Running times of implemented algorithms computing values and standardized indices for CD and CBL.

<p>For each implementation and for each tree size, the figures illustrate the time that it takes for the function to process a set of one hundred samples.</p

The residual number of species per cell of the spatial auto-regressive error (SAR) models of the clade species richness against the environmental predictors.

<p>Plotted against a latitudinal gradient for both the Caniformia and Feliformia models run on all environmental predictors.</p

Environmental variables.

<p>1: Climate variables was taken from (<a href="http://www.worldclim.org/" target="_blank">http://www.worldclim.org/</a>) and calculated from 2.5′ resolution data,</p><p>2: (<a href="http://glcf.umd.edu/data/gimms/" target="_blank">http://glcf.umd.edu/data/gimms/</a>),</p><p>3: (<a href="http://sedac.ciesin.columbia.edu/wildareas/downloads.jsp" target="_blank">http://sedac.ciesin.columbia.edu/wildareas/downloads.jsp</a>),</p><p>4: (<a href="http://www.gofc-gold.uni-jena.de/wg_biomass/sites/globcover.php" target="_blank">http://www.gofc-gold.uni-jena.de/wg_biomass/sites/globcover.php</a>),</p><p>5: (<a href="http://www2.jpl.nasa.gov/srtm/" target="_blank">http://www2.jpl.nasa.gov/srtm/</a>) via worldclim.</p

Macroecological Evidence for Competitive Regional-Scale Interactions between the Two Major Clades of Mammal Carnivores (Feliformia and Caniformia)

<div><p>Geographical gradients in species diversity are often explained by environmental factors such as climate and productivity. Biotic interactions play a key role in evolutionary diversification and may therefore also affect diversity patterns, but this has rarely been assessed. Here, we investigate whether negative competitive interactions shape the diversity patterns of the two major mammalian clades of carnivores, the suborders Caniformia (dogs and allies) and Feliformia (cats and allies) within the order Carnivora. We specifically test for a negative effect of feliform species richness on caniform species richness by a natural experiment, The Great American Interchange, which due to biogeographic lineage history and climate patterns caused tropical South America to be colonized by most caniform families, but only one feliform family. To this end we used regression modelling to investigate feliform and caniform richness patterns and their determinants with emphasis on contrasting the Old and New World tropics. We find that feliform richness is elevated in the Old World Tropics, while caniform richness is elevated in the New World Tropics. Models based on environmental variables alone underpredict caniform richness and overpredict feliform richness in the New World and vice versa in the Old World. We further show that models including feliform richness as a predictor for caniform species richness significantly improve predictions at the continental scale, albeit not at finer scales. Our results are consistent with a negative effect of feliforms on regional-scale caniform diversification within the tropics, probably indicating that niche space occupancy by the one clade constrains diversification in the other in the build-up of regional faunas, while negative interactions at smaller scales may be unimportant due to niche differentiation within the regional faunas.</p></div

Latitudinal <i>Carnivora</i> species richness distribution.

<p>The average number of species for both Feliformia and Caniformia was calculated on each latitudinal height, in 5 degree grid-cells, for both the New World, the Old World and combined. Islands and areas with lower than 50% land was excluded.</p

Feliform species richness distribution in a Behrmann Equal Area projection of 5° equivalents (482 km×482 km) grid size, with the tropics marked.

<p>Feliform species richness distribution in a Behrmann Equal Area projection of 5° equivalents (482 km×482 km) grid size, with the tropics marked.</p

Results of the simultaneous auto-regressive (SAR) error models of feliform and caniform species richness against environmental (ENV) predictors or these plus species richness of the competing group (Env + Feliformia).

<p>Spatial simultaneous autoregressive (SAR) error models were run for feliform species richness against all our environmental predictors (Acronyms as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100553#pone-0100553-t001" target="_blank">Table 1</a>), and the same was done for caniform species richness. Caniform species richness was furthermore modelled against all environmental predictors and feliform species richness, though here <b>not</b> showing feliform species richness as a significant predictor of their species richness. Significance codes:</p><p>*** (p<0.001),</p><p>** (p<0.01) and</p><p>* (p<0.05), unmarked are not significant (p>0.05).</p

Caniformia species richness distribution in a Behrmann Equal Area projection of 5° equivalents (482 km×482 km) grid size, with the tropics marked.

<p>Caniformia species richness distribution in a Behrmann Equal Area projection of 5° equivalents (482 km×482 km) grid size, with the tropics marked.</p

Results of the Ordinary-least-squares (OLS) multiple linear regression models of feliform and caniform species richness against environmental (ENV) predictors or these plus species richness of the competing group (Env + Feliformia).

<p>Ordinary-least-squares (OLS) multiple linear regression models were run for feliform species richness against all our environmental predictors (Acronyms as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100553#pone-0100553-t001" target="_blank">Table 1</a>), and the same was done for caniform species richness. Caniform species richness was furthermore modelled against environmental predictors and feliform species richness, showing feliform species richness as a significant predictor. Significance codes:</p><p>*** (p<0.001),</p><p>** (p<0.01) and</p><p>* (p<0.05), unmarked are not significant (p>0.05).</p

Appendix B. Testing the null models for the functional-diversity–area relationship.

Testing the null models for the functional-diversity–area relationship