Integrating conservation planning and landuse planning in urban landscapes

Landscape and Urban Planning 91 (2009) 183–194

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Integrating conservation planning and landuse planning in urban landscapes Ascelin Gordon a,∗ , David Simondson a , Matt White b , Atte Moilanen c , Sarah Adine Bekessy a a

School of Global Studies, Social Science and Planning, RMIT University, GPO Box 2476V, Melbourne 3001, Australia The Arthur Rylah Institute for Environmental Research, Department of Sustainability and Environment, PO Box 137, Heidelberg 3084, Australia c Metapopulation Research Group, Department Biological and Environmental Sciences, PO Box 65 (Biocenter III), University of Helsinki, Finland b

a r t i c l e

i n f o

Article history: Received 16 October 2007 Received in revised form 12 November 2008 Accepted 30 December 2008 Available online 23 February 2009 Keywords: Conservation planning Zonation Landscape prioritisation Connectivity Urban planning Threatened species

a b s t r a c t The rapid growth of cities around the world is now seen as a major contributor to global biodiversity loss and many governments include biodiversity conservation as an explicit policy goal. To help prevent further loss of biodiversity, there is an urgent need for more strategic approaches to conservation planning in urban environments based on a scientific understanding of landscape patterns, species requirements and development pressures. In this study, we demonstrate the use of new conservation planning tools to better integrate information on threatened species into landuse planning. We present a case study in the Greater Melbourne area that utilises the Zonation conservation planning tool with data for 30 threatened fauna species. We perform a multi-species spatial prioritisation that incorporates species-specific connectivity requirements and demonstrate the use of this information in a number of landuse planning contexts. First, we quantitatively assess the differences between Melbourne’s current conservation areas with the locations prioritised by Zonation and determine priority areas for their extension. We then show how the prioritisation can be used in decisions regarding Melbourne’s Urban Growth Boundary and in rezoning land for development. Finally, we demonstrate how the prioritisation can be used to identify areas of conservation significance within individual developments that account for the wider landscape context. These results demonstrate how conservation planning tools can be better integrated into the different stages of landuse planning for future urban growth. © 2009 Elsevier B.V. All rights reserved.

1. Introduction The global biodiversity crisis (Western, 1992) has come about due to major landuse changes resulting from human activities (Hilty et al., 2006). This is particularly prevalent in urban areas where high levels of fragmentation are common and the greater intensity of land modification in the matrix surrounding remaining habitat is generally unsuitable for many species. The expansion of urban areas is a major threat to the biodiversity values of peri-urban areas (Williams et al., 2001), and can lead to a simplification of indigenous biodiversity (Knight, 1999). Important values are at stake; for example, in Australia, it is estimated that more than 50% of threatened species have habitat in and around major cities or in population growth areas (Yencken and Wilkinson, 2000). Mitigating the impacts of the growth of cities on natural systems and processes is complicated by high land values, a diversity of stakeholders and land tenures (Bekessy and Gordon, 2007), and is hampered by a

∗ Corresponding author. Tel.: +61 3 9925 9930; fax: +61 3 9925 3088. E-mail addresses: [email protected] (A. Gordon), [email protected] (D. Simondson), [email protected] (M. White), [email protected].fi (A. Moilanen), [email protected] (S.A. Bekessy). 0169-2046/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.landurbplan.2008.12.011

lack of practical research concerning the threat of urbanisation to biodiversity (Miller and Hobbs, 2002). In addition to ecological imperatives, retaining biodiversity in urban and peri-urban areas provides a number of important ecosystem services (Binning et al., 2001) and benefits for human well-being (Tzoulas et al., 2007). Furthermore, the conservation of native ecological communities close to where people live and work provides opportunities for formal education and enhances community awareness of environmental issues (Miller and Hobbs, 2002). In Australia, the responsibility for protecting biodiversity rests with all levels of government. The Federal Government and all Australian State and Territory Governments are signatories to the National Strategy for the Conservation of Australia’s Biological Diversity (Department of Environment, Sport and Territories, 1996). The Federal Government also has the power to restrict activities (including urban development) that may have a significant impact on threatened species and communities though the Environment Protection and Biodiversity Conservation (EPBC) Act 1999. Despite these commitments, conflicts between biodiversity conservation and the development of land for population and economic growth are acute (Bekessy and Gordon, 2007). The causal chain of events that leads to development activities around cities can be grouped into three general stages, namely

