Earth, Wind, and Fire: Resource Allocation and Dispersal Strategies of Rhynchospora knieskernii (Cyperaceae) in a Disturbance-Dependent Ecosystem

A Thesis

Submitted to the Faculty

of

Drexel University

by

Marilyn Carolyn Sobel

in partial fulfillment of the

requirements for the degree of

Doctor of Philosophy

June 2015

ii

Dedication

To my wonderful and supportive family

Elizabeth Amanda Sobel

Benjamin Carmen Sobel

Marcus J Sobel

iii

Acknowledgements

The pursuit of a doctoral degree is a long task that involves the help and support of many people. As a returning student burdened with a full-time job, I benefited greatly from a wide network of people that made it possible for me to complete my thesis.

I would like to thank my advisor, Dr. Walter F. Bien for sharing his vast knowledge of the New Jersey Pine Barrens as well as many other areas of ecology. I am so grateful for his support, kindness, friendship, and endless supply of jokes. From the first day of his Pine Barrens

Ecology class to the day of my defense Dr. Bien worked to enhance, develop, and polish my scientific understanding and knowledge of ecology. Dr. Bien was always willing to take time to explain concepts and procedures, share books and articles, and teach me a wide range of techniques. He was understanding about my time constraints and willing to work around my schedule. He truly made me a scientist.

I also received expert help from my wonderful committee. I am especially grateful to Dr.

Dennis M. Gray. He taught me many techniques for analyzing nitrogen and phosphorus, assisted directly in the most technical areas of analysis, made several fruitful suggestions for my research that took it in interesting directions, and generously shared his knowledge and ideas in many discussions of my results. My committee chair, Dr. Michael O’Connor, patiently explained several statistical concepts and was also a great teacher of biophysical ecology, modeling, physiological ecology, and biostatistics. I used information from his classes every single day while writing my thesis. Dr. Gerry Moore shared his botanical expertise about Rhynchospora knieskernii as well as the many associated graminoids. His experience in the field and knowledge of plant structure and taxonomy were invaluable. Dr. James Spotila kindly shared both knowledge and equipment. I could not have conducted the water potential studies without him. iv

It would not have been possible for me to write my chapter on community assembly

without the help of several New Jersey Pine Barrens botanists. I would like to thank Terry

Schmidt, Mark Szutarsky, Donna McBride, and Tom Besselman for patiently sorting through

hundreds of plants to ensure all the associated species were correctly identified. Their good

humor and expertise turned a tedious task into a special experience.

Our laboratory of mavericks provided friendship, support, laughter, and strength when

the going got tough. From day one Dane Ward made me feel truly a part of the lab. Thanks to

him I got to play king of the mountain, pine cone softball, and broom hockey. We had long

discussions about science and conservation and I benefited from his insight and his leadership.

Ryan Rebozo was always willing to share data and statistical techniques; he was also quite nice

about having a much more attractive study plant. Alina Freire-Fierro shared her extensive

botanical knowledge, her specialized taxonomic books, and her sunny personality. Kevin Smith

was always helpful with questions about photography and was very supportive during seminars

and presentations. Our lab would fall apart without Emily Ostrow, undergraduate extraordinaire.

Many thanks for her dedication and willingness to do all kinds of grunt work that kept the lab on

track.

I am lucky enough to have a very kind and understanding supervisor without whom I

could not have taken on this task. I thank Mary Ellen Taggart-Ford of the Steinbright Career

Development Center at Drexel University with all my heart for her flexibility, support, and understanding. She even took an interest in snakes, frogs, and my special plant. I am also very appreciative of the support of my colleagues Lauren McHale, Angela Brennan, Nancy LeClair,

Catherine Rooney, and Jeanine Rastatter. Mr. Peter Franks, Director of the SCDC, was an early facilitator of my dreams.

Many volunteers helped me in the laboratory and field, including Gabrielle Farrell, Jodi

Baumgarten, Stuart Berg, Donna Bridger, Christopher Ball, Christopher Funk, and Holly Hagy. I would also like to thank Dr. Roger Marino of Drexel University for his help with the hydrochory v

experiments using the flume tank, Robert Cartica of the New Jersey Department of

Environmental Protection for assistance with Rhynchospora knieskernii site locations, and Jeremy

Markuson of the United States Fish and Wildlife Service for assistance in receiving my permit.

Also warm thanks to Major Richard DeFeo and the personnel at Warren Grove Range for

logistical support.

Last, but certainly not least, I have to thank my wonderful family. My son Ben Sobel and

daughter Lizzy Sobel were excited about my doctoral work from the very beginning. They were

only 12 and 10 when I started and could not have known what they were going to have to put up

with, but they were always gracious and acted as if they did not mind how often I was away in the

field. They also helped me check my data, weighed plants, and assisted with my research in

many other ways. My husband Marc Sobel put up with having no car most weekends, answered

endless questions about statistics, and took care of Ben and Lizzy when I was in the field. I also

want to thank my mother-in-law, Florence Sobel for her interest, support, and good wishes. I am

sorry my own parents are not alive to enjoy this day, but I am grateful for their focus on the

importance of education. I owe a great deal to my mother Jane Carter for demonstrating how

happiness is something you create through optimism, hard work, and personal effort. Her

example has been a life-long inspiration for me and helped to make this thesis possible.

My research received financial support from the 177th Fighter Wing of the New Jersey

Air National Guard, the Laboratory of Pinelands Research at Drexel University, and the

Philadelphia Botanical Club (Bayard Long Award).

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TABLE OF CONTENTS

LIST OF TABLES ...... x

LIST OF FIGURES ...... xii

ABSTRACT ...... xix

INTRODUCTION ...... 1

1.1. The Genera Rhynchospora ...... 1

1.1.1 Study Species ...... 2

1.1.2 Study Sites ...... 4

1.2 Dissertation Structure ...... 5

1.2.1 Chapter 2: Earth, Wind, and Fire: Rhynchospora knieskernii Allocation Strategies under Differing Conditions of Resource Availability ...... 5

1.2.2 Chapter 3: Interactions of Rhynchospora knieskernii with Associated Species and Impacts on Community Assembly ...... 6

1.2.3 Chapter 4: Dispersal Strategies and Germination Cues in a Fire- Dependent Ecosystem ...... 7

1.2.4 Chapter 5: Conservation Implications and Future Directions ...... 8

Literature Cited ...... 10

2. EARTH, WIND AND FIRE: RHYNCHOSPORA KNIESKERNII ALLOCATION STRATEGIES UNDER DIFFERING CONDITIONS OF REOSOURCE AVAILABILITY ...... 15

Abstract ...... 15

2.1 Introduction ...... 16

2.1.1 Nitrogen Cycling ...... 17

2.1.2 Phosphorus Cycling ...... 18

2.1.3 Prescribed Burning and Fire Effects ...... 19

2.1.4 Rare Plants ...... 20

2.1.5 Resource Allocation ...... 23

2..2 Methods ...... 25

2.2.1 Soil Sampling ...... 25 vii

2.2.2 Plant Measurements ...... 26

2.2.3 Reproductive Output ...... 27

2.2.4 Plant Sampling ...... 28

2.2.5 Population Monitoring ...... 29

2.2.6 Canopy Cover ...... 30

2.2.7 Soil Moisture ...... 30

2.2.8 Weather ...... 31

2.2.9 Data Analysis ...... 31

2.3. Results ...... 32

2.3.1 Data Analysis ...... 32

2.3.2 Soil Sampling ...... 33

2.3.3 Plant Measurements ...... 35

2.3.4 Reproductive Output ...... 35

2.3.5 Plant Sampling ...... 36

2.3.6 Population Monitoring ...... 38

2.3.7 Canopy Cover ...... 38

2.3.8 Soil Moisture and Weather Data ...... 39

2.4. Discussion ...... 39

2.5 Literature Cited ...... 52

3. INTERACTIONS OF RHYNCHOSPORA KNIESKERNII WITH ASSOCIATED SPECIES AND IMPACTS ON COMMUNITY ASSEMBLY ...... 103

Abstract ...... 103

3.1 Introduction ...... 104

3.1.1 Lottery Model ...... 105

3.1.2 Community Assembly in Nutrient-Poor Communities ...... 105

3.1.2 Disturbance and Community Assembly ...... 106

3.2 Methods ...... 107

3.2.1 Associated Species Identification ...... 107

3.2.2 Study Sites ...... 108 viii

3.2.3 Soil Sampling ...... 108

3.2.4 Plant Measurements and Nutrient Concentrations ...... 109

3.2.5 Soil Water Potential ...... 110

3.2.6 Canopy Cover ...... 111

3.2.7 Weather ...... 112

3.2.8 Data Analysis ...... 113

3.3 Results ...... 113

3.3.1 Associated Species Identification ...... 113

3.3.2 Soil Sampling ...... 114

3.3.3 Plant Sampling ...... 115

3.3.4 Water Potential and Soil Moisture ...... 116

3.3.5 Canopy Cover ...... 117

3.3.6 Weather ...... 117

3.4 Discussion ...... 117

3.5 Literature Cited ...... 125

4. DISPERSAL STRATEGIES AND GERMINATION CUES IN A FIRE- DEPENDENT ECOSYSTEM ...... 159

Abstract ...... 159

4.1 Introduction ...... 160

4.2 Methods ...... 167

4.2.1 Wind Dispersal (Anemochory) ...... 167

4.2.2 Water Dispersal (Hydrochory) ...... 168

4.2.3 Animal Dispersal (Epizoochory) ...... 169

4.2.4 Germination ...... 171

4.2.5 Data Analysis ...... 172

4.3 Results ...... 173

4.3.1 Wind Dispersal (Anemochory) ...... 173

4.3.2 Water Dispersal (Hydrochory) ...... 175

4.3.3 Animal Dispersal (Epizoochory) ...... 176 ix

4.3.4 Germination ...... 177

4.4 Discussion ...... 177

4.5 Literature Cited ...... 189

5. CONSERVATION IMPLICATIONS AND FUTURE DIRECTIONS ...... 218

5.1 Conservation Implications ...... 218

5.2 Future Directions ...... 222

5.3 Literature Cited ...... 225

VITA ...... 227

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LIST OF TABLES

Table 1.1 Site descriptions for sites used in soil, plant, population, and canopy cover sampling. All sites are found within a pitch pine lowland matrix except Runway, which is a highly disturbed site adjacent to a forest...... 14

Table 2.1. Results of model testing for mixed-effects models comparing null models with full models to determine whether site characteristics influenced patterns of soil nutrient availability...... 90

Table 2.2 Statistical results of ANOVAs and regressions for a wide range of soil, plant, and site variables ...... 91

Table 2.2 (continued). Statistical results of ANOVAs and regressions for a wide range of soil, plant, and site variables...... 92

Table 2.3. Summary of means for data collected for a wide range of site variables, including soil and plant characteristics, canopy, population, and wind speed...... 93

Table 2.4. Soil nutrient concentrations (means and standard errors) from August 2011 to November 2013 at study sites on WGR. Data pooled for all sites; all concentrations in µg/g...... 94

Table 2.5. Soil nutrient concentrations (means and standard errors) for all study sites on WGR, 2011-2013. Data pooled for all months and years...... 95

Table 2.6. Plant measurements by year, site, and month (means and standard deviations) for five study sites on WGR, 2011-2013...... 96

Table 2.7. Measurements of reproductive output and related R. knieskernii plant measurements (means only) at five monitored sites on WGR, 2012-2013...... 97

Table 2.8. Plant nutrient concentrations (mean and SE) by year, season, and structure. Results pooled from five monitored sites on WGR, 2011-2013 ...... 98

Table 2.9. Rhynchospora knieskernii population measures (means and standard deviations) for ten transects at eight monitored sites on WGR, 2010-2014 ...... 99

Table 2.10. Measures of light availability (means and standard deviations) for ten transects at eight monitored sites on WGR, 2011-2013 ...... 100

Table 2.11. Soil moisture results (means and standard errors) for eight monitored sites on WGR, 2011-2013 ...... 101

Table 2.12. Monthly precipitation data (means only) from WGR weather tower (2010-2013) and Oswego weather station (2014)...... 102

Table 2.13. Monthly air temperature data (means only) from WGR weather tower, 2010-2013 ...... 102

Table 3.1. All identified species collected from 0.0625 m2 plots on WGR, 2012. Species richness shown by site ...... 148

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Table 3.2. All identified species collected from 0.0625 m2 plots on WGR, 2013. Species richness shown by site ...... 149

Table 3.3 Data on conservation status, growth form, and metabolic pathway for all identified species found at monitored sites on WGR, 2012-2013 ...... 150

Table 3.4. Summary of statistical results ...... 151

Table 3.5. Summary of descriptive statistics for plant height, root length, and root:shoot ratios for study sites on WGR in 2012 ...... 152

Table 3.6. Summary of descriptive statistics for plant height, root length, and root:shoot ratios for study sites on WGR in 2013 ...... 153

Table 3.7. Summary statistics for aboveground and belowground biomass and root:shoot ratios for five study sites on WGR in 2012 ...... 154

Table 3.8. Summary statistics for aboveground and belowground biomass and root:shoot ratios for five study sites on WGR in 2013 ...... 155

Table 3.9. List of dominant species at each site, as determined by percent biomass of all collected species for each plot at each site ...... 156

Table 3.10. Comparison of number of individual species and the number of sites at which they were collected for monitored sites in 2012 (5) and 2013 (4) on WGR...... 157

Table 3.11. Species found at all monitored sites on WGR in 2012 (5 sites) and 2013 (4 sites) ...... 157

Table 3.12. Comparison of changes to species biomass from 2012 to 2013 at five monitored sites on WGR...... 158

Table 4.1. Summary of statistical results ...... 215

Table 4.2. Summary statistics for wind speeds measured at sites during fall 2013 at WGR...... 216

Table 4.3. Summary statistics for wind speeds and distances measured during wind dispersal test...... 216

Table 4.4. Results of ten trials of achene buoyancy at variable shaking speeds and not shaken ...... 217

Table 4.5. Results deer attachment trials in which achenes were directly attached to deer legs. Data represent combined results from all five trials (10 achenes for each trial, N=50)...... 217

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LIST OF FIGURES

Figure 1.1 (Left) Illustration showing single plant (left), typical axillary arrangement of spikelets along stem; note spikelet close to base of plant (right), achenes within spikelet cluster (top center) and single achene with retrorsely barbellate bristles (bottom center). Illustration courtesy of the Flora of North America Association, Barbara Alongi, illustrator. (Right) Photograph of R. knieskernii showing spikelet close to base of plant (M. Sobel). This distinctive proximal spikelet is one of the primary means of identifying R. knieskernii in the field ...... 12

Figure 1.2. Map showing locations of subpopulations of R. knieskernii on WGR (above) and location of study sites (below). The plots for the germination experiment were located close to the Runway site ...... 13

Figure 2.1. Changes in concentrations of soil extractable carbon (top) and extractable total nitrogen (bottom) from August 2011 to November 2013 at five sites with R. knieskernii populations on WGR. All concentrations in µg/g. Error bars removed to minimize clutter; SEM for sites and months can be found in Tables 2.4 and 2.5 ...... 60

Figure 2.2. Changes in concentrations of soil extractable NH4-N (top), and nitrates/nitrites (bottom) from August 2011 to November 2013 at five sites with R. knieskernii populations on WGR. All concentrations in µg/g. Error bars removed to minimize clutter; SEM for sites and months can be found in Tables 2.4 and 2.5 ...... 61

Figure 2.3. Changes in concentrations of soil extractable total inorganic nitrogen (top) and total organic nitrogen (bottom) from August 2011 to November 2013 at five sites with R. knieskernii populations on WGR. All concentrations in µg/g. Error bars removed to minimize clutter; SEM for sites and months can be found in Tables 2.4 and 2.5 ...... 62

Figure 2.4. Changes in concentrations of soil extractable PO4-P from August 2011 to November 2013 at five sites with R. knieskernii populations on WGR. All concentrations in µg/g. Error bars removed to minimize clutter; SEM for sites and months can be found in Tables 2.4 and 2.5 ...... 63

Figure 2.5. Potential relationship between precipitation and soil extractable PO4-P concentrations. Precipitation (blue line) closely tracked PO4-P at moderate rainfall levels and was inversely related during periods of high rainfall. All concentrations in µg/g. Error bars removed to minimize clutter; SEM for phosphorus concentrations for sites and months can be found in Table 2.5 ...... 64

Figure 2.6. Relationship between plant height and number of achenes produced per plant. Taller plants produce a greater abundance of achenes, although the relationship is strongest at the extremes ...... 65

Figure 2.7. Comparison of total number of achenes produced at each of five monitored sites at WGR. Totals are for September (fruiting) and November (senescing) in 2012 and for August (flowering) and September (fruiting) in 2013. Values represent means ± 1 SEM ...... 66

Figure 2.8. Comparison of estimated mass of individual achenes produced at five monitored sites on WGR, 2012 to 2013. Achenes were measured in September (fruiting) and November (senescing) in 2012 and in August (flowering) and September (fruiting) in 2013. Values represent means ± 1 SEM...... 67

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2 Figure 2.9. Relationship between areal soil PO4-P concentrations (mg/m ) and the total number of achenes produced per plant (log transformed). Each point represents the total soil P concentration for a sampled site as it relates to the mean of the total number of achenes produced by R. knieskernii plants at that site...... 68

Figure 2.10. Relationship between soil moisture and individual achene mass in micrograms (estimated). Each point represents the percent of soil moisture at a site as it relates to the mean estimated achene mass for that site. Relationship is statistically significant (F1,13=14.77, p<0.01)...... 69

Figure 2.11. Comparison of annual differences in maximum seedling emergence depth for R. knieskernii achenes, based on the scaling equation y = 27.0*(achene weight)0.33 . Achene weight is estimated from weight of multiple achenes, and thus does not take into account individual variability. Box and whisker plot shows median (line), first and third quartiles (box), and points within 1.5 interquartile range (IQR) of the lower and upper quartiles (whiskers). Dots represent outliers...... 70

Figure 2.12. Influence of the interaction of soil moisture and soil extractable PO4-P concentrations on nitrogen concentrations in plant reproductive structures (log transformed). Soil moisture is indicated in the ribbon on top of x-axis; numbers indicate sequence of increasing soil moisture, beginning with 11% (1) and increasing to 20% (8). The two highest soil moisture levels (7 and 8) are represented by only one data point (line). Phosphorus concentrations (x-axis) increase from 0.0 to 1.5 µg/g in increments of 0.5 µg. Median plant nitrogen concentrations (log-transformed) are lowest at medium soil moisture levels and relatively high phosphorus concentrations, and are highest at high soil moisture levels and low phosphorus concentrations. Box and whisker plot shows median (line), first and third quartiles (box), and points within 1.5 interquartile range (IQR) of the lower and upper quartiles (whiskers). The interaction is significant 2 (F2,21 = 14.28, p < 0.0001, R = 0.54) ...... 71

Figure 2.13. Percent allocation of carbon to key R. knieskernii plant structures: reproductive, stems (culms and leaves), and storage (winter bud and root) during flowering (August), fruiting (September), and senescing (November) for five monitored sites at WGR, 2011-2012. Ditch site was dropped in 2012 and LSL site was added in 2012. ... 72

Figure 2.14. Percent allocation of nitrogen to key R. knieskernii plant structures: reproductive, stems (culms and leaves), and storage (winter bud and root) during flowering (August), fruiting (September), and senescing (November) for five monitored sites at WGR, 2011-2012. Ditch site was dropped in 2012 and LSL site was added in 2012...... 73

Figure 2.15. Percent allocation of phosphorus to key R. knieskernii plant structures: reproductive, stems (culms and leaves), and storage (winter bud and root) during flowering (August), fruiting (September), and senescing (November) for five monitored sites at WGR, 2011-2012. Ditch site was dropped in 2012 and LSL site was added in 2012 ...... 74

Figure 2.16. Comparison of seasonal changes in allocation of R. knieskernii carbon, nitrogen, and phosphorus concentrations at five monitored sites on WGR in 2012. The Ditch site was dropped in 2012 and LSL was added in 2012 (see Methods)...... 75

Figure 2.17. Differences in allocation of nitrogen (TN, top) and phosphorus (PO4-P, bottom) for different R. knieskernii plant structures. Box and whisker plot shows median (line), first and third quartiles (box), and points within 1.5 interquartile range (IQR) of the lower and upper quartiles (whiskers). Circles represent outliers. Data are pooled for all sites and years...... 76 xiv

Figure 2.18. Relationship between total phosphorus concentrations within theR. knieskernii plants and the percentage allocated to the reproductive structures for all sites between 2011 and 2012 ...... 77

Figure 2.19. Relationship between nitrogen concentrations within R. knieskernii plants and the proportion allocated to the reproductive structures for all sites between 2011 and 2012. Note difference in scales for x-axis between 2011 values (15-35) and 2012 values (50-250). Proportion of allocation was significantly different at different total concentrations (p<0.01) , but proportions did not differ significantly between years (p=0.70) .... 78

Figure 2.20. Seasonal allocation of nitrogen at monitored burned and unburned sites on WGR, 2011 to 2012. Note difference in scales on y-axis for 2011 unburned sites (middle row). Box and whisker plot shows median (line), first and third quartiles (box), and points within 1.5 interquartile range (IQR) of the lower and upper quartiles (whiskers). Dots represent outliers ...... 79

Figure 2.21. Seasonal allocation of phosphorus at monitored burned and unburned sites on WGR, 2011 to 2012. Box and whisker plot shows median (line), first and third quartiles (box), and points within 1.5 interquartile range (IQR) of the lower and upper quartiles (whiskers). Dots represent outliers ...... 80

Figure 2.22. Differential allocation of nitrogen to different plant structures at burned and burned sites during fruiting at five monitored sites on WGR, 2011 to 2013. Box and whisker plot shows median (line), first and third quartiles (box), and points within 1.5 interquartile range (IQR) of the lower and upper quartiles (whiskers). Dots represent outliers...... 81

Figure 2.23. Differential allocation of phosphorus to different plant structures at burned and unburned sites during fruiting at five monitored sites on WGR, 2011 to 2013. Box and whisker plot shows median (line), first and third quartiles (box), and points within 1.5 interquartile range (IQR) of the lower and upper quartiles (whiskers). Dots represent outliers...... 82

Figure 2.24. Comparison of nitrogen and phosphorus concentrations (all structures) with total plant nitrogen and phosphorus (mass x concentration) between 2011 and 2012. 2 Differences in N concentrations (F1,25 = 120.7, p < 0.0001, R = 0.82) and total plant N 2 (F1,22 = 17.61, p < 0.001, R = 0.42) between years were significant. Differences in total 2 plant P between years was not significant (F1,22 = 0.641, p = 0.43, R = -0.02). It was not possible to test for annual differences in P concentrations, as errors could not be normalized, even after log transformation...... 83

Figure 2.25. Relationship between soil N:P ratio and plant N:P ratio. The category “Roots” includes both plant roots and the winter bud. Each point represents the N:P ratio of a plant at a site as it relates to the N:P ratio of the soil at the same site...... 84

Figure 2.26. Relationship between soil N:P ratio and the total plant N:P ratio based on summing mass x concentration for all plant structures (square root-transformed). Each point represents the N:P ratio of a plant at a site as it relates to the N:P ratio of the soil at the same site...... 85

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Figure 2.27. Relationship between soil moisture and nitrogen concentrations in the reproductive structures. Each point represents the relationship between soil moisture at a site and the total nitrogen concentration of R. knieskernii reproductive structures at that site. .... 86

Figure 2.28. Fruiting population decrease in relation to increased soil moisture as a quadratic function at burned (right) and unburned (left) sites (left) on WGR, 2011-2013. It was not possible to determine a relationship for burned sites due to an inadequate number of degrees of freedom...... 87

Figure 2.29. Relationship between height and the percent of total light (direct + diffuse light, see Methods) measured by canopy photographs for eleven sites on WGR, 2011 and 2013; five were prescribed burned (two in 2011 and three in 2012. Each data point represents one subplot where fruiting population was measured and canopy values were photographed...... 88

Figure 2.30. Comparison of relationship between height and the percent of total light (direct + diffuse light, see Methods) as measured by canopy photographs for the same eleven sites on WGR, 2011 and 2013 (left); relationship between canopy and soil moisture (right)...... 89

Figure 3.1. Sites where associated species and R. knieskernii plants were collected on WGR in 2012 and 2013. Red circles pinpoint site locations...... 128

Figure 3.2. Representative examples of species associated with R. knieskernii sites on WGR. Note the similarity in form among species; most have slender stems, are low in height, and are graminoid functional types...... 129

Figure 3.3. Above: Total plant nitrogen (mass x concentration x site area) for five study sites on WGR in October 2013. AllRk = R. knieskernii concentrations; AllSite = sum of R. knieskernii and the associated species; ASA= associated species aboveground; ASB = associated species belowground. Below: Total soil nitrogen availability (mass x concentration x site area) for the same five sites on WGR in October 2013...... 130

Figure 3.4. Above: Total plant phosphorus (mass x concentration x site area) for five study sites on WGR in October 2013. AllRk = R. knieskernii concentrations; AllSite = sum of R. knieskernii and the associated species; ASA= associated species aboveground; ASB = associated species belowground. Below: Total soil phosphorus availability (mass x concentration x site area) for the same five sites on WGR in October 2013...... 131

Figure 3.5. Comparisons of plant height for several commonly occurring species at five study sites on WGR from 2012 to 2013. Results represent mean ± 1 SEM for five samples of 0.0625 m2 subplots at each site...... 132

Figure 3.6. Comparisons of root length for several commonly occurring species at five tudy sites on WGR from 2012 to 2013. Results represent mean ± 1 SEM for five samples of 0.0625 m2 subplots at each site...... 133

Figure 3.7. Comparisons of root:shoot ratios (mass) for several commonly occurring species at five monitored sites on WGR from 2012 to 2013. Results represent mean ± 1 SD for five samples of 0.0625 m2 subplots at each site...... 134

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Figure 3.8. Comparison of total phosphorus (mass x concentration) between pooled associated species (AS) and R. knieskernii (Rk). Box and whisker plot shows median (line), first and third quartiles (box), and points within 1.5 interquartile range (IQR) of the lower and upper quartiles (whiskers). Dots represent outliers...... 135

Figure 3.9. Comparison of total plant nitrogen (mass x concentration) between pooled associated species (AS) and R. knieskernii (Rk). Box and whisker plot shows median (line), first and third quartiles (box), and points within 1.5 interquartile range (IQR) of the lower and upper quartiles (whiskers). Dots represent outliers...... 136

Figure 3.10. Results of principal component analysis using the most commonly encountered species. Variables that may not offer clear identification are: LSM, late spring moisture; MM, mean seasonal moisture, AM, August moisture, PWP, permanent wilting point; FC, field capacity; ASP, associated species phosphorus; ASN, associated species nitrogen. .... 137

Figure 3.11. Relationship between the first principal component axis and several potential explanatory variables. There is no relationship between the first axis and plant species (upper left), but there appears to be a gradient related to soil moisture, inorganic nitrogen, and canopy cover...... 138

Figure 3.12. Relationship between first principal component axis and measures of plant water potential. FC = field capacity, which is the point at which the micropores in the soil pore water are filled and plant roots can uptake water under ideal conditions. PWP = permanent wilting point, the point at which water adheres so tightly to soil particles that plant roots cannot uptake the existing soil pore water...... 139

Figure 3.13. Results of principal component analysis using all species. Circles indicate separation between Aristida sp. (green) and R. knieskernii (blue). Variables that may not offer clear identification are: LSM, late spring moisture; MM, mean seasonal moisture, AM, August moisture, PWP, permanent wilting point; FC, field capacity; ASP, associated species phosphorus; ASN, associated species nitrogen. Above: scree plot of principal components (left) and eigenvalues for each of the factors (right)...... 140

Figure 3.14. Comparison of percent biomass of the most dominant species for each plot sampled on WGR in 2012 and 2013 based on whether plots were located in sites that had been prescribed burn the previous year, the current year, or not burned at all. Data pooled for both years for sites that were not prescribed burned...... 141

Figure 3.15. Fitted curves for water potential for Dead End site at WGR...... 142

Figure 3.16. Fitted curves for water potential for Double site at WGR...... 142

Figure 3.17. Fitted curves for water potential for LSL site at WGR...... 143

Figure 3.18. Fitted curves for water potential for Runway site at WGR...... 143

Figure 3.19. Fitted curves for water potential for Sight Line site at WGR ...... 144

Figure 3.20. Fitted curves for water potential for Sand site at WGR. This is an upland site where we would not expect to ever find R. knieskernii ...... 144

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Figure 3.21. Fitted curves for water potential for Dry site at WGR This was a xeric site located in proximity to known R. knieskernii populations, but which did not have any R. knieskernii. Little vegetation grew at the site...... 145

Figure 3.22. Fitted curves for water potential for Wet site at WGR. This was a hydric site located in proximity to known R. knieskernii populations, but which did not have any R. knieskernii. The site is a wet ditch filled with decaying plant matter...... 145

Figure 3.23. Combined curves for all sites showing moisture gradient. This gradient is also reflected in soil moisture readings taken from April to September 2013 (Chapter 2)...... 146

Figure 3.24. Comparison of relationship between height and the percent of total light (direct + diffuse light, see Methods) as measured by canopy photographs for the same eleven sites on WGR, 2011 and 2013 (left); relationship between canopy and soil moisture (right)...... 147

Figure 4.1. Experimental setup for testing wind dispersal distances showing 18 cm platform. The bags of sand kept the platform stable...... 197

Figure 4.2. Flume tank (inset) and experimental setup for testing hydrochory dispersal distances. Note the pebbles distributed every 10 cm along tank surface...... 197

Figure 4.3. Experimental setup for testing seed attachment to deer fur...... 198

Figure 4.4. Experimental germination plot. Dark and light colors represent buried and surface seeds. Orange represents heat; grey represents smoke; brown represents charred wood; blue represents controls...... 198

Figure 4.5. Comparison of mean (left) and maximum (right) wind speeds 40 cm above the ground (Air) and 4 cm above the ground surface (Ground). Asterisks indicate site differences...... 199

Figure 4.6. Comparison of mean (left) and maximum (right) wind speeds 40 cm above the ground (Air) and 4 cm above the ground surface (Ground). Asterisks indicate site differences...... 200

Figure 4.7. Wind dispersal kernel densities for R. knieskernii seeds released from two different platform heights...... 201

Figure 4.8. Comparison of wind dispersal kernel densities for R. knieskernii at different maximum wind speeds. Differences in wind speeds were significant for all speeds above 2.0 m/s and highly significant for wind speeds at 2.6 m/s...... 202

Figure 4.9. Wind direction during fall testing of wind dispersal of R. knieskernii seeds. Rose diagram on left shows wind direction as measured by weather tower on Warren Grove Range over 24-hour period; rose diagram on right shows wind direction during dispersal testing...... 202

Figure 4.10. Kernel density plot for showing distance distribution of R. knieskernii seeds based on experimental results from point source dispersal (above) and simulations using increasing densities of seeds and assuming the same conditions (below). Note differences in bandwith. Black line shows experimental seed density based on wind dispersal tests...... 203

Figure 4.11. Comparison of seed buoyancy under different movement conditions. For shaken seeds, movement was 40 RPM for 30 minutes, 70 RPM for 150 minutes and 90 RPM for 90 minutes...... 204 xviii

Figure 4.12. Boxplots showing statistically significant differences between water velocity. Asterisks indicate highly significant difference between velocities...... 205

Figure 4.13. Comparison of hydrochoric dispersal distances for R. knieskernii seeds at two different depths with water velocity held constant...... 206

Figure 4.14. Comparison of dispersal distances for R. knieskernii seeds tested at two different water velocities with depth held constant...... 207

Figure 4.15. Kernel density plot showing distance distribution of R. knieskernii seeds based on experimental results from point source dispersal (above) and simulations using increasing densities of seeds and assuming the same conditions (below). Note differences in bandwith. Black line shows experimental achene density based on water dispersal tests...... 208

Figure 4.16. Distance distributions comparison of wind (red) and water (black) dispersal of R. knieskernii seeds. Although peak of wind distribution is not visible, it is close to zero. .... 209

Figure 4.17. Comparison of R. knieskernii seed attachment when deer model traveled through high-density (643 plants/m2) and low-density (147 plants/m2) patches...... 210

Figure 4.18. Bimodal dispersal distances for R. knieskernii seeds based on calculations of known deer movement and five tests of attachment rates using deer attachment model...... 211

Figure 4.20. Kernel density plot showing distance distribution of R. knieskernii seeds based on extrapolated results from deer attachment rates (above) and simulations using increasing densities of seeds and assuming the same conditions (below). Note differences in bandwith. Black line shows experimental achene density based on deer dispersal tests...... 212

Figure 4.20. Comparison of dispersal distances for three potential R. knieskernii dispersal vectors. Water and wind dispersal based on experimental point source dispersals; deer dispersal extrapolated from published deer movement rates and tested seed attachment rates. ... 213

Figure 4.21. Interaction plot with boxplots depicting interaction between location and germination for each of four treatments of R. knieskernii seeds (control = no treatment; charred wood, heat and smoke treatments described in methods). There were four subplots within each type and location (N = 32). Lines depict slope of interaction between treatment and location...... 214

xix

Abstract

Earth, Wind, and Fire: Resource Allocation and Dispersal Strategies of Rhynchospora knieskernii (Cyperaceae) in a Disturbance-Dependent Ecosystem By Marilyn Carolyn Sobel Walter F. Bien, Ph.D, Advisor

Rhynchospora knieskernii J. Carey (Cyperaceae) is a federally-threatened sedge that is endemic to the New Jersey Pine Barrens, a fire-dependent and nutrient-poor ecosystem.

Although global rare, little is known about its nutrient requirements, dispersal mechanisms, and population dynamics. Autecological studies that elucidate environmental requirements are essential for effective rare plant conservation.

Depending on availability, R. knieksernii preferentially allocated nitrogen to either reproduction or storage. When plant N concentration was elevated more N was allocated to storage than to reproduction. Achene mass and volume were less when plant N concentration was

higher. Plants in habitat that had been prescribed burned had the greatest N concentrations. In

contrast, there was no difference in phosphorus allocation to either reproduction or storage

regardless of the level of soil P. Plants in habitat where the canopy was opened following a prescribed burned were taller and produced more achenes. Although there was no difference in the nutrient allocation strategy between R. knieskernii and associate plants, R. knieskernii was the dominant species in habitat that was prescribed burned either the current or previous year.

Plant dispersal is often facilitated by specific plant structures. Although the bristles on R.

knieskernii achenes suggest adhesive dispersal, I found that achenes were also dispersed by wind

and water. Simulated dispersal kernels, using known or estimated dispersal distances, suggested

that deer were the vector for the longest -distance dispersal with water being a more effective

dispersal vector than wind. xx

Fire suppression and hydrological alteration pose the greatest threat to the stability of R. knieskernii populations. Potential changes to nutrient cycling as a result of climate change may also lead to population declines, particularly if increased precipitation results in lower phosphorus availability. In the absence of wildfire, frequent prescribed burns that mimic natural fire return intervals are recommended for the maintenance of R. knieskernii populations.

1

Chapter 1: Introduction

1.1 The Genera Rhynchospora

The genus Rhynchospora (Vahl) is widespread in neotropical and temperate environments, with about 270 species worldwide. The genus derives its name from the unique shape of the propagule, which is crowned by a triangular tubercule that is often characteristic for a particular species (Gleason and Cronquist 1991). Most species in the genera also sport bristles that begin at the base of the achene and may subtend it (Gleason and Cronquist 1991). These bristles may indicate adhesive dispersal, a common strategy in the Cyperaceae family (Sorenson

1986; Leck and Schütz 2005). There have been taxonomic (Moore 1992), phylogenetic (Thomas et al. 2009) and structural (Lucero and Vegetti 2012; Ueno 2013; Lucero et al. 2014) treatments of Rhynchospora, species, but only a handful of ecological studies (Ohlson and Malmer 1990;

Kesel and Urban 1999; Costa and Machado 2012); with only one treatment of a species found in the United States (Busch et. al. 2004). There are few examples of Rhynchospora that serve as key elements of ecosystems, which may explain the paucity of studies.

Cyperaceae are often components of the food web for both humans (Dominy 2012) and wildlife (Magliocca and Gautier‐Hion 2002). However, the only Rhynchospora known to play a key role in food web dynamics is Rhynchospora corymbosa, which is strongly preferred by lowland gorillas (Magliocca and Gautier‐Hion 2002). Given that little research has been directed at Rhynchospora species, it is possible that their roles in ecosystem function have yet to be elucidated. Recently R. alba was found to be an important host plant for an endangered butterfly

(Weking et al. 2013).

