken in

the field, compared with specimens at the North Carolina State University Herbarium, and

14

verified with regional botanical experts (pers. comm. T. Wentworth, J. Stucky, B. Miley, and

A. Krings). If we were still unable to identify plant species (less than 3% of all species could

not be identified), they were removed from all datasets prior to analyses. Fourteen species

could not be identified to species, however, these unknown species were able to be

recognized consistently as unique in the field. These species were given unique names as

unknown species (Appendix A) and retained in the datset.

In a few cases two or more species were lumped into a single group for analyses because

they could not be differentiated in the field. Species groups that were combined were: 1)

Centrosema virginianum and Clitoria mariana, and 2) Cyperus retrorsus and another

unknown Cyperus spp. Also, because Dichanthelium spp. are notoriously difficult to

distinguish in the field, all questionable individuals of Dichanthelium spp. were categorized

with four easily recognizable species (listed in Appendix A) based on size and morphology.

Analyses were conducted without removing or lumping species, confirming that these

changes did not alter the direction or significance of our results.

Plant species were categorized by dispersal mode by searching regional flora and guides

(Radford et al. 1964, Miller and Miller 1999, Weakley 2005), conducting a primary literature

search for each species (ISI Web of Science and Biological Abstracts), and searching for

each species in three plant databases: 1) USDA PLANTS Database (USDA 2004), 2)

NatureServe Explorer (NatureServe 2005), and 3) The Illinois Plant Network (Iverson et al.

1999). If species dispersal modes could not be found using these methods (~25%), they were

deduced from seed morphology and congener comparisons. All species could be classified

with reasonable certainty.

Species were divided into two categories based on dispersal mode: 1) biotically dispersed

15

species as those actively dispersed through the ingestion of fruits or seeds by birds or

mammals, and 2) abiotically dispersed species as those dispersed passively by wind, gravity,

or explosive seed capsules (e.g., some pods in Fabaceae). Species were not broken into more

specific dispersal modes (e.g., bird, mammal, wind, gravity, etc.) because the number of

species within subgroups became too small to analyze separately. There were eight species

dispersed by adhesion to animals that were eliminated from analyses because these seeds

readily attached themselves to observers in our system, possibly altering dispersal pathways.

Soil Moisture

Soil moisture holding capacity has been found to be a strong predictor of plant species

richness in southeastern pine forests (Kirkman et al. 2001) and at the Savannah River Site

(Bryan Foster pers. comm.). Soil moisture holding capacity was measured at each permanent

plot using the methods of Salter and Williams (1967). Eight 2.5 x 15 cm soil cores were

taken along the perimeter of each 1 x 1 m vegetation plot. The cores from each plot were

homogenized and placed in soil tins that were 5 cm tall and 6.5 cm in diameter. In the

bottom of each tin, we drilled fifteen 1 mm diameter holes and lined the tin with a piece of

filter paper before filling it with soil. All tins were placed in a shallow pan of water, allowed

to saturate for 24 hours, and then placed on a wire rack until they stopped dripping. All

saturated tins were weighed and subsequently placed in a drying oven at 105° C until they

reached constant mass and then weighed again. The percent soil moisture by weight was

calculated for each sample as: {% soil moisture by weight = [(wet weight – dry weight) *

100%] / dry weight}. Estimating soil moisture in this way provides an estimate for how

much water the soil could potentially hold over long time periods rather than discrete time

16

periods that can be biased by weather events and climatic conditions. At large experimental

scales (over 25 km between some sites in our system), this method allows for the feasible

estimation of soil moisture holding ability with a limited number of observers. Averages of

the soil moisture by weight for all plots in each patch were used as covariates in analyses.

This is an appropriate covariate because estimates were obtained for how much water the soil

could potentially hold rather than over discrete time intervals, which would have possibly

been influenced by our treatment effect (patch type).

Data analysis

Total species richness

To test our predictions about how corridors affect species diversity, we used species

richness (the cumulative count of all species in each patch) as our response variable. We

calculated species richness separately from each of our two survey types: 1) presence-

absence, and 2) a cumulative list from all permanent plots within a patch. To test for corridor

effects on groups of species that vary with dispersal mode, we also subdivided total richness

into groups of species with biotic or abiotic dispersal and analyzed these groups separately.

All response variables were checked for normality and homogeneity of variances prior to

conducting analyses.

The center patch in each experimental unit was removed from analyses so that

comparisons were always made between patches of equal area. In addition, the two duplicate

patch types (winged or rectangle) in each experimental unit were averaged prior to analyses

so as not to not inflate degrees of freedom. We conducted each analysis twice, first using the

entire area of each patch (dark and light gray in Figure 1.3), and second, using only the

17

central 100 x 100m area of each patch (dark gray only in Figure 1.3). We did this to assess

the relative importance of additional areas (i.e., the corridors, extra area in rectangles, or the

wings) in contributing to the patterns we observed.

Species richness was used as a response variable in a repeated measures randomized block

design ANOVA with year as the repeated measure, each of the eight experimental units as

blocks, patch type (connected, rectangular or winged) as a treatment effect, and the natural

log of soil moisture by weight as a covariate. We also tested for an interaction between year

and patch type. We used a mixed model design with block as a random effect and year,

patch type, and the year by patch type interaction as fixed effects. When overall F-values for

patch type were significant, we used two planned linear contrasts to test our hypotheses about

corridor functioning (Table 1.1) by 1) comparing connected to winged and rectangular

patches, and 2) comparing both winged and connected patches to rectangular patches. The

Dunn-Šidák procedure was used to adjust the alpha level according to the number of post-hoc

comparisons (α’ = 1 - (1 - α) 1/k for k comparisons, Sokal and Rohlf (1995)).

Species richness by dispersal mode

Species were divided into two groups, those with passive vs. active dispersal modes, and

analyzed in the same way as described above. Because empirical results from our study sites

have demonstrated that there is greater deposition of bird-dispersed seeds in connected

patches vs. either rectangle or winged unconnected patches (Tewksbury et al. 2002, Haddad

et al. 2003, Levey et al. 2005) and models have been able to predict landscape seed dispersal

patterns from local movement behaviors of birds (Levey et al. 2005), we use a one-tailed test

for the specific linear contrast testing if corridors act through between-patch connectivity

18

effects for biotically dispersed species to test this clear directional prediction. All other

analyses use two-tailed tests. All of the above analyses were conducted using SAS v. 8.02.

Within- and between-patch effects on species richness, evenness, and similarity

We determined the impact of within-patch edge effects on diversity and evenness by

calculating species richness and evenness for each 1 x 1 m plot using Williams’s evenness

index (Williams 1964), measured as [ (1 / D) / S ] where D = ∑ (the proportion of individuals

in the ith species)2 and S is the number of species in the sample. Williams’s evenness index is

a robust measure of evenness that is independent from species richness (Smith and Wilson

1996). We used the PC-Ord Software System (McCune and Mefford 1999) to calculate D

and S prior to determining William’s index. Plots were compared between three locations:

edges, centers, and other areas depending on the patch type (i.e., either corridors, wings, or

rectangles; see Figure 1.3 for plot locations). To analyze these response variables, we used a

mixed model repeated measured randomized block design with year as the unit of replication,

the experimental site as the block (a random effect), patch type as a treatment effect (fixed

effect), plot location nested within patch type (fixed effect), and the natural log of soil

moisture as a covariate. Species richness was natural log transformed and William’s

evenness index was square root transformed prior to analyses to improve normality and the

homogeneity of variances.

To examine if patch type affects the similarity between plots from different locations, we

conducted a Multivariate Analysis of Variance (MANOVA) using the 2003 permanent plot

data. MANOVA allows for multiple response variables to be examined, especially if they

are not independent from one another (i.e., between-plot similarity measures use data from

19

the same plot locations). We measured the similarity between plots at different locations

(edge, center, other) to see if there were gross differences in the composition of these

locations. Two similarity measures were used: Bray-Curtis, which incorporates species

abundances, and Sorensen’s, which is based on presence-absence data. We generated

similarity values between each plot and every other plot within and between locations using

EstimateS software (Colwell 2005). We averaged the similarity values obtained from all

pairwise comparisons of plots between groups (edge to center, edge to other, and center to

other) and used these three means as multiple response variables in the MANOVA. We

included experimental unit and patch type as independent variables, and the variance in soil

moisture at the patch level as a covariate.

Supplemental edge survey

We determined the impact of patch edges by conducting two analyses. First, species

richness was used as a response variable in a mixed model ANOVA. Experimental unit was

used as a random block effect and transect location as a fixed treatment effect. We utilized a

repeated measures analysis to account for the multiple transects within each patch. Second,

we calculated the species similarity using the number of overlapping species between each

transect and its same location as well as between the opposite location (center-center, center-

edge, edge-edge). Since there were four possible center-edge comparisons, we used the

average of the four comparisons in our analyses. We used a mixed model ANOVA with the

number of overlapping species as the response variable, experimental site as a random

blocking factor, and the type of comparison (center-center, center-edge, edge-edge) as a fixed

20

independent variable. All response variables were checked for normality and homogeneity

of variances.

Occurrences of rare and common species by patch type

For each species, we calculated the proportion of all patches (40 total) it occupied from

2001-2003 during our presence-absence surveys. We then created a list of all species, rank-

ordered by the frequency of patches each species occupied (from 40/40 to 1/40 patches). We

conducted chi-square analyses for four groups of species to determine if species rarity was

related to increased frequencies in connected, winged, or rectangular patches. The first chi-

square analysis used species that occur in 75% or more of the patches, the second used those

in 50-74% of the patches, the third included species occurring in 25-49% of the patches, and

the fourth used species in 24% or less of the patches. For those groups that occurred more

often than expected in any patch type, we subsequently examined the frequency of species

with different dispersal and pollination modes, again using chi-square analyses. In addition

to dispersal, we examined pollination because it is essential for sexual reproduction to occur

and pollination modes can be easily assigned to species.

Results

Total species richness

During a pilot study in 2000 (see Permanent Plot Surveys for methods), no difference in

overall species richness was found between any patch type (Tables 1.2 and 1.3, Figure 1.4),

suggesting that the patterns we see later in the study are a result of our experimental

manipulations. A total of 285 species were identified from 2001 until 2003 in all survey

21

types (Appendix A). Experimental units differed in their species richness and composition,

however, since we included this as a random effect, we do not report means or significance

values. The fact that different sites (experimental units) varied in their diversity and

composition strengthens our finding since our treatment effects were still strongly significant

after accounting for these site-to-site differences in our statistical model.

In the whole patch, total species richness in presence-absence surveys differed

significantly by patch type with greater richness in winged and connected patches than in

rectangular patches (Table 1.4, Figure 1.4). There were also significant year effects (Table

1.4), but there was no interaction between year and patch type. Species richness from the

central 100 x 100 m of each patch did not significantly differ (Table 1.2), but the trends were

in the same direction as analyses of the whole patch (Figure 1.5). Likewise, patch species

richness measured from the accumulation of all species recorded in permanent plots resulted

in non-significant, but similar directional trends (Figures 1.5 and 1.6).

Species richness by dispersal mode

Our pilot study in 2000 also showed no difference in species richness of abiotically

dispersed species or biotically dispersed species as a function of patch type, again indicating

that the results we observed were due to our experimental manipulation. When species

richness from 2001-2003 was subdivided into abiotic and biotic dispersal modes, two unique

patterns emerged. When the whole patch was analyzed, abiotically dispersed species had

consistently higher species richness in connected and winged patches than in rectangular

patches (Table 1.5, Figure 1.5c). Our survey of species in Poaceae, Juncaceae, Cyperaceae,

and Asteraceae in the fall of 2002 confirms these same trends (Table 1.6, Figure 1.7a). For

22

biotically dispersed species, richness was lower in rectangular patches than in the other two

patch types, but was also significantly higher in connected patches than winged patches

(Table 1.5, Figure 1.5e). Species richness from the central 100 x 100 m area of each patch

did not significantly differ (Table 1.3), but the trends were in the same direction (Figure

1.6d). Our survey of species in Poaceae, Juncaceae, Cyperaceae, and Asteraceae in the late

summer-fall of 2002 also shows a trend for increased species richness of abiotically

dispersed species in connected and winged patches vs. rectangular patches, but in this case,

the pattern was only significant when analyzing the central 100 x 100 m area of each patch

(Table 1.6, Figure 1.7).

When analyses were conducted using patch species richness from a cumulative list of

species from permanent plots, there was no significant difference between patch types for

total richness or for either dispersal mode group (either for the whole patch or for the central

100 x 100 m). However, the directional trends were consistent with the patch-wide presence-

absence survey (Tables 1.4 and 1.5, Figures 1.5b, 1.5d, 1.5f and 1.6b, 1.6d, 1.6f)

corroborating the patterns we found.

Within- and between-patch effects on species richness, evenness, and similarity

Mean species richness per meter2 was 11.47 ± 1.063 in 2001, 12.16 ± 1.06 in 2002, and

15.10 ± 1.06 in 2003. Species richness/m2 did not differ by patch type (F = 1.112, 17.2, p

=0.3526 and F = 1.602, 14.7, p = 0.2348 respectively) or plot location (F = 0.626, 33.4, p =

0.7106 and F = 0.626, 45.6, p = 0.7105 respectively), but did differ by year

(p ≤ 0.0001), with communities increasing in species richness each year. Species evenness

measured by William’s index did not differ by year, patch type, or plot location (p > 0.05).

23

The average similarity in composition between plots located in different groups (edge,

center, other) was not affected by patch type when measured using either Bray-Curtis

(Pillai’s trace, F = 0.956, 38, p = 0.4694) or Sorensen (Pillai’s trace, F = 1.526, 38, p = 0.1991)

similarity measures. Pairwise similarities between any two plots always differed in

composition, however, the similarity between plots within the same location (e.g., all center

plots) and plots between different locations (e.g., between edge and center plots) were the

same (Bray-Curtis ~0.23, Sorensen ~0.39). This indicates that from one 1 x 1 m plot to the

next, there is always some amount of compositional dissimilarity, but this difference does not

change because of specific locations or patch types.

Supplemental edge study

Species richness did not differ between edge and center locations (37.49 species at

centers, 34.08 at edges, F = 4.681, 3.61, p = 0.1039). In addition, the number of overlapping

species also did not differ between location types (22.00 overlapping species between center

locations, 18.83 between edge locations, and 19.04 between edge and center locations; F =

1.262,10, p = 0.3256). This pattern did not change when other similarity measures, such as the

Sorensen or Jaccard Indices, were used.

