Volume No. 12, 2003/2004
Applications
While we (FD and DS) were developing the California Gap Analysis Project in the mid-1990s, we naturally became interested in using our GAP data to design reserve networks to fill the gaps we were identifying. We began collaborating on various reserve selection methods for choosing sets of sites that would achieve conservation targets efficiently. These efforts were satisfying intellectually as they became more sophisticated, but we were somewhat frustrated that this kind of systematic approach to conservation planning was not being adopted widely in public land use planning. An opportunity to rethink the conservation planning problem arose after California’s legislative watchdog agency had been critical of the state’s conservation program. They cited a lack of coordination among agencies with different agendas and an inability to formally evaluate properties when they were offered for acquisition. Was the state moving cost-effectively toward some desired endpoint? As a result, the California Resources Agency contracted with the National Center for Ecological Analysis and Synthesis (NCEAS) to convene a working group that would bring systematic conservation planning theory and methods to bear on the design and implementation of the state’s conservation programs through their California Legacy Project (CLP, http://www.legacy.ca.gov). Because California’s conservation programs (like many others) act on voluntary offers of private lands to be acquired from a fixed budget (e.g., proceeds from a bond initiative), they did not need or want a process to develop a long-range plan that may take decades to implement. Rather they needed a process to evaluate and prioritize the set of properties that are currently available for conservation. In this paper we provide a brief overview of a planning framework produced by the NCEAS working group. A detailed technical description of the framework can be found online at http://www.nceas.ucsb.edu/nceas-web/projects/4040/TerrBiod_framework-report.pdf.
What makes this framework different from earlier examples? It differs primarily in three aspects: the overall focus of systematic conservation planning, multiple rather than single objectives, and the measure of site conservation value.
Most examples of conservation planning tools follow a reserve selection paradigm that either meets conservation goals for protection at minimum cost (or area) or maximizes biodiversity protected for a limited budget. The performance measure is how much biodiversity is contained (represented) in the reserve network. Biodiversity outside of reserves is not credited. In our framework we shifted the focus to maximizing how much biodiversity is expected to remain in the future (whether in reserves or not). Adding new reserves becomes the means to that end rather than the end in itself.
The planning framework is organized into a hierarchy of five conservation objectives for terrestrial biodiversity currently used in conservation practice:
Each objective represents a different policy for prioritizing conservation investments, and each invokes a somewhat distinctive set of ecological and spatial criteria.
Arguably the most important feature of any planning approach is the specification of the performance measure by which sites can be prioritized. The performance measure may also be seen as a “marginal utility function.” In traditional reserve selection approaches, this measure is often some form of complementarity. Because our framework is concerned with maximizing the amount of biodiversity that remains at some future time, it requires a change in how we measure the value of individual sites. Instead of measuring how much biodiversity a site has today, we project how much biodiversity would be lost if it were not protected. Or more concisely, what difference would conservation make? If a site is not threatened, its biodiversity is likely to remain even without formal protection, and so our framework would assign it a low priority. Therefore, the framework requires a spatially explicit scenario of future land use that is used to estimate the potential loss of biodiversity in the site and the region. Specifically, we compute the difference between the area of each element with and without conservation as an index of threat.
The framework establishes a relationship between the level or amount of a resource (e.g., the area of a particular habitat type) and the “utility” associated with it. Economists recognize that the utility of the next unit of some resource depends on how much you already have. If there were a million hectares of habitat for a species, protecting the first hectare probably has more social utility than protecting the millionth. In a gap analysis context, planners set goals of how much habitat to protect, which is usually an estimate of the minimum area needed (with some degree of confidence) for the species or community to persist. The implicit relationship between resource amount and marginal utility is a step function, where marginal utility of remaining unprotected sites becomes zero once minimum conservation goals are achieved. That implies that society would be satisfied with a set of reserves surrounded by intense land use, which we believe grossly oversimplifies the social demand for conservation. This is rather like being satisfied with a subsistence diet instead of recognizing that as a bare minimum. To reserve selection algorithms, the goal is treated as the ceiling (this much and no more) whereas to us, the goal is like a floor (at least this much for persistence but the more the better). The step function is a special case of our more general diminishing marginal utility function.
To measure a site’s overall value for conserving terrestrial biodiversity, we estimate the site’s marginal utility for each of the five conservation objectives listed above. These are combined by weighting each objective (according to stakeholders’ values) and summing the weighted values for each site. The final step in our framework is a budget allocation model. Our approach to measuring conservation value is based on cost-effectiveness, with the cost for whatever action is deemed necessary to remove threat. The problem the allocation model solves is to invest a fixed budget in a set of sites that, if conserved, would minimize the loss of terrestrial biodiversity during the planning period.
The framework we have developed for the California Legacy Project has not been fully vetted with the relevant state agencies or other stakeholder groups, so it remains to be seen whether the ideas and methods will prove useful in real planning efforts. We believe the strengths of the framework are its generality, explicitness, modest data requirements (such as GAP), flexibility for exploring alternatives, formal consideration of threats and costs, and―perhaps most importantly―its ability to help in choosing among competing projects. For many organizations, this may be more useful than optimizing grand conservation plans that are often out of date the moment they are adopted. We believe the framework could be adapted to other regions, scales, and ownerships (e.g., prioritizing acquisitions for National Wildlife Refuges, National Forest or BLM land management planning, land exchanges), restoration projects, and aquatic biodiversity. We are eager to explore these opportunities further with you (contact stoms@bren.ucsb.edu).
The framework has only been implemented sufficiently to demonstrate the concepts and therefore is currently rather cumbersome. NatureServe (http://www.natureserve.org/) is developing a planning support system, and we are working with them to codify our process.
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