963 research outputs found

    Disentangling the effects of key innovations on the diversification of Bromelioideae (bromeliaceae).

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    The evolution of key innovations, novel traits that promote diversification, is often seen as major driver for the unequal distribution of species richness within the tree of life. In this study, we aim to determine the factors underlying the extraordinary radiation of the subfamily Bromelioideae, one of the most diverse clades among the neotropical plant family Bromeliaceae. Based on an extended molecular phylogenetic data set, we examine the effect of two putative key innovations, that is, the Crassulacean acid metabolism (CAM) and the water-impounding tank, on speciation and extinction rates. To this aim, we develop a novel Bayesian implementation of the phylogenetic comparative method, binary state speciation and extinction, which enables hypotheses testing by Bayes factors and accommodates the uncertainty on model selection by Bayesian model averaging. Both CAM and tank habit were found to correlate with increased net diversification, thus fulfilling the criteria for key innovations. Our analyses further revealed that CAM photosynthesis is correlated with a twofold increase in speciation rate, whereas the evolution of the tank had primarily an effect on extinction rates that were found five times lower in tank-forming lineages compared to tank-less clades. These differences are discussed in the light of biogeography, ecology, and past climate change

    Estimating Alpha, Beta, and Gamma Diversity Through Deep Learning

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    The reliable mapping of species richness is a crucial step for the identification of areas of high conservation priority, alongside other value and threat considerations. This is commonly done by overlapping range maps of individual species, which requires dense availability of occurrence data or relies on assumptions about the presence of species in unsampled areas deemed suitable by environmental niche models. Here, we present a deep learning approach that directly estimates species richness, skipping the step of estimating individual species ranges. We train a neural network model based on species lists from inventory plots, which provide ground truth data for supervised machine learning. The model learns to predict species richness based on spatially associated variables, including climatic and geographic predictors, as well as counts of available species records from online databases. We assess the empirical utility of our approach by producing independently verifiable maps of alpha, beta, and gamma plant diversity at high spatial resolutions for Australia, a continent with highly heterogeneous diversity patterns. Our deep learning framework provides a powerful and flexible new approach for estimating biodiversity patterns, constituting a step forward toward automated biodiversity assessments

    Bayesian estimation of multiple clade competition from fossil data

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    Background: The diversification dynamics of clades is governed by speciation and extinction processes and is likely affected by multiple biotic, abiotic, and stochastic factors. Using quantitative methods to analyse fossil occurrence data, one may infer rates of speciation and extinction in a Bayesian framework. Moreover, Silvestro et al. (2015a) recently developed a Multiple Clade Diversity Dependence birth-death model (MCDD) to determine whether diversification dynamics can be explained by positive or negative effects of interactions within or between co-existing clades. However, the power and accuracy of this model and its general applicability have yet to be thoroughly investigated. Aims: Explore the properties of the existing MCDD implementation, which is based on Bayesian variable selection. Introduce an alternative parameterization based on the Horseshoe prior and show the properties of this approach for Bayesian shrinkage in complex models. Test the ability of the model to correctly identify within and between diversification interference under a suite of different diversification scenarios. Methods: Use simulations to assess and compare the power and accuracy of the two algorithms. Results: Diversity dependence within and between clades can be inferred with confidence in a wide range of scenarios using the MCDD model. The two implementations provide comparable results, but the new Horseshoe prior estimator appears to be more reliable, albeit slightly more conservative. The MCDD model is a powerful framework to analyse the putative effects of ecological interactions on macroevolutionary dynamics using fossil data and provides a sound statistical basis for future method developments

    Publisher Correction: The impact of endothermy on the climatic niche evolution and the distribution of vertebrate diversity.

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    In the version of this Article originally published, in Fig. 3a the first boundary was incorrectly labelled the "K/T boundary"; it should have read the "K/Pg boundary". The two equations in the main text were incorrectly omitted from the HTML. In the description of the posterior distribution of an ancestral state, the normal distribution was incorrectly described as being "assigned as prior to the node value"; it should have read "assigned as calibration to the node value". In the associated equation (the second equation in the text), the denominator of the last term was incorrectly given as "Node prior"; it should have read "Node calibration". In the same equation, the numerator of the third term on the right-hand side of the equation contained incorrect superscript notation on the x and this is shown in the full equation in the notice below.In the Acknowledgements, the following two sentences were incorrectly omitted: "The authors thank the Vital-IT facilities of the Swiss Institute of Bioinformatics for the computational support" and "This work was funded by the University of Lausanne and the Swiss National Science Foundation (CRSIII3-147630) to N.S." In the Author contributions section, the first sentence was incorrectly given as "J.R. designed the study. J.R., N.S. and D. Silvestro designed the methodology and ran the analyses"; it should have read "J.R., D.S. and N.S. designed the study and the methodology". In the Supplementary Information, all three instances of the word "prior" were incorrect and should have read "calibration".These errors have now been corrected in all versions of the Article

    Clownfishes evolution below and above the species level.

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    The difference between rapid morphological evolutionary changes observed in populations and the long periods of stasis detected in the fossil record has raised a decade-long debate about the exact role played by intraspecific mechanisms at the interspecific level. Although they represent different scales of the same evolutionary process, micro- and macroevolution are rarely studied together and few empirical studies have compared the rates of evolution and the selective pressures between both scales. Here, we analyse morphological, genetic and ecological traits in clownfishes at different evolutionary scales and demonstrate that the tempo of molecular and morphological evolution at the species level can be, to some extent, predicted from parameters estimated below the species level, such as the effective population size or the rate of evolution within populations. We also show that similar codons in the gene of the rhodopsin RH1, a light-sensitive receptor protein, are under positive selection at the intra and interspecific scales, suggesting that similar selective pressures are acting at both levels

    An Efficient Method to Take into Account Forecast Uncertainties in Large Scale Probabilistic Power Flow

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    The simulation of uncertainties due to renewable and load forecasts is becoming more and more important in security assessment analyses performed on large scale networks. This paper presents an efficient method to account for forecast uncertainties in probabilistic power flow (PPF) applications, based on the combination of PCA (Principal Component Analysis) and PEM (Point Estimate Method), in the context of operational planning studies applied to large scale AC grids. The benchmark against the conventional PEM method applied to large power system models shows that the proposed method assures high speed up ratios, preserving a good accuracy of the marginal distributions of the outputs

    The past and future human impact on mammalian diversity

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    To understand the current biodiversity crisis, it is crucial to determine how humans have affected biodiversity in the past. However, the extent of human involvement in species extinctions from the Late Pleistocene onward remains contentious. Here, we apply Bayesian models to the fossil record to estimate how mammalian extinction rates have changed over the past 126,000 years, inferring specific times of rate increases. We specifically test the hypothesis of human-caused extinctions by using posterior predictive methods. We find that human population size is able to predict past extinctions with 96% accuracy. Predictors based on past climate, in contrast, perform no better than expected by chance, suggesting that climate had a negligible impact on global mammal extinctions. Based on current trends, we predict for the near future a rate escalation of unprecedented magnitude. Our results provide a comprehensive assessment of the human impact on past and predicted future extinctions of mammals
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