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Patterns of genetic variation in anthropognically impacted populations


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Genetic variation is considered critical for allowing natural populations to adapt to their changing environment, and yet the effects of human disturbance on genetic variation in the wild are poorly understood. Different types of human disturbances may genetically impact natural populations in a predictable manner and so the aim of this study was to provide an overview of these changes using a quantitative literature review approach. I examined both allozyme and microsatellite estimates of genetic variation from peer-reviewed journals, using the mean number of alleles per locus and expected heterozygosity as standardized metrics. Populations within each study were categorized according to the type of human disturbance experienced (“hunting/harvest”, “habitat fragmentation”, or “pollution”), and taxon-specific, as well as time- and context-dependent disturbance effects were considered. I found that human disturbances are associated with weak, but consistent changes in neutral genetic variation within natural populations. The direction of change was dependent on the type of human disturbance experienced, with some forms of anthropogenic challenges consistently decreasing genetic variation from background patterns (e.g., habitat fragmentation), whereas others had no effect (e.g., hunting/harvest) or even slightly increased genetic variation (e.g., pollution). These same measures appeared sensitive to both the time of origin and duration of the disturbance as well. This suggests that the presence or absence, strength, type, as well as the spatial and temporal scale of human disturbance experienced may warrant careful consideration when conservation management plans are formulated for natural populations, with particular attention paid to the effects of habitat fragmentation.
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Patterns of genetic variation in anthropogenically impacted
Joseph D. DiBattista
Received: 7 October 2006 / Accepted: 26 February 2007 / Published online: 12 April 2007
Ó Springer Science+Business Media B.V. 2007
Abstract Genetic variation is considered critical for
allowing natural populations to adapt to their changing
environment, and yet the effects of human disturbance on
genetic variation in the wild are poorly understood. Dif-
ferent types of human disturbances may genetically impact
natural populations in a predictable manner and so the aim
of this study was to provide an overview of these changes
using a quantitative literature review approach. I examined
both allozyme and microsatellite estimates of genetic var-
iation from peer-reviewed journals, using the mean number
of alleles per locus and expected heterozygosity as stan-
dardized metrics. Populations within each study were cat-
egorized according to the type of human disturbance
experienced (‘‘hunting/harvest’’, ‘‘habitat fragmentation’’,
or ‘‘pollution’’), and taxon-specific, as well as time- and
context-dependent disturbance effects were considered. I
found that human disturbances are associated with weak,
but consistent changes in neutral genetic variation within
natural populations. The direction of change was dependent
on the type of human disturbance experienced, with some
forms of anthropogenic challenges consistently decreasing
genetic variation from background patterns (e.g., habitat
fragmentation), whereas others had no effect (e.g., hunting/
harvest) or even slightly increased genetic variation (e.g.,
pollution). These same measures appeared sensitive to both
the time of origin and duration of the disturbance as well.
This suggests that the presence or absence, strength, type,
as well as the spatial and temporal scale of human distur-
bance experienced may warrant careful consideration when
conservation management plans are formulated for natural
populations, with particular attention paid to the effects of
habitat fragmentation.
Keywords Conservation genetics Genetic variation
Heterozygosity Human disturbance Mean number of
alleles per locus
Genetic variation is the raw material on which selection
acts and thus critical for evolutionary change. Genetic
variation may be particularly important in the case of rapid
environmental change, where evolution must also be rapid
if a population is to persist (Burger and Lynch 1995; Lande
and Shannon 1996). However, as dramatic environmental
changes are often associated with human activities (e.g., De
Pippo et al. 2006), it is here that genetic variation may be
most important. Indeed, human impacts themselves are
thought to decrease genetic variation (Caizergues et al.
2003; Kang et al. 2005), thus compromising necessary
evolutionary change. The aim of this study is therefore to
examine how human activities influence genetic variation
in natural populations.
The ideal experiment to examine human impacts on
genetic variation in nature is to screen a population before
and after a disturbance. However, it is often not possible to
carry out such experiments, therefore, as an alternative I
have examined a large number of published studies to find
a consensus on the effects of different types of human
disturbance on genetic variation. This consideration is
motivated in part by the conflicting results from different
studies of genetic variation. In particular, some studies
report reductions in genetic variation as a result of human
J. D. DiBattista (&)
Redpath Museum and Department of Biology, McGill
University, 859 Sherbrooke St. West, Montreal, QC H3A 2K6,
Conserv Genet (2008) 9:141–156
DOI 10.1007/s10592-007-9317-z
disturbance (Caizergues et al. 2003; Kang et al. 2005),
whereas others find no such effect (Berckmoes et al. 2005;
Goosens et al. 2005). Genetic variation will reflect a bal-
ance between selection, mutation, and drift, and so human
activities that differentially impact these forces may have
very different effects on genetic variation. Human impacts
that reduce population size and increasingly isolate popu-
lations may increase genetic drift and thereby reduce ge-
netic variation. Human impacts that change environmental
conditions may increase selection and thereby also reduce
genetic variation. Human impacts that increase mutation
rates (e.g., Chernoble; Ellegren et al. 1997) may increase
genetic variation. To examine these effects, I divide dif-
ferent types of human impacts in accordance with the
primary deterministic factors that contribute to modern
population extinction events (for review see Frankham
Hunting and harvesting reduce population size and at
least sometimes cause significant declines in neutral
genetic variation (Frankham 1996; Godt et al. 1996). In
these cases, genetic variation may be lost through random
genetic drift as the effective population size decreases
(Lacy 1997). Further, inbreeding may increase the pro-
portion of homozygous individuals within a population,
which ultimately leads to a reduction in fitness (Crnokrak
and Roff 1999). Trophy hunting in particular may also
exert strong directional selection by targeting animals with
the largest ornaments, which may then remove specific
alleles or genotypes from a population (Fitzsimmons et al.
1995; Coltman et al. 2003). The prediction here would
therefore be a decrease in genetic variation for hunted and
harvested populations.
Habitat fragmentation, due to human settlements,
fenced motorways, channels, and habitat clearing, results in
the subdivision of populations into smaller, more discrete
units, with limited dispersal among them. These changes
can, in at least some cases, erode genetic variation due to
increased inbreeding and genetic drift within fragments,
and to reduced gene flow among fragmented units (Young
et al. 1996; Frankham et al. 2002). The prediction here
would therefore also be a decrease in genetic variation for
fragmented populations.
Pollution may influence genetic variation, although the
outcome is much less certain here than for the factors
mentioned above (Bickham et al. 2000). On the one hand
pollution might decrease genetic variation owing to genetic
drift and inbreeding, particularly in cases of increased
mortality that decrease population size (Posthuma and Van
Straalen 1993; Belfiore and Anderson 2001). Genetic var-
iation may also decrease owing to selection for pollution-
tolerant genotypes (Keane et al. 2005). On the other hand,
populations chronically exposed to chemical pollutants
may experience an increase in genetic variation due to
increased mutation rates (Yauk and Quinn 1996; Baker
et al. 2001) or selection for heterozygotes (i.e., overdomi-
nant hypothesis; see Bickham et al. 2000). Because of this
complexity, it remains uncertain as to the type of effects
that pollution will have on average.
Given our interest in evolutionary potential, we would
most like to track changes in genetic variation at fitness
related traits. This information, however, is largely lacking
for natural populations. Instead, it is sometimes possible to
use neutral genetic variation as a surrogate (Frankham
et al. 2002). This can be tenuous when examining variation
among populations (McKay and Latta
2002), but it is often
defensible within populations (Gilligan et al. 2005). In-
deed, neutral genetic variation largely appears associated
with population fitness and extinction risk (Frankham
2003, 2005; Reed and Frankham 2003). I will therefore
analyze patterns of neutral genetic variation in hope that it
also informs the amount of variation for traits and genes
under selection.
In the present study, I specifically test the null hypoth-
esis that estimates of neutral genetic variation are not
significantly different between populations in habitats not
disturbed by humans versus those in habitat subject to the
above types of human disturbance. My analyses are based
on a compilation of studies examining allozyme and mi-
crosatellite variation across a wide range of species. Other
studies have performed similar analyses (see Garner et al.
2005), but mine differs in (1) explicitly examining different
types of human disturbance, (2) excluding cases of dis-
turbances not directly related to human activity (i.e., sto-
chastic factors) (3) including more studies (and from a
wider range of taxa), and (4) examining effects of the age
and duration of disturbance.
I searched the literature for allozyme and microsatellite
data on genetic variation in disturbed or undisturbed pop-
ulations in nature. This process took the form of keyword
searches (genetic variation, heterozygosity, allelic diver-
sity, natural population, and population size) in Pubmed,
Web of Science, BIOSIS Previews, and BioOne databases.
Note that no keyword suggestive of disturbance was in-
cluded, thus avoiding a bias toward studies specifically
examining this effect. Keyword searches were then sup-
plemented by examining the literature cited section of
papers thus revealed.
Studies were included in the database if they met spe-
cific criteria. First, at least one of two relevant measures of
genetic variation had to be reported: mean number of al-
leles per locus or heterozygosity. The mean number of
alleles per locus is representative of the potential genetic
142 Conserv Genet (2008) 9:141–156
polymorphism, dictating the true limit of the response to
selection (Schoen and Brown 1993; Bataillon et al. 1996).
Heterozygosity is often thought of as a measure of actual
genetic diversity (Nei 1987). For each study, I averaged
population-specific values to obtain an overall value within
each study. Mean heterozygosities were arc-sine square
root transformed and number of alleles were log
formed, which improved normality. Second, I avoided
pseudoreplication by using only a single study for a given
species, specifically the most recent study. Third, genetic
variation had to be reported for at least five microsatellite
or polymorphic allozyme loci. Fourth, at least ten indi-
viduals had to be sampled per population. Fifth, the pop-
ulations examined had to be natural, rather than domestic,
captive, or experimental.
Information recorded from each study included the
species, the number of populations sampled, the average
number of individuals per population, the type of marker
used, the number of loci, the mean number of alleles per
locus, and the mean observed and expected heterozygosity.
When loci deviated from Hardy–Weinberg equilibrium,
heterozygosity values were recalculated, where possible,
after eliminating those loci. This was done because the
causes of deviation from Hardy–Weinberg equilibrium
could be many (null alleles, admixture, selection), and the
specific cause is rarely known. Expected heterozygosities
were reported in most studies (87% of all papers collected),
and when they were not, I instead used observed hetero-
zygosities, which should be similar at equilibrium (Hedrick
Human disturbance within each study was categorized
as ‘‘hunting/harvest’’, ‘‘habitat fragmentation’’ (including
habitat loss), or ‘‘pollution’’. Studies of populations
experiencing natural disturbances, such as disease, preda-
tion, natural disasters, and fire, were excluded in an attempt
to restrict the focus to anthropogenic factors. If a popula-
tion suffered more than one type of disturbance (29% of
studies), it was included in the analysis for only the pri-
mary disturbance type mentioned in the publication (thus
preventing non-independent data points). Papers in which
the primary disturbance type was either not explicitly sta-
ted or unclear were excluded. When no disturbance was
noted in a study, the populations were considered
‘‘undisturbed’’. This was confirmed by reading relevant
references also cited within these papers. When studies
included both disturbed and undisturbed populations of the
same species, both were included in the analysis (see also
below). In the end, a total of 220 relevant publications were
identified (Appendix).
In order to consider the long-term effects of human
activity on genetic variation, disturbances were further
classified as to their time of origin and duration. A dis-
turbance was deemed ‘‘historic’’ if it had occurred and
ended prior to 1900. A disturbance was considered
‘‘recent’’ if it occurred after 1950. Few disturbances began
between 1900 and 1950 and were therefore not here con-
sidered. Further, a ‘‘short-term’’ disturbance is one that
occurred after 1950 and is still present, whereas a ‘‘long-
term’’ disturbance is one which began prior to 1950 and
persists to the present. These distinctions could not be
made for 26 studies, which were therefore excluded from
this part of the analysis.
Statistical analyses
Formal meta-analytic approaches require that studies report
measures of variability from which effect sizes can be
calculated (Arnqvist and Wooster 1995; Gurevitch and
Hedges 1999). This was not the case for many studies in
the database, and so I instead relied on conventional sta-
tistical tests. These tests may have lower power than formal
meta-analyses, but Type I error rates are at least similar
when the pattern of sampling-error variances is not sub-
stantially different among categories (Gurevitch and
Hedges 1999).
I first evaluated the relationship between the mean
number of individuals sampled in a study and the mean
number of alleles per locus (Von Segesser et al. 1999).
