Expert knowledge as a foundation for the management of secretive species and their habitat
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Abstract
In this chapter, we share lessons learned during the elicitation and application of expert knowledge in the form of a belief network model for the habitat of a waterbird, the King Rail (Rallus elegans). A belief network is a statistical framework used to graphically represent and evaluate hypothesized cause and effect relationships among variables. Our model was a pilot project to explore the value of such a model as a tool to help the US Fish and Wildlife Service (USFWS) conserve species that lack sufficient empirical data to guide management decisions. Many factors limit the availability of empirical data that can support landscape-scale conservation planning. Globally, most species simply have not yet been subject to empirical study (Wilson 2000). Even for well-studied species, data are often restricted to specific geographic extents, to particular seasons, or to specific segments of a species’ life history. The USFWS mandates that the agency’s conservation actions (1) be coordinated across regional landscapes, (2) be founded on the best available science (with testable assumptions), and (3) support adaptive management through monitoring and assessment of action outcomes. Given limits on the available data, the concept of “best available science” in the context of conservation planning generally includes a mix of empirical data and expert knowledge (Sullivan et al. 2006).
| Publication type | Book chapter |
|---|---|
| Publication Subtype | Book Chapter |
| Title | Expert knowledge as a foundation for the management of secretive species and their habitat |
| DOI | 10.1007/978-1-4614-1034-8 |
| Year Published | 2012 |
| Language | English |
| Publisher | Springer New York |
| Contributing office(s) | Coop Res Unit Atlanta |
| Description | 21 p. |
| Larger Work Type | Book |
| Larger Work Title | Expert knowledge and its application in landscape ecology |
| First page | 87 |
| Last page | 107 |
| Google Analytic Metrics | Metrics page |