Improved inferences about landscape connectivity from spatial capture–recapture by integration of a movement model

Abstract

Understanding how broad-scale patterns in animal populations emerge from individual-level processes is an enduring challenge in ecology that requires investigation at multiple scales and perspectives. Complementary to this need for diverse approaches is the recent focus on integrated modeling in statistical ecology. Population-level processes represent the core of spatial capture-recapture (SCR), with many methodological extensions that have been motivated by standing ecological theory and data-integration opportunities. The extent to which these recent advances offer inferential improvements can be limited by the data requirements for quantifying individual-level processes. This is especially true for SCR models that use non-Euclidean distance to relax the restrictive assumption that individual space use is stationary and symmetrical to make inferences about landscape connectivity. To meet the challenges of scale and data quality, we propose integrating an explicit movement model with non-Euclidean SCR for joint estimation of a shared cost parameter between individual and population processes. Here, we define a movement kernel for step selection that uses "ecological distance" instead of Euclidean distance to quantify availability for each movement step in terms of landscape cost. We compare performance of our integrated model to that of existing SCR models using realistic animal movement simulations and data collected on black bears. We demonstrate that an integrated approach offers improvements both in terms of bias and precision in estimating the shared cost parameter over models fit to spatial encounters alone. Simulations suggest these gains were only realized when step lengths were small relative to home range size, and estimates of density were insensitive to whether or not an integrated approach was used. By combining the fine spatiotemporal scale of individual movement processes with the estimation of population density in SCR, integrated approaches such as the one we develop here have the potential to unify the fields of movement, population, and landscape ecology and improve our understanding of landscape connectivity.

Mechanistic home range capture–recapture models for the estimation of population density and landscape connectivity

“…Joint statistical modelling of animal movement trajectories obtained by GPS loggers and spatial capture-recapture data is an important approach for understanding detailed animal movement processes, while ensuring the generality of results (Dupont et al, 2022). Both ADCR and the step selection function are based on biassed random walk models (Duchesne et al, 2015), and ADCR can be applied to joint modelling, retaining consistency between the animal movement model and resultant home range.…”

“…Individual movement is an important driver of spatial patterns of populations, and incorporating movement processes in spatial capture-recapture analysis will facilitate studies of the interplay between individual- and population-level ecological processes (Royle et al, 2018; McClintock et al, 2022). The integration of LCP modelling with spatial capture recapture analysis (Royle et al, 2013; Sutherland et al, 2015) was a great methodological advancement for the simultaneous estimation of population density and ecological distance, but accurate estimation of movement parameters exclusively from capture-recapture data is still difficult (Dupont et al, 2022). In this study, I developed a formal framework for a spatial capture-recapture model including mechanistic home range formation with an explicit theoretical link to animal movement models.…”

“…Although ADCRs and LCP-based spatial capture-recapture models differ with respect to connectivity assumptions (LCP-SCR, Royle et al, 2013; Sutherland et al, 2015; Royle et al, 2018) and cannot be directly compared, evaluations of the performance of estimators are helpful for users. Applying statistical models to simulated data generated from a mechanistic animal movement model is a straightforward way to test the unbiasedness of estimators and robustness to deviation from assumptions (Dupont et al, 2022; Theng et al, 2022). In particular, deviations in the spatial resolution of landscapes can often alter the results of landscape ecology (Qi & Wu, 1996; Turner et al, 1989), and the robustness of models to differences in spatial resolution between data generation and model estimation should be evaluated.…”

Integrating opportunistic and structured non-invasive surveys with spatial capture-recapture models to map connectivity of the Pyrenean brown bear population

“…This framework offers an efficient and cost-effective alternative to the use of expert opinion or telemetry data to quantify connectivity (Royle et al, 2018; Zeller et al, 2012). However, if GPS or telemetry data are available, they can be integrated with capture-recapture data to provide more precise estimate of movement and inform the estimation of the scale parameter (Dupont et al, 2021).…”

Special Feature: Linking capture–recapture and movement

“…Focusing on landscape connectivity, Dupont et al (2021) extend spatial capture–recapture models to accommodate a movement kernel based on so‐called ecological distance instead of Euclidean distance. Unlike other integrated approaches in the Special Feature (i.e., Chandler et al, 2021; Gardner et al, 2022; Hostetter et al, 2022; McClintock et al, 2021), Dupont et al (2021) use a step‐selection model and discrete‐space approximation for movement that can be fitted using maximum likelihood methods. Though it incorporates some restrictive assumptions, the model reduces computational burdens by avoiding the need to integrate over the latent movement paths during model fitting.…”