<%BANNER%>

Conservation Genetics of the Florida Black Bear


PAGE 1

CONSERVATION GENETICS OF THE FLORIDA BLACK BEAR By JEREMY DOUGLAS DIXON A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2004

PAGE 2

Copyright 2004 by Jeremy Dixon

PAGE 3

“The bears are yet too numerous; they are a strong creature and prey on fruits of the country.” William Bartram commenting on the abundance of black bears during his trip through Florida (1773-74).

PAGE 4

iv ACKNOWLEDGMENTS Without the continued support from several groups, organizations, and people, this project would not be possible. Funding and logistic suppo rt was provided by the Florida Fish and Wildlife Conservation Commi ssion (FWC), Florida Department of Transportation, Wildlife Foundation of Florida, Natural Future Founda tion, Safari Club International, University of Florida (UF) School of Natural Resources and Environment and UF Department of Wildlife Ecology and Conservation. I am especially thankful to Walter McCown for being my field supervisor and mentor; he has been a constant source of in spiration. I am grateful to Mark Cunningham, Stephanie Simek, and Brian Schieck for thei r advice and support of my research. Without Thomas Eason’s foresight and dedica tion, no bear research in Florida would be possible. Special thanks goe s to bear students Elina Ga rrison and Melissa Moyer and graduate students Arpat Ozgul, Heidi Richter, Justyn Stahl, and Tom Hoctor. I enjoyed our friendships and discussions on ever y aspect of ecology. I would like to also thank Alanna Fren ch for her love, support, and understanding during this complicated time. I thank my family whose support and encouragement have fueled my desire for higher education. My mother’s sense of adventure and my father’s teachings of the importance of hard work molded the path that I have chosen. I thank my sisters, Jodi and Becca, for always supporting my bear interests, however strange they might seem.

PAGE 5

v I am grateful for volunteers Bill Henteg es and Tanya DiBenedetto; and wildlife technicians, Billy McKinstry, Chris Long, a nd Darrin Masters. These people worked very hard under harsh conditi ons, dealing with biting insect s; uncooperative barbed wire; and the hot, humid conditions of the Floridian landscape. This project would not be possible without the dozens of indivi duals who collected genetic samples and volunteered those sample s for my project. Melvin Sunquist (UF), David Maehr (University of Kentucky), Mark Cunningham (FWC), and other FWC biologists provided samples for genetic analysis. I also appreciate the agencies and landowne rs who allowed me to place hair snares on their lands: Plum Creek Timber Compa ny, Raiford State Prison (Pride Forestry), Florida Division of Forestry, Florida Na tional Guard, Matthew Kenyan, Suwannee River Water Management District, St. Johns Ri ver Water Management District, Florida Greenways and Trails, UF Ordway Preserve, and FWC. I also thank Dave Dorman, Matt Pollock, Erin Myers, Scott Weaver, Bill Sumpter, Jim Garrison, Scott Crosby, Adele Mills, Bobby Jackson, Paul Catlett, Steve Coates, Dan Miller, Tim Hannon, Bob Heeke, Bill Bossuot, John Ault, Allan Hallman, and Charlie Peterson for in-kind support. I thank Wildlife Genetics International (Nelson, British Columbia, Canada) for performing the genetic analyses; and especi ally David Paetkau and Jennifer Weldon for their professional and courteous service. Their dedication to the intricate processes of DNA analysis was critical to this project. I thank my committee (Dr. Melvin Sunquist, Dr. Thomas Eason, and Dr. Michael Wooten) for their advice and direction. Fi nally, I would like to thank my committee

PAGE 6

vi chair, Dr. Madan Oli, for being a good role model, and giving me the chance to do research on such an exciting and elusive carnivore.

PAGE 7

vii TABLE OF CONTENTS Page ACKNOWLEDGMENTS.................................................................................................iv LIST OF TABLES.............................................................................................................ix LIST OF FIGURES.............................................................................................................x ABSTRACT....................................................................................................................... xi CHAPTER 1 INTRODUCTION........................................................................................................1 2 GENETIC CONSEQUENCES OF HABITAT FRAGMENTATION AND LOSS........................................................................................................................... ..4 Introduction................................................................................................................... 4 Methods........................................................................................................................ 6 Sample Collection.................................................................................................6 Statistical Analyses................................................................................................7 Results........................................................................................................................ ...8 Discussion...................................................................................................................13 Genetic Variation.................................................................................................13 Genetic Structure.................................................................................................14 Conclusion..................................................................................................................18 3 EVALUATING THE EFFECTIVENESS OF A REGIONAL BLACK BEAR CORRIDOR................................................................................................................21 Introduction.................................................................................................................21 Methods......................................................................................................................26 Results........................................................................................................................ .29 Discussion...................................................................................................................34 Conclusion..................................................................................................................38

PAGE 8

viii 4 CONCLUSIONS AND MANAGEMENT RECOMMENDATIONS.......................40 Conclusions.................................................................................................................40 Management Recommendations.................................................................................42 Recommendations for Further Research....................................................................43 APPENDIX A HISTORY OF THE FL ORIDA BLACK BEAR........................................................45 General........................................................................................................................ 45 Regulations.................................................................................................................47 B MICROSATELLITE ANALYSIS.............................................................................49 C GENETIC VARIATION AMO NG BEAR POPULATIONS....................................50 D MICROSATELLITE DATA FOR FLORIDA BLACK BEARS..............................55 LITERATURE CITED......................................................................................................79 BIOGRAPHICAL SKETCH.............................................................................................94

PAGE 9

ix LIST OF TABLES Table page 1 Measures of genetic variation (mean 1 SE) at 12 microsatellite loci in nine Florida black bear populations (sam ple sizes are in parentheses)..............................9 2 Pairwise FST (below diagonal) and RST (above diagonal) estimates for nine Florida black bear populations (standard errors are in parentheses).....................................11 3 Assignment of individuals using the Ba yesian clustering technique using the program STRUCTURE without any prior information on population of origin.....31 4 Microsatellite genetic va riation in bear populations................................................51 5 Individual 12-loci genotypes for blac k bears sampled in Florida, 1989-2003.........56 6 Allele frequencies for 12 microsatellite loci in 10 populations of Florida black bears.........................................................................................................................7 3

PAGE 10

x LIST OF FIGURES Figure page 1 Distribution of black bears in Florida........................................................................6 2 Relationship between estimated populati on size (N) and measures of genetic variation (mean 1 SE) in nine Florida black bear populations..............................10 3 An unrooted phylogenetic tree depicting the genetic relationships among Florida black bear populations..............................................................................................12 4 Area proposed as a regional corridor be tween the Ocala and Osceola black bear populations...............................................................................................................26 5 Locations of samples collected in the Osceola-Ocala corridor................................30 6 Bubble plot of trap success in the Osceola-Ocala corridor......................................30 7 Assignment of black bears to a population of origin without regard to sample locations using STRUCTURE.................................................................................32 8 Spatial pattern of the proportion of memb ership (q) for bears sampled in Osceola, Ocala and the Osceola-O cala corridor using the program STRUCTURE...............33 9 Historic distribution of black bears in the southeastern United States.....................46 10 Current populations of the Florida black bear ( Ursus americanus floridanus ).......48

PAGE 11

xi Abstract of Thesis Presen ted to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science CONSERVATION GENETICS OF THE FLORIDA BLACK BEAR By Jeremy Douglas Dixon May 2004 Chair: Madan K. Oli Major Department: Natural Resources and Environment Habitat loss and fragmentation can influen ce the genetic structure of biological populations. I studied the genetic consequen ces of historical a nd contemporary patterns of habitat fragmentation in nine Florida black bear ( Ursus americanus floridanus ) populations. A total of 305 bears from nine populations was genotyped for 12 microsatellite loci to characterize genetic va riation and structure. None of the nine populations deviated from Har dy-Weinberg equilibrium. Genetic variation, quantified by mean expected heterozygosity ( HE), ranged from 0.27–0.71, and was substantially lower in smaller populations. Low le vels of gene flow (global FST = 0.227; global RST = 0.249) and high values of the likelihood ra tio genetic distance (average DLR = 16.255) suggest that fragmentation of once-contiguous habita t has resulted in genetically distinct populations. There was no isolation-by-dist ance relationship among Florida black bear populations. Barriers such as roads, cities and residential areas limit the dispersal capabilities of black bears in Florida, ther eby reducing the probability of gene flow among populations. Regional corridors or tran slocation of bears may be needed to restore historical levels of genetic variation.

PAGE 12

xii Corridors have been suggested to mitig ate the adverse effects of habitat fragmentation, by restoring or mainta ining connectivity among once-contiguous populations. However, the role of corridors for large carnivores has rarely been evaluated objectively. I us ed non-invasive sampling, microsatellite analysis, and population-assignment tests to evaluate the effectivene ss of a regional corridor (Osceola-Ocala corri dor) in connecting two Florida black bear populations. I sampled 31 bears (28 males, 3 females) within the corri dor. Because bear dispersal is male-biased, the gender disparity suggests that the Osceola-Ocal a corridor functions as a conduit for dispersal and other seasonal movements. Of the 31 bears sampled in the Osceola-Ocala corridor, 28 had genotypes that were assigne d to the Ocala population. I found a mostly unidirectional pattern of move ment from Ocala, with a lim ited mixing of Ocala-assigned individuals with Osceola-assi gned individuals in one area of the corridor. I also documented the presence of bears in Osceola as signed to Ocala, and the presence of bears in Osceola that may be Osceola-Ocala hybrids. My results indicate that the Osceola-Ocala corridor provide s a conduit for gene flow between these populations. However, residential and industrial deve lopment and highways may reduce movements of bears within the Osceola-Ocala corridor. The methods used here may provide a means of evaluating corridor effectiveness, and id entifying gaps in connectivity. Regional corridors should be reestablished or maintain ed where such connectiv ity occurred in the recent past, to increase the viability of populations, and maintain metapopulation structure.

PAGE 13

1 CHAPTER 1 INTRODUCTION Habitat fragmentation and loss is one of th e greatest threats to the conservation of biodiversity in the world (H arris 1984; Meffe & Carroll 1997). The effect of habitat fragmentation on animal populations can have several demographic and genetic consequences. The reduction of population size and connectivity can create conditions where genetic variation is lost at a rapid rate. The loss of genetic variation within populations may lead to inbreed ing depression, a reduction in evolutionary potential, and greater extinction pr obability (Frankham et al. 2002). The most serious threat to the continue d existence of the Florida black bear ( Ursus americanus floridanus ) is fragmentation and loss of habitat (Wesley 1991; Hellgren & Maehr 1993; Hellgren & Vaughan 1994). Habita t fragmentation and loss is driven by human population growth. An estimated 16.3 million people lived in Florida in 2001. This number is projected to increase to mo re than 20 million by 2015 (US Census 2000). Roads, and agricultural, commercial and reside ntial developments continue to encroach on (and further degrade) remaining black bear habitat. The distri bution of the Florida black bear has been reduced by 83% from its historic distribution (Wooding 1993). Currently, Florida black bears occur in severa l populations, mostly restricted within the state of Florida (Appendix A) (Pelton & Van Manen 1997). The reduction of size and connectivity of populations has caused concern regarding the genetic health of Florida black bears. Most extant Fl orida black bear populations are small compared to historic size, and are relatively isolated. Theory suggests that small,

PAGE 14

2 isolated populations are at a higher risk of extinction than large, well-connected populations (Frankham 1995; Meffe & Carroll 1 997; Ebert et al. 2002; Frankham et al. 2002). Because Florida black bear populations are fragme nted from their original relatively contiguous distribu tion, the level of gene flow among populations may be important in maintaining levels of genetic va riation and evolutionary potential of Florida black bears. Although aspects of the populati on genetics of the Florida black bear have been investigated previously (Warrilow et al. 2001; Dobey 2002; Edwards 2002) using microsatellite analyses (Appendix B), these studies did not provide estimates of gene flow among populations, or pr ovide data on the genetic consequences of habitat fragmentation and loss on Florida black bear populations. Little is known about the level of genetic variation within (or gene flow am ong) populations of the Fl orida black bear. It has been suggested that fragment ed populations are best managed as a metapopulation, where local populations are func tionally connected with corridors that facilitate movement. The large home ranges and long-distance disper sal capabilities of black bears have been used as a rati onale for implementation of corridors among populations (Hellgren & Vaughan 1994; Bowker & Jacobson 1995; Hoctor et al. 2000). The Osceola-Ocala corridor has been suggested as the best option in connecting any two of the populations of Florida black bear. Howe ver, the efficacy of this corridor or other corridors for large carnivo res is relatively unknown. Objectives The objectives of my study were to inves tigate genetic variation and gene flow among Florida black bear populations, and to obj ectively evaluate the functionality of the Osceola-Ocala corridor in facilitating demogr aphic and genetic connectivity. Chapter 2

PAGE 15

3 discusses the effects of populat ion size on within-population ge netic variability, estimates levels of gene flow among populations, and examines relationships among measures of genetic differentiation and geographic distances between pairs of populations. Chapter 3 discusses the effectiveness of a regional corridor in connecting two Florida black bear populations using non-invasive geneti c sampling and recently developed population-assignment tests. Taken together, these chapters provide mu ch-needed data on the genetic variation within (and gene flow among) populations of the Florida black bear; and an objective evaluation of the functionality of the Osceola-Ocala corridor. These data are expected to be important for the formulation and impleme ntation of a management plan to ensure long-term persistence of Fl orida black bear populations.

PAGE 16

4 CHAPTER 2 GENETIC CONSEQUENCES OF HAB ITAT FRAGMENTATION AND LOSS Introduction Fragmentation and loss of habitat is one of the most serious problems facing the conservation of biodiversity worldwide (Har ris 1984; Meffe & Carroll 1997). Habitat fragmentation can increase mortality rate s (Jules 1998), reduce abundance (Flather & Bevers 2002), alter movement patterns (Br ooker & Brooker 2002), and disrupt the social structure of populations (Ims & Andreasse n 1999; Cale 2003); and may reduce the probability of persistence (Harrison & Bruna 1999; Davies et al. 2001). Additionally, habitat fragmentation can influence genetic structure and persiste nce of populations in several ways. First, isolation and reduction of populations can decrea se genetic variation (Hudson et al. 2000; Kuehn et al. 2003), which may reduce the ability of individuals to adapt to a changing environment, cause inbree ding depression (Ebert et al. 2002), reduce survival and reproduction (Frankham 1995; R eed & Frankham 2003), and increase the probability of extinction (Saccher i et al. 1998; Westemeier et al. 1998). Secondly, habitat fragmentation can create dispersal barriers which can deter gene flow (Hitchings & Beebee 1997; Gerlach & Musolf 2000) or ot herwise alter geneti c structure of the population (Hale et al. 2001). Thus, efforts to conserve plant and animal populations should take into account the genetic c onsequences of habitat fragmentation. Large mammalian carnivores are particular ly vulnerable to habitat loss and fragmentation because of their relative ly low numbers, large home ranges, and interactions with humans (Noss et al. 1996; Crooks 2002). The Florida panther ( Puma

PAGE 17

5 concolor coryi ) and giant panda ( Ailuropoda melanoleuca ) are examples of large carnivores that were reduced to small numbe rs largely because of impacts of habitat fragmentation and loss (Roelke et al. 1993; Lu et al. 2001). Another large carnivore that has been negatively impacted by habitat fr agmentation is the Florida black bear ( Ursus americanus floridanus ) (Hellgren & Maehr 1993). The Florida black bear historically roam ed throughout the peninsula of Florida and southern portions of Georgia, Alabama, and Mississippi (Brady & Maehr 1985). From the 1800s to the 1970s, numbers of Florida bl ack bears were significantly reduced by loss and fragmentation of habitat, and unregulat ed hunting (Cory 1896; Hendry et al. 1982). Only an estimated 300 to 500 bears were le ft in the state of Florida in the 1970s (McDaniel 1974; Brady & M aehr 1985). Consequently, the Florida Game and Freshwater Fish Commission classified the Flor ida black bear as a threatened species in most Florida counties, in 1974 (Wooding 1993) Destruction and fragmentation of once-contiguous habitat has reduced the distribution of Florida black bears to nine areas: Eglin (EG), Apalachicola (AP), Aucilla (AU), Osceola (OS), Ocala (OC), St. Johns (SJ), Chassahowitzka (CH), Glades/Highlands ( GH), and Big Cypress (BC) (Fig. 1). Fragmentation of populations can reduce gene tic variation (Sherwin & Moritz 2000) and increase the probability of extinction (Saccher i et al. 1998; Westemeier et al. 1998), but the genetic consequences of the histori cal and contemporary patterns of habitat fragmentation on Florida bl ack bear populations are unknow n. Using microsatellite analyses, my goal was to investigate the gene tic consequences of habitat fragmentation on Florida black bear populations. My specific objectives were to estimate within-population genetic variation, and inves tigate the level of genetic differentiation

PAGE 18

6 among Florida black bear populations. Theory predicts a positiv e correlation between genetic variation and population si ze (Frankham 1996), and between genetic differentiation and geographic distance among popul ations (Slatkin 1993). Thus, I tested these predictions by examining the relationshi p between measures of genetic variation and recent estimates of population size, and be tween measures of genetic differentiation and geographic distances among populations. Figure 1. Distribution of black bears in Fl orida: Eglin (EG), Apalachicola (AP), Aucilla (AU), Osceola (OS), Ocala (OC), St Johns (SJ), Chassahowitzka (CH), Highlands/Glades (HG), and Big Cypre ss (BC). The distribution map was compiled by the Florida Fish and Wildlife Conservation Commission. Methods Sample Collection Hair and tissue samples from individual bear s were collected from each of the nine Florida black bear populations during 19892003. Most samples were collected from field studies, some using noninvasive techniques (Woods et al. 1999); but samples also were collected from translocated animals, a nd from bears killed on roadways. Hair and tissue samples were sent to Wildlife Genetic s International (Nelson, British Columbia,

PAGE 19

7 Canada) ( www.wildlifegenetics.ca/ ) for microsatellite anal ysis. DNA was extracted using QIAGEN’s DNeasy Tissue kits (Val encia, California), as per QIAGEN's instructions ( http://www.qiagen.com/ literature/genomlit.asp ); and microsatellite loci were amplified using polymearse chain reacti on (PCR). Each indivi dual was genotyped for 12 microsatellite loci (G1A, G10B, G10C, G1D, G10L G10M, G10P, G10X, G10H, MU50, MU59, and G10J). Laboratory methods us ed in my study are described in detail by Paetkau et al. (1995, 1998a, 1998b, 1999) and Paetkau & Strobeck (1994). Statistical Analyses Departures from Hardy-Weinberg equilib rium (HWE) were tested using the HWE probability test in Genepop 3.4 (Raymond & Rousset 1995). Exact p-values were computed using the complete enumeration met hod for loci with fewer than four alleles (Louis & Dempster 1987), and the Markov ch ain method (dememorization 1,000; batches 100; iterations per batch 1,000) for loci with more than f our alleles (Guo & Thompson 1992). Using this same program, linkage-dise quilibrium tests were used to test for nonrandom associations among alleles of differe nt loci, using the Markov chain method. Within each bear population, genetic vari ation was measured as the observed average heterozygosity ( HO), expected average heterozygosity ( HE), and the average number of alleles per locus ( A ). Spearman’s rank correlati on was used to test for the correlation between genetic va riation and estimated populatio n size. To characterize nonrandom mating within populations, FIS was calculated according to Weir & Cockerham (1984) in Genepop 3.4 (Raymond & R ousset 1995). Global estimates (across all populations) of FIS, FIT (characterizes nonrandom ma ting within populations and genetic differentiation among populations), and FST (characterizes genetic differentiation among populations) were also calculat ed using these methods.

PAGE 20

8 Genetic differentiation among populati ons was estimated using Genepop 3.4 (Raymond & Rousset 1995) with pairwise FST (Weir & Cockerham 1984) and pairwise RST (Michalakis & Excoffier 1996). The RST was estimated because microsatellites are thought to conform to the step wise-mutational mode l better than to the infinite-alleles model on which FST is based (Slatkin 1995). The significance of population differentiation was tested using the genic differentiation test in Genepop 3.4 (Raymond & Rousset 1995). The likelihood ratio genetic distance, DLR (Paetkau et al. 1995) was estimated for each pair of populations usi ng the Doh assignment calculator from the website, http://www2.biology.ualberta .ca/jbrzusto/ Doh.php This genetic distance is based on the ratio of genotype likelihoods be tween pairs of populations. The software program Phylip 3.5c (Felsenstein 1993) a nd the subprogram FITCH (Fitch & Margolia 1967) were used to generate an unrooted phylogenetic tree, with branch lengths corresponding to DLR values. Geographic distances among populations we re estimated as the shortest land distance between population centroids using le ast-cost path analysis in ArcGIS 8.1.2 (McCoy & Johnston 2000). Cent roids were estimated as the harmonic mean of the sample collection locations in each study s ite. The subprogram ISOLDE in Genepop 3.4 (Raymond & Rousset 1995) was used to te st for a relationship between geographic distances, and FST, RST, and DLR values. Statistical signif icance of these relationships was tested using a Mantel (1967) test with 10,000 permutations. Results A total of 305 individual bears was genotyped for 12 micr osatellite loci in nine Florida black bear populations (Table 1). There were no significant departures from HWE for any locus or population (p > 0.05). The linkage disequilibr ium test indicated

PAGE 21

9 that only 15% of loci pairings had significant nonrandom associations (p < 0.05). Loci used in this analysis were found to be inde pendent (D. Paetkau, pers. comm.). Thus, any significant linkage observed among loci pa irs may be a result of nonrandom mating, sampling bias, recent admixture, or genetic drift (Frankham et al. 2002). The population with the highest mean number of alleles per locus ( A ) was Osceola (mean 1SE; 6.667 + 0.225); whereas Chassahowitz ka had the lowest value (2.250 + 0.179). Observed average heterozygosity ( HO) ranged from 0.287 + 0.058 in Chassahowitzka to 0.705 + 0.030 in Osceola. Similarly, exp ected average heterozygosity ( HE) ranged from 0.271 + 0.054 in Chassahowitzka to 0.713 + 0.027 in Osceola (Table 1). Estimated population sizes ranged from 20 in Chassahowitzka to 830 in Osceola (Kasbohm & Bentzein 1998; Maehr et al. 2001; Florida Fish and Wildlife Conservation Commission (FWC ), unpublished data). All three measures of genetic variation were positively correlate d with estimated population size ( A : rs = 0.845, p = 0.004; HO: rs = 0.778, p = 0.014; HE: rs = 0.728, p = 0.026) (Fig. 2). Table 1. Measures of geneti c variation (mean 1 SE) at 12 microsatellite loci in nine Florida black bear populations (sample size s are in parentheses). Measures of genetic variation are: observ ed average heterozygosity ( HO), expected average heterozygosity ( HE), and mean alleles per locus ( A ). Values of FIS (a measure of nonrandom mating w ithin populations) + 1 SE are also given. Population HO HE A FIS Apalachicola (38) 0.686 + 0.036 0.706 + 0.031 5.92 + 0.358 0.027 + 0.025 Aucilla (9) 0.556 + 0.063 0.616 + 0.054 3.83 + 0.322 0.097 + 0.062 Big Cypress (41) 0.642 + 0.036 0.650 + 0.026 5.50 + 0.435 0.013 + 0.034 Chassahowitzka (29) 0.287 + 0.058 0.271 + 0.054 2.25 + 0.179 -0.057 + 0.028 Eglin (40) 0.613 + 0.071 0.537 + 0.062 4.08 + 0.379 -0.141 + 0.024 Highlands/Glades (27) 0.327 + 0.049 0.385 + 0.051 2.75 + 0.250 0.149 + 0.059 Ocala (40) 0.579 + 0.045 0.610 + 0.045 4.75 + 0.305 0.051 + 0.024 Osceola (41) 0.705 + 0.030 0.713 + 0.027 6.67 + 0.225 0.010 + 0.033 St. Johns (40) 0.650 + 0.048 0.663 + 0.041 5.58 + 0.379 0.020 + 0.028

PAGE 22

10 A N 0200400600800 H O 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 B N 0200400600800 H E 0.2 0.3 0.4 0.5 0.6 0.7 0.8 C N 0200400600800 A 1 2 3 4 5 6 7 8 Apalachicola Aucilla Big Cypress Chassahowitzka Eglin Highlands/Glades Ocala Osceola St. Johns Figure 2. Relationship between estimated population size (N) and measures of genetic variation (mean 1 SE) in nine Flor ida black bear populations. A) N and Observed average heterozygosity ( HO), B) N and Expected average heterozygosity ( HE), and C) N and Averag e alleles per locus ( A ). Curves were fitted using a sigmoid 4-parameter regression in Sigmaplot.

PAGE 23

11 FIS ranged from -0.141 + 0.024 in Eglin to 0.149 + 0.059 in Highlands/Glades (Table 1). These results give evidence of random mating within these populations. The global estimate of FIS was 0.010 and the global estimate of FIT was 0.235. The relatively high FIT values encompass relatively insubsta ntial effects of mating between close relatives within populations; and also the exte nsive effects of restricted gene flow among the populations (Hartl & Clark 1997). Global FST, the measure of population subdivi sion across all populations, was 0.227. Estimates of FST ranged from 0.009 to 0.574 and RST ranged from 0.010 to 0.629. The pairwise comparisons between Ocala and St Johns had highest le vels of gene flow whereas Highlands/Glades and Chassahowitzka had the lowest levels of gene flow (Table 2). All tests of genic differentia tion among populations were highly significant (p < 0.001). Table 2. Pairwise FST (below diagonal) and RST (above diagonal) estimates for nine Florida black bear populations (standa rd errors are in parentheses). Populations are: Apalachicola (AP) Aucilla (AU), Big Cypress (BC), Chassahowitzka (CH), Eglin (EG), Hi ghlands/Glades (HG), Ocala (OC), Osceola (OS), and St. Johns (SJ). Fig. 1 contains the geographic locations of these populations. AP AU BC CH EG HG OC OS SJ AP 0.0546 (+ 0.034) 0.1356 (+ 0.034) 0.3427 (+ 0.067) 0.1572 (+ 0.063) 0.4197 (+ 0.046) 0.2017 (+ 0.050) 0.0727 (+ 0.044) 0.2225 (+ 0.049) AU 0.1223 (+ 0.019) 0.2073 (+ 0.053) 0.5953 (+ 0.101) 0.1946 (+ 0.066) 0.4966 (+ 0.097) 0.2348 (+ 0.065) 0.1388 (+ 0.054) 0.2714 (+ 0.062) BC 0.1379 (+ 0.026) 0.2010 (+ 0.018) 0.3342 (+ 0.074) 0.3026 (+ 0.073) 0.2435 (+ 0.062) 0.1053 (+ 0.050) 0.1422 (+ 0.051) 0.0848 (+ 0.037) CH 0.3609 (+ 0.041) 0.4449 (+ 0.061) 0.3748 (+ 0.046) 0.5472 (+ 0.087) 0.6292 (+ 0.075) 0.3723 (+ 0.087) 0.3443 (+ 0.078) 0.3449 (+ 0.061) EG 0.1653 (+ 0.029) 0.1961 (+ 0.026) 0.2348 (+ 0.032) 0.4846 (+ 0.065) 0.5176 (+ 0.088) 0.2847 (+ 0.071) 0.1477 (+ 0.055) 0.3207 (+ 0.073) HG 0.2972 (+ 0.038) 0.3841 (+ 0.064) 0.2431 (+ 0.038) 0.5737 (+ 0.064) 0.4000 (+ 0.068) 0.2269 (+ 0.056) 0.3787 (+ 0.050) 0.1576 (+ 0.049) OC 0.1617 (+ 0.030) 0.1960 (+ 0.036) 0.1360 (+ 0.029) 0.3906 (+ 0.067) 0.2299 (+ 0.034) 0.2707 (+ 0.035) 0.0842 (+ 0.014) 0.0101 (+ 0.029) OS 0.1167 (+ 0.022) 0.1463 (+ 0.023) 0.1277 (+ 0.032) 0.3483 (+ 0.049) 0.1792 (+ 0.032) 0.3050 (+ 0.036) 0.1062 (+ 0.029) 0.1351 (+ 0.042) SJ 0.1419 (+ 0.033) 0.1790 (+ 0.042) 0.1212 (+ 0.018) 0.3585 (+ 0.052) 0.2240 (+ 0.035) 0.2232 (+ 0.036) 0.0099 (+ 0.005) 0.0942 (+ 0.028)

PAGE 24

12 An unrooted phylogenetic tree based on DLR values suggested that the Ocala and St. Johns populations were cl osely related, whereas Chassa howitzka, Highlands/Glades, and Eglin were the most divergent of a ll the populations (Fig. 3). There was no significant relationship between geographi c distance and measures of genetic differentiation [FST (p = 0.211), RST (p = 0.104), or DLR (p = 0.073)]. Figure 3. An unrooted phyl ogenetic tree depicting the genetic relationships among Florida black bear populations. Branch lengths correspond to the likelihood ratio genetic distance, DLR. Populations are: Egli n (EG), Apalachicola (AP), Aucilla (AU), Osceola (OS), Ocala (O C), St. Johns (SJ), Chassahowitzka (CH), Highlands/Glades (HG) and Big Cypress (BC).

PAGE 25

13 Discussion Genetic Variation Habitat fragmentation can reduce genetic variation, which can adversely influence fitness [e.g., the Florida panther (Roelke et al. 1993) and lion (Panthera leo) ], increase susceptibility to disease [e.g., cheetah ( Acinonyx jubatus ) (O'Brien et al. 1994)], and decrease population viability (Sherwin & Moritz 2000). Habitat fragmentation and hunting are thought to be responsible for losse s in genetic variation in wolverines ( Gulo gulo ) (Kyle & Strobeck 2001), lynx ( Lynx lynx ) (Spong & Hellborg 2002), mountain lions ( Puma concolor ) (Ernest et al. 2003), Ethiopian wolves ( Canis simenesis ) (Gottelli et al. 1994) and brown bears ( U. arctos ) (Miller & Waits 2003). Large carnivores may be much more susceptible than other taxa to losses in genetic vari ation due to habitat fragmentation because of their large home ranges and low population densities (Paetkau & Strobeck 1994). The measures of genetic vari ation reported for most Flor ida black bear populations were within the range of othe r populations of bears using 8 of the same microsatellite loci (Waits et al. 2000) However, gene tic variation in Chassahowitzka and Highlands/Glades are among the lowest repo rted for any bear population (Appendix C, Table 4). The three measures of genetic vari ation for Florida black bear populations were positively correlated with population size. Ch assahowitzka was characterized by a small population size, and accordingly, this population had the lowest level of genetic diversity. Osceola was characterized by a large population size because of its connection with the Okefenokee National Wildlife Refuge, and had th e highest levels of genetic diversity. Presumably, the effects of genetic drift on lo ss of genetic variation are much greater in

PAGE 26

14 Chassahowitzka and Highlands/Glades because of small population sizes, whereas the effects of genetic drift are not as substantial in the larger populations. One of the only bear populations that have a reported genetic variation lower than Chassahowitzka is that of brown bears on K odiak Island, Alaska. Kodiak bears have remained isolated from the mainland brow n bear populations for >10,000 years (Paetkau et al. 1998b). The Chassahowitzka and Hi ghlands/Glades populations are thought to have remained isolated from other Florida black bear populations for a longer period than any other Florida black bear populations. The isolation of these populations is remarkable because it has resulted in the subs tantial loss of geneti c variation that has occurred in presumably < 100 years. The declines in local abundance and ge netic variability of Chassahowitzka and Highlands/Glades bear populations raise the possibility that inbr eeding depression could reduce fitness, surviv al, and evolutionary potential (Reed & Frankham 2003), and that these populations may face an increased risk of local extinction (Frankham 1995; Ebert et al. 2002). Although not within these populations some characteristic signs of inbreeding depression were observed in Florida black bears in the western panhandle of Florida (Dunbar et al. 1996) and southern Alabama (K asbohm & Bentzien 1998). However, low FIS values and lack of deviations from Hardy-Weinberg Equilibrium suggest that random mating is operating within studied popul ations of the Florida black bear. Genetic Structure The tests of genetic differentiation, FST, RST, and DLR indicated that there was extensive differentiation among Florida black bear populations. This differentiation was most evident with pairwise comparisons of Chassahowitzka, Highlands/Glades, or Eglin with any other population. The high rate of genetic drift within these populations most

PAGE 27

15 likely contributed to the ex tensive genetic differentiati on among populations. The level of genetic differentiation between Florid a black bear populations was substantially greater than between other la rge carnivore populations (e.g., b ears: (Paetkau et al. 1997), the Asian black bear [ U. thibetanus ]: (Saitoh et al. 2001), moun tain lions: (Ernest et al. 2003) wolverines: (Kyle & Strobe ck 2001; Walker et al. 2001) and lynx: (Hellborg et al. 2002; Schwartz et al. 2002). The global estimate of FST, the measure of population subdivision across all populations, was 0.227. This degree of subdivi sion is expected if there are on average 0.85 successful migrants [Nm = (1/FST-1)/4] entering each population per generation (approximately 8 years for black bears) assuming an island model of migration (Frankham et al. 2002). Therefore, on averag e, across all Florida black bear populations, there is one successful migrant every 10 years, a relatively low level of gene flow. There have been dozens of bear tr anslocations among populations due to management activities during the last 20 years (T. Eason, pers. comm.). Due to the relatively recent history of th ese artificial movements, it is unknown what effects they will have on the genetic structure of these populations. Some studies suggest that most translocations of carnivores are unsuccessful, and probably do not contribute to the gene pool of the population in which they we re released (Linnell et al. 1997). In large natural populations occupying a mo stly contiguous habitat, a pattern of isolation by distance is expect ed (Wright 1931). This relati onship has been reported for other bear populations (Paetkau et al. 1997). However, there was no relationship between geographic distance and measures of geneti c differentiation among Florida black bear populations. However, nearly signifi cant relationships of pairwise RST and DLR values

PAGE 28

16 with geographic distances sugge st that exclusion of valu es associated with small populations (i.e., Chassahowitzka and Highla nds/Glades) may gene rate a significant isolation-by-distance re lationship among “larger” populations of Florida black bears. Interestingly, two pairs of populations se parated by comparable geographic distances (Ocala-St. Johns and ApalachicolaAucilla) had very different FST values, 0.009 and 0.122 respectively, suggesting that there is a high level of ge ne flow between Ocala and St. Johns, but not between Apalachicola and Aucilla. The genetic differentiation among Florida bl ack bears was substantial, although the average distance between nearest neighbori ng populations (134 km ) is within the dispersal capabilities of black bears (Rogers 1987; Maehr et al. 1988). Dispersal of bears is sex-biased, and males typically disperse fa rther than females, who tend to establish home ranges near their mother’s home ra nge (Rogers 1987; Schwartz & Franzmann 1992). It has been suggested that dispersi ng black bears may be able to maintain connectivity among populations even when popul ations are fragmented (Noss et al. 1996; Maehr et al. 2001). Why, then, was there su ch a high level of ge netic differentiation among Florida black bear populations? Fu rthermore, why did I fail to find isolation-by-distance re lationship in Florida black bear s, which has been reported for other black bear populations occ upying contiguous habitat? I suggest that the substantial genetic differentiation and the lack of isol ation-by-distance rela tionship among Florida black bear populations is primarily due to the reduction of bear numbers by habitat fragmentation, and by human-made barriers to dispersal. The presence of natural barriers, such as mountain ranges or large rivers, has historically determined the limits of sp ecies distribution (C hesser 1983). Habitat

PAGE 29

17 fragmentation in the form of anthropogenic barriers such as roads or other human development can further limit species distri bution and gene flow (Mader 1984). The separation of populations with roads reduced the level of gene flow in the moor frog ( Rana arvalis ) (Vos et al. 2001), ground beetle ( Carabus violaceus ) (Keller & Largiader 2003), and bank vole ( Clethrionomys glareolus ) (Gerlach & Musolf 2000). Additionally, habitat fragmentation is respons ible for altering the genetic st ructure of the red squirrel ( Sciurus vulgaris ) (Hale et al. 2001) and black grouse ( Tetrao tetrix ) (Caizergues et al. 2003). Although large carnivores are thought to be highly va gile (Paetkau et al. 1999; Schwartz et al. 2002), some studi es suggest they may be limite d in distribution because of anthropogenic barriers (Kyle & Strobeck 2001; Sinclair et al. 2001; Walker et al. 2001; Ernest et al. 2003; Miller & Waits 2003). Black bear movement does not seem to be limited by topographical features of the native Floridian landscape; however, human-made barriers such as roads, cities and residential areas, appear to limit the successful dispersal of black bears (Br ody & Pelton 1989; Hellgren & Maehr 1993) in Florida. Although bears are able to cross some hi ghways (McCown et al. 2001), the impact of highways on mortality of bears can be detrimental. From 2000 to 2002, 346 bears were documented as killed on roads in Florida. Most of these were young males that may have been attempting dispersal or migrat ion to distant popula tions (FWC, unpublished data). Additionally, highways and development can act as partial or complete barriers. Some bears may avoid interstate highways (Brody & Pelton 1989; Proctor et al. 2002), and other forms of human development may a lter movement patterns (Maehr et al. 2003), further decreasing the probability of movement of bears among populations.

