<br>This release consists of flux tower measurements of the exchange of energy and mass between the surface and the atmospheric boundary-layer using eddy covariance techniques. Data were processed using PyFluxPro (v3.4.7) as described by Isaac et al. (2017). PyFluxPro produces a final, gap-filled product with Net Ecosystem Exchange (NEE) partitioned into Gross Primary Productivity (GPP) and Ecosystem Respiration (ER).</br> <br>The Cumberland Plain flux station is located in a dry sclerophyll forest. The Cumberland Plain Woodland is now an endangered ecological community that encompasses distinct groupings of plants growing on clayey soils. The canopy is dominated by <em>Eucalyptus moluccana</em> and <em>Eucalyptus fibrosa</em>, which host an expanding population of mistletoe. Average canopy height is 23&nbsp;m, the elevation of the site is 20&nbsp;m and mean annual precipitation is 800&nbsp;mm. Fluxes of water vapour, carbon dioxide and heat are quantified with the open-path eddy flux technique from a 30&nbsp;m tall mast. Additional measurements above the canopy include temperature, humidity, wind speed and direction, rainfall, incoming and reflected shortwave and longwave radiation and net, diffuse and direct radiation and the photochemical reflectance index. In addition, profiles of humidity and CO₂ are measured at eight levels within the canopy, as well as measurements of soil moisture content, soil heat fluxes, soil temperature, and 10&nbsp;hr fuel moisture dynamics. In addition, regular monitoring of understory species abundance, mistletoe infection, leaf area index and litterfall are also performed.

The qualities of these data include: (i) sound experimental design to detect a change between confounding factors, (ii) large sample size, (iii) microchipped animals, (iv) validated heamatological processing on the wild Australian lizard Tiliqua rugosa involving a collaboration between wildlife ecologists and veterinary scientists. Its reuse potential may involve a comparative analysis of body size, haematological parameters with other long-lived, medium-sized lizards, ectoparasite studies (Aponomma hydrosauri, Amblyomma libatum) for different host populations, and background justification for ecotoxicological (pesticide) studies in farmland. Using a using a multivariate, one-way nested Type I PERMANCOVA (analysis of covariance) design, body size, blood samples and ectoparasite presence was collected on a total of 119 animals from two different populations in southern Australia. One population was from an intensively managed cropping environment and one was from an adjacent a less intensively managed grazing environment. This study took place in extensive rangelands and the fragmented landscapes of the South Australian Murray Mallee cereal cropland in southern Australia. Adult and juvenile T. rugosa were captured for sampling at one rangeland (baseline) site and three severely modified (severe) landscape-scaled sites (LS1, LS2, LS3) over a large area (68 km × 84 km or 571,200 ha) across the croplands. Two animal sampling designs were used to collect data on physiological health (Design 1: Baseline vs Severe and Design 2 - Severe only). Data collected: Record No., Animal No., Treatment, Habitat Type, Landscape No., Connectivity Class, Age Class, Linear Body Size Index (LBSI), Heterophil (H) Field of View, Heterophil per microlitre, Total White Blood Cell Count, Absolute Heterophil Count, % Heterophil Count, Absolute Lymphocyte (L) Count, % Lymphocytes, H:L Ratio (Absolute), H:L Ratio (%), Absolute Monocytes, % Monocytes , Absolute Other Granulocytes , % Other Granulocytes, % Polychromasia, Snout-Vent Length (mm), Total No. Ectoparasites per Animal.

The dataset contains records of Robber Crab (<i>Birgus latro</i>) mortality across Christmas Island, including location co-ordinates and details of sex and thoracic length. To manage the impact of road mortality on the species, this monitoring project is designed to assess spatial variation in road mortality. Basic data are collected at the site (sex, size, date, coordinates).

<p>This dataset shows the crops grown in Queensland's main cropping areas, for the winter and summer growing-seasons, from 1988 to the current year. The winter growing-season is defined as June to October, and the summer growing-season is November to May. The basis of the maps is imagery from the (when available) Landsat-5 TM, Landsat-7 ETM+, Landsat-(8,9) OLI, and Sentinel-2(A,B) satellites; MODIS MOD13Q1 imagery was used as a backup in the case of large, temporal data gaps. Clusters of temporally similar pixels, termed 'segments', were identified in the imagery for each growing season, and served as an approximation of field boundaries. Per-segment phenological information, derived from the satellite imagery, was then combined with a tiered, tree-based statistical classifier, using >10000 field observations as training data, and >4000 independent observations for validation. The dataset supersedes a former crop-mapping effort <a href ="https://doi.org/10.3390/rs8040312">(Schmidt et al., 2016)</a>.</p> <p>Each season has 2 maps: an end-of-season prediction and a mid-season prediction. The mid-season prediction is labelled "_vInterim" to indicate that it is based on a relatively short time series, and should be used with caution.</p> <p>For optimum display symbology files have been provided for both QGIS and ArcGIS.</p>

Gentry transects were established to monitor the vegetation abundance, cover and structure of the mid-stratum and subordinate stratum of the core 1 ha plot in the Warra Tall Eucalypt site in 2014.

This one file dataset contains the information on the Long-haired rats (<i>Rattus villosissimus</i>) used in this study, i.e. data that was collected between October 2011 and May 2013. It contains the exact date (Date) for when a rat was released (Trip_type Release, Trip_number 0) or trapped (Trip_type = Seasonal Trapping, Trip >/= 1) in each of the two enclosures (Enclosure = Enclosure I or Enclosure II), as well as the treatments (Treatment regarding the access of cats into the enclosure: high_fence (no access for cats) or low_fence (access for cats), including information on a rats gender (Sex = M (for male) or F (for female), a rats weight (Animal_weight measured in g), body condition (Body_condition theoretically ranging from 1 (emaciated) to 5 (obese), but only categories 2 (underconditioned), 3 (well-conditioned) and 4 (overconditioned) were scored) and individual identification (PIT.ID) as well as whether they had been recaptured (New_firsttripcap_recap indicating whether the animal was new= released/ caught the very first time, was a firsttripcap = captured before, but first captured during a trapping session, or a recap = recaptured during the same trip).

