TY - JOUR
T1 - Rarity-weighted richness
T2 - A simple and reliable alternative to integer programming and heuristic algorithms for minimum set and maximum coverage problems in conservation planning
AU - Albuquerque, Fabio
AU - Beier, Paul
N1 - Publisher Copyright:
© 2015 Albuquerque, Beier.
PY - 2015/3/17
Y1 - 2015/3/17
N2 - Here we report that prioritizing sites in order of rarity-weighted richness (RWR) is a simple, reliable way to identify sites that represent all species in the fewest number of sites (minimum set problem) or to identify sites that represent the largest number of species within a given number of sites (maximum coverage problem). We compared the number of species represented in sites prioritized by RWR to numbers of species represented in sites prioritized by the Zonation software package for 11 datasets in which the size of individual planning units (sites) ranged from <1 ha to 2,500 km2. On average, RWR solutions were more efficient than Zonation solutions. Integer programming remains the only guaranteed way find an optimal solution, and heuristic algorithms remain superior for conservation prioritizations that consider compactness and multiple near-optimal solutions in addition to species representation. But because RWR can be implemented easily and quickly in R or a spreadsheet, it is an attractive alternative to integer programming or heuristic algorithms in some conservation prioritization contexts.
AB - Here we report that prioritizing sites in order of rarity-weighted richness (RWR) is a simple, reliable way to identify sites that represent all species in the fewest number of sites (minimum set problem) or to identify sites that represent the largest number of species within a given number of sites (maximum coverage problem). We compared the number of species represented in sites prioritized by RWR to numbers of species represented in sites prioritized by the Zonation software package for 11 datasets in which the size of individual planning units (sites) ranged from <1 ha to 2,500 km2. On average, RWR solutions were more efficient than Zonation solutions. Integer programming remains the only guaranteed way find an optimal solution, and heuristic algorithms remain superior for conservation prioritizations that consider compactness and multiple near-optimal solutions in addition to species representation. But because RWR can be implemented easily and quickly in R or a spreadsheet, it is an attractive alternative to integer programming or heuristic algorithms in some conservation prioritization contexts.
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U2 - 10.1371/journal.pone.0119905
DO - 10.1371/journal.pone.0119905
M3 - Article
C2 - 25780930
AN - SCOPUS:84924956400
SN - 1932-6203
VL - 10
JO - PLoS ONE
JF - PLoS ONE
IS - 3
M1 - e0119905
ER -