Camera trapping as a method for estimating abundance of Mexican wolves

Abstract

AbstractEstimating wildlife abundance, particularly for rare and elusive species, is challenging because of time, cost, and methodological constraints. The Mexican wolf (Canis lupus baileyi), a federally‐listed endangered subspecies of gray wolf, is currently monitored using ground and aerial methods to obtain a minimum known population count. As the Mexican wolf population has grown and expanded, the time and cost required to monitor the subspecies has increased. We investigated the efficacy of camera trapping for estimating Mexican wolf abundance by comparing the accuracy, precision, and cost of camera trapping to those obtained with current monitoring techniques. Between 1 November 2019 and 31 July 2020, we collected 13,317 photos of wolves from 124 camera traps in Arizona where Mexican wolves were known to occur, excluding tribal lands. We used a spatial mark‐resight analysis to estimate abundance for both winter (November 2019 through February 2020) and summer (April through July 2020) seasons, with and without the assistance of global positioning system (GPS) telemetry data to identify individual wolves. Combined with GPS data, camera trapping provided a summer abundance estimate ( = 50, 95% CI = 37–64) that was 14% lower than the 2019 minimum known population count (N = 59), but included the minimum known population count in the 95% confidence interval. The summer no telemetry abundance estimate was 27% below the minimum known population count ( = 43, 95% CI = 30–56). During winter, abundance estimates obtained from camera trapping (no telemetry: = 33, 95% CI = 15–52; telemetry: = 45, 95% CI = 28–62), were much lower than the 2019 Mexican wolf minimum known population count (winter: N = 62), but included the minimum known population count in the 95% confidence interval for the winter telemetry dataset. A cost comparison indicated that the first‐year camera trapping equipment expenses were 1.7 times the equipment cost of the current method and that camera trapping equipment expenses in subsequent years were equivalent to the equipment cost of the current method, and required at least 1.4 times the labor hours. We provide recommendations to potentially improve Mexican wolf abundance estimates and minimize camera trapping expenses. The results of our project may help managers make appropriate decisions for their population monitoring needs, while considering budget, staffing capabilities, precision, and accuracy.