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Many spatiotemporal data record the time of birth and death of individuals, along with their spatial trajectories during their lifetime, whether through continuous‐time observations or discrete‐time observations. Natural applications include epidemiology, individual‐based modelling in ecology, spatiotemporal dynamics observed in bioimaging and computer vision. The aim of this article is to estimate in this context the birth and death intensity functions that depend in full generality on the current spatial configuration of all alive individuals. While the temporal evolution of the population size is a simple birth–death process, observing the lifetime and trajectories of all individuals calls for a new paradigm. To formalise this framework, we introduce spatial birth–death–move processes, where the birth and death dynamics depends on the current spatial configuration of the population and where individuals can move during their lifetime according to a continuous Markov process with possible interactions. We consider non‐parametric kernel estimators of their birth and death intensity functions. The setting is original because each observation in time belongs to a non‐vectorial, infinite dimensional space and the dependence between observations is barely tractable. We prove the consistency of the estimators in the presence of continuous‐time and discrete‐time observations, under fairly simple conditions. We moreover discuss how we can take advantage in practice of structural assumptions made on the intensity functions and we explain how data‐driven bandwidth selection can be conducted, despite the unknown (and sometimes undefined) second order moments of the estimators. We finally apply our statistical method to the analysis of the spatiotemporal dynamics of proteins involved in exocytosis in cells, providing new insights on this complex mechanism. [ABSTRACT FROM AUTHOR]

A genus is a taxonomic unit that may contain one species (monotypic) or thousands. Yet counts of genera or families are used to quantify diversity where species-level data are not available. High frequencies of monotypic genera (~30% of animals) have previously been scrutinized as an artefact of human classification. To test whether Linnean taxonomy conflicts with phylogeny, we compared idealized phylogenetic systematics in silico with real-world data. We generated highly replicated, simulated phylogenies under a variety of fixed speciation/extinction rates, imposed three independent taxonomic sorting algorithms on these clades (2.65 × 108 simulated species) and compared the resulting genus size data with quality-controlled taxonomy of animal groups (2.8 × 105 species). 'Perfect' phylogenetic systematics arrives at similar distributions to real-world taxonomy, regardless of the taxonomic algorithm. Rapid radiations occasionally produce a large genus when speciation rates are favourable; however, small genera can arise in many different ways, from individual lineage persistence and/or extinctions creating subdivisions within a clade. The consistency of this skew distribution in simulation and real-world data, at sufficiently large samples, indicates that specific aspects of its mathematical behaviour could be developed into generalized or nomothetic principles of the global frequency distributions of higher taxa. Importantly, Linnean taxonomy is a better-than-expected reflection of underlying evolutionary patterns. [ABSTRACT FROM AUTHOR]

Insects rely on chemical information to locate food, choose mates, and detect potential predators. It has been hypothesized that adaptive changes in the olfactory system facilitated the diversification of numerous insect lineages. For instance, evolutionary changes of Odorant Receptor (OR) genes often occur in parallel with modifications in life history strategies. Corbiculate bees display a diverse array of behaviors that are controlled through olfaction, including varying degrees of social organization, and manifold associations with floral resources. Here we investigated the molecular mechanisms driving the evolution of the OR gene family in corbiculate bees in comparison to other chemosensory gene families. Our results indicate that the genomic organization of the OR gene family has remained highly conserved for ~80 Myr, despite exhibiting major changes in repertoire size among bee lineages. Moreover, the evolution of OR genes appears to be drivenmostly by lineage-specific gene duplications in few genomic regions that harbor large numbers of OR genes. A selection analysis revealed that OR genes evolve under positive selection, with the strongest signals detected in recently duplicated copies. Our results indicate that chromosomal translocations had a minimal impact on OR evolution, and instead local molecular mechanisms appear to be main drivers of OR repertoire size. Our results provide empirical support to the longstanding hypothesis that positive selection shaped the diversification of the OR gene family. Together, our results shed new light on the molecular mechanisms underlying the evolution of olfaction in insects. [ABSTRACT FROM AUTHOR]

