This dataset depicts a national map of available ASS mapping and ASS qualification inferred from surrogate datasets. ASS mapping is classified with a nationally consistent legend that includes risk assessment criteria and correlations between Australian and International Soil Classification Systems.

Existing digital datasets of ASS mapping have been sourced from each coastal state and territory and combined into a single national dataset. Original state classifications have been translated to a common national classification system by the respective creators of the original data and other experts. This component of the Atlas is referred to as the “Coastal” ASS mapping. The remainder of Australia beyond the extent of state ASS mapping has been “backfilled” with a provisional ASS classification inferred from national and state soils, hydrography and landscape coverages. This component is referred to as the “Inland” ASS mapping.

For the state Coastal ASS mapping, the mapping scale of source data ranges from 1:10K aerial photography in SA to 1:250K vegetation mapping in WA and NT, with most East coast mapping being at the 1:100K scale. For the backfilled inferred Inland ASS mapping the base scale is 1:2.5 million (except Tas.) overlaid with 1:250k hydography. As at 06/08, the Tasmanian inland mapping has been re-modelled using superior soil classification map derived from 1:100k landscape unit mapping.

NOTE: This is composite data layer sourced from best available data with polygons depicted at varying scales and classified with varying levels of confidence. Great care must be taken when interpreting this map and particular attention paid to the “map scale” and confidence rating of a given polygon. It is stressed that polygons rated with Confidence = 4 are provisional classifications inferred from surrogate data with no on ground verification. Also some fields contain a “-“, denoting that a qualification was not able to be made, usually because a necessary component of source mapping coverage did not extend to the given polygon. Lineage: Coastal ASS component:

Existing state CASS mapping was received and processed to varying degrees to conform to the NatCASS national ASS classification system. Spatially, all datasets were reprojected from their original projections to geographic GDA94. Classification of state mapping polygons to the NatCASS classification system was as follows. In the case of SA, NSW, Qld and WA it was a matter of directly translating the original state ASS classifications to the NatCASS classifications. These translations were undertaken by the creators of the state data and other experts within the respective states.

Due to the more broad classifications of the original Vic and Tas ASS mapping, polygons for these two states were initially translated to a NatCASS classification group (eg Tidal, Non-Tidal) by the data custodians then subsequently differentiated further through intersecting with other layers. These included the 3 second SRTM DEM and North Coast Mangrove mapping GIS datasets. The former being used to differentiate within the Non-Tidal zones (ie classes Ae-j and Be-j) and the latter used to differentiate the Tidal zones (ie Ab-d, Bb-d).

Mapping of the Tidal-Zone classes was augmented for all states except SA and NSW with 1:100K Coastal Waterways Geomorphic Habitat Mapping (Geoscience Australia). This dataset was used to infer additional areas of subaqueous material in subtidal wetland (class Aa & Ba) and Intertidal Flats (class Ab & Bb).

Inland ASS component:

Provisional Inland ASS classifications are derived from National and (in the case of Tasmania) state soil classification coverages combined with 1:250K series 3 Hydrography and Multiresolution Valley Bottom Floor Index (MrVBF).

Layer of regional and subregional linking corridors for fauna of the Upper North East (UNE) and Lower North East (LNE) NSW RFA regions. A new GIS program, NPWS CORRIDORS, was used to derive potential landscape linkages (habitat corridors) based on the predicted distributions of priority fauna species assemblages (see metadata for fauna key habitats). These ESRI grid outputs were refined, under a series of decision rules, to derive final corridor ESRI shapefile polygons. The final corridors map layer is a regional representation displaying the most likely occurrence of linking corridors for fauna consolidated at the regional scale. The mapping and derivation has been based on state-of-the-art data and GIS tools combined with qualitative interpretation based on ecological principles and expertise. As of April 2001, the mapping has not been formally field tested and the methods have not been peer-reviewed outside several conference and workshop presentations, all well received. A journal paper and project report are in preparation.

