The marine benthic habitats of seabed areas between the reefs of Torres Strait were classified with data collected on epibenthos abundance, seagrass presence or absence, substrate type and water depth from 1984 to 1989 at 984 sites in an 12,347 square km study area in central Torres Strait. An idex of habitat diversity, calculated as the variety of distinct habitats within a 10 x 10 km window passed over the study area indicated that the areas with highest diversity of habitats were among the reefs and islands that formed two bands; one from Cape York to Buru Island, and the other from Cape York to Daru Island that included the Warrior reef complex. Refer to the compiled report: Long BG and Taranto TJ. (1997) Habitat Classification Of Seabed Areas Of Torres Strait, Northern Australia. CSIRO Division of Marine Research, QLD, Australia.

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

This dataset summarises benthic surveys of Western Torres, Torres Strait in December 2020 into 3 GIS shapefiles: (1) The site shapefile describes (a) seagrass presence/absence, (b) species composition, (c) algae cover and (d) benthic macro-invertebrate cover at 542 sites. (2) The meadow shapefile describes subtidal seagrass communities. (3) The interpolation shapefile describes variation in subtidal seagrass biomass across the survey area. Carter AB, McKenna SA and Shepherd L (2021), “Subtidal seagrass of western Torres Strait”, Centre for Tropical Water & Aquatic Ecosyormastem Research Report no. 21/11, James Cook University, Cairns, 36 pp. This project is a baseline survey of subtidal benthic habitats, including seagrass, algae and coral, in the Western Cluster of Torres Strait. Torres Strait’s Western Cluster is an ecologically important region due to extensive seagrass habitat, and high densities of turtle and dugong. This survey provides essential information to the TSRA, Australian and Queensland governments for dugong and turtle management plans, complementing dugong and turtle research studies in the region. The sampling methods used to study, describe and monitors seagrass meadows were developed by the TropWATER Seagrass Group and tailored to the location and habitat surveyed; these are described in detail in the relevant publications (https://research.jcu.edu.au/tropwater). 1 Location Sites were surveyed by helicopter. At each site latitude and longitude was recorded by GPS. Sediment type was recorded. 2 Seagrass metrics At each site observers estimated the percent cover of seagrass, then for three quadrats within each site using boat-based free diving or camera drop equipment, ranked seagrass biomass and estimated the percent contribution of each species to that biomass are provided. Seagrass above-ground biomass was determined using the “visual estimates of biomass” technique (Mellors 1991) using trained observers. This involves ranking seagrass biomass while referring to a series of quadrat photographs of similar seagrass habitats for which the above-ground biomass has been previously measured. Three separate biomass scales are used: low biomass, high biomass, and Enhalus biomass. The percent contribution of each seagrass species to total above-ground biomass within each quadrat is also recorded. At the completion of sampling each observer ranks a series of calibration quadrats. A linear regression is then calculated for the relationship between the observer ranks and the harvested values. This regression is used to calibrate above-ground biomass estimates for all ranks made by that observer during the survey. Biomass ranks are then converted to above-ground biomass in grams dry weight per square metre (g DW m-2). 3 Benthic macro-invertebrates At each site a visual estimate of benthic macro-invertebrate (BMI) percent cover was recorded each site according to four broad taxonomic groups: • Hard coral – All scleractinian corals including massive, branching, tabular, digitate and mushroom. • Soft coral – All alcyonarian corals, i.e. corals lacking a hard limestone skeleton. • Sponge. • Other BMI – Any other BMI identified, e.g. hydroid, ascidian, barnacle, oyster, mollusc. Other BMI are listed in the “comments” column of the GIS site layer. 4 Algae A visual estimate of algae percent cover was recorded at each site. When present, algae were categorised into five functional groups and the percent contribution of each functional group was estimated: • Erect macrophyte – Macrophytic algae with an erect growth form and high level of cellular differentiation, e.g. Sargassum, Caulerpa and Galaxaura species. • Erect calcareous – Algae with erect growth form and high level of cellular differentiation containing calcified segments, e.g. Halimeda species. • Filamentous – Thin, thread-like algae with little cellular differentiation. • Encrusting – Algae that grows in sheet-like form attached to the substrate or benthos, e.g.

Seagrass biomass, density and morphometrics were measured at sites around Groote Eylandt and Albatross Bay in the Gulf of Carpentaria. Environmental data such as depth were also collected at a number of sites. Transect data was collected to give a qualitative assessment of these parameters.

This dataset contains raw sampling data beginning in 1989 for a long-term environmental and seagrass monitoring station in Laguna Madre (“LM-151”). This project served to understand environmental drivers of long-term changes in seagrass condition within the Upper Laguna Madre, Texas. Environmental parameters measured within the water column include water depth, dissolved oxygen concentration, underwater light level, pH, salinity, Secchi depth (turbidity), water temperature, total suspended solids, and dissolved inorganic nitrogen and ammonium. Seagrass biological parameters measured are above/belowground biomass and shoot density. Typical seagrass species represented within the data include Halodule wrightii (shoal grass) and Syringodium filiforme (manatee grass). Sampling was dependent on funding/resource availability and local weather conditions, therefore temporal gaps in data may exist. Data are provided in CSV format.

