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 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).
Simple
Identification info
- Date (Creation)
- 2014-07-30
- Date (Publication)
- 2017-08-08
- Date (Revision)
- 2024-04-30
- Edition
- 1
Identifier
Publisher
Author
- Website
- https://www.tern.org.au/
- Purpose
- This dataset relates to Chapter 3 of the author's PhD thesis: Conservation of large, relatively intact landscapes in the face of widespread development such as resource extraction is a challenge of global conservation significance. Growing human populations and economies and increasing scarcity of natural resources are pushing the frontiers of rapidly proliferating development into areas that have remained relatively intact until recent times; diminishing or degrading those landscapes.In this thesis I present research that aims to provide approaches, information and insights that can be used to ameliorate these impacts, using the largest and most intact remaining temperate woodland on earth as a case study. The Great Western Woodlands (GWW) is a region of international ecological significance and also a highly productive mining province, with a rich history of gold, nickel, and iron ore mining evidenced by numerous exploration tracks, drilling remains, mine pits, and waste dumps. I investigated the following overarching questions in the context of mining in the GWW: 1. What is the scope of ecological impacts that require mitigation to successfully conserve intact landscapes? 2. How significant is linear infrastructure (e.g. roads, tracks, and railways) as a component of disturbance? 3. How does linear infrastructure affect key ecosystem processes, such as predation and water movement? I developed a conceptual framework (Chapter 2), used spatial analysis techniques (Chapter 3), and conducted extensive field work (Chapters 4 and 5) to address these questions. Chapter 2 proposes a framework for conceptualising enigmatic ecological impacts: impacts that are often overlooked or inadequately addressed in impact evaluations. Enigmatic impacts include those that are small but act cumulatively (cumulative impacts); those outside of the area directly considered (offsite impacts); those not detectable with the methods or spatiotemporal scales used (cryptic impacts); those facilitated, but not directly caused, by the development (secondary impacts); and synergistic impact interactions. Potential solutions to these enigmatic impacts include strategic broad-scale planning, improving professional practice and decision-making processes, and environmental insurance schemes. This framework sets the context for the following chapters which explore various enigmatic effects of development in the GWW. In Chapter 3 I characterised and quantified the cumulative development footprint in the GWW, with extensive digitisation from aerial imagery across a random stratified sample of the region. In contrast to common perceptions of mining impacts as primarily consisting of mine pits and associated hub infrastructure, I found that approximately 67% of the disturbance footprint consists of linear infrastructure. I estimated that 150,000 km of tracks, roads, and railways exist in the region and that beyond the ~690 km2 total disturbance footprint, a further 4,00055,000 km2 (335% of the GWW) lies within offsite risk zones. Moreover, the majority of linear infrastructure is unmapped, indicating that available data sources are not comprehensive and can lead to false conclusions about ecological impacts. To explore the effect of linear infrastructure on predator activity, I used a combination of motion-sensor cameras and spoor inspections to compare dingo, fox and cat activity on vehicle tracks and for three kilometres into the surrounding vegetation matrix (Chapter 4). I found strong effects of roads on activity for all species studied: on-road activity was generally far higher than off-road activity, and roads appeared to affect predator activity even up to 2.5 km away. I also explored the effects of extensive track, road, and rail networks on water movement (Chapter 5). I assessed over 1100 km of linear infrastructure and off-road transects and 300 stream crossings, and found strong associations between linear infrastructure and evidence of altered surface and near-surface hydrology. Ninety-eight percent of stream crossings showed evidence of flow impedance, flow concentration, flow diversion and/or channel initiation. A number of engineering and environmental factors influence the frequency and severity of these impacts, which I estimate number at least 335,000 across the region. This research indicates that pervasive ecological impacts exist but are commonly overlooked in conventional impact evaluations, and undermine the potential for successful impact mitigation. Linear infrastructure can be the elephant in the room with regard to such impacts, affecting both top-down (predation) and bottom-up (water availability) ecosystem regulation across substantial parts of the landscape. Nevertheless, there is substantial scope for mitigating these impacts and conserving large, relatively intact landscapes such as the Great Western Woodlands in perpetuity.