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strategic, rezoning, and development stages. The strategic stage involves long-term strategic planning to determine where landuse change should be occurring at a regional level. An Australian example is the planning document Melbourne 2030, which was development by the Victorian State Government to address longterm planning across the greater Melbourne region (Department of Sustainability and Environment, 2002). The second category of activities comprises the rezoning of specific tracts of land. The zoning of land designates permitted and prohibited uses, and the uses for which additional formal authorisation is required. In a peri-urban setting, this would typically result in areas with rural (farming) zonings being changed to allow residential development. The development stage involves fine scale activities, such as the subdivision of land for urban development. For effective biodiversity conservation, input is required at all three stages of the development process. Systematic conservation planning (Margules and Pressey, 2000) is a suite of methods used to determine, implement and manage a set of areas containing desired conservation targets with the minimum expenditure of resources. A range of quantitative tools have been used to address the problem of prioritising areas for protection and these tools have potential to improve the way biodiversity is incorporated into urban planning. Methods used to address this problem include simple procedures such as ranking sites by a set of criteria such as species richness and presence of threatened species (Margules et al., 1991), to more sophisticated measures such as irreplaceability (Ferrier et al., 2000). Integer programming techniques have been applied to provide exact solutions to a range of coverage problems related to area prioritisation, where the aim is to determine the smallest set of locations needed to represent all species, or to maximise the number of species represented for a given budget (ReVelle et al., 2002, Williams et al., 2004). Spatial attributes have also been incorporated into this type of approach (J.C. Williams et al., 2005). Freely available software such as MARXAN (Ball and Possingham, 2000), C-Plan (NSW NPWS, 1999) and SITES (Andelman et al., 1999) can be used to determine networks of conservation reserves that meet specified targets for reservation (e.g. 30% of the habitat for all species) while minimising other constraints such as economic cost and the reserve boundary length. Systematic conservation planning techniques have been applied in many areas around the world (Cabeza and Moilanen, 2001; Cowling and Pressey, 2003; Lombard et al., 2003; Margules and Pressey, 2000; Pressey et al., 1993), however they have generally been conducted in non-urban regions due to the preference of scientists to study more pristine landscapes and the difficulties of undertaking conservation activities in urban areas (Miller and Hobbs, 2002; Marzluff, 2002; Crossman et al., 2007; Bekessy and Gordon, 2007). Applying systematic conservation planning in human dominated landscapes poses significant challenges, including the need to address multiple objectives, the likelihood that many priority areas will not be available for conservation and may degrade and alter in their availability over time (Haight et al., 2005; van Langevelde et al., 2002; Weber et al., 2006). Other obstacles include political pressures for development, high land prices and small land parcels (Bekessy and Gordon, 2007). Furthermore, conservation planning in urban areas is often ancillary to planning for the provision of recreation and landscape amenity and has focussed on creating open space (Ahern, 2004; Ruliffson et al., 2003). This paper focuses on the area prioritisation aspect of systematic conservation planning, and how it can be used to integrate information on threatened species into landuse planning at multiple scales. We present the results of a case study in the Greater Melbourne area which uses the Zonation conservation planning tool (Moilanen and Kujala, 2006) to identify conservation priorities for thirty threat-

ened fauna species. We demonstrate how the tool can be used to quantitatively compare the current arrangement of conservation areas to the areas prioritised by Zonation, and to identify areas for their extension. We also demonstrate how the tool can be used to prioritise areas to extend Melbourne’s Urban Growth Boundary (UGB) and to prioritise land not yet zoned for development, but under high (or certain) risk of being developed. 2. Methods 2.1. Study area The city of Melbourne is located in the Port Phillip and Westernport region of the state of Victoria (Fig. 1). The Greater Melbourne area has a population of over 3.5 million people (Australian Bureau of Statistics, 2007) and is expected to grow by one million people by 2030 (Department of Sustainability and Environment, 2002). The area is highly variable in terms of climate, geology and vegetation, spanning five bioregions that contain a diversity of vegetation types and associated habitats for flora and fauna species. The development of Melbourne since European settlement has resulted in major loss of habitat through agriculture and urban development, with only 10% of the original vegetation remaining (excluding outer water catchment areas) (Wong, 2005). Areas immediately north and west of Melbourne contain remnants of lowland native grassland, one of Australia’s most endangered vegetation types with over 99.5% of its original distribution having been lost or substantially altered (N.S.G. Williams et al., 2005). Within the Melbourne region there are 146 rare and threatened plant species and 105 rare and threatened fauna species (Department of Sustainability and Environment, 2004). To address strategic long-term planning across the greater Melbourne region, the Victorian State Government produced a planning document titled Melbourne 2030 (Department of Sustainability and Environment, 2002). This plan established an Urban Growth Boundary around Melbourne to limit urban sprawl and protect agricultural, environmental and recreational values from urban development. Several areas within the UGB have been designated as growth corridors, where new land will be released for development in coordination with local and regional infrastructure. A component of the biodiversity conservation plan embodied in Melbourne 2030 is the protection of Green Wedge Zones outside the UGB that are provided special protection under State legislation in the Planning and Environment (Metropolitan Green Wedge Protection) Act 2003 (Fig. 1). However, the Green Wedge Zones are not specifically for conservation purposes and allow for a range of landuse activities (Department of Planning and Community Development, 2007) and the design of conservation areas within the Green Wedges has not been assessed in terms of the comprehensiveness or adequacy that they offer for the protection of threatened species. 2.2. Habitat maps Habitat maps were developed for 30 rare or threatened fauna species comprising of 19 birds, 4 reptiles, 3 mammals, 2 amphibians, 1 fish and 1 invertebrate (see Appendix A). The species were selected if they were known to have significant populations or habitat within the study area and were listed under state or federal threatened species legislation (the Flora and Fauna Guarantee Act 1988 and EPBC Act, respectively). All species included in the analysis have declining populations and are sensitive to further habitat loss due to development. The habitat maps were derived from simple models of habitat requirements, which resulted in binary distribution maps indicating the presence or absence of ‘potential habitat’ for each species. Potential habitat was defined as including all land uses and all vegetation and wetland types that may support individ-

