Statistical ecology comes of age

Gimenez, Olivier and Buckland, Stephen T. and Morgan, Byron J. T. and Bertrand, Sophie and Choquet, Rémi and Dray, Stéphane and Etienne, Marie-Pierre and Fewster, Rachel and Gosselin, Frédéric and Mérigot, Bastien and Monestiez, Pascal and Morales, Juan M. and Mortier, Frédéric and Munoz, François and Ovaskainen, Otso and Pavoine, Sandrine and Pradel, Roger and Schurr, Frank M. and Thomas, Len and Thuiller, Wilfried and Trenkel, Verena and de Valpine, Perry and Rexstad, Eric and Bez, Nicolas (2014) Statistical ecology comes of age. Biology Letters, 10 (11). ISSN 1744-9561. (doi:https://doi.org/10.1098/rsbl.2014.0698) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)

Official URL http://dx.doi.org/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. Impor- tant 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. Stat- istical ecology has come of age: it now provides a general and mathematically rigorous framework linking ecological theory and empirical data.

Item Type:	Article
Subjects:	Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics Q Science > QH Natural history > QH541 Ecology
Divisions:	Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User:	Byron Morgan
Date Deposited:	09 Jun 2015 13:09 UTC
Last Modified:	12 Jun 2015 13:27 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/48965 (The current URI for this page, for reference purposes)