The values of the Great Barrier Reef World Heritage Area (GBRWHA) are threatened by coastal development through habitat loss and runoff of sediment, nutrients and pollutants. Future coastal development is difficult to predict because it depends on volatile socio-economic and political factors. With this in mind, we developed a research project that uses spatially explicit scenario planning to identify plausible futures to 2035 for the GBRWHA coastal zone. The method used to produce maps of these scenarios is land-use change modelling using the GIS Idrisi. A governance analysis for the GBR coast is part of the project and is included in the scenario-planning process. Using a conservation planning approach, this project sets out to identify key priorities for restoring and protecting coastal ecosystems in the GBRWHA coastal zone. The objectives are to: - Compile spatial data on the coastal ecosystems and socio-economic characteristics of the GBRWHA coastal zone - Produce spatially explicit land use scenarios for the GBRWHA coastal zone to 2035 - Analyse the governance system in the GBRWHA coastal zone - Identify conservation goals for the GBRWHA coastal zone - Assess impacts of development for assets related to the conservation goals - Bring all the above information together to determine spatial options for allocating protection and restoration actions to achieve goals

This dataset consist of inputs and intermediate results from the coastal scenario modelling. It is an analysis of the bio-physical factors that best explain the changes in QLUMP land use change between 1999 and 2009 along the Queensland coastal region for the classifications used in the future coastal modelling. Methods: The input layers (variables etc) were produced using a range of sources as shown in Table 1. Source datasets were edited to produce raster dataset at 50m resolution and reclassified to suit the needs for the analysis. The analysis was made using the IDRISI Land Use Change Modeler using multi-layer perceptron neural network with explanatory power of bio-physical variables. In this process a range of bio-physical layers such as slope, rainfall, distance to roads etc (see full list in Table 1) are used as potential explanatory variables for the changes in the land use. The neutral network is trained on a subset of the data then tested against the remaining data, thereby giving an estimate of the accuracy of the prediction. This analysis produces suitability maps for each of the transitions between different land use classifications, along with a ranking of the important bio-physical factors for explaining the changes. The 1999 - 2009 Land use change was analysed with of which 4 were found to be the strongest predictors of the change for various transitions between one land use and another. This dataset includes the rasters of the 4 best predictors along with a sample of the highest accuracy transition probability maps. Format: Table 1 (Table 1 NERP 9_4 e-atlas dataset) This table contains the list of names, short descriptions, data source and data manipulation for the input rasters for the land use change model All GIS files are in GDA 94 Albers Australia coordinate system. 1999.tif This layer shows a rasterised form of the QLUMP land use (clipped to the GBR coastal zone as defined in 9.4) for 1999 used for analysis of bio-physical predictors of land use change. The original QLUMP data was re-classified into 18 classes then rasterised at 50m resolution. This raster was then resampled to a 500m resolution. 2009.tif This layer shows a rasterised form of the QLUMP land use (clipped to the GBR coastal zone as defined in 9.4) for 2009 used for analysis of bio-physical predictors of land use change. The original QLUMP data was re-classified into 18 classes (with addition of tourism land use) then rasterised at 50m resolution. This raster was then resampled to a 500m resolution. Rainfall.rst This layer shows the average annual rainfall (in mm) sourced from the Average Yearly Rainfall Isohyets Queensland dataset (clipped to the GBR coastal zone as defined in 9.4) used for analysis of bio-physical predictors of land use change. The data was re-classified and resampled at 50m resolution. Slope.rst This layer shows the slope (in degrees) value at 50m pixel resolution (clipped to the GBR coastal zone as defined in 9.4) used for analysis of bio-physical predictors of land use change. The slope was derived from the Australian Digital Elevation Model in ArcGIS (using the Slope tool of the 3D analyst Tools) at a 200m resolution. The data was resampled at 50m resolution. SeaDist.rst This layer shows the distance (in m) to the nearest coastline (including estuaries) at 50m pixel resolution used for analysis of bio-physical predictors of land use change. It was created by applying an Euclidean distance function (in ArcGIS in the Spatial Analyst toolbox) to the “Mainland coastline” feature in the GBR features dataset available from GBRMPA. UrbanDist.rst This layer shows