Abstract This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied. The metadata was not provided by the data supplier and has …Show full descriptionAbstract This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied. The metadata was not provided by the data supplier and has been compiled by the programme based on known details. Originally digitised very poorly, this dataset has been fixed as much as posssible. Important feeding habitats and roosting sites for seven species of migratory shorebirds (waders) were plotted on GIS software using air photos as templates. Data for this process was obtained from a variety of sources to provide numbers of birds recorded at each of the sites. The habitat mapping was done from first hand experience in the field by the author and/or from local ornithologists with particular skills in shorebird identification and local knowledge of habitats used by shorebirds. The accuracy of the data plotted will not always match the topography, such as shoals, sand spits or shorelines because these are constantly changing (the air photos used are at least 10 years old).However this is the most precise shorebird mapping available. Important feeding habitats and roosting sites for seven species of migratory shorebirds (waders) were plotted on GIS software using air photos as templates. Data for this process was obtained from a variety of sources to provide numbers of birds recorded at each of the sites. The habitat mapping was done from first hand experience in the field by the author and/or from local ornithologists with particular skills in shorebird identification and local knowledge of habitats used by shorebirds. The accuracy of the data plotted will not always match the topography, such as shoals, sand spits or shorelines because these are constantly changing (the air photos used are at least 10 years old). However this is the most precise shorebird mapping available. The coastline template provided by DEC was not of sufficient accuracy for the purpose of this project and has not been used in any of the maps other than to illustrate the whole of coast maps. Data were sorted and any obvious errors removed before being used. Only data since 1990 has been used for the purpose of this report due to changes in habitats and population estimates over time. Habitat and species management can only be carried out using the most up to date data and habitat mapping. However as there are no regular population estimates for all coastal estuaries and some data over a 15 year period was used to provide a pattern of habitat usage by threatened migratory shorebirds for the whole coast. There has been no attempt to assess wetlands habitats away from the coast or estuarine habitats due to lack of data for inland sites. However the species concerned are largely coastal during their non-breeding seasons, when the birds are in Australia. This project was commissioned by the Department of Environment and Conservation to identify and map all known shorebird feeding habitat and roost sites along coastal NSW, specifically that of the Sanderling Calidris alba, Great Knot Calidris tenuirostris, Greater Sand Plover Charadrius leschenaultii, Lesser Sand Plover Charadrius mongolus, Broad-billed Sandpiper Limicola falcinellus, Black-tailed Godwit Limosa limosa and Terek Sandpiper Xenus cinereus. The results of the project was a series of shape files and data files using Arcview 9.1 and Microsoft Excel-,compatible with those of the DEC Spatial Analysis & Information Section, to map GIS layers for: the known feeding habitat of Sanderling, Great Knot, Greater Sand Plover, Lesser Sand Plover, Broad-billed Sandpiper, Black-tailed Godwit and Terek Sandpiper along the NSW coast; the boundaries of key foraging sites of the above species along the NSW coast; and the boundaries of roosting sites of the above species along the NSW coast. This dataset has been provided to the BA Programme for use within the programme only. Third parties should contact the NSW Office of Environment & Heritage. Purpose The degree of accuracy of mapping is governed to a large extent on the maps and air photos that are used as a template. Although topographic maps are drawn from air photos the information on the map is what is interpreted by the person responsible for interpreting and plotting the information onto a map. The majority of shorebird habitat is subtidal and as a consequence of this is rarely mapped, unless it happens to be part of a major sand bar. Coastlines are largely delineated by the high tide mark. This in itself is variable depending on whether the tide is high at the time the photograph was taken and whether the tides are spring tides or neap tides at the time. Furthermore the accuracy of the coastline drawn depends on the dedication of the cartographer and at which scale the map is drawn. The coastline provided be DEC for this project is of little use fro drawing shape files to the degree of accuracy that would be required by local government and for drawing b Dataset History Methods: All existing data on shorebird distribution (foraging and roosting records) held by bird groups, specialists and DEC Wildlife Atlas were reviewed and updated where information was available and checked for inaccuracies. , Data was also updated where required as a result of changes to estuarine habitats through the use of aerial photography. Where necessary face to face meetings were arranged with relevant people to obtain input from other shorebird specialists, failing this communication was by phone or email. Digitised air photos provided by DEC were used to map all known shorebird foraging habitat and roost sites along the NSW coast, noting habitat utilised by threatened migratory shorebirds, specifically Sanderling, Great Knot, Greater Sand Plover, Lesser Sand Plover, Broad-billed Sandpiper, Black-tailed Godwit and Terek Sandpiper. Dataset Citation NSW Department of Environment, Climate Change and Water (2010) Threatened Migratory Shorebird Mapping NSW DECCW 2006. Bioregional Assessment Source Dataset. Viewed 31 May 2018, http://data.bioregionalassessments.gov.au/dataset/92a85a3e-11e9-43cf-96d6-f8cd4fb65d03.

