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Application of a numerical algorithm to the selection of reserves in semi-arid New South Wales
Biological Conservation 50 (1989) 263-278
Application of a Numerical Algorithm to the Selection of Reserves in Semi-arid New South Wales R. L. Pressey New South Wales National Parks and Wildlife Service, PO Box 1967, Hurstville, NSW 2220, Australia
& A. O. Nicholls CSIRO, Division of Wildlife and Ecology, PO Box 84, Lyneham, ACT 2602, Australia (Received 9 January 1989; revised version received 31 January 1989; accepted 1 February 1989)
ABSTRACT An iterative analysis was used to identify the number and area of pastoral properties needed to contain the full range of natural environments (land systems) in part of western New South Wales. A minimum of 32 properties (3"1% of total number) and 7980 km z (5" 7% of total area) was required to represent every land system at least once. Full representation required a greater overall number and area when the analysis began with properties already acquired for conservation. Excluding land systems atypical of the study area did not greatly reduce the number and area of properties required. Complete representation of land systems in each geographical subdivision in which they occur required a much greater overall number and area of properties than when the analysis was run for the study area as a whole. The algorithm used in this study produced very similar results to other iterative analyses applied to reserve selection in Australia. INTRODUCTION S y s t e m a t i c a p p r o a c h e s to c o n s e r v a t i o n e v a l u a t i o n a n d reserve selection are typically multi-criteria scoring procedures. Sites are r a n k e d in o r d e r o f 263 Biol. Conserv. 0006-3207/89/$03"50 © 1989 Elsevier Science Publishers Ltd, England. Printed in Great Britain
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significance or priority for protection according to scores derived from various criteria. Criteria are most commonly indices of rarity, diversity, size and naturalness (Margules & Usher, 1981; Smith & Theberge, 1986; Usher, 1986). These procedures provide a definition of conservation value and treat sites explicitly and consistently. Scoring procedures have an important limitation, however, as methods for achieving the basic conservation goal of protecting the full range of natural environments or species. If sites are conserved in order of their positions on a list of priorities, very large numbers and areas of sites may be required to contain samples of all natural environments or species (Pressey & Nicholls, this issue; Margules et al., in press). Scoring procedures do not sample the range of site attributes (environments or species) efficiently. Any set of highest ranking sites derived from scoring duplicates some attributes many times and may miss others. Alternative systematic approaches to reserve selection are being developed in Australia. These can be termed 'iterative' approaches because they proceed stepwise with all steps after the first taking into account the attributes of sites already selected. They are also specifically directed at sampling the full range of attributes in a relatively small number of sites. Kirkpatrick & Harwood (1983) first proposed such an approach and applied it to the conservation of wetland plants in Tasmania. An elaboration of this basic approach, taking into account the extent to which attributes are already reserved and the degree to which they are restricted to the study area, has been used by Kirkpatrick (1983). An alternative iterative analysis was applied by Margules et al. (1988) to identify the areas needed to conserve all wetland plants in part of northern New South Wales. These iterative approaches are generally much more efficient at representing the full range of regional biophysical variation than scoring procedures (Pressey & Nicholls, this issue). In this paper, a modification of the algorithm used by Margules et al. (1988) is presented and the revised analysis applied to the problem of conserving natural environments in western New South Wales. The results reported are from a large trial study area. The procedure will be applied to a larger region after further refinement. THE STUDY AREA The trial study area is within the Western Division of New South Wales. The Western Division occupies some 320 000 km 2 or 40% of the State (Fig. 1). Nearly all of the area is leasehold land administered by the Western Lands Commission. Relatively small areas are in State Forest, dedicated as Crown land, or under freehold tenure. The main land use is sheep grazing.
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Numerical algorithm in reserve selection
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R. L. Pressey, A. O. Nicholls
Broad land types in the Western Division have been summarised by the Western Division Select Committee (1983). In terms of surficial geology, some 25% of the total area is bedrock, 15% alluvial plains and 60% aeolian deposits. Major vegetation types are mallee (tall shrubland) eucalypts (7%), chenopod shrubs 0 5 % ) and a range of structural forms, most commonly woodland, low open forest, low woodland and tall shrubland, dominated by Eucalyptus microtheca-E, largiflorens (15%), Acacia aneura (28%), Eucalyptus populnea-Callitris glaucophylla (21%) and Casuarina cristataHeterodendrum oleifolium (14%). Average annual rainfall varies from 450 mm in the north-eastern corner to less than 150 m m in the far west. The reserve system in the Western Division consists of six national parks, ten nature reserves, two historic sites and one Aboriginal area with a total extent of 8300km 2 or 2-6% of the region. Additional acquisitions for conservation, yet to be gazetted, will bring the reserved area up to about 9400 km 2 or 2.9% of the region. As in other parts of Australia and elsewhere in the world, these areas have been selected in an ad hoc manner, largely in response to availability and perceived threat. The reserve system is not fully representative of the Western Division since a significant number of natural environments are unreserved or poorly reserved (Pressey & Nicholls, in press). TABLE 1 Natural Regions and Sub-regions within the Study Area (After Morgan, 1986)
Region~sub-region
2 3 4 5A 5B 5C 6A
(Warrego Fan) (White Cliffs Plateau) (Tibooburra Downs) (Bulloo Overflow) (Western Sands) (Eastern Sands) (Darling Riverine Plains)
Dominant geology
Alluvium Bedrock Bedrock Alluvium Aeolian Aeolian Bedrock
No. provinces
3 1 3 1 4 5 4
The study area extends over 138 915 km 2 or 43% of the Western Division. With the remainder of western New South Wales, it has been subdivided hierarchically into natural regions, subregions and provinces by Morgan (1986), mainly on the basis of geology, vegetation and rainfall. Figure 1 shows the boundaries of regions and subregions. Seven regions/subregions and 21 provinces are wholly or partly within the study area (Table 1). Gazetted reserves in the study area are Sturt National Park (3257 k m 2) in the far north-west, Nocoleche Nature Reserve (758 km 2) in the central west, and
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Numerical algorithm in reserve selection
N a r r a n Lake Nature Reserve (46km 2) in the east. Additions to Sturt (507 km 2) have been acquired for conservation but not yet gazetted.
