Using Forward-Looking Infrared Radiography to Estimate Elk Density and Distribution in Eastern Kentucky

University of Kentucky UKnowledge

University of Kentucky Master's Theses Graduate School

2008

USING FORWARD-LOOKING INFRARED RADIOGRAPHY TO ESTIMATE ELK DENSITY AND DISTRIBUTION IN EASTERN KENTUCKY

Lauren M. Dahl University of Kentucky

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Recommended Citation Dahl, Lauren M., "USING FORWARD-LOOKING INFRARED RADIOGRAPHY TO ESTIMATE ELK DENSITY AND DISTRIBUTION IN EASTERN KENTUCKY" (2008). University of Kentucky Master's Theses. 570. https://uknowledge.uky.edu/gradschool_theses/570

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ABSTRACT OF THESIS

USING FORWARD-LOOKING INFRARED RADIOGRAPHY TO ESTIMATE ELK DENSITY AND DISTRIBUTION IN EASTERN KENTUCKY

Elk (Cervus elaphus) in eastern Kentucky appear to have increased in number since reintroduction in 1997, but rugged landscapes and cryptic elk behavior have precluded use of typical population survey methods to accurately estimate population size. In December 2006, I used forward-looking infrared radiography (FLIR) to survey the elk population in eastern Kentucky. Elk locations identified by FLIR were used to create a landscape based model to estimate the density distribution of elk within a 7,088 km2 core area of the elk restoration zone. FLIR detected 76% of elk groups of < 10 individuals and 100% of elk groups of ≥ 10 individuals. The density of elk was positively associated with the amount of herbaceous area, herbaceous edge, herbaceous area weighted mean patch fractal dimensions, proximity to release sites, the number of elk released at each site and urban core area index, and negatively associated with road density. My model estimated the elk population at 7,001 (SE = 772, 95% CI = 5,488- 8,514) individuals within the core area, 53% of which were < 10 km from release sites. The predicted elk distribution pattern and abundance estimate derived from this model will be important for wildlife managers in successfully managing the Kentucky elk population.

KEY WORDS: Elk, Forward-Looking Infrared Radiography, Kentucky, Population Model, Wildlife Survey

Lauren M. Dahl _ 12/19/08 _

USING FORWARD-LOOKING INFRARED RADIOGRAPHY TO ESTIMATE ELK DENSITY AND DISTRIBUTION IN EASTERN KENTUCKY

Lauren M. Dahl

John J. Cox _ Co-Director of Thesis

Michael J. Lacki _ Co-Director of Thesis

David B. Wagner _ Director of Graduate Studies

12/19/08 _

RULES FOR THE USE OF THESIS

Unpublished theses submitted for the Master’s degree and deposited in the University of Kentucky Library are, as a rule, open for inspection but are to be used only with due regard to the rights of the authors. Bibliographical references may be noted, but quotations or summaries of parts may be published only with the permission of the author, and with the usual scholarly acknowledgments.

Extensive copying or publication of the thesis in whole or in part also requires the consent of the Dean of the Graduate School of the University of Kentucky.

A library that borrows this thesis for use by its patrons is expected to secure the signature of each user.

Name Date

THESIS

Lauren Marie Dahl

The Graduate School

University of Kentucky

2008

USING FORWARD-LOOKING INFRARED RADIOGRAPHY TO ESTIMATE ELK DENSITY AND DISTRIBUTION IN EASTERN KENTUCKY

THESIS

A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in the College of Agriculture at the University of Kentucky

Lauren M. Dahl

Lexington, Kentucky

Directors: Dr. John J. Cox and Dr. Michael J. Lacki

Lexington, Kentucky

2008

MASTER’S THESIS RELEASE

I authorize the University of Kentucky Libraries to reproduce this thesis in whole or in part for purposes of research.

Signed: Lauren Dahl _

Date: 12/19/08 _

To every supportive hand that lent itself when I needed it most.

Thanks

ACKNOWLEDGEMENTS

The research presented in this thesis would not have been possible without the financial and logistical support of Kentucky Department of Fish and Wildlife Resources,

The Rocky Mountain Elk Foundation, The Turner Foundation, United States Department of Agriculture, and the Department of Forestry at the University of Kentucky.

I sincerely thank my family for their constant support and encouragement, as they have been my source of stability throughout my ever changing career.

I am grateful to my advisors for their dedication to the education of the wildlife biologists of tomorrow. Dr. John Cox has provided me with guidance and direction throughout my tenure at the University of Kentucky, and has supported me through the highs and lows of my research project. He has kept me grounded and diligent till the end.

Dr. Dave Maehr, rest his soul, instilled in me a need to broaden my experiences and think critically about every situation in which I become involved. He never allowed me to slide by on “good enough” and constantly challenged me to strive for excellence.

My deepest gratitude goes to Dr. Karen Alexy. Her belief in my abilities and encouragement to continue my education brought me to Kentucky to tackle a challenging and rewarding master’s research topic. My committee members, Dr. Mike Lacki and Dr.

Songlin Fei, deserve many thanks as their expertise proved invaluable as it strengthened my analysis and my understanding of modeling and scientific thought. While I have made every effort to thank Dr. Joe Duchamp in every correspondence that I have had with him, I would like to continue that trend here. His statistical expertise helped me to condense an overwhelmingly large amount of data into a manageable and interpretable model to estimate Kentucky’s elk population.

iii

Thank you to my FLIR crew, Will Bowling, John Hast, Dr. Dave Unger and Ben

Augustine. These trained professionals gathered an immense amount of quality data and

were each a great asset to the project. Each of you has had an immense impact on my experience in Kentucky and on my life. I would also like to thank the multitude of volunteers that assisted this FLIR crew with data collection. I am grateful to the support from everyone at the University of Kentucky’s Robinson Forest.

I want to extend my gratitude to the Forestry graduate students, many of whom assisted me with logistics and data collection throughout my project. This group of students not only provided an enriching environment in which to bounce project ideas around, but also provided a source of friendship, relaxation and support which enabled us to hold one another up through our toughest of struggles. To the many residents of the

T.P. Cooper building, thank you for making this place a community.

Thank you to everyone at the Kentucky Department of Fish and Wildlife

Resources that supported this project, especially Tina Brunjes, Keith Wethington, and the entire GIS lab. The support of biologists Dan Crank and Charlie Logsdon has helped me broaden my field experience and understanding of issues involving Kentucky’s elk herd.

Thanks, I am indebted to each of you.

TABLE OF CONTENTS

Acknowledgements ...... iii

List of Tables ...... vii

List of Figures ...... viii

List of Files ...... ix

Section 1: Introduction ...... 1 Taxonomy and History of Kentucky Elk ...... 1 Elk Population Modeling and Monitoring ...... 3

Section 2: Study Area ...... 10 Kentucky Elk Restoration Zone ...... 11 FLIR Detection Rate Study Site ...... 11

Section 3: Methods ...... 15 FLIR Technology ...... 15 FLIR Detection Rate ...... 16 FLIR Elk Population Survey ...... 18 Elk Population Modeling ...... 19

Section 4: Results ...... 35 FLIR Detection Rate ...... 35 FLIR Elk Population Survey ...... 35 PCA Analysis ...... 35 Elk Population Modeling ...... 36 Distribution Pattern ...... 37

Section 5: Discussion ...... 48 FLIR Detection Rate ...... 48 FLIR Elk Population Model ...... 50 Roads ...... 50 Habitat ...... 51 Site Influence ...... 52 Urban Core Area Index ...... 53 Assumptions and Caveats ...... 54 Accuracy of KEM ...... 55 Utility of FLIR ...... 56 Conclusions ...... 58

Reference:………………...………………………………………………………..64

VITA ...... 74

LIST OF TABLES

Table 2.1 : Description of the 8 elk release sites located in the elk restoration zone in eastern Kentucky, USA, 2006...... 13 Table 3.1 : Comparison of reclassified Kentucky Land Cover Data Set 2001 (KLCD-01) Anderson Level II (Frankfort, KY) map and FLIR identified land cover...... 25 Table 3.2: Independent variables considered for analysis in a multiple regression model of elk densities in eastern Kentucky USA, 2006...... 26 Table 3.3 : Correlation matrix for variables considered in model building of forward-looking infrared radiography-based elk density model (r ≥ 0.06)...... 28 Table 3.4: Models present in the top 10 model selection from each of 100 iterations used to adjust elk counts observed using forward-looking infrared radiography in December 2006 in eastern Kentucky, USA...... 31 Table 4.1: Observed densities of elk in forward-looking infrared radiography transect blocks, December 2006 in eastern Kentucky, USA...... 38 Table 4.2: Elk density (elk/km2) observed using forward-looking infrared radiography within 4 km2 sample cells located in transect blocks surveyed December 2006 in eastern Kentucky, USA...... 39 Table 4.3: Principal component loadings for ordination of correlated variables and subsequent derived independent variables from forward-looking infrared radiography survey of elk in eastern Kentucky, USA, 2006...... 40 Table 4.4: Model AICc weights and delta AICc for the four best multiple linear regression models derived from forward-looking infrared radiography to estimate the density of elk in 2006 in eastern Kentucky, USA...... 41 Table 4.5: Standard beta coefficients and standard deviation for final multiple linear regression model estimating elk density in 2006 in eastern Kentucky, USA...... 42 Table 4.6: Estimated elk densities within 400 km2 areas surrounding release sites from the December 2006 forward-looking infrared radiography survey of eastern Kentucky, USA...... 43 Table 4.7: Sites of low estimated elk density within a 400 km2 area in 2006 in eastern Kentucky, USA...... 44 Table 5.1: Cost break down of the forward-looking infrared radiography (FLIR) survey conducted in December 2006 in eastern Kentucky, USA...... 61 Table 5.2: Cost comparison of forward-looking infrared radiography (FLIR) to similar ungulate population survey techniques...... 62

vii

LIST OF FIGURES

Figure 2.1: Original 14-county elk restoration zone and 8 elk release sites in southeastern Kentucky, USA. Starfire Complex inset was the site of the detection rate study in December 2006...... 14 Figure 3.1: Forward-looking infrared radiography survey transect blocks and core area of elk restoration zone in relation to elk release sites in southeastern Kentucky, USA, December 2006...... 33 Figure 3.2: Blocks (20 x 20 km) surrounding each elk release site used to estimate elk density in eastern Kentucky, USA, December 2006...... 34 Figure 4.1: Detection rate samples depicting “hits” (◊ = successful detection of ground identified elk groups by forward-looking infrared radiography (FLIR)) and “misses” (□ = unsuccessful detection of ground identified elk groups by FLIR) for the 2006 and 2007 detection rate samples in eastern Kentucky, USA...... 45 Figure 4.2: Distribution of elk group sizes detected by forward-looking infrared radiography, in eastern Kentucky, USA, December 2006...... 46 2 Figure 4.3: Elk density distribution estimated within 4 km cells in the core area of the restoration zone in southeastern Kentucky, USA, December 2006...... 47 Figure 5.1: Influence of road density on estimated distribution of elk in eastern Kentucky, USA, December 2006...... 63

viii

LIST OF FILES

File 1: ...... Dahl Thesis.pdf

SECTION 1: INTRODUCTION

Taxonomy and History of Kentucky Elk

Elk (Cervus elaphus) are gregarious ungulates of the family Cervidae with a dark neck and light rump patch. Elk are large bodied, cursorial species able to escape predation by covering wide expanses at a sustained run (Geist 2002). Males average 300 kg, and typically weigh 20% more than females (Hudson and Haigh 2002). Males gain weight reaching 500 kg and grow antlers in the spring in preparation for the breeding season in the fall (Murie 1951, Geist 2002, Hudson and Haigh 2002). North American elk display long spreading antlers and straight branches with adults typically having 5 - 7

tines per antler (O’Gara 2002). Elk are ruminants allowing them to be generalist feeders,

typically grazing on forbs and grasses during spring, summer, and fall while browsing in

the winter (Cook 2002, Schineider et al. 2006).

The North American elk shares its scientific name with several subspecies of

Eurasian red deer, all of which share a common ancestor (Cervus elaphus acornatus)

originating in the Pliocene in central Asia (O’Gara and Dundas 2002). Cervus spp.

spread throughout Europe, Asia and across the Bering Straight to North America by the

Middle Pleistocene (Li 1996). The North American elk were divided into 6 different

subspecies based on morphology and geographic range, and were distributed from the

Pacific to the Atlantic coasts and from northern Alberta to central New Mexico (Murie

1951). The eastern elk subspecies (Cervus elaphus canadensis) that occurred throughout

the eastern third of the continental U.S., and the Merriam’s elk (Cervus elaphus

merriami) that occurred in the southwest are considered extinct (O’Gara 2002).

Journals, letters and other written accounts from early European settlers indicated that elk were found throughout Kentucky, particularly in the savannas, barren, and floodplains west of the Cumberland Plateau (Walker 1750, McClung 1832, Ranck 1901,

Funkhouser 1925, Shoemaker 1939, Anrow 1960, Hall 1981, Wharton and Barbour 1991,

Cox et al. 2002). European settlement led to rapid conversion of native ecological communities to agriculture and unregulated hunting that caused the extirpation of several vertebrate species and decreased the range of others (Guilday 1984). Elk were extirpated from Kentucky before the end of the 19th century (Funkhouser 1925, Shoemaker 1939,

Arnow 1960, Barbour and Davis 1974, Belue 2003).

