tools for bioregional conservation assessment

4

TOOLS FOR

BIOREGIONALCONSERVATION

ASSESSMENT

Photographic images by:

Wil AllenMurray EllisGary Bridle

4

TOOLS FOR

BIOREGIONAL

CONSERVATION

ASSESSMENT

The development of a bioregional network of siteshas traditionally been undertaken by experts witha working knowledge of biological communities orspecies of interest. More recently computer-

assisted methods have been used. The systematiccombination of the two offers the best way of unifyingdiverse priorities to develop a cohesive network of sites uponwhich to begin planning.This Section outlines methods for effectively obtaining expert inputand discusses one example of the software currently available forbioregional conservation assessment in NSW.

4.1 EXPERT INPUT Expert input can help fill gaps in information at all stages of abioregional conservation assessment. Input by experts can occur onan individual basis or in groups to:

■ Source information in general;

■ Verify information where there are doubts about quality;

■ Better articulate potential problems with data layers oranalytical techniques;

■ Provide specific information;

■ Review or check various stages of the assessment; and

■ Decide on goals.

Some important considerations and ways of eliciting input:

■ Establish a context for the informants;

■ Identify how the information will be used;

■ Break information gathering into smaller steps and preferablyover several occasions to provide time for revision andreflection;

■ If possible gather information in several formats, for example,written or verbal (including group and individual discussion).This can enhance the capture of the unwritten and complexknowledge of your informants;

■ Where information is being provided, seek someconsistency/checking of information both by the informant andacross informants; and

■ Document who provided information and when so that it canaccurately referenced.

The Delbecq and Van de Ven *Nominal GroupTechnique (NGT) and the *Delphi TechniqueThese techniques are used as ways of soliciting information fromgroups of people, that is, where individual judgements must betapped and combined to arrive at solutions to problems or togenerate ideas. They were created specifically for situations wherethere is an incomplete knowledge and pooled knowledge isrequired.

74 Tools for bioregional conservation assessment

NGT summarised involves the following steps:

■ Silent generation of ideas in writing by the group of individuals;

■ Feedback from each individual in the group such that each presents one idea from their list and the recorder writes the idea in view of others on a board;

■ Discussion of each idea for clarification and evaluation; and

■ Individual voting on priority ideas by rating and then pooling the group decision.

The aim of this process is to balance participation amongmembers (by silently generated individual contribution ofideas) and to ensure that both private thinking and groupdiscussion occur thus catering for creative thinking whilereducing errors by aggregating individual judgements.

The Delphi technique does not require face-to-faceinteraction. It is a method for seeking and collatingjudgements through a set of carefully designed sequentialquestionnaires interspersed with summarised information andfeedback of opinions.

Delphi techniques can be used to:

■ Explore the underlying assumptions or information leading to different judgements;

■ Seek out information which may generate consensus; and/or

■ Correlate informed judgements on a topic spanning a wide range of disciplines.

It is possible to vary the format in which the Delphi technique isused, based on:

■ Whether the respondent group is anonymous;

■ Whether the questions are open-ended or structured;

■ How many iterations of questionnaires and feedback reports areneeded; and

■ What decision rules used to aggregate the group judgements or information.

At its simplest the following steps occur:

■ A questionnaire is developed and sent to the group

■ The results are summarised and a feedback report and a second questionnaire is sent to the group;

■ Respondents are asked to independently vote on priority ideas included in the second questionnaire; and

■ A final summary and feedback report is then mailed to the respondent group.

75Tools for bioregional conservation assessment

The NGT is preferred when verbal clarification is required andtime is not available to send, collate and report on questionnaires.The Delphi technique requires the least amount of time forparticipants. Where the dynamics of a group is likely to result insome information not being made available (that is, because it issensitive, for example, land management allocation) then aDelphic approach may be preferable.

* The term nominal was adopted to refer to processes which bringgroups together but do not allow the individuals to communicateverbally. The Delphi technique was named after the DelphicOracle (Delbecq et al. 1975).

4.2 MATHEMATICAL MODELS There are two broad types of mathematical models which can beused to assess bioregion-wide data sets: optimality and heuristic.

OptimisingMathematically, this technique identifies the smallest set of sitesin terms of the number or total area needed to represent thetargeted feature (Pressey et al. 1996). They do this by exploringand comparing all the possible combinations of sites in the set ofdata. There may be more than one optimal suite of sites as aresult of these combinations (Csuti et al. 1997) (depending on thesize of the data set, the narrowness of the targets or theoccurrence of the targeted feature). While the outcome of thesealgorithms is reliable they can take hours, days or longer to becomputed. However with advances in computer technology,processing time is likely to be reduced.

