Temporal effects and genetic diversity : an example from the southern plains woodrat (Neotoma micropus)

2016·2.3 MB·English

by  Méndez-Harclerode, Francisca M., author

TEXAS TECH UNIVERSITY
Natural Science Research Laboratory
Occasional Papers
Museum of Texas Tech University Number 340 27 September 2016
Temporal Effects and Genetic Diversity: An Example from the Southern
Plains Woodrat (Neotoma micropus)
M. E. F. L.
Francisca Mendez-Harclerode, Richard Strauss, Charles Fulhorst, Mary Milazzo, Donald
III, D. D.
C. Ruthven Christopher Dunn, and Robert Bradley
Abstract
Temporal effects on genetic diversity were investigated within a large, panmictic population
of Southern Plains Woodrats {Neotoma micropus). Microsatellite genotypes obtained from 549
individuals, collected during a 4-year period, were assigned to groups according to capture site
and year of capture. Most (99.84%) of the genetic variation was attributable to among-individual
comparisons. No significant differences were observed among years relative to allelic richness,
observed heterozygosity, gene diversity, FIS, FST, and relatedness values. However, pairwise tests
of genetic differentiation indicated significant differences in allele frequencies among collecting
sites during the 4-year period, suggesting that even though levels of genetic diversity between
collecting sites were fairly homogeneous for a given year, allele frequencies within each site
evolved independently with respect to time. Low heterozygosity values associated with the
number of alleles in each group were indicative of a pattern consistent with a recent (within the
last 3,000 years) population expansion.
Key words: genetic diversity, microsatellites, Neotoma micropus, population genetics,
Southern Plains Woodrat, temporal effects
Introduction
Estimates of parameters of genetic diversity and unless examined under a temporal sampling scheme
structure in vertebrate populations can be expected (Wang et al. 2002). Moreover, temporal factors can
to change over time due to a number of biological influence estimates of population diversity, underesti¬
events including natural fluctuations in population size, mating the actual long-term genetic structure present
demographic stochasticity, genetic drift (Tessier and in populations (Wang et al. 2002). For example, a
Bernatchez 1999; Planes and Lenfant 2002), selection population may appear to be genetically homoge¬
(Planes and Lenfant 2002), and non-random sampling neous when samples from multiple cohorts (different
of populations (Allendorf and Phelps 1981; Tessier and sampling periods of the same population) are pooled.
Bernatchez 1999). These events can remain undetected However, if samples are divided according to cohort,
2 Occasional Papers, Museum of Texas Tech University
significant amounts of structure may be detected in studies included samples collected from a period that
the data. Therefore, it is necessary to examine the exceeded 10 years, they did not find high levels of
potential impact of temporal effects to obtain accurate temporal variation. Conversely, Wang et al. (2002)
assessments of genetic diversity in natural populations. detected considerable genetic variation among years in
a large unrestricted population of Roe Deer in Europe.
Although there have been several studies per¬ However, their study included samples spanning three
taining to temporal variation and genetic diversity of decades that could have resulted in more accurate es¬
vertebrates, especially fish (Lacson and Morizot 1991; timates of temporal variation.
Jordan et al. 1992; Jorde and Ryman 1996; Laikre et al.
1998; Tessier and Bernatchez 1999; Garant et al. 2000; Recently, Mendez-Harclerode et al. (2007) ex¬
Planes and Lenfant 2002), with temporal patterns rang¬ amined genetic diversity in a large, well-established
ing from no significant change to considerable change population of relatively long-lived rodents (maximum
over time, few have been reported for mammalian recorded life-span in the wild is about three years), the
populations. This is due to the difficulty in obtaining Southern Plains Woodrat (Neotoma micropus). This
sufficiently large sample sizes from several consecutive study provided an opportunity to investigate temporal
generations (and consequent problems with low statis¬ effects in a single population of mammals, which
tical power) and caveats associated with overlapping possessed high levels of genetic variation and lacked
generations. Similar to the studies on fish (above), significant amounts of genetic structure (Mendez-
the few examples pertaining to mammals (Rossiter Harclerode et al. 2007). Additionally, results of genetic
et al. 2000; Queney et al. 2000; Wisely et al. 2002; diversity analyses (low nucleotide and high haplotype
Wang et al. 2002; Hoffman et al. 2009; Phillips et al. diversity) were consistent with a recent population
2011; Forcada and Hoffman 2014) reported varying expansion (Mendez-Harclerode et al. 2005, 2007).
