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Despite evolutionary biology’s obsession with natural selection, few studies have evaluated multigenerational series of patterns of selection on a genome-wide scale in natural populations. Here, we report on a 10-y population-genomic survey of the microcrustaceanDaphnia pulex.The genome sequences of$>$800 isolates provide insights into patterns of selection that cannot be obtained from long-term molecular-evolution studies, including the following: the pervasiveness of near quasi-neutrality across the genome (mean net selection coefficients near zero, but with significant temporal variance about the mean, and little evidence of positive covariance of selection across time intervals); the preponderance of weak positive selection operating on minor alleles; and a genome-wide distribution of numerous small linkage islands of observable selection influencing levels of nucleotide diversity. These results suggest that interannual fluctuating selection is a major determinant of standing levels of variation in natural populations, challenge the conventional paradigm for interpreting patterns of nucleotide diversity and divergence, and motivate the need for the further development of theoretical expressions for the interpretation of population-genomic data.

 

Permeable sediments, which make up almost half of the continental shelf worldwide, are potential sources of the important greenhouse gas N2O from coastal regions. Yet, the extent to which interactions between these sediments and anthropogenic pollution produce N2O is still unknown. Here we use laboratory experiments and modeling to explore the factors controlling N2O production at a eutrophic site in a temperate shallow marine embayment (Port Phillip Bay, Australia). Our results show that denitrification is the main source of N2O production within permeable sediments, but the extent to which N2O is actually released is determined by the rate of seawater exchange with the sediment bed (which governs solute residence time within the bed). In wave‐dominated coastal areas, shallower water with more intense waves (wave height >> 1 m) release the most N2O, with up to 0.5% of dissolved inorganic nitrogen pumped into biologically active eutrophic sediment being released as N2O. Our results suggest rates of N2O production in coastal permeable sediments are generally low compared to other environments. 

The field of genomics has ushered in new methods for studying molecular-genetic variation in natural populations. However, most population-genomic studies still rely on small sample sizes (typically, <100 individuals) from single time points, leaving considerable uncertainties with respect to the behavior of relatively young (and rare) alleles and, owing to the large sampling variance of measures of variation, to the specific gene targets of unusually strong selection. Genomic sequences of ∼1,700 haplotypes distributed over a 10-year period from a natural population of the microcrustacean Daphnia pulex reveal evolutionary-genomic features at a refined scale, including previously hidden information on the behavior of rare alleles predicted by recent theory. Background selection, resulting from the recurrent introduction of deleterious alleles, appears to strongly influence the dynamics of neutral alleles, inducing indirect negative selection on rare variants and positive selection on common variants. Temporally fluctuating selection increases the persistence of nonsynonymous alleles with intermediate frequencies, while reducing standing levels of variation at linked silent sites. Combined with the results from an equally large metapopulation survey of the study species, classes of genes that are under strong positive selection can now be confidently identified in this key model organism. Most notable among rapidly evolving Daphnia genes are those associated with ribosomes, mitochondrial functions, sensory systems, and lifespan determination.

 

In many applications of hierarchical models, there is often interest in evaluating the inherent heterogeneity in view of observed data. When the underlying hypothesis involves parameters resting on the boundary of their support space such as variances and mixture proportions, it is a usual practice to entertain testing procedures that rely on common heterogeneity assumptions. Such procedures, albeit omnibus for general alternatives, may entail a substantial loss of power for specific alternatives such as heterogeneity varying with covariates. We introduce a novel and flexible approach that uses covariate information to improve the power to detect heterogeneity, without imposing unnecessary restrictions. With continuous covariates, the approach does not impose a regression model relating heterogeneity parameters to covariates or rely on arbitrary discretizations. Instead, a scanning approach requiring continuous dichotomizations of the covariates is proposed. Empirical processes resulting from these dichotomizations are then used to construct the test statistics, with limiting null distributions shown to be functionals of tight random processes. We illustrate our proposals and results on a popular class of two-component mixture models, followed by simulation studies and applications to two real datasets in cancer and caries research.