HeuristicA heuristic technique is essentially a trial-and-error technique forlearning or problem solving. Here, the decision-making isprogressive. It applies particularly when the conservation assessoris progressively selecting sites and must choose between ‘tied sites’(that is, two sites having the same value). At this point decisionsare either made randomly by selecting the first site on the list orby implementing a rule which is either thought to be the mostefficient solution or the best choice in order to achieve someother goal. Because the ‘best rule’ cannot always be identified‘before hand’ they are called heuristic (Pressey et al. 1997). Theyare quicker in terms of processing time than optimising algorithmsessentially because they do not explore every combinationavailable before selecting an option. In order to explore thecontribution of other sites or suites of sites to the conservationgoal using a heuristic algorithm it is necessary to re-run theprocess with a different set of conditions. Variations in a rule maylead to variations in results.


The truest expression of each of these algorithm types is anon-interactive analysis. Where there is a capacity for a userto interact with the computer software, there will inevitablybe a divergence from the ‘ideal network of sites’. The valueof these tools lies in their capacity to evaluate multiple datasets within a regional context according to a plannedanalytical strategy that is systematically applied to the data.Heuristic type algorithms are more reliant than optimal typemodels on a sound analytical strategy, but both can utiliseexpert input to establish goals and select features and rely onsound data and the user’s understanding of that data. At thecrudest level, users of these tools should understand that inmost cases (all else being equal) the results might differ withdifferent algorithms and within the same heuristic algorithmdepending on the decision or selection rules used. Anunderstanding of the subtleties of the approach used isimportant for understanding the results. Providing thatdecisions are documented, these techniques have anenhanced capacity to be explicit and repeatable.

C-PlanThe heuristic conservation assessment software C-Plan (Pressey andLogan 1995) is one type of software that has become available andused for conservation assessment in NSW. Others are available, butgiven its usage we will highlight in general terms the way in whichC-Plan guides the construction of a network of sites and anyconsequences of this.

The idea of choice and the concept of irreplaceabilityIn any bioregion, where targets are applied that are a subset of theavailable area, (that is, less than 100% of the available area) there islikely to be some choice in how targets are met for someenvironments while for others there will be no choice (that is, thetarget will be greater than or equal to 100% of the available area).

There are at least three consequences of this for conservationassessment:

■ Where there is no choice the conservation target can only bereached if the site(s) containing the targeted feature is added tothe network of sites;

■ Where there is a choice, there needs to be a basis for decidingbetween sites; and

■ Changing the conservation goal and targets will alter theavailable choices. For example, increasing the area of anecosystem to be conserved will lessen the number of choicesavailable.

This process becomes more complicated as more features (forexample, a combination of fauna and vegetation targets) to beaccounted for at any given site are increased.

If using a model such as C-Plan it is important to distinguishbetween the measure of choice and conservation value. For allindices, a site that has low irreplaceability means that there areopportunities to make a contribution to the conservation goal usingother sites but it does not necessarily equate to no conservation value.


Irreplaceability

C-Plan can calculate a range of indices for what is termed‘irreplaceability’. The term has been variously interpreted outsidethe use of the C-Plan software (Ferrier et al. 2000) and the detailof its measurement has evolved within the package. The conceptand its measurement is discussed below as it is used within thissoftware package.

Most simply stated, irreplaceability predicts the number ofavailable choices to meet a conservation goal or goals. Itmeasures how much a site could contribute to the conservationgoal/s relative to any other site and conversely the extent towhich the options for achieving the conservation goal/s arereduced if that site is not available.

The original formulation of irreplaceability predicted how manycombinations of sites which met targets for all features areincluded in a given site. Ferrier et al. (2000) report that this hasbeen further refined to measure how essential the site is, based on:

■ How much of the feature is contained in other sites; and

■ How many times a given site is essential to be part of a combination with other sites in order to meet the target.

C-Plan addresses adequacy ‘automatically’ in terms of dealingwith the area or amount of area required to meet the conservationtargets, however application of design has to date been toocomplicated to automate and has been left to the user to apply.Conforming to the heuristic model, C-Plan predicts thesecombinations as a way of reducing the computational time. Thisprediction, the so-called ‘predictor’, is based on certain theory,assumptions and rules. These include the following:

■ The population being sampled is finite;

■ When sites are selected they are not replaced (that is, they are not sampled again);

■ That randomly sampled combinations of sites will approach a normal distribution for combination sizes larger than 30 (Hogg and Tanis 1977 in Ferrier et al. 2000);

■ That where there is more than one feature, these behave independently of each other; and

■ That our understanding of how to establish an appropriate number of combinations (combination size) for each data set isnot unduly influencing the accuracy of the results.

Our understanding of the validity and/or effects of theseassumptions for applied use will evolve.

There is more than one index of irreplaceability within the C-Plan software. A different understanding of the data is providedby each measure. The user needs to understand the way in whicheach manipulates the data fully, prior to use. A précis of some ofthese indices is contained in Appendix 7 but for more detail referto NPWS 1999 and Ferrier et al. 2000.


