Inbreeding load, bet hedging, and the evolution of sex-biased dispersal

Inbreeding load affects not only the average fecundity of philopatric individuals but also its variance. From bet-hedging theory, this should add further dispersal pressures to those stemming from the mere avoidance of inbreeding. Pressures on both sexes are identical under monogamy or promiscuity. Under polygyny, by contrast, the variance in reproductive output decreases with dispersal rate in females but increases in males, which should induce a female-biased dispersal. To test this prediction, we performed individual-based simulations. From our results, a female-biased dispersal indeed emerges as both polygyny and inbreeding load increase. We conclude that sex-biased dispersal may be selected for as a bet-hedging strategy.

Statistical properties of population differentiation estimators under stepwise mutation in a finite island model.

Microsatellite loci mutate at an extremely high rate and are generally thought to evolve through a stepwise mutation model. Several differentiation statistics taking into account the particular mutation scheme of the microsatellite have been proposed. The most commonly used is R(ST) which is independent of the mutation rate under a generalized stepwise mutation model. F(ST) and R(ST) are commonly reported in the literature, but often differ widely. Here we compare their statistical performances using individual-based simulations of a finite island model. The simulations were run under different levels of gene flow, mutation rates, population number and sizes. In addition to the per locus statistical properties, we compare two ways of combining R(ST) over loci. Our simulations show that even under a strict stepwise mutation model, no statistic is best overall. All estimators suffer to different extents from large bias and variance. While R(ST) better reflects population differentiation in populations characterized by very low gene-exchange, F(ST) gives better estimates in cases of high levels of gene flow. The number of loci sampled (12, 24, or 96) has only a minor effect on the relative performance of the estimators under study. For all estimators there is a striking effect of the number of samples, with the differentiation estimates showing very odd distributions for two samples.

Sex-chromosome turnovers induced by deleterious mutation load.

In sharp contrast with mammals and birds, many cold-blooded vertebrates present homomorphic sex chromosomes. Empirical evidence supports a role for frequent turnovers, which replace nonrecombining sex chromosomes before they have time to decay. Three main mechanisms have been proposed for such turnovers, relying either on neutral processes, sex-ratio selection, or intrinsic benefits of the new sex-determining genes (due, e.g., to linkage with sexually antagonistic mutations). Here, we suggest an additional mechanism, arising from the load of deleterious mutations that accumulate on nonrecombining sex chromosomes. In the absence of dosage compensation, this load should progressively lower survival rate in the heterogametic sex. Turnovers should occur when this cost outweighs the benefits gained from any sexually antagonistic genes carried by the nonrecombining sex chromosome. We use individual-based simulations of a Muller's ratchet process to test this prediction, and investigate how the relevant parameters (effective population size, strength and dominance of deleterious mutations, size of nonrecombining segment, and strength of sexually antagonistic selection) are expected to affect the rate of turnovers.

Genetic bottlenecks driven by population disconnection.

Connectivity among populations plays a crucial role in maintaining genetic variation at a local scale, especially in small populations affected strongly by genetic drift. The negative consequences of population disconnection on allelic richness and gene diversity (heterozygosity) are well recognized and empirically established. It is not well recognized, however, that a sudden drop in local effective population size induced by such disconnection produces a temporary disequilibrium in allelic frequency distributions that is akin to the genetic signature of a demographic bottleneck. To document this effect, we used individual-based simulations and empirical data on allelic richness and gene diversity in six pairs of isolated versus well-connected (core) populations of European tree frogs. In our simulations, population disconnection depressed allelic richness more than heterozygosity and thus resulted in a temporary excess in gene diversity relative to mutation drift equilibrium (i.e., signature of a genetic bottleneck). We observed a similar excess in gene diversity in isolated populations of tree frogs. Our results show that population disconnection can create a genetic bottleneck in the absence of demographic collapse.

Senescence rates are determined by ranking on the fast-slow life-history continuum.

Comparative analyses of survival senescence by using life tables have identified generalizations including the observation that mammals senesce faster than similar-sized birds. These generalizations have been challenged because of limitations of life-table approaches and the growing appreciation that senescence is more than an increasing probability of death. Without using life tables, we examine senescence rates in annual individual fitness using 20 individual-based data sets of terrestrial vertebrates with contrasting life histories and body size. We find that senescence is widespread in the wild and equally likely to occur in survival and reproduction. Additionally, mammals senesce faster than birds because they have a faster life history for a given body size. By allowing us to disentangle the effects of two major fitness components our methods allow an assessment of the robustness of the prevalent life-table approach. Focusing on one aspect of life history - survival or recruitment - can provide reliable information on overall senescence.

