State-space models for the dynamics of wild animal populations

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Metapopulation consequences of site fidelity for colonially breeding mammals and birds (2005, methodology): “Such time-saving devices are needed if annual movement models are to be incorporated in population models whose parameters are estimated using computerintensive statistical algorithms, such as Markov chain Monte Carlo (O’Hara et al. 2002; Drechsler et al. 2003) or sequential importance sampling ( Bu”
ESTIMATING DENSITY DEPENDENCE, PROCESS NOISE, AND OBSERVATION ERROR (2006, methodology): “More recently, Buckland et al. (2004) constructed detailed state-space models with multiple state variables, and Newman et al. (2006) implemented such a model for fisheries data.”
Integrating demographic data: towards a framework for monitoring wildlife populations at large spatial scales (2014, background): “…of state-space, or hidden-process, models offers the opportunity to develop integrated analyses of population count and demographic data, giving the potential to incorporate unmeasured processes directly in the population models (Buckland et al. 2004; King et al. 2009; Newman et al. 2009).”