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    At the inception of our project, no study had examined particle fluxes in the Subantarctic Zone (SAZ) of the Southern Ocean, despite the fact that the SAZ represents a large portion of the total area of the Southern Ocean, serve as a strong sink for atmospheric (~1G t C yr-1 [Metzl et al., 1999]), and is central to hypotheses linking particle fluxes and climate change [Francois et al., 1997; Kumar et al., 1995; Sigman et al., 1999]. The SAZ serves as an interface between the cold nutrient-rich waters to its south and the nutrient-depleted subtropical gyres to its north. SAZ upper layers are marked by a thick layer of relatively homogenous Subantarctic Mode Water (SAMW), which overlies Antarctic Intermediate Water (AAIW). Both water masses are subducted northward beneath the subtropical gyres. Thus particles leaving the surface in these regions contribute to carbon redistribution via both the fraction that reaches the deep sea by settling and the fraction that is remineralized within SAMW or AAIW and subsequently subducted. The SAZ exhibits surface water carbon dioxide partial pressures well below atmospheric equilibrium, but PFZ waters are closer to atmospheric equilibrium in this sector [Metal et al., 1999; Poppet al., 1999]. The relative physical and biological contributions to these carbon dioxide partial pressure variations are unclear, but it is important to determine them because physical and biological carbon dioxide transfers are expected to show different responses to climate change [ Matear et al., 1999; Sarmiento and LeQuere, 1996]. For these reasons we focused on the SAZ and, for comparative purposes, on the PFZ to its south. We measured particle fluxes using moored sinking particle traps at three sites in the SAZ, in the PFZ, and beneath the Subantarctic Front (SAF), which separates them. This record describes particle flux data collected between 2000 and 2001. The NetCDF data contains the following variables. Please note not all variables are supplied in all files, specifically there are not uncertainty estimates and no quality control flags for this data. -----DATA DICTIONARY----- Name, description, units, standard name TIME, time, YYYY-MM-DD, time of sample midpoint TIME_START, time sample open, YYYY-MM-DD, time sample open NOMINAL_DEPTH, depth, m, nominal depth LATITUDE, latitude, degrees_north, latitude of anchor LONGITUDE, longitude, degrees_east, longitude of anchor pressure_actual, actual, dbar, actual pressure sample, sample number, 1, sample number sample_quality_control, quality flag for sample number, unitless, quality flag for sample number mass_flux, <1mm, mg m-2 d-1, particulate total mass flux mass_flux_uncertainty, uncertainty for particulate total mass flux, mg m-2 d-1,), uncertainty for particulate total mass flux mass_flux_quality_control, quality flag for particulate total mass flux, unitless, quality flag for particulate total mass flux SAL_BRINE, supernatant, 1, sample supernatant practical salinity SAL_BRINE_uncertainty, uncertainty for sample supernatant practical salinity, 1, uncertainty for sample supernatant practical salinity SAL_BRINE_quality_control, quality flag for sample supernatant practical salinity, unitless, quality flag for sample supernatant practical salinity pH_BRINE, supernatant, 1, sample supernatant pH NBS scale pH_BRINE_uncertainty, uncertainty for sample supernatant pH NBS scale, 1, uncertainty for sample supernatant pH NBS scale pH_BRINE_quality_control, quality flag for sample supernatant pH NBS scale, unitless, quality flag for sample supernatant pH NBS scale PC_mass_flux, <1mm, mg m-2 d-1, particulate total carbon mass flux PC_mass_flux_uncertainty, uncertainty for particulate total carbon mass flux, mg m-2 d-1, uncertainty for particulate total carbon mass flux PC_mass_flux_quality_control, quality flag for particulate total carbon mass flux, unitless, quality flag for particulate total carbon mass flux PN_mass_flux, <1mm, mg m-2 d-1, particulate total nitrogen mass flux PN_mass_flux_uncertainty, uncertainty for particulate total nitrogen mass flux, mg m-2 d-1, uncertainty for particulate total nitrogen mass flux PN_mass_flux_quality_control, quality flag for particulate total nitrogen mass flux, unitless, quality flag for particulate total nitrogen mass flux POC_mass_flux, <1mm, mg m-2 d-1, particulate organic carbon mass flux POC_mass_flux_uncertainty, uncertainty for particulate organic carbon mass flux, mg m-2 d-1, uncertainty for particulate organic carbon mass flux POC_mass_flux_quality_control, quality flag for particulate organic carbon mass flux, unitless, quality flag for particulate organic carbon mass flux PIC_mass_flux, <1mm, mg m-2 d-1, particulate inorganic carbon mass flux PIC_mass_flux_uncertainty, uncertainty for particulate inorganic carbon mass flux, mg m-2 d-1, uncertainty for particulate inorganic carbon mass flux PIC_mass_flux_quality_control, quality flag for particulate inorganic carbon mass flux, unitless, quality flag for particulate inorganic carbon mass flux BSi_mass_flux, <1mm, mg m-2 d-1, particulate biogenic silicon mass flux BSi_mass_flux_uncertainty, uncertainty for particulate biogenic silicon mass flux, mg m-2 d-1, uncertainty for particulate biogenic silicon mass flux BSi_mass_flux_quality_control, quality flag for particulate biogenic silicon mass flux, unitless, quality flag for particulate biogenic silicon mass flux TIME_END, time sample closed, YYYY-MM-DD, time sample closed Reference, citable reference DOI, DOI

