Biologic invasions can have important ecological, economic and social consequences, particularly when they involve the introduction and spread of plant invasive pathogens, as they can threaten natural ecosystems and jeopardize the production of human food. Examples include the grapevine downy mildew, caused by the oomycete Plasmopara viticola, an invasive species native to North America, introduced into Europe in the 1870s. We investigated the introduction and spread of this invasive pathogen, by analysing its genetic structure and diversity in a large sample from European vineyards. Populations of P. viticola across Europe displayed little genetic diversity, consistent with the occurrence of a bottleneck at the time of introduction. Bayesian coalescent analyses revealed a clear population expansion signal in the genetic data. We detected a weak, but significant, continental-wide population structure, with two geographically and genetically distinct clusters in Western and Eastern European vineyards. Approximate Bayesian computation, analyses of clines of genetic diversity and of isolation-by-distance patterns provided evidence for a wave of colonization moving in an easterly direction across Europe. This is consistent with historical reports, first mentioning the introduction of the disease in Bordeaux vineyards (France) and sub-sequently documenting its rapid spread across Europe. This initial introduction in the west was probably followed by a 'leap-frog' event into Eastern Europe, leading to the formation of the two genetic clusters we detected. This study shows that recent population genetics methods within the Bayesian and coalescence frameworks are extremely powerful for increasing our understanding of pathogen population dynamics and invasion histories.
The basidiomycete genus Armillaria s.l. (Armillaria s.s. and Desarmillaria) has a worldwide distribution and plays a central role in the dynamics of numerous woody ecosystems, including natural forests, tree plantations for timber production, orchards, vineyards, and gardens. Early studies have shown that all Armillaria species are capable of degrading dead woody substrates causing white rot. Moreover, most species exhibit a parasitic ability, and can be considered as facultative necrotrophs. Although over the years extensive research has been conducted on the phylogeny, biology, and ecology of different Armillaria species, numerous theoretical and applied questions remain open. Recently published studies have provided new perspectives, the most significant of which we present in this review. First, new investigations have highlighted the importance of a multilocus approach for depicting the phylogeny of the genus Armillaria. Second, the importance of clonality and sexuality for the different species is now better described, enabling a more accurate prediction of population dynamics in various environments. Third, genome sequencing has provided new insights into genome evolution and the genetic basis of pathogenicity and wood degradation ability. Fourth, several new studies have pointed out the possible influence of climate change on Armillaria distribution, biology and ecology, raising questions regarding the future evolution of Armillaria species and their effect on ecosystems. In this review, we also give a state-of-the-art overview of the control possibilities of parasitic Armillaria species. Finally, we outline some still open questions in Armillaria research, the investigation of which will strongly benefit from recent methodological advances.
Identification of partial sweeps, which include both hard and soft sweeps that have not currently reached fixation, provides crucial information about ongoing evolutionary responses. To this end, we introduce partialS/HIC, a deep learning method to discover selective sweeps from population genomic data. partialS/HIC uses a convolutional neural network for image processing, which is trained with a large suite of summary statistics derived from coalescent simulations incorporating population-specific history, to distinguish between completed versus partial sweeps, hard versus soft sweeps, and regions directly affected by selection versus those merely linked to nearby selective sweeps. We perform several simulation experiments under various demographic scenarios to demonstrate partialS/HIC’s performance, which exhibits excellent resolution for detecting partial sweeps. We also apply our classifier to whole genomes from eight mosquito populations sampled across sub-Saharan Africa by the Anopheles gambiae 1000 Genomes Consortium, elucidating both continent-wide patterns as well as sweeps unique to specific geographic regions. These populations have experienced intense insecticide exposure over the past two decades, and we observe a strong overrepresentation of sweeps at insecticide resistance loci. Our analysis thus provides a list of candidate adaptive loci that may be relevant to mosquito control efforts. More broadly, our supervised machine learning approach introduces a method to distinguish between completed and partial sweeps, as well as between hard and soft sweeps, under a variety of demographic scenarios. As whole-genome data rapidly accumulate for a greater diversity of organisms, partialS/HIC addresses an increasing demand for useful selection scan tools that can track in-progress evolutionary dynamics.
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