DIET AND BODY MASS IN TERRESTRIAL MAMMALS


et al. (2014). Dietary classification otherwise followed the same criteria as in our main data set. The resulting data set included 2835 mammal species (Supplementary Table 2). Additionally, we correlated the percentage


of fruit in the diet of the frugivore species in our data set with the values for the same species provided by Wilman et al. (2014). The correla- tion was poor (r = 0.27; see Supplementary Fig. 1). All of our data comes from stomach content studies, which suggests that mixing different methodologies might have biased the Wilman et al. (2014) data set and therefore that adding information from that data set would bias our analysis. We used the R statistical environment (R


Core Team 2013) to perform analyses and construct tables and figures. A Shapiro-Wilk test for normality showed that the body-mass distribution of the species in some dietary categories was nonnormal. We therefore performed Kruskal-Wallis tests to determine whether differences in body mass existed among the feeding categories. We applied a pairwise Wilcoxon rank-sum test to the whole data set and to the rodent data set to show whether body-mass differences existed between mammals with different diet speciali- zations. We carried out principal components analysis (PCA) to explore the relationship between dietary specialization and body mass. PCA was based on covariance matrices instead of correlation matrices, as is standard practice (Bro and Smilde 2014). Unfortunately, raw percentage values are nonindependent because they must add up to 100, which makes them computationally unsuitable for PCA. More importantly, PCA tends to underweight variables if the percentages are consistently low, because the lower bound of zero com- presses potential variance. This property masks the contribution of rare dietary prefer- ences. We solved the problems of nonindepen- dence and underweighting by rescaling the percentage values as z-scores before carrying out the PCA. Factor analysis was also applied, but the results were similar and so are not discussed further. To test the relationship between degree of


food mixing and body mass, we calculated a dietary diversity index. We applied the inverse


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of the Simpson index (Simpson 1949) to the resource percentage data extracted from Pineda-Munoz and Alroy (2014). In this way, the percentage contribution of every resource in the diet of a species was treated as analogous to the percentage abundance of a given species in an ecosystem. Similar methods were proposed in earlier decades to infer dietary diversity and niche breadth (MacArthur and Pianka 1966; Schwartz and Ellis 1981). Weplotted the correlation between body mass


and dietary diversity to visually evaluate ecolo- gical patterns in feeding behavior. Additionally, the species were classified into 14 ranked body- mass categories using a base 10 logarithm scale (from log10 of body mass = 0.5 to log10 of body mass = 7 with 0.5-unit increments). The maximum degree of dietary diversity for every categorywas then plotted.The brown bear Ursus arctos was excluded from this particular analysis for reasons discussed below. All the same analyses (Kruskal-Wallis tests,


PCA, and comparisons of diet diversity with bodymass)were applied to themodified data of Wilman et al. (2014) using the same parameters. We evaluated frugivory further because


it was one of the few dietary specializations observable in the medium body-size range. Frugivore species were classified as either pure frugivores or mixed frugivores following Pineda-Munoz and Alroy (2014). Fruit constitu- tes 50–80%of thedietofamixedfrugivore’sdiet and more than 80% of a pure frugivore’sdiet. Geographical distribution maps for mixed fru- givore and pure frugivore species were extracted from Map of Life (2016). The geogra- phical distribution of tropical and subtropical moist broadleaf forestswas adapted fromOlson et al. (2001). The intersection between these maps and their areas were calculated using ArcMap (ArcGIS, Esri, U.S.A.). Because of the poor correlation between our frugivore data and those given by Wilman et al. (2014), this analysis was not performed using their data. Our comparisons of body mass and dietary


categories assume that data points represent- ing species are statistically independent. We recognize that phylogenetic autocorrelation could cause these comparisons to reflect shared inheritance instead of direct causal relationships.We have, however, not taken an


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