In a just published Opinion paper in Trends in Ecology & Evolution, we advocate that a next-generation, global-scale, ecological approach to biomonitoring will emerge in the coming decade, which can detect ecosystem change accurately, cheaply and generically. Next-generation sequencing (NGS) of DNA sampled from the Earth’s environments, would provide data for the relative abundance of operational taxonomic units or ecological functions. Machine-learning methods would then be used to reconstruct the ecological networks of interactions implicit in the raw NGS data in order to detect and predict ecosystem change. In this Next Generation Biomonitoring (NGB) project, we will examine whether NGS samples from five distinct ecosystems undergoing global change can be used to reconstruct hypothetical networks of interaction using machine learning. We will then compare these reconstructed networks with the current state of knowledge for these systems to test whether NGS and machine learning approaches can be used to reconstruct valid ecological networks. These tests will include examining the NGS networks for specific, established interactions through to detailed comparisons against already-known ecological networks, built using classic network construction approaches. The five systems we will work on represent a cross-section of the organisational scales, drivers of change and data quality we would expect that a NGB approach could be applied to. From microbial interaction networks to macro-biome networks of interacting invertebrates, and across drivers of change such as invasion, disease, conservation, management and climate, the project will determine whether ecosystem change can be detected using an NGB approach. We will troubleshoot many of the technical, methodological and ecological problems facing the development of an NGB approach, such as the variable quality of NGS databases, taxa biases, identification errors, zero-rich data and asymmetric abundance distributions, and develop statistical approaches for detecting change and determining the size and power of biomonitoring programs. Ultimately, we envision the development of autonomous samplers that would sample nucleic acids and upload NGS sequence data to the cloud for network reconstruction, using methods that we will develop in the project. Large numbers of these samplers, in a global array, would allow sensitive automated biomonitoring of the Earth’s major ecosystems at high spatial and temporal resolution, revolutionising our understanding of ecosystem change.

Pesticides are of limited use against bacterial diseases in crops due to a lack of effective and non-toxic molecules. Thus, genetic selection of resistant crops remains the most effective approach to control bacterial pathogens. Resistance breeding requires a conceptual jump to efficiently design significant and durable resistance to a large variety of pathogens in a large number of crops simultaneously. The CROpTAL project aims at identifying plant susceptibility hubs in major crops (cereals, citrus, legumes and brassicaceae) targeted by Xanthomonas virulence-promoting TAL (Transcription Activator-Like) type III effectors. These conserved susceptibility targets could then be used for marker-assisted breeding of loss-of-susceptibility by selection of inactive variants of those hubs. These results will contribute to the development of durable resistance to a broad range of bacterial pathogens in the selected crops.

Emergent diseases of plants, a high proportion of which are caused by phytoviruses, are a significant burden on the food security and economic stability of societies. However, no studies have provided a comprehensive view of the geographical distribution of phytovirus diversity to date, including both the numbers or richness of virus species and the evenness of their distribution in any individual environment on Earth. Our capacity to detect phytoviruses in the early phases of emergence is strongly dependent on our ability to determine the frequencies and geographical distributions of both new introductions of virus and plant species to environments, and the new virus-host encounters that ensue within these environments. This gap in our knowledge undermines our understanding of virus adaptation and limits our capacity to derive truly general predictive models of phytovirus emergence. Recent viral metagenomics studies, which have leveraged methodological innovations to achieve the relatively unbiased sampling and sequencing of viral genomes within natural environments have paved the way towards the analyses of phytovirus biodiversity in these environments in sufficient detail to drive major advances in our understanding of the evolutionary processes that underlie the emergence of phytoviruses as agricultural pathogens. These studies have already revealed that uncultivated areas within agricultural settings are key-players in the ecology and evolution of agriculturally relevant phytoviruses. The two overarching hypotheses that we propose testing in this project will extend the findings of these pioneering virus biodiversity studies: - Plant community structure influences phytoviral community structure: We hypothesize that plant community species richness, composition, density and biomass are predictors of phytovirus species richness. - The rate of molecular evolution of viruses is slower in uncultivated areas than in cultivated areas: We hypothesize that land uses changes and cropping practices are likely to select for fast-growing, early-transmitted, and more virulent viruses. The PHYTOVIRUS project has three scientific and technological objectives that will aim at testing whether plant species richness influences phytovirus species richness in natural and cultivated areas (objective 1), studying experimentally the effect of plant communities on phytoviral species richness (objective 2), and searching for evolutionary fingerprints associated with emergence within phytovirus genomes (objective 3). The first work package (WP1) will yield a substantially expanded inventory of known phytoviruses and provide detailed comparative data on the species richness of plants and phytoviruses in several natural unmanaged ecosystems and managed agricultural systems. WP2 will experimentally test whether associations exist between the species richness of phytovirus and plant communities. Finally, WP3 will explore sequence data generated in WP1/2 to detect and characterise evolutionary footprints (evolutionary rates, recombination patterns and natural selection patterns) that are associated with emergence. Besides providing the first assessments of phytovirus species richness in selected environments, this project also aims at defining and demonstrating a standardized experimental approach to measure phytovirus species richness that could then be universally used at scales ranging from defined ecosystems through to entire continents. The project brings together research groups that are specialized in plant virology, viral metagenomics, plant ecology, and the computational analysis of virus evolution. This multidisciplinary consortium has the ability to implement a holistic research program that is without equivalent at the international levels with respect to its focus on phytovirus species richness and the plant community and viral evolutionary parameters that have shaped this species richness.