This product has been superseded and will not be processed from early 2023. Please find the updated version 3 of this product at https://portal.tern.org.au/metadata/23884. The seasonal fractional ground cover product shows the proportion of bare ground, green and non-green ground cover and is derived directly from the seasonal fractional cover product, also produced by Queensland's Remote Sensing Centre. The seasonal fractional cover product is a spatially explicit raster product, which predicts vegetation cover at medium resolution (30 m per-pixel) for each 3-month calendar season. However, the seasonal fractional cover product does not distinguish tree and mid-level woody foliage and branch cover from green and dry ground cover. As a result, in areas with even minimal tree cover (>15%), estimates of ground cover become uncertain. With the development of the fractional cover time-series, it has become possible to derive an estimate of ‘persistent green’ based on time-series analysis. The persistent green vegetation product provides an estimate of the vertically-projected green-vegetation fraction where vegetation is deemed to persist over time. These areas are nominally woody vegetation. This separation of the 'persistent green' from the fractional cover product, allows for the adjustment of the underlying spectral signature of the fractional cover image and the creation of a resulting 'true' ground cover estimate for each season. The estimates of cover are restricted to areas of <60% woody vegetation. Currently, this is an experimental product which has not been fully validated.

These datasets provide the data underlying the publication on <i>"Lines in the sand: quantifying the cumulative development footprint in the world’s largest remaining temperate woodland"</i> <em> https://link.springer.com/article/10.1007/s10980-017-0558-z. </em>. The datasets are: (A) data in csv format: [1] development footprint by sample area: Information on the 24, ~490 km^2 sample areas assessed in the study, including the different infrastructure types (roads, railways, mapped tracks, un-mapped tracks which have been manually digitized in the study using aerial imagery and hub infrastructure such as mine pits and waste rock dumps, also manually digitized in the study). Also contains some key co-variables assessed as potential explanatory variables for development footprint. The region-wide modelling of development footprint found strong positive effects of mining project density and pastoralism, as well as a highly significant negative interaction between the two. At low mining project densities, development footprints are more extensive in pastoral areas, but at high mining project densities, pastoral areas are relatively less developed than non-pastoral areas, on average. [2] Great Western Woodlands (GWW) 20 km grid: The datasets provides data for the 20x20 km grid placed over the whole GWW and used for the regional estimation of development footprint, linear infrastructure density, and linear infrastructure type based on the region-wide analysis. Data is for each cell in the grid and provides the total length of roads in that grid cell, MINEDEX mining projects, pastoral status, etc. This dateset was used to project the data from the 24 study areas across the whole of the Great Western Woodlands and calculate region-wide estimates of development footprint and linear infrastructure lengths. [3] disturbance by patch: This dataset provides the data for each patch for the analysis of patch-level drivers of development footprint, which was performed to gain further insights into the effects of other landscape variables that what could be gleaned from the region-wide analysis. For this analysis, we divided sample areas into polygonal patch types, each with a unique combination of the following categorical co-variables: pastoral tenure, greenstone lithology, conservation tenure, ironstone formation, schedule-1 area clearing restrictions, environmentally sensitive area designation, vegetation formation, and sample area. For each patch type (n=261), we calculated the following attributes: a) number of mining projects, b) number of dead mineral tenements, c) sum of duration of all live and dead tenements, d) type of tenements (exploration/prospecting tenement, mining and related activities tenement, none), e) primary target commodity (gold, nickel, iron-ore, other), f) distance to wheatbelt, and g) distance to the nearest town. [4] mapped versus digitized tracks: This dataset provides mapped and un-mapped track widths, measured using high-resolution aerial imagery at at least 20 randomly-generated locations within each of 24 sample areas. Pastoral tenure and mining intensity for each sample area are included for analysis purposes. [5] edge effect scenarios: Hypothetical edge effect zones were created, based on effect zones gleaned from the literature and arranged under three scenarios, to reflect potential risks of offsite impacts in areas adjacent to development footprints observed (see appendix 3 of article). The calculated proportion of the entire GWW within edge effect zones varied from ~3% under the conservative scenario to ~35% under the maximal scenario. Within the range of development footprints observed in this study, the proportion of a landscape that lies within edge effect zones increases hyperbolically with the number of mining projects, and approaches 100% in the maximal scenario, 60% in the moderate scenario, and ~20% under the conservative scenario. shapefiles: [6] Great Western Woodlands boundary, [7] sample areas (layer file shows sample areas by category).

We generated a total of 2,313,977 16S archaeal raw reads across the 36 replicates (64,277 ± 23,335 SD per replicate). A total of 2,299,955 archaeal sequences (63,888 ± 23,473 SD per replicate) and 1,937 archaeal OTUs (54 ± 20 SD per replicate) remained for further analysis after quality filtering. The OTU data provide information on archaeal flux at an active restoration site at Mt Bold, a water catchment reserve of the Mt Lofty Ranges in South Australia, through a stagger of years and can be used accordingly.

This data contains diameter, volume and biomass measurements of all coarse woody debris pieces and standing dead trees within the core 1 ha plot at the Cumberland Plain site in 2014 - 2015.