 

By revealing the influence of recombinational activity beyond what can be achieved with controlled crosses, measures of linkage disequilibrium (LD) in natural populations provide a powerful means of defining the recombinational landscape within which genes evolve. In one of the most comprehensive studies of this sort ever performed, involving whole-genome analyses on nearly 1,000 individuals of the cyclically parthenogenetic microcrustacean Daphnia pulex, the data suggest a relatively uniform pattern of recombination across the genome. Patterns of LD are quite consistent among populations; average rates of recombination are quite similar for all chromosomes; and although some chromosomal regions have elevated recombination rates, the degree of inflation is not large, and the overall spatial pattern of recombination is close to the random expectation. Contrary to expectations for models in which crossing-over is the primary mechanism of recombination, and consistent with data for other species, the distance-dependent pattern of LD indicates excessively high levels at both short and long distances and unexpectedly low levels of decay at long distances, suggesting significant roles for factors such as nonindependent mutation, population subdivision, and recombination mechanisms unassociated with crossing over. These observations raise issues regarding the classical LD equilibrium model widely applied in population genetics to infer recombination rates across various length scales on chromosomes. 

Genetic variants of mitochondrial DNA at the individual (heteroplasmy) and population (polymorphism) levels provide insight into their roles in multiple cellular and evolutionary processes. However, owing to the paucity of genome-wide data at the within-individual and population levels, the broad patterns of these two forms of variation remain poorly understood. Here, we analyze 1,804 complete mitochondrial genome sequences from Daphnia pulex, Daphnia pulicaria, and Daphnia obtusa. Extensive heteroplasmy is observed in D. obtusa, where the high level of intraclonal divergence must have resulted from a biparental-inheritance event, and recombination in the mitochondrial genome is apparent, although perhaps not widespread. Global samples of D. pulex reveal remarkably low mitochondrial effective population sizes, <3% of those for the nuclear genome. In addition, levels of population diversity in mitochondrial and nuclear genomes are uncorrelated across populations, suggesting an idiosyncratic evolutionary history of mitochondria in D. pulex. These population-genetic features appear to be a consequence of background selection associated with highly deleterious mutations arising in the strongly linked mitochondrial genome, which is consistent with polymorphism and divergence data suggesting a predominance of strong purifying selection. Nonetheless, the fixation of mildly deleterious mutations in the mitochondrial genome also appears to be driving positive selection on genes encoded in the nuclear genome whose products are deployed in the mitochondrion. 

In many applications of zero-inflated models, score tests are often used to evaluate whether the population heterogeneity as implied by these models is consistent with the data. The most frequently cited justification for using score tests is that they only require estimation under the null hypothesis. Because this estimation involves specifying a plausible model consistent with the null hypothesis, the testing procedure could lead to unreliable inferences under model misspecification. In this paper, we propose a score test of homogeneity for zero-inflated models that is robust against certain model misspecifications. Due to the true model being unknown in practical settings, our proposal is developed under a general framework of mixture models for which a layer of randomness is imposed on the model to account for uncertainty in the model specification. We exemplify this approach on the class of zero-inflated Poisson models, where a random term is imposed on the Poisson mean to adjust for relevant covariates missing from the mean model or a misspecified functional form. For this example, we show through simulations that the resulting score test of zero inflation maintains its empirical size at all levels, albeit a loss of power for the well-specified non-random mean model under the null. Frequencies of health promotion activities among young Girl Scouts and dental caries indices among inner-city children are used to illustrate the robustness of the proposed testing procedure. 

In many applications of two‐component mixture models such as the popular zero‐inflated model for discrete‐valued data, it is customary for the data analyst to evaluate the inherent heterogeneity in view of observed data. To this end, the score test, acclaimed for its simplicity, is routinely performed. It has long been recognised that this test may behave erratically under model misspecification, but the implications of this behaviour remain poorly understood for popular two‐component mixture models. For the special case of zero‐inflated count models, we use data simulations and theoretical arguments to evaluate this behaviour and discuss its implications in settings where the working model is restrictive with regard to the true data‐generating mechanism. We enrich this discussion with an analysis of count data in HIV research, where a one‐component model is shown to fit the data reasonably well despite apparent extra zeros. These results suggest that a rejection of homogeneity does not imply that the underlying mixture model is appropriate. Rather, such a rejection simply implies that the mixture model should be carefully interpreted in the light of potential model misspecifications, and further evaluated against other competing models.