Layer of regional and subregional linking corridors for fauna of the Upper North East (UNE) and Lower North East (LNE) NSW RFA regions. A new GIS program, NPWS CORRIDORS, was used to derive potential landscape linkages (habitat corridors) based on the predicted distributions of priority fauna species assemblages (see metadata for fauna key habitats). These ESRI grid outputs were refined, under a series of decision rules, to derive final corridor ESRI shapefile polygons. The final corridors map layer is a regional representation displaying the most likely occurrence of linking corridors for fauna consolidated at the regional scale. The mapping and derivation has been based on state-of-the-art data and GIS tools combined with qualitative interpretation based on ecological principles and expertise. As of April 2001, the mapping has not been formally field tested and the methods have not been peer-reviewed outside several conference and workshop presentations, all well received. A journal paper and project report are in preparation. Data and Resources

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied:

Layer of regional and subregional linking corridors for fauna of the Upper North East (UNE) and Lower North East (LNE) NSW RFA regions. A new GIS program, NPWS CORRIDORS, was used to derive potential landscape linkages (habitat corridors) based on the predicted distributions of priority fauna species assemblages (see metadata for fauna key habitats). These ESRI grid outputs were refined, under a series of decision rules, to derive final corridor ESRI shapefile polygons. The final corridors map layer is a regional representation displaying the most likely occurrence of linking corridors for fauna consolidated at the regional scale. The mapping and derivation has been based on state-of-the-art data and GIS tools combined with qualitative interpretation based on ecological principles and expertise. As of April 2001, the mapping has not been formally field tested and the methods have not been peer-reviewed outside several conference and workshop presentations, all well received. A journal paper and project report are in preparation.

Additional metadata

Gilmore, A. M. and Parnaby, H. E., 1994. Vertebrate Fauna of Conservation Concern in North-East NSW Forests. North East Forests Biodiversity Study, Report No. 3e, unpublished report, NSW National Parks and Wildlife Service. Metadata statement for UNE/LNE Key Habitats. Metadata statement for UNE/LNE RFA Centres of Endemism. NPWS, 1994a. Environmental GIS database for north-east NSW. North East Forests Biodiversity Study, Report No. 2, unpublished report, NSW National Parks and Wildlife Service. NPWS, 1994b. Fauna of North-East NSW Forests. North East Forests Biodiversity Study, Report No. 3, unpublished report, NSW National Parks and Wildlife Service. NPWS 1999. Modelling areas of habitat significance for vertebrate fauna and vascular flora in north east NSW. A project undertaken for the Joint Commonwealth NSW Regional Forest Agreement Steering Committee as part of the NSW Comprehensive Regional Assessments. Scotts, D., Drielsma, M, Whish, G. and Kingma, L. in prep. Regional key habitats and corridors for forest fauna of north-east New South Wales; a framework to focus conservation planning, assessment and management.

