DEER AND BROWSING IN PILOT MOUNTAIN: AN EVALUATION OF

POPULATION SURVEY METHODS AND BROWSING PRESSURE IN A NORTH

CAROLINA STATE PARK

BY

ROBERT W. BALDWIN

A Thesis Submitted to the Graduate Faculty of

WAKE FOREST UNIVERSITY GRADUATE SCHOOL OF ARTS AND SCIENCES

in Partial Fulfillment of the Requirements

for the Degree of

MASTER OF SCIENCE

Biology

May 2019

Winston-Salem, NC

Approved by:

T. Michael Anderson, Ph.D., Advisor

Miles R. Silman, Ph.D., Chair

Robert Erhardt, Ph.D.

ii

ACKNOWLEDGEMENTS

I’d first like to thank my advisor, Dr. Michael Anderson for his guidance in experimental

design, analysis methods, thoughtful edits, and selecting the correct brand of Craftsman

pliers. His help has been invaluable for all aspects of my graduate experience. I’d also

like to thank my committee members Dr. Miles Silman and Dr. Robert Erhardt for

providing me with assistance, edits, and enthusiasm. I owe a huge thanks to Dr. Jared

Beaver for his guidance throughout my graduate and undergraduate research. I wouldn’t

be where I am today without Jared and I’m eternally grateful he agreed to show me the

ropes three years.

I’m permanently indebted to the many lab-mates, graduate students, undergraduate

assistants, and technicians that braved the slopes of Pilot Mountain with me; it takes a

village to do any sort of fieldwork, even when the field site is only twenty minutes from

campus. Thank you for helping collect data, providing feedback, and most importantly

keeping a smile on my face when faced with seemingly insurmountable tasks. Lastly, I’d

like to thank my loving family that has supported me every step of my journey in

graduate education.

iii

TABLE OF CONTENTS

List of Illustrations and Tables ........................................................................................v

List of Abbreviations ....................................................................................................... ix

Abstract ..............................................................................................................................x

Introduction: ................................................................................................................... xi

Chapter 1: Evaluating the use of drones equipped with thermal imaging technology

as an effective method for estimating wildlife .................................................................... 1

Abstract ............................................................................................................................2

Introduction ......................................................................................................................4

Study Area .................................................................................................................................. 8

Methods ...........................................................................................................................9

Results ............................................................................................................................13

Discussion ......................................................................................................................14

Management Implications ..............................................................................................19

Acknowledgements ........................................................................................................20

Literature Cited ..............................................................................................................21

Figures and Tables .................................................................................................................. 30

Chapter 2: N-mixture models provide reliable population density estimates of a

large, free-ranging ungulate from camera trap data ........................................................ 35

Abstract ..........................................................................................................................36

Introduction ....................................................................................................................38

Methods .........................................................................................................................42

Results ...........................................................................................................................46

Discussion .....................................................................................................................48

Literature Cited ..............................................................................................................53

Figures and Tables .........................................................................................................63

Supplemental Methods ...................................................................................................68

iv

Chapter 3: Sustained browsing pressure structures vegetation communities in a

North Carolina State Park ...................................................................................................... 70

Abstract ..........................................................................................................................70

Introduction ...................................................................................................................71

Methods .........................................................................................................................74

Results ...........................................................................................................................78

Discussion .....................................................................................................................80

Literature Cited ..............................................................................................................83

Figures and Tables ........................................................................................................90

Supplementary Figures ..................................................................................................96

Curriculum Vitae ............................................................................................................97

v

ILLUSTRATIONS AND TABLES

Chapter 1

Figure 1. Flight path (white lines) and transects flown (highlighted in yellow and

labelled) and at Auburn University deer research facility, Alabama USA, 16–17 March

2017 for white-tailed deer thermal drone surveys.

Figure 2. Screenshot illustrating ESRI’s Full Motion Video add-in tool used to identify

and georeference individuals (or group of animals) detected during aerial thermal drone

flights, within the Auburn University deer research facility in Alabama, USA, 16–17

March 2017. Full Motion Video allowed us to observe the flight footage and current

location of the aircraft along our flight path when marking locations of each individual

and for comparison among observers. Yellow lines indicate programed aircraft flight path

and transects and purple lines indicate actual aircraft flight path. Colored dots indicate

marked white-tailed deer observations.

Figure 3. Thermal white-tailed deer population estimates by flight and observer for

Auburn University deer research facility, Alabama USA, 16–17 March 2017. Estimates

were calculated using 2 separate approaches: transect-specific density and total count.

Boxes represent interquartile range of transect-specific population estimates, midline

estimate, and dashed lines represent the range. Dotted lines indicate the total count

estimates. Slash line indicated known deer population within the high-fenced research

facility.

Figure 4. Kernel-smoothed probability density distribution of recorded white-tailed deer

observation times for surveys conducted at Auburn University deer research facility,

vi

Alabama USA, 16–17 March 2017. Identically shaped curves indicate consistency of

observation between observers.

Figure 5. Thermal images of white-tailed deer observations in both open vegetative

cover and mixed-deciduous forest from an altitude of 100 m above ground level during

both morning (AM; sunrise) and evening (PM; sunset) flights conducted within the

Auburn University deer research facility in Alabama, USA, 16–17 March 2017.

Mornings flights occurred within 0619–0719 and evening flights within 1821–1921.

Confident thermal signatures of animals are marked with solid line circles and dashed

line circles marked unclear signatures. Time (AM vs PM) and cover type for each image:

(a) AM, open; (b) AM, forested; (c) PM, open; (d) PM, forested.

Chapter 2

Figure 1. Locations of camera sites in Pilot Mountain State Park mountain section, North

Carolina USA.

Figure 2. Mean deer population density estimated by N-mixture modelling (NMM),

mark-recapture (MR), and un-manned aerial FLIR (UAI) methods. Error bars represent

95% confidence intervals of estimates.

Figure 3. NMM estimated abundance by topographic aspect of camera site. Boxes

represent interquartile range of abundances, midline represents median, and points

represent outliers.

vii

Figure 4. The NMM estimated mean deer population density of PMSP with increased

survey length. The solid line represents LOESS best fit line to visualize trends. The

shaded region represents the 95% confidence interval of UAI validation. Error bars

represent 95% credible intervals.

Figure 5. Temporal analysis of PMSP deer population dynamics in relation to seasonal

vegetational changes. Three-month bins were used to evaluate seasonal trends in local

populations and resource availability. Green points represent mean NDVI value by

season, black points represent mean deer density. Lines represent LOESS best fit line to

visualize trends. Error bars represent 95% CI on mean best unbiased predictor site

abundance.

Chapter 3

Figure 1. Herbivore exclosure sites at Pilot Mountain State Park, NC USA. Each point

represents a paired fenced and un-fenced plot.

Figure 2. NMDS ordinated using Bray-Curtis dissimilarities between sites. Low stress

(<0.2) indicates the validity of the ordination. Boxplots represent interquartile ranges of

NMDS axis values by treatment. Midlines of boxes represent median values, dashed lines

represent extent and circles represent outliers.

Figure 3. Topographic aspect effects on site composition. Aspect values represent a

gradient from due south (-1) to due north (+1). Aspect was the sole significant predictor

viii

of site composition (β = 38.78, χ2 = 8.81, df = 1, P = 0.003). There was no evidence of a

treatment effect on site composition.

Figure 4. The effects of elevation on site diversity. Elevation values were scaled to

prevent difficulties in model convergence due to large absolute differences in predictors.

Elevation was the sole significant predictor of site diversity (β = 0.81, χ2 = 13.95, df = 1,

P = 0.00019).

Figure 5. Effects of elevation on species evenness. High evenness values represent lower

levels of species dominance. Although elevation was the best fitting predictor of

evenness, this relationship was not significant (β = 0.08, χ2 = 1.24, df = 1, P = 0.27).

Figure 6. Effects of local deer abundance on vegetation structure. AGB was square-root

transformed to conform to the assumptions of normality. AGB significantly decreased

with increased deer abundance (β = -0.39, χ2 = 9.51, df = 1, P = 0.0025).

Figure S1. Caging treatment effect on species evenness. Boxes represent interquartile

range of abundances, midline represents median, and points represent outliers

ix

ABBREVIATIONS

AGB: Aboveground biomass

AGL: Above ground level

AIC: Aikake’s information criteria

AICc: Aikake’s information criteria corrected for small sample size

ANOVA: Analysis of variance

FLIR: Forward-looking infrared

FMV: Full motion video

GSD: Ground sample distance

MODIS: Moderate Resolution Imaging Spectroradiometer

MR: Mark-resight

NDVI: Normalized difference vegetation index

NMDS: Non-metric multidimensional scaling

NMM: N-mixture modelling

PMSP: Pilot Mountain State Park, NC USA

RGB: Red-green-blue

UAI: Unmanned aerial infrared

VLOS: Visual line of sight

x

ABSTRACT

Eastern hardwood forests are becoming increasingly homogenous largely due to the

persistent browse effects of white-tailed deer. Deer browse selectively and place heavy

herbivory pressure on understory vegetation communities when populations are high. In

order to adequately control deer populations, however, managers must first be able to

accurately and reliably estimate populations. Even the best-informed management actions

may fail to mitigate community effects of browsing in areas throughout the eastern

United States due to potential browse legacy effects. The broader objectives of this thesis

were to develop a validation technique for free-ranging populations of deer, compare

different methods of quantifying deer populations using remote photography surveys, and

explore potential legacy effects of chronic herbivory using large herbivore exclosures.

Using drones equipped with thermal imagers, we were able to quantify captive

populations up to 97% accurately, allowing us to effectively validate other methods. All

of the remote photography survey methods investigated generated statistically

indistinguishable results with our drone validations, giving us confidence in all methods.

Lastly, we saw no significant vegetative regeneration in our exclosures, indicating that

historic browse pressure in our study area likely is exhibiting legacy effects on its

vegetation community.

xi

INTRODUCTION

The floral and faunal diversity of eastern hardwood forests (EHFs) are declining

at an alarming rate (Webster et al., 2018). Both understory and overstory communities are

becoming increasingly homogenous across space and do not necessarily reflect their

ecological legacy (Rooney et al., 2004). The continued degradation of these habitats

results in the replacement of endemics adapted to localized pressures with more

generalist species and invasive aliens (Hobbs and Mooney, 1998). In order for natural

resource managers to better protect the biodiversity and endemism of EHFs, a more

comprehensive understanding of the factors contributing to homogenization is needed.

White-tailed deer (Odocoileus virginianus; hereafter deer) are keystone

herbivores in the eastern United States (Waller and Alverson, 1997) and have profound

homogenizing effects on understory composition and structure due to selective browsing

pressure (Rossell et al., 2007; Rossell Jr. et al., 2005). Over-abundance of deer limits

vegetative cover and food availability to many wildlife species found in forested areas

(deCalesta, 1994; Côte et al., 2004) and ultimately reduces both floral and faunal

diversity (Hester et al., 2000; Webster et al., 2005; Rossell et al., 2007). Despite the

extensive literature documenting understory damage caused by deer browse, vegetation

and animal communities are managed independently, with little regard to their

connectivity and trophic relationships (Wisdom et al., 2006). This disconnect between

population management and natural ecological processes is particularly concerning given

the disproportionate role of humans in deer population management.

In order to adequately manage deer populations in a landscape without large

predators, land managers need better understanding of reliability and accuracy of their

xii

baseline population data. Mid-Atlantic EHFs historically were home to a diverse array of

large predators before widespread range contractions from habitat encroachment and

active extirpation by humans greatly limited local predator populations (Laliberte and

Ripple, 2004). Today, gray wolves (Canis lupus), red wolves (Canis rufus) and eastern

cougars (Puma concolor couguar) have been completely eradicated from the area,

whereas black bears (Ursus americanus) only exist in fragmented patches. As a result,

prey populations, particularly deer, have dramatically increased due to reduced predation

pressure (Côte et al., 2004), despite attempted top down control by humans. Hunting is

the most common form of deer population reduction, but low hunter participation,

restrictive bag limits, and short hunting seasons limit hunters from exercising the same

top-down control over deer populations as do natural predators (Brown et al., 2000)

Current population management prioritizes hunter satisfaction (i.e equitable distribution

of antlered harvest and hunter success) over the comprehensive management of browse

effects in many areas (Riley et al., 2003). Yet, even those management efforts targeting

browse effects are further limited by a lack of understanding of the biases associated with

common techniques of population estimation; managers need reliable and accurate

methods to quantify populations in order to establish adequate management goals

(Engeman, 2005).

The broad aim of this thesis was to foster holistic management of both population

management and understory regeneration by evaluating and exploring the population

survey methods and the legacy effects of historically over-browsed areas. My specific

objectives were to 1) establish a validation method for population surveys using

unmanned aerial vehicles, 2) evaluate the efficacy of N-mixture modelling as a reliable

xiii

replacement for traditional camera survey methods, and 3) evaluate the compositional

and structural vegetation changes elicited by sustained browsing pressure at PMSP.

1

CHAPTER 1: EVALUATING THE USE OF DRONES EQUIPPED WITH

THERMAL IMAGING TECHNOLOGY AS AN EFFECTIVE METHOD FOR

ESTIMATING WILDLIFE

JARED T. BEAVER1, Department of Biology and Center for Energy, Environment, and

Sustainability, Wake Forest University, Winston-Salem, NC 27109, USA

ROBERT W. BALDWIN1, Department of Biology and Center for Energy, Environment,

and Sustainability, Wake Forest University, Winston-Salem, NC 27109, USA

MAX MESSINGER, Department of Biology and Center for Energy, Environment, and

Sustainability, Wake Forest University, Winston-Salem, NC 27109, USA

CHAD NEWBOLT, School of Forestry and Wildlife Sciences, Auburn University,

Auburn, AL 36849, USA

STEPHEN S. DITCHKOFF, School of Forestry and Wildlife Sciences, Auburn

University, Auburn, AL 36849, USA

MILES R. SILMAN, Department of Biology and Center for Energy, Environment, and

Sustainability, Wake Forest University, Winston-Salem, NC 27109, USA

1These authors contributed equally to this work

Note: This chapter is formatted according to the guidelines of the Journal of Wildlife

Management. RWB curated data, analyzed data, created figures, and contributed to the

drafting and editing of the manuscript.

2

ABSTRACT Drones equipped with thermal sensors have shown ability to overcome

some of the limitations often associated with traditional human-occupied aerial surveys

(e.g., low detection, high operational cost, human safety risk). However, their accuracy

and reliability as a valid population technique has not been adequately tested. We tested

the effectiveness of using a miniaturized thermal sensor equipped to a drone (thermal

drone) for surveying white-tailed deer (Odocoileus virginianus) populations using a

captive deer population of known abundance at Auburn University’s deer research

facility, Alabama, USA, 16–17 March 2017. We flew 3 flights beginning 30 minutes

prior to sunrise and sunset (1 morning and 2 evening) consisting of 15 non-overlapping

parallel transects (18.8 km) using a small fixed-wing aircraft equipped with a non-

radiometric thermal infrared imager. Deer were identified by 2 separate observers by

their contrast against background thermal radiation and body shape. Our average thermal

drone density estimate (69.8 deer/km2, 95% CI: 52.2–87.6), was 78% of the mean known

value of 90.2 deer/km2, exceeding most sighting probabilities observed with thermal

surveys conducted using human-occupied aircraft. However, during evening flights

thermal contrast was improved and our drone-based density estimate (89.9 deer/km2) was

nearly identical (99.7%) to the mean known value, indicating that time of flight, in

conjunction with local vegetation types, determines video contrast and influences ability

to distinguish deer. Human observers had consistency in the time of observations and

resulting deer density estimates differed only 5% between observers. The method

provides the ability to perform accurate and reliable population surveys in a safe and

cost-effective manner compared to traditional aerial surveys and are only expected to

continue to improve as sensor technology and machine learning analytics continue to

3

advance. Furthermore, the replicability of autonomous flight results in methodology with

superior precision that increases statistical power to detect population trends across

surveys.

KEY WORDS aerial, deer, density estimation, drones, Odocoileus virginianus,

population methods, thermal imaging, wildlife surveys.

4

INTRODUCTION

One of the primary tenets of population management is that abundance and monitoring

information accurately describe the current population state or trend (Collier et al. 2013).