PAGE 30

18 Given the unprecedented rate of human population growth in Florida, wildlife habitat will continue to be converted for commercial or residential purposes. Consequently, further fragmenta tion or isolation of Florida bl ack bears and other wildlife population is likely. My results indicate th at habitat fragmentation and human-made dispersal barriers may have substantially a ltered the genetic structure of Florida black bears. The effects of habitat fragmentation and isolation are li kely to be even greater in species with limited dispersal capabilities. It is imperative that management plans for the conservation of black bears in Florida consider measures to mitigate genetic (and most likely, demographic) consequences of habita t fragmentation and anthropogenic dispersal barriers. Conclusion I conclude that the loss and fragmentati on of once contiguous habitat has caused the loss of genetic variation in the Florida black bear, and that genetic variation in smaller populations is among the lowest reported for any species of bear. Th is substantial loss of genetic variation has contribu ted to extensive genetic di fferentiation among populations. Additionally, roads with high traffic volume and commercial and residential developments apparently act as barriers to gene flow, contributing to genetic differentiation among populations. Loss of genetic variation is a concern for the long-term survival and adaptation of Florida black bears. What constitutes historical levels of genetic variation for Florida black bear populations? Evid ence suggests that at one time Florida black bears were distributed throughout the state (Brady & Maehr 1985). Most contiguous mainland populations of black bears have high levels of genetic variation ( HE ~ 0.76) (Paetkau et

PAGE 31

19 al. 1998b). Thus, efforts should be made to re store historic levels of genetic variation within Florida black bear populations, us ing mainland figures as a baseline. To prevent the further loss of genetic variation, efforts should be made to increase the size of Florida black bear populations. It has been suggested that a minimum of 50 effective breeders is needed to prevent inbr eeding depression and popul ation levels in the hundreds or thousands to maintain evolutio nary potential (Franklin 1980; Lande 1995). However, keeping bears at high population levels may be increasingly difficult due to the rapid rate of development over much of the state. Given that Florida black bear populations have been reduced in size, gene flow among bear populations is needed to restore and maintain genetic variation (Waits 1999). A minimum of one and a maximum of ten succes sful migrants per generation have been suggested as a rule of thumb to maintain le vels of genetic varia tion (Mills & Allendorf 1996). I suggest that Florida black b ear populations should be managed as a metapopulation so that gene flow can occur among populations connected with conservation corridors (Craighead & Vyse 1996; Maehr et al. 2001; Larkin et al. 2004). However, the effectiveness of corridors in maintaining gene fl ow among populations of carnivores is not well unders tood (Beier & Noss 1998). Rece nt data suggest that one such corridor between the O cala and Osceola populations may facilitate gene flow between these populations (F WC, unpublished data). Additionally, wildlife crossing structures may be needed to allow safe passage of bears across roadways that pose significant barriers to bear movement (Foster & Humphrey 1995). In situations where populati on connection via corridors is impractical, artificial transloca tion of animals should be consid ered (Griffith et al. 1989).

PAGE 32

20 Translocation of animals has been successful in curbing some effects of inbreeding depression and increasing levels of gene tic variation in some animal populations (Mansfield & Land 2002). Conservation biologi sts should be cognizant of the fact that the effects of translocated animals on population structure an d hierarchy are not understood. Finally, further reduction or fr agmentation of habitat likely will have detrimental impact on demographic and gene tic health of the Florida black bear populations, and efforts to conserve remaining habita t cannot be overemphasized.

PAGE 33

21 CHAPTER 3 EVALUATING THE EFFECTIVENESS OF A REGIONAL BLACK BEAR CORRIDOR Introduction The effect of habitat fragmentation on na tural populations is one of the greatest threats to biodiversity conservation (Fah rig & Merriam 1994; Meffe & Carroll 1997; Fahrig 2001). Habitat fragmentation can subdivide and isolate populations, reduce genetic diversity, and in crease the chances of local extinc tion (Harris 1984; Saccheri et al. 1998; Westemeier et al. 1998). B ecause most wildlife populations in human-dominated landscapes occur in fragmented habitats, attempts have been made to identify measures that can reduce the adve rse influences of ha bitat fragmentation. Corridors have been proposed to mitigate the negative effects of habitat fragmentation by connecting once isolated populations (No ss & Harris 1986). Corridors can increase movement of organisms among patches (H ass 1995; Aars & Ims 1999; Haddad 1999; Sieving et al. 2000; Mech & Hallett 2001; Ha ddad et al. 2003; Kirchner et al. 2003), thereby providing additional habitat (Perault & Lomolino 2000), facilitate plant-animal interactions (Tewksbury et al 2002), and increase recoloniza tion potential (Hale et al. 2001), survival (Coffman et al. 2001), gene flow (Harris & Gallagher 1989) and the probability of persistence (Fahrig & Merriam 19 85; Beier 1993). The use of corridors in conservation stems from the equilibrium theory of isla nd biogeography (MacArthur & Wilson 1967), landscape ecology (Forma n & Godron 1986), and the metapopulation paradigm (Levins 1970; Hanski 1994). Severa l authors have suggest ed that conservation

PAGE 34

22 of fragmented populations requires a meta population approach (Hanski & Simberloff 1997; Dobson et al. 1999). Managing fragment ed or spatially-st ructured populations requires functional corridors that permit exchange of individuals among populations. Discussions regarding the role of corri dors in conservation biology is confused by the many definitions of this concept (Ros enberg et al. 1997; Beier & Noss 1998; Hess & Fischer 2001). Corridors range in scale from small transects linking patches of habitat to regional complexes linking ecosystems and waters heds. Noss et al. (1996) suggested that “connectivity will be best provided by broad, heterogeneous landscapes, not narrow, strictly defined corridors.” Thus, evalua ting the effectiveness of corridors requires a consideration of the entire landscape mosaic and the functional/structural aspects of the corridor for the focal species. Large carnivores are highly susceptible to the effects of habitat fragmentation, because of the potential for conflicts with humans, large home ranges, and low population densities (Noss et al. 1996; Cr ooks 2002). Many populations of large carnivores exist within fragmented habita ts, encompassing areas much too small to support viable populations (Woodroffe & Gins berg 1998). Additionally, the conservation of large carnivores that are flagship and umbrella species provides a means of protecting biodiversity at smaller scales (Cox et al. 1994; Noss et al. 199 6). It has been suggested that carnivore populations in fragmented habitats operate as metapopulations (Poole 1997; Ferreras 2001; Palomares 2001). For ma ny carnivore species, movement among populations is vital for metapopul ation persistence (e.g., lynx [ Lynx spp. ]: Ferreras 2001; Ganona et al. 1998; Palomares 2001; and brown bears [ Ursus arctos ]: Craighead & Vyse 1996).

PAGE 35

23 The long-distance movements of large car nivores suggest that they are more likely to use corridors for movements than species with limited dispersal capabilities (Lidicker & Koenig 1996; Harrison & Voller 1998). Corridors we re recommended as management tools for connecting populat ions of lynx (Poole 1997; Ferreras 2001; Palomares 2001), cougars ( Puma concolor ) (Beier 1995; Ernest et al. 2003), wolves ( Canis lupus ) (Duke et al. 2001), brown bears (P icton 1987; Craighead & Vyse 1996; Weaver et al. 1996), and black bears ( U. americanus ) (Cox et al. 1994; Hoctor 2003; Larkin et al. 2004). However, the effectiven ess of corridors for larg e carnivores has not been tested on a regional scale. One challenge in testing the effectiven ess of regional corridors for carnivores using traditional techniques, such as radio te lemetry, is that the long-distance movements of carnivores make it difficult to locate and observe animals. In many species, long-distance dispersal is of ten rare, and there is no guara ntee that the sample of radio-instrumented animals will contain dispersing animals (Koenig et al. 1996). Moreover, the dispersal of an animal fr om population to population does not indicate effective dispersal; genetic data are much more suited to provide that information (Frankham et al. 2002). The use of relati vely inexpensive, non-invasive sampling techniques, such as hair snares, and geneti c analyses may help overcome these limitations of radio telemetry-based studies. Such tech niques provide data necessary for evaluation of the functionality of corrido rs by elucidating genetic stru cture and effective dispersal (Foran et al. 1997). Recent a dvances in genetic analyses a nd statistical t echniques (e.g., population-assignment tests) have made it possi ble to identify the or igin of animals by assigning them to a population based on their multilocus genot ypes (Paetkau et al. 1995;

PAGE 36

24 Waser & Strobeck 1998; Waser et al. 2001). P opulation-assignment te sts have been used to identify immigrants within populations of cougars (Ernest et al. 2003), otters ( Lutra lutra ) (Dallas et al. 2002), wolv es (Flagstad et al. 2003; V ila et al. 2003), marten ( Martes americana ) (Small et al. 2003), wolverines ( Gulo gulo ) (Cegelski et al. 2003), and bears (Paetkau et al. 1995). These techniques can identify dispersal pa tterns and cryptic boundaries, which may indicate breaks in th e gene flow across populations or the reconnection of once isolated populations (M anel et al. 2003). Additionally, some assignment tests detect not only immigrants into a population, but also their offspring, which enables researchers to directly detect and monitor gene flow (Rannala & Mountain 1997; Pritchard et al. 2000). A carnivore species that c ould benefit from the implementation of regional corridors is the Florida black bear ( U. a. floridanus ). The Florida black bear was once distributed throughout Florida, and the sout hern portions of Georgia, Alabama, and Mississippi. Human activities significantly reduced the number of black bears from the 1850s to the 1970s through extensive fragme ntation of habitat and excessive hunting (Brady & Maehr 1985). Consequently, Florid a black bears now occur in fragmented populations. The long-term isolation of popul ations could lead to a loss of genetic variation and evolutionary pot ential, and may also reduce population viability (Harris 1984; Frankham 1995; Reed & Frankham 2003 ). However, some populations are expanding as bears recolonize suitable vacant habitat (Eason 2000). Black bears have large home ranges and dispersing bears can travel hundreds of kilometers from their natal home range (Alt 1979; Rogers 1987; Maeh r et al. 1988; Wooding & Hardisky 1992; Hellgren & Maehr 1993; McCown et al. 2001; Lee & Vaughan 2003). However,

PAGE 37

25 development throughout much of the state of Fl orida has created formidable obstacles to movements such as towns, commercial/res idential developments, and major highways (Brody & Pelton 1989; Maehr et al. 2003). C onsequently, regional corridors may be needed to mitigate the detrimental demographic and genetic effects of habitat fragmentation in Florida bl ack bear populations (Harri s & Scheck 1991; Noss 1993). Documented dispersal and movement of individual bears (Florida Fish and Wildlife Conservation Commission (FWC ), unpublished data) and Geographic Information Systems (GIS) analysis (Hoc tor 2003) suggest that the Osceola-Ocala regional corridor may be the best option for connecting two of the largest Florida black bear populations. The Osceola-Ocala corridor is a patchwork of public and private lands within a matrix of roads and development stretching for 90 km from the Ocala National Forest to Osceola National Forest (Fig. 4). This proposed corridor contains a mosaic of flatwoods, pine plantations, forested we tlands, riparian hammocks, scrub, and sandhill covering over 80,000 ha (Maehr et al. 2001). Osceola and Ocala are two of the largest populations of Florida black bear (Eas on 2000), and establishing or maintaining connectivity between these populations may be necessary to ensure the long-term persistence of the Fl orida black bear. The goal of my study was to evaluate th e effectiveness of the Osceola-Ocala corridor for the Florida black bear. I used non-invasive sampling to obtain genetic material from bears within the Osceol a-Ocala corridor and genotyped bears for 12 microsatellite loci. I also sampled bears from the Osceola and Ocala populations and from seven other areas throughout Florida. I used population-assignment tests to assign individuals sampled from the corridor to a popu lation of origin (Osceo la or Ocala) based

PAGE 38

26 on their multilocus genotypes. These techniques allowed me to characterize the dispersal of bears from the source populations, and id entify gaps in connectivity within the Osceola-Ocala corridor. Figure 4. Area proposed as a regional corr idor between the Ocala and Osceola black bear populations. Crosshatched areas represent primary black bear habitat (presence of breeding females) and sti ppled areas represent secondary black bear habitat from a recent distribu tion map (Florida Fish and Wildlife Conservation Commission (FWC), unpublished data). Populations are abbreviated as: Eglin (EG), Apalachicola (AP), Aucilla (AU), Osceola (OS), Ocala (OC), St. Johns (SJ), Chassahow itzka (CH), Highlands/Glades (HG), and Big Cypress (BC). Methods I used a map of secondary black bear ha bitat (FWC, unpublished data; Fig. 4) and results from a least-cost path analysis (Hocto r 2003) to identify area s that might serve as a potential regional corridor between O cala and Osceola. These habitat patches

PAGE 39

27 represented areas that bears most likel y travel through to avoid commercial and residential development. I overlaid a grid of 20 km2 cells on a map of available lands within the potential corridor and placed at least one hair snare (Woods et al. 1999) within each cell. Each hair snare was construc ted of two strands of standa rd 4-prong barbed wire at heights of approximately 30 cm and 55 cm, attach ed to a perimeter of three or more trees encompassing a total area of 10-30 m2. I baited the center of th e snare with pastries and corn, and placed two attractants (pastries and raspberry extract) > 2.44 m above the snare. As bears entered the hair snare, the barbed wi re snagged hair samples that were used in genetic analyses. I operated each hair snare for an average of seven times with a mean period of 26 days between baiting and sampling from May to November of 2002 and May to August of 2003. I collected hair samp les using the protocol of Eason et al. (2001). Additionally, I collected hair samples within the corridor opportunistically from a complementary hair snare project in Os ceola (May-August, 2002-03), existing fences (2001-03) and bears killed on roads (1998-2003). Black bear tissue and hair samples collect ed from previous research studies and highway mortalities during 1989-2003 were available for the Osceola and Ocala populations (n = 41 and n = 40 i ndividual bears, respectively) To provide comparative data, individuals also were sampled fr om other Florida black bear populations: Apalachicola (n = 40), Aucilla (n = 9), Big C ypress (n = 41), Chassahowitzka (n = 29), Eglin (n = 40), Highlands/Glades (n = 28), and St. Johns (n = 40). I sent hair and tissue samples to Wildlife Genetics International (Nelson, British Columbia, Canada) ( http://www.wildlifegenetics.ca/ ), where individuals were genotyped

PAGE 40

28 using microsatellite analysis. DNA was ex tracted using QIAGEN’s DNeasy Tissue kits (Valencia, California), as per QIAGEN's instructions ( http://www.qiagen.com/literature/genomlit.asp ). Microsatellite loci were amplified using polymerase chain reaction (PCR) prim ers (G1A, G10B, G10C, G1D, G10L, G10M, G10P, G10X, G10H, MU50, MU59, and G10J). The gender of each bear was determined using the length polymorphism in the amel ogenin gene (D. Paetkau, pers. comm.). Laboratory analyses were performed as de scribed in Paetkau et al. (1995, 1998a, 1998b, 1999) and Paetkau & Strobeck (1994). I used the software program STRUCTUR E to assign individuals to a population of origin using Bayesian clustering tec hniques (Pritchard et al. 2000). STRUCTURE assumes Hardy-Weinberg equilibrium (HWE) within populations and linkage equilibrium between loci. I used Genepop 3.4 (Raymond & R ousset 1995) to test for deviations from Hardy-Weinberg equilibrium (HWE). For lo ci with fewer than four alleles, exact p-values were computed using the comple te enumeration method (Louis & Dempster 1987), and for loci with more than four alleles the Markov chain method (dememorization 1,000; batches 100; iterati ons per batch 1,000) was used (Guo & Thompson 1992). Using Genepop 3.4, I used li nkage disequilibrium tests to identify nonrandom association between al leles of different loci using the Markov chain method. I assigned bears sampled from the corrido r and from other populations to a cluster or population based on their genotypes, wit hout regard to where the samples were collected, using the program STRUCTURE. I used the admixture model, which assumes that each individual draws some proportion of membership (q) from each of K clusters.

PAGE 41

29 Allele frequencies were assumed independe nt and analyses were conducted with a 100,000 burn-in period and 100,000 repetitions of Markov Chain Monte Carlo. I conducted population-assignment tests us ing STRUCTURE at two levels. For comparative purposes, the first analysis wa s conducted on the statewide level with individuals sampled from the nine populations and the corridor (K = 8 clusters). A second analysis was conducted on a regional le vel; only individuals sampled from Ocala, Osceola, and the corridor were included (K = 2 clusters). An indivi dual bear was placed into a cluster if q > 0.85 for that cluster. If q > 0.40 for both cluste rs, the genotype profile indicated mixed ancestry, suggesting the indi vidual may be an offspring of a mating between the two clusters. I plotted the a ssigned individuals on a map of north-central Florida using ArcGIS 8.1.2 to examine the ge ographic patterns of congruence (Ormsby et al. 2001). Results A total of 598 hair samples was collected at 44 out of 86 hair snare sites within the Osceola-Ocala corridor (Fig. 5). Overa ll, trap success for hair snares was 23.33%, with substantially lower trapping success toward s the center of the corridor (Fig. 6). Within the corridor, 31 black bears were samp led at 50 locations; 11 of the 31 bears were sampled at multiple locations. Only three of the 31 bears sampled in the corridor were females, and these were within 20 km of the Ocala population. There were no significant departures from HWE for any locus or population (p > 0.05), and the linkage disequili brium test indicated that 10% of loci pairings had significant nonrandom associations (p < 0.05). These significant loci pairings may be a result of nonrandom mating, sampling bias, re cent admixture, or ge netic drift (Frankham et al. 2002).

PAGE 42

30 Figure 5. Locations of samples collected in the Osceola-Ocala corridor. Dark circles represent hair snares visited by bears, whereas open circles represent hair snares not visited by bears. Squa res represent samples collected opportunistically. Distance from Osceola (km) 20406080100120140 Distance from Ocala (km) 20 40 60 80 100 120 140 Trap success of hair snares Hair snares not visited Figure 6. Bubble plot of trap success in the Osceola-Ocala corridor. The size of the bubble represents the number of bear visi ts relative to the number of trapping sessions. Squares represent hair snares not visited by bears. The distance was estimated as the linear distance from the population’s centroids (the harmonic mean of sample locations in the Ocal a and Osceola populations) to the hair snare sites in the corridor.

PAGE 43

31 For the statewide analysis, the 31 individua ls sampled in the corridor, along with the 308 individuals sampled statewide, we re analyzed using STRUCTURE. The 10 predefined populations had 79% or more of th eir membership assigned to a single cluster. Individuals sampled from Ocala, St. Johns and the Osceola-Ocala corridor were assigned to the same cluster (q > 0.85), suggesting no significant genetic differentiation among these three populations (Table 3). Table 3. Assignment of individuals using the Bayesian clustering technique using the program STRUCTURE (Pritc hard et al. 2000) without any prior information on population of origin. The average proportion of membership for individuals sampled in predefined populat ions for each of 8 clusters (highest average proportion of membership assi gned to a single cluster is in bold italics). Sample sizes are in parentheses. Average proportion of membership in 8 clusters Population 1 2 3 4 5 6 7 8 Apalachicola (40) 0.846 0.0880.0130.0060.0210.007 0.011 0.009 Aucilla (9) 0.121 0.835 0.0080.0090.0070.006 0.006 0.008 Big Cypress (41) 0.0120.006 0.887 0.0060.0100.021 0.045 0.012 Chassahowitzka (29) 0.0020.0040.004 0.977 0.0030.004 0.004 0.003 Eglin (40) 0.0100.0060.0100.005 0.947 0.007 0.006 0.010 Highlands/Glades (28) 0.0030.0030.0240.0040.003 0.954 0.006 0.003 Ocala (40) 0.0060.0050.0100.0040.0080.011 0.947 0.009 Corridor (31) 0.0080.0070.0090.0060.0090.008 0.848 0.105 Osceola (41) 0.0190.0160.0350.0140.0150.020 0.085 0.796 St. Johns (40) 0.0190.0210.0240.0140.0090.035 0.853 0.025 For the regional analysis, I conducted population-assignment tests including only individuals sampled from Ocala, Osceola, a nd the corridor, and es timated the proportion of membership of each bear to the two clusters (Ocala and Osceola). All bears sampled in Ocala were assigned to cluster 1 (q > 0.90), indicating that no immigrants from Osceola were sampled in Ocala. Bears samp led in Osceola had ancestry in both clusters, with 36 of the 41 bears assigned to cluster 2 (q > 0.85). Two individuals sampled in Osceola (OS31 and OS41) were assigned to cl uster 1 (q > 0.99), suggesting they were immigrants from Ocala. Additionally, tw o bears sampled in the Osceola population

PAGE 44

32 (OS14 and OS20) were assigned to both cl usters (q > 0.40), indicating that these individuals were offspring from an Osceola and Ocala mating (Fig. 7). Black bears sampled in OcalaIndividual bears OC1 OC2 OC3 OC4 OC5 OC6 OC7 OC8 OC9 OC10 OC11 OC12 OC13 OC14 OC15 OC16 OC17 OC18 OC19 OC20 OC21 OC22 OC23 OC24 OC25 OC26 OC27 OC28 OC29 OC30 OC31 OC32 OC33 OC34 OC35 OC36 OC37 OC38 OC39 OC40 q 0.0 0.2 0.4 0.6 0.8 1.0 Cluster 1 Cluster 2 Black bears sampled in th e Osceola-Ocala CorridorIndividual bears OO1 OO2 OO3 OO4 OO5 OO6 OO7 OO8 OO9 OO10 OO11 OO12 OO13 OO14 OO15 OO16 OO17 OO18 OO19 OO20 OO21 OO22 OO23 OO24 OO25 OO26 OO27 OO28 OO29 OO30 OO31 q 0.0 0.2 0.4 0.6 0.8 1.0 Cluster 1 Cluster 2 Black bears sampled in OsceolaIndividual bears OS1 OS2 OS3 OS4 OS5 OS6 OS7 OS8 OS9 OS10 OS11 OS12 OS13 OS14 OS15 OS16 OS17 OS18 OS19 OS20 OS21 OS22 OS23 OS24 OS25 OS26 OS27 OS28 OS29 OS30 OS31 OS32 OS33 OS34 OS35 OS36 OS37 OS38 OS39 OS40 OS41 q 0.0 0.2 0.4 0.6 0.8 1.0 Cluster 1 Cluster 2 Figure 7. Assignment of black bears to a popu lation of origin without regard to sample locations using STRUCTURE (Pritchard et al. 2000). Each individual bear sampled in Ocala, Osceola and the corrido r is represented by a single vertical line, which is partitioned into segments that represent that individual’s proportion of membership (q) in the two clusters.

PAGE 45

33 Of the 31 black bears sampled in the corri dor, 28 were assigned to cluster 1 (Ocala) with q > 0.85, suggesting a predomin ately one-way movement by bears from Ocala into the corridor. However, there were three individuals sampled in the corridor (OO20, OO26, and OO31) that were assigned to cluster 2 (q > 0.98), suggestive of origins in the Osceola population (Fig. 7). The sample locations of these bears plotted on a map of north-central Flor ida revealed a spatial patt ern in the distribution of genotypes with limited mixing of Osceola and Ocala bears within the corridor (Fig. 8). Figure 8. Spatial pattern of the proporti on of membership (q) for bears sampled in Osceola, Ocala and the Osceola-Ocala corridor using the program STRUCTURE (Pritchard et al. 2000). For Osceola and Ocala, 41 and 40 individuals respectively, ar e displayed. Within the Osceola-Ocala corridor, 31 bears sampled at 50 different locations are displayed. Black bears with q > 0.85 in a cluster are labeled as belongi ng to that cluster. Individuals with mixed ancestry have q > 0.40 in both clusters.

PAGE 46

34 Discussion The role of corridors in conservation pla nning has been controversial, due largely to the lack of empirical studies evaluating the effectiveness of corridors (Simberloff & Cox 1987; Simberloff et al. 1992; Rosenberg et al. 1997; Niemela 2001). Despite the paucity of data supporting the function of corridors, many conserva tion biologists argue that corridors should be reestablished or ma intained where such c onnectivity occurred in the recent past (Noss & Ha rris 1986; Noss 1987; Beier & Noss 1998). Nowhere has the corridor controversy been more intense th an in the state of Florida (Noss 1987; Simberloff & Cox 1987; Simber loff et al. 1992). Plans for a regional network of connected lands have been undert aken with little knowledge of the efficiency of corridors in facilitating movement s of animals (Noss & Harris 1986; Hoctor et al. 2000; Larkin et al. 2004). The effectiveness of corridors in connecting carnivore populations is a question of considerable conser vation importance. Large carn ivores provide flagship and umbrella mechanisms for conservation and are sensitive to the effects of habitat fragmentation (Noss et al. 1996; Woodroffe & Ginsberg 1998). Thus, corridors that provide connectivity among large carnivore populations are lik ely to be beneficial to other species with smaller home ranges. I documented the presence of black bears throughout the Osceola-Ocala corridor, indicating that perhaps a small population inhabits this ar ea. Male black bears disperse long distances due to competition for reso urces (Rogers 1987; Schwartz & Franzmann 1992), and the substantial disparity in the se x ratio of bears (28 males, 3 females) sampled in the Osceola-Ocala corridor suggests that the corridor is primarily used as a conduit for gender-biased dispersal.

PAGE 47

35 For a dispersal corridor to be functiona l, the distance between populations should be within dispersal capabilities of the focal species. The average dispersal distance observed for male black bears is roughly half the distance of the Osceola-Ocala corridor (Alt 1979; Rogers 1987; Maehr et al. 1988; Schwartz & Franzmann 1992; Wooding & Hardisky 1992; Wertz et al. 2001; Lee & Vaughan 2003). However, black bears can move great distances, occasi onally dispersing > 100 km (Alt 1979; Rogers 1987; Maehr et al. 1988). Long-distance di spersal is difficult to meas ure and often underestimated (Koenig et al. 1996). However, the range of dispersal dist ances for black bears suggest that it is possible for bears to travel the length of the Osceola-Ocala corridor. The effectiveness of a dispersal corridor w ould require that animals use the area for natal dispersal, seasonal mi gration, foraging or searching for a mate (Harris & Scheck 1991; Noss 1993; Rosenberg et al. 1997; Hess & Fischer 2001). Many studies suggest that there are directional pattern s of dispersal related to the presence of habitat suitable for dispersal corridors (Smith 1993; Poole 1997; McLellan & Hove y 2001; Wertz et al. 2001; Maehr et al. 2002; Lee & Vaughan 2003). For instance, bears used the Osceola-Ocala corridor for dispersal because there is available habitat in which to disperse. Additionally, the presence of the bears, including some females, in multiple locations suggests that some individuals may be residents with home ranges within the corridor. Although there were only thr ee females sampled, a reproducing population within the corridor would be tter facilitate movement among populations (Noss 1993; Noss et al. 1996; Rosenberg et al. 1997; Beier & Noss 1998). Most individuals were assigned to the population in which they were sampled, verifying the validity of usi ng population-assignment tests fo r Florida black bears (Table

PAGE 48

36 3). However, two male bears sampled in Osceola had genotype combinations most consistent with those assigned to Ocala. Additionally, two in dividuals had genotypes assigned as hybrids, indicating that bears born in Ocala may have bred successfully in Osceola. There is a possibil ity that some bears identifie d as immigrants within the Osceola population may be nuisanc e bears that were translocated from Ocala. However, the relatively small number of documented tr anslocations and the know n fates of most of these translocated bears sugge sts that one or both bears sa mpled in Osceola that were assigned to Ocala are disperse rs from the latter population that used the corridor for movement. Most bears sampled within the Osceola-Oca la corridor were assigned to Ocala, with a predominantly unidirectional pattern of movement. There was a limited mixing of Ocala-assigned individuals with Osceola-assign ed individuals in one area of the corridor (Fig. 8). Three of the Ocala-assigned bear s were previously sampled in the Ocala population; these are clear examples of l ong-distance dispersal (30-100 km) into the corridor and further validate the accuracy of assignment tests. The use of the Osceola-Ocala corridor by bears has increased in recent years (J. Garrison, pers. comm.), a pattern similar to recoloniza tion rates of black bears in the Trans-Pecos (Mexico-Texas border) (Onorato & Hellgren 2001) and red squirrels ( Sciurus vulgaris ) in Scotland (Hale et al. 2001). Expansion of the Ocala populat ion into the Osceola-Ocala corridor likely will continue as long as habitat is availa ble and there are no significant barriers to movement. The spatial pattern of trap success of the hair snares (Fig. 5) and assignment tests (Fig. 8) indicated a limited gap in connectivity. This gap may have been caused by a

PAGE 49

37 significant habitat bottleneck caused by residential developm ent and a four-lane highway (S.R. 301). Development near the city of St arke, the expansion of unincorporated areas of Jacksonville (especially near Middleburg) and extensive surface mines in those areas may also have contributed to a break in connectivity (Hoctor 2003). Extensive habitat alteration by residential and industrial devel opments have been identified as potential deterrents for bear dispersal (McLellan & Shackleton 1988; Maehr et al. 2003), and this may be the situation for bears in the Osceola -Ocala corridor. However, there remains a possibility that bears have not had suffi cient time to recolonize these areas. Only three bears with Osceola genotypes were sampled south of the interstate highway (I-10), despite the large population of bears (Osceola) just north of I-10. One of those three bears also was sampled north of I-10 (FWC, unpublished data) suggesting that while the highway is not a complete barrier to movement, it may represent a significant filter allowing only a few individuals to cros s successfully. Large, high-speed highways have been known to alter m ovement patterns of bears (Br ody & Pelton 1989; Wertz et al. 2001; Proctor et al. 2002; Kaczensky et al. 2003). My results were consistent with the hypothesis that high-speed interstate highw ays can significantly reduce movements of Florida black bears. Roads can have a more significant effect on bear movements with in the corridor. From 1979 to 2002, 32 bears (28 males, 3 females, 1 unknown) were documented as killed on highways within the Osceola-Ocala co rridor. High mortalit y rates of dispersing carnivores are not uncommon (e .g., San Joaquin kit foxes [ Vulpes macrotis mutica ]: Koopman et al. 2000; tigers [ Panthera tigris ]: Smith 1993; brown bears: McLellan & Hovey 2001; and black bears: Alt 1979, Sc hwartz & Franzmann 1992). Clearly,

PAGE 50

38 maintaining or restoring effective conn ectivity between the Osceola and Ocala populations will require measures to reduce mortality of dispersing animals. Taken together, my results show that the Osceola-Ocala corridor is functional. My study provides one of the first empirical eval uations of the effectiveness of a regional corridor in connecting populations of a large carnivore. The methods used in my study provide a framework for using non-invasive sa mpling and genetic analysis for evaluating the effectiveness of corridors in providing demographic and genetic connectivity between wildlife populations. These techniques allow researchers to identify the genetic signatures of connectivity by identifying im migrants and hybrids, and these methods should be useful in evaluating the effectiveness of other pot ential corridors for connecting wildlife populations. Conclusion My results suggest that the Ocala and Osceola black bear populations were recently re-connected, primarily through unidirec tional movement of bears from Ocala to Osceola, and that some of the dispersers ma y have successfully reproduced. Moreover, I found a small black bear populati on currently inhabits the Osce ola-Ocala corridor itself. Based on these results, I concl ude that the Osceola-Ocala co rridor is functional, and provides genetic and demogra phic connectivity between Ocal a and Osceola black bear populations. The connection of the Osceola and Ocala populations allows gene flow between these populations through male-med iated dispersal, the maintenance of metapopulation structure, and may increase population viability. However, increasing development pressure in this regional corr idor may thwart functional connectivity of these populations if the ha bitat within the corridor is not protected.

PAGE 51

39 Maintaining or restoring connectivity ma y require multiple strategies including encouraging recolonization of the corridor by maintaining high densities in the source populations, minimizing habitat loss and frag mentation, and managing for a high quality habitat. Very short distances separate mo st of Osceola and Ocala bears within the corridor; these breaks in conn ectivity should be minimized su ch that a bear could cross the area in a single dispersal event (Beier & Loe 1992). Ho wever, sufficient habitat for recolonization requires easements, purchasing conservation lands, fostering agreements with private landowners, and reducing human activity (Beier 1995; Duke et al. 2001). Providing connectivity may also require retrof itting highways to allow safe passage of bears (Foster & Humphrey 1995). I found that the use of non-invasive hair snares and population-assignment tests could serve as an appropriate and efficient method for evaluating the effectiveness of a regional corridor. Although my study was not re plicated, it did provi de useful insights into the functionality of a regional corridor for large carnivores. A fully replicated, experimental approach is rarely practical in conservation settings. Design limitations aside, I do view consistent use of a corri dor as sufficient evidence to justify the conservation value of these areas (Beier & Noss 1998). Given the rapid pace of development in Florida, the connection of populations with corridors may be the best option in mitigating the adverse impacts of habitat fragmentation on black bears and other wildlife.

PAGE 52

40 CHAPTER 4 CONCLUSIONS AND MANAGEMENT RECOMMENDATIONS In my study, I used microsatellite analys is of complete 12-lo cus genotypes of 339 bears to investigate the conservation gene tics of Florida black bear populations (Appendix D, Table 5). Allele frequencies for these bears varied substantially across 10 study areas (Appendix D, Table 6). I used thes e microsatellite data to investigate the genetic consequences of hab itat fragmentation and to examine the functionality of the Osceola-Ocala corridor. Conclusions Genetic variation is an im portant consideration for th e long-term survival and adaptation of Florida black bears. My resu lts indicate that most Florida black bear populations had genetic varia tion within the range reporte d for other bear populations (Appendix C, Table 3). However, Florida bl ack bear populations w ith < 200 individuals were characterized by low leve ls of genetic variation. Th e level of genetic variation within the Chassahowitzka and Highlands /Glades populations are among the lowest reported for any species or popul ation of bears (Appendix C, Table 3). The reduction of genetic variation in the Chassahowitzk a and Highlands/Glades populations could adversely influence evolutionary potentia l and increase inbreeding depression, which may lead to the eventual ex tirpation of these populations. My results indicated low levels of gene flow among most populati ons of the Florida black bear. However, there was a high leve l of gene flow between the St. Johns and

PAGE 53

41 Ocala populations, and for genetic management, these populations could be considered as the same population unit. Genetic differentiation among Florida black bear populations is greater than that reported for other bear populat ions separated by greater geog raphic distances (Paetkau et al. 1998b; Paetkau et al. 1999; Waits et al. 2000; Lu et al. 2001; Saitoh et al. 2001; Warrillow et al. 2001; Marshall & Ritland 2002 ). Additionally, ther e was no significant pattern of isolation by distance in Florida black bear populations. This pattern has been observed among other populations of bears (Paetk au et al. 1997). Roads with high traffic volume and anthropogenic development apparently act as barriers to gene flow among populations of bears in Florida. Data presented in Chapter 3 clearly in dicate that the Osceola-Ocala corridor provides demographic and genetic connectiv ity between two of the largest bear populations via unidirectiona l movement of bears from Ocala into Osceola. I documented the presence of bears in Osceola with Ocala genotypes and others that may be Osceola-Ocala hybrids. There was a preponderance of male bears within the Osceola-Ocala corridor, sugges ting that the corridor is pr imarily used as a conduit for dispersal. The recolonization of the corrido r likely will continue as long as sufficient habitat is available and there are no signifi cant barriers to movement. However, there were some gaps in black bear distribution w ithin the corridor, possi bly due to barriers such as residential and industrial development. The methods used in my study provide a framework for evaluating functionality of corridors for connec ting other wildlife populations.

PAGE 54

42 Management Recommendations Efforts should be made to restore historic levels of genetic vari ation within Florida black bear populations. For the smaller, mo re isolated populations (i.e., Chassahowitzka and Highlands/Glades) to persist into the fo reseeable future, it may be necessary to increase levels of genetic vari ation within these populations. I recommend two ways to increase or main tain genetic variation in Florida black bear populations. The first is to increase the size of the populations, and to prevent further loss and fragmentation of their habita t. Efforts should be made to maintain or increase populations to > 200 individuals to prevent substantial loss of genetic variation. The increase in population size would minimi ze the loss of genetic variation due to genetic drift, and would increas e the number of dispersers, po tentially increasing the level of gene flow among populations. My second recommendation is to increase gene flow among populations. This may be accomplished in two ways: genetic augm entation and the connection of populations with corridors. Genetic augmentation woul d require the translocation of bears among populations. For augmentation to be successful these bears must ma te with members of the target population. The Florida Fish and Wildlife Conservati on Commission has a policy that requires the movement of nuisance bears among populati ons. A study is needed to determine the fate and reproductive success of these transloc ated bears. If the findings suggest that translocated nuisance bears successfully bree d, this method could be used to genetically augment populations. Translocation of pregna nt female bears may be a better option than nuisance bears because they have a higher prob ability of staying in the area where they are released (Eastridge & Clark 2001). A dditionally, the stocking of bears in the Big

PAGE 55

43 Bend of Florida (north of Chassahowitzka and east of Aucilla) would increase the probability of gene flow into the Cha ssahowitzka and Aucilla populations (Wooding & Roof 1996). Gene flow among populations via natural di spersal would require the connection of populations with conservation co rridors. This method is preferred because it would restore historical connectivit y, increase probability of long-term persistence, and maintenance of metapopulation structure. Howe ver, little habitat that could potentially serve as corridors is available because of the high rate of commercial and residential development throughout much of the state of Florida. The Osceola-Ocala corridor may be the only corridor that can provide demographic and genetic connectivity of the Florida black bear. As noted above, this corridor is functional, and efforts should be made to enha nce the quality of habitat and minimize the effects of potential barriers. The protec tion and conservation of lands within the Osceola-Ocala corridor will be needed to en sure functional connectivity between these populations. The large number of landowners requires a consortium to manage these lands effectively. Management actions to re duce mortality and increase safe movement across highways also may include the in stallation of wildlife underpasses and/or overpasses (Foster & Humphrey 1995; Roof & Wooding 1996). Additionally, a reproducing population within th e Osceola-Ocala corridor woul d provide a better means of facilitating movement of b ears between the Osceola and Ocala populations. Therefore, efforts should be made to encourage fe male recolonization of the corridor. Recommendations for Further Research Genetic monitoring of Florida black bear populations is needed to examine changes in levels of genetic variation over time. Th ese investigations coul d be coordinated with

PAGE 56

44 the statewide population mon itoring program of the Florida Fish and Wildlife Conservation Commission (Eason et al. 2001). A relatedness analysis using microsatell ites would help clarify the relationships among individuals within a populations (Sch enk et al. 1998; Spong et al. 2002). This method could be used to create a pedigree of sampled individuals in a population, thereby determining the levels of inbreeding. A comprehensive mitochondrial DNA (mt DNA) study is needed for a better understanding of the genetic st atus of these populations. These investigations could better elucidate female disper sal and population structure. Finally, comprehensive demographic studi es are needed to conduct a population viability analysis (PVA). These analyses coul d be used to predict the impact of further habitat fragmentation and loss on the viabil ity of Florida black bear populations. (Cox et al. 1994; Kasbohm & Bentzien 1998; Dobey 2002; Edwards 2002)

PAGE 57

45 APPENDIX A HISTORY OF THE FLORIDA BLACK BEAR General The American black bear ( Ursus americanus ) has maintained a broad distribution throughout much of its history, and fossil evid ence indicates that bl ack bears have been present in North America for at least 3 million years (Kurten & Anderson 1980). The Florida black bear ( U. a. floridanus ) is one of three subspecies of North American black bears, and was first described in Key Bis cayne by Merriam (1896). The Florida black bear historically ranged throughout Florida a nd southern portions of Georgia, Alabama, and Mississippi (Hall 1981) (Fig. 9). Black bears have large body size and need considerable expanses of land to maintain home ranges. They use a wide vari ety of habitats, including pine flatwoods, hardwood swamp, cypress swamp, cabbage pa lm forest, sand pine scrub, and mixed hardwood hammock (Maehr et al. 2001). The omnivorous diet of bl ack bears includes mostly plant and some animal ma terial (Maehr & Brady 1984). Seminole Indians hunted black bears in Florid a, using meat, skin and fat for various consumptive, ornamental, and traditional purposes (Bartram 1980; Bakeless 1989). In the past, cattle ranchers and beekeepers cons idered the Florida black bear a nuisance; consequently, the shooting and poisoning of bears was common (Hendry et al. 1982). Hunting for sport and food was intensive and unregulated prior to 1950 (Cory 1896). Regulated bear hunting was initiated in Fl orida in 1950 (Wooding 1993), but was stopped in most counties in 1971 and in all counties in 1993 (M aehr et al. 2001).

