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Highways as potential barriers to movement and genetic exchange 

in small mammals 

Final Report 
February 2003 



Submitted by 

L. Scott Mills 

Associate Professor 

Wildlife Biology Program 

University of Montana, School of Forestry 

Missoula, MT 59812 

Reesa Yale Conrey 

M.S. Student, Wildlife Biology Program and 

Montana Cooperative Wildlife Research Unit 

University of Montana 

Missoula, MT 59812 



Submitted to 

Montana Department of Transportation 

Research Section 

2701 Prospect Avenue 

Helena, MT 59620 



TECHNICAL REPORT DOCUMENTATION PAGE 



1. Report No. FHWA/MT-02-013/8152 



2. Government Accession No. 



3. Recipient's Catalog No. 



4. Title and Subtitle 



Highways as potential barriers to movement and genetic 
excliange in small mammals 



5. Report Date 13 February 2003 



6. Performing Organization Code 



7. Autlior(s) 

Reesa Yale Conrey and L. Scott Mills 



8. Performing Organization Report No. 



9. Performing Organization Name and Address 

Wildlife Biology Program 
School of Forestry 
University of Montana 
Missoula, MI 59812 



10. Worl< Unit No. 



11. Contract or Grant No. 8152 



12. Sponsoring Agency Name and Address 

Research Section 

Montana Department of Transportation 

2701 Prospect Avenue 

PO Box 201001 

Helena MI 59620-1001 



13. Type of Report and Period Covered 

Final Report: May 2000 - December 2002 



14. Sponsoring Agency Code 5401 



15. Supplementary Notes Research performed in cooperation with the Montana Department of Transportation and the 
US Department of Transportation, Federal Highway Administration. 



16. Abstract Small mammal populations separated by highways may be partially or completely isolated from one 
another due to low dispersal capabilities, low probability of surviving highway crossing attempts, and/or 
avoidance of areas adjacent to highways. Our objective was to determine how population connectivity is 
influenced by highways of different widths and traffic levels for several small mammal species that may 
experience varying success in crossing highways. We used mark-recapture techniques to compare movement 
adjacent to highways to movement across highways for southern red-backed voles (Clethrionomys gapperi), 
deer mice (Peromyscus maniculatus), yellow pine chipmunks (Tamias amoenus), and red-tailed chipmunks 
(Tamias ruficaudus) in forested areas of western Montana. In addition, we used genetic techniques to compare 
gene flow (movement plus reproduction) adjacent to highways to gene flow across highways for red-backed 
voles, deer mice, and vagrant shrews (Sorex vagrans). Overall, 2.5 times more individuals moved adjacent to 
highways than across highways, and more crossed 2-lane than 4-lane highways. Observed movements varied 
among species, with forest-associated species (red-backed voles and chipmunks) more inhibited by highways 
than habitat generalists (deer mice). However, decreased movement has not yet led to genetic divergence for 
voles separated by highways. Gene flow across highways in deer mice was highly variable among sites, with an 
11% decline in gene flow evident at one 4-lane highway site, in spite of relatively high numbers of observed 
movements at this site. Shrew gene flow was reduced by both 2- and 4-lane highways, and surprisingly, effect 
sizes (up to 37% decline) were largest for this habitat generalist. 



17. Key Words 

chipmunk, deer mouse, fragmentation, gene flow, 
highway, movement, population connectivity, red-backed 
vole, small mammal, vagrant shrew 



18. Distribution Statement 

Unrestricted. This document is available through 
the National Technical Information Service, 
Springfield, VA 21161. 



19. Security Classlf. (of this report) 

Unclassified 



20. Security Classlf. (of this page) 

Unclassified 



21. No. of Pages 116 



22. Price 



PREFACE 

Disclaimer 

This document is disseminated under the sponsorship of the Montana Department of 
Transportation and the United States Department of Transportation in the interest of 
information exchange. The State of Montana and the United States Government assume 
no liability of its contents or use thereof 

The contents of this report reflect the views of the authors, who are responsible for the 
facts and accuracy of the data presented herein. The contents do not necessarily reflect 
the official policies of the Montana Department of Transportation or the United States 
Department of Transportation 

The State of Montana and the United States Government do not endorse products of 
manufacturers. Trademarks or manufacturers' names appear herein only because they are 
considered essential to the object of this document 

This report does not constitute a standard, specification, or regulation. 

Alternative Format Statement 

The Montana Department of Transportation attempts to provide reasonable 
accommodations for any known disability that may interfere with a person participating 
in any service, program, or activity of the Department. Alternative accessible formats of 
this document will be provided upon request. For further information, call (406) 444- 
7693 or TTY (406) 444-7696. 



Ill 



TABLE OF CONTENTS 

Introduction 1 

Problem Statement 1 

Background Summary 1 

Roads and Habitat Fragmentation 1 

Small Mammals as Appropriate Study Organisms 2 

Roads and Small Mammals 4 

Small Mammal Study Species 6 

Combining Approaches 7 

Objectives 8 

Materials and Methods 9 

Study Area 9 

Trapping Grid Design 12 

Trapping Protocol 15 

Demographic Analyses 16 

Movement 16 

Abundance 17 

Genetic Analyses 18 

Genotyping 18 

Genetic Variation 19 

Gene Flow 21 

Results 27 

Demography 27 

Movement 27 

Abundance 30 

Genetics 32 

Testing Assumptions 32 

Genetic Variation 35 

Gene Flow 36 

Discussion 45 

Acknowledgements 57 

Literature Cited 58 

Appendix 1 - Small Mammal Abundance and Vegetation Surveys 67 

Appendix 2 -Marking Animals 72 

Appendix 3 - Microsatellites 73 

Appendix 4 - Mutation Models 74 

Appendix 5 - Hardy- Weinberg and Linkage Disequilibrium 76 

Appendix 6 -Measuring Genetic Differentiation 78 

Genetic Distance: Wright's Fst 78 

Assignment Test 81 

Appendix 7 -Captures and Movement 86 

Appendix 8 -Genetic Tests 88 

Appendix 9 -Heterozygosity and Allelic Diversity 99 

Appendix 10 - Genetic Differences Among Sites 107 

Appendix 11 - Gene Flow within Sites 114 



IV 



LIST OF TABLES 

1. Site information 9 

2. Sample size for genetic analyses 32 

3. Genetic differences among sites 37 

4. Number of migrants detected across highways 40 

5. Effect of culverts on gene flow 44 

6. Do highways reduce small mammal movement? 45 

LIST OF FIGURES 

1. Study area 10 

2. Trapping grid layout 14 

3. Comparing gene flow adjacent to and across highways 25 

4. Percentage of individuals that moved 29 

5. Abundance 31 

6. Gene flow at 2- and 4-lane highways 38 

7. Gene flow for red-backed voles 39 

8. Gene flow for deer mice 41 

9. Gene flow for vagrant shrews 43 



LIST OF TABLES IN APPENDICES 

1.1. Vegetation patterns across sites 69 

1.2. Vegetation patterns within sites 70 

7.1. Individuals captured 86 

7.2. Individuals that moved 87 

7.3. Movement adjacent to versus across highways 87 

8.1. Population differentiation among 2000 and 2001 red-backed vole samples. ..88 

8.2. Tests indicating Hardy-Weinberg disequilibrium for red-backed voles 88 

8.3. Tests indicating linkage disequilibrium for red-backed voles 89 

8.4. Percentage of tests in which linkage was detected for red-backed voles 90 

8.5. Population differentiation among 2000 and 2001 deer mouse samples 91 

8.6. Tests indicating Hardy-Weinberg disequilibrium for deer mice 91 

8.7. Tests indicating linkage disequilibrium for deer mice 93 

8.8. Percentage of tests in which linkage was detected for deer mice 96 

8.9. Population differentiation among 2000 and 2001 vagrant shrew samples 97 

8.10. Tests indicating Hardy-Weinberg disequilibrium for vagrant shrews 97 

8.11. Tests indicating linkage disequilibrium for vagrant shrews 98 

9.1. Average heterozygosity 99 

9.2. Site specific heterozygosity for red-backed voles 101 

9.3. Number of alleles per locus for red-backed voles 102 

9.4. Site specific heterozygosity for deer mice 103 

9.5. Number of alleles per locus for deer mice 104 

9.6. Site specific heterozygosity for vagrant shrews 105 

9.7. Number of alleles per locus for vagrant shrews 106 

10.1. FsT for red-backed voles among sites 107 

10.2. Assignments of red-backed voles among sites 108 

10.3. FsT for deer mice among sites 109 

10.4. Assignments of deer mice among sites 110 

10.5. FsT for vagrant shrews among sites Ill 

10.6. Assignments of vagrant shrews among sites 112 

10.7. Misassignments among sites 113 

11.1. Gene flow across highways for red-backed voles 114 

11.2. Gene flow across highways for deer mice 115 

11.3. Gene flow across highways for vagrant shrews 116 



VI 



INTRODUCTION 
Problem Statement 

Roads, especially large highways, can adversely impact wildlife populations by 
increasing mortality due to vehicular collisions and by discouraging crossing attempts. 
Small mammal populations separated by highways may be partially or completely 
isolated from one another due to low dispersal capabilities, low probability of surviving 
highway crossing attempts, and/or avoidance of areas adjacent to highways. Threats to 
small mammals are problematic at the ecosystem level because of their importance in 
ecosystem processes, such as seed and sporocarp dispersal, and because of their role as 
prey for predators such as lynx, marten, fisher, and raptors. Thus, as the human 
population continues to increase, and pressures mount for wider roads to accommodate 
more traffic, it is imperative that transportation planners understand the potential negative 
effects of these roads on wildlife and how to mitigate them. 

Background Summary 

Roads and Habitat Fragmentation 

Habitat loss and fragmentation are the primary threats to wildlife today. One 
potential agent of habitat loss and fragmentation that was long ignored is our system of 
roads and highways. Roads occupy a considerable area of land and form extensive 
longitudinal obstacles. Permeability of these potential barriers to wildlife depends on 
road width, traffic volume, the placement of fencing or concrete barriers, vegetation 
characteristics along the road corridor, the existence of culverts or overpasses, and 
physical abilities and behavioral characteristics of potential crossers. 

1 



Harmful effects of roads on wildlife may include mortality, disturbance due to 
emissions and noise, habitat loss and modification, intrusion of edge effects, and 
subdivision of populations (Andrews 1990). Of course, not all species experience 
negative effects; generalists and species adapted to open areas may benefit from the 
altered environment, responding positively to novel food sources or edge effects. Roads 
can cause changes in home ranges, movement, reproductive success, escape response, 
and physiological state, and have been correlated with changes in species composition 
and population sizes (Trombulak and Frissell 2000). 

Small Mammals as Appropriate Study Organisms 

Small mammals are both important ecological interactors and tractable study 
organisms for investigating the effects of highways on population connectivity. Deer 
mice (Peromyscus maniculatus) are the primary predator on seeds that reach the forest 
floor (Adams 1950; Schmidt and Shearer 1971), and chipmunks (Tamias spp.) are also 
important predators on conifer seeds. Chipmunks cache seeds and forget the location of 
some caches, facilitating seed dispersal and tree reproduction. Seeds from more than 20 
species of pine are dispersed by birds and rodents (Vander Wall 1993). They improve 
germination by carrying seeds away from the parent tree, allowing colonization of new 
habitats, and by burying them. In contrast, seeds dispersed by wind tend to fall on the 
surface near the parent tree. Tevis (1953) suggested that the yellow pine chipmunk 
(Tamias amoenus) was important in affecting stand regeneration in northeastern 
California, and Vander Wall (1993) showed that yellow pine chipmunks were more 
effective, higher quality seed dispersers than wind. 

2 



Shrews (Sorex spp.) and deer mice are important insect predators, and may 
control insect pest populations in some circumstances (Buckner 1958; Frank 1967). Piatt 
and Blakley (1973) suggested that the masked shrew (Sorex cinereus) functioned as a 
keystone predator by suppressing populations of dominant insect competitors. Anderson 
and Folk (1993) showed that shrews and deer mice reduced survival in acorn weevil 
populations, important forest pests. 

Many higher plant species depend on a symbiotic relationship with mycorrhizal 
fungi to meet their nutritional requirements (Marks and Kozlowski 1973; Sanders et al. 
1975). Most ectomycorrhizal fungi produce fruiting bodies that develop underground, 
relying on mammals to dig up and eat the fruiting bodies, dispersing spores in their feces 
(Johnson 1996). The northern flying squirrel (Glaucomys sabrinus) and the California 
red-backed vole (Clethrionomys californicus) are almost exclusive mycophagists (Maser 
et al. 1978; Maser et al. 1985; Hayes et al. 1986). The southern red-backed vole 
(Clethrionomys gapperi) replaces the California red-backed vole in the Rocky 
Mountains, and is also an important mycophagist (Ure and Maser 1982; Gunther et al. 
1983), although this species is more opportunistic in its consumption of truffles. Deer 
mice and chipmunks also eat sporocarps opportunistically, and are more likely to deposit 
spore-containing feces in adjacent nonforested areas than are voles (Maser et al. 1978; Li 
etal. 1986). 

Many predators depend on a small mammal prey base for at least part of the year. 
For example, red-backed voles are the primary food source for American pine marten 
(Martes americana) (Weckwerth and Hawley 1962; Buskirk and Ruggiero 1994) and 
boreal owls (Aegolius funereus: Hayward et al. 1993). Small mustelids such as least 

3 



weasels (Mustela nivalis) also prey heavily on small mammals (Norrdahl and Korpimaki 
1998). Numerous other mammalian and avian predators prey opportunistically on small 
mammals. 

In addition to being ecologically important, small mammals are tractable 
organisms for studying the effects of highways on population connectivity in wildlife 
over a short time frame. Due to relatively high densities and willingness to go into traps, 
small mammals can be captured in relatively high numbers, and handling is not difficult. 
Small mammals have short generation times, so they have the potential to show the 
signature of population subdivision long before it becomes detectable in longer-lived 
species. 

Roads and Small Mammals 

Roads may hinder some small mammal species very little, while presenting near 
absolute barriers to others. For example, Mader (1984) found that none of the 121 
marked small rodent individuals in his study crossed a 6 m two-lane paved highway in 
Germany, although numerous movements occurred parallel to the highway, and several 
species could theoretically have crossed within a few seconds. Even very small roads can 
function as barriers to wildlife. On a narrow (3 m) dirt road that received only 10-20 
vehicles per day, Swihart and Slade (1984) observed inhibition of movement across the 
road in prairie voles (Microtus ochrogaster) and cotton rats (Sigmodon hispidus), 
although movement rates were likely high enough in this case to maintain gene flow 
across the road. Oxley et al. (1974) trapped small mammals adjacent to roads ranging 
from gravel to 4-lane divided highways. In the two most abundantly captured species, 

4 



only 3% of white-footed mice (Peromyscus leucopus) crossed roads, and only one of 
these crossed a road as large as a 2-lane highway. Only 3% of eastern chipmunks 
(Tamias striatus) crossed, and these crossed only gravel, not paved, roads. However, 
Oxley et al. (1974) found that mammals adapted to open country ventured onto roads 
with apparent readiness in comparison to the caution shown by small forest-adapted 
mammals. In the first study to investigate the genetic effects of roads, Gerlach and 
Musolf (2000) demonstrated genetic subdivision in bank voles (Clethrionomys glareolus) 
separated by a 4-lane highway in Germany that had been present for 25 years (although 
the degree of subdivision was small). Several authors have suggested that divided 
highways with clearances of 90 m may be barriers as effective in hindering the dispersal 
of small forest mammals as bodies of fresh water twice as wide (Werner 1956; Sheppe 
1965). 

The impacts of road width and traffic volume on wildlife crossings have also been 
investigated in a few areas. A significant correlation was found between number of 
vehicles and number of snakes found dead or mortally injured on roads in the Everglades 
(Bernardino and Dalrymple 1992). In a study that attempted to identify the most 
important factors inhibiting small mammal movement across roads, Oxley et al. (1974) 
concluded that the clearance distance between habitats on either side of the road was 
more important than traffic volume or any other factor. Wilkins and Schmidly (1980) 
reported that highway mortality for mammals was highest on a highway with 
intermediate traffic volume, lowest on a highway with low volume, and intermediate at 
high volume, presumably because mammals did not attempt to cross high volume 
highways as often as those with low or intermediate traffic volume. Similar to Wilkins 

5 



and Schmidly's (1980) finding for mammals, Fahrig et al. (1995) found that the total 
number of frogs and toads on roads decreased as traffic intensity increased, but the 
proportion of dead to living anurans increased as traffic intensity increased. 

Small Mammal Study Species 

We chose our study species based on both ecological interest and logistical 
concerns. We focused on red-backed voles (Clethrionomys gapperi), deer mice 
(Peromyscus maniculatus), yellow pine chipmunks (Tamias amoenus), red-tailed 
chipmunks (Tamias ruficaudus), and vagrant shrews (Sorex vagrans) because they 
exhibit a wide range of ecological roles, habitat requirements, and behavioral 
characteristics (Pearson 1999; Foresman 2001^). Along a continuum from strong forest- 
associate -^ habitat generalist, the order of these species would be red-backed vole, red- 
tailed chipmunk, yellow pine chipmunk, vagrant shrew, and deer mouse. Deer mice are 
extremely widely distributed and are found in a wide variety of habitats (Pattie and 
Verbeek 1967; Hoffmann and Pattie 1968; Foresman 2001 a), as are vagrant shrews, 
although shrews seem to require a somewhat wetter environment with a more developed 
understory (Clothier 1955; Spencer and Pettus 1966; McCracken 1990; Foresman 2001 a). 
In contrast, red-backed voles live mainly in moist, densely-forested areas with a 
developed understory and abundant coarse woody debris (Gunderson 1959; Pearson 
1994; Foresman 2001 a), and are indicators for old growth conditions in the Rocky 
Mountains (USDA Forest Service 1985). Yellow pine chipmunks occur in dry, open 
forest stands (locally, in fairly open low elevation ponderosa pine/Douglas fir forests), 
while red-tailed chipmunks occur only in denser stands (Foresman 2001 a). Deer mice 

6 



typically respond positively to edges and to clearcuts (Sullivan 1979; Sekgororoane and 
Dilworth 1995; Tallmon et al. In Prep), while voles tend to prefer the forest interior (or 
sometimes edge habitat: Lair 2001), and shrews and chipmunks tend to show no 
avoidance of or attraction to edges (Sekgororoane and Dilworth 1995). 

We analyzed data only for those small mammal species that were abundant 
enough in our sample to make the analyses statistically possible and biologically 
meaningful. Therefore, our study species are abundant and widely-distributed, unlikely 
to face extinction even if movement across highways is quite rare. However, by focusing 
on this group of species, our goal was to represent a wide range of potential responses to 
highways, thereby providing information that might be useful in considering the effects 
of highways on rarer species of concern for which data collection was impossible. 

Combining Approaches 

Although several studies have investigated abundance and movement of small 
mammals near roads (reviews by Andrews 1990; Bennett 1991; Spellerberg 1998; 
Trombulak and Frissell 2000), and one has measured genetic effects of roads (Gerlach 
and Musolf 2000), none has done both. Demographic (mark-recapture) data can provide 
information about current movement and characteristics of study organisms (such as sex 
and age), but the limitation is that movement can only be inferred where it is detected 
through trapping. Unless an individual is captured before and after it moves, the event 
will go unrecorded. Moreover, mark-recapture data cannot reveal whether individuals 
that move actually breed, linking populations through gene flow. While genetic data can 
address these concerns, it may be difficult to detect recent population fragmentation using 

7 



genetics, because all tests of population subdivision depend on differences in gene 
frequencies to detect an effect, and these differences take time to develop, even in 
isolated populations. Thus, we have combined demographic and genetic approaches to 
gain insight into past and present movement and gene flow (Mills and Tallmon 1999; 
Mills et al. In Press) across highways. 

Objectives 

Our objective was to determine how movement and gene flow are affected by 
highways of different widths and traffic levels for several small mammals with varying 
habitat associations. We used a mark-recapture approach to compare movement adjacent 
to highways to movement across highways for southern red-backed voles, deer mice, 
yellow pine chipmunks, and red-tailed chipmunks in forested areas of western Montana. 
We also examined gene flow (movement plus reproduction) adjacent to versus across 
highways in red-backed voles, deer mice, and vagrant shrews. Our goal was to assess the 
barrier effect of highways of different widths on these species, so that these negative 
impacts can be identified and mitigated in the future. 

We tested the following hypotheses: 

1) Movement rates (and gene flow) across highways are decreased relative to movement 
rates adjacent to highways. 

2) 4-lane highways are a more significant barrier than 2-lane highways. 

3) Impacts of highways are stronger for forest-associates than for habitat generalists. 
Specifically, we predicted that red-backed voles (preferring dense forest cover), would be 



more deterred by highways than deer mice (using a wide variety of habitats), and that 
chipmunks and shrews would have an intermediate response. 

MATERIALS AND METHODS 
Study Area 

We established replicate trapping grids at three 2-lane and two 4-lane forested 
highway sites in western Montana (Table 1; Figure 1). Two-lane sites were located on 1) 
Highway 12 just west of Lolo Hot Springs, 2) State Highway 200 in the Lubrecht 
Experimental Forest, and 3) Highway 83 between Lake Alva and Rainy Lake. Four-lane 
sites were located on L90, one just east of St. Regis, and the other near Tarkio. These 
sites will hereafter be referred to as Lolo, Lubrecht, Rainy Lake, St. Regis, and Tarkio. 



