An empirical evaluation of four variants of a universal species-area relationship

TL;DR

This study empirically compares four variants of the Maximum Entropy Theory of Ecology for predicting species-area relationships, finding that a non-recursive approach with a theoretical abundance distribution performs best across diverse datasets.

Contribution

It provides the first empirical evaluation of the four METE variants, guiding optimal application for scale-dependent biodiversity predictions.

Findings

METE accurately predicts species richness with R^2 > 0.94

Recursive approach under-predicts richness

Using theoretical SAD is as effective as observed SAD

Abstract

The Maximum Entropy Theory of Ecology (METE) predicts a universal species-area relationship (SAR) that can be fully characterized using only the total abundance (N) and species richness (S) at a single spatial scale. This theory has shown promise for characterizing scale dependence in the SAR. However, there are currently four different approaches to applying METE to predict the SAR and it is unclear which approach should be used due to a lack of empirical evaluation. Specifically, METE can be applied recursively or a non-recursively and can use either a theoretical or observed species-abundance distribution (SAD). We compared the four different combinations of approaches using empirical data from 16 datasets containing over 1000 species and 300,000 individual trees and herbs. In general, METE accurately downscaled the SAR (R^2> 0.94), but the recursive approach consistently under-predicted richness, and METEs accuracy did not depend strongly on using the observed or predicted SAD. This suggests that best approach to scaling diversity using METE is to use a combination of non-recursive scaling and the theoretical abundance distribution, which allows predictions to be made across a broad range of spatial scales with only knowledge of the species richness and total abundance at a single scale.