Estimating wolf abundance from cameras. Issue 2 (6th February 2022)

• Record Type: Journal Article
• Authors: Ausband, David E.; Lukacs, Paul M.; Hurley, Mark; Roberts, Shane; Strickfaden, Kaitlyn; Moeller, Anna K.
• Is Part Of: Ecosphere. Volume 13:Issue 2(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-02-06
• Subjects: abundance -- camera -- Canis lupus -- density -- monitoring -- space‐to‐event -- wolf; Ecology -- Periodicals; Ecology; Periodicals; 577.05
• DOI: 10.1002/ecs2.3933
• Languages: English
• ISSNs: 2150-8925

Abstract

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 low‐density 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 ).