These data provide a baseline inventory of submerged aquatic vegetation within Long Island's South Shore bays. The data were derived from conventional-color metric film diapositives obtained in June 2002 from the New York Department of State's Division of Coastal Resources. Benthic classifications follow the System for Classification of Habitats in Estuarine and Marine Environments (SCHEME). The study area spans approximately 443 square kilometers, extending from the west end of Long Beach Island in Nassau County eastward to Heady Creek at the east end of Shinnecock Bay in Suffolk County. The creation of this baseline inventory was a critical need identified in the Comprehensive Management Plan for the Long Island South Shore Estuary Reserve. Established following the state legislature's passage of the Long Island South Shore Estuary Reserve Act in 1993, the management plan aimed to protect and improve the estuary's ecosystem, enhance public access, and support sustainable economic activities. Ultimately, the goal was to sustain existing high-quality habitats and restore degraded areas to support the productivity of commercially and ecologically important estuarine species. The management plan also mandated a long-term monitoring program to evaluate progress toward estuarine resource improvement goals, building upon this foundational benthic habitat data. The layers available within the data download include biotic, geoform, and substrate. Partners: New York Department of State's Division of Coastal Resources

A supervised classification was applied to a Landsat TM5 image. This image was acquired 9:40 am, on the 27th July 2011 (5.14 am low tide at Brisbane Bar). The image classification was applied on areas of clear waters up to three metres depth and for exposed regions of Moreton Bay. Field validation data was collected at 4797 survey sites by UQ. GPS referenced field data were used as training areas for the image classification process. For this training the substrate DN signatures were extracted from the Landsat 5 TM image for field survey locations of known substrate cover, enabling a characteristic "spectral reflectance signature" to be defined for each target. The Landsat TM image, containing only those pixels in water < 3.0m deep, was then subject to minimum distance to means algorithm to group pixels with similar DN signatures (assumed to correspond to the different substrata). This process enabled each pixel to be assigned a label of either seagrass cover (0, 1-25 %, 25-50 %, 50-75 % and 75-100 %). The resulting raster data was then converted into a vector polygon file. Species information was added based on the field data and expert knowledge. Both polygon files were joined by overlaying features of remote sensing files with the EHMP field data to produce an output theme that contains the attributes and full extent of both themes. If polygons of remote sensing were within polygons of field data the assumption was made that the remote sensing polygon was showing more detail and the underlying field polygon was deleted.

This dataset shows a raster spatial model of the distribution and relative density of dugongs (Dugong dugong) in the Torres Strait region based on an aggregate of 24 years (1987 - 2011) of systematic aerial surveys. Aerial surveys were conducted using the strip transect method described by Marsh and Sinclair (1989). The survey region was divided into blocks containing systematic transects of varying length, which were typically perpendicular to the coast across the depth gradient. Tandem teams with two observers on each side of the aircraft independently recorded sightings of dugongs, including information on group size and calf numbers. Transects were 200 m wide at the water’s surface on either side of the aircraft. The spatial data from all the aerial surveys in the region (1987, 1991, 1996, 2001, 2005, 2006, and 2011) were corrected for differences in sampling intensity and area sampled between surveys. The corrected data was then interpolated using universal kridging over the spatial extent of the aerial surveys. The modelled abundance and distribution show the relative density of dugongs (areas where there are more or less dugongs) and NOT the absolute dugong density as corrections for perception bias (animals that are available to, but missed by, observers) and availability bias (animals that are unavailable to observers because of water turbidity) can only be applied at the spatial scale of entire surveys (thousands of square kilometres), making them inappropriate for the spatial scale for this dataset. Nonetheless, the relative densities among regions should be approximately comparable (H. Marsh, personal communication). Planning units were classified as low (1), medium (2), high (3) and very high (4) dugong density on the basis of the relative density of dugongs estimated from the models and a frequency analysis. Low density areas: 0 dugongs per square km; medium density areas 0.0015 - 0.25 dugongs dugongs per square km; high density areas 0.25 - 0.5 dugongs per square km; very high density areas > 0.5 dugongs per square km. The spatial model is 134x118 pixels with a pixel size of 2kmx2km and a spatial reference of WGS84 UTM Zone 54S. The original dataset is stored in ESRI GRID format (60 KB), which was converted to a GeoTiff for use in the eAtlas (26 kB). Both datasets are available under a creative commons attribution license.

The seabed habitat, marine plant and sessile megabenthos cover of the 50,000 km² area of the Torres Strait Protected Zone and adjacent shelf seabed was observed by a 500 m transect of a Drop-Camera video system at 173 sites, representing a wide range of known physical environments, during one 1-month-long voyage on the James Cook University vessel James Kirby. Continuous underway coding during transects recorded cover of 9 substrata types, 26 biohabitat types, and occurrence of 12 faunal classes. Laboratory post-analysis of the video recorded more detail at ~30 random frames per transect, including: 20 substratum types, 92 biological types -- the dataset comprises ~28,000 site-by-type records. In addition, during most transects, digital still photographs were taken at 5-15 second intervals with strobes, and CTD data were recorded.

A supervised classification was applied to a Landsat TM5 image. This image was acquired on the 8th August 2004, 15 minutes after low tide. The image classification was applied on areas of clear waters up to three metres depth and for exposed regions of Moreton Bay. Field validation data was collected at 2800 survey sites by UQ, 18 Seagrass-Watch sites and 60 Port of Brisbane Corporation survey sites. GPS referenced field data were used as training areas for the image classification process. For this training the substrate DN signatures were extracted from the Landsat 5 TM image for field survey locations of known substrate cover, enabling a characteristic "spectral reflectance signature" to be defined for each target. The Landsat TM image, containing only those pixels in water < 3.0m deep, was then subject to minimum distance to means algorithm to group pixels with similar DN signatures (assumed to correspond to the different substrata). This process enabled each pixel to be assigned a label of either seagrass cover (0, 1-25 %, 25-50 %, 50-75 % and 75-100 %). The resulting raster data was then converted into a vector polygon file. Species information was added based on the field data and expert knowledge. Both polygon files were joined by overlaying features of remote sensing files with the EHMP field data to produce an output theme that contains the attributes and full extent of both themes. If polygons of remote sensing were within polygons of field data the assumption was made that the remote sensing polygon was showing more detail and the underlying field polygon was deleted.