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Dissertations and Theses

Fall 12-16-2017

Plant Species Richness and Diversity of Northern White-Cedar (Thuja occidentalis) Swamps in Northern New York: Effects and Interactions of Multiple Variables

Robert Smith SUNY College of Environmental Science and Forestry, [email protected]

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PLANT SPECIES RICHNESS AND DIVERSITY OF NORTHERN WHITE-CEDAR (Thuja occidentalis) SWAMPS IN NORTHERN NEW YORK: EFFECTS AND INTERACTIONS OF

MULTIPLE VARIABLES

by

Robert L. Smith II

A thesis submitted in partial fulfillment of the requirements for the Master of Science Degree State University of New York College of Environmental Science and Forestry Syracuse, New York November 2017

Department of Environmental and Forest Biology

Approved by: Donald J. Leopold, Major Professor René H. Germain, Chair, Examining Committee Donald J. Leopold, Department Chair S. Scott Shannon, Dean, The Graduate School

ACKNOWLEDGEMENT

I would like to thank my major professor, Dr. Donald J. Leopold, for his great advice during our many meetings and email exchanges. In addition, his visit to my study site and recommended improvements to my thesis were very much appreciated. I also thank my committee members, Dr. H. Brian Underwood and Dr. Gregory McGee, for taking some of your valuable time to meet with me, answering my many questions, and providing me with some great advice during these meetings and ecolunch. I also thank Jason Wagner, Chief, Fort Drum Natural Resources branch, for sponsoring me and allowing me to conduct my research on Fort Drum. I thank Dr. Steve Stehman for his statistical advice, which was thoroughly used during my analysis, and both Dr. Russell Briggs and Chuck Schirmer for answering questions I had concerning my soil analysis.

This work would not be possible without the funding from the Edna Bailey Sussman Foundation and the TA positions that kept me financially secure for two years of my graduate studies.

Much thanks need to go to my lab mates, past and present, (Grete Bader, Kristen Haynes, Toby Liss, Kali Mattingly, Jim Molloy, Alex Petzke, Jess Saville, and Justine Weber) for the very valuable conversations we had during meetings, ecolunch, and the office. Also, many thanks to Geoff Griffiths and Margaret Roberts who took the time to talk to me, look through my data, and provided me with sound advice on how to go forward with my analysis. I would like to especially thank Chellby Kilheffer for the great amount of time she took out of her very busy schedule to go over my research data, discuss its significance, and teach me how to code in R. Thanks to all the ecolunch participants who watched me present my research four times and were still very attentive and helpful.

Extra special thanks must go to my wife, Gala, who has supported me for the seven years that I have been going to school and proofread all my papers during this time. This support came after all the support she gave me during my 20 years in the Army! I truly could not have done this without her.

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TABLE OF CONTENTS LIST OF TABLES……………………………………………………...……...... vi LIST OF FIGURES……...... ……………….…………….…..…………………...... vii LIST OF APPENDICES...... ix ABSTRACT...... x PREFACE...... 1 CHAPTER 1. Investigating the Effects of Abiotic Factors on Vascular Plant Species Richness and Diversity in Northern White-Cedar Swamps Abstract...... 2 1. Introduction…...... 2

2. Methods...... 6 2.1. Study Area...... 6 2.2. Disturbance History...... 8 2.3. Plot Sampling Design...... 8 2.4. Vegetation Sampling Methods ...... 10 2.5. Soil Specific Conductivity and pH Sampling Methods...... 10 2.6. Water Table Depth and Fluctuation Sampling Methods...... 11 2.7. Canopy Gap Sampling Method...... 11 2.8. Microtopography Sampling Method...... 11 2.9. Statistical Analyses...... 12 3. Results...... 13 3.1. Disturbance History...... 13 3.2. Environmental Variables...... 13 3.3. Species Richness...... 14 3.4. Linear Relationship between Environmental Variables and Plot Richness...... 15

3.5. Multiple Regression Model for Plot Richness and Variable Correlation…...... 19 3.6. Species Composition...... 20 3.7. Wetland Status...... 23 3.8. State Protected Species...... 23 3.9. Dominant Species...... 25

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3.10. Non-Native Species...... 28 3.11. Swamp Richness and Variable Heterogeneity...... 28 4. Discussion...... 29 4.1. Disturbance History...... 29 4.2. Area and Species Richness...... 32 4.3. Linear Relationship between Environmental Variables and Plot Richness...... 33 4.4. Multiple Regression Model for Plot Richness and Variable Correlation...... 35 4.5. Species Composition...... 37 4.6. Wetland Status...... 38 4.7. State Protected Species...... 38 4.8. Dominant Species...... 39 4.9. Non-Native Species...... 40 4.10. Swamp Richness and Variable Heterogeneity...... 41 5. Conclusion...... 43 Literature Cited...... 45 CHAPTER 2. Investigating the Effects of Deer Density on the Species Richness/Diversity in Northern White-Cedar Swamps. Abstract...... 54 1. Introduction...... 54 2. Methods...... 57 2.1. Plot Sample Design...... 57 2.2. Deer Density Estimation...... 58 2.3. Vegetation Plot Sampling...... 58 2.4. Proximity to Fort Drum Impact Area and Relative Deer Density...... 59 2.5. Statistical Analyses...... 59 3. Results...... 60 3.1. Relationship of Relative Deer Density and Plant Species Richness...... 60 3.2. Deer Herbivory...... 61 3.3. Proximity to Fort Drum Impact Area and Relative Deer Density...... 65

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4. Discussion...... 66 4.1. Relationship of Relative Deer Density and Plant Species Richness...... 66 4.2. Deer Herbivory...... 68 4.3. Proximity to Fort Drum Impact Area and Relative Deer Density...... 69 5. Conclusion...... 70 Literature Cited...... 72 SUMMARY...... 77 APPENDICES...... 80 CURRICULUM VITAE...... 88

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LIST OF TABLES

Chapter 1

Table 1.1. Mean and standard deviation of environmental variables for each swamp...... 16

Table 1.2. Area, perimeter/area ratio for each swamp along with mean/standard deviation for richness indices...... 16

Table 1.3. Statistics for regression model: Plot Richness = 7.07 + 0.0850 Cond. - 0.0396 DtoWT - 0.000132 Cond2 - 0.000801 Cond * DtoWT...... 21

Table 1.4. New York state protected plant species present in the 10 Fort Drum Swamps...... 25

Table 1.5. Dominant species in the understory and sub-canopy according to importance values (IV) ...... 26

Table 1.6. Non-native plant species present in the 10 Fort Drum Swamps...... 28

Chapter 2

Table 2.1. Relative Deer Density and Mean/Standard Deviation for the Swamp and Plot Understory and Subcanopy Richness in the Research Swamps on Fort Drum, NY...... 61

Table 2.2. Number of subplots with species present and percentage browsed in the 10 Fort Drum Research Swamps...... 64

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LIST OF FIGURES

Chapter 1

Figure 1.1. Location of the 10 northern white-cedar research swamps (in red) on Fort Drum in northern New York...... 7

Figure 1.2. Northern white-cedar swamp on Fort Drum with plots established along transects in a cardinal direction to the nearest road...... 9

Figure 1.3. Main and microtopography plots including four small understory plant vegetation subplots and one medium sized subcanopy vegetation subplot...... 9

Figure 1.4. Histogram of the age of 45 black ash trees cored in 9 of the 10 research swamp on Fort Drum...... 13

Figure 1.5. Comparison of standardized (48 subplots) swamp richness (w/95% C.I.) with area of the 10 research swamps on Fort Drum...... 17

Figure 1.6. Comparison of standardized (48 subplots) swamp richness (w/95% C.I.) of the 10 research swamps on Fort Drum...... 17

Figure 1.7. Scatterplot of pH and understory plot richness m-2 with linear regression model for 172 plots in the research swamps on Fort Drum...... 18

Figure 1.8. Scatterplot of conductivity and understory plot richness m-2 with linear regression model for 172 plots in the research swamps on Fort Drum...... 18

Figure 1.9. Scatterplot of average depth to water table and understory plot richness m-2 with linear regression model for 172 plots in the research swamps on Fort Drum...... 19

Figure 1.10. Observed understory plot richness m-2 for 172 plots in the 10 research swamps plotted against predicted values using the following regression equation: Plot Richness m-2 = 7.07 + 0.0850 Conductivity - 0.0396 DtoWT - 0.000132 Conductivity2 - 0.000801 Conductivity*DtoWT...... 22

Figure 1.11. Structural model of indirect and direct effects on understory plot richness in the 10 research swamps on Fort Drum...... 22

Figure 1.12. Percentage of wetland category in each of the 10 research swamps on Fort Drum. Classification is in accordance with the USDA Wetland Plant List...... 24

Figure 1.13. Mean and standard deviation of conductivity and depth to water table for each of the 10 research swamps on Fort Drum. Swamps are in order from low to high richness...... 30

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Figure 1.14. Scatterplot of standard deviation of conductivity and standardized swamp richness with linear regression model for the 10 research swamps on Fort Drum...... 30

Figure 1.15. Scatterplot of standard deviation of average depth to water table and standardized swamp richness with linear regression model for the 10 research swamps on Fort Drum...... 31

Chapter 2

Figure 2.1. Scatterplot with trend line of relative deer density and mean plot richness (w/95% CI) in the 10 Research Swamps on Fort Drum, NY...... 61

Figure 2.2. Percentage of all species browsed by deer in the 10 Fort Drum swamps (# plots “x” species browsed/# plots “all” species browsed)...... 62

Figure 2.3. Percentage of individual species browsed by deer in the 10 Fort Drum swamps (# plots “x” species browsed/# plots “x” species present)...... 63

Figure 2.4. Scatterplot of percentage of subplots browsed of the subplots with browsed understory species present on relative deer density of the 10 Fort Drum Swamps; Point labels indicate mean plot richness values...... 65

Figure 2.5. Scatterplot of deer density and distance from the nearest portion of the impact area to the 10 Fort Drum swamps; Point labels indicate percentage of subplots browsed of the subplots with browsed understory species present...... 66

Figure 2.6. Map of Fort Drum Training Area including all roads, impact area, installation border, firing ranges, research swamps, and training areas that include at least one research swamp...... 67

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LIST OF APPENDICES

Appendix 1. List of species found in the understory of the 10 Fort Drum Swamps during the summer 2015 and 2016...... 80

Appendix 2. List of species found in the subcanopy of the 10 Fort Drum Swamps during the summer 2015 and 2016...... 86

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ABSTRACT

R.L. Smith. Plant Species Richness and Diversity of Northern White-Cedar (Thuja occidentalis) Swamps in Northern New York: Effects and Interactions of Multiple Variables, 87 Pages, 8 Tables, 21 Figures. 2017. APA style guide used. Northern white-cedar swamps have been shown to have relatively high plant species richness. The northern-white cedar swamps on Fort Drum in northern New York, have no known study of understory plant richness. My interest was in the abiotic and biotic conditions that promote richness in these wetlands. A survey of the herbaceous and woody plant species under 10.16- centimeter dbh in the 10 northern white-cedar swamps revealed 211 plant species, 26 of which were on the New York state protected plant list. Standardized swamp richness ranged from 78 to 99 vascular plant species. Multiple regression analysis revealed that soil conductivity and depth to water table most influenced the richness. Results of relative deer density estimates revealed that higher relative deer density swamps had lower species richness than lower relative deer density swamps. Swamps closer to the Fort Drum Impact Area were found to have lower deer density than those further away. Keywords: plot richness, abiotic, water depth, conductivity, deer density, swamp richness, fluctuation, canopy gap, microtopography, pH, browsed.

