Global pattern and local variation in species–area relationships

Authors


Péter Sólymos, Alberta Biodiversity Monitoring Institute, Department of Biological Sciences, CW 405, Biological Sciences Bldg., University of Alberta, Edmonton, AB T6G 2E9, Canada. E-mail: solymos@ualberta.ca

ABSTRACT

Aim  We conducted a meta-analysis of species–area relationships (SARs) by combining several data sets and important covariates such as types of islands, taxonomic groups, latitude and spatial extent, in a hierarchical model framework to study global pattern and local variation in SARs and its consequences for prediction.

Location  One thousand nine hundred and eighteen islands from 94 SAR studies from around the world.

Methods  We developed a generalization of the power-law SAR model, the HSARX model, which allows: (1) the inclusion of multiple focal parameters (intercept, slope, within-study variance), (2) use of multiple effect modifiers based on a collection of SAR studies, and (3) modelling of the between- and within-study variability.

Results  The global pattern in the SAR was the average of local SARs and had wide confidence intervals. The global SAR slope was 0.228 with 90% confidence limits of 0.059 and 0.412. The intercept, slope and within-study variability of local SARs showed great heterogeneity as a result of the interaction of modifying covariates. Confidence intervals for these SAR parameters were narrower when other covariates in addition to area were accounted for, thus increasing the accuracy of the predictions for species richness. The significant effect of latitude and the interaction of latitude, taxa and island type on the SAR slope indicated that the ‘typical’ latitudinal diversity gradient can be reversed in isolated systems.

Main conclusions  The power-law relationship underlying the HSARX model provides a good fit for non-nested SARs across vastly different spatial scales by taking into account other covariates. The HSARX framework allows researchers to explore the complex interactions among SAR parameters and modifying variables, to explicitly study the scale dependence, and to make robust predictions on multiple levels (island, study, global) with associated prediction intervals. From a prediction perspective, it is not the global pattern but the local variation that matters.

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