OF BUGS AND BIRDS: MARKOV CHAIN MONTE CARLO FOR HIERARCHICAL MODELING IN WILDLIFE RESEARCH

By W. Link, E. Cam, James D. Nichols, Evan G. Cooch. 2002

226 citations recorded by the source corpus.

Subfields: Ecology

View Paper: https://www.semanticscholar.org/paper/696823af1e1a2ac7bac290121f9bfe910a30819a

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In Ecology

Citations in the corpus

A Hierarchical Model for Regional Analysis of Population Change Using Christmas Bird Count Data, with Application to the American Black Duck (2006, methodology): “We generated chains of length 10 000, discarding the first 4000 as a burn-in (see Link et al. 2002 for a simple introduction to MCMC and our Discussion for some comments on the length of chains used in MCMC calculations).”
Chapter 3 Modeling Individual Animal Histories with Multistate Capture–Recapture Models (2009, methodology): “The two main methods are Markov Chain Monte‐Carlo (MCMC) methods (Brooks et al., 2002; Clark et al., 2005; Link et al., 2002a) and recursive filtering (Buckland et al., 2004).”
A HIERARCHICAL SPATIAL MODEL OF AVIAN ABUNDANCE WITH APPLICATION TO CERULEAN WARBLERS (2004, methodology): “We conducted model fitting in WinBUGS 1.3 (Speigelhalter et al. 2000; Supplement), a statistical package conducting Bayesian inference with MCMC methods (Gibbs Sampling) (Link et al. 2002), following specific methodology described by Royle et al. (2002:630–631).”
Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling. (2009, methodology): “The utility of such a decomposition is that it allows us to account formally for uncertainty within each stage, where the stages are linked in a probabilistically consistent fashion, resulting in a Bayesian analysis (e.g., Link et al. 2002).”
A Bayesian approach to combining animal abundance and demographic data (2004, methodology): “We demonstrate how the state space model used to describe the index data can be easily fitted using Markov chain Monte Carlo (MCMC; Gamerman, 1995; Gilks et al., 1996; Brooks, 1998) and implemented via WinBUGS (Spiegelhalter et al., 2002b; Gentleman, 1997; Link et al., 2002).”
Modelling occurrence and abundance of species when detection is imperfect (2005, unknown): “With the introduction of the metapopulation concept ( Levins 1969, Hanski 1999), considerable theoretical and practical attention has been focused on patch occupancy and related metapopulation concepts.”
Density Estimation in a Wolverine Population using Spatial Capture-Recapture Models (2011, methodology): “Bayesian analysis proceeds using a simulation-based method (i.e., MCMC; Link et al. 2002), which requires that we simulate realizations of the underlying point-process.”
Hierarchical Bayes estimation of species richness and occupancy in spatially replicated surveys (2008, background): “typically is intractable analytically (Brooks 2003; Link et al. 2002).”
Alternatives to statistical hypothesis testing in ecology: a guide to self teaching. (2006, background): “Hierarchical models, which are analogous to traditional random effects models, are growing in popularity (Link et al. 2002, Sauer and Link 2002, Calder et al. 2003, Clark 2003), and provide a framework for describing the variability among individuals or populations.”
WinBUGS for population ecologists: bayesian modeling using markov chain Monte Carlo methods (2009, methodology): “Indeed, the application of the Bayesian theory in population ecology has been greatly facilitated by the implementation of algorithms known as Markov chain Monte Carlo (MCMC) methods (Gilks et al. 1996 and Link et al. 2002 for an introduction for ecologists) in flexible and reliable software.”
On thinning of chains in MCMC (2012, methodology): “We write this note assuming readers have some acquaintance with MCMC methods; for more details on fundamentals, we refer readers to Link et al. (2002) or to texts by Gelman et al. (2004) and Link & Barker (2010).”
Population-level impact of white-nose syndrome on the endangered Indiana bat (2012, methodology): “For each model, we ran three chains for 60,000 iterations, discarding the first 40,000 iterations as burn-in and thinning by 5 to reduce serial correlation (Link et al. 2002, Lunn et al. 2000).”