Dataset History

Lineage: Lineage The process employed in deriving fauna corridors is explicit and repeatable in as much as: * The fauna species models, which are the basic biodiversity entities that the project seeks to summarise and integrate are stored and held by NPWS; * All relevant data layers, developed at each stage of the project, are stored and held by NPWS; * The Geographic Information System (GIS) tools developed for the analyses are available as extensions to the ARCVIEW GIS. At numerous stages of the analyses, informed interpretation of outputs and assignment of thresholds has been required to move the process along or to finalise an output. Any qualitative decisions taken have been based on the project manager's ecological expertise and knowledge of the data sets being considered. Habitat corridors have been mapped across public and private lands. The process of deriving and mapping regional corridors for fauna has involved the use of fauna assemblage distributions and fauna key habitats (see additional metadata referenced below), as surrogates for areas of high fauna conservation, and as the actual habitats to be linked. This involved a 4 step process which is detailed below: STEP 1. UNDERTAKE LEAST COST PATHWAYS ANALYSES TO DERIVE POTENTIAL REGIONAL AND SUB-REGIONAL CORRIDORS A technique has been developed and refined by the Research and Development Unit of the NPWS GIS Division to aid with the delineation of habitat corridors; NPWS CORRIDORS is used as an extension to the ARCVIEW GIS program. CORRIDORS is used to identify the pathways that most efficiently link identified significant landscape elements or habitats. The program operates under the principle that species, and their constituent genes, are most likely to move (while foraging, dispersing, breeding, migrating) along gradients of preferred habitat; non-preferred habitats representing varying levels of impedance or even barriers. For any particular biodiversity entity, in this case species assemblages, the most efficient landscape links are those that exact the "least cost", in terms of energy expenditure, for their use. More favourable habitats, be it for foraging, roosting, nesting or as transitory movements, are assumed to exact less cost for their use than less favourable marginal or non-habitats. Non-habitats may include areas of native vegetation that are simply not suitable for use by the species assemblage concerned. They also include areas that have been cleared of native vegetation and developed for human uses such as agriculture and urban expansion. The basic requirement of the CORRIDORS program is a "cost grid". This is a continuous probability surface covering the entire study area and describing the relative costs, to a particular biodiversity entity (e.g. a species or species assemblage), of utilizing each grid cell within the area as habitat, or as a potential linking pathway. Cost grids were derived for the KHC Project through a combination of the assemblage habitat map layer and existing maps of extant vegetation and land tenure. The derived cost grids reflect levels of habitat suitability and tenure class for every grid cell available as a potential linking pathway. Predicted habitats for the assemblage are deemed the least costly pathways, the best predicted habitat class (class 3) carrying the least cost. Extant vegetation that is not predicted habitat represents a less costly path than cleared land. Within each habitat suitability class, tenure is weighted to place greater cost on private lands as opposed to public lands and, within public lands, a greater cost on state forests as opposed to NPWS estate and Crown Reserves managed by NPWS. The effect of tenure weightings is to favour reserved lands over state forests over private lands as corridor links, all else being equal. Additional costs were applied to mapped estuaries making it more "costly", but not impossible, for the program to link across these features, relative to alternative links, all else being equal. The CORRIDORS program utilizes paired reference points, assigned in an iterative manner and apportioned within focal habitat types (e.g. assemblage habitats and key habitats), which it works to via the most efficient pathways available according to the cost grid. The reference points are directed into identified strategic areas, making them focal areas for landscape links. For the purposes of the KHC Project analyses 10,000 reference points were used and assigned to the predicted assemblage habitats with a minimum proportion directed into fauna core habitats. In seeking to establish the most ecologically valid corridor network for the KHC Project study areas the LCP analyses were undertaken at two levels: Level 1: a CORRIDORS analysis for each of the each identified fauna assemblage independently (7 for UNC, 7 for LNC, 6 for TAB and 5 for SYD); Level 2: a CORRIDORS analysis for the combined assemblages within each study area. These two levels were selected in order to pursue the goal of enhancing overall landscape connectivity. The first level will establish potential corridor links for species within each assemblage, a clear goal of landscape ecology. The second level will consolidate the landscape approach, whereby the mosaics of habitats and species assemblages across a landscape are treated as one functional system, another ecological requirement enhancing overall landscape connectivity. These between assemblage corridors are also intended to provide for larger scale dispersal and movement (e.g. migration) between predicted assemblage habitats. The CORRIDORS outputs are continuous probability surface models (map layers) depicting the pathways of least cost linking habitats, and particularly core habitats, of each fauna assemblage individually, plus a combined assemblages run for each KHC study area. These map layers can be used as planning entities in their own right or, as in this project, can be combined and weighted to derive regional and sub-regional corridors. STEP 2. DERIVING REGIONAL AND SUB-REGIONAL CORRIDOR GRIDS FROM "CORRIDORS" PROGRAM OUTPUTS The CORRIDORS outputs represent potential corridors; assessing them and moving them from potential corridors to Regional and Sub-regional corridors followed another set process for each KHC study area: A. Reclassify the continuous probability surface layers depicting the potential corridors for each assemblage to five classes; 0,1,2,3,4, based on perceived thresholds of significance, with class 4 being those potential corridors at the highest probability end of the scale, and of the highest priority for that assemblage; B. Do the same for the between assemblage potential corridors for each KHC study area; C. For each KHC study area, combine the classified assemblage, and between assemblage corridor grids and sum the combined classes; D. Apply thresholds to delineate Regional and Sub-regional corridors; E. For interim display purposes (prior to final conversion of the grid map layers to polygon map layers) use existing vegetation mapping to intersect the derived corridors map layers and display vegetated and non-vegetated portions of the regional and sub-regional corridors. Regional and sub-regional corridors extend across all tenures with certain private lands being crucial links in the network. In many instances, the least costly pathway to link some assemblage habitats crossed cleared lands. The potential regional and sub-regional corridor grid map layers depicting potential corridors linking predicted fauna assemblage habitats are available

