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Bergmanns rule and the mammal fauna of northern North America

Tim M. Blackburn and Bradford A. Hawkins

Blackburn, T. M. and Hawkins, B. A. 2004. Bergmanns rule and the mammal fauna ofnorthern North America. / Ecology 27: 715/724.

The observation that on the whole. . . larger species live farther north and the smallerones farther south was first published by Carl Bergmann in 1847. However, whyanimal body mass might show such spatial variation, and indeed whether it is a general

feature of animal assemblages, is currently unclear. We discuss reasons for thisuncertainty, and use our conclusions to direct an analysis of Bergmanns rule in themammals in northern North America, in the communities of species occupying areasthat were covered by ice at the last glacial maximum. First, we test for the existence ofBergmanns rule in this assemblage, and investigate whether small- and large-bodiedspecies show different spatial patterns of body size variation. We then attempt toexplain the spatial variation in terms of environmental variation, and evaluate theadequacy of our analyses to account for the spatial pattern using the residuals arisingfrom our environmental models. Finally, we use the results of these models to testpredictions of different hypotheses proposed to account for Bergmanns rule.Bergmanns rule is strongly supported. Both small- and large-bodied species exhibitthe rule. Our environmental models account for most of the spatial variation in mean,minimum and maximum body mass in this assemblage. Our results falsify predictionsof hypotheses relating to migration ability and random colonisation and diversification,but support predictions of hypotheses relating to both heat conservation and starvationresistance.

T. M. Blackburn ([email protected]), School of Biosciences, Univ. of Birming-ham, Edgbaston, Birmingham UK B15 2TT. / B. A. Hawkins, Dept of Ecology andEvolutionary Biology, Univ. of California, Irvine, CA 92697, USA.

The observation that the body sizes of animal species

vary spatially was first made by Bergmann (1847), who

noted that if we could find two species of animals which

would only differ from each other with respect to size, . . .

the geographical distribution of the two species would

have to be determined by their size. . .

If there are generain which the species differ only in size, the smaller species

would demand a warmer climate, to the exact extent of

the size difference. He concluded that although it is

not as clear as we would like, it is obvious that on the

whole the larger species live farther north and the

smaller ones farther south (Bergmann 1847, translated

in James 1970). Spatial variation in body size of this sort

across species is now known as Bergmanns rule (see

Blackburn et al. 1999). Here we focus on Bergmanns

rule in the northern Nearctic region.

Studies of Bergmanns rule can take one of two

methodological approaches. First, they can examine

how body size differs amongst areas within a region by

comparing summary statistics for body size of the faunasinhabiting these areas. We term this the community

approach (cf. Stevens 1989). For example, Cousins

(1989) studied how the average mass of bird species

inhabiting 10)/10 km grid squares across Britain varied

with the latitude of the squares. Other studies have

considered the faunas of bands of latitude over a region

(Barlow 1994, Blackburn and Gaston 1996), or of point

communities (Zeveloff and Boyce 1988, Cotgreave and

Accepted 26 June 2004

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Stockley 1994). Most such studies tend to find evidence

for an increase in body size with latitude (Lindsey 1966,

Cousins 1989, Cushman et al. 1993, McDowall 1994,

Blackburn and Gaston 1996), although there are excep-

tions (Barlow 1994, Hawkins and Lawton 1995, Loder

1997).

The alternative approach to studying Bergmanns rule

involves examining how body size varies with the spatial

(typically latitudinal) midpoint of a species geographic

range. We term this the midpoint approach (cf. Rohde

et al. 1993). Such comparisons can be made either by

plotting midpoint against body size across species

(ideally accounting for phylogenetic non-independence)

(Hawkins 1995, Hawkins and Lawton 1995, Poulin and

Hamilton 1995, Blackburn and Gaston 1996, Blackburn

and Ruggiero 2001), or by comparing summary statistics

for body size of species whose range midpoints fall

within a given area (typically, a latitudinal band;

Kaspari and Vargo 1995, Blackburn and Gaston 1996).

An advantage of these methods is that each species

contributes only once to the overall comparison, which

circumvents the problem of pseudoreplication. However,

midpoint methods do not use data from all species

inhabiting every area (only those whose mid-points lie

within it), and seem to produce results that are more

equivocal in their support for Bergmanns rule (Hawkins

1995, Hawkins and Lawton 1995, Kaspari and Vargo

1995, Poulin and Hamilton 1995, Blackburn and Gaston

1996, Loder 1997, Blackburn and Ruggiero 2001).

