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ORIGINALARTICLE
Effects of ecogeographic variables ongenetic variation in montane mammals:implications for conservation in a globalwarming scenario
Amy M. Ditto1 and Jennifer K. Frey2*
1Department of Biology, University of New
Mexico, Albuquerque, NM, USA and2Department of Biology and Museum of
Southwestern Biology, University of New
Mexico, Albuquerque, NM, USA
*Correspondence and present address: Jennifer
K. Frey, Department of Fishery & Wildlife
Science, MSC 4901, PO Box 30003, New Mexico
State University, Las Cruces, NM 88003-8003,
USA.
E-mail: [email protected]
ABSTRACT
Aim Evolutionary theory predicts that levels of genetic variation in island
populations will be positively correlated with island area and negatively correlated
with island isolation. These patterns have been empirically established for oceanic
islands, but little is known about the determinants of variation on habitat islands.
The goals of this study were twofold. Our first aim was to test whether published
patterns of genetic variation in mammals occurring on montane habitat islands in
the American Southwest conformed to expectations based on evolutionary
theory. The second aim of this research was to develop simple heuristic models to
predict changes in genetic variation that may occur in these populations as a
result of reductions in available mountaintop habitat in response to global
warming.
Location Habitat islands of conifer forest on mountaintops in the American
Southwest.
Methods Relationships between island area and isolation with measures of
allozyme variation in four species of small mammal, namely the least
chipmunk (Tamias minimus), Colorado chipmunk (Tamias quadrivittatus), red
squirrel (Tamiasciurus hudsonicus), and Mexican woodrat (Neotoma mexicana),
were determined using correlation and regression techniques. Significant
relationships between island area and genetic variation were used to develop
three distinct statistical models with which to predict changes in genetic
variation following reduction in insular habitat area arising from global
warming.
Results Patterns of genetic variation in each species conformed to evolutionary
predictions. In general, island area was the most important determinant of
heterozygosity, while island isolation was the most important determinant of
polymorphism and allelic diversity. The heuristic models predicted widespread
reductions in genetic variation, the extent of which depended on the population
and model considered.
Main conclusions The results support a generalized pattern of genetic variation
for any species with an insular distribution, with reduced variation in smaller,
more isolated populations. We predict widespread reductions in genetic variation
in isolated populations of montane small mammals in the American Southwest as
a result of global warming. We conclude that climate-induced reductions in the
various dimensions of genetic variation may increase the probability of
population extinction in both the short and long term.
Keywords
Area, conservation, genetic variation, global warming, island biogeography,
isolation, montane mammals, Rocky Mountains, southwestern North America.
Journal of Biogeography (J. Biogeogr.) (2007) 34, 1136–1149
1136 www.blackwellpublishing.com/jbi ª 2007 The Authorsdoi:10.1111/j.1365-2699.2007.01700.x Journal compilation ª 2007 Blackwell Publishing Ltd
INTRODUCTION
Considerable effort has been made to document levels of
genetic variation in wild organisms in order to understand
evolutionary and ecological phenomena. Such studies have
increased in importance because, along with ecosystems and
species, genes are recognized as a priority for biodiversity
conservation (Hedrick & Miller, 1992; Caldecott et al., 1994;
Sherwin & Moritz, 2000). Genotypic variation is important to
the survivorship, adaptive flexibility and evolution of popula-
tions, and may be of particular importance to the viability of
small, isolated populations (e.g. Landweber & Dobson, 1999;
Young & Clarke, 2000). In addition, insular populations may
contribute greatly to overall diversity within a species as a
result of a tendency for genetic divergence of small, isolated
populations. Consequently, it is of critical importance to
understand the factors that influence patterns of genetic
variation within these populations.
Expectations about geographic patterns of genetic variation
in insular systems can be derived from evolutionary theory
(e.g. Futuyma, 1986). Mutation generates genetic variation,
sexual reproduction re-assorts it, and this existing variation
can be maintained through balancing selection or be enhanced
through gene flow. Loss of genetic variation can occur through
fixation of alleles through genetic drift or natural selection.
Consequently, levels of genetic variation in insular systems are
predicted to be positively correlated with island area and
negatively correlated with island isolation (Jaenike, 1973).
Genetic variation is predicted to decrease through genetic drift
and inbreeding as area decreases, assuming a correlation
between area and effective population size (Reed, 2005).
Similarly, genetic variation is predicted to decrease as popu-
lation isolation increases, owing to a corresponding decrease in
gene flow, which may also contribute to the effects of genetic
drift and inbreeding.
Previous studies have established a generalized positive
relationship between population size and three estimates of
genetic variation: polymorphism, heterozygosity, and allelic
diversity (e.g. Stangel et al., 1992; Frankham, 1996; Sun,
1996). In insular systems, it is assumed that population size is
correlated with island area, and empirical data support this
idea (e.g. Nevo, 1978; Berry, 1986; Frankham, 1996, 1997a).
Oceanic islands have been the primary focus of these studies.
For example, Frankham (1997a) found that island area was a
highly correlated determinant of genetic variation for a
variety of taxa inhabiting oceanic islands. In addition,
previous studies have provided less well-supported empirical
evidence (e.g. Brussard, 1984; Holderegger & Schneller, 1994;
Siikamaki & Lammi, 1998), and theoretical models (e.g.
Jaenike, 1973) indicating that isolation contributes to reduced
genetic variation.
Despite the number of studies on oceanic islands, little is
known about the determinants of variation on habitat islands.
Habitat islands are areas of a distinctive habitat type
surrounded by dissimilar habitat. In western North America
a classic example of this type of system consists of ‘islands’ of
conifer forest on mountaintops surrounded by ‘seas’ of lower-
elevation desert and grassland habitat (e.g. Findley, 1969;
Brown, 1971, Brown, 1978; Lomolino et al., 1989; Fig. 1).
These montane habitat islands were formed when climatic
warming during the late Quaternary caused the fragmentation
of formerly widespread Pleistocene conifer forests as they
retreated to higher elevations, which remained relatively cool
and wet. Habitat islands exhibit fundamental differences from
their oceanic counterparts. A primary distinction lies in the
nature and degree of isolation. For habitat islands, isolation is
determined not only by the distance from the mainland or
between islands, but also by the harshness of the intervening
habitat relative to the species of interest. In many cases,
intervening habitat may serve not as a barrier to dispersal, but
Figure 1 Islands of conifer forest in the
American Southwest (map modified from
Brown & Lowe, 1980). Numbers refer to
small mammal populations examined for
estimates of genetic variation: (1a) San Juan
Range of the Rocky Mountains, (1b) Jemez
Range of the Rocky Mountains, (1c) Sangre
de Cristo Range of the Rocky Mountains,
(2) Chuska Mountains, (3a) Zuni Mountains,
(3b) Cebolleta Mountains, (4) Mogollon
Plateau, (5) Black Range, (6) San Mateo
Mountains, (7) Pinaleno Mountains, (8)
Chiricahua Mountains, (9) Animas Moun-
tains, (10a) Sandia Mountains, (10b) Man-
zano Mountains, (11a) Capitan Mountains,
(11b) Sacramento Mountains, (12) Guada-
lupe Mountains, (13) Oscura Mountains,
(14) Organ Mountains. Scale bar is in
kilometres.
