State-space modelling of data on marked individuals

Giménez, Olivier; Rossi, Vivien; Choquet, Rémi; Dehais, Camille; Doris, Blaise; Varella, Hubert; Vila, Jean-Pierre; Pradel, Roger

doi:10.1016/j.ecolmodel.2007.03.040

Cited by 165 publications

(173 citation statements)

References 33 publications

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“…Recently, a proposal was made for incorporating individual heterogeneity in open CR models via a continuous mixture using random effects (Royle 2008; see also Clark et al 2005, Gimenez et al 2006a. A statespace formulation of the Cormack-Jolly-Seber (CJS) model (Lebreton et al 1992) was adopted, in line with previous work (Clark et al 2005, Gimenez et al 2007 We offer an alternative to MCMC methods for the incorporation of individual heterogeneity in open CR models. We refer to this class of models as capturerecapture mixed models (CR2Ms) as they include both fixed and random effects.…”

Section: Introductionmentioning

confidence: 90%

Individual heterogeneity in studies on marked animals using numerical integration: capture–recapture mixed models

Giménez¹,

Choquet

2010

Ecology

111

167

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Abstract. In conservation and evolutionary ecology, quantifying and accounting for individual heterogeneity in vital rates of open populations is of particular interest. Individual random effects have been used in capture-recapture models, adopting a Bayesian framework with Markov chain Monte Carlo (MCMC) to carry out estimation and inference. As an alternative, we show how numerical integration via the Gauss-Hermite quadrature (GHQ) can be efficiently used to approximate the capture-recapture model likelihood with individual random effects. We compare the performance of the two approaches (MCMC vs. GHQ) and finite mixture models using two examples, including data on European Dippers and Sociable Weavers. Besides relying on standard statistical tools, GHQ was found to be faster than MCMC simulations. Our approach is implemented in program E-SURGE. Overall, capturerecapture mixed models (CR2Ms), implemented either via a GHQ approximation or MCMC simulations, have potential important applications in population biology.

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Section: Introductionmentioning

confidence: 90%

Individual heterogeneity in studies on marked animals using numerical integration: capture–recapture mixed models

Giménez¹,

Choquet

2010

Ecology

111

167

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“…In standard MR models, assessing the influence of individual covariates on age-specific survival probabilities is possible, but individual random effects are not yet implemented routinely when detectability is !1 see, however, Gimenez et al 2006 andRoyle 2008). A new formulation of MR models that disentangle demographic processes (e.g., survival) from their observation (i.e., detection;Pradel 2005;Gimenez et al 2007) makes us confident that those tools will soon become available.…”

Section: Discussionmentioning

confidence: 99%

The Risk of Flawed Inference in Evolutionary Studies When Detectability Is Less than One

Giménez¹,

Viallefont²,

Charmantier³

et al. 2008

The American Naturalist

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100

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“…However, the utility of the model reaches far beyond simply estimating density and includes the investigation of important questions about population and landscape ecology which we describe next. Moreover, an enormous number of extensions to SCR models can be accommodated, both to the structure of the ecological processes and the types of observation method, including acoustic sampling (Dawson and Efford 2009), sampling continuous space instead of using traps (Royle andYoung 2008, Royle et al 2011), sampling continuous time instead of discrete sampling intervals (Borchers et al 2014, Dorazio andKaranth 2017) and modeling population dynamics such as survival and recruitment using individual level or statespace formulations of classical Jolly-Seber and CormackJolly-Seber models (Gimenez et al 2007, Gardner et al 2010. We discuss some of these extensions below.…”

Section: Box 2 Core Elements Of Spatial Capture-recapturementioning

confidence: 99%

Unifying population and landscape ecology with spatial capture–recapture

Royle¹,

Fuller

Sutherland³

2017

Ecography

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172

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Spatial heterogeneity in the environment induces variation in population demographic rates and dispersal patterns, which result in spatio-temporal variation in density and gene flow. Unfortunately, applying theory to learn about the role of spatial structure on populations has been hindered by the lack of mechanistic spatial models and inability to make precise observations of population state and structure. Spatial capture-recapture (SCR) represents an individual-based analytic framework for overcoming this fundamental obstacle that has limited the utility of ecological theory. SCR methods make explicit use of spatial encounter information on individuals in order to model density and other spatial aspects of animal population structure, and they have been widely adopted in the last decade. We review the historical context and emerging developments in SCR models that enable the integration of explicit ecological hypotheses about landscape connectivity, movement, resource selection, and spatial variation in density, directly with individual encounter history data obtained by new technologies (e.g. camera trapping, non-invasive DNA sampling). We describe ways in which SCR methods stand to advance the study of animal population ecology.

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State-space modelling of data on marked individuals

Cited by 165 publications

References 33 publications

Individual heterogeneity in studies on marked animals using numerical integration: capture–recapture mixed models

Individual heterogeneity in studies on marked animals using numerical integration: capture–recapture mixed models

The Risk of Flawed Inference in Evolutionary Studies When Detectability Is Less than One

Unifying population and landscape ecology with spatial capture–recapture

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