the distance (in m) to the nearest pixel of urban land use at 50m pixel resolution used for analysis of bio-physical predictors of land use change. It was created by applying an Euclidean distance function (in ArcGIS in the Spatial Analyst toolbox) to the QLUMP 2009 dataset on the selected urban polygons. Transition_potential_Other_to_DryHorticulture.rst This layer shows the probability for each pixel (50m resolution) of the coastal to transition from the land use class Other to Rain-fed Horticulture. Areas originally of a different land use class are given no values. This was produced by analysing the patterns of land use change between 1999 and 2009 in IRDISI as part of the Land Use Change Modeler where the main bio-physical variables affecting the pattern of change were identified. See details in the model results file. A high accuracy rate of 92% was calculated during testing. Land Change Modeler MLP Model Results_Rain-fed_horticulture.docx This shows the results of the analysis of change from land use Others to rain-fed horticulture between 1999 and 2009 using four variables: Distance to existing horticulture, Rainfall, Soil type and Slope. Transition_potential_Other_to_Drysugar.rst This layer shows the probability for each pixel (50m resolution) of the coastal to transition from the land use class Other to Rain-fed Sugar cane. Areas originally of a different land use class are given no values. This was produced by analysing the patterns of land use change between 1999 and 2009 in IRDISI as part of the Land Use Change Modeler where the main bio-physical variables affecting the pattern of change were identified. See details in the model results file. A high accuracy rate of 84% was calculated during testing. Land Change Modeler MLP Model Results_Rain-fed_sugar.docx This shows the results of the analysis of change from land use Others to rain-fed sugar between 1999 and 2009 using three variables: Rainfall, Soil type and Slope. Transition_potential_Other_to_Forestry.rst This layer shows the probability for each pixel (50m resolution) of the coastal to transition from the land use class Other to Forestry. Areas originally of a different land use class are given no values. This was produced by analysing the patterns of land use change between 1999 and 2009 in IRDISI as part of the Land Use Change Modeler where the main bio-physical variables affecting the pattern of change were identified. See details in the model results file. A good accuracy rate of 73% was calculated during testing. Land Change Modeler MLP Model Results_Forestry.docx This shows the results of the analysis of change from land use Others to Forestry between 1999 and 2009 using three variables: Rainfall, Soil type and Proximity to existing forestry. Transition_potential_Other_to_Urban.rst This layer shows the probability for each pixel (50m resolution) of the coastal to transition from the land use class Other to Urban. Areas originally of a different land use class are given no values. This was produced by analysing the patterns of land use change between 1999 and 2009 in IRDISI as part of the Land Use Change Modeler where the main bio-physical variables affecting the pattern of change were identified. See details in the model results file. A good accuracy rate of 75% was calculated during testing. Land Change Modeler MLP Model Results_Urban.docx This shows the results of the analysis of change from land use Others to Urban between 1999 and 2009 using two variables: Slope and Proximity to existing urban areas.

This dataset consists of different possible land use configurations along the Great Barrier Reef coastal zone for the future (year 2035) under eight different scenarios (plausible futures). Scenarios are not predictions and do not intend to show what the coastal zone will look like (this is impossible as the future of coastal development I highly uncertain, even on a short term basis, let alone 25 years). Instead, scenarios are used to depict some plausible futures under certain circumstances so that managers can understand the system better and the various ways the future might unfold. Scenarios are built to broaden the extremities of possibilities in order to assert the main differences. The scenarios are then used to conduct impact assessments and this helps managers better understand the connections between land spatial planning and the effects on the values of the GBR World Heritage Area. The results can also be incorporated in the process for spatial planning of coastal development. These scenarios were built using workshops and literature reviews and are based on different combinations of socio-economic factors driving the amount of change of the different land uses and of strength of governance systems (strong or weak). Figure 1 contains summaries of the storylines of the scenarios. These were translated in quantitative and spatial rules of change for land uses (transition matrix of land use change were created for each scenario and GIS layers