A Bayesian approach to combining animal abundance and demographic data

By Steve P. Brooks, Ruth King, Byron J. T. Morgan. Animal Biodiversity and Conservation, 2004

177 citations recorded by the source corpus.

In studies of wild animals, one frequently encounters both count and mark-recapture-recovery data. Here, we consider an integrated Bayesian analysis of ring¿recovery and count data using a state-space model. We then impose a Leslie-matrix-based model on the true population counts describing the natural birth-death and age transition processes. We focus upon the analysis of both count and recovery data collected on British lapwings (Vanellus vanellus) combined with records of the number of frost days each winter. We demonstrate how the combined analysis of these data provides a more robust inferential framework and discuss how the Bayesian approach using MCMC allows us to remove the potentially restrictive normality assumptions commonly assumed for analyses of this sort. It is shown how WinBUGS may be used to perform the Bayesian analysis. WinBUGS code is provided and its performance is critically discussed.

Subfields: Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/53842cf54fad2837cf67694c0233a8624703eed3

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In Ecology

In Nature and Landscape Conservation

Citations in the corpus

Massive immigration balances high anthropogenic mortality in a stable eagle owl population: Lessons for conservation (2010, methodology): “We fitted the integrated model in the Bayesian framework as it provides more flexibility than the frequentist framework (Brooks et al., 2004; Schaub et al., 2007) and exact measures of parameter uncertainty (Link and Barker, 2010).”
Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling. (2009, background): “Hierarchical models address complex problems for which there may be several sources of information and hidden variables (Wikle et al. 1998, Abbitt and Breidt 2002, Brooks et al. 2004, Ogle et al. 2004, Clark 2005, Royle and Dorazio 2006).”
Integrated population models facilitate ecological understanding and improved management decisions (2018, methodology): “Once the joint likelihood has been constructed, inference can be obtained using Kalman filters within the frequentist framework (Besbeas et al. 2002) or Markov chain Monte Carlo (MCMC) simulation within the Bayesian framework (Brooks et al. 2004, K ery and Schaub 2012:chapter 11).”
WinBUGS for population ecologists: bayesian modeling using markov chain Monte Carlo methods (2009, background): “These ideas open the area to numerous applications including the integration of several sources of information (recovery and recapture data, see Catchpole et al. 1998; count data and demographic data, see Besbeas et al. 2002, 2005, 2008; Brooks et al. 2004; Maunder 2004; Schaub et al. 2007).”