D A T A BASE Land system mapping by the N e w South Wales Soil Conservation Service provides the most consistent and detailed delineation of natural environments for the entire Western Division. Land systems are areas or groups of areas throughout which there are recurring patterns of topography, soils and vegetation (Christian & Stewart, 1968) and are used here as surrogates for ecosystems. They are mapped by interpretation of patterns on aerial photographs with subsequent field work to confirm boundaries and identification and to describe characteristics and internal variation. A series of 24 land system maps at 1:250000 scale covers the Western Division. The data base for the analysis reported here was derived by recording the identity and extent of each land system in each management or cadastral unit shown on the maps. M o s t units are pastoral properties or discrete parts of properties but some are smaller areas such as public watering places or travelling stock reserves. All are subsequently referred to as properties. The data base is a matrix of 1026 properties and 128 land systems. Average property size varies inversely with rainfall from a b o u t 40 km 2 in the far north-eastern corner to about 520 km 2 in the far west. TABLE 2
Occurrence of Land Systems in the Study Area in Terms of Frequency (% Total Number of Properties) and Extent (% Total Area) Percentage class
< 1-0 1-1-2"0 2"1-3"0 3.1---4-0 4.1-5"0 5.1-10-0 10.1-20-0 >20"0
No. of land systems Frequency
Extent
38 (30) 26 (20) 15 (12) 11 (8) 9 (7) 23 (18) 5 (4) 1 (1)
90 (70) 28 (22) 7 (5) 2 (2) -1 (1) ---
Bracketed figures are numbers of land systems as percentages of total number.
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R. L. Pressey, A. O. Nicholls TABLE 3 Percentages of the Total Extent of Land Systems within the Study Area as Opposed to Elsewhere in the Western Division % Extent in study area
No. land systems
O--lO
13 (lo)
11-20 21-30 31-40 41-50 51~0 61-70 71-80 81-90 91-100
5 (4) 1 (1) 3 (2) 7 2 4 7 86
(6) (2) (3) (6) (67)
Bracketed figures are numbers of land systems as percentages of total number.
The frequency of occurrence of land systems, in terms of the number of properties in which they occur, varies from I to 219. The total area covered by any land system in the study area ranges from 1 to about 11000 km 2. Most land systems are relatively infrequent and restricted in extent (Table 2). Half the land systems have frequencies of 2% or less o f the total number of TABLE 4 Distribution of Land Systems in Terms of Numbers of Regions/Subregions and Provinces in which they Occur No. regions/subregions or provinces
1 2 3 4 5 6 7 8 9 10 >10
Distribution o f land systems (Regions/subregions)
(Provinces)
48 (38) 40 (31) 27 (21) 8 (6) 4 (3) 1 (1)
23 (18) 26 (20) 23 (18) 13 (10) 12 (9) 10 (8) 7 (6) 6 (5) 3 (2) 4 (3) 1 (1)
Bracketed figures are numbers of land systems as percentages of total.
Numerical algorithm in reserve selection
269
properties and 70% of land systems extend over only 1% or less of the study area.
Most land systems can be regarded as typical of the study area, having more than 90% of their mapped extent in the Western Division within the study area (Table 3). Most land systems occur in only one or two of the seven regions/ subregions, although the maximum distribution is over six (Table4). The distribution over provinces is generally much broader with some land systems occurring in 10 or more of the 21.
D A T A ANALYSES A numerical algorithm was applied to the property data to identify small sets of complementary areas that would represent the full range of land systems a specified number of times. One representation required all land systems at least once. Five representations required all land systems with five or more occurrences at least five times and all the occurrences of less frequent land systems. The algorithm was a modification of that used by Margules et al. (1988) on wetland data from the north coast of New South Wales. It had the following steps: (1)
Select property or properties which contain those land systems with an individual frequency less than or equal to the required level of representation. Where there is a choice of properties that can represent a land system, use rules 2 to 5 below. (2) Select property which adds the next rarest (in terms of frequency within the data matrix) unrepresented or under-represented land system and which maximises the number of additional representations of currently unrepresented or under-represented land systems. (3) If a choice exists at rule 2 then select property which adds the next rarest unrepresented or under-represented land system and contains the most infrequent additional land systems that are currently unrepresented or under-represented. (4) If a choice exists after rule 3 has been applied then select the smallest property from the list of possible properties. (5) Ifa choice exists after rule 4 has been applied then select the property which comes first in the list of remaining possible properties to represent the land systems. The modification of the original algorithm is a new fourth step which
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selects the smallest property from the range of alternatives. This was intended to reduce the overall area required to represent all the land systems. The original algorithm had a fourth and final step which was orderdependent, as is the new fifth step. The algorithm was used to test the effects of altering three sets of conditions. First, the set of properties needed to represent all land systems the required number of times was identified by starting the analysis with and without the areas already acquired for conservation. The land system content of the properties selected in the latter case was quantified in two ways: the actual amount of replication of land systems relative to the minimum number of representations required, and the degree to which land systems are under- or over-represented in terms of their proportional areas in selected properties. Areal representation was assessed by calculating the 'selection ratio' for each land system (1.s.). The selection ratio was defined as: selected area of 1.s. as % total selected area total 1.s. area as % of study area Ratios of less than one indicate under-representation in the selected properties. Ratios of more than one indicate over-representation. In the second comparison, the properties required for at least one and five representations of each land system were identified when land systems atypical of the study area were excluded from the analysis. These are land systems which are more extensive in that part of the Western Division outside the study area. Two arbitrary levels of occurrence were used to identify alternative sets of atypical land systems: < 10% and < 40% of total extent within the study area. Thirteen land systems (10% of the total) were excluded in the first case and 22 (17%) in the second. In the third comparison, the properties required for one representation of each land system were identified when the algorithm was applied to the study area as a whole, to individual regions or subregions and to individual provinces. An additional comparison was made between the results of the new algorithm reported here and those of two other iterative analyses for reserve selection. One is the algorithm used by Margules et al. (1988) and the other is the analysis applied in Tasmania by Kirkpatrick & Harwood (1983). The latter analysis starts by identifying the site with the highest number of attributes. This site is considered to be nominally reserved and is removed from subsequent analysis along with its attributes. The site with the next highest number of'unreserved' attributes is then treated in the same way and the procedure repeated until all attributes are in nominal reserves.
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Numerieal algorithm in reserve selection
RESULTS Comparisons with and without existing reserves Without reference to existing reserves, the algorithm selected 32 properties (3.1% of total number) with a total area of 7980 km 2 (5"7% of the study area) to represent every land system at least once. These figures increased to 171 properties (16-7%) and 28 726 km 2 (20.7%) for at least five representations of every possible land system. When the analysis was run by starting with the 13 properties already acquired for conservation, greater overall numbers and areas of properties were required for all representations (Table 5). With acquired areas included, 31% more properties and a 44% larger area were required to contain every land system at least once. The acquired areas therefore lower the efficiency with which all land systems can be reserved. This reduced efficiency is also demonstrated by the relatively minor contribution to one representation of all land systems made by the acquired areas. The number and area of additional properties required for one representation is almost as large as those required when the analysis ignores acquired properties. For five representations, the contribution of acquired areas to sampling of land systems is greater and their reduction of efficiency smaller. TABLE 5
Properties Selected by the Algorithm Starting with and without Properties Already Acquired for Conservation 1 Representation No.
Area
(km 2) 1. Analysis without acquired properties 2. Analysis with acquired properties 3. 2/1 4. Acquired properties 5. (2-4)a 6. (1-5) b
5Representations No.