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. Receptor impact models (RIMs) use inputs from surface water and groundwater models. For a given node, there is a value for each combination of hydrological response variable, future, and replicate or run number. RIMs are developed for specific landscape classes. The hydrological response variables that a RIM within a landscape class requires are organised by the R script RIM_Prediction_CreateArray.R into an array. The formatted data is available as an R data file format called RDS and can be read directly into R. The R script IMIA_HUN_RIM_predictions.R applies the receptor model functions (RDS object as part of Data set 1: Ecological expert elicitation and receptor impact models for the HUN subregion) to the HRV array for each landscape class (or landscape group) to make predictions of receptor impact varibles (RIVs). Predictions of a receptor impact from a RIM for a landscape class are summarised at relevant AUIDs by the 5th through to the 95th percentiles (in 5% increments) for baseline and CRDP futures. These are available in the HUN_RIV_quantiles_IMIA.csv data set. RIV predictions are further summarised and compared as boxplots (using the R script boxplotsbyfutureperiod.R) and as (aggregated) spatial risk maps using GIS. Dataset History Receptor impact models (RIMs) are developed for specific landscape classes. The hydrological response variables that a RIM within a landscape class requires are organised by the R script RIM_Prediction_CreateArray.R into an array. The formatted data is available as an R data file format called RDS and can be read directly into R. The R script IMIA_HUN_RIM_predictions.R applies the receptor model functions (RDS object as part of Data set 1: Ecological expert elicitation and receptor impact models for the HUN subregion) to the HRV array for each landscape class (or landscape group) to make predictions of receptor impact varibles (RIVs). Predictions of a receptor impact from a RIM for a landscape class are summarised at relevant AUIDs by the 5th through to the 95th percentiles (in 5% increments) for baseline and CRDP futures. These are available in the HUN_RIV_quantiles_IMIA.csv data set. RIV predictions are further summarised and compared as boxplots (using the R script boxplotsbyfutureperiod.R) and as (aggregated) spatial risk maps using GIS. Dataset Citation Bioregional Assessment Programme (XXXX) HUN Ecological expert elicitation and receptor impact models v01. Bioregional Assessment Derived Dataset. Viewed 28 August 2018, http://data.bioregionalassessments.gov.au/dataset/8974bf25-b681-43ac-bf32-6fde00406391. Dataset Ancestors Derived From Natural Resource Management (NRM) Regions 2010 Derived From NSW Wetlands Derived From NSW Office of Water Surface Water Entitlements Locations v1_Oct2013 Derived From HUN Landscape Classification v02 Derived From Travelling Stock Route Conservation Values Derived From Darling River Hardyhead Predicted Distribution in Hunter River Catchment NSW 2015 Derived From Threatened migratory shorebird habitat mapping DECCW May 2006 Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only Derived From Climate Change Corridors for Nandewar and New England Tablelands Derived From National Groundwater Dependent Ecosystems (GDE) Atlas Derived From Asset database for the Hunter subregion on 27 August 2015 Derived From Hunter CMA GDEs (DRAFT DPI pre-release) Derived From Estuarine Macrophytes of Hunter Subregion NSW DPI Hunter 2004 Derived From Geofabric Surface Network - V2.1.1 Derived From Birds Australia - Important Bird Areas (IBA) 2009 Derived From Camerons Gorge Grassy White Box Endangered Ecological Community (EEC) 2008 Derived From NSW Office of Water Surface Water Licences Processed for Hunter v1 20140516 Derived From Asset database for the Hunter subregion on 24 February 2016 Derived From Fauna Corridors for North East NSW Derived From Gosford Council Endangered Ecological Communities (Umina woodlands) EEC3906 Derived From NSW Office of Water Surface Water Offtakes - Hunter v1 24102013 Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA) Derived From Asset list for Hunter - CURRENT Derived From Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental Significance Database (BA subset - RESTRICTED - Metadata only) Derived From Bioregional Assessment areas v01 Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb) Derived From Ramsar Wetlands of Australia Derived From Native Vegetation Management (NVM) - Manage Benefits Derived From GEODATA TOPO 250K Series 3 Derived From NSW Catchment Management Authority Boundaries 20130917 Derived From Geological Provinces - Full Extent Derived From Hunter subregion boundary Derived From Commonwealth Heritage List Spatial Database (CHL) Derived From GW Element Bores with Unknown FTYPE Hunter NSW Office of Water 20150514 Derived From Greater Hunter Native Vegetation Mapping with Classification for Mapping Derived From Atlas of Living Australia NSW ALA Portal 20140613 Derived From Bioregional Assessment areas v03 Derived From Spatial Threatened Species and Communities (TESC) NSW 20131129 Derived From HUN Landscape Classification v03 Derived From Bioregional Assessment areas v02 Derived From National Heritage List Spatial Database (NHL) (v2.1) Derived From Climate Change Corridors (Dry Habitat) for North East NSW Derived From Groundwater Entitlement Hunter NSW Office of Water 20150324 Derived From Asset database for the Hunter subregion on 20 July 2015 Derived From NSW Office of Water combined geodatabase of regulated rivers and water sharing plan regions Derived From Asset database for the Hunter subregion on 22 September 2015 Derived From Asset database for the Hunter subregion on 16 June 2015 Derived From Australia World Heritage Areas Derived From HUN River Perenniality v01 Derived From Lower Hunter Spotted Gum Forest EEC 2010 Derived From New South Wales NSW Regional CMA Water Asset Information WAIT tool databases, RESTRICTED Includes ALL Reports Derived From Greater Hunter Native Vegetation Mapping Derived From HUN AssetList Database v1p2 20150128 Derived From Climate Change Corridors Coastal North East NSW Derived From Groundwater Economic Elements Hunter NSW 20150520 PersRem v02 Derived From NSW Office of Water - GW licence extract linked to spatial locations for North and South Sydney v2 20140228 Derived From Asset database for the Hunter subregion on 12 February 2015 Derived From New South Wales NSW - Regional - CMA - Water Asset Information Tool - WAIT - databases Derived From Climate Change Corridors (Moist Habitat) for North East NSW Derived From Operating Mines OZMIN Geoscience Australia 20150201 Derived From NSW Office of Water - National Groundwater Information System 20141101v02 Derived From Bioregional_Assessment_Programme_Catchment Scale Land Use of Australia - 2014 Derived From Groundwater Economic Assets Hunter NSW 20150331 PersRem Derived From Australia - Species of National Environmental Significance Database Derived From Monitoring Power Generation and Water Supply Bores Hunter NOW 20150514 Derived From Northern Rivers CMA GDEs (DRAFT DPI pre-release) Derived From NSW Office of Water GW licence extract linked to spatial locations for NorthandSouthSydney v3 13032014 Derived From Australia, Register of the National Estate (RNE) - Spatial Database (RNESDB) Internal Derived From NSW Office of Water Groundwater Entitlements Spatial Locations Derived From NSW Office of Water Groundwater Licence Extract, North and South Sydney - Oct 2013 Derived From Directory of Important Wetlands in Australia (DIWA) Spatial Database (Public) Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 (Not current release) Derived From Groundwater Dependent Ecosystems supplied by the NSW Office of Water on 13/05/2014

