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Andreas Spillner,
Binh T. Nguyen and
Vincent Moulton. Computing phylogenetic diversity for split systems. In TCBB, Vol. 5(2):235-244, 2008.
Keywords: abstract network, diversity, phylogenetic network, phylogeny, split.
Note: http://dx.doi.org/10.1109/TCBB.2007.70260, slides available at http://www.newton.cam.ac.uk/webseminars/pg+ws/2007/plg/plgw01/0906/spillner/.
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"In conservation biology it is a central problem to measure, predict, and preserve biodiversity as species face extinction. In 1992 Faith proposed measuring the diversity of a collection of species in terms of their relationships on a phylogenetic tree, and to use this information to identify collections of species with high diversity. Here we are interested in some variants of the resulting optimization problem that arise when considering species whose evolution is better represented by a network rather than a tree. More specifically, we consider the problem of computing phylogenetic diversity relative to a split system on a collection of species of size $n$. We show that for general split systems this problem is NP-hard. In addition we provide some efficient algorithms for some special classes of split systems, in particular presenting an optimal $O(n)$ time algorithm for phylogenetic trees and an $O(nlog n + n k)$ time algorithm for choosing an optimal subset of size $k$ relative to a circular split system. © 2006 IEEE."
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Bui Quang Minh,
Steffen Klaere and
Arndt von Haeseler. Taxon Selection under Split Diversity. In Systematic Biology, Vol. 58(6):586-594, 2009.
Keywords: abstract network, circular split system, diversity, from network, phylogenetic network, split network.
Note: http://dx.doi.org/10.1093/sysbio/syp058.
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"The phylogenetic diversity (PD) measure of biodiversity is evaluated using a phylogenetic tree, usually inferred from morphological or molecular data. Consequently, it is vulnerable to errors in that tree, including those resulting from sampling error, model misspecification, or conflicting signals. To improve the robustness of PD, we can evaluate the measure using either a collection (or distribution) of trees or a phylogenetic network. Recently, it has been shown that these 2 approaches are equivalent but that the problem of maximizing PD in the general concept is NP-hard. In this study, we provide an efficient dynamic programming algorithm for maximizing PD when splits in the trees or network form a circular split system. We illustrate our method using a case study of game birds (Galliformes) and discuss the different choices of taxa based on our approach and PD."
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Magnus Bordewich and
Charles Semple. Budgeted Nature Reserve Selection with diversity feature loss and arbitrary split systems. In JOMB, Vol. 64(1):69-85, 2012.
Keywords: abstract network, approximation, diversity, phylogenetic network, polynomial, split network.
Note: http://www.math.canterbury.ac.nz/~c.semple/papers/BS11.pdf.
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"Arising in the context of biodiversity conservation, the Budgeted Nature Reserve Selection (BNRS) problem is to select, subject to budgetary constraints, a set of regions to conserve so that the phylogenetic diversity (PD) of the set of species contained within those regions is maximized. Here PD is measured across either a single rooted tree or a single unrooted tree. Nevertheless, in both settings, this problem is NP-hard. However, it was recently shown that, for each setting, there is a polynomial-time (1-1/e)-approximation algorithm for it and that this algorithm is tight. In the first part of the paper, we consider two extensions of BNRS. In the rooted setting we additionally allow for the disappearance of features, for varying survival probabilities across species, and for PD to be measured across multiple trees. In the unrooted setting, we extend to arbitrary split systems. We show that, despite these additional allowances, there remains a polynomial-time (1-1/e)-approximation algorithm for each extension. In the second part of the paper, we resolve a complexity problem on computing PD across an arbitrary split system left open by Spillner et al. © 2011 Springer-Verlag."
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Donovan H. Parks and
Robert G. Beiko. Measuring Community Similarity with Phylogenetic Networks. In MBE, Vol. 29(12):3947-3958, 2012.
Keywords: abstract network, diversity, phylogenetic network, phylogeny, split network.
Note: poster available at http://dparks.wdfiles.com/local--files/publications/SMBE_BetaDiversity_2011.pdf.
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"Environmental drivers of biodiversity can be identified by relating patterns of community similarity to ecological factors. Community variation has traditionally been assessed by considering changes in species composition and more recently by incorporating phylogenetic information to account for the relative similarity of taxa. Here, we describe how an important class of measures including Bray-Curtis, Canberra, and UniFrac can be extended to allow community variation to be computed on a phylogenetic network. We focus on phylogenetic split systems, networks that are produced by the widely used median network and neighbor-net methods, which can represent incongruence in the evolutionary history of a set of taxa. Calculating β diversity over a split system provides a measure of community similarity averaged over uncertainty or conflict in the available phylogenetic signal. Our freely available software, Network Diversity, provides 11 qualitative (presence-absence, unweighted) and 14 quantitative (weighted) network-based measures of community similarity that model different aspects of community richness and evenness. We demonstrate the broad applicability of network-based diversity approaches by applying them to three distinct data sets: pneumococcal isolates from distinct geographic regions, human mitochondrial DNA data from the Indonesian island of Nias, and proteorhodopsin sequences from the Sargasso and Mediterranean Seas. Our results show that major expected patterns of variation for these data sets are recovered using network-based measures, which indicates that these patterns are robust to phylogenetic uncertainty and conflict. Nonetheless, network-based measures of community similarity can differ substantially from measures ignoring phylogenetic relationships or from tree-based measures when incongruent signals are present in the underlying data. Network-based measures provide a methodology for assessing the robustness of β-diversity results in light of incongruent phylogenetic signal and allow β diversity to be calculated over widely used network structures such as median networks. © 2012 The Author 2012."
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- forked on GitHub.