There are 31 Rhynchospora species that are considered rare, threatened or endangered at the state level in the United States (USDA PLANTS database 2015), but the only one that is 2

federally threatened is Knieskern’s beaksedge (Rhynchospora knieskernii J. Carey), which is an endemic of the New Jersey Pine Barrens. Endemism is common in Rhynchospora, which include the largest number of endemic species (41) growing along the Atlantic Coastal Plain (Sorrie and

Weakley 2001). Most Rhynchospora are obligate wetland plants and many are structurally similar, with slender culms and clusters of spikelets that may be cespitose (terminal) or found along several portions of the stem (Kral 1993), suggesting similar types of ecosystem function and adaptation (Reich et al. 2003). Therefore a study of a single threatened Rhynchospora endemic may have wider conservation applications.

1.1.1 Study Species

Rhynchospora knieskernii was first described by Carey (1847) and named in honor of Dr.

Robert Knieskern, the first botanist to identify the species. It is currently found only in New

Jersey, although there are historic reports of the species in Delaware, where it is now considered extirpated. It is a perennial sedge that grows up to 60 cm in height (Figure 1.1). It is an early-

successional species endemic to the New Jersey Pine Barrens, where it is found in moist mineral

soils and often appears after fires or mechanical disturbance of its microhabitat (USFWS 2008).

The species is well described by Kral (1993) in the Flora of North America: The culms are erect

to arching, leafy, linear to filiform, nearly triangular. The leaves are ascending, overtopped by the

culm; leaf blades are flat, linear to filiform, to 1.8 mm wide; the apex is distally involute,

trigonous, and setaceous. The inflorescences are terminal and axillary; there are 2-4 spikelet

clusters which are widely spaced. The lowest spikelet cluster is near the plant base and is one of

the primary means of identifying R. knieskernii in the field. The clusters are compact, broadly

turbinate to hemispheric, to 1.5 cm wide; the leafy bracts are curved, setaceous, and overtop

subtended compounds to varying degrees. The spikelets are dark brown with a lance-ellipsoid

shape and are 2–3 mm long. Fertile scales are 2 mm long, with an acute apex; a short-excurrent

midrib may be present. The flowers have approximately six perianth bristles as long as the fruit 3

body which are retrorsely barbellate; the distinctive achene is the most reliable way to positive identify R. knieskernii (Figure 1.1). The fruits are mostly two per spikelet, 1.5–1.9 mm long; the body is brown with a yellowish center and ellipsoid in shape with a triangular tubercle 0.3-0.6 mm long, distinctly shorter than the fruit body. There is a lenticular distal to short stipe, 1–1.3 long × 0.6–0.8 mm wide (Kral 1993). The mean weight of the achenes is 122 µg (M. Sobel, unpublished data).

The new meristem emerges from the winter bud in late spring (April-May); in cases where multiple culms appear it is unclear at what point additional culms emerge. Plant growth to full seasonal height takes place in early summer (May to July). Achenes form early in the flowering season (early August) and begin dispersing in early September. Leaf senescence coincides with emergence of the winter bud in late August; culms turn brown and senescent in

October, but there is no dieback until late in winter. Achenes disperse throughout the fall and some remain on plants as late as December (T. Schmidt and T. Gordon, pers. comm.). The winter bud is composed of a rosette of small (1-3 mm) blunt coriaceous blades that remain photosynthetic throughout the winter months. Rhynchospora knieskernii is a perennial but unlike many Rhynchospora it is not rhizomatous. Single or multiple culms emerge from the winter bud in a yearly iterative process, giving the plant the appearance of an annual. In the field the plant may appear to be one large perennial with multiple winter buds attached to a single plant, but collected plants were all individuals with one or multiple culms attached to a single root system with one (sometimes a second) winter bud. Each plant thus forms a single unit annually, simplifying the process of examining allocation to growth, reproduction and storage. Although found only in the New Jersey Pine Barrens, it can grow densely in suitable habitat, making it possible to collect a small number of plants at each site without negatively impacting populations.

This research was approved by the United States Fish and Wildlife Service, which issued an

Endangered Species Recovery Permit (#TE-697823, 2011). 4

1.1.2 Study Sites

Warren Grove Range (WGR) is an active air-to-ground gunnery range operated by the

177th Fighter Wing of the New Jersey Air National Guard and located in Burlington County, New

Jersey (39o41’48”N; 79o24’0”W). Several habitat types are found on WGR, but R. knieskernii

sites were all located within or adjacent to pitch pine lowland habitat type (sensu McCormick

1979). The predominant soil types (Woodmansie, Lakehurst, Lakewood) are part of the

Woodmansie-Lakehurst association (USDA 1971). These soils are characterized by low pH, low

CEC and the presence of high quantities of aluminum (Tedrow 1986). The upland soils in the surrounding forest matrix are sandy and droughty, but the low topography and high water table of typical R. knieskernii sites contributes to the presence of moist soils that are often silty.

Rhynchospora knieskernii is widely distributed throughout WGR (Bien et al. 2009; Figure 1.2). I selected and established five monitoring sites at several widely separated locations in August

2011 (Table 1.1; Figure 1.2). All sites had relatively dense populations of R. knieskernii in 2010.

However, flooding scoured one of the selected sites in 2012 (Ditch). Additionally, it became

clear that a prescribed burn would be executed in an area with a second site that had initially been

considered too small for sampling. I added the LSL site in November 2011 and dropped the

Ditch site in August 2012. Site descriptions are found in Table 1.1.

5

1.2 Dissertation Structure

1.2.1 Chapter 2: Earth, Wind, and Fire: Rhynchospora knieskernii Allocation Strategies under Differing Conditions of Resource Availability

In this chapter I examined an array of resources that could influence population dynamics

and reproductive output in Rhynchospora knieskernii. I collected and analyzed soil for N and P

nutrient concentrations and soil moisture and used analysis of hemispheric canopy photographs to

determine elements of the light environment. I also looked at how prescribed burns influenced

resource dynamics and R. knieskernii response to changes in available resources. To determine

resource allocation, I collected and analyzed plant samples during flowering fruiting and

senescing at five sites on WGR in 2011 and 2012 and five samples from each site during the

fruiting season only in 2012 and 2013. I asked the following questions:

1. How does R. knieskernii allocate resources to different plant parts? Does this allocation

follow established patterns of resource allocation?

2. How does R. knieskernii utilize available soil nitrogen and phosphorus resources?

3. Does fire increase available soil nutrients and do plant resource allocation patterns

change in the aftermath of fire?

4. Is R. knieskernii nutrient-limited in its environment?

5. Are there resource allocation differences among seasons, sites, years, or plant parts

6. What variables have the most influence on resource allocation to different plant parts?

7. Are there potential trade-offs influencing resource allocation?

8. What are the strategies R. knieskernii uses to allocate resources efficiently?

My data suggested that R. knieskernii allocated substantial resources to reproduction and that soil moisture and prescribed burning both strongly affected allocation patterns; increased moisture generally led to increased allocation of nutrients to reproductive structures, but fruiting 6

populations decreased when soil moisture was above or below ten percent. Plants growing at sites that were prescribed burned allocated more N resources to storage and less to reproduction, but P allocation patterns did not change.

1.2.2 Chapter 3: Interactions of Rhynchospora knieskernii with Associated Species and Impacts on Community Assembly

In this chapter I investigated the role of R. knieskernii within its specified habitat. I was interested in whether plant community dynamics contributed to its rarity, which could occur if associated species were more competitive for certain resources such as soil nutrients, light, or moisture. I examined habitat characteristics and how they influenced community composition and biomass. I compared height, biomass, and aboveground and belowground nutrient concentrations separately for R. knieskernii and the associated species found in that habitat. I was interested in the following questions:

1. What seasonal moisture conditions exist in established populations?

2. Does an increase in canopy cover decrease population densities of R. knieskernii? If so,

what percentage of canopy cover negatively impacts population density?3.

3. Does R. knieskernii differ in patterns of above and below ground allocation in

comparison to associated species in the same habitat?

4. Does R. knieskernii differ in patterns of population density or plant biomass in

comparison to associated species in the same habitat?

5. What common characteristics of R. knieskernii and associated species may be influencing

plant assembly at these sites?

6. What are some potential competitive interactions based on aboveground and

belowground differences or similarities in resource allocation?

7. What are the negative impacts of soil moisture changes at sites? Are soils more likely to

become too dry or too saturated and how will this affect population density? 7

8. What potential strategies does R. knieskernii use to successfully increase population

density relative to associated species?

9. What do patterns of species richness and abundance tell us about methods of community

assembly (neutral, niche, stochastic niche) at these sites?

Surprisingly, there were few differences between R. knieskernii and associated species.

My study suggested that low nutrient resource availability may create environmental constraints on plant functional types that can successfully inhabit these sites, limiting the development of competitive mechanisms in favor of similar mechanisms of resource use efficiency and stress tolerance (sensu Grime 1977). Prescribed burning led to increased R. knieskernii biomass in comparison with associated species, suggesting that R. knieskernii resource allocation and dispersal strategies may increase dominance at fire-disturbed sites by storing resources in the winter bud and in achenes (seed bank).

1.2.3 Chapter 4: Dispersal Strategies and Germination Cues in a Fire-Dependent Ecosystem

In this chapter I examined three potential dispersal mechanisms and three potential germination cues for R. knieskernii. The presence of bristles on R. knieskernii achenes suggested animals dispered achenes to new habitats, and that dispersal could also occur via water or wind. I examined charred wood, smoke, and heat as germination cues because increases in R. knieskernii populations are often observed after a fire. Because light and changing temperatures are often cues in early-successional species, I also looked at whether there were germination differences if achenes were sown on the soil or below it. I was interested in these questions:

1. Does fire influence germination of R. knieskernii?

2. How do achenes from R. knieskernii disperse? Does it have one or more mechanisms for

long distance dispersal? How common is long distance dispersal? 8

3. What are the most likely potential vectors for long-distance dispersal?

4. Does R. knieskernii exhibit bet-hedging strategies in its dispersal mechanisms?

5. What environmental conditions influence changes in achene production or variation in short

and long distance dispersal?

6. What environmental factors influence dispersal success?

7. How do stochastic changes and mosaic landscapes in the fire-dependent NJPB ecosystem

influence dispersal mechanisms?

My study suggests that R. knieskernii achenes have the ability to disperse both long- and

short-distances, providing colonization advantages in disturbance-dependent ecosystems.

Although I did not demonstrate that germination cues were related to fire effects, I did find

differences between surface and buried achenes, suggesting light may be a germination cue.

1.2.4 Chapter 5: Conservation Implications and Future Directions

In this chapter I reviewed the conservation implications of R. knieskernii resources allocation patterns, interactions with community and habitat, and dispersal mechanisms. My study identified hydrological regimes and fire return intervals as important components of R. knieskernii population dynamics. I reviewed different management strategies that can help to

preserve and enhance habitat. I was interested in the following questions:

1. What site conditions promote greater densities of R. knieskernii populations?

2. How are population changes related to differences in light availability, soil moisture,

water potential, or nutrient availability?

3. Are there significant differences in density among sites and among years?

4. What threats have the potential to negatively affect R. knieskernii population densities or

available habitat?

5. What conservation strategies are best suited to preventing negative impacts? 9

6. What variables and interactions have the most significant impact on recruitment and

establishment?

7. What changes to habitats might negatively impact dispersal?

My study raises as many questions as it answers and more research is needed on the contribution of plant litter to nutrient cycling, variability in plant species tolerance for flooding as a potential factor influencing habitat composition, different metapopulation dynamics, the level of genetic interaction among relatively isolated populations, and the functions of the winter bud. 10

1.3 Literature Cited

Bien, W., J. Spotila and T. Gordon 2009. Distribution trends of rare plants at the Warren Grove Gunnery Range. Bartonia 64:1-18.

Busch, J., I. A. Mendelssohn, B. Lorenzen, H. Brix, and S. Miao 2004. Growth responses of the Everglades wet prairie species Eleocharis cellulosa and Rhynchospora tracyi to water level and phosphate availability. Aquatic Botany, 78:37-54.

Carey, J. 1847. A new species of Rhynchospora. American Journal of Science and Arts 4:25.

Costa, A. C. G. and I. C. Machado 2012. Flowering dynamics and pollination system of the sedge Rhynchospora ciliata (Vahl) Kükenth (Cyperaceae): does ambophily enhance its reproductive success? Plant Biology, 14:881-887.

Dominy, N. J. 2012. Hominins living on the sedge. Proceedings of the National Academy of Sciences, 109:20171-20172.

Gleason, H. A. and A. Cronquist 1991. Manual of Vascular Plants of Northeastern United States and Adjacent Canada. New York Botanical Garden. New York. 910 pp.

Kesel, R. and K. Urban 1999. Population dynamics of Gentiana pneumonanthe and Rhynchospora fusca during wet heathland restoration. Applied Vegetation Science 2:149- 156.

Kral, Robert 1993+. Rhynchospora. In: Flora of North America Editorial Committee, eds. Flora of North America North of Mexico. 18+ vols. New York and Oxford. Vol. 23, pp. 202, 211-12.

Leck, M. A., and W. Schütz 2005. Regeneration of Cyperaceae, with particular reference to seed ecology and seed banks. Perspectives in Plant Ecology, Evolution and Systematics, 7:95- 133.

Lucero, L. E., and A. C. Vegetti 2012. Inflorescence structure in Rhynchospora Vahl (Cyperaceae). Flora-Morphology, Distribution, Functional Ecology of Plants, 207:47-56.

Lucero, L. E., A. C. Vegetti and R. Reinheimer 2014. Evolution and Development of the Spikelet and Flower of Rhynchospora (Cyperaceae). International Journal of Plant Sciences, 175:186-201.

Magliocca, F., and A. Gautier‐Hion 2002. Mineral content as a basis for food selection by western lowland gorillas in a forest clearing. American Journal of Primatology, 57:67-77.

Moore, A. G. 1997. A taxonomic investigation of Rhynchospora section Longirostres Kunth. Vanderbilt University, Tennessee.: PhD Thesis, 199.

Ohlson, M., and N. Malmer 1990. Total nutrient accumulation and seasonal variation in resource allocation in the bog plant Rhynchospora alba. Oikos, 100-108.

11

Reich, P. B., I. J. Wright, J., Cavender‐Bares, J. M. Craine, J. Oleksyn, M. Westoby, and M. B. Walters 2003. The evolution of plant functional variation: traits, spectra, and strategies. International Journal of Plant Sciences, 164:S143-S164.

Simpson, D. A., and C. A. Inglis 2001. Cyperaceae of economic, ethnobotanical and horticultural importance: a checklist. Kew Bulletin, 257-360.

Sorenson, A.E. 1986. Seed dispersal by adhesion. Annual Review of Ecological Systems 17:443-463.

Sorrie, B.A., and A.S. Weakley 2001. Coastal Plain vascular plant endemics: phytogeographic patterns. Castanea 66:50-82.

Strong, M. T. 2006. Taxonomy and distribution of Rhynchospora (Cyperaceae) in the Guianas, South America. Contributions from the United States National Herbarium, 53, 1-225.

Ueno, O. 2013. Ultrastructure and Carbon Isotope Ratios of Leaves in C4 Species of Rhynchospora (Cyperaceae) That Differ in the Location of Kranz Cells. International Journal of Plant Sciences, 174:702-709.

Tedrow, J. C. F. 1986. Soils of New Jersey. Malabar, Florida: Robert E. Krieger Publishing Company, Inc.

Thomas, W. W., A. C. Araújo, and M. V. Alves 2009. A preliminary molecular phylogeny of the Rhynchosporeae (Cyperaceae). The Botanical Review, 75:22-29.

Weking, S., G. Hermann, and T. Fartmann 2013. Effects of mire type, land use and climate on a strongly declining wetland butterfly. Journal of Insect Conservation, 17:1081-1091. 12

Figures

Figure 1.1 (Left) Illustration showing single plant (left), typical axillary arrangement of spikelets along stem; note spikelet close to base of plant (right), achenes within spikelet cluster (top center) and single achene with retrorsely barbellate bristles (bottom center). Illustration courtesy of the Flora of North America Association, Barbara Alongi, illustrator. (Right) Photograph of R. knieskernii showing spikelet close to base of plant (M. Sobel). This distinctive proximal spikelet is one of the primary means of identifying R. knieskernii in the field.

13

Figure 1.2. Map showing locations of subpopulations of R. knieskernii on WGR (above) and location of study sites (below). The plots for the germination experiment were located close to the Runway site. 14

Tables

Table 1.1 Site descriptions for sites used in soil, plant, population, and canopy cover sampling. All sites are found within a pitch pine lowland matrix except Runway, which is a highly disturbed site adjacent to a forest.

Site Description Sampling Years Soil, Plant, Dead End Site was a long shallow ditch found at the end of a sand road. Population, Canopy Cover 2011-2014 Site was an anthropogenic ditch formed during a road-building project, Soil (dropped) , which was abandoned after Rhynchospora knieskernii was Plant (dropped), Ditch discovered in the area. The site was within 100 m of a wetland. It Population,Canopy had two transects adjacent to one another. Cover 2011-2014 Site had a small difference in elevation and thus I put in two opposite Soil, Plant, Double transects to examine microhabitat differences. The site was within 20 Population, Canopy meters of a wetland. Cover 2010-2014 Site was within same fire block as Sight Line. Site formed a small Soil (added), Plant LSL circular dip that remained open with little canopy advancement for (added), Population, several years. Canopy Cover 2011-2014 Site was adjacent to a road which was used minimally but periodically. Population, Canopy Road The road was within 100 meters of a wetland. Cover 2011-2014 Soil, Plant, Large site adjacent to a long stretch of the WGR runway. It was at the Runway Population, Canopy bottom of a gentle slope. Cover 2009-2014 Site found along an old sight line adjacent to a gravel road which is Soil, Plant, Sight Line frequently used. The site was within 100 meters of a wetland and Population, Canopy contained a relatively dense population of R. knieskernii . Cover 2006-2014 Smallest site. It was on a slope and was adjacent to an ephemeral Population, Canopy Slope pond. Cover 2011-2014

15

Chapter 2: Earth, Fire and Water: Rhynchospora knieskernii Allocation Strategies under Differing Conditions of Resource Availability

Abstract

Nutrient availability and allocation are important for plant fitness. Capturing and

sequestering nutrients in habitats where they are scarce can be challenging. However,

plants in nutrient-poor environments that are frequently disturbed (i.e., fire) have

developed strategies that facilitate nutrient acquisition. I examined nitrogen and

phosphorus allocation strategies in Rhynchospora knieskernii, a globally rare sedge,

endemic to the fire-prone New Jersey Pine Barrens. I compared burned and unburned

sites for differences in soil and plant nutrient concentrations. Soils in areas that were

prescribed burned did not have significant differences in nutrient concentrations, but soil

2 2 (F2,276 = 27.15, p < 0.0001, R = 0.15) and plant (F2,82 = 59.59, p < 0.001, R = 0.38) N

concentrations were significantly higher in 2012 than in 2011, and plants at burned sites

2 allocated more nitrogen to storage than at unburned sites (F1,11 = 8.56, p < 0.01, R =

0.39). Nitrogen allocation to reproductive structures decreased at high total plant

2 nitrogen concentration (F1,11 = 8.31, p < 0.05, R = ), and plants produced fewer (F1,178 =

2 2 78.62, p < 0.0001, R = 0.30) but heavier achenes (F1,178 = 175, p < 0.0001, R = 0.53).

There was a positive relationship between prescribed burning, soil moisture content, and

2 fruiting response (F2,7 = 6.48, p < 0.05, R = 0.55). Although low concentrations of soil P

were not correlated with plant phosphorus, a low reproductive output in 2012 may have

been the result of P limitation. There was a correlation between the soil N:P ratio and

2 total plant N:P ratio (F2, 17 = 8.7, p < 0.01, R = 0.45) and plant storage N:P ratio (F1,22 =

17.01, p = 0.001, R2 = 0.44). These data suggest that R. knieskernii utilized a plastic 16

nutrient allocation strategy in response to periodic fire related N pulses where there was a

trade-off between seed size and mass for increased allocation to storage to maintain N:P

ratios that mitigated the negative effect of a low-nutrient environment. My study

suggested that increased nitrogen deposition could impact the nutrient allocation strategy

of R. knieskernii.

2.1 Introduction

Resource availability and allocation are important to plant fitness. Sprengel first

formulated and Liebig developed the “law of the minimum” in the early 1800s, which

states that plant growth is limited by whichever resource is least available to the plant.

The study of wild plants has led to an alternative explanation, which is the theory of multiple limitation first developed by Bloom, Chapin and Mooney (1985). This theory

states that plants will optimize their resource use if they adjust allocation so that each resource is equally limiting for growth (Bloom et al. 1985).

The multiple limitation theory was further modified by the concept of resource

ratios introduced by Tilman and Gleeson (1992). This theory has strongly influenced

views on the relationship between resource allocation, species diversity, and competition

(Miller et al. 2005; Jabot and Potter 2012). Recently, however, the explanatory power of

the resource ratio model has been questioned (Rubio et al. 2003; Weiner 2004). Actual

resource allocation does not always respond to theoretical predictions (Rubio et al.2003).

Furthermore, resource allocation may be related to allometric trajectories that mask plant

responses to resource availability (Weiner 2004). In fact, the ability of plants to allocate

scare resources to structures performing a multitude of functions makes it difficult in

some cases to discern tradeoffs and to precisely specify how resource allocation is being 17

optimized. For example, many biomechnical properties serve multiple purposes and produce numerous feedbacks (Read and Stokes 2006). Furthermore, plants are able to store nutrients in their vacuoles, limiting the ability of researchers to find direct relationships (Rubio et al. 2003). Many resource allocation studies use biomass as a proxy for nutrients, which can magnify the role of allometric relationships (Weiner

2004). Several studies have shown plants may partition nutrients differently than they partition biomass, most notably in reproduction (Abrahamson and Caswell 1982; Nault and Gagnon 1988; Ohlson and Mahlmer 1990). Thus, allocation of critical nutrients best defines plant strategies (Abrahamson and Caswell 1982).

2.1.1 Nitrogen Cycling

Nitrogen (N) is a limiting nutrient in most ecosystems (Vitousek and Howarth

1991). Nitrogen is freely available in the atmosphere, but few plants can obtain nitrogen directly. Instead, microorganisms and fungi mediate the cycling of nitrogen. Free living bacteria break down available organic nitrogen from a variety of sources (including amino acids, proteins, and chitin), producing dissolved organic nitrogen in the process

(DON). There are recent data that plants may be able to uptake DON, especially in the form of simple amino acids (Jones et al. 2005; Bardgett and Wardle 2010). The microbial biomass competes for nitrogenous compounds, but also converts organic compounds to inorganic nitrogen through mineralization (Bardgett and Wardle 2010).

During ammonification, chemical bonds linking carbon compounds are broken and NH4

is released. Ammonium ions are not very labile, making it likely that NH4 is taken up immediately by adjacent plant roots and the soil biota. Ammonium may be further

transformed through nitrification into NO2 and NO3 (Gurevitch et al. 2006). Plants use 18

both NH4 and NO3, although they may selectively uptake different forms of nitrogen to

maintain ion balance (von Wiren et al. 1997). Plants must compete with microorganisms

for all forms of nitrogen available in the soil solution and microbial competition can significantly reduce plant nutrient availability (Bardgett and Wardle 2010). Nitrogen is much more mobile in the soil than phosphorus and uptake through root hairs occurs through both diffusion and mass flow (von Wiren et al. 1997). Soil acidity slows

denitrification processes (Brady and Weil 1996), and NO2 and NO3 are readily leached

through the soil column under low pH conditions (Neary et al. 1999).

2.1.2 Phosphorus Cycling

Phosphorus (P) availability is determined by the nature of the parent material and

a variety of abiotic factors, of which pH is the most important (Schachtman et al. 1998).

Phosphorus tends to be more limited in acidic soils because it binds rapidly with Al and

Fe cations to form trivalent complexes which immobilize the phosphorus and make it

unavailable to plants (Brady and Weil 1999). Under anaerobic conditions, phosphorus

sorbed to iron can sometimes be solubilized and made available to plants, although the

minimum time interval for the process is unclear (Patrick and Khalid 1974). Phosphorus is taken up by the roots through active transport, although transport mechanisms have not

been completely elucidated (Schachtman et al. 1998). When the supply of P is limited,

plants strategies include increasing root growth, increasing the rate of P uptake by roots

from the soil, translocating P from older leaves, and depleting vacuolar stores of P

(Jeschke et al. 1996; Schactman et al. 1998). Although P is relatively immobile in soil, it is quite mobile in plants and circulates freely (Bielseski 1973). The meristematic tissues 19

are the main phosphorus sinks in growing plants (Bieleski 1973). Large amounts of P are

commonly stored in seeds in the form of phytic acid (Schactman et al. 1998).

Soil nutrient availability, especially P availability, may be influenced by

mycorrhizal associations (Cornwell et al. 2001). Although wetland plants were once

thought to lack mycorrhizal relationships, an increasing number of sedges, including

many Rhynchospora, are found to have mycorrhizal associates (Muthukumar et al. 2004).

These associations are frequently assumed to be beneficial, especially in nutrient-poor environments (Tuininga and Dighton 2004; Dighton 2009). While many benefits have been documented, fire effects can create complicated interactions that may induce negative plant responses to mycorrhizae, depending on plant functional type and dependence on mycorrhizae (Hartnett et al. 1994).

2.1.3 Prescribed Burning and Fire Effects

Wildfire is a frequent natural disturbance of habitats worldwide (Bond and Keeley

2005). Many plants and animals have adaptations that enable them to flourish in fire-

disturbed habitats. These adaptations often confer a competitive advantage, such that

organisms living in a fire-dependent habitat may suffer population declines when fire is

suppressed (Bond and Keeley 2005). However, anthropogenic alterations of the natural

landscape (e.g., development) have created the need for controlling fire regimes in

populated areas (Luque et al. 1994). Prescribed burning is a common strategy for

controlling fuel loads and mimicking natural disturbance regimes in fire-dependent

habitats (Little 1979). Prescribed burning is usually conducted in the winter months

when the weather is cooler, fuel loads are smaller, and there is less danger of fire 20

escaping into the larger community (Buell and Cantlon 1953; Forman and Boerner 1981;

Theobald and Romme 2007). Wildfires are more intense than prescribed burns, occur

more frequently in summer, and are often ignited by lightning (Iverson and Hutchinson

2002). However, prescribed burns usually have strong impacts on plant communities,

both directly through removal of vegetation and plant litter, and indirectly through

alteration of biochemical cycles in response to soil heating and other factors (Certini

2005). Among the most widely reported effects of fire on soil are increases in pH,

ammonium, nitrates+nitrites, and phosphorus (Certini 2005). Fire often has direct,

negative effects on the soil microbial community, especially in wetlands, where the

greater moisture availability of hydric soils often leads to increases in soil heating and

consequent death (Neary 1999; Certini 2005).

As a major component of many ecosystems, fire has long been associated with

changes in nutrient cycling and nutrient availability (Boerner 1982). Fire is common in coniferous forested habitats, where the adaptations of the vegetation to fire may increase fuel loads and create negative feedback systems (Boerner 1982) Evergreen trees and shrubs also produce leaf litter with low nutrient quality and high levels of secondary compounds (Aerts and Chapin 2000). This slows the rate of decomposition and contributes to low levels of soil fertility (Aerts and Chapin 2000). Fire can have a direct positive impact through the release of nutrients from burned vegetation into the habitat and it can have an indirect positive impact through increased mineralization or decreased leaching (Gray 2006). Fire can also have negative impacts through volatilization of nutrients (especially nitrogen), reduced mineralization, or increased leaching (Boerner

1982). However, most studies of nutrient-poor system have reported increased nutrient 21

concentrations in the soil and sometimes in plants as well (Wilbur and Christensen 1983)

Organic phosphorus that is not immobilized by plants and microbes will be quickly

adsorbed to iron and aluminum ligands and be unavailable for plant use. It may take up

to a year for arbuscular mycorrhizal (AM) communities to recover from fire (Pattinson et

al. 1999). Fire duration is often the key to fungal survival, with fast-moving fires causing relatively little mortality (Neary et al. 1999). However, nutrient pulses sometimes result in the decline of mycorrhizal relationshps, as plants can uptake the newly available nutrients directly through their roots and therefore resist mycorrhizal colonization.

Plants in nutrient-poor soils have limited access to nitrogen and phosphorus, suggesting that they will utilize trade-offs to ensure the effective allocation of resources

(Lee and Fenner 1989; Gleeson and Tilman 1990; Aerts and Chapin 2000). The New

Jersey Pine Barrens (NJPB) is a nutrient poor ecosystem (Zampella et al. 1992; Gray and

Dighton 2006; Yu and Ehrenfeld 2010). However, frequent fires contribute to nutrient pulses and prescribed burns can increase the availability of nitrogen and phosphorus

(Gray and Dighton 2006). This may influence changes in plant resource allocation in the following post-fire season. In the NJPB, fire is also an important selective pressure likely to influence resource allocation patterns. Soil nutrient quality can be linked to plant resource allocation at broad scales (Fujita et al 2014), but studies which examine the influence of soil nutrient availability on plant nutrient concentrations have mixed results

(Abrahamson and Caswell 1982; Güsewell and Koerselman 2002).

2.1.4 Rare Plants

Rare plants are often found in marginal environments where resources may be

limited (Lee and Fenner 1989; Pykälä et al. 2005; Wassen et a. 2007). Plant response to 22

environmental resource limitation usually involves trade-offs that buffer the negative

consequences to plants during periods of reduced allocation of resources to critical plant structures (Lee and Fenner 1989; Venable 1992). Plants are often able to store nutrients and translocate resources from one structure to another during resource shortages (Chapin et al. 1986). Some plants are also capable of taking up more resources than are needed for current biological demand, a phenomenon known as “luxury consumption” (Thomas and Sadras 2001). Shrubs and trees have long-term storage structures, but herbaceous plants are less able to respond to resource losses over extensive periods of the growing season. Environments that are frequently disturbed are commonly associated with ephemeral, early-successional plant communities that are often biologically diverse and

may be especially rich in rare species (Pykälä et al. 2005). However, rare plants often

display habitat specificity and therefore may not easily adapt to major environmental

changes in disturbance regimes (Reier 2005; Fujita et al. 2014). In disturbed

environments, resource allocation strategies may buffer habitat changes and enable plants

to resist displacement by colonizing plants arriving in newly disturbed habitats (Fynn et

al.2005). Thus, resource allocation patterns may provide insight into the strategies rare

plants utilize to cope with environmental stress and to maintain populations in potentially

competitive environments (Pykälä et al. 2005).

Rare endemic plants are often associated with particular edaphic conditions, such

as serpentine barrens (Harrison et al. 2000) or chalk grasslands (Janssens et al. 1998). In

some cases, edaphic conditions may be widespread but of low quality (such as nutrient-

poor soils) or higher quality but of limited availability (Maschinski et al. 2004).

Understanding the edaphic conditions that constitute the microenvironment of rare plants 23

is important for developing management plans (Wiser et al. 1998; Mashchinski et al.

2004; Wassen et al. 2005; Hajkova et al. 2009) and finding new populations (Sperduto

and Congalton 1996; Bourg et al. 2005). Soil moisture availability is often one of the

most important edaphic requirements for rare plants (Fox et al. 2006; Maschinski et al.

2004; Hajkova et al. 2009; Laidig et al. 2009). Disturbance associated with gap

dynamics can influence light and hydrological regimes as well as alter nutrient cycling

and thus availability (Ehrenfeld 1995).

2.1.5 Resource Allocation

Many studies have examined plant resource allocation for a range of habitats and plant functional types (Fujita et al. 2014). The study of seasonal resource allocation can increase our understanding of the role of trade-offs in the annual cycle of a plant

(Abrahamson and Caswell 1982; Nault and Gagnon 1988; Ohlson and Mahlmer 1990).

Herbaceous plants in nutrient-poor environments are among the most limited in terms of resource availability and thus make excellent candidates for studies of allocation strategies. Yet studies of rare plants are often limited to comparisons of traits of rare and common congeners (Bevill and Louda 1999). Such studies may include elements of resource allocation such as reproductive output, but do not specifically examine how allocation changes over time or under differing habitat conditions. These lack of data on

rare species may stem from a focus on plants that appear to contribute most to ecosystem

functioning. However, rare species can sometimes be major contributors to ecosystems

and may play an influential role in ecosystem resilience (Marsh et al. 2000; Lyons and

Schwartz 2001; Lyons et al. 2005). The resource allocation strategies of rare plants may

influence competitive ability (Lee and Fenner 1989; Poot and Lambers 2003; Euliss et al. 24

2007) or other aspects of population dynamics (Roucheleau and Houle 2001). There are a limited number of studies that examine resource allocation in rare plants (Hegazy 2000;

Moora and Jogar 2006; Euliss 2007), but none have focused on rare graminoids, although many rare, threatened and endangered taxa belong to the Cyperaceae, Juncaceae and

Poaceae families (USDA PLANTS database). These families are often important components of ecosystems that contribute significantly to ecosystem functioning (Dı́az and Cabido 2001; Leck and Schutz 2005).

Rhynchospora knieskernii is an ideal species to study the resource allocation of a

rare herbaceous plant. It is a federally-threated sedge that usually occurs in disturbed

habitat where plants typically occur in low densities but under favorable light and

moisture conditions can reach 4000 plants per m2 (Baumgarten and Palmer 2009).

Although R. knieskernii is a perennial that stores nutrients in overwintering buds, it

resembles an annual in its rapid seasonal growth and large seed output. I conducted a

series of field studies on R. knieskernii that examined the influence of habitat factors

(light, moisture, and soil nutrient availability) on seasonal resource allocation. I hypothesized that prescribed burns would influence soil resource availability, plant nutrient uptake, and that increased N would confound plant nutrient allocation patterns.

It is critical to understand resource allocation strategies in rare plant populations, as they drive plant response to habitat variability and thus strongly influence population dynamics. Understanding linkages beween rare plants and key habitat factors will be essential for developing a conservation strategy as outlined by the USFWS Rhynchospora knieskernii recovery plan.

25

2.2 Methods

Please see the Chapter 1 (Introduction) for a summary of the study species and

study sites.

2.2.1 Soil Sampling

I sampled eight sites in August 2011 and nine sites in November 2011, March

2012 and May 2012. I then reduced the number of sites sampled to five sites, which I sampled in August, September, and November of 2012 and 2013. I extracted five mineral cores (5 cm diameter, 15 cm deep) from each site. I began processing immediately and all extractions were performed within 24-48 hours. I weighed cores, gently mixed the soil from each core and removed the fine roots and pebbles, and then took aliquots for gravimetric moisture determinations, extraction with 2 M KCI for NH4

- - and NO (= NO3 + NO2 ) concentrations, and extraction with Bray's solution (0.03 N

NH4F + 0.1 N HCl) for PO4 concentrations. All extractions were conducted with 30 mL

extractant:10 g fresh weight soil. I froze filtered extractants and stored them until analysis. I analyzed extractions colorimetrically for NH4 and NO using an Astoria

Pacific AP3 autoanalyzer (Clakamas, WA) and for PO4-P colorimetrically (Allen 1989).

I analyzed total carbon (TC) and total nitrogen (TN) using a Shimadzu TOC-Vcsh, non-

dispersive infrared gas analyzer and TNM1 chemiluminescence nitrogen monoxide

analyzer (Kyoto, Japan). I initially sampled soil cores randomly without regard to R.

knieskernii plant density, but in September and November of 2012 and in August of 2013

I observed the density of R. knieskernii plants within 25 cm of soil sample sites and took

additional samples as needed to ensure at least three samples were adjacent to dense

patches of R. knieskernii and three samples were adjacent to patches of limited or no 26

density of R. knieskernii. In order to further examine relationships between soil nutrients and plant nutrients, I also calculated soil concentrations by area, scaling up from µg/cm2 to mg/m2. All reported soil nutrient concentrations are for extractable nutrients and are expressed per microgram dry mass unless otherwise noted.

In March 2012, two sites (LSL and Sight Line) and in March 2013 one monitored site (Double) and two other sites used in population and canopy cover sampling (Road and Slope) were prescribed burned as part of the fire management program at Warren

Grove Range. In 2012 the burns were classified as low-intensity, singing the pitch pine needles in the canopy and either charring or combusting leaf litter. In 2013 the burn was classified as high-intensity, killing some surrounding trees, and combusting all leaf litter.