What matters for predicting spatial distributions of trees: techniques, data, or species' characteristics?

Data characteristics and species traits are expected to influence the accuracy with which species' distributions can be modeled and predicted. We compare 10 modeling techniques in terms of predictive power and sensitivity to location error, change in map resolution, and sample size, and assess whether some species traits can explain variation in model performance. We focused on 30 native tree species in Switzerland and used presence-only data to model current distribution, which we evaluated against independent presence-absence data. While there are important differences between the predictive performance of modeling methods, the variance in model performance is greater among species than among techniques. Within the range of data perturbations in this study, some extrinsic parameters of data affect model performance more than others: location error and sample size reduced performance of many techniques, whereas grain had little effect on most techniques. No technique can rescue species that are difficult to predict. The predictive power of species-distribution models can partly be predicted from a series of species characteristics and traits based on growth rate, elevational distribution range, and maximum elevation. Slow-growing species or species with narrow and specialized niches tend to be better modeled. The Swiss presence-only tree data produce models that are reliable enough to be useful in planning and management applications.

The evolution of judgement bias in indirect reciprocity.

Indirect reciprocity is a form of reciprocity where help is given to individuals based on their reputation. In indirect reciprocity, bad acts (such as not helping) reduce an individual's reputation while good acts (such as helping) increase an individual's reputation. Studies of indirect reciprocity assume that good acts and bad acts are weighted equally when assessing the reputation of an individual. As different information can be processed in different ways, this is not likely to be the case, and it is possible that an individual could bias an actor's reputation by putting more weight to acts of defection (not helping) than acts of co-operation (helping) or vice versa. We term this difference 'judgement bias', and build an individual-based model of image scoring to investigate the conditions under which it may evolve. We find that, if the benefits of co-operation are small, judgement bias is weighted towards acts perceived to be bad; if the benefits are high, the reverse is true. Our result is consistent under both scoring and standing strategies, and we find that allowing judgement bias to evolve increases the level of co-operation in the population.

Deposits of the sandy braided South Saskatchewan River: Implications for the use of modern analogs in reconstructing channel dimensions in reservoir characterization

Estimation of the dimensions of fluvial geobodies from core data is a notoriously difficult problem in reservoir modeling. To try and improve such estimates and, hence, reduce uncertainty in geomodels, data on dunes, unit bars, cross-bar channels, and compound bars and their associated deposits are presented herein from the sand-bed braided South Saskatchewan River, Canada. These data are used to test models that relate the scale of the formative bed forms to the dimensions of the preserved deposits and, therefore, provide an insight as to how such deposits may be preserved over geologic time. The preservation of bed-form geometry is quantified by comparing the Alluvial architecture above and below the maximum erosion depth of the modem channel deposits. This comparison shows that there is no significant difference in the mean set thickness of dune cross-strata above and below the basal erosion surface of the contemporary channel, thus suggesting that dimensional relationships between dune deposits and the formative bed-form dimensions are likely to be valid from both recent and older deposits. The data show that estimates of mean bankfull flow depth derived from dune, unit bar, and cross-bar channel deposits are all very similar. Thus, the use of all these metrics together can provide a useful check that all components and scales of the alluvial architecture have been identified correctly when building reservoir models. The data also highlight several practical issues with identifying and applying data relating to cross-strata. For example, the deposits of unit bars were found to be severely truncated in length and width, with only approximately 10% of the mean bar-form length remaining, and thus making identification in section difficult. For similar reasons, the deposits of compound bars were found to be especially difficult to recognize, and hence, estimates of channel depth based on this method may be problematic. Where only core data are available (i.e., no outcrop data exist), formative flow depths are suggested to be best reconstructed using cross-strata formed by dunes. However, theoretical relationships between the distribution of set thicknesses and formative dune height are found to result in slight overestimates of the latter and, hence, mean bankfull flow depths derived from these measurements. This article illustrates that the preservation of fluvial cross-strata and, thus, the paleohydraulic inferences that can be drawn from them, are a function of the ratio of the size and migration rate of bed forms and the time scale of aggradation and channel migration. These factors must thus be considered when deciding on appropriate length:thickness ratios for the purposes of object-based modeling in reservoir characterization.

