The biogeography of Montana black bear genetics
by William Allen Ostheimer
A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in
Biological Sciences
Montana State University
© Copyright by William Allen Ostheimer (1998)
Abstract:
Traditional studies of Island Biogeography focused on the diversity of species found on different island
systems. The application of genetic diversity studies to island biogeography is a relatively recent
development. Microsatellite DNA allele heterozygosity was used to assay the genetic diversity of black
bears living on isolated mountain ranges in Montana, and compare those heterozygosity levels to levels
found in contiguous black bear habitat that represented a mainland. Probability of identity was also
calculated and the presence of unique alleles in some of the populations helped illuminate possible
histories of those populations. Due to very small sample sizes (n=10, 8, 5, 5) a bootstrap approach was
used to resample the larger mainland population (n=38) at the smallest sample size. There were no
significant differences between the mainland populations and the mountain islands, with the
heterozygosity levels of the mountain islands fitting the bootstrapped mainland population with 95 %
confidence. Possible histories of black bear occupation of central Montana were discussed. 
THE BIOGEOGRAPHY OF MONTANA
BLACK BEAR GENETICS
by
William Allen Ostheimer
A thesis submitted in partial fulfillment 
o f the requirements for the degree
of
Master of Science 
in
Biological Sciences
MONTANA STATE UNIVERSITY 
Bozeman, Montana
April 1998
ii
APPROVAL 
of a thesis submitted by 
William Allen Ostheimer
The thesis has been read by each member of the thesis committee and has been found to be 
satisfactory regarding content, English usage, format, citations, bibliographic style, and consistency, and is 
ready for submission to the College of Graduate Studies.
Ernest Vyse -fT fA ff
Date
Ernest Vyse
Approved for the Department of Biology.
/  Date
Approved for the College of Graduate Studies.
Dafe '
Joseph JFedock
iii
STATEMENT OF PERMISSION TO USE
In presenting this thesis in partial fulfillment o f the requirements for a master’s degree at Montana State 
University-Bozeman, I agree that the Library shall make it available to borrowers under rules of the Library.
If I  have indicated my intention to copyright this thesis by including a copyright notice page, 
copying is allowable only for scholarly purposes, consistent with “fair use” as prescribed in the US. 
Copyright Law. Requests for permission for extended quotation from or reproduction of this thesis in whole 
or in parts may be granted only by the copyright holder.
Signature
Date
J
iv
ACKNOWLEDGMENTS
Thanks to Montana Fish, Wildlife and Parks for funding this study. I would like to thank my 
parents, family and friends for their support. Thanks to Ernest Vyse for his role as mentor and friend. Dr. 
Curtis Strobeck, Dr. John CofBn, Greg Wilson, and most everyone in the genetics lab at the University of 
Alberta, Edmonton were most generous with their time, patience and knowledge. Dr. Robert Garrott and 
the Biology 580 class read various drafts of this manuscript and provided excellent feedback.
Thanks also to Tracey Mader, Chris and Maiji Mills.
■ V
TABLE OF CONTENTS
INTRODUCTION.................................................................................................................  I
STUDY AREA..........................................................................  ..........................................  4
METHODS ............................................................................................................................ 6
RESULTS ............................................................................................................................... 9
DISCUSSION............................................................................................................................ 16
LITERATURE CITED..............................................................................................................21
APPENDIX 25
LIST OF TABLES
Table Page
1. Locus designation and primer sequences for six Ursid microsatellites..................................... 7
2. Allele distributions, frequencies and number of alleles at six Ursid microsatellite primers for six
black bear populations in M ontana............................................................................................  11
3. Levels of heterozygosity and probability of identity at six microsatellite loci for six black
bear populations in M ontana......................................................................................................  13
LIST OF FIGURES
Figure Page
1. Two island system model showing different assemblages of alleles...........................................  3
2. Study area map.....................................................................................................................................  5
3. Lines of best fit for area of mountain range and distance to nearest suitable habitat
to expected heterozygosity and probability of identity for black bears on four mountain
ranges in central Montana..................................................................................................................... 14
VUl
ABSTRACT
Traditional studies of Island Biogeography focused on the diversity of species found on different 
island systems. The application o f genetic diversity studies to island biogeography is a relatively recent 
development. Microsatellite DNA allele heterozygosity was used to assay the genetic diversity of black 
bears living on isolated mountain ranges in Montana, and compare those heterozygosity levels to levels 
found in contiguous black bear habitat that represented a mainland. Probability o f identity was also 
calculated and the presence o f unique alleles in some of the populations helped illuminate possible histories 
of those populations. Due to veiy small sample sizes (n=10, 8, 5, 5) a bootstrap approach was used to 
resample the larger mainland population (n=38) at the smallest sample size. There were no significant 
differences between the mainland populations and the mountain islands, with the heterozygosity levels of the 
mountain islands fitting the bootstrapped mainland population with 95 % confidence. Possible histories of 
black bear occupation of central Montana were discussed.
IINTRODUCTION
In 1967, Robert MacArthur of Princeton University and Edward Wilson o f Harvard University 
published The Theory of Island Biogeographv. MacArthur and Wilson’s work has profoundly influenced 
the study of species and their relationship to the spatial structure o f their environment (Rosensweig 1995). 
MacArthur and Wilson reported that the number of species found on an island was dependent upon that 
island’s size and degree of isolation. Islands which were small and isolated had lower species diversity than  
larger islands occurring closer to a mainland. Species diversity was dependent on the interaction between 
immigration and extinction. Investigations of true islands (Adler 1992, Patterson 1987, Diamond 1975, 
Abbott & Black 1978) and habitat islands (Rosensweig 1995, Brown 1971, Thompson 1974, Picton 1979) 
have widely supported the tenants set forth in 1967.
Similar to the classical island biogeography studies which addressed community diversity on islands 
and isolated habitats, studies investigating genetic diversity of single species with respect to island 
biogeography have shown reduced genetic diversity in isolated populations of pool frogs (Rana temporarid) 
in Germany (Reh & Seitz 1990), black bears (Ursus americanus) on the island o f Newfoundland (Paetkau & 
Strobeck 1994), and brown bears (Urstis arctos) on Kodiak Island (Paetkau & Strobeck, in press). The 
advent of genetic fingerprinting techniques has greatly increased the sensitivity of intraspecies analyses (Hill 
1987, Weber & May 1989, Taberlet Sc Bouvet 1992a,). Genetic studies of black bears have successfully 
employed multilocus probes (Schenk Sc Kovacs 1996), mitochondrial sequences (Paetkau Sc Strobeck 
1996), and microsatellites (Paetkau Sc Strobeck 1994).
Microsatellites are di, tri, tetra or pentanucleotide “words” (ie: GGCCG) repeated five to thirty 
times and occurring some 30,000 times throughout the genome of vertebrates, they constitute non-coding 
regions ofnDNA and are not under the same correction procedures as coding regions and subsequently are 
subject to higher rates of mutation (Jeffreys 1985). Due to their high rate of mutation microsatellites exist 
in multiple alleles and prove to be indicators of genetic diversity and population markers in many organisms 
including bears (Craighead 1994, Paetkau & Strobeck 1994, 1995).
