This record details the mapping of marine 'landforms' (geomorphic features), 'substrate type', and 'ecosystems' classified using multibeam echosounder and marine LiDAR data for the Commonwealth Solitary Islands Marine Park (SIMP) during 2022-23. Mapping was conducted using multibeam echosounder (MBES), towed-video and sediment surveys. A bathymetry mosaic was generated using data sourced from the NSW DCCEEW bathymetry mosaic (https://datasets.seed.nsw.gov.au/dataset/aa8f268e-a23d-4d27-b046-f60c45f8349b), updated with MBES data collected within SIMP in 2023. Coupled with sediment sampling and towed video surveys, the data was used to: 1) ground-truth the MBES data, 2) map the extent and characterise the diversity of unconsolidated seabed types; and, 3) map the extent of rocky reefs and characterise sessile invertebrate diversity within these reef-dominated areas. Seabed ‘landforms’ were derived from the bathymetry mosaic using the Seabed Landforms Classification Toolbox (Linklater et al. 2023). Landform features were subsequently grouped into 'hard' and 'soft' features according to the Seamap Australia National Benthic Habitat Classification Scheme (Lucieer et al. 2019), and additionally labelled with depth zonation to conform to the NESP Natural Values Common Language (Hayes et al. 2021). This package contains a synthesised seabed classification dataset, with three additional contextual datasets: • ‘SIMP_SeabedClassified’ defines seabed landforms, and reef and sediment areas delineated by depth intervals (10 m increments) classified according to the Parks Australia Natural Values Ecosystems and Seamap Australia Substratum component. See also https://datasets.seed.nsw.gov.au/dataset/f0e83f61-3790-4707-8dfe-2e505fbf3fd3 • ‘SIMP_BathyMosaicSources’ outlines the source coverages of the input bathymetric mosaic (also appended to the synthesised seabed classification dataset described above). See https://dx.doi.org/10.26186/149091 for access to bathymetry and backscatter survey data. • 'SIMP_TowedVideoSubClass' provides point classifications of the primary seabed substrate from still images derived from towed videos. See https://squidle.org/geodata/explore#map for annotated imagery. • 'SIMP_Sediments_Metadata' provides the location and associated metadata of sediment grabs. See https://pid.geoscience.gov.au/dataset/ga/69869 for access to the analysed sediment data in the MARS database. The 'Lineage' section of this record provides full methodology and a data dictionary. Surveys were funded by Parks Australia's Director of Marine Parks (Department of Climate Change, Energy, the Environment and Water) and completed under contract to the New South Wales Department of Climate Change, Energy, the Environment and Water. See Final Project Report: https://australianmarineparks.gov.au/static/734c97e56886d93a15c611222d227b33/amp-simp2024-report.pdf References: Lucieer, V., Barrett, N., Butler, C. et al. (2019). A seafloor habitat map for the Australian continental shelf. Sci Data 6, 120. https://doi.org/10.1038/s41597-019-0126-2 Hayes, K.R., Dunstan, P., Woolley, S. et al. (2021). Designing a targeted monitoring program to support evidence based management of Australian Marine Parks: A pilot on the South-East Marine Parks Network. Report to Parks Australia and the National Environmental Science Program, Marine Biodiversity Hub. Parks Australia, University of Tasmanian and CSIRO, Hobart, Australia, https://www.nespmarine.edu.au/system/files/Hayes%20et%20al_SS2_M8_D7_M4_Designing%20a%20targeted%20monitoring%20program%20to%20support%20evidence-based%20management%20of%20AMPs.pdf. Linklater, M, Morris, B.D. and Hanslow, D.J. (2023). Classification of seabed landforms on continental and island shelves. Frontiers of Marine Science, 10, https://doi.org/10.3389/fmars.2023.1258556.

Hardy Inlet was surveyed by underwater drop camera observations in December 2018 and January 2020. Seagrass species distribution and cover (as percentage cover in categories: 0, 1-10%, 10-25%, 25-50%, 50-75%, 75-90%, 90-100%) were recorded. The canopy height (in 10 cm intervals) and epiphytic cover (as low, medium, high) of seagrass was also estimated. Macroalgae distribution and cover (as percentage cover in categories: 0, 1-10%, 10-25%, 25-50%, 50-75%, 75-90%, 90-100%) were recorded. At approximately a third of the sites, physical water profiles and secchi depth were recorded. The datasets making up the Hardy Inlet Seagrass Survey data are: Hardy_Seagrass - point dataset Hardy_Seagass_Extent - polygon showing presence/absence derived from points Hardy_Seagrass_Cover - polygon showing percentage cover derived from points Layers: Hardy_Seagrass_Dec_2018 - Hardy_seagrass points where Year = '2018' Hardy_Seagrass_Jan_2020 - Hardy_seagrass points where Year = '2020' Hardy_Seagrass_Extent_2018 - Hardy_Seagrass_Extent where Year = '2018' Hardy_Seagrass_Extent_Jan_2020 - Hardy_Seagrass_Extent where Year = '2020' Hardy_Seagrass_Cover_2018 - Hardy_Seagrass_Cover where Year = '2018' Hardy_Seagrass_Cover_Jan_2020 - Hardy_Seagrass_Cover where Year = '2020'

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.

Hardy Inlet was surveyed by underwater drop camera observations in December 2018 and January 2020. Seagrass species distribution and cover (as percentage cover in categories: 0, 1-10%, 10-25%, 25-50%, 50-75%, 75-90%, 90-100%) were recorded. The canopy height (in 10 cm intervals) and epiphytic cover (as low, medium, high) of seagrass was also estimated. Macroalgae distribution and cover (as percentage cover in categories: 0, 1-10%, 10-25%, 25-50%, 50-75%, 75-90%, 90-100%) were recorded. At approximately a third of the sites, physical water profiles and secchi depth were recorded. The datasets making up the Hardy Inlet Seagrass Survey data are: Hardy_Seagrass - point dataset Hardy_Seagass_Extent - polygon showing presence/absence derived from points Hardy_Seagrass_Cover - polygon showing percentage cover derived from points Layers: Hardy_Seagrass_Dec_2018 - Hardy_seagrass points where Year = '2018' Hardy_Seagrass_Jan_2020 - Hardy_seagrass points where Year = '2020' Hardy_Seagrass_Extent_2018 - Hardy_Seagrass_Extent where Year = '2018' Hardy_Seagrass_Extent_Jan_2020 - Hardy_Seagrass_Extent where Year = '2020' Hardy_Seagrass_Cover_2018 - Hardy_Seagrass_Cover where Year = '2018' Hardy_Seagrass_Cover_Jan_2020 - Hardy_Seagrass_Cover where Year = '2020'

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.

Seascapes describing a layer of ecologically meaningful biophysical variable that spatially represent potential seabed habitats have been derived for the Australian margin and adjacent seabed in a new analysis of existing biophysical data. A total of 13 seascapes were derived for the continental shelf and nine seascapes for regions beyond the continental shelf using the unsupervised ISOCLASS classification in the software package ERMapper. The ecological significance of the seascapes is assessed at the national, regional and local scale using existing biological data. Options and avenues for future development are also described.