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116 results for “Phylogenetics: methods”
Phylogenetic comparative methods are problematic when applied to gene trees with speciation and duplication nodes: correcting for biases in testing the ortholog conjecture
<p>This repository contains “manuscript_dunn.RData” file, which is reproduced by using the files and scripts of Dunn et al. (Dunn CW, Zapata F, Munro C, Siebert S, Hejnol A (2018) Pairwise comparisons across species are problematic when analyzing functional genomic data. Proc Natl Acad Sci U S A 115: E409–E417. <a href="http://dx.doi.org/10.1073/pnas.1707515115">doi:10.1073/pnas.1707515115</a>).</p> <p>In this repository, we also supplied “Data_TMRR_latest.rda” file, containing the results generated by using our own scripts. Our scripts are available on GitHub: <a href="https://github.com/tbegum/Testing_the_ortholog_conjecture">https://github.com/tbegum/Testing_the_ortholog_conjecture</a>.</p> <p> </p>
FIGURE 10 in Comparative morphology of the eggs from the eight species in the genus Agathemera Stål (Insecta: Phasmatodea), through phylogenetic comparative method approach
FIGURE 10. Phylogenetic hypothesis based on the morphological characters from the Agathemera eggs. a. Unrooted tree considering all the Agathemera species' eggs. b. rooted tree considering just the species in clade 1 (sensu Vera et al. 2012) and A. grylloidea as outgroup. c. Rooted tree considering only the species in clade 2 (sensu Vera et al. 2012) and A. luteola as outgroup. Black circles and their respective numbers indicate synapomorphies. Jacknife/Bootstrap values are shown for each node.
FIGURE 6 in Comparative morphology of the eggs from the eight species in the genus Agathemera Stål (Insecta: Phasmatodea), through phylogenetic comparative method approach
FIGURE 6. External morphology of the eggs from clade 2 and their respective operculum. a1-c1, dorsal view; a2-c2 operculum. The order of the eggs and operculum from right to left is A. grylloidea, A. elegans, A. mesoauriculae (escale = 1mm).
FIGURE 7 in Comparative morphology of the eggs from the eight species in the genus Agathemera Stål (Insecta: Phasmatodea), through phylogenetic comparative method approach
FIGURE 7. Ultrastructure surface of the micropylar plate of the eggs from clade 1 and their respective operculum. A1-E1, micropylar plate ultrastructure; A2-E2 ultrastructure surface of the operculum. The order of the micropylar plates and operculum from top to bottom is A. luteola, A. maculafulgens, A. crassa. A. millepunctata, A. claraziana.
FIGURE 8 in Comparative morphology of the eggs from the eight species in the genus Agathemera Stål (Insecta: Phasmatodea), through phylogenetic comparative method approach
FIGURE 8. Ultrastructure surface of the micropylar plate of the eggs from clade 2 and their respective operculum. A1-C1, micropylar plate ultrastructure; A2-C2 ultrastructure surface of the operculum. The order of the micropylar plates and operculum from top to botom is A. grylloidea, A. elegans, A. mesoauriculae.
FIGURE 9 in Comparative morphology of the eggs from the eight species in the genus Agathemera Stål (Insecta: Phasmatodea), through phylogenetic comparative method approach
FIGURE 9. Character states reconstruction by maximum parsimony over the molecular phylogeny (sensu Vera et al. 2012). The character states for both the ancestral nodes A-G and actual species (H-O) are represented by color-coded boxes, where numbers indicate the character and colors the state. Besides, examples of the micropylar plate open (A. maculafulgens) and closed (A. elegans) are shown. Note: character numbers are consistent with those throughout the text.
FIGURE 5 in Comparative morphology of the eggs from the eight species in the genus Agathemera Stål (Insecta: Phasmatodea), through phylogenetic comparative method approach
FIGURE 5. External morphology of the eggs from clade 1 and their respective operculum. a1-e1, dorsal view; a2-e2 operculum. The order of the eggs and operculum from right to left is A. luteola, A. maculafulgens, A. crassa. A. millepunctata, A. claraziana (escale = 1mm).
