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DESIGNING BIORESERVE NETWORKS TO SATISFY MULTIPLE, CONFLICTING DEMANDS
Author(s) -
Rothley K. D.
Publication year - 1999
Publication title -
ecological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.864
H-Index - 213
eISSN - 1939-5582
pISSN - 1051-0761
DOI - 10.1890/1051-0761(1999)009[0741:dbntsm]2.0.co;2
Subject(s) - compromise , computer science , robustness (evolution) , social connectedness , set (abstract data type) , variety (cybernetics) , operations research , rank (graph theory) , representation (politics) , selection (genetic algorithm) , risk analysis (engineering) , mathematics , business , machine learning , artificial intelligence , psychology , social science , biochemistry , chemistry , combinatorics , sociology , politics , political science , law , psychotherapist , gene , programming language
Reserve designers typically strive to create reserves that satisfy a variety of potentially conflicting criteria. Rather than optimizing with respect to just one criterion, reserve planners are likely to seek some compromise. To facilitate bioreserve design, I propose the use of multiobjective programming to identify these compromise alternatives, and then the use of the simple multiattribute rating technique to rank these alternatives and to explore the sensitivity of the rankings to the relative value placed on the individual criteria. An example is provided for the selection of a reserve system in Nova Scotia, Canada, based on three criteria: (1) connectedness, (2) area, and (3) rare species representation. First, multiobjective programming was used to reduce the set of over 15000 potential reserve‐system alternatives to a list of 36 candidate systems representing the optimal trade‐offs among the three criteria. The simple attribute‐rating technique was then used to identify a single best solution for an arbitrary set of relative criteria values and to test the robustness of this solution to changes in relative preferences for the criteria. The techniques presented here can simplify the evaluation of reserve alternatives, enabling planners to refocus their efforts on the complex biological, social, and economic aspects of reserve design.