R. L. Smith II Candidate for the degree of Master of Science, November 2017 Donald J. Leopold, Ph.D. Department of Environmental and Forest Biology State University of New York College of Environmental Science and Forestry, Syracuse, New York

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PREFACE

This thesis is comprised of two chapters. The first chapter addresses this main question: Which of the abiotic variables measured are having the greatest influence on the swamp-level richness, plot-level richness and composition of vascular plants within northern white-cedar (Thuja occidentalis) swamps? This chapter includes a summary of the history of the area surrounding 10 swamps and evidence of significant disturbance. The abiotic conditions measured include depth to water table and conductivity. These factors are compared to plant compositional descriptors such as total swamp richness, plot richness, dominant species, and rarity occurring in each swamp. A condensed version of this chapter will be submitted to Forest Ecology and Management, with Donald J. Leopold as a coauthor.

Chapter 2 addresses this main research question: How do deer densities within these swamps affect the total swamp richness and plot richness? It includes a discussion of the known relationship that white-tailed deer (Odocoileus virginianus) have with northern white-cedar (Thuja occidentalis) swamps. The primary data analyzed were relative deer density calculated from deer pellet counts and the degree and type of plant species browsed in the vegetation plots. A condensed version will be submitted to Forest Ecology and Management, with Donald J. Leopold as a coauthor.

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Chapter 1. Effects of Abiotic Factors on Vascular Plant Species Richness and Diversity in Northern White-Cedar (Thuja occidentalis) Swamps.

Abstract

Vegetation sampling and measurements of abiotic factors within 10 northern white-cedar swamps on Fort Drum in northern New York were made to determine the influence of these factors on plant species richness, diversity, and composition. Fort Drum is an active 43,408-ha

Army installation, of which at least two thirds is training area for various tactical operations.

Until a government buyout in 1941, much of the training area was occupied by five villages and farmland. Coring of black ash (Fraxinus nigra) trees revealed that the most recent significant disturbance occurred in these swamps during the Great Appalachian Storm of 1950. One hundred and seventy-two 25 m2 plots containing four 1-m2 vegetation plots were set up along transects to determine plant species richness, diversity and composition. Depth to water table, pH, conductivity, and water fluctuation were measured at plot center. In addition, microtopography was measured in 20-m2 plots adjacent to the 25-m2 plots. The average depth to the water table and conductivity had significant influence on the plot richness with conductivity being the greater influence of the two factors. No relationship was could be determined between area and richness. Although not included in the regression model, water table fluctuation, percentage canopy gap, and soil pH were significantly correlated with each other and to the predictors within the model and appeared to have an indirect effect on the plot richness, diversity, and composition. Conductivity and depth to water table had similar relationships with richness at the plot and swamp scale.

1. Introduction

Much plant ecology research is focused on the factors that most affect plant species richness and diversity. With such information, species of conservation concern can be better

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managed. Factors examined often include light, nutrients, water level, soil moisture, pH, and specific conductivity, as well as how natural and anthropogenic disturbance and microtopography influence these and other factors. Some of this research has focused on a single variable, while others have considered a combination of variables and their interactions.

Some studies have found community species richness declines as nutrient availability goes above a certain threshold (Bedford et al., 1999). A study of wetlands in northern New York showed that species richness increased with higher soil conductivity to a peak of 350-480 micro- siemens and then declined in soils that surpassed this conductivity range (Johnson and Leopold,

1994). The explanation for this trend in species richness is that few species tolerate extremely low nutrient levels and that species richness increases as nutrient levels increase to a certain level after which diversity then declines due to the dominance of a few highly competitive species

(Pausas and Austin, 2001).

Areas with microtopographic heterogeneity (e.g., hummocks-hollows) have greater plant species diversity than areas with homogenous microtopography (flat surfaces) (Beatty, 1984;

Vivian-Smith, 1997; Moser et al., 2007). An experiment involving created wetlands in Virginia had 30 vascular plant species for disked (i.e., heterogeneous landscape) versus 19 for non-disked

(i.e., homogenous landscape) land within the same study site (Moser et al., 2007). This greater diversity has been attributed to the abundance of microhabitats which could meet a greater number of plant species habitat preferences and allowed for coexistence through inhibition of competitive exclusion (Vivian-Smith, 1997).

Several studies on the effects of light intensity on plant richness have involved canopy gaps. A study involving created gaps in northern Hungary concluded that total plant cover and

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richness increased with greater amounts of light and moisture introduced by gap formation; it also found that plant species differentiated within the gaps according to different light availabilities (Galhidy et al., 2006). This result, as with the findings with microtopography, indicates that greater heterogeneity supports higher plant species richness.

Soil moisture has also been related to plant diversity as a significant driver in plant species occurrence and abundance. In a vegetation removal experiment, water-limited areas declined from original species richness, while low elevation areas without water limitation actually increased from original species richness (Xiong et al., 2003). This experiment demonstrated the importance water plays when in sufficient quantity, but can be a disturbance when introduced in great quantities. In a study concerning microtopography, it was found that although pits had more favorable growing conditions than mounds, the longevity of spring flooding allowed for only those plant species tolerant to these conditions (Beatty, 1984).

Throughout the research into the many factors that affect plant species richness and diversity, an underlying commonality seems to support the general unimodal relationship of

Grime’s (1973) “humpbacked” model. This model suggests that stress and disturbance are the key factors in determining plant species richness. These factors limit richness at both low and high levels in that low stress or disturbance leads to dominance by a few highly competitive plant species, while high stress or disturbance leads to the presence of only a few extreme disturbance or stress adapted species. Those communities with intermediate stress or disturbance levels should lead to the greatest species richness (Grime, 1973). Another model that is supported by these researchers is the centrifugal organization model, which proposes that a landscape consists of multiple gradients of limiting factors that control species richness. In areas with large

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numbers of extreme environments, these multiple gradients will likely lead to an overall greater species diversity (Keddy, 2005).

In order to further substantiate or possibly dispute the hypothesis that intermediate disturbance or stress leads to higher species richness and that those landscapes with a large degree of heterogeneity have greater species richness, more experiments into how these abiotic and biotic factors affect species diversity in various ecosystems need to be conducted. An ecosystem that has not been thoroughly explored and could benefit from such an analysis is northern white-cedar (NWC) swamps. The primary range of these swamps extends from southeastern Canada to northeastern United States and west to the Great Lakes region (USDA

NRCS, 2002). These forested wetlands typically occur on organic soils in cool, poorly drained depressions along lakes and streams and are often enriched by minerotrophic groundwater, resulting in a stable water table and continually saturated soils with influence from calcium in bedrock or glacial material (Edinger et al., 2017). These wetlands feature a great amount of micro-topography in the form of hummocks and hollows resulting from windthrown trees. The water table is typically at or near the surface with the exception of higher hummocks and mounds over coarse woody debris and has a pH close to neutral (>6.0). Water-filled hollows frequently become dry during drought (Minnesota DNR, 2015).

NWC swamps consist of many woody and herbaceous plant species. The characteristic tree is northern white-cedar (Thuja occidentalis L.), which makes up more than 30% of the canopy cover. Common associate tree species include balsam fir (Abies balsamea (L.) Mill.), red maple (Acer rubrum L.), yellow birch (Betula alleghaniensis Britton), black ash (Fraxinus nigra Marshall), eastern white pine (Pinus strobus L.), and eastern hemlock (Tsuga canadensis

(L.) Carrière) (NYNHP, 2017). Shrubs found in these swamps include red-osier dogwood

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(Cornus sericea L.), speckled alder (Alnus incana (L.) Moench), and mountain holly

(Nemopanthus mucronatus (L.) Trel.) (NYNHP, 2017). Botanists (e.g. Curtis, 1959), naturalists, and others have long noted that the understory of these swamps includes many species listed of conservation concern (i.e. exploitably vulnerable or rare in New York), demonstrating that NWC swamps could possibly have the highest concentration of this class of protected species of any temperate ecosystem or at least those in the Northeast. Terrestrial orchid species are especially abundant in some NWC swamps, including the showy lady’s slipper (Cypripedium reginae

Walter), yellow lady’s slipper (Cypripedium parviflorum Salisb.), and ram’s head lady’s slipper

(Cypripedium arietinum R.Br.) (NH Division of Forest & Lands, 2017).

The objectives of this research were to: 1) Investigate the effects of abiotic factors (pH, conductivity, depth to water table, water table fluctuation, microtopography, and percentage canopy gap) as well as area and perimeter/area ratio on the plant species richness; and 2)

Conduct a survey of these swamps to determine species composition, dominance, and rarity. To my knowledge there has never been a study of the vascular plants in the NWC swamps at Fort

Drum, nor in this region. It was hypothesized that spatial heterogeneity of the abiotic factors in

NWC swamps has a greater impact on plant species richness/diversity than any single factor including area.

2. Methods

2.1. Study Area

The 10 NWC swamps in this study are located on Fort Drum, which is a military installation located approximately 16 km northeast of Watertown, NY in northern New York

(44°02′N 75°45′W) (Figure 1.1). It is 43,408 ha with the majority of this area consisting of training area. Prior to 1941, much of the training area was the site of five villages and over 360

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farms. Three of the five villages were established around iron manufacturing (Fort Drum

Cultural Resources, 2017). Iron ore was processed using charcoal fueled blast furnaces that

exhausted much of the original timber in the area (Peterson, 2002). The source for this iron was

a swamp located to the north of the current training in Antwerp (Peterson, 2002). The current

land use of the training area surrounding these swamps includes firing ranges, impact area,

maneuver area, and recreational sites all connected by many unpaved roads (Fort Drum Website,

2017). Selection of the 10 NWC swamps within the Fort Drum Training Area was based on Fort

Drum data of forestry stands that were dominantly or co-dominantly NWC and that met the New

York State Department of Environmental Conservation’s definition of a wetland (NYSDEC,

1975) (Figure 1.1). Two of the ten stands consisted of upland and wetland forest and were

delineated to isolate the study wetlands within. These swamps ranged from 1.4 to 8.1 ha (as

calculated in ArcGIS Desktop 10.5 (ESRI, 2016)). All swamps in this study will be referred to

by their Fort Drum forest stand number.

Figure 1.1. Location of the 10 northern white-cedar research swamps (in red) on Fort Drum in northern New York.

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2.2. Disturbance History

No records could be found that indicated specific anthropogenic disturbance within these swamps. As a proxy for disturbance, 45 black ash (Fraxinus nigra) trees were selected from clusters (five or more) of black ash trees in nine of the ten swamps and cored to determine the age of the clusters. This species is a moderately shade intolerant, forest canopy “gap” species which establishes in clusters after a disturbance to the forest canopy, so are best suited for determining disturbance events within these swamps (USDA, 2017).

2.3. Plot Sampling Design

Initial transects were established in a cardinal direction to the nearest road and at 80 m spacing (Figure 1.2). Irregularity in the shape of some swamps required additional transects be established midway between the initial transects. Plot centers were located along each transect at

40 m intervals. At least four plots per ha were established in each swamp to ensure a relatively equal sampling effort. Two types of plots (i.e., main and microtopography) were constructed from plot center (Figure 1.3). Main plots were 5 x 5 m (25-m2) quadrats with four understory 1 x

1 m (1-m2) vegetation subplots located in the corners, and one subcanopy 2.5 x 2.5 m (6.25-m2) vegetation subplot located in the left side corner farthest from the road. Microtopography plots were 2 x 10 m (20-m2) and were placed 2.5 m from plot center along the transect in the direction away from the main road. The microtopography plots were established in the opposite direction in circumstances where the plot would go beyond the border of the NWC swamp. In addition, the microtopography plots were used for deer density estimates

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Figure 1.2. Northern white-cedar swamp on Fort Drum with plots established along transects in a cardinal direction to the nearest road.

