Estimating Competition in Cladocerans Using Data on Dynamics of Clutch Size and Population Density

Negative correlations between clutch size and population density are proposed to be considered as indices of intra- and interspecific competition in cladocerans if they are revealed while analyzing population dynamics and clutch size and time lags are taken into account. The proper correlation analysis of summer populations of Diaphanosoma brachyurum, Bosmina coregoni, Daphnia cucullata and D. galeata from mesotrophic Lake Glubokoye (Moscow Region) in 1975, 1978, and 1979 indicates the important role of competition of both types for the community studied. High niche overlap in food and space in the four populations was also observed.     

Estimating the annual number of breeding attempts from breeding dates using mixture models

Well-established statistical methods exist to estimate variation in a number of key demographic rates from field data, including life-history transition probabilities and reproductive success per attempt. However, our understanding of the processes underlying population change remains incomplete without knowing the number of reproductive attempts individuals make annually; this is a key demographic rate for which we have no satisfactory method of estimating. Using census data to estimate this parameter from requires disaggregating the overlying temporal distributions of first and subsequent breeding attempts. We describe a Bayesian mixture method to estimate the annual number of reproductive attempts from field data to provide a new tool for demographic inference. We validate our method using comprehensive data on individually-marked song sparrows Melospiza melodia , and then apply it to more typical nest record data collected over 45 years on yellowhammers Emberiza citrinella . We illustrate the utility of our method by testing, and rejecting, the hypothesis that declines in UK yellowhammer populations have occurred concurrently with declines in annual breeding frequency.     

Bayesian latent multi‐state modeling for nonequidistant longitudinal electronic health records

Large amounts of longitudinal health records are now available for dynamic monitoring of the underlying processes governing the observations. However, the health status progression across time is not typically observed directly: records are observed only when a subject interacts with the system, yielding irregular and often sparse observations. This suggests that the observed trajectories should be modeled via a latent continuous‐time process potentially as a function of time‐varying covariates. We develop a continuous‐time hidden Markov model to analyze longitudinal data accounting for irregular visits and different types of observations. By employing a specific missing data likelihood formulation, we can construct an efficient computational algorithm. We focus on Bayesian inference for the model: this is facilitated by an expectation‐maximization algorithm and Markov chain Monte Carlo methods. Simulation studies demonstrate that these approaches can be implemented efficiently for large data sets in a fully Bayesian setting. We apply this model to a real cohort where patients suffer from chronic obstructive pulmonary disease with the outcome being the number of drugs taken, using health care utilization indicators and patient characteristics as covariates.     

Estimation of Plant Demographic Parameters from Stage‐Structured Censuses

Summary This article presents some statistical methods for estimating the parameters of a population dynamics model for annual plants. The model takes account of reproduction, immigration, seed survival in a seed bank, and plant growth. The data consist of the number of plants in several developmental stages that were measured in a number of populations for a few consecutive years; they are incomplete since seeds could not be counted. It is assumed that there are no measurement errors or that measurement errors are binomial and not frequent. Some statistical methods are developed within the framework of estimating equations or Bayesian inference. These methods are applied to oilseed rape data.     

Sperm ultrastructure of shrimps from the family Penaeidae (Crustacea: Dendrobranchiata) in a phylogenetic context

We describe the sperm ultrastructure of six penaeid species, including at least one member of each tribe (Penaeini, Parapenaeini and Trachypenaeini). Fragments of the vas deferens of the Penaeidae Farfantepenaeus brasiliensis, Farfantepenaeus paulensis, Litopenaeus schmitti, Parapenaeus americanus, Rimapenaeus constrictus and Xiphopenaeus kroyeri were fixed and processed according to the routine for transmission electron microscopy. The morphological results were contextualized in an evolutionary perspective using molecular markers for the phylogenetic reconstruction of this group. A phylogram was proposed by Bayesian inference based on 1007 bp of 33 sequences of the combined genes (16S rDNA and COI mtDNA) from 27 dendrobranchiate specimens. Our findings show that morphological differences in the sperm ultrastructures of members among the tribes of Penaeidae can be used as a baseline to understand their evolutionary relationships. Individuals from the Penaeini tribe show plesiomorphic characteristics in the sperm ultrastructure compared to the Trachypenaeini tribe from which they were derived, such as shrimp from family Sicyoniidae. The morphological complexity of the sperm of the different penaeid members corroborated with the genetic phylogeny, which showed different clades for each tribe and the close relationship with Sicyoniidae. The sperm features of the selected species studied here reflected their evolutionary history. These features confirm the previous phylogenetic hypothesis and question the monophyly of Penaeidae, which should be verified in the future with a more complete set of representative members of each tribe.     