Occurrences of rare and common species by patch type

Chi-square analyses revealed that the species with “rare to intermediate” occupancy

frequencies (49% or less of all patches; Figure 1.8) had the strongest influence on increased

species richness in connected and winged patches vs. rectangular patches (χ2 = 23.72, p <

0.0001), while “common” species (50% or greater of patches; Figure 1.8) did not show a

24

significant influence (χ2 = 2.00, p = 0.3687). Two life history traits, dispersal mode and

pollination mode, were significantly different between rare and intermediately distributed

species (χ2 = 23.72, p < 0.0001 and χ2 = 5.3703, p < 0.0205 for dispersal and pollination

modes respectively), while no difference existed for common species. We subsequently

evaluated the frequency of species with different dispersal (bird, mammal, ballistic, gravity,

and wind) and pollination (insect, wind) modes in each patch type (Table 1.7) and found that

rare and intermediate species with ballistic, gravity, and wind dispersal as well as species

pollinated by insects (vs. wind), were more likely to be found in connected and winged

patches. Some combinations of these traits could also be analyzed together (e.g., gravity

dispersed and insect pollinated species), which indicated that insect pollination is important

for generating responses in gravity and wind-dispersed species.

Finally, we examined the importance of within-patch edge effects for these species by

assessing the distribution of rare and intermediate species in permanent plot locations. We

used a chi-square analysis to compare the frequency of species occurrences at edge, center,

and other plots and determined that these species did not occur in any plot type more than in

the others, corroborating our previous findings. In other words, within-patch edge effects do

not seem to driving the distribution of intermediate and rare species occurrences.

Discussion

Corridors increased plant species richness through both connectivity and patch shape

effects. The degree to which each of these effects increased species richness was related to

dispersal modes. Actively-dispersed species responded to corridors mainly through

connectivity effects and less so to patch shape effects, while passively-dispersed species

25

responded mainly to patch shape effects. Plant community evenness and composition were

unaffected by corridors or patch shape. The higher species richness in connected and winged

patches can be explained by the increased presence of species with rare and intermediate

patch occupancy frequencies, suggesting that corridors operate by increasing the probability

of uncommon events that lead to greater colonization or persistence and that there may be

specific life history traits or interactions that are more or less affected by corridors.

Separating drift-fence and edge effects

We did not detect effects of edges on species richness, evenness, or similarity in either our

permanent plots or in our supplemental edge study. Rare and intermediately common species

were equally common in all plot types (edge plot frequencies = center plot frequencies),

suggesting that the increased amount of edge in connected and winged patches is not driving

the increase in diversity that we observe. This was surprising, given that other studies have

found strong impacts of edges on community composition of plants (e.g., Murcia 1995, Fox

et al. 1997, Didham and Lawton 1999, Restrepo and Vargas 1999, Euskirchen et al. 2001,

Bruna 2002, Benitez-Malvido and Martinez-Ramos 2003, Ries et al. 2004, Chapter 3) and

other species (Davies et al. 2001). Nearly all of these studies, however, have been performed

in late-successional environments such as tropical forests. In early-successional

environments like ours, effects of edges on individual species may be diluted at the

community level due to the relatively strong influences of environmental heterogeneity,

historical influences, and species interactions. Another study of edge effects across clearcuts

in red and jack pine forests corroborates our findings (Euskirchen et al. 2001). The latter

study also did not detect consistent shifts in species richness, evenness, or similarity between

26

edge and core habitat locations within clearcuts (Euskirchen et al. 2001). In light of the

findings of these other studies, the fact that we detect corridor and patch shape effects above

this “ecological noise” makes our seemingly small total effect sizes quite large in

comparison.

The lack of a discernable edge effect suggests that the increased species richness in

winged patches is not because of dissimilarity in plant communities at edges and center

locations within the patch. It also suggests that “drift-fence effects” may be operating. This

could be due to increased colonization probabilities caused by changes in wind dynamics at

edges. If edges increase wind turbulence, an important factor for dispersal of wind-dispersed

plants (Soons et al. 2004a, Soons et al. 2004b), then patches that have more edge could more

effectively redistribute seeds from plants within the patch. This could increase species

diversity by increasing the proportion of seeds that remain in a patch that would otherwise be

lost to the surrounding landscape matrix.

Applicability of island biogeography

The idea of corridors initially arose from applying island biogeography to terrestrial

landscapes, and these theoretical underpinnings have given corridor implementation intuitive

appeal and led to their widespread use. However, many empirical studies have found

evidence that runs contrary to theory (e.g., Jules et al. 1999, Holl and Crone 2004), casting

doubt on the ability of island biogeography to be applied in terrestrial settings. The results

from our study suggest that applied island biogeography indeed operates in terrestrial

systems, but that it works in conjunction with, and is often weaker than, within- and between-

patch environmental heterogeneity. Moreover, the relative strength of applied biogeography

27

relative to environmental processes can depend on the organism of interest and may be

largely predictable by life history traits. For plants in our study system, the effects of applied

island biogeography were relatively strong for biotically dispersed species when compared to

abiotically dispersed species. It is possible that the degree to which island biogeography can

be applied to any particular organism depends on its dispersal mode in conjunction with the

scale of the landscape and the quality and degree of contrast between the habitat of interest

and the surrounding matrix. Theoretical predictions of applied biogeography may be met for

species that can actively direct their dispersal through a landscape via behavioral choices.

But, species that are dispersed passively through a landscape are subject to the speed and

direction of winds or currents and any landscape feature (i.e., patch shape) that alters these

conditions may be more important than connectivity effects per se. Additionally, life stage

specific processes may be differentially affected, making some life history traits or species

interactions more or less limiting. The frequency of rare and intermediate species with

abiotic dispersal not only is limited by dispersal, but also pollination. Other studies in our

system have also suggested that traits such as seed size can differentially impact predation

rates in different patch types (Orrock and Damschen 2005). Further studies are needed to

assess the relative importance of each of these processes for specific species groups.

Generality of observed patterns

Our experimental design uses openings within managed pine plantation created by cutting

and burning. Early colonists in disturbance-generated openings in the southeastern United

States are often considered to be widespread generalists and “good colonizers” and are

therefore assumed to be less affected by habitat isolation. If this assumption is true, we

28

provide a conservative test of the effects of corridors. That is, if corridors can impact species

that are generally not considered to be dispersal limited, then they may have greater impacts

on those species that are less mobile, especially over long time periods. Additionally, most

of the species found in our study sites are native to the region (Appendix A) and over half of

the species are native to longleaf pine forests, a threatened ecosystem once covering the

coastal plain of the southeastern United States (Frost 1993, Smith et al. 2000). As early-

successional communities, our experimental patches may be much closer to historical

environmental conditions of longleaf pine forests, which had low tree densities, high light

penetration, and open understories (Frost 1993, Smith et al. 2000). The planted pine

plantations surrounding our experimental patches, on the other hand, do not allow for many

of the species dependent on these historical conditions to germinate or persist due mainly to

increased root competition with mature trees (Brockway and Outcalt 1998, Battaglia et al.

2003, Chapter 3, Harrington et al. 2003). For these reasons, it is likely that the results we

observe from 285 species in our experimental landscapes are generally applicable to other

systems, especially those dependent on open habitats such as longleaf pine forest, tallgrass

prairie, or oak savanna. Our results for abiotically dispersed species may also have

implications that extend beyond terrestrial environments to marine systems, where a large

proportion of organisms also experience passive dispersal and where alterations of currents

and flow could occur with changes in the spatial configuration of habitat.

Conservation implications and recommendations for future work

As reserve designs are considered for particular habitats or species, the life history

characteristics of individual species as well as the types of traits commonly found in general

29

community types need to be considered carefully in conjunction with any proposed reserve

design.

While we have primarily shown that dispersal and pollination modes can be useful

categories for understanding the ways corridors function, there are many other traits that

could be affected by corridors. Such traits may be more important for individual species or

at specific successional stages (i.e., dispersal traits initially important after a disturbance, but

competitive and reproductive traits later becoming more important). For example, other

studies have shown that seed predation is affected by corridors and patch shape (Orrock et al.

2003, Orrock and Damschen 2005), so advantages in dispersal could be outweighed by losses

due to predation. Thus far, studies have either considered specific mechanisms individually

(e.g., Haddad 1999b, Haddad and Baum 1999, Haddad 2000, Tewksbury et al. 2002, Orrock

et al. 2003, Orrock and Damschen 2005, Townsend and Levey 2005, Levey et al. In Review)

or have examined general patterns in community composition (described here).

Additionally, most studies span only a few years, making it difficult to understand the

relative importance of specific mechanisms, like dispersal, pollination, or predation,

temporally over the course of succession. Future research should experimentally test for the

relative importance of specific mechanisms (e.g., dispersal, species interactions, local

environment, etc.) to community diversity and composition over longer time periods.

Acknowledgements

This work was made possible by funding from NSF DEB-9907365, the D.O.E. –Savannah

River Operations Office through the Forest Service Savannah River under the Interagency

Agreement DE-1A09-76SR00056 (a National Environmental Research Park), Sigma Xi, and

30

North Carolina State University. We are especially thankful for the leadership provided by

John Blake and Ed Olson and the quality assistance of Kim Hale at the Forest Service

Savannah River. Thank you to the members of The Corridor Project for your insight and

support: Jory Brinkerhoff, Brent Danielson, Doug Levey, John Orrock, Sarah Sargent, Josh

Tewksbury, Patricia Townsend, and Aimee Weldon. Tremendous thanks to the support of

John Orrock and many outstanding field technicians: Justin Cooper, Julie Davis, Dominique

Donato, Jennifer Freeman, Sarah Gabris, Bethany Honeyager, Daniel Kuefler, Elizabeth

Long, Jenny Mayer, and Sara Woolard. We are also most grateful for support and site

maintenance provided by Dave Coyle, Chris Hobson, Dan Shea, Kim Hale, Iola Hallock,

Steve Polk, Tim Kolnik, Kaye Mills, Travis McCollough, Bob Morgan, Ron Mosely, Jarvis

Brown, Diana Johnston, Bobby Phillips, Jamie Scott, Tim Travis, Nick Vines, Pat Wright,

and the Savannah River Forest Service Fire Crew. Thanks to Don Imm, Alexander Krings,

Brett Miley, Jon Stucky, Tom Wentworth, and the North Carolina State Herbarium for

assistance with specimen identification and to Jonathan Levine for providing logistical

support to EID during the writing process. Thank you to John Orrock, Mark Vellend, Kevin

Gross, members of the Haddad Lab, and the Terrestrial Plant Ecology Seminar at UCSB for

your most helpful suggestions and statistical guidance. The manuscript benefited from the

helpful comments of John Orrock, Jim Gilliam, George Hess, Kristen Rosenfeld, Tom Rufty,

Mark Vellend, and Tom Wentworth

31

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42

Table 1.1. Predictions for how plant community diversity will respond to corridors. “Connected” = connected patch type, “Winged” = winged patch type, and “Rectangular”= rectangular patch type. Within- or

between-patch

process

Corridor effect

type Experimental prediction

Species predicted to exhibit

this response

Between-patch Connectivity effect Connected > (Winged =

Rectangular)

Biotically dispersed species

Between-patch Drift-fence effect (Connected = Winged) >

Rectangular, no within-patch

effects

Abiotically dispersed species

Within-patch Area effect Connected = Rectangular =

Winged

All species

Within-patch Edge effects (Connected = Winged) >

Rectangular, patch edges > patch

centers, and/or edges dissimilar

from centers

All species

43

Table 1.2. Results from repeated measures ANOVA for total species richness in the central 100 x 100 m of each patch. Results are shown for species richness estimates from each survey type (presence-absence and permanent plots) separately. Factors for 2000 include patch type (connected, unconnected winged, unconnected rectangular) and the natural log of log soil moisture by weight. Factors for 2001-2003 include year (2001, 2002, 2003), patch type (connected, unconnected winged, unconnected rectangular), an interaction between year and patch type, and the natural log of soil moisture by weight for each patch. Numerator and denominator degrees of freedom are listed as subscripts for each F-value. Presence absence surveys were not conducted in 2000.

2000 2001-2003

F p F p

Presence-Absence Surveys

Source of Variance

Year n.a. n.a. 27.822, 42 < 0.0001

Patch type n.a. n.a. 1.382, 13.2 0.2852

Year*Patch type n.a. n.a. 0.914, 42 0.4685

Soil moisture n.a. n.a. 2.271, 15.4 0.1524

Planned linear contrasts

Connected > (Rectangular = Winged) n.a. n.a. 1.621, 13.2 0.2244

(Connected = Winged) > Rectangular n.a. n.a. 1.211, 13.2 0.3977

Permanent Plot Data

Source of Variance

Year n.a. n.a. 23.012, 40.6 < 0.0001

Patch type 0.522, 13 0.6040 0.472, 13.8 0.6329

Year* Patch type n.a. n.a. 1.104, 40.6 0.3685

Soil moisture 0.291, 13 0.6001 2.261, 15.1 0.1531

Planned linear contrasts

Connected > (Rectangular = Winged) 0.031, 13 0.8722 0.161, 13.8 0.6915

(Connected = Winged) > Rectangular 1.031, 13 0.3288 0.761, 13.8 0.3977

44

Table 1.3. Results from repeated measures ANOVA for species richness by dispersal mode (abiotic and biotic) in the central 100 x 100 m of each patch. Results are shown for species richness estimates from each survey type (presence-absence and permanent plots) separately. Factors for 2000 include patch type (connected, unconnected winged, unconnected rectangular) and log soil moisture. Factors for 2001-2003 include year (2001, 2002, 2003), patch type (connected, unconnected winged, unconnected rectangular), an interaction between year and patch type, and the log soil moisture by weight for each patch. Numerator and denominator degrees of freedom are listed as subscripts for each F-value. Presence absence surveys were not conducted in 2000.

