25,499 research outputs found

    Non-stationary patterns of isolation-by-distance: inferring measures of local genetic differentiation with Bayesian kriging

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    Patterns of isolation-by-distance arise when population differentiation increases with increasing geographic distances. Patterns of isolation-by-distance are usually caused by local spatial dispersal, which explains why differences of allele frequencies between populations accumulate with distance. However, spatial variations of demographic parameters such as migration rate or population density can generate non-stationary patterns of isolation-by-distance where the rate at which genetic differentiation accumulates varies across space. To characterize non-stationary patterns of isolation-by-distance, we infer local genetic differentiation based on Bayesian kriging. Local genetic differentiation for a sampled population is defined as the average genetic differentiation between the sampled population and fictive neighboring populations. To avoid defining populations in advance, the method can also be applied at the scale of individuals making it relevant for landscape genetics. Inference of local genetic differentiation relies on a matrix of pairwise similarity or dissimilarity between populations or individuals such as matrices of FST between pairs of populations. Simulation studies show that maps of local genetic differentiation can reveal barriers to gene flow but also other patterns such as continuous variations of gene flow across habitat. The potential of the method is illustrated with 2 data sets: genome-wide SNP data for human Swedish populations and AFLP markers for alpine plant species. The software LocalDiff implementing the method is available at http://membres-timc.imag.fr/Michael.Blum/LocalDiff.htmlComment: In press, Evolution 201

    Visibility graphs and landscape visibility analysis

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    Visibility analysis based on viewsheds is one of the most frequently used GIS analysis tools. In this paper we present an approach to visibility analysis based on the visibility graph. A visibility graph records the pattern of mutual visibility relations in a landscape, and provides a convenient way of storing and further analysing the results of multiple viewshed analyses for a particular landscape region. We describe how a visibility graph may be calculated for a landscape. We then give examples, which include the interactive exploration ofa landscape, and the calculation of new measures of a landscape?s visual properties based on graph metrics ? in particular, neighbourhood clustering coefficient and path length analysis. These analyses suggest that measures derived from the visibility graph may be of particular relevance to the growing interest in quantifying the perceptual characteristics of landscapes

    REinforcement learning based Adaptive samPling: REAPing Rewards by Exploring Protein Conformational Landscapes

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    One of the key limitations of Molecular Dynamics simulations is the computational intractability of sampling protein conformational landscapes associated with either large system size or long timescales. To overcome this bottleneck, we present the REinforcement learning based Adaptive samPling (REAP) algorithm that aims to efficiently sample conformational space by learning the relative importance of each reaction coordinate as it samples the landscape. To achieve this, the algorithm uses concepts from the field of reinforcement learning, a subset of machine learning, which rewards sampling along important degrees of freedom and disregards others that do not facilitate exploration or exploitation. We demonstrate the effectiveness of REAP by comparing the sampling to long continuous MD simulations and least-counts adaptive sampling on two model landscapes (L-shaped and circular), and realistic systems such as alanine dipeptide and Src kinase. In all four systems, the REAP algorithm consistently demonstrates its ability to explore conformational space faster than the other two methods when comparing the expected values of the landscape discovered for a given amount of time. The key advantage of REAP is on-the-fly estimation of the importance of collective variables, which makes it particularly useful for systems with limited structural information

    Factors shaping community assemblages and species co-occurrence of different trophic levels

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    Species assemblages are the results of various processes, including dispersion and habitat filtering. Disentangling the effects of these different processes is challenging for statistical analysis, especially when biotic interactions should be considered. In this study, we used plants (producers) and leafhoppers (phytophagous) as model organisms, and we investigated the relative importance of abiotic versus biotic factors that shape community assemblages, and we infer on their biotic interactions by applying three-step statistical analysis. We applied a novel statistical analysis, that is, multiblock Redundancy Analysis (mbRA, step 1) and showed that 51.8% and 54.1% of the overall variation in plant and leafhopper assemblages are, respectively, explained by the two multiblock models. The most important blocks of variables to explain the variations in plant and leafhopper assemblages were local topography and biotic factors. Variation partitioning analysis (step 2) showed that pure abiotic filtering and pure biotic processes were relatively less important than their combinations, suggesting that biotic relationships are strongly structured by abiotic conditions. Pairwise co-occurrence analysis (step 3) on generalist leafhoppers and the most common plants identified 40 segregated species pairs (mainly between plant species) and 16 aggregated pairs (mainly between leafhopper species). Pairwise analysis on specialist leafhoppers and potential host plants clearly revealed aggregated patterns. Plant segregation suggests heterogeneous resource availability and competitive interactions, while leafhopper aggregation suggests host feeding differentiation at the local level, different feeding microhabitats on host plants, and similar environmental requirements of the species. Using the novel mbRA, we disentangle for the first time the relative importance of more than five distinct groups of variables shaping local species communities. We highlighted the important role of abiotic processes mediated by bottom-up effects of plants on leafhopper communities. Our results revealed that in-field structure diversification and trophic interactions are the main factors causing the co-occurrence patterns observed.Fil: Trivellone, Valeria. Swiss Federal Institute for Forest, Snow and Landscape Research; SuizaFil: Bougeard, Stephanie. French Agency for Food, Environmental and Occupational Health Safety; FranciaFil: Giavi, Simone. Swiss Federal Institute for Forest, Snow and Landscape Research; SuizaFil: Krebs, Patrik. Swiss Federal Institute for Forest, Snow and Landscape Research; SuizaFil: Balseiro, Diego. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigaciones en Ciencias de la Tierra. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Centro de Investigaciones en Ciencias de la Tierra; ArgentinaFil: Dray, Stephane. Université Claude Bernard Lyon 1; FranciaFil: Moretti, Marco. Swiss Federal Institute for Forest, Snow and Landscape Research; Suiz
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