2014/07/09 by Matteo Adorisio, Adorisio, Matteo, Jacopo Grilli +9
Biochemistry, Genetics and Molecular Biology · Environmental Science · Physics and Astronomy · #Ecology and Vegetation Dynamics Studies #FOS: Biological sciences #FOS: Physical sciences #Populations and Evolution (q-bio.PE) #Quantitative Methods (q-bio.QM) #Remote Sensing in Agriculture #Species Distribution and Climate Change #Statistical Mechanics (cond-mat.stat-mech) #cond-mat.stat-mech #q-bio.PE #q-bio.QM
paper · pdf · doi:10.48550/arxiv.1407.2425
arxiv created 2014/07/09 · openalex publication_date 2014/07/09 · arxiv updated 2014/07/10 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
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.