Tidal wetlands are vulnerable to accelerated rates of sea-level rise projected by climate models. The Surface Elevation Table (SET) is a technique applied globally to assess the extent of vertical adjustment of tidal wetlands to sea-level rise over decadal timescales. This record describes the SET data from the Australian network (OzSET). This data can be used for analyzing wetlands elevation change at the study sites

Biological ocean data collected from ships find reuse in aggregations of historical data. These data are heavily relied upon to document long term change, validate satellite algorithms for ocean biology and are useful in assessing the performance of autonomous platforms and biogeochemical models. There is a need to combine subsurface biological and physical data into one aggregate data product to support reproducible research. Existing aggregate products are dissimilar in source data, have largely been isolated to the surface ocean and most omit physical data. These products cannot easily be used to explore subsurface bio-physical relationships. We present the first version of a biological ocean data reformatting effort (BIO-MATE, https://gitlab.com/KBaldry/BIO-MATE). BIO-MATE uses R software that reformats openly sourced published datasets from oceanographic voyages. These reformatted biological and physical data from underway sensors, profiling sensors and pigments analysis are stored in an interoperable and reproducible BIO-MATE data product for easy access and use.

Out-of-range observations of significant rafts of giant kelp (Macrocystis pyrifera) washing ashore in southern NSW in winter 2020. On 9 August 2020, two local marine naturalists on the south coast of New South Wales, Australia noticed a significant amount of a large unfamiliar kelp washed up on a local beach. Following some quick confirmations via phone and email, it was revealed that the unfamiliar seaweed was giant kelp (Macrocystis pyrifera): a species whose closest known populations are ~450 km away to the south (in Tasmania and western Victoria) and whose transport to New South Wales would have required oceanic rafting over several weeks and hundreds of kilometres against the prevailing south-flowing East Australian Current. Subsequent community-led searches over the following days confirmed four more locations of often-substantial amounts of giant kelp wrack, as well as many more anecdotal and unconfirmed accounts.

We investigated the effects of seabird presence and seasonality on ground-active spider community structure (activity-density, family-level richness, age class and sex structure) and composition at the family-level across five short-tailed shearwater breeding islands around south-eastern Tasmania, Australia. Using 75 pitfall traps (15 per island), spiders were collected inside, near and outside seabird colonies on each island, at five different stages of the short-tailed shearwater breeding cycle over a year. 3. Pitfall traps were deployed for a total of 2,674 days, capturing 1,592 spiders from 26 families with Linyphiidae and Lycosidae the most common.

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

Benthic habitat annotations of stereo Baited Remote Underwater Video (Stereo-BRUV) and panoramic drop camera imagery, were completed as part of a report funded by the NESP Marine & Coastal Hub. This report focussed on an IUCN II zone in the South-west Corner Marine Park off the 'Capes region' near Margaret River. These data were analysed in TransectMeasure using a modified version of the CATAMI scheme.

This data is from the 2021 Seeds for Snapper season which is a community volunteer seed based seagrass restoration program located in Perth, Western Australia. It details the effort that went into the collection of Posidonia australis seagrass fruit including number of divers, number of shore support personnel, volunteered hours, and fruit collection metrics (volume, estimated number).

This is a database of nature-based ("soft" or "hybrid") coastal protection projects from across Australia. The database will act as a tool to help coastal managers make informed decisions by providing as many examples as possible of where these solutions have been used, how they were used and how effective they are in different contexts. The information in this database was collected through direct engagement with coastal managers and stakeholders, a literature search, and informal internet searching.