Conservation Genetics of Black Bears in Arizona and Northern Mexico

Item Type text; Electronic Dissertation

Authors Varas-Nelson, Angela Cora

Publisher The University of Arizona.

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Link to Item http://hdl.handle.net/10150/195033 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

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

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. Lopéz- Gonzalez 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.

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.

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

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 non- invasive 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.

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

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

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

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

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

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

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).

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

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).

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 Ajos-

Bavispe 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

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. 1998 b, Woods et al. 1999). Primer information and polymerase chain reaction (PCR) conditions and population genetic analyses are presented in

Appendix C.

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

Arizona and Sierra Madre Occidental in México, with the highest F ST 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

populations in Coahuila, México (source population) and Texas (subpopulations) (Doan-

Crider 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.

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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

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

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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

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

(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

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

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. 2004 a,

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

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

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

(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

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 (F ST = 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

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 F ST = 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

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 (F ST = 0.07 to 0.12). Populations that are separated by a narrow land mass had higher gene flow (F ST = 0.07) than populations isolated by

ocean water and ice (F ST = 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

bears, has substantial genetic isolation (mean pairwise F ST 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 (G ST = 0.97) (Mills et al. 2003) with approximately 3 migrants per generation moving across the valley. High allelic

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

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

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

and one mainland (lakeshore) population in Wisconsin (Stockton, Sand Island, and Oak

Island). Results show higher bear density in Stockton (0.64 bears/km 2) than on Sand

2 Island (0.50 bears/km ). The genetic variation within islands was high (mean H E ≥ 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/km 2. 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/ km 2 for the National Park and 0.59-1.00 black bears/ km 2 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/km 2 to 0.23 individuals/km. 2 Settlage et al. (2008)

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/km 2 in the mountains and 0.089 bears/km 2 in the plateau (Mowat et al. 2005).

The abundance values are similar to the ones presented by (Miller et al. 1997) using non-

genetic methods. Conversely, Peacock (2004) reported densities of 1.5 bears/km 2 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

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 sub- sampling 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 sub- samples 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. 2004 b). 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

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

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

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 mark- recapture 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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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

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.

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

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 high- latitude 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

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.

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.

2004 a). 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

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

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 ),

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 ), Silver-

Leaf 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 Ajos-

Bavispe 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

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 Ajos-

Bavispe 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 oC to 14.1 oC; with a mean precipitation of 795 mm. In the Huachuca Mountains the temperature ranges from 15 oC to 33 oC 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 18 oC 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.

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

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

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

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 (P i)

(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

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.

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

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

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).

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

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

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

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

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

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 non- invasively 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

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

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.

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

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.

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Fig. 1. Sampling locations

Fig. 2. Neighbor-Joining tree of 11 sampled population in the sky islands in Arizona

and northern México, based on pairwise Euclidean (Edwards and Cavalli-

Sforza 1964) and 1,000 bootstrap replicates (only bootstrap supported nodes

of 40 or higher are displayed on the tree nodes/branches).

Fig. 3. Maximum Likelihood tree of 11 sampled population in the sky islands in

Arizona and northern México, based on pairwise Euclidean (Edwards and Cavalli-

Sforza 1964) and 1,000 bootstrap replicated (only bootstrap supported nodes of 40 or higher are displayed on the tree nodes/branches).

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.

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.

Fig 6. Neighbor Joining Tree of CR region haplotypes, with branch lengths and bootstrap values.

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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).

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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

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Fig 9. Geographic distribution of the 5 Control Region (CR) haplotypes by sampling location.

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Fig 10. Geographic distribution of the 6 ATP8 haplotypes by sampling location.

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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

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Table 1. Samples used for the analysis by type and localities.