Figure 1.3. Main and microtopography plots including four 1-m2 understory plant vegetation subplots and one 6.25-m2 sized subcanopy vegetation subplot.

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2.4. Vegetation Sampling Methods

Within each of the 1-m2 subplots, an inventory of understory plant species (under 2.54 cm diameter at breast height (dbh)) composition, cover, stem count, and deer browse was conducted. Percent cover was estimated visually with the aid of cardboard cutouts of 1%, 5%, and 10% cover. Deer browse was recorded for each species as present or absent. Gleason and

Cronquist (1991) was used in identifying unknown species and nomenclature was according to

“The Plant List” website (2013). Within the 6.25-m2 subplot, an inventory of subcanopy plant species (2.54-10.16 cm dbh) composition, and stem count was conducted in the same manner as with the understory. Cover was estimated visually without aids. Shrubs with at least one 2.54 to

10.16 cm dbh stem were considered subcanopy. Plot richness was calculated by averaging the richness of the four 1-m2 subplots within the 25-m2 plot. This calculation was made to prevent the selection of a single quadrat that was atypical of the main plot vegetation. Importance values for each species were calculated by summing the relative plot density, frequency, and cover and dividing by 3. Swamp evenness was calculated with the following equation: E = H/ln S, where E

푠 = evenness, H = Shannon diversity index = − ∑푖=1 푝푖 푙푛 푝푖 (pi = proportion of stems of S made up of the ith species), and S = total number of species.

2.5. Soil Specific Conductivity and pH Sampling Methods

Soil specific conductivity, an indicator of nutrient availability (USDA, 2017), and pH at each plot were measured as close to and no further than 1 m from plot center with a YSI Model

63 Handheld pH and Conductivity Meter. A hole was dug to a depth of 40 cm or until enough water infiltrated the hole to a depth suitable to submerge the probe. A wait period of 20 minutes was chosen in order to allow for water to infiltrate the hole and suspended soil sediment to settle.

Measurements were recorded when the conductivity and pH results retained a reading for one

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minute. Nine plots had insufficient water infiltration and values were estimated using the inverse distance weighting method of interpolation in ArcGIS Desktop 10.5 (ESRI, 2016).

2.6. Water Table Depth and Fluctuation Sampling Methods

Depth to water table was measured from the same hole used for measuring soil specific conductivity and pH. A wait period of at least 20 minutes was implemented in order to allow for water levels to reach their maximum level. Distance from the top of the water table to the lowest point along the top of the hole was measured with a meter stick. Water table measurements were conducted in May and September 2016 and were used to calculate average depth to water table

((i.e., early + late season depth to water table)/2) and water table fluctuation (i.e., difference between early and late season depth to water table). In the event of insufficient water infiltration at a hole depth of 40 cm, the depth to water table was recorded as 50 cm.

2.7. Canopy Gap Sampling Method

Percentage canopy gap is a measure of the proportion of openness in the canopy and was used to estimate available light at each plot center. These percentages were determined by taking hemispherical photographs at 1 m above each plot center and then analyzing these pictures with the Gap Light Analyzer (GLA) software program (Frazer et al., 1999).

2.8. Microtopography Sampling Method

In order to measure microtopography, a string was first attached at 1 m in height to a wooden stake, run for 10 m along the transect in each 10-m2 microtopography plot, and then attached to a second wooden stake at 1 m in height. Upon completion of this setup, a meter stick was used to measure the distance between the string and the ground at 1 m increments. The standard deviation of these distances were calculated and used as the index for microtopography.

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2.9. Statistical Analyses

Initial analysis was derived from descriptive characteristics of each swamp’s abiotic variables (pH, conductivity, average depth to water, water table fluctuation, microtopography, and percentage canopy gap), geographic variables (area, perimeter/area ratio), plot richness, and swamp richness. Swamp richness values were standardized to the swamp with the smallest

푇 − 푌푖 sample size (n=48) using the following equation: 푆푠푎푚푝푙푒(푡)(푒푠푡) = 푆표푏푠 − ∑ ⌊( )⌋ / 푦푖>0 푡

푇 ( ) in EstimateS version 9.1.0 (Colwell, 1997), where S is the estimated swamp richness, t is 푡 the number of sampling units (48 subplots), T is number of sampling units defining the incidence reference sample (total subplots in swamp), and Yi is the observed species incidence frequencies.

Linear regression was applied to compare individual environmental (predictor) variables to the plot richness (response) variable. Multiple regression was used to determine the relationship between all environmental variables and plot richness using forward selection stepwise regression with consideration of models including interactions and quadratic forms of the explanatory variables. Analysis at the swamp scale was conducted by comparing the standardized swamp richness values with the mean and standard deviations of the plot abiotic variables. Due to the small sample size (n=10) involved in swamp scale richness, analysis was conducted both statistically and graphically. All environmental variables were investigated for correlation to each other and considered as possibly having indirect effects on plot richness. A structural model was constructed to illustrate the causal relationship of the direct and indirect effects with plot richness. All analyses were completed in Minitab version 17.3.1 (Minitab 17

Statistical Software, 2010).

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3.0. Results

3.1. Disturbance History

Results of the coring of 45 trees revealed a mean of 63.7 (STD = 17.3) years with the majority (24) between 45 to 65 years in age (Figure 1.4). The youngest tree was 31 years and was found in Swamp 1900F, while the oldest was 115 years and was found in Swamp 3016.

Twelve trees established prior to 1941, the year that Fort Drum acquired the area where these swamps are located.

Figure 1.4. Histogram of the age of 45 black ash trees cored in 9 of the 10 research swamps on

Fort Drum.

3.2. Environmental Variables

Each swamp in this study was quite distinct in mean and range of individual plot values for some of the variables, while other variables were more homogeneous within each swamp

(Table 1.1). The mean acidity among the swamps was narrow, from pH 5.4 in Swamp 774 to

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6.4 in Swamps 4009B and 1021, while the range within the swamps was very distinct from 5.9-

7.7 in 4009B to 4.0-6.3 in Swamp 1660B. Conductivity was quite variable between swamps with a mean of 50.3 uS cm-1 in Swamp 1660B and 223.6 uS cm-1 in Swamp 2561. Within swamp variation in conductivity was also diverse from 70.7-133.2 uS cm-1 in Swamp 774 to

77.2-315.3 uS cm-1 in Swamp 1021. Mean depth to water table for these swamps was highest in

Swamp 414 at 34.9 cm and lowest at 7.8 cm in Swamp 774, while within swamp range was the largest in Swamp 1344 from 1.2-50.0 cm and smallest in Swamp 4009B from 23.8-32.2 cm.

Fluctuation of the water table throughout the growing season varied greatly from a mean of 5.9 cm in Swamp 774 to 44.7 cm in Swamp 4009B. Within swamp fluctuation also had a wide variation from 0.0-49.9 cm in Swamp 1900F to a more uniform loss in water table levels from

52.5-38.0 cm in Swamp 4009B. Microtopography and percentage canopy gap were more uniform in mean and range and can be seen in Table 1.1.

3.3. Species Richness

Estimated plot richness of vascular plant species across all swamps averaged 12.0 with a minimum average number of 3.8 species in one of the plots in Swamp 414 and a maximum average number of 21.0 found in a plot in Swamp 2561. When the swamps were considered separately, the lowest average plot richness was 7.6 in Swamp 1660B, while the highest average plot richness was 16.1 in Swamp 774. The standardized swamp richness with 95% confidence intervals was plotted with the area for each swamp (Figure 1.5). The highest standardized swamp richness value was 99 in Swamp 4009B with the lowest standardized swamp richness value being 70.4 in Swamp 1660B. Significant differences in these richness values were found among the four highest richness swamps (4009B, 1021, 1344, 774) and the four lowest richness

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swamps (1660B, 2033B, 1900F, 3016) (Figure 1.6). Area ranged from 1.42 ha in Swamp 2033B to 8.17 ha in Swamp 1660B and had a negative relationship with standardized richness (R2 =

0.48, p = 0.03). This relationship, however, was considered statistically inconclusive at n=10.

An analysis of Perimeter/Area ratio and standardized swamp richness did not reveal a significant relationship (p = 0.18, R2 = 0.21). Evenness in the swamps was fairly consistent with values ranging from 0.66 in Swamp 774 to 0.74 in Swamp 2561. Subcanopy plot richness among all swamps was 0.51 and ranged from zero to three species. Individual subcanopy plot richness averaged from 0.14 species in Swamp 1021 to 1.05 species in Swamp 3016. The highest standardized swamp richness value was 5.16 in Swamp 774 and the lowest was 1.8 in Swamp

1344. Margin of error was greatly varied for these standardized values (Table 1.2) and no apparent relationship could be discerned between subcanopy swamp richness and area.

3.4. Linear Relationship between Environmental Variables and Plot Richness

Of the six environmental variables considered in this study, four had a statistically significant linear relationship with understory plot richness. pH had a weak positive relationship with understory plot richness m-2 (R2 = 0.09, p < 0.01) (Figure 1.7), while conductivity had a stronger positive relationship with understory plot richness m-2 (R2 = 0.28, p < 0.01) (Figure 1.8).

Average depth to water table showed a significant negative relationship with understory plot richness m-2 (R2 = 0.19, p < 0.01) (Figure 1.9), while percentage canopy gap had an extremely low positive relationship with understory plot richness m-2 (R2 = 0.02, p < 0.05.). Both fluctuation and microtopography did not have a significant linear relationship with understory plot richness m-2. No significant linear relationship was found between the environmental variables and subcanopy plot richness.

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Figure 1.5. Comparison of standardized (48 subplots) swamp richness (w/95% C.I.) with area of the 10 research swamp on Fort Drum.

Figure 1.6. Comparison of standardized (48 subplots) swamp richness (w/95% C.I.) of the 10 research swamps on Fort Drum.

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Figure 1.7. Scatterplot of pH and understory plot richness m-2 with linear regression model for the 172 plots in the 10 research swamps on Fort Drum.

Figure 1.8. Scatterplot of conductivity and understory plot richness m-2 with linear regression model for the 172 plots in the 10 research swamps on Fort Drum.

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Figure 1.9. Scatterplot of average depth to water table and understory plot richness m-2 with linear regression model for the 172 plots in the 10 research swamps on Fort Drum.

3.5. Multiple Regression Model for Plot Richness and Variable Correlation

The best model for understory plot richness included only the environmental variables conductivity and average depth to water table. A curvilinear (conductivity2) and 2-way

interaction (conductivity*DtoWT) relationship was also shown to exist within this model

(Table 1.3). Total variance explained by the regression equation was 47% (R2). In addition, the equation retained most of its value as a predictor of new observations with an R2 predicted value of 43%. Conductivity had a positive relationship with understory plot richness m-2 and explained

28% of the variance. Average depth to water table had a negative relationship (i.e. drier conditions lead to lower richness) with understory plot richness m-2 and explained an additional

15% of the variance. The last two variables of conductivity2 and conductivity*depth to water table only explained an additional 2%, but did demonstrate a slight curvilinear relationship and a decreased positive effect of increasing conductivity on understory plot richness m-2 with increased depth to water table. A scatterplot comparing the observed with the predicted understory plot richness m-2 can be seen in Figure 1.10.

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Although four of the six environmental variables were not part of the model, three of these variables were correlated with the independent variables in the model and to each other.

These indirect effects are shown in a structural model (Figure 1.11). pH was shown to have a significant positive linear relationship when considered in a model by itself, but was eliminated from the final model since it was collinear with conductivity (r = 0.67, p < 0.01) and did not explain as much of the variance in understory plot richness m-2. Fluctuation was positively correlated with average depth to water table (r = 0.27, p < 0.01) while canopy gap was positively correlated with conductivity (r = 0.18, p < 0.05) and negatively correlated with average depth to water table (r = -0.18, p < 0.05). In addition to the correlations with the variables in the model, canopy gap was negatively correlated with fluctuation (r = -0.28, p < 0.01) and fluctuation was positively correlated with pH (r = 0.21, p < 0.01).