Site Information 





Lolo 


Lubrecht 


Rainy Lake 


St. Regis 


Tarkio 


Number of lanes 


2 lanes 


2 lanes 


2 lanes 


4 lanes 


4 lanes 


Highway ID 


12: mile 6.5 


200: mile 22.5 


83: mile 26.5 


1-90: mile 36 


1-90: mile 63.5 


Pavement width 


12m 


14m 


12m 


24 m 


24 m 


Median width 


none 


none 


none 


51m 


12m 


Traffic volume 


1300veh/day 


2900 veh/day 


1100 veh/day 


5900 veh/day 


6500 veh/day 


Year 


1954/- 


1941/- 


1956/- 


1920S/1980 


1920S/1982 


paved/widened 












Distance from 


5m 


10m 


2m 


25 m 


30 m 


pavement edge 












to forest edge 












Distance from 


22 m 


34 m 


16m 


125 m 


96 m 


forest edge to 












forest edge 












Species captured 


RB voles 


RB voles 


RB voles 


RB voles 


Deer mice 


and analyzed 


Chipmunks 


Deer mice 


Deer mice 


Deer mice 


Chipmunks 




Shrews 


Chipmunks 


Chipmunks 
Shrews 


Chipmunks 
Shrews 





Table 1. Information on areas where small mammals were sampled. 



Study area 



■■i.OrJETgfti 




Figure 1 . Study sites are marked by stars, and nearby towns are noted in italics. Study sites starting at St. 
Regis (1-90) and moving counter-clockwise are Tarkio (1-90), Lolo (Highway 12), Lubrecht (State 
Highway 200), and Rainy Lake (Highway 83). 



10 



These five sites represented the only areas within two hours drive of Missoula that 
were suitable for this study. We selected only undeveloped forested sites that were 
relatively flat and situated around a fairly straight stretch of road, avoiding steep, rocky 
terrain and curvy stretches of road that would have complicated the establishment of four 
square, equidistant trapping grids around the highway. The predominance of rivers, 
railroad tracks, and frontage roads near highways restricted the pool of sites, because they 
form potential barriers that would have confounded our analyses of highway effects. We 
chose sites that were undeveloped and lacked concrete barriers or fencing that might 
restrict small mammal movement, because we wanted to maximize our chances of 
detecting movement across highways where it was likely to occur. Three of our sites 
contained culverts. At Rainy Lake, a culvert conducted a permanent stream under the 
road bed at the southernmost end of our trapping grids. The other two culverts remained 
mostly dry during our study. At St. Regis, a culvert connected the northwest trapping 
grid to the median strip, but went no farther. At Tarkio, a culvert near our eastern 
trapping grids ran between the north and south sides of the highway, but did not open 
directly into our trapping grids. 

The vegetation at these sites is somewhat variable, but all sites are dominated by 
ponderosa pine and Douglas-fir (for information on vegetation surveys in this study, see 
Appendix 1). Rainy Lake and St. Regis are dense, moist sites near the highway, but both 
of these sites have been thinned starting around 75 m from the highway. Tarkio is a very 
dry, open, managed stand of ponderosa pine, with very little undergrowth. Lubrecht has 
vegetative structure intermediate to that of the previously mentioned sites. Lolo has 
variable vegetation due to the presence of Lolo Creek on the south side of the highway. 

11 



Dryer ponderosa pine - Douglas-fir forest dominates on the north side of the highway 
and near the highway on the south side, but riparian vegetation is common near the creek. 

At our 2-lane sites, the forest edge was quite near the pavement edge; distances 
were larger at our 4-lane sites (Table 1). At Lolo, there was a small grassy area coming 
down from the slightly raised roadbed to the forest (or to a mesic area with high shrub 
cover, for the easternmost part of our eastern trapping grids). At Lubrecht, the slope from 
the road bed to the forest edge varied from a slight slope up, to level, to a very slight 
slope down to the forest; this area between the pavement and the forest edge was grassy, 
except that the up-slope area was sparsely covered by seedlings and small saplings. 
There was essentially no shoulder at our Rainy Lake site, so the forest edge was only 1 - 
2 m from the pavement edge, separated by a strip of grass. In contrast, the area 
bordering our 4-lane highway, 1-90, was mowed periodically by the Montana Department 
of Transportation, and the forest edge was 25 - 30 m from the pavement edge. At St. 
Regis, our first one or two rows of traps were set in an open, grassy area with few trees or 
shrubs. These open areas sloped gently up or down from the roadbed. At Tarkio, a level, 
grass and gravel area bordered the highway. The grass and gravel blended into an area 
covered in spotted knapweed and sparse ponderosa pine trees. Our first two rows of traps 
were set in this knapweed / ponderosa pine area, with the rest of the traps located within 
the pine forest. 

Trapping Grid Design 

We live-trapped small mammals in the summers of 2000 and 2001. At each site, 
there were four equidistant highway trapping grids (Figure 2), two on one side of the 

12 



highway and two on the other. This design permitted the comparison of movement rates 
adjacent to the highway versus across the highway. Each grid was square and contained 
seven traps by seven traps, for a total of 49 traps per grid and 196 traps per site. Traps 
were 15 m apart, and grids were 75 m apart. The distance of grids from the highway 
varied from site to site due to differences in highway width. We chose to maintain 
constant distance between grids, rather than constant distance from grids to the highway, 
so that we could remove distance as a nuisance variable that would have confounded the 
analysis of movement rates across 2- and 4-lane highways. 

In 2001, we added to the study design at three sites (Lubrecht, Rainy Lake, and 
St. Regis): two additional grids in the forest interior, 75 m from the original highway 
grids on one side of the highway, for a total of six trapping grids. The addition of forest 
interior grids essentially provided another within-site replicate, and also allowed 
comparisons of small mammal abundance near the highway versus the forest interior; 
logistical constraints limited us to only one side of the highway and three sites. This 
expansion of the project was possible because of a collaboration with a student 
completing his undergraduate senior thesis, Jeremy Moran. In addition, J. Moran 
assessed a number of vegetation variables (Appendix 1). 



13 



Trapping grid layout 





G F 


E D C 


B 


A 


























14 
13 


* 
* 


* 
* 


* =1 

* =1 


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— 


15 


m 




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* 






12 


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* * * 


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-90 m 


FOREST INIERIOR 


11 
10 


* 
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* 






75 


m 


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* * * 


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GRIDS 










9 


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75 


m 


















75 


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7 


* 


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* 


=H 


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6 


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Figure 2. Layout of trapping grids at a site. Each dot represents a Sherman live trap. Four square (7 traps 
by 7 traps), equidistant grids were bisected by the highway; two additional grids were located in the forest 
interior on one side of the highway at three sites (Lubrecht, Rainy Lake, and St. Regis) in 2001 only. 
There were 49 traps per grid, for a total of 294 traps at these three sites and 196 traps at Tarkio and Lolo. 
Traps were spaced 15 m apart, and grids were 90 m by 90 m. Grids were 75 m apart, so distance from the 
edge of the highway varied with highway width. At each site, highway trapping grids were denoted NE, 
NW, SE, and SW, based on their position around the highway. Highways run east-west except at Rainy 
Lake, where the highway runs north-south. Forest interior grids were denoted NEl, NWl, and so on. 



14 



Trapping Protocol 

During summer 2000 and 2001, we used large baited Sherman traps (9 x 8 x 23 
cm) to live-trap small mammals. Traps were baited with sunflower seeds, oat groats, and 
apple (for moisture). Traps also contained water-repellent polyester batting for warmth, 
and were placed inside treated cardboard milk cartons to regulate temperatures inside the 
traps and repel water. Whenever possible, we placed traps near woody debris, trees, or 
some other form of cover within aim radius of the grid point location, in order to 
maximize probability of capture. We followed this trap placement protocol at every trap 
location at every site. At each site, we prebaited for two to four days to increase capture 
probability. 

Trapping sessions consisted of three consecutive nights of trapping separated by 
25 days. We opened traps between 17:30 and 20:00, and closed traps after checking 
them the next morning, starting at 7:00. Because trapping effort was concentrated during 
the night, capture probability was maximized for mostly nocturnal species like red- 
backed voles, deer mice, and shrews. We were willing to sacrifice some daylight 
trapping hours that may have increased diurnal chipmunk captures in order to minimize 
mortality in other species. Our goal was to trap each site at least three times per year, 
which is necessary for estimation of survival and movement in open populations. 
However, logistical limitations, including forest closures due to fire, meant that in 
summer 2000 Lubrecht was trapped four times, Lolo was trapped twice, and all other 
sites were trapped three times; during summer 2001, we trapped each site three times. 

For each captured individual, we recorded species, ID number, sex, reproductive 
condition, weight, tail length, and any other unique characteristics or comments that 

15 



described the animal's condition. The ID number was assigned at first capture via toe 
clipping or ear tags (Appendix 2). Mice and voles were marked by toe clipping, which 
has the additional benefit of providing a tissue sample for genetic analysis (tissues were 
stored in silica gel). Chipmunks and bushy-tailed woodrats, which are too large for toe 
clipping, were marked with small numbered metal ear tags. Although we did not assess 
gene flow for these species, we archived a small hair sample for possible future genetic 
analyses. 

Shrews are strict insectivores, and were not targeted for capture (traps were baited 
with seeds and fruit). Nevertheless, we captured many shrews, 70% of which had 
perished overnight in the trap due to extremely high metabolic rates that require shrews to 
move and feed almost constantly. Live shrews were released, and deceased shrews were 
collected for genetic analysis. Species determination for shrews occurred in the lab 
through an examination of dentition (Foresman 2001b), because species are not easily 
distinguished in the field. 

Demographic Analyses 
Movement 

To assess the effect of highways on small mammal movement, we compared 
movement adjacent to highways to movement across highways, where a movement was 
defined as an initial capture in one trapping grid, and subsequent capture in a different 
trapping grid. Therefore a movement was of minimum length 75 m, the distance between 
trapping grids. If the grid of previous capture was diagonal (across the highway) from 
the grid of subsequent capture, then two movements were noted: one adjacent to and one 

16 



across the highway. Some individuals moved more than once. We used logistic 
regression to determine which factors best explained movement, where the response 
variable had two possible values: movement adjacent to or movement across highways. 
We investigated the importance of several factors, including highway width (2- or 4- 
lane), species (red-backed vole, deer mouse, or chipmunk), sex, site, and year. We used a 
chi-square to test whether movement adjacent to highways was more or less common 
than movement across highways for each species separately, and for all species 
combined, comparing observed movements to the null hypothesis of half of the 
movements adjacent to and half across the highway. 

Abundance 

For his undergraduate senior thesis, Jeremy Moran estimated the abundance of 
red-backed voles, deer mice, and chipmunks in the highway/forest edge trapping grids 
and in the forest interior grids. He predicted that an ecological "fence effecf (Krebs et 
al. 1969; Lidicker 1975; Gliwicz 1980; Gaines and Johnson 1987) was occurring: 
dispersing individuals encounter highways that frustrate their dispersal, so abundances 
are higher near highways than in the forest interior. Lincoln-Petersen estimates (Seber 
1982) of monthly abundance were calculated, assuming that populations were closed 
within 3-day trapping sessions (meaning that no individuals were added to or removed 
from the population during this time). The fence effect hypothesis was tested indirectly 
by comparing abundance in highway edge grids to abundance in forest interior grids, and 
was evaluated relative to the alternative hypothesis that abundance patterns are 
determined purely by chance or by vegetation characteristics. For each vegetation 

17 



variable (Appendix 1), plot averages were calculated and combined for mean (and 
standard error) values across the 14 plots (7 per grid) making up the two interior and two 
highway grids at each site (on one side of the highway). 

Genetic Analyses 

In addition to demographic analyses of mark-recapture data, we analyzed genetic 
information obtained from tissue samples from each red-backed vole, deer mouse, and 
vagrant shrew that we captured. Our goal was to quantify relative genetic differences 
between animals in trapping grids on the same side of highways versus those separated 
by highways. Genetic differences between populations (defined here as trapping grids) 
result when gene flow is limited by some factor, such as a physical barrier, causing allele 
frequencies in different populations to drift apart (Allendorf and Phelps 1981; Mills and 
Allendorf 1996). We used microsatellite loci for our genetic analyses because they are 
highly variable, resulting in high power to distinguish between individuals and/or 
populations (Appendix 3 and 4). 

Genotyping 

In summary, the genotyping procedure was as follows: 1) extract DNA from 
tissue, 2) run the polymerase chain reaction (PCR) to amplify a nuclear microsatellite 
region of the DNA, 3) use gel electrophoresis to visualize and score alleles for each 
individual at each microsatellite locus. DNA was extracted using standard tissue 
protocols in the Dneasy Tissue Kit (Qiagen Inc.). Samples were left to digest overnight 
so that maximum product was obtained. 

18 



Our goal was to mn six microsatellite loci per species. For P. maniculatus, we 
used the six best amplifying primers for microsatellite loci from Chirhart et al. 2000 
(Pml-1, 4, 5, 6, 10, and 12). Primers for C. gapperi and S. vagrans were not available, so 
primers developed for closely related species were used (see also Mech and Hallett 2001; 
Tallmon et al. 2002). Primers used for C. gapperi were designed by Gockel et al. (1997) 
for C. glareolus (MSCgl-4, MSCgl-15, and MSCgl-19) and by Ishibashi et al. (1995) for 
C. rufocanus (MSCRB-4, MSCRB-5, and MSCRB-6). Primers used for S. vagrans were 
developed by Maldonado (unpublished data) for S. ornatus. Out the 10 primer sequences 
that we tested, five amplified well in our S. vagrans samples: A3-5, A3-35, A4-20, A4-5, 
and SH-22. We also screened primers developed for the European common shrew (Sorex 
araneus) by Wyttenbach et al. (1997) and Balloux et al. (1998) but were unsuccessful in 
getting high quality, polymorphic product from any of these 13 primer sequences. 

PCR products were separated in a 6.5% acrylamide gel for 2-2.5 hours using the 
Li-cor Global IR- System and visualized using Li-cor SAGA genotyping software (Li- 
cor. Inc. 2002). To ensure accuracy, gels were manually scored and compared to SAGA 
scores, and hand scores were double-checked by a second person. To facilitate scoring 
and allow comparisons between gels, we ran ladder and at least three positive controls 
(individuals run on each gel for that species at that locus) on each gel. 

Genetic Variation 

We first tested for genotypic differences between years with GENEPOP Internet 
Version 3.1c (Raymond and Rousset 1995) to evaluate whether or not we could pool data 
from 2000 and 2001 in order to maximize sample sizes and statistical power. We used a 

19 



log-likelihood G-based exact test (Goudet et al. 1996), which estimates p-values using a 
Markov chain (Haldane 1954; Weir et al. 1990; Guo and Thompson 1992a). The values 
entered into the Markov chain were a dememorization number of 1000, with 1000 
batches and 10,000 iterations per batch. 

We calculated expected heterozygosity (H^: based on Hardy-Weinberg 
proportions) and observed heterozygosity (Ho) for each locus at each site, as well as 
allelic diversity (number of alleles) within GENEPOP Internet Version 3.1c (Raymond 
and Rousset 1995). Heterozygosity is equal to the proportion of individuals that are 
heterozygous at a given locus. If a locus is in H-W equilibrium. Ho should nearly equal 
He. Heterozygosity is important because it sets an upper limit on Fst. Hedrick (1999) 
showed that Fst can never exceed the overall homozygosity = [1 - heterozygosity], even 
if there is no gene flow between populations and they have completely different 
nonoverlapping sets of alleles. This is especially important for microsatellite loci, where 
heterozygosity may be quite high, and means that caution is required when comparing 
FsT-values among studies that make use of markers with different levels of heterozygosity 
(Balloux et al. 2000; Balloux and Lugon-Moulin 2002). 

We tested for Hardy-Weinberg and linkage disequilibrium (Appendix 5) using 
GENEPOP Internet Version 3.1c (Raymond and Rousset 1995). We used a two-way 
probability test entering the same values into the Markov Chain as noted above (Guo and 
Thompson 1992a,b). For H-W tests, a small p-value indicates that a locus may not be in 
H-W proportions in a particular population. For linkage tests, a small p-value indicates 
that a locus pair may not be independent in a given population. To be conservative, we 
report all tests that resulted in p < 0.05, but a certain number of significant tests are 

20 



expected by chance, due to the large number of tests performed (one test per locus or 
locus pair per trapping grid). Therefore, we also include the results of a sequential 
Bonferroni procedure (Rice 1989). 

Gene Flow 

We analyzed gene flow using two measures: Fst and the assignment test (see 
Appendix 6 for details on both approaches). Fst is a measure of population subdivision 
that is calculated from allele frequencies of animals dispersed across a landscape (Wright 
1951). Because Fst is the proportion of total genetic variation due to divergence among 
subpopulations, it is inversely related to the number of migrants per generation. Fgi 
varies between zero and one, with higher numbers indicating more differentiation and 
less gene flow between populations. A number of different measures of genetic 
differentiation have been developed (for example: Wright 1931, 1951, 1969; Cavalli- 
Sforza and Edwards 1967; Nei 1972; Weir and Cockerham 1984; Slatkin 1985; 
Chakraborty and Jin 1993; Goldstein et al. 1995,, t; Slatkin 1995; Shriver et al. 1997), but 
most make the same basic assumptions and yield the same kind of information, 
quantifying the distinctness of populations. We chose to use Fst because it has been used 
for many years to examine genetic divergence and gene flow, facilitating comparisons 
between studies, and because interpretation is straightforward. In addition, statistics 
developed specifically for microsatellites have performed poorly when fragmentation is 
more recent (Paetkau et al. 1997; Balloux and Lugon-Moulin 2002), as with highways. 
Our goal was to examine relative differences in Fsi, comparing genetic divergence 
between populations on the same side of highways to divergence between populations 

21 



separated by highways. Therefore, our choice of measures was not of crucial importance, 
because we were more interested in relative differences within sites than in the actual 
value of FsT. In short, unlike most studies testing for genetic divergence, we had a control 
value (connectivity adjacent to highways) to compare to the treatment value (connectivity 
across highways), rather than relying entirely on a determination of what value of Fst 
should be considered "significanf for populations separated by a barrier (the highway). 

The assignment test can provide insight into current gene flow by identifying 
likely migrants based on the multi -locus likelihood of their genotypes (Paetkau et al. 
1995; Waser and Strobeck 1998). The assignment test assigns individuals to their 
populations of origin according to the likelihood of their genotypes occurring in each 
population. Misassignments provide an index of gene flow: a misassignment is an animal 
captured in one population, but assigned to another population because its genotype is 
more likely to come from the other population. A lower proportion of misassignments 
indicates less gene flow. (Note that some misassignments will arise as statistical 
artifacts, leading us to interpret relative differences in misassignments and not absolute 
levels). We compared misassignment rates between trapping grids on the same side of 
the highway to misassignment rates between grids separated by the highway. 

We used GENEPOP Internet Version 3.1c (Raymond and Rousset 1995) to 
calculate Fst between population pairs using a weighted analysis of variance (Cockerham 
1973; Weir and Cockerham 1984). For comparison, we report pairwise Fgi-values 
calculated using all loci, as well as those calculated using only those loci that were in 
Hardy-Weinberg equilibrium. We used GeneClass (Comuet et al. 1999) to assign 
individuals to their most likely population of origin. We report misassignment rates 

22 



calculated using two approaches: a likelihood-based test using Bayesian probabilities 
(Rannala and Mountain 1997), with and without loci deviating from H-W proportions, 
and a distance-based test that does not require H-W or linkage equilibrium (Cornuet et al. 
1999) using Nei's Da statistic (Nei 1987). 

We first report misassignment rates obtained by considering only the most likely 
population of origin, where each individual was assigned to exactly one population. For 
animals that moved between trapping grids during our study, the capture population was 
defined as the population where the individual was first captured. Some individuals were 
only marginally more likely to come from one population than another, or were unlikely 
to come from any of the populations sampled; therefore, we also present misassignments 
that are more likely to represent true migrants (Proctor et al. In Press), using likelihood 
ratios of 10 (where a misassigned individual was at least 10 times more likely to come 
from the assigned population than from the population where it was captured), 20, and 
100 as cutoff values. We used a distance-based assignment test (Nei's Da statistic), 
simulating 10,000 individuals, where the lower threshold for assignment was a 
probability of occurrence of 0.01. 

To help us calibrate the biological relevance of our estimates of gene flow across 
highways using the assignment test, we first estimated misassignment rates at the 
regional level (among sites) before proceeding with within site analyses. Because sites 
were 50 - 320 km apart (30 - 200 miles), we knew that direct migration between these 
sites was impossible. Thus we expected a fairly high Fst value and a very low 
misassignment rate, theoretically, zero misassignments. 



23 



Within sites, we treated trapping grids as populations and compared Fsi-values 
and misassignment rates between grids on the same side of highways (75 m apart) to 
values between grids separated by highways (75 m apart: Figure 3). For each site, our 
grid design gave us two values for grids on the same side, and two values for grids on 
opposite sides. To maintain equivalent distances between trapping grids when comparing 
gene flow adjacent to versus across highways, we only compared grids that were directly 
across the highway from one another (for example, we did not analyze gene flow 
between the NE and SW grids, or between the NW and SE grids, because the distances 
here were larger; Figure 3). Values reported for each site are means (N = 2) for values 
between individual grids on the same side of the highway and those on opposite sides, as 
well as standard errors around the means. Finally, we averaged values for 2-lane sites 
and for 4-lane sites to facilitate comparisons based on highway width. The averaged 
data, comparing gene flow differences for voles, mice, and shrews at 2- versus 4-lane 
highways, are presented along with more detailed genetic data from each site. 



24 



Comparing gene flow adjacent to and across highways 





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Figure 3. For clarity, grids are named as at Lubrecht and St. Regis. There were two ways to measure gene 
flow on the same side of (adjacent to) the highway (NE-NW and SE-SW) and two ways to measure gene 
flow on opposite sides of (across) the highway (NE-SE and NW-SW). Similarly for the forest interior, 
there were two ways to measure gene flow on the same side of the highway (NEl -NWl and SE-SW) and 
two ways to measure gene flow on opposite sides of the highway (NEl-SE and NWl-SW). We compared 
gene flow adjacent to and across highways by averaging these Fgi-values and misassignment rates for 
individuals captured in grids on the same side of the highway (75 m apart), grids directly across the 
highway (75 m apart), and grids across the highway into the forest interior (240 m apart). We then 
averaged these values across sites to compare 2- and 4-lane highways for each species. 