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Fig. 1. The study area is located in the city of Melbourne in the state of Victoria, Australia. The hatching shows the area enclosed by Melbourne’s Urban Growth Boundary.

uals of the species. Rules to delimit the model for each species were elicited from specialist ecologists with local field experience by posing the question, “What land uses, vegetation types and/or wetland types as defined by the available spatial data, would never comprise habitat for this species?” Once a satisfactory consensus was determined, the residual landscape within the study area became ‘potential habitat’. The habitat maps were also supplemented with limited field assessments. The species habitat models did not consider the spatial context and arrangement of habitat or the various temporal aspects of habitat suitability. The habitat maps were compiled within a GIS, using 1:25 000 vector data relating to landuse, vegetation type, wetlands and watercourses. The resulting raster habitat maps had a cell size of 80 m × 80 m, with the study area containing approximately 800 000 cells. The cells in each species’ habitat map can take a value of zero (no habitat) or one (potential habitat). This data and all subsequent data relating to landuse and zoning were supplied through the Victorian Government Department of Sustainability and Environment’s Corporate Geospatial Data Library (Department of Sustainability and Environment, 2004a). 2.3. Landscape prioritisation with Zonation The Zonation conservation planning tool (Moilanen et al., 2005; Moilanen and Kujala, 2006) provides a range of analyses for spatial multi-species conservation prioritisation based on observed or predicted species distributions maps. According to metapopulation-dynamic principles (Hanski, 1998), Zonation assumes that persistence is correlated with abundance and connectivity, and provides a number of ways of incorporating the connectivity of priority areas in a species-specific manner (Moilanen et al., 2005). Here, Zonation was used to determine priority areas using 30 threatened fauna habitat maps for Greater Melbourne. The algorithm used by Zonation is a reverse stepwise heuristic which iteratively removes cells from the landscape in an order that minimises marginal loss (Moilanen et al., 2005) while main-

taining connectivity (Cabeza et al., 2004). The algorithm is based on the principle that minimising the loss of conservation value, results in the greatest conservation value in the remaining areas. Several analysis variants are available in Zonation and here we have used the core-area Zonation analysis, as this gives priority to obtaining some high-quality areas for all species (Moilanen et al., 2005). Zonation measures ‘conservation value’ through its definition of marginal loss. Mathematically, the definition of marginal loss (ıi ) for core-area Zonation is given by ıi = max j

wj rij ci Qj (S)

,

(1)

where wj is the weight (or priority) of species j, ci is the cost of adding cell i to the reserve network and rij is the representation level of species j in cell i. The representation rij for each species j is expressed as a normalized proportion of the total  distribution of the species across the landscape, meaning that i rij = 1, summing over all cells i. The term Qj (S) = k∈S rkj is the proportion of the full distribution of species j located in the remaining set of cells, S. Thus, when a cell is removed in a given iteration, Qj (S) is reduced if species j occurs in that cell. The core-area definition of marginal loss is based on principles that jointly produce good conservation outcomes. Heuristically, it (i) prefers inexpensive locations to expensive ones (low ci ), (ii) gives emphasis to species with high priority (high wj ), (iii) prefers locations with highest local occurrence levels (high rij ), and (iv), increases the emphasis given to a species when it loses more of its distribution (decreased Qj (S)). Presently, the objective of the corearea Zonation algorithm has not been described mathematically as an objective function. This is because the min-max structure from minimising Eq. (1) results in a discontinuous function, complicating its conversion to an objective function. The aims of the core-area Zonation algorithm are given via the definition of marginal loss and the starting condition that the full landscape is selected. Thus, the points (i)–(iv) above can be thought of as a description of the objectives of the prioritisation.

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Zonation differs fundamentally from target-based prioritisation software such as MARXAN (Ball and Possingham, 2000). In Zonation, representation targets do not need to be specified for each species as a nested hierarchy of solutions is produced (best 1% within best 2% and so on). Species-specific weights and connectivity responses result in a balanced (but not pre-specified) distribution of representation levels for species throughout the hierarchy of solutions. Relevant features of Zonation include (i) a deterministic algorithmic approach, (ii) direct workflow between GIS, statistical species distribution modelling and Zonation (Moilanen et al., 2005; Kremen et al., 2008), (iii) an ability to set connectivity requirements in a species-specific manner, and (iv) an ability to work with data sets having millions of selection units (grid cells). The analysis presented here used over 800 000 selection units, which is beyond the maximum data set size that can be that be handled using methods such as integer programming (Williams et al., 2004). Species-specific connectivity was introduced into core-area Zonation via the boundary quality penalty (BQP) method built into Zonation (Moilanen and Wintle, 2007). The BQP assumes that increased disturbance, edge effects or a disruption of spatial dynamics may lead to a reduction in the local density of the species if habitat is lost from the neighbourhood of the focal cell. Once the BQP is included, the effect removing a cell is not only the loss of the value in the cell itself, but also a (species-specific) reduction in quality in the neighbouring cells. Specifically, the BQP is implemented by replacing the term rij in Eq. (1) with a species-specific weighted sum over a species-specific neighbourhood. This accounts for the original representation levels in cells belonging to the neighbourhood, their original connectivity levels, their connectivity in the remaining landscape (S) and the species-specific effects of further connectivity reduction via the loss of the focal cell i. Details on the BQP implementation are given in Moilanen and Wintle (2007). The effect radius and response functions (Fig. 2) that characterise the BQP are specified for each species in Appendix A. The radius would be large for a species with a large home-range, such as the swift parrot (Lathamus discolor) and small if the species has localised requirements, such as the striped legless lizard (Delma impar). The