Genetic variation in montane mammals
Journal of Biogeography 34, 1136–1149 1137ª 2007 The Authors. Journal compilation ª 2007 Blackwell Publishing Ltd
rather as a filter of varying effectiveness. A second distinction is
that habitat archipelagos may be more prone to geographic
alterations in area, and subsequently to isolation, and hence
are expected to be less temporally and spatially durable.
Because of the importance of genetic diversity to conserva-
tion and the pervasiveness of habitat destruction and frag-
mentation, investigations into the potential effects of
alterations of island geography on levels of genetic variation
are prudent. Any factors that decrease the area or increase the
isolation of habitat islands have the potential to negatively
impact levels of genetic variation and are of conservation
concern. One of the most important factors potentially
affecting island area and relative isolation of montane habitats
in western North America is climate change. Current predic-
tions suggest that average global temperatures will increase
between 1.4 and 5.8�C before 2100, and that the average
temperature in southwestern North America will increase by 3
to 5�C (Allen et al., 2001; Houghton et al., 2001; IPCC, 2001;
Reilly et al., 2001; Wigley & Raper, 2001). Moreover, it is likely
that both temperature and precipitation will be more variable
(United States Environmental Protection Agency, 1998).
Consequently, it has been predicted that the size of conifer
forest habitats in the American Southwest will be reduced as a
result of this warming (United States Environmental Protec-
tion Agency, 1998; IPCC, 2001). As a result, levels of genetic
variation could be eroded in species occupying these small,
isolated habitat islands.
The goals of our study were twofold. Our first objective was
to investigate geographic patterns of genetic variation in
species occupying habitat islands and compare them with
predictions based on evolutionary theory. We analysed allo-
zyme variation in small mammals occupying montane conifer
forest habitat islands in southwestern North America. We
found that all species conformed to evolutionary predictions.
Genetic variation was positively correlated with island area and
negatively correlated with island isolation. Therefore, our
second objective was to develop three simple statistical models
based on our results that could be utilized to exemplify
potential impacts of global warming on levels of genetic
variation in these populations. We conclude that global
warming might result in reductions to all measures of genetic
variation (polymorphism, heterozygosity, allelic diversity) and
that these reductions are of concern for the conservation of
diversity in both the short and the long term.
METHODS
Study system
Islands and ecogeographic variables
In south-western North America conifer forests, which are
dominated by various pines (Pinus spp.), Douglas-fir (Pseu-
dotsuga menziesii), firs (Abies spp.) and spruces (Picea spp.),
are found on mountaintops and high plateaus at elevations
above 2200 m (Brown, 1982). In contrast, lower elevations
play host to a variety of non-forested biotic communities,
which include woodland, grassland, and desert. The mid-
elevation conifer woodland zone, which is dominated by
juniper (Juniperus spp.) and pinon (Pinus spp.), is often
considered a filter barrier to dispersal by conifer forest species,
while lower-elevation grassland and desert habitats are usually
considered more complete barriers to dispersal (e.g. Lomolino
et al., 1989; Lomolino & Davis, 1997). Thus, the geography of
the American Southwest results in a large ‘mainland’ of conifer
forest situated in the southern Rocky Mountains, with smaller
isolated or semi-isolated mountaintop ‘islands’ of conifer
forest distributed south and west of this mainland (Fig. 1). The
largest of the habitat islands south of the Rocky Mountains is
the Mogollon Plateau, which has sometimes been considered
to have become a possible ‘mainland’ of conifer forest habitat
during the latest Pleistocene pluvial period (Lomolino et al.,
1989).
Similar to previous biogeographical studies conducted in
this region (e.g. Patterson, 1984; Lomolino et al., 1989), we
used the Brown & Lowe (1980) map of biotic communities to
define montane conifer forest habitat islands. We defined the
islands as those areas mapped by Brown & Lowe (1980) as
Petran montane conifer forest [habitat classification code
(HCC) 122.3 in Brown (1982)] and inclusions of smaller
patches of associated higher-elevation habitats (e.g. alpine
tundra). Estimates of island area (km2) were taken from
Lomolino et al. (1989). The area estimate for the southern
Rocky Mountains was taken from Patterson (1984) and
was the sum of all contiguous montane habitats (Rocky
Mountains, Pike’s Peak Massif, Sangre de Cristo Mountains,
San Juan Mountains, and Uncompaghre Plateau).
In addition to area estimates we determined three measures
of isolation for each island, namely distance (km) to the Rocky
Mountain mainland (I-Rocky), distance to the Mogollon
Plateau habitat island (I-Mogollon), and distance to the
nearest habitat island with the species in question present
(I-Nearest). For I-Nearest, geographic distributions were based
on Findley et al. (1975), Hoffmeister (1986), and additional
records in the Museum of Southwestern Biology. Isolation
measures were derived using a ruler to measure the shortest
straight-line distance between mapped patches of Petran
montane conifer forest on the Brown & Lowe (1980) map.
Species
We collected estimates of genetic variation for populations of
montane mammals occurring on habitat islands of coniferous
forest in south-western North America for which data were
available for the majority of the species’ distribution within the
study area (Table 1). Furthermore, we restricted our analysis to
those species for which there were genetic data for at least four
mountaintop populations in the American Southwest. Pub-
lished studies on four species of small mammals met these
criteria, namely the least chipmunk (Tamias minimus, Bachman
1839; Sullivan, 1985; Sullivan & Petersen, 1988); Colorado
chipmunk (Tamias quadrivittatus [Say 1823]; Sullivan, 1996);
A. M. Ditto and J. K. Frey
1138 Journal of Biogeography 34, 1136–1149ª 2007 The Authors. Journal compilation ª 2007 Blackwell Publishing Ltd
red squirrel (Tamiasciurus hudsonicus [Erxleben 1777]; Sullivan
& Yates, 1995); and Mexican woodrat (Neotoma mexicana, Baird
1855; Sullivan, 1994). Each of the species typically is associated
with montane conifer forest habitats in the American Southwest,
although occasionally some may be associated with lower-
elevation habitats (e.g. Findley et al., 1975; Hoffmeister, 1986;
Frey & Yates, 1996; Wilson & Ruff, 1999). Tamias minimus,
Tamias quadrivittatus, and Tamiasciurus hudsonicus reach the
southern edge of their distribution in the study area (Wilson &
Ruff, 1999). Tamiasciurus hudsonicus is the most specialized in
habitat, being essentially restricted to mixed conifer and boreal
forests (Findley et al., 1975; Hoffmeister, 1986). Tamias quad-
rivittatus also is typically restricted to conifer forests (Findley
et al., 1975). However, endangered peripheral populations of
Tamias quadrivittatus in the Oscura and Organ mountains occur
in extremely marginal conifer forest, woodland, and montane
scrub (Sullivan, 1996; Fig. 1). In contrast, Tamias minimus is
closely tied to high elevations within and above the conifer forest
zone at the southern periphery of its range. It occasionally occurs
in lower-elevation non-forested habitats in the northern part of
the study area. Finally, the centre of distribution for Neotoma
mexicana is in Mexico, with a range that extends north of the
study area into Utah and Colorado (Wilson & Ruff, 1999). This
species occasionally occurs in conifer woodlands and other low-
elevation sites throughout the study area that maintain
cryomesic microhabitats (Findley et al., 1975).