2For this study, I investigated genetic diversity of black bears in Montana, and questioned if the 
isolated mountain ranges in central Montana act as genetic islands for black bears. Genetic diversity was 
assayed in six populations of black bears and was expected to decrease with increased isolation and 
decreased area. Specifically, I predicted the microsatellite allele heterozygosity of black bears would be 
greater in the “continental” portion of Montana than in the isolated mountains. Among the “island” 
mountains, the smaller, more isolated ranges would show the lowest heterozygosities. Heterozygosity 
would be related to the size of the mountain range and inversely related to the degree of isolation. In 
addition to testing black bears microsatellite heterozygosity in relation to size of occupied area and degree of 
isolation, I investigated the probability that two individuals from the same mountain range would share the 
identical sampled genotype. This measure, probability of identity (Paetkau & Strobeck 1994), has been used 
to estimate the ability to identify individuals. Knowledge o f heterozygosity levels and probabilities of 
identity would be useful for identifying genetically depauperate populations that may need special 
management strategies. The allele distribution information presented here may provide information 
concerning the history of black bears in central Montana.
The distribution of microsatellite alleles in black bears occupying island habitats in central Montana 
may provide information about how those islands became occupied. There are two simplistic models to 
explain species diversity found in island systems. In the colonization (or stepping stone) model, species 
colonize islands either from the mainland or the next proximal island. In the refiigia model, a once 
contiguous habitat is fragmented into island habitats. Species assemblages on colonized islands should be 
subsets of the mainland assemblage and subsets of any island that is closer to the mainland. The refiigia 
islands should have the same assemblage as the mainland when the island was formed and over time the size 
of the island determines which species will persist (Brown 1971 & Lomolino et. al. 1989) (Fig. I).
By comparing shared and unique alleles between the populations I investigated, I hoped to shed
light on the history of those populations. If  the isolated mountain populations are the result o f colonization 
events, I would expect them to contain only alleles found in the mainland population, and more distant 
mountain island populations would contain only alleles found in the populations closer to the mainland. If
3the isolated mountain populations are refugia, then the bears on them would have a random mix of alleles 
found on the mainland and other islands.
Figure I.
Two island system models showing different assemblages of alleles. The top model depicts a colonization 
(or stepping stone) model where alleles are brought to the islands from the island closer to the mainland, 
ach distant island, 2 and 3, contain a population with a subset of the alleles found on the next proximal 
island, I and 2 respectivly. The bottom model depicts allele distributions on islands that are refugia of a 
past contiguous habitat. Alleles found on the islands are a random assortment of the alleles found on the 
mainland.
4STUDY AREA
The study area encompasses the Madison mountain range, Gallatin mountain range, Absaroka- 
Beartooth mountain complex. Rocky Mountain Front and northwestern mountains, Bridger Mountains, 
Crazy Mountains, Little Belt Mountains, and Snowy Mountains of Montana. The Rocky Mountain Front is 
continuous with Glacier National Park and the Canadian Rocky Mountains. The Gallatin-Madison complex 
is continuous with Yellowstone National Park to the south and the Absaroka-Beartooth range to the east. 
This conglomeration of mountain ranges represents continuous black bear habitat extending north to Alaska 
and south to southern Wyoming, and acts as a mainland for this study.
To the east of the Rocky Mountain Front, and north-east of the Madison-Gallatin-Absaroka 
mountain complex, there are mountain ranges of varying size. The Little Belt Mountains occupy 3,200 km^ 
and are 3 km east o f the Big Belt mountains which are contiguous with the Rocky Mountain Front. The 
Bridger Mountains are 660 km^ and are separated from the Madison-Gallatin mountain complex by 
Interstate 90. The Crazy mountains occupy 1,100 km^ and are 21 km easfof the Bridger mountains, and 28 
km north of the Absaroka-Beartooth range. The Snowy mountains are the eastern most mountain range 
investigated, occupying 700 km^ they are 65 km north east of the Crazy Mountains and 13 km east of the 
Little Belt Mountains (Fig. 2). Forested land was used to define suitable black bear habitat for computing
areas.
5Rocky Mountain 
Front-northwest 
mountain 
complex
Montana
Hdena Crazy
Mountains
lridger Mountains
Madison-GaDatin-Absaroka 
mountain complex_______ 2d km
Figure 2. Western and central Montana, showing contiguous black bear habitat in the western and southern 
portion of the state, as well as the isolated mountain ranges containing black bear populations analyzed for 
genetic diversity at six microsatellite loci.
6METHODS
AU bears killed in the state o f Montana require verification from the Montana Department of Fish 
Wildlife and Parks (FWP). Ninety four black bear samples were collected from hunter harvested bears in 
1995, 1996 and 1997. Thirty eight samples came from the Madison-GaUatin-Absaroka- Beartooth mountain 
complex, 28 from the Rocky Mountain Front and Northwest mountains, 10 from the Bridget mountains, 8 
from the Crazy mountains, 5 from the Little Belt mountains and 5 from the Snowy mountains. Fish WUdlife 
and Parks personnel coUected samples from harvested bears and sent them to the FWP Wildlife Research 
Lab in Bozeman, Montana. In addition to hunter kiUed animals, Animal Damage Control agents that tilled 
bears in response to bear - Uvestock conflict are required to turn in carcasses to FWP. Samples were stored 
frozen until DNA could be extracted. Whenever possible I used striated or cardiac muscle for DNA 
extraction. A few samples came from liver, kidney, or spleen, or hair.
In 1995 and 1996, DNA was extracted from tissue samples using phenol/chloroform. This 
procedure yielded DNA, however the presence of organic material, phenol, chloroform, or combinations of 
these, prohibited adequate amplification and resolution of genotypes. In 1997, all DNA was extracted with 
Qiagen ™ tissue tits  following the protocol provided by the manufacturer. DNA extracted in 1995 and 1996 
was reextracted in 1997 using these tits. After DNA extraction, microsatellite loci were amplified using six 
primers developed by David Paetkau (Table I). These primers have proven very successful in providing 
genetic profiles of Ursids (Craighead 1994, Paetkau & Strobeck 1994, Paetkau et al. 1995). Primers were 
labeled with florescence which allowed for non-radioactive imaging. G lA  and GlOL were labeled with TET 
green, GlOB1G lOC, and G lD  with 6FAM (ABI) blue, and GlOP with HEX (ABI) yellow (Paetkau et al 
1995).
