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  • The prolonged rainfall reduction in South West Western Australia (SWWA) in recent decades has previously been reported to be unprecedented in the past 750 years. This rainfall reduction has reduced the water supply for both residents and agriculture in SWWA. However, the cause of this rainfall reduction is unclear. The relatively short length of the SWWA instrumental rainfall record limits long term studies of SWWA rainfall. In this study, SWWA rainfall is reconstructed based on a statistically significant negative correlation between SWWA rainfall and snowfall at Dome Summit South (DSS), East Antarctica. The 2000-year DSS snow accumulation record is used to reconstruct SWWA rainfall from 22 BCE to 2015 CE. With Cumulative Summation (CUSUM) analysis applied to the rainfall reconstruction, it is found that SWWA rainfall started to reduce around 1971 CE. This prolonged rainfall reduction is unprecedented during the past 750 years, but there have been two prior droughts of similar duration and intensity during the past 2000 years. Applying statistical techniques to compare the rainfall reconstruction with climate model simulations, it is found that greenhouse gases are likely to be the dominant driver of the SWWA rainfall drying trend after 1971 CE. This record describes the Honours Thesis [available for download in 'Online Resources' section of thsi record]. For the data generated by this project, see https://metadata.imas.utas.edu.au/geonetwork/srv/eng/catalog.search#/metadata/d7d5ea56-f972-435d-b44b-44fea598150c

  • The ETAS (Eastern TASmania) model is a high-resolution (~2 km in the horizontal) ocean model for eastern Tasmania, providing three-dimensional estimates of daily temperature, salinity, and circulation over the 1993-2014 period. This dataset consists of temperature, salinity, density, sea level, eastward (u) and northward (v) currents organised into timeseries files.

  • The ETAS (Eastern TASmania) model is a high-resolution (~2 km in the horizontal) ocean model for eastern Tasmania, providing three-dimensional estimates of daily temperature, salinity, and circulation over the 1993-2014 period. This dataset consists of hourly temperature, salinity, sea level, eastward (u) and northward (v) currents (at depth and vertically-averaged) at selected locations.

  • This projects' data on abalone translocations is stored in two databases containing data collected for abalone aggregation and larval surveys in Tasmania. Genetic information has also been generated using microsatellites.

  • Interaction uncertainties between tidal energy devices and marine animals have the potential to disrupt the tidal energy industry as it advances. Best-practices for environmental impact assessments (EIAs) must be explored that are able to provide conclusive recommendations for mitigating environmental impact concerns of tidal energy developments. As the tidal energy industry is moving closer to commercial-scale array installations, the development of standardised EIAs would allow for potential impact concerns for the marine environment to be identified and minimised early in the site-development process. In an effort to help formulate a standardised EIA framework that addresses knowledge gaps in fish-current interactions at tidal energy candidate sites, this study investigated changes in fish aggregations in response to tidal currents at a tidal energy candidate site in Australia prior to turbine installation. Here, we present the dataset collected for this study that includes tidal current information from Acoustic Doppler Current Profiler (ADCP) measurements, volume backscattering strength from a four-frequency biological echosounder (Acoustic Zooplankton and Fish Profiler – AZFP) as an indicator for fish biomass, and fish aggregation metrics calculated from volume backscatter in post-processing. ADCP and AZFP were installed on a bottom-mounted mooring and engaged in a concurrent sampling plan for ~2.5 months from December 2018 to February 2019. The mooring was deployed in the Banks Strait, a tidal energy candidate site located in the northeast of Tasmania, Australia, at a location favourable for tidal turbine installations considering current speed, depth, substrate, sediment type and proximity to shore. The ADCP dataset includes current velocity and direction measurements at a 1 m vertical and 1-sec time intervals. The raw AZFP dataset includes volume backscattering strength collected at 4-sec time intervals with a vertical resolution of 0.072 m in raw, and 0.1 m in pre-processed form. Fish aggregation metrics were derived in post-processing and are presented by the minute along with corresponding environmental conditions for current speed, shear, temperature, diel stage, and tidal stage compiled from both AZFP and ADCP datasets.