Bone, Blood, muscle, Hair, Scats teeth cheek cells hide

Sierra San Luis 215

Sierra El Nido 37

Sierra Los Ajos 6 3 16

Huchuca Mountains 21 5 7 8

Chiricahua

Mountains 15 4 4 1

Peloncillo Mountains 21 1

Pinaleno Mountains 8 11 2 5

Rincon Mountains 75 2 38

Mazatzal Mountains 35

Apache National

Forest 1 5 4 2

Nutrioso Mountains 6 7

TOTAL 405 29 61 70 565

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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

Total 536 363 332 220

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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 ATP8 K π K π Sierra 0.05 0.0014 2.196 0.0099 Madre Occidental Sierra 0.25 0.0007 0 0 Madre Oriental Total 1.30 0.004 2.197 0.0099

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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)

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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.

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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

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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 non-

spatial 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 F st 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.

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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 upper-

elevation 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).

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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. 1999 b), 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 (Moctezuma-

Orozco 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

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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.

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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;1995 a, Taberlet et al. 1996, Taberlet et al. 1997, Paetkau et al. 1998 b, 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 ), Silver-

Leaf oak (Q. hypoleucoides ), Oneseed juniper (Juniperus monosperma ), and Mexican

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

119

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

120

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 H 2O, 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 MgCl 2; loci G10M, G10L, G10J, Mu50 used 2.5 mM MgCl 2; and loci G1A, G10X

used 3.5 mM MgCl 2. We used five thermal profiles that differed only in their annealing

temperature: 94 oC 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 oC 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. 2000 a, Pritchard et al. 2000 b). 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. 2005 a,

Guillot et al. 2005 b, 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. 2005 a), 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. Locus- specific 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 (admixture- correlated 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 H o 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

2 59% (R = 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 non-

invasive 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 F ST value is 0.06, whereas between Sierra Los Ajos and Sierra San Luis the F ST 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 F ST value. As long

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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

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stepping-stones between them.

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Fig 1. Black bear sample areas

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Fig 2. Geographic distance among sky islands in Arizona and Northern México

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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.

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Fig 4. Geographic ranges of genetically distinct groups detected using Assignment tests. a. TESS. Two populations: 1. Mazatzal Mountains, 2. All the sky islands of Arizona and northern México.

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b. STRUCTURE and GENELAND. Three populations: 1. Mazatzal Mountains, 2. Apache N.F., Pinalenos, Rincons, Huachucas, Peloncillos, Sierra Los Ajos and Sierra San Luis; 3. Sierra El Nido.

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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).

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 Cheek cells 7 7 7 7 Total 536 363 332 220

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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 log- likelihood 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. ∆K 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

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Table 3. Inbreeding values in the sky islands in Arizona and northern México

Multilocus estimates

Locus Fwc (is) Fwc (st) Fwc (it)

G10B 0.4188 0.123 0.4903 G10H 0.4452 0.1868 0.5488 G10L 0.0672 0.0332 0.0982 G10M 0.4277 0.0163 0.437 G1A 0.2399 0.0693 0.2926 Mu50 0.4387 0.016 0.4477 CXX20 0.321 0.0326 0.3431 G10J 0.222 0.1193 0.3148 G1D 0.1007 0.0885 0.1803 G10O 0.4024 0.034 0.4227 All 0.263 0.0732 0.3169

Table 4. Fst values among 11 locations of black bears from Arizona and northern México.

1 2 3 4 5 6 7 8 9 10 1 San Luis 2 El Nido 0.04 3 Los Ajos 0.02 0.03 4 Chiricahua 0.02 0.02 0.003 5 Huachuca 0.06 0.09 0.05 0.02 6 Apache National Forest 0.07 0.09 0.002 0.02 0.07 7 Rincons 0.07 0.02 0.04 0.02 0.091 0.06 8 Mazatzal 0.14 0.14 0.1 0.09 0.14 0.08 0.09 9 Nutrioso 0.05 0.05 0.06 0.03 0.11 0.04 0.006 0.09 10Peloncillo 0.03 0.02 0.01 0.02 0.04 0.03 0.04 0.08 0.03 11 Pinaleno 0.05 0.06 0.004 0.03 0.08 0.04 0.02 0.07 0.01 0.04

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Table 5. Migration rate among black bears in 11 sample locations of black bears from Arizona and northern México.

