A new approach for selecting fully representative reserve networks: addressing efficiency, reserve design and land suitability with an iterative analysis

Biological Conservation 1992, 62, 115-125

A new approach for selecting fully representative reserve networks: addressing efficiency, reserve design and land suitability with an iterative analysis Michael Bedward, Robert L. Pressey & David A. Keith N S W National Parks and Wildlife Service, PO Box 1967, Hurstville, N S W 2220, Australia (Received 18 October 1991; accepted 11 November 1991)

Recent developments in procedures for selecting nature reserves have emphasized the goal of representing the full range of conservation features (e.g. species, communities, land systems) in a region. Iterative selection algorithms have proved to be very efficient at this task, but until now have not taken criteria for reserve design or land suitability into account. A new interactive computer program, C O D A (Conservation Options and Decisions Analysis), overcomes these limitations. C O D A is conceptually simple, yet powerful. A broad range of conservation objectives can be met and alternative reserve configurations can be displayed and compared. In this paper we outline the C O D A procedure and apply it to a demonstration reserve planning exercise in southeastern New South Wales, Australia. The cost of reserve design criteria and of representing conservation features to different extents is assessed in terms of the total reserve area required. We discuss the implications of our results for formulating conservation proposals, especially with regard to competing land uses.

1978; White & Bratton, 1980). On the other hand, a greater balance between conservation and exploitative land uses can be encouraged by reserve selection procedures which are explicit in their operation, powerful enough to handle large amounts of data, and flexible enough to accommodate different criteria for reserve design. For many areas the time remaining for sound conservation planning at regional scales is very limited. Recent developments in systematic procedures for selecting nature reserves in Australia have emphasized the need for reserve networks to be as representative as possible of the natural features in a region, i.e. encompass the range of regional variation in species or natural environments (Austin & Margules, 1986; Margules & Nicholls, 1987; Margules, 1989). These developments have concentrated on iterative procedures in which the potential contributions of unselected sites to full representation are recalculated each time a site is

INTRODUCTION Throughout the world the remaining wild populations of plants and animals are under intense pressure from exploitative land uses. The creation of nature reserves is one solution to this problem but its effectiveness depends on an adequate database of natural features and well-founded methods for locating reserves in relation to those features and other land uses. In areas where the degradation of natural areas outside reserves is rapid, the failure to locate reserves sensibly with regard to distribut ion of species and their habitats will leave species at risk of at least local extinction in the next few )ears. A poorly planned reserve system will also be less effective in protecting the natural features contained within it (e.g. Pickett & Thompson, Biological Conservation 0006-3207/92/$05.00 ~ 1992 Elsevier Science Publishers Ltd. 115

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added to the reserve network (e.g. Kirkpatrick, 1983; Kirkpatrick & Harwood, 1983; Margules et al., 1988; Pressey & Nicholls, 1989a). Iterative reserve selection algorithms are more efficient than a range of conservation scoring procedures in selecting representative reserve networks (Pressey & NichoUs, 1989b). Highest efficiency, in this context, refers to the ability to represent all features in the smallest number of sites--an important property given the need to defend reserve proposals against competing land uses. Even so, the proportion of the total area needed for representation of all the features in a region can be large. Estimates vary from about 8% (Pressey & Nicholls, 1989a) to 45% (Margules et al., 1988) depending on the scale of definition of the features and the size of the areas being examined as potential reserves. This explicit identification of reserve requirements is another advantage of iterative procedures. Until now there have been two major obstacles to the use of these algorithms for formulating actual reserve proposals. First, they generally select a diffuse scatter of sites which, although efficiently representing landscape or biological diversity, fails to address well-founded criteria for reserve design. There is broad agreement on the need for large and contiguous reserves (e.g. Frankel & Soul6, 1981; Burkey, 1989) and the advantages of locating reserves within buffer areas of sympathetic land uses wherever possible (UNESCO, 1974). Secondly, these algorithms only identify a single network of sites without displaying alternative networks of the same size or larger. This makes it difficult to justify the inclusion or exclusion of particular sites when defending the reserve proposal and precludes the selection of alternative sites that are more desirable in terms of location, tenure or other considerations. In this paper we introduce an interactive, mapbased computer program, CODA (Conservation Options and Decisions Analysis), that overcomes these limitations. CODA allows the user to modify the diffuse network of sites selected by an iterative algorithm to address reserve design and land suitability criteria. It also uses a refinement of the algorithms previously applied by Margules et al. (1988) and Pressey and Nicholls (1989a) which identifies a set of sites needed to represent a specified area of each feature. This paper outlines the general procedure and describes the application of CODA to a reserve planning exercise in the Eden region of southeastern New South Wales in which there is

considerable variation in land tenure and condition of the remaining vegetation, and where conflicts exist between forestry and nature conservation.