Prior to reintroduction in 1997, free-roaming elk were absent from Kentucky for over 150 years (Maehr et al. 1999). Kentucky Department of Fish and Wildlife

Resources (KDFWR) conducted several public meetings in eastern Kentucky that indicated strong support (98% approval) for elk reintroduction (Maehr et al. 1999). For many residents of the area, elk were viewed as a potential source of economic revenue in the forms of ecotourism and increased hunting and wildlife viewing opportunities. From

1997 - 2001, 1,547 elk (Cervus elaphus nelsoni) were transported from western states and released at 8 sites within eastern Kentucky. An initial sample of 415 of the reintroduced elk were fitted with radio-collars to monitor survival and reproductive rates (Larkin et al.

2003), movement, dispersal, and activity patterns (Larkin 2004, Wichrowski et al. 2005,

Olsson et al. 2007), disease (Larkin 2003b, Alexy 2004), food habits (Schineider et al.

2006), and interspecies relations with white-tailed deer (Odocoileus virginianus) and coyote (Canis latrans, Cox 2003). Initial studies suggested that Kentucky elk had a low

mortality rate (0.07) and a high natality rate (0.86) during the initial 3 years post-

reintroduction (Cox 2003, Larkin et al. 2003).

By 2001, the population was estimated at ca. 2,000 animals (K. Alexy personal communication) and a small elk hunt (n = 12) was conducted to simultaneously establish an elk hunting season in Kentucky, yet allow continuation of herd growth. Hunting is the primary tool to manage the growing elk population in Kentucky as established eastern elk populations have low predation rates and high survival and reproductive rates (Cox 2003,

Larkin et al. 2003). Hunting regulations and the number of elk hunting permits in

Kentucky have been updated and increased, respectively, as elk management has shifted from population growth to stabilization of the population near the target of 10,000 animals (K. Alexy personal communication). Elk population management in Kentucky will therefore be heavily dependent on reasonably accurate population models and reliable, cost-effective survey techniques.

Elk Population Modeling and Monitoring

Many wildlife management decisions are driven by a species’ population size, density, and distribution (Lancia et al. 1996, Meffee and Carroll 1997). Population models have been used to identify effects of habitat manipulation, climate changes, carrying capacity, predator/prey relationships, threatened and endangered species management objectives, and socio-economic impacts on wildlife (Buckland and Elston

1993, Lopez et al. 2004, Finley et al. 2005). Wildlife population models can be conducted inductively or deductively and are considered either descriptive or analytical

(Corsi et al. 2000, Skalski 2005). A strong population model holds true to its assumptions, is built from the most accurate data samples, minimizes bias, and uses

biologically meaningful criteria in the selection of scale and influential variables (Corsi et al. 2000, Wiens 2002, Thuiller et al. 2004). Statistical assumptions are specific to each modeling technique and sampling strategy, and should be considered prior to implementation. The most accurate survey techniques should be chosen based on the study animal, landscape accessibility, and the research question posed. Bias in estimating population size and density may occur due to 4 factors: the amount of the population covered by the survey, population closure, misidentification of non-target species, and the ratio of animals observed versus the number present (detection rate: Bart et al. 2004).

Bias due to population coverage can be minimized by conducting habitat based models to extrapolate surveyed results to non-surveyed areas (Bart et al. 2004). The selection of appropriate scale and inclusion of specific variables into the model selection process should be based on reviews of previous research and unbiased observation. Surveys completed within a narrow time interval decrease the bias due to violations of population closure. Simultaneous double sampling by two independent observers can be used to minimize bias due to detection rate and misidentification error (Borchers et al. 1998, Bart et al 2004).

With any visual survey there is a probability of missing animals, therefore a detection rate estimate must be considered. Detection is the ability of a survey method to accurately count the number of animals present in the survey area. Detection rates vary depending on time of year or day, altitude above ground level, observer fatigue, weather conditions, activity patterns, vegetation cover, and group size (Caughley 1974, Newsome et al. 1981, Gasaway et al. 1985, Pollock and Kendall 1987, Samuels et al. 1987, Bodie et al. 1995, Bernatas and Nelson 2004). Two methods used to quantify detection rates of

surveys for species include sightability modeling and the sight-resight method (Skalski et al. 2005). The sightability model is used when detection is a function of group size, hence, leading to size bias sampling (Thompson 2002). This method involves two independent observers, one surveying the population and the other locating radio-collared

animals missed by the initial survey (Samuel et al. 1987, Unsworth et al. 1990, White and

Garrott 1990, Bernatas and Nelson 2004, Skalski et al. 2005). Detection probabilities are derived from logistic regression, and an estimate of abundance can be based on the size

of individual groups located during a visual survey using the Horwitz-Thompson estimator (Cochran 1977). The sight-resight method is based on two nearly simultaneous independent surveys of relatively static species, that when compared, the animals witnessed by each individual and by both observers can be identified (Geibel and Miller

1984, Estes and Jameson 1988, Anthony et al. 1999, Nichols et al. 2000, Bart and Earnst

2002, Pollock et al. 2002). Skalski et al. (2005) presents the equations that use the number of individuals witnessed by each observer and the number of individuals witnessed by both observers to reveal the population estimate for the sampled area. Once the detection rate of a survey technique is identified it is applied to the survey results ultimately raising the estimates to more realistic levels.

A reliable, cost-effective population survey that is able to provide data to estimate the distribution and density of elk on the landscape is an effective tool for wildlife managers (Lancia et al. 1996, Meffee and Carroll 1997). This information is critical to the establishment of harvest guidelines and the achievement of management goals that include maintaining ecological and social carrying capacity, habitat manipulation, disease control, land acquisition, nuisance mitigation, and identification of recreational

opportunities. Although research and management efforts have identified potential elk

preferences to landscape patterns and estimated the total abundance of the elk population, these findings have not been useful for quantification of the density and distribution

patterns of Kentucky’s elk population. Landscape use by elk has been analyzed by

Wichrowski (2001) and Larkin et al. (2004), but findings were localized to a few sites

and may not be applicable to the entire population’s range.

To date, estimates of the total elk abundance in Kentucky have been derived from

a KDFWR-developed Kentucky elk population model (KEM). In 2006, this model

estimated the population to be 5,700, suggesting that the population had nearly

quadrupled in < 10 years (T. Brunjes, KDFWR, personal communication). KEM relies

on demographic data collected within the first 3 years post-elk release and annual elk

harvest data collected since 2001. While the KEM has been able to provide a baseline

population estimate, predictive models are often temporally limited and sensitive to

changes in the variables originally used for model development and should be verified

periodically (Sargeant and Oehler 2007). Major limitations of KEM include: reliance on

mortality and reproductive data collected during the initial reintroduction phase (Larkin

2001, Larkin 2003), lack of consideration for landscape characteristics that could affect

elk abundance, and lack of scalability and associated inability to predict local population

density fluctuations; KEM only provides an overall abundance estimate for the entire elk

restoration zone, thereby precluding site specific density-based adjustments of harvest

recommendations. Given the inherent limitations of KEM, there is a need for

implementation of a survey method that uses inductive-analytical modeling to identify

landscape patterns that are associated with elk density, and ultimately, estimate the

population’s distribution and abundance patterns (Corsi et al. 2000, Guisan and Thuiller

2005). Data can then be used as a baseline to compare with future trend data (Msoffe et

al. 2007), to validate or update the 2006 KEM estimate, and influence management

decisions.

Techniques typically used to visually survey large mammal populations include:

drive counts, aerial photography, double sampling, mark-resight, line-transects, and aerial visual surveys (Cochran 1977, Lancia et al. 1996). Detection problems arising from the rugged forested landscape of eastern Kentucky limit the efficacy of these sampling techniques. While line-transects, double sampling, and mark-resight would work well in open areas, it may prove difficult to visually detect, identify, or confirm distinguishing marks of animals in forested habitats (Miller et al. 1987, Garshelis 1992). Drive counts and aerial photo techniques can become logistically difficult and expensive as the size of the study area increases. While aerial visual surveys are widely used to obtain population estimates of large animals (Woolf 1973, Samuel et al. 1987, White et al. 1989, Thompson et al. 1998, Dunn et al. 2002, Rabe et al. 2002), inherent weaknesses in the technique can skew estimates (Gassaway et al. 1985). These weaknesses include observer bias

(Haroldson et al. 2003) and factors that affect elk detection such as dense vegetation, group size, habitat-specific activity patterns, snow cover, and weather conditions

(Caughley 1974, Newsome et al. 1981, Gasaway et al. 1985, Pollock and Kendall 1987,

Samuels et al. 1987, Bodie et al. 1995). Thus, these survey techniques are limited in their ability to provide an accurate estimate of the elk population in eastern Kentucky.

An alternative technique that addresses many of the weaknesses of conventional survey methods is aerial-based forward-looking infrared radiography (FLIR), a method

that utilizes an infrared scope attached to the underside of an aircraft to detect thermal

radiation of target species. FLIR has been used on elk (Dunn et al. 2002), moose (Alces

alces, Adams et al. 1997), white-tailed deer (Odocoileus virginianus, Wiggers and

Beckerman 1993, Garner et al. 1995, Haroldson et al. 2003), bighorn sheep (Ovis

canadensis, Bernatas and Nelson 2004, Potvin and Breton 2005), polar bear (Ursus maritimus: Amstrup et al. 2004), walrus (Odobenus rosmarus divergens: Udevitz et al.

2008), sandhill crane (Grus canadensis, Kinzel et al. 2006), and wild turkey

(Meleagrididae spp., Wakeling et al. 1991, Garner et al. 1995). FLIR is able to overcome many of the weaknesses of visual surveys by its ability to penetrate through most habitat cover types and indiscriminately detect cryptically colored animals that otherwise may not be seen (Adams et al. 1997, Bernatas and Nelson 2004, Claridge et al. 2004).

Advances in FLIR technology since its initial use (Croon et al. 1968, McCullough et al.

1969, Graves et al. 1972, Parker and Driscoll 1972) have improved image quality and heightened thermal sensitivity, and thus improved animal identification (Wiggers and

Beckerman 1993, Garner et al. 1995). FLIR survey footage can also be recorded during flight transects, thus permitting post-flight review and verification of animal counts.

FLIR surveys appear particularly appropriate for Kentucky’s elk population because they can be conducted during: 1) crepuscular and nocturnal hours, when elk are typically found in open areas (Larkin 2001, Wichrowski et al. 2005, Olsson et al. 2007), and 2) during early winter months, when leaf-off conditions reduce reflective vegetative cover and elk are typically in large groups. For these reasons, FLIR appears to provide a survey method that more reliably detects elk in the rugged eastern Kentucky landscape than more traditional survey methods.

Previous studies using FLIR have investigated the detection rate for a species

(Wiggers and Beckerman 1993, Garner et al. 1995, Havens and Sharp 1998, Bernatas and

Nelson 2004), ascertained minimum elk counts (Dunn et al. 2002), identified deer population trend fluctuations (Diefenbach 2005), and estimated the density of deer populations at a small geographic scale (Naugle et al. 1996). However, FLIR has not

been used to produce a landscape scale species population model to estimate density and

distribution. This study tested the use of FLIR as a suitable survey method for detecting

and subsequent modeling of the elk population in southeastern Kentucky. My study is

unique because it combines many of the previously tested uses of FLIR into a

comprehensive and inclusive population model of a species at a landscape scale.

The main objectives of this study were to:

1) Identify the detection rate of FLIR for elk in eastern Kentucky. 2) Conduct a large scale population survey using FLIR. 3) Use the FLIR-located elk, associated landscape characteristics and detection rates to produce an elk population model. 4) Use the population model to estimate elk density, distribution and abundance.

SECTION 2: STUDY AREA

Eastern Kentucky consisted mostly of continuous mixed-mesophytic forest covering ridges, steep slopes, and winding narrow valleys (McFarlan 1943, Braun 1950,

Overstreet 1984). Moist, well drained sites supported dominant overstory species which included American beech (Fagus grandifolia), yellow-poplar (Liriodendron tulipifera),

American basswood (Tilia americana), sugar maple (Acer saccharum), northern red oak

(Quercus rubra), white oak (Q. alba), eastern hemlock (Tsuga Canadensis), and yellow buckeye (Aesculus octandra) (Braun 1950). Understory trees included dogwood (Cornus florida), magnolia (Magnolia sp.), sourwood (Oxydendrum arboretum), striped maple

(Acer pennsylvanicum), redbud (Cercis canadensis), ironwood (Carpinus caroliniana), hop-hornbeam (Ostrya virginiana), holly (Ilex opaca), and service-berry (Amelanchier arborea) (Braun 1950). Areas of rocky soil, ridge tops, and upper southwestern slopes facilitated oak-hickory (Quercus-Carya), oak-chestnut (Q.-Castanea) and oak-pine (Q.-

Pinus) climax communities (Braun 1950, Overstreet 1984). The latter 3 communities were typically found in the southeast corner of the study area which included Pine

Mountain, Black Mountain, and the Cumberland Mountains. Hemlock dominated some ravines and was normally accompanied by Rhododendron spp. and Magnolia tripetala in the mountainous areas.

Surface coal mines and subsequent reclamation has converted 10% of the elk restoration zone from forest to herbaceous openings of up to 5,000 ha (Larkin 2003).

