Historical biogeography of the widespread macroalga Sargassum (Fucales,Phaeophyceae)

Sargassum is a cosmopolitan brown algal genus spanning the three ocean basins of the Atlantic, Pacific and Indian Oceans, inhabiting temperate, subtropical and tropical habitats. Sargassum has been postulated to have originated in the Oligocene epoch approximately 30 mya according to a broad phylogenetic analysis of brown macroalgae, but its diversification to become one of the most widespread and speciose macroalgal genera remains unclear. Here, we present a Bayesian molecular clock study, which analyzed data from the order Fucales of the brown algal crown radiation (BACR) group to reconstruct a time-calibrated phylogeny of the Sargassum clade. Our phylogeny included a total of 120 taxa with 99 Sargassum species sampled for three molecular markers – ITS-2, cox3 and rbcLS – calibrated with an unambiguous Sargassaceae fossil from between the lower and middle Miocene. The analysis revealed a much later origin of Sargassum than expected at about 6.7 mya, with the genus diversifying since approximately 4.3 mya. Current geographic distributions of Sargassum species were then analyzed in conjunction with the time-calibrated phylogeny using the dispersal-extinction-cladogenesis (DEC) model to estimate ancestral ranges of clades in the genus. Results strongly support origination of Sargassum in the Central Indo-Pacific (CIP) region with subsequent independent dispersal events into other marine realms. The longer history of diversification in the ancestral CIP range could explain the much greater diversity there relative to other marine areas today. Analyses of these dynamic processes, when fine-tuned to a higher spatial resolution, enable the identification of evolutionary hotspots and provide insights into long-term dispersal patterns.     

Summer and Winter Marine Heatwaves Favor an Invasive Over Native Seaweeds

Marine heatwaves (MHWs) are emerging as forceful agents of ecosystem change and are increasing in frequency, duration, and intensity with climate change. During MHWs, physiological thresholds of native species may be exceeded while the performance of invasive species with warm affinities may be enhanced. As a consequence, MHWs could significantly alter an ecosystem's invasive dynamics, but such interactions are poorly understood. Following a 10-d acclimation period, we investigated the physiological resistance and resilience of an intertidal rock pool assemblage invaded by the seaweed Sargassum muticum to realistic 14-d marine heatwave scenarios (+1.5°C, +2.0°C, +3.5°C) followed by a 14-d recovery period. We conducted mesocosm experiments in both summer and winter to investigate temporal variability of MHWs. MHW treatments had clear negative impacts on native seaweeds (Fucus serratus and Chondrus crispus) while enhancing the performance of S. muticum. This pattern was consistent across season indicating that acclimation to cooler ambient temperatures results in winter MHWs having significant impacts on native species. As climate warming advances, this may ultimately lead to changes in competitive interactions and potentially exclusion of native species, while invasive species may proliferate and become more conspicuous within temperate rocky shore environments.     

The impact of fossil stratigraphic ranges on tip-calibration,and the accuracy and precision of divergence time estimates

The molecular clock provides the only viable means of establishing realistic evolutionary timescales but it remains unclear how best to calibrate divergence time analyses. Calibrations can be applied to the tips and/or to the nodes of a phylogeny. Tip-calibration is an attractive approach since it allows fossil species to be included alongside extant relatives in molecular clock analyses. However, most fossil species are known from multiple stratigraphical horizons and it remains unclear how such age ranges should be interpreted to codify tip-calibrations. We use simulations and empirical data to explore the impact on precision and accuracy of different approaches to informing tip-calibrations. In particular, we focus on the effect of using tip-calibrations defined using the oldest vs youngest stratigraphic occurrences, the full stratigraphical range, as well as confidence intervals on these data points. The results of our simulations show that using different calibration approaches leads to different divergence-time estimates and demonstrate that concentrating tip-calibrations near the root of the dated phylogeny improves both precision and accuracy of estimated divergence times. Finally, our results indicate that the highest levels of accuracy and precision are achieved when fossil tips are calibrated based on the fossil occurrence from which the morphological data were derived. These trends were corroborated by analysis of an empirical dataset for Ursidae. Overall, we conclude that tip-dating analyses should, in particular, employ tip calibrations close to the root of the tree and they should be calibrated based on the age of the fossil used to inform the morphological data used in Total Evidence Dating.     

Mimicking SIV chimpanzee viral evolution toward HIV-1 during cross-species transmission

HIV-1 evolved from SIV during cross-species transmission events, though viral genetic changes are not well understood. Here, we studied the evolution of SIVcpzLB715 into HIV-1 Group M using humanized mice. High viral loads, rapid CD4⁺ T-cell decline, and non-synonymous substitutions were identified throughout the viral genome suggesting viral adaptation.     

Adjustment for exploratory cut-off selection in randomized clinical trials with survival endpoint

Defining the target population based on predictive biomarkers plays an important role during clinical development. After establishing a relationship between a biomarker candidate and response to treatment in exploratory phases, a subsequent confirmatory trial ideally involves only subjects with high potential of benefiting from the new compound. In order to identify those subjects in case of a continuous biomarker, a cut-off is needed. Usually, a cut-off is chosen that resulted in a subgroup with a large observed treatment effect in an exploratory trial. However, such a data-driven selection may lead to overoptimistic expectations for the subsequent confirmatory trial. Treatment effect estimates, probability of success, and posterior probabilities are useful measures for deciding whether or not to conduct a confirmatory trial enrolling the biomarker-defined population. These measures need to be adjusted for selection bias. We extend previously introduced Approximate Bayesian Computation techniques for adjustment of subgroup selection bias to a time-to-event setting with cut-off selection. Challenges in this setting are that treatment effects become time-dependent and that subsets are defined by the biomarker distribution. Simulation studies show that the proposed method provides adjusted statistical measures which are superior to naïve Maximum Likelihood estimators as well as simple shrinkage estimators.     