levels of temporal variation. For example, Rossiter
et al. (2000) found no evidence of temporal variation The purpose of this study was to reexamine the
among populations of bats from Europe during a 5-year population described by Mendez-Harclerode et al.
study period. Queney et al. (2000) and Wisely et al. (2005, 2007) under a temporal framework and further
(2002) reported low levels of genetic variation within test for patterns of population expansion or contrac¬
rabbits and Black-footed Ferrets, respectively, even tion. This was achieved by partitioning genotypes
though the populations they studied were known to temporally and spatially, by season, year, and capture
have undergone severe bottlenecks. Although these site, respectively.
Materials and Methods
All specimens were collected as part of studies Appendix) and served as the DNA source as described
on the ecology of the Catarina virus (Arenaviridae) in Mendez-Harclerode et al. (2007). Five microsatellite
in southern Texas (Cajimat et al. 2007; Milazzo et al. loci (NmaOl, Nma04, Nma06, NmalO, and Nmall)
2013). Ruthven and Synatzske (2002), Fulhorst et al. were amplified using the methods described by Castle¬
(2002), and Suchecki et al. (2004) provided detailed berry et al. (2000) as reported in Mendez-Harclerode
descriptions of the 36-km2 study area (Chaparral et al. (2007). Microsatellite genotypes were obtained
Wildlife Management Area - CWMA), located 85 km from 549 individuals collected during 16 trapping pe¬
north of Laredo, Texas. Three mark-recapture webs riods (once per season over four years) and are listed
(Anderson et al. 1983) were used to collect samples by Mendez-Harclerode (2005). Individuals were geno-
following methods outlined by Mendez-Harclerode et typed upon initial capture; however, if an individual
al. (2005, 2007) and Milazzo et al. (2013). Toe and was captured during more than one trapping period, its
ear punches obtained from individuals were deposited corresponding genotype was included in the appropriate
in the Genetic Resources Collection of the Natural time-frame to represent their presence over the span of
Science Research Laboratory, Museum of Texas Tech the study (Table 1, Fig. 1). Specimen catalog numbers
University (Mendez-Harclerode et al. 2005, 2007 and are available in Mendez-Harclerode et al. (2007).
Mendez-Harclerode et al.—Genetic Diversity in the Southern Plains Woodrat 3
Table 1. Sample size (number of individuals) from each capture site (web) ac¬
cording to trapping period. Sample size is adjusted to include recaptures as well
as novel individuals; therefore, the total number of individuals included is greater
than 549, as many individuals were recaptured across several trapping periods.
Capture Site
Trapping Period Web I Web II Web III Total
January 2001 3 0 5 8
March 2001 11 10 11 32
June 2001 29 15 32 76
October 2001 20 19 27 66
January 2002 4 21 8 33
March 2002 11 11 4 26
June 2002 4 4 12 20
October 2002 11 22 18 51
January 2003 11 16 7 34
March 2003 8 8 11 27
June 2003 22 39 14 75
October 2003 27 36 18 81
January 2004 21 17 8 46
March 2004 16 9 13 38
June 2004 18 21 20 59
October 2004 10 16 10 36
Total 226 264 218 708
H t—H t—H n Cl Cl Cl m m m m ■'d- 'd- 'd-
o o o o o o o o o o o o o o o o
o o o o o o o o o o o o o o o o
CM ci CM Cl Cl Cl CM Cl Cl CM Cl Cl CM Cl Cl Cl
dcd sc3 dd o dcd scd dd o dcd scd dd o dcd scd dd o
>—> 1—s O •—5 •—s O •—5 1—) O •—5 O
Trapping session
Figure 1. Histogram depicting cumulative woodrat captures (new and
recaptured) by date. Dark portions of each bar reflect new captures
and light portions depict recaptures.
