1. Using marine mammal necropsy data in animal health surveillance: the case of the harbor porpoise in the Southern North Sea.
- Author
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IJsseldijk, Lonneke L., van den Broek, Jan, Kik, Marja J. L., Leopold, Mardik F., Rebolledo, Elisa Bravo, Gröne, Andrea, and Heesterbeek, Hans
- Subjects
ANIMAL health surveillance ,HARBOR porpoise ,MARINE mammals ,AUTOPSY ,SUPERVISED learning ,PORPOISES ,MARINE ecology - Abstract
Rapid changes of marine ecosystems resulting from human activities and climate change, and the subsequent reported rise of infectious diseases in marine mammals, highlight theurgency for timelydetectionof unusualhealtheventsnegatively affecting populations. Studies reportingpathological findings in thecommonly strandedharbor porpoise (Phocoena phocoena) on North Atlantic coastlines are essential to describe newandemergingcauses of mortality. However,suchstudies oftencannotbeused as long-term health surveillance tools due to analytical limitations. We tested 31 variables gained from stranding-, necropsy-, dietary-andmarine debris data from 405 harbor porpoises using applied supervised and unsupervisedmachine learning techniques to explore and analyze this large dataset. We classified and cross-correlated the variables and characterized the importance of the different variables for accurately predicting cause-of-death categories, to allow trend assessment for good conservation decision. The variable 'age class' seemed most influential in determining cause-ofdeath categories, and it became apparent that juveniles diedmore often due to acute causes, including bycatch, grey-seal-predation and other trauma, while adults of infectious diseases. Neonateswere found in summer, and mostly without prey in their stomach and more often stranded alive. The variables assigned as part of the external examinationof carcasses,suchas imprints from net sandlesionsinducedbypredators, as well as nutritional condition were most important for predicting cause-of-death categories,with a model prediction accuracyof 75%. Future porpoisemonitoring, and in particular the assessment of temporal trends, should predominantly focus on influential variables as determined in this study. Pathogen- and contaminant assessment data was not available for all cases, but would be an important step to further complete the dataset. This could be vital for drawing population-inferences and thus for long-term harbor porpoise population health monitoring as an early warning tool for population change. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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