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This data is part of the 2013 report "Synthesis of seagrass mapping studies conducted by the Water Science Branch of the Department of Water".
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.
The spatial and temporal dynamics of seagrasses have been studied from the leaf to patch (100 m**2) scales. However, landscape scale (> 100 km**2) seagrass population dynamics are unresolved in seagrass ecology. Previous remote sensing approaches have lacked the temporal or spatial resolution, or ecologically appropriate mapping, to fully address this issue. This paper presents a robust, semi-automated object-based image analysis approach for mapping dominant seagrass species, percentage cover and above ground biomass using a time series of field data and coincident high spatial resolution satellite imagery. The study area was a 142 km**2 shallow, clear water seagrass habitat (the Eastern Banks, Moreton Bay, Australia). Nine data sets acquired between 2004 and 2013 were used to create seagrass species and percentage cover maps through the integration of seagrass photo transect field data, and atmospherically and geometrically corrected high spatial resolution satellite image data (WorldView-2, IKONOS and Quickbird-2) using an object based image analysis approach. Biomass maps were derived using empirical models trained with in-situ above ground biomass data per seagrass species. Maps and summary plots identified inter- and intra-annual variation of seagrass species composition, percentage cover level and above ground biomass. The methods provide a rigorous approach for field and image data collection and pre-processing, a semi-automated approach to extract seagrass species and cover maps and assess accuracy, and the subsequent empirical modelling of seagrass biomass. The resultant maps provide a fundamental data set for understanding landscape scale seagrass dynamics in a shallow water environment. Our findings provide proof of concept for the use of time-series analysis of remotely sensed seagrass products for use in seagrass ecology and management.
[This data has been superseded by a synthesised global dataset which includes additional ecological data contributed by non-RLS entities (National Reef Monitoring Network). Please visit the corresponding NRMN Collection (IMOS - National Reef Monitoring Network Sub-Facility - Global off-transect species observations || Global reef fish abundance and biomass) for the most current version of this data. See "Downloads and Links" section below.] This dataset contains records of bony fishes and elasmobranchs collected by Reef Life Survey (RLS) divers along 50m transects on shallow rocky and coral reefs, worldwide. Abundance information is available for all records found within quantitative survey limits (50 x 5 m swathes during a single swim either side of the transect line, each distinguished as a Block), and out-of-survey records are identified as presence-only (Method 0). Although surveys are undertaken as part of monitoring programs at particular locations (mostly in Australia), this dataset contains does not include repeat surveys of sites.
2003 Habitat Maps derived from Grey Nurse Shark (GNS) Project of Wolf Rock, Double Island Point, Queensland, Australia
Surveying habitats critical to the survival of grey nurse sharks in South-East Queensland has mapped critical habitats, gathered species inventories and developed protocols for ecological monitoring of critical habitats in southern Queensland. This information has assisted stakeholders with habitat definition and effective management. In 2002 members of UniDive applied successfully for World Wide Fund for Nature, Threatened Species Network funds to map the critical Grey Nurse Shark Habitats in south east Queensland. UniDive members used the funding to survey, from the boats of local dive operators, Wolf Rock at Double Island Point, Gotham, Cherub's Cave, Henderson's Rock and China Wall at North Moreton and Flat Rock at Point Look Out during 2002 and 2003. These sites are situated along the south east Queensland coast and are known to be key Grey Nurse Shark aggregation sites. During the project UniDive members were trained in mapping and survey techniques that include identification of fish, invertebrates and substrate types. Training was conducted by experts from the University of Queensland (Centre of Marine Studies, Biophysical Remote Sensing) and the Queensland Parks and Wildlife Service who are also UniDive members. The monitoring methods (see methods) are based upon results of the UniDive Coastcare project from 2002, the international established Reef Check program and research conducted by Biophysical Remote Sensing and the Centre of Marine Studies. This record describes the digitised habitat features for Wolf Rock. View the original metadata record at https://doi.pangaea.de/10.1594/PANGAEA.864211 for the full data collection.