Table 1 The threat weightings, ti , assigned to each of the four threat categories in Victoria’s Flora and Fauna Guarantee (FFG) Act 1988. FFG Act threat category in Victoria

Threat weight (ti )

Critically endangered Endangered Vulnerable Lower risk-near threatened

4 3 2 1

response function specifies how the local quality changes when suitable habitat is lost from within the effect radius. We estimated the effect radius and response function for each species using the expert opinion of the same ecologists who assisted in the production of the habitat maps. The effect radius estimates ranged from 20 to 10 000 m and each species was categorised as having one of the following response functions: no effect, weak effect, medium effect or strong effect (Fig. 2; Appendix A). Replacement cost analysis (Cabeza and Moilanen, 2006) can be used to compare the conservation value of areas prioritised by Zonation with any other set of areas, including those generated by other conservation planning tools. This is achieved by supplying a mask layer to Zonation that specifies areas to be forcibly ranked highest in the landscape. This feature was used to quantify the ecological value (for threatened species) of the current conservation areas on public land. These conservation areas consist of parks and conservation reserves as well as special use areas such as sewage treatment plants. 2.4. Species weights Zonation allows each species to be assigned a weight, which is used to prioritise a species relative to the others. The weighting influences the balance of representation for species at any level of landscape removal. Giving a species a higher weight results in an elevated representation level, but this is negatively compensated by losses in representation of other species. One advantage of Zonation is that it allows the implications of alternative species weightings to be explored. For example, a ‘triage’ approach would weight critically endangered species higher than those less threatened. Alternatively, the least endangered species might be prioritised if the focus of a biodiversity policy is to preserve those species that are most likely to become critically endangered in years to come (McIntyre et al., 1992). Policy choices of this kind are typically made without explicitly exploring the implications of different trade-offs (but see Arthur et al., 2004). In this study we use a weighting scheme that incorporates both threat and uncertainty. Specifically, the weight for species i, wi , is calculated by summing two factors: wi = ui + ti . The value of ui can be 0, 1 or 2, which is a measure of the relative uncertainly in the habitat map for species i relative to the other habitat maps used in the study, with zero representing the highest level of uncertainty. These estimates were based on the expert opinion of the ecologists involved in the production of the habitat maps. The level of threat faced by species i is given by ti and is determined by the category in which the species is listed in Victoria’s Flora and Fauna Guarantee Act 1988 as shown in Table 1. The weights assigned to each species fall in a range of one to six and are shown in Appendix A. 3. Results

Fig. 2. The four response categories to the boundary quality penalty (BQP), describing the sensitivity of a species to loss of habitat in surrounding cells. The x-axis shows the percent of habitat lost from the neighbourhood surrounding the focal cell, specified by the effect radius (see text). The y-axis shows the resulting loss in habitat suitability of the focal cell. Each species was categorised into one of the four categories.

3.1. Species richness Fig. 3 presents the species habitat richness of the study area and provides an overview of how the species are distributed in the

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Fig. 3. Map of the species habitat richness of the study area. Richness values vary from 1 to 23, with the darker areas depicting higher richness.

landscape. Species habitat richness (henceforth referred to simply as species richness) varies from 1 to 23 with the areas of highest species richness occurring in the north-eastern and north-western parts of the study area, with scattered areas in the north and along riparian zones. The areas that comprise habitat for more that twenty species make up 0.8% of the total habitat for all species, while areas that comprise habitat for more that 15 species make up 9.3% the total habitat. Although the areas with high species richness are important for conservation planning, selecting areas based solely on species richness will neglect species whose habitat does not overlap species rich areas.

3.2. Zonation prioritisation Fig. 4 shows the output raster prioritisation surface produced by Zonation with a colour gradation indicating the value between zero and one assigned to each cell. A comparison with Fig. 3 shows that many of the areas with high species richness are also given a high priority by Zonation. However, high priority areas have also been designated to the west and south-east of the study area, where species richness was lower. This is due to Zonation retaining habitat for all species even when they occur in species-poor areas. The ranking of cells is also affected by the relative weightings of species

Fig. 4. The Zonation landscape prioritisation for Greater Melbourne. Each cell is assigned a value between zero and one, indicating its conservation value as determined by the Zonation algorithm (see text). The black line depicts Melbourne’s Urban Growth Boundary.