One reason why midpoint methods may be more

equivocal than community methods is that the former donot consider information from all species present in any

given area. Comparisons based only on species whose

range midpoints fall within a given region thus exclude

information from all species whose ranges overlap that

region but are centred elsewhere. Moreover, mid-points

may be poor descriptors of a species range for wide-

spread species. For example, large-bodied species

expected by Bergmanns rule to occupy high latitudes

are often also widespread (Brown and Maurer 1987,

Gaston and Blackburn 1996, 2000), and hence will tend

to have ranges centred at mid-latitudes. An example

is Puma concolor, which ranges throughout most of theNew World. This could introduce a systematic bias

against recovering the rule in cases where the community

approach would reveal it. The most appropriate way

to study Bergmanns rule would therefore seem to be to

use a community approach, but to employ statistics that

evaluate the sources of the autocorrelation in the

data (Diniz-Filho et al. 2003). This is the approach we

adopt here.

The difficulties entailed in adequately quantifying the

relationship between body size and geography may also

go some way to explaining why there is no current

consensus on the cause of the effect. To date, at least sixplausible hypotheses have been suggested to explain why

Bergmanns rule might exist (summarised in Cushman

et al. 1993, Loder 1997, Blackburn et al. 1999, Gaston

and Blackburn 2000, Ashton et al. 2000, Meiri and

Dayan 2003). Two of these suggest that spatial patterns

in body size are artefacts, either 1) of the selective

advantage of some trait other than body mass but with

which mass is coupled, or 2) of random colonisation of

certain areas by large-bodied ancestral species followed

by subsequent clade diversification.

The remaining four hypotheses for Bergmanns rule

can be considered biological. The dispersal hypothesis

(3) suggests that small-bodied species are under-repre-

sented in certain areas because they have failed to

disperse there as often as have large-bodied species in

the period since these areas became habitable. The heat

conservation hypothesis (4) suggests that large body size

might allow species to occupy cooler areas, such as high

latitudes, because it increases heat conservation via lower

surface area to volume ratios (Bergmann 1847), or by

allowing thicker and heavier layers of insulation. A

related idea (5) is that body size varies in response to the

demands of keeping cool (James 1970). Individuals

living in warm moist environments cannot take advan-

tage of evaporative cooling. An alternative strategy is to

increase their rate of heat loss by increasing their surface

area: volume ratio. One way to do this is to be small-

bodied. Finally, Bergmanns rule has been hypothesized

to be a response to seasonal scarcity of resources. Larger

body mass may increase starvation resistance in such

circumstances (hypothesis 6), because fat reserves in-

crease more rapidly with body size than does metabolic

rate (Calder 1984, Lindstedt and Boyce 1985).

Given these issues in understanding pattern and

process with respect to Bergmanns rule, this paper has

the following four aims. First, we test for the existence of

Bergmanns rule in the mammals in northern North

America, specifically in the communities of species

occupying those areas that were covered by ice at the

last glacial maximum (see map in Hawkins and Porter

2003). Restricting the analysis to this region has the

important property that the fauna and flora have been

entirely structured by recolonisation in the last 20 000 yr,

and so should be largely unaffected by patterns ofancestral colonisation and diversification. Second, we

investigate whether small- and large-bodied species show

different spatial patterns of body size variation. Freckle-

ton et al. (2003) showed that the intra-specific pattern

was stronger within large- than small-bodied mammal

species (see also Ashton et al. 2000, Meiri and Dayan

2003, Ochocinska and Taylor 2003); we perform the

parallel test for the interspecific pattern. Third, we

evaluate the adequacy of our analyses to account for

the spatial pattern using the residuals arising from our

environmental models. This approach is being increas-

ingly used in analyses of gradients of species richness(Badgley and Fox 2000, Hawkins and Porter 2003,

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Diniz-Filho et al. 2003, Hawkins et al. 2003) and is

appropriate for evaluating almost all macroecological

patterns. Our philosophy follows that of Legendre

(1993), Legendre and Legendre (1998) and Legendre

et al. (2002), who argue that the autocorrelation

structure of spatial data represents an important source

of information rather than a source of error that has to

be removed or corrected for (see also Diniz-Filho et al.