25 



As with FsT-values, misassignments were "pairwise": in any given run, the 
assignment test had only two trapping grids from which to choose a population of origin 
(that in which the individuals were captured, plus the grid adjacent to or across the 
highway). If we had run the assignment test on whole sites at once, individuals might 
have been assigned to any of four (or six) trapping grids. As noted above, this would 
have compromised the balanced nature of the experimental design (equidistant trapping 
grids). Thus, misassignment rates are an index of movement, since an individual's true 
population of origin may not have been one of the two tested. In contrast, when using 
likelihood ratios to identify "true" migrants, individuals could be assigned to any trapping 
grid (or none), since an animal could legitimately have originated anywhere in the study 
area. 

The presence of culverts within several sites may increase population connectivity 
across highways. It was not possible to explicitly test for the effects of culverts within 
this study (or to control for culverts by choosing only sites that lacked them), but within 
sites containing culverts, we do report Fst and misassignment values for grids connected 
by culverts and for grids lacking culverts, for the reader's comparison. Three of our sites 
contained culverts: Rainy Lake, St. Regis, and Tarkio. The culvert at Rainy Lake 
conducted a stream under the highway at the southern edge of our trapping grids, and was 
always flooded. St. Regis and Tarkio contained dry culverts. The culvert at St. Regis ran 
from the NW trapping grid into the median strip, but went no farther. The culvert at 
Tarkio did not open into our trapping grids, but ran under the highway within our site 
from a point south of our NE trapping grid to a point north of our SE trapping grid. 



26 



By combining Fst and misassignment estimates with demographic analyses (see 
review by Mills et al. In Press), we maximized inference strength. In addition, we 
replicated both sites and trapping grids (populations) within sites. A third strength of the 
experimental design was the inclusion of controls (grids adjacent to highways) that we 
could compare to treatment (grids across highways) values. By choosing study sites 
nonrandomly, we sacrificed inference scope for inference strength. We eliminated from 
consideration sites with human development and sites where concrete barriers, railroad 
tracks, frontage roads, or rivers would have bisected the study area, because this would 
obviously have confounded our analyses of highway effects. Therefore, our scope of 
inference does not extend to such sites. However, if these types of obstructions influence 
population fragmentation in small mammals, they would be likely to magnify barrier 
effects of highways. 

RESULTS 

Demography 

Movement 

Over two summers we recorded 3,812 captures of 1,609 individuals of 15 
different small mammal species (Appendix 7, Table 7.1). Deer mice were the species 
most often captured (504 individuals), followed by vagrant shrews (355 individuals), red- 
backed voles (318 individuals), red-tailed chipmunks (138 individuals), and yellow pine 
chipmunks (95 individuals). For those individuals that were marked and released, we 
averaged 2.6 captures per individual, ranging from one to 15 captures. Results for yellow 
pine and red-tailed chipmunks were similar, so they were pooled to simplify presentation. 

27 



For all red-backed vole, deer mouse, and chipmunk data pooled, more marked 
animals moved adjacent to highways than across highways {yj = 18.38; p < 0.001): 69 
animals moved adjacent to highways and 27 animals moved across highways. Some of 
these moved more than once, and most were deer mice. The only small mammals that 
crossed 4-lane highways were deer mice (with the exception of one male vole who 
permanently dispersed during fall or winter). 

As predicted, there were fewer crossings of 4-lane highways than of 2-lane 
highways, and species differed in their response to highways (Figure 4). Logistic 
regression showed that species (p = 0.036) and highway width (p = 0.004) were 
significantly related to whether movement occurred adjacent to versus across the 
highway, while site (p = 0.191), sex (p = 0.624), and year (p = 0.695) were not. 
Therefore, data for males and females in 2000 and 2001 were pooled among sites to 
examine differences among species at 2- and 4-lane highways. The demographic data 
suggest that forest-associated species (red-backed voles and chipmunks) were more 
inhibited by highways than habitat generalists (deer mice) (Figure 4; Appendix 7, Tables 
7.2 and 7.3). Every time we saw an effect, it was in the expected direction: more 
movement adjacent to highways than across for red-backed voles and chipmunks at 2- 
lane highways, and for deer mice and chipmunks at 4-lane highways. Unfortunately, our 
sample size of moving voles was too small to detect differences for voles at 4-lane sites. 
We observed only one vole and no chipmunks crossing 4-lane highways. 



28 



Percentage of individuals that moved 



12 



2-lane highways 



4-lane highways 



16* 






(123) 


(88) 






9 






r 










(1 

8 


57) 

ft 


5 








5 






1 





















(414) 
28" 


(1 
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(139) 




11 












1 1 














red-backed voles deer mice chipmunks 



red-backed voles deer mice chipmunks 



adjacent 
across 



I adjacent 
I across 



Figure 4. The numbers above the bars are the actual numbers of individuals that moved; the numbers in 
parentheses are the total captures. We tested for differences using a chi-square test, comparing observed 
movement to the null expectation of equal numbers of movements adjacent to and across highways. For 
example, 5% of the voles at 2-lane sites moved adjacent to the highway, 8 of 157 captured. 
*p<0.05;**p<0.01 



For all sites and years combined, 75% of the animals that moved between 
trapping grids were male, but males did not move across the highway (as opposed to 
adjacent to the highway) proportionately more than females. In other words, not many 
females move, but those that do are just as likely to cross the highway as males. The 
preponderance of movements by males is not surprising, because dispersal is male-biased 
in many mammal species (Greenwood 1980; Wolff 1993; Lambin 1994; Petri et al. 1997; 
Bowne et al. 1999; Tallmon et al. 2002; but see Goertz 1964; Kozakiewicz 1976; Favre et 
al. 1997). 



29 



Abundance 

Deer mice were more abundant near highways than in the forest interior (Figure 
5: Moran 2001), with no apparent relationship to vegetation patterns. Red-backed vole 
and chipmunk abundance appeared unrelated to either the presence of the highway or 
vegetation patterns. 



30 



Red-backed vole abundance 




25 



20 



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Deer mouse abundance 



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5 






T 


- 


-■- T 1 -r 









Lubrecht Rainy Lal(e St. Regis 



Chipmunk abundance 



J 


h P 

T 


k 



Lubrecht 



Rainy Lal<e 



St. Regis 



highiway 
interior 



Figure 5. Average per grid abundance estimates were calculated using 
a Lincoln-Petersen estimator. These figures represent mean abundance, 
averaged over three 3 -day trapping sessions during summer 2001, and 
include estimates from forest interior grids and only those highway 
grids adjacent to them (not grids across the highway). 



31 



Genetics 

We genotyped 314 red-backed voles at six microsatellite loci, 503 deer mice at six 
loci, and 355 vagrant shrews at five loci (Table 2). Sample sizes in the forest interior 
trapping grids were too small to analyze gene flow across highways, except at St. Regis, a 
4-lane site. As determined by dentition, shrews fi'om Lolo and St. Regis were almost all 
S. vagrans, but our sample fi'om Lubrecht and Rainy Lake consisted of fairly equal 
numbers of ^S. vagrans and S. cinereus (masked shrews), as well as several S. monticolus 
(montane shrews). For shrews with extremely worn teeth, such that species 
determination was uncertain, if the genotype included unusual alleles (suggesting that 
they were unlikely to be S. vagrans), the individuals were excluded from further analysis. 

Sample size for genetic analyses 



RB voles 
Deer mice 
Vagrant shrews 


2-lane 


4-lane 


Total 


Lolo 


Lubrecht 


Rainy 


St. Regis 


Tarkio 




32 
13* 
98 


52 

45 

4*** 


79 
29* 
26 


151 
178** 

227 


238** 


314 
503 

355 



Table 2. Number of individuals that were genotyped does not necessarily equal the total number captured, 
since not all individuals amplified well. Not all species occurred at all sites. * Sample sizes were too low 
to analyze mouse gene flow at Lolo (mice captured on only one side of the highway) and Rainy Lake (only 
two mice from northern grids). ** Samples from 2000 and 2001 were pooled, except that data for deer 
mice from St. Regis and Tarkio were not pooled. These mice were treated as separate populations: 46 mice 
m St. Regis 2000, 132 m St. Regis 2001, 81 m Tarkio 2000, and 157 m Tarkio 2001. *** Shrew numbers 
were quite low at Lubrecht (most were not S. vagrans), and thus were not analyzed. 



Testing Assumptions 

Red-backed voles: We tested for genotypic differences between years and found 
that out of six loci and four sites (6*4 = 24 tests), there were two instances of differences 
between years: one locus at Lubrecht and one locus at St. Regis. After a sequential 



32 



Bonferroni procedure was run, none remained significant with "table-wide" a = 0.05. 
Therefore, we pooled 2000 and 2001 samples for all analyses that follow. 

Among individual trapping grids, tests for Hardy- Weinberg proportions found 
several departures: locus 4 (2/17 tests), locus 5 (1/17 tests), locus 15 (2/17 tests), locus 6 
(4/17 tests), and locus 4B (2/17 tests). After a sequential Bonferroni procedure, several 
departures remained: locus 15 (1/17 tests), locus 6 (4/17 tests), and locus 4B (1/17 tests). 
Thus, we were mainly concerned about locus 6. We report Fgi-values and misassignment 
rates calculated with and without locus 6. 

Linkage disequilibrium was detected in 9% of tests (21/227 tests), and in just 4% 
of tests (9/227 tests) after a sequential Bonferroni correction for multiple comparisons. 
No locus pair was consistently found to be in linkage disequilibrium across populations; 
no pairs were detected more than three times (no more than once after Bonferroni 
correction). 

Deer mice: We tested for genotypic differences between years and found that out 
of six loci and three sites (6*3 = 18 tests), 13/18 tests suggested differences: four loci at 
Lubrecht and Tarkio, and five loci at St. Regis. After a sequential Bonferroni procedure, 
10/18 remained significant: two loci at Lubrecht, five at St. Regis, and three at Tarkio. 
Due to low sample sizes at Lubrecht, we pooled data from 2000 and 2001. However, for 
the purposes of within site analyses of highway effects, we considered St. Regis 
2000/2001 and Tarkio 2000/2001 to be four separate populations. 

We found a number of departures from H-W proportions: locus 1 (17/22 tests), 
locus 6 (8/22 tests), locus 4 (6/22 tests), locus 5 (6/22 tests), locus 10 (19/22 tests) and 
locus 12 (4/22 tests). After a sequential Bonferroni correction, several deviations from 

33 



H-W equilibrium remained: locus 1 (15/22 tests), locus 6 (4/22 tests), locus 4 (3/22 tests), 
locus 5 (3/22 tests), locus 10 (19/22 tests) and locus 12 (2/22 tests). Because loci 1 and 
10 were especially problematic, we analyzed gene flow with and without these loci. 

Linkage disequilibrium was detected in 33% of tests (86/261 tests), and in 24% of 
tests (63/261 tests) after a sequential Bonferroni correction for multiple comparisons. 
Linkage was particularly high at St. Regis 2001 and Tarkio 2001 (36 and 68%), 
respectively, 30 and 53%) after Bonferroni correction), where sample sizes were high and 
many family groups may have been sampled; these sites experienced a large recruitment 
event that summer, and many young mice were captured, sometimes two or three to a 
trap. No locus pair was consistently found to be in linkage disequilibrium across 
populations, except for 4 and 5, which were in disequilibrium 75%) of the time (63%) of 
the time after Bonferroni correction). We investigated the effect of linkage 
disequilibrium on gene flow estimates by comparing results from a distance-based 
assignment test (which does not assume linkage equilibrium) to results from the Bayesian 
assignment procedure. 

Vagrant shrews: We tested for genotypic differences between years and found 
that out of five loci and three sites (5*3 = 15 tests), 7/15 tests suggested differences: one 
locus at Lolo, five loci at Rainy Lake (all loci), and one locus at St. Regis. After a 
sequential Bonferroni procedure, 6/15 remained significant, those for Rainy Lake and St. 
Regis. Differences at Rainy Lake are most likely a result of differences in sample sizes 
between years: because we captured only five shrews in 2000 and 21 shrews in 2001, 
allele and genotype frequencies are almost certainly different. Given this explanation, it 
made sense to pool data from 2000 and 2001, and sample size at Rainy Lake was too 

34 



small to do otherwise. A3-35 was the only locus that fluctuated between years at more 
than one site, so data were pooled 

Among individual trapping grids, tests for H-W proportions revealed several 
departures: locus A3-5 (10/12 tests), locus SH-22 (7/12 tests), locus A4-5 (3/12 tests), 
and locus A3-35 (1/12 tests). After a sequential Bonferroni procedure, only A3-5 and 
SH-22 deviated from H-W proportions (only 1/12 tests for A4-5 remained significant). 
Thus, we were mainly concerned about locus A3 -5 and SH-22 and report Fgi-values and 
misassignment rates calculated with and without these loci. 

Linkage disequilibrium was detected in just 4% of tests (4/109 tests), and in < 1% 
of tests (1/109 tests) after a sequential Bonferroni correction for multiple comparisons. 
Although linkage appears to be less of a concern for shrews than for voles or mice, there 
may be weak linkage between A3-5 and A4-20, as this pair was in disequilibrium in 27% 
of tests (3/1 1 tests) before the Bonferroni procedure. 

Details are reported in Appendix 8. 

Genetic Variation 

Genetic variation at microsatellite loci was quite high for all three species 
(Appendix 9). Averaging across sites and loci, expected heterozygosity ranged from 
0.762 - 0.816 for voles, 0.899 - 0.909 for mice, and 0.858 - 0.874 for shrews, depending 
on whether loci out of Hardy-Weinberg proportions were included in the calculation. 
When loci that deviated from H-W proportions were removed, heterozygosity increased 
somewhat and observed heterozygosity (Ho) more closely resembled expected 
heterozygosity (He). Due to high heterozygosities, the upper bound on Fst was 

35 



constrained to be rather low: 0.184 -0.234 for voles, 0.091 -0.101 for mice, and 0.126- 
0.142 for shrews, again dependent upon which loci were used to compute Fst. This 
should be kept in mind when comparing Fst values from this study to those from other 
studies. 

Heterozygosity did not vary much from site to site, in spite of large differences in 
sample size. Nor did the mean number of alleles per locus, which like heterozygosity 
values, tended to be rather high. This is probably an indication that overall population 
sizes in the region were high. Our trapping grids were situated within large blocks of 
contiguous forest, but we sampled only those individuals that were captured in our grids. 
Allelic diversity ranged from four alleles at vole locus 4B (Lubrecht) to 38 alleles at 
mouse locus 6 (Tarkio). The mean number of alleles per locus per site was 12.2 for 
voles, 19.7 for mice, and 14 for shrews. Heterozygosity and allelic diversity are 
presented in more detail in Appendix 9. 

Gene Flow 

Differences among sites: At the regional level (among sites), Fst averaged 0.035 
for voles, 0.048 for mice, and 0.029 for shrews, regardless of whether loci out of H-W 
equilibrium were included (Table 3; Appendix 10). The percentage of individuals 
correctly assigned to their population of capture averaged 70% for voles, 88% for deer 
mice, and 75%) for shrews. None of the misassignments represent true migrants, because 
sites were 50 - 320 km (30 - 200 miles) apart and small mammals do not disperse over 
such large distances. These results indicate that for our within site comparisons, we will 
focus on relative differences adjacent to versus across highways instead of on absolute 

36 



values, because many misassignments are statistical, not actual, migrants. We reduced 
spurious misassignments (statistical migrants) by using a likelihood ratio of 10, 20, or 
100 as a cutoff value: the percentage of individuals correctly assigned was > 99% among 
sites for voles and shrews and > 96% for mice (Appendix 10: Table 10.7). 

Genetic differences among sites 





Voles 


Mice 


Shrews 


Loci analyzed (test) 


FsT 


assign 


FsT 


assign 


FsT 


assign 


all loci (Bayesian) 
less 1 locus (Bayesian) 
less 2 loci (Bayesian) 
all loci (Nei Da) 


0.043 

0.035 

n/a 

n/a 


0.742 

0.691 

n/a 

0.704 


0.047 

0.046 

0.048 

n/a 


0.893 
0.889 
0.885 
0.867 


0.028 

0.029 

0.029 

n/a 


0.772 
0.761 
0.755 
0.749 



Table 3. Genetic differentiation across the study region in western Montana was rather low. Averages for 
pairwise Fsx-values among sites and for the proportion of individuals correctly assigned are shown. 
Misassignments cannot be actual migrants, since sites were 50 - 320 km (30 - 200 miles) apart. Fgx was 
calculated with and without loci that deviated from Hardy-Weinberg proportions. The likelihood-based 
Bayesian assignment test was used with and without loci that deviated from H-W proportions, and the 
distance -based assignment test (Nei's D^ distance statistic), which does not assume H-W equilibrium, was 
used with all loci. Choice of methods, with or without loci not in H-W equilibrium, had little effect on 
these results. 



The exclusion of loci that deviated from Hardy-Weinberg proportions had little 
effect on results, nor did the choice of assignment methods. This was true within sites, as 
well as between sites. Therefore, values reported in the text are for Fgi calculated only 
with those loci in H-W proportions, and for misassignment rates calculated using the 
distance-based test using Nei's Da distance statistic (a conservative approach, particularly 
appropriate for deer mice where linkage was relatively frequent). Full matrices showing 
pairwise Fsi-values and misassignment rates between sites are located in Appendix 10. 

Gene flow within sites: Relative differences in gene flow adjacent to highways 
versus across highways were largest for vagrant shrews, variable for deer mice, and 
insignificant for red-backed voles. Effect size varied widely among sites (Figure 6). 



37 



Gene flow at 2-lane highways 

0.10 



0.08 



0.06 



0.04 



0.02 



0.00 



'jiM 



RB voles deer mice vagrant shrews 



adjacent 
across 



%30 




RB voles deer mice vagrant shrews 



adjacent 
across 



Figure 6a. Gene flow at 2-lane highways was reduced for vagrant shrews only (higher Fgx and lower 
misassignment rate across highways). We compared grids on the same side of the highway (75 m apart) to 
grids on opposite sides of the highway (75 m apart) to show no isolation by distance. Numbers of 
comparisons (grids adjacent versus grids across) are in parentheses. Fsx-values were calculated using only 
those loci that were in Hardy -Weinberg proportions. Misassignment rates were calculated using a distance- 
based test based on Nei's D^ distance statistic. 



Gene flow at 4-lane highways 

0.10 



0.08 



0.06 



0.04 



0.02 



0.00 



^ 



^ 



RB voles deer mice vagrant shrews 



adjacent 
across 



%30 




RB voles deer mice vagrant shrews 



adjacent 
across 



Figure 6b. Gene flow at 4-lane highways was reduced for deer mice and vagrant shrews (higher Fgi and 
lower misassignment rate across highways). We compared grids on the same side of the highway (75 m 
apart) to grids on opposite sides of the highway (75 m apart) to show no isolation by distance. Numbers of 
comparisons (grids adjacent versus grids across) are in parentheses. Fgx-values were calculated using only 
those loci that were in Hardy-Weinberg proportions. Misassignment rates were calculated using a distance- 
based test based on Nei's D^ distance statistic. 



38 



Vole gene flow was not reduced across highways (Figures 6 and 7; Appendix 1 1). 
Within sites, voles appeared to form one large panmictic (randomly mating) population. 
However, the number of migrants across highways sampled over two summers (as 
determined by the assignment test) was rather low: across sites, the average ranged from 
zero to three migrants, depending on the likelihood ratio (LR) used as a cutoff value, 
above which individuals were considered "true" migrants (Table 4). 

Gene flow for red-backed voles 

0.10 
0.08 
0.06 



0.04 



0.02 



0.00 




Lolo Lub 



RL StR hwy StR int 



adjacent 
across 



Lolo Lub 



adjacent 
across 



RL StR hwy StR int 



Figure 7. Gene flow in red-backed voles did not appear to be influenced by highways. We compared 
genetic differences between animals captured on the same side of the highway (75 m apart), versus those 
captured on opposite sides of the highway (75 m apart). Numbers of comparisons (grids adjacent versus 
grids across) are in parentheses. Fgi-values were calculated using only those loci that were in Hardy- 
Weinberg proportions. Misassignment rates were calculated using a distance -based test based on Nei's D^ 
distance statistic. Lolo, Lubrecht, and Rainy Lake are 2-lane highway sites, while St. Regis is a 4-lane 
highway site. At St. Regis in 2001, we made comparisons directly across the highway (75 m) and across 
the highway into the forest interior (240 m). 



39 



Number of red-backed vole migrants detected across highways 






Migrants 


Migrants 


Migrants 


Total 




LR>10 


LR>20 


LR>100 


Analyzed 


Lolo 


1 (3.2%) 


1 (3.2%) 





31 


Lubrecht 


4 (8.5%) 


4 (8.5%) 





47 


Rainy Lake 


2 (2.6%) 


2 (2.6%) 





78 


St. Regis: hwy 


3 (2.9%) 








102 


St. Regis: far 


5 (6.5%) 


3 (3.9%) 





77 



Number of deer mouse migrants detected across highways 





Migrants 


Migrants 


Migrants 


Total 




LR>10 


LR>20 


LR>100 


Analyzed 


Lubrecht 


2(5.1%) 


2(5.1%) 


2(5.1%) 


39 


St. Regis: hwy 


14 (8.5%) 


12 (7.3%) 


9 (5.5%) 


164 


St. Regis: far 


1 (1.3%) 


1 (1.3%) 





80 


Tarkio 


16 (6.7%) 


11 (4.6%) 


7 (2.9%) 


238 



Number of vagrant shrew migrants detected across highways 





Migrants 


Migrants 


Migrants 


Total Analyzed 




LR>10 


LR>20 


LR>100 




Lolo 


5 (5.2%) 


1 (1.0%) 


1 (1.0%) 


97 


Rainy Lake 


1 (3.8%) 


1 (3.8%) 


1 (3.8%) 


26 


St. Regis: hwy 


6(3.2%) 


3(1.6%) 


1 (0.5%) 


190 


St. Regis: far 


4(3.3%) 


3 (2.5%) 


2(1.7%) 


121 



Table 4. Migrants were identified by the assignment test, using a distance -based procedure based on Nei's 
Da distance statistic. These individuals were assigned to a population across the highway from their 
population of capture, with odds of 1 : 1 , 20 : 1 , and 1 00 : 1 of originating in the assigned population versus 
the capture population. Migrants moved a minimum of 75 m across highways, except that migrants 
between the forest interior and the opposite side of the highway (St. Regis: far) moved a minimum of 240 
m. This is an index of movement across highways over two years (except that the forest interior was 
sampled only in 2001), and the actual number of migrants (some probably went unsampled) may be higher. 
We do not show the number of migrants per year, because individuals may have migrated across highways 
at any time prior to initial capture; we do not show the number of migrants per generation, because of the 
uncertainly involved in estimating generation time. Lolo, Lubrecht, and Rainy Lake are 2-lane sites; St. 
Regis and Tarkio are 4-lane sites. 