  • These files contain the data recorded from a mesocosm experiment conducted in Bergen, Norway 2022 which assessed the effect of simualted mineral-based (silicate or calcium) ocean alkalinity enhancement (OAE) on diatom silicification. Ten mesocosms were used in total, divided into two groups either the silicate- or calcium based group and alkalinity was increased by either 0, 150, 300, 450 or 600 µmol L-1 above natrually occuring levels. The PDMPO-fluorescence (an appropriate proxy for silicification) of diatoms was recorded on eight seperate days during the experiment. Accompanying data includes measured; macronutrients (nitrate, nitrite, phophate, silicate), total alkalinity, biogenic silica in the water column and sediment trap.

  • 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.

  • 1. Workforce Tasmania’s commercial fishing industry workforce is defined as those people engaged in economic activity (work) within the sector across or at a given time, either in paid employment or self-employment. For fisheries this includes skippers and crew employed as sub-contractors and paid on a share of catch arrangement. It can include people engaged in unpaid work undertaken as part of these activities, although this has not been included in this assessment. Monitoring workforce changes is important because these changes indicate changes in social and economic benefits at a statewide and regional community level. Factors which affect workforce size include the extent to which a policy of maximizing technical efficiency is pursued through management, which typically reduces the fleet size and therefore the number of people employed. Other factors include the level of stock availability and access, the cost of entry into the fishery, and the financial profitability of fishing. Because of these factors, many fishers are engaged in employment in multiple fisheries or other marine sectors in order to supplement fishing incomes and pursue full-time employment. 1.1. Abalone The commercial harvesters catching abalone species operate within the Tasmanian Abalone Fishery. Assessment of workforce indicators is undertaken at fishery level. The data provided for this fishery is for the Tasmanian Abalone Fishery as a whole, which includes harvesting activity for this species as well as all other species caught in this fishery. 1.2. Commercial Dive species The commercial harvesters catching these species operate within the Tasmanian Commercial Dive Fishery. Assessment of workforce indicators is undertaken at fishery level. The data provided here is for the Tasmanian Commercial Dive species as well as all other species caught in this fishery. 1.3. Giant crab species The commercial harvesters catching giant crab operate within the Tasmanian Giant Crab Fishery. Assessment of workforce indicators is undertaken at fishery level. 1.4. Scalefish species The commercial harvesters catching this scalefish species operate within the Tasmanian Scalefish Fishery. Assessment of workforce indicators is undertaken at fishery level. The data provided here is for the Tasmanian Scalefish Fishery as a whole, which includes harvesting activity for this species as well as all other species caught in this fishery. 1.5. Scallop species The commercial harvesters catching species of scallop operate within the Tasmanian Scallop Fishery. Assessment of workforce indicators is undertaken at fishery level. 1.6. Southern rock lobster The commercial harvesters catching southern rock lobster operate within the Tasmanian Rock Lobster Fishery. Assessment of workforce indicators is undertaken at fishery level. 2. Workforce Indicators 2.1. Persons Workforce size (the total number of people directly employed) includes both skippers and crew, and those employed full time and part time. 2.2. Employment FTE The number of Full Time Equivalent (FTE) positions in each fishery is also estimated. This indicator shows that while a higher number of people may be employed in a fishery, some of these jobs may be part-time. Therefore, the number of FTEs is typically lower than the number of people in the workforce. In this iteration of the dataset, this value is unavailable for the abalone fishery in 2016, 2017, and 2019, and does not apply to the scallop fishery in any of the years available (2016-2020). 2.3. Active Supers The number of supervisors (skippers) employed in the fishery. 2.4. Harvest Units (TAS HP) The number of harvest units (combination of licensed vessel and fishing entitlement) active in a fleet and the number of people who actively harvest fish as supervisors (skippers) in a commercial fishery are directly linked to the size of the workforce in each fishery. In many cases, multiple supervisors may be linked to the same harvest unit, so the number of supervisors is often higher.

