This dataset contains images from 21 individual Antarctic research campaigns between 1985 and 2019, collated as part of the Antarctic Seafloor Annotated Imagery Database (AS-AID). A total of 632,252 expert annotations are associated with these seafloor images. This dataset is a static snapshot of the AS-AID images at the time of publication of the data descriptor. All images are also accessible from https://data.imas.utas.edu.au/imagery/IMAS_Antarctic/ The dataset contains images and associated tracklog metadata for each research campaign.

This dataset contains image annotations for 21 Antarctic research campaigns between 1985 and 2019, recorded as part of the Antarctic Seafloor Annotated Imagery Database (AS-AID). This snapshot of the AS-AID annotations comprises of a total of 632,252 expert annotations. Annotations are based on the Collaborative and Automated Tools for Analysis of Marine Imagery (CATAMI) classification scheme and have been reviewed by experts. Three annotation sets exist for each campaign: point grid annotations of 108 points per image, bounding box annotations for mobile fauna, and bounding box annotations for select benthic species which are classified as Vulnerable Marine Ecosystem species. All annotations are also accessible through Squidle+ by following this persistent link: https://squidle.org/geodata/explore/map?filters=%7B%22platform_ids%22%3A%5B%2222%22%5D%7D This dataset can be used to investigate species distributions, community patterns, provide a reference to assess change through time, and can be used to train algorithms to automatically detect and annotate marine fauna.  

These data were collected in 2024 for a PhD project, including the dissolved trace metal data and particulate trace metal data from the diatom-Antarctic krill experiment. The particulate trace metal data were collected from diatom culture grown in the lab and Antarctic krill culture sourced from AAD.

Globally, terrestrially-breeding marine predators have experienced shifts in species distribution, prey availability, breeding phenology, and population dynamics due to climate change. These central-place foragers are restricted within proximity of their breeding colonies during the breeding season, making them highly susceptible to any changes in both marine and terrestrial environments. While ecologists have developed risk assessments to assess likely climate risk in various contexts, these often overlook critical breeding biology data. To address this knowledge gap, we developed a trait-based risk assessment framework, focusing on the breeding season and applying it to marine predators breeding in parts of Australian territory and Antarctica. Our objectives were to quantify climate change risk, identify specific threats, and establish an adaptable framework. The assessment considered 25 criteria related to three risk components: vulnerability, exposure, and hazard, while accounting for uncertainty. We employed a scoring system that integrated a systematic literature review and expert elicitation for the hazard criteria. Monte Carlo sensitivity analysis was conducted to identify key factors contributing to overall risk. Our results identified shy albatross (Thalassarche cauta), southern rockhopper penguins (Eudyptes chrysocome), Australian fur seals (Arctocephalus pusillus doriferus), and Australian sea lions (Neophoca cinerea) with high climate urgency. Species breeding in lower latitudes as well as certain eared seal, albatross, and penguin species were particularly at risk. Hazard and exposure explained the most variation in relative risk, outweighing vulnerability. Key climate hazards affecting most species include extreme weather events, changes in habitat suitability, and prey availability. We emphasise the need for further research, focusing on at-risk species, and filling knowledge gaps (less-studied hazard criteria, and/or species) to provide a more accurate and robust climate change risk assessment. Our findings offer valuable insights for conservation efforts, given monitoring and implementing climate adaptation strategies for land-dependent marine predators is more feasible during their breeding season.

This project aimed to identify and map critical habitats for Australian sea lions (Neophoca cinerea) to assess the ecological value of different habitats, and identify risks to their populations. Video imagery, GPS, time-depth and accelerometer/magnetometer data was captured from eight adult female Australian sea lions from Olive Island (n=4) on the western Eyre Peninsula and Seal Bay (n=4) on Kangaroo Island in South Australia. Sea lions were instrumented with animal-borne cameras with integrated accelerometers/magnetometers (CATS Cam, 135 x 96 x 40 mm, 400 g) and satellite-linked GPS loggers with integrated time-depth recorders (SPLASH-10, Wildlife Computers, 100 x 65 x 32 mm, 200 g). Sea lions were sedated and anaesthetised and bio-logging instruments were glued to the pelage on the dorsal midline. Bio-logging instruments were recovered after a single foraging trip (~1-6 days). The data collected in this project provides fundamental information on critical benthic habitats for Australian sea lions, the differences in foraging behaviour of individual sea lions, and their prey preferences. This information improves our understanding of threats to sea lion populations and will support future conservation actions to recover the species.