4 Occasional Papers, Museum of Texas Tech University
Although the major reproductive activity of N. all other webs per year. This resulted in a 12x12 matrix,
micropus in south Texas occurs primarily in the spring with each sample representing a web-year combination,
and early summer, a lesser amount occurs throughout and p-values were estimated after 1,320 randomized
the year (Conditt and Ribble 1997; Merkelz and Kerr permutations of the original dataset. Second, all webs
2002; Suchecki et al. 2004). In order to delineate a were grouped according to year (disregarding web of
set of biologically meaningful periods to utilize in the provenance), resulting in a 4x4 matrix, with ^-values
temporal analysis (given the problem presented by estimated after 120 permutations. Significance of the
the continuous reproductive activity), F-statistics (FIT, results of each of these tests was adjusted to compensate
FIS, and FST; Weir and Cockerham 1984) and related¬ for multiple comparisons following sequential Bonfer-
ness parameters (Queller and Goodnight 1989) were roni corrections (Holm 1979; Rice 1989). Tests for
estimated for each of the 16 trapping sessions using Hardy-Weinberg equilibrium were performed using
the software program FSTAT (Goudet 2001). These the software program FSTAT (Goudet 2001) as fol¬
estimates were then graphed with their corresponding lows: individual samples were partitioned according
confidence intervals (Fig. 2). No obvious temporal to web and year, and two estimates of Hardy-Weinberg
trend that would aid in the separation of cohorts was equilibrium were produced (per locus and among loci).
detected in the variation of the parameters across trap¬ Estimates of p- values for this test were obtained after
ping periods; therefore, genotypes were examined by 1,200 random permutations and significance levels
web and year in order to investigate temporal effects. were adjusted after sequential Bonferroni corrections.
Years were used, not seasons, because small sample
sizes per trapping web per season could result in a lack The possibility of a recent reduction or expansion
of statistical power. in the effective population size was investigated under
the 2-phased (TPM - Valdes et al. 1993; Di Rienzo et
Genetic variability through time (years) and al. 1994), step-wise mutation (SMM - Kimura and Ohta
space (trapping webs) was examined by means of an 1978), and infinite-alleles (IAM - Kimura and Crow
Analysis of Molecular Variance (AMOVA; Excoffier 1964) mutation models using the software program
et al. 1992) using the software program Arlequin 2.0 Bottleneck (Comuet and Luikart 1996; Piry et al. 1999).
(Schneider et ah 2000). To investigate the possibility This analysis is based on the observation that during
of temporal effects, null hypotheses of no significant initial phases of a bottleneck the number of alleles is re¬
differences among years were tested with respect to duced faster than corresponding heterozygosity values
allelic richness, observed heterozygosity, gene diversity (Cornuet and Luikart 1996; Piry et al. 1999). Because
(Nei 1987), relatedness (Queller and Goodnight 1989), heterozygosity is largely dependent on the frequency
and FIS and FST (Weir and Cockerham 1984) using the of common alleles, and rare alleles are probabilisti¬
software program FSTAT (Goudet 2001), and 2-sided cally the first to disappear at the onset of a bottleneck,
^-values were estimated after 10,000 random permuta¬ heterozygosity is expected to decrease at a slower rate
tions generated from the original dataset. In an effort than the number of alleles. A Wilcoxon signed-rank
to separate age cohorts (juvenile, sub-adult, and adult), test performed with the software program Bottleneck
this test was repeated as described above with only (Cornuet and Luikart 1996) was used to determine
adult individuals—identified as such by pelage color whether a significant number of loci per web exhibited
(evidence of completion of molting) and size (nose-to- an excess of observed gene diversity (heterozygosity)
rump length greater than 250 mm). per year. Results of the bottleneck analysis also were
used to determine which of the three models of evolu¬
An additional test of heterogeneity in allele fre¬ tion (IAM, TPM, or SMM) best fit the data. Because
quencies based on the G-statistic (Goudet et al. 1996) of the null hypothesis (no relationship between the data
was performed by utilizing the pairwise test of differen¬ and the model), a low p-\alue indicates the fit of the
tiation in the software program FSTAT (Goudet 2001) model. Therefore, a count of the significant ^-values
to examine differences among webs and among years. for each of the mutation models produced by this test
Unlike the previous tests, this analysis was performed was used as a non-linear indicator of the lack of fit of
under two schemes. First, each web was compared to the models (Stevens et al. 2006).