Dupuytren disease is a fibroproliferative disorder with unknown aetiology that often progresses and eventually can cause permanent contractures of the fingers affected. We provide a computationally efficient Bayesian framework to discover potential risk factors and investigate which fingers are jointly affected. Our Bayesian approach is based on Gaussian copula graphical models, which provide a way to discover the underlying conditional independence structure of variables in multivariate data of mixed types. In particular, we combine the semiparametric Gaussian copula with extended rank likelihood to analyse multivariate data of mixed types with arbitrary marginal distributions. For structural learning, we construct a computationally efficient search algorithm by using a transdimensional Markov chain Monte Carlo algorithm based on a birth-death process. In addition, to make our statistical method easily accessible to other researchers, we have implemented our method in C++ and provide an interface with R software as an R package BDgraph, which is freely available from . [ABSTRACT FROM AUTHOR]

The total-evidence approach to divergence time dating uses molecular and morphological data from extant and fossil species to infer phylogenetic relationships, species divergence times, and macroevolutionary parameters in a single coherent framework. Current model-based implementations of this approach lack an appropriate model for the tree describing the diversification and fossilization process and can produce estimates that lead to erroneous conclusions. We address this shortcoming by providing a total-evidence method implemented in a Bayesian framework. This approach uses a mechanistic tree prior to describe the underlying diversification process that generated the tree of extant and fossil taxa. Previous attempts to apply the total-evidence approach have used tree priors that do not account for the possibility that fossil samples may be direct ancestors of other samples, that is, ancestors of fossil or extant species or of clades. The fossilized birth-death (FBD) process explicitly models the diversification, fossilization, and sampling processes and naturally allows for sampled ancestors. This model was recently applied to estimate divergence times based on molecular data and fossil occurrence dates. We incorporate the FBD model and amodel of morphological trait evolution into a Bayesian total-evidence approach to dating species phylogenies. We apply this method to extant and fossil penguins and show that the modern penguins radiated much more recently than has been previously estimated, with the basal divergence in the crown clade occurring at ∼12.7 Ma and most splits leading to extant species occurring in the last 2 myr. Our results demonstrate that including stem-fossil diversity can greatly improve the estimates of the divergence times of crown taxa. The method is available in BEAST2 (version 2.4) software www.beast2.org with packages SA (version at least 1.1.4) and morph-models (version at least 1.0.4) installed. [ABSTRACT FROM AUTHOR]

Classic null models for speciation and extinction give rise to phylogenies that differ in distribution from empirical phylogenies. In particular, empirical phylogenies are less balanced and have branching times closer to the root compared to phylogenies predicted by common null models. This difference might be due to null models of the speciation and extinction process being too simplistic, or due to the empirical datasets not being representative of random phylogenies. A third possibility arises because phylogenetic reconstruction methods often infer gene trees rather than species trees, producing an incongruity between models that predict species tree patterns and empirical analyses that consider gene trees. We investigate the extent to which the difference between gene trees and species trees under a combined birth-death and multispecies coalescent model can explain the difference in empirical trees and birth-death species trees. We simulate gene trees embedded in simulated species trees and investigate their difference with respect to tree balance and branching times. We observe that the gene trees are less balanced and typically have branching times closer to the root than the species trees. Empirical trees from Tree Base are also less balanced than our simulated species trees, and model gene trees can explain an imbalance increase of up to 8% compared to species trees. However, we see a much larger imbalance increase in empirical trees, about 100%, meaning that additional features must also be causing imbalance in empirical trees. This simulation study highlights the necessity of revisiting the assumptions made in phylogenetic analyses, as these assumptions, such as equating the gene tree with the species tree, might lead to a biased conclusion. [ABSTRACT FROM AUTHOR]