Population Dynamics and Structural Effects at Short and Long Range Support the Hypothesis of the Selective Advantage of the G614 SARS-CoV-2 Spike Variant

SARS-CoV-2 epidemics quickly propagated worldwide, sorting virus genomic variants in newly established propagules of infections. Stochasticity in transmission within and between countries or an actual selective advantage could explain the global high frequency reached by some genomic variants. Using statistical analyses, demographic reconstructions, and molecular dynamics simulations, we show that the globally invasive G614 spike variant 1) underwent a significant demographic expansion in most countries explained neither by stochastic effects nor by overrepresentation in clinical samples, 2) increases the spike S1/S2 furin-like site conformational plasticity (short-range effect), and 3) modifies the internal motion of the receptor-binding domain affecting its cross-connection with other functional domains (long-range effect). Our results support the hypothesis of a selective advantage at the basis of the spread of the G614 variant, which we suggest may be due to structural modification of the spike protein at the S1/S2 proteolytic site, and provide structural information to guide the design of variant-specific drugs.     

Hierarchical Modeling for Estimating Relative Risks of Rare Genetic Variants: Properties of the Pseudo‐Likelihood Method

Summary Many major genes have been identified that strongly influence the risk of cancer. However, there are typically many different mutations that can occur in the gene, each of which may or may not confer increased risk. It is critical to identify which specific mutations are harmful, and which ones are harmless, so that individuals who learn from genetic testing that they have a mutation can be appropriately counseled. This is a challenging task, since new mutations are continually being identified, and there is typically relatively little evidence available about each individual mutation. In an earlier article, we employed hierarchical modeling ( Capanu et al., 2008 , Statistics in Medicine 27 , 1973–1992) using the pseudo‐likelihood and Gibbs sampling methods to estimate the relative risks of individual rare variants using data from a case–control study and showed that one can draw strength from the aggregating power of hierarchical models to distinguish the variants that contribute to cancer risk. However, further research is needed to validate the application of asymptotic methods to such sparse data. In this article, we use simulations to study in detail the properties of the pseudo‐likelihood method for this purpose. We also explore two alternative approaches: pseudo‐likelihood with correction for the variance component estimate as proposed by Lin and Breslow (1996, Journal of the American Statistical Association 91 , 1007–1016) and a hybrid pseudo‐likelihood approach with Bayesian estimation of the variance component. We investigate the validity of these hierarchical modeling techniques by looking at the bias and coverage properties of the estimators as well as at the efficiency of the hierarchical modeling estimates relative to that of the maximum likelihood estimates. The results indicate that the estimates of the relative risks of very sparse variants have small bias, and that the estimated 95% confidence intervals are typically anti‐conservative, though the actual coverage rates are generally above 90%. The widths of the confidence intervals narrow as the residual variance in the second‐stage model is reduced. The results also show that the hierarchical modeling estimates have shorter confidence intervals relative to estimates obtained from conventional logistic regression, and that these relative improvements increase as the variants become more rare.     

Estimating the proportion of identifiable individuals and group sizes in photographic identification studies

Simple Bayesian statistical models are introduced to estimate the proportion of identifiable individuals and group sizes in photographic identification, or photo‐ID, studies of animals that are found in groups. The models require a simple random photographic sampling of animals, where the photographic captures are treated as sampling with replacement within each group. The total number of images, including those that cannot be identified, and the number of images that contain identifiable individuals are used to make inference about the proportion of identifiable individuals within each group and as the population when a number of groups are sampled. The numbers of images for individuals within each group are used to make inference about the group size. Based on analyses of simulated and real data, the models perform well with respect to accuracy and precision of posterior distributions of the parameters. Widths of posterior intervals were affected by the number of groups sampled, sampling duration, and the proportion of identifiable individuals in each group that was sampled. The structure of the models can accommodate covariates, which may affect photographic efficiency, defined in this study as the probability of photographically capturing individuals.     