2000 2001-2003

F p F p

Presence-Absence Surveys – Biotically Dispersed

Source of Variance

Year n.a. n.a. 7.412, 42 0.0018

Patch type n.a. n.a. 2.392, 20 0.1177

Year*Patch type n.a. n.a. 0.304, 42 0.8771

Soil moisture n.a. n.a. 0.051, 20 0.8256

Planned linear contrasts

Connected > (Rectangular = Winged) n.a. n.a. 4.271, 20 0.0520

(Connected = Winged) > Rectangular n.a. n.a. 0.561, 20 0.4619

Permanent Plot Data – Biotically Dispersed

Source of Variance

Year n.a. n.a. 3.132, 40.6 0.0545

Patch type 1.662, 13 0.2272 1.302, 13.4 0.3046

Year* Patch type n.a. n.a. 1.664, 40.6 0.1788

Soil moisture 4.091, 13 0.0641 4.541, 18.5 0.0469

Planned linear contrasts

Connected > (Rectangular = Winged) 1.851, 13 0.1967 2.471, 13.5 0.1391

(Connected = Winged) > Rectangular 1.391, 13 0.2591 0.161, 13.4 0.6979

45

Presence-Absence Surveys – Abiotically Dispersed

Source of Variance

Year n.a. n.a. 32.732, 21 <0.0001

Patch type n.a. n.a. 1.082, 12.5 0.3697

Year*Patch type n.a. n.a. 0.984, 21 0.4389

Soil moisture n.a. n.a. 8.551, 13.8 0.0112

Planned linear contrasts

Connected > (Rectangular = Winged) n.a. n.a. 0.601, 12.5 0.4536

(Connected = Winged) > Rectangular n.a. n.a. 1.591, 12.4 0.2309

Permanent Plot Data – Abiotically Dispersed

Source of Variance

Year n.a. n.a. 18.012,

40.8

<0.0001

Patch type 0.552, 13 0.5876 1.102, 14.3 0.3593

Year* Patch type n.a. n.a. 0.754, 40.8 0.5665

Soil moisture 0.031, 13 0.8582 0.731, 15.9 0.4052

Planned linear contrasts

Connected > (Rectangular = Winged) 0.611, 13 0.4494 1.471, 14.3 0.2457

(Connected = Winged) > Rectangular 0.531, 13 0.4815 0.681, 14.3 0.4230

46

Table 1.4. Results from repeated measures ANOVA for total species richness in the whole patch. Results are shown for species richness estimates from each survey type (presence-absence and permanent plots) separately. Factors include year (2001, 2002, 2003), patch type (connected, unconnected winged, unconnected rectangular), an interaction between year and patch type, and the log soil moisture by weight for each patch. Numerator and denominator degrees of freedom are listed as subscripts for each F-value. Presence absence surveys were not conducted in 2000. 2001-2003

F p

Presence-Absence Surveys

Source of Variance

Year 43.922, 40.9 <.0.0001

Patch type 4.442, 15.2 0.0303

Year*Patch type 0.324, 40.9 0.8608

Soil moisture 5.901, 17.2 0.0263

Planned linear contrasts

Connected > (Rectangular = Winged) 0.871, 15.3 0.3649

(Connected = Winged) > Rectangular 8.381, 15.2 0.0110

Permanent Plot Data

Source of Variance

Year 26.272, 21 <0.0001

Patch type 2.212, 9.71 0.1616

Year* Patch type 1.114, 21 0.3802

Soil moisture 2.001, 14.3 0.1791

Planned linear contrasts

Connected > (Rectangular = Winged) 0.651, 9.8 0.4384

(Connected = Winged) > Rectangular 4.001, 9.68 0.0744

47

Table 1.5. Results from repeated measures ANOVA for species richness by dispersal mode (abiotic or biotic) in the whole patch. Results are shown for species richness estimates from each survey type (presence-absence and permanent plots) separately. Factors include year (2001, 2002, 2003), patch type (connected, unconnected winged, unconnected rectangular), an interaction between year and patch type, and the log soil moisture by weight for each patch. Numerator and denominator degrees of freedom are listed as subscripts for each F-value. Presence absence surveys were not conducted in 2000.

2001-2003

F p

Presence-Absence Surveys – Biotically Dispersed

Source of Variance

Year 2.862, 42 0.0687

Patch type 3.442, 20 0.0521

Year*Patch type 0.294, 42 0.8808

Soil moisture 0.591, 20 0.4506

Planned linear contrasts

Connected > (Rectangular = Winged) 2.981, 20 0.0498

(Connected = Winged) > Rectangular 4.251, 20 0.0526

Permanent Plot Data – Biotically Dispersed

Source of Variance

Year 8.622, 42 0.5684

Patch type 0.592, 13.7 0.5684

Year* Patch type 0.874, 42 0.4871

Soil moisture 2.771, 20 0.1116

Planned linear contrasts

Connected > (Rectangular = Winged) 0.791, 13.8 0.3879

(Connected = Winged) > Rectangular 0.451, 13.6 0.5132

48

Presence-Absence Surveys – Abiotically Dispersed

Source of Variance

Year 61.862, 40.5 <0.0001

Patch type 5.152, 15 0.0199

Year*Patch type 0.364, 40.5 0.8358

Soil moisture 6.301, 16.3 0.0229

Planned linear contrasts

Connected > (Rectangular = Winged) 0.241, 15 0.6330

(Connected = Winged) > Rectangular 10.241, 15 0.0060

Permanent Plot Data – Abiotically Dispersed

Source of Variance

Year 17.542, 41.7 <0.0001

Patch type 2.232, 15.8 0.1406

Year* Patch type 0.374, 41.7 0.8295

Soil moisture 1.051, 18 0.3194

Planned linear contrasts

Connected > (Rectangular = Winged) 1.581, 15.9 0.2275

(Connected = Winged) > Rectangular 2.531, 15.8 0.1318

49

Table 1.6. Results from repeated measures ANOVA for total species richness of all species in Poaceae, Juncaceae, Cyperaceae, and Asteraceae in the whole patch. Species richness was estimated from a survey in the fall of 2002. Factors include patch type (connected, unconnected winged, unconnected rectangular) and the log soil moisture by weight for each patch. Numerator and denominator degrees of freedom are listed as subscripts for each F-value.

2002 Fall Survey of Species in Poaceae, Juncaceae, Cyperaceae, and

Asteraceae

F p

Whole Patch

Source of Variance

Patch type 1.932, 13 0.1848

Soil moisture 4.031, 13 0.6600

Planned linear contrasts

Connected > (Rectangular = Winged) 0.001, 13 0.9447

(Connected = Winged) > Rectangular 3.811, 13 0.1729

Central 100 x 100 m Area

Source of Variance

Patch type 3.812, 13 0.0498

Soil moisture 2.891, 13 0.1130

Planned linear contrasts

Connected > (Rectangular = Winged) 1.641, 13 0.2224

(Connected = Winged) > Rectangular 6.151, 13 0.0276

50

Table 1.7. Chi-square analyses of species traits by patch type for rare and intermediate species. Expected frequencies come from the number of patches of each type (connected - 16, rectangular - 12, winged – 12) out of the total 40 patches in our experimental landscape. Species were characterized by pollination mode and dispersal mode and separate chi-square analyses were performed for each category. For those pollination and dispersal modes that were significant, categories that overlapped were further subdivided and analyzed separately (note: we did not analyze categories that did not exist like wind pollinated and bird dispersed species). Connected Rectangle Winged

Expected Frequencies: 16 (40%) 12 (30%) 12 (30%)

Observed Frequencies:

Dispersal Pollination χ2 p-value

Insect 281 (45.03%) 138 (22.12%) 205 (32.85%) 18.5735 <0.0001

Wind 66 (42.31%) 36 (23.08%) 54 (34.62%) 3.0877 0.1490

Ballistic 42 (52.50%) 16 (20.00%) 22 (22.50%) 5.9583 0.0508

Bird 31 (41.33%) 21 (28.00%) 23 (30.37%) 0.1444 0.9303

Mammal (& bird) 28 (44.44%) 13 (20.63%) 22 (34.92%) 2.6614 0.2643

Gravity Alone 116 (45.85%) 53 (20.95%) 84 (33.22%) 9.9381 0.0069

Wind 130 (42.07%) 71 (22.98%) 108 (34.95%) 7.9364 0.0189

Gravity Alone Insect 92 (46.94%) 40 (20.41%) 64 (32.65%) 8.8299 0.0121

Gravity Alone Wind 24 (42.11%) 13 (22.81%) 20 (35.09%) 1.5380 0.4635

Wind Insect 98 (42.06%) 55 (23.61%) 80 (34.33%) 4.8827 0.0807

Wind Wind 32 (42.11%) 16 (21.01%) 28 (30.00%) 3.2982 0.1922

51

Figure 1.1. The proportion of corridor studies that have focused on: a) animals vs. plants, and b) individual- or population-level processes vs. multiple-species or community processes (including any study examining two or more interacting species). Proportions were generated from an ISI Web of Science search on March 22, 2005 with the key word “corridor(s)” in combination with one or more of the following words: “landscape”, “movement”, “plant(s)”, “animal(s)”, “reserve”, “population”, “community”, and “fragmentation”. In addition, studies that the authors knew about from other sources were added to this list.

52

Figure 1.2. Experimental landscape located at the Savannah River Site near Aiken, SC. Eight experimental units each contain a system of five connected and unconnected patches. Center patches are 100 x 100 m and connected to a patch of equal size by a 150 x 25 m corridor. Three unconnected patches are either “rectangular” to test for “area effects” or “winged” to test for “drift-fence effects” and alterations of edge:area ratios.

53

Figure 1.3. Patch and plot design. The center patch (shown in white) was never included in analyses, while light gray areas were included only in analyses of the central 100 x 100 m area of each patch, and both light and dark gray areas were included for whole patch analyses. The black squares represent one nested plot with a 1 x 1 m, 3 x 3 m, and 10 x 10 m plot sampling herbs, shrubs, and trees, respectively. Plots are located in “edges”, “centers” and in three additional areas: “corridors”, “wings”, and “rectangles”.

54

Figure 1.4. Species richness measured by 544 permanent plots in edge and center plot locations only during July-August 2000. Species richness for connected, unconnected rectangular, and unconnected winged patches is shown from a) the total plant community, b) abiotically dispersed species, and c) biotically dispersed species. Error bars indicate standard errors and corresponding F and p-values are in Tables 1.2 and 1.3.

55

Figure 1.5. Species richness from 2001-2003 measured from patch-level presence-absence suveys (a, c, e) and a cumulative list of species from permanent plots (b, d, f) for the whole patch. Species richness for connected, unconnected rectangular, and unconnected winged patches is shown for the total plant community (a, b), abiotically dispersed species (c, d), and biotically dispersed species (e, f). Error bars indicate standard errors and corresponding F and p-values are in Tables 1.4 and 1.5.

56

Figure 1.6. Species richness from 2001-2003 measured from patch-level presence-absence surveys (a, c, e) and a cumulative list of species from permanent plots (b, d, f) for the central 100 x 100 m of each patch. Species richness for connected, unconnected rectangular, and unconnected winged patches is shown for the total plant community (a, b), abiotically dispersed species (c, d), and biotically dispersed species (e, f). Error bars indicate standard errors and corresponding F and p-values are in Tables 1.2 and 1.3.

57

Figure 1.7. Species richness measured from patch-level presence-absence surveys of species in Poaceae, Juncaceae, Cyperaceae, and Asteraceae in the fall of 2002. Species richness for connected, unconnected rectangular, and unconnected winged patches is shown from a) the whole patch, b) the central 100 x 100 m of each patch. Error bars indicate standard errors and corresponding F and p-values are in Tables 1.6.

58

Figure 1.8. The proportion of total patches (40) occupied by each species found in presence-absence surveys from 2001-2003. Species richness of common species (occupying 50 % or more of all patches) was not significantly affected by patch type, while species richness of intermediately common and rare species (occupying 49% or less of all patches) were significantly affected by patch type. All species are included in the figure but the x-axis only lists a portion of these species due to space constraints.

59

CHAPTER 2: HABITAT CONFIGURATION AFFECTS A SEEDBANK

A paper to be submitted to Journal of Ecology

Ellen I. Damschen, Kristen M. Rosenfeld, Nick M. Haddad, and Thomas R. Wentworth

Abstract

Seedbanks can make large contributions to the composition of standing plant

communities, especially following disturbance. The spatial configuration of a landscape may

affect processes that add or remove seeds from the seedbank, such as dispersal, germination

conditions, or seed predation. In a large-scale habitat fragmentation experiment, we

evaluated the influence of corridors and patch shape on the seedbank. These landscapes were

created by clearing and burning, and the resulting early-successional community grew and

dispersed seeds over the next four growing seasons. During the fourth growing season, we

sampled the soil seedbank to assess whether the experimental configuration of habitat had

altered the density and diversity of seeds in the soil. The soil seedbank was most similar to

the plant community established following disturbance and progressively became more

dissimilar over time. Corridors and patch shape did not affect seed density or diversity, but

the edges of all patch types had significantly fewer species of seeds than the centers. Of the

species that were only present at edges or centers of patches, but not both, wind and gravity

dispersed species were more likely to be at patch centers than at patch edges. These changes

in seedbank composition are likely due to the combined influence of several edge effects on

seed dispersal, predation, and germination requirements.

60

Introduction

Fragmentation impacts and seedbanks

Plant community composition can be affected substantially by inputs from the seedbank

(the pool of germinable seeds laying dormant in the soil), which can be especially strong

under particular climatic conditions or after disturbance events (e.g., Kalamees and Zobel

1997, 1998, Bossuyt et al. 2002, Kalamees and Zobel 2002). Propagules enter the seedbank

through dispersal and exit through germination or mortality via seed senescence or seed

predation (Figure 2.1).

One factor that may have important effects on seedbanks, but has received little attention,

is habitat fragmentation. Habitat loss and alteration is one of the biggest contributors to the

endangerment of species across the globe (Wilcove 1998, Yiming and Wilcove 2005).

Among other effects, habitat fragmentation has been shown to influence critical plant-animal

interactions such as pollination and seed dispersal (Rathcke and Jules 1993, Aizen and

Feinsinger 1994, Tewksbury et al. 2002, Townsend and Levey 2005), as well as negative

interactions such as seed predation (Orrock et al. 2003, Chacoff et al. 2004, Orrock and

Damschen 2005). Because propagule inputs, survivorship, and seedling emergence from the

seedbank depend on processes that are known to be affected by spatial alterations of habitat

(Figure 2.1), the composition of the seedbank may be altered in fragmented landscapes.

In this study, we tested for the effects of two habitat characteristics that are altered by

fragmentation: patch shape and connectivity. We specifically test for the effects of corridors,

thin strips of habitat connecting two otherwise isolated habitat patches, on the density and

diversity of seeds in a seedbank. Corridors can operate through three main between-patch

processes (see Chapter 1 for further description): First, corridors can act through

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“connectivity effects”, where they serve to increase movement rates between the patches they

connect. Second, corridors can operate solely by increasing habitat area (“area effects”).

And, finally, they can operate through “drift-fence effects” (after Haddad and Baum 1999) by

filtering organisms moving through the surrounding landscape into a connected patch simply

because of the expanse of the corridor in the landscape, intercepting dispersing organisms

and directing them into a connected patch. Additionally, corridors can alter within-patch

processes by changing the prevalence of local edge effects because corridors are two long,

linear edges. Increased edge-to-area ratios are a major result of habitat fragmentation, which

can have dramatic consequences for the distribution and abundance of organisms due to

shifts in habitat types and available resources at ecotones (reviewed in Ries et al. 2004, Ries

and Sisk 2004).

At the Savannah River Site, near Aiken, South Carolina, we have worked with the U.S.

Forest Service to create experimental landscapes to test for the effects of habitat

fragmentation on plant communities through alterations to connectivity and patch shape (see

Methods for study site description). These sites were created by harvesting and burning

(typical forest management practice in this region) and allowed to undergo succession for

four growing seasons. During this time, the herbaceous understory grew, reproduced, and

dispersed propagules under our imposed spatial landscape. We have evidence that spatial

processes in this landscape altered the three main factors that could affect seedbank

composition, including seed dispersal (Tewksbury et al. 2002), local microsite conditions

(Orrock unpublished data), and seed mortality by predation (Orrock et al. 2003, Orrock and

Damschen 2005). The deposition of bird-dispersed seeds was greater in connected patches

than in any other unconnected patch type (Tewksbury et al. 2002), and edge effects and patch

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shape affected both rodent and arthropod seed predation rates (Orrock et al. 2003, Orrock and

Damschen 2005). Other studies have shown that seedbanks can differ across habitat edges

(Dougall and Dodd 1997, Cubina and Aide 2001, Lamb and Mallik 2003, Landenberger and

McGraw 2004, Devlaeminck et al. 2005) and that dispersal can be an important determinant

of seedbank composition (Cubina and Aide 2001, Landenberger and McGraw 2004).

