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
ii
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.
iv
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.
vi
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
....................................................................................
ix x xi 1 4 4 6 7 8 9 9 11 12 16 16 16 19
24
vii
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
.............................................................................
31
34 37 40
46
viii
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
17
18
22
22
24
27
ix
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
20
21
23
25
28
29
30
x
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
xi
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.
2
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).
3
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
6
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).
8
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