Plant species found in these openings included Kentucky-31 tall fescue (Festuca arundinacea), autumn olive (Elaeagnus umbellata), bush clover (Lespedeza spp.), bird- foot trefoil (Lotus corniculatus), crown vetch (Coronilla varia), perennial ryegrass

(Lolium perenne), orchardgrass (Dactulis glomerata), black alder (Alnus glutinosa), and

black locust (Robinia pseudoacacia) (Larkin 2003, Alexy 2004, Olsson et al. 2007).

Surface mines varied widely in intensity of mining activity, size, traffic flow, and stage of

reclamation.

Eastern Kentucky had a low human population density (29 - 58 people/km2:

Ulack et al. 1998) and limited agriculture (1% of the restoration zone; Maehr et al. 1999).

The majority of the human population and agriculture development was concentrated in the lower elevations near the river bottoms in order to access fertile flat ground.

Elevations ranged from 244 to 1,260 m and the climate was temperate humid continental

(Overstreet 1984). Average yearly snow fall was 51 cm and the average yearly rainfall was 81 cm (McDonald 1965).

Kentucky Elk Restoration Zone

Kentucky’s original elk restoration zone was 1.06 million ha and included 14 southeastern counties (Larkin 2001) (Figure 2.1). Elk were released at 8 sites distributed throughout this area. Each release site varied in landscape composition and the number of elk released (Table 2.1). In 2004, the restoration zone was expanded to include Whitley and McCreary Counties to facilitate and manage the movement of elk between Kentucky and Tennessee (K. Alexy personal communication). This study only focused on the original 14-county restoration zone.

FLIR Detection Rate Study Site

Elk detection rate surveys were conducted on the ca. 65 km2 area Starfire

Complex – a collection of surface mines that included Paul Van Booven Wildlife

Management Area (WMA), International Coal Group property (ICG), Kentucky River property (Beech Fork), and Big Elk Mining Company property (previously called Cyprus or Addington WMA) (Figure 2.1). The landscape of this area is dominated by reclaimed surface mines with little to no active mining. Big Elk Mining Company property is the only exception with intermittent mining activity on ca. ¼ of its area. The topography consisted of open herbaceous rolling hills divided by deep forested ravines and some un- mined forested hill tops.

Table 2. 1 : Description of the 8 elk release sites located in the elk restoration zone in eastern Kentucky, USA, 2006. Road Relative amount Release No. elk Year of Percent area covered by each land cover typea density of human site released release activity Forest Herbaceous Agriculture Urban Water Martin 307 2002 69 22 3 6 0.9 Low Large

Starfire 322 1998 – 01 71 22 0.6 5 0.1 Low Large Complex Orr 322 1999 – 01 85 9 0.4 5 0.3 Moderate Moderate

Blue 232 2001 – 02 85 9 0.2 5 0.1 Moderate Moderate

13 Diamond

Pike 55 2000 – 02 72 17 3 7 0.4 High Moderate

Raven 145 2000 80 12 2 6 0.05 High Small Letcherb 54 2001 75 17 0 8 0.2 High Small Redbird 110 2000 87 7 1 5 0.3 Low Small

a The proportion of land within a 16 km radius of the release site that has a specific land cover type based on reclassified Kentucky Land Cover Data Set 2001 (KLDC-01) Anderson Level II (Frankfort, KY). b The 16 km surrounding Letcher is outside of the restoration zone for 36% of the area and has been scaled accordingly.

Figure 2. 1 : Original 14-county elk restoration zone and 8 elk release sites in southeastern Kentucky, USA. Starfire Complex inset was the site of the detection rate study in December 2006.

SECTION 3: METHODS

FLIR Technology

The infrared radiometer used for this study was a PolyTech, Kelvin 350 II

(PolyTech Airborne Remote Sensing, Stockholm, Germany) which consisted of a

Thermovision 1000 sensor. This system consisted of a long wave (8-12 µ), high resolution (800 x 400 pixels) sensor that discriminates thermal variation of <1º C (S.

Bernatas, unpublished data). To maintain quality performance and maneuverability the sensor was attached to the underside of a Cessna 206 fixed wing aircraft (Cessna Aircraft

Company, Wichita, Kansas) by a 4-axis gyro-stabilized system (S. Bernatas, unpublished data). A monitor within the cabin of the plane displayed the real-time infrared images which assisted the operator in directing the view of the sensor and zooming-in on target species to assist in identification. The images were digitally recorded for later analysis

following completion of the flights. The plane was flown at an above ground level

(AGL) of ca. 300 m providing a field of view (FOV) of ca. 100 x 70 m at the wide angle

(20º) and ca. 25 x 15 m at the narrow angle (5º: S. Bernatas, unpublished data). The

sensor was systematically swept from side to side covering a swath of ca. 500 m.

Airspeed was maintained at or near 120 km/hour with consideration given to wind speed

and direction.

The digitally recorded footage collected during the flights was analyzed following

the completion of the entire survey. Each hour of footage required ca. 4 hrs of analysis time, in which the sensor operator reviewed the footage to locate and identify species

throughout the tape using morphological characteristics. The sensor operator verified the

number of animals in each group, identified the general habitat characteristics in which

they were found, and recorded the activity of the majority of the group. Activity of the

majority of the herd was classified as feeding if their heads were lowered, moving if they

were walking, or bedded if their legs were not visible. The FLIR operator defined a

group as a cluster of individuals that were visible within the field of view of the sensor.

The location of each elk group sighting was determined by combining the spatial

reference data of the location of the airplane with the estimated look angle of the infrared

sensor to produce a final location using a geographical information system (GIS).

Locations were delivered to us beginning in June 2007 as a GIS shapefile.

FLIR Detection Rate

I conducted the FLIR detection rate study during December of 2006 and 2007 at

the Starfire Complex (Figure 2.1). This site was chosen to exploit over 70 individual

VHF radio-collared elk in the area. The survey was conducted on 3 - 4 consecutive

nights each year for ca. 4 hrs between 1800 - 0100. A ground crew of 3 - 15 people

visually located and recorded data on 5 - 16 unique elk groups per night, using telemetry to identify radio-collared elk found in each group, spotlights, and handheld infrared scopes (Thermal Eye 250D, L-3 Communications Infrared Products, Dallas, TX, USA).

Elk groups were also opportunistically located using spotlight searches. The ground crew defined a group to be a cluster of individuals each within ca. 300 m of the next closest individual and potentially containing one or more radio-collared elk. For each group the crew recorded the global positioning system (GPS) location, number of individuals per group, the habitat in which they were found, and the activity of the majority of the group.

These visual ground locations were collected while the same area was censused using FLIR. The digitally recorded footage was analyzed as described above, and the

sensor operator collected the same variables as the ground crew. I used a combination of

sightability and sight-resight methods (Skalski et al. 2005) because each method alone

was unable to allow quantification of detection rates for this study. Due to limited

communication with the FLIR operator during the survey flights, sightability techniques were limited as FLIR sightings of VHF-collared elk were unable to be immediately verified. Also, since FLIR works best during nocturnal hours, I was unable to conduct a simultaneous visual survey to count the elk as required for the sight-resight method.

Therefore, I determined the detection rate of FLIR for elk by identifying the proportion of times that the FLIR operator detected an elk group that corresponded to an independent ground crew identified elk group. FLIR and ground identified locations were compared based on criteria including: the distance between locations, the time-lag between the identification times, the number of elk in a group, the habitat in which the elk were located, and the activity of the majority of the group. The differences between the definition of a group by the FLIR operator and the ground crew was alleviated for this comparison by considering all FLIR identified groups surrounding each ground identified group. For example, if the composition of the surrounding FLIR elk groups matched the single ground identified elk group based on the above criteria, it was considered the same group. Unclear comparisons between FLIR locations and ground locations, due to a time lag of > 30 minutes (within which an elk group could move or reconfigure their grouping structure), were removed from consideration. I used ArcGIS 9.2 (ArcGIS:

Environmental Systems Research Institute, Redland, CA. USA) to plot both ground and

FLIR identified locations to determine the distance between locations of the same group.

The successful location of a ground identified elk group by FLIR was considered a “hit.”

The criteria of distinguishing a hit required both ground and FLIR identified groups to have a similar number of elk (> 90% agreement) within a maximum distance of 400 m.

This distance buffer was selected based on sample trials that tested the error of FLIR GPS

locations of static objects which suggested a 200 m (n = 30, SE = 131) error radius. This distance also allowed for potential elk movement between the two observations. Habitat and activity of the group were only considered within a 5-min time difference, as elk may move into new habitats.

To evaluate the potential misidentification of the morphologically similar species deer, both the ground crew and FLIR operator identified deer within the survey area. The ground crew opportunistically observed and recorded the locations of individual white- tailed deer. These animals were monitored until the FLIR sensor was directly over head.

By comparing deer and elk identified by both the ground crew and FLIR, I estimated the proportion of deer witnessed by the ground crew for which FLIR did not detect a matching deer location within 400 meters, but instead identified an elk at the same location as the deer.

FLIR Elk Population Survey

I surveyed the elk restoration zone in eastern Kentucky using FLIR over a 10 day period in December 2006. I choose a systematic sampling design (Cochran 1977) because it provided an effective means of gaining a minimum count of elk at each release site, the ability to map their distribution surrounding the release sites, and allowed a general population estimate to be derived (Caughley 1974). While a simple random sample may have provided a more statistically robust estimate of the abundance and distribution of the entire population, this sampling strategy allowed multiple scientific

and management objectives to be simultaneously addressed. Survey transect blocks originated at the 8 elk release sites, and were constructed to extend 2 x 16 km in the 4 cardinal directions (Figure 3.1). Three sites (Raven, Letcher, and Starfire Complex) have some variation to this design and have missing transects or were flown in a different area than around the release site (Figure 3.1). These inconsistencies were due to poor weather conditions, alterations in the design of transect block placement, or miscommunication.

The FLIR survey was conducted using several transects to cover the entire area within the transect blocks and to minimize travel time between flight paths. The digital footage was analyzed as stated above.

Elk Population Modeling

I used inductive modeling to identify landscape characteristics that were associated with elk densities in eastern Kentucky (Bart et al. 2004, Salski 2005). The landscape characteristics were measured at two different spatial scales because the distribution of animals is a function of several biological interactions and cannot be described at any one specific scale (Levin 1992, Noss 1992, Buckland and Elston 1993,

Barbosa et al. 2003, Anderson et al. 2005, Boyce 2006, Stubblefield et al. 2006). I divided survey transect blocks into 2 x 2 km (4 km2) grid cells which encompass the average estimated daily movement (1,200 m) of elk in eastern Kentucky during

December (W. Bowling, University of Kentucky, unpublished data). I also collected landscape characteristics from a 6 x 6 km (36 km2) grid cell that surrounded each 4 km2 cell. This larger cell encompassed the area of the average home range (31 km2) of elk in eastern Kentucky as calculated from recent telemetry data (W. Bowling, University of

Kentucky, unpublished data), and allowed the adjoining nine 4 km2 cells to be analyzed

collectively. Landscape characteristics gathered from both spatial scales were used to predict elk densities within the 4 km2 cell. The 4 km2 cells were positioned within the survey transect blocks. I alternated the selection of the cells to be included in the model building and model validation process to ensure an even distribution of cells for both datasets. I then randomly selected cells from the validation data set to be included into the model building data set to create a 70:30 ratio of model building to model validation cells. This enabled the model to be built from more sample cells and only tested on the

30% that were reserved for later model validation.

I reclassified the Kentucky Land Cover Data Set 2001, Anderson Level II

(KLCD-01, Frankfort, KY) into 5 cover type categories: urban, agriculture, herbaceous, forest, and water. The decision to reclassify the land cover into these 5 categories was based on elk land use patterns (Skovlin et al. 2002, Larkin 2004), and allowed the final analysis to be simplified for application to a larger spatial scale. The urban classification included all categories of developed land; the agriculture classification included cropland and hay/pasture land; the herbaceous classification included herbaceous, shrub, open land mined and mined barren land; and the forest classification included all deciduous, pine and wetland forests. The mined land classifications were included in the herbaceous reclassification category to account for the likely changes in land use as a result of reclamation practices that occurred between land cover data and the FLIR surveys. A sample of 488 habitat observations by FLIR in 2006 produced 76% agreement with the reclassified habitat categories selected for this study (Table 3.1).

I selected landscape variables based on published research and perceived biological importance to Kentucky’s elk distribution (Cook 2002, Geist 2002, Skoulin et

al. 2002, Larkin 2004, Schneider 2006, Stubblefield 2006, Sawyer 2007, Olsson et al.

2007, Telesco et al. 2007). I collected variables for each sample cell at both spatial scales using Patch Analyst (Elkie et al. 1999) ArcGIS 9.2, and ArcView 3.2 (ArcView:

Environmental Systems Research Institute, Redlands, CA. USA) (Table 3.2). Variables

measured included class area of forest (ForCA) and herbaceous (HerbCA) land cover,

herbaceous edge density (HerbED), herbaceous mean patch size (HerbMPS), forest mean

patch size (ForMPS), herbaceous mean nearest neighbor (HerbMNN), herbaceous area

weighted mean patch fractal dimensions (HerbAWMPFD), and urban core area index

(UrbCAI). Additionally, I calculated the road densities at both spatial scales (4kmRdDen and 36kmRdDen), and site influence (SiteInflu) a variable that combined the distance from each site and the number of elk released at each of those sites (Table 3.2). Site influence was calculated by taking the inverse of the distance (km) from each site to a given cell, multiplied by the relative index of elk released at that site (total number elk released divided by the minimum number of elk released at a site). Once derived for each site these values were added together within each cell. The road coverage was obtained from the University of Kentucky GIS portal data created by the Kentucky

Natural Resources and Environmental Protection Cabinet (www.eppc.ky.gov, accessed

18 November 2007). Road density was calculated for all state maintained major paved roads within each 4 km2 cell. Euclidean distance from each cell to each release site was

calculated using the Euclidean distance tool in ArcGIS 9.2.