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    In collaboration with the Tasmanian State Emergency Service, water level monitoring instruments were installed to enable the collection of data in four estuaries identified as being vulnerable to coastal and compound flooding: Derwent Estuary, Huon Estuary, Georges Bay, and Macquarie Harbour. These instruments recorded fluctuations in water levels due to the combined influences of tide, river discharge, and weather events. The effects of the January 2022 Hunga Tonga-Hunga Ha’apai tsunami following a significant submarine volcanic explosion was also recorded in three out of the four estuaries. The datasets, comprising reduced water level observations, predicted water level, and residuals, are available from the IMAS Data Portal. Water level observations of varying duration were recorded between November 2020 – November 2022 for 14 sites in four Tasmanian estuaries. This work was undertaken by Karen Palmer as part of a PhD candidature at the University of Tasmania under the supervision of Dr Christopher Watson, Dr John Hunter, Assoc Prof Hannah Power (University of Newcastle), and Dr Rebecca Harris.

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    At the inception of our project, no study had examined particle fluxes in the Subantarctic Zone (SAZ) of the Southern Ocean, despite the fact that the SAZ represents a large portion of the total area of the Southern Ocean, serve as a strong sink for atmospheric (~1G t C yr-1 [Metzl et al., 1999]), and is central to hypotheses linking particle fluxes and climate change [Francois et al., 1997; Kumar et al., 1995; Sigman et al., 1999]. The SAZ serves as an interface between the cold nutrient-rich waters to its south and the nutrient-depleted subtropical gyres to its north. SAZ upper layers are marked by a thick layer of relatively homogenous Subantarctic Mode Water (SAMW), which overlies Antarctic Intermediate Water (AAIW). Both water masses are subducted northward beneath the subtropical gyres. Thus particles leaving the surface in these regions contribute to carbon redistribution via both the fraction that reaches the deep sea by settling and the fraction that is remineralized within SAMW or AAIW and subsequently subducted. The SAZ exhibits surface water carbon dioxide partial pressures well below atmospheric equilibrium, but PFZ waters are closer to atmospheric equilibrium in this sector [Metal et al., 1999; Poppet al., 1999]. The relative physical and biological contributions to these carbon dioxide partial pressure variations are unclear, but it is important to determine them because physical and biological carbon dioxide transfers are expected to show different responses to climate change [ Matear et al., 1999; Sarmiento and LeQuere, 1996]. For these reasons we focused on the SAZ and, for comparative purposes, on the PFZ to its south. We measured particle fluxes using moored sinking particle traps at three sites in the SAZ, in the PFZ, and beneath the Subantarctic Front (SAF), which separates them. This record describes particle flux data collected between 2004 and 2005. The NetCDF data contains the following variables. Please note not all variables are supplied in all files, specifically there are not uncertainty estimates and no quality control flags for this data. -----DATA DICTIONARY----- Name, description, units, standard name TIME, time, YYYY-MM-DD, time of sample midpoint TIME_START, time sample open, YYYY-MM-DD, time sample open NOMINAL_DEPTH, depth, m, nominal depth LATITUDE, latitude, degrees_north, latitude of anchor LONGITUDE, longitude, degrees_east, longitude of anchor pressure_actual, actual, dbar, actual pressure sample, sample number, 1, sample number sample_quality_control, quality flag for sample number, unitless, quality flag for sample number mass_flux, <1mm, mg m-2 d-1, particulate total mass flux mass_flux_uncertainty, uncertainty for particulate total mass flux, mg m-2 d-1,), uncertainty for particulate total mass flux mass_flux_quality_control, quality flag for particulate total mass flux, unitless, quality flag for