We predicted that the seedbank community would differ by patch type (i.e., connected,

winged, or rectangular) and by its proximity to a patch edge. However, while we have

learned much about how space influences the processes that contribute to the seedbank

composition in our landscape, directional predictions for seedbank community response are

not clear because the relative strength of each of these forces for population and community

dynamics is unknown. Here, we tested for the effects of patch shape and within-patch

location (edge, center, “other”; Figure 2.2) on the density and diversity of the seedbank and

evaluated the importance of dispersal, microsite limitation, seed predation, and senescence in

light of our findings. We also hypothesized that the seedbank community would be most

similar in composition to the established plant community established in the first year after

our sites were cleared and burned and would become progressively increasingly dissimilar

from the established growing plant community in subsequent years.

Methods

Study site

Our study took place at the Savannah River Site near Aiken, South Carolina, a National

Environmental Research Park (NERP). The Savannah River Site lies in the Upper Coastal

Plain Physiographic Province and is characterized by sandy, well-drained soils. The U.S.

63

Forest Service manages the SRS for Pinus taeda (loblolly pine) and Pinus palustris (longleaf

pine) (Workman and McLeod 1990). In the winter of 1999-2000, the Forest Service helped

to create a large-scale experimental landscape consisting of open habitat patches within a

surrounding matrix of mature pine forest (Figure 2.2). The landscape consisted of 8

experimental units made up of 5 patches each. One central 100 x 100 m (1 ha) patch was

surrounded by four 150 m peripheral patches (Figure 2.2). A 150 x 25 m corridor connected

the central patch to a single peripheral patch while the other three peripheral patches were

unconnected (Figure 2.2). The cardinal direction of the corridor was randomized to adjust

for plants or animals that exhibit daily or seasonal directionality in their movement.

Connectivity effects were tested using the connected patches, while the two unique

unconnected patch shapes allowed us to test for the other possible corridor functions.

“Rectangular” patches tested for area effects by adding the area equivalent to a corridor to

100 x 100 m patch but not providing a connection. “Winged” patches have two 75 x 25 m

projections off either patch side to test for the impacts of corridors as drift-fences. These

changes in patch shape may alter within-patch processes through local edge effects, or they

may affect between-patch processes by filtering dispersing individuals into patches because

of their expanse across the landscape. Four of the experimental units had one rectangular and

two winged patches and the other four experimental units had two rectangular and one

winged patch.

Seed Bank Sampling

In 2003, 3.5 years after patch creation, we collected soil seedbank samples from the 1280

permanent 1 x 1 m plots used to sample the standing vegetation (described below, Figure 2.2)

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using a modified version of Gross’s (1990) “germination following cold-stratification seed

bank estimation method.” From 16 – 29 June 2003, eight soil cores (15 cm deep and 2.5 cm

in diameter) were extracted at every 0.5 m around the perimeter of each 1 x 1 m plot (Figure

2.2). The litter layer was brushed away prior to taking each core. The eight cores were

combined and homogenized into one sample per plot and then cold stratified for 6 weeks at

2.2 - 3.3o C. Other studies have found that a greater number of combined small samples are

more appropriate than a small number of large samples for estimating the composition of the

seedbank, which is subject to small scale heterogeneity (e.g., Benoit et al. 1989, Cohen et al.

2004)

From 15 September – 30 November 2003, samples were allowed to germinate under

greenhouse conditions at North Carolina State University, Raleigh, NC. 27.94 x 44.88 cm

plastic trays with several small drainage holes were filled 2.54 cm deep with Metro Mix 360

sterile potting medium and separated into thirds using plastic dividers. Each sample was

spread across the sterile potting mix in one section of a divided tray, resulting in a sample

layer approximately 1.5 - 2.5 cm deep on top of the sterile soil medium. Samples were

randomly assigned to trays and tables within three greenhouses. To control for possible seed

contaminants present in greenhouses, eleven trays were randomly placed throughout the

greenhouses and filled only with sterile soil. Only one species, Erigeron canadensis,

emerged in trays close to an intake fan in one greenhouse, so this species was removed from

data collected from all trays within 5 m of this fan (this species did not emerge in any other

control tray that was farther than 1.5 m from the fan, so this contamination distance is

conservative). Because E. canadensis was a species commonly found in the established plant

community, we did not want to eliminate the species from the dataset, because most

65

individuals were not likely to be contaminants.

All trays were watered gently until the soil was saturated. Watering took place one to two

times per day to keep the soil consistently moist for the duration of the experiment. We

fertilized all trays once a month for two reasons: 1) some seeds may require fertilization for

germination (Baskin and Baskin 1998), and 2) to assure the survivorship of seedlings

growing in trays awaiting identification (see monitoring methods below). We fertilized all

trays with a solution of 0.25 teaspoon of Peters 20 : 20 : 20 (N : P2O5 : K2O) fertilizer per

gallon of water on 9 September and again on 8 October 2003. Similarly, to promote the

germination of seeds beneath the soil surface, we disturbed each sample once from 29 - 31

September 2003 by gently turning-over the soil with a clean plastic fork, taking care not to

combine the soil sample with the underlying sterile potting mix or to disturb already growing

seedlings. While it is possible that germination requirements for all species were met, our

method mimics natural field conditions by providing high light and moisture conditions for

seed germination and is a common method used in other seedbank studies (Thompson and

Grime 1979, Gross 1990).

Seedling monitoring

Seedling monitoring began on 26 September 2003 and continued weekly until 30

November - 3 December 2003. All seedlings were identified to species, counted, and then

removed from the trays. If we were unable to identify a seedling, it was counted and marked

with a unique combination of colored straight pins until identification was possible. If a

species began to flower and was still unidentified, it was removed and pressed for later

identification. Those seedlings that had not flowered and were still unidentified at the

66

conclusion of the experiment were transplanted to larger pots and allowed to mature until 15

August 2004, when all seedlings were identified or pressed. If a germinant died before

identification was possible, it was recorded as emerging, but was not included in any

analyses. Analyses were also performed that included dead individuals, but doing so did not

alter the direction or significance of our findings.

We followed the nomenclature of Radford et al. (1964) except for the genera

Chamaecrista and Gnapthalium, where the updated nomenclature of Weakley (2005) was

used. A complete species list can be found in Appendix B. Because grasses in the genus

Dichanthelium are notoriously difficult to identify, we classified all Dichanthelium spp.

individuals into three groups based on size. All Rubus spp. were also grouped together as

one species because identification was dependent upon reproductive parts and none of these

individuals ever flowered. Similarly, we lumped both Andropogon ternarius and A.

virginicus into one species. All species were matched to specimens at the North Carolina

State University Herbarium and verified by regional experts (pers. comm. J. Stucky, and A.

Krings). We could not identify eleven species to genus or species, but recognized them as

unique. These species were given a unique identifying name for use in analyses (see

Appendix B).

Dispersal modes and other life history traits were determined by conducting primary

literature searches, searching the USDA PLANTS Database (USDA 2004), NatureServe

Explorer (NatureServe 2005), and The Illinois Plant Network (Iverson et al. 1999), and using

local flora and regional plant guides (Radford et al. 1964, Miller and Miller 1999, Weakley

2005). For less than 25% of the species, the primary dispersal mode was not indicated in

these sources, so a best guess was made based on field observations of morphological traits

67

and comparisons with congeners. We were able to categorize all known species by dispersal

mode with reasonable certainty.

Standing Vegetation Survey

At each of the 1280 plots where we sampled the seedbank, we also surveyed the

established plant community from 2001-2003 (Figure 2.2). Plots were a nested design, in

which three concentric plot sizes were used to accommodate plant species with different

vegetative habits: 1 x 1 m for herbs, 3 x 3 m for shrubs, and 10 x 10 for trees. Here, we

compare the seedbank community to species found in the 1 x 1 m plots only, which included

all species less than 30 cm tall (both woody and herbaceous). Surveys took place during the

height of plant production from 15 July – 20 August 2001-2003. All species were recorded

and assigned one of ten percentage cover classes following the methods of Peet et al. (1998):

0-0.01%, 0.01-1%, 1-2%, 2-5%, 5-10%, 10-25%, 25-50%, 50-75%, 75-95%, >95%, which

allows for consistency among multiple observers. The median value of each cover class was

used for analyses as recommended by Peet et al. (1998).

Data analysis

We used a mixed model randomized block design to analyze two separate response

variables: the density of individuals and the number of species (species richness) that

emerged from each plot. Both of these variables were square-root transformed to improve

normality by using X′ = X1/2 + (X+1)1/2 prior to analysis (Zar 1999). Random variables

included the experimental site the sample came from and the greenhouse a sample

germinated in, as well as the interactions between greenhouse and site, greenhouse and patch

68

type, and greenhouse by plot location within a patch. Fixed variables included patch type

and plot location (edge, center, other; Figure 2.2) nested within patch type. The natural log

of soil moisture by weight (see Chapter 1 for methods) and the natural log of the nearest

distance to a heater in the greenhouse were included as covariates.

To assess the degree of similarity in seedbank composition between plots in different

locations within a patch (i.e., edges, centers, and other), we calculated pairwise similarity

distances between each plot and all other plots using Bray-Curtis and Sorensen distance

measures calculated with EstimateS software (Colwell 2005). The Bray-Curtis similarity

measure is based on species abundances while the Sorensen similarity measure is based on

presence-absence data and therefore more heavily weights rare species. We averaged the

pairwise similarity values for all comparison types (center and edge, edge and other, center

and other) for each similarity measure (Bray-Curtis and then Sorenson). These distance

comparisons were used as response variables in a Multivariate Analysis of Variance

(MANOVA) with experimental unit and patch type as independent variables and the variance

in soil moisture in each patch included as a covariate. Bray-Curtis similarity measures were

natural log transformed and Sorensen similarity values were square-root transformed to

improve normality and homogeneity of variances.

To assess the similarity of the seedbank and standing vegetative communities from 2001-

2003, we ordinated species presence-absence data for each species occurring in any survey

type (seedbank, standing vegetation 2001-2003). We used non-metric multidimensional

scaling (NMS) as recommended by McCune and Grace (2002) using the PC-Ord Software

System (McCune and Mefford 1999) with the default settings (Kruskal 1964, McCune and

Mefford 1999). We utilized the Sorensen distance measure with 3 axes and the ordination

69

was rotated with varimax rotation so that the first axis accounted for the majority of the

solution.

We also examined the frequency of species occurrences in each patch type and location

and whether specific species life history traits were associated with the observed patterns.

Four life history traits were assigned to each species: dispersal mode (bird, ballistic, gravity,

mammal, or wind), pollination mode (insect or wind), life cycle (annual or perennial), and

habit (herb, shrub, tree, or vine). Traits were assigned by searching regional flora and guides

(Radford et al. 1964, Miller and Miller 1999, Weakley 2005), conducting a primary literature

search for each species (ISI Web of Science and Biological Abstracts), and searching for

each species in three plant databases: 1) USDA PLANTS Database (USDA 2004), 2)

NatureServe Explorer (NatureServe 2005), and 3) The Illinois Plant Network (Iverson et al.

1999). If we could not determine species life history characteristics using these methods

(~25%), we deduced them from plant morphology and congener comparisons. We classified

all species with reasonable certainty. Chi-square analyses were used to test the relative

frequency of species with the four life history traits between edge and center plot locations.

Results

A total of 9029 individuals and 76 species were found in the germinable soil seedbank

(Appendix B). On average, there were 2.82 ± 0.10 individuals and 2.49 ± 0.04 species per

sample (588.72 cm3). Of all species found in either the seedbank the established plant

community from 2001-2003, or both, 3% were unique to the seedbank, 39% were shared, and

58% were unique to the established community (Figure 2.3). Ordination revealed that the

seedbank community was most similar to the community established in 2001, one year after

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the initial disturbance, and became progressively more dissimilar over time (Figure 2.4).

This is evident in the ordination plot, where the clustering of plots in any given year moves

progressively away from the initial year after disturbance (years are different symbols).

There were differences between experimental units in the diversity and composition of

seeds, however, we do not report means or significance values since the experimental unit

was included as a random effect in our statistical model. There was no effect of patch type or

location on the density of seeds in the seedbank (Table 2.1, Figure 2.5). Species richness was

also unaffected by patch type, but location had a significant effect (Table 2.1). Linear

contrasts revealed that patch centers had more species than either edges or other areas

(corridors, wings, rectangles) within each patch type (Table 2.1, Figure 2.5). Location-to-

location similarities analyzed with MANOVA were not affected by patch type using either

Bray-Curtis (Pillai’s trace F = 0.462,21, p = 0.6401) or Sorensen measures (Pillai’s trace F =

0.822,21, p = 0.4536). Chi-square analyses indicated that of species that only occur in one

location (either edges or centers, but not both), wind- or gravity-dispersed species are more

likely to occur at centers than at edges (14 species at center, 3 species at the edge, χ2 = 7.12,

df = 1, p = 0.0076). All other dispersal modes, pollination modes, life cycles, and habits did

not differ between edges and center locations.

Discussion

Seedbanks in our study system were more species rich at centers of patches than edges or

other locations, but were not affected by patch type. The density of seeds in the seedbank

was also unaffected by patch type or location and there was no effect of patch type on the

similarity of the seedbank community between different locations. The composition of the

71

seedbank was most similar to the established plant community that initially arose after the

sites were created via disturbance and then became progressively more dissimilar over time.

Fragmentation and the seedbank

The increased species richness of seeds at patch centers suggests that multiple processes

are at work. Below, we consider the evidence for each of the three possible mechanisms

driving our observed response: seed dispersal, seed predation, and microsite limitation. Seed

dispersal could have led to the increased species richness we saw at patch centers because

centers capture greater proportions of seed rain when compared to edges. However, we did

not observe an increase in seed density, which would also be expected to increase if seed rain

alone were driving the pattern. Small mammal and arthropod seed predators have been

shown to forage more intensively at patch centers in our experimental landscape (Orrock et

al. 2003, Orrock and Damschen 2005). Small mammals avoid edges, which are perceived as

places of high predation risk, while arthropods are more active in locations with greater solar

load (also away from edges). There could be an interaction between increased dispersal and

seed predation, nullifying an effect of increased seed rain via seed predation. Furthermore,

seed predators could preferentially forage on common species, making it more likely that

rarer species persist and emerge. Alternatively, arthropod seed predators are known to

disperse and cache seeds without consuming them (Christian and Stanton 2004). If ants are

bringing greater numbers of seeds to patch centers, they could increase the probability of

unique species in the seedbank at patch centers. Finally, microsite conditions could reduce or

enhance the persistence of seeds within the seedbank. Other studies have documented shifts

in light, temperature, soil moisture, and root production along gap and clearing edges in

72

longleaf pine forests (Brockway and Outcalt 1998, Harrington et al. 2003, Chapter 3). If

such changes affect seed survival, microsite conditions could affect seedbank composition.

Future studies should explicitly control each of these factors by using a combination of seed

additions (dispersal), seed predator exclosures (predation), and abiotic manipulations of

shading and soil moisture (microsite).