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species, which is useful for quantitatively comparing the effectiveness of different scenarios across all species. 3.4. Assessing the current reserve system

Fig. 5. Representation curves for a range of species. The x-axis shows the proportion of the landscape outside the Zonation solution and y-axis shows the proportion of habitat remaining. The mean value for all species describes how robust any given level of cell removal is to the mean loss of habitat across all species. The growling grass frog tends to have a greater proportional loss of habitat for a given level of cell removal compared to the mean of all species, whereas the swamp skink exhibits a lower than average proportional loss of habitat.

and the boundary quality penalty, which adjusts the value a cell based on the spatial configuration of surrounding habitat and the sensitivity of the species to habitat loss (see Appendix A). The black line in Fig. 4 depicts the Urban Growth Boundary and several high priority areas exist within or adjacent to this boundary. In many instances these areas follow riparian zones, which often represent the only areas with remnant vegetation remaining in urban areas. The land outside the UGB is within Melbourne’s Green Wedge Zone (Fig. 1) and contains a large proportion of the priority areas. Due to government commitments to continue to supply land for greenfield development, the UGB is likely to be altered at some future stage. 3.3. Assessing the prioritisation for individual species Taking the prioritisation surface produced by Zonation (Fig. 4), it is possible to determine the proportion of any species’ original habitat area remaining when a given proportion of the landscape is selected. Fig. 5 shows the proportion of the landscape remaining in the solution plotted against the proportion of habitat remaining for a selection of species. There can be considerable variation in the proportion of habitat retained for a given species as cells are removed from the landscape. This proportion is determined by the distribution of a species and the extent to which its habitat overlaps with other species habitat. Species whose habitat is nested within the distribution of other priority species will have the highest proportion of habitat remaining when any given proportion of the landscape is selected. The lowest proportions are retained for species that are widely and evenly distributed, which results in an approximately linear decline in retained habitat as cells are removed. In this analysis, the swamp skink (Egernia coventryi) retains all of its habitat until over 90% of the landscape is lost, while the growling grass frog (Litoria raniformis) has a linear decline in representation after 15% of the landscape is lost (Fig. 5). These curves can be used to quantitatively determine the conservation importance of a selected area for each species. The dashed line in Fig. 5 shows the mean proportion of habitat remaining for all

Fig. 6(a) shows the Zonation solution with Melbourne’s current conservation areas overlaid as black hatching. The UGB is shown in the figure and most land directly outside the UGB is within Melbourne’s Green Wedge Zones (Fig. 1). A large proportion of the conservation areas are located in the Green Wedge Zones and overlap with some of the medium to high priority areas predicted by Zonation. Replacement cost analysis can be used to quantify the difference between the current conservation areas and the Zonation prioritisation. Fig. 6(b) shows the proportion of the landscape remaining in the solution plotted against the mean and minimum proportions of habitat remaining for all species. This is shown for the case where Zonation is constrained to include the existing reserves in the optimisation (dashed line) and for the case where Zonation is allowed to find its solution without any constraints (solid line). These two solutions give a quantitative measure of how the current reserve system compares to the unconstrained Zonation solution. From Fig. 6(b) it is apparent that managing 20% of the habitat (on average) for all species would require 9.5% of the landscape using the unconstrained result, where as extending the current reserve system to cover 20% of the habitat (on average) for all species requires 14.5% of the of the landscape. Fig. 7 shows the predicted priorities for extending existing conservation areas. This result was obtained from the constrained solution by selecting the top 10% of cells outside the current conservation areas. 3.5. Prioritising future growth Fig. 8 demonstrates the potential use of Zonation in incorporating conservation values into planning decisions at the strategic, rezoning and development stages of the landuse change process. Fig. 8(a) shows cells ranked in both the upper and lower 10% of the solution that occur outside the UGB. The cells ranked in the lower 10% solution (shown in red) are areas where the UGB could be extended into the Green Wedge Zones with the minimum level of disruption to threatened species habitat, while the cells ranked in the top 10% (shown in blue) represent areas to avoid. Fig. 8(b) shows a close up of Melbourne’s northern growth corridor. The areas inside the UGB that are not yet zoned for urban development (predominantly rural zonings) are shown overlapping the top 10% of the solution. Being inside the UGB, these areas are at high risk of being rezoned for urban development, and the intersection of these two layers can help prioritise where conservation areas, open space and wildlife corridors should be located when subdivisions for development are planned. 4. Discussion Despite the high conservation value of urban areas in Australia (Yencken and Wilkinson, 2000), systematic planning to identify land that will adequately represent species of concern is rarely undertaken at the strategic planning stage or at appropriate scales (Fallding, 2004). As a result, urban sprawl continues to lead to the loss of habitat, and populations of threatened species in many urban areas are facing extinction (Bekessy and Gordon, 2007). This paper presents the use of conservation planning tools in an urban context and provides examples of how these methods can be integrated into urban planning and landuse planning to protect threatened species habitat in these areas. Our approach to area prioritisation is innovative as we incorporate species-specific connectivity into multi-species conservation planning within an urban context.

