Generalized estimators of avian abundance from count survey data

By J. Andrew Royle. Animal Biodiversity and Conservation, 2004

113 citations recorded by the source corpus.

I consider modeling avian abundance from spatially referenced bird count data collected according to common protocols such as capture-recapture, multiple observer, removal sampling and simple point counts. Small sample sizes and large numbers of parameters have motivated many analyses that disregard the spatial indexing of the data, and thus do not provide an adequate treatment of spatial structure. I describe a general framework for modeling spatially replicated data that regards local abundance as a random process, motivated by the view that the set of spatially referenced local populations (at the sample locations) constitute a metapopulation. Under this view, attention can be focused on developing a model for the variation in local abundance independent of the sampling protocol being considered. The metapopulation model structure, when combined with the data generating model, define a simple hierarchical model that can be analyzed using conventional methods. The proposed modeling framework is completely general in the sense that broad classes of metapopulation models may be considered, site level covariates on detection and abundance may be considered, and estimates of abundance and related quantities may be obtained for sample locations, groups of locations, unsampled locations. Two brief examples are given, the first involving simple point counts, and the second based on temporary removal counts. Extension of these models to open systems is briefly discussed.

Subfields: Ecology

View Paper: https://www.semanticscholar.org/paper/47bfbd388c42d9451d7d899cfd65284b979f3a06

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In Ecology

Citations in the corpus

Predicting species distributions from checklist data using site‐occupancy models (2010, background): “Finally, it has become clear that site-occupancy models are just a special case of a general hierarchical metapopulation modelling framework (Royle, 2004; Royle & Dorazio, 2006, 2008).”
Spatially explicit models for inference about density in unmarked or partially marked populations (2011, unknown): “So-called N -mixture models (Royle, 2004b,a; Royle, Dawson and Bates, 2004) can be applied to a repeatedmeasures type of data structure wherein data are collected at R sites, with J replicate surveys are conducted at each.”
unmarked: An R Package for Fitting Hierarchical Models of Wildlife Occurrence and Abundance (2011, methodology): “Abundance from repeated counts pcount unmarkedFramePCount Royle (2004b)”