Our results could be influenced by the depth of the soil cores that we used to sample the

seedbank (15 cm). We used this depth because there have been few studies of seedbanks in

the southeastern United States (but see Cohen et al. 2004) and 15 cm was a conservative limit

for the range of seedbank depths sampled in other studies (Moles and Drake 1999, Cubina

and Aide 2001, Cohen et al. 2004, Decocq et al. 2004). It is possible that the persistent

seedbank, which we would not expect to be affected by the spatial configuration of the

landscape over the past four years, overwhelmed any effect we may have seen in the transient

seedbank in the uppermost soil depths. If this is the case, we may only be detecting the

strongest trends, which are the resulting changes in seedbank species richness as a result of

edge effects. If seedbanks are affected by edges, we would expect that changes in the

amount and configuration of edges would lead to patch-level consequences. It may be that

these effects have not had enough time to accumulate. Because the early-successional

species we observed are dependent on natural (fire for longleaf pine forest species) and

human disturbances (clearing and burning in forest management) for reproduction and

dispersal, additional disturbance events might be needed before patch-level responses are

able to be detected.

73

Relationship between the seedbank and established plant community

The established vegetative community in this experimental landscape has become more

diverse in connected and winged patches vs. rectangular over time (Chapter 1). One of the

main hypotheses we tested was the effect of increased edge-to-area ratios in connected

patches (since corridors are effectively two long, linear edges) leading to the increases in

diversity we observed. We found no difference, however, in established plant species

richness, evenness, or composition between locations within a patch (edge, center, other) for

the established plant community. This discrepancy between observing edge effects in the

seedbank but not in the established plant community suggests several possible mechanisms

are at work, which would be worth exploring in the future. First, differential abiotic factors

at edges and centers that are present in the field (but not in the greenhouse) could cause

differential germination patterns that essentially nullify any compositional differences in the

seedbank as species germinate. Second, species interactions in more mature life stages could

eliminate any initial edge effect. And finally, site history (i.e., present community

composition, disturbance, land use, etc.) may be more important in determining the

composition of the standing community by limiting the number or type of seedlings that

emerge.

Conservation implications

Longleaf pine forests were historically the dominant community type across the

southeastern United States (Frost 1993, Smith et al. 2000). These forests extended across a

moisture gradient, with more mesic forests defined as savannas and more xeric forests

defined as sandhill or scrub communities. These forests are some of the most species rich

74

ecosystems in North America and harbor many rare and threatened species (Frost 1993,

Smith et al. 2000). Today, less than 3% of longleaf pine forest remains (Frost 1993,

Schwartz 1994, Smith et al. 2000) and there are active restoration efforts underway to restore

habitat for many of the critical plant species found in these forests. Our study suggests that

native longleaf pine species are infrequent members of the seedbank community (Appendix

B) and that seed additions may be necessary when trying to restore longleaf pine habitats.

Our results are contrary to those of Cohen et al. (2004), who compared the seedbanks of

disturbed and undisturbed native longleaf pine savannas and found that over half of the

species in the seedbank of highly disturbed former longleaf pine sites were indicative of

native longleaf pine communities. Both of our studies occurred in disturbed sites that have

undergone similar forest management strategies (including the use of mechanical clearing,

herbicide, and burning), soil cores were taken at similar depths (10 cm and 15 cm), and we

both used similar seedling emergence methods. The major difference between our studies

was that Cohen et al. sampled forests occurring in the more mesic end of the soil moisture

gradient for longleaf pine communities, while we sampled communities at the more xeric

end. The differences we found in overall composition suggest that the usefulness of

seedbanks as a restoration tool in longleaf pine forests may depend on hydrological gradients.

Acknowledgements

This work was made possible by funding from NSF DEB-9907365, the D.O.E. –Savannah

River Operations Office through the Forest Service Savannah River under the Interagency

Agreement DE-1A09-76SR00056 (a National Environmental Research Park), Sigma Xi, and

North Carolina State University. We are especially thankful for the leadership provided by

75

John Blake and Ed Olson at the Forest Service Savannah River. Thanks to Julie Davis,

Jennifer Freeman, and Bethany Honeyager for your patience, persistence, and tenacity while

collecting copious quantities of dirt. Thank you to John Orrock for your continued support

and helpful advice throughout the completion of this project. Jamie Scott provided

refrigeration space for the cold stratification process and Ron Mosely and Aimee Weldon

assisted in transporting the samples to NCSU. Vann Covington, George Kennedy, and Fred

Gould provided greenhouse space and logistical support. Thanks to Nate Bacheler, Becky

Bartel, Salinda Daley, Julie Davis, Jennifer Freeman, Faith Inman, Christy Prenger, Wayne

Rostant, and Sunny Snider for help with greenhouse planting, watering, and sampling.

Alexander Krings, Jon Stucky, and the North Carolina State Herbarium provided tremendous

assistance with specimen identification. Thanks to Marcia Gumpertz and Kevin Gross for

statistical advice. The intellectual and logistical support provided to E.I.D. by Jonathan

Levine during manuscript preparation is greatly appreciated. The manuscript benefited from

the helpful comments of Jim Gilliam, George Hess, John Orrock, and Tom Rufty.

76

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Table 2.1. Results from ANOVA for total number of individuals and species richness in seedbank (both square root transformed). Fixed effects included patch type (connected, unconnected winged, unconnected rectangular), location (center, edge, other) nested within patch type. We used covariates of the natural log of average soil moisture by weight for each patch and the natural log of the distance from a heater in the greenhouse. Random variables included the experimental site the sample came from and the greenhouse a sample germinated in, as well as the interactions between greenhouse and site, greenhouse and patch type, and greenhouse by plot location within a patch. Numerator and denominator degrees of freedom are listed as subscripts for each F-value.

F p

Number of Individuals / m2

Source of Variance

Patch type 0.962, 22.8 0.3977

Patch type (location) 1.336, 1044 0.2415

Soil moisture 6.631, 1071 0.0102

Distance from greenhouse heater 0.031, 981 0.8632

Linear Contrasts

C = E 2.271, 1044 0.1320

C = O 0.051, 1041 0.8209

E = O 1.481, 1042 0.2246

Species richness / m2

Source of Variance

Patch type 0.672, 22.6 0.5200

Patch type (location) 2.606, 1045 0.0165

Soil moisture 4.341, 1067 0.0374

Distance from greenhouse heater 1.911, 984 0.1673

Linear Contrasts

C = E 8.701, 1046 0.0033

C = O 3.811, 1044 0.0511

E = O 0.751, 1045 0.3874

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Figure 2.1. Conceptual diagram showing how the spatial configuration of habitat can affect processes that determine seedbank composition.

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Figure 2.2. Study design for seedbank study in an experimental landscape at the Savannah River Site, South Carolina. There are eight experimental units, three patch types (connected, winged, rectangular), and 34 plots in three locations (center, edge, and “other” – wings, corridor, or rectangle). Eight soil cores were taken around a 1x1m plot and homogenized for one seedbank sample per plot.

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Figure 2.3. The percentage of unique and overlapping species between the seedbank and standing vegetation.

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NMS of Seed Bank and Standing Vegetation 2001 - 2003

Axis 1

Axi

s 2

YEAR200120022003Seed bank

Figure 2.4. NMS ordination of the seedbank and established vegetative community in 2001, 2002, and 2003. The first two axes are shown and each point is coded by survey year or type.

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Figure 2.5. Species richness and total number of individuals in the seedbank of patch types and locations. Both response variables were square root transformed by using X′ = X1/2 + (X+1)1/2 for analysis (Zar 1999). Error bars indicate standard errors and corresponding F and p-values can be found in Table 2.1.

a) b)

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CHAPTER 3: EDGE EFFECTS ON LONGLEAF PINE FOREST HERBS

A paper to be submitted to Ecological Applications

Ellen I. Damschen, John L. Orrock, Sara O. Woolard, Elizabeth Y. Long,

Sarah H. Gabris, and Don Imm

Abstract

The increased prevalence of abrupt edges is a well-known consequence of habitat

fragmentation. Edge effects have been widely studied in forested habitats, but edge effects

for historically open habitats have been largely ignored. We examined the impact of an open

habitat and forest edge on nine species of native longleaf pine forest herbs and grasses. We

planted equal densities of seedlings of nine species in plots at six distances into an opening

(0, 6, 12, 25, 50, and 100 m) and monitored their performance. Photosynthetically active

radiation (PAR) and soil temperature decreased at 0 and 6 m into the opening, while soil

nutrients and texture did not vary with respect to distance from edge. Three edge responses

were observed: the largest species (Sorghastrum secundum Desmodium ciliare,

Schizachyrium scoparium, and Aristida longespica) had increased growth and flowering at 0

and 6 m, the smallest species (Galactia regularis and Baptisia lanceolata) had decreased

growth and flowering at 0 and 6 m, while two species (Carphephorous bellidifolius and

Dalea pinnata) showed relatively little response. Both abiotic conditions and biotic

interactions could be responsible for the observed tradeoffs in plant growth and flowering,

but further manipulative experiments will be needed to determine the importance of species

interactions in governing the community patterns we observed. Our results corroborate

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findings from other studies by indicating that overstory thinning and the creation of multi-

generational stands may be necessary for the successful conservation and restoration of many

herbaceous species.

Introduction

Fragmentation and edge effects in open habitats

There are several well-known consequences of habitat loss and fragmentation, including

decreases in total habitat area, increases in habitat isolation, and increases in the prevalence

of edge habitat (Harrison and Bruna 1999, Fahrig 2003). This increased landscape

“edgyness” has led to a host of studies on edge effects, which have documented shifts in

abiotic conditions and species interactions at these ecotones (reviewed in Murcia 1995, Ries

et al. 2004).

Most edge studies have focused on forested habitats, while edges of historically open

habitats, such as prairie and savanna, have received little attention (Ries et al. 2004, Ries and

Sisk 2004). Both forested and open habitats have been fragmented by disturbance and

agriculture, but open habitats are also threatened by newly planted forests (e.g., Frost 1993,

Brockway and Lewis 2003, Harrington et al. 2003, Lett and Knapp 2005). In the

southeastern United States, these recently planted forests are characterized by dense pine

overstories, thick hardwood middlestories, and the increased presence of shade tolerant

species. These habitats result from typical forest management practices as well as the loss of

historical fire regimes (Frost 1993, Harrington et al. 2003).

Large areas of native open habitat have been lost because they are ideal lands for

agriculture and grazing. Today, only small remnants of once widespread open habitats

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remain, including less than 1% of the tallgrass prairie (Samson and Knopf 1994) and less

than 3% of longleaf pine-dominated ecosystems (Frost 1993, Schwartz 1994, Smith et al.

2000, Figure 3.1). Many of the species associated with these native communities are now

relegated to old fields, forest gaps, and clearings where the environmental conditions most

closely resemble their original habitat.

Herbaceous plants in longleaf pine forests

Longleaf pine forests are among the most diverse plant communities in North America, in

some cases containing more than 40 species per square-meter (Frost 1993, Peet and Allard

1993, Smith et al. 2000). Historically, longleaf pine forests contained low densities of

patchily-distributed canopy trees (often below 50 stems / ha), an open understory, and were

maintained by frequent, low-intensity, surface fires (Frost 1993, Noel et al. 1998, Smith et al.

2000, Harrington et al. 2003). Current management of pine forests in the southeastern U.S.

typically includes longleaf, loblolly, or slash pine planted at high densities, where low light

levels, thick pine litter layers, and competition with overstory trees decrease the survivorship,

abundance, and diversity of herbaceous plants native to longleaf pine-dominated ecosystems

(Frost 1993, Means 1997, Brockway and Outcalt 1998, Harrington and Edwards 1999,

Harrington et al. 2003).

Study goals

Most edge studies are observational, in that they record the presence or state of naturally

occurring organisms along an edge gradient (Ries et al. 2004). Such studies are crucial for

understanding the effect of habitat edges on abiotic conditions and species interactions, but

90

lack controls over the abundances of the species they observe. Additionally, few edge

studies have used adequate replication over space or time to test for the consistency of

observed effects (Ries et al. 2004). We advanced the study of edge effects by experimentally

controlling the abundances of plants at consistent distances from each edge and replicating

our study across multiple locations. Our goals were to: 1) assess the effect of edges between

open habitats and forest on the growth and performance of several plant species, and to 2)

identify possible mechanisms underlying any observed edge effects.

Methods

Study site

To test for edge effects, we established five experimental sites (i.e., openings) at the

Savannah River Site (SRS), a National Environmental Research Park (NERP), near Aiken,

South Carolina (Figure 3.2). The landscape surrounding each site consists of mature longleaf

or loblolly pine stand and sites were created according to typical U.S. Forest Service

management protocols. In 1999, each site was created by harvesting pine trees. In 2003,

sites were prepared by the U.S. Forest Service using standard management protocols. From

13 May – 14 August 2003, they applied herbicide to eliminate woody vegetation resprouts

using a mix of Arsenal AC and Garlon 4 at a rate of 35 gal / acre. From 16 August – 28

September 2003, they burned all sites. All sites were subsequently planted with longleaf pine

(Pinus palustris) seedlings (except one site was planted with loblolly (Pinus taeda) as well)

from 1 January – 25 February 2004 at a density of ~700 seedlings / acre. While tree densities

were higher than historical longleaf pine forests in both the clearing and the forest, young

longleaf pine remain in a short-statured “grass stage” for 2-5 years before bolting into a small

91

tree. This maintained a large discrepancy between the forested habitat with large trees,

closed canopies, and mature root systems, and the open habitat with small seedlings,

immature root systems, and no canopy.

In May 2004, two 1 x 1 m plots were established at each of six distance categories (0, 6,

12, 25, 50, and 100 m) from the western opening-forest edge into the open site, for a total of

60 plots (Figure 3.3). We consistently used the same edge direction in order to control for

differences in edge effects due to cardinal direction. The western edge was chosen for two

reasons. First, it provides a more conservative test of edge effects by minimizing the contrast

in light and temperature along an edge (as opposed to north or south-facing edges). And

second, all sites also had western edges that met our selection criteria of being adjacent to

mature pine forest and being farther than 100 m from any other edge. The sites ranged in

total area from 16 to 25 hectares and differed in their shape. However, our plots were closer

to the western edge used for this study than to any other edge surrounding the clearing,

making these differences in shape and size negligible for our study goals.

Species and planting methods

During winter 2003, seedlings for nine species (Table 3.1) were propagated from seed

collected locally at the Savannah River Site using the methods developed by the U.S. Forest

Service (Marshall, In prep.). All are native to the understory of longleaf pine forest

communities. The species chosen still occur in the area, but were absent from the sites

chosen for our study. We used three species from the Poaceae (Aristida longespica,

Schizachyrium scoparium, and Sorghastrum secundum), four species from the Fabaceae

(Baptisia lanceolata, Dalea pinnata, Desmodium ciliare, and Galactia regularis), and two

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species from the Asteraceae (Carphephorous bellidifolius and Liatris elegans) plant families.

