François Rousset

Summary: Cryptic female choice - female-mediated bias in fertilization after mating - is well established in animals and can also occur in plants when multiple pollens compete on the same pistil. However, whether interactions between pollen and pistil tissues after pollen deposition contribute to this process remains unknown. Here, we experimentally test whether such interactions mediate cryptic female choice in the angiosperm Brassica rapa . We quantified fertilization success of pollen donors competing on the same pistil using paternity analyses, and in parallel, made semi- in vivo assays to measure pollen tubes trajectories emerging from the excised styles and growing toward unfertilized ovules for each donor-recipient pair. We show that pollen tube growth towards ovules predicts higher fertilization success under pollen competition. Thus, we document a previously unobserved mechanism of cryptic female choice based on physical interactions between male and female components of reproduction. In addition, different recipient plants favour different pollen donors, consistent with non-directional female choice. Plants with longer styles bias paternities more strongly towards the most successful pollen donor. Overall, our study demonstrates that interactions between pollen tubes and pistil tissues after pollen germination enable plants to bias paternity toward particular donors.

Simulation-based methods such as approximate Bayesian computation (ABC) are widely used to infer the evolutionary history of populations from molecular genetic data. We describe and evaluate a new iterative method of statistical inference about model parameters, which revisits the idea of inferring a likelihood surface using simulation when the likelihood function cannot be evaluated. It is based on combining the random forest machine learning method, and multivariate Gaussian mixture (MGM) models, in an effective inference workflow, here used to fit models with up to 15 variable parameters. In addition to the traditional assessment of precision in terms of bias and mean square error, we also evaluate the coverage of confidence intervals. The method is compared with approximate Bayesian computation using random forests (ABC-RF), a non-iterative method sharing some technical features with the proposed approach, across scenarios of historical demographic inference from population genetic data. It is also compared to another iterative method, sequential neural likelihood estimation (SNLE). These comparisons highlight the importance of an iterative workflow for exploring the parameter space efficiently. For equivalent simulation effort of the data-generating process, the new summary-likelihood method provides intervals whose coverage is better controlled than the marginal coverage of intervals provided by ABC with random forests, and than generally reported for ABC methods. The iterative workflow can also yield greater improvements in estimator precision when larger datasets are used.

Introgression, the incorporation of foreign variants through hybridization and repeated backcross, is increasingly being studied for its potential evolutionary consequences, one of which is adaptive introgression (AI). In recent years, several statistical methods have been proposed for the detection of loci that have undergone adaptive introgression. Most of these methods have been tested and developed to infer the presence of Neanderthal or Denisovan AI in humans. Currently, the behaviour of these methods when faced with genomic datasets from evolutionary scenarios other than the human lineage remains unknown. This study therefore focuses on testing the performance of the methods using test data sets simulated under various evolutionary scenarios inspired by the evolutionary history of human, wall lizard ( Podarcis ) and bear ( Ursus ) lineages. These lineages were chosen to represent different combinations of divergence and migration times. We study the impact of these parameters, as well as migration rate, population size, selection coefficient and presence of recombination hotspots, on the performance of three methods (VolcanoFinder, Genomatnn and MaLAdapt) and a standalone summary statistic (Q95( w , y )). Furthermore, the hitchhiking effect of an adaptively introgressed mutation can have a strong impact on the flanking regions, and therefore on the discrimination between the genomic windows classes ( i.e. AI/non-AI). For this reason, three different types of non-AI windows are taken into account in our analyses: independently simulated neutral introgression windows, windows adjacent to the window under AI, and windows coming from a second neutral chromosome unlinked to the chromosome under AI. Our results highlight the importance of taking into account adjacent windows in the training data in order to correctly identify the window with the mutation under AI. Finally, our tests show that methods based on Q95 seem to be the most efficient for an exploratory study of AI.