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    This dataset summarises 30 years of seagrass data collection (1984-2014) within the Great Barrier Reef World Heritage Area. The Meadow data describes seagrass at 1,169 individual or composite meadows. The data includes information on species, meadow type and age and reliability of the data. Data represented in this dataset has been collected by the TropWATER Seagrass Group and CSIRO in a GIS database. In making this data publicly available for management, the authors from the TropWATER Seagrass Group request being contacted and involved in decision making processes that incorporate this data, to ensure its limitations are fully understood. The site and meadow GIS available on eAtlas should be considered a “living” document that will be updated and modified as new data become available. Additional 'point' site data, and alternative download formats are available from eAtlas. https://eatlas.org.au/geonetwork/srv/eng/catalog.search#/metadata/77998615-bbab-4270-bcb1-96c46f56f85a A later version (2020) of this dataset including 35 years of data has been published under "Seagrass mapping synthesis: A resource for coastal management in the Great Barrier Reef (NESP TWQ Project 3.2.1 and 5.4, TropWATER, James Cook University)." https://eatlas.org.au/data/uuid/5011393e-0db7-46ce-a8ee-f331fcf83a88 Data Dictionary: Seagrass meadow data: - NRM_REGION: The NRM region in which the survey occurred. - SURVEY_DAT: Survey month and year, or a list of survey dates for meadows repeatedly sampled. - METHOD: Sampling and mapping methods – GPS/aerial photography, helicopter, walking, boat with camera, diver, grab and/or sled. - PERSISTENC: Meadows were classed according to four categories: Stable, Variable, Highly variable ephemeral, or Unknown. - MEADOW_LOC: Meadows were classed according to three categories, although some meadows cover a range of these locations: Intertidal, Shallow subtidal, or Deep subtidal. - DENSITY: Meadow density categories (light, dense, variable among years, unknown) were determined by the consistency of mean above-ground biomass of the dominant species among all years sampled. - DOMINANT_S: Dominant species and species present. - SPP_PRESEN: All species present. - MEAN_BIOMA: Mean meadow biomass in g DW m-2 (+ standard error if available), or the minimum and maximum biomass recorded for meadows sampled more than once. - AREA_HA: Meadow area in hectares (+ reliability estimate if available), or the minimum and maximum area recorded for meadows sampled more than once. - HECTARES: Total extent of meadow (HA) - PERCENT_CO: Meadow percent cover - this value represents mean seagrass percent cover, or the range of percent cover (if >1 number in the data cell). Meadow percent cover was most commonly calculated in pre-1990s surveys and recorded as “n/a” if not available. - CUSTODIAN: Data custodians - COMMENTS Meadow Persistence: - Stable: enduring meadow form; seagrass presence, biomass and area expected to be stable over time and seagrass meadow expected to be a permanent feature apart from extreme events or sustained long term impacts; - Variable: meadow presence, biomass and area expected to fluctuate within and among years, but generally some seagrass expected to be present apart from extreme events or sustained long term impacts; - Highly variable ephemeral: meadow not persistent over time; at some time periods seagrass will be present and at other times absent. Ephemeral meadows that have a naturally extreme level of variation in area and biomass within and among years; - Unknown: undetermined persistence as meadow sampled only once. Meadow Location: - Intertidal - all sites surveyed by helicopter or walking within a meadow and/or comments in field books identified an intertidal meadow, - Shallow subtidal - meadows where free divers SCUBA, sled collection, or cameras were used to sample and water depth was generally <10 m; - Deep subtidal - for this project meadows >10 m deep were included as deep subtidal.

