Amburgey SM, AA Yackel Adams, B Gardner, NJ Hostetter, SR Siers, BT McClintock, and SJ Converse. 2021. Validation of camera trap-based abundance estimators for unmarked populations. Ecological Applications 31: e02410.
Estimates of species abundance are critical to understand population processes and to assess and plan management actions. However, capturing and marking individuals for abundance estimation can be economically and logistically prohibitive, particularly for cryptic species. Camera traps can collect data at temporal and spatial scales necessary for estimating abundance, but the use of camera traps comes with limitations when target species are not uniquely identifiable (i.e., “unmarked”). Abundance estimation is particularly useful in the management of invasive species, and herpetofauna are being recognized as some of the most pervasive and detrimental invasive species. However, the use of camera traps for these taxa comes with additional challenges with relevancy across multiple taxa. It is often necessary to use lures to attract animals in order to obtain sufficient observations, yet lure-attraction can influence species’ landscape use and potentially induce bias in abundance estimators. We investigated these challenges and assessed the feasibility of obtaining reliable abundance estimates using camera trapping data on a population of invasive brown treesnakes (Boiga irregularis) in Guam. Data were collected using camera traps in an enclosed area where snakes were subject to high-intensity capture-recapture effort, resulting in presumed abundance of 116 snakes (density = 23/ha). We then applied Unmarked Spatial Capture-Recapture (USCR), Random Encounter and Staying Time, Space to Event, and Instantaneous Sampling estimators to photo-capture data to estimate abundance and compared estimates to our presumed abundance. We found that all estimators for unmarked populations performed poorly, with inaccurate or imprecise abundance estimates that limit their usefulness for management. We further investigated the sensitivity of these estimators to increasing lure attraction (i.e., violating the assumption that animal behavior is unchanged by sampling) and camera density. Increasing lure attraction and camera density both resulted in higher abundance estimates, with the USCR estimator particularly sensitive to changing camera density while other estimators were more sensitive to increasing lure attraction. Each estimator rarely recovered truth or suffered from convergence issues. Our results indicate that, when limited to unmarked estimators and the use of lures, camera traps alone are unlikely to produce abundance estimates with utility to managers.