PAGE 58

46 Figure 9. Historic distributi on of black bears in the southe astern United States (after Eason 1995) The greatest reduction of Florida black bear was a result of extensive habitat loss and fragmentation during the 19th century (Wesley 1991; Pelton & Van Manen 1997). Forests were cleared for timber and agricu lture, wetlands were dr ained, and large areas were mined (Myers & Ewel 1991). In the 1970’s, there were only an estimated 300-500 bears in Florida (McDaniel 1974; Brady & Maehr 1985). Under the assumption that bears once occupied nearly al l the state’s land area (34.5 million acres), they have been eliminated from approximately 83% of their range (Wooding 1993).

PAGE 59

47 Currently, Florida black bears occur in several populations that are mostly relegated to public lands within Florida (Apalachicola, Auc illa, Big Cypress, Chassahowitzka, Eglin, Highlands/Glades, Ocala, Osceola, and St. Johns), Georgia (Okefenokee), and Alabama (South Alabama) (Fig. 10). Regulations The Florida Game and Freshwater Fish Commission classified the Florida black bear as a threatened species in most Fl orida counties in 1974 (W ooding 1993). Florida black bears in Georgia are considered a game animal and are subject to a limited hunting season, but are listed as an endangered species on the state-level in Alabama (Pelton & Van Manen 1997; Kasbohm 2004). The U.S. Fish and Wildlife Service (U SFWS) was petitioned in 1990 to list the Florida black bear as a fede rally threatened sp ecies under the Endange red Species Act of 1973. The USFWS findings of 1991 concluded that the petition to list the Florida black bear was warranted, but was precluded by wo rk on other species having higher priority for listing (Wesley 1991). A subsequent reexamination by the USFWS in 1998 concluded that listing the Flor ida black bear as federally threatened or endangered was not warranted based on existing data (Bentzie n 1998). This decision was challenged in court by several conservation organizations, and the USFWS was ordered to clarify the documentation of the adequacy of existing regu latory mechanisms to protect the Florida black bear. The findings c oncluded that the existing re gulatory mechanisms were sufficient and that listing the Florida black be ar as a threatened or endangered species was not warranted (Kasbohm 2004).

PAGE 60

48 Figure 10. Current populations of the Florida black bear ( Ursus americanus floridanus ). Abbreviations are as follows: SA (South Alabama), EG (Eglin), AP (Apalachicola), AU (Aucilla), CH (Chassahowitzka), OC (Ocala), HG (Highlands/Glades), BC (Big Cypress), SJ (St. Johns), OS (Osceola), and OK (Okefenokee) (after Pelton and van Manen 1997).

PAGE 61

49 APPENDIX B MICROSATELLITE ANALYSIS Microsatellites are a class of nuclear DNA markers that have a rapid mutation rate and are ideal for studies of genetic conseque nces of habitat fragmentation (Lindenmayer & Peakall 2000). Microsatellites consist of a variety of tandem repeat loci that involve a base motif of 1-6 base pairs repeated up to 100 times. Microsatellites are abundant, widely disbursed in eukaryotic genomes, a nd are highly polymorphic. Individual loci are amplified using polymearse chain reaction (PCR). This allows resolution of alleles that differ by as little as 1 base pair, and several loci can be analyzed simultaneously (Hedrick 2000). Microsatellite analysis has frequently been used in conservation studies for estimating within-population genetic variation and gene flow among populations of black bears ( Ursus americanus ) (Paetkau & Strobeck 1994; Sait oh et al. 2001; Warrillow et al. 2001; Marshall & Ritland 2002; Csik i et al. 2003), brown bears ( U. arctos ) (Kohn et al. 1995; Taberlet et al. 1997; P aetkau et al. 1998a; Paetkau et al. 1998b; Waits et al. 2000; Miller & Waits 2003), polar bears ( U. maritimus ) (Paetkau et al. 1995; Paetkau et al. 1999), spectacled bears ( Tremarctos ornatus ) (Ruiz-Garcia 2003) and giant pandas ( Ailuropoda melanoleuca ) (Lu et al. 2001). Microsatellite analysis has also been used to estimate population density of black and br own bear populations using mark-recapture models (Woods et al. 1999; Mowat & Strobeck 2000; Boerson et al. 2003).

PAGE 62

APPENDIX C GENETIC VARIATION AMONG BEAR POPULATIONS

PAGE 63

51Table 4. Microsatellite genetic vari ation in bear populations. Sample size (n), observed averag e heterozygosity (HO), expected average heterozygosity (HE), mean alleles per locus (A), and nu mber of loci used in the study (L). Species Population n HO HE A L Citation Florida black bear Apalachicola 40 0.6900.708 5.92 12 my study (Ursus americanus floridanus) Aucilla 9 0.5560.616 3.83 12 my study Big Cypress 41 0.6420.650 5.50 12 my study Chassahowitzka 29 0.2870.271 2.25 12 my study Eglin 40 0.6130.537 4.08 12 my study Highlands/Glades 28 0.3270.384 2.75 12 my study Ocala 40 0.5790.610 4.75 12 my study Osceola 41 0.7050.713 6.67 12 my study St. Johns 40 0.6500.663 5.58 12 my study Osceola/Ocala Corridor 31 0.6290.629 5.42 12 my study Mobile River 13 0.3900.350 2.88 7 Warrilow et al. 2001 South Alabama 19 0.316N/A 2.88 8 Edwards 2002 Okefenokee 39 0.663N/A 6.13 8 Dobey 2002 Osceola 37 0.679N/A 5.75 8 Dobey 2002 Louisiana black bear Pointe Coupee Parish 16 0.5460.686 5.60 5 Csiki et al. 2003 (Ursus americanus luteolus) Southern Coastal Louisiana 20 0.3800.428 4.20 5 Csiki et al. 2003 Tensas River 14 0.5300.480 3.57 7 Warrilow et al. 2001 Upper Atchafalaya Basin 20 0.5500.660 6.00 7 Warrilow et al. 2001 Tensas River 36 0.576N/A 3.80 12 Boersen et al. 2003 Lower Atchafalaya Basin 26 0.4200.540 6.14 7 Warrilow et al. 2001 American black bear North Carolina (treatment) 66 0.667N/A 6.00 10 Thompson 2003 (Ursus americanus) North Carolina (control) 115 0.664N/A 6.90 10 Thompson 2003 Ozarks 13 0.7230.761 5.80 5 Csiki et al. 2003 Ouachitas 6 0.7330.754 4.60 5 Csiki et al. 2003

PAGE 64

52Table 4. Continued Species Population n HO HE A L Citation American black bear White River 18 0.4470.317 1.80 5 Csiki et al. 2003 (Ursus americanus) Minnesota 10 0.5760.772 5.60 5 Csiki et al. 2003 Cook County 36 0.5400.770 8.71 7 Warrilow et al. 2001 Ozarks 14 0.5400.730 6.00 7 Warrilow et al. 2001 Ouachitas 6 0.5600.730 4.86 7 Warrilow et al. 2001 White River 22 0.3800.330 2.43 7 Warrilow et al. 2001 La Mauricie 32 0.7830.800 8.75 4 Paetkau & Strobeck 1994 Banff (West Slope) 31 0.8010.800 8.00 4 Paetkau & Strobeck 1994 Terra Nova 23 0.3600.360 2.25 4 Paetkau & Strobeck 1994 West Slope 116 0.8000.806 9.50 8 Paetkau et al. 1998b Newfoundland Island 33 0.4270.414 3.00 8 Paetkau et al. 1998b Nimpkish 19 N/A 0.621 4.40 10 Marshall et al. 2002 Hawkesbury Island 20 N/A 0.699 5.70 10 Marshall et al. 2002 Gribbell Island 16 N/A 0.664 5.40 10 Marshall et al. 2002 Princess Royal Island 50 N/A 0.707 6.50 10 Marshall et al. 2002 Roderick Island 11 N/A 0.668 4.80 10 Marshall et al. 2002 Pooley Island 10 N/A 0.692 5.00 10 Marshall et al. 2002 Yeo Island 10 N/A 0.725 5.10 10 Marshall et al. 2002 West of Hawkesbury 6 N/A 0.724 4.20 10 Marshall et al. 2002 East of Princess Royal 21 N/A 0.747 6.30 10 Marshall et al. 2002 North of Roderick 13 N/ A 0.673 6.60 10 Marshall et al. 2002 Don Peninsula 23 N/A 0.667 5.90 10 Marshall et al. 2002 Terrace 17 N/A 0.793 7.50 10 Marshall et al. 2002 brown bear NN 29 0.6600.660 5.50 19 Waits et al. 2000 (Ursus arctos) NS 108 0.6600.660 6.20 19 Waits et al. 2000 M 88 0.6500.660 5.80 19 Waits et al. 2000

PAGE 65

53Table 4. Continued Species Population n HO HE A L Citation S 155 0.7100.660 6.80 19 Waits et al. 2000 brown bear Kluane 50 0.7880.761 7.38 8 Paetkau et al. 1998b (Ursus arctos) Richardson Mountains 119 0.7660.755 7.50 8 Paetkau et al. 1998b Brooks Range 148 0.7740.749 7.63 8 Paetkau et al. 1998b Flathead River 40 0.6940.694 6.50 8 Paetkau et al. 1998b Kushoskim Range 55 0.7000.682 6.13 8 Paetkau et al. 1998b West Slope 41 0.6680.678 6.38 8 Paetkau et al. 1998b East Slope 45 0.6440.670 7.00 8 Paetkau et al. 1998b Paulatuk 58 0.6570.650 5.75 8 Paetkau et al. 1998b Coppermine 36 0.6110.605 5.75 8 Paetkau et al. 1998b Yellowstone 57 0.5530.554 4.38 8 Paetkau et al. 1998b Kodiak Island 34 0.2980.265 2.13 8 Paetkau et al. 1998b Admirality Island 30 0.6460.628 n/a 17 Paetkau et al. 1998a Alaska Range 28 0.7590.779 n/a 17 Paetkau et al. 1998a Baranof and Chicagof Island 35 0.4930.496 n/a 17 Paetkau et al. 1998a Yellowstone 136 N/A 0.560 5.50 8 Miller & Waits 2003 polar bear Western Hudson Bay 33 N/A 0.670 6.00 16 Paetkau et al. 1999 (Ursus maritimus) Foxe Basin 30 N/A 0.660 6.00 16 Paetkau et al. 1999 Davis Strait-Labrador 30 N/A 0.630 6.30 16 Paetkau et al. 1999 Baffin Bay 31 N/A 0.680 6.30 16 Paetkau et al. 1999 Kane Basin 30 N/A 0.710 6.70 16 Paetkau et al. 1999 Lancaster Sound 30 N/A 0.700 6.90 16 Paetkau et al. 1999 Gulf of Boothia 30 N/A 0.720 6.70 16 Paetkau et al. 1999 M'Clintock Channel 15 N/A 0.680 5.50 16 Paetkau et al. 1999 Viscount Melville Sound 30 N/A 0.660 6.30 16 Paetkau et al. 1999 Norwegian Bay 30 N/A 0.670 6.20 16 Paetkau et al. 1999

PAGE 66

54Table 4. Continued Species Population n HO HE A L Citation Northern Beaufort Sea 30 N/A 0.700 6.80 16 Paetkau et al. 1999 Southern Beaufort Sea 30 N/A 0.690 6.40 16 Paetkau et al. 1999 Chukchi Sea 30 N/A 0.700 6.80 16 Paetkau et al. 1999 Franz Josef L.Novaja Z. 32 N/A 0.660 6.70 16 Paetkau et al. 1999 Svalbard 31 N/A 0.690 6.90 16 Paetkau et al. 1999 East Greenland 31 N/A 0.690 6.80 16 Paetkau et al. 1999 spectacled bear Venezula 8 N/A 0.607 2.00 4 Ruiz-Garcia 2003 (Tremarctos oranatus) Colombia 32 N/A 0.392 2.80 4 Ruiz-Garcia 2003 Ecuador 42 N/A 0.245 3.00 4 Ruiz-Garcia 2003 Asian black bear Western C hugoku 52 0.2720.300 2.00 6* Saitoh et al. 2001 (Ursus thibetanus) Eastern Chugoku 24 0.2430.301 2.50 6* Saitoh et al. 2001 Western N. Kinki 66 0.3110.324 3.33 6* Saitoh et al. 2001 Eastern N. Kinki 67 0.445 0.450 4.17 6* Saitoh et al. 2001 giant panda Qinling 14 0.570n/a 3.30 18* Lu et al. 2001 (Ailuropoda melanoleuca) Minshan 7 0.580n/a 3.50 18* Lu et al. 2001 Qionglai 15 0.490n/a 4.30 18* Lu et al. 2001 *Suite of microsatellite markers different than other studies

PAGE 67

APPENDIX D MICROSATELLITE DATA FOR FLORIDA BLACK BEARS

PAGE 68

56 Table 5. Individual 12-loci genotypes fo r black bears sampled in Florida, 1989-2003. Apalachicola Loci Bear # G1A G10B G10C G1D G10H G10J G10L G10M G10P G10X MU50 MU59 A1 194 200 156 156 211 211186 186241 241187 203135 157 212 212155 163147 147124 138239 239 A2 190 198 160 162 211 211176 188239 241187 187153 153 212 216151 159147 155124 124243 243 A3 190 198 158 162 215 215188 190241 253187 199151 157 206 212157 157141 149122 122239 239 A4 190 198 156 156 215 215176 182241 241187 187135 157 214 216159 163149 151124 138239 239 A5 196 200 156 156 209 215186 190241 241187 203135 155 214 216163 163149 149124 138239 243 A6 190 202 160 164 209 209176 186241 241185 187155 157 212 216159 163149 155138 142239 243 A7 190 198 156 164 211 211176 176241 253187 187143 157 214 214155 159147 155124 138231 239 A8 190 194 156 156 211 211186 188251 253187 199135 157 214 216159 159141 149124 124231 239 A9 190 190 156 156 207 209176 176241 253203 203155 155 214 216155 161149 155126 138239 239 A10 200 200 160 160 215 215176 182241 241203 203153 155 212 214161 163147 149126 138243 243 A11 200 202 156 156 209 209176 190241 241185 187155 155 206 206159 163141 149126 126239 243 A12 194 200 156 156 209 209176 176241 251187 203157 157 206 214159 161141 147124 138239 239 A13 194 194 156 160 209 215176 190241 251187 203153 157 206 214155 159147 147124 138239 243 A14 190 196 156 156 209 215176 186241 241203 203135 157 216 216163 163147 149126 138239 243 A15 190 200 156 156 215 215176 188241 241187 187135 157 216 218159 159141 155124 138239 243 A16 200 202 160 164 209 209176 182241 241203 203135 155 206 218163 163139 155126 142239 243 A17 200 200 156 156 209 211176 188241 241185 199153 155 218 218159 161147 149124 126239 241 A18 198 200 162 162 211 215188 190239 253187 187143 157 214 214155 157141 147124 124243 243 A19 198 200 156 162 207 215176 190241 241187 203135 143 212 214157 159147 155126 126239 243 A20 200 202 160 162 207 215176 186239 241187 203143 153 214 214151 155155 155124 138239 243 A21 198 202 156 156 215 215176 186239 247187 203157 157 214 216151 155141 149124 126239 243

PAGE 69

57Table 5. Continued Apalachicola Loci Bear # G1A G10B G10C G1D G10H G 10J G10L G10M G10P G10X MU50 MU59 A22 190 200 156 162 211 215176 188241 251187 199135 153 212 214155 159149 155124 124239 243 A23 194 198 156 160 209 215188 190241 241203 203135 153 206 214155 159147 149124 138239 239 A24 190 200 156 160 211 215176 186239 241187 187153 157 212 214155 159147 147122 124243 243 A25 196 200 156 156 209 209176 176241 241203 203153 157 214 216151 163149 149124 126239 243 A26 202 202 156 160 215 215186 188241 249199 199135 143 214 214155 155137 149124 138239 239 A27 196 200 160 162 215 215176 182239 241187 199153 157 214 218155 159137 155124 128239 243 A28 192 200 156 162 207 215176 182239 241187 203135 153 214 218159 163137 155128 136239 243 A29 190 198 158 160 205 211172 188241 241187 199143 157 206 212163 163137 141122 122239 243 A30 190 200 160 162 209 211176 186241 253203 203153 153 212 216151 155141 147122 128239 243 A31 190 192 156 156 209 215182 186241 241187 187143 157 214 216155 155137 141124 124239 239 A32 196 200 156 162 209 211182 188239 241187 187143 153 212 214155 163141 149124 128243 243 A33 190 198 156 162 205 209182 184241 241185 187141 157 212 216155 157137 141124 124239 239 A34 196 200 162 164 211 211176 176241 241187 199135 157 214 216151 159147 149128 138239 239 A35 198 198 156 156 209 215176 186239 241187 203157 157 216 216159 159147 149124 124239 239 A36 200 202 156 160 207 215186 186241 241187 199141 153 212 216155 159137 147124 126243 243 A37 194 198 156 160 211 215188 190241 241187 203157 157 206 214155 159147 147122 124239 243 A38 194 198 160 164 209 215176 182241 252187 203153 157 212 214155 159147 155124 138239 243 A39 194 198 156 160 211 215182 190241 251199 203153 157 214 214155 163141 147124 124239 243 A40 192 194 162 162 205 215184 190241 241185 187141 143 212 214157 157141 155124 138239 239

PAGE 70

58Table 5. Continued Aucilla Loci Bear # G1A G10B G10C G1D G10H G 10J G10L G10M G10P G10X MU50 MU59 AU1 190 190 156 158 205 215180 188235 241187 187151 151 206 216159 163141 147122 126239 239 AU2 198 200 158 162 215 215180 188235 241187 199143 157 206 206159 163137 149122 126239 239 AU3 198 198 158 162 205 205172 176241 241187 199151 151 212 212163 163155 155122 122239 239 AU4 198 200 158 164 215 215180 190235 241187 187141 151 212 212157 157141 141122 124239 239 AU5 198 200 162 162 205 205172 180241 241199 199143 151 206 212157 157147 155122 122239 239 AU6 198 200 156 158 205 215190 190241 241191 191143 151 212 212157 157141 149122 124239 239 AU7 194 198 156 164 211 215176 188241 251187 199135 153 212 214159 159149 155124 124239 243 AU8 190 200 158 164 215 215180 190235 241187 187141 143 212 212157 157141 147122 124239 239 AU9 198 200 158 158 207 215180 188241 241187 191151 151 206 212157 159141 141122 122239 239 Big Cypress Bear # G1A G10B G10C G1D G10H G 10J G10L G10M G10P G10X MU50 MU59 B1 190 198 164 164 207 207176 184253 253187 199139 139 212 214159 163147 149134 134231 243 B2 190 190 152 154 207 213172 176241 253187 199153 161 212 214161 163149 151134 134239 239 B3 190 198 152 164 207 207176 176253 261185 199135 139 212 214147 163141 147134 134239 239 B4 198 198 154 164 207 215186 188235 253199 199139 155 214 214163 163141 149134 134239 243 B5 190 190 156 156 207 207176 186249 253187 199139 155 212 214159 159149 149122 134239 247 B6 190 190 156 156 207 215172 184241 241187 199155 155 210 210159 161147 149134 134239 243 B7 190 190 156 156 207 215178 184241 253199 199139 157 212 214147 163149 149126 134239 243 B8 190 192 152 164 207 215176 176253 261185 199135 155 214 214147 163141 147134 134239 239 B9 190 200 152 154 207 207186 186241 253185 187135 157 212 214147 159141 149122 134235 239 B10 200 200 152 156 207 207176 186235 253199 199153 155 212 214147 159149 149134 134243 243 B11 190 194 156 160 207 215176 186235 249185 199139 139 212 212159 163141 149122 122243 243

PAGE 71

59Table 5. Continued Big Cypress Loci Bear # G1A G10B G10C G1D G10H G 10J G10L G10M G10P G10X MU50 MU59 B12 190 198 154 156 207 207176 184235 253187 199139 157 212 214147 159149 149126 126239 239 B13 190 200 152 152 207 215176 176253 259185 203139 153 210 212147 159143 149134 134243 243 B14 190 200 156 160 207 207176 186253 253185 187139 157 212 212147 163147 149126 126241 243 B15 190 190 152 156 207 207178 186253 253199 199153 157 212 214159 163147 149122 134243 243 B16 190 190 154 156 207 207176 176253 253185 199139 157 214 214147 163141 147126 134239 243 B17 190 200 152 154 207 215176 188253 253199 199139 157 212 212147 163141 149122 134239 243 B18 190 200 154 160 207 215176 184241 253185 199135 139 212 212147 147149 149122 134243 243 B19 190 198 154 154 207 207176 184235 253187 187155 155 210 214159 163149 149122 122239 243 B20 190 190 152 156 207 207176 184241 241187 199155 155 210 212159 163149 149122 134239 239 B21 190 190 154 160 207 215176 188253 253185 199135 153 212 214163 163149 149126 134239 243 B22 190 196 156 158 207 213176 186251 253185 199135 157 212 214163 163141 157122 134241 243 B23 190 190 152 160 215 215176 186249 253187 203139 161 210 214159 163149 149134 134243 243 B24 190 190 156 164 207 213176 186243 249185 187139 153 210 212147 159149 149122 126239 239 B25 190 200 152 152 213 215176 178241 253187 199139 139 212 212147 147147 149134 134243 243 B26 192 200 152 164 207 207176 186235 261185 187135 141 212 214147 163149 149122 126239 239 B27 190 190 154 154 207 215176 184241 253187 203135 139 210 212159 163149 149122 126239 239 B28 190 190 156 164 215 215176 184253 253187 199153 153 212 214163 163149 149134 134231 239 B29 190 190 156 160 215 215186 190241 253185 187157 157 214 216159 163141 149122 134231 239 B30 198 200 156 164 207 215176 184253 253187 187139 153 210 212159 163149 149134 134231 239 B31 190 192 152 152 207 215176 186241 253187 199139 139 214 214159 163149 149134 134235 235 B32 190 200 154 156 207 215184 186235 253187 187139 153 210 214159 159149 149134 134239 239 B33 190 200 154 164 215 215176 186241 253187 199139 155 212 214147 161141 149122 134243 243

PAGE 72

60Table 5. Continued Big Cypress Loci Bear # G1A G10B G10C G1D G10H G 10J G10L G10M G10P G10X MU50 MU59 B34 190 196 152 156 207 207176 184 241 253187 187157 161 214 214159 163149 149122 126239 241 B35 190 200 156 164 207 215186 186 241 253187 199135 157 214 214163 163147 149122 134239 243 B36 190 198 152 154 207 207176 184 241 241185 187139 153 212 214163 163147 149122 122239 239 B37 190 190 152 156 213 215184 186 241 253187 199155 157 212 212163 163147 149122 126239 239 B38 190 190 160 160 207 215176 186 235 241185 187139 157 212 214147 163149 149126 126239 243 B39 194 200 154 156 207 207184 186 253 253185 203155 157 212 212147 159149 149126 134243 243 B40 190 190 154 156 215 215172 184 253 253185 187139 157 212 214163 163147 149122 134239 243 B41 194 198 154 156 215 215184 184 241 241187 203139 139 206 212159 163141 149122 128239 239 Chassahowitzka Bear # G1A G10B G10C G1D G10H G 10J G10L G10M G10P G10X MU50 MU59 C1 198 198 154 156 211 213172 172241 247187 199137 149 212 212159 159149 149140 140239 239 C2 198 198 156 156 207 211172 184241 241187 199139 149 212 212157 159141 149140 140239 239 C3 198 198 154 156 207 211172 172241 247199 199137 149 212 212159 159141 149140 140239 239 C4 198 198 156 156 211 211172 172247 247199 199137 149 212 212159 159141 149140 140239 239 C5 190 198 154 156 211 211172 172241 241199 199137 149 212 212157 159141 149140 140239 239 C6 198 198 156 156 207 211172 184241 247199 199139 149 212 212159 159149 149140 140239 239 C7 198 198 154 156 207 211172 184241 241199 199139 139 212 212159 159149 149140 140239 239 C8 198 198 154 156 211 211172 172241 247199 199149 149 212 212159 159149 149140 140239 239 C9 198 198 156 156 211 211172 186241 247199 199149 149 212 212159 159141 149140 140239 239 C10 198 198 156 156 211 211184 186241 247199 199139 149 212 212159 159149 149140 140239 239

PAGE 73

61Table 5. Continued Chassahowitzka Loci Bear # G1A G10B G10C G1D G10H G 10J G10L G10M G10P G10X MU50 MU59 C11 190 198 156 156 211 211172 172241 247199 199137 149 212 212157 159141 141140 140239 239 C12 198 198 156 156 207 211172 184241 241187 199149 149 212 212157 157141 141140 140239 239 C13 198 198 156 156 211 211172 184241 241199 199149 149 212 212157 159141 149140 140239 239 C14 198 198 156 156 207 211172 184241 247199 199149 149 212 212159 159149 149140 140239 239 C15 198 198 154 156 211 211172 184241 247199 199139 149 212 212159 159149 149140 140239 239 C16 198 198 156 156 207 211172 184247 247199 199139 149 212 212159 159141 149140 140239 239 C17 198 198 156 156 207 211172 172241 247199 199139 149 212 212159 159149 149140 140239 239 C18 198 198 156 156 211 211172 172241 247199 199149 149 212 212159 159141 149140 140231 239 C19 198 198 154 156 207 211172 172241 247199 199149 149 212 212159 159149 149140 140239 239 C20 198 198 154 154 207 211172 172247 247199 199137 149 212 212159 159141 149140 140239 239 C21 190 198 156 156 211 211172 172241 241199 199137 149 212 212159 159149 149140 140239 239 C22 190 198 156 156 211 211172 172247 247199 199149 149 212 212159 159141 141140 140239 239 C23 198 198 154 156 207 211172 184241 241199 199139 149 210 212159 159149 149140 140239 239 C24 198 198 156 156 211 211172 184241 247199 199139 149 212 212159 159149 149140 140239 239 C25 198 198 156 156 211 211172 186241 247199 199149 149 212 212159 163149 149140 140231 239 C26 198 198 154 156 211 211172 172241 247199 199149 149 212 212159 159141 149140 140239 239 C27 190 198 154 156 211 211172 172241 247199 199137 137 212 212157 159149 149140 140239 239 C28 198 198 154 156 211 211184 186241 241199 199139 149 212 212159 159149 149140 140239 239 C29 198 198 156 156 207 211172 172247 247199 199149 149 212 212159 163141 149140 140231 239

PAGE 74

62Table 5. Continued Eglin Loci Bear # G1A G10B G10C G1D G10H G 10J G10L G10M G10P G10X MU50 MU59 E1 196 200 160 164 215 217178 190247 247187 199153 157 214 218163 163139 141122 142239 239 E2 190 196 156 160 215 217186 190241 241187 199153 155 214 214155 163137 139122 122239 239 E3 196 200 160 164 217 217176 176241 241187 187149 155 210 214159 163139 139122 122239 239 E4 200 202 160 160 215 217178 190241 255187 199151 155 210 218163 163137 149122 142239 239 E5 196 202 160 160 215 217176 186241 241187 187153 155 210 214155 163141 141122 142239 239 E6 200 200 160 164 215 215178 186241 247187 187149 153 206 210159 163139 141122 122239 239 E7 196 200 156 160 209 215178 190241 247187 187155 155 210 214163 163137 139122 142239 239 E8 196 200 160 164 215 217178 186241 241187 187153 155 206 214159 163137 141122 142239 239 E9 196 202 156 160 215 217176 176241 247187 199153 155 210 214155 163139 141122 124239 239 E10 196 196 156 164 209 217186 190241 241187 187153 153 214 218163 163137 139122 142239 239 E11 200 200 156 164 215 215190 190241 247199 199153 157 214 216163 163139 141122 142239 239 E12 190 200 156 164 215 215176 186241 241187 199151 153 206 214159 163137 137122 142239 239 E13 200 202 156 164 215 217186 190241 247187 187153 155 206 214155 163137 139122 124239 239 E14 196 196 160 164 215 217178 190241 241187 187151 153 210 214163 163137 139122 122239 239 E15 196 202 156 164 215 217186 190241 247187 187153 153 206 214155 163137 141122 142239 239 E16 196 200 160 160 215 217190 190241 241187 199151 153 210 218155 163137 139122 142239 239 E17 196 200 156 164 215 217178 190241 241187 187155 155 212 218163 163137 139122 122239 243 E18 196 200 160 164 215 217176 186241 247187 187153 155 214 214163 163137 139122 122239 239 E19 190 196 156 164 215 217186 190241 247187 199153 153 210 218163 163137 139122 126239 239 E20 196 202 156 160 215 215178 190241 247187 199153 153 214 216163 163139 141142 142239 239 E21 196 200 156 160 215 217176 186241 247187 187151 153 206 214159 163139 141122 142239 239 E22 194 196 160 164 215 217178 190241 241187 187149 155 210 214163 163137 137122 126239 239

PAGE 75

63Table 5. Continued Eglin Loci Bear # G1A G10B G10C G1D G10H G 10J G10L G10M G10P G10X MU50 MU59 E23 196 196 156 156 209 215186 190241 241187 187151 153 206 214163 163137 141122 142239 239 E24 196 200 156 164 215 215178 190241 241187 187153 155 212 214163 163139 149122 122239 239 E25 196 196 160 164 215 217178 190241 241187 187153 155 210 218163 163137 139122 122239 239 E26 196 196 160 164 215 215176 186241 247187 187149 153 214 214159 163137 139122 122239 239 E27 196 200 160 164 215 215176 178241 247187 199135 155 206 214159 163139 149122 142239 239 E28 198 200 160 160 215 215190 190241 241199 205153 155 216 218163 163139 149122 122239 239 E29 196 196 160 164 215 217176 190241 241187 187149 155 214 214159 163139 141122 142239 239 E30 196 196 156 164 215 217178 178241 241187 205153 155 212 218159 163137 149122 126239 239 E31 196 200 152 160 215 217176 178241 241187 187155 157 210 214163 163137 141122 122239 239 E32 196 202 156 160 215 215176 186241 241187 205149 155 210 218155 159139 141122 122239 239 E33 190 196 156 164 215 215186 190241 247187 199153 155 214 218155 163139 141122 142239 239 E34 200 200 156 160 215 215178 190241 241187 199157 157 212 216159 163137 141122 122239 239 E35 200 200 160 160 215 217176 178241 253187 199135 149 210 214159 163139 149122 142239 239 E36 196 200 156 164 215 215176 178241 247187 187153 155 214 214159 163137 141122 122239 239 E37 196 196 160 164 215 217178 186241 241187 187151 153 214 214163 163137 139122 122239 239 E38 196 200 160 164 215 217176 178241 253187 187149 149 210 210159 159139 139122 122239 239 E39 196 196 160 160 215 215176 186241 247187 187149 153 214 214163 163137 139122 122239 239 E40 196 200 152 164 209 217178 178241 247187 187149 153 210 218159 163137 137122 122239 239

PAGE 76

64Table 5. Continued Highlands/Glades Loci Bear # G1A G10B G10C G1D G10H G 10J G10L G10M G10P G10X MU50 MU59 H1 190 198 152 156 215 215184 184241 255203 203139 139 210 212159 163141 141122 122243 243 H2 190 198 152 156 215 215176 176253 253203 203139 139 210 212159 163141 141122 134243 243 H3 190 190 156 156 215 215176 176253 255199 203139 139 212 212163 163141 141122 122243 243 H4 198 198 152 156 215 215176 184241 255203 203139 139 210 210163 163141 149122 122239 243 H5 190 198 156 156 215 215184 184255 255199 203139 139 212 212147 163141 141122 122243 243 H6 190 190 156 156 215 215184 184255 255199 203139 139 212 212163 163141 141122 134239 243 H7 190 190 156 156 215 219176 176241 253199 199139 139 210 210147 163141 141122 122243 243 H8 190 198 156 156 207 219184 184241 255203 203139 139 212 212163 163141 149122 122239 239 H9 190 198 152 152 215 215176 176255 255199 199139 139 212 212163 163141 141122 122243 243 H10 190 190 152 156 207 215178 184255 255203 203139 139 210 212161 163141 141122 122243 243 H11 190 198 156 156 207 215176 176241 241203 203139 139 210 212163 163149 149122 122241 243 H12 190 198 152 156 207 215176 176255 255203 203139 139 212 212147 159141 149122 122243 243 H13 190 198 152 156 215 215176 178241 241203 203139 139 210 212163 163141 149122 122243 243 H14 190 198 154 156 215 215176 188253 253199 203139 139 212 214163 163141 141134 134241 243 H15 190 190 152 156 207 207176 176253 255199 203139 139 210 210163 163141 149122 122243 243 H16 190 198 152 156 215 215176 184255 255203 203139 139 212 212163 163141 149122 122243 243 H17 198 198 152 156 215 215176 184255 255203 203139 139 212 212163 163141 149122 122243 243 H18 190 198 152 152 207 215176 176255 255199 203139 139 212 212147 159141 141122 122243 243 H19 190 190 152 156 215 215176 176255 255199 203139 139 212 212159 163141 141122 122243 243 H20 190 190 152 156 215 215184 184241 253199 203139 139 210 212163 163141 141122 122243 243 H21 190 190 156 156 215 215176 184255 255203 203139 139 210 212147 163141 141122 122243 243 H22 198 198 152 156 215 215176 184255 255185 203139 139 210 210147 161141 141122 122239 243

PAGE 77

65Table 5. Continued Highlands/Glades Loci Bear # G1A G10B G10C G1D G10H G 10J G10L G10M G10P G10X MU50 MU59 H23 190 198 152 156 207 215176 184253 255203 203139 139 210 212163 163141 141122 122243 243 H24 190 198 156 156 215 215176 176241 241199 203139 139 210 210147 163141 141122 122243 243 H25 190 198 156 156 207 215184 184241 253203 203139 139 210 212163 163141 141122 122243 243 H26 190 198 156 156 207 215176 176241 241203 203139 139 212 212163 163149 149122 122243 243 H27 198 198 152 156 207 215176 176241 255203 203139 139 210 210161 163141 149122 122243 243 H28 190 198 156 156 215 215184 184241 255199 203139 139 210 210163 163141 141122 122239 243 Ocala Bear # G1A G10B G10C G1D G10H G 10J G10L G10M G10P G10X MU50 MU59 OC1 192 192 156 156 207 215184 184241 241187 205139 139 212 214159 159137 149128 134239 239 OC2 194 198 152 156 217 217184 184241 253187 203139 139 206 212159 159141 149122 134245 245 OC3 194 194 160 164 215 217176 184241 241187 187139 139 206 212159 163149 149122 138231 245 OC4 192 194 160 164 217 217176 188241 249187 187139 139 212 214163 163149 149134 138245 245 OC5 190 198 152 160 215 215184 190249 249203 203139 139 212 214161 161141 149122 134231 239 OC6 192 194 152 156 215 215184 184241 253203 203139 139 212 214159 163139 149134 134231 245 OC7 190 194 156 156 215 215172 186241 243187 205139 139 206 214159 159149 149122 134239 245 OC8 192 194 156 156 215 215184 184241 241187 187139 139 212 212159 163149 149134 144239 239 OC9 194 194 156 164 215 217176 184241 241187 205139 155 212 212159 163149 149122 122231 245 OC10 192 198 154 160 215 215172 188243 249187 187139 139 206 212161 163149 149128 134231 235 OC11 192 198 160 160 215 217190 190243 249187 203139 139 206 212159 161141 147122 134235 243 OC12 192 198 156 156 215 215184 184241 241187 205139 139 206 214159 163147 149122 134231 239 OC13 192 192 152 160 215 215172 176241 249187 187139 149 212 214159 159149 149122 138231 245 OC14 192 198 154 156 215 215184 190249 253187 187149 155 212 214159 163137 149134 134231 239

