ORIGINAL Effects of ecogeographic variables on ARTICLE genetic …web.nmsu.edu/~jfrey/36 Ditto and Frey 2007.pdf · 2008-03-07 · ORIGINAL ARTICLE Effects of ecogeographic variables

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

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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

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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

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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


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


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



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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



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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.



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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).



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80

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



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81

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


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


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.



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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


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


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.



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83

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



Jennifer

84

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

content or functionality of any supplementary materials

supplied by the authors. Any queries (other than missing

material) should be directed to the corresponding author for

the article

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



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