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371 results for “Likelihood”
FIGURE 1. Maximum likelihood tree generated from three mitochondrial genes shows a in Disentangling vines: a study of morphological crypsis and genetic divergence in vine snakes (Squamata: Colubridae: Ahaetulla) with the description of five new species from Peninsular India
FIGURE 1. Maximum likelihood tree generated from three mitochondrial genes shows a number of new lineages (L1–L13) identified in our study. The lineages are marked with grey bars which represent the criteria used to delimit species boundaries, where three gene bPTP (P3), genetic p-distance (GD), morphological separation (M) and geographic isolation (G) are used to predict the number of putative lineages. The nodes below 70% parametric bootstrap support are indicated with asterisk (*). "+" indicates that the status of A. nasuta cf. isabellina needs further work (see Deepak et al. 2019).
FIGURE 2. Maximum likelihood tree using 28S D2 & D3–5, ITS2 in Read between the lineata: A revision of the tattooed wasps, Zagrammosoma Ashmead (Hymenoptera: Eulophidae), with descriptions of eleven new species
FIGURE 2. Maximum likelihood tree using 28S D2 & D3–5, ITS2, and COI NJ. Bootstrap values shown. The varying colors within Zagrammosoma indicate different species.
Data from: Likelihood of tree topologies with fossils and diversification rate estimation
Since the diversification process cannot be directly observed at the human scale, it has to be studied from the information available, namely the extant taxa and the fossil record. In this sense, phylogenetic trees including both extant taxa and fossils are the most complete representations of the diversification process that one can get. Such phylogenetic trees can be reconstructed from molecular and morphological data, to some extent. Among the temporal information of such phylogenetic trees, fossil ages are by far the most precisely known (divergence times are inferences calibrated mostly with fossils). We propose here a method to compute the likelihood of a phylogenetic tree with fossils in which the only considered time information is the fossil ages, and apply it to the estimation of the diversification rates from such data. Since it is required in our computation, we provide a method for determining the probability of a tree topology under the standard diversification model. Testing 21 our approach on simulated data shows that the maximum likelihood rate estimates from the phylogenetic tree topology and the fossil dates are almost as accurate as those obtained by taking into account all the data, including the divergence times. Moreover, they are substantially more accurate than the estimates obtained only from the exact divergence times (without taking into account the fossil record). We also provide an empirical example composed of 50 Permo-carboniferous eupelycosaur (early synapsid) taxa ranging in age from about 315 Ma (Late Carboniferous) to 270 Ma (shortly after the end of the Early Permian). Our analyses suggest a speciation (cladogenesis, or birth) rate of about 0.1 per lineage and per My, a marginally lower extinction rate, and a considerable hidden paleobiodiversity of early synapsids.
Data from: A maximum likelihood approach to generate hypotheses on the evolution and historical biogeography in the Lower Volga Valley regions (southwest Russia)
The evolution of the diverse flora in the Lower Volga Valley (LVV) (southwest Russia) is complex due to the composite geomorphology and tectonic history of the Caspian Sea and adjacent areas. In the absence of phylogenetic studies and temporal information, we implemented a maximum likelihood (ML) approach and stochastic character mapping reconstruction aiming at recovering historical signals from species occurrence data. A taxon-area matrix of 13 floristic areas and 1018 extant species was constructed and analyzed with RAxML and Mesquite. Additionally, we simulated scenarios with numbers of hypothetical extinct taxa from an unknown palaeoflora that occupied the areas before the dramatic transgression and regression events that have occurred from the Pleistocene to the present day. The flora occurring strictly along the river valley and delta appear to be younger than that of adjacent steppes and desert-like regions, regardless of the chronology of transgression and regression events that led to the geomorphological formation of the LVV. This result is also supported when hypothetical extinct taxa are included in the analyses. The history of each species was inferred by using a stochastic character mapping reconstruction method as implemented in Mesquite. Individual histories appear to be independent from one another and have been shaped by repeated dispersal and extinction events. These reconstructions provide testable hypotheses for more in-depth investigations of their population structure and dynamics.
