Genetic variation of pronghorn across US Route 89 and State Route 64

              Genetic Variation of Pronghorn
across US Route 89 and
State Route 64
Final Report 659
March 2012
Arizona Department of Transportation
Research Center
Genetic Variation of Pronghorn
across US Route 89 and
State Route 64
Final Report 659
March 2012
Prepared by:
Tad Theimer, Scott Sprague, Ellyce Eddy, and Russell Benford
Department of Biological Sciences
Northern Arizona University
Box 5640
Flagstaff, AZ 86011
Prepared for:
Arizona Department of Transportation
In cooperation with
U.S. Department of Transportation
Federal Highway Administration
The contents of this report reflect the views of the authors who are responsible for the
facts and the accuracy of the data presented herein. The contents do not necessarily
reflect the official views or policies of the Arizona Department of Transportation or the
Federal Highway Administration. This report does not constitute a standard,
specification, or regulation. Trade or manufacturers’ names that may appear herein are
cited only because they are considered essential to the objectives of the report. The US
government and the State of Arizona do not endorse products or manufacturers.
Cover photos courtesy of Wikipedia Commons.
Technical Report Documentation Page
1. Report No.
FHWA-AZ-12-659
2. Government Accession No.
3. Recipient's Catalog No.
5. Report Date
March 2012
4. Title and Subtitle
GENETIC VARIATION OF PRONGHORN ACROSS US ROUTE 89 AND
STATE ROUTE 64
6. Performing Organization Code
7. Author
Tad Theimer, Scott Sprague, Ellyce Eddy, Russell Benford
8. Performing Organization Report No.
10. Work Unit No.
9. Performing Organization Name and Address
Northern Arizona University
Box 5640, Beaver Street
Flagstaff, AZ 86011 11. Contract or Grant No.
SPR-PL-1(173) 659
13.Type of Report & Period Covered
FINAL (05/2008 – 12/2011)
12. Sponsoring Agency Name and Address
Arizona Department of Transportation
206 S. 17th Avenue
Phoenix, AZ 85007
ADOT Project Manager: Dr. Estomih M Kombe
14. Sponsoring Agency Code
15. Supplementary Notes
Prepared in cooperation with the U.S. Department of Transportation, Federal Highway Administration
16. Abstract
This study investigated whether highways acted as barriers to gene flow for pronghorn in northern Arizona. DNA
samples from 132 pronghorn were analyzed using eight polymorphic microsatellite loci. Samples represented
animals living on opposite sides of US Route 89 (US 89) and State Route 64 (SR 64). Two different modeling
approaches indicated that both US 89 and SR 64, and to a lesser extent US Route 180 (US 180), acted as
barriers to gene flow. The genetic structuring caused by highways, especially across US 89, is consistent with
behavioral data that demonstrated pronghorn rarely cross this highway. This study found no evidence of
inbreeding or reduced genetic variation in any of the populations examined, but those effects may take longer to
appear. Based on these results, the researchers recommend future genetic monitoring of these populations or
assessment of genetic variation across highways with larger traffic volumes or longer histories to determine
whether the barrier effects documented here lead to loss of genetic diversity.
17. Key Words
Pronghorn, Antilocapra americana, gene flow, genetics,
highways, microsatellites
18. Distribution Statement
Document is available to the U.S. public through the
National Technical Information Service, Springfield, Virginia,
22161
19. Security Classification
Unclassified
20. Security Classification
Unclassified
21. No. of Pages
38
22. Price
SI* (MODERN METRIC) CONVERSION FACTORS
APPROXIMATE CONVERSIONS TO SI UNITS
Symbol When You Know Multiply By To Find Symbol
LENGTH
in inches 25.4 millimeters mm
ft feet 0.305 meters m
yd yards 0.914 meters m
mi miles 1.61 kilometers km
AREA
in2 square inches 645.2 square millimeters mm2
ft2 square feet 0.093 square meters m2
yd2 square yard 0.836 square meters m2
ac acres 0.405 hectares ha
mi2 square miles 2.59 square kilometers km2
VOLUME
fl oz fluid ounces 29.57 milliliters mL
gal gallons 3.785 liters L
ft3 cubic feet 0.028 cubic meters m3
yd3 cubic yards 0.765 cubic meters m3
NOTE: volumes greater than 1000 L shall be shown in m3
MASS
oz ounces 28.35 grams g
lb pounds 0.454 kilograms kg
T short tons (2000 lb) 0.907 megagrams (or "metric ton") Mg (or "t")
TEMPERATURE (exact degrees)
oF Fahrenheit 5 (F-32)/9 Celsius oC
or (F-32)/1.8
ILLUMINATION
fc foot-candles 10.76 lux lx
fl foot-Lamberts 3.426 candela/m2 cd/m2
FORCE and PRESSURE or STRESS
lbf poundforce 4.45 newtons N
lbf/in2 poundforce per square inch 6.89 kilopascals kPa
APPROXIMATE CONVERSIONS FROM SI UNITS
Symbol When You Know Multiply By To Find Symbol
LENGTH
mm millimeters 0.039 inches in
m meters 3.28 feet ft
m meters 1.09 yards yd
km kilometers 0.621 miles mi
AREA
mm2 square millimeters 0.0016 square inches in2
m2 square meters 10.764 square feet ft2
m2 square meters 1.195 square yards yd2
ha hectares 2.47 acres ac
km2 square kilometers 0.386 square miles mi2
VOLUME
mL milliliters 0.034 fluid ounces fl oz
L liters 0.264 gallons gal
m3 cubic meters 35.314 cubic feet ft3
m3 cubic meters 1.307 cubic yards yd3
MASS
g grams 0.035 ounces oz
kg kilograms 2.202 pounds lb
Mg (or "t") megagrams (or "metric ton") 1.103 short tons (2000 lb) T
TEMPERATURE (exact degrees)
oC Celsius 1.8C+32 Fahrenheit oF
ILLUMINATION
lx lux 0.0929 foot-candles fc
cd/m2 candela/m2 0.2919 foot-Lamberts fl
FORCE and PRESSURE or STRESS
N newtons 0.225 poundforce lbf
kPa kilopascals 0.145 poundforce per square inch lbf/in2
*SI is the symbol for th International System of Units. Appropriate rounding should be made to e comply with Section 4 of ASTM E380.