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. Receptor impact models (RIMs) use inputs from surface water and groundwater models. For a given node, there is a value for each combination of hydrological response variable, future, and replicate or run number. RIMs are developed for specific landscape classes. The hydrological response variables that a RIM within a landscape class requires are organised by the R script RIM_Prediction_CreateArray.R into an array. The formatted data is available as an R data file format called RDS and can be read directly into R. The R script IMIA_XXX_RIM_predictions.R applies the receptor model functions (RDS object as part of Data set 1: Ecological expert elicitation and receptor impact models for the XXX subregion) to the HRV array for each landscape class (or landscape group) to make predictions of receptor impact varibles (RIVs). Predictions of a receptor impact from a RIM for a landscape class are summarised at relevant AUIDs by the 5th through to the 95th percentiles (in 5% increments) for baseline and CRDP futures. These are available in the XXX_RIV_quantiles_IMIA.csv data set. RIV predictions are further summarised and compared as boxplots (using the R script boxplotsbyfutureperiod.R) and as (aggregated) spatial risk maps using GIS. Dataset History Receptor impact models (RIMs) are developed for specific landscape classes. The hydrological response variables that a RIM within a landscape class requires are organised by the R script RIM_Prediction_CreateArray.R into an array. The formatted data is available as an R data file format called RDS and can be read directly into R. The R script IMIA_XXX_RIM_predictions.R applies the receptor model functions (RDS object as part of Data set 1: Ecological expert elicitation and receptor impact models for the XXX subregion) to the HRV array for each landscape class (or landscape group) to make predictions of receptor impact varibles (RIVs). Predictions of a receptor impact from a RIM for a landscape class are summarised at relevant AUIDs by the 5th through to the 95th percentiles (in 5% increments) for baseline and CRDP futures. These are available in the XXX_RIV_quantiles_IMIA.csv data set. RIV predictions are further summarised and compared as boxplots (using the R script boxplotsbyfutureperiod.R) and as (aggregated) spatial risk maps using GIS. Dataset Citation Bioregional Assessment Programme (XXXX) GLO Ecological expert elicitation and receptor impact models v01. Bioregional Assessment Derived Dataset. Viewed 12 July 2018, http://data.bioregionalassessments.gov.au/dataset/76fb9d24-b8db-4251-b944-f69f983507ff. Dataset Ancestors Derived From Groundwater Dependent Ecosystems supplied by the NSW Office of Water on 13/05/2014 Derived From Greater Hunter Native Vegetation Mapping with Classification for Mapping Derived From BA ALL mean annual flow for NSW - Choudhury implementation of Budyko runoff v01 Derived From Bioregional Assessment areas v06 Derived From Bioregional Assessment areas v04 Derived From Bioregional Assessment areas v02 Derived From Gippsland Project boundary Derived From Natural Resource Management (NRM) Regions 2010 Derived From GLO subregion boundaries for Impact and Risk Analysis 20160712 v01 Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb) Derived From Bioregional_Assessment_Programme_Catchment Scale Land Use of Australia - 2014 Derived From GEODATA TOPO 250K Series 3 Derived From Australian Geological Provinces, v02 Derived From NSW Catchment Management Authority Boundaries 20130917 Derived From Geological Provinces - Full Extent Derived From GLO Preliminary Assessment Extent Derived From Australian Coal Basins Derived From Gloucester River Types v01 Derived From Bioregional Assessment areas v03

