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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. 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