(Revised March 2003)
TABLE OF CONTENTS
I. EXECUTIVE SUMMARY .................................................................................................1
II. INTRODUCTION ...............................................................................................................3
Background.......................................................................................................................3
Research Justification ........................................................................................................5
Research Objectives...........................................................................................................5
III. METHODS .........................................................................................................................7
Study Area .........................................................................................................................7
Sample Collection..............................................................................................................8
Genetic Analyses ..............................................................................................................8
IV. RESULTS .........................................................................................................................11
V. DISCUSSION....................................................................................................................15
VI. CONCLUSIONS................................................................................................................17
VII. RECOMMENDATIONS.................................................................................................19
APPENDIX A - DETAILED GENETIC METHODS ...................................................21
APPENDIX B - DETAILED GENETIC RESULTS.......................................................23
REFERENCES ................................................................................................................29
LIST OF TABLES
TABLE 1 Summary of Genetic Results .................................................................................23
TABLE 2 Δk Values Produced by Program Structure for K Values from 1-8.......................25
TABLE 3 Allelic Richness Across All Loci for Each Population Based on Minimum
Sample Size of Nine Individuals............................................................................26
TABLE 4 Results of Tests of Heterozygote Deficiency.........................................................27
LIST OF FIGURES
FIGURE 1 A female (left) and male (right) American pronghorn (Antilocapra
americana) photographed in northern Arizona........................................................3
FIGURE 2 US 89 north of Flagstaff, Arizona, bisects continuous grassland habitat
appropriate for pronghorn (route of highway is shown by the dark line)................7
FIGURE 3 Capture/kill locations of 132 pronghorn in northern
Arizona……………………………………………………………….....................8
FIGURE 4 Probability of population membership of 132 pronghorn based on the
modeling program STRUCTURE…......................................................................11
FIGURE 5 Population assignment of individual pronghorn based on results using the
program STRUCTURE.…….................................................................................12
FIGURE 6 A. Collection locations of 132 pronghorn.
B. Pronghorn populations for an iteration when modeling program
GENELAND yielded four populations as most likely.
C. Pronghorn populations for an iteration that yielded three populations as
the most likely.…………………………….....................................................13
ACKNOWLEDGEMENTS
This project would not have been possible without the assistance and support of
numerous individuals and agencies. We thank Estomih Kombe, Project Manager -
Arizona Department of Transportation (ADOT) Research Center, John Harper and Chuck
Howe of ADOT’s Flagstaff District, Justin White, Siobhan Nordhaugen, Bruce Eilerts,
and Todd Williams of ADOT’s Office of Environmental Services and Doug Eberline of
ADOT’s Multimodal Planning Division. We also acknowledge Steve Thomas and K.
Kelly LaRosa of the Federal Highway Administration (FHWA) for the critical role this
agency played in providing research funding and support for this project and reviewing
the final report. We acknowledge general laboratory support from Catherine Gehring,
Zsusi Kovacs, and the EnGGen facility at Northern Arizona University (NAU) and
Nashelley Meneses and Joe Busch for valuable advice on specific aspects of the genetic
analyses. We also acknowledge the Arizona Game and Fish Department for providing
critical logistical support. Specifically we thank the following personnel for assisting in
pronghorn captures: Rob Nelson, Chad Loberger, Fenner Yarborough, Kirby Bristow,
Michelle Crabb, Thorry Smith (all Research Branch), Larry Phoenix and Carl Lutch
(Region II) and Bill David, Gary Labanow, Steve Dubois, and Steve Sunde (Aviation
Branch) and the following for general logistical support: Chasa O’Brien and Sue Boe
(Research Branch) and Ron Seig, Andi Rogers, and Tom McCall (Region II). Finally we
thank the pronghorn hunters of Arizona who donated samples for this study.
