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Accounting for geographical variation in species–area relationships improves the prediction of plant species richness at the global scale
Author(s) -
Gerstner Katharina,
Dormann Carsten F.,
Václavík Tomáš,
Kreft Holger,
Seppelt Ralf
Publication year - 2014
Publication title -
journal of biogeography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.7
H-Index - 158
eISSN - 1365-2699
pISSN - 0305-0270
DOI - 10.1111/jbi.12213
Subject(s) - species richness , biome , body size and species richness , ecology , land cover , biodiversity , vascular plant , geography , habitat , scale (ratio) , range (aeronautics) , physical geography , land use , ecosystem , biology , cartography , composite material , materials science
Aim The species–area relationship ( SAR ) is a prominent concept for predicting species richness and biodiversity loss. A key step in defining SAR s is to accurately estimate the slope of the relationship, but researchers typically apply only one global (canonical) slope. We hypothesized that this approach is overly simplistic and investigated how geographically varying determinants of SAR s affect species richness estimates of vascular plants at the global scale. Location Global. Methods We used global species richness data for vascular plants from 1032 geographical units varying in size and shape. As possible determinants of geographical variation in SAR s we chose floristic kingdoms and biomes as biogeographical provinces, and land cover as a surrogate for habitat diversity. Using simultaneous autoregressive models we fitted SAR s to each set of determinants, compared their ability to predict the observed data and large‐scale species richness patterns, and determined the extent to which varying SAR s differed from the global relationship. Results Incorporating variation into SAR s improved predictions of global species richness patterns. The best model, which accounts for variation due to biomes, explained 46.1% of the species richness variation. Moreover, fitting SAR s to biomes produced better results than fitting them to floristic kingdoms, supporting the hypothesis that energy availability complements evolutionary history in generating species richness patterns. Land cover proved to be less important than biomes, explaining only 36.4% of the variation, possibly owing to the high uncertainty in the data set. The incorporation of second‐order interactions of area, land cover and biomes did not improve the predictive ability of the models. Main conclusions Our study contributes to a deeper understanding of SAR s and improves the applicability of SAR s through regionalization. Future models should explicitly consider geographically varying determinants of SAR s in order to improve our assessment of the impact of global change scenarios on species richness patterns.