  • This dataset summarises 40 years of seagrass data collection (1983-2022) within Torres Strait and the Gulf of Carpentaria. The full dataset is available through eAtlas at https://doi.org/10.26274/2CR2-JK51 and contains point and polygon packages. This record describes the meadow extent polygons at 641 individual or composite meadows. Managing seagrass resources in northern Australia requires adequate baseline information on where seagrass is (presence/absence), the mapped extent of meadows, what species are present, and date of collection. This baseline is particularly important as a reference point against which to compare seagrass loss or change through time. The scale of northern Australia and the remoteness of many seagrass meadows from human populations present a challenge for research and management agencies reporting on the state of seagrass ecological indicators. Broad-scale and repeated surveys/studies of areas are logistically and financially impractical. However seagrass data is being collected through various projects which, although designed for specific reasons, are amenable to collating a picture of the extent and state of the seagrass resource. In this project we compiled seagrass spatial data collected during surveys in Torres Strait and the Gulf of Carpentaria into a standardised form with point-specific and meadow-specific spatial and temporal information. We revisited, evaluated, simplified, standardised, and corrected individual records, including those collected several decades ago by drawing on the knowledge of one of our authors (RG Coles) who led the early seagrass data collection and mapping programs. We also incorporate new data, such as from photo records of an aerial assessment of mangroves in the Gulf of Carpentaria in 2017. This project was funded by the National Environmental Science Programme (NESP) Marine and Coastal Hub and Torres Strait Regional Authority (TSRA) in partnership with the Centre for Tropical Water and Aquatic Ecosystem Research (TropWATER), James Cook University. The project follows on from TropWATER’s previous work compiling 35 years of seagrass spatial point data and 30 years of seagrass meadow extent data for the Great Barrier Reef World Heritage Area (GBRWHA) and adjacent estuaries, funded through successive NESP Tropical Water Quality Hub Projects 3.1 (2015-2016) and 5.4 (2018-2020). These data sets are now publicly available through the eAtlas data portal: https://doi.org/10.25909/y1yk-9w85 . In making this data publicly available for management, the authors and data custodians request being contacted and involved in decision making processes that incorporate this data, to ensure its limitations are fully understood. Polygons in the meadow layer are drawn from extent data collected during some surveys. Not all surveys collected meadow extent data (e.g., Torres Strait lobster surveys). The seagrass meadow layer is a composite of all the spatial polygon data we could access where meadow boundaries were mapped as part of the survey. All spatial layers were compiled into a single spatial layer using the ArcToolbox ‘merge’ function in ArcMap. Where the same meadow was surveyed multiple times as part of a long-term monitoring program, the overlapping polygons were compiled into a single polygon using the ‘merge’ function in ArcMap. Because meadows surveyed more than once were merged, there were some cases where adjacent polygons overlap each other. Meadow Data Includes: 1. Temporal survey details – Survey month and year, or a list of survey dates for meadows repeatedly sampled; 2. Survey methods; 3. Meadow persistence – Classified into three categories: a. Unknown – Unknown persistence as the meadow was surveyed less than five times; b. Enduring – Seagrass is present in the meadow ≥90% of the surveys; c. Transitory – Seagrass is present in the meadow <90% of the surveys; 4. Meadow depth – Classified into three categories: a. Intertidal – Meadow was mapped on an exposed bank during low tide, e.g. Karumba monitoring meadow; b. Subtidal – Meadow remains completely submerged during spring low tides, e.g. Dugong Sanctuary meadow; c. Intertidal-Subtidal – Meadow includes sections that expose during low tide and sections that remain completely submerged, e.g. meadows adjacent to the Thursday Island shipping channel; 5. Dominant species of the meadow based on the most recent survey; 6. Presence or absence of individual seagrass species in a meadow; 7. Meadow density categories – Seagrass meadows were classified as light, moderate, dense, variable or unknown based on the consistency of mean above-ground biomass of the dominant species among all surveys, or percent cover of all species combined (see Table 2 in Carter et al. 2022). For example, a Halophila ovalis dominated meadow would be classed as “light” if the mean meadow biomass was always <1 gram dry weight m-2 (g DW m-2) among years, “variable” if mean meadow biomass ranged from <1 - >5 g DW m-2, and “dense” if mean meadow biomass was always >5 g DW m-2 among years. For meadows with density assessments based on both percent cover (generally from older surveys) and biomass, we assessed density categories based on the biomass data as this made the assessment comparable to a greater number of meadows, and comparable to the most recent data. Meadows with only one year of data were assigned a density category based on that year but no assessment of variability could be made and these are classified as “unknown”; 8. The minimum and maximum annual mean above-ground biomass measured in g DW m-2 (+ standard error if available) for each meadow is included for meadows with >1 year of biomass data. For meadows that were only surveyed once the mean meadow biomass (+ standard error if available) is presented as the minimum and maximum biomass of the meadow. “-9999” represents meadows where no above-ground biomass data was collected.; 9. The minimum and maximum annual mean percent cover is included for each meadow with >1 year of percent cover data. For meadows that were only surveyed once the mean meadow percent cover is presented as the minimum and maximum percent cover of the meadow. Older surveys (e.g., 1986 Gulf of Carpentaria surveys) used percent cover rather than biomass. For some surveys percent cover was estimated as discrete categories or ‘data binning’ (e.g., <10% - >50%). “-9999” represents meadows where no percent cover data was collected; 10. Meadow area survey details – The minimum, maximum and total area (hectares; ha) for each meadow: a. Total area - Total area of each meadow was estimated in the GDA 1994 Geoscience Australia Lambert projection using the ‘calculate geometry’ function in ArcMap. For meadows that were mapped multiple times, meadow area represents the merged maximum extent for all surveys. 