1
I. EXECUTIVE SUMMARY
This study investigated whether highways acted as barriers to gene flow for
pronghorn in northern Arizona. DNA samples from 132 pronghorn were analyzed using
eight polymorphic microsatellite loci. Samples represented animals living on opposite
sides of US Route 89 (US 89) and State Route 64 (SR 64). Two different modeling
approaches indicated that both US 89 and SR 64, and to a much lesser extent US Route
180 (US 180), acted as barriers to gene flow. The genetic structuring caused by
highways, especially across US 89, is consistent with behavioral data collected by
Arizona Game and Fish Department (AZGFD) that demonstrated pronghorn rarely cross
this highway.
This study found no evidence of inbreeding or reduced genetic variation in any of
the populations examined, but those effects may take longer to appear. The data reported
here provide a solid baseline of current genetic diversity and population structure that can
be used in future comparisons. Future evaluation of genetic variation in these
populations or in those separated by highways with greater traffic volumes or longer
histories could clarify whether isolation by highways eventually leads to loss of genetic
diversity. Future studies may suggest that certain types of habitat mitigation would
alleviate the barrier effects documented here, but this study alone does not support that
recommendation.
2
3
II. INTRODUCTION
BACKGROUND
Highways can block animal movements between seasonal ranges or other vital
habitats and limit the movement of individuals between subpopulations (Forman and
Alexander 1998). Population isolation and fragmentation caused by roads could result in
reduced genetic variation that could lead to both short-term genetic effects, such as lower
fertility and higher juvenile mortality, and long-term inability to adapt to stochastic
environmental challenges (Lacy 1997). Isolating effects of roads have been documented
in amphibians (Reh and Seiz 1990), terrestrial insects (Keller and Largiadér 2003;
Vandergast et al. 2007), rodents (Gerlach and Musolf 2000; Metcalfe et al. 2001),
bobcats and coyotes (Riley et al. 2006), grizzly bears (Proctor et al. 2005), and bighorn
sheep (Epps et al. 2005).
One caveat of studies demonstrating the impact of highways on the genetic
structure of animal populations is that other factors may conceal the effect of highways.
Highways often follow physiographic features such as rivers, valleys, and escarpments
that could have historically acted as barriers to animal movement or are concordant with
geologic discontinuities that could have historically shaped genetic structure (e.g. fault
lines, ancient shorelines) (Vandergast et al. 2007). Thus, distinguishing the relative
importance to evolutionary history on population structure from recent anthropogenic
effects like highways can be difficult (Vandergast et al. 2007).
American pronghorn (Antilocapra americana) (Fig. 1) avoid crossing highways,
particularly those with fenced right-of-ways (Ockenfels et al. 1994; Van Riper and
Ockenfells 1998; Ockenfels et al. 2006). They live on extensive grassland habitats many
of which are crossed by highways. Thus, they represent a species in which the effects of
highways on genetic structure are less likely to be concealed by other factors.
FIGURE 1. A Female (Left) and Male (Right) American Pronghorn (Antilocapra
americana). Photographed in Northern Arizona.
4
Although pronghorn are sometimes referred to as “antelope,” they are a uniquely
American species in the family Antilocapridae, evolutionarily distinct from African and
Asian antelope in the family Bovidae (Baccus et al. 1983; Kraus and Miyamoto 1991;
Matthee et al. 2001). With an appearance somewhere between a white-tailed deer
(Odocoileus virginianus) and a bighorn sheep (Ovis canadensis), pronghorn are typically
smaller than either. Pronghorn primarily feed on forbs and supplement their diet with
browse and grass species. Keen eyesight and the capacity to reach and sustain speeds
unequalled by other North American land mammals help these grassland-dwelling
ruminants avoid predation (O’Gara and Yoakum 2004a).
Pronghorn band composition in uninterrupted habitat has a fluidity that is marked
by general seasonal trends. Leading up to a late summer breeding period, mature males
establish territories and attempt to hold together groups of up to a dozen or so mature
females. Bachelor bands are comprised of younger males unable to defend a territory.
Over the fall and winter, pronghorn tend to form larger herds, often moving to wintering
grounds distinct from summer home ranges. As the end of winter and gestation
approach, winter herds break up into smaller bands as they move to summer grounds.
Pregnant females (“does”) disperse to bear their young (“fawns”). After a few weeks,
does and their fawns come together to form nursery bands. Despite these general trends,
a healthy pronghorn population has significant variability in size and individual make-up
of groups on a day-to-day basis (O’Gara and Yoakum 2004b); therefore, designating
specific herds or herd locations requires long-term monitoring of many individuals across
large landscapes.
Behavioral observations suggest that pronghorn are more sensitive to highways’
barrier effects than most other large mammal species. Extensive VHF-telemetry studies
in northern Arizona over several years never documented pronghorn crossing a paved and
fenced highway (Ockenfels et al. 1994; Van Riper and Ockenfels 1998; Ockenfels et al.