Area
(kin 2)
32 (3-1)
7980 (5-7)
171 (16"7) 28726 (20.7)
42 (4.1) 1.31 13 (1"3) 29 (2"8) 3 (0.3)
11 503 (8-3) 1.44 4611 (3"3) 6892 (5-0) 1 088 (0.8)
176 (17'2) 30065 (21.6) 1"03 1-05 13 (1-3) 4611 (3"3) 163 (15-9) 25454 (18-3) 8 (0.8) 3 272 (2.4)
Bracketed figures are percentages of total number and total area of properties. a Properties selected by analysis 2 in addition to acquired properties. b Reduction of additional properties required by analysis 2 attributable to acquired properties (this provides a measure of the relative contribution of acquired areas to the more efficient sampling of analysis 1).
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TABLE 6 Actual Replication of Land Systems in Properties Selected as Nominal Reserves Actual replications
No. land systems (1 representation)
No. land systems (5 representations)
1 2 3 4 5 6 7 8 9 l0 11-20 > 20
82 (64) 23 (18) 10 (8) 6 (5) 2 (1) 2 (1) 3 (3)
10 (8)
4 (3) 1 (1) 4 (3) 51 (40) 10 (8) 9 (7) 8 (6) 2(1) 4 (3) 20 (16) 5 (4)
Bracketed figures are numbers of land systems as percentages of total. Actual land s y s t e m content o f selected areas
The actual replication o f land systems in properties for one a n d five representations (without existing reserves) is s u m m a r i s e d in Table 6. F o r one representation, 82 land systems (64%) were replicated only once, ten o f which only occur once in the study area. Forty-six land systems (36%) were actually replicated m o r e often t h a n the m i n i m u m required. In the case o f five representations, 19 land systems (15%) were under-representated because they occur less t h a n five times in the s t u d y area. Fifty-one land systems (40%) were represented the required five times and 58 (45%) were replicated m o r e often t h a n required, up to 26 times. TABLE 7 Actual Replication (rpln) of Infrequent and Frequent Land Systems in Properties Selected as Nominal Reserves 1 Representation
Infrequent Frequent
5 Representations
1 repln
> 1 repln
<_5 replns
> 5 replns
51 (80) 31 (48)
13 (20) 33 (52)
49 (77) 21 (33)
15 (23) 43 (67)
Bracketed figures are numbers of land systems as percentages of total in each frequency class.
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Numerical algorithm in reserve selection TABLE $
Selection Ratios of Land Systems in Properties Selected as Nominal Reserves Selection ratio
04).10 0.11-1.00 1.01-10.00 > 10.00
No. land systems (1 representation)
No. land systems (5 representations)
16 (13) 46 (35) 50 (39) 16 (13)
3 (2) 47 (37) 78 (61) --
Bracketed figures are numbers of land systems as percentages of total. The a m o u n t of replication of land systems in the selected properties is at least partly linked to their frequency of occurrence in the study area. Land systems can be defined as either frequent or infrequent according to whether they occur on more or fewer properties than the median of 21.5. Infrequent land systems were more often replicated the minimum number of times and less often replicated more than the minimum required when compared to frequent land systems (Table 7). The proportional areal representations (selection ratios) of land systems varied widely. Ratios in the set of properties for one representation ranged from 0.01 (greatly under-represented) to 17.41 (greatly over-represented). F o r five representations, ratios varied from 0"01 to 7.91. The number of land systems with extreme ratios was higher for one representation than for five (Table 8). Land systems with m a x i m u m selection ratios were those with frequencies less than or equal to the required number of representations. These were completely contained within the selected properties. Land systems with minimum selection ratios were those which are relatively frequent and TABLE 9
Selection Ratios (SRs) for Infrequent and Frequent Land Systems in Properties Selected as Nominal Reserves 1 Representation
Infrequent Frequent
5 Representations
SR< 1
SR> 1
SR< 1
SR> 1
17 (27) 45 (70)
47 (73) 19 (30)
5 (8) 45 (70)
59 (92) 19 (30)
Bracketed figures are numbers of land systems as percentages of total in each frequency class.
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R. L. Pressey, A. O. Nieholls
extensive in the study area. Most infrequent land systems had high selection ratios and most frequent land systems had low selection ratios (Table 9). The same pattern is evident when land systems are classed as restricted or extensive about the median total area. The algorithm therefore selects sets of properties in which infrequent and restricted land systems tend to be proportionally over-represented and frequent, extensive land systems are proportionally under-represented.
Comparisons with other iterative analyses The new five-step algorithm, reported here, selected smaller overall areas to represent the range of land systems than did the earlier version applied by Margules et al. (1988) (Table 10). More properties were required by the new algorithm, however, for five representations. The analysis of Kirkpatrick & Harwood (1983), with selection based on diversity, produced very similar results to the two algorithms based on rarity. It required slightly larger areas for both one and five representations and selected slightly more properties for one representation and fewer for five representations.
The effect of excluding atypical land systems Among the atypical land systems excluded at the 10 or 40% levels were most of those with single occurrences and several others with relatively low frequencies in the study area. Their apparent rarity was due to their marginal locations. Excluding atypical land systems from the analysis resulted in relatively minor reductions in the numbers of properties required for one and five representations (Table 11). The areas required were slightly reduced in most cases, although a slightly increased area was required for one representation when land systems with less than 40% of their total extent in the study area were excluded. This increase is a function of the specific properties selected TABLE 10 Numbers of Properties and Percentages of Study Area Required by Three Iterative Analyses to Represent All Possible Land Systems
Five-step algorithm Margules et al. (1988) Kirkpatrick & Harwood (1983)
1 Representation
5 Representations
No.
% area
No.
% area
32 32 35
5"7 6"1 6"6
171 159 151
20"7 26'3 27-6
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Numerical algorithm in reserve selection
TABLE 11 Numbers of Properties and Percentages of Study Area Required to Represent Three
Alternative Sets of Land Systems
All land systems (128 in) < 10% excluded (ll5in) <40% excluded (106 in)
l Representation
5 Representations
No.
No.
% area
% area
32
5.7
171
20'7
27 (84)
5"0 (88)
168 (98)
19-7 (95)
25 (78)
5'8 (102)
163 (95)
19-0 (92)
Bracketed figures are percentages of numbers on the top line of the table.
when the algorithm addressed the relative rarity of a different set of land systems. Not all of the land systems excluded from the analysis were actually missed by the selection procedure. In the case of 13 land systems excluded, seven and nine of these were sampled incidentally for one and five representations, respectively. After the exclusion of 22 land systems, four and 17 of them were sampled incidentally for one and five representations, respectively. The influence of context for reserve selection
Constraining the analysis to achieve one representation of all land systems present within each region or subregion and within each province greatly increased the overall number and area of properties needed. Narrowing the context for reserve selection in this way effectively required multiple representations of the many land systems occurring in more than one geographical subdivision (Table 4). Representing every land system within each region/subregion in which it occurred required 67 properties (209% of the number required by the unconstrained analysis on the whole study area) and a total area of 19591km / (246% of the unconstrained result). Representation within provinces required 123 properties (384% of unconstrained number) and 31 720 km z (397% of unconstrained area).