These variables were weakly, but significantly correlated
for microsatellites (r
= 0.061, P < 0.0001) and not sig-
nificant for allozymes (r
= 0.032, P = 0.10). Sample size
variation was therefore unlikely to affect interpretations
based on alleles per locus. I nevertheless repeated all
analyses (see below) after standardizing the number of
alleles by the number of sampled individuals. Standardized
and un-standardized estimates were significantly and pos-
itively correlated with each other (Pearson Product Mo-
ment: r = 0.58, P < 0.0001), and observed patterns were
similar in all cases. Analyses of numbers of alleles were
therefore based on unstandardized values.
Two types of analyses were performed. First, I com-
pared genetic variation among studies, which itself
involved several analyses. Second, I compared genetic
variation among populations within studies. All statistical
analyses were performed using SPSS v11.1 software, at the
a = 0.05 level of significance.
Genetic variation among studies was primarily analyzed
with MANOVAs. The dependent variables were numbers
of alleles and heterozygosity (referred to jointly as
‘‘genetic variation’’). The independent variables were
disturbance and molecular marker type (both fixed). These
analyses were supplemented by separate univariate ANO-
VAs for each marker type and genetic variance measure,
followed by Fisher’s LSD post hoc tests. This analysis was
repeated for only the two best-represented groups in the
database: mammals and plants, which ensured that
Conserv Genet (2008) 9:141–156 143
observed patterns were not dependant on particular dis-
turbance types having a disproportionate number of data
points from a particular taxon. In all instances, a full model
was first run and non-significant interactions were then
removed. Overall inferences about changes in genetic
variation were based on the MANOVAs, whereas infer-
ences about specific response variables were based on the
ANOVAs. These analyses should be broadly similar given
that the two genetic variation measures were strongly
correlated with each other (Pearson Product Moment,
r = 0.905, P < 0.0001). The data were treated in a similar
manner when the temporal effects of human disturbance
were considered (with time of origin or duration of a dis-
turbance as fixed factors).
Opposing effects in different taxa, however, may cancel
each other out in a metaanalysis (e.g., disturbance may lead
to a decrease in genetic diversity in mammals, but an in-
crease in birds, and thus no effect overall), and so a com-
parison among individual taxa is important. To test for
taxonomic effects, species were grouped into mammals,
birds, fish, herp-fauna (i.e., amphibians and reptiles),
invertebrates, and plants. These analyses included only
taxa with at least two disturbed and undisturbed species
and pooled the various disturbance types (to ensure suffi-
cient sample size). Similar to above, MANOVAs were
employed, with the numbers of alleles and heterozygosity
as dependent variables. In this case, however, the inde-
pendent variables were disturbance, molecular marker
type, and taxon (all fixed factors). These analyses were also
supplemented by separate univariate ANOVAs for each
marker type and genetic variation measure.
Variation within studies was analyzed by considering
differences between disturbed and undisturbed populations
within a given study (N = 50 studies). This analysis thus
controls for differences in the methodology employed by
each individual study (e.g., marker loci used, study species,
and sample size). Further, 11 of these data sets actually
included the same populations before and after human
disturbance, thus controlling for site-specific differences. In
particular, I used Wilcoxon Signed Rank t tests to assess
the relationship between mean heterozygosity in disturbed
versus undisturbed reference populations within the same
study and species. Heterozygosity is measured on a scale
ranging from 0 to 1 and thus lends itself to this type of
In analyses of variation among studies, genetic variation
was much higher for microsatellites than for allozymes
= 290.98, P < 0.0001), and was also
influenced by disturbance type (MANOVA: F
= 2.86,
P = 0.009); different types of human disturbance had dif-
ferent genetic effects. In general, genetic variation in
undisturbed populations was significantly higher than that
in fragmented populations, non-significantly higher than
that in hunted/harvested populations, and non-significantly
lower than that in polluted populations (Table 1). For
allozyme markers in particular, disturbance had a signifi-
cant effect on the mean number of alleles per locus
= 6.75, P < 0.0001) but not heterozygosity
= 2.33, P = 0.08); fragmented populations had fewer
allozyme alleles than did polluted (P = 0.001), hunted/
harvested (P = 0.041), or undisturbed (P < 0.0001) popu-
lations. The same was true for the number of alleles
= 3.23, P = 0.024) and heterozygosity
= 2.24, P = 0.085) estimated with microsatellite
markers; fragmented populations had fewer microsatellite
alleles than did polluted (P = 0.041) or undisturbed
(P = 0.011) populations, although in this case, not hunted/
harvested (P = 0.459) populations. Thus, habitat frag-
mentation clearly had the strongest effect, consistently
decreasing genetic variation from background patterns.
The above trends were maintained when accounting for
possible effects of taxon. First, when species were grouped
into distinct taxa, genetic variation was typically (but not
always) lower in disturbed versus undisturbed populations
(Fig. 1A,B,C,D). Although marker type (MANOVA:
= 145.17, P < 0.0001) and taxon (MANOVA:
= 3.47, P < 0.0001) had a significant effect on
genetic variation, surprisingly disturbance did not (MA-
= 1.88, P = 0.155). Disturbance effects
increased (and were significant), however, after removal
of pollution studies (MANOVA: marker type,
= 131.71, P < 0.0001; taxon, F
= 2.86,
P = 0.002; disturbed versus undisturbed, F
= 3.33,
P = 0.043), reinforcing the idea that pollution had quali-
tatively different effects than other types of disturbance
here considered. Following this modification, the mean
number of alleles per locus (allozyme: F
= 9.41,
P < 0.0001; microsatellite: F
= 3.62, P = 0.029), but
not heterozygosity (allozyme: F
= 1.98, P = 0.15;
microsatellite: F
= 2.52, P = 0.084), was significantly
lower in disturbed populations across all taxa. Second,
trends in genetic variation among the disturbance types
were similar (pollution > undisturbed > hunting/
harvest > fragmented) and significant (MANOVA:
= 2.12, P = 0.05), albeit marginally, when compar-
ing genetic variation estimates strictly within plants and
mammals (Table 2). Fragmented plant populations (dis-
turbance type: F
= 4.042, P = 0.031) had significantly
fewer allozyme alleles per locus than undisturbed
(P = 0.015) or polluted (P = 0.027) populations, whereas
fragmented mammalian populations (disturbance type:
= 14.59, P = 0.032) had significantly fewer allozyme
144 Conserv Genet (2008) 9:141–156
alleles than undisturbed populations (P = 0.032). Thus, the
observed differences in genetic variation among distur-
bance categories did not appear to be dictated by a single
taxonomic group.
Possible long-term effects of human disturbance on
genetic variation were also assessed, but only for micro-
satellite markers (Fig. 2A,B) due to small sample sizes for
allozymes. A subtle trend for a decrease in genetic varia-
Table 1 Mean allozyme and microsatellite genetic variation esti-
mates in ‘‘undisturbed’’ and ‘‘disturbed’’ populations (disturbance
categories: Hunting/Harvest, Habitat Fragmentation, and Pollution),
characterized as the mean number of alleles per locus (A) and
expected heterozygosity (H
Undisturbed Hunting/Harvest Fragmentation Pollution
A 2.13 ± 0.09
(46) 2.076 ± 0.34 (7) 1.56 ± 0.088
(30) 2.19 ± 0.17 (13)
0.19 ± 0.016 (48) 0.16 ± 0.028 (8) 0.14 ± 0.02
(33) 0.22 ± 0.036 (14)
A 8.84 ± 0.57 (80) 6.89 ± 0.46 (49) 6.83 ± 0.52
(60) 13.12 ± 4.03 (6)
0.65 ± 0.018 (82) 0.60 ± 0.02 (50) 0.59 ± 0.023 (62) 0.70 ± 0.088 (7)
Values are means ± 1 SEM (N)
MANOVA tests were carried out for both estimators of genetic diversity (A and H
together), using disturbance and molecular marker type as
fixed factors. Univariate ANOVA tests were also conducted to identify case specific differences
An asterisk ‘‘*’’ indicates a significant difference from all other disturbance types, P < 0.05, whereas a double asterisk ‘‘**’’ indicates a
significant difference from polluted (P = 0.041) and undisturbed populations (P = 0.011) only
Mean heterozygosity
Mean Heterozygosity
Mean number of alleles per locus
Mean number of alleles per locus
Fig. 1 Number of alleles per locus (A, B) and heterozygosity (C, D)
across a wide range of animal taxa as a function of human
disturbance, investigated using both allozyme (A, C) and microsat-
ellite markers (B, D). Numbers in parentheses represent sample sizes
(N). All values are means ± SEM
Conserv Genet (2008) 9:141–156 145
tion with increasing time since disturbance was evident
(Fig. 2A) but non-significant (MANOVA: F
= 2.22,
P = 0.066). It was significant, however, when the number
of alleles (F
= 4.36, P = 0.014) and heterozygosity
= 3.45, P = 0.034) were considered separately;
undisturbed populations had significantly more alleles
(P = 0.007) and higher heterozygosity (P = 0.017) than
populations that had experienced disturbances prior to the
1900s, which was not the case for more recent disturbances
(mean number of alleles per locus, P = 0.07; heterozy-
gosity, P = 0.099). Human disturbances of increasing
duration (Fig. 2B) also decreased genetic variation overall
= 2.38, P = 0.045). It was only significant, how-
ever, for the mean number of alleles (F
= 3.77,
P = 0.025) and not heterozygosity (F
= 1.62, P = 0.2)
when considered separately. Populations experiencing
long-term disturbances had significantly fewer alleles than
undisturbed (P = 0.007) populations, and populations
subject to short-term disturbances (P = 0.046).
A more rigorous test of the effects of human disturbance
on genetic variation was performed by correlating hetero-
zygosity estimates from both disturbed and undisturbed
reference populations of the same species, reported within
the same study (Fig. 3). Variation within studies included
analyses for 8 mammals, 3 birds, 12 fishes, 3 herp-fauna,
10 invertebrates, and 14 plants. As might be expected,
genetic variation in disturbed and undisturbed populations
was strongly correlated across studies (Pearson Product
Moment: r = 0.93, P < 0.0001), but no consistent trend for
differences (i.e., disturbed versus undisturbed) was evident
when all disturbance types were considered together
(Wilcoxon Signed Rank t test: P = 0.31). However, nine of
the 12 studies showing qualitatively higher values in dis-
turbed populations were for instances of pollution, and so
polluted populations on their own had significantly higher
genetic variation than their undisturbed counterparts
(Wilcoxon Signed Rank t test: P = 0.045). When pollution
data were removed from the analysis, disturbed and
undisturbed heterozygosity estimates were significantly
different among the remaining categories (Wilcoxon
Signed Rank t test: P = 0.004), although, in this case,
indicating a consistent negative impact of human distur-
bance on genetic variation. I observed the same pattern
when the mean number of alleles per locus was analyzed in
this manner (data not shown).
My goal was to evaluate the genetic impacts of different
types of human disturbance. I found that the direction of
responses, in terms of changes in neutral genetic variation
from undisturbed background patterns, were dependent on
the type of disturbance experienced. In general, fragmen-
tation reduced genetic variation, hunting/harvesting had no
appreciable effect, and pollution may actually increase
genetic variation, although this last effect was not signifi-
cant when tested directly. These results were largely con-
sistent across different taxa (Fig. 1, Table 2), and were
robust to differences in molecular marker types (allozymes
or microsatellites) and genetic variation estimators (num-
bers of alleles or heterozygosity). Interestingly, however,
the mean number of alleles per locus was more likely to
show significant differences than was heterozygosity. This
result fits with work showing that allelic diversity is af-
fected more by demographic disturbances than are other
estimates of neutral genetic variation (Hartl and Pucek
1994). Further, the observed patterns remained when the
number of alleles was expressed as a ratio of sample size,
indicating that my results were not driven simply by dif-
ferences in sampling effort.
Table 2 Mean allozyme and microsatellite genetic variation
estimates in ‘‘undisturbed’’ and ‘‘disturbed’’ populations of mam-
mals and plants (disturbance categories: Hunting/Harvest, Habitat
Fragmentation, and Pollution), characterized as the mean number of
alleles per locus (A) and expected heterozygosity (H
Undisturbed Hunting/Harvest Fragmentation Pollution
Mammals: Allozyme
A 2.75 ± 0.46
(2) NA 1.43 ± 0.13 (3) NA
0.34 ± 0.16 (2) NA 0.11 ± 0.051 (3) NA
Mammals: Microsatellite
A 8.18 ± 0.69 (49) 6.59 ± 0.55 (34) 6.17 ± 0.54 (38) NA
0.65 ± 0.026 (49) 0.60 ± 0.024 (35) 0.59 ± 0.029 (39) NA
Plants: Allozyme
A 1.99 ± 0.11
(20) 2.23 ± 0.49 (2) 1.68 ± 0.13 (18) 2.17 ± 0.21
0.21 ± 0.018 (22) 0.18 ± 0.03 (2) 0.16 ± 0.022 (20) 0.21 ± 0.035 (5)
Plants: Microsatellite
A 8.31 ± 1.074 (8) 8.27 ± 2.24 (5) 5.53 ± 2.00 (3) NA
0.62 ± 0.031 (8) 0.57 ± 0.061 (5) 0.51 ± 0.18 (3) NA
Values are means ± 1 SEM (N)
MANOVA tests were carried out for both estimators of genetic variation (A and H
together), using disturbance type, molecular marker type,
and taxon as fixed factors. Only categories represented by at least two samples were included in the analysis
An asterisk ‘‘*’’ indicates a significant difference from the fragmented group, P < 0.05
146 Conserv Genet (2008) 9:141–156
Could my findings be the result of a publication bias?