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Fig. 6. (a) The Zonation landscape prioritisation for Greater Melbourne from Fig. 4, with Melbourne’s current public conservation areas overlaid. (b) The mean representation curves for the constrained and unconstrained Zonation solutions. The plot indicates that managing 20% of the habitat for all species (on average) requires 9.5% of the landscape for the unconstrained solution and 14.5% of the landscape when Zonation is constrained to have the current public conservation areas ranked highest in the landscape.

In peri-urban areas there are three identifiable junctures in the development of land to more intensive urban related uses. We have categorised these as strategic, rezoning, and development stages. Biodiversity values can be difficult to transpose and need to be considered at large scales (Briggs, 2001) and at the earliest possible juncture (Fallding, 2004). Therefore, it is especially important that biodiversity values are incorporated at the strategic planning stage. Due to the considerable time lags between strategic reviews of appropriate landuse, it is also important that biodiversity considerations also play a part in rezoning and development actions undertaken to accommodate landuse change at a finer scale. Greater Melbourne contains many valuable areas for threatened species conservation, despite significant transformations to the landscape (Wong, 2005). Maintaining and enhancing these areas is a policy objective of Australian Governments at all lev-

els (e.g. Commonwealth of Australia, 2003; Department of Natural Resources and Environment, 2002). Using the Zonation conservation planning tool, we determined priority areas for 30 rare or threatened fauna species that will be impacted by habitat loss due to urban development. We demonstrated the use of Zonation with examples from the three stages of the development processes that occur in urban and peri-urban areas. These examples demonstrate how typical obstacles to protecting biodiversity in urban areas, such as timing and scale (Fallding, 2004), can potentially be addressed. The output from Zonation includes the species representation curves (Fig. 5) and a raster prioritisation output surface. Each cell in the output surface is assigned a value between zero and one representing its relative conservation value and the priority areas for a given proportion of the landscape can be determined by selecting the cells over an appropriate threshold. Aggregation was achieved

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Fig. 7. Using Zonation to determine priority areas for extending the current conservation reserve system. The hatched areas show the location of current conservation areas, and the blue cells show the predicted priority areas for extending the current conservation areas to cover a further 10% of the landscape. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the article.)

through the boundary quality penalty feature using estimates of the likely ranges and effects of roads and development on each of the species. This method has an advantage over more generic methods of aggregation (such as minimising boundary length), in that it allows fragmented areas to be retained in the solution where species are not sensitive to the effects of fragmentation, and generates aggregated areas where species are sensitive to fragmentation. We argue that some form of aggregation should always be applied when using small selection units as it is likely that neighbouring cells will have linked population dynamics for many species of concern. Several studies have also shown that even when the negative consequences of fragmentation are not explicitly modelled, reserve network aggregation can be achieved with a minor loss of biological value, due to the lower quality connecting habitat that needs to be included in the solution (McDonnell et al., 2002; Fischer and Church, 2003; Cabeza et al., 2004; Moilanen and Wintle, 2007). Fig. 8(a) indicates how Zonation can be used for large scale strategic planning by identifying areas outside the Urban Growth Boundary that are given the highest and lowest priorities by Zonation. The figure demonstrates that there are locations where the UGB could be extended to avoid high priority areas for threatened species. Clearly, other objectives, such as transport infrastructure, must also be considered in planning urban growth and it is possible to explore the impact of additional objectives using Zonation. In this study, conservation status was used to weight some species relative to others (see Appendix A), but other factors can also be incorporated. Conservation planning tools can also be used to assess the efficacy of current conservation areas and to identify land that is more likely to protect species. The current conservation areas in Greater Melbourne are in place for a range of historical reasons, including their scenic and recreational values, their (un)suitability for various land uses, the influence of lobby groups and the tenure of the land. In 1970, the Victorian Government established the Land Conservation Council (LCC; now called the Victorian Environmental Assessment Council) to make recommendations regarding the use of public land. While the LCC has influenced the current

location of conservation areas, its recommendations incorporate the social and financial costs of a range of competing land uses and are not developed solely for biodiversity considerations (Land Conservation Council, 1987, 1994). Melbourne’s current conservation areas are depicted in Fig. 6(a). It is evident that a substantial area of high value threatened species habitat is located outside current conservation areas. The conservation value for selected areas can be assessed with species representation curves (Figs. 5 and 6(b)), allowing quantitative comparison of different planning scenarios (Cabeza and Moilanen, 2006). Fig. 7 presents the Zonation predictions for the locations to extend current conservation areas. The sites identified include a large contiguous area to the south-west of Melbourne and many other predominantly riparian areas throughout the region. The area to the south-west contains lowland native grassland, and comprises habitat for species that are very sensitive to urbanisation, such as the fat-tailed dunnart (Sminthopsis crassicaudata), the grassland earless dragon (Tympanocryptis pinguicolla) and the striped legless lizard (D. impar) (see Appendix A). Fig. 8(b) presents a detailed analysis of Melbourne’s northern growth corridor to demonstrate the use of conservation planning tools at the development and rezoning stages of activity. The analysis identifies areas prioritised by Zonation that are not yet zoned for urban development. This information could be used to determine areas inside the UGB where the rezoning of land should not occur, and at the single development level scale, to determine the configuration of open space within a proposed development. Using the priority areas in these situations takes into account the wider landscape context because the Zonation algorithm ensures that habitat for all species is represented and that areas are aggregated in a species-specific way. The conservation planning tools demonstrated in this study can provide urban planners with more sophisticated information beyond simple rules of thumb (Marzluff, 2002). However, the analyses presented here required an initial process of obtaining reasonable distribution data for the species of interest. For this study the distribution maps were derived from data on landuse, wetlands and watercourses using expert opinion and field