11. Minimum and maximum area – For meadows surveyed more than once the minimum and maximum annual area of the meadow was reported. For meadows that were only surveyed once the meadow area is presented as the minimum and maximum area of the meadow. Where possible, we retained area data reported in the original shapefiles (and calculated using original projections). Where area data did not exist in original shapefiles (e.g., 1986 Gulf of Carpentaria surveys), we calculated area using the ‘calculate geometry’ function in ArcMap in the GDA 1994 Geoscience Australia Lambert projection; Data custodians. Spatial Limits: Data was limited to that collected in the Gulf of Carpentaria between Cape Arnhem (Northern Territory) and Cape York (Queensland). Torres Strait data was restricted to north of Queensland’s Great Barrier Reef World Heritage Area boundary, and includes data collected along the Papua New Guinea coastline. Taxonomy: Seagrass taxonomic revision over the last couple of decades has resulted in the revision and reclassification of some tropical species. For example, Zostera capricorni has been reclassified as Zostera muelleri subsp. capricorni, Halophila minor a synonym of Halophila ovalis, and Halodule pinifolia a synonym of Halodule uninervis. Field surveys have at times grouped species that are difficult to distinguish in the field. To address these issues, we amalgamated some species into complexes: Halophila minor and Halophila ovalis are included as Halophila ovalis complex. Zostera muelleri subsp. capricorni has been abbreviated to Zostera capricorni throughout for simplicity. Data Sets: Data was limited to that collected in the Gulf of Carpentaria between Cape Arnhem (Northern Territory) and Cape York (Queensland). Torres Strait data was restricted to north of Queensland’s Great Barrier Reef World Heritage Area boundary, and includes data collected along the Papua New Guinea coastline. Further information in the data sets included in this synthesis can be found in this report: Carter A, McKenna S, Rasheed M, Taylor H, van de Wetering C, Chartrand K, Reason C, Collier C, Shepherd L, Mellors J, McKenzie L, Roelofs A, Smit N, Groom R, Barrett D, Evans S, Pitcher R, Murphy N, Duke NC, Carlisle M, David M, Lui S, Torres Strait Indigenous Rangers (led by Pearson L, Laza T, Bon A), and Coles RG (2022). Four Decades of Seagrass Spatial Data from Torres Strait and Gulf of Carpentaria. Report to the National Environmental Science Program. Centre for Tropical Water and Aquatic Ecosystem Research (TropWATER), James Cook University. Limitations of the Data: Data included extends back to the mid-1980s. Large parts of the coast have not been mapped for seagrass presence since that time. Technology and methods for mapping and position fixing have improved dramatically in 40 years. This layer represents the most reliable interpretation of that early data. Not all surveys collected meadow data, including Torres Strait lobster and beche de mer surveys and Torres Strait intertidal transect monitoring collected only site data. Because of this, the meadow layer represents an underestimate of seagrass extent, particularly in deeper subtidal waters. The meadow layer should be used in conjunction with the site layer to determine where meadows have been mapped, and where seagrass surveys have occurred but no seagrass has been found. See Table 1 and Data age, Limitations and Variability section in Carter et al. (2022). Data Dictionary: Torres Strait and Gulf of Carpentaria seagrass meadows, 1984-2022 (polygon data) - ID (numeric) - Unique identifier representing a single meadow - SURVEY_NAME (text) – Name of survey location - SURVEY_DATE (text) – A list of survey date(s) for meadows repeatedly sampled; month and year - PERSISTENCE (text) – Meadow form on three categories: enduring, transitory, unknown - DEPTH (text) – Classified into three categories (intertidal, subtidal, intertidal-subtidal) - DENSITY (text) – Meadow density categories (light, moderate, dense, variable, unknown) - AREA (text) - Area of the composite meadow in a given location in hectares, represents merged maximum extent - SURVEY METHOD (text) – e.g., helicopter, walking, hovercraft, boat-based including camera, free diving, scuba diving, van Veen grab, sled net - COMMENTS (text) – Any comments for that site - CUSTODIAN (text) – Custodian/owner of the data set - NO_SURVEYS (numeric) – Number of times meadow has been surveyed - DOMINANT_SPP [DOM_SPP] (text) – The dominant species in the meadow - EA_PA (numeric) – Absence (0) or Presence (1) of Enhalus acoroides - TC_PA (numeric) – Absence (0) or Presence (1) of Thalassodendron ciliatum - TH_PA (numeric) – Absence (0) or Presence (1) of Thalassia hemprichii - CS_PA (numeric) – Absence (0) or Presence (1) of Cymodocea serrulata - CR_PA (numeric) – Absence (0) or Presence (1) of Cymodocea rotundata - ZC_PA (numeric) – Absence (0) or Presence (1) of Zostera muelleri subsp. capricorni - HU_PA (numeric) – Absence (0) or Presence (1) of Halodule uninervis - SI_PA (numeric) – Absence (0) or Presence (1) of Syringodium isoetifolium - HS_PA (numeric) – Absence (0) or Presence (1) of Halophila spinulosa - HT_PA (numeric) – Absence (0) or Presence (1) of Halophila tricostata - HO_PA (numeric) – Absence (0) or Presence (1) of Halophila ovalis - HC_PA (numeric) – Absence (0) or Presence (1) of Halophila capricorni - HD_PA (numeric) – Absence (0) or Presence (1) of Halophila decipiens - BIO_MIN (numeric) – The minimum mean meadow biomass measured in g DW m-2 for meadows sampled more than once, or -9999 where biomass data not recorded - BIO_MIN_SE (numeric) – Standard error of the minimum mean meadow biomass measured in g DW m-2, or -9999 where biomass data not recorded - BIO_MAX (numeric) – The maximum mean meadow biomass measured in g DW m-2 for meadows sampled more than once, or -9999 where biomass data not recorded - BIO_MAX_SE (numeric) – Standard error of the maximum mean meadow biomass measured in g DW m-2, or -9999 where biomass data not recorded - PER_CO_MIN (numeric) – The minimum mean meadow percent cover for meadows sampled more than once, or -9999 where percent cover data not recorded - PER_CO_MAX (numeric) – The maximum meadow percent cover for meadows sampled more than once, or -9999 where percent cover data not recorded - AREA_MIN (numeric) – The minimum area for meadows sampled more than once - AREA_MAX (numeric) – The maximum area for meadows sampled more than once