2006). More recently, ADOT-funded telemetry studies of pronghorn along US 89 have
demonstrated extremely low rates of highway crossing compared to elk and white-tailed
deer (Dodd and Gagnon pers. comm.).
Two aspects of pronghorn behavior likely contribute to the low passage rates
across highways compared to other native ungulates. First is a reluctance to jump over
intact barbed-wire fencing. Pronghorn cross under fences and are known to run along
fence lines for long distances seeking areas where the lower fence wire is high enough to
crawl under. Fence structure can make suitable crossings very rare to absent. Thus
highways bordered by fencing can act as a double barrier to pronghorn movement
(Ockenfels et al. 2006). Second, unlike deer and elk, which are often active at night and
can therefore cross highways when traffic volumes are typically at their lowest,
pronghorn are active during the day (O’Gara and Yoakum 2004c) and therefore must
attempt highway crossings when traffic volumes are often high.
Recent declines in pronghorn populations have been attributed, in part, to
fragmentation and isolation of pronghorn herds by highways, railways, and canals
5
(O’Gara and Yoakum 1992; Sawyer and Rudd 2005). Several studies, within and outside
of Arizona, have examined pronghorn genetics (Rhodes et al. 1999, 2001; Carling et al.
2003b; Stephen et al. 2005), but none have investigated the impacts of roadways on
genetic structure.
RESEARCH JUSTIFICATION
The final US 89 Antelope Hills – Junction US 160 Environmental Assessment
(ADOT 2005) notes that the primary environmental effect of a proposed reconstruction of
US 89 on pronghorn populations would be an increase in the barrier effect of the widened
highway and increased traffic, which could contribute to a higher degree of population
fragmentation and potential impacts on genetic structure. To date, no studies have
assessed whether highways in Arizona may be retarding gene flow among populations.
This project represents the first attempt to do so.
RESEARCH OBJECTIVES
The objectives of this project were to determine the following:
1) Whether northern Arizona pronghorn populations exhibited evidence of reduced
genetic diversity or increased inbreeding caused by the isolating effects of highways.
2) Whether pronghorn populations in northern Arizona exhibited patterns of genetic
structuring consistent with reduced gene flow across highways.
6
7
III. METHODS
STUDY AREA
This study focused on US 89 north of Flagstaff and SR 64 north of Williams in
northern Arizona. US 89 is the primary highway connecting Flagstaff and Interstate 40
(I-40) with Utah, while also serving the Navajo Nation and popular recreation areas north
of Flagstaff (e.g., Sunset Crater Volcano and Wupatki national monuments, Grand
Canyon National Park, Page, and Lake Powell). US 89 was built in 1932 and is primarily
two lanes, with traffic volume currently averaging 7500 vehicles per day with a modal
speed limit of 65 mph along the areas where samples were collected. SR 64 is the
entrance road to the South Rim of Grand Canyon National Park, connecting Williams to
Grand Canyon Village. Two lanes wide, it also was built in 1932 and averages 4700
vehicles per day with a modal speed limit of 65 mph in the areas sampled. Additional
samples were collected in the area between I-40 and US 180 northwest of Flagstaff. US
180 is the major connection between Flagstaff and SR 64, linking Flagstaff with Grand
Canyon Village and the South Rim of the Grand Canyon. Built in 1960, US 180 is two
lanes and averages 1900 vehicles per day with a modal speed of 65 mph. In each case,
these highways pass through continuous pronghorn habitat, dividing that habitat
independent of other physiographic features that likely influence pronghorn movements
(Fig. 2).
FIGURE 2. US 89 North of Flagstaff, Arizona, Bisects Continuous Grassland Habitat
Appropriate for Pronghorn. (Route of Highway Is Shown by the Dark Line).
8
SAMPLE COLLECTION
Muscle tissue from hunter-killed pronghorn and ear-punches from animals
captured as part of radio-telemetry studies carried out by the Arizona Game and Fish
Department (AZGFD) were the sources of tissue samples. Sample collection was
concentrated east and west of US 89 north of Flagstaff and east and west of SR 64 (Fig.
3). Thirteen samples were from animals in the area bounded by I-40 and US 180 and SR
64. Samples were assigned to one of eight arbitrarily designated a priori “populations”
(A-W on Fig. 3).
FIGURE 3. Capture/Kill Locations for the 132 Pronghorn from Northern Arizona Used
in This Study. Letters Indicate the Eight “Populations” to Which Samples
Were Assigned. Each Circle Represents a Pronghorn and Circles of the
Same Color Were Assigned to the Same Population.
GENETIC ANALYSES
DNA was extracted from ear and muscle samples using standard techniques
(Appendix A). Eight microsatellite DNA markers previously developed for pronghorn
(Carling et al. 2003a, Stephen et al. 2005) were used to type all pronghorn samples (listed
in Table 1 in Appendix B).