All species are perennials except A. longespica, which is an annual. Our intention was to

plant Aristida beyrichiana (a native perennial grass); however, seeds from A. longespica (a

native annual grass) were inadvertently procured by seed collection volunteers and not

discovered until after seedling propagation.

Five individuals of each species were planted in each 1 x 1 m study plot from May 13-20,

2004. We established a 7 x 7 cell grid for each plot to ensure equal spacing of seedlings and

to allow us to generate a random planting order for each plot (Figure 3.3).

Since there were a total of 45 seedlings (9 species x 5 individuals each), but 49 possible grid

cells, four randomly chosen cells in each plot were left unplanted. To standardize the

planting procedure and facilitate accurate seedling placement, seedlings were planted using a

dibble bar that matched the size of the seedling container. Soil was gently back-filled around

each planted core. Plots were supplemented with water for the first 9 weeks of growth to

ensure the establishment of seedlings. Plots were watered for the first two days following

planting with 11.36 liters / plot each day (see rationale for amount below). After this time,

we watered plots only when the weekly average rainfall was not met (the average rainfall

from May-August at the Savannah River Site is 2.5 cm / week, unpublished weather station

data). Rain gauges at each site were checked twice each week and if 1.25 cm of rain was not

present in the gauge, plots were watered with 11.36 liters / plot (equivalent to the volume of

water during a 1.25 cm rainfall in a 1 m2 area). If this rainfall had been achieved, plots did

not receive any additional water. All watering ceased on August 12, 2004 after seedlings

were fully established. While we supplemented with water for seedling establishment,

evapotranspiration rates likely still differed as a function of edge, allowing for soil moisture

93

differences along the edge gradient. Plots were also weeded for the first nine weeks,

removing all non-focal species within each plot.

Plot sampling

Measurements were taken on 21-24 May, 6-9 August, and 2-8 October 2004. We

measured plant height at all time periods. When species began to flower, we counted the

number of flowers. The number of flowers was defined as the number of flowering heads for

the Asteraceae species, the number of flowering stems for the three grass species, and the

total linear length of flowering spikes for L. elegans because individual flowers for these

species were too small to count individually. Only one species, B. lanceolata, did not flower

or fruit during 2004, because it flowers in the spring. After the completion of our study, B.

lanceolata was observed in fruit in June 2005, so this species did flower in the spring one

year after planting. For each plot, values from the five individuals of each species were

averaged prior to analyses.

Environmental variables

We measured several abiotic variables at each plot. On 6 July 2004, the amount of

incident Photosynthetically Active Radiation (PAR) was recorded with a SunScan SS1 Delta-

T Devices Ltd. (Cambridge, UK) light meter from 10:30h - 14:30h. Two light readings were

taken for each plot and subsequently averaged. Soil temperature was recorded for every plot

using Onset Computer Corporation (Pocasset, MA) HOBO temperature loggers. Two

loggers were placed at every plot (one on the north and one on the south side of each plot) in

each site and recorded the soil temperature every five minutes from 08:30h – 16:30h. The

94

average daily soil temperature from the two loggers at each plot was used in analyses. Each

site was measured on a different day, but all plots within a site were measured concurrently

within a single day. Eight soil cores (2.5 cm diameter x 15 cm deep) were taken along the

border of each 1 x 1 m plot and homogenized. Soil samples from each plot were analyzed

for standard nutrient and textural content by Brookside Laboratories, Incorporated in New

Knoxville, Ohio using the Mehlich III extraction solution.

Data analysis

We first examined the affect of the edge gradient on several abiotic variables. Two

separate analyses were used for the abiotic variables because we expected that soil properties

would respond much more slowly (relative to the timeframe of our study) to edge effects

than light and temperature. Soil properties including soil exchange capacity (TEC), percent

organic matter, and estimated nitrogen release (ENR) were used as response variables in a

Multivariate Analysis of Variance (MANOVA). Independent variables included distance as

a discrete categorical treatment effect and site as a block effect. A second MANOVA was

performed with average daily soil temperature (C°) and incident Photosynthetically Active

Radiation (PAR) with the same independent variables. Soil temperature was natural log

transformed and incident PAR was square-root transformed to improve normality and

homogeneity of variances. Unplanned linear contrasts were used for each analysis to discern

how far the edge effect penetrated into the opening. The Dunn-Šidák procedure was used to

adjust the alpha level according to the number of post-hoc comparisons (α’ = 1 - (1 - α) 1/k for

k comparisons, Sokal and Rohlf (1995)).

95

The impact of edge distance on plant growth was determined by performing a MANOVA

with the difference in height between 21 May and 9 August 2004 for each of the nine species

as multiple response variables. Independent variables were distance and site, and post-hoc

linear contrasts were also employed as described above to understand how far the edge effect

penetrated. We used this same type of analysis described above for a separate analysis of the

number of flowers produced by each of the nine species. Flowers were counted at the peak

of flowering for each species, which occurred from 2-8 October for A. longespica, C.

bellidifolius, D. ciliare, D. pinnata, L. elegans, S. scoparium, and S. secundum and 9 August

for G. regularis. B. lanceolata was the only species not to flower, so it was not used in the

MANOVA for flower production. All analyses were conducted with SAS v. 8.02.

Results

Soil TEC, organic matter, and ENR did not change with distance from the edge (Pillai’s

trace, F = 1.2015, 150, p = 0.2750). Light and soil temperature, however, were significantly

affected by distance from the edge (Pillai’s trace, F = 1.5325, 250, p = 0.0240, Figure 3.4).

Means for light and temperature indicated that these abiotic factors were exhibiting relatively

shallow edge response, where only plots at 0 and 6 m differed from all remaining classes.

We used five post-hoc linear contrasts corrected with the Dunn-Šidák procedure (α’ =

0.0102) to compare the difference between the edge (0 m) and all other distance classes.

Zero meters was not different from the 6 m distance class (p = 0.0214), but was significantly

different from all other distance classes (p ≤ 0.0040 for comparing 0 m to 12 m, 25 m, 50 m

and 100 m).

96

Plant growth for the nine species, measured as the change in height over the growing

season from 21 May to 9 August, was significantly affected by distance from the edge

(Pillai’s trace, F = 1.54545, 230, p = 0.0434). Standardized canonical coefficients reported

from the MANOVA provide a comparison of the relative strength and correlation among

response variables (but not the specific direction of the response) across treatments (Scheiner

and Gurevitch 2001). Canonical coefficients for the nine plant species indicated that the

growth of S. secundum, D. ciliare, L. elegans, S. scoparium, and A. longespia responded to

distance similarly but in opposition to G. regularis and B. lanceolata, while C. bellidifolius

and D. pinnata showed relatively little response (Table 3.2, Figure 3.5). Species means show

that at 0 and 6 m, S. secundum D. ciliare, L. elegans, S. scoparium, and A. longespia all had

increased growth, while G. regularis and B. lanceolata had decreased growth compared to all

other distances (Figure 3.5).

The number of flowers was also significantly affected by distance from the edge (Pillai’s

trace, F = 1.7145, 235, p = 0.0079). Canonical coefficients for the eight species that flowered

during our study indicate that D. pinnata, G. regularis, and L. elegans responded similarly

and in opposition to A. longespica, D. ciliare, S. scoparium, and S. secundum, while C.

bellidifolius showed relatively little response in the number of flowers produced (Table 3.2,

Figure 3.5). Species means show that S. secundum had the greatest number of flowers at 0

m, while D. ciliare and S. scoparium had the greatest number of flowers at 6 m (Figure 3.5).

All other species that flowered showed an opposite pattern, having the fewest number of

flowers at 0 and 6 m (Figure 3.5).

97

Discussion

The nine study species showed two main responses to the distance of the opening edge.

The growth and flower production of the four largest species, the three grass species and D.

ciliare (a legume), increased within 0 – 6 m from the clearing edge. The remaining three

species of legumes and one of the two species in Asteraceae exhibited the opposite pattern

with either increased growth or flowering (or both) away from the edge. Only one species,

C. bellidifolius (an Asteraceae species), showed little response to the edge in either growth or

flowering.

Ascribed mechanisms

Other researchers have generally observed a decrease in herbaceous cover and diversity

up to 18 m from the base of mature pine trees (Brockway and Outcalt 1998, Harrington and

Edwards 1999, 2001, McGuire et al. 2001, Harrington et al. 2003). This pattern has been

ascribed primarily to increased root competition for soil moisture and nitrogen (Brockway

and Outcalt 1998, Harrington et al. 2003, Jones et al. 2003). Brockway and Outcalt (1998)

found that litterfall only increased 4 m inside forest gaps, canopy coverage up to 4 – 5 m, and

that there was no change in PAR as a function of edge. However, fine root production

increased within 12 – 16 m of surrounding mature pine trees and most closely matched the

observed edge effect distance for the herbaceous community (Brockway and Outcalt 1998).

Other studies have found similar edge effects. The distribution and abundance of legumes

in longleaf pine forests was positively correlated with soil moisture and negatively correlated

with pine basal area (Hainds et al. 1999). Jones et al. (2003) found that total soil moisture

and nitrogen mineralization rates decreased when the production of pine root mass increased.

98

They also found that when the number of gaps increased in a pine forest, the total production

of fine roots by non-pine species increased (Jones et al. 2003). When Harrington et al.

(2003) experimentally removed 50% of the pine tree cover, belowground competition for soil

water was reduced and the performance of understory herbs improved.

Given the findings of other studies, we propose that the increased performance of the

three grass species (especially S. secundum) and D. ciliare at 0 and 6 m from the forest edge

are likely due to species-specific responses to increased fine root mass closer to the opening-

forest edge. Because we did not find soil nitrogen content, organic matter, or exchange

capacity to be affected by our edge gradient, it is most probable that soil moisture was the

limiting resource driving the patterns we observed. This may be especially true for our sites,

which occur at the more xeric end of the longleaf pine moisture gradient.

Recent results from a study manipulating root competition, litterfall, and stand density in

longleaf pine forests predicted that belowground competition would be most limiting for the

herbaceous understory when the overstory was relatively open, while aboveground

competition would be most limiting with dense overstories and middlestories (Harrington et

al. 2003). Our results confirm their predictions in that the three largest species performed

best near the forest edge, where belowground competition was most severe and where

aboveground competition likely led to their superior performance. It is probable that these

species, in turn, limited the others via competition, but future work will be needed to separate

these biotic interactions from abiotic factors.

99

Relationship to historical diversity patterns and conservation

Old-growth longleaf pine forests have been shown to have lower densities of overstory

pine trees (often less than 50 stems/ha) with very patchy distributions, as well as smaller

numbers of hardwoods than second-growth longleaf pine stands (Noel et al. 1998, Harrington

et al. 2003). The death of older, larger trees in old-growth stands creates openings within the

forest that promote juvenile longleaf recruitment, a characteristic that has been lost in`

second-growth forests (Noel et al. 1998).

Our study provides additional evidence that the uniform spacing and size of trees in

modern forests may limit species richness in longleaf pine forests (Oliver and Larson 1996,

Harrington et al. 2003). Patchy distributions of overstory trees likely result in greater

variability in soil moisture availability and competitive relationships, which could lead to

high levels of diversity at intermediate scales. Our study provides evidence that high

densities of trees in forest plantations will favor the performance of species that are superior

aboveground competitors, reducing overall understory diversity. Our findings support

others’ recommendations that the conservation and restoration of native longleaf pine forests

would benefit from thinning overstory trees and creating uneven size distributions of trees

that allow for open areas of recruitment (Noel et al. 1998, Harrington et al. 2003). Previous

researchers have suggested that canopy gaps larger than 0.1 ha may be necessary to allow for

understory restoration in young plantations, while mature pine stands may require gaps at

least 0.15 - 0.20 ha (Harrington et al. 2003). Furthermore, planting trees in clumped

distributions will increase the prevalence of larger gaps (Noel et al. 1998, Harrington et al.

2003).

100

Our study was conducted in a clearing created by typical forest management practices:

clearcutting, herbiciding, burning, and planting an even-aged pine stand. Clearings, like

those used in our study, are important refuges for species once associated with historical

longleaf pine forests. However, we do not advocate the use of large-scale and pervasive

clearcutting for conservation and restoration of these species. Land use history, including

agriculture and burning regimes, can have larger impacts on understory composition than

pine densities per se (Hedman et al. 2000). Forests regenerating from clearcuts have been

found to have higher percentages of woody cover, lower species richness, and more

predictable species composition (species evenness) than those that have only been thinned

(Brockway and Lewis 2003). Furthermore, Brockway and Lewis (2003) found that while

cover of grass species like S. scoparium and Andropogon spp. remained the same over time

in both clearcut and thinned habitats, the abundance of forbs decreased in the clearcut areas

while it did not in the thinned areas. The clearcuts used in our study are early in their

rotation, making the temperature, light, and soil moisture conditions more similar to those

found in at least portions of historical longleaf pine forests. Over time, however, these stands

may undergo succession and become less suitable habitat for native longleaf pine forest

species. Planting multi-generational stands at lower overall densities and maintaining regular

low-intensity burns will be key for the conservation of these species.

Future work

Our study is one of the first to document edge effects on experimentally established plant

communities in open habitats and to propose possible mechanisms behind these responses.

Future work should focus on manipulating soil moisture, nutrients and species interactions to

101

understand the mechanistic controls behind the patterns we observed. To determine the role

of competitive interactions, species should be planted in monoculture and in paired

combinations while soil moisture and nitrogen are manipulated in a factorial experiment.

Additionally, field studies over greater time periods will be important because some

interactions (i.e., the influence of nitrogen-fixing legumes) and successional processes may

become increasingly important over time.

Acknowledgements

This work was made possible by funding from the D.O.E. – Savannah River Operations

Office through the Forest Service Savannah River (a National Environmental Research Park)

and North Carolina State University. We are especially thankful for the leadership and

support provided by John Blake, Don Imm, and Ed Olson at the Forest Service Savannah

River and to Nick Haddad at North Carolina State University. Tremendous thanks go to

Doug Marshall for producing the seedlings and for the field support and advice. Field

assistance was provided by Becky Bartel-Sexton, Allison Leidner, and Kristen Rosenfeld.

Thanks to Jamie Scott, Andrew Freeman, Bob Morgan, and Ron Mosely for logistical

support. The manuscript benefited from the helpful comments of Nick Haddad, Becky

Bartel, Jim Gilliam, George Hess, Kristen Rosenfeld, and Tom Wentworth.

102

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Table 3.1. Names and characteristics for the nine species used in the edge study.

Species Family Common Name U.S. Nativity Duration

Aristida longespica Poir. Poaceae Slimspike threeawn Native Annual Baptisia lanceolata (Walter) Ell. Fabaceae Gopherweed Native Perennial Carphephorus bellidifolius (Michx.) Torr. & Gray Asteraceae Sandywoods chaffhead Native Perennial Dalea pinnata (J.F. Gmel.) Barneby var pinnata Fabaceae Summer farewell Native Perennial Desmodium ciliare (Muhl. Ex Willd.) DC. Fabaceae Hairy small-leaf ticktrefoil Native Perennial Galactia regularis (L.) BSP. Fabaceae Eastern milkpea Native Perennial Liatris elegans (Walt.) Michx. Asteraceae Pinkscale blazing star Native Perennial Schizachyrium scoparium (Michx.) Nash Poaceae Little bluestem Native Perennial Sorghastrum secundum (Ell.) Nash Poaceae Lopsided indiangrass Native Perennial

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Table 3.2. Standardized canonical coefficients from MANOVA analyses for plant growth and flower production.

