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).

River sites were sampled during the summers of 2008/09 and 2009/10 in a survey designed to identify correlations between commonly used river condition variables and grazing land-use. Potential stream sites in northern Tasmania were screened by catchment size, northing and slope, and according to attributes aimed at minimising confounding variables, maintaining broad consistency in landscape and geomorphological context, and promoting independence among sites. A set of 27 survey sites was selected across a gradient from low to high proportion of land under grazing in their upstream catchments. Catchment sizes varied from 20-120 km2 and proportion grazing from 0-80%. Macroinvertebrates were sampled using Surber sampler. All macroinvertebrates within a 20% sub-sample identified to family and counted, with individuals from the insect orders Ephemeroptera, Plecoptera and Trichoptera identified to genus/species (by Laurie Cook, UTAS). Algal abundance was estimated at each site as the proportion of algal cover and as areal density of benthic chlorophyll a. Physical data variables collected were: water temperature, conductivity, turbidity, pH, total alkalinity, nitrate+nitrate, dissolved reactive phosphorus, total nitrogen, total phosphorus, overhead shading, the proportion of fine sediments within the sampled riffle zone, accumulated abstraction index and accumulated regulation index. For more information see: See Magierowski RH, Read SM, Carter SJB, Warfe DM, Cook LS, Lefroy EC and Davies PE. Inferring landscape-scale land-use impacts on rivers using data from mesocosm experiments and artificial neural networks. PLOS ONE.

The data set contains count data of amphibians from surveys of grazing properties in the Central and Southern Tablelands of NSW, Australia. Amphibians were surveyed using pitfall and funnel trapping along transects. Twelve properties were surveyed for the data set. Each property was surveyed 5 times for five trap nights on each survey between 2014 and 2015. A total of 2378 amphibians were captured from 11 different species during the surveys. All species captured were from one of three families: Limnodynastidae (three species), Myobatrachidae (four species) and Hylidae (four species).

This data set contains the count data of reptiles captured through pitfall and funnel trapping in surveys of grazing agricultural properties in the Central Tablelands of NSW, Australia. Experimental treatments were examined and additional environmental variables were recorded. Each of the 12 sites (farms) was surveyed five times, once between January and March 2014 and four times between October 2014 and March 2015. Each survey consisted of five trap nights. In total 5,040 traps were surveyed giving a combined total of 25,200 trapping nights. 1,242 captures were recorded from 28 species of reptiles. The majority of the species (19) were from the family Scincidae.

The QBEIS survey database (formerly CORVEG) contains ecosystem physical and vegetation characteristics, including structural and floristic attributes as well as descriptions of landscape, soil and geologic features, collected at study locations across Queensland since 1982. The resulting survey database provides a comprehensive record of areas ground-truthed during the regional ecosystems mapping process and a basis for future updating of mapping or other relevant work such as species modelling.<br /><br /> Only validated survey data is made publicly available and all records of confidential taxa have been masked from the dataset. Data is accessible from the TERN Data Infrastructure, which provides the ability to extract subsets of vegetation, soil and landscape data across multiple data collections and bioregions for more than 100 variables including basal area, crown cover, growth form, stem density and vegetation height.