Regional conservation planning and reserve design

Reserve Design for the Natural Communities Conversation Planning Program
Reserve Selection as a Maximal Covering Location Problem
Biodiversity Management Area Selection Model for the Sierra Nevada Ecosystem Project
Intermountain Semi-Desert Ecoregion Gap Analysis and Reserve Siting

Reserve Design for the Natural Communities Conversation Planning Program

Southwestern California , specifically the region extending from Point Conception through the Los Angeles and Riverside basins to San Diego and the Mexican border, is rapidly approaching an ecological state of emergency brought about by destruction of native habitats along the coast and in inland basins. Roughly 17 million people or 56% of California's total population reside in the southwestern 8% of the state. Literally hundreds of biodiversity planning and conservation initiatives are underway in this region, ranging from region-wide conservation risk assessments to landscape-level recovery planning for endangered species such as the Stephens kangaroo rat (Dipodomys stephensi), to local restoration and mitigation projects for native grasslands, riparian woodlands, and coastal wetlands. One of the most far-reaching and innovative of these, which is part of California's Natural Communities Conservation Planning (NCCP) Program, has been underway since 1990 in response to the proposed listing of the California gnatcatcher (Polioptila californica) as a federally endangered species. At that time, local municipalities, developers and public agencies joined forces to design a rational reserve network for coastal sage scrub vegetation, which is the primary habitat for the gnatcatcher. Their goal was to achieve a change in listing of the gnatcatcher from endangered to threatened status and more generally to sustain the coastal sage scrub ecosystem while permitting development of some remaining areas

NCCP began with a regional assessment by a state-appointed team of research biologists, who used the California Gap Analysis vegetation map along with other information to establish the regional distribution and ownership status of remaining extensive tracts of coastal sage scrub. This provided a broad framework for more detailed reserve design studies and assessments that are being conducted by individual counties. IBM-ERP Collaborator Peter Stine was directly involved in producing an ambitious plan for a coastal sage scrub reserve system designed for three target species, the California gnatcatcher, the Cactus wren (Campylorhynchus bruneicapillus), and the Orange-throated whiptail lizard (Cnemidophorus hyperythrus). This plan entailed the production of detailed vegetation maps, compilation of existing collection data for the targeted vertebrate species, a large field sampling effort, and use of a GIS to facilitate modeling of coastal sage scrub habitat conservation value based on vegetation patch size, condition and connectedness to other habitat patches. The product of this effort is a proposed reserve system for western San Diego County comprised of core conservation areas and interlinking corridors (Stine 1995).

Reserve Selection as a Maximal Covering Location Problem

Over the past decade, conservation planners have developed a number of methods for selecting priority areas for potential nature reserves. The evolution of approaches has proceeded from simple scoring, where sites were ranked to iterative heuristic methods. Although these methods differ in the objectives they emphasize and the algorithms used, they all shared the desire to select sites in an explicit, objective, repeatable, and efficient manner. That is, to select a reserve system that accomplishes the most protection for the least cost (or area). While the heuristic methods follow a logic designed to achieve efficiency, they do not guarantee an optimal solution nor can they measure how far from optimality they are. In fact, for most classes of location/resource allocation problems, the greedy style heuristics often find suboptimal solutions.

After a thorough review of existing literature, we concluded that most approaches could be subsumed within a more general framework known as the "maximal covering location problem" (MCLP), described in the operations research and regional science literature twenty years ago. The MCLP can be solved optimally, that is, no better solution exists, and most problems of the size described for reserve selection can be solved within reasonable computer resources. In the terminology of Operations Research, a selected site could be said to "cover" a species if suitable habitat for the species exists at that site.

To formulate the Maximal Covering Location Model, consider the following notation:

i = index for species to be protected

j = index for areas that can be selected for the reserve system

p = the number of areas that are to be selected for the reserve system

Ni = { j | where species i is present in area j }

Z = number of species covered by the set of sites selected for the reserve system

The formulation of the MCLP for reserve selection is:

One note of caution is in order. The MCLP does fall within the class of problems known as n-p hard. This means that a proven bound on computational requirements to solve a given problem instance to optimality can not be written as a polynomial of the problem characteristics. Thus, problems may exist that are inherently hard to solve and may require a significant amount of computer time to solve. In simple terms, worst case examples may exist which may not be solvable to optimality within any available computer resources. For most practical applications, MCLP problems can be easily solved.