To avoid multicolinearity during the model selection process I examined a correlation matrix (Table 3.3) of all the covariates using Pearson’s product moment correlation coefficient and addressed correlation (r ≥ 0.6). I found that groups of

variables measuring aspects of forest and herbaceous land covers were correlated.

Within this group, the variables HerbMNN and ForMPS were removed from the dataset because they were perceived to be biologically less important than the variables they were correlated with (Larkin et al. 2004, Stubblefield et al. 2006). I then conducted a principle component analysis (PCA) to transform the remaining correlated variables into an independent set of composite variables (Osborne and Tigar 1992, Buckland and Elston

1993). The variables (HerbCA, ForCA, HerbED, HerbMPS, and HerbAWMPFD, each measured at both spatial scales) included in this PCA analysis were likely interdependent and of equal perceived importance.

To estimate the uncertainty that the undetected elk groups contribute to the habitat model building process, I estimated the number and size of unobserved groups by using the estimated group detection rate. To estimate the number of undetected elk groups, I multiplied the total number of observed groups with < 10 individual by the rate of non- detection. I then assigned each of these undetected groups to a size class from 1 - 9 based on the distribution curve of group sizes detected by FLIR. I then added these unobserved groups to the FLIR observed population by randomly distributing them among the sample cells to account for missed elk with unknown distribution on the landscape. Due to the lack of an observed pattern in the locations of the radio-collared sample of elk groups that were undetected during the detection survey, I assumed that undetected elk groups had an equal probability of occurring among the sample cells irrespective of surrounding elk density or landscape patterns. I repeated this step prior to regression analysis for a total of 100 possible elk population estimates among the sample cells (Efron 1979, Buckland and Elston 1993).

I performed a model selection procedure that tested all possible combinations of

the 8 landscape variables for multiple linear regression models that included 1 – 7

variables. By limiting regression models to a maximum of 7 variables, I ensured that there were at least 20 samples per estimated parameter. I ranked models according to

their Akaike’s Information Criterion corrected for small sample size (AICc : program R:

v. 2.2.1, package ‘stats’; Burham and Anderson 2002, Venables and Ripley 2002). I

retained the 10 best models based on their AICc values, with the delta AICc values for

most of the 100 population iterations ranging from 0 – 2 (Table 3.4).

Because the population estimates varied between separate iterations, the AICc values between these iterations were not directly comparable. Alternatively, to summarize the relative support for each model across population iterations, I calculated the AICc model weights for the 10 best models within each iteration and then ranked models based on the sum of their weights across iterations (Buckland and Elston 1993,

Burham and Anderson 2002). The strongest models should be robust to the redistribution of the estimated error, and would repeatedly be ranked among the 10 best models. I

retained the most highly ranked models until a cumulative sum of the model weights surpassed 50% of the total weights across all models. I then re-weighted the retained

models so the total weight summed to one. I applied these model weights, the coefficients, and standard errors of each model to arrive at the final parameter estimates and standard errors for the averaged multiple linear regression model (Burham and

Anderson 2002). By using model averaging, standard errors represent model selection

uncertainty and detection uncertainty (White 2008).

I validated the model by predicting the elk density for each validation cell, the

30% that were held back to validate the model, using the averaged multiple linear regression model. The predicted elk densities were compared to a set of 100 estimated elk density simulations, where the FLIR observed elk densities within the validation cells were augmented by distributing the estimated unobserved groups across the sample cells.

For each elk population simulation, I compared the predicted elk density estimates produced by the model to the detection adjusted observed elk densities derived for each validation 4 km2 cell. I calculated the root mean square prediction error (RMSPE) of the validation sample cell. I then extrapolated this error to the overall population estimate to derive the standard error of the prediction and 95% confidence intervals.

I applied the model derived estimates of elk density to each 4 km2 cell throughout the core area (7,076 km2) of the restoration zone (i.e. the total area between the 8 original elk release sites and the area within a buffer of 16 km around each site: Figure 3.1). This area encompassed all of the survey transect blocks flown by FLIR, and represents the extent of area to which the model can be reliably extrapolated. Overall population estimates in this area were based on the total number of predicted animals across the sample cells. Error estimates were based on the accuracy of predicting the augmented population estimates. Estimated site specific elk densities were collected within a 20 x

20 km block (400 km2) surrounding each site similar to Larkin’s (2001) analysis of site fidelity (Figure 3.2). These blocks combined to cover 45% of the core area of the restoration zone.

Table 3. 1 : Comparison of reclassified Kentucky Land Cover Data Set 2001 (KLCD-01) Anderson Level II (Frankfort, KY) map and FLIR identified land cover.

KLDC-01 FLIR FLIR FLIR Percent reclassified land identified identified identified correctly cover categories forest herbaceousa otherb classified

Forest 222 84 3 91

Herbaceousa 15 146 0 61

Otherb 7 11 0 0

Total 244 241 3 76 a Includes grass and shrub cover types. b Includes urban, agriculture, and water.

Table 3. 2 : Independent variables considered for analysis in a multiple regression model of elk densities in eastern Kentucky USA,

2006.

Land cover Scale Name Variable Description type 4 km2 36 km2 X¯ SD min max X¯ SD min max Herb Area Herbaceous 1.12 0.05 0 1.28 1.15 0.04 1.07 1.29 Measure of patch AWMPFD weighted complexity mean fractal weighted by the dimensions size of the patch 1=simple shape 2=complex shape 26

UrbCAI Core area Urban 2 6.04 0 77 3 6.98 0 63 Percent of core index area in the landscape HerbMNN Mean Herbaceous 79 42 0 2,295 64 32 39 236 Average distance nearest from one neighbor herbaceous patch to the next nearest from edge to edge (m) HerbCA Herbaceous Herbaceous 49 50 0 364 430 319.8 35 2,067 Total area of class area herbaceous land cover type (ha) ForCA Forest class Forest 319 58 19 303 2,816 370.6 1,076 3,447 Total area of forest area land cover type (ha)

Table 3. 2 (continued)

Land cover Scale Name Variables Description type 4 km2 36 km2 X¯ SD min max X¯ SD min max HerbMPS Herbaceous Herbaceous 1 3.19 0 112 1.19 1.08 0.24 12 Average size of mean patch herbaceous patches size (ha) ForMPS Forest mean Forest 69 1.52 1 404 69 0.92 4 1,699 Average size of patch size forest patches (ha)

HerbED Edge Herbaceous 51 26.5 0 149 50 18.7 4 120 Amount of density herbaceous edge relative to the 27 herbaceous area (m/ha)

RdDen Road 1,065 916 0 11,138 1,059 566.6 0 4,725 Density of major density paved road (m/km2) SiteInflua Site 0.71 0.28 0.32 4.78 0.71 0.28 0.32 4.78 The relationship influence between the number of elk released and the distance of each cell to each release sites.

a The equation to derive SiteInflu = ∑ (1/distance from each cell to a release site) x (No. elk released/minimum No. elk released at one release site).

Table 3. 3 : Correlation matrix for variables considered in model building of forward-looking infrared radiography-based elk density

model (r ≥ 0.06).

Scale Variables 4RdDen 4ForCA 4HerbCA 4HerbMPS 4HerbED 4HerbAWMPFD 4UrbCAI 4 km Road density 1.00 -0.04 -0.28 -0.28 0.00 -0.19 0.61 Forest class area -0.04 1.00 -0.93 -0.69 -0.71 -0.65 -0.03 Herbaceous class area -0.28 -0.93 1.00 0.74 0.71 0.73 -0.20 Herbaceous mean patch size -0.28 -0.69 0.74 1.00 0.31 0.44 -0.20 Herbaceous edge density 0.00 -0.71 0.71 0.31 1.00 0.80 0.01

28 Herbaceous AWMPFD (a) -0.19 -0.65 0.73 0.44 0.80 1.00 -0.12

Urban core area index 0.61 -0.03 -0.20 -0.20 0.01 -0.12 1.00 36 km Road density 0.40 -0.01 -0.13 -0.13 -0.04 -0.14 0.23 Forest class area 0.01 0.82 -0.79 -0.48 -0.72 -0.61 0.01 Herbaceous class area -0.25 -0.79 0.86 0.57 0.69 0.66 -0.18 Herbaceous mean patch size -0.35 -0.63 0.70 0.68 0.35 0.45 -0.25 Herbaceous edge density 0.06 -0.59 0.59 0.17 0.84 0.62 0.07 Herbaceous AWMPFD -0.24 -0.68 0.76 0.47 0.71 0.68 -0.16 Urban core area idex 0.11 -0.03 0.02 -0.05 0.12 0.06 0.05 Site influence -0.29 -0.30 0.37 0.34 0.29 0.38 -0.19

(a) AWMPFD = Area weighted mean patch fractal dimensions

Table 3. 3 (continued)

Scale Variables 36RdDen 36ForCA 36HerbCA 36HerbMPS 36HerbED 36HerbAWMPFD 4 km Road density 0.40 0.01 -0.25 -0.35 -0.06 0.24 Forest class area -0.01 0.82 -0.79 -0.63 -0.59 -0.68 Herbaceous class area -0.13 -0.79 0.86 0.70 -0.59 0.76 Herbaceous mean patch size -0.13 -0.48 0.57 0.68 0.17 -0.47 Herbaceous edge density -0.04 -0.72 0.69 0.35 0.84 0.71 Herbaceous AWMPFD (a) -0.14 -0.61 0.66 0.45 0.62 0.68

Urban core area index 0.23 0.01 -0.18 -0.25 -0.07 0.16 29

36 km Road density 1.00 0.00 -0.13 -0.19 -0.06 0.12 Forest class area 0.00 1.00 -0.93 -0.67 -0.80 -0.81 Herbaceous class area -0.13 -0.93 1.00 0.80 0.73 -0.89 Herbaceous mean patch size -0.19 -0.67 0.80 1.00 0.27 -0.65 Herbaceous edge density 0.06 -0.80 0.73 0.27 1.00 0.75 Herbaceous AWMPFD -0.12 -0.81 0.89 0.65 0.75 1.00 Urban core area idex 0.09 0.00 -0.03 -0.08 0.10 0.01 Site influence -0.14 -0.36 0.46 0.58 0.22 -0.42

(a) AWMPFD = Area weighted fractal dimensions

Table 3. 3 (continued)

Scale Variables 36UrbCAI SiteInflu 4 km Road density 0.11 -0.29 Forest class area 0.03 -0.30 Herbaceous class area 0.02 0.37 Herbaceous mean patch size 0.05 0.34 Herbaceous edge density 0.12 0.29 Herbaceous AWMPFD (a) 0.06 0.38

Urban core area index 0.05 -0.19 30

36 km Road density 0.09 -0.14 Forest class area 0.00 -0.36 Herbaceous class area 0.03 0.46 Herbaceous mean patch size 0.08 0.58 Herbaceous edge density 0.10 0.22 Herbaceous AWMPFD 0.01 0.42 Urban core area idex 1.00 -0.07 Site influence 0.07 1.00

(a) AWMPFD = Area weighted fractal dimensions

Table 3. 4 : Models present in the top 10 model selection from each of 100 iterations

used to adjust elk counts observed using forward-looking infrared radiography in

December 2006 in eastern Kentucky, USA.

No. of iterations in Average AICc Average AICc Average AICc Modela which model is value weight delta value present v1v2v3v6 1 428.4 0.07 2.09

v1v2v4v6v7v8 1 416.7 0.06 1.67

v1v2v5v6v8 1 423.5 0.06 2.50

v2v3v6 1 420.5 0.08 1.91

v2v5v6v7 1 421.0 0.06 2.37

v1v2v5v6v7 2 427.6 0.07 2.02

v1v2v3v5v6 3 430.5 0.06 2.44

v1v2v6v8 3 433.1 0.07 1.74

v2v6v8 3 426.2 0.07 1.66

v1v2v6v7 4 433.1 0.06 1.82

v2v6v7 6 432.4 0.07 1.66

v2v3v4v5v6 11 430.0 0.06 2.35

v2v4v5v6v8 12 428.6 0.07 2.09

v2v4v5v6v7 17 428.9 0.06 2.13

v1v2v3v4v5v6 25 429.0 0.07 2.07 a v# represents the specific variable included in the model such that: 1 = site influence, 2 = 4 km2 road density, 3 = 4 km2 urban core area, 4 = 36 km2 road density, 5 = 36 km2 urban core area, 6 = PCA1, 7 = PCA2, 8 = PCA3

Table 3. 4 (continued)

No. of iterations in Average AICc Average AICc Average AICc Modela which model is value weight delta value present v1v2v3v4v6 30 429.2 0.07 2.05 v2v3v4v6 30 431.2 0.06 2.00 v1v2v4v5v6v8 32 429.0 0.07 2.00 v2v4v6v8 36 430.0 0.07 1.78 v1v2v4v5v6v7 39 429.6 0.07 1.97 v2v5v6 42 429.8 0.08 1.39 v1v2v5v6 47 430.2 0.09 1.30 v1v2v4v6v8 49 430.0 0.07 1.83 v2v4v6v7 49 429.6 0.07 1.82 v2v6 53 430.3 0.10 1.07 v1v2v6 54 430.3 0.10 1.16 v1v2v4v6v7 63 430.3 0.07 1.83 v2v4v6 93 428.7 0.14 0.40 v1v2v4v6 96 428.9 0.14 0.48 v2v4v5v6 97 428.9 0.13 0.67 v1v2v4v5v6 99 428.8 0.14 0.46 a v# represents the specific variable included in the model such that: 1 = site influence, 2 = 4 km2 road density, 3 = 4 km2 urban core area, 4 = 36 km2 road density, 5 = 36 km2 urban core area, 6 = PCA1, 7 = PCA2, 8 = PCA3

Figure 3. 1: Forward-looking infrared radiography survey transect blocks and core area

of elk restoration zone in relation to elk release sites in southeastern Kentucky, USA,

December 2006.