PAGE 78

66 Table 5. Continued Ocala Loci Bear # G1A G10B G10C G1D G10H G 10J G10L G10M G10P G10X MU50 MU59 OC15 192 194 154 156 215 215172 176241 249187 187139 139 212 212159 161147 147134 134239 245 OC16 192 200 156 156 215 215184 184253 259187 187139 139 212 214159 163141 141122 134231 243 OC17 190 192 154 156 215 215184 190249 253187 187139 155 206 212159 159149 149128 134239 239 OC18 190 198 156 156 215 215172 176253 259187 187139 139 212 212159 159141 149122 134239 243 OC19 192 192 156 164 215 217176 186241 249187 187139 139 214 214159 163141 149122 138231 245 OC20 190 194 152 160 215 215176 176253 259187 187139 149 206 206159 159149 149134 134231 241 OC21 192 192 156 156 215 215172 188241 249187 205139 139 212 212159 163149 149128 134239 239 OC22 194 198 152 156 215 215172 190249 249205 205139 139 212 218159 161137 149122 128239 245 OC23 192 194 154 156 215 215176 188241 259187 187139 155 206 212163 163141 149128 134239 243 OC24 190 198 152 156 213 215184 184241 249187 203139 155 212 212159 163137 141122 128239 241 OC25 194 194 156 156 215 217172 176241 241187 203139 139 206 206159 159149 149122 134239 243 OC26 190 198 156 160 215 217184 190249 249187 187139 139 206 212157 159137 147122 134231 237 OC27 194 198 154 154 215 215172 184259 259187 187139 149 212 214159 163141 149122 134239 243 OC28 192 194 154 156 213 215176 190241 253187 187139 139 212 218161 163149 149122 134231 239 OC29 192 194 152 156 215 215176 188241 241187 203139 149 212 212159 159137 137122 122237 239 OC24 190 198 152 156 213 215184 184241 249187 203139 155 212 212159 163137 141122 128239 241 OC25 194 194 156 156 215 217172 176241 241187 203139 139 206 206159 159149 149122 134239 243 OC26 190 198 156 160 215 217184 190249 249187 187139 139 206 212157 159137 147122 134231 237 OC27 194 198 154 154 215 215172 184259 259187 187139 149 212 214159 163141 149122 134239 243 OC28 192 194 154 156 213 215176 190241 253187 187139 139 212 218161 163149 149122 134231 239 OC29 192 194 152 156 215 215176 188241 241187 203139 149 212 212159 159137 137122 122237 239

PAGE 79

67Table 5. Continued Ocala Loci Bear # G1A G10B G10C G1D G10H G 10J G10L G10M G10P G10X MU50 MU59 OC30 194 194 156 156 215 215172 172241 253187 187139 139 214 218159 159147 149122 134239 239 OC31 190 198 152 160 215 217172 176241 249187 187139 139 206 212159 163147 149122 134239 245 OC32 190 200 156 160 215 217176 176241 259187 205149 155 206 212159 159137 147122 122243 243 OC33 190 190 156 164 215 215172 184241 241187 187139 139 206 212159 159141 149134 134235 239 OC34 192 192 152 156 215 215172 172259 259187 187139 139 206 214159 159137 137122 122231 239 OC35 190 190 156 156 213 215184 186249 259187 187139 149 206 206159 163137 149134 134231 239 OC36 192 192 154 156 215 217172 176241 241187 205139 139 206 212159 163137 141134 134235 243 OC37 194 194 156 164 215 215172 176241 241187 187139 139 212 212161 163149 149122 122231 243 OC38 190 192 152 156 213 215184 186241 259187 187139 155 206 214159 159137 149122 134231 239 OC39 192 192 154 156 215 217172 184241 249187 187139 155 206 206159 159147 149122 134231 239 OC40 192 194 152 156 215 215186 188243 253187 187149 157 206 212159 163137 141134 134231 231 Osceola Bear # G1A G10B G10C G1D G10H G 10J G10L G10M G10P G10X MU50 MU59 OS1 196 200 154 158 215 217176 184235 243187 187151 153 212 214155 159141 149124 134239 239 OS2 198 198 160 160 217 217176 186241 243187 187149 155 218 218159 159141 149124 124239 239 OS3 190 200 160 164 213 217184 186241 243191 203143 151 206 206157 159141 147126 134239 239 OS4 198 198 154 160 207 217176 186243 243187 187139 143 214 214159 159141 149124 134239 243 OS5 194 198 152 154 215 217186 188243 253187 199151 153 212 218159 159141 149122 134239 241 OS6 196 198 152 152 215 217176 188245 251187 187149 157 210 216147 159141 149122 134231 239 OS7 194 198 166 166 217 217176 188243 243187 189151 151 212 212159 159141 149134 134239 239 OS8 194 200 158 160 217 217176 188243 243187 187139 153 210 214147 159141 141122 134231 231

PAGE 80

68Table 5. Continued Osceola Loci Bear # G1A G10B G10C G1D G10H G 10J G10L G10M G10P G10X MU50 MU59 OS9 194 194 156 156 217 219176 184241 243187 189151 153 210 218147 159149 155122 134231 241 OS10 190 198 156 160 215 217178 188235 243187 199135 139 214 218159 159141 149144 144239 239 OS11 194 200 160 160 217 217184 188249 255187 189139 151 212 212159 163147 151134 134239 243 OS12 194 194 154 166 207 217176 180241 241187 189151 155 210 214159 159149 149124 134231 243 OS13 200 200 156 158 215 217176 184235 243189 203139 149 210 214157 159141 149134 134231 243 OS14 192 196 156 156 215 215176 186241 255187 187139 149 206 214157 159141 149122 144231 243 OS15 194 198 160 164 217 217176 186243 243185 187135 153 214 214159 159149 149134 144239 243 OS16 190 194 160 164 213 219176 176235 241187 187143 151 206 214147 159149 149122 126239 239 OS17 194 198 156 160 207 217178 184243 245187 199139 155 214 218159 159149 151124 144231 239 OS18 194 198 156 164 213 219176 184243 245187 189143 151 206 210159 163149 149122 126231 239 OS19 196 196 152 156 215 215188 190241 241187 189135 139 206 214159 159141 149126 144239 249 OS20 192 198 152 160 215 215186 188241 243187 199139 153 206 210159 161139 149124 134231 241 OS21 194 198 160 164 217 217176 186243 243187 199153 155 216 218159 159149 149124 134239 239 OS22 196 200 156 160 211 217176 176245 255187 187135 151 212 212159 163141 149140 144239 239 OS23 194 200 154 160 217 217176 184241 243187 189151 153 210 214159 159141 147122 134239 241 OS24 196 200 156 160 217 219176 188241 255187 189139 157 206 212159 163141 147124 134239 243 OS25 194 198 160 160 215 217176 188235 253187 205153 153 212 212157 159141 141122 122231 239 OS26 194 200 160 164 217 217176 186243 255187 199139 155 206 218159 161141 149134 144239 239 OS27 194 196 164 166 217 217176 184241 241191 199143 151 206 214159 159141 149122 134239 241 OS28 194 198 160 160 217 217176 176241 243187 199153 153 214 214157 163141 147126 134239 243 OS29 194 194 156 160 217 217188 188235 251187 205153 153 212 214157 159141 141122 122231 231 OS30 194 200 156 160 217 217184 184241 241199 205151 153 206 212157 159141 141122 126239 243

PAGE 81

69Table 5. Continued Osceola Loci Bear # G1A G10B G10C G1D G10H G 10J G10L G10M G10P G10X MU50 MU59 OS31 190 192 156 156 215 215184 186243 249187 203139 157 212 214161 163149 149128 134243 243 OS32 194 194 154 156 217 217184 184243 253187 191151 153 214 216157 159141 149126 134231 231 OS33 194 198 160 160 215 217176 188251 253187 199135 149 212 212159 159141 149122 122239 249 OS34 190 194 156 160 211 217184 188235 241189 205149 153 212 214147 159149 151122 134239 239 OS35 194 196 160 160 215 217176 186241 251187 199149 151 206 206157 159141 149126 134231 239 OS36 194 194 160 160 217 217176 188235 253187 189135 143 212 212159 163141 141122 140239 249 OS37 194 194 160 160 217 217176 186241 243185 187153 153 214 214159 159149 149122 124239 249 OS38 194 196 156 164 215 217178 186241 243187 199139 143 212 218159 161141 141134 144239 239 OS39 194 200 160 164 217 217176 184245 253187 205135 139 212 214157 163147 149126 134239 243 OS40 194 194 160 160 217 217186 186243 251187 199151 153 206 214159 159141 149134 144231 249 OS41 192 192 156 156 207 215184 188243 253187 187139 139 212 212159 163147 149134 134241 245 St. Johns Bear # G1A G10B G10C G1D G10H G 10J G10L G10M G10P G10X MU50 MU59 S1 190 194 152 156 215 215184 186249 255187 205139 139 206 212159 163141 149122 134231 239 S2 190 194 154 156 207 215176 184241 253187 203139 139 212 214159 163147 149122 122239 245 S3 190 194 152 160 213 217176 184241 253187 203139 155 212 218159 163141 141122 138239 243 S4 194 198 156 156 215 215172 184241 249187 205139 139 206 206159 159137 141122 144245 245 S5 190 194 156 156 215 217176 190241 241187 205139 139 212 218159 163141 149134 138239 239 S6 194 200 152 156 211 213172 188241 249185 187139 139 214 216159 159147 147122 134241 243 S7 192 194 154 156 215 217172 186249 259187 205139 155 212 212161 161141 149122 134237 239 S8 192 200 152 156 211 215184 188241 241187 205139 139 212 214159 159137 147122 128241 243

PAGE 82

70Table 5. Continued St. Johns Loci Bear # G1A G10B G10C G1D G10H G10J G10L G10M G10P G10X MU50 MU59 S9 194 200 152 156 211 215172 184249 249187 187139 139 214 214159 159147 147122 134241 247 S10 192 192 156 156 215 215172 184241 249203 205139 149 212 212159 161137 149122 134241 245 S11 192 192 156 156 215 217172 184241 241187 203139 155 206 206159 159137 141122 128231 239 S12 186 198 152 152 207 215176 180241 241187 187139 139 212 214163 163137 141134 134239 239 S13 186 194 152 160 215 215176 188241 259187 189139 139 214 214159 159141 141122 134243 243 S14 194 198 152 156 213 215176 184241 249187 187139 139 206 218159 163147 149122 122231 239 S15 190 192 154 156 215 215172 176253 259187 187139 139 212 212159 159149 149122 128231 231 S16 194 194 154 156 211 215184 184249 259187 203149 155 212 212159 159141 147122 122237 245 S17 192 194 152 156 207 215176 184249 249187 203139 149 212 212159 163137 137134 134231 243 S18 190 198 152 152 215 215176 184249 249185 203139 139 212 214163 163137 137134 140231 243 S19 194 194 152 156 207 215172 172249 259187 187139 139 212 218159 159141 141122 134231 239 S20 194 194 156 156 217 217172 176241 253187 187139 139 212 218159 159141 149122 134239 239 S21 194 198 152 156 207 207180 184249 259187 187139 139 214 214161 161137 147138 138243 247 S22 192 192 156 156 211 215172 184249 259187 187139 139 206 214159 159141 149122 122235 243 S23 194 198 152 156 215 215172 176235 241185 187139 139 212 214159 163137 155122 138231 239 S24 190 194 152 156 215 215176 190241 253185 203139 139 212 214157 163147 149122 134245 245 S25 194 198 154 160 215 215184 190259 259187 205139 139 206 214159 163149 149128 134231 239 S26 186 192 156 160 207 215172 184241 249187 189139 139 206 214159 159147 149122 138239 243 S27 190 194 152 152 215 215176 186241 255187 191139 143 206 212159 163149 149138 140239 239 S28 190 192 152 156 211 215172 186241 249187 191139 143 206 212159 159149 149122 140239 243 S29 190 192 152 156 211 215176 184255 259187 187139 139 206 206159 159141 149122 138235 239 S30 186 194 152 154 207 213184 184241 259187 189139 155 206 214161 163137 147122 126231 239

PAGE 83

71Table 5. Continued St. Johns Loci Bear # G1A G10B G10C G1D G10H G10J G10L G10M G10P G10X MU50 MU59 S31 190 192 156 160 217 217176 184241 241187 203155 155 214 218159 163141 149122 122241 243 S32 194 198 154 156 215 217172 184241 249187 205139 139 206 214159 159149 149134 134239 241 S33 190 194 152 156 213 217176 176241 253187 187139 155 218 218159 163141 149122 138239 243 S34 190 198 152 154 215 215184 190241 259203 203139 139 212 214157 159149 149122 122237 245 S35 192 198 152 160 213 217184 184241 249205 205139 139 212 212159 163137 141134 134239 245 S36 190 194 156 156 207 215172 184253 259187 187139 139 206 212159 159141 147134 134231 239 S37 198 198 156 156 215 217172 184241 249187 203139 139 212 212159 163149 149122 134239 241 S38 194 198 156 156 215 215176 184241 259189 205139 139 206 214159 159137 147134 144231 245 S39 198 198 152 156 207 215176 184259 259187 189139 139 206 214159 159137 147122 134231 239 S40 198 198 156 156 215 217176 184249 259187 187139 139 212 212159 163149 149122 138231 239 Osceola/Ocala Corridor Bear # G1A G10B G10C G1D G10H G 10J G10L G10M G10P G10X MU50 MU59 OO1 192 194 154 156 215 215172 184241 241187 203149 155 212 214159 159149 149122 134239 243 OO2 190 198 152 156 215 215172 184241 259187 205139 149 214 214159 159137 141122 134231 231 OO3 192 194 156 156 215 215172 188241 243187 187139 139 206 212159 163137 139122 134231 245 OO4 192 194 156 156 215 215184 186249 259187 205139 157 214 218159 159141 149122 122239 243 OO5 192 194 156 156 213 215176 188243 249187 187139 149 212 214161 163141 149122 124231 239 OO6 192 192 154 156 215 215176 184241 249187 187139 139 212 214161 163149 149134 134241 241 OO7 192 192 154 160 215 215172 176241 249187 187139 139 206 212159 163137 149122 122231 239 OO8 192 200 154 160 215 217172 190241 259187 205139 139 212 212159 161139 149122 122231 231 OO9 192 198 152 158 215 217172 184241 253187 187139 155 212 214159 159141 149134 134231 239

PAGE 84

72Table 5. Continued Osceola/Ocala Corridor Loci Bear # G1A G10B G10C G1D G10H G10J G10L G10M G10P G10X MU50 MU59 OO10 190 192 154 156 211 215188 188249 249187 187139 149 212 212159 159137 149134 134235 243 OO11 190 198 152 156 215 215172 186241 249187 205139 155 212 214163 163137 141134 134231 239 OO12 194 198 152 154 215 215172 172241 249187 205139 139 206 212159 159149 149134 134231 237 OO13 192 194 152 156 215 215172 190241 259187 203139 149 206 212159 159137 149122 134239 239 OO14 192 198 156 164 213 215172 172241 249187 203139 139 212 214159 159149 149122 134243 245 OO15 190 194 156 160 207 215184 188243 259187 205139 155 212 214159 159141 149134 138231 245 OO16 198 198 152 156 215 215172 172241 259187 203139 139 212 212159 159141 149134 134231 243 OO17 192 192 152 156 213 215186 186249 259187 187139 139 206 214159 159149 149122 134231 239 OO18 190 192 156 156 215 215176 188241 259187 187135 139 206 214159 163141 149134 134231 231 OO19 192 192 156 164 215 217176 184241 249187 187139 139 212 212163 163149 149122 134239 239 OO20 200 200 156 158 215 217176 186243 243187 187143 153 214 218159 159149 149134 144231 239 OO21 192 194 156 156 215 215186 188243 249203 205139 139 206 212159 163139 147122 134231 239 OO22 194 198 152 160 215 215172 184241 249187 203139 139 212 212159 159141 149122 134239 239 OO23 190 192 160 160 215 215172 176249 259187 205139 149 206 214159 163141 149134 138231 239 OO24 198 198 156 160 215 217190 190241 249187 205139 139 214 214157 159137 149134 134241 245 OO25 192 194 154 156 215 215184 188241 253187 187139 139 206 212159 159149 149134 144231 239 OO26 196 198 160 160 217 217176 186241 241187 199149 153 206 218159 159141 149124 134231 239 OO27 192 192 156 160 213 215172 184259 259187 187139 149 212 214159 159141 141134 134231 243 OO28 190 192 152 154 207 215186 188241 249203 205139 149 206 212159 159141 149134 138231 235 OO29 190 192 152 156 207 215176 186241 241187 205139 139 206 214159 163137 141122 122231 243 OO30 198 198 154 156 215 215172 188241 241203 203139 139 212 212159 159137 141134 134231 235 OO31 200 200 156 160 215 217188 188241 243187 189143 143 212 218159 159149 149134 134231 239

PAGE 85

73Table 6. Allele frequencies for 12 microsatellite lo ci in 10 populations of Florida black bears. Alleles at locus G1A Population 186190192194196198200202 Apalachicola 00.20.0380.1250.0750.1880.2750.1 Aucilla 00.16700.05600.4440.3330 Big Cypress 00.6220.0370.0370.0240.110.1710 Chassahowitzka 00.0860000.91400 Eglin 00.0500.0130.5120.0130.3250.087 Highlands/Glades 00.5710000.42900 Ocala 00.1750.3630.28700.150.0250 Osceola/Ocala Corridor 00.1290.4030.1610.0160.210.0810 Osceola 00.0610.0610.4150.1220.1950.1460 St. Johns 0.050.1750.1880.33800.2130.0380 Alleles at locus G10B Population 152154156158160162164166 Apalachicola 00.20.0380.1250.0750.1880.2750.1 Aucilla 00.16700.05600.4440.3330 Big Cypress 00.6220.0370.0370.0240.110.1710 Chassahowitzka 00.0860000.91400 Eglin 00.0500.0130.5120.0130.3250.087 Highlands/Glades 00.5710000.42900 Ocala 00.1750.3630.28700.150.0250 Osceola/Ocala Corridor 00.1290.4030.1610.0160.210.0810 Osceola 00.0610.0610.4150.1220.1950.1460 St. Johns 0.050.1750.1880.33800.2130.0380

PAGE 86

74Table 6. Continued Alleles at locus G10C Population 205207209211213215217219 Apalachicola 0.0380.0620.2750.23700.38700 Aucilla 0.3330.05600.05600.55600 Big Cypress 00.585000.0610.35400 Chassahowitzka 00.20700.7760.017000 Eglin 000.05000.6250.3250 Highlands/Glades 00.1960000.76800.036 Ocala 00.013000.050.7620.1750 Osceola/Ocala Corridor 00.04800.0160.0650.7420.1290 Osceola 00.04900.0240.0370.220.6220.049 St. Johns 00.12500.0870.0750.550.1620 Alleles at locus G1D Population 172176178180182184186188190 Apalachicola 0.0130.375000.1250.0250.1880.150.125 Aucilla 0.1110.11100.3330000.2220.222 Big Cypress 0.0370.4020.037000.220.2560.0370.012 Chassahowitzka 0.72400000.2070.06900 Eglin 00.2130.2750000.21300.3 Highlands/Glades 00.5710.036000.37500.0180 Ocala 0.2250.2250000.3120.0620.0750.1 Osceola/Ocala Corridor 0.290.1450000.1610.1450.1940.065 Osceola 00.3540.0370.01200.2070.1830.1950.012 St. Johns 0.2130.2500.02500.3750.050.0380.05

PAGE 87

75Table 6. Continued Alleles at locus G10H Population 235239241243245247249251252253255259261 Apalachicola 00.1120.712000.0130.0130.0620.0130.075000 Aucilla 0.22200.72200000.05600000 Big Cypress 0.09800.2560.012000.0490.01200.52400.0120.037 Chassahowitzka 000.552000.4480000000 Eglin 000.738000.2250000.0250.01300 Highlands/Glades 000.2860000000.1790.53600 Ocala 000.450.05000.237000.12500.1380 Osceola/Ocala Corridor 000.4190.113000.258000.03200.1770 Osceola 0.09800.2560.3540.06100.0240.06100.0850.06100 St. Johns 0.01300.3750000.275000.0870.0380.2130 Alleles at locus G10J Population 185187189191199203205 Apalachicola 0.0620.475000.1380.3250 Aucilla 00.55600.1670.27800 Big Cypress 0.2070.378000.3540.0610 Chassahowitzka 00.052000.94800 Eglin 00.775000.18800.038 Highlands/Glades 0.0180000.250.7320 Ocala 00.7750000.1120.112 Osceola/Ocala Corridor 00.6450.01600.0160.1450.177 Osceola 0.0240.5490.1340.0370.1590.0370.061 St. Johns 0.050.5750.0620.02500.150.138

PAGE 88

76Table 6. Continued Alleles at locus G10L Population 135137139141143149151153155157161 Apalachicola 0.162000.0380.11200.0130.2250.1120.3380 Aucilla 0.056000.1110.22200.50.05600.0560 Big Cypress 0.1100.3540.0120000.1340.1590.1950.037 Chassahowitzka 00.1550.19000.65500000 Eglin 0.02500000.1380.0870.40.2870.0620 Highlands/Glades 00100000000 Ocala 000.788000.1000.10.0130 Osceola/Ocala Corridor 0.01600.67700.0480.14500.0320.0650.0160 Osceola 0.08500.19500.0850.0850.2070.2440.0610.0370 St. Johns 000.83700.0250.038000.100 Alleles at locus G10M Population 206210212214216218 Apalachicola 0.11200.20.3870.2250.075 Aucilla 0.27800.6110.0560.0560 Big Cypress 0.0120.1220.4510.4020.0120 Chassahowitzka 00.0170.983000 Eglin 0.10.2130.050.4380.050.15 Highlands/Glades 00.4110.5710.01800 Ocala 0.300.4750.18800.038 Osceola/Ocala Corridor 0.19400.4520.2900.065 Osceola 0.1710.0980.280.3050.0370.11 St. Johns 0.22500.3870.2750.0130.1

PAGE 89

77Table 6. Continued Alleles at locus G10P Population 147151155157159161163 Apalachicola 00.0750.2750.0870.30.050.213 Aucilla 0000.50.27800.222 Big Cypress 0.2320000.280.0370.451 Chassahowitzka 0000.1210.84500.034 Eglin 000.100.200.7 Highlands/Glades 0.1250000.0890.0540.732 Ocala 0000.0130.6130.10.275 Osceola/Ocala Corridor 0000.0160.7420.0480.194 Osceola 0.06100.0120.1220.6460.0490.11 St. Johns 0000.0250.650.0750.25 Alleles at locus G10X Population 137139141143147149151155157 Apalachicola 0.0870.0130.17500.2870.250.0130.1750 Aucilla 0.05600.38900.1670.16700.2220 Big Cypress 000.1340.0120.1460.6830.01200.012 Chassahowitzka 000.293000.707000 Eglin 0.3380.3750.213000.075000 Highlands/Glades 000.786000.214000 Ocala 0.1750.0130.16200.1120.538000 Osceola/Ocala Corridor 0.1450.0480.25800.0160.532000 Osceola 00.0120.40200.0850.4510.0370.0120 St. Johns 0.18800.2500.1880.36300.0130

PAGE 90

78Table 6. Continued Alleles at locus MU50 Population 122124126128134136138140142144 Apalachicola 0.0870.450.150.06200.0130.21300.0250.087 Aucilla 0.6110.2780.1110000000.611 Big Cypress 0.2800.1830.0120.52400000.28 Chassahowitzka 0000000100 Eglin 0.7120.0250.038000000.2250.712 Highlands/Glades 0.9290000.07100000.929 Ocala 0.375000.0870.47500.05000.375 Osceola/Ocala Corridor 0.290.032000.59700.048000.29 Osceola 0.2320.1220.110.0120.378000.02400.232 St. Johns 0.43800.0130.050.31200.1250.03800.438 Alleles at locus MU59 Population 231235237239241243245247249 Apalachicola 0.025000.5750.0130.387000 Aucilla 0000.94400.056000 Big Cypress 0.0490.03700.4880.0370.37800.0120 Chassahowitzka 0.052000.94800000 Eglin 0000.98800.013000 Highlands/Glades 0000.1070.0360.857000 Ocala 0.250.050.0250.3630.0250.1250.16200 Osceola/Ocala Corridor 0.4030.0480.0160.3060.0480.1130.06500 Osceola 0.207000.50.0730.1460.01200.061 St. Johns 0.1880.0250.0380.350.0870.1620.1250.0250

PAGE 91

79 LITERATURE CITED Aars, J., and R. A. Ims. 1999. The effect of habitat corridors on rates of transfer and interbreeding between vole demes. Ecology 80 :1648-1655. Alt, G. 1979. Dispersal patterns of black bears in northeastern Pennsylvania a preliminary report. Pages 186-199 in R. D. Hugie, editor. Fourth Eastern Black Bear Workshop, Bangor, Maine. Bakeless, J. E. 1989. America as seen by its fi rst explorers: the eyes of discovery. Dover Publications, New York. Bartram, W. 1980. William Bartram travels in W. Howarth, and F. Bergon, editors. Literature of the American wilderness Peregrine Smith, Inc., Salt Lake City. Beier, P. 1993. Determining minimum habita t areas and habitat corridors for cougars. Conservation Biology 7 :94-108. Beier, P. 1995. Dispersal of juvenile cougars in fragmented habitat. Journal of Wildlife Management 59 :228-237. Beier, P., and S. Loe. 1992. A checklist for evaluating impacts to wildlife movement corridors. Wildlife Society Bulletin 20 :434-440. Beier, P., and R. F. Noss. 1998. Do habitat corridors provide conn ectivity? Conservation Biology 12 :1241-1252. Bentzien, M. M. 1998. Endangered and threat ened wildlife and plants; new 12-month finding for a petition to list the Fl orida black bear. Federal Register 63 :6761367618. Boerson, M. R., J. D. Clark, and T. L. King. 2003. Estimating black bear population density and genetic diversity at Tensas River, Louisiana us ing microsatellite DNA markers. Wildlife Society Bulletin 31 :197-207. Bowker, B., and T. Jacobson. 1995. Louisiana bl ack bear recovery plan. United States Fish and Wildlife Servic e, Jackson, Mississippi. Brady, J. R., and D. S. Maehr. 1985. Distributi on of black bears in Florida. Florida Field Naturalist 13 :1-7.

PAGE 92

80 Brody, A. J., and M. R. Pelton. 1989. Effects of roads on black bear movements in western North Carolina. W ildlife Society Bulletin 17 :5-10. Brooker, L., and M. Brooker. 2002. Dispersa l and population dynamics of the bluebreasted fairy-wren, Malurus pulcherrimus in fragmented habitat in the Western Australian wheatbelt. Wildlife Research 29 :225-233. Caizergues, A., O. Ratti, P. Helle, L. Ro telli, L. Ellison, and J. Y. Rasplus. 2003. Population genetic structur e of male black grouse ( Tetrao tetrix ) in fragmented vs. continuous landscapes. Molecular Ecology 12 :2297-2305. Cale, P. G. 2003. The influence of soci al behaviour, dispersal and landscape fragmentation on population structure in a sedentary bird. Biol ogical Conservation 109 :237-248. Cegelski, C. C., L. P. Waits, and N. J. Anderson. 2003. Assessing population structure and gene flow in Montana wolverines ( Gulo gulo ) using assignment-based approaches. Molecular Ecology 12 :2907-2918. Chesser, R. K. 1983. Isolation by distance: re lationship to the management of genetic resources. Pages 66-77 in C. M. Schonewald-Cox, S. M. Chambers, B. MacBryde, and W. L. Thomas, editors. Genetics a nd conservation: a reference for managing wild animal and plant populations. Th e Benjamin/Cummings Publishing Company, Inc., London. Coffman, C. J., J. D. Nichols, and K. H. Pollock. 2001. Population dynamics of Microtus pennsylvanicus in corridor-linked patches. Oikos 93 :3-21. Cory, C. B. 1896. Hunting and fishing in Fl orida. Estes and Laur iet Publishing, Boston. Cox, J., R. Kautz, M. MacLaughlin, and T. Gilbert 1994. Closing the gaps in Florida's wildlife habitat conservation system. Florida Game and Fresh Water Fish Commission, Tallahassee. Craighead, F. L., and E. R. Vyse. 1996. Br own/grizzly bear me tapopulations. Pages 325351 in D. R. McCullough, editor. Meta populations and wildlife conservation. Island Press, Washington, D.C. Crooks, K. R. 2002. Relative sensitivities of mammalian carnivores to habitat fragmentation. Conservation Biology 16 :488-502. Csiki, I., C. Lam, A. Key, E. Coulter, J. D. Clark, R. M. Pace, K. G. Smith, and D. D. Rhoads. 2003. Genetic variation in black b ears in Arkansas and Louisiana using microsatellite DNA marker s. Journal of Mammalogy 84 :691-701. Dallas, J. F., F. Marshall, S. B. Piertney, P. J. Bacon, and P. A. Racey. 2002. Spatially restricted gene flow and reduced microsatel lite polymorphism in the Eurasian otter ( Lutra lutra ) in Britain. Conservation Genetics 3 :15-29.

PAGE 93

81 Davies, K. F., C. Gascon, and C. R. Margules. 2001. Habitat fragmentation: consequences, management, and future res earch priorities. Pages 81-97 in M. E. Soule', and G. H. Orians, editors. Conserva tion biology: research priorities for the next decade. Island Press, Washington. Dobey, S. T. 2002. Abundance and density of Florida black bears in the Okefenokee National Wildlife Refuge and Osceola National Forest. M.S. thesis. The University of Tennessee, Knoxville. Dobson, A., K. Ralls, M. Foster, M. E. Soule', D. Simberloff, D. Doak, J. A. Estes, L. S. Mills, D. Mattson, R. Dirzo, H. Arita, S. Ryan, E. A. Norse, R. F. Noss, and D. Johns. 1999. Corridors: reconnecti ng fragmented landscapes. in M. E. Soule', and J. Terbough, editors. Continental conservation: scientific foundations of a regional reserve network. Island Press, Washington, D.C. Duke, D. L., M. Hebblewhite, P. C. Paquet, C. Callaghan, and M. Persy. 2001. Restorating a large carnivore corridor in Ban ff National Park. in D. S. Maehr, R. F. Noss, and J. L. Larkin, editors. Larg e mammal restoration. Island Press, Washington, D.C. Dunbar, M. R., M. W. Cunningham, J. B. Wooding, and R. P. Roth. 1996. Cryptorchidism and delayed testicular des cent in Florida black bears. Journal of Wildlife Diseases 32 :661-664. Eason, T. H. 1995. Weights and morphometrics of black bears in the southeastern United States. M.S. thesis. The University of Tennessee, Knoxville. Eason, T. H. 2000. Black bear status report: a staff report to the commissioners. Florida Fish and Wildlife Conservation Commission, Tallahassee. Eason, T. H., S. L. Simek, and D. Zeigler. 2001. Statewide assessmen t of road impacts on bears in Florida. Florida Fish and Wi ldlife Conservation Commission, Tallahassee. Eastridge, R., and J. D. Clark. 2001. Eval uation of 2 soft-release techniques to reintroduce black bears. Wildlife Society Bulletin 29 :1163-1174. Ebert, D., C. Haag, M. Kirkpatrick, M. Rie k, J. W. Hottinger, and V. I. Pajunen. 2002. A selective advantage to immigrant gene s in a Daphnia metapopulation. Science 295 :485-488. Edwards, A. S. 2002. Status of the black bear in southwestern Alabam a. M.S. thesis. The University of Tennessee, Knoxville. Ernest, H. B., W. M. Boyce, V. C. Bleich, B. May, S. J. Stiver, and S. G. Torres. 2003. Genetic structure of mountain lion ( Puma concolor ) populations in California. Conservation Genetics 4 :353-366. Fahrig, L. 2001. How much habitat is enough? Biological Conservation 100 :65-74.

PAGE 94

82 Fahrig, L., and G. Merriam. 1985. Habitat pa tch connectivity and population survival. Ecology 66 :1762-1768. Fahrig, L., and G. Merriam. 1994. Conservati on of fragmented popul ations. Conservation Biology 8 :50-59. Felsenstein, J. 1993. PHYLIP (Phylogeny Infere nce Package), version 3.5c. Department of Genetics, University of Washington, Seattle. Ferreras, P. 2001. Landscape structure and asy mmetrical inter-patch connectivity in a metapopulation of the endangered Ib erian lynx. Biological Conservation 100 :125136. Fitch, W. M., and E. Margolia. 1967. Cons truction of phylogenetic trees. Science 155 :279-284. Flagstad, O., C. W. Walker, C. Vila, A. K. Sundqvist, B. Fernholm, A. K. Hufthammer, O. Wiig, I. Koyola, and H. Ellegren. 2003. Two centuries of the Scandinavian wolf population: patterns of geneti c variability and migration during an era of dramatic decline. Molecular Ecology 12 :869-880. Flather, C. H., and M. Bevers. 2002. Patchy reaction-diffusion and population abundance: the relative importance of habitat amount and arrangement. American Naturalist 159 :40-56. Foran, D. R., S. C. Minta, and K. S. Hein emeyer. 1997. DNA-based analysis of hair to identify species and individuals for p opulation research and monitoring. Wildlife Society Bulletin 25 :840-847. Forman, R. T., and M. Godron 1986. Landscap e ecology. John Wiley & Sons, New York. Foster, M. L., and S. R. Humphrey. 1995. Use of highway underpasses by Florida panthers and other wildlife. Wildlife Society Bulletin 23 :95-100. Frankham, R. 1995. Inbreeding and extinction: a threshold effect. Conservation Biology 9 :792-799. Frankham, R. 1996. Relationship of genetic va riation to population size in wildlife. Conservation Biology 10 :1500-1508. Frankham, R., J. Ballou, and D. Brisco e 2002. Introduction to conservation genetics. Cambridge University Press, New York. Franklin, I. R. 1980. Evolutiona ry change in small populations. Pages 135-150 in M. E. Soule', and B. A. Wilcox, editors. C onservation biology: an evolutionary-ecological perspective. Sinaue r, Sunderland, Massachusetts.

PAGE 95

83 Ganona, P., P. Ferreras, and M. Delibe s. 1998. Dynamics and viability of a metapopulation of the en dangered Iberian lynx ( Lynx pardinus ). Ecological Monographs 68 :349-370. Gerlach, G., and K. Musolf. 2000. Fragmenta tion of landscape as a cause for genetic subdivision in bank voles Conservation Biology 14 :1066-1074. Gottelli, D., C. Sillerozubiri, G. D. A pplebaum, M. S. Roy, D. J. Girman, J. Garciamoreno, E. A. Ostrander, and R. K. Wayne. 1994. Molecular genetics of the most endangered canid: the Ethiopian wolf ( Canis simensis ). Molecular Ecology 3 :301-312. Griffith, B., J. Scott, J. Carpenter, an d C. Reed. 1989. Translocation as a species conservation tool: status and strategy. Science 245 :477-480. Guo, S. W., and E. A. Thompson. 1992. Perfor ming the exact test of Hardy-Weinberg proportion for multiple alleles. Biometrics 48 :361-372. Haddad, N. M. 1999. Corridor use predicted from behaviors at habitat boundaries. American Naturalist 153 :215-227. Haddad, N. M., D. R. Bowne, A. Cunningham, B. J. Danielson, D. J. Levey, S. Sargent, and T. Spira. 2003. Corridor use by diverse taxa. Ecology 84 :609-615. Hale, M. L., P. W. W. Lurz, M. D. F. Shir ley, S. Rushton, R. M. Fuller, and K. Wolff. 2001. Impact of landscape management on th e genetic structur e of red squirrel populations. Science 293 :2246-2248. Hall, E. R. 1981. The mammals of North America. John Wiley and Sons, New York. Hanski, I. 1994. Patch-occupancy in frag mented landscapes. Trends in Ecology & Evolution 9 :131-135. Hanski, I., and D. Simberloff. 1997. The metapo pulation approach, its history, conceptual domain, and application to conservation. in I. Hanski, and M. E. Gilpin, editors. Metapopulation biology: ecology, genetics a nd evolution. Academic Press, San Diego, California. Harris, L. D. 1984. The fragmented forest : island biogeography theory and the preservation of biotic diversity. The Univer sity of Chicago Press, Chicago, Illinois. Harris, L. D., and P. B. Gallagher. 1989. Ne w initiatives for wild life conservation: the need for movement corridors. in G. Mackintosh, editor. Preserving communities and corridors. Defenders of Wildlife, Washington, D.C.

PAGE 96

84 Harris, L. D., and J. Scheck. 1991. From imp lications to applications: the dispersal corridor principle appl ied to the conservation of bi ological diversity. in D. A. Saunders, and R. J. Hobbs, editors. Nature conservation 2: the role of corridors. Surrey Beatty & Sons, Chipping Norton, New South Wales, Australia. Harrison, S., and E. Bruna. 1999. Habitat frag mentation and largescale conservation: what do we know for sure? Ecography 22 :225-232. Harrison, S., and J. Voller. 1998. Connectivity. in S. Harrison, and J. Voller, editors. Conservation biology principles for fore sted landscapes. UBC Press, Vancouver, British Colombia. Hartl, D. L., and A. G. Clark 1997. Prin ciples of population genetics. Sinauer, Sunderland, Massachusetts. Hass, C. A. 1995. Dispersal and use of co rridors by birds in wooded patches on an agricultural landscape. Conservation Biology 9 :845-854. Hedrick, P. H. 2000. Applications of populati on genetics and molecular techniques to conservation biology. Conservation Biology 4. Pages 438-450 in A. G. Young, and G. M. Clarke, editors. Genetics, demography, and viability of fragmented populations. Cambridge University Press, Cambridge, United Kingdom. Hellborg, L., C. W. Walker, E. K. Rueness, J. E. Stacy, I. Kojola, H. Valdmann, C. Vila, B. Zimmermann, K. S. Jakobsen, and H. Ellegren. 2002. Differen tiation and levels of genetic variation in northern European lynx ( Lynx lynx ) populations revealed by microsatellites and mitochondrial DNA analysis. Conservation Genetics 3 :97-111. Hellgren, E. C., and D. S. Maehr. 1993. Ha bitat fragmentation and black bears in the eastern United States. Pages 154-165 in E. P. Orff, editor. Eastern Black Bear Workshop for Research and Management Waterville Valley, New Hampshire. Hellgren, E. C., and M. R. Vaughan. 1994. Cons ervation and management of isolated black bear populations in the southeaste rn Coastal Plain of the United States. Proceedings of the Annual Conference Sout heastern Association Fish and Wildlife Agencies 48 :276-285. Hendry, L. A., T. M. Goodwin, and R. F. Labisky. 1982. Florida's vanishing wildlife. Circular 485 (Revised). Florida Cooperative Extension Service, Gainesville, Florida. Hess, G. R., and R. A. Fischer. 2001. Communicating clearly about conservation corridors. Landscape and Urban Planning 55 :195-208. Hitchings, S. P., and T. J. C. Beebee. 1997. Ge netic substructuring as a result of barriers to gene flow in urban Rana temporaria (common frog) populations: implications for biodiversity conservation. Heredity 79 :117-127.