Such a bias could occur if studies reporting significant
results are more likely to be published (Arnqvist and
Wooster 1995; Gurevitch and Hedges 1999). This would be
a problem in my study if there was a bias toward publi-
cation of disturbed populations that show reductions in
genetic variation. Some such bias is possible but seems
unlikely to explain all the main trends. First, patterns of
genetic change were largely consistent across taxa,
molecular marker type, and genetic variation estimators.
Second, genetic changes owing to human disturbances are
likely underrepresented in this study, as species or popu-
lations driven to extinction by human activities were not
considered. Third, many of the studies included in the
database collected data for purposes mostly unrelated to
assessing the impacts of human disturbances on genetic
variation (e.g., social structure, breeding biology, or iso-
lation by distance). Fourth, the pollution data actually seem
to suggest an increase in genetic variation, indicating that
the decrease in fragmentation studies is unlikely to be just
the result of a bias.
Do my results reflect human effects? I specifically
examined disturbances attributable to humans, and so my
results clearly apply to that context. It is also possible,
however, that natural disturbances could have similar ef-
fects. Indeed, previous studies did not separate these effects
(Garner et al. 2005). My main goal, however, is to compare
different types of human disturbance, and so here infer-
ences do not depend on an understanding of the effects of
natural disturbances.
sucol rep selella fo rebmun naeM
ytisogyzoreteh naeM
Mean number of alleles per locus
Mean heterozygosity
Historic disturbance
Recent disturbance
sucol rep selella fo rebmun naeM
Mean number of alleles per locus
Mean heterozygosity
Short-term disturbance
Long-term disturbance
Fig. 2 Genetic variation (± SEM) in populations subject to historical
or recent (A) in addition to short-term or long-term human
disturbances (B) relative to undisturbed populations, considering
microsatellite marker data only. MANOVA tests were carried out for
both estimators of genetic variation (mean number of alleles per locus
and heterozygosity together), using time of origin or duration of
disturbance as fixed factors. Univariate ANOVA tests were also
conducted to identify case specific differences. An asterisk ‘‘*’’
indicates a significant difference from the undisturbed group only
(P < 0.05), whereas a double asterisk ‘‘**’’ indicates a significant
difference from both the undisturbed and short-term disturbance
group (P < 0.05). Numbers in parentheses represent sample sizes (N)
Habitat fragmentation
Undisturbed heterozygosity
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Disturbed heterozygosity
Habitat fragmentation
Fig. 3 The relationship between disturbed and undisturbed hetero-
zygosity estimates reported within the same study (Pearson Product
Moment Correlation: r = 0.93, P < 0.0001), considering all catego-
ries of disturbance (N = 50). The line in bold represents a line of
unity, which is the point at which heterozygosity estimates in
disturbed and undisturbed populations are equal. Data points below
the line of unity indicate a negative impact of disturbance, whereas
points falling above the line are positively impacted by human
Conserv Genet (2008) 9:141–156 147
Disturbance types
Fragmentation clearly decreases genetic variation. One
possible driver of this effect is reductions in population size
(Young et al. 1996). Another is reduced gene flow as a
result of habitat fragmentation (Frankham et al. 2002; Toro
and Caballero 2005). Habitat fragmentation may reduce
population size the most out of all disturbance types con-
sidered in this study, thus producing statistically significant
reductions in genetic variation. Unfortunately, few studies
provided estimates of census or effective population size,
preventing a proper test of the idea that population size is
heavily influencing the outcome. Alternatively, population
size may decrease substantially with all disturbance types,
and so the pronounced negative effect on genetic variation
in fragmented populations may be due to reduced dispersal.
Although previous work has shown a significant and po-
sitive relationship between population size and genetic
diversity (Frankham 1996), further studies, comparing
undisturbed and fragmented populations while controlling
for population size, would indicate whether factors above
and beyond population size are responsible for a lowering
of genetic variation. Nonetheless, habitat fragmentation
clearly has a significant impact on genetic variation in
natural populations, and so conservation case studies
involving fragmentation should be given priority.
Hunting/harvesting appeared to have little effect on
genetic variation. This is surprising given the rapid
reductions in population size generally associated with
hunting and harvesting practices. Thus, I would expect a
decrease in genetic variation owing to effects associated
with bottlenecks (i.e., genetic drift and inbreeding), and yet
I do not find this in my study. However, it should also be
noted that this relationship is not always as straightforward
as assumed, with past work identifying relatively abundant
species having limited variability and other endangered
populations maintaining high variability (for review see
Frankham 1995; Amos and Harwood 1998). Thus, other
factors may be involved, such as selection acting on spe-
cific genotypes, which are indirectly targeted by hunters
(Fitzsimmons et al. 1995; Coltman et al. 2003; Hartl et al.
2003). One possible explanation for our results, however, is
that hunting/harvest reduces population size to a lesser
extent than other types of disturbance (i.e., fragmentation),
and so the effects are weaker or more inconsistent (and thus
Pollution appeared as though it might have a positive
impact on genetic variation. I make this inference because
every genetic variation measure was qualitatively greater
for populations subject to pollution than for those in
undisturbed conditions, although only some of these were
significant owing to small sample sizes (Table 1). More-
over, comparisons within studies suggested a similar effect
(Fig. 3), and negative genetic impacts of human distur-
bance were only evident when pollution data were removed
from several analyses. Whether or not pollution increases
genetic variation, it clearly has a qualitatively different
effect than fragmentation, as evidenced by the significantly
greater number of alleles and higher heterozygosity in
polluted populations (Table 1). Thus, I suggest that pollu-
tion can have both positive and negative effects through
different mechanisms. On the one hand, pollution may
decrease population size (Posthuma and Van Straalen
1993) or increase selection for homozygous genotypes
(Keane et al. 2005), which would decrease genetic varia-
tion. Indeed, some studies have clearly found reductions in
genetic variation because of pollution (e.g., Ma et al. 2000;
Belfiore and Anderson 2001). On the other hand, pollution
could increase mutation rates at marker loci (Yauk and
Quinn 1996; Baker et al. 2001) or increase selection for
heterozygotes (Falconer and MacKay 1996). The net effect
of pollution on genetic variation should therefore reflect a
balance between these various forces.
That being said, conservation biologists may need to
consider genetic threats from pollution carefully, separat-
ing them from other forms of human disturbance. Given the
general belief that the maintenance of genetic variation is
healthy in natural populations, in the short term, polluted
populations may appear to be doing well genetically. Long-
term effects of pollution, however, which may include
adverse effects on the physiology of an organism and its
environment as well as a possible increase in mutational
load, are all detrimental to a population’s viability.
Time of origin and duration of disturbance
The level of genetic variation maintained within a popu-
lation may also be dependant on both the time of origin and
duration of a particular human disturbance (Frankham
2003, 2005). Although rare alleles are likely the first to be
lost, a long-term disturbance, acting over many genera-
tions, will cause the loss of more common alleles and a
steeper decline in genetic variation (Lande 1988). In fact, a
prolonged disturbance would likely leave a more distinct
genetic ‘‘footprint’’ within a population than a transient
challenge. My findings support this idea, with short-term
disturbances having a lesser effect on genetic variation than
long-term ones. Further, populations that had experienced
historic disturbances were associated with a lower level of
genetic variation than those disturbed only recently, sug-
gesting that within-population genetic variation may be
sensitive to the temporal scale of human-related activities.
Although, increased conservation efforts in recent years
could also explain the trend for higher genetic variation in
populations disturbed only within the last 50 years.
148 Conserv Genet (2008) 9:141–156
Future considerations
The loss of genetic variation may not only affect organisms at
the population level but lead to the loss of entire species
given enough time, thus, the maintenance of genetic variation
is of critical importance. But why is it important to under-
stand genetic effects in natural populations specifically
attributable to human activity? First, in order to mitigate
against loss of genetic variation, it is essential we understand
the source or cause. Second, by identifying specific human
activities related to detrimental genetic effects we can either
eliminate the source of the impact altogether or seek viable,
less intrusive alternatives. Finally, a more comprehensive
knowledge of past or current genetic impacts on natural
populations may increase our predictive power and ability to
control future impacts. This information would be of par-
ticular use to incorporate into existing models and simulation
programs directed at threatened or endangered populations,
where direct sampling is limited or often impossible. Al-
though this issue merits further consideration, my study has
provided essential baseline information which will facilitate
future comparisons, and presents the most comprehensive
assessment of genetic variation in human impacted popula-
tions to date.
The weak patterns of neutral genetic change observed in
this study, despite large sample sizes in general, do raise
one concern. Genetic variation is overwhelmingly moni-
tored by neutral molecular variation in natural populations
(Frankham et al. 2002) and so it was used in this study.
However, there is a growing debate about whether
molecular measures of genetic variation reflect adaptive
differences among populations, or even the ability to re-
spond to future environmental changes (Reed and Frank-
ham 2001). Most environmental changes associated with
human activities will affect different morphological or life-
history traits of particular species, thus quantitative genetic
variation may serve as a more sensitive bioindicator. In
fact, a recent simulation study found that some human
impacts on genetic variation could not be detected with
neutral molecular markers, but only become apparent when
changes in quantitative genetic variation were assessed
guez et al. 2005). Thus, although logisti-
cally difficult, a comprehensive assessment of quantitative
genetic variation in natural populations may be the only
means of estimating the ‘‘true’’ magnitude of human-re-
lated genetic effects.
Acknowledgments This study was supported by a postgraduate
fellowship from NSERC. Thanks are extended to A. Garner, J.L.
Rachlow, and J.F. Hicks for providing a complete reference list from
their recent paper in Conservation Biology. Thanks also to A.P.
Hendry, K.A. Feldheim, J. Bates, K. Gilmour, the Field museum
journal club, and two anonymous referees for providing comments on
an earlier version of this manuscript.
Appendix Table A1 References for genetic variation data reviewed by DiBattista (2007)
Abbreviated reference Complete reference
Allen et al., 1995 Allen, P. J., Amos, W., Pomeroy, P. P., and Twiss, S. D. (1995). Molecular Ecology 4: 653–662.
Andersen et al., 1998 Andersen, L. W., Born, E. W., Gjertz, I., Wiig, Ø., Holm, L. E., and Bendixen, C. (1998). Molecular
Ecology 7: 1323–1336.
Andersen et al., 2001 Andersen, L. W., Ruzzante, D. E., Walton, M., Berggren, P., Bjørge, A., and Lockyer, C. (2001).
Conservation Genetics 2: 309–324.
Arigoni and Largiade
r, 2000 Arigoni, S. and Largiade
r, C. R. (2000). Molecular Ecology 9: 2155–2169.
Arnaud et al., 2003 Arnaud, J., Madec, L., Guiller, A., and Deunff, J. (2003). Heredity 90: 451–458.
Barcia et al., 2005 Barcia, A. R., Lo
pez, G. E., Herna
ndez, D., and Garcı
a-Machado, E. (2005). Molecular Ecology 14: 2933–
Batista and Sosa, 2002 Batista, F. and Sosa, P. A. (2002). Annals of Botany 90: 725–733.
Beaumont et al., 2001 Beaumont, M., Barratt, E. M., Gottelli, D., Kitchenere, A. C., Daniels, M. J., Pritchard, J. K., and Bruford,
M. W. (2001). Molecular Ecology 10: 319–336.
Becher and Griffiths, 1998 Becher, S. A. and Griffiths, R. (1998). Molecular Ecology 7: 1599–1604.
Beheregaray et al., 2000 Beheregaray, L. B., Sunnucks, P., Alpers, D. L., Banks, S. C., and Taylor, A. C. (2000). Conservation
Genetics 1: 89–92.
Belant et al., 2005 Belant, J. L., Van Stappen, J. F., and Paetkau, D. (2005). Ursus 16: 85–92.
Bell and Okamura, 2005 Bell, J. J. and Okamura, B. (2005). Proceedings of the Royal Society of London B-Biological Sciences 272:
Benton et al., 1994 Benton, M. J., Diamond, S. A., and Guttman, S. I. (1994). Ecotoxicology and Environmental Safety. 29: 20–
Conserv Genet (2008) 9:141–156 149
Appendix Table A1 continued
Abbreviated reference Complete reference
Benton et al., 2002 Benton, M. J., Malott, M. L., Trybula, J., Dean, D. M., and Guttman, S. I. (2002). Environmental Toxicology
and Chemistry 21: 584–589.