Minimum set analysis, MinsetMinset stands for ‘minimum set analysis’. This term is alsoused outside the C-Plan software. In general terms, theMinset algorithm used in C-Plan is designed to select thesmallest number of sites that ‘would fulfil’ a specific aim (alsocalled the stopping condition) (NPWS 1999). An exampleof this aim would be to achieve the conservation goal for asmany features as possible while minimising the total land areaoccupied by the network of conservation sites.

To achieve this aim, the Minset uses a set of rules to selectone or more sites in an iterative search. In other words, aftereach selection C-Plan recalculates all values and a new site isselected in the same way as the last. What is important tounderstand is that the first choice or the initial rule will affectthe outcomes the most, assuming that significant numbers ofsites meet the condition of the rule. Where there are ties,subsequent rules will be necessary. An example of a set ofrules is identified in Appendix 8.

A minimum set analysis can provide a snapshot of patterns inirreplaceability to guide planning. They can also be used to comparethe influence of different structuring of bioregional data sets and toclarify these influences. The most useful points to look at whencomparing Minsets are:

■ The number of iterations needed before the stopping conditionor goal is reached. If one iteration means that one site is added(which is usually but not always the case) then more iterationsmeans a less efficient Minset; and

■ The rate of accumulation of sites and sites selected early or atcritical points in the analysis (for example, the majority of theconservation goals may be met early in one compared to anotherMinset).

Minsets do not provide a capacity to address the configurationaspects of Adequacy criterion, that is, there is no capacity to build inrules based on proximity or adjacency.

Interactive analysisIn addition to the Minset feature, C-Plan provides the capacity forthe user to select sites and for successive recalculation of the potentialcontribution of unselected sites following successive selections. Inthis way the user can address some of the design considerations notautomatically considered by C-Plan. An initial Minset analysis canbe used to understand the efficiency of interactive selections.

The importance of the first choiceThe starting point (or starting points) is very important when addingsites to a network since these influence subsequent decisionsbetween otherwise equally valued sites. It is possible to exploreoptions for different starting points, yet in practice starter sites areusually based on:

■ Sites for which there are no other choices (for example,irreplaceability is equal to 1);


■ Areas already under secure conservation management (such asexisting national parks); and

■ Areas which are most threatened.

An example of a rule sequence used in C-Plan is provided in Section 3.

Using mathematical modelsTo use analytical tools such as the C-Plan software, the user shouldhave sufficient skills to understand the implications of usingparticular functions with the available data. As with similar analysesusing GIS there is a constant risk and perhaps temptation to conductanalyses beyond the capacity of the data to provide meaningful andaccurate information. This temptation exists simply because thecomputer software functions are available. These analyses can bemade regardless of the suitability of the data or the skill of the userand can easily result in analyses whose maps are convincing but areactually meaningless, or which serve to confuse eventual users ratherthan to provide insight. Eardley (1999) has referred to this aspect ofcredibility of computer-based software assessment as the ‘facade ofcredibility’. This, we should add, is not a fault of the software butserves to emphasise that users also require a healthy dose ofpracticality in their tool kit (Smart 2000).

KEY GUIDELINES, PRINCIPLES AND STANDARDS■ Expert input is required in any bioregional conservation

assessment even if mathematical models are used to conductanalysis;

■ The techniques used to elicit information from experts willdepend on the type of information required, the size of the groupof experts and the latitude given experts for decision;

■ The value of mathematical models lies in their capacity toevaluate multiple data sets within a regional context accordingto a planned analytical strategy which is systematically appliedto the data. As with any technique it is important that the usersunderstand the way in which data is manipulated and theunderlying philosophy of the model so that the results are wellunderstood.

FURTHER READING AND REFERENCESBarrett, T. W. (1999) An Overview of the C-Plan SummedIrreplaceability Site Indices and their use in Search Queries.Unpublished report by National Parks and Wildlife Service (NSW),Hurstville.

Csuti, B., Polarsky, S., Williams, P.H., Pressey, R.L., Camm, J.D.,Kershaw, M., Kiester, A.R., Downs, B., Hamilton, R., Huso, M. andSahr, K. (1997) A Comparison of Reserve Selection AlgorithmsUsing Data on Terrestrial Vertebrates in Oregon. BiologicalConservation 80: 83-97.


Delbecq, A.L., Van de Ven, A. H. and Gustafson, D.H.(1975). Group techniques for Program Planning. A guide tonominal group and delphi processes. Scott, Foresman andCompany, Glenview, Illinois.

Dick, R. ed. (2000) A multi-faceted approach to regionalconservation assessment on the Cobar Peneplain BiogeographicRegion - an overview. Unpublished report by National Parksand Wildlife Service (NSW), Hurstville.