%
7Table 1. Locus designation and primer sequences for six Ursid microsatellites._______
Locus (GT) strand primer___________________________ (CA) strand primer________
G1A GACCCTGCAT ACTCCTCT GAT G GCACTGTCCTTGCGTAGAAGTGA
G1D GATCTGTGGGTTTATAGGTTACA CTACTCTTCCTACTCTTTAAGAG
GIOB GCCTTTTAATGTTCTGTTGAATTT G AC AAAT C AC AGAAAC CTC C ATC C 
GIOC AAAGCAGAAGGCCTTGATTTCCT GGGGACATAAACACCGAGACAGC
G10L GTACTGATTTAATTCACATTTCCC GGGGACATAAACACCGAGACAGC
GIOP AGGAGGAAGGAAAGATGGAAAAC TCATGTGGGGAAATACTCTGAA
Amplification mixtures for the polymerase chain reaction are in the appendix. Amplified PCR 
products were run on a polyacrylamide gel (Weber & May 1989) in an ABI 373 automated sequencer and 
run for 6 hours (Paetkau & Srtobeck 1994). Each lane in the gel contained the PCR product and a standard 
(genescan 2500). Each gel could hold 36 samples but no more than 32 were loaded. The 373 used a laser 
to scan for each loci primer, biotintilated with yellow, green, blue and red, and the biotintilated primers were 
scored as they passed the laser. Alleles at different loci that were similar in size contained primers dyed 
different colors, allowing all six loci to be run in the same lane of the gel.
Immediately before loading the gel, 1.5 pi of the Gl A, GlOL mix were placed in the GlOB1 G10C, 
GlD tube and 1.5 pi of that mixture was added to the GlOP tube. From the GlOP tube mixture, 1.2 pi was 
mixed with 2.0 pi of formamide buffer, and 0.5 pi of an internal standard (Genescan 2500, biotintilated with 
ROX (ABI)). Each lane of the gel was loaded with this diluted DNA mixture from a single individual 
(Paetkau & Strobeck 1995). The computer accompanying the sequencer, using Genescan 672 software, 
scored each band with respect to the standard in each lane and formed an image of the whole gel as well as a 
suite of electropherograms for each lane. This information was then imported into Genotyper software and 
the bands were assigned as alleles. This process was be visually inspected to account for missed alleles, 
incorrectly assigned alleles, and non-alleles scored as alleles.
Chi square tests ( X^) were used to test for discrepancies between observed and expected numbers 
of heterozygous individuals for each population at each loci and for each population at all loci combined.
8Expected heterozygosities and probabilities of identity were calculated using the formulae: 
h=l-(nZpi2-l)/(n-l) (Nei & Roychoudhuiy 1974), and I= Epj4+ZZ(2pjPj)2 (Paetkau & Strobeck 1995) 
respectively, where p; and pj are the i*  and Jt*1 alleles in a population.
To investigate the impact of sample size on levels o f heterozygosity or probability of identity, I 
resampled with replacement the Madison-Gallatin-Absaroka population 38 times at n=5. This process gave 
me a distribution of values that I could then compare with the mountain island populations. If the values for 
the mountain island population fit within the 95 % confidence they were considered not significantly 
different from the mainland Madison-Gallatin-Absaroka population.
9RESULTS
I analyzed six microsatellite loci in 94 black bears from six geographic areas in Montana.
Genotypes for all bears used for this study (as well as bears left out o f the analysis due to location 
uncertainty or incomplete genotypes) can be found in the appendix. Allele distributions, frequencies, and 
number of alleles at each locus for each population are summarized in Table 2. Although both the Rocky 
Mountain Front-Northwest and Madison-Gallatin-Absaroka populations represent a mainland, I treated them 
separately in order to elicit possible allelic commonalties between them and the mountain island populations 
of the Bridget Mountains, Little Belt Mountains, Crazy Mountains and Snowy Mountains.
There were no significant differences between the expected and observed heterozygosities for each 
population: MadisonrGallatin-Absaroka complex = 5.27, p = 0.38, Rocky Mountain Front-northwest 
mountains %% = 0.89, p = 0.97, Bridget mountains %% = 2.83, p = 0.73, Little Belt mountains %% = 0.95,
p = 0.97, Crazy Mountains %% = 0.95, p = 0.97, Snowy mountains = 0.58, p= 0.99. These values 
suggest no significant deviation from the expected Hardy-Weinberg equilibria. Null alleles, reported in some 
microsatellite analyses, were not present in significant numbers. Null alleles are alleles that do not amplify in 
the polymerase chain reaction, thereby causing a larger proportion o f homozygotes (Craighead 1994, 
Paetkau & Strobeck 1995).
Heterozygosities ranged between 0.761 for the Rocky Mountain Front- North West, and 0.633 for 
the Snowy Mountains (Table 3). These values are similar to published microsatellite heterozygosities for 
mainland black bears in Canada (Paetkau & Strobeck 1994), where values were reported (at 4 of the loci 
used in this study G1A, G1D, G10B, and G10L) between 0.801 and 0.783. Probability of identity (Table 3) 
values ranged between I in 1.5 million for the Rocky Mountain Front- North West, to I in 6,289 for the 
Snowy Mountains. Heterozygosity levels for the isolated mountain ranges were not outside the 95%
confidence interval established for the resampled Madison-Gallatih-Absaroka population (0.632< mean = 
0.682 <0.732). Although heterozygosity levels and probabilities of identity did not differ significantly 
between the sampled populations, linear regressions showed trends relating heterozygosity and probability of 
identity to area and distance to nearest suitable habitat (Fig. 3). The R2 values ranged from 0.52, p = 0.27, 
for the probability of identity against area to a R2 0.00, p = 0.94 for probability of identity against distance 
to nearest habitat. Values for Heterozygosity against area and distance to habitat were R2 = 0.07, p = 0.73 
and R2 = 0.12, p = 0.65 respectively.
Table 2. Allele distributions, frequencies and number of alleles at six Ursid microsatellite primers for six black bear populations in Montana: 
Madison-Gallatin-Absaroka mountain complex n=38 (MGA), Rocky Mountain Front-north western mountain complex n=28 (NWF),
Bridger Mountains n=10 (BR), Little Belt Mountains n=5 (LB), Crazy Mountains n=8 (CR), and Snowy Mountains n=5 (SN).______ ’
1 2 3 4 5 6
Locus
G10B
MGA 0.013
NWF 0.089
BR
LB
CR
SN
Alleles
8 9 10 11 12 13
0.645 0.039 0.092 0.197
0.321 0.143 0.107 0.286
0.700 0.050 0.050 0.200
0.100 0.500 0.100 0.300
0.125 0.750 0.125
0.200 0.700 0.100
14 15 16 17 18 No. of alleles
0.013 
0.054
Locus
G10C
MGA 0.368 0.013 0.500 0.066 0.023 0.013 0.013
NWF 0.375 0.482 0.054 0.036 0.036 0.018
BR 0.450 0.450 0.100
LB 0.900 0.100
CR 0.688 0.250 0.062
SN 0.400 0.400 0.200
Locus
G10L
MGA 0.026 0.039 0.105 0.184 0.013 0.053 0.026 0.224 0.026 0.171 0.013 0.118
NWF 0.054 0.125 0.071 0.018 0.054 0.036 0.321 0.018 0.071 0.089 0.143
BR 0.250 0.050 0.200 0.050 0.300 0.050 0.100
LB 0.100 0.100 0.100 0.200 0.300 0.200
CR 0.062 0.250 0.125 0.125 0.062 0.062 0.250
SN 0.100 0.300 0.400 0.200
2
3
3
12
11
7 
6
8 
4
<£><£> 
"i* 
CO CO 
CD CD 
CO
1 2 3 4 5 6 7 8 9
____________________________________________ Alleles
Locus
G1A
MGA 0.026 0.013 0.013 0.026 0.355 0.500 0.039
NWF 0.054 0.036 0.339 0.393 0.014
BR 0.350 0.450 0.250
LB 0.100 0.100 0.400 0.300
CR 0.125 0.062 0.438 0.312
SN 0.100 0.100 0.500 0.100
Locus
G1D
MGA 0.263 0.329 0.145 0.026 0.053 0.039
NWF 0.179 0.179 0.018 0.393 0.054 0.018
BR 0.150 0.300 0.300 0.100 0.100
LB 0.600 0.100 0.300
CR 0.438 0.125 0.250 0.062
SN 0.500 0.300 0.200
Locus
G10P
MGA 0.013 0.026 0.211
NWF 0.018 0.018 0.018 0.250
BR 0.200 0.100
LB 0.100
CR 0.375
SN 0.800
10 11 12 13 14 15 16 17 18 No. of alleles
0.026 0.026 9
0.036 0.036 7
3
0.100 5
0.062 5
0.200 5
0.013 0.132 8
0.089 0.018 0.054 9
0.050 0.063 7
3
0.125 5
3
0.355 0.145 0.145 0.105 7
0.143 0.179 0.250 0.071 0.054 9
0.350 0.050 0.100 0.200 6
0.500 0.300 0.100 4
0.375 0.062 0.062 0.062 0.062 6
0.200 2
13
Table 3. Levels of heterozygosity and probability of identity at six microsatellite DNA loci 
for six black bear populations in Montana.
