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ABSTRACT The shifting climatic regime of maritime Antarctica is driving complex changes across trophic levels that are manifesting differentially across its resident species and regions. Land‐breeding pinnipeds have increased their seasonal attendance near Palmer Station since the earliest observations in the mid‐1900s, and Antarctic fur seals (Arctocephalus gazella) now represent a significant but unstudied predator population in the region during the austral summer. To characterize the timing of abundance and the fine‐scale distribution of this seasonal attendance, we carried out regular drone surveys of terrestrial habitats near Palmer Station in the austral summer of 2020. Using repeat animal counts and photogrammetric data products, we modeled fur seal abundance at survey sites over the period of observation, modeled habitat suitability based on fine‐scale topographic habitat characteristics, and estimated abundance across terrestrial habitats near Palmer Station as a function of these products. High habitat suitability was most associated with low‐slope and low‐elevation inshore terrain and with relatively dry, sun‐exposed, and wind‐sheltered locations, and estimated peak abundance occurred on March 11 (day 71) of 2020. Models estimated 2289–5544 (95% confidence interval) fur seals on land across all potential terrestrial habitats (41 discrete sites) near Palmer Station and Wylie Bay on the south coast of Anvers Island during peak abundance. This constitutes a first estimate of the aggregate timing, abundance, and distribution of Antarctic fur seals in the terrestrial habitats of this region—a critical first step in understanding the phenology and ecological role of this largely nonbreeding predator population. These findings additionally establish a baseline from which to estimate future changes in this seasonal population and its effects on sympatric terrestrial and marine biota, as the physical environment and food chain of the western Antarctic Peninsula transform under long‐term climatic changes. 

Body condition is a crucial and indicative measure of an animal’s fitness, reflecting overall foraging success, habitat quality, and balance between energy intake and energetic investment toward growth, maintenance, and reproduction. Recently, drone-based photogrammetry has provided new opportunities to obtain body condition estimates of baleen whales in one, two or three dimensions (1D, 2D, and 3D, respectively) – a single width, a projected dorsal surface area, or a body volume measure, respectively. However, no study to date has yet compared variation among these methods and described how measurement uncertainty scales across these dimensions. This associated uncertainty may affect inference derived from these measurements, which can lead to misinterpretation of data, and lack of comparison across body condition measurements restricts comparison of results between studies. Here we develop a Bayesian statistical model using known-sized calibration objects to predict the length and width measurements of unknown-sized objects (e.g., a whale). We use the fitted model to predict and compare uncertainty associated with 1D, 2D, and 3D photogrammetry-based body condition measurements of blue, humpback, and Antarctic minke whales – three species of baleen whales with a range of body sizes. The model outputs a posterior predictive distribution of body condition measurements and allows for the construction of highest posterior density intervals to define measurement uncertainty. We find that uncertainty does not scale linearly across multi-dimensional measurements, with 2D and 3D uncertainty increasing by a factor of 1.45 and 1.76 compared to 1D, respectively. Each standardized body condition measurement is highly correlated with one another, yet 2D body area index (BAI) accounts for potential variation along the body for each species and was the most precise body condition metric. We hope this study will serve as a guide to help researchers select the most appropriate body condition measurement for their purposes and allow them to incorporate photogrammetric uncertainty associated with these measurements which, in turn, will facilitate comparison of results across studies.