- Credit
- We at TERN acknowledge the Traditional Owners and Custodians throughout Australia, New Zealand and all nations. We honour their profound connections to land, water, biodiversity and culture and pay our respects to their Elders past, present and emerging.
- Credit
- Suzanne Prober, Richard Hobbs, and Hugh Possingham were co-authors to the paper which these datasets underly, and provided important guidance to the analysis. Gondwana Link and the Wilderness Society (and the Great Western Woodlands Collaboration) provided the Great Western Woodlands boundary and also provided immense support and guidance on the analysis and desired outcomes. The Department of Biodiversity, Conservation and Attractions (previously DPaW and DEC), Landgate, Geoscience Australia, Department of Mines, Industry Regulation and Safety (previously Department of Mines and Petroleum), all provided data which was used in this analysis. We gratefully acknowledge support from the Gledden Postgraduate Research Scholarship, the Australian Research Council Centre of Excellence for Environmental Decisions, The Wilderness Society, Gondwana Link, the Natural Environmental Research Program Environmental Decisions Hub, and the Great Western Woodlands Supersite, part of Australia's Terrestrial Ecosystem Research Network. We thank Ophir Levin, Julia Waite, Brad Desmond, and Rachel Omodei for assistance in digitising the unmapped development footprint. We also thank Fiona Westcott for her assistance in ground-truthing, Cliffs Natural Resources for in-kind 500 support in the field, and Amanda Keesing (Gondwana Link), Judith Harvey (DPaW) and Katherine Zdunic (DPaW) for their assistance in supplying spatial information. Ashley Sparrow, Richard Forman, Andrew Bennett, John Bissonette, and two anonymous reviewers provided valuable reviews that improved this manuscript. All fieldwork was carried out under Department of Parks and Wildlife Regulation 4 lawful authority CE003548.
- Status
- Completed
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- Biota
Extent
- Description
- IBRA: Great Western Woodlands The dataset covers selected sample areas (~490 km2) in the Great Western Woodlands, located in the southwest of Australia.
Temporal extent
- Time period
- 2012-07-02 2014-07-30
- Title
- Lines in the sand: quantifying the cumulative development footprint in the world’s largest remaining temperate woodland
- Website
-
Lines in the sand: quantifying the cumulative development footprint in the world’s largest remaining temperate woodland
Related documentation
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- QUDT Units of Measure
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- Keywords (Discipline)
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- Chenopod Shrublands
- Samphire Shrublands And Forblands
- Disturbance
- Eucalypt Open Woodlands
- Eucalypt Woodlands
- Geology/Lithology Hub
- Linear Infrastructure
- Mallee
- Open Woodlands And Sparse Mallee Shrublands
- Native Vegetation
- Development Impacts
- Habitat Fragmentation
- Land-use change
- Mineral Exporation
- Mining
- Natural Resource Use
- Over-consumption
- Roads
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- The Creative Commons Attribution 4.0 International (CC BY 4.0) license allows others to copy, distribute, display, and create derivative works provided that they credit the original source and any other nominated parties. Details are provided at https://creativecommons.org/licenses/by/4.0/
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- TERN services are provided on an "as-is" and "as available" basis. Users use any TERN services at their discretion and risk. They will be solely responsible for any damage or loss whatsoever that results from such use including use of any data obtained through TERN and any analysis performed using the TERN infrastructure. <br />Web links to and from external, third party websites should not be construed as implying any relationships with and/or endorsement of the external site or its content by TERN. <br /><br />Please advise any work or publications that use this data via the online form at https://www.tern.org.au/research-publications/#reporting
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- Please cite this dataset as {Author} ({PublicationYear}). {Title}. {Version, as appropriate}. Terrestrial Ecosystem Research Network. Dataset. {Identifier}.
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- (C)2017 University of Western Australia. Rights owned by University of Western Australia.