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
Abundance and distribution of coastal, inshore zooplankton in the Huon Estuary and D'Entrecasteaux Channel, Tasmania
Mesozooplankton community composition and structure were examined throughout the D’Entrecasteaux Channel, Huon Estuary and North West Bay, Tasmania, from November 2004 to October 2005. The composition of the mesozooplankton community was typical of inshore, temperate marine habitats, with seasonally higher abundance in summer and autumn and lower numbers in winter and spring. Copepods were the largest contributors to total abundance across all seasons and stations, while cladocerans and appendicularians were proportionally abundant in spring and summer. The faecal pellets of these three main groups, along with those of krill and amphipods, also contributed significantly to material recovered from sediment traps. Meroplanktonic larvae of benthic animals showed short-term peaks in abundance and were often absent from the water column for long periods. Spatially, North West Bay and the Channel had a higher representation of typically marine species, including Calanus australis and Labidocera cervi, while truly estuarine species, such as the copepod Gladioferens pectinatus, were more important in the Huon Estuary.
The spatial extent of the long spined sea urchin, Centrostephanus rodgersii, was estimated by divers using underwater visual census methods to survey rocky reef habitats at 13 regions along Tasmania's east coast (between Eddystone Point and Recherche Bay). Within each region 3 subsites were surveyed, and within each subsite 4 belt transects were surveyed. Divers recorded depth, percent substrata type, percent C. rodgersii barrens habitat; abundance of urchins (C. rodgersii and H. erythrogramma), rock lobster (J. edwardsii) and abalone (H. rubra). Divers also recorded algal cover (estimated), C. rodgersii barrens and substratum type using set categories (see below).
A pilot management program to maximise Tasmania's Sea Urchin (Heliocidaris erythrogramma) resource: Meredith Point, urchin density manipulations
The main aim of this research program was to determine the potential for reducing the density of urchins to encourage the return of seaweeds and an improvement in urchin roe quality and quantity from remaining urchins. Tasmanian Sea Urchin Developments used two widely-separated sub-tidal experimental lease areas. One of these areas was at Meredith Point, on the east coast, and the other at Hope Island, on the south coast. Both sites had been subject to some overgrazing by urchins. At Meredith Point, the study area was divided into plots containing urchins at three densities: artificially enhanced, continually harvested and control (undisturbed). At Hope Island, controlled clearings of urchins and limpets from barrens areas were conducted. Recovery of vegetation was monitored as well as urchin roe quality and quantity. The data represented by this record was collected at Meredith Point.
The data is the quantitative abundance of megafaunal invertebrates derived from underwater visual census methods involving transect counts at rocky reef sites around Tasmania. This data forms part of a larger dataset that also surveyed fish abundance and algal cover for the area. The aggregated dataset allows examination of changes in Tasmanian shallow reef floral and faunal communities over a decadal scale - initial surveys were conducted in 1992-1995, and again at the same sites in 2006-2007. There are plans for ongoing surveys. An additional component was added in the latter study - a boat ramp study looking at the proximity of boat ramps and their effects of fishing. We analysed underwater visual census data on fishes and macroinvertebrates (abalone and rock lobsters) at 133 shallow rocky reef sites around Tasmania that ranged from 0.6 - 131 km from the nearest boat ramp. These sites were not all the same as those used for the comparison of 1994 and 2006 reef communities. The subset of 133 sites examined in this component consisted of only those sites that were characterized by the two major algal (kelp) types (laminarian or fucoid dominated). Sites with atypical algal assemblages were omitted from the 196 sites surveyed in 2006. This study aimed to examine reef community data for changes at the community level, changes in species richness and introduced species populations, and changes that may have resulted from ocean warming and fishing. The methods are described in detail in Edgar and Barrett (1997). Primarily the data are derived from transects at 5 m depth and/or 10 m depth at each site surveyed. The underwater visual census (UVC) methodology used to survey rocky reef communities was designed to maximise detection of (i) changes in population numbers and size-structure (ii) cascading ecosystem effects associated with disturbances such as fishing, (iii) long term change and variability in reef assemblages.