were created for each spatial rule later combined to fit each scenario). The land use change modelling took several steps, all conducting the IDRISI Land Use Change Modeler. The first stage was to establish the best bio-physical predictors of change in land use. This was done using [see Bio-physical predictors of coastal Land use change between 1999 and 2009]. The outputs were transition probability maps for each change between one land use to another. The second stage was to use these transition probability maps, the quantitative matrices of change and the spatial rule layers to assign change and produce land use change configurations (the scenarios) to 2035. The modelling of the land use change was done at a 50m pixel scale. This was however aggregated to a resolution of 500m as presented here to ensure that the dataset is not seen as an accurate set of maps. The “predictive” power of change of the system is not expected to be accurate at the scale of 50m as the land use changes are applied in a probabilistic manner. As a result new allocated land may follow random pattern at the small scale and so the shape of the new areas do not necessarily have a structure that is natural for each land use type. An example of this is that in reality new residential developments tend to be rectangular in shape. However transitions to residential areas in the model tend to be scattered. To get around the excessive precision of the model this final dataset is aggregated to a resolution of 500m, where each pixel corresponds to the majority land use for that area. Format: The following rasters (GRID format) constitute the dataset of the spatially-explicit scenarios. They each represent land use maps for the terrestrial part of the Great Barrier Reef coastal zone in the past in 1999 (QLUMP data) and in 2009 (QLUMP data) and in the 8 scenarios produced as part of the NERP 9.4 project. See Figure 1 for detail of scenarios. 1999 : QLUMP 1999 data 2009 : QLUMP 2009 data AWG : Scenario As We Go (Business as usual in weak governance) EM : Scenario Export Management (Food and Minerals in strong governance) ER : Scenario Eco-Revolution (Green in strong governance) GW : Green Washing (Green in weak governance) RTC : Red Tape Cutting (Food and Minerals in weak governance) TH : Tourism Heaven (Tourism in strong governance) TOT : Twist on Trend (Business as Usual in strong governance) WFR : Way For Resorts (Tourism in weak governance) The classification of all rasters is as follow: PIXEL VALUE | PIXEL CLASS | NOTES 1 | Ocean | Not coastal zone 2 | Land | Not coastal zone 3 | Waterstorage | Human-made water storage (lakes/reservoirs) 4 | Water | Rivers, lakes estuaries etc 5 | Others | Mainly natural vegetation with grazing 6 | Conservation | Areas protected under IUCM status up to level IV 7 | Defence | Land with no entry permitted held by the Armies 8 | Forestry | Commercial plantations for timber 9 | IrrigS | Sugar cane fields requiring irrigation 10 | IntAnim | Indoor or improved pastures for high-density animal farms 11 | DryH | Crops, orchards, nut plantations, cotton with no irrigation 12 | DryS | Sugar cane fields with no irrigation 13 | IrrigH | Crops, orchards, nut plantations, cotton requiring irrigation 14 | Aquaculture | Land-based only 15 | Tourism | Areas built-up or used for tourism purposes 16 | Ports | Areas of commercial port infrastructure 17 | Mining | Areas under mining and related infrastructures 18 | Urban | Residential, retail and industrial areas (cities, towns) Part of the dataset includes a symbology layer filer that will automatically format the display in the best format for visualisation. classificationfromidrisi.lyr: This file contains the symbology (colour scheme for land use classes) to display the land maps and scenarios of the GBR coastal zone.

This dataset corresponds to a database of datasets that are relevant for the development of coastal development scenarios and impact assessments GBR. It corresponds to a list of all the datasets that were sourced as part of project 9.4. It contains basic information about each dataset along with the license that each dataset was obtained under and where the data can be sourced. This database is an excellent starting point for any others looking at obtaining data relevant for coastal management. Methods: Datasets were sourced from a large and various. They are presented in the database in their raw condition as downloaded or obtained. The database includes all metadata that was associated with them. The database is currently hosted on a server at the Centre of Excellence for Coral Reef Studies at James Cook University as is backed up weekly. Format: Coastal_zone_GIS_database.xlsx This is an excel file containing name and full description of all datasets. Each row corresponds to a different GIS file (either shapefile or raster). When dataset can be downloaded from a website with or without an open access licence, the link is included. For others, the best contact point is included.