PAGE 97

85 Hoctor, T. S. 2003. Regional landscape analysis and reserve design to conserve Florida's biodiversity. Ph.D. dissertation. Univ ersity of Florida, Gainesville. Hoctor, T. S., M. H. Carr, and P. D. Zw ick. 2000. Identifying a linked reserve system using a regional landscape approach: the Florida ecological network. Conservation Biology 14 :984-1000. Hudson, Q. J., R. J. Wilkins, J. R. Waas, a nd I. D. Hogg. 2000. Low genetic variability in small populations of New Zealand kokako ( Callaeas cinerea wilsoni ). Biological Conservation 96 :105-112. Ims, R. A., and H. P. Andreassen. 1999. Eff ects of experimental habitat fragmentation and connectivity on root vole dem ography. Journal of Animal Ecology 68 :839-852. Jules, E. S. 1998. Habitat fragmentation a nd demographic change for a common plant: trillium in old-growth forest. Ecology 79 :1645-1656. Kaczensky, P., F. Knauer, B. Krze, M. Jonozovic, M. Adamic, and H. Gossow. 2003. The impact of high speed, high volume traffic axes on brown bears in Slovenia. Biological Conservation 111 :191-204. Kasbohm, J. W. 2004. Endangered and threaten ed wildlife and plants; reexamination of regulatory mechanisms in relation to th e 1998 Florida black bear petition finding. Federal Register 69 :2100-2108. Kasbohm, J. W., and M. M. Bentzien. 1998. The status of the Florid a black bear. United States Fish and Wildlife Serv ice, Jacksonville, Florida. Keller, I., and C. R. Largiader. 2003. Recent habitat fragmentation caused by major roads leads to reduction of gene flow and loss of genetic variability in ground beetles. Proceedings of the Royal Society of London Series B-Biological Sciences 270 :417423. Kirchner, F., J. B. Ferdy, C. Andalo, B. Cola s, and J. Moret. 2003. Role of corridors in plant dispersal: an exam ple with the endangered Ranunculus nodiflorus Conservation Biology 17 :401-410. Koenig, W. D., D. Van Vuren, and P. H. Hooge. 1996. Detectability, philopatry, and the distribution of dispersal di stances in vertebrates. Tr ends in Ecology & Evolution 11 :514-517. Kohn, M., F. Knauer, A. Stoffella, W. Schroder, and S. Paabo. 1995. Conservation genetics of the European brown bear a study using excremental PCR of nuclear and mitochondrial sequences. Molecular Ecology 4 :95-103. Koopman, M. E., B. L. Cypher, and J. H. Scrivner. 2000. Dispersal patterns of San Joaquin kit foxes ( Vulpes macrotis mutica ). Journal of Mammalogy 81 :213-222.

PAGE 98

86 Kuehn, R., W. Schroeder, F. Pirchner, a nd O. Rottmann. 2003. Genetic diversity, gene flow and drift in Bavarian red deer populations ( Cervus elaphus ). Conservation Genetics 4 :157-166. Kurten, B., and E. Anderson 1980. Pleistocen e mammals of North America. Columbia University Press, New York. Kyle, C. J., and C. Strobeck. 2001. Genetic structure of North American wolverine ( Gulo gulo ) populations. Molecular Ecology 10 :337-347. Lande, R. 1995. Mutation and conservation. Conservation Biology 9 :782-791. Larkin, J. L., D. S. Maehr, T. S. Hoctor, M. A. Orlando, and K. Whitney. 2004. Landscape linkages and conservation planning for the black bear in west-central Florida. Animal Conservation 7 :1-12. Lee, D. J., and M. R. Vaughan. 2003. Disper sal movements by subadult American black bears in Virginia. Ursus 12 :162-170. Levins, R. 1970. Some mathematical quest ions in biology 2. Pages 77-107 in M. Gerstenhaber, editor. Lectures on mathematics in the life sciences. American Mathemathics Society, Providence. Lidicker, W. Z., and W. D. Koenig. 1996. Respons es of terrestrial vertebrates to habitat edges and corridors. in D. R. McCull ough, editor. Metapopula tions and wildlife conservation. Island Press, Washington, D.C. Lindenmayer, D., and R. Peakall. 2000. The Tumet experimentintegrating demographic and genetic studies to unravel fragmentati on effects: a case study of the native bush rat. Conservation Biology 4. Pages 173-202 in A. G. Young, and G. M. Clarke, editors. Genetics, demography, and viabi lity of fragmented populations. Cambridge University Press, Cambridge, United Kingdom. Linnell, J. D. C., J. Odden, M. E. Smith, R. Aanes, and J. Swenson. 1997. Translocation of carnivores as a method for problem an imal management: a review. Biodiversity and Conservation 6 :1245-1257. Louis, E. J., and E. R. Dempster. 1987. An exact test for Hardy-Weinberg and multiple alleles. Biometrics 43 :805-811. Lu, Z., W. E. Johnson, M. Menotti-Raymond, N. Yuhki, J. S. Martenson, S. Mainka, H. Shi-Qiang, Z. Zhihe, G. H. Li, W. S. Pan, X. R. Mao, and S. J. O'Brien. 2001. Patterns of genetic divers ity in remaining giant pa nda populations. Conservation Biology 15 :1596-1607. MacArthur, R. H., and E. O. Wilson 1967. The theory of island biogeography. Princeton University Press, Princeton, New Jersey.

PAGE 99

87 Mader, H. J. 1984. Animal habitat isolation by roads and agricultural fields. Biological Conservation 29 :81-96. Maehr, D. S., and J. R. Brady. 1984. Food habi ts of Florida black bears. Journal of Wildlife Management 48 :230-234. Maehr, D. S., T. S. Hoctor, L. J. Quinn, and J. S. Smith. 2001. Black bear habitat management guidelines for Florida. Techni cal report 17. Florida Fish and Wildlife Conservation Commission, Tallahassee. Maehr, D. S., E. D. Land, D. B. Shindle, O. L. Bass, and T. S. Hoctor. 2002. Florida panther dispersal and conser vation. Biological Conservation 106 :187-197. Maehr, D. S., J. E. Layne, E. D. Land, J. W. McCown, and J. Roof. 1988. Long distance movements of a Florida black be ar. Florida Field Naturalist 16 :1-6. Maehr, D. S., J. S. Smith, M. W. Cunningham, M. E. Barnwell, J. L. Larkin, and M. A. Orlando. 2003. Spatial characteri stics of an isolated Flor ida black bear population. Southeastern Naturalist 2 :433-446. Manel, S., M. K. Schwartz, G. Luikart, and P. Taberlet. 2003. Landscape genetics: combining landscape ecology and populati on genetics. Trends in Ecology & Evolution 18 :189-197. Mansfield, K. G., and E. D. Land. 2002. Cryptor chidism in Florida panthers: prevalence, features, and influence of genetic rest oration. Journal of Wildlife Diseases 38 :693698. Mantel, N. 1967. The detection of disease clustering and a generalized regression approach. Cancer Research 27 :209-220. Marshall, H. D., and K. Ritland. 2002. Genetic diversity and differentiation of Kermode bear populations. Molecular Ecology 11 :685-697. McCown, J. W., T. H. Eason, and M. W. Cunningham. 2001. Black bear movements and habitat use relative to roads in Ocala National Forest. Final Report. Florida Fish and Wildlife Conservation Commission, Tallahassee. McCoy, J., and K. Johnston 2000. Using ArcG IS spatial analyst. ESRI Publishing, Redlands, California. McDaniel, J. 1974. Florida report on black be ar management and research. Pages 157162 in M. R. Pelton, and D. Conley, edito rs. Proceedings of the Second Eastern Workshop on Black Bear Management a nd Research, Gatlinburg, Tennessee. McLellan, B. N., and F. W. Hovey. 2001. Nata l dispersal of grizzly bears. Canadian Journal of Zoology 79 :838-844.

PAGE 100

88 McLellan, B. N., and D. M. Shackleton. 1988. Grizzly bears and resource-extraction industries: effects of roads on behavior habitat use and demography. Journal of Applied Ecology 25 :451-460. Mech, S. G., and J. G. Hallett. 2001. Evaluati ng the effectiveness of corridors: a genetic approach. Conservation Biology 15 :467-474. Meffe, G. K., and C. R. Carroll 1997. Prin ciples of conserva tion biology. Sinauer, Sunderland, Massachusetts. Merriam, C. H. 1896. Preliminary synopsis of the American bears. Proceedings of the Biological Society of Washington 10 :65-83. Michalakis, Y., and L. Excoffier. 1996. A ge neric estimation of population subdivision using distances between alleles with special interest to microsatellite loci. Genetics 142 :1061-1064. Miller, C. R., and L. P. Waits. 2003. The hist ory of effective populat ion size and genetic diversity in the Yellowstone grizzly ( Ursus arctos ): implications for conservation. Proceedings of the National Academy of Scie nces of the United States of America 100 :4334-4339. Mills, L. S., and F. W. Allendorf. 1996. The one-migrant-per-generation rule in conservation and management. Conservation Biology 10 :1509-1518. Mowat, G., and C. Strobeck. 2000. Estimating po pulation size of grizzl y bears using hair capture, DNA profiling, and mark-recapture analysis. Journal of Wildlife Management 64 :183-193. Myers, R. L., and J. J. Ewel. 1991. Ecosystems of Florida. University of Central Florida Press, Orlando. Niemela, J. 2001. The utility of movement corridors in forested landscapes. Scandinavian Journal of Forest Research 3 :70-78. Noss, R. F. 1987. Corridors in real la ndscapes: a reply to Simberloff and Cox. Conservation Biology 1 :159-164. Noss, R. F. 1993. Wildlife corridors. in D. S. Smith, and P. C. Hellmund, editors. Ecology of greenways. University of Minnesota Press, Minneapolis. Noss, R. F., and L. D. Harris. 1986. Nodes, ne tworks, and mums preserving diversity at all scales. Environmental Management 10 :299-309. Noss, R. F., H. B. Quigley, M. G. Hornoc ker, T. Merrill, and P. C. Paquet. 1996. Conservation biology and carnivore c onservation in the Rocky Mountains. Conservation Biology 10 :949-963.

PAGE 101

89 O'Brien, S. J. 1994. A role for molecula r genetics in biological conservation. Proceedings of the National Academy of Scie nces of the United States of America 91 :5748-5755. Onorato, D. P., and E. C. Hellgren. 2001. Black bear at the border: natural recolonization of the Trans-Pecos. Pages 245-259 in D. S. Maehr, R. F. Noss, and J. L. Larkin, editors. Large mammal restoration. Island Press, Washington, D.C. Ormsby, T., E. Napoleon, R. Burke, C. Gr oessl, and L. Feaster. 2001. Getting to know ArcGIS Desktop. ESRI Press, Redlands, California. Paetkau, D., S. C. Amstrup, E. W. Born, W. Ca lvert, A. E. Derocher, G. W. Garner, F. Messier, I. Stirling, M. K. Taylor, O. Wiig, and C. Strobeck. 1999. Genetic structure of the world's polar be ar populations. Molecular Ecology 8 :1571-1584. Paetkau, D., W. Calvert, I. Stirling, and C. Strobeck. 1995. Microsatellite analysis of population structure in Canadian polar bears. Mo lecular Ecology 4 :347-354. Paetkau, D., G. F. Shields, and C. Strobeck. 1998a. Gene flow between insular, coastal and interior populations of brown bears in Alaska. Molecular Ecology 7 :12831292. Paetkau, D., and C. Strobeck. 1994. Microsatellite analysis of geneti c variation in black bear populations. Molecular Ecology 3 :489-495. Paetkau, D., L. P. Waits, P. L. Clarkson, L. Craighead, and C. Strobeck. 1997. An empirical evaluation of genetic distance st atistics using microsatellite data from bear ( Ursidae ) populations. Genetics 147 :1943-1957. Paetkau, D., L. P. Waits, P. L. Clarkson, L. Craighead, E. Vyse, R. Ward, and C. Strobeck. 1998b. Variation in genetic divers ity across the range of North American brown bears. Conservation Biology 12 :418-429. Palomares, F. 2001. Vegetation structure and prey abundance requirements of the Iberian lynx: implications for the design of rese rves and corridors. Journal of Applied Ecology 38 :9-18. Pelton, M. R., and F. T. Van Manen. 1997. Stat us of black bears in the southeastern United States. Pages 31-44 in A. L. Gaski, and D. F. Williamson, editors. Proceedings of the Second Internationa l Symposium on Trade of Bear Parts, Washington, D.C. Perault, D. R., and M. R. Lomolino. 2000. Corridors and mammal community structure across a fragmented, old-growth fore st landscape. Ecological Monographs 70 :401422. Picton, H. D. 1987. A possible link between Yellowstone and Glaci er grizzly bear populations. Bears -Their Biology and Management 6 :7-10.

PAGE 102

90 Poole, K. G. 1997. Dispersal patterns of l ynx in the Northwest Te rritories. Journal of Wildlife Management 61 :497-505. Pritchard, J. K., M. Stephens, and P. D onnelly. 2000. Inference of population structure using multilocus genotype data. Genetics 155 :945-959. Proctor, M. F., B. N. McLellan, and C. Strobeck. 2002. Population fragmentation of grizzly bears in southeastern British Columbia, Canada. Ursus 13 :153-160. Rannala, B., and J. L. Mountain. 1997. De tecting immigration by using multilocus genotypes. Proceedings of the National Acad emy of Sciences of the United States of America 94 :9197-9201. Raymond, M., and F. Rousset. 1995. Gene pop (Version-1.2) Population genetics software for exact tests and ecumenicism. Journal of Heredity 86 :248-249. Reed, D. H., and R. Frankham. 2003. Correla tion between fitness a nd genetic diversity. Conservation Biology 17 :230-237. Roelke, M. E., J. S. Martenson, and S. J. Obrien. 1993. The consequences of demographic reduction and genetic depleti on in the endangered Florida panther. Current Biology 3 :340-350. Rogers, L. L. 1987. Factors influencing dispersa l in the black bear. Pages 75-84 in B. D. Chepko-Sade, and Z. T. Halpin, editors. Ma mmalian dispersal patterns: the effects of social structure on population genetics. University of Chicago Press, Chicago, Illinois. Roof, J., and J. Wooding. 1996. Evaluation of S.R. 46 wildlife crossing. Florida Cooperative Fish and Wildlife Research Unit, Gainesville. Rosenberg, D. K., B. R. Noon, and E. C. Meslow. 1997. Biological corridors: form, function, and efficacy. Bioscience 47 :677-687. Ruiz-Garcia, M. 2003. Molecular population ge netic analysis of the spectacled bear ( Tremarctos ornatus ) in the northern Andean area. Hereditas 138 :81-93. Saccheri, I., M. Kuussaari, M. Kankare, P. Vikman, W. Fortelius, and I. Hanski. 1998. Inbreeding and extinction in a butterfly metapopulation. Nature 392 :491-494. Saitoh, T., Y. Ishibashi, H. Kanamori, a nd E. Kitahara. 2001. Genetic status of fragmented populations of the Asian black bear ( Ursus thibetanus ) in Western Japan. Population Ecology 43 (3):221-227. Schenk, A., M. E. Obbard, and K. M. Kov acs. 1998. Genetic relatedness and home-range overlap among female black bears ( Ursus americanus ) in northern Ontario, Canada. Canadian Journal of Zoology 76 :1511-1519.

PAGE 103

91 Schwartz, C. C., and A. W. Franzmann. 1992. Dispersal and survival of subadult black bears from the Kenai Peninsula, Alas ka. Journal of Wildlife Management 56 :426431. Schwartz, M. K., L. S. Mills, K. S. McKe lvey, L. F. Ruggiero, and F. W. Allendorf. 2002. DNA reveals high dispersal synchr onizing the population dynamics of Canada lynx. Nature 415 :520-522. Sherwin, W. B., and C. Moritz. 2000. Managi ng and monitoring genetic erosion. Pages 934 in A. G. Young, and G. M. Clarke, editors. Genetics, demography, and viability of fragmented populations. Cambridge University Press, New York. Sieving, K. E., M. F. Willson, and T. L. De Santo. 2000. Defining corridor functions for endemic birds in fragmented south-te mperate rainforest. Conservation Biology 14 :1120-1132. Simberloff, D., and J. Cox. 1987. Consequen ces and costs of conservation corridors. Conservation Biology 1 :63-71. Simberloff, D., J. A. Farr, J. Cox, and D. W. Mehlman. 1992. Movement corridors conservation bargains or poor i nvestments. Conservation Biology 6 :493-504. Sinclair, E. A., E. L. Swenson, M. L. Wolfe, D. C. Choate, B. Bates, and K. A. Crandall. 2001. Gene flow estimates in Utah's cougars imply management beyond Utah. Animal Conservation 4 :257-264. Slatkin, M. 1993. Isolation by distance in equilibrium and nonequilibrium populations. Evolution 47 :264-279. Slatkin, M. 1995. A measure of population subdivision based on microsatellite allele frequencies. Genetics 139 :457-462. Small, M. P., K. D. Stone, and J. A. Cook. 2003. American marten ( Martes americana ) in the Pacific Northwest: population differentia tion across a landscape fragmented in time and space. Molecular Ecology 12 :89-103. Smith, J. L. D. 1993. The role of dispersal in structuring the Ch itwan tiger population. Behaviour 124 :165-195. Spong, G., and L. Hellborg. 2002. A near-extincti on event in lynx: do microsatellite data tell the tale? Conservation Ecology 6:Art. No. 15. Spong, G., J. Stone, S. Creel, and M. Bj orklund. 2002. Genetic structure of lions ( Panthera leo L. ) in the Selous Game Reserve: implications for the evolution of sociality. Journal of Evolutionary Biology 15 :945-953.

PAGE 104

92 Taberlet, P., J. J. Camarra, S. Griffin, E. Uhres, O. Hanotte, L. P. Waits, C. DuboisPaganon, T. Burke, and J. Bouvet. 1997. Noninvasive ge netic tracking of the endangered Pyrenean brown b ear population. Molecular Ecology 6 :869-876. Tewksbury, J. J., D. J. Levey, N. M. Haddad, S. Sargent, J. L. Orrock, A. Weldon, B. J. Danielson, J. Brinkerhoff, E. I. Da mschen, and P. Townsend. 2002. Corridors affect plants, animals and their interacti ons in fragmented landscapes. Proceedings of the National Academy of Sciences of the United States of America 99 :1292312926. Thompson, L. M. 2002. Abundance and genetic st ructure of two black bear populations prior to highway construction in easte rn North Carolina. M.S. thesis. The University of Tennessee, Knoxville. United States Census (US Census). Projections of the total populati ons of states: 1995 to 2025. United States Census, Washington, D.C. Available from http://quickfacts.census .gov/qfd/states/120001k.html (accessed 05 February 2004). Vila, C., A. K. Sundqvist, O. Flagstad, J. Se ddon, S. Bjornerfeldt, I. Kojola, A. Casulli, H. Sand, P. Wabakken, and H. Ellegren. 2003. Rescue of a severely bottlenecked wolf ( Canis lupus ) population by a single immigrant. Proceedings of the Royal Society of London Series B 270 :91-97. Vos, C. C., A. G. Antonisse-De Jong, P. W. Goedhart, and M. J. M. Smulders. 2001. Genetic similarity as a measure for conn ectivity between fragmented populations of the moor frog ( Rana arvalis ). Heredity 86 :598-608. Waits, L. P. 1999. Molecular genetic app lications for bear research. Ursus 11 :253-260. Waits, L., P. Taberlet, J. E. Swenson, F. Sandegren, and R. Franzen. 2000. Nuclear DNA microsatellite analysis of genetic divers ity and gene flow in the Scandinavian brown bear ( Ursus arctos ). Molecular Ecology 9 :421-431. Walker, C. W., C. Vila, A. Landa, M. Li nden, and H. Ellegren. 2001. Genetic variation and population structure in Scandinavian wolverine ( Gulo gulo ) populations. Molecular Ecology 10 :53-63. Warrillow, J., M. Culver, E. Hallerman, a nd M. Vaughan. 2001. Subspecific affinity of black bears in the White River National Wildlife Refuge. Journal of Heredity 92 :226-233. Waser, P. M., and C. Strobeck. 1998. Genetic signatures of interpopulation dispersal. Trends in Ecology & Evolution 13 :43-44. Waser, P. M., C. Strobeck, and D. Paet kau. 2001. Estimating interpopulation dispersal rates. Pages 484-497 in J. L. Gittleman, S. M. Funk, D. Macdonald, and R. K. Wayne, editors. Carnivore conservation. Ca mbridge University Press, Cambridge, United Kingdom.

PAGE 105

93 Weaver, J. L., P. C. Paquet, and L. F. Ruggiero. 1996. Resilience and conservation of large carnivores in the Rocky Mountains. Conservation Biology 10 :964-976. Weir, B. S., and C. C. Cockerham. 1984. Estimating F-statistics for the analysis of population structure. Evolution 38 :1358-1370. Wertz, T. L., J. J. Akenson, M. J. Henj um, and E. J. Bull. 2001. Home range and dispersal patterns of subadult black b ears in northeastern Oregon. Pages 93-100. Seventh Western Black Bear Workshop, Coos Bay, Oregon. Wesley, D. J. 1991. Endangered and threaten ed wildlife and plants; finding on the petition to list the Florida black bear as a threatened species. Federal Register 56 :596-600. Westemeier, R. L., J. D. Brawn, S. A. Simpson, T. L. Esker, R. W. Jansen, J. W. Walk, E. L. Kershner, J. L. Bouzat, and K. N. Paige. 1998. Tracking th e long-term decline and recovery of an isolated population. Science 282 :1695-1698. Wooding, J. B. 1993. Management of the black be ar in Florida, a staff report to the commissioners. Florida Game and Fres h Water Fish Commission, Tallahassee. Wooding, J. B., and T. S. Hardisky. 1992. Home range, habitat use, and mortality of black bears in North-central Florida. In ternational Conference on Bear Research and Management 9 :349-356. Wooding, J. B., and J. Roof. 1996. Feasibility of stocking black bears in the Big Bend Region. Final Performance Report, Study No. 7554. Florida Game and Fresh Water Fish Commission, Gainesville. Woodroffe, R., and J. R. Ginsberg. 1998. Edge effects and the extinction of populations inside protected areas. Science 280 :2126-2128. Woods, J. G., D. Paetkau, D. Lewis, B. N. McLellan, M. Proctor, and C. Strobeck. 1999. Genetic tagging of free-ra nging black and brown bears. Wildlife Society Bulletin 27 :616-627. Wright, S. 1931. Evolution in mendelian populations. Genetics 16 :97-159.

PAGE 106

94 BIOGRAPHICAL SKETCH Jeremy Douglas Dixon was born in Jasper Florida, on July 14, 1977. Son of a teacher and farmer, he grew up near Madi son, Florida. He attended Madison County High School and graduated in May of 1995. In August of 1995, he began college at the University of West Florida, where he r eceived his A.A. degree. Focus on wildlife ecology brought Jeremy to the University of Florida in fall of 1997. He enrolled in the Department of Wildlife Ecology and Conserva tion and graduated in December of 1999 with a Bachelor of Science degree in wildlife ecology and management. He began graduate school in August of 2001 at the Un iversity of Florida, College of Natural Resources and Environment, and received his Master of Science degree in interdisciplinary ecology in May of 2004.


Permanent Link: http://ufdc.ufl.edu/UFE0004705/00001

Material Information

Title: Conservation Genetics of the Florida Black Bear
Physical Description: Mixed Material
Copyright Date: 2008

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0004705:00001

Permanent Link: http://ufdc.ufl.edu/UFE0004705/00001

Material Information

Title: Conservation Genetics of the Florida Black Bear
Physical Description: Mixed Material
Copyright Date: 2008

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0004705:00001


This item has the following downloads:


Full Text










CONSERVATION GENETICS OF THE FLORIDA BLACK BEAR


By

JEREMY DOUGLAS DIXON
















A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE

UNIVERSITY OF FLORIDA


2004
































Copyright 2004

by

Jeremy Dixon


































"The bears are yet too numerous; they are a strong creature and prey on fruits of the
country." William Bartram commenting on the abundance of black bears during his trip
through Florida (1773-74).
















ACKNOWLEDGMENTS

Without the continued support from several groups, organizations, and people, this

proj ect would not be possible. Funding and logistic support was provided by the Florida

Fish and Wildlife Conservation Commission (FWC), Florida Department of

Transportation, Wildlife Foundation of Florida, Natural Future Foundation, Safari Club

International, University of Florida (UF) School of Natural Resources and Environment

and UF Department of Wildlife Ecology and Conservation.

I am especially thankful to Walter McCown for being my field supervisor and

mentor; he has been a constant source of inspiration. I am grateful to Mark Cunningham,

Stephanie Simek, and Brian Schieck for their advice and support of my research.

Without Thomas Eason's foresight and dedication, no bear research in Florida would be

possible. Special thanks goes to bear students Elina Garrison and Melissa Moyer and

graduate students Arpat Ozgul, Heidi Richter, Justyn Stahl, and Tom Hoctor. I enjoyed

our friendships and discussions on every aspect of ecology.

I would like to also thank Alanna French for her love, support, and understanding

during this complicated time. I thank my family whose support and encouragement have

fueled my desire for higher education. My mother' s sense of adventure and my father' s

teachings of the importance of hard work molded the path that I have chosen. I thank my

sisters, Jodi and Becca, for always supporting my bear interests, however strange they

might seem.









I am grateful for volunteers Bill Henteges and Tanya DiBenedetto; and wildlife

technicians, Billy McKinstry, Chris Long, and Darrin Masters. These people worked

very hard under harsh conditions, dealing with biting insects; uncooperative barbed wire;

and the hot, humid conditions of the Floridian landscape.

This proj ect would not be possible without the dozens of individuals who collected

genetic samples and volunteered those samples for my project. Melvin Sunquist (UF),

David Machr (University of Kentucky), Mark Cunningham (FWC), and other FWC

biologists provided samples for genetic analysis.

I also appreciate the agencies and landowners who allowed me to place hair snares

on their lands: Plum Creek Timber Company, Raiford State Prison (Pride Forestry),

Florida Division of Forestry, Florida National Guard, Matthew Kenyan, Suwannee River

Water Management District, St. Johns River Water Management District, Florida

Greenways and Trails, UF Ordway Preserve, and FWC. I also thank Dave Dorman, Matt

Pollock, Erin Myers, Scott Weaver, Bill Sumpter, Jim Garrison, Scott Crosby, Adele

Mills, Bobby Jackson, Paul Catlett, Steve Coates, Dan Miller, Tim Hannon, Bob Heeke,

Bill Bossuot, John Ault, Allan Hallman, and Charlie Peterson for in-kind support.

I thank Wildlife Genetics International (Nelson, British Columbia, Canada) for

performing the genetic analyses; and especially David Paetkau and Jennifer Weldon for

their professional and courteous service. Their dedication to the intricate processes of

DNA analysis was critical to this proj ect.

I thank my committee (Dr. Melvin Sunquist, Dr. Thomas Eason, and Dr. Michael

Wooten) for their advice and direction. Finally, I would like to thank my committee










chair, Dr. Madan Oli, for being a good role model, and giving me the chance to do

research on such an exciting and elusive carnivore.






















TABLE OF CONTENTS


Page


ACKNOWLEDGMENT S .............. .................... iv


LI ST OF T ABLE S ............. ...... .__ .............. ix..


LIST OF FIGURES .............. ...............x.....


AB STRAC T ................ .............. xi


CHAPTER


1 INTRODUCTION ................. ...............1.......... ......


2 GENETIC CONSEQUENCES OF HABITAT FRAGMENTATION AND
LO SS ............... ...............4..


Introducti on ................. ...............4.................
Method s .................. ...............6...

Sample Collection .............. ...............6.....
Statistical Analyses............... ...............7
Re sults ................ ...............8.................
Discussion ................. ...............13.................
Genetic Variation............... ...............1
Genetic Structure ................. ...............14.................
Conclusion ................ ...............18.................


3 EVALUATING THE EFFECTIVENESS OF A REGIONAL BLACK BEAR
C ORRID OR ............. ...... .__ ...............21..


Introducti on ............. ...... ._ ...............21...
M ethods .............. ...............26....
Re sults............. ...... ._ ...............29...
Discussion ............. ...... ._ ...............34...
Conclusion ............. ...... ...............38...












4 CONCLUSIONS AND MANAGEMENT RECOMMENDATIONS .......................40


Conclusions............... ...... ..........4

Management Recommendations ................. ...............42.................
Recommendations for Further Research .............. ...............43....


APPENDIX


A HISTORY OF THE FLORIDA BLACK BEAR ................. ........................_45


General ........._._ ...... ._ __ ...............45....

Regulations .............. ...............47....


B MICROSATELLITE ANALYSIS .............. ...............49....


C GENETIC VARIATION AMONG BEAR POPULATIONS .............. ...............50


D MICRO SATELLITE DATA FOR FLORIDA BLACK BEARS ............... ...............55


LITERATURE CITED .............. ...............79....


BIOGRAPHICAL SKETCH .............. ...............94....











































V111

















LIST OF TABLES


Table pg

1 Measures of genetic variation (mean + 1 SE) at 12 microsatellite loci in nine
Florida black bear populations (sample sizes are in parentheses) ................... ...........9

2 Pairwise FST (below diagonal) and RST (above diagonal) estimates for nine Florida
black bear populations (standard errors are in parentheses). ............. ................11

3 Assignment of individuals using the Bayesian clustering technique using the
program STRUCTURE without any prior information on population of origin .....31

4 Microsatellite genetic variation in bear populations .............. ....................5

5 Individual 12-loci genotypes for black bears sampled in Florida, 1989-2003 .........56

6 Allele frequencies for 12 microsatellite loci in 10 populations of Florida black
bears ............. .............73......

















LIST OF FIGURES


Figure pg

1 Distribution of black bears in Florida ..........._......___ .....__ ...........

2 Relationship between estimated population size (N) and measures of genetic
variation (mean + 1 SE) in nine Florida black bear populations ................... ...........10

3 An unrooted phylogenetic tree depicting the genetic relationships among Florida
black bear populations ................. ...............12........... ....

4 Area proposed as a regional corridor between the Ocala and Osceola black bear
populations .............. ...............26....

5 Locations of samples collected in the Osceola-Ocala corridor ............... .... ........._..30

6 Bubble plot of trap success in the Osceola-Ocala corridor ................. ................. 30

7 Assignment of black bears to a population of origin without regard to sample
locations using STRUCTURE .............. ...............32....

8 Spatial pattern of the proportion of membership (q) for bears sampled in Osceola,
Ocala and the Osceola-Ocala corridor using the program STRUCTURE ...............33

9 Historic distribution of black bears in the southeastern United States ................... ..46

10 Current populations of the Florida black bear (Ursus americanus flori~dd~~dd~~dddans .......48









Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science

CONSERVATION GENETICS OF THE FLORIDA BLACK BEAR

By

Jeremy Douglas Dixon

May 2004

Chair: Madan K. Oli
Major Department: Natural Resources and Environment

Habitat loss and fragmentation can influence the genetic structure of biological

populations. I studied the genetic consequences of historical and contemporary patterns

of hab itat fragm entati on i n ni ne Flori da black b ear ( Ursus amnericanus floridanus)~~dddd~~~ddd~~~

populations. A total of 305 bears from nine populations was genotyped for 12

microsatellite loci to characterize genetic variation and structure. None of the nine

populations deviated from Hardy-Weinberg equilibrium. Genetic variation, quantified by

mean expected heterozygosity (HE), ranged from 0.27-0.71, and was substantially lower

in smaller populations. Low levels of gene flow (global FST = 0.227; global RST = 0.249)

and high values of the likelihood ratio genetic distance (average DLR = 16.255) suggest

that fragmentation of once-contiguous habitat has resulted in genetically distinct

populations. There was no isolation-by-di stance relationship among Florida black bear

populations. Barriers such as roads, cities, and residential areas limit the dispersal

capabilities of black bears in Florida, thereby reducing the probability of gene flow

among populations. Regional corridors or translocation of bears may be needed to

restore historical levels of genetic variation.










Corridors have been suggested to mitigate the adverse effects of habitat

fragmentation, by restoring or maintaining connectivity among once-contiguous

populations. However, the role of corridors for large carnivores has rarely been

evaluated objectively. I used non-invasive sampling, microsatellite analysis, and

population-assignment tests to evaluate the effectiveness of a regional corridor

(Osceola-Ocala corridor) in connecting two Florida black bear populations. I sampled 31

bears (28 males, 3 females) within the corridor. Because bear dispersal is male-biased,

the gender disparity suggests that the Osceola-Ocala corridor functions as a conduit for

dispersal and other seasonal movements. Of the 31 bears sampled in the Osceola-Ocala

corridor, 28 had genotypes that were assigned to the Ocala population. I found a mostly

unidirectional pattern of movement from Ocala, with a limited mixing of Ocala-assigned

individuals with Osceola-assigned individuals in one area of the corridor. I also

documented the presence of bears in Osceola assigned to Ocala, and the presence of bears

in Osceola that may be Osceola-Ocala hybrids. My results indicate that the

Osceola-Ocala corridor provides a conduit for gene flow between these populations.

However, residential and industrial development and highways may reduce movements

of bears within the Osceola-Ocala corridor. The methods used here may provide a means

of evaluating corridor effectiveness, and identifying gaps in connectivity. Regional

corridors should be reestablished or maintained where such connectivity occurred in the

recent past, to increase the viability of populations, and maintain metapopulation

structure.















CHAPTER 1
INTTRODUCTION

Habitat fragmentation and loss is one of the greatest threats to the conservation of

biodiversity in the world (Harris 1984; Meffe & Carroll 1997). The effect of habitat

fragmentation on animal populations can have several demographic and genetic

consequences. The reduction of population size and connectivity can create conditions

where genetic variation is lost at a rapid rate. The loss of genetic variation within

populations may lead to inbreeding depression, a reduction in evolutionary potential, and

greater extinction probability (Frankham et al. 2002).

The most serious threat to the continued existence of the Florida black bear (Grsus

americanus floridd~~dd~ddanus) is fragmentation and loss of habitat (Wesley 1991; Hellgren &

Machr 1993; Hellgren & Vaughan 1994). Habitat fragmentation and loss is driven by

human population growth. An estimated 16.3 million people lived in Florida in 2001.

This number is proj ected to increase to more than 20 million by 2015 (US Census 2000).

Roads, and agricultural, commercial and residential developments continue to encroach

on (and further degrade) remaining black bear habitat. The distribution of the Florida

black bear has been reduced by 83% from its historic distribution (Wooding 1993).

Currently, Florida black bears occur in several populations, mostly restricted within the

state of Florida (Appendix A) (Pelton & Van Manen 1997).

The reduction of size and connectivity of populations has caused concern regarding

the genetic health of Florida black bears. Most extant Florida black bear populations are

small compared to historic size, and are relatively isolated. Theory suggests that small,









isolated populations are at a higher risk of extinction than large, well-connected

populations (Frankham 1995; Meffe & Carroll 1997; Ebert et al. 2002; Frankham et al.

2002). Because Florida black bear populations are fragmented from their original

relatively contiguous distribution, the level of gene flow among populations may be

important in maintaining levels of genetic variation and evolutionary potential of Florida

black bears.

Although aspects of the population genetics of the Florida black bear have been

investigated previously (Warrilow et al. 2001; Dobey 2002; Edwards 2002) using

microsatellite analyses (Appendix B), these studies did not provide estimates of gene

flow among populations, or provide data on the genetic consequences of habitat

fragmentation and loss on Florida black bear populations. Little is known about the level

of genetic variation within (or gene flow among) populations of the Florida black bear.

It has been suggested that fragmented populations are best managed as a

metapopulation, where local populations are functionally connected with corridors that

facilitate movement. The large home ranges and long-distance dispersal capabilities of

black bears have been used as a rationale for implementation of corridors among

populations (Hellgren & Vaughan 1994; Bowker & Jacobson 1995; Hoctor et al. 2000).

The Osceola-Ocala corridor has been suggested as the best option in connecting any two

of the populations of Florida black bear. However, the efficacy of this corridor or other

corridors for large carnivores is relatively unknown.

Obj ectives

The obj ectives of my study were to investigate genetic variation and gene flow

among Florida black bear populations, and to obj ectively evaluate the functionality of the

Osceola-Ocala corridor in facilitating demographic and genetic connectivity. Chapter 2









discusses the effects of population size on within-population genetic variability, estimates

levels of gene flow among populations, and examines relationships among measures of

genetic differentiation and geographic distances between pairs of populations. Chapter 3

discusses the effectiveness of a regional corridor in connecting two Florida black bear

populations using non-invasive genetic sampling and recently developed

population-assignment tests.

Taken together, these chapters provide much-needed data on the genetic variation

within (and gene flow among) populations of the Florida black bear; and an obj ective

evaluation of the functionality of the Osceola-Ocala corridor. These data are expected to

be important for the formulation and implementation of a management plan to ensure

long-term persistence of Florida black bear populations.















CHAPTER 2
GENETIC CONSEQUENCES OF HABITAT FRAGMENTATION AND LOSS

Introduction

Fragmentation and loss of habitat is one of the most serious problems facing the

conservation of biodiversity worldwide (Harris 1984; Meffe & Carroll 1997). Habitat

fragmentation can increase mortality rates (Jules 1998), reduce abundance (Flather &

Bevers 2002), alter movement patterns (Brooker & Brooker 2002), and disrupt the social

structure of populations (Ims & Andreassen 1999; Cale 2003); and may reduce the

probability of persistence (Harrison & Bruna 1999; Davies et al. 2001). Additionally,

habitat fragmentation can influence genetic structure and persistence of populations in

several ways. First, isolation and reduction of populations can decrease genetic variation

(Hudson et al. 2000; Kuehn et al. 2003), which may reduce the ability of individuals to

adapt to a changing environment, cause inbreeding depression (Ebert et al. 2002), reduce

survival and reproduction (Frankham 1995; Reed & Frankham 2003), and increase the

probability of extinction (Saccheri et al. 1998; Westemeier et al. 1998). Secondly, habitat

fragmentation can create dispersal barriers, which can deter gene flow (Hitchings &

Beebee 1997; Gerlach & Musolf 2000) or otherwise alter genetic structure of the

population (Hale et al. 2001). Thus, efforts to conserve plant and animal populations

should take into account the genetic consequences of habitat fragmentation.

