This dataset provides understorey herbaceous biomass, ground cover and overstorey woody cover response to different fire regimes over a twenty year period at a grassland and open woodland in the tropical savannas of northern Australia. BOTANAL was used to assess understorey herbaceous biomass. Woody canopy cover was derived from digital analysis of oblique aerial imagery taken from a helicopter at the site in 1995 and again in 2013. Woody cover (tree basal area and canopy cover) was also assessed using a bitterlich gauge on BOTANAL ground based transects in 2009. The data could be used to calibrate models of herbaceous growth and woody cover change in response to long term fire. It may be useful for assessing climate change impacts on aboveground carbon sequestration. The fire regimes tested were of varying frequency (every 2, 4 and 6 years) and season (June vs. October) of fire compared to unburnt controls on woody cover and pasture composition. Sites were open to grazing by cattle.

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).

The dataset can be reused for contintental-wide synthesis of the cover of Australian grasses. It consists of high quality, well-described plot-based data extracted from TERN repository on 13/3/2014. The data includes vegetation records for the Poaceae family from the following dataset: ABARES Ground Cover Reference Sites Database, Biological Survey of South Australia - Vegetation Survey, Biological Database of South Australia, Corveg (Queensland), TERN AusPlots Rangelands Survey Program, Biological Survey of the Ravensthorpe Range (Western Australia).The entire content of the portal was initially extracted using the portal's download feature to obtain the full extent of available data for the following all datasets. These data were loaded into a PostgreSQL database. Subsequently, a SQL query was built for each of the cited datasets which produced a flat table containing information about the survey name, site identifier, visit date, coordinates, species, abundance, biomass and/or cover class, filtering on species of the Poaceae family using a genus list obtained from the website of the Atlas of Living Australia (http://www.ala.org.au/).

The authors analyzed a total of 3,002,411 quality-filtered bacterial 16S rRNA gene sequences in the 48 technical replicates across 8 revegetation chronosequence sites, consisting of 3,316 OTUs. Nine bacterial phyla dominated this dataset, including Acidobacteria, Actinobacteria, Bacteroidetes, Chloroflexi, Firmicutes, Gemmatimonadetes, Planctomycetes, Proteobacteria and Verrucomicrobia.The OTU data provide information on bacterial flux at this restoration site through a stagger of years and can be used accordingly.

The AEKOS Australian Vegetation sPlot dataset consists of high quality, well-described plot-based data extracted from the AEKOS (portal.aekos.org.au) on 11/11/2014. The data includes vegetation records for the following datasets: Australian Ground Cover Reference Sites Database, Biological Survey of South Australia - Vegetation Survey - Biological Database of South Australia, Atlas of NSW database: VIS flora survey module, Queensland CORVEG Database, TERN AusPlots Rangelands, Transect for Environmental Monitoring and Decision Making (TREND), AusCover Supersites SLATS Star Transects, Biological Survey of the Ravensthorpe Range (Western Australia).The portal's vegetation plot data was extracted using the portal's download feature to obtain the full extent of available data for the all datasets. In addition, an average cover value was calculated for each site using a slight modification of the ingestion scripts normally used to ingest the source data into AEKOS. The altitude values derived from a map layer using the site coordinates were obtained from the AEKOS index. Finally, land use and vegetation type were derived from map layers using the site coordinates. These data were loaded in different tables of a PostgreSQL database. Subsequently, two SQL queries were built to centralise the available data in two tables: table r_site containing the site specific data and table r_speciesobservations containing the individual data on observed specimen. A PostgreSQL backup file containing these two table was then built using the pg_dump tool. The dataset can be reused for contintental-wide or global synthesis of the cover of Australian vegetation.