Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
16
datasets available to search
ShareScore release 0.9.0
Dataset results
16 results for “Bayesian species delimitation”
Data from: Evaluating multilocus Bayesian species delimitation for discovery of cryptic mycorrhizal diversity
The increasing availability of DNA sequence data enables exciting new opportunities for fungal ecology. However, it amplifies the challenge of how to objectively classify the diversity of fungal sequences into meaningful units, often in the absence of morphological characters. Here, we test the utility of modern multilocus Bayesian coalescent-based methods for delimiting cryptic fungal diversity in the orchid mycorrhiza morphospecies Serendipita vermifera. We obtained 147 fungal isolates from Caladenia, a speciose clade of Australian orchids known to associate with Serendipita fungi. DNA sequence data for 7 nuclear and mtDNA loci were used to erect competing species hypotheses by clustering isolates based on: (a) ITS sequence divergence, (b) Bayesian admixture analysis, and (c) mtDNA variation. We implemented two coalescent-based Bayesian methods to determine which species hypothesis best fitted our data. Both methods found strong support for eight species of Serendipita among our isolates, supporting species boundaries reflected in ITS divergence. Patterns of host plant association showed evidence for both generalist and specialist associations within the host genus Caladenia. Our findings demonstrate the utility of Bayesian species delimitation methods and suggest that wider application of these techniques will readily uncover new species in other cryptic fungal lineages.
Data from: A rapid and scalable method for multilocus species delimitation using Bayesian model comparison and rooted triplets
Multilocus sequence data provide far greater power to resolve species limits than the single locus data typically used for broad surveys of clades. However, current statistical methods based on a multispecies coalescent framework are computationally demanding, because of the number of possible delimitations that must be compared and time-consuming likelihood calculations. New methods are therefore needed to open up the power of multilocus approaches to larger systematic surveys. Here, we present a rapid and scalable method that introduces 2 new innovations. First, the method reduces the complexity of likelihood calculations by decomposing the tree into rooted triplets. The distribution of topologies for a triplet across multiple loci has a uniform trinomial distribution when the 3 individuals belong to the same species, but a skewed distribution if they belong to separate species with a form that is specified by the multispecies coalescent. A Bayesian model comparison framework was developed and the best delimitation found by comparing the product of posterior probabilities of all triplets. The second innovation is a new dynamic programming algorithm for finding the optimum delimitation from all those compatible with a guide tree by successively analyzing subtrees defined by each node. This algorithm removes the need for heuristic searches used by current methods, and guarantees that the best solution is found and potentially could be used in other systematic applications. We assessed the performance of the method with simulated, published, and newly generated data. Analyses of simulated data demonstrate that the combined method has favorable statistical properties and scalability with increasing sample sizes. Analyses of empirical data from both eukaryotes and prokaryotes demonstrate its potential for delimiting species in real cases.
FIGURE 10. Bayesian inference phylogenetic reconstruction using the mitochondrial gene cox1 in Molecular delimitation of the seasonal killifishes of the Hypsolebias antenori species group (Cyprinodontiformes, Rivulidae), with description of two new species from the Caatinga coastal basins, northeastern Brazil
FIGURE 10. Bayesian inference phylogenetic reconstruction using the mitochondrial gene cox1 of the Hypsolebias antenori species-group. Vertical bars represent species complexes. Numbers next to nodes represent posterior probability values for the relevant nodes; values <0.5 are not shown.
Figure 11. Phylogenetic relationships within the Xiphinema americanum-group complex. Bayesian 50 in Cryptic diversity and species delimitation in the Xiphinema americanum-group complex (Nematoda: Longidoridae) as inferred from morphometrics and molecular markers
Figure 11. Phylogenetic relationships within the Xiphinema americanum-group complex. Bayesian 50% majority rule consensus tree as inferred from partial cytochrome c oxidase subunit I (coxI) sequence alignment under a transversional of invariable sites and gamma-shaped distribution model TVM + I + G model. Posterior probabilities more than 65% are given for appropriate clades; bootstrap values greater than 50% are given on appropriate clades in the maximum likelihood analysis. Sequences newly obtained in this study in this study are in bold. Scale bar = expected changes per site.
