The blubber adipocyte index: A nondestructive biomarker of adiposity in humpback whales ( Megaptera novaeangliae)

• Published in: Ecology and Evolution
• Main Authors: Castrillon, Juliana, Huston, Wilhelmina, Bengtson Nash, Susan
• Other Authors: Pacific Life Ocean Foundation
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2017
• Subjects: baleen whales; Humpback Whale; Megaptera novaeangliae
• Online Access: http://dx.doi.org/10.1002/ece3.2913; https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fece3.2913; https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.2913; https://onlinelibrary.wiley.com/doi/full-xml/10.1002/ece3.2913

Abstract The ability to accurately evaluate the energetic health of wildlife is of critical importance, particularly under conditions of environmental change. Despite the relevance of this issue, currently there are no reliable, standardized, nonlethal measures to assess the energetic reserves of large, free‐roaming marine mammals such as baleen whales. This study investigated the potential of adipocyte area analysis and further, a standardized adipocyte index ( AI ), to yield reliable information regarding humpback whale ( Megaptera novaeangliae ) adiposity. Adipocyte area and AI , as ascertained by image analysis, showed a direct correlation with each other but only a weak correlation with the commonly used, but error prone, blubber lipid‐percent measure. The relative power of the three respective measures was further evaluated by comparing humpback whale cohorts at different stages of migration and fasting. Adipocyte area, AI , and blubber lipid‐percent were assessed by binary logistic regression revealing that adipocyte area had the greatest probability to predict the migration cohort with a high level of redundancy attributed to the AI given their strong linear relationship (r = −.784). When only AI and lipid‐percent were assessed, the performance of both predictor variables was significant but the power of AI far exceeded lipid‐percent. The sensitivity of adipocyte metrics and the rapid, nonlethal, and inexpensive nature of the methodology and AI calculation validate the inclusion of the AI in long‐term monitoring of humpback whale population health, and further raises its potential for broader wildlife applications.