3.6. Species Composition

Of the 209 understory species found among all the swamps, only 27 species were common to all swamps (12.9%), while 49 species were found only in one of the swamps

(23.4%). Dividing the remaining 133 species into more frequently and less frequently occurring reveals the presence of 48 of these species in 6 to 9 swamps (23.0%) and 85 species present in 2 to 5 swamps (40.7%). Swamp 1660B and 414 were found to have the highest percentage of species only found in one swamp (10.9% & 9.8%). Swamp 774 and 1021 were most similar in species composition with 73 species in common, while Swamp 1660B and 2561 were the most dissimilar in species composition with 49 species in common (Appendix 1). In addition to the species found in the plots, Corallorhiza trifida Châtel. and Sarracenia purpurea L. were found in Swamp1660B.

In the subcanopy, 15 species were found, 13 of which were found in the understory.

None of these species were found in all the swamps, while seven of these species (46.7%) were

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Figure 1.10. Observed understory plot richness m-2 for 172 plots in the 10 research swamps plotted against predicted values using the following regression equation: Plot Richness m-2 = 7.07 + 0.0850 Conductivity - 0.0396 DtoWT - 0.000132 Conductivity2 - 0.000801 Conductivity*DtoWT

Figure 1.11. Structural model of indirect and direct effects on understory plot richness in the 10 research swamps on Fort Drum.

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found in only one swamp. Dividing each swamp into more and less frequent species, as was done with the understory, revealed 3 species (20.0%) were found in 6 to 9 swamps, while five species (33.3%) were found in 2 to 5 swamps. Species richness for the subcanopy of the swamps ranged from two species in Swamp 1344, 4009B and 1021 to six in Swamp 774 and 3016

(Appendix 2).

3.7. Wetland Status

In accordance with the USDA wetland plant list, of the 204 vascular plant species found in the understory of all ten swamps, 57 (27.9%) were wetland obligate plants, 47 (23.0%) were facultative wetland plants, 32 (15.7%) were facultative plants, 57 (27.9%) were facultative upland plants, and 11 (5.4%) were upland plants (Appendix 1). Within these swamps, the proportion of obligate plant species varied widely from 16.0% in Swamp 414 to 33.3% in

Swamp 2561. Facultative wetland plants in most swamps were in similar proportion (27.1% to

32.0%) with the exception being Swamp 1660B with 21.6%. Facultative plant species were found to exist in a narrow range of 14.4% to 20.8%. Facultative upland plant species were also found to exist in most swamps in a narrow, but higher range of 18.0% to 25.9% with the exceptions being Swamp 1660B and Swamp 414 with 34.1% and 33.0% respectively. Species that are considered upland were a small proportion of each swamp from 1.1% in Swamp 2561 to

6.0 % in Swamp 414. Figure 1.12 is a scatterplot showing the proportion of wetland category by swamp.

3.8. State Protected Species

There were 26 state protected species found in these swamps (Table 1.4). Twenty-five of these are classified as exploitably vulnerable and one species, Sparganium natans L., is classified as threatened. Swamp 1344 had the highest occurrence of these species with 15, while

Swamp 774 had the lowest occurrence with eight species. Four of these species (Dryopteris

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Figure 1.12. Percentage of wetland category in each of the 10 research swamps on Fort Drum. Classification is in accordance with the USDA Wetland Plant List.

carthusiana (Vill.) H.P. Fuchs, Ilex verticillata (L.) A. Gray, Osmunda cinnamomea L., and

Thelypteris palustris (A. Gray) Schott) were found in all swamps, whereas six of these species

(Botrychium virginianum (L.) Sw., Cypripedium parviflorum Salisb., Cypripedium

reginae Walter, Malaxis monophyllos (L.) Sw., Sanguinaria canadensis L., and Sparganium

natans) were found in only one swamp. According to the New York Flora Atlas and Fort

Drum’s lists of known plant species, the discovery of Malaxis monophyllos in Swamp 3016 is

the first known occurrence of this species on Fort Drum and all of Lewis County. This species

was only found in one plot, which had the following environmental values: pH of 6.0,

conductivity of 105.4 uS cm-1, depth to water table of 14.6 cm, and water table fluctuation of

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27.9 cm. Species found with the greatest coverage in this plot were Tiarella cordifolia L.,

Onoclea sensibilis L., and Carex gracillima Steud. & Hochst. ex Steud. Sparganium natans was found in four plots in Swamp 2561 with the following mean environmental values: pH of 6.17, conductivity of 229.08 uS cm-1, depth to water table of 8.0 cm, and water table fluctuation of 4.7 cm. Associated species include Fraxinus nigra, Bidens connata Muhl. ex Willd., and

Lysimachia thyrsiflora L..

3.9. Dominant Species

The importance values for the top five most dominant understory and subcanopy species

(Table 1.5) were used in the following section for swamp comparison. Among all the swamps, four species (Coptis trifolia (L.) Salisb., Onoclea sensibilis, Carex bromoides Willd., and

Osmunda cinnamomea) were on the list of dominant species in five or more swamps. Fourteen species were found to be dominant in only one swamp. Swamps 1660B and 1900F were the most similar in dominant understory composition with 3 out of 5 species in common.

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Conductivity and depth to water table, the variables found to have significant influence on understory plot richness in these swamps, were also similar in mean and standard deviation with

50.3 uS cm-1 (19.5) and 24.5 cm (11.8) in Swamp16660B and 87.0 uS cm-1 and 31.2 cm (10.7) in

Swamp 1900F. Swamp 1344 and 3016 as well as Swamp 1660B and 2561 were completely dissimilar in dominant species composition. Swamp 1344 conductivity value was slightly higher at 131.0 uS cm-1 compared to 100.8 uS cm-1 in Swamp 3016 and had a slightly higher depth to water table of 20.7 cm when compared to 14.1 cm in Swamp 3016, but had its greatest difference in standard deviation in depth to water table at 17.7 cm compared to 6.2 cm in Swamp 3016.

Unlike Swamp 1344 and 3016, Swamp 1660B and 2561 were quite different in conductivity with values of 50.3 uS cm-1 in Swamp 1660B versus 223.6 uS cm-1 in Swamp 2561. Depth to water table values were also more dissimilar with a mean of 24.5 cm in Swamp 1660B and 11.4 cm in

Swamp 3016. Standard deviation was similar for conductivity, but was slightly higher in distance to water table with 11.8 cm in Swamp 1660B vs. 6.6 cm in Swamp 3016.

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3.10. Non-Native Species

Fourteen non-native species found in these swamps (Table 1.6). Swamp 414 had the highest number of non-native species with seven, while Swamp 1660B had the lowest number with only one non-native species. Two of these species, Epipactis helleborine (L.)

Crantz and Solanum dulcamara L. were found in seven of these swamps, while six of these species (Epilobium hirsutum L., Persicaria hydropiper (L.) Delarbre, Pilosella aurantiaca (L.)

F.W.Schultz & Sch.Bip., Ranunculus acris L., Tussilago farfara L., and Veronica officinalis

L.) were found in only one swamp. The species with the greatest frequency in any swamp was

Frangula alnus Mill. with 90.9% presence in the sample plots of Swamp 3016. The second and third greatest plot presence was Solanum dulcamara with 52.9% in Swamp 2561 and Epipactis helleborine with 50% presence in Swamp 1900F. Many of the non-native species such as

Lysimachia nummularia L. in Swamp 3016 and Veronica officinalis in Swamp 414 occurred in low frequency with 4.5% and 5.9% plot presence, respectively.

3.11. Swamp Richness and Variable Heterogeneity

In order to extrapolate the effects of conductivity and depth to water table from understory plot richness to understory swamp richness, a scatterplot showing the variable means and standard deviations for each swamp and its richness was created (Figure 1.13). Except for

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Swamps 414 and 2561, conductivity showed a generally upward trend with increased species richness. This increase in conductivity with increased swamp richness matched well with the predictive model for plot richness. Depth to water table was also generally greater in three out of the four swamps with the lowest richness than the four swamps with the highest richness.

Just as with conductivity, this matched well with the predictive model for plot richness. The differences in richness values between the four lowest richness swamps and four highest richness swamps were also significant (Figure 1.6). Swamps 2561 and 414 had intermediate richness values and did not trend in environmental variables in the same manner as previously mentioned.

Swamp 2561 had high conductivity like the swamps with the highest richness, but also had a very low depth to water table. Swamp 414 also had high conductivity, but had the highest depth to water table. An analysis of the standard deviation of conductivity and depth to water table, as measures of heterogeneity, with standardized swamp richness showed no significant relationship

(p = 0.47, R2 = .07; p = 0.69, R2 = 0.02 respectively) (Figure 14) (Figure 15). Swamp 1021 had the highest standard deviation in conductivity with a value of 67.4 uS cm-1 and a richness value of 98.5 species, while Swamp 1344 had the highest standard deviation in distance to water table with a value of 17.7 cm and a richness value of 97.6 species. Swamp 774 had the lowest standard deviation in conductivity with a value of 17.7 uS cm-1 and richness value of 95.1 species, whereas Swamp 4009B had the lowest standard deviation in distance to water table with a value of 3.1 cm and a richness value of 99 species.

4. Discussion

4.1. Disturbance History

The mean age of 64 years for the black ash trees cored correlated well with the Great

Appalachian storm (known locally as the Big Blowdown) of 1950. The closest area reporting considerable damage was the Adirondack Park, the border of which is less than 10 miles to the

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Figure 1.13. Mean and standard deviation of conductivity and depth to water table for each of the 10 research swamps on Fort Drum. Swamps are in order from low to high richness.

Figure 1.14. Scatterplot of standard deviation of conductivity and standardized swamp richness with linear regression model for the 10 research swamps on Fort Drum. 30

Figure 1.15. Scatterplot of standard deviation of average depth to water table and standardized swamp richness with linear regression model for the 10 research swamps on Fort Drum. east of Fort Drum. The worst of this storm was reported in the western and central portions with losses estimated at two million cords of softwood and forty million board feet of hardwood

(McMartin, 1994). While I could find no reports of damage on Fort Drum, the shallow root structures of trees in swamps make them more prone to windthrow than surrounding upland forests (Smith & Smith, 2001). Since the Chief, Fort Drum Natural Resources Branch stated that no tree harvesting occurs in these swamps (Personal Communication, J. Wagner, January 23,

2015), this storm seems to be the most likely explanation for the tree ages. There were some ash trees found that established prior to the creation of the training area that could not be correlated with a major weather event. These could possibly be the result of anthropogenic disturbance during the time period of the five villages.

Unfortunately, these sites of former disturbance may once again be disturbed with the arrival of the emerald ash borer (Agrilus planipennis). This invasive beetle from Asia has been responsible for the death of tens of millions of ash trees in 22 states and 2 Canadian provinces, and although it has not been found in northern New York (Bauer et al., 2014), it will likely reach

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this area in the near future. A study by Slesak et al. (2014) proposes that group selection cuts of black ash and planting of alternative tree species prior to the invasion by the emerald ash borer may be necessary to maintain ecosystem processes in these forested wetlands. Some alternative trees recommended in a study by Looney et al. (2016) are Acer rubrum, Quercus bicolor

Willd., and Populus balsamifera L..