Tens of thousands of phylogenetic trees, describing the evolutionary relationships between hundreds of thousands of taxa, are readily obtainable from various databases. From such trees, inferences can be made about the underlying macroevolutionary processes, yet remarkably these processes are still poorly understood. Simple and widely used evolutionary null models are problematic: Empirical trees show very different imbalance between the sizes of the daughter clades of ancestral taxa compared to what models predict. Obtaining a simple evolutionary model that is both biologically plausible and produces the imbalance seen in empirical trees is a challenging problem, to which none of the existing models provide a satisfying answer. Here we propose a simple, biologically plausible macroevolutionary model in which the rate of speciation decreases with species age, whereas extinction rates can vary quite generally. We show that this model provides a remarkable fit to the thousands of trees stored in the online database TreeBase. The biological motivation for the identified age-dependent speciation process may be that recently evolved taxa often colonize new regions or niches and may initially experience little competition. These new taxa are thus more likely to give rise to further new taxa than a taxon that has remained largely unchanged and is, therefore, well adapted to its niche. We show that age-dependent speciation may also be the result of different within-species populations following the same laws of lineage splitting to produce new species. As the fit of our model to the tree database shows, this simple biological motivation provides an explanation for a long standing problem in macroevolution. [ABSTRACT FROM AUTHOR]

The study of paleopolyploidies requires the comparison of multiple whole genome sequences. If the branches of a phylogeny on which a whole-genome duplication (WGD) occurred could be identified before genome sequencing, taxa could be selected that provided a better assessment of that genome duplication. Here, we describe a likelihood model in which the number of chromosomes in a genome evolves according to a Markov process with one rate of chromosome duplication and loss that is proportional to the number of chromosomes in the genome and another stochastic rate at which every chromosome in the genome could duplicate in a single event. We compare the maximum likelihoods of a model in which the genome duplication rate varies to one in which it is fixed at zero using the Akaike information criterion, to determine if a model with WGDs is a good fit for the data. Once it has been determined that the data does fit the WGD model, we infer the phylogenetic position of paleopolyploidies by calculating the posterior probability that a WGD occurred on each branch of the taxon tree. Here, we apply this model to a molluscan tree represented by 124 taxa and infer three putative WGD events. In the Gastropoda, we identify a single branch within the Hypsogastropoda and one of two branches at the base of the Stylommatophora. We also identify one or two branches near the base of the Cephalopoda. [ABSTRACT FROM AUTHOR]

Quantitative traits have long been hypothesized to affect speciation and extinction rates. For example, smaller body size or increased specialization may be associated with increased rates of diversification. Here, I present a phylogenetic likelihood-based method (quantitative state speciation and extinction [QuaSSE]) that can be used to test such hypotheses using extant character distributions. This approach assumes that diversification follows a birth–death process where speciation and extinction rates may vary with one or more traits that evolve under a diffusion model. Speciation and extinction rates may be arbitrary functions of the character state, allowing much flexibility in testing models of trait-dependent diversification. I test the approach using simulated phylogenies and show that a known relationship between speciation and a quantitative character could be recovered in up to 80% of the cases on large trees (500 species). Consistent with other approaches, detecting shifts in diversification due to differences in extinction rates was harder than when due to differences in speciation rates. Finally, I demonstrate the application of QuaSSE to investigate the correlation between body size and diversification in primates, concluding that clade-specific differences in diversification may be more important than size-dependent diversification in shaping the patterns of diversity within this group. [ABSTRACT FROM PUBLISHER]

Time-calibrated molecular phylogenies provide a valuable window into the tempo and mode of species diversification, especially for the large number of groups that lack adequate fossil records. Molecular phylogenetic data frequently suggest an initial “explosive speciation” phase, leading to widespread speculation that ecological niche-filling processes might govern the dynamics of species diversification during evolutionary radiations. However, these patterns are difficult to reconcile with the fossil record. The fossil record strongly suggests that extinction rates have been high relative to speciation rates, but such elevated background extinction should erase the signal of early, rapid speciation from molecular phylogenies. For this reason, extinction rates in molecular phylogenies are frequently estimated as zero under the widely used birth–death model. Here, I construct a simple model that combines phylogenetically patterned extinction with pulsed turnover dynamics and constant diversity through time. Using approximate Bayesian methods, I show that heritable extinction can easily explain the phenomenon of explosive early diversification, even when net diversification rates are equal to zero. Several assumptions of the model are more consistent with both the fossil record and neontological data than the standard birth–death model and it may thus represent a viable alternative interpretation of phylogenetic diversification patterns. These results suggest that variation in the absolute rate of lineage turnover through time, in conjunction with phylogenetically nonrandom extinction, may underlie the apparent diversity-dependent speciation observed in molecular phylogenies. [ABSTRACT FROM PUBLISHER]