Madison-Gallatin-Absaroka mountain complex n=38 (MGA),
Rocky Mountain Front-north western mountain complex n=28 (NWF)1
Bhdger Mountains n=10 (BR), Little Belt Mountains n=5 (LB), Crazy Mountains n=8 (CR).
Snowy Mountains n=5 (SN)
Heterozygosity
Locus MGA NWF BR LB CR SN
G1A 0.610 0.718 0.668 0.800 0.733 0.755
G10B 0.542 0.794 0.489 0.711 0.433 0.511
G1D 0.773 0.772 0.816 0.600 0.758 0.689
G10L 0.857 0.855 0.832 0.889 0.883 0.779
G10C 0.585 0.616 0.616 0.200 0.492 0.711
G10P 0.778 0.813 0.816 0.733 0.783 0.356
6 Loci 0.691 0.761 0.706 0.655 0.680 0.633
top 4 0.695 0.785 0.707 0.702 0.750 0.683
Probability of Identity
Locus MGA NWF BR LB CR SN
G1A 0.226 0.135 0.197 0.123 0.149 0.140
G10B 0.258 0.081 0.268 0.188 0.388 0.341
G1D 0.094 0.090 0.075 0.285 0.126 0.217
G10L 0.042 0.041 0.061 0.068 0.051 0.145
G10C 0.263 0.230 0.244 0.689 0.354 0.206
G10P 0.089 0.070 0.081 0.180 0.111 0.134
6 Loci 5.40 x 10'6 6 .58x10"7 4.71 XlQ-G 5.59x10-: 1.46x10-3 1.59 x 10"
top 4 1.72x10" 2.42 x 10 5 1.07x10" 3.49 x 10"5 7.16x10-3 1.88x10"
Pr
ob
ab
ili
ty
 o
f I
de
nt
ity
f r  0.47
0 00000
0.00001
OlOOOOI
0.00002
0.00003
0.00004
0.00003
a 00003
0.00006 2000
A ru  (bn2)
Dutince (km)
Figure 3. Lines of best fit for area o f mountain range and distance to nearest suitable habitat to expected heterozygosity (top two 
and probability o f identity (bottom two) for black bears on four mountain ranges in central Montana.
15
Heterozygosity decreased with increasing distance from suitable habitat and increasing area. Probability of 
identity increased with increasing distance from suitable habitat and increasing area.
At the six loci combined, the Madison-Gallatin-Absaroka bears (n=38) contained 50 alleles, the 
Rocky Mountain Front-Northwest (n=28) had 48 alleles, the Bridger population (n=10) had 30 alleles, the 
Little Belt Mountains (n=5) had 24 alleles, the Crazy Mountains (n=8) had 30 alleles, and the Snowy 
Mountains (n=5) had 20 alleles represented. The Madison-Gallatin-Absaroka bears held 5 unique alleles 
(alleles not represented in other populations), the Rocky Mountain Front-Northwest bear sample showed 8 
unique alleles, and the Crazy Mountain population. Little Belt Mountain population, and Snowy Mountain 
population each had I unique allele. The Bridger Mountain population held 3 alleles that were represented 
in the Madison-Gallatin-Absaroka population but not represented in the Rocky Mountain Front-Northwest 
population and I allele represented in the NWF but not the MGA The Crazy Mountain bears showed 2 
alleles represented in the Rocky Mountain Front-Northwest population, that were not represented in the 
Madison-Gallatin-Absaroka population, and 2 alleles represented in the MGA population, but not in the 
NWF. The Little Belt bears and Snowy Mountain population contained one allele that was present in the 
Madison-Gallatin-Absaroka population but not the Rocky Mountain Front population.
16
DISCUSSION
Island Biogeography theory states that species diversity on islands is lower than on the mainland, 
and that species diversity goes down with relation to the size of the island and the isolation of the island 
(MacArthur & Wilson 1967). The data presented here suggests that the genetic diversity of a species 
follows a similar pattern. Although not significant, black bear heterozygosity is lower on the mountain 
islands (except the Bridger range) when compared to the mainland. None of the mountain islands 
investigated produced samples with atypical genetic diversity, however there were some interesting trends. 
Heterozygosities among the mountain islands do not follow a predictable pattern. The presence of unique 
alleles in the island mountain ranges does suggest that these populations are isolated to some degree.
The black bears of the Bridger mountains show higher heterozygosity than the bears of the Madison- 
Gallatin-Absaroka mountain complex, and share an allele with the Rocky Mountain Front-Northwest bears 
that is not found in the Madison-Gallatin-Absaroka population. This suggests that the Bridger mountains 
provide a link, or corridor, between the Madison-Gallatin-Absaroka mountain complex and the Rocky 
Mountain Front-Northwest via the Big Belt mountains. The movement of bears between the Madison- 
Gallatin-Absaroka complex and the Bridger mountains has been supported by the number of black bears 
killed on Interstate 90 between Bozeman and Livingston (3 during this study). Interstate 90 runs between 
the Bridger mountains and the Madison-Gallatin-Absaroka complex, and is the only barrier to black bear 
travel between the areas. The Bridger mountains have been used by Montana FWP for release sites for 
black bears trapped in the Gallatin mountains. These transplants may increase the microsatellite allele 
heterozygosity in Bridger mountain bears.
The Snowy mountains, being second most isolated and second smallest, showed the lowest 
reported heterozygosity. The Snowy mountains are the eastern most habitat studied and may represent a
17
peninsula for dispersal of individuals. In contrast to the Bridger mountains, fewer bears would be expected 
to colonize the Snowy mountains from other populations simply because the Snowy mountains are not 
between two populations of black bears.
