Evaluating unmarked abundance estimators using remote cameras and aerial surveys

Abstract

Reliable population abundance estimates are invaluable to wildlife management programs. Mark-recapture techniques are regarded as the gold standard for estimating abundance but can be financially or logistically prohibitive. Recent developments in remote camera analytical approaches have provided alternative methods to estimate unmarked populations efficiently but have undergone limited field testing. In September and October 2020, I assessed 2 camera methods that use the same set of time-lapse photographs; a space to event (STE) model and instantaneous sampling (IS), applied to a case study of feral sheep (Ovis aries) at the US Army's Pōhakuloa Training Area (PTA) on Hawai`i Island. Results of the camera methods were compared to an abundance estimate calculated from an aerial distance sampling survey completed concurrently with camera sampling. A comparison of confidence intervals indicated no statistical difference between estimates, but the relative imprecision of IS suggested the STE model to be a more reliable estimator. The cost of camera sampling was approximately 3 times that of the aerial survey, but camera sampling became more economical if the area was resurveyed 3 or more times. My study showed the STE model to be a cost effective, practical abundance estimator of feral sheep when compared to well-established aerial distance sampling techniques, and therefore should offer considerable potential for the conservation and management of other easily identifiable species.

Comparing camera‐based ungulate density estimates: a case study using island populations of bighorn sheep and mule deer

“…An alternate approach that addresses heterogeneity in capture probability is to collect time-lapse pictures (e.g., taken every 5 minutes), which are sampled and recorded without motion triggers. The instantaneous sampling (IS) and space-to-event (STE) estimators were developed to evaluate time-lapse sampled data (Moeller et al 2018) and have been applied across a diversity of study systems (Amburgey et al 2021, Ausband et al 2022, Leo et al 2022. This approach removes components of camera-based sampling that lead to high heterogeneity in capture probability (i.e., variation in the effectiveness of motion-triggered photography) and overall reduction of conditions under which capture probability is <1 (Moeller et al 2023).…”

“…Moeller et al (2018) presented a case study wherein camera‐based IS and STE estimates both underestimated sightability‐corrected counts of elk ( Cervus canadensis ), yet they admittedly violated assumptions of random camera placement (IS and STE), perfect capture probability (IS and STE), and independent movement among individuals (STE only) with unmeasured effects (Lyet et al 2024). Leo (2022) found IS and STE camera‐based point estimates of feral sheep ( Ovis aries ) abundance to be slightly higher and lower, respectively, to a population estimate produced from aerial distance sampling; however, confidence intervals of all estimates overlapped. Grouping behavior of sheep in this study may have violated the assumption of independent movement for STE estimates and only daytime pictures were analyzed, both of which may have affected results.…”