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Keywords:

  • Alpha diversity;
  • beta diversity;
  • community;
  • compositional dissimilarity;
  • Panama;
  • prediction;
  • rain forest;
  • richness;
  • simulation

Abstract

Aim

The relationship between species richness (α-diversity) and area is well studied; however, the way in which compositional dissimilarity between pairs of sites (β-diversity) scales with area has only recently attracted research attention. The aim of this study was to improve the understanding of how both α- and β-diversity scale with area, to illuminate ecological processes structuring the distribution of biodiversity and enable prediction of α- and β-diversity for large regions from much smaller samples.

Location

We examined both simulated spatial community data and measurements from tropical forest tree plots in Panama.

Methods

We applied the simulated and measured community data to assess how both α- and β-diversity scale with area. Then we examined how accurately community α-diversity and pairwise β-diversity can be extrapolated from small sample areas of different size within each community, using the species–area power relationship.

Results

For both the simulated and tree plot data, pairwise β-diversity scaled with area in a corresponding manner to the much more familiar species–area relationship. By altering the attributes of the simulated communities, we found that α- and β-diversity saturated at smaller areas where abundances were more even, species distributions were less aggregated and regional richness was lower. Estimates of α- and β-diversity for a pair of communities generally increased in accuracy with the size of the local sample areas from which extrapolations were made.

Main conclusions

These analyses suggest that the most robust estimates of α- and β-diversity for a larger area will be obtained by local samples that are greater than 10% the size of that larger area. Our results emphasize the fundamental link in how both α- and β-diversity scale with area, and demonstrate how simple knowledge of these scaling relationships can be used to predict the diversity of larger areas from smaller samples.