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Browsing by Subject "Bayesian method"

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    Open Access
    A multilocus phylogeny of the world Sycoecinae fig wasps (Chalcidoidea: Pteromalidae)
    (Public Library of Science, 2013) Cruaud, Astrid; Underhill, Jenny G; Huguin, Maïlis; Genson, Gwenaëlle; Jabbour-Zahab, Roula; Tolley, Krystal A; Rasplus, Jean-Yves; van Noort, Simon
    The Sycoecinae is one of five chalcid subfamilies of fig wasps that are mostly dependent on Ficus inflorescences for reproduction. Here, we analysed two mitochondrial ( COI , Cyt b ) and four nuclear genes (ITS2, EF-1α, RpL27a, mago nashi ) from a worldwide sample of 56 sycoecine species. Various alignment and partitioning strategies were used to test the stability of major clades. All topologies estimated using maximum likelihood and Bayesian methods were similar and well resolved but did not support the existing classification. A high degree of morphological convergence was highlighted and several species appeared best described as species complexes. We therefore proposed a new classification for the subfamily. Our analyses revealed several cases of probable speciation on the same host trees (up to 8 closely related species on one single tree of F. sumatrana ), which raises the question of how resource partitioning occurs to avoid competitive exclusion. Comparisons of our results with fig phylogenies showed that, despite sycoecines being internally ovipositing wasps host-switches are common incidents in their evolutionary history. Finally, by studying the evolutionary properties of the markers we used and profiling their phylogenetic informativeness, we predicted their utility for resolving phylogenetic relationships of Chalcidoidea at various taxonomic levels.
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    A variational Bayes approach to the analysis of occupancy models
    (Public Library of Science, 2016) Clark, Allan E; Altwegg, Res; Ormerod, John T
    Detection-nondetection data are often used to investigate species range dynamics using Bayesian occupancy models which rely on the use of Markov chain Monte Carlo (MCMC) methods to sample from the posterior distribution of the parameters of the model. In this article we develop two Variational Bayes (VB) approximations to the posterior distribution of the parameters of a single-season site occupancy model which uses logistic link functions to model the probability of species occurrence at sites and of species detection probabilities. This task is accomplished through the development of iterative algorithms that do not use MCMC methods. Simulations and small practical examples demonstrate the effectiveness of the proposed technique. We specifically show that (under certain circumstances) the variational distributions can provide accurate approximations to the true posterior distributions of the parameters of the model when the number of visits per site ( K ) are as low as three and that the accuracy of the approximations improves as K increases. We also show that the methodology can be used to obtain the posterior distribution of the predictive distribution of the proportion of sites occupied (PAO).
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