Global change poses adaptive challenges to organisms, which have to face various forms of stress: habitat loss and fragmentation, invasive species pollutant or yet climate change. The main characteristic of environmental changes caused by human activities is that they occur faster and larger than those that organisms have likely experienced in their evolutionary past. Understanding how species tackle fast evolutionary challenges posed by global change is therefore more than ever crucial. Genetic adaptation, although sometimes surprisingly fast, is not the only way organisms may react to rapid environmental changes. Adaptive phenotypic plasticity is an alternative evolutionary solution adopted by organisms facing natural and human-induced fast-changing environments. Recently, plasticity was found to occur across generations (transgenerational plasticity, TGP) with the phenotype of a generation influenced by the environment experienced by the previous generation(s). TGP is taxonomically widespread and can occur in response to a wide range of biotic and abiotic environmental cues. TGP may be adaptive if offspring’s selective pressures resemble their parents’, as it prepares the offspring phenotype to them, even when they are not able to perceive the conditions by themselves. However, while more and more studies document TGP, we still have limited understanding of its evolutionary dynamics and underlying mechanisms. The objective of TEATIME is to investigate the dynamics, evolutionary potential and mechanisms of TGP in the context of predator-induced defences. We recently demonstrated TGP of inducible defences in the freshwater snail Physa acuta. This snail is able to detect the predator (crayfish) odours, which induced morphological and behavioural defences in both the exposed snail and their offspring. This system is ideal to study transgenerational plasticity. P. acuta has a relatively short generation time (6 weeks) and is easy to rear in the laboratory. Our approach will be integrative. We will combine experimental approaches on adaptive traits (life-history, morphology, behaviour), ambitious experimental evolution protocols and cutting-edge investigation of molecular mechanisms at the genome scale, based on transcriptomics and epigenetics (chromatin openness). TEATIME will be organized along three axes: (1) As TGP is a form of memory of past environments, what is the temporal dynamics of this memory? Firstly, we will investigate the sensitive developmental windows at which parents experience the environmental cues and at which the offspring express the response. Secondly, we will determine how much time (in generations) will this memory last; (2) As TGP is expected adaptive when parents and offspring are exposed to similar selection pressures, can it evolve in response to variation of the parent-offspring correlation in selective environments? We will conduct experimental evolution and evaluate local adaptation in transgenerational plasticity in natural populations; (iii) Can we detect the molecular bases of TGP. We will test in parallel whether the environmental experience of parents may change the gene expression patterns of offspring over generations and whether the environmental signal perceived by parents may be imprinted and transmitted via changes in chromatin openness. Emilien Luquet, a young evolutionary ecologist working on plasticity for several years, has gathered partners with highly complementary skills to propose TEATIME. The project has the potential to shed a new light onto TGP that may position TGP as a process filling the gap between within-generational plasticity and long-term genetic adaptation, usually represented as a binary dichotomy in evolutionary theory, though they may end up being two extremes of a continuum from fast and ephemerous to slow and permanent responses.

Climate change strongly affects wetlands and their plant communities. It leads to temperature increase which may modify, directly or indirectly through the alteration of biotic interactions, the plant species performances. Project PONDS aims at determining how annual temperature increase modifies the coexistence mechanisms of aquatic plant species and subsequently affects the functioning of a pond ecosystem. The role of the intraspecific variability of morphological and metabolomics traits of aquatic plants in their resistance to the thermal constraints of their habitat combined to plant-plant interactions, will be identified. Consequences of the variations in