2003).

Finally, we use our data to test predictions of the

hypotheses proposed to explain Bergmanns rule. These

predictions are as follows:

Hypothesis 1 (covariation with mass): we think this is

virtually untestable. Any relationship between body size

and latitude could be argued to arise because of the

confounding effect of an unmeasured true predictor

strongly coupled to size. Even controlling for phyloge-

netic non-independence is not an infallible guard against

this possibility.

Hypothesis 2 (ancestral colonisation): this predicts no

relationship between size and any spatially patterned

environmental variables in our data, because not enough

time has elapsed for the pattern of ancestral colonisation

and diversification hypothesised. Note that the existence

of Bergmanns rule would not invalidate this hypothesis

if the process of ancestral colonisation and diversifica-

tion had structured this mammal community in the

millennia prior to the first period of glaciation, and then

the original community reconstituted itself every time

the ice retreated. However, in this circumstance, we

would have to ask why this reconstitution occurs?Whatever the answer, it would clearly invalidate the

concept of random colonisation. The existence of

Bergmanns rule would also not invalidate this hypoth-

esis if random recolonisation had by chance given us the

size gradient following the last glacial retreat (and the

diversification was still to come). However, this again

would be untestable, and anyway would also be highly

unlikely (indeed, we can measure its likelihood by the

strength of observed correlations between body size and

spatially patterned environmental variables).

Hypothesis 3 (dispersal): this predicts that average

body size should be most closely related to the time sincean area was covered by glaciers. It also predicts a weaker

relationship between this time and size for large-bodied

than for small-bodied species, because large-bodied

species should have been able to colonise even those

areas that were most recently covered by glaciers. In

contrast, small-bodied species should be absent from

younger areas.

Hypothesis 4 (heat conservation): this predicts that

temperature should be the best environmental explana-

tory variable for average body size. It also predicts that

covariation of size with temperature should be stronger

for small-bodied than large-bodied species, because theirgreater surface area to volume ratios mean that small-

bodied species face more of a challenge in keeping warm

than do large-bodied species.

Hypothesis 5 (heat dissipation): this predicts that

variables related to both temperature and moisture

should best describe average body size. It also predicts

that covariation of size with these variables should be

stronger for large-bodied than small-bodied species,

because small-bodied species do not need to respond

to the challenge of dissipating heat and keeping cool,

whereas large-bodied species do.

Hypothesis 6 (resource availability): this predicts that

average body size should respond to variation in

resource availability. Thus, we might expect to see

covariation of average size with measures of productiv-

ity, as higher productivity ought to lead to greater

resource availability. We might also expect to see

covariation of average size with measures of seasonality,

if total productivity is less important than how much its

availability varies: in these data, seasonality will benegatively correlated with annual average temperature,

and possibly positively with range in elevation (as

mountainous areas have greater elevational ranges and

tend to show greater seasonality, all else being equal).

Methods

Variables

We generated six potential explanatory variables derived

from a range of sources (see also Hawkins and Porter2003, Hawkins et al. 2003). The variables were: 1) time

since glacial retreat (age), estimated by changes in ice

cover in the temporal series of maps generated by Dyke

and Prest (1987), supplemented with maps atB/members.

cox.net/quaternary/nercNORTHAMERICA.html/; 2)

range in elevation (derived from a combination of spot

heights and contour lines in the topographical maps in

the Pergamon world atlas (Anon. 1968), estimated to the

nearest 50 m); 3) mean monthly temperature (temp-

erature: B/www.grid.unep.ch/data/grid/gnv15.php/); 4)

annual precipitation (precipitation: B/www.grid.unep.ch/

data/grid/gnv174.php/); 5) annual Generalized Vege-

tation Index (GVI) (B/www.ngdc.noaa.gov/seg/eco/

cdroms/gedii_a/datasets/a01/mgv.htm#top/: Kineman

and Hastings 1992). This estimate of plant biomass is

derived from 1-km resolution Normalized Difference

Vegetation Index (NDVI) converted into 10-min grid

cells, composited monthly and rescaled. This coarser

resolution was considered more appropriate for the grain

size we use here (48 400 km2). We used the monthly data

extending from April 1985 to December 1988 (the entire

dataset available) to generate average monthly GVI

across all months. We also generated data for annual

range in GVI as a measure of seasonality. However, this

variable is highly correlated with annual GVI, as there isessentially no variation in GVI across our region in

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winter, and does not displace annual GVI in any of our

multivariate models. Thus, we do not consider it further;

6) landcover diversity (the number of landcover types

calculated from 8 km resolution AVHRR data, NOAA

pathfinder land [PAL] program) (B/www.geog.umd.edu/

landcover/8km-map.html/). We include this even

though a relationship to body mass is not predicted by

any of the hypotheses we test.