The testing and adoption of methods that provide rigorous, accurate estimates of

population size is not only relevant, but requisite for informed population management of

many wildlife species (Williams et al. 2002, Collier et al. 2013). However, true

population size for most wild populations is unknown (Yoccoz et al. 2001, Hodgson et al.

2016), and unless a survey method has been tested on a population of known size it is not

possible to directly assess the accuracy of any count method.

No single species has had more work focused on population size estimation and

methodological evaluation than the white-tailed deer (Odocoileus virginianus; hereafter

deer; Gill et al. 1997, Lancia et al. 2005, Collier et al. 2013). A variety of survey

techniques have been developed for estimating deer population size: browse surveys

(Aldous 1944, Tremblay et al. 2005), harvest data reconstruction (Roseberry and Woolf

1991, Millspaugh et al. 2009), pellet counts (Eberhardt and Van Etten 1956, Van Etten

and Bennett 1965, Goode et al. 2014), ground-based infrared and thermal imaging

surveys (Wiggers and Beckerman 1993, Gill et al. 1997, Collier et al. 2007), spotlight

surveys (McCullough 1982, Mitchell 1986, DeYoung 2011), and camera surveys

(Jacobson et al. 1997, Koerth and Kroll 2000) among others. However, aerial surveys

remain the best option for counting large mammals, especially over large areas (Caughley

1974, Jachmann 1991, Linchant et al. 2015). Aerial surveys provide more reliable

estimates of population size than ground-based techniques (Naugle et al. 1996, Beaver et

al. 2014) because of high detection rates (Bernatas and Nelson 2004, Millette et al. 2011)

5

and their ability to randomly sample across the landscape, avoiding biases inherent to

road-based sampling (Diefenbach 2005, Drake et al. 2005, Kissell and Nimmo 2011,

Beaver et al. 2014).

Aerial surveys conducted using human-occupied aircraft have been primarily

based on human visual detection (Caughley 1974, Poole et al. 2013, Chrétien et al. 2016),

are logistically difficult to implement, costly (Watts et al. 2010, Linchant et al. 2015), and

pose a health risk for operators (Jones et al. 2006, Watts et al. 2010). Aviation accidents

are the most common cause of work-related death for wildlife biologists in the United

States (Sasse 2003). The risks and costs associated with use of human-occupied aircraft

for aerial surveys makes drones (also known as unmanned aerial vehicles/systems,

remotely piloted aircraft) particularly attractive for wildlife population assessments. Early

use of drones has shown a vast array of diverse ecological applications (e.g. Vermeulen et

al. 2013, Christiansen et al. 2016, Evans et al. 2016, Wich et al. 2016) and the ability to

reduce cost and risk to humans (Watts et al. 2010, Seymour et al. 2017). Additionally,

they provide easier logistics and manipulation than manned aircraft (Linchant et al. 2015)

and avoid disturbances associated with ground surveys (Hodgson et al. 2018). These

benefits have led many practitioners to label drones as a powerful tool for wildlife

ecology (Chabot and Bird 2012, Linchant et al. 2015, Christie et al. 2016, Seymour et al.

2017).

An emerging area in drone research is miniaturized sensors allowing data to be

collected at extremely fine spatial and temporal resolutions highly suited to ecological

applications (Watts et al. 2012, Anderson and Gaston 2013, Messinger et al. 2016,

Hodgson et al. 2018). One of the more popular is the use of commercially available

6

thermal infrared sensors (hereafter thermal; Kissell and Nimmo 2011), providing a high-

contrast method of discriminating endotherms from their surroundings (Stark et al. 2014,

Burke et al. 2019) and allowing for detection of animals at night and in low light

conditions, which is an advantage over conventional multispectral (red-green-blue; RGB)

cameras (Israel et al. 2011, Witczuk et al. 2018). However, previous approaches using

thermal have experienced visibility limitations similar to those found in traditional

photographic methods, with the landscape obscuring animals not only through occlusion,

but also through isothermality (Witczuk et al. 2018).

Early results indicate the use of drones equipped with RGB and/or thermal

cameras in wildlife monitoring may be a viable alternative to typical field methods (e.g.

Christie et al. 2016, Seymour et al. 2017, Hodgson et al. 2018, Linchant et al. 2018,

Witczuk et al. 2018). Furthermore, as drone platforms, sensors and computer vision

techniques develop, the accuracy and cost-effectiveness of drone-based approaches will

also likely improve (Hodgson et al. 2018). Yet, important issues must be resolved prior to

wider drone application, including short flight endurance, optimizing resolution with area

coverage, and regulatory hurdles such as the requirement in many jurisdictions to

maintain visual light of sight (VLOS) with the aircraft at all times (Linchant et al. 2018).

Additionally, methods for estimating wildlife populations remain poorly understood

because most drone-based surveys to date have focused on the plausibility of wildlife

monitoring applications rather than their effectiveness as a viable improvement upon

current survey methods (Linchant et al. 2015).

The primary objective of this study was to determine the effectiveness of using

drones equipped with thermal for surveying wildlife populations by estimating deer

7

density and abundance for a captive deer population of known abundances. We predicted

this method would provide improved detection rates and more reliable population

estimates than current aerial survey methods while also providing biologists and wildlife

managers with a benchmark by which to compare and validate their existing survey

methods. We also identify strengths and weaknesses of the drone-based thermal approach

as a real-time survey tool and suggest paths for improvements and future directions.

8

STUDY AREA

We conducted our study at Auburn University’s deer research facility (Fig. 1) located in

Lee County which lies in the Piedmont region of east-central Alabama, USA. The facility

was constructed in October 2007 and consisted of 174 ha enclosed by 2.5-m steel fence.

The enclosed population was comprised of wild animals captured during the construction

of the facility and their descendants. Vegetation was approximately 40% open fields

maintained for hay production, 13% bottomland hardwoods (various oak [Quercus spp.]),

26% mature, naturally regenerated mixed hardwoods (various oak and hickory [Carya

spp.]) and loblolly pine (Pinus taeda), 11% early regenerated thicket areas consisting

primarily of Rubus spp., sweetgum (Liquidambar styraciflua), eastern red cedar

(Juniperus virginina), and Chinese privet (Ligustrun sinense), and 10% 10–15 year old

loblolly pine. A second order creek bisected the property and provided a stable source of

water year-round. For a more complete description of the facility, see Neuman et al.

(2016) and Newbolt et al. (2017).

9

METHODS

Data Collection

We conducted all surveys over the study area using the Ritewing Drak aircraft, a small

flying-wing style fixed-wing aircraft with a 1.5-m wingspan and maximum takeoff

weight of 4.3 kg. The aircraft was controlled by a Cube Autopilot (Hex Technology Ltd,

Hong Kong, CN) running the ArduPlane software stack (ArduPilot,

http://firmware.ardupilot.org/Plane/, accessed 10 March 2017). Flight paths were

programmed using Mission Planner (Mission Planner Version 1.3,

http://ardupilot.org/planner/, accessed 10 March 2017) using the integral terrain-

following feature to allow for precise, repeatable flights at a fixed altitude above ground

level (AGL). The drone was hand-flown during take-off and landing using the remote-

control system while all other phases of flight were under autopilot control and monitored

using Mission Planner.

The aircraft was equipped with the 640 × 480-pixel non-radiometric thermal

infrared imager (FLIR Vue Pro 640, 13-mm lens, 45° horizontal FOV, 30 hz). The

camera was mounted looking vertically downward to obtain the nadir view, and the

horizontal axis of the camera was aligned perpendicular to the flight path. This sensor

was self-calibrating, with a precision of 0.05 °C (< 50 mk).

Three flights were made in total, with 1 morning flight on 16 March 2017 and 2

evening flights on 16 and 17 March 2017, all of which were initiated 30 minutes prior to

sunrise and sunset. Mean temperature during the morning flight was -2° C while the

mean temperatures during the evening flights were 13° C on 16 March and 21° C on 17

10

March (Auburn Airport weather station, Auburn, AL). Weather was calm (winds <6

km/hr) and clear on both days (Auburn Airport weather station, Auburn, AL).

Flights were performed at 100 m AGL, providing an 84-m horizontal field of

view and a 13-cm ground sample distance. The aircraft flew at a constant speed of

approximately 21 m/s (75 km/hr) in an east-west oriented transect pattern, with 15

parallel transects, totaling 18.8 km in length, spaced evenly 100 m apart across the study

area (Fig. 2). Video and telemetry data were collected continuously in flight. After flight,

telemetry logs from the autopilot were trimmed to correspond with video clips,

reformatted for ingestion into ArcGIS Full Motion Video (FMV; Environmental Systems

Research Institute, Inc., Redlands, CA), and the two were multiplexed in FMV to create

georeferenced video.

Video Analysis

Georeferenced video footage from each of the 3 flights was analyzed at half speed by 2

separate observers to compare the effects of observer performance and flight conditions

on resulting density estimates. Deer were identified by their contrast against background

thermal radiation and body shape. When a thermal signature of an animal (or group of

animals) was detected, the location and time of each observation were marked and

recorded using FMV to mark locations of each individual and ensure density estimates

were generated from observations of the same animals (Fig. 2). We evaluated observation

agreement among observers by binning individual observations within 5 second windows

to control for small differences in observer recognition time of animals and compared

between observers using the kernel-smoothed probability density of observation time

11

using package sm (Bowman and Azzalin 2018) in program R (R Version 3.4.0, www.r-

project.org, accessed 2 Feb 2019).

Population Estimation

Data analysis was conducted in program R using package car (Fox and Weisberg 2011).

We used 2 methods to assess deer abundance from thermal footage. The first used

transects to provide a sample-based density estimate (deer/km2). Deer observations were

summed by flight transect and divided by their respective areas (transect length x

estimated strip width [field of view]) to calculate transect-specific densities per observer

(Naugle et al. 1996). Transect densities were then subsetted into 3 groups of every third

transect so that each used transect was 300 m apart to avoid the possibility of multiple

observations of animals across consecutive transects (i.e. transects for group 1: 1, 4, 7,

10, 13; group 2: 2, 5, 8, 11, 14; group 3: 3, 6, 9, 12, 15). However, it is worth noting that

multiple detection of the same animals on different transects does not introduce bias if

animal movement is random relative to the transect lines (Buckland et al. 2001). We then

calculated a mean density per flight by averaging the 3 mean group densities. This

process was repeated per observer.

The second method to assess abundance used the total number of deer counted per

complete flight. Total count values were adjusted by flight area to account for incomplete

area coverage (88%) of flights. Confidence intervals (95%, CI) of calculated abundances

and densities were generated from a 10,000-iteration bootstrapped distribution of transect

densities. Densities were compared between observers using a two-sample, two-tailed t-

test. Densities were compared by flight, and the interaction of flight and observer using

analysis of variance (ANOVA) with type two sum of squares. Both estimates were

12

conducted without prior knowledge of the known population size.

Accuracy Assessment

The deer population at Auburn University’s deer research facility was intensively

monitored, with capturing and camera surveys occurring every year to track the

population. Individuals within the facility had previously been captured and assigned a

unique number, freeze branded, and tagged in both ears with cattle ear tags (see Neuman

et al. [2016] and Newbolt et al. [2017] for specific capture and handling techniques).

Capture techniques followed American Society of Mammalogists’ guidelines (Sikes and

Gannon 2011) and were in accordance with Auburn University’s Institutional Animal

Care and Use Committee (PRNs 2008-1417, 2008-1421, 2010-1785, 2011-1971, 2013-

2372, 2016-2964, 2016-2985). A number (10–15 individuals) of young-of-the-year deer

were captured and released outside the facility at ~6 months of age each year to control

deer density and maintain appropriate numbers of individuals across age classes. Natural

mortalities further mediated the population density of the facility. The deer within the

facility had no movement restrictions outside of the high fence boundary. Occasionally, a

deer was missed during annual tagging, and the vegetative characteristics and large size

of the facility did not allow for continuous monitoring of all individuals over the course

of the study. As such, we used a combination of methods to estimate our known deer

abundance as was used in Keever et al. (2017). No hunting occurred within the research

facility.

13

RESULTS

At the time of our study, the Auburn deer facility contained approximately 151–163 deer

(86.8–93.7 deer/km2), with a mean value of 157 deer (90.2 deer/km2; Fig. 3). The average

density estimate from the thermal camera equipped drone (hereafter, thermal drone) was

69.8 deer/km2 (95% CI: 52.3–87.40; Fig. 3), resulting in an average sighting probability

of 78% (56–100%) of the known estimate or a correction factor of 1.28. Total abundance

from thermal drone counts, adjusted for incomplete coverage (88%) of the study area,

was 120 or (95% CI: 90.9–142.2; Fig. 3), 56–94% of the known estimate.

Estimated densities differed only 5% between observers and were not

significantly different (P = 0.68). The probability density distributions of observations

differed between observers in the morning flight but the shape and structure were nearly

identical for evening flights (Fig. 4), indicating consistent identification of heat signatures

by separate observers.

Video analysis indicated differing video contrast between morning and evening

flights (Fig. 5). The mean estimated population density was significantly less in the

morning flight (44.4 deer/km2, 95% CI: 28.9–52.4) than in evening flights (89.9

deer/km2, 95% CI 67.2–97.8, F = 3.70, P = 0.05; Fig. 3) and evening flight density and

total abundance estimates (142 deer) were nearly identical (99.7%, 90.4%) to the mean

known density and total abundance values (90.2 deer/km2, 157 deer; Fig. 3). Despite the

differing contrast between timing of the flights, there was no significant difference

between observers (t = -0.47, P = 0.68), or the interaction of flight and observer (F =

0.89, P = 0.43; Fig. 3).

14

DISCUSSION

Performance Assessment

Thermal drone surveys provided an accurate, time efficient, and highly repeatable

method of quantifying deer populations resulting in density estimates and total counts

(89.9 deer/km2, 142 deer) for our high-contrast evening flights nearly identical to known

values (90.2 deer/km2, 157 deer). Averaged thermal drone density estimates had a

sighting probability of 78% of present individuals, exceeding those often reported from

thermal surveys (<75%) conducted using human-occupied aircraft (Parker and Driscoll

1972, Caughley 1974, Bartmann et al. 1986, Beasom et al. 1986), and the high-contrast

evening flights achieved a sighting probability of nearly 100%. Several studies have

found that the detection of deer with thermal imagery from human-occupied aerial flights

offered a varied performance ranging from 37% to 98% (Croon et al. 1968, Wiggers and

Beckerman 1993, Naugle et al. 1996, Potvin and Breton 2005). In fact, detection rates

reported specifically for deer are usually 60–80% under optimal conditions, and below

50% when detection conditions are less favorable (Bartmann et al. 1986, Beasom et al.

1986, DeYoung et al. 1989, Potvin et al. 1992, Chrétien et al. 2016). Recently, some

attempts utilizing drone platforms equipped with either thermal or RGB sensors have

been made to overcome these limitations influencing sighting probability.

The sighting probabilities obtained in this study lead to correction factors ranging

from 0 for the evening flights to 1.28 overall. Linchant et al. (2018) used drones equipped

with RGB for counting hippopotamuses (Hippopotamus amphibious) over a homogenous

landscape and found an almost identical correction factor (1.22) to ours that averaged

both morning and evening flights. However, one advantage of thermal imagers over

15

conventional RGB cameras is the ability to distinguish animals based on their body heat

and increased detection of animals at night and during low light conditions (Burke et al.

2019). Chrétien et al. (2016) found that detection of thermal objects was more accurate

than RGB. Witczuk et al. (2018) showed that terrestrial ungulates could be identified in

both leafless deciduous forests and coniferous forests using thermal cameras. We also

detected deer in all major vegetation types (deciduous/mixed/and coniferous forest, open

areas). However, heat emission is not constant throughout the day (Felton et al. 2010) and

can fluctuate depending on a variety of factors such as weather condition (e.g., ambient

temperature, cloud cover; Burke et al. 2019) and environment (e.g., land cover, aspect;

Franke et al. 2012, Chrétien et al. 2016, Witczuk et al. 2018, Burke et al. 2019), making

timing of surveys a critical study component.