Large mammalian carnivores are particularly vulnerable to habitat loss and

fragmentation because of their relatively low numbers, large home ranges, and

interactions with humans (Noss et al. 1996; Crooks 2002). The Florida panther (Puma









concolor coryi) and giant panda (Ailuropoda melan2oleuca) are examples of large

carnivores that were reduced to small numbers largely because of impacts of habitat

fragmentation and loss (Roelke et al. 1993; Lu et al. 2001). Another large carnivore that

has been negatively impacted by habitat fragmentation is the Florida black bear (Ursus

americanus floridd~~dd~ddanus) (Hellgren & Machr 1993).

The Florida black bear historically roamed throughout the peninsula of Florida and

southern portions of Georgia, Alabama, and Mississippi (Brady & Machr 1985). From

the 1800s to the 1970s, numbers of Florida black bears were significantly reduced by loss

and fragmentation of habitat, and unregulated hunting (Cory 1896; Hendry et al. 1982).

Only an estimated 300 to 500 bears were left in the state of Florida in the 1970s

(McDaniel 1974; Brady & Machr 1985). Consequently, the Florida Game and

Freshwater Fish Commission classified the Florida black bear as a threatened species in

most Florida counties, in 1974 (Wooding 1993). Destruction and fragmentation of

once-contiguous habitat has reduced the distribution of Florida black bears to nine areas:

Eglin (EG), Apalachicola (AP), Aucilla (AU), Osceola (OS), Ocala (OC), St. Johns (SJ),

Chassahowitzka (CH), Glades/Highlands (GH), and Big Cypress (BC) (Fig. 1).

Fragmentation of populations can reduce genetic variation (Sherwin & Moritz 2000) and

increase the probability of extinction (Saccheri et al. 1998; Westemeier et al. 1998), but

the genetic consequences of the historical and contemporary patterns of habitat

fragmentation on Florida black bear populations are unknown. Using microsatellite

analyses, my goal was to investigate the genetic consequences of habitat fragmentation

on Florida black bear populations. My specific obj ectives were to estimate

within-population genetic variation, and investigate the level of genetic differentiation










among Florida black bear populations. Theory predicts a positive correlation between

genetic variation and population size (Frankham 1996), and between genetic

differentiation and geographic distance among populations (Slatkin 1993). Thus, I tested

these predictions by examining the relationship between measures of genetic variation

and recent estimates of population size, and between measures of genetic differentiation

and geographic distances among populations.












O 990




Kilometers



Figure 1. Distribution of black bears in Florida: Eglin (EG), Apalachicola (AP), Aucilla
(AU), Osceola (OS), Ocala (OC), St. Johns (SJ), Chassahowitzka (CH),
Highlands/Glades (HG), and Big Cypress (BC). The distribution map was
compiled by the Florida Fish and Wildlife Conservation Commission.

Methods

Sample Collection

Hair and tissue samples from individual bears were collected from each of the nine

Florida black bear populations during 1989-2003. Most samples were collected from

field studies, some using non-invasive techniques (Woods et al. 1999); but samples also

were collected from translocated animals, and from bears killed on roadways. Hair and

tissue samples were sent to Wildlife Genetics International (Nelson, British Columbia,









Canada) (www.wildlifegenetics.ca/) for microsatellite analysis. DNA was extracted

using QIAGEN's DNeasy Tissue kits (Valencia, California), as per QIAGEN's

instructions (http://www. qiagen.com/ literature/genomlit. asp); and microsatellite loci

were amplified using polymearse chain reaction (PCR). Each individual was genotyped

for 12 microsatellite loci (G1A, G10B, G10C, GlD, G10L, G10M, G10P, G10X, G10H,

MU50, MU59, and G10J). Laboratory methods used in my study are described in detail

by Paetkau et al. (1995, 1998a, 1998b, 1999) and Paetkau & Strobeck (1994).

Statistical Analyses

Departures from Hardy-Weinberg equilibrium (HWE) were tested using the HWE

probability test in Genepop 3.4 (Raymond & Rousset 1995). Exact p-values were

computed using the complete enumeration method for loci with fewer than four alleles

(Louis & Dempster 1987), and the Markov chain method (dememorization 1,000; batches

100; iterations per batch 1,000) for loci with more than four alleles (Guo & Thompson

1992). Using this same program, linkage-disequilibrium tests were used to test for

nonrandom associations among alleles of different loci, using the Markov chain method.

Within each bear population, genetic variation was measured as the observed

average heterozygosity (Ho), expected average heterozygosity (HE), and the average

number of alleles per locus (A). Spearman's rank correlation was used to test for the

correlation between genetic variation and estimated population size. To characterize

nonrandom mating within populations, FIs was calculated according to Weir &

Cockerham (1984) in Genepop 3.4 (Raymond & Rousset 1995). Global estimates (across

all populations) of FIs, FIT (characterizes nonrandom mating within populations and

genetic differentiation among populations), and FST (characterizes genetic differentiation

among populations) were also calculated using these methods.









Genetic differentiation among populations was estimated using Genepop 3.4

(Raymond & Rousset 1995) with pairwise FST (Weir & Cockerham 1984) and pairwise

RST (Michalakis & Excoffler 1996). The RST WAS estimated because microsatellites are

thought to conform to the stepwise-mutational model better than to the infinite-alleles

model on which FST is based (Slatkin 1995). The significance of population

differentiation was tested using the genic differentiation test in Genepop 3.4 (Raymond &

Rousset 1995). The likelihood ratio genetic distance, DLR (Paetkau et al. 1995) was

estimated for each pair of populations using the Doh assignment calculator from the

web site, http://www2.bi ol ogy.ualb erta.ca/j brzusto/ Doh.php. This genetic distance is

based on the ratio of genotype likelihood between pairs of populations. The software

program Phylip 3.5c (Felsenstein 1993) and the subprogram FITCH (Fitch & Margolia

1967) were used to generate an unrooted phylogenetic tree, with branch lengths

corresponding to DLR ValUeS.

Geographic distances among populations were estimated as the shortest land

distance between population centroids using least-cost path analysis in ArcGIS 8. 1.2

(McCoy & Johnston 2000). Centroids were estimated as the harmonic mean of the

sample collection locations in each study site. The subprogram ISOLDE in Genepop 3.4

(Raymond & Rousset 1995) was used to test for a relationship between geographic

distances, and FST, RST, and DLR ValUeS. Statistical significance of these relationships

was tested using a Mantel (1967) test with 10,000 permutations.

Results

A total of 305 individual bears was genotyped for 12 microsatellite loci in nine

Florida black bear populations (Table 1). There were no significant departures from

HWE for any locus or population (p > 0.05). The linkage disequilibrium test indicated









that only 15% of loci pairings had significant nonrandom associations (p < 0.05). Loci

used in this analysis were found to be independent (D. Paetkau, pers. comm.). Thus, any

significant linkage observed among loci pairs may be a result of nonrandom mating,

sampling bias, recent admixture, or genetic drift (Frankham et al. 2002).

The population with the highest mean number of alleles per locus (A) was Osceola

(mean a 1SE; 6.667 + 0.225); whereas Chassahowitzka had the lowest value

(2.250 + 0. 179). Observed average heterozygosity (Ho) ranged from 0.287 + 0.058 in

Chassahowitzka to 0.705 + 0.030 in Osceola. Similarly, expected average heterozygosity

(H)ran ed from 0.271 + 0.054 in Chassahowitzka to 0.713 + 0.027 in Osceola

(Table 1). Estimated population sizes ranged from 20 in Chassahowitzka to 830 in

Osceola (Kasbohm & Bentzein 1998; Machr et al. 2001; Florida Fish and Wildlife

Conservation Commission (FWC), unpublished data). All three measures of genetic

variation were positively correlated with estimated population size (A: rs = 0.845,

p = 0.004; Ho: rs = 0.778, p = 0.014; HE: rs = 0.728, p = 0.026) (Fig. 2).

Table 1. Measures of genetic variation (mean & 1 SE) at 12 microsatellite loci in nine
Florida black bear populations (sample sizes are in parentheses). Measures of
genetic variation are: observed average heterozygosity (Ho), expected average
heterozygosity (HE), and mean alleles per locus (A). Values of FIs (a measure
of nonrandom mating within populations) + 1 SE are also given.
Population Ho HE A Frs
A alachicola (38) 0.686 + 0.036 0.706 + 0.031 5.92 + 0.358 0.027 + 0.025
Aucilla ( 0.556 + 0.063 0.616 + 0.054 3.83 + 0.322 0.097 + 0.062
Big C ress (41) 0.642 + 0.036 0.650 + 0.026 5.50 + 0.435 0.013 + 0.034
Chassahowitzka (29) 0.287 + 0.058 0.271 + 0.054 2.25 + 0.179 -0.057 + 0.028
BE lin (40) 0.613 + 0.071 0.537 + 0.062 4.08 + 0.379 -0.141 + 0.024
Hi hlands/Glades (27) 0.327 + 0.049 0.385 + 0.051 2.75 + 0.250 0.149 + 0.059
Ocala (40) 0.579 + 0.045 0.610 + 0.045 4.75 + 0.305 0.051 + 0.024
Osceola (41) 0.705 + 0.030 0.713 + 0.027 6.67 + 0.225 0.010 + 0.033
St. Johns (40) 0.650 + 0.048 0.663 + 0.041 5.58 + 0.379 0.020 + 0.028













* Apalachicola
o Aucilla
v Big Cypress
v Chassahowitzka
m Eglin
SHighlands/Glades
+Ocala
Osceola
SSt. Johns


0 200 400 600 800
N


0.8

0.7

0.6

S0.5

0.4

0.3

0.2
0 200


400 600 800
N


0 200


400 600 800


Figure 2. Relationship between estimated population size (N) and measures of genetic
variation (mean + 1 SE) in nine Florida black bear populations. A) N and
Observed average heterozygosity (Ho), B) N and Expected average
heterozygosity (HE), and C) N and Average alleles per locus (A). Curves were
fitted using a sigmoid 4-parameter regression in Sigmaplot.










FIs ranged from -0.141 + 0.024 in Eglin to 0.149 + 0.059 in Highlands/Glades

(Table 1). These results give evidence of random mating within these populations. The

global estimate of FIs was 0.010 and the global estimate of FIT WAS 0.23 5. The relatively

high FIT ValUeS encompass relatively insubstantial effects of mating between close

relatives within populations; and also the extensive effects of restricted gene flow among

the populations (Hartl & Clark 1997).

Global FST, the measure of population subdivision across all populations, was

0.227. Estimates of FST ranged from 0.009 to 0.574 and RST ranged from 0.010 to 0.629.

The pairwise comparisons between Ocala and St. Johns had highest levels of gene flow

whereas Highlands/Glades and Chassahowitzka had the lowest levels of gene flow

(Table 2). All tests of genic differentiation among populations were highly significant

(p < 0.001).

Table 2. Pairwise FST (below diagonal) and RST (above diagonal) estimates for nine
Florida black bear populations (standard errors are in parentheses).
Populations are: Apalachicola (AP), Aucilla (AU), Big Cypress (BC),
Chassahowitzka (CH), Eglin (EG), Highlands/Glades (HG), Ocala (OC),
Osceola (OS), and St. Johns (SJ). Fig. 1 contains the geographic locations of
these populations.
AP AU BC CH EG HG OC OS SJ
AP 0.0546 0.1356 0.3427 0.1572 0.4197 0.2017 0.0727 0.2225
(+0.034) (+0.034) (+0.067) (+0.063) (+0.046) (+0.050) (+0.044) (+0.049)
AU 0.1223 0.2073 0.5953 0.1946 0.4966 0.2348 0.1388 0.2714
(+0.019) (+0.053) (+0.101) (+0.066) (+0.097) (+0.065) (+0.054) (+0.062)
BC 0.1379 0.2010 0.3342 0.3026 0.2435 0.1053 0.1422 0.0848
(+0.026) (+0.018) (+0.074) (+0.073) (+0.062) (+0.050) (+0.051) (+0.037)
CH 0.3609 0.4449 0.3748 0.5472 0.6292 0.3723 0.3443 0.3449
(+0.041) (+0.061) (+0.046) (+0.087) (+0.075) (+0.087) (+0.078) (+0.061)
EG 0.1653 0.1961 0.2348 0.4846 0.5176 0.2847 0.1477 0.3207
(+0.029) (+0.026) (+0.032) (+0.065) (+0.088) (+0.071) (+0.055) (+0.073)
HG 0.2972 0.3841 0.2431 0.5737 0.4000 0.2269 0.3787 0.1576
(+0.038) (+0.064) (+0.038) (+0.064) (+0.068) (+0.056) (+0.050) (+0.049)
OC 0.1617 0.1960 0.1360 0.3906 0.2299 0.2707 0.0842 0.0101
(+0.030) (+0.036) (+0.029) (+0.067) (+0.034) (+0.035) (+0.014) (+0.029)
OS 0.1167 0.1463 0.1277 0.3483 0.1792 0.3050 0.1062 0.1351
(+0.022) (+0.023) (+0.032) (+0.049) (+0.032) (+0.036) (+0.029) (+0.042)
SJ 0.1419 0.1790 0.1212 0.3585 0.2240 0.2232 0.0099 0.0942
(+0.033) (+0.042) (+0.018) (+0.052) (+0.035) (+0.036) (+0.005) (+0.028)











An unrooted phylogenetic tree based on DLR ValUeS suggested that the Ocala and

St. Johns populations were closely related, whereas Chassahowitzka, Highlands/Glades,

and Eglin were the most divergent of all the populations (Fig. 3). There was no

significant relationship between geographic distance and measures of genetic

differentiation [FST (p = 0.211), RST (p = 0.104), or DLR (p = 0.073)].


os


Figure 3. An unrooted phylogenetic tree depicting the genetic relationships among
Florida black bear populations. Branch lengths correspond to the likelihood
ratio genetic distance, DLR. Populations are: Eglin (EG), Apalachicola (AP),
Aucilla (AU), Osceola (OS), Ocala (OC), St. Johns (SJ), Chassahowitzka
(CH), Highlands/Glades (HG), and Big Cypress (BC).









Discussion

Genetic Variation

Habitat fragmentation can reduce genetic variation, which can adversely influence

fitness [e.g., the Florida panther (Roelke et al. 1993) and lion (Panthera leo)], increase

susceptibility to disease [e.g., cheetah (Acinonyx jubatus) (O'Brien et al. 1994)], and

decrease population viability (Sherwin & Moritz 2000). Habitat fragmentation and

hunting are thought to be responsible for losses in genetic variation in wolverines (Gulo

gulo) (Kyle & Strobeck 2001), lynx (Lynx lynx) (Spong & Hellborg 2002), mountain

lions (Puma concolor) (Ernest et al. 2003), Ethiopian wolves (Canzis simenesis) (Gottelli

et al. 1994) and brown bears (U. arctos) (Miller & Waits 2003). Large carnivores may be

much more susceptible than other taxa to losses in genetic variation due to habitat

fragmentation because of their large home ranges and low population densities (Paetkau

& Strobeck 1994).

The measures of genetic variation reported for most Florida black bear populations

were within the range of other populations of bears using 8 of the same microsatellite loci

(Waits et al. 2000) However, genetic variation in Chassahowitzka and

Highlands/Glades are among the lowest reported for any bear population (Appendix C,

Table 4). The three measures of genetic variation for Florida black bear populations were

positively correlated with population size. Chassahowitzka was characterized by a small

population size, and accordingly, this population had the lowest level of genetic diversity.

Osceola was characterized by a large population size because of its connection with the

Okefenokee National Wildlife Refuge, and had the highest levels of genetic diversity.

Presumably, the effects of genetic drift on loss of genetic variation are much greater in









Chassahowitzka and Highlands/Glades because of small population sizes, whereas the

effects of genetic drift are not as substantial in the larger populations.

One of the only bear populations that have a reported genetic variation lower than

Chassahowitzka is that of brown bears on Kodiak Island, Alaska. Kodiak bears have

remained isolated from the mainland brown bear populations for >10,000 years (Paetkau

et al. 1998b). The Chassahowitzka and Highlands/Glades populations are thought to

have remained isolated from other Florida black bear populations for a longer period than

any other Florida black bear populations. The isolation of these populations is

remarkable because it has resulted in the substantial loss of genetic variation that has

occurred in presumably < 100 years.

The declines in local abundance and genetic variability of Chassahowitzka and

Highlands/Glades bear populations raise the possibility that inbreeding depression could

reduce fitness, survival, and evolutionary potential (Reed & Frankham 2003), and that

these populations may face an increased risk of local extinction (Frankham 1995; Ebert et

al. 2002). Although not within these populations, some characteristic signs of inbreeding

depression were observed in Florida black bears in the western panhandle of Florida

(Dunbar et al. 1996) and southern Alabama (Kasbohm & Bentzien 1998). However, low

FIs values and lack of deviations from Hardy-Weinberg Equilibrium suggest that random

mating is operating within studied populations of the Florida black bear.

Genetic Structure

The tests of genetic differentiation, FST, RST, and DLR indicated that there was

extensive differentiation among Florida black bear populations. This differentiation was

most evident with pairwise comparisons of Chassahowitzka, Highlands/Glades, or Eglin

with any other population. The high rate of genetic drift within these populations most









likely contributed to the extensive genetic differentiation among populations. The level

of genetic differentiation between Florida black bear populations was substantially

greater than between other large carnivore populations (e.g., bears: (Paetkau et al. 1997),

the Asian black bear [U. thibetanus]: (Saitoh et al. 2001), mountain lions: (Ernest et al.

2003) wolverines: (Kyle & Strobeck 2001; Walker et al. 2001) and lynx: (Hellborg et al.

2002; Schwartz et al. 2002).

The global estimate of FST, the measure of population subdivision across all

populations, was 0.227. This degree of subdivision is expected if there are on average

0.85 successful migrants [Nm = (1/FST-1)/4] entering each population per generation

(approximately 8 years for black bears) assuming an island model of migration

(Frankham et al. 2002). Therefore, on average, across all Florida black bear populations,

there is one successful migrant every 10 years, a relatively low level of gene flow.

There have been dozens of bear translocations among populations due to

management activities during the last 20 years (T. Eason, pers. comm.). Due to the

relatively recent history of these artificial movements, it is unknown what effects they

will have on the genetic structure of these populations. Some studies suggest that most

translocations of carnivores are unsuccessful, and probably do not contribute to the gene

pool of the population in which they were released (Linnell et al. 1997).

In large natural populations occupying a mostly contiguous habitat, a pattern of

isolation by distance is expected (Wright 1931). This relationship has been reported for

other bear populations (Paetkau et al. 1997). However, there was no relationship between

geographic distance and measures of genetic differentiation among Florida black bear

populations. However, nearly significant relationships of pairwise RST and DLR ValUeS









with geographic distances suggest that exclusion of values associated with small

populations (i.e., Chassahowitzka and Highlands/Glades) may generate a significant

isolation-by-di stance relationship among "larger" populations of Florida black bears.

Interestingly, two pairs of populations separated by comparable geographic distances

(Ocala-St. Johns and Apalachicola-Aucilla) had very different FST ValUeS, 0.009 and

0. 122 respectively, suggesting that there is a high level of gene flow between Ocala and

St. Johns, but not between Apalachicola and Aucilla.

The genetic differentiation among Florida black bears was substantial, although the

average distance between nearest neighboring populations (134 km) is within the

dispersal capabilities of black bears (Rogers 1987; Machr et al. 1988). Dispersal of bears

is sex-biased, and males typically disperse farther than females, who tend to establish

home ranges near their mother' s home range (Rogers 1987; Schwartz & Franzmann

1992). It has been suggested that dispersing black bears may be able to maintain

connectivity among populations even when populations are fragmented (Noss et al. 1996;

Machr et al. 2001). Why, then, was there such a high level of genetic differentiation

among Florida black bear populations? Furthermore, why did I fail to find

isolation-by-di stance relationship in Florida black bears, which has been reported for

other black bear populations occupying contiguous habitat? I suggest that the substantial

genetic differentiation and the lack of isolation-by-di stance relationship among Florida

black bear populations is primarily due to the reduction of bear numbers by habitat

fragmentation, and by human-made barriers to dispersal.

The presence of natural barriers, such as mountain ranges or large rivers, has

historically determined the limits of species distribution (Chesser 1983). Habitat









fragmentation in the form of anthropogenic barriers such as roads or other human

development can further limit species distribution and gene flow (Mader 1984). The

separation of populations with roads reduced the level of gene flow in the moor frog

(Rana arvalis) (Vos et al. 2001), ground beetle (Carabus violaceus) (Keller & Largiader

2003), and bank vole (Clethrl ne inysllll. glareolus) (Gerlach & Musolf 2000). Additionally,

habitat fragmentation is responsible for altering the genetic structure of the red squirrel

(Sciurus vulgaris) (Hale et al. 2001) and black grouse (Tetrao tetrix) (Caizergues et al.

2003). Although large carnivores are thought to be highly vagile (Paetkau et al. 1999;

Schwartz et al. 2002), some studies suggest they may be limited in distribution because of

anthropogenic barriers (Kyle & Strobeck 2001; Sinclair et al. 2001; Walker et al. 2001;

Ernest et al. 2003; Miller & Waits 2003). Black bear movement does not seem to be

limited by topographical features of the native Floridian landscape; however,

human-made barriers such as roads, cities, and residential areas, appear to limit the

successful dispersal of black bears (Brody & Pelton 1989; Hellgren & Machr 1993) in

Florida.

Although bears are able to cross some highways (McCown et al. 2001), the impact

of highways on mortality of bears can be detrimental. From 2000 to 2002, 346 bears

were documented as killed on roads in Florida. Most of these were young males that may

have been attempting dispersal or migration to distant populations (FWC, unpublished

data). Additionally, highways and development can act as partial or complete barriers.

Some bears may avoid interstate highways (Brody & Pelton 1989; Proctor et al. 2002),

and other forms of human development may alter movement patterns (Machr et al. 2003),

further decreasing the probability of movement of bears among populations.










Given the unprecedented rate of human population growth in Florida, wildlife

habitat will continue to be converted for commercial or residential purposes.

Consequently, further fragmentation or isolation of Florida black bears and other wildlife

population is likely. My results indicate that habitat fragmentation and human-made

dispersal barriers may have substantially altered the genetic structure of Florida black

bears. The effects of habitat fragmentation and isolation are likely to be even greater in

species with limited dispersal capabilities. It is imperative that management plans for the

conservation of black bears in Florida consider measures to mitigate genetic (and most

likely, demographic) consequences of habitat fragmentation and anthropogenic dispersal

barriers.

Conclusion

I conclude that the loss and fragmentation of once contiguous habitat has caused

the loss of genetic variation in the Florida black bear, and that genetic variation in smaller

populations is among the lowest reported for any species of bear. This substantial loss of

genetic variation has contributed to extensive genetic differentiation among populations.

Additionally, roads with high traffic volume and commercial and residential

developments apparently act as barriers to gene flow, contributing to genetic

differentiation among populations.

Loss of genetic variation is a concern for the long-term survival and adaptation of

Florida black bears. What constitutes historical levels of genetic variation for Florida

black bear populations? Evidence suggests that at one time Florida black bears were

distributed throughout the state (Brady & Machr 1985). Most contiguous mainland

populations of black bears have high levels of genetic variation (HE ~ 0.76) (Paetkau et









al. 1998b). Thus, efforts should be made to restore historic levels of genetic variation

within Florida black bear populations, using mainland figures as a baseline.

To prevent the further loss of genetic variation, efforts should be made to increase

the size of Florida black bear populations. It has been suggested that a minimum of 50

effective breeders is needed to prevent inbreeding depression and population levels in the

hundreds or thousands to maintain evolutionary potential (Franklin 1980; Lande 1995).

However, keeping bears at high population levels may be increasingly difficult due to the

rapid rate of development over much of the state.

Given that Florida black bear populations have been reduced in size, gene flow

among bear populations is needed to restore and maintain genetic variation (Waits 1999).

A minimum of one and a maximum of ten successful migrants per generation have been

suggested as a rule of thumb to maintain levels of genetic variation (Mills & Allendorf

1996). I suggest that Florida black bear populations should be managed as a

metapopulation so that gene flow can occur among populations connected with

conservation corridors (Craighead & Vyse 1996; Machr et al. 2001; Larkin et al. 2004).

However, the effectiveness of corridors in maintaining gene flow among populations of

carnivores is not well understood (Beier & Noss 1998). Recent data suggest that one

such corridor between the Ocala and Osceola populations may facilitate gene flow

between these populations (FWC, unpublished data).

Additionally, wildlife crossing structures may be needed to allow safe passage of

bears across roadways that pose significant barriers to bear movement (Foster &

Humphrey 1995). In situations where population connection via corridors is impractical,

artificial translocation of animals should be considered (Griffith et al. 1989).









Translocation of animals has been successful in curbing some effects of inbreeding

depression and increasing levels of genetic variation in some animal populations

(Mansfield & Land 2002). Conservation biologists should be cognizant of the fact that

the effects of translocated animals on population structure and hierarchy are not

understood. Finally, further reduction or fragmentation of habitat likely will have

detrimental impact on demographic and genetic health of the Florida black bear

populations, and efforts to conserve remaining habitat cannot be overemphasized.















CHAPTER 3
EVALUATING THE EFFECTIVENESS OF A REGIONAL BLACK BEAR
CORRIDOR

Introduction

The effect of habitat fragmentation on natural populations is one of the greatest

threats to biodiversity conservation (Fahrig & Merriam 1994; Meffe & Carroll 1997;

Fahrig 2001). Habitat fragmentation can subdivide and isolate populations, reduce

genetic diversity, and increase the chances of local extinction (Harris 1984; Saccheri et

al. 1998; Westemeier et al. 1998). Because most wildlife populations in

human-dominated landscapes occur in fragmented habitats, attempts have been made to

identify measures that can reduce the adverse influences of habitat fragmentation.

Corridors have been proposed to mitigate the negative effects of habitat fragmentation by

connecting once isolated populations (Noss & Harris 1986). Corridors can increase

movement of organisms among patches (Hass 1995; Aars & Ims 1999; Haddad 1999;

Sieving et al. 2000; Mech & Hallett 2001; Haddad et al. 2003; Kirchner et al. 2003),

thereby providing additional habitat (Perault & Lomolino 2000), facilitate plant-animal

interactions (Tewksbury et al. 2002), and increase recolonization potential (Hale et al.

2001), survival (Coffman et al. 2001), gene flow (Harris & Gallagher 1989) and the

probability of persistence (Fahrig & Merriam 1985; Beier 1993). The use of corridors in

conservation stems from the equilibrium theory of island biogeography (MacArthur &

Wilson 1967), landscape ecology (Forman & Godron 1986), and the metapopulation

paradigm (Levins 1970; Hanski 1994). Several authors have suggested that conservation









of fragmented populations requires a metapopulation approach (Hanski & Simberloff

1997; Dobson et al. 1999). Managing fragmented or spatially-structured populations

requires functional corridors that permit exchange of individuals among populations.

Discussions regarding the role of corridors in conservation biology is confused by

the many definitions of this concept (Rosenberg et al. 1997; Beier & Noss 1998; Hess &

Fischer 2001). Corridors range in scale from small transects linking patches of habitat to

regional complexes linking ecosystems and watersheds. Noss et al. (1996) suggested that

"connectivity will be best provided by broad, heterogeneous landscapes, not narrow,

strictly defined corridors." Thus, evaluating the effectiveness of corridors requires a

consideration of the entire landscape mosaic and the functional/structural aspects of the

corridor for the focal species.

Large carnivores are highly susceptible to the effects of habitat fragmentation,

because of the potential for conflicts with humans, large home ranges, and low

population densities (Noss et al. 1996; Crooks 2002). Many populations of large

carnivores exist within fragmented habitats, encompassing areas much too small to

support viable populations (Woodroffe & Ginsberg 1998). Additionally, the conservation

of large carnivores that are flagship and umbrella species provides a means of protecting

biodiversity at smaller scales (Cox et al. 1994; Noss et al. 1996). It has been suggested

that carnivore populations in fragmented habitats operate as metapopulations (Poole

1997; Ferreras 2001; Palomares 2001). For many carnivore species, movement among

populations is vital for metapopulation persistence (e.g., lynx [Lynx spp.]i: Ferreras 2001;

Ganona et al. 1998; Palomares 2001; and brown bears [Grsus arctos]: Craighead & Vyse

1996).









The long-distance movements of large carnivores suggest that they are more

likely to use corridors for movements than species with limited dispersal capabilities

(Lidicker & Koenig 1996; Harrison & Voller 1998). Corridors were recommended as

management tools for connecting populations of lynx (Poole 1997; Ferreras 2001;

Palomares 2001), cougars (Puma concolor) (Beier 1995; Emnest et al. 2003), wolves

(Canis lupus) (Duke et al. 2001), brown bears (Picton 1987; Craighead & Vyse 1996;

Weaver et al. 1996), and black bears (U. amnericanus) (Cox et al. 1994; Hoctor 2003;

Larkin et al. 2004). However, the effectiveness of corridors for large carnivores has not

been tested on a regional scale.

One challenge in testing the effectiveness of regional corridors for carnivores

using traditional techniques, such as radio telemetry, is that the long-distance movements

of carnivores make it difficult to locate and observe animals. In many species,

long-distance dispersal is often rare, and there is no guarantee that the sample of

radio-instrumented animals will contain dispersing animals (Koenig et al. 1996).

Moreover, the dispersal of an animal from population to population does not indicate

effective dispersal; genetic data are much more suited to provide that information

(Frankham et al. 2002). The use of relatively inexpensive, non-invasive sampling

techniques, such as hair snares, and genetic analyses may help overcome these limitations

of radio telemetry-based studies. Such techniques provide data necessary for evaluation

of the functionality of corridors by elucidating genetic structure and effective dispersal

(Foran et al. 1997). Recent advances in genetic analyses and statistical techniques (e.g.,

population-assignment tests) have made it possible to identify the origin of animals by

assigning them to a population based on their multilocus genotypes (Paetkau et al. 1995;









Waser & Strobeck 1998; Waser et al. 2001). Population-assignment tests have been used

to identify immigrants within populations of cougars (Ernest et al. 2003), otters (Lutra

lutra) (Dallas et al. 2002), wolves (Flagstad et al. 2003; Vila et al. 2003), marten (Martes

americana) (Small et al. 2003), wolverines (Gulo gulo) (Cegelski et al. 2003), and bears

(Paetkau et al. 1995). These techniques can identify dispersal patterns and cryptic

boundaries, which may indicate breaks in the gene flow across populations or the

reconnection of once isolated populations (Manel et al. 2003). Additionally, some

assignment tests detect not only immigrants into a population, but also their offspring,

which enables researchers to directly detect and monitor gene flow (Rannala & Mountain

1997; Pritchard et al. 2000).

A carnivore species that could benefit from the implementation of regional

corridors is the Florida black bear (U. a. floridanus).~dddd~~~ddd~~~ The Florida black bear was once

distributed throughout Florida, and the southern portions of Georgia, Alabama, and

Mississippi. Human activities significantly reduced the number of black bears from the

1850s to the 1970s through extensive fragmentation of habitat and excessive hunting

(Brady & Machr 1985). Consequently, Florida black bears now occur in fragmented

populations. The long-term isolation of populations could lead to a loss of genetic

variation and evolutionary potential, and may also reduce population viability (Harris

1984; Frankham 1995; Reed & Frankham 2003). However, some populations are

expanding as bears recolonize suitable vacant habitat (Eason 2000). Black bears have

large home ranges and dispersing bears can travel hundreds of kilometers from their natal

home range (Alt 1979; Rogers 1987; Machr et al. 1988; Wooding & Hardisky 1992;

Hellgren & Machr 1993; McCown et al. 2001; Lee & Vaughan 2003). However,









development throughout much of the state of Florida has created formidable obstacles to

movements such as towns, commerce al/re si denti al developments, and maj or highway s

(Brody & Pelton 1989; Machr et al. 2003). Consequently, regional corridors may be

needed to mitigate the detrimental demographic and genetic effects of habitat

fragmentation in Florida black bear populations (Harris & Scheck 1991; Noss 1993).

Documented dispersal and movement of individual bears (Florida Fish and

Wildlife Conservation Commission (FWC), unpublished data) and Geographic

Information Systems (GIS) analysis (Hoctor 2003) suggest that the Osceola-Ocala

regional corridor may be the best option for connecting two of the largest Florida black

bear populations. The Osceola-Ocala corridor is a patchwork of public and private lands

within a matrix of roads and development stretching for 90 km from the Ocala National

Forest to Osceola National Forest (Fig. 4). This proposed corridor contains a mosaic of

flatwoods, pine plantations, forested wetlands, riparian hammocks, scrub, and sandhill

covering over 80,000 ha (Machr et al. 2001). Osceola and Ocala are two of the largest

populations of Florida black bear (Eason 2000), and establishing or maintaining

connectivity between these populations may be necessary to ensure the long-term

persistence of the Florida black bear.

The goal of my study was to evaluate the effectiveness of the Osceola-Ocala

corridor for the Florida black bear. I used non-invasive sampling to obtain genetic

material from bears within the Osceola-Ocala corridor and genotyped bears for 12

microsatellite loci. I also sampled bears from the Osceola and Ocala populations and

from seven other areas throughout Florida. I used population-assignment tests to assign

individuals sampled from the corridor to a population of origin (Osceola or Ocala) based
























Osceola-POcalad Corridor

Priar blc er a ia
Seconda~ry blac berhai
Conservationand


on their multilocus genotypes. These techniques allowed me to characterize the dispersal

of bears from the source populations, and identify gaps in connectivity within the

Osceola-Ocala corridor.


IEC:
-i,
SJ
-4PI IAli
loc
oc


aH i~I~:
I I BCr :
0 300 km


Figure 4. Area proposed as a regional corridor between the Ocala and Osceola black
bear populations. Crosshatched areas represent primary black bear habitat
(presence of breeding females) and stippled areas represent secondary black
bear habitat from a recent distribution map (Florida Fish and Wildlife
Conservation Commission (FWC), unpublished data). Populations are
abbreviated as: Eglin (EG), Apalachicola (AP), Aucilla (AU), Osceola (OS),
Ocala (OC), St. Johns (SJ), Chassahowitzka (CH), Highlands/Glades (HG),
and Big Cypress (BC).

Methods

I used a map of secondary black bear habitat (FWC, unpublished data; Fig. 4) and

results from a least-cost path analysis (Hoctor 2003) to identify areas that might serve as

a potential regional corridor between Ocala and Osceola. These habitat patches










represented areas that bears most likely travel through to avoid commercial and

residential development. I overlaid a grid of 20 km2 CellS on a map of available lands

within the potential corridor and placed at least one hair snare (Woods et al. 1999) within

each cell.

Each hair snare was constructed of two strands of standard 4-prong barbed wire at

heights of approximately 30 cm and 55 cm, attached to a perimeter of three or more trees

encompassing a total area of 10-30 m2. I baited the center of the snare with pastries and

corn, and placed two attractants (pastries and raspberry extract) > 2.44 m above the snare.

As bears entered the hair snare, the barbed wire snagged hair samples that were used in

genetic analyses. I operated each hair snare for an average of seven times with a mean

period of 26 days between baiting and sampling from May to November of 2002 and

May to August of 2003. I collected hair samples using the protocol of Eason et al.

(2001). Additionally, I collected hair samples within the corridor opportunistically from

a complementary hair snare proj ect in Osceola (May-August, 2002-03), existing fences

(2001-03) and bears killed on roads (1998-2003).

Black bear tissue and hair samples collected from previous research studies and

highway mortalities during 1989-2003 were available for the Osceola and Ocala

populations (n = 41 and n = 40 individual bears, respectively). To provide comparative

data, individuals also were sampled from other Florida black bear populations:

Apalachicola (n = 40), Aucilla (n = 9), Big Cypress (n = 41), Chassahowitzka (n = 29),

Eglin (n = 40), Highlands/Glades (n = 28), and St. Johns (n = 40).

I sent hair and tissue samples to Wildlife Genetics International (Nelson, British

Columbia, Canada) (http ://www.wildlifegenetics .ca/), where individuals were genotyped









using microsatellite analysis. DNA was extracted using QIAGEN' s DNeasy Tissue kits

(Valencia, California), as per QIAGEN's instructions

(http://www.qiagen. com/literature/genomlit. asp). Microsatellite loci were amplified

using polymerase chain reaction (PCR) primers (G1A, G10B, G10C, GlD, G10L, G10M,

G10P, G10X, G10H, MU50, MU59, and G10J). The gender of each bear was determined

using the length polymorphism in the amelogenin gene (D. Paetkau, pers. comm.).

Laboratory analyses were performed as described in Paetkau et al. (1995, 1998a, 1998b,

1999) and Paetkau & Strobeck (1994).

I used the software program STRUCTURE to assign individuals to a population

of origin using Bayesian clustering techniques (Pritchard et al. 2000). STRUCTURE

assumes Hardy-Weinberg equilibrium (HWE) within populations and linkage equilibrium

between loci. I used Genepop 3.4 (Raymond & Rousset 1995) to test for deviations from

Hardy-Weinberg equilibrium (HWE). For loci with fewer than four alleles, exact

p-values were computed using the complete enumeration method (Louis & Dempster

1987), and for loci with more than four alleles the Markov chain method

(dememorization 1,000; batches 100; iterations per batch 1,000) was used (Guo &

Thompson 1992). Using Genepop 3.4, I used linkage disequilibrium tests to identify

nonrandom association between alleles of different loci using the Markov chain method.

I assigned bears sampled from the corridor and from other populations to a cluster

or population based on their genotypes, without regard to where the samples were

collected, using the program STRUCTURE. I used the admixture model, which assumes

that each individual draws some proportion of membership (q) from each of K clusters.









Allele frequencies were assumed independent and analyses were conducted with a

100,000 burn-in period and 100,000 repetitions of Markov Chain Monte Carlo.

