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30 results for “ancestral states”
Bayesian Methods for Ancestral State Reconstruction in Morphosyntax
<p>Supplementary files to accompany journal submission.</p> <p>Files are:</p> <p> </p> <p>tree.pdf - pdf consensus tree, for illustration</p> <p>data.txt - coding file</p> <p>TREE_Set.t - nexus format sample of trees.</p> <p>sources.pdf - source materials used for languages</p>
Chronogram or phylogram for ancestral state estimation? Model-fit statistics indicate the branch lengths underlying a binary character's evolution: R scripts and simulated trees
<p>All R scripts used in this study, and the set of simulated phylogenetic trees used in the study.</p> <p>1. Modern methods of ancestral state estimation (ASE) incorporate branch length information, and it has been demonstrated that ASEs are more accurate when conducted on the branch lengths most correlated with a character's evolution; however, a reliable method for choosing between alternate branch length sets for discrete characters has not yet been proposed.<br><br>2. In this study, we simulate paired chronograms and phylograms, and generate binary characters that evolve in correlation with one of these. We then investigate (1) the effect of alternate branch lengths on ASE error, and (2) whether phylogenetic signal statistics and/or model-fit statistic can be used to select the branch lengths most correlated with a binary character.<br><br>3. In agreement with previous studies, we find that ASEs are more accurate when conducted on the branch lengths most correlated with the character. Phylogenetic signal statistics show limited utility for selecting the correct branch lengths, but model-fit statistics are found to be more accurate, with the correct branch lengths generally returning greater model-fit (lower AICc and BIC values). Using this method to choose between alternate branch length sets is more accurate when tree and character properties are more favorable for model optimization, and when shape differences between alternate phylogenies are greater.<br><br>4. Our results indicate that researchers conducting ASEs on discrete characters should carefully consider which branch lengths are appropriate, and, in the absence of other evidence, we suggest estimating model-fit values over alternate branch length sets and evolutionary models and choosing the branch length/model combination that returns better model fit.</p>
Evolutionary insights into Felidae iris color through ancestral state reconstruction
<p>There have been almost no studies with an evolutionary perspective on eye (iris) color, outside of humans and domesticated animals. Extant members of the family Felidae have a great interspecific and intraspecific diversity of eye colors, in stark contrast to their closest relatives, all of which have only brown eyes. This makes the felids a great model to investigate the evolution of eye color in natural populations. Through machine learning cluster image analysis of publicly available photographs of all felid species, as well as a number of subspecies, five felid eye colors were identified: brown, hazel/green, yellow/beige, gray, and blue. Using phylogenetic comparative methods, the presence or absence of these colors was reconstructed on a phylogeny. Additionally, through a new color analysis method, the specific shades of the ancestors' eyes were quantitatively reconstructed. The ancestral felid population was predicted to have brown-eyed individuals, as well as a novel evolution of gray-eyed individuals, the latter being a key innovation that allowed the rapid diversification of eye color seen in modern felids, including numerous gains and losses of different eye colors. It was also found that the loss of brown eyes and the gain of yellow/beige eyes is associated with an increase in the likelihood of evolving round pupils, which in turn influence the shades present in the eyes. Along with these important insights, the unique methods presented in this work are widely applicable and will facilitate future research into phylogenetic reconstruction of color beyond irises.</p>
Evolutionary insights into Felidae iris color through ancestral state reconstruction
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Chronogram or phylogram for ancestral state estimation? Model-fit statistics indicate the branch lengths underlying a binary character’s evolution: R scripts and simulated trees
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Data from: Opsin genes of select treeshrews resolve ancestral character states within Scandentia
Treeshrews are small, squirrel-like mammals in the order Scandentia, which is nested together with Primates and Dermoptera in the superordinal group Euarchonta. They are often described as living fossils, and researchers have long turned to treeshrews as a model or ecological analogue for ancestral primates. A comparative study of colour vision-encoding genes within Scandentia found a derived amino acid substitution in the long-wavelength sensitive opsin gene (OPN1LW) of the Bornean smooth-tailed treeshrew (Dendrogale melanura). The opsin, by inference, is red-shifted by ca. 6 nm with an inferred peak sensitivity of 561 nm. It is tempting to view this trait as a novel visual adaptation; however, the genetic and functional diversity of visual pigments in treeshrews is unresolved outside of Borneo. Here we report gene sequences from the northern smooth-tailed treeshrew (Dendrogale murina) and the Mindanao treeshrew (Tupaia everetti, the senior synonym of Urogale everetti). We found that the opsin genes are under purifying selection and that D. murina shares the same substitution as its congener, a result that distinguishes Dendrogale from other treeshrews, including T. everetti. We discuss the implications of opsin functional variation in light of limited knowledge about the visual ecology of smooth-tailed treeshrews.