OUTLINE OF THE CODA P R O C E D U R E

Objectives of the procedure To represent all natural features in the reserve network Species, communities and land systems are all examples of natural features that can be the subject of conservation planning exercises. Several different types of natural features may be considered simultaneously, e.g. plant communities and individual plant species of particular conservation significance. CODA requires an explicit target for the number or extent of each feature that is required in the reserve network. Ideally, the targets are an expression of the level of representation in reserves required adequately to protect natural features. Insights from population viability analyses (e.g. Murphy et al., 1990) could be used to formulate targets for the representation of species and habitat types. The likely fate of adjacent, unreserved areas can also be important. Where reserves are intended to provide refuges for fauna in a region largely devoted to a land use sympathetic to conservation goals, the extent of habitat required would be less than if all unreserved areas in the region were expected to be permanently cleared, leaving the only available habitat in reserves. To satisfy reserve design criteria This is entirely flexible: the network can be completely unconstrained or can be based on a range of reserve design criteria. For reserves intended to protect populations of a particular species, our knowledge of the biology of that species will determine appropriate design criteria. Where reserves are intended to conserve as many as possible of the species in a region, the following general design principles are relevant:

(a) reserves should be large, compact and connected; (b) where a choice exists, reserves should be drawn from the least disturbed areas; (c) where possible, reserves and corridors between reserves should be located so that the management of surrounding lands is the most compatible of those available, e.g. forestry rather than intensive agriculture.

Selecting representative reserve networks To make reserve proposals defensible in the light of competing land uses For areas where there is significant competition between conservation and exploitative land uses, the following are examples of criteria that can be applied: (a) the selection of areas for reserves should be as efficient as possible, i.e. the cost of the reserve network, in terms of resources no longer available for other land uses, is not greater than was necessary to accomplish conservation goals; (b) where possible, additional reserves should be drawn from publicly owned lands to minimise costs of acquisition. Data requirements Selection units These are the units of land that are examined during the procedure and used to construct reserves. Selection units used in previous studies have included grid cells (Purdie et al., 1986), vegetation remnants (Game & Peterken, 1984; Margules & Nicholls, 1987) and tenure units (Pressey & Nicholls, 1989a). Conservation data base t h i s contains information on the location and extent of conservation features, e.g. species, communities, land systems. It can contain information on more than one type of feature, e.g. a m a p of plant communities together with specific locality data for rare species. Land suitability data base l'his divides the study area into classes of suitability for reservation. For example, the classes can be cleared lands, pristine vegetated areas and other areas modified to various degrees. The boundaries of land suitability classes might coincide with those of the selection units or they might be quite independent. Units of cost t h e s e are the currency used to determine the cost of acquiring reserves or the opportunity costs of alternative reserve networks to competing land uses. The simplest units of cost are the number or total area of the selection units reserved. Other possible units are timber volumes or tonnages of mineral ore, both of which would require a detailed knowledge of resources.

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Steps in the analysis The basic C O D A procedure consists of nine steps which can be grouped into three stages: preprocessing of the data; preliminary selection of new reserves with an iterative algorithm; and modification of the network.

Preprocessing STEP 1. Exclude inappropriate conservation features This refers to conservation features that should not have an influence on the placement of new reserves. Examples include features that are marginal in their distribution to the region and well-conserved in adjacent regions, and features that are present only as isolated remnants in highly modified areas and best protected by means other than reservation. STEP 2. Allocate conservation features and units of cost to selection units Each selection unit is described in terms of the identity and, ideally, the extent or number of conservation features that it contains. Each unit is also assigned a cost of reservation, e.g. market value, extent, or the volume of standing timber. STEP 3. Allocate land suitability classes to selection units and, optionally, exclude unsuitable units Some selection units might be inappropriate for reservation due to gross modification by agriculture or residential development or prior commitment to other land uses. These can be excluded at this stage so that they do not influence the selection of new reserves. Where such units contain features of particular significance such as a remnant population of a rare species or the only remaining patches of a plant community, they may be more appropriately dealt with by dedicating small reserves or imposing land use constraints. For the remaining selection units, land suitability information will be used in conjunction with units of cost to compare alternative reserve networks. STEP 4. Define the representation target for each feature The targets indicate the number or extent of each feature that is required in the reserve network. Formulation of targets is flexible and different types of targets can be set for different features. For example, a target could be a given number of occurrences of a rare plant community that is