4.2. Area and Species Richness

The species area relationship of greater species richness with greater area is one of the most consistent patterns in ecology (May, 1975; Rosenzweig, 1995; Holt et al., 1999). This area richness trend, however, was not apparent in the swamps of this study. A possible explanation for this discrepancy might be surmised through review of the two main explanatory hypotheses for this phenomenon. The “area per se hypothesis” (Preston, 1960; MacArthur & Wilson, 1967) proposes that larger area is associated with larger populations which decreases the chance of extinction and also increases the likelihood of immigration (Kallimanis et al., 2008). This hypothesis does not reflect what was observed in the research and ignores the varied environmental conditions that exist in the areas of each of these swamps. The “habitat heterogeneity hypothesis” (Williams, 1964) proposes that new habitats are correlated with increased area and that increased species richness occurs with the new plants associated with this additional habitat (Kallimanis et al., 2008). Although this hypothesis still equates greater species richness with greater area, it can be interpreted to vary among communities according to habitat types or heterogeneity of environmental conditions. This variation in species richness with area environmental heterogeneity could be seen in the standardization of richness values in these swamps in that new species occurred with additional plot sampling (area), but at different rates in accordance with habitat suitability for other types of plant species. The difference in the environmental values of these swamps support this interpretation of the habitat heterogeneity

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hypothesis and aligns well with both the general unimodal relationship “humpbacked” model and centrifugal organization models. Together, these models predict that more kinds of gradients and greater range of conditions in these gradients led to higher diversity (Keddy, 2005). In addition, a study of 16,143 quadrats in multiple habitat protected areas of Greece also supported this rationale in that it found that richness was related to area when habitat diversity was held constant, but was related to habitat diversity when the area was held constant (Kallimanis et al.

2008).

4.3. Linear Relationship between Environmental Variables and Plot Richness

Of the variables measured, pH, conductivity, depth to water table, and percentage canopy gap showed a statistically significant linear relationship when considered individually with understory plot richness m-2. pH showed a positive relationship with understory plot richness m-2, but was weak and only explained 9% of the variance in understory plot richness m-2. Other wetland types like the mires in western Carpathian showed that pH was responsible for 24% of the variance (Hajkova & Hajek, 2004), while a study of fens in New York State was similar to the results of the 10 swamps with pH explaining 6% of the variance in vascular species richness

(Bedford et al., 1999). A large number of the plots had pH values between 5.5 and 7.0 with varied levels of richness. This variation of richness most likely indicates that most of the plant species in these swamps are adapted to this range of pH values and that these values are not a competitive advantage or disadvantage for these plants.

Conductivity had the strongest positive relationship with an explanatory variance for understory plot richness m-2 of 28%. This result is similar to other studies of wetlands such as mires in Finland which showed species richness increased substantially at conductivity levels above 70 uS cm-1 to the soil maximum of around 160 uS cm-1 (Narhi et al., 2010). A study of

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wetlands in northern New York State showed a positive relationship of conductivity with species richness with a peak in richness from 350 uS cm-1 to 480 uS cm-1 (Johnson & Leopold, 1994).

The range of conductivity in both of these studies also aligned well with the range of the 10 swamps in this study (26-315 uS cm-1). This stronger relationship with conductivity versus pH concurs with studies by Vitt & Chee (1990) who observed that vascular plants generally respond to nutrient gradients, while bryophytes respond to pH gradients.

Depth to water table showed a negative relationship with understory plot richness m-2 and explained 19% of the variance. This result is nearly identical to a study of a mixed conifer swamp in central New York which showed depth to water table was negatively related to species richness with an explanatory variance of 15% (Anderson & Leopold, 2002). Other wetlands such as montane riparian meadows (Dwire et al. 2006) and marshes (Wilson et al., 1993), where average water levels often are found above the surface by up to tens of centimeters, show a decline in species richness with increase in water levels above those measured in the 10 NWC swamps. This upward trend in species richness with water levels approaching the surface followed by a decrease in richness as the water continues to rise above the surface works well with the prediction of the unimodal “humpbacked” model for species richness. Unlike depth to water table, water fluctuation was not shown to be a significant factor. This lack of significance indicates that the richness in these swamps is more influenced by the average depth to the water table during the growing season than a fluctuation value which does not indicate an actual starting depth, i.e. a downward fluctuation of 20 cm may be from 0 cm to 20 cm below the surface or from 30 cm to 50 cm below the surface.

Percentage canopy gap showed a positive relationship with understory plot richness m-2, but only explained 2% of the variance. Other studies involving canopy gaps such as one conducted in a mixed deciduous forest in eastern New York (Goldblum, 1997), another in a northern hardwood stand in northwestern Pennsylvania (Collins & Pickett, 1987), and a third in

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a mixed conifer swamp in central New York (Anderson and Leopold, 2002) demonstrated an association of canopy gaps with greater species richness and this effect is believed to be the case in these 10 NWC swamps.

Microtopography did not show a significant relationship to understory plot richness m-2.

This result did not correspond well with studies such as those in boreal swamps in Norway

(Okland et al., 2007) and a conifer swamp in Central New York (Paratley & Fahey, 1986), which showed microtopography to be vital in maintaining species richness in these types of wetlands.

Another study in a restored wetland in North Carolina, which recorded richness and composition by the microtopographic components of flats, hummocks and hollows, demonstrated how each component of microtopography contributed to greater overall richness (Bruland & Richardson,

2005). Based on my personal observation of these 10 swamps, this same pattern exists and contributes to the richness in the same manner, but similarity in the quantities of each of these components among all these swamps made statistical significance impossible to ascertain.

4.4. Multiple Regression Model for Plot Richness and Variable Correlation

The best explanatory model for plot richness included only conductivity and depth to water table. As was expected, water table fluctuation, microtopography, and percentage canopy gap were eliminated as predictors for understory plot richness m-2. pH, which was shown to have a significant positive relationship with richness m-2 when considered as the sole explanatory variable, was also eliminated from inclusion in the best model. The reason for this exclusion appears to be a weak direct relationship of pH to richness m-2 and its correlation with conductivity (r = 0.67). A study by Vitt and Slack (1984) found a similar correlation between pH and conductivity (r = 0.63) in northern Minnesota peatlands. Based on additional strong correlations between calcium and conductivity (r = 0.91) and magnesium and conductivity (r =

0.91), the authors attributed the quantities of these two elements in the soil with the level of

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conductivity. These two elements are known to regulate soil pH (Brady & Weill, 2008), and although not measured in the 10 swamps, are most likely the cause for the correlation of pH and conductivity. A small correlation of pH and water table fluctuation was also found (r = 0.21).

These fluctuations indicated a drop in the water table and may reflect an increase in calcium and magnesium concentrations with lower water table levels as was found in a study involving an intermediate fen peatland in northern Ontario (McLaughlin & Webster, 2010). This explanation, however, cannot explain why conductivity was not also found to have a correlation with water table fluctuation.

The majority of the variance (R2 (pred.) = 41%) of the multiple regression model could be attributed to linear relationship of conductivity (positive) and depth to water table

(negative). The other two variables, although small in R2 value, were important in that they indicated a decreased effect of higher conductivity and lower depth to water table on species richness (curvilinear relationship). This model concurs with the unimodal (humpbacked) model in that it predicts low richness when the factors are either at high or low values with the highest richness occurring at moderate values. The data for these 10 swamps could confirm this model at low values of conductivity and high depths to water table, but only approached the values necessary to demonstrate the other extreme of the unimodal curve.

Other correlations found in this study were canopy gap with depth to water table and fluctuation, fluctuation with depth to water table and canopy gap with conductivity. Canopy gap had a negative correlation with fluctuation (r = -0.28) and depth to water table (r= -0.18) and most likely relates to the effects of trees on the water table. Many studies such as those by

Jutras et al. (2006) and Sun et al. (2000) show a relationship between removal of trees and higher water table. It seems reasonable to believe this effect occurs at small scales where disturbance has knocked over trees and created gaps in the canopy. Fluctuation was positively correlated

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with depth to water table (r = 0.27) and reflects the fact that most plots with large downward fluctuation had high depths to the water table and most plots with low fluctuation had low depths to the water table. Canopy Gap was positively correlated to conductivity (r = 0.18). A likely explanation for this correlation is that decomposition and mineralization are occurring at higher rates in the larger canopy gaps. The relationship of canopy gaps to these processes has been shown in studies by many researchers including Denslow et al. (1998) and Zhang & Zak (1995), but results have been mixed as to effect of gap size (Muscolo et al., 2014).

4.5. Species Composition

The results of this study demonstrated the high richness of the understory as well as the degree of difference in species composition among these swamps. A total of 209 species were found among these swamps in a total area of 32.6 ha. Of these 209 species, only 27 were common to all the swamps, while 49 were found in only one swamp. The large species richness and compositional difference in these swamps are qualities not often mentioned or emphasized by other studies or government agencies. Often the information provided is the existence of common species such as Maianthemum canadense Desf., Trientalis borealis Raf., and Thelypteris confluens (Thunb.) C.V. Morton (Michigan Natural Features Inventory, 2007;

Edinger et al., 2014) and/or rare species like Cypripedium reginae and Cypripedium arietinum

(Rooney et al., 2002). This study revealed that NWC swamps can support a large number of plant species and that these swamps are not homogenous in species content. This dissimilarity in species composition may also speak to the loose definition of a NWC swamp. The NY Natural

Heritage Program defines these swamps as a forested peatland with at least 30% NWC canopy cover that possesses hummocks and hollows. Maine’s Department of Agriculture, Conservation, and Forestry (2013) has a similar definition with a requirement of at least 60% canopy cover with the dominant species being northern white-cedar. The Vermont Department of

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Environmental Conservation and Vermont’s Nongame and Natural Heritage Program (2003) conducted a statewide inventory in 1996 and 1997 and divided NWC swamps into three types according to topography, hydrology, and other abiotic factors. Given the range in values for some of the abiotic factors measured in this study, compositional differences could be indicative of different types or variants of NWC swamps.

4.6. Wetland Status

Plant species in all wetland classes were present in every swamp in this study. These results demonstrate the large gradient in moisture levels that exist in these swamps.

Microtopography, which has been demonstrated by Sleeper & Ficklin (2016) and Bruland &

Richardson (2005) to create spatial differences in species composition according to moisture levels (i.e. wetlands classifications), is present in large quantities in these swamps and although microtopography was not shown to be significant in determining species richness, it does appear to have a great influence on species composition. Distance to water table also appears to be influential on the proportion of different moisture adapted species. Swamps 2561 and 3016, which had the greatest proportion of obligate plant species, had the lowest depth to water table, while Swamp 414, which had the greatest proportion of upland plant species, had the highest depth to water table. As these proportions demonstrate the habitat suitability within these swamps, they may also relate to the species richness. Swamp 4009B, which had the greatest species richness, was also the most evenly distributed in wetland type species, while Swamp

1660B, which had the lowest species richness, had unusually low proportion of facultative wetland plant species and unusually high proportion of facultative upland plant species.

4.7. State Protected Species

Of the 26 New York State protected species in these swamps, 25 are exploitably

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vulnerable species and one, Sparganium natans, is listed as a threatened species. The majority of the protected species are not actually rare, but are considered at risk of becoming threatened through being picked for commercial and personal purposes (NYDEC, 2017a). This classification is the lowest in New York State and may make these 10 swamps seem less important for conservation efforts, but are actually very important if one considers that they contain 16.3% of all exploitably vulnerable species listed in New York State (NYDEC, 2017b).

The reason for the large percentage of exploitably vulnerable species in these swamps can be attributed to the habitat diversity that supports protected species with various environmental adaptations. Examples of these species include Botrychium virginianum (L.) Sw., a facultative upland plant, Athyrium filix-femina (L.) Roth, a facultative plant, and Chelone glabra L., an obligate plant. This large percentage of exploitably vulnerable species indicates that these NWC swamps could be used as centers for proactive conservation efforts. Managing these swamps as a proactive conservation effort would follow the recommendations in papers by such authors as

Poiani et al. (2000) and Groves et al. (2002), which promote a multilevel (landscape, ecosystem, communities, etc.) approach to conservation, rather than efforts focused on just a rare or endangered species.

