  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.