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M. Bedward, R. L. Pressey, D. A. Keith

present in the region only as isolated remnants. For a widespread plant community, the target could be set as a proportion of its total extent. Where there are existing reserves within the study area the a m o u n t of each feature already reserved is subtracted from the initial targets. Similarly, the targets are updated as new areas are added to, or taken out of, the reserve network. At any stage of the procedure, features with positive targets are under-represented in the reserve network while those with zero or negative targets are fully represented. STEP 5. Identify focal selection units A focal selection unit is one that (a) is wholly or partly within an existing reserve; (b) contains a feature of high conservation significance that must be included in the reserve network; (c) must be included in the reserve network for other reasons. The focal selection units are nuclei about which the expanded reserve network will be constructed. Preliminary selection o f new reserves STEP 6. Apply the reserve selection algorithm In this step, additional selection units are chosen to give a network in which all features are fully represented. We suggest the use of one of the family of iterative selection algorithms (e.g. Margules et al., 1988; Pressey & Nicholls, 1989a; A. O. Nicholls, pers. comm.) which can be adapted to a variety of types of data and optimised for different cost units. Other types of selection algorithms, e.g. linear programming, could be employed. The network selected by such algorithms will usually be a diffuse scatter of units throughout the region. The C O D A mapping module is used to display the location of the selection units relative to focal selection units. Those units that meet design and land suitability criteria are notionally reserved. Modification o f the network STEP 7. Replace unsuitable selection units Some units selected in Step 6 may not satisfy reserve design and land suitability criteria. C O D A provides two facilities to search for alternatives for such units. The first of these temporarily removes the unit from the network and checks to see which features fall below full representation. The system then displays the location of any currently unselected units which individually contain

enough of these features to return them to full representation if included in the network. Where no single replacement units are available or suitable, a second facility is used to indicate which selection units contain features that will fall below full representation when the unsuitable unit is removed from the network. Two or more selection units can then be identified which, together, replace the original unit. STEP 8. Check for any unnecessary increase in the cost of the network After Step 6, the network is, or is close to, the most efficient way to represent fully all conservation features. As it is modified to conform to criteria of land suitability and reserve design it is likely that the total cost of the network (e.g. the total area of land or the volume of timber unavailable to industry) will increase. Where there is significant competition from other land uses it is important to show that such cost increases are essential to fulfil conservation goals. C O D A provides facilities to search for avoidable cost increases. The first of these searches for redundant selection units within the network, i.e. any units that can be removed from the network without any conservation features falling below full representation. Where the inclusion of such a unit is not justified by its contribution to sensible reserve design, it can be removed from the network. A second facility displays the current targets for conservation features. A large negative target indicates that the amount of a feature in the network is much greater than the original goal. C O D A can be used to search for units that contain such features and make only a small contribution to the representation of other features. These can then be assessed in a similar manner to redundant units. Step 8 can be applied repeatedly during Step 7 to assess the cost effectiveness of alternative modifications to the network. STEP 9. Rationalise reserve boundaries This optional step applies to cases where the boundaries of selection units are not suitable boundaries for reserves. For example, if a large selection unit was included to represent features that occupy only a small part of the unit, it could be appropriate to exclude the remainder of the unit from the reserve network. Also at this step, previously unselected units may be brought into the reserve network to give greater connectivity between reserves or to form boundaries that are more suitable for management or acquisition.