Mendez-Harclerode et al.—Genetic Diversity in the Southern Plains Woodrat 5
0.16
D 0.12
0.08 1—

> Li
0.04 t
—is
<>—$—i
0.00
-0.04
-0.08
Figure 2. Estimates of relatedness (A), FIT (B), FIS(C), and FST (D), along with their
respective 95% confidence intervals, and number of sampled individuals (E) for each of
the trapping periods during the 4-year study.
6 Occasional Papers, Museum of Texas Tech University
A contiguous clustering method (Chesser and this method assumes trapping homogeneity and can
Van Den Bussche 1988) was employed to test for analyze only one locus at a time, each locus from each
the presence of statistically significant clusters of trapping web was analyzed separately with genotypes
genotypes relevant to geographic proximity. Because grouped by year.
Results
Genetic variation was partitioned as follows: years (Table 3). The second test, which combined data
among years (0.00%), among webs within years from the three webs and compared allelic frequencies
(0.03%), among individuals within webs (0.13%), and among years, revealed significant differences among
among individuals (99.84%). Ranges for population all sample pairs with two exceptions: 2002 was not
genetic parameters estimated from the whole sample significantly different from 2003, and 2003 was not
from each year were: allelic richness (12.647-13.631), significantly different from 2004 (Table 4). Only
observed heterozygosity (0.787-0.806), gene diversity one deviation from Hardy-Weinberg equilibrium was
(0.821-0.845), FIS (0.019-0.056), FST (0.016-0.018), observed after corrections, suggesting the possible
and relatedness (0.031-0.033). None of the variation presence of heterozygote deficiency at locus NmalO
observed in any of these parameters was significant in web III-2001 0 = 0 .001).
(Table 2). Ranges for these same parameters estimated
from only the adults were similar to those obtained from The Bottleneck analysis produced 18,17, and 24
the entire sample (Table 2); however, the variation in statistically significant results under the three mutation
observed heterozygosity and FIS was significant {p = models (IAM, TPM, and SMM, respectively). There¬
0.049 andp = 0.009, respectively). Additionally, tests fore, following the rationale employed by Stevens et
of genetic heterogeneity (G-statistic) produced signifi¬ al. (2006), the SMM was considered to be the most
cant values under two schemes. The first examined appropriate model for this data. This was assumed
differences in allele frequencies among webs and years because analyses under this model yielded the highest
and revealed significant differences (p = 0.001) among number of instances (24) in which the null hypothesis
all sample pairs with three exceptions: webs I and III of no relationship between the data and the model was
were not significantly different in three of the four rejected. Under the SMM, the Wilcoxon test identified
Table 2. Allelic richness (AR), observed heterozygosity (H0), gene diversity (Hs), FIS, FST, relatedness (R) and their
respectivep-values by year and by year including only adults. Sample sizes (SS) for each period are provided.
Year SS AR F R
Ho Hs F,s ST
2001 182 13.631 0.800 0.845 0.053 0.017 0.032
2002 130 12.998 0.789 0.835 0.056 0.018 0.033
2003 217 12.647 0.787 0.827 0.048 0.018 0.033
2004 179 12.753 0.806 0.821 0.019 0.016 0.031
p-value 0.596 0.794 0.506 0.121 0.994 0.998
2001 adults 83 10.798 0.757 0.842 0.101 0.018 0.033
2002 adults 66 10.924 0.833 0.839 0.007 0.007 0.014
2003 adults 87 10.146 0.802 0.824 0.026 0.010 0.020
2004 adults 78 10.727 0.821 0.819 -0.002 0.025 0.049
p-value adults 0.701 0.049 0.731 0.009 0.468 0.461
Mendez-Harclerode et al.—Genetic Diversity in the Southern Plains Woodrat 7
Table 3. Matrix of pairwise G-statistics from the test of pairwise differentiation among webs and years. Roman nu¬
merals refer to web designation. Above the diagonal is pairwise significance after standard Bonferroni corrections at
the 5% nominal level {p = 0.001) obtained after 1,320 permutations. Below the diagonal are non-adjustedp- values for
each pairwise comparison. NS = non-significant; * = significant.