Noise does not equal bias in assessing the evolutionary history of the angiosperm flora of China: A response to Qian (2019)

In response to our paper on the evolutionary history of the Chinese flora, Qian suggests that certain features of the divergence time estimation employed might have led to biased conclusions in Lu et al (2018). Here, we consider Qian's specific criticisms, explore the extent of uncertainty in the data and demonstrate that (i) no systematic bias toward dates that are too young or too old is detected in Lu et al.; (ii) constraint of the crown age of angiosperms does not bias the generic ages estimated by Lu et al.; and (iii) ages derived from the Chinese regional phylogeny do not bias the conclusions reported by Lu et al. All these analyses confirm that the conclusions reported previously are robust. We argue that, like many large-scale biodiversity analyses, sources of noise in divergence time estimation are to be expected, but these should not be confused with bias.     

Large scale maximum average power multiple inference on time-course count data with application to RNA-seq analysis

Experiments that longitudinally collect RNA sequencing (RNA-seq) data can provide transformative insights in biology research by revealing the dynamic patterns of genes. Such experiments create a great demand for new analytic approaches to identify differentially expressed (DE) genes based on large-scale time-course count data. Existing methods, however, are suboptimal with respect to power and may lack theoretical justification. Furthermore, most existing tests are designed to distinguish among conditions based on overall differential patterns across time, though in practice, a variety of composite hypotheses are of more scientific interest. Finally, some current methods may fail to control the false discovery rate. In this paper, we propose a new model and testing procedure to address the above issues simultaneously. Specifically, conditional on a latent Gaussian mixture with evolving means, we model the data by negative binomial distributions. Motivated by Storey (2007) and Hwang and Liu (2010), we introduce a general testing framework based on the proposed model and show that the proposed test enjoys the optimality property of maximum average power. The test allows not only identification of traditional DE genes but also testing of a variety of composite hypotheses of biological interest. We establish the identifiability of the proposed model, implement the proposed method via efficient algorithms, and demonstrate its good performance via simulation studies. The procedure reveals interesting biological insights, when applied to data from an experiment that examines the effect of varying light environments on the fundamental physiology of the marine diatom Phaeodactylum tricornutum.     

Sequential adaptive variables and subject selection for GEE methods

Modeling correlated or highly stratified multiple-response data is a common data analysis task in many applications, such as those in large epidemiological studies or multisite cohort studies. The generalized estimating equations method is a popular statistical method used to analyze these kinds of data, because it can manage many types of unmeasured dependence among outcomes. Collecting large amounts of highly stratified or correlated response data is time-consuming; thus, the use of a more aggressive sampling strategy that can accelerate this process—such as the active-learning methods found in the machine-learning literature—will always be beneficial. In this study, we integrate adaptive sampling and variable selection features into a sequential procedure for modeling correlated response data. Besides reporting the statistical properties of the proposed procedure, we also use both synthesized and real data sets to demonstrate the usefulness of our method.     

When sinks become sources: Adaptive colonization in asexuals*

The establishment of a population into a new empty habitat outside of its initial niche is a phenomenon akin to evolutionary rescue in the presence of immigration. It underlies a wide range of processes, such as biological invasions by alien organisms, host shifts in pathogens, or the emergence of resistance to pesticides or antibiotics from untreated areas. We derive an analytically tractable framework to describe the evolutionary and demographic dynamics of asexual populations in a source-sink system. We analyze the influence of several factors on the establishment success in the sink, and on the time until establishment. To this aim, we use a classic phenotype-fitness landscape (Fisher's geometrical model in n dimensions) where the source and sink habitats have different phenotypic optima. In case of successful establishment, the mean fitness in the sink follows a typical four-phases trajectory. The waiting time to establishment is independent of the immigration rate and has a “U-shaped” dependence on the mutation rate, until some threshold where lethal mutagenesis impedes establishment and the sink population remains so. We use these results to get some insight into possible effects of several management strategies.     

Female-specific resource limitation does not make the opportunity for selection more female biased

Competition for limiting resources and stress can magnify variance in fitness and therefore selection. But even in a common environment, the strength of selection can differ across the sexes, as their fitness is often limited by different factors. Indeed, most taxa show stronger selection in males, a bias often ascribed to intense competition for access to mating partners. This sex bias could reverberate on many aspects of evolution, from speed of adaptation to genome evolution. It is unclear, however, whether stronger opportunity for selection in males is a pattern robust to sex-specific stress or resource limitation. We test this in the model species Callosobruchus maculatus by comparing female and male opportunity for selection (i) with and without limitation of quality oviposition sites, and (ii) under delayed age at oviposition. Decreasing the abundance of the resource key to females or increasing their reproductive age was challenging, as shown by a reduction in mean fitness, but opportunity for selection remained stronger in males across all treatments, and even more so when oviposition sites were limiting. This suggests that males remain the more variable sex independent of context, and that the opportunity for selection through males is indirectly affected by female-specific resource limitation.