CONSERVATION GENETICS OF BLACK BEARS IN ARIZONA AND NORTHERN MÉXICO By

CONSERVATION GENETICS OF BLACK BEARS IN ARIZONA AND NORTHERN MÉXICO  By
CONSERVATION GENETICS OF BLACK BEARS IN ARIZONA AND NORTHERN
MÉXICO
By
Angela Cora Varas-Nelson
___________________________________
A Dissertation Submitted to the Faculty of the
SCHOOL OF NATURAL RESOURCES AND THE ENVIRONMENT
In partial Fulfillment of the Requirements
For the Degree of
DOCTOR OF PHILOSOPHY
WITH A MAJOR IN WILDLIFE AND FISHERIES SCIENCE
In the Graduate College
THE UNIVERSITY OF ARIZONA
2010
2
THE UNIVERSITY OF ARIZONA
GRADUATE COLLEGE
As members of the Dissertation Committee, we certify that we have read the dissertation
prepared by Angela Cora Varas-Nelson
entitled Conservation genetics of back bears in Arizona and northern México
and recommend that it be accepted as fulfilling the dissertation requirement for the Degree of
Doctor of Philosophy
____________________________________________________________Date: 12/14/09
Melanie Culver
____________________________________________________________Date: 12/14/09
Paul R. Krausman
___________________________________________________________Date: 12/14/09
William Shaw
Final approval and acceptance of this dissertation is contingent upon the candidate's
submission of the final copies of the dissertation to the Graduate College.
I hereby certify that I have read this dissertation prepared under my direction and recommend
that it be accepted as fulfilling the dissertation requirement.
____________________________________________________________Date: 12/14/09
Dissertation Director: Melanie Culver
____________________________________________________________Date: 12/14/09
Dissertation Director: Paul R. Krausman
3
STATEMENT BY AUTHOR
This dissertation has been submitted in partial fulfillment of requirements for an
advance degree at the University of Arizona and is deposited in the University Library to
be made available to borrowers under rules of the Library.
Brief quotations from this dissertation are allowable without special permission,
provided that accurate acknowledgment of source is made. Request for permission for
extended quotation from or reproduction of this manuscript in whole or in part may be
granted by the head of the major department or the Dean of the Graduate College when in
his or her judgment the proposed use of the material is in the interest of scholarship. In all
other instances, however, permission must be obtained from the author.
SIGNED: _________________________________________
Angela Cora Varas-Nelson
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ACKNOWLEDGEMENTS
Many people helped with the research for my degree. I thank to the United States
Geological Survey (USGS), The Arizona Cooperative Fish and Wildlife Research Unit,
and the Arizona Game and Fish Department for providing the finantial support for this
reseach. Arizona Game and Fish Department provided hunters names and address for
sample collection. I thank M. Cirrett for his support collecting hair samples. C. LopézGonzalez collected bear scats; his students were instrumental in the sample collection in
México. Carlos also provided ideas for this research project. I also thank all the Arizona
hunters that sent samples for this research. S. Bonar, C. Conway and C. Yde, provided
their support and enthusiasm. The Minority Training Program undergraduates C.
Contreras, J. Camarena, helped with laboratory work. J. Ramirez helped with laboratory
and fieldwork. She has been an incredible friend and a great person to work with.
Technical support was provided by UAGC laboratory at the University of
Arizona; M. Kaplan, T. Edwards, Hans-Werner Herrmann, S. Miller and G. Nelson. I
thank D. Swann and other personel at the Saguaro National Park for their help with scat
and hair collection and the training of J. Camarena, a minority training student that was
part with this project. R. Thompson provided friendship and contagious enthusiasm for
carnivore conservation.
Many faculty and staff at the University of Arizona contributed to this research.
A. Honaman, T. Edwards, A. Quijada, P. Sherman, M. Reed, and L. Lopez-Hoffman. I
thank the faculty members who have served on my committee: M. Hammer, R.
Robichaux, M. Culver, W. Shaw, and P. R. Krausman. M. Culver and P. R. Krausman
provided guidance and encouragement through out my tenure.
I am very grateful for the support of my laboratory mates: A. Munguia, K. Peltz,
R. Fitak, S. Amirsultan, A. Nadiu, J. Ramirez, A. Carlson, S. Carrillo, L. Haynes. My
friends, and fellow graduate students, A. Marcias-Duarte, K. Monroe, M. Moreno, A.
Cinty, G. Soria, C. Chiquete, M. Altricter, X. Bazurto, R. Cudney and J. Marshall.
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DEDICATION
To my husband Gavin Nelson, our kids Juan Carlos, Daniel Ricardo and Cora Sabrina
and to my parents Cora Cevallos de Varas and Roberto Varas, and Barbara and Ernest
Nelson.
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TABLE OF CONTENTS
ABSTRACT........................................................................................................................ 7
INTRODUCTION .............................................................................................................. 8
PRESENT STUDY ........................................................................................................... 15
Objectives ......................................................................................................... 15
Study area.......................................................................................................... 15
Molecular Markers ............................................................................................ 18
CONCLUSION ................................................................................................................. 19
REFERENCES ................................................................................................................. 22
APPENDIX A.
BLACK BEAR GENETIC LITERATURE REVIEW ..................................................... 28
APPENDIX B.
PHYLOGEOGRAPHY AND CONSERVATION IMPLICATION OF BLACK BEARS
IN THE SKY ISLANDS OF ARIZONA AND NORTHERN MÉXICO. ....................... 58
APPENDIX C.
GENETIC STRUCTURE OF THE AMERICAN BLACK BEAR IN THE SKY
ISLANDS, ARIZONA AND NORTHERN MÉXICO .................................................. 110
APPENDIX D.
DENSITY, POPULATION SIZE AND CONSERVATION OF BLACK BEAR IN
SIERRA SAN LUIS, SONORA, MÉXICO ................................................................... 162
7
ABSTRACT
Because American black bears (Ursus americanus) are an important game species
in Arizona and are endangered in México, an understanding of the population structure,
gene flow, and connectivity are important for effective management. Black bears inhabit
coniferous and broadleaf deciduous woodlands in southern Arizona and northern México,
usually in sky islands (sky islands are mountains that rise from the desert and are isolated
from each other). Because a single sky island is too small to support a viable bear
population, black bears move through desert lowlands to reach other sky islands. My
objective was to assess genetic structure, connectivity, and conservation implications for
sky island black bears in southern Arizona and northern México. I addresses 4
components of bear ecology and genetics: a literature review of genetic information
available for black bears in North America; the use of 2 mitochondrial DNA genes
(Control Region and ATP synthase protein 8) to study the phylogenetic relationship of
black bears from the sky islands of southern Arizona and northern México relative to all
North America; the use of 10 microsatellite loci to detect the current genetic structure of
black bears in the sky islands in Arizona and northern México; and the use of noninvasive samples collected from the field to determine bear density and population size
for black bear in Sierra San Luis, Sonora, México. These studies provide information that
can be used by biologists, land managers, and others to assist in the conservation of black
bears and their habitat.
8
INTRODUCTION
American black bears (Ursus americanus) were first described in Arizona in the
early 1800s. From the 1930s to the 1950s black bears were classified as predators
(Hoffmeister 1986) and some populations were nearly extirpated. From 1958 to 1968
black bears were classified as small game and protected. In 1968 their status was
changed to big game (Hoffmeister 1986). From the 1950s until 2001, the black bear
population in Arizona was thought to be stable (N = 2,500-3,500) (Cunningham et al.
2001, McCracken et al. 1995).
Reducing bear numbers is detrimental to their long-term survival in Arizona
(LeCount and Yarchin 1990). Bear populations that are reduced in numbers,
(intentionally or not) may take years to recover (Miller 1990) due to their long life span
(> 20 years), delayed reproductive maturity (first breeding at 3-7 years), a low
reproductive rate (2 cubs every 2 to 6 years) (LeCount 1982a, 1983), and energetically
demanding parental investment (Kolinosky 1990). Consequently, populations recover
slowly.
Concerns for black bears in Arizona include harvest numbers, anthropogenic use
of land that could threaten population connectivity, and inaccurate population estimates.
From 1964 to 1989 a mean of 239 bears were harvested annually (6.8% of the maximum
population estimate), which increased to 368 bears in 2001 (10.5% of the maximum
population estimate).
Since the 1980s, the Arizona black bear season length has been based on the
number of females harvested within a given game management unit. When the harvest
9
objective of 5% of total females is reached the bear season is closed in that game
management unit. However, in the 1970s > 5% females in some populations were
harvested (R. Olding and T. Waddell, Arizona Game and Fish Department, unpublished
data). Also, the black bear population in east-central Arizona was over harvested in the
1980s with 15% adult annual mortality affecting recruitment by reducing the breeding
age of females and, therefore, reducing the number of cubs available for replacement
(LeCount 1982 b). Additionally, liberal hunting seasons in the sky islands (i.e., isolated
mountains surrounded by desert and grasslands) combined with limited habitat available
produced low population numbers in the Coronado National Forest (R. Olding and T.
Waddell, unpublished data).
In México, black bears are endangered (Servheen et al. 1999). There are records
of black bears in Sonora, Chihuahua, Coahuila, Nuevo León, Zacatecas, and Durango
(Sierra-Corona et al. 2005). However, the published information is from populations in
the northern Sonora and Coahuila (Doan-Crider and Hellgren 1996, Sierra-Corona et al.
2005, Onorato et al. 2007), and bear occurrence is not well documented in other parts of
México. Although little scientific information is available for México, it is known that
black bears have lost ≤ 30% of their historical range (Pelton et al. 1997). The main
factors threatening black bear survival in northern México are habitat loss and poaching
(MacCracken et al. 1995); in addition the poor economy prevents enforcement of
poaching and habitat destruction regulations. The lack of information about migration
patterns and connectivity among black bear populations within the sky island region of
México and neighboring Arizona further hampers potential conservation and
10
management efforts.
Primary habitats for black bears are coniferous and broadleaf deciduous
woodlands in southern Arizona and northern México. These habitats occur in mountain
sky islands. These sky islands rise from the desert and are isolated from each other, and
since a single sky island is too small to support a viable black bear population, black
bears move through the desert lowlands to other sky islands (LeCount and Yarching
1990).
Sky islands of the desert Southwest have produced population isolation in many
species occupying the region resulting in morphological and genetic differentiation of
flora and fauna. Morphological diversity has been demonstrated in lemon lily (Lillium
parryi) (Linhart and Premoli 1993), snails (Sonorella sp.) (Bequaert and Miller 1973),
beetles (Scamphontus petersi) (Ball 1966), the jumping spider (Habronattus pugilis)
(Maddison and McMahon 2000), mountain spiny lizards (Sceloporus jarrovii) (Stebbins
1985), canyon treefrog (Hyla arenicolor) (Barber 1999), and the Mount Graham red
squirrel (Tamasciuris hudsonicus grahamensis) (Riddle et al. 1992).
Molecular genetic studies have been used to investigate the mechanisms of sky
island isolation and how they affect population structure of the species that inhabit them
(Dixon et al. 2007). Genetic differentiation has been studied in terms of isolation due to
biogeographic barriers, distance to the source of migrants, and sky island size with
respect to population structure. Also, genetic analyses have been useful to estimate
whether time of speciation is concordant with island formation. Molecular studies have
not been reported in the literature for large mammals in the sky islands. And there is no
11
knowledge of how sky island size, configuration, distance, and proximity to barriers
affects connectivity (gene flow) of large mammals such as black bears.
Factors such as distance, which influence the dispersal of plants, insects, reptiles,
and small mammals, could have little or no effect on black bears due to their capacity for
long distance dispersal up to 230 km. Also, barriers such as rivers or patches of desert
that affect smaller species could have little effect on bear movement. However, a
combination of distance and unsuitable habitat (e.g., human use of desert lowlands
including housing developments in the valleys between mountain ranges, recreational use
of the land, agricultural land use, summer home developments, and highways) may cause
significant barriers for black bears (Schenk 1996) and disrupt connectivity among bear
populations.
Bear populations are difficult to inventory and monitor because the animals occur
in low densities and are secretive by nature. A variety of techniques have been used to
obtain population numbers, density, and movement estimates for bears. Direct
observation can be used to estimate small population sizes and trends as with the brown
bears (Ursus arctos) in Glacier, and Yellowstone National Parks (Hayward 1989).
Capture-mark-recapture (Kolenosky 1986) and radio telemetry (Vashon et al. 2003) have
been the most commonly used techniques. Recently, molecular markers in combination
with non-invasive sampling techniques have provided an inexpensive and efficient
method to resolve relationships at the level of species and population.
Molecular techniques have been informative to delineate evolutionary
relationships among the 8 species of bears (Ursidae), where paleontological and
12
morphological data have revealed inconclusive results. The giant panda (Ailuropoda
melanoleuca) is the most ancestral followed by the spectacled bear (Tremarctos ornatus)
determined using 6 gene segments of the mitochondrial DNA (Waits et al. 1999). The
American and Asiatic black bear (Ursus thibetanus) are closely related, and the youngest
group includes brown and polar bears (Ursus maritimus).
During the Pleistocene, the most recent glaciation, deciduous forests occurred
mainly in eastern and western refugia in North America. Mitochondrial DNA studies of
black bears have confirmed the existence of these 2 refugia by identifying 2 major groups
(i.e., clades): one east of the Rocky Mountains (including the southern Rocky
Mountains), and another west of the Rocky Mountains (California and southern British
Columbia), with an area of contact where both clades are present in northern British
Columbia and Alberta (Wooding and Ward 1997). This suggests that, at least in part, the
extant patterns of diversity in black bears is due to post Pleistocene colonization followed
by woodlands retreating to higher elevations in the southwestern U.S.
Analysis of population genetic structure in black bears has identified evolutionary
history based on the level of genetic differentiation among populations (Peacock et al.
2007, Robinson 2007). Genetic structure of black bear populations has been examined in
several studies using microsatellite DNA (highly variable regions of nuclear DNA that
are not usually contained within genes) fragment analysis and mitochondrial DNA
(maternally inherited extra-nuclear DNA) sequence analysis.
Microsatellite DNA variation has been used in black bears to understand how
population fragmentation affects genetic structure of populations. For example, black
13
bears on Newfoundland Island, Canada had lower levels of genetic variation than
mainland populations (Paetkau and Strobeck 1994). In Florida, black bears have currently
≥ 8 genetically distinct subpopulations from what once was a large single population
(Dixon et al. 2007). Black bears in Luisiana showed a significant population
differentiation between the coastal and inland populations, and it was determined the
genetic integrity of the coastal population needed protection (Triant et al. 2004).
Microsatellite DNA loci were useful to detect the origins of black bear
populations after reintroduction programs from Minnesota and Manitoba to Arkansas and
Louisiana. Bears from Ozark and Ouachita in Arkansas and inland Louisiana descended
from reintroduced bears; whereas, bears from southeastern Arkansas and coastal
Louisiana were genetically unique and isolated populations (Csiki et al. 2003).
Mitochondrial DNA is also useful to detect population isolation; for example, black bears
in the Kenai Peninsula and adjacent coastal populations are not closely related, showing
the lack of connectivity between the peninsula and the coastal populations (Robinson et
al. 2007). Finally, black bear in the Alexander Archipelago and the mainland of southeast
Alaska, confirmed the lack of connectivity among bear populations of the islands with the
continent (Peacock et al. 2007, Stone and Cook 2000). In contrast, a lack of
differentiation has been observed in 1 bear study. Black bears from northern Sierra Madre
Oriental in México and western Texas show connectivity between them via desert
corridors (Onorato et al. 2007).
Genetic analyses have been useful in examining phylogenetic relationships and
level of connectivity among bear populations. The earliest studies using allozymes were
14
mostly uninformative due to the little genetic variability detected. Microsatellite loci and
mtDNA control region sequences, used more recently in black bear population studies,
have revealed substantial genetic variation. Genetic data has been used to develop
augmentation plans in conservation planning and bear management (Waits et al. 2001).
15
PRESENT STUDY
The following is a summary of the objectives, methods and the most important
findings of these papers. Complete details on methods, results, and conclusions of this
study are presented in the papers appended to this dissertation.
Objectives
My objectives for this research included the use of 2 mitochondrial DNA genes
(Control Region and ATP synthase protein 8) to study the phylogenetic relationship of
black bears from the sky islands of southern Arizona and northern México relative to all
North America, to determine the phylogenetic relationships of black bears among the sky
islands of Arizona and northern México. Also, using 12 microsatellite loci, my objective
was to detect the current genetic structure of black bears in the sky islands in Arizona and
northern México; and finally, to determine bear density and population size for black bear
in Sierra San Luis, Sonora, México.
Study area
This research occurred in the State of Arizona and in the northern part of México.
In Arizona, our study sites included: the Huachuca, Peloncillo, Pinaleno, Chiricahua,
Catalina, and Rincon Mountains, and a continuous habitat range in northern Arizona,
which includes the Mazatzal Mountains (i.e., Four Peaks and Mount Ord), Nutrioso
Mountains, and Apache National Forest. In northern México, our study sites included
Sierra Los Ajos, Sierra San Luis, and Sierra El Nido. These mountains are part of a group
of approximately 40 mountains between the Mongollon Rim and the Sierra Madre
16
Occidental (Warshall 1995). These sky islands were formed from continental rifting that
started about 13 million years ago. The tallest peak is Mount Graham in the Pinalenos
3,246 m above the sea level (a.s.l.). Distances between the valleys and the peaks are
378.8 to 2,045 m a.s.l.
Plant species are similar across sky islands in Arizona and México in the Sonoran
Desert Sky islands, and includes pinyon (Pinus spp.), juniper (Juniperus spp.), pine-oak
(Quercus spp.) forests, oak woodland with second growth, open low forest, mesquite
Prosopis spp.) grasslands, riparian forest, and chaparral ecosystems (Palacio-Prieto et al.
2000). In the Sonoran Desert, sky island plant species include: southwestern white pine
(Pinus strobiformis), western yellow pine (P. ponderosa), alder (Alnus tenuiflolia),
Rocky Mountain fir (Abies Lasiocarpa), Engelmann spruce (Pices engelmanni), netleaf
oak (Quercus rugosa), silverleaf oak (Q. hypoleucoides), Rocky Mountain white oak (Q.
gambelii), Arizona white Oak (Q. Arizonica), basketgrass (Nolina microcarpa), Rocky
Mountain maple (Acer glabrum), bigtooth maple (A. grandidentatum), alligator juniper
(Juniperus deppeana), desert agave (Agave palmeri), Arizona smooth cypress (Cypressus
arisonica), among others (Wallmo 1950, Bowers and McLaughlin 1987).
In the Chihuahuan Desert, sky islands plant species include: Mexican pinyon
(Pinus cembroides), emory oak (Quercus emoryi), black oak (Q. mcvaughii), silver-leaf
oak (Q. hypoleucoides), oneseed juniper (Juniperus monosperma), and Mexican
manzanita (Arctostaphyllos pungens), blue grama (Bouteloua gracilis), sideoats grama
(B. curtipendula), annual muhly (Muhlenbergia minutissima), and wolfstail (Lycurus
phleoides) (Shreve 1939, LeSueur 1945, Villarreal and Yoolt 2008).
17
The Sierra Los Ajos, located east of Cananea, Sonora, are situated between
México’s Sierra Madre Occidental and the Rocky Mountain region of the western United
States. Elevations of Sierra Los Ajos vary from 1,050 to 2,625 m. Biological and floristic
diversity is high, related in part to its unique geographic location (Fishbein et al. 1994).
Black bear hair samples were collected in the northern portion of the protected AjosBavispe National Forest and Wildlife Refuge.
In the Sierra San Luis, our study was in El Pinito ranch, which is located in the
Sierra San Luis, Sonora, between 108° 56’ 46’’ N latitude and 31° 11’ 49’’ W longitude
(Sierra-Corona et al. 2005). In the Sierra el Nido, scat samples were collected in Rancho
Santa Monica located (29° 33' 0 N, 106° 47' 60 W), with elevation ranging from 2,500 to
3,040 m.
Land use in the Arizona Sky islands ecosystem includes urban and farming in the
valleys with species such as cotton, alfalfa, citrus fruits, melons, and head lettuce. Other
agricultural activities across the ecosystem include cattle and sheep raising. In the
mountains, a large part is owned by the United States Forest Service, and is used for
forestry, skiing, hunting, camping, fishing, rock climbing, and car-based tourism. There
are also some privately owned areas, mostly used for summer homes. In México, land use
patterns are a matrix of large vs. small parcels of private ownership mixed with protected
areas, for example, Sierra Los Ajos is part of the Ajos-Bavispe National Forest and
Wildlife Refuge.
The weather conditions in the Arizona sky islands vary depending on the altitude.
For example, in the Mazatzal Mountains, temperatures range from 4 to 20 ˚C and rainfall
18
is from 250 to 635 mm annually. In the Pinaleno Mountains the temperature ranges from
-13 to 44 ˚C. In the Chiricahua Mountains, temperature ranges from 5.7 to 14.1 ˚C; with
a mean precipitation of 795 mm. In the Huachuca Mountains the temperature ranges
from 15 to 33 ˚C with a mean precipitation of 3,750 mm.
On the Mexican side, Sierra Los Ajos and Sierra San Luis have an annual
temperature range from 8 to 18 ˚C and an annual mean precipitation of 2,200 mm. In
Sierra El Nido, the annual rain precipitation is 400 mm and the average annual
temperatures range from 12 to 14 ˚C.
Molecular Markers
We amplified and sequence two regions of the mitochondrial DNA genome.
A 360 base pair (bp) fragment of the mitochondrial DNA control region (mtDNACR)
(Varas et al 2006), and a 224 bp fragment from the ATP synthase subunit 8 (ATP8),
which included 54 bp of the tRNA-Lys and 170 bp of the ATP8 region. Primer
information and polymerase chain reaction (PCR) conditions as well as genetic
identification and phylogenetic relationships are outlined in Appendix B.
We amplified 12 ursid microsatellite loci: G10B, G10H, G10L, G10M, G1A,
G10J, G1D, G10O, CXX20, G10X, Mu59, and Mu50 (Paetkau and Strobeck 1994;
1995, Paetkau et al. 1998b, Woods et al. 1999). Primer information and polymerase
chain reaction (PCR) conditions and population genetic analyses are presented in
Appendix C.