2001 2002 2003 2004
Year Web I II III I II III I II III I II III
2001 I * * NS * * * * * * * *
II 0.001 * * NS * * * * * NS *
III 0.001 0.001 * * NS * * * * * *
2002 I 0.158 0.001 0.001 * * NS * * NS * *
II 0.001 0.005 0.001 0.001 * * NS * * NS *
III 0.001 0.001 0.036 0.001 0.001 * * NS * * NS
2003 I 0.001 0.001 0.001 0.217 0.001 0.001 * * NS * *
II 0.001 0.001 0.001 0.001 0.442 0.001 0.001 * * NS *
III 0.001 0.001 0.001 0.001 0.001 0.533 0.001 0.001 * * NS
2004 I 0.001 0.001 0.001 0.036 0.001 0.001 0.497 0.001 0.001 * *
II 0.001 0.002 0.001 0.001 0.006 0.001 0.001 0.506 0.001 0.001 *
III 0.001 0.001 0.001 0.001 0.001 0.316 0.001 0.001 0.881 0.001 0.001
Table 4. Matrix of pairwise G-statistics from the test of pairwise differentiation among years. Above the
diagonal is pairwise significance after standard Bonferroni corrections at the 5% nominal level (p = 0.001)
obtained after 120 permutations. Below the diagonal are non-adjustedp-values for each pairwise comparison.
NS = non-significant; * = significant.
2001 2002 2003 2004
2001 * * *
2002 0.001 NS *
2003 0.001 0.267 NS
2004 0.001 0.001 0.517
five web/year samples exhibiting a significant number The contiguous clustering analysis (Chesser and
of loci with a deficiency of observed gene diversity Van Den Bussche 1988) failed to detect statistically
(heterozygosity), suggesting population expansion significant clusters of genotypes during any year. That
(Cornuet and Luikart 1996). These samples were: is, clusters of closely related genotypes were not found
web 1-2001 {p = 0.031), web 1-2002 (p = 0.016), web in geographically close proximity relative to year of
1-2003 (p = 0.016), web III-2003 (p = 0.031), and web collection.
1-2004 (p = 0.031).
8 Occasional Papers, Museum of Texas Tech University
Discussion
Analyses of five microsatellite loci indicated that Only web I possessed a significant heterozygosity
the probability of unique identity was one individual deficiency over all loci across all years even though
in 65,005,424; further, the high levels of allelic varia¬ many loci in the web/year samples produced significant
tion, polymorphic information content, and low overall results in the bottleneck analysis. Motro and Thomson
relatedness values between individuals suggest that a (1982) theorized that bottlenecks combined with cyclic
sufficient number of loci appear to have been exam¬ populations would result in a reduction in heterozygos¬
ined (Mendez-Harclerode et al. 2007). Based on the ity levels. Conversely, Cornuet and Luikart (1996)
results from the AMO VA, most of the genetic variation reported that heterozygosity deficiency for microsatel¬
(99.84%) was among individuals. The lack of signifi¬ lite data under the SMM was indicative of population
cant differences among years relative to allelic richness, expansion. Based on these findings (no evidence of a
gene diversity, relatedness, and FST reported in the pres¬ bottleneck), we interpreted these data as evidence that
ent study supported the hypothesis that the majority of this population of woodrats has undergone a recent
genetic variation was among individuals. However, expansion. We define “recent” as approximately 3,284
it is of interest that when only adult individuals were years ago following the formula provided by Piry et
included in analyses, the observed heterozygosity and al. (1999). Further support for population expansion
FIS each differed significantly across time (p = 0.049 was provided by Mendez-Harclerode et al. (2005),
and p = 0.009, respectively). This indicates that the where patterns indicative of population expansion were
temporal variation in this population may be obscured detected with a nested-clade analysis (Templeton et
by the overlapping generations (relatively high survi¬ al. 1987; Templeton et al. 1995) and Fu’s Fs statistic
vorship and longevity) inherent to N. micropus. (Fu 1996). Additionally, Mendez-Harclerode et al.