Berckmoes et al., 2005 Berckmoes, V., Scheirs, J., Jordaens, K., Blust, R., Backeliau, T., and Verhagen, R. (2005). Environmental
Toxicology and Chemistry 24: 2898–2907.
et al., 1998 Be
, M., Aguilar, A., Dendanto, D., Larsen, F., Notarbartolo Di Sciara, G., Sears, R., Sigurjo
nsson, J.,
Urban-R., J., and Palsbøll, P. J. (1998). Molecular Ecology 7: 585–599.
et al., 2000 Be
, M., Jørgensen, H., McEwing, R., and Palsbøll, P. J. (2000). Molecular Ecology 9: 2181–2183.
Billington 1991 Billington, H. L. (1991). Conservation Biology 5: 115–119.
Billot et al., 1998 Billot, C., Rousvoal, S., Estoup, A., Epplen, J. T., Saumitou-Laprade, P., Valero, M., and Kloareg, B. (1998).
Molecular Ecology 7: 1771–1788.
Blundell et al., 2002 Blundell, G. M., Ben-David, M., Groves, P., Bowyer, R. T., and Geffen, E. (2002). Molecular Ecology 11:
Bottin et al., 2005 Bottin, L., Verhaegen, D., Tassin, J., Olivieri, I., Vaillant, A., and Bouvet, J. M. (2005). Molecular Ecology
14: 1989.
Bowyer et al., 2002 Bowyer, J. C., Newell, G. R., and Eldridge, M. D. B. (2002). Conservation Genetics 3: 61–69.
Bradley et al., 2000 Bradley, B. J., Boesch, C., and Vigilant, L. (2000). Conservation Genetics 1: 289–292.
Browning et al., 2001 Browning, T. L., Taggart, D. A., Rummery, C., Close, R. L., and Eldridge, M. D. B. (2001). Conservation
Genetics 2: 145–156.
Buchanan et al., 1996 Buchanan, F. C., Friesen, M. K., Littlejohn, R. P., and Clayton, J. W. (1996). Molecular Ecology 5: 571–
Buchert et al., 1997 Buchert, G. P., Rajora, O. M. P., Hood, J. V., and Dancik, B. P. (1997). Conservation Biology 11: 747–758.
Bulgin et al., 2003 Bulgin, N. L., Gibbs, H. L., Vickery, P., and Baker, A. J. (2003). Molecular Ecology 12: 831–844.
Burland et al., 1998 Burland, T. M., Barratt, E. M., and Racey, P. A. (2002). Molecular Ecology 7: 133–140.
Burton et al., 2002 Burton, C., Krebs, C. J., and Taylor, E. B. (2002). Molecular Ecology 11: 1689–1701.
Caizergues et al., 2003 Caizergues, A., Ra
tti, O., Helle, P., Rotelli, L., Ellison, L., and Rasplus, J. (2003). Molecular Ecology 12:
Castella et al., 2000 Castella, V., Ruedi, M., Excoffier, L., Iba
ez, C., Arlettaz, R., and Hausser, J. (2000).
Molecular Ecology 9:
Cespedes et al., 2003 Ce
spedes, M., Gutierrez, M. V., Holbrook, N. M., and Rocha, O. J. (2003). Molecular Ecology 12: 3201–
Chase et al., 1996 Chase, M., Kesseli, R., and Bawa, K. (1996). American Journal of Botany 83: 51–57.
Chen et al., 2003 Chen, X., Li, N., Shen, L., and Li, Y. (2003). Environmental Pollution 124: 449–455.
Cimmaruta et al., 2003 Cimmaruta, R., Scialanca, F., Luccioli, F., and Nascetti, G. (2003). Oceanologica Acta 26: 101–110.
Ciofi et al., 2002 Ciofi, C., and Bruford, M. W. (2002). Molecular Ecology 7: 133–140.
Colson and Hughes, 2004 Colson, I., and Hughes, R. N. (2004). Molecular Ecology 13: 2223–2233.
Coltman et al., 1998 Coltman, D. W., Bowen, W. D., and Wright, J. M. (1998). Proceedings of the Royal Society of London B-
Biological Sciences 265: 803–809.
Coltman et al., 1999 Coltman, D. W., Bancroft, D. R., Robertson, A., Smith, J. A., Clutton-Brock, T. H., and Pemberton, J. M.
(1999). Molecular Ecology 8: 1199–1209.
Comer et al., 2005 Comer, C. E., Kilgo, J. C., D’Angelo, G. J., Glenn, T. C., Miller, K. V. (2005). Journal of Wildlife
Management 69: 332–344.
Cronin et al., 2005 Cronin, M. A., Shideler, R., Waits, L., and Nelson, R. J. (2005). Ursus 16: 70–84.
Cross and Rebordinos, 2003 Cross, I., and Rebordinos, L. (2003). Ciencias Marinas 29: 239–250.
Davis and Strobeck, 1998 Davis, C. S., and Strobeck, C. (1998). Molecular Ecology 7: 1771–1788.
De Oliveira et al., 2005 de Oliveira, M., Russo, C., Lazoski, C., Vianna, P., and Sole
-Cava, A. (2005). Genetics and Molecular
Research 4: 197–202.
Descimon and Napolitano, 1993 Descimon, H. and Napolitano, M. (1993). Biological Conservation 66: 117–123.
Dhuyvetter et al., 2005 Dhuyvetter, H., Gaublomme, E., Verdyck, P., and Desender, K. (2005). Journal of Heredity 96: 381–387.
Diniz et al., 2005 Diniz, F. M., Maclean, N., Ogawa, M., Paterson, I. G., and Bentzen, P. (2005). Conservation Genetics 6:
Edwards et al., 2003 Edwards, T., Goldberg, C. S., Kaplan, M. E., Schwalbe, C. R., and Swann, D. E. (2003). Molecular Ecology
Notes 3: 589–591.
150 Conserv Genet (2008) 9:141–156
Appendix Table A1 continued
Abbreviated reference Complete reference
Eggert et el, 2003 Eggert, L. S., Eggert, J. A., and Woodruff, D. S. (2003). Molecular Ecology 12: 1389–1402.
Eizirik et al., 2001 Eizirik, E., Kim, J., Menotti-Raymond, M., Crawshaw Jr., P. G., O’Brien, S. J., and Johnson, W. E. (2001).
Molecular Ecology 10: 65–79.
Eldridge et al., 2004 Eldridge, M. D., Kinnear, J. E., Zenger, K. R., McKenzie, L. M., and Spencer, P. B. (2004). Conservation
Genetics 5: 325–328.
o and Dizon, 2000 Escorza-Trevin
o, S., and Dizon, A. E. (2000). Molecular Ecology 9: 1049–1060.
Favre and Balloux, 1997 Favre, L., and Balloux, F. (1997). Molecular Ecology 6: 595–596.
Fickel et al., 2005 Fickel, J., Schmidt, A., Putze, M., Spittler, H., Ludwig, A., Juergen Streich, W., and Pitra, C. (2005). Journal
of Wildlife Management 69: 760–770.
Fitzsimmons et al., 1995 Fitzsimmons, N. N., Buskirk, S. W., and Smith, M. H. (1995). Conservation Biology 9: 314–323.
Flagstad et al., 2000 Flagstad, O., Syvertsen, P., Stenseth, N., Stacy, J. E., Olsaker, I., Roed, K. H., and Jakobsen, K. S. (2000).
Conservation Biology 14: 254–264.
Forbes and Boyd, 1996 Forbes, S. H., and Boyd, D. K. (1996). Conservation Biology 10: 1082–1090.
et al., 1992 Fore
, S. A., Hickey, R. J., Vankat, J. L., Guttman, S. I., and Schaeffer, R. L. (1992). Canadian Journal of
Botany 70: 1659–1668.
et al., 1995 Fore
, S. A., Guttman, S. I., Bailer, A. J., Altfater, D. J., and Counts, B. V. (1995). Ecotoxicology and
Environmental Safety 30: 24–35.
Fraser et al., 2005 Fraser, D. J., Duchesne, P., and Bernatchez, L. (2005). Molecular Ecology 14: 3133–3146.
Frati et al., 1992 Frati, F., Fanciulli, P. P., and Posthuma, L. (1992). Biochemical Systematics and Evolution 20: 297–310.
Freville et al., 2001 Freville, H., Justy, F., and Olivieri, I. (2001). Molecular Ecology 10: 879–889.
Fullard et al., 2000 Fullard, K. J., Early, G., Heide-Jørgensen, M. P., Bloch, D., Rosing-Asvid, A., and Amos, W. (2000).
Molecular Ecology 9: 949–958.
Funk et al., 1999 Funk, W. C., Tallmon, D. A., and Allendorf, F. W. (1999). Molecular Ecology 8: 1633–1640.
Galeuchet et al., 2005 Galeuchet, D. J., Perrett, and C., Fischer, M. (2005). Molecular Ecology 14: 991–1000.
Gao 2005 Gao, L. (2005). Molecular Ecology 14: 4287–4297.
Garcia-Rodriguez et al., 2000 Garcia-Rodriguez, A. I., Moraga-Amador, D., Farmerie, W., McGuire, P., and King, T. L. (2000). Molecular
Ecology 9: 2155–2234.
Garza et al., 1997 Garza, J. C., Dallas, J., Duryadi, D., Gerasimov, S., Croset, H., and Boursot, P. (1997). Molecular Ecology 6:
Ge et al., 1998 Ge, S., Wang, K., Hong, D., Zang, W., and Zu, Y. (1999).
Conservation Biology 13: 509–513.
Gerlach and Hoeck, 2001 Gerlach, G., and Hoeck, H. N. (2001). Molecular Ecology 10: 2307–2317.
Gerlach and Musolf, 2000 Gerlach, G., and Musolf, K. (2000). Conservation Biology 14: 1066–1074.
Girman et al., 2001 Girman, D. J., Vila
, C., Geffen, E., Creel, S., Mills, M. G. L., McNutt, J. W., Ginsberg, J., Kat, P. W.,
Mamiya, K. H., and Wayne, R. K. (2001). Molecular Ecology 10: 1703–1723.
Godt et al., 1996 Godt, M. J. W., Johnson, B. R., and Hamrick, J. L. (1996). Conservation Biology 10: 796–805.
Godt and Hamrick, 1998 Godt, M. J. W., and Hamrick, J. L. (1998). American Journal of Botany 85: 802–810.
Goldberg et al., 2003 Goldberg, C. S., Edwards, T., Kaplan, M. E., and Goode, M. (2003). Molecular Ecology Notes 3: 539–541.
lez-Astorga and Castillo-
Campos, 2004
lez-Astorga, J., and Castillo-Capos, G. (2004). Annals of Botany 93: 521–528.
lez-Astorga and Nu
n, 2001
lez-Astorga, J., and Nu
n, J. (2001). Evolutionary Ecology Research 3: 861–872.
Goodman et al., 2001 Goodman, S. J., Tamate, H. B., Wilson, R., Nagata, J., Tatsuzawa, S., Swanson, G. M., Pemberton, J. M.,
and McCullough, D. R. (2001). Molecular Ecology 10: 1357–1370.
Goosens et al., 2001 Goosens, B., Chikhi, L., Taberlet, P., Waits, L. P., and Allaine
, D. (2001). Molecular Ecology 10: 41–52.
Goossens et al., 2005 Goosens, B., Chikhi, L., Jalil, F., Ancrenaz, M., Lackman-Ancrenaz, I., Mohamed, M., Andau, P., and
Bruford, M. W. (2005). Molecular Ecology 14: 441–456.
Gottelli et al., 1994 Gottelli, D., Sillerozubiri, C., Applebaum, G. D., Roy, M. S., Girman, D. J., Garciamoreno, J., Ostrander, E.
A., and Wayne, R. K. (1994). Molecular Ecology Notes 3: 301–312.
Guillemin et al., 2000 Guillemin, M., Lavergne, A., and Catzeflis, F. (2000). Molecular Ecology 9: 1433–1449.
Guinand et al., 2003 Guinand, B., Scribner, K. T., Page, K. S., and Burnham-Curtis, M. K. (2003). Proceedings of the Royal
Society of London B-Biological Sciences270: 425–433.
Conserv Genet (2008) 9:141–156 151
Appendix Table A1 continued
Abbreviated reference Complete reference
rrez-Espeleta et al., 2000 Gutie
rrez-Espeleta, G. A., Kalinowski, S. T., Boyce, W. M., and Hedrick, P. W. (2000). Conservation
Genetics 1: 3–15.
guez and Lasker,
guez, C., and Lasker, H. R. (2004). Molecular Ecology 13: 2211–2221.