Selecting representative reserve networks

AN A P P L I C A T I O N IN T H E S O U T H E A S T F O R E S T S OF N E W S O U T H WALES Study area

The study area occupies approximately 780 000 ha in the southeastern corner of New South Wales, Australia (Fig. 1). It encompasses a low coastal range of up to 300 m in elevation, with some isolated taller peaks, and undulating terrain immediately inland. Further west a mountain range rises to over 1000 m and then flattens out to form a plateau of low relief. The area is geologically diverse with rocks of sedimentary, metamorphic and igneous origin as well as alluvial deposits. Vegetation includes rainforest, heath, swamps, and forests and woodlands with canopies dominated by species of Eucalyptus. Keith and Sanders (1990) give detailed descriptions of the area and its vegetation communities. The major land uses in the area are forestry, agriculture and conservation. State forests occupy approximately 45% of the area while approximately 9% is dedicated as conservation reserves. About 36% of the area has been cleared and is mostly private agricultural land.

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vation reserves. The conservation data base for this study consisted of two data sets. The first was a map of 'environmental domains' derived from a numerical classification of climatic, terrain and soil attributes (Richards et al., 1990). A total of 109 domains were described for the study area, most with localised distributions, with a median total extent of 5800 ha (values varied from 8 to 29 900 ha). The use of any m a p units for actual conservation planning assumes that they are informative about the distribution and abundance of the biota. Environmental domains are used here only as an example map base to explore the C O D A procedure. The second data set consisted of known locations of species and plant communities of particular conservation significance which could have been omitted from reserves selected only on the basis of environmental domains. Rare plant species included those endemic to the study area (from Keith, 1990) and those listed as rare or threatened in Australia (Briggs & Leigh, 1988). Rare animal species considered were the koala Phascolarctos cinereus and the long-footed potoroo Potorous longipes. Plant communities of conservation significance were those identified as poorly reserved by Keith and Sanders (1990).

Conservation data base Selection units

Detailed distributional information is lacking for the majority of the area's biota. A large set of data from vegetation sites throughout the area has been compiled but a detailed vegetation m a p is not yet available. Distributional data for fauna are patchy and often restricted to existing conser-

These consisted of 590 stream catchments, the boundaries of which were derived from a digital terrain model of the area. Median catchment area was 1000 ha with values ranging from less than 10 ha to over 6000 ha.

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Fig. 1. The study area located in the southeastern corner of New South Wales, Australia.

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M. Bedward, R. L. Pressey, D. A. Keith

Units of cost Detailed data on the volume of standing timber in the region were unavailable. The geographical extent of selection units was taken as the reservation cost.

Land suitability data base Data on land suitability came from a variety of sources. Richards et al. (1990) provided a map of vegetative cover based on satellite imagery which was used to identify cleared land. They also provided information on land tenure. A map of 'naturalness' was prepared jointly by staff of New South Wales National Parks and Wildlife Service and the Centre for Resource and Environmental Studies at the Australian National University, using a procedure described by Leslie et al. (1988). Naturally vegetated areas were assigned to one of five naturalness categories that ranged from unlogged and ungrazed to subject to clearfell logging and/or intensive grazing. The boundaries of plantations of the introduced conifer Pinus radiata were taken from the unpublished data provided by staff of the Forestry Commission of NSW.

Objectives for this application Six objectives were set for this exercise. The first two define our representation goals: (1) to represent each rare species and significant community in at least three catchments (or to represent all occurrences where the species or community is present in three or fewer catchments); (2) to represent 1000 ha of each environmental domain plus 5% of the remaining extent in reserves. Only the uncleared extent of domains was considered. A second analysis was undertaken to represent 1000 ha plus 20% of the remaining extent of each domain. These will be referred to as the 5% and 20% analyses. For both analyses, all occurrences of domains with a total extent of less than 1000 ha were required in reserves. The next two objectives define our goals for reserve design: (3) where possible, representation of conservation features should be achieved by reserving contiguous catchments to give reserves with

low perimeter to area ratios and a high degree of connectivity; (4) where possible, reserves should represent the most undisturbed occurrences of environmental domains. The final objectives were set to make the reserve proposals defensible: (5) the area of additional reserves should be minimised where this does not compromise representation and reserve design objectives; (6) where possible, reserves should be drawn from publicly owned land to minimise the cost of acquisition.