Timing of surveys and topographical complexity can result in unequal warming of

areas (thermal cluttering) mediated by exposure to sunlight (i.e. background thermal

radiation varies across the landscape). This is especially true in areas with objects with

thermal inertia such as boulders and wet ground or water (Burke et al. 2019). Witczuk et

al. (2018) indicated that in forested areas, the contrast between trees and ground can

influence our ability to detect animals, and found that during morning flights, some sun-

exposed tree trunks and branches were just beginning to heat up, resulting in thermal

cluttering and low contrast. We observed that the residual heat captured during the day by

vegetation allowed better distinction of foliage in forested habitats during evening flights,

causing the ground signature to be relatively cooler. Thus, evening images were clearer

and had a homogenous background, such that animal signatures stood out with enough

contrast for relatively easy detection (Witczuk et al. 2018). The accuracy of our data

16

collected during evening flights suggests a correction factor may not be needed when

flight conditions are optimized for a particular area. Conversely, during morning flights,

vegetation was cold due to thermal radiation exchange with the clear sky at night while

the ground remained relatively warm, deer became difficult to distinguish from the

background during this period due to decreased contrast.

In this study we used a non-radiometric sensor that did not allow temperature

thresholds to be set. Thermal exchange between animals and their environment is well

understood (Porter and Gates 1969), and using a radiometric sensor thermal threshold

that can be tailored to the calculated radiative properties of the organism(s) of interest can

greatly increase the thermal contrast between an animal and the terrain and likely

improve accuracy of this technique.

Human Observers

The huge amount of data and the need for multiple observers to minimize potential

observer bias are often described as an important drawback for using drones for wildlife

surveys (Linchant et al. 2015). However, Linchant et al. (2018) noted that experienced

observers needed smaller correction factors to estimate the total population of deer and

that estimates from separate observers quickly converge with small amounts of

experience. Our results support this conclusion as our experienced human observers had

consistency in both the time of deer observations and resulting density estimates of their

respective video analyses. Observer-specific probability density curves of observation

time were nearly identically shaped for high-contrast evening flights, indicating observers

generally counted the same individual animals during the study. However, there was no

difference between flight and observer observations overall, despite the low-contrast

17

morning flight. Meaning during the morning flight that while both observers identified

approximately the same number of deer in lower quality footage, they did so at different

times, suggesting increased false positive observations were made due to the difficulty in

separating the thermal image of the deer from the thermal clutter in the landscape.

Thermal and RGB drone surveys can collect detailed information rapidly

(Linchant 2015), potentially creating scenarios where the time and cost of manual

classification of imagery from aerial surveys becomes exorbitant. Our study area was

small (each flight provided near complete coverage of our study area and the complete

video footage for each survey was approximately 20 minutes only) and our observers

only had 64 total minutes of video footage to analyze. Larger study areas and more

complex data sets might necessitate automated image classification, which would save

time and costs associated with human-observer analysis (Chabot and Francis 2016,

Seymour et al. 2017). However, the development of these technologies is still in its

infancy, and these approaches are of limited use if models must be re-trained for new

datasets or require extensive set up (Seymour et al. 2017). Additionally, Hodgson et al.

(2018) illustrated that while semi-automated counts did streamline the process after

algorithm training, manual counts performed equally well. However, when the number of

subjects is high or repeat counts are required at different time points, labor investment

needed for manual counting can be substantial and the incorporation of machine-learning

capabilities will be more necessary (Chabot and Francis 2016, Burke et al. 2019).

Due to the enclosed nature of our study area and the sole dedication of the facility

to research with deer, we were unable to evaluate the ability to distinguish between

multiple species, a potential limitation of the method in areas with multiple species of

18

large mammals. Further research on characteristics of thermal signatures for other large

land-dwelling mammal species is needed, and future studies should assess and integrate a

correction method that considers the sighting probability of deer for different cover types

(e.g., deciduous forest, evergreen forest, open/field.; Chrétien et al. 2016). With further

development of drone platforms, sensors, and computer vision techniques, drone-based

approaches to wildlife estimation are also expected to continue to improve and become

more widespread (Longmore et al. 2017, Seymour et al. 2017, Hodgson et al. 2018).

19

MANAGEMENT IMPLICATIONS

Autonomous drone-based surveys using thermal sensors allow precisely replicated

surveys and population estimates that equal or exceed traditional airborne techniques.

This in turn gives wildlife managers greater ability to detect relatively small changes in

populations, thereby improving the ability to apply and evaluate management efforts

while simultaneously allowing surveys of difficult terrain and drastic reductions in risk to

personnel. However, for thermal aerial surveys to be successfully applied to wildlife

management and conservation issues, certain aspects of these surveys must be

considered. First, observer bias and experience must be accounted for during data

analysis. The impact of training on observer performance is poorly understood, but

common sense dictates that performance will benefit from training. Second, data quality

will be a function of vegetative cover, topography, and thermal contrast, and the

interactions of these variables will vary with season, time of day, and region. It will be

imperative to have an adequate understanding of the thermal landscape of a study area

and how these variables influence thermal video contrast before attempting thermal drone

surveys. Finally, study area size will influence the successful collection of data because

of technological limitations with drones. Currently, small drones are limited to flights of

several minutes to a few hours depending on the model and payload, forcing managers to

balance coverage area with the minimum resolution for animal identification. However,

expansion of the survey coverage to larger areas can be expected in the near future as

regulations for civilian use of drones are constantly changing. As drone surveys are

expanded to larger areas, data processing and analysis workflow will become of

increasing importance.

20

ACKNOWLEDGMENTS

We thank Wake Forest University’s Center for Energy, Environment and Sustainability,

the Wake Forest Unmanned Systems Laboratory, and the Department of Biology for

financial support and other contributions to this project. Thank you to Patrick Fox for his

assistance conducting flights and ensuring proper and safe flight operations and to Kyle

Blackburn for his assistance with data organization and image selection.

21

LITERATURE CITED

Aldous, S. E. 1944. A deer browse survey method. Journal of Mammalogy 25:130–136.

Anderson, K., and K. J. Gaston. 2013. Lightweight unmanned aerial vehicles will

revolutionize spatial ecology. Frontiers in Ecology and the Environment 11:138–

146.

Bartmann, R. M., L. H. Carpenter, R. A. Garrott, and D. C. Bowden. 1986. Accuracy of

helicopter counts of mule deer in pinyon-juniper woodland. Wildlife Society

Bulletin 14:356–363.

Beasom, S. L., F. G. Leon III, and D. R. Synatzske. 1986. Accuracy and precision of

counting white-tailed deer with helicopters at different sampling intensities.

Wildlife Society Bulletin 14:364–368.

Beaver, J. T., C. A. Harper, R. E. Kissell, L. I. Muller, P. S. Basinger, M. J. Goode, F. T.

Van Manen, W. Winton, and M. L. Kennedy. 2014. Aerial vertical-looking

infrared imagery to evaluate bias of distance sampling techniques for white-tailed

deer. Wildlife Society Bulletin 38:419–427.

Bernatas, S., and L. Nelson. 2004. Sightability model for California bighorn sheep in

canyonlands using forward-looking infrared (FLIR). Wildlife Society Bulletin

32:638–647.

Bowman, A. W., and A. Azzalini. 2018. R package 'sm': nonparametric smoothing

methods. version 2.2-5.6 URL: http://www.stats.gla.ac.uk/~adrian/sm.

Buckland, S. T., D. R. Anderson, K. P. Burnham, J. L. Laake, D. L. Borchers, and L.

Thomas. 2001. Introduction to distance sampling: Estimating abundance of

biological populations. Oxford University Press, Oxford, United Kingdom.

22

Burke, C., M. Rashman, S. Wich, A. Symons, C. Theron, and S. Longmore. 2019.

Optimizing observing strategies for monitoring animals using drone-mounted

thermal infrared cameras. International Journal of Remote Sensing 40:439–467.

Caughley, G. 1974. Bias in aerial survey. The Journal of Wildlife Management 38:921–

933.

Chabot, D., and D. M. Bird. 2012. Evaluation of an off-the-shelf unmanned aircraft

system for surveying flocks of geese. Waterbirds 35:170–174.

Chabot, D., and C. M. Francis. 2016. Computer-automated bird detection and counts in

high- resolution aerial images: A review. Journal of Field Ornithology 87:343–

359.

Chrétien, L.-P., J. Théau, and P. Ménard. 2016. Visible and thermal infrared remote

sensing for the detection of white-tailed deer using an unmanned aerial system.

Wildlife Society Bulletin 40:181–191.

Christiansen, F., A. M. Dujon, K. R. Sprogis, J. P. Y. Arnould, and L. Bejder. 2016.

Noninvasive unmanned aerial vehicle provides estimates of the energetic cost of

reproduction in humpback whales. Ecosphere 7:e01468.

Christie, K. S., S. L. Gilbert, C. L. Brown, M. Hatfield, and L. Hanson. 2016. Unmanned

aircraft systems in wildlife research: Current and future applications of a

transformative technology. Frontiers in Ecology and the Environment 14:241–

251.

Collier, B. A., S. S. Ditchkoff, J. B. Raglin, and J. M. Smith. 2007. Detection probability

and sources of variation in white-tailed deer spotlight surveys. Journal of Wildlife

Management 71:277–281.

23

Collier, B. A., S. S. Ditchkoff, C. R. Ruth, and J. B. Raglin. 2013. Spotlight surveys for

white-tailed deer: Monitoring panacea or exercise in futility? The Journal of

Wildlife Management 77:165–171.

Croon, G. W., D. R. McCullough, C. E. Olsen, and L. M. Queal. 1968. Infrared scanning

techniques for big game censusing. Journal of Wildlife Management 32:751–759.

DeYoung, C. A. 2011. Population dynamics. Pages 147–180 in D. G. Hewitt, editor.

Biology and management of white-tailed deer. CRC Press, Boca Raton, Florida,

USA.

DeYoung, C. A., F. S. Guthery, S. L. Beasom, S. P. Coughlin, and J. R. Heffelfinger.

1989. Improving estimates of white-tailed deer abundance from helicopter

surveys. Wildlife Society Bulletin 17:275–279.

Diefenbach, D. R., 2005. The ability of aerial surveys using thermal infrared imagery to

detect changes in abundance of white-tailed deer on Pennsylvania state forest.

Commonwealth of Pennsylvania, Department of Conservation and Natural

Resources, Bureau of Forestry, Harrisburg, USA.

Drake D., C. Aquila, and G. Huntington. 2005. Counting a suburban deer population

using forward-looking infrared radar and road counts. Wildlife Society Bulletin

33:656–661.

Eberhardt, L., and R. C. Van Etten. 1956. Evaluation of the pellet group count as a deer

census method. The Journal of Wildlife Management 20:70–74.

Evans, L. J., T. H. Jones, K. Pang, S. Saimin, and B. Goossens. 2016. Spatial ecology of

estuarine crocodile (Crocodylus porosus) nesting in a fragmented landscape.

Sensors, 16.

24

Felton, M., K. P. Gurton, J. L. Pezzaniti, D. B. Chenault, and L. E. Roth. 2010. Measured

comparison of the crossover periods for mid- and long- wave IR (MWIR and

LWIR) polarimetric and conventional thermal imagery. Optics Express

18:15704–15713.

Fox, J., and S. Weisberg. 2011. An {R} Companion to Applied Regression. Second

Edition. Thousand Oaks CA: Sage. URL:

http://socserv.socsci.mcmaster.ca/jfox/Books/Companion.

Franke, U., B. Goll, U. Hohmann, and M. Heurich. 2012. Aerial ungulate surveys with a

combination of infrared and high-resolution natural colour images. Animal

Biodiversity and Conservation 35:285–293.

Gill, R. M. A., M. L. Thomas, and D. Stocker. 1997. The use of portable thermal imaging

for estimating deer population density in forest habitats. Journal of Applied

Ecology 34:1273–1286.

Goode, M. J., J. T. Beaver, L. I. Muller, J. D. Clark, F. T. van Manen, C. A. Harper, and

P. S. Basinger. 2014. Capture-recapture of white-tailed deer using DNA from

fecal pellet groups. Wildlife Biology 20:270–278.

Hebblewhite, M., and D. T. Haydon. 2010. Distinguishing technology from biology: A

critical review of the use of GPS telemetry data in ecology. Philosophical

Transactions of the Royal Society of London. Series B: Biological Sciences

365:2303–2312.

Hodgson, J. C., R. Mott, S. M. Baylis, T. T. Pham, S. Wotherspoon, A. D. Kilpatrick, R.

Raja Segaran, I. Reid, A. Terauds, and L. P. Koh. 2018. Drones count wildlife

more accurately and precisely than humans. Methods in Ecology and Evolution

25

9:1160–1167.

Hodgson, J. C., S. M. Baylis, R. Mott, A. Herrod, and R. H. Clarke. 2016. Precision

wildlife monitoring using unmanned aerial vehicles. Scientific Reports 6:1–7.

Israel, M. 2011. A UAV-based roe deer fawn detection system. International Archives of

the Photogrammetry. Remote Sensing and Spatial Information Sciences

38(1/C22):51–55.

Jachmann, H. 1991. Evaluation of four survey methods for estimating elephant densities.

African Journal of Ecology 29:188–195.

Jacobson, H. A., J. C. Kroll, R. W. Browning, B. H. Koerth, and M. H. Conway. 1997.

Infrared-triggered cameras for censusing white-tailed deer. Wildlife Society

Bulletin 25:547–556.

Jones, G. P., L. G. Pearlstine, and H. F. Percival. 2006. An assessment of small

unmanned aerial vehicles for wildlife research. Wildlife Society Bulletin 34:750–

758.

Keever, A. C., C. P. Mcgowan, S. S. Ditchkoff, P. K. Acker, J. B. Grand, and C. H.

Newbolt. 2017. Efficacy of N-mixture models for surveying and monitoring

white-tailed deer populations. Mammal Research 62:413–422.

Kissell, Jr., R. E., and S. K. Nimmo. 2011. A technique to estimate white-tailed deer

density using vertical-looking infrared imagery. Wildlife Biology 17:85–92.

Koerth, B. H., and J. C. Kroll. 2000. Bait type and timing for deer counts using cameras

triggered by infrared monitors. Wildlife Society Bulletin 28:630–635.

Lancia, R. A., W. L. Kendall, K. H. Pollock, and J. D. Nichols. 2005. Estimating the

number of animals in wildlife populations. Pages 106–133 in C. E. Braun, editor.

26

Techniques for wildlife investigations and management, sixth edition. The

Wildlife Society, Bethesda, Maryland, USA.

Linchant, J., J. Lisein, J. Semeki, P. Lejeune, and C. Vermeulen. 2015. Are unmanned

aircraft systems (UASs) the future of wildlife monitoring? A review of

accomplishments and challenges. Mammal Review 45:239–252.

Linchant, J., S. Lhoest, S. Quevauvillers, P. Lejeune, C. Vermeulen, J. Semeki

Ngabinzeke, B. Luse Belanganayi, W. Delvingt, and P. Bouché. 2018. UAS

imagery reveals new survey opportunities for counting hippos. PLoS ONE

13:e0206413.

Longmore, S. N., R. P. Collins, S. Pfeifer, S. E. Fox, M. Mulero-Pázmány, F. Bezombes,

A. Goodwin, M. De Juan Ovelar, J. H. Knapen, and S. A. Wich. 2017. Adapting

astronomical source detection software to help detect animals in thermal images

obtained by unmanned aerial systems. International Journal of Remote Sensing

38:2623–2638.

McCullough, D. R. 1982. Evaluation of night spotlighting as a deer study technique.

Journal of Wildlife Management 46:963–973.

Messinger, M., G. P. Asner, and M. Silman. 2016. Rapid assessments of Amazon forest

structure and biomass using small unmanned aerial systems. Remote Sensing

8(8):615.

Millette, T. L., D. Slaymaker, E. Marcano, C. Alexander, and L. Richardson. 2011.

AIMS-Thermal - a thermal and high resolution color camera system integrated

with GIS for aerial moose and deer census in northeastern Vermont. Alces 47:27–

37.

27

Millspaugh, J. J., J. R. Skalski, R. L. Townsend, D. R. Diefenbach, M. S. Boyce, L. P.

Hansen, and K. Kammermeyer. 2009. An evaluation of sex-age-kill (SAK) model

performance. Journal of Wildlife Management 73:442–451.

Mitchell, W. A. 1986. Deer spotlight census: section 6.4.3, U.S. Army Corp of Engineers

wildlife resources management manual. U.S. Army Engineer Waterways

Experiment Station Technical Report EL-86-53, Vicksburg, Mississippi, USA.

Neuman, T.J., C. H. Newbolt, S. S. Ditchkoff, and T. D. Steury. 2016. Microsatellites

reveal plasticity in reproductive success of white-tailed deer. Journal of

Mammalogy 97:1441–1450.