Eardley, K.A. (1999) A Foundation for Conservation In theRiverina Bioregion. Unpublished Report. National Parks andWildlife Service (NSW), Hurstville.

Ferrier, S., Pressey, R.L. and Barrett, T.W. (2000): A newpredictor of the irreplaceability of areas for achieving aconservation goal, its application to real-world planning anda research agenda for further refinement. BiologicalConservation 93: 303-325.

Hogg, R.V. and Tanis, E.A. (1977). Probability and StatisticalInference. Macmillan, New York. In Ferrier, S., Pressey, R.L. andBarrett, T.W. (2000): A new predictor of the irreplaceability of areasfor achieving a conservation goal, its application to real-worldplanning and a research agenda for further refinement. BiologicalConservation 93: 303-325.

NPWS (1999) C-Plan Conservation Planning Software User Manualfor C-Plan Version 2.2. Unpublished report by National Parks andWildlife Service (NSW), Hurstville.

NPWS (1999) Development of C-Plan functionality to guideachievement of spatial configuration objectives. A project undertaken aspart of the NSW Comprehensive Regional Assessments. Projectnumber NA 60/EH. National Parks and Wildlife Service (NSW),Hurstville.

Pressey, R. L. and Logan, V. S. (1995) Reserve coverage andrequirements in relation to partitioning and generalization of landclasses: analysis for western New South Wales. Conservation Biology9 (6) pp.1506-1517.

Pressey, R.L., Possingham, H.L. and Margules, C.R. (1996)Optimality in Reserve Selection Algorithms: When does it matterand how much? Biological Conservation 76: 259-267.

Pressey, R.L., Possingham, H.P. and Day, J.R. (1997) Effectiveness ofalternative heuristic algorithms for identifying indicative minimumrequirements for conservation reserves. Biological Conservation 80:207-209.

Pressey, R.L. and Taffs, K.H. (in press) Priority Conservation Areas:a definition for the real world applied to western NSW.

Smart, J. M., Knight, A.T. and Robinson, M. (2000) A ConservationAssessment for the Cobar Peneplain Biogeographic Region - Methods andOpportunities. Report by National Parks and Wildlife Service(NSW), Hurstville.


5COLLECTING

INFORMATIONAND

MANAGING IT


Wil AllenBrian McLachlan

Greg Croft

5

COLLECTING

INFORMATION

AND MANAGING

IT

Amajor component of bioregional conservationassessment is taken up with sourcing,gathering, assessing the appropriateness andformatting information.

5.1 BEFORE YOU START – STRUCTURING YOUR DATA GATHERING

The data-gathering phase can easily eat into the project timelinesleaving little time for formatting, detailed analysis and gooddocumentation let alone communication of project outcomes. Tomanage this:

■ Only collect data at a scale that is reasonable for use in abioregional conservation assessment and that can be analysedwithin the resources of the assessment;

■ Undertake a data audit of information but don’t collect everypiece of information on the bioregion if that is not needed.However, if you have the resources, an audit of the availableinformation and its quality can be useful for future users;

■ Decide which information is relevant. The description of theconservation target will be influenced by the data available todescribe and measure progress towards meeting them and viceversa;

■ Try to request information from one source at the one time toenhance the supplier’s efficiency. Document all requests,agreements and responses;

■ Be aware of memoranda of understanding or data exchangeagreements which may operate between your group and theother group and anticipate that if these are not available theymay need to be negotiated;

■ Make the data suppliers aware of your deadlines and maintaincommunication with an appropriate data supplier contact to tryto avoid delays in supply;

■ Be realistic about promises of new data sets being developed orimproved by the suppliers. Don’t count on using any data setwhich is not immediately available;

■ If costs of data are prohibitive investigate the opportunity fordata exchange; and

■ Request metadata with your data.

5.1.1 Information sources

Although Appendix 9 describes current data directories and Internetaddresses for key information sources, word-of-mouth is still animportant way of finding appropriate information. This is wherereference groups can be of assistance in the beginning of anassessment as a source of further contacts or information. Bioregionalconservation assessments of adjacent regions are also useful startingpoints for information.

84 Collecting information and managing it

5.1.2 Gathering new data

If, after investigating available data, you still need to collectdata, consider the following:

■ Be aware of balancing resources devoted to the collectionof new data versus the planning and development ofapproaches to use the available data. You need to avoidusing a large proportion of your resources (both time andmoney) collecting new data if there is no time toeffectively use the available data. Most first timebioregional conservation assessments will progress in theface of identified gaps in information. That being said, itmay be possible to gather some new data as the basis forfuture work if the resources allow for both activities; anassessment of the available data and some new butincomplete data gathering; and

■ Is the collection of new data possible at all within thetime frame and does your project budget provide for rawdata collection?