CODA analysis The following sequence was followed for both the '5% and 20% analyses. STEP 1. Five environmental domains which were only marginal in their distribution to the study area, and which were well-reserved outside the area, were excluded from the analysis. STEP 2. The following statistics were determined for each catchment: (a) total area (the unit of cost); (b) presence of rare species and significant plant communities; (c) area within existing reserves; (d) proportion of the catchment cleared; (e) identity and uncleared extent of environmental domains for reserved and/or unreserved parts of the catchment. STEP 3. 134 catchments which had more than 90% of their area cleared were excluded from the analysis. The highly fragmented nature of any natural vegetation in these catchments made them unsuitable for broad-scale reserve planning. The total extent of each environmental domain was adjusted by subtracting the area that occurred in excluded catchments. STEP 4. Preliminary targets for domain representation were calculated using the formulae outlined in objective (2). STEP 5. Focal units included catchments wholly or partly within existing reserves plus a set of 58 catchments within which rare species and significant plant communities were represented as outlined in objective (1). The iterative selection algorithm of Margules et al. (1988) was used to select these 58 catchments from the set of all

Selecting representative reserve networks

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catchments which contained rare species or significant communities. The representation targets for domains were adjusted by subtracting the domain content of the focal units.

STEP 8. Checks for redundant catchments were made. Those which did not contribute to reserve shape or connectedness were removed from the network.

STEP 6. An iterative selection algorithm (based on that o f A. O. Nicholls, pers. comm.) was used to select the preliminary reserve network in which all environmental domains were fully represented. This is a stepwise algorithm which applies the following rules:

STEP 9. After the completion of modifications to the network using whole catchments, the following steps were taken to rationalise reserve boundaries:

(1) select all catchments which contain the only occurrence of a currently under-represented domain; (2) select the catchment containing the next rarest, currently under-represented domain; (3) if there is a choice, select the catchment which contains a sufficient area of the domain to represent it fully in the reserve network; (4) if there is a choice, or if no catchment contains enough of the domain to represent it fully, select the catchment which most lowers the area still required for other domains; (5) if there is a choice at Step 4, select the first catchment on the list. Each selected catchment was examined for its condition, tenure and location. Catchments that were contiguous with existing reserves and on relatively undisturbed, publicly owned land, in accordance with objectives (3), (4) and (6), were notionally reserved. STEP 7. Replacements were sought for selected catchments which were not notionally reserved at Step 6 using the facilities described in the general outline of the CODA procedure. During this step it became evident that a number of catchments which were mostly cleared had been selected to represent domains whose naturally vegetated occurrence was highly fragmented. Some of these domains could only be represented by reserving a aumber of catchments which did not contribute other conservation features or enhance the shape or connectedness of reserves. In such cases the domains were excluded from the exercise. Fragmented domains which occurred in catchments contiguous with the notional reserve network were dealt with by reserving the whole catchment at this step and then removing that part of the catchment which did not contain vegetated occurrences of the domain, or contribute to the network in other ways, at Step 9.

(a) some catchment boundaries were adjusted to compensate for anomalies between boundaries derived from the digital terrain model of the area and boundaries delineated on topographic maps of the area; (b) a small number of catchments were added to the network to link narrowly separated reserves and to reduce the perimeter to area ratios of reserves in accordance with objective (3); (c) cleared portions of notionally reserved catchments were removed from the network where this did not compromise objective (3).

RESULTS Comparison of the reserve networks

The final networks from the 5% and 20% analyses are shown in Fig. 2. The similarity in the basic form of the two networks is a result of the constraints imposed by the large areas of cleared agricultural land, the smaller areas that have been cleared for Pinus radiata plantations, and building each network on the same set of focal units. Figure 3 shows the representation of domains at various stages of the 5% and 20% analyses. The two reserve networks were identical for the first two stages shown for each analysis and the difference in the two histograms is due to the different representation targets. A larger number of domains were over-represented in existing reserves plus other focal areas in the 5% analysis than in the 20% analysis. With the addition of catchments selected by the iterative algorithm all domains were fully represented in both analyses. At this stage, representing the domains to the extent required in the 20% analysis required only 3% more of the study area than was required in the 5% analysis. There was still a greater degree of overrepresentation of domains in the 5% analysis, with 39 domains having more than twice the required

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Fig. 2. Final reserve networks for the 5% and 20% analyses. Solid, black areas are existing reserves; hatched areas are additional reserves selected using the CODA procedure.