Oceanic islands contribute disproportionately to global biodiversity and harbor a high number of endemic species that exhibit unique evolutionary and functional adaptations, shaped by their life in isolation. Owing to their exceptional endemism and diversity, high vulnerability to multiple global change drivers, and under-representation in many global biodiversity databases and initiatives, oceanic islands are a top priority for assessing the status and monitoring the trends of biodiversity. Thus, in BioMonI, we will provide unique insights into past, present, and future trends of plant-related essential biodiversity variables and essential ecosystem function variables by leveraging long-term paleoecological archives, vegetation monitoring plots, remote sensing, and modeling of future scenarios. Our goal is to uncover trends in biodiversity across elusive dimensions, expanding the scope of conservation and monitoring efforts by incorporating evolutionary and functional perspectives, as well as biotic associations. We will develop a harmonized monitoring scheme specifically tailored to the challenges of island biodiversity, assemble BioMonI-PLOT – a unique vegetation plot network of oceanic islands, and foster the BioMonI e-infrastructure – making information across archipelagos easily accessible for stakeholders including researchers, citizen scientists, conservation managers, (non-)governmental organizations and public institutions.

Many bacteria secrete small peptides with antimicrobial properties called bacteriocins that contribute to their competitiveness in the environment. The spectrum of action of bacteriocins is narrow and most often targets genetic lineages of the same bacterial species. Therefore, bacteriocins have already found their application in food industry as natural preservatives, in animal production as probiotics and they represent a credible alternative to antibiotics and constitute a serious solution to multidrug-resistant bacteria. In agriculture, although many diseases are caused by bacteria attacking plants, studies on bacteriocins are still very limited. The bacterial Ralstonia solanacearum species complex (RSSC) is the causal agent of bacterial wilt, one of the most important disease of crop plants worldwide and is included in the A2 (high risk) list of quarantine organisms in Europe. Recently we showed that the two RSSC lineages, I-18 and I-31, mostly prevalent in the South-West Indian Ocean (SWIO) have bacteriocin-like activity against less prevalent lineages suggesting a role for these molecules in the epidemic success. Interestingly, the bacteriocin activities of some I-18 strains target the I-31 lineage which is a considerable threat to solanaceous crops in the small islands of the SWIO and in Africa. The objectives of the BAOBAB project are : (i) to widely screen the diversity of RSSC strains for their ability to produce bacteriocins (ii) to characterize for the first time bacteriocins from the RSSC, using both a biochemical purification pipeline based on bio-guided fractionation, and genomic comparison methods to identify bacteriocin-encoding genes, (iii) to estimate their role in the success of epidemic strains by performing competition assays between active and susceptible strains or depleted mutants for bacteriocins (iv) to provide a proof of concept for the use of bacteriocins as efficient biocontrol agent to prevent bacterial infections in plants.