FINE SCALE GENETIC STRUCTURE DRIVEN BY HABITAT-DEPENDENT SELECTION IN A MESOCARNIVORE

BY ROBERT C. LONSINGER, B.S.

A thesis submitted to the Graduate School in partial fulfillment of the requirements for the degree of Master of Science

Major Subject: Wildlife Science Minor Subject: Experimental Statistics

New Mexico State University Las Cruces, New Mexico May 2010

Fine scale genetic structure driven by habitat-dependent selection in a mesocarnivore, a thesis prepared by Robert Lonsinger in partial fulfillment of the requirements for the degree, Master of Science, has been approved and accepted by the following:

Linda Lacey Dean of the Graduate School

Gary W. Roemer Chair of the Examining Committee

Date

Committee in Charge: Dr. Gary W. Roemer Dr. William Gould Dr. Caitriana Steele

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ACKNOWLEDGMENTS

I owe many thanks to my graduate advisor and friend, Dr. Gary W. Roemer, whose impact on me has been unsurpassed. His passion for ecology and teaching is contagious. He has provided me with support and guidance, from which I have grown into a better person both personally and professionally. His guidance and friendship has been irreplaceable. I thank the many friends and colleagues who provided invaluable assistance and guidance. Aaron Bueno Cabrera, James Doyle, Aaron Facka, Martin Moses, Missy Powell, James Ward and Bradford Westrich each assisted in the field. Fred Armstrong, Hildy Rieser and Renee West assisted with securing funding and logistical planning. Jack Kincaid and his mules were imperative to our backcountry stints. Funding was provided by the National Park Service and T&E, Inc. Assistantship support was provided by the Department of Fish, Wildlife and Conservation Ecology. Dr. Caiti Steele provided guidance with GIS modeling. Drs. David Daniel and William Gould provided guidance in the statistical analyses. Drs. Roemer, Gould and Steele reviewed and consequently greatly improved this thesis. I would like to thank my wife, Desiree Lonsinger, who endured many nights alone as I chased my ringtail quarry, for her unconditional support, both emotionally and financially and her continued encouragement throughout. My parents instilled in me a love for wild places, for which I am truly grateful.iii

VITA

1979 2002 2003-2004

Born in West Chester, Pennsylvania B.S. Biology (Magna cum Laude) Gannon University, Erie, Pennsylvania Employed seasonally: Telemetry Assistant, USFWS Red Wolf Recovery Field Assistant, Nez Perce Tribe Gray Wolf Recovery Wildlife Technician, Turner Endangered Species Fund Wildlife Assistant, Arizona Game and Fish Department Black-footed Ferret Reintroduction Project Graduate Assistant, Department of Fish, Wildlife and Conservation Ecology, New Mexico State University Sigma Xi The Wildlife Society American Society of Mammalogists

2004-2006

2006-2010

Professional Societies

Technical Publications

Facka AN, Lonsinger RC, Roemer GW (2008) Estimates of population size of Gunnisons prairie dogs in the Aubrey Valley, Arizona based on a new monitoring approach. Final report to the Arizona Game and Fish Department. 26pp. King C, Broecher J, Siniawski A, Lonsinger RC, Pebworth J, Van Pelt WE (2005) Results of the 2004 Black-footed Ferret Release Effort in Aubrey Valley, Arizona. Arizona Game and Fish Department, Nongame and Endangered Wildlife Program Technical Report. 20pp.

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ABSTRACT

FINE SCALE GENETIC STRUCTURE DRIVEN BY HABITAT-DEPENDENT SELECTION IN A MESOCARNIVORE By Robert C. Lonsinger

Master of Science New Mexico State University Las Cruces, New Mexico, 2010 Dr. Gary W. Roemer, Chair

Habitat preferences and prey specializations influence interspecific partitioning and the distribution of species. Heterogeneity among conspecifics and the affinity of individuals to settle in habitats similar to where they were born may, in the absence of physical barriers to dispersal, influence the genetic structure of populations. We aimed to evaluate levels of population genetic structuring in a mesocarnivore, the ringtail (Bassariscus astutus), and hypothesized that fine-scale genetic structure could occur in this species and may be related to habitat-dependent selection that would result in genetically identifiable clusters. We used 15v

microsatellite loci and two programs, STRUCTURE and GENELAND, to assess levels of population genetic structure. Our findings reveal complex hierarchical population genetic structure in absence of physical barriers to dispersal; STRUCTURE and GENELAND identified two and six subpopulations, respectively. Discriminant function analyses were then used to test for differences in habitat among clusters identified a priori by GENELAND. All the DAs proved to be robust, assigning a significantly high proportion (>80%) of individuals to their observed genetic cluster, indicating discriminant power that cannot be explained by random chance alone. Finally, using the ringtail as a short-range dispersal generalist we evaluated the degree of connectivity between two protected areas, Guadalupe Mountains National Park and Carlsbad Caverns National Park. Observed levels of population genetic structure could be differentiated with confidence based exclusively on habitat and landscape characteristics suggesting that this structure is driven by habitat-dependent selection during dispersal and settlement, despite a high degree of connectivity across the study region.