Genetic variation
Published data on genetic variation for each species were based
on the analyses of allozymes. Comparable molecular DNA
studies of a suite of species meeting the criteria were not
Table 1 Genetic and ecogeographic data for montane populations of small mammals in the American Southwest. Symbols and variable
definitions are as follows: n, number of individuals studied per population; P, polymorphism, H, heterozygosity; A, allelic diversity. Indices
of genetic variation were based on allozymes. Isolation measures represent straight-line distances to the Rocky Mountains (I-Rocky), the
Mogollon Rim (I-Mogollon), and the next-nearest montane complex where the species is present (I-Nearest). See Fig. 1 for localities.
Species
Genetic variation indices Ecogeographic variables
n P H A Area (km2)
Isolation (km)
Mountain range I-Rocky I-Mogollon I-Nearest
Tamias minimus (Bachman 1839)
Rocky 99 14.6 0.027 1.2 134642 0 198 44
Chuska 11 12.5 0.008 1.2 1508 117 142 117
Mogollon Plateau 21 12.5 0.048 1.1 11134 198 0 142
Sandia 18 4.2 0.016 1.1 44 44 186 44
Sacramento 2 4.2 0.000 1.0 1350 204 208 181
Tamias quadrivittatus (Say 1823)
Rocky 9 13.33 0.030 1.14 134642 0 198 40
Chuska 14 16.67 0.031 1.17 1508 117 142 45
Cebolleta-Zuni 15 10.00 0.007 1.10 1091 40 87 40
Sandia-Manzano 16 13.32 0.019 1.13 185 44 158 44
Oscura 18 3.33 0.000 1.03 0 223 156 127
Organ 16 0.04 0.000 1.00 9 345 188 127
Tamiasciurus hudsonicus (Erxleben 1777)
Rocky 24 0.0 0.000 1.0 134642 0 198 40
Chuska 14 7.7 0.019 1.1 1508 117 142 45
Cebolleta-Zuni 7 0.0 0.000 1.0 1091 40 87 40
Mogollon Plateau 17 11.5 0.007 1.1 11134 198 0 10
Pineleno 5 0.0 0.000 1.0 87 425 79 79
Capitan-Sacramento 7 0.0 0.000 1.0 1435 193 208 126
Neotoma mexicana (Baird 1855)
Rocky 7 29.2 0.600 1.3 134642 0 198 40
Cebolleta-Zuni 16 29.2 0.081 1.3 1091 40 87 40
Mogollon Plateau 24 20.8 0.620 1.2 11134 198 0 10
Black 7 25.0 0.096 1.2 873 240 10 10
San Mateo 20 25.0 0.070 1.2 366 196 39 21
Pinaleno 15 16.7 0.006 1.2 87 425 79 68
Chiricauhua 9 20.8 0.051 1.2 68 467 144 48
Animas 3 16.7 0.083 1.2 10 482 168 48
Sandia-Manzano 9 29.2 0.056 1.3 185 44 158 44
Capitan-Sacramento 9 20.8 0.059 1.2 1435 193 208 24
Guadalupe 8 20.8 0.042 1.2 26 375 353 94
Genetic variation in montane mammals
Journal of Biogeography 34, 1136–1149 1139ª 2007 The Authors. Journal compilation ª 2007 Blackwell Publishing Ltd
available. Utilization of allozyme studies has several benefits.
Each study included used similar methodology and data
presentation, data were available for a relatively large number
of species and populations, and a relatively large number of
loci in the nuclear genome were sampled. Results are easily
comparable both within this study and with other similar
studies.
We examined three distinct indices of genetic variation for
each population of each species, namely polymorphism (P),
which is the estimated proportion of loci that are polymorphic
(for which the most common allele has a frequency of < 0.95);
observed (¼ direct count or individual) heterozygosity (H),
which is the proportion of individuals sampled that are
heterozygous; and allelic diversity (A), which is the mean
number of alleles per locus. These measures represent different
dimensions of genetic variation, and changes in each may have
different ramifications for a population. In instances where
indices of genetic variation were available for more than one
local population within a habitat island, we found no
relationship between the number of populations sampled
and the amount of genetic variation observed (P > 0.05).
Thus, intra-island measures were averaged. When indices of
genetic variation were available only for pooled populations of
neighbouring mountain ranges, we summed the areas of the
respective islands to generate the corresponding habitat island
area estimate.
Statistical analyses
Sample size can influence measures of genetic variation, and
various methods to correct for sample size have been
developed (Nei, 1978; Gorman & Renzi, 1979; El Mousadik
& Petit, 1996; Kalinowski, 2004). The published measures of
genetic variation utilized in this study were uncorrected for
sample size. Furthermore, it was not possible to apply most
corrections post hoc owing to the nature of the published data.
A correction for allelic diversity whereby the smallest samples
are eliminated from analyses was technically possible (Leberg,
2002). However, this method is most effective for highly
polymorphic loci, a condition that our data did not exhibit.
This method was further deemed inappropriate because of the
low number of populations surveyed in each species (i.e. 5 in
Tamias minimus, 6 in Tamias quadrivittatus, 6 in Tamiasciurus
hudsonicus, and 11 in Neotoma mexicana). In lieu of sample
size corrections, we used correlation, regression, and Fisher’s
combined probability tests (Sokal & Rohlf, 1981) to investigate
the extent of the relationship between sample size and genetic
variation.
Geographic relationships between measures of area and
isolation were investigated using correlation and multivariate
regressions. Area estimates were log-transformed to normalize
the data to meet parametric test assumptions, as is conven-
tional in island biogeographic studies (MacArthur & Wilson,
1967).
Correlation and regression techniques were used to
investigate relationships between genetic variation and ecogeo-
graphic variables. For these analyses, we added 1 to all area
estimates in order to include the Oscura Mountain population
of Tamias quadrivittatus: this range had no mapped area of
Petran montane conifer forest. Linear regressions were run on
each species individually, and alternative non-linear models
were considered where deemed appropriate. Stepwise multiple
regressions were used to determine the most important
predictor variable for each index of genetic variation for each
species. Owing to low sample sizes, and because of the
possibility of bias arising from methodological differences in
this cross-study analysis, we considered a probability value of
P £ 0.10 to signify biologically meaningful relationships or
predictors (Johnson, 1999). As a further means for compen-
sating for low statistical power in individual analyses, we used
Fisher’s combined probability tests (Sokal & Rohlf, 1981) to
assess relationships across all species for each ecogeographic
variable and measure of genetic variation. For these analyses,
we utilized a more stringent probability value (P < 0.05) as
representative of biological meaning. Unless otherwise
indicated, only biologically meaningful results are presented.
We used biologically meaningful regression equations
between area of montane habitat island and genetic variation
to predict the potential effects of global warming on genetic
variation in existing populations. Because correlation coeffi-
cients (r) are often poor indicators of the predictive power of
independent variables, percent prediction errors (PPE;
PPE ¼ 100[(observed variation ) current predicted vari-
ation)/current predicted variation]) were calculated for each
regression model as an additional measure of the efficacy of the
linear model in predicting genetic variation. Percent prediction
error represents the percent difference between actual and
predicted current levels of variation as estimated by the model
(Van Valkenburg, 1990). Mean percent prediction errors for
each regression model were calculated irrespective of sign and
used to compare the predictive accuracy of the various models.
