Verification of fisheries sustainability credentials is essential to increase consumer confidence, market access and community benefit. Sector performance currently centres on monitoring fish stocks and economic performance. However, markets and stakeholder organisations increasingly require traceable evidence of Environmental, Social and Governance (ESG) indicators such as provenance, safety, diversity, animal welfare, carbon, biodiversity to inform decisions. To meet this need, this project activates CSIROs Healthcheck ESG Fisheries data system by engaging industry and Indigenous leaders, management agencies and researchers to identify targeted indicators, collect data, prioritise data gaps to enable more comprehensive ESG reporting. The reporting system is designed to collect and report data which is ready for ingestion into existing catalogues and exchanges (e.g., Ag Food data Exchange). Data is compatible and interoperable for publishing to recognised sustainability framework reporting (e.g. Status of Australian Fish Stocks, Marine Stewardship Certification, National Fisheries Plan, UN SDGs, Taskforce for Nature-related Financial Disclosure, Australian Agricultural Sustainability Framework, Agricultural Innovation Australia (AIA) Environmental Accounting Platform), and ready for supplying relevant indicators and data for Australia’s Fisheries and Aquaculture Sustainability Framework development. We demonstrate the capability of this sustainability data reporting system with selected fisheries. New indicators address climate impacts and adaptation responses, food safety systems, modern slavery protections, sector-led initiatives to improve ESG outcomes, Indigenous sector participation and economic development, among others. Using and building new digital and LLM (large language model) technologies to identify, screen and verify data sources, the sustainability reporting data system reflects global standards in traceability of data itself. Data provenance pipelines provide a pathway for repeatable, routine data extraction and reporting, and increase data accessibility for the Australian fisheries sector. Construction of these pipelines has highlighted critical gaps to address and what actions to take to overcome remaining limitations on data accessibility and shareability for key ESG reporting areas.

Robust prediction of population responses to changing environments requires the integration of factors controlling population dynamics with processes affecting distribution. This is true everywhere but especially in polar pelagic environments. Biological cycles for many polar species are synchronised to extreme seasonality, while their distributions may be influenced by both the prevailing oceanic circulation and sea-ice distribution. Antarctic krill (𝘌𝘶𝘱𝘩𝘢𝘶𝘴𝘪𝘢 𝘴𝘶𝘱𝘦𝘳𝘣𝘢) is one such species exhibiting a complex life history that is finely tuned to the extreme seasonality of the Southern Ocean. Dependencies on the timing of optimal seasonal conditions has led to concerns over the effects of future climate on krill’s population status, particularly given the species’ important role within Southern Ocean ecosystems. Under a changing climate, established correlations between environment and species may breakdown. Developing the capacity for predicting krill responses to climate change therefore requires methods that can explicitly consider the interplay between life history, biological conditions, and transport. The Spatial Ecosystem And Population Dynamics Model (SEAPODYM) is one such framework that integrates population and general circulation modelling to simulate the spatial dynamics of key organisms. Here, we describe a modification to SEAPODYM, creating a novel model – KRILLPODYM – that generates spatially resolved estimates of krill biomass and demographics. This new model consists of three major components: (1) an age-structured population consisting of five key life stages, each with multiple age classes, which undergo age-dependent growth and mortality, (2) six key habitats that mediate the production of larvae and life stage survival, and (3) spatial dynamics driven by both the underlying circulation of ocean currents and advection of sea-ice. Here we present the first results of KRILLPODYM, using published deterministic functions of population processes and habitat suitability rules. Initialising from a non-informative uniform density across the Southern Ocean our model independently develops a circumpolar population distribution of krill that approximates observations. The model framework lends itself to applied experiments aimed at resolving key population parameters, life-stage specific habitat requirements, and dominant transport regimes, ultimately informing sustainable fishery management. ____ This dataset represents KRILLPODYM modelled estimates of Antarctic krill circumpolar biomass distribution for the final year of a 12-year spin up. Biomass distributions are given for each of the five key life stages outlined above. The accompanying background, model framework and initialisation description can be found in the following reference paper: 𝗚𝗿𝗲𝗲𝗻 𝗗𝗕, 𝗧𝗶𝘁𝗮𝘂𝗱 𝗢, 𝗕𝗲𝘀𝘁𝗹𝗲𝘆 𝗦, 𝗖𝗼𝗿𝗻𝗲𝘆 𝗦𝗣, 𝗛𝗶𝗻𝗱𝗲𝗹𝗹 𝗠𝗔, 𝗧𝗿𝗲𝗯𝗶𝗹𝗰𝗼 𝗥, 𝗖𝗼𝗻𝗰𝗵𝗼𝗻 𝗔, & 𝗟𝗲𝗵𝗼𝗱𝗲𝘆 𝗣. (2023) KRILLPODYM: a mechanistic, spatially resolved model of Antarctic krill distribution and abundance. 𝘍𝘳𝘰𝘯𝘵𝘪𝘦𝘳𝘴 𝘪𝘯 𝘔𝘢𝘳𝘪𝘯𝘦 𝘚𝘤𝘪𝘦𝘯𝘤𝘦, 10 Article 1218003. https://doi.org/10.3389/fmars.2023.1218003