Figure 10. Phylogenetic relationships within the Xiphinema americanum-group complex. Bayesian 50 in Cryptic diversity and species delimitation in the Xiphinema americanum-group complex (Nematoda: Longidoridae) as inferred from morphometrics and molecular markers
Figure 10. Phylogenetic relationships within the Xiphinema americanum-group complex. Bayesian 50% majority rule consensus tree as inferred from internal transcribed spacer 1 (ITS1) rRNA sequence alignment under the general timereversible and gamma-shaped distribution model. Posterior probabilities more than 65% are given for appropriate clades; bootstrap values greater than 50% are given on appropriate clades in the maximum likelihood analysis. Sequences newly obtained in this study are in bold. Scale bar = expected changes per site.
Data from: Evaluating multilocus Bayesian species delimitation for discovery of cryptic mycorrhizal diversity
Open the record for dataset details and reuse information.
Data from: A rapid and scalable method for multilocus species delimitation using Bayesian model comparison and rooted triplets
Open the record for dataset details and reuse information.
Data from: Bayesian species delimitation combining multiple genes and traits in a unified framework
Open the record for dataset details and reuse information.
Data from: Bayesian species delimitation can be robust to guide tree inference errors
The Bayesian method of species delimitation (Yang and Rannala, 2010) uses a so-called guide tree to reduce the number of models to be evaluated in the reversible-jump Markov chain Monte Carlo (rjMCMC) algorithm (Green, 1995). It has been pointed out that the method tends to over-split if a random population tree is used as the guide tree (Fujita and Leaché, 2011). Here we conduct a simulation study to examine the performance of the method under more realistic scenarios, that is, when the guide tree is inferred from the sequence data. We found that Bayesian species delimitation is in general robust to errors in the inferred guide tree.
Data from: The species versus subspecies conundrum: quantitative delimitation from integrating multiple data types within a single Bayesian approach in Hercules beetles
With the recent attention and focus on quantitative methods for species delimitation, an overlooked but equally important issue regards what has actually been delimited. This study investigates the apparent arbitrariness of some taxonomic distinctions, and in particular how species and subspecies are assigned. Specifically, we use a recently developed Bayesian model-based approach to show that in the Hercules beetles (genus Dynastes) there is no statistical difference in the probability that putative taxa represent different species, irrespective of whether they were given species or subspecies designations. By considering multiple data types, as opposed to relying exclusively on genetic data alone, we also show that both previously recognized species and subspecies represent a variety of points along the speciation spectrum (i.e., previously recognized species are not systematically further along the continuum than subspecies). For example, based on evolutionary models of divergence, some taxa are statistically distinguishable on more than one axis of differentiation (e.g., along both phenotypic and genetic dimensions), whereas other taxa can only be delimited statistically from a single data type. Because both phenotypic and genetic data are analyzed in a common Bayesian framework, our study provides a framework for investigating whether disagreements in species boundaries among data types reflect (i) actual discordance with the actual history of lineage splitting, or instead (ii) differences among data types in the amount of time required for differentiation to become apparent among the delimited taxa. We discuss what the answers to these questions imply about what characters are used to delimit species, as well as the diverse processes involved in the origin and maintenance of species boundaries. With this in mind, we then reflect more generally on how quantitative methods for species delimitation are used to assign taxonomic status.
