Download Bergmann`s rule and the mammal fauna of northern North America

ECOGRAPHY 27: 715 /724, 2004
Bergmann’s rule and the mammal fauna of northern North America
Tim M. Blackburn and Bradford A. Hawkins
Blackburn, T. M. and Hawkins, B. A. 2004. Bergmann’s rule and the mammal fauna of
northern North America. / Ecology 27: 715 /724.
The observation that ‘‘on the whole. . . larger species live farther north and the smaller
ones farther south’’ was first published by Carl Bergmann in 1847. However, why
animal 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 this
uncertainty, and use our conclusions to direct an analysis of Bergmann’s rule in the
mammals in northern North America, in the communities of species occupying areas
that were covered by ice at the last glacial maximum. First, we test for the existence of
Bergmann’s rule in this assemblage, and investigate whether small- and large-bodied
species show different spatial patterns of body size variation. We then attempt to
explain the spatial variation in terms of environmental variation, and evaluate the
adequacy of our analyses to account for the spatial pattern using the residuals arising
from our environmental models. Finally, we use the results of these models to test
predictions of different hypotheses proposed to account for Bergmann’s rule.
Bergmann’s rule is strongly supported. Both small- and large-bodied species exhibit
the rule. Our environmental models account for most of the spatial variation in mean,
minimum and maximum body mass in this assemblage. Our results falsify predictions
of hypotheses relating to migration ability and random colonisation and diversification,
but support predictions of hypotheses relating to both heat conservation and starvation
resistance.
T. M. Blackburn ([email protected]), School of Biosciences, Univ. of Birmingham, Edgbaston, Birmingham UK B15 2TT. / B. A. Hawkins, Dept of Ecology and
Evolutionary 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 genera
in 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 ‘‘Bergmann’s rule’’ (see
Blackburn et al. 1999). Here we focus on Bergmann’s
rule in the northern Nearctic region.
Studies of Bergmann’s 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 faunas
inhabiting 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
Copyright # ECOGRAPHY 2004
ISSN 0906-7590
ECOGRAPHY 27:6 (2004)
715
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 exceptions (Barlow 1994, Hawkins and Lawton 1995, Loder
1997).
The alternative approach to studying Bergmann’s 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 Bergmann’s 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 do
not 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 widespread species. For example, large-bodied species
expected by Bergmann’s 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 the
New 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 Bergmann’s 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 six
plausible hypotheses have been suggested to explain why
716
Bergmann’s 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 Bergmann’s rule
can be considered biological. The dispersal hypothesis
(3) suggests that small-bodied species are under-represented 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 advantage 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 smallbodied. Finally, Bergmann’s 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 increase 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 Bergmann’s rule, this paper has
the following four aims. First, we test for the existence of
Bergmann’s 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 of
ancestral colonisation and diversification. Second, we
investigate whether small- and large-bodied species show
different spatial patterns of body size variation. Freckleton 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 increasingly used in analyses of gradients of species richness
(Badgley and Fox 2000, Hawkins and Porter 2003,
ECOGRAPHY 27:6 (2004)
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 Bergmann’s 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 phylogenetic 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 Bergmann’s rule would not invalidate this hypothesis
if the process of ancestral colonisation and diversification 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
Bergmann’s rule would also not invalidate this hypothesis 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 since
an 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 explanatory 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 their
greater surface area to volume ratios mean that smallECOGRAPHY 27:6 (2004)
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 productivity, 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 be
negatively 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 Porter
2003, 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 at B/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 (temperature: 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 Vegetation 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 is
essentially no variation in GVI across our region in
717
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 logtransformed 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 138
native 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 (mean /42.3). Each cell was
220/220 km, except for adjacent coastal cells which
were 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 conducted 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 regression 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. Nevertheless, 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 calculated 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 p B/0.05. Non-significant values are
presented in bold.
Variable
Age
Precipitation
Range in elevation
Temperature
Annual GVI
Landcover
718
Age
Precipitation
1
0.225
0.349
0.604
0.628
0.585
1
0.366
0.513
0.469
0.369
Range in elevation
1
/0.008
0.004
0.331
Temperature
Annual GVI
1
0.878
0.593
1
0.591
ECOGRAPHY 27:6 (2004)
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 variables, 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
(F4,159 /116.7, pB/0.001). In general, body mass tends
to increase to the north and to the west. Models with
latitude and longitude alone explain 77% of the variation
in minimum body mass (including squared terms,
F4,159 /141.0, pB/0.001), and 47% of the variation in
maximum body mass (linear terms only, F2,161 /73.9,
pB/0.001). This suggests that some aspect of environmental 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 appropriate) 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). Temperature 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 (a/0.05)
decline in model fit. Latitude (but not longitude)
explains significant additional variation if added to
this model (F1,156 /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 temperature, 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 variation if added to this model (F1,157 /6.1, p/0.014), but
increases the variance explained by only 0.6%.
Log maximum body mass shows univariate curvilinear
relationships 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 additional variation if added to this model (F1,159 /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 order
polynomial 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) when
introduced last into a multiple regression model that already includes all other variables (also known as adjusted or ‘‘type III’’ F).