Figure 3. 2: Blocks (20 x 20 km) surrounding each elk release site used to estimate elk density in eastern Kentucky, USA, December 2006.

SECTION 4: RESULTS

FLIR Detection Rate

The ground crew identified 64 unique elk group locations, 44 (10 from 2006 and

34 from 2007) of which were visually observed and within a 30 min time delay. These

44 locations were used to determine the detection rate of FLIR for elk in Kentucky.

Thirty-four percent of these locations had a time delay of ≤ 5 minutes, 39% were > 5 and

≤ 15 minutes, and 27% were > 15 and ≤ 30 minutes (Figure 4.1). FLIR detection of elk was 76% (n = 33) for groups of < 10 individuals and 100% (n = 11) for groups ≥ 10 individuals. Logistic regression was unable to identify a relationship between group size and detection rates (P = 0.974). FLIR misidentified 2 of 31 (6%) ground observed white- tailed deer as elk during the detection study.

FLIR Elk Population Survey

A total of 1,981 elk were detected using FLIR within the survey transect blocks

(Table 4.1). The distribution curve shows 55% of elk groups contained 1 - 2 individuals and 75% of groups were < 5 individuals (Figure 4.2). The average elk density detected by FLIR in each 4 km2 sample cells within the survey transect blocks ranged from 0 -

28.8 elk/km2 (X¯ = 2.19, SE = 0.27) (Table 4.2). Seventy percent of elk were located in

herbaceous land cover, 29% in forested land cover, and 1% in urban areas. Elk were not located in agricultural areas or water.

PCA Analysis

Three PCA variables were included in the model building process (Table 4.3).

Only PCA1, which accounted for 70% of the variation, was included in the final model.

This variable was explained by the interaction of HerbCA and ForCA, with some

influence from HerbED and HerbAWMPFD.

Elk Population Modeling

The top 10 models selected from each iteration consisted of 31 competing models

(Table 3.4). The final averaged model derived from FLIR survey results was constructed from the 4 best models (Table 4.4) using averaged multiple linear regressions. Five variables were found to be strong predictors of elk densities (Table 4.5). The model was as follows:

Elk Density = 1.61+0.07x1-0.36x2-0.18x3+0.06x4+0.33x5

Where: x1=SiteInflu, x2=4kmRdDen, x3=36kmRdDen, x4=36kmUrbCAI, and x5=PCA1

The final model suggested elk density was positively associated with SiteInflu, PCA1 and

36kmUrbCAI, and negatively associated with 4mRdDen and 36kmRdDen.

Model validation procedures produced an estimate of elk within model validation

cells of 295 (SE = 145.6) while the number of elk detected by FLIR, and adjusted for

detection rates, for these cells was 615. I derived a root mean squared prediction error to

be 18.34. I extrapolated results from the model to the core area of the Kentucky elk

restoration zone (Figure 4.3) and calculated 7,001 (SE = 772, 95% CI = 5,488 - 8,514)

individuals for 2006. The range of elk density for each 4 km2 cell derived using the

model was 0 - 8.75 elk/km2 (X¯ = 0.99, SE= 0.02) for the entire core area of the restoration zone.

Distribution Pattern

The distribution of elk on the landscape is based on land use, habitat

configuration, and the influence of each release site. The distribution of high elk density

areas was associated with areas of extensive herbaceous cover, near release sites with

large initial release numbers, and having low road densities. Fifty three percent of elk were estimated to be within 10 km from release sites. The estimated elk density within this 400 km2 area around each release site ranged from 0.66 - 2.01 elk/km2, with the

highest densities located around the Martin and Starfire release sites (Table 4.6). The lowest densities of elk were found south of the Starfire release site near Hazard, KY; east

of the Letcher release site near Whitesburg, KY; and northeast of the Redbird release site

near Hyden, KY. (Table 4.7).

Table 4. 1 : Observed densities of elk in forward-looking infrared radiography transect blocks, December 2006 in eastern Kentucky, USA.

No. FLIR- Area surveyed Observed elk density within Elk release site observed elk (km2) transect blocks (elk/km2)

Starfire 385 96 4.01

Orr 399 128 3.12

Martin 392 128 3.06

Raven 308 139 2.22

Blue Diamond 245 128 1.91

Pike 139 128 1.09

Redbird 91 128 0.71

Letcher 22 64 0.34

Total 1,981 939 2.06

Table 4. 2 : Elk density (elk/km2) observed using forward-looking infrared radiography within 4 km2 sample cells located in transect blocks surveyed December 2006 in eastern

Kentucky, USA.

Site X¯ SD min max

Starfire 3.94 1.0 1 14.5

Orr 3.20 0.86 0 16

Martin 2.93 0.61 0 10.5

Raven 2.51 1.19 0 28.8

Blue Diamond 2.15 0.58 0 13.5

Pike 1.08 0.45 0 12.3

Redbird 0.75 0.13 0 2.5

Letcher 0.42 0.16 0 1.8

Table 4. 3 : Principal component loadings for ordination of correlated variables and subsequent derived independent variables from

forward-looking infrared radiography survey of elk in eastern Kentucky, USA, 2006.

Scale 4 km2 36 km2 Composite Forest Herba Herb Herb Forest Herb Herb Herb Description variable Herb Herb class class edge AWM class class edge AWM MPSb MPS Area area density PFDc area Area density PFD

PCA1 -0.34 0.35 0.24 0.31 0.30 -0.35 0.36 0.28 0.29 0.33 Axis of variation in the ratio of herbaceous to Portion of forest area at the 4 km2 variance and 36 km2 spatial 40 = 0.71 scale

PCA2 0.12 -0.16 -0.56 0.39 0.18 -0.08 -0.04 0.45 0.18 0.10 Axis of variation due to the contrast of patch Portion of size and edge density variance = 0.13

PCA3 -0.22 0.22 0.33 0.30 0.50 0.32 -0.34 -0.37 -0.16 -0.28 Axis of variation due to the contrast between 4 Portion of km2 and 36 km2 variance = 0.06 a Herb=herbaceous b MPS=mean patch size c AWMPFD=area weighted mean patch fractal dimension

Table 4. 4 : Model AICc weights and delta AICc values for the four best multiple linear regression models derived from forward-

looking infrared radiography to estimate the density of elk in 2006 in eastern Kentucky, USA.

Re- Summed Averaged weighted Model Variables AICc Delta AICc weight values weights

2 2 2

41 Site 4 km road 36 km road 36 km urban

Intercept PCA1 influence density density core area

Elk01 X X X X X X 14.10 0.46 0.306

Elk02 X X X X X 13.41 0.48 0.296

Elk03 X X X X 13.36 0.40 0.286

Elk04 X X X X X 12.31 0.67 0.295

Table 4. 5 : Standard beta coefficients and standard deviation for final multiple linear regression model estimating elk density in 2006 in eastern Kentucky, USA.

Variable Coefficient SD t p

Intercept 1.61 0.08 19.27 <0.001

Site influence 0.07 0.10 0.76 0.450

4 km2 road -0.36 0.10 -3.80 <0.001 density

36 km2 road -0.18 0.09 -1.91 0.059 density

36 km2 urban 0.06 0.08 0.72 0.471 core area index

PCA1 0.33 0.09 3.46 <0.001

Table 4. 6 : Estimated elk densities within 400 km2 areas surrounding release sites from the December 2006 forward-looking infrared radiography survey of eastern Kentucky,

USA.

Elk release Estimated Mean estimated min Max SD site No. of elk number of elk/km2

Martin 802 718 886 2.01 1.42

Starfire 785 701 869 1.96 1.51

Orr 456 381 540 1.14 0.64

Blue 421 337 505 1.05 0.73 Diamond

Pike 415 331 499 1.04 0.78

Raven 323 239 407 0.81 0.43

Redbird 273 189 357 0.68 0.29

Letcher 264 180 348 0.66 0.42

Table 4. 7 : Sites of low estimated elk density within a 400 km2 area in 2006 in eastern

Kentucky, USA.

Estimated No. Mean estimated Site min max SD elk number of elk/km2

Hyden 262 178 346 0.655 1.21

Hazard 234 150 318 0.585 0.54

Whitesburg 232 148 316 0.580 0.43

450

400

350

300

250

200

Distance (m) Distance 150 45

100

0 0:00 0:07 0:14 0:21 0:28 Time Difference (min)

Figure 4. 1 : Detection rate samples depicting “hits” (◊ = successful detection of ground identified elk groups by forward-looking

infrared radiography (FLIR)) and “misses” (□ = unsuccessful detection of ground identified elk groups by FLIR) for the 2006 and

2007 detection rate study in eastern Kentucky, USA.

0.3

0.25

Detected elk groups 0.2

0.15 46 Frequency

0.1

0.05

0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 Group Size

Figure 4. 2 : Distribution of elk group sizes detected by forward-looking infrared radiography, in eastern Kentucky, USA, December

2006.

Figure 4. 3 : Elk density distribution estimated within 4 km2 cells in the core area of the restoration zone in southeastern Kentucky, USA, December 2006.

SECTION 5: DISCUSSION

FLIR Detection Rate

Elk are a gregarious species that form large herds from the onset of the rut usually through winter (Geist 2002, Wichrowski et al. 2005); a seasonal behavior that, coupled with leaf-off conditions, make elk particularly easy to locate using FLIR. In this study,

FLIR detection rates of elk in southeastern Kentucky were high (76-100%) and

comparable to other FLIR-based aerial surveys of ungulates, including elk (54.3-91%,

Dunn et al. 2002), white-tailed deer (31-89%, Naugle et al. 1996, Haroldson 2003, Potvin and Breton 2005), bighorn sheep (89%, Bernatas and Nelson 2004) and moose (88%,

Adams et al. 1997).

FLIR detection rates for large heterogeneous study areas should be evaluated

throughout the survey area to confirm reliable detection estimates across the various

habitat types (Skalski et al. 2005). I was unable to apply this practice due to the lack of

existing collared elk located within the various habitat types, the prohibitive cost of travel

and applying radio-collars to elk in areas throughout the survey transect blocks, the

limited ground access to many of the areas within the survey blocks, and the possible

dispersal of newly collared elk to areas outside the boundaries of the survey blocks. I

attempted to locate elk in forested cover during the detection study, but most of the radio-

collared elk were found in open herbaceous habitat during the nocturnal flights as

expected (Larkin et al. 2004, Olssen et al. 2007). This suggested that the majority of the

population surveyed was likely located in open herbaceous cover types similar to the

detection rate study area. This is further supported by the proportion of elk identified by

FLIR within herbaceous cover types (70%). While I was unable to assess the detection

rate of elk in forested cover types, it may have caused only minimal effects on the final results of the model. By applying the potentially missed elk groups in a random distribution, I assumed that each elk group had an equal probability of being missed regardless of landscape patterns or surrounding elk density. This allowed a conservative model to be built based on overriding landscape associations with elk density. This method accounted for my uncertainty as to where these missed elk were located and allowed the model to be built on more than just the distribution of the FLIR observed elk groups.

The comparison of FLIR identified elk and deer with ground identified deer locations suggested that FLIR misidentified deer as elk 6% of the time, a finding that may initially be perceived as low. However, when extrapolated over the larger study area

(7,088 km2) the 6% misclassification rate could impact population estimation of elk. For

example, the misidentification rate of 6% factored with deer density estimates of 1.5 - 7

deer/km2 (K. Alexy, KDFWR, personal communication) could inflate the estimated

number of elk within the core area of the restoration zone by 14 – 64% (954 - 4,452). I

was unable to quantify the extent to which the misidentification of deer affected the final

elk population estimates because local deer density data, and deer population estimate for

the core area of the restoration zone, were unknown. Because of the gregarious behavior

of elk during this season, I expected that there would be a higher proportion of groups of

≥ 10 individuals. I speculate, based on the distribution of elk group sizes detected by

FLIR (Table 4.2), that some observed small elk groups may have been deer that were

misidentified. Summing observations by group size revealed that 55% of locations were

groups of 1 - 2 individuals and 75% were groups of < 5 individuals. While these small

groups may represent smaller bachelor herds or lone male elk, some may have been deer

that were misidentified as elk.