2.2.2 Plant Measurements

I selected ten plants randomly from all plants collected for resource allocation analysis from each site during flowering, fruiting, and senescence in 2011 and 2012. I measured plant height, wet mass, root length, terminal spikelet length, and number of spikelets. Using known wet mass/dry mass ratios and percentages of mass allocated to roots/winter buds, stems/leaves, and reproductive structures from the total plant sample for each site, I then estimated the percent mass allocated to these parts for each individual plant measured. I also used these mass percentages to estimate total nutrient concentrations in the respective plant parts. I compared estimated mass allocated to reproductive structures against known mass of reproductive structures based on achene mass for plants in 2012 and 2013.

27

2.2.3 Reproductive Output

In September 2012, November 2012, August 2013, and September 2013, I randomly selected ten flowering plants from each of the five monitored sites for measurements related to achene production. I measured plant height and root length, plant dry mass, number of spikelets, number of achenes on each spikelet on the tallest culm (if there were multiple culms), number of culms (stems), total number of achenes on the tallest culm, total number of spikelets, and total number of achenes on all stems. I separated the achenes from the enclosing reproductive material and weighed the achenes.

I counted the number of achenes for each spikelet on the main (tallest) culm, in order to make comparison between plants with single and plants with multiple culms. If a plant had multiple culms, all achenes from spikelets on the additional culms were combined together. After I counted the achenes, I combined and weighed them on a Citizen CX265 semi microbalance with an accuracy of 0.01/0.1 mg (Mumbai, India). I then divided the total number of achenes by the mass of all the achenes for each plant. Additionally, I calculated the estimated reproductive mass per plant per site based on the known percent mass allocated to reproduction in 2011 and 2012 for each site and divided it by the number of achenes per plant. For example, if a plant weighing 0.28 mg allocated 15% of its mass to reproduction, the reproductive mass was estimated to be 0.042 g. If the plant produced 45 achenes, the estimated individual achene mass was 0.00093 g. I then compared the two different methods for estimating individual achene mass using a t-test.

Because the t-test found no significant difference between the two measures (see Results)

I used reproductive mass to estimate individual achene mass for 2012, when equipment problems prevented measurement of achene mass. I calculated the seed enrichment ratio 28

using allocation to reproductive structures/allocation to shoots following the method of

Lee and Fenner (1989). Bond et al. (1999) developed an allometric calculation for

determining the depth at which a buried seed would still be able to germinate based on

seed size. I used this scaling relationship (depth (y) = 27*achene mass (x)0.334) to

calculate maximum burial depth for seed survival to germination based on estimated seed

mass.

2.2.4 Plant Sampling

In 2011 and 2012 I collected R. knieskernii plants within one week of soil sampling dates in August (flowering), September (fruiting), and November (senescing), using the same five sites as those that were sampled for soil. I gently cleaned plants of soil in deionized water and plants were allowed to air dry. I randomly selected ten plants from each site and measured wet weight, height (from base of plant to tip of terminal spikelet), root length, length of terminal spikelet, and counted the number of spikelets on the main stem. If plants had additional stems I noted that as well as the total number of spikelets. Then all plants were divided into three groups for each site: reproductive structures (achenes, reproductive organs, peduncles), stems (stems and leaves), and storage (roots and winter buds). I weighed parts after separation and dried them at 70oC

for 48 hours, then reweighed them. Separated plant parts were cut into fine pieces, which were weighed to obtain 0.25 g (± 0.006 g) plant material for each sample. I used the total

available if the total mass was less than 0.25 g (mean = 0.13 g). In 2012 and 2013 I

established five 0.0625 m2 quadrats at five randomly selected patches within each of the

same five sites. All R. knieskernii biomass was collected from within these quadrats in

early October and dried at 70oC for 48 hours. I measured height and root length for five 29

randomly selected R. knieskernii plants from each species per plot (all plants if there were

less than five in a plot). I separated plant material into belowground (roots, winter buds)

and aboveground (stems, leaves, reproductive structures) biomass and weighed it. I cut

plant material into fine pieces and weighed to 0.25 g and the total used if there was

insufficient plant material. The dried plant material was analyzed for total nitrogen (TN),

total carbon (TC) and total phosphate PO4 after digestion in a selenium catalyzed, sulfuric acid–hydrogen peroxide solution, using a Tecator block digestor, with subsequent colorimetric PO4 analysis and analysis of TN and TC using a Shimazdu TOC-V analyzer

(Allen 1989). All plant nutrients are reported per milligram dry mass except when

otherwise noted.

2.2.5 Population Monitoring

I selected eight subpopulations for monitoring over three years (Figure 1). To monitor each subpopulation, I established six sampling quadrats (0.042 m2) along a

permanent line transect. I counted all R. knieskernii plants within each quadrat and

classified them either as fruiting (with achenes) or not fruiting (without achenes). I also

noted if winter buds were present. Five plants within each quadrat were non-randomly

selected (four plants nearest each corner of the quadrat and the tallest plant in the

quadrat) and measured for height and number of spikelets. Data were collected in

October 2011, 2012, 2013 and 2014 when plants began dispersing achenes (seeds). See

Figure 1.2 for site locations and Table 1.1 for site names and descriptions.

30

2.2.6 Canopy Cover

I took hemispheric photographs above each quadrat used in population sampling

sites in October 2011, August 2013, and October 2013 on partly cloudy days between 10

am and 2 pm using a Nikon Coolpix 4500 camera with an FC-E8 fisheye converter

attached to a tripod approximately 1 m in height above the ground. I centered and

leveled the tripod over the midpoint of the subplot area as established by the quadrat and

used a compass to determine north. I attached fiber optic strands to the north and south

points of the lens as determined by a baseplate compass (two strands for north; one for

south) and recorded the time and number of the photograph for later use in processing

photographs. I prepared photographs for analysis using the software program Gap Light

Analyzer Version 2.0, which has been developed and used specifically for purposes of

determining canopy cover and other factors associated with the light environment (Frazer

et al. 1999). I first configured the photograph by identifying north; then I created a grey-

scaled image which I edited to screen out darker pixels which sometimes appeared in

open-canopy areas after processing. Once the image was edited, the software produced

four assessments: percent open canopy, percent direct light, percent diffuse light, percent

total light (direct and diffuse). I then compared plant population and site soil

characteristics with different elements of the light environment.

2.2.6 Soil Moisture

I measured soil moisture gravimetrically in August and November 2011 and

March, May, August, September, and November 2012 with other soil characteristics using the difference between the wet mass and dry mass of 10-gram subsamples of soil.

In 2013, I measured soil moisture gravimetrically (again using 10-gram soil subsamples) 31

every two weeks from April to November for the five monitored sites and two sites that

were adjacent to known R. knieskernii populations but appeared either too hydric or too

xeric to support R. knieskernii.

2.2.7 Weather

I used data from a weather tower located on WGR to find monthly means for air

temperature minima and maxima and 24-hour precipitation totals from 2011 to 2013.

The weather tower recorded data every 30 minutes. Weather tower data from WGR was not available in 2014 so precipitation data were collected from the nearest state mesonet site (Oswego Lake) located approximately five kilometers from study sites.

2.2.8 Data Analysis

Site, plant, and population data were all collected from the same sites over time.

In order to separate the effect of site, a series of mixed-effects models were developed.

For soil, site was used as the random effect and soil nutrient variables, date (month and

year), and fire as fixed effects. For plants, both site and structure (type) were considered

random effects and were tested against plant nutrient variables and season as fixed

effects. For population, site was again considered the random effect and fruiting and total

populations and mean plant height were considered fixed effects. Null models were

created using random effects only and were tested against the full model with both fixed

and random effects. Model performance was based on an assessment of the results from

the Bayesian Information Criterion (BIC); the full model also need to have a significant

p-value (less than 0.05). In mixed-effects models the model with the lower BIC number

is considered the superior model; greater differences in BIC number are usually 32

considered indications of more confidence associated with model differences (Schwarz

1978).

One-way and two-way ANOVA and linear regressions tested differences in soil

and plant nutrient concentrations between months (seasons) and years and tested

relationships between soil nutrient concentrations and plant nutrient concentrations. One-

way and two-way ANOVAs and linear regressions tested differences in variables related to reproductive output (total achenes, achene weight, total spikelets) between months and years and tested relationships between reproductive output, plant nutrient concentrations, and soil nutrient concentrations. One-way and two-way ANOVAs and linear regressions tested differences and potential relationships between several variables and soil nutrient concentrations at sites that were prescribed burned or not burned. These sites (LSL and

Sight Line in 2012) were not randomly distributed in space, but were part of the same burn block (Figure 1.2). The Double site which was prescribed burned the following year (2013) was separated from most sites but was close to two sites used only in canopy and population analyses (Road and Slope). Data that did not meet assumptions of normality and homoscedasticity were log-transformed; soil and plant N:P ratios were square-root transformed. Figures show untransformed data unless otherwise noted.

2.3 Results

2.3.1 Data Analysis

For soil, the full model performed better than the null model in all cases except

phosphorus and total inorganic nitrogen (Table 2.1). For plants, the full model performed

better than the null model in all cases (Table 2.1). For population, the null model 33

performed better than the full model, indicating site was a significant effect (Table 2.1).

In all cases where site was not considered a factor, statistical tests of differences between

months and years were performed with pooled data.

I calculated summary descriptive statistics (mean, SD, SE) for all variables. A t-

test determined if there was a difference in estimated achene mass based on two different

calculation methods (see Methods). Results of ANOVAs, regressions-and t-tests can be found in Table 2.2; as there were a large number of variables used in this study, a summary of means for the most significant variables can be found in Table 2.3; descriptive statistics are summarized in Tables 2.4 to 2.13. All statistical analyses were performed in R (R Core Development Team 2015).

2.3.2 Soil Sampling

All sites displayed similar patterns of increasing or decreasing nutrient

concentrations over the three year sampling period with few exceptions (Figures 2.1 to

2.4; Tables 2.4 to 2.5). Mean concentrations for extractable total carbon and extractable

total nitrogen were highest in August 2011 and 2012, May 2012 and September 2013

with the exception of Sight Line, which was the only site to also show high

concentrations of both nutrients in March (Figure 2.1). Mean concentrations for

extractable ammonium (NH4) were the most variable among sites. Concentrations of

NH4 were low in August 2011 and August 2013 but high in August 2012 and September

2013. However, this pattern of soil NH4 availability was different at the Dead End and

Sight Line sites. The Dead End site exhibited consistently low concentrations of

ammonium in 2011 and early 2012; concentrations did not begin increasing until August

2012. Sight Line exhibited relatively high concentrations of ammonium in November 34

2011 and March 2012; concentrations were high in August along with the other sites but

peaked in September before declining to low levels in the 2013 growing season (Figure

2.2). Mean extractable NOx (nitrates and nitrites) concentrations were high in November

2011, very low in March 2012, but increased in May 2012 and again in August and

September before declining slightly at all sites in November 2012. Mean concentrations

remained low in August except for the Sight Line site; other sites peaked in September

2012, with Dead End exhibiting the largest increase. Concentrations of NOx at all sites

declined in November 2012 (Figure 2.2). Concentrations of total inorganic nitrogen

(TIN) were similar to patterns of NH4 concentration at most sites (Figure 2.3); concentrations of total organic nitrogen were similar to total nitrogen, with higher concentrations in 2012 and 2013 (Figure 2.3). Mean concentrations of extractable PO4-P

peaked in November 2011 and remained high in March and May before declining

precipitously throughout the 2012 growing season; in 2013 concentrations peaked in

August, declined in September, and increased in November except at the Dead End site

(Figure 2.4). Results from the analysis of the mixed-effects models indicated that site

could be considered a random effect in analyses of all soil nutrient concentrations except

for PO4-P and TIN. This included comparisons of sites with prescribed burns compared

to those without. Further analysis of site differences for PO4-P based on results from the

mixed models comparisons revealed that only the Ditch site had significantly different P

concentrations; as this site was only used in 2011 it was removed for from analyses used

in annual comparisons. Both the Runway (p < 0.001) and Double (p < 0.05) sites

displayed significant site differences for total inorganic nitrogen based on Tukey HSD

results of site comparisons. Although the variations in nutrients appeared to display 35

strong seasonal patterns, there were no statistically significant correlations with

temperature, precipitation, or any interactions of the two weather variables for any soil

nutrients. However, precipitation patterns were closely related to soil PO4-P concentrations except during periods of above-average seasonal precipitation, when there was an inverse relationship between PO4-P concentrations and precipitation (Figure 2.5).

There was a statistically significant difference in the ratio of ammonium to total organic

2 nitrogen among years (F1,58 = 16.02, p < 0.01, R = 0.2) although not among sites (F5,54 =

2 2 0.8906, p = 0.49, R = -0.0009) or months (F1,58 = 2.02, p = 0.1416, R = 0.02).

Descriptive statistics are in Tables 2.4 and 2.5.

2.3.4 Plant Measurements

There were statistically significant differences in the number of spikelets per plant

and plant height for 2011 compared to 2012 and 2013 (Table 2.2). There were no

difference among sites between root length, root:shoot ratios, or plant dry mass.

Descriptive statistics are in Table 2.6.

2.3.5 Reproductive Output

There was a positive relationship between plant height and total number of

2 achenes (F1,82 = 101.30, p < 0.0001, R = 0.55; Figure 2.6). There were significant annual differences in reproductive output between years for the total number of achenes (Figure

2.7), and the estimated mass of individual achenes (Figure 2.8). The number of achenes

increased in 2013 but estimated individual achene mass decreased. A one-tailed t-test found no significant differences between the two methods of estimating individual achene mass (df = 96, t-critical = 1.66, p = 0.11). There were some relationships between

soil nutrient concentrations and reproductive output. Plants produced more achenes when 36

2 soil PO4-P concentrations increased (F2,17 = 8.7, p < 0.01, R = 0.45, Figure 2.9) and

2 when soil moisture increased (F1,13 = 14.77, p < 0.1, R = 0.53, Figure 2.10). There was also a significant difference between years for the maximum estimated burial depth

2 achenes could achieve and still germinate (F1,198 = 34.42, p < 0.0001, R = 0.14, Figure

2.11). Tukey post-hoc tests did not reveal any difference between depth months. There was a complex interaction between soil moisture, soil PO4-P concentrations, and nitrogen concentrations in the reproductive structures; nitrogen concentrations decreased as soil moisture and PO4-P increased, but then increased as soil moisture increased but PO4-P

2 concentrations decreased (F2,21 = 14.28, p < 0.0001, R = 0.54; Figure 2.12). Descriptive statistics are in Table 2.7.

2.3.6 Plant Sampling

Allocation of total carbon (C) and total Kjedahl nitrogen (N) displayed similar patterns among sites in 2011, but patterns were highly variable among sites in 2012

(Figures 2.13 and 2.14). However, these patterns were not statistically significant (Table

2.2). Seasonal nutrient concentrations followed similar patterns in 2011 but not in 2012 for C and N; however patterns remained similar for P for both years (Figures 2.13 to

2.15). There were no significant differences in allocation of N to any of the three plant structural groups, but for P there was significantly higher allocation to the reproductive structures (Figure 2.16). Allocation of P to plant structures was consistent across sites, seasons, and years; the highest proportion was allocated to reproduction and the lowest to the stems (Figure 2.17). Allocation to reproduction increased as total plant P concentrations increased for P in both 2011 and 2012 (Figure 2.18), but N allocation differed between years; the slope for the proportion of N allocated to the reproductive 37

structures was positive in 2011 and negative in 2012 (Figure 2.19). Differences between years for these allocation patterns was significant for phosphorus (p < 0.0001), but not for nitrogen (p = 0.58).

Plants at sites that were prescribed burned exhibited significantly higher total

2 nitrogen concentrations, both seasonally (F19,64 = 2.45,p < 0.01, R = 0.24) and annually

2 (F28,55 = 3.0, p < 0.001, R = 0.40) (Figure 2.20). There were significantly higher

concentrations of total nitrogen in plant storage structures (roots/winter bud) (F5,78 = 8.3,

2 2 p < 0.0001, R = 0.31) during fruiting and senescing (F17,66 = 3.6, p < 0.001, R = 0.35,

Figure 2.20). There were also significant annual differences in plant N concentrations

2 (F2,82 = 50.59, p < 0.0001, R = 0.38) and in allocation of nitrogen to storage for all sites

for 2012 compared to 2011 (Figure 2.20). There were no significant differences in

allocation among sites seasons, and years for phosphorus at sites that were prescribed

burned compared to those that were not (Table 2.2; Figure 2.21). In 2013, plants were

only collected during the fruiting season. Comparisons of plant resource allocation for all

three years (2011-2013) again showed significant differences in N allocation to storage at

burned sites in 2012 and 2013, but no significant differences for P (Figures 2.22 and

2.23). There were significant annual differences in plant N concentrations (F1,25 = 120.7,

2 p < 0.0001, R = 0.82) and total plant N (mass x concentration, F1,22 = 17.61, p < 0.001,

2 R = 0.42), but differences in total plant P were not significant ((F1,22 = 0.641, p = 0.43,

R2 = -0.02; Figure 2.24; Table 2.2). It was not possible to test for annual differences in P

concentrations, as errors could not be normalized, even after log transformation. The soil

N:P ratio was weakly correlated with both the N:P ratio in the storage structures (Figure

2.25), and with total plant N:P (Figure 2.26). The concentration of N in the reproductive 38

structures increased with increasing soil moisture (Figure 2.27). Summary plant nutrient

concentration results are in Table 2.8.

2.3.7 Population Monitoring

Mean fruiting density was significantly higher in 2013 compared to other years

(p < 0.01, Table 2.2). There was a negative relationship between soil moisture and

fruiting population density, but the relationship was different for burned and unburned

2 sites (F2,7 = 6.48, p < 0.05, R = 0.55, Figure 2.28). Burned sites exhibited a negative

linear relationship, although it was not possible to develop confidence intervals due to an

insufficiency of degrees of freedom (Figure 2.28). Unburned sites exhibited a negative

parabola; the lowest and highest moisture levels had the lowest fruiting densities (Figure

2.28). Summary population results are in Table 2.9.

2.3.8 Canopy Cover

There were significant differences in canopy cover, direct light, diffuse light, and

total light for burned sites compared to unburned sites (Table 2.2). Height increased as

2 total light increased (Figure 2.29, F2,79 = 32.39, p < 0.0001, R = 0.31). There were

significant differences in plant height (F2,79 = 32.39, p < 0.001) and fruiting populations

(F2,80 = 13.91, p < 0.001) as light availability was greater for sites that were burned

compared to those that were unburned. Mean plant height also was also greater as mean

2 canopy cover decreased (F1,11 = 13.27, p < 0.01, R = 0.51). Summary light results are in

Table 2.11.

2.3.9 Soil Moisture and Weather Data 39

Plant height also decreased as soil moisture increased (Figure 2.30, F1,11 = 5.58,

p < 0.05, R2 = 0.28). All sites exhibited seasonal declines in soil moisture. There were

no significant relationships between precipitation, temperature, and soil or plant nutrient

concentrations. Summary data for moisture and weather measurements are in Tables 12

to 14.

2.4. Discussion

My study was the first to detect potential trade-offs between immediate and future

reproductive output in a globally rare plant based on patterns of N and P allocation.

Studies of resource allocation in herbaceous plants typically analyze nutrient

concentrations once annually and seldom follow plants for several years (Abrahamson

and Caswell 1982; Nault and Gagnon 1988; Abrahamson; Ohlson and Malmer 1990). By

following a single plant species over three years, I was able to document seasonal and

annual changes in resource availability to better elucidate allocation strategies and their

impacts on rare plant population dynamics. My study indicated that complex

relationships between site characteristics, disturbance, and seasonal changes in soil

moisture and nutrients influenced site resource availability, making it challenging to

discern plant responses. Nevertheless, a few patterns emerged providing support for my

hypothesis that R. knieskernii allocated available N and P resources to plant structures

differentially and that sites that were prescribed burned exhibited changes in allocation patterns as a result of increased N availability and differences in soil moisture conditions. 40

It has been postulated that preferential allocation to reproduction enables early

successional plants to successfully colonize newly disturbed habitats through increased

dispersal of numerous seeds, but reduces allocation to other key structures, which affects

nutrient storage, carbon capture, and other functions, making plants less competitive in

later seres (Coomes and Grubb 2003; Fynn et al. 2005). Studies of seeds grown in soils

of varying fertility generally demonstrate reduced plant performance for small-seeded

species growing in low-fertility soils (Milberg and Lamont 1997), while plants from

nutrient-poor soils may produce seeds with higher nutrient content than closely related

plants typically found in more fertile soils (Lee and Fenner 1989).

Rhynchospora knieskernii preferentially allocated N and P resources to reproduction in all three years, but changes in allocation patterns led to the production of fewer, heavier seeds with higher nitrogen concentrations in 2012 compared to 2013

(Figures 2.7 and 2.8). In a study of a closely related species (Chionochloa) along a soil fertility gradient, Lee and Fenner (1989) also found trade-offs in allocation to reproduction and growth; plants growing in low-nutrient habitats produced larger seeds with higher seed enrichment ratios (seed allocation/shoot allocation) but allocated fewer resources to shoots; the study did not examine root concentrations. Seeds in the low- fertility soils had a greater absolute amount of nutrients, which may assist with germination and establishment in nutrient-poor soils (Lee and Fenner 1989).

Heavier seeds are also more likely to emerge from greater depths, an important quality for seed banking; seeds at greater depths may also survive better at burned sites

(Bond et al. 1999). Using calculations based on seed mass, the heavier R. knieskernii seeds produced in 2012 were predicted to emerge more successfully from greater depths 41

than those produced in 2013 (Bond et al. 1999). As small seeds are more likely to reach

greater depths in the soil column (Chambers and MacMahon 1994), the production of

fewer achenes with higher nutrient concentrations and greater mass could be seen as

another manifestation of allocation to storage. In these disturbed, oligotrophic habitats, a

strategy favoring storage of nitrogen (both within the winter bud and potentially within

the seed bank) may increase plant survival while achieving maximum allocation to

reproduction under the constraints of low soil nutrient supply and maintenance of N:P

ratios.

The decreased proportion of nitrogen allocated to reproduction when plant

nitrogen concentrations were high probably reflected soil P limitation, especially since

the opposite pattern existed for phosphorus, but may also have reflected physiological

constraints on the translocation of nitrogen to seeds. Fenner (1986) found that seeds

suffered the greatest declines when nitrogen was eliminated from nutrient solutions in

bioassays testing seed survival under element limitations. However, seeds had higher

phosphorus than nitrogen concentrations, even when they were produced from nitrogen-

fixing legumes, suggesting that there are limits to the ability of seeds to concentrate

nitrogen (Fenner 1986). Given that many species inhabiting early successional habitats

use light and temperature changes to cue germination (Bazzaz 1979), there may also be a

trade-off between increasing the potential for seed survival through larger, heavier seeds

and increasing germination rates by distributing larger numbers of lighter seeds that

remain closer to the surface and are more likely to germinate the following spring.

Muller-Landau (2010) has proposed a fecundity-tolerance trade-off, with larger seeds better able to tolerate stressful microsites. Rhynchospora knieskernii sites are 42

characterized by fluctuating soil moisture levels (USFWS 2008) which may lead to microsites that are stressful for emerging seedling because conditions are too dry or too wet.

While there were clear annual differences in both soil and plant N and P in my study, there were few relationships between extractable soil nutrient concentrations and plant nutrient concentrations. Previous studies also have reported a lack of congruence between extractable soil and plant N and P concentrations (Abrahamson and Caswell;

Güsewell and Koerselman 2002). Soil nutrient concentrations are not static and can be expected to be highly variable over time, depending on the influences of temperature, precipitation, timing of plant senescence, microbial community interactions, and other factors (Ehrenfeld et al. 2005). Plant nutrient concentrations are less mobile and more likely to be the result of seasonal accumulations (Güsewell and Koerselman 2002). The fact that positive relationships were found in my study between plant and soil N:P ratios

(Figures 28 and 29), even though there were no significant relationships between specific plant and extractable soil nutrient concentrations, suggested R. knieskernii nutrient uptake was at least partially regulated by soil nutrient supply, which was consistent with that reported for other studies (Craine et al. 2002; Willby et al. 2005).

Although total soil N and organic N concentrations steadily increased over time across monitored sites (Figures 2.1 and 2.3), extractable soil nitrogen concentrations did not increase at rates that would explain the high plant nutrient concentrations observed in

2012. Furthermore, some measures of extractable soil nitrogen were higher in 2013 when plant nitrogen concentrations were similar to 2011 results (Figure 2.22). It is possible that nitrogen deposition patterns changed in 2012 and that plant and microbial 43

uptake of nitrogen from increased deposition was rapid, given prevailing nutrient-poor conditions (Magill et al. 1997). Nitrogen deposition in the NJPB increases from the southwest to the northeast and WGR is close to the path of nitrogen deposition traced by

Dighton et al. (2004). Alternatively, increased deposition of post-fire nitrogen laden ash in 2012 may be attributable to the increased winter prescribed burning as a result of more favorable fire weather conditions in 2012. Gray (2006) found that increased ash deposition from burned litter increased mineralized N. Although I did not identify the N source, the greater N concentrations in plants in 2012 compared to 2011 and 2013 offered the opportunity to compare the plant nutrient status under different site conditions.

Although soil P concentration was higher and soil N concentration lower in 2011 compared to 2012, total plant N (concentration x mass) was greater in 2012 than in 2011 with no difference in plant P between years (Figure 2.24). These data indicated that P uptake increased such that plants maintained N:P ratios but uptake was constrained by limited soil P resources. These altering allocation patterns were consistent with those reported by Güsewell (2004). In a greenhouse study of pine seedlings under different simulated burn regimes, Gray (2006) did not find indications of N limitation but did suggest P could be limiting in the NJPB. In a study of nitrogen deposition in the NJPB,

Dighton et al. (2004) reported that P is limited when nitrogen levels are elevated.

Although in my study P soil concentrations changed seasonally and annually, plant P concentration was relatively constant. These data provided support for my hypothesis that although P can be seasonally limiting, R. knieskernii has adapted to low P soil conditions through preferential allocation of P to reproduction, a common strategy of early successional plants (Bazzaz 1979) but less frequently encountered in rare wetland plants 44

in P-limited systems (Fujita et al. 2014). In addition, the increased N in reproductive

structures observed in 2011 suggested that nitrogen may be co-limiting in the

oligotrophic soils of the NJPB. Thus, R. knieskernii may utilize plastic allocation

strategies that maximize allocation to reproductive output when P is high and to storage

when P is limited and/or N availability increases. Double was the only site prescribed

burned in 2013 that showed increased nutrient allocation to plant storage even though

plant N, plant P, and soil P concentrations were similar to 2011 concentrations (Figures 6

and 25). Although some forms of N are more readily stored in plants (Millard 1988),

there were no significant differences in soil N between the burned and unburned sites in

my August soil assay, although I hypothesized there would be following the prescribed

burn. A meta-analysis of fire impacts on soil N pools found most effects were short term

(Wan et al. 2001).

Many studies report a pulse of increased soil N and P after a fire (Raison 1979;

Wilbur and Christensen 1983; Wan et al. 2001) although some see no change

(Abrahamson 1984). Thus, it remains unclear in my study why changes in soil N and P

were not detected, even though I sampled the soil within four days of the prescribed burn

in 2012. The most likely explanation is that there was a low mass and nutrient quality of

leaf litter at most R. knieskernii sites. Litter quality is generally considered poor in

evergreen species, forbs, and graminoids and may even create a negative feedback system

limiting nutrient availability (Aerts and Chapin 1999). Since many lowlands of the NJPB

are dominated by pitch pine (Pinus rigida) the litter quality is lacking compared to richer

soils that support a greater abundance of deciduous trees. Pine needles are lower in nutrients compared to oak tree and huckleberry species that predominant upland habitats 45

in the NJPB (Gray and Dighton 2006). Pine needles made up the majority of litter mass at my study sites. Fire consumes litter and in the process breaks down many recalcitrant compounds (Raison 1979). However, in oligotrophic systems, plants characteristically retain nutrients and senesced leaves will often have very low nutrient concentrations

(Aerts and Chapin 2000). At my sites a low intensity prescribed burn in 2012 that only scorched and did not consume leaves may have contributed to minimizing the release of nutrients. In addition, my study sites were all located on moist mineral soils in proximity to pitch pine lowlands (sensu McCormick 1979) where the high percentage of volumetric soil moisture may have inhibited the growth of more nutrient and litter rich vegetation

(unpublished data, Chapter 2).

Post-fire studies have reported conflicting results on the impact of how fire mediates phosphorus: decreases (Duran et al. 2009), increases (Wilbur and Christensen

1983), and no effect (Murphy et al. 2006). The difference in these results may be attributable to how fire impacts the microbial and vegetation communities, as organic phosphorus that is not immobilized by plants and microbes will be quickly adsorbed to iron and aluminum ligands and be unavailable for plant use (Kooijman et al. 1998).

Erosion may also facilitate losses of soil P (DeBano and Conrad 1978). Extractable soil phosphorus was low at burned and unburned sites in 2012, suggesting that at a landscape scale precipitation may be more likely to influence soil nutrient concentrations than the latent effects of prescribed burns acting at a more temporal and smaller spatial scale

(Figure 2.4). Temporal and spatial effects of the 2013 prescribed burn may explain why

Double, where the prescribed burn was intense, had greater plant P concentrations compared to plants at other less intense burned sites (Figure 2.23). Recently Dighton et 46

al. (2013) demonstrated that there is an increase in mychorrhizae colonization with R.

knieskernii at natural versus degraded sites, which may be due to soil P availability. An

increase in the abundance of mycorrhizae in a low nutrient soil environment increases the potential for P plant uptake. Schachtman et al. (1998) reported that roots colonized by mycorrhizal fungi facilitate nutrient uptake at rates three to five times greater than roots not infected with mycorrhizae. However, phosphorus can also be taken up directly through plant roots, as when mycorrhizal colonization in R. knieskernii is weak and facultative (Dighton et al. 2013)

Plants at sites that were prescribed burned allocated a greater proportion of N to storage rather than to reproduction during fruiting and senescing regardless of the nitrogen availability compared to unburned sites (Figure 2.20 to 2.22). Although burned sites had increased extractable soil nitrogen, seasonal (i.e., monthly and annual) changes in extractable soil N were similar among sites (Figures 2.1 to 2.3). Inorganic soil N was greatest in the year of the burn and decreased seasonally (Figure 2.3), but in 2012 concentrations were relatively high at the end of the growing season (November) compared to other years. As microbial respiration decreased with the ensuing cooler temperature, it is plausible that when plants sequestered a high concentration of N they released more root nitrogen exudate as plants senesced in November, thus maintaining high seasonal concentrations of extractable soil nitrogen throughout 2012 (Figures 2.1 to

2.3).

Disturbance that opens closed canopy can have effects on plant populations

similar to fire by increasing light availability (Pykälä et al. 2005). Although rare plants may derive increased benefit from an open canopy, as low height is one of the few traits 47

commonly associated with rare plants across a wide range of species (Hedge and

Ellstrand 1989; Lavergne et al. 2004), several studies report that plant response to fire

differs qualitatively to other forms of disturbance (Howe 1999; Clarke et al. 2001). In addition, intense burns that kill the shrub layer also help to maintain more open shrub canopy gaps (Ehrenfeld et al. 1992). The significantly taller R. knieskernii plants at

burned sites were correlated with increased light availability (Figures 35 and 36).

However, the increased light availability did not entirely explain the increased plant

height, because plant height increased among all sites and biomass increased at most sites

in 2013. My observations in the field indicate that increased plant height in 2013 was

more associated with increased precipitation rather than increased light and nutrients.

However, the combination of higher soil nutrient concentration, greater light availability,

and increased precipitation (=water availability) probably facilitated the increased plant

height, as was reported (for light and nutrients) in a similar study of Houstonia montana

(Euliss 2007).

The areal and population size of R. knieskernii populations decreased with

increased canopy cover, although light was not physically measured for cause and effect

(USFWS 2008). Walck et a. (1999) found light to be a limiting factor for Solidago shortii

but P-limitation had a greater influence on population dynamics. In contrast, Houstonia

montana plants had greater growth when shaded (Euliss 2007). Although R. knieskernii is

an obligate wetland plant, there was a strong negative correlation between site moisture

and fruiting (Figure 2.28) and between soil moisture and canopy cover (Figure 2.30).

Presumably these shaded clay-enriched soils (Woodmansie and Lakehurst) retained more

moisture than open sites and were unfavorable for germination and winter bud 48

meristematic growth. A study of Rhynchospora tracyi found reduced plant performance under conditions of lower soil redox potential (Eh) conditions, which is the result of an oxygen-deficient root medium in response to flooding (Busch et al. 2004). An unpublished greenhouse study of R. knieskernii seedling response to differing water table levels found a similar response curve; seedling growth increased as soil moisture increased, but then decreased at the most flooded levels (Frank 2007). Thus, the widely observed decrease in R. knieskernii population density with increased canopy cover

(USFWS 2008) may be more related to plant-water status maintenance than to light levels or competition.

Except for soil moisture, there were no significant correlations between population and site factors (light, soil N and P availability). Fruiting was greatest in 2013, when plants produced high quantities of seeds with low seed mass compared to 2012.

Because there was a positive relationship between seed mass and soil moisture (Table

2.2), it is plausible that the production of a large number of low mass seeds in 2013 was in response to lowered soil moisture content during seed set in August and September

2013 (Table 2.12). Although seed abundance was greatest in 2013, there was no difference in time of flowering and fruiting, suggesting that nutrient allocation may have been fixed earlier in the season when soil total nitrogen levels and moisture content were higher (Figure 2.1; Table 2.12). Under similar environmental conditions in 2011, R. knieskernii allocated more nutrients to reproduction (Figures 2.22 and 2.23).

There were seasonal shifts in plant nitrogen and phosphorus allocation. In 2011, plants had more N partitioned to reproductive parts than to other plant parts (Figure 2.19).

However, throughout the season, there was a sequential shift in N allocation from 49

reproductive parts to storage in winter buds (during fruiting and senescence most of the

plant dry mass is composed of the winter bud). Nitrogen allocation in 2013 was similar

to 2011; plants had similar N concentration in stems and allocated greater N to

reproductive parts than to winter buds (Figures 2.20 and 2.22).

Higher soil nitrogen levels in 2012 facilitated an increase in plant mass late in the

fruiting season. The limited increase in nitrogen allocation to reproduction and the

negative slope at high nitrogen concentrations may relate to low soil P availability, which

could have been affected by precipitation conditions (Figure 2.5; Table 2.13). Although

Gray (2006) did not find data indicating P leaching in sandy NJPB soils even under

above-average moisture conditions, the above-average precipitation in August and

September 2012 could have altered nutrient availability in other ways. For example,

DeBano and Conrad (1978) reported higher P losses due to leaching after fire and erosion

following a winter rainfall. In September 2012, I observed iron precipitation in the water

at all ponded sites. Since bioreducible iron does not undergo a rapid decrease in redox

potential (intensity) following flooding (Pezeshki and DeLaune 2012), the unusually high

concentrations of iron may have led to higher soil immobilization of P through the

binding of P with Fe (Kooijman et al. 2000).

Nitrogen addition experiments almost always lead to increased growth in plants

although extremely high levels may lead to flat or reduced growth, especially as other

nutrients become limiting (Xia and Wan 2008). Annuals are generally less responsive to

soil nutrient additions than perennials (van Andel and Vera 1977). Presumably this is related to the ability of perennials to take up and store nutrients beyond their current biological demand (Thomas and Sadras 2001). Plants also have mechanisms for the 50

accumulation and storage of nitrogen (Millard 1988). To alleviate nutrient stress, plants can mobilize nutrients from their leaves during translocation (Millard 1988). The development and appearance of the R. knieskernii winter bud coincided with leaf

senescence, suggesting that N from in leaves was translocated to winter buds. This process supported my hypothesis that R. knieskernii was not N limited. The large increases in plant N suggested the presence of increased nitrogen deposition, even though this was not captured in soil sampled. Thus, the increased allocation to storage and other responses of R. knieskernii to increased N may be compared to plants in environments experiencing alterations to nutrient cycling due to excess atmospheric N deposition (Xia and Wan 2008). Herbaceous plants were found to respond with increases in aboveground biomass at low increases of N supply, but did not respond to high increases in N (Xia and Wan 2008). Phosphorus was often found to be a limiting nutrient as N deposition increased and plants were no longer N-limited (Xia and Wan 2008). A broad review of species richness and rare plant occurrence that examined patterns related to N and P limitation found that 70% of rare plant species in several European countries occurred at P-limited sites (Wassen et al. 2005). Recent reports have raised concern that rare plants adapted to low-P conditions will lose their competitive advantage and experience population declines (Wassen et al. 2005; Klejn et al. 2008; Xia and Wan

2008). Climate change in the NJPB is predicted to increase nitrogen limitation over the next few decades through an interaction between fire and increased warming that will affect soil nutrient cycling in complex ways (Lucash et al. 2012), but my study shows that at smaller scales there may be differences between patch and landscape patterns of nitrogen availability. Thus, it is critically important to better understand how rare plants 51

will respond to potential nutrient limitations so that an effective management strategy can be implemented to mitigate any negative affects to population dynamics.