Selection on a genetic polymorphism counteracts ecological speciation in a stick insect.

The interplay between selection and aspects of the genetic architecture of traits (such as linkage, dominance, and epistasis) can either drive or constrain speciation [1-3]. Despite accumulating evidence that speciation can progress to "intermediate" stages-with populations evolving only partial reproductive isolation-studies describing selective mechanisms that impose constraints on speciation are more rare than those describing drivers. The stick insect Timema cristinae provides an example of a system in which partial reproductive isolation has evolved between populations adapted to different host plant environments, in part due to divergent selection acting on a pattern polymorphism [4, 5]. Here, we demonstrate how selection on a green/melanistic color polymorphism counteracts speciation in this system. Specifically, divergent selection between hosts does not occur on color phenotypes because melanistic T. cristinae are cryptic on the stems of both host species, are resistant to a fungal pathogen, and have a mating advantage. Using genetic crosses and genome-wide association mapping, we quantify the genetic architecture of both the pattern and color polymorphism, illustrating their simple genetic control. We use these empirical results to develop an individual-based model that shows how the melanistic phenotype acts as a "genetic bridge" that increases gene flow between populations living on different hosts. Our results demonstrate how variation in the nature of selection acting on traits, and aspects of trait genetic architecture, can impose constraints on both local adaptation and speciation.

The effect of learning on the evolution of new courtship behavior: a simulation model

The fact that individuals learn can change the relationship between genotype and phenotype in the population, and thus affect the evolutionary response to selection. Here we ask how male ability to learn from female response affects the evolution of a novel male behavioral courtship trait under pre-existing female preference (sensory drive). We assume a courtship trait which has both a genetic and a learned component, and a two-level female response to males. With individual-based simulations we show that, under this scenario, learning generally increases the strength of selection on the genetic component of the courtship trait, at least when the population genetic mean is still low. As a consequence, learning not only accelerates the evolution of the courtship trait, but also enables it when the trait is costly, which in the absence of learning results in an adaptive valley. Furthermore, learning can enable the evolution of the novel trait in the face of gene flow mediated by immigration of males that show superior attractiveness to females based on another, non-heritable trait. However, rather than increasing monotonically with the speed of learning, the effect of learning on evolution is maximized at intermediate learning rates. This model shows that, at least under some scenarios, the ability to learn can drive the evolution of mating behaviors through a process equivalent to Waddington's genetic assimilation.

The Association of H1N1 Pandemic Influenza with Congenital Anomaly Prevalence in Europe: An Ecological Time Series Study.

BACKGROUND: In the context of the European Surveillance of Congenital Anomalies (EUROCAT) surveillance response to the 2009 influenza pandemic, we sought to establish whether there was a detectable increase of congenital anomaly prevalence among pregnancies exposed to influenza seasons in general, and whether any increase was greater during the 2009 pandemic than during other seasons. METHODS: We performed an ecologic time series analysis based on 26,967 pregnancies with nonchromosomal congenital anomaly conceived from January 2007 to March 2011, reported by 15 EUROCAT registries. Analysis was performed for EUROCAT-defined anomaly subgroups, divided by whether there was a prior hypothesis of association with influenza. Influenza season exposure was based on World Health Organization data. Prevalence rate ratios were calculated comparing pregnancies exposed to influenza season during the congenital anomaly-specific critical period for embryo-fetal development to nonexposed pregnancies. RESULTS: There was no evidence for an increased overall prevalence of congenital anomalies among pregnancies exposed to influenza season. We detected an increased prevalence of ventricular septal defect and tricuspid atresia and stenosis during pandemic influenza season 2009, but not during 2007-2011 influenza seasons. For congenital anomalies, where there was no prior hypothesis, the prevalence of tetralogy of Fallot was strongly reduced during influenza seasons. CONCLUSIONS: Our data do not suggest an overall association of pandemic or seasonal influenza with congenital anomaly prevalence. One interpretation is that apparent influenza effects found in previous individual-based studies were confounded by or interacting with other risk factors. The associations of heart anomalies with pandemic influenza could be strain specific.

Tipping the scales: Evolution of the allometric slope independent of average trait size.

