A machine-learning approach to assign species to ‘unidentified’ entangled whales

• Published in: Endangered Species Research
• Main Author: Carretta, JV
• Format: Article in Journal/Newspaper
• Language: English
• Published: Inter-Research 2018
• Subjects: Zoology; QL1-991; Botany; QK1-989; Balaenoptera acutorostrata; Fin whale; Humpback Whale; Megaptera novaeangliae; Physeter macrocephalus; Sperm whale
• Online Access: https://doi.org/10.3354/esr00894; https://doaj.org/article/89a56a3cbea14931abfd5aeb66fc1f85

Whale entanglements in US west coast fishing gear are largely represented by opportunistic sightings, and some reports lack species identifications due to rough seas, distance from whales, or a lack of cetacean identification expertise. Unidentified entanglements are often ignored in species risk assessments and thus, entanglement risk is underestimated. To address this negative bias, a species identification model was built from random forest (RF) classification trees using 199 identified entanglements (‘model data’). Humpback Megaptera novaeangliae and gray whales Eschrichtius robustus represented 92% of identified entanglements; the remaining 8% were minke whales Balaenoptera acutorostrata, fin whales B. physalus, blue whales B. musculus, and sperm whales Physeter macrocephalus. Predictor variables included year, gear type, location, season, sea surface temperature, water depth, and a multivariate El Niño index. Cross-validated species classifications were correct in 78% (155/199) of cases, significantly higher (p < 0.001, permutation test) than the 49% correct classification rate expected by chance. The RF model correctly classified 91% of humpback whale cases, 64% of gray whale cases, and 100% of sperm whale cases, but misclassified all minke, blue, and fin whale cases. The cross-validated RF classification-tree species model was used to classify 35 entanglements without species identifications (‘novel data’) and each case was assigned a probability of belonging to each of 6 model data species. This approach eliminates the negative bias associated with ignoring unidentified entanglements in species risk assessments. Applications to other wildlife studies where some detections are unidentified include fisheries bycatch, line-transect surveys, and large-whale vessel strikes.