ORCID Profile
0000-0003-2059-4355
Current Organisations
University of Hawai'i at Mānoa
,
University of Hawai'i
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Publisher: Wiley
Date: 06-2023
DOI: 10.1002/ECE3.10082
Abstract: Understanding the population health status of long‐lived and slow‐reproducing species is critical for their management. However, it can take decades with traditional monitoring techniques to detect population‐level changes in demographic parameters. Early detection of the effects of environmental and anthropogenic stressors on vital rates would aid in forecasting changes in population dynamics and therefore inform management efforts. Changes in vital rates strongly correlate with deviations in population growth, highlighting the need for novel approaches that can provide early warning signs of population decline (e.g., changes in age structure). We tested a novel and frequentist approach, using Unoccupied Aerial System (UAS) photogrammetry, to assess the population age structure of small delphinids. First, we measured the precision and accuracy of UAS photogrammetry in estimating total body length (TL) of trained bottlenose dolphins ( Tursiops truncatus ). Using a log‐transformed linear model, we estimated TL using the blowhole to dorsal fin distance (BHDF) for surfacing animals. To test the performance of UAS photogrammetry to age‐classify in iduals, we then used length measurements from a 35‐year dataset from a free‐ranging bottlenose dolphin community to simulate UAS estimates of BHDF and TL. We tested five age classifiers and determined where young in iduals ( years) were assigned when misclassified. Finally, we tested whether UAS‐simulated BHDF only or the associated TL estimates provided better classifications. TL of surfacing dolphins was overestimated by 3.3% ±3.1% based on UAS‐estimated BHDF. Our age classifiers performed best in predicting age‐class when using broader and fewer (two and three) age‐class bins with ~80% and ~72% assignment performance, respectively. Overall, 72.5%–93% of the in iduals were correctly classified within 2 years of their actual age‐class bin. Similar classification performances were obtained using both proxies. UAS photogrammetry is a non‐invasive, inexpensive, and effective method to estimate TL and age‐class of free‐swimming dolphins. UAS photogrammetry can facilitate the detection of early signs of population changes, which can provide important insights for timely management decisions.
Publisher: Wiley
Date: 13-07-2023
Abstract: Researchers can investigate many aspects of animal ecology through noninvasive photo–identification. Photo–identification is becoming more efficient as matching in iduals between photos is increasingly automated. However, the convolutional neural network models that have facilitated this change need many training images to generalize well. As a result, they have often been developed for in idual species that meet this threshold. These single‐species methods might underperform, as they ignore potential similarities in identifying characteristics and the photo–identification process among species. In this paper, we introduce a multi‐species photo–identification model based on a state‐of‐the‐art method in human facial recognition, the ArcFace classification head. Our model uses two such heads to jointly classify species and identities, allowing species to share information and parameters within the network. As a demonstration, we trained this model with 50,796 images from 39 catalogues of 24 cetacean species, evaluating its predictive performance on 21,192 test images from the same catalogues. We further evaluated its predictive performance with two external catalogues entirely composed of identities that the model did not see during training. The model achieved a mean average precision (MAP) of 0.869 on the test set. Of these, 10 catalogues representing seven species achieved a MAP score over 0.95. For some species, there was notable variation in performance among catalogues, largely explained by variation in photo quality. Finally, the model appeared to generalize well, with the two external catalogues scoring similarly to their species' counterparts in the larger test set. From our cetacean application, we provide a list of recommendations for potential users of this model, focusing on those with cetacean photo–identification catalogues. For ex le, users with high quality images of animals identified by dorsal nicks and notches should expect near optimal performance. Users can expect decreasing performance for catalogues with higher proportions of indistinct in iduals or poor quality photos. Finally, we note that this model is currently freely available as code in a GitHub repository and as a graphical user interface, with additional functionality for collaborative data management, via Happywhale.com.
Location: United States of America
No related grants have been discovered for Philip Patton.