Biodiversity survey guidelinesBiodiversity Survey Guidelines are being developed by theNPWS. The Biodiversity Survey Guidelines aim to deal with:

■ The first steps of a regional biodiversity assessment, that is, survey (including aims and methods of survey, design of survey, using systematic survey effort, and making use of existing survey information); and

■ The first stage of assessing survey data (such as limitations on how far you can push the data, interpreting absences, the importance of looking at the data at wider or geographic scales).

The Biodiversity Survey Guidelines will contain:

■ General principles that should be applied to any kind of biodiversity survey (such as stratification, replication, systematic effort, etc); and

■ Technical information which can be adapted to suit the survey in different kinds of environments and for different aims.

The Biodiversity Survey Guidelines are designed to be used byanyone embarking on a biodiversity survey because the principlesthey contain (regarding stratification, replication and systematiceffort) can be applied to any kind of biodiversity survey. They willnot, however, be a ‘recipe’ for survey in every type of environmentor for every end-purpose (which would be impossible given thatsurveys have to be individually tailored to suit the survey’s aims).

To this end, the Biodiversity Survey Guidelines are intended to beguidelines, not standards, and so they will not carry any legislativeor regulatory authority. They will be promoted to councils,consultants and agencies to adopt in survey work, though it is alsoacknowledged that it may not actually be possible that a single set ofguidelines will suit all the different needs and views of stakeholders.

85Collecting information and managing it

Vegetation survey standardsBenson (1999) describes categories for assessing quality ofvegetation mapping for different end uses in NSW.

Ideally, sound vegetation management planning requires:

■ Adequate plot data;

■ A robust vegetation classification; and

■ Mapping of that classification over a region. (Keith et al. 1997, Benson 1995, Benson 1999.)

Map unit definitions are greatly influenced by scale and over theyears in NSW, different projects have mapped vegetation atdifferent scales. These inconsistencies make it difficult to edge-match or overlay vegetation maps that have been derived fromdifferent projects. This is why plot data are so important.Variables recorded at a particular place and time can be used todefine vegetation patterns in the future. In contrast, aerialphotographic interpretation (API) of vegetation patterns is highlysubjective and difficult to standardise. API is best used inconjunction with adequate sampling of the vegetation as thesampling provides a sound basis for extrapolating photo patternsthat reflect structure and floristic variation across the landscape.

Depending on the purpose of the study and its scale, the level ofdetail of vegetation mapping and survey will vary. Scale does notnecessarily relate to quality because different scale vegetation mapsmay have different purposes. However, projects that map vegetationat a fine scale (say 1:25,000) and adequately sample the vegetationin survey plots are more likely to produce high standard data suitablefor local and regional planning. Several categories of the most likelyquality and their usefulness are summarised below:

High standard – full floristic, stratified plot samplingand fine scale mapping■ Scale of 1:25,000 in the Eastern Division, 1:50,000 in the

Central Division and 1:100,000 scale in the Western Division;

■ Vegetation sampled in standard-sized plots, using stratified sampling methods (Benson 1995);

■ All vascular plant species recorded in plots;

■ Vegetation numerically classified and analysed against environmental variables;

■ Mapping of vegetation units by API with ground traverses and/or using modelling;

■ Vegetation units should be linked to numerical analysis as much as possible; and

■ Capacity for modelling ecosystems, vegetation communities and species across the landscape to produce pre-European vegetation maps.

UsesRegional vegetation management planning, property planning,species and ecosystem recovery planning, identify sites ofsignificance, species and ecosystem modelling for rehabilitation.


High-medium standard – full or part floristic,stratified or non-stratified plot sampling,medium scale mapping■ Scale of 1:100,000 in the Eastern Division, 1:100,000 –

1:250,000 in the Central and Western Divisions;

■ Plot data less than required for high quality analysis; limited number of plot data recording all vascular plant species, or large number of canopy species plot data;

■ Vegetation numerically or subjectively classified;

■ Remote sensing by API and/or SPOT satellite images;

■ Thorough ground truthing; and

■ Capacity to model species from data except canopy species.

UsesProvides regional perspective for regional vegetation planning,basis for more detailed survey, identifies sites of significance.

Medium standard – part floristic survey – oftencanopy only, medium to coarse scale mapping■ Scale of 1:100,000 in Eastern Division, 1:250,000 in Central

and Western Divisions;

■ Limited non-stratified plot data, often only canopy species noted;

■ Data not subjected to numerical analysis;

■ Vegetation subjectively classified;

■ Remote sensing by API, SPOT or LANDSAT imagery;

■ Moderate ground truthing;

■ Limited capacity to model species from data except canopy species.

UsesSetting regional perspective, overview for regional vegetationplanning, identifying some sites of significance.