We also generated three measures of the average log-

transformed body sizes (in grams) of the mammals,

using mass data from Burt and Grossenheider (1976): 1)

average mass (Avemass) / average log mass of all

species; 2) minimum body mass (Minmass) / mass of

the single species representing the 25th percentile of the

body masses in a grid cell, and 3) maximum body mass

(Maxmass) / mass of the single species representing the

75th percentile. We use these species to remove the

influence of the absolute smallest and largest species,

which may be outliers. The distributions of the 138native mammal species found within the study region

were delimited using the range maps in Hall and Kelson

(1959).

Values for all body mass and environmental variables

were generated for each of 164 grid cells comprising the

part of North America completely covered by ice during

the glacial maximum 20 000 yr before present. A species

was included in the species list for a grid cell if any part

of its geographic range, as delimited by Hall and Kelson

(1959), overlapped the cell. The number of species per

cell ranged from 12 to 73 (mean0/42.3). Each cell was

220)/220 km, except for adjacent coastal cells whichwere often combined to keep total land area in each cell

as constant as possible. All islands were excluded. This

cell size was selected to make the grain generally

comparable with other macroecological analyses con-

ducted in this region (Currie 1991, Kerr and Packer

1999, Hawkins and Porter 2003).

Analyses

Most of the predictor variables in our analyses co-vary

(Table 1). Although the correlation coefficients between

most of the predictors are not especially high (r/0.63

only for temperature versus annual GVI), it is possible in

some cases that the influence of a variable in multiple

regression analysis will be influenced by which other

variables are included in the model. To assess the

importance of the various predictor variables given this

problem of collinearity, we report both sequential and

adjusted sums of squares for minimum adequate regres-

sion models generated by stepwise backwards deletion

from a full model including all variables (and their

squared terms where univariate analysis suggested that

this may explain additional variation). Adjusted sums of

squares are calculated from entering each variable last

into a model that already includes the other variables

(also known as type III sums of squares). Adjusted

sums of squares that are much lower than sequential are

indicative of collinearity, as they suggest that a predictor

already in the model accounts for much of the variability

previously ascribed to that added last. While collinearity

does affect the significance of some of the variables in

our minimum adequate models, this does not affect any

of the conclusions drawn from those models. Never-

theless, we present F-values for individual predictor

variables on the basis of the adjusted values, to assess

significance controlling for all other variables in a model.

We also examined adjusted r2 as a measure of model fit,

to clarify the trade-off between goodness of fit

and number of model parameters. Statistics were calcu-

lated using R version 1.8.0 (R Foundation, B/http://

www.r-project.org/).

We also evaluate the adequacy of our regression

models to explain the spatial pattern of body size using

the technique described by Diniz-Filho et al. (2003). The

pattern of spatial structure in the body size data was

described by generating spatial correlograms using

SAAP 4.3 (Wartenburg 1989). Correlograms were then

generated on residual body mass after fitting minimum

adequate multiple-regression models. Reduction in the

level of spatial autocorrelation in any distance class after

fitting the predictor variables represents the ability of the

model to explain body mass at that distance. Any

remaining spatial autocorrelation after fitting the models

indicates that additional spatially patterned variables not

included in the model may also be contributing to the

spatial pattern. Even so, some unexplained residual

spatial pattern might be expected, since none of our

models explain 100% of the variance in body mass. The

presence of significant residual spatial autocorrelation

also means that significance levels associated with the

predictor variables in the regression models are too

Table 1. Correlation matrix for predictor variables. All values of r/0.160 are significant at pB/0.05. Non-significant values arepresented in bold.