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TABLE OF CONTENTS

LIST OF TABLES ................................................................................ LIST OF FIGURES .............................................................................. ABBREVIATIONS .............................................................................. INTRODUCTION ................................................................................ METHODS ........................................................................................... Study Area ................................................................................ Genetic Sampling ...................................................................... Landscape and Habitat Sampling.............................................. Genetic Analysis ....................................................................... Standard Genetic Measures....................................................... STRUCTURE Analysis ............................................................ GENELAND Analysis .............................................................. Assessment of Habitat-Dependent Genetic Structure ............... RESULTS ............................................................................................. Trapping and Habitat Sampling ................................................ Genetic Sampling and Standard Genetic Measures .................. Bayesian Clustering Analyses................................................... Discriminant Analysis of Habitat-Dependent Genetic Structure ....................................................................................

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DISCUSSION ....................................................................................... Ringtails as a Model for Assessing Fine Scale Genetic Structure .................................................................................... Discriminant Analysis of Habitat-Specific Clustering ............. REFERENCES ..................................................................................... APPENDIX A: R Programming Language Code for Discriminant Analyses, Testing for Violations of Model Assumptions and Randomization Tests .............................................................................

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LIST OF TABLES Table 1. Range, Median, Mean and Standard Deviation of Habitat and Landscape Variables .................................................... 2. Standard Genetic Measures and Tests for HardyWeinberg Equilibrium Across 15 loci ................................ 3. Mean Number of Alleles Per Locus, Observed and Expected Heterozygosity, Fixation Indices and Tests of Heterozygote Deficiency for Clusters Identified by GENELAND ....................................................................... 4. Pairwise FST Matrix for Clusters Identified by GENELAND ....................................................................... 5. Eigenvalues, Proportion of Variation Explained, Wilks and APER for Two Linear Discriminant Analyses............. 6. Scaling Coefficients of Habitat and Landscape Variables for Two Linear Discriminant Analyses............................... Page

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LIST OF FIGURES Figure 1. Study Region and Ringtails Trapping Locations ................ 2. Representation of STRUCTURE Results ........................... 3. Proportion of Individual Ancestry in Each Cluster Identified by STRUCTURE ................................................ 4. Maps of Probability of Population Membership for Each of Six Clusters Identified by GENELAND ........................ 5. Maps of Probability of Population Membership for Each of Three Subdivisions of Cluster 3 ..................................... 6. Distribution of APERs From Randomization Tests of Two Linear and One Quadratic Discriminant Analyses ............. 7. Scatter Plots of Individuals Against the Two Linear Discriminants with the Greatest Discrimination for Two Linear Discriminant Analyses............................................. 8. Three-dimensional Scatter Plot of Individuals Against All Three Linear Discriminants for LDA2 ............................................... 9. Photographs of Habitat Typically Characterizing Each of Four Clusters ....................................................................... Page 5 19

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ABBREVIATIONS

CAVE ................................................ Carlsbad Caverns National Park GUMO ....................................... Guadalupe Mountains National Park GRDL................ Lincoln National Forest Guadalupe Ranger District HWE ..................................................... Hardy-Weinberg Equilibrium MCMC ..................................................... Monte Carlo Markov Chain K ................................................. Number of Distinct Genetic Clusters DA .....................................................................Discriminant Analysis LDA ...................................................... Linear Discriminant Analysis QDA ................................................. Quadratic Discriminant Analysis LD ........................................................................ Linear Discriminant

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INTRODUCTION Individuals vary in their response to the environment they inhabit: individual trees within a species respond differently to fluctuations in light, moisture and nutrients thereby lessening competition and perhaps contributing to high species biodiversity (Clark 2010); experimental manipulation of density in three-spine sticklebacks (Gasterosteus aculeatus) resulted in individuals diversifying their diets to reduce intraspecific competition (Svanbck and Bolnick 2007); and sea otters (Enhydra lutris) differ in their ability to process foods of different size and type, leading to variable foraging strategies and diet specialization that most likely optimizes energetic return (Estes et al. 2003, Tinker et al. 2007). Understanding how diverse individuals contribute to the range of variation characterizing a populations response to a common environment, and to what degree such variation is genetically inherited or culturally transmitted, promises to link individual heterogeneity to population response and community dynamics for a greater understanding of the mechanisms driving ecological patterns (Bolnick et al. 2007). Heritable differences among individuals in foraging or settlement strategies may influence how genes are spatially distributed across the landscape within a species. Tundra/taiga wolves (Canis lupus) are specialist predators on migratory barren-ground caribou (Rangifer tarandus groenlandicus). These wolves are behaviorally, morphologically and genetically distinct from conspecific populations of wolves that inhabit boreal forest regions to the south (Musiani et al. 2007). The1

