Dothistroma pini, D. septosporum, and Lecanosticta acicola are fungal pathogens that cause severe foliage diseases in conifers. All three pathogens are listed as quarantine organisms in numerous countries throughout the world and, thus, are subject to specific monitoring. Detection and identification of these pathogens still often relies on cumbersome and unsatisfactory methods that are based upon the morphological characterization of fungal fruiting bodies and conidia. In this study, we present the development of several new molecular tools that enable a rapid and specific in planta detection of each of these pathogens. Several DNA extraction procedures starting from infected needles were compared and four commercial DNA extraction kits provided DNA of satisfactory quality for amplification by polymerase chain reaction (PCR). In addition, we developed several sets of conventional PCR primers, dual-labeled probes (DLPs), and duplex-scorpion probes (DSPs), all of which targeted each pathogen. Their ability to detect the pathogens in a series of naturally infected needle samples was compared. The quadruplex DLP real-time assay proved to be more sensitive than the DSP assay and conventional PCR but the two real-time probe formats yielded identical results in the naturally infected samples. Both real-time assays proved to be significantly superior to the technique of humid chamber incubation, which often failed to produce spores for the accurate identification of the pathogens.

Wild and cultivated plants represent very different habitats for pathogens, especially when cultivated plants bear qualitative resistance genes. Here, we investigated to what extent the population genetic structure of a plant pathogenic fungus collected on its wild host can be impacted by the deployment of resistant cultivars. We studied one of the main poplar diseases, poplar rust, caused by the fungus Melampsora larici-populina. A thousand and fifty individuals sampled from several locations in France were phenotyped for their virulence profile (ability to infect or not the most deployed resistant cultivar 'Beaupré'), and a subset of these was genotyped using 25 microsatellite markers. Bayesian assignment tests on genetic data clustered the 476 genotyped individuals into three genetic groups. Group 1 gathered most virulent individuals and displayed evidence for selection and drastic demographic changes resulting from breakdown of the poplar cultivar 'Beaupré'. Group 2 comprised individuals corresponding to ancestral populations of M. larici-populina naturally occurring in the native range. Group 3 displayed the hallmarks of strict asexual reproduction, which has never previously been demonstrated in this species. We discuss how poplar cultivation has influenced the spatial and genetic structure of this plant pathogenic fungus, and has led to the spread of virulence alleles (gene swamping) in M. larici-populina populations evolving on the wild host.

Sphaeropsis shoot blight, caused by Diplodia pinea and Diplodia scrobiculata, damage conifers throughout the world. In France, the first disease outbreaks were reported during the 1990s. The factors associated with the pathogen presence in stands and the relationship between pathogen and disease distributions were analysed in order to understand the Sphaeropsis emergence. Eighty-two stands of Pinus nigra, Pinus sylvestris, Pinus pinaster and Pinus radiata were visited. Cones were collected on the ground to assess the pathogen frequency. Diplodia spp were isolated and determined by a species-specific PCR test. The role of potential explaining factors of D. pinea prevalence on cones was analysed by logistic regression. D. pinea was the dominant species in visited stands. The main factors influencing the pathogen presence selected in the models were host species (the pathogen being less frequent on P. pinaster than on P. nigra and P. sylvestris cones), winter temperature and summer rain, which were both positively correlated with cone colonization. The climate became more favourable to D. pinea presence within the last 15 years compared with the previous 30-year period. By contrast, future climatic changes over the next 40 years should have far less impact on the pathogen presence.

Host-parasite systems provide convincing examples of Red Queen co-evolutionary dynamics. Yet, a key process underscored in Van Valen's theory - that arms race dynamics can result in extinction - has never been documented. One reason for this may be that most sampling designs lack the breadth needed to illuminate the rapid pace of adaptation by pathogen populations. In this study, we used a 25-year temporal sampling to decipher the demographic history of a plant pathogen: the poplar rust fungus, Melampsora larici-populina. A major adaptive event occurred in 1994 with the breakdown of R7 resistance carried by several poplar cultivars widely planted in Western Europe since 1982. The corresponding virulence rapidly spread in M. larici-populina populations and nearly reached fixation in northern France, even on susceptible hosts. Using both temporal records of virulence profiles and temporal population genetic data, our analyses revealed that (i) R7 resistance breakdown resulted in the emergence of a unique and homogeneous genetic group, the so-called cultivated population, which predominated in northern France for about 20 years, (ii) selection for Vir7 individuals brought with it multiple other virulence types via hitchhiking, resulting in an overall increase in the population-wide number of virulence types and (iii) - above all - the emergence of the cultivated population superseded the initial population which predominated at the same place before R7 resistance breakdown. Our temporal analysis illustrates how antagonistic co-evolution can lead to population extinction and replacement, hence providing direct evidence for the escalation process which is at the core of Red Queen dynamics.

Summary1. Biological invasions are a major consequence of globalization and pose a significant threat to biodiversity. Because only a small fraction of introduced species become invasive, identification of those species most likely to become invasive after introduction is highly desirable to focus management efforts. The predictive potential of species-specific traits has been much investigated in plants and animals. However, despite the importance of fungi as a biological group and the potentially severe effects of pathogenic fungi on agrosystems and natural ecosystems, the specific identification of traits correlated with the invasion success of fungi has not been attempted previously. 2. We addressed this question by constructing an ad hoc data set including invasive and noninvasive species of forest pathogenic fungi introduced into Europe. Data were analysed with a machine learning method based on classification trees (Random Forest). The performance of the classification rule based on species traits was compared with that of several random decision rules, and the principal trait predictors associated with invasive species were identified. 3. Invasion success was more accurately predicted by the classification rule including biological traits than by random decision rules. The effect of species traits was maintained when confounding variables linked to residence time and habitat availability were included. The selected traits were unlikely to be affected by a phylogenetic bias as invasive and non-invasive species were evenly distributed in fungal clades. 4. The species-level predictors identified as useful for distinguishing between invasive and non-invasive species were traits related to long-distance dispersal, sexual reproduction (in addition to asexual reproduction), spore shape and size, number of cells in spores, optimal temperature for growth and parasitic specialization (host range and infected organs). 5. Synthesis and applications. This study demonstrates that some species-level traits are predictors of invasion success for forest pathogenic fungi in Europe. These traits could be used to refine current pest risk assessment (PRA) schemes. Our results suggest that current schemes, which are mostly based on sequential questionnaires, could be improved by taking into account trait interactions or combinations. More generally, our results confirm the interest of machine learning methods, such as Random Forest, for species classification in ecology.