Spatial maximum entropy modeling from presence/absence tropical forest data

TL;DR

This paper introduces a spatially explicit maximum entropy model that effectively captures spatial patterns in presence/absence tropical forest data, improving understanding of species distribution and ecological assembly.

Contribution

It develops a novel maximum entropy modeling approach that explicitly incorporates spatial information, enabling better analysis of spatial ecological patterns from presence/absence data.

Findings

Model accurately describes species area and endemic area relationships

Spatial correlations improve model predictions

Effective interactions suggest ecological clustering mechanisms

Abstract

Understanding the assembly of ecosystems to estimate the number of species at different spatial scales is a challenging problem. Until now, maximum entropy approaches have lacked the important feature of considering space in an explicit manner. We propose a spatially explicit maximum entropy model suitable to describe spatial patterns such as the species area relationship and the endemic area relationship. Starting from the minimal information extracted from presence/absence data, we compare the behavior of two models considering the occurrence or lack thereof of each species and information on spatial correlations. Our approach uses the information at shorter spatial scales to infer the spatial organization at larger ones. We also hypothesize a possible ecological interpretation of the effective interaction we use to characterize spatial clustering.