We solved the MCLP model for a real application using vertebrate distribution data prepared for the Gap Analysis of southwestern California. The database consists of lists of vertebrate species in each of 280 quadrangles that correspond to the United States Geological Survey 7.5' topographic map sheets covering the southwestern region of California, USA. The regional species pool contains 333 native vertebrates, and therefore the species-site matrix contains of 281 columns by 333 rows.

The Maximal Covering Location Problem representing the reserve design problem was solved in a two-step process. First species by site data were read by a program that created a Mathematical Programming System (MPS) formatted file. The MPS file represents a specific MCLP problem. The MPS format is an industry standard and can be read by most general purpose integer-linear programming systems. The second stage involved the application of a special purpose Fortran program that utilized IBM's Optimization Subroutine Library (OSL) subroutines to set-up and solve each MCLP. Solutions on an IBM RS-6000 workstation took an average of 2.8 seconds of CPU time over the 12 solutions, with none taking more than 9 seconds. The rate of additional species coverage rapidly diminishes with increasing numbers of sites, such that 75% of the vertebrate species are covered in the first site while five sites are required to cover the last six species (Figure 12).

Figure 12. Accumulation curve showing the maximum number of new species covered by 1 through 12 sites. The solid line with squares indicates the curve for the MCLP model; the dashed line with triangles shows the results of a greedy adding heuristic model.

For comparison, the tradeoff curve for a greedy adding heuristic is also shown in Figure 12. The greedy model also selects the Catclaw Flat quadrangle in the single site solution. Since the greedy heuristic's choice will be optimal for the one-site solution, this selection is not surprising. However, subsequent selections are likely to be suboptimal. Beginning with the two-site solution, the two methods diverge as the greedy model covers fewer species than the MCLP. This divergence in outcomes occurs because once a site is selected in the greedy method, it remains a part of all subsequent larger solutions. Thus, the first site selected by the greedy appears in all twelve solutions, the site added in step 2 appears in solutions 2-11, and so on. In steps two through seven, the greedy heuristic solution covers between three and eight fewer species than MCLP solutions for the same number of sites. The worst greedy solution (relative to the MCLP solution) is for the four-site solutions, which covers 97.4% as many species. The eight- through twelve-site solutions of both models cover the same number of species. The greedy model took 0.63 CPU seconds.

In contrast, the MCLP selects an optimal solution set for each value of p independently of solutions for different values of p. The first site selected does not appear in all twelve solution sets. The 12-site solution that covers all 333 species is displayed as a map in Figure 13. Other sites that appear in one or more of the other eleven solutions are outlined in white. These sites generally are clustered near the sites in the 12-site solution. Twelve sites comprises only 4% of the candidate sites in the study area.

Figure 13. Map of solution to the maximal covering location problem in which all species are covered at least once. The twelve sites selected are shown in cross-hatching. Sites selected in one or more of the other eleven solutions are outlined in white.

Although the basic MCLP model proposed and applied in this report is not the final word on reserve selection algorithms, we believe that it simplifies the increasingly complex heuristic rule methods and gives a simple benchmark measure of the most efficient allocation of sites. Having this benchmark makes the trade-offs to achieve other conservation objectives and to accommodate planning realities clear and measurable and avoids the competition for creating the best approximation. Solution times of well under a minute make it feasible to operate the model in an interactive spatial decision support system. Such an interactive environment in which planners and decision makers can explore alternatives sets based on differing objectives is greatly needed. Furthermore, we believe that other conservation objectives such as habitat quality, site configuration, emphasis on rare species or additional biodiversity elements, or flexibility can all be accommodated within the basic structure of the MCLP model, whether by additional constraints, objectives, or weights. The MCLP approach to reserve selection is described in greater detail in Church et al. (1996).

We also conducted a sensitivity analysis on the size of planning units. We would expect larger planning units to generate less efficient solutions because relatively few new species are accumulated as the unit grows larger. That is, a large site would cover fewer species than four smaller sites of the same total area if those four sites were widely distributed. In conservation biology terms, the dispersed sites "complement" one another more than four contiguous sites (i.e., the larger site) does. In our analysis, we tested this hypothesis by aggregating the southwestern California vertebrate data from units equal to the 7.5' quadrangle grid into 15' and 30' grids, each representing a fourfold increase in area. At the 30' grid size, more than ten times the area was required to cover all species at least once, relative to the total area with 7.5' grids (Davis and Stoms, 1996a). The tradeoff curve showing the relative coverage by the three grid sizes is shown in Figure 14.