DISCUSSION The results of this study are indicative only. Data on the trial study area have been used to explore the implications of a simple conservation goal: to
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conserve the full range of natural environments, defined in this case by land systems. Many considerations are necessary in the selection of nature reserves apart from the sampling of natural environments. Scenic, recreational, historic and archaeological attributes are all important, as are the occurrences of species which might not be catered for by an approach based solely on mapping units. Nevertheless, the delineation of natural environments can provide a useful framework for the distribution of these other attributes. In addition, initial reserve selection to represent natural environments can provide a basic network of sites on which other considerations can be superimposed. Similarly, the efficiency of sampling in reserves is not the only consideration in conserving natural environments. The relative priority of natural environments for conservation and the condition and manageability of alternative sites are among the other concerns. Efficiency in reserve selection is very important, however, in maximising the likelihood of conserving the full range of natural environments. The same applies to conserving species or other attributes. The iterative analysis applied in this study, like others of its type in Australia, is a more efficient way of sampling species or natural environments in reserves than the more widely used scoring approaches (Pressey & Nicholls, this issue). A comparison of results starting with and without the existing reserves shows that ad hoc reservations, without regard to efficiency, have increased the minimum area needed to represent all land systems in the study area. Further such reservations will continue to raise the minimum requirement and may result in the areal limit of conservation, however ill-defined, being reached before the reserve system becomes fully representative. Despite the relative efficiency of the analysis, large areas are still required for even one representation of each land system. The addition of 6892 km 2 to the already extensive reserve system is needed and would bring the total reserved area to 8.3% of the study area. Moreover, this large area satisfies only a very basic goal. Significantly larger areas would be required for reservation of a minimum areal proportion of each land system and for the protection of localised species, not catered for by this coarse approach, in addition to land systems. Attempting to develop fully representative reserve systems within geographical subdivisions of the study area will make overall conservation still more expensive. The large areas needed for complete regional or provincial reservation stand against the benefits of increasing the combinations and geographical variants of land systems conserved. Only relatively small reductions in required areas can be achieved by excluding atypical land systems from the analysis. Apart from its efficiency, the analysis presented here has other advantages
Numerical algorithm m reserve selection
277
over scoring procedures. It identifies a whole complementary set of potential reserves in one process. Any required alteration to the selection, for example by excluding inappropriate sites from the analysis or beginning the analysis with desirable sites, can be accommodated. The areal cost of such changes can be assessed by comparing the results with those from the unconstrained analysis, as for the comparisons beginning with and without the existing reserves. The algorithm of Margules et al. (1988) and the modification reported here actually facilitate these changes by effectively identifying two categories of sites: those with unique attributes, which could be regarded as non-negotiable, and others which can be substituted to represent attributes. Another advantage of the iterative approaches is that they bring land managers face to face with the implications of conservation goals. A minimally representative reserve system in our study area, beginning with acquired properties, requires the dedication of 8-3% of the land. Is the acquisition of this area feasible? If not, then which sites and which land systems should be excluded from the reserve network? Which sites should be acquired and which ones should be protected in other ways? The traditional adhoc approach to reserve selection and the widely used scoring approaches do not define the costs of conservation goals so clearly. They therefore inhibit the assessment of the feasibility of those goals and of the best means of achieving them. The analysis presented here is a very basic one. Work is currently underway to develop an algorithm which samples a minimum percentage of the extent of each land system. A data base on vascular plants and vertebrates with localised occurrences in the Western Division is being compiled to complement the broad approach based on land systems. Additional work is planned which will compare the efficacy of alternative selection units (properties, component leases, arbitrary grid cells) and which will develop adjacency constraints to select large blocks of properties as potential reserves. A longer term aim is to use the analysis as an operational aid to decision-making on the conservation or development of specific sites in the Western Division.
ACKNOWLEDGEMENTS Development of the data base for this study was made possible by a grant from the Australian National Parks and Wildlife Service and by the patient and accurate work of Peter Rubacek. Michael Bedward prepared the figure and assisted in the compilation and interpretation of the tables. Mike Austin, Andrew Burbidge and Doug Cocks commented on the draft manuscript.
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Christian, C. S. & Stewart, G. A. (1968). Methodology of integrated surveys. In Proceedings of the Toulouse Conference on Aerial Surveys and Integrated Studies. UNESCO, Paris, pp. 233-80. Kirkpatrick, J. B. (1983). An iterative method for establishing priorities for the selection of nature reserves: an example from Tasmania. Biol. Conserv., 25, 127-34. Kirkpatrick, J. B. & Harwood, C. E. (1983). Conservation of Tasmanian macrophytic wetland vegetation. Proc. R. Soc. Tas., 117, 5-20. Margules, C. R. & Usher, M. B. (1981). Criteria used in assessing wildlife conservation potential: a review. Biol. Conserv., 21, 79-109. Margules, C. R., Nicholls, A. O. & Pressey, R. L. (1988). Selecting networks of reserves to maximise biological diversity. Biol. Conserv., 43, 63-76. Margules, C. R., Pressey, R. L. & Nicholls, A. O. (in press). Selecting nature reserves. In Cost-effective Survey Methods for Nature Conservation, ed. C. R. Margules & M. P. Austin. CSIRO Division of Wildlife and Ecology and New South Wales National Parks and Wildlife Service, Canberra. Morgan, G. (1986). Nature conservation in western New South Wales. Natn. Parks J., 30, 12-17. Pressey, R. L. & Nicholls, A. O. (1989). Efficiency in conservation evaluation: scoring versus iterative approaches. Biol. Conserv., 50, 199-218. Pressey, R. L. & Nicholls, A. O. (in press). Reserve selection in the Western Division of New South Wales: development of a new procedure based on land system mapping. In Cost-effective Survey Methods for Nature Conservation, ed. C. R. Margules & M. P. Austin. CSIRO Division of Wildlife and Ecology and New South Wales National Parks and Wildlife Service, Canberra. Smith, P. G. R. & Theberge, J. B. (1986). A review of criteria for evaluating natural areas. Environ. Manage., 10, 715-34. Usher, M. B. (1986). Wildlife conservation evaluation: attributes, criteria and values. In Wildlife Conservation Evaluation, ed. M. B. Usher. Chapman and Hall, London, pp. 344. Western Division Select Committee (1983). First Report of the Joint Select Committee of the Legislative Council and Legislative Assembly to Enquire into the Western Division of New South Wales. Government Printer, Sydney.
