Challenges in the estimation of extinction from molecular phylogenies: A response to Beaulieu and O'Meara

Time‐calibrated phylogenies that contain only living species have been widely used to study the dynamics of speciation and extinction. Concerns about the reliability of phylogenetic extinction estimates were raised by Rabosky (2010), where I suggested that unaccommodated heterogeneity in speciation rate could lead to positively biased extinction estimates. In a recent article, Beaulieu and O'Meara (2015a) correctly point out several technical errors in the execution of my 2010 study and concluded that phylogenetic extinction estimates are robust to speciation rate heterogeneity under a range of model parameters. I demonstrate that Beaulieu and O'Meara underestimated the magnitude of speciation rate variation in real phylogenies and consequently did not incorporate biologically meaningful levels of rate heterogeneity into their simulations. Using parameter values drawn from the recent literature, I find that modest levels of heterogeneity in speciation rate result in a consistent, positive bias in extinction estimates that are exacerbated by phylogenetic tree size. This bias, combined with the inherent lack of information about extinction in molecular phylogenies, suggests that extinction rate estimates from phylogenies of extant taxa only should be treated with caution.     

Evolutionary timescale of monocots determined by the fossilized birth‐death model using a large number of fossil records

Although the phylogenetic relationships between monocot orders are sufficiently understood, a timescale of their evolution is needed. Several studies on molecular clock dating are available, but their results have been biased by their calibration schemes. Recently, the fossilized birth‐death model, a type of Bayesian dating method, was proposed, and it does not require prior calibration and allows the use all available fossils. Using this model, we conducted divergence‐time estimations of monocots to explore their evolutionary timeline without calibration bias. This is the first application of this model to seed plants. The dataset contained the matK and rbcL chloroplast genes of 118 monocot genera covering all extant orders. We employed information from 247 monocot fossils, which exceeded previous dating analyses that used a maximum of 12 monocot fossils. The crown group of monocots was dated to approximately the Late Jurassic–Early Cretaceous periods, and most extant monocot orders were estimated to diverge throughout the Early Cretaceous. Our results overlapped with the divergence time of insect lineages, such as beetles and flies, suggesting an association with pollinators in early monocot evolution. In addition, we proposed three new orders based on divergence time: Orchidales separated from Asparagales and Tofieldiales and Arales separated from Aslimatales.     

Bayesian Estimation of the Time‐Varying Sensitivity of a Diagnostic Test with Application to Mother‐to‐Child Transmission of HIV

Summary We present a Bayesian model to estimate the time‐varying sensitivity of a diagnostic assay when the assay is given repeatedly over time, disease status is changing, and the gold standard is only partially observed. The model relies on parametric assumptions for the distribution of the latent time of disease onset and the time‐varying sensitivity. Additionally, we illustrate the incorporation of historical data for constructing prior distributions. We apply the new methods to data collected in a study of mother‐to‐child transmission of HIV and include a covariate for sensitivity to assess whether two different assays have different sensitivity profiles.     

Dynamic clinical prediction models for discrete time‐to‐event data with competing risks—A case study on the OUTCOMEREA database

The development of clinical prediction models requires the selection of suitable predictor variables. Techniques to perform objective Bayesian variable selection in the linear model are well developed and have been extended to the generalized linear model setting as well as to the Cox proportional hazards model. Here, we consider discrete time‐to‐event data with competing risks and propose methodology to develop a clinical prediction model for the daily risk of acquiring a ventilator‐associated pneumonia (VAP) attributed to P. aeruginosa (PA) in intensive care units. The competing events for a PA VAP are extubation, death, and VAP due to other bacteria. Baseline variables are potentially important to predict the outcome at the start of ventilation, but may lose some of their predictive power after a certain time. Therefore, we use a landmark approach for dynamic Bayesian variable selection where the set of relevant predictors depends on the time already spent at risk. We finally determine the direct impact of a variable on each competing event through cause‐specific variable selection.     

An evaluation of behavior inferences from Bayesian state‐space models: A case study with the Pacific walrus

State‐space models offer researchers an objective approach to modeling complex animal location data sets, and state‐space model behavior classifications are often assumed to have a link to animal behavior. In this study, we evaluated the behavioral classification accuracy of a Bayesian state‐space model in Pacific walruses using Argos satellite tags with sensors to detect animal behavior in real time. We fit a two‐state discrete‐time continuous‐space Bayesian state‐space model to data from 306 Pacific walruses tagged in the Chukchi Sea. We matched predicted locations and behaviors from the state‐space model (resident, transient behavior) to true animal behavior (foraging, swimming, hauled out) and evaluated classification accuracy with kappa statistics (κ) and root mean square error (RMSE). In addition, we compared biased random bridge utilization distributions generated with resident behavior locations to true foraging behavior locations to evaluate differences in space use patterns. Results indicated that the two‐state model fairly classified true animal behavior (0.06 ≤ κ ≤ 0.26, 0.49 ≤ RMSE ≤ 0.59). Kernel overlap metrics indicated utilization distributions generated with resident behavior locations were generally smaller than utilization distributions generated with true foraging behavior locations. Consequently, we encourage researchers to carefully examine parameters and priors associated with behaviors in state‐space models, and reconcile these parameters with the study species and its expected behaviors.