The AusSeabed Survey Coordination Tool is a web-based tool that provides a central location for, and improve consistency in, the specification of bathymetric data acquisition for scientific research purposes in the Australian marine estate. It was developed to support more coordinated planning of bathymetric, seabed mapping and marine biodiversity data acquisition, recognising that these data are time-consuming and costly to collect and that future survey effort should be directed toward areas of shared end-user priority. This record describes the source code and associated mapping services for the AusSeabed Survey Coordination Tool, including the front-end submission tool and WMS/WFS services used to publish submitted spatial information. The tool was developed by Geoscience Australia and FrontierSI in collaboration with the broader seabed mapping community, supported by NESP Marine and Coastal Hub Project 1.2. The Survey Coordination Tool supports three key functions; 1) Survey planning: allows the marine research community to publicise planned surveys by submitting a spatial outline of the intended survey area, target data types, survey focus, chief investigator contact details and anticipated survey dates. Once published, these survey plans are visible through the upcoming surveys spatial layer on the AusSeabed portal. 2) Submission of Hydroscheme Industry Partnership Program (HIPP) requests: the tool hosts the online form for survey requests to the Australian Hydrographic Office for consideration under the Hydroscheme Industry Partnership Programme. 3) Areas of Interest submission: allows users to describe their seabed mapping or biodiversity characterisation data needs with spatial context. These submissions help identify regions of mutual interest, support collaborative multi-disciplinary survey planning, and inform high-value survey activities priorities for legacy data release. The current maintained source code is available from: https://github.com/ausseabed/survey-request-and-planning-tool The front-end of the tool is accessible to registered users at: https://coordination.ausseabed.gov.au This record provides a static archival snapshot of the source code associated with activities supported by NESP Marine and Coastal Hub Project 1.2. For the most current version of the source code, please refer to the GitHub repository.

Seagrass meadow extent and meadow-scape was mapped using three alternative approaches at Midge Point, a coastal turbid water habitat, in the central section of the Great Barrier Reef, in September/October 2017. Approach 1 included mapping meadow boundaries and meadow-scape during low spring tides on foot using a handheld Garmin GPS. Approach 2 was where the meadows were surveyed at low tide with observations from a helicopter, with observational spot-checks conducted at a number haphazardly scattered points. Approach 3 used PlanetScope Dove imagery captured on 09 October 2017 coinciding as close as possible to the field-surveys, with 3.7 m x 3.7 m pixels (nadir viewing) acquired from the PlanetScope archive. This record describes meadow extent data collected using Approach 3 (PlanetScope imagery). View the original metadata record at https://doi.org/10.1594/PANGAEA.946606 for the full data collection.

Seagrass meadow extent and meadow-scape was mapped using two alternative approaches at Green Island, a reef clear water habitat, in the Cairns section of the Great Barrier Reef, in November 2020. Approach 1 included mapping seagrass meadow-scape using imagery captured during low spring tides with a DJI Mavic 2 Pro UAV at an altitude of 100 m, with a resolution of 2.45cm/pixel. Approach 2 used PlanetScope Dove imagery captured on 05 November 2020 coinciding as close as possible to the field-surveys from 25 to 27 November 2020, with 3.7 m x 3.7 m pixels (nadir viewing) acquired from the PlanetScope archive. This record describes meadow extent data collected using Approach 2 (PlanetScope imagery). View the original metadata record at https://doi.pangaea.de/10.1594/PANGAEA.946605 for the full data collection.

***This record contains a subset of benthic habitat data from https://doi.org/10.25959/E4S6-GE74 (NESP MaC Project 3.6) rehosted for the purposes of the Seamap Australia collaborative project.*** Seagrass beds are a dominant marine ecosystem of Tayaritja (the Furneaux Group of Islands) in the north-eastern waters off Tasmania. Historical coarse mapping has indicated extensive beds of Posidonia, Amphibolis, Heterozostera, and Zostera species, potentially comprising some of the largest and deepest seagrass extents found in temperate Australian waters. However, limited data on the distribution and ecological value of these seagrass habitats represents a significant knowledge gap in understanding Australia's wetland natural assets. This project mapped the extent, ecological composition, population structure, and blue carbon value of seagrass beds around Tayaritja, in partnership with the Tasmanian Aboriginal Centre, as part of NESP Marine and Coastal Hub Project 3.6. The study area focused on the coastal waters surrounding Flinders Island in the western Furneaux Group, with mapping extending from the high tide line to the depth limit of reliable optical detection (approximately 30 m), based on analysis of field data and satellite imagery capabilities in the region. This metadata record specifically describes the benthic mapping component of the study. A combination of close-range remote sensing methods was used to map the extent and ecological values of seagrass beds. High-resolution satellite imagery from Sentinel-2 (10 m) sensors, combined with bathymetric LiDAR data and oceanographic variables, was used to map baseline seagrass extent and composition. A field campaign deployed a Benthic Observation Survey System (BOSS) and unBaited Remote Underwater stereo-Video system (stereo-uBRUV) at approximately 400 locations to validate remote sensing outputs, collecting field photo quadrats and rhizome cores. From these data, maps were produced showing the extent and coverage of seagrass, sand, and macroalgae, and where possible, seagrass species composition, subject to water depth and clarity constraints. See the "Lineage" section of this record for full methodology.

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.

An increasing number of studies are considering Fe and ligand concentrations, providing data of trace element availability across the remote Southern Ocean region (Ardiningsih et al., 2021, Gerringa et al., 2020, Hassler et al., 2017, Thuroczy et al., 2012, Thuroczy et al., 2011, Caprara et al., 2016 and references therein). However, studies seldom focus on polar coastal environments which are especially sensitive to climate-induced changes. To anticipate how these changes may impact Fe availability, we must first understand the drivers of ligand supply to the Antarctic coast and offshore. The newly compiled Southern Ocean Ligand (SOLt) Collection includes all publicly available Fe complexation datasets for the Southern Ocean including dissolved Fe concentrations, Fe-binding ligand concentrations, and complexation capacities for 25 studies between 1995 - 2019.