MeteR: an r package for testing the maximum entropy theory of ecology

Abstract

Summary Macroecological patterns appear to follow consistent forms across a range of natural systems; however, the origin of their regularity remains obscured. The maximum entropy theory of ecology (METE) predicts macroecological patterns of abundance, metabolic rates and their distribution within communities and across space using an information theoretic approach. METE's success in predicting empirical patterns demands that we further press the theory's predictions to determine how (or whether) predictability depends on attributes of the system and the temporal, spatial and biological scales at which we study it. Maximum entropy theory of ecology predicts multiple macroecological metrics using statistical idealizations from information theory; thus, confronting METE with data represents a strong test of the underlying biological mechanisms that could drive real communities away from statistical idealizations. METE has remained somewhat inaccessible due to its highly mathematical nature and a lack of software for model construction/evaluation. To remedy this, we have developed an r package implementation of METE. Our open‐source (GNU General Public License v2) r package, meteR (version 1.2; https://cran.r-project.org/package=meteR), (i) directly calculates all of METE's predictions from a variety of data formats; (ii) automatically handles approximations and other technical details; and (iii) provides high‐level plotting and model comparison functions to explore and interrogate models. With these tools in hand, ecologists can more readily test the predictions of METE for their data sets. By facilitating tests of METE, we expect that a better understanding of its strengths and limitations will emerge. A better understanding of the strengths and limitations of METE will offer insight into how biological mechanisms and statistical constraints combine to drive macroecological patterns.