1 2 3 4 5 6 7 8 9 10 11

1 San Luis 0.98 0.131 0.024 0.135 0.159 0.062 0.114 0.001 0.062 0.154 0.1 2 El Nido 0.31 0.878 0.041 0.078 0.056 0.066 0.064 0.001 0.048 0.036 0.061 3 Los Ajos 0.02 0.001 0.707 0.011 0.009 0.014 0.021 0.001 0.013 0.011 0.013 4 Chiricahua 0.001 0.001 0.015 0.736 0.028 0.015 0.026 0.001 0.016 0.005 0.014 5 Huachuca 0.002 0.01 0.015 0.014 0.699 0.016 0.015 0.001 0.016 0.004 0.014 6 Apache National Forest 0.001 0.012 0.016 0.011 0.009 0.703 0.015 0.001 0.016 0.004 0.013 7 Rincon 0.001 0.009 0.014 0.01 0.009 0.015 0.703 0.001 0.016 0.004 0.015 8 Mazatzal 0.001 0.01 0.017 0.01 0.008 0.031 0.022 0.989 0.078 0.011 0.021 9 Nutrioso 0.001 0.01 0.015 0.013 0.009 0.017 0.014 0.001 0.703 0.004 0.014 10 Peloncillo 0.001 0.01 0.016 0.181 0.221 0.18 0.16 0.001 0.12 0.954 0.194 11 Pinaleno 0.001 0.009 0.015 0.013 0.011 0.016 0.012 0.001 0.015 0.004 0.701

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Appendix 1. Arizona Game and Fish Department black bears in Arizona from 1998 to 2007

Release site Descript Capture site (GMU) Sex Date Situation (GMU) ion PINALENO CHIRICAHUA F Juv 7/4/98 (Unit 31) (Unit 29) SANTA RITAS Unit 36 M Ad 5/23/99 (Unit 34A) NORTH OF SAFFORD PINALENO M Juv * 5/29/99 Ft Thomas, AZ (Unit 31) HUACHUCA EAST Green Valley, AZ M Ad 6/1/99 (Unit 35B) PELONCILLO PINALENO (Unit 31) M Juv * 7/9/99 * same bear (Unit 30) PINALENO Eating in a Fort Thomas, AZ F Juv 5/24/00 (Unit 31) orchard GILA MOUNTAINS/SAN GALIURO CARLOS INDIAN M Juv 7/1/00 (Unit 32 ) RESERVATION (Unit 28) PINALENO GALIURO F Cub 7/6/00 Nuisance (Unit 31) (Unit 32) RINCONS GALIURO F Ad 3 cubs 8/1/00 Eating trash (Unit 33) (Unit 32) Released same Claridge Ranch Claridge Ranch F Juv 8/4/00 location RINCONS PELONCILLO F 8/19/00 Nuisance (Unit 33) (Unit 30) SOUTH OF TONTO N.F. Inappropriate Phoenix Zoo F 9/17/00 (UNIT 37) habitat SANTA RITAS Inappropriat Nogales, AZ M Juv 11/3/00 (Unit 34A) e habitat SANTA RITAS HUACHUCA EAST Inappropriate M Juv 11/10/00 (Unit 34A) (Unit 35B) habitat Alvernon and 29th in Inappropriate Adobe Mountain M Juv 11/28/00 Tucson habitat CHIRICAHUA GALIURO M Ad 5/9/01 Nuisance (Unit 29) (Unit 32) Inappropriate SOUTH OF TONTO N.F. PELONCILLO M Ad 8/8/01 habitat (urban (UNIT 37) (Unit 30) Tucson) PINALENO CHIRICAHUA Near tomato M Juv 9/10/01 (Unit 31/32) (Unit 29) nursery In chicken PELONCILLO North of Wilcox 5/10/03 coop n. of (Unit 30) Wilcox. Residence east of Safford GALIURO F Juv 5/19/03

