Over the last decade, spatial capture-recapture (SCR) models have become widespread for estimating demographic parameters in ecological studies. However, the underlying assumptions about animal movement and space use are often not realistic. This is a missed opportunity because ecological questions related to animal space use, habitat selection, and behavior cannot be addressed with most SCR models, despite the fact that the data collected in SCR studies -- individual animals observed at specific locations and times -- can provide a rich source of information about how these processes relate to demographic rates. We developed SCR models that integrate complex movement processes that are typically inferred from telemetry data, including a simple random walk, correlated random walk (i.e., short-term directional persistence), and habitat-driven Langevin diffusion. We demonstrated how to formulate, simulate from, and fit these models with standard SCR data using Bayesian analysis methods. We evaluated their performance through a simulation study, where we varied the detection, movement, and resource selection parameters. We also examined different numbers of sampling occasions and assessed performance gains when including auxiliary location data collected from telemetered individuals. Across all scenarios, the integrated SCR movement models performed well in terms of abundance, detection, and movement parameter estimation. We found little difference in bias for the simple random walk model when reducing the number of sampling occasions from T=25 to T=15. We found some bias in movement parameter estimates under several of the correlated random walk scenarios, but incorporating auxiliary location data improved parameter estimates and significantly improved mixing during model fitting. The Langevin movement model was able to recover resource selection parameters from standard SCR data, which is appealing because it explicitly links the individual-level movement process with habitat selection and population density. We focused on closed population models, but movement models developed here could be extended to open SCR models. The movement process models could also be extended to accommodate additional "building blocks'' of random walks, such as central tendency (e.g., territoriality) or multiple movement behavior states, thereby providing a flexible and coherent framework for linking animal movement behavior to population dynamics, density, and distribution.

We provide a set of R scripts to simulate data and fit models using NIMBLE for integrated movement and spatial capture recapture models. For details on the simulation and models that are fitted within the script, please see Gardner et al. 2022.

There are no specific datasets included here, instead we provide the R scripts to simulate data under 3 different animal movement models.

To run either the random walk or the directional persistence movement models, use the sim_SCR_RandomWalks.R script. The script is set to simulate data and fit the model for Scenario 7a in the Gardner et al. 2022 manuscript. To simulate data from the directional persistence movement model, you will set gamma to be a value between 0 and 1; to run the random walk/bivarate normal movement, you will set gamma to be 0. This script will source the file: SCR_RandomWalks_Distributions_Samplers_and_Functions.R

To run the Langevin movement model, use the sim_SCR_Langevin.R script. The script is set to simulate data and fit the model for Scenario 13 in the Gardner et al. 2022 manuscript. This script will source the file: SCR_Langevin_Mvmt_Distributions_Samplers_and_Functions.R

Annotation within the scripts describe the parameters and values that can be changed. The various functions, samplers, and distributions are described in the Readme.pdf file.