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Prudent allocation of scarce conservation resources requires rigour in the identification of conservation areas. We sought to broadly identify areas within the wheatbelt that would efficiently represent assemblages derived from the biological survey considered susceptible to the threat of salinity. We inferred the spatial distribution of each assemblage’s species richness using a simple interpolation procedure. After omitting assemblages considered not to be threatened by salinity, as well as those for which the interpolation procedure failed to discriminate areas of high and low richness, analyses were undertaken using 17 aquatic and 6 terrestrial assemblages. To gain an appreciation of the magnitude of resourcing required for different levels of representation, we described the number of subcatchments and the area required to satisfy a range of conservation targets, where targets were based on the number of species associated with each assemblage. Nearoptimal solutions were identified using a heuristic algorithm principally driven by a measure of irreplaceability for each of 829 subcatchments comprising the study area. Results identified a subset of core areas for conservation investment that efficiently represented assemblage diversity. However, when on-ground management activities commence in each of the identified areas, field reconnaissance and verification will be necessary to account for localised variation in species richness and for vagaries in the distribution of remnant vegetation patches, wetlands and salinity risk. After canvassing limitations of the methodological approach, we conclude that the use of systematic planning tools involving heuristic algorithms provides a sound basis for estimating the extent and configuration of areas needed to satisfy specified conservation targets. INTRODUCTION Approximately 75% of the native vegetation of the wheatbelt region of Western Australia has been cleared for agriculture over the last 150 years (George at al., 1995). The viability of the region’s biodiversity is threatened by a suite of processes, with habitat loss, fragmentation and salinisation associated with broad-scale land clearance considered especially important (Saunders et al., 1993; Kay et al., 2001; Cramer and Hobbs, 2002; Halse et al., 2003; McKenzie et al., 2003). Within the Southwest Agricultural Zone, it is estimated that 1.8 million ha of land are currently affected by salinity, and in the absence of intervention, 6 million ha are predicted to be affected by the time a new hydrological equilibrium is reached (Ferdowsian et al., 1996). The scale of threats to the wheatbelt region’s biodiversity and the high costs associated with their effective remediation demands prioritisation and rigour in the investment of public resources (Pannell, 2001; Possingham, 2001). Yet the prudent allocation of scarce conservation resources is a fundamental challenge to management agencies and the broader community (Burgman et al., 1993; Kristjanson and Hobbs, 2001). In an attempt to reduce the impact of salinisation on biodiversity (as well as agricultural productivity and rural infrastructure) the Western Australian government announced a Salinity Action Plan in 1996 (Anonymous 1996). One of the recommendations of the plan was that a biological survey of the wheatbelt be undertaken to assist in selecting catchments of high biodiversity value, termed originally Biodiversity Recovery Catchments but now referred to as natural diversity recovery catchments, where public money would be invested to control salinisation and maintain conservation values. The biological survey revealed substantially 366 T. V. Walshe, S. A. Halse, N. L. McKenzie, N. Gibson approach and formulation of the problem, and the inefficiency of an approach based on richest exemplars. Suppose the biota of a region comprises n = 120 species that are characterised by k = 3 assemblages. The number of species associated with Assemblages A, B and C is nA = 40, nB = 20 and nC = 60. There are m = 6 candidate sites for recovery catchment selection. The assemblage-specific species richness at each of the six sites is described in a site-by-assemblage matrix (m × k) with elements aij, for i = 1,m and j = 1,k, as shown in Table 1. The vector X with dimension m and elements xi describes whether or not a site is included in the recovery catchment system, where Now, suppose we set minimum summed species richness representation targets for each assemblage j equivalent to its corresponding number of associated species, nj. That is, the summed species richness target for Assemblage A = nA = 40. The targets for Assemblages B and C are 20 and 60, respectively. Our objective is to find the minimum set of sites that will simultaneously satisfy the three targets. More formally, the problem can be stated as, After some consideration, it is reasonably easy to see from Table 1 that the minimum number of sites that will satisfy the objective is two – Sites 3 and 5. Note that although Site 1 