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    Author(s): M.J. Conroy; B.R. Noon
    Date: 1996
    Source: Ecological Applications. 6(3): 763-773
    Publication Series: Miscellaneous Publication
    PDF: Download Publication  (280.0 KB)


    Biodiversity mapping (e.g., the Gap Analysis Program [GAP]), in which vegetative features and categories of land use are mapped at coarse spatial scales, has been proposed as a reliable tool for land use decisions (e.g., reserve identification, selection, and design). This implicitly assumes that species richness data collected at coarse spatiotemporal scales provide a first-order approximation to community and ecosystem representation and persistence. This assumption may be false because (1) species abundance distributions and species richness are poor surrogates for community/ecosystem processes, and are scale dependent; (2) species abundance and richness data are unreliable because of unequal and unknown sampling probabilities and species-habitat models of doubtful reliability; (3) mapped species richness data may be inherently resistant to "scaling up" or "scaling down"; and (4) decision-making based on mapped species richness patterns may be sensitive to errors from unreliable data and models, resulting in suboptimal conservation decisions. We suggest an approach in which mapped data are linked to management via demographic models, multiscale sampling, and decision theory. We use a numerical representation of a system in which vegetation data are assumed to be known and mapped without error, a simple model relating habitat to predicted species persistence, and statistical decision theory to illustrate use of mapped data in conservation decision-making and the impacts of uncertainty in data or models on the decision outcome.

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    Conroy, M.J.; Noon, B.R. 1996. Mapping of species richness for conservation of biological diversity: conceptual and methodological issues. Ecological Applications. 6(3): 763-773.


    biological diversity, conservation, decision theory, gap analysis, GIS, landscape modeling, mapping, population modeling, reserve design, sampling, scaling

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