I conducted population-assignment tests using STRUCTURE at two levels. For

comparative purposes, the first analysis was conducted on the statewide level with

individuals sampled from the nine populations and the corridor (K = 8 clusters). A

second analysis was conducted on a regional level; only individuals sampled from Ocala,

Osceola, and the corridor were included (K = 2 clusters). An individual bear was placed

into a cluster if q > 0.85 for that cluster. If q > 0.40 for both clusters, the genotype profie

indicated mixed ancestry, suggesting the individual may be an offspring of a mating

between the two clusters. I plotted the assigned individuals on a map of north-central

Florida using ArcGIS 8. 1.2 to examine the geographic patterns of congruence (Ormsby et

al. 2001).

Results

A total of 598 hair samples was collected at 44 out of 86 hair snare sites within

the Osceola-Ocala corridor (Fig. 5). Overall, trap success for hair snares was 23.33%,

with substantially lower trapping success towards the center of the corridor (Fig. 6).

Within the corridor, 3 1 black bears were sampled at 50 locations; 11 of the 3 1 bears were

sampled at multiple locations. Only three of the 3 1 bears sampled in the corridor were

females, and these were within 20 km of the Ocala population.

There were no significant departures from HWE for any locus or population (p >

0.05), and the linkage disequilibrium test indicated that 10% of loci pairings had

significant nonrandom associations (p < 0.05). These significant loci pairings may be a

result of nonrandom mating, sampling bias, recent admixture, or genetic drift (Frankham

et al. 2002).






































Figure 5. Locations of samples collected in the Osceola-Ocala corridor. Dark circles
represent hair snares visited by bears, whereas open circles represent hair
snares not vi sited by bears. Squares represent samples collected
opp ortuni sti cally .


140

120

m 100 -~

80

a60



20

20 40 60 80 100 120 140
Distance from Osceola (km)
O Trap success of hair snares
o Hair snares not visited


Figure 6. Bubble plot of trap success in the Osceola-Ocala corridor. The size of the
bubble represents the number of bear visits relative to the number of trapping
sessions. Squares represent hair snares not visited by bears. The distance was
estimated as the linear distance from the population's centroids (the harmonic
mean of sample locations in the Ocala and Osceola populations) to the hair
snare sites in the corridor.









For the statewide analysis, the 31 individuals sampled in the corridor, along with

the 308 individuals sampled statewide, were analyzed using STRUCTURE. The 10

predefined populations had 79% or more of their membership assigned to a single cluster.

Individuals sampled from Ocala, St. Johns and the Osceola-Ocala corridor were assigned

to the same cluster (q > 0.85), suggesting no significant genetic differentiation among

these three populations (Table 3).

Table 3. Assignment of individuals using the Bayesian clustering technique using the
program STRUCTURE (Pritchard et al. 2000) without any prior information
on population of origin. The average proportion of membership for
individuals sampled in predefined populations for each of 8 clusters (highest
average proportion of membership assigned to a single cluster is in bold
italics). Sample sizes are in parentheses.
Average proportion of membership in 8 clusters
Population 1 2 3 4 5 6 7 8
Apalachicola (40) 0.846 0.088 0.013 0.006 0.021 0.007 0.011 0.009
Aucilla (9) 0.121 0.835 0.008 0.009 0.007 0.006 0.006 0.008
Big Cypress (41) 0.012 0.006 0.887 0.006 0.010 0.021 0.045 0.012
Chassahowitzka (29) 0.002 0.004 0.004 0.977 0.003 0.004 0.004 0.003
Eglin (40) 0.010 0.006 0.010 0.005 0.947 0.007 0.006 0.010
Highlands/Glades (28) 0.003 0.003 0.024 0.004 0.003 0.954 0.006 0.003
Ocala (40) 0.006 0.005 0.010 0.004 0.008 0.011 0.947 0.009
Corridor (31) 0.008 0.007 0.009 0.006 0.009 0.008 0.848 0.105
Osceola (41) 0.019 0.016 0.035 0.014 0.015 0.020 0.085 0.796
St. Johns (40) 0.019 0.021 0.024 0.014 0.009 0.035 0. 853 0.025

For the regional analysis, I conducted population-assignment tests including only

individuals sampled from Ocala, Osceola, and the corridor, and estimated the proportion

of membership of each bear to the two clusters (Ocala and Osceola). All bears sampled

in Ocala were assigned to cluster 1 (q > 0.90), indicating that no immigrants from

Osceola were sampled in Ocala. Bears sampled in Osceola had ancestry in both clusters,

with 36 of the 41 bears assigned to cluster 2 (q > 0.85). Two individuals sampled in

Osceola (OS31 and OS41) were assigned to cluster 1 (q > 0.99), suggesting they were

immigrants from Ocala. Additionally, two bears sampled in the Osceola population











(OS14 and OS20) were assigned to both clusters (q > 0.40), indicating that these

individuals were offspring from an Osceola and Ocala mating (Fig. 7).


Black bears sampled in Ocala


0.8

q 0.6
0.4
0.2
0.0








1.0
0.8

q 0.6
0.4
0.2
0.0


0000000000000000000000000UUUUUUUUUUUUUUUU
0000000000000000000000000000000
EE Cluster 1 Individual bears
I Cluster 2


Em Cluster 1
I Cluster 2


Individual bears


Black bears sampled in Osceola


0.8

q 0.6
0.4
0.2
0.0


,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
00000000000000000000000000000000
EE Cluster 1 Individual bears
I Cluster 2


Figure 7. Assignment of black bears to a population of origin without regard to sample
locations using STRUCTURE (Pritchard et al. 2000). Each individual bear
sampled in Ocala, Osceola and the corridor is represented by a single vertical
line, which is partitioned into segments that represent that individual's
proportion of membership (q) in the two clusters.









Of the 3 1 black bears sampled in the corridor, 28 were assigned to cluster 1

(Ocala) with q > 0.85, suggesting a predominately one-way movement by bears from

Ocala into the corridor. However, there were three individuals sampled in the corridor

(OO20, OO26, and OO31) that were assigned to cluster 2 (q > 0.98), suggestive of

origins in the Osceola population (Fig. 7). The sample locations of these bears plotted

on a map of north-central Florida revealed a spatial pattern in the distribution of

genotypes with limited mixing of Osceola and Ocala bears within the corridor (Fig. 8).





xx35R 01








0xA 35 70 kmr
Indivdualmembeshipin clster
q > 0.85 in clstrer Oaa
0 q > 0.85 in : cutr 1i 2 Oseoa
0 04 ec n lstr1 n (psiblyrd

Figure~~~"-i 8. Spta pater oftepooto fmmeshp()frbassmldi
OselOaaadteOceoaOaacrio sn h rga
STUCUR (richrdetal 200.Fo Ocelaan Oal, 1 nd4
indivdual repcivlae ipayd ihi h scoaOcl orio,3
bassmleda 50 different loain r ipae.Bakbaswt



m ixe id acetr hae q > 0.40 in bohclusters.









Discussion

The role of corridors in conservation planning has been controversial, due largely

to the lack of empirical studies evaluating the effectiveness of corridors (Simberloff &

Cox 1987; Simberloff et al. 1992; Rosenberg et al. 1997; Niemela 2001). Despite the

paucity of data supporting the function of corridors, many conservation biologists argue

that corridors should be reestablished or maintained where such connectivity occurred in

the recent past (Noss & Harris 1986; Noss 1987; Beier & Noss 1998). Nowhere has the

corridor controversy been more intense than in the state of Florida (Noss 1987;

Simberloff & Cox 1987; Simberloff et al. 1992). Plans for a regional network of

connected lands have been undertaken with little knowledge of the efficiency of corridors

in facilitating movements of animals (Noss & Harris 1986; Hoctor et al. 2000; Larkin et

al. 2004). The effectiveness of corridors in connecting carnivore populations is a

question of considerable conservation importance. Large carnivores provide flagship and

umbrella mechanisms for conservation and are sensitive to the effects of habitat

fragmentation (Noss et al. 1996; Woodroffe & Ginsberg 1998). Thus, corridors that

provide connectivity among large carnivore populations are likely to be beneficial to

other species with smaller home ranges.

I documented the presence of black bears throughout the Osceola-Ocala corridor,

indicating that perhaps a small population inhabits this area. Male black bears disperse

long distances due to competition for resources (Rogers 1987; Schwartz & Franzmann

1992), and the substantial disparity in the sex ratio of bears (28 males, 3 females)

sampled in the Osceola-Ocala corridor suggests that the corridor is primarily used as a

conduit for gender-biased dispersal.









For a dispersal corridor to be functional, the distance between populations should

be within dispersal capabilities of the focal species. The average dispersal distance

observed for male black bears is roughly half the distance of the Osceola-Ocala corridor

(Alt 1979; Rogers 1987; Machr et al. 1988; Schwartz & Franzmann 1992; Wooding &

Hardisky 1992; Wertz et al. 2001; Lee & Vaughan 2003). However, black bears can

move great distances, occasionally dispersing > 100 km (Alt 1979; Rogers 1987; Machr

et al. 1988). Long-distance dispersal is difficult to measure and often underestimated

(Koenig et al. 1996). However, the range of dispersal distances for black bears suggest

that it is possible for bears to travel the length of the Osceola-Ocala corridor.

The effectiveness of a dispersal corridor would require that animals use the area for

natal dispersal, seasonal migration, foraging or searching for a mate (Harris & Scheck

1991; Noss 1993; Rosenberg et al. 1997; Hess & Fischer 2001). Many studies suggest

that there are directional patterns of dispersal related to the presence of habitat suitable

for dispersal corridors (Smith 1993; Poole 1997; McLellan & Hovey 2001; Wertz et al.

2001; Machr et al. 2002; Lee & Vaughan 2003). For instance, bears used the

Osceola-Ocala corridor for dispersal because there is available habitat in which to

disperse. Additionally, the presence of the bears, including some females, in multiple

locations suggests that some individuals may be residents with home ranges within the

corridor. Although there were only three females sampled, a reproducing population

within the corridor would better facilitate movement among populations (Noss 1993;

Noss et al. 1996; Rosenberg et al. 1997; Beier & Noss 1998).

Most individuals were assigned to the population in which they were sampled,

verifying the validity of using population-assignment tests for Florida black bears (Table










3). However, two male bears sampled in Osceola had genotype combinations most

consistent with those assigned to Ocala. Additionally, two individuals had genotypes

assigned as hybrids, indicating that bears born in Ocala may have bred successfully in

Osceola. There is a possibility that some bears identified as immigrants within the

Osceola population may be nuisance bears that were translocated from Ocala. However,

the relatively small number of documented translocations and the known fates of most of

these translocated bears suggests that one or both bears sampled in Osceola that were

assigned to Ocala are dispersers from the latter population that used the corridor for

movement.

Most bears sampled within the Osceola-Ocala corridor were assigned to Ocala,

with a predominantly unidirectional pattern of movement. There was a limited mixing of

Ocala-assigned individuals with Osceola-assigned individuals in one area of the corridor

(Fig. 8). Three of the Ocala-assigned bears were previously sampled in the Ocala

population; these are clear examples of long-distance dispersal (30-100 km) into the

corridor and further validate the accuracy of assignment tests. The use of the

Osceola-Ocala corridor by bears has increased in recent years (J. Garrison, pers. comm.),

a pattern similar to recolonization rates of black bears in the Trans-Pecos (Mexico-Texas

border) (Onorato & Hellgren 2001) and red squirrels (Sciurus vulgaris) in Scotland (Hale

et al. 2001). Expansion of the Ocala population into the Osceola-Ocala corridor likely

will continue as long as habitat is available and there are no significant barriers to

movement.

The spatial pattern of trap success of the hair snares (Fig. 5) and assignment tests

(Fig. 8) indicated a limited gap in connectivity. This gap may have been caused by a










significant habitat bottleneck caused by residential development and a four-lane highway

(S.R. 301). Development near the city of Starke, the expansion of unincorporated areas

of Jacksonville (especially near Middleburg) and extensive surface mines in those areas

may also have contributed to a break in connectivity (Hoctor 2003). Extensive habitat

alteration by residential and industrial developments have been identified as potential

deterrents for bear dispersal (McLellan & Shackleton 1988; Machr et al. 2003), and this

may be the situation for bears in the Osceola-Ocala corridor. However, there remains a

possibility that bears have not had sufficient time to recolonize these areas.

Only three bears with Osceola genotypes were sampled south of the interstate

highway (I-10), despite the large population of bears (Osceola) just north of I-10. One of

those three bears also was sampled north of I-10 (FWC, unpublished data) suggesting that

while the highway is not a complete barrier to movement, it may represent a significant

filter allowing only a few individuals to cross successfully. Large, high-speed highways

have been known to alter movement patterns of bears (Brody & Pelton 1989; Wertz et al.

2001; Proctor et al. 2002; Kaczensky et al. 2003). My results were consistent with the

hypothesis that high-speed interstate highways can significantly reduce movements of

Florida black bears.

Roads can have a more significant effect on bear movements within the corridor.

From 1979 to 2002, 32 bears (28 males, 3 females, 1 unknown) were documented as

killed on highways within the Osceola-Ocala corridor. High mortality rates of dispersing

carnivores are not uncommon (e.g., San Joaquin kit foxes [Vulpes macrotis mutica]:

Koopman et al. 2000; tigers [Panthera tigris]: Smith 1993; brown bears: McLellan &

Hovey 2001; and black bears: Alt 1979, Schwartz & Franzmann 1992). Clearly,









maintaining or restoring effective connectivity between the Osceola and Ocala

populations will require measures to reduce mortality of dispersing animals.

Taken together, my results show that the Osceola-Ocala corridor is functional. My

study provides one of the first empirical evaluations of the effectiveness of a regional

corridor in connecting populations of a large carnivore. The methods used in my study

provide a framework for using non-invasive sampling and genetic analysis for evaluating

the effectiveness of corridors in providing demographic and genetic connectivity between

wildlife populations. These techniques allow researchers to identify the genetic

signatures of connectivity by identifying immigrants and hybrids, and these methods

should be useful in evaluating the effectiveness of other potential corridors for connecting

wildlife populations.

Conclusion

My results suggest that the Ocala and Osceola black bear populations were

recently re-connected, primarily through unidirectional movement of bears from Ocala to

Osceola, and that some of the dispersers may have successfully reproduced. Moreover, I

found a small black bear population currently inhabits the Osceola-Ocala corridor itself.

Based on these results, I conclude that the Osceola-Ocala corridor is functional, and

provides genetic and demographic connectivity between Ocala and Osceola black bear

populations. The connection of the Osceola and Ocala populations allows gene flow

between these populations through male-mediated dispersal, the maintenance of

metapopulation structure, and may increase population viability. However, increasing

development pressure in this regional corridor may thwart functional connectivity of

these populations if the habitat within the corridor is not protected.









Maintaining or restoring connectivity may require multiple strategies including

encouraging recolonization of the corridor by maintaining high densities in the source

populations, minimizing habitat loss and fragmentation, and managing for a high quality

habitat. Very short distances separate most of Osceola and Ocala bears within the

corridor; these breaks in connectivity should be minimized such that a bear could cross

the area in a single dispersal event (Beier & Loe 1992). However, sufficient habitat for

recolonization requires easements, purchasing conservation lands, fostering agreements

with private landowners, and reducing human activity (Beier 1995; Duke et al. 2001).

Providing connectivity may also require retrofitting highways to allow safe passage of

bears (Foster & Humphrey 1995).

I found that the use of non-invasive hair snares and population-assignment tests

could serve as an appropriate and efficient method for evaluating the effectiveness of a

regional corridor. Although my study was not replicated, it did provide useful insights

into the functionality of a regional corridor for large carnivores. A fully replicated,

experimental approach is rarely practical in conservation settings. Design limitations

aside, I do view consistent use of a corridor as sufficient evidence to justify the

conservation value of these areas (Beier & Noss 1998). Given the rapid pace of

development in Florida, the connection of populations with corridors may be the best

option in mitigating the adverse impacts of habitat fragmentation on black bears and

other wildlife.















CHAPTER 4
CONCLUSIONS AND MANAGEMENT RECOMMENDATIONS

In my study, I used microsatellite analysis of complete 12-locus genotypes of 339

bears to investigate the conservation genetics of Florida black bear populations

(Appendix D, Table 5). Allele frequencies for these bears varied substantially across 10

study areas (Appendix D, Table 6). I used these microsatellite data to investigate the

genetic consequences of habitat fragmentation and to examine the functionality of the

Osceola-Ocala corridor.

Conclusions

Genetic variation is an important consideration for the long-term survival and

adaptation of Florida black bears. My results indicate that most Florida black bear

populations had genetic variation within the range reported for other bear populations

(Appendix C, Table 3). However, Florida black bear populations with < 200 individuals

were characterized by low levels of genetic variation. The level of genetic variation

within the Chassahowitzka and Highlands/Glades populations are among the lowest

reported for any species or population of bears (Appendix C, Table 3). The reduction of

genetic variation in the Chassahowitzka and Highlands/Glades populations could

adversely influence evolutionary potential and increase inbreeding depression, which

may lead to the eventual extirpation of these populations.

My results indicated low levels of gene flow among most populations of the Florida

black bear. However, there was a high level of gene flow between the St. Johns and









Ocala populations, and for genetic management, these populations could be considered as

the same population unit.

Genetic differentiation among Florida black bear populations is greater than that

reported for other bear populations separated by greater geographic distances (Paetkau et

al. 1998b; Paetkau et al. 1999; Waits et al. 2000; Lu et al. 2001; Saitoh et al. 2001;

Warrillow et al. 2001; Marshall & Ritland 2002). Additionally, there was no significant

pattern of isolation by distance in Florida black bear populations. This pattern has been

observed among other populations of bears (Paetkau et al. 1997). Roads with high traffic

volume and anthropogenic development apparently act as barriers to gene flow among

populations of bears in Florida.

Data presented in Chapter 3 clearly indicate that the Osceola-Ocala corridor

provides demographic and genetic connectivity between two of the largest bear

populations via unidirectional movement of bears from Ocala into Osceola. I

documented the presence of bears in Osceola with Ocala genotypes and others that may

be Osceola-Ocala hybrids. There was a preponderance of male bears within the

Osceola-Ocala corridor, suggesting that the corridor is primarily used as a conduit for

dispersal. The recolonization of the corridor likely will continue as long as sufficient

habitat is available and there are no significant barriers to movement. However, there

were some gaps in black bear distribution within the corridor, possibly due to barriers

such as residential and industrial development. The methods used in my study provide a

framework for evaluating functionality of corridors for connecting other wildlife

populations.










Management Recommendations

Efforts should be made to restore historic levels of genetic variation within Florida

black bear populations. For the smaller, more isolated populations (i.e., Chassahowitzka

and Highlands/Glades) to persist into the foreseeable future, it may be necessary to

increase levels of genetic variation within these populations.

I recommend two ways to increase or maintain genetic variation in Florida black

bear populations. The first is to increase the size of the populations, and to prevent

further loss and fragmentation of their habitat. Efforts should be made to maintain or

increase populations to > 200 individuals to prevent substantial loss of genetic variation.

The increase in population size would minimize the loss of genetic variation due to

genetic drift, and would increase the number of dispersers, potentially increasing the level

of gene flow among populations.

My second recommendation is to increase gene flow among populations. This may

be accomplished in two ways: genetic augmentation and the connection of populations

with corridors. Genetic augmentation would require the translocation of bears among

populations. For augmentation to be successful, these bears must mate with members of

the target population.

The Florida Fish and Wildlife Conservation Commission has a policy that requires

the movement of nuisance bears among populations. A study is needed to determine the

fate and reproductive success of these translocated bears. If the findings suggest that

translocated nuisance bears successfully breed, this method could be used to genetically

augment populations. Translocation of pregnant female bears may be a better option than

nuisance bears because they have a higher probability of staying in the area where they

are released (Eastridge & Clark 2001). Additionally, the stocking of bears in the Big









Bend of Florida (north of Chassahowitzka and east of Aucilla) would increase the

probability of gene flow into the Chassahowitzka and Aucilla populations (Wooding &

Roof 1996).

Gene flow among populations via natural dispersal would require the connection of

populations with conservation corridors. This method is preferred because it would

restore historical connectivity, increase probability of long-term persistence, and

maintenance of metapopulation structure. However, little habitat that could potentially

serve as corridors is available because of the high rate of commercial and residential

development throughout much of the state of Florida.

The Osceola-Ocala corridor may be the only corridor that can provide demographic

and genetic connectivity of the Florida black bear. As noted above, this corridor is

functional, and efforts should be made to enhance the quality of habitat and minimize the

effects of potential barriers. The protection and conservation of lands within the

Osceola-Ocala corridor will be needed to ensure functional connectivity between these

populations. The large number of landowners requires a consortium to manage these

lands effectively. Management actions to reduce mortality and increase safe movement

across highways also may include the installation of wildlife underpasses and/or

overpasses (Foster & Humphrey 1995; Roof & Wooding 1996). Additionally, a

reproducing population within the Osceola-Ocala corridor would provide a better means

of facilitating movement of bears between the Osceola and Ocala populations. Therefore,

efforts should be made to encourage female recolonization of the corridor.

Recommendations for Further Research

Genetic monitoring of Florida black bear populations is needed to examine changes

in levels of genetic variation over time. These investigations could be coordinated with









the statewide population monitoring program of the Florida Fish and Wildlife

Conservation Commission (Eason et al. 2001).

A relatedness analysis using microsatellites would help clarify the relationships

among individuals within a populations (Schenk et al. 1998; Spong et al. 2002). This

method could be used to create a pedigree of sampled individuals in a population, thereby

determining the levels of inbreeding.

A comprehensive mitochondrial DNA (mtDNA) study is needed for a better

understanding of the genetic status of these populations. These investigations could

better elucidate female dispersal and population structure.

Finally, comprehensive demographic studies are needed to conduct a population

viability analysis (PVA). These analyses could be used to predict the impact of further

habitat fragmentation and loss on the viability of Florida black bear populations.















APPENDIX A
HISTORY OF THE FLORIDA BLACK BEAR

General

The American black bear (Ursus americanus) has maintained a broad distribution

throughout much of its history, and fossil evidence indicates that black bears have been

present in North America for at least 3 million years (Kurten & Anderson 1980). The

Florida black bear (U. a. floridanus)~~dddd~~~ddd~~~ is one of three subspecies of North American black

bears, and was first described in Key Biscayne by Merriam (1896). The Florida black

bear historically ranged throughout Florida and southern portions of Georgia, Alabama,

and Mississippi (Hall 1981) (Fig. 9).

Black bears have large body size and need considerable expanses of land to

maintain home ranges. They use a wide variety of habitats, including pine flatwoods,

hardwood swamp, cypress swamp, cabbage palm forest, sand pine scrub, and mixed

hardwood hammock (Machr et al. 2001). The omnivorous diet of black bears includes

mostly plant and some animal material (Machr & Brady 1984).

Seminole Indians hunted black bears in Florida, using meat, skin and fat for various

consumptive, ornamental, and traditional purposes (Bartram 1980; Bakeless 1989). In

the past, cattle ranchers and beekeepers considered the Florida black bear a nuisance;

consequently, the shooting and poisoning of bears was common (Hendry et al. 1982).

Hunting for sport and food was intensive and unregulated prior to 1950 (Cory 1896).

Regulated bear hunting was initiated in Florida in 1950 (Wooding 1993), but was stopped

in most counties in 1971 and in all counties in 1993 (Machr et al. 2001).












C-



i
I


1






'
'' '-i
i
V
r
1-
r


Ir


I


i_


O'C ~

I
? i. I

I I













i i


C.

'I


I d


P9


American Black Bear

Florida Black Bear

Louisiana Black Bear



Figure 9. Historic distribution of black bears in the southeastern United States (after
Eason 1995)


The greatest reduction of Florida black bear was a result of extensive habitat loss


and fragmentation during the 19th century (Wesley 1991; Pelton & Van Manen 1997).


Forests were cleared for timber and agriculture, wetlands were drained, and large areas


were mined (Myers & Ewel 1991). In the 1970's, there were only an estimated 300-500


bears in Florida (McDaniel 1974; Brady & Machr 1985). Under the assumption that


bears once occupied nearly all the state's land area (34.5 million acres), they have been


eliminated from approximately 83% of their range (Wooding 1993).









Currently, Florida black bears occur in several populations that are mostly

relegated to public lands within Florida (Apalachicola, Aucilla, Big Cypress,

Chassahowitzka, Eglin, Highlands/Glades, Ocala, Osceola, and St. Johns), Georgia

(Okefenokee), and Alabama (South Alabama) (Fig. 10).

Regulations

The Florida Game and Freshwater Fish Commission classified the Florida black

bear as a threatened species in most Florida counties in 1974 (Wooding 1993). Florida

black bears in Georgia are considered a game animal and are subj ect to a limited hunting

season, but are listed as an endangered species on the state-level in Alabama (Pelton &

Van Manen 1997; Kasbohm 2004).

The U. S. Fish and Wildlife Service (USFWS) was petitioned in 1990 to list the

Florida black bear as a federally threatened species under the Endangered Species Act of

1973. The USFWS findings of 1991 concluded that the petition to list the Florida black

bear was warranted, but was precluded by work on other species having higher priority

for listing (Wesley 1991). A subsequent reexamination by the USFWS in 1998

concluded that listing the Florida black bear as federally threatened or endangered was

not warranted based on existing data (Bentzien 1998). This decision was challenged in

court by several conservation organizations, and the USFWS was ordered to clarify the

documentation of the adequacy of existing regulatory mechanisms to protect the Florida

black bear. The findings concluded that the existing regulatory mechanisms were

sufficient and that listing the Florida black bear as a threatened or endangered species

was not warranted (Kasbohm 2004).






48






S SJOS~S
EG AU CH
AP
OC/

HG

BC~


Figure 10. Current populations of the Florida black bear (Lhsus americanus floridd~~dd~ddanus)
Abbreviations are as follows: SA (South Alabama), EG (Eglin), AP
(Apalachicola), AU (Aucilla), CH (Chassahowitzka), OC (Ocala), HG
(Highlands/Glades), BC (Big Cypress), SJ (St. Johns), OS (Osceola), and OK
(Okefenokee) (after Pelton and van Manen 1997).















APPENDIX B
MICROSATELLITE ANALYSIS

Microsatellites are a class of nuclear DNA markers that have a rapid mutation rate

and are ideal for studies of genetic consequences of habitat fragmentation (Lindenmayer

& Peakall 2000). Microsatellites consist of a variety of tandem repeat loci that involve a

base motif of 1-6 base pairs repeated up to 100 times. Microsatellites are abundant,

widely disbursed in eukaryotic genomes, and are highly polymorphic. Individual loci are

amplified using polymearse chain reaction (PCR). This allows resolution of alleles that

differ by as little as 1 base pair, and several loci can be analyzed simultaneously (Hedrick

2000).

Microsatellite analysis has frequently been used in conservation studies for

estimating within-population genetic variation and gene flow among populations of black

bears (Ursus amnericanus) (Paetkau & Strobeck 1994; Saitoh et al. 2001; Warrillow et al.

2001; Marshall & Ritland 2002; Csiki et al. 2003), brown bears (U. arctos) (Kohn et al.

1995; Taberlet et al. 1997; Paetkau et al. 1998a; Paetkau et al. 1998b; Waits et al. 2000;

Miller & Waits 2003), polar bears (U. maritimus) (Paetkau et al. 1995; Paetkau et al.

1999), spectacled bears (Tremarctos ornatus) (Ruiz-Garcia 2003) and giant pandas

(Ailuropoda melan2oleuca) (Lu et al. 2001). Microsatellite analysis has also been used to

estimate population density of black and brown bear populations using mark-recapture

models (Woods et al. 1999; Mowat & Strobeck 2000; Boerson et al. 2003).














APPENDIX C
GENETIC VARIATION AMONG BEAR POPULATIONS






















ya






oo













ha
N






cor


cOO


a~ 00
~Om



m HH


CC
8



O
c0


co
at tt tt t a~ ~ ~ ~ ~ ~ ~ ~
333333333 "
EEEEEEmEmEEE


N OmanahmN ammooBOOVOOOO
NO Boot WHHONOOMMODUC~~


0o


cOO
a~c


0000 O OO


o


EO 3OO OMO >00400000~







Od h
ac 0 U

-~ O one o,


ne me wo 00 0 .oot a
e .- M.o a OOO R OO N
mumodao HQH ZZOO ~

































o-commoncocoocoocoocoocoo


lo


a 000


O33~3 O33 OO
N N N


Ilo O\Db NNMM
33CNamN CC) CC


o
a u


or
eE


000 ~000000000000
000 ~000000000000
~~~N~NN~NNNNNNNN
cooit
Voooooooooo


o~~ m e, -. o o,
chm 3 X


~ '2;
e
o
c~ N
3
e a,

o
'Z x
















CC

0000 0,00 0000 0000 0000
coa coa co mme co cco mm
tttttttttttttttttt
MOd d ~d d d 0\0\0\0\d


O O O ~~~


00000000000
~oo N~~m


o
003


0\00 \0
0 0~3~~~~60~~ m003000~00
36 CCI
b~bb~m~mm~m mmmmmmm3mm
33 3


fi
,
B
fi
e
x


~
ct d
c,
o ~dh O
~P F9
a o
f~ a
-d O dC~C~
a --, ~ U ~d
~'ua F9
3 Saa Q) a
-, O c~ ~ ~ vl
o oo c~
818 .~~"8-bm,
F9
~c~O~ F9
~~~ld~a "h ~
c~ ~C d Q, m d 3
ao OX~ rc~
o o a a x o
a=~ E ~ ~ d d U m
,""""5
au~~~~~~ n~~~~ C~~


r~~ia
~FL~ ~
Q)~m~m
d c~ ~C Q, O
5c~ ~o 0,6 rd
V.I~L~FL~Fq~







54










O\\OO\\O0000000
333330000000









000 000 000 0 0

o< < < <

ZZZZZZ %0000000



m~~~~m~mbmmb em










OO 900~00
01 a > om~~ -d 020




X155555"""














APPENDIX D
MICRO SATELLITE DATA FOR FLORIDA BLACK BEARS














QMQAM~~MMAAQMM3MMMMMMM
~mbmmbbmmmbbmbbbbbbbbb
NNNNNNNNNNNNNNNNNNNNN
~mbmmmmmmbmVmmmmmatamm
NNNNNNNNNNNNNNNNNNNNN
ONOUbm~mtmmmbe~mmaN~










EN~N~NN~NNN~NNNNNNNNNN
NNCYWNYWYNCOCOCC WNYY

6 MMOMMMMMMMOOOMMOMMMMM
0 NNNNNNNNNNNNNNNNNNNNN
O ~ 3 ~ ~ ~ 33 ~ ~ 3~
Nbbbbbbbbmb~



33 a 3 co3oco a coo3
m~mm ~ ~ NN~m m NNN NN
0133 3300333 3330 O 3







CCO OO UNOCOD N 000033333


O
0 0
NNNNNNNNNNNNNNNNNNNNN





rbO


0 00 000 0000000~00

e ~~~00~~0~00000000000

O 3M3MMM33333MMMMNMMMNM
O b~b~~b~b b~b

313 OMN333 33333 3 mow








57







~mmmmmmmmmbmemmmmmme
bMYWAYWWWM~amatWWWM
NNNNNNNNNNNNNNNNNNN,
NNNNNNNNNNNNNNNNNNN

NANbbN NNNNN NNNMNb
33N333 NN333 33N333
NNNNNNNNNNNNNNNNNNN








NNNNNNNNNNNNNNNNNNN~









moso ammons manames


NNNNNNNNNNNNNNNNNNN3
NN N N N N N N N N
MOCOUNND3OMWOOCONOO



333O3333333O3333333
NNNNNNNNNNNNNNNNNNN
EMOMOMMOOOOOOMOOMOMO
NNNNNNNNNNNNNNNNNNN~
NOOCONNONONNYWOOVON3

m mO O O N~~m~~

01 O OONOO ONO O N3



e ~ma~m~mo~~m~memm
0 ----"-------- N00
Uo ~ ~ 3~~3 3~~~
*M
O m m~~~~~~~
UN0~~0 ~ ~ ~ ~ ~
M NMWO3bamo-Nat Ch O
33 NNNNNN3M3333333mmV





0\0
ccrc
NN ~
0\0
NN cc
CON
NN ~













ON ,
by
NN








NN v

NN


ON CC









O cc
O i
N


0\0
cccc
NNC
0\0
NNcc
NYC












CNC













vOO

NO

3
NN
OO

NNv
N



OO ,
OO
NN ~


MO
bee
NN ~
mm \
NN cc
cWc
NN
cYN
NN








cYN
NN

NN
NN








NN

NN
O3


O~v


NN

vMv
NN

WW



O c
O c
N
O c


OAcc
ccr
NNc
0\0
NNcc
cWc
bb
cccc
33








bb
NNcc
NY

NN








NN

NN




NN
00
NNCC
iD\




O


ba
cWc
NN \
mm cc
NN ~

WW
ccrc
NY
NM c







WO

NN \




















00


0\0


OO ,

OO


0\0
cccc
NNC
Omv
NNcc
CWC
bb
ccNc
3N








ccrc
NN

bN
NN








NN

NN




03






NOrO
Ogv
N3
00


CCCC
bb
NNc
cccc
NN
cYN

ccNc
3N
ccNc







3N


















0\
NN
00

OOcc

N~i

O3
Oy
N3

00


vO\


03


Cm cbAmmememe








59





~mbbbbbbmbbmbmmememmmem



NAbbbmbmmmbmbmmmN Nmmmmm

0 YONO N N N NY NYN NY N W W
NAN N NN N N NN NA~mNNAamm






W N N W N Y NY W N Y NYU Y W
N N N N N NN NN NN N N N N
ANON NY NNOON NOONNONV O YO
N N N N N NN NN NN N N N N







~moommemmemmemmo mm mam



N1 N 3 3 3 3 3 3 3 3 3 N NNNNNNN
N
4 0 0 00 V W 00 00 0 W O 9 0
0 b~b 00b 0 0 0 0 b0 00 00 00


3 3 0 0 0 0 0 3 3 3 3 3
N N N N N NN NN NN N N N N
OOOOOOOOOOOMOMOOMMOOOMi
N N N N N NN NN NN N N N N
e com <0 roomov v v vo v e

~m~~~~~m~3N N N~m

0 0 0 0 0 0 OO OOOOOOOONO
01 commoo comemomeo3 3
N N N N N N N N N~~~
. OOOOOOOOOOOOOONOOO 0 0




3


HOMMMMMMMMMQMQMMMMMQMQMMQ
































































































bvi400\000
ccrcrcrccccrcr
F909FF9F99F9


O
cHN Mv W D~oO
0000000000


CcO
bee
NN ~
0\m
NN cc
cWN

ccN
NN
NN ~







cWc
NN
0\N
NN







NN

NN




o




N CC

O \
O
CC C
OO D


mm O
bee
NN ~
0\0
NN cc
cWc
bN
NN c
NN







cYN























0\0
bb
03


0\0
ccrc
NN ~
0\0
NN cc
OO ~
WW
OO
WW







NN
NN
NN

NN \







NN
NN
NY

NN


NN

o 0
NN ,


0\0





00
bb

00
bb







NN

NN0
NN
NN








NN

NN
NN

NN0


NNv
NN


0\0





00


00
WW







NN

NN \
NN
NN

















0\0
NN ,


0\0
ccmc
NNC
0\m
NNcc
OOC
00
OO
33







NN
NN
NN











0\0

NW


NN

NN\


CCCC
bb




Occr
NNr







cWN

NNb
NNcc
NN






















0\0


0\0








61





m a m m m m m m m m m
N N N N N N N N N N

0\O\O\O\O\O\O\30\O\3
N N N NN NN N N NN NN N N

0 0 00 00 00 00 00 00 00 000
0 0 0 00 00 0 0 00 00 0 0







N N N N N N N N N N
N N N N N N N N N N
N N N NN N N NN NO N NN N
N N N N N N N N N N







N N N N N N N N N N
N N N NN NN N N NN NN N N
NY3 W W WW N NN N NNY W N N
N N N NN N N NN N N NN N


N N N NN NN N N NN NN N N
3 3 3 0 0 0 0 3 3 3

N1 N N N N NN NN NN N N N




. c
3 3 3 3 3 3 3 3 3 3

U O3 3 3 3 3 3 3 3 3

4 N
m & a N N N N N N N N

HUM U U U U U U U U U








62




mam mmmmmmmmmmmmcramm

N NN NN NN NN NN NN NN NN






Xmambbbmbbmmmmmbmmmbbmm
N1 N 3 3 3 3 3 3 3 3 3 N NNNNNNN

N N NN N N NYN N Y N NN N ON N










MMMMMOMOMMMMOOMOMMMMMON
N1 N 3 3 3 3 3 3 3 3 3 N NNNNNNN







N
N N N N N NN NN NN N N N N
0 0 0 0 0 00 00 00 0 0 0 0



N N N N N NN NN NN N N N N

N N N N N NN NN NN N N N N
3 o co vo v v vv v v ov v co
o -3303030330
0 0 0 0 0 00 00 00 0 0 0 0

01 OCON NOOONDOONONOOO W NO
0 O 0 0 00 00 00 0 0 0 00 0OO
N MN N N N NN MN N MN N NN MN N
.- cococommemoooememoose <
4 e o om e o o
a1 N N -- N N
U

