An evaluation of density estimation methods using a multiyear dataset of localized bowhead whale calls

Abstract

For eight years (2007–2014), Greeneridge Sciences deployed, collected, and analyzed an immense passive acoustic dataset to study the response of bowhead whales (Balaena mysticetus) to oil exploration activities along a 280 km swath of the Alaskan Arctic continental shelf. Each year, up to 40 Directional Autonomous Seafloor Acoustic Recorders (DASARs) detected bowhead calls during the whales’ annual fall migration, resulting in over 2.4 million localized calls over the study period. These enormous sample sizes, combined with the unique localization capability of the DASARs, have enabled the investigation and cross-validation of several acoustic density estimation (DE) methods. Here, we compared results among three density estimation methods: (i) direct census, (ii) distance sampling, and (iii) spatially explicit capture recapture (SECR). In the course of our investigation, we encountered and addressed two challenges: (i) how to apply point transect theory to distributed array systems, and (ii) how to implement SECR in situations where detections between sensors are not statistically independent or where false positives exist on single sensors. These density estimates were then used to estimate relative abundance of the Western Arctic bowhead whale population over multiple years. [Work sponsored by the Joint Industry Programme.]