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Fig. 8. Integrating the priority areas determined by Zonation into landuse planning at two different scales. (a) Cells ranked in the upper (blue) and lower (red) 10% of the Zonation solution that occur outside the UGB. This information can be used to help identify where the UGB could be extended with the minimum impact on threatened species habitat. (b) Close up of the northern growth corridor, indicated by the black rectangle in (a). The top 10% of the Zonation solution (green area) is shown along with land that is yet to be zoned for urban development (pink cells) but that is inside the UGB. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the article.)

assessment. Thus, the maps represent conservative estimates of the potential habitat for each species. The resulting distributions were significantly fragmented for many species indicating that they may be persisting in metapopulations. In these situations, unoccupied but suitable habitat can be important for conservation (Hanski, 1998; Moilanen and Cabeza, 2002), hence it is appropriate to include potential habitat in landscape prioritisation exercises. A more rigorous approach to developing species distribution maps would involve the development of predictive habitat distribution models for each of the species based on presence/absence

data. There has been a rapid development of techniques for habitat distribution modelling (HDM; Guisan and Zimmermann, 2000) and these approaches have also been applied in peri-urban areas (Wintle et al., 2005). With adequate datasets for both calibration and validation, improved distribution maps could be obtained for each of the species using HDM techniques. Although the Victorian Government has presence data for many of the species used in this study, the current records are scarce, highly biased and would pose a significant challenge for use in HDMs. Further field work would be required to provide presence/absence records needed for the development of robust HDMs.

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While the output of this study could be used to help prioritise investment in further conservation areas, it is likely that public land reservation will not be the only tool used to integrate biodiversity conservation in urban and peri-urban areas (Norton, 2000). As many of the priority areas predicted by Zonation occur on private land, only a small proportion could be added to the public conservation network due to high land prices. In some cases, it may be more cost effective to combine both restrictions and incentive schemes to encourage private landholders to manage biodiversity. In Australia, there has been a general move towards market based methods and the Victoria Government has a range of incentive programs in place such as BushBroker, a native vegetation credit and trading system and BushTender, an auction based program that pays landholders to manage biodiversity assets above legislative requirements (Department of Sustainability and Environment, 2007). The priority areas determined by Zonation could be used to help direct such programs to determine where to carry out actions on private land to enhance habitat for multiple threatened species. Due to the clearing and fragmentation of native vegetation that occurs in peri-urban areas, extensive revegetation and restoration may also be necessary to ensure the viability of some species. Strategies for systematic landscape restoration (Crossman and Bryan, 2006; Crossman et al., 2007) are required and will be complementary to the analysis presented here.

tific understanding of landscape patterns, species requirements and development pressures. This study provides examples for how priority areas for threatened species can be incorporated into the various stages of urban landuse planning to achieve better outcomes for biodiversity. Output from conservation planning tools can be used to plan for the future growth of cities at an ecologically relevant scale. Furthermore, these tools can help prevent inappropriate rezoning of high value sites within growth corridors and can assist in prioritising investment in further conservation areas.

5. Conclusion

A list of the species used in the study, along with the parameter settings used for each species in the Zonation conservation planning software. Victorian FFG Act status indicates the category under which the species is listed in Victoria’s Flora and Fauna Guarantee Act 1988. The weight column indicates the relative weighting assigned to each species. The boundary quality penalty was parameterised using a strength category (Fig. 2) and an effect radius.

Landuse change and development around the world’s urban centres is likely to be particularly pervasive in the coming years. Preventing further loss of biodiversity in urbanising areas will require a focus on scientifically derived planning at strategic stages, prior to the assessment of development plans. Urban planners require tools to assist with strategic decision making, based on a scien-

Acknowledgements We would like to thank Grant Dickins for help with preparation of GIS data. This research was funded by a Commonwealth Environment Research Facility (Applied Environmental Decision Analysis) and the Australian Research Council under linkage projects LP0454979 and LP0882780 with the following industry partners: the Department of Sustainability and Environment, Hume City Council, the City of Whittlesea, Mornington Peninsula Shire, the Port Phillip and Western Port Catchment Management Authority, Parks Victoria and Stockland Incorporated. A.M. was supported the Academy of Finland, project 1118518. Appendix A

Common name

Scientific name

Taxa

Victorian FFG Act status

Weight

BQP strength

BQP effect radius (m)

Black falcon Barking owl Brown quail Brown treecreeper Black-chinned honeyeater Black-eared cuckoo Bibron’s toadlet Bush stone-curlew Diamond firetail Grassland earless dragon Fat-tailed dunnart Grey goshawk Growling grass frog Tree goanna Hooded robin Helmeted honeyeater Masked owl New holland mouse Painted honeyeater Powerful owl Macquarie perch Plains-wanderer Spotted quail-thrush Regent honeyeater Swamp skink Speckled warbler Southern brown bandicoot Striped legless lizard Golden sun moth Swift parrot