Predictions for reductions in area of conifer forest on each
mountain range in the American Southwest are not currently
available for different global warming scenarios. However, for
19 mountain ranges in the Great Basin, McDonald & Brown
(1992) predicted a mean reduction of 74% (range 35–95%) in
conifer forest area based on a projected 500-m upward
displacement of vegetation zones [data calculated from area
measurements presented in McDonald & Brown (1992)].
These predictions were based on a 3�C increase in temperature,
which was considered an intermediate figure for global
warming projections over the next century, but may be
conservative in light of IPCC projections for southwestern
North America that suggest increases ranging from 3 to 5�C
(McDonald & Brown, 1992; IPCC, 2001). Because McDonald
and Brown based their predictions on topography, the range of
predicted habitat loss reflected differences in the shape of each
mountain range. It is likely that other deterministic features
(e.g. geographic location, elevation, bulk, proximity to other
mountains) also influence rates of habitat loss. We defined
islands based on habitat rather than elevation, and thus the
McDonald & Brown (1992) methodology could not be applied
A. M. Ditto and J. K. Frey
1140 Journal of Biogeography 34, 1136–1149ª 2007 The Authors. Journal compilation ª 2007 Blackwell Publishing Ltd
to the current study. Furthermore, because predictions for
reductions in conifer forest are lacking and could not be
generated, and because the deterministic features of each
mountain range influence habitat loss, the potential influence
of global warming was illustrated by reducing current habitat
areas by 25%, 75% and 95%. These figures provided a range of
habitat-loss scenarios, which essentially spanned those predic-
ted for the Great Basin based on an intermediate level of
warming.
We developed three models (regression, residual, and
percent loss) to derive predictions for levels of genetic
variation following reduction in island area. In the regression
model, predicted levels of genetic variation were calculated by
solving the regression equation using the reduced-area esti-
mates (predicted genetic variation ¼ F(Area)). Our residual
model was modified from a method described in McDonald &
Brown (1992), whereby levels of genetic variation were
predicted by preserving the residuals around the regression
line (predicted genetic variation ¼ F(Area) + ei; where ei is the
residual of the ith case; Fig. 2). This model assumes that the
relative magnitude of the deviation of the observed value from
the predicted value reflects deterministic features of each
mountain range that are not impacted by global warming, and
that each population will vary in a regular, linear fashion
parallel to the regression equation [see Skaggs & Boecklen
(1996) for a criticism of this method]. Finally, we developed a
percent loss model (predicted genetic variation ¼ F(Area)/
(F(100% area) · observed variation); Fig. 2). This model assumes
that the residual varies proportionately with area such that,
when area is small, the residual is also small, and when area is
large, the residual value, and hence the importance of other
outside factors, increases. This suggests that small effective
population size (Ne) is of ultimate importance in determining
the amount of variation observed in smaller populations, and
allows for the slope of the relationship between area and Ne to
vary in different populations. This model also assumes that
variation within a population will vary in a regular, linear
manner. Predictions were not made for the Oscura Mountain
population of Tamias quadrivittatus because the current
mapped area of Petran montane conifer forest is zero. In
order to compare the predictions of the three models,
predicted percent losses in genetic variation for the various
scenarios were calculated for each (1 ) variation predicted
with habitat loss/observed variation).
RESULTS
Area of conifer forest habitat island [ln(Area)] had a negative
relationship with straight-line distance to the Rocky Moun-
tains (I-Rocky ¼ 384.131–30.800 [ln(Area)]; P ¼ 0.000,
d.f. ¼ 26; Adj. r2 ¼ 0.359), but no relationship with
I-Mogollon (P ¼ 0.514) or I-Nearest (P ¼ 0.080). There were
no meaningful positive relationships between sample size and
each measure of genetic variation in any species based on
correlation and regression techniques (see Table S1 in Sup-
plementary Material). Likewise, combined probabilities from
individual regressions revealed no meaningful relationships
between sample sizes with any of the measures of genetic
variation (P > 0.4, d.f. ¼ 8). Although no sample-size effects
were evident, we caution that because corrections for sample
size were not made, reported measures of genetic variation
could be underestimated for populations with small sample
sizes or overestimated for populations with large sample sizes.
Patterns of genetic variation
For relationships between ecogeographic variables and genetic
variation, partial F-tests revealed that linear models were most
appropriate in all cases. Of the four ecogeographic variables,
island area [ln(Area)] was the most consistent predictor of
genetic variation (Fig. 3; see Table S2 for statistical details of
relationships). In general, there was a positive relationship
Figure 2 The (a) residual model and (b) percent loss model used to estimate genetic variation in response to the reduction in habitat island
area as a result of global warming. Equations characterizing the relationship between area and genetic variation for these models are
presented in the text. Bold lines represent the regression equation from which the model was derived. Numbers represent hypothetical
populations. Filled dots represent actual data points used to calculate the regression line. Open dots represent examples of other data points
the models assume to exist if we could sample genetic variation for each population at all possible areas.
Genetic variation in montane mammals
Journal of Biogeography 34, 1136–1149 1141ª 2007 The Authors. Journal compilation ª 2007 Blackwell Publishing Ltd
between island area and genetic variation. Biologically mean-
ingful relationships with area occurred in all species except
Tamiasciurus hudsonicus, and for all indices of genetic variation.
Relationships between area and polymorphism were most
common and were found in Tamias minimus (PTm ¼ 1.3707–
1.2171 [ln(Area)], Tamias quadrivittatus (PTq ¼ 2.8450 +
1.1788 [ln(Area)]), and N. mexicana (PNm ¼ 17.2364 + 0.9535
[ln(Area)]; Fig. 3a). Relationships between area and heterozyg-
osity were found in two species, namely Tamias quadrivittatus
(HTq ¼ )0.0012 + 0.0028[ln(Area)] and N. mexicana (HNm ¼)0.2520 + 0.0670 [ln(Area)]; Fig. 3d). Finally, a relationship
between area and allelic diversity was found only in Tamias
quadrivittatus (ATq ¼ 1.0245 + 0.0126 [(ln(Area)]; Fig. 3f).
In general, there was a negative relationship between measures
of island isolation and indices of genetic variation (see Table S2
for statistical details of relationships). Biologically meaningful
relationships between genetic variation and isolation were
exhibited in three of four species (except Tamias minimus) for
two isolation measures, I-Rocky and ln (I-Nearest). No
relationships occurred between I-Mogollon and indices of
genetic variation. Had the Mogollon Plateau served as a
‘mainland’ source population during the Pleistocene, we would
have expected a reduction in genetic variation with increasing
distance from the plateau. Relationships with isolation measures
were most common for polymorphism and least common for
heterozygosity. Tamias quadrivittatus and N. mexicana
both exhibited negative relationships between I-Rocky and
both polymorphism [PTq ¼ 14.6867–0.0409 (I-Rocky);
PNm ¼ 28.9268–0.0241 (I-Rocky); Fig. 3b] and allelic diversity
[ATq ¼ 1.1489–0.0004 (I-Rocky); ANm ¼ 1.2782–0.0002 (I-
Rocky); Fig. 3g]. Negative relationships with ln(I-Nearest) were
found for polymorphism in Tamias quadrivittatus
[PTq ¼ 51.5748–10.2510 1n (I-Nearest)] and Tamiasciurus
hudsonicus [PTh ¼ 20.5718–4.5931 1n (I-Nearest); Fig. 3c], for
heterozygosity in Tamias quadrivittatus [HTq ¼ 0.929–0.0191
1n (I-Nearest); Fig. 3e], and for allelic diversity in Tamias
quadrivittatus [ATq ¼ 1.5301–0.1059 1n (I-Nearest); Fig. 3h].