If highways reduced gene flow among populations, the geographic pattern of
genetic variation would reflect this. The samples were tested two ways to see if highways
affected the population's genetic structure. First, the Bayesian clustering algorithm in
program STRUCTURE (Pritchard et al. 2000) was used to assign individuals to gene-based
populations. STRUCTURE calculates the probability of each individual's
9
population membership based on any number (K) of hypothesized populations. The
study team tested values from K = 1 (all samples were part of a single population) to K =
8 (samples represented eight different populations). Results for each K value can be
compared by computing a ΔK statistic and the K giving the highest ΔK value is
considered the most likely number of distinct genetic populations (Evanno et al. 2005).
The ΔK for this test showed the highest probability that the pronghorn came from three
distinct gene pools. Outputs of these analyses are in the form of bar graphs, in which each
vertical bar represents an individual pronghorn and different shades in each panel
represent the K different populations. The relative proportion of each individual’s bar that
is a given shade represents the probability of that individual being a member of that
population, as depicted in Results - Figure 4.
Second, the program GENELAND was used to produce maps of genetically
distinct populations across geographic space (Francois et al. 2006; Guillot et al. 2008).
This program uses a different modeling approach (colored tessellation) to determine the
geographic distribution of genetic population clusters. This method differs from that
used in STRUCTURE because it uses the spatially explicit Universal Transverse
Mercator (UTM) coordinates of each sample rather than any a priori population
assignments. As in the program STRUCTURE, GENELAND uses repeated iterations to
generate probabilities for each hypothesized number of populations (K). The output of
this program is a map that shows the number of genetically distinct populations and their
geographic extent. Most relevant to this study was whether boundaries between the
estimated populations coincided with highways, as would be expected if highways were
barriers to gene flow.
Reduced gene flow among populations can result in loss of genetic variation, both
within populations (genetic or allelic diversity) and within individuals (heterozygosity).
Evidence of reduced genetic diversity caused by isolation was examined by comparing
genetic (allelic) diversity across the eight sample populations. If genetic diversity had
been reduced in one or more of these populations, then this would be reflected in lower
allelic diversity across all eight microsatellites in that population.
When populations are small and isolated, inbreeding will lead to reduced within-individual
genetic variation (increased homozygosity). Evidence of this was tested by
comparing levels of within-individual genetic variation (heterozygosity) to that expected
if all individuals were mating randomly, without barriers to gene flow. Levels of within-individual
genetic variation (heterozygosity) were compared to those expected if all
individuals were randomly mating without barriers to gene flow to see if there were
evidence of high homozygosity.
10
11
IV. RESULTS
Program STRUCTURE found greatest support for the hypothesis that the
pronghorn samples in this study were drawn from three genetically distinct clusters
(Fig.3; Table 2, Appendix B). The two populations on the eastern side of US 89 (A and
V) were consistently and strongly grouped in the same cluster (Fig. 4). Though
individuals from other populations were sometimes also assigned to this cluster, it was
never with the consistently high probability that these two populations showed.
Individuals from population B were consistently assigned to a cluster distinct from those
east of US 89 or west of SR 64. Other individuals from populations between US 89 and
SR 64 (C and E) were not strongly associated with any one of the three clusters, while
populations west of SR 64 were associated with the same cluster roughly 60-70% of the
time (Fig. 5). These results were consistent with the hypothesis that both US 89 and SR
64 acted as barriers to gene flow but that this effect was greater for US 89.
FIGURE 4. Probability of Population Membership of 132 Pronghorn Based on the
Modeling Program STRUCTURE
Note: The blue, green, and red columns represent the likelihood of membership to
each of the three clusters, represented by a probability value that is greater than 0
and less than 1 for each animal.
US 89 SR 64
US 180
Probability of Membership
12
FIGURE 5. Population Assignment of Individual Pronghorn Based on Results Using the
Program STRUCTURE.
Notes: Circles with the same colors indicate that those animals were consistently grouped
together based on genetic similarity. Note that one genetically distinct group (blue) lies on
the eastern side of US 89 while another lies on the western side (green).
When geographic patterns of genetic variation were estimated based on individual
UTM locations for each sample using the program GENELAND, iterations yielded most
support for either three or four populations (Appendix C). All iterations had a consistent
population boundary between individuals occupying opposite sides of US 89 (Fig. 6 B
and C). A population boundary was also roughly concordant with SR 64, though the
placement of this boundary varied more than that of US 89. The pattern of genetic
structuring in some iterations was strikingly concordant with all three highways (Fig 6
B).
The analyses found no evidence of reduced genetic variation or inbreeding in any
of the eight populations examined. Allelic richness averaged across the eight
microsatellite markers in each population ranged from 3.2 to 3.9, reflecting similar
genetic diversity across populations (Table 3, Appendix B). Likewise, no significant
reduction in within-individual variation (heterozygosity) was detected in any of the eight
populations (Table 4, Appendix B).
13
FIGURE 6. A. Collection Locations of 132 Pronghorn.
B. Pronghorn Populations for an Iteration When Modeling Program
GENELAND Yielded Four Populations as Most Likely.
C. Pronghorn Populations for an Iteration that Yielded Three Populations
as the Most Likely.