Variable Age Precipitation Range in elevation Temperature Annual GVI

Age 1Precipitation 0.225 1Range in elevation 0.349 0.366 1Temperature 0.604 0.513 /0.008 1

Annual GVI 0.628 0.469 0.004 0.878 1Landcover 0.585 0.369 0.331 0.593 0.591

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liberal, although the fact that most of the spatial pattern

in body mass is explained by the models indicates that

this bias is slight and has no influence on interpretation

of the significance of the major predictors. Finally, it

should be remembered that the goal of the analysis is to

explain variance in the geographic distributions of body

mass, not fix significance levels of environmental vari-

ables, however weak their association with mass.

Results

Mammal body mass shows spatial variation in northern

North America. A trend surface analysis of log body

mass in terms of both latitude and longitude and their

squared terms explains 74% of the variation in mass

(F4,1590/116.7, pB/0.001). In general, body mass tends

to increase to the north and to the west. Models withlatitude and longitude alone explain 77% of the variation

in minimum body mass (including squared terms,

F4,1590/141.0, pB/0.001), and 47% of the variation in

maximum body mass (linear terms only, F2,1610/73.9,

pB/0.001). This suggests that some aspect of environ-

mental variation influences the body masses of the

species able to occupy mammalian communities in this

region.

Several of the environmental predictor variables are

related to average log mass in a curvilinear fashion

(Table 2), although the additional variance explained by

the second order terms is usually small (B/0.08).

Variables explain up to 69% of the variation in body

mass when they (and their squared term, where appro-

priate) are the sole predictor. The relationship of cell age

to body mass is negative but linear, whereas range in

elevation is not related to body mass. Landcover, GVI,

range in elevation and age also explain no significant

variation in body mass when entered last into the full

model including all other predictors (Table 2). Tempera-

ture and precipitation both explain significant amounts

of variation in body mass independent of the other

predictors. However, the F-values for these variables

decline markedly when they are added last to the model

compared to when they are added alone, due to the

effects of collinearity.

Sequential deletion of variables from the full model

gives a minimum adequate model that includes annual

average temperature and its squared term, rainfall and

annual GVI (Table 3). The three variables combined

explain 72.6% of the variation in average log mass, only

3.6% more than is explained by temperature alone,

which is consistently the strongest predictor of average

body mass in these data (Fig. 1). Nevertheless, removal

of any of these predictors leads to a significant (a0/0.05)

decline in model fit. Latitude (but not longitude)

explains significant additional variation if added to

this model (F1,1560/17.5, pB/0.001), but only increases

the variance explained to 75.2%.

Log minimum body mass shows a univariate linear

relationship with age, and curvilinear relationships with

precipitation, annual average temperature, annual GVI

and landcover. The full model for minimum body mass

including all the variables and their squared terms

reveals significant effects of age, annual average tem-

perature, precipitation and annual GVI. Sequential

deletion of variables from the full model leaves a

minimum adequate model (Table 4) that explains

80.5% of the variation in log minimum body mass and

identifies strong negative effects of annual average

temperature, annual GVI and precipitation. Latitude

(but not longitude) explains significant additional varia-

tion if added to this model (F1,1570/6.1, p0/0.014), but

increases the variance explained by only 0.6%.

Log maximum body mass shows univariate curvilinearrelationships with all variables bar range in elevation.

The full model for maximum body mass including all the

variables reveals significant effects of precipitation,

annual average temperature and range in elevation.

Sequential deletion of variables from the full model

leaves a minimum adequate model (Table 5) that

explains 45.1% of the variation in log maximum body

mass, and suggests a strong effect of annual average

temperature, precipitation and range in elevation.

Latitude (but not longitude) explains significant addi-

tional variation if added to this model (F1,1590/11.9,

Table 2. The relationship between average log mass and each of the variables in the first column. (x, 2) indicates that a second orderpolynomial regression for x is a significantly better fit than the linear regression. All significant univariate relationships are negative.r2 and univariate F-values in this table relate to the univariate (or polynomial where appropriate) relationship to average log mass,and are adjusted for number of parameters. F last measures the effect of a predictor (and its squared term where indicated) whenintroduced last into a multiple regression model that already includes all other variables (also known as adjusted or type III F).DF0/degrees of freedom.