FIGURE 3 in Comparative morphology of the eggs from the eight species in the genus Agathemera Stål (Insecta: Phasmatodea), through phylogenetic comparative method approach
FIGURE 3. Principal component analysis for the Agathemera eggs. The percentage of the variance explained by each principal component plotted is in parentheses. Every line connects a data point with its corresponding centroid. a. PCA for the eight Agathemera species; b. PCA for the species from clade 1; c. PCA for species from clade 2.
FIGURE 2 in Comparative morphology of the eggs from the eight species in the genus Agathemera Stål (Insecta: Phasmatodea), through phylogenetic comparative method approach
FIGURE 2. Distribution of the nine morphometric variables measured. the species are ordered following the phylogenetic relationships. Boxes represent the values between the 25 and 75 percentiles respectively, the horizontal line is the median and the point within each box is the mean and the whiskers indicate the sample range. Light grey boxplots correspond to species from clade 1 and dark grey boxplots correspond to species from clade 2. a. Capsule width; b. Capsule length; c. Capsule height; d. Micropylar plate length; e. Micropylar plate width; f. Operculum length; g. Operculum width; h. Operculum height; i. Opercular angle.
FIGURE 1 in Comparative morphology of the eggs from the eight species in the genus Agathemera Stål (Insecta: Phasmatodea), through phylogenetic comparative method approach
FIGURE 1. schematic drawing of an Agathemera egg showing the different variables measured. a. dorsal view. b. lateral view. c. upper view. Abbreviations: w = width; mpl = micropylar plate width; mpl = micropylar plate length; h = capsule height; l = capsule length; opl = operculum length; opa = opercular angle; opw = operculum width; operculum height.
Data from: The impact of reconstruction methods, phylogenetic uncertainty and branch lengths on inference of chromosome number evolution in American daisies (Melampodium, Asteraceae)
Chromosome number change (polyploidy and dysploidy) plays an important role in plant diversification and speciation. Investigating chromosome number evolution commonly entails ancestral state reconstruction performed within a phylogenetic framework, which is, however, prone to uncertainty, whose effects on evolutionary inferences are insufficiently understood. Using the chromosomally diverse plant genus Melampodium (Asteraceae) as model group, we assess the impact of reconstruction method (maximum parsimony, maximum likelihood, Bayesian methods), branch length model (phylograms versus chronograms) and phylogenetic uncertainty (topological and branch length uncertainty) on the inference of chromosome number evolution. We also address the suitability of the maximum clade credibility (MCC) tree as single representative topology for chromosome number reconstruction. Each of the listed factors causes considerable incongruence among chromosome number reconstructions. Discrepancies between inferences on the MCC tree from those made by integrating over a set of trees are moderate for ancestral chromosome numbers, but severe for the difference of chromosome gains and losses, a measure of the directionality of dysploidy. Therefore, reliance on single trees, such as the MCC tree, is strongly discouraged and model averaging, taking both phylogenetic and model uncertainty into account, is recommended. For studying chromosome number evolution, dedicated models implemented in the program ChromEvol and ordered maximum parsimony may be most appropriate. Chromosome number evolution in Melampodium follows a pattern of bidirectional dysploidy (starting from x = 11 to x = 9 and x = 14, respectively) with no prevailing direction.
Data from: Rethinking phylogenetic comparative methods
As a result of the process of descent with modification, closely related species tend to be similar to one another in a myriad different ways. In statistical terms, this means that traits measured on one species will not be independent of traits measured on others. Since their introduction in the 1980s, phylogenetic comparative methods (PCMs) have been framed as a solution to this problem. In this paper, we argue that this way of thinking about PCMs is deeply misleading. Not only has this sowed widespread confusion in the literature about what PCMs are doing but has led us to develop methods that are susceptible to the very thing we sought to build defenses against --- unreplicated evolutionary events. Through three Case Studies, we demonstrate that the susceptibility to singular events is indeed a recurring problem in comparative biology that links several seemingly unrelated controversies. In each Case Study we propose a potential solution to the problem. While the details of our proposed solutions differ, they share a common theme: unifying hypothesis testing with data-driven approaches (which we term ``phylogenetic natural history'') to disentangle the impact of singular evolutionary events from that of the factors we are investigating. More broadly, we argue that our field has, at times, been sloppy when weighing evidence in support of causal hypotheses. We suggest that one way to refine our inferences is to re-imagine phylogenies as probabilistic graphical models; adopting this way of thinking will help clarify precisely what we are testing and what evidence supports our claims.