extent in the reserve network compared to 15 domains in the 20% analysis. Some of the domains fell below full representation in each analysis after Step 8 and again after the final step. As discussed above, these domains were only present as vegetation remnants in highly modified areas that were not suitable for inclusion in the reserve network. The reduction in the total extent of the network in each analysis is mainly due to removing catchments that had been selected by the iterative algorithm to represent these fragmented domains. The difference in area between the two networks is not great. The final network from the 5% analysis occupied 26% of the study area (202 687 ha), whereas only an additional 5% of the study area was required to fulfil the higher representation targets of the 20% analysis (for a total reserve area of 239 094 ha). This is a consequence of the unavoidable over-representation of some domains in the 5% analysis. Some domains were already overrepresented in the existing reserve network. Other domains became over-represented when other focal catchments were selected to represent significant species and communities. In addition, the inclusion of catchments to achieve minimal representation of rare domains can cause incidental over-representation of more c o m m o n domains also present in those catchments. Over-representation in the 5% analysis was such that the reserved extent of a number of the domains was sufficient to fulfil the targets of the 20% analysis, and so the

additional area required for the 20% reserve network was small. The cost of reserve design criteria

Modifying the diffuse network of sites selected by the iterative algorithm in Step 6 into a network which fulfils our objectives for reserve design will generally involve an increase in cost. This is not evident in Fig. 3 because of the decrease in the extent of the reserve networks that occurred when highly fragmented domains were allowed to fall below full representation. If all of the domains had been widespread in contiguous naturally vegetated areas it is likely that the area of the network would have increased between Step 6 and Step 8. To investigate this further, we used the iterative selection algorithm to construct networks in which domain representation was equivalent to that in the 5% and 20% networks after Step 8, b u t where no reserve design constraints were imposed. The representation target for each domain in these 'unconstrained' networks was set equal to the target in the original analysis or the area actually achieved after Step 8, whichever was the least. For the 5% analysis the area of the reserve network after Step 8 (262 457 ha) was 4.4% larger than that of the unconstrained 5% network (251 340 ha). For the 20% analysis the area of the network after Step 8 (317 850 ha) was 4.2% larger than that of the 20% unconstrained network (305 029 ha).

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M. Bedward, R. L. Pressey, D. A. Keith

DISCUSSION The strength of the C O D A procedure lies in its explicitness, simplicity and flexibility. The techniques employed are powerful yet conceptually straightforward. Apart from clear advantages to the user, this simplicity also ensures that the results of C O D A analyses, and the procedure itself, can be readily explained to, and repeated by, others. C O D A forces the user to be explicit about the representation goal for each conservation feature and about criteria for reserve design. Parameters can be set to address a broad range of conservation objectives. Where two or more objectives are in conflict, alternatives can be displayed and evaluated. A range of data and map bases can be used. In addition, C O D A is equally applicable to conservation planning outside reserves. Fulfilling our reserve design criteria required an additional 4% of reserve area for both the 5% and 20% analyses. This result will vary considerably with different applications and design objectives, but in all cases C O D A can be used to estimate the cost of fulfilling the design objectives or a subset of them. The simplest cost units are the number or extent of selection units reserved. Where substantial land use conflicts exist, the most appropriate cost units are those that will reflect the opportunity cost of reserves for other land uses. For this study, the ideal cost units would have been sawlog and pulpwood resources foregone; however, these data were not made available. More sophisticated cost units are also possible, such as estimates of management costs of reserves based on ease of access and the types of neighbouring land uses. It is also possible to assess the cost of a reserve network using more than one type of cost unit, e.g. the amount of a resource no longer available for other land uses and the monetary cost of acquisition of reserves. The size of selection units will have a bearing on the results of C O D A analyses. Where units are very small it is likely that there will be little overrepresentation of features in the initial reserve network chosen by the selection algorithm in Step 6. However, it is also likely that large modifications would need to be made to such a network in Steps 7 and 8, particularly the merging of small selection units into large blocks, to achieve reserve design objectives. The converse would generally be the case where selection units are large. In practice, there will often be constraints on the choice of the type and size of selection units, such as