Data from: Marginal likelihood estimate comparisons to obtain optimal species delimitations in Silene sect. Cryptoneurae (Caryophyllaceae)
Coalescent-based inference of phylogenetic relationships among species takes into account gene tree incongruence due to incomplete lineage sorting, but for such methods to make sense species have to be correctly delimited. Because alternative assignments of individuals to species result in different parametric models, model selection methods can be applied to optimise model of species classification. In a Bayesian framework, Bayes factors (BF), based on marginal likelihood estimates, can be used to test a range of possible classifications for the group under study. Here, we explore BF and the Akaike Information Criterion (AIC) to discriminate between different species classifications in the flowering plant lineage Silene sect. Cryptoneurae (Caryophyllaceae). We estimated marginal likelihoods for different species classification models via the Path Sampling (PS), Stepping Stone sampling (SS), and Harmonic Mean Estimator (HME) methods implemented in BEAST. To select among alternative species classification models a posterior simulation-based analog of the AIC through Markov chain Monte Carlo analysis (AICM) was also performed. The results are compared to outcomes from the software BP&P. Our results agree with another recent study that marginal likelihood estimates from PS and SS methods are useful for comparing different species classifications, and strongly support the recognition of the newly described species S. ertekinii.
Data from: Likelihood-based inference of population history from low coverage de novo genome assemblies
Short-read sequencing technologies have in principle made it feasible to draw detailed inferences about the recent history of any organism. In practice, however, this remains challenging due to the difficulty of genome assembly in most organisms and the lack of statistical methods powerful enough to discriminate among recent, non-equilibrium histories. We address both the assembly and inference challenges. We develop a bioinformatic pipeline for generating outgroup-rooted alignments of orthologous sequence blocks from de novo low-coverage short-read data for a small number of genomes, and show how such sequence blocks can be used to fit explicit models of population divergence and admixture in a likelihood framework. To illustrate our approach, we reconstruct the Pleistocene history of an oak-feeding insect (the oak gallwasp Biorhiza pallida) which, in common with many other taxa, was restricted during Pleistocene ice ages to a longitudinal series of southern refugia spanning theWestern Palaearctic. Our analysis of sequence blocks sampled from a single genome from each of three major glacial refugia reveals support for an unexpected history dominated by recent admixture. Despite the fact that 80% of the genome is affected by admixture during the last glacial cycle, we are able to infer the deeper divergence history of these populations. These inferences are robust to variation in block length, mutation model, and the sampling location of individual genomes within refugia. This combination of de novo assembly and numerical likelihood calculation provides a powerful framework for estimating recent population history that can be applied to any organism without the need for prior genetic resources.
Data from: Rate of inter-sex interactions affects injury likelihood in Tasmanian devil contact networks
Identifying the types of contacts that result in disease transmission is important for accurately modelling and predicting transmission dynamics and disease spread in wild populations. We investigated contacts within a population of adult Tasmanian devils (Sarcophilus harrisii) over a six-month period and tested whether individual-level contact patterns were correlated with accumulation of bite wounds. Bite wounds are important in the spread of devil facial tumour disease (DFTD), a clonal cancer cell line transmitted through direct inoculation of tumour cells when susceptible and infected individuals bite each other. We used multi-model inference and network autocorrelation models to investigate the effects of individual-level contact patterns, identities of interacting partners, and position within the social network on the propensity to be involved in bite-inducing contacts. We found that males were more likely to receive potentially disease-transmitting bite wounds than females, particularly during the mating season when males spend extended periods mate-guarding females. The number of bite wounds individuals received during the mating season was unrelated to any of the network metrics examined. Our approach illustrates the necessity for understanding which contact types spread disease in different systems to assist the management of this and other infectious wildlife diseases.
Data from: Bayesian and likelihood phylogenetic reconstructions of morphological traits are not discordant when taking uncertainty into consideration: a comment on Puttick et al
Puttick et al. (2017 Proc. R. Soc. B 284, 20162290 (doi:10.1098/rspb.2016.2290)) performed a simulation study to compare accuracy among methods of inferring phylogeny from discrete morphological characters. They report that a Bayesian implementation of the Mk model (Lewis 2001 Syst. Biol. 50, 913–925 (doi:10.1080/106351501753462876)) was most accurate (but with low resolution), while a maximum-likelihood (ML) implementation of the same model was least accurate. They conclude by strongly advocating that Bayesian implementations of the Mk model should be the default method of analysis for such data. While we appreciate the authors' attempt to investigate the accuracy of alternative methods of analysis, their conclusion is based on an inappropriate comparison of the ML point estimate, which does not consider confidence, with the Bayesian consensus, which incorporates estimation credibility into the summary tree. Using simulation, we demonstrate that ML and Bayesian estimates are concordant when confidence and credibility are comparably reflected in summary trees, a result expected from statistical theory. We therefore disagree with the conclusions of Puttick et al. and consider their prescription of any default method to be poorly founded. Instead, we recommend caution and thoughtful consideration of the model or method being applied to a morphological dataset.
