Fretwell, P. T. | bioRxiv Science

bioRxiv2025

Dynamic emperor penguin colonies

Here we report on the discovery of five emperor penguin breeding locations in West Antarctica, all of which have moved significant distances over the last decade. Three of the colonies could be considered previously undiscovered, while the other two are likely to be relocations due to ice shelf calving events. One of the sites, on the northwest tip of the Stancomb-Wills Ice Tongue has formed or significantly increased in size since 2022, possibly due to immigration from one or both of the two nearest colonies at Halley Bay and Stancomb-Wills colonies. Another colony near Case Island in the Bellinghausen Sea that has recently form is likely due to emigration from other colonies in the region that have suffered recent breeding failures due to early fast ice loss. We discuss the movement of these colonies and the implications for the wider metapopulation and our understanding of emperor penguins colony dispersal.

ecology↗

bioRxiv2025

Semi-Automated Seal Detection on the Western Antarctic Peninsula: An Unsupervised Machine Learning Approach for Detecting Ice Seals in Aerial Survey Data

Over the past 25 years, the Western Antarctic Peninsula (WAP) has experienced dramatic shifts in sea ice extent. This change has coincided with rapid alterations in ice-dependent ecosystems, including those supporting crabeater seals - the most abundant Antarctic seal and one of the largest mammalian consumers of krill. Despite their ecological importance, population estimates for ice seals remain scarce due to the difficulty of surveying large-scale, remote, ice-covered habitats. In 2023, during an abnormally low sea ice year, we conducted aerial surveys over Crystal Sound and Marguerite Bay during the end of the breeding season, flying over 1,000 km of transects. Seals were extremely sparse in the resulting imagery - occupying less than 1% of the surveyed area. This posed a significant challenge for both manual annotation and automated detection. Here we present a semi-automated, rules-based image analysis pipeline to substantially reduce human annotation time. Our method leverages hierarchical clustering with just two tunable parameters, avoiding the computational burden and opacity of deep learning models. Using this method, we identified 758 seals within a [~]350 km2 survey subset, achieving a test recall of 82%. In the absence of concurrent tagging data to estimate haul-out corrections, we refrain from extrapolating to a population estimate. However, the low observed densities highlight the urgent need for continued monitoring. Our improved data processing pipeline is a key step in facilitating the large-scale analysis required to inform conservation strategies for this key species.

ecology↗