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Being confounding factors, directional trends are likely to make two quantitative traits appear as spuriously correlated. By determining the probability distributions of independent contrasts when traits evolve following Brownian motions with linear trends, we show that the standard independent contrasts can not be used to test for correlation in this situation. We propose a multiple regression approach which corrects the bias caused by directional evolution. We show that our approach is equivalent to performing a Phylogenetic Generalized Least Squares (PGLS) analysis with tip times as covariables by providing a new and more general proof of the equivalence between PGLS and independent contrasts methods. Our approach is assessed and compared with three previous correlation tests on data simulated in various situations and overall outperforms all the other methods. The approach is next illustrated on a real dataset to test for correlation between hominin cranial capacity and body mass., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Despite its intrinsic difficulty, ancestral character state reconstruction is an essential tool for testing evolutionary hypothesis. Two major classes of approaches to this question can be distinguished: parsimony- or likelihood-based approaches. We focus here on the second class of methods, more specifically on approaches based on continuous-time Markov modeling of character evolution. Among them, we consider the most-likely-ancestor reconstruction, the posterior-probability reconstruction, the likelihood-ratio method, and the Bayesian approach. We discuss and compare the above-mentioned methods over several phylogenetic trees, adding the maximum-parsimony method performance in the comparison. Under the assumption that the character evolves according a continuous-time Markov process, we compute and compare the expectations of success of each method for a broad range of model parameter values. Moreover, we show how the knowledge of the evolution model parameters allows to compute upper bounds of reconstruction performances, which are provided as references. The results of all these reconstruction methods are quite close one to another, and the expectations of success are not so far from their theoretical upper bounds. But the performance ranking heavily depends on the topology of the studied tree, on the ancestral node that is to be inferred and on the parameter values. Consequently, we propose a protocol providing for each parameter value the best method in terms of expectation of success, with regard to the phylogenetic tree and the ancestral node to infer., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Using the fossil record yields more detailed reconstructions of the evolutionary process than what is obtained from contemporary lineages only. In this work, we present a stochastic process modeling not only speciation and extinction, but also fossil finds. Next, we derive an explicit formula for the likelihood of a reconstructed phylogeny with fossils, which can be used to estimate the speciation and extinction rates. Finally, we provide a comparative simulation-based evaluation of the accuracy of estimations of these rates from complete phylogenies (including extinct lineages), from reconstructions with contemporary lineages only and from reconstructions with contemporary lineages and the fossil record. Results show that taking the fossil record into account yields more accurate estimates of speciation and extinction rates than considering only contemporary lineages., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Choosing an ancestral state reconstruction method among the alternatives available for quantitative characters may be puzzling. We present here a comparison of seven of them, namely the maximum likelihood, restricted maximum likelihood, generalized least squares under Brownian, Brownian-with-trend and Ornstein–Uhlenbeck models, phylogenetic independent contrasts and squared parsimony methods. A review of the relations between these methods shows that the maximum likelihood, the restricted maximum likelihood and the generalized least squares under Brownian model infer the same ancestral states and can only be distinguished by the distributions accounting for the reconstruction uncertainty which they provide. The respective accuracy of the methods is assessed over character evolution simulated under a Brownian motion with (and without) directional or stabilizing selection. We give the general form of ancestral state distributions conditioned on leaf states under the simulation models. Ancestral distributions are used first, to give a theoretical lower bound of the expected reconstruction error, and second, to develop an original evaluation scheme which is more efficient than comparing the reconstructed and the simulated states. Our simulations show that: (i) the distributions of the reconstruction uncertainty provided by the methods generally make sense (some more than others); (ii) it is essential to detect the presence of an evolutionary trend and to choose a reconstruction method accordingly; (iii) all the methods show good performances on characters under stabilizing selection; (iv) without trend or stabilizing selection, the maximum likelihood method is generally the most accurate. [ABSTRACT FROM AUTHOR]