particulate total mass flux SAL_BRINE, supernatant, 1, sample supernatant practical salinity SAL_BRINE_uncertainty, uncertainty for sample supernatant practical salinity, 1, uncertainty for sample supernatant practical salinity SAL_BRINE_quality_control, quality flag for sample supernatant practical salinity, unitless, quality flag for sample supernatant practical salinity pH_BRINE, supernatant, 1, sample supernatant pH NBS scale pH_BRINE_uncertainty, uncertainty for sample supernatant pH NBS scale, 1, uncertainty for sample supernatant pH NBS scale pH_BRINE_quality_control, quality flag for sample supernatant pH NBS scale, unitless, quality flag for sample supernatant pH NBS scale PC_mass_flux, <1mm, mg m-2 d-1, particulate total carbon mass flux PC_mass_flux_uncertainty, uncertainty for particulate total carbon mass flux, mg m-2 d-1, uncertainty for particulate total carbon mass flux PC_mass_flux_quality_control, quality flag for particulate total carbon mass flux, unitless, quality flag for particulate total carbon mass flux PN_mass_flux, <1mm, mg m-2 d-1, particulate total nitrogen mass flux PN_mass_flux_uncertainty, uncertainty for particulate total nitrogen mass flux, mg m-2 d-1, uncertainty for particulate total nitrogen mass flux PN_mass_flux_quality_control, quality flag for particulate total nitrogen mass flux, unitless, quality flag for particulate total nitrogen mass flux POC_mass_flux, <1mm, mg m-2 d-1, particulate organic carbon mass flux POC_mass_flux_uncertainty, uncertainty for particulate organic carbon mass flux, mg m-2 d-1, uncertainty for particulate organic carbon mass flux POC_mass_flux_quality_control, quality flag for particulate organic carbon mass flux, unitless, quality flag for particulate organic carbon mass flux PIC_mass_flux, <1mm, mg m-2 d-1, particulate inorganic carbon mass flux PIC_mass_flux_uncertainty, uncertainty for particulate inorganic carbon mass flux, mg m-2 d-1, uncertainty for particulate inorganic carbon mass flux PIC_mass_flux_quality_control, quality flag for particulate inorganic carbon mass flux, unitless, quality flag for particulate inorganic carbon mass flux BSi_mass_flux, <1mm, mg m-2 d-1, particulate biogenic silicon mass flux BSi_mass_flux_uncertainty, uncertainty for particulate biogenic silicon mass flux, mg m-2 d-1, uncertainty for particulate biogenic silicon mass flux BSi_mass_flux_quality_control, quality flag for particulate biogenic silicon mass flux, unitless, quality flag for particulate biogenic silicon mass flux TIME_END, time sample closed, YYYY-MM-DD, time sample closed Reference, citable reference DOI, DOI

  • Data to accompany publication on wild diet of southern rock lobster on the east coast of Tasmania. In this study we collected 64 lobsters and analysed the diet of each individual using stomach contents, stable isotope analysis and DNA identification of prey species in faecal samples.

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    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.

  • This record contains the R code and bibliographic data used in the publication 'Reciprocal knowledge exchange between climate-driven species redistribution and invasion ecology' (doi:10.21425/F5FBG60804). The aim of this study was to examine the current degree of cross-fertilisation between range shift ecology and invasion ecology, as a first step in determining the level of need for increasing connection between the two fields. To that end, here we examine (1) the structure and degree of similarity of themes explored within range shift and invasion ecology publications, (2) the extent that range shift and invasion publications draw on a common pool of research, and (3) the extent that range shift and invasion publications directly cite publications from the other field of study. This dataset includes: 1) R code used in the litsearchr package to generate a semi-automated search string, 2) publication data used for bibliographic analysis, and 3) R code used with the bibliometrix package for keyword co-occurrence analysis.

  • 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.