Coarse standard – limited field plots and/or coarsescale, structural rather than floristic classification■ Scale >1:100,000 in Eastern Division, generally >1:250,000 in

Central and Western Divisions;

■ No stratified sampling, few plots, emphasis on noting canopy species;

■ Subjective classification of floristic elements or only structure measured remotely;

■ Remote sensing by LANDSAT or API but often for structure only;

■ No capacity to model species.

UsesState or regional overview can show extant vegetation bystructural classes such as delineating areas of woody/non-woodyvegetation.


5.2 DOCUMENTING AND ARCHIVING INFORMATION

5.2.1 Establish a data management system – digital data

■ Store all map data in electronic form (digitally) where possible;

■ Establish a database of all data for the bioregion;

■ Store all data sets and any metadata (in the form established bythe Australian New Zealand Land Information Council ANZLIC)in this database as well as any additional documentation receivedwith the dataset. This database should be able to provide asnapshot of all available data and key aspects of this data;

■ Store all data in the original format received. However alldatasets must be reprojected to the same projection (as well asthe same datum) to be compatible;

■ If data sets are amalgamated or joined in any way ensure that theprocess is explicitly documented to ensure that deconstructionand reconstruction is simple;

■ Set up a data index spreadsheet in the directory listing all dataand documents stored in the database, as well as key informationabout the data (custodian restrictions, date received etc). Thiscan also be used to list multiple versions and indicate what hasto be done to each version;

■ Set up a standard naming system for datasets and versions,according to what processes have been carried out;

■ Set up an system for quick viewing of datasets;

■ Keep the most up-to-date datasets in one directory and alwayskeep the same naming systems so data can be found;

■ Store all old versions in an ‘archive’ folder to avoid confusion;

■ All custodians of data are required to summarise metadataaccording to an international standard (ANZLIC 1996). Aproforma for a metadata statement is available from both theNatural Resource Information Management System (NRIMS)and ANZLIC (http://www.auslig.gov.au/pipc/anzlic/anzlicma.h)web sites.

5.2.2 Data agreements and sensitive information

When using digital data created by another agency or group, the usermust be licensed to use their data set; this is done through a LicenseAgreement or Memorandum of Understanding. The license bothidentifies the terms and conditions of use for the data set and is usedto track the distribution of data. The custodian of the data definesthe terms of the license and its conditions and limitations on use.When a user makes a request for a data set, the custodian asks theuser to sign the license (This is sometimes done directly through theWorld Wide Web, depending on the custodian.) By following theterms outlined in the license, the user is assured that the data set isvalid and is being used correctly.


There are a number of issues related to the use of ecologicalinformation provided by Aboriginal communities toconservation assessments/surveys and ongoing storage of thatinformation and use for mapping and other documents.Although additional information on some of these issues isprovided in Section 7 of this Guide, It is relevant to identifykey issues here (after Smart et al. 2000a) as:

■ Intellectual property (in Australia, the CommonwealthGovernment's (1996) National Strategy for theConservation of Australia’s Biodiversity)), recognises theintellectual property rights of Aboriginal people withrespect to traditional ecological knowledge in thescientific, commercial and public domains. It states thatthe use of such knowledge should be determined by thetraditional owners and remains under their control. Thisguides ‘best practice’.

■ Storing and interpreting information so that it can onlybe accessed and used according to culturally based protocols;

■ Ensuring that the meaning of information provided is notaltered by either misunderstanding, attachment of other valuessuch as legal jargon with more specific interpretations, or overabstracted in maps and analyses;

■ The particular requirements for conservation assessments and inparticular GIS-based assessments which require relativelycomplete coverage of the area being analysed, as well asinformation of consistent quality;

■ Understanding the differences between Aboriginal ecologicalknowledge and that obtained through non-indigenous surveytechniques; and

■ Access to the technology by Aboriginal people.

The collection, use and storage of data obtained from Aboriginalcommunities should address these issues.

5.2.3 Non-digital information

Establish a paper filing system for the project, subdivided into majorcomponents of the project. Use standard filing procedures. Crossreference between files.


KEY GUIDELINES, PRINCIPLES AND STANDARDS

Structure data gathering ■ Only collect data at a scale that is reasonable and relevant for

use in a bioregional conservation assessment and that can beanalysed within the resources of the assessment;

■ Consider the collection of new data carefully. Avoid using alarge proportion of your resources (both time and money)collecting new data if there is no time to effectively use theavailable data. Most first time bioregional conservationassessments will progress in the face of identified gaps ininformation; and

■ Collect vegetation and fauna data according to establishedstandards.

