Identification, Assessment and Protection of National Estate Part a natural Values



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2. PROCESS

2.1 Criteria


Natural values in the Lower North-East NSW CRA region were assessed against the national estate criteria. Identification and treatment of natural values follows three broad subdivisions:


  • extensive natural values;

  • localised biodiversity values;

  • other natural values, including those relating to geology, geomorphology and soils.

In the regional context, assessment of the national estate requires a comparative appraisal of the significance of places having one or more attributes or values. The values are derived from the national estate criteria, which are reproduced in Appendix A.


Indicators of significance vary across the national estate natural values and include:


  • rarity or threat;

  • distribution pattern;

  • condition and integrity;

  • diversity or richness;

  • outstanding example.

  • representativeness


2.2 Thresholds


In order to identify areas of potential and indicative significance for natural national estate values a threshold level is set. Areas that exceed this threshold are regarded as meeting the criteria required for national estate listing. Thresholds are set in relation to the significance indicators and are specific to each national estate value. The development of thresholds for national estate significance varies depending on the level of current knowledge about the nature and extent of the values and their distribution in the landscape at a local, regional or national level.


2.3 Data


The area being assessed for natural national estate included all the forested public and private lands in the Lower North-East NSW CRA region. Adjacent areas in central, western and upper north-eastern NSW were used as context information, particularly where area thresholds were used in assessing criteria.
The major data sets used were:


  • flora - flora data was provided by the NSW National Parks and Wildlife Service and comprises a subset of the total sum of flora data. This sub-set has had basic validation done including the removal of gross errors. For the purposes of the work conducted for national estate, the data set was assumed to have an average spatial reliability of 1 km. The use of point records was avoided wherever possible because of the problems of spatial uncertainty. This data-set was primarily used for richness analysis. The data-set was not comprehensive south of the Hunter River.




  • fauna - fauna data was provided by the NSW National Parks and Wildlife Service and consisted of NSW Wildlife Atlas data with no reliability or accuracy work conducted on it. Rudimentary auditing on this data-set was conducted by Environment Australia and consisted of culling gross errors and spurious records. An additional CRA fauna data set for priority species was also provided. This data has had reliability and accuracy auditing done on it. Data-sets were taken to have an average spatial reliability of 1 km. The use of point records was avoided because of the problems of spatial uncertainty. These data-sets were primarily used for richness analysis.




  • forest ecosystems - the CRA forest ecosystem data-set was used, the version being that provided to the environment and heritage technical committee. This data-set was used to provided forest ecosystem landscape information. The data-set was available in 100 m grid-cells and is the output of forest ecosystem modelling conducted for the CRA This data set was not field validated at the time of report writing.




  • disturbance and old growth forest - disturbance was derived from biophysical naturalness (generated for wilderness analysis according to the methodology of the National Wilderness Inventory). The biophysical naturalness data relies heavily on aerial photo interpretation done as part of the Broad Old Growth Mapping Project (BOGM) undertaken in 1993. Biophysical naturalness was used to generate undisturbed catchments, natural landscapes and was used to filter data in some other analyses. The BOGM data-set applies to public land only and does not assess the level of disturbance to rainforest. The old growth data used is known to contain errors including errors relating to post 1993 disturbance. The extent of these errors is uncertain and has not been fully field-validated.




  • digital elevation model - 1:25,000 scale digital elevation model. The model was used to provide information on the escarpment, steep areas and general elevational information.

All mapped indicative national estate natural areas have been digitised and are held in ARC format on a Geographic Information System platform held by Environment Australia. Appendix E lists the data layers that have contributed to the lower north-eastern NSW national estate assessment, and the meta-data statements for each layer.




2.4 Methodology


The lower north-eastern NSW methodology was developed using best available data. It may not necessarily form the basis for, or be similar to, the formulation of requirements for other CRA regions in NSW. The detail of the process by which each of the national estate values was assessed is provided in Section 3.
For extensive natural values, the methodology closely followed the approach adopted in other CRAs. Undisturbed catchments were derived from data provided by the Wild Rivers Database. Natural landscapes were derived from the biophysical naturalness layer used to generate NWI wilderness. Old-growth forest was derived from the draft old-growth forest data provided to the data warehouse. These layers were driven entirely by data.
Areas of indicative potential national estate significance were delineated for local national estate values (ie not extensive natural values) for each criteria (see Attachment A) using the following information:


  • Species point locality data based on information provided by experts and literature review (Appendix B);

  • Landscape elements nominated by experts as being foci for the particular national estate criteria (Appendix C); and

  • Particular sites nominated by experts as being important for criteria (Appendix D).

Experts were consulted during the Conservation Requirements and Response to Disturbance Workshops held between June 1st and 31st July 1998 and were given additional time to submit information outside these forums.


The three data sources were compared (where available) to cross-validate the approaches used and refine and increase confidence in the outcome. Where two or more of the information sources described were available and comparable, the composite of the information was taken (ie, point data was merged with areas nominated by experts and landscape elements nominated by experts).
For species with disjunct ranges, species at the limit of their range, species richness, vegetation community richness, remnant vegetation communities, rare vegetation communities and old-growth forest, no landscape elements were nominated and the analysis was based entirely on species or forest ecosystem data. In this case, data was used to delineate indicative areas and checked for coverage of specific areas identified by experts.
All three data sources were available for endemic species, rare species, primitive and relictual species, refugia, migratory species and important habitat. For rare species and migratory species, species data adequately covered sites nominated and remained the primary data source used. For primitive and relictual species and refugia, the composite of point data and landscape values was taken, subject to coverage of areas identified by experts.
Most species related values were thresholded by displaying species point location data as a richness map across the landscape. Areas that had concentrations of relevant species more than two standard deviations above the average number of species in the landscape were identified as above threshold. The choice of 2 standard deviations was based on the need to delineate areas of significance that are clearly above the average level of variance in the landscape.
Principle characteristics of class and successional stages were felt to be best represented through the JANIS criteria.



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