Abstract

AbstractAimThe maximum entropy theory of ecology (METE) is a unified theory of biodiversity that attempts to simultaneously predict patterns of species abundance, size and spatial structure. The spatial predictions of this theory have repeatedly performed well at predicting diversity patterns across scales. However, the theoretical development and evaluation of METE has focused on predicting patterns that ignore intersite spatial correlations. As a result the theory has not been evaluated using one of the core components of spatial structure. We develop and test a semi‐recursive version of the spatially explicit predictions of METE for the distance–decay relationship of community similarity and compare its performance with the classic random placement model of completely random species distributions. This provides a better understanding and stronger test of the spatial community predictions of METE.LocationNew World tropical and temperate plant communities.MethodsWe analytically derived and simulated the spatially explicit expectations of METE for the Sørensen index of community similarity. We then compared the distance–decay of community similarity of 16 mapped plant communities with METE and the random placement model.ResultsThe version of METE we examined was successful at capturing the general functional form of the observed distance–decay relationships, a negative power function relationship between community similarity and distance. However, the semi‐recursive approach consistently predicted lower intercepts and higher slopes than observed in the empirical distance–decay relationships and yielded worse predictions than the random placement model.Main conclusionsOur results suggest that while the current spatial models of METE accurately predict the spatial scaling of species occupancy, and therefore core ecological patterns like the species–area relationship, its semi‐recursive form does not accurately characterize spatially explicit patterns of correlation. More generally, this suggests that tests of spatial theories based only on the species–area relationship may appear to support the underlying theory despite significant deviations in important aspects of spatial structure.

Highlights

  • Community structure can be characterized using a variety of macroecological relationships such as the species-abundance, body size, and species spatial distributions

  • The Random Placement Model (RPM) is known to be a poor model for distance decay because it does not exhibit a decrease in similarity with distance

  • The semi-recursive Maximum Entropy Theory of Ecology (METE) distance decay relationship (DDR) was well approximated by a decreasing power function, and consistent with the general form of empirical DDRs, but it provided a poor fit to empirical data

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Summary

Introduction

Community structure can be characterized using a variety of macroecological relationships such as the species-abundance, body size, and species spatial distributions. A new class of constraint-based models suggest that similar patterns may be produced by different sets of processes because the form of the predicted pattern is due to the existence of statistical constraints rather than directly reflecting detailed biological processes (Frank, 2009, 2014; McGill & Nekola, 2010; Locey & White, 2013). METE uses the principle of entropy maximization, that the most likely distribution is the one with the least information (i.e., the one closest to the uniform distribution) subject to a set of constraints (i.e., prior information), to predict distributions of species abundance, body size, and spatial structure. In contrast to detailed biological models of community assembly, METE has no free parameters and only requires information on total community area, total number of individuals, total number of species, and total metabolic rate of all individuals to generate its predictions

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