52

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Figures

Figure 2.1. Changes in concentrations of soil extractable carbon (top) and extractable total nitrogen (bottom) from August 2011 to November 2013 at five sites with R. knieskernii populations on WGR. All concentrations in µg/g. Error bars removed to minimize clutter; SEM for sites and months can be found in Tables 2.4 and 2.5.

61

Figure 2.2. Changes in concentrations of soil extractable NH4-N (top), and nitrates/nitrites (bottom) from August 2011 to November 2013 at five sites with R. knieskernii populations on WGR. All concentrations in µg/g. Error bars removed to minimize clutter; SEM for sites and months can be found in Tables 2.4 and 2.5 62

Figure 2.3. Changes in concentrations of soil extractable total inorganic nitrogen (top) and total organic nitrogen (bottom) from August 2011 to November 2013 at five sites with R. knieskernii populations on WGR. All concentrations in µg/g. Error bars removed to minimize clutter; SEM for sites and months can be found in Tables 2.4 and 2.5.

63

Figure 2.4. Changes in concentrations of soil extractable PO4-P from August 2011 to November 2013 at five sites with R. knieskernii populations on WGR. All concentrations in µg/g. Error bars removed to minimize clutter; SEM for sites and months can be found in Tables 2.4 and 2.5.

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8.11 11.11 3.12 5.12 8.12 9.12 11.12 8.13 9.13 11.13

Date

Figure 2.5. Potential relationship between precipitation and soil extractable PO4-P concentrations. Precipitation (blue line) closely tracked PO4-P at moderate rainfall levels and was inversely related during periods of high rainfall. All concentrations in µg/g. Error bars removed to minimize clutter; SEM for phosphorus concentrations for sites and months can be found in Table 2.5.

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R2 = 0.53 y = 6.2004x – 79.1466

Figure 2.6. Relationship between plant height and number of achenes produced per plant. Taller plants produce a greater abundance of achenes, although the relationship is strongest at the extremes.

66

Figure 2.7. Comparison of total number of achenes produced at each of five monitored sites at WGR. Totals are for September (fruiting) and November (senescing) in 2012 and for August (flowering) and September (fruiting) in 2013. Values represent means ± 1 SEM.

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Figure 2.8. Comparison of estimated mass of individual achenes produced at five monitored sites on WGR, 2012 to 2013. Achenes were measured in September (fruiting) and November (senescing) in 2012 and in August (flowering) and September (fruiting) in 2013. Values represent means ± 1 SEM.

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y = 0.004404x+1.650123 R2 =0.44

2 Figure 2.9. Relationship between areal soil PO4-P concentrations (mg/m ) and the total number of achenes produced per plant (log transformed). Each point represents the total soil P concentration for a sampled site as it relates to the mean of the total number of achenes produced by R. knieskernii plants at that site.

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yy == --57.44x57.44x + 2231.07 + 2231.07 RR22== 0.50 0.50 g) Estimated achene mass ( µ

Soil Moisture (%)

Figure 2.10. Relationship between soil moisture and individual achene mass in micrograms (estimated). Each point represents the percent of soil moisture at a site as it relates to the mean estimated achene mass for that site. Relationship is statistically significant (F1,13=14.77, p<0.01).

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Figure 2.11. Comparison of annual differences in maximum seedling emergence depth for R. knieskernii achenes, based on the scaling equation y = 27.0*(achene weight)0.33 . Achene weight is estimated from weight of multiple achenes, and thus does not take into account individual variability. Box and whisker plot shows median (line), first and third quartiles (box), and points within 1.5 interquartile range (IQR) of the lower and upper quartiles (whiskers). Dots represent outliers.

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Figure 2.12. Influence of the interaction of soil moisture and soil extractable PO4-P concentrations on nitrogen concentrations in plant reproductive structures (log transformed). Soil moisture is indicated in the ribbon on top of x-axis; numbers indicate sequence of increasing soil moisture, beginning with 11% (1) and increasing to 20% (8). The two highest soil moisture levels (7 and 8) are represented by only one data point (line). Phosphorus concentrations (x-axis) increase from 0.0 to 1.5 µg/g in increments of 0.5 µg. Median plant nitrogen concentrations (log-transformed) are lowest at medium soil moisture levels and relatively high phosphorus concentrations, and are highest at high soil moisture levels and low phosphorus concentrations. Box and whisker plot shows median (line), first and third quartiles (box), and points within 1.5 interquartile range (IQR) of the lower and upper quartiles (whiskers). The interaction is significant 2 (F2,21 = 14.28, p < 0.0001, R = 0.54)

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Figure 2.13. Percent allocation of carbon to key R. knieskernii plant structures: reproductive, stems (culms and leaves), and storage (winter bud and root) during flowering (August), fruiting (September), and senescing (November) for five monitored sites at WGR, 2011-2012. Ditch site was dropped in 2012 and LSL site was added in 2012.

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Figure 2.14. Percent allocation of nitrogen to key R. knieskernii plant structures: reproductive, stems (culms and leaves), and storage (winter bud and root) during flowering (August), fruiting (September), and senescing (November) for five monitored sites at WGR, 2011-2012. Ditch site was dropped in 2012 and LSL site was added in 2012.

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Figure 2.15. Percent allocation of phosphorus to key R. knieskernii plant structures: reproductive, stems (culms and leaves), and storage (winter bud and root) during flowering (August), fruiting (September), and senescing (November) for five monitored sites at WGR, 2011-2012. Ditch site was dropped in 2012 and LSL site was added in 2012.

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Figure 2.16. Comparison of seasonal changes in allocation of R. knieskernii carbon, nitrogen, and phosphorus concentrations at five monitored sites on WGR in 2012. The Ditch site was dropped in 2012 and LSL was added in 2012 (see Methods).

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Figure 2.17. Differences in allocation of nitrogen (TN, top) and phosphorus (PO4-P, bottom) for different R. knieskernii plant structures. Box and whisker plot shows median (line), first and third quartiles (box), and points within 1.5 interquartile range (IQR) of the lower and upper quartiles (whiskers). Circles represent outliers. Data are pooled for all sites and years. 77

Figure 2.18. Relationship between total phosphorus concentrations within theR. knieskernii plants and the percentage allocated to the reproductive structures for all sites between 2011 and 2012.

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y = 0.015833x + 0.008758 y = -0.0015694x + 0.5429113 R2 = 0.68 R2 = 0.38

Figure 2.19. Relationship between nitrogen concentrations within R. knieskernii plants and the proportion allocated to the reproductive structures for all sites between 2011 and 2012. Note difference in scales for x- axis between 2011 values (15-35) and 2012 values (50-250). Proportion of allocation was significantly different at different total concentrations (p<0.01) , but proportions did not differ significantly between years (p=0.70).

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Figure 2.20. Seasonal allocation of nitrogen at monitored burned and unburned sites on WGR, 2011 to 2012. Note difference in scales on y-axis for 2011 unburned sites (middle row). Box and whisker plot shows median (line), first and third quartiles (box), and points within 1.5 interquartile range (IQR) of the lower and upper quartiles (whiskers). Dots represent outliers.

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Figure 2.21. Seasonal allocation of phosphorus at monitored burned and unburned sites on WGR, 2011 to 2012. Box and whisker plot shows median (line), first and third quartiles (box), and points within 1.5 interquartile range (IQR) of the lower and upper quartiles (whiskers). Dots represent outliers.

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Figure 2.22. Differential allocation of nitrogen to different plant structures at burned and burned sites during fruiting at five monitored sites on WGR, 2011 to 2013. Box and whisker plot shows median (line), first and third quartiles (box), and points within 1.5 interquartile range (IQR) of the lower and upper quartiles (whiskers). Dots represent outliers.

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Figure 2.23. Differential allocation of phosphorus to different plant structures at burned and unburned sites during fruiting at five monitored sites on WGR, 2011 to 2013. Box and whisker plot shows median (line), first and third quartiles (box), and points within 1.5 interquartile range (IQR) of the lower and upper quartiles (whiskers). Dots represent outliers.

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Figure 2.24. Comparison of nitrogen and phosphorus concentrations (all structures) with total plant nitrogen and phosphorus (mass x concentration) between 2011 and 2012. Differences in N concentrations 2 2 (F1,25 = 120.7, p < 0.0001, R = 0.82) and total plant N (F1,22 = 17.61, p < 0.001, R = 0.42) between years were significant. Differences in total plant P between years was not significant (F1,22 = 0.641, p = 0.43, R2 = -0.02). It was not possible to test for annual differences in P concentrations, as errors could not be normalized, even after log transformation.

84

y = 0.38386x + 3.85096 R2 = 0.41

Figure 2.25. Relationship between soil N:P ratio and plant N:P ratio. The category “Roots” includes both plant roots and the winter bud. Each point represents the N:P ratio of a plant at a site as it relates to the N:P ratio of the soil at the same site.

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y = 0.4041x + 1.8595 R2 = 0.44

Figure 2.26. Relationship between soil N:P ratio and the total plant N:P ratio based on summing mass x concentration for all plant structures (square root-transformed). Each point represents the N:P ratio of a plant at a site as it relates to the N:P ratio of the soil at the same site.

86

y = – 464.5x + 422.4 70 R2 = 0.36 60 50 TKN Nutrient Concentration, Concentration, TKN Nutrient 40 30 20 10

0.12 0.14 0.16 0.18 0.20

Soil Moisture (%)

Figure 2.27. Relationship between soil moisture and nitrogen concentrations in the reproductive structures. Each point represents the relationship between soil moisture at a site and the total nitrogen concentration of R. knieskernii reproductive structures at that site.

87

y=-2596.26x2 + 522.39x -16.39 R2 = 0.55

Figure 2.28. Fruiting population decrease in relation to increased soil moisture as a quadratic function at burned (right) and unburned (left) sites (left) on WGR, 2011-2013. It was not possible to determine a relationship for burned sites due to an inadequate number of degrees of freedom.

88

2

Total Light (%)

Figure 2.29. Relationship between height and the percent of total light (direct + diffuse light, see Methods) measured by canopy photographs for eleven sites on WGR, 2011 and 2013; five were prescribed burned (two in 2011 and three in 2012. Each data point represents one subplot where fruiting population was measured and canopy values were photographed.

89

Figure 2.30. Comparison of relationship between height and the percent of total light (direct + diffuse light, see Methods) as measured by canopy photographs for the same eleven sites on WGR, 2011 and 2013 (left); relationship between canopy and soil moisture (right). 90

Tables

Table 2.1. Results of model testing for mixed-effects models comparing null models with full models to determine whether site characteristics influenced patterns of soil nutrient availability.

Models Model Type df BIC p-value TC~(1|Site) Null 3 688.50 TC~Date*Fire+(1|Site) Full 6 656.14 0.00 TN~(1|Site) Null 3 633.11 TN~Date*Fire+(1|Site) Full 6 615.06 0.00 P~(1|Site) Null 3 548.48 P~Date*Fire+(1|Site) Full 6 560.93 0.19

NH4~(1|Site) Null 3 792.22

NH4~Date*Fire+(1|Site) Full 6 765.70 0.00 NOX~(1|Site) Null 3 1052.70 NOX~Date*Fire+(1|Site) Full 6 1047.10 0.00 TIO~(1|Site) Null 3 166.31 TIO~Date*Fire+(1|Site) Full 6 177.30 0.10 TO~+(1|Site) Null 3 665.39 TO~Date*Fire+(1|Site) Full 6 650.32 0.00 TC~(1|Site) Null 3 86.76 TC~Season+Year+(1|Type)+(1|Site) Full 7 -6.40 0.00 TN~(1|Site) Null 3 91.76 TN~Season+Year+(1|Type)+(1|Site) Full 7 14.03 0.00 P~(1|Site) Null 3 86.76 P~Season+Year+(1|Type)+(1|Site) Full 7 -57.13 0.00 Fruiting Population~(1|Site) Null 3 1666.50 Fruiting Population~Year+(1|Site) Full 4 1669.90 0.16 Total Population~(1|Site) Null 3 1722.10 Total Population~Year+(1|Site) Full 4 1727.50 0.91 Mean Height~(1|Site) Null 3 1218.00 Meant Height~Year+(1|Site) Full 4 1221.70 0.22 Phosphorus/m2~(1|Site) Null 3 199.20 Phosphorus/m2~Achenes+(1|Site) Full 4 190.43 0.00 91

Table 2.2 Statistical results of ANOVAs and regressions for a wide range of soil, plant, and site variables.

log- 2 Comparison df Fstat pvalue R Significance Comme nt tranformed NH4:TO Ratio~Year 1, 58 16.02 0.01 0.20 ** No NH4:TO Ratio~Site 5,54 0.8906 0.49 0.00 ns No NH4:TO Ratio~Month 1,58 2.02 0.14 0.02 ns No NH4~Year 2, 277 11.75 0.00 0.07 *** All years Yes TN~Year 2, 276 27.15 0.00 0.15 *** All years Yes P~Year 2, 277 5.25 0.01 0.03 ** 2011 only Yes Spikelets ~ Year 2 and 355 15.76 0.00 0.08 *** No Height~Year 2 and 357 4.858 0.01 0.02 No Root Length ~DW*Site 9,348 7.961 0.05 0.15 * Runway No DW ~ Year 1 and 358 0.1511 0.70 0.00 : 0.549 Total Achenes~Height 1, 178 127.8 0.00 0.41 *** Yes LSL 0.003; Total Achenes~Site Runway 4 and 195 2.768 0.03 0.03 * 0.011 No Total Achenes~P (mg/m2) 2, 17 8.7 0.01 0.45 ** No Estimated achene weight ~ Soil moisture 1 and 13 14.77 0.01 0.53 ** No Reproductive N~ Soil P + Soil Moisture (%) 2, 21 14.58 0.00 0.54 *** Yes Soil Depth ~ Year 1, 198 34.42 0.00 0.14 *** No

PO4-P~Plant Structure 2, 82 45.73 0.00 0.52 *** No

PO4-P~Year*Plant Structure 5, 79 18.52 0.00 0.51 *** No

PO4-P~Site*Plant Structure 17, 67 7.98 0.00 0.59 *** No

PO4-P~Season*Type 8, 76 13.15 0.00 0.54 *** No

PO4-P~Year 1, 83 0.2101 0.65 -0.01 ns No

PO4-P~Site 5, 79 0.4337 0.82 0.03 ns No

PO4-P~Season 2, 82 0.09477 0.91 -0.02 ns No Nitrogen in Reproductive Structures~Total Soil Nitrogen 1, 22 8.314 0.01 0.26 * No Nitrogen in Reproductive Structures~Soil Moisture 1, 20 12.61 0.00 0.36 ** No Nitrogen in Stem Structures~Total Soil Nitrogen 1, 22 1.144 0.30 0.01 ns No Nitrogen in Root Structures~Total Soil Nitrogen 1, 22 2.032 0.17 0.05 ns No Total Kjedahl Nitrogen~Plant Structure 2, 81 1.047 0.36 0.00 ns No Total Kjedahl Nitrogen~Season 2, 81 1.822 0.17 0.04 ns No Total Kjedahl Nitrogen~Year 2, 82 50.59 0.00 0.38 *** No Nitrogen ~ Plant Structure 2, 82 3.149 0.05 0.04 ns Yes PO4-P ~ Plant Structure 2, 82 38.93 0.00 0.47 *** Yes

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Table 2.2 (continued). Statistical results of ANOVAs and regressions for a wide range of soil, plant, and site variables.

log- 2 Comparison df Fstat pvalue R Significance Comme nt tranformed Reproductive P ~ Total Plant P in 2011 1, 12 0.3041 0.59 -0.06 ns No Reproductive P ~ Total Plant P in 2012 1, 11 8.49 0.05 0.38 * No Reproductive P ~ Total Plant P in 2011 and 2012 2, 24 6.92 0.01 0.31 * No Reproductive N ~ Total Plant N in 2011 1, 12 29.14 0.00 0.68 ** No Reproductive N ~ Total Plant N in 2012 1, 11 8.31 0.05 0.38 * No Reproductive N ~ Total Plant N in 2011 and 2012 2, 24 6.34 0.01 0.29 * No Root N:P Ratio ~ Soil N:P Ratio 1,22 17.01 0.00 0.44 ** No Total Plant N:P ~ Soil N:P 2, 17 8.7 0.01 0.45 Nitrogen in Reproductive Structures~Soil Moisture 1, 20 12.61 0.00 0.36 ** No Reproductive P ~ Stem C:N Ratio 1, 22 12.03 0.00 0.33 ** Yes Nstorage ~ Fire 1 and 11 8.56 0.01 0.39 * No Pstorage ~ Fire 1 and 11 1.895 0.20 0.07 ns No

Fruiting Population ~ Year+Site 12, 198 2.12 0.05 0.06 * Year 2013 No Double site in Fruiting Population ~ Year*Site 2013; Ditch site in 2014 34, 176 4.58 0.00 0.37 *** No Fruiting Population ~ Moisture * Fire 2, 7 6.48 0.05 0.55 Yes Fruiting~ Year + Prepro + Pstem + Canopy 4 and 8 8.33 0.01 0.71 ** No Height~Total Light (%), all years 1, 85 38.84 0.00 0.31 No r2 = 0.32 Fruiting Population~Total Light (%) Fire; 0.05 2, 80 13.91 0.00 No fire No 2 Plant Height ~ Total Light (%) r 0.02 Fire; 2, 79 32.39 0.00 0.32 No fire No Fruiting~Height, 2011 1, 50 21.50 0.00 0.29 No Fruiting~Height, August 2013 1, 33 17.04 0.01 0.32 No Fruiting~Height, October 2013 1, 49 18.51 0.00 0.26 No Fruiting~Direct Light (%), 2011 1, 50 4.56 0.05 0.07 No Height~Total Light (%), 2011 1, 50 22.43 0.00 0.30 No Height~Diffuse Light (%), August 2013 1, 34 6.19 0.05 0.13 No Height~Total Light (%), 2013 1, 34 6.09 0.05 0.13 No Height~Open Canopy (%), all years 1, 11 13.27 0.01 0.51 No Soil Moisture (%) ~Open Canopy (%) 1, 11 5.58 0.05 0.28 No 93

Table 2.3. Summary of means for data collected for a wide range of site variables, including soil and plant characteristics, canopy, population, and wind speed.

Dead Sight Site End Ditch Double LSL Road Runway Line Slope Mean Mean pH 3.84 4.13 4.12 4.04 4.21 4.62 4.19 4.09 4.16 Mean Bulk Density (g/cc) 1.44 1.50 1.54 1.52 1.55 1.61 1.40 1.38 1.49 Mean Moisture (%) 0.15 0.17 0.13 0.11 0.14 0.10 0.14 0.14 0.14 Mean PWP (%) 6.8 3.3 4.0 8.0 2.7 5.5 5.05 Mean FC (%) 12.0 9.0 6.0 18.0 6.0 11.0 10.33 Mean Open Canopy, 2011 56.75 55.05 48.92 61.53 59.94 98.51 48.90 42.55 59.02 Mean Open Canopy, 2013 62.35 77.22 74.18 72.13 99.23 62.92 59.67 72.53 Mean Fruiting Population (2011-2014) 5.4 11.9 10.2 9.9 4.8 6.1 6.1 1.5 6.99 Mean Total Population (2011-2014) 8.3 15.7 15.0 14.7 5.8 7.7 6.8 2.1 9.51 Mean Plant Height (cm) 10.0 13.9 11.0 14.6 10.9 18.4 12.5 13.5 13.10 Mean Root Length (cm) 2.27 2.43 1.91 2.15 3.12 2.59 2.41 Mean Root:Shoot Ratio 0.09 0.09 0.10 0.13 0.12 0.11 0.11 Mean Total Achenes/Plant 95.47 65.53 51.20 57.83 88.68 71.74 Mean Soil N:P Ratio 20.69 13.09 11.98 14.74 2.57 8.56 11.64 3.47 10.84 Total Soil Carbon (µg/cc) 49.10 31.23 25.78 33.06 28.63 17.20 39.38 31.55 31.99 Total Soil Nitrogen (µg/cc) 11.89 9.76 6.81 9.58 2.21 4.90 8.42 2.35 6.99

PO4-P (µg/cc) 0.57 0.75 0.57 0.65 0.86 0.57 0.72 0.68 0.67

NH4-N (µg/cc) 2.26 3.64 1.90 2.59 1.38 1.52 2.26 1.31 2.11 NOx-N (µg/cc) 0.58 0.40 0.38 0.42 0.46 0.39 0.44 0.42 0.43 Total Inorganic Nitrogen (µg/cc) 2.84 4.05 2.28 3.01 1.84 1.91 3.30 1.73 2.62 Total Organic Nitrogen (µg/cc) 9.24 5.84 4.67 6.73 1.52 3.84 5.93 1.90 4.96 Mean Plant N:P Ratio (mg/g) 13.44 6.97 19.76 34.56 21.18 20.27 19.51 Mean Total Carbon (mg/g) 16.36 6.94 20.64 41.20 20.97 32.56 23.11 Mean Total Nitrogen (mg/g) 24.04 9.69 30.85 71.57 27.08 44.53 34.63 Mean PO4-P (mg/g) 1.72 1.44 1.69 2.19 1.55 1.51 1.68 Wind Speed, m/s (Air), 2013 0.53 0.73 0.40 1.16 0.88 0.74 Wind Speed, m/s (Ground), 2013 0.48 0.28 0.20 0.92 0.72 0.52

94

Table 2.4. Soil nutrient concentrations (means and standard errors) from August 2011 to November 2013 at study sites on WGR. Data pooled for all sites; all concentrations in µg/g.

Mean Mean Mean Mean Mean Mean Extractable Extractable Mean Mean Extractable Extractable Mean Mean Ye ar Month Extractable Extractable Total Total Bulk N:P Total Total NO C:N Ratio PO -P NH x Inorganic Organic Density Ratio Carbon Nitrogen 4 4 Nitrogen Nitrogen 2011 August 48.32 4.02 0.23 1.19 0.29 1.48 2.54 1.44 14.42 17.77 2011 November 16.35 1.84 0.98 1.29 0.87 2.16 1.91 1.57 27.98 3.04 2012 March 13.47 3.40 0.65 1.59 0.01 1.59 1.93 1.48 4.52 10.86 2012 May 32.89 3.91 0.42 0.99 0.02 1.02 2.89 1.52 8.48 10.38 2012 August 14.80 7.26 0.16 2.30 0.20 2.50 4.89 1.46 2.06 58.53 2012 September 11.62 6.69 0.15 1.64 0.27 1.86 6.10 1.48 1.81 56.51 2012 November 13.17 5.75 0.13 1.27 0.25 1.53 4.22 1.39 2.46 52.16 2013 August 11.14 6.78 0.82 1.36 0.29 1.64 5.53 1.49 1.40 12.59 2013 November 12.37 4.45 0.40 0.85 0.22 1.08 3.38 1.58 2.37 11.77 2013 September 53.32 13.19 0.62 2.34 0.52 2.85 10.34 1.50 3.65 45.77 SE SE SE SE SE SE Extractable Extractable SE Extractable Extractable SE Bulk SE Ye ar Month Extractable Extractable SE NO Total Total N:P Total Total x Density C:N Ratio PO -P NH Inorganic Organic Ratio Carbon Nitrogen 4 4 Nitrogen Nitrogen 2011 August 4.33 0.63 0.03 0.05 0.01 0.06 0.60 0.04 0.76 4.80 2011 November 2.02 0.35 0.09 0.08 0.03 0.08 0.29 0.02 7.03 0.74 2012 March 1.20 0.55 0.33 0.64 0.00 0.64 0.16 0.02 0.24 1.70 2012 May 2.41 0.21 0.02 0.07 0.00 0.07 0.18 0.03 0.39 0.97 2012 August 1.49 0.65 0.02 0.52 0.02 0.54 0.56 0.04 0.08 10.87 2012 September 1.08 0.69 0.02 0.23 0.02 0.24 1.20 0.03 0.17 5.98 2012 November 1.07 0.55 0.01 0.15 0.01 0.16 0.42 0.05 0.14 6.91 2013 August 2.31 1.03 0.17 0.17 0.05 0.20 0.81 0.03 0.17 2.09 2013 November 3.69 0.64 0.04 0.07 0.00 0.07 0.58 0.03 0.36 2.42 2013 September 7.28 1.38 0.30 0.19 0.15 0.28 1.15 0.04 0.29 7.59

95

Table 2.5. Soil nutrient concentrations (means and standard errors) for all study sites on WGR, 2011-2013. Data pooled for all months and years.

Mean Mean Mean Mean Mean Mean Extractable Extractable Extractable Extractable Mean Mean Extractable Extracta Total Total Mean Bulk Mean Site Total Total Nox N:P PO -P ble NH Inorganic Organic Density C:N Ratio Carbon Nitrogen 4 4 (µg/g) Ratio (µg/g) (µg/g) Nitrogen Nitrogen (µg/g) (µg/g) (µg/g) (µg/g) Dead End 34.81 8.48 0.45 1.58 0.40 1.98 6.83 1.44 4.54 34.41 Ditch 24.50 3.57 0.50 1.25 0.29 1.53 2.38 1.50 7.26 15.19 Double 18.51 4.61 0.36 1.24 0.25 1.49 3.61 1.53 4.66 24.14 LSL 22.98 6.49 0.40 1.79 0.28 2.07 4.76 1.50 4.21 43.14 Road 18.56 1.71 1.22 0.89 0.28 1.18 1.32 1.55 17.12 4.90 Runway 11.77 3.60 0.34 1.01 0.24 1.24 3.60 1.61 8.25 22.03 Sight Line 28.08 6.67 0.41 2.14 0.29 2.43 4.70 1.40 6.56 33.63 Slope 23.71 2.42 0.47 0.96 0.31 1.27 1.70 1.38 21.18 5.27 SE SE SE SE SE SE Extractable Extractable Extractable Extractable SE SE Extractable Extracta Total Total SE Bulk SE Site Total Total Nox N:P PO -P ble NH Inorganic Organic Density C:N Ratio Carbon Nitrogen 4 4 (µg/g) Ratio (µg/g) (µg/g) Nitrogen Nitrogen (µg/g) (µg/g) (µg/g) (µg/g) Dead End 3.89 0.77 0.15 0.11 0.08 0.16 0.63 0.02 0.47 4.73 Ditch 3.53 0.39 0.06 0.12 0.07 0.16 0.24 0.03 0.77 6.69 Double 2.96 0.48 0.04 0.11 0.03 0.11 0.37 0.03 0.62 3.92 LSL 4.18 0.93 0.09 0.33 0.04 0.35 0.81 0.03 0.50 7.95 Road 2.83 0.27 0.65 0.06 0.08 0.12 0.22 0.03 5.99 0.86 Runway 1.09 0.38 0.04 0.07 0.03 0.08 0.78 0.03 3.18 4.06 Sight Line 3.09 0.60 0.07 0.50 0.04 0.49 0.39 0.02 1.29 4.46 Slope 3.80 0.32 0.03 0.10 0.08 0.15 0.19 0.04 8.61 0.82

96

Table 2.6. Plant measurements by year, site, and month (means and standard deviations) for five study sites on WGR, 2011-2013.

Mean SD Mean SD Mean SD Mean Ye ar Month Site Height Height Root:Shoot Root:Shoo SD DW Spikelets Spikelets DW (g) (cm) (cm) Ratio t Ratio 2011 8 Dead End 33.56 8.77 4.40 0.97 2.55 0.67 0.11 0.05 2011 8 Double 27.86 5.97 4.50 0.71 1.66 0.64 0.09 0.05 2011 8 Runway 30.84 4.48 4.70 0.67 4.02 1.21 0.32 0.17 2011 8 Sight Line 32.62 6.16 4.70 0.48 3.63 0.83 0.14 0.06 2011 9 Dead End 29.51 6.89 3.80 0.79 2.45 0.58 0.16 0.05 2011 9 Double 26.15 3.00 4.20 0.42 2.32 0.92 0.14 0.04 2011 9 Runway 30.36 4.75 6.20 4.05 2.43 0.87 0.20 0.14 2011 9 Sight Line 29.77 5.14 4.50 0.53 2.30 0.96 0.23 0.09 2011 11 Dead End 19.09 7.87 3.90 0.57 1.89 0.66 0.04 0.03 2011 11 Double 13.07 3.64 3.00 0.47 2.72 0.79 0.04 0.02 2011 11 Runway 16.18 3.41 3.50 0.71 1.71 0.73 0.05 0.04 2011 11 Sight Line 16.18 3.41 4.10 0.57 2.54 0.74 0.08 0.06 2012 8 Dead End 31.46 11.11 3.20 1.55 2.25 1.11 0.22 0.16 2012 8 Double 19.47 5.08 3.40 0.52 1.39 0.67 0.04 0.02 2012 8 Runway 26.20 9.07 4.44 0.53 3.48 1.31 0.33 0.27 2012 8 Sight Line 28.69 9.21 4.20 0.63 2.96 1.40 0.16 0.08 2012 9 Dead End 27.23 15.39 9.90 6.06 2.30 0.71 0.39 0.41 2012 9 Double 21.08 4.56 3.70 1.06 1.54 0.87 0.10 0.04 2012 9 LSL 22.12 5.20 13.50 6.08 2.20 0.66 0.13 0.05 2012 9 Runway 22.85 4.77 11.22 6.63 3.89 1.47 0.36 0.21 2012 9 Sight Line 24.40 7.83 4.20 0.63 1.87 0.48 0.22 0.18 2012 11 Dead End 15.60 4.38 8.20 6.70 2.19 1.31 0.05 0.04 2012 11 Double 14.82 3.97 3.50 0.71 1.81 0.46 0.03 0.01 2012 11 LSL 13.43 2.64 4.30 1.95 2.09 0.96 0.03 0.02 2012 11 Runway 25.36 5.10 10.90 7.78 3.20 1.47 0.16 0.11 2012 11 Sight Line 17.37 5.43 8.40 7.57 2.21 0.57 0.07 0.05 2013 8 Dead End 29.13 11.40 9.20 7.60 2.90 1.31 0.16 0.17 2013 8 Double 24.38 7.21 10.00 5.58 2.48 1.23 0.15 0.11 2013 8 LSL 22.50 4.53 4.50 1.72 2.16 0.55 0.07 0.03 2013 8 Runway 12.42 8.64 4.20 1.93 3.35 1.96 0.17 0.11 2013 8 Sight Line 28.84 6.10 8.80 5.63 2.71 1.25 0.12 0.08 2013 9 Dead End 19.28 8.68 9.60 5.40 1.35 0.84 0.13 0.08 2013 9 Double 21.27 4.34 12.90 5.72 1.82 0.87 0.16 0.09 2013 9 LSL 21.78 3.83 6.33 2.40 2.68 0.41 0.08 0.02 2013 9 Runway 25.67 5.68 4.50 1.35 2.83 1.10 0.10 0.07 2013 9 Sight Line 24.91 6.34 4.90 1.73 1.29 0.53 0.08 0.03

97

Table 2.7. Measurements of reproductive output and related R. knieskernii plant measurements (means only) at five monitored sites on WGR, 2012-2013.

Achenes Achenes Achenes Achenes Subtotal Additional Total (All Site Terminal Second Third Proximal (Single Culms Spikelets) Spikelet Spikelet Spikelet Spikelet Culm) Year Month 2012 8 Dead End 16.44 11.56 15.89 6.50 25.60 48.22 62.44 2012 8 Double 13.91 6.36 5.00 1.00 26.25 25.73 35.27 2012 8 LSL 14.30 9.50 9.10 5.13 10.67 37.00 40.20 2012 8 Runway 18.40 14.20 14.60 6.13 37.20 52.10 70.70 2012 8 Sight Line 29.40 13.80 15.00 10.00 64.50 67.80 80.70 2012 11 Dead End 0.20 1.10 1.13 9.00 3.00 3.10 4.90 2012 11 Double 0.40 0.30 0.00 0.00 0.00 0.70 0.70 2012 11 LSL 2.70 0.40 0.20 0.00 0.00 3.30 3.30 2012 11 Runway 1.30 1.00 0.70 1.00 4.33 3.20 5.80 2012 11 Sight Line 1.50 2.20 1.13 2.00 6.17 5.00 8.70 2013 8 Dead End 41.90 26.80 26.20 32.43 58.80 117.60 144.00 2013 8 Double 28.40 13.50 12.60 11.00 61.20 54.80 93.60 2013 8 LSL 25.70 16.40 14.25 5.60 7.40 56.30 60.00 2013 8 Runway 25.60 10.67 12.00 3.00 10.83 43.50 50.00 2013 8 Sight Line 30.90 14.40 14.20 6.56 5.80 65.40 68.30 2013 9 Dead End 41.90 25.80 23.50 18.00 76.50 109.70 155.60 2013 9 Double 46.50 26.40 19.00 17.33 57.60 108.70 166.30 2013 9 LSL 24.00 15.10 14.10 7.89 29.00 60.30 77.50 2013 9 Runway 28.20 19.00 20.70 16.40 17.00 77.90 81.30 2013 9 Sight Line 42.80 27.70 22.90 18.78 20.00 110.70 126.70 Estimated Estimated Estimated Root Percent Percent Mean Depth Height Weight Site Length Terminal Proximal Weight Survival to (cm) Reproductive (cm) Spikelet Spikelet Single Germination Output Year Month Achene (cm) 2012 8 Dead End 26.32 2.22 0.35 0.11 0.02 0.0003 1.88 2012 8 Double 18.57 1.61 0.55 0.08 0.01 0.0002 1.60 2012 8 LSL 20.34 2.20 0.44 0.14 0.01 0.0003 1.69 2012 8 Runway 21.30 3.89 0.35 0.10 0.02 0.0004 1.87 2012 8 Sight Line 22.76 1.87 0.44 0.15 0.05 0.0006 2.23 2012 11 Dead End 15.61 2.19 0.02 0.43 0.00 0.0008 2.16 2012 11 Double 15.31 1.81 0.67 0.00 0.00 0.0025 1.08 2012 11 LSL 13.43 2.09 0.86 0.00 0.01 0.0044 3.77 2012 11 Runway 24.06 3.20 0.39 0.10 0.00 0.0011 1.71 2012 11 Sight Line 17.37 2.21 0.27 0.25 0.01 0.0018 2.62 2013 8 Dead End 32.93 2.73 0.38 0.25 0.02 0.0002 1.45 2013 8 Double 29.58 3.62 0.54 0.20 0.01 0.0001 1.13 2013 8 LSL 22.50 2.08 0.47 0.08 0.01 0.0001 1.19 2013 8 Runway 24.15 3.19 0.66 0.09 0.00 0.0001 1.21 2013 8 Sight Line 24.91 1.29 0.45 0.09 0.01 0.0002 1.48 2013 9 Dead End 27.38 2.25 0.40 0.14 0.02 0.0001 1.33 2013 9 Double 38.29 2.85 0.44 0.15 0.02 0.0001 1.39 2013 9 LSL 21.78 2.68 0.41 0.13 0.01 0.0002 1.46 2013 9 Runway 25.67 2.83 0.38 0.16 0.01 0.0001 1.37 2013 9 Sight Line 28.84 2.71 0.41 0.14 0.01 0.0001 1.20

98

Table 2.8. Plant nutrient concentrations (mean and SE) by year, season, and structure. Results pooled from five monitored sites on WGR, 2011-2013.