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- Embargo date: 31/08/2017
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- Unclassified
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- OnLine resource
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- Statement
- All spatial analyses were performed in ArcGIS 10.3 and Geospatial Modelling Environment (Version 0.7.4.0; available from www.spatialecology.com), and all data analyses were performed in R (version 3.2.0, R Core Team 2015). We created a 20 x 20 km grid overlying the study area and categorised each grid cell into one of 8 categories made up of four levels of mining activity and a binary indicator for pastoral status (Figure 2). Density of mining projects, calculated using the Minedex layer provided by Department of Mines and Petroleum, was used as a proxy for mining activity level. Pastoral status was based on pastoral datasets provided by Department of Agriculture and Food of Western Australia (DAFWA) and included former pastoral areas that are currently in transition to conservation tenure. 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. Circular sample areas also helped to reduce sampling bias which may occur due to coincidental alignment of the sample area boundary with tenure boundaries or roads, and better reflect the landscape as a whole. Sample area diameters of 25 km were selected to balance capture of the landscape variability with logistics of digitisation. The sample area locations were all randomly selected within each category, except for two which were placed in areas of particular interest to conservation needs in the region (Lake Cronin and Helena-Aurora Range). Grid cells with towns were excluded, as were some grid cells to the far east where high-resolution imagery was not available. In total, the sample areas represented 11,729 km2 (7.34%) of the GWW by area. We created a spatial layer containing all mapped linear infrastructure based on 23 unique datasets obtained from Department of Parks and Wildlife (DPaW), DAFWA, Landgate, and Geosciences Australia. Mapped linear infrastructure elements were classified as railway, major road, paved minor road, unpaved road, track, fence line, relegated route and unknown based on information provided in the source layers. The latter four classifications were merged with tracks for the purposes of this study. No region-wide spatial data for hub infrastructure were available. KR and a team of volunteers digitised all physical anthropogenic disturbances visible from high-resolution ortho-rectified aerial imagery (orthophotos) that weren't already mapped, for the full extent of each sample area, at an average scale of 1:2000. KR maintained consistency between the digitisation performed by different people by providing every volunteer with identical training in the methodology to be used, close supervision, and close review of all data outputs. Corrections and supplementation of the digitised dataset was performed where necessary by KR. The orthophoto sets used were provided by Landgate and were the two most recent available for each area, dating between 2003 and 2014, and with 50-140 cm pixel resolution. All unmapped linear infrastructure was classified as track. Mapped features generally matched up with observable features although there were some tracks that were difficult to distinguish, and the locational accuracy of some was low. All features which were polygonal (i.e. not linear: e.g. mine pits, waste-rock dumps, dams, homesteads and mine worker camps) were grouped as hub infrastructure. Hub infrastructure was digitised with polygon feature classes by tracing the approximate edge of the disturbance feature. Linear infrastructure was digitised as line features and then both mapped and unmapped linear infrastructure features were converted to polygon features using their average widths in order to calculate the area of their development footprints. To calculate average widths of linear infrastructure features, points were randomly placed along each type of linear infrastructure within each sample area and the width of the linear infrastructure at each random point was measured by zooming in to ~1:100 scale. Where there was only one feature per type of linear infrastructure, 5 width measurements were taken; otherwise between 20 and 50 width measurements were taken per feature type per sample area, amounting to 1237 width measurements in total. To determine whether there was a significant difference between mapped and unmapped tracks, their widths were modelled using linear mixed models, with mining activity level and pastoral status as possible fixed factors and sample area as the random factor (Appendix 2). Ground-truthing We ground-truthed a selection of all types of mapped and digitised infrastructure, as well as areas in which no disturbance features were observed from orthophotos along a travel route of approximately 500 km. The travel route included travel along some unmapped tracks and approximately 100 km of walking off-road. Extensive fieldwork in the study area (for related studies) gave KR further experience in assessing disturbance