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Abstract Aerial photogrammetry is a popular non‐invasive tool to measure the size, body morphometrics and body condition of wild animals. While the method can generate large datasets quickly, the lack of efficient processing tools can create bottlenecks that delay management actions. We developed a machine learning algorithm to automatically measure body morphometrics (body length and widths) of southern right whales (Eubalaena australis, SRWs) from aerial photographs (n = 8,958) collected by unmanned aerial vehicles in Australia. Our approach utilizes two Mask R‐CNN detection models to: (i) generate masks for each whale and (ii) estimate points along the whale's axis. We annotated a dataset of 468 images containing 638 whales to train our models. To evaluate the accuracy of our machine learning approach, we compared the model‐generated body morphometrics to manual measurements. The influence of picture quality (whale posture and water clarity) was also assessed. The model‐generated body length estimates were slightly negatively biased (median error of −1.3%), whereas the body volume estimates had a small (median error of 6.5%) positive bias. After correcting both biases, the resulting model‐generated body length and volume estimates had mean absolute errors of 0.85% (SD = 0.75) and 6.88% (SD = 6.57), respectively. The magnitude of the errors decreased as picture quality increased. When using the model‐generated data to quantify intra‐seasonal changes in body condition of SRW females, we obtained a similar slope parameter (−0.001843, SE = 0.000095) as derived from manual measurements (−0.001565, SE = 0.000079). This indicates that our approach was able to accurately capture temporal trends in body condition at a population level. 

Abstract During their nonbreeding period, many species of swallows and martins (family: Hirundinidae) congregate in large communal roosts. Some of these roosts are well-known within local birdwatching communities; however, monitoring them at large spatial scales and with day-to-day temporal resolution is challenging. Community-science platforms such as the Purple Martin Conservation Association’s project MartinRoost and eBird have addressed some of these challenges by centralizing data collected from regional communities. Additionally, due to the high densities of birds within these aggregations, their early morning dispersals are systematically detected by weather radars, which have also been used to collect data about roost timing and location. An important issue, however, limits spatiotemporal scope of previous radar-based studies: finding the roost signatures on millions of rendered reflectivity images is extremely time-consuming. Recent advances in computer vision, however, have allowed us to reduce this effort. The rise of this technology makes it necessary that we assess whether our biological definition of a roost matches what the machine-learning models are capturing. We do so by comparing eBird detections of roosts in the Great Lakes region with those obtained by a human-supervised machine-learning model from 2000 to 2022. With more than two decades of data, we assess the ability of these two tools to detect roosts on a day-to-day basis, and we compare the phenology of dispersals to investigate whether radar detections correspond to swallow and martin roosts or if they are associated with other well-known birds that form large aggregations. Our comparison of these datasets strongly suggests that swallows and martins are responsible for the dispersals we observe on the radars from July to late September; however, the alternative species we examined could be causing some of the detections in October.