19
CONCLUSION
The analysis of genetic diversity within species is essential to understand
evolutionary processes at the species and population level. The use of genetic tools has
become increasingly central in wildlife research and management. Molecular markers are
being use to answer critical questions in the conservation of wildlife species. Black bears
live in sky islands within a desert matrix. Molecular markers such as mitochondrial and
microsatellite DNA are important to understand the evolutionary pattern and gene flow
among black bears in the sky island region.
Our mitochondrial DNA data shows that black bears from Arizona are closely
related to black bears in western New México and along the eastern portion of the Rocky
Mountains. This is reasonable because there is a geographical connection between
northern Arizona (Mogollon Rim) and the southern Rocky Mountains. Our results
indicate the Arizona populations are not closely related to populations further east than
western New México. This pattern of diversity likely represents historical dispersal since
the last glaciation.
Black bears in the sky islands in Arizona and in the Sierra Madre Occidental in
northern México are closely related. Mitochondrial DNA and microsatellites show the
Arizona sky islands and Sierra Madre Occidental populations share mitochondrial DNA
haplotypes and many microsatellites alleles. Therefore, we could consider the sky island
region in Arizona and northern México as a connected population for management and
conservation.
Microsatellite data shows a moderate level of gene flow among the sky islands in
20
Arizona and Sierra Madre Occidental in México, with the highest FST value being 0.18
between the populations in the Sierra Madre Occidental and the population in the north of
the Mogollon Rim in Arizona (the populations separated by the longest distance). These
results suggest that the primary factor influencing gene flow among bear populations is
the distance between populations. Therefore, neighboring populations are less
differentiated than distant ones.
Black bears in the major Mexican mountain ranges, Sierra Madre Oriental
(Coahuila/Sierra el Burro) (Onorato et al. 2007), and the Sierra Madre Occidental (Sierra
San Luis) (Varas et al. 2006), are not closely related. It seems likely that these mountains
ranges were historically separate and have not experienced significant gene flow since the
last glaciation. Consequently, there are ≥ 2 different black bear lineages occurring in
México.
Our results indicate a connected population of black bears in the sky islands in
Arizona and the Sierra Madre Occidental in northern México. Black bears are moving
among these sky islands within and between the U.S. and México. Management options
need to consider genetic differentiation and levels of gene flow among populations, and
strive to maintain genetic variability of populations to promote long-term survival of
wildlife. The increased militarization in the border may be disrupting and reducing the
movement of bears across the border. Also, the addition of an impermeable fence across
the border would stop bear migration between the U.S. and México. Arizona populations
may be the only source of migrants to the endangered black bear populations in Sonora,
México. Habitat connectivity between Texas and México has allowed dispersal between
21
populations in Coahuila, México (source population) and Texas (subpopulations) (DoanCrider and Hellgren 1996, Onorato at al. 2007). As a result this enhances the long-term
viability of the metapopulation in Big Bend National Park, Texas provided that the border
remains open to bear migration. Two-way movement between source populations and
subpopulations is vital to the survival of the black bear in the desert Southwest.
Challenges are huge in terms of preserving the connectivity among black bear
populations in the sky islands. This connectivity is vital so that large mammals in general
have the genetic variability they need to adapt to rapidly changing environments.
International cooperation, binational agreements, and education of the public are the keys
to maintaining the rich biodiversity we have in this unique sky island ecosystem.
22
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28
APPENDIX A
BLACK BEAR GENETIC LITERATURE REVIEW
Cora Varas
School of Natural Resources
University of Arizona
P.O. Box 210043
Tucson, Arizona 85721-0043
Phone 520 621 2161; Fax 520 621 8801
Email [email protected]
RH: Varas et al. • Black bear genetics. Review
CORA VARAS, School of Natural Resources and the Environment, University of
Arizona, Tucson, Arizona, 85721, USA
PAUL R. KRAUSMAN, Boone and Crockett Program in Wildlife Conservation.
University of Montana, Missoula, Montana 59812, USA
MELANIE CULVER, School of Natural Resources and the Environment, University of
Arizona, Tucson, Arizona, 85721, USA
Abstract, The analysis of genetic diversity within species is essential to understand
evolutionary processes at the population level. It has become increasingly central in
wildlife research and management, as demonstrated by the increased number of papers
29
using molecular markers, and of genetics to answer critical questions in the conservation
of wildlife species. We describe the most common genetic techniques and how they have
been used to understand black bear evolution, population genetics, and ecology. In the
1990s, allozymes were the molecular markers of choice to understand genetic variability,
and later mtDNA markers took their place and were used to address questions from
evolutionary patterns to distribution of species. Today, microsatellite DNA markers are
highly variable markers that have been used to produce information about closely related
populations. The information provided by molecular markers has been crucial in the
management of black bears.
KEYWORDS black bears, North America, allozymes, mitochondrial DNA,
microsatellites, literature review, Ursus americanus.
URSUS: 00(0): 000-000, 20XX
________________________________________________________________________
Genetic variability is the raw material that species need to allow adaptation to
changing environments. Therefore, measuring levels of genetic variability is an important
aspect of conservation of wildlife species. Genetic variability is reduced when a
population becomes isolated from gene flow, when the number of individuals becomes
too low in a population, or when increased breeding among closely related individuals
occurs. In these cases variability is reduced due to a decrease in heterozygosity or a
random loss and fixation of some alleles. Habitat fragmentation over a short period of
time, due to human activities, is a concern because it usually causes a population
30
reduction and genetic isolation in wildlife species. Black bears are especially influenced
by habitat fragmentation because they have naturally low levels of gene flow, low
population numbers, and low effective population sizes (Lecount 1982a). The effective
population size is the actual number of individuals in a population that breed in a given
year. Other reproductive characteristics that contribute to the black bears’ vulnerability
to habitat fragmentation include: a long life span (> 20 years), delayed reproductive
maturity (first breeding at 3-7 years), a low reproductive rate (2 cubs every 2 to 6 years;
Lecount 1982b;1983), and energetically demanding parental investment (Kolinosky
1990). Consequently, they often take years to recover from a significant population
reduction and low genetic variability (Miller 1990).
Genetic markers are useful to detect some of the factors that increase extinction
probability and help with the management required to minimize these risks. For example
genetic markers have been used in black bears to detect the origin of isolated populations
in Arkansas and Louisiana and identify a population of concern (Csiki et al. 2003), to
resolve population structure in fragmented ecosystems (Belant et al. 2005, Onorato et al.
2007, Robinson et al. 2007) or in endangered and threaten black bears populations
(Warrillow et al. 2001), to define management units within species, to detect paternity
(Sinclair et al. 2003, Dixon et al. 2007), and to understand species biology (e.g., mating
patterns, dispersal, migration, population size, density; Dixon et al. 2006, 2007).
Accurate population-level and ecological information is important to make critical
management decisions that ensure the survival of wildlife species. The field of wildlife
management has evolved over the past 30 years, and in the process, wildlife research
31
(including that performed on black bears) has moved towards more efficient and low cost
techniques. Some of the most commonly used techniques to obtain survival estimates,
sex ratios, number of individuals, density, and movement estimates for black bears
include: direct observation (Koehler and Pierce 2005); use of hunter-harvest reports
(Koehler and Pierce 2005); capture-mark-recapture (Bales et al. 2005), and radio
telemetry (Miller et al. 1997). However, free-ranging black bear populations are difficult
to inventory and monitor because they exhibit low population densities and are secretive
in nature. In the search for new techniques to obtain ecological information on wildlife
populations, genetic markers provided potential to effectively monitor black bear
populations. The increasing affordability of molecular techniques, easier sample
collection, and more efficient DNA extraction has allowed researchers to increase sample
size and increase the use of non-invasively collected samples (feathers, hair, scat).
Consequently, the use of molecular techniques can provide answers to questions in
wildlife conservation and management that were previously not available. Our objectives
herein are to demonstrate how molecular markers are a tool for addressing questions
about black bear conservation and management, to describe the use of allozymes,
mitochondrial DNA (mtDNA) and nuclear microsatellite DNA in black bear research,
and to describe the increased use of non-invasive sampling to obtain DNA, and the use of
multiple independent genetic markers to produce stronger support and more robust
results.
Allozymes
The development of protein electrophoresis in the 1950s (Powell 1994) allowed
32
researchers to identify individuals as “homozygotes” or “heterozygotes” at a given locus.
Allozymes are variant forms of an enzyme that are coded by different alleles at the same
locus. Once the researcher has homogenized the tissue, the enzymes are electrophoresed
through an electric current in a starch or cellulose gel. The electric current causes each
protein to move through the gel at a different speed determined by its size and charge;
therefore, after they are stained they can be visualized as colored bands on the gel.
Studies that employ allozyme polymorphism as a genetic marker contributed greatly to
our understanding of population processes; they were used in black bear genetic studies
in the 1990s. For example, allozymes were used to establish that black bears in Great
Smoky Mountains National Park belong to a large and continuous population over much
of the southern Appalachian region, a large expanse of bear habitat in North Carolina
(Wathen et al. 1985) and to determine paternity and degree of relatedness among female
black bears in Chapleau Crown Game Preserve (CCGP) Ontario, Canada (Schenk et al.
1998). The analysis resolved the likely father of each of two litters, out of many potential
fathers, and showed no relationship between spatial proximity and average genetic
relatedness (range = 0.032-0.120) for bears in their study area. Results also showed that
extensive home-range overlap exhibited by individuals in the population is not a
consequence of natal phylopatric tendencies (Schenk et al. 1998). However, many
researchers have recently selected to use DNA-based techniques such as mtDNA and
microsatellite loci due to the following limitations of allozymes: limited variation present
at allozyme loci, allozymes are not a random sample of the genome and may bias genetic
inferences, allozymes reflect variability in protein coding sequences and may be
33
selectively constrained compared to non-coding regions, balancing selection can act on
allozymes resulting in overestimation of allelic similarity among populations compared to
neutral loci such as microsatellites, and allozymes are not practical to generate
information about genetic relatedness among individuals within a population.
Mitochondrial DNA
MtDNA sequence analyses have been used to infer phylogenetic relationships
among ursid species (Pages et al. 2008), and among populations in relation to geographic
distribution (Cronin et al. 1991, Wooding and Ward 1997, Ishibashi and Saitoh 2004) .
Also, mtDNA sequence analyses have been used to understand the role of geographic
barriers and their degree of importance to reduction in gene flow (Onorato et al. 2004a,
Peacock et al. 2007, Robinson et al. 2007).
To complete the resolution of evolutionary patterns of black bears in North
America, Delisle and Strobeck (2002) developed a series of primers based on conserved
mtDNA regions to amplify the entire mtDNA genome of three bear species (i.e., Ursus
arctos, U. americanus, and U. maritimus). Further, using a combination of mtDNA
sequence and restriction enzyme variation from black bears, two closely related groups
with relatively low divergence were found in Montana and Oregon (P = 0.031-0.057).
Researchers confirmed the presence of similar haplotypes across the United States,
therefore, there has been considerable wide range gene flow for this species (Cronin et al.
1991).
Moreover, the existence of two refugia of deciduous forest during the Pleistocene
was confirmed by mtDNA analysis in black bears. Wooding and Ward (1997) found two
34
major mtDNA clades: one east of the Rocky Mountains (including the southern Rocky
Mountains), and one west of the Rocky Mountains (California and southern British
Columbia), and an area where both clades overlap in northern British Columbia and
Alberta. These eastern and western groups have an estimated divergence time of about
1.8 million years ago. The results suggest the extant patterns of diversity in black bears
are due to post-Pleistocene colonization followed by woodlands retreating to higher
elevations in the Southwestern United States. Regional differences in lineage distribution
suggest that mixing in recent years has not eliminated the historical genetic signal.
Therefore, black bears have been isolated for a long term, but more recently they have
had contact and hybridization among the populations (Wooding and Ward 1997).
The existence of a third refugia in Haida Gwaii, Queen Charlotte Islands, was
reported by Byun et al. (1997). MtDNA haplotypes in bears from Haida Gwaii are
indistinguishable from coastal bears of British Columbia and Vancouver Island, but are
highly distinct from bears further inland. The coastal mtDNA lineage occurs in each of
the three recognized coastal subspecies suggesting that the morphological characteristics
that differentiate these taxa may be post-glacially derived. These data are consistent with
the recent suggestions that a glacial refuge existed on the now submerged continental
shelf connecting Haida Gwaii, Vancouver Island, and the coastal fringe of mainland
British Columbia. Therefore, this refuge would have been an additional source for postglacial recolonization of northwestern North America. More recently, mtDNA was used
to resolve the factors that influenced genetic diversity of black bear within Alexander
Archipelago (i.e., Kuiu, Kupreanof, Prince of Wales, Mitkof, Revillagigedo) and
35
mainland of southeast Alaska (i.e., Yacutat, Chilkat Peninsula, Skagway, Juneau)
(Peacock et al. 2007). Results show nine mtDNA genetic groups and two nuclear DNA
genetic clusters suggesting that contemporary movement since colonization, most likely
beginning 18,000 MYA, has not been sufficient to eliminate genetic differences between
the highly divergent lineages. Results also suggest that the pattern of genetic diversity in
black bears is related to contemporary biogeographic regions. For instance, narrow
saltwater straits, expansive ice fields, narrow beach fringes, and saltwater inland bays
separate genetically distinct groupings of black bears.
A fourth refugia, in the northeastern part of North America, was proposed but not
substantiated using mtDNA sequence analysis due to the lack of unique haplotypes for
the region. Paetkau and Strobeck (1996) found that all black bears from insular
Newfoundland, New Brunswick, Quebec, and most individuals from Alberta (comprising
a possible fourth refugia), had closely related haplotypes. Black bears from
Newfoundland were more similar to those in eastern Canada than to Alberta. The split
between the two groups of bears significantly predates the Wisconsin glaciation;
therefore, data suggests that the reduced genetic diversity of black bears in
Newfoundland likely arose through rapid genetic drift associated with a founder effect
during postglacial colonization of the island, and not through long periods of isolation of
a glacial refugia.
mtDNA has also been valuable to detect different degrees of population isolation.
For example, black bears in the Kenai Peninsula are differentiated from adjacent coastal
populations showing a lack of connectivity between the peninsula and coastal populations
36
(Robinson et al. 2007). Similarly, black bear populations from the Alexander Archipelago
are differentiated from the mainland of southeast Alaska (Stone and Cook 2000, Stone et
al. 2002, Peacock et al. 2007). In contrast, a lack of differentiation has been observed in
black bears populations from the northern Sierra Madre Oriental in México and western
Texas. Results show connectivity between these populations via desert corridors
(Onorato et al. 2007). Although at a fine scale, Onorato et al. (2007) also showed that
there are three subpopulations among the 6 areas sampled; with high genetic diversity
within the metapopulation of black bears in northern México. This study confirmed the
panmictic nature of bear populations in the binational borderland region and the
importance of maintaining corridors to allow for gene flow.
Microsatellites
Microsatellites are highly variable regions of nuclear DNA consisting of tandemly
repeated units of 1-6 base pairs. Microsatellites are typically neutral and co-dominant,
and are an order of magnitude more variable than allozymes (Paetkau and Strobeck
1994), making them useful for population level studies. Since Paetkau and Strobeck
(1994) developed the first set of microsatellite DNA loci to be used in bear studies,
microsatellite use has continually increased. Currently, there is a set of about 28
dinucleotide and 21 tetranucleotide bear microsatellites available for researchers to use in
black bear studies (Paetkau and Strobeck 1994, Paetkau et al. 1995, Taberlet et al. 1996,
Taberlet et al. 1997, Paetkau et al. 1998b, Paetkau 1999, Kitahara et al. 2000, Wilson et
al. 2003, Sanderlin et al. 2009). Microsatellites have been used to answer a wide-range of
ecological questions with direct application to the field of wildlife conservation and
37
management (Waits and Paetkau 2005). Especially in black bears, microsatellites have
been useful to understand the role of population fragmentation of threatened and
endangered populations that occur on islands, coastal areas, inland areas, and those that
inhabit complex landscapes. Microsatellites have been helpful to investigate dispersal
and the effectiveness of habitat corridors. In addition, microsatellite markers are
particularly well suited to resolve population parameters important to conservation such
as relatedness, inbreeding, population size, and density.
Case Studies
Population subdivision, fragmentation, and gene flow
Low genetic variability and lack of population connectivity is of particular
concern for threatened or endangered black bear populations (Boersen et al. 2003). For
example, the endangered Louisiana black bear (Ursus americanus luteolus) once
occupied a contiguous range across the southeastern U.S. but were extirpated from most
of Arkansas and Louisiana by the early 1950s. Reintroduction programs in the late 1950s
and 1960s brought bears from Minnesota and Manitoba into the southeastern U.S.
Microsatellites were used to detect the degree of connectivity between the Louisiana
coastal and inland bear populations, and to detect the origins of black bear populations in
the area. Results showed low connectivity with significant population differentiation
between the coastal and inland populations (FST = 0.206) (Triant et al. 2004). Further,
bears from Ozark and Ouachita in Arkansas, and inland Louisiana, descended from
introduced bears; whereas, bears from southeastern Arkansas and coastal Louisiana were
38
genetically unique and represented isolated fragments of a remnant population (Csiki et
al. 2003). By defining which populations represented the original “Louisiana black bear,”
these two studies provided managers with the knowledge of which populations to protect,
to maintain genetic integrity, of the Louisiana coastal population (Csiki et al. 2003, Triant
et al. 2004).
Microsatellites were also used to detect the levels of genetic differentiation of
nine remaining populations of the endangered Florida black bear (Ursus americanus
floridanus), and to evaluate the effectiveness of a regional corridor to connect two
populations (i.e., Osceola and Ocala). Florida black bears have at least eight genetically
distinct subpopulations from what once was a large single population, confirmed by the
high levels of genetic differentiation among subpopulations (global FST = 0.224) and a
wide range of genetic variability (heterozygosity 27% to 71%; Dixon et al. 2007). Based
on estimates of gene flow, the established corridor appears to be functional and is
allowing gene flow between the Osceola and Ocala populations, but the flow is
predominantly in one direction with limited mixing in one area of the corridor (Dixon et
al. 2006).
Microsatellites have been useful to investigate patterns of population connectivity
in complex landscapes including genetic isolation in island populations versus coastal
populations. For example, microsatellites were used to detect black bear genetic
variability in three Canadian National Parks: La Mauricie in Quebec, Banff in Alberta,
and Terra Nova on the Island of Newfoundland. Bears from the island population had
low levels of variation (36%) compared to the high genetic variation in the two
39
continental populations (80%) (Paetkau and Strobeck 1994).
A combination of microsatellite and mtDNA markers were employed to study
black bear population structure and phylogeographic patterns between the Kenai
Peninsula, Prince William area, and the Alaska mainland (Robinson et al. 2007).
Microsatellite loci revealed substantial population substructure with three distinct groups
(i.e., Kenai Peninsula, Prince William area, Alaska mainland). The three populations
have moderate gene flow among them (FST = 0.07 to 0.12). Populations that are separated
by a narrow land mass had higher gene flow (FST = 0.07) than populations isolated by
ocean water and ice (FST = 0.12). As estimate of only one male bear migrant was
consistently assigned from the mainland to the Kenai Peninsula. Additionally, five
mtDNA haplotypes were detected; the two limited to the Kenai are not deeply divergent
from mainland haplotypes. Results suggest that the Kenai population should be
considered a distinct management unit because it represents an important subset of
genetic variation not represented elsewhere in the subspecies/species (Moritz 1994,2002,
Robinson et al. 2007).
Another study used microsatellite and mtDNA markers to study population
structure of the Kermodei black bear (Ursus Americanus kermodei) (Marshall and
Ritland 2002). The Kermodei black bear is one of the five subspecies of black bears in
British Columbia that inhabits the coastal British Columbia and islands, particularly
Princess Royal and Gribbell. These islands include the highest frequency of white phase
bear specimens. The island populations with high frequencies of Kermodeis have 4% less
genetic diversity than mainland populations. Gribbel Island, with the most white phase
40
bears, has substantial genetic isolation (mean pairwise FST of 0.14 with other localities).
Additionally, the mtDNA analyses indicate that Kermode bears belong to the western
black bear lineage that predates the Wisconsin glaciation (Marshall and Ritland 2002). It
appears that “kermodism” was established and maintained in the populations by a
combination of genetic isolation, reduced population size, and possible selective pressure
or nonrandom mating. The authors showed that genetic drift and natural selection were
responsible for maintaining the white phase bear in appreciable frequencies in this region.
Kermode populations represent a component of the coastal lineage of black bears whose
current distribution could be a result of a glacial refugium and has been maintained by
small population size and isolation in insular habitat, in combination with possible
selection pressure on the coat-color locus associated with the white phase. Therefore
black bears on these islands are of conservation concern and managers should take into
consideration the possibility that neighboring immigrant black bears could affect the
mating opportunities of the “white bears”.
Microsatellites have been used to examine population structure and genetic
variation of black bears in fragmented landscapes. For example, Schwartz et al. (2006)
used non-invasive genetic sampling (hair snares) to estimate gene flow between two
subpopulations in Idaho (i.e., Idaho Panhandle National Forest-Selkirk Mountains and the
Purcell Mountains). A large agricultural valley separates the two subpopulations which
could act as a barrier to slow gene flow. Genetic results indicated a moderate level of
gene flow between the two subpopulations (GST = 0.97) (Mills et al. 2003) with
approximately 3 migrants per generation moving across the valley. High allelic
41
variability occurs in both subpopulations (Purcell Ho = 0.76 and He =0.78, and Selkirk
Ho and He = 0.80) (Schwartz et al. 2006). In the same area, Cushman et al. (2006)
microsatellite DNA analyses that showed only one population in the Selkirk and Purcell
Mountains, and genetic structure of black bears in the area correlates to elevational
landscape gradients. Finally, Onorato et al. (2007) assessed gene flow among black bear
populations in the borderlands of México, New México and Texas. Black bears were
sampled from 5 areas (Mogollon Mountains, New México; Davis Mountains; Black Gap,
Texas; Big Bend National Park, Texas; Carmen Mountains, México; and Burro
Mountains, México). Genetic distances between the Mogollon Mountains, Burro
Mountains, and Big Bend National Park were high (Ds = 1.65 and 1.61), while the
distance between Big Bend National Park and Burro Mountains was lower (Ds = 0.18).
mtDNA results showed three groups: 1) Big Bend National Park, Texas; 2) Mogollons
Mountains, New México; 3) Burro and Carmen Mountains, México. Microsatellite
analysis and mtDNA results suggest black bears that recolonize western Texas
populations come from the Burro and Carmen Mountains, México. Therefore, efforts to
conserve black bears in this transborder area have to include corridors between México
and Texas to ensure adequate gene flow to maintain the historical genetic variability of
these populations (Onorato et al. 2004a, Onorato et al. 2007).