Mean Mean Mean Mean Mean Total Total Ye ar Season Plant Structure PO -P C:N N:P Carbon Nitrogen 4 (mg/g) Ratio Ratio (mg/g) (mg/g) 2011 Flowering Reproduction 8.85 17.81 2.17 0.48 8.26 2011 Fruiting Reproduction 5.80 11.05 2.06 0.53 5.20 2011 Senescing Reproduction 3.03 9.95 2.61 0.32 3.79 2012 Flowering Reproduction 22.60 32.73 2.84 0.68 11.46 2012 Fruiting Reproduction 25.29 44.65 2.46 0.58 20.02 2012 Senescing Reproduction 29.89 38.51 2.98 0.76 10.06 2011 Flowering Roots/Winter Buds 5.87 7.25 1.32 0.81 6.02 2011 Fruiting Roots/Winter Buds 6.97 8.87 1.29 0.80 6.89 2011 Senescing Roots/Winter Buds 6.84 11.26 1.38 0.60 8.21 2012 Flowering Roots/Winter Buds 22.99 26.75 1.66 0.90 16.66 2012 Fruiting Roots/Winter Buds 39.13 61.82 1.85 0.72 33.46 2012 Senescing Roots/Winter Buds 37.98 58.69 1.46 0.66 42.01 2011 Flowering Stems/Leaves 7.40 7.43 1.01 0.98 7.40 2011 Fruiting Stems/Leaves 6.58 5.23 0.84 1.26 6.21 2011 Senescing Stems/Leaves 5.57 4.28 0.60 1.31 7.41 2012 Flowering Stems/Leaves 16.47 16.92 1.55 0.99 11.48 2012 Fruiting Stems/Leaves 35.88 47.02 1.68 0.86 20.60 2012 Senescing Stems/Leaves 32.37 34.84 0.90 1.21 27.67 SE SE SE SE SE Ye ar Season Plant Structure Total Total C:N N:P PO -P Carbon Nitrogen 4 Ratio Ratio 2011 Flowering Reproduction 1.51894 1.04 0.09 0.09 0.54 2011 Fruiting Reproduction 1.75133 2.45 0.18 0.08 0.91 2011 Senescing Reproduction 0.09142 1.12 0.17 0.03 0.22 2012 Flowering Reproduction 1.21988 5.44 0.42 0.04 0.52 2012 Fruiting Reproduction 0.39435 8.95 0.23 0.06 6.13 2012 Senescing Reproduction 0.25393 31.31 0.96 0.05 27.04 2011 Flowering Roots/Winter Buds 0.87036 0.44 0.20 0.06 0.86 2011 Fruiting Roots/Winter Buds 0.27041 0.78 0.09 0.05 0.38 2011 Senescing Roots/Winter Buds 0.68752 0.88 0.04 0.02 0.73 2012 Flowering Roots/Winter Buds 5.40877 5.12 0.21 0.08 3.53 2012 Fruiting Roots/Winter Buds 4.91957 21.53 0.14 0.08 10.99 2012 Senescing Roots/Winter Buds 6.82564 21.46 0.09 0.05 16.14 2011 Flowering Stems/Leaves 1.08931 0.82 0.05 0.14 0.92 2011 Fruiting Stems/Leaves 3.92565 0.35 0.03 0.05 0.39 2011 Senescing Stems/Leaves 7.17018 0.20 0.06 0.09 0.71 2012 Flowering Stems/Leaves 3.53371 1.38 0.15 0.08 1.87 2012 Fruiting Stems/Leaves 10.2078 10.50 0.24 0.12 3.75 2012 Senescing Stems/Leaves 13.3963 12.20 0.19 0.26 7.00 99

Table 2.9. Rhynchospora knieskernii population measures (means and standard deviations) for ten transects at eight monitored sites on WGR, 2010-2014.

Mean SD Mean SD Mean SD Ye ar Site Fruiting Fruiting Total Total Height Height 2010 Double 1 17.33 14.72 28.00 18.41 2.90 1.35 2010 Double 2 16.00 11.83 28.33 19.46 3.33 1.96 2010 LSL 23.00 24.95 22.70 24.38 2010 Runway 5.00 7.82 6.67 10.40 2010 Sight Line 5.90 7.59 6.29 7.77 2011 Dead End 4.17 2.79 6.17 4.26 8.13 3.17 2011 Ditch 1 11.33 10.88 2011 Ditch 2 6.00 4.00 12.17 7.49 8.89 4.74 2011 Double 1 6.17 7.78 9.67 8.87 6.95 5.09 2011 Double 2 8.67 8.31 11.67 10.05 8.15 2.75 2011 LSL 0.10 0.32 15.30 15.47 13.46 3.67 2011 Road 5.00 6.26 7.17 5.71 5.87 2.71 2011 Runway 8.33 3.56 8.67 3.93 18.86 5.39 2011 Sight Line 6.00 5.97 9.00 7.64 10.16 4.78 2011 Slope 0.50 0.55 2.67 1.97 4.78 3.09 2012 Double 1 1.83 1.94 7.83 5.49 6.94 3.97 2012 Double 2 0.50 1.22 3.00 3.35 2012 LSL 8.67 6.12 11.17 5.71 10.57 3.39 2012 Road 0.00 0.00 0.17 0.41 2012 Runway 9.83 6.52 13.33 8.96 15.02 8.56 2012 Sight Line 4.50 4.72 5.67 4.46 2012 Slope 0.00 0.00 0.17 0.41 2013 Dead End 7.17 8.80 7.67 9.20 10.87 4.04 2013 Ditch 2 1.00 1.55 1.83 2.86 2013 Double 1 27.83 16.51 31.33 18.06 19.76 7.42 2013 Double 2 17.83 12.14 18.50 11.64 21.59 8.93 2013 LSL 10.33 7.76 10.33 7.76 2013 Road 13.00 6.78 13.33 6.53 13.40 3.16 2013 Runway 6.50 4.18 7.50 4.04 16.80 4.06 2013 Sight Line 9.50 8.26 9.67 8.09 15.69 6.50 2013 Slope 5.17 3.82 5.33 3.61 20.05 8.55 2014 Dead End 4.83 3.37 11.17 10.55 2014 Ditch 2 33.00 54.44 2014 Double 1 1.67 2.73 5.50 5.79 2.27 0.32 2014 Double 2 3.83 5.08 6.00 6.84 2014 LSL 5.33 3.44 8.17 2.48 12.53 4.65 2014 Road 1.17 1.17 2.50 2.51 2014 Runway 5.33 3.27 6.33 2.94 10.68 3.23 2014 Sight Line 5.67 5.16 5.83 5.04 2014 Slope 0.33 0.52 0.33 0.52

100

Table 2.10. Measures of light availability (means and standard deviations) for ten transects at eight monitored sites on WGR, 2011-2013.

Mean SD Mean SD Mean SD Mean SD Open Open Direct Direct Direct Diffuse Direct Diffuse Ye ar Month Site Canopy Canopy Light Light Light Light Light Light (%) (%) (%) (%) (%) (%) (%) (%) 2010 10 Sight Line 46.08 19.03 57.67 17.89 56.19 24.42 56.75 20.89 2011 10 Dead End 39.83 4.37 46.44 14.76 57.90 6.13 55.17 3.47 2011 10 Ditch 1 49.43 6.45 44.06 13.81 65.80 9.87 54.93 11.27 2011 10 Ditch 2 42.00 11.25 44.00 22.42 54.94 15.69 49.47 15.95 2011 10 Double 1 37.61 11.50 43.58 18.79 53.19 15.94 48.38 16.42 2011 10 Double 2 39.66 3.14 66.49 7.82 56.58 4.95 61.53 4.99 2011 10 Road 52.70 1.41 52.38 2.71 67.50 3.46 59.94 2.65 2011 10 Runway 91.03 1.03 99.65 0.11 97.43 0.47 98.51 0.29 2011 10 Sight Line 39.13 11.70 45.68 12.25 47.92 18.32 46.60 14.61 2011 10 Wet 35.03 1.51 35.00 9.67 48.43 4.55 42.55 5.77 2013 8 Dead End 40.10 2.43 56.49 3.21 58.37 3.48 57.41 2.01 2013 8 Double 1 62.57 2.57 77.10 7.07 80.54 3.43 78.82 5.23 2013 8 Double 2 60.73 4.21 79.67 6.12 77.79 4.76 78.73 5.15 2013 8 LSL 52.47 1.63 68.86 2.02 70.88 2.10 69.87 1.93 2013 8 Runway 90.11 1.56 99.74 0.09 97.44 0.53 98.59 0.29 2013 8 Sight Line 39.36 13.05 38.61 19.82 48.34 19.81 43.47 19.31 2013 10 Dead End 44.08 2.81 65.04 5.91 59.66 4.06 62.35 3.56 2013 10 Double 1 62.80 1.32 72.80 8.68 78.92 1.75 75.86 5.04 2013 10 Double 2 63.33 3.90 77.38 4.81 79.80 3.38 78.59 2.48 2013 10 LSL 59.69 1.38 71.34 3.48 77.02 1.39 74.18 2.42 2013 10 Road 63.04 2.37 69.14 8.39 75.12 2.79 72.13 4.70 2013 10 Runway 94.46 0.87 99.90 0.11 98.55 0.28 99.23 0.18 2013 10 Sight Line 51.14 9.88 67.14 10.70 58.70 15.74 62.92 12.89 2013 10 Wet 43.73 2.90 62.46 6.34 56.86 3.90 59.67 3.52

101

Table 2.11. Soil moisture results (means and standard errors) for eight monitored sites on WGR, 2011- 2013.

Mean SD Mean SD Ye ar Month Site Moisture Moisture Ye ar Month Site Moisture Moisture (%) (%) (%) (%) 2011 August Dead End 0.13 0.02 2012 August Dead End 0.14 0.06 2011 August Ditch 0.16 0.02 2012 August Double 0.15 0.02 2011 August Double 0.13 0.02 2012 August LSL 0.13 0.02 2011 August Road 0.15 0.03 2012 August Runway 0.11 0.03 2011 August Runway 0.12 0.02 2012 August Sight Line 0.14 0.05 2011 August Sight Line 0.15 0.02 2012 September Dead End 0.20 0.05 2011 August Slope 0.16 0.02 2012 September Double 0.15 0.02 2011 November Dead End 0.14 0.01 2012 September LSL 0.14 0.02 2011 November Ditch 0.14 0.04 2012 September Runway 0.13 0.02 2011 November Double 0.15 0.01 2012 September Sight Line 0.20 0.04 2011 November LSL 0.09 0.03 2012 November Dead End 0.16 0.03 2011 November Road 0.13 0.01 2012 November Double 0.15 0.02 2011 November Runway 0.14 0.01 2012 November LSL 0.14 0.05 2011 November Sight Line 0.15 0.03 2012 November Runway 0.13 0.03 2011 November Slope 0.13 0.04 2012 November Sight Line 0.16 0.04 2012 March Dead End 0.21 0.06 2013 August Dead End 0.12 0.03 2012 March Ditch 0.21 0.08 2013 August Double 0.12 0.02 2012 March Double 0.16 0.01 2013 August Runway 0.08 0.03 2012 March LSL 0.12 0.01 2013 August Sight Line 0.13 0.01 2012 March Road 0.17 0.03 2013 September Dead End 0.11 0.03 2012 March Runway 0.15 0.03 2013 September Double 0.09 0.03 2012 March Sight Line 0.19 0.03 2013 September LSL 0.06 0.01 2012 March Slope 0.15 0.00 2013 September Runway 0.05 0.02 2012 May Dead End 0.17 0.06 2013 September Sight Line 0.07 0.02 2012 May Ditch 0.15 0.04 2013 November Dead End 0.11 0.01 2012 May Double 0.13 0.01 2013 November Double 0.28 0.40 2012 May LSL 0.10 0.04 2013 November LSL 0.10 0.03 2012 May Road 0.11 0.01 2013 November Runway 0.07 0.01 2012 May Runway 0.07 0.05 2013 November Sight Line 0.10 0.02 2012 May Sight Line 0.15 0.04 2012 May Slope 0.14 0.02

102

Table 2.12. Monthly precipitation data (means only) from WGR weather tower (2010-2013) and Oswego weather station (2014).

Precipitation (cm) Month 2010 2011 2012 2013 2014 January 6.48 6.45 5.05 5.44 5.64 February 23.37 5.77 4.34 9.52 10.90 March 26.26 11.56 3.78 12.09 10.62 April 7.06 10.52 7.14 4.98 11.35 May 17.73 5.82 8.23 6.76 5.69 June 10.16 4.72 11.35 15.85 5.82 July 15.85 11.35 9.22 10.26 15.77 August 8.48 13.97 13.00 9.96 18.19 September 21.84 8.43 23.01 5.49 11.73 October 19.18 8.74 15.11 18.80 8.00 November 9.42 8.56 3.23 11.99 5.69 December 4.78 8.66 16.48 32.61

Table 2.13. Monthly air temperature data (means only) from WGR weather tower, 2010-2013.

o Temperature ( C) Month 2010 2011 2012 2013 January 0.27 -1.26 3.73 2.40 February 0.05 3.50 4.88 1.85 March 8.64 5.90 10.30 4.37 April 13.61 12.76 12.76 11.57 May 18.45 18.04 18.88 16.95 June 24.09 22.97 21.74 22.38 July 26.69 26.08 25.77 25.82 August 24.14 23.82 24.48 22.65 September 19.72 22.67 20.25 18.75 October 13.05 13.99 14.93 15.14 November 7.58 10.92 6.46 7.08 December -0.63 6.46 6.53 4.22

103

Chapter 3: Interactions of Rhynchospora knieskernii with Associated Species and Impacts on Community Assembly

Abstract

The establishment of diverse plant communities in disturbed nutrient-poor systems is of

particular interest to ecologists because it offers opportunities for the study of plant

interactions under conditions of both resource and dispersal limitation. The study of rare

plant interactions with associated species that may be more dominant can offer insight

into the processes by which communities assemble. In this study I tested the hypothesis

that rarity of the endemic sedge Rhynchospora knieskernii was a function of diminished

competitive ability often associated with rare plants by comparing several measures of R.

knieskernii plant performance with those of associated species within the habitat. I

examined the potential influence of several resources (soil N and P availability, canopy

cover, and soil moisture) on plant height, root length, biomass, root:shoot ratios, and

plant N and P concentrations. I found few significant differences in plant measurement

or in nutrient concentrations, but did find some differences in biomass dominance of

microsites. Rhynchospora knieskernii was dominated sites that were prescribed burned in

the current or previous year. The remarkable uniformity of several biotic indices among

species inhabiting sites favorable to R. knieskernii establishment suggests that abiotic

constraints limit establishment on these sites to plants of similar functional types with

low metabolic needs that achieve dominance through fortuitous capture of annual

available space rather than the development of traits associated with superior competitive

ability. The changes in dominance related to prescribed burning have relevance to the 104

development of effective management plants, which should consider natural fire return intervals as a key element of R. knieskernii management and recovery.

3.1 Introduction

Mechanisms of plant community assembly and the maintenance of species diversity are critical issues that have occupied ecologists for decades (Fägerstrom 1988;

Grace and Tilman 1990; Chesson 2000; Silvertown 2004; Tilman 2004; Rosindell et al.

2011; HilleRisLambers 2012). Part of the fascination with community assembly stems from the paradox that interspecific interactions should eventually lead to a monoculture of the most competitive species, yet many habitats are highly diverse and often includes species that superficially appear to be functionally similar (Silvertown et al. 1999;

Rosindell et al. 2011). Niche theory is a fundamental ecological paradigm used to explain how competitive interactions facilitate plant coexistence (Silvertown 2004). In its essence this theory posits that biodiversity exists because no plant can be equally competitive in all niches. However, plants compete for the same resources within a limited space using a small range of adaptations to acquire resources, which would seem to require a larger number of niches than actually exist (Chesson 2000; Silvertown 2004).

Gleeson and Tilman (1990) developed the resource ratio theory, which suggested that multiple plants could compete differentially for resources as different resources become limiting. However, experimental results by other investigators did not always support theoretical predictions (Rubio et al. 2003). The complexity of ecological interactions is often a confounding element in an effort to develop a unified theory of biodiversity. For example, Grubb (1977) argued that differences in age class requirements may be an important part of niche differentiation 105

3.1.1 Lottery Model

In contrast, lottery models suggested a random process for species co-existence that gave the advantage to species that were the first to arrive in an unoccupied space within a habitat (Fägerstrom 1988). Neutral theory could be seen as a type of lottery model (Rosindell et al. 2011). Classic neutral theory is a mathematical model predicting the spatial distribution of species. Species are assumed to be neutral in that their functional traits do not determine their distribution at the community level; all species within the community have an equal chance of developing within any particular habitat

(Rosindell et al. 2011). Dispersal limitation controls species arrival at suitable habitat and this creates biodiversity, as species will occupy habitats more or less randomly depending on species dispersal distance in relation to habitat availability (McGill et al.

2006; Rosindell et al. 2011). The lack of a mechanism for environmental filtering seemed at odds with most ecological studies of community interactions, leading to some modifications of the theory (McGill et al. 2006; Rosindell et al. 2011). Models attempting to reconcile niche and neutral theory suggest that relaxing some of assumptions of neutral theory to allow for niche influences may better explain community assembly processes (Tilman 2004).

3.1.2 Community Assembly in Nutrient-Poor Communities

Not all plant interactions are necessarily competitive and recent studies offer support for the ways in which plants may facilitate rather than eliminate coexistence

(McIntyre and Fajardo 2014). The lack of agreement on how diversity is maintained suggests that more empirical studies of community assembly and community interactions are needed to help resolve this important issue. Grime (1977) considered nutrient-poor 106

communities to be examples of habitats where competitive interactions were less

important than stress and disturbance tolerance.

3.1.3 Disturbance and Community Assembly

The establishment of diverse plant communities in disturbed nutrient-poor

systems is of particular interest to ecologists because it offers opportunities for the study

of plant interactions under conditions of both resource and dispersal limitation. Rare and

endemic plants are often members of marginal communities that are nutrient-deficient

(Wassen et al. 2005; Fujita et al. 2014). In cases where nutrient-poor plant communities are part of disturbance-dependent ecosystems, disturbance often acts to alter community relationships, facilitating opportunities for germination and colonization that may increase rare plant densities (Walck et al. 1999; Safford and Harrison 2004; Reier et al.

2005). Rare plants are often seen as poor competitors that benefit from disturbance because it serves as a release from competitive exclusion if plants are able to colonize newly disturbed habitats. Yet there are only a few studies of rare plant community interactions; their results indicate rare plants may in fact be able to compete with more widespread species (Rabinowitz et al. 1984; Imbert et al 2012). However, these are experimental studies that only examine a limited number of interactions. There are few field studies of rare plant interactions with potential competitors (Keddy et al. 2002). In my study I examined the interactions between a rare endemic, Rhynchospora knieskernii,

and the associated species within the plant community. There have been no experimental

studies of R. knieskernii, but local reports frequently cite light competition as a factor in

its rarity (USFWS 2008). I hypothesized that R. knieskernii was rare because it was less

competitive with other species within the habitat (referred to below as associated species) 107

for one or more resources (light, water, nutrients, space). Although increased canopy

cover is often cited as a cause for R. knieskernii population declines, it does not explain

competitive interactions within the early successional sere itself. I hypothesized that

because it lives in a nutrient-poor system, R. knieskernii was mainly outcompeted for

nutrients by associated species.

3.2 Methods

3.2.1 Associated Species Identification

In 2012 and 2013 I collected all plant biomass in early October from five 0.0625

m2 quadrats placed at randomly selected patches within the five monitored sites used for

soil and R. knieskernii resource allocation sampling. Plants were identified to species

when possible; a few plants could only be identified to genera or family due to a lack of

reproductive material. Most plants in these communities shared similar phenologies.

They began growing in late spring, flowered in summer, fruited in fall, and senesced at the beginning of winter. Thus, most plants were sampled when they were in the same part of the reproductive cycle as R. knieskernii. However, a few herbaceous species were missed because of earlier phenologies. Many plants made up a very small part of the biomass and were excluded from several of the analyses. The six plant genera that were consistently compared were Andropogon, Aristida, Dicanthelium, Eleocharis,

Muhlenbergia and Rhynchospora. Different species from these genera were combined together to facilitate analysis, but species were also analyzed separately for plant measurements and nutrient concentrations. Species from the genus Dicanthelium were not identified to species except for D. wrightianum. 108

3.2.2 Study Sites

Warren Grove Range (WGR) is an active air-to-ground gunnery range operated

by the 177th Fighter Wing of the New Jersey Air National Guard and located in

Burlington County, New Jersey (39o41’48”N; 79o24’0”W). All sites were originally selected for their relatively dense populations of R. knieskernii in 2010 (Figure 3.1). All sites were all located within or adjacent to pitch pine lowland habitat type (sensu

McCormick 1979). The predominant soil types (Woodmansie, Lakehurst, and

Lakewood) are part of the Woodmansie-Lakehurst association (USDA 1971). These soils are characterized by low pH, low CEC and the presence of high quantities of aluminum (Tedrow 1986). Upland soils in the surrounding forest matrix are sandy and droughty, but the low topography and high water table at these sites contributes to the presence of moist soils that are often silty. Site descriptions are in Table 1.1.

Site conditions were ideal for the study of community interactions. Sites consisted of distinct gaps within a forested matrix that were clearly delimited and maintained by differences in soil moisture resulting from the low topography within the habitat and proximity to wetland complexes or bog seeps (USFWS 2008). The small size of the community, which was spatially differentiated from the surrounding community and the functional similarity of species within the associated plant community, simplified the process of comparisons between species.

3.2.3 Soil Sampling

See Chapter 2 for soil sampling methods

109

3.2.4 Plant Measurements and Nutrient Concentrations

I randomly selected five plants from each species from the group and measured root and shoot lengths and masses; then I dried plants at 70oC for 48 hours. I separated

plant material into belowground (roots, winter buds) and aboveground (stems, leaves,

reproductive structures) biomass and weighed it.

I separated plants into two groups for nutrient analysis: one composed solely of R.

knieskernii structures and the other of pooled associated species structures. I cut plant

material for R. knieskernii into fine pieces and weighed it out to a total of 0.25 g. For the

associated species, I cut all plant material into fine pieces and then mixed it thoroughly

(separately for aboveground and belowground material) before weighing out a total of

0.25 g to be used for analysis; I used the total amount available if there was insufficient

plant material for analysis. This method was used to preserve the species biomass

proportions found in each plot. In most plots one species constituted 50% or more of the

biomass, which meant that each sample was highly representative of the most dominant

species, and that species would have the most influence on the nutrient concentration for

that sample. It was not possible to analyze each species separately, due to a lack of

sufficient plant material for individual species. I analyzed the dried plant material for

total phosphate PO4-P, total nitrogen (TN), and total carbon (TC). I digested plant

material in a selenium catalyzed, sulfuric acid–hydrogen peroxide solution, using a

Tecator block digestor, with subsequent colorimetric PO4-P analysis and analysis of TN

and TC using a Shimazdu TOC-V analyzer (Allen 1989). All plant nutrients are reported per milligram dry mass except when otherwise noted. 110

I made direct comparisons between aboveground and belowground nutrient

concentrations for the pooled associated species and for R. knieskernii. I used the

nutrient concentrations from each plot to estimate plant nutrient concentrations for

individual species. While this method did not reflect the variability found in individual

species, it did make possible comparisons at a finer scale. I also took the known mass of the pooled associated species biomass and the R. knieskernii biomass from each plot and

multiplied it by the N and P concentrations for each plot to calculate the total plant N and

P per m-2 for each site.

3.2.5 Community Relationships

I used the vegan package in R (Oksanen et al. 2015) to conduct two principal

component analyses to examine potential relationships between species and the following

variables: site, plant dry mass, root:shoot ratio (dry mass), N and P concentrations in the

associated species and in R. knieskernii, soil extractable nutrient concentrations (TC, TN,

P, NH4, NO (nitrates+nitrites), total inorganic nitrogen (TIN), total organic nitrogen

(TO), soil C:N ratio, soil N:P ratio, soil volumetric moisture (May, August, mean), and

canopy cover. One analysis used all species and the other only the most widespread

species to make comparisons more tractable. I used the results of the principal

component analysis to examine potential gradients and to select variables for statistical

analysis.

I compared species richness between sites and years and examined changes in

species composition and the number of species found at one or more sites. I determined 111

which species had the greatest dry biomass in each plot and characterized that species as dominant.

3.2.6 Soil Water Potential

I measured the soil water potential of the five sites I used for soil sampling, along with a mesic and a xeric site in proximity to known R. knieskernii sites, and an upland xeric site where R. knieskernii is not known to ever occur. I pulled three soil cores from each site to a depth of three centimeters using a bulb corer. I then created a dry hysteresis curve for each sample (modified from Augé et al. 2003) I dried samples at 70oC for 48 hours, followed by sieving with a 4 mm mesh standard soil sieve. I prepared 10-gram samples with water content ranging from field capacity (0.0 MPa) to permanent wilting point (-1.5 MPa) by adding gradually increasing increments of DI water to soil samples and mixing thoroughly. I used a Decagon WP4-T dewpoint potentiometer (Decagon

Devices, Inc., Pullman, WA) to determine water potential and fitted curves using the

MATLAB curve fitting toolbox. The exponential function y = ae(b*x) where a and b were fitting parameters, provided the best fit for all curves except for the dry upland site

(“Sand”), where a power function provided a better fit (a*xb). I sampled soil every two weeks at the same seven sites sampled for water potential from April 2013 to November

2013. I determined soil moisture gravimetrically and then compared the results to field capacity and permanent wilting point as determined by the standardized dry hysteresis curve for each site.

112

3.2.7 Canopy Cover

I took hemispheric photographs above each quadrat used in population sampling

sites in August 2013 and October 2013 on partly cloudy days between 10 am and 2 pm

using a Nikon Coolpix 4500 camera with an FC-E8 fisheye converter attached to a tripod approximately 1 meter in height above the ground. I centered and leveled the tripod over the midpoint of the subplot area as established by the quadrat and used a compass to determine north. I attached fiber optic strands to the north and south points of the lens as determined by a baseplate compass (two strands for north; one for south) and recorded the time and number of the photograph for later use in processing photographs. I prepared photographs for analysis using the software program Gap Light Analyzer

Version 2.0, which has been developed and used specifically for purposes of determining canopy cover and other factors associated with the light environment (Frazer et al. 1999).

I first configured the photograph by identifying north; then I created a grey-scaled image which I edited to screen out darker pixels which sometimes appeared in open-canopy areas after processing. Once the image was edited, the software produced four assessments: percent open canopy, percent direct light, percent diffuse light, percent total light (direct and diffuse).

3.2.8 Weather

I used data from a weather tower located on WGR to find monthly means for air

temperature minima and maxima and 24-hour precipitation totals from 2011 to 2013.

The weather tower recorded data every 30 minutes continuously.

113

3.2.9 Data Analysis

I performed one-way ANOVAs and linear regressions on a range of plant variables, including root and shoot length and mass, root:shoot ratios, and N and P nutrient concentrations to compare potential species differences and to compare R. knieskernii with pooled associated species variables. I also compared changes to biomass among sites and species. I conducted two-way ANOVAs to determine potential interactions between site variables among sites and between years. I used the results of the principal component analysis to focus on canopy, soil moisture, and inorganic nitrogen as potential explanatory variables for differences among sites and species. I conducted one-way and two-way ANOVAs and linear regressions to test for interactions between species and these potential explanatory variables. Summary descriptive statistics (mean, SD, SE) were calculated for all variables. I conducted all statistical analyses in R (R Core Development Team 2015).

3.3 Results

3.3.1 Associated Species

There were approximately 44 species found over two years (2012 to 2013) at the monitored sites, but many belonged to the same few genera (Tables 3.1 and 3.2). Almost all species were graminoids belonging to the Cyperaceae or Poaceae families. There was one member of the Juncaceae, Juncus pelocarpus. Members of the Cyperaceae family included the genera Cyperus, Eleocharis, and Rhynchospora. Members of the Pocaceae family included Andropogon, Aristida, Muhlenbergia, and Panicum. There were also a few herbaceous species (Hypericum, Diodia teres, Hieracium), including some 114

carnivorous plants (Drosera, Utricularia). There were two genera representing non-

vascular plants (Sphagnum and Polytrichum). Trees and shrubs were present only as seedlings. There were no lichens in sample plots. A few species that were observed earlier in the year were not found in sampled plots (e.g., Viola spp.) and some species that

I often observed at sites were seldom sampled (Hypericum gentianoides and Diodia teres); this is probably due to plots being sampled only once in October. All species nomenclature followed Gleason and Cronquist (1991) or in the case of recent taxonomic changes, Flora of North America (1993+).

Species characteristics were similar for most plants at the five monitored sites.

Plants were generally characterized by herbaceous habit, low plant height, shallow root systems, long thin leaves, and slender stems. See Figure 3.2 for illustrations of several characteristic and common species found in plots. Most plants were perennials with a C3 metabolic pathway, although it was not always possible to determine C3 or C4 status.

Several species were rare, threatened or endangered in at least one state. One species

(Dicanthelium wrightianum) is a New Jersey state species of concern. See Table 3.3 for

data on plant conservation status, metabolic pathway, and life history status.

3.3.2 Soil Sampling

There were few relationships between species and soil variables. There was a

2 strong relationship between site NH4 soil concentration (g/m ) and whether a sites was

2 prescribed burned or not (F1,34 = 43.72, p < 0.0001,R = 0.55). There were significant

annual and site differences for soil N and P (Figures 3.3 and 3.4; Table 3.4). There was a

2 weak relationship between soil N:P ratio and plant N:P ratio (F1,34 = 7.67, p < 0.01, R =

0.16). There was also a positive relationship between soil extractable N and P and total 115

plant total N and P (mass x concentration) for both R. knieskernii and the associated

species, but only the relationship between soil P and plant P was significant (F15,20 = 2.40,

p < 0.03, R2 = 0.37). See Table 2.5 and Figures 2.1 to 2.4 for all soil nutrient

concentration results.

3.3.3 Plant Sampling

There were several differences in plant height, root length, aboveground biomass,

belowground biomass, and root:shoot ratios when all species were compared, but these

were related to broad differences in plant traits such as size at maturity (e.g.,

bunchgrasses are expected to be taller and more massive than carnivorous plants under all

circumstances) and thus did not form part of the analysis. Analysis of the six most

commonly encountered species (see Methods) found few statistically significant

differences for individual species when plant height, root lengths, and root:shoot ratios

were compared among sites and years (Figures 3.5 to 3.7; Tables 3.5 to 3.8) among any

of the six species. In all cases differences were specific to one site for one year. There

2 were no significant annual or site differences in root (F71,144 = 4.48, p = 0.12, r = 0.41) or

2 shoot (F72, 147 = 3.11, p = 0.30, r = 0.53) mass between species, except at the Double site,

where several species (Aristida sp., P. rigida, R. knieskernii) exhibited significant annual

differences in measured characteristics (Table 3.4). There were also no significant

differences in nutrient concentrations between R. knieskernii and the associated species

(Figures 3.8 and 3.9; Table 3.4). There was a significant difference between the species representing the dominant plant in the plot and plant P concentration (F1,133 = 4.39,

p < 0.05, R2 = 0.02). There were no significant differences between biomass or nutrient

concentrations for aboveground and belowground structures. 116

3.3.4 Community Relationships

The principal component analysis of the most commonly encountered species did

not indicate potential explanatory variables that might explain community composition.

Most soil variables appeared associated with spring moisture, while TIO and NH4

appeared associated with seasonal moisture (Figure 3.10). The variables which

appeared most closely related to the first principal component were canopy, total soil

extractable inorganic nitrogen, mean moisture (Figure 3.11), and moisture at permanent

wilting point and field capacity (Figures 3.12). The first axis explained approximately

27% of the variation, with soil variables and associated species nutrient concentrations

most strongly associated with the first axis (Figure 3.13). The second axis explained

approximately 20% of the variation. It was strongly associated with root and shoot mass

and more strongly associated with R. knieskernii nutrient concentrations than with those

of associated species. Canopy values were distinct from other explanatory variables

(Figures 3.10 and 3.13). There was no relationship between species and the first or

second principal component (Figure 3.13).

Most plots had a clearly dominant species that formed over 50% of the biomass

(Table 3.9). There were significant differences in dominant plant species and in biomass

for plants at sites based on whether they were prescribed burned within the current or

2 2 previous year (F2,42 = 3.46, p<0.05, R = 0.10 (species); F2,42 = 4.65, p<0.05, R =0.14

(biomass)). In comparing whether sites were burned the previous year, burned the

current year, or not burned, R. knieskernii was more likely to be the dominant plant and plant biomass was greater at plots that were taken from sites that were prescribed burned

(Figures 3.14). 117

There were few changes in species composition, richness or biomass between

2012 and 2013 (Tables 3.10 to 3.12). Few species were found at all five sites (Table

3.11). There were minimal changes in percent of site biomass for most species between

2012 and 2013, but only Aristida spp. and Andropogon spp. showed large changes (Table

3.12).

3.3.5 Water Potential and Soil Moisture

There was some microsite variation in water potential at all sites except Runway,

where all three samples produced very similar curves (Figures 3.15 to 3.22). Fitted

curves displayed a clear gradient of differing values for moisture levels at which sites

would reach permanent wilting point and field capacity (Figure 3.23). Both spring and

mean moisture values indicated a general gradient along sites (Figure 3.24).

3.3.6 Canopy Cover

There were no significant relationships between species and canopy cover (Table

3.4). There was a weak interaction between canopy values and P concentration (F3,163 =

3.12, p < 0.03, R2 = 0.04), with R. knieskernii showing slightly increased P concentrations at greater canopy openness.

3.4 Discussion

Natural field studies offer the opportunity to explore complex ecological

interactions that are difficult to replicate in traditional experimental settings (Fayle et al.

2015). I collected data through extensive sampling of a wide range of habitat and plant 118

variables which may provide interactions insights into potential competitive interactions and community assembly mechanisms for these NJPB habitats. Measures of height, root length, biomass, and nutrient concentrations did not differ between R. knieskernii and associated species across a range of sites, years, and potentially dominant species. These data did not provide support for my hypothesis that R. knieskernii is rare because it is a weak competitor compared to associated species in the same habitat. Nor did I find support for my hypothesis that associated species out-competed R. knieskernii for nutrients. Thus, this study suggested that the rarity of R. knieskernii was not due to weak competitive ability, but may have been related to abiotic qualities that limited the types of habitat in which most species can survive and in which small graminoid species could thrive. More broadly, my study indicated that competitive interactions may not have been as important as plant functional type and dispersal ability in assembling communities in nutrient-poor habitats (Grime 1977). These data also offered support for a lottery model of community assembly in extremely nutrient-poor habitats such as those encountered in the NJPB (Fägerstrom 1988).

Given the low soil concentrations of N and P in these systems and the low volume and quality of plant litter (see Chapter 2), nutrient limitation was likely to have the greatest impact on species composition and community assembly. Yet plants exhibited few or no differences in aboveground or belowground nutrient concentrations (Figures

3.8 to 3.9). In fact, the remarkable uniformity of several biotic indices among species inhabiting sites favorable to R. knieskernii establishment suggested that abiotic constraints determined the type of species that could establish in these nutrient-poor sites.

In particular, total soil nutrient availability and plant nutrient content was similar between 119

types (associated species versus R. knieskernii) for most of the five sites over two years

(Figures 3 and 4), suggesting that nutrient resource availability was so low that plant uptake was barely sufficient for plant metabolic needs. Thus, abiotic qualities filtered out plants with adaptations that promoted resource acquisition in favor of plants that conserved resources (Aerts and Chapin 2000). However, the similarity of abiotic qualities among sites, which were only minimally differentiated along light and moisture gradients (Figures 3.11, 3.12 and 3.24), suggested that all five sites should have been equally suitable for most or all of the graminoid species typically found in this habitat.

Yet the majority of species were found at only one of the five sites. Therefore seed dispersal may have played an important role (see below). Although moisture was not a limiting resource, differences in redox potential may have limited plant establishment

(Busch et. al. 2004). These habitats were characterized by fluctuating soil moisture conditions where ponding and flooding of microhabitats was common (USFWS 2008; M.

Sobel, pers. obs.). In fact, Silvertown et al. (1999) found niche differentiation along a hydrological gradient that was strongly related to differences in species ability to tolerate flooding versus drying.

In resource-poor communities storage will be a key element of resource allocation

(Millard 1988). Several of the other perennial plants (M. uniflora, Rubus sp., P. rigida, I. glabra, J. pelocarpus) invested considerable resources in storage structures such as rhizomes, which are at risk during fires, especially if they are intense. The disappearance of M. uniflora from Double in 2013 when it was dominant at some plots in

2012 was probably due to the intensity of the prescribed burn at that site. Rhynchospora knieskernii stored nutrients in the winter bud. These winter buds can survive even an 120

intense burn if they are in a sufficiently moist microhabitat (M. Sobel, pers. obs.).

Combined with its small size, the winter bud was thus less vulnerable than a network of

rhizomes, which may have given R. knieskernii an advantage is establishing on suitable

microsites after a site was burned. The decline in Aristida spp., (both species commonly

found at the sites are annuals), may have been related to differences in plant response to

prescribed burning (Figure 3.14; Table 3.9), as most other species did not exhibit similar

declines.

