Split diversity in constrained conservation prioritization using integer linear programming.
Olga Chernomor, Bui Quang Minh, Félix Forest, Steffen Klaere, Travis Ingram, Monika Henzinger, Arndt von Haeseler
Author Information
Olga Chernomor: Center for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, University of Vienna, Medical University Vienna Vienna, Austria ; Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna Vienna, Austria.
Bui Quang Minh: Center for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, University of Vienna, Medical University Vienna Vienna, Austria.
Félix Forest: Jodrell Laboratory, Royal Botanic Gardens Kew, Richmond, UK.
Steffen Klaere: Department of Statistics, School of Biological Sciences, University of Auckland Auckland, New Zealand.
Travis Ingram: Department of Zoology, University of Otago Dunedin, New Zealand.
Monika Henzinger: Theory and Applications of Algorithms, Faculty of Computer Science, University of Vienna Vienna, Austria.
Arndt von Haeseler: Center for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, University of Vienna, Medical University Vienna Vienna, Austria ; Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna Vienna, Austria.
Phylogenetic diversity (PD) is a measure of biodiversity based on the evolutionary history of species. Here, we discuss several optimization problems related to the use of PD, and the more general measure split diversity (SD), in conservation prioritization.Depending on the conservation goal and the information available about species, one can construct optimization routines that incorporate various conservation constraints. We demonstrate how this information can be used to select sets of species for conservation action. Specifically, we discuss the use of species' geographic distributions, the choice of candidates under economic pressure, and the use of predator-prey interactions between the species in a community to define viability constraints.Despite such optimization problems falling into the area of NP hard problems, it is possible to solve them in a reasonable amount of time using integer programming. We apply integer linear programming to a variety of models for conservation prioritization that incorporate the SD measure.We exemplarily show the results for two data sets: the Cape region of South Africa and a Caribbean coral reef community. Finally, we provide user-friendly software at http://www.cibiv.at/software/pda.