(2005,2007) reported high haplotype diversity and low
Significant differences in allele frequencies were nucleotide diversity based on mitochondrial D-loop
detected among most web/year pairs; however, when sequence data, also indicative of a population expansion
webs were compared among years none of the compari¬ as proposed by Grant and Bowen (1998). Alternatively,
sons among webs within a year were significant. This null alleles or the Wahlund effect (Wahlund 1928) may
result suggests that although webs were homogeneous be responsible for the significant heterozygosity defi¬
during the same year, each web evolved independently ciency observed in this study. However, the fact that
with respect to time. Within-web and among-year multiple analyses based on different markers indicate
comparisons revealed interesting trends. None of the this population is expanding suggests that the Wahlund
webs was significantly different from itself during effect is not likely to account for this pattern.
immediately subsequent years (e.g. web 1-2001 was
not significantly different from web 1-2002, but was Although no obvious temporal trend was ob¬
significantly different from web 1-2003). When webs served when population-genetic parameters were
were grouped according to year, 2001 was significantly compared across the 16 trapping periods (Fig. 2), a
different from all other years and 2002 was significantly few interesting patterns were observed. For example,
different from 2004. This may be a product of several Fit and FIS produced nearly identical patterns with FIT
factors. First, it may be a result of a heterogeneous values being slightly higher than FIS values. This sug¬
distribution of allele frequencies by year; for example, gests that genetic variabilities between the individual
the highest number of alleles (111) was observed during and the subpopulation and between the individual and
2001 and the lowest (93) during 2004. Second, web the total population were comparable and, therefore,
I was the only web that exhibited a pattern consistent that most of the variability can be attributed to be
with population expansion (see below) during all four among individuals. This supports the presence of a
years and may have produced significant differences single population as reported by Mendez-Harclerode
in allele frequencies. Third, this may have been due to et al. (2007). A similar conclusion can be drawn from
environmental conditions as the study site experienced the pattern observed during June 2002. During this
a drought during 2001 and 2002 (Bradley et al. 2006), trapping period, the highest values were recorded for
which may have produced a shift in allele frequencies. relatedness Frp FIS, and Fsr This suggests that the in-
Mendez-Harclerode et al.—Genetic Diversity in the Southern Plains Woodrat 9
crease in genetic structure concomitant with increased bottleneck population (N2), pre-bottleneck population
relatedness was proportionally distributed across all (N3), and ancestral population (N4). The selection of
hierarchical levels, thereby indicating a nearly pan- an upper limit for the population size under a bottle¬
mictic population. The increase in population genetic neck (N2) was based on the results from the LDNE
parameters observed during June 2002 coincided with program (Waples et al. 2008), which estimated an
the lowest sample size during the 4-year study. This effective population size of 407 individuals at which
decrease in the number of trapped individuals prob¬ time a bottleneck effect could be detected. Scenario
ably was caused by the 13-month drought that ended 1 allowed for a possible constant population size,
in July 2002 (Bradley et al. 2006). The nature of the whereas scenarios 2-10 involved a step-wise popula¬
apparent correlation between the increase in population tion reduction from 500 individuals (scenario 2) and
parameters and the decrease in sample size, however, concluded with two individuals (scenario 10). Three
is unclear. It could be that the reduction in sample size million simulated datasets were generated and posterior
produced a bottleneck effect resulting in higher levels of probabilities were estimated for each of the scenarios
genetic structure. Although no bottleneck was detected, using the software package DIYABC (Cornuet et al.
the microsatellite markers may be indirectly reflecting 2014). The posterior probabilities were then used to
some selective pressure that could have acted on the rank the accuracy of the 10 scenarios and infer the af¬
low population size (pre-July 2002) enabling a drought- fect of possible bottleneck events.
hardy lineage to thrive during the drought conditions.
Therefore, a simulation was conducted to estimate the Results of these simulations indicated that sce¬
effective population size required to generate a statis¬ nario 1 produced the highest posterior probability value
tically detectable bottleneck that would be capable of (prob = 0.972; Table 5) of matching the observed data.
impacting genetic diversity in subsequent generations. The remaining nine scenarios based on population sizes
Approximate Bayesian Computation (ABC, Cornuet < 500 produced much lower posterior probabilities
et al. 2014) was used to simulate 10 scenarios (Table (prob < 0.060; Table 5). This finding indicates that
5) involving possible population sizes ranging from a bottleneck reducing the total population size to an
one to 10,000 individuals. In these scenarios, four effective population size of < 500 would produce a
sets of hypothetical population conditions (N1-N4) significant difference in genetic diversity. Further, the
were selected to represent the current population (Nl), simulations support the contention that the drought
Table 5. Information used to conduct the simulation analyses as outlined in the text. Abbreviations are as follows:
N1-N4 = population size, ranging from lowest to upper set limit; T1-T3 = generation size, ranging from lowest to
upper set limit; PV = posterior probability values; and Cl = confidence interval values.