Application of a Numerical Algorithm to the Selection of Reserves in Semi-arid New South Wales R. L. Pressey New South Wales National Parks and Wildlife Service, PO Box 1967, Hurstville, NSW 2220, Australia
& A. O. Nicholls CSIRO, Division of Wildlife and Ecology, PO Box 84, Lyneham, ACT 2602, Australia (Received 9 January 1989; revised version received 31 January 1989; accepted 1 February 1989)
ABSTRACT An iterative analysis was used to identify the number and area of pastoral properties needed to contain the full range of natural environments (land systems) in part of western New South Wales. A minimum of 32 properties (3"1% of total number) and 7980 km z (5" 7% of total area) was required to represent every land system at least once. Full representation required a greater overall number and area when the analysis began with properties already acquired for conservation. Excluding land systems atypical of the study area did not greatly reduce the number and area of properties required. Complete representation of land systems in each geographical subdivision in which they occur required a much greater overall number and area of properties than when the analysis was run for the study area as a whole. The algorithm used in this study produced very similar results to other iterative analyses applied to reserve selection in Australia. INTRODUCTION S y s t e m a t i c a p p r o a c h e s to c o n s e r v a t i o n e v a l u a t i o n a n d reserve selection are typically multi-criteria scoring procedures. Sites are r a n k e d in o r d e r o f 263 Biol. Conserv. 0006-3207/89/$03"50 © 1989 Elsevier Science Publishers Ltd, England. Printed in Great Britain
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significance or priority for protection according to scores derived from various criteria. Criteria are most commonly indices of rarity, diversity, size and naturalness (Margules & Usher, 1981; Smith & Theberge, 1986; Usher, 1986). These procedures provide a definition of conservation value and treat sites explicitly and consistently. Scoring procedures have an important limitation, however, as methods for achieving the basic conservation goal of protecting the full range of natural environments or species. If sites are conserved in order of their positions on a list of priorities, very large numbers and areas of sites may be required to contain samples of all natural environments or species (Pressey & Nicholls, this issue; Margules et al., in press). Scoring procedures do not sample the range of site attributes (environments or species) efficiently. Any set of highest ranking sites derived from scoring duplicates some attributes many times and may miss others. Alternative systematic approaches to reserve selection are being developed in Australia. These can be termed 'iterative' approaches because they proceed stepwise with all steps after the first taking into account the attributes of sites already selected. They are also specifically directed at sampling the full range of attributes in a relatively small number of sites. Kirkpatrick & Harwood (1983) first proposed such an approach and applied it to the conservation of wetland plants in Tasmania. An elaboration of this basic approach, taking into account the extent to which attributes are already reserved and the degree to which they are restricted to the study area, has been used by Kirkpatrick (1983). An alternative iterative analysis was applied by Margules et al. (1988) to identify the areas needed to conserve all wetland plants in part of northern New South Wales. These iterative approaches are generally much more efficient at representing the full range of regional biophysical variation than scoring procedures (Pressey & Nicholls, this issue). In this paper, a modification of the algorithm used by Margules et al. (1988) is presented and the revised analysis applied to the problem of conserving natural environments in western New South Wales. The results reported are from a large trial study area. The procedure will be applied to a larger region after further refinement. THE STUDY AREA The trial study area is within the Western Division of New South Wales. The Western Division occupies some 320 000 km 2 or 40% of the State (Fig. 1). Nearly all of the area is leasehold land administered by the Western Lands Commission. Relatively small areas are in State Forest, dedicated as Crown land, or under freehold tenure. The main land use is sheep grazing.
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Numerical algorithm in reserve selection
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R. L. Pressey, A. O. Nicholls
Broad land types in the Western Division have been summarised by the Western Division Select Committee (1983). In terms of surficial geology, some 25% of the total area is bedrock, 15% alluvial plains and 60% aeolian deposits. Major vegetation types are mallee (tall shrubland) eucalypts (7%), chenopod shrubs 0 5 % ) and a range of structural forms, most commonly woodland, low open forest, low woodland and tall shrubland, dominated by Eucalyptus microtheca-E, largiflorens (15%), Acacia aneura (28%), Eucalyptus populnea-Callitris glaucophylla (21%) and Casuarina cristataHeterodendrum oleifolium (14%). Average annual rainfall varies from 450 mm in the north-eastern corner to less than 150 m m in the far west. The reserve system in the Western Division consists of six national parks, ten nature reserves, two historic sites and one Aboriginal area with a total extent of 8300km 2 or 2-6% of the region. Additional acquisitions for conservation, yet to be gazetted, will bring the reserved area up to about 9400 km 2 or 2.9% of the region. As in other parts of Australia and elsewhere in the world, these areas have been selected in an ad hoc manner, largely in response to availability and perceived threat. The reserve system is not fully representative of the Western Division since a significant number of natural environments are unreserved or poorly reserved (Pressey & Nicholls, in press). TABLE 1 Natural Regions and Sub-regions within the Study Area (After Morgan, 1986)
Region~sub-region
2 3 4 5A 5B 5C 6A
(Warrego Fan) (White Cliffs Plateau) (Tibooburra Downs) (Bulloo Overflow) (Western Sands) (Eastern Sands) (Darling Riverine Plains)
Dominant geology
Alluvium Bedrock Bedrock Alluvium Aeolian Aeolian Bedrock
No. provinces
3 1 3 1 4 5 4
The study area extends over 138 915 km 2 or 43% of the Western Division. With the remainder of western New South Wales, it has been subdivided hierarchically into natural regions, subregions and provinces by Morgan (1986), mainly on the basis of geology, vegetation and rainfall. Figure 1 shows the boundaries of regions and subregions. Seven regions/subregions and 21 provinces are wholly or partly within the study area (Table 1). Gazetted reserves in the study area are Sturt National Park (3257 k m 2) in the far north-west, Nocoleche Nature Reserve (758 km 2) in the central west, and
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Numerical algorithm in reserve selection
N a r r a n Lake Nature Reserve (46km 2) in the east. Additions to Sturt (507 km 2) have been acquired for conservation but not yet gazetted.
D A T A BASE Land system mapping by the N e w South Wales Soil Conservation Service provides the most consistent and detailed delineation of natural environments for the entire Western Division. Land systems are areas or groups of areas throughout which there are recurring patterns of topography, soils and vegetation (Christian & Stewart, 1968) and are used here as surrogates for ecosystems. They are mapped by interpretation of patterns on aerial photographs with subsequent field work to confirm boundaries and identification and to describe characteristics and internal variation. A series of 24 land system maps at 1:250000 scale covers the Western Division. The data base for the analysis reported here was derived by recording the identity and extent of each land system in each management or cadastral unit shown on the maps. M o s t units are pastoral properties or discrete parts of properties but some are smaller areas such as public watering places or travelling stock reserves. All are subsequently referred to as properties. The data base is a matrix of 1026 properties and 128 land systems. Average property size varies inversely with rainfall from a b o u t 40 km 2 in the far north-eastern corner to about 520 km 2 in the far west. TABLE 2
Occurrence of Land Systems in the Study Area in Terms of Frequency (% Total Number of Properties) and Extent (% Total Area) Percentage class
< 1-0 1-1-2"0 2"1-3"0 3.1---4-0 4.1-5"0 5.1-10-0 10.1-20-0 >20"0
No. of land systems Frequency
Extent
38 (30) 26 (20) 15 (12) 11 (8) 9 (7) 23 (18) 5 (4) 1 (1)
90 (70) 28 (22) 7 (5) 2 (2) -1 (1) ---
Bracketed figures are numbers of land systems as percentages of total number.
