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Statistical ecology comes of age.
Gimenez, Olivier; Buckland, Stephen T; Morgan, Byron J T; Bez, Nicolas; Bertrand, Sophie; Choquet, Rémi; Dray, Stéphane; Etienne, Marie-Pierre; Fewster, Rachel; Gosselin, Frédéric; Mérigot, Bastien; Monestiez, Pascal; Morales, Juan M; Mortier, Frédéric; Munoz, François; Ovaskainen, Otso; Pavoine, Sandrine; Pradel, Roger; Schurr, Frank M; Thomas, Len; Thuiller, Wilfried; Trenkel, Verena; de Valpine, Perry; Rexstad, Eric.
Afiliación
  • Gimenez O; CEFE UMR 5175, CNRS, Université de Montpellier, Université Paul-Valéry Montpellier, EPHE, 1919 Route de Mende, 34293 Montpellier Cedex 5, France olivier.gimenez@cefe.cnrs.fr.
  • Buckland ST; Centre for Research into Ecological and Environmental Modelling, University of St Andrews, St Andrews KY16 9LZ, UK.
  • Morgan BJ; School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, Kent CT2 7NF, UK.
  • Bez N; IRD, UMR EME 212, Sète, France.
  • Bertrand S; IRD, UMR EME 212, Sète, France.
  • Choquet R; CEFE UMR 5175, CNRS, Université de Montpellier, Université Paul-Valéry Montpellier, EPHE, 1919 Route de Mende, 34293 Montpellier Cedex 5, France.
  • Dray S; Université de Lyon, F-69000, Lyon; Université Lyon 1; CNRS, UMR5558, Laboratoire de 18 Biométrie et Biologie Evolutive, F-69622, Villeurbanne, France.
  • Etienne MP; AgroParisTech, UMR MIA 518, Paris, France.
  • Fewster R; Department of Statistics, University of Auckland, Private Bag 92019, Auckland, New Zealand.
  • Gosselin F; Irstea, UR EFNO, Centre de Nogent-sur-Vernisson, 45290 Nogent-sur-Vernisson, France.
  • Mérigot B; Université Montpellier 2, UMR EME 212, Sète, France.
  • Monestiez P; INRA, BioSP, Avignon, France.
  • Morales JM; Laboratorio Ecotono, CRUB, INIBIOMA-CONICET, Bariloche, Argentina.
  • Mortier F; UPR Bsef, CIRAD, Montpellier, France.
  • Munoz F; UM2, UMR AMAP, Bd de la Lironde, TA A-51/PS2, 34398 Montpellier Cedex 5, France.
  • Ovaskainen O; Department of Biosciences, University of Helsinki, Helsinki, Finland.
  • Pavoine S; UMR 7204 CNRS UPMC, Centre for Ecology and Conservation Sciences, Muséum National d'Histoire Naturelle, 55-61 rue Buffon, 75005 Paris, France Mathematical Ecology Research Group, Department of Zoology, University of Oxford, Oxford OX1 3PS, UK.
  • Pradel R; CEFE UMR 5175, CNRS, Université de Montpellier, Université Paul-Valéry Montpellier, EPHE, 1919 Route de Mende, 34293 Montpellier Cedex 5, France.
  • Schurr FM; Institute of Landscape and Plant Ecology, University of Hohenheim, 70593 Stuttgart, Germany.
  • Thomas L; Centre for Research into Ecological and Environmental Modelling, University of St Andrews, St Andrews KY16 9LZ, UK.
  • Thuiller W; Laboratoire d'Ecologie Alpine, UMR CNRS 5553, Université Joseph Fourier, Grenoble I, BP 53, 38041 Grenoble Cedex 9, France.
  • Trenkel V; Ifremer, Rue de l'île d'Yeu, BP 21105, 44311 Nantes Cedex 3, France.
  • de Valpine P; Environmental Science, Policy and Management, University of California, Berkeley, CA 94720, USA.
  • Rexstad E; Centre for Research into Ecological and Environmental Modelling, University of St Andrews, St Andrews KY16 9LZ, UK.
Biol Lett ; 10(12): 20140698, 2014 Dec.
Article en En | MEDLINE | ID: mdl-25540151
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Modelos Estadísticos / Ecología Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Animals Idioma: En Revista: Biol Lett Asunto de la revista: BIOLOGIA Año: 2014 Tipo del documento: Article País de afiliación: Francia

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Modelos Estadísticos / Ecología Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Animals Idioma: En Revista: Biol Lett Asunto de la revista: BIOLOGIA Año: 2014 Tipo del documento: Article País de afiliación: Francia