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      Statistical ecology comes of age

      research-article
      1 , 2 , 3 , 4 , 4 , 1 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 1 , 17 , 2 , 18 , 19 , 20 , 2
      Biology Letters
      The Royal Society
      citizen science, hidden Markov model, hierarchical model, movement ecology, software package, spatially explicit capture–recapture, species distribution modelling, state–space model

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          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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          Most cited references26

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          Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)

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            State-space models of individual animal movement.

            Detailed observation of the movement of individual animals offers the potential to understand spatial population processes as the ultimate consequence of individual behaviour, physiological constraints and fine-scale environmental influences. However, movement data from individuals are intrinsically stochastic and often subject to severe observation error. Linking such complex data to dynamical models of movement is a major challenge for animal ecology. Here, we review a statistical approach, state-space modelling, which involves changing how we analyse movement data and draw inferences about the behaviours that shape it. The statistical robustness and predictive ability of state-space models make them the most promising avenue towards a new type of movement ecology that fuses insights from the study of animal behaviour, biogeography and spatial population dynamics.
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              Equivalence of MAXENT and Poisson point process models for species distribution modeling in ecology.

              Modeling the spatial distribution of a species is a fundamental problem in ecology. A number of modeling methods have been developed, an extremely popular one being MAXENT, a maximum entropy modeling approach. In this article, we show that MAXENT is equivalent to a Poisson regression model and hence is related to a Poisson point process model, differing only in the intercept term, which is scale-dependent in MAXENT. We illustrate a number of improvements to MAXENT that follow from these relations. In particular, a point process model approach facilitates methods for choosing the appropriate spatial resolution, assessing model adequacy, and choosing the LASSO penalty parameter, all currently unavailable to MAXENT. The equivalence result represents a significant step in the unification of the species distribution modeling literature. Copyright © 2013, The International Biometric Society.
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                Author and article information

                Journal
                Biol Lett
                Biol. Lett
                RSBL
                roybiolett
                Biology Letters
                The Royal Society
                1744-9561
                1744-957X
                December 2014
                December 2014
                : 10
                : 12
                : 20140698
                Affiliations
                [1 ]CEFE UMR 5175, CNRS, Université de Montpellier, Université Paul-Valéry Montpellier , EPHE, 1919 Route de Mende, 34293 Montpellier Cedex 5, France
                [2 ]Centre for Research into Ecological and Environmental Modelling, University of St Andrews , St Andrews KY16 9LZ, UK
                [3 ]School of Mathematics, Statistics and Actuarial Science, University of Kent , Canterbury, Kent CT2 7NF, UK
                [4 ]IRD, UMR EME 212 , Sète, France
                [5 ]Université de Lyon, F-69000, Lyon; Université Lyon 1 ; CNRS, UMR5558, Laboratoire de 18 Biométrie et Biologie Evolutive, F-69622, Villeurbanne, France
                [6 ]AgroParisTech, UMR MIA 518 , Paris, France
                [7 ]Department of Statistics, University of Auckland , Private Bag 92019, Auckland, New Zealand
                [8 ]Irstea, UR EFNO, Centre de Nogent-sur-Vernisson , 45290 Nogent-sur-Vernisson, France
                [9 ]Université Montpellier 2, UMR EME 212 , Sète, France
                [10 ]INRA, BioSP , Avignon, France
                [11 ]Laboratorio Ecotono, CRUB, INIBIOMA-CONICET , Bariloche, Argentina
                [12 ]UPR Bsef, CIRAD , Montpellier, France
                [13 ]UM2, UMR AMAP, Bd de la Lironde , TA A-51/PS2, 34398 Montpellier Cedex 5, France
                [14 ]Department of Biosciences, University of Helsinki , Helsinki, Finland
                [15 ]UMR 7204 CNRS UPMC, Centre for Ecology and Conservation Sciences, Muséum National d'Histoire Naturelle , 55-61 rue Buffon, 75005 Paris, France
                [16 ]Mathematical Ecology Research Group, Department of Zoology, University of Oxford , Oxford OX1 3PS, UK
                [17 ]Institute of Landscape and Plant Ecology, University of Hohenheim , 70593 Stuttgart, Germany
                [18 ]Laboratoire d'Ecologie Alpine, UMR CNRS 5553, Université Joseph Fourier , Grenoble I, BP 53, 38041 Grenoble Cedex 9, France
                [19 ]Ifremer, Rue de l’île d'Yeu , BP 21105, 44311 Nantes Cedex 3, France
                [20 ]Environmental Science, Policy and Management, University of California , Berkeley, CA 94720, USA
                Author notes
                Author information
                http://orcid.org/0000-0001-7269-7490
                Article
                rsbl20140698
                10.1098/rsbl.2014.0698
                4298184
                25540151
                0cfa3474-7e85-4d9b-a806-b71228f96240

                © 2014 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

                History
                : 29 August 2014
                : 4 December 2014
                Categories
                1001
                60
                Population Ecology
                Custom metadata
                December, 2014

                Life sciences
                citizen science,hidden markov model,hierarchical model,movement ecology,software package,spatially explicit capture–recapture,species distribution modelling,state–space model

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