Deer mouse gene flow was reduced by 4-lane highways, only at St. Regis 
(Figures 6 and 8; Appendix 1 1). Gene flow, as measured by Fst and misassignment rates, 
was similar between the forest interior and the opposite side of the highway (240 m), 
versus between grids separated only by the highway (75 m). This seems to indicate that 
it is the presence of the highway itself that decreases gene flow, and not distance, but 



40 



when likelihood ratios were used to identify real migrants, the difference was quite large: 
for LR =10 there were 14 migrants directly across the highway versus only one migrant 
between the interior and opposite side of the highway. Gene flow differences across the 
4-lane highway were apparent at St. Regis, but not at Tarkio. Both the number of "true" 
migrants and the proportion of migrants per site were higher, on average, for mice than 
for voles or shrews, ranging from six to 1 1 migrants across highways (Table 4). Due to 
particularly high levels of genetic variation in deer mice (Appendix 9), some alleles were 
unique to some sites (and to some trapping grids), resulting in higher accuracy in the 
assignment test than was achieved for voles or shrews. There was less impact on Fst, 
because Fst does not consider allele identity when measuring proportional variation. 



Gene flow for deer mice 



0.10 



0.08 - 



0.06 



0.04 - 



0.02 



0.00 



^ 



W ^^ I w 



(l^ 



■ I ■ 



6 



60 
50 
40 
%30 
20 
10 





-- 


T 


X 




1 








^ 


^ 




X 


-^ 






■ ■ - 


■ ■ 


■ 



Lub SIR 00 StRhwy StRint Tar 00 Tar 01 

^ 1 01 01 

adjacent 



Lub SIR 00 StRhwy StRint Tar 00 Tar 01 

— -. 1 01 01 

adjacent 



Figure 8. Gene flow in deer mice was reduced by 4-lane highways only, and only at St. Regis (higher Fst - 
except for St. Regis highway grids in 2001- and lower misassignment rate across highways). We compared 
genetic differences between animals captured on the same side of the highway (75 m apart), versus those 
captured on opposite sides of the highway (75 m apart). Numbers of comparisons (grids adjacent versus 
grids across) are in parentheses. FgT-values were calculated using only those loci that were in Hardy- 
Weinberg proportions. Misassignment rates were calculated using a distance -based test based on Nei's D^ 
distance statistic. Lubrecht is a 2-lane highway site, while St. Regis and Tarkio are 4-lane highway sites. 
At St. Regis in 2001, we made comparisons directly across the highway (75 m) and across the highway into 
the forest interior (240 m). 



41 



Unlike voles and deer mice, shrew gene flow was reduced across both 2- and 4- 
lane highways (but only at one of two 2-lane sites), and the relative differences adjacent 
to versus across highways were large (Figures 6 and 9; Appendix 1 1). Gene flow was 
reduced most severely at Rainy Lake, where relative differences in FsT-values adjacent to 
versus across the highway were almost an order of magnitude greater than Fst differences 
at the other two sites. This was also reflected in the misassignment rates, which showed a 
37% decrease across highways at Rainy Lake, compared to no difference at Lolo and a 
15% decrease at St. Regis. As was the case with deer mice, data from St. Regis show that 
the presence of the highway is more important than distance in reducing gene flow, 
because the FsT-values and misassignment rates are similar between the south side of the 
highway and the north side of the highway (75 m), and between the south side of the 
highway and the forest interior on the north side (240 m). The number of "true" migrants 
across highways within sites ranged from one to four shrews, depending on the LR that 
was specified (Table 4). 



42 



Gene flow for vagrant shrews 

0.10 



0.08 



0.06 



0.04 



0.02 



0.00 




Lolo 

adjacent 
across 



RL SIR hwy SIR int 



50 ■ 
40 ■ 














r- 


^ 


1 




T 


T 




/oSO ■ 


























20 ■ 


























10 ■ 


























n ■ 





























Lolo 

adjacent 
across 



RL SIR hwy StR int 



Figure 9. Gene flow in vagrant shrews was reduced by both 2-lane (at Rainy Lake only) and 4-lane 
highways (higher Fst and lower misassignment rate across highways). We compared genetic differences 
between animals captured on the same side of the highway (75 m apart), versus those captured on opposite 
sides of the highway (75 m apart). Numbers of comparisons (grids adjacent versus grids across) are in 
parentheses. Fgx-values were calculated using only those loci that were in Hardy -Weinberg proportions. 
Misassignment rates were calculated using a distance-based test based on Nei's Da distance statistic. Lolo 
and Rainy Lake are 2-lane highway sites, while St. Regis is a 4-lane highway site. At St. Regis in 2001, we 
made comparisons directly across the highway (75 m) and across the highway into the forest interior (240 
m). 



43 



Culverts may promote movement across highways for vagrant shrews and deer 
mice (Table 5). Shrews at St. Regis, where a culvert connected the NW grid to the 
median strip, showed the largest differences between grids partially connected by the 
culvert and adjacent grids lacking a culvert: Fst decreased by 0.015 and the 
misassignment rate increased by 27% near the culvert. Mice at St. Regis (both years) 
also exhibited lower FsT-values and higher misassignment rates in grids partially 
connected by the culvert. However, the culvert at Tarkio, which did not open directly 
into our trapping grids, did not appear to influence Fgi-values or misassignment rates 
between grids near the culvert. Nor did voles at Rainy Lake (where the culvert was 
permanently flooded) or St. Regis seem to benefit from culverts. 

Effect of culverts on gene flow 



Species and site 


Nearer culvert 


Farther from culvert 


Effect? 




Fst 


misassign 


Fst 


misassign 




Voles: Rainy 2-ln (wet) 


0.002 


0.500 


0.002 


0.500 


N 


Voles: St. Regis 4-ln 


0.023 


0.329 


0.000 


0.485 


N 


Mice: St. Regis 2000 4-ln 


0.000 


0.375 


0.003 


0.318 


Y? 


Mice: St. Regis 2001 4-ln 


0.038 


0.167 


0.072 


0.114 


Y 


Mice: Tarkio 2000 4-ln 


0.045 


0.244 


0.018 


0.325 


N 


Mice: Tarkio 2001 4-ln 


0.010 


0.169 


0.024 


0.270 


N 


Shrews: St. Regis 4-ln 


0.000 


0.459 


0.016 


0.191 


Y 



Table 5. Culverts may promote movement across highways for deer mice and vagrant shrews. We 
compared Fgx-values (calculated with only those loci in H-W proportions) and misassignment rates 
(distance -based test) between trapping grids that contained openings to culverts or were near openings, to 
values between adjacent trapping grids that were not connected by culverts. If culverts facilitated gene 
flow, we expected lower Fgx-values and higher misassignment rates nearer to the culvert. Three sites 
contained culverts, but the culvert at St. Regis was the only dry culvert that opened directly into our 
trapping grids. 



44 



DISCUSSION 

Movement was reduced across highways as predicted, more so for 4-lane than for 
2-lane highways (Table 6). Species responded differently to highways. We predicted 
that red-backed voles, strong forest-associates, would be the species most severely 
affected by highways, followed by chipmunks (also forest-associates), vagrant shrews 
(more generalist but associated with mesic areas and undergrowth), and deer mice 
(adaptable habitat generalists). The support for these predictions varied, and a fence- 
effect (leading to higher abundance near highways) seems likely only for deer mice (but 
alternative explanations cannot be excluded). 

Do highways reduce small mammal movement? 





RB voles 


Deer mice 


Chipmunks 


Vagrant shrews 


2-lane 


4-lane 


2-lane 


4-lane 


2-lane 


4-lane 


2-lane 


4-lane 


Mark-Recapture 
Genetics 


Y 


7* 


N 
N 


Y 
Y 


Y 

? 


Y 

? 


? 
Y 


? 
Y 



Table 6. Summary of highway effects. Movement was reduced across highways, moreso for 4-lane than 
for 2-lane highways. However, species did not respond to highways as predicted. Vagrant shrews were the 
species most significantly affected by highways (of both types), followed by chipmunks or voles, and 
finally, deer mice. * Sample size = one moving vole ** Lower variation (compared to mice and shrews) 
may have limited our power to detect highway effects, so genetic effects may not be detectable yet, or 
limited movement may maintain levels of genetic exchange sufficient to prevent significant divergence. 



Results from red-backed voles are the most difficult to interpret, and deviated 
most from our predictions. We captured red-backed voles at just one 4-lane site, St. 
Regis, where our sample size of moving voles was too small to test for highway effects 
using mark-recapture. In contrast, mark-recapture data showed that vole movement 
declined across 2-lane highways, but the genetic data showed no effect; nor were there 
significant effects on gene flow at 4-lane highways. Three explanations are possible. 1) 
Populations are large, so drift acts slowly and divergence is slow. Therefore, not enough 



45 



vole generations have passed to allow us to detect population fragmentation using genetic 
techniques. 2) A small amount of movement - sufficient to maintain similar gene 
frequencies across highways - is occurring. 3) Additional voles crossed 2-lane highways 
during fall, winter, or spring and bred there, but were not recaptured in summer 2001. 
Explanation 3 is less likely to be true, because we would have expected to see this pattern 
for movement adjacent to the highway as well: we detected eight voles moving adjacent 
to highways, and only one moving across them. In addition, the mean number of "true" 
migrants per site across highways was rather low, ranging from zero to three voles. 
However, our prediction that highway effects would be strongest on a forest-associate 
was not confirmed; we detected reduced movement, but not reduced gene flow. 

We did not detect any decrease in movement for deer mice at 2-lane highways 
through mark-recapture data or genetic analyses (but genetic data could be analyzed at 
only one 2-lane site due to low sample sizes at other sites). Both approaches did indicate 
reduced movement across the 4-lane highway at St. Regis. Effect size varied between 
sites; although we attempted to control for sources of variation such as rivers and 
frontage roads, many differences between sites remained. We found much stronger 
evidence of highway effects at St. Regis than at Tarkio (but the number of "true" 
migrants across the highway was similar), perhaps because the median strip was so much 
thinner at Tarkio, making the total crossing distance shorter by almost 40 m. In addition, 
Tarkio is a much more open site overall than St. Regis, so perhaps mice adapted to this 
open environment do not perceive the highway as a significant barrier. 

Because we detected four individual deer mice crossing 1-90 at St. Regis during 
just 18 nights of trapping, it was surprising to see reduced gene flow. It may be that deer 

46 



mice frequently move relatively large distances to forage or explore, but do not often 
breed there. Deer mice occurred at higher densities within our study areas than did voles 
or chipmunks and are thought to be more social than many other rodents, nesting 
communally to offset cold temperatures (Millar and Derrickson 1992; Foresman 2001a). 
On multiple occasions, we captured two and three deer mice per trap, which is possible 
only if they travel in tight groups. Perhaps a high degree of socialization or low resource 
demand means that few mice disperse away from their birthplace to breed, at least when a 
4-lane highway exists as a deterrent. 

In the absence of genetic data for chipmunks (not analyzed due to time 
constraints) or mark-recapture data for shrews, our interpretation of highway effects on 
chipmunks and shrews is somewhat challenging. The mark-recapture data strongly 
suggest that chipmunk movement was reduced across both 2- and 4-lane highways. It is 
difficult to predict how the genetic data would look for chipmunks, but it is unlikely that 
we would see genetic differences across 2-lane highways; we detected five individuals 
that crossed 2-lane highways with limited diurnal trapping. On the other hand, we never 
detected a chipmunk crossing of a 4-lane highway, so we would expect gene fiow to be 
decreased there. 

Evidence of highway effects was stronger for vagrant shrews than for voles or 
mice. Gene flow was reduced across both 2-and 4-lane highways (at 2/3 sites), and 
effects were particularly strong at Rainy Lake, a 2-lane site, where only one migrant was 
identified regardless of the likelihood ratio specified. One 2-lane site, Lolo, did not show 
the same pattern of reduced gene flow across the highway as did our other highway sites. 
At Lolo, wet swampy habitat extended right up to the raised road bed on both sides of the 

47 



highway. Because we frequently captured shrews here near the highway edge, we 
speculate that this may have facilitated movement at Lolo. Since the genetic data showed 
strong effects of both highway types, it is likely that mark-recapture data would reflect 
the same pattern. (The logistics of such a study would be considerable because hourly 
trap checks during the night would be necessary to keep shrews alive). 

In addition, the magnitude of the differences between gene flow adjacent to and 
gene flow across highways was larger for shrews than for voles or mice, particularly the 
movement index based on misassignment rates. This result was somewhat unexpected 
since vagrant shrews occur in a variety of habitats, including meadows. We had expected 
that species occurring in areas with low forest cover would be more likely to successfully 
cross open pavement. However, we may have underestimated the importance of grass 
and shrub cover, which may be preferred for foraging and/or provide lower perceived 
predation risk for shrews. Even when we captured shrews in areas lacking trees, they 
could potentially remain concealed by grass or brush, which is obviously not possible on 
an asphalt surface. 

The type of vegetation bordering the highway may influence the willingness or 
ability of small mammals to cross. The vegetation bordering our 4-lane highway (at both 
sites) was more open and grassy than the vegetation bordering our 2-lane highways. The 
Montana Department of Transportation mows along the highway edge at St. Regis and 
Tarkio, and the forest edge is 15 - 28 m farther from the highway edge than at 2-lane 
sites. Among our 2-lane sites, the vegetation is sparsest, and the distance from forest 
edge to pavement largest, at Lubrecht. However, these vegetation differences did not 



48 



lead to decreased movement across the 2-lane highway at Lubrecht, relative to our other 
2-lane sites. 

In several cases, our estimates of real migrants seemed to conflict with our results 
comparing misassignment rates (both real and statistical migrants) adjacent to versus 
across highways. First, vole gene flow was not reduced across highways (whereas gene 
flow was reduced for shrews and mice at some sites), yet we detected fewer individuals 
that were likely to be true migrants than for mice or shrews, particularly for LR = 100. 
This counter-intuitive result may be related to lower genetic variation in voles that would 
cause fewer individuals to be considered real migrants when the stringent cutoff value of 
LR =100 was imposed. Also, we discussed the possibility that reduced movement 
(evident in the mark-recapture data from 2-lane highways) may not yet be detectable 
using genetic techniques. Second, Fgx-values and misassignment rates were similar for 
deer mice at St. Regis separated by 75 m and for those separated by 240 m, yet we 
detected only one real migrant at 240 m, versus 14 migrants at 75 m with LR = 10. There 
may be a subtle increase in differentiation at the larger distance, such that most 
misassignments can be shown to be statistical, not actual migrants. Third, the number of 
"true" mouse migrants was similar for St. Regis and Tarkio, yet Fgi and misassignment 
data show decreased gene flow only at St. Regis. This is explained by the fact that 
increased differentiation (due to decreased gene flow) at St. Regis increases statistical 
power and thus the odds that the likelihood ratio for potential migrants exceeds a cutoff 
value of 10 or more. The likelihood ratio misassignment data provide an index to 
movement, but not a true estimate, because some migrants probably went unsampled. 



49 



Culverts may increase gene flow for both deer mice and vagrant shrews. Gene 
flow was higher for mice and shrews captured near a culvert at St. Regis (which opened 
into a high cover area within our trapping grid) than for individuals captured in grids 
lacking a culvert. This was particularly true for shrews, and may explain why gene flow 
across the 4-lane highway was higher than gene flow across the 2-lane highway at Rainy 
Lake. In contrast, culverts did not appear to influence gene flow for red-backed voles or 
for deer mice at Tarkio (where a nearby culvert did not actually open into our trapping 
grids, but instead opened into an open grass and gravel area near the highway); mouse 
gene flow was higher across highways at Tarkio than at St. Regis, but high gene flow did 
not appear to be attributable to the culvert at Tarkio. While the data suggest that mice 
and shrews use culverts, this study was not designed specifically to measure the effects of 
culverts, and all evidence comes from one 4-lane site. However, our results are 
consistent with the preliminary observations of Foresman (pers. comm.), in a study of 
culvert use along Highway 93 (4-lane) south of Missoula, Montana. He has 
photographed approximately 22 deer mice (some were likely photographed more than 
once) per month per culvert, as well as several shrews and meadow voles (Microtus 
pennsylvanicus) where there is vegetation near culvert entrances. 

Overall levels of genetic differentiation across the study region were low. We 
were somewhat surprised to see 15 - 30% of individuals misassigning among sites 50 - 
320 km (30 - 200 miles) apart (but only 1 - 4% misassigned when a likelihood ratio of 
10 was required). This may be the result of large, widely distributed populations in 
contiguous forest, where high variation is maintained within subpopulations such that 
differentiation between them is low. Genetic drift would act slowly in such large 

50 



populations. This conclusion is supported by our finding of extremely high levels of 
genetic variation at all sites. Even sites where few individuals were captured maintained 
high allelic diversity, probably because the true extent of populations was much greater 
than the boundaries of our trapping grids. 

Overall low levels of genetic differentiation limited our ability to quantify 
movement. Since there were low Fsi-values and erroneous misassignments among sites, 
we assume that this effect is magnified within sites. For this reason, we do not attempt to 
turn FsT-values into estimates of the number of migrants per generation (Wright 1969). 
This approach would be expected to overestimate the amount of movement across 
highways. However, we did try to distinguish real migrants from statistical migrants 
within the assignment test by specifying the odds that a migrant must "beaf before being 
considered real. In addition, we know that Fst is constrained to be low (Charlesworth 
1998; Hedrick 1999), because heterozygosity was so high for all three species. However, 
this high level of genetic variation was a huge advantage when we compared gene flow 
adjacent to highways to gene flow across highways within sites. Power was high enough 
that similar values were obtained even when one or two loci were excluded from analysis 
(Appendix 10-1 1). For example, shrew misassignments across the 4-lane highway at St. 
Regis increased by only 4% when we went from five loci to just three loci in the analysis. 
Also reassuring was the fact that results changed very little when we used different 
assignment tests (Appendix 10 - 11). These results would be much less informative or 
useful if conclusions depended on which methods were used to estimate gene flow (after 
screening out inappropriate methods or those that performed poorly). 



51 



To put these results in perspective, we compare them to several other studies. In a 
study similar to ours, Gerlach and Musolf (2000) studied bank voles (Clethrionomys 
glareolus) near various roadways in Germany, including a 4-lane highway. C. glareolus 
are closely related to C. gapperi, and this particular German highway had been paved 
more recently than the 4-lane highway in our study; thus we expected similar or greater 
levels of genetic divergence in our study. However, at a 50 m 4-lane highway (much 
wider than our 4-lane highway, unless the median is included) with a total sample size of 
102 voles, they calculated a mean Fst of 0.025. Only our largest Fst estimates exceeded 
this value (0.03 - 0.04 for deer mice and shrews); our estimates for red-backed voles 
were much smaller, implying more gene flow for voles in our study. The difference may 
be that the surrounding landscape in Montana is largely unfragmented, and our 
(narrower) section of highway experiences just 1/5 the traffic volume of the German 
highway. 

Proctor et al. (In Press) estimated Fst and misassignment rates for grizzly bears 
(Ursus arctos), a species with slower generation time but smaller populations than ours, 
across a 4-lane highway, British Columbia-Alberta Highway 3 (which has similar traffic 
levels to our 4-lane highway, up to about 7000 vehicles per day). Grizzlies have slower 
generation times than small mammals, which might cause a delay in detectable genetic 
divergence, but smaller population sizes, which would accelerate divergence. Their mean 
Fst was 0.034, with 29 total misassignments out of 219 bears (13%). Again, our largest 
FsT-values were comparable, but our misassignment rates were on average much higher 
than Proctor et al. (In Press) found, 20 - 40% compared to their 13%). When they used a 
log-likelihood cutoff value of 3 (bears had to be 1000 times more likely to be from a 

52 



population different from the population of capture), they found only four bears to be true 
migrants. Using such a high cutoff value for our study seemed inappropriate, since the 
overall lower levels of differentiation that we observed provide us with less power to 
distinguish real migrants with such certainty. However, if for comparison we specify LR 
= 1000, we would identify no voles, eight mice (two at Lubrecht, four at St. Regis, and 
two at Tarkio), and one shrew (at Rainy Lake) as real migrants across highways, over two 
summers of sampling. 

Galbusera et al. (2000) estimated gene flow in the endangered Taita thrush 
(Turdus helleri) in a fragmented landscape, the uplands of southeast Kenya. They 
sampled birds at sites separated by 5 - 25 km of unsuitable arid habitat. Birds are 
obviously more mobile than small mammals and sites were closer together than ours, so 
we might expect higher gene flow, but small population size would be expected to have 
the opposite effect. Their pairwise Fst estimates were 0. 103 and 0.238, with no migrants 
detected from the current generation out of 260 thrushes sampled (but 2-3 descendants 
of migrants: Rannala and Mountain (1997)). Our estimates indicated much more gene 
flow than occurred with endangered thrushes, and in fact, it would have been impossible 
for us to get Fgi-values so large except perhaps for voles, with such low homozygosities. 