14
15
V. DISCUSSION
Two of the major concerns about genetically isolated populations is that they will
lose genetic variation from the population overall (reduced genetic diversity) and that
they will lose genetic diversity within individuals (reduced heterozygosity). Reduced
genetic variation leaves populations less able to respond evolutionarily to changes in their
environment, thereby increasing the chance of population extinction. Reduced
heterozygosity (or its reciprocal, increased homozygosity) due to inbreeding allows
expression of deleterious recessive forms of genes, leading to reduced fertility and other
genetic anomalies. None of the eight populations examined exhibited evidence of
reduced genetic variation or loss of heterozygosity. Therefore, pronghorn in the study
area do not appear to be threatened by these genetic consequences of reduced gene flow
at this time. However, reduced genetic variation and deleterious effects of inbreeding
may take considerable time to be expressed, for which no timeline is available. The
relatively young age of highways may mean that isolation effects simply have not yet
developed.
The geographic pattern of genetic structuring concordant with highways
documented here, especially along US 89, indicates that highways are acting as barriers
to gene flow. The alternative hypothesis, that location of highways is correlated with
some other physiographic feature that limits movement, seems highly unlikely given the
uniformity of the habitat between sampling locations. The genetic structuring of
populations on either side of US 89 is consistent with recent behavioral data indicating
that pronghorn rarely cross this highway.
The patterns of population structuring concordant with highways were strongest
across US 89, were weaker for SR 64 and weakest for US 180. The ages of US 89 and
SR 64 are similar (roughly 75 years) but traffic volume on US 89 is roughly 1.5 times
higher. Recent improvements to US 89 have also widened the roadway along many
stretches. US 180 is younger and has lower traffic volume than either US 89 or SR 64.
Collaborative efforts between ADOT and AZGFD in the 1990s also shifted highway
fencing farther from US 180 to improve the ability of pronghorn to cross the highway.
Both roads and fences can act as barriers to pronghorn movement, but close proximity of
the two can make crossing even less likely. Taken together, these patterns suggest an
increasing barrier effect of highways with increasing age and traffic volume. If true, the
effects of highways on pronghorn population subdivision would increase as traffic
volumes increase and highways are upgraded.
It is difficult to predict the genetic consequences of the genetic structuring
detected in this study. Gene flow, though reduced by highways, may still be high enough
to prevent further population differentiation and offset deleterious effects of reduced
genetic variation and inbreeding. Populations on the east side of US 89 may exchange
genes with populations farther east while populations on the west side of SR 64 may do
the same with populations farther west. The populations of greatest concern would
therefore be those between the two highways. Allelic richness in these populations was
as high as that in any of the other populations, as were levels of heterozygosity. Thus, the
16
data do not show loss of genetic diversity in these populations due to reduced gene flow.
Whether this will remain the case over longer periods, especially in the face of increasing
traffic volumes and highway modifications, is unknown.
Deleterious effects of reduced gene flow and increased isolation are only a few of
many biologically important impacts that highway-caused reduced movement of
pronghorn could have. Most wild populations are not continuous across the landscape,
but rather are comprised of a set of smaller subpopulations connected by animal
movement, often termed “metapopulations” (Hanski 1998). Maintaining connectivity
among these subunits is important not only for maintaining genetic diversity and avoiding
deleterious effects of inbreeding, but also for maintaining subpopulations through time
and “rescuing” those that undergo local extinction due to catastrophic events such as
drought or heavy snowfall. Minimizing the barrier effect of highways through
construction of effective wildlife crossing structures would be prudent to guard against
potential effect on pronghorn genetics and also shorter-term demographic challenges
(Jackson and Griffen 2000).
17
VI. CONCLUSIONS
This project was initiated to investigate effects of SR 64 and US 89 on gene flow
among pronghorn populations on either side of these highways. The key conclusions
from this research project are:
 Two independent modeling approaches revealed geographic patterns of genetic
variation consistent with US 89, SR 64, and US 180 acting as barriers to gene
flow. The barrier effect was strongest for US 89, weaker for SR 64, and weakest
for US 180. This pattern could suggest that barrier effects of roadways increase
mostly with traffic volume but also to a lesser extent with highway age and width.
This would be consistent with behavioral data on highways and pronghorn
movement.
 Populations examined did not differ in genetic diversity nor show excess
homozygosity that would indicate inbreeding caused by population isolation.
However, these effects may take longer to manifest than the length of time
highways have been present.
 Consequences of highway barrier effects are difficult to predict. Over time,
reduced gene flow could lead to deleterious genetic effects, especially if increased
traffic or highway upgrades increases the barrier effect. Alternatively, the
reduction in gene flow caused by highways may not be great enough to cause
significant losses of genetic diversity. The data reported here provide a baseline
of current genetic diversity and population structure that can be used in future
comparisons to determine which of these outcomes occurs.
18
19
VII. RECOMMENDATIONS
This project investigated effects of SR 64 and US 89 on gene flow among
pronghorn populations on either side of these highways. The key recommendation from
this research project is to undertake future genetic analyses of pronghorn populations,
either in this study area or across highways with a longer history or higher traffic
volumes, for an assessment of whether the barrier effects documented here lead to
reduced genetic diversity.