Hanfling et al., 2004 Ha
nfling, B., Durka, W., and Brandl, R. (2004). Conservation Genetics 5: 247–257.
Hansen et al., 2002 Hansen, M. M., Ruzzante, D. E., Nielsen, E. E., Bekkevold, D., and Mensberg, K. D. (2002). Molecular
Ecology 11: 2523–2535.
Harley et al., 2005 Harley, E. H., Baumgarten, I., Cunningham, J., and O’Ryan, C. (2005). Molecular Ecology 14: 2981–2990.
Hauser et al., 2002 Hauser, L., Adcock, G. J., Smith, P. J., Ramirez, J. H., and Carvalho, G. R. (2002). Proceedings of the
National Academy of Sciences of the United States of America 99: 11742–11747.
Heath et al., 2002 Heath, D. D., Busch, C., Kelly, J., and Atagi, D. Y. (2002). Molecular Ecology 11: 197–214.
Heckel et al., 2000 Heckel, G., Achmann, R., and Mayer, F. (2000). Molecular Ecology 9: 242.
Hedgecock 1978 Hedgecock, D. (1978). Evolution 32: 271–286.
Hellborg et al., 2002 Hellborg, L., Walker, C. W., Rueness, E. K., Stacy, J. E., Kojola, I., Valdmann, H., Vila
, C., Zimmermann,
B., Jakobsen, K. S., and Ellegren, H. (2002). Conservation Genetics 3: 97–111.
Heuertz et al., 2001 Heuertz, M., Hausman, J. F., Tsvetkov, I., Frascaria-Lacoste, N., and Vekemans, X. (2001). Molecular
Ecology 10: 1615–1623.
Houlden et al., 1996 Houlden, B. A., England, P. R., Taylor, A. C., Greville, W. D., and Sherwin, W. B. (1996). Molecular
Ecology 5: 269–281.
Hughes et al., 1998 Hughes, C. R., Melland, R. R., and Beissinger, S. R. (1998). Molecular Ecology 7: 1247–1248.
Hughes et al., 2003 Hughes, J. M., Mather, P. B., Toon, A., Ma, J., Rowley, I., and Russell, E. (2003). Molecular Ecology 12:
Ishibashi et al., 1996 Ishibashi, Y., Saitoh, Y., Abe, S., and Yoshida, M. C. (1995). Molecular Ecology 5: 589–590.
Israel et al., 2004 Israel, J. A., Cordes, J. F., Blumberg, M. A., and May, B. (2004). North American Journal of Fisheries
Management 24: 922–931.
Jekielek and Strobeck, 1999 Jekielek, J., and Strobeck, C. (1999). Molecular Ecology 8: 895–906.
Johnson et al., 1999 Johnson, W. E., Slattery, J. P., Eizirik, E., Kim, J., Raymond, M. M., Bonacic, C., Cambre, R., Crawshaw,
P., Nunes, A., Seua
nez, H. N., Moreira, M., Seymour, K. L., Simon, F., Swanson, W., and O’Brien, S. J.
Molecular Ecology 8: S79-S94.
Johnson et al., 2003 Johnson, J. A., Tpe[fer, J. E., and Dunn, P. O. (2003). Molecular Ecology 12: 3335–3347.
Jones and Gliddon, 1999 Jones, B., and Gliddon, C. (1999). Plant Ecology 141: 151–161.
Kang et al., 2005 Kang, M., Jiang, M., and Huang, H. (2005). Annals of Botany 95: 1145–1151.
Kays et al., 2000 Kays, R. W., Gittleman, J. L., and Wayne, R. K. (2000). Molecular Ecology 9: 743–751.
Keklak et al., 1994 Keklak, M. M., Newman, M. C., and Mulvey, M. (1994). Archives of Environmental Contamination and
Toxicology 27: 20–24.
Keller and Largiade
r, 2003 Keller, I. and Largiade
r, C. R. (2003). Proceedings of the Royal Society of London B-Biological Sciences
270: 417–423.
Ketmaier et al., 2003 Ketmaier, V., Scapini, F., and De Matthaeis, E. (2003). Estuarine, Coastal and Shelf Studies 58S: 159–167.
Kim and Sappington, 2005 Kim, K. S., and Sappington, T. W. (2005). Environmental Entomology 34: 494–503.
Kirchhoff et al., 1999 Kirchhoff, S., Se
vigny, J. M., and Couillard, C. M. (1999). Marine Environmental Research 47: 261–283.
Knaepkens et al., 2004 Knaepkens, G., Bervoets, L., Verheyen, E., and Eens, M. (2004). Biological Conservation 115: 403–410.
Korfanta et al., 2005 Korfanta, N. M., McDonald, D. B., and Glenn, T. C. (2005). The auk 122: 464–478.
Kraaijeveld-Smit et al., 2005 Kraaijeveld-Smit, F. J. L., Beebee, T. J. C., Griffiths, R. A., Moore, R. D., and Schley, L. (2005). Molecular
Ecology 14: 3307–3315.
Kronforst and Fleming, 2001 Kronforst, M. R. and Fleming, T. H. (2001). Heredity 86: 243–250.
Krutzen et al., 2004 Kru
tzen, M., Barre
, L. M., Connor, R. C., Mann, J., and Sherwin, W. B. (2004). Molecular Ecology 13:
Kuehn et al., 2004 Kuehn, R., Haller, H., Schroeder, W., and Rottmann, O. (2004). Journal of Heredity 95: 136–143.
Kyle et al., 2004 Kyle, C. J., Weir, R. D., Newhouse, N. J., Davis, H., and Strobeck, C. (2004). Journal of Mammology 85:
Lacey 2001 Lacey, E. A. (2001). Heredity 86: 629–637.
Lade et al., 1996 Lade, J. A., Murray, N. D., Marks, C. A., and Robinson, N. A. (1996). Molecular Ecology 5: 81–87.
152 Conserv Genet (2008) 9:141–156
Appendix Table A1 continued
Abbreviated reference Complete reference
Lampert et al., 2003 Lampert, K. P., Rand, A. S., Mueller, U. G., and Ryan, M. J. (2003). Molecular Ecology 12: 3325–3334.
Larno et al., 2001 Larno, V., Laroche, J., Launey, S., Flammarion, P., and Devaux, A. (2001). Ecotoxicology 10: 145–158.
Larson et al., 2002 Larson, S., Jameson, R., Bodkin, J., Staedler, M., and Bentzen, P. (2002). Journal of Mammology 83: 893–
Lee et al., 2001 Lee, P. L. M., Bradbury, R. B., Wilson, J. D., Flanagan, N. S., Richardson, L., Perkins, A. J., and Krebs, J. R.
(2001). Molecular Ecology 10: 1633–1644.
Lefant 2003 Lefant, P. (2003). Comptes Rendus Biologies 326: 751–760.
rres et al., 2003 Lesbare
rres, D., Pagano, A., and Lode
, T. (2003). Comptes Rendus Biologies 326: S68-S72.
Libants et al., 2000 Libants, S., Olle, E., Oswald, K., and Scribner, K. T. (2000). Molecular Ecology 9: 1433–1449.
Longauer et al., 2004 Longauer, R., Go
ry, D., Paule, L., Blada, I., Popescu, F., Mankovska, B., Mu
ller-Starck, G., Schubert, R.,
Percy, K., Szaro, R. C., and Karnosky, D. F. (2004). Environmental Pollution 130: 85–92.
Lu et al., 2001 Lu, Z., Johnson, W. E., Menotti-Raymond, M., Yuhki, N., Martenson, J. S., Mainka, S., Shi-Qiang, H.,
Zhihe, Z., Li, G., Pan, W., Mao, X., and O’Brien, S. J. (2001). Conservation Biology 15: 1596–1607.
Luijten et al., 2000 Luijten, S. H., Dierick, A., Gerard, J., Oostermeijer, B., Raijmann, L. E. L., and Den Nijs, H. (2000).
Conservation Biology 14: 1776–1787.
Maes et al., 2005 Maes, G. E., Raeymaekers, J. A. M., Pampoulie, C., Seynaeve, A., Goemans, G., Belpaire, C., and
Volckaert, F. A. M. (2005). Aquatic Toxicology 73: 99–114.
Makeeva et al., 2005 Makeeva, V. M., Belokon, M. M., and Malyuchenko, O. P. (2005) Russian Journal of Genetics 41: 1495–
Marshall et al., 1999 Marshall, T. C., Sunnucks, P., Spalton, J. A., Greth, A., and Pemberton, J. M. (1999). Animal Conservation
2: 269–278.
Martinez-Cruz et al., 2004 Martı
nez-Cruz, B., Godoy, J. A., and Negro, J. J. (2004). Molecular Ecology 13: 2243–2255.
Mateu-Andres 2004 Mateu-Andre
s, I. (2004). Annals of Botany 94: 797–804.
Maudet et al., 2002 Maudet, C., Miller, C., Bassano, B., Breittenmoser-Wu
rsten, C., Gauthier, D., Obexer-Ruff, G., Michallet,
J., Taberlet, P., and Luikart, G. (2002). Molecular Ecology 11: 421–436.
McCrae et al., 2005 McCrae, B. H., Beier, P., Dewald, L. E., Huynh, L. Y., and Keim, P. (2005). Molecular Ecology 14: 1965–
McQuown et al., 2003 McQuown, E., Krueger, C. C., Kincaid, H. L., Gall, G. A. E., and May, B. (2003). Journal of Great Lakes
Research 29:
Miller and Kapuscinski, 1997 Miller, L. M. and Kapuscinski, A. R. (1997). Genetics 147: 1249–1258.
Millis 2000 Millis, A. L. (2000). Molecular Ecology 9: 1661–1686.
Mills et al., 2004 Mills, H. R., Moro, D., and Spencer, P. B. S. (2004). Animal Conservation 7: 387–395.
Moritz et al., 1997 Moritz, C., Heideman, A., Geffen, E., and McCrae, P. (1997). Molecular Ecology 6: 925–936.
Murphy et al., 2000 Murphy, R. W., Fu, J., Upton, D. E., De Lama, T., and Zhao, E. (2000). Molecular Ecology 9: 1539–1547.
Nesje and Røed, 2000 Nesje, M., and Røed, K. H. (2000). Molecular Ecology 9: 1433–1449.
Nichols et al., 2001 Nichols, R. A., Bruford, M. W., and Groombridge, J. J. (2001). Molecular Ecology 10: 593–602.
Nievergelt et al., 1998 Nievergelt, C. M., Mundy, N. I., and Woodruff, D. S. (1998). Molecular Ecology 7: 1431–1439.
Ohnishi et al., 1998 Ohnishi, N., Ishibashi, Y., Saitoh, T., Abe, S., and Yoshida, M. C. (1998). Molecular Ecology 7: 1431–1439.
Olsen and Spearman, 2004 Olsen, J. B., and Spearman, W. J. (2004). Transactions of the American Fisheries Society 133: 476–483.
Olsen et al., 1998 Olsen, J. B., Bentzen, P., and Seeb, J. E. (1998). Molecular Ecology 7: 1083–1090.
Paetkau et al., 1999 Paetkau, D., Amstrup, S. C., Born, E. W., Calvert, W., Derocher, A. E., Garner, G. W., Messier, F., Stirling,
I., Taylor, M. K., Wiig, Ø., and Strobeck, C. (1999). Molecular Ecology 8: 1571–1584.
Palo et al., 2001 Palo, J. U., Ma
kinen, H. S., Helle, E., Stenman, O., and Va
, R. (2001). Heredity 86: 609–617.
Pampoulie et al., 2004 Pampoulie, C., Gysels, E. S., Maes, G. S., Hellemans, B., Leentjes, V., Jones, A. G., and Volckaert, F. A. M.
(2004). Heredity 92: 434–445.
Paschke et al., 2002 Paschke, M., Abs, C., and Schmid, B. (2002). Conservation Genetics 3: 131–144.
Peterson and Heaney, 1993 Peterson, A. T., and Heaney, L. R. (1993). Biological Journal of the Linnean Society 49: 203–218.
Pertoldi et al., 2001 Pertoldi, C., Hansen, M. M., Loeschcke, V., Madsen, A. B., Jacobsen, L., and Baagoe, H. (2001).
Proceedings of the Royal Society of London B-Biological Sciences 268: 1775–1781.
Pertoldi et al., 2005 Pertoldi, C., Loeschcke, V., Randi, E., Madsen, A. B., Hansen, M. M., Bijlsma, R., and Van de Zande, L.
(2005). Journal of Zoology London 265: 387–394.
Pfeiler and Markow, 2001 Pfeiler, E., and Markow, T. A. (2001). Molecular Ecology 10: 1787–1791.
Conserv Genet (2008) 9:141–156 153
Appendix Table A1 continued
Abbreviated reference Complete reference
Piertney et al., 2000 Piertney, S. E., Dallas, J. F., Hawkins, C. E., and Racey, P. A. (2000). Molecular Ecology 9: 489–504.