Reproductive behavior
Microsatellites have been used to examine inbreeding avoidance in black bears
(Costello et al. 2008). Black bears in the Sangre de Cristo and Mogollon Mountains of
New México showed that the degree of relatedness among females, and the proportion of
42
female relatives, decreased as a function of distance. Little change was observed with
increased distance among male pairs or opposite-sex pairs, and the genetic structure was
consistent with male-biased dispersal. This evidence suggests that male bears may
decrease their rate of dispersal, or dispersal distance, in good quality habitat, but
increases their rate of dispersal to reduce competition and avoid inbreeding. Results also
suggest that competition for mates or resources modifies dispersal patterns. (Zedrosser et
al. 2007).
Population size and density
Estimation of population size is important for effective conservation and
management of wildlife species. However, it is difficult to identify and track individual
animals in the field, and rely on hunting effort information, which might not always
accurate. To identify individuals, wildlife researches have used unique natural markings
(possible in some species), or different kinds of ear tags, collars, and radio transmitters. A
common non-genetic method for population size estimates used in black bears includes
the use of identified individuals with hunting data. Individual bears are captured annually
and identified through transmitters or markings, and when bears are harvested population
size estimates are obtained using the proportion of harvested black bears recorded during
the hunting season. For example, if 20 of 100 tagged bears are harvested and the total
harvest is 1,000 bears, population size would be estimated to be 5,000 bears (1,000
divided by 20%). There are two major limitations to this technique. First, it is difficult to
mark enough bears annually so that estimates are relatively accurate; and second, a
primary assumption, that bears marked or unmarked have an equal chance of being
43
harvested, may not always be accurate. Genetic tags are an alternate technique because
they can identify individual bears consistently and accurately, they are inexpensive, and
permanent (Woods et al. 1999). Combining the use of microsatellite DNA loci analysis
and statistical models allows researchers to estimate black bear population size more
accurately (Woods et al. 1999, Tredick et al. 2007). Moreover, this technique has become
more popular with the increased use of non-invasive sampling, in which researchers
collect hair, scat, shells, scales, and feathers, from the field without distressing the
animals.
Non-invasive genetic sampling has advantages over traditional techniques (e.g.,
live-trapping, radio collars) because non-invasive genetic sampling increases capture
probability, requires less intense field effort, provides larger sample sizes, and the ability
to study populations without handling animals. However, the low quantity and/or quality
of DNA that can be extracted from non-invasive samples can cause errors in the resulting
data. For example, the identification of too few or too many individuals will bias the final
population estimation. However, discussions of the errors and solutions have been
published (Taberlet et al. 1996, Mills et al. 2000, Waits et al. 2001, Miller et al. 2002,
Paetkau 2003, Creel et al. 2003, McKelvey and Schwartz 2004, Roon et al. 2005).
Following are examples of microsatellites employed together with non-invasive sampling
to estimate population densities for black bear populations, using both rarefaction and
capture-recapture models.
Belant et al. (2005) used hair samples, microsatellite loci, and mark-recapture
models to detect black bears population structure and population density in two island
44
and one mainland (lakeshore) population in Wisconsin (Stockton, Sand Island, and Oak
Island). Results show higher bear density in Stockton (0.64 bears/km2) than on Sand
Island (0.50 bears/km2). The genetic variation within islands was high (mean HE ≥ 0.77)
suggesting substantial immigration from the mainland population. Black bears on Sand
Island were more genetically variable than on Stockton and Oak Island (Ho 0.94 versus
Ho 0.84 and Ho 0.83). Bears from Oak Island were genetically intermediate between the
other two islands. Results suggest that bears in these islands are genetically distinct but
have had bear immigrants from the mainland population (Belant et al. 2005). Boersen et
al. (2003) used similar methodology to detect the number of individuals in a population
of the Louisiana black bear in the Tensas River Tract, Louisiana. Results estimated a
density of 0.36 bears/km2. The outcome also estimated a low effective population size at
Tensas River Tract (as few as 32 individuals); and it has been suggested that this
population exhibits characteristics consistent with inbreeding and genetic drift.
Additionally, Dreher et al. (2007) and Settlage (2008) estimated black bear population
density and home range size in the southern Appalachians (portions of Great Smoky
Mountains National Park in Tennessee, and a U.S. Forest Service land that includes
portions of North Carolina, South Carolina and Georgia). They reported a density of
0.62-0.71 black bears/ km2 for the National Park and 0.59-1.00 black bears/ km2 for the
forest service (National Park area). Finally, using similar methodology (non-invasive hair
samples and capture-recapture analysis), Immell and Anthony (2008) estimated black
bear population densities in the Steamboat and Toketee Umpqua National Forests,
Oregon to range from 0.18 individuals/km2 to 0.23 individuals/km.2 Settlage et al. (2008)
45
estimated the population density of two study areas in the southern Appalachians (Great
Smoky Mountains National Park and the neighboring National Forest) and found 97 to
114 bears in the National Forest study area and 197 to 330 bears in the National Park.
Other studies have used a combination of statistical models to detect population
size. For example, black bear density was estimated in two ecosystems, Parsnip Plateau
and Parsnip Mountains, near the Canadian Rocky Mountains. Mowat et al. (2005) used
non-invasive sampling (hair) and microsatellite DNA loci to compare results using two
statistical methods (rarefraction indices and capture-recapture methods). Microsatellite
analysis resulted in the identification of 275 black bears, 194 for the plateau (sex ratio of
45M:55F) and 85 in the mountains (sex ratio 41M:59F). Results yielded a density of
0.257 bears/km2 in the mountains and 0.089 bears/km2 in the plateau (Mowat et al. 2005).
The abundance values are similar to the ones presented by (Miller et al. 1997) using nongenetic methods. Conversely, Peacock (2004) reported densities of 1.5 bears/km2 in the
Pacific Northwest and suggested that black bears occur at higher densities in the interior
populations than in the coastal populations, and that black bear density is higher where
grizzly bears are scarce or not present. This value is comparable with the estimate from
Lindzey and Meslow (1977) of black bears on Long Island.
Non-invasive sampling techniques can produced a large amount of samples that
can be overwhelming to work with, and can become prohibitively expensive to analyze in
the laboratory. Tredick et al. (2007) investigated how accurate population density values
can be obtained with a small budget. They used sub-sample data analysis to understand
how the use of less data can affect the precision and accuracy of population estimate
46
results. They used two genetic datasets from hair samples in the southeastern U.S. (i.e.,
Pocosin Lakes National Wildlife Refuge, northeastern North Carolina and the St. Johns
area, northeastern Florida). The authors compared different subsets from the data sets
with estimates produced from the complete data sets. Results suggest that bias and
precision of estimates improved as the proportion of total samples used increased. Using
the full data set, heterogeneity models were robust enough to detect number of
individuals: 39 bears (95% CI = 29-80) in St. Johns and 108 bears (95% CI = 104-182) in
Pocosin Lakes National Wildlife Refuge. However, the actual estimates using the subsampling replicates were close to those obtained with the whole data set. They varied
from 32 to 39 bears in St. Johns, and 29 to 124 bears in Pocosin. The use of the subsamples ranged from a quarter to half of the total samples and resulted in reducing the
budget by one third. However, an important recommendation is the use of high-quality
samples (a minimum of 5 hair follicles) and extra effort in maximizing capture and
recapture rates in the field when using sub-sampling for population estimation.
DISCUSSION
The loss of genetic variability is one of the factors threatening the survival of
small populations (Onorato et al. 2004b). Consequently, genetic studies have become an
important component in research in the field of wildlife management. Genetic studies
started with the development of electrophoretic techniques in the 1950s. First, researches
used allozymes, and then, DNA-based markers, which provide information on fine scale
genetic variation among population. There is not a single best technique to study
47
variation in natural populations; the most appropriate technique for a particular study
depends on the question being asked, usually the variability of the genetic marker will
determine its use. Also, the use of more than one molecular marker produces more
information and, therefore, allows for a better understanding of the populations under
study.
Allozymes
For most of the 20th century, geneticists struggled to measure genetic variation in
natural populations. From the 1900s to the 1970s researchers used laboratory mating
studies and chromosomes analyses to detect genetic variablity. Then, in the 1960s and
1970s with the discovery of protein electrophoresis (Powell 1994), allozyme variation
was a commonly used methodology to detect genetic differentiation among individuals in
local populations, and among populations within the same species (Allendorf and Luikart
2007). By 1992 the average heterozigosities of 1,111 species had been published (Nevo
et al. 1984). For over 30 years allozymes contributed to the field of black bear ecology
and conservation (Wathen et al. 1985, Schenk et al. 1998). Allozymes were useful
because a large number of nuclear loci could be studied at low cost, in a short amount of
time. Additionally, the variation can be seen directly from electrophoretic patterns and
different laboratories can examine the same loci and use identical allelic designations so
laboratories can combine or compare data sets. On the other hand, allozyme analyses
include only a small set of genes (the ones that code for water-soluble enzymes), so
cannot detect “silent substitutions” or genetic changes that do not produce changes in
amino acids. The allozyme technique requires a large amount of tissue, as a result, often
48
the individuals have to be sacrificed. With the discovery of new techniques such as the
Polymerase Chain Reaction (PCR) the detection of genetic variation in wildlife species
moved toward detecting variability at the DNA level, which requires small amounts of
tissue sample from the individual.
Mitchondrial DNA
mtDNA gene sequences have been useful to understand black bear distribution
and evolution in North America. The black bear diversity across U.S. is the combined
product of historical events and contemporary forces. Post-Pleistocene colonization and
landscape fragmentation explains the current genetic patterns of black bears subpopulations (Peacok 2007). Landscapes are becoming more fragmented due to human
activities. Historically large black bear populations have become further isolated, such as
in Louisiana and Florida (Csiki et al. 2003, Robinson et al. 2007). Genetic information
has showed the importance of preserving genetically “unique” populations (Csiki et al.
2003, Marshall et al. 2002), creating and monitoring regional corridors, and
translocations to restore the historical levels of genetic variation to ensure the long-term
persistence of the black bears (Onorato et al. 2004, 2007).
Microsatellite DNA
Microsatellite DNA analyses has provided information to aid the management and
conservation of black bears in North America, from detecting the genetic variability in
individuals within a population (Robinson et al. 2007) to estimating the degree of gene
flow among populations in naturally fragmented landscapes. Gene flow has been
estimated for island populations (Robinson et al. 2007) and for artificially fragmented
49
landscapes (Onorato et al. 2007). Microsatellites have also been useful to detect the
degree of relatedness and inbreeding avoidance (Costello et al. 2008) and finally to detect
population size, density, and home range of individuals.
Microsatellites combined with non-invasive sampling, using hair or scats from the
field, have shown to be a reliable method to obtain population density estimates.
However, the accuracy of the results depends on the quality of data (to avoid genotyping
errors) and the maximization of capture-recapture rates in the field. The precision of
density estimates increase as the number of samples increase (Tredick et al. 2007).
Microsatellites used to detect population size and density determined that DNA markrecapture studies require a high density of sampling sites for black bear, higher than what
has been necessary for grizzly bears, to obtain similar levels of accuracy and precision.
Also, it is important to use high quality hair samples at each snare site to minimize error
rates. Finally, it is critical to consider the presence of capture heterogeneity when
choosing a model for the statistical analysis.
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58
APPENDIX B
PHYLOGEOGRAPHY AND CONSERVATION IMPLICATION OF BLACK BEARS
IN THE SKY ISLANDS OF ARIZONA AND NORTHERN MÉXICO.
CORA VARAS, School of Natural Resources and the Environment, University of
Arizona, Tucson, Arizona, 85721, USA
PAUL R. KRAUSMAN, Boone and Crockett Program in Wildlife Conservation.
University of Montana, Missoula, Montana 59812, USA
MELANIE CULVER, School of Natural Resources and the Environment, University of
Arizona, Tucson, Arizona, 85721, USA.
School of Natural Resources
University of Arizona
P.O. Box 210043
Tucson, Arizona 85721-0043
Phone 520 621 2161; Fax 520 621 8801
Email [email protected]
KEY WORDS: Black bears, mitochondrial DNA, phylogeography, sky islands
59
Abstract
The black bear (Ursus americanus) has been present in North America for at least
3 million years. Climatic fluctuations during the most recent glaciation forced black bears
into geographic locations called refugium, which influenced the present-day population
genetic structure of black bears. Several refugia have been reported for black bears;
however, previous analyses, which indicated refugia, did not include sequences from the
Arizona and northern México border. Here we report a phylogeographic study of black
bears. We examined mitochondrial DNA (control region) sequence variation at two
levels: a U.S. wide analysis, which included downloaded sequences of 33 haplotypes
previously detected from natural black bear populations across their entire range, and a
more local level analysis of the phylogenetic relationship between Arizona populations
and between the Sierra Madre Occidental and Sierra Madre Oriental populations in
northern México. Phylogenetic analysis revealed the presence of two major groups in
North America, one west of the Rocky Mountains and the other east of, and including,
the Rocky Mountains. However, the eastern group is separated into two subgroups, one
along the Rockies and the other east of the Rockies. These results confirm previous
studies suggesting black bear genetic variability in North America is the result of
multiple refugia south of the most recent ice sheets. In a more regional analysis,
phylogenetic structure was not detected between Arizona and northern México because
haplotypes are shared extensively in this area. However the analysis between black bears
in the Sierra Madre Occidental and Sierra Madre Oriental in México showed a strong
genetic discontinuity between those two major mountain ranges.
60
Patterns of mitochondrial DNA genetic diversity in black bears of the Southwest
suggest a refugium existed in the Sierra Madre Occidental, México, in addition to the two
southern refugia already suggested in the literature for Florida and California. Neutrality
test of the Control Region within the black bears in Arizona and northern México
identified five haplotypes with tracks of population expansion This pattern suggest a
population expansion northward following the retreat of the most recent glacial event in
North America. In addition, genetic data indicates recolonization proceeded northward
from three lineages (California, México, and Florida) with mixing of these lineages
occurring in the northern U.S. and Canada.
INTRODUCTION
The black bear (Ursus americanus) was one of the most widely distributed
carnivores in North America at the time of European settlement, but was over-hunted to
limit damage to crops and livestock (Miller 1990). After government regulations were
established in the U.S., black bear populations recovered and have persisted in North
America. However, their range has decreased about 50% in the U.S. and up to 70% in
México. Furthermore, some black bears currently exist in fragmented populations that are
threatened or endangered in the U.S. (Ritland et al. 2001, Larkin et al. 2004, Dixon et al.
2006) and México (Medellin et al. 2005).
Black bears have been present in North America for at least three million years
(Wooding and Ward 1997) and they adapted to changing ecological conditions (Stirling
1989). There is widespread agreement that glacial cycles affected continental biota in the
61
high-latitude continental areas such as northern Europe, Asia, and North America
(Waltari et al. 2007). The Pleistocene changed the landscape and ecology of these highlatitude areas in the northern hemisphere as a result of the expansion of large ice sheets
(Lessa et al. 2003). During this time, many species migrated and were able to persist at
lower latitudes in North America south of the ice sheets (Graham et al. 1996).
Consequently, current black bear genetic diversity patterns in North America are the
result of climate and habitat changes during the Pleistocene over the last two million
years (myr), and more recently to anthropogenic factors such as habitat fragmentation,
unrestricted harvesting, and predator control (Van Den Busshe et al. 2009).
The Pleistocene and associated climate cycles produced refugia, areas that
escaped ecological changes occurring elsewhere, providing suitable habitat for relict
species and populations (Pielou 1992). However, controversy and uncertainty remains
regarding the number, locations, and significance of biotic refugia as sources for the
colonization of the higher latitudes of North America after the Pleistocene (Byun et al.
1997, Demboski 1999, Lessa et al. 2003).
Black bears are associated with forest habitats that through the late Pleistocene
glaciation remained in southern North America. This information combined with the
observed pattern of genetic diversity in black bears suggested a long-term fragmentation
of forests into eastern and western refugia (Wooding and Ward 1997). Two clades of
black bear were identified by Wooding and Ward (1997) in northwestern North America.
The first lineage distributed from southeast Alaska to north of California (Mendocino
County). The second clade extended from the interior of Alaska to southern Oregon, and
62
east of Newfoundland (Morrison 1991) to the southwest United States and northwest of
México. As recently as 100 years ago, black bears occurred in all forested regions of
North America from the tree line in Alaska and Canada, to Florida and northern México
(Lecount 1982b), occupying a diverse array of very heterogeneous habitats.
Among the most exceptional are the sky islands of Arizona and northern México
(Warshall 1995). These are defined as a mix of montane forests and woodland immersed
in a matrix of desert, grasslands or scrublands. The sky islands in the Sonoran Desert
region of North America (Southwestern archipelago) were formed by a tectonic event
that began around 12 mya and ended roughly 6 mya (Morrison 1991). Estimates from
studies with radio carbon-dating using material found in pack rat (Neotoma spp.) middens
show that woodland communities in the Sonoran sky islands region were continuous
habitat as recently as 8,000 to 10,000 years ago (Van Devender 1977). Since then, the
forested mountain ranges have become relatively isolated from each other. Prior to this
time, the ice sheets of the Pleistocene glaciation covered North America forcing black
bears into forested refugia, one in California and one in Florida (Wooding and Ward
1997).
Studies in the Sierra Madre Occidental have confirmed that the area is the most
diverse for conifer-oak forest species in México; it is a region with high endemism for
plants (Bye 1994), and vertebrates (Escalante-Pliego et al. 1993). It has also been shown
to be the center of origin of rattle snakes (Crotalus spp. and Sistrurus spp.) (Aaron and
Charles 2004). It has also been suggested that in the last glaciation the area served as
refugia for plants and animals (Lessa et al. 2003, McCormack et al. 2008, Sosa et al.
63
2009). For example, Lessa et al. (2003) used mitochondrial DNA d-loop sequences to
show the Sierra Madre Occidental was a refugia for voles (Microtus longicaudus) and
after the glaciation they showed dramatic expansion in the Southwest region (Sierra
Madre Occidental).
Genetic analyses using mitochondrial DNA have been useful to study historical
and contemporary population structure for American black bears (Paetkau and Strobeck
1996, Byun et al. 1997, Wooding and Ward 1997, Stone and Cook 2000, Onorato et al.
2004a). Mitochondrial DNA has also been useful to detect population isolation due to
natural fragmentation. For example, black bears in the Kenai Peninsula and adjacent
coastal populations showed lack of connectivity between them (Robinson et al. 2007).
Similarly, black bears in the Alexander Archipelago show lack of gene flow with black
bears in the mainland of southeast Alaska (Stone and Cook 2000, Peacock et al. 2007). In
contrast, mitochondrial DNA studies reported population connectivity and black bear
expansion through natural dispersal from the mountains in the Sierra Madre Oriental
(México) into western Texas (McKinney and Pittman 1999, Onorato and Hellgren 2001).
However, in these wide range studies in North America black bears from Arizona and
northern México in the Sierra Madre Occidental were not included in the analysis.
It is important to employ multiple independent molecular genetic markers to study
evolution and population structure for a species. Nuclear genes such as microsatellites,
can be used as several independent markers to corroborate phylogenetic relationships
among taxa produced by mtDNA (Takezaki and Nei 1996). Microsatellites are random
repeats of 1 to 5 base pairs (bp) that are widely distributed in the genome and highly
64
polymorphic; their mode of inheritance is co-dominant. These characteristics have made
microsatellites the marker of choice in many molecular ecology studies. Also
microsatellite data has been used as a source of genetic data to resolve relationships
among populations. Therefore, microsatellites have been used to infer phylogenetic
relationships in plants, insects, and vertebrates (Takezaki and Nei 1996, Angers and
Bernatchez 1998, Richard and Thorpe 2001, Orsini et al. 2004).
Because the Sierra Madre Occiental, in México served as a refugium for other
plant and animal species, we suggest this area could have been a refugium for black bears
during the Pleistocene. Populations from the mountains in México could have colonized
habitats further north as they became available once the ice sheets retreated. Under this
scenario, we should see a historic genetic signal of a rapid expansion since the retreat of
the ice sheets, rather than a signal of populations having been gradually reduced and
subdivided since the last glaciation.
Our goals are: 1) to determine the phylogenetic relationships of black bears
among the sky islands of Arizona and northern México. 2) To resolve the population
structure of black bears in Arizona and northern México, particularly to determine
whether black bears in the Southwest are more closely related to the western group
(California clade) or to the eastern group (Florida clade) previously described by
Wooding and Ward (1997) . 3) To determine whether there is evidence of a demographic
processes related to an hypothetical refugium in the Sierra Madre Occidental for black
bears during the last glaciation. 4) To determine the relationship between black bears in
Sierra Madre Occidental and Sierra Madre Oriental in México. 5) To place our findings
65
in a meaningful historical context, over the last two myr, during the Pleistocene epoch,
and in modern times.
MATERIALS AND METHODS
Study area
In Arizona, we collected samples from the Huachuca, Peloncillo, Pinaleno,
Chiricahua, Catalina, and Rincon mountains, and from a continuous habitat range in
northern Arizona, which includes the Mazatzal Mountains (i.e., Four Peaks and Mount
Ord), Nutrioso Mountains, and Apache National Forest. In northern México, we collected
samples from Sierra Los Ajos (SLA), Sierra San Luis and Sierra El Nido (Fig. 1). These
mountains are part of a group of approximately 40 mountains between the Mongollon
Rim and the Sierra Madre Occidental (Warshall 1995). These sky islands were formed
from continental rifting that started about 13 million years ago. The tallest peak is Mt.