features based on orthophotos. Region-wide analysis of development footprint For each sample area we calculated mining project density, distances to the nearest town and to the edge of the wheatbelt (Figure 1), and total development footprint. The wheatbelt is an agricultural area with a much higher population density relative to that of the GWW and the areas that lie to the north and east of the GWW. In addition to proximity to towns, proximity to the wheatbelt may act as a proxy for human accessibility and therefore be associated with increased disturbance (e.g. for recreational use, prospecting, sandalwood harvesting, as well as mineral exploration for explorers that prefer to target more accessible; resources that might be therefore cheaper to transport to ports or markets). To explore potential drivers of development extent, we modelled footprint area (square-root transformed; response variable) against mining project density (log-transformed), pastoral status, and shortest distance to the wheatbelt (explanatory variables), using linear models in the lme4 package. We also tested for an interaction between mining project density and pastoral status. Distance to town was excluded due to high collinearity (-0.6) with mining project density, and showed no trend with the model residuals. We selected the optimal model based on comparison of adjusted R2 and Akaike information criterion with a correction for finite sample sizes (AICc) values, both of which indicated the same optimal model. We used the optimal model to estimate the development footprint across the rest of the GWW for each 20 x 20 km grid cell. We also estimated linear infrastructure density for the GWW and proportion accounted for by each infrastructure type using average densities and proportions for each infrastructure type and analysis category and extrapolation based on the 20 x 20 km grid. Patch-level predictors of development footprint To gain further insights into the effects of other landscape variables, 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 (Appendix 1). The vegetation formations dataset was created by grouping the vegetation types in the source layer. 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. Presence of a mining tenement overrode the presence of any exploration tenements. We modelled the proportion area under development footprint (logit transformed) as a function of the variables listed above using linear mixed models in nlme package. The logit function is the inverse of the sigmoidal logistic function, which is bound by 0 and 1, making it effective for transforming skewed proportional data into a model-ready distribution. Where variables were collinear (r >0.6) they were alternated to identify the most significant variable to include. The top-ranking model was selected from 4096 models including the global model and all possible subsets with an information-theoretic approach using AICc, in MuMin package (Barton 2015). Edge effect zones We created zones around the direct development footprint representing 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, as species-specific data for the GWW was not available (Appendix 3). This methodology is an adaptation of the approach presented by Liu et al. (2008), and although it represents a simplistic generalisation, it offers a useful starting point for calculating the likely extent of ecological impacts outside of the direct development footprint. Edge effects include reduced habitat quality adjacent to disturbed areas; groundwater contamination; chemical, dust, sound and light pollution and changes; introduction of invasive organisms; and barriers to ecological flows and processes (Beyer et al. 2014; Karlson and Mortberg 2015; Roche and Mudd 2014; Tyler et al. 2016). We buffered the various infrastructure features by different widths to represent edge effect risk under conservative, medium, and maximal scenarios and plotted the proportion of the landscape within these zones by mining project density for each scenario (Appendix 3; Table A3.1).
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Reference System Information
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- EPSG/EPSG:3577
- Reference system type
- Geodetic Geographic 2D
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urn:uuid/165881eb-1547-405b-af0d-131cf39fc4c6
- Title
- TERN GeoNetwork UUID
- Language
- English
- Character encoding
- UTF8
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- Resource scope
- Dataset
- Metadata linkage
-
https://geonetwork.tern.org.au/geonetwork/srv/eng/catalog.search#/metadata/165881eb-1547-405b-af0d-131cf39fc4c6
Point-of-truth metadata URL
- Date info (Creation)
- 2022-10-24T00:00:00
- Date info (Revision)
- 2024-04-30T00:00:00
Metadata standard
- Title
- ISO 19115-1:2014/AMD 1:2018 Geographic information - Metadata - Fundamentals
- Edition
- 1
Metadata standard
- Title
- ISO/TS 19115-3:2016
- Edition
- 1.0
Metadata standard
- Title
- ISO/TS 19157-2:2016
- Edition
- 1.0
- Title
- Terrestrial Ecosystem Research Network (TERN) Metadata Profile of ISO 19115-3:2016 and ISO 19157-2:2016
- Date (published)
- 2021
- Edition
- 1.0