Systematically record transactions and manipulationsof data■ When requesting data from suppliers, document all requests,

agreements and responses;

■ Request metadata and store all data sets and any metadata (inthe form established by the Australian New Zealand LandInformation Council ANZLIC) in a database as well as anyadditional documentation received or developed for datasets;

■ Store all data in the original format received;

■ Explicitly document any deconstruction and reconstruction ofdata sets and establish an index of all data and documents storedin the database, as well as key information about the data(custodian restrictions, date received etc) and multiple versions;and

■ Set up a standard naming system for datasets and versionsaccording to what processes have been carried out.

Follow and, if not established, develop MOU or dataexchange agreements for sensitive data■ Be aware of memoranda of understanding or data exchange

agreements which may operate between your group and othergroups and anticipate that if these are not available they mayneed to be negotiated; and

■ Identify the intellectual property of information provided byAboriginal communities and manage the data according to aprotocol developed with the community.


FURTHER READING AND REFERENCES

ANZLIC (1996) Guidelines:core metadata elements Version 1.Metadata for high level land and geographic data directories inAustralia and New Zealand. Prepared for the Australia andNew Zealand Land Information Council by the ANZLICWorking Group on Metadata, July 1996.

Benson, J.S. (1995) Sampling strategies and costs of regionalvegetation mapping. Journal of the Australian Map Circle.The Globe 43: 18-28.

Benson, J. (1999) Setting the Scene. The Native Vegetation ofNew South Wales. A background paper of the NativeVegetation Advisory Council of New South Wales.Background paper number 1. Department of Land and WaterResources, Sydney.

Eardley, K.A. (1999) A Foundation for Conservation In theRiverina Bioregion. National Parks and Wildlife Service(NSW), Hurstville.

Ferrier, S., Manion, G., Drielsma, M and Smith, J. (2000) Paper forthe Nandewar Bioregional Scoping Report. National Parks and WildlifeService (NSW), Hurstville.

Keith, D.A., Sivertsen, D.P., Carrit, R. Bolton, M., Smith, P.,Wilson, N., Watt, A., Goody, B., Benson, J., Peacock, R. and Clark,S. (1997) Forest ecosystem classification and mapping for EdenComprehensive Regional Assessment. Unpublished report to CRASteering Committee. National Parks and Wildlife Service (NSW),Hurstville.

Commonwealth of Australia (1996) The National Strategy for theConservation of Australia's Biodiversity. AGPS, Canberra.

NSW Department of Land and Water Conservation (1999) InterimGuidelines for Targeted and General Flora and Fauna Surveys Under theNative Vegetation Conservation Act 1997, Centre for NaturalResources, NSW DLWC, Parramatta.

NPWS (1999) Modelling Areas of Habitat Significance for VertebrateFauna and Vascular Flora in North East NSW. A project undertakenas part of the NSW Comprehensive Regional Assessments projectnumber NA 23/EH.

Smart, J., Creaser, P. and Monaghan, D. (2000a) LinkingConservation Assessment and Aboriginal Ecological Knowledge on theCobar Peneplain. Report by National Parks and Wildlife Service(NSW), Hurstville.

Smart J. M., Knight, A.T. and Robinson, M. (2000b) AConservation Assessment for the Cobar Peneplain Biogeographic Region -Methods and Opportunities. National Parks and Wildlife Service(NSW), Hurstville.


6

COLLECTINGTHE RIGHT

INFORMATION -DATA THEMESAND QUALITY


Murray Ellis

6

COLLECTING

THE RIGHT

INFORMATION -

DATA THEMES

AND QUALITY

There is a misconception that, given the greatvariety of data that exists, there is an unlimitedamount of analysis that can be done with it. Whatyou are able to do with it however, is determined

by the characteristics of the data. Data sets are collected toanswer specific questions under specific circumstances.These govern the type of information gathered. The methodand standards used for collection and compilation of theinformation produces these characteristics as much as theoriginal information and govern its applicability or qualityfor the bioregional conservation assessment.

6.1 DATA THEMESAn overview of the themes of data used in most bioregionalconservation assessments is provided here. More specific detail onthe rationale and standards that should be adopted for each of thethemes is provided in the following section.

6.1.1 A surrogate for biodiversity

As discussed in Section 3, the biodiversity surrogate will most likelybe developed from a combination of biological and non-biologicaldata layers which are drivers of biodiversity distribution in thebioregion.

6.1.2 Information on the ecological requirements of biodiversity orthe biodiversity surrogate

The need for this information was identified in Section 3. It willusually be text-based information and could be based on single ormultiple population viability analyses.

6.1.3 Information on the current and predicted condition ofbiodiversity or the biodiversity surrogate

Information on the current condition of biodiversity or particularcommunities at a bioregional scale are usually based on maps of theextent of clearing and the extent of fragmentation and, if available,maps of the extent of land degradation (salinity, soil structure decline).The most common predictor available for condition is usuallyprovided by maps of land tenure and management plans (whichindicate what land uses are possible). These may be accompanied byinformation on new land uses entering the bioregion (based onassessments of new land-use trends) or an assessment of historicaltrends in land use related to land condition. The latter tend only tobe useful to identify lag effects in condition loss following previousland use or where the historical trend is likely to continue.