3 3 3 3O 3 3 N 3 3 3 3 3 N








63





Q\m m m m m m m m
N N N N NN NN NN N N N






Xbbmmmmmbbbbbmmmmmm
N1 N N N N N N N N N

N N N NN NO N N NN N N
W N NNY N N NNY N N NN N

N N N NN N N NN N N NN N
N1 N N N N N N N N N











31iNC~O d iD iD d NO O d N~O d d O d
~0 0 * * *
N N N N NN NN NN N N N




013o a 033 3 33 3 3
33m~~m N N Nm~ ~


N N N N NN NN NN N N N

N~ N N N NN N N N NN N N
0 OO



N N N N NN NN NN N N N
N1 N N N N N~ N ~ N N~ N N N N N N N N
3 c c

0 0 0 0 0 N O OOON

C COCCOOOCONDOOOCOCO
X 0 OQAOOQAOO0 0 0 0O 0
31 NMMN NMMN MN N NMNM
.- commememmecooemem

a mammemememmoom mee
0 -- -- -- 3 -N N
U


mY me b mmO Natme am

U3 N NN N N N333333 mm
H NWW W W WW W W WW W W





MO
NN r
cmc
NN O
NN r
NN ~

NN ~
NN ~







ON
NN ~
ON ~
NN



mm
b
3
O
N
O
N CC

NN D

NN





NN r
bO










0\0


MM CC
NN
cmc
NN cc
WN
cMN

NN c
NN








NN ~
NN ~
ON

NN


mm
MM

mm




O cc

NN D

NN





NN ~
MM
NN ~





OO


CCCC
NN
cmc
NNrO
NYe
NMc

NNcc
NN








NNc
NNc
NN

NN


bb
MM

mm
OO
NN



NNCC

NN\




NN
MMc
NND



O3

00c


CCIC
NN
cmc
NN cc
NN
NN ~

NN ~
NN ~







NN
NN ~
NO ~
NN



0\0
MM
mm
OO
NN
OO
NN c

NN D

NN





NN r
OM
NN


NN ~


OO


CCCC
NN
cmc
NNcc
NN
NNc

NNc
NNc







ON
NNc







0\0
NN
O3
N3

NN

NN





NNCC
OM\
NN


NNCC


OO D


CCCC
NN
cmc
NNcc
NN
NNc

NNc
NNc







NN
NNc







003
NN













ccOO
NNv


NN



0cr


CCCC
NN
cmc
NNcc
NN
NNc

NNc
NNc







NN
NNc
NOc
NN



33
bb
33
00
NN


NN














OOcc

00i


*4
O


~MN OMN03


O
N~~ ""





NN A
MY
NN ~
31C
ccr
NY ~
NM o







NY cc
NN
cUN
O
NN \







NN
NN
0\



NN
NN


OO CC


N \


NNI
bb
NNc
30\
CCCC
cWN
bN







cccc
NN
NOrc
O












\0\












NOrO


NN ,
ccr
NN ~
0N
CCN C
cYN

mN








NN c
NN
NN \
NN




O
N 0


NN
NN

CCIC



NN D
NN





N 0


NN\
bee
NNc
IC)
CCCC
NNc
NN







NYcc
NN
cOc
OOc
NN





OO
NN



NN
NN


0\0



NNv
NN

O

O c



NN\


NNO
bee
NNc
33
CCCC
NYc
NMb







cccc
NN













0\0



NN
NN


OOcc

NYi


NN


NN CC
WW
NN ~
NN cc
NN
NN ~
NN ~







ON ~
NN
OO ~
NN ~




OO
NN
O
N

NN

NN
04




NN CC
O \
NN





OO r


NN CC
VM
NN ~
NN O
NN r
NN ~
NN ~







OO ~
NN
OO ~
NN ~




OO
NN
Ob
N3

NN




3CC



NN D
O3
NN





O


o
i-;1






























a


p

U~3j
a
v~ d


UU
OO


UU
OO


NNNNNN


UUUUUUUUU
OOOOOOOOO





NNcc
ccr
NNc
0\0
cccc
N~c
NN











0 0
NN







NN



0\0



NNv
NN





OO0


NN 3
mm
NN ~
WW
CCIC
NY ~
NM











00











300



NN
NN





NO O


NN\
bee
NNc
0\3
CCCC
NNc
NN







NNcc
NN
NNc







NNc
NN

0\O



NN
NN





N0


NN3
mmr
NNc

N Zcc
NNc

NNo
NNc







NN
NNc
NNc
NN





00r



NN

NNcc
33




NNcc
NNi




NO


NN r
cmc
NN
CCIC
cac

NN
NN cc







N
NN ~
cC ~
00














0\0



NN ,
NN





O v


NN0
ccmc
NNC

CCNC
cmN

NNrc
NN







N~c
NNc
NN














cO\



NNv
NN





NN\


0\0
NM cc
NN ~
NN











30
NN




OO








0\0



NN ,
NN





O i


N
U




















HO


UU
OO


0\
UU
OO


by
UU
OO


UU
OO


UU
OO


UU
OO


UU
OO








67





~mbbmmatbmmm mmmbbmmmm

mmmmtmmmmmmm ~mmmmmmmm
NNNNNNNNNNN NNNNNNNN




e~-~- ---




NNNYONNYONb NWb~b
~~~0 0~~ a~~~m
NNNNNNNNNNN NNNNNNNN

XbbbbmbbNbbb N OWNONO
0 O OO OM O 3 33O 33 103 333






3o a









NNONNYNNYNC0 ~3033333


N N N N N N N N N N N N N N N 0 \vv I erv C)C) C
~mm~m bmm ~ ~ ~ ~~b~~0




0 obmmmemmmb a 0 ab~

cOOOOOOOOONON ON O3000oooo
o 1 ~333C 1 1033C
U

o a 3 c o 3 3 3 3 3

coUUUUUUUUUUMMMM3M
HOMOOOOOOOOOOOOMOOOOOOO








68





bmbbbbbmmmbbmmbbmmbbmbM



Oammmmmbbbmmbmmmbmmmmmm
N N N N N NN NN NN N N N N
W W W WW W0 4 4 4 44 4NY WW N
~~mYm~m~~~b~mVWN N m~ m Y m
NY W WW NY N NW WYONY N NON
NY N N N N N N NYN N N N N






X~b~b bO O N N N Nbb
N1 N 3 3 3 3 3 3 3 3 3 N NNNNNNN
O W NOOD 0 YOCOUN UNCC W N
~bbbb b 0 0 0 0 0 0 bb
N N N N N NN NN NN N N N N




~~m~~~~~m3~m~ m3 ~ O O
013~~~ ~ ~ ~ N33 3 3 3 33 3 3 3


N

N N N N N NN NN NN N N N N

W 0 O 90 0 W Y MCO 0 00 4 0
0 0 0 0 b 0 0 0 0 0 b 0 0 0 0


N







NMNMMMM3MMMN N NMNMMMN
O W Y NV O W CN 0 4 4 4 4 4
013 3333 333 3333 333 3
330~~~~m3mm m

~~mm~mOm Nammmmb 0 b O
33N N N N N N N N N N3
O O O O O O O O O O O O








69





VM3~mmYYMYWW ~mYWWAY


YammmmmmmmmY mmmbmbmbm
NNNNNNNNNNN NNNNNNNN
O,,N,,OYWWWW OmN W WW








~-~-- 0-e- -- -
NNNN NNNN N NN NNNN
NYNNONYNNONm CNNONYNN
3 3O3 3 3 3 3 o3 3 3 3 3 3 3
NNNN NNNN N NN NNNN



Ob bbb b b O O 00000
N1 N N NN N NN 1 101


3NN3NN3NNNNN 33NNNNNNN








o so
N N N N N N N N N N N N N N 3 3 1 1 3 3 3 3 3

cocoo vae a comme
E~O 000 ii OOiO ii

3 ~ dC~ ~ i ~~dC~ ~~i C~31i C~C~ 0 90dC~ ~
~3333 3333 3 ~03 0 o3 o
~ e 0 er3 CC C)C) \CC \I I I \O\I) \0\O I) O





cOO
HOMOOO vOOO0 ~OO ,OOOMIIv ererv v ,v








70





W Wm mmbmmbbmbV V m mY m mm

W Wmmmm mmmmmbm mbm mmmmmm

W W W W N N O W W N W OO 0
OmN ~,bmN N N mm N Wm
N0 N N N N NN N N N NN N N N
N N N N N NN N NN N N N N






NW N N ~m~~N N NW W
o a -33 3 3 3 3 3 3 3
N N N N N NN NN NN N N N N
X NONYUN N N N NY N NCbCOC
013 33 3 0 0 0 0 0 03
N N N N N NN NN NN N N N N



OO 0 0 0 OObbbb bb
3 3 3 3 3 3 3 3 3 3 3 3

0 won~ m~3mm~ m~
01 33 3 3 3 3 3 3 3 3 33

N N N N N NN NN NN N N N N

N N N N N NN NN NN N N N N

W W OMV 4 44 NWW W 0 04 0 4
E~0 i i i i i i ii0 00000b000bb00bQ
N N NCCON OUN NON N W NONE

~3303033 333033000o

N N N N N NN NN NN N N N N







O
- N N N O 0 0 0~33~
0 ~ 0~~0~~~0 ~ ~
U ~3 3~~3 3~
03 N 3 3 O3 33 N33
O
3NN N N N N N N N N N3
HM manamanamanamanamamma
































































































3CMN DO 00\
ccrcrcrcrcrccccccrc
VmVm mVIVIm IVII


CMN bl\~00
OOOOOOOOO
OOOOOOOOO


NNO
bee
NNc
0\3
CCCC
cYc
bb







NNcc
NN
NO
cc c
NN








ccO







0\0
vWN


NN




30




NO ~


NN )
mm
NN ~
0\
CCIC
NY ~
NM







NY b
NN
NC c
3

NN




OO









0\0








30


NNC
ccr
NNc
33
CCCC
NNc
NN












30







O cc







0\0








30




ONc


NN
mm r
NN ~
NY
NN CC
NN ~

NN ~







ZW
NN ~
cWN












0\0
00
YC


NN




0\




NN D


NN3
CCmC
NNc
NN
NNCC
NNc
NNc












0\0
NN












COI
iD\
NN


NN0

NNv
OO



NOc
O3
NNc


NN I
mm
NN ~
0\0
ccrc
cWN
bN











0\0

0 3






O cc






0\0
vUv
NW



NN









O v





NN cc
Cmm
NN
CCIC
cmc

WW
ccrc







WN
NN








300

NN
NN

ccN
NN


NN O
igv
NN


NN O





30


NNI
bb
NNc
CCI
bee
NYc
Nao
















0\0
NN


0\0

NNv
NN







\0\
vCv


No


NN
mm r
NN ~
@@
CCIC
cWN
mN
















0 0
N

30

NN
NN








0\0
NN ,


NN \
ccm c
NN ~
30
CCIC
NN ~
NN







NN cc
NN
cON
OM ~
NN









ccO





0\0
vON


NN

cMc
NN
cO ~

iON


NNO
bee
NNc
33
bee
cWc
bb







ccN
NN
bb
ccOc
NN













0\0
~v,


NN





30

cYc


NNcc
mmr
NNc
33
CCCC
cWc
Na







cccc
NN
bN












0\3
bN


NN
33
NN

0\0
OUv

N3
0\0
N~v


NNCC
mmr
NNc
3N










CCIC
OOc









0\3

NN
NN







OOcc


\0\
NNv

NN

33
OOc


co














ou


UO



S o

Ho


N0
co


00,
00


00
00


NN ~
co
co


00
00


00
00


00
00






































NM
N


oO
O


OOOM
oO
O


O


00
O
O


O


O
O


3bCCIO\IA31ACCI
333C~3C~0\3
0o~o.b.dd~c~
000 00


3bCCIO\IA31ACCI
333C~3C~0\3
0o~o.b.dd~c~
000 00


Ob
N
O

O ~

vOcc
OO
OO


OBr
O
O


O


OOW0
O
O


NO
N
vM
O C

bee
VM

OO

100
OM
OO


Ob
O
O


O


OOW0
O
O


NO
N
vMo
OCC

bee
00

OO0

100
OM
OO


O N
O O




O


O N
O O




O


OBr
O
O


o
o\
1,I~


IC)31C)0\31C)
O~~C~iD~
~3303
0
00000


~
1,I~


IC)31C)0\31C)
O~~C~iD~
~3303
0
00000


a


O

o O

a

0 ~


m ZO o


O



com a


. o


<






























NO
O


OM
O


000\0
a


RO


0000
a


OO

O


ON
O
O



vco


O


O


ON>
NO


OO 0>~
O\ccr
co


0\0

O


000\0
O
O


Occ

O


OO>O
ee
a


OO>O
ee
a


000v
0


OO


IC)

OO 3











O
OO






Sc~
.


Occ

O


000

O


Occ
N
O













m


OM


0


o


O

o O


0 O
o o



00<0 d


o O

m ~

mu OO















OON~
O
O


OO0OM

O


Occ

OO
OO


04

O


O
O


OOON
N r
O


O


90
o
00

0\
O,


OO 0

a


OON
ee
c


0\0
O
O


O
O


OON~
-
9
a


0-<
on


O


v~O\
63
d
O
O


On,

O


ON







O






o
.- c



<<


OB
O


OO
O


0


o
. c



<<


O

o O

o a

0 ~


mo oo


SO


C a

o



mommoo





















































































SO

C o

o> 0
0



mommoo


ON
O
O


o

O


OMc
O
O


031C

O


OON~
O
O


O
VO
N


O


vOOOB
O
O


OOOBO
O
N


000 ,
IA


OO
0


000 ,


000
CC
c


j0
o
o


000~

ae


O


O 0

Oo
a r


corc


0

OO


ry


O0oo
00

0


O


O~bb NOO
000 a


000 ,


O


000

NOM3
O
O


00o000
O\
O


00\0
-
O
O


Om

O


O
O


v0
00
O
O


a


o
.- c



<<


SO


~ o



a0 d


Umd moo






























OON~
O
O


0
c


631C
0\Co
o


ON
O


O r
O


O
O


o


O


001N

O


00
O
0


OM
3
0


OOcc
O
0


OON~
O
O


OO


OO0v,
ee
0


O
0

00
3
0


NO
O
0

00o
oo
0


OO








.- c



<<


00

00













.- c



<<


C ~0v 0





3 4

O m





mmZO


0


0000

0


OZ

m O
Q 4

O m


muC O




























ON


co


000v,


O


OON~
O
O


IA00
Ob
O
O
O


a ,


000
000



000

OOM


OOV

O


OBr
O
O


OOD
O r
O


OOMO )
OW
OO

OOOB
oO
O
O


O
O


O
O


\O ,


CU
OY


OO

a


OOO~O
0


000

CC
00
N

ov


OOv,
N
0


OON~
ee
0


NO


00>
ee
a


000v000
OW
c


ON


00


OO
V
a


OOvMm>
N~OO
co


.- c



<<


. c



<<


O

o O


0
0 o



momo d


SO


C a

o



mommoo

















LITERATURE CITED

Aars, J., and R. A. Ims. 1999. The effect of habitat corridors on rates of transfer and
interbreeding between vole demes. Ecology 80:1648-1655.

Alt, G. 1979. Dispersal patterns of black bears in northeastern Pennsylvania a
preliminary report. Pages 186-199 in R. D. Hugie, editor. Fourth Eastern Black
Bear Workshop, Bangor, Maine.

Bakeless, J. E. 1989. America as seen by its first explorers: the eyes of discovery. Dover
Publications, New York.

Bartram, W. 1980. William Bartram travels in W. Howarth, and F. Bergon, editors.
Literature of the American wilderness. Peregrine Smith, Inc., Salt Lake City.

Beier, P. 1993. Determining minimum habitat areas and habitat corridors for cougars.
Conservation Biology 7:94-108.

Beier, P. 1995. Dispersal of juvenile cougars in fragmented habitat. Journal of Wildlife
Management 59:228-237.

Beier, P., and S. Loe. 1992. A checklist for evaluating impacts to wildlife movement
corridors. Wildlife Society Bulletin 20:434-440.

Beier, P., and R. F. Noss. 1998. Do habitat corridors provide connectivity? Conservation
Biology 12:1241-1252.

Bentzien, M. M. 1998. Endangered and threatened wildlife and plants; new 12-month
finding for a petition to list the Florida black bear. Federal Register 63:67613-
67618.

Boerson, M. R., J. D. Clark, and T. L. King. 2003. Estimating black bear population
density and genetic diversity at Tensas River, Louisiana using microsatellite DNA
markers. Wildlife Society Bulletin 31:197-207.

Bowker, B., and T. Jacobson. 1995. Louisiana black bear recovery plan. United States
Fish and Wildlife Service, Jackson, Mississippi.

Brady, J. R., and D. S. Machr. 1985. Distribution of black bears in Florida. Florida Field
Naturalist 13:1-7.










Brody, A. J., and M. R. Pelton. 1989. Effects of roads on black bear movements in
western North Carolina. Wildlife Society Bulletin 17:5-10.

Brooker, L., and M. Brooker. 2002. Dispersal and population dynamics of the blue-
breasted fairy-wren, Malurus pulcherrimus, in fragmented habitat in the Western
Australian wheatbelt. Wildlife Research 29:225-233.

Caizergues, A., O. Ratti, P. Helle, L. Rotelli, L. Ellison, and J. Y. Rasplus. 2003.
Population genetic structure of male black grouse (Tetrao tetrix) in fragmented vs.
continuous landscapes. Molecular Ecology 12:2297-2305.

Cale, P. G. 2003. The influence of social behaviour, dispersal and landscape
fragmentation on population structure in a sedentary bird. Biological Conservation
109:237-248.

Cegelski, C. C., L. P. Waits, and N. J. Anderson. 2003. Assessing population structure
and gene flow in Montana wolverines (Gulo gulo) using assignment-based
approaches. Molecular Ecology 12:2907-2918.

Chesser, R. K. 1983. Isolation by distance: relationship to the management of genetic
resources. Pages 66-77 in C. M. Schonewald-Cox, S. M. Chambers, B. MacBryde,
and W. L. Thomas, editors. Genetics and conservation: a reference for managing
wild animal and plant populations. The Benj amin/Cummings Publishing Company,
Inc., London.

Coffman, C. J., J. D. Nichols, and K. H. Pollock. 2001. Population dynamics of2~icrotus
pennsylvanicus in corridor-linked patches. Oikos 93:3-21.

Cory, C. B. 1896. Hunting and fishing in Florida. Estes and Lauriet Publishing, Boston.

Cox, J., R. Kautz, M. MacLaughlin, and T. Gilbert 1994. Closing the gaps in Florida's
wildlife habitat conservation system. Florida Game and Fresh Water Fish
Commission, Tallahassee.

Craighead, F. L., and E. R. Vyse. 1996. Brown/grizzly bear metapopulations. Pages 325-
351 in D. R. McCullough, editor. Metapopulations and wildlife conservation.
Island Press, Washington, D.C.

Crooks, K. R. 2002. Relative sensitivities of mammalian carnivores to habitat
fragmentation. Conservation Biology 16:488-502.

Csiki, I., C. Lam, A. Key, E. Coulter, J. D. Clark, R. M. Pace, K. G. Smith, and D. D.
Rhoads. 2003. Genetic variation in black bears in Arkansas and Louisiana using
microsatellite DNA markers. Journal of Mammalogy 84:691-701.

Dallas, J. F., F. Marshall, S. B. Piertney, P. J. Bacon, and P. A. Racey. 2002. Spatially
restricted gene flow and reduced microsatellite polymorphism in the Eurasian otter
(Lutra lutra) in Britain. Conservation Genetics 3:15-29.










Davies, K. F., C. Gascon, and C. R. Margules. 2001. Habitat fragmentation:
consequences, management, and future research priorities. Pages 81-97 in M. E.
Soule', and G. H. Orians, editors. Conservation biology: research priorities for the
next decade. Island Press, Washington.

Dobey, S. T. 2002. Abundance and density of Florida black bears in the Okefenokee
National Wildlife Refuge and Osceola National Forest. M. S. thesis. The University
of Tennessee, Knoxville.

Dobson, A., K. Ralls, M. Foster, M. E. Soule', D. Simberloff, D. Doak, J. A. Estes, L. S.
Mills, D. Mattson, R. Dirzo, H. Arita, S. Ryan, E. A. Norse, R. F. Noss, and D.
Johns. 1999. Corridors: reconnecting fragmented landscapes. in M. E. Soule', and J.
Terbough, editors. Continental conservation: scientific foundations of a regional
reserve network. Island Press, Washington, D.C.

Duke, D. L., M. Hebblewhite, P. C. Paquet, C. Callaghan, and M. Persy. 2001.
Restorating a large carnivore corridor in Banff National Park. in D. S. Machr, R. F.
Noss, and J. L. Larkin, editors. Large mammal restoration. Island Press,
Washington, D.C.

Dunbar, M. R., M. W. Cunningham, J. B. Wooding, and R. P. Roth. 1996.
Cryptorchidism and delayed testicular descent in Florida black bears. Journal of
Wildlife Diseases 32:661-664.

Eason, T. H. 1995. Weights and morphometrics of black bears in the southeastern United
States. M. S. thesis. The University of Tennessee, Knoxville.

Eason, T. H. 2000. Black bear status report: a staff report to the commissioners. Florida
Fish and Wildlife Conservation Commission, Tallahassee.

Eason, T. H., S. L. Simek, and D. Zeigler. 2001. Statewide assessment of road impacts on
bears in Florida. Florida Fish and Wildlife Conservation Commission, Tallahassee.

Eastridge, R., and J. D. Clark. 2001. Evaluation of 2 soft-release techniques to
reintroduce black bears. Wildlife Society Bulletin 29:1163-1174.

Ebert, D., C. Haag, M. Kirkpatrick, M. Riek, J. W. Hottinger, and V. I. Pajunen. 2002. A
selective advantage to immigrant genes in a Daphnia metapopulation. Science
295:485-488.

Edwards, A. S. 2002. Status of the black bear in southwestern Alabama. M. S. thesis. The
University of Tennessee, Knoxville.

Emnest, H. B., W. M. Boyce, V. C. Bleich, B. May, S. J. Stiver, and S. G. Torres. 2003.
Genetic structure of mountain lion (Puma concolor) populations in Califomnia.
Conservation Genetics 4:353-366.

Fahrig, L. 2001. How much habitat is enough? Biological Conservation 100:65-74.










Fahrig, L., and G. Merriam. 1985. Habitat patch connectivity and population survival.
Ecology 66:1762-1768.

Fahrig, L., and G. Merriam. 1994. Conservation of fragmented populations. Conservation
Biology 8:50-59.

Felsenstein, J. 1993. PHYLIP (Phylogeny Inference Package), version 3.5c. Department
of Genetics, University of Washington, Seattle.

Ferreras, P. 2001. Landscape structure and asymmetrical inter-patch connectivity in a
metapopulation of the endangered Iberian lynx. Biological Conservation 100: 125-
136.

Fitch, W. M., and E. Margolia. 1967. Construction of phylogenetic trees. Science
155:279-284.

Flagstad, O., C. W. Walker, C. Vila, A. K. Sundqvist, B. Fernholm, A. K. Hufthammer,
O. Wiig, I. Koyola, and H. Ellegren. 2003. Two centuries of the Scandinavian wolf
population: patterns of genetic variability and migration during an era of dramatic
decline. Molecular Ecology 12:869-880.

Flather, C. H., and M. Bevers. 2002. Patchy reaction-diffusion and population abundance:
the relative importance of habitat amount and arrangement. American Naturalist
159:40-56.

Foran, D. R., S. C. Minta, and K. S. Heinemeyer. 1997. DNA-based analysis of hair to
identify species and individuals for population research and monitoring. Wildlife
Society Bulletin 25:840-847.

Forman, R. T., and M. Godron 1986. Landscape ecology. John Wiley & Sons, New York.

Foster, M. L., and S. R. Humphrey. 1995. Use of highway underpasses by Florida
panthers and other wildlife. Wildlife Society Bulletin 23:95-100.

Frankham, R. 1995. Inbreeding and extinction: a threshold effect. Conservation Biology
9:792-799.

Frankham, R. 1996. Relationship of genetic variation to population size in wildlife.
Conservation Biology 10:1500-1508.

Frankham, R., J. Ballou, and D. Briscoe 2002. Introduction to conservation genetics.
Cambridge University Press, New York.

Franklin, I. R. 1980. Evolutionary change in small populations. Pages 135-150 in M. E.
Soule', and B. A. Wilcox, editors. Conservation biology: an evolutionary--
ecological perspective. Sinauer, Sunderland, Massachusetts.










Ganona, P., P. Ferreras, and M. Delibes. 1998. Dynamics and viability of a
metapopulation of the endangered Iberian lynx (Lynx pardinus). Ecological
Monographs 68:349-370.

Gerlach, G., and K. Musolf. 2000. Fragmentation of landscape as a cause for genetic
subdivision in bank voles. Conservation Biology 14:1066-1074.

Gottelli, D., C. Sillerozubiri, G. D. Applebaum, M. S. Roy, D. J. Girman, J.
Garciamoreno, E. A. Ostrander, and R. K. Wayne. 1994. Molecular genetics of the
most endangered canid: the Ethiopian wolf (Canis simensis). Molecular Ecology
3:301-312.

Griffith, B., J. Scott, J. Carpenter, and C. Reed. 1989. Translocation as a species
conservation tool: status and strategy. Science 245:477-480.

Guo, S. W., and E. A. Thompson. 1992. Performing the exact test of Hardy-Weinberg
proportion for multiple alleles. Biometrics 48:361-372.

Haddad, N. M. 1999. Corridor use predicted from behaviors at habitat boundaries.
American Naturalist 153:215-227.

Haddad, N. M., D. R. Bowne, A. Cunningham, B. J. Danielson, D. J. Levey, S. Sargent,
and T. Spira. 2003. Corridor use by diverse taxa. Ecology 84:609-615.

Hale, M. L., P. W. W. Lurz, M. D. F. Shirley, S. Rushton, R. M. Fuller, and K. Wolff.
2001. Impact of landscape management on the genetic structure of red squirrel
populations. Science 293:2246-2248.

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

Hanski, I. 1994. Patch-occupancy in fragmented landscapes. Trends in Ecology &
Evolution 9:131-135.

Hanski, I., and D. Simberloff. 1997. The metapopulation approach, its history, conceptual
domain, and application to conservation. in I. Hanski, and M. E. Gilpin, editors.
Metapopulation biology: ecology, genetics and evolution. Academic Press, San
Diego, California.

Harris, L. D. 1984. The fragmented forest: island biogeography theory and the
preservation of biotic diversity. The University of Chicago Press, Chicago, Illinois.

Harris, L. D., and P. B. Gallagher. 1989. New initiatives for wildlife conservation: the
need for movement corridors. in G. Mackintosh, editor. Preserving communities
and corridors. Defenders of Wildlife, Washington, D.C.










Harris, L. D., and J. Scheck. 1991. From implications to applications: the dispersal
corridor principle applied to the conservation of biological diversity. in D. A.
Saunders, and R. J. Hobbs, editors. Nature conservation 2: the role of corridors.
Surrey Beatty & Sons, Chipping Norton, New South Wales, Australia.

Harrison, S., and E. Bruna. 1999. Habitat fragmentation and large-scale conservation:
what do we know for sure? Ecography 22:225-232.

Harrison, S., and J. Voller. 1998. Connectivity. in S. Harrison, and J. Voller, editors.
Conservation biology principles for forested landscapes. UBC Press, Vancouver,
British Colombia.

Hartl, D. L., and A. G. Clark 1997. Principles of population genetics. Sinauer,
Sunderland, Massachusetts.

Hass, C. A. 1995. Dispersal and use of corridors by birds in wooded patches on an
agricultural landscape. Conservation Biology 9:845-854.

Hedrick, P. H. 2000. Applications of population genetics and molecular techniques to
conservation biology. Conservation Biology 4. Pages 438-450 in A. G. Young, and
G. M. Clarke, editors. Genetics, demography, and viability of fragmented
populations. Cambridge University Press, Cambridge, United Kingdom.

Hellborg, L., C. W. Walker, E. K. Rueness, J. E. Stacy, I. Kojola, H. Valdmann, C. Vila,
B. Zimmermann, K. S. Jakobsen, and H. Ellegren. 2002. Differentiation and levels
of genetic variation in northern European lynx (Lynx lynx) populations revealed by
microsatellites and mitochondrial DNA analysis. Conservation Genetics 3:97-111.

Hellgren, E. C., and D. S. Machr. 1993. Habitat fragmentation and black bears in the
eastern United States. Pages 154-165 in E. P. Orff, editor. Eastern Black Bear
Workshop for Research and Management, Waterville Valley, New Hampshire.

Hellgren, E. C., and M. R. Vaughan. 1994. Conservation and management of isolated
black bear populations in the southeastern Coastal Plain of the United States.
Proceedings of the Annual Conference Southeastern Association Fish and Wildlife
Agencies 48:276-285.

Hendry, L. A., T. M. Goodwin, and R. F. Labisky. 1982. Florida's vanishing wildlife.
Circular 485 (Revised). Florida Cooperative Extension Service, Gainesville,
Florida.

Hess, G. R., and R. A. Fischer. 2001. Communicating clearly about conservation
corridors. Landscape and Urban Planning 55:195-208.

Hitchings, S. P., and T. J. C. Beebee. 1997. Genetic substructuring as a result of barriers
to gene flow in urban RanaRRRRR~~~~~~~RRRRRR temporaria (common frog) populations: implications
for biodiversity conservation. Heredity 79: 117-127.










Hector, T. S. 2003. Regional landscape analysis and reserve design to conserve Florida's
biodiversity. Ph.D. dissertation. University of Florida, Gainesville.

Hector, T. S., M. H. Carr, and P. D. Zwick. 2000. Identifying a linked reserve system
using a regional landscape approach: the Florida ecological network. Conservation
Biology 14:984-1000.

Hudson, Q. J., R. J. Wilkins, J. R. Waas, and I. D. Hogg. 2000. Low genetic variability in
small populations of New Zealand kokako (Callaea~s cinerea wilsoni). Biological
Conservation 96: 105-112.

Ims, R. A., and H. P. Andreassen. 1999. Effects of experimental habitat fragmentation
and connectivity on root vole demography. Journal of Animal Ecology 68:839-852.

Jules, E. S. 1998. Habitat fragmentation and demographic change for a common plant:
trillium in old-growth forest. Ecology 79:1645-1656.

Kaczensky, P., F. Knauer, B. Krze, M. Jonozovic, M. Adamic, and H. Gossow. 2003. The
impact of high speed, high volume traffic axes on brown bears in Slovenia.
Biological Conservation 111:191-204.

Kasbohm, J. W. 2004. Endangered and threatened wildlife and plants; reexamination of
regulatory mechanisms in relation to the 1998 Florida black bear petition finding.
Federal Register 69:2100-2108.

Kasbohm, J. W., and M. M. Bentzien. 1998. The status of the Florida black bear. United
States Fish and Wildlife Service, Jacksonville, Florida.

Keller, I., and C. R. Largiader. 2003. Recent habitat fragmentation caused by maj or roads
leads to reduction of gene flow and loss of genetic variability in ground beetles.
Proceedings of the Royal Society of London Series B-Biological Sciences 270:417-
423.

Kirchner, F., J. B. Ferdy, C. Andalo, B. Colas, and J. Moret. 2003. Role of corridors in
plant dispersal: an example with the endangered Ranunculus nodiflorus.
Conservation Biology 17:401-410.

Koenig, W. D., D. Van Vuren, and P. H. Hooge. 1996. Detectability, philopatry, and the
distribution of dispersal distances in vertebrates. Trends in Ecology & Evolution
11:514-517.

Kohn, M., F. Knauer, A. Stoffella, W. Schroder, and S. Paabo. 1995. Conservation
genetics of the European brown bear a study using excremental PCR of nuclear
and mitochondrial sequences. Molecular Ecology 4:95-103.

Koopman, M. E., B. L. Cypher, and J. H. Scrivner. 2000. Dispersal patterns of San
Joaquin kit foxes (Vulpes macrotis mutica). Journal of Mammalogy 81:213-222.










Kuehn, R., W. Schroeder, F. Pirchner, and O. Rottmann. 2003. Genetic diversity, gene
flow and drift in Bavarian red deer populations (Cervus elaphus). Conservation
Genetics 4:157-166.

Kurten, B., and E. Anderson 1980. Pleistocene mammals of North America. Columbia
University Press, New York.

Kyle, C. J., and C. Strobeck. 2001. Genetic structure of North American wolverine (Gulo
gulo) populations. Molecular Ecology 10:337-347.

Lande, R. 1995. Mutation and conservation. Conservation Biology 9:782-791.

Larkin, J. L., D. S. Machr, T. S. Hector, M. A. Orlando, and K. Whitney. 2004.
Landscape linkages and conservation planning for the black bear in west-central
Florida. Animal Conservation 7:1-12.

Lee, D. J., and M. R. Vaughan. 2003. Dispersal movements by subadult American black
bears in Virginia. Ursus 12:162-170.

Levins, R. 1970. Some mathematical questions in biology 2. Pages 77-107 in M.
Gerstenhaber, editor. Lectures on mathematics in the life sciences. American
Mathemathics Society, Providence.

Lidicker, W. Z., and W. D. Koenig. 1996. Responses of terrestrial vertebrates to habitat
edges and corridors. in D. R. McCullough, editor. Metapopulations and wildlife
conservation. Island Press, Washington, D.C.

Lindenmayer, D., and R. Peakall. 2000. The Tumet experiment- integrating demographic
and genetic studies to unravel fragmentation effects: a case study of the native bush
rat. Conservation Biology 4. Pages 173-202 in A. G. Young, and G. M. Clarke,
editors. Genetics, demography, and viability of fragmented populations. Cambridge
University Press, Cambridge, United Kingdom.

Linnell, J. D. C., J. Odden, M. E. Smith, R. Aanes, and J. Swenson. 1997. Translocation
of carnivores as a method for problem animal management: a review. Biodiversity
and Conservation 6:1245-1257.

Louis, E. J., and E. R. Dempster. 1987. An exact test for Hardy-Weinberg and multiple
alleles. Biometrics 43:805-811.

Lu, Z., W. E. Johnson, M. Menotti-Raymond, N. Yuhki, J. S. Martenson, S. Mainka, H.
Shi-Qiang, Z. Zhihe, G. H. Li, W. S. Pan, X. R. Mao, and S. J. O'Brien. 2001.
Patterns of genetic diversity in remaining giant panda populations. Conservation
Biology 15:1596-1607.

MacArthur, R. H., and E. O. Wilson 1967. The theory of island biogeography. Princeton
University Press, Princeton, New Jersey.










Mader, H. J. 1984. Animal habitat isolation by roads and agricultural fields. Biological
Conservation 29:81-96.

Machr, D. S., and J. R. Brady. 1984. Food habits of Florida black bears. Journal of
Wildlife Management 48:230-234.

Machr, D. S., T. S. Hector, L. J. Quinn, and J. S. Smith. 2001. Black bear habitat
management guidelines for Florida. Technical report 17. Florida Fish and Wildlife
Conservation Commission, Tallahassee.

Machr, D. S., E. D. Land, D. B. Shindle, O. L. Bass, and T. S. Hector. 2002. Florida
panther dispersal and conservation. Biological Conservation 106:187-197.

Machr, D. S., J. E. Layne, E. D. Land, J. W. McCown, and J. Roof. 1988. Long distance
movements of a Florida black bear. Florida Field Naturalist 16:1-6.

Machr, D. S., J. S. Smith, M. W. Cunningham, M. E. Barnwell, J. L. Larkin, and M. A.
Orlando. 2003. Spatial characteristics of an isolated Florida black bear population.
Southeastern Naturalist 2:433-446.

Manel, S., M. K. Schwartz, G. Luikart, and P. Taberlet. 2003. Landscape genetics:
combining landscape ecology and population genetics. Trends in Ecology &
Evolution 18:189-197.

Mansfield, K. G., and E. D. Land. 2002. Cryptorchidism in Florida panthers: prevalence,
features, and influence of genetic restoration. Journal of Wildlife Diseases 38:693-
698.

Mantel, N. 1967. The detection of disease clustering and a generalized regression
approach. Cancer Research 27:209-220.

Marshall, H. D., and K. Ritland. 2002. Genetic diversity and differentiation of Kermode
bear populations. Molecular Ecology 11:685-697.

McCown, J. W., T. H. Eason, and M. W. Cunningham. 2001. Black bear movements and
habitat use relative to roads in Ocala National Forest. Final Report. Florida Fish
and Wildlife Conservation Commission, Tallahassee.

McCoy, J., and K. Johnston 2000. Using ArcGIS spatial analyst. ESRI Publishing,
Redlands, California.

McDaniel, J. 1974. Florida report on black bear management and research. Pages 157-
162 in M. R. Pelton, and D. Conley, editors. Proceedings of the Second Eastemn
Workshop on Black Bear Management and Research, Gatlinburg, Tennessee.

McLellan, B. N., and F. W. Hovey. 2001. Natal dispersal of grizzly bears. Canadian
Journal of Zoology 79:838-844.










McLellan, B. N., and D. M. Shackleton. 1988. Grizzly bears and resource-extraction
industries: effects of roads on behavior, habitat use and demography. Journal of
Applied Ecology 25:451-460.

Mech, S. G., and J. G. Hallett. 2001. Evaluating the effectiveness of corridors: a genetic
approach. Conservation Biology 15:467-474.

Meffe, G. K., and C. R. Carroll 1997. Principles of conservation biology. Sinauer,
Sunderland, Massachusetts.

Merriam, C. H. 1896. Preliminary synopsis of the American bears. Proceedings of the
Biological Society of Washington 10:65-83.

Michalakis, Y., and L. Excoffler. 1996. A generic estimation of population subdivision
using distances between alleles with special interest to microsatellite loci. Genetics
142:1061-1064.

Miller, C. R., and L. P. Waits. 2003. The history of effective population size and genetic
diversity in the Yellowstone grizzly (Ursus arctos): implications for conservation.
Proceedings of the National Academy of Sciences of the United States of America
100:4334-4339.

Mills, L. S., and F. W. Allendorf. 1996. The one-migrant-per-generation rule in
conservation and management. Conservation Biology 10:1509-1518.

Mowat, G., and C. Strobeck. 2000. Estimating population size of grizzly bears using hair
capture, DNA profiling, and mark-recapture analysis. Journal of Wildlife
Management 64:183-193.

Myers, R. L., and J. J. Ewel. 1991. Ecosystems of Florida. University of Central Florida
Press, Orlando.

Niemela, J. 2001. The utility of movement corridors in forested landscapes. Scandinavian
Journal of Forest Research 3:70-78.

Noss, R. F. 1987. Corridors in real landscapes: a reply to Simberloff and Cox.
Conservation Biology 1:159-164.

Noss, R. F. 1993. Wildlife corridors. in D. S. Smith, and P. C. Hellmund, editors.
Ecology of greenways. University of Minnesota Press, Minneapolis.

Noss, R. F., and L. D. Harris. 1986. Nodes, networks, and mums preserving diversity at
all scales. Environmental Management 10:299-309.

Noss, R. F., H. B. Quigley, M. G. Hornocker, T. Merrill, and P. C. Paquet. 1996.
Conservation biology and carnivore conservation in the Rocky Mountains.
Conservation Biology 10:949-963.