268
R. L. Pressey, A. O. Nicholls TABLE 3 Percentages of the Total Extent of Land Systems within the Study Area as Opposed to Elsewhere in the Western Division % Extent in study area
No. land systems
O--lO
13 (lo)
11-20 21-30 31-40 41-50 51~0 61-70 71-80 81-90 91-100
5 (4) 1 (1) 3 (2) 7 2 4 7 86
(6) (2) (3) (6) (67)
Bracketed figures are numbers of land systems as percentages of total number.
The frequency of occurrence of land systems, in terms of the number of properties in which they occur, varies from I to 219. The total area covered by any land system in the study area ranges from 1 to about 11000 km 2. Most land systems are relatively infrequent and restricted in extent (Table 2). Half the land systems have frequencies of 2% or less o f the total number of TABLE 4 Distribution of Land Systems in Terms of Numbers of Regions/Subregions and Provinces in which they Occur No. regions/subregions or provinces
1 2 3 4 5 6 7 8 9 10 >10
Distribution o f land systems (Regions/subregions)
(Provinces)
48 (38) 40 (31) 27 (21) 8 (6) 4 (3) 1 (1)
23 (18) 26 (20) 23 (18) 13 (10) 12 (9) 10 (8) 7 (6) 6 (5) 3 (2) 4 (3) 1 (1)
Bracketed figures are numbers of land systems as percentages of total.
Numerical algorithm in reserve selection
269
properties and 70% of land systems extend over only 1% or less of the study area.
Most land systems can be regarded as typical of the study area, having more than 90% of their mapped extent in the Western Division within the study area (Table 3). Most land systems occur in only one or two of the seven regions/ subregions, although the maximum distribution is over six (Table4). The distribution over provinces is generally much broader with some land systems occurring in 10 or more of the 21.
D A T A ANALYSES A numerical algorithm was applied to the property data to identify small sets of complementary areas that would represent the full range of land systems a specified number of times. One representation required all land systems at least once. Five representations required all land systems with five or more occurrences at least five times and all the occurrences of less frequent land systems. The algorithm was a modification of that used by Margules et al. (1988) on wetland data from the north coast of New South Wales. It had the following steps: (1)
Select property or properties which contain those land systems with an individual frequency less than or equal to the required level of representation. Where there is a choice of properties that can represent a land system, use rules 2 to 5 below. (2) Select property which adds the next rarest (in terms of frequency within the data matrix) unrepresented or under-represented land system and which maximises the number of additional representations of currently unrepresented or under-represented land systems. (3) If a choice exists at rule 2 then select property which adds the next rarest unrepresented or under-represented land system and contains the most infrequent additional land systems that are currently unrepresented or under-represented. (4) If a choice exists after rule 3 has been applied then select the smallest property from the list of possible properties. (5) Ifa choice exists after rule 4 has been applied then select the property which comes first in the list of remaining possible properties to represent the land systems. The modification of the original algorithm is a new fourth step which
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R. L. Pressey, A. O. Nicholls
selects the smallest property from the range of alternatives. This was intended to reduce the overall area required to represent all the land systems. The original algorithm had a fourth and final step which was orderdependent, as is the new fifth step. The algorithm was used to test the effects of altering three sets of conditions. First, the set of properties needed to represent all land systems the required number of times was identified by starting the analysis with and without the areas already acquired for conservation. The land system content of the properties selected in the latter case was quantified in two ways: the actual amount of replication of land systems relative to the minimum number of representations required, and the degree to which land systems are under- or over-represented in terms of their proportional areas in selected properties. Areal representation was assessed by calculating the 'selection ratio' for each land system (1.s.). The selection ratio was defined as: selected area of 1.s. as % total selected area total 1.s. area as % of study area Ratios of less than one indicate under-representation in the selected properties. Ratios of more than one indicate over-representation. In the second comparison, the properties required for at least one and five representations of each land system were identified when land systems atypical of the study area were excluded from the analysis. These are land systems which are more extensive in that part of the Western Division outside the study area. Two arbitrary levels of occurrence were used to identify alternative sets of atypical land systems: < 10% and < 40% of total extent within the study area. Thirteen land systems (10% of the total) were excluded in the first case and 22 (17%) in the second. In the third comparison, the properties required for one representation of each land system were identified when the algorithm was applied to the study area as a whole, to individual regions or subregions and to individual provinces. An additional comparison was made between the results of the new algorithm reported here and those of two other iterative analyses for reserve selection. One is the algorithm used by Margules et al. (1988) and the other is the analysis applied in Tasmania by Kirkpatrick & Harwood (1983). The latter analysis starts by identifying the site with the highest number of attributes. This site is considered to be nominally reserved and is removed from subsequent analysis along with its attributes. The site with the next highest number of'unreserved' attributes is then treated in the same way and the procedure repeated until all attributes are in nominal reserves.
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Numerieal algorithm in reserve selection
RESULTS Comparisons with and without existing reserves Without reference to existing reserves, the algorithm selected 32 properties (3.1% of total number) with a total area of 7980 km 2 (5"7% of the study area) to represent every land system at least once. These figures increased to 171 properties (16-7%) and 28 726 km 2 (20.7%) for at least five representations of every possible land system. When the analysis was run by starting with the 13 properties already acquired for conservation, greater overall numbers and areas of properties were required for all representations (Table 5). With acquired areas included, 31% more properties and a 44% larger area were required to contain every land system at least once. The acquired areas therefore lower the efficiency with which all land systems can be reserved. This reduced efficiency is also demonstrated by the relatively minor contribution to one representation of all land systems made by the acquired areas. The number and area of additional properties required for one representation is almost as large as those required when the analysis ignores acquired properties. For five representations, the contribution of acquired areas to sampling of land systems is greater and their reduction of efficiency smaller. TABLE 5
Properties Selected by the Algorithm Starting with and without Properties Already Acquired for Conservation 1 Representation No.
Area
(km 2) 1. Analysis without acquired properties 2. Analysis with acquired properties 3. 2/1 4. Acquired properties 5. (2-4)a 6. (1-5) b
5Representations No.
