Raudino, Holly C., Cleguer, Christophe, Hamel, Melanie A., Swaine, Michael, and Waples, Kelly A. (2022) Species identification of morphologically similar tropical dolphins and estimating group size using aerial imagery in coastal waters. Mammalian Biology, 102. pp. 829-839.

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Abstract

Detecting dolphins, accurately identifying species, and estimating group size during manned aerial surveys can be challenging. We used images obtained during a manned aerial survey over 13 non-consecutive days and covering a total of 3732 km of transects, in a high-wing aircraft flown at 185 km/h and an altitude of 152 m, equipped with three Single Lens Reflex cameras, to supplement estimates of dolphins recorded by observers. We used this opportunity to assess the adequacy of the resulting images for species identification of two morphologically similar dolphin species (i.e., with similar coloration and body size): the Indo-Pacific bottlenose dolphin (Tursiops aduncus) and the Australian humpback dolphin (Sousa sahulensis) and to accurately estimate dolphin group size. We also compared dolphin group estimates and species identification made by observers during the aerial survey, with manual review of the aerial images, where images overlapped with the observers’ field of view from the aircraft. We collected and reviewed 413000 images and found that an image resolution of 1.25 cm per pixel was adequate to distinguish between the two targeted dolphin species. Our findings suggest that aerial images can improve species identification and estimates of group size > 5. The camera setup and photography parameters that we describe here, with the survey altitude and speed that were used, has the potential to be used to identify, and accurately estimate other morphologically similar small-bodied (< 3 m) species in the marine environment. This will be an important consideration for future aerial surveys of low density, threatened species, that form small and sometimes mixed species groups, including humpback and bottlenose dolphins where their distribution overlaps.

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