Transmission of Leptosphaeria maculans from a cropping season to the following one

Current modelling of inoculum transmission from a cropping season to the following one relies on the extrapolation of kernels estimated on data at short distances from punctual sources, because data collected at larger distances are scarce. We estimated the dispersal kernel of Leptosphaeria maculans ascospores from stubble left after harvest in the summer previous to newly sown oilseed rape fields, using phoma stem canker autumn disease severity. We built a dispersal model to analyse the data. Source strengths are described in the spatial domain covered by source fields by a log‐Gaussian spatial process. Infection potentials in the following season are described in the space consisting of the target fields, by a convolution of sources and a power‐exponential dispersal kernel. Data were collected on farmers' fields considered as sources in 2009 and 2011 (72 and 39 observation points) and as targets in 2010 and 2012 (172 and 200 points). We applied the Bayesian approach for model selection and parameter estimation. We obtained fat tail kernels for both data sets. This estimation is the first from data acquired over distances of 0 to 1000 m, using several non‐punctual inoculum sources. It opens the prospect of refining the existing simulators, or developing disease risk maps.     

Oscillations in continuous culture populations of Streptococcus pneumoniae: population dynamics and the evolution of clonal suicide

Agents that kill or induce suicide in the organisms that produce them or other individuals of the same genotype are intriguing puzzles for ecologists and evolutionary biologists. When those organisms are pathogenic bacteria, these suicidal toxins have the added appeal as candidates for the development of narrow spectrum antibiotics to kill the pathogens that produce them. We show that when clinical as well as laboratory strains of Streptococcus pneumoniae are maintained in continuous culture (chemostats), their densities oscillate by as much as five orders of magnitude with an apparently constant period. This dynamic, which is unanticipated for single clones of bacteria in chemostats, can be attributed to population-wide die-offs and recoveries. Using a combination of mathematical models and experiments with S. pneumoniae, we present evidence that these die-offs can be attributed to the autocatalytic production of a toxin that lyses or induces autolysis in members of the clone that produces it. This toxin, which our evidence indicates is a protein, appears to be novel; S. pneumoniae genetic constructs knocked out for lytA and other genes coding for known candidates for this agent oscillate in chemostat culture. Since this toxin lyses different strains of S. pneumoniae as well as other closely related species of Streptococcus, we propose that its ecological role is as an allelopathic agent. Using a mathematical model, we explore the conditions under which toxins that kill members of the same clone that produces them can prevent established populations from invasion by different strains of the same or other species. We postulate that the production of the toxin observed here as well as other bacteria-produced toxins that kill members of the same genotype, ‘clonal suicide’, evolved and are maintained to prevent colonization of established populations by different strains of the same and closely related species.     

Elucidating the Population Histories and Transmission Dynamics of Papillomaviruses Using Phylogenetic Trees

Using gene genealogies constructed from gene sequence data, we show that both the mucosal and cutaneous papillomaviruses (PV)—supergroups A and B—appear to have been transmitted through susceptible populations faster than exponentially. The data and methods involved (1) examining the PV database for phylogenetic signal in an L1 open reading frame (ORF) fragment and an E1 ORF segment, (2) demonstrating that the same two fragments have evolved in a way consistent with a molecular clock, and (3) applying methods of phylogenetic tree analysis that test different scenarios for the dynamics of viral transmission within populations. The results indicate increases in PV populations of both supergroups A and B in the recent past. This form of the increases, which fit a null model of population growth with an exponent increasing in time, is compatible with the fact that human populations have grown at a faster than exponential rate, thus increasing the numbers of susceptible hosts for HPVs. There are, however, indications that the population of supergroup A has now stopped increasing in size. Received: 4 June 1996 / Accepted: 12 August 1996     

Genome-based identification and characterization of a putative mucin-binding protein from the surface of Streptococcus pneumoniae