Variables r2 Univariate F DF F last DF

Temperature, 2 0.690 182.6*** 2,161 9.24*** 2,153Range in elevation 0.003 0.5 1,162 0.05 1,153Precipitation, 2 0.417 59.2*** 2,161 7.46*** 2,153Age 0.200 41.7*** 1,162 0.02 1,153Annual GVI, 2 0.608 127.2*** 2,161 2.43 2,153Landcover, 2 0.379 50.7*** 2,161 1.82 2,153

*pB/0.05, **pB/0.01, ***pB/0.001.

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pB/0.001), but increases the variance explained by only

3.5%.

Most of the species present in the region occupy a

relatively low percentage of all possible grid cells (Fig. 2),

suggesting that the results are not influenced by a high

proportion of ubiquitous species. The number of cells

occupied is weakly positively correlated with body mass

(log-transformed data, r20/0.05, n0/138, p0/0.01). The

spatial autocorrelation patterns for all three measures ofbody mass are typical of a cline, with strong positive

autocorrelation at shorter distances, gradually becoming

negative at longer distances (Fig. 3). Fitting the mini-

mum adequate models successfully removed from 59 to

80% of the overall autocorrelation, indicating that the

environmental models describe the spatial patterns well.

However, the correlograms of the residuals for all three

models (mean, minimum and maximum) remain sig-

nificant at some distance classes, indicating that the

variation unexplained by the models in these cases

contains some spatial structure. The strongest residual

autocorrelation tends to occur in the shortest distance

class, indicating that processes not modelled by our

broad-scale environmental predictors influence body size

patterns at more local scales.

Discussion

In the Introduction, we identified two main issues

concerning Bergmanns rule. First, do data support its

existence? Second, if it is supported, what causes it? With

regard to the mammalian fauna of the northern

Nearctic, the answer to the first question is a definite

affirmative. There is a strong and clear tendency for the

average body mass of species occupying equal-area grid

cells in this region to increase with latitude, as required

by Bergmanns rule (Bergmann 1847, Blackburn et al.

1999). Combined with the results of recent reviews of

spatial variation in body mass within species (Ashton et

al. 2000, Meiri and Dayan 2003, Freckleton et al. 2003),

this suggests that Bergmanns rule applies to mammals

whether it is considered to be an inter- or intraspecific

pattern.

The results for maximum and minimum body mass

potentially suffer from an artefact due to the general

tendency for species richness to vary spatially. If there

were no general tendency for mass to vary across space,

we would still expect to see variation in minimum and

maximum mass if the number of species varied, because

the smaller samples of species in species-poor areas

would be expected by chance alone to exhibit a smaller

range of body masses. However, there is good reason to

believe that this artefact is not driving patterns of body

mass variation in these data. If this artefact were

operating, spatial variation in maximum and minimum

body mass should be mirror images of each other

(because areas with more species should have higher

maxima and lower minima). Our results provide no

evidence for this: for example, both maximum and

minimum body mass decrease with temperature acrossgrid cells.

Freckleton et al. (2003), (see also Ashton et al. 2000,

Meiri and Dayan 2003) found that large-bodied species

tend to follow the intraspecific version of the rule more

closely than do small-bodied species when body size

variation was compared to temperature, but not when it

was compared to latitude. Our analyses differ in that we

do not consider spatial variation in the body size of a

given species, but rather in the assemblage of species

living in different areas. Nevertheless, the best models

for the relationships between maximum body mass

and both latitude (adj. r20/0.44) and annual averagetemperature (adj. r20/0.36) are weaker than the

Table 3. Minimum adequate model for average log mass. Adj. r20/0.726, F4,1590/109.2, pB/0.001. The coefficients for first orderregression terms are listed first in each cell. F is calculated from the adjusted (type III) sum of squares.

Variables Coefficients DF Seq. SS Adj. SS MS F

Temperature, 2 (/1.51, 0.48 2 5.47 0.79 0.39 29.9***Precipitation (/0.0001 1 0.23 0.22 0.22 16.8***Annual GVI (/0.003 1 0.07 0.07 0.07 5.6*

157 2.10 2.10 0.013

*pB/0.05, **pB/0.01, ***pB/0.001.

2.4

2.6

2.8

3.0

3.2

3.4

3.6

Averagelogbodym

ass

20 15 10 5 0 5 10 15

Annual average temperature

Fig. 1. The relationship between average log mass (g) andannual average temperature (8C). The regression line is for thebest-fit polynomial model (r20/0.690, n0/164, pB/0.001).