4.8. Dominant Species

Dominant species in the understory were varied among these swamps with only four species dominant in five or more swamps (Coptis trifolia, Onoclea sensibilis, Carex bromoides, and Osmunda cinnamomea). This difference in dominant species composition corresponded well with the dissimilarity in total understory species found between these swamps and once again demonstrates how different these swamps can be in composition. Similarity in the environmental factors significantly related to richness m-2 (conductivity and depth to water table) appeared to have an influence on dominance as the most similar swamps were also similar

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in these values (Swamps 1660B and 1900F). The most dissimilar swamps, however, were not always significantly different in the mean values, but did show a difference in environmental heterogeneity in the form of different standard deviations. (Swamps 1344 & 3016, 1660B &

2561). These results concur with both the environmental heterogeneity hypothesis (Williams,

1964; Ricklefs, 1977) and the unimodal “humpbacked” model (Grime, 1973) in that both heterogeneity and environmental gradients are controlling factors in species richness and composition.

4.9. Non-Native Species

Of the 14 non-native species found in these swamps, none of these species were found in all swamps and only two of these species, Epipactis helleborine and Solanum dulcamara were found in seven of these swamps. Epipactis helleborine is a European orchid that has not been reported, so far, to have negative effects on native species (Canadian Wildlife Service, 1999;

Kolanowska, 2013). Solanum dulcamara, however, has a moderate rank of invasiveness in New

York, which is just below the status of regulatory action, and is considered problematic enough to be removed from natural areas (NYIS Info, 2017). Other species found in these swamps with a moderate rank of invasiveness are Epilobium hirsutum, Lysimachia nummularia, Tussilago farfara, and Veronica officinalis (NYIS Info, 2017).

The non-native species of biggest concern is Frangula alnus and was found in Swamps

3016 and 414. This species has a high category of invasiveness (NYIS Info, 2017) and is known to form dense thickets that shade out tree seedlings, saplings, and herbaceous species (Fagan &

Peart, 2004). Of these two swamps, Frangula alnus was only found in one plot of Swamp 414, while it was found in 90.9% of the plots in Swamp 3016. This swamp is also the site of the only known occurrence of Malaxis monophyllos on Fort Drum and Lewis County. Attempts to

40

remove this invasive species or minimize its presence may be necessary in order to retain the population of Malaxis monophyllos in this part of New York State. In addition, Swamp 3016 has thirteen other exploitably vulnerable species, including the orchids Platanthera psycodes (L.)

Lindl. and Cypripedium parviflorum, that may be lost if the Frangula alnus population continues to increase. Chemical control is recommended for removal/suppression of this invasive species.

Forms of application that are not known to affect non-target species include: stump application of 20% glyphosate in August/September, wick application of 2 ½- 3% glyphosate in May, mist application of 2.4 kg/ha fosamine (ammonium salt) in September, and basal application of 2,4 D in diesel fuel at 2-4% or 12.5% during the first half of the growing season (Converse, 1984;

NYIS Info 2017). A combination of cutting and an application of glyphosate herbicide to the cut stump is also recommended and resulted in a 92% to 100% kill of Frangula alnus during

December through March in a study by Reinartz (1997) (NYIS Info, 2017).

4.10. Swamp Richness and Variable Heterogeneity

This study has demonstrated that the environmental factors of conductivity and depth to water table have a significant relationship with understory plot richness. In an attempt to reveal how these factors contribute to richness at the larger swamp scale, a scatterplot was constructed.

Earlier in this paper, significant difference in the richness estimates was shown between the four swamps with the lowest richness and the four swamps with the highest richness. The means and standard deviations of the two significant variables for these two groups were then compared in order to look for trends at the swamp level. After making this comparison, mean conductivity was found to be higher in the high richness group than the low richness group. This pattern of higher mean conductivity with higher plant species richness demonstrates that mean conductivity is a good indicator of richness at both the plot and swamp level. Mean depth to water table was found to be higher in the low richness group than the high richness group. This pattern of

41

higher water table depth with low richness is also consistent with the model for plot richness.

An exception to this connection between richness at the plot and swamp levels was Swamp

3016, which had a water table level similar to those of the high richness group. A possible explanation for this result is that Swamp 3016 had a greater species richness prior to the inundation by the invasive species Frangula alnus. The two swamps that were intermediate in richness, 414 and 2561, were unique in their conditions when compared to the other eight swamps. Swamp 414 had the highest conductivity and the highest depth to water table. This combination in environmental conditions appear to be suitable to more plant species than the low richness group, but not to the level of the high richness group. Swamp 2561, however, is an anomaly in that it has the highest conductivity and the second lowest depth to water table and should have a higher richness value. Overall, the mean values for the swamps in these two environmental factors followed the same pattern as the model for plot richness m-2 and the predictions of the unimodal “humpbacked” model. Comparison of the heterogeneity values for these two environmental factors with the standardized swamp richness in regression analyses did not reveal a significant relationship. As was the case with microtopography, the true influence of environmental heterogeneity appears to be masked by the similar values. Of those swamps with very different standard deviations in these factors, there did not appear to be a relationship between heterogeneity and species richness. This lack of relationship between heterogeneity and species richness can be seen when comparing the standard deviations of distance to water table in Swamps 1344 and 4009B. Swamp 4009B, which had the highest species richness, was low in standard deviations for both factors, while Swamp 1344, which had the third highest species richness, had the largest standard deviation for depth to water table. Many studies such as those by Dufour et al. (2006) and Pausas et al. (2003) have demonstrated the positive relationship between increased environmental heterogeneity and species richness and this study does not intend to contradict those results. Instead, it is believed that both environmental heterogeneity

42

and the humpbacked model which predicts maximum richness at intermediate resource values can lead to overall higher richness at the swamp scale. While plots with optimal resources for richness may have many combinations of species composition, the presence of other habitats, whether low or high in conductivity or depth to water table, are opportunities for those plant species that are well adapted to these conditions.

5. Conclusion

This study investigated the effects of abiotic factors (pH, conductivity, depth to water table, water fluctuation, microtopography, and canopy gap) as well as area and perimeter/area ratio on the plant species richness. It was hypothesized that spatial heterogeneity of the abiotic factors in NWC swamps has a greater impact on plant species richness/diversity than any single factor including area. This hypothesis was not supported in that most of the abiotic factors had similar levels of heterogeneity and those with dissimilar values did not show a pattern of greater species richness with greater heterogeneity. Results of this study showed that conductivity and distance to water table explained 46.65% of the variance for richness at the plot level. Richness at the swamp level appeared to follow the same pattern of higher conductivity and lower distance to water table leading to greater species richness. In addition, greater area and perimeter/area ratio did not correlate with higher species richness. This lack of correlation of species richness with area or perimeter/area demonstrates the importance of considering environmental conditions and not just area when making decisions on where to focus conservation efforts.

Another important consideration is the distinct plant species composition found in each of these wetlands. With only 27 plant species common to the swamps, but over 200 species found among the swamps, these forested wetlands need to be protected as a group if the large species richness is to be conserved.

43

To ensure a complete statistical analysis at the swamp scale, it is recommended that future research include a greater number of swamps. Additional swamps for this research could include two much larger swamps (66.6 & 56.3 ha stands) on Fort Drum as well as other swamps that may have been discovered during the forest inventory that was being conducted by Fort

Drum Natural Resources staff at the time of the study. Swamps from the surrounding St.

Lawrence, Jefferson, and Lewis county area could also be incorporated into the research. This recommendation would require a group of researchers or a collaborative effort from different environmental organizations, but is necessary if quality management and conservation of this unique ecosystem are to be ensured.

44

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Chapter 2. The Effects of White-Tailed Deer on the Plant Species Richness/Diversity in Northern White-Cedar Swamps.

Abstract

White-tailed deer (Odocoileus virginianus) have been increasing in numbers since the early 20th century and now often exceed 10 deer per km2. These ungulates are known to use northern white-cedar swamps (Thuja occidentalis) as preferred winter habitat. This preference is due to the thermal protection provided by conifer swamps and the nutritional benefit that only the northern white-cedar trees provide. Browsing of understory plants has also been found to occur on rarer species of lilies and orchids. To determine relative deer densities in each swamp, 20-m2 deer pellet plots were established along transects adjacent to 25-m2 vegetation plots. During vegetation sampling, species browsed in the subplots were recorded. The resulting relative deer densities in these swamps ranged from 0 to 1.5. Analysis of relative deer density and mean plot richness m-2 revealed that swamps with higher relative deer density had lower mean plot richness m-2 than those with lower relative deer density. Swamps closer to the Fort Drum Impact Area were found to have lower deer densities than those further away. Symphyotrichum puniceum,

Chelone glabra, and Lysimachia thyrsiflora were the most commonly browsed plant species.

1. Introduction The history of white-tailed deer (Odocoileus virginianus) populations in the United States and southern Canada can be best explained through the use of Aldo Leopold’s three historical stages. These three stages are pre-settlement, exploitation, and present (Leopold 1943, Rooney

2001). During the pre-settlement stage (pre-1700), deer densities were estimated to be as low as

2-4 deer per km2 (Alverson et al., 1988). This low abundance of deer was attributed to severe winters, predators such as the wolf (Canis lupus) and cougar (Felis concolor), hunting from

Native Americans, and thick forests with little understory vegetation (Witmer & deCalesta,

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1991). The exploitation stage (1700-1900) saw a decline in deer populations to near extirpation in much of the eastern forests (Horsley et al., 2003). This decline in population was attributed to the introduction of market/commercial hunting (McCabe & McCabe, 1984). The present stage

(1900-) is marked by an increase in deer density that often exceeds 10 deer per km2 (Cote et al.,

2004). This can be attributed to extensive habitat modification, strict hunting regulations, and extirpation of predators (Horsley et al., 2003).

Impacts of over-browsing by deer on NWC swamps involve all aboveground vegetative components, from herbs and shrubs to trees (Rooney, 2001). NWC trees are particularly vulnerable in the winter since conifer stands are used as thermal cover and NWC trees are a premier winter food source (Miller, 1990). In a study conducted in northern Wisconsin and

Michigan, NWC recruitment showed significant decline in recruitment of both 10-29 cm tall saplings and 30-300 cm tall saplings (Rooney, 2001). Another reason why these trees are particularly susceptible is their slow growth rate. NWC trees in Vermont, including those in more favorable upland sites, averaged 8-16 m in height at 50 years (Hannah, 2004; Hofmeyer,

2009). NWC swamps, which are poorly drained, will likely have a slower growth rate and will not grow beyond the reach of deer browsing for decades (Alverson, 1988).

Unique shrub and forb species restricted to conditions found in NWC swamps are also susceptible to over-browsing (Callan, 2013). This susceptibility may be either a direct or indirect effect of this herbivory. Browsing directly affects reproduction in many plants, particularly if deer preferentially forage on reproductive plants or consume flowers (Cote, 2004). A 1992 report on 98 rare species concluded that a high proportion of rare lilies (40%), orchids (39%), and dicots (56%) were adversely affected by deer herbivory (Waller & Alverson, 1997). This preferred browsing makes the understory of NWC swamps particularly vulnerable since they

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contain regionally rare species such as calypso orchid (Calypso bulbosa (L.) Oakes), showy lady’s slipper (Cypripedium reginae Walter), and the globally rare ram’s head lady’s slipper

(Cypripedium arietinum R.Br.) (Rooney et al., 2002; NYNHP, 2017). These plants may not die, as has been shown in studies of white trillium (Trillium grandiflorum (Michx.) Salisb.), but may instead regress in size and become non-reproductive (Cote, 2004). Browsing of the NWC tree saplings may also have indirect effects on the understory plant composition by changing the canopy composition. If evergreen conifers, such as white cedar, lose canopy dominance to deciduous tree species, light regimes will change drastically, leading to loss of many species restricted to this habitat (Alverson, 1988).