contained the greatest total species richness, it does not form part of the minimum set. Note also that if we had used the richest exemplar criterion as the basis for site selection (Site 4 for Assemblage A, Site 5 for Assemblage B, and Site 6 for Assemblage C) the target would not be achieved for any of the assemblages, despite inclusion of one more site than the minimum set solution. It is important to emphasise that defining summed species richness targets on the basis of nj does not imply that all species associated with an assemblage are represented in the recovery catchment system. For example, if 50% of the Assemblage C species at Site 3 also occur at Site 5 greater biodiversity than previously recorded (Keighery, 2001). The terrestrial survey of 304 quadrats found 2,878 species, which on the basis of patterns of co-occurrence could be grouped into 17 assemblages after exclusion of singletons (McKenzie et. al., 2004). The aquatic survey conducted at 197 wetlands found 1,887 species which, after excluding singletons, were characterised by 19 assemblages (Halse et al., 2004). The number of species comprising any one assemblage varied considerably. The focus of analyses presented here is efficient capture of the richness of wetland and terrestrial assemblages considered susceptible to the threat of salinisation in notional natural diversity recovery catchments. The taxonomic breadth included in the biological survey (Halse et al., 2004; McKenzie et al., 2004) suggests that conservation targets might better utilise assemblages as the entities of interest rather than other surrogates such as vegetation type or abiotic gradients. Distal surrogates may be poorly correlated with many taxonomic groups (Ferrier and Watson, 1997; Moritz et al., 2001). Given that assemblages show strongly nested patterns in their species composition (Table 6 in McKenzie et al., 2004; Table 4 in Halse et al., 2004) species richness can be regarded as a reasonable surrogate for composition. Some of the wheatbelt’s outstanding biological diversity had already been identified and protected in six natural diversity recovery catchments using a combination of preliminary survey results and expert opinion (Department of Environment, 2003; Figure 1). However, a more formal analysis was required to determine which other areas in the wheatbelt might best complement these existing recovery catchments in meeting conservation goals. Species richness data can be used for conservation planning in several ways. A simple and unambiguous method is to conserve the richest exemplars of each assemblage (see Figure 2 in Halse et al., 2004). Although attractive in its simplicity and transparency, this approach is unlikely to provide an efficient outcome. A better outcome may be obtained if the ‘richest exemplar’ criterion is relaxed and consideration is given to areas where reasonably high richness values for two or more assemblages coincide (e.g. see Figures 11 and 12 in McKenzie et al., 2004). In this paper we adopt the objective of selecting a recovery catchment system that has a minimum representation threshold for each assemblage, where we use the number of species associated with each assemblage as the basis for determining minimum representation. We seek to achieve the objective in an efficient way such that our objective is achieved with as few as possible new areas added to the current system of natural diversity recovery catchments. A simple hypothetical example illustrates the Potential natural diversity recovery catchments 367 (see Table 1) the total number of C species represented in the minimum set system is 30 + (0.5 × 30) = 45. If data describing the individual spatial distributions of all 120 species throughout the region of our hypothetical example were available, the minimum set analysis could be undertaken at the species-level rather than the assemblage-level. However, collation of such data is commonly impractical, making a coarse approach necessary (Burgman and Lindenmayer, 1998; Ferrier, 2002). Targets defined as multiples of nj can be increased to reduce the effect of analyses undertaken at the assemblage-level compromising the representation of species-level diversity. Instead of setting assemblage-specific targets equivalent to 1 × nj, a larger multiplier can be nominated. For targets equivalent to 2 × nj, the threshold summed species richness for Assemblages A, B and C is 80, 40 and 120, respectively. Again, after consideration of Table 1, the minimum set for the 2 × nj targets can be seen to be five – Sites 1, 2, 3, 4 and 5. The solution to the simple example provided in Table 1 can be solved by inspection. For larger data sets, the computational task can be formulated as an optimisation linear programming problem, with the objective function of minimising the set of selected sites subject to a set of constraints, as defined above. However, for most real-life data sets, linear programming

Towards identification of an efficient set of natural diversity recovery catchments in the Western Australian wheatbelt