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respective relationships for minimum mass (latitude: adj.

r20/0.75; annual average temperature: adj. r20/0.75).

Variation in body mass across the region thus is driven

by the gain or loss of both large- and small-bodied

species from assemblages, but small-bodied species more

strongly follow the interspecific version of Bergmanns

rule. These results are not incompatible with the patterns

identified within species, however. Large-bodied species

may also respond to the demands of higher latitudes by

altering their body mass, while small-bodied species may

respond by adopting other strategies less accessible tolarger-bodied species (e.g. hibernation or torpor) that

do not necessitate body mass changes. The factors that

drive spatial variation in body mass may thus cause

variation both within individual species and across entire

communities.

Our results concur with most other investigations

of Bergmanns rule using community approaches

(Lindsey 1966, Cousins 1989, Cushman et al. 1993,

McDowall 1994, Blackburn and Gaston 1996). Most of

these studies concern vertebrates, whereas those that do

not find the predicted pattern using this method

generally refer to invertebrates (Barlow 1994, Hawkins

1995, Hawkins and Lawton 1995, Loder 1997). This may

provide a clue as to causation. Such clues are certainly

needed, as there is currently no consensus on the cause

of the effect exhibited by our data (and others).

One problem in distinguishing among hypotheses for

the interspecific Bergmanns rule is that while they may

predict associations between body mass and different

independent variables, those independent variables are

often highly collinear. Therefore, comparative tests of

association are often likely to be inconclusive. Thus, for

example, the temperature regulation, dispersal ability

and starvation hypotheses predict that body mass should

depend principally on environmental temperature, re-

gional age and productivity, respectively. However, these

three predictors are likely to be significantly inter-

correlated, and in the case of temperature and plant

biomass the correlation in our data is 0.878 (Table 1). Itis thus not surprising that it is difficult to disentangle

effects. While this could be taken as an argument for

abandoning the comparative approach in favour of a

manipulative experimental paradigm, that approach

would also not be without problems. Notably, experi-

ments would have to be performed on a sufficient range

of species to draw meaningful conclusions about inter-

specific patterns / no small challenge. Moreover, in our

data set, the correlations among most predictor variables

are not excessively high, while for transparency our

statistical analyses document the effects of collinearity

through comparison of sequential and adjusted sum ofsquares.

Table 5. Minimum adequate model for log maximum body mass. Adj. r20/0.451, F3,1600/45.7, pB/0.001. F is calculated from theadjusted (type III) sum of squares.


Temperature (/0.012 1 1.60 0.59 0.59 39.3***Range in elevation 0.0001 1 0.19 0.38 0.38 25.1***Precipitation (/0.0002 1 0.26 0.26 0.26 17.5***

160 2.40 2.40 0.015

*pB/0.05, **pB/0.01, ***pB/0.001.

Table 4. Minimum adequate model for log minimum body mass. Adj. r20/0.805, F5,1580/135.5, pB/0.001. The coefficients for firstorder regression terms are listed first in each cell. F is calculated from the adjusted (type III) sum of squares.


Precipitation (/0.0001 1 1.16 0.10 0.10 21.9***Temperature, 2 (/0.66, 0.42 2 1.85 0.16 0.08 16.6***Annual GVI, 2 (/0.056, (/0.0003 2 0.16 0.16 0.08 17.1***

158 0.74 0.74 0.005

*pB/0.05, **pB/0.01, ***pB/0.001.

0

5

10

15

20

25

30

35

40

45

Numberofspe

cies

0 24 48 72 96 120 144 168

Number of grid squares occupied

Fig. 2. Frequency distribution of grid cell occupancy for the138 mammal species in the analysis. The maximum possibleoccupancy is 164 cells.

ECOGRAPHY 27:6 (2004) 721


8/10

Although Bergmanns rule is often expressed in terms

of latitude, most workers realize that latitude is of itself

meaningless in terms of understanding spatial drivers of

body mass. The important question is what are the

spatially patterned environmental forces driving spatial

variation in body mass for which latitude is a surrogate?We think that our results are informative about cause as

well as effect for Bergmanns rule.