Streptococcus pneumoniae open reading frame SP1492 encodes a surface protein that contains a novel conserved domain similar to the repeated fragments of mucin-binding proteins from lactobacilli and lactococci. To investigate the functional role(s) of this protein and its potential adhesive properties, the surface-exposed region of SP1492 was expressed in Escherichia coli, purified to homogeneity, and partially characterized by biophysical and immunological methods. Circular dichroism and sedimentation measurements confirmed that SP1492 is an all-beta protein that exists in solution as a monomer. The SP1492 protein has been shown to be expressed by S. pneumoniae and was experimentally localized to its surface. The protein functional domain binds to mucins II and III from porcine stomach and to purified submaxillary bovine gland mucin. It appears to be one of the very few unambiguous pneumococcal adhesin molecules known to date. A hypothetical model constructed by ab initio techniques predicts a novel beta-sandwich protein structure.     

Uncovering the drivers of host‐associated microbiota with joint species distribution modelling

In addition to the processes structuring free‐living communities, host‐associated microbiota are directly or indirectly shaped by the host. Therefore, microbiota data have a hierarchical structure where samples are nested under one or several variables representing host‐specific factors, often spanning multiple levels of biological organization. Current statistical methods do not accommodate this hierarchical data structure and therefore cannot explicitly account for the effect of the host in structuring the microbiota. We introduce a novel extension of joint species distribution models (JSDMs) which can straightforwardly accommodate and discern between effects such as host phylogeny and traits, recorded covariates such as diet and collection site, among other ecological processes. Our proposed methodology includes powerful yet familiar outputs seen in community ecology overall, including (a) model‐based ordination to visualize and quantify the main patterns in the data; (b) variance partitioning to assess how influential the included host‐specific factors are in structuring the microbiota; and (c) co‐occurrence networks to visualize microbe‐to‐microbe associations.     

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.     

Assembly Dynamics and Stability of the Pneumococcal Epsilon Zeta Antitoxin Toxin (PezAT) System from Streptococcus pneumoniae

The pneumococcal epsilon zeta antitoxin toxin (PezAT) system is a chromosomally encoded, class II toxin antitoxin system from the human pathogen Streptococcus pneumnoniae. Neutralization of the bacteriotoxic protein PezT is carried out by complex formation with its cognate antitoxin PezA. Here we study the stability of the inhibitory complex in vivo and in vitro. We found that toxin release is impeded in Escherichia coli and Bacillus subtilis due to the proteolytic resistance of PezA once bound to PezT. These findings are supported by in vitro experiments demonstrating a strong thermodynamic stabilization of both proteins upon binding. A detailed kinetic analysis of PezAT assembly revealed that these particular features of PezAT are based on a strong, electrostatically guided binding mechanism leading to a stable toxin antitoxin complex with femtomolar affinity. Our data show that PezAT complex formation is distinct to all other conventional toxin antitoxin modules and a controlled mode of toxin release is required for activation.     

Search and foraging behaviors from movement data: A comparison of methods

Search behavior is often used as a proxy for foraging effort within studies of animal movement, despite it being only one part of the foraging process, which also includes prey capture. While methods for validating prey capture exist, many studies rely solely on behavioral annotation of animal movement data to identify search and infer prey capture attempts. However, the degree to which search correlates with prey capture is largely untested. This study applied seven behavioral annotation methods to identify search behavior from GPS tracks of northern gannets (Morus bassanus), and compared outputs to the occurrence of dives recorded by simultaneously deployed time–depth recorders. We tested how behavioral annotation methods vary in their ability to identify search behavior leading to dive events. There was considerable variation in the number of dives occurring within search areas across methods. Hidden Markov models proved to be the most successful, with 81% of all dives occurring within areas identified as search. k‐Means clustering and first passage time had the highest rates of dives occurring outside identified search behavior. First passage time and hidden Markov models had the lowest rates of false positives, identifying fewer search areas with no dives. All behavioral annotation methods had advantages and drawbacks in terms of the complexity of analysis and ability to reflect prey capture events while minimizing the number of false positives and false negatives. We used these results, with consideration of analytical difficulty, to provide advice on the most appropriate methods for use where prey capture behavior is not available. This study highlights a need to critically assess and carefully choose a behavioral annotation method suitable for the research question being addressed, or resulting species management frameworks established.