The need to balance economic development with impacts to Arctic wildlife has been a prominent subject since petroleum exploration began on the North Slope of Alaska, USA, in the late 1950s. The North Slope region includes polar bears (Ursus maritimus) of the southern Beaufort Sea subpopulation, which has experienced a long-term decline in abundance. Pregnant polar bears dig dens in snow drifts during winter and are vulnerable to disturbance, as den abandonment and mortality of neonates may result. Maternal denning coincides with the peak season of petroleum exploration and construction, raising concerns that human activities may disrupt denning. To minimize disturbance of denning polar bears, aerial infrared (AIR) surveys are routinely used to search for dens within planned industry activity areas and that information is used to implement mitigation. Aerial infrared surveys target the heat signature emanating from dens. Despite use by industry for >15 years, the efficacy of AIR and the factors that impact its ability to detect dens remains uncertain. Here, we evaluate AIR using artificial dens and observers naïve to locations to estimate detection probability and its relationship with covariates including weather variables, den characteristics, infrared sensor and altitude, and survey order to identify potential evidence of in-flight observer learning occurring between surveys. In December 2019 we constructed 14 dens (each with an artificial heat source), and 11 control sites (disturbed sites without dens). Between December 2019 and January 2020, 3 survey crews flew 6 independent AIR surveys within the vicinity of dens and control sites and video-recorded AIR imagery. Observers identified putative dens either in flight or during post-flight review of recordings. We assessed detection probability with a simple Bayesian model using 3 subsets of data: 1) all detection/non-detection data; 2) detection/non-detection data restricted to instances where sample sites were confirmed to have been properly scanned by AIR during post-study verification (i.e., when den locations were known); and 3) all dens visible on the recorded imagery during post-study verification, even if they were not seen during the survey or during post-flight review. Subsets 1 and 2 most closely resembled den surveys flown for oil and gas industry and had detection probabilities of 0.15 (95% CI = 0.08–0.23) and 0.24 (95% CI = 0.13–0.37), respectively. Detection probability was 0.41 (95% CI = 0.25–0.58) for subset 3. Higher wind speeds and larger den volume negatively influenced detection probability. Our low detection rate compared to previous studies could partially be the result of differences in study design, such as survey flight patterns. Our results suggest that AIR, as it is currently used, is unlikely to detect most polar bear dens in surveyed areas. Resource managers who use AIR should consider a suite of additional methods (e.g., habitat mapping, probabilistic den distribution, AIR methodology improvements) for minimizing impacts of industry on denning polar bears.
|Publication Subtype||Journal Article|
|Title||Evaluating the efficacy of aerial infrared surveys to detect artificial polar bear dens|
|Series title||Wildlife Society Bulletin|
|Publisher||The Wildlife Society|
|Contributing office(s)||Alaska Science Center Biology MFEB|
|Description||e1324, 20 p.|
|Google Analytic Metrics||Metrics page|