Data from: Assessing species boundaries and the phylogenetic position of the rare Szechwan Ratsnake, Euprepiophis perlacea (Serpentes: Colubridae), using coalescent-based methods
Delimiting species and clarifying phylogenetic relationships are the main goals of systematics. For species with questionable taxonomic status, species delimitation approaches using multi-species coalescent models with multiple loci are recommended if morphological data are unavailable or unhelpful. Moreover, these methods will also reduce subjectivity based on genetic distance or requirement of monophyletic genetic lineages. We determine the validity and phylogenetic position of a rare and long controversial species of Chinese reptile, the Szechwan ratsnake (Euprepiophis perlacea), using multi-locus data from multiple individuals and coalescent-based approaches. Species were first delimited using Bayesian Phylogenetics & Phylogeography (BP&P), Brownie and Bayes Factor model comparison approaches, while relationships among species were estimated using species tree inference in *BEAST. Results indicate that Euprepiophis perlacea is a distinct species sister to Euprepiophis mandarinus. Despite gene tree discrepancy, the coalescent model-based approaches used here demonstrate the taxonomic validity and the phylogenetic position of Euprepiophis perlacea. These approaches objectively test the validity of questionable species diagnoses based on morphological characters and determine their phylogenetic position.
Data from: A phylogenetic comparative method for evaluating trait coevolution across two phylogenies for sets of interacting species
Evaluating trait correlations across species within a lineage via phylogenetic regression is fundamental to comparative evolutionary biology, but when traits of interest are derived from two sets of lineages that co-evolve with one another, methods for evaluating such patterns in a dual-phylogenetic context remain underdeveloped. Here we extend multivariate permutation-based phylogenetic regression to evaluate trait correlations in two sets of interacting species while accounting for their respective phylogenies. This extension is appropriate for both univariate and multivariate response data, and may utilize one or more independent variables, including environmental covariates. Imperfect correspondence between species in the interacting lineages can also be accommodated, such as when species in one lineage associate with multiple species in the other, or when there are unmatched taxa in one or both lineages. For both univariate and multivariate data, the method displays appropriate type I error, and statistical power increases with the strength of the trait covariation and the number of species in the phylogeny. These properties are retained even when there is not a 1:1 correspondence between lineages. Finally, we demonstrate the approach by evaluating the evolutionary correlation between traits in fig species and traits in their agaonid wasp pollinators. R computer code is provided.
Data from: Multivariate phylogenetic comparative methods: evaluations, comparisons, and recommendations
Recent years have seen increased interest in phylogenetic comparative analyses of multivariate datasets, but to date the varied proposed approaches have not been extensively examined. Here we review the mathematical properties required of any multivariate method, and specifically evaluate existing multivariate phylogenetic comparative methods in this context. Phylogenetic comparative methods based on the full multivariate likelihood are robust to levels of covariation among trait dimensions and are insensitive to the orientation of the dataset, but display increasing model misspecification as the number of trait dimensions increases. This is because the expected evolutionary covariance matrix (V) used in the likelihood calculations becomes more ill-conditioned as trait dimensionality increases, and as evolutionary models become more complex. Thus, these approaches are only appropriate for datasets with few traits and many species. Methods that summarize patterns across trait dimensions treated separately (e.g., SURFACE) incorrectly assume independence among trait dimensions, resulting in nearly a 100% model misspecification rate. Methods using pairwise composite likelihood are highly sensitive to levels of trait covariation, the orientation of the dataset, and the number of trait dimensions. The consequences of these debilitating deficiencies is that a user can arrive at differing statistical conclusions, and therefore biological inferences, simply from a dataspace rotation, like principal component analysis. By contrast, algebraic generalizations of the standard phylogenetic comparative toolkit that use the trace of covariance matrices are insensitive to levels of trait covariation, the number of trait dimensions, and the orientation of the dataset. Further, when appropriate permutation tests are used, these approaches display acceptable Type I error and statistical power. We conclude that methods summarizing information across trait dimensions, as well as pairwise composite likelihood methods should be avoided, while algebraic generalizations of the phylogenetic comparative toolkit provide a useful means of assessing macroevolutionary patterns in multivariate data. Finally, we discuss areas in which multivariate phylogenetic comparative methods are still in need of future development; namely highly multivariate Ornstein-Uhlenbeck models and approaches for multivariate evolutionary model comparisons.