6.1.4 Contextual information

This includes information such as scale, grids, orientation,projection, datum, rivers, roads and infrastructure to orient the user.

94 Collecting the right information- data themes and quality

6.2 FORMS AND FORMATSThere are various forms of data or information and it is heldin multiple formats.

6.2.1 Maps and plans

■ Digital data can come in three forms: raster (grid cell),vector (line) or point; and

■ Hardcopy maps, if these are not also digitised (they maybe archival).

6.2.2 Non-mapped data and information

■ Databases that may contain text, accompanying lists (forexample, of species) and point locations. Databases canusually be queried to provide statistics; and

■ Literature can include documentary descriptions, forexample survey reports, which may also be accompaniedby maps.

6.2.3 Satellite imagery and aerial photography

■ Digital satellite imagery is available in raster format althougheach satellite product will gave different computer formats andeach satellite sensor captures different types of information;

■ Satellite images are available in hardcopy; and

■ Air photographs are available as hardcopy and can be scanned.

It should be noted that mapped data may be stored in different mapprojections and digital data may be stored in different softwareformats.

Reformatting digital data■ All mapped data should be used in a digital format;

■ Convert digital data to a common map projection and format;

■ If converting from raster to vector format be aware that some detail will be lost; and

■ If converting from vector to raster be aware that the accuracy of the resultant vector map will have an error factor at least equal to or less than the size of the raster grid cell of the original data set.

6.3 SCALEDetail on the ecological scales for bioregional conservationassessment has been provided in Section 3. A few points should bemade at this more general level:

■ Quality aside, the scale of the original survey will limit the detailat which you can determine accurate boundaries;

■ There can be errors introduced through digitising from hardcopy maps;

95Collecting the right information- data themes and quality

■ Following from this, the scale of the subsequent map compilationwill also limit the detail at which you can determine accurateboundaries;

■ If you are using digital data be aware of the scale of the originalsurvey and mapping; and

■ In general, the scale of the coarsest layer of informationdetermines the detail of the whole assessment (this appliesprimarily to the data layers used for decision making whether itis describing environments, their condition or management andnot to primarily contextual data).

6.4 SURVEY AND CLASSIFICATION METHOD The method used to collect survey data will affect:

■ The uniformity of information across the data set;

■ The accuracy and hence reliability of the data set; and

■ Whether your geographic area is 100% covered withinformation or whether there are gaps in the coverage (thesemay be consistent or inconsistent).

The method used to subsequently classify, map or otherwise store thedata can also affect the quality of the information or its quality foryour purpose. Examples include:

■ Raw information grouped into broader more heterogeneousgroupings than required;

■ Raw information grouped into categories or approximations, thatis, 10-20% when a more precisely defined classification isrequired; and

■ Information may be too finely classified.

Joining data setsIf joining or stitching data sets together, inspect the join linesbetween the data sets for gaps, mismatched lines or overlaps.Joined lines between data sets can be messy owing to potentiallydiffering characteristics of each data set. Rules governing howdifferent joins are made may need to be established to ensure thatthe joining is consistent. Edge matching data sets to tidy up a joinline may or may not be an appropriate manipulation of the datadepending on the confidence and consistency with which thematching can be conducted.

6.5 CAN YOU USE THE INFORMATION?All the above issues affect the ultimate quality of the data for use ina bioregional conservation assessment. In dealing with these qualityissues consider whether:

■ The effect of consistent gaps in coverage on your final analysiscan be quantified and hence other trends identified;

■ Questions can be answered but only to a degree, for example youmay be able to identify trends in clearing of woody vegetationbut not of grassy vegetation;

96 Collecting the right information- data themes and quality

■ Raw information can be accessed and reclassified;

■ Too fine classifications can be reliably amalgamated intobroader classes; and

■ Different data sets can be joined to provide a bioregion-wide coverage.

Using coarse scale information and incompletecoverages – or how much data is adequate toproceed with an assessment?Coarse scale information does not have to be overlookedand in fact if some trends become apparent at a coarse scale(for example, an environmental type is endemic to thebioregion and has been almost cleared) then little additionalinformation should be required to initiate conservationaction for that environmental type.

Finer scale information may provide a better definiteboundary of occurrence of the area requiring protecting as well asinformation on the most appropriate management strategy butwill not alter the need to undertake conservation of some form.

As long as the gaps in an environmental coverage are quantifiable(and hence the information in the remainder of the coverage isquantifiable) it is possible to determine priorities with theremainder of the coverage. It may mean that new priorities will beadded once the gaps are filled.

97Collecting the right information- data themes and quality

tools for bioregional conservation assessment

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