FLIR Elk Population Model

Elk distribution was associated with land use, habitat configuration, and the

influence of each release site. I found the highest elk densities at the Martin and Starfire

Complex release sites, followed by Orr, Blue Diamond, Pike, Raven, Redbird and

Letcher release sites, respectively (Table 4.6). Low densities of elk were typically located near urban centers and along major roadways (Table 4.7, Figure 5.1) and suggest that these features may represent potential barriers to elk movement and dispersal

(Trombulak and Fissell 2000).

The most influential variables used to estimate the elk density distribution were

4kmRdDen and PCA1, followed by 36kmRdDen, SiteInflu and 36kmUrbCAI, respectively (Table 4.5). This suggests that the Kentucky elk distribution was strongly associated with avoidance of major paved roads and an affinity to herbaceous patches with high edge density and complex shapes. Elk aversion to high road density at the home range scale was slightly less influential. The elk distribution was only slightly positively associated with distance to and the number of elk released at each site, and the relative proportion of urban core area in the landscape.

Roads

The influence of roads on elk distribution and behavior has been extensively documented and suggest that the amount of traffic on a specific road is ultimately what affects elk distribution and habitat use surrounding roads (Perry and Overly 1976, Lyon

1983, Johnson et al. 1991, Cole et al. 1997, Rowland et al. 2000, Trombulak and Frissell

2000, Spellerberg 2002, Forman et al. 2003, Gagnon et al. 2007). Roads can also affect

elk density due to mortality from vehicle collision, increased human activity/harassment,

and increased hunting/poaching access (Trombulak and Frissell 2000, Larkin 2001). The

road variable used in this model was restricted to major paved roads to reflect higher

traffic levels. The model demonstrated a strong association between high road densities

and low elk densities at both spatial scales. This negative association was evident along

the Kentucky highway corridors of US Hwy 23, State Rd 80, US Hwy 119, and US Hwy

421, suggesting these roads may represent potential elk movement barriers and restrict

overall connectivity within the restoration zone (Figure 5.1) (Cox 2003). Thus, elk herds

confined within areas bordered by these roads, especially around the Martin and Starfire

Complex sites, could reach their local carrying capacities before the entire restoration

zone reaches its goal of 10,000 elk (Trombulak and Fissell 2000). This could lead to local overabundance, increased human-elk conflict, and degradation of ecological components and services that could affect elk and a multitude of organisms within these communities (Schoenecker et al. 2004, Bradford and Hobbs 2008).

Habitat

In the mixed-mesophytic forests of Appalachia, herbaceous openings and reclaimed surface mines provide elk with abundant forage of high nutrient levels and extended growing seasons when compared to the habitats in the western United States

(Witmer and Cogan 1989, Maehr et al. 1999, Larkin et al. 2004, Wichrowski et al. 2005,

DeBerti 2006, Schneider 2006, Stubblefield et al. 2006). While elk are generalist feeders,

the majority of their diet consists of grasses and forbs found in these openings (Schneider

2006). As such, I expected and found PCA1 to be a strong variable in estimating elk density distribution. Within this principal component analysis variable, not only was the high proportion of herbaceous area associated with high elk densities, but also high edge density and complexity of patch shape (Table 4.3). Elk typically use areas with higher edge density between herbaceous and forest patches because these habitats provide a diversity of forage within close proximity to escape and thermal cover (Kersch 1963,

Leckenby 1984, Skovlin et al. 2002). This is consistent with higher elk densities in landscapes dominated by herbaceous openings that have complex patch shapes that further maximize edge density with forests, which was supported by my findings.

Site Influence

Overall, release site influence demonstrated only a weak positive association with elk density, which was lower than I expected. This is partially attributed to release sites being chosen based on landscape characteristics such as low road density, mixture of herbaceous and forested land cover, and low human population/disturbance (Larkin

2001); the first two were strongly associated with estimating elk density using my model.

Site influence showed a positive association with the number of elk released at each site, in combination with a negative relationship with increasing distances from the release sites. Therefore, the closer a cell was to the sites that had high numbers of elk released, the higher the estimated elk density in that cell. Also, areas between neighboring release sites also had increased elk densities. The influence of the number of elk released at each site was expected, as higher releases of individuals have shown higher successful establishment of reintroduced animals (Witmer 1990). This relationship between site

influence and elk density also suggested that the spread of elk around each site was not uniform, and was strongly dependent on the surrounding habitat and road density.

While methodologically different, my findings about site influence and elk habitat preferences seemed contradictory to Larkin et al. (2004). By recording movement patterns of elk released at 3 sites, Larkin et al. (2004) was able to determine the proportion of elk remaining within a 10 - km buffer one year post-reintroduction. Their results suggested that the elk at the Redbird and Starfire Complex release sites had low release site fidelity (53%, n = 79 and 55%, n = 110), while those at the Orr release site displayed high site fidelity (82%, n = 131). Larkin et al. (2004) attributed the low site fidelity of some sites to large land cover patches (i.e., forest at Redbird, herbaceous at

Starfire Complex), low edge density, and high human disturbance. Although my results reflect low densities at Redbird release site (0.68 elk/km2), and moderate densities at Orr release site (1.14 elk/km2), which was similar to Larkin et al. (2004), the high Starfire

Complex density estimate (1.96 elk/km2) suggested that this site has since become favored by elk. A possible explanation for this inconsistency could be related to human disturbance, rather than to land cover characteristics. Elk have been observed to become habituated to human disturbance and hazing activities (Thompson and Henderson 1998).

Kentucky elk may have become habituated to human activity such as that found at active mine sites. This habituation may have allowed elk to recolonize sites such as Starfire

Complex.

Urban Core Area Index

The proportion of urban core area within each 36 km2 cell (36kmUrbCAI) had a weak positive association with elk density. This seems counter intuitive as I expected elk

to avoid urban areas and development along major roadways. However, elk in Kentucky

have been observed to migrate into bottom land and towns in the winter, typically

spending time in golf courses, hayfields, and backyards in towns such as Rowdy,

Prestonsburg, and Whitesburg, Kentucky. (C. Logsdon personal communication). The season in which the FLIR survey was conducted in 2006 was consistent with these

observed activities and potentially caused the model to predict slightly higher elk

densities in urban areas than at other times of the year.

Assumptions and Caveats

The overall estimated elk abundance derived from this FLIR-based model suggested that the elk population within the core area of the restoration zone was between

5,488 - 8,514 elk in 2006. There are 4 factors that I was unable to includ in the model and should be considered when interpreting the results. First, the deer misidentification rate, while unable to be quantified, if incorporated into the model would have produced a more conservative estimate because only elk would be included in the model. Second, model validation procedures produced a lower estimate for validation cells than detected by FLIR with adjustments made for detection rates. Since the model did not estimate densities over 8.75 elk/km2 while FLIR detected up to 28.8 elk/km2, there was potential

for underestimation of the population. This lower density estimation by the model may

be due to the spreading out of these high density areas into several neighboring cells.

While the model may be unable to estimate high peaks in density, it may show a larger

area of moderate density equal to the peak seen by FLIR. Third, the KLCD-01 map used in this analysis was created in 2001, which may bias the population model due to landscape changes between this map and the FLIR observed 2006 land cover. Also, the

KLCD-01 map itself had a 30 x 30 m pixel resolution that diminished its precision and

included inherent bias from sampling methods used in its creation. Despite these

shortcomings, my assessment of the agreement between the reclassification of the 2001

map and observed 2006 land cover found 76% agreement. Most of the error (70%) was explained by the misclassification of the 2006 identified herbaceous habitat as forest on

the 2001 map (Table 3.1). This error, most likely, is due to the ever changing land cover

of eastern Kentucky as forest is cleared to access coal seams and then later reclaimed to

herbaceous openings. The comparisons between the 2001 map and the 2006 FLIR

identified land cover suggests that the map, while unable to maintain complete accuracy

in these frequently altered landscapes, remains fairly accurate overall. Finally, the

method I chose to apply the detection rate, which incorporated the random distribution of

potentially missed groups throughout the transect blocks, may have masked slight habitat

associations with elk densities. This process was necessary in order to evaluate the

potential distribution of missed elk groups and their effect on the model. Without this

process I would have had to assume that the distribution of elk was based solely on the

groups observed by FLIR, and that the unobserved elk would have had no effect on the

final model. The use of this random distribution of missed elk allowed the strong,

overriding habitat associations to stand out through the redistribution of these random

locations, which was ultimately the foundation of this model.

Accuracy of KEM

The 2006 KEM population estimate of 5,700 was at the lower end of the range

estimated by my FLIR model. Although the KEM estimate was within the range of the

FLIR-based population estimate, this may suggest that KEM needs to be periodically

updated with current mortality and reproduction rates to enhance its future accuracy.

KEM should also be checked periodically by a similar survey method such as FLIR to identify population fluctuations, potential changes in landscape influence, and local density distribution changes. FLIR-based population model estimates enable managers to identify high elk density areas and potential landscape patterns associated with elk density distribution. Regardless, this method was a temporal snapshot and is not capable of predicting future populations or distribution patterns, therefore necessitating the use of both models (Lancia et al. 1996).

Utility of FLIR

Although analysis of detection rates using FLIR has been extensively documented

(Bodie et al. 1995, Naugle et al. 1996, Haroldson et al. 2003, Bernatas and Nelson 2004,

Potvin and Breton 2005, Kinzel et al. 2006), none used radio-collared animals during nocturnal hours to test the ability of FLIR to detect animals. Because the population survey was conducted during nocturnal hours, I tested the detection rate of FLIR during the same time period, resulting in limited visibility for the ground crew. Therefore, I experienced pitfalls and a steep learning curve to assess the detection rate. The detection survey in 2006 consisted of 3 ground crew members. This crew was unable to locate some of the target animal in a timely manner, leading to some time lags of over 30 min, after which it was impossible to identify the FLIR-identified group that included the target collared animal, especially if several groups were present. I corrected this limitation in the 2007 by using 6 ground crew leaders each with 1 - 3 volunteers to assist in data recording. This permitted ground crews to locate and record habitat information more efficiently, enabled them to minimize time lag between ground and FLIR

observations, and monitor the group until the FLIR operator passed by to insure that the

group did not move or change in number of individuals. This adjustment resulted in a

sample size three-fold of that obtained in the first year and enabled the collection of deer locations used to evaluate misidentification rates between deer and elk. If I had radio- collared deer in the same area as the detection survey this may have further enhanced the ability to test species identification accuracy. Moreover, it would have been helpful to have an estimate of the deer population within the elk restoration zone to which the misidentification rate could be applied. Finally, the fact that elk were gregariously distributed, and identified by FLIR based on groups, not individuals, made the application of the detection rate to density data difficult.

The FLIR footage analysis process posed some pitfalls that I was unaware of at the onset of the project. Footage analysis was conducted after all surveys were completed in late December 2006. This allowed the FLIR operator to focus on collecting data within a short time span, and decreased the amount of time the operator needed to be in Kentucky. The footage analysis process required ca. 4 hrs for each hour of survey footage, which created some time lag prior to data delivery. I experienced at least a 5 month time delay for the delivery of results as they began to arrive in June 2007, and the final results arrived in June 2008. Footage analysis for this survey required extended time and increased expenses when compared to a minimum count analysis, due to the

landscape data collection required for detection comparisons and modeling purposes.

While incurring these time delays may have affected timely acquisition of results, the

data collected was important and required the expertise of the FLIR operator to ensure

accuracy. For example, special equipment was required to link the footage to the file of

the plane’s GPS locations to geo-reference the footage. Future use of FLIR requires attention to these limitations.

The total cost of this population survey was $105,632 (Table 5.1) at a rate of

$112.50/km2. The survey time ($700/hr) and the footage analysis time ($100/hr) alone totaled $62,905, which did not include the detection rate survey time. This FLIR survey cost more than comparable survey techniques due to the footage analysis cost, which averaged $400 for each hour of footage collected and was necessary to produce the final survey results (Table 5.2). Most other survey techniques do not require a review of footage with results immediately available after the survey has been conducted.

Conclusions

While this project was expensive and had several time delays and logistical pitfalls to overcome, my findings may be useful to managers of elk in eastern Kentucky.

Based on my study, it appears that FLIR is a useful tool for surveying elk in the rugged and forested eastern Kentucky landscape. FLIR demonstrated a high detection rate for elk, a low misidentification rate of white-tailed deer, and was able to provide a relatively fast survey of a large geographic area. The potential downfalls of this survey technique included the time delay for analysis of results, the relatively higher expense as compared to other survey techniques, and the coordination of a logistically complicated detection study.

The estimates of Kentucky’s elk density distribution derived from FLIR surveys could be used for several management decisions. First, the total estimated population within the core area of the restoration zone can be used to validate the KEM or other

Kentucky elk population estimates. Second, the FLIR-based model can identify

landscape associations linked to elk density. Third, the extrapolation of this model can provide distribution information that may prove useful in the delineation of elk hunting units, relative harvest limits; and the identification of lands important for elk-related habitat protection, recreation, nuisance mitigation, and maintaining connectivity among elk herds.

Given the distribution and density estimates derived in this study, I recommend that several hypotheses be further investigated. Potential barriers to elk movement should be tested to evaluate possible isolation of portions of the herd. Further evaluation of habitat preference and requirements of elk in Kentucky could lead to fine scale density estimates that are more applicable to specific habitat manipulation or land acquisition endeavors. The results from these investigations could lead to a better understanding of the social and biological carrying capacities at multiple spatial and temporal scales.

