Richness patterns, species distributions and the principle of extreme deconstruction
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Data
2009-03
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Resumo
Aim To analyse the global patterns in species richness of Viperidae snakes throughthe deconstruction of richness into sets of species according to their distributionmodels, range size, body size and phylogenetic structure, and to test if environmentaldrivers explaining the geographical ranges of species are similar to those explainingrichness patterns, something we called the extreme deconstruction principle. Location Global. Methods We generated a global dataset of 228 terrestrial viperid snakes, whichincluded geographical ranges (mapped at 1 ° resolution, for a grid with 7331 cellsworld-wide), body sizes and phylogenetic relationships among species. We usedlogistic regression (generalized linear model; GLM) to model species geographicalranges with five environmental predictors. Sets of species richness were also generatedfor large and small-bodied species, for basal and derived species and for four classesof geographical range sizes. Richness patterns were also modelled against the fiveenvironmental variables through standard ordinary least squares (OLS) multipleregressions. These subsets are replications to test if environmental factors driving speciesgeographical ranges can be directly associated with those explaining richness patterns. Results Around 48% of the total variance in viperid richness was explained by theenvironmental model, but richness sets revealed different patterns across the world. Thesimilarity between OLS coefficients and the primacy of variables across species geographicalrange GLMs was equal to 0.645 when analysing all viperid snakes. Thus, in general,when an environmental predictor it is important to model species geographicalranges, this predictor is also important when modelling richness, so that the extremedeconstruction principle holds. However, replicating this correlation using subsets ofspecies within different categories in body size, range size and phylogenetic structure gavemore variable results, with correlations between GLM and OLS coefficients varying from–0.46 up to 0.83. Despite this, there is a relatively high correspondence ( r = 0.73) betweenthe similarity of GLM-OLS coefficients and R 2 values of richness models, indicatingthat when richness is well explained by the environment, the relative importance ofenvironmental drivers is similar in the richness OLS and its corresponding set of GLMs. Main conclusions The deconstruction of species richness based on macroecologicaltraits revealed that, at least for range size and phylogenetic level, the causes underlyingpatterns in viperid richness differ for the various sets of species. On the other hand,our analyses of extreme deconstruction using GLM for species geographical rangesupport the idea that, if environmental drivers determine the geographical distributionof species by establishing niche boundaries, it is expected, at least in theory, that theoverlap among ranges (i.e. richness) will reveal similar effects of these environmentaldrivers. Richness patterns may be indeed viewed as macroecological consequences ofpopulation-level processes acting on species geographical ranges.
Descrição
v. 18, p. 123-136, mar. 2009
Palavras-chave
Distribution modelling, Extreme deconstruction, Range size, Snakes, Species richness, Viperidae
Citação
TERRIBILE, Levi Carina; DINIZ FILHO, José Alexandre Felizola; RODRÍGUEZ, Miguel Ángel; RANGEL, Thiago Fernando L. V. B. Richness patterns, species distributions and the principle of extreme deconstruction. Global Ecology and Biogeography, v. 18, p. 123-136, Mar. 2009. Disponível em: <http://onlinelibrary.wiley.com/doi/10.1111/j.1466-8238.2008.00440.x/epdf>.