boreal coniferous forest wolves are territorial, have a much lower incidence of a white coat color morph and differ from tundra wolves at three genetic markers, so much so, that the two ecotypes cluster into genetically diagnosable units. Coyotes (Canis latrans) also exhibit phylogeographic structure that can potentially be explained by individual heterogeneity in dispersal preference for particular habitats (Sacks et al. 2004). The underlying premise is that animals born into a specific habitat type will preferentially search for and settle in a similar habitat when dispersing. Such tendencies would result in a landscape genetic structure that is explained by habitat-specific breaks. Coyote genetic structure determined using genetic clustering approaches was concordant with specific bioregions and supportive of habitat-specific affinities in dispersal patterns resulting in habitat-dependent selection (Sacks et al. 2004). Each of these studies involved a generalist, highly vagile carnivore whose genetic structure was assessed across an expansive landscape. If individuals differ in their potential to settle in habitats where they were born or have learned to forage on specific prey that results in dietary specialization that could lead to genetic distinctiveness then the process should be independent of scale; these processes should operate at fine scales as long as habitat heterogeneity occurs within the pertinent scale.

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Ringtails (Bassariscus astutus) are small (~1kg), nocturnal carnivores in the Family Procyonidae. The small size of ringtails suggests they have relatively limited vagility, making them an ideal model carnivore to assess more fine-scale genetic structure and whether such structure may be explained by preferences for specific habitat types. Ranging from southern Mexico to southern Oregon, ringtails are widespread across much of the southwestern United States (Poglayen-Neuwall and Toweill 1988). Ringtails are typically associated with steep rocky terrain, canyons, or mountain slopes (Trapp 1972, Callas 1987, Ackerson and Harveson 2006), but they are capable of exploiting virtually all habitat types within their range (Lacy 1983, Poglayen-Neuwall and Toweill 1988). In the Edwards Plateau region of western Texas, nearly every type of habitat available to ringtails was occupied (Taylor 1954). Despite their ability to exploit different habitats, ringtails do not necessarily use available habitats proportionally (Lacy 1983, Yarchin 1990, Ackerson 2001), suggesting that some habitats may be preferred over others and that habitat structure may play an important role in their distribution. Ringtail denning and home range size differs both within and between the sexes. Mean denning range varied from 40 to 278 ha for males and 20 to 124 ha for females, with average distances traveled between consecutively used dens ranging from 344 to 1080 m and 284 to 628 m, respectively (Toweill and Teer 1981, Callas 1987). Home ranges reported ranged from 22.7 to 139 ha for males and 16.9 and 129 ha for females (Trapp 1978, Yarchin 1990).

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Here, we use the habitat generalist ringtail as a model small carnivore with limited vagility to, (1) evaluate levels of hierarchical genetic population structuring and (2) test for patterns of habitat-dependent clustering between genetically differentiated subpopulations. To assess population structure and connectivity, we used two Bayesian clustering techniques, implemented in the programs STRUCTURE and GENELAND, which determine the most likely number of genetically distinct subpopulations based on genetic data (Pritchard et al. 2000, Falush et al. 2003, Guillot et al. 2005). We then employed a discriminant function analysis to test for differences in habitat among clusters identified a priori by GENELAND that support the hypothesis of habitat-specific clustering. Finally, a corollary objective was to evaluate the degree of connectivity between two protected areas, Guadalupe Mountains National Park and Carlsbad Caverns National Park, using the ringtail as a short-range dispersal generalist.

METHODS Study Area We live-trapped ringtails in the Guadalupe Mountains of southern New Mexico and west Texas and focused on areas both within and between Carlsbad Caverns (CAVE) and Guadalupe Mountains (GUMO) National Parks. The area between the two parks is the Lincoln National Forests Guadalupe Ranger District.4

The Guadalupe Mountains extend from west TX northeast into southern NM to the eastern border of CAVE. The entire mountain range is approximately 110 km long and 25 km wide (Hill 1996; Figure 1). Part of an ancient fossilized reef formed during the Permian period, these mountains rise dramatically from the floor of the Delaware Basin resulting in complex topography and steep and abrupt cliff faces along its entire length. Elevations range from 1100 m in both CAVE and GUMO, to 1900 m in CAVE and 2667 m in GUMO at the summit of Guadalupe Peak.