Newbolt, C. H., P. K. Acker, T. J. Neuman, S. I. Hoffman, S. S. Ditchkoff, ans T. D.

Steury. 2017. Factors influencing reproductive success in male white-tailed deer.

Journal of Wildlife Management 81:206–217.

Naugle, D. E., J. A. Jenks, and B. J. Kernohan. 1996. Use of thermal infrared censing to

estimate density of white-tailed deer. Wildlife Society Bulletin 24:37–43.

Parker, Jr., H. D., and R. S. Driscoll. 1972. An experiment in deer detection by thermal

scanning. Journal of Range Management 25:480–481.

Poole, K. G., C. Cuyler, and J. Nymand. 2013. Evaluation of caribou Rangifer tarandus

groenlandicus survey methodology in West Greenland. Wildlife Biology 19:225–

239.

Porter, W. P., and D. M. Gates. 1969. Thermodynamic equilibria of animals with

environment. Ecological Monographs 39:245–270.

Potvin, F., and L. Breton. 2005. From the field: testing 2 aerial survey techniques on deer

in fenced enclosures—visual double-counts and thermal infrared sensing. Wildlife

28

Society Bulletin 33:317–325.

Potvin, F., L. Breton, L. P. Rivest, and A. Gingras. 1992. Application of a double-count

aerial survey technique for white-tailed deer, Odocoileus virginianus, on Anticosti

Island, Quebec. Canadian Field-Naturalist 106:435–442.

Roseberry, J. L., and A. Woolf. 1991. A comparative evaluation of techniques for

analyzing white-tailed deer harvest data. Wildlife Monographs 3–59.

Sasse, D. B. 2003. Job-related mortality of wildlife workers in the United States, 1937–

2000. Wildlife Society Bulletin 31:1015–1020.

Seymour, A. C., J. Dale, M. Hammill, P. N. Halpin, and D. W. Johnston. 2017.

Automated detection and enumeration of marine wildlife using unmanned aircraft

systems (UAS) and thermal imagery. Scientific Reports 7:1–10.

Sikes R. S., and W. L. Gannon. 2011. Guidelines of the American Society of

Mammalogists for the use of wild mammals in research. Journal of Mammalogy

92:235–253.

Stark, B., B. Smith, and Y. Chen. 2014. Survey of thermal infrared remote sensing for

Unmanned Aerial Systems. IEEE 1294–1299.

Tremblay, J.-P., I. Thibault, C. Dussault, J. Huot, and S. D. Côté. 2005. Long-term

decline in white-tailed deer browse supply: can lichens and litterfall act as

alternative food sources that preclude density-dependent feedbacks. Canadian

Journal of Zoology 83:1087–1096.

Van Etten, R. C., and C. L. Bennett. 1965. Some sources of error in using pellet-group

counts for censusing deer. The Journal of Wildlife Management 29:723–729.

Vermeulen C., P. Lejeune, J. Lisein, P. Sawadogo, P. Bouché. 2013. Unmanned aerial

29

survey of elephants. PLoS ONE 8:e54700.

Watts, A. C., V. G. Ambrosia, and E. A. Hinkley. Unmanned aircraft systems in remote

sensing and scientific research: Classification and considerations of use. Remote

Sensing 4:1671–1692.

Watts, A. C., J. H. Perry, S. E. Smith, M. A. Burgess, B. E. Wilkinson, Z. Szantoi, P. G.

Ifju, and H. F. Percival. 2010. Small unmanned aircraft systems for low-altitude

aerial surveys. Journal of Wildlife Management 74:1614–1619.

Wich, S., and L.P. Koh. 2018. Conservation drones, mapping and monitoring

biodiversity. Oxford University Press, Oxford, United Kingdom.

Wiggers, E. P., and S. F. Beckerman. 1993. Use of thermal infrared sensing to survey

white-tailed deer populations. Wildlife Society Bulletin 21:263–268.

Williams, B. K., J. D. Nichols, and M. J. Conroy. 2002. Analysis and management of

animal populations. Academic Press, San Diego, California, USA.

Witczuk, J., S. Pagacz, A. Zmarz, and M. Cypel. 2018. Exploring the feasibility of

unmanned aerial vehicles and thermal imaging for ungulate surveys in forests -

preliminary results. International Journal of Remote Sensing 39:5504–5521.

Yoccoz, N. G., J. D. Nichols, and T. Boulinier. 2001. Monitoring of biological diversity

in space and time. Trends in Ecology & Evolution 16:446–453.

30

FIGURES AND TABLES

Figure 1. Flight path (white lines) and transects flown (highlighted in yellow and

labelled) and at Auburn University deer research facility, Alabama USA, 16–17 March

2017 for white-tailed deer thermal drone surveys.

31

Figure 2. Screenshot illustrating ESRI’s Full Motion Video add-in tool used to identify

and georeference individuals (or group of animals) detected during aerial thermal drone

flights, within the Auburn University deer research facility in Alabama, USA, 16–17

March 2017. Full Motion Video allowed us to observe the flight footage and current

location of the aircraft along our flight path when marking locations of each individual

and for comparison among observers. Yellow lines indicate programed aircraft flight path

and transects and purple lines indicate actual aircraft flight path. Colored dots indicate

marked white-tailed deer observations.

32

Figure 3. Thermal white-tailed deer population estimates by flight and observer for

Auburn University deer research facility, Alabama USA, 16–17 March 2017. Estimates

were calculated using 2 separate approaches: transect-specific density and total count.

Boxes represent interquartile range of transect-specific population estimates, midline

estimate, and dashed lines represent the range. Dotted lines indicate the total count

estimates. Slash line indicates known deer population within the high-fenced research

facility.

33

Figure 4. Kernel-smoothed probability density distribution of observation time for

surveys conducted at Auburn University deer research facility, Alabama

34

USA, 16–17 March 2017. Identically shaped curves indicate consistency of observation

between observers.

Figure 5. Thermal images of deer observations in both open vegetative cover and mixed-

deciduous forest from an altitude of 100 m above ground level during both morning (AM;

sunrise) and evening (PM; sunset) flights conducted within the Auburn University deer

research facility in Alabama, USA, 16–17 March 2017. Sunrise flights occurred within

0619–0719 and sunset flights within 1821–1921. Confident thermal signatures of animals

are marked with solid line circles and dashed line circles marked unclear signatures. Time

(AM vs PM) and cover type for each image: (a) AM, open; (b) AM, forested; (c) PM,

open; (d) PM, forested.

35

CHAPTER 2: N-MIXTURE MODELS PROVIDE RELIABLE POPULATION

DENSITY ESTIMATES OF A LARGE, FREE-RANGING UNGULATE FROM

CAMERA TRAP DATA

ROBERT W. BALDWIN*, Department of Biology and Center for Energy, Environment,

and Sustainability, Wake Forest University, Winston-Salem, NC 27109

JARED T. BEAVER, Department of Biology and Center for Energy, Environment, and

Sustainability, Wake Forest University, Winston-Salem, NC 27109

MATT WINDSOR, Pilot Mountain State Park, North Carolina State Parks, 1792 Pilot

Knob Park Rd, Pinnacle, NC 27043

MAX MESSINGER, Department of Biology and Center for Energy, Environment, and

Sustainability, Wake Forest University, Winston-Salem, NC 27109

MILES R. SILMAN, Department of Biology and Center for Energy, Environment, and

Sustainability, Wake Forest University, Winston-Salem, NC 27109

T. MICHAEL ANDERSON, Department of Biology and Center for Energy,

Environment, and Sustainability, Wake Forest University, Winston-Salem, NC 27109

*Corresponding author email: baldrw13@wfu.edu

Note: This chapter is formatted according to the guidelines of the Journal of Applied

Ecology. RWB helped conceive study, conducted fieldwork, analyzed data, and wrote the

manuscript.

36

ABSTRACT

1. Camera traps are an increasingly popular species monitoring tool yet remain

controversial for estimating populations due to inherent data interpretation

difficulties. The objectives of our study were to compare two commonly used

population estimation analysis methods using camera trap data, N-mixture

modelling and mark-resight, and validate their accuracy with an independent

dataset.

2. We evaluated these methods using observations of white-tailed deer (Odocoileus

virgnianus) from a camera-trapping study at Pilot Mountain State Park, NC USA.

Deer densities derived from unmanned aerial thermal imagery were used to

independently validate both methods. Environmental covariates were included in

N-mixture models to explore spatial abundance relationships and seasonal

abundance models were fit to 18 months of camera observations to explore

temporal dynamics in local populations.

3. N-mixture modelling, mark-resight, and unmanned aerial surveys converged on

similar density estimates, indicating the accuracy of both camera survey methods.

Our N-mixture modelling estimates were robust over a large range of temporal

survey lengths (from 1 – 168 h sample units). Mark-resight methods required less

samples and provided demographic information valuable in management but

required more survey effort to identify individuals and suffered from a lack of

measurable precision.

4. Our N-mixture modelling approach additionally allowed us to robustly measure

spatial and temporal dynamics within our study area. Deer in our study area

37

preferred southern aspects, likely due to the greater amount of browse in these

areas. Temporal patterns showed two-fold seasonal fluctuations of deer density

and suggest our study area acts as a hunting season refuge, presumably due to

tradeoffs between forage availability and mortality risk.

5. Synthesis and applications. Both N-mixture modelling and mark-resight analysis

provide reliable deer population estimates from camera trap data. N-mixture

modelling allows for robust population estimates of commonly occurring species

and allows for the exploration of spatial and temporal hypotheses.

KEYWORDS

Camera trapping, N-mixture modelling, density estimates, population, mark-resight,

North Carolina, spatial occupancy models, white-tailed deer, demographic methods,

imperfect detection

38

INTRODUCTION

Population surveys provide fundamental baseline information for a wide spectrum of

ecological processes, including population dynamics (Freckleton et al., 2005), species

interactions (Vásquez et al., 2007), disease prevalence (Hochachka and Dhondt, 2000),

and biodiversity (Gotelli and Colwell, 2001). However, the accuracy of these estimates

often remains unexplored due to the high cost and effort of validating data collected at

large, population-level, scales.

Remote photography surveys have surged in popularity since the development of

affordable commercially available infrared-triggered camera traps (Cutler and Swann,

1999; Koerth and Kroll, 2000; Rowcliffe and Carbone, 2008). Established cameras serve

as permanent observers, operating continuously in a wide range of environmental and

climatic conditions with minimal human attention (Larrucea et al., 2007). As a result,

camera traps are less invasive, less labor intensive, and more cost effective than other

ecological monitoring techniques (Silveira et al., 2003; Heilbrun et al., 2006; Rowcliffe et

al., 2008). Despite their widespread use, however, generating accurate population

estimates from camera trap data remains a challenge due to inherent sampling error

(Meek et al., 2015). For example, camera traps capture a limited portion of their assumed

sampling area, confounding true absences of the target species with their non-detection

(Burton et al., 2015, Kéry & Royle 2016). Estimates not accounting for ‘imperfect

detection’ will misrepresent target populations and lead to poor management decisions.

Although several analytical methods account for imperfect detection of camera traps

(Rowcliffe et al., 2008; Howe et al., 2017), modified mark and recapture surveys are

those most commonly used by managers.

39

Mark and re-sight surveys (MR) are commonly used in coordination with camera

traps for estimating populations but are limited by the need to identify individuals. The

method applies traditional mark and recapture analysis to camera trap data by using the

photographic capture rates of individual animals to estimate abundance (Burton et al.,

2015). Traditional MR methods operate under two assumptions: that individuals are

correctly identified and have equal probabilities of detection (Pollock et al., 1990). The

necessity of individual identification also restricts the application of MR to species with

visual markings such as large, patterned felids (Karanth & Nichols, 1998) or ungulates

with conspicuous antler patterns (Jacobson et al. 1997). Recent improvements of MR

methods account for heterogenous detection of individuals, space use of individuals, and

imperfect detection (Royle & Young, 2008), but the necessity of individual identification

and fine-scaled movement data prevents their application to many wildlife species and

warrants the exploration of other methods.

Another approach, N-mixture modelling (NMM), uses spatially and temporally

replicated point counts to estimate abundance without the need for identification of

individuals (Royle, 2004; Mackenzie & Royle, 2005). NMM treats the probability of

observing individuals (i.e., ‘detection’) and true occupancy separately by using variation

in site-specific, temporally replicated point counts to estimate the probability that

individuals are detected (p). NMM assumes that variation in point counts are due to p

alone (e.g., Royle, 2004), that populations are closed, there are no false positive species

identifications, that detections are independent, and there is a constant, homogeneous

detection probability for all individuals (Kéry & Royle, 2016). NMM allows for the

incorporation of environmental covariates into both detection and occupancy models and

40

distinguishes non-detection from true absences (Dénes et al., 2015). In doing so, NMMs

allow biologists to investigate temporal and spatial relationships with fine-scale

population fluctuations, allowing for precisely targeted management actions. Although

NMMs have been used to estimate population characteristics of birds, amphibians, and

tropical mammals (Kéry et al. 2005; Ahumada et al., 2013; Ficetola et al. 2018), their

accuracy remains questionable because true population estimates are typically

unavailable for many free-ranging species.

Despite their popularity, NMMs have been recently criticized due to their reliance

on unrealistic assumptions listed above (Barker et al., 2017). Double counting of

individuals, population fluctuations, and heterogenous detection probability constitute

major violations of the assumptions of the NMM approach, yet are unavoidable, to some

degree, within field surveys (Link et al., 2018). Recent simulation studies show that these

violations only marginally affect model fit, but radically change resulting population

estimates (Duarte et al., 2018; Link et al. 2018). Previous validations of NMM have

relied upon simulated data conforming to the underlying assumptions of the model

(Royle, 2004) and do not necessarily validate the model in situations where modelling

assumptions may not be realistic (i.e. free-ranging populations of animals). Further

validation of the NMM approach using alternative techniques is needed in this field.

The broad goals of this study were to compare survey methods using a free-

ranging population of white-tailed deer (Odocoileus virginianus; hereafter deer) and use

resulting estimates to connect spatial and temporal population fluctuations with

ecological processes within a moderately-sized (10 km2) protected area. Deer are

considered keystone herbivores in the eastern United States (Waller & Alverson 1997)

41

and at elevated densities can have profound homogenizing effects on understory

composition and structure due to selective browsing pressure (Rossell et al., 2007;

Rossell Jr. et al., 2005). Male deer, identifiable by their antler characteristics (Jacobson et

al., 1997), serve as an ecologically relevant model for the assessment of both MR and

NMM population methods. MR surveys have been used widely to study deer (Jacobson et

al., 1997; McCoy et al., 2011), but NMMs, although widely used for other species, have

been sparsely applied to deer in a management context due to their potential to violate

modelling assumptions (Keever et al., 2017).

Our approach is novel in that we apply a third method, unmanned aerial vehicles

equipped with infrared imagers (UAI), as a means of validating the MR and NMM

camera survey estimates. The UAI method has been validated using captive populations

of deer in a natural setting (Baldwin et. al, in review), thus providing an ideal benchmark

for comparison. Our study had three specific aims: (1) validate camera survey estimates

with UAI analysis, (2) compare the estimates obtained by N-mixture modelling with

those of MR estimates and (3) use resulting estimates to connect population fluctuations

to spatial and temporal ecological processes at a local scale.

42

MATERIALS AND METHODS

Study Area

This study was conducted on the ~10 km2 (1024 ha) mountain section of Pilot Mountain

State Park (PMSP), NC USA, 36.3425° N, 80.4768° W (Figure 1). The mountain section

of the park consists of a quartzite monadnock that rises 738 meters above sea level and

427 meters above the surrounding hillside. The differing levels of soil moisture and

exposure amongst Pilot Mountain’s south/west and north/east slopes drive vegetational

differences along these aspects. Pine-oak/heath dominate on the south/west facing slopes

as a result of xeric conditions from prevailing southwestern winds and intense sun

exposure. Dominant species include Table Mountain pine (Pinus pungens) and pitch pine

(P. rigida), with an understory of mountain laurel (Kalmia latifolia), and purple

rhododendron (Rhododendron catawbiense). Scrub oak (Quercus ilicifoli), blackjack oak

(Q. marilandica), and chestnut oak (Q. montana) are also present, with an herb layer

comprised mainly of beetleweed (Galax urceolata) and trailing arbutus (also known as

mayflower; Epigaea repens). On more mesic east- and north-facing slopes dominance

shift from pines (P. pungens, P. rigida) to oaks (predominantly Q. montana), with a

poorly-developed herbaceous layer of primarily beetleweed (Galax urceolata; Randall et

al. 1995).