When all species were plotted along the axes of the PCA there was some

differentiation between Aristida spp. and R. knieskernii, which was not discernible when

I examined only the most commonly occurring species (Figure 3.13). Rhynchospora knieskernii only dominated at plots that were prescribed burned in the year of sampling or in the previous year, even though sites were originally selected because of high densities of R. knieskernii (Figure 3.14; Table 3.9). The perennial habit of R. knieskernii would be clearly advantageous because nutrient storage would enable it to develop more quickly

(Millard 1988). This strategy may also explain why species of Andropogon were also found across sites (Table 3.11). Drosera species were found at all sites excepted Double in 2013, which was the site of the intense burn; carnivorous species are less limited by soil nutrient availability. However, the Aristida species found in my plots are annuals.

Perhaps the decline in in Aristida biomass in 2013 was related to lower soil nitrogen availability, as reflected in plant nutrient concentrations (even though total soil nitrogen concentrations for October were higher in 2013).

My data suggested that similar functional traits enabled successful colonization of these habitats by a wide range of species, yet it was not clear why so many species were 121

found in only one habitat (Table 3.7). While these are considered early successional sites, most of them exhibited a relatively high degree of canopy cover (Table 2.10).

Nevertheless, light was unlikely to have been a limiting resource, as these habitats were usually in small or large gaps in a pitch pine lowland matrix or along roadsides in forested environments (USFWS 2008). Plants also exhibited similar height, root length, and root:shoot ratios, indicating that plants in these specific habitats were not light- limited and that there were no species differences in stress tolerance (Figures 3.5 to 3.7).

Grime (1977) considers stress tolerance and disturbance response to be more important than competitive ability in determining plant community interactions in nutrient-poor habitats. In this scenario, plants gain their advantage by arriving at a site first, but there are so few resources in the habitat that plants do not benefit by developing traits that increase their ability to uptake resources. Thus stress-tolerant plants share certain traits, such as slow growth, low height, and low allocation to reproduction, while plants classified as ruderals show rapid growth and high allocation to reproduction

(Grime 1977). My study suggests that plants in these habitats cannot be comfortably placed in either category, but showed an overlap between stress-tolerant and ruderal plants. Yet these plants did exhibit traits that could be considered adaptations to low nutrient availability. High root:shoot ratios are usually considered signs of stress, yet plants in my study had low root:shoot ratios (Figure 3.7). Plants also grew to similar heights and had similar root lengths, suggesting a tightly controlled allometric allocation of nutrients to plant structures to maximize growth and reproductive potential in this low- nutrient environment (Weiner 2004). Density-dependent interactions may also play a role and this could explain changes in plant biomass at sites where disturbance may have 122

led to nutrient pulses and greater availability of microsites. Given that plots were almost

invariably dominated by one species I infer that microsite resource availability influenced plant success, enabling a particular species to dominate that microenvironment.

However, of 44 different species found at five sites over two years, only eight

dominated plots more than once (Table 3.9). Plant dominance often changed annually at

sites, even those that were not burned (Tables 3.9 and 3.10). These data suggested

colonization exerted a strong influence on plant establishment. As sites were widely

separated by both forests and highly altered habitats (Figure 3.1), plants may have been

limited by several factors in their ability to reach suitable habitat. These were small

seeded plants that were probably dispersed by wind (Grubb 1977). Adhesive dispersal

may be more effective for long-distance dispersal and is often found in disturbed habitats

(Sorenson 1986). The propagules of Eleocharis and R. knieskernii exhibit structures that facilitate adhesive dispersal and many Cyperaceae utilize this dispersal mechanism (Leck and Schütz 2005).

Most rare plants in P-limited wetlands allocate few resources to reproduction in order to conserve P (Fujita 2014), but R. knieskernii allocated substantial resources to reproduction and a single plant produced as many as 500 achenes. This strategy could explain the ability of R. knieskernii to establish at sites widely separated in space and to achieve microsite dominance in favorable years. Presumably this strategy requires a trade-off if rare plants normally conserve P; in the case of R. knieskernii, the trade-off may be reduced allocation to growth. It is one of the smallest species in the genus

(Gleason and Cronquist 1990). 123

When I examined plots within sites for differences in biomass, I found that no one

species dominated every plot in every habitat or even every plot in every site (Table 3.9).

Yet plots were, with few exceptions, dominated by one species rather than a pair or group of species with similar biomasses (Table 3.9). This could have indicated some microsite

variability within sites. Reich et al. (2003) in a broad study of plant functional traits

found that plant traits within a site varied at least as much as traits across sites (Reich et

al. 2003). The authors suggested this result was due to the large range of niches produced

by variation in microsites and disturbance history. However, in my study the sites were

very small and widely separated, where I would expect there to be less niche

differentiation within than between sites. Thus, differences in species dominance among

plots could have indicated differences in germination success related to seed rain and

seed banking (Grubb 1977). Germination is affected by microsite qualities, but given

that all seeds do not germinate, the number of seeds dispersed to a site will also affect

species germination success and consequent adult density (Grubb 1977). I did not study

reproductive output for any of the species except R. knieskernii, which exhibited

substantial allocation of resources to reproduction. However, observation of these plants

indicated many produce large quantities of small seeds. Differences in seed rain as well

as dispersal limitation may ultimately limit colonization potential (Grime 1977).

My study offered support for lottery theory (Fägerstrom 1988) as a mechanism for

community assembly of these small, marginal habitats. However, my data also

suggested that plant functional traits contributed to species distribution on the landscape

scale, as sites were almost exclusively inhabited by graminoids and small herbaceous

annuals. Tilman (2004) recently tried to reconcile some of the conflicts between neutral 124

and niche theory. I did not see niche differentiation as modeled in his work, but some site measures indicated that there may be room for niche differentiation in traits related to hydrological tolerance (Silvertown 1999). The lack of agreement on how diversity is maintained suggests that more empirical studies of community assembly and community interactions are needed to help resolve this important issue. 125

3.5 Literature Cited

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Chesson, P. 2000. Mechanisms of maintenance of species diversity. Annual Review of Ecology and Systematics, 343-366.

Fagerström, T. 1988. Lotteries in communities of sessile organisms. Trends in Ecology & Evolution, 3:303-306.

Fayle, T. M., Turner, E. C., Basset, Y., Ewers, R. M., Reynolds, G., & Novotny, V. (2015). Whole-ecosystem experimental manipulations of tropical forests. Trends in ecology & evolution.

Flora of North America Editorial Committee, eds. 1993+. Flora of North America North of Mexico. 16+ vols. New York and Oxford. Vol. 1, 1993; vol. 2, 1993; vol. 3, 1997; vol. 4, 2003; vol. 5, 2005; vol. 7, 2010; vol. 8, 2009; vol. 19, 2006; vol. 20, 2006; vol. 21, 2006; vol. 22, 2000; vol. 23, 2002; vol. 24, 2007; vol. 25, 2003; vol. 26, 2002; vol. 27, 2007; vol 28, 2014; vol. 9, 2014.

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Figures

Figure 3.1. Sites where associated species and R. knieskernii plants were collected on WGR in 2012 and 2013. Red circles pinpoint site locations.

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Andropogon Aristida Dicanthelium Drosera Eleocharis glomeratus longespica wrightianum filiformis microcarpa

Hypericum Muhlenbergia Rhynchospora Sphagnum Sphagnum gentianoides uniflora knieskernii cyclophyllum tenerum

Figure 3.2. Representative examples of species associated with R. knieskernii sites on WGR. Note the similarity in form among species; most have slender stems, are low in height, and are graminoid functional types.

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Figure 3.3. Above: Total plant nitrogen (mass x concentration x site area) for five study sites on WGR in October 2013. AllRk = R. knieskernii concentrations; AllSite = sum of R. knieskernii and the associated species; ASA= associated species aboveground; ASB = associated species belowground. Below: Total soil nitrogen availability (mass x concentration x site area) for the same five sites on WGR in October 2013.

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Figure 3.4. Above: Total plant phosphorus (mass x concentration x site area) for five study sites on WGR in October 2013. AllRk = R. knieskernii concentrations; AllSite = sum of R. knieskernii and the associated species; ASA= associated species aboveground; ASB = associated species belowground. Below: Total soil phosphorus availability (mass x concentration x site area) for the same five sites on WGR in October 2013.

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Figure 3.5. Comparisons of plant height for several commonly occurring species at five study sites on WGR from 2012 to 2013. Results represent mean ± 1 SEM for five samples of 0.0625 m2 subplots at each site.

133

Figure 3.6. Comparisons of root length for several commonly occurring species at five study sites on WGR from 2012 to 2013. Results represent mean ± 1 SEM for five samples of 0.0625 m2 subplots at each site.

134

Figure 3.7. Comparisons of root:shoot ratios (mass) for several commonly occurring species at five monitored sites on WGR from 2012 to 2013. Results represent mean ± 1 SD for five samples of 0.0625 m2 subplots at each site.

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Figure 3.8. Comparison of total phosphorus (mass x concentration) between pooled associated species (AS) and R. knieskernii (Rk). Box and whisker plot shows median (line), first and third quartiles (box), and points within 1.5 interquartile range (IQR) of the lower and upper quartiles (whiskers). Dots represent outliers.

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Figure 3.9. Comparison of total plant nitrogen (mass x concentration) between pooled associated species (AS) and R. knieskernii (Rk). Box and whisker plot shows median (line), first and third quartiles (box), and points within 1.5 interquartile range (IQR) of the lower and upper quartiles (whiskers). Dots represent outliers.

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Figure 3.10. Results of principal component analysis using the most commonly encountered species. Variables that may not offer clear identification are: LSM, late spring moisture; MM, mean seasonal moisture, AM, August moisture, PWP, permanent wilting point; FC, field capacity; ASP, associated species phosphorus; ASN, associated species nitrogen.

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Figure 3.11. Relationship between the first principal component axis and several potential explanatory variables. There is no relationship between the first axis and plant species (upper left), but there appears to be a gradient related to soil moisture, inorganic nitrogen, and canopy cover.

139

Figure 3.12. Relationship between first principal component axis and measures of plant water potential. FC = field capacity, which is the point at which the micropores in the soil pore water are filled and plant roots can uptake water under ideal conditions. PWP = permanent wilting point, the point at which water adheres so tightly to soil particles that plant roots cannot uptake the existing soil pore water. 140

Figure 3.13. Results of principal component analysis using all species. Circles indicate separation between Aristida sp. (green) and R. knieskernii (blue). Variables that may not offer clear identification are: LSM, late spring moisture; MM, mean seasonal moisture, AM, August moisture, PWP, permanent wilting point; FC, field capacity; ASP, associated species phosphorus; ASN, associated species nitrogen. Above: scree plot of principal components (left) and eigenvalues for each of the factors (right). 141

Figure 3.14. Comparison of percent biomass of the most dominant species for each plot sampled on WGR in 2012 and 2013 based on whether plots were located in sites that had been prescribed burn the previous year, the current year, or not burned at all. Data pooled for both years for sites that were not prescribed burned.

142

Figure 3.15. Fitted curves for water potential for Dead End site at WGR.

Figure 3.16. Fitted curves for water potential for Double site at WGR. 143

Figure 3.17. Fitted curves for water potential for LSL site at WGR.

Figure 3.18. Fitted curves for water potential for Runway site at WGR. 144

Figure 3.19. Fitted curves for water potential for Sight Line site at WGR

Figure 3.20. Fitted curves for water potential for Sand site at WGR. This is an upland site where we would not expect to ever find R. knieskernii 145

Figure 3.21. Fitted curves for water potential for Dry site at WGR This was a xeric site located in proximity to known R. knieskernii populations, but which did not have any R. knieskernii. Little vegetation grew at the site.

Figure 3.22. Fitted curves for water potential for Wet site at WGR. This was a hydric site located in proximity to known R. knieskernii populations, but which did not have any R. knieskernii. The site is a wet ditch filled with decaying plant matter. 146

Figure 3.23. Combined curves for all sites showing moisture gradient. This gradient is also reflected in soil moisture readings taken from April to September 2013 (Chapter 2).

147

Figure 3.24. Moisture gradient.

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Tables

Table 3.1. All identified species collected from 0.0625 m2 plots on WGR, 2012. Species richness shown by site.

2012 Species Dead End Double LSL Runway Sight Line All Amphicarpum amphicarpon x Andropogon x x x x x x Aristida x x x Aristida dichotoma x Aristida longespica x x x x Comptonia peregrina x Cyperaceae x x x x Cyperus dentatus x Dicanthelium x x x x Dicanthelium wrightianum x Diodia teres x Drosera filiformis x Drosera intermedia x x Eleocharis microcarpa x x Eleocharis tenuis x x Eleocharis tuberculosa x Hypericum canadense x x x Hypericum gentianoides x x Ilex glabra x x x Muhlenbergia uniflora x x Panicum x Panicum verrucosum x Paspalum x Pinus rigida x x x Poaceae x x x x x x Polytrichum commune x Rhynchospora knieskernii x x x x x x Rubus hispida x Sphagnum x Sphagnum tenerum x x x Sphagnum cuspitdatum x x Sphagnum cyclophyllum x x Utricularia subulata x x Xyris sp. x x Xyris caroliniana var. caroliniana x 149

Table 3.2. All identified species collected from 0.0625 m2 plots on WGR, 2013. Species richness shown by site.

2013 Species Dead End Double Runway Sight Line

Amphicarpum amphicarpon x Andropogon x x x x Andropogon glomeratus x Andropogon virginicus x Aristida x x x x Aristida dichotoma x x x x Aristida longespica x x x Aristida longespica var. genticulata x Cyperaceae x

Cyperus x Cyperus dentatus x Cyperus grayii x Dicanthelium x x x x Dicanthelium wrightianum x Diodia teres x Drosera filiformis x x

Drosera intermedia x x x x Eleocharis microcarpa? x Eleocharis olivaceae x Eleocharis tuberculosa x Hieracium venosum x Hieracium x Hypericum canadense x x x Hypericum mutilum x Ilex glabra x x

Juncus pelocarpus x x x Muhlenbergia uniflora x x Panicum x Panicum verrucosum x Panicum virgatum x x Pinus rigida x x Poaceae x x

Rhynchospora knieskernii x x x x Sphagnum tenerum x Sphagnum cuspidatum x Sphagnum cyclophyllum x x x Utricularia subulata x x Utricularia subulata forma cleistogama x Xyris x x x x Xyris difformis x Xyris torta x 150

Table 3.3 Data on conservation status, growth form, and metabolic pathway for all identified species found at monitored sites on WGR, 2012-2013.

Conservation Growth C3 or Species Status Level Form C4 Mycorrhizal Amphicarpum amphicarpon Endangered State Perennial C3 Andropogon glomeratus Not threatened Perennial C4 Andropogon virginicus Not threatened Perennial C4 Aristida Not threatened Annual C4 Aristida dichotoma Not threatened Annual C4 Probably no Aristida longespica Threatened State Annual C4 Probably no Aristida longespica var. genticulata Not threatened Annual C4 Probably no Comptonia peregrina Endangered State Perennial C3 Cyperus dentatus Endangered State Perennial C3 Yes Cyperus grayii Endangered State Perennial C3 Dicanthelium Not threatened Perennial C3 Possibly no Dicanthelium wrightianum Endangered State Perennial C3 Possibly no Diodia teres Not threatened Perennial C3 Drosera filiformis Endangered State Perennial C3 Drosera intermedia Endangered State Perennial C3 Eleocharis microcarpa Endangered State Perennial C3 Probably yes Eleocharis olivaceae TERS State Perennial Eleocharis tenuis Subspecies threatened State Perennial Eleocharis tuberculosa Endangered State Perennial Hieracium Not threatened Perennial C3 Probably yes Hieracium venosum Endangered State Perennial Hypericum canadense Not threatened Annual Hypericum gentianoides Not threatened Annual C3 Hypericum mutlium Not threatened Annual C3 Ilex glabra Not threatened Perennial C3 Juncus pelocarpus Not threatened Perennial C3 Muhlenbergia uniflora Endangered (PA) Perennial Panicum Not threatened Annual/Perennial C4? Panicum verrucosum Endangered State Annual C3/C4 Panicum virgatum Not threatened Perennial C3 Paspalum Not threatened Perennial C4 Pinus rigida Not threatened Perennial C3 Yes Poaceae Not threatened Annual/Perennial C3/C4 Usually Polytrichum commune Not threatened Perennial C3 No Rhychospora knieskernii Federally-threatened Perennial C3 Yes Rubus Not threatened Perennial C3 Sphagnum Not threatened Perennial C3 Sphagnum cyclophyllum Species of concern State Perennial C3 Sphagnum cuspidatum Not threatened Perennial C3 Sphagnum tenerum Not threatened Perennial C3 Utricularia subulata Not threatened Annual C3 Utricularia subulata forma cleistogama Not threatened Annual C3 Xyris Not threatened Perennial C3 Xyris caroliniana var. caroliniana Not threatened Perennial C3 Xyris difformis Not threatened Perennial C3 Xyris torta Threatened Perennial C3 151

Table 3.4. Summary of statistical results.

Comparison df Fstat p-value R 2 Sig Comment p-value Sig Dominant Plant~Plant P Concentration (mg/g) 1,133 4.39 0.04 0.02 * Soil N/m2~Year 1, 34 27.57 0.00 0.43 *** Soil N/m2~Year 1, 34 88.33 0.00 0.71 *** Soil N/m2~Site 4, 31 5.05 0.01 0.32 ** Soil P/m2~Site 4, 31 5.80 0.01 0.35 ** Soil N:P Ratio ~ Plant N:P Ratio 1, 34 7.67 0.01 0.16 * Soil Extractable TN ~ Plant N*Type 7, 28 2.03 0.17 0.34 Soil Extractable P ~ Plant P*Type 15, 20 2.40 0.03 0.37 Soil Extractable NH4 ~ Burn 1, 34 43.72 0.00 0.55 ** DWRoot~TIO 1, 257 0.05 0.83 0.00 Significant Difference for Shoot Length~Species*Site*Year 70, 829 6.59 2.23-16 0.30 Aristida at Double Site 0.00 ** Root Length~Species*Site*Year 70, 825 10.80 0.00 0.43 Significant Difference for 13.39 R:S Ratio (length)~Species 70, 828 0.00 0.49 P. rigida at Double Site 0.03 * Significant Difference for R. knieskernii at Double Shoot Mass~Species*Site*Year 72,147 3.11 3.01 0.41 Site 0.03 ** Significant Difference for Root Mass~Species*Site*Year 71,144 4.48 1.25 0.53 Aristida at Double Site 0.00 ** Significant Difference for 1.01 R. knieskernii at Double R:S Ratio (mass)~Species*Site*Year 72,147 0.47 0.00 Site 0.03 *** Dominant Plant~Plant P Concentration (mg/g) 1,133 4.39 0.05 0.02 * Dominant Species~Prescribed Burned 2, 42 3.46 0.05 0.10 * Percent Biomass~ Prescribed Burned 2, 42 4.65 0.05 0.14 * DWRoot~TIO 1, 257 0.05 0.83 0.00 Species~Site 4, 275 1.38 0.24 0.01 Runway(0.001); Sight 0.03 0.03 ** Species~Site*Year 8,271 2.18 line (0.056) DWRoot~LSM 1,257 0.04 0.84 0.00 Species~PWP 1, 165 1.62 0.20 0.00 Species~FC 1, 165 1.30 0.25 0.00 DWShoot~Canopy 1, 277 0.39 0.53 0.00 DWRS~Canopy 1, 266 1.25 0.26 0.00 Associated Species N~Canopy 1,165 0.10 0.76 -0.01 Associated Species P~Canopy 3,163 3.12 0.03 0.04

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Table 3.5. Summary of descriptive statistics for plant height, root length, and root:shoot ratios for study sites on WGR in 2012.

Shoot Root Shoot R:S Mean Mean Root SD RS SD SD Ratio Ye ar Site Species (cm) (cm) 2012 Dead End Andropogon 7.48 3.87 3.65 0.90 0.58 0.29 2012 Dead End Aristida 8.68 1.12 2.26 0.96 0.26 0.08 2012 Dead End Dicanthelium 11.73 4.91 7.02 2.05 0.70 0.30 2012 Dead End Eleocharis tenuis 12.80 5.68 1.40 1.05 0.11 0.05 2012 Dead End Muhlenbergia uniflora 13.19 5.35 5.62 2.13 0.48 0.26 2012 Dead End Rhynchospora knieskernii 14.12 10.41 2.39 1.36 0.23 0.14 2012 Dead End Rubus 56.80 NA 10.20 NA 0.18 NA 2012 Dead End Xyris 26.18 7.18 1.90 1.59 0.08 0.07 2012 Double Andropogon 11.92 6.17 3.94 0.74 0.37 0.13 2012 Double Aristida 11.34 3.52 2.96 1.18 0.28 0.16 2012 Double Aristida longespica 16.74 5.59 3.44 1.46 0.21 0.06 2012 Double Dicanthelium 5.41 2.57 3.32 1.37 0.81 0.75 2012 Double Muhlenbergia 15.13 5.28 5.46 1.54 0.38 0.11 2012 Double Muhlenbergia uniflora 25.92 5.23 5.34 1.18 0.21 0.04 2012 Double Pinus rigida 7.70 1.27 2.85 0.49 0.37 0.00 2012 Double Rhynchospora knieskernii 8.11 4.24 1.24 0.77 0.19 0.15 2012 LSL Andropogon 12.56 6.96 2.07 0.77 0.20 0.10 2012 LSL Aristida 7.32 2.12 2.02 1.38 0.26 0.15 2012 LSL Aristida longespica 14.00 1.70 3.18 0.55 0.23 0.04 2012 LSL Cyperaceae 9.03 1.47 1.48 0.43 0.17 0.07 2012 LSL Dicanthelium 1.10 NA 2.80 NA 2.55 NA 2012 LSL Hypericum gentianoides 11.60 2.26 0.90 0.47 0.08 0.04 2012 LSL Muhlenbergia 10.80 NA 5.50 NA 0.51 NA 2012 LSL Pinus rigida 1.00 NA 1.80 NA 1.80 NA 2012 LSL Poaceae 7.96 3.07 2.73 1.72 0.48 0.70 2012 LSL Rhynchospora knieskernii 8.65 2.00 1.64 0.45 0.20 0.05 2012 Runway Amphicarpum purshii 4.12 1.64 3.82 1.69 0.96 0.37 2012 Runway Andropogon 8.46 3.91 6.56 3.85 0.86 0.48 2012 Runway Aristida 7.02 1.86 3.83 1.14 0.57 0.20 2012 Runway Aristida longespica 9.48 3.63 4.47 0.71 0.53 0.22 2012 Runway Cyperaceae 4.63 2.33 2.58 0.96 0.65 0.32 2012 Runway Cyperus dentatus 14.46 5.55 7.52 4.03 0.58 0.29 2012 Runway Dicanthelium 17.00 NA 10.70 NA 0.63 NA 2012 Runway Diodia teres 4.63 1.97 3.24 1.50 0.83 0.54 2012 Runway Drosera filiformis 3.20 NA 1.00 NA 0.31 NA 2012 Runway Eleocharis 14.35 0.92 5.50 1.84 0.39 0.16 2012 Runway Eleocharis tuberculosa 13.97 2.45 6.48 3.08 0.46 0.18 2012 Runway Hypericum canadense 2.34 2.18 2.42 0.99 2.04 1.87 2012 Runway Panicum 8.92 2.41 4.70 1.53 0.59 0.32 2012 Runway Paspalum 1.94 0.24 2.30 1.26 1.19 0.68 2012 Runway Pinus rigida 4.80 NA 5.10 NA 1.06 NA 2012 Runway Rhynchospora knieskernii 20.66 6.17 4.45 1.15 0.22 0.07 2012 Runway Utricularia subulata 3.07 0.88 0.83 0.44 0.24 0.10 2012 Runway Xyris caroliniana var. caroliniana 8.93 4.72 3.15 0.68 0.44 0.25 2012 Sight Line Andropogon 13.92 15.57 5.42 2.00 0.55 0.23 2012 Sight Line Aristida 8.37 2.03 2.06 1.01 0.25 0.09 2012 Sight Line Aristida dichotoma 9.99 3.30 2.31 1.33 0.26 0.17 2012 Sight Line Aristida longespica 12.82 4.70 2.44 0.88 0.20 0.07 2012 Sight Line Cyperaceae 3.34 1.02 1.68 0.42 0.53 0.21 2012 Sight Line Eleocharis microcarpa 10.84 5.53 2.08 0.79 0.20 0.04 2012 Sight Line Hypericum gentianoides 9.50 NA 1.00 NA 0.11 NA 2012 Sight Line Ilex glabra 1.90 0.62 1.80 0.78 0.98 0.48 2012 Sight Line Pinus rigida 6.22 2.30 4.40 2.03 0.80 0.52 2012 Sight Line Rhynchospora knieskernii 13.66 5.32 1.67 0.78 0.14 0.10 153

Table 3.6. Summary of descriptive statistics for plant height, root length, and root:shoot ratios for study sites on WGR in 2013.

Shoot Root Shoot R:S Mean Mean Root SD RS SD SD Ratio Ye ar Site Species (cm) (cm) 2013 Dead End Andropogon 19.90 13.28 3.90 2.17 0.24 0.10 2013 Dead End Aristida 8.10 2.73 1.10 0.49 0.16 0.09 2013 Dead End Aristida dichotoma 23.60 3.36 3.90 1.32 0.16 0.04 2013 Dead End Dicanthelium 10.56 7.72 3.39 1.86 0.39 0.23 2013 Dead End Dicanthelium 1.17 1.25 0.63 0.76 0.58 0.42 2013 Dead End Dicanthelium wrightianum 8.31 2.39 2.00 0.79 0.31 0.16 2013 Dead End Drosera intermedia 2.82 2.43 0.63 0.54 0.33 0.60 2013 Dead End Eleocharis 5.15 1.02 0.55 0.16 0.11 0.03 2013 Dead End Hieracium 1.20 0.42 2.30 0.85 2.18 1.48 2013 Dead End Hypericum 12.53 5.54 1.07 1.03 0.08 0.06 2013 Dead End Hypericum canadense 8.57 3.69 0.73 0.25 0.08 0.03 2013 Dead End Hypericum mutilum 1.90 1.84 0.50 NA 0.08 0.11 2013 Dead End Ilex glabra 5.60 0.14 9.00 0.71 1.61 0.17 2013 Dead End Muhlenbergia uniflora 21.44 8.24 3.86 1.46 0.20 0.09 2013 Dead End Rhynchospora knieskernii 22.09 11.24 2.11 1.37 0.10 0.07 2013 Dead End Utricularia subulata 1.40 NA 0.83 NA 0.76 NA 2013 Dead End Xyris 8.90 5.76 2.15 0.83 0.45 0.45 2013 Double Andropogon 9.52 3.56 8.22 7.78 1.03 0.91 2013 Double Aristida 21.98 6.09 3.10 1.81 0.11 0.07 2013 Double Aristida dichotoma 15.71 10.71 2.11 0.99 0.19 0.14 2013 Double Aristida longespica 15.90 3.57 2.46 0.88 0.16 0.09 2013 Double Dicanthelium 2.56 1.71 2.94 1.59 1.46 0.75 2013 Double Drosera intermedia 2.30 1.39 1.10 0.00 0.12 0.17 2013 Double Hypericum canadense 9.45 4.47 1.41 0.73 0.20 0.18 2013 Double Juncus pelocarpus 23.80 4.49 7.44 1.39 0.32 0.10 2013 Double Pinus rigida 3.09 1.38 4.06 2.41 1.33 0.49 2013 Double Rhynchospora knieskernii 22.48 6.36 1.97 1.08 0.09 0.05 2013 Double Utricularia subulata 2.55 2.05 0.83 0.00 0.13 0.10 2013 Double Xyris 4.47 1.11 1.18 0.66 0.22 0.17 2013 Runway Andropogon 18.89 21.58 7.02 3.31 0.64 0.37 2013 Runway Aristida 7.06 2.21 2.89 1.15 0.42 0.15 2013 Runway Aristida dichotoma 4.30 NA 3.50 NA 0.81 NA 2013 Runway Aristida longespica 11.58 6.25 3.90 1.35 0.42 0.27 2013 Runway Cyperus 13.18 5.05 4.87 2.04 0.44 0.32 2013 Runway Cyperus dentatus 13.25 5.94 6.17 2.99 0.66 0.71 2013 Runway Cyperus grayii 9.31 2.35 4.25 1.65 0.47 0.16 2013 Runway Dicanthelium 2.10 1.13 2.90 2.07 1.34 0.61 2013 Runway Diodia teres 3.92 1.34 1.92 1.97 0.37 0.35 2013 Runway Drosera filiformis 4.45 1.34 1.20 NA 0.22 NA 2013 Runway Drosera intermedia 4.63 3.68 1.80 NA 0.23 NA 2013 Runway Eleocharis 7.10 NA NaN NA NaN NA 2013 Runway Eleocharis olivaceae 12.83 6.10 3.38 1.63 0.26 0.02 2013 Runway Eleocharis tuberculosa 18.64 4.62 3.78 1.96 0.22 0.13 2013 Runway Hypericum canadense 8.87 2.37 1.20 0.36 0.15 0.08 2013 Runway Juncus pelocarpus 17.31 2.23 5.19 1.28 0.30 0.07 2013 Runway Muhlenbergia uniflora 10.75 6.31 5.58 2.73 0.55 0.17 2013 Runway Panicum 8.90 NA 6.60 NA 0.74 NA 2013 Runway Rhynchospora knieskernii 15.21 10.20 3.29 1.75 0.26 0.13 2013 Runway Utricularia subulata 1.48 0.52 0.67 0.32 0.42 0.10 2013 Runway Xyris 15.52 11.08 2.84 1.06 0.45 0.49 2013 Runway Xyris difformis 19.66 3.06 5.00 2.02 0.25 0.10 2013 Runway Xyris torta 15.08 4.98 3.32 2.30 0.20 0.09 2013 Sight Line Andropogon 10.09 5.66 4.31 2.62 0.44 0.18 2013 Sight Line Aristida 11.56 5.68 2.31 1.16 0.21 0.10 2013 Sight Line Aristida dichotoma 16.76 4.35 2.89 1.09 0.19 0.09 2013 Sight Line Aristida longespica 12.65 3.93 2.11 0.55 0.17 0.05 2013 Sight Line Dicanthelium 9.64 2.72 6.44 2.83 0.79 0.56 2013 Sight Line Drosera intermedia 8.30 0.28 0.65 0.07 0.08 0.01 2013 Sight Line Hieracium 2.40 NA 10.80 NA 4.50 NA 2013 Sight Line Ilex glabra 3.60 NA 6.60 NA 1.83 NA 2013 Sight Line Panicum verrucosum 7.73 3.27 3.93 1.06 0.58 0.24 2013 Sight Line Panicum virgatum 15.99 5.40 3.93 1.31 0.25 0.03 2013 Sight Line Pinus rigida 1.55 0.49 1.45 0.21 1.01 0.46 2013 Sight Line Rhynchospora knieskernii 13.26 4.42 2.16 0.80 0.18 0.09 2013 Sight Line Xyris 6.90 NA 2.00 NA 0.29 NA 154

Table 3.7. Summary statistics for aboveground and belowground biomass and root:shoot ratios for five study sites on WGR in 2012.

Shoot Root Mean Shoot Mean Root R:S

Year Site Species (g) SD (g) SD Ratio RS SD 2012 Dead End Andropogon 0.09 0.06 0.01 0.01 0.11 0.03 2012 Dead End Aristida 0.01 0.00 0.01 0.00 0.37 0.00 2012 Dead End Dicanthelium 0.56 0.36 0.24 0.15 0.46 0.16

2012 Dead End Dicanthelium wrightianum 0.04 NA 0.02 NA 0.43 NA 2012 Dead End Eleocharis 0.29 0.39 0.12 0.16 0.33 0.12

2012 Dead End Hypericum 0.10 0.18 0.01 0.02 0.09 0.00 2012 Dead End Muhlenbergia 0.27 0.11 0.09 0.06 0.39 0.42

2012 Dead End Panicum 0.01 NA 0.00 NA 0.43 NA 2012 Dead End Rhynchospora knieskernii 0.56 0.75 0.19 0.24 0.42 0.13 2012 Dead End Xyris 0.62 1.01 0.08 0.13 0.11 0.02 2012 Double Andropogon 0.16 0.17 0.07 0.07 0.45 0.08 2012 Double Aristida 0.27 0.25 0.03 0.04 0.11 0.07 2012 Double Cyperaceae 0.06 0.00 0.02 0.01 0.41 0.19 2012 Double Dicanthelium 0.20 0.16 0.06 0.06 0.29 0.04 2012 Double Hypericum 0.01 0.00 0.00 0.00 0.06 0.05 2012 Double Muhlenbergia 0.45 0.16 0.14 0.10 0.29 0.10 2012 Double Pinus rigida 0.08 0.05 0.02 0.01 0.16 0.07 2012 Double Rhynchospora knieskernii 0.09 0.04 0.10 0.07 1.11 0.60 2012 LSL Andropogon 0.05 0.03 0.01 0.00 0.20 0.16 2012 LSL Aristida 2.09 NA 0.02 NA 0.01 NA 2012 LSL Cyperaceae 0.08 0.07 0.02 0.02 0.73 0.87 2012 LSL Dicanthelium 0.07 NA 0.02 NA 0.33 NA 2012 LSL Hypericum 0.31 0.26 0.03 0.02 0.08 0.00 2012 LSL Pinus rigida 0.22 NA 0.01 NA 0.05 NA 2012 LSL Rhynchospora knieskernii 0.34 0.35 0.08 0.07 0.31 0.14 2012 Runway Andropogon 0.41 0.38 0.15 0.14 0.37 0.14 2012 Runway Aristida 0.15 0.12 0.04 0.04 0.28 0.03 2012 Runway Cyperaceae 0.12 0.08 0.06 0.05 0.55 0.28 2012 Runway Cyperus 0.97 0.43 0.87 0.24 1.02 0.51 2012 Runway Dicanthelium 0.01 NA NaN NA 0.00 NA 2012 Runway Eleocharis 0.51 0.61 0.13 0.17 0.46 0.55 2012 Runway Hypericum 0.01 0.01 0.00 0.00 0.06 0.05 2012 Runway Panicum 0.25 0.32 0.12 0.12 0.86 0.61 2012 Runway Pinus rigida 0.08 NA 0.02 NA 0.19 NA 2012 Runway Rhynchospora knieskernii 1.11 1.47 0.21 0.13 0.30 0.12 2012 Runway Xyris 0.10 0.11 0.02 0.02 0.17 0.03 2012 Sight Line Andropogon 0.19 0.25 0.09 0.06 0.28 0.29 2012 Sight Line Aristida 0.60 0.61 0.11 0.12 0.17 0.03 2012 Sight Line Cyperaceae 0.13 0.07 0.06 0.01 0.47 0.17 2012 Sight Line Eleocharis 0.80 NA 0.14 NA 0.17 NA 2012 Sight Line Hypericum 0.02 0.02 0.00 0.00 0.04 0.01 2012 Sight Line Pinus rigida 0.44 0.46 0.08 0.08 0.20 0.03 2012 Sight Line Rhynchospora knieskernii 1.20 1.13 0.21 0.22 0.16 0.04

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Table 3.8. Summary statistics for aboveground and belowground biomass and root:shoot ratios for five study sites on WGR in 2013.

