Hongo, S. | bioRxiv Science

bioRxiv2021

A practical guide for estimating animal density using camera traps: Focus on the REST model

Camera traps are increasingly popular in wildlife research and have the potential to be reliable and cost-effective for estimating animal density. Although density estimation using this automatic technique has long been restricted to species with individually recognizable markings, several analytical approaches have been proposed to target animals lacking such markings (i.e., unmarked populations). Among these approaches, the random encounter and staying time (REST) model may be an efficient and cost-effective approach, even though the procedures for the implementations have not yet been shared with researchers. This paper presents a working protocol for implementing the REST model our research group has been developing. We also present the R code to perform parameter estimation with a maximum likelihood and Bayesian approach. We suggest that this model has potential for further development. We strongly hope that this paper will encourage many researchers to use the REST for density estimation in a wide variety of species in various habitats and make a significant contribution to advancing wildlife conservation and management.

ecology↗

bioRxiv2021

Double-observer approach with camera traps: Towards an unbiased density estimation of unmarked animal populations

Camera traps are a powerful tool for wildlife surveys. However, camera traps may not always detect animals passing in front. This constraint may create a substantial bias in estimating critical parameters such as the density of unmarked populations. We proposed the double-observer approach with camera traps to counter the constraint, which involves setting up a paired camera trap at a station and correcting imperfect detection with a hierarchal capture-recapture model for stratified populations. We performed simulations to evaluate this approachs reliability and determine how to obtain desirable data for this approach. We then applied it to 12 mammals in Japan and Cameroon. The results showed that the approach could correct imperfect detection as long as paired camera traps detect animals nearly independently (Correlation coefficient < 0.2). Camera traps should be installed to monitor a predefined small focal area from different directions to satisfy this requirement. The field surveys showed that camera trap might miss animals by 3 %-40%, suggesting that current density estimation models relying on perfect detection may underestimate animal density by the same order of magnitude. We hope that our approach will be incorporated into existing density estimation models to improve their accuracy.

ecology↗