Graham in the Pinalenos 3,246 meters over the sea level (m.o.s.l.). Distances between the
valleys and the Peaks are around 378.8 and 2,045 m.o.s.l.
Black bear habitat is similar across sky islands in Arizona and México, and
includes pinyon (Pinus spp.)-juniper (Juniperus spp.), Pine-oak (Quercus spp.)
forests, oak woodland with second growth, open low forest, mesquite (Prosopis spp.)
grasslands, riparian forest, and chaparral ecosystems (Palacio-Prieto et al. 2000). In
the Sonoran Desert, sky islands plant species include: Southwestern White pine
(Pinus strobiformis), Western Yellow pine (P. ponderosa), alder (Alnus tenuiflolia),
Rocky Mountain fir (Abies Lasiocarpa), Engelmann spruce (Pices engelmanni),
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Netleaf oak (Quercus rugosa), Silver-Leaf oak (Q. hypoleucoides), Rocky Mountain
white oak (Q. gambelii), Arizona white oak (Q. arizonica), basketgrass (Nolina
microcarpa), Rocky Mountain maple (Acer glabrum), bigtooth maple (A.
grandidentatum), alligator juniper (Juniperus deppeana), desert agave (Agave
palmeri), Arizona smooth cypress (Cypressus arisonica), among others (Wallmo
1950, Bowers and McLaughlin 1987).
In the Chihuahuan Desert, sky islands plant species include: Mexican pinyon
(Pinus cembroides), Emory oak (Quercus emoryi), Black oak (Q. mcvaughii), SilverLeaf oak (Q. hypoleucoides), Oneseed juniper (Juniperus monosperma), and Mexican
Manzanita (Arctostaphyllos pungens), grasslands: blue grama, (Bouteloua gracilis)
and Sideoats grass (B. curtipendula), annual muhly (Muhlenbergia minutissima),
wolfstail (Lycurus phleoides) (Shreve 1939, LeSueur 1945, Villarreal and Yoolt
2008).
The Sierra Los Ajos, located east of Cananea, Sonora, are situated between
México’s Sierra Madre Occidental and the Rocky Mountain region of the western United
States. Elevations of the SLA range from 1,050 m to 2,625 m. Biological and floristic
diversity is high, related in part to its unique geographic location (Fishbein et al. 1994).
Black bears hair samples were collected in the northern portion of the protected AjosBavispe National Forest and Wildlife Refuge.
In the Sierra San Luis, samples were collected in El Pinito ranch, which is located
in the Sierra San Luis, Sonora, between 108° 56’ 46’’ N latitude and 31° 11’ 49’’ W
longitude (Sierra-Corona et al. 2005). In the Sierra el Nido, scat samples were collected
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in Rancho Santa Monica located at Latitude: 29° 33' 0 N, Longitude: 106° 47' 60 W, with
elevation ranging from 2,500 to 3,040 m.
Land use in the Arizona sky islands ecosystem includes urban and farming in the
valleys (with species such as cotton, alfalfa, citrus fruit, melons, head lettuce). Other
agricultural activities across the ecosystem include cattle and sheep rising. In the
mountains, a large part is own by the United States Forest Service, and is used for skiing,
hunting, camping, fishing, rock climbing, and car-based tourism. There are also some
privately owned areas, mostly used for summer homes.
In México, land patterns are a matrix of large vs. small parcels of private
ownership mixed with protected areas, for example, Sierra Los Ajos is part of the AjosBavispe National Forest and Wildlife Refuge.
The weather conditions in the Arizona sky islands varies depending on the
altitude. For example, in the Mazatzal Mountains, temperature ranges from 4 to 20 oC
and rainfall is from 250 to 635 mm annually. In the Pinaleno Mountains the temperature
ranges from -13 oC to 44 oC. In the Chiricahua Mountains, temperature ranges from 5.7
o
C to 14.1 oC; with a mean precipitation of 795 mm. In the Huachuca Mountains the
temperature ranges from 15 oC to 33oC with a mean precipitation of 3,750 mm.
On the Mexican side, Sierra Los Ajos and Sierra San Luis have an annual
temperature range from -8 oC to 18oC and an annual mean precipitation of 2,200 mm. In
Sierra El Nido, the annual rain precipitation is 400mm and the average annual
temperatures range from 12 to 14 oC.
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Sample collection
México
We collected scat and hair samples using non-invasive techniques. In Sierra los
Ajos, we set 20 hair traps using Simpson Strong Tie mending plates (3" x 6")
(http://www.lowes.com). We attached the plates with nails to trees at about 1.5 m from
the ground. We used punctured cans with sardines in them as bait attached by a line from
a branch making it difficult for a bear to reach it, but allowing the smell to attract bears.
We set 20 hair traps approximately 1 km from each other.
In Sierra San Luis, Ranch El Nidito and Sierra El Pinito, we set up transects of 3
km, that we walked every other week looking for scat samples. Scat samples were
collected for 28 days in October and November (2002) in El Pinito Ranch, for 22 days in
June and July (2006) in Sierra San Luis, and for 20 days in October and December (2007)
in Sierra El Nido. We obtained locality data through a portable Global Positioning
System (GPS) for each scat and hair sample. We collected bone, tissue, and hair samples
donated by the Los Ajos-Bavispe National Forest and Wildlife Refuge. Tissue samples
were collected in 2 ml Eppendorf tubes with blood buffer and kept at room temperature.
Each hair and bone sample was collected in a small paper envelope and scat samples
were collected in a paper bag. All scat, bone and hair samples were stored at room
temperature until transported to the University of Arizona for long-term storage at -20˚C
until they were used for DNA extraction.
Arizona
We obtained blood, buccal cells, bone, and hide from hunter-killed bears, scat
69
from transects, and hair from hair snares we set on public lands. We obtained buccal cells
and blood from black bears trapped by the Arizona Game and Fish Department. When
samples were obtained from hunters, names were provided by the Arizona Game and
Fish Department and the location of each sample was recorded according to the verbal
description by the hunter and located on a map. Tissue samples were collected in blood
buffer in a 2 ml screw cap cryotube and stored at -20˚C until DNA extraction was
performed. Non-invasive samples were collected as described previously with their
recorded Universe Transverse Mercator (UTM) coordinate system. The UTM for each
sample was plotted using a manual ArcGIS 9.0 (ESRI, Redlands, California). In the case
of samples without an exact location, we constructed a 500-m buffer around the location
and randomly located the sample points at unique locations within that buffer. This point
relocation was used to facilitate visualization of sample points (Robinson et al. 2009).
DNA isolation
We extracted DNA from 536 samples. All DNAs were stored at the University of
Arizona. Whole genomic DNA was extracted from tissue, blood, bone, scat, and hair
following different protocols.
We purified DNA using approximately 25 mg of tissue, 100 ul of blood, 50 ul of
buccal cell dilution, or 1–10 hair follicles using the Qiagen DNeasy tissue extraction kit
following the manufacturer's protocol (Qiagen Ltd., Crawley, West Sussex, United
Kingdom). We used a protocol adjustment for hair samples. In step one we used X1
buffer (10mM Tris–HCl buffer, pH 8.0, 10mM EDTA, 100mM NaCl, 40mM
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dithiothreitol, 2% SDS, 250 ug/mL Proteinase K) instead of the ATL buffer
recommended by the protocol (Suenaga and Nakamura 2005). Bone samples were
pulverized into powder and 25 mg of powder was decalcified with EDTA (0.5M pH 8.0
ph Amnion Catalog number AM9260G) for 5 days. DNA was then extracted as
described previously for tissues.
We extracted DNA from scats, hair, and bone in a laboratory exclusively used to
process samples with low DNA yield. This laboratory is located in a building separate
from where other DNA samples are processed to avoid contamination. We scraped
between 0.40 g to 0.60 g of the scat surface to obtain epithelial cells for DNA extraction.
The QIAmp® Stool Mini Kit (Qiagen Inc., Valencia, California) was used following the
manufacturer’s protocol with one adjustment to the DNA elution step. For DNA elution,
we added 50 µl of buffer AE to the Qiagen column, centrifuged, washed with 50 µl of
H2O, and centrifuged once more eluting DNA in a final volume of 100 µl. We used 1
negative control for every 15 samples extracted.
The final data set included 173 black bears from Arizona and northern México
(Sierra Madre Occidental) and 9 samples from the Sierra Madre Oriental in México.
DNA amplification
Control Region
We amplified and sequenced a 360 base pair (bp) fragment of the
mitochondrial DNA control region (mtDNACR). We used the following forward
primer mtDNACRf (CTCCACTATCAGCACCCAAAG) and the reverse primer
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mtDNACRr (GGAGCGAGAGGTACACGT) (Varas et al 2006). We edited each
sequence using forward and reverse sequences with Sequencher 4.6 (Gene Codes
Corporation, 2006), uploaded to the NCBI web page (http://www.ncbi.nlm.nih.gov)
to confirm that our samples belong to black bears, then aligned sequences using
CLUSTAL in Mesquite 2.7 (Maddison and Maddison 2009). We used BLASTN
2.2.22 (Zheng et al. 2000) to download 33 previously published black bear sequences
deposited into the GenBank data base (Paetkau and Strobeck 1996, Byun et al. 1997,
Wooding and Ward 1997, Onorato et al. 2004a, Yu et al. 2004, Robinson et al. 2007,
Yu et al. 2007, Van Den Busshe et al. 2009). Details on geographic location of the
haplotypes, haplotype names, and GenBank Accession numbers were recorded
(Appendix 1. A) for each sample.
ATP synthase subunit 8
We amplified and sequenced a 224 bp fragment from the ATP synthase subunit 8
(ATP8), which included 54 bp of the tRNA-Lys and 170 bp of the ATP8 region. The
ATP8 is one of three mitochondrial DNA genes the makes up the ATP synthase complex
(which makes energy for that cell by generating ATP from ADP and phosphorus (Pi)
(Boyer 1997 ).
We used forward primer ATP8f (GCATTAACCTTTTAAGTTAA) and reverse
primer ATP8r (GGCGAATAGATTTTCGTTCA) (Delisle and Strobeck 2002). We
amplified the two mtDNA fragments from samples from 8 areas in Arizona (Huachuca,
Pinalenos, Chiricahua, Peloncillo, Rincon, Mazatzal and Escudillo mountains, and
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Apache National Forest) and 4 in México (3 in the Sierra Madre Occidental: Sierra Los
Ajos, Sierra El Nido and Sierra San Luis and 1 in Sierra Madre Oriental).
We use our sequences with BLASTN 2.2.22 (Zheng et al. 2000) to confirmed
species identification (ID), and download similar sequences (published haplotypes) from
GenBank (Delisle and Strobeck 2002, Hsieh et al. 2006, Hou et al. 2007), to use as
outgroups in the analysis . Details on geographic location of the haplotypes, haplotype
names, and GenBank Accession numbers were recorded for each sample (Appendix 1.B).
Phylogeny
Microsatellite phylogeny
We used data from ten microsatellites loci across all sampling areas in Arizona
and northern México, and we calculated Euclidean pairwise distances (Cavalli-Sforza and
Edwards 1967). The average distance across all loci was calculated by taking the square
root of the sum of the squared distances for individual loci, using the Pythagoras theorem
(Edwards and Cavalli-Sforza 1964). All pairwise distances among black bears were used
to create a phylogenetic tree. We analyzed black bear data from 11 sampling sites (Fig. 1)
in PHYLIP 3.69 (Felsenstein 2009). We generated a rooted neighbor-joining tree in the
NEIGHBOR subroutine of PHYLIP, and a maximum-likelihood (ML) tree using the ML
subroutine (CONTML) in PHYLIP. We used black bears samples from the Sierra Madre
Oriental in México as the outgroup for both analyses. We performed 1,000 bootstrap
replications to calculate percentage of support for individual nodes.
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Mitochondrial DNA phylogeny
Bayesian analysis
We applied Bayesian methods, to mtDNACR and to ATP8 regions fragments as
implemented on MrBAYES 3.1 (Ronquist and Huelsenbeck 2003). To select the
probabilistic model of evolution that best fitted the DNA sequence data, we used
MrMODELTEST 2.2 (Nylander 2004). The best-fit model for both regions was chosen
based on the Akaike information criterion (AIC). MrModeltest slected as HKY + I + G
model for mtNACR, which assumes non-varying nucleotide frequencies [statefreqpr =
dirichlet (1, 1, 1, 1)], with two types of substitutions (i.e. transitions and transversions, nst
= 2), and a proportion of invariant sites and a rate variation among sites described by a
gamma distribution (rates = invgamma). For ATP8, the model of evolution selected was
GTR + G, with six types of substitutions (Nst = 6), and non-varying nucleotide
frequencies [statefreq pr = dirichlet (1, 1, 1, 1)]. Because in the Bayesian analysis
(Markov chain Monte Carlo) integrates over the uncertainty in parameter values, the
parameter values were not fixed and only the general structure of the model was
specified.
Two independent runs were analyzed simultaneously, each with four Markov
chain Monte Carlo (MCMC) iterations during 2,000,000 generations and a sample
frequency of 1,000, resulting in 2,000 samples. I assumed independent runs converged
when the standard deviation of split frequencies from the two independent runs was
below 0.01, which occurred at generation ˜1, 210,000 and discarded the first 25% of the
samples collected to this point as the “burn in.” In addition, stationary was verified by a
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visual inspection of the estimated parameter values of the MCMC with TRACER 1.3
(Rambaut and Drummond 2005), to check for divergence in the estimated values from
the two independent runs or disparate fluctuations on the estimates. The topologies from
the remaining 2,798 trees in the CR and of 2,520 trees for ATP8 trees from each of the
two runs were used to generate a 50% majority rule consensus tree on MrBayes with a
clade support indicated as posterior probabilities.
We analysed CR region haplotypes from Arizona and northern México (Sierra
Madre Occidental) and haplotypes from the Sierra Madre Oriental from this study and
from (Onorato et al. 2007) in MEGA4 (Tamura et al. 2007). Neighbor-Joining method
(Saitou and Nei 1987), and Maximum Composite Likelihood (MCL) and bootstrap values
for the estimated phylogenetic tree.
Regional Genetic Differentiation
North America
To detected DNA polymorphism within the populations, we used DnaSP version
5 (Rozas et al. 2009) with the same CR sequences described in the methods. We
calculated the average number of nucleotide differences per site among haplotypes,
nucleotide diversity, Pi (Nei 1987), the average number of nucleotide differences, k
(Tajima 1983), Theta = 4Nu (where N is the effective population size, and u is the
mutation rate per nucleotide (or per sequence and per generation) (Nei 1987), and, Theta
is the per nucleotide under the finite sites model (Tajima 1996). We also used DnaSP to
produce several measures of the extent of DNA divergence between populations. For
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example, the nucleotide diversity of each population, the average number of nucleotide
substitutions per site between populations, Dxy (Nei 1987), and the number of net
nucleotide substitutions per site between populations, Da (Nei 1987).
Arizona and northern México
Network analysis
We used the 5 mtDNACR haplotypes found in Arizona and northern México as
input for NETWORK 3.5.1.0 (Bandelt et al. 1999). We used the median-joining (MJ)
network algorithm to draw the network, which allowed for multi-state data with the
default weight (10) and used epsilon value of 10, 20 and 30 to see network difference.
Sierra Madres, México
To determine genetic differentiation among black bears in the Sierra Madre
Occidental and Sierra Madre Oriental we used DnaSP version 5 (Rozas et al. 2009) with
CR sequences and with ATP8 sequences. DnaSP computes the nucleotide diversity of
each population, the average number of nucleotide substitutions per site between
populations, Dxy (Nei 1987), and the number of net nucleotide substitutions per site
between populations, Da (Nei 1987). We also used DnaSP to produce several measures
of the extent of DNA divergence between populations. DnaSP computes the nucleotide
diversity of each population, the average number of nucleotide substitutions per site
between populations, Dxy (Nei 1987), and the number of net nucleotide substitutions per
site between populations, Da (Nei 1987).
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Population Expansion
Using the program DnaSP version 4.10.4 (Rozas et al. 2003) we detected the
levels of mitochondrial haplotype diversity (h) and nucleotide diversity (π) for each group
(excluding indels). We performed FS (Fu 1997) and Tajima’s D to test for geographic
expansion within Arizona-Northern México and New México group, where significant
positive values indicate long-term isolation and negative values indicate recent
population expansion. Significance was determined based on 10,000 coalescent
simulations under a model of population growth-decline size.
RESULTS
Sample collection
We collected 565 samples: 405 (71.7 %) scat samples; 70 (12.4%) hair or hide
samples; 61 (10.8%) were blood, muscle tissue or cheek cells, and 29 were bone and
teeth samples (5.1 %; Fig.1).
DNA isolation
We did not extract DNA from 29 samples. We did not extract DNA from hair
samples without hair roots or less than 4 hair follicles, also we did not extract DNA from
scats that had grown mold; as a result we did not extract DNA from 29 samples. We
extracted DNA from 536 samples (94.8%).
DNA amplification
Samples (n = 363) were amplified for mitochondrial DNA (64.2%) for both
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mtDNA genes. Samples (n = 220) were amplified for 7 or more microsatellites (38.9%;
Table 2).
DNA sequence analyses
Mitochondrial DNA Control Region
We used 107 clean mtDNA-CR sequences in Arizona and northern México. We
detected five haplotypes in the 360 bp segment of mtDNA-CR (Fig. 3). Haplotypes were
distinguished by a single cytosine–thymine transition substitution and 3 insertion–
deletion mutations. The most common Arizona and northern México haplotype coincided
with haplotype D found in New México (Onorato et al. 2004), the second most common
Arizona and northern México haplotype coincided with haplotype E found in New
México (Onorato et al. 2004). One haplotype from the Arizona dataset, found only in the
Pinalenos Mountains, coincided with haplotype 19B (Wooding and Ward 1997) found
originally in California.
The 5 CR region haplotypes were found in Arizona and northern México (Fig. 9).
Haplotype D was the most common and widespread, occurring throughout the study area
at a frequency of about 80%. Haplotype E was also common whereas haplotype 19B and
two other haplotypes were rare but they were found in Arizona and northern México. The
haplotype found in the Sierra Madre Oriental coincided with haplotype C previously
found by Onorato et al. (2004).
The phylogenetic analysis using MrBayes produced a majority rule 50%
consensus tree. Two main clades were evident, one ancestral which contained black bear
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haplotypes from western North America including California and the Canadian Rockies,
the other group contained black bear haplotypes along the Rocky Mountains and east of
the Rocky Mountains. A subgroup of haplotypes within the eastern group includes black
bears from Texas, the México-Texas border, and the Sierra Madre Oriental in México.
An unresolved group of black bear haplotypes along the Rockies includes samples from
Arizona, the México-Arizona border, and the Canadian Rockies (Fig. 4). Similarly an
analysis using haplotypes found in Arizona, Sierra Madre Occidental (México), New
México, Texas and Sierra Madre Oriental (México) produced two groups; one for the
samples in Arizona, New México and Sierra Madre Occidental and one for Texas and
Sierra Madre Oriental. Haplotypes in Arizona and Sierra Madre Occidental are closely
related, demonstrated by the short branch lengths in the phylogeny (Fig. 6).
The Neighbor Joining tree with CR region haplotypes, in which we only included
samples from New México, Arizona and the north-western México (Sierra Madre
Occidental) and haplotypes found in Texas, north-eastern México (Sierra Madre
Oriental), showed two groups, one with the Sierra Madre Occidental haplotypes ancestral
to the second group, which included the haplotypes from the Sierra Madre Oriental.
ATP8
We used 101 clean sequences of the ATP8 gene including 213, which code for 71
amino acids. Nucleotide sequences were used for the phylogenetic analyses and amino
acids were used to detect synonymous and non-synonymous substitutions. The nucleotide
sequences produced 7 haplotypes, 1 present in the Sierra Madre Oriental and 6 present in
Arizona and northern México (Sierra Madre Occidental). Most substitutions were
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transitions in the third base position, and were non-synonymous; there were 2 amino acid
changes. Although six haplotypes were reported for the sky islands of Arizona and
northern México, only two of those were in Arizona (Fig. 10).
The phylogenetic analysis using MrBayes produced a majority rule 50%
consensus tree. Two clades are evident in the tree, one with black bears from the Sierra
Madre Oriental in México, and one with a polytomy of all the samples from Arizona and
Sierra Madre Occidental in México. Most haplotypes are shared between Arizona and the
Sierra Madre Occidental, however, some haplotypes are unique to the Sierra Mare
Occidental, México (Fig. 5).
Regional Genetic Differentiation
North America
Genetic diversity of the 33 downloaded sequences (haplotypes) indicated 29
variable nucleotide positions. The mean number of nucleotide differences per site
between sequences (π) was 0.027 and all observed substitutions were either transitions or
indels (insertions or deletions) of thymines. The phylogeny shows a split between
samples that originated from the California refuge, western clade, and the central and
eastern clade, which includes haplotypes that are found along the Rockies as a subgroup
of unresolved relationships, and the samples that most likely repopulated the U.S. from
the Florida refuge.
Sierra Madres, México
In the Control Region analysis, black bears from the Sierra Madre Occidental and
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Sierra Madre Oriental share no haplotypes; the average number of nucleotides differences
between black bears in the two areas is small (K = 1.3 and π = 0.04) (Table 3). The theta
per nucleotide under the finite site model was low (θ = 0.386).
The ATP8 region analysis showed no shared mutations, the average number of
nucleotide differences between black bears from Sierra Madre Occidental and Sierra
Madre Oriental is 2.24, the average number of nucleotide differences (K) 2.2, and π =
0.009 (Table 3). The number of net nucleotides per site between populations was low
(Da = 0.005).
The CR and the ATP8 regions show that black bears from the Sierra Madre
Occidental and from the Sierra Madre Oriental are not closely related. Our analyses
suggest that gene flow is not occurring between the two regions (microsatellite
information); and has not been in the recent past (mitochondrial DNA).
Network analysis
The most common CR haplotype was present in all sampling localities and it
seems to be the origin of the other 4 haplotypes that differ by 1 or 2 bp. The exception is
one haplotype, which was found only once, and it differed from all other haplotypes by a
minimum of 10 bp differences (Fig. 7).