Figure 14. Accumulation curve showing the maximum number of species covered for three sizes of planning units. The line symbols for the curves correspond to: solid line with squares = 7.5' quadrangles, the dashed lines with triangles = 15' quadrangles, and dotted lines with circles = 30' quadrangles.

While the number of covering-type models has increased, most require custom software or sophisticated optimization packages. Linkages with GIS have been awkward and usually limited to data pre-processing and display of map results or to specialized, privately distributed codes. Solving an optimal conservation siting model has been severely limited or impossible within a widely-used, general-purpose GIS product. Even the interactive modeling environment described by Pressey et al. had the GIS call an external model. To facilitate the integration of covering models into the decision making environment, the modeling systems must be made more compatible with the computing operations and technical skills of resource agency staff.

The MCLP model can also be reformulated as a p-median problem. This reformulation gave us the opportunity to develop an integrated tool for reserve selection using existing GIS software. Therefore, instead of a model requiring external optimization software that most gap analysis projects would not have access to and passing data back and forth with the GIS, conservation planners could solve the problem entirely within their GIS. The application was accomplished within ARC/INFO's Location-Allocation software module. We constructed a logical network structure (Figure 15) recognizable by ARC/INFO, that translates our problem of selecting land areas to represent species into a problem of selecting "facilities" (equivalent to reserves) that cover "demand nodes" (equivalent to species). Finally, we extended our approach, using the same logical network, to a weighted species coverage model that allows us to trade-off the representation of endemic and non-endemic species in different configurations of protected areas. The weighted model is a move away from a simple species richness objective and demonstrates how the MCLP (and the ARC/INFO software) can easily be extended to optimizing more sophisticated measures of the value of a reserve system. The approach was successfully applied to the same southwestern California data set used by Church et al. (1996, in review) with 333 species and 281 sites. See Gerrard et al. (in press) or http://www.biogeog.ucsb.edu/projects/ibm/arcmodel.html for details. More recently this approach was extended to a national set of rare species and communities representing 3,016 elements and 5,562 sites (Chapin and Gerrard 1998).

Figure 15. The logical network between site nodes and species "nodes" created to integrate the reserve selection model into an existing GIS location-allocation module.

While development of the MCLP model and the GIS-based version of it were undertaken as general methodological advance, we had the opportunity to apply the methods to a real conservation planning problem, namely selecting potential sites for Research Natural Areas (RNAs) for the U. S. Forest Service. Selecting new RNA sites is currently somewhat ad hoc, based on the local knowledge of Forest Service staff. Through a grant from the Forest Service, we devised a more systematic approach to the selection process which uses a variation of the MCLP model and applied it on Los Padres National Forest in central coastal California. One advance over the basic MCLP model that was developed for this application was to include a weighting on the representativeness of each site for each forest type. Various alternatives (Figure 16) were modeled to explore trade-offs between efficiency (i.e., most types in a fixed number of sites) and suitability (i.e., the representativeness of the site in terms of biophysical conditions). A manuscript is currently in review (Stoms et al. in review).

Figure 16. Sample alternatives for selecting sets of sites as potential Research Natural Areas using a modified Maximal Covering Location Problem model.

Biodiversity Management Area Selection Model for the Sierra Nevada Ecosystem Project

In 1994 the U. S. Forest Service initiated the Sierra Nevada Ecosystem Project (SNEP) to assess the current state of the Sierran ecosystem and to evaluate alternative management strategies. As participants in SNEP, we were able to build on our understanding of the strengths and limitations of the MCLP model and develop a new model for siting conservation areas. The Sierra Nevada region differs from southern California in that biodiversity in the Sierra is not restricted to islands of protected habitat in a sea of urbanization. Rather, the landscape matrix in the Sierra Nevada, though often managed for intensive resource extraction, still provides habitat for many species. We proposed that a system of core biodiversity management areas be instituted in the region that would not serve as a comprehensive reserve system, but rather would reduce the vulnerability of native biodiversity to human activities.