Based on multivariate analyses, relationships with ecogeo-
graphic variables were most common for polymorphism,
which occurred in all species (Fig. 3; see Table S2 for statistical
details). However, no single ecogeographic variable was
consistently predictive of genetic variation indices. For poly-
morphism, predictors were ln(Area) in Tamias minimus, 1n
(I-Nearest) in Tamias quadrivittatus and Tamiasciurus hud-
sonicus, and I-Rocky in N. mexicana. In contrast, ln(Area) was
the only predictor for heterozygosity, which occurred in two
species. The variable ln(I-Nearest) was always a predictor of
allelic diversity in biologically meaningful multiple regressions
between ecogeographic variables with allelic diversity. How-
ever, I-Rocky also contributed as an additional predictor of
allelic diversity in N. mexicana.
Global warming: predictive models
Models to predict the response of genetic variation to climate-
induced reductions in montane forest habitat were developed
Figure 3 Biologically meaningful relationships between ecogeographic variables and indices of genetic variation (see Table S2 for statistical
details). Indices of genetic variation include: (a–c) polymorphism (P); (d, e) heterozygosity (H); and (f–h) allelic diversity (A). Ecogeo-
graphic variables include: (a, d, f) the natural log of the area of Petran montane conifer forest [ln(Area)]; (b, g) the shortest distance to the
Rocky Mountains (I-Rocky); and (c, e, h) the natural log of the shortest distance to the nearest island containing the species [ln(I-Nearest)].
The original units for area were km2 and for isolation were km. Species include: least chipmunk (Tamias minimus; open triangles); Colorado
chipmunk (Tamias quadrivittatus; closed circles); red squirrel (Tamiasciurus hudsonicus; open circles); and Mexican woodrat (Neotoma
mexicana; open squares).
A. M. Ditto and J. K. Frey
1142 Journal of Biogeography 34, 1136–1149ª 2007 The Authors. Journal compilation ª 2007 Blackwell Publishing Ltd
for three species that exhibited a meaningful relationship
between island area and genetic variation: Tamias minimus,
Tamias quadrivittatus, and N. mexicana. The accuracy of the
regression equations for predicting current levels of genetic
variation in each population varied (Table 2). Considering
mean percent prediction errors, the equations for allelic
diversity in Tamias quadrivittatus (x ¼ 0.10, s ¼ 4.17) and
polymorphism in N. mexicana (x ¼ 13.30, s ¼ 9.89) were the
best predictors of genetic variation, despite a low adjusted R2
for polymorphism in N. mexicana (see Table S2). The equation
for polymorphism in Tamias minimus also generally per-
formed well but had a high standard deviation (x ¼ 21.81,
s ¼ 23.03) as a result of relatively low predictive power for the
Chuska and Sacramento mountains populations. The models
for heterozygosity, including those for N. mexicana
(x ¼ 84.10, s ¼ 63.38) and Tamias quadrivittatus (x ¼ 54.00,
s ¼ 34.26) had the lowest predictive accuracy based on high
mean percent prediction error values and high standard
deviations, followed by the model for polymorphism in Tamias
quadrivittatus (x ¼ 44.60, s ¼ 34.60).
Predictions obtained from each model generally were
similar. However, the similarity of predictions across models
varied with how effectively the regression model predicted
current variation (Table 2): the lower the percent prediction
error, the more similar the predictions of all models. Under
the regression model, 15 of 21 total populations (71%),
including all three species, were predicted to experience
reduction in at least one index of genetic variation owing to a
25% reduction in area of conifer forest island (Table 2). With
a 75% reduction in island area, all populations except three
(86%) were predicted to experience reductions in at least one
index of genetic variation. At a 95% reduction in island area,
only Tamias minimus in the Sacramento Mountains was
predicted to maintain its current level of genetic variation. In
cases where no losses were predicted, percent prediction errors
often were high and the regression model predicted more
variation than is currently observed. The conservatism in the
number of populations predicted to exhibit losses according to
the regression model is offset in that this model generally
predicts the greatest magnitude of loss when a loss is
predicted.
Losses of genetic variation under the residual and percent
loss models were ubiquitous across all populations and
habitat-reduction scenarios (Tables 2 and 3). Owing to
underlying assumptions, these models always predicted a
reduction in genetic variation relative to current observed
values, regardless of the habitat-loss scenario applied. The
percent loss model was the most conservative with regard to
the magnitude of predicted losses in genetic variation. This
model also predicted that the smallest populations would
exhibit the greatest losses. In contrast, the residual model
tended to predict greater losses in variation when the
regression model predicted a gain (i.e. no loss) in genetic
variation for a population as a result of the prediction error
and usage of the residual error of the regression model in
calculating these figures.
DISCUSSION
We found that observed geographic patterns of genetic
variation in populations of small mammals on montane
habitat islands were consistent with predictions derived from
evolutionary theory and findings of previous studies for other
organisms on oceanic islands (Brussard, 1984; Stangel et al.,
1992; Holderegger & Schneller, 1994; Frankham, 1996; Sun,
1996; Siikamaki & Lammi, 1998). Our findings suggest a
generalized pattern of reduced genetic variation for popula-
tions occurring on both increasingly small and isolated islands.
Furthermore, this reduction involved multiple dimensions of
genetic variation, including polymorphism, heterozygosity,
and allelic diversity.
Observed geographic patterns of genetic variation are
probably a product of both contemporary and historical
processes and may reflect nuances unique to the species or
population under consideration. Relationships between eco-
geographic variables and genetic variation may not have been
ubiquitous because of several factors. These include a lag time
between geographic and genetic changes, an inconsistency in
the relationship between area and the amount of genetic
variation at the time of island isolation, the occasional pooling
of genetic variation estimates from neighbouring mountaintop
islands, and the small number of populations studied in some
species. Furthermore, individualistic differences in natural
history can also influence the relationship between population
size and genetic variation (Hadly et al., 2004). Finally, the
ecogeographic variables we used may not represent ideal
measures of area or isolation across all species owing to subtle
differences in habitat and life history. The use of straight-line
distances for measures of isolation may be unrealistic and
overly conservative. Contemporary intermontane movement
of conifer forest species may be more likely to occur through
corridors of the most cryomesic habitat, rather than through
the shortest, straight-line route.
When current levels of genetic variation are compared with
values predicted under the various habitat-loss scenarios, our
models suggest widespread reductions in variation resulting
from habitat loss attributable to global warming. Although we
made specific predictions only for those species and measures
of genetic variation that exhibited significant relationships with
island area, we anticipate that reductions in genetic variation
would be virtually universal for any species that occupies
habitat that could experience fragmentation or reduction
arising from climate change or other intrinsic factors. Thus,
erosion of genetic variation of such populations should be a
major conservation concern in the face of global warming.
