Exploring the spatially explicit predictions of the Maximum Entropy Theory of Ecology

Abstract

AimThe 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.Location New 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.

Disturbance macroecology: a comparative study of community structure metrics in a high‐severity disturbance regime

“…), but some spatial distribution and metabolic predictions are not supported (see McGlinn et al. , Newman et al. , Xiao et al.…”

Measurement of Biodiversity (MoB): A method to separate the scale‐dependent effects of species abundance distribution, density, and aggregation on diversity change

“…Thus, it is critical that we look beyond richness as a single metric, and develop methods to disentangle its underlying components that have more mechanistic links to processes (e.g., Vellend, 2016). While there are other mathematically valid decompositions of species richness and its change, the three components above (density, SAD and aggregation) are well-studied properties of ecological systems, and provide insights into mechanisms behind changes in richness and community structure (Harte, Zillio, Conlisk, & Smith, 2008;McGlinn, Xiao, Kitzes, & White, 2015;Supp, Xiao, Ernest, & White, 2012). We note that the SAD and density components are also sometimes referred to as the column and row sums, respectively, of the abundance-based community matrix (e.g., Ulrich & Gotelli, 2010).…”

MoB (Measurement of Biodiversity): a method to separate the scale-dependent effects of species abundance distribution, density, and aggregation on diversity change

“…While there are other mathematically valid decompositions of species richness and its change, 75 the three components above (density, SAD and aggregation) are well-studied properties of 76 ecological systems, and provide insights into mechanisms behind changes in richness and 77 community structure (Harte et al 2008, Supp et al 2012, McGlinn et al 2015. We note that the 78 SAD and N components are also sometimes referred to as the column and row sums respectively 79 of the abundance-based community matrix (e.g., Ulrich and Gotelli 2010).…”