Statistical ecology comes of age.

Olivier Giménez ,Frank M Schurr,Sandrine Pavoine,Frédéric Gosselin,François Munoz,Stéphane Dray,Nicolas Bez,Bastien Mérigot,Otso Ovaskainen,Frédéric Mortier,Rémi Choquet,Len Thomas,Sophie Bertrand,Pascal Monestiez,Stephen T Buckland,Wilfried Thuiller,Byron J T Morgan,Roger Pradel,Perry De Valpine,Juan Manuel Morales ,Verena M Trenkel ,Marie‐Pierre Étienne ,Rachel M Fewster ,Eric A Rexstad

doi:10.1098/rsbl.2014.0698

Abstract

The desire to predict the consequences of global environmental change has been the driver towards more realistic models embracing the variability and uncertainties inherent in ecology. Statistical ecology has gelled over the past decade as a discipline that moves away from describing patterns towards modelling the ecological processes that generate these patterns. Following the fourth International Statistical Ecology Conference (1–4 July 2014) in Montpellier, France, we analyse current trends in statistical ecology. Important advances in the analysis of individual movement, and in the modelling of population dynamics and species distributions, are made possible by the increasing use of hierarchical and hidden process models. Exciting research perspectives include the development of methods to interpret citizen science data and of efficient, flexible computational algorithms for model fitting. Statistical ecology has come of age: it now provides a general and mathematically rigorous framework linking ecological theory and empirical data.

Highlights

Variability is challenging ecology, from genes to individuals, species or ecosystems: quantifying and explaining biological variation is an ever-important goal
Research centres especially devoted to statistical ecology have been created in the USA (Statistical and Applied Mathematical Sciences Institute) and the UK (National Centre for Statistical Ecology)
The reasons for this recent rise of statistical ecology are manifold and include the societal demand for scientists to address pressing issues such as global change and the current biodiversity crisis, the need to analyse the massive datasets and the novel data types generated by new technologies, and the popularization of methods through free statistical packages and the increase in computing power