The strengths of the three predictive models are likely to
vary with island (population) size. The percent loss model is
expected to perform well for small populations but would
probably over- or under-predict variation in larger popula-
tions. In contrast, the residual model is characterized by a
tendency to predict more variation than expected in small
populations because variation is not always predicted to fall to
zero with zero population size. While this model probably does
Genetic variation in montane mammals
Journal of Biogeography 34, 1136–1149 1143ª 2007 The Authors. Journal compilation ª 2007 Blackwell Publishing Ltd
Table 2 Current and predicted levels of genetic variation in populations of montane small mammals following climate-induced reductions
in habitat island area. Equations used for making the predictions are in the text.
Species
Mountain range
Prediction
error (%)
Model
Current variation Regression Residual Percent loss
Genetic
variation Observed Predicted
)25%
habitat
)75%
habitat
)95%
habitat
)25%
habitat
)75%
habitat
)95%
habitat
)25%
habitat
)75%
habitat
)95%
habitat
Tamias minimus (Bachman 1839)
Polymorphism Rocky 14.6 14.97 )2.5 14.6 13.1 10.9 14.2 12.7 10.5 14.2 12.8 10.6
Chuska 12.5 8.82 41.8 8.4 6.9 4.7 12.1 10.6 8.4 11.9 9.8 6.7
Mogollon 12.5 11.55 8.2 11.2 9.7 7.5 8.1 10.6 8.4 12.1 10.4 8.1
Sandia 4.2 4.00 5.1 3.6 2.1 0.0b 3.8 2.3 0.1 3.8 2.2 0.0b
Sacramento 4.2 8.66 )51.5 8.3 6.8 4.6 3.8 2.3 0.1 4.0 3.3 2.2
Tamias quadrivittatusa (Say 1823)
Polymorphism Rocky 13.33 16.77 )20.5 16.43 15.13 13.24 12.99 11.70 9.80 13.06 12.03 10.52
Chuska 16.67 11.47 45.3 11.13 9.84 7.94 16.33 15.04 13.14 16.18 14.30 11.54
Cebolleta-Zuni 10.00 11.09 )9.8 10.75 9.46 7.56 9.66 8.37 6.47 9.69 8.53 6.82
Sandia-Manzano 13.32 9.00 48.0 8.66 7.36 5.47 12.98 11.69 9.79 12.82 10.89 8.10
Organ 0.04 5.38 )99.3 5.04 3.74 1.85 0.00b 0.00b 0.00b 0.04 0.03 0.01
Heterozygosity Rocky 0.030 0.032 )5.9 0.031 0.028 0.023 0.029 0.026 0.022 0.029 0.026 0.022
Chuska 0.031 0.019 60.7 0.018 0.015 0.011 0.030 0.027 0.023 0.029 0.025 0.018
Cebolleta-Zuni 0.007 0.018 )61.9 0.018 0.015 0.010 0.006 0.003 0.000b 0.007 0.006 0.004
Sandia-Manzano 0.019 0.013 41.6 0.013 0.010 0.005 0.018 0.015 0.011 0.019 0.015 0.007
Organ 0.000 0.005 )100.0 0.004 0.001 0.000b 0.000b 0.000b 0.000b 0.000b 0.000b 0.000b
Allelic diversity Rocky 1.14 1.17 )2.8 1.17 1.16 1.14 1.14 1.12 1.10 1.14 1.13 1.11
Chuska 1.17 1.12 4.8 1.11 1.10 1.08 1.17 1.15 1.13 1.16 1.15 1.13
Cebolleta-Zuni 1.10 1.11 )1.1 1.11 1.10 1.07 1.10 1.08 1.06 1.10 1.09 1.06
Sandia-Manzano 1.13 1.09 3.6 1.09 1.07 1.05 1.13 1.11 1.09 1.13 1.11 1.09
Organ 1.00 1.05 )4.9 1.05 1.03 1.01 1.00 0.98 0.96 1.00 0.98 0.96
Neotoma mexicana (Baird 1855)
Polymorphism Rocky 29.2 28.5 2.5 28.2 27.2 25.6 28.9 27.9 26.3 28.9 27.9 26.2
Cebolleta-Zuni 29.2 23.9 22.1 23.6 22.6 21.0 28.9 27.9 26.3 28.8 27.6 25.7
Mogollon Plateau 20.8 26.1 )20.4 25.8 24.8 23.3 20.5 19.5 17.9 20.6 19.8 18.6
Black 25.0 23.7 5.5 23.4 22.4 20.8 24.7 23.7 22.1 24.7 23.6 21.9
San Mateo 25.0 22.9 9.3 22.6 21.5 20.0 24.7 23.7 22.1 24.7 23.5 21.8
Pinaleno 16.7 21.5 )22.3 21.2 20.2 18.6 16.4 15.4 13.8 16.5 15.7 14.5
Chiricauhua 20.8 21.3 )2.2 21.0 19.9 18.4 20.5 19.5 17.9 20.5 19.4 18.0
Animas 16.7 19.5 )14.3 19.2 18.2 16.6 16.4 15.4 13.8 16.4 15.6 14.2
Sandia-Manzano 29.2 22.2 31.4 21.9 20.9 19.4 28.9 27.9 26.3 28.8 27.5 25.5
Capitan-Sacramento 20.8 24.2 )13.9 23.9 22.8 21.3 20.5 19.5 17.9 20.5 19.6 18.3
Guadalupe 20.8 20.3 2.3 20.1 19.0 17.5 20.5 19.5 17.9 20.6 19.5 17.9
Heterozygosity Rocky 0.600 0.539 11.3 0.520 0.446 0.339 0.581 0.507 0.399 0.579 0.497 0.377
Cebolleta-Zuni 0.081 0.217 )62.6 0.197 0.124 0.016 0.062 0.000b 0.000b 0.074 0.046 0.006
Mogollon Plateau 0.620 0.372 66.5 0.353 0.279 0.172 0.601 0.527 0.419 0.588 0.465 0.287
Black 0.096 0.202 )52.4 0.182 0.109 0.001 0.077 0.003 0.000b 0.087 0.052 0.000b
San Mateo 0.070 0.144 )51.2 0.124 0.051 0.000b 0.051 0.000b 0.000b 0.060 0.025 0.000b
Pinaleno 0.006 0.047 )87.3 0.028 0.000b 0.000b 0.000b 0.000b 0.000b 0.004 0.000 0.000b
Chiricauhua 0.051 0.031 65.4 0.012 0.000b 0.000b 0.032 0.000b 0.000b 0.020 0.000 0.000b
Animas 0.083 0.000b )188.3 0.000b 0.000b 0.000b 0.064 0.000b 0.000b c c c
Sandia-Manzano 0.056 0.098 )42.7 0.078 0.005 0.000b 0.037 0.000b 0.000b 0.045 0.003 0.000b
Capitan-Sacramento 0.059 0.235 )74.9 0.216 0.142 0.034 0.040 0.000b 0.000b 0.054 0.036 0.000b
Guadalupe 0.042 0.000b )221.9 0.000b 0.000b 0.000b 0.023 0.000b 0.000b c c c
aPredictions were not made for the Oscura Mountain population because the current area of petran montane conifer forest is zero.bPredicted values were negative, but are reported as zero.cNo predictions were made for the Animas and Guadalupe mountains populations under the percent loss model because the regression model
predicted zero current variation, hence a model could not be devloped.
A. M. Ditto and J. K. Frey
1144 Journal of Biogeography 34, 1136–1149ª 2007 The Authors. Journal compilation ª 2007 Blackwell Publishing Ltd
Table 3 Predicted percent loss of genetic variation in montane small mammals according to three statistical models and three scenarios of
climate-induced habitat loss.