Arizona and Sierra Madre Occidental, México
Microsatellite phylogeny
The Neighbor Joining and Maximum Likelihood trees both indicate the outgroup
samples (Sierra Madre Oriental) to be the most ancestral group, followed by the Sierra El
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Nido samples (Sierra Madre Occidental). The remaining populations form the ingroup.
The split of Sierra El Nido with the rest of the ingroup samples has bootstrap values of
62% and 52% (Neighbor Joining and Maximum Likelihood, respectively) (Figs. 2, 3).
Areas closer together geographically consistently cluster together in both trees. For
example, Mazatzal and Nutrioso mountains; Peloncillo and Chiricahua mountains;
Rincon and Pinaleno mountains; and the Huachuca Mountains and Sierra San Luis that
are geographically close but separated by the U.S.-México international border.
Population Expansion
The mtDNA-CR region was used to test for geographic expansion in all
Arizona and northern México (Sierra Madre Occidental) sequences and results
indicated a recent population expansion (Tajima’s D -2.3877, P < 0.00001) (Fig. 8).
The test was performed using the ATP8 gene and results also suggested population
expansion but not at a statisticaly significant level (-1.088, P < 0.06) (Fig. 11).
DISCUSSION
This study has limitations given the non-invasive nature of our sampling. Samples
such as hair, bone, cured hide, and scat have reduced amounts of usable DNA. The
quality and quantity of DNA from samples such as scat and hairs, collected noninvasively from the field, depends on time since the sample was deposited and the
amount of direct sun, humidity, rain, and other environmental conditions the sample was
exposed to. Under these circumstances, there are a limited number of PCR reactions
obtainable from each sample, thus a limited amount of data, from samples that contain
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any usable DNA at all.
The use of non-invasive techniques, such as scats collected in the field, avoids the
handling of animals under study. This is especially important for endangered species. But
black bears are not endangered. This study successfully used non-invasive techniques to
provide genetic samples to study a secretive large mammal that occurs at relatively low
densities, that otherwise would be more difficult to study. In this study we obtained
amplifiable DNA for 213 out of 354 (60.1%) of all the scats processed with mtDNA.
From the samples that amplified for mtDNA, 112 samples amplified for 7 or more
microsatellites.
Previous studies reported the mtDNA-CR as a useful marker to study species
phylogeny, population structure, and genetic diversity within and among individuals
(Waits 1997, Wooding and Ward 1997, Onorato 2004). The combined usefulness of the
mtDNACR and mitochondrial ATP8 gene in this study proved useful to detect genetic
variability, phylogeny, and recolonization patterns for black bears in southwestern North
America.
In this study, we re-examined patterns of genetic diversity and phylogeographic
structure in American black bears across their range using haplotypes downloaded from
GenBank and newly generated haplotypes from Arizona and northern México. Black
bears have been able to adapt to the changing environment for the past three millions
years, and it is the most abundant bear species in North America. Some populations show
high levels of genetic variability across North America, and high dispersal capabilities.
Phylogenetic analyses show 2 monophyletic clades. The “West” clade includes bears
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from California, the Kenai Peninsula, and the Canadian Rockies; this group is more
ancestral to other North American black bears. The second is an “East” clade that
includes black bears from Florida, Texas, Sierra Madre Oriental in México, and the
México-Texas border. This group also contains a “Central” subgroup which contains
bears from the Sierra Madre Occidental in northern México, Arizona, and the Rocky
Mountains. From this analysis, black bears in Arizona and northern México are more
closely related to the East clade (group “B” from Wooding and Ward 1997). One
Arizona sample provided by a hunter does not fit this relationship. This sample was from
a bear harvested in the Pinalenos, has a haplotype identical a haplotype found in
California (West clade). This haplotype is 10 mutations apart for all other haplotypes
found in Arizona and the East clade. Possible explanations of why the sample is so
different to all the other found in Arizona are not clear. Perhaps the taxidermist confused
the hide with other bear hides not taken in Arizona or possibly the haplotype is present in
Arizona but we did not detect it because our sample size is small.
The mtDNA-CR sequence divergence we found among all the sequences across
the U.S., including the black bears in sky islands (5.2%), similar to the previously
documented divergence of at least 5% (Wooding and Ward 1997) using the same genetic
marker. Intraspecific genetic divergence of large mammals is typically low (average
2.4%, Avise et al. 1998), making this level of divergence among black bears significant
and may result from the naturally fragmented nature of these Southwestern sky islands,
combined with fragmentation due to human factors. Fragmentation of habitat, whether it
natural or human caused, can lead to higher levels of genetic divergence.
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In the Arizona and northern México study area, we found 5 mtDNA-CR
haplotypes. These haplotypes did not vary in frequency across sampling areas (sky
islands versus mainland). The most frequent haplotype (1) accounted for over 60% of all
samples while the other haplotypes ranged in frequency from 0.7 to 0.1. The most
common haplotypes in Arizona and the México-Arizona borderlands coincided with
haplotype D (Onorato 2004). The network analysis of the CR haplotypes found in the
study area shows that the most common haplotype could have been the source of the
other more recently evolved haplotypes (Fig. 9).
The mismatch distribution analysis of the mtDNA-CR data set, which includes all
sequences from Arizona, northern México, and New México suggested the Arizona black
bear maternal lineage was the result of a population expansion from México (P < 0.01).
Similarly, the ATP8 mistmatch test suggested a population expansion (P < 0.06) although
barely significant. These results are based on two regions of mitochondrial DNA, 360 bp
of mtDNA-CR and 224 bp of ATP8, therefore the level of resolution is low.
Corroborating this result is that both microsatellite phylogenetic trees indicate Sierra El
Nido, the farthest south México population, to be more ancestral relative to other
populations in the study other than the outgroup population. Taken together, results from
mitochondrial DNA phylogenies, network analysis, the neutrality test, and microsatellite
phylogenies all suggest that black bears survived in the mountains of Sierra Madre
Occidental during the last glaciation (a third glacial refugium), probably in a small
number (demonstrated by the presence of 1 common haplotype that seems to have given
origin to the 3 close related haplotypes found in the area) and expanded northward after
85
the glaciers retreated.
Interestingly, black bears from the México-Texas border and Sierra Madre
Oriental (Madres and Cohahuila) do not share any haplotypes with black bears from the
Arizona-México border and Sierra Madre Occidental (Sierra San Luis, Sierra El Nido and
Sierra Los Ajos). In phylogenetic analyses using mtDNA-CR and ATP8, these two
mountains ranges also separated into different clades. It is noteworthy that the level of
mtDNA divergence found between these lineages suggests a long-term historical
isolation and divergence between lineages (π = 1.3, 0.36% divergence). Based on the
2.8% divergence per million years (Wooding and Ward 1997), we estimated that the two
black bear populations in México have been separated for about 130,000 thousand years.
If we considered 5 years for a bear generation, these two populations have been apart for
about 26,000 generations. Therefore, even though our sample size is small, our results
indicate the possibility that these two Mexican populations do not appear to have had
significant gene flow since the split about 130,000 years ago.
ACKNOWLEDGMENTS
We want to thank Mario Cirrett from the Los Ajos-Bavispe National Forest and
Wildlife Refuge for his assistance with fieldwork and The University of Arizona Genetics
Core (UAGC) for all their support during the laboratory work phase of this project. Stan
Cunningham helped with sample collection, Adrian Quijada help with edits to this
document.
86
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Fig. 1. Sampling locations
95
Fig. 2. Neighbor-Joining tree of 11 sampled population in the sky islands in Arizona
and northern México, based on pairwise Euclidean (Edwards and CavalliSforza 1964) and 1,000 bootstrap replicates (only bootstrap supported nodes
of 40 or higher are displayed on the tree nodes/branches).
96
Fig. 3. Maximum Likelihood tree of 11 sampled population in the sky islands in
Arizona and northern México, based on pairwise Euclidean (Edwards and CavalliSforza 1964) and 1,000 bootstrap replicated (only bootstrap supported nodes of 40 or
higher are displayed on the tree nodes/branches).
97
Fig. 4. MrBayes black bear phylogeny with mtDNA Control Region. The outgroup
common name (Ursus thibetanus) samples are indicated in red; the more ancestral
California lineage is indicated by green; black and brown indicate the second clade,
with the two shades of brown showing the haplotypes from the east, inside the group,
the dark brown is showing the haplotypes from Sierra Madre Oriental. Black are
samples in the Central group, which includes samples along the Rockies Mountains
and Arizona.
98
Fig. 5. MrBayes black bear phylogeny of ATP synthase subunit 8 (ATP8). In red
are shown outgroups, in brown the haplotypes from Sierra Oriental in México;
black shows all the haplotypes from Arizona and the north of the Sierra Madre
Occidental in México.
99
Fig 6. Neighbor Joining Tree of CR region haplotypes, with branch lengths and
bootstrap values.
100
Fig. 7. Haplotype network and distribution of 5 mitochondrial DNA Control
Region haplotypes identified in black bears in Arizona and Northern México. A)
haplotypes symbols shown in the haplotypes network. Symbol size is proportional
to haplotypes prevalence. B) Map that shows the distribution of the 5 haplotype
and solid colors (green, red and blue represent México and pattern colors
represent Arizona sky islands).
101
Fig. 8. Mismatch distribution for the clade Arizona, Northen México, New
México using mtDNA Control Region. Results of neutrality tests:
Raggedness index r = 0.0275, P < 0.01; Tajima’s test = -2.3877, P <
0.00001; Fu’s Fs = -23.6969, P < 0.00001
102
Fig 9. Geographic distribution of the 5 Control Region (CR) haplotypes by
sampling location.
103
Fig 10. Geographic distribution of the 6 ATP8 haplotypes by sampling location.
104
Fig 11. Mismatch distribution for the clade Arizona and Northen México
with ATP8. Results of neutrality tests: Raggedness index r = 0.067, P <
0.01; Tajima’s test = -1.088, P < 0.06; Fu’s Fs = -11.172, P < 0.001
105
Table 1. Samples used for the analysis by type and localities.
Bone,
Blood, muscle,
Hair,
teeth
cheek cells
hide
Scats
Sierra San Luis
215
Sierra El Nido
37
Sierra Los Ajos
6
3
Huchuca Mountains
21
5
7
8
Mountains
15
4
4
1
Peloncillo Mountains
21
1
Pinaleno Mountains
8
11
2
5
Rincon Mountains
75
2
38
16
Chiricahua
35
Mazatzal Mountains
Apache National
Forest
1
Nutrioso Mountains
6
TOTAL
405
5
4
2
7
29
61
70
565
106
Table 2. Samples from which DNA was extracted and successfully amplified with
mitochondrial DNA and with microsatellites.
7 or more
Sample Type
N
MtDNA
1 microsatellite
microsatellites
Scat
354
213
184
112
Bone-teeth
29
29
29
24
Blood
35
35
35
35
Hair
84
55
54
20
Hide
8
5
4
3
Muscle tissue
19
19
19
19
Cheeck cells
7
7
7
7
536
363
332
220
Total
107
Table 3. Genetic difference between black bears in the Sierra Madre Occidental
and Sierra Madre Oriental showing the average number of nucleotide differences
(K) and nucleotide diversity (π)
CR
Sierra
Madre
Occidental
Sierra
Madre
Oriental
Total
ATP8
K
0.05
π
0.0014
K
2.196
π
0.0099
0.25
0.0007
0
0
1.30
0.004
2.197
0.0099
108
Appendix 1.
A. Haplotypes for mtDNA Control Region analyses
1. Haplotypes 1-19 (GenBank accession numbers AF012305-AF012323) from
Fairbanks, Alaska; Banff National Park, Alberta; La Maurice National Park, Quebec;
Terra Nova National Park, Newfoundland; Fundy National Park, New Brunswick;
Yellowstone National Park, Wyoming; Bridger-Teton National Forest, Wyoming;
Florida; New México, Book Cliffs, Utah; Mendocino County, California; Yaak River,
British Columbia; South Fork of the Flahead River, Montana; North Fork of the Flathead
River, Montana; West Slope Ecosystem, British Columbia (Wooding and Ward 1997).
2. Haplotypes A-E (GenBank accession numbers AY334363-AY334367) from
northern Serranias del Burro and Sierra del Carmen -Sierra Madre Oriental, México; Big
Bend National Park, Black Gap Wildlife Management Area, and the Trans-Pecos region,
Texas (Onorato et al. 2004a). Sequence (GenBank accessions numbers WF198756)
(Robinson et al. 2007) (from the Kenai Peninsula).
3. Haplotypes F-M (GenBank accession numbers FJ619652-FJ619659) samples
from Manitoba, Canada; Cook County, Minnesota; White River National Wildlife
Refuge, Ozark Mountain; Quachita Mountains, Arkansas; Quichita Mountains,
Oklahoma; Tensas River and Inland, Luisiana (Van Den Busshe et al. 2009).
4. Haplotypes from Ursus americanus from Newfoundland (GenBank accession
number UAU34260-UAU34266) (Paetkau and Strobeck 1996)
5. Haplotypes from Ursus americanus kermoidei (GeneBank accession number
AF007936) (Byun et al. 1997)
109
6. Haplotypes from Ursus thibetanus mupinensis (Yu et al. 2007) GenBank
accession number DQ402378 to be used as outgroup in the analysis.
B. Haplotypes downloaded for ATP8 analyses
Haplotype 1. Accession numbers AF303109 and AF303111(Ursus americanus,
Alberta, Canada) (Delisle and Strobeck 2002),
Haplotype 2. EF076773 (Ursus thibetanus formasanus), (Hsieh et al. 2006)
(Ursus thibetanus mupinensis),
Haplotype 3. DQ402478 (Hou et al. 2007) to use as outgroups in the analysis.
110
APPENDIX C
GENETIC STRUCTURE OF THE AMERICAN BLACK BEAR IN THE SKY
ISLANDS, ARIZONA AND NORTHERN MÉXICO
CORA VARAS, School of Natural Resources and the Environment, University of
Arizona. Tucson, Arizona, 85721, USA
CARLOS LOPEZ-GONZALES, Universidad Autónoma de Querétaro, Querétaro C. P.
76010
PAUL R. KRAUSMAN, Boone and Crockett Program in Wildlife Conservation.
University of Montana, Missoula, Montana 59812, USA
MELANIE CULVER, School of Natural Resources and the Environment, University of
Arizona, Tucson, Arizona, 85721, USA
Cora Varas
School of Natural Resources
University of Arizona
P.O. Box 210043
Tucson, Arizona 85721-0043
Phone 520 621 2161; Fax 520 621 8801
Email [email protected]
KEY WORDS: Black bears, microsatellites, gene flow, sky islands, fragmentation
111
Abstract
Habitat fragmentation, both natural and due to increased human impacts in the
sky islands region of Arizona and northern México, has important implications to genetic
diversity and population structure of local taxa. Black bears (Ursus americanus) inhabit
the sky islands of the Madrean archipelago and are currently a species of public interest
and management focus in the U.S., and a species of special concern in México. We used
10 nuclear DNA (nDNA) microsatellite markers to investigate population structure of
black bears in the sky islands of Arizona and northern México. We used spatial and nonspatial Bayesian assignment models to evaluate nDNA genetic structure and cluster
individuals into genetically distinct groups. Subtle population structure was detected
indicating high levels of gene flow in recent generations, especially in the sky islands,
while lower gene flow was detected between the “mainland” Mazatzal Mountains and the
sky islands. The GENELAND non-spatial analysis indicated two populations separating
the sky islands and the Mazatzal Mountains, with an average Fst of 0.474; while three
populations were found using STRUCTURE and TESS and Connectivity between the
three groups that included the Arizona and northern México sky islands was high with an
FST of 0.07 (range = 0.004 to 0.097). These results suggest that in the Arizona sky
islands’ black bears should be considered as a single population for conservation
purposes instead of the smaller Game Management Units used to manage populations in
Arizona. Also our data shows that black bears from Arizona and northern México belong
to the same population, therefore an international agreement should be in place to
maintain the long-term survival of black bears in the sky island region.
112
Introduction
The black bear (Ursus americanus) is the most common bear species in North
America. It lives throughout much of the continent, from northern Alaska to the northern
part of México and from the east to the west coast. Black bears were first described in
Arizona in the early 1800s. Primary habitats for black bears are coniferous and broadleaf
deciduous woodlands. In Arizona and northern México, black bears inhabit upperelevation coniferous forests, or “sky islands,” that rise from the Sonoran and Chihuahuan
deserts. The sky islands have been isolated from each other by desert and scrub
vegetation for about 9,000 years (Turner et al. 1995, Warshall 1995). Furthermore, a
single sky island is too small to support a viable black bear population; therefore, black
bears migrate between sky islands through the desert lowlands (Hoffmeister 1986,
LeCount and Yarchin 1990).
The fragmented nature of sky islands has produced isolated populations of many
species that occupy them. This separation results in morphological and genetic
differentiation of flora and fauna. Population differentiation has been demonstrated in
other sky island species including the lemon lily (Lillium parryi; Linhart and Premoli
1994), snails (Sonorella sp; Miller 1967), beetles (Scamphontus petersi; Ball 1966), the
jumping spider (Habronattus pugilis; Maddison and McMahon 2000, Masta 2000), the
mountain spiny lizard (Sceloporus jarrovii; Colwell and Gatz 1993), the lizard malaria
(Sceloporus jarrovii isolate; Mahrt 1987) the canyon treefrog (Hyla arenicolor; Barber
1999), the Mt. Graham red squirrel (Tamasciuris hudsonicus grahamensis; Sullivan et al.
1994), and the skunk (Mephitis mephitis; Rheude 2008).
113
Factors, such as distance, that influence the dispersal of plants, insects, reptiles,
and small mammals may have little or no effect on large mammals such as black bears
due to their capacity for long-distance dispersal. Dispersing bears can travel hundreds of
km from where they were born (Rogers and Hoagland 1995, Beckmann and Lackey
2004). The potential for long-range dispersal offers a mechanism by which population
connectivity, and metapopulation structure can be maintained. Similarly, natural barriers
such as rivers or patches of desert that preclude movement of smaller species may have
little effect on bear movement. A combination of distance or human created barriers (e.g.
human use of desert lowlands, housing developments in the valleys between mountain
ranges, recreational use of the land, agricultural, highways, and the recently constructed
security international U.S.-México border fence, may be important barriers to the
movements of black bears (LeCount and Yarchin 1990, Schenk and Kovacs 1995,
Schenk et al. 1998) between sky islands. Therefore, natural and anthropogenic barriers
can disrupt the connectivity among bear populations, which is critical for long-term
viability especially in the fragile sky-island ecosystem that transcends the U.S.-México
border.
In México, black bears are listed as endangered (Servheen et al. 1999b), and they
have lost at least 30% of their historical range (Pelton and van Manen 1997). However,
the availability of scientific literature on black bears in México is limited (MoctezumaOrozco and Doan-Crider 2005). Research has primarily investigated populations in
northern Sonora (Sierra-Corona et al. 2005), Coahuila (Doan-Crider and Hellgren 1996,
Onorato et al. 2007), and Nuevo Leon (Zepeda-Gonzalez et al. 1997). Bear occurrence is
114
poorly known in other parts of México; however there are additional records of black
bears in the states of Chihuahua, Zacatecas and Durango (Sierra-Corona et al. 2005). The
main factors threatening black bear survival in northern México are habitat loss and
poaching (Baker and Greer 1962, Medellin et al. 2005). Additionally, economic priorities
make it difficult for the government to enforce existing regulations about poaching and
habitat destruction. The lack of information about migration patterns and connectivity
among populations within México (Sonora in particular) further inhibits conservation and
management efforts.
Black bear populations are difficult to inventory because they occur in relatively
low densities and are secretive by nature. A variety of techniques have been used to
obtain population and ecological information. For example, direct observation has been
used to estimate small population sizes and trends of grizzly bears (Ursus arctos) in
Glacier and Yellowstone National Parks (Hayward 1989); other techniques such as,
capture-mark-recapture (Clark and Eastridge 2006), bait stations (Clark et al. 2005),
mark-resight (Matthews et al. 2008), and radiotelemetry (Miller et al. 1997, Vashon et al.
2003) are commonly used to detect population size and home ranges in bear species.
Recently, however, molecular markers alone or in combination with non-invasive
techniques have provided an inexpensive and efficient alternative methodology to answer
a full range of population ecology questions about bears (Breck et al. 2008, Kendall et al.
2009).
The first attempt to measure genetic diversity and genetic structure in bear
populations was made using allozymes and restriction digestion of mtDNA (Wathen et al.
115
1985, Shields and Kocher 1991). These methods proved to be uninformative because they
detected very little genetic variability. Microsatellites and mtDNA control region
sequences have been used more recently in population studies of American black bears
and have uncovered substantial genetic variation. To date, 28 dinucleotide and 21
tetranucleotide microsatellites are available for researchers to use in black bear studies
(Paetkau and Strobeck 1994;1995a, Taberlet et al. 1996, Taberlet et al. 1997, Paetkau et
al. 1998b, Paetkau 1999, Kitahara et al. 2000, Wilson et al. 2005, Sanderlin et al. 2009).
Microsatellite analyses have detected population structure in black bears and have
identified populations that have evolved independently and described the genetic
structure among black bear populations in fragmented environments (Marshall and
Ritland 2002, Csiki et al. 2003, Belant et al. 2005, Craighead et al. 2006, Onorato et al.
2007, Boulanger et al. 2008, Kendall et al. 2009). Despite evidence that sky islands have
produced genetic isolation in plants, insects, reptiles, and small mammals; no molecular
studies have been done with large mammals. Also, the influence of factors such as
variable distances among sky islands, and different proximities of barriers to gene flow,
for black bears remain unknown.
Our objective is to detect the overall level of genetic diversity and population
structure of black bears in Arizona and northern México. Our results will distinguish
whether bear populations in the region are panmictic (connected and interbreeding) or
whether there is population subdivision. If structure exists, then bears in this study area
are composed of smaller groups such as evolutionarily significant units (ESUs) or areas
of management. To further characterize the structure, we will identify any restrictions to
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gene flow between populations, and establish if any connectivity exists between the sky
island and “mainland” populations. This data, combined with estimates of genetic
diversity, will be placed in the context of bear management to assist wildlife managers in
the development of a scientifically based bear management strategy for Arizona and
northern México.
