Premium
Quantitative assessment of species identification in aerial transect surveys for ice‐associated seals
Author(s) -
McClintock Brett T.,
Moreland Erin E.,
London Joshua M.,
Dahle Shawn P.,
Brady Gavin M.,
Richmond Erin L.,
Yano Kymberly M.,
Boveng Peter L.
Publication year - 2015
Publication title -
marine mammal science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.723
H-Index - 78
eISSN - 1748-7692
pISSN - 0824-0469
DOI - 10.1111/mms.12206
Subject(s) - phoca , aerial survey , biology , ecology , abundance (ecology) , transect , identification (biology) , fishery , geography , cartography
Technological advances have facilitated collection of vast quantities of photographic data from aerial surveys of marine mammals. However, when it is difficult to distinguish species from a distance, reliable identification from aerial images can often be challenging. This is the case for ice‐associated seals, species for which global climate change has motivated intensive monitoring efforts in recent years. We assess species and age class identification from aerial images of four ice seal species (bearded seals, Erignathus barbatus ; ribbon seals, Histriophoca fasciata ; ringed seals, Pusa hispida ; spotted seals, Phoca largha ) in the Bering Sea. We also investigate the specific phenomenological and behavioral traits commonly associated with species identification and observer confidence. We generally found species and age class misidentification occurred at relatively low levels, but only 83% of spotted seals tended to be correctly identified (with 11% mistaken as ribbon seals). We also found certain traits were strong predictors for observed species, age class, or observer confidence. Our findings add to the growing body of evidence that species misidentification is pervasive in passive sampling of animal populations. Even low levels of misidentification have been demonstrated to induce substantial biases in estimators of species distribution and abundance, and it is important that statistical models account for such errors.