If a FLIR survey is proposed to validate future Kentucky elk estimates it could be conducted in a more streamlined manner and with fewer transects as compared to this study. Survey transect blocks should be repositioned to create a random sample throughout the entire restoration zone. However, detection rates may still need to be assessed if the sensor technology improves, and as different FLIR operators conduct these surveys.

I recommend to anyone considering the use of FLIR in future survey projects to consider the following suggestions for both financial and logistic reasons. First, a detection rate study is the most labor and resource intensive portion of the entire survey process, but it is extremely important in assessing sampling error when estimating wildlife populations (Salski et al. 2005). Not only should the detection rate of the target

species be evaluated, but also the misidentification rates for similar morphological species located within the survey area. Second, service contracts should clearly communicate expected services, anticipated costs, a realistic timelines for survey completion and data delivery. Finally, communication between the FLIR contractor and contractees must be effective and timely to insure quality control of data and successful execution of project objectives.

Table 5. 1 : Cost break down of the forward-looking infrared radiography (FLIR) survey conducted in December 2006 in eastern Kentucky, USA.

Activity Amount of Time Total Cost

Detection surveya 35 hours $27,090

Population surveya 41 hours $33,120

Footage analysis 339 hours $33,900

Weather days 2 nights $1,600

Ferry time 6 trips $9,921

Total: $105,631

a Includes hanger fees, hotel cost, car rental and per-deim for FLIR operator and pilot. Survey time was charged at a rate of $700/hour.

Table 5. 2 : Cost comparison of forward-looking infrared radiography (FLIR) to similar ungulate population survey techniques.

Source Survey method Cost/hour

Ogutu et al. 2006 Road strip and line transects $29a

Potvin et al. 2002 Pellet count survey $41b

Noyes et al. 2000 Fixed wing visual survey $70c

Noyes et al. 2000 Helicopter visual survey $270d

Potvin et al. 2002 Double count helicopter survey $1000e

This study Aerial FLIR survey $1100f a Includes fuel, salaries and housing b Includes salaries and travel expenses c Includes airplane fee d Includes helicopter fee e Includes helicopter fee f Includes airplane, infrared sensor use and footage analysis

Figure 5. 1 : Influence of road density on estimated distribution of elk in eastern

Kentucky, USA, December 2006.

REFERENCES

Adams, K. P., P. J. Pekins, K. A. Gustapson, and K. Bontaites. 1997. Evaluation of infrared technology for aerial moose surveys in New Hampshire. Alces 33: 129- 139.

Alexy, K. J. 2004. Meningeal worm (Parelaphostrongylus tenuis) and ectoparasite issues associated with elk restoration in southeastern Kentucky. PhD. Dissertation. Clemson University, Clemson, SC.

Amstrup, S. C., G. York, T. L. McDonald, R. Nielson, and K. Simac. 2004. Detecting denning polar bears with forward-looking infrared (FLIR) imagery. BioScience 54: 337-344.

Anderson, D. P., M. G. Turner, J. D. Forester, J. Zhu, M. S. Boyce, H. Beyer, and L. Stowell. 2005. Scale-dependent summer resource selection by reintroduced elk in Wisconsin, USA. Journal of Wildlife Mangement 69: 298-310.

Anrow, H. S. 1960. Seedtime on the Cumberland. New York: New York.

Anthony, R. G., M. G. Garrett, and F. B. Isaacs. 1999. Double-survey estimates of bald eagle population in Oregon. Journal of Wildlife Management 63: 794-802.

Barbosa, A. M., R. Real, J. Olivero, and J. M. Vargas. 2003. Otter (Lutra lutra) distribution modeling at two resolution scales suited to conservation planning in the Iberian Peninsula. Biological Conservation 114: 377-387.

Barbour, R. W., W. H. Davis. 1974. Mammals of Kentucky. University Press of Kentucky, Lexington, KY.

Bart, J., S. Droege, P. Geissler, B. Peterjohn, and C. J. Ralph. 2004. Density estimation in wildlife surveys. Wildlife Society Bulletin 32:1242-1247.

Bart, J., and S. L. Earnst. 2002. Double sampling to estimate bird density and population trends. Auk 119:36-45.

Belue, T. F. 2003. The hunters of Kentucky: a narrative history of America’s first far west 1750-1792. Stackpole Books. Mechanicsburg, PA.

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.

Bodie, W. L., E. O. Garton, E. R. Taylor, and M. McCoy. 1995. A sightability model for bighorn sheep in canyon habitats. Journal of Wildlife Management 59:832-840.

Borchers, D. L., S. T. Buckland, P. W. Goedhart, E. D. Clarke, and S. L. Hedley. 1998. Horvitz-Thompson estimators for double-platform line transect surveys. Biometrics 54:1221-1237.

Boyce, M. S. 2006. Scale for resource selection functions. Diversity and Distribution 12: 269-276.

Braun, E. L. 1950. Deciduous forests of eastern North America. Hafner, New York, NY.

Bradford, J. B., and N. T. Hobbs. 2008. Regulating overabundant ungulate populations: an example for elk in Rocky Mountain National Park, Colorado. Journal of Environmental Management 86:520-528.

Buckland, S. T., and D. A. Elston. 1993. Empirical models for the spatial distribution of wildlife. Journal of Applied Ecology 30:478-495.

Burnham, K. P., and D. R. Anderson. 2002. Model selection and inference: a practical information-theoretic approach. 2nd edition. Springer-Verlag, New York, NY.

Caughley, G. 1974. Bias in aerial survey. Journal of Wildlife Management. 38:921-933.

Claridge, A. W., G. Mifsud, J. Dawson, and M. J. Saxon. 2004. Use of infrared digital cameras to investigate the behavior of cryptic species. Wildlife Research 31:645- 650.

Cochran, W. G. 1977. Sampling techniques. John Wiley and Sons, New York, New York.

Cole, E. K., M. D. Pope, and R. G. Anthony. 1997. Effects of road management on movement and survival of Roosevelt elk. Journal of Wildlife Management 61: 1115-1126.

Cook, J. G. 2002. Nutrition and food. Pages 259-350 in D. E. Toweill and J. W. Thomas, editors. North American elk: ecology and management. Smithsonian Institute Press, Washington, D. C.

Corsi, F., J. D. Leeuw, and A. Skidmore. 2000. Modeling species distribution with GIS. Pages 389-426 in L. Boitani and T. K. Fuller, editors. Research Techniques in Animal Ecology: Controversies and Consequences. Columbia University Press, New York.

Cox, J. J., D. S. Maehr, and J. L. Larkin. 2002. The biogeography of faunal place names in the United States. Conservation Biology 16:1143-1150.

Cox, J. J. 2003. Community dynamics among reintroduced elk, white-tailed deer, and coyote in southeastern Kentucky. PhD Dissertation. University of Kentucky, Lexington, KY.

Croon, G. W., D. R. McCullough, C. E. Olson, and L. M. Queal. 1968. Infrared scanning techniques for big game censusing. Journal of Wildlife Management 32: 751-759.

DeBerti, J. M. 2006. Management plan for elk in Pennsylvania. Pennsylvania Game Commission. Jersey Shore, Pennsylvania.

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 Forests. U.S. Geological Survey, Pennsylvania Cooperative Fish and Wildlife Research Unit, The Pennsylvania State University, University Park, PA.

Dunn, W. C., J. P. Donnelly, and W. J. Krausmann. 2002. Using infrared sensing to count elk in southwestern United States. Wildlife Society Bulletin 30:963-967.

Efron, B. 1979. Bootstrap methods: another look at the jackknife. Annals of Statistics 7:1-26.

Elkie, P., R. Rempel, and A. Carr. 1999. Patch analyst user’s manual. Ontario Ministry of Natural Resources. Northwest Science and Technology, Thunder Bay, Canada.

Estes, J. A. and R. J. Jameson. 1988. A double-sampling estimate for sighting probability of sea otters in California. Journal of Wildlife Management 52: 70-76.

Finley, D. J., G. C. White, and J. P. Fitzgerald. 2005. Estimation of swift fox population size and occupancy rates in eastern Colorado. Journal of Wildlife Management 69:861-873.

Forman, R. T. T., D. Sperling, J. A. Bissonette, A. P. Clevenger, C. D. Cutshall, V. H. Dale, L. Fahrig, R. France, C. R. Goldman, K. Heanue, J. A. Jones, F. J. Swanson, T. Turrentine, and T. C. Winter. 2003. Road Ecology. Island Press. Washington, D.C.

Freddy, D. J., G. C. White, M. C. Kneeland, R. H. Kahn, J. W. Unsworth, W. J. deVergie, V. K. Graham, J. H. Ellenberger, and C. H. Wagner. 2004. How many mule deer are there? Challenges of credibility in Colorado. Wildlife Society Bulletin 32: 916-927.

Funkhouser, W. D. 1925. Wildlife in Kentucky. Frankfort: Kentucky Geological Survey.

Gagnon, J. W., T. C. Theimer, N. L. Dodd, S. Boe, and R. E. Schweinsburg. 2007. Traffic volume alters elk distribution and highway crossings in Arizona. The Journal of Wildlife Management 71:2318-2323.

Garner, D. L., H. B. Underwood, and W. F. Porter. 1995. The use of modern infrared thermography for wildlife population surveys. Journal of Environmental Management 19:233-238.

Garshelis, D. L. 1992. Mark-recapture density estimation for animals with large home ranges. Pages 1151-1160 in McCullough D. R., Barrett R. H. Wildlife 2001: Populations. Elsevier Applied Science. New York, NY.

Gassaway, W. C., S. D. Dubois, and S. J. Harbo. 1985. Biases in aerial transect surveys for moose during May and June. Journal of Wildlife Management 49:777-784.

Geibel, J. J., and Miller. 1984. Estimation of sea otter, Enhadra lutris, population with confidence bounds, from air and ground counts. California Fish and Game 70: 223-225.

Geist, V. 2002. Adaptive behavioral strategies. Pages 389-434 in D. E. Toweill and J. W. Thomas, editors. North American elk: ecology and management. Smithsonian Institution Press, Washington, D.C.

Graves, H. B., E. D. Bellis, and W. M. Knuth. 1972. Censusing white-tailed deer by airborne thermal infrared imagery. Journal of Wildlife Management 36: 875-884.

Guilday, J. E. 1984. Pleistocene extinction and environmental change: case study of the Appalachians. Pages 250-258 in P. S. Martin and R. G. Klein, editors. Quaternary extinctions: a prehistoric revolution. The University of Arizona Press, Tucson, AZ.

Guisan, A., and W. Thuiller. 2005. Predicting species distribution: offering more than simple habitat models. Ecology Letters 8: 993-1009.

Hall, E. R. 1981. The mammals of North America. 2nd edition. John Wiley and Sons, New York, NY.

Haroldson, B. S., E. P. Wiggers, J. Beringer, L. P. Hansen, and J. B. McAninch. 2003. Evaluation of aerial thermal imaging for detecting white-tailed deer in a deciduous forest environment. Wildlife Society Bulletin 31:1188-1197.

Havens, K. J., and E. J. Sharp. 1998. Using thermal imagery in the aerial survey of animals. Wildlife Society Bulletin 26:17-23.

Hudson, R. J. and J. C. Haigh. 2002. Physical and physiological adaptations. Pages 199-258 in D.E. Toweill and J. W. Thomas, editors. North American elk: ecology and management. Smithsonian Institute Press, Washington, D.C.

Johnson, B. K., J. H. Noyes, J. W. Thomas and L. D. Bryant. 1991. Overview of the Starskey Project: current measures of elk vulnerability. Pages 225-228 in A. G. Christensen, L. J. Lyon and T. N. Lonner, editors, Proceedings from the Elk Vulnerability Symposium. Montana State University, Bozeman, MT.

Kinzel, P. J., J. M. Nelson, R. S. Parker, and L. R. Davis. 2006. Spring census of mid- continent sandhill cranes using aerial infrared videography. Journal of Wildlife Mangement 70: 70-77.

Kirsch, J. B. 1963. Range use, relationship to logging and food habits of the elk in the Little Belt Mountains, Montana. M.S. Thesis, Montana State University, Bozeman.

Lancia, R. A., J. D. Nichols, and K. H. Pollock. 1996. Estimating the numbers of animals in wildlife populations. Pages 215-253 in T. A. Bookhout, editors. Research and Management Techniques for Wildlife and Habitats. Allen Press, Inc., Lawrence, KS.

Larkin, J. L. 2001. Demographic and spatial characteristics of a reintroduced elk population. PhD Dissertation. University of Kentucky, Lexington, KY.

Larkin, J. L., D. S. Maehr, J. J. Cox, D. C. Bolin, and M. W. Wichrowski. 2003. Demographic characteristics of reintroduced elk population in Kentucky. Journal of Wildlife Management 67:467-476.

Larkin, J. L., K. J. Alexy, D. C. Bolin, D. S. Maehr, J. J. Cox, M. W. Wichrowski, and N. W. Seward. 2003b. Meningeal worm in a reintroduced elk population in Kentucky. Journal of Wildlife Diseases 39: 588-592.

Larkin, J. L., J. J. Cox, M. W. Wichrowsiki, M. R. Dzialak, and D. S. Maehr. 2004. Influences on release-site fidelity of translocated elk. Restoration Ecology 12:97- 105.

Leckenby, D. A. 1984. Elk use and availability of cover and forage habitat components in the Blue Mountains, northeast Oregon, 1976-82. Wildlife Resources Report No. 14, Oregon Department of Fish and Wildlife.