Species traits may influence rates of speciation and extinction, affecting both the patterns of diversification among lineages and the distribution of traits among species. Existing likelihood approaches for detecting differential diversification require complete phylogenies; that is, every extant species must be present in a well-resolved phylogeny. We developed 2 likelihood methods that can be used to infer the effect of a trait on speciation and extinction without complete phylogenetic information, generalizing the recent binary-state speciation and extinction method. Our approaches can be used where a phylogeny can be reasonably assumed to be a random sample of extant species or where all extant species are included but some are assigned only to terminal unresolved clades. We explored the effects of decreasing phylogenetic resolution on the ability of our approach to detect differential diversification within a Bayesian framework using simulated phylogenies. Differential diversification caused by an asymmetry in speciation rates was nearly as well detected with only 50% of extant species phylogenetically resolved as with complete phylogenetic knowledge. We demonstrate our unresolved clade method with an analysis of sexual dimorphism and diversification in shorebirds (Charadriiformes). Our methods allow for the direct estimation of the effect of a trait on speciation and extinction rates using incompletely resolved phylogenies. [ABSTRACT FROM PUBLISHER]

Many hormones are secreted in pulses. The pulsatile relationship between hormones regulates many biological processes. To understand endocrine system regulation, time series of hormone concentrations are collected. The goal is to characterize pulsatile patterns and associations between hormones. Currently each hormone on each subject is fitted univariately. This leads to estimates of the number of pulses and estimates of the amount of hormone secreted; however, when the signal-to-noise ratio is small, pulse detection and parameter estimation remains difficult with existing approaches. In this article, we present a bivariate deconvolution model of pulsatile hormone data focusing on incorporating pulsatile associations. Through simulation, we exhibit that using the underlying pulsatile association between two hormones improves the estimation of the number of pulses and the other parameters defining each hormone. We develop the one-to-one, driver–response case and show how birth–death Markov chain Monte Carlo can be used for estimation. We exhibit these features through a simulation study and apply the method to luteinizing and follicle stimulating hormones. [ABSTRACT FROM AUTHOR]

Determining whether speciation and extinction rates depend on the state of a particular character has been of long-standing interest to evolutionary biologists. To assess the effect of a character on diversification rates using likelihood methods requires that we be able to calculate the probability that a group of extant species would have evolved as observed, given a particular model of the character's effect. Here we describe how to calculate this probability for a phylogenetic tree and a two-state (binary) character under a simple model of evolution (the "BiSSE" model, binary-state speciation and extinction). The model involves six parameters, specifying two speciation rates (rate when the lineage is in state 0; rate when in state 1), two extinction rates (when in state 0; when in state 1), and two rates of character state change (from 0 to 1, and from 1 to 0). Using these probability calculations, we can do maximum likelihood inference to estimate the model's parameters and perform hypothesis tests (e.g., is the rate of speciation elevated for one character state over the other?). We demonstrate the application of the method using simulated data with known parameter values. [ABSTRACT FROM AUTHOR]

In many chronic conditions, subjects alternate between an active and an inactive state, and sojourns into the active state may involve multiple lesions, infections, or other recurrences with different times of onset and resolution. We present a biologically interpretable model of such chronic recurrent conditions based on a queueing process. The model has a birth–death process describing recurrences and a semi-Markov process describing the alternation between active and inactive states, and can be fit to panel data that provide only a binary assessment of the active or inactive state at a series of discrete time points using a hidden Markov approach. We accommodate individual heterogeneity and covariates using a random effects model, and simulate the posterior distribution of unknowns using a Markov chain Monte Carlo algorithm. Application to a clinical trial of genital herpes shows how the method can characterize the biology of the disease and estimate treatment efficacy. [ABSTRACT FROM AUTHOR]