Estimating wolf abundance from cameras

Abstract

Monitoring the abundance of rare carnivores is a daunting task for wildlife biologists. Many carnivore populations persist at relatively low densities, public interest is high, and the need for population estimates is great. Recent advances in trail camera technology provide an unprecedented opportunity for biologists to monitor rare species economically. Few studies, however, have conducted rigorous analyses of our ability to estimate abundance of lowdensity carnivores with cameras. We used motion-triggered trail cameras and a space-to-event model to estimate gray wolf (Canis lupus) abundance across three study areas in Idaho, USA, 2016-2018. We compared abundance estimates between cameras and noninvasive genetic sampling that had been extensively tested in our study areas. Estimates of mean wolf abundance from camera and genetic surveys were within 22% of one another and 95% CIs overlapped in 2 of the 3 years. A single camera with many detections appeared to bias camera estimates high in 2018. A subsequent bootstrapping procedure produced a population estimate from cameras equal to that derived from genetic sampling, however. Camera surveys were less than half the cost of genetic surveys once initial camera purchases were made. Our results suggest that cameras can be a viable method for estimating wolf abundance across broad landscapes (>10,000 km 2 ).

How to Choose? Comparing Different Methods to Count Wolf Packs in a Protected Area of the Northern Apennines

“…Interestingly, the total number of 99 detected genotypes is very close to the CT total number of 97 individuals (86 from identified packs, plus two couples and nine dispersers recognised). In Idaho [ 59 ], researchers have already found comparable NGS and CT results in terms of estimations of wolves’ numbers. On the other side, with CT, we obtained a higher average number of individuals per pack than NGS (7.2 vs. 5.3 wolves/pack).…”

“…In IESA, the three integrated methods identified the same five packs as the single techniques applied alone in 100% of the nine cells. This indicates that a greater sampling effort for each technique is fundamental to get closer to the actual number of packs, and with sufficient sampling, different techniques are able to provide comparable results [ 59 ].…”

Assessment of the residential Finnish wolf population combines DNA captures, citizen observations and mortality data using a Bayesian state-space model

“…We believe that the seamless integration of different information sources using a Bayesian state-space model can be beneficial also in other cases, where different types of data are collected to infer the wolf population size. For example, Ausband et al (2022) compared the use of camera traps and DNA samples, and Stenglein et al (2010) compared the use of DNA samples and GPS surveys. In both cases, it would be possible to embed both data sources into a single model in the same way as we did for citizen-provided DNA samples and point observations.…”

Best practices to account for capture probability and viewable area in camera‐based abundance estimation

“…In contrast to the circular sector, the trapezoidal deployment strategy results in a viewable area with definitive end points and therefore is more easily defined and measured. However, this approach has rarely been applied in camera trap research, possibly because of the greater effort required to deploy elevated cameras and a perceived decrease in animal captures (Ausband et al, 2022, although see Jacobs & Ausband, 2018). The calculation of viewable area for either camera setup requires accurate measurements of the relevant parameters in the field (i.e., s 1 , s 2 , and h for the elevated camera setup and θ and r for the circular sector setup).…”

“…In contrast to the circular sector, the trapezoidal deployment strategy results in a viewable area with definitive end points and therefore is more easily defined and measured. However, this approach has rarely been applied in camera trap research, possibly because of the greater effort required to deploy elevated cameras and a perceived decrease in animal captures (Ausband et al., 2022, although see Jacobs & Ausband, 2018).…”