First, the explanation based on ancestral colonisation

can be excluded here. Mammals exhibit Bergmanns rule

in an area that was covered by glaciers within the last

20 000 yr, far too recently for speciation to have caused

the patterns (see also Cushman et al. 1993, Blackburn

and Gaston 1996). Although random recolonisation

could by chance have produced spatial patterning in

body mass following the last glacial retreat, the strength

of the correlations between body size and environmental

variables makes this implausible: indeed, the probabilityof obtaining such a strong relationship between average

log body mass and temperature by chance is /0.001.

Second, the dispersal hypothesis garners no support

from these data. There is no relationship between cell age

and any measure of log body mass when other variables

are also included in the analysis (Tables 2 /5).

Third, the data provide support for the idea that body

mass is responding to temperature. Annual average

temperature explains significant variance in average

mass independent of other predictors (Table 2) and is

included in the minimum adequate model for average

(Table 3), minimum (Table 4) and maximum (Table 5)mass. All this suggests that temperature influences body

mass variation. However, temperature has been hypothe-

sised to influence mass through the demands of either

heat conservation (Bergmann 1847, Scholander et al.

1950, Herreid and Kessel 1967, Calder 1984), or heat

dissipation (James 1970).

The heat dissipation hypothesis proposes that body

size varies in response to the demands of keeping cool,

rather than keeping warm. It predicts that body mass

should on average be less in warmer, wetter regions, as

observed (Table 3). This result might seem surprising, as

we only consider high northern latitudes for which theannual average temperature for /70% of the grid cells is

below zero. However, the heat dissipation hypothesis

also predicts that the response to climate should be

greatest amongst the largest species. In contrast, the heat

conservation hypothesis predicts a greater response to

temperature amongst small-bodied mammals, as they

face more of a challenge in keeping warm than do

large-bodied species (see Introduction). In fact, tempera-

ture explains more than twice as much of the spatial

variation in the masses of small-bodied than large-

bodied species (see above). Thus, our results are more

consistent with the requirements of heat conservationthan heat dissipation.

Fig. 3. Correlograms of Morans I showing patterns of spatialautocorrelation of raw mammal body mass and residualautocorrelation after fitting the respective minimum adequate

model (MAM), for a) log average body mass, b) log minimumbody mass and c) log maximum body mass.

722 ECOGRAPHY 27:6 (2004)


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Nevertheless, our results also suggest that heat con-

servation may not be the only driver of Bergmanns rule.

Our measure of plant biomass (GVI) enters the MAMs

for both minimum and average body mass (Tables 3 and

4). This suggests that resource availability might also be

an important driver of spatial variation in body mass,

especially for small-bodied mammals. Small-bodied

species may have low starvation resistance, because fat

reserves increase more rapidly with body size than does

metabolic rate (Calder 1984, Lindstedt and Boyce 1985),

which may be an advantage where resources are scarce.

This logic has been questioned, however, because small-

bodied species may be better able to ameliorate the

stresses of seasonal shortage by exploiting stored food

reserves, microclimatic refugia, or hibernation or torpor

(Dunbrack and Ramsay 1993). Nevertheless, the general

increase in the body mass of the smallest mammal

species inhabiting areas with lower annual GVI suggests

that other strategies for ameliorating the stresses ofseasonal shortage are insufficient. The positive relation-

ship between range in elevation and body mass for large-

bodied mammals (Table 5) is also consistent with the

influence of resource availability (see Introduction).

Trying to distinguish the influences of temperature

versus resource availability is inherently difficult due to

the fact that temperature may influence both animal

body sizes and levels of plant productivity. Given this

biological reality, for the time being we conclude that the

patterns of covariation among our variables suggest that

both heat conservation and resource availability may be

important influences on Bergmanns rule, but otherapproaches will be needed to be sure. Future analyses

that examine the biological and ecological characteristics

of the mammals found in each part of the region with

respect to body size may be informative. Until then, we

can conclude that there can be no doubt that Bergmanns

rule applies to mammals in northern latitudes, at least in

the Nearctic region, and we are narrowing down the

possible explanations for it. We are optimistic that the

increasing availability of large-scale data will allow us to

soon understand this and related macroecological pat-

terns.

Acknowledgements / We thank Kyle Ashton, Richard Duncanand Robert Poulin for comments that improved this manuscript.

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