Area
(kin 2)
32 (3-1)
7980 (5-7)
171 (16"7) 28726 (20.7)
42 (4.1) 1.31 13 (1"3) 29 (2"8) 3 (0.3)
11 503 (8-3) 1.44 4611 (3"3) 6892 (5-0) 1 088 (0.8)
176 (17'2) 30065 (21.6) 1"03 1-05 13 (1-3) 4611 (3"3) 163 (15-9) 25454 (18-3) 8 (0.8) 3 272 (2.4)
Bracketed figures are percentages of total number and total area of properties. a Properties selected by analysis 2 in addition to acquired properties. b Reduction of additional properties required by analysis 2 attributable to acquired properties (this provides a measure of the relative contribution of acquired areas to the more efficient sampling of analysis 1).
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TABLE 6 Actual Replication of Land Systems in Properties Selected as Nominal Reserves Actual replications
No. land systems (1 representation)
No. land systems (5 representations)
1 2 3 4 5 6 7 8 9 l0 11-20 > 20
82 (64) 23 (18) 10 (8) 6 (5) 2 (1) 2 (1) 3 (3)
10 (8)
4 (3) 1 (1) 4 (3) 51 (40) 10 (8) 9 (7) 8 (6) 2(1) 4 (3) 20 (16) 5 (4)
Bracketed figures are numbers of land systems as percentages of total. Actual land s y s t e m content o f selected areas
The actual replication o f land systems in properties for one a n d five representations (without existing reserves) is s u m m a r i s e d in Table 6. F o r one representation, 82 land systems (64%) were replicated only once, ten o f which only occur once in the study area. Forty-six land systems (36%) were actually replicated m o r e often t h a n the m i n i m u m required. In the case o f five representations, 19 land systems (15%) were under-representated because they occur less t h a n five times in the s t u d y area. Fifty-one land systems (40%) were represented the required five times and 58 (45%) were replicated m o r e often t h a n required, up to 26 times. TABLE 7 Actual Replication (rpln) of Infrequent and Frequent Land Systems in Properties Selected as Nominal Reserves 1 Representation
Infrequent Frequent
5 Representations
1 repln
> 1 repln
<_5 replns
> 5 replns
51 (80) 31 (48)
13 (20) 33 (52)
49 (77) 21 (33)
15 (23) 43 (67)
Bracketed figures are numbers of land systems as percentages of total in each frequency class.
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Numerical algorithm in reserve selection TABLE $
Selection Ratios of Land Systems in Properties Selected as Nominal Reserves Selection ratio
04).10 0.11-1.00 1.01-10.00 > 10.00
No. land systems (1 representation)
No. land systems (5 representations)
16 (13) 46 (35) 50 (39) 16 (13)
3 (2) 47 (37) 78 (61) --
Bracketed figures are numbers of land systems as percentages of total. The a m o u n t of replication of land systems in the selected properties is at least partly linked to their frequency of occurrence in the study area. Land systems can be defined as either frequent or infrequent according to whether they occur on more or fewer properties than the median of 21.5. Infrequent land systems were more often replicated the minimum number of times and less often replicated more than the minimum required when compared to frequent land systems (Table 7). The proportional areal representations (selection ratios) of land systems varied widely. Ratios in the set of properties for one representation ranged from 0.01 (greatly under-represented) to 17.41 (greatly over-represented). F o r five representations, ratios varied from 0"01 to 7.91. The number of land systems with extreme ratios was higher for one representation than for five (Table 8). Land systems with m a x i m u m selection ratios were those with frequencies less than or equal to the required number of representations. These were completely contained within the selected properties. Land systems with minimum selection ratios were those which are relatively frequent and TABLE 9
Selection Ratios (SRs) for Infrequent and Frequent Land Systems in Properties Selected as Nominal Reserves 1 Representation
Infrequent Frequent
5 Representations
SR< 1
SR> 1
SR< 1
SR> 1
17 (27) 45 (70)
47 (73) 19 (30)
5 (8) 45 (70)
59 (92) 19 (30)
Bracketed figures are numbers of land systems as percentages of total in each frequency class.
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R. L. Pressey, A. O. Nieholls
extensive in the study area. Most infrequent land systems had high selection ratios and most frequent land systems had low selection ratios (Table 9). The same pattern is evident when land systems are classed as restricted or extensive about the median total area. The algorithm therefore selects sets of properties in which infrequent and restricted land systems tend to be proportionally over-represented and frequent, extensive land systems are proportionally under-represented.
Comparisons with other iterative analyses The new five-step algorithm, reported here, selected smaller overall areas to represent the range of land systems than did the earlier version applied by Margules et al. (1988) (Table 10). More properties were required by the new algorithm, however, for five representations. The analysis of Kirkpatrick & Harwood (1983), with selection based on diversity, produced very similar results to the two algorithms based on rarity. It required slightly larger areas for both one and five representations and selected slightly more properties for one representation and fewer for five representations.
The effect of excluding atypical land systems Among the atypical land systems excluded at the 10 or 40% levels were most of those with single occurrences and several others with relatively low frequencies in the study area. Their apparent rarity was due to their marginal locations. Excluding atypical land systems from the analysis resulted in relatively minor reductions in the numbers of properties required for one and five representations (Table 11). The areas required were slightly reduced in most cases, although a slightly increased area was required for one representation when land systems with less than 40% of their total extent in the study area were excluded. This increase is a function of the specific properties selected TABLE 10 Numbers of Properties and Percentages of Study Area Required by Three Iterative Analyses to Represent All Possible Land Systems
Five-step algorithm Margules et al. (1988) Kirkpatrick & Harwood (1983)
1 Representation
5 Representations
No.
% area
No.
% area
32 32 35
5"7 6"1 6"6
171 159 151
20"7 26'3 27-6
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Numerical algorithm in reserve selection
TABLE 11 Numbers of Properties and Percentages of Study Area Required to Represent Three
Alternative Sets of Land Systems
All land systems (128 in) < 10% excluded (ll5in) <40% excluded (106 in)
l Representation
5 Representations
No.
No.
% area
% area
32
5.7
171
20'7
27 (84)
5"0 (88)
168 (98)
19-7 (95)
25 (78)
5'8 (102)
163 (95)
19-0 (92)
Bracketed figures are percentages of numbers on the top line of the table.
when the algorithm addressed the relative rarity of a different set of land systems. Not all of the land systems excluded from the analysis were actually missed by the selection procedure. In the case of 13 land systems excluded, seven and nine of these were sampled incidentally for one and five representations, respectively. After the exclusion of 22 land systems, four and 17 of them were sampled incidentally for one and five representations, respectively. The influence of context for reserve selection
Constraining the analysis to achieve one representation of all land systems present within each region or subregion and within each province greatly increased the overall number and area of properties needed. Narrowing the context for reserve selection in this way effectively required multiple representations of the many land systems occurring in more than one geographical subdivision (Table 4). Representing every land system within each region/subregion in which it occurred required 67 properties (209% of the number required by the unconstrained analysis on the whole study area) and a total area of 19591km / (246% of the unconstrained result). Representation within provinces required 123 properties (384% of unconstrained number) and 31 720 km z (397% of unconstrained area).
