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On which targets should we compromise in conservation prioritization problems?
Author(s) -
Kaim Andrea,
Watts Matthew E.,
Possingham Hugh P.
Publication year - 2017
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/2041-210x.12812
Subject(s) - ranking (information retrieval) , solver , integer programming , computer science , mathematical optimization , rank (graph theory) , context (archaeology) , linear programming , simulated annealing , set (abstract data type) , total cost , data mining , machine learning , mathematics , geography , economics , archaeology , combinatorics , microeconomics , programming language
Systematic conservation planning is an essential part of biodiversity preservation. In the context of conservation prioritization problems, the total cost of the entire reserve system is highly dependent on how big we set targets (e.g. 10% or 30%) for conservation features (e.g. species or habitats). Thus, it is of interest to conservation planners how targets could be adjusted in a reasonable way, in order to decrease total cost. The aim of this paper is to rank features based on their influence on total cost. Focusing on the minimum set coverage problem—an integer linear optimization problem (ILP)—we developed a method to rank features according to their influence on total cost. Since the computation time is often too high to solve the ILP, we approximated its optimal solutions by the results of a linear optimization problem (LP). For the feature ranking, we used the shadow prices of the LP, validating it with rankings created by methods which used an ILP solver and the software Marxan, which uses a simulated annealing algorithm. The results show that shadow prices can be used to rank features in terms of their impact on the total cost of the reserve system thereby identifying those feature targets that could yield better compromises. Thus, the shadow price ranking provides a novel tool for conservation planners to help set feature targets in conservation prioritization problems. Furthermore, the results of the study can be used to improve Marxan.