This collection contains the data, processes and descriptions of workflows required to produce the representative species sets for vascular plants used in the NSW Biodiversity Indicator Program first assessment. The labels given to the datasets in this collection are defined in the workflow diagram and data links spreadsheet. This is a supplementary dataset that was used as an input to the three derived indicators for vascular plants: 1.2a expected survival of all known species 2.1a within-species genetic diversity (for all known species) 2.1b extant area occupied (for all known species). Details are given in the explanatory notes attached with this package and the method implementation report (Nipperess DA, Faith DP, Williams KJ, King D, Manion G, Ware C, Schmidt R, Love J, Drielsma M, Allen S & Ware C 2019, Expected survival and state of all known species: Data packages for the Biodiversity Indicator Program, first assessment.) accessed through the NSW Biodiversity Indicator Program website (see related links).

This data collection contains the tabular data, R scripts and methods used to generate three indicators specific to vascular plants for the NSW Biodiversity Indicator Program's first assessment (prior to the date of commencement of the Biodiversity Conservation Act 2016): 1.2a expected survival of all known species; 2.1a within-species genetic diversity (for all known species); 2.1b extant area occupied (for all known species). These indicators use representative species sets (provided in a related data collection). The habitat condition indicators (related data collections) are used to infer reduction in geographic range size. These indicators are an application of the ‘expected diversity’ framework. Reduction in the geographic range size of a species due to habitat loss, alteration and fragmentation is well known to decrease within-species genetic diversity and increase extinction risk. Therefore, current range size and proportion of range lost from habitat loss, alteration and fragmentation were estimated for vascular plant species known to occur naturally in New South Wales. The area of effective habitat (i.e. high quality habitat able to support biodiversity) remaining for each species was estimated from two alternative habitat condition indicators (Love et al. 2020): ecological condition of terrestrial habitat and ecological carrying capacity of terrestrial habitat. Because most species in New South Wales have not been formally assessed for possible threatened status (i.e. at heightened risk of extinction), a provisional risk assessment using a limited set of criteria was completed for all NSW vascular plant species for which adequate data were available from the Atlas of Living Australia. For consistency with IUCN recommended Red List methods, the expected survival of all known species uses area of occupancy within 2km grids to classify all species into four categories: lowest risk, lower risk, higher risk and highest risk. Each category was assigned a probability of survival, allowing the proportion of NSW vascular plant species expected to survive in 100 years to be estimated. Extrapolating trends in the rate of biodiversity loss requires that the list of species used in analyses are representative of the overall biodiversity of New South Wales. A subset of NSW vascular plant species that uniformly represent the full variety of natural habitats for vascular plants in New South Wales (called the representative species set) was selected to represent all vascular plant species, including those yet to be discovered. Ecological environments defined by a generalised dissimilarity model of vascular plants were used as a surrogate for the variety of natural habitats. Based on the proportion of remaining effective habitat in each species’ original range, within-species genetic diversity is also estimated. A range of values is given because each species will respond to loss of range size differently, depending on factors like dispersal ability and degree of adaptation to local environmental conditions, and these differences are not precisely known. The data and scripts provided in the data collection will allow the pre-commencement analyses of these indicators to be re-run. The method as applied in the scripts is designed to allow future iterations of the indicators to be run using updated input data. Guidelines on how to re-run the analyses using the scripts and adapt the data package for future iterations of the indicators is provided in the implementation report (Nipperess DA, Faith DP, Williams KJ, King D, Manion G, Ware C, Schmidt R, Love J, Drielsma M, Allen S & Gallagher R 2020. Expected survival and state of all known species, first assessment. Department of Planning, Industry and Environment NSW, Sydney, Australia.). The relevant guidelines extracted from that report are provided with this data package.

This collection contains the data, processes and descriptions of workflows required to produce the representative species sets for vascular plants used in the NSW Biodiversity Indicator Program first assessment. The labels given to the datasets in this collection are defined in the workflow diagram and data links spreadsheet. This is a supplementary dataset that was used as an input to the three derived indicators for vascular plants: 1.2a expected survival of all known species 2.1a within-species genetic diversity (for all known species) 2.1b extant area occupied (for all known species). Details are given in the explanatory notes attached with this package and the method implementation report (Nipperess DA, Faith DP, Williams KJ, King D, Manion G, Ware C, Schmidt R, Love J, Drielsma M, Allen S & Ware C 2019, Expected survival and state of all known species: Data packages for the Biodiversity Indicator Program, first assessment.) accessed through the NSW Biodiversity Indicator Program website (see related links).

This collection contains 3-arcsecond gridded datasets (ESRI binary float format in WGS84) showing the baseline (1990-centred) predicted potential distribution of 102 (class numbers range between 1 and 125) "Keith" Vegetation Classes for New South Wales based on their correlation with baseline ecological environments (c.1990 climates, substrate and landform). The vegetation patterns and classification derive from a map for NSW compiled by David Keith. A kernel regression was used with a geographically even sample of 9,951 locations of training classes for the 102 classes attributed with 21 GDM-scaled environmental predictors for Vascular Plants representing baseline ecological environments. The training class data input to the kernel regression is provided with this package. The GDM-scaled environmental predictors, source biological data and model fit parameters are also provided with the data package. Using the 1990 baseline training class data, and without constraining the prediction to pre-existing map boundaries, the kernel regression predicted the potential distribution of the 102 Vegetation Classes using 1990-centred (30 year average) baseline climates derived from ANUCLIM v6.1 (Xu and Hutchinson 2011) and soil/geology/landform attributes. The kernel regression generates unconstrained probabilities varying in the range from 0 and up to 1 for each of the 102 classes. The data are provided as 3-arcsecond (approximately 90m), ESRI binary float grid format in WGS84. Each class is denoted “UNCON###”, where the number refers to the code originally assigned to that class in the vegetation map. A lookup table linking the vegetation classes to the output codes and descriptive title is provided. The methods are described in "Doerr, VAJ, Williams, KJ, Drielsma, M, Doerr, ED, Davies, MJ, Love, J, Langston, A, Low Choy, S, Manion, G, Cawsey, EM, McGinness, HM, Jovanovic, T, Crawford, D, Austin, M & Ferrier, S 2013, Designing landscapes for biodiversity under climate change: Final report, National Climate Change Adaptation Research Facility, Gold Coast, 260 pp.". A plain English description of the method used (but applied Nationally) can be found in the AdaptNRM Guide “Helping biodiversity adapt to climate change: a community-level modelling approach”, available online at: www.adaptnrm.org. Source of vegetation class data: KEITH, D. A. (2002) A compilation map of native vegetation for New South Wales. NSW Biodiversity Strategy, New South Wales Government. KEITH, D. A. (2004) Ocean shores to desert dunes, Hurstville, Department of Environment and Conservation (NSW).