Résultats de recherche pour « Phylogenetics »

Ecologists are increasingly adopting an evolutionary perspective, and in recent years, the idea that closely related species are ecologically similar has become widespread. In this regard, phylogenetic signal must be distinguished from phylogenetic niche conservatism. Phylogenetic niche conservatism results when closely related species are more ecologically similar that would be expected based on their phylogenetic relationships; its occurrence suggests that some process is constraining divergence among closely related species. In contrast, phylogenetic signal refers to the situation in which ecological similarity between species is related to phylogenetic relatedness; this is the expected outcome of Brownian motion divergence and thus is necessary, but not sufficient, evidence for the existence of phylogenetic niche conservatism. Although many workers consider phylogenetic niche conservatism to be common, a review of case studies indicates that ecological and phylogenetic similarities often are not related. Consequently, ecologists should not assume that phylogenetic niche conservatism exists, but rather should empirically examine the extent to which it occurs.

UNLABELLED: Analysis of Phylogenetics and Evolution (APE) is a package written in the R language for use in molecular evolution and phylogenetics. APE provides both utility functions for reading and writing data and manipulating phylogenetic trees, as well as several advanced methods for phylogenetic and evolutionary analysis (e.g. comparative and population genetic methods). APE takes advantage of the many R functions for statistics and graphics, and also provides a flexible framework for developing and implementing further statistical methods for the analysis of evolutionary processes. AVAILABILITY: The program is free and available from the official R package archive at http://cran.r-project.org/src/contrib/PACKAGES.html#ape. APE is licensed under the GNU General Public License.

Conservation biologists and community ecologists have increasingly begun to quantify the phylogenetic diversity and phylogenetic dispersion in species assemblages. In some instances, the phylogenetic trees used for such analyses are fully bifurcating, but in many cases the phylogenies being used contain unresolved nodes (i.e. polytomies). The lack of phylogenetic resolution in such studies, while certainly not preferred, is likely to continue particularly for those analyzing diverse communities and datasets with hundreds to thousands of taxa. Thus it is imperative that we quantify potential biases and losses of statistical power in studies that use phylogenetic trees that are not completely resolved. The present study is designed to meet both of these goals by quantifying the phylogenetic diversity and dispersion of simulated communities using resolved and gradually 'unresolved' phylogenies. The results show that: (i) measures of community phylogenetic diversity and dispersion are generally more sensitive to loss of resolution basally in the phylogeny and less sensitive to loss of resolution terminally; and (ii) the loss of phylogenetic resolution generally causes false negative results rather than false positives.

We developed a theoretical framework based on phylogenetic comparative methods to integrate phylogeny into three measures of biodiversity: species variability, richness, and evenness. These metrics can be used in conjunction with permutation procedures to test for phylogenetic community structure. As an illustration, we analyzed data on the composition of 58 lake fish communities in Wisconsin. The fish communities showed phylogenetic underdispersion, with communities more likely to contain closely related species. Using information about differences in environmental characteristics among lakes, we demonstrated that phylogenetic underdispersion in fish communities was associated with environmental factors. For example, lakes with low pH were more likely to contain species in the same clade of acid-tolerant species. Our metrics differ from existing metrics used to calculate phylogenetic community structure, such as net relatedness index and Faith's phylogenetic diversity. Our metrics have the advantage of providing an integrated and easy-to-understand package of phylogenetic measures of species variability, richness, and evenness with well-defined statistical properties. Furthermore, they allow the easy evaluation of contributions of individual species to different aspects of the phylogenetic organization of communities. Therefore, these metrics should aid with the incorporation of phylogenetic information into strategies for understanding biodiversity and its conservation.

1 Analysing the phylogenetic structure of natural communities may illuminate the processes governing the assembly and coexistence of species in ecological communities. 2 Unifying previous works, we present a statistical framework to quantify the phylogenetic structure of communities in terms of average divergence time between pairs of individuals or species, sampled from different sites. This framework allows an additive partitioning of the phylogenetic signal into alpha (within-site) and beta (among-site) components, and is closely linked to Simpson diversity. It unifies the treatment of intraspecific (genetic) and interspecific diversity, leading to the definition of differentiation coefficients among community samples (e.g. IST, PST) analogous to classical population genetics coefficients expressing differentiation among populations (e.g. FST, NST). 3 Two coefficients which express community differentiation among sites from species identity (IST) or species phylogeny (PST) require abundance data (number of individuals per species per site), and estimators that are unbiased with respect to sample size are given. Another coefficient (ΠST) expresses the gain of the mean phylogenetic distance between species found in different sites compared with species found within sites, and requires only incidence data (presence/absence of each species in each site). 4 We present tests based on phylogenetic tree randomizations to detect community phylogenetic clustering (PST > IST or ΠST > 0) or phylogenetic overdispersion (PST < IST or ΠST < 0). In addition, we propose a novel approach to detect phylogenetic clustering or overdispersion in different clades or at different evolutionary time depths using partial randomizations. 5 I ST, PST or ΠST can also be used as distances between community samples and regressed on ecological or geographical distances, allowing us to investigate the factors responsible for the phylogenetic signal and the critical scales at which it appears. 6 We illustrate the approach on forest tree communities in Equatorial Guinea, where a phylogenetic clustering signal was probably due to phylogenetically conserved adaptations to the elevation gradient and was mostly contributed to by ancient clade subdivisions. 7 The approach presented should find applications for comparing quantitatively phylogenetic patterns of different communities, of similar communities in different regions or continents, or of populations (within species) vs. communities (among species).

In phylogenetics, the unrooted model of phylogeny and the strict molecular clock model are two extremes of a continuum. Despite their dominance in phylogenetic inference, it is evident that both are biologically unrealistic and that the real evolutionary process lies between these two extremes. Fortunately, intermediate models employing relaxed molecular clocks have been described. These models open the gate to a new field of "relaxed phylogenetics." Here we introduce a new approach to performing relaxed phylogenetic analysis. We describe how it can be used to estimate phylogenies and divergence times in the face of uncertainty in evolutionary rates and calibration times. Our approach also provides a means for measuring the clocklikeness of datasets and comparing this measure between different genes and phylogenies. We find no significant rate autocorrelation among branches in three large datasets, suggesting that autocorrelated models are not necessarily suitable for these data. In addition, we place these datasets on the continuum of clocklikeness between a strict molecular clock and the alternative unrooted extreme. Finally, we present analyses of 102 bacterial, 106 yeast, 61 plant, 99 metazoan, and 500 primate alignments. From these we conclude that our method is phylogenetically more accurate and precise than the traditional unrooted model while adding the ability to infer a timescale to evolution.