Understanding the selection of breeding sites in cities is key for the effective management of urban bird populations. We analysed 16 years of data collected within a relocation program for female mallards in Berlin (Germany), with the goal of (i) characterizing the breeding output and breeding sites in urban environments, (ii) identifying factors correlating with probabilities of individuals to return to previously used breeding sites (i.e. breeding site fidelity) and (iii) assessing the consequences of ongoing management strategies on the selection of breeding sites. Mallards using anthropogenic structures for breeding primarily chose balconies, roof terraces and courtyards. In 22.8% of cases, mallards selected the same sites in subsequent breeding events. Breeding site fidelity decreased with higher human population density at that location, and decreased as mallard families were relocated further away from the breeding site to a suitable water body. In contrast, return probabilities were higher for subsequent broods within the same breeding season than across two successive seasons. Above all, the identity of the breeding location and the identity of the female best predicted whether an individual would return to a given breeding site. We conclude that breeding site fidelity in urban mallards is strongly shaped by micro-scale location-specific properties (e.g. balcony characteristics), as well as individual variation due to an intrinsic propensity for site fidelity. We argue that adequate management for waterfowl in urban environments depends on why birds choose to breed on anthropogenic structures, on their conservation status and the availability of breeding sites around urban waters.

Simulation-based methods such as approximate Bayesian computation (ABC) are widely used to infer the evolutionary history of populations from molecular genetic data. We describe and evaluate a new iterative method of statistical inference about model parameters, which revisits the idea of inferring a likelihood surface using simulation when the likelihood function cannot be evaluated. It is based on combining the random forest machine learning method, and multivariate Gaussian mixture (MGM) models, in an effective inference workflow, here used to fit models with up to 15 variable parameters. In addition to the traditional assessment of precision in terms of bias and mean square error, we also evaluate the coverage of confidence intervals. The method is compared with approximate Bayesian computation using random forests (ABC-RF), a non-iterative method sharing some technical features with the proposed approach, across scenarios of historical demographic inference from population genetic data. It is also compared to another iterative method, sequential neural likelihood estimation (SNLE). These comparisons highlight the importance of an iterative workflow for exploring the parameter space efficiently. For equivalent simulation effort of the data-generating process, the new summary-likelihood method provides intervals whose coverage is better controlled than the marginal coverage of intervals provided by ABC with random forests, and than generally reported for ABC methods. The iterative workflow can also yield greater improvements in estimator precision when larger datasets are used.

Introgression, the incorporation of foreign variants through hybridization and repeated backcross, is increasingly being studied for its potential evolutionary consequences, one of which is adaptive introgression (AI). In recent years, several statistical methods have been proposed for the detection of loci that have undergone adaptive introgression. Most of these methods have been tested and developed to infer the presence of Neanderthal or Denisovan AI in humans. Currently, the behaviour of these methods when faced with genomic datasets from evolutionary scenarios other than the human lineage remains unknown. This study therefore focuses on testing the performance of the methods using test data sets simulated under various evolutionary scenarios inspired by the evolutionary history of human, wall lizard ( Podarcis ) and bear ( Ursus ) lineages. These lineages were chosen to represent different combinations of divergence and migration times. We study the impact of these parameters, as well as migration rate, population size, selection coefficient and presence of recombination hotspots, on the performance of three methods (VolcanoFinder, Genomatnn and MaLAdapt) and a standalone summary statistic (Q95( w , y )). Furthermore, the hitchhiking effect of an adaptively introgressed mutation can have a strong impact on the flanking regions, and therefore on the discrimination between the genomic windows classes ( i.e. AI/non-AI). For this reason, three different types of non-AI windows are taken into account in our analyses: independently simulated neutral introgression windows, windows adjacent to the window under AI, and windows coming from a second neutral chromosome unlinked to the chromosome under AI. Our results highlight the importance of taking into account adjacent windows in the training data in order to correctly identify the window with the mutation under AI. Finally, our tests show that methods based on Q95 seem to be the most efficient for an exploratory study of AI.