Maximum likelihood methods and their application in neighbourhood models

Abstract

Gómez-Aparicio, L., Ávila, J.M, Cayuela, L. 2013. Maximum likelihood methods and their application in neighbourhood models. Ecosistemas 22(3):12-20. Doi.: 10.7818/ECOS.2013.22-3.03

Maximum likelihood methods and their application in neighborhood models. Maximum likelihood methods (MLM) offer an alternative framework to the traditional frequentist approach of data analysis, where the use of p-values to reject a single null (usually trivial) hypothesis is replaced by the use of likelihoods to evaluate the support in the data for a set of alternative working hypotheses of scientific relevance. These methods have been widely applied in the ecological framework of the neighborhood models. These models use a spatially-explicit approach to describe demographic or ecosystem processes as a function of the attributes of neighboring plants. They are therefore phenomenological models that serve as a tool of synthesis of the multiple mechanisms by which species can interact and modify its immediate environment, offering an estimate of the per-capita influence of individuals of different characteristics (e.g. size, species, functional traits) on the processes of study. A fundamental advantage of applying MMV in the framework of the neighborhood models is that it allows fitting and comparing multiple models that use contrasting neighbor attributes and/or functional forms to select the one with the largest empirical support. In this way, each model works as a “virtual experiment” to answer questions related with the magnitude and spatial extent of the effects of different coexisting species and their potential implications for the function of communities and ecosystems. This paper reviews the use of MMV and neighborhood models in terrestrial ecology, synthesizing the state of the art and emphasizing new avenues of application.