Levin, S. A. 1992. The problem of pattern and scale in ecology. Ecology. 73: 1943- 1967.

Li, M. 1996. Study of phylogeny among deer in China. Ph.D. Dissertation, East China Normal University. Shanghahi, English Abstract. 80 pp.

Lopez, R. R., N. J. Silvy, B. L Pierce, P. A. Frank, M. T. Wilson, and K. M. Burke. 2004. Population density of the endangered Florida Key Deer. Journal of Wildlife Management 68:570-575.

Lyon, J. L. 1979. Habitat effectiveness for elk as influenced by roads and cover. Journal of Forestry 77: 658-660.

Lyon, J. L. 1983. Road density models describing habitat effectiveness for elk. Journal of Forestry 81: 592-594.

Maehr, D. S., R. Grimes, and J. L. Larkin. 1999. Initiating elk restoration in the east: the Kentucky case study. Proceedings of the Annual Conference of Southeastern Fish and Wildlife Agencies 53:350-363.

McClung, J. A. 1832. Sketches of western adventure. Maysville, KY.

McCullough, D. R., C. E. Olson, Jr., and L. M. Queal. 1969. Progress in large animal census by thermal mapping. Pages 138-147 in P. L. Johnson, editor. Remote sensing in ecology. University of Georgia Press, Athens, GA.

McDonald, H. P., and R. L. Blevins. 1965. Reconnaissance soil survey of fourteen counties in eastern Kentucky. U.S. Department of Agriculture, Washington, D.C.

McFarlan, A. C. 1943. Geology of Kentucky. Waverly Publishing, Baltimore, MD.

Meffe, G. K. and C. R. Carroll. 1997. Principles of conservation biology. Sinauer Associates Inc, Sanderland, MA.

Miller, S. D., E. F. Becher, W. B. Ballard. 1987. Black and brown bear density estimates using modified capture-recapture techniques in Alaska. International Conference on Bear Research and Management. 7: 23-35.

Msoffe, F., F. A. Mturi, V. Galanti, W. Tosi, L. A. Wauters, and G. Tosi. 2007. Comparing data from different survey methods for sustainable wildlife management in hunting areas: the case of Tarangire-Manyara ecosystem, northern Tanzania. European Journal of Wildlife Research 53: 112-124.

Murie, A. 1951. The elk of North America. Stackpole Co., Harrisburg, PA.

Naugle, D. E., J. A. Jenks, and B. J. Kernohan. 1996. Use of thermal infrared sensing to estimate density of white-tailed deer. Wildlife Society Bulletin 24:32-36.

Nelson, J. R., and D. G. Burnell. 1976. Elk-cattle competition in central Washington Pages 172-173 in Range multiple use management. University of Idaho, Moscow, ID.

Newsome, A. E., M. L. Dudzinski, and W. A. Low. 1981. Measuring bias in aerial surveys due to dispersion of animals. ILCA Monographs 4. Narobi, Kenya, Africa.

Nichols, J. D., J. E. Hines, J. R. Sauer, F. W. Fallon, J. E. Fallon, and P. J. Heglund. 2000. A double-observer approach for estimating detection probability and abundance from point counts. The Auk 117: 393-408.

Noss, R. S. 1992. Issues of scale in conservation biology. Pages 240-250 in P. L. Fiedler and S. K. Jain, editors. Conservation biology: The theory and practice of nature conservation, preservation and management. Chapman and Hall, NY.

Noyes, J. H., B. K. Johnson, R. A. Riggs, M. W. Schlegel, and V. L. Coggins. 2000. Assessing aerial survey methods to estimate elk populations: a case study. Wildlife Society Bulletin 28: 636-642.

O’Gara, B. W. 2002. Taxonomy. Pages 3-66 in De. E. Toweill and J. W. Thomas, editors. North American elk: ecology and management. Smithsonian Institute Press, Washington, D. C.

O’Gara, B. W., and R. G. Dundas. 2002. Distribution: past and present. Pages 67-120 in D. E. Toweill and J. W. Thomas, editors. North American elk: ecology and management. Smithsonian Institute Press, Washington, D.C.

Ogutu, J. O., N. Bhola, H. P. Piepho, and R. Reid. 2006. Efficiency of strip- and line- transect surveys of African savanna mammals. Journal of Zoology 269: 149-160.

Olsson, P. M. O., J. J. Cox, J. L. Larkin, D. S. Maehr, P. Widen, and M. W. Wichrowski. 2007. Movement and activity patterns of translocated elk (Cervus elaphus nelsoni) on an active coal mine in Kentucky. Wildlife Biology in Practice 3:1-8.

Osborne, P. E., and B. J. Tigar. 1992. Interpreting bird atlas data using logistic models: an example from Lesotho, Southern Africa. Journal of Applied Ecology 29:55-62.

Overstreet, J. C. 1984. Robinson forest inventory: 1980-1982. University of Kentucky, College of Agriculture, Lexington, KY.

Parker, H. D., Jr. and R. S. Driscoll. 1972. An experiment in deer detection by thermal scanning. Journal of Range Management. 25: 480-481.

Perry, C., and R. Overly. 1976. Impact of roads on big game distribution in portions of the Blue Mountains of Washington. Pages 62-68 in S. R. Hieb, editor. Proceedings of the Elk –Logging-Roads Symposium. University of Idaho, Moscow, ID.

Pollock, K. H., and W. L. Kendall. 1987. Visibility bias in aerial surveys: a review of estimation procedures. Journal of Wildlife Management 51:502-510.

Pollock, K. H., J. D. Nichols, T. R. Simons, G. L. Farnsworth, L. L. Bailey, and J. R. Sauer. 2002. Large scale wildlife monitoring studies: statistical methods for design and analysis. Allen Press, New York, New York.

Potvin, F., and L. Breton. 2005. Testing 2 aerial survey techniques on deer in fenced enclosures, visual double-counts and thermal infrared sensing. Wildlife Society Bulletin 33: 317-325.

Rabe, M. J., S. S. Rosenstock, and J. C. deVos, Jr. 2002. Review of big-game survey methods used by widlilfe agencies of the western United States. Wildlife Society Bulletin 30:46-52.

Ranck, G. W. 1901. Boonesborough. Filson Club Publication No. 16. John P. Morton, Louisville, KY.

Rowland, M. M., M. J. Wisdom, B. K. Johnson, and J. G. Kie. 2000. Elk distribution and modeling in relation to roads. Journal of Wildlife Management 64: 672-684.

Samuel, M. D., E. O. Garton, M. W. Schlegel, and R. G. Carson. 1987. Visibility bias during aerial surveys of elk in north-central Idaho. Journal of Wildlife Management 51:622-630.

Sargeant, G.A., and M.W. Oehler Sr. 2007. Dynamics of newly established elk population. Journal of Wildlife Management 71:1141-1148.

Sawyer, H., R. M. Nielson, F. G. Lindzey, L. Keith, J. H. Powell, and A. A. Abraham. 2007. Habitat selection of Rocky Mountian elk in a nonforested environment. Journal of Wildlife Management 71:868-874.

Schineider, J., D. S. Maehr, K. J. Alexy, J. J. Cox, J. L. Larkin, and B. C. Reeder. 2006. Food habits of reintroduced elk in Southeastern Kentucky. Southeastern Naturalist 5: 535-546.

Shoemaker, H. W. 1939. Vanishing game. Pages 15-34 in A. Ely, H. E. Anthony, and R. M. Carpenter, editors. North American big game. Scribner, NY.

Shoenecher, K. A., F. J. Singer, L. C. Zeignefuss, D. Binkley, and R. S. C. Meneze. 2004. Effects of elk herbivory on vegetation and nitrogen processes. Journal of Wildlife Management 68:837-849.

Skalski, J. R., K. E. Ryding, and J. J. Millspaugh. 2005. Wildlife demography: analysis of sex, age and count data. Elsvier Academic Press. Burlington, MA.

Skovlin, J. M., P. Zager, B. K. Johnson. 2002. Elk habitat selection and evaluation. Pages 531-556 in D. E. Toweill and J. W. Thomas, editors. North American elk: ecology and management. Smithsonian Institute Press, Washington, D. C.

Spellerberg, I. F. 2002. Ecological effects of roads. Science Publishers. Enfield, NH.

Stubblefield, C. H., K. T. Vierling, and M. A. Rumble. 2006. Landscape-scale attributes of elk centers of activity in the central Black Hills of South Dakota. Journal of Wildlife Management 70:1060-1069.

Telesco, R. L., F. T. Van Manen, J. D. Clark, M. E. Cartwright. 2007. Identifying sites for elk restoration in Arkansas. Journal of Wildlife Management 71:1393-1403.

Thompson, S. K. 2002. Sampling 2nd edition. John Wiley & Sons, New York, New York.

Thompson, M. and R. Henderson. 1998. Elk habituation as a credibility challenge for wildlife professionals. Wildlife Society Bulletin 26: 477-483.

Thompson, W. L., G. C. White, and C. Gowan. 1998. Monitoring vertebrate populations. Academic, San Diego, CA.

Thuiller, W., M. B. Araujo, and S. Lavorel. 2004. Do we need land-cover data to model species distributions in Europe? Journal of Biogeography 31:353-361.

Trombulak, S. C. and C. A. Frissell. 2000. Review of ecological effects of roads on terrestrial and aquatic communities. Conservation Biology 14: 18-30.

Udevitz, M. S., D. M. Burn, and M. A. Webber. 2008. Estimation of walrus populations on sea ice with infrared imagery and aerial photography. Marine Mammal Science 24: 57-70.

Ulack, R., K. Raitz, and G. Pauer. 1998. Atlas of Kentucky. University of Kentucky Press, Lexington, KY.

Unsworth, J. W., L. Kuck, and E. O. Garton. 1990. Elk sightability model validation at the National Bison Range, Montana. Wildlife Society Bulletin 18:113-115.

Venables, W. N. and B. D. Ripley. 2002. Modern applied statistics 4th edition. Springer-Verlag, New York, NY.

Wakeling, B. F., D. N. Cagle, and N. H. Witham. 1991. Performance of aerial forward- looking infrared surveys on cattle, elk and turkey in northern Arizona. Arizona Game and Fish Department Wildlife Bulletin Proceedings of the 4th Biennial Conference of Research on the Colorado Plateau 4: 77-88.

Walker, T. 1750. Journal of an exploration in the spring of the year 1750. Accession # F 516.W18 1888, Special Collections, University of Virginia Library, Charlottesville, VA. Little, Brown, and Company. Boston, MA.

Wharton, M. E. and R. W. Barbour. 1991. Bluegrass land and life: land character, plants, and animals of the inner Bluegrass region of Kentucky. University of Kentucky Press, Lexington, KY.

White, G. C. 2008. Closed population estimation models and their extensions in Program MARK. Environmental and Ecological Statistics 15: 89-99.

White, G. C., R. M. Bartmann, L. C. Carpenter, and R. A. Garrot. 1989. Evaluation of aerial transects for estimating mule deer densities. Journal of Wildlife Management 53:625-635.

White, G. C., and R. A. Garrott. 1990. Analysis of wildlife radio-tracking data. Academic Press, San Diego, CA.

Whitmer, G. W., and R. Cogan. 1989. Elk and crop damage in Pennsylvania. Fourth Eastern Wildlife Damage Control Conference. Lincoln, NE.

Wichrowski, M. W. 2001. Activity, movement, and habitat use of a reintroduced elk population in Appalachia. MS Thesis. University of Kentucky. Lexington, KY.

Wichrowski, M. W., D. S. Maehr, J. L. Larkin, J. J. Cox, and M. P. O. Olsson. 2005. Activity and movement of reintroduced elk in southeastern Kentucky. Southeastern Naturalist 4:365-374.

Wiens, J. A. 2002. Predicting species occurrences: progress, problems and prospects. Pages 739-750 in J. M. Scott, P. J. Heglund, M. L. Morrison, J. B. Haufler, M. G. Raphael, W. A. Wall and F. B. Samson, editors. Predicting species occurrences: issues of accuracy and scale. Island Press, Covelo, CA.

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.

Woolf, A. 1973. Population dynamics and remote censusing of a large, captive, white- tailed deer herd. Ph.D. Thesis. Cornell University, Ithaca, NY.

Witmer, G. W. 1990. Re-introduction of elk in the United States. Journal of Pennsylvania Academy of Science. 64: 131-135.

VITA

Date and Place of Birth:

February 4, 1981; Saint Louis, MO

Education:

2003 – B.A. in Wildlife Biology; Colorado State University, Fort Collins, CO

Awards and Honors:

2007 – 2008 Board member of Graduate Student Association

2007 Graduate Student Award for Excellence in Research, Service and Academics

Professional Experience:

September 2006 – present: Elk research assistant for David S. Maehr and John J. Cox at

the University of Kentucky.

May 2006-September 2006: Bison research assistant for the Nature Conservancy.

May 2005-May 2006: Bighorn sheep research assistant for Idaho Fish and Game.

September 2004 – May 2005: Elk research technician for David S. Maehr at the

University of Kentucky and the Kentucky Department of Fish and Wildlife

Resources.

February 2004 – September 2004: Northern bobwhite research assistant at Auburn

University.

September 2002-May 2003: Wildlife technician for the Division of Wildlife Foothills

Research Facility.