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23 results for “coalescent approaches”
Using a coalescent approach to assess gene flow and effective population size of Acrocomia aculeata (Jacq.) Lodd. Ex Mart. in the Brazilian Atlantic Forest
<p><i>Acrocomia aculeata</i> is a tropical palm tree native to Central and South America that has significant economic, social, and environmental potential. However, land encroachment due to the expansion of agribusiness, and other factors such as urban sprawl, have resulted in the fragmentation and destruction of its habitat, leading to the loss of genes and genotypes in <i>A. aculeata</i> populations. In this context, the objective of this study was to characterize the genetic variability of <i>A. aculeata</i> populations by estimating gene flow and effective population size using an approach based on coalescent theory. Four populations located in the municipalities of Teodoro Sampaio (TSI and TSII), Rosana (RA), and Amparo (AP) in São Paulo State, Brazil, were genotyped with nine microsatellite markers. Gene flow and effective population size were estimated using a coalescent-based Bayesian inference implemented in the MIGRATE-N software. The effective population size (<i>N<sub>e</sub></i>) was obtained considering an assumed mutation rate of <a name="_Hlk6565387">5x10<sup>-5</sup>. </a>Gene flow (<i>Nm</i>) for pairwise populations ranged from 0.28 to 1.17, with higher levels of migration between the three geographically proximal locations (TSI, TSII, and RA). The estimates of effective population size (<i>N<sub>e</sub></i>) were 444, 835, 838, and 874 for AP, TSII, RA, and TSI, respectively, showing that the effects caused by genetic drift may be more pronounced when <i>N<sub>e</sub></i> is smaller. The coalescent-based results add to our understanding of <i>A. aculeata</i> population genetics and suggest that some traditional assessment methods may be ineffective in characterizing historical evolutionary processes.</p>
Using a coalescent approach to assess gene flow and effective population size of Acrocomia aculeata (Jacq.) Lodd. Ex Mart. in the Brazilian Atlantic Forest
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Data from: The phylogeny of pikas (Ochotona) inferred from a multilocus coalescent approach
The clarification of the systematics of pikas (genus Ochotona) has been hindered by largely overlapping morphological characters among species and the lack of a comprehensive molecular phylogeny. Here we estimate the first multilocus phylogeny of the genus to date, by analysing 12 nuclear DNA markers (total of 7.5 Kb) in 11 species of pikas from the four classified subgenera (Pika, Ochotona, Lagotona and Conothoa) using a multispecies coalescent-based framework. The species-tree confirmed the subgeneric classification by retrieving as monophyletic the subgenera represented here by more than one species. Contrary to previous phylogenies based on mtDNA alone, Lagotona was found to be sister to Pika. Also, support for the monophyly of the alpina group was not strong, thus caution should be used in future analyses of this group. A relaxed molecular clock calibrated using the Ochotonidae-Leporidae divergence resulted in more recent estimates of divergence times relative to previous studies. Strong concordance with inferences based on fossil records was found, suggesting that the initial diversification of the genus took place by the end of late Miocene. Finally, this work sets up methodologies and gathers molecular markers that can be used to extend the understanding of the evolutionary history of the genus.
Figure 11 in Fantastic beasts and how to delimit them: an integrative approach using multispecies coalescent methods reveals two new, endemic Dugesia species (Platyhelminthes: Tricladida) from Corsica and Sardinia
Figure 11. Dugesia hoidi: A, holotype RMNH.VER.21056.1, photomicrograph showing the penial fold (pf) in sagiưal section; B, paratype RMNH.VER.21056.2, photomicrograph showing the penis papilla (pp) and the penial fold (pf) in transverse section.
Figure 10. Dugesia hoidi. Holotype RMNH.VER.21056.1 in Fantastic beasts and how to delimit them: an integrative approach using multispecies coalescent methods reveals two new, endemic Dugesia species (Platyhelminthes: Tricladida) from Corsica and Sardinia
Figure 10. Dugesia hoidi. Holotype RMNH.VER.21056.1: A, sagiưal reconstruction of the male copulatory apparatus (anterior to the right); B, sagiưal reconstruction of the penial fold and female copulatory apparatus; C, photomicrograph of sagiưal section, showing penis bulb (pb) with the seminal vesicle (sv), right (rvd) and the less (lvd) vas deferens, penis papilla (pp) with the pointed diaphragm (d), and the ejaculatory duct (ed).