Pope et al., 2000 Pope, L. C., Estoup, A., and Moritz, C. (2000). Molecular Ecology 9: 2041–2053.
Prober and Brown, 1994 Prober, S. M., and Brown, A. H. D. (1994). Conservation Biology 8: 1003–1013.
Prus-Glowacki et al., 1999 Prus-Glowacki, W., Wonicka-Poltorak, A., Oleksyn, J., and Reich, P. B. (1999). Water, Air, and Soil
Pollution 116: 395–402.
Queney et al., 2001 Queney, G., Ferrand, N., Weiss, S., Mougel, F., and Monnerot, M. (2001). Molecular Biology and Evolution
18: 2169–2178.
Raijmann et al., 1994 Raijmann, L. E. L., Van Leeuwen, N. C., Kersten, R., Oostermeijer, J. G. B., Den Nijs, H. C. M., and
Menken, S. B. J. (1994). Conservation Biology 8: 1014–1026.
Rajora et al., 2000 Rajora, O. P., Rahman, M. H., Buchert, G. P., and Dancik, B. P. (2000). Molecular Ecology 9: 339–348.
Ranker et al., 1994 Ranker, T. A. (1994). Biological Conservation 70: 19–24.
Reding and Guttman, 1991 Reding, M. E., and Guttman, S. I. (1991). American Midland Naturalist 126: 322–337.
Reinartz et al., 2000 Reinartz, G. E., Karron, J. D., Phillips, R. B., Weber, J. L. (2000). Molecular Ecology 9: 315–328.
Ribeiro et al., 2005 Ribeiro, R., Ramos, A., Filho, J., and Lovato, M. (2005). Annals of Botany 95: 1171–1177.
Richard et al., 1996 Richard, K. R., Whitehead, H., and Wright, J. M. (1996). Molecular Ecology 5: 313–315.
Riffaut et al., 2005 Riffaut, L., McCoy, K. D., Tirard, C., Friesen, V. L., and Boulinier, T. (2005). Marine Ecology Progress
Series 291: 263–273.
Roach et al., 2001 Roach, J. L., Stapp, P., Van Horne, B., and Antolin, M. F. (2001). Journal of Mammology 82: 946–959.
Roark and Brown 1996 Roark, S., and Brown, K. (1996). Environmental Toxicology and Chemistry 15: 921–927.
Roark et al., 2001 Roark, S. A., Andrews, J. F., and Guttman, S. I. (2001). Ecotoxicology 10: 223–227.
Røed and Midthjell, 1998 Røed, K. H., and Midthjell, L. (1998). Molecular Ecology 7: 1771–1788.
Rooney et al., 1999 Rooney, A. P., Honeycutt, R. L., Davis, S. K., and Derr, J. N. (1999). Journal of Molecular Evolution 49:
Rossiter et al., 2000 Rossiter, S. J., Jones, G., Ransome, R. D., and Barratt, E. M. (2000). Proceedings of the Royal Society of
London Series B-Biological Sciences 267: 545–551.
Roy et al., 1994 Roy, M. S., Geffen, E., Smith, D., Ostrander, E. A., and Wayne, R. K. (1994). Molecular Biology and
Evolution 11: 553–570.
Roy et al., 1996 Roy, M. S., Geffen, E., Smith, D., and Wayne, R. K. (1996). Conservation Biology 10: 1413–1424.
Salgueiro et al., 2003 Salgueiro, P., Carvalho, G., Collares-Pereira, M. J., and Coelho, M. M. (2003). Biological Conservation 109:
Sarno et al., 2001 Sarno, R. J., Franklin, W. L., O’Brien, S. J., and Johnson, W. E. (2001). Animal Conservation 4: 93–101.
Schmidt 1999 Schmidt, C. A. (1999).
Journal of Mammology 80: 522–529.
Schroeder et al., 2000 Schroeder, J. W., Honeycutt, R. L., Rooney, A. P., Han, G., Begall, S., and Gallardo, M. H. (2000).
Molecular Ecology 9: 1433–1449.
Schulte-Hostedde et al., 2001 Schulte-Hostedde, A. I., Gibbs, H. L., and Millar, J. S. (2001). Molecular Ecology 10: 1625–1631.
Segarra-Moragues and Catala
Segarra-Moragues, J. G., and Catala
n, P. (2002). International Journal of Plant Sciences 163: 159–166.
Segelbacher and Storch, 2002 Segelbacher, G. and Storch, I. (2002). Molecular Ecology 11: 1669–1677.
Sharma et al., 2003 Sharma, I. K., Jones, D. L., and French, C. J. (2003). Biochemical Systematics and Ecology 31: 513–526.
Spencer et al., 1997 Spencer, P. B. S., Adams, M., Marsh, H., Miller, D. J., and Eldridge, M. D. B. (1997). Australian Journal of
Zoology 45: 199–210.
Spong et al., 2000 Spong, G., Johansson, M., and Bjo
rklund, M. (2000). Molecular Ecology 9: 1773–1782.
Stangel et al., 1992 Stangel, P. W., Lennartz, M. R., and Smith, M. H. (1992). Conservation Biology 6: 283–292.
Stevens et al., 1997 Stevens, S., Coffin, J., and Strobeck, C. (1997). Molecular Ecology 6: 493–495.
Stewart et al., 1999 Stewart, W. A., Dallas, J. F., Piertney, S. B., Marshall, F., Lambin, X., and Telfer, S. (1999). Biological
Journal of the Linnean Society 68: 159–171.
Stow et al., 2001 Stow, A. J., Sunnucks, P., Briscoe, D. A., and Gardner, M. G. (2001). Molecular Ecology 10: 867–878.
Sumner et al., 2004 Sumner, J., Jessop, T., Paetkau, D., and Moritz, C. (2004). Molecular Ecology 13: 259–269.
Tallmon et al., 2002 Tallmon, D. A., Draheim, H. M., Mills, L. S., and Allendorf, F. W. (2002). Molecular Ecology 11: 699–709.
Taylor et al., 2000 Taylor, A. C., Cowan, P. E., Fricke, B. L., Cooper, and D. W. (2000). Molecular Ecology 9: 869–879.
Tessier and Bernatchez, 1999 Tessier, N., and Bernatchez, L. (1999). Molecular Ecology 8: 169–179.
154 Conserv Genet (2008) 9:141–156
Amos W, Harwood J (1998) Factors affecting levels of genetic
diversity in natural populations. Philos Trans R Soc Lond B Biol
Arnqvist G, Wooster D (1995) Meta-analysis: Synthesizing research
findings in ecology and evolution. TREE 10:236–240
Baker RJ, Bickham AM, Bondarkov M, Gaschak SP, Matson CW,
Rodgers BE, Wickliffe JK, Chesser RK (2001) Consequences of
polluted environments on population structure: The bank vole
(Clethrionomys glareolus) at Chernobyl. Ecotoxicology 10:
Bataillon TM, David JL, Schoen, DJ (1996) Neutral genetic markers
and conservation genetics: simulated germplasm collections.
Genetics 144:409–417
Belfiore NM, Anderson SL (2001) Effects of contaminants on genetic
patterns in aquatic organisms: a review. Mutat Res 489:97–122
Berckmoes V, Scheirs J, Jordaens K, Blust R, Backeliau T, Verhagen
R (2005) Effects of environmental pollution on microsatellite
DNA diversity in wood mouse (Apodemus sylvaticus) popula-
tions. Environ Toxicol Chem 24:2898–2907
Bickham JW, Sandhu S, Hebert PD, Chikhi L, Athwal R (2000)
Effects of chemical contaminants on genetic diversity in natural
populations: implications for biomonitoring and ecotoxicology.
Mutat Res 463:33–51
Burger R, Lynch M (1995) Evolution and extinction in a changing
environment: A quantitative-genetic analysis. Evolution 49:
Caizergues A, Ra
tti O, Helle P, Rotelli L, Ellison L, Rasplus J (2003)
Population genetic structure of male black grouse (Tetrao tetrix
Appendix Table A1 continued
Abbreviated reference Complete reference
Thomas et al., 1999 Thomas, B. R., Macdonald, S. E., Hicks, M., Adams, D. L., and Hodgetts, R. B. (1999). Theoretical and
Applied Genetics 98: 793–801.
Tsuda and Ide, 2005 Tsuda, Y., and Ide, Y. (2005). Molecular Ecology 14: 3929–3941.
Van de Zande et al., 2000 Van de Zande, L., Van Apeldoorn, R. C., Blijdenstein, A. F., De Jong, D., Van Delden, W., and Bijlsma, R.
(2000). Molecular Ecology 9: 1651–1656.
Van den Bussche et al., 2003 Van den Bussche, R. A., Hoofer, S. R., Wiedenfeld, D. A., Wolfe, D. H., and Sherrod, S. K. (2003).
Molecular Ecology 12: 675–683.
Van der Strate et al., 2000 Van der Strate, H. J., Olsen, J. L., Van de Zande, L., Edwards, K. J., and Stam, W. T. (2000). Molecular
Ecology 9: 1433–1449.
Van Dongen et al., 1998 Van Dongen, S., Backeljau, T., Matthysen, E., and Dhondt, A. A. (1998).Heredity 80: 92–100.
Van Hooft et al., 2000 Van Hooft, W. F., Groen, A. F., and Prins, H. H. T. (2000). Molecular Ecology 9: 2017–2025.
Veit et al., 2005 Veit, M. L., Robertson, R. J., Hamel, P. B., and Friesen, V. L. (2005). Conservation Genetics 6: 159–174.
Vernesi et al., 2003 Vernesi, C., Crestanello, B., Pecchioli, E., Tartari, D., Caramelli, D., Hauffe, H., and Bertorelle, G. (2003).
Molecular Ecology 12: 585–595.
von Segesser et al., 1999 Von Segesser, F., Menard, N., Gaci, B., and Martin, R. D. (1999). Molecular Ecology 8: 433–442.
Vos et al., 2001 Vos, C. C., Antonisse-De Jong, A. G., Goedhart, P. W., and Smulders, M. J. M. (2001). Heredity 86 598–
Waits et al., 2000 Waits, L., Taberlet, P., Swenson, J. E., Sandergren, F., and Franze
n, R. (2000). Molecular Ecology 9: 421–
Waldick et al., 2002 Waldick, R. C., Kraus, S., Brown, M., and White, B. N. (2002). Molecular Ecology 11: 2241–2249.
Walker et al., 2001 Walker, C. W., Vila
, C., Landa, A., Linde
n, M., and Ellegren, H. (2001). Molecular Ecology 10: 53–63.
Wang and Schreiber, 2001 Wang, M., and Schreiber, A. (2001). Heredity 86: 703–715.
White et al., 1999 White, G. M., Boshier, D. H., and Powell, W. (1999). Molecular Ecology 8: 1899–1909.
Wilson and Strobeck, 1999 Wilson, G. A., and Strobeck, C. (1999). Genome 42: 483–496.
Wisely et al., 2002 Wisely, S. M., Buskirk, S. W., Fleming, M. A., McDonald, D. B., and Ostrander, E. A. (2002). The Journal
of Heredity 93: 231–237.
Wisely et al., 2004 Wisely, S. M., Buskirk, S. W., Russell, G. A., Aubry, K. B., and Zielinski, W. J. (2004). Journal of
Mammology 85: 640–648.
Withler et al., 2000 Withler, R. E., Le, K. D., Nelson, R. J., Miller, K. M., and Beacham, T. D. (2000). Canadian Journal of
Fisheries and Aquatic Sciences 57: 1985–1998.
Wooten et al., 1999 Wooten, M. C., Scribner, K. T., and Krehling, J. T. (1999). Molecular Ecology 8: 167–168.
Wyttenbach et al., 1997 Wyttenbach, A., Favre, L., and Hausser, J. (1997). Molecular Ecology 6: 797–800.
Xu et al., 2001 Xu, Z. K., Primavera, J. H., de la Pena, L. D., Pettit, P., Belak, J., and Alcivar-Warren, A. (2001).
Aquaculture 199: 13–40.
Yap et al., 2004 Yap, C. K., Tan, S. G., Ismail, A., and Omar, H. (2004). Environment International 30: 39–46.
Young and Brown, 1996 Young, A. G., and Brown, A. H. D. (1996). Conservation Biology 10: 1220–1228.
Young et al., 1999 Young, A. G., Brown, A. H. D., and Zich, F. A. (1999). Conservation Biology 13: 256–265.
Conserv Genet (2008) 9:141–156 155
L.) in fragmented vs. continuous landscapes. Mol Ecol 12:
guez A, Rola
n-Alvarez E, Caballero A (2005) Quan-
titative variation as a tool for detecting human-induced impacts
on genetic diversity. Biol Conserv 124:1–13
Coltman DW, O’Donoghue P, Jorgenson JT, Hogg JT, Strobeck C,
Festa-Bianchet M (2003) Undesirable evolutionary conse-
quences of trophy hunting. Nature 426:655–658
Crnokrak P, Roff DA (1999) Inbreeding depression in the wild.
