Institute for Marine and Antarctic Studies
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This file contains data and associated R code for producing the figures, tables and analysis/models within the manuscript Ferderer et al., Carbonate chemistry fitness landscapes inform diatom resilience to future perturbations. Data was collected at IMAS by Aaron Ferderer.
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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. For habitat restoration to be effective, the cause of habitat decline must be understood and overcome. But 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, identified warmwater-tolerant strains of giant kelp (Macrocystis pyrifera) from remnant patches in eastern Tasmania, where the species has experienced severe declines over the past half-century due to climate change and ocean-warming. While these strains have high potential to assist with ‘future-proofing’ of kelp forest restoration activities, it is still unclear what the physiological mechanisms are that provide their improved thermal tolerance. Here we cultivated the warm-tolerant giant kelp strains, along with giant kelp strains of normal tolerance, at both cool (16 °C) and warm temperatures (20 °C). We then harvested the juvenile kelp, and examined a suite of physiological traits that may be responsible for their differences in thermal tolerance, including 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. For the first time, we also demonstrate that their improved thermal performance 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, our 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. This 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.
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This record describes the outputs of two different modelling exercises that were used to characterise the seafloor habitats for temperate Australian waters. The modelled area includes all shelf waters (<250m depth) in southern Australia south of the Tropic of Capricorn. Bioregional benthic habitat maps were constructed using (1) the Geoscience Australia 250m 2023 grid (ref); (2) ground-truthing observations derived from horizontally facing imagery from stereo-BRUV and BOSS camera systems; and (3) several physical datasets as covariates in model development (all oceanographic variables smoothed to 250m resolution). Source data is available from Geoscience Australia's eCat: https://doi.org/10.26186/148758 (bathymetry), Squidle+: (benthic imagery annotations), and (3) AODN Portal: https://portal.aodn.org.au/search (IMOS oceanographic datasets). The specific subset of GA observations used in this modelling exercise is available from https://github.com/UWA-Marine-Ecology-Group-projects/nesp-2.1/blob/main/data/tidy/NESP-2.1_broad-habitat.csv. See the NESP Mac Project 2.1 final report for a description of the sampling design for ground-truthing observations and annotation technique. -----Functional Reef model (binomial)----- This model discriminates ‘functional reef’ from sediment (non-reef) ecosystem types. Functional reef is defined by this project as “any seabed area functioning as a reef, which may include dense beds of sessile invertebrates or molluscs”. This term was chosen because much of the continental shelf is dominated by sediment yet is stable enough to support emergent sessile biota that provide structure and resources for “reef-affiliated” species. The modelling approach uses a Bayesian representation of a Binomial generalised linear model. For ground-truthing benthic annotations, the following benthic categories were collapsed into the ‘functional reef’ classification: sessile invertebrates, bare rocky reef (consolidated), macroalgae, Amphibolis spp. and Thalassodendron spp. All other benthic classifications were assigned to the ‘non-reef’ category. -----Ecosystem Component model (multinomial)----- This model discriminates between five broad habitat types (hereafter ‘ecosystem components’): seagrass, macroalgae, sessile invertebrates, bare consolidated substrata, bare unconsolidated substrata. The modelling approach uses a Bayesian implementation of a Multinomial generalised linear model. For ground-truthing benthic annotations, benthic annotations for mobile species (e.g. echinodermata) were discarded. All remaining annotations were collapsed into the five broad ecosystem components. A selection of mapping (WMS) services are listed in the 'Downloads & Links' section of this record. See the 'Lineage' section for a full description of the data packages available for download, and for more visualisation options.
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This dataset contains oceanographic profiles of dissolved iron (DFe) at the Southern Ocean Time Series collected during voyages IN2018_V02 and IN2019_V02 aboard RV Investigator, CSIRO Marine National Facility. Profiles of DFe were collected using a 12-bottle trace metal rosette (TMR; Seabird Scientific) equipped with acid washed, externally sprung Niskin bottles (Ocean Test Equipment). GEOTRACES cleaning, sampling, analytical and intercalibration procedures (Cutter et al., 2017) were followed where possible. Full sampling and analytical details for these profiles can be found in Ellwood et al., (2020a) and Ellwood et al., (2020b)
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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: • ‘Marine geomorphology, ecosystems and substrates’ defines seabed landforms, and reef and sediment areas delineated by depth intervals (10 m increments). See also https://datasets.seed.nsw.gov.au/dataset/f0e83f61-3790-4707-8dfe-2e505fbf3fd3 • ‘Bathymetry sources’ 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. • 'Towed video substrate classification' 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. • 'Sediments retrieval' 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. 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.
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This dataset is a geomorphic classification of the shelf features surrounding Lord Howe Island and Balls Pyramid. This dataset provides information on the size, extent and type of features which occur around the shelves, which can be used for a broad range of marine planning and research purposes. Shelves were classified into shelf region (inner, mid, outer) and geomorphic features. Features include an extensive submerged fossil reefs, ridges and patch reefs, sandy basins, paleochannels, modern fringing reef, shallow lagoon, shelf edge terraces and shelf break. Broad seafloor features were visually interpreted through digitisation in ArcGIS v10.1 using terminology consistent with international nomenclature and national standards. The classification of geomorphic features extends upon the interpretation of Balls Pyramid shelf undertaken by Linklater et al. (2015). Full description of methods is outlined in the following open-access publication, accessible by the following link: https://doi.org/10.3390/geosciences8010011