20
21
APPENDIX A
DETAILED GENETIC METHODS
Genomic DNA was extracted from approximately 5 mg of ear or muscle tissue
using DNEasy Tissue KitsTM (Qiagen). Eight microsatellite loci developed or modified
for pronghorn (Carling et al. 2003a, Stephen et al. 2005) were used to type all pronghorn
samples (loci are listed in Appendix B). Microsatellites were amplified using polymerase
chain reactions (PCR) with samples heated to 94°C for 5 minutes followed by 35 cycles
of 94°C for 20 seconds, 60°C for 30 seconds, and 72°C for 5 minutes. The process
utilized a fluorescent dye-labeled forward primer and unlabeled reverse primers. The
resulting PCR products were sized on Applied Biosystems ABI 3100 genetic sequencer.
Electropherograms were analyzed and manually scored using Genescan® (version 3.7,
Applied Biosystems 2001) and Genotyper® (version 3.7, Applied Biosystems 2000).
STRUCTURE analyses were run using 30,000 iterations for burn-in followed by
100,000 repetitions (Pritchard et al 2000). The “Model with prior population
information” was used with individual samples grouped by population name, as
recommended when inferring weak population structure (Hubisz et al. 2009). K values
ranging from 1-8 were tested with four iterations at each value of K to confirm that log-likelihood
values had converged. The most likely number of population clusters (K) was
assumed to be that which resulted in the highest mean log-likelihood value across the
four iterations (Pritchard et al. 2000).
GENELAND estimates of population structure were based on 100,000 Markov
Chain Monte Carlo iterations, with thinning set to 100, and K ranging from 1-8 (Francois
et al. 2006; Guillot et al. 2008). Allele frequencies were assumed to be correlated, as this
is a more likely scenario for populations arising from the same ancestral panmictic
population (Balding 2003).
Allelic diversity, observed and expected heterozygosity, deviation from Hardy–
Weinberg expectations, and fixation indices were calculated for each sample population
using the program GenAlEx (Peakall and Smouse 2006). Allelic richness with correction
for variable sample size was calculated for each locus in each population using the
program FSTAT (Goudet 2001). Tests for heterozygote deficiency in each population
were carried out using the program GENEPOP (Raymond and Rousset 1995).
22
23
APPENDIX B
DETAILED GENETIC RESULTS
TABLE 1. Summary of Genetic Results.
Pop Locus N Na Ho He HW
F
A Aam1 21 2.000 0.571 0.499 ns -0.145
Aam2 21 5.000 0.857 0.719 ns -0.192
Aam3 21 6.000 0.524 0.529 ns 0.011
PrM65 21 3.000 0.429 0.398 ns -0.077
Aam5 21 4.000 0.524 0.502 *** -0.043
Aam6 21 2.000 0.571 0.499 ns -0.145
Aam7 21 5.000 0.524 0.593 ns 0.117
Aam8 21 4.000 0.619 0.700 ns 0.115
B Aam1 27 3.000 0.667 0.504 ns -0.322
Aam2 27 7.000 0.815 0.757 ns -0.077
Aam3 27 10.000 0.667 0.750 ** 0.111
PrM65 27 4.000 0.593 0.571 * -0.037
Aam5 27 2.000 0.370 0.417 ns 0.112
Aam6 27 2.000 0.407 0.475 ns 0.143
Aam7 27 6.000 0.778 0.706 ns -0.102
Aam8 27 5.000 0.741 0.666 ns -0.112
C Aam1 20 4.000 0.700 0.578 ns -0.212
Aam2 20 7.000 0.800 0.789 ns -0.014
Aam3 20 7.000 0.600 0.656 ns 0.086
PrM65 20 4.000 0.300 0.303 ns 0.008
Aam5 20 4.000 0.350 0.336 *** -0.041
Aam6 20 2.000 0.600 0.495 ns -0.212
Aam7 20 6.000 0.300 0.458 *** 0.344
Aam8 20 4.000 0.500 0.636 ns 0.214
D Aam1 18 4.000 0.611 0.611 ** 0.000
Aam2 18 7.000 0.833 0.792 ns -0.053
Aam3 18 7.000 0.667 0.694 *** 0.040
PrM65 18 5.000 0.500 0.481 ns -0.038
Aam5 18 3.000 0.333 0.356 ns 0.065
Aam6 18 2.000 0.444 0.500 ns 0.111
Aam7 18 5.000 0.500 0.634 ns 0.212