Data from: Environmental conditions and biotic interactions acting together promote phylogenetic randomness in semi-arid plant communities: new methods help to avoid misleading conclusions
QUESTIONS: Molecular phylogenies are increasingly used to better understand the mechanisms structuring natural communities. The prevalent theory is that environmental factors and biotic interactions promote the phylogenetic clustering and over-dispersion of plant communities, respectively. However, both environmental filtering and biotic interactions are very likely to interact in most natural communities, jointly affecting community phylogenetic structure. How do environmental filters and biotic interactions jointly affect the phylogenetic structure of plant communities across environmental gradients? LOCATION: Eleven Stipa tenacissima L. grasslands located along an environmental gradient from central to southeast Spain, covering the core of the distribution area of this vegetation type in Europe. METHODS: We jointly evaluated the effects of environmental conditions and plant–plant interactions on the phylogenetic structure – measured with the mean phylogenetic distance index of the studied communities. As an indicator of environmental conditions, we used a PCA ordination including eight climatic variables. Different metrics were used to measure the following processes: (1) competition/facilitation shifts at the entire community level (species combination index), and (2) the effect of microclimatic amelioration provided by the two most important nurse plants on neighbour composition (similarity indices and comparison of the phylogenetic pattern between canopy patches and bare ground areas). RESULTS: Biotic interactions and, to a less extent, environmental conditions affected the phylogenetic pattern of the studied communities. While positive plant–plant interactions (both at community level and the scale of individual nurse plants) increased phylogenetic overdispersion, higher rainfall increased phylogenetic clustering. The opposing effects of environmental conditions and biotic interactions could be the main cause of the overall random phylogenetic structure found inmost of these communities. CONCLUSIONS: Our results illustrate, for the first time, how an overall random phylogenetic pattern may not only be promoted by the lack of influence of either environmental filtering or biotic interactions, but rather by their joint and opposing effects. They caution about making inferences on the underlying mechanisms shaping plant communities from the sole use of their phylogenetic pattern. We also provide a comprehensive set of easy-to-measure tools to avoid misleading conclusions when interpreting phylogenetic structure data obtained from observational studies.
FIGURE 3. Acutihumerus petronius n in Tanaidacea from Brazil. II. A revision of the subfamily Hemikalliapseudinae (Kalliapseudidae; Tanaidacea; Crustacea) using phylogenetic methods
FIGURE 3. Acutihumerus petronius n. sp. Female paratype. A, antennule; B, antenna; C, labrum; D, labium; E, maxillule; F, Epignath; G, right mandible; H, left mandible; I, maxilla; J, maxilliped. Scale bars = 0.5 mm and 0.1 mm.
FIGURE 6 in Tanaidacea from Brazil. II. A revision of the subfamily Hemikalliapseudinae (Kalliapseudidae; Tanaidacea; Crustacea) using phylogenetic methods
FIGURE 6. Paraleiopus macrochelis Silva-Brum, 1978. Manca II. A, antennas; B, telson and uropods; C, maxilliped; D, cheliped; E, pereopod 1; F, pereopod 2; G, pereopod 3; H, pereopod 4; I, pereopod 5. Scale bars = 0.1 mm.
FIGURE 2. Acutihumerus petronius n in Tanaidacea from Brazil. II. A revision of the subfamily Hemikalliapseudinae (Kalliapseudidae; Tanaidacea; Crustacea) using phylogenetic methods
FIGURE 2. Acutihumerus petronius n. sp. A, female holotype, dorsal view; B, male allotype, dorsal view. C, pleotelson; D, pleopod; E, pleonites 1–3 lateral view; 1. Scale bars = 1 mm and 0.1 mm.
FIGURE 4. Acutihumerus petronius n in Tanaidacea from Brazil. II. A revision of the subfamily Hemikalliapseudinae (Kalliapseudidae; Tanaidacea; Crustacea) using phylogenetic methods
FIGURE 4. Acutihumerus petronius n. sp. Female paratype and male allotype. A, cheliped female; B, cheliped male, allotype; C–H female paratype. C, pereopod 1; D, pereopod 2; E, pereopod 3; F, pereopod 4; G, pereopod 5; H, pereopod 6. Scale bars = 0.5 mm.
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