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Data from: Bayesian estimation of speciation and extinction from incomplete fossil occurrence data
- 负责人：
- DOI：
- doi:10.5061/dryad.87d8s
- 摘要：
- The temporal dynamics of species diversity are shaped by variations in the rates of speciation and extinction, and there is a long history of inferring these rates using first and last appearances of taxa in the fossil record. Understanding diversity dynamics critically depends on unbiased estimates of the unobserved times of speciation and extinction for all lineages, but the inference of these parameters is challenging due to the complex nature of the available data. Here, we present a new probabilistic framework to jointly estimate species-specific times of speciation and extinction and the rates of the underlying birth-death process based on the fossil record. The rates are allowed to vary through time independently of each other, and the probability of preservation and sampling is explicitly incorporated in the model to estimate the true lifespan of each lineage. We implement a Bayesian algorithm to assess the presence of rate shifts by exploring alternative diversification models. Tests on a range of simulated data sets reveal the accuracy and robustness of our approach against violations of the underlying assumptions and various degrees of data incompleteness. Finally, we demonstrate the application of our method with the diversification of the mammal family Rhinocerotidae and reveal a complex history of repeated and independent temporal shifts of both speciation and extinction rates, leading to the expansion and subsequent decline of the group. The estimated parameters of the birth-death process implemented here are directly comparable with those obtained from dated molecular phylogenies. Thus, our model represents a step towards integrating phylogenetic and fossil information to infer macroevolutionary processes.
Data from: Genealogical working distributions for Bayesian model testing with phylogenetic uncertainty
- 负责人：
- DOI：
- doi:10.5061/dryad.8tm76
- 摘要：
- Marginal likelihood estimates to compare models using Bayes factors frequently accompany Bayesian phylogenetic inference. Approaches to estimate marginal likelihoods have garnered increased attention over the past decade. In particular, the introduction of path sampling (PS) and stepping-stone sampling (SS) into Bayesian phylogenetics has tremendously improved the accuracy of model selection. These sampling techniques are now used to evaluate complex evolutionary and population genetic models on empirical data sets, but considerable computational demands hamper their widespread adoption. Further, when very diffuse, but proper priors are specified for model parameters, numerical issues complicate the exploration of the priors, a necessary step in marginal likelihood estimation using PS or SS. To avoid such instabilities, generalized SS (GSS) has recently been proposed, introducing the concept of “working distributions” to facilitate - or shorten - the integration process that underlies marginal likelihood estimation. However, the need to fix the tree topology currently limits GSS in a coalescent-based framework. Here, we extend GSS by relaxing the fixed underlying tree topology assumption. To this purpose, we introduce a “working” distribution on the space of genealogies, that enables estimating marginal likelihoods while accommodating phylogenetic uncertainty. We propose two different “working” distributions that help GSS to outperform PS and SS in terms of accuracy when comparing demographic and evolutionary models applied to synthetic data and real-world examples. Further, we show that the use of very diffuse priors can lead to a considerable overestimation in marginal likelihood when using PS and SS, while still retrieving the correct marginal likelihood using both GSS approaches. The methods used in this paper are available in BEAST, a powerful user-friendly software package to perform Bayesian evolutionary analyses.
Data from: Incorporating sampling uncertainty in the geospatial assignment of taxa for virus phylogeography
- 负责人：
- DOI：
- doi:10.5061/dryad.7jr85rj
- 摘要：
- Discrete phylogeography using software such as BEAST considers the sampling location of each taxon as fixed; often to a single location without uncertainty. When studying viruses, this implies that there is no possibility that the location of the infected host for that taxa is somewhere else. Here, we relaxed this strong assumption and allowed for analytic integration of uncertainty for discrete virus phylogeography. We used automatic language processing methods to find and assign uncertainty to alternative potential locations. We considered two influenza case studies: H5N1 in Egypt; H1N1 pdm09 in North America. For each, we implemented scenarios in which 25% of the taxa had different amounts of sampling uncertainty including 10%, 30%, and 50% uncertainty and varied how it was distributed for each taxon. This includes scenarios that: (i) placed a specific amount of uncertainty on one location while uniformly distributing the remaining amount across all other candidate locations (correspondingly labeled 10, 30, and 50); (ii) assigned the remaining uncertainty to just one other location; thus “splitting” the uncertainty among two locations (i.e. 10/90, 30/70, and 50/50) and; (iii) eliminated uncertainty via two pre-defined heuristic approaches: assignment to a centroid location (CNTR) or the largest population in the country (POP). We compared all scenarios to a reference standard in which all taxa had known (absolutely certain) locations. From this, we implemented five random selections of 25% of the taxa and used these for specifying uncertainty. We performed posterior analyses for each scenario, including: (a) virus persistence, (b) migration rates, (c) trunk rewards, and (d) the posterior probability of the root state. The scenarios with sampling uncertainty were closer to the reference standard than CNTR and POP. For H5N1, the absolute error of virus persistence had a median range of 0.005 – 0.047 for scenarios with sampling uncertainty – (i) and (ii) above - versus a range of 0.063 – 0.075 for CNTR and POP. Persistence for the pdm09 case study followed a similar trend as did our analyses of migration rates across scenarios (i) and (ii). When considering the posterior probability of the root state, we found all but one of the H5N1 scenarios with sampling uncertainty had agreement with the reference standard on the origin of the outbreak whereas both CNTR and POP disagreed. Our results suggest that assigning geospatial uncertainty to taxa benefits estimation of virus phylogeography as compared to ad-hoc heuristics. We also found that, in general, there was limited difference in results regardless of how the sampling uncertainty was assigned; uniform distribution or split between two locations did not greatly impact posterior results. This framework is available in BEAST v.1.10. In future work, we will explore viruses beyond influenza. We will also develop a web interface for researchers to use our language processing methods to find and assign uncertainty to alternative potential locations for virus phylogeography.