Figure 7. Dugesia benazzii s.s., CGAS Pla 25.1 in Fantastic beasts and how to delimit them: an integrative approach using multispecies coalescent methods reveals two new, endemic Dugesia species (Platyhelminthes: Tricladida) from Corsica and Sardinia
Figure 7. Dugesia benazzii s.s., CGAS Pla 25.1: A, sagiưal reconstruction of the male copulatory apparatus (anterior to the right); B, sagiưal reconstruction of the fold and female copulatory apparatus; C, photomicrograph of sagiưal section, showing the penis bulb (pb), penis papilla (pp) with conical, pointed diaphragm (d), ejaculatory duct (ed), penial fold (pf), and 'angled' bursal canal (abc).
Figure 6 in Fantastic beasts and how to delimit them: an integrative approach using multispecies coalescent methods reveals two new, endemic Dugesia species (Platyhelminthes: Tricladida) from Corsica and Sardinia
Figure 6. Karyogram of (A) Dugesia benazzii s.s. from Su Rizzolu River (Oưi, loc. 13) and (B) Dugesia hoidi.
Figure 1. A in Fantastic beasts and how to delimit them: an integrative approach using multispecies coalescent methods reveals two new, endemic Dugesia species (Platyhelminthes: Tricladida) from Corsica and Sardinia
Figure 1. A, sampling localities of the present study. Numbers correspond to population codes listed in Appendix, Table A1 and coincide with those in Dols-Serrate et al. (2020). Red circles indicate populations used for morphological analyses. B, rectangular inset: enlargement of the Bunnari–Mascari confluence area. Ŋe map was created using Q-GIS v.3.2.2 (hưps://qgis.org/es/site/ last accessed September 2023) and edited in ILLUSTÞTOR CC v.22.0.1 (hưps://www.adobe.com/products/illustrator.html last accessed September 2023).
Figure 14 in Fantastic beasts and how to delimit them: an integrative approach using multispecies coalescent methods reveals two new, endemic Dugesia species (Platyhelminthes: Tricladida) from Corsica and Sardinia
Figure 14. Dugesia mariae: A, CGAS Pla 27. 1, photomicrograph showing the penis bulb with the seminal vesicle (sv), less (lvd) and right (rvd) vas deferens, the penial papilla (pp) and the two atrial folds (af) in horizontal section; B, CGAS Pla 27. 4, photomicrograph showing the penis papilla (pp) and the penial fold (pf) in transverse section.
Figure 9. Dugesia benazzii s.s in Fantastic beasts and how to delimit them: an integrative approach using multispecies coalescent methods reveals two new, endemic Dugesia species (Platyhelminthes: Tricladida) from Corsica and Sardinia
Figure 9. Dugesia benazzii s.s.: A, CGAS Pla 25. 6, photomicrograph showing the penial fold (pf) and the two atrial folds (af) in sagiưal section; B, CGAS Pla 25. 4, photomicrograph showing the penis papilla (pp) and the penial fold (pf) in transverse section.
Figure 3 in Fantastic beasts and how to delimit them: an integrative approach using multispecies coalescent methods reveals two new, endemic Dugesia species (Platyhelminthes: Tricladida) from Corsica and Sardinia
Figure 3. mtDNA (dataset I) phylogenetic tree and species discovery delimitation schemes for ABGD, GMYC, and mPTP, as well as PSHs and PSCs. Ultrametric tree from BEAST is shown only for visual purposes; posterior probabilities (pp) and bootstrap support values (bs) relate to MrBayes and ÞxML analyses, respectively; pp and bs node support values represented by squares and circles, filled with white (unsupported), grey (supported), and black (maximum support), respectively.
Figure 8. Dugesia benazzii s.s., CGAS Pla 25.1 in Fantastic beasts and how to delimit them: an integrative approach using multispecies coalescent methods reveals two new, endemic Dugesia species (Platyhelminthes: Tricladida) from Corsica and Sardinia
Figure 8. Dugesia benazzii s.s., CGAS Pla 25.1. Photomicrograph of the copulatory bursa with a ciliate parasite (cp) and a spermatophore (sp) in a sagiưal section.
