DOI: 10.5061/dryad.pc866t1r9
title: Integrated animal movement and spatial capture-recapture models: simulation, implementation, and inference

description: 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.
description: 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.
description: 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.