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(Unit 32) SW Center for Bear Elgin, AZ F Juv 9/9/03 Nuisance Rehab. HUACHUCA WEST GALIURO M Juv 4/28/05 In town (Unit 35A) (Unit 32) Udall Park, Urban Unit 6 M Ad 6/8/05 In town Tucson. Rehabilitator in PINALENO (Unit 31) Juv 1/24/07 Phoenix. GILA GILA MOUNTAINS/SAN MOUNTAINS/SAN CARLOS INDIAN CARLOS INDIAN Juv 1/12/07 RESERVATION RESERVATION (Unit 28) (Unit 28) Linda Searle, HUACHUCA WEST Rehabilitator in M Cub 10/24/06 (Unit 35A) Phoenix HUACHUCA WEST GALIURO M Juv 10/12/06 (Unit 35A) (Unit 32 ) HUACHUCA WEST Rehabber in Phoenix F Cub 10/7/06 (Unit 35A) HU ACHUCA WEST Unit 36C F Ad 10/4/06 (Unit 35A) HUACHUCA WEST Region VI M Juv 10/1/06 (Unit 35A) HUACHUCA WEST Deer Creek F Ad 9/30/06 (Unit 35A) HUACHUCA WEST HUACHUCA M Juv 9/24/06 Tag 164 (Unit 35A) Guajalote Flat CHIRICAHUA Miller Spring M Juv 9/16/06 (Unit 29) PINALENO (Unit 31) SAGUARO N.M/SANXAVIER/MAR SANTA RITAS 9/3/06 ANA (Unit 34A) (Unit 38M) SAGUARO SANTA RITAS N.M/SANXAVIER/MAR F Ad 9/3/06 (Unit 34A) ANA (Unit 38M) SAGUARO SANTA RITAS N.M/SANXAVIER/MAR Cub 9/3/06 (Unit 34A) ANA (Unit 38M) SAGUARO SANTA RITAS N.M/SANXAVIER/MAR Cub 9/3/06 (Unit 34A) ANA (Unit 38M) HUACHUCA WEST Guajalote Flat M Ad 8/25/06 Tag 171 (Unit 35A) PELONCILLO PELONCILLO M Juv 6/24/06 (Unit 30) (Unit 30) PINALENO (Unit 31) Rehabber in Phoenix Cub 6/21/06 GALIURO Rehabber in Phoenix Cub 6/21/06 (Unit 32 )

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RINCONS Rehabber in Phoenix Juv 1/24/2007 (Unit 33) SAGUARO SAGUARO N.M/SANXAVIER/MAR N.M/SANXAVIER/M Juv 1/12/07 ANA (Unit 28) ARANA (Unit 28) PINALENO PINALENO M Juv 5/23/07 (Unit 31) (Unit 31) PINALENO PINALENO F Juv 5/23/07 (Unit 31) (Unit 31) HUACHUCA WEST CHIRICAHUA M Ad 6/4/07 (Unit 35A) (Unit 29)

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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

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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/km 2. 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. 1999 a, 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. Non- genetic 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.

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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).

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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 km 2 and is located between 108 o 56’ 46’ N latitude and

31 o 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

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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.

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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;1995 b, Paetkau et al.

1998 a, 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,

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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 MgCl 2; four microsatellite loci (G10M,

G10L, G10J, Mu50) used 2.5 MgCl 2; and one microsatellite locus (G1A) used 3.5 MgCl 2.

We used five thermal cycling profiles that differed only in their annealing temperature.

All cycling profiles included 94 oC for 3 minutes, and 40 cycles of 94 oC for 30 seconds, annealing temperature for 30 seconds (60 oC for G10B, G10H; 62 oC for G1A; 54 oC for

G1D, G10J; 52 oC for G10O, G10M, G10L, and 50 oC for CXX20, Mu50), and 72 oC for

30 seconds, followed by a final extension at 72 oC 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

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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,

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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

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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 (+/-

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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.

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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.

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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

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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 km 2 resulting in a density of 0.22 bears/ km 2. 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/ km 2.

A density of 0.22 to 0.38 bears/km 2 found in this study area in México is higher that the 0.06 bears/ km 2 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/ km 2 (Doan-Cridder and

Hellgreen 1996), and also within the density found in similar habitat in Arizona 0.13-

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0.738/ km 2 (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/ km 2) (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. 2004 a, Onorato et al.

2004 b), 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.

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