Aam8 18 3.000 0.611 0.508 ns -0.204
E Aam1 13 2.000 0.308 0.260 ns -0.182
Aam2 13 8.000 0.846 0.754 ns -0.122
Aam3 13 5.000 0.538 0.491 ns -0.096
PrM65 13 4.000 0.692 0.530 ns -0.307
Aam5 13 2.000 0.462 0.355 ns -0.300
Aam6 13 2.000 0.462 0.497 ns 0.071
Aam7 13 4.000 0.308 0.435 ns 0.293
Aam8 13 4.000 0.846 0.663 ns -0.277
24
Table I
(cont)
Pop
Locus
N
Na
Ho
He
HW
F
F Aam1 13 4.000 0.462 0.530 ns 0.128
Aam2 13 6.000 0.692 0.796 ns 0.130
Aam3 13 5.000 0.385 0.444 ns 0.133
PrM65 13 4.000 0.231 0.388 ns 0.405
Aam5 13 2.000 0.385 0.311 ns -0.238
Aam6 13 2.000 0.538 0.500 ns -0.077
Aam7 13 5.000 0.538 0.494 ns -0.090
Aam8 13 4.000 0.231 0.275 *** 0.161
V Aam1 11 2.000 0.545 0.496 ns -0.100
Aam2 11 5.000 0.545 0.504 ns -0.082
Aam3 11 4.000 0.455 0.442 ns -0.028
PrM65 11 3.000 0.636 0.525 ns -0.213
Aam5 11 2.000 0.273 0.236 ns -0.158
Aam6 11 2.000 0.364 0.496 ns 0.267
Aam7 11 5.000 0.636 0.616 ns -0.034
Aam8 11 4.000 0.636 0.678 ns 0.061
W Aam1 9 3.000 0.222 0.204 ns -0.091
Aam2 9 5.000 0.667 0.765 ns 0.129
Aam3 9 4.000 0.667 0.512 ns -0.301
PrM65 9 5.000 0.667 0.580 * -0.149
Aam5 9 2.000 0.333 0.278 ns -0.200
Aam6 9 2.000 0.333 0.278 ns -0.200
Aam7 9 5.000 0.778 0.679 ns -0.145
Aam8 9 4.000 0.556 0.562 ns 0.011
Pop = population designation as in Fig. 1, Locus = each of the eight microsatellite loci,
N = sample size, Na = number of alleles, Ho = observed heterozygosity, He = expected
heterozygosity, HW = probability of G-test for deviation from Hardy-Weinberg
expectations, F = fixation index. Ns = not significantly different from expectations, *
different at p<0.05, ** p<0.01, ***p<0.001.
25
TABLE 2. ΔK Values Produced by Program Structure for K Values from 1-8.
K
Run1
L(K) L'(K) L"(K)
Run2
L(K)
Run3
L(K)
Run4
L(K)
Run5
L(K) ΔK
1 -2406.4 -2406.4 2445.3 -2406.6 -2406.5 -2406.6 -2406.4 24389
2 -2367.5 38.9 31.9 -2378.5 -2368.4 -2377.4 -2378.7 3.596
3 -2360.5 7 24.1 -2360.1 -2363.1 -2366.1 -2360.2 12.881
4 -2377.6 -17.1 29.4 -2385.4 -2375.6 -2369.6 -2409 1.220
5 -2424.1 -46.5 71.5 -2419.8 -2388 -2387.9 -2417.5 1.580
6 -2399.1 25 51.1 -2428 -2415.2 -2415.1 -2405.5 3.060
7 -2425.2 -26.1 2.6 -2378 -2420.1 -2428.3 -2416 1.377
8 -2448.7 -23.5 23.5 -2387.6 -2411.6 -2406.5 -2396.6 0.707
Results from 20 iterations of STRUCTURE (5 each at K = 1 – 8) and calculation of ΔK for
estimation of the true number of population clusters. The modal ΔK is in bold, indicating the true
value of K is 3. K = the number of inferred population clusters, L(K) = Ln P(D)’ = the maximum
posterior probability of the data returned for each run in STRUCTURE, L’(K) and L”(K) =
intermediate stages in the calculation of ΔK as described by Evanno et al. (2005)
26
TABLE 3. Allelic Richness Across All Loci for Each Population Based on Minimum
Sample Size of Nine Individuals.
POPULATION
LOCUS A B C D E F V W
AAM1 2.000 3.000 2.333 3.294 3.746 1.995 3.888 2.000
AAM2 4.862 5.000 5.599 5.828 5.902 6.882 5.866 4.608
AAM3 4.742 4.000 6.722 5.112 4.990 4.342 4.277 3.790
PRM650 2.427 5.000 3.767 2.860 3.876 3.888 3.585 2.997
AAM5 3.108 2.000 2.000 2.881 2.496 2.000 1.999 1.997
AAM6 2.000 2.000 2.000 2.000 2.000 2.000 2.000 2.000
AAM7 4.012 5.000 4.993 4.546 4.611 3.601 4.498 4.610
AAM8 3.946 4.000 4.225 3.764 2.886 3.691 3.298 3.818
27
TABLE 4. Results of Tests of Heterozygote Deficiency.
POPULATION P-VALUE
A 0.8223
B 0.3648
C 0.9954
D 0.8963
E 0.1388
F 0.9619
V 0.4529
W 0.3648
Markov chain parameters for all tests: Dememorization:10000; Batches:20; Iterations per
batch:5000
28
29
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