547 research outputs found

    Phylogenetic Resolution and Quantifying the Phylogenetic Diversity and Dispersion of Communities

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    Conservation biologists and community ecologists have increasingly begun to quantify the phylogenetic diversity and phylogenetic dispersion in species assemblages. In some instances, the phylogenetic trees used for such analyses are fully bifurcating, but in many cases the phylogenies being used contain unresolved nodes (i.e. polytomies). The lack of phylogenetic resolution in such studies, while certainly not preferred, is likely to continue particularly for those analyzing diverse communities and datasets with hundreds to thousands of taxa. Thus it is imperative that we quantify potential biases and losses of statistical power in studies that use phylogenetic trees that are not completely resolved. The present study is designed to meet both of these goals by quantifying the phylogenetic diversity and dispersion of simulated communities using resolved and gradually ‘unresolved’ phylogenies. The results show that: (i) measures of community phylogenetic diversity and dispersion are generally more sensitive to loss of resolution basally in the phylogeny and less sensitive to loss of resolution terminally; and (ii) the loss of phylogenetic resolution generally causes false negative results rather than false positives

    Converting Endangered Species Categories to Probabilities of Extinction for Phylogenetic Conservation Prioritization

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    Categories of imperilment like the global IUCN Red List have been transformed to probabilities of extinction and used to rank species by the amount of imperiled evolutionary history they represent (e.g. by the Edge of Existence programme). We investigate the stability of such lists when ranks are converted to probabilities of extinction under different scenarios.Using a simple example and computer simulation, we show that preserving the categories when converting such list designations to probabilities of extinction does not guarantee the stability of the resulting lists.Care must be taken when choosing a suitable transformation, especially if conservation dollars are allocated to species in a ranked fashion. We advocate routine sensitivity analyses

    Evidence, Content and Corroboration and the Tree of Life

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    We examine three critical aspects of Popper’s formulation of the ‘Logic of Scientific Discovery’—evidence, content and degree of corroboration—and place these concepts in the context of the Tree of Life (ToL) problem with particular reference to molecular systematics. Content, in the sense discussed by Popper, refers to the breadth and scope of existence that a hypothesis purports to explain. Content, in conjunction with the amount of available and relevant evidence, determines the testability, or potential degree of corroboration, of a statement; content distinguishes scientific hypotheses from metaphysical assertions. Degree of corroboration refers to the relative and tentative confidence assigned to one hypothesis over another, based upon the performance of each under critical tests. Here we suggest that systematists attempt to maximize content and evidence to increase the potential degree of corroboration in all phylogenetic endeavors. Discussion of this “total evidence” approach leads to several interesting conclusions about generating ToL hypotheses

    Recognizing Treelike k-Dissimilarities

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    A k-dissimilarity D on a finite set X, |X| >= k, is a map from the set of size k subsets of X to the real numbers. Such maps naturally arise from edge-weighted trees T with leaf-set X: Given a subset Y of X of size k, D(Y) is defined to be the total length of the smallest subtree of T with leaf-set Y . In case k = 2, it is well-known that 2-dissimilarities arising in this way can be characterized by the so-called "4-point condition". However, in case k > 2 Pachter and Speyer recently posed the following question: Given an arbitrary k-dissimilarity, how do we test whether this map comes from a tree? In this paper, we provide an answer to this question, showing that for k >= 3 a k-dissimilarity on a set X arises from a tree if and only if its restriction to every 2k-element subset of X arises from some tree, and that 2k is the least possible subset size to ensure that this is the case. As a corollary, we show that there exists a polynomial-time algorithm to determine when a k-dissimilarity arises from a tree. We also give a 6-point condition for determining when a 3-dissimilarity arises from a tree, that is similar to the aforementioned 4-point condition.Comment: 18 pages, 4 figure

    Why and how might genetic and phylogenetic diversity be reflected in the identification of key biodiversity areas?

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    ‘Key biodiversity areas' are defined as sites contributing significantly to the global persistence of biodiversity. The identification of these sites builds from existing approaches based on measures of species and ecosystem diversity and process. Here, we therefore build from the work of Sgró et al. (2011 Evol. Appl. 4, 326–337. (doi:10.1111/j.1752-4571.2010.00157.x)) to extend a framework for how components of genetic diversity might be considered in the identification of key biodiversity areas. We make three recommendations to inform the ongoing process of consolidating a key biodiversity areas standard: (i) thresholds for the threatened species criterion currently consider a site's share of a threatened species' population; expand these to include the proportion of the species' genetic diversity unique to a site; (ii) expand criterion for ‘threatened species' to consider ‘threatened taxa’ and (iii) expand the centre of endemism criterion to identify as key biodiversity areas those sites holding a threshold proportion of the compositional or phylogenetic diversity of species (within a taxonomic group) whose restricted ranges collectively define a centre of endemism. We also recommend consideration of occurrence of EDGE species (i.e. threatened phylogenetic diversity) in key biodiversity areas to prioritize species-specific conservation actions among sites
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