We now turn to the question of biological significance: how much of a reduction 
in population connectivity across highways is considered biologically significant? There 
are no standards for evaluating the extent to which populations should be connected. The 
only rule of thumb is the one migrant per generation rule (OMPG), a level of movement 
which allows local adaptation while minimizing the loss of variation due to drift (Wright 
193 1; Mills and Allendorf 1996). Although 1-10 migrants per generation may be 

53 



sufficient for genetic considerations (Hedrick and Harrison 1995; Mills and Allendorf 
1996), higher levels of connectivity may be required for ecological and demographic 
reasons (Mills et al. In Press). Higher levels of connectivity across highways are 
desirable, since it is unlikely that local adaptation will occur across this type of barrier. 
In most cases, conservation biologists and wildlife managers strive to increase 
population connectivity, mitigating the effects of habitat fragmentation (small, isolated 
populations with dwindling habitat available and few opportunities for dispersal, mate- 
finding, or recolonization of empty areas). Although natural barriers such as rivers or 
mountain ranges may fragment populations (and we do not typically try to increase 
wildlife movement across such barriers), we are often interested in increasing movement 
across man-made artificial barriers like highways. However, there are several reasons 
why increased connectivity may not be beneficial. First, disease transfer is facilitated 
when connectivity is high (Cunningham 1996). Second is the issue of demographic 
decoupling: extinction risk may be higher when population connectivity is high 
(Burgman et al. 1993). This takes us back to the "single large versus several small" 
debate in reserve design; the risk may be higher when all your eggs are in one basket. 
Third, there are examples in the animal behavior literature of cases when increased 
movement of certain individuals is not beneficial to other individuals. For territorial 
species, interactions with non-resident animals may lead to decreased stability or fitness 
among residents. Although the potential negative effects of connectivity should be 
considered, it seems likely that the benefits of population connectivity far outweigh the 
costs in most situations. 



54 



If we consider any decline in observed movement to be significant, then highways 
present a significant barrier to all species examined in this study, except deer mice at 2- 
lane highways. Even if only a 50% reduction in observed movement is considered 
significant, our conclusions remain the same. If however, we require a detectable 
reduction in gene flow before we consider highways a significant barrier, then only 
shrews are affected by 2-lane highways, while both deer mice and shrews may be 
affected by 4-lane highways. If we decide that a 10% decrease in misassignment rates is 
required for biological significance, then only shrew connectivity is inhibited by 
highways of any width. In any event, high abundance, high levels of genetic variation, 
and the continued presence of these species in the study region suggests that these small 
mammals can sustain some reduction in population connectivity by highways. However, 
we cannot predict how ecosystem processes such as seed and mycorrhizal spore dispersal 
might be affected by decreased connectivity in small mammal populations, or how 
predators that rely on small mammals as prey might be impacted. To summarize, our 
results show that highways reduce small mammal population connectivity, but it is 
difficult to predict how important the effects might be on small mammal survival or 
reproduction, or on ecosystem processes. 

Our goal was to use a suite of species with varying habitat requirements and life 
histories to illustrate a range of potential effects of highways on small mammals. 
Although these species are unlikely to face extinction due to highways, other species of 
special concern (Montana Natural Heritage Program 2001) such as Preble's shrew (Sorex 
preblei) or the pygmy shrew (Sorex hoyi: on review), that were impossible for us to study 
may face more significant risks. We recommend that research be done into mitigation 

55 



measures such as underpasses or overpasses, their design and location, and our resuhs 
suggest that even a generalist species such as a shrew or deer mouse should be 
considered. Vegetation around crossing structures may be important, as we found 
increased connectivity only for a culvert that opened into dense cover, and not for a 
culvert that opened into an open, short grass and gravel area or for a permanently flooded 
culvert. Obviously, forest species will only cross highways that run through forested 
areas, so the placement of crossing structures is important. We strongly recommend that 
reptiles and amphibians be especially targeted, as they may be particularly vulnerable to 
highway mortality (for example: Wilkins and Schmidly 1980; Bernardino and Dalrymple 
1992; Rosen and Lowe 1994; Fahrig et al. 1995; Fowle 1996; Gibbs 1998; Vos and 
Chardon 1998; Lehtinen et al. 1999; Hels and Buchwald 2001), and may sometimes be 
forced to cross highways to access overwintering or breeding sites. Highways are 
ubiquitous across the landscape, and effects on wildlife will only be accentuated as the 
human population continues to increase and pressures mount for wider roads to 
accommodate more traffic. 



56 



ACKNOWLEDGEMENTS 

Funding for this project was provided by the Montana Department of 
Transportation. The National Science Foundation provided an equipment grant for the 
Li-cor Global IR- System. I am grateful to my committee, L. Scott Mills, Roland 
Redmond, and Kerry Foresman, as well as to Jeremy Moran, Kristy Pilgrim, Michael 
Schwartz, Mark Lindberg, and Elizabeth Crone for their input and advice. Douglas 
Conrey provided technical support and Melissa Hart donated her map-making skills. 
Ann Riddle, Alicia Awes, and Hope Draheim were instrumental in performing the lab 
work. Hector Kent, Ben Cummins, Heidi Hagen, Brian Laub, Justin Carnecchia, Erin 
Clevidence Hill, and Aya Tanigami spent long hours in the field. Thanks also to the 
Mills lab for providing invaluable feedback: Karen Hodges, Jenny Woolf, Paul Griffin, 
Sue Cox, Tammy Mildenstein, and John Citta. 



57 



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66 



APPENDIX 1 - SMALL MAMMAL ABUNDANCE & VEGETATION SURVEYS 

This work resulted from a collaboration with Jeremy Moran, reported in his 
undergraduate senior thesis (Moran 2001), funded by the National Science Foundation 
EPSCoR (Experimental Program to Stimulate Competitive Research), and an Irene Evers 
Scholarship from the University of Montana School of Forestry. J. Moran' s goal was to 
determine whether abundance patterns were correlated with differences in vegetation 
between edge and interior, or whether the presence of the highway itself seemed to be the 
determining factor. 

Vegetation Surveys: We collected vegetation data within circular plots of 10 m 
radius and along 20 m transects passing through the plot center, oriented parallel to the 
highway. For logistical reasons, we centered vegetation plots on trap locations, using 
stratified random sampling to choose among trap locations. Using each row (rows are 
parallel to the highway) as a stratum, and randomly choosing one trap location from each 
stratum, we sampled seven plots per trapping grid (Figure 2). Stratifying in this way 
made sense because we expected vegetation structure to change from forest edge 
(highway) to interior. Specifically, we measured: 

1) Dominant species (2 or 3) in three categories: > 2 m (trees), 0.5-2 m (shrubs and 

seedlings), and < 0.5 m (ground layer). 

2) Percent of ground covered by tree canopy in size categories: very large > 76.2 cm (30 

inches) dbh; large 53.3-76.2 cm (21-30 inches) dbh; medium 22.9-53.2 cm (9-20.9 
inches) dbh; pole 12.7-22.8 cm (5-8.9 inches) dbh; sapling >1.5 m (59 inches) tall, < 
12.7 cm (5 inches) dbh; seedling < 1.5 m (59 inches) tall. 

3) Percent cover of trees, shrubs, forbs, grass, ferns, dead wood, moss, and bare ground. 

67 



4) Horizontal understory cover, defined as the percent of a 1 m square piece of fabric that 

was covered by vegetation when held vertically, touching the ground. An observer, 1 
m from the canvas, differentiated between the bottom and top 0.5 m. Horizontal 
cover was measured in four directions at 2.5, 7.5, 12.5, and 17.5 m along the transect. 

5) Ground cover by lifeform (tree, shrub, forb, grass - live, grass - dead, fern, moss, 

rock, dead wood, and bare ground), plus the coverage and depth of the litter duff layer 
and number of conifer cones in 1 m square plots placed at 5 m and at 15 m along the 
20 m transect. 

6) Canopy cover measured every 2 m along the 20 m transect. 

7) Length and diameter of all coarse woody debris (> 1 cm in diameter) crossing the 20 m 
transect. 

8) Species composition for those species occupying > 5% of the plot. 

9) Slope and aspect, averaged over the plot. 

Average per grid abundance estimates are presented in the main text. Figure 5. 
Means and standard deviations for canopy cover, horizontal understory cover, ground 
cover, and dead wood are shown in table 1.1. Means are reported for each site and for 
trapping grids within sites (Tablel.2). 



68 



Vegetation patterns across sites 



Cover (%) 


Lolo 


Lubrecht 


Rainy 


Lake 


St. Re 


!gis 


Tarkio 


Mean 


SD 


Mean 


SD 


Mean 


SD 


Mean 


SD 


Mean SD 


Canopy 


14.5 


7.5 


14.1 


11.6 


16.0 


6.6 


13.3 


8.5 


10.6 6.3 


Horizontal 


12.2 


19.3 


13.3 


16.2 


16.3 


20.1 


19.1 


20.8 


10.4 10.8 


Ground 


99.3 


2.3 


99.0 


4.5 


98.3 


6.0 


98.6 


4.9 


95.4 12.8 


Dead Wood 


7.2 


12.6 


15.2 


17.5 


20.8 


23.0 


19.4 


20.5 


6.5 10.7 



Table 1.1. Vegetation patterns differed little among sites, except that cover tended to be slightly lower at Tarkio 
(dead wood cover was also low at Lolo). Variation was high and was partly attributable to changes from the 
highway edge to the forest interior, particularly at St. Regis where the first 1-2 rows of traps were in an open, 
mowed grassy area. Only the highway trapping grids (not the forest interior grids) were averaged here. Canopy 
cover is the area covered by tree canopy. Horizontal cover refers to the percent of a 1 m wide piece of fabric 
that was covered by vegetation when held vertically, from the soil surface to 0.5 m above the ground. Ground 
cover could be any type of vegetation or rock (non-bare ground). Dead wood cover was a part of the ground 
cover measurement, recorded in two 1 m square areas per vegetation plot. More detailed information on coarse 
woody debris is available: length and width of all debris (>1 cm in diameter) encountered along 20 m transects. 
Sample size was 280 for canopy cover, 112 for horizontal cover, and 56 for ground and dead wood cover. At 
each point where measurements were taken, we recorded horizontal cover for all four cardinal directions; these 
four measurements were highly correlated and non-independent, so the four subsamples were averaged at each 
of 1 12 locations for a sample size of 112. 



69 



Vegetation patterns at Lolo 


(2-lane) 












Cover (%) 


NE 


NW 


SE 


SW 


Mean 


SD 


Mean 


SD 


Mean 


SD 


Mean 


SD 


Canopy 
Horizontal 
Ground 
Dead Wood 


16.5 
7.9 

98.4 
6.4 


4.3 

11.3 

4.1 

5.6 


20.2 
10.3 
99.8 
11.1 


2.8 
13.1 

0.8 
19.0 


9.1 
10.2 
99.9 

1.9 


7.9 

16.5 

0.3 

2.2 


11.9 

20.6 

99.3 

9.4 


8.3 
29.6 

1.5 
15.1 



Vegetation patterns at Lubrecht (2-lane) 



Cover (%) 


NE 


NW 


SE 


SW 


NEl 


NWl 


Mean 


SD 


Mean 


SD 


Mean 


SD 


Mean 


SD 


Mean 


SD 


Mean SD 


Canopy 


13.2 


4.9 


9.4 


6.6 


20.8 


19.5 


12.9 


4.9 


12.5 


6.1 


15.0 5.2 


Horizontal 


7.3 


8.8 


7.5 


8.1 


18.8 


18.2 


19.6 


21.4 


11.1 


15.1 


8.1 11.5 


Ground 


99.8 


0.8 


96.4 


8.6 


100 





99.9 


0.5 


95.6 


7.0 


99.2 2.7 


Dead Wood 


18.9 


24.3 


12.6 


14.4 


14.4 


14.3 


14.8 


16.5 


6.6 


7.8 


4.6 2.6 



Vegetation patterns at Rainy Lake (2-lane) 



Cover (%) 


NE 


NW 


SE 


SW 


NEl 


SEl 


Mean 


SD 


Mean 


SD 


Mean 


SD 


Mean 


SD 


Mean 


SD 


Mean 


SD 


Canopy 


14.0 


7.1 


18.5 


5.9 


13.2 


6.9 


18.2 


4.4 


9.7 


6.1 


9.5 


6.7 


Horizontal 


20.4 


26.0 


20.1 


22.9 


13.9 


15.2 


10.9 


12.7 


9.4 


9.4 


7.7 


7.4 


Ground 


98.4 


4.0 


100 





94.6 


10.8 


99.9 


0.3 


89.9 


18.8 


99.1 


1.8 


Dead Wood 


16.6 


24.4 


18.8 


24.5 


21.0 


21.5 


26.9 


22.8 


8.1 


9.5 


6.5 


7.9 



70 



Vegetation patterns at St. Regis (4-lane) 



Cover (%) 


NE 


NW 


SE 


sw 


NEl 


NWl 


Mean 


SD 


Mean 


SD 


Mean 


SD 


Mean 


SD 


Mean 


SD 


Mean SD 


Canopy 


12.1 


7.9 


13.8 


8.5 


13.3 


9.1 


14.0 


8.5 


14.0 


5.3 


8.6 5.9 


Horizontal 


12.2 


14.5 


33.2 


29.0 


14.3 


15.1 


16.8 


14.7 


14.4 


15.0 


11.2 15.2 


Ground 


97.1 


9.3 


98.1 


2.9 


99.6 


0.9 


99.7 


0.6 


97.0 


10.7 


95.3 6.9 


Dead Wood 


15.4 


15.2 


23.6 


20.3 


18.4 


26.0 


20.3 


20.4 


41.1 


27.4 


22.8 22.0 



Vegetation patterns at Tarkio (4-lane) 



Cover (%) 


NE 


NW 


SE 


SW 


Mean 


SD 


Mean 


SD 


Mean 


SD 


Mean 


SD 


Canopy 


11.6 


8.0 


11.6 


5.0 


10.3 


5.2 


8.8 


6.4 


Horizontal 


16.4 


13.6 


10.0 


11.8 


9.9 


8.3 


5.2 


4.6 


Ground 


96.4 


8.2 


98.9 


2.9 


92.8 


18.1 


93.6 


16.3 


Dead Wood 


4.4 


8.2 


6.1 


9.0 


2.2 


2.9 


13.4 


16.0 



Table 1.2. Vegetation patterns differed little within sites. A meadow covered about half of the SE grid at Lolo, which is apparent 
from the low canopy and dead wood cover. Cover was low in the forest interior grids at Rainy Lake because this area had been 
logged, and only small trees remained. Canopy cover was somewhat low in the NWl interior grid at St. Regis because this area 
had been logged. Variation was high and was partly attributable to changes from the highway edge to the forest interior. Trapping 
grids were denoted according to their position around the highway; those ending in " 1 " were forest interior trapping grids. Canopy 
cover is the area covered by tree canopy. Horizontal cover refers to the percent of a 1 m wide piece of fabric that was covered by 
vegetation when held vertically, from the soil surface to 0.5 m above the ground. Ground cover could be any type of vegetation or 
rock (non-bare ground). Dead wood cover was a part of the ground cover measurement, recorded in two 1 m square areas per 
vegetation plot. More detailed information on coarse woody debris is available: length and width of all debris (>1 cm in diameter) 
encountered along 20 m transects. Sample size per trapping grid was 70 for canopy cover, 28 for horizontal cover, and 14 for 
ground and dead wood cover. At each point where measurements were taken, we recorded horizontal cover for all four cardinal 
directions; these four measurements were highly correlated and non-independent, so the four subsamples were averaged at each of 
28 locations for a sample size of 28. 



71 



APPENDIX 2 - MARKING ANIMALS 

Toe clipping has not been shown to affect survival (Pavone and Boonstra 1985), 
body mass, residence time in trapping grids, or long-term recapture rates relative to other 
marking techniques (Wood and Slade 1990). We clipped two toes per animal and never 
more than one per foot. Toes healed quickly after clipping, and we never observed any 
infection. Furthermore, toe clipping provides a high quality genetic sample (Tallmon et 
al. 2002). Thus, we felt that toe clipping was a humane and practical technique, unlikely 
to bias estimation of movement rates or abundance. 

Ear tagging was somewhat more problematic, because sometimes ears became 
infected or torn. This did not occur often enough for us to stop ear tagging, but it resulted 
in tag loss for four chipmunks at St. Regis in 2000, and several more in 2001. Additional 
tags were lost between the end of the 2000 field season and the beginning of the 2001 
field season. In some cases within a field season, we could determine the identity of 
animals that had lost tags based on which ear was torn, sex, weight, location, and whether 
the animal with that number continued to be captured with ear tag intact. Tag loss is not 
an issue when toe clipping is used to permanently mark individuals. 



72 



APPENDIX 3 - MICROSATELLITES 

Since small mammals have short generation times (1-4 generations per year), it 
is possible that genetic divergence between populations separated by highways has 
occurred, even though highways are relatively recent features of the landscape. Low 
levels of gene flow between populations will prevent complete fixation of certain alleles, 
but more substantial flow is necessary to prevent genetic differentiation due to genetic 
drift (Allendorf and Phelps 1981; Mills and Allendorf 1996). We have chosen to use 
microsatellites to study gene flow and subdivision, because their high mutation rates and 
high variation give relatively high power to detect genetic subdivision between recently 
fragmented populations. 

Microsatellites are highly variable non-coding regions of nuclear DNA consisting 
of a series of repeats of two to six base pairs. They are codominant and are inherited in a 
mendelian fashion (Ashley and Dow 1994). Microsatellites are typically highly 
polymorphic, with average expected heterozygosity well above 50% in most cases 
(Bowcock et al. 1994; Jarne and Lagoda 1996). Microsatellite loci mutate rapidly; the 
average mutation rate is 10"^ per generation, two to three orders of magnitude higher than 
values known for allozymes (Weber and Wong 1993; Jarne and Lagoda 1996). There has 
been much discussion about the appropriate mutation model for microsatellites 
(Appendix 4), with implications for the way that genetic distance is calculated. Although 
microsatellites are thought to be neutral in most cases, there may be an upper limit to the 
number of repeats, and some microsatellites have been associated with disease (Goldstein 
and Pollock 1997). Microsatellites are easily scorable by measuring the number of base 
pairs relative to a ladder of known size. 

73 



APPENDIX 4 - MUTATION MODELS 

Analysis of population stmcture depends on correctly identifying the mutation 
rate and model, although Comuet et al. (1999) show that methods such as the assignment 
test are somewhat robust to choice of mutation model (but higher power is achieved when 
the assumed mutation model is correct and is the infinite alleles model). There has been 
much discussion of the appropriate mutation model for microsatellites (Shriver et al. 
1993; Valdes et al. 1993; Jame and Lagoda 1996; Goldstein and Pollock 1997). The two 
most commonly discussed models are the infinite alleles model (lAM: Kimura and Crow 
1964) and the stepwise mutation model (SMM: Ohta and Kimura 1973). The lAM states 
that there are an infinite number of possible alleles, with no restriction on allele size. 
Each mutation creates a new allele at rate [i, and all possible alleles are equally likely to 
occur upon mutation. The SMM states that each mutation adds or subtracts (with equal 
probability fj.) a single unit to or from the current allele. A likely mechanism is DNA 
slippage, whereby the repeat units on the two DNA strands may anneal out of register, 
resulting in expansion or contraction of the microsatellite following replication or repair 
(Levinson and Gutman 1987; Ashley and Dow 1994; Schlotterer and Pemberton 1994). 

Modifications to the SMM allow occasional additions and subtractions of more 
than one unit at a time (Di Rienzo et al. 1994), suggest a bias toward adding or 
subtracting units (Ashley and Warren 1995), and indicate a possible upper limit on allele 
size (Garza et al. 1995). The favored mutation model is the SMM (Valdes et al. 1993), 
recognizing that this is a simplification of reality, but no consensus has emerged. Shriver 
et al. (1993) found that computer simulations suggested that the true mutation model for 
microsatellites was intermediate to the lAM and SMM, because the SMM predictions 

74 



matched the patterns for microsatellites, except that the SMM underestimated the number 
of alleles per locus. Some differentiation measures were developed under the lAM, and 
some under the SMM. For example, Wright's Fst (Wright 193 1) and Nei's Gst (Nei 1973) 
are based on the lAM, while Slatkin's Rst (Slatkin 1995), Goldstein's ASD and (5^)' 
(Goldstein et al. 1995,, b) are based on the SMM. 

Nauta and Weissing (1996) theorized that because mutation will eventually return 
allele frequencies to previous conditions by chance, constraining the amount of 
divergence that can occur, microsatellites should only be used when the time scale of 
interest is short (i.e., not for phylogenetic inference). This recommendation does not 
conflict with our usage of microsatellite data. 



75 



APPENDIX 5 - HARDY-WEINBERG AND LINKAGE DISEQUILIBRIUM 

Before proceeding with analyses of gene flow, we had to first test for violations of 
two required assumptions: Hardy-Weinberg (H-W) equilibrium and linkage equilibrium. 
H-W equilibrium refers to the fact that in a randomly mating population, genotypic 
frequencies will be equal to the product of the frequencies of the two alleles making up 
the genotype. A homozygote has two copies of the same allele, so the expected genotype 
frequency of the homozygote in the population is the square of the frequency of that 
allele. A heterozygote has one copy of two different alleles, so the expected genotype 
frequency of the heterozygote in the population is twice the product of the frequency of 
these two alleles. Most tests of genetic differentiation and gene flow assume that 
populations are in H-W equilibrium, and they use these expected proportions in 
calculations. 