Standardized Canonical Coefficients

Species Plant Growth Flower Production Aristida longispica -0.2487 -0.3061 Baptisia lanceolata 0.3051 n.a. Carphephorous bellidifolius -0.0006 0.0993 Dalea pinnata -0.0387 0.4090 Desmodium ciliare -0.4670 -0.4708 Galactia regularis 0.7083 0.9707 Liatris elegans -0.3144 0.5077 Schizachyrium scoparium -0.2771 -0.1163 Sorghastrum secundum -0.7886 -0.6448

108

Figure 3.1. Historical extent of longleaf pine forest in relation to the study site location at The Savannah River Site, SC. Modified from Outcalt and Sheffield (1998).

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Figure 3.2. Location of edge study sites at the Savannah River Site near Aiken, SC.

110

Figure 3.3. Plot layout at each site. Plots were placed at six distance categories away from the western edge of the clearing. All sites contained mature longleaf or loblolly pine forest on the western side of the edge and a recently planted clearing on the eastern side of the edge (all treated under the same management protocols for planting preparations and times). The black squares each represent one 1 x 1 m plot of experimental seedlings. Two plots at each distance category were randomly placed in four possible locations that were 25 m apart (potential locations indicated by horizontal lines). The planting arrangement for a 1 x 1 m plot is detailed in the circular inset. Seedlings were uniformly spaced in a 7 x7 grid. The black circles indicate four randomly chosen cells that were left blank. The species order and blank cells were randomly determined for each plot.

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Figure 3.4. Mean light availability and temperature for each distance category. Light was measured as Photosynthetically Averaged Radiation (PAR) and was square root transformed. The average daily soil temperature was natural log transformed. Error bars represent 95% confidence intervals.

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Figure 3.5. Mean plant growth and flowering for nine study species. The difference in plant height from 21 May to 9 August 2004 is shown as a solid line and refers to the left axis. The log of the number of flowers produced is indicated by the dotted line and refers to the right axis. Error bars represent 95% confidence limits. Error bars for the growth G. regularis are slightly offset to the right for visual clarity.

113

Appendix A. Plant species found in either presence-absence (P/A) or permanent plot surveys (Plots) in 2001, 2002, or 2003. Shown here are the unique species codes given to each species, what survey type the species was found in (an asterisk indicates that it was present), if the species is a monocot (M) or dicot (D), the family, genus, and specific epithet, the dispersal type (AD = adhesive, BL = ballistic, BD = bird, GR = gravity, MA = mammal, WI = wind), the dispersal type (A = abiotic, B = biotic), life cycle (A = annual, B = biennial, P = perennial, ABP = annual, biennial, or perennial), habit (F = forb, G = graminoid, R = fern, S= shrub, T = tree, TS = tree or shrub, V = vine), and origin (NV = native, ID = introduced, IV = invasive, EN = endemic). Life history information was found by using a primary literature search, searching three databases, and consulting regional guides and floras (see methods for details and citations). All plants were identified using the nomenclature of Radford et al. (1964) except the genera Chamaecrista, Dichanthelium, and Panicum, which follow the updated nomenclature of Weakley (2005).

Unique Code Plots P/A

Mono/Dicot Family Genus ssp.

Dispersal Type

Dispersal Mode Life Cycle Habit Origin

DYSOBL * * D Acanthaceae Dyschoriste oblongifolia BL P P F NV ACERUB * * D Aceraceae Acer rubrum WI P P T NV RHUCOP * * D Anacardiaceae Rhus copallina BD A P TS NV RHUTOX * * D Anacardiaceae Rhus toxicodendron BD A P S NV SANCAN * D Apiaceae Sanicula canadensis AD A B F NV APOCAN * D Apocynaceae Apocynum cannabinum WI P P F NV ILEGLA * D Aquifoliaceae Ilex glabra BD A P S NV ILECAS * D Aquifoliaceae Illex cassine var. cassine BD A P TS NV ILEOPA * * D Aquifoliaceae Illex opaca BD A P T NV ILEVOM * D Aquilifoliaceae Ilex vomitoria BD A P TS NV ARASPI * * D Araliaceae Aralia spinosa BD A P TS NV ASCAMP * * D Asclepiadaceae Asclepias amplexicaulis WI P P F NV ASCTUB * * D Asclepiadaceae Asclepias tuberosa WI P P F NV ASCVIR * D Asclepiadaceae Asclepias viridiflora WI P P F AMBART * * D Asteraceae Ambrosia artemisiifolia WI P A F NV ASTCON * * D Asteraceae Aster concolor WI P P F ASTDUM * * D Asteraceae Aster dumosa WI P P F NV ASTPAT * * D Asteraceae Aster patens WI P P F NV ASTTOR * D Asteraceae Aster tortifolius WI P P F ASTUND * * D Asteraceae Aster undulatus WI P P F BACHAL * * D Asteraceae Baccharis halimifolia WI P P F NV BERPUM * * D Asteraceae Berlandiera purnila WI P P F Unknown 1 * * D Asteraceae Carduus unknown WI P A F CARBEL * D Asteraceae Carphephorus bellidifolius WI P P F CIRREP * * D Asteraceae Cirsium repandum WI P P F NV, ~EN

114

CORMAJ * * D Asteraceae Coreopsis major WI P P F NV CRODIV * * D Asteraceae Croptilion divaricatum WI P A F NV ELETOM * * D Asteraceae Elephantopus tomentosus WI P P F NV EREHIE * * D Asteraceae Erechtites hieracifolis WI P A F NV ERICAN * * D Asteraceae Erigeron canadensis WI P A F ERISTR * * D Asteraceae Erigeron strigosus WI P ABP F NV EUPCAP * * D Asteraceae Eupatorium capillifolium WI P A F NV EUPCOM * * D Asteraceae Eupatorium compositifolium WI P P F NV EUPCUN * * D Asteraceae Eupatorium cuneifolium WI P P F EUPHYS * D Asteraceae Eupatorium hyssopifolium WI P P F EUPROT * * D Asteraceae Eupatorium rotundifolium WI P P F EUPSER * * D Asteraceae Eupatorium serotinum WI P P F NV GNAOBT * * D Asteraceae Gnapthalium obtusifolium WI P ABP F NV GNAPUR * * D Asteraceae Gnapthalium purpureum WI P ABP F NV HELAMA * D Asteraceae Helenium amarum WI P A F NV HELDIV * * D Asteraceae Helianthus divaricatus WI P P F HELAMP * D Asteraceae Heliotropium amplexicaule WI P P F HETGOS * * D Asteraceae Heterotheca gossypinus WI P P F HETSUB * D Asteraceae Heterotheca subaxillaris WI P ABP F NV HIEGRO * * D Asteraceae Hieracium gronovii WI P P F NV HYMSCA * * D Asteraceae Hymenopappus scabiosaeus WI P P F IVAMIC * D Asteraceae Iva microcephala WI P A F LACCAN * * D Asteraceae Lactuca canadensis WI P B F NV LACGRA * * D Asteraceae Lactuca graminifolia WI P B F NV LIAELE * * D Asteraceae Liatris elegans WI P P F NV LIAGRA * D Asteraceae Liatris graminifolia WI P P F NV LIASEC * * D Asteraceae Liatris secunda WI P P F NV LIASPI * * D Asteraceae Liatris spicata WI P P F LIASQU * D Asteraceae Liatris squarrosa WI P P F NV LIATEN * * D Asteraceae Liatris tenuifolia WI P P F NV PITGRA * * D Asteraceae Pityopsis graminifolia WI P P F NV RUDHIR * D Asteraceae Rudbeckia hirta WI P ABP F NV Unknown 2 * * D Asteraceae Senecio unknown WI P P F SILCOM * * D Asteraceae Silphium compositum var. compositum WI P P F NV SILDEN * * D Asteraceae Silphium dentatum WI P P F

115

SOLALT * * D Asteraceae Solidago altissima WI P P F SOLGIG * * D Asteraceae Solidago gigantea WI P P F SOLMIC * * D Asteraceae Solidago microcephela WI P P F NV SOLODO * * D Asteraceae Solidago odora WI P P F NV SOLPET * D Asteraceae Solidago petiolaris WI P P F SOLRUG * D Asteraceae Solidago rugosa WI P P F SOLSTR * D Asteraceae Solidago stricta WI P P F SOLTOR * * D Asteraceae Solidago tortifolia WI P P F Unknown 3 * * D Asteraceae Solidago unknown WI P P F Unknown 4 * D Asteraceae Unknown unknown WI P P F Unknown 5 * D Asteraceae Unknown unknown WI P A F Unknown 6 * D Asteraceae Unknown unknown WI P P F Unknown 7 * D Asteraceae Unknown unknown WI P P F Unknown 8 * D Asteraceae Unknown unknown WI P A F Unknown 9 * * D Asteraceae Unknown unknown WI P P F VERANG * * D Asteraceae Veronia angustifolia WI P P F CAMRAD * * D Bignoniaceae Campsis radicans WI P P V NV CATSPE * D Bignoniaceae Catalpa speciosa WI P P T NV LITCAR * * D Boraginaceae Lithospermum carolinense GR P P F NV OPUCOM * * D Cactaceae Opuntia compressa BD, MA A P C NV SPEPER * * D Campanulaceae Specularia perfoliata GR P A F NV LONJAP * * D Caprifoliaceae Lonicera japonica BD A P V ID, IV VIBPRU * D Caprifoliaceae Viburnum prunifolium BD A P S STISET * * D Caryophyllaceae Stipulicida setacea GR P A F HELCAN * * D Cistaceae Helianthemum canadense GR P P F HELROS * * D Cistaceae Helianthemum rosmarinifolium GR P P F NV LECLEG * D Cistaceae Lechea leggettii GR P P F LECVIL * * D Cistaceae Lechea villosa GR P P F NV BONPAT * * D Convoluvulaceae Bonamia patens GR P P V IPOHED * * D Convolvulaceae Ipomea heduraceae GR P A V ID IPOPAN * * D Convolvulaceaea Ipomoea pandurata GR P P V NV CORFLO * * D Cornaceae Cornus florida BD, MA A P T NV LECTEN * * D Costaceae Lechea tenuifolia GR P P F NV CHIMAC * D Ericaceae Chimaphila maculata GR P P F LYOLIG * D Ericaceae Lyonia ligustrina BD A P S NV

116

VACARB * * D Ericaceae Vaccineum arboreum BD A P TS NV VACCOR * * D Ericaceae Vaccineum corymbosum BD A P S VACELL * D Ericaceae Vaccineum ellottii BD A P S VACSTA * * D Ericaceae Vaccineum stamineum BD, MA A P S NV ACAGRA * * D Euphorbiaceae Acalypha gracilens GR P A F NV CNISTI * * D Euphorbiaceae Cnidoscolus stimulosus GR P P TS NV CROGLA * * D Euphorbiaceae Croton glandulosus GR P A F NV EUPCOR * * D Euphorbiaceae Euphorbia corollata GR P P F NV EUPIPE * * D Euphorbiaceae Euphorbia ipecacuahae GR P P F STISYL * * D Euphorbiaceae Stillingia sylvatica GR P P F NV TRAURE * * D Euphorbiaceae Tragia urens GR P P F NV TRAURT * * D Euphorbiaceae Tragia urticifolia GR P P F NV ALBJUL * D Fabaceae Albizia julibrissin GR P P T ARAHYP * D Fabaceae Arachis hypogaea GR P A F BAPALB * D Fabaceae Baptisia alba BL P P F BAPPER * * D Fabaceae Baptisia perfoliata BL P P F BAPTIN * * D Fabaceae Baptisia tinctoria BL P P F NV CASOBT * D Fabaceae Cassia obtusifolia BL P A F NV, ID CENVIR * * D Fabaceae Centrosema virginianum GR P P V NV CHAFAS * * D Fabaceae Chamaecrista fasiculata BL P A F NV

CHANIC * * D Fabaceae Chamaecrista nictitatans ssp. nictans var. nictitans BL P A F NV

CLIMAR * * D Fabaceae Clitoria mariana GR P P V NV CROANG * * D Fabaceae Crotalaria angulata BL P ABP F CROSPE * * D Fabaceae Crotalaria spectabilis BL P A F ID DESFLO * * D Fabaceae Desmodium floridanum AD A P F DESMAR * * D Fabaceae Desmodium marilandicum AD A P F NV DESPAN * * D Fabaceae Desmodium paniculatum var. paniculatum AD A P F NV DESSTR * * D Fabaceae Desmodium strictum AD A P F NV DESTOR * * D Fabaceae Desmodium tortuosum AD A A F GALVOL * * D Fabaceae Galactia volubilis GR P P V NV INDCAR * * D Fabaceae Indigofera caroliniana BL P P S NV LESBIC * * D Fabaceae Lespedeza bicolor GR P P S ID, IV LESCUN * * D Fabaceae Lespedeza cuneata GR P P F ID LESHIR * * D Fabaceae Lespedeza hirta GR P P F NV

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LESREP * * D Fabaceae Lespedeza repens GR P P F NV LESSTR * * D Fabaceae Lespedeza striata GR P A F ID LESSTU * * D Fabaceae Lespedeza stuevei GR P P F NV LESVIR * * D Fabaceae Lespedeza virginica GR P P F NV LUPDIF * * D Fabaceae Lupine diffusus BL P P F PETPIN * D Fabaceae Petalostemum pinnata GR P P F PHASIN * D Fabaceae Phaseolus sinnatus GR P P V RHYDIF * * D Fabaceae Rhyncosia difformis GR P P F NV RHYTOM * D Fabaceae Rhyncosia tomentosa BL P P F NV RHYREN * * D Fabaceae Rynchosia reniformus BL P P F NV SCHMIC * * D Fabaceae Schrankia microphylla AD A P V NV STYBIF * * D Fabaceae Stylosanthes biflora GR P P F NV TEPFLO * * D Fabaceae Tephrosia florida BL P P F NV Unknown 10 * D Fabaceae Unknown Unkonwn BL P P F WISFRU * D Fabaceae Wisteria fructescens BL P P V NV CASPUM * D Fagaceae Castanea pumilla MA A P S QUEALB * D Fagaceae Quercus alba MA A P TS NV QUEFAL * * D Fagaceae Quercus falcata MA A P T NV QUEINC * * D Fagaceae Quercus incana MA A P TS NV QUELAE * * D Fagaceae Quercus laevis MA A P T NV QUELAU * * D Fagaceae Quercus laurifolia MA A P T NV QUEMAG * * D Fagaceae Quercus margaretta MA A P T QUEMAR * * D Fagaceae Quercus marilandica MA A P T NV QUENIG * * D Fagaceae Quercus nigra MA A P T NV QUESTE * * D Fagaceae Quercus stellata MA A P T NV QUEVEL * * D Fagaceae Quercus velutina MA A P T QUEVIR * * D Fagaceae Quercus virginiana MA A P T NV SABDIF * D Gentianaceae Sabatia difformis GR P P F Unknown 11 * D Geraniaceae Geranium Unknown GR P P F LIQSTY * * D Hamamelidaceae Liquidambar styraciflua GR P P T NV HYPGEN * * D Hypericaceae Hypericum gentianoides GR P A F NV HYPHYP * * D Hypericaceae Hypericum hypercoides WI P P S NV CARALB * * D Juglandaceae Carya alba MA A P T CARPAL * * D Juglandaceae Carya pallida MA A P T NV PRUVUL * D Lamiaceae Prunella vulgaris var. lanceola GR P P F NV