Species
Mountain range
Habitat-loss scenario
25% habitat loss 75% habitat loss 95% habitat loss
Genetic variation Regression Residual
Percent
loss Regression Residual
Percent
loss Regression Residual
Percent
loss
Tamias minimus (Bachman 1839)
Polymorphism Rocky 0.14 2.67 2.60 10.48 13.01 12.67 25.55 28.15 27.40
Chuska 32.64 3.20 4.48 44.64 15.20 21.52 62.32 32.88 46.56
Mogollon 10.72 3.12 3.44 22.80 15.20 16.48 40.40 32.80 35.52
Sandia 14.29 9.52 9.76 50.00 45.24 47.62 102.62 97.86 102.86
Sacramento )96.90 9.29 4.52 )60.95 45.24 21.90 )8.57 97.62 47.38
Tamias quadrivittatusa (Say 1823)
Polymorphism Rocky )23.26 2.55 2.03 )13.50 12.23 9.75 0.68 26.48 21.08
Chuska 33.23 2.04 2.94 40.97 9.78 14.22 52.37 21.18 30.77
Cebolleta-Zuni )7.50 3.40 3.10 5.40 16.30 14.70 24.40 35.30 31.80
Sandia-Manzano 34.98 2.55 3.75 44.74 12.24 18.24 58.93 26.50 39.19
Organ )12500.00 100.00 0.00 )9250.00 100.00 25.00 )4525.00 100.00 75.00
Heterozygosity Rocky )3.33 3.33 3.33 6.67 13.33 13.33 23.33 26.67 26.67
Chuska 41.94 3.23 6.45 51.61 12.90 19.35 64.52 25.81 41.94
Cebolleta-Zuni )157.14 14.29 0.00 )114.29 57.14 14.29 )42.86 114.29 42.86
Sandia-Manzano 31.58 5.26 0.00 47.37 21.05 21.05 73.68 42.11 63.16
Organ b b b b b b b b b
Allelic diversity Rocky )2.63 0.00 0.00 )1.75 1.75 0.88 0.00 3.51 2.63
Chuska 5.13 0.00 0.85 5.98 1.71 1.71 7.69 3.42 3.42
Cebolleta-Zuni )0.91 0.00 0.00 0.00 1.82 0.91 2.73 3.64 3.64
Sandia-Manzano 3.54 0.00 0.00 5.31 1.77 1.77 7.08 3.54 3.54
Organ )5.00 0.00 0.00 )3.00 2.00 2.00 )1.00 4.00 4.00
Neotoma mexicana (Baird 1855)
Polymorphism Rocky 3.42 1.03 1.03 6.85 4.45 4.45 12.33 9.93 10.27
Cebolleta-Zuni 19.18 1.03 1.37 22.60 4.45 5.48 28.08 9.93 11.99
Mogollon Plateau )24.04 1.44 0.96 )19.23 6.25 4.81 )12.02 13.94 10.58
Black 6.40 1.20 1.20 10.40 5.20 5.60 16.80 11.60 12.40
San Mateo 9.60 1.20 1.20 14.00 5.20 6.00 20.00 11.60 12.80
Pinaleno )26.95 1.80 1.20 )20.96 7.78 5.99 )11.38 17.37 13.17
Chiricauhua )0.96 1.44 1.44 4.33 6.25 6.73 11.54 13.94 13.46
Animas )14.97 1.80 1.80 )8.98 7.78 6.59 0.60 17.37 14.97
Sandia-Manzano 25.00 1.03 1.37 28.42 4.45 5.82 33.56 9.93 12.67
Capitan-Sacramento )14.90 1.44 1.44 )9.62 6.25 5.77 )2.40 13.94 12.02
Guadalupe 3.37 1.44 0.96 8.65 6.25 6.25 15.87 13.94 13.94
Heterozygosityc Rocky 13.33 3.17 3.50 25.67 15.50 17.17 43.50 33.50 37.17
Cebolleta-Zuni )143.21 23.46 8.64 )53.09 100.00 43.21 80.25 100.00 92.59
Mogollon Plateau 43.06 3.06 5.16 55.00 15.00 25.00 72.26 32.42 53.71
Black )89.58 19.79 9.38 )13.54 96.88 45.83 98.96 100.00 98.96
San Mateo )77.14 27.14 14.29 27.14 100.00 64.29 100.00 100.00 100.00
Pinaleno )366.67 100.00 33.33 100.00 100.00 100.00 100.00 100.00 100.00
Chiricauhua 76.47 37.25 60.78 100.00 100.00 100.00 100.00 100.00 100.00
Animas 100.00 22.89 c 100.00 100.00 c 100.00 100.00 c
Sandia-Manzano )39.29 33.93 19.64 91.07 100.00 94.64 100.00 100.00 100.00
Capitan-Sacramento )266.10 32.20 8.47 )140.68 100.00 38.98 42.37 100.00 84.75
Guadalupe 100.00 45.24 c 100.00 100.00 c 100.00 100.00 c
aPredictions were not made for the Oscura Mountain population because the current area of mapped petran montane conifer forest is zero.bNo percent loss is reported for the Organ mountain population because current variation was reported as zero.cNo predictions were made for the Animas and Guadalupe mountains under the percent loss model because the regression model predicted zero
current variation, and hence a model could not be devloped.
Genetic variation in montane mammals
Journal of Biogeography 34, 1136–1149 1145ª 2007 The Authors. Journal compilation ª 2007 Blackwell Publishing Ltd
not predict genetic variation as effectively at either extreme, it
may do as well as or better than the regression model at
predicting changes in genetic variation for intermediate
population sizes. Considering percent prediction errors, the
linear regression model also may be best at making predictions
for populations of intermediate size. Controlled experimental
studies will be required to test the relative performance of these
models.
Potential flaws with the predictive models include the
assumption that genetic variation will vary in a regular manner
across all populations. Furthermore, the residual and percent
loss models make the assumption that certain unidentified
factors, which are represented by the residual produced by the
regression equation, are important intrinsic factors affecting
genetic variation in each population. This method, as used by
McDonald & Brown (1992) for predicting losses of species
richness in montane mammal communities, was criticized on
the basis that preserving the residual variation about the
regression line violated the assumptions of the regression
model (Skaggs & Boecklen, 1996). However, this technique
may be seen as reasonable because it is essentially a mixed
model in which every site has a different intercept (E. Bedrick,
personal communication). Finally, the emphasis on area as the
sole predictor of variation, while statistically valid, may fail to
consider other important influences on the amount of genetic
variation present in a population and downplay the unique
circumstances shaping the variation inherent to natural
populations. Regardless, there is no doubt that the size of
the available habitat and associated Ne represents an important
factor in predicting the amount of genetic variation present in
a population.
Reductions in each of the three measures of genetic variation
may result in negative consequences for a population.
