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Anthropogenic global change is occurring at alarming rates, leading to increased urgency in the ability to monitor wildlife health in real time. Monitoring sentinel marine species, such as bottlenose dolphins, is particularly important due to extensive anthropogenic modifications to their habitats. The most common non-invasive method of monitoring cetacean health is documentation of skin lesions, often associated with poor health or disease, but the current methodology is inefficient and imprecise. Recent advancements in technology, such as machine learning, can provide researchers with more efficient ecological monitoring methods to address health questions at both the population and the individual levels. Our work develops a machine learning model to classify skin lesions on the understudied Tamanend's bottlenose dolphins (Tursiops erebennus) of the Chesapeake Bay, using manual estimates of lesion presence in photographs. We assess the model's performance and find that our best model performs with a high mean average precision (65.6 %–86.8 %), and generally increased accuracy with improved photo quality. We also demonstrate the model's ability to address ecological questions across scales by generating model-based estimates of lesion prevalence and testing the effect of gregariousness on health status. At the population level, our model accurately estimates a prevalence of 72.1 % spot and 27.3 % fringe ring lesions, with a slight underprediction compared to manual estimates (82.2 % and 32.1 %). On the other hand, we find that individual-level analyses from the model predictions may be more sensitive to data quality, and thus, some individual scale questions may not be feasible to address if data quality is inconsistent. Manually, we do find that lesion presence in individuals suggests a positive relationship between lesion presence and gregariousness. This work demonstrates that object detection models on photographic data are reasonably successful, highly efficient, and provide initial estimates on the health status of understudied populations of bottlenose dolphins. 

Abstract Barnacles can reveal much about the physiology, health, and spatial ecology of their cetacean hosts. Here, we examine how temperature and hydrodynamic factors impact presence ofXenobalanus globicipitis, a pseudo‐stalked barnacle that attaches exclusively to cetaceans. We hypothesized that temperature is a key environmental factor (i.e., water temperature) and physiological factor, in thatX. globicipitisprefers the warmest skin temperature for attachment, possibly as a mechanism for survival in colder waters. First, we demonstrate a global relationship between spatial ecology of host species and presence ofX. globicipitis. Notably,X. globicipitisis absent in the four species occupying waters with the lowest sea surface temperature (SST) year‐round, but present in migratory species that likely acquire the barnacle in waters with higher SST. Second, barnacle attachment location on common bottlenose dolphin (Tursiops truncatus) dorsal fins corresponds with fin temperature and hydrodynamics. Although body temperature may influence attachment location on the body of the animal, hydrodynamic forces, as previously proposed, determine how well barnacles can remain attached during the adult stage.X. globicipitisprevalence likely provides important bioindicator, ecological, and physiological information about its host. As parasitic infestation has some cost, these results have implications for cetacean health in warming seas.