The Little Belt mountains, between the Snowy mountains and the Rocky Mountain Front, are well 
connected and large yet produced an unexpectedly low heterozygosity. It is possible that the habitat 
between the Rocky Mountain Front-northwestern mountain bears and the Little Belts is not suitable for 
black bears and therefore there is not much genetic exchange between these populations. Given the 
proximity of the Snowy mountains to the Rocky Mountain Front, it is interesting that they shared more 
alleles with the more distant Madison-Gallatin-Absaroka population, and had one allele that was only seen in 
the Crazy mountain population.
A bear walking from the Bridger mountains (edge of the Madison-Gallatin-Absaroka) through the 
Little Belt Mountains to the Snowy mountains would cover about 250 km by staying in forested habitat. By 
crossing upland rangelands, a bear could walk 65 km from the Crazy mountains directly to the Snowy 
Mountains. The long route through the Little Belt mountains could be spread out through generations, 
however the shorter route would probably have to be covered in a single generation given the openness of 
the intervening lands. By following the Musselshell river from the Crazy mountains, a bear could stay in the 
gallery forest to Harlowtown and then go up one of the side drainages to the Snowy mountains. This route 
seems most likely since the bear would have cover for the majority of the trip. The Musselshell river was 
known as a “bear river” in the 1800s (Picton, pers. com.). By increasing sample sizes in the central Montana 
mountain islands, some of these uncertainties concerning relatedness of bears between mountain ranges 
could be resolved. Increasing sample size would also give better estimates of levels of heterozygosity and 
probability of identity.
Probabilities of identity for each population may be useful tools for managers in both mark 
recapture population estimates from hair, and forensic work. Recently there has been interest in using 
microsatellite genotypes to first ‘mark’ and then ‘recapture’ individuals in a population, thereby gaining a
18
population estimate without handling animals. The benefits to this procedure are the nonintrusive nature, 
and the ability to check many trap cites in one sampling since there is not the added time handling bears. 
Probability o f identity allows the researcher to express confidence that two identical genotypes are indeed 
the same bear. For example, the probability to sample two bears that share the same genotype in the Snowy 
mountains is I in 20,000. In addition to mark recapture studies, probability of identity can be used to 
support prosecution of crimes against wildlife. For example, a bear carcass found in the Snowy Mountains, 
and blood on a suspect’s boots, if genotypically identical, there is a 19999/20000 chance that the blood came 
from the bear. This same logic could be employed for bears that are suspected of being repeat management 
problems. By taking hair from two cabin windows for example, managers could identify a single bear, or 
multiple bears.
The presence of unique alleles in the mountain island populations (Little Belts I, Crazy Mountains 
I, and I allele shared between the Crazy Mountains and the Snowy Mountains) can be explained either 
through mutation or a relic allele that existed in other populations in the past. Regardless o f the origin of 
the unique alleles, their presence suggests some degree of isolation of black bear populations occupying 
mountain islands in central Montana. The most widely accepted theory for mutation in microsatellites is a 
stepwise model, where new mutations are I or 2 base pairs larger or smaller than an established allele. 
Microsatellite DNA is non-coding, and it is assumed there is no selection for or against any alleles. 
Microsatellites can mutate at 1/1000 generations (Weber & Wong 1993). With a population size of 100, 
and breeding every 4-5 years there could be a mutation every second generation. In order for a unique allele 
to persist in a population, however, it must be passed from parent to offspring. There is a probability of 0.5 
that an offspring will inherit the allele. The next generation will have the same probabilities for inheritance, 
or a probability of 0.25 for the allele to make it two generations (assuming I cub/generation), so it is unlikely
that unique alleles persist in populations. If  the population was reduced at some point in the past that was 
concurrent with the mutation of a unique allele, that allele would have a higher probability to become
19
established (Nei, 1975). The presence of an allele found only in the Crazy and Snowy Mountains suggests 
either a relic, or a mutation in one population and subsequent dispersal to the other population.
The number of alleles in each of the island populations that are shared with other island populations 
and the mainland can shed light on the possible histories of the study area (Taberlet 1992). Colonized 
islands tend to have species assemblages that are subsets of the species assemblages occurring on the 
mainland (Patterson 1986). The allele distribution of a single species should follow this pattern, therefore, 
populations on colonized islands tend to have allele distributions that are subsets o f the allele distributions 
occurring on the mainland. Refugia islands contain species assemblages that are a random sampling of the 
species present in the mainland population (Brown 1971, Lomolino et. al. 1989). Using the same logic, 
populations on refiigia islands would show allelic distributions that are a random sampling of the alleles 
present in the mainland population.
The presence of unique alleles in the mountain island populations suggest a refugia with enough 
time for establishment of a mutation. The presence of the same allele found only in the Snowy and Crazy 
mountains suggests genetic interchange between these populations since it is doubtful both populations 
produced the same mutation independently. It is possible that the unique alleles are present in other 
populations of black bears living to the east, south east, or north of the central Montana mountain island 
black bears. North of central Montana, in Alberta is the stronghold for black bear in North America, and it 
is possible that in the past this population reached into Montana, and as the population retreated north there 
remained relic populations in central Montana. In order to understand the origin of the unique alleles seen in 
the central Montana mountain island black bear populations, microsatellite bear genotypes from central 
Alberta and the Black Hills of South Dakota should be investigated. As well samples from the central 
Montana mountain islands should continue to be collected and analysed.
In addition to sampling populations that may help explain the history o f Montana’s mountain island 
bears, the microsatellite loci investigated in this study and in Canada should be employed in the southern 
portions of black bears in the west. There appears to be a decline in heterozygosity in more southern bears.
20
From Banff to the Northwest part of Montana to the southern portion of Montana the heterozygosity of 
sampled black bears drops from 80% to 76% to 69%. It would be interesting to investigate this trend 
farther south in the black bear range. If supported, this trend would support the peninsular theory for the 
low heterozygosity seen in the Snowy mountains of Montana in this study.
This study supports that black bear populations on island mountain ranges in Montana are isolated 
to some degree. By combining a genetic study such as this with a more traditional ecological study of black 
bears, managers would be better able to identify the demographic structure and dynamics of these black bear 
populations. BCnowledge of black bear movements in central Montana is quite limited (Stivers pers. com ), 
and larger sample sizes would allow for a better understanding o f the degree of isolation between black bear 
populations in central Montana. There have been a number of studies in the north west portion of the state 
and Idaho (Aune & Kasworm 1995, Unsworth 1984, Beechan 1980, Armstrup & Beecham 1976, Jonkel & 
Cowan 1971), as well as along the Beartooth Front south o f Big Timber (Greer 1987, Mack 1985, Rosguard 
& Simmons 1982). These studies documented home ranges for bears but did not address colonization or 
dispersal movements, however, one bear did disperse from the Absaroka range to the Crazy mountains 
(Mack pers. com.) There is need for a study of the ecology and movement patterns of black bears in central 
Montana.