H-W disequilibrium can result from any condition related to nonrandom mating, 
such as population subdivision. One common cause of H-W disequilibrium is the 
existence of a null allele, which results in a deficit of heterozygotes. Null alleles refer to 
mutations in the primer regions flanking the microsatellite sequence, which result in a 
lack of amplification. Individuals that fail to amplify may be homozygous for a null 
allele, and some individuals that appear to be homozygotes may in fact be heterozygous 
for the observed allele and the null allele. 

Linkage disequilibrium refers to the nonrandom association of loci, such that 
individuals with a particular genotype at one locus will tend to have a certain genotype at 
a different locus. Linkage disequilibrium can result when loci are physically linked: they 
are close together on the chromosome, such that recombination during meiosis does not 

76 



often split them apart. However, linkage is not always physical, and can have a number 
of other causes. Linkage disequilibrium is problematic because it means that loci are not 
independent of one another, and therefore should not be treated as independent replicates. 
Most tests of genetic differentiation and gene flow assume that there is no linkage 
equilibrium and treat loci as if they were independent. 

Linkage disequilibrium can result from actual physical linkage of loci that are 
close together on the chromosome, but there are several other possible causes. First, 
genetic drift in small populations can cause nonrandom associations between gametes. 
Second, although microsatellites are neutral, non-coding regions, natural selection may 
operate on an adjacent region, resulting in genetic hitchhiking of the microsatellite and 
apparent linkage. Third, subdivision within the defined population can result in linkage 
disequilibrium, if alleles are not mixing and recombining at random. In this case, genetic 
structure may suggest different population boundaries than those chosen by the 
researcher. Finally, sampling family groups may be a common cause of linkage 
disequilibrium. 



77 



APPENDIX 6 - MEASURING GENETIC DIFFERENTIATION 
Genetic Distance: Wright's Fst 

FsT is defined as the loss of heterozygosity due to population subdivision (Wright 
1951). It is also the proportion of total genetic variation that exists among 
subpopulations (as opposed to within subpopulations). Fst ranges from to 1, with low 
Fst indicating high gene flow, and high Fst indicating low gene flow. Sewell Wright 
(1969) showed that Fst ~ l/(4mN + 1) when the migration rate (m) is very small. The 
amount of divergence among subpopulations depends only on the total number of 
migrants (mN), not the migration rate (m) (Allendorf and Phelps 1981). Gst is the 
multiple alleles version of Fst, which was modeled for a 2-allele locus (Nei 1972). Weir 
and Cockerham (1984) created a commonly used estimate for Fst (9wc)- 

Fst is a useful statistic, but it is flawed in several ways that are especially relevant 
to microsatellites and populations experiencing recent changes. First, Fst is built on the 
island model of migration, which assumes that migration is equal between all 
subpopulations. This is obviously an invalid assumption, and we predicted that the 
opposite would in fact be true, that populations separated by highways would have lower 
migration between them. However, Fst is fairly robust to violations of this assumption 
(Mills and Allendorf 1996). 

Second, Charlesworth (1998) and Hedrick (1999) observed that because 
microsatellite loci often have very high within-population heterozygosity, the magnitude 
of differentiation measures can be quite small. This is because Fst and similar measures 
are strongly influenced by the level of within-population diversity (which is often high 
with microsatellites), such that the proportion of diversity between populations (= genetic 

78 



distance) is constrained to be small even when divergence is high, and must be smaller 
than overall homozygosity (Charlesworth 1998; Hedrick 1999). Slatkin (1995) noted that 
FsT will tend to indicate genetic similarity even when this conclusion is not justified, 
because there is memory in the mutation process (Appendix 4) indicated by the SMM, 
which constrains the differentiation that can occur more than is implied by measures 
based on the lAM. Like Fst, the maximum value of Gst may be quite small and must be 
less than the average within-population homozygosity. 

Finally, Fst assumes migration drift equilibrium. The primary factors affecting 
genetic variation in recently subdivided populations are gene flow, which tends to keep 
populations from becoming differentiated, and genetic drift, which promotes 
differentiation (divergence). Fsi-type measures assume that the population has already 
reached genetic equilibrium between migration and drift, but this can take many 
generations if the population size is large and/or the migration rate is small (Varvio et al. 
1986; Steinberg and Jordan 1997; Whitlock and McCauley 1999). 

Numerous geneticists have formulated their own measures of genetic distance 
(Wright 1931, 1951, 1969; Cavalh-Sforza and Edwards 1967; Nei 1972; Weir and 
Cockerham 1984; Slatkin 1985; Chakraborty and Jin 1993; Goldstein et al. 1995,, t; 
Slatkin 1995; Shriver 1997), and most if not all of these measures have later been 
criticized by other geneticists (Goldstein et al. 1995,, b; Slatkin 1995; Takezaki and Nei 
1996; Charlesworth 1998; Nagylaki 1998; Hedrick 1999). For example, Nagylaki (1998) 
concludes that Fst is an appropriate index of genetic differentiation only if genetic 
diversity is low. Nevertheless, conclusions reached by considering Fsi are often similar 



79 



to conclusions reached by consideration of other distance measures (Paetkau et al. 1997; 
Firestone et al. 2000), and no distance method is immune to all problems. 

One alternative suggested by Hedrick (1999) is Slatkin's rare alleles method. 
However, Slatkin himself admitted that his rare alleles method, which says that the 
number of migrants per generation is linearly related to the logarithm of the average 
frequency of private (unshared) alleles (Slatkin 1985), is vulnerable to sampling 
problems. This occurs because a rare allele determined to be absent from a particular 
population may simply have been missed (Slatkin 1995). Steinberg and Jordan (1997) 
also noted that a private allele could result from the miscoding of a more common allele. 
Slatkin created another alternative to Fgx called Rst (1995), Goldstein et al. (1995b) 
created (5[j,)-, and Shriver et al. (1997) created another statistic, all designed specifically 
for microsatellites under the SMM. Although these SMM distance methods appear to be 
more appropriate than distance measures designed earlier under lAM, SMM distances 
tend to perform more poorly than lAM distances (Paetkau et al. 1997), presumably 
because of the higher variance associated with SMM statistics (Paetkau et al. 1997; 
Balloux and Lugon-Moulin 2002). These authors found lAM statistics like Fgi to be 
more sensitive to more recent divergence, while SMM statistics better reflected much 
older splits (i.e., for phylogenetic inference). We chose to use Fst because interpretation 
is straightforward, fragmentation by highways is recent, and Fst is commonly used in 
studies of population structure and fragmentation, facilitating comparisons between 
studies. 

For reasons discussed above, assignment tests may be a more reliable measure of 
population differentiation and gene flow than Fst. However, the assignment test relies 

80 



upon genetic differences among populations, as quantified by Fst, in order to correctly 
assign individuals, making these methods complementary. 

Assignment Test 

The assignment test is a relatively new method for assessing gene flow (Paetkau 
et al. 1995; Waser and Strobeck 1998). The assignment test attempts to assign captured 
individuals to their population of origin based on their genotype, considering genotype 
frequencies in all sampled populations. The individual is assigned to the population 
where its expected frequency is highest, where it has the greatest probability of 
occurrence. If the test consistently does this successfully, low gene flow is inferred. 
Whereas Fsi-type measures quantify genetic distance that has resulted from past levels of 
gene flow, the assignment test provides more current estimates, theoretically identifying 
migrants and quantifying current gene flow. 

The assignment test as originally designed by Paetkau et al. (1995) assigns 
individuals according to their genotype through the following procedure (Waser and 
Strobeck 1998): 

1) Remove the test individual's genotype from the population in which it was sampled 
and estimate allele frequencies at each locus. 

2) Calculate the genotype's expected frequency in the population of capture at each locus. 

3) Multiply across loci and log-transform the product. 

4) Repeat to estimate the genotype's expected frequency in other potential source 
populations. 



5) Assign the genotype to the population in which it has the highest log-likelihood of 

occurrence. 
Since 1995, other assignment methods have been developed (Rannala and Mountain 
1997; Cornuet et al. 1999; Pritchard et al. 2000). The frequency-based likelihood 
assignment test corrects for apparently unique alleles by adding the unique allele to all 
populations at a small frequency, while Bayesian and distance-based assignment avoid 
this problem altogether. 

Assignment tests can be broken down into two basic categories: likelihood-based 
assignment and distance-based assignment. Paetkau et al. (1995), Rannala and Mountain 
(1997), and Pritchard et al. (2000) all described different likelihood-based assignment 
methods. Paetkau et al. (1995) were the first to describe assignment tests as they are 
currently used (but Bowcock et al. (1994) and Estoup et al. (1995) used a similar shared- 
allele index). Although the frequency-based assignment methodology has been refined 
since 1995, the basic idea remains the same: individuals are assigned where their 
expected genotype frequency is highest (highest likelihood of occurrence). The Bayesian 
assignment method of Rannala and Mountain (1997) is similar, but incorporates Bayesian 
probabilities. Pritchard et al. (2000) developed the Bayesian clustering method, which 
allows users to choose whether or not to include "prior knowledge" of where an 
individual was captured, and considers all individuals simultaneously. This method is 
particularly useful when there is no prior information on population structure that would 
facilitate the determination of population boundaries. In this study, the Bayesian 
clustering method did not seem particularly useful, since we were not trying to determine 



82 



the genetic structure of small mammals in western Montana, but were instead testing the 
specific hypothesis that highways form population boundaries. 

Distance-based assignment methods (Cornuet et al. 1999) calculate the genetic 
distance between a given individual and all other individuals in each population. 
Individuals are assigned where the average genetic distance is lowest. The major 
advantage to this method is that Hardy-Weinberg and linkage equilibrium are not 
assumed or required. Within GeneClass (Cornuet et al. 1999), it is possible to assign 
individuals based on several distance statistics: Nei Da, Nei Standard, and Nei Minimum 
(reviewed in Nei 1987), Cavalli-Sforza (Cavalli-Sforza and Edwards 1967), Das, shared 
allele distance (Chakraborty and Jin 1993), and Goldstein's distance (Goldstein et al. 
1995b). Cornuet et al. (1999) evaluated these methods and compared them to the 
likelihood assignment methods, varying time of population divergence, mutation model, 
sample size, and number of loci. They found that likelihood-based methods, particularly 
the Bayesian method, always outperformed distance assignment methods. All 
assignment methods performed better when the mutation model was the Infinite Alleles 
Model (lAM), as opposed to the Stepwise Mutation Model (SMM), when sample size 
and number of loci were higher, and when Fst was larger. 

When first considering which assignment test to use, we tested a variety of 
options within GeneClass to assign individuals to their site of origin (sites were separated 
by 50 - 320 km, 30 - 200 miles): the frequency-based likelihood method of Paetkau et al. 
(1995), the Bayesian likelihood method of Rannala and Mountain (1997), and several 
distance-based assignment methods (Cornuet et al.l999). In theory, no individuals 
should have misassigned, since small mammals do not disperse over such large distances. 

83 



We found little variation in the proportion of individuals correctly assigned, except that 
Goldstein's distance statistic performed badly. The Bayesian likelihood method tended 
to perform best across our red-backed voles, deer mice, and vagrant shrews. Cornuet et 
al. (1999) also reported that the Bayesian likelihood method performed best on a 
simulated dataset. Because of superior performance, we used the Bayesian method both 
with and without loci that violated the Hardy-Weinberg assumption. For comparison, we 
also conducted assignment tests using distance assignment (Cornuet et al. 1999) based on 
Nei's Da distance statistic, because distance-based assignment does not assume H-W or 
linkage equilibrium. 

In practice, our choice of assignment method had little impact on conclusions 
(Tables 3-6; Appendix 10). For example, when assigning vagrant shrews to their site of 
origin, the Bayesian likelihood method correctly assigned 75 - 77% of individuals, 
depending on the number of loci used, with highest accuracy when all loci were used. 
The frequency-based likelihood method correctly assigned 73 - 77%, depending on both 
the number of loci used and the constant value assumed for null frequencies. Nei's Da 
and the Cavalli-Sforza distance statistic correctly assigned 74 - 75%), while the other 
distance measures (Das, Nei Standard, and Nei minimum) had slightly lower accuracy, 
around 70%), and Goldstein's method correctly assigned only 31%) of individuals. This is 
an interesting result, considering that Goldstein's method was developed especially for 
microsatellites. 

One major advantage is that the assignment test does not assume genetic 
equilibrium between migration and drift. It does, however, assume that the population is 
in Hardy-Weinberg equilibrium and that there is no linkage disequilibrium (with the 

84 



exception of distance-based assignment: Cornuet et al. 1999). One potential problem 
with the assignment test that we encountered in this study is related to the fact that we 
artificially designated populations as those animals living in and around our trapping 
grids. The populations in our grids are adjacent to contiguous forest, with the exception 
of the edge bordering the highway. Thus, we did not sample throughout the true 
population, and the assignment test likely had difficulty assigning individuals to their 
population of origin simply because it was not covered by trapping grids. In part, we 
were able to get around this potential problem because we were not actually concerned 
with true population structure across the landscape; we were interested only in how 
highways affect population structure. Therefore, we have related misassignment rates 
adjacent to highways to misassignment rates across highways, providing a basis for 
comparison. A second issue to consider is that assignment tests tend to be inaccurate 
when populations are not distinct. Power is improved by testing more individuals at 
more loci (Rannala and Mountain 1997; Cornuet et al. 1999). 



85 



APPENDIX 7 - CAPTURES AND MOVEMENT 
Individuals captured 



Species 


Individuals 


Deer mice 


504 


Vagrant shrews 


355 


Southern red-backed voles 


318 


Red-tailed chipmunks 


138 


Yellow pine chipmunks 


95 


Masked shrews 


59 


Western jumping mice 


44 


Meadow voles 


25 


Shrews spp.* 


19 


Chipmunks spp.* 


14 


Bushy-tailed woodrats 


13 


Short-tailed weasels 


8 


Montane shrews 


7 


Northern flying squirrels 


6 


Pygmy shrews 


2 


Golden-mantled ground squirrels 


1 


Northern pocket gophers 


1 


Total Individuals 


1609 



Table 7.1. Number of individuals captured per species. 
* Some chipmunks could not be identified to species due to 
indistinct coloration, and some shrews could not be 
identified due to worn teeth. 



86 



Individual 


s that moved 










RB voles 
Deer mice 
Chipmunks 
All species 


2-lane highways 


4-lane highways 


Total Moved Moved 
Captured Adjacent Across 


Total Moved Moved 
Captured Adjacent Across 


157 
88 
123 
368 


8 
5 

17 
30 


1 
9 
5 
15 


139 
414 
123 
676 


1 
29 

9 
39 


1 

11 

12 



Table 7.2. Total number of individuals captured and released, as well as those that moved 
between highway trapping grids. Individuals that were dead in trap upon first capture are not 
included, since it wasn't possible to detect movement. 



Movement 


adjacent 


to versus across highways 








RB voles 
Deer mice 
Chipmunks 
All species 


2-lane highways 




4-lane hig 


hways 


affected? 


X p -value 


affected? 


T 


p-value 


Y 
N 
Y 
Y 


5.44 
1.14 
6.55 
5.00 


0.010 <p<0.025 

p> 0.100 
0.010 <p<0.025 
0.025 <p<0.050 


N/A* 
Y 
Y 
Y 


8.10 
9.00 
7.15 


0.001<p< 0.005 
0.001 <p<0.005 
0.005 <p<0.010 



Table 7.3. A chi-square test was used to compare the number of individuals moving between trapping grids 
adjacent to the highway versus across the highway. The null hypothesis was that movement was equal 
adjacent to and across highways. * Small sample size of individuals moving between trapping grids (one 
vole moved between grids on the same and opposite sides of the highway) precluded testing. 



87 



APPENDIX 8 - GENETIC TESTS 



Red-backed voles 



Population differentiation among 2000 and 2001 red-backed vole samples 



Site 


Locus 


p-value 


SE 


Lubrecht 


15 


0.034 


0.000 


St. Regis 


15 


0.047 


0.001 



Table 8. 1 . Tests indicating population differentiation among 2000 and 2001 samples where a = 0.5. 
2/24 tests were significant when considered individually, but none were significant after a sequential 
Bonferroni correction for multiple tests. 



Tests indicating 


Hardy-Weinberg 


disequilibrium for red-backed voles 


Site 


Grid 


Locus 


p-value 


SE 


Lolo 


SE 


6 


*0.012 


0.000 


Rainy 


SE 


15 


0.042 


0.001 


Rainy 


SW 


6 


*0.000 


0.000 


Rainy 


SW 


4B 


*0.007 


0.000 


St. Regis 


NEl 


4 


0.037 


0.001 


St. Regis 


NEl 


15 


*0.007 


0.000 


St. Regis 


NW 


5 


0.011 


0.001 


St. Regis 


NW 


6 


*0.000 


0.000 


St. Regis 


NW 


4B 


0.047 


0.002 


St. Regis 


SE 


4 


0.035 


0.001 


St. Regis 


SW 


6 


*0.000 


0.000 



Table 8.2. Tests indicating H-W disequilibrium among voles where a = 0.05. There were 17 tests per 
locus. * p < 0.05 after sequential Bonferroni procedure 



88 



Tests indicating linkage disequilibrium for red-backed voles 




Site 


Grid 


Locus 1 


Locus 2 


p-value 


SE 


Lubrecht 


SW 


19 


4 


*0.004 


0.000 


Lubrecht 


sw 


19 


5 


*0.001 


0.000 


Lubrecht 


SE 


19 


4B 


0.038 


0.000 


Lubrecht 


SW 


4B 


4 


*0.004 


0.000 


Lubrecht 


SW 


4B 


5 


*0.006 


0.000 


Lubrecht 


SW 


4B 


15 


*0.007 


0.000 


Lubrecht 


SW 


4 


5 


*0.001 


0.000 


Lubrecht 


SW 


4 


15 


0.038 


0.002 


Lubrecht 


SW 


5 


15 


*0.003 


0.000 


Rainy 


NW 


5 


6 


0.046 


0.001 


Rainy 


SW 


5 


6 


0.038 


0.001 


Rainy 


SE 


19 


15 


*0.011 


0.001 


Rainy 


NW 


4 


15 


0.023 


0.002 


Rainy 


SE 


4 


15 


0.015 


0.001 


Rainy 


SE 


6 


4B 


0.041 


0.000 


Rainy 


SW 


6 


4B 


0.030 


0.001 


St. Regis 


NEl 


4B 


5 


0.036 


0.001 


St. Regis 


NW 


4B 


5 


0.010 


0.001 


St. Regis 


SW 


5 


19 


0.038 


0.003 


St. Regis 


NW 


15 


6 


0.022 


0.001 


St. Regis 


SW 


19 


6 


*0.000 


0.000 



Table 8.3. Tests indicating linkage disequilibrium amonj 
per locus pair, depending on sample size and variation at 
sequential Bonferroni procedure 



; voles where a = 0.05. There were 14 - 17 tests 
those loci in each trapping grid. * p < 0.05 after 



89 



% of tests 


in which 


inkage disequilibrium 


was detected for red-backed voles 


Locus 1 


Locus 2 


Lolo 


Lubrecht 


Rainy 


St. Regis 


Total 


% 


4 


5 





1 








1 


7.1 


4 


15 





1 


2 





3 


20.0 


4 


19 





1 








1 


6.3 


4 


6 




















4 


4B 





1 








1 


6.3 


5 


15 





1 








1 


7.1 


5 


19 





1 





1 


2 


21.4 


5 


6 








2 





2 


14.3 


5 


4B 





1 





2 


3 


21.4 


15 


19 








1 





1 


6.3 


15 


6 











1 


1 


5.9 


15 


4B 





1 








1 


6.3 


19 


6 











1 


1 


11.8 


19 


4B 





1 








1 


5.9 


6 


4B 








2 





2 


11.8 


Total 







9 


7 


5 


21 




% 







16.4 


11.7 


5.6 




9.3 



Table 8.4. Percentage of times linkage disequilibrium was detected for red-backed voles when a = 0.05. 