In 1986, the River Murray Riparian Vegetation Survey was initiated by the Murray-Darling Basin Commission (MDBC) to assess the present status of the vegetation along the River Murray, to identify causes of degradation, and to develop solutions for its rehabilitation and long term stability. The Study area was the floodplain of the River Murray and its anabranches, including the Edward-Wakool system, from below Hume Dam to the upper end of Lake Alexandrina, a total of nearly 9,000 square kilometres (900,000 hectares). The survey was conducted by Margules and Partners Pty Ltd, P and J Smith Ecological Consultants, and the then Victorian Department of Conservation, Forests and Lands (DCFL). The results were then compiled by DCFL, a report published (see References) and a GIS was constructed. Please note that the vegetation mapping uses a mixed floristic/structural classification. VIS_ID 4156

Data quality

Lineage:

The process employed in deriving fauna corridors is explicit and repeatable in as much as:

• The fauna species models, which are the basic biodiversity entities that the project seeks to summarise and integrate are stored and held by NPWS;

• All relevant data layers, developed at each stage of the project, are stored and held by NPWS;

• The Geographic Information System (GIS) tools developed for the analyses are available as extensions to the ARCVIEW GIS.

At numerous stages of the analyses, informed interpretation of outputs and assignment of thresholds has been required to move the process along or to finalise an output. Any qualitative decisions taken have been based on the project manager's ecological expertise and knowledge of the data sets being considered.

Habitat corridors have been mapped across public and private lands.

The process of deriving and mapping regional corridors for fauna has involved the use of fauna assemblage distributions and fauna key habitats (see additional metadata referenced below), as surrogates for areas of high fauna conservation, and as the actual habitats to be linked.

