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  • This dataset shows the location of the BEMP monitoring sites and was provided by EPA Tasmania. The Broadscale Environmental Monitoring Program (BEMP) was initiated in 2009 by the State Government to provide knowledge and information on ecosystem function in the D’Entrecasteaux Channel and Huon Estuary. A similar BEMP was established for Macquarie Harbour in 2011 and for Storm Bay in 2019. The objective of each program is to document (on an ongoing basis) broadscale spatial and temporal trends for key environmental parameters, allowing assessment of the environmental effects of finfish aquaculture in the region. Marine farming licence conditions include participation in respective BEMPs.

  • This is a collection of data in East Antarctica from Southern Elephant seal's between 2004 and 2009. The monthly data set has been further classified by polynya and year. Additionally, we provide a dataset of the polynyas contours defined following a criteria of 75% of sea-ice concentration for each individual month between 2004 and 2019. Data are provided in .mat format

  • Carbon and nitrogen isotope data for J. edwardsii lobsters from eight sites in SE Australia.

  • This data record contains findings from bottom trawl dredging by the FV Southern Champion voyage SC26 (April to May 2003). The petrographic, geochemical and geochronological data provided here was organised by Prof. Pat Quilty and Dr. Trevor Falloon. Further research is underway to characterise dredged granites and gneisses as part of a PhD project facilitated by the Institute for Marine and Antarctic Studies (IMAS), University of Tasmania. The metadata record will be updated to reflect further publications related to this dataset.

  • Southeastern Australia's marine waters are undergoing a trend of increased warming, surpassing the global average. This area has emerged as an alluring location for research on planktic microfossils, particularly dinoflagellate cysts, which are abundant in contemporary and Late Quaternary sediments. The composition of dinoflagellate cyst assemblages offers valuable information about the physical and biogeochemical properties of mid-latitude waters in this region. This study presents an analysis of cyst assemblages from marine sediment cores from waters inshore and offshore Maria Island, Tasmania, southeast Australia, up to 9 kyrs BP. The dominant cysts were Protoceratium reticulatum, Protoperidinium spp. (P. avellana, P. conicum, P.minutum, P. oblongum, P. subinerme, P. shanghaiense) and Spiniferites spp. (S. bulloideus, S. hyperacanthus, S. membranaceus, S. mirabilis, S. pachydermus, and S. ramosus). Inshore, Spiniferites spp. were more abundant (up to 61%), while P. reticulatum was dominant (up to 80%) at the offshore site. Impagidinium spp. and Nematosphaeropsis labyrinthus were exclusively detected offshore, with their increasing occurrence from 6 kyrs BP to present suggesting a transition from shallow coastal to stable deep-water habitat. Cysts of the Alexandrium tamarense complex were detected over the past 140 years and 9 kyrs BP at the inshore and offshore sites respectively, indicating an endemic long-term presence. Low abundances of Gymnodinium catenatum cysts were detected exclusively inshore from 50 years ago to present, suggesting recent bloom events. The limited southward penetration of the East Australian Current is indicated by the lack of warm-water cyst taxa such as Lingulodinium machaerophorum. Unlike coccolithophores, previously studied in the same sediment core, no discernible shift from cold to warm-water dinoflagellate cyst species was observed. The documentation of dinoflagellate cyst assemblages presented in this study will aid in predicting the effects of climate change, eutrophication, and introduction of novel species on local and broader community dynamics.

  • We compare the formulation and emergent dynamics of 11 CMIP6 IPCC marine biogeochemical models. We find that the largest source of uncertainty across model simulations of marine carbon cycling is grazing pressure (i.e. the phytoplankton specific loss rate to grazing). Variability in grazing pressure is driven by large differences in zooplankton specific grazing rates, which are not sufficiently compensated for by offsetting differences in zooplankton specific mortality rates. Models instead must tune the turnover rate of the phytoplankton population to balance large differences in top-down grazing pressure and constrain net primary production. We then run a controlled sensitivity experiment in a global, coupled ocean-biogeochemistry model to test the sensitivity of marine carbon cycling to this uncertainty and find that even when tuned to identical net primary production, export and secondary production remain extremely sensitive to grazing, likely biasing predictions of future climate states and food security.

  • 'Weather@home ANZ' is a global citizen science distributed computing project being run as part of the Oxford-based 'weather@home' project, which is part of ''. In this experiment, a detailed limited area (regional) climate model is embedded within the less detailed 'driving' global model. This higher-resolution regional model is able to tell us in unprecedented detail about potential changes to patterns of weather as climate changes. In the initial 'weather@home' experiment launched in 2010, the project team released this regional modelling capability for three regions: Europe, Southern Africa and the Western USA. This capability has been extended to other regions around the world and the first such new region to be developed was the Australasian region encompassing Australia, New Zealand and surrounding areas, which was launched to the public in 2014. This particular part of the project - 'weatherathome ANZ' - has received support from the University of Oxford (U.K.), the U.K. Met. Office, the Universities of Melbourne and Tasmania (Australia), the Tasmanian Partnership for Advanced Computing and the New Zealand National Institute for Water and Atmospheric Research (NIWA). 'weather@home' has also been supported by Microsoft Research.

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    Data pertain to analysis of GRACE time series of mass change in both gridded and basin format. The GRACE data are based on the COST-G solution obtained from Both 50km regular gridded data and basin-level time series are included, as well as the SAM and ENSO climate indices on which the regressions were based. The gridded datasets provided here include derived quantities from the regressions such as trends, uncertainties, and regression coefficients in NetCDF format.

  • 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 (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, D. B., Titaud, O., Bestley, S., Corney, S. P., Hindell, M. A., Trebilco, R., Conchon, A. and Lehodey, P. in review. KRILLPODYM: a mechanistic, spatially resolved model of Antarctic krill distribution and abundance. - Frontiers in Marine Science

  • Phytoplankton indirectly influence climate through their role in the ocean biological carbon pump. In the Southern Ocean, the subantarctic zone represents an important carbon sink, yet variables limiting phytoplankton growth are not fully constrained. Using three shipboard bioassay experiments on three separate voyages, we evaluated the seasonality of iron (Fe) and manganese (Mn) co-limitation of subantarctic phytoplankton growth south of Tasmania, Australia. We observed a strong seasonal variation in a phytoplankton Fe limitation signal, with a summer experiment showing the greatest response to Fe additions. An autumn experiment suggested that other factors co-limited phytoplankton growth, likely low silicic acid concentrations. The phytoplankton responses to Mn additions were subtle and readily masked by the responses to Fe. Using flow cytometry, we observed that Mn may influence the growth of some small phytoplankton taxa in late summer/autumn, when they represent an important part of the phytoplankton community. In addition, Mn induced changes in the bulk photophysiology signal of the spring community. These results suggest that the importance of Mn may vary seasonally, and that its control on phytoplankton growth may be associated with specific taxa.