Figure 1. Guadalupe Mountains National Park (GUMO), Carlsbad Caverns National Park (CAVE) and the Guadalupe Ranger District of the Lincoln National Forest (GRDL) are located in southeastern NM and western TX. The black circles represent locations where ringtails were successfully captured.5

The Guadalupe Mountains offer a unique environment to look at landscape connectivity and fine scale population genetic structure because the regions convoluted topography, range of elevations and edaphic interfaces provide for an array of different habitat types juxtaposed within a small geographic area (Northington and Burgess 1979). The lower elevations of CAVE and GUMO are uniquely located where the Chihuahuan Desert transitions to plains grasslands incorporating elements of both into the region (Northington and Burgess 1979). Higher elevations support oak-juniper-pion woodlands and coniferous forests, all of which are incised by both permanent and ephemeral riparian zones; transitional slopes incorporate characteristics of many habitats (Powell 1998). Genetic Sampling Ringtails were trapped using standard procedures (e.g., Roemer et al. 2000). Trapping took place from May 2006 to April 2009, inclusive. Depending on the transect size, from 6 to 29 carnivore live-traps (30 x 11 x 12; Safeguard, New Holland, PA 17557) were used. Traps were set approximately 250 m apart along transects positioned adjacent to roads, trails and washes for up to 10 nights (range = 2-10, mean = 4.54, SD = 1.69). Traps were baited with dry cat food and a scent bait, either loganberry paste or sardines; the scent bait was also placed outside the trap within one meter of the entrance. Traps were checked daily at sunrise. Ringtails were anesthetized initially

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using a solution of medetomidine hydrochloride (50 g/kg) and ketamine hydrochloride (5 mg/kg) injected intramuscularly (Orion Corporation, Espoo, Finland). If sedation was incomplete, additional doses of 0.05 ml of the above were used in sequence. After processing, an antagonist to the medetomidine, antisedan hydrochloride, was administered (~ 200 250 g/kg; Orion Corporation, Espoo, Finland). Processing included the collection of a snip of ear tissue for genetic analysis, up to 10 ml of blood for disease assay, hair and standard physical measures. Individuals were marked either with the subcutaneous insertion of a Passive Integrated Transponder (PIT) tag (Biomark, Inc., Boise, ID 83702) or with an ear tag (National Band and Tag Company, Newport, KY 41072) and allowed to recover from anesthesia in the safety of the trap before being released. All animals captured were handled and released without complication in accordance with procedures sanctioned by the NMSU Institutional Animal Care and Use Committee (Permit # 2006 006). Landscape and Habitat Sampling Landscape and habitat characteristics were recorded at each trap location. Landscape features included slope, aspect, elevation, landform (i.e., valley, canyon, ridge, etc.) and land cover. Slope, aspect and elevation were measured with a clinometer, compass and Global Positioning System, respectively. Land cover was determined from existing vegetation maps created by the NM SWReGAP and TX GAP projects using ArcGIS (ESRI, Redlands, CA 92373). Vegetation classifications for land cover differed, with the TX GAP vegetation layer containing 21 land cover7

types and the NM SWReGAP layer containing 52 land cover types. The two layers were condensed into a single layer by matching land cover types based on their descriptions. The resulting layer included five major (grassland, shrubland, riparian, woodland, forest) and five minor (bare soil, sand flats, dunes with sparse vegetation, consolidated rock with sparse vegetation, cropland) cover types. This generalization of habitat types removed some of the uncertainty typically associated with remotely sensed data. Habitat characteristics were also measured using a spoke design centered on each trapping location. The three spokes (transects) were 50 m in length, with equal angles (120) between each transect. The first angle was selected randomly. At 5 m intervals along each transect the plant species or microhabitat feature (i.e., bare soil, rock outcrop) that intercepted the line was recorded. For each site, the vegetative form recorded was characterized (tree, shrub, subshrub, forb, or grass) providing additional information on land cover. Genetic Analysis Tissue and blood samples collected for genetic analysis were stored in a -80C freezer prior to DNA extraction. A total of 153 ringtails were genotyped for fifteen tetranucleotide microsatellite markers; details regarding sample extraction, amplification and scoring can be found in Schweizer et al. (2009).

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Standard Genetic Measures We calculated observed and expected levels of heterozygosity across all loci with the program SPAGEDI 1.3 (Hardy and Vekemans 2002). We calculated FIS values (Weir and Cockerham 1984) and tested for departure from Hardy-Weinberg equilibrium (HWE) across all loci using a Monte Carlo Markov Chain (MCMC) method as implemented in GENEPOP 4.0.10 (Raymond and Rousset 1995). Dememorization, number of batches and iterations per batch were increased to 10000, 500 and 8000, respectively, to achieve standard errors of