Shoot Root Mean Shoot Mean Root R:S Year Site Species (g) SD (g) SD Ratio RS SD 2013 Dead End Andropogon 2.55 3.26 0.95 1.32 0.23 0.23 2013 Dead End Aristida 0.38 NA 0.02 NA 0.06 NA 2013 Dead End Dicanthelium 0.98 NA 0.35 NA 0.36 NA 2013 Dead End Dicanthelium wrightianum 0.15 NA 0.01 NA 0.09 NA 2013 Dead End Muhlenbergia 1.07 0.91 0.09 0.04 0.11 0.05 2013 Dead End Rhynchospora knieskernii 0.87 0.82 0.20 0.06 0.37 0.28 2013 Dead End Xyris 0.04 NA 0.01 NA 0.18 NA 2013 Double Andropogon 0.13 0.13 0.08 0.09 0.71 0.97 2013 Double Aristida 0.29 0.29 0.04 0.05 0.11 0.06 2013 Double Cyperaceae NaN NA NaN NA NaN NA 2013 Double Dicanthelium 0.07 0.09 0.02 0.03 0.14 0.15 2013 Double Hypericum 0.04 0.05 0.01 0.01 0.16 0.04 2013 Double Pinus rigida 0.23 0.32 0.11 0.16 0.86 0.50 2013 Double Rhynchospora knieskernii 3.31 1.30 1.43 0.60 0.43 0.02 2013 Double Xyris 0.00 0.00 0.01 0.02 3.50 5.22 2013 Runway Andropogon 1.84 1.07 0.54 0.47 0.26 0.09 2013 Runway Aristida 0.11 0.04 0.03 0.01 0.33 0.19 2013 Runway Cyperus 0.72 0.55 0.60 0.53 2.58 3.87 2013 Runway Eleocharis 1.20 0.77 0.46 0.35 0.38 0.17 2013 Runway Muhlenbergia 0.13 0.09 0.04 0.00 0.41 0.28 2013 Runway Panicum 0.20 NA 0.16 NA 0.80 NA 2013 Runway Rhynchospora knieskernii 0.22 0.24 0.16 0.20 0.68 0.11 2013 Runway Xyris 0.50 0.62 0.09 0.09 0.30 0.18 2013 Runway Xyris difformis 1.44 NA 0.43 NA 0.30 NA 2013 Runway Xyris torta 0.36 NA 0.08 NA 0.21 NA 2013 Sight Line Andropogon 0.36 0.28 0.14 0.12 0.39 0.02 2013 Sight Line Aristida 0.65 0.78 0.09 0.12 0.16 0.09 2013 Sight Line Dicanthelium 0.18 NA 0.04 NA 0.20 NA 2013 Sight Line Panicum 0.04 0.03 0.03 0.03 0.59 0.41 2013 Sight Line Pinus rigida 0.02 NA 0.01 NA 0.47 NA 2013 Sight Line Rhynchospora knieskernii 0.68 0.29 0.38 0.17 0.56 0.15

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Table 3.9. List of dominant species at each site, as determined by percent biomass of all collected species for each plot at each site.

Total Percent Year Site Plot Biomass Dominant Species Biomass 2012 Dead End 1 3.543 Rhynchospora knieskernii 0.47 2012 Dead End 2 4.015 Rhynchospora knieskernii 0.55 2012 Dead End 3 4.854 Eleocharis 0.46 2012 Dead End 4 2.287 Dicanthelium 0.66 2012 Dead End 5 3.523 Dicanthelium 0.47 2012 Double 1 1.886 Aristida 0.47 2012 Double 2 2.654 Muhlenbergia uniflora 0.42 2012 Double 3 2.96 Muhlenbergia uniflora 0.47 2012 Double 4 1.557 Aristida 0.42 2012 Double 5 1.703 Muhlenbergia uniflora 0.68

2012 LSL 1 0.973 Rhynchospora knieskernii 0.43 2012 LSL 2 1.56 Rhynchospora knieskernii 0.82 2012 LSL 3 0.756 Rhynchospora knieskernii 0.86 2012 LSL 4 3.872 Aristida longespica 0.67 2012 LSL 5 1.619 Poaceae 0.62 2012 Runway 1 4.135 Cyperus dentatus 0.61 2012 Runway 2 5.41 Cyperus dentatus 0.32 2012 Runway 3 8.261 Eleocharis tuberculosa 0.28 2012 Runway 4 10.132 Rhynchospora knieskernii 0.53 2012 Runway 5 3.745 Cyperus dentatus 0.49 2012 Sight Line 1 1.136 Andropogon 0.53 2012 Sight Line 2 7.069 Aristida longespica 0.37 2012 Sight Line 3 5.813 Rhynchospora knieskernii 0.7 2012 Sight Line 4 3.934 Rhynchospora knieskernii 0.41 2012 Sight Line 5 2.752 Aristida longespica 0.49 2013 Dead End 1 5.52 Muhlenbergia uniflora 0.46 2013 Dead End 2 21.162 Sphagnum tenerum 0.47 2013 Dead End 3 4.383 Muhlenbergia uniflora 0.75 2013 Dead End 4 4.4515 Eleocharis 0.63 2013 Dead End 5 2.9 Rhynchospora knieskernii 0.62 2013 Double 1 5.102 Rhynchospora knieskernii 0.91 2013 Double 2 5.589 Rhynchospora knieskernii 0.97 2013 Double 3 7.614 Rhynchospora knieskernii 0.95

2013 Double 4 5.93 Rhynchospora knieskernii 0.4 2013 Double 5 17.063 Juncus pelocarpus 0.53

2013 Runway 1 5.448 Eleocharis tuberculosa 0.54 2013 Runway 2 7.617 Eleocharis tuberculosa 0.38 2013 Runway 3 6.468 Andropogon 0.52 2013 Runway 4 12.923 Andropogon virginicus 0.41 2013 Runway 5 9.506 Eleocharis tuberculosa 0.35 2013 Sight Line 1 5.001 Aristida 0.52 2013 Sight Line 2 9.412 Rhynchospora knieskernii 0.49 2013 Sight Line 3 2.265 Rhynchospora knieskernii 0.51 2013 Sight Line 4 0.473 Dicanthelium 0.49 2013 Sight Line 5 3.497 Rhynchospora knieskernii 0.52 157

Table 3.10. Comparison of number of individual species and the number of sites at which they were collected for monitored sites in 2012 (5) and 2013 (4) on WGR.

Sites 2012 2013 1 14 23 2 9 7 3 4 4 4 3 All 5 7 Total 35 41

Table 3.11. Species found at all monitored sites on WGR in 2012 (5 sites) and 2013 (4 sites).

2012 2013 Andropogon spp. Andropogon spp. Aristida spp. Aristida spp. Aristida dichotoma Dicanthelium sp. Poaceae spp. Drosera intermedia

Rhynchospora knieskernii Rhynchospora knieskernii

Sphagnum spp. Xyris spp.

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Table 3.12. Comparison of changes to species biomass from 2012 to 2013 at five monitored sites on WGR.

Percent Species 2012 2013 Change Andropogon 0.06 0.19 +13

Aristida 0.14 0.07 -7 Cyperaceae spp. 0.02 0.00 -2

Cyperus 0.12 0.07 -5 Dicanthelium spp. 0.07 0.02 -5 Diodia teres 0.02 0.00 -2 Drosera intermedia 0.00 0.00 0

Eleocharis 0.08 0.10 +2 Hypericum 0.01 0.00 -1

Ilex glabra 0.00 0.00 0 Juncus pelocarpus 0.08 +8 Muhlenbergia uniflora 0.05 0.05 0 Panicum 0.01 0.01 0

Pinus rigida 0.02 0.01 -1 Poaceae spp. 0.09 0.00

Rhynchospora knieskernii 0.26 0.29 +3 Sphagnum 0.02 0.08 +6 Utricularia subulata 0.00 0.00 Xyris spp. 0.01 0.03 +2

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Chapter 4: Dispersal Strategies and Germination Cues in a Fire-Dependent Ecosystem

Abstract

The stochasticity of disturbed environments can be either positive or negative for plant communities. Plants have responded to this conundrum through the development of dispersal mechanisms that increase the probability that seeds will reach suitable habitat. I examined three potential dispersal vectors (wind, water, deer) and three potential germination cues (heat, smoke, charred wood) for Rhynchospora knieskernii, a globally rare sedge that is endemic to the fire-prone New Jersey Pine Barrens ecosystem. The location and habitat specificity of extant R. knieskernii populations suggests that seeds are dispersed long distances. Although my data demonstrated that R. knieskernii was dispersed by the three vectors, dispersal distance was greatest at higher wind speeds (F =

10.16, p < 0.0001) and water velocities (F = 24.18, p < 00001). Fat-tailed water dispersal kernels (empirical and simulated) indicated there was a positive relationship between seed density and moist habitats. Deer fur-seed-attachment experiments demonstrated that deer were an epizoochoric dispersal vector. Although there were no significant differences among germination treatments (F = 0.067, p = 0.80), there was a significant increase in germination (F = 6.06, p < 0.05) and flowering (F = 8.47, p < 0.01) for seeds sown on the soil surface compared to seeds sown below the soil surface. The increased seedling density for seeds sown on the soil surface was attributable to increased light as the germination cue. These data suggested that R. knieskernii seeds have the ability to disperse both long and short distances, thus providing an opportunistic advantage for seeds to reach newly disturbed habitat where there is appropriate light and moisture 160

requirements for germination. Although a recent simulated model suggests that dual-

dispersal (long- and short distance) can be a competitive advantage in disturbed

ecosystems, my study using empirical data was the first to validate this positive effect in

a federally-threatened plant.

4.1 Introduction

Disturbance-dependent rare plants face a paradox: although disturbance creates

habitat, is also makes habitat loss inevitable, while the overall probability of a seed

arriving at an appropriate habitat is small. While plant dispersal mechanisms facilitate

expansion into new areas of suitable habitat (Howe and Smallwood 1982; Nathan et al.

2008), limited dispersal abilities may explain plant rarity (Quinn et al. 1994). There are

only a few studies on rare plant dispersal and results have been mixed (Rabinowitz and

Rapp 1981; Laughlin 2003; Burmeier and Jensen 2008). Rabinowitz and Rapp (1981)

found a strong relationship between seed abundance and seed movement capabilities for

several rare prairie grasses, although field tests did not find significant differences

between dispersal distances of rare and common species. Dispersal and regeneration site

limitation were hypothesized to explain the rarity of creeping marshwort (Apium repens),

but field tests did not support these explanations (Burmeier and Jensen 2008). However,

loss of a key animal disperser was cited as the probable cause of the current rarity of

sideoats grama (Bouteloua curtipendula) (Laughlin 2003). Given the importance of dispersal to rare plants, it is imperative to identify dispersal mechanisms and to protect dispersal relationships to maintain and enhance the conservation of plant populations

(Farwig and Berens 2012). 161

The ability to disperse seeds is paramount for plant populations, especially when the current habitat becomes unsuitable. In response to environmental stochasticity plants have developed a variety of dispersal strategies that allow them to transport reproductive units through time (seed bank) and space. Recruitment to a new site can be accomplished through either dispersal of plant parts (vegetative) or through seed dispersal. Dispersal is commonly defined as the movement of seeds (propagules) away from the parent plant

(Howe and Smallwood 1982; Kiviniemi and Telenius 1998; Nathan et al. 2008). A vector is any dispersal agent that moves a propagule (typically the entire structure that assists in dispersal) from one place to another (Cousens 2008). There are six recognized dispersal vectors (ant, ballistic, vertebrate (endozoochory and epizoochory), wind and water), but my research focused on the three vectors most commonly associated with sedges: wind (anemochory); water (hydrochory); and vertebrate (epizoochory) (Willson et al. 1990; Leck and Schütz 2005; Cousens 2008). In some cases, investigators use the term adhesive dispersal rather than epizoochory to refer to all dispersal through animal, human, and mechanical agents involving direct seed attachment to the vector (Sorenson

1982). Many plants invest considerable energy developing elaborate propagule morphologies, although some have no obvious dispersal adaptations (Willson 1993;

Hintze et al. 2013). Specialized structures are often effective at dispersing propagules, leading to the frequent identification of a particular dispersal pathway based solely on propagule structure (Willson et al. 1990; Hintze et al. 2013; Tamme et al. 2014).

Although morphology alone is insufficient to determine the dispersal mechanism of a particular species, general trends are strong enough that a recent model used morphological classifications to delineate dispersal distances for a wide range of species 162

(Tamme et al. 2014). Furthermore, a study of adhesive seed dispersal found that while

many plants adhered to wild boar fur, only seeds with specific adaptations could adhere

to deer fur (Courveur et al. 2004). Since seeds can disperse in several ways over time,

the initial movement stage is termed primary dispersal and all subsequent movements are

considered secondary dispersal (Chambers and MacMahon 1994; Cousens 2008). Many

plants utilize several types of dispersal mechanisms, but studies of specific species find it

more tractable to focus on one primary dispersal mechanism (Jongejans and Telenius

2001; Laughlin 2003; Burmeier and Jensen 2008).

Although morphological structures for dispersal are energetically costly to

produce, they provide a method for propagules to be dispersed from the natal area

(Cousens 2008; Bonte et. al 2012). Dispersal can reduce competition with parents and

siblings and provides opportunities for new subpopulations to develop, potentially

reducing inbreeding depression and increasing the number of source populations (Howe

and Smallwood 1982; Ronce 2007). Plants that disperse greater distances increase their

chance of colonizing appropriate new habitat (Willson et al. 1990; Boulanger et al. 2011).

However, dispersal can involve risks (Bonte et al. 2012). For example, founder

populations can be small and at a great distance from the parent population. Thus, they

may lose genetic variation or suffer from Allee effects (Ghazoul 2005). Also, seeds may

disperse into unsuitable habitat and die (Ronce 2007; Bonte et al. 2012). Plants that

produce a large number of seeds are more likely to overcome this problem, but this

involves a trade-off between seed size and seed number (Leishman 2003). Plants in

environments that are subject to periodic disturbance are often associated with r-dispersal strategies that minimize risk of population extirpation through catastrophic habitat loss 163

while increasing opportunities for colonizing newly created habitat (Kisdi 2002; Bullock

et al. 2003; Kallimanis et al. 2006; Ronce 2007).

Dispersal mechanisms and distributions have been difficult to study in situ,

especially in small-seeded species (Willson et al. 1990; Nathan and Muller-Landau

2000). Thus, models have been used to examine species performance under various

constraints and ecological conditions (Greene and Johnson 1989; Mouissie et al. 2005;

Schurr et al. 2005). However, these models are not applicable to all environments and

plant functional types. Many wind dispersal models are based on tree canopy dynamics

and have limited use for herbaceous plants (Nathan and Muller-Landau 2000). Animal dispersal models frequently utilize domestic animals for testing, making it difficult to translate results to more natural environments dominated by wild herbivores (Couvreur et al. 2004; Will and Tackenberg 2008). However, recent developments in the use of more generalized modeling methods and the availability of online databases with specific data from species of several different functional types offers the opportunity to apply the insights derived from models to field studies under a greater range of conditions (Hintze et al. 2013; Tamme et al. 2014).

Many theoretical dispersal models have explored the circumstances under which dispersal can be advantageous (Cousens et al. 2008). In heterogeneous landscapes, suitable habitat is patchy, so long-distance dispersal increases the risk of landing in unsuitable habitat. Thus, some models have accounted for conditions that change dispersal distances (Kallimanis et al. 2006). A long-distance dispersal strategy appears beneficial for plant populations in ephemeral habitats (Kisdi 2002; Cousens et al. 2008;

Buchi and Vuilleumier 2014). One model comparing competition, disturbance regime, 164

and landscape pattern found the disturbance regime to be more influential than the

landscape pattern in determining dispersal distance (Kallimanis et al. 2006).

The dispersal kernel and the distance distribution are spatial patterns formed by

propagules dispersing from the parent plant (Nathan et al. 2008). Most dispersal kernels

follow an exponential decay model, with the majority of propagules found within a short

distance of the parent plant and the number of propagules decreasing as distance

increases from the parent plant (Nathan and Muller-Landau 2000). Dispersal kernels measure densities (the number of seeds per unit area of distance) while distance distributions examine frequencies (the number of seeds that achieve a certain distance).

Distance and density distributions are sharply left-skewed for most plants, meaning that long-distance dispersal is almost always rare, but the tails of the dispersal kernel and distance distribution often vary (Portnoy and Willson 1993; Nathan and Muller-Landau

2000). Long tails indicate infrequent long-distance dispersal, while fat tails indicate more frequent long-distance dispersal (Nathan et al. 2008).

Adhesive dispersal is more frequently encountered in disturbed environments than other types of environments (Sorenson 1986). This may be due to the potential of many animals to move among similar habitats that are spatially distant (Couvreur et al. 2004;

Cousens et al. 2008). Adhesive dispersal tends to be more difficult to model than wind dispersal because there are less available data on animal movement, but high quality models show that adhesive dispersal can move seeds relatively long distances from source patches (Will and Tackenberg 2008). Adhesive dispersal occurred more frequently in rare than common plants in a study of the New Zealand flora, suggesting it 165

may be an effective means for dispersing plants to specialized habitats (Thorson et al.

2009).

The stochastic nature of wildfire creates periodic disturbance at a larger scale

(landscape) than smaller, less intense prescribed burns. In fire dependent systems, it is not uncommon to find plants that employ long distance dispersal strategies, including early- successional plants that have evolved fire adapted traits that take advantage of recently disturbed habitat for germination (Turner et al. 1998; Coffey and Kirkman 2006). Even when dispersal is effective in distributing seeds to suitable habitats, recruitment requires plants to germinate successfully. Factors affecting seed germination in fire-dependent ecosystems have been well-studied and smoke, heat, and charred wood have all been identified as key factors stimulating germination (Christensen and Muller 1975; Keely and Pizzorno 1986; Keely and Fotheringham 1998; Wills and Read 2002). However, less data exist about the response of wetland plants and seeds to fire (Bolin 2007).

Rhynchospora kniesknerii is a globally rare sedge, endemic to the New Jersey

Pine Barrens (NJPB), a fire-dependent ecosystem. Little is known about factors affecting either dispersal or germination of R. knieskernii seeds. The presence of bristles on the seeds suggests adhesive dispersal, although Rhynchospora bristles may also facilitate hydrochoric movement (Moore 1997). Seed germination rates of up to 68% occurred after cold/wet stratification and exposure to light, but there was no description of methods or data on variability in germination rates (Yurlina 1998). A controlled germination study examining the impact of scarification found no difference between scarified and control seeds, with germination rates for both groups of approximately 10% (Frank

2007). Mycorrhizal relationships may be important to R. knieskernii and this may 166

influence the difference in germination rates (Dighton et al. 2013). Seedling growth was enhanced by exposure to moist, but not flooded soil (Frank 2007). Lower plant densities are associated with above-average site moisture levels at burned and unburned sites

(unpublished data, Chapter 1), suggesting R. knieskernii has very specific moisture requirements that could be affected by fire, limiting available habitat for establishment.

Fire-related germination cues may promote recruitment at recently burned sites, as there have been observed increases in density after fire (USFWS 2008). If R. knieskernii seeds show higher germination rates in response to treatments with charred wood, smoke, or heat, this suggests that fire plays an important role in enhancing these populations (Van

Staden et al. 2004).

The two main goals of my research involved examining R. knieskernii dispersal patterns and fire-related germination cues of achenes (= seeds). Because long distance dispersal is particularly critical for rare plants with specific habitat requirements, I hypothesized that R. knieskernii has developed mechanisms for long-distance dispersal and fire-related germination cues that increase its potential to recruit and establish in recently burned sites. Thus, I examined three potential dispersal mechanisms (wind, water, deer) and three potential germination cues (heat, charred wood, smoke). My research addressed the following questions: 1) What are the specific dispersal mechanisms for R. knieskernii? 2) Does R. knieskernii have mechanisms for long- distance dispersal? 3) How do the dispersal mechanisms of R. knieskernii relate to its habitat requirements? 4) Does R. knieskernii gain any advantage from the particular dispersal mechanisms it utilizes? 5) Does R. knieskernii have fire-related germination 167

cues? The answers to these questions will elucidate the role that R. knieskernii dispersal

and germination mechanisms play in metapopulation dynamics.

4.2 Methods

4.2.1 Wind dispersal (anemochory)

Site Measurements: I measured wind speed at five sites on 13 October 2013, 14 October

2013, and 23 November 2013 using a Kestrel 3000 wind meter (Nielsen-Kellerman

Company, Boothwyn, PA) held at a height of 40 cm and approximately 4 cm above

ground level. I took three readings for one minute each at each height for each of the five

sites. I measured wind speed at least twice in the morning and twice in the afternoon over the season. I calculated mean and maximum wind velocities (m/s, ± 0.1 m/s) that mature R. knieskernii plants would encounter during dispersal. Maximum wind velocities measure gusts while mean wind velocities average out the range of wind velocities encountered at the site.

Dispersal Tests: I conducted wind dispersal tests to calculate achene dispersal distance

and shape of the distribution kernel. I covered the test area (approximately 4.5 m2) with

white muslin cloth in order to detect the distance of dispersed achenes. I used two

wooden platforms, 18 cm high and 35 cm in height, and covered the top of each platform

with balsa wood to minimize friction. I placed ten propagules in the center of each

platform and covered them with a small container (Figure 4.1). I conducted six trials at

each height. At the start of each trial I removed the container from the platform, recorded

the ambient wind speeds (m/s) for two minutes, determined the direction that achenes

dispersed from the platform, and measured the distance that the achenes travelled from 168

the platform to their point of attachment on the white muslin cloth, I compared my wind

directional data with data from the WGR weather tower which collects data every 30

minutes.

4.2.2 Water dispersal (hydrochory)

Buoyancy Test: I prepared ten samples for each of the two treatments (still and shaken) by

filling a 120 ml Nalgene bottle with 50 ml of deionized water and placing ten propagules

in each bottle. I placed ten bottles on a lab bench where they remained for the duration of

the experiment and ten bottles on a Model 4633 shaker table (Lab Line Instruments,

Melrose Park, IL) set to variable speeds (40 RPM for 30 minutes, 70 RPM for 150

minutes, 90 RPM for 120 minutes). Every 30 minutes I examined the bottles and

recorded the number of floating propagules before returning them to the shaker table. The experiment ended after 270 minutes for the still propagules and 300 minutes for the shaken propagules.

Water Dispersal Experiment: I used an HM 160 experimental flume (Gunt Corporation,

Hamburg, Germany) to measure propagule dispersal distance under two types of

experimental manipulation: (1) Depth at 2 cm and 3 cm, flow velocity held constant at

0.2 m/s; (2) Flow velocity at 0.11 m/s and 0.19 m/s, depth held constant at 2 cm. The

flume capacity was 219 cm x 8.6 cm x 60 cm with a slope of 3%. I lined the flume bed

with 2 mm mesh wire and placed small pebbles (approximately 12-15 mm long) 10 cm apart to simulate the natural environment, which generally consists of small pebbles and plant litter scattered over open sandy areas (Figure 4.2). I filled the flume to one of two depths (2 cm and 3 cm) and measured velocity by calculating the time a 3 mm2 paper 169

traveled the complete distance of the flume (219 cm) for three trials preceding each experiment. Water flow was regulated through a hose and the water drained continuously, maintaining water depth for the duration of the experiment. For each trial I placed ten propagules on a small plastic weir (12 cm x 8.6 cm x 6 cm) approximately five cm from the water’s edge before initiating water flow. Water flow lasted for 60 seconds, after which I located propagules and measured dispersal distances from the base of the weir. I noted propagule movement obstructed by pebbles. I conducted ten trials for each manipulation (N=40).

4.2.3 Animal dispersal (epizoochory):

I tested the potential for deer to disperse R. knieskernii propagules. Deer have much larger home ranges that other potential mammal dispersers (Trent and Rongstad

1974; Jones and Sherman 1983) and deer tracks are encountered at all R. knieskernii study sites. Birds have large home ranges (Sanzenbacher and Haig 2001), but known bird dispersers of wetland plants have all been highly aquatic birds such as ducks (Leck and Schütz 2005).

Attachment Potential: First I used a plastic toy car with fresh deer legs attached to simulate deer movement through patches of dispersing seeds (Figure 4.3). I obtained the deer legs from a dead deer found on the side of the road. Deer legs were cut just below the hock to approximate the leg height that would brush against R. knieskernii plants, which have a mean height of 12 cm ± 0.5 cm (M. Sobel, unpublished data). I moved the apparatus at a steady pace of 50 cm/s by pulling it past a set of flags placed evenly over lengths of 1, 3, 6, 10, and 20 meters at four different patches of R. knieskernii in October 170

2013, when ripe seeds were dispersing. Not all patches were the same length, due to

natural differences in patch sizes and variability in plant density over the length of R.

knieskernii sites. Low density patches had mean densities of 167 plants/m2; high density

patches had mean densities of 643 plants/m2. I completed five trials at each of two

patches at one low-density site and one high-density site (N=20).

Attachment Time: I tested the length of time seeds remain attached on deer legs. I placed

five seeds on different parts of the cannon and fetlock (n=10) of fresh deer legs. I again

used the plastic toy car and pulled the apparatus over a 6 m x 3 m layer of felt using flags

to maintain an even pace of 50 cm/s. The felt was adjusted in places to simulate

microtopography. I pulled the apparatus continuously for 15 minutes per trial for five

timed trials. Each time a seed fell onto the felt, I recorded the time. Seeds that remained

on the deer legs at the end of the 15 minute trial were left attached for the next trial; I

recorded the total length of time the seed remained attached over the course of multiple

trials. Data from tests of attachment potential and attachment rates were used to calculate dispersal curves by multiplying the mean deer movement rate of 1.4 km/hour (Nelson et al. 2004; Root et al. 1988) by the number of seconds seeds remained attached.

Dispersal kernels: I created dispersal density kernels for both wind and water dispersal

using the data from the anemochory (N = 120) and hydrochory (N = 147) experiments.

These data best fitted a gamma distribution. I then estimated the shape (peak) and rate

(tail) parameters that most closely matched the empirical distribution. For wind dispersal

the shape = 1 and the rate 5; for water dispersal the shape = 4 and the rate = 1. For the

deer dispersal distribution I was not able to use field data to determine the shape and rate

parameters. Instead I used the length of time a seed was attached and multiplied it by 171

average deer movement rate, so that a seed attached for one minute was estimated to

travel 26.2 meters. I then used the collective distances to create a dispersal density kernel

(N=50) which also best fitted a gamma distribution. I then estimated the appropriate

parameters as I did for the wind and water distributions (shape = 1.5, rate = 4.2). I used

the shape and rate parameters for all three dispersal density kernels to create simulated

densities using progressively larger numbers of seeds (50, N, 200, 300, 500, 1000, 5000,

10000) and generated density plots using the random gamma distribution function in R

3.1.3 to compare results (R Core Development Team 2015).

4.2.4 Germination

I collected seeds from ten plants at the Runway site and ten plants at the Double

site (Table 1) in October 2012, air dried them for two months (October-November); then

separated, counted, and wrapped them in damp paper towels before placing them in small

plastic bags (Cullina 2002). I cold-stratified seeds at 0oC in a home freezer for two

months (December-January) before exposing them to two months of ambient basement

temperatures (February-March, varying from approximately -8oC to 8oC). I selected one

site on WGR based on a visual assessment of soil type and moisture status, and proximity

to a known population of R. knieskernii. I cleared the site of existing vegetation in late

winter to create four contiguous plots. Each of the four plots had eight subplots placed in

a randomized block design (Figure 4.4). Subplots were 30 cm x 25 cm and were separated from each other by a distance of 10 cm. There was a 5 cm edge of soil around the perimeter of the plot (3.6 m x 1.5 m).

In April 2013 I treated seeds with either heat, smoke or charred wood or left seeds untreated (control) before placing seeds either on the surface or 1 cm below the surface 172

for a block design with four treatments at each level (n=32 subplots). I planted 30

achenes in each subplot 3 cm apart in 3 rows of 10 seeds each (n=960). The heat

treatment involved substantial mixing of soil, so plots not receiving heat treatment also

had soil turned over to a depth of 2 cm before inserting seeds.

I collected and heated the top 2 cm of soil to 60oC for 1 hour (modified from

Wills and Read 2002) in a Boekel 133000 oven (Boekel Scientific, Feasterville, PA),

returned soil to the site and inserted seeds into the soil to a depth of 1 cm or placed them

on the surface. The seeds are small and it was not clear what temperature would damage

seeds if heated directly. While some seeds are exposed directly to fire, in many cases (as

in the prescribed burn in 2012), fire heats the soil while consuming little plant litter.

For the smoke treatment I placed seeds in small trays at the top of a slow smoker approximately 60 cm from the heat source to minimize heating of seeds and smoked them for 15 minutes (adapted from Shebitz et al., 2003) using local vegetation as the smoke source: pitch pine (Pinus rigida); blackjack oak (Quercus marilandica), and switchgrass

(Panicum virgatum). I sprayed seeds once with DI water after the smoke treatment to increase ambient adherence of smoke particles (adapted from Shebitz et al. 2003) before placing them on the surface or inserting them in the soil.

I soaked seeds receiving charred wood treatment for 24 hours in an aqueous solution of charred wood. I prepared the aqueous solution by collecting charred P. rigida branches from a recently burned site, cutting them approximately 1 cm in diameter, grinding branches in a household blender, and sifting the material through a #18 (1 mm) sieve screen and then soaking 2.5 mg charred wood in 20 mL DI water for 18 hours and filtering the solution through a Whatman #1 filter paper (Keely and Pizzorno 1986). 173

I covered plots with lightweight transparent fabric to prevent predation or

movement of surface seeds until plants began germinating, I removed the fabric when

seedlings emerged and recorded the number of R. knieskernii seedlings per plot. I

removed all plants other than R. knieskernii and monitored study plots until the end of the

growing season (October 2013).

4.2.5 Data Analysis

I tested differences in how platform height, wind speed, water depth, and water

velocity affected dispersal distances using one way and two way ANOVAs followed by

Tukey HSD post-hoc tests to identify significant factors. I also conducted one-way

ANOVAs to identify significant differences in wind speeds at different sites, changes in

buoyancy at different RPM settings, and the impact of achene density on attachment

rates. I tested differences in germination rates for treatments, levels and treatment x level

interactions using one way and two way ANOVAs and Tukey HSD post-hoc tests. All statistical analyses were performed using R 3.1.3 (R Core Development Team 2015). As part of my analysis of dispersal dynamics, I tested for clustering of extant R. knieskernii populations at the landscape scale by computing simple spatial statistics using the

Average Nearest Neighbor tool in ArcView 10.0 (ESRI Corporation). This tool uses the difference between the observed and expected distances between points within a specified area to calculate a z-statistic that indicates whether neighbors are clustered or dispersed and a p-value based on the normal distribution. I tested potential site clustering using centroid points from all known sites on WGR. I also tested potential site clustering at a larger landscape scale by using centroid points from all known sites within

the New Jersey Pine Barrens (including sites on WGR). Location data for R. knieskernii 174

populations (statewide) were provided by the New Jersey Department of Environment

Protection, Office of Natural Land Management.

4.3 Results

4.3.1 Wind Dispersal

Site Measurements: Mean wind speeds among the five study sites ranged from 0.20 to

1.16 m/s ( X ± 1 SE = 0.6 ± 0.02) while maximum wind speed ranged from 0.75 to 3.88

m/s ( X ± 1 SE = 2.2 ± 0.02). There was a statistically significant difference in mean

2 wind speed (F9,68 = 6.4,p<0.0001, R = 0.39) for the Runway site (p<0.01) compared to

2 other sites and for maximum wind speed (F9,68 – 13.34, p<0.0001, R = 0.59) at the

Runway and Sight Line sites (p<0.0001, Figure 5; Tables 1 and 2) compared to other

sites. There were no statistically significant within-site differences for either mean or

maximum wind speed measurements when comparing wind speeds at air and ground

levels, although there were between-site differences (Figures 5 and 6; Table 1).

Wind Dispersal Experiment: There were significant differences in distances propagules

traveled during wind dispersal based on mean and maximum wind speed; differences

2 were highly significant for maximum wind speed (F7,112 = 10.16, p<0.0001, r = 0.35).

There was an interaction between platform height and wind speed (F3,116 = 16.46,

p<0.0001), but there were no significant differences between platform heights (F1,118 =

1.94, p = 0.17, Tukey HSD, p=0.76, Table 1). However, density distributions showed a

bimodal distribution for the taller platform (Figure 4.7). Distance density kernels for

different maximum wind speeds revealed a pattern of peak densities at greater distances 175

as wind speeds increased (Figure 4.8). Differences in distances propagules traveled were moderately significant for several levels of mean and maximum wind speeds, and highly significant for the penultimate mean and maximum wind speeds (p<0.01 for mean wind speed of 0.6 m/s; p < 0.0001 for maximum wind speed of 2.6 m/s; Tables 4.1 and 4.3). A comparison of wind direction data collected during the experiment with data collected by the weather tower at WGR revealed similar patterns, although the tower data were collected over a longer time period, resulting in a greater density and spread of wind directions (Figure 4.9). Gamma distribution simulations for wind dispersal indicated that higher propagule densities peaked tightly around zero, but also had much longer and thinner tails than lower densities (Figure 4.10).

Buoyancy: There was a statistically significant difference in the rate at which propagules

sank in still water compared to moving water (Figure 4.11; Table 4.4). Propagules sank

more rapidly as the rate of water movement increased (Figure 4.11; Table 4.4). Over a

four hour period, the mean number of buoyant propagules in moving water declined from

ten to eight, while movement changed from 40 to 70 to 90 RPM (Figure 4.11). Over a

one hour period at 90 RPM, the mean number of buoyant propagules in moving water

declined from eight to five (Figure 4.11). During this same four hour period, the mean

number of buoyant propagules in still water declined from ten to three (Figure 4.11).

Water Dispersal: There was a statistically significant difference in the distance that

propagules moved at the two velocities (0.11 and 0.19 m/s; F1,72=24.18, p<0.0001,

r2=0.24, Figure 4.12); there was no significant difference for the two different depths (2

cm and 3 cm; F1,72=1.01, p=0.32). The peak and tail of the distance density distributions

were similar for the two depths (Figure 4.13) but different for the two velocities (Figure 176

4.14). The gamma distribution simulation showed a trend toward fatter tails for lower

propagule densities, but the distance densities were similar for all densities (Figure 4.15).

Comparison of Wind and Water Dispersal: In a comparison of water and wind kernel

density distributions, peak density was clustered around zero for both dispersal vectors,

but the tail of the water dispersal distribution was fatter and longer, while the tail of the

wind distribution was short and ended abruptly at ~ 100 cm (Figure 4.16).

Deer dispersal: In the attachment potential experiment, I tested the number of propagules

that attached to deer legs as the device moved through patches. The mean number (± 1

SE) of attached propagules per trial for all sites was 6.05 ± 0.93; it was 3.2 ± 0.95 for

low-density patches (167 plants/m2) and 8.9 ± 0.97 for high-density patches (643

plants/m2). Significantly more propagules attached to deer legs in higher density patches

2 (F1,18 = 17.56, p<0.0001, r = 0.47; Figure 4.17). Distances were extrapolated as

described in the methods section. Kernel densities for four of five tests exhibited

bimodal distributions, with peaks near zero and at 400 meters (Figure 4.18); propagules

tended to either detach within five minutes or remain attached for 15 or more minutes

(Table 4.5). Simulated dispersal distances exhibited fat tails, particularly at low

propagule densities; at higher propagule densities simulations trended toward long thin

tails, although this was not seen in the experimental condition (Figure 4.20). A

comparison of all three dispersal vectors exhibited different kernel densities; both wind

and water dispersal had highly concentrated kernel densities near the source spot when

compared to deer dispersal. Deer dispersal exhibited several peaks; all peaks were at

greater distances than the other dispersal vectors (Figure 4.21). 177

Spatial Analysis: The nearest neighbor analysis indicated that subpopulations of R.

knieskernii were clustered within the landscape at both spatial scales tested: at WGR, (z

<-2.58 and p<0.0001) and throughout the New Jersey Pine Barrens (z<-2.58, p<0.01).

Germination: There was no statistically significant difference among treatments or

between treatment plots and control plots (Figure 4.21; Table 1). There was a trend

toward increased germination for the heat and charred wood treatments compared to the

control and smoke treatments; this increase was higher for treatments where seeds were

buried 1 cm below the surface (Figure 4.21; Table 1). There was a statistically

significant difference between seeds sown on the surface and seeds buried at 1 cm (F1,30 =

8.468, p<0.01,r2 = 0.19), although there was no significant interaction between depth and

treatment (F1,30 = 2.941, p=0.5).

4.5 Discussion

Few studies compare multiple potential dispersal mechanisms for a single species, even though a species may utilize more than one dispersal mechanism (Willson 1993;

Bullock et al. 2006; Tamme et al. 2014). It is important to look at multiple dispersal modes to understand the factors limiting rare species distribution (Burmeier and Jensen

2008). In disturbance-dependent ecosystems where propagule dispersal may play a role in metapopulation dynamics, it is also important to consider how disturbance may relate to germination and recruitment (Levine and Murrell 2003). My data suggests that specialized morphological characteristics on R. knieskernii facilitate several different

dispersal mechanisms. Although the bristled propagules of R. knieskernii are generally

associated with adhesive dispersal, they utilize wind, water, and animal dispersal 178

mechanisms; additionally, the variation within these mechanisms has the potential to

influence population dynamics. Studies which examine only one dispersal mechanism in

detail may not adequately describe the range of dispersal options available to plants

(Burmeier and Jensen 2008). Long-distance dispersal is often seen as beneficial in

disturbed habitats (Kisdi 2002; Kallimanis et al. 2006; Büchi, and Vuilleumier 2014), but

there is also a high risk of dispersing to unsuitable habitats (Bonte et al. 2012). In this

context, it is important to understand the germination requirements of a species after

dispersal. While I did not find fire-related germination cues, I did demonstrate that R. knieskernii seeds respond strongly to light cues. Light availability increased after fire, indirectly influencing germination in burned patches. In a study that analyzed fire effects

in boreal forests at multiple landscape scales, dispersal and burn intensity were found to

be influential at the smallest scale (patch), although other biotic and abiotic factors were

more influential at larger scales (Boiffen et al. 2015). The interrelationships of

disturbance, multiple dispersal mechanisms, and recruitment to patches with higher light

availability may have ecological consequences for species conservation, metapopulation

dynamics, and community interactions (Willson 1993; Levine and Murrell 2003; Büchi,

and Vuilleumier 2014; Boiffen et al. 2015).

