Humphries, H.C., Bourgeron, P.S., Reynolds, K.M.
NWT Accession Number: NWT1705
The availability of spatially continuous data layers can have a strong impact on selection of land units for conservation purposes. The suitability of ecological conditions for sustaining the targets of conservation is an important consideration in evaluating candidate conservation sites. We constructed two fuzzy logic-based knowledge bases to determine the conservation suitability of land units in the interior Columbia River basin using NetWeaver software in the Ecosystem Management Decision Support application framework. Our objective was to assess the sensitivity of suitability ratings, derived from evaluating the knowledge bases, to fuzzy logic function parameters and to the removal of data layers (land use condition, road density, disturbance regime change index, vegetation change index, land unit size, cover type size, and cover type change index). The amount and geographic distribution of suitable land polygons was most strongly altered by the removal of land use condition, road density, and land polygon size. Removal of land use condition changed suitability primarily on private or intensively-used public land. Removal of either road density or land polygon size most strongly affected suitability on higher-elevation US Forest Service land containing small-area biophysical environments. Data layers with the greatest influence differed in rank between the two knowledge bases. Our results reinforce the importance of including both biophysical and socio-economic attributes to determine the suitability of land units for conservation. The sensitivity tests provided information about knowledge base structuring and parameterization as well as prioritization for future data needs.
sensitivity analysis, map removal, knowledge base, conservation suitability, land suitability, fuzzy logic, ecosystem management decision support
Humphries, H.C., Bourgeron, P.S., Reynolds, K.M., (2010) Sensitivity analysis of land unit suitability for conservation using a knowledge-based system. Environmental Management 46 :225-236 , DOI: 10.1007/s00267-010-9515-1
This material is based upon work supported by the National Science Foundation under Cooperative Agreement #DEB-1637686. Any opinions, findings, conclusions, or recommendations expressed in the material are those of the author(s) and do not necesarily reflect the views of the National Science Foundation.
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