Military installations, like areas used for recreation, mining, and logging, are sites of human disturbance (Stephenson et al. 1996). These human disturbances have the potential to influence the distribution, behavior, and abundance of many animal species (Clair & Forrest,

2009). The types of disturbance found in military training areas include vehicular movement/noise (air/ground) and noise produced by gunnery and bombing ranges. While studies of the effects of these disturbances are limited for white-tailed deer (Odocoileus virginianus), one study by Dorrance et al. (1975) involving deer response to snowmobile activity showed an aversion to areas along trails at all levels of snowmobile traffic. Studies involving other ungulates are more frequent and may be good indicators of the responses that occur with white-tailed deer. A study of Roosevelt Elk (Cervus elaphus rooselvelti) by Witmer and deCalesta (1985) showed a positive correlation between elk densities and distance from forest roads. Another study by Landon et al. (2003) involving Sonoran pronghorn (Antilocapra americana sonoriensis) on the Barry M. Goldwater military range in southwestern Arizona revealed that these ungulates used areas with lower noise levels more often than areas with high

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noise levels. Other potential impacts of noise disturbance on terrestrial animals include increased stress, decreased immune response, hearing loss, reduced reproductive success, increased risk of predation, and degradation in species communication (Larkin et al., 1996; Pater et al., 2009).

The objective of this research was to: 1) Investigate the effects of relative deer density on plant species richness; 2) Determine the plant species most frequently browsed by white-tailed deer; and 3) Explore the relationship between distance to military features and deer density; The following hypotheses were proposed for this chapter: 1) NWC swamps with relatively fewer deer would exhibit higher species richness than those with relatively higher deer abundance; and

2) NWC swamps closer to the impact area will exhibit lower relative deer density than swamps further away.

2. Methods

2.1. Plot Sampling Design

Plots used in chapter 1 were also used in chapter 2. Main plots were 5 x 5 m (25-m2) quadrats with one 6.25-m2 subcanopy subplot located in the far-left side corner, relative to the road, and four 1-m2 understory subplots located in the corners of the main plot. Deer pellet plots were the same as the microtopography plots of chapter 1 and were 2 by 10 m (20-m2) in size and placed 2.5 m from plot center along the transect in the direction away from the main road. The deer pellet plots were set up in the opposite direction in circumstances where the plot would go beyond the border of the NWC swamp.

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2.2. Deer Density Estimation

Estimation of deer density took place during two summers since deer pellets in swamps are known to take several years to decompose (H. B. Underwood, personal communication).

During the first visit, each deer plot was cleared of all deer fecal pellet groups. Deer pellets observed along the perimeter of the deer plots were removed in order to prevent these deer pellets from drifting into the plot during flood events. Deer pellet groups, which were defined as having at least 10 deer pellets and centered within the plot, were recorded during the first summer for a comparative analysis with the following summer. All pellets collected were disposed of outside of each swamp perimeter. The following summer (i.e., second visit), deer pellet groups which had accumulated in the intervening period were recorded at each plot.

Relative deer density was calculated for each swamp using the following equation:

2 푛표. 푝푒푙푙푒푡 푔푟표푢푝푠 deer/km = 푝푒푙푙푒푡 푔푟표푢푝푠 25 푥 푑푎푦푠 푠푖푛푐푒 푑푒푒푟 푝푙표푡 푐푙푒푎푟푖푛푔 푥 푠푞푢푎푟푒 푘푖푙표푚푒푡푒푟푠 표푓 푎푙푙 푝푙표푡푠 푑푎푦

Estimated defecation rate of 25 pellet groups/day was based on studies conducted by Rogers

(1987) and Sawyer et al. (1990). Because fecal pellet group defecation rate is highly variable in wild deer (Van Etten & Bennett, 1965; Neff, 1968), it is simply treated as a constant here and the focus is on different fecal pellet accumulations among swamps relative to each other.

2.3. Vegetation Plot Sampling

Within each 1-m2 subplot, an inventory of understory plant species (under 2.54 cm dbh) composition, stem count, and deer browse was conducted. Foliar cover (%) was estimated visually with the aid of cardboard cutouts of 1%, 5%, and 10% cover. Deer browse for each species was recorded as present or absent in each plot. Gleason and Cronquist (1991) was used

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in identifying unknown species and nomenclature was according to “The Plant List” website

(2013). Within the 6.25-m2 subplot, an inventory of subcanopy plant species (2.54-10.16 cm dbh) composition, stem count, and deer browse was conducted in the same manner as with the understory. Shrubs with at least one 2.54 to 10.16 cm dbh stem were considered subcanopy.

Plot richness was calculated by averaging the richness of the four 1-m2 subplots within the 25-m2 plot. This calculation was made to prevent the selection of a single quadrat that was atypical of the main plot vegetation.

2.4. Proximity to Fort Drum Impact Area and Relative Deer Density

Shapefiles of the impact area, firing ranges, Fort Drum perimeter, major roads

(route3/3A), range roads, and training area with swamps were created in ArcGIS 10.5 in accordance with Fort Drum’s recreational use map and aerial imagery. Shapefiles for the firing ranges were positioned at the approximate location of the firing line with a set area of 23,500 m2.

Fort Drum roads shapefile was imported from the New York State GIS Clearinghouse. Swamp shapefiles were created by the forestry section of Fort Drum’s Natural Resource Branch with the exception of Swamp 414 and 1021 which were created through delineation with GPS of two Fort

Drum forestry stands. An ArcGIS 10.5 basemap was used for the aerial view of the training area. Proximity from the swamps to the Fort Drum Impact Area was determined using the Near tool in ArcGIS 10.5. Bi-variate plots of relative deer density on near distances to the Fort Drum

Impact Area were used to test my hypotheses.

2.5. Statistical Analyses

Linear regression analysis of relative deer density with swamp richness, browsing, and distance to Fort Drum’s Impact Area were conducted. Scatterplots, trendlines, and a map were

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constructed for these and will be considered graphically. These analyses were completed in

Minitab version 17.3.1 (Minitab 17 Statistical Software, 2010). Swamp richness values were standardized to the swamp with the smallest sample size (n=48) using the following equation:

푇 − 푌푖 푇 푆푠푎푚푝푙푒(푡)(푒푠푡) = 푆표푏푠 − ∑ ⌊( )⌋ / ( ) in EstimateS version 9.1.0 (Colwell, 1997), 푦푖>0 푡 푡 where S is the estimated swamp richness, t is the number of sampling units (48 subplots), T is number of sampling units defining the incidence reference sample (total subplots in swamp), and

Yi is the observed species incidence frequencies.

3. Results

3.1. Relationship between Deer Density and Plant Species Richness

Relative deer density ranged from a low of 0 in Swamp 414 and 3016 to a high of 1.34 in

Swamp 1344. Mean understory plot richness ranged from 7.59 in Swamp 1660 to 17.65 in

Swamp 2561, while mean understory swamp richness was lowest in Swamp 1660B at 70.4 and highest in Swamp 4009B at 99. Mean subcanopy plot richness ranged from 0.1 in Swamp 1021 to 1.1 in Swamp 3016, while mean subcanopy swamp richness was lowest in Swamp 1021 at 1.7 and highest in Swamp 4009B at 5.2 (Table 2.1).

A scatterplot of relative deer density and mean understory plot richness m-2 (w/95% CI) revealed that understory plot richness m-2 increased from low to intermediate relative deer densities and declined at high relative deer densities (Figure 2.1). The best linear regression model for mean plot richness m-2 was Y= -9.3649x2 + 10.492x + 11.656, but was not significant at α = .05 with a sample size of 10 (p = 0.15, R2 = 0.42). A similar pattern of lower richness with higher relative deer density occurred at the swamp richness scale (p = 0.67, R2 = 0.11) and can be seen as labels adjacent to the scatterplot points on Figure 2.1. A scatterplot of relative deer density and mean subcanopy plot richness showed a negative relationship, but was not

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significant at α = .05 with a sample size of 10 (p = 0.26, R2 = 0.16), while deer density and

swamp richness had no relationship (p = 0.96, R2 < .01).

Figure 2.1. Scatterplot with trend line of relative deer density and mean plot richness (w/95% CI) in the 10 Research Swamps on Fort Drum, NY; Point labels indicate swamp richness values.

3.2. Deer Herbivory

There were 22 species found to be browsed by deer in the understory of all ten swamps.

Of these 22 species, 19 species were not commonly browsed with values less than 4% of all

species browsed. Symphyotrichum puniceum (L.) Á.Löve & D.Löve, Chelone glabra L., and

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Lysimachia thyrsiflora (L.) Pohl were the three most browsed species with values of 37%, 16%, and 5%, respectively, of all species browsed in the swamps (i.e. # plots “x” species browsed/# plots “all” species browsed) (Figure 2.2). Only one of these three species, however, was among the three highest in percentage of individual species browsed (i.e. # plots “x” species browsed/# plots “x” species present) (Figure 2.3). That species was Symphyotrichum puniceum with 17% of the species being browsed in the 1.0-m2 subplots where it was present. The other two species were Hamamelis virginiana L. and Sambucus canadensis L. with 25% and 18%, respectively, being browsed in the subplots where it was present. Chelone glabra and Lysimachia thyrsiflora were browsed in 12% and 5%, respectively, of the 1.0-m2 subplots where it was present.

Figure 2.2. Percentage of all species browsed by deer in the 10 Fort Drum swamps (# plots “x” species browsed/# plots “all” species browsed).

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Figure 2.3. Percentage of individual species browsed by deer in the 10 Fort Drum swamps (# plots “x” species browsed/# plots “x” species present).

Percentage of species browsed in each swamp and across all swamps can be seen in Table 2.2.

Swamps 2033B, 1021, and 774 had the highest number of species browsed at six species, while

Swamps 1900F and 414 had the lowest number of species browsed at one species. Results of plotting relative deer density and percentage of all 1.0-m2 subplots browsed in each swamp did not reveal any trend, however, using percentage browsed of just the 1.0-m2 subplots including browse species from this study showed a positive relationship between the two variables (Figure

2.4). The linear regression model for this relationship was Y = 9.5x + 4.64, and was significant at α = .05 (p = 0.02, R2 = 0.51) with a sample size of 10. Recent deer browse in the subcanopy subplots was not found.

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Figure 2.4. Scatterplot of percentage of subplots browsed of the subplots with browsed understory species present on relative deer density of the 10 Fort Drum Swamps; Point labels indicate mean plot richness values.

3.3. Proximity to Fort Drum Impact Area and Relative Deer Density

Comparisons of relative deer density and proximity to the impact area revealed that those

swamps less than 2700 m from the impact area had relative deer densities from 0 to 0.6, while

four out of the five swamps that were further than 2700 m from the impact area had relative deer

densities of 0.8 to 1.4 (Figure 2.5). Labels with percentages of subplots browsed with known

browse species were placed on the scatterplot in Figure 2.5 and revealed that higher browse in

swamps was related to higher relative deer density and further distance to the impact area. A

map is presented in Figure 2.6 as an additional visual display of the proximities of the swamps to

the Fort Drum Impact Area.

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Figure 2.5. Scatterplot of deer density and distance from the nearest portion of the impact area to the 10 Fort Drum swamps; Point labels indicate percentage of subplots browsed of the subplots with browsed understory species present.

4. Discussion

4.1. Relationship between Deer Density and Plant Species Richness

-2 I documented a lower mean understory plot richness m and standardized swamp richness at higher relative deer density in NWC swamps of the Fort Drum area. While estimating actual deer density proved challenging in my study, high deer densities have been shown to negatively impact species richness (Nuttle et al., 2014). This decline in species richness has been attributed to preferential browsing of plant species intolerant to deer browse.