90 



Deer Mice 



Population differentiation among 2000 and 2001 deer mouse samples 


Site 


Locus 


p-value 


SE 


Lubrecht 


6 


*0.005 


0.000 


Lubrecht 


5 


0.047 


0.000 


Lubrecht 


10 


*0.009 


0.000 


Lubrecht 


12 


0.037 


0.000 


St. Regis 


1 


*0.021 


0.000 


St. Regis 


6 


*0.000 


0.000 


St. Regis 


5 


*0.000 


0.000 


St. Regis 


10 


*0.003 


0.000 


St. Regis 


12 


*0.003 


0.000 


Tarkio 


1 


*0.001 


0.000 


Tarkio 


4 


0.043 


0.001 


Tarkio 


5 


*0.000 


0.000 


Tarkio 


12 


*0.000 


0.000 



Table 8.5. Tests indicating population differentiation among 2000 and 2001 samples where a = 0.5. 13/18 
tests were significant when considered individually, and 10/18 remained significant after a sequential 
Bonferroni correction for multiple tests. * p < 0.05 after sequential Bonferroni procedure 



Tests indicating 


Hardy-Weinberg disequilibrium for deer mice 


Site 


Grid 


Locus 


p-value 


SE 


Lubrecht 


NE 


5 


0.048 


0.001 


Lubrecht 


NW 


1 


*0.002 


0.000 


Lubrecht 


NW 


6 


*0.010 


0.001 


Lubrecht 


NW 


10 


*0.000 


0.000 


Lubrecht 


SE 


1 


0.048 


0.001 


Lubrecht 


SE 


10 


*0.010 


0.000 


Lubrecht 


SW 


1 


0.035 


0.001 


Lubrecht 


SW 


4 


0.042 


0.001 


Lubrecht 


SW 


10 


*0.000 


0.000 


St. Regis2000 


NE 


4 


0.014 


0.000 


St. Regis2000 


NE 


10 


*0.047 


0.001 


St. Regis2000 


NW 


1 


*0.000 


0.000 


St. Regis2000 


NW 


10 


*0.000 


0.000 


St. Regis2000 


SE 


10 


*0.018 


0.000 


St. Regis2000 


SW 


10 


*0.000 


0.000 


St. Regis2001 


NE 


1 


*0.000 


0.000 


St. Regis2001 


NE 


6 


*0.002 


0.000 


St. Regis2001 


NE 


4 


*0.001 


0.000 


St. Regis2001 


NE 


5 


*0.000 


0.000 


St. Regis2001 


NE 


10 


*0.000 


0.000 


St. Regis2001 


NW 


1 


*0.000 


0.000 


St. Regis2001 


NW 


10 


*0.008 


0.000 



91 



Site 


Grid 


Locus 


p-value 


SE 


St. Regis2001 


NW 


12 


*0.008 


0.000 


St. Regis2001 


NWl 


1 


*0.013 


0.000 


St. Regis2001 


SE 


1 


*0.004 


0.000 


St. Regis2001 


SE 


4 


*0.003 


0.000 


St. Regis2001 


SE 


5 


*0.003 


0.000 


St. Regis2001 


SE 


10 


*0.000 


0.000 


St. Regis2001 


SE 


12 


0.017 


0.000 


St. Regis2001 


SW 


1 


*0.012 


0.001 


St. Regis2001 


SW 


6 


*0.000 


0.000 


St. Regis2001 


SW 


5 


0.042 


0.001 


St. Regis2001 


SW 


10 


*0.000 


0.000 


Tarkio2000 


NE 


1 


*0.024 


0.001 


Tarkio2000 


NE 


6 


0.015 


0.001 


Tarkio2000 


NE 


10 


*0.000 


0.000 


Tarkio2000 


NW 


1 


*0.000 


0.000 


Tarkio2000 


NW 


4 


0.031 


0.001 


Tarkio2000 


NW 


10 


*0.000 


0.000 


Tarkio2000 


SE 


1 


*0.003 


0.000 


Tarkio2000 


SE 


5 


*0.000 


0.000 


Tarkio2000 


SE 


10 


*0.000 


0.000 


Tarkio2000 


SW 


1 


*0.000 


0.000 


Tarkio2000 


SW 


6 


0.043 


0.002 


Tarkio2000 


SW 


10 


*0.000 


0.000 


Tarkio2001 


NE 


1 


*0.000 


0.000 


Tarkio2001 


NE 


6 


0.021 


0.001 


Tarkio2001 


NE 


10 


*0.000 


0.000 


Tarkio2001 


NE 


12 


*0.000 


0.000 


Tarkio2001 


NW 


1 


*0.000 


0.000 


Tarkio2001 


NW 


6 


*0.004 


0.001 


Tarkio2001 


NW 


4 


*0.003 


0.000 


Tarkio2001 


NW 


5 


0.013 


0.001 


Tarkio2001 


NW 


10 


*0.000 


0.000 


Tarkio2001 


SE 


1 


*0.017 


0.00 


Tarkio2001 


SE 


6 


0.039 


0.002 


Tarkio2001 


SE 


10 


*0.000 


0.000 


Tarkio2001 


SE 


12 


0.036 


0.001 


Tarkio2001 


SW 


1 


*0.013 


0.001 


Tarkio2001 


SW 


10 


*0.000 


0.000 



Table 8.6. 
per locus. 



Tests 
*p< 



indicating H-W disequilibrium among mice where a = 0.05. There were 22 tests 
0.05 after sequential Bonferroni procedure 



92 



Tests indicatin 


g linkage diseq 


luilibrium for deer mice 






Site 


Grid 


Locus 1 


Locus 2 


p-value 


SE 


Lubrecht 


NW 


6 


4 


0.042 


0.002 


Lubrecht 


NW 


6 


5 


0.038 


0.002 


Lubrecht 


NW 


4 


5 


0.013 


0.001 


Lubrecht 


NW 


10 


12 


*0.003 


0.000 


St. Reg 


S2000 


NE 


4 


5 


0.042 


0.003 


St. Reg 


S2000 


NE 


6 


12 


0.031 


0.002 


St. Reg 


IS2000 


sw 


6 


5 


0.040 


0.003 


St. Reg 


S2001 


NE 


6 


4 


*0.008 


0.001 


St. Reg 


S2001 


NE 


6 


5 


*0.005 


0.000 


St. Reg 


S2001 


NE 


4 


5 


*0.000 


0.000 


St. Reg 


S2001 


NE 


6 


12 


*0.000 


0.000 


St. Reg 


S2001 


NE 


4 


12 


*0.013 


0.001 


St. Reg 


S2001 


NE 


5 


12 


*0.003 


0.000 


St. Reg 


S2001 


NE 


10 


12 


*0.013 


0.001 


St. Reg 


S2001 


NW 


4 


5 


*0.000 


0.000 


St. Reg 


S2001 


NW 


4 


10 


0.0137 


0.001 


St. Reg 


S2001 


NWl 


4 


5 


*0.028 


0.001 


St. Reg 


S2001 


SE 


1 


4 


*0.008 


0.001 


St. Reg 


S2001 


SE 


6 


4 


*0.000 


0.000 


St. Reg 


S2001 


SE 


1 


5 


*0.003 


0.001 


St. Reg 


S2001 


SE 


4 


5 


*0.000 


0.000 


St. Reg 


S2001 


SE 


1 


10 


0.040 


0.002 


St. Reg 


S2001 


SE 


4 


12 


*0.001 


0.000 


St. Reg 


S2001 


SE 


5 


12 


0.032 


0.002 


St. Reg 


S2001 


SE 


10 


12 


*0.000 


0.000 


St. Reg 


IS2001 


SW 


1 


6 


*0.000 


0.000 


St. Reg 


IS2001 


SW 


6 


4 


*0.000 


0.000 


St. Reg 


IS2001 


SW 


6 


5 


*0.002 


0.001 


St. Reg 


IS2001 


SW 


4 


5 


*0.000 


0.000 


St. Reg 


IS2001 


SW 


6 


12 


*0.007 


0.001 


St. Reg 


IS2001 


SW 


4 


12 


0.030 


0.003 


Tarkio2000 


NE 


1 


6 


*0.007 


0.001 


Tarkio2000 


NE 


1 


4 


0.014 


0.001 


Tarkio2000 


NE 


6 


4 


*0.001 


0.000 


Tarkio2000 


NE 


1 


5 


*0.013 


0.001 


Tarkio2000 


NE 


6 


5 


0.047 


0.002 


Tarkio2000 


NE 


1 


12 


*0.006 


0.001 


Tarkio2000 


NW 


1 


5 


*0.002 


0.001 


Tarkio2000 


NW 


4 


5 


*0.000 


0.000 


Tarkio2000 


SE 


6 


4 


*0.006 


0.001 


Tarkio2000 


SE 


4 


5 


*0.000 


0.000 


Tarkio2000 


SE 


5 


10 


0.021 


0.002 



93 



Site 


Grid 


Locus 1 


Locus 2 


p-value 


SE 


Tarkio2000 


SW 


6 


5 


0.041 


0.003 


Tarkio2000 


sw 


4 


5 


*0.013 


0.002 


Tarkio2000 


SW 


10 


12 


*0.002 


0.000 


Tarkio2001 


NE 


1 


6 


*0.000 


0.000 


Tarkio2001 


NE 


1 


4 


*0.021 


0.002 


Tarkio2001 


NE 


1 


5 


*0.000 


0.000 


Tarkio2001 


NE 


4 


5 


*0.000 


0.000 


Tarkio2001 


NE 


1 


10 


0.015 


0.002 


Tarkio2001 


NE 


6 


10 


*0.008 


0.001 


Tarkio2001 


NE 


4 


10 


*0.000 


0.000 


Tarkio2001 


NE 


5 


10 


*0.000 


0.000 


Tarkio2001 


NE 


5 


12 


*0.005 


0.001 


Tarkio2001 


NE 


10 


12 


0.046 


0.003 


Tarkio2001 


NW 


1 


6 


*0.000 


0.000 


Tarkio2001 


NW 


1 


4 


*0.000 


0.000 


Tarkio2001 


NW 


6 


4 


*0.001 


0.000 


Tarkio2001 


NW 


1 


5 


*0.000 


0.000 


Tarkio2001 


NW 


6 


5 


*0.002 


0.001 


Tarkio2001 


NW 


4 


5 


*0.000 


0.000 


Tarkio2001 


NW 


1 


10 


0.045 


0.004 


Tarkio2001 


NW 


5 


10 


0.033 


0.004 


Tarkio2001 


NW 


1 


12 


*0.002 


0.001 


Tarkio2001 


NW 


6 


12 


*0.000 


0.000 


Tarkio2001 


NW 


4 


12 


*0.000 


0.000 


Tarkio2001 


NW 


5 


12 


*0.000 


0.000 


Tarkio2001 


NW 


10 


12 


0.037 


0.004 


Tarkio2001 


SE 


6 


4 


*0.005 


0.001 


Tarkio2001 


SE 


6 


5 


*0.000 


0.000 


Tarkio2001 


SE 


4 


5 


*0.000 


0.000 


Tarkio2001 


SE 


1 


10 


0.036 


0.003 


Tarkio2001 


SE 


6 


10 


*0.005 


0.001 


Tarkio2001 


SE 


4 


10 


*0.000 


0.000 


Tarkio2001 


SE 


5 


10 


*0.003 


0.001 


Tarkio2001 


SE 


6 


12 


*0.007 


0.001 


Tarkio2001 


SE 


10 


12 


*0.004 


0.001 


Tarkio2001 


SW 


1 


6 


0.030 


0.003 


Tarkio2001 


SW 


1 


4 


*0.005 


0.001 


Tarkio2001 


SW 


6 


4 


*0.006 


0.002 


Tarkio2001 


SW 


1 


5 


0.033 


0.004 


Tarkio2001 


SW 


6 


5 


*0.021 


0.003 


Tarkio2001 


SW 


4 


5 


*0.000 


0.000 


Tarkio2001 


SW 


1 


12 


0.046 


0.004 


Tarkio2001 


SW 


4 


12 


*0.011 


0.002 



94 



Site 


Grid 


Locus 1 


Locus 2 


p-value 


SE 


Tarkio2001 


SW 


5 


12 


*0.011 


0.002 



Table 8.7. Tests indicating linkage disequilibrium among deer mice where a = 0.05. There were 17 - 20 
tests per locus pair, depending on sample size and variation at those loci in each trapping grid. * p < 0.05 
after sequential Bonferroni procedure 



95 



Percentage of tests in ' 


tvhich linka 


ge disequili 


brium was detected for deer mice 






Locus 1 


Locus 2 


Lubrecht 


St. Regis 
2000 


St. Regis 
2001 


Tarkio 
2000 


Tarkio 
2001 


Total 


% 




6 








1 


1 


o 
J 


5 


29.4 




4 








1 


1 


3 


5 


23.8 




5 








1 


2 


3 


6 


30.0 




10 








1 





3 


4 


21.1 




12 











1 


2 


o 
J 


16.7 


6 


4 


1 





3 


2 


o 
J 


9 


50.0 


6 


5 


1 


1 


2 


2 


3 


9 


52.9 


6 


10 














2 


2 


11.1 


6 


12 





1 


2 





2 


5 


27.8 


4 


5 


1 


1 


5 


3 


4 


14 


73.7 


4 


10 








1 





2 


o 
J 


16.7 


4 


12 








3 





2 


5 


26.3 


5 


10 











1 


3 


4 


22.2 


5 


12 








2 





3 


5 


27.8 


10 


12 


1 





2 


1 


o 
J 


7 


41.8 


Total 




4 


3 


24 


14 


41 


86 




% 




10.8 


8.1 


35.8 


23.3 


68.3 




33.0 



Table 8.8. Percentage of times linkage disequilibrium was detected for deer mice when a = 0.05. 



96 



Vagrant shrews 



Population differentiation among 2000 and 2001 vagrant shrew 


samples 


Site 


Locus 


p-value 


SE 


Lolo 


A3 -3 5 


0.030 


0.000 


Rainy 


A3 -5 


*0.049 


0.000 


Rainy 


A3 -3 5 


*0.009 


0.000 


Rainy 


A4-20 


*0.001 


0.000 


Rainy 


A4-5 


*0.002 


0.000 


Rainy 


SH-22 


*0.007 


0.000 


St. Regis 


A3 -3 5 


*0.000 


0.000 



Table 8.9. Tests indicating population differentiation among 2000 and 2001 samples where a = 0.5. 7/15 
tests were significant when considered individually, including 5/5 tests at Rainy Lake (due to large changes 
in small sample sizes from 2000 to 2001). 6/15 remained significant after a sequential Bonferroni 
correction for multiple tests. * p < 0.05 after sequential Bonferroni procedure 



Tests indicating 


; Hardy-Weinberg disequilibrium for vagrant shrews 


Site 


Grid 


Locus 


p-value 


SE 


Lolo 


NE 


A3 -5 


*0.002 


0.000 


Lolo 


SE 


A3 -5 


*0.000 


0.000 


Lolo 


SW 


A3 -5 


*0.000 


0.000 


Lolo 


NE 


A4-5 


0.023 


0.001 


Lolo 


NE 


SH-22 


*0.002 


0.000 


Lolo 


SE 


SH-22 


*0.017 


0.001 


Lolo 


SW 


SH-22 


*0.000 


0.000 


Rainy 


NW 


A3 -5 


*0.001 


0.000 


Rainy 


NW 


A3 -3 5 


0.026 


0.000 


Rainy 


SW 


A4-5 


*0.005 


0.001 


St. Regis 


NE 


A3 -5 


*0.000 


0.000 


St. Regis 


NEl 


A3 -5 


*0.000 


0.000 


St. Regis 


NW 


A3-5 


*0.000 


0.000 


St. Regis 


NWl 


A3 -5 


*0.000 


0.000 


St. Regis 


SE 


A3 -5 


*0.000 


0.000 


St. Regis 


SW 


A3 -5 


*0.000 


0.000 


St. Regis 


NEl 


A4-5 


0.048 


0.002 


St. Regis 


NW 


SH-22 


*0.000 


0.000 


St. Regis 


NWl 


SH-22 


*0.006 


0.000 


St. Regis 


SE 


SH-22 


*0.002 


0.000 


St. Regis 


SW 


SH-22 


*0.000 


0.000 



Table 8. 10. Tests indicating El- 
tests per locus. * p < 0.05 after 



W disequilibrium among shrews where a = 0.05. There were 12 
sequential Bonferroni procedure 



97 



Tests indicating linkage disequilibrium for vagrant shrews 



Site 


Grid 


Locus 1 


Locus 2 


p-value 


SE 


Rainy 


SW 


A3 -5 


A4-20 


*0.023 


0.001 


St. Regis 


NE 


A3 -5 


A4-20 


0.009 


0.001 


St. Regis 


NWl 


A3 -5 


A4-20 


0.027 


0.002 


St. Regis 


NEl 


A3 -5 


A4-5 


0.036 


0.003 



Table 8. 1 1 . Tests indicating linkage disequilibrium among shrews where a = 0.05. There were 10-11 
tests per locus pair, depending on sample size and variation at those loci in each trapping grid. * p < 0.05 
after sequential Bonferroni procedure 



98 



APPENDIX 9 - HETEROZYGOSITY AND ALLELIC DIVERSITY 



Average heterozygosity for red-backed voles 








Site 


All loci 


w/out 6 




Het exp 


Het obs 


Het exp 


Het obs 


Lolo 


0.788 


0.739 


0.850 


0.830 


Lubrecht 


0.749 


0.736 


0.779 


0.806 


Rainy 


0.756 


0.764 


0.800 


0.820 


St. Regis 


0.755 


0.731 


0.836 


0.821 


Total 


0.762 


0.742 


0.816 


0.819 



Table 9. la. Heterozygosity was averaged over all six loci, and was then recalculated, leaving out locus 6. 
Locus 6 was out of Hardy-Weinberg equilibrium and exhibited a deficit of heterozygotes consistent with 
the existence of a null allele. Expected heterozygosity exceeded observed heterozygosity in most cases, but 
differences were quite small without locus 6. Fst has an upper limit equal to the average homozygosity: 
0.234 calculated with locus 6, and 0. 184 calculated without locus 6. 

Average heterozygosity for deer mice 



Site 


All] 


oci 


w/out 10 


w/out 


1 or 10 




Het exp 


Het obs 


Het exp 


Het obs 


Het exp 


Het obs 


Lubrecht 


0.914 


0.753 


0.914 


0.842 


0.922 


0.911 


Rainy 


0.897 


0.875 


0.908 


0.924 


0.914 


0.948 


St. Regis 


0.876 


0.749 


0.881 


0.815 


0.882 


0.861 


Tarkio 


0.908 


0.790 


0.918 


0.859 


0.918 


0.905 


Total 


0.899 


0.792 


0.905 


0.860 


0.909 


0.906 



Table 9. lb. Heterozygosity was averaged over all six loci, and was then recalculated, leaving out locus 10 
and leaving out both 1 and 10. These loci were out of Hardy-Weinberg equilibrium and exhibited a deficit 
of heterozygotes consistent with the existence of a null allele. Expected heterozygosity exceeded observed 
heterozygosity in most cases, but differences were quite small when only those loci in H-W equilibrium 
were considered. Fgx has an upper limit equal to the average homozygosity: 0. 101 calculated with all loci, 
0.095 without locus 10, and 0.091 without 1 or 10. 

Average heterozygosity for vagrant shrews 



Site 


All 


oci 


w/out A3 -5 


w/outA3-5orSH-22 




Het exp 


Het obs 


Het exp 


Het obs 


Het exp 


Het obs 


Lolo 


0.857 


0.693 


0.848 


0.758 


0.865 


0.840 


Rainy 


0.880 


0.720 


0.877 


0.750 


0.896 


0.782 


St. Regis 


0.857 


0.707 


0.849 


0.771 


0.862 


0.825 


Total 


0.865 


0.706 


0.858 


0.759 


0.874 


0.816 



Table 9. Ic. Heterozygosity was averaged over all five loci, and was then recalculated, leaving out locus 
A3 -5 and leaving out both A3 -5 and SH-22. These loci were out of Hardy-Weinberg equilibrium and 
exhibited a deficit of heterozygotes consistent with the existence of a null allele. Expected heterozygosity 
exceeded observed heterozygosity in all cases, but differences were rather small (except for 1 1% difference 
at Rainy Lake where gene flow across the highway was low) when only those loci in H-W equilibrium 
were considered. Fgx has an upper limit equal to the average homozygosity: 0.135 calculated with all loci, 
0. 142 without locus A3-5, and 0. 126 without A3-5 or SH-22. 



99 



In most cases, He still exceeded Ho, even after loci deviating from H-W 
proportions were excluded, probably because we pooled grids together to calculate 
heterozygosity for sites; if highways reduce small mammal movement, then sites do not 
represent one panmictic (randomly mating) population. He therefore exceeds Ho, because 
alleles are less often shared among individuals in a subdivided population than in a single 
panmictic population. This appeared to be especially true for shrews, particularly at 
Rainy Lake, where gene flow measurements confirm this conclusion. 



100 



Red-backed voles 



Lolo red-backed voles 



Locus 


Expected 
heterozygosity 


Observed 
heterozygosity 


4 


0.890 


0.893 


5 


0.874 


0.844 


15 


0.924 


0.906 


19 


0.829 


0.750 


6 


0.480 


0.281 


4B 


0.733 


0.759 


Average 


0.788 


0.739 


Average 
w/out 6 


0.850 


0.830 


Lubrecht red-backed voles 


Locus 


Expected 
heterozygosity 


Observed 
heterozygosity 


4 


0.840 


0.824 


5 


0.856 


0.882 


15 


0.839 


0.922 


19 


0.702 


0.712 


6 


0.601 


0.385 


4B 


0.660 


0.692 


Average 


0.749 


0.736 


Average 
w/out 6 


0.779 


0.806 


Rainy Lake red-backed voles 


Locus 


Expected 
heterozygosity 


Observed 
heterozygosity 


4 


0.826 


0.893 


5 


0.880 


0.922 


15 


0.920 


0.899 


19 


0.785 


0.810 


6 


0.536 


0.481 


4B 


0.590 


0.577 


Average 


0.756 


0.764 


Average 
w/out 6 


0.800 


0.820 



101 



St. Regis red-backed voles 



Locus 


Expected 
heterozygosity 


Observed 
heterozygosity 


4 


0.866 


0.847 


5 


0.860 


0.867 


15 


0.918 


0.881 


19 


0.803 


0.768 


6 


0.348 


0.285 


4B 


0.736 


0.740 


Average 


0.755 


0.731 


Average 
w/out 6 


0.836 


0.821 



Table 9.2. Site specific heterozygosity for red-backed voles, 
averaged over loci with and without locus 6. Locus 6 
showed deviations from Hardy-Weinberg proportions. 

Number of alleles per locus for red-backed voles 





4 


5 


15 


19 


6 


4B 


Lolo 


14 


15 


17 


14 


4 


8 


Lubrecht 


12 


11 


13 


8 


5 


4 


Rainy 


11 


15 


21 


18 


7 


10 


St. Regis 


13 


17 


22 


16 


6 


11 


Total 


17 


20 


27 


25 


9 


13 



Table 9.3. Allelic diversity for red-backed voles, including the total number of alleles recorded in this 
study. 