Survey design

Twenty-two camera sites were established throughout the mountain section of

PMSP, creating a camera density of approximately one camera per 0.41 km2 (40.5

hectares). Original site locations were chosen using a systematic design on a randomly

generated grid, with minor adjustments made to minimize slope and ease of camera

43

access (Jacobson et al., 1997). Cuddeback E3 model cameras (Non Typical, Inc., Green

Bay, WI) were used and set with “high” trigger sensitivity and “optimal” detection range.

Camera sites were established in June of 2016 and maintained continuously thereafter;

memory cards were replaced monthly and batteries weekly. Cameras were mounted

facing north to reduce backlighting at sunrise and sunset. Obstructing vegetation was

cleared from the camera view to reduce the likelihood of empty captures; plastic

identification tags were mounted opposite cameras to facilitate site recognition. Photos

from January 1 – March 31, 2018 (90 days) were used to compare models to maintain

assumptions of closed populations and equal detectability of animals with the minimal

canopy coverage necessary for UAI flights. Photos from June 2016 – November 2017

were consolidated and used to investigate temporal trends in NMM estimated population

density.

Image analyses

All photos were initially classified by number of adult males, adult females, fawns, and

individuals of indeterminable demographic characteristics. Subsequently, we applied the

method of Jacobson et al. (1997): the ratio of unique adult males, identified by antler

characteristics, to total adult male captures was applied to the total captures of adult

females and fawns, providing an estimate of unique number of individuals in both

classes. Population density was calculated by dividing the resulting estimates by the total

study area.

Model creation

Captures were tallied in photos collected at least 10 minutes apart to avoid double

counting and aggregated by hour over the course of the 90-day study period (j = 2,160;

44

Royle, 2004). Aggregations of increasing length (1 - 168 h) were used to analyze the

sensitivity of null models to changes in temporal discretization of continuous camera

detections. Elevation data were extracted from the USGS National Elevation Dataset and

used to calculate site-specific slope and aspect (U.S. Geological Survey, 2013). Distance

to edge, distance to road, and distance to trail were calculated for each site. The

hierarchical model used was in the form:

1. 𝑁𝑖 ~ 𝑃𝑜𝑖𝑠𝑠𝑜𝑛(𝜆𝑖); with log(𝜆𝑖) = 𝛽0 + 𝛽1

2. 𝐶𝑖𝑗|𝑁𝑖 ~ 𝐵𝑖𝑛𝑜𝑚𝑖𝑎𝑙(𝑁𝑖, 𝑝𝑖𝑗); with log(𝑝𝑖𝑗) = 𝛼0 + 𝛼1

Where 𝑁 is the latent abundance of site 𝑖 , 𝐶 is the point count at site i and time 𝑗, 𝜆 is

mean abundance and 𝑝 is the detection probability of individuals at site i and time 𝑗.

Covariates (𝛽, 𝛼) are incorporated into the observation and occupancy portions of the

model using log-link functions.

Candidate models were created using environmental covariates in both the

observation and occupancy processes and top model was selected using Aikake’s

information criterion (AIC). An empirical Bayes best unbiased predictor of mean site

abundance was calculated from a simulated posterior distribution of the top performing

model along with 95% credible intervals. Population density was calculated by summing

mean abundance by site and dividing by the area of PMSP. Upon identification and

validation of top-performing model, we used modelling parameters to estimate deer

abundances from September 2016 – December 2017. Observation data were grouped into

three-month windows for modelling in order to control for seasonal differences in

detection probability and animal behavior. Normalized difference vegetation index

(NDVI) values were extracted from 16 day, 250m resolution MODIS data for each

45

camera site and averaged by season to portray vegetation dynamics over time. Data

analysis was performed using packages unmarked (Fiske & Chandler, 2011),

AICcmodavg (Mazerolle, 2017), raster (Hijmans, 2019), and MODIS (Mattiuzzi &

Detsch, 2019) in R version 3.4.0 (R Core Team, 2018).

UAI validation

We conducted five flights using the Linn Aerospace Hummingbird quadcopter equipped

with a 640 x 480-pixel non-radiometric thermal infrared imager (FLIR Vue Pro 640; 13-

mm lens, 45° horizontal FOV, 30 hz). Three morning and two evening flights were

conducted within one hour of sunrise or sunset on February 8 – 13, 2018. All flights were

performed at 120 m above ground level (AGL), providing a 100 m horizontal field of

view. The aircraft flew in an east-west oriented transect pattern, with 12 parallel

transections, totaling 13.75 km in length, spaced evenly 300 m apart across the study area

to avoid possible double counts. Flight coverage area (transect length x width of view)

accounted for 13.5% (1.38 km2ha) of the study area. Video was collected continuously in

flight and was processed and analyzed according to the methods of Baldwin et al. 2019

(SI Methods). Densities were averaged between transects and flights to provide validation

density estimates. An analysis of variance (ANOVA) using type 2 sum of squares was

used to test for significant differences between flight estimates to ensure the reliability of

the benchmark.

46

RESULTS

We obtained 10,234 deer photos over our total 21-month study period and 1695 photos

during our three-month validation period.

Both camera survey methods generated overlapping density estimates with our

UAI validation. The UAI method estimated the deer population density of PMSP to be

31.0 deer / km2 (95% CI: 23.7 – 40.0). There was no significant difference in estimates

between flight-specific estimates (F4,25 = 1.98, P = 0.13), giving us confidence in the

reliability of UAI as a validation benchmark. NMM estimated the population density at

34.6 deer / km2 (95% CI: 27.7 – 42.3), which differed only 26% from the MR estimate of

25.3 deer / km2 . We determined all estimates to be statistically indistinguishable due to

the inclusion of all means within the 95% CIs of the UAI estimate (Figure 2).

Our top performing NMM contained significant predictors within both

observation and occupancy portions of the model. The detection model contained an

interaction between slope and aspect (β = 0.365, SE = 0.128, P = 0.004), which was

expected due to variability in topography and vegetation cover which modified the

sampling area of the camera traps. Aspect was also a significant (β = 0.757, SE = 0.237,

P = 0.0005) driver of site abundance, with higher deer abundances strongly associated

with the southern aspects of the study area (Figure 3).

By grouping the camera trap captures into time periods of differing lengths, we

were able to conduct a sensitivity analysis on how survey length influenced our estimates

of deer density. Different discretization lengths of surveys had no significant effect on

resulting estimates. Increasing length of survey decreased NMM density estimates by up

to 5% from surveys of one hour and approached an asymptote of 25 deer / km2 (Figure

47

4). There were no significant differences amongst mean density estimates and all

discretizations generated density estimates overlapping our UAI validation (Figure 4).

The precision of these estimates, however, increased with survey length.

Our NMM temporal analysis revealed consistent seasonal fluctuations in local

deer density over the course of our 18-month study period. PMSP deer density increased

steadily in summer months, peaked in autumn months, and slowly decreased throughout

the winter and spring – varying inversely with seasonal NDVI patterns (Figure 5).

Although the amplitude of change differed across years, the general trends remained

consistent between years. While camera detection was likely lower during periods of

abundant understory growth and standing biomass (i.e., May – September), our higher

estimates of deer density demonstrate that this did not likely introduce bias into our

results.

48

DISCUSSION

Both camera survey methods used in this study generated population density estimates

that were comparable to our UAI validation, signaling that either method may be

considered a viable option for estimating deer populations. The small differences between

our estimates were likely due to the various assumptions of the respective methods. MR

deer surveys rely on photographic capture of all individual male animals and only

account for imperfect detection by inflating estimates 10% as determined by baited

surveys of captive populations (Jacobson et al.,1997). Proper correction factors have not

been explored for un-baited MR surveys; failure to capture or correctly identify all

individual males present within our study area likely resulted in lower density estimates

than other methods (Figure 3). Likewise, although correction factors improve the

accuracy of resulting parameter estimates, they also conflate observation and site

occupancy, which limits the possible application of parameter estimates to ecological

processes. NMM accounts for imperfect detection by modelling detection probability and

abundance separately (Royle, 2004), which results in more accurate density estimates

given the assumptions of the model are upheld. A recent study validated extensions of

NMMs on large, free-ranging ungulates using telemetry data to estimate immigration and

emigration rates (Ketz et al., 2018), but individual movement data are time intensive,

costly, and rarely available to stakeholders. Our findings demonstrate that basic NMM

(i.e., using model estimation procedures in freely available, open source software) can

provide accurate abundance estimates for free-ranging ungulates and can be used to test

hypotheses of abundance relationships with spatial and temporal predictors.

49

Within our study area topographic aspect influenced deer abundance, thus

highlighting the importance of abundance-space relationships. Topographic aspect

directly impacts soil nutrient and organic matter content due to differing exposure and

moisture regimes (Yimer et al., 2006), resulting in contrasting vegetation community

composition between aspects (Bennie et al., 2006). Deer preferentially select areas of

higher relative nutritive quality when the costs of selection of other available areas are

equivalent (Pauley, 1993). Within our study area, individuals occupied southern slopes

more heavily than northern slopes (Figure 3), which is likely due to higher amounts of

available browse within the mixed forest overstory area (Williams & Oosting, 1944). The

southern slopes of our study area also have easier access to surrounding agricultural

lands, whereas the northern slopes are bounded by more suburban areas. Although habitat

preferences of deer are well-documented and easily explained, our results demonstrate

that NMM can illuminate spatial relationships of abundance in a robust manner to test

hypotheses of space-abundance relationships. These benefits are compounded by the ease

of expanding NMM throughout time to investigate temporal process of local population

dynamics.

Our results suggest that PMSP experiences seasonal fluctuations in deer

population density due to the interplay of risk and resource availability. Ungulates trade-

off mortality risk and forage quality when selecting habitat (Hebblewhite & Merrill,

2009), valuing safety over forage when risks of predation are especially high (Lone et al.,

2015). In the resource-rich agricultural landscapes surrounding our study area, human

recreational hunting pressure poses a substantial risk to deer during the autumn hunting

season. Public lands shelter disproportionate numbers of ungulates to their size in the

50

western united states during hunting seasons due to hunting restriction and prohibition

(Proffitt et al., 2013; DeVoe et al. 2019); our temporal analysis suggests that PMSP

serves as a similar mortality refuge for local deer. Conversely, during the months of the

early growing season, the deer population density of PMSP gradually decreases as

surrounding agricultural lands with higher forage availability become more favorable.

These seasonal fluctuations in population density demonstrate the importance of

considering behavior and spatial configurations of the landscape when interpreting

population survey data. Survey methods and designs must be carefully selected based on

behavioral characteristics of focal species and management goals of estimates (Burton et

al., 2015).

Temporal and spatial replication are necessary for NMM but represent a tradeoff

in practitioner effort (Shannon et al., 2014). Greater spatial replication facilitates the

detection of rare or cryptic species, whereas greater temporal replication allows for more

precise estimates of common species (Mackenzie & Royle, 2005). The similarity of our

estimates results, in part, from detailed knowledge of deer space use; home ranges and

core area sizes of deer are extensively detailed (Stewart et al., 2011). In Appalachian

forests, deer have an average core area of use of 0.24 km2 and home range size of 10 km2

during winter (Campbell et al., 2010), but home ranges and space use are also known to

vary with habitat quality (Marchinton & Hirth, 1984) and season (Nicholson et al., 1997).

Our camera density of 1 camera per 4 km2 minimized the possibility of detecting animals

at more than one camera site within our study area while maintaining adequate coverage

for the capture of individual animals necessary for MR analysis. However, as we cannot

ensure complete closure between our camera sites, our interpretation of abundance

51

changes from absolute abundance to one of ‘use’ (Latif et al., 2015). Site specific

abundances better represent the mean number of animals using a specific area since

double counting of some animals across sites likely resulted in greater NMM estimates.

Although the validation of our NMM approach suggests that minimal double-counting

occurred, the possibility cannot be completely discounted.

Our results demonstrate the robust nature of NMM by showing that different

discretization of survey lengths only marginally affected our resulting estimates. Camera

traps provide a continuous record of detection, but camera surveys are often discretized

for computational and analytical ease (Guillera-Aroita, 2011). Practitioners are advised to

select the smallest possible time unit of ecological inference to prevent unnecessary data

reduction, but the effects of changing discretization periods are poorly understood

(Mackenzie & Royle, 2005). Previous studies show that lengthening discretization

periods increase estimated detection probability while occupancy estimates remain

constant (Steenweg et al., 2016). Our results suggest that increasing the length of our

effective survey periods had only a marginal effect on the resulting abundance estimates

and all survey lengths generated population estimates within the range of our UAI

validation (Figure 4). Hence, NMM presents a flexible option for the population

estimation of commonly occurring species from camera data; practitioners may discretize

camera surveys as determined by data availability without fear of manipulation of

resulting estimates.

The goals of management action should be the determining factor for choosing

camera trap survey methods. MR surveys allow for easier calculation of demographic

information, such as population age and sex structure, compared to NMM due to lower

52

sampling requirements for analysis. MR surveys can be imperative for population

management decisions which oftentimes necessitate demographic targeting (Gordon et

al., 2004). However, calculating precision for MR surveys can be difficult; due to the

logistics involved, replicate survey data are often unavailable. Managers need the ability

to compare their results across space and time in order to evaluate the effects of past

decisions and plan for future action (Engeman, 2005). NMM provide robust estimates of

precision and allow for better comparison of estimates across space and time. A

combination of approaches may be necessary to achieve all management goals of a given

area; our findings suggest that managers may choose either MR and NMM methods of

analysis with confidence that their results accurately reflect the population of their study

area.

53

LITERATURE CITED

Ahumada, J. A., Hurtado, J., & Lizcano, D. (2013). Monitoring the Status and Trends of

Tropical Forest Terrestrial Vertebrate Communities from Camera Trap Data:

A Tool for Conservation. PLOS ONE, 8(9), e73707.

doi:10.1371/journal.pone.0073707

Barker, R. J., Schofield, M. R., Link, W. A., & Sauer, J. R. (2018). On the reliability of

N-mixture models for count data. Biometrics, 74(1), 369–377. doi:

10.1111/biom.12734

Bennie, J., Hill, M. O., Baxter, R., & Huntley, B. (2006). Influence of slope and aspect on

long-term vegetation change in British chalk grasslands. Journal of Ecology,

94(2), 355–368. doi: 10.1111/j.1365-2745.2006.01104.x

Burton, A. C., Neilson, E., Moreira, D., Ladle, A., Steenweg, R., Fisher, J. T., … Boutin,

S. (2015). REVIEW: Wildlife camera trapping: a review and recommendations

for linking surveys to ecological processes. Journal of Applied Ecology, 52(3),

675–685. doi: 10.1111/1365-2664.12432

Campbell, T. A., Laseter, B. R., Ford, W. M., & Miller, K. V. (2004). Feasibility of

localized management to control white-tailed deer in forest regeneration areas.

Wildlife Society Bulletin, 32(4), 1124–1131. doi: 10.2193/0091-

7648(2004)032[1124:FOLMTC]2.0.CO;2

Christie K. S, Gilbert S. L, Brown C. L, Hatfield M., & Hanson L. (2016). Unmanned

aircraft systems in wildlife research: current and future applications of a

transformative technology. Frontiers in Ecology and the Environment, 14(5),

241–251. doi: 10.1002/fee.1281

54

Côté, S. D., Rooney, T. P., Tremblay, J.-P., Dussault, C., & Waller, D. M. (2004).

Ecological Impacts of Deer Overabundance. Annual Review of Ecology,

Evolution, and Systematics, 35(1), 113–147. doi:

10.1146/annurev.ecolsys.35.021103.105725

Cutler, T. L., & Swann, D. E. (1999). Using Remote Photography in Wildlife Ecology: A

Review. Wildlife Society Bulletin (1973-2006), 27(3), 571–581.

deCalesta, D. S. (1994). Effect of White-Tailed Deer on Songbirds within Managed

Forests in Pennsylvania. The Journal of Wildlife Management, 58(4), 711–718.

doi: 10.2307/3809685

Dénes, F. V., Silveira, L. F., & Beissinger, S. R. (2015). Estimating abundance of

unmarked animal populations: accounting for imperfect detection and other

sources of zero inflation. Methods in Ecology and Evolution, 6(5), 543–556. doi:

10.1111/2041-210X.12333

Diefenbach, D. R., Finley, J. C., Luloff, A. E., Stedman, R., Swope, C. B., Zinn, H. C., &

Julian, G. J. S. (2005). Bear and Deer Hunter Density and Distribution on Public

Land in Pennsylvania. Human Dimensions of Wildlife, 10(3), 201–212. doi:

10.1080/10871200591003445

Duarte, A., Adams, M. J., & Peterson, J. T. (2018). Fitting N-mixture models to count

data with unmodeled heterogeneity: Bias, diagnostics, and alternative approaches.