Heredity 83:260–270
De Pippo T, Donadio C, Guida M, Petrosino C (2006) The case of
Sarno river (southern Italy): Effects of geomorphology on the
environmental impacts. Environ Sci Pollut Res Int 13:
Ellegren H, Lindgren G, Primmer CR, Møller AP (1997) Fitness loss
and germline mutations in barn swallows breeding in Chernobyl.
Nature 389:593–596
Falconer DS, MacKay TF (1996) Introduction to quantitative
genetics. Addison Wesley Publishing, Essex
Fitzsimmons NN, Buskirk SW, Smith MH (1995) Population history,
genetic variability, and horn growth in bighorn sheep. Conserv
Biol 9:314–323
Frankham R (1995) Conservation genetics. Annu Rev Genet 29:
Frankham R (1996) Relationship of genetic variation to population
size in wildlife. Conserv Biol 10:1500–1508
Frankham R (2003) Genetics and conservation biology. C R Biol 326:
Frankham R (2005) Genetics and extinction. Biol Conserv 126:
Frankham R, Ballou JD, Briscoe DA (2002) Introduction to conser-
vation genetics. Cambridge University Press, Cambridge
Garner A, Rachlow JL, Hicks JF (2005) Patterns of genetic diversity
and its loss in mammalian populations. Conserv Biol 19:
Gilligan DM, Briscoe DA, Frankham R (2005) Comparative losses of
quantitative and molecular genetic variation in finite populations
of Drosophila melanogaster. Genet Res Camb 85:47–55
Godt MJ, Johnson BR, Hamrick JL (1996) Genetic diversity and
population size in four southern Appalachian plant species.
Conserv Biol 10:796–805
Goosens B, Chikhi L, Jalil F, Ancrenaz M, Lackman-Ancrenaz I,
Mohamed M, Andau P, Bruford MW (2005) Patterns of genetic
diversity and migration in increasingly fragmented and declining
orang-utan (Pongo pygmaeus) populations from Sabah, Malay-
sia. Mol Ecol 14:441–456
Gurevitch J, Hedges LV (1999) Statistical issues in ecological meta-
analyses. Ecology 80:1142–1149
Hartl GB, Pucek Z (1994) Genetic depletion in the European bison
(Bison bonasus) and the significance of electrophoretic hetero-
zygosity for conservation. Conserv Biol 8:167–174
Hartl GB, Zachos F, Nadlinger K (2003) Genetic diversity in
European red deer (Cervus elaphus L.): Anthropogenic influ-
ences on natural populations. C R Biol 326: S37-S42
Hedrick PW (2000) Genetics of populations. Jones and Bartlett,
Kang M, Jiang M, Huang H (2005) Genetic diversity in fragmented
populations of Berchemiella wilsonii var. pubipetiolata (Rhamn-
aceae). Ann Bot (Lond) 95:1145–1151
Keane B, Collier MH, Rogstad SH (2005) Pollution and genetic
structure of North American populations of the common
dandelion (Taraxacum officinale). Environ Monit Assess
Lacy RC (1997) Importance of genetic variation to the viability of
mammalian populations. J Mammal 78:320–335
Lande R (1988) Genetics and demography in biological conservation.
Science 241:1455–1460
Lande R, Shannon S (1996) The role of genetic variation in
adaptation and population persistence in a changing environ-
ment. Evolution 50:434–437
Ma LM, Cowles DL, Carter RL (2000) Effect of pollution on genetic
diversity in the bay mussel Mytilus galloprovincialis and the
acorn barnacle Balanus glandula. Mar Environ Res 50:559–563
McKay JK, Latta RG (2002) Adaptive population divergence:
Markers, QTL and traits. TREE 17:285–291
Nei M (1987) Molecular evolutionary genetics. Colombia University
Press, New York
Posthuma L, Van Straalen NM (1993) Heavy-metal adaptation in
terrestrial invertebrates: A review of occurrence, genetics,
physiology and ecological consequences. Comp Biochem Phys-
iol 106C:11–38
Reed DH, Frankham R (2001) How closely correlated are molecular
and quantitative measures of genetic variation? A meta-analysis.
Evolution 55:1095–1103
Reed DH, Frankham R (2003) Correlation between fitness and genetic
diversity. Conserv Biol 17:230–237
Schoen DJ, Brown, AH (1993) Conservation of allelic richness in
wild crop relatives is aided by assessment of genetic markers.
PNAS 90:10623–10627
Toro MA, Caballero A (2005) Characterization and conservation of
genetic diversity in subdivided populations. Philos Trans R Soc
Lond B Biol Sci 360:1367–1378
Von Segesser F, Menard N, Gaci B, Martin RD (1999) Genetic
differentiation within and between isolated Algerian subpopula-
tions of Barbary macaques (Macaca sylvanus): Evidence from
microsatellites. Mol Ecol 8:433–442
Yauk CL, Quinn JS (1996) Multilocus DNA fingerprinting reveals
high rate of heritable genetic mutation in herring gulls nesting in
an industrialized urban site. PNAS 93:12137–12141
Young A, Boyle T, Brown T (1996) The population genetic
consequences of habitat fragmentation for plants. TREE 11:
156 Conserv Genet (2008) 9:141–156
... Moreover, changes in nuclear genetic diversity following habitat disturbance are variable across taxa. For example, mammals generally lose diversity in highly urbanized areas, but at different rates depending on species (DiBattista, 2008;Habrich et al., 2021;Schmidt et al., 2020). Bird species either lose or gain genetic diversity in more urban areas (Schmidt et al., 2020), whereas changes in amphibian genetic diversity are more idiosyncratic depending on species and location (Schmidt & Garroway, 2021b). ...
The International Union for Conservation of Nature (IUCN) Red List is an important and widely used tool for conservation assessment. The IUCN uses information about a species’ range, population size, habitat quality and fragmentation levels, and trends in abundance to assess extinction risk. Genetic diversity is not considered, although it affects extinction risk. Declining populations are more strongly affected by genetic drift and higher rates of inbreeding, which can reduce the efficiency of selection, lead to fitness declines, and hinder species’ capacities to adapt to environmental change. Given the importance of conserving genetic diversity, attempts have been made to find relationships between red‐list status and genetic diversity. Yet, there is still no consensus on whether genetic diversity is captured by the current IUCN Red List categories in a way that is informative for conservation. To assess the predictive power of correlations between genetic diversity and IUCN Red List status in vertebrates, we synthesized previous work and reanalyzed data sets based on 3 types of genetic data: mitochondrial DNA, microsatellites, and whole genomes. Consistent with previous work, species with higher extinction risk status tended to have lower genetic diversity for all marker types, but these relationships were weak and varied across taxa. Regardless of marker type, genetic diversity did not accurately identify threatened species for any taxonomic group. Our results indicate that red‐list status is not a useful metric for informing species‐specific decisions about the protection of genetic diversity and that genetic data cannot be used to identify threat status in the absence of demographic data. Thus, there is a need to develop and assess metrics specifically designed to assess genetic diversity and inform conservation policy, including policies recently adopted by the UN's Convention on Biological Diversity Kunming‐Montreal Global Biodiversity Framework.
... Genetic variation underpins population fitness and adaptive potential and is key in terms of species extinction risk (Hoffmann et al., 2017;Reed & Frankham, 2003). Importantly, habitat loss and fragmentation decreases the size and connectivity of populations, with a consequent loss of genetic diversity at both species and population levels (Allendorf et al., 2012;DiBattista, 2008). Safeguarding genetic variation is therefore essential to mitigating biodiversity loss (Leigh et al., 2019;Pereira et al., 2013;Sarre & Georges, 2009). ...
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We apply an environmental domains approach to identify environmentally heterogeneous characteristics defining a landscape matrix. We built environmental layers for national, regional, and local scales, considering the different scales studies can have. We used a numerical classification of explicit spatial layers and performed a multivariate classification. Based on the domains obtained, we mapped the landscape’s climatic heterogeneity and identified a comprehensive set of environmental variables that defined the landscape matrix at each scale. We specifically tested our approach for its suitability to define a sampling strategy for a landscape genetics study, using as focal species the rodent Heteromys pictus. Namely, from the domains obtained at the local scale, we selected sampling localities that comprised the broadest habitat heterogeneity, which we corroborated in the field. The landscape matrix thus generated was used with genetic data previously obtained for H. pictus. Our approach allowed identification of environmental variables significantly associated with dispersal (gene flow) of H. pictus individuals in their natural habitat. We demonstrate its adequacy to efficiently determine sampling localities —or landscape sites— that encompass the highest environmental heterogeneity, in explored and unexplored landscapes, enabling rapid identification of localities and their environmental characteristics where in situ information is scarce.
... Lack of genetic diversity is often associated with higher levels of inbreeding and reduced fitness (Brook et al. 2002, Keller & Waller 2002 adaptive potential (Nei et al. 1975, Corti et al. 2011, Abascal et al. 2016, Parra et al. 2018. Severe population declines, due to anthropo genic pressures for example, can precipitate genetic bottlenecks (Leigh et al. 2019), with the resulting small, fragmented populations likely to become genetically depauperate, more susceptible to genetic drift and often lacking incoming gene flow that may help to maintain genetic diversity (Willi et al. 2006, DiBattista 2008, Robinson et al. 2016. This situation can be exacerbated by harvesting (Allendorf et al. 2008), habitat degradation (Xu et al. 2019) and philopatric behaviours such as residency (Flowers et al. 2016, Monti et al. 2018), thereby minimising dispersal success, reducing the number of migrants and ultimately leading to local extirpation. ...
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Stability and long-term persistence of a species rely heavily on its genetic diversity, which is closely allied to its capacity for adaptation. In threatened species, population connectivity can play a major role in maintaining that diversity, and genetic assessments of their populations can be crucial for the design of effective spatial conservation management. Not only is it worth evaluating the amount of diversity in a candidate population for protection, but the magnitude of outgoing gene flow can provide insight into its potential to replenish others via emigrants. The critically endangered flapper skate Dipturus intermedius receives protection in the Loch Sunart to the Sound of Jura Marine Protected Area (MPA) in Scotland. However, there is insufficient knowledge of genetic diversity and connectivity across its range. Recent tagging studies in the MPA suggest the presence of animals with high levels of site fidelity and residency, as well as transient individuals, raising concerns of limited connectivity to populations beyond the MPA. In this study, a newly developed mitochondrial haplotype marker allowed use of DNA sourced from fin clips, mucus and egg cases to investigate population structure and mitochondrial variability across several sites around the British Isles, including the MPA. Unfortunately, results characterized the MPA as having particularly low haplotype diversity and significant population differentiation from other sample sites. More than a quarter of its individuals carry a haplotype rarely observed elsewhere, leaving outgoing gene flow questionable. The MPA appears unlikely to sustain the species’ existing mtDNA genetic diversity or act as an effective source population.
... A synthesis comparing genetic diversity estimates from wild populations facing different direct drivers found that populations whose habitat had been fragmented by landuse change have around 17% less genetic diversity than undisturbed populations (DiBattista, 2008); that study found no effect of direct exploitation on genetic diversity, but another meta-analysis reported that populations of fish species that have been overfished in the last 50 years had significantly lower genetic diversity than populations of closely related species (Pinsky & Palumbi, 2014). The declines in range size, numbers of populations, and population sizes of many species (Section will all tend to reduce their genetic diversity (Frankham, 1996). ...
... For most populations, 'maintain sufficient genetic diversity for adaptive potential' implies near zero loss of current genetic diversity (or when needed, restored genetic diversity through active management) which can be reported on using indicators for effective population size of 500 within each population to mitigate loss from genetic drift (see Implementation and Reporting below). No loss is consistent with CBD's Mission ("To take urgent action across society. . . to put biodiversity on a path to recovery by 2030 for the benefit of planet and people" and Vision ("living in harmony with nature"), especially as many species already suffered high genetic diversity loss (DiBattista 2008;Leigh et al. 2019;Exposito-Alonso et al. 2022). Ne [?] 500 (or, as Frankham (2022) suggests, Ne > 1000) is important for preventing future losses for all populations within species regardless of past lossesthough we acknowledge it does not address the extent of losses over previous decades and centuries. ...
... Fragmented habitats are associ-ated with a reduction in genetic diversity by a decrease in population size due to habitat isolation and matrix impermeability [64]. Fragmentation is also associated with higher selfing rates [50,[65][66][67], which is consistent with our findings. We detected a significant reduction in invaders' genetic diversity in self-compatible species. ...