Heterozygosity refers to the presence of more than one allele
at a locus and it can be measured at a variety of levels,
including individual heterozygosity (¼ observed heterozygos-
ity or direct-count heterozygosity) and Hardy–Weinberg
expected heterozygosity of a population (¼ heterozygosity or
gene diversity). Expected heterozygosity and reproductive
fitness are correlated at the population level (Reed &
Frankham, 2003). As effective population size declines,
expected heterozygosity is lost as a result of genetic drift and
inbreeding, resulting in a reduction in fitness owing to
inbreeding depression (Dudash & Fenster, 2000; Reed &
Frankham, 2003; Reed, 2005). In contrast, evidence for a
relationship between individual heterozygosity and fitness is
weaker and may be restricted to populations experiencing
inbreeding (Frankham et al., 2002). However, given the
demonstration of a positive relationship between population
size and fitness (Reed, 2005), it is expected that the predicted
declines in individual heterozygosity as a result of global
warming would translate into reduced fitness for these insular
populations. The processes of reduced population size,
decreased heterozygosity, inbreeding depression, and reduced
fitness can increase the probability of population extinction,
especially in the short term (Frankham, 1997b, 2005).
Polymorphism (a measure of the proportion of genes with at
least two alleles) and allelic diversity (a measure of the relative
number of alleles for each gene) are measures of the amount of
genetic raw material in the genome. Although other explana-
tions are possible (e.g. assortative mating), a reduction in
polymorphism and allelic diversity indicates an erosion of
genetic raw material with possible fixation through the loss of
alleles. There are empirical examples of the relationship between
a reduction of genetic raw material and a lowered probability of
population survivorship (see Fernandez et al., 2004; Frankham,
2005). However, in general the consequences of such reductions
are poorly understood and the short-term influences on
population survivorship are thought to be weaker than those
related to inbreeding depression as a result of reductions in
heterozygosity (Frankham, 2005). Conversely, reductions in
polymorphism and allelic diversity are potentially important
threats to the long-term viability of populations, because the
loss of alleles can compromise the ability of a population to
adapt to changing conditions and its overall evolutionary
potential (Fernandez et al., 2004; Frankham, 2005).
A caveat to these conclusions is that, while molecular
markers such as allozymes are well justified for examining
patterns of genetic variation, these nearly neutral genetic
markers generally do not exhibit high correlations with fitness
(Reed & Frankham, 2001) and may be more reflective of
genetic drift than of variation in quantitative traits. The
controversy regarding the strength of the correlation between
variation in molecular markers and quantitative genetic traits,
which are more subject to selection and hence more indicative
of evolutionary processes and potential, remains unresolved
(Reed & Frankham, 2003; Gilligan et al., 2005). Consequently,
similar studies utilizing quantitative traits directly associated
with fitness are warranted in order to evaluate extinction risk
more thoroughly in both the short and long term.
We anticipate that predicted reductions in genetic variation
arising from global warming will compromise both the short-
and long-term survival of montane mammal populations in the
American Southwest. Results of multiple regression analyses
indicated that area was the primary predictor of heterozygosity.
This suggests that reduction in habitat island size as a result of
global warming may most directly affect the amount of
observable heterozygosity in our study populations, and hence
impact their short-term viability. Area also was the most
important predictor of polymorphism in Tamias minimus.
Thus, losses in area will probably have long-term effects for some
species as well. In contrast, isolation was the most important
predictor of polymorphism in three of four species and it was a
significant factor in predicting allelic diversity in two of four
species. Of the three isolation measures, I-Nearest was the
significant predictor of genetic variation in most cases, and was a
contributing factor in predicting allelic diversity in N. mexicana.
These relationships suggest that polymorphism and allelic
diversity may be especially affected by local extinctions, which
would have the effect of increasing isolation by eliminating
neighbouring populations. Local extinction is a likely conse-
quence of global warming and may further exacerbate the
A. M. Ditto and J. K. Frey
1146 Journal of Biogeography 34, 1136–1149ª 2007 The Authors. Journal compilation ª 2007 Blackwell Publishing Ltd
negative effects of areal reductions in available habitat for
surviving populations by increasing relative isolation.
There is reason to believe that our predicted estimates of losses
to genetic variation are overly conservative. In addition to
habitat loss resulting from upward retraction of the lower
boundary of conifer forest, global warming is anticipated to lead
to increased isolation by both distance and harshness and
therefore may have an even greater impact on more isolated
ranges. Furthermore, our models posit a general upward
retraction of the lower border of conifer forest. For montane
habitats, the initial phases of global warming may represent a
greater potential loss of area (and hence genetic variation)
because of the conical shape of mountains. It is also likely that
global warming will result in a generalized degradation in the
quality of conifer forest habitats, which would effectively
increase actual habitat area losses and hence potentially pose
even more threats to existing populations.
While we do not necessarily recommend that our models
be employed directly in decision-making policies regarding
conservation of these species, we offer them as a heuristic
example that tests the validity of assumptions about the
effects of habitat fragmentation and reduction on genetic
variation in montane systems. Global warming is predicted to
reduce the areal extent and increase the isolation of montane
habitats, resulting in a decrease in genetic variation in
mammal populations associated with these habitats in the
American Southwest and elsewhere. There is growing evi-
dence of ecophenotypic and genetic change associated with
global warming events (Hadly et al., 1998, 2004; Consuegra
et al., 2002; Barnosky et al., 2003). The ability of mammal
species and communities to adapt and respond to these
changes is unknown (Houghton et al., 2001; Reilly et al.,
2001; Wigley & Raper, 2001). Consequently, elevated extinc-
tion rates may be predicted (Barnosky et al., 2003). Although
there is still considerable disagreement about the extent to
which reduced genetic variation is a cause or symptom of
endangerment (e.g. Gilpin & Soule, 1986; Caro & Laurenson,
1994; Young & Clarke, 2000), it is clear that reductions in
genetic variation do not bode well for a population and it
should continue to be recognized as a fundamental aspect of
biodiversity that warrants conservation.
ACKNOWLEDGEMENTS
Partial financial support of this research was provided by the
United States Geological Survey (grant 1448-0009-93-978). We
extend thanks to M. Bogan and T. Yates for help in facilitating
this research and constructive discussions, and to J. H. Brown,
F. A. Smith, and two anonymous reviewers for helpful
criticisms on a previous version of this paper. Statistical advice
came from G. Brock and E. Bedrick of the Mathematics and
Statistics Clinic at the University of New Mexico. We are
especially indebted to F. A. Smith for significant statistical and
philosophical advice. Thanks also go to S. Chakerian, T. Frey,
and W. Goldman for proofreading, statistical advice, and
support.
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SUPPLEMENTARY MATERIAL
The following supplementary material is available for this
article online:
Table S1. Statistical relationships between sample size and
genetic variation.
Table S2. Regression analyses between ecogeographic varia-
bles and measures of genetic variation.
This material is available as part of the online article
from: http://www.blackwell-synergy.com/doi/abs/10.1111/j.1365-
2699.2007.01700.x
Please note: Blackwell Publishing are not responsible for the
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BIOSKETCHES
Amy M. Ditto recgfeived her PhD from the Department of
Biology at the University of New Mexico in 2006. Her primary
research interests include geographic and ecological predictors
of genotypic and phenotypic variation in isolated populations
of montane mammals.
Jennifer K. Frey is a College Associate Professor in the
Department of Fishery and Wildlife Science and the Depart-
ment of Biology at New Mexico State University. Her primary
research focuses on the biogeography and conservation of
mammals in the American Southwest.
Editor: Brett Riddle
Genetic variation in montane mammals
Journal of Biogeography 34, 1136–1149 1149ª 2007 The Authors. Journal compilation ª 2007 Blackwell Publishing Ltd