MATERIALS AND METHODS
Study area
In Arizona, we collected black bear samples from the Huachuca, Peloncillo,
Pinaleno, Chiricahua, Catalinas and Rincon mountains, and from a continuous habitat
range (the Mogollon Rim) in northern Arizona which includes Tonto National Forest
(Four Peaks and Mount Ord), Escudillo Mountains, and Prescott National Forest. In
northern México, we collected samples from the sky islands Sierra Los Ajos (SLA) and
Sierra San Luis (SSL), and Sierra El Nido (SEL) representing a more continuous habitat
range (Fig1).
Black bear habitat is similar across sky islands in Arizona and México, and
includes pinyon (Pinus spp.)-juniper (Juniperus spp.), pine-oak (Quercus spp.)
forests, oak woodland with second growth, open low forest, mesquite (Prosopis spp.)
grasslands, riparian forest, and chaparral ecosystems (Palacio-Prieto et al. 2000). In
the Sonoran Desert, sky islands plant species include: southwestern white pine (Pinus
strobiformis), Western Yellow pine (P. ponderosa), alder (Alnus tenuiflolia), Rocky
Mountain fir (Abies Lasiocarpa), Engelmann spruce (Pices engelmanni), Netleaf oak
(Quercus rugosa), Silver-Leaf oak (Q. hypoleucoides), Rocky Mountain white oak
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(Q. gambelii), Arizona white oak (Q. arizonica), basketgrass (Nolina microcarpa),
Rocky Mountain maple (Acer glabrum), bigtooth maple (A. grandidentatum),
alligator juniper (Juniperus deppeana), desert agave (Agave palmeri), Arizona
smooth cypress (Cypressus arisonica), among others (Wallmo 1950, Bowers and
McLaughlin 1987).
In the Chihuahuan Desert, sky islands plant species include: Mexican pinyon
(Pinus cembroides), Emory oak (Quercus emoryi), Black oak (Q. mcvaughii), SilverLeaf oak (Q. hypoleucoides), Oneseed juniper (Juniperus monosperma), and Mexican
Manzanita (Arctostaphyllos pungens), grasslands: blue grama, (Bouteloua gracilis)
and Sideoats grass (B. curtipendula), annual muhly (Muhlenbergia minutissima),
wolfstail (Lycurus phleoides) (Shreve 1939, LeSueur 1945, Villarreal and Yoolt
2008).
The Sierra los Ajos (SLA), located east of Cananea, Sonora, is situated
between México’s Sierra Madre Occidental and the Rocky Mountain region of the
western United States. Elevation of the Sierra los Ajos ranges from 1050 m to 2625
m. Biological and floristic diversity is known to be high, due to its unique geographic
location (Fishbein et al. 1994).
In the Sierra San Luis (SSL), our study are was El Pinito Ranch, Sonora, which is
located in the Sierra San Luis between 108° 56’ 46’’ N latitude and 31° 11’ 49’’ W
longitude (Sierra-Corona et al. 2005). In the Sierra el Nido (SEN), samples were
collected in (Santa Monica Ranch) located at Latitude: 29° 33' 0 N, Longitude: 106° 47'
60 W, with elevation from 2,500 to 3,040 m.
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Sample collection
Sonora, México
We collected scat and hair samples using non-invasive techniques. In SLA, we set
20 hair traps using Simpson Strong Tie mending plates (3" x 6"). We attached the plates
with nails to trees at about 1 m. above the ground level. We used punctured sardine cans
suspended in trees as bait. We set 20 hair traps in the SLA approximately 1 km from each
other, and we collected bone, tissue and hair samples donated by the Los Ajos-Bavispe
National Forest and Wildlife Refuge. In SSL, we sampled at Ranch El Pinito by
establishing 3 km long transects. We walked the trails every other week looking for bear
scat. Samples were collected for 22 days in June and July (2007), and for 28 days in
October and November 2002 in El Pinito Ranch and from October to December 2007 in
Sierra El Nido. We recorded locations of scat and hair samples with a portable Global
Positioning System (GPS) for each scat and hair sample. Tissue samples (i.e., hair, bone,
muscle, blood) were collected in 2 ml Eppendorf tubes with lyses buffer. Each hair and
bone sample was collected in a small paper envelope and scat samples were collected in a
paper bag. All scat, bone, and hair samples transported to the laboratory for long-term
storage and kept at -20° C until they were used for DNA extraction.
Arizona, U.S.
Black bear samples were collected from hunter-killed bears and supplemented
with non-invasive scats and hair snare collection on public lands. For samples obtained
from hunters, we recorded the location of each sample according to the verbal description
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of the hunting location on the Arizona Game and Fish Department game management
maps. Tissue samples were collected in blood buffer in a 2 ml screw cap plastic tube and
stored at -20˚C until DNA extraction was performed. Non-invasive samples locations
were recorded with the UTM (Universe Transverse Mercator coordinate system). The
UTM for each sample was plotted using a manual ArcGIS 9.0 (ESRI, Redlands,
California). When exact locations were not available, we constructed a 500-m buffer
around the estimated location and randomly located the sample points at unique locations
within that buffer. This point relocation was used to facilitate visualization of sample
points. Using ArcGIS 9.0, we constructed spatial distance models with the UTMs from
each population. The error in plotting reported hunt locations was expected to be minimal
in comparison to the home range of a black bear, which would extend several kilometers
beyond the point of capture (Kernohan et al. 2001).
DNA isolation.
We extracted all DNA in the Culver Conservation Genetics laboratory at the
University of Arizona. Whole genomic DNA was extracted from muscle tissue, blood,
bone, scat, and hair following different protocols. We extracted and amplified DNA from
532 samples.
We purified DNA from approximately 25 mg of muscle tissue, 100 ul of blood,
50 ul of a cheek cell suspension, or 1–10 hair follicles using the Qiagen DNeasy tissue
extraction kit following the manufacturer's protocol (Qiagen Ltd., Crawley, West Sussex,
United Kingdom). We pulverized bone samples into powder using a steel mortar and
pestle and 25 mg was decalcified with EDTA (concentration, ph and company) for 5
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days. DNA was then extracted also using the Qiagen DNeasy tissue kit.
Bone samples were processed by pulverizing bone pieces and decalcifying 25 mg
with EDTA (0.5M pH 8.0 ph Amnion Catalogue number AM9260G) for 5 days
(Hagelberg and Clegg 1991). DNA was then extracted using the Qiagen DNeasy tissue
kit.
For scat samples, we scraped between 0.40 to 0.60 grams of the surface of the
scats to obtain epithelial cells for extraction. The QIAmp® Stool Mini Kit (Qiagen Inc.,
Valencia, California) was used following the manufacturer’s protocol with one
adjustment to the DNA elution step. For DNA elution, we added 50 µl of buffer AE to
the Qiagen column, centrifuged, washed with 50 µl of H2O, and centrifuged once more
eluting DNA in a final volume of 100 µl. We used 1 negative control with every 15
samples extracted.
Because of poor DNA yield from scat and insufficient geographic information,
not every sample was used in the final analysis. The final data set included 173 black
bears genotyped for a minimum of seven microsatellite loci and the sex identification
locus.
We confirmed the species from scat and hair samples using a length
polymorphism in the mitochondrial DNA (mtDNA) control region (Paetkau and Strobeck
1996). We extracted DNA from scats and hair in a laboratory exclusively used to process
samples with low DNA yield. This laboratory is located in a building separate from
where other animal DNA samples are processed to avoid contamination.
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Microsatellite DNA data collection
Black bear DNA was amplified using 12 ursid microsatellite loci: G10B, G10H,
G10L, G10M, G1A, G10J, G1D, G10O, CXX20, G10X, Mu59, and Mu50 (Paetkau and
Strobeck 1994;1995a;b, Paetkau et al. 1998b, Woods et al. 1999). Fluorescently labeled
forward and un-labeled reverse primers were synthesized by Invitrogen (Life
Technologies, Carlsbad, California).
Three PCR reactions were optimized and all contained 1.5 µl Promega 10 X
buffer, 0.3 ul of 10 mM dNTPs, 0.08 units of Taq DNA polymerase (5 units/µl), 0.25 ul
of 20 µM forward and reverse primers, and 5 µl template DNA in a final reaction volume
of 10 µl. Microsatellite DNA loci G10O, G10B, G10H, G1D, CXX20, Mu59 used 1.5
mM MgCl2; loci G10M, G10L, G10J, Mu50 used 2.5 mM MgCl2; and loci G1A, G10X
used 3.5 mM MgCl2. We used five thermal profiles that differed only in their annealing
temperature: 94oC for 3 minutes, 40 cycles of 94 oC for 30 seconds, 30 seconds of
annealing temperature (60 oC for G10B, G10H; 62 oC for G1A; 54 oC for G1D, G10J; 52
o
C for G10O, G10M, G10L, G10X, and 50 oC for CXX20, Mu50, and Mu59), 72 oC for
30 s, followed by a final extension of 72 oC for 5 minutes. All loci were genotyped using
fluorescence fragment analysis technology (ABI Prism 3730 Genetic Analyzer, Applied
Biosystems, Foster City, California) at the University of Arizona Genetics Core
(http://uagc.arl.arizona.edu). Microsatellites alleles were scored with Genotyper 3.7
(Applied Biosystems, Foster City, California) software.
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Microsatellite data precautions
To minimize microsatellite genotyping errors we followed the error testing
procedures outlined in Woods et al. (1999) and Paetkau (2003). To control for allelic
dropout, each PCR amplification was repeated three times. Samples were scored as
heterozygotes at a locus if both alleles appeared clearly distinguishable twice among the
three replicates. Homozygotes were scored if at least two replicates showed identical
homozygote profiles.
Two people independently scored each genotype, for each locus. These scores
were compared, and one final data set was constructed for further analyses. Genotypes
from different samples were considered to represent the same individual when all alleles
at all loci were identical.
Species identification
We amplified and sequenced 360 base pair fragment of the mitochondrial DNA
control region. We used the program BLAST to compare our sequences with those
previously deposited in Genbank (www.ncbi.com) to identify the species of origin based
on a maximum identity cutoff value of 99 % or higher.
Power analysis
We used the program CERVUS 3.0 (Kalinowski et al. 2007) to quantify the
power of this set of ten microsatellite loci by computing the probability of identity (PI) the overall probability that two individuals drawn at random from a given population
share identical genotypes at all typed loci (Paetkau and Strobeck 1994). Also, because
bears frequently travel in siblings groups (Robinson et al. 2007), there is the possibility
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that full siblings may have been sampled within the study area. Thus, we also computed
the PI between siblings.
Population genetic analysis
Variation at 10 microsatellite DNA loci was summarized by allele frequencies
and observed and expected heterozygosity using GENALEX 6.2 (Peakall and Smouse
2006). We examined all genotype frequencies for deviations from Hardy-Weinberg
Equilibrium (HWE) and all pairwise combination of loci for linkage disequilibrium in
GENEPOP 4.0 (Raymond and Rousset 1995, Rousset 2008). We estimated the frequency
of null alleles in MICRO-CHECKER 2.2.3 (Van Oosterhout et al. 2004) with
dememorization = 1000, batches = 100, and iterations per batch = 1000 (Guo and
Thompson 1992), and included only samples that have seven or more scored loci. We
adjusted all P-values using Bonferroni correction for multiple comparisons (Bonferroni
1936 ). We calculated inbreeding coefficients FIS and FST for each locus in each sampling
group (Weir and Cockerham 1984); we also calculated pairwise FST using ARLEQUIN
3.01 (Excoffier et al. 2005) to measure differentiation between groups found in the
analysis. The calculated genetic distance based on pairwise FST was visually assessed by
producing a multidimensional monotonic plot (MDS) with NTSYS (Exeter Software,
NTSYS pc 2.1, Setauket, NY). Goodness of fit was measured by using the stress test
(Kruskal and Wish 1978). We measured allelic richness using Fstat 2.9.3.2 (Goudet
2001). We also inferred the rate of recent migration between the sampling locations and
among the resulting genetic groups in BayesAss 1.3 (Wilson and Rannala 2003).
Samples that amplified for four or more microsatellites were selected for sex
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determination using polymorphism in the amelogenin gene. Primers SE47 and SE48
(Yamamoto et al. 2002) were used to PCR amplify the X and Y chromosome amelogenin
gene products. The PCR reaction and cycling conditions are described in Yamamoto et
al. (2002). To determine sex of individuals, we examined the fragment size of PCR
products on a 2% agarose gel stained with ethidium bromide.
Population structure analysis
STRUCTURE
We used STRUCTURE 2.3.1 (Hubisz et al. 2009) to assign individual bears to a
cluster or population of origin based on their multi-locus genotypes with regard to where
the samples were collected. Allele frequencies were assumed independent and analysis
were conducted with 100,000 iterations of burn-in followed and 200,000 repetitions of
Markov Chain Monte Carlo. We did four different analyses: admixture analysis with
correlated allele frequencies, admixture with frequencies non-correlated, non-admixture
with correlated frequencies, and non-admixture with non-correlated frequencies to
compare results. The admixture model assumes that each individual draws some
proportion of membership (q) from each of (K) clusters (Pritchard et al. 2000a, Pritchard
et al. 2000b). An individual bear was placed in a cluster if q > 0.85 for that cluster. If q >
0.40 for both clusters, the genotype profile indicated mixed ancestry, suggesting the
individual may be the result of mating between individuals from the two clusters. In this
analysis we used the sampling location to modify the prior probability of the clustering
analysis. The models in STRUCTURE 2.3.1 allow much better performance on some
data sets where there are too few loci or individuals, or not enough divergence, for the
standard structure models to assign individuals to clusters.
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To infer the number of populations, we proceed with successive runs (from K=1
to K= 12) by increasing the number of clusters, and selected the number of clusters with
the highest likelihood (we used the mean for the 20 iterations for each run). Because this
method might not be always accurate, we also used the ∆K (base on the rate of change in
the log probability of data between successive K values) measure to provide a better
estimate of the true K (Evanno et al. 2005).
TESS
We used TESS 2.1 (Chen et al. 2007, Durand et al. 2009) to assess the benefit of
including geographical coordinates into more classical analyses such as STRUCTURE.
TESS applies principles of Bayesian computation, a well-defined background with a long
tradition in statistics (Gelman 2003). It is based on highly validated methods combining
Gibbs sampling and Metropolis–Hastings algorithms. The algorithms provide an optimal
means for comparisons with the results of STRUCTURE (Chen et al. 2007). We used
UTMs for each sky island in the center of the sample distribution and asked TESS to
randomly assign the individual's UTMs around this point. Then, we repeated the analysis
using 35 dummy UTMs coordinates in the sky islands from areas that we did not sample.
We first used the model without admixture and Kmax = 2, then we increased the number of
K (clusters) until the DIC values were low and stable or varied little. Then, we performed
the analysis using the admixture model with the inferred K, and ran 100,000 MCMC runs
proceeded by 25,000 burn-in sweeps with Kmax = non-admixture value first, and then with
Kmax +1. We repeated each of the runs 100 times. STRUCTURE assumes HWE within
populations and linkage equilibrium between loci (Hubisz et al. 2009).
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GENELAND
We used the population analysis using R and GENELAND (Guillot et al. 2005a,
Guillot et al. 2005b, Guillot and Santos 2009). This method is an efficient tool for
inferring the number of populations at HWE, and also for locating the genetic
discontinuities within a landscape between those populations (Guillot et al. 2005a), and
we account for the presence of null alleles. For the analysis we used one GIS value for
each population by processing 10 independent MCMC runs of the spatial D-model. We
used priors on K-uniform between 1 and populations occupy spatial domains rather
similar to 12. Each run was done with 200,000 iterations and a 100,000 burn-in. We ran
the model with correlated and uncorrelated frequencies. The posterior distribution gave a
mode at K = 4. Then the model was rerun along 50,000 iterations with a fixed value for K
= 6. We derived maps of the posterior probability for any sample to belong to each
population.
When using multiple assignment tests, the representation of population structure
may differ among methods. We used the same criteria as Robinson et al. (2007) for
selecting among options: admixture between groups was minimal, linkage disequilibrium
and HWE deviations were minimal, allele frequencies differed significantly between all
groups, FST values indicated significant divergence between all groups, and geographic
overlap between groups was minimal.
Isolation by distance analysis
We conducted population-based Mantel test Genepop 4.0 (Raymond and Rousset
1995, Rousset 2008). The significance of IBD was assessed through 999 randomizations.
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The Mantel test assumes that a single process is generating the pattern of correlation
between variables. This assumption, termed stationary, may be violated if the sample
population is subdivided into distinct units each governed by different processes (Fortin
and Dale 2005). In the population genetic context this means that, if different processes in
distinct genetic groups govern gene flow and genetic distance, separate tests within each
continuous group may be more appropriate.
Human-Mediated Black Bear Translocations
We compiled data from the Arizona Game and Fish Department detailing
translocations of black bears within Arizona between 1998 and 2007.
RESULTS
Sample collection, DNA isolation, and microsatellite amplification
We collected 536 samples; scats (66.2%) and hair (15.7%), bone and teeth
(5.4%), hair (15.6%), muscle tissue (3.5%), hide (1.8%), and check cells (1.3%) (Table
1).
Species identification, microsatellite amplification, and individual identification
We obtained 363 purified DNA samples were obtained, all were submitted to the
program BLAST (citation here) and 363 were identified as black bear. We uploaded
sequences to the NCBI web page (http://www.ncbi.nlm.nih.gov) to confirm that our
samples belong to black bears. We amplified 220 black bear DNA samples for 7 or more
ursid microsatellite DNA loci and 332 samples were genotyped for at least one
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microsatellite. For robust population genetic and population structure analyses we needed
a minimum of seven microsatellite loci genotypes per sample. We generated a composite
genotype of 7 loci for 220 DNA samples. Our analysis to identify all unique individuals
resulted in 173 individuals from those 220 DNA samples.
Power analysis
We distinguished closely related individuals or recaptured individuals based on
the low probability of identity (PI = 1.06 x 10-6) and the probability of siblings (PS = 2.19
x 10-3) with 7 or more microsatellites.
Population genetic analysis
We surveyed 12 loci, locus Mu50 did not amplify consistently across individuals,
and loci G10X showed insufficient variation in this dataset, therefore, these two loci were
excluded from our analyses. The 10 loci surveyed in 173 bears were highly variable and
informative; the mean number of alleles for locus was 13.9 (range of 8 to 23). The total
allele-based error rates were 0.4% for hair samples and 1.7% for scat samples. Locusspecific error rates averaged 0.8% (range = 0% at locus G10B to 3.04% at locus G10H).
Null allele presence averaged 10% (range = 0.04 at loci G10L to 0.22 at locus G10H, and
G10B). The same loci that showed higher presence of null alleles showed presence of
allele drop out.
Using Genepop 4.0 (Raymond and Rousset 1995, Rousset 2008), the analysis
showed eight of the sampled populations were in HWE (Sierra El Nido, Sierra Los Ajos,
Apache National Forest, Prescott National Forest, Chiricahuas, Peloncillo mountains and
Pinalenos mountains) and four populations were out of HWE (Sierra San Luis, Mazatzal
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mountains, and Huachuca, and Rincon mountains). The mean number of alleles for
locus was 13.9 (range = 8 to 23)
The mean migration rate for black bears among sky islands as calculated by
BayesAss is a rate of 0.0207 (4.53 x10 -10 to 0.13). However, there is a much lower
migration rate between populations that are more than 500 km apart (e.g., Mazatzal
Mountains and San Luis populations have a migration rate of 0.0066 (SD = 0.0060, CI =
0.00017 to 0.022)) (Table 5).
A 2-dimentional, monotonic MDS plot displayed little population differentiation
among sample groups (Figure 3). It had a stress value of 1.7, a good to fair fit by
Kruskal’s and Wish’s (1978) index. The 11 sampling locations cluster complementary to
their geographic proximities, as anticipated when assuming gene flow. For example, the
three sampling localities in the north of the study site, Mazatzal Mountains, Nutrioso
Mountains, and Apache National Forest clustered close together, also the Huachuca
Mountains clustered with the Sierra San Luis. However, the Rincon, Pinaleno and
Peloncillo Mountains did not group together as expected and neither did the three
Mexican sampling localities. Additionally, geographically distant groups are separated
from the other groups.
Human mediated translocations
Arizona Game and Fish Department relocated 46 black bears between 1998 and
2007. Six were moved to captive facilities (1 to the Phoenix Zoo and 5 to a rehabilitator)
and 40 were relocated to natural habitat. Six bears were moved to the same sky island
were they were tapped (3 males, 2 females, 1 unknown), 3 bears were moved to a nearby
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sky island (1 female and 1 male were moved from the Pinalenos to the Chiricahuas, and 1
male from the Chiricahuas to the Pinalenos). Other bears were moved a different area
from where they were trapped, for example a male bear was moved from Thomas,
Arizona to the Pinalenos, a female was moved from the Rincons to the Peloncillos, three
bears (1 female with a cub and an unknown bear) were moved from Game Management
Unit 38M (between Saguaro National Park and the San Xavier Reservation) to the Santa
Rita Mountains. For a complete list see Appendix 2.
Population structure analysis
STRUCTURE 2.3.1 showed that black bears in Arizona sky islands and northern
Sonora belong to three genetic groups using ∆K analysis (Evanno et al 2005). The results
were consistent among the four models used (e.g., admixture-correlated, admixture-no
correlated, non-admixture-correlated and non-admixture-non correlated) (Table 2).
Our TESS 2.1.1 analysis with the first two models (no-admixture correlated and
no-correlated models with no-dummy variables) produced one population that included
black bears in all Arizona sky islands and México. The last two models (admixturecorrelated and no-correlated models) with 35 dummy variables (35 UTM locations where
black bears were not sampled) resulted in two populations (best DIC value= -3776) (Fig
4a).
STRUCTURE 2.3.1. Resolved three populations structured as follows: Population
1, Mazatzal Mountains; Population 2, Rincon, Huachuca, Pinaleno, Galiuros, Chiricahua,
Escudillo, and Peloncillo mountains, with the Mexican sky islands, Sierra Los Ajos and
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Sierra San Luis; and Population 3 that included Sierra el Nido.