  • This record provides an overview of the scope and research output of NESP Marine Biodiversity Hub Project A6 - "Prioritisation of research and management needs for Australian elasmobranch species". For specific data outputs from this project, please see child records associated with this metadata. -------------------- NERP successfully demonstrated new ways to get the raw ingredients for evidence-based management of previously intractable species: abundance, survival, connectivity. But there is still a need to explore/demonstrate how management can use these tools (e.g. adaptive control of bycatch, or deciding if more monitoring is needed), and which species are suitable. This project comprises (i) a workshop to re-assess Australian shark and ray species in terms of degree-of-concern, state-of-knowledge-for-management, and feasibility-of-filling-knowledge-gaps; and (ii) a desk study exemplifying one pathway to management use. In 2016, we will work with DoE to prioritize species for research and explore more management pathways. Planned Outputs • A report outlining workshop findings, recommendations relative to data gaps and effective research approaches to address these gaps. • A paper demonstrating how management can use new methods to examine adaptive monitoring of bycatch to assess impact • Presentation of results to key stakeholders and end users

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    This dataset describes seagrass at 34 individual meadows from surveys of Dugong and Turtle habitats in the North-West Torres Strait for November 2015 and January 2016. The data includes information on seagrass species, biomass, diversity, and BMI and algae percent cover. This meadow (polygon) layer provides summary information for all survey sites within the 34 individual seagrass meadows mapped in 2015-2016 with information including individual meadow ID, meadow location (intertidal/shallow subtidal/subtidal), meadow density based on mean biomass, meadow area, dominant seagrass species, seagrass species present, survey dates, survey method, and data custodian. ESRI and Landsat satellite image basemaps were used as background source data to check meadow and site boundaries, and re-map where required. The data described by this record is current as of 01/12/2016 for use in the Seamap Australia project. Newer versions of the data, additional 'point' data for 853 sites, and alternative download formats are available from eAtlas. http://eatlas.org.au/geonetwork/srv/eng/metadata.show?uuid=034ce816-0777-4bbd-aefc-8b73bd540245

  • Bathymetry flythrough of Perth Canyon using data acquired by Schmidt Ocean Institute in 2015 on RV Falkor (University of Western Australia et al.). The flythrough highlights geomorphic features mapped by Geoscience Australia, including landslides, escarpments and bedform fields and biodiversity associated with the canyon (benthic and pelagic). Produced as a science communication product for the Marine Biodiversity Hub (National Environmental Science Program). This research is supported by the National Environmental Science Program (NESP) Marine Biodiversity Hub through Project D1.