DISCUSSION The results of this study are indicative only. Data on the trial study area have been used to explore the implications of a simple conservation goal: to
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R. L. Pressey, A. O. Nicholls
conserve the full range of natural environments, defined in this case by land systems. Many considerations are necessary in the selection of nature reserves apart from the sampling of natural environments. Scenic, recreational, historic and archaeological attributes are all important, as are the occurrences of species which might not be catered for by an approach based solely on mapping units. Nevertheless, the delineation of natural environments can provide a useful framework for the distribution of these other attributes. In addition, initial reserve selection to represent natural environments can provide a basic network of sites on which other considerations can be superimposed. Similarly, the efficiency of sampling in reserves is not the only consideration in conserving natural environments. The relative priority of natural environments for conservation and the condition and manageability of alternative sites are among the other concerns. Efficiency in reserve selection is very important, however, in maximising the likelihood of conserving the full range of natural environments. The same applies to conserving species or other attributes. The iterative analysis applied in this study, like others of its type in Australia, is a more efficient way of sampling species or natural environments in reserves than the more widely used scoring approaches (Pressey & Nicholls, this issue). A comparison of results starting with and without the existing reserves shows that ad hoc reservations, without regard to efficiency, have increased the minimum area needed to represent all land systems in the study area. Further such reservations will continue to raise the minimum requirement and may result in the areal limit of conservation, however ill-defined, being reached before the reserve system becomes fully representative. Despite the relative efficiency of the analysis, large areas are still required for even one representation of each land system. The addition of 6892 km 2 to the already extensive reserve system is needed and would bring the total reserved area to 8.3% of the study area. Moreover, this large area satisfies only a very basic goal. Significantly larger areas would be required for reservation of a minimum areal proportion of each land system and for the protection of localised species, not catered for by this coarse approach, in addition to land systems. Attempting to develop fully representative reserve systems within geographical subdivisions of the study area will make overall conservation still more expensive. The large areas needed for complete regional or provincial reservation stand against the benefits of increasing the combinations and geographical variants of land systems conserved. Only relatively small reductions in required areas can be achieved by excluding atypical land systems from the analysis. Apart from its efficiency, the analysis presented here has other advantages
Numerical algorithm m reserve selection
277
over scoring procedures. It identifies a whole complementary set of potential reserves in one process. Any required alteration to the selection, for example by excluding inappropriate sites from the analysis or beginning the analysis with desirable sites, can be accommodated. The areal cost of such changes can be assessed by comparing the results with those from the unconstrained analysis, as for the comparisons beginning with and without the existing reserves. The algorithm of Margules et al. (1988) and the modification reported here actually facilitate these changes by effectively identifying two categories of sites: those with unique attributes, which could be regarded as non-negotiable, and others which can be substituted to represent attributes. Another advantage of the iterative approaches is that they bring land managers face to face with the implications of conservation goals. A minimally representative reserve system in our study area, beginning with acquired properties, requires the dedication of 8-3% of the land. Is the acquisition of this area feasible? If not, then which sites and which land systems should be excluded from the reserve network? Which sites should be acquired and which ones should be protected in other ways? The traditional adhoc approach to reserve selection and the widely used scoring approaches do not define the costs of conservation goals so clearly. They therefore inhibit the assessment of the feasibility of those goals and of the best means of achieving them. The analysis presented here is a very basic one. Work is currently underway to develop an algorithm which samples a minimum percentage of the extent of each land system. A data base on vascular plants and vertebrates with localised occurrences in the Western Division is being compiled to complement the broad approach based on land systems. Additional work is planned which will compare the efficacy of alternative selection units (properties, component leases, arbitrary grid cells) and which will develop adjacency constraints to select large blocks of properties as potential reserves. A longer term aim is to use the analysis as an operational aid to decision-making on the conservation or development of specific sites in the Western Division.
ACKNOWLEDGEMENTS Development of the data base for this study was made possible by a grant from the Australian National Parks and Wildlife Service and by the patient and accurate work of Peter Rubacek. Michael Bedward prepared the figure and assisted in the compilation and interpretation of the tables. Mike Austin, Andrew Burbidge and Doug Cocks commented on the draft manuscript.
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R. L. Pressey, A. O. Nicholls REFERENCES
Christian, C. S. & Stewart, G. A. (1968). Methodology of integrated surveys. In Proceedings of the Toulouse Conference on Aerial Surveys and Integrated Studies. UNESCO, Paris, pp. 233-80. Kirkpatrick, J. B. (1983). An iterative method for establishing priorities for the selection of nature reserves: an example from Tasmania. Biol. Conserv., 25, 127-34. Kirkpatrick, J. B. & Harwood, C. E. (1983). Conservation of Tasmanian macrophytic wetland vegetation. Proc. R. Soc. Tas., 117, 5-20. Margules, C. R. & Usher, M. B. (1981). Criteria used in assessing wildlife conservation potential: a review. Biol. Conserv., 21, 79-109. Margules, C. R., Nicholls, A. O. & Pressey, R. L. (1988). Selecting networks of reserves to maximise biological diversity. Biol. Conserv., 43, 63-76. Margules, C. R., Pressey, R. L. & Nicholls, A. O. (in press). Selecting nature reserves. In Cost-effective Survey Methods for Nature Conservation, ed. C. R. Margules & M. P. Austin. CSIRO Division of Wildlife and Ecology and New South Wales National Parks and Wildlife Service, Canberra. Morgan, G. (1986). Nature conservation in western New South Wales. Natn. Parks J., 30, 12-17. Pressey, R. L. & Nicholls, A. O. (1989). Efficiency in conservation evaluation: scoring versus iterative approaches. Biol. Conserv., 50, 199-218. Pressey, R. L. & Nicholls, A. O. (in press). Reserve selection in the Western Division of New South Wales: development of a new procedure based on land system mapping. In Cost-effective Survey Methods for Nature Conservation, ed. C. R. Margules & M. P. Austin. CSIRO Division of Wildlife and Ecology and New South Wales National Parks and Wildlife Service, Canberra. Smith, P. G. R. & Theberge, J. B. (1986). A review of criteria for evaluating natural areas. Environ. Manage., 10, 715-34. Usher, M. B. (1986). Wildlife conservation evaluation: attributes, criteria and values. In Wildlife Conservation Evaluation, ed. M. B. Usher. Chapman and Hall, London, pp. 344. Western Division Select Committee (1983). First Report of the Joint Select Committee of the Legislative Council and Legislative Assembly to Enquire into the Western Division of New South Wales. Government Printer, Sydney.