Small, light propagules of herbaceous plants tend to be dispersed by wind and

have morphological characteristics that increase movement across soil surfaces (Sheldon

and Burrows 1973; Chambers 2000; Jongejans and Telenius 2001). Special seed

structures serve to increase dispersal distance by lowering terminal velocity (Sheldon and

Burrows 1973; Jongejans and Schippers 1999). In R. knieskernii, the structures are

oriented around the main axis of the achene, rather than spreading from the summit as is 179

seen in many wind-dispersed propagules (Sheldon and Burrows 1973; Schurr et al. 2005).

In the wind dispersal experiment, R. knieskernii exhibited bimodal dispersal curves for higher wind speeds; propagules either remained on the platform or moved relatively long distances (Figure 9). Little empirical data exist on the behavior of low-mass wind- dispersed propagules; most propagules are at least 0.1 mg in weight (Greene and Johnson

1989; Jongejans and Schippers 1999), while the mean weight of R. knieskernii seeds is

122 µg (M. Sobel, unpublished data). Johnson and Fryer (1992) reported that a small percentage of seeds did not move across a smooth wood surface, although no reason was given. Paradoxically, very small seeds are predicted to disperse for shorter distances than larger, heavier seeds due to differences in wind velocity experienced by the seed when accounting for roughness length (Schurr et al. 2005). Alternatively, the bristles may have adhered to the balsa wood, inadvertently mimicking the behavior of propagules that do not disperse until wind speed reaches a release threshold (Soons and Bullock

2008). However, in my study, resistance alone did not seem to adequately explain the comparatively large number of propagules that did not move at higher maximum wind speeds (Figure 4.8). Although I only measured primary dispersal, secondary dispersal may be limited if propagules disperse on a rough, textured surface (Johnson and Fryer

1992; Schurr et al. 2005). Propagules in my study dispersed greater distances at higher wind speeds and greater platform height; this has also been observed in other studies

(Jongejans and Telenius 2001; Cousens 2008). It is thought that taller plants would have an advantage in dispersing propagules further from the mother plant, as this would reduce kin competition (Ronce 2007). The strongly skewed and truncated seed rain patterns in 180

my study indicate that wind primarily disperses propagules within R. knieskernii habitats where parent plants are established.

Many Cyperaceae are found in wetland environments and are associated with hydrochorous dispersal traits (Leck and Schutz 2005). Although R. knieskernii is an obligate wetland plant, it typically inhabits ecotones characterized by saturated mineral soils rather than inundated organic soils (Bien et al. 2009). Patches occur under forested canopy gaps along shallow ditches, depressions and animal trails; they often terminate abruptly, presumably due to changes in hydrology (M. Sobel, pers. obs.). While sites can be ponded for several weeks and in rare cases months, most sites do not have standing water (USFWS 2008; M. Sobel, pers. obs.). Rhynchospora knieskernii propagules were found to be buoyant and to remain on the surface for several hours, particularly under conditions of slow water movement (Figure 4.11). In a similar test of buoyancy for a range of species over a longer time period, there was a positive relationship between propagule flotation time and the hydric quality of the species’ habitat; propagules of species living in wetter habitats floated longer than species living in drier habitats (van der Broek et al. 2005). Although buoyancy tests are usually conducted in the laboratory,

Burmeier and Jensen (2008) used greenhouse and field experiments to test buoyancy.

Propagules placed in open containers in the field sank quickly after exposure to wind and rain (Burmeier and Jensen 2008). Similarly, R. knieskernii propagules sank when water movement increased to 90 RPM. These data suggest precipitation could have a similar effect on floating time for R. knieskernii propagules.

Buoyancy time is a standard measure in water dispersal studies, but ecological relationships are also important in assessing hydrochory. In a study of the rare herb 181

Apium repens, the authors found that propagules could float for up to 28 days, but speculated that this was not ecologically relevant given that plants grow mainly in isolated wetland complexes such as vernal pools (Burmeier and Jensen 2008). Although the buoyancy period exhibited by R. knieskernii was short compared to many highly aquatic species, it is sufficient to disperse propagules within hydrologically isolated sites.

It is also plausible that R. knieskernii propagules are transported downslope by precipitation events that sometimes lead to water currents moving through R. knieskernii sites (M. Sobel, pers. obs.). This potential transport vector was simulated in the flume tank experiment by testing propagule transport at velocities of 0.11 m/s and 0.19 m/s; there was a positive relationship between increased water velocity and increased dispersal distances (Figure 14). These data suggest that precipitation events that facilitate water currents within saturated sites could play an important role in dispersing R. knieskernii seeds. A study of plants that release seeds into drainage ditches where water moves slowly with few surface obstructions (Soomers et al. 2010; Soomers et al. 2013) may prove more comparable to R. knieskernii than those of aquatic species that often live in riparian habitats adjacent to streams and other features with swifter currents and more complex obstructions than propagules will typically encounter in the seeps and wet ditches R. knieskernii inhabits. The mean water velocity of the ditches in the study was ~

0.4 m/s (Soomers et al. 2010). However, my results contrast with those found for propagules in these ditches, where wind speed was the key variable determining dispersal distance; propagules were pushed by wind until reaching a deposition point, which was often an obstruction in the water (Soomers et al. 2010; Soomers et al 2013). Even at the shallow depths of the flume experiment, the small size of R. knieskernii propagules 182

facilitated movement at the water’s surface, allowing them to float past the pebbles

presented as potential obstructions and avoid the frictional forces of the substrate. Water

dispersal distances were greater than wind dispersal distances, even though the

experimental dispersal distance was limited by the length of the flume tank. These

distance data are also consistent with the fat tails of the water dispersal distributions,

suggesting that hydrochory is a long-distance dispersal vector within wetland complexes.

This could explain the distribution of R. knieskernii at larger landscape scales, where known patches often occur at several locations along seeps and small streams.

These data comparing wind and water dispersal kernels suggest that primary wind dispersal vectors are less effective at dispersing R. knieskernii propagules long distances than secondary hydrochoric dispersal vectors. This may be related to the difference in frictional forces, which are less likely to impede propagule movement in water. Greene and Johnson (1989) developed a theoretical model which predicated a flattening of dispersal curves with changes to either wind speed or release height that would also increase tail length, while Groves et al. (2009) found a strikingly similar result for water dispersal based on changes to either water velocity or depth. The water and wind dispersal curves show a similar flattening as wind speed and water velocity increase

(Figures 4.8 and 4.15). However, at high propagule densities, simulated wind dispersal kernels exhibited very long tails, suggesting that for dense patches wind could be more effective for infrequent long-distance dispersal of propagules to new habitats (Figure

4.10).

Although wind has can disperse R. knieskernii propagules long distances under certain conditions, deer are thought to be the most effective long-distance dispersal 183

vector. Deer and other large herbivores have been found to be effective propagule dispersers in several studies (Heinken and Raudnitschka 2002; Couvreur et al. 2004;

Mouissie et al. 2005). Field studies are valuable in demonstrating how deer transport propagules, but they are limited by the environments where the studies are conducted

(Heinken and Raudnitschka 2002; Couvreur et al. 2004). For example, few studies have reported on deer as vectors for wetland plants, including Cyperaceae with adhesive dispersal characteristics (Leck and Schutz 2005). In cases where dead animals had their fur combed for seeds it is not clear how far animals transported propagules (Heinken and

Raudnitschka 2002; Picard and Baltzinger 2012). Models using domesticated animals

(i.e., sheep, cattle) have elucidated which factors facilitate dispersal, of which fur type is the most important (Mouissie et al. 2005; Will and Tackenberg 2008). However, the domestic animals tested have thick, curly hair not usually found on wild animals. In studies using deer and small mammal fur, seed morphology becomes more important

(Mouissie et al. 2005).

Clearly, R. knieskernii propagules attach to deer fur and can remain attached long enough to be moved to sites hundreds of meters away, based on published deer movement rates (Nelson et al. 2004; Root et al. 1998). Studies used to validate attachment models have used several mechanical devices to estimate attachment rates

(Mouissie et al. 2005; Römermann et al. 2005; Will et al. 2007; Mouissie et al. 2005), while studies in the field involve either simulating attachment (Laughlin 2003) or sampling fur from dead animals (Heinken et al. 2002; Picard and Baltzinger 2012). In my experiment to determine if deer were a long-distance dispersal vector I attached fresh deer legs to a toy car, and pulled the device through dispersing patches of R. knieskernii 184

propagules to simulate deer movement. As in my model, other studies using mechanical means to shake fur also had high percentages of propagules remaining attached for long time periods (Will et al. 2007). It would appear that once a propagule is firmly attached it does not detach easily. If the percentage of propagules remaining attached to deer fur for at least 15 minutes (22%) reflects real attachment rates, the mean number of propagules that could potentially be dispersed at least 280 meters after a deer moved through a patch would be less than one propagule per deer for low-density patches, but two propagules for high density patches (Figure 4.17). Although movement of a few propagules between patches may seem inconsequential, an examination of data comparing deer and rare plant abundance over 30 years found strong support for deer dispersal of the species Cynoglossum germanicum, which was originally rare in the environment but has now become widespread (Boulanger et al. 2011).

Because deer move among isolated wetlands within pitch pine lowlands, they are capable of directed dispersal to these sites. Although it is not known if routes are direct or circuitous, the frequency of fresh deer tracks at my R. knieskernii plots suggests that deer move often between these sites. Deer frequently browse New Jersey Pine Barrens

(NJPB) wetland habitats (Zampella and Lathrop 1997). Deer are also known to browse preferentially at recently burned sites (Hallisey and Wood 1976), providing another means of directed dispersal, although many prescribed burns do not take place until well after fall seed dispersal.

Spatial statistics can provide additional insight into dispersal dynamics. The results of the nearest neighbor analysis showed clustering at both the local scale (WGR) and the landscape scale (NJPB). The mean nearest neighbor distance for R. knieskernii 185

among subpopulations on WGR was 357 m, while the greatest distance between subpopulations was 1300 m. For the entire extent of the NJPB, the mean nearest neighbor distance was 4.8 km and the greatest distance was 34.6 km. Spatial analysis of sites revealed clustering with a high degree of probability (p < 0.01 for WGR; p < 0.0001 for NJPB), indicating that habitats are usually in proximity to one another. Deer movement within home ranges would frequently cover the distances encountered between nearest neighbors (25-225 hectares; Tierson et al. 1985; Rhoads et al. 2010). Thus deer represent a plausible means for distributing R. knieskernii propagules throughout the

NJPB, while hydrochorous dispersal can be effective at maintaining plant density within highly suitable habitats, minimizing the risks presented by long-distance dispersal. While

I found strong support for my hypothesis that R. knieskernii uses long-distance dispersal to colonize new sites, further research and genetic studies are needed to delineate dispersal dynamics.

Germination at suitable sites is necessary for seed dispersal to be successful

(Middleton 2000; Eichberg et al. 2005; Schupp et al. 2010). Many fire-disturbed ecosystems support species that increase in density after fires (Carrington 1999;

Clemente et al. 2007; Ne’eman et al. 2009). Investigations have demonstrated that fire often increases light availability (Peterson et al. 2007), reduces competition (Safford and

Harrison 2004), and indirectly stimulates germination (Keeley and Pizzorno 1986; Wills and Read 2002. In my study, there were no significant differences in germination for any of the three fire-related treatments, but there was a significant difference in germination rates for seeds on the surface compared to seeds below the surface. This would suggest that light is an important cue triggering R. knieskernii germination. However, 186

germination rates were highest for the heat and charred wood treatments, providing a potential pathway fore fire effects to facilitate germination.

Burn severity strongly influenced community dynamics in a study of boreal forests (Boiffen et al. 2015). However, wildfires usually result in greater burn severity than prescribed burns (Certini 2005), which could in turn affect germination positively by increasing nutrient availability or negatively by killing seeds through excessively heating.

A study in mixed oak forests during a cool winter burn found that the highest flame temperatures were 10 cm above the soil, while the highest soil temperatures were less than 40oC (Irvin and Hutchinson 2002). More significant was the effect of fire on long- term soil temperatures, which remained higher than on unburned sites for several months

(Irvin and Hutchinson 2002). Heating soil generally increases germination response under experimental conditions because heat shock plays a role in breaking physiological dormancy in hard-seeded plants (Van Staden et al. 2000; Christensen and Muller 1975).

This may explain why heat had a more positive impact on germination rates for R. knieskernii seeds below the surface in comparison to smoke or control plots. In actual fires the soil remains heated, while in my experiment the heat probably dissipated rapidly, which may have diminished its overall effect on seeds.

Studies of charred wood show variable germination responses that are not always positive (Keely and Pizzorno 1986; Perez-Fernandez and Rodriguez-Echeverria 2003).

Both smoke and charred wood may stimulate hormone production (Von Staden et al.

2000). Charred wood also increases pH (temporarily), which may increase soil microbial activity and the production of nitrates, which can enhance seed germination (Von Staden et al. 2000). The slightly higher germination rates recorded for buried seeds treated with 187

charred wood may have been the result of changes in belowground processes that were

obscured aboveground where there was greater light availability to cue germination.

Both smoke and heat combined with smoke increased germination response

(Keely and Fotheringham 1998; Wills and Read 2002; Shebitz et al. 2003). The effect of smoke can persist for up to five years, because the active compound is very stable and can adsorb to soil particles (Von Staden et al. 2000). The direct effect smoke compounds have on germination has not been elucidated. Nitrogenous compounds have proven to be important in cueing germination but have inhibited germination in other studies or have had no effect (Keely and Pizzorno 1986; Von Staden et al. 2000). In my study, smoke-

treated seeds had particularly low germination rates, suggesting that compounds in the

smoke either inhibited or did not facilitate R. knieskernii seed germination.

Although I did not find statistical support for my hypothesis that R. knieskernii

uses fire-related germination cues to establish in recently burned sites, it remains unclear

if fire plays a role in stimulating germination in R. knieskernii. Indirectly, fire can influence patch dynamics by creating openings with higher light availability and potentially increasing nutrient availability (unpublished data, Chapters 1 and 2). In the

NJPBs, where there is high beta diversity associated with frequent fires and infrequent flooding, short and long-distance dispersal mechanisms provide a bet-hedging strategy that allows R. knieskernii to exploit new habitats while maintaining high densities that may exclude competitors in relatively resource-rich sites. However, there are trade-offs

involved in long-distance dispersal strategies; selection pressure that favors habitat

specificity may decrease the fitness of seeds by dispersing them into new habitats where

they are less likely to germinate (Kisdi 2002; Bonte et al. 2012). 188

Rhynchospora knieskernii may employ a recruitment strategy which allows it to establish and even dominate within specific habitats while maintaining the capacity to colonize new and distant habitats. Unfortunately, there is little empirical evidence to support the benefits of such a strategy (Rabinowitz et al. 1984). Models have demonstrated that competitive dispersal strategies of rare plants can contribute to the maintenance of species diversity (Büchi and Vuilleumier 2014). More generally, Bolker

and Pacala (1999) modeled the conditions under which short-term and long-term dispersal are beneficial to species. They found that specialization for colonization, exploitation, and tolerance are all possible spatial strategies with potential for success based on landscape characteristics and spatial dynamics. Generally plants must select for one strategy, but in some circumstances it is possible to have dual-dispersal strategies of

exploitation and colonization (Bolker and Pacala 1999). Based on their results they

speculate that “a dual dispersal strategy combining mostly short-distance dispersal with a

few long-dispersing propagules gives the largest increase in variance with the smallest

decrease in performance in the mean environment.” (Bolker and Pacala 1999). Similarly,

R. knieksernii exhibits short- and long-distance strategies. The results of my study

suggested that dual dispersal strategies facilitate the maintenance of populations.

However, further research will be necessary to elucidate how R. knieskernii dispersal,

metapopulation dynamics, and associated plant competition (e.g., light, nutrients, space)

interact with disturbance regimes. 189

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Figures

Figure 4.1. Experimental setup for testing wind dispersal distances showing 18 cm platform. The bags of sand kept the platform stable.

Figure 4.2. Flume tank (inset) and experimental setup for testing hydrochory dispersal distances. Note the pebbles distributed every 10 cm along tank surface.

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Figure 4.3. Experimental setup for testing seed attachment to deer fur.

Figure 4.4. Experimental germination plot. Dark and light colors represent buried and surface seeds. Orange represents heat; grey represents smoke; brown represents charred wood; blue represents controls. 199

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Figure 4.5. Comparison of mean (left) and maximum (right) wind speeds 40 cm above the ground (Air) and 4 cm above the ground surface (Ground). Asterisks indicate site differences.

200

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Figure 4.6. Comparison of mean (left) and maximum (right) wind speeds 40 cm above the ground (Air) and 4 cm above the ground surface (Ground). Asterisks indicate site differences.

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Figure 4.7. Wind dispersal kernel densities for R. knieskernii seeds released from two different platform heights.

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Figure 4.8. Comparison of wind dispersal kernel densities for R. knieskernii at different maximum wind speeds. Differences in wind speeds were significant for all speeds above 2.0 m/s and highly significant for wind speeds at 2.6 m/s.

Figure 4.9. Wind direction during fall testing of wind dispersal of R. knieskernii seeds. Rose diagram on left shows wind direction as measured by weather tower on Warren Grove Range over 24-hour period; rose diagram on right shows wind direction during dispersal testing. 203

Figure 4.10. Kernel density plot for showing distance distribution of R. knieskernii seeds based on experimental results from point source dispersal (above) and simulations using increasing densities of seeds and assuming the same conditions (below). Note differences in bandwith. Black line shows experimental seed density based on wind dispersal tests.

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Figure 4.11. Comparison of seed buoyancy under different movement conditions. For shaken seeds, movement was 40 RPM for 30 minutes, 70 RPM for 150 minutes and 90 RPM for 90 minutes.

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Figure 4.12. Boxplots showing statistically significant differences between water velocity. Asterisks indicate highly significant difference between velocities.

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Figure 4.13. Comparison of hydrochoric dispersal distances for R. knieskernii seeds at two different depths with water velocity held constant.

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Figure 4.14. Comparison of dispersal distances for R. knieskernii seeds tested at two different water velocities with depth held constant.

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Figure 4.15. Kernel density plot showing distance distribution of R. knieskernii seeds based on experimental results from point source dispersal (above) and simulations using increasing densities of seeds and assuming the same conditions (below). Note differences in bandwith. Black line shows experimental achene density based on water dispersal tests.

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Figure 4.16. Distance distributions comparison of wind (red) and water (black) dispersal of R. knieskernii seeds. Although peak of wind distribution is not visible, it is close to zero.

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Figure 4.17. Comparison of R. knieskernii seed attachment when deer model traveled through high-density (643 plants/m2) and low-density (147 plants/m2) patches.

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Figure 4.18. Bimodal dispersal distances for R. knieskernii seeds based on calculations of known deer movement and five tests of attachment rates using deer attachment model.

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Figure 4.20. Kernel density plot showing distance distribution of R. knieskernii seeds based on extrapolated results from deer attachment rates (above) and simulations using increasing densities of seeds and assuming the same conditions (below). Note differences in bandwith. Black line shows experimental achene density based on deer dispersal tests.

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Figure 4.20. Comparison of dispersal distances for three potential R. knieskernii dispersal vectors. Water and wind dispersal based on experimental point source dispersals; deer dispersal extrapolated from published deer movement rates and tested seed attachment rates.

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Figure 4.21. Interaction plot with boxplots depicting interaction between location and germination for each of four treatments of R. knieskernii seeds (control = no treatment; charred wood, heat and smoke treatments described in methods). There were four subplots within each type and location (N = 32). Lines depict slope of interaction between treatment and location.

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Tables

Table 1. Summary of statistical results.

2 Comparison df Fstat p R significance Mean Wind Speed ~ Site 5 and 76 8.14 0.0000 0.31 *** Maximum Wind Speed ~Site 5 and 76 25.81 0.0000 0.61 *** Maximum Wind Speed ~ Site * Level (Air or Ground) 9 and 68 13.34 0.0000 0.59 *** Distance ~ Mean Wind Speed 1 and 118 12.01 0.0007 0.08 ** Distance ~ Maxiumum Wind Speed 1 and 118 42.62 0.0000 0.26 ***

2.1 (*), 2.5 (*), Distance ~ Wind Speed Groups 7 and 112 10.16 0.0000 0.35 2.6 (***), 2.7 (**) Distance ~ Height 1 and 118 1.94 0.1660 0.01 ns Distance ~ Height*Mean Wind Speed 3 and 116 7.25 0.0002 0.14 ** Distance ~ Height*Maximum Wind Speed 3 and 116 16.46 0.0000 0.28 *** Buoyant ~ Type 1 and 187 10.98 0.0011 0.05 * Buoyant~Type*Minutes 3 and 185 41.01 0.0000 0.39 *** Distance ~ Velocity 1 and 72 24.18 0.0000 0.24 *** Distance~Depth 1 and 71 1.01 0.3174 0.00 ns Achenes ~ Density 1 and 18 17.56 0.0005 0.47 ** Germination (Flowering) ~ Position 1 and 30 8.47 0.0068 0.19 * Germination (Total) ~ Position 1 and 30 6.06 0.0198 0.14 * Germination (Flowering) ~ Position~Type 3 and 28 2.94 0.5000 0.16 ns Germination ~ Type (Flowering) 1 and 30 0.10 0.7582 -0.03 ns Germination ~ Type (Total) 1 and 30 0.07 0.7971 -0.07 ns

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Table 2. Summary statistics for wind speeds measured at sites during fall 2013 at WGR.

Maximum Mean Wind Wind Speed Speed (m/s) Site Level N SD SE (m/s) SD SE Dead End Air 6 0.53 0.08 0.03 1.77 0.47 0.19 Dead End Ground 6 0.48 0.10 0.04 1.47 0.14 0.06 Double Air 12 0.73 0.51 0.15 1.33 0.42 0.12 Double Ground 12 0.28 0.18 0.05 1.05 0.54 0.16 LSL Air 6 0.40 0.35 0.14 1.27 0.92 0.38 LSL Ground 6 0.20 0.22 0.09 0.75 0.36 0.15 Runway Air 9 1.16 0.47 0.16 3.88 1.96 0.65 Runway Ground 9 0.92 0.45 0.15 3.46 1.49 0.50 Sight Line Air 6 0.88 0.24 0.10 3.85 0.27 0.11 Sight Line Ground 6 0.72 0.23 0.09 3.53 0.93 0.38

Table 3. Summary statistics for wind speeds and distances measured during wind dispersal test.

Mean Platform Mean Wind Maximum Wind Distance SD Height N Speed (m/s) Speed (m/s) (cm) (Distance) SE (Distance) 18 10 0.4 0.5 11.06 12.78 4.04 18 10 0.5 1.7 11.91 16.19 5.12 18 20 0.6 2 30.35 18.32 4.10 18 20 0.7 2.1 20.27 15.05 3.36 35 10 0.3 0.9 0.00 0.00 0.00 35 10 0.4 2.5 0.00 0.00 0.00 35 10 0.5 2.6 64.06 20.50 6.48 35 20 0.6 2.7 31.88 37.22 8.32 35 10 0.7 2.7 38.26 34.28 10.84

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Table 4. Results of ten trials of achene buoyancy at variable shaking speeds and not shaken.

Shaken Still

Mean Mean Time RPM (buoyant) SD RPM (buoyant) SD 30 40 95 1.58 0 96 0.95 60 70 95 1.58 0 95 0.97 90 70 94 1.58 0 93 0.95

120 70 93 1.57 0 81 2.60 150 70 89 2.13 0 72 2.66 180 70 86 2.01 0 62 3.36 210 90 86 2.01 0 54 3.60

240 90 84 2.01 0 41 3.35 270 90 69 2.51 0 34 3.44

300 90 47 2.67

Table 5. Results deer attachment trials in which achenes were directly attached to deer legs. Data represent combined results from all five trials (10 achenes for each trial, N=50).

Number Number Percent Attached Percent Detached Detached Achenes Attached Time (m) Time (s) Achenes Achenes (N=50) Achenes 1 60 11 0.22 39 0.78 2 120 5 0.1 45 0.9 3 180 1 0.02 49 0.98 5 300 4 0.08 46 0.92 15 900 18 0.36 32 0.64 30 1800 7 0.14 43 0.86 75 4500 4 0.08 39 0.78

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Conservation Implications and Future Directions

5.1 Conservations Implications

Rhynchospora knieskernii is a federally-threatened species endemic to the New

Jersey Pine Barrens (NJPB). Although the U.S. Fish and Wildlife Service considers the species to be relatively secure and the need for action to be of low priority (USFWS

2008), my study suggests that populations may be at greater risk than previously supposed. The widespread perception that R. knieskernii is an early-successional species

often found along roadsides has led to an assumption that R. knieskernii will be able to

establish easily in disturbed environments and that the increase in canopy cover that is a

result of changes in successional seres poses the greatest risk to populations (USFWS

2008). My study instead suggests that dense populations are generally found in forested

environments under canopies and that R. knieskernii can only grow in habitats within a

small range of soil moisture availability. This may explain why populations are only

infrequently encountered even along moist roadside environments (R. Cartica, NJDEP,

pers. comm.). Thus proper protection of this species should focus on the critical

ecological relationships which I identified in my study. These relationships were most

closely associated with soil hydrology and with prescribed burning. The pace of

development in the NJPB is increasing and the extent of fires has contracted in the last

several decades (Forman and Boerner 1981; Luque et al. 1994). As increased

development generally leads to fire suppression and potentially to a loss of hydrological

integrity (Bunnell et al. 2003; Hansen et al. 2005), management of R. knieskernii must

include protection of these complex relationships. 219

Clearly any early-successional species is at risk from later seres that utilize long- term competitive strategies, but R. knieskernii habitats are often kept open for decades through either management or naturally moist conditions that limit vegetation (Schyler

1990; Obee 1995; Gordon 1996). This makes it critically important to maintain hydrological relationships within wetland complexes and to ensure that the high water table is not negatively affected by activities within the proximity of R. knieskernii populations. Although an obligate wetland plant, dense populations are very seldom found adjacent to streams or even large ponds (M. Sobel, pers. obs.). Instead, populations are frequently encountered in areas of low topography proximate to intermittent ponds (M. Sobel, pers. obs.). These areas may not be as well protected as larger wetlands, particularly since the small, low-density graminoid vegetation is easily overlooked in surveys (USFWS 2008).

My data suggests that hydrochory is one of the main mechanisms of dispersal in

R. knieskernii. Ponding and water movement which would disperse achenes along hydrological corridors does occur, but experimental data and simulations suggest most hydrochoric dispersal will be within habitats (Chapter 4). However, seasonal hurricane activity, though limited, coincides with fall dispersal and may facilitate transport along larger wetland drainages when flooding is extensive. Since R. knieskernii achenes stayed afloat and traveled further in moving water (Chapter 4), this increases the likelihood that under these favorable conditions achenes may be transported relatively long distances.

Long-distance dispersal is often critical in disturbed environments and dispersal along wetland drainages increases the likelihood that achenes will be deposited in suitably moist habitats. 220

A rather surprising result of my study was that there was a quadratic relationship between soil moisture and fruiting populations; populations were smaller at both low and high soil volumetric moisture levels and were at their densest at sites with moisture levels close to 10% (Chapter 2). These data have important conservation implications.

Botanists have previously associated R. knieskernii populations with a fluctuating moisture table, but my study suggests that R. knieskernii has much more precise yet complex moisture requirements than previously supposed (Chapters 2 and 3).

Populations are densest within a very narrow soil moisture range, yet soil moisture may also facilitate R. knieskernii establishment through mechanisms that limit species lacking the capacity to live in flooded environments (Chapters 2 and 3). Thus it is extremely important to maintain all the hydrological relationships within R. knieskernii habitats, including less visible but essential connections between ground water, intermittent ponds, and wetland drainages. Anthropogenic disturbance in moist habitats undoubtedly contributes to the establishment of populations on WGR, but certain types of disturbance may increase erosion, leading to small but steep elevation declines that may increase flooding of R. knieskernii habitat at small scales (M. Sobel, pers. obs.).

Hydrological changes may also negatively impact species composition, nutrient cycling, and rare plant populations (Laidig et al.2009; Zhang et al. 2011). The limited moisture range of R. knieskernii sites places populations at risk if land use changes alter hydrological regimes, as may happen when wetlands are disturbed. Although wetlands are protected under New Jersey law, R. knieskernii occupies moist mineral sites along wetland edges that may be impacted by indirect disturbances to its habitat. In fact, these 221

small sites may not even be considered protected habitat and do not fit within any habitat

classification schemes (Breden et al. 2009).

Reproductive output is a key component of plant fitness. Predicted increases in precipitation may impact soil P availability at R. knieskernii sites. Although there was a only a tenuous relationship between extremely high levels of precipitation and low P availability, P is immobilized when it forms trivalent compounds with aluminum and iron; soils in the NJPB are high in both (Brady and Weil 1999). Thus it is plausible that predicted increases in precipitation in the NJPB could negatively impact R. knieskernii

populations through increased P-limitation (Lucash et al. 2014). Rare plants are often associated with P-limited environments, where they often conserve resources to minimize

P-losses (Fujita et al. 2014). Rhynchospora knieskernii allocates preferentially to

reproduction, but P-limitation may have negative impacts on reproductive output

(Chapter 2). Depending on the plasticity of R. knieskernii response to increased P- limitation, reduced reproductive output could lead to population declines. Nitrogen

Within the nutrient-poor ecosystem of the NJPB, R. knieskernii competes with other species with similar form and reproductive biology (Chapter 3). One mechanism that may positively influence R. knieskernii site biomass is prescribed burning; intense burns had the greatest effect on density (Chapters 2 and 3). Changes in fire regime that accompany population growth and concomitant development of areas adjacent to natural fire-dependent ecosystems have been a cause of ecological concern for several decades

(Forman and Boerner 1981; Luque et al. 1994; Hansen et al. 2005). Fire suppression may be the single greatest threat to R. knieskernii populations outside of WGR. Many of the peripheral populations are found in developed areas where prescribed burning is 222

likely to be limited. Management and recovery plans should include consideration of

localized, small-scale prescribed burns to enhance declining populations.

My study suggests that R. knieskernii can use multiple dispersal mechanisms and

that these mechanisms may offer an advantage in a disturbance-dependent landscape

where observed patterns of plant composition in preferred habitat suggests a lottery

model of community assembly (Chapter 3). I inferred through experimental testing of

achene adherence to deer fur that adhesive dispersal was a viable mechanism for long-

distance movement of achenes to suitable habitat far from current populations (Chapter

4). As deer are extremely common in the NJPB, it is unlikely there is any danger of dispersal corridors being disturbed. However, increased landscape fragmentation could eventually lead to the disruption of long-distance dispersal. However, Warren Grove

Range has a large number of populations that exist in a relatively undisturbed landscape that will probably not be developed over the next several decades (Bien et al. 2009). It also has a long-term protection agreement with the U. S. Fish and Wildlife Service.

Military installations frequently provide additional protection for rare species through limitations on access to military land and through conservation and management plans that are required through the Sykes Act (Stein et al. 2008).

5.2 Future Directions

My study confirmed many observations made by botanists over several years,

although it also may contradict some common perceptions about the ecological

relationships that influence populations. There are still many questions about this species

and several promising directions for research. One area which merits extensive 223

investigation is whether the winter bud may act as a form of vegetative dispersal. As with achenes, dispersal may be both through time and space. When monitoring and counting populations, I observed that in some years almost the entire population consisted of fruiting plants and in other years many plants were vegetative. Although there were no statistically significant relationships, larger vegetative populations seemed to be associated with sites where nutrient availability was low (even within the context of a low-nutrient habitat). I only counted populations once in October. By counting and marking winter buds at their first appearance (usually in early September), it may be possible to follow populations long enough to discern patterns. I did mark plants, but most markers were trampled, lost, or burned. Clearly a much larger study is needed that focuses on the winter bud itself.

I observed that the winter bud detached easily from the parent plant and was equipped with a pair of very tiny roots, suggesting that buds as well as achenes can be dispersed. However, in November 2012 I observed and marked ten winter buds that detached from the parent plant in an unusual event in which a large number of buds were scattered throughout the site, presumably either by deer trampling or flooding. Only one bud grew into a fruiting plant. Vegetative dispersal remains a possibility that needs to be explored further.

The surprising result that greater soil volumetric moisture was related to lower population density deserves further study, particularly since precipitation in the NJPB is expected to increase due to global warming (Lucash et al. 2014). It would be useful to conduct more rigorous experiments with a field and greenhouse component to validate 224

this relationship. It would also be useful to determine soil moisture conditions with greater precision.

The negative consequences of increased P-limitation would have wide impacts on rare plant populations. Although there was a tenuous relationship between extremely high levels of precipitation and low P availability, this relationship is far from certain. A greenhouse study that simulated natural leaching found only minimal leaching, but immobilization may increase when precipitation is too high and rapid for plants and microorganisms to uptake available P. Experiments that examined the potential for P limitation under differing precipitation regimes and soil iron and aluminum content could contribute to a better understanding of the impact of global warming on P cycling in the

NJPB. It would also be important to look at the impact of N limitation or co-limitation on herbaceous plants, which have limited capacity for storing N and may be the most impacted by predicted climatological changes in the N cycle in the NJPB (Lucash et al.

2014). 225

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Obee, E. M. 1995. Conservation plans for Rhynchospora knieskernii: Big Doughnut and Shark River populations. Unpublished report. New Jersey Department of Environmental Protection, Division of Parks and Forestry, Office of Natural Lands Management, Trenton, New Jersey. 6 pp. + Appendices.

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Vita

Marilyn Carolyn Sobel [email protected] ∙ 610.766.1166 ∙ 329 Haverford Place ∙ Swarthmore, PA 19081

Education

Ph.D. in Environmental Science, Drexel University, Philadelphia, Pennsylvania, June 2015

Master of Arts in Elementary Education , Teachers College, Columbia University, New York, New York, January 1985

Bachelor of Arts in Anthropology , University of California at Berkeley, Berkeley, California, June 1981

Thesis: Earth, Wind, and Fire: Resource Allocation and Dispersal Strategies of Rhynchospora knieskernii (Cyperaceae) in a Disturbance-Dependent Ecosystem

Advisor: Dr. Walter F. Bien, Drexel University Committee: Dr. James R. Spotila, Drexel University; Dr. Michael O’Connor, Drexel University; Dr. Dennis M. Gray, Pinelands Field Station, Rutgers University; Dr. Gerry Moore, Team Leader, NRCS, USDA PLANTS database

Research Experience

Research Assistant , Laboratory of Pinelands Research, Drexel University, Philadelphia, Pennsylvania November 2008 to Present

Participate in field research at Warren Grove Range (WGR) under direction of Dr. Walter Bien for multiple projects involving rare plants, fire management, snakes, butterflies, and wetlands

Teaching Assistant, Pine Barrens Ecology, Drexel University, Spring 2010 to 2014 Assisted Dr. Walter Bien with instruction in field techniques and ecological principles

Grants and Awards

United States Army Corps of Engineers. “Determination of Germination and Microhabitat Requirements and Role of Disturbance on Knieskern’s Beaked Rush (Rhynchospora knieskernii) at Warren Grove Range, New Jersey.” 2012-2014. Awarded September 2012 via New Jersey Air National Guard.

Bayard Long Award for Botanical Research, Philadelphia Botanical Club, March 2011

Publications/Technical Reports

Dighton, John, T. Gordon, R. Mejia, and M. Sobel (2013) Mycorrhizal status of Knieskern’s beaksedge (Rhynchospora knieskernii) in the New Jersey pine barrens, Bartonia 66, 24-27.

Bien, Walter F., H. W. Avery, J. R. Spotila, and M. Sobel (2011) “Rare Plant Monitoring Report”, Ecological Studies in Support of the Warren Grove Gunnery Range Integrated Natural Resources Management Plan, Final Report, 114 pages.

Bien, Walter F., H. W. Avery, J.R. Spotila, K. Brooks, M. Sobel (2009) “Comprehensive Avifauna Survey”, Ecological Studies in Support of the Warren Grove Gunnery Range Integrated Natural Resources Management Plan, Final Report, 108 pages.