The scaling of body parts is central to the expression of morphology across body sizes and to the generation of morphological diversity within and among species. Although patterns of scaling-relationship evolution have been well documented for over one hundred years, little is known regarding how selection acts to generate these patterns. In part, this is because it is unclear the extent to which the elements of log-linear scaling relationships-the intercept or mean trait size and the slope-can evolve independently. Here, using the wing-body size scaling relationship in Drosophila melanogaster as an empirical model, we use artificial selection to demonstrate that the slope of a morphological scaling relationship between an organ (the wing) and body size can evolve independently of mean organ or body size. We discuss our findings in the context of how selection likely operates on morphological scaling relationships in nature, the developmental basis for evolved changes in scaling, and the general approach of using individual-based selection experiments to study the expression and evolution of morphological scaling.

Evaluation of occupational exposure: comparison of biological and environmental variabilities using physiologically based toxicokinetic modeling

PURPOSE: Few studies compare the variabilities that characterize environmental (EM) and biological monitoring (BM) data. Indeed, comparing their respective variabilities can help to identify the best strategy for evaluating occupational exposure. The objective of this study is to quantify the biological variability associated with 18 bio-indicators currently used in work environments. METHOD: Intra-individual (BV(intra)), inter-individual (BV(inter)), and total biological variability (BV(total)) were quantified using validated physiologically based toxicokinetic (PBTK) models coupled with Monte Carlo simulations. Two environmental exposure profiles with different levels of variability were considered (GSD of 1.5 and 2.0). RESULTS: PBTK models coupled with Monte Carlo simulations were successfully used to predict the biological variability of biological exposure indicators. The predicted values follow a lognormal distribution, characterized by GSD ranging from 1.1 to 2.3. Our results show that there is a link between biological variability and the half-life of bio-indicators, since BV(intra) and BV(total) both decrease as the biological indicator half-lives increase. BV(intra) is always lower than the variability in the air concentrations. On an individual basis, this means that the variability associated with the measurement of biological indicators is always lower than the variability characterizing airborne levels of contaminants. For a group of workers, BM is less variable than EM for bio-indicators with half-lives longer than 10-15 h. CONCLUSION: The variability data obtained in the present study can be useful in the development of BM strategies for exposure assessment and can be used to calculate the number of samples required for guiding industrial hygienists or medical doctors in decision-making.

Preliminary regional rockfall hazard mapping using LIDAR-based slope frequency distribution and CONEFALL modeling

A factor limiting preliminary rockfall hazard mapping at regional scale is often the lack of knowledge of potential source areas. Nowadays, high resolution topographic data (LiDAR) can account for realistic landscape details even at large scale. With such fine-scale morphological variability, quantitative geomorphometric analyses become a relevant approach for delineating potential rockfall instabilities. Using digital elevation model (DEM)-based ?slope families? concept over areas of similar lithology and cliffs and screes zones available from the 1:25,000 topographic map, a susceptibility rockfall hazard map was drawn up in the canton of Vaud, Switzerland, in order to provide a relevant hazard overview. Slope surfaces over morphometrically-defined thresholds angles were considered as rockfall source zones. 3D modelling (CONEFALL) was then applied on each of the estimated source zones in order to assess the maximum runout length. Comparison with known events and other rockfall hazard assessments are in good agreement, showing that it is possible to assess rockfall activities over large areas from DEM-based parameters and topographical elements.

A flow cytometry based oligotrophic pollutant exposure test to detect bacterial growth inhibition and cell injury.

Toxicity of chemical pollutants in aquatic environments is often addressed by assays that inquire reproductive inhibition of test microorganisms, such as algae or bacteria. Those tests, however, assess growth of populations as a whole via macroscopic methods such as culture turbidity or colony-forming units. Here we use flow cytometry to interrogate the fate of individual cells in low-density populations of the bacterium Pseudomonas fluorescens SV3 exposed or not under oligotrophic conditions to a number of common pollutants, some of which derive from oil contamination. Cells were stained at regular time intervals during the exposure assay with fluorescent dyes that detect membrane injury (i.e., live-dead assay). Reduction of population growth rates was observed upon toxicant insult and depended on the type of toxicant. Modeling and cell staining indicate that population growth rate decrease is a combined effect of an increased number of injured cells that may or may not multiply, and live cells dividing at normal growth rates. The oligotrophic assay concept presented here could be a useful complement for existing biomarker assays in compliance with new regulations on chemical effect studies or, more specifically, for judging recovery after exposure to fluctuating toxicant conditions.