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    This dataset contains predictions of the aboveground biomass density (AGBD) for Australia for 2020. Data were generated by the Global Ecosystem Dynamics Investigation (GEDI) NASA mission, which used a full-waveform LIDAR attached to the International Space Station to provide the first global, high-resolution observations of forest vertical structure. Data include both Level 4A (~25&nbsp;m footprints) and Gridded Level 4B (1&nbsp;km x 1&nbsp;km) Version 2. The Australian portion of the data was extracted from the original global datasets <a href="https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=2056">GEDI L4A Footprint Level Aboveground Biomass Density</a> and <a href="https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=2299">GEDI L4B Gridded Aboveground Biomass Density</a>.

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    The dataset consists of results from two stream mesocosm experiments that were conducted in the summer-autumn of 1996 and 1997 to distinguish the influence of fine sediment loads and nutrient concentrations on benthic macro-invertebrate and algal communities. 11 biological variables were extracted from the results of this experiment and were standardized for the purpose of training neural networks that could be used to diagnose nutrient and fine sediment impacts in field surveys. The 11 variables were selected according to how well they correlated with the experimental treatment levels (high and low values of both nutrients and fine sediments). The 11 variables were: chlorophyll a (mg/m2), macro-invertebrate familial richness, total abundance, and the abundance of <em>Oligochaeta, Leptoperla varia (Gripopterygidae), Nousia spp. (Leptophlebiidae), Austrophlebioides spp. (Leptophlebiidae), Orthocladiinae, Tanypodinae, Tipulidae</em> and larval <em>Scirtidae</em>. These taxa were abundant within and among the stream mesocosm communities and are common in a wide range of Tasmanian rivers. Values for each of 11 biological response variables were standardized by dividing by their average value observed in the experimental controls mesocosm samples from that year. See Magierowski RH, Read SM, Carter SJB, Warfe DM, Cook LS, Lefroy EC, et al. (2015) <i>Inferring Landscape-Scale Land-Use Impacts on Rivers Using Data from Mesocosm Experiments and Artificial Neural Networks.</i> PLoS ONE 10(3): e0120901. https://doi.org/10.1371/journal.pone.0120901 https://doi.org/10.1371/journal.pone.0120901. This data was collected for the purpose of training artificial neural networks that could diagnose nutrient and sediment impacts in Tasmanian rivers. Each of the 11 variables were standardized by their average value observed in the experimental control samples from that year and some experimental treatment effects (Light) were ignored to simplify the neural network training process. Therefore, these data should not be used to make conclusions about the impacts of fine sediments and nutrients in Tasmanian rivers.

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    <p>The dataset contains raw records on the frequency and % cover of Australian plant species stored in TERN's AEKOS as at 23 February 2017. There is information on basal area data in addition.The data includes plant records for the following datasets: [1] Australian Ground Cover Reference Sites Database, [2] Biological Survey of South Australia - Vegetation Survey - Biological Database of South Australia, [3] Atlas of NSW database: VIS flora survey module, [4] Queensland CORVEG Database, [5] TERN AusPlots Rangelands, [6] Transects for Environmental Monitoring and Decision Making (TREND) (2013-present) and the [7] TREND-Biome of Australia Soil Environments (BASE). </p> Soil samples for physical structure and chemical analysis (14 sites) throughout Australia were also incorporated in addition (starting 2013). The sites were: [1] AusCover Supersites SLATS Star Transects, [2] Biological Survey of the Ravensthorpe Range (Western Australia), [3] Biological Survey of South Australia - Roadside Vegetation Survey, [4] Biological Database of South Australia, [5] South-Western Australian Transitional Transect (SWATT), [6] Koonamore Vegetation Monitoring Project (1925-present), [7] Desert Ecology Research Group Plots (1990-2011) and Long Term Ecological Research Network (2012-2015), Simpson Desert, [8] Western Queensland, Australia (plants only) and [9] the TERN AusPlots Forest Monitoring Network - Large Tree Survey - 2012-2015. In total, 97,035 sites were extracted and downloaded for individual and population levels. The download package contains site location files, separate data files for individual and population levels, citation details for individual surveys and notes on how to interpret the download.

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    <p>The dataset comprises well-designed survey data from the first fuel load survey across 192 transects within the 48 AusPlots Forests, 1-ha monitoring plots across Australia. Data includes: [1] Site identifiers (ID and Site Name) and site- or transect- specific notes from the fuel survey campaign; [2] Transect survey dates; [3] Transect photograph numbers and attributes (Bearing, Slope and Aspect); [4] Fuel measurements (Grass and Litter height; Duff depth; Fine Woody fuel counts and Coarse Woody fuel counts and diameter; Projective cover for biomass components (Grass, Litter, Herbs, Vines and Shrubs), and Mass of biomass components (Grass, Litter, Herbs and Vines)); [5] Moisture content for biomass components (Grass, Litter, Herbs and Vines).</p> Descriptions of the data and coding protocols used in the database are explained in (a) the database itself; (b) the explanatory file attached to this dataset and (c) the Ausplots Forest Monitoring Network Manual. The protocols and coding used in this module are drawn directly from international forest fuel survey protocols and are consistent with other Australian forest fuel inventory methodologies.

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    The dataset comprises data from the first survey of ~24,000 large trees (>10 cm diameter at breast height; DBH) within 48 1 ha forest monitoring plots established across Australia between 2011 and 2015. Data includes: [1] Site identifiers (ID and Site Name); [2] Plot Establishment Dates; [3] Tree identifiers and descriptors (ID, Species, Status, Growth Stage, Crown Class); [4] Tree measurements (Diameter, Point of Measurement, Height, Location); [5] Comments and ancillary information; and [6] List of Metagenomic Sample Identifiers.

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    The dataset contains information from the first initial trial of the AusPlots Fauna Protocol conducted at Calperum Station, Renmark, South Australia. Selected proposed methodologies and fauna survey techniques were trialled for logistical purposes. After the field trials, the proposed methodologies and techniques were refined. The dataset contains species information on fauna species captures, observations, and specimen collections from the April-May 2015 field trials. The data can be used to review the outcomes of the survey methodologies, presence data of the species recorded, morphological details of the animals recorded, and relate field data to the whole specimen and tissue specimens collected. The Enhancing Long-term Surveillance Monitoring Across Australia Programme will enhance the breadth and depth of Australia's terrestrial ecosystem condition monitoring and reporting at national and regional scales through building on the Terrestrial Ecosystem Research Network (TERN) AusPlots Facility. Specifically, this will be achieved by increasing the range and type of AusPlots field sites and monitoring, and through providing guidelines, protocols manuals or standards that will enhance environmental data quality.

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    The TREND (PSRF)- Terrestrial Ecosystems project initiated a landscape-scale monitoring transect along the Adelaide Geosyncline region in southern Australia, initially spanning approximately 550 km. The aim was to examine spatial drivers of species composition and to isolate the influence of climate on whole vegetation community composition and therefore inform on-going monitoring of the impact of climate change. Specific questions were: 1. What are the most important spatial drivers of species and phylogenetic composition along landscape-scale environmental gradients? 2. Can the answer to Question 1. inform selection of suitable spatial analogues for climate change? 3. How can a framework for assessing spatial drivers be used to monitor and interpret shifts in species composition due to climate change? The dataset consists of site and species records (see attachments) for plots established along the Adelaide Geosyncline for the TREND project. Data consist of vascular plant species composition (presence-abundance/absence) within 900m<sup>2</sup> plots plus site data, including aspect and soil properties. Data have been used to analyze changes in composition with geographic and environmental differences and as a baseline for monitoring.