where cadastral units will be the smallest unit of acquisition for reserves. In this study, 1000 ha was chosen as the median size of selection units so that any isolated reserves in the final network would be likely to be of viable size. The question of over-representation of conservation features in the reserve network needs to be addressed explicitly in conservation planning exercises. For instance, our results showed that representing environmental domains to the level required in our 5% analysis required far more than 5% of the area. This could not be avoided without abandoning our objectives for large and contiguous reserves wherever possible. Even if this were done, some over-representation of features would still have occurred. It is crucial to our ability to defend reserve proposals that the reasons for over-representation are understood and can be expressed in terms of the distribution of conservation features with respect to units of selection, and the requirements of reserve design. This study also showed that a relatively small increase in the area of the reserve network was required to fulfil much higher representation targets for domains. Although this result will be datadependent, it will generally be the case that marked over-representation of conservation features will occur where targets are small, with the consequence that meeting increased targets will not require a commensurate increase in costs. Understanding and quantifying this effect will give conservation planners confidence to propose and defend more than the minimum option. Where the conservation data base includes m a p units as a major component we need to consider the extent to which the map reflects the distribution of the primary conservation features which are usually individual species. For example, the utility of the environmental domains of Richards et al. (1990) as predictors of the distributions of flora or fauna species has not been adequately demonstrated. Until such an analysis is undertaken we would not advocate using this m a p base for a real conservation evaluation exercise. Even where maps are derived directly from data on species distributions, such as vegetation types derived from a numerical classification of vegetation survey data, care must be exercised. Pressey and Bedward (1991) have suggested that it is possible to reserve samples of all units from such a m a p while still leaving a large proportion of mostly u n c o m m o n species unrepresented. Supplementing the conservation data base with distributional

Selecting representative reserve networks

i n f o r m a t i o n o n i n d i v i d u a l c o n s e r v a t i o n features o f p a r t i c u l a r significance, as in the a b o v e a p p l i c a t i o n , will e n s u r e t h a t these are r e p r e s e n t e d . W e are c u r r e n t l y u n d e r t a k i n g f u r t h e r d e v e l o p m e n t o f C O D A a n d research into the utility o f different types o f m a p bases for c o n s e r v a t i o n . Inh e r e n t in this w o r k is o u r p e r c e p t i o n o f the need f o r c o n s e r v a t i o n p l a n n i n g to be n o t o n l y systematic a n d explicit, b u t also assertive. M a r g u l e s et al. (1991) h a v e d o c u m e n t e d the large e x t e n t to which o p p o r t u n i s t i c a l l y selected reserves fail to p r o v i d e an a d e q u a t e r e p r e s e n t a t i o n o f c o n s e r v a t i o n features. Pressey (1990) p o i n t e d to the pitfalls o f p u r e l y o p p o r t u n i s t i c reserve selection which include p e r p e t u a t i n g a submissive a t t i t u d e in res p o n s e to o t h e r l a n d uses, while S a x o n (1983) refers to the n e e d f o r d y n a m i c c o n s e r v a t i o n planning m e a s u r e s m e e t i n g e v e r - c h a n g i n g needs. P r o c e d u r e s such as C O D A are a step in this direction. T h e t e r m 'coda' refers to the final m o v e m e n t o f a musical c o m p o s i t i o n t h a t brings the w h o l e to a s a t i s f a c t o r y c o n c l u s i o n . T o achieve this in conserv a t i o n , new p r o c e d u r e s such as C O D A m u s t be supported by accurate and appropriate data, and the flexibility a n d v i g o u r with which we c o n c e i v e of, a n d c o m m u n i c a t e , o u r p r o p o s a l s .

ACKNOWLEDGEMENTS W e wish to t h a n k Prof. H e n r y N i x a n d the staff o f the C e n t r e for R e s o u r c e a n d E n v i r o n m e n t a l Studies, A u s t r a l i a n N a t i o n a l University, for p r o viding essential d a t a f o r this exercise. E l i z a b e t h A s h b y p r o v i d e d technical assistance a n d helpful c o m m e n t s o n the m a n u s c r i p t . T h a n k s also to D a n i e l L u n n e y , Chris M a r g u l e s , P e t e r M y e r scough, N i c k N i c h o l l s a n d H u g h P o s s i n g h a m for their criticisms a n d suggestions.