Bayesian species delimitation in Pleophylla chafers (Coleoptera) – the importance of prior choice and morphology
<p>Input files for the analyses associated with the following study publish in BMC Evolutionary Biology: </p> <p> </p> <p><em>Bayesian species delimitation in Pleophylla chafers (Coleoptera) – the importance of prior choice and morphology</em></p> <p><strong>Background</strong></p> <p>Defining species units can be challenging, especially during the earliest stages of speciation, when phylogenetic inference and delimitation methods may be compromised by incomplete lineage sorting (ILS) or secondary gene flow. Integrative approaches to taxonomy, which combine molecular and morphological evidence, have the potential to be valuable in such cases. In this study we investigated the South African scarab beetle genus <em>Pleophylla</em> using data collected from 110 individuals of eight putative morphospecies. The dataset included four molecular markers (<em>cox1</em>, 16S, <em>rrnL</em>, ITS1) and morphometric data based on male genital morphology. We applied a suite of molecular and morphological approaches to species delimitation, and implemented a novel Bayesian approach in the software iBPP, which enables continuous morphological trait and molecular data to be combined.</p> <p><strong>Results</strong></p> <p>Traditional morphology-based species assignments were supported quantitatively by morphometric analyses of the male genitalia (eigenshape analysis, CVA, LDA). While the ITS1-based delineation was also broadly congruent with the morphospecies, the <em>cox1</em> data resulted in over-splitting (GMYC modelling, haplotype networks, PTP, ABGD). In the most extreme case morphospecies shared identical haplotypes, which may be attributable to ILS based on statistical tests performed using the software JML. We found the strongest support for putative morphospecies based on phylogenetic evidence using the combined approach implemented in iBPP. However, support for putative species was sensitive to the use of alternative guide trees and alternative combinations of priors on the population size (<em>θ</em>) and rootage (<em>τ</em><sub><em>0</em></sub>) parameters, especially when the analysis was based on molecular or morphological data alone.</p> <p><strong>Conclusions</strong></p> <p>We demonstrate that continuous morphological trait data can be extremely valuable in assessing competing hypotheses to species delimitation. In particular, we show that the inclusion of morphological data in an integrative Bayesian framework can improve the resolution of inferred species units. However, we also demonstrate that this approach is extremely sensitive to guide tree and prior parameter choice. These parameters should be chosen with caution – if possible – based on independent empirical evidence, or careful sensitivity analyses should be performed to assess the robustness of results. Young species provide exemplars for investigating the mechanisms of speciation and for assessing the performance of tools used to delimit species on the basis of molecular and/or morphological evidence.</p>
Data from: The species versus subspecies conundrum: quantitative delimitation from integrating multiple data types within a single Bayesian approach in Hercules beetles
Open the record for dataset details and reuse information.
Data from: Bayesian species delimitation can be robust to guide tree inference errors
Open the record for dataset details and reuse information.
Figure 3. Bayesian 50 in Phylogeny, species delimitation and population structure of the steppe-inhabiting land snail genus Helicopsis in Eastern Europe
Figure 3. Bayesian 50% majority-rule consensus tree of European Helicopsis based on concatenated sequences of the mitochondrial COI, 12S rDNA and 16S rDNA. Support values at the nodes correspond to Bayesian posterior probabilities (left), maximum likelihood (middle) and maximum parsimony (right) bootstrap values. Extraction voucher numbers are given at the tips of the tree. For information about sequenced specimens, see Supporting Information, TableS1.
Figure 2. Bayesian 50 in Phylogeny, species delimitation and population structure of the steppe-inhabiting land snail genus Helicopsis in Eastern Europe
Figure 2. Bayesian 50% majority-rule consensus tree of Helicopsis and related groups based on sequences of the mitochondrial 16S rDNA. Nodes supported by posterior probabilities ≥ 0.95 are marked by a dot. For information about sequenced specimens and detailed support values, see Supporting Information, Figure S1 and Table S1.
Figure 9. Phylogenetic relationships within the Xiphinema americanum-group complex. Bayesian 50 in Cryptic diversity and species delimitation in the Xiphinema americanum-group complex (Nematoda: Longidoridae) as inferred from morphometrics and molecular markers
Figure 9. Phylogenetic relationships within the Xiphinema americanum-group complex. Bayesian 50% majority rule consensus tree as inferred from D2-D3 expansion segments of 28S rRNA sequence alignment under the general time reversible model with invariable sites and gamma-shaped distribution. Posterior probabilities more than 65% are given for appropriate clades; bootstrap values greater than 50% are given on appropriate clades in the maximum likelihood analysis. Sequences newly obtained in this study are in bold. Scale bar = expected changes per site.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.