We used Digital Soil Mapping (DSM) technologies combined with the real-time collations of soil attribute data from TERN's recently developed Soil Data Federation System, to produce a map of Australian Soil Classification Soil Order classes with quantified estimates of mapping reliability at a 90m resolution.

<p>This database contains occurrence data for vertebrates across the Australian Wet Tropics. Species occurrence point data has been collected during field intensive surveys using a variety of sampling methods as well as from the literature and institutional databases. The records are divided into two tables: Misc_records and STD_records. The first contains records collated opportunistically, as well as records collected from literature. The latter is a collection of standardized surveys conducted by Steve E. Williams (JCU). </p> <p> All occurrences were vetted for positional and taxonomic accuracy, and for sensitivity at the state and national levels. Sensitive species records are withheld or have their location generalised following sensitive species rules for processing these records. </p>

This dataset includes volumetric soil water content measured across soil pits in the lowland rainforest of Cape Tribulation. Data were acquired using time-domain reflectometry (TDR) probes recording at soil surface (10 cm) and at depths (50, 100 and 150 cm) at 4 control points - PB1 and PB8 are in the SW quadrant of the crane plot, PB2 and PB5 are in the NW quadrant of the crane plot.

This data release consists of flux tower measurements of the exchange of energy and mass between the surface and the atmospheric boundary-layer in semi-arid eucalypt woodland using eddy covariance techniques. It been processed using PyFluxPro (v3.3.0) as described in Isaac et al. (2017), <a href="https://doi.org/10.5194/bg-14-2903-2017">https://doi.org/10.5194/bg-14-2903-2017</a>. PyFluxPro takes data recorded at the flux tower and process this data to a final, gap-filled product with Net Ecosystem Exchange (NEE) partitioned into Gross Primary Productivity (GPP) and Ecosystem Respiration (ER). For more information about the processing levels, see <a href="https://github.com/OzFlux/PyFluxPro/wiki">https://github.com/OzFlux/PyFluxPro/wiki</a>. <br /> <br /> The Tumbarumba flux station is located in the Bago State Forest in south eastern New South Wales. It was established in 2000 and is managed by CSIRO Marine and Atmospheric Research. The forest is classified as wet sclerophyll, the dominant species is Eucalyptus delegatensis, and average tree height is 40m. Elevation of the site is 1200m and mean annual precipitation is 1000mm. The Bago and Maragle State Forests are adjacent to the south west slopes of southern New South Wales and the 48,400 ha of native forest have been managed for wood production for over 100 years. The instrument mast is 70m tall. Fluxes of heat, water vapour and carbon dioxide are measured using the open-path eddy flux technique. Supplementary measurements above the canopy include temperature, humidity, wind speed, wind direction, rainfall, incoming and reflected shortwave radiation and net radiation. Profiles of temperature, humidity and CO2 are measured at seven levels within the canopy. Soil moisture content is measured using Time Domain reflectometry, while soil heat fluxes and temperature are also measured. Hyper-spectral radiometric measurements are being used to determine canopy leaf-level properties. The Tumbarumba flux station is supported by TERN and the DCCEE through the ACCSP. <br />For additional site information, see https://www.tern.org.au/tern-observatory/tern-ecosystem-processes/tumbarumba-wet-eucalypt-supersite/. <br /><br />

We selected nine study sites, each incorporating three vegetation states: (a) fallow cropland, representing the restoration starting point, (b) planted old field (actively restored site), and (c) reference York gum (E. loxophleba) woodland. Plant species richness and cover All annual and perennial plant species were recorded in spring 2017 within each plot and identified to genus and species level where possible. Nomenclatures follow the Western Australian Herbarium (2017). A point intercept method previously demonstrated to provide objective and repeatable measures of cover (Godínez-Alvarez, Herrick, Mattocks, Toledo & Van Zee 2009; Prober, Standish & Wiehl 2011) was used to quantify cover of individual plant species, total vegetation cover and substrate types (i.e., bare ground, litter cover, plant cover). Ground cover, individual species, and canopy cover intercepting at every 2 m along four parallel, evenly spaced 50 m transects across each plot were recorded using a vertically placed dowel (8 mm wide, 2 m tall), resulting in 100 intercepting points per plot. For planted old fields, transects were placed parallel to planting rows, with two centred on rows and two centred between rows. This approximately represented the relative abundance of planted rows and non-planted inter-rows. If a species was recorded in the plot but did not intercept the dowel on any transect it was assigned 0.5 points. This method provided a measure of relative abundance (percentage cover) of plant species across the plot. To calculate species richness and cover across different life history and growth forms, species were classified into the following groups: total, native trees, native shrubs, native non – planted shrubs, native grasses, native perennial forbs, native annual forbs, exotic grasses and exotic annual forbs using the Western Australian Herbarium (2017) classification. Woody debris and leaf litter surveys Leaf-litter dry mass was estimated by collecting leaf-litter from five randomly placed 25 cm x 25 cm quadrats along two 50 m transects across each plot. Litter was stored in paper bags for transportation and then oven dried for 36 hours at 60 °C. The dried litter was weighed to 3 decimal points. Cover of fine and coarse woody debris and litter depth was estimated at every meter along two 20 m transects for each plot. Woody debris was classified by diameter. Length, max and min diameter was measured for all logs with a diameter greater than 10 cm.