REFERENCES Austin, M. P. & Margules, C. R. (1986). Assessing representativeness. In Wildlife Conservation Evaluation, ed. M. B. Usher. Chapman and Hall, London, pp. 45-67. Briggs, J. D. & Leigh, J. H. (1988). Rare or Threatened Australian Plants. Australian National Parks and Wildlife Service, Canberra. Burkey, T. V. (1989). Extinction in nature reserves: the effect of fragmentation and the importance of migration between reserve fragments. Oikos, 55, 75-81. Frankel, O. H. & Soul6, M. E. (1981). Conservation and Evolution. Cambridge University Press, Cambridge. Game, M. & Peterken, G. F. (1984). Nature reserve selection strategies in the woodlands of central Lincolnshire, England. Biol. Conserv., 29, 157-81.

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Keith, D. A. (1990). Rare and regionally significant vascular plant species of the Eden region, south-eastern New South Wales: a listing for the 'fine filter' approach. Proc. Linn. Soc. N.S.W., 112, 111-32. Keith, D. A. & Sanders, J. M. (1990). Vegetation of the Eden region, south-eastern Australia: species composition, diversity and structure. J. Veget. Sci., 1,203-32. 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 macrophyte wetland vegetation. Pap. Proc. R. Soc. Tasmania, 117, 5-20. Leslie, R. G., Mackey, B. G. & Preece, K. M. (1988). A computer based method of wilderness evaluation. Environ. Conserv., 15, 225-32. Margules, C. R. (1989). Selecting nature reserves in South Australia. In Mediterranean Landscapes in Australia: Mallee Ecosystems and their Management, ed. J. C. Noble & R. A. Bradstock. CSIRO, Melbourne, pp. 398405. Margules, C. R. & Nicholls, A. O. (1987). Assessing the conservation value of remnant habitat islands: mallee patches on the western Eyre Peninsula, South Australia. In Nature Conservation, the Role of Remnants of Native Vegetation, ed. D. A. Saunders, G. W. Arnold, A. A. Burbidge & A. J. M. Hopkins. Surrey Beatty and Sons, Sydney, pp. 89-102. Margules, C. R., Nicholls, A. O. & Pressey, R. L. (1988). Selecting networks of reserves to maximise biological diversity. Biol. Conserv., 43, 663-76. Margules, C. R., Pressey, R. L. & Nicholls, A. O. (1991). Selecting nature reserves. In Nature Conservation: Costeffective Biological Surveys and Data Analysis, ed. C. R. Margules & M. P. Austin. CSIRO, Canberra, pp. 90-7. Murphy, D. D., Freas, K. E. & Weiss, S. B. (1990). An environment-metapopulation approach to population viability analysis for a threatened invertebrate. Conserv. Biol., 4, 41-51. Pickett, S. T. A. & Thompson, J. N. (1978). Patch dynamics and the design of nature reserves. Biol. Conserv., 13, 27-37. Pressey, R. L. (1990). Reserve selection in New South Wales: where to from here? Aust. Zool., 26, 70-5. Pressey. R. L. & Bedward, M. (1991). Mapping the environment at different scales: benefits and costs for nature conservation. In Nature Conservation: Cost-effective Biological Surveys and Data Analysis, ed. C. R. Margules & M. P. Austin. CSIRO, Canberra, pp. 7-13. Pressey, R. L. & Nicholls, A. O. (1989a). Application of a numerical algorithm to the selection of reserves in semiarid New South Wales. Biol. Conserv., 50, 263-78. Pressey, R. L. & Nicholls, A. O. (1989b). Efficiency in conservation evaluation: scoring versus iterative approaches. Biol. Conserv., 50, 199-218. Purdie, R. W., Blick, R. & Bolton, M. P. (1986). Selection of a conservation reserve network in the Mulga Biogeographic Region of south-western Queensland, Australia. Biol. Conserv., 38, 369-84. Richards, B. N., Bridges, R. G., Curtin, R. A., Nix, H. A., Shepherd, K. R. & Turner, J. (1990). Biological Conservation of the South-East Forests: Report of the Joint Scientific Committee. Australian Government Publishing Service, Canberra. Saxon, E C. (1983). Parks and reserves. In What Future for Australia's Arid LandsL ed. J. Messer & G. Mosley. Australian Conservation Foundation, Melbourne, pp. 165-8. UNESCO (1974). Criteria and guidelines for the choice and establishment of biosphere reserves. Man & Biosphere Rep. Ser., No. 22. UNESCO, Paris. White, P. S. & Bratton, S. P. (1980). After preservation: the philosophical and practical problems of change. Biol. Conserv., 18, 241-55.