We investigated the effects of seabird presence and seasonality on ground-active spider community structure (activity-density, family-level richness, age class and sex structure) and composition at the family-level across five short-tailed shearwater breeding islands around south-eastern Tasmania, Australia. Using 75 pitfall traps (15 per island), spiders were collected inside, near and outside seabird colonies on each island, at five different stages of the short-tailed shearwater breeding cycle over a year. 3. Pitfall traps were deployed for a total of 2,674 days, capturing 1,592 spiders from 26 families with Linyphiidae and Lycosidae the most common.

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.

Invasive mammal eradications are widely used for managing island ecosystems. However, tracking the outcomes of such large-scale, whole ecosystem projects is challenging and costly, and monitoring all components of an ecosystem is near impossible. Instead, indicators of ecosystem change may provide more practical and integrated measures of ecosystem response to eradications. As high-order marine predators, seabirds subsidise island ecosystems with nutrients isotopically enriched in nitrogen. Invasive mammals have caused a global decline of seabirds on islands, reducing this nutrient subsidisation. Following eradications, nitrogen stable isotope analysis may provide a useful and resource-efficient indicator of ecosystem functional change on eradicated islands. However, isotope ratios are affected by a myriad of factors, with potential sources of variation being introduced by spatial and temporal variation in sampling, and within and between different taxa and ecosystem components. To correctly attribute isotopic change to post-eradication ecosystem function change, these confounding variables need to be understood. To address this need, we analysed stable isotopes of nitrogen in soil, plant, spider, and seabird guano samples collected at different distances from seabird colonies and at different stages of the short-tailed shearwater breeding cycle on six island sites around south-eastern Tasmania, Australia. Across these cool, temperate islands we detected no temporal variability in δ15N throughout the breeding season. However, there was notable spatial variability in δ15N values. The effects of seabird-derived nutrient subsidisation were highly localised with high δ15N values found inside seabird colonies and then rapidly decreasing from the colony boundary. Higher δ15N values also occurred in areas of higher burrow density within a colony. Variability in δ15N values also existed both within and between ecosystem components. Our results highlight the importance of context dependency when using ecological indicators and have important implications for the design, implementation and interpretation of studies employing stable isotopes as indicators for ecosystem change. We provide recommendations for designing future stable isotope studies on seabird islands.

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

The effect of ocean alkalinity enhancement on a coastal phytoplankton community was assessed via a microcosm experiment. The effect of alkalinity enhancement in two scenarios (i) when enclosed seawater was in equilibrium with atmospheric CO2 and (ii) when enclosed seawater was not in equilibrium with atmospheric CO2 were explored. Alkalinity was increased by ~497 umol/kg in these two treatments and plankton communities, carbonate chemistry, dissolved inorganic nutrients, particulate matter and chlorophyll a dynamics monitored over a 22 day period where a spring bloom occurred.

This record provides an overview of the NESP Marine and Coastal Hub project "Aerial survey of the Southern Right Whale ‘western’ sub-population off southern Australia". For specific data outputs from this project, please see child records associated with this metadata. -------------------- Southern right whales are listed as Endangered under the EPBC Act and are a species of national conservation significance. Monitoring of their recovery is guided by the Southern Right Whale Conservation Management Plan, which aims to improve the population’s conservation status through regular assessment of population size, calving intervals, and spatial trends. This project continued the long-term aerial survey program of southern right whales along the southern Australian coast, spanning from Perth (WA) to Ceduna (SA). Annual surveys have been conducted since 1993, providing a continuous long-term dataset for the 'western' population and supporting national assessments of connectivity with the smaller ‘eastern’ population. The surveys contribute essential data on population trends, calving rates, and movements of individuals. The August 2022 aerial survey ensured an uninterrupted time series in the long-term population trend data. This is particularly important given the species' non-annual breeding cycle (typically every three years). Annual surveys are essential to maintain an acceptable level of precision in estimating population trends and key life history parameters. A total of 526 whales were recorded, including 247 cow–calf pairs, 31 unaccompanied adults and one yearling. Based on long-term models, this equated to a population estimate of approximately 2,675 individuals, with an average annual growth rate of ~5.3%. While this represents a continued population growth, results suggest a possible slowing in the rate of growth over the past 13 years (from 7.5% in 2009). The 2022 survey also recorded the lowest number of unaccompanied animals in the entire time series, extending a five-year trend of low sightings in this group. Continued monitoring of the population is needed to assess whether these changes represent longer-term shifts in population dynamics and calving intervals, and to inform adaptive management for this long-lived, slow-recovering species. Outputs • Estimate of relative abundance and population trend compared to long-term aerial survey sightings [dataset] • Individual whale photo-identification data - 2021-22 season [imagery - published to ARWPIC] • Final technical report detailing overall numbers of southern right whales observed within the survey region, their gender (and life stage where possible) and spatial distribution of individuals [written]