Modeling abundance, distribution, movement and space use with camera and telemetry data. Issue 10 (16th December 2021)

• Record Type: Journal Article
• Authors: Chandler, Richard B.; Crawford, Daniel A.; Garrison, Elina P.; Miller, Karl V.; Cherry, Michael J.
• Is Part Of: Ecology. Volume 103:Issue 10(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-16
• Subjects: home range -- Ornstein–Uhlenbeck bridge -- spatial population dynamics -- spatio‐temporal models -- utilization distributions; Ecology -- Periodicals; Ecology -- Periodicals; Écologie -- Périodiques; Ecologie; Écologie; Écologie animale; Écologie végétale; Ecology; Periodicals; 577.05
• DOI: 10.1002/ecy.3583
• Languages: English
• ISSNs: 0012-9658

Abstract

Abstract: Studies of animal abundance and distribution are often conducted independently of research on movement, despite the important links between processes. Movement can cause rapid changes in spatial variation in density, and movement influences detection probability and therefore estimates of abundance from inferential methods such as spatial capture–recapture (SCR). Technological developments including camera traps and GPS telemetry have opened new opportunities for studying animal demography and movement, yet statistical models for these two data types have largely developed along parallel tracks. We present a hierarchical model in which both datasets are conditioned on a movement process for a clearly defined population. We fitted the model to data from 60 camera traps and 23, 572 GPS telemetry locations collected on 17 male white‐tailed deer in the Big Cypress National Preserve, Florida, USA during July 2015. Telemetry data were collected on a 3–4 h acquisition schedule, and we modeled the movement paths of all individuals in the region with a Ornstein–Uhlenbeck process that included individual‐specific random effects. Two of the 17 deer with GPS collars were detected on cameras. An additional 20 male deer without collars were detected on cameras and individually identified based on their unique antler characteristics. Abundance was 126 (95% CI: 88–177) in the 228 km 2 region, only slightly higher than estimated using a standard SCR model: 119 (84–168). The standard SCR model, however, was unable to describe individual heterogeneity in movement rates and space use as revealed by the joint model. Joint modeling allowed the telemetry data to inform the movement model and the SCR encounter model, while leveraging information in the camera data to inform abundance, distribution and movement. Unlike most existing methods for population‐level inference on movement, the joint SCR‐movement model can yield unbiased inferences even if non‐uniform sampling is used to deploy transmitters. Potential extensions of the model include the addition of resource selection parameters, and relaxation of the closure assumption when interest lies in survival and recruitment. These developments would contribute to the emerging holistic framework for the study of animal ecology, one that uses modern technology and spatio‐temporal statistics to learn about interactions between behavior and demography.