In Arizona, where the black bear habitat is fragmented similarly to central Montana’s mountain 
island system, black bears have been documented moving over 100 Km in one study (Lecount 1982) and not 
moving off a single mountain in another study (Waddel & Brown 1984). In central Montana, the black bears 
are isolated, but it is unclear to what degree. It is impossible to predict what the genetic diversity of bears 
occupying isolated mountain ranges in Montana will be at any given point in the future. Presently, there 
does not seem to be any concern for inbreeding depression or very small population sizes in the mountain 
ranges addressed here. Should the landscape and land use surrounding black bears on mountain islands 
change, or harvest rates increase substantially, there could be concern for the continued genetic health of 
those populations(Reh & Seitz 1990, T aberle t & Bouvet 1992).
21
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Factors determining the number of land bird species on islands around south-western 
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Adler GH (1992)
Endemism in birds of tropical Pacific islands.
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Heterozygosity and fitness in natural populations of animals.
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Armstrup SC, Beecham J  (1976)
Activity patterns of radiodllared black bears in Idaho.
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Aune K, Kasworm W (1995)
Final report: East Front grizzly studies.
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Helena, Montana
Beecham J  (1980)
Some population characteristics of two black bear populations in Idaho.
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Brown JH (1971)
Mammals on mountain tops: nonequilibrium insular biogeog.raphy.
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Conservation genetics of grizzly bears.
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The Island Dilemma: lessons of modem biogeographic studies for the design of natural 
reserves.
Biological Conservation, 7, 129-146.
22
Greer SQ (1987)
Home range, habitat use, and food habits of black bears in south central Montana. 
Master Thesis, Montana State University 
Bozeman, MT
Hill WG (1987)
DNA Fingerprints applied to animal and bird populations.
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Jeffireys AJ, Wilson V, Thein SI, (1985)
Hypervariable 'minisatellite' regions in human DNA.
Nature, 314, 67-73.
Jonkel CJ, Cowan I (1971)
The black bear in the spruce-fir forest.
Wildlife monographs 27.
Lecount A (1982)
Characteristics of a central Arizona black bear population.
Journal of wildlife management, 46 (4) 861-868
Lomolino MV, Brown JH, Davis R (1989)
Island biogeography of the montane forest mammals in the American southwest. 
Ecology, 70, 180-194.
MacArthur RH, Wilson EO, (1967)
The Theory of Island Biogeography. Monographs in Population Biology. 
Princeton University Press.
Princeton, NJ.
Mack JA (1985)
Ecology of black bear on the Beartooth face, south central Montana.
Master Thesis, Montana State University 
Bozeman, MT
Nei M, Roychoudhury AK (1974)
Sampling variances of heterozygosities and genetic distance.
Genetics, 76, 379-390.
Nei M, Maruyama T, Chakraborty (1975)
The bottleneck effect and genetic variability in populations 
Evolution, 29 (I), 1-10
Patterson BD, (1987)
The principle of Nested Subsets and its implications for biological conservation. 
Conservation Biology, I, 323-333.
Paetkau D, Strobeck C, (1994)
Microsatellite analysis of genetic variation in black bear populations.
Molecular Ecology, 3,489-495.
23
Paetkau D, Strobeck C, (1995)
The molecular basis and evolutionary history of a microsatellite null allele in bears. 
Molecular Ecology, 4 , 519-520.
Paetkau D, Calvert W, Stirling I, Strobeck C (1995)
MicrosateUite analysis of population structure in Canadian polar bears.
Molecular Ecology, 4, 347-354.
Paetkau D, Strobeck C (1996)
Mitochondrial DNA and the phylogeography of Newfoundland black bears.
Canadian Journal of Zoology, 74, 1992-1996.
Pelton, M. 1982 
Black bear.
In Wild Mammals of North America, 
pages 504-514
Eds: Chapman JA, Feldhamer GA 
John Hopkins Univrsity Press 
Baltimore. MD
Picton HD (1979)
The application of insular biogeographic theory to the conservation of large mammals in the 
Northern Rocky Mountains.
Biological Conservation, 15, 73-79.
Ralls K, Harvey P, Lyles A
Inbreeding in natural populations of birds and mammals.
In, Conservation Biology: The science of scarcity and diversity, 
pages 35-57.
Ed. Michael Soule
Sinauer Associates Inc., Sunderland, MA 
Reh W, Seitz A (1990)
The influence of land use on the genetic structure of population of the common frog Rana 
temporaria.
Biological Conservation, 54, 239-249.
Rosensweig ML (1995)
Species diversity in space and time.
Chapters 8 and 9 
Cambridge University Press.
Cambridge, MA.
Rosguard, Simmons (1982)
East Boulder big game technical report.
Montana Fish, Wildlife and Parks 
Helena, MT
Schenk A, Kovacs KM (1996)
Genetic variation in a population of black bears as revealed by DNA fingerprinting.
Journal of Mammalogy, 77, 942-950.
Taberlet P, Bouvet J  (1992) 
Bear conservation genetics. 
Nature, 358, 197.
24
Thompson LS (1974)
Insular distribution of Montana birds the Sweetgrass Hills, Montana and other Island' 
Mountain ranges of the northern Great Plains.
,MS Thesis, Washington State University.
Puhnan, WA.
Unsworth JW
Black bear habitat use in west central Idaho.
Master Thesis, Montana State University 
Bozeman, MT
Waddel TE, Brown DE (1984)
Exploitation of two subpopulations of black bear in an isolated mountain range.
Journal of wildlife management, 48 (3) 933-938
Weber JL, May PE (1989)
Abundant class of human DNA polymorphisms which can be typed using the polymerase 
chain reaction.
American Journal of Human Genetics 44, 388-396.
Weber JL, Wong (1993)
Mutation of human short tandem repeats.