Scenario Nl T1 N2 T2 N3 T3 N4 PV Cl
1 (1, 10,000) (1,20) (1, 10,000) (1,20) (1, 1000) (1,300) (1, 10,000) 0.972 (0.961, 0.983)
2 (1, 10,000) (1,20) (1,500) (1,20) (1, 1000) (1,300) (1, 10,000) 0.060 (0.000, 0.268)
3 (1, 10,000) (1,20) (1,250) (1,20) (1, 1000) (1,300) (1, 10,000) 0.060 (0.000, 0.737)
4 (1, 10,000) (1,20) (1, 125) (1,20) (1, 1000) (1,300) (1, 10,000) 0.000 (0.000, 0.000)
5 (1, 10,000) (1,20) (1,62.5) (1,20) (1, 1000) (1,300) (1, 10,000) 0.000 (0.000, 0.000)
6 (1, 10,000) (1,20) (1,31.3) (1,20) (1, 1000) (1,300) (1, 10,000) 0.000 (0.000, 0.000)
7 (1, 10,000) (1,20) (1, 15.6) (1,20) (1, 1000) (1,300) (1, 10,000) 0.000 (0.000, 0.000)
8 (1, 10,000) (1,20) (1,7.8) (1,20) (1, 1000) (1,300) (1, 10,000) 0.006 (0.000, 0.074)
9 (1, 10,000) (1,20) (T4) (1,20) (1, 1000) (1,300) (1, 10,000) 0.002 (0.000, 0.041)
10 (1, 10,000) (1,20) (1,2) (1,20) (1, 1000) (1,300) (1, 10,000) 0.000 (0.000, 0.000)
10 Occasional Papers, Museum of Texas Tech University
(Bradley et al. 2006) did not adversely affect this southern Texas are known to breed year round (Conditt
population. and Ribble 1997; Merkelz and Kerr 2002; Suchecki
et al. 2004) and have a life-span in the wild of at least
Overall, little evidence of temporal variation three years (Raymond et al. 2003; Mendez-Harclerode
was identified in this study. This could have been due et al. 2007). Consequently, it is likely we were unable
to at least four factors. First, the length of the study to obtain congruent results across all the analyses due
may have been insufficient to detect any significant to a mixing of cohorts. Third, this population may be,
temporal variation. A 5-year study on bats failed to in fact, sufficiently large and stable over time, that tem¬
detect evidence of temporal variation among popula¬ poral variation could not be detected during the study
tions (Rossiter et al. 2000); other mammalian studies time. Analyses detected no evidence of a bottleneck
encompassing samples collected from a period of despite the occurrence of a substantial drought causing
time exceeding 10 years have found little evidence of a reduction in population size during the study. Fourth,
temporal variation (Queney et al. 2000; Wisely et al. examination of only five microsatellite loci may not
2002). The fact that allele frequencies in web I and have been sufficient to detect changes in genetic diver¬
web III were significantly different at the end points sity over such a short time-span. However, given the
of the study (2001 and 2004) suggested that a longer- probability of unique identification (1 in 65,005,424)
term study may have been more successful in detecting this seems unlikely. Additional samples collected dur¬
temporal variation. Second, failure to detect significant ing a greater period of time in conjunction with a greater
temporal variation could be due to the inherent overlap number of microsatellite loci may be needed to clarify
of generations in our species of study. Woodrats in possible patterns of temporal variation in N. micropus.
Acknowledgments
The authors thank Ronald K. Chesser for pro¬ contributed helpful suggestions. This research was
viding help with the contiguous clustering analysis; financially supported by National Institutes of Health
Stacey Davis and Ronald A. Van Den Bussche assisted grant AI-41435, entitled “Ecology and Epidemiology
with data analysis; and Michelle L. Haynie, B. Dnate’ of Emerging Arenaviruses in the Southwestern United
Baxter, John Delton Hanson, and Nevin D. Durish States”.
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