Estimating population density and connectivity of American mink using spatial capture–recapture

Abstract

Estimating the abundance or density of populations is fundamental to the conservation and management of species, and as landscapes become more fragmented, maintaining landscape connectivity has become one of the most important challenges for biodiversity conservation. Yet these two issues have never been formally integrated together in a model that simultaneously models abundance while accounting for connectivity of a landscape. We demonstrate an application of using capture-recapture to develop a model of animal density using a least-cost path model for individual encounter probability that accounts for non-Euclidean connectivity in a highly structured network. We utilized scat detection dogs (Canis lupus familiaris) as a means of collecting non-invasive genetic samples of American mink (Neovison vison) individuals and used spatial capture-recapture models (SCR) to gain inferences about mink population density and connectivity. Density of mink was not constant across the landscape, but rather increased with increasing distance from city, town, or village centers, and mink activity was associated with water. The SCR model allowed us to estimate the density and spatial distribution of individuals across a 388 km² area. The model was used to investigate patterns of space usage and to evaluate covariate effects on encounter probabilities, including differences between sexes. This study provides an application of capture-recapture models based on ecological distance, allowing us to directly estimate landscape connectivity. This approach should be widely applicable to provide simultaneous direct estimates of density, space usage, and landscape connectivity for many species.

Unifying population and landscape ecology with spatial capture–recapture

“…For example, spatial sampling must be sufficient to ensure that individuals are observed at multiple spatial locations. Such spatial recaptures provide direct information about the model parameter s, or other parameters that might affect the distance function such as connectivity (Fuller et al 2015). Moreover, sampling should occur so that a population of home ranges is exposed to sampling, i.e.…”

“…The asymmetric space use model described here and in Box 4 in general, can be extended to multiple landscape characteristics as would be done with any loglinear regression model, and requires only that the landscape covariates are defined at the pixel level. While current applications have focused on river networks (Fuller et al 2015), the approach should be highly relevant for any species for which one or more landscape features act to impede or facilitate movement (Morin et al 2017) (e.g. extreme topographic variation, well-defined networks of roads and trails used by a species, Box 4).…”

“…Therefore, SCR can serve as an empirical framework for characterizing the utility of landscapes to populations. In particular, when combined with explicit models of connectivity (previous section), spatially explicit metrics which integrate information about both density and connectivity (Fuller et al 2015, Sutherland et al 2015, Morin et al 2017) can be estimated, thus providing information about quality of the landscape for maintaining connectivity and also for maintaining source populations of important species. Corridors are increasingly used as conservation tools, designed to facilitate movement of individuals between habitat patches, or between two nodes or habitat blocks separated by some distance (e.g.…”

“…We are aware of only one application of using capturerecapture data for formal inference about landscape resistance for a species (Fuller et al 2015), in which effective distance was parameterized by least-cost path. Further, corridor conservation has been devoid of explicit consideration of local population density.…”

“…SCR promises the integration of models (hypotheses) of within-population dynamics with 'population level' parameters and dynamics. Thus, SCR has proven to be more than simply an extension of a technique, but has emerged as a flexible framework that allows ecologists to test hypotheses about a wide range of ecological theories including landscape and network connectivity (Fuller et al 2015, Sutherland et al 2015, demography (Ergon and Gardner 2013, Whittington and Sawaya 2015, Muñoz et al 2016, resource selection (Royle et al 2013b, Proffitt et al 2015, and movement and dispersal , Lagrange et al 2014, Schaub and Royle 2014, Royle et al 2016. While classical capture-recapture methods focus on population level quantities, SCR models allow for the 'downscaling' of population structure from coarse summaries (spatial and/or demographic) into finer-scale components by the use of a spatially explicit individual-based point process model.…”