Our results with GENELAND using the null allele model with non-spatial
analysis produced three populations. The spatial partition in three populations with high
posterior probabilities considerably (but not entirely) decreased genetic structure within
samples from FIS = 0.180 to 0.088 (Table 3), with single-population FIS values ranging
from 0.038 to 0.110. HWE could not be rejected in two of the three inferred populations
(Fisher’s exact test, P = 0.05; Raymond and Rousset 1995). Hence, the spatial method
gives strong evidence for the presence of three populations; this confirms previously
detected populations when uusing non-spatial statistical approaches. The analysis of
genetic variability within and among the three groups (BayesAss 1.3) showed that most
of the genetic variability was among individuals within the sub-populations (0.807) than
among populations (0.1872).
Black bears in the area formed three clusters as stated above. The Mazatzal
Mountains group had a Ho of 0.76 and He of 0.78 and the two groups (east and west) sky
islands had similar values, 0.80 for Ho and He. Overall FIS was non-significant in all
groups and the FST = 0.072 (0.046 – 0.097) (Table 4). Analysis showed a medium
amount of movement of three migrants per generation across the desert grassland
between the groups. The movement between populations of 1 to 10 migrants is a medium
level of movement per generation (Mills et al. 2003).
Isolation by distance analysis.
Analysis of the correlation between the linear distance among the sky islands
(km) and the genetic differentiation (FST /1-FST) show a strong positive and significant
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correlation between distance and genetic differentiation (P = 0.02). Distance explained
59% (R2 = 0.59) of the genetic differentiation among the sampled areas (FST values).
DISCUSSION
Forty-one samples were not used for DNA amplification: 38 were scat samples
and 3 were hair samples. Extracting DNA from scats is not always possible from noninvasive samples because of the limited amount of DNA, for example, 5 hair roots are
needed for each hair sample to amplify microsatellites, scats that are not kept dry can
have mold and therefore quality and quantity of the DNA is compromised.
Our DNA amplification success rate from black bear scat samples for
mitochondrial DNA regions was similar to previously reported (62%) (Adams et al. 2003,
Prigioni et al. 2006, Vine et al. 2009). We had 363 samples that amplified for mtDNA
genes and the number was reduced to 332 samples that amplified for at least 1
microsatellite, and 220 (66%) samples amplified for 7 or more microsatellites (and were
used for the final analysis. This success rate of PCR amplification for microsatellites is
typical for molecular genetic studies of scat and hair samples and our results were similar
to success rates from other carnivore studies, including gray wolves (Canis Lupus) 53%
at 6 loci (Lucchini et al. 2002), coyotes (Canis Lantrans) 48% at 3 loci (Kohn et al.
1999). The higher success rate amplifying mtDNA in comparison with microsatellites, is
largely due to the fact that there are about 10,000 mitochondrial DNA genomes compared
to one nuclear genome per cell; therefore, there is a greater opportunity to amplify
mitochondrial than nuclear DNA.
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The low probability of identity for random black bears and for siblings in this
study make us confidant that using our data set of 10 polymorphic microsatellite loci we
can detect unique individuals.
Descriptive Statistics for Microsatellites
Our results also showed four sampling locations out of HWE, Sierra San Luis,
Mazatzal Mountains, and Huachuca, and Rincon Mountains, due to heterozygote
deficiencies (or excess of homozygotes). Any locus with null alleles would show an
excess of homozygotes, resulting in departures from HWE. Three microsatellite loci in
this study showed evidence of null alleles (G10B, G10L, and G10H), which could be
affecting HWE if null alleles happened to be present at high frequencies in these
populations. Null alleles are alleles that do not amplify due to mutation in the priming
site, or due to extreme shift in allele size so they are not detected on the gel run. Other
explanations for heterozygote deficiencies would be non-amplifying alleles due to allele
drop out caused by low amounts of DNA, such as with DNA extracted from scat samples.
However, our estimates of allele dropout rates too low to make this a significant cause of
error. Finally, this result could be due to sampling subpopulations that are actually part
of a larger population, the Wahlund effect (Wahlund 1928).
Gene flow
Our results show that black bears in the sky island ecosystem of Arizona and
northern México have high gene flow among them (Average FST = 0.07). Also, most of
the genetic variability is within the populations instead of among populations, and based
on population structure analysis, we conclude that we have a weak population structure
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(demonstrated by analysis with STRUCTURE 2.3.1, TESS 2.1.1 and GENELAND).
Because we had presence of two loci in our data set (G10H, and G10B) with about 20%
of null alleles, we implemented the null alleles model in GENELAND to test if that could
potentially have produced errors in the structure analysis, however, this additional
analysis produced similar results and grouped all the sampling areas in three groups.
It has been reported that STRUCTURE 2.3.1, TESS 2.1.1 and GENELAND
reliably detect substructure in populations with high gene flow (Latch et al. 2006; Chen et
al.2007). Our analysis produced 2 or 3 subgroups from all sampling locations, however,
they each show a similar pattern, in which, the individual assignment frequencies changes
slowly from north to south (Fig 4a and 4b).
In our study area, genetic structuring is strongly associated with geography
(Slatkin and Maddison 1990); the farther the bears have to travel, the more genetic
differentiation is present. Isolation by Distance (IBD) analysis shows a significant
association between geographic distance and genetic differentiation (Mantel test; R2 =
0.59, P = 0.02). Barriers, human induced or otherwise, are important to consider in the
long-term survival of a species. Especially for a species like the black bear that has to
move across long patches of grasslands or deserts, from one sky island to another, to find
mates and resources.
A close examination of the land cover map of Arizona shows that the spatial
domains of our populations are separated by an extent of natural desert ecosystem cities,
freeways and man-made habitats (i.e., farms) that may have reduced black bear
movements. For example, there are sampling locations that have a higher FST than
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expected from distance alone. For instance, between the Pinaleno Mountains and the
southern extreme of the Apache National Forest (Game Management Unit 27) the FST
value is 0.06, whereas between Sierra Los Ajos and Sierra San Luis the FST value is 0.02,
and both are similar geographic distances. From a detail map, in the Pinaleno Mountain
example it is noticeable that the Gram Canal, the Tiwell Canal, the City of Safford and
two Interstate Highways (U.S.-70 and U.S.-191) could act as barriers for free bear
movement between the two areas, whereas there are no apparent barriers between the two
Sierras. Therefore, geographic distance is not the only factor affecting black bear
connectivity in the sky island region. In particular, human caused barriers can also stop
gene flow and affect the long term viability of bear populations.
Regardless of the subtle sub-structure we found in this study; it is clear that black
bears are moving and dispersing across great distances (more than 400 km), and in large
numbers, among the sky islands in Arizona and with the sky islands in northern México
(Sierra Madre Occidental). In addition, the existence of some genetic differentiation,
despite high potential population movement, argues for greater attention to dispersal
behavior when considering the species’ population dynamics. While the black bear does
not currently persist as a metapopulation (the chance of extinction on any sky island is
not high) our findings of large amount of gene flow among Arizona and México sky
islands, within two or three subdivided populations, implies that black bears in this area
might be treated as a large population with migration among them.
The translocation and release of black bears in Arizona could have affected our
results, particularly in the estimation of gene flow by decreasing the FST value. As long
136
as the translocations were not extensive, and keeping in mind that a translocated bear
needs to be translocated, set up a territory, and successfully reproduce, in order to be
considered a migrant from a genetic standpoint. There is extensive literature that many
translocated animals do not survive let alone reproduce in the new location, or they return
to the original home range (Beckmann and Lackey 2004). A genetic rule of thumb is that
one individual migrant per generation is appropriate to maintain genetic diversity and
prevent inbreeding depression in fragmented populations (Wright 1931, Slatkin 1985).
An extensive number of studies have shown that connectivity among subpopulations is
essential to allow local and global adaptation (e.g., Frankel and Soule 1981, Mills and
Allendorf 1996). So a small number of translocation to nearby populations is not likely
to negatively affect the natural genetic structure of a population.
Black bears could benefit from a statewide management program instead of the
smaller game management units. Also, because there is a clear connection between
Arizona and northern México (Sierra Madre Occidental) as shown by our results
(population structure, FST values, and migration rates), black bears could benefit from
management that facilitates transboundary movements. Although closer sky islands have
a higher migration rate than farther apart ones, as expected, there is some connectivity
even in the farthest apart populations (FST values are 0.14 – 0.18 from the farthest apart
populations). Additionally, it seems that bears are moving north (the migration rate from
El Nido to Sierra San Luis is about 30% from Sierra San Luis to El Nido is 12%).
Therefore the sky islands are important for connectedness of the larger “mainlands
(Mogollon Rim, Arizona and Sierra Madre Occidental, México) as they may be acting as
137
stepping-stones between them.
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Fig 1. Black bear sample areas
151
Fig 2. Geographic distance among sky islands in Arizona and Northern México
152
Fig 3. A 2-dimentional scaling plot of genetic distances (FST) for 11 sample locations of
black bears (Ursus americanus) from Arizona and northern México.
153
Fig 4. Geographic ranges of genetically distinct groups detected using Assignment tests.
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northern México.
154
b. STRUCTURE and GENELAND. Three populations: 1. Mazatzal Mountains, 2.
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Sierra San Luis; 3. Sierra El Nido.
155
Fig 5. Isolation by distance
Table 1. Number of samples collected by type and number that amplified with
mitochondrial DNA, with at least 1 microsatellite and with 7 or more microsatellites.
Number of samples (N).
Sample Type
Scat
Bone-teeth
Blood
Hair
Hide
Muscle tissue
Cheek cells
Total
N
354
29
35
84
8
19
7
536
MtDNA
213
29
35
55
5
19
7
363
1 microsatellite
184
29
35
54
4
19
7
332
7 or more
microsatellites
112
24
35
20
3
19
7
220
156
Table 2. Results showing the most likely number of genetically distinct groups within
the data set according to the output of the Bayesian test in STRUCTURE 2.3.1, TESS
2.1.1 and GENELAND. For each possible number of distinct groups (K) the loglikelihood L(K) and the probability (Prob) are presented. For STRUCTURE results we
also calculated ∆K statistic for further verification of the most likely partition.
STRUCTURE
K
L(K)
Prob.
1
-4767.5
<<0.001
2
-4448.9
<<0.001
3.66
3
-4187.1
1.00
138.58 *
4
-4031.4
<<0.001
22.35
5
-3903.2
<<0.001
19.75
6
-3822.0
<<0.001
6.64
7
-3752.2
<<0.001
2.12
8
-3693.0
<<0.001
2.36
9
-3671.0
<<0.001
1.54
10
-3698.6
<<0.001
3.77
11
-3726.1
<<0.001
0.51
12
-3804.3
<<0.001
∆K
157
Table 3. Inbreeding values in the sky islands in Arizona and northern México
Multilocus
estimates
Locus
Fwc (is)
Fwc (st)
0.123
0.1868
0.0332
0.0163
0.0693
0.016
0.0326
0.1193
0.0885
0.034
0.0732
0.4188
0.4452
0.0672
0.4277
0.2399
0.4387
0.321
0.222
0.1007
0.4024
0.263
G10B
G10H
G10L
G10M
G1A
Mu50
CXX20
G10J
G1D
G10O
All
Fwc (it)
0.4903
0.5488
0.0982
0.437
0.2926
0.4477
0.3431
0.3148
0.1803
0.4227
0.3169
Table 4. Fst values among 11 locations of black bears from Arizona and northern
México.
1
1 San Luis
2 El Nido
3 Los Ajos
4 Chiricahua
5 Huachuca
6 Apache
National
Forest
7 Rincons
8 Mazatzal
9 Nutrioso
10Peloncillo
11 Pinaleno
2
3
4
0.04
0.02
0.02
0.06
0.03
0.02
0.09
0.003
0.05
0.02
0.07
0.07
0.14
0.05
0.03
0.05
0.09
0.02
0.14
0.05
0.02
0.06
0.002
0.04
0.1
0.06
0.01
0.004
0.02
0.02
0.09
0.03
0.02
0.03
5
0.07
0.091
0.14
0.11
0.04
0.08
6
0.06
0.08
0.04
0.03
0.04
7
0.09
0.006
0.04
0.02
8
0.09
0.08
0.07
9
0.03
0.01
10
0.04
158
Table 5. Migration rate among black bears in 11 sample locations of black bears from
Arizona and northern México.
1 San Luis
2 El Nido
3 Los Ajos
4 Chiricahua
5 Huachuca
6 Apache
National Forest
7 Rincon
8 Mazatzal
9 Nutrioso
10 Peloncillo
11 Pinaleno
1
2
3
4
5
6
7
8
9
10
11
0.98
0.31
0.02
0.001
0.002
0.131
0.878
0.001
0.001
0.01
0.024
0.041
0.707
0.015
0.015
0.135
0.078
0.011
0.736
0.014
0.159
0.056
0.009
0.028
0.699
0.062
0.066
0.014
0.015
0.016
0.114
0.064
0.021
0.026
0.015
0.001
0.001
0.001
0.001
0.001
0.062
0.048
0.013
0.016
0.016
0.154
0.036
0.011
0.005
0.004
0.1
0.061
0.013
0.014
0.014
0.001
0.001
0.001
0.001
0.001
0.001
0.012
0.009
0.01
0.01
0.01
0.009
0.016
0.014
0.017
0.015
0.016
0.015
0.011
0.01
0.01
0.013
0.181
0.013
0.009
0.009
0.008
0.009
0.221
0.011
0.703
0.015
0.031
0.017
0.18
0.016
0.015
0.703
0.022
0.014
0.16
0.012
0.001
0.001
0.989
0.001
0.001
0.001
0.016
0.016
0.078
0.703
0.12
0.015
0.004
0.004
0.011
0.004
0.954
0.004
0.013
0.015
0.021
0.014
0.194
0.701
159
Appendix 1. Arizona Game and Fish Department black bears in Arizona from 1998 to
2007
Capture site (GMU)
PINALENO
(Unit 31)
Unit 36
NORTH OF SAFFORD
Ft Thomas, AZ
Green Valley, AZ
PINALENO (Unit 31)
Fort Thomas, AZ
GILA
MOUNTAINS/SAN
CARLOS INDIAN
RESERVATION
(Unit 28)
PINALENO
(Unit 31)
RINCONS
(Unit 33)
Release site
(GMU)
CHIRICAHUA
(Unit 29)
SANTA RITAS
(Unit 34A)
PINALENO
(Unit 31)
HUACHUCA EAST
(Unit 35B)
PELONCILLO
(Unit 30)
PINALENO
(Unit 31)
GALIURO
(Unit 32 )
GALIURO
(Unit 32)
GALIURO
(Unit 32)
Sex
Descript
ion
Date
F
Juv
7/4/98
M
Ad
5/23/99
M
Juv *
5/29/99
M
Ad
6/1/99
M
Juv *
7/9/99
* same bear
F
Juv
5/24/00
Eating in a
orchard
M
Juv
7/1/00
F
Cub
7/6/00
Nuisance
F
Ad 3 cubs
8/1/00
Eating trash
Juv
8/4/00
Released same
location
Nuisance
Claridge Ranch
Claridge Ranch
F
RINCONS
(Unit 33)
SOUTH OF TONTO N.F.
(UNIT 37)
PELONCILLO
(Unit 30)
F
8/19/00
Phoenix Zoo
F
9/17/00
Nogales, AZ
SANTA RITAS
(Unit 34A)
HUACHUCA EAST
(Unit 35B)
M
Juv
11/3/00
M
Juv
11/10/00
Adobe Mountain
M
Juv
11/28/00
GALIURO
(Unit 32)
M
Ad
5/9/01
SOUTH OF TONTO N.F.
(UNIT 37)
PELONCILLO
(Unit 30)
M
Ad
8/8/01
PINALENO
(Unit 31/32)
CHIRICAHUA
(Unit 29)
M
Juv
9/10/01
North of Wilcox
PELONCILLO
(Unit 30)
Residence east of Safford
GALIURO
SANTA RITAS
(Unit 34A)
Alvernon and 29th in
Tucson
CHIRICAHUA
(Unit 29)
5/10/03
F
Juv
5/19/03
Situation
Inappropriate
habitat
Inappropriat
e habitat
Inappropriate
habitat
Inappropriate
habitat
Nuisance
Inappropriate
habitat (urban
Tucson)
Near tomato
nursery
In chicken
coop n. of
Wilcox.
160
Elgin, AZ
HUACHUCA WEST
(Unit 35A)
Udall Park, Urban
Tucson.
PINALENO (Unit 31)
GILA
MOUNTAINS/SAN
CARLOS INDIAN
RESERVATION
(Unit 28)
HUACHUCA WEST
(Unit 35A)
HUACHUCA WEST
(Unit 35A)
HUACHUCA WEST
(Unit 35A)
HUACHUCA WEST
(Unit 35A)
HUACHUCA WEST
(Unit 35A)
HUACHUCA WEST
(Unit 35A)
HUACHUCA WEST
(Unit 35A)
CHIRICAHUA
(Unit 29)
SAGUARO
N.M/SANXAVIER/MAR
ANA
(Unit 38M)
SAGUARO
N.M/SANXAVIER/MAR
ANA (Unit 38M)
SAGUARO
N.M/SANXAVIER/MAR
ANA (Unit 38M)
SAGUARO
N.M/SANXAVIER/MAR
ANA (Unit 38M)
HUACHUCA WEST
(Unit 35A)
PELONCILLO
(Unit 30)
PINALENO (Unit 31)
GALIURO
(Unit 32 )
(Unit 32)
SW Center for Bear
Rehab.
GALIURO
(Unit 32)
F
Juv
9/9/03
Nuisance
M
Juv
4/28/05
In town
M
Ad
6/8/05
In town
Juv
1/24/07
Juv
1/12/07
M
Cub
10/24/06
M
Juv
10/12/06
Rehabber in Phoenix
F
Cub
10/7/06
Unit 36C
F
Ad
10/4/06
Region VI
M
Juv
10/1/06
Deer Creek
F
Ad
9/30/06
M
Juv
9/24/06
M
Juv
9/16/06
Unit 6
Rehabilitator in
Phoenix.
GILA
MOUNTAINS/SAN
CARLOS INDIAN
RESERVATION
(Unit 28)
Linda Searle,
Rehabilitator in
Phoenix
GALIURO
(Unit 32 )
HUACHUCA
Guajalote Flat
Miller Spring
PINALENO (Unit 31)
SANTA RITAS
(Unit 34A)
SANTA RITAS
(Unit 34A)
9/3/06
Ad
9/3/06
SANTA RITAS
(Unit 34A)
Cub
9/3/06
SANTA RITAS
(Unit 34A)
Cub
9/3/06
M
Ad
8/25/06
M
Juv
6/24/06
Cub
6/21/06
Cub
6/21/06
Guajalote Flat
PELONCILLO
(Unit 30)
Rehabber in Phoenix
Rehabber in Phoenix
Tag 164
F
Tag 171
161
RINCONS
(Unit 33)
SAGUARO
N.M/SANXAVIER/MAR
ANA (Unit 28)
PINALENO
(Unit 31)
PINALENO
(Unit 31)
HUACHUCA WEST
(Unit 35A)
Rehabber in Phoenix
SAGUARO
N.M/SANXAVIER/M
ARANA (Unit 28)
PINALENO
(Unit 31)
PINALENO
(Unit 31)
CHIRICAHUA
(Unit 29)
Juv
1/24/2007
Juv
1/12/07
M
Juv
5/23/07
F
Juv
5/23/07
M
Ad
6/4/07
162
APPENDIX D
DENSITY, POPULATION SIZE AND CONSERVATION OF BLACK BEAR IN
SIERRA SAN LUIS, SONORA, MÉXICO
Cora Varas
School of Natural Resources
University of Arizona
P.O. Box 210043
Tucson, Arizona 85721-0043
520-621-2161
Email [email protected]
RH: Varas • Sonora, México Bear Population and Density using DNA markers
CORA VARAS, School of Natural Resources and the Environment, University of
Arizona, Tucson, Arizona, 85721, USA
CARLOS LOPEZ-GONZALES, Universidad Autónoma de Querétaro, Querétaro C. P.
76010
PAUL R. KRAUSMAN, Boone and Crockett Program in Wildlife Conservation.
University of Montana, Missoula, Montana 59812, USA
MELANIE CULVER, School of Natural Resources and the Environment, University of
Arizona, Tucson, Arizona, 85721, USA
163
Abstract: Effective management of black bears throughout their range requires an
understanding of population characteristics including the size of a population. We used
scats collected from the field to obtain DNA to estimate density and population size for
the endangered black bear (Ursus americanus) from a population in the Sierra San Luis,
Sonora, México, located in the northern part of the Sierra Madre Occidental. We
collected 223 scats, 49 (21.87%) were amplified for 10 microsatellites and one set of sex
determination primers. We discovered 33 unique genotypes used to estimate a population
size of about 55 ±7 (SD) individuals and a density of 0.38 bears/km2. The high density in
this study suggests the population has been established for a long time, and could
potentially be a source of individuals to recolonize available historical habitat elsewhere.
The Journal of Wildlife Management: 00(0): 000-000, 200X
Key words: black bears, density, microsatellites, population, rarefaction analysis, scat,
Sierra San Luis, Sonora, Ursus americanus.
________________________________________________________________________
The black bear (Ursus americanus), an endangered species in México (Servheen
et al. 1999a, SEMARNAT 2002), was historically present in most forested areas in
northern and central México, in the Sierra Madre Occidental, from the northern border of
Zacatecas, Nayarit, Jalisco, and Aguascalientes, and Sierra Madre Oriental, Coahuila,
Nuevo Leon, Tamaulipas, and San Luis Potosi (Dalquest 1953, Leopold 1959, Baker and
Greer 1962, Tinker 1978, Hall 1981). Currently, black bears are only present in Sonora,
Chihuahua, Coahuila, Nuevo León, Tamaulipas (Castro 1984, Nino Ramirez 1989,
164
Zepeda-Gonzalez et al. 1997), Zacatecas, and Durango (Sierra-Corona et al. 2005).
However, the complete historical and current black bear distribution and abundance in
México is unknown.
