The data set contains distance measures of primary (wind-borne) and secondary (on ground) seed dispersal during spring, summer and autumn, using empirical observations and detailed measurement of wind characteristics. Seeds were collected from populations of <i>Callitris verrucosa</i> within the reserve and was placed parallel to, and 100 m from the burn edge within the burnt site. For the empirical observation of seed dispersal we chose six release locations, three locations in each of the two sites, about 6 km apart that had both recently undergone a planned burn, one in spring 2009 and the other in autumn 2011. Within those two sites the three release locations were positioned 800 m apart from each other along a transect that was placed parallel to, and 100 m from the burn edge within the burnt site. To assess primary (wind-borne) seed dispersal, 20 randomly chosen seeds were released from each of three different heights (1 m, 2 m and 3 m) at each of the six sites, giving a total of 360 seeds released per season. Seeds were only released within a horizontal wind speed range of 8 - 25 km/h. At lower wind speeds seeds would not take-off and at higher wind speeds seeds could not be relocated. This data set could be reused in a similar study carried out for the same species in a different location. <br> To understand the effect of standing vegetation on the secondary (on-ground) seed dispersal, we established groups of 10 seeds on the ground within 10 m of each of the six previous release locations. Seed were left for 4 days before relocated and distances to the starting point were measured. This was repeated during all 3 seasons. Out of the 180 seeds released,161 (89%) seeds could be relocated. <br> Wind measurements were taken on a sand dune crest in the site that was burned during autumn 2011 using an ultrasonic anemometer (Model WindMaster (Part 1590-PK-020), Gill Instruments Ltd, Lymington, UK). Measurements continued for two weeks in spring, summer and autumn. The anemometer measured horizontal wind speed, horizontal wind direction, and vertical wind speed every 0.1 s, producing a dynamic, three dimensional wind speed vector. Measurements were taken at 2 m height. The data can be used for studies dealing with wind movements in mallee during Spring, Summer and Autumn as well as comparative seed dispersal studies using the same or other wind dispersed plant species.

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).