Ecological Modelling, 374, 51–59. doi: 10.1016/j.ecolmodel.2018.02.007

Engeman, R. M. (2005). Indexing principles and a widely applicable paradigm for

indexing animal populations. Wildlife Research, 32(3), 203–210. doi:

10.1071/WR03120

55

Ficetola, G. F., Barzaghi, B., Melotto, A., Muraro, M., Lunghi, E., Canedoli, C., …

Manenti, R. (2018). N -mixture models reliably estimate the abundance of small

vertebrates. Scientific Reports, 8(1), 10357. doi: 10.1038/s41598-018-28432-8

Freckleton, R. P., Gill, J. A., Noble, D., & Watkinson, A. R. (2005). Large-scale

population dynamics, abundance–occupancy relationships and the scaling from

local to regional population size. Journal of Animal Ecology, 74(2), 353–364. doi:

10.1111/j.1365-2656.2005.00931.x

Gordon, I. J., Hester, A. J., & Festa‐Bianchet, M. (2004). REVIEW: The management of

wild large herbivores to meet economic, conservation and environmental

objectives. Journal of Applied Ecology, 41(6), 1021–1031. doi: 10.1111/j.0021-

8901.2004.00985.x

Gotelli, N. J., & Colwell, R. K. (2001). Quantifying biodiversity: procedures and pitfalls

in the measurement and comparison of species richness. Ecology Letters, 4(4),

379–391. doi: 10.1046/j.1461-0248.2001.00230.x

Guillera-Arroita, G., Morgan, B. J. T., Ridout, M. S., & Linkie, M. (2011). Species

Occupancy Modeling for Detection Data Collected Along a Transect. Journal of

Agricultural, Biological, and Environmental Statistics, 16(3), 301–317. doi:

10.1007/s13253-010-0053-3

Halls, L. K. (1984). White-tailed Deer: Ecology and Management. Stackpole Books.

Hebblewhite, M., & Merrill, E. H. (2009). Trade-offs between predation risk and forage

differ between migrant strategies in a migratory ungulate. Ecology, 90(12), 3445–

3454. doi: 10.1890/08-2090.1

56

Heilbrun, R. D., Silvy, N. J., Peterson, M. J., & Tewes, M. E. (2006). Estimating Bobcat

Abundance Using Automatically Triggered Cameras. Wildlife Society Bulletin,

34(1), 69–73. doi: 10.2193/0091-7648(2006)34[69:EBAUAT]2.0.CO;2

Hester, A. J., Edenius, L., Buttenschøn, R. M., & Kuiters, A. T. (2000). Interactions

between forests and herbivores: the role of controlled grazing experiments.

Forestry: An International Journal of Forest Research, 73(4), 381–391. doi:

10.1093/forestry/73.4.381

Hijmans, Robert J. (2019). raster: Geographic Data Analysis and Modeling.

R package version 2.8-19. https://CRAN.R-project.org/package=raster

Hochachka, W. M., & Dhondt, A. A. (2000). Density-dependent decline of host

abundance resulting from a new infectious disease. Proceedings of the National

Academy of Sciences, 97(10), 5303–5306. doi: 10.1073/pnas.080551197

Hodgson, J. C., Mott, R., Baylis, S. M., Pham, T. T., Wotherspoon, S., Kilpatrick, A. D.,

… Koh, L. P. (2018). Drones count wildlife more accurately and precisely than

humans. Methods in Ecology and Evolution, 9(5), 1160–1167. doi: 10.1111/2041-

210X.12974

Howe, E. J., Buckland, S. T., Després‐Einspenner, M.-L., & Kühl, H. S. (2017). Distance

sampling with camera traps. Methods in Ecology and Evolution, 8(11), 1558–

1565. doi: 10.1111/2041-210X.12790

Jacobson, H. A., Kroll, J. C., Browning, R. W., Koerth, B. H., & Conway, M. H. (1997).

Infrared-Triggered Cameras for Censusing White-Tailed Deer. Wildlife Society

Bulletin (1973-2006), 25(2), 547–556.

57

Johnson, A. S., Hale, P. E., Ford, W. M., Wentworth, J. M., French, J. R., Anderson, O.

F., & Pullen, G. B. (1995). White-Tailed Deer Foraging in Relation to

Successional Stage, Overstory Type and Management of Southern Appalachian

Forests. The American Midland Naturalist, 133(1), 18–35. doi: 10.2307/2426344

Karanth, K. U., & Nichols, J. D. (1998). Estimation of Tiger Densities in India Using

Photographic Captures and Recaptures. Ecology, 79(8), 2852–2862. doi:

10.1890/0012-9658(1998)079[2852:EOTDII]2.0.CO;2

Kéry, M., & Royle, J. A. (2016). Applied Hierarchical Modeling in Ecology (Vol. 1).

Elsevier.

Kéry, M., Royle, J. A., & Schmid, H. (2005). Modeling Avian Abundance from

Replicated Counts Using Binomial Mixture Models. Ecological Applications,

15(4), 1450–1461. doi: 10.1890/04-1120

Ketz, A. C., Johnson, T. L., Monello, R. J., Mack, J. A., George, J. L., Kraft, B. R., …

Hobbs, N. T. (2018). Estimating abundance of an open population with an N-

mixture model using auxiliary data on animal movements. Ecological

Applications, 28(3), 816–825. doi: 10.1002/eap.1692

Kissell, R. E., & Nimmo, S. K. (2011). A technique to estimate white-tailed deer

Odocoileus virginianus density using vertical-looking infrared imagery. Wildlife

Biology, 17(1), 85–92. doi: 10.2981/10-040

Koerth, B. H., & Kroll, J. C. (2000). Bait Type and Timing for Deer Counts Using

Cameras Triggered by Infrared Monitors. Wildlife Society Bulletin (1973-2006),

28(3), 630–635.

58

Larrucea, E. S., Brussard, P. F., Jaeger, M. M., & Barrett, R. H. (2007). Cameras,

Coyotes, and the Assumption of Equal Detectability. Journal of Wildlife

Management, 71(5), 1682–1689. doi: 10.2193/2006-407

Latif, Q. S., Ellis, M. M., & Amundson, C. L. (2016). A broader definition of occupancy:

Comment on Hayes and Monfils. The Journal of Wildlife Management, 80(2),

192–194. doi: 10.1002/jwmg.1022

Linchant, J., Lisein, J., Semeki, J., Lejeune, P., & Vermeulen, C. (2015). Are unmanned

aircraft systems (UASs) the future of wildlife monitoring? A review of

accomplishments and challenges. Mammal Review, 45(4), 239–252. doi:

10.1111/mam.12046

Link W. A., Schofield M. R., Barker R. J., & Sauer J. R. (2018). On the Robustness of N‐

mixture models. Ecology, 0(ja). doi: 10.1002/ecy.2362

Lone, K., Loe, L. E., Meisingset, E. L., Stamnes, I., & Mysterud, A. (2015). An adaptive

behavioural response to hunting: surviving male red deer shift habitat at the onset

of the hunting season. Animal Behaviour, 102, 127–138. doi:

10.1016/j.anbehav.2015.01.012

Mackenzie, D. I., & Royle, J. A. (2005). Designing occupancy studies: general advice

and allocating survey effort. Journal of Applied Ecology, 42(6), 1105–1114. doi:

10.1111/j.1365-2664.2005.01098.x

Mattiuzzi, M. & Detsch, F. (2019). MODIS: Acquisition and Processing of

MODIS Products. R package version 1.1.5.

Mazerolle, M. J. (2017) AICcmodavg: Model selection and multimodel inference

basedon (Q)AIC(c). R package version 2.1-1.

59

Mccoy, J. C., Ditchkoff, S. S., & Steury, T. D. (2011). Bias associated with baited camera

sites for assessing population characteristics of deer. The Journal of Wildlife

Management, 75(2), 472–477. doi: 10.1002/jwmg.54

Meek, P. D., Ballard, G., Claridge, A., Kays, R., Moseby, K., O’Brien, T., … Townsend,

S. (2014). Recommended guiding principles for reporting on camera trapping

research. Biodiversity and Conservation, 23(9), 2321–2343. doi: 10.1007/s10531-

014-0712-8

Meek, Paul D., Ballard, G.-A., & Fleming, P. J. S. (2015). The pitfalls of wildlife camera

trapping as a survey tool in Australia. Australian Mammalogy, 37(1), 13–22. doi:

10.1071/AM14023

Nicholson, M. C., Bowyer, R. T., & Kie, J. G. (1997). Habitat Selection and Survival of

Mule Deer: Tradeoffs Associated with Migration. Journal of Mammalogy, 78(2),

483–504. doi: 10.2307/1382900

ORNL DAAC. (2018). MODIS and VIIRS Land Products Global Subsetting and

Visualization Tool. ORNL DAAC, Oak Ridge, Tennessee, USA. Accessed 04 04,

2019. Subset obtained for product at 36.3425° N, 80.4768° W, time period: 01

June 2016 to 16 January 2017, and subset size: 4 x 4 km. doi:

10.3334/ORNLDAAC/1379

Parsons, A. W., Simons, T. R., Pollock, K. H., Stoskopf, M. K., Stocking, J. J., &

O’connell, A. F. (2015). Camera traps and mark-resight models: The value of

ancillary data for evaluating assumptions. The Journal of Wildlife Management,

79(7), 1163–1172. doi: 10.1002/jwmg.931

60

Pauley, G. R., Peek, J. M., & Zager, P. (1993). Predicting White-Tailed Deer Habitat Use

in Northern Idaho. The Journal of Wildlife Management, 57(4), 904–913. doi:

10.2307/3809096

Pollock, K. H., Nichols, J. D., Brownie, C., & Hines, J. E. (1990). Statistical Inference for

Capture-Recapture Experiments. Wildlife Monographs, (107), 3–97.

Proffitt, K. M., Gude, J. A., Hamlin, K. L., & Messer, M. A. (2013). Effects of hunter

access and habitat security on elk habitat selection in landscapes with a public and

private land matrix. The Journal of Wildlife Management, 77(3), 514–524. doi:

10.1002/jwmg.491

Rossell, C. R., Patch, S., & Salmons, S. (2007). Effects of Deer Browsing on Native and

Non-native Vegetation in a Mixed Oak-Beech Forest on the Atlantic Coastal

Plain. Northeastern Naturalist, 14(1), 61–72. doi: 10.1656/1092-

6194(2007)14[61:EODBON]2.0.CO;2

Rossell Jr., C. R., Gorsira, B., & Patch, S. (2005). Effects of white-tailed deer on

vegetation structure and woody seedling composition in three forest types on the

Piedmont Plateau. Forest Ecology and Management, 210(1–3), 415–424. doi:

10.1016/j.foreco.2005.02.035

Rowcliffe, J. M., Field, J., Turvey, S. T., & Carbone, C. (2008). Estimating animal

density using camera traps without the need for individual recognition. Journal of

Applied Ecology, 45(4), 1228–1236. doi: 10.1111/j.1365-2664.2008.01473.x

Royle, J. A., & Young, K. V. (2008). A Hierarchical Model for Spatial Capture–

Recapture Data. Ecology, 89(8), 2281–2289. doi: 10.1890/07-0601.1

61

Royle J. A. (2004). N‐Mixture Models for Estimating Population Size from Spatially

Replicated Counts. Biometrics, 60(1), 108–115. doi: 10.1111/j.0006-

341X.2004.00142.x

Shannon, G., Lewis, J. S., & Gerber, B. D. (2014). Recommended survey designs for

occupancy modelling using motion-activated cameras: insights from empirical

wildlife data. PeerJ, 2. doi: 10.7717/peerj.532

Silveira, L., Jácomo, A. T. A., & Diniz-Filho, J. A. F. (2003). Camera trap, line transect

census and track surveys: a comparative evaluation. Biological Conservation,

114(3), 351–355. doi: 10.1016/S0006-3207(03)00063-6

Steenweg, R., Whittington, J., Hebblewhite, M., Forshner, A., Johnston, B., Petersen, D.,

… Lukacs, P. M. (2016). Camera-based occupancy monitoring at large scales:

Power to detect trends in grizzly bears across the Canadian Rockies. Biological

Conservation, 201, 192–200. doi: 10.1016/j.biocon.2016.06.020

Vázquez, D. P., Melián, C. J., Williams, N. M., Blüthgen, N., Krasnov, B. R., & Poulin,

R. (2007). Species abundance and asymmetric interaction strength in ecological

networks. Oikos, 116(7), 1120–1127. doi: 10.1111/j.0030-1299.2007.15828.x

Waller, D. M., & Alverson, W. S. (1997). The White-Tailed Deer: A Keystone

Herbivore. Wildlife Society Bulletin (1973-2006), 25(2), 217–226.

Webster, C. R., Jenkins, M. A., & Rock, J. H. (2005). Long-term response of spring flora

to chronic herbivory and deer exclusion in Great Smoky Mountains National

Park, USA. Biological Conservation, 125(3), 297–307. doi:

10.1016/j.biocon.2005.03.027

62

Williams, R. M., & Oosting, H. J. (1944). The Vegetation of Pilot Mountain, North

Carolina: A Community Analysis. Bulletin of the Torrey Botanical Club, 71(1),

23–45. doi: 10.2307/2481485

Yimer, F., Ledin, S., & Abdelkadir, A. (2006). Soil organic carbon and total nitrogen

stocks as affected by topographic aspect and vegetation in the Bale Mountains,

Ethiopia. Geoderma, 135, 335–344. doi: 10.1016/j.geoderma.2006.01.005

63

FIGURES AND TABLES

Figure 1. Locations of camera sites in Pilot Mountain State Park mountain section, North

Carolina USA.

64

Figure 2. Mean deer population density estimated by N-mixture modelling (NMM),

mark-recapture (MR), and un-manned aerial infrared (UAI) methods. Error bars represent

95% confidence intervals of estimates.

65

Figure 3. NMM estimated abundance by topographic aspect of camera site. Boxes

represent interquartile range of abundances, midline represents median, and points

represent outliers.

66

Figure 4. The NMM estimated mean deer population density of PMSP with increased

survey length. The solid line represents LOESS best fit line to visualize trends. The

shaded region represents the 95% confidence interval of UAI validation. Error bars

represent 95% credible intervals.

67

Figure 5. Temporal analysis of PMSP deer population dynamics in relation to seasonal

vegetational changes. Three-month bins were used to evaluate seasonal trends in local

populations and resource availability. Green points represent mean NDVI value by

season, black points represent mean deer density. Lines represent LOESS best fit line to

visualize trends. Error bars represent 95% CI on mean best unbiased predictor site

abundance.

68

SUPPLEMENTARY METHODS

Flight parameters

Our Linn Aerospace Hummingbird quadcopter was controlled by a Cube Autopilot (Hex

Technology Ltd, Hong Kong, CN) running the Arduplane software stack. Flight paths

were programmed using Mission Planner (Michael Oborne, 2019). This flight control

system provides for fully autonomous flight and terrain following, allowing for precise,

repeatable flight paths. The drone was hand-flown during take-off and landing using the

Pixhawk flight controller while all other phases of flight were under autopilot control and

monitored using Mission Planner. The aircraft was equipped with a 640 x 480-pixel non-

radiometric thermal infrared imager (FLIR Vue Pro 640; 13-mm lens, 45° horizontal

FOV, 30 hz). The camera was mounted looking vertically downward to obtain the nadir

view and the camera horizontal axis was aligned perpendicular to the flight path. This

sensor is self-calibrating, with a marketed precision of 0.05 °C (<50 mk). Comparisons

with ground-based temperature measurements indicate that it is accurate within +/- 5 °C.