This dataset contains the extent of the coastal zone for the Great Barrier Reef. This extent was identified as the most suitable area to be investigated on land and at sea as part of a conservation planning project looking at coastal development along the GBR coast. Methods: The terrestrial part of the coastal zone was initially defined in consultation with stakeholders and included the Queensland Government’s definition as “either: five kilometres from the coastline; or where land first reaches the height of 10 metres Australian Height Datum (AHD), whichever is further from the coast” (Queensland Coastal Management Plan). However, the Queensland government definition corresponds to a narrow strip of coastline in most areas and does not encompass most of coastal land uses (and their potential future expansion) that have a significant impact on the marine environmental such as agriculture. In the context of biodiversity conservation and water quality, workshop participants were asked what the coastal zone meant to them. The answers were summarised into four main categories: 1- Intensive coastal-based agriculture, 2- Geomorphological characteristics, 3- Typical coastal vegetation, and 4- Human presence and activities. The coastal zone was defined to include all features that would match these four categories, and that also included the Government’s definition. The marine part of the coastal zone was also defined during this process and the offshore boundary was described as the maximum flood plume extent (secondary flood plume) as in Álvarez-Romero et al. 2013. The extent was smoothed to allow a clear area to be defined. This covers the areas that are have the greatest level of impacts from coastal development, in particular from run-off. Format: GBR_coastal_zone.shp (and all associated files as part of the shapefile) This dataset contains a polygon that defines the extent of the area identified as the most appropriate coastal zone of the Great Barrier Reef to investigate conservation planning, in particular spatial planning of coastal development to ensure the health for the GBR World Heritage Area. References: Álvarez-Romero, J. G., M. Devlin, E. Teixeira da Silva, C. Petus, N. C. Ban, R. L. Pressey, J. Kool, J. J. Roberts, S. Cerdeira-Estrada, and A. S. Wenger. 2013. A novel approach to model exposure of coastal-marine ecosystems to riverine flood plumes based on remote sensing techniques. Journal of environmental management 119:194-207.

This dataset corresponds to the polygon digitisation of tourism sites and sugar mills. The locations of the tourism sites were obtained from a commercial database of tourism operators (Australian Tourism Warehouse). The location of sugar mills was obtained from information gathered on the internet and point locations were created for each mill. These datasets were developed to fill in missing source datasets for the scenario modelling used for the coastal development modelling. The tourism dataset was incorporated to the 2009 QLUMP to provide inclusion of tourism land use in this dataset. The sugar mill dataset was developed to provide the distances from sugar land use to mill and determine the restrictions for future expansion. Methods: Tourism dataset: Tourism corresponds to the official definition used by the World Trade Organisation as "Tourism comprises the activities of persons travelling to and staying in places outside their usual environment for not more than one consecutive year for leisure, business and other purposes." It is generally accepted that in most instances tourism activities are conducted from approximately 50km of the domicile. The definition of tourism land use in this study is areas with infrastructure and areas of cleared land that would not have existed if it were not for demand from tourism and hospitality and the ability to gain economic benefits from that tourism as well as urban zones created to accommodate required staff and services, green areas etc. Tourism land use is not part of the QLUMP land use map. It was manually added to this dataset using data on tourism infrastructure from a private company (the Australian Tourism Data Warehouse) that registers tourism-related businesses and records geographical coordinates of businesses. Buffers around point locations were created to acknowledge for different sizes of buildings/carparks etc. Governmental data of recreational areas and airport facilities obtained from GeoScience Australia were added. The tourism layer was improved but is still not inclusive of all tourism area in 2013. Sugar mill dataset: The location of sugar mills was obtained from information gathered on the internet and point location were created for each mill. It includes Mossman mill, Pleystowe mill, Raceway mill, Marian mill, Farleigh mill, Tableland mill, Mulgrave central mill, Babinda mill, South Johnstone mill, Tully mill, Victoria mill, Macknade mill, Invicta mill, Pioneer mill, Kalamia mill, Inkerman mill, Prosperine mill, Plane creek mill. Format: GBR_TourismLandUse2013.shp (and associated files as part of shapeflie) This layer is a polygon shapefile representing all areas of land use associated with tourism in the GBR coastal zone. GBR_CoastSugarMills2013.shp (and associated files as part of shapeflie) This layer is a point shapefile containing all locations of sugar mills in operation as in March 2013 in the GBR coastal region or in close proximity.