The decline of the black bear population in México began in the mid-1980s due to
over hunting and habitat encroachment (Leopold 1959, Baker and Greer 1962). Small
populations survived in the remote mountains of northern México (DoanCrider and
Hellgren 1996). Some populations were able to increase due to changes in Mexican law
and in public attitude towards large predators in the 1960s and 1970s. Despite
improvements in attitudes, there are still many existing threats that can negatively impact
the survival of black bears, including poaching, habitat encroachment, loss of habitat, and
anthropogenic fragmentation of habitat (Baker 1956, Leopold 1959, Medellin et al.
2005). Human induced fragmentation is a rapid process to which black bears have little
time to adapt. Young bears need to travel from their natal area to find food or mates, and
may not be able to overcome newly created barriers (e.g., security fence); as a result, they
are not able to successfully disperse. In México, black bears occupy 30% of their
historical range (Pelton and vanManen 1996). To maintain healthy black bear
populations in México, ones that can possibly expand their range to historical area, and
ensure their protection, Medellin et al. (2005) suggested 3 steps: ensure that established
populations remain secure; use abundance, location, and land-use patterns to identify
those populations not yet secure but with the potential for long-term persistence; and
determine black bear population structure and protect dispersing individuals. Therefore,
a key factor for protecting black bears in México is to estimate black bear population size
165
(Medellin et al. 2005).
Obtaining information about population size and abundance is crucial in the
conservation of many species. Population size, is a key factor that determines if a species
will be listed as endangered or threatened. Population size also assists biologists with
conservation efforts for populations or species of concern. Black bears are generally
difficult to detect, and can travel long distances from their natal area (up to 200 km). As
a result, data from single records of individuals are problematic to interpret for an
accurate population census (Medellin et al. 2005).
Field observation, radio collars, capture-recapture of live animals, or hunter data
have traditionally been used to obtain black bear abundance. For example, black bear
abundance and density was obtained using sighting techniques with cameras in Great
Smoky Mountains National Park in North Carolina (Martorello et al. 2001); and using
capture-recapture techniques in Minnesota (Garshelis and Noyce 2006), Alaska (Miller
et al. 2005) and Hoopa Valley, California (Matthews et al. 2008), among others. Nongenetic capture-recapture provides an estimate of population size, although there are
factors that could potentially bias the estimation of the population size, such as variation
of capture probabilities among individuals or sexes, small data sets, and geographic
closure (Otis et al. 1978, Matthews et al. 2008). Also, there are times when capture of
individuals is difficult or not an option. More recently, researchers have developed
genetic techniques (Woods et al. 1999) to accurately determine bear population size and
abundance, a technique that is especially useful in rare, low density, or endangered
species.
166
A combination of genetic markers (Woods et al. 1999) and non-invasive sampling
has recently been used to successfully identify individuals and estimate population
density for bears (Frantz et al. 2004). For example, hair samples have been used to
estimate population density for black bears in Oregon (Immell and Anthony 2008), in
Kenai Fjords National Park, Alaska (Robinson et al. 2009), and in the southern
Appalachians of North Carolina, South Carolina, and Georgia (Settlage et al. 2008).
Another non-invasive sample than can be used in black bears is scats. Scats collected in
the field provide a viable alternative to other field techniques because scat is highly
visible, abundant, inexpensive, and provides sufficient DNA to perform genetic studies.
The use of non-invasive samples is especially useful when working with endangered,
dangerous, or rare species.
Scats have been used for a variety of other population genetic or ecological
applications such identifying individual wallabies (Petrogale penincillata) (Frantz et al.
2004), chimpanzees (Pan troglodytes) (Morin and Woodruf 1992), brown bears (Ursus
arctos) (Csiki et al. 2003), and black bears (Ursus americanus) (Csiki et al. 2003, Frantz
et al. 2004), among other species. The combination of non-invasive sampling to obtain
DNA, and microsatellite loci as genetic markers, has other applications in wildlife studies
e.g., detection of rare species (Perovic et al. 2003), evaluation of social genetic structure
(Morin et al. 1993), estimation of genetic diversity and gene flow (Gerloff et al. 1999),
detection of hybridization (Adams et al. 2003), diet analysis (Hoss et al. 1992)
identification of predator kills (Ernest et al. 2000, Ernest et al. 2002), and population size
estimation (Poole et al. 2001, Frantz et al. 2004, Paetkau et al. 2004).
167
Given information on individual genotypes, the true population size can be
estimated using different methods such as capture-mark-recapture (White and Burnham
1999, Robinson et al. 2009) and rarefaction indices (Kohn et al. 1999, Valiere 2002,
Kalinowski 2004). The rarefaction methodology has been used to successfully estimate
population size for brush-tailed rock-wallaby (Petrogale penicillata) in Australia (Piggott
et al. 2006), and brown bears in Sweden (Bellemain et al. 2005), and Pakistan (Bellemain
et al. 2007). Consequently, our objectives for this study are to use genetic methods and 3
different rarefaction algorithms to estimate population size and density of black bears for
a population in Sierra San Luis, and to discuss the implications for conservation of black
bears in the Sierra Madre Occidental, México.
STUDY AREA
The Pinito Ranch (Rancho el Pinito) is located in the Sierra San Luis, Sonora,
México. It has an area of 68.8 km2 and is located between 108o 56’ 46’ N latitude and
31o 11’ 49” W longitude. The climate is dry desert and dry deserted with summer rains,
but in the mountains the climate is less dry with a precipitation more than 500 mm a year.
The topography of the area includes a chain of volcanic mountain ranges that are part of
the Sierra Madre Occidental with altitudes that range from 1,050 m to 2,625 m. The
hydrology of the area includes 5 dams in the El Pinito Ranch, which are filled during the
rainy season; they are located in the dry riverbeds and are used to store water. The land
use in the area has a matrix of large and small parcels of private ownership; mostly with
livestock ranching. The vegetation of the study area is mainly composed by mixed forest
168
of pine-oak, pine-juniper, oak-grassland. The most common species present are: acorns
(Quercus emoryi), oak (Quercus reticulata), Mountain oak (Quercus undulata), cypress
(Cupressus glabra), juniper (Juniperus deppeana), (Juniperus monosperma), Apache
pine (Pinus engelmannii), Ponderosa pine (Pinus ponderosa), Chihuahuan pine (Pinus
chichuahua) Pinon pine (Pinus edulis), manzanita (Arctostaphylos pungens), cholla
(Opuntia spinosior) Agave (Agave parryi), sotol (Dasylirion wheeleri), yucca (Yucca
schottii), bear grass (Nolina microcarpa), catclaw mimosa (Mimosa biuncifera) (Mas et
al. 2002, Sierra-Corona et al. 2005). The fauna of the study area includes the cottontail
rabbit (Sylvilagus floridanus), white-tailed deer (Odocoileus virginianus), coati (Nasua
narica), gray fox (Urocyon cinereoargenteus), puma (Puma concolor) turkey (Meleagris
gallopavo), Mexican jays (Aphelocoma ultramarine), rattlesnake (Crotalus willardi, C.
molossus), spiny lizard (Sceloporus jarrovii, S. clarkii), whiptail lizard (Cnemidophorus
uniparens) (Brown 1994, Silva-Hurtado 2004).
METHODS
Sample collection and Preservation Methods
We established 3 km long transects at “El Pinito Ranch”. We walked transects
every other week looking for scat samples. Samples were collected for 22 days in June
and July, and 28 days in October and November 2002. Locality data were obtained
through a portable Global Positioning System (GPS). Each scat sample was placed in a
paper bag and stored at room temperature until transported to the University of Arizona
and frozen until they were used for DNA extraction.
169
DNA Purification
We extracted DNA from 223 scat samples in a laboratory dedicated to processing
samples with very low DNA yield (e.g., hair and scats). To avoid contamination, this
laboratory is located in a separate building isolated from any animal DNA or PCR work.
We scraped the surface of the scats to obtain epithelial cells and used between 0.40 to
0.60 grams of scraped scat for DNA extraction. We used the QIAmp® Stool DNA Mini
Kit (Qiagen Inc., Valencia, CA) following the manufacturer’s protocol for isolation of
DNA. We included 1 water (blank) sample for each 15-scat samples processed for a
contamination check, and aerosol-resistant pipette tips were used during the procedure.
DNA analysis for species ID
We amplified and sequenced 360 base pair fragment of the mitochondrial DNA
control region. We compared our sequences with those previously deposited in Genbank
(www.ncbi.com) and identified the species based upon maximum identity > 99 %.
Genotyping for individual identification
The extracted DNA known to originate from black bears was amplified using 10
black bear specific microsatellite DNA loci: G10B, G10H, G10L, G10M, G1A, G10J,
G1D, G10O, CXX20, and Mu50 (Paetkau and Strobeck 1994;1995b, Paetkau et al.
1998a, Woods et al. 1999). Forward fluorescently labeled primers and reverse unlabeled
primers were synthesized by Invitrogen (Life Technologies, www.invitrogen.com).
Polymerase Chain Reaction (PCR) conditions were optimized for chemistry and
cycling conditions. Each of the 10 microsatellite loci amplified with 3 different PCR
reaction conditions. All contained 1.5 ul Promega 10X buffer, 0.3µl of 10mM dNTPs,
170
0.08 units of Taq DNA polymerase (5 units/ul), 0.25ul of 20uM forward and reverse
primers and 5 ul template DNA in a final volume of 10 ul. Five microsatellite loci
(G10O, G10B, G10H, G1D, CXX20) used 1.5 MgCl2; four microsatellite loci (G10M,
G10L, G10J, Mu50) used 2.5 MgCl2; and one microsatellite locus (G1A) used 3.5 MgCl2.
We used five thermal cycling profiles that differed only in their annealing temperature.
All cycling profiles included 94oC for 3 minutes, and 40 cycles of 94oC for 30 seconds,
annealing temperature for 30 seconds (60oC for G10B, G10H; 62oC for G1A; 54oC for
G1D, G10J; 52oC for G10O, G10M, G10L, and 50oC for CXX20, Mu50), and 72oC for
30 seconds, followed by a final extension at 72oC for 5 minutes. The PCR products were
sized using fluorescence fragment analysis technology (ABI Prism 3100, Applied
Biosystems, Foster City, CA). Microsatellite fragment sizes were collected and scored
using Genotyper 1.0 (Applied Biosystems) software.
Population genetic analysis
Genetic variation at 10 microsatellite loci was described by allele frequencies and
observed and expected heterozygosities. Hardy-Weingberg equilibrium was assessed by
Markov chain permutations using the program GENEPOP 4.0 (Raymond and Rousset
1995, Rousset 2008). Since these analyses assume that all loci are independent, we tested
for genotypic disequilibrium among pairs of loci using GENEPOP. We applied the
Bonferroni correction (Rice 1989) for multiple comparisons.
Sex determination analysis
We selected all samples that amplified for 7 or more microsatellites were selected
for sex determination using length polymorphism in the Amelogenin gene. Primers SE47
171
and SE48 (Yamamoto et al. 2002) differentiate the X and Y chromosome amelogenin
gene products based on PCR product length. Polymerase Chain Reaction protocol and
cycling conditions were used as described in Yamamoto et al. (2002). Five microliters of
the PCR product was electrophoresed in a 2% agarose gel with a1 Kb Plus DNA Ladder
standard and visualized by staining with ethidium bromide.
Reliability of DNA results
To minimize microsatellite genotyping errors we followed the error testing
procedures outlined in (Woods et al. 1999, Paetkau 2003). To control for allelic dropout
each PCR amplification was repeated three times, for each sample and microsatellite
locus. Samples were typed as heterozygotes at a locus, if both alleles appeared very
clearly two times among the three replicates, and they were typed as homozygotes if at
least 2 replicates showed identical homozygote profiles.
Individual genotypes were scored twice at each locus, by different people, and
then compared; one final data set was constructed for the analyses. Genotypes from
different samples were considered to represent a single bear when all alleles at all loci
were identical. We used program Micro-checker (Van Oosterhout et al. 2004) to detect
genotyping errors due to non-amplified alleles (null alleles) and allele dropout.
We used the program CERVUS 3.0 (Kalinowski et al. 2007) to quantify the
power of this set of ten microsatellite loci by computing the probability of identity (PI)
the overall probability that two individuals drawn at random from a given population
share identical genotypes at all typed. Also, because bears frequently travel in sibling
groups, there is the possibility that full siblings may be sampled within the study area,
172
thus, we also computed the PI between potential siblings using the program CERVUS
(Waits et al. 2001, Kalinowski et al. 2007).
Population Size estimation
Only samples that amplified successfully, for a subset of seven microsatellite loci
(G10L, G10M, G1A, Mu50, CXX20, G10J, G1D), were used for population size
estimates. We used the program GIMLET 1.3.3 (Valière 2002) to generate rarefaction
curves. Estimates are obtained by plotting the accumulation curves of the number of
scats samples against the cumulative number of new profiles. Population size
corresponds to the projected asymptote of the curve determined by the accumulation of
unique genotypes. There are three main suggested equations: (1) Kohn’s equation y =
ax/(b + x), where y = cumulative number of genetic profiles, x = number of genotypes
sampled, a = asymptote (or population size estimate) and b = nonlinear slope of the
function (Kohn et al. 1999); (2) Eggert’s equation y = a(1 − e(bx)) (Eggert et al. 2003);
and (3) Chessel’s equation, y = a – a(1 − [1/a])x, corresponding to the expectation of the
number of full boxes when x balls are distributed among a boxes (Valiere 2002).
Parameters in (2) and (3) are the same as for (1). The formulas were used for the
cumulative number of genotypes, and the number of genotyped DNA samples, to produce
an asymptote which is the population estimate (Frantz et al. 2006). Simulation studies
showed that the three equations do not perform equally well when estimating population
size. Eggert’s and Kohn, do well when using a large data set. When using simulations
with a small data set, Kohn and Chessel’s consistently overestimated the population size
(Frantz and Roper 2006). Results of simulation analyses showed that the median values
173
obtained from Eggert’s equation were consistently accurate, and the variance of the
estimates were the smallest when using the small data set, however the mean values from
Eggert’s equation overestimated the population size. We used all three equations for the
function to be fitted to the accumulation plot, as the software documentation suggests.
We used the program R (Ihaka and Gentlemen 1996) to perform analyses of the
rarefaction curves. The program GENODIVE (Meirmans and Van Tienderen: 2004) was
used to compare data produced by GIMLET 1.3.3 (Valiere 2002) in terms of number and
frequencies of unique genotypes (using the script and data input file generated in
GIMLET). The GENODIVE data input file is generated by regrouping and counting the
samples that have an identical genetic profile. The order in which the samples are added
to the analysis, and the profiles in the data set, was randomized 10000 times. Using the
three rarefaction equations described above. For each randomization, the asymptote was
projected. The mean value of all iterations for the asymptote, a, was taken to be the
population estimate. The variance of the a estimate was analyzed by calculating the
standard deviation (SD) and the 95% confidence intervals (CI) of that mean.
Relatedness estimates
We used program GENALEX 6.0 (Peakall and Smouse 2006) as an alternate
algorithm to estimate population size. If two or more samples had a P value for sibling
match of 0.8 or higher, they were considered the same bear (i.e. excluding the loci that
were incomplete for either animal). We compared our relatedness value to other studies
such as Wood et al. (1999), which reported a sibling relationship of 0.5 and Sinclair et al.
(2003) which reported a mean pairwise relatedness for known full-siblings of 0.453 (+/-
174
0.173) and known mother-cubs of 0.553 (+/- 0.212).
RESULTS
Population size estimate
We collected 223scat samples, and 49 samples (21.87%) successfully amplified
for a subset of seven microsatellite loci, which were used for population estimates. These
49 samples were used for bear density analysis, which produced 33 unique genotypes.
Each genotype was found 1 to 5 times. Twenty-three genotypes were found only once
(69.7%). Samples for which sex could not be determined were included in the analysis
and considered as the same bear if they matched at all microsatellite alleles. Chessel’s
equation produced a minimum population size of 38 and median of 55 ± 7; Eggert’s
equation yielded a minimum population of 39 and median of 37 ± 9; Kohn’s a population
size of 49 and median of 69 ± 25 (Fig 1).
The numbers of individual bears were not different when using unique genotypes
(GIMLET) versus relatedness coefficient of 0.80 or higher (GenAlex). The rarefaction
analysis produced similar results with a minimum population size of 35 and 37 (Chessel’s
and Eggert’s respectively).
Reliability of DNA Results
The PI using the seven amplified microsatellite loci was low (1.06 x 10-6), which
means there is a low probability that two random individuals will have the same genotype
by chance, therefore this dataset can reliably identify each individual. The probability of
sibling identity was 2.19 x 10-3 indicating a low probability of two random individuals
sharing one allele at each locus by chance, as true siblings would.
175
One microsatellite locus (G10M) did not conform to Hardy-Weinberg equilibrium
and the GIMLET analysis confirmed allele dropout for that same locus (G10M) which
may be the reason for non-equilibrium. The number of microsatellite alleles per locus
ranged from 4 to 8 with a mean number of alleles per locus of 6.86. The mean expected
heterozygosity was 0.56 and the mean observed heterozygosity was 0.50.
Sex Identification
The Amelogenin PCR primers for sex determination amplified fragments of 210
and 291 base pairs. We accurately determined sex for five positive control bear samples
of known sex. We identified 16 males, 10 females and could not determine the sex for 7
bears.
DISCUSSION
The utility of genetic data to detect population size depends on the accuracy of
DNA genotyping. The set of seven ursid microsatellite loci used in this study
successfully identified unique individuals; nevertheless genotyping errors are a potential
problem when working with scats samples that typically yield small amounts of DNA
(Taberlet et al. 1996). Conducting multiple PCR amplifications from each sample to
confirm allele sizes and genotypes can minimize errors. In our case, we conducted three
PCR reactions for each sample to confirm allele sizes. Also, to minimize errors we only
used samples that amplified for 7 or more microsatellites. Moreover, Paetkau (2003)
found that the reduction or gain of individuals was insignificant when marginal samples,
or not confirmed samples, were excluded from the analysis so we feel that eliminating
our samples that amplified for fewer than 7 loci will not compromise our outcome.
176
Even with these conservative methods, genotyping errors may have occurred,
such as false matches and false identification. False matches occur when a false allele or
an artefact is genotyped, and two samples are identified as the same individual when in
reality they are not the same individual. False identification results from allele dropout,
where one of the two alleles of a heterozygous individual fails to amplify – usually due to
a degraded or low quantity DNA sample (Eggert et al. 2003). False identification results
in individuals that are actually the same individual not matching in genotype. When
using scat samples false matches and false identification errors can happen due to low
amounts of DNA in scat. As a result, these errors can bias population density numbers
either high or low. Significant differences between the observed and expected number of
heterozygotes and homozygotes in our data set would be indicative errors or bias yet we
find this problem at only 1 microsatellite locus, G10M. In addition, the overall PI for our
population was low (PI=1.06 x 10-6) which is acceptable for mark-recapture studies (Mills
et al. 2000); therefore, we are confident that our assignment of individual genotypes was
as accurate based on 6/7 loci conforming to HW equilibrium and the corroborating PI
scores.
We provided data to support the value of rarefaction models in short-duration
non-invasive genetic projects. We have also reported that non-invasive genetic sampling
and DNA-based genetic analysis provide a tool for proactive monitoring and
management of black bear populations. However, there are two things to consider:
rarefaction analysis does not provide an estimator of population size variance but only
gives an indication of the sampling variance and rarefaction indices assume all samples
177
have constant and equal detection probabilities. In this study, because bears are not
hunted, we should have been able to find the same bears in both sampling periods,
resembling a closed population, so detection probabilities may have been equal and
constant. A non-invasively assigned (genetic) tag does not produce behavioral response
and therefore should not affect the probability of recapturing bears. Temporal variation
may have occurred due to changes in weather conditions, however weather conditions
were consistent through the study, therefore scats’ detection probability was similar. In
addition, all the sampling periods were in the same calendar year.
Kohn’s equation estimated the largest bear population and also produced the
largest difference between the minimum and maximum population estimation and the
largest standard deviation (SD = 25). Chessel’s and Eggert’s equations produced similar
results and considering previous simulation studies, Eggert’s equation likely produced the
most accurate estimate. We estimate the minimum population to be 38 bears in our study
area of 148.9 km2 resulting in a density of 0.22 bears/ km2. Frantz and Roper (2006)
suggested Eggert’s median values as the closest to the true population size when using
small data sets like ours; in our study area, that would mean a population of 57 bears
resulting in a density of 0.38 bears/ km2.
A density of 0.22 to 0.38 bears/km2 found in this study area in México is higher
that the 0.06 bears/ km2 density found previously in the area using camera trapping
(Sierra Corona et al. 2005). However, it is within the densities found in the Sierra Madre
Oriental population (Chisos Mountains) of 0.33 to 0.78 bears/ km2 (Doan-Cridder and
Hellgreen 1996), and also within the density found in similar habitat in Arizona 0.13-
178
0.738/ km2 (Cunningham and Ballard 2004). The black bear density estimation in the
Sierra San Luis population is similar to the density found in recolonizing populations of
large carnivores, such as black bears in the Ouachita Mountains, OK (0.26/ km2) (Bales
et al. 2005). The population estimates we reported in this study indicate Sierra San Luis
sustains a reasonably large population, and as in Los Chisos, where bears moved across
the border and repopulated Big Bend National Park (Onorato et al. 2004a, Onorato et al.
2004b), the population could be a source of bears to re-colonize historical habitat in
nearby areas of México.
It is essential to identify priorities for carnivore conservation in México, to ensure
that established populations remain secure, and to create ways to protect dispersing
individuals to aid natural recolonization. Black bear recolonization however, faces
challenges such as conflict with people and loss of habitat. More research is needed to
understand the role of natural and human induced fragmentation surrounding this
population, and to determine the economic and social factors facing recovery of black
bear populations.
179
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FIGURE AND TABLE CAPTIONS
Fig. 1. Genotypes versus number of feces. Black bear population estimates using
rarefaction analysis of genotypes from scats. Regression curves correspond to the
median of the coefficients calculated for three equations after 10,000 iteration of
the regression, with the sample order randomized each time.
Table 1. Population estimates, including median and Standard Deviation, produced by
three different equations for rarefaction analysis.
209
Minimum pop size
Median
SD
Chessel's equation
38
55
7
Eggert's equation
35
57
9
Kohn's equation
46
69
25
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