Data processing

Video was collected continuously in flight and after flight, data flash logs from the

autopilot were trimmed to correspond with video clips, reformatted for ingestion into

ArcGIS Full Motion Video (ESRI 2017), and the two were multiplexed in Full Motion

Video (FMV) to create georeferenced video.

Georeferenced video footage from each of the five flights was analyzed at half

speed by three separate observers. Deer were identified by their contrast against

background thermal radiation and body shape. The location and time of each observation

were marked and recorded using the Full Motion Video plugin for ArcGIS to geotag each

69

individual and ensure all observations remained within the known study area. Deer

observations of each observer were summed by flight transect and divided by their

respective areas to calculate transect-specific densities (Baldwin et al., in review).

70

CHAPTER 3: SUSTAINED BROWSING PRESSURE STRUCTURES

VEGETATION COMMUNITIES IN A NORTH CAROLINA STATE PARK

ABSTRACT

White-tailed deer (Odocoileus virginianus) dramatically alter forests through selective

browsing pressure, reducing species diversity, structural complexity, and shifting

dominance towards shade and browse tolerant invasive species. Previous studies suggest

that prolonged, intense browsing may exert considerable legacy effects on vegetative

communities for decades after the alleviation of browsing and may be the cause of

alternative vegetative stable states within eastern forests. This study investigated the

legacy of intensive browsing in a North Carolina State Park using paired herbivore

exclosures. Relationships of ordination axis values, Simpson’s dominance (D2),

Simpson’s evenness, and aboveground biomass with environmental covariates and

exclosure treatments were evaluated using linear mixed models. There were no

significant differences in composition, diversity, or structure between exclosure

treatments, indicating possible browse legacy effects within our study area. Our study

provides baseline data for longitudinal exploration of the vegetative regeneration in

heavily browsed areas.

KEYWORDS

Deer, browsing, species diversity, community composition, vegetative structure,

exclosure, North Carolina

71

INTRODUCTION

Large populations of deer create more simplified and less diverse vegetative communities

through sustained browsing pressure (Nuzzo et al. 2017). In areas of high deer

population, palatable species are rapidly defoliated, resulting in low probabilities of

survival and reproduction (Augustine and Frelich 1998, Augustine and Decalesta 2003,

Côte et al. 2004). Intense browsing shifts community composition away from native

palatable species; long-lived herbaceous perennials are often dispersal-limited and are

quickly lost from heavily browsed communities (Webster et al. 2018), allowing for the

invasion of alien species and reductions of floral diversity (Vavra et al. 2007). As such,

intensive deer browsing comprises a substantial threat to present-day biodiversity

(Rooney & Waller, 2003) These ecological effects of sustained browsing pressure are

compounded by the direct effects of browsing on vegetational structure.

Chronic herbivory of high deer populations reduces structural complexity of

understory vegetation, affecting the faunal composition of the area. High populations of

deer drastically reduce standing palatable vegetation biomass (Webster et al., 2018),

shrinking surviving adult plants in stature and size variance (Knight et al., 2009). Heavy

browsing often suppresses nitrogen-fixing plants, which dramatically alters nutrient

cycling of the surrounding ecosystem and reduces the overall primary productivity of a

given area (Hobbs 1996, Schoenecker et al. 2004). Small mammals rely on vegetational

structural complexity for cover from predators (Rossell & Rossell, 1999; Cote et al.

2004) and bird populations fluctuate in direct accordance with changes in structure

(McShea & Rappolle, 2001; Martin et al., 2011). As such, reductions in vegetation

biomass ultimately reduce the faunal diversity of the area and ultimately result in

72

cascading trophic effects (Nuttle et al. 2014). However, the reduction of vegetative

structural complexity is not a mere product of current browse pressure; areas exhibit

legacy effects of historic browsing pressure decades after its alleviation.

Sustained intensive browsing can create a vegetative legacy of browsing that

extends far past the alleviation of browsing pressure. Browse-tolerant species often

dominate heavily browsed areas and may competitively exclude heavily browsed species

even decades after the alleviation of acute browsing pressure (Carson et al. 2005,

Tanentzap et al. 2012, Nuttle et al. 2014). Reduced light availability, increased soil

compaction, and increased thickness of soil E horizon elicited by long-term browsing

further prevent the re-establishment of understory communities (Sabo et al. 2017).

Additionally, chronic herbivory pressure can also result in the depletion of palatable

native species from the seed bank due to the mortality of adult plants (Beauchamp et al.

2013, Bertiller 1996). Invasion of invasive species may further exacerbate compositional

changes, while remaining masked in quantitative investigations of diversity due to their

replacement of natives (Habeck and Schultz 2015). These compounding effects of

sustained browse pressure can affect successional processes (Stromayer and Warren,

1997) and may ultimately result in alternative vegetative stable states (Augustine et al.,

1998). While the lack of longitudinal data precludes the investigation of successional

states in this initial study, the exclusion of herbivores from certain areas may be used to

investigate both immediate browse effects and the potential for long term regeneration.

Herbivore exclosures are amongst the most popular methods of quantifying white-

tailed deer browse effects on vegetation structure and composition (Horsley et al. 2003).

The technique spatially excludes herbivores from areas using fencing in order to compare

73

vegetation between areas subject to browse and areas alleviated from browsing pressure.

Although herbivore exclosure studies have a long history in the research of vegetation-

herbivore dynamics (eg. Hough, 1949; Anderson & Katz, 1993; Kain et al. 2011), recent

studies have questioned the technique due to its reduction of the gradient of browsing

pressure to a binary response and difficulty in interpreting non-linear relationships

(Rooney & Waller, 2003). Although previous studies have experimentally manipulated

deer populations to investigate browse effects across deer density gradients (Tremblay &

Potvin 2006), a better understanding of the relationship between deer herbivory and

vegetation dynamics is needed across a heterogeneous landscape of herbivory.

The objectives of this study were to assess the legacy effects of sustained deer

browse on understory vegetation of PMSP by comparing the community composition,

diversity, and structure of aboveground plants in herbivore exclosures to those within

deer-accessible plots. Our approach was novel in that we were able to estimate site-

specific deer abundances using an N-mixture modelling, allowing us to explore potential

relationships of browse pressure across a gradient of sustained browsing pressure. We

expected that fenced and un-fenced plots would show no significant differences in

composition and structure due to the short-term nature of our study and the legacy of

sustained intensive browse within our study area.

74

METHODS

Study Area

This study was conducted on the ~10 km2 mountain section of Pilot Mountain State Park

(PMSP), NC USA, 36.3425° N, 80.4768° W. The mountain section of the park consists

of a quartzite monadnock that rises 738 meters above sea level and 427 meters above the

surrounding hillside.

Pilot Mountain’s south/west and north/east slopes exhibit easily observable

vegetational differences. Pine-oak/heath dominate on the south/west facing slopes likely

as a result of xeric conditions from prevailing southwestern winds and intense sun

exposure. Dominant species include table mountain pine (Pinus pungens) and pitch pine

(P. rigida), with an understory of mountain laurel (Kalmia latifolia), and purple

rhododendron (Rhododendron catawbiense). Scrub oak (Quercus ilicifoli), blackjack oak

(Q. marilandica), and chestnut oak (Q. montana) are also present, with an herb layer

comprised mainly of beetleweed (Galax urceolata) and trailing arbutus (also known as

mayflower; Epigaea repens). On more mesic east- and north-facing slopes dominance

shift from pines (P. pungens, P. rigida) to oaks (predominantly Q. montana), with a

poorly-developed herbaceous layer of primarily beetleweed (Galax urceolata).

Prior to the advent of the prescribed burning plan in 2009, PMSP had been fire-

suppressed since the 1940s (M. Windsor, personal communication). Fire scars dating to

1859 reveal an average historical fire return interval of ~ 4 years (D. Cuppinger, personal

communication) before the suppression of fire within the property to preserve logging

interests. The current prescribed burning plan replicates the historic burn interval to

maintain a low fuel load on the property and facilitate the recovery of the understory

75

community. Implementation of burning, however, has failed to regenerate the understory,

except in those areas of the park naturally excluded from deer herbivory by geological

characteristics.

Recent deer population surveys using an N-mixture modelling approach (Royle,

2004) indicate that PMSP supports a mean population density of 35 deer / km2, although

the distribution of that population is not uniform across the landscape (Baldwin et al., in

review). Southern slopes contain relatively higher deer populations than northern slopes

likely due to increased amounts of available forage.

Survey design

Ten pairs of 10m x 10m vegetation plots separated by 2m were established June-July

2018 in a random stratified design (Figure 1). Plots were randomly assigned a fenced or

unfenced treatment. Fenced plots were surrounded by 2m high wire fencing and uncaged

plots were marked by fluorescently painted rebar posts. Each vegetation plot was divided

into four 5m x 5m subplots for sampling.

Vegetation samples were collected in September of 2018. Each subplot was

sampled once at a random location determined by a blind bean-bag toss. An additional

sample was taken at a random location within the plot for a total of five samples per plot.

At each sampling location, community composition was evaluated using a modified

Daubenmire method of coverage estimation (Daubenmire 1953, USDA 1997). Aerial

percent cover for each species present was visually estimated to the nearest 0.5% within a

50 x 50 cm frame. Cardboard cutouts representing 1 – 25% of the total frame area were

used to assist with visual estimation. Note that total cover may exceed 100% because

76

species cover is estimated independently for each species. Aboveground biomass was

sampled at each randomly selected location by clipping all individual plants rooted within

two 0.1 m2 strips bordering the sampling frame. Samples were dried overnight in

convection drying oven and dry mass was measured and classified by plant functional

type for each site.

Data analysis

Environmental covariates (slope, aspect, elevation) were calculated from USGS National

Elevation (USGS, 2019) dataset for inclusion in statistical analyses. All covariates were

scaled for analysis to reduce difficulties in model convergence due to drastic differences

in raw values. Topographic aspect was cosine transformed in order to control for

circularity of measurements; cosine-transformed aspect values represent a gradient from

due south to due north (-1 - +1). Time since last burn was collated by site from prescribed

burn records. Site-specific deer abundances were extracted from the nearest available

survey site as an index of relative browsing pressure. Non-metric multidimensional

scaling (NMDS) was used to quantify community composition along environmental

gradients using Bray-Curtis dissimilarities. This analysis was performed in Program R

version 3.4.0 (R Core Team 2018) using the package vegan (Oksanen et al. 2019). Site

diversity was calculated using Simpsons dominance index (D2) due to its ability to

evaluate changes in diversity in areas with high levels of species dominance (Morris et al.

2014). Simpson’s evenness was also calculated for each plot to test for shifts in

dominance relationships, with lower values representing higher levels of species

dominance in plots.

77

Community composition (NMDS axis values), diversity, and evenness were

modelled as a function of environmental covariates, browsing pressure, fire frequency,

fencing treatment, and biologically relevant interactions using linear mixed models,

treating site as a random effect to control for multiple observations. AGB was square-root

transformed in order to adhere to assumptions of normality. Mixed effect models were

created using the package lme4 (Bates et al. 2015). Top models were chosen from an a

priori determined suite of candidate models using Aikake’s information criteria corrected

for small sample size (AICc) and significance of terms were assessed using Wald χ2

analysis of deviance with type two sum of squares. Models were created, selected and

evaluated for statistical significance using the packages car (Fox & Weisberg 2011) and

AICcmodavg (Mazerolle 2017) in program R version 3.4.0 (R Core Team, 2018).

78

RESULTS

Thirty-two total species were identified from eight of the paired vegetation plots; sites

two and six were completely devoid of vegetation both inside and outside of exclosures,

indicative of the extreme browsing pressure of the area. Plots ranged in species richness

from 2-10 species present. Red maple (Acer rubrum), small black blueberry (Vacinium

tenellum), and mountain laurel were present at the highest number of sites. Japanese

stiltgrass (Microstegium vimineum), an invasive grass, dominated where present.

Our ordination and composition modelling indicated that site composition was

largely driven by topographic aspect. NMDS ordination demonstrated high fit (non-

metric R2 = 0.87) and low stress (0.11), demonstrating its reliability as a quantification of

community composition (Figure S1). Our top model included aspect as the sole

significant predictor of NMDS axis values (β = 38.78, χ2 = 8.81, df = 1, P = 0.003). There

was no evidence of a treatment effect on composition (Figure 3).

Similarly, plot diversity and evenness were best predicted by elevation. Site

diversity significantly increased with elevation (β = 0.81, χ2 = 13.95, df = 1, P = 0.00019)

(Figure 4). There were no other significant predictors of diversity, despite AICc support

for a model containing aspect and elevation as additive fixed effects. Although our top

fitting model for species evenness contained elevation as a covariate (Figure 5), species

evenness did not show a significant relationship with any predictors (β = 0.08, χ2 = 1.24,

df = 1, P = 0.27). Trends from our evenness analysis support increased levels of

dominance with un-fenced treatment (Figure S2) and increased browsing pressure, but

these relationships were not significant.

79

Local browsing pressure drove plot AGB regardless of treatment. Our top model

included deer abundance as the sole predictor of AGB. Biomass significantly decreased

with increased browsing pressure (β = -0.39, χ2 = 9.51, df = 1, P = 0.0025) and this

relationship explained high amounts of variance (Figure 6). Again, there was no evidence

of a treatment effect; there were no significant differences in biomass between fenced and

unfenced plots (β = 0.15, χ2 = 0.064, df = 1, P = 0.80).

80

DISCUSSION

Compositional changes between plots in our study area were only significantly associated

with topographic aspect. Aspect influences the amount of solar radiation an area receives,

resulting in micro-climatic differences in air temperature (Desta et al., 2004), soil

moisture (Bennie et al. 2008), and soil nutrient content (Hicks & Frank, 1984). Southern-

facing slopes receive more direct sunlight than northern facing slopes, resulting in higher

light exposure and lower soil moisture conditions than northern facing slopes. Within our

study area, these moisture and sunlight regimes drive compositional difference between

our plots (Figure 2) and is a more significant predictor than browsing pressure,

management regime, elevation or caging treatment. This demonstrates potential legacy

effects of chronic browsing pressure within the area; sustained browsing pressure at

PMSP has resulted in the homogenous defoliation of palatable species across the

landscape (Rossell et al., 2005). The remaining vegetative communities within our study

area contain species of little nutritional value for deer and hence are structured by

environmental gradients instead of browsing pressure.

Diversity analyses revealed that elevation is the sole significant predictor of both

D2. Site diversity significantly increased at higher elevations (Figure 3), demonstrating

trends consistent with previous studies detailing elevational-diversity gradients. Plant

species diversity has been shown to have a hump-shaped relationship with elevation in

ecosystems around the world; intermediate elevations associated with transitional zones

contain the highest amounts of biodiversity (Lomolino, 2008). Although most studies

focusing on elevational gradients explore diversity trends scaling thousands of meters,

rather than hundreds, subtle differences in elevation can still influence diversity patterns

81

due to micro-climatic and soil chemistry changes (Noss et al., 2014). In our study area,

higher elevations are associated with community shifts from predominantly heath-oak

forest to open canopy pine communities (Williams and Oosting, 1944), due likely to

changes in temperature and soil moisture content with elevation.

Simpson’s evenness follows a similar general pattern as D2, with higher elevation

sites showing lower amounts of species dominance. Although elevation was the best

fitting predictor of evenness, it was not a significant relationship (Figure 4). There were

no significant predictors of plot evenness amongst our environmental covariates and

caging treatments. Unfenced plots did show higher levels of species dominance than plots

inside fencing treatments (Figure S1), potentially indicating the beginning of a treatment

effect. Deer are known to facilitate the dominance of shade tolerant, unpalatable species

and ferns by defoliating palatable species (Webster et al. 2018). In the lower elevations of

our study area, browse-tolerant invasive Japanese stiltgrass outcompetes heavily-browed

species and thrives in areas of high shade and soil moisture. Unless browsing pressure is

alleviated from these areas, it is likely that stiltgrass and other invasive species will

increase their dominance, particularly within the vegetative biomass of the area.

Site-specific deer abundance showed the strongest relationship with AGB,

demonstrating the propensity of browsing pressure to elicit structural vegetation changes.

Higher browsing pressures significantly decreased the AGB of plots due to consistent

removal of palatable forage (Figure 6). In areas with high resident deer populations,

much of the standing biomass <2m high is depleted resulting in a clear browse line

designating the physiological limits of deer browse (Bressette et al., 2012). Heavily

browsed areas, such as our study area, may be primed to maintain high deer populations

82

at the expense of the system due to complex aboveground and belowground trophic

cascades resulting from original reductions of biomass (Bressette et al., 2012). As a

result, areas with chronic herbivory pressure can experience similar legacy effects of

browse decades after the alleviation of browsing (White, 2012).

