Spatial analysis data for 'Lines in the sand: quantifying the cumulative development footprint in the world's largest remaining temperate woodland'

These datasets provide the data underlying the publication (see abstract below).
The datasets are:

data in csv format:
1. development footprint by sample area:

Information on the 24 ~490 km2 sample areas assessed in the study, including the different infrastructure types (roads, railways, mapped tracks, unmapped tracks which have been manually digitised in the study using aerial imagery and hub infrastructure such as mine pits and waste rock dumps, also manually digitised in the study). Also contains some key covariables 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. gww 20 km grid:

This dataset provides data for the 20x20 km grid placed over the whole Great Western Woodlands 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 dataset 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 covariables: 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: number of mining projects, number of dead mineral tenements, sum of duration of all live and dead tenements, type of tenements (exploration/prospecting tenement, mining and related activities tenement, none), primary target commodity (gold, nickel, iron-ore, other), distance to wheatbelt, and distance to nearest town.

4. mapped versus digitised tracks:

This dataset provides mapped and unmapped 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.

This data was analysed as follows: we used a t-test to test for a difference between mapped and unmapped track width, conducted data exploration as per (Zuur et al. 2009), and modelled track widths using linear mixed models with _?lme4' package in R. We created a global model containing the following fixed variables: mapped/unmapped status; mining activity level for the relevant sample area, and pastoral status. Sample area identity was included as the random effect in all models after testing for its significance.

We used the _?dredge' function in _?MuMin' package to model all possible subsets of the global model and rank them based on AICc values. The optimal model included only mapped/unmapped status as a fixed effect, and the other top-ranking model also included a positive effect of pastoral tenure on track width. Mapped tracks were found to be on average ~1 m wider than unmapped tracks (p U 0026lt; 0.001) (Figure A2.1). Average widths of mapped and unmapped tracks were 6.06 m (s.e. 0.15 m) and 4.92 m (s.e. 0.10 m) respectively. No effect of mining activity was included in the top-ranking models.

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 used stratified random sampling to distribute 24 circular sample areas, each 25 km in diameter, among the 8 mining and pastoral categories. We used circular sample areas to minimise the edge-to-area ratio of the sample areas and therefore maximise the extent to which the sample areas reflected the category represented rather than the adjacent landscape.

8. linear infrastructure extending beyond gww boundary by ~100 km. This is a dataset compiled from 23 different sources that represents the most comprehensive spatial dataset for the GWW available at the time of publication, to KR's knowledge. However, it does contain a number of different sources of error and should not be considered to necessarily reflect an updates, accurate dataset (note there is a more detailed metadata document inside this folder).

9. linear infrastructure footprints. Linear features buffered by average width of that linear infrastructure type for each sample area. Linear features include paved roads and railways, unpaved roads, mapped tracks, and unmapped tracks (digitized from aerial images in this study).

10. digitised tracks All linear infrastructure that hadn't already been mapped in #8 above. Manually digitised from high-resolution aerial images in this study.

11. digitised hub infrastructure Development footprints of all non-linear (i.e. polygonal) anthropogenic disturbance, including mine pits, waste rock dumps, mining camps and accommodation villages, dams, and other cleared areas, manually digitised from high-resolution aerial imagery in this study.

12. edge effect zones Polygons created by creating buffers around the development footprint as described in Appendix 3 of the article. These zones around the direct development footprint represent offsite impact risk for each type of infrastructure, using a hypothesized set of risk buffers. These were based on edge effect distances reported in the literature for species and processes from around the world. Three scenarios are represented: a conservative, moderate, and maximal scenario.

The abstract for the publication is as follows:

Context
The acceleration of infrastructure development presents many challenges for the mitigation of ecological impacts. The type, extent, and cumulative effects of multiple developments must be quantified to enable mitigation.

Objectives
We quantified anthropogenic development footprints in a globally significant and relatively intact region. We identified the proportion accounted for by linear infrastructure (e.g. roads) including infrastructure that is currently unmapped; investigated the importance of key landscape drivers; and explored potential ramifications of offsite impacts (edge effects).

Methods
We quantified direct development footprints of linear and 'hub' infrastructure in the Great Western Woodlands (GWW) in south-western Australia, using digitisation and extrapolation from a stratified random sample of aerial imagery. We used spatial datasets and literature resources to identify predictors of development footprint extent and calculate hypothetical _?edge effect zones'.

Results
Unmapped linear infrastructure, only detectable through manual digitisation, accounts for the greatest proportion of the direct development footprint. Across the 160,000 km2 GWW, the estimated development footprint is 690 km2, of which 67% consists of linear infrastructure and the remainder is _?hub' infrastructure. An estimated 150,000 km of linear infrastructure exists in the study area, equating to an average of ~1 km per km2. Beyond the direct footprint, a further 4,000_?55,000 km2 (3_?35% of the region) lies within edge effect zones.

Conclusions
This study highlights the pervasiveness of linear infrastructure and hence the importance of managing its cumulative impacts as a key component of landscape conservation. Our methodology can be applied to other relatively intact landscapes worldwide.