Based on this goal, we reformulated the reserve selection problem with three significant refinements. First, specific target levels of biodiversity management as a percentage of the distributions of each biodiversity element were to be met, and these targets could be varied between alternatives or even between elements (e.g., rare vs. widespread). In the MCLP model, the only requirement was that each element be "covered" or represented one or more times. Second, suitability of the sites for biodiversity management was incorporated into the objective function of the model, whereas suitability was not considered in the basic MCLP model. This modification discouraged the model from selecting sites that were heavily impacted by roads and human settlements or that would be difficult to manage for biodiversity because they were on private land or had extensive fragmentation of public and private ownership. Third, the management class definitions were refined relative to GAP standards by incorporating data on grazing and timber management from existing land use plans from the Forest Service (Figure 17). We were able both to change assumptions between alternatives about the level of protection different management classes provided for biodiversity protection and also could compare the amounts of each class required by the solution for each alternative. Figure 18 shows one alternative solution and the suitability index of candidate sites. The MCLP model did not consider current management status, either in the solution or in its evaluation of solutions. We named the new model the Biodiversity Management Area Selection (BMAS) model. This model was described in a chapter of the final SNEP Report to Congress (Davis et al. 1996) and was recently submitted to a journal (Davis et al. in review).

Figure 17. Comparison of the three standard levels used for GAP in other regions of California (top) and the five class schema used for the Sierra Nevada study (bottom). The maps show a small portion of the northern Sierra Nevada region. Level 1 = formally designated for biodiversity management; 2 = other public lands; 3 = private lands. Class 1 = formally designated for protection of biodiversity, grazing not permitted; 2 = not formally designated but no grazing or commercial timber harvest; 3 = within existing grazing allotments but no commercial timber harvest; 4 = allocated to commercial timber harvest on public lands; and 5 = private lands on which development, grazing, and timber harvest may be permitted.

Figure 18. Map of one alternative solution to the Biodiversity Management Area Selection (BMAS) problem (selected watersheds shown in bold outline). The gray tone indicates the level of suitability for biodiversity management as defined by road and human population densities, percentage of private land ownership, and density of boundary between public and private lands.

Intermountain Semi-Desert Ecoregion Gap Analysis and Reserve Siting

The nation's first multi-state gap analysis of the management status of plant communities was conducted for the Intermountain Semi-Desert Ecoregion (Figure 6). Land cover data were compiled from nine western states, using the image compositing and map-guided classification techniques described above. Forty-eight land-cover types were mapped at the regional level, many of which are at the alliance level of classification. Twenty types were determined to be the highest conservation priorities as they are especially vulnerable to future losses or degradation in the absence of formal designation or active intervention for long-term biodiversity management. Over 96% of the terrestrial environment within the region is potentially available to intensive human uses for resources, recreation, or urbanization; the proportions are similar for the Columbia Plateau and Wyoming Basin subregions. We hasten to add that findings regarding individual vegetation types from this assessment must be carefully validated by regional field investigation to better determine their true level of representation and actual vulnerability to threats before policy decisions are made and implemented.

Beyond the initial conservation assessment, these findings can be applied in at least two additional directions. First they can be provide a regional perspective when the impacts of specific land use proposals are investigated. GAP data can quantify how rare a community type is, where else it occurs, and whether it is well-represented in biodiversity management areas. Second, the data from GAP can play a significant role in follow-up conservation planning efforts at a statewide or regional level (Crowe 1996, Vickerman 1996). For instance, GAP data can provide the missing biodiversity dimension to discussions about alternative wilderness and national park proposals (Wright et al. 1994, Merrill et al. 1995, Merrill et al. 1996, Wright and Scott 1996).

By fortuitous circumstances, as we completed the gap analysis of this ecoregion, The Nature Conservancy was just beginning their regional conservation planning for the Columbia Plateau portion of the ecoregion and used the GAP database as a coarse-filter to identify candidate areas to ensure adequate representation of all community types. The Nature Conservancy planning leader for the project was impressed with the BMAS model as a planning tool. Therefore we adapted BMAS to incorporate rare species occurrence data along with plant community maps and ran several alternative plans for them. The Nature Conservancy planning team was pleased with their preferred alternative as solved by BMAS. They reviewed each selected site and made modifications when they had local knowledge that a site was unsuitable for reasons not documented in the regional GIS database. Thus what began as a methodological development for the Sierra Nevada Ecosystem Project was applied to help solve a real-world conservation problem. The project also gave us the opportunity to develop better interfaces for processing GIS data for the model and post-processing the BMAS reports for display in the GIS environment. (see http://www.biogeog.ucsb.edu/projects/tnc/tnc.html for details).

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