Dinoflagellates are one of the most widespread and abundant unicellular eukaryotes in aquatic ecosystems, in which they play key roles in carbon cycling. Their ecological success can be related to the extraordinary diversity of their roles, interactions, but also trophic modes, as they can be strictly photosynthetic, strictly heterotrophic, or perform both (mixotrophy) depending on external conditions. Dinoflagellates also exhibit remarkable molecular features including Gb-sized nuclear genomes, permanently condensed chromosomes, and a 10- fold lower ratio of protein to DNA than other eukaryotes. Genomic and transcriptomic studies from the last decade based on cultivated lineages confirmed their remarkable sequence divergence and complexity, revealing millions of « dark » proteins (i.e. they do not share significant similarity to any known sequences). Are these dark proteins lineage-specific? Are dark proteins and genomic novelty linked to organismal traits (e.g., trophic modes, life style, toxicity)? Are dark proteins abundant in the ecosystems? Do they show biogeographical structure and are they particularly expressed in certain niches? DIVEDINO will study the functional diversity of dinoflagellates, in order to link their hyper diversification (e.g., high molecular evolutionary rates, high gene copy numbers, large dark proteomes) to their evolution and ecology. Existing datasets will be gathered and complemented with new transcriptomes obtained from under-represented lineages. The objectives will be:1) Exploring to an unprecedented scale functional diversity using comparative omics, phylogenomics and structure prediction; 2) Linking functional diversity to organismal traits; 3) Investigating how functional diversity and genomic novelty correlate to biotic/abiotic factors in the marine ecosystems. DIVEDINO will constitute the most extensive eco-evolutionary study of these highly divergent lineage, shedding light on the origins of genomic and functional diversity.

This project will bridge gaps between micro and macro evolution by ascertaining the evolutionary feedbacks between trait and species diversification, focusing on closely-related species living in sympatry. Sympatric species often differ in suites of traits involved in niche partitioning: how do ecological interactions induce the sequential evolution of series of traits? In turn, how does phenotypic divergence open up new niches and fuel sympatric speciation? The project focuses on the neo-tropical butterfly genus Morpho where multiple behavioural and morphological traits strikingly differ between sympatric species living in the canopy vs. understorey. Studying trait variations within and among these closely-related species living in sympatry allows reconstructing the evolutionary steps leading to the divergence in suites of traits linked to niche specialization. Within the understorey clade, striking parallel geographic variations are observed among sympatric species, resulting in repeated local convergences in iridescent blue wing patterns, that may be driven by mimicry among these fast-flying, conspicuous butterflies. Such evasive mimicry may induce costly reproductive interferences, favouring segregation of circadian activities between species and thus contributing to the speciation process. The project relies on the original combination of both up-to-date and field-based approaches: (1) cutting-edge phenotypic characterisation of complex traits (eg. iridescence, flight) (2) empirical estimations of selective forces with wild butterflies (3) machine learning-based population genomics applied to demographic inferences (4) and mathematical modelling of density-dependent processes with stochasticity. These innovative approaches will shed light on unrevealed ecological interactions between species, impacting diversification of traits and species, therefore bringing major scientific breakthrough and attracting society attention on biodiversity loss in Amazonia.

A major challenge in evolutionary biology is to understand and predict the evolution of phenotypic traits influenced by many genes, a.k.a. quantitative traits, which represent the majority of adaptive traits. For this, we require an accurate knowledge of the ‘genetic architecture’ of a trait, here defined as the statistical distribution of the effects of the genes on the phenotype. However, it has not been possible to firmly check theoretical predictions against empirical data, due to a lack of method to accurately infer genetic architecture. In this project, I will develop novel statistical methodology to accurately infer the genetic architecture of traits in the wild, by leveraging the statistical correlation between neighbouring sites in the genome, or linkage disequilibrium. Using the power of a new linked-read sequencing to obtain information on recombination, I will apply this novel methodology to study the link between the genetic architecture of the traits, and the ‘evolutionary regime’, i.e. characteristics of selective and neutral factors. First, I will perform an in-depth study of the link between selection and genetic architecture on a long-term-studied wild population of common lizards. Second, I will apply my method to analogous traits across more than 20 species to infer their genetic architecture and use knowledge about the evolutionary regime and phylogenetic context, to assess the influence of those components on the variation in genetic architecture. By combining novel methodology with analysis within and across species, this project will provide a firm empirical basis for thinking about genetic architecture. In turn, this understanding of the expected distribution of the gene effects, depending on the evolutionary context, will improve our ability to forecast adaptation, predict phenotype from genomic data and locate genes in diverse fields such as evolution, agronomy, conservation and human health.

Species with limited dispersal can overcome the challenges of climate change by changing their phenotype (phenotypic plasticity) or by adapting at the genetic level (adaptive evolution). Predicting the long-term ability of a species to adapt to climate is challenging, because most of the relevant traits are influenced by many genes. Quantitative genetics is able to provide short-term predictions without the knowledge of these genes, but for longer-term predictions, we require more information regarding the genetic architecture of the traits. The ORACLE project will tackle this issue by developing a framework to predict adaptation on the longer run and apply it on a well-studied common lizard (Zootoca vivipara) population, in order to predict its response to changes in its thermal environment cause by climate change. Thermal environment is a key aspect of the environment for ectotherms such as the common lizard, and the evolutionary potential of reptiles to changes in the thermal environment is scarcely known. I will (i) characterise the link between the phenotype and thermal environment, through natural selection, phenotypic plasticity and their interaction; (ii) characterise the genetic and phenotypic variances of the traits, as well as covariances between traits; and (iii) use whole-genome sequencing to characterise the genetic architecture of the traits. These inferences on key evolutionary features of the traits and their relationship with the thermal environment will then be used to (iv) predict the long-term evolution of traits and its impact on population dynamics, regarding long-term projections on the thermal environment of the population. By bridging the gap between quantitative genetics and population genomics, this project will deepen our knowledge about the accuracy of the predictive approaches typical from each field, but also provide new, hybrid tools to predict long-term evolution. I expect this approach to be applicable, to a large extent, to a wide range of species, where genomic analysis is possible. Being an umbrella species of concern, the results on the evolutionary potential of the common lizard also have a value for conservation.

Host plant shift is a major diversification process in herbivorous insects, which constitute one fourth of the Earth’s biodiversity. However, the underlying genetic and evolutionary mechanisms remain unclear. Of particular interest are: (1) the degree of genomic modularity controlling different fitness attributes on the new host, (2) the genetic linkage between host use and reproductive isolation traits, and (3) the repeatability of genetic changes underlying the convergent specialization on the same host. In spite of significant progress, the lack of powerful genetic and genomic tools capable to identify genes underlying host plant use and/or reproductive isolation in classical herbivorous insect models has hindered the resolution of these questions. Here, we propose to investigate an interesting case, where two species, Drosophila sechellia and D. yakuba mayottensis have independently become specialists on the toxic fruits of noni (Morinda citrifolia) in the Seychelles and Mayotte islands, respectively. In both cases, noni specialization was accompanied by partial reproductive isolation. We will combine precise phenotypic analyses with population and quantitative genomics in D. yakuba to identify genes potentially underlying noni use and reproductive isolation in D. y. mayottensis. We will then leverage advanced genome editing and transgenesis tools (e.g., CRISPR\Cas9) that have recently been developed in both species, to functionally dissect those candidate genes. The expected results will improve our understanding of the genetic and evolutionary mechanisms underlying convergent host shift and ecological speciation, which could go beyond the Drosophila-noni relationship towards the identification of neuronal circuits or detoxification pathways that are common among herbivorous insects.