102 



Deer mice 



Lubrecht deer mice 



Locus 


Expected 
heterozygosity 


Observed 
heterozygosity 


1 


0.882 


0.565 


6 


0.930 


0.933 


4 


0.903 


0.867 


5 


0.926 


0.889 


10 


0.912 


0.308 


12 


0.930 


0.956 


Average 


0.914 


0.753 


Average 
w/out 10 


0.914 


0.842 


Average 
w/out 1 or 10 


0.922 


0.911 


Rainy Lake d( 


;er mice 




Locus 


Expected 
heterozygosity 


Observed 
heterozygosity 


1 


0.885 


0.828 


6 


0.894 


0.931 


4 


0.907 


0.966 


5 


0.934 


0.931 


10 


0.838 


0.630 


12 


0.922 


0.966 


Average 


0.897 


0.875 


Average 
w/out 10 


0.908 


0.924 


Average 
w/out 1 or 10 


0.914 


0.948 



103 



St. Regis deer mice 



Locus 


Expected 
heterozygosity 


Observed 
heterozygosity 


1 


0.876 


0.629 


6 


0.913 


0.837 


4 


0.891 


0.893 


5 


0.904 


0.888 


10 


0.854 


0.419 


12 


0.819 


0.826 


Average 


0.876 


0.749 


Average 
w/out 10 


0.881 


0.815 


Average 
w/out 1 or 10 


0.882 


0.861 


Tarkio deer m 


lice 




Locus 


Expected 
heterozygosity 


Observed 
heterozygosity 


1 


0916 


0.674 


6 


0.939 


0.940 


4 


0.849 


0.824 


5 


0.913 


0.916 


10 


0.860 


0.447 


12 


0.973 


0.941 


Average 


0.908 


0.790 


Average 
w/out 10 


0.918 


0.859 


Average 
w/out 1 or 10 


0.918 


0.905 



Table 9.4. Site specific heterozygosity for deer mice, 
averaged over loci with and without locus 1 and locus 1 . 
These loci showed deviations from Hardy -Weinberg 
proportions. 

Number of alleles per locus for deer mice 





1 


6 


4 


5 


10 


12 


Lubrecht 


16 


25 


14 


24 


17 


24 


Rainy 


11 


18 


14 


19 


14 


19 


St. Regis 


15 


28 


15 


24 


15 


22 


Tarkio 


21 


38 


18 


24 


14 


24 


Total 


23 


54 


22 


45 


25 


37 



Table 9.5. Allelic diversity for deer mice, including the total number of alleles recorded in this study. 



104 



Vagrant shrews 



Lolo vagrant shrews 



Locus 


Expected 
heterozygosity 


Observed 
heterozygosity 


A3 -5 


0.893 


0.432 


A3 -3 5 


0.904 


0.918 


A4-20 


0.771 


0.765 


A4-5 


0.919 


0.837 


SH-22 


0.796 


0.510 


Average 


0.857 


0.693 


Average w/out 

A3 -5 


0.848 


0.758 


Average w/out 
A3 -5 or SH-22 


0.865 


0.840 


Rainy Lake vag 


rant shrews 




Locus 


Expected 
heterozygosity 


Observed 
heterozygosity 


A3 -5 


0.891 


0.600 


A3 -3 5 


0.883 


0.808 


A4-20 


0.881 


0.808 


A4-5 


0.925 


0.731 


SH-22 


0.821 


0.654 


Average 


0.880 


0.720 


Average w/out 

A3 -5 


0.877 


0.750 


Average w/out 
A3 -5 or SH-22 


0.896 


0.782 



105 



St. Regis vagrant shrews 



Locus 


Expected 
heterozygosity 


Observed 
heterozygosity 


A3 -5 


0.891 


0.451 


A3 -3 5 


0.863 


0.859 


A4-20 


0.805 


0.797 


A4-5 


0.919 


0.819 


SH-22 


0.808 


0.606 


Average 


0.857 


0.707 


Average w/out 

A3 -5 


0.849 


0.771 


Average w/out 
A3 -5 or SH-22 


0.862 


0.825 



Table 9.6. Site specific heterozygosity for vagrant shrews, 
averaged over loci with and without locus A3 -5 and locus SH- 
22. These loci showed deviations from Hardy-Weinberg 
proportions 



Number of al 


eles per locus 1 


For vagrant shrews 








A3-5 


A3-35 


A4-20 


A4-5 


SH-22 


Lolo 


16 


18 


9 


20 


11 


Rainy 


14 


13 


12 


19 


7 


St. Regis 


18 


13 


9 


19 


12 


Total 


22 


22 


16 


27 


15 



Table 9.7. Allelic diversity for vagrant shrews, including the total number of alleles recorded in this study. 



106 



APPENDIX 10 - GENETIC DIFFERENCES AMONG SITES 



Red-backed voles 



FsT for red-backed voles among sites: all loci 





Lolo 


Lubrecht 


Rainy 


Lubrecht 


0.056 






Rainy 


0.056 


0.041 




St. Regis 


0.029 


0.051 


0.023 



Table 10.1a. Pairwise Fst among sites averaged 0.043 when all loci were included in the 
calculation. 



Fst for red-backed voles among sites: without locus 6 





Lolo 


Lubrecht 


Rainy 


Lubrecht 


0.045 






Rainy 


0.061 


0.033 




St. Regis 


0.028 


0.025 


0.021 



Table 10.1b. Pairwise Fst among sites averaged 0.035 when locus 6 was excluded. Locus 6 
showed deviations from Hardy-Weinberg proportions. 



107 



Assignments of red-backed voles among sites: Bayesian likelihood method w/ all loci 





Lolo 


Lubrecht 


Rainy 


St. Regis 


Lolo 


0.594 


0.039 


0.051 


0.060 


Lubrecht 


0.094 


0.750 


0.101 


0.086 


Rainy 


0.063 


0.192 


0.722 


0.073 


St. Regis 


0.250 


0.0192 


0.127 


0.781 



Table 10.2a. Proportion of voles correctly assigned to their population of capture or misassigned to a 
different site using Bayesian probabilities. Voles were captured in [columns], but assigned in [rows]. 
Overall 74.2% classified correctly. 

Assignments of red-backed voles among sites: Bayesian method w/out locus 6 





Lolo 


Lubrecht 


Rainy 


St. Regis 


Lolo 


0.531 


0.039 


0.025 


0.093 


Lubrecht 


0.125 


0.731 


0.101 


0.132 


Rainy 


0.063 


0.173 


0.759 


0.099 


St. Regis 


0.281 


0.058 


0.114 


0.675 



Table 1 0.2b. Proportion of voles correctly assigned to their population of capture or misassigned to a 
different site using Bayesian probabilities, locus 6 excluded. Locus 6 showed deviations from Hardy- 
Weinberg proportions. Voles were captured in [columns], but assigned in [rows]. Overall 69. 1% classified 
correctly. 



Assignments of red-backed voles i 


imong sites: distance method (Nei Da) w/ all loci 




Lolo 


Lubrecht 


Rainy 


St. Regis 


Lolo 


0.594 


0.000 


0.038 


0.073 


Lubrecht 


0.094 


0.808 


0.152 


0.146 


Rainy 


0.094 


0.173 


0.709 


0.093 


St. Regis 


0.219 


0.0192 


0.101 


0.689 



Table 10.2c. Proportion of voles correctly assigned to their population of capture or misassigned to a 
different site using Nei's Da genetic distance statistic. This test does not require Hardy -Weinberg 
equilibrium. Voles were captured in [columns], but assigned in [rows]. Overall 70.4% classified correctly. 



108 



Deer mice 



FsT for deer mice among sites: all loci 





Lolo 


Lubrecht 


Rainy 


St. Regis 


Lubrecht 


0.054 








Rainy 


0.058 


0.017 






St. Regis 


0.074 


0.029 


0.036 




Tarkio 


0.075 


0.036 


0.041 


0.047 



Table 10.3a. Pairwise Fst among sites averaged 0.047 when all loci were included in the 
calculation. 



Fst for deer mice among sites: 


without locus 10 






Lolo 


Lubrecht 


Rainy 


St. Regis 


Lubrecht 


0.049 








Rainy 


0.063 


0.017 






St. Regis 


0.066 


0.030 


0.034 




Tarkio 


0.073 


0.037 


0.044 


0.049 



Table 10.3b. Pairwise Fst among sites averaged 0.046 when locus 10 was excluded. Locus 10 
showed deviations from Hardy-Weinberg proportions. 

Fst for deer mice among sites: without locus 1 or 10 





Lolo 


Lubrecht 


Rainy 


St. Regis 


Lubrecht 


0.052 








Rainy 


0.067 


0.017 






St. Regis 


0.072 


0.036 


0.040 




Tarkio 


0.072 


0.035 


0.045 


0.049 



Table 10.3c. Pairwise Fgx among sites averaged 0.048 when locus 1 and locus 10 were excluded 
due to deviations from Hardy-Weinberg proportions. 



109 



Assignments of deer mice among sites: Bayesian likelihood method w/ all loci 




Lolo 


Lubrecht 


Rainy 


St. Regis 


Tarkio 


Lolo 


0.769 


0.067 


0.000 


0.000 


0.013 


Lubrecht 


0.154 


0.711 


0.103 


0.011 


0.000 


Rainy 


0.000 


0.111 


0.759 


0.034 


0.013 


St. Regis 


0.077 


0.067 


0.035 


0.955 


0.029 


Tarkio 


0.000 


0.044 


0.103 


0.056 


0.945 



Table 10.4a. Proportion of deer mice correctly assigned to their population of capture or misassigned to a 
different site using Bayesian probabilities. Mice were captured in [columns], but assigned in [rows]. 
Overall 89.3% classified correctly. 



Assignments of deer mice among sites: Bayesian likelihood method w/out locus 10 




Lolo 


Lubrecht 


Rainy 


St. Regis 


Tarkio 


Lolo 


0.769 


0.044 


0.000 


0.000 


0.008 


Lubrecht 


0.154 


0.711 


0.103 


0.023 


0.004 


Rainy 


0.000 


0.133 


0.690 


0.028 


0.017 


St. Regis 


0.077 


0.044 


0.069 


0.893 


0.021 


Tarkio 


0.000 


0.067 


0.138 


0.056 


0.950 



Table 10.4b. Proportion of deer mice correctly assigned to their population of capture or misassigned to a 
different site using Bayesian probabilities, excluding locus 10. Locus 10 showed deviations from Hardy- 
Weinberg proportions. Mice were captured in [columns], but assigned in [rows]. Overall 88.9% classified 
correctly. 

Assignments of deer mice among sites: Bayesian method w/out locus 1 or 10 





Lolo 


Lubrecht 


Rainy 


St. Regis 


Tarkio 


Lolo 


0.846 


0.044 


0.000 


0.000 


0.017 


Lubrecht 


0.077 


0.733 


0.103 


0.028 


0.013 


Rainy 


0.000 


0.089 


0.655 


0.023 


0.008 


St. Regis 


0.077 


0.067 


0.103 


0.893 


0.025 


Tarkio 


0.000 


0.067 


0.138 


0.056 


0.937 



Table 10.4c. Proportion of deer mice correctly assigned to their population of capture or misassigned to a 
different site using Bayesian probabilities, excluding locus 1 and 10. These loci showed deviations from 
Hardy -Weinberg proportions. Mice were captured in [columns], but assigned in [rows]. Overall 88.5%) 
classified correctly. 



Assignments of deer mice among sites: distance method (N 


ei Da) w/ all loci 




Lolo 


Lubrecht 


Rainy 


St. Regis 


Tarkio 


Lolo 


0.846 


0.133 


0.069 


0.000 


0.025 


Lubrecht 


0.077 


0.644 


0.103 


0.011 


0.004 


Rainy 


0.000 


0.111 


0.690 


0.023 


0.017 


St. Regis 


0.077 


0.067 


0.069 


0.927 


0.067 


Tarkio 


0.000 


0.044 


0.069 


0.039 


0.887 



Table 10. 4d. Proportion of deer mice correctly assigned to their population of capture or misassigned to a 
different site using Nei's D^ genetic distance statistic. This test does not require Hardy -Weinberg 
equilibrium. Mice were captured in [columns], but assigned in [rows]. Overall 86.7%) classified correctly. 



110 



Vagrant shrews 

FsT for shrews among sites: all loci 





Lolo 


Rainy 


Rainy 


0.034 




St. Regis 


0.012 


0.036 



Table 10.5a. Pairwise Fst among sites 
averaged 0.028 when all loci were 
included in the calculation. 

Fst for shrews among sites: 
without locus A3-5 





Lolo 


Rainy 


Rainy 


0.035 




St. Regis 


0.015 


0.036 



Table 10.5b. Pairwise Fst among sites 
averaged 0.029 when A3-5 was excluded. 
Locus A3 -5 showed deviations from 
Hardy -Weinberg proportions. 

Fst for shrews among sites: 
without locus A3-5 or SH-22 





Lolo 


Rainy 


Rainy 


0.036 




St. Regis 


0.019 


0.033 



Table 10.5c. Pairwise Fst among sites 
averaged 0.029 when A3-5 and SH-22 
were excluded. These loci showed 
deviations from Hardy -Weinberg 
proportions. 



Ill 



Assignments of shrews among sites: Bayesian likelihood method w/ all loci 





Lolo 


Rainy 


St. Regis 


Lolo 


0.673 


0.154 


0.181 


Rainy 


0.031 


0.808 


0.009 


St. Regis 


0.296 


0.039 


0.811 



Table 10.6a. Proportion of shrews correctly assigned to their population of capture or 
misassigned to a different site using Bayesian probabilities. Shrews were captured in 
[columns], but assigned in [rows]. Overall 77.2% classified correctly. 

Assignments of shrews among sites: Bayesian likelihood method w/out A3-5 





Lolo 


Rainy 


St. Regis 


Lolo 


0.673 


0.192 


0.198 


Rainy 


0.020 


0.769 


0.004 


St. Regis 


0.306 


0.039 


0.797 



Table 10.6b. Proportion of shrews correctly assigned to their population of capture or 
misassigned to a different site using Bayesian probabilities, excluding locus A3-5. A3-5 
showed deviations from Hardy-Weinberg proportions. Shrews were captured in [columns], 
but assigned in [rows]. Overall 76.1% classified correctly. 

Assignments of shrews among sites: Bayesian method w/out A3-5 or SH-22 





Lolo 


Rainy 


St. Regis 


Lolo 


0.663 


0.154 


0.185 


Rainy 


0.041 


0.692 


0.013 


St. Regis 


0.296 


0.154 


0.802 



Table 10.6c. Proportion of shrews correctly assigned to their population of capture or 
misassigned to a different site using Bayesian probabilities, excluding locus A3-5 and SH-22. 
These loci showed deviations from Hardy-Weinberg proportions. Shrews were captured in 
[columns], but assigned in [rows]. Overall 75.5%) classified correctly. 



Assignments of shrews among sites: 


distance method (> 


ei Da) w/ all loci 




Lolo 


Rainy 


St. Regis 


Lolo 


0.633 


0.115 


0.189 


Rainy 


0.041 


0.808 


0.018 


St. Regis 


0.327 


0.077 


0.793 



Table 10. 6d. Proportion of shrews correctly assigned to their population of capture or 
misassigned to a different site using Nei's Da genetic distance statistic. This test does not 
require Hardy-Weinberg equilibrium. Shrews were captured in [columns], but assigned in 
[rows]. Overall 74.9%) classified correctly. 



112 



Misassignments among sites 





Misassign 
All 


Misassign 
LR>10 


Misassign 
LR>20 


Misassign 
LR > 100 


Total 
Analyzed 


red-backed voles 


0.296 (93) 


0.006 (2) 


0.003 (1) 


0.000 


314 


deer mice 


0.133(67) 


0.032 (16) 


0.026(13) 


0.018 (9) 


503 


vagrant shrews 


0.251 (88) 


0.003 (1) 


0.000 


0.000 


351 



Table 10.7. The proportion of individuals misassigned among sites was greatly reduced when a likelihood 
ratio (LR) of at least 10 was used as a cutoff value, below which point individuals were not considered 
"true" migrants. LR = [probability of originating in assigned population / probability of originating in 
capture population]. 



113 



APPENDIX 11 - GENE FLOW WITHIN SITES 



Gene flow across highways for red-backed voles 








FsT and Bayesian assignment test 


Distance 
assignmt 


Site 


All loci 


No 6 


All loci 




FsT 


misassign 


FsT 


misassign 


misassign 


Lolo (2-lane) 












same side 


0.120 


0.080 


0.057 


0.080 


0.120 


opposite side 


0.089 


0.154 


0.069 


0.231 


0.231 


Lubrecht (2-lane) 












same side 


0.019 


0.266 


0.011 


0.302 


0.292 


opposite side 


0.014 


0.274 


0.008 


0.300 


0.300 


Rainy (2-lane) 












same side 


0.007 


0.423 


0.007 


0.433 


0.423 


opposite side 


0.002 


0.409 


0.002 


0.395 


0.500 


St. Regis (4-lane) 












same side, hwy 


0.006 


0.393 


0.006 


0.370 


0.401 


opposite side, hwy 


0.011 


0.407 


0.011 


0.420 


0.407 


same side, far 


0.009 


0.393 


0.010 


0.381 


0.369 


opposite side, far 


0.009 


0.407 


0.010 


0.390 


0.423 



Table 11.1. Gene flow in red-backed voles did not appear to be influenced by highways. We 
compared genetic differences between animals captured on the same side of the highway, versus 
those captured on opposite sides of the highway. Comparisons indicating more differentiation and 
less gene flow are shown in bold: higher Fsx and lower misassignment rate. At St. Regis in 2001, we 
made comparisons directly across the highway (75 m) and across the highway into the forest interior 
(240 m). We present values for Fst and the Bayesian likelihood-based assignment test with and 
without locus 6, which showed deviations from Hardy -Weinberg proportions, as well as a genetic 
distance -based assignment test (Nei's Da distance statistic) that does not require H-W equilibrium. 



114 



Gene flow across highways for deer 


mice 
















Fsi 


and Bayesian assignmt test 




Distance 
assignmt 


Site 


All 


oci 


Nolo 


Nol 


or 10 


All loci 




FsT 


misassign 


FsT 


misassign 


FsT 


misassign 


misassign 


Lubrecht (2-lane) 
















same side 


0.021 


0.409 


0.030 


0.254 


0.024 


0.437 


0.337 


opposite side 


0.022 


0.423 


0.023 


0.385 


0.029 


0.385 


0.404 


St. Regis 2000 (4-lane) 
















same side 


0.000 


0.474 


0.002 


0.518 


0.002 


0.494 


0.494 


opposite side 


0.005 


0.392 


0.001 


0.479 


0.002 


0.458 


0.347 


St. Regis 2001 (4-lane) 
















same side, hwy 


0.037 


0.223 


0.039 


0.205 


0.043 


0.223 


0.209 


opposite side, hwy 


0.057 


0.113 


0.050 


0.130 


0.055 


0.150 


0.141 


same side, far 


0.037 


0.213 


0.038 


0.277 


0.041 


0.213 


0.220 


opposite side, far 


0.029 


0.150 


0.035 


0.150 


0.030 


0.175 


0.150 


Tarkio 2000 (4-lane) 
















same side 


0.025 


0.235 


0.028 


0.235 


0.029 


0.222 


0.259 


opposite side 


0.023 


0.273 


0.030 


0.211 


0.031 


0.235 


0.285 


Tarkio 2001 (4-lane) 
















same side 


0.026 


0.172 


0.025 


0.162 


0.027 


0.182 


0.201 


opposite side 


0.022 


0.155 


0.019 


0.186 


0.017 


0.224 


0.220 



Table 1 1 .2. Gene flow in deer mice was reduced by 4-lane highways only, and only at St. Regis. We compared genetic differences 
between animals captured on the same side of the highway, versus those captured on opposite sides of the highway. Comparisons 
indicating more differentiation and less gene flow are shown in bold: higher Fg-p and lower misassignment rate. At St. Regis in 2001, we 
made comparisons directly across the highway (75 m) and across the highway into the forest interior (240 m). We present values for Fg-p 
and the Bayesian likelihood-based assignment test with and without locus 1 and locus 10, which showed deviations from Hardy-Weinberg 
proportions, as well as a genetic distance -based assignment test (Nei's Da distance statistic) that does not require H-W equilibrium. 
Values shown in Figure 6 are averages across sites for Fg-p computed without 1 or 10 and distance -based misassignment rates. Sample 
sizes at Lolo and Rainy Lake were too small for analyses to be meaningful. 



115 



Gene flow across highways for vagrant shrews 





FsT and Bayesian assignment test 


Distance 
assignmt 


Site 


All loci 


No A3 -5 


No A3 -5 or SH-22 


All loci 




FsT 


misassign 


FsT 


misassign 


FsT 


misassign 


misassign 


Lolo (2-lane)* 
















same side 


0.001 


0.480 


0.002 


0.440 


0.002 


0.427 


0.467 


opposite side 


0.000 


0.563 


0.000 


0.542 


0.006 


0.479 


0.479 


Rainy (2-lane)* 
















same side 


0.000 


0.381 


0.000 


0.381 


0.011 


0.286 


0.429 


opposite side 


0.061 


0.063 


0.056 


0.063 


0.081 


0.063 


0.063 


St. Regis (4-lane) 
















same side, hwy 


0.004 


0.429 


0.005 


0.467 


0.004 


0.450 


0.478 


opposite side, hwy 


0.014 


0.342 


0.013 


0.317 


0.008 


0.360 


0.325 


same side, far 


0.000 


0.453 


0.000 


0.473 


0.000 


0.442 


0.523 


opposite side, far 


0.007 


0.376 


0.008 


0.372 


0.008 


0.374 


0.374 



Table 11.3. Gene flow in vagrant shrews was reduced by both 2-lane (at Rainy Lake only) and 4-lane highways. We compared 
genetic differences between animals captured on the same side of the highway, versus those captured on opposite sides of the 
highway. Comparisons indicating more differentiation and less gene flow are shown in bold: higher Fst and lower misassignment 
rate. At St. Regis in 2001, we made comparisons directly across the highway (75 m) and across the highway into the forest 
interior (240 m). The reduction in gene flow at St. Regis appeared to be unrelated to distance, attributable to the presence of the 
highway. Highway effects were especially pronounced at Rainy Lake. We present values for Fg-p and the Bayesian likelihood- 
based assignment test with and without locus A3-5 and locus SH-22, which showed deviations from Hardy -Weinberg 
proportions, as well as a genetic distance -based assignment test (Nei's Da distance statistic) that does not require H-W 
equilibrium. 



116