FIGURE 5 Ancestral state reconstructions. A. Whorl count. B. Body length. C in Phylogeny and systematic revision of the helicarionid semislugs of eastern Queensland (Stylommatophora, Helicarionidae)
FIGURE 5 Ancestral state reconstructions. A. Whorl count. B. Body length. C. Altitude.
Estimating ancestral states of complex characters: A case study on the evolution of feathers
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Data from: Opsin genes of select treeshrews resolve ancestral character states within Scandentia
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Data and R Script from: The ancestor of sharks and rays laid eggs, but ancestral state reconstructions need empirically supported traits and transparent reporting
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Ancestral state reconstruction for regeneration and autotomy in arthopods and reptiles
<p>Some form of regeneration occurs in all lifeforms and extends from single-cell organisms to humans. The degree to which regenerative ability is distributed across different taxa, however, is harder to ascertain given the potential for phylogenetic constraint or inertia, and adaptive processes to shape this pattern. Here, we examine the phylogenetic history of regeneration in two groups where the trait has been well-studied: arthropods and reptiles. Because autotomy is often present alongside regeneration in these groups, we performed ancestral state reconstructions for both traits to more precisely assess the timing of their origins and the degree to which these traits coevolve. Using an ancestral trait reconstruction, we find that autotomy and regeneration were present at the base of the arthropod and reptile trees. We also find that when autotomy is lost it does not re-evolve easily. Lastly, we find that the distribution of regeneration is intimately connected to autotomy with the association being stronger in reptiles than in arthropods. While these patterns suggest that decoupling autotomy and regeneration at a broad phylogenetic scale may be difficult, the available data provides useful insight into their entanglement. Ultimately, our reconstructions provide important groundwork to explore how selection may have played a role during the loss of regeneration in specific lineages.</p>
Human genome ancestral state files
<p>Files containing ancestral states at all positions of the human genome, inferred based on consensus support among three ape species; Gorilla, Chimpanzee and Orangutan.</p>
Data from: Rate heterogeneity across Squamata, misleading ancestral state reconstruction and the importance of proper null model specification
The binary-state speciation and extinction (BiSSE) model has been used in many instances to identify state-dependent diversification and reconstruct ancestral states. However, recent studies have shown that the standard procedure of comparing the fit of the BiSSE model to constant-rate birth–death models often inappropriately favours the BiSSE model when diversification rates vary in a state-independent fashion. The newly developed HiSSE model enables researchers to identify state-dependent diversification rates while accounting for state-independent diversification at the same time. The HiSSE model also allows researchers to test state-dependent models against appropriate state-independent null models that have the same number of parameters as the state-dependent models being tested. We reanalyse two data sets that originally used BiSSE to reconstruct ancestral states within squamate reptiles and reached surprising conclusions regarding the evolution of toepads within Gekkota and viviparity across Squamata. We used this new method to demonstrate that there are many shifts in diversification rates across squamates. We then fit various HiSSE submodels and null models to the state and phylogenetic data and reconstructed states under these models. We found that there is no single, consistent signal for state-dependent diversification associated with toepads in gekkotans or viviparity across all squamates. Our reconstructions show limited support for the recently proposed hypotheses that toepads evolved multiple times independently in Gekkota and that transitions from viviparity to oviparity are common in Squamata. Our results highlight the importance of considering an adequate pool of models and null models when estimating diversification rate parameters and reconstructing ancestral states.
FIGURE 1. Ancestral character-state reconstructions for Characters 1–9 in Concentrated evolutionary novelties in the foot musculature of Odontophrynidae (Anura: Neobatrachia), with comments on adaptations for burrowing
FIGURE 1. Ancestral character-state reconstructions for Characters 1–9. Ambiguities in Macrogenioglottus alipioi and Odontophrynus carvalhoi in Characters 5–7 are due to polymorphism.