For more detailed information about lineage see http://www.bionet.nsw.gov.au/website/keyhabs

Positional accuracy:

Species assemblage distributions, key habitats and corridors have been derived from interpolated species distributions generated by modelling point locality species records (with a spatial accuracy of approximately 100m) in relation to mapped environmental layers (with a map scale of 1:100 000 to 1:250 000) (see additional metadata). The CORRIDORS analysis was undertaken at the 500m grid cell size in order to reduce computational burden. The outputs were re-sampled to 100m grid cell for display and storage.

In applying and interpreting the key habitats map layer it should always be remembered that they are based on modelled data and have been developed at the regional scale, to inform regional land, water and vegetation reform programs. The mapped products represent a state-of-the-art consolidation of fauna information for UNE and LNE areas but should be interpreted in terms of a likelihood of occurrence of fauna key habitats; they are indicative representations (see mapping caveat). It should also be noted that the process of development of the key habitats layer has necessarily included qualitative judgements relating to interpretations and setting of thresholds; these have been made based on ecological expertise and explicit decision rules.

In 1986, the River Murray Riparian Vegetation Survey was initiated by the Murray-Darling Basin Commission to assess the present status of the vegetation along the River Murray, to identify causes of degradation, and to develop solutions for its rehabilitation and long term stability. The Study area was the floodplain of the River Murray and its anabranches, including the Edward-Wakool system, from below Hume Dam to the upper end of Lake Alexandrina, a total of nearly 9,000 square kilometres (900,000 hectares). The survey was conducted by Margules and Partners Pty Ltd, P and J Smith Ecological Consultants, and the then Victorian Department of Conservation, Forests and Lands (DCFL). The results were then compiled by DCFL, a report published (see References) and a GIS was constructed.

Please note that the vegetation mapping uses a mixed floristic/structural classification. VIS_ID 3964

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

Originally digitised very poorly, this dataset has been fixed as much as posssible. Important feeding habitats and roosting sites for seven species of migratory shorebirds (waders) were plotted on GIS software using air photos as templates. Data for this process was obtained from a variety of sources to provide numbers of birds recorded at each of the sites. The habitat mapping was done from first hand experience in the field by the author and/or from local ornithologists with particular skills in shorebird identification and local knowledge of habitats used by shorebirds. The accuracy of the data plotted will not always match the topography, such as shoals, sand spits or shorelines because these are constantly changing (the air photos used are at least 10 years old).However this is the most precise shorebird mapping available.

This dataset has been provided to the BA Programme for use within the programme only. Third parties should contact the NSW Office of Environment & Heritage (http://www.environment.nsw.gov.au/).

Dataset History

Lineage: GIS data created by a consultant who innacurately indicated that he knew how to use GIS. All existing data on shorebird distribution (foraging and roosting records) held by bird groups, specialists and DEC Wildlife Atlas were reviewed and updated where information was available and checked for inaccuracies. , Data was also updated where required as a result of changes to estuarine habitats through the use of aerial photography. Where necessary face to face meetings were arranged with relevant people to obtain input from other shorebirdspecialists, failing this communication was by phone or email. Digitised air photos provided by DEC were used to map all known shorebird foraging habitat and roost sites along the NSW coast, noting habitat utilised by threatened migratory shorebirds, specifically Sanderling, Great Knot, Greater Sand Plover, Lesser Sand Plover, Broad-billed Sandpiper, Black-tailed Godwit and Terek Sandpiper.

Dataset Citation

NSW Department of Environment, Climate Change and Water (2010) Threatened migratory shorebird habitat mapping DECCW May 2006. Bioregional Assessment Source Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/cc0b62a0-ded7-4c14-b954-1552337b395e.

Vegetation Distribution Map for the western portion of Kempsey Shire Council. Project undertaken by GHD consultants in 2007. The western portion is generally considered to be those lands within the shire boundary on the western side of the Pacific Highway.; ; The vegetation mapping was carried out using Landsat 5 TM (imagery taken between Dec 2004 and August 2005) scene and topographic variables as a basis for multi-variate statistical classification techniques. The image was 'seeded' with areas of known vegetation type 'samples', as determined by the field survey, from which spectral and topographic similarity was used to predict the presence of those types elsewhere in the image. A few iterations of model and map refinement and validation were then used to produce a final map that was then converted to the preferred GIS format for display.; ; VIS_ID 244