DF/degrees of freedom.
r2
Variables
Temperature, 2
Range in elevation
Precipitation, 2
Age
Annual GVI, 2
Landcover, 2
0.690
0.003
0.417
0.200
0.608
0.379
Univariate F
182.6***
0.5
59.2***
41.7***
127.2***
50.7***
DF
2,161
1,162
2,161
1,162
2,161
2,161
F last
9.24***
0.05
7.46***
0.02
2.43
1.82
DF
2,153
1,153
2,153
1,153
2,153
2,153
*p B/0.05, **pB/0.01, ***pB/0.001.
ECOGRAPHY 27:6 (2004)
719
Table 3. Minimum adequate model for average log mass. Adj. r2 /0.726, F4,159 /109.2, pB/0.001. The coefficients for first order
regression terms are listed first in each cell. F is calculated from the adjusted (‘‘type III’’) sum of squares.
Variables
Coefficients
DF
/1.51, 0.48
/0.0001
/0.003
Temperature, 2
Precipitation
Annual GVI
2
1
1
157
Seq. SS
Adj. SS
MS
5.47
0.23
0.07
2.10
0.79
0.22
0.07
2.10
0.39
0.22
0.07
0.013
F
29.9***
16.8***
5.6*
*p B/0.05, **pB/0.01, ***pB/0.001.
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, r2 /0.05, n/138, p/0.01). The
spatial autocorrelation patterns for all three measures of
body mass are typical of a cline, with strong positive
autocorrelation at shorter distances, gradually becoming
negative at longer distances (Fig. 3). Fitting the minimum 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 significant 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.
3.6
Average log body mass
3.4
3.2
3.0
2.8
2.6
2.4
–20
–15
–10
–5
0
5
10
15
Annual average temperature
Fig. 1. The relationship between average log mass (g) and
annual average temperature (8C). The regression line is for the
best-fit polynomial model (r2 /0.690, n /164, p B/0.001).
720
Discussion
In the Introduction, we identified two main issues
concerning Bergmann’s 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 Bergmann’s 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 Bergmann’s 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 across
grid 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. r2 /0.44) and annual average
temperature (adj. r2 /0.36) are weaker than the
ECOGRAPHY 27:6 (2004)
Table 4. Minimum adequate model for log minimum body mass. Adj. r2 /0.805, F5,158 /135.5, pB/0.001. The coefficients for first
order regression terms are listed first in each cell. F is calculated from the adjusted (‘‘type III’’) sum of squares.
Variables
Coefficients
DF
/0.0001
/0.66, 0.42
/0.056, /0.0003
Precipitation
Temperature, 2
Annual GVI, 2
Seq. SS
Adj. SS
MS
1.16
1.85
0.16
0.74
0.10
0.16
0.16
0.74
0.10
0.08
0.08
0.005
1
2
2
158
F
21.9***
16.6***
17.1***
*p B/0.05, **pB/0.01, ***pB/0.001.
Table 5. Minimum adequate model for log maximum body mass. Adj. r2 /0.451, F3,160 /45.7, p B/0.001. F is calculated from the
adjusted (‘‘type III’’) sum of squares.
Variables
Coefficients
DF
/0.012
0.0001
/0.0002
Temperature
Range in elevation
Precipitation
1
1
1
160
Seq. SS
Adj. SS
MS
1.60
0.19
0.26
2.40
0.59
0.38
0.26
2.40
0.59
0.38
0.26
0.015
F
39.3***
25.1***
17.5***
*p B/0.05, **pB/0.01, ***pB/0.001.
respective relationships for minimum mass (latitude: adj.
r2 /0.75; annual average temperature: adj. r2 /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 Bergmann’s
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 to
larger-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.
45
40
Number of species
35
30
25
20
15
10
5
0
0
24
48
72
96
120
144
168
Number of grid squares occupied
Fig. 2. Frequency distribution of grid cell occupancy for the
138 mammal species in the analysis. The maximum possible
occupancy is 164 cells.
ECOGRAPHY 27:6 (2004)
Our results concur with most other investigations
of Bergmann’s 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 Bergmann’s 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, regional age and productivity, respectively. However, these
three predictors are likely to be significantly intercorrelated, and in the case of temperature and plant
biomass the correlation in our data is 0.878 (Table 1). It
is 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, experiments would have to be performed on a sufficient range
of species to draw meaningful conclusions about interspecific 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 of
squares.
721
Fig. 3. Correlograms of Moran’s I showing patterns of spatial
autocorrelation of raw mammal body mass and residual
autocorrelation after fitting the respective minimum adequate
model (MAM), for a) log average body mass, b) log minimum
body mass and c) log maximum body mass.
722
Although Bergmann’s 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 Bergmann’s rule.
First, the explanation based on ancestral colonisation
can be excluded here. Mammals exhibit Bergmann’s 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 probability
of 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 hypothesised 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 the
annual 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, temperature explains more than twice as much of the spatial
variation in the masses of small-bodied than largebodied species (see above). Thus, our results are more
consistent with the requirements of heat conservation
than heat dissipation.
ECOGRAPHY 27:6 (2004)
Nevertheless, our results also suggest that heat conservation may not be the only driver of Bergmann’s 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 smallbodied 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 of
seasonal shortage are insufficient. The positive relationship between range in elevation and body mass for largebodied 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 Bergmann’s rule, but other
approaches 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 Bergmann’s
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 patterns.
Acknowledgements / We thank Kyle Ashton, Richard Duncan
and Robert Poulin for comments that improved this manuscript.
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