<p>Field measurements were made on the 5^th September 2000 along a 90 m long transect for a section of mangroves located between the beach and main road just north of Cape Tribulation (Queensland).</p> <p>The transect was 90 m long and 10 m wide. The start and end points for the transect were measured using a hand-held GPS. Since tree cover reduced the GPS signal at the start and end points, GPS measurements were taken near the start and end points of the transect using the GPS (where the canopy was more open), then a distance and bearing made to the end points. For trees within the transect area having a circumference at breast height (CBH) greater than 15 cm, their location, CBH and an estimate of height were measured. Only the location was recorded for trees having a CBH below 15 cm (equivalent to a diameter at breast height (DBH) < 5cm). Tree height was measured <em>ad libitum</em> with a hand-held laser system. Mangrove species was also recorded when known.</p>

This dataset includes upper and lower thermal limits, voluntary exposure to extreme cold and warm temperatures, ATP levels, and longevity of <i>Acyrtociphom pisum</i> and <i>Hippodamia convergens</i>. Pathogens can modify many aspects of host behavior or physiology, with cascading impacts across trophic levels in terrestrial food webs. These changes include thermal tolerance of hosts, however, the effects of fungal infections on thermal tolerances and behavioral responses to extreme temperatures of prey (<i>Acyrtociphon pisum</i>) and predator (<i>Hippodamia convergens</i>) insect species have rarely been studied. We measured the impacts of fungal infection (at two levels: low and high spore load) on thermal tolerance (critical thermal maximum and minimum), voluntary exposure, energetic cost, and survival of both insect species. Fungal infection reduced thermal tolerance to heat in both insect species, but only reduced tolerance to cold of the predator. Voluntary exposure to extreme temperatures was modified by the infection, energetic cost increased with infection and thermal conditions, and survival was significantly reduced in both insect species.

These datasets consist of soil maps generated to assess baselines, drivers and trends for soil health and stability within the NSW Regional Forest Agreement (RFA) regions. <br> The maps are organised into empirical soil maps, digital soil maps, and data cube maps. <br> Empirical soil maps consists of four products. Maps include topsoil pH, carbon, Emerson Aggregate Stability and Soil Profile Quality Confidence. Each map consists of 2,162 units. Maps were generated using the most representative soil profile for each unit available within the Soil and Land Information System (SALIS). The 2008 woody vegetation coverage was used as baseline. Maps reflect values when the sampling occurred with temporal changes not being accounted for. Locations with missing or of poor quality data are identified, providing a confidence rating map as part of the evaluation process.<br> Digital soil maps include map products of key soil condition indicators covering the Regional Forest Agreement regions of eastern NSW. Raster maps of key soil indicators, such as soil carbon, pH, bulk density, hillslope erosion and others, were created at 100 m resolution. For each key soil indicator, maps include baseline (approximately 2008) levels as well as trends of change resulting from different human and natural disturbances such as forest harvesting, uncontrolled stock grazing, climate change and bush fire. <br> Data cube maps include time series of soil organic carbon (SOC) between January 1990 and December 2020 for the Regional Forest Agreement regions of eastern NSW. Products provide estimates of SOC concentrations and associated trends through time. Modelling was carried out using a data cube platform incorporating machine learning space-time framework and geospatial technologies. Important covariates required to drive this spatio-temporal modelling were identified using the Recursive Feature Elimination algorithm (RFE). <br> A web mapping application on the NSW Spatial Collaboration Portal depicts these datasets. Access the webapp through the link below:<br> https://portal.spatial.nsw.gov.au/portal/home/item.html?id=af9c71935f024f4a8f64cb39f5eba007

Ground layer vascular plant species identity and projective foliage cover (PFC) data were collected from four permanently marked 50x10 metre plots in north Queensland on a three monthly frequency for three years. Ten 0.5 square metre quadrats were used for sampling at each occasion at each site and the data pooled and averaged. Refer to Neldner, V.J., Kirkwood, A.B. and Collyer, B.S. (2004). Optimum time for sampling floristic diversity in tropical eucalypt woodlands of northern Queensland. The Rangeland Journal 26: 190-203 for more information. Note: Spreadsheet compiled in 2021 from original data collection records.

This data release consists of flux tower measurements of the exchange of energy and mass between the surface and the atmospheric boundary-layer in semi-arid eucalypt woodland using eddy covariance techniques. It been processed using PyFluxPro (v3.3.0) as described in Isaac et al. (2017), <a href="https://doi.org/10.5194/bg-14-2903-2017">https://doi.org/10.5194/bg-14-2903-2017</a>. PyFluxPro takes data recorded at the flux tower and process this data to a final, gap-filled product with Net Ecosystem Exchange (NEE) partitioned into Gross Primary Productivity (GPP) and Ecosystem Respiration (ER). For more information about the processing levels, see <a href="https://github.com/OzFlux/PyFluxPro/wiki">https://github.com/OzFlux/PyFluxPro/wiki</a>. <br /> <br /> The Collie flux station was located approximately 10km southeast of Collie, near Perth, Western Australia. It was established in August 2017 and stopped measuring in November 2019. <br /><br />