The lack of a treatment effect within any of our responses indicate that PMSP

may either be subject to legacy effects from long-term deer overpopulation or that the

timing of our fence installation did not sufficiently protect plants during the growing

season. As we expected, we saw no fencing treatment effects in any of our measured

response variables, which may indicate that the legacy of sustained browse at our study

area will hinder vegetative recovery despite active management efforts (Banta et al.,

2005). Given the clear browse line at our study area, and its function as a hunting refuge

during autumn months (Baldwin et al., chapter 2), it likely that the vegetative

communities of PMSP will exhibit substantial legacy effects for the near future.

However, our herbivore exclosures were established only three months prior to

vegetation sampling and were not installed before the growing season. Early vegetative

growth provides highly nutritious forage (Blair et al. 1977) and can be depleted at the

seedling life stages in areas of particularly high browsing pressure (Cote et al. 2004). In

order to evaluate these hypotheses and determine the regenerative potential of PMSP, this

study should serve as community baseline for comparison to annual collections of

vegetation data.

83

LITERATURE CITED

Anderson, R. C., & Katz, A. J. (1993). Recovery of browse-sensitive tree species

following release from white-tailed deer Odocoileus virginianus zimmerman

browsing pressure. Biological Conservation, 63(3), 203–208. doi: 10.1016/0006-

3207(93)90713-B

Augustine, D. J., & Decalesta, D. (2003). Defining deer overabundance and threats to

forest communities: From individual plants to landscape structure. Écoscience,

10(4), 472–486. doi: 10.1080/11956860.2003.11682795

Augustine, D. J., & Frelich, L. E. (n.d.). Effects of White-Tailed Deer on Populations of

an Understory Forb in Fragmented Deciduous Forests. Conservation Biology, 12(5),

995–1004. doi: 10.1046/j.1523-1739.1998.97248.x

Banta, J. A., Royo, A. A., Kirschbaum, C., & Carson, W. P. (2005). Plant communities

growing on boulders in the Allegheny National Forest: Evidence for boulders as

refugia from deer and as a bioassay of overbrowsing. Natural Areas Journal. 25(1):

10-18., 25(1), 10–18.

Bennie, J., Huntley, B., Wiltshire, A., Hill, M. O., & Baxter, R. (2008). Slope, aspect and

climate: Spatially explicit and implicit models of topographic microclimate in chalk

grassland. Ecological Modelling, 216(1), 47–59. doi:

10.1016/j.ecolmodel.2008.04.010

Bergström, R., & Edenius, L. (2003). From twigs to landscapes – methods for studying

ecological effects of forest ungulates. Journal for Nature Conservation, 10(4), 203–

211. doi: 10.1078/1617-1381-00020

84

Blair, R. M., Short, H. L., & Epps, E. A. (1977). Seasonal Nutrient Yield and

Digestibility of Deer Forage from a Young Pine Plantation. The Journal of Wildlife

Management, 41(4), 667–676. doi: 10.2307/3799988

Bressette, J. W., Beck, H., & Beauchamp, V. B. (2012). Beyond the browse line:

complex cascade effects mediated by white-tailed deer. Oikos, 121(11), 1749–1760.

doi: 10.1111/j.1600-0706.2011.20305.x

Côté, S. D., Rooney, T. P., Tremblay, J.-P., Dussault, C., & Waller, D. M. (2004).

Ecological Impacts of Deer Overabundance. Annual Review of Ecology, Evolution,

and Systematics, 35(1), 113–147. doi: 10.1146/annurev.ecolsys.35.021103.105725

Daubenmire, R.F. (1959). A canopy cover method of vegetational analysis. Northwest

Science. 33: 43-46

Desta, F., Colbert, J. J., Rentch, J. S., & Gottschalk, K. W. (2004). Aspect Induced

Differences in Vegetation, Soil, and Microclimatic Characteristics of an

Appalachian Watershed. Castanea, 69(2), 92–108. doi: 10.2179/0008-

7475(2004)069<0092:AIDIVS>2.0.CO;2

Habeck, C. W., & Schultz, A. K. (2015). Community-level impacts of white-tailed deer

on understorey plants in North American forests: a meta-analysis. AoB PLANTS, 7.

doi: 10.1093/aobpla/plv119

Hicks, R. R., & Frank, P. S. (1984). Relationship of aspect to soil nutrients, species

importance and biomass in a forested watershed in West Virginia. Forest Ecology

and Management, 8(3), 281–291. doi: 10.1016/0378-1127(84)90060-4

Hobbs, N. T. (1996). Modification of Ecosystems by Ungulates. The Journal of Wildlife

Management, 60(4), 695–713. doi: 10.2307/3802368

85

Horsley, S. B., Stout, S. L., & deCalesta, D. S. (2003). White-Tailed Deer Impact on the

Vegetation Dynamics of a Northern Hardwood Forest. Ecological Applications,

13(1), 98–118. doi: 10.1890/1051-0761(2003)013[0098:WTDIOT]2.0.CO;2

Hough, A. F. (1949). Deer and Rabbit Browsing and Available Winter Forage in

Allegheny Hardwood Forests. The Journal of Wildlife Management, 13(1), 135–141.

doi: 10.2307/3796131

Kain, M., Battaglia, L., Royo, A., & Carson, W. P. (2011). Over-browsing in

Pennsylvania creates a depauperate forest dominated by an understory tree: Results

from a 60-year-old deer exclosure. The Journal of the Torrey Botanical Society,

138(3), 322–326. Retrieved from JSTOR.

Knight, T. M., Caswell, H., & Kalisz, S. (2009). Population growth rate of a common

understory herb decreases non-linearly across a gradient of deer herbivory. Forest

Ecology and Management, 257(3), 1095–1103. doi: 10.1016/j.foreco.2008.11.018

Lomolino, M. V. (2001). Elevation gradients of species-density: historical and

prospective views. Global Ecology and Biogeography, 10(1), 3–13. doi:

10.1046/j.1466-822x.2001.00229.x

Martin, T. G., Arcese, P., & Scheerder, N. (2011). Browsing down our natural heritage:

Deer impacts on vegetation structure and songbird populations across an island

archipelago. Biological Conservation, 144(1), 459–469. doi:

10.1016/j.biocon.2010.09.033

Mazerolle, M. J. (2017) AICcmodavg: Model selection and multimodel inference

basedon (Q)AIC(c). R package version 2.1-1.

86

McShea, W. J., & Rappole, J. H. (2000). Managing the Abundance and Diversity of

Breeding Bird Populations through Manipulation of Deer Populations. Conservation

Biology, 14(4), 1161–1170. doi: 10.1046/j.1523-1739.2000.99210.x

Morris, E. K., Caruso, T., Buscot, F., Fischer, M., Hancock, C., Maier, T. S., … Rillig,

M. C. (n.d.). Choosing and using diversity indices: insights for ecological

applications from the German Biodiversity Exploratories. Ecology and Evolution,

4(18), 3514–3524. doi: 10.1002/ece3.1155

Noss, R. F., Platt, W. J., Sorrie, B. A., Weakley, A. S., Means, D. B., Costanza, J., &

Peet, R. K. (2015). How global biodiversity hotspots may go unrecognized: lessons

from the North American Coastal Plain. Diversity and Distributions, 21(2), 236–

244. doi: 10.1111/ddi.12278

Nuttle, T., Ristau, T. E., & Royo, A. A. (2014). Long-term biological legacies of

herbivore density in a landscape-scale experiment: forest understoreys reflect past

deer density treatments for at least 20 years. Journal of Ecology, 102(1), 221–228.

doi: 10.1111/1365-2745.12175

Nuzzo, V., Dávalos, A., & Blossey, B. (2017). Assessing plant community composition

fails to capture impacts of white-tailed deer on native and invasive plant species.

AoB PLANTS, 9(4). doi: 10.1093/aobpla/plx026

Rooney, T. P., & Waller, D. M. (2003). Direct and indirect effects of white-tailed deer in

forest ecosystems. Forest Ecology and Management, 181(1), 165–176. doi:

10.1016/S0378-1127(03)00130-0

Rossell, C. Reed, Gorsira, B., & Patch, S. (2005). Effects of white-tailed deer on

vegetation structure and woody seedling composition in three forest types on the

87

Piedmont Plateau. Forest Ecology and Management, 210(1), 415–424. doi:

10.1016/j.foreco.2005.02.035

Rossell, C.R., & Rossell, I. M. (1999). Microhabitat selection by small mammals in a

southern Appalachian fen in the USA. Wetlands Ecology and Management, 7(4),

219–224. doi: 10.1023/A:1008473029564

Royle J. Andrew. (2004). N‐Mixture Models for Estimating Population Size from

Spatially Replicated Counts. Biometrics, 60(1), 108–115. doi: 10.1111/j.0006-

341X.2004.00142.x

Royo Alejandro A., Collins Rachel, Adams Mary Beth, Kirschbaum Chad, & Carson

Walter P. (2010). Pervasive interactions between ungulate browsers and disturbance

regimes promote temperate forest herbaceous diversity. Ecology, 91(1), 93–105. doi:

10.1890/08-1680.1

Sabo, A. E., Frerker, K. L., Waller, D. M., & Kruger, E. L. (2017). Deer‐mediated

changes in environment compound the direct impacts of herbivory on understorey

plant communities. Journal of Ecology, 105(5), 1386–1398. doi: 10.1111/1365-

2745.12748

Schoenecker, K. A., Singer, F. J., Zeigenfuss, L. C., Binkley, D., Menezes, R. S. C., &

Krausman. (2004). Effects of elk herbivory on vegetation and nitrogen processes.

Journal of Wildlife Management, 68(4), 837–849. doi: 10.2193/0022-

541X(2004)068[0837:EOEHOV]2.0.CO;2

Stromayer, K. A. K., & Warren, R. J. (1997). Are Overabundant Deer Herds in the

Eastern United States Creating Alternate Stable States in Forest Plant Communities?

Wildlife Society Bulletin (1973-2006), 25(2), 227–234.

88

Tanentzap, A. J., Kirby, K. J., & Goldberg, E. (2012). Slow responses of ecosystems to

reductions in deer (Cervidae) populations and strategies for achieving recovery.

Forest Ecology and Management, 264, 159–166. doi: 10.1016/j.foreco.2011.10.005

Thébault, E., & Fontaine, C. (2010). Stability of Ecological Communities and the

Architecture of Mutualistic and Trophic Networks. Science, 329(5993), 853–856.

doi: 10.1126/science.1188321

Tremblay, J.-P., Huot, J., & Potvin, F. (2006). Divergent nonlinear responses of the

boreal forest field layer along an experimental gradient of deer densities. Oecologia,

150(1), 78–88. doi: 10.1007/s00442-006-0504-2

Vavra, M., Parks, C. G., & Wisdom, M. J. (2007). Biodiversity, exotic plant species, and

herbivory: The good, the bad, and the ungulate. Forest Ecology and Management,

246(1), 66–72. doi: 10.1016/j.foreco.2007.03.051

Webster, C. R., Dickinson, Y. L., Burton, J. I., Frelich, L. E., Jenkins, M. A., Kern, C. C.,

… Willis, J. L. (2018a). Promoting and maintaining diversity in contemporary

hardwood forests: Confronting contemporary drivers of change and the loss of

ecological memory. Forest Ecology and Management, 421, 98–108. doi:

10.1016/j.foreco.2018.01.010

Webster, C. R., Dickinson, Y. L., Burton, J. I., Frelich, L. E., Jenkins, M. A., Kern, C. C.,

… Willis, J. L. (2018b). Promoting and maintaining diversity in contemporary

hardwood forests: Confronting contemporary drivers of change and the loss of

ecological memory. Forest Ecology and Management, 421, 98–108. doi:

10.1016/j.foreco.2018.01.010

89

White, M. A. (2012). Long-term effects of deer browsing: Composition, structure and

productivity in a northeastern Minnesota old-growth forest. Forest Ecology and

Management, 269, 222–228. doi: 10.1016/j.foreco.2011.12.043

Williams, R. M., & Oosting, H. J. (1944). The Vegetation of Pilot Mountain, North

Carolina: A Community Analysis. Bulletin of the Torrey Botanical Club, 71(1), 23–

45. doi: 10.2307/2481485

Wisdom, M. J., Vavra, M., Boyd, J. M., Hemstrom, M. A., Ager, A. A., & Johnson, B. K.

(2006). Understanding Ungulate Herbivory–Episodic Disturbance Effects on

Vegetation Dynamics: Knowledge Gaps and Management Needs. Wildlife Society

Bulletin, 34(2), 283–292. doi: 10.2193/0091-7648(2006)34[283:UUHDEO]2.0.CO;2

90

FIGURES AND TABLES

Figure 1. Herbivore exclosure sites at Pilot Mountain State Park, NC USA. Each point

represents a paired fenced and un-fenced plot.

91

Figure 2. NMDS ordinated using Bray-Curtis dissimilarities between sites. Low stress

(<0.2) indicates the validity of the ordination. Boxplots represent interquartile ranges of

NMDS axis values by treatment. Midlines of boxes represent median values, dashed lines

represent extent and circles represent outliers.

92

Figure 3. Topographic aspect effects on site composition. Aspect values represent a

gradient from due south (-1) to due north (+1). Aspect was the sole significant predictor

of site composition (β = 38.78, χ2 = 8.81, df = 1, P = 0.003). There was no evidence of a

treatment effect on site composition.

93

Figure 4. The effects of elevation on site diversity. Elevation values were scaled to

prevent difficulties in model convergence due to large absolute differences in predictors.

Elevation was the sole significant predictor of site diversity (β = 0.81, χ2 = 13.95, df = 1,

P = 0.00019).

94

Figure 5. Effects of elevation on species evenness. High evenness values represent lower

levels of species dominance. Although elevation was the best fitting predictor of

evenness, this relationship was not significant (β = 0.08, χ2 = 1.24, df = 1, P = 0.27).

95

Figure 6. Effects of local deer abundance on vegetation structure. AGB was square-root

transformed to conform to the assumptions of normality. AGB significantly decreased

with increased deer abundance (β = -0.39, χ2 = 9.51, df = 1, P = 0.0025).

96

SUPPLEMENTARY FIGURES

Figure S1. Caging treatment effect on species evenness. Boxes represent interquartile

range of abundances, midline represents median, and points represent outliers.

97

CURRICULUM VITAE

Robert W. Baldwin

Winston-Salem, NC | baldrw13@wfu.edu | 312.316.7050

Education

2019 M.S. Biology, Wake Forest University, Winston-Salem, NC

Thesis: Deer and browsing in Pilot Mountain: an exploration of population

estimation and browse effects of white-tailed deer at a North Carolina state park.

Advisor: Dr. T. Michael Anderson

PA: 3.9

2017 B.S. Biology, Wake Forest University, Winston-Salem, NC, May 2017

Bachelor of Science in Biology, minors in Economics and Environmental Science

Thesis: An evaluation of camera survey methods for white-tailed deer

(Odocoileus virginianus).

Advisor: Dr. Miles R. Silman

GPA: 3.5

Honors in Biology, Cum laude

Research Interests

My research pursues a combination of applied and basic science questions about

the population and spatial ecology of large ungulates. Specifically, I’ve evaluated

different methods of quantifying populations and investigated vegetative

community browse effects in overpopulated areas.

98

Research Experience

2017 – 2019 M.S Research, Pilot Mountain State Park, NC, USA

2016 – 2017 Undergraduate Research Assistant, Pilot Mountain State Park, NC,

USA

Selected Honors, Awards, and Fellowships

2018 Elton C. Cocke Travel Award

2017 Carolina Biological Supply Award for Best Undergraduate Research

Conference Presentations and Posters

2018 Southeastern Deer Study Group, Nashville, TN

2017 Undergraduate Honors and Awards, Winston-Salem, NC

2017 Poster, Southeastern Deer Study Group, St. Louis, MO

Teaching Experience

2017 – 2019 Teaching Assistant, Introduction to Evolutionary Biology and Ecology,

Department of Biology, Wake Forest University

2016 – 2017 Econometrics Tutor, Wake Forest Learning Assistance Center, Wake

Forest University

Extramural Professional Activities

Peer reviewer for Journal of Wildlife Management