Falco subniger Ninox connivens Coturnix ypsilophora Climacteris picumnus Melithreptus gularis Chrysococcyx osculans Pseudophryne bibronii Burhinus grallarius Stagonopleura guttata Tympanocryptis pinguicolla Sminthopsis crassicaudata Accipiter novaehollandiae Litoria raniformis Varanus varius Melanodryas cucullata Lichenostomus melanops cassidix Tyto novaehollandiae Pseudomys novaehollandiae Grantiella picta Ninox strenua Macquaria australasica Pedionomus torquatus Cinclosoma punctatum Xanthomyza phrygia Egernia coventryi Chthonicola sagittata Isoodon obesulus obesulus Delma impar Synemon plana Lathamus discolor

Bird Bird Bird Bird Bird Bird Amphibian Bird Bird Reptile Mammal Bird Amphibian Reptile Bird Bird Bird Mammal Bird Bird Fish Bird Bird Bird Reptile Bird Mammal Reptile Invertebrate Bird

Vulnerable Endangered Near threatened Near threatened Near threatened Near threatened Endangered Endangered Vulnerable Critically endangered Near threatened Vulnerable Endangered Vulnerable Near threatened Critically endangered Endangered Endangered Vulnerable Vulnerable Endangered Critically endangered Near threatened Critically endangered Vulnerable Vulnerable Near threatened Endangered Endangered Endangered

2 5 3 3 3 3 3 5 4 4 2 2 4 3 2 5 5 5 3 3 3 5 2 6 4 3 2 3 3 4

None Weak Weak Med Weak Weak Strong Med Weak Strong Strong None Strong Med Weak Strong None Strong Weak Weak None Med Strong Weak Strong Strong Strong Strong Med Weak

– 5000 1000 300 3000 3000 50 1000 500 100 400 – 100 500 500 100 – 100 5000 10000 – 2000 1000 5000 20 1000 100 20 20 5000

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Department of the Environment and Water Resources, Canberra. http://www.environment.gov.au/soe/2001/publications/ theme-reports/biodiversity/index.html (accessed July 5, 2007). Williams, J.C., ReVelle, C.S., Levin, S.A., 2004. Using mathematical optimization models to design nature reserves. Front. Ecol. Environ. 2, 98–105. Williams, J.C., ReVelle, C.S., Levin, S.A., 2005. Spatial attributes and reserve design models: a review. Environ. Model. Assess. 10, 163–181. Williams, N.S.G., McDonnell, M.J., Seager, E.J., 2005. Factors influencing the loss of an endangered ecosystem in an urbanising landscape: a case study of native grasslands from Melbourne, Australia. Landscape Urban Plan. 71, 35–49. Wintle, B.A., Elith, J., Potts, J.M., 2005. Fauna habitat modelling and mapping: a review and case study in the Lower Hunter Central Coast region of NSW. Austral Ecol. 30, 719–738. Wong, V., 2005. Environmental Indicators for Metropolitan Melbourne—Bulletin 8. Australian Institute of Urban Studies, Melbourne. http://www.melbourne. vic.gov.au/rsrc/PDFs/Research/AIUSEnvironmentBulletin.pdf (accessed October 10, 2007). Yencken, D., Wilkinson, D., 2000. Resetting the Compass: Australia’s Journey Towards Sustainability. CSIRO Publishing, Melbourne. Ascelin Gordon works as a post doctoral research fellow at RMIT University, Australia. His research interests are in measuring and modelling biodiversity and its relationship to urban development. Currently he is working on a project that aims to develop a more strategic approach to conservation planning in urban environments using recent advancements in ecological modelling and robust optimisation. Ascelin comes from a physics background and completed his PhD at the University of Melbourne in 2004.

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David Simondson completed a Masters in environment and planning at RMIT University, Australia. He has worked as an ecological consultant and is currently employed as an environmental planner with a local government authority.

ics (Techn. lic.) and spatial population ecology (PhD). His present main research interest is development of methods, theory and software for the purpose of spatial conservation assessment and prioritization.

Matt White is an ecologist with the Victorian Department of Sustainability and Environment. His key research interests are vegetation circumscription, spatial modelling and environmental history.

Sarah Bekessy is a senior lecturer in environmental studies at RMIT University, Australia. Sarah and is involved in an interdisciplinary range of research projects, including two Australian Research Council research projects—Reimagining the Australian Suburb: biodiversity planning in urban fringe landscapes, and Building Capacity for a Sustainable Future: embedding education for sustainability into universities. Sarah is also involved in a new Commonwealth Environmental Research Facility titled Applied Environmental Decision Analysis that seeks to develop and test tools to support transparent decision-making for environmental management.

Atte Moilanen is a research fellow of the Academy of Finland and he works in the Metapopulation Research Group at the University of Helsinki. He also is associated with the Australian Centre of Excellence for Risk Analysis (Univ. Melbourne) and the Commonwealth research hub in Applied Environmental Decision Analysis (Univ. Queensland). Atte has a background in computer science (MSc), applied mathemat-