HumanMolecular Genetics, 2, 1123-1128
25
APPENDIX
26
Black Bear Genotypes
Sam ple Location G10B G 10B G 10C  G 10C  G10 G10 G1A G1A G1D G1D G 10P  G 10P
B T o rs ten sen  B racke t Cr. 158 164 103 103 145 169 192 194 172 176 155 155
J  Cotterrell HD 319 158 164 99 99 159 163 192 194 180 184 149 153
E. Martin Hatfield Mt. 158 162 99 103 145 167 192 194 174 176 155 159
Trum an Cr B ridger Mts. 158 158 103 103 155 159 194 196 178 180 153 163
E .L S e lf R e e s e  Cr. 164 164 103 113 165 165 192 196 174 178 163 163
M. M cC artne  HD 319 158 160 99 113 145 145 192 194 172 176 155 157
181533 Bridger canyon 158 158 99 103 165 169 194 196 174 176 149 155
S eav ey B ridger Mts. 158 158 99 99 159 165 194 196 174 176 149 163
181511 Main B oulder Rv. 158 164 103 105 137 139 192 192 174 176 153 163
181505 Low er B older Rv. 162 164 99 99 165 137 194 194 172 172 153 157
181508 I 90  Sp ringda le 158 164 99 99 145 167 192 194 172 180 153 157
181510 Main B oulder Rv. 158 164 99 105 145 165 192 194 174 174 155 159
181217 Main B oulder Rv. 158 164 99 103 165 165 194 194 172 174 155 155
181526 Main B oulder Rv. 158 158 99 99 159 165 192 194 172 172 153 155
181492 B oulder Rv. 158 164 99 105 165 165 192 194 174 174 159 163
181494 C oun trym an  Cr. 162 164 99 105 139 139 192 194 172 174 153 153
181499 B oulder Rv. 162 164 99 99 169 169 192 194 172 172 153 157
181500 W. B oulder Rv. 158 158 99 103 155 159 196 196 174 174 155 159
181485 C olum bus 158 162 99 103 155 159 194 194 172 178 153 159
181578 B oulder Rv. 158 162 99 99 165 169 188 194 172 174 155 163
181538 Sp ringda le 158 158 99 99 159 165 192 198 172 174 153 163
181516 C olum bus 99 99 159 165 192 194 159 163
181517 R eed  Point 103 105 143 145 192 194 153 155
181506 Low er B oulder R d . 164 164 99 99 165 169 194 194 172 172
181509 Main B oulder Rv. 160 162 103 105 155 157 190 198 175 175
181204 Big T im ber R d . 158 158 99 99 159 169 192 198 172 172
181218 Main B oulder Rv. 156 158 99 99 155 165 194 194 174 176
181493 B ou lder Rv. 162 164 99 103 165 165 194 194 172 172
181528 hd 560  U pper D eer Cr. 158 162 99 99 137 155 194 194 172 174 155 157
181214 hd 560  P e te rs o n  Cr. 158 158 99 103 159 159 194 194 172 174 157 163
181539 hd 540  Efk. H opley  Cr 158 164 99 99 169 169 192 194 172 176 159 159
181553 Efk H opley  Cr 158 158 99 99 141 159 192 194 172 176 155 159
181496 A m erican  Fk. hd 580 158 158 99 99 145 159 192 194 172 184 153 155
181501 W. B oulder Cr 158 158 103 103 192 194 172 174 155 157
181330 ? 158 158 99 105 165 169 192 194 174 176
181488 Big T im ber C n . hd 570 158 162 99 99 141 163 194 198 172 172
D eer Cr D eer Cr. 158 158 99 103 141 141 192 194 174 176 159 159
R ischer D eer Cr. 158 158 103 103 155 159 194 198 176 184 155 157
Goldthwait T im beriine Cr. 158 166 99 103 159 169 192 194 184 184 155 155
D. W ard E lbow  Cr. 158 158 103 103 159 165 194 194 174 184 153 159
Holland P a p o o se  C r 158 164 99 103 145 145 192 194 172 176 153 155
27
Sam p le Location GIOB G 10B G IO C  G IO C  G10 G10 G IA G1A G ID G ID G 10P  G 10P
181328 Lew istow n 156 158 99 99 169 169 184 198 172 174 153 153
181205 C razy  M ounta ins 158 158 99 103 155 169 194 194 172 176 153 155
181206 C razy  M ounta ins 158 158 99 99 155 169 192 194 176 184 155 155
R .Shu lts hd 341 B eav e r Cr. 160 160 101 103 145 145 192 192 176 178 153 155
W hitm an B eav e r Cr. 158 164 99 103 141 145 184 194 174 174 153 155
Griffis B eav e r Cr. 158 164 103 103 169 169 194 198 174 176 155 155
B eav e r Cr B eav e r cr 164 164 99 103 159 165 192 194 172 174 155 159
S hrou t hd 341 M eadow  Cr. 158 164 103 103 141 141 192 194 177 177 155 155
G reen B ear Cr. 158 158 103 103 141 160 194 194 174 174 155 157
H ettinger hd  341 S p an ish  Cr. 158 158 103 113 149 160 194 194 174 184 153 155
C ooper hd 341 S p an ish  Cr. 158 158 99 103 145 145 192 194 176 184 153 163
F isher hd 341 158 158 103 109 145 145 190 192 174 184 157 159
Dvorak hd 341 W .Fk Gallatin 158 162 103 103 141 141 190 192 172 176 155 157
F Ja s aw hd 341 156 158 99 103 165 169 192 192 178 180 149 157
181201 h w y 191 YNP 158 164 99 103 163 169 192 194 172 172 153 159
McCall hd 301 158 158 103 103 145 155 192 194 176 184 155 157
181234 S tick land  Cr. 158 158 103 103 145 145 192 194 172 180 155 157
V idm ar Mt. Ellis 158 158 103 103 159 163 194 196 174 178 155 163
181225 E. B o zem an  P a s s 158 164 99 103 145 145 192 192 174 174 159 163
181554 B ozem an  P a s s 156 162 103 103 159 159 192 192 174 176 155 155
J  B ausch hd 317  S ugario af Mt. 158 164 99 103 145 159 192 192 172 184 155 159
L G eahrin hd 313 160 164 99 103 159 159 194 194 174 176 153 153
181520 h d 3 1 5  C ra zy M ts . 158 164 99 99 155 169 188 198 172 176 155 157
181203 W hite Sulfur Springs 154 162 99 99 165 165 184 192 172 172 155 163
181238 hwy 2. m m 188 158 164 103 103 139 139 192 192 174 182 153 155
181202 Fish  Cr. C rop Mt. 158 164 99 103 155 159 194 198 172 172 155 155
D.Martin hd 106. Kalispel 158 164 99 103 159 159 184 194 172 188 157 157
Jenk in s hd 109. Buck Cr. 160 164 99 103 143 159 194 194 172 174 153 159
S trong M ission Cr. 158 158 99 99 159 165 192 194 174 176 153 163
181237 Fortine 162 166 105 113 165 184 194 176 180 153 155
A sb es to s  Cr. A sb e s to s  Cr. 158 158 99 103 184 192 172 174 153 155
Spaid B ear T rap  C anyon 158 158 99 103 159 165 192 194 172 174 153 163
B earT rap S Z  B ear T rap  C anyon 158 158 99 103 165 169 184 194 176 184 155 159
G. M ajors B ea r T rap  C anyon 158 162 103 103 159 159 192 192 172 172 155 155
M adison Riv 05 /01 /95 158 158 99 109 159 165 194 194 172 184 159 163
B ear Trap  9 / 09 /23 /95 158 158 103 105 194 194 172 174 153 159