Data from: Divergence and diversification in North American Psoraleeae (Fabaceae) due to climate change
- 负责人：
- DOI：
- doi:10.5061/dryad.sr927n43
- 摘要：
- MCMC sampling in BEAST based on eight DNA regions (ITS, waxy, matK, trnD-trnT, trnL-trnF, trnK, trnS-trnG, and rpoB-trnC). We also test the hypothesis
Data from: Model choice, missing data and taxon sampling impact phylogenomic inference of deep Basidiomycota relationships
- 负责人：
- 关键词：
- DOI：
- doi:10.5061/dryad.g0db883
- 摘要：
- (e.g. fast-evolving sites and long-branch taxa) to inferences of basal Basidiomycota relationships. Bayesian MCMC and likelihood mapping analyses reject
Data from: Is BAMM flawed? Theoretical and practical concerns in the analysis of multi-rate diversification models
- 负责人：
- DOI：
- doi:10.5061/dryad.36g21
- 摘要：
- BAMM (Bayesian Analysis of Macroevolutionary Mixtures) is a statistical framework that uses reversible jump MCMC to infer complex macroevolutionary
Data from: The evolution of defense mechanisms correlate with the explosive diversification of autodigesting Coprinellus mushrooms (Agaricales, Fungi)
- 负责人：
- DOI：
- doi:10.5061/dryad.665vv1c7
- 摘要：
- hypothesis, by analyzing the resolvability of internal nodes of the backbone of the putative radiation using Reversible-Jump MCMC. We discuss
Data from: Modelling flight heights of lesser black-backed gulls and great skuas from GPS: a Bayesian approach
- 负责人：
- DOI：
- doi:10.5061/dryad.dp2ms
- 摘要：
- Wind energy generation is increasing globally, and associated environmental impacts must be considered. The risk of seabirds colliding with offshore wind turbines is influenced by flight height, and flight height data usually come from observers on boats, making estimates in daylight in fine weather. GPS tracking provides an alternative and generates flight height information in a range of conditions, but the raw data have associated error. Here, we present a novel analytical solution for accommodating GPS error. We use Bayesian state-space models to describe the flight height distributions and the error in altitude measured by GPS for lesser black-backed gulls and great skuas, tracked throughout the breeding season. We also examine how location and light levels influence flight height. Lesser black-backed gulls flew lower by night than by day, indicating that this species would be less likely to encounter turbine blades at night, when birds’ ability to detect and avoid them might be reduced. Gulls flew highest over land and lowest near the coast. For great skuas, no significant relationships were found between flight height, time of day and location. We consider four ‘collision risk windows’, corresponding to the airspace swept by rotor blades for different offshore wind turbine designs. We found the highest proportion of birds at risk for a 22–250 m turbine (up to 9% for great skuas and 34% for lesser black-backed gulls) and the lowest for a 30–258 m turbine. Our results suggest lesser black-backed gulls are at greater risk of collision than great skuas, especially by day. Synthesis and applications. Our novel modelling approach is an effective way of resolving the error associated with GPS tracking data. We demonstrate its use on GPS measurements of altitude, generating important information on how breeding seabirds use their environment. This approach and the associated data also provide information to improve avian collision risk assessments for offshore wind farms. Our modelling approach could be applied to other GPS data sets to help manage the ecological needs of seabirds and other species at a time when the pressures on the marine environment are growing.
