Spatially explicit models for inference about density in unmarked or partially marked populations

By R. Chandler, J. Andrew Royle. 2011

299 citations recorded by the source corpus.

Recently developed spatial capture-recapture (SCR) models represent a major advance over traditional capture-recapture (CR) models because they yield explicit estimates of animal density instead of population size within an unknown area. Furthermore, unlike nonspatial CR methods, SCR models account for heterogeneity in capture probability arising from the juxtaposition of animal activity centers and sample locations. Although the utility of SCR methods is gaining recognition, the requirement that all individuals can be uniquely identified excludes their use in many contexts. In this paper, we develop models for situations in which individual recognition is not possible, thereby allowing SCR concepts to be applied in studies of unmarked or partially marked populations. The data required for our model are spatially referenced counts made on one or more sample occasions at a collection of closely spaced sample units such that individuals can be encountered at multiple locations. Our approach includes a spatial point process for the animal activity centers and uses the spatial correlation in counts as information about the number and location of the activity centers. Camera-traps, hair snares, track plates, sound recordings, and even point counts can yield spatially correlated count data, and thus our model is widely applicable. A simulation study demonstrated that while the posterior mean exhibits frequentist bias on the order of 5-10% in small samples, the posterior mode is an accurate point estimator as long as adequate spatial correlation is present. Marking a subset of the population substantially increases posterior precision and is recommended whenever possible. We applied our model to avian point count data collected on an unmarked population of the northern parula (Parula americana) and obtained a density estimate (posterior mode) of 0.38 (95% CI: 0.19-1.64) birds/ha. Our paper challenges sampling and analytical conventions in ecology by demonstrating that neither spatial independence nor individual recognition is needed to estimate population density - rather, spatial dependence can be informative about individual distribution and density.

Subfields: Ecology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/dd8888a9f00bfd0b834fbff6af8bbb34f3fd5c8a

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Citations in the corpus

Spatial capture–recapture with partial identity: An application to camera traps (2018, methodology): “Chandler & Clark (2014) probabilistically associate unidentified detections or counts to individuals identified by mark-recapture using their spatial location and a latent SCR model and Chandler et al. (2013) consider the situation where 100% of the samples are of unknown identity and use the spatia”
Synthesizing multiple data types for biological conservation using integrated population models (2018, background): “Both capture-recapture and survey (either count or detection-nondetection) data can be modeled conditional on the point process (Chandler and Royle, 2013; Royle et al., 2013), thus avoiding the conventional and restrictive requirement that these two datasets be independent.”
Estimating animal density without individual recognition using information derivable exclusively from camera traps (2018, background): “Chandler and Royle (2013) proposed spatial capture–recapture models to estimate animal density for unmarked populations using spatially correlated point count data.”
Restoring apex predators can reduce mesopredator abundances (2019, methodology): “For the Egyptian mongoose, individual recognition was not possible, so we used spatial count (SC) models (Chandler and Royle, 2013; Royle et al., 2014).”
Spatially explicit integrated population models (2014, result): “…precision that we observed resulted from the fact that patterns in abundance and distribution are manifest in both the capture–recapture data and the detection data, a finding that agrees with previous theoretical and empirical work (He & Gaston 2003; Royle & Nichols 2003; Chandler & Royle 2013).”
An invasive‐native mammalian species replacement process captured by camera trap survey random encounter models (2016, background): “Recently developed spatial capture-recapture (SCR) models provide a means of estimating population densities for unmarked populations from a variety of survey data (Chandler and Royle 2013).”