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SALAZE * * D Lamiaceae Salvia azurea GR P ABP F PERBOR * * D Lauraceae Persea borbonia BD A P T NV SASALB * * D Lauraceae Sassafras albidum BD A P T NV LINSTR * D Linaceae Linum striatum GR P A F LINUSI * * D Linaceae Linum usitatissimum GR P A F ID GELSEM * * D Logainiaceae Gelsemium sempervirens GR P P V NV POLPRO * * D Loganiaceae Polypremum procumbens GR P P F NV LIRTUL * * D Magnoliaceae Liriodendron tulipifera WI P P T NV MAGGRA * D Magnoliaceae Magnolia grandiflora GR P P T SIDRHO * D Malvaceae Sida rhombifolia GR P A F RHEMAR * D Melastomataceae Rhexia mariana GR P P F NV MELAZE * * D Meliaceae Melia azedarach GR P P T ID MORRUB * D Moraceae Morus rubra BD A P T NV MYRCER * * D Myricaceae Myrica cerifera BD A P T NV NYSSYL * D Nyssaceae Nyssa sylvatica BD A P T LIGSIN * * D Oleaceae Ligustrum sinense BD A P TS ID OSMAME * D Oleaceae Osmanthus americana BD A P T NV GAUFIL * * D Ongraceae Gaura filipes Spach. GR P P F NV OENBIE * * D Ongraceae Oenethera biennis BL P ABP F NV OSMCIN * D Osmundaceae Osmunda cinnamomea WI P P R OXASTR * * D Oxalidaceae Oxalis stricta BL P P F NV PASINC * * D Passifloraceae Passiflora incarnata MA A P V NV PASLUT * * D Passifloraceae Passiflora lutea BD, MA A P V NV PHYAME * * D Phytolaccaceae Phytolacca americana BD A P F NV PINPAL * * D Pinaceae Pinus palustris WI P P T NV PINTAE * * D Pinaceae Pinus taeda WI P P T ID PLAOCC * * D Platanaceae Platanus occidentalis GR P P T NV CHEAMB * * D Polygonaceae Chenopodium ambrosioides GR P ABP F ID ERITOM * * D Polygonaceae Eriogonum tomentosum GR P P F POLAME * * D Polygonaceae Polygonella americuana WI P P F RUMHAS * * D Polygonaceae Rumex hastatulus WI P P F NV BERSCA * * D Rhamnaceae Berchemia scandens BD, MA A P V NV CEAAME * * D Rhamnaceae Ceanothus americanus BL P P S NV AGRPAR * D Rosaceae Agrimonia parviflora GR P P F CRAFLA * * D Rosaceae Crataegus flava BD, MA A P TS NV

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CRAUNI * * D Rosaceae Crataegus uniflora BD, MA A P S NV MALANG * * D Rosaceae Malus angustifolia BD, MA A P TS NV POTCAN * * D Rosaceae Potentilla canadensis BD, MA A P V NV PRUANG * * D Rosaceae Prunus angustifolia BD, MA A P TS NV PRUCAR * * D Rosaceae Prunus caroliniana BD A P TS NV PRUSER * * D Rosaceae Prunus serotina BD A P T NV RUBARG * * D Rosaceae Rubus argutus BD, MA A P S NV RUBCUN * * D Rosaceae Rubus cunefolia BD, MA A P S NV RUBFLA * * D Rosaceae Rubus flagellaris BD, MA A P V NV SORARB * D Rosaceae Sorbus arbutifolia BD, MA A P S DIOTER * * D Rubiaceae Diodia teres GR P A F NV GALTIN * * D Rubiaceae Galium tinctorium GR P P F NV RICSCA * * D Rubiaceae Richardia scabra GR P A F NR DIOVIR * * D Sapotaceae Diospyros virginiana BD, MA A P T NV AGAFAS * * D Scrophulariaceae Agalinis fasciculata GR P A F NV AURPEC * * D Scrophulariaceae Aureolaria pectinata GR P A F LINCAN * * D Scrophulariaceae Linaria canadensis GR P B F NV, NR PENAUS * * D Scrophulariaceae Penstemon australis GR P P F NV AILALT * D Simaroubaceae Ailanthus altissima WI P P T DATSTR * D Solanaceae Datura stramonium GR P A F ID PHYHET * * D Solanaceae Physalis heterophylla MA A P F NV SOLAME * D Solanaceae Solanum americanum BD A ABP F NV SOLCAR * * D Solanaceae Solanum carolinense BD, MA A P F NV PIRCAR * * D Turneraceae Piriqueta cistoides ssp. caroliniana GR P ABP S NV CELTEN * * D Ulmaceae Celtis tenuifolia BD, MA A P TS NV ULMALA * * D Ulmaceae Ulmus alata WI P P T NV Unknown 12 * D Unknown Unknown unknown GR P P F CALAME * * D Verbenaceae Callicarpa americana BD A P S NV VERSCA * D Verbenaceae Verbena scabra GR P P F Unkonwn 13 * * D Violaceae Viola unknown GR P ABP F NV AMPARB * * D Vitaceae Ampelopsis arborea BD, MA A P V NV PARQUI * * D Vitaceae Parthenocissus quinquefolia BD A P V NV VITAES * * D Vitaceae Vitis aestivalis BD A P V NV VITROT * * D Vitaceae Vitis rotundifolia BD A P V NV POLACR * F Aspidiaceae Polystichum acrostichoides WI P P F

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ASPPLA * F Aspleniaceae Asplenium platyneuron WI P P R NV PTEAQU * * F Pteridaceae Pteridium aquilinum WI P P F NV COMERE * * M Commelinaceae Commelina erecta GR P ABP F ID, NV TRAROS * M Commelinaceae Tradescantia rosea GR P P F BULCIL * * M Cyperaceae Bulbostylis ciliatifolia GR P A G CARALA * * M Cyperaceae Carex alata GR P P G CYPALA * M Cyperaceae Cyperus alata GR P P G CYPFIL * * M Cyperaceae Cyperus filiculmis GR P P G CYPRET * * M Cyperaceae Cyperus retrorsus GR P A G NV RHYGRA * M Cyperaceae Rhyncospora grayi GR P P G NV RHYINE * * M Cyperaceae Rhyncospora inexpansa WI P P G NV SCICYP * M Cyperaceae Scirpus cyperinus WI P P G NV SCLCIL * * M Cyperaceae Scleria ciliata WI P P G JUNDIC * * M Juncaceae Juncus dichotomous GR P P F ASPOFF * * M Liliaceae Asparagus officinalis BD A P F ID SMIBON * * M Liliaceae Smilax bona-nox BD A P V NV SMIGLA * * M Liliaceae Smilax glauca BD A P V NV SMIROT * * M Liliaceae Smilax rotundifolia BD A P V NV SMISMA * * M Liliaceae Smilax smallii BD A P V NV YUCFIL * * M Liliaceae Yucca filamentosa GR P P U NV ALLCAN * M Lilliaceae Allium canadense var. canadense GR P P F ANDSPP * * M Poaceae Andropogon ternarius and virginicus WI P P G NV ARIPUR * * M Poaceae Aristida purpurascens var. tenuispica WI P P G NV ARITUB * * M Poaceae Aristida tuberculosa WI P A G NV Unknown 14 * * M Poaceae Aristida unknown WI P ABP G ARUGIG * * M Poaceae Arundinaria gigantea WI P P G NV CENSPI * * M Poaceae Cenchrus spinifex AD A A F CYNDAC * * M Poaceae Cynodon dactylon GR P P G ID DANSER * * M Poaceae Danthonia sericea WI P P G DICACU + + M Poaceae Dichanthelium acuminatum var. acuminatum GR P P G NV DICCOM + + M Poaceae Dichanthelium commutatum GR P P G NV DICLEU + + M Poaceae Dichanthelium leucothrix GR P P G NV DICVOL + + M Poaceae Dichanthelium volubis GR P P G NV DIGCOG * * M Poaceae Digitaria cognata GR P A G ERACAP * * M Poaceae Eragrostis capillaris WI P A G NV

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ERAMEX * * M Poaceae Eragrostis mexicana WI P A G EREOPH * * M Poaceae Eremochloa ophiuroides WI P P G GYMAMB * * M Poaceae Gymnopogon ambiguus GR P P G NV MUHEXP * * M Poaceae Muhlenbergia expansa WI P P G PANANC * * M Poaceae Panicum anceps var. anceps WI P P G NV PANVER * * M Poaceae Panicum verrucosum WI P A G NV PASBOS * * M Poaceae Paspalum bosicanum GR P A G PASFLO * * M Poaceae Paspalum floridanum GR P P G NV PASLAE * * M Poaceae Paspalum laeve GR P P G NV SACBRE * * M Poaceae Saccharum brevibarbe WI P P G NV SETGLA * M Poaceae Setaria glauca WI P A G NR SORNUT * M Poaceae Sorghastrum nutans WI P P G NV STIAVE * M Poaceae Stipa avenaceae WI P P G

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Appendix B. Vascular plant taxa recorded in the seedbank from experimental sites at the Savannah River Site near Aiken, South Carolina. We utilized the nomenclature of Radford (1964) except for the genera Chamaecrista and Gnapthalium, where the updated nomenclature of Weakley (2005) was used.

Unique Code

Mono- or Dicot Family Genus Species

Dispersal Type

Dispersal Mode

Life Cycle Habit Origin

ACAGRA D Euphorbiaceae Acalypha gracilens GR A A F NV ANDSPP M Poaceae Andropogon ternarius and virginicus WI A P G NV ARITUB M Poaceae Aristida tuberculosa WI A A G NV BACHAL D Asteraceae Baccharis halimifolia WI A P F NV BULCIL M Cyperaceae Bulbostylis ciliatifolia GR A A G CALAME D Verbenaceae Callicarpa americana BI B P S NV CENSPI M Poaceae Cenchrus spinifex AD B A F CHAFAS D Fabaceae Chamaecrista fasiculata BA A A F NV

CHANIC D Fabaceae Chamaecrista nictitatans ssp. nictans var. nictitans BA A A F NV

CHEAMB D Polygonaceae Chenopodium ambrosioides GR A ABP F ID CYPFIL M Cyperaceae Cyperus filiculmis GR A P G CYPHAS M Cyperaceae Cyperus haspan GR A P G CYPRET M Cyperaceae Cyperus retrorsus GR A A G NV DICLAR M Poaceae Dichanthelium large spp. GR A P G NV DICMED M Poaceae Dichanthelium medium spp. GR A P G NV DICSMA M Poaceae Dichanthelium small spp. GR A P G NV DIGCOG M Poaceae Digitaria cognata GR A A G DIGFIL M Poaceae Digitaria filiformis GR A A G NV

DIGISC M Poaceae Digitaria ischaemum var. Ischaemum GR A A G

DIOTER D Rubiaceae Diodia teres GR A A F NV ERACAP M Poaceae Eragrostis capillaris WI A A G NV EREHIE D Asteraceae Erechtites hieracifolis WI A A F NV EREOPH M Poaceae Eremochloa ophiuroides WI A P G ERICAN D Asteraceae Erigeron canadensis WI A A F ERISTR D Asteraceae Erigeron strigosus WI A ABP F NV EUPCAP D Asteraceae Eupatorium capillifolium WI A A F NV EUPCOM D Asteraceae Eupatorium compositifolium WI A P F NV GELSEM D Logainiaceae Gelsemium sempervirens GR A P V NV GNAOBT D Asteraceae Gnapthalium obtusifolium WI A ABP F NV GNAPRF D Asteraceae Gnapthalium purpureum var. falcata WI A ABP F GNAPRS D Asteraceae Gnapthalium purpureum var. spa WI A ABP F HYPGEN D Hypericaceae Hypericum gentianoides GR A A F NV HYPHYP D Hypericaceae Hypericum hypercoides WI A P S NV IVAMIC D Asteraceae Iva microcephala WI A A F JUNDIC M Juncaceae Juncus dichotomous GR A P F LECVIL D Cistaceae Lechea villosa GR A P F NV LESCUN D Fabaceae Lespedeza cuneata GR A P F ID LESSTR D Fabaceae Lespedeza striata GR A A F ID LINCAN D Scrophulariaceae Linaria canadensis GR A B F NV LINUSI D Linaceae Linum usitatissimum GR A A F ID MOLVER D Aizoaceae Mollugo verticillata GR A A F ID NICTAB D Solanaceae Nicotiana tabacum GR A F OENBIE D Ongraceae Oenethera biennis BA A ABP F NV OPUCOM D Cactaceae Opuntia compressa BM B P C NV OXASTR D Oxalidaceae Oxalis stricta BA A P F NV

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PASBOS M Poaceae Paspalum bosicanum GR A A G PASFLO M Poaceae Paspalum floridanum GR A P G NV PASINC D Passifloraceae Passiflora incarnata MA B P V NV PHYAME D Phytolaccaceae Phytolacca americana BI B P F NV PHYHET D Solanaceae Physalis heterophylla MA B P F NV POLPRO D Loganiaceae Polypremum procumbens GR A P F NV RHUCOP D Anacardiaceae Rhus copallina BI B P TS NV RHUTOX D Anacardiaceae Rhus toxicodendron BI B P S NV RHYINE M Cyperaceae Rhyncospora inexpansa WI A P G NV RICSCA D Rubiaceae Richardia scabra GR A A F NV RUBSPP D Rosaceae Rubus spp. BM B F SETGLA M Poaceae Setaria glauca WI A A G ID SIDRHO D Malvaceae Sida rhombifolia GR A A F SOLPTY D Solanaceae Solanum ptycanthum BI B A F NV UNKN01 D Unknown Unknown unknown 1 A UNKN02 D Unknown Unknown unknown 2 A UNKN03 M Poaceae Unknown unknown 3 A UNKN04 D Asteraceae Unknown unknown 4 A UNKN05 D Poaceae Unknown unknown 5 A UNKN06 M Poaceae Unknown unknown 6 A UNKN07 M Poaceae Unknown unknown 7 A UNKN08 M Poaceae Unknown unknown 8 A UNKN09 M Poaceae Unknown unknown 9 A UNKN10 M Poaceae Unknown unknown 10 A UNKN11 D Asteraceae Unknown unknown 11 A VITROT D Vitaceae Vitis rotundifolia BI B P V NV