181456 ? 158 162 105 111 157 157 186 192 178 183 151 151
181616 B lackfoot Rv. 156 162 103 105 145 145 192 194 174 176 137 153
181617 R egion  2 156 160 99 105 139 139 192 198 176 176 155 157
181618 T hom pson  Falls 164 164 99 103 163 169 192 194 176 176 159 159
181619 T hom pson  Falls 162 164 103 103 159 161 188 196 176 184 159 161
181620 S w ee n ey  Cr. 158 160 103 111 163 163 194 196 184 184 157 163
28
Sam p le Location GIOB G10B G IO C  G 10C  GIO G10 G IA G1A G ID G ID G 10P  G IO P
181636 I 90  a t  Turah 158 166 99 99 141 141 194 194 174 188 153 153
181637 R egion  2 158 164 99 103 159 159 184 194 176 176 151 153
181512 S ec tio n h o u se  Cr. 156 158 99 99 172 172 155 157
181600 R egion  1 158 158 139 159 192 194 172 172 155 161
181602 R egion  I 160 164 103 103 141 141 194 194 174 176 153 159
181559 hd 103 164 166 103 103 137 159 192 194 176 184 155 157
181564 hd 122 158 164 99 99 145 159 192 192 172 176 159 161
181567 C olum bia  Falls 158 166 103 111 139 165 188 198 176 176 159 159
181562 Mfk. F la thead 156 160 113 113 141 159 196 196 174 188 155 157
181572 Mfk F la thead 158 160 99 103 137 159 196 196 174 176 153 155
181568 Mfk F la thead 158 158 103 115 149 149 194 196 172 174 159 159
181523 S . Livingston 6mi. 103 105 145 145 192 192
181547 R egion  4 158 158 99 103 192 194
181310 I 90  a t  Rock C reek 158 158 99 99 137 159 184 194 174 176
18185 I 90  a t  Big T im ber 156 158 99 99 164 167 192 194 172 174 159 163
181970 C razy  M ountains 158 158 103 105 155 169 192 192 172 179 159 161
181971 C razy  M ountains 156 164 99 103 159 163 184 192 172 174 155 163
181986 B ridget M ountains 158 158 99 99 159 165 192 194 174 176 149 155
taxiderm y C razy  M ountains 156 158 99 99 165 171 192 194 172 174 153 153
Brown Snow y M ountains 158 164 103 115 139 167 196 198 174 176 153 159
C asp e r Snow y  M ountains 158 158 103 103 137 167 194 194 172 172 153 159
Martin 97 B ridget M ountains 158 158 103 103 145 165 192 194 172 174 155 159
Chord Snow y M ountains 158 158 99 115 139 167 194 194 174 176 153 153
Birdwell Snow y  M ountains 156 158 99 103 139 167 192 194 172 172 153 153
B orgreen Little Belt M ountains 158 164 99 99 145 165 188 192 174 176 153 155
S ere rak C razy  M ountains 158 158 99 103 163 167 184 192 172 176 153 157
29
PCR mixture for six microsatellite loci. All units are micoliters.
GIAand G10L
Primers 0.54
dNTPs 1.32
MgCI 1.28
Buffer 1.68
Water 9.72
TAQ enzyme 0.045
DNA 2
Loci
G10B.G10C and G1D G10P
0.81 0.27
1.32 1.01
1.28 1.32
1.68 1.68
9.42 10.17
0.049 0.135
2 2
30
Sample clectropherogram showing the GlOB1 GlOC and GlOD loci.
bdlbcd Rssults 19 Blue 181578 B C D  Siam blue
babcd flWUto 20 Blue 181544 B C D  61am Mue
losvspl
binbcd Rasuto 21 Blue 181543 B C O 61am blue
JjC t Z^yoos
1103.851
billbed Results 22 Blue 181538 B C D  61am blue
p 3 0 0 0
Iisa.ail 1172.841
fia2.S3|
ITFtas)
H ^ 4 0 0 0
- 3 0 0 0
-2000
-1000
1178 .57 |
rt-thrv jiW U r. 
| lS 8 .3 9 |  | l 7 2 .64 |
| l6 3 .7 l | fT 7 6 .5 7 |
- 4 0 0 0
- 3 0 0 0
-2 0 0 C
- 1 0 0 0
I  A u . A  0
|99!75|
billbed Results 23 Blue 181572 B C D  61am blue
11 58.391 Q tzTss]
QTtlaI
- 3 0 0 0
-2 0 0 0
-1 0 0 C
-A L -
|9 9 .7 S | 
f l 03.771
JlJl
11 58.36| 1174.65|
11 6 0 .5 1| 1178.57|
- 2 0 0 0
-1000
b'Hbed Results 24 Blue 181569 B C D  61am blue
- 2 0 0 0  
- I  500 
-1000 
- 5 0 0
31
Alleleic assignments for microsatellite" loci G I OB, GlOC..
GlOB
140 / (X) Highest peak from 139.30 to 140.30 bp in blue
142 2 ( X ) Highest peak from 141.30 to 142.30 bp in blue
144 J f - (X) Highest peak from 143.40 to 144.40 bp in blue
146 H- (X) Highest peak from 145.40 to 146.40 bp in blue
148 r (X) Highest peak from 147.50 to 148.50 bp in blue
150 4 (X) Highest peak from 149.60 to 150.60 bp in blue
152 7 (X) Highest peak from 151.60 to 152.60 bp in blue
154 < (X) Highest peak from 153.70 to 154.70 bp in blue
156? (X) Highest peak from 155.80 to 156.80 bp in blue
158 /f (X) Highest peak from 157.90 to 158.90 bp in blue
160 " (X) Highest peak from 160.00 to .161.00 bp in blue
162 /i (X> Highest peak from 162.20 to 163.20 bp in blue
164 (X) Highest peak from 164.40 to 165.40 bp in blue
166 (X) Highest peak from 166.40 to 167.40 bp in blue
101  7 
103 y 
105 '
107 c 
109 9 
H l  i 
113 i 
115 
117 //
119 %
121 / j
97 A  I 
99 
GlOH
GlOH (FAM) 
GlOJ
GlOJ long 
GlOL / .
'139 2 
141 3 
143 V 
145 S'
147 <
149 
151 8 
153 7 
155 /?
157 ,(
159 /2 
161 /3
Highest
Highest
Highest
Highest
Highest
Highest
Highest
Highest
Highest
Highest
Highest
Highest
Highest
peak
peak
peak
peak
peak
peak
peak
peak
peak
peak
peak
peak
peak
from 101.20 to 102.20 bp in blue
from 103.10 to 104.10 bp in blue
from 105.10 to 106.10 bp in blue
from 107.00 to 108.00 bp in blue
from 109.00 to 110.00 bp in blue
from 110.80 to 111.80 bp in blue
from 112.50 to 113.50 Ip in blue
from 114.40 to 115.40 bp in blue
from 116.30 to 117.30 bp in blue
from 118.20 to 119.20 bp in blue
from 120.00 to 121.00 bp in blue
from 97.10 to 98.10 bp in blue
from 99.20 to 100.20 bp in blue
Highest peak at 139.70 ± 0.50 bp in green
Highest peak from 140.60 to 142.30 bp in "green
Highest peak from 142.60 to 144.30 bp in green
Highest peak from 144.70 to 146.40 bp in green
Highest peak from 146.70 to 148.40 bp in green
Highest peak from 148.80 to 150.50 bp in green
Highest peak from 150.90 to 152.60 bp in green
Highest peak from 153.00 to 154.70 bp in green
Highest peak from 155.10 to 156.80 bp in green
Highest peak from 157.20 to 158.90 bp in green
Highest peak from 159.30 to 161.00 bp in green
Highest peak from 161.40 to 163.10 bp in green
MONTANA STATE UNIVERSITY LIBRARIES
3  I /O Z  I UZOUUUb b