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501 results for “Phylogenetic tree”
FIG. 9. — Most parsimonious tree T1 in A revision of the Upper Jurassic-Lower Cretaceous dragonfly family Tarsophlebiidae, with a discussion on the phylogenetic positions of the Tarsophlebiidae and Sieblosiidae (Insecta, Odonatoptera, Panodonata)
FIG. 9. — Most parsimonious tree T1 (obtained with PAUP4.0b10, Branch and Bound option), Consistency Index CI: 0.9375, CI excluding uninformative characters: 0.9231, Retention Index RI: 0.9375, and RC (RC = CI × RI): 0.8789.
Text-fig. 10. Phylogenetic relationships of Miocene hyaenodonts (for definitions of character states see Table 2). The data matrix was compiled in MacClade 4.05 and run in PAUP 4.0b10 (Macintosh version). We chose Cimolestes magnus CLEMENS et RUSSELL, 1965, (additional data from Lillegraven 1969), as the outgroup. The unordered and unweighted analysis produced 16 trees. a: Majority-rule consensus. b: Strict consensus. Consistency index (CI): 0.5882; Homoplasy index (HI): 0.4118; Retention index (RI): 0.7742. in New Hyaenodonts (Ferae, Mammalia) From The Early Miocene Of Napak (Uganda), Koru (Kenya) And Grillental (Namibia)
Text-fig. 10. Phylogenetic relationships of Miocene hyaenodonts (for definitions of character states see Table 2). The data matrix was compiled in MacClade 4.05 and run in PAUP 4.0b10 (Macintosh version). We chose Cimolestes magnus CLEMENS et RUSSELL, 1965, (additional data from Lillegraven 1969), as the outgroup. The unordered and unweighted analysis produced 16 trees. a: Majority-rule consensus. b: Strict consensus. Consistency index (CI): 0.5882; Homoplasy index (HI): 0.4118; Retention index (RI): 0.7742.
Fig. 22. Strict consensus tree from 2004 in Redescription and Phylogenetic Position of the Early Miocene Penguin Paraptenodytes antarcticus from Patagonia
Fig. 22. Strict consensus tree from 2004 trees of 148 steps, osteological dataset. Absolute Bremer values are above branches, relative values are below branches. Bremer values were calculated from a sample of 16,000 trees (see Methods). CI = 0.60, RI = 0.90.
Text-fig. 2. Species of Masillamys considered on the phylogenetic tree of theridomorphs (Vianey-Liaud and Marivaux 2017: fig. 7), within the basal Theridomorpha, before the polyphyletic genus Protadelomys. Position inferred from their dental features (see text). in A Reevaluation Of The Taxonomic Status Of The Rodent Masillamys Tobien, 1954 From Messel (Germany, Late Early To Early Middle Eocene, 48-47 M.Y.)
Text-fig. 2. Species of Masillamys considered on the phylogenetic tree of theridomorphs (Vianey-Liaud and Marivaux 2017: fig. 7), within the basal Theridomorpha, before the polyphyletic genus Protadelomys. Position inferred from their dental features (see text).
Figure 7. The strict consensus tree obtained from the parsimony analysis with 35 in Descriptions and phylogenetic relationships of two new genera and four new species of Oligo-Miocene waterfowl (Aves: Anatidae) from Australia
Figure 7. The strict consensus tree obtained from the parsimony analysis with 35 characters ordered. Support values above lines at each node show bootstrap> 50% and Bayesian credibility values> 70% (100% = *). Values below lines are numbers of unambiguous synapomorphies for each node. Clades A, B, and C are referred to in text and Table 4.
Figure 32. Two most parsimonious trees, A and B in A phylogenetic study of the neotropical catfish family Cetopsidae (Osteichthyes, Ostariophysi, Siluriformes), with a new classification
Figure 32. Two most parsimonious trees, A and B (L = 272, CI = 54, RI = 83) of relationships among Cetopsidae and outgroups, based on matrix in Appendix 2. Note in particular alternative topologies within Cetopsidium.
