Prioritizing populations for conservation using phylogenetic networks

Volkmann, Logan, Martyn, Iain, Moulton, Vincent ORCID:, Spillner, Andreas and Mooers, Arne O. (2014) Prioritizing populations for conservation using phylogenetic networks. PLoS One, 9 (2). ISSN 1932-6203

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In the face of inevitable future losses to biodiversity, ranking species by conservation priority seems more than prudent. Setting conservation priorities within species (i.e., at the population level) may be critical as species ranges become fragmented and connectivity declines. However, existing approaches to prioritization (e.g., scoring organisms by their expected genetic contribution) are based on phylogenetic trees, which may be poor representations of differentiation below the species level. In this paper we extend evolutionary isolation indices used in conservation planning from phylogenetic trees to phylogenetic networks. Such networks better represent population differentiation, and our extension allows populations to be ranked in order of their expected contribution to the set. We illustrate the approach using data from two imperiled species: the spotted owl Strix occidentalis in North America and the mountain pygmy-possum Burramys parvus in Australia. Using previously published mitochondrial and microsatellite data, we construct phylogenetic networks and score each population by its relative genetic distinctiveness. In both cases, our phylogenetic networks capture the geographic structure of each species: geographically peripheral populations harbor less-redundant genetic information, increasing their conservation rankings. We note that our approach can be used with all conservation-relevant distances (e.g., those based on whole-genome, ecological, or adaptive variation) and suggest it be added to the assortment of tools available to wildlife managers for allocating effort among threatened populations.

Item Type: Article
Additional Information: Copyright: © 2014 Volkmann et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Faculty \ School: Faculty of Science > School of Computing Sciences
UEA Research Groups: Faculty of Science > Research Groups > Computational Biology > Computational biology of RNA (former - to 2018)
Faculty of Science > Research Groups > Computational Biology > Phylogenetics (former - to 2018)
Faculty of Science > Research Groups > Computational Biology
Faculty of Science > Research Groups > Norwich Epidemiology Centre
Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre
Depositing User: Pure Connector
Date Deposited: 09 Jun 2014 14:00
Last Modified: 16 Apr 2024 13:30
DOI: 10.1371/journal.pone.0088945


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