Such species lack defense mechanisms, either chemical or morphological, and are more vulnerable to browsing than those that do have these defenses (Cote et al., 2004). Given that

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Figure 2.6. Map of Fort Drum Training Area including all roads, impact area, installation border, firing ranges, research swamps, and training areas that include at least one research. swamp.

species known to be browse intolerant, such as Trillium spp. (Anderson, 1994) and Chelone glabra (Williams, et al., 2000), are present in these swamps, an association of less richness with higher relative deer density is of concern. This association could explain the negative relationship of higher relative deer density swamps and mean understory richness documented in this study. Subcanopy plot richness also exhibited a negative relationship, although not statistically significant, with relative deer density. This may reflect over-browsing of the browse

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intolerant northern white-cedar saplings in the understory and subcanopy of these swamps during use as winter habitat for white-tailed deer.

4.2. Deer Herbivory

I found that Symphyotrichum puniceum, Chelone glabra, and Lysimachia thyrsiflora were three most browsed species with values of 36.5%, 15.9%, and 4.8%, respectively, of all species browsed in the swamps. Of these three species, only Chelone glabra is a protected species in

New York state. This species is found near streambanks and other areas with damp ground in much of the eastern half of the United States (USDA Forest Service, 2017b). It is also known to be highly browsed by white-tailed deer and has been used as an indicator of browse intensity

(Williams et al., 2000). Chelone glabra is currently a common species found in nine of the ten research swamps, but may decline in numbers with continued browsing pressure.

Symphyotrichum puniceum is a common species in swamps, marshes, fens, wet thickets, stream banks, and ditches (NY Flora Association, 2017). Although studies about deer browse of this species could not be found, studies by McCaffery et al. (1974) in northern Wisconsin and

Stormer & Bauer (1980) in northern Michigan did find that plants in the aster family were commonly browsed by white-tailed deer. Lysimachia thyrsiflora was not browsed nearly as much as the Symphyotrichum puniceum or Chelone glabra and no study could be found concerning deer browse preference.

Of the three species most commonly browsed, only Symphyotrichum puniceum was among the three highest in percentage of individual species browsed with 17.2% of the 1.0-m2 subplots, where it was present, being browsed. This proportional consideration of species browsed may be more important than commonly browsed species since it could indicate a greater vulnerability to extirpation from the swamps (Rooney & Waller, 2003). Although

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Symphyotrichum puniceum is currently abundant in these swamps, continued deer browsing may significantly deplete the population.

The other two species that were heavily browsed were Hamamelis virginiana and

Sambucus canadensis with 25.0% and 18.2%, respectively. Hamamelis virginiana is a common species in mesic woods and bottomlands (USDA Forest Service, 2017a) and is considered of low importance in a deer’s diet (5-10%) (USDA NRCS, 2017a). This species, however, is only in four subplots in Swamp 1344 and may be extirpated from this swamp with continued browsing pressure. Sambucus canadensis is a common species to riparian areas and canopy gaps of moist forests and is considered a minor part of a deer’s diet (2-5%) (USDA NRCS, 2017b). Unlike

Hamamelis virginiana, this species is found in five swamps and its loss would have a greater impact on the average species richness of these research swamps.

4.3. Proximity to Fort Drum Impact Area and Relative Deer Density

The five swamps closest to the impact area exhibited a lower relative deer density than four out of five of the swamps furthest away. The reason for this avoidance may be due to the impact area being surrounded by military activity. A study by Stephenson et al. (1996) showed that mule deer (Odocoileus hemionus) on Piñon Canyon Maneuver Site in southeastern Colorado either moved out of their normal home range or increased their core home range during periods of military activity. The authors of the study also suggested that unpredictable human activity elicits more of a response from deer than predictable activity. Given the varied daily use by many military organizations in the areas around the impact area, this greater response would be expected. Two military activities around the impact that could be causing this response are noise

(gunfire, explosions) and human presence. As was mentioned in the introduction, Sonoran

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pronghorn used areas with lower noise levels more often than areas with high noise levels

(Landon et al., 2003). It seems reasonable to believe that white-tailed deer also respond to high noise levels around the impact area in the same manner. Human presence is also a great deterrent for white-tailed deer as people are frequently perceived as threatening and create a strong flight response (Stankowich, 2008). Studies by Kucera (1976) and Freddy et al. (1986) showed that white tailed deer and mule deer, respectively, fled at further distances from humans on foot than those driving in vehicles. The large groups of military personnel conducting training around the impact area should also elicit a strong flight response.

5. Conclusion

This study investigated the effects of relative deer density on plant species richness, the most frequently browsed plant species by white-tailed deer, and the relationship between distance to military features and relative deer density. The hypothesis that NWC swamps with relatively fewer deer would exhibit higher species richness than those with relatively higher deer abundance was supported. Continued monitoring of deer density in these swamps is recommended as further decline in plant species richness is likely to occur at higher deer densities

It was also hypothesized that swamps in closer proximity to the impact area will have lower deer densities than those further away. Measured distances from the swamps to the impact area showed a positive relationship with deer density and supported this hypothesis.

Recommended methods for exploring the impact of military activities on the behavior and abundance of deer include radio telemetry, behavioral observations, noise level measurements, and monitoring of daily activity around the impact area.

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Another important consideration in this study was the types of plant species browsed by white-tailed deer in these swamps and the intensity that these species were browsed. Of the 209 species found in these swamps, 22 species were found to be browsed by white-tailed deer. This result demonstrated that NWC swamps have many plant species that are considered a food source by white-tailed deer. Symphyotrichum puniceum, Chelone glabra, and Lysimachia thyrsiflora were the most frequently browsed species, while the species most browsed in proportion to their presence in the swamps were Symphyotrichum puniceum, Hamamelis virginiana, and Sambucus canadensis. This latter index of browsing pressure may be more important in that it demonstrates the likelihood of a species declining in population or being extirpated from the swamps. It is recommended that species on either of these lists be monitored for species abundance within these swamps.

As was stated in Chapter 1, future research will need a greater number of swamps to provide statistically significant results at the swamp scale. Additional swamps for this research could come from Fort Drum and the surrounding St. Lawrence, Jefferson, and Lewis county area. This study would require a collaborative effort from different environmental organizations.

Testing of the impact area’s effect on deer density could also be conducted in other ecosystems and used for comparative analysis with the NWC swamps.

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SUMMARY

The northern white-cedar swamps on Fort Drum were found to have high plant species richness. The overall objective of this research was to determine the factors that contributed to this species richness and which of these were having the greatest impact. These factors were pH, conductivity, depth to water table, water fluctuation, microtopography, canopy gap, area, perimeter/area, and deer density. Effects of proximity to the Fort Drum Impact Area on relative deer density in these swamps was also investigated. In addition, species composition, rarity, and vulnerability were also considered in this research.

The results of Chapter 1 indicated that conductivity had a positive relationship, while depth to water table had a negative relationship with plot richness m-2. Correlations of fluctuation with depth to water table, canopy gap with conductivity and depth to water table, canopy gap with fluctuation, and fluctuation with pH were found to have indirect effects on richness m-2. Richness at the swamp level appeared to follow the same pattern of higher conductivity and lower distance to water table leading to greater species richness. A relationship of greater abiotic variable heterogeneity with greater richness, however, was not found in these swamps. Area and perimeter/area did not correlate with swamp richness. This lack of correlation demonstrates the importance of focusing on environmental conditions and not just area when making decisions on where to focus conservation efforts.

Only 27 of the 209 plant species were found to be in common in these swamps. This relatively low proportion speaks to the importance of protecting these forested wetlands as a group and not as individual swamps. Included in the 209 species are 16.3% of all exploitably vulnerable species in New York State. This proportion of exploitably vulnerable species

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demonstrates that these forested wetlands could possibly have the highest concentration of this class of protected species of any temperate ecosystem or at least those in the Northeast. Besides these exploitably vulnerable species in these swamps are 14 non-native species. Of these,

Frangula alnus is the biggest concern since it is known to shade out herbaceous species (Fagan

& Peart 2004) and is found in large quantities in a swamp with 14 exploitably vulnerable species including the only known occurrence of Malaxis monophyllos in Lewis County and Fort Drum.

The results of chapter 2 indicated that relative deer density has a significant effect on understory plot richness m-2. Plots in swamps with relatively low deer densities had higher species richness m-1 than plots in swamps with higher relative deer densities. Deer density and richness at the swamp scale followed a similar trend of relatively low and high deer density swamps having lower plant species richness than moderate deer density swamps. Further monitoring of deer densities and the species richness in these swamps is recommended.

Swamps closer to the impact area had relatively lower deer density than those further away. The reason for this avoidance may be due to the impact area being surrounded by military activity. Two military activities around the impact area that could be causing this response are noise (gunfire, explosions) (Landon et al. 2003) and human presence (Kucera 1976, Freddy et al.

1986). Further investigation into deer activity in the training area is necessary before any solid conclusion can be made.

An investigation of the deer herbivory in these swamps revealed that 22 of the 209 species found in these swamps were browsed by deer. This quantity of browsed species demonstrates that white-tailed deer consider many plant species in these swamps as a source of food. Calculations of the species most browsed in the swamps and those species most browsed proportionally to their presence were calculated. This latter index of browsing pressure may be

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the most important one since it indicates species susceptible to population decline or extirpation from these swamps. Symphyotrichum puniceum, Chelone glabra, and Lysimachia thyrsiflora were the top three most browsed species, while Symphyotrichum puniceum, Hamamelis virginiana, and Sambucus canadensis were the most browsed by proportion of presence in the swamp. Symphyotrichum puniceum was on both lists and although very common to these swamps, should still be monitored as a proactive conservation effort. A positive relationship of relative deer density with percentage of browsed plots was seen only when subplots that had browsed species from this study were considered. This positive relationship of relative deer density with percentage of browsed plots matched well with distance to the Fort Drum Impact

Area in that the most browsed swamps were also found to be those further away from the impact area.

In order to provide statistically significant results at the swamp scale, future research will require a greater number of swamps. These additional research swamps could include two larger swamps found on Fort Drum, other swamps that might have been found during a forest inventory occurring at the time of this study, and swamps from the surrounding St. Lawrence, Jefferson, and Lewis counties. Collaborative efforts from different environmental organizations would be required for this research to be accomplished. In order to make solid conclusions concerning the relationship between relative deer density and the impact area, future research will require a more thorough investigation involving some or all of these methods: radio telemetry, behavioral observations, noise level measurements, and monitoring of daily activities around the impact area. Testing of the impact area’s effect on relative deer density could also be conducted in other ecosystems and used for comparative analysis with the northern white-cedar swamps.

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APPENDICES

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Curriculum Vitae

Name: Robert Lee Smith II Birth Date: October 21, 1968 Birth Place: Ballston Spa, NY

Education

Name and Location Dates Degree

Ballston Spa High School 1982-1986 Regents Diploma Ballston Spa, NY

Jefferson Community College 2010-2011 Associate of Science Watertown, NY

SUNY College of Environmental Science & Forestry 2012-2014 Bachelor of Science Syracuse, NY

SUNY College of Environmental Science & Forestry 2014-2017 Master of Science Syracuse, NY

Employment

Employer Dates Job Title

U. S. Army 1989-2010 Administrative Specialist

SUNY College of Environmental Science & Forestry Summer 2012 Field Assistant

Fort Drum Natural Resources Branch Summer 2014 Field Biologist

Edna Bailey Sussman Foundation Summer 2015 Intern

SUNY College of Environmental Science & Forestry 2014-2016 Teaching Assistant

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