Figure 3. A, the single most parsimonious tree derived from 2129 in Phylogenetic relationships and biogeographical history of the genus Rhinoclemmys Fitzinger, 1835 and the monophyly of the turtle family Geoemydidae (Testudines: Testudinoidea)
Figure 3. A, the single most parsimonious tree derived from 2129 aligned characters of mitochondrial genes (12S, 16S, cyt-b) (CI = 0.40; TL = 31; RI = 0.58) using maximum parsimony. Of these, 1229 characters are constant and 708 characters are parsimony-informative. Numbers above branches are bootstrap values and below are Bremer values. B, strict consensus of 96 trees generated from 1244 aligned characters of nuclear genes (Rag1 and Cmos) (CI = 0.82; TL = 205; RI = 0.84) using maximum parsimony. Of these, 1086 characters are constant and 90 are parsimonyinformative. Numbers above branches are bootstrap values and below are Bremer values.
Fig. 1. Bayesian phylogenetic tree obtained with the cox1 in Rare, deep-water and similar: revision of Sibogasyrinx (Conoidea: Cochlespiridae)
Fig. 1. Bayesian phylogenetic tree obtained with the cox1 dataset. Posterior probabilities (> 0.95) and bootstraps (> 90) are shown for each node. The boxes in front of the lineages of Sibogasyrinx Powell, 1969 represent the ABGD PSHs, numbered from 1 to 10. Alternative PSH partitions obtained in the second and third-best ASAP partitions are shown with dashed lines. The colors refer to the locality; * = illustrated shells.
Data for: rtrees: An R package to assemble phylogenetic trees from megatrees
<p>Despite the increasingly available phylogenetic hypotheses for multiple taxonomic groups, most of them do not include all species. In phylogenetic ecology, there is still strong demand to have phylogenies with all species in a study included. The existing software tools to graft species to backbone megatrees, however, are mostly limited to a specific taxonomic group such as plants or fishes. Here, I introduce a new user-friendly R package `rtrees` that can assemble phylogenies from existing or user-provided megatrees. For most common taxonomic groups, users can only provide a vector of species' scientific names to get a phylogeny or a set of posterior phylogenies from megatrees. It is my hope that `rtrees` can provide an easy, flexible, and reliable way to assemble phylogenies from megatrees, facilitating the progress of phylogenetic ecology.</p>
Machine learning can be as good as maximum likelihood when reconstructing phylogenetic trees and determining the best evolutionary model on four taxon alignments
<p><span>Machine learning can be as good as maximum likelihood when reconstructing phylogenetic topologies and determining the best evolutionary model on four taxon alignments.</span></p> <p><span>Phylogenetic tree reconstruction with molecular data is important in many fields of life science research. The gold standard in this discipline is the Maximum Likelihood tree reconstruction method. Here we show that for quartet trees, Machine Learning using neural networks can be as good as the Maximum Likelihood method to infer the best tree topology and the best model of sequence evolution for nucleotide as well as amino acid sequences. For this purpose we simulated data sets for a wide range of branch lengths, evolutionary models and model parameters and compared the topologies and inferred models obtained with Machine learning with those obtained with the Maximum Likelihood and the Neighbour Joining method. Our results show that neural networks are a promising avenue for determining relatedness between taxa, which is likely to accelerate the construction of phylogenetic trees in the future, while maintaining a high accuracy.</span></p>
Figure 4. Bayesian Inference phylogenetic tree inferred from 1039 in Genetic Relationships of Long-nosed Potoroos Potorous tridactylus (Kerr, 1792) from the Bass Strait Islands, with Notes on the Subspecies Potorous tridactylus benormi Courtney, 1963
Figure 4. Bayesian Inference phylogenetic tree inferred from 1039 bp of concatenated CO1 and ND2 mitochondrial DNA sequence data. Posterior probabilities for major lineages are shown. A similar tree topology was also inferred from Maximum Likelihood.
Figure 3. Maximum Likelihood phylogenetic tree inferred from 695 in Genetic Relationships of Long-nosed Potoroos Potorous tridactylus (Kerr, 1792) from the Bass Strait Islands, with Notes on the Subspecies Potorous tridactylus benormi Courtney, 1963
Figure 3. Maximum Likelihood phylogenetic tree inferred from 695 bp of CO1 mtDNA sequence, including data from the Potorous tridactylus benormi Holotype (AM M.8319) and Paratype (AM M.8373). Bootstrap values for major lineages are shown. A similar tree topology was inferred from Bayesian inference.
