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

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

SYRPHIDAE (DIPTERA) FROM LAMPEDUSA ISLAND

Diptera of Lampedusa are poorly known, and the only published data are those of VENTURI (1960) and PISCIOTTA et al. (2008). In the present paper the authors report new records of Syrphidae (Diptera) for Lampedusa island discovered during field investigations carried out for a broader research project in the island. Data are updated to November 2009. Eleven species of Syrphidae new for Lampedusa have been found of which one is new for the Sicilian fauna

Bayesian estimation of multiple clade competition from fossil data

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.

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.

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

Estimating Age-Dependent Extinction: Contrasting Evidence from Fossils and Phylogenies.

The estimation of diversification rates is one of the most vividly debated topics in modern systematics, with considerable controversy surrounding the power of phylogenetic and fossil-based approaches in estimating extinction. Van Valen's seminal work from 1973 proposed the "Law of constant extinction," which states that the probability of extinction of taxa is not dependent on their age. This assumption of age-independent extinction has prevailed for decades with its assessment based on survivorship curves, which, however, do not directly account for the incompleteness of the fossil record, and have rarely been applied at the species level. Here, we present a Bayesian framework to estimate extinction rates from the fossil record accounting for age-dependent extinction (ADE). Our approach, unlike previous implementations, explicitly models unobserved species and accounts for the effects of fossil preservation on the observed longevity of sampled lineages. We assess the performance and robustness of our method through extensive simulations and apply it to a fossil data set of terrestrial Carnivora spanning the past 40 myr. We find strong evidence of ADE, as we detect the extinction rate to be highest in young species and declining with increasing species age. For comparison, we apply a recently developed analogous ADE model to a dated phylogeny of extant Carnivora. Although the phylogeny-based analysis also infers ADE, it indicates that the extinction rate, instead, increases with increasing taxon age. The estimated mean species longevity also differs substantially, with the fossil-based analyses estimating 2.0 myr, in contrast to 9.8 myr derived from the phylogeny-based inference. Scrutinizing these discrepancies, we find that both fossil and phylogeny-based ADE models are prone to high error rates when speciation and extinction rates increase or decrease through time. However, analyses of simulated and empirical data show that fossil-based inferences are more robust. This study shows that an accurate estimation of ADE from incomplete fossil data is possible when the effects of preservation are jointly modeled, thus allowing for a reassessment of Van Valen's model as a general rule in macroevolution

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

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

Pre-impact fall detection: optimal sensor positioning based on a machine learning paradigm

The aim of this study was to identify the best subset of body segments that provides for a rapid and reliable detection of the transition from steady walking to a slipping event. Fifteen healthy young subjects managed unexpected perturbations during walking. Whole-body 3D kinematics was recorded and a machine learning algorithm was developed to detect perturbation events. In particular, the linear acceleration of all the body segments was parsed by Independent Component Analysis and a Neural Network was used to classify walking from unexpected perturbations. The Mean Detection Time (MDT) was 3516123 ms with an Accuracy of 95.4%. The procedure was repeated with data related to different subsets of all body segments whose variability appeared strongly influenced by the perturbation-induced dynamic modifications. Accordingly, feet and hands accounted for most data information and the performance of the algorithm were slightly reduced using their combination. Results support the hypothesis that, in the framework of the proposed approach, the information conveyed by all the body segments is redundant to achieve effective fall detection, and suitable performance can be obtained by simply observing the kinematics of upper and lower distal extremities. Future studies are required to assess the extent to which such results can be reproduced in older adults and in different experimental conditions