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Plant invasions have a huge impact on the health of ecosystems and human well-being. The invasion risk varies with the introduction pathway, the propagule pressure, and the genetic diversity of the founding population. We performed a systematic review and meta-analysis of 30 studies reporting the genetic diversity of 31 plant species in their invasive and native ranges. We evaluated if patterns of genetic diversity differ between ranges and whether these responses are influenced by life-history traits, hybridization, polyploidization, and habitat condition. We found that invasive populations had significantly lower genetic diversity and higher inbreeding than native populations. In fragmented and degraded habitats, the genetic diversity of invaders was lower, but inbreeding was not affected. Polyploid invaders with hybrid capacity also showed lower genetic diversity. Invasive herbs with vegetative propagation were more sensitive to the loss of genetic diversity and had higher levels of inbreeding. Our synthesis showed that the genetic response in the invaded range could result from historical processes, such as founder and bottleneck events. Traits such as selfing are more likely to preserve the signatures of founder events and influence the genetic diversity in invasive populations. Additionally, clonality seems to be the predominant reproduction system in the invaded range.
... Habitat loss and fragmentation have negative impacts on populations, and are considered as one of the main causes of biodiversity loss and therefore a major issue in conservation biology (Fischer and Lindenmayer 2007;Wilson et al. 2016;Wu 2013). In particular, anthropogenic habitat fragmentation has modified the distribution and population sizes in many different organisms (Crooks et al. 2017;Haddad et al. 2015) with local and/or global reduction of genetic diversity and connectivity (DiBattista 2008;Leigh et al. 2019). Monitoring the genetic consequences of human activities that increase habitat fragmentation is therefore important to develop appropriate conservation and management strategies (Hoban et al. 2020). ...
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The negative impact of habitat fragmentation due to human activities may be different in different species that co-exist in the same area, with consequences on the development of environmental protection plans. Here we aim at understanding the effects produced by different natural and anthropic landscape features on gene flow patterns in two sympatric species with different specializations, one generalist and one specialist, sampled in the same locations. We collected and genotyped 194 wood mice (generalist species) and 199 bank voles (specialist species) from 15 woodlands in a fragmented landscape characterized by different potential barriers to dispersal. Genetic variation and structure were analyzed in the two species, respectively. Effective migration surfaces, isolation-by-resistance (IBR) analysis, and regression with randomization were used to investigate isolation-by-distance (IBD) and the relative importance of land cover elements on gene flow. We observed similar patterns of heterozygosity and IBD for both species, but the bank vole showed higher genetic differences among geographic areas. The IBR analysis suggests that (i) connectivity is reduced in both species by urban areas but more strongly in the specialist bank vole; (ii) cultivated areas act as dispersal corridors in both species; (iii) woodlands appear to be an important factor in increasing connectivity in the bank vole, and less so in the wood mouse. The difference in dispersal abilities between a generalist and specialist species was reflected in the difference in genetic structure, despite extensive habitat changes due to human activities. The negative effects of fragmentation due to the process of urbanization were, at least partially, mitigated by another human product, i.e., cultivated terrains subdivided by hedgerows, and this was true for both species.
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Invertebrate populations are amongst the most widespread species, inhabiting a variety of habitats, however there is limited conservation effort due to the scarce knowledge on their population genetics. Here, we assess levels of genetic diversity and population structure of the Epirus dancing grasshopper (Chorthippus lacustris), a steno-endemic species, located in Northwest Greece, exhibiting a fragmented distribution. By utilizing two mitochondrial genes and amplified fragment length polymorphisms (AFLPs), we detected moderate to high levels of genetic diversity of the focal populations. Haplotype network analysis revealed the existence of private haplotypes with low genetic differentiation suggesting a sudden expansion of the species in the study area with subsequent isolations on suitable habitats. Despite the low genetic differentiation between the studied populations, our data further suggest a subtle subdivision of the populations and the existence of three genetic clusters. Implications for insect conservation: Our study is the first to provide insights into the population genetics of the steno-endemic grasshopper C. lacustris, highlighting the importance of preserving focal populations. The species inhabits areas subject to high changes in land use and fragmentation. We argue that the preservation and management of suitable habitats is essential for the viability of the grasshopper populations.
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Chondrichthyes (including sharks, rays, and chimaeras) are a class of jawed cartilaginous fishes (with skeletons composed primarily of cartilage), with major relevance to the marine ecosystems and to humanity. However, cartilaginous fishes are facing various threatens, inflicting abrupt declines in their populations. Thus, critical assessment of available molecular genetic variation, particularly retrieved from Chondrichthyans' transcriptomic analyses, represents a major resource to foster genomics research in this ancient group of vertebrate species. Briefly, RNA-Seq involves the sequencing of RNA strands present on a target tissue, which can assist genome annotation and elucidate genetic features on species without a sequenced genome. The resulting information can unravel responses of an individual to environmental changes, evolutionary processes, and support the development of biomarkers. We scrutinized more than 800 RNA-Seq entries publicly available, and reviewed more than one decade of available transcriptomic knowledge in chondrichthyans. We conclude that chondrichthyans’ transcriptomics is a subject in early development, since not all the potential of this technology has been fully explored, namely their use to prospectively preserve these endangered species. Yet, the transcriptomic database provided findings on the vertebrates’ evolution, chondrichthyans’ physiology, morphology, and their biomedical potential, a trend likely to expand further in the future.
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Genetic diversity among and within populations of all species is necessary for people and nature to survive and thrive in a changing world. Over the past three years, commitments for conserving genetic diversity have become more ambitious and specific under the Convention on Biological Diversity’s (CBD) draft post-2020 global biodiversity framework (GBF). This Perspective article comments on how goals and targets of the GBF have evolved, the improvements that are still needed, lessons learned from this process, and connections between goals and targets and the actions and reporting that will be needed to maintain, protect, manage and monitor genetic diversity. It is possible and necessary that the GBF strives to maintain genetic diversity within and among populations of all species, to restore genetic connectivity, and to develop national genetic conservation strategies, and to report on these using proposed, feasible indicators.
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Nine out of 57 bovine and caprine microsatellites investigated have proved polymorphic in roe deer populations from Central Europe. The polymorphism of four to nine microsatellites (with two to 16 alleles each) has been screened in 492 roe deer from 27 sample locations in Germany, the Netherlands and France, and 10 allozyme loci have been investigated in 118 roe deer from Germany. These studies have revealed a genetically homogeneous population, but with a local scatter of allele frequencies. The mean genetic distance among sample pairs, and the overall fixation index for the 27 population samples were D = 0.1638 and GST = 0.0972 for four microsatellite loci, and D = 0.0598 and G(ST) = 0.1459 for 10 allozyme loci. No isolation-by-distance was observed. Roe deer from isolated habitats could be distinguished by various measures of genetic variability. The expected heterozygosity and the allelic diversity were higher in male than in female roe deer, but mean genetic distances and fixation indices were higher in females. The fixation indices of pairs of adjacent samples, and the genetic distance among these samples correlated highly significantly with the density of human settlement, measured by the percentage of land surface covered by roads and villages. The utility of allozymes and microsatellites for population genetic studies in cervids are compared.
The biological diversity of our planet is being depleted due to the direct and indirect consequences of human activity. As the size of animal and plant populations decrease, loss of genetic diversity reduces their ability to adapt to changes in the environment, with inbreeding depression an inevitable consequence for many species. This textbook provides a clear and comprehensive introduction to the importance of genetic studies in conservation. The text is presented in an easy-to-follow format with main points and terms clearly highlighted. Each chapter concludes with a concise summary, which, together with worked examples and problems and answers, emphasise the key principles covered. Text boxes containing interesting case studies and other additional information enrich the content throughout, and over 100 beautiful pen and ink portraits of endangered species help bring the material to life.
We have developed microsatellite or simple sequence repeat (SSR) genetic markers for the tropical tree Pithecellobium elegans (Mimosoideae). The frequency of this class of marker is estimated and the level and distribution of variability at these markers is assessed and contrasted to that found at isozyme markers in the same populations. The results indicate that SSR loci are powerful tools for the analysis of population structure and that, in these populations, they provide a means of accurately examining two important parameters in conservation biology, gene flow and paternity.
Seven enzyme loci were analyzed in three natural populations of Crassostrea angulata located on the southern Atlantic coast of the Iberian Peninsula. Two of the populations showed distinct levels of contamination by heavy metals, whereas the third was not contaminated and served as control. These seven loci were shown to be very variable in terms of the number of alleles, polymorphism and average heterozygosity. The Lap and Mdh1 loci presented null alleles. A significant positive correlation was found between the number of alleles and the concentration of iron that was fitted to a model of linear regression. However, this correlation was negative for the heterozygosity, and significant for cadmium and zinc. The Em, Lap, Mdh1 and Xdh loci showed a deficit of heterozygotes in all the populations. The values of heterozygotic deficit (D) were statistically significant between the contaminated populations and the control for Mdh1 and very close to a significant level for Em. In Pgm, a heterozygotic excess appeared in the control population and a deficit, which was correlated to the increased levels of metal concentration, occurred in the other two populations. The differences between the D values of the three populations were also significant in this locus. Positive, negative and significant relationships were obtained between the concentration of metals and some alleles of the Em, Lap and Pgm loci. Also, the homozygotic genotypes of the alleles with positive correlation values were selected in the contaminated areas, while the heterozygotes were more favoured in the control population, showing an adaptive behavior and corroborating the utility of some of these loci as biomarkers in studies of population dynamics in areas subjected to environmental contamination.
Because of the ubiquity of genetic variation for quantitative traits, virtually all populations have some capacity to respond evolutionarily to selective challenges. However, natural selection imposes demographic costs on a population, and if these costs are sufficiently large, the likelihood of extinction will be high. We consider how the mean time to extinction depends on selective pressures (rate and stochasticity of environmental change, and strength of selection), population parameters (carrying capacity, and reproductive capacity), and genetics (rate of polygenic mutation). We assume that in a randomly mating, finite population subject to density-dependent population growth, individual fitness is determined by a single quantitative-genetic character under Gaussian stabilizing selection with the optimum phenotype exhibiting directional change, or random fluctuations, or both. The quantitative trait is determined by a finite number of freely recombining, mutationally equivalent, additive loci. The dynamics of evolution and extinction are investigated, assuming that the population is initially under mutation-selection-drift balance. Under this model, in a directionally changing environment, the mean phenotype lags behind the optimum, but on the average evolves parallel to it. The magnitude of the lag determines the vulnerability to extinction. In finite populations, stochastic variation in the genetic variance can be quite pronounced, and bottlenecks in the genetic variance temporarily can impair the population's adaptive capacity enough to cause extinction when it would otherwise be unlikely in an effectively infinite population. We find that maximum sustainable rates of evolution or, equivalently, critical rates of environmental change, may be considerably less than 10% of a phenotypic standard deviation per generation.
Genetic divergence and gene flow among closely related populations are difficult to measure because mutation rates of most nuclear loci are so low that new mutations have not had sufficient time to appear and become fixed. Microsatellite loci are repeat arrays of simple sequences that have high mutation rates and are abundant in the eukaryotic genome. Large population samples can be screened for variation by using the polymerase chain reaction and polyacrylamide gel electrophoresis to separate alleles. We analyzed 10 microsatellite loci to quantify genetic differentiation and hybridization in three species of North American wolflike canids. We expected to find a pattern of genetic differentiation by distance to exist among wolflike canid populations, because of the finite dispersal distances of individuals. Moreover, we predicted that, because wolflike canids are highly mobile, hybrid zones may be more extensive and show substantial changes in allele frequency, relative to nonhybridizing populations. We demonstrate that wolves and coyotes do not show a pattern of genetic differentiation by distance. Genetic subdivision in coyotes, as measured by theta and Gst, is not significantly different from zero, reflecting persistent gene flow among newly established populations. However, gray wolves show significant subdivision that may be either due to drift in past Ice Age refugia populations or a result of other causes. Finally, in areas where gray wolves and coyotes hybridize, allele frequencies of gray wolves are affected, but those of coyotes are not. Past hybridization between the two species in the south-central United States may account for the origin of the red wolf.
Horizontal starch gel electrophoresis of enzymes and multivariate analysis of morphometric variations were used to compare the genetic structure of cicada populations, Magicicada cassini, collected from a radionuclide contaminated site with those collected from four reference sites. Homogeneity was observed between sites at the phosphoglucomutase (PGM) and alpha-glycerophosphate dehydrogenase (α-GPDH) loci. Heterogeneity was found among populations within the contaminated site at the α-GPDH loci. The esterase (EST-2,3,5) loci examined were heterogeneous between sites and among populations. With few exceptions little morphometric variation was discovered between cicada populations. However, more morphometric variation was observed among populations within sites. The data indicate that population genetic structure of cicadas from the uranium production facility was not affected by soil contamination. Morphological patterns were unique in cicadas from one population at the facility.