Fig. 6. Bayesian phylogenetic tree obtained with the 18S rRNA and 28S in New record and new species of Laubierpholoe Pettibone, 1992 (Annelida, Sigalionidae) from the soft bottom of submarine caves near Marseille (Mediterranean Sea) with discussion on phylogeny and ecology of the genus
Fig. 6. Bayesian phylogenetic tree obtained with the 18S rRNA and 28S rRNA concatenated dataset showing position of Laubierpholoe massiliana Zhadan sp. nov. within Sigalionidae Kinberg, 1856. Posterior probabilities and bootstrap values are shown for each medium supported node.
The limits of the constant-rate birth-death prior for phylogenetic tree topology inference
<p>Birth-death models are stochastic processes describing speciation and extinction through time and across taxa and are widely used in biology for inference of evolutionary timescales. Previous research has highlighted how the expected trees under constant-rate birth-death (crBD) tend to differ from empirical trees, for example with respect to the amount of phylogenetic imbalance. However, our understanding of how trees differ between crBD and the signal in empirical data remains incomplete. In this Point of View, we aim to expose the degree to which crBD differs from empirically inferred phylogenies and test the limits of the model in practice. Using a wide range of topology indices to compare crBD expectations against a comprehensive dataset of 1189 empirically estimated trees, we confirm that crBD trees frequently differ topologically compared with empirical trees. To place this in the context of standard practice in the field, we conducted a meta-analysis for a subset of the empirical studies. When comparing studies that used crBD priors with those that used other non-BD Bayesian and non-Bayesian methods, we do not find any significant differences in tree topology inferences. To scrutinize this finding for the case of highly imbalanced trees, we selected the 100 trees with the greatest imbalance from our dataset, simulated sequence data for these tree topologies under various evolutionary rates, and re-inferred the trees under maximum likelihood and using crBD in a Bayesian setting. We find that when the substitution rate is low, the crBD prior results in overly balanced trees, but the tendency is negligible when substitution rates are sufficiently high. Overall, our findings demonstrate the general robustness of crBD priors across a broad range of phylogenetic inference scenarios but also highlight that empirically observed phylogenetic imbalance is highly improbable under crBD, leading to systematic bias in data sets with limited information content.</p>
Figures 4–5. Phylogenetic tree for Aedes vexans populations constructed from 395 in Global phylogeography of the flood mosquito, Aedes vexans (Diptera: Culicidae), from mitochondrial DNA
Figures 4–5. Phylogenetic tree for Aedes vexans populations constructed from 395 haplotypes from the COI gene by using Bayesian Inference, BI (4) and Maximum Likelihood, ML (5). The evolutionary history for both analyses was inferred by using the GTR + G model, as suggested by jModelTest version 2.1.10. The BI tree was obtained by using 2-million generations, while the ML used 1,000 replicas. For BI, the support of the branches is indicated by the subsequent probability values, while for ML the bootstrap values are shown. Numbers in blue represent sequences from the A. nipponii subspecies. In both figures the colors below denote the continents and their respective countries: (z) America (Canada and USA) + Europe (Turkey); (z) Asia (China, India, Japan, Singapore and South Korea); (z) Europe (Sweden and Belgium) + Asia (China); (z) Eurasia (Romania, Sweden, Belgium, Russia, Kosovo, the Netherlands, China, Spain, Germany, Iran, Austria and Hungary); (z) Africa (South Africa); (z) America (USA) + Africa (South Africa).
Phylogenetic conservatism in the relationship between functional and demographic characteristics in Amazon tree taxa
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Data for: rtrees: An R package to assemble phylogenetic trees from megatrees
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The limits of the constant-rate birth-death prior for phylogenetic tree topology inference
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Data and Supplement from: Phylogenetic tree instability after taxon addition: Empirical frequency, predictability, and consequences for online inference
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Data from: Enriching the ant tree of life: enhanced UCE bait set for genome-scale phylogenetics of ants and other Hymenoptera
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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.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
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.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.