Figure 5 in Fantastic beasts and how to delimit them: an integrative approach using multispecies coalescent methods reveals two new, endemic Dugesia species (Platyhelminthes: Tricladida) from Corsica and Sardinia
Figure 5. BFD results for the array of models tested (1–6), with different a priori species-delimitation hypotheses tested on three datasets (I, IV, and V). Each column represents a model with a unique combination of lineages (rows). Star symbol indicates reassignment of PSC4. Marginal-likelihood estimates (MLE) and Bayes' factors comparison results (2lnBf) from the combined analyses of five independent runs with PS (path-sampling) and SS (stepping stone) are represented with circles and a grey-scale scheme. 2lnBf comparison support indicates differences from the best model: non-significant indicates no difference in support for the two models; positively supported and decisively-supported indicate clear support in favour of the best-fiưing model over its alternative.
Figure 4 in Fantastic beasts and how to delimit them: an integrative approach using multispecies coalescent methods reveals two new, endemic Dugesia species (Platyhelminthes: Tricladida) from Corsica and Sardinia
Figure 4. Schematic representation of BPP results on multi-locus data for two topologies. Colour scheme and squares represent posterior probability (pp) for each node under four different prior combinations, using two algorithms (A0 and A1) and three datasets (I, IV, and V). *Unsupported node with a pp of 0.94.
Figure 2 in Fantastic beasts and how to delimit them: an integrative approach using multispecies coalescent methods reveals two new, endemic Dugesia species (Platyhelminthes: Tricladida) from Corsica and Sardinia
Figure 2. Simplified Bayesian (A–E1) and ML dendrograms (E2). A, dataset I: COI–NADH4–tRNAW–COII; B, dataset II: Dunuc10; C, dataset III: Dunuc12; D, dataset IV: Dunuc10 * 12; E, dataset V: mtDNA * nDNA. Posterior probabilities (pp) are indicated by filled squares and bootstrap support values (bs) with filled circles. Full trees are represented in Figure 3 (dataset I) and Supporting Information, Figure S1 (datasets I–V).
Figure 13. Dugesia mariae. Holotype RMNH.VER.21056.1 in Fantastic beasts and how to delimit them: an integrative approach using multispecies coalescent methods reveals two new, endemic Dugesia species (Platyhelminthes: Tricladida) from Corsica and Sardinia
Figure 13. Dugesia mariae. Holotype RMNH.VER.21056.1: A, sagiưal reconstruction of the copulatory apparatus (anterior to the right); B, photomicrograph showing, in a sagiưal section, the penis bulb (pb) with seminal vesicle (sv), penis papilla (pp) with the pointed diaphragm (d), pleated ejaculatory duct (ed), and the penial fold (pf).
Figure 12 in Fantastic beasts and how to delimit them: an integrative approach using multispecies coalescent methods reveals two new, endemic Dugesia species (Platyhelminthes: Tricladida) from Corsica and Sardinia
Figure 12. Dugesia mariae. Photomicrograph of a preserved sexual specimen from the Golo River (Barcheưa, loc. 24).
Data from: The phylogeny of pikas (Ochotona) inferred from a multilocus coalescent approach
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Data from: An empirical comparison of a character-based and a coalescent-based approach to species delimitation in a young avian complex
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Data from: Delimiting species using single-locus data and the Generalized Mixed Yule Coalescent approach: a revised method and evaluation on simulated data sets
DNA barcoding-type studies assemble single-locus data from large samples of individuals and species, and have provided new kinds of data for evolutionary surveys of diversity. An important goal of many such studies is to delimit evolutionarily significant species units, especially in biodiversity surveys from environmental DNA samples. The Generalized Mixed Yule Coalescent (GMYC) method is a likelihood method for delimiting species by fitting within- and between-species branching models to reconstructed gene trees. Although the method has been widely used, it has not previously been described in detail or evaluated fully against simulations of alternative scenarios of true patterns of population variation and divergence between species. Here, we present important reformulations to the GMYC method as originally specified, and demonstrate its robustness to a range of departures from its simplifying assumptions. The main factor affecting the accuracy of delimitation is the mean population size of species relative to divergence times between them. Other departures from the model assumptions, such as varying population sizes among species, alternative scenarios for speciation and extinction, and population growth or subdivision within species, have relatively smaller effects. Our simulations demonstrate that support measures derived from the likelihood function provide a robust indication of when the model performs well and when it leads to inaccurate delimitations. Finally, the so-called single-threshold version of the method outperforms the multiple-threshold version of the method on simulated data: we argue that this might represent a fundamental limit due to the nature of evidence used to delimit species in this approach. Together with other studies comparing its performance relative to other methods, our findings support the robustness of GMYC as a tool for delimiting species when only single-locus information is available.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.