The abundance of macroinvertebrates associated with 28 experimental artificial reefs supporting different patch sizes and density of kelp (Ecklonia radiata) off Maria Island, Tasmania. Macroinvertebrates were assessed by diver-based visual census conducted between November 2015 and December 2016. This data was collected to examine how the patch size and density of kelp influences the establishment of macroinvertebrate assemblages.

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

1. Seabird species worldwide are integral to both marine and terrestrial environments, connecting the two systems by transporting vast quantities of marine-derived nutrients and pollutants to terrestrial breeding, roosting, and nesting grounds via the deposition of guano and other allochthonous inputs (e.g., eggs, feathers). 2. We conducted a systematic review and meta-analysis and provide insight into what types of nutrients and pollutants seabirds are transporting, the influence these subsidies are having on recipient environments, with a particular focus on soil, and what may happen if seabird populations decline. 3. The addition of guano to colony soils substantially increased nutrient levels compared to control soils for all seabirds studied, with cascading positive effects observed across a range of habitats. Deposited guano sometimes led to negative impacts, such a guanotrophication, or guano-induced eutrophication, which was often observed where there was an excess of guano or in areas with high seabird densities. 4. While the literature describing nutrients transported by seabirds is extensive, literature regarding pollutant transfer is comparatively limited, with a focus on toxic and bioaccumulative metals. Research on persistent organic pollutants and plastics transported by seabirds is likely to increase in coming years. 5. Studies were limited geographically, with hotspots of research activity in a few locations, but data were lacking from large regions around the world. Studies were also limited to seabird species generally listed as Least Concern on the IUCN Red List. As seabird populations are impacted by multiple threats and steep declines have been observed for many species worldwide, gaps in the literature are particularly concerning. The loss of seabirds will impact nutrient cycling at localised levels and potentially on a global scale as well, yet it is unknown what may truly happen to areas that rely on seabirds if these populations disappear. The information in this record includes three spreadsheets and R code. Descriptions are included below: - The spreadsheets contain all information extracted from the publications that were critically reviewed (n = 181). The first spreadsheet contains information regarding each publication (1 publication per row), such as study location, sampling methods. The second spreadsheet contains information about the seabird species studied in each publication (1 row per seabird species per publication). The third spreadsheet contains data for the meta-analysis (1 row per publication, except if the publication studied multiple species, then it would be 1 row per species per publication). - The R code is for the meta-analyses that were undertaken. Comments are included within the code plus detailed information can be found in the Methods section of the paper.

Data collected from Southern Ocean phytoplankton laboratory culture experiments to examine the effect of iron limitation on the Chlorophyll fluorescence (F) to chlorophyll (Chl) ratio. Irradiance levels at which cultures were grown are indicated by the photon flux density (PFD). Growth rates of Fe limited cultures (-Fe) relative to Fe replete cultures (+Fe) are referred to as μ / μmax (unitless).

This record described kelp growth and ecophysiological data relevant to the thermal tolerance of specific warm-tolerant and 'normal' family-lines of giant kelp (Macrocystis pyrifera) from Tasmania, Australia. Australia’s giant kelp forests are listed as a Threatened Ecological Community under the Environment Protection and Biodiversity Conservation Act 1999. Habitat restoration is a potential tool for the conservation and management of giant kelp ecosystems. For habitat restoration to be effective, the cause of habitat decline must be understood and overcome. This is problematic when climate change is driving habitat loss since it cannot be reversed or ameliorated prior to restoration. A previous NESP project led by this team (Project E7, Marine Biodiversity Hub) identified warm-tolerant strains of giant kelp from remnant patches in eastern Tasmania, where the species has experienced precipitous declines due to ocean-warming. These strains have high potential to assist with ‘future-proofing’ kelp forest restoration, however it is still unclear what the physiological mechanisms are that provide their improved thermal tolerance. This work cultivated the warm-tolerant strains of giant kelp previously identified, along with giant kelp strains of normal tolerance, at both cool (16 °C) and warm temperatures (20 °C). The juvenile kelp was then harvested, and a suite of physiological traits that may be responsible for their differences in thermal tolerance were examined. These included nutrient usage (carbon and nitrogen content), cellular membrane processes (fatty acid contents), and photosynthesis (PAM fluorometry and photosynthetic pigments). The cultivation trials again illustrated the improved ability of the warm-tolerant strains to develop at stressful warm temperatures relative to normal giant kelp. This work demonstrated for their first time that the improved thermal performance of these strains may extend to the development and fertilisation of the earlier kelp ‘gametophyte’ life-stage. Despite the clear differences in growth between the two test groups, the physiological assessments illustrated a complex pattern of responses, some of which are contrary to expected based on prior knowledge of thermal performance in kelps. Nonetheless, these results indicate that the warm-tolerant strains of giant kelp have a greater capacity to alter the composition of their fatty acids and may be more efficient users of nitrogen (a key nutrient for growth and development). This new information will help inform ongoing kelp breeding and selection programs for future-proofing kelp restoration in Australia and globally. The improved understanding of the physiology of kelp thermal tolerance might also help with identifying individuals and populations of Macrocystis, and other kelps, that may be resilient to (or especially threatened by) ocean warming and climate change.

Tidal wetlands are vulnerable to accelerated rates of sea-level rise projected by climate models. Assessing the resilience, or vulnerability, of these environments requires measurements of rates of vertical accretion, subsidence and elevation gain across a range of coastal settings. The Surface Elevation Table (SET) technique is applied globally to assess the extent of vertical adjustment of tidal wetlands to sea-level rise over decadal timescales. This enables measurement of whether wetlands are keeping pace with sea level rise (measured at tide gauges), or subsiding relative to local sea level rise and thus vulnerable to permanent inundation and loss. Australia’s network of Surface Elevation Tables is one of the most extensive in the world, consisting of over 200 benchmark monitoring stations from Westernport Bay, Victoria to Darwin Harbour, NT. This record describes the consolidated SET data collated from the Australian network (OzSET) as at 2022. This data can be used for analysing change to the elevation of wetlands wetlands at the study sites encompassed by OzSET.

Efforts to restore Posidonia seagrass meadows in NSW are reliant on collecting beachcast fragments as collection of donor material from extant beds is prohibited. However, to maximise the collection efforts it necessary to understand where to collect fragments from and what environmental conditions (e.g. wind direction, wind strength, tidal height) increase the availability of fragments and where to collect the most healthy fragments. This data set captures the abundance of fragments at 7 sites in Gamay (Botany Bay), an area of interest for restoration of Posidonia australis. It investigates how characteristics of wind (speed and direction), tidal height and swell (height, direction) influence the availability (abundance) and health (as determined by observations of necrosis) of shoots at sites throughout Botany Bay. The Excel data workbook is comprised of two sheets: Fragments_data sheet shows the number of P. australis fragments collected at different sites, when they were collected, and the environmental conditions at collection (see data attributes section). Shoot_data sheet shows the proportion of necrosis of shoots attached to collected fragments.

Decision-makers seek to account for the socioeconomic values of environmental assets. However, understanding the available frameworks and data can be a barrier. We address this here by summarising the data used across four case studies (3 geographic regions and 3 socio-economic value frameworks) to demonstrate what data are available and how they are applied to support decisions in varied contexts. See 'Lineage' section of this record for additional methodology.

Collection of processed BGC-Argo float profiles, used to calculate phytoplankton phenology from chlorophyll, phytoplankton carbon and nitrate.