Figure 2 in The tight genome size of ants: diversity and evolution under ancestral state reconstruction and base composition
Figure 2. Bayesian consensus tree resulting from the LW-Rh and Wg gene alignments (871 bp). Coloured dots on the branches indicate the values of posterior probability (PP): green dots represent values between 1.00 and 0.95, yellow dots between 0.94 and 0.90, and red dots ≤ 0.89. The nodes are indicated with numbers. Values above and below the branches represent the ancestral genome size (GS; 1C-values, in picograms) at particular nodes: in blue is the value generated by the maximum likelihood (ML) [asterisks are related to confidence interval (CI) values shown in Supporting Information, Table S4]; orange is the value generated by maximum parsimony (MP); and black, given below the branches, is the value generated by Bayesian inference (BI). Genome size data (1C-values) were obtained in the present work (pink dots) or taken from the literature (grey dots).
Figure 1 in The tight genome size of ants: diversity and evolution under ancestral state reconstruction and base composition
Figure 1. Fluorescence intensity histograms obtained from three different species, with Drosophila melanogaster as internal standard, stained with propidium iodide (PI; A–C) or 4,6-diamidino-2-phenylindole (DAPI; D–F). The x-axis corresponds to the scale of fluorescence intensity, and the y-axis represents the number of nuclei with that fluorescence intensity.
Figure 3 in The tight genome size of ants: diversity and evolution under ancestral state reconstruction and base composition
Figure 3. Mean genome size (in picograms and megabase pairs) estimated for Formicidae subfamilies. The phylogenetic tree generated in the present study was redrawn, with collapsed branches corresponding to species of the same subfamily.
Figure 3 in The tight genome size of ants: diversity and evolution under ancestral state reconstruction and base composition
Figure 3. Mean genome size (in picograms and megabase pairs) estimated for Formicidae subfamilies. The phylogenetic tree generated in the present study was redrawn, with collapsed branches corresponding to species of the same subfamily.
Figure 2 in The tight genome size of ants: diversity and evolution under ancestral state reconstruction and base composition
Figure 2. Bayesian consensus tree resulting from the LW-Rh and Wg gene alignments (871 bp). Coloured dots on the branches indicate the values of posterior probability (PP): green dots represent values between 1.00 and 0.95, yellow dots between 0.94 and 0.90, and red dots ≤ 0.89. The nodes are indicated with numbers. Values above and below the branches represent the ancestral genome size (GS; 1C-values, in picograms) at particular nodes: in blue is the value generated by the maximum likelihood (ML) [asterisks are related to confidence interval (CI) values shown in Supporting Information, Table S4]; orange is the value generated by maximum parsimony (MP); and black, given below the branches, is the value generated by Bayesian inference (BI). Genome size data (1C-values) were obtained in the present work (pink dots) or taken from the literature (grey dots).
Modeling pulsed evolution and time-independent variation improves the confidence level of ancestral and hidden state predictions
<p><span><span><span><span><span><span><span><span><span><span>Ancestral state reconstruction is not only a fundamental tool for studying trait evolution, but also very useful for predicting the unknown trait values (hidden states) of extant species. A well-known problem in ancestral and hidden state predictions is that the uncertainty associated with predictions can be so large that predictions themselves are of little use. Therefore, for meaningful interpretation of predicted traits and hypothesis testing, it is prudent to accurately assess the uncertainty of the predictions. Commonly used constant-rate Brownian motion (BM) model fails to capture the complexity of tempo and mode of trait evolution in nature, making predictions under the BM model vulnerable to lack-of-fit errors from model misspecification. Using empirical data (mammalian body size and bacterial genome size), we show that the distribution of residual Z-scores under the BM model is neither homoscedastic nor normal as expected. Consequently, the 95% confidence intervals (CIs) of predicted traits are so unreliable that the actual coverage probability ranges from 33% (strongly permissive) to 100% (strongly conservative). Alternative methods such as BayesTraits and StableTraits that allow variable rates in evolution improve the predictions but are computationally expensive. Here we develop RasperGade, a method of ancestral and hidden state prediction that uses the Levy process to explicitly model gradual evolution, pulsed evolution and time-independent variation. Using the same empirical data, we show that RasperGade outperforms both BayesTraits and StableTraits and is orders-of-magnitude faster. Our results suggest that, when predicting the ancestral and hidden states of continuous traits, the tempo and mode of evolution should always be assessed and the quality of confidence estimates should always be examined.</span></span></span></span></span></span></span></span></span></span></p>
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Allen Brain Atlas
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