Data from: A likelihood-based approach for assessment of extra-pair paternity and conspecific brood parasitism in natural populations
Genotypes are frequently used to assess alternative reproductive strategies such as extra-pair paternity and conspecific brood parasitism in wild populations. However, such analyses are vulnerable to genotyping error or molecular artefacts that can bias results. For example, when using multilocus microsatellite data, a mismatch at a single locus, suggesting the offspring was not directly related to its putative parents, can occur quite commonly even when the offspring is truly related. Some recent studies have advocated an ad-hoc rule that offspring must differ at more than one locus in order to conclude that they are not directly related. While this reduces the frequency with which true offspring are identified as not directly related young, it also introduces bias in the opposite direction, wherein not directly related young are categorized as true offspring. More importantly, it ignores the additional information on allele frequencies which would reduce overall bias. In this study, we present a novel technique for assessing extra-pair paternity and conspecific brood parasitism using a likelihood-based approach in a new version of program cervus. We test the suitability of the technique by applying it to a simulated data set and then present an example to demonstrate its influence on the estimation of alternative reproductive strategies.
Figure 1. Maximum likelihood phylogenetic tree obtained from 514 in Target-enriched DNA sequencing from historical type material enables a partial revision of the Madagascar giant stream frogs (genus Mantidactylus)
Figure 1. Maximum likelihood phylogenetic tree obtained from 514 bp of the mitochondrial 16S rRNA gene. The values at the nodes are the bootstrap supports (not given for intra-lineage nodes for improved clarity). The type specimens of M. guttulatus and M. grandidieri from the London and Paris museum collections are highlighted in red and brown, respectively.
FIGURE 6. Maximum likelihood tree built with 405 in New Leucettidae de Laubenfels, 1936 (Porifera, Calcarea) from Western Australia
FIGURE 6. Maximum likelihood tree built with 405 bp of LSU (C-region) rRNA marker, using a GTR+G+I correction model. Numbers represent support values (bootstrap, 1000 pseudo-replicates).
FIGURE 3. Maximumlikelihood tree using 1041 in Description of a new endemic species of mountain lizard from Northwestern Spain: Iberolacerta galani sp. nov. (Squamata: Lacertidae)
FIGURE 3. Maximumlikelihood tree using 1041 base pairs of the mitochondrial genes cytb and 12SrRNA and the nuclear gene cmos. Support values are presented above the branches (posterior probabilities, only asterisk if value is equal to or above 95%) and below the branches (left bootstrap value for ML and right bootstrap value for MP). Datations are presented for some nodes and are indicated by a dot and a value in My (millions of years).
FIGURE 1. Maximum likelihood phylogram derived from a Bayesian backbone constraint consensus tree constructed using only taxa for which 12S, 16S, cytochrome b and cytochrome oxidase I in A new species of Dendrobates (Anura: Dendrobatidae) from the Amazonian lowlands in Perú
FIGURE 1. Maximum likelihood phylogram derived from a Bayesian backbone constraint consensus tree constructed using only taxa for which 12S, 16S, cytochrome b and cytochrome oxidase I sequence data were available. Numbers indicate posterior probabilities from the Bayesian analysis. Species of the Ventrimaculatus group are denoted with s.s. (sensu stricto), s.l. (sensu lato) and sp. aff (species affinis).
FIGURE 4. Maximum-likelihood tree using 352 in Genetic and morphological differentiation of Mosor rock lizards, Dinarolacerta mosorensis (Kolombatović, 1886), with the description of a new species from the Prokletije Mountain Massif (Montenegro) (Squamata: Lacertidae)
FIGURE 4. Maximum-likelihood tree using 352 base pairs of the mitochondrial gene 12S rRNA. Support values are presented above the branches (left bootstrap value for ML and right bootstrap value for MP). Numbers between square brackets after locality names refer to population numbers shown in Fig. 1.
FIGURE 1. Maximum Likelihood tree with bootstrap values over 50 in Onthophagus (Palaeonthophagus) medius (Kugelann, 1792) — a good western palaearctic species in the Onthophagus vacca complex (Coleoptera: Scarabaeidae: Scarabaeinae: Onthophagini)
FIGURE 1. Maximum Likelihood tree with bootstrap values over 50% shown above the branches that resulted from the analysis of the mitochondrial DNA sequences. Collecting sites: Bulgaria: L1—Sakar Mts: Topolovgrad, L2—Strandza Mts.: Zvezdec, L3—Bakadzicite: Vojnika, L4—Sinemorec (8 km S Ahtopol); Germany: L5—Klein Schmölen, L6— Mecklenburg: Sternberg; Italy: L7—Torino (Ipla), L8—Sardinia: Rio Antas: Flumini-magg.-Tempio di Antas, L9—St. Antíoco, Tonnara, L10—Cala Gonone, L11—Monte Lupone, L12—Rocca Massima, L13—Lago Sefro, L14—Piane della Regna, L15—Colle dell'Orso, L16—Campodimele env., L17—Pantano della Zottola, Iserina; Spain: L18— Higuera de la Sierra, L19—Avila to El Barraco, L20—Guadix, L21—Extremadura: 1 km E Jarandilla.
FIGURE 8. Maximum likelihood tree showing the relationships among partial cytochrome b in Geographic variation, phylogeny and systematic status of Gracilinanus microtarsus (Mammalia: Didelphimorphia: Didelphidae)
FIGURE 8. Maximum likelihood tree showing the relationships among partial cytochrome b sequences of Gracilinanus spp. Bootstrap support and Bayesian posterior probabilities based on molecular dataset are provided above each branch. Bayesian posterior probabilities based on the combined (morphological + molecular) dataset is provided below each branch. The numbers beside the localities correspond to the same numbers shown on the map (Figure 11). The symbols represent: (●) "small microtarsus", (․) "large microtarsus" and (˔) "ehrhardti".
FIGURE 3. The Maximum Likelihood tree reconstructed from 16S in Inter- and intra-island divergence in Odorrana ishikawae (Anura, Ranidae) of the Ryukyu Archipelago of Japan, with description of a new species
FIGURE 3. The Maximum Likelihood tree reconstructed from 16S and sequence divergence data. The most parsimonious tree has the same topology.
FIGURE 18. Maximum Likelihood tree estimated from 1044 in Bythaelurus bachi n. sp., a new deep-water catshark (Carcharhiniformes, Scyliorhinidae) from the southwestern Indian Ocean, with a review of Bythaelurus species and a key to their identification
FIGURE 18. Maximum Likelihood tree estimated from 1044 aligned sites of the mitochondrial NADH2 gene using a General Time Reversible model and an accommodation for among site rate variation and Invariant sites (GTR+I+G model).
FIGURE 2. Maximum likelihood phylogram generated from a in Mitochondrial DNA reveals a new species of parachuting frog (Rhacophoridae: Rhacophorus) from Sumatra
FIGURE 2. Maximum likelihood phylogram generated from a fragment of 16S mitochondrial DNA. Unless otherwise noted, support values appear above branches and correspond to posterior probabilities (BAYES), maximum likelihood bootstrapping (ML), parsimony bootstrapping (MP), and minimum evolution bootstrapping (ME), respectively. Nodes not receiving topological support are indicated with "NS".
FIGURE 7. Maximum Likelihood best tree for 33 in Revision of the genus Devadatta Kirby, 1890 in Borneo based on molecular and morphological methods, with descriptions of four new species (Odonata: Zygoptera: Devadattidae)
FIGURE 7. Maximum Likelihood best tree for 33 specimens of Devadatta and one outgroup taxon from the combined COI+16S+ITS+28S data set. Bootstrap support values below 100 are superimposed on the tree. RMNH collection codes are shown for each specimen, with the RMNH.INS. prefix omitted for clarity.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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