Statistical ecology comes of age
| Type | Article | ||||||||||||
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| Date | 2014-12 | ||||||||||||
| Language | English | ||||||||||||
| Author(s) | Gimenez Olivier1, Buckland Stephen T.2, Morgan Byron J. T.3, Bez Nicolas4, Bertrand Sophie4, Choquet Remi1, Dray Stephane5, 6, 7, Etienne Marie-Pierre8, Fewster Rachel9, Gosselin Frederic10, Merigot Bastien11, Monestiez Pascal12, Morales Juan M.13, Mortier Frederic14, Munoz Francois15, Ovaskainen Otso16, Pavoine Sandrine17, 18, Pradel Roger1, Schurr Frank M.19, Thomas Len2, Thuiller Wilfried20, Trenkel Verena 21, de Valpine Perry22, Rexstad Eric2 |
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| Affiliation(s) | 1 : Univ Montpellier 3, CNRS, CEFE UMR 5175, EPHE, F-34293 Montpellier 5, France. 2 : Univ St Andrews, Ctr Res Ecol & Environm Modelling, St Andrews KY16 9LZ, Fife, Scotland. 3 : Univ Kent, Sch Math Stat & Actuarial Sci, Canterbury CT2 7NF, Kent, England. 4 : IRD, UMR EME 212, Sete, France. 5 : Univ Lyon, F-69000 Lyon, France. 6 : Univ Lyon 1, F-69622 Villeurbanne, France. 7 : CNRS, UMR5558, Lab Biometrie & Biol Evolut 18, F-69622 Villeurbanne, France. 8 : AgroParisTech, UMR MIA 518, Paris, France. 9 : Univ Auckland, Dept Stat, Auckland 1, New Zealand. 10 : Irstea, UR EFNO, Ctr Nogent Sur Vernisson, F-45290 Nogent Sur Marne, France. 11 : Univ Montpellier 2, UMR EME 212, Sete, France. 12 : INRA, BioSP, Avignon, France. 13 : Consejo Nacl Invest Cient & Tecn, INIBIOMA, CRUB, Lab Ecotono, San Carlos De Bariloche, Rio Negro, Argentina. 14 : CIRAD, UPR Bsef, Montpellier, France. 15 : UMR AMAP, UM2, F-34398 Montpellier 5, France. 16 : Univ Helsinki, Dept Biosci, Helsinki, Finland. 17 : Museum Natl Hist Nat, Ctr Ecol & Conservat Sci, UMR CNRS UPMC 7204, F-75005 Paris, France. 18 : Univ Oxford, Dept Zool, Math Ecol Res Grp, Oxford OX1 3PS, England. 19 : Univ Hohenheim, Inst Landscape & Plant Ecol, D-70593 Stuttgart, Germany. 20 : Univ Grenoble 1, UMR CNRS 5553, Lab Ecol Alpine, F-38041 Grenoble 9, France. 21 : IFREMER, F-44311 Nantes 3, France. 22 : Univ Calif Berkeley, Environm Sci Policy & Management, Berkeley, CA 94720 USA. |
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| Source | Biology Letters (1744-9561) (Royal Soc), 2014-12 , Vol. 10 , N. 12 , P. - | ||||||||||||
| DOI | 10.1098/rsbl.2014.0698 | ||||||||||||
| WOS© Times Cited | 35 | ||||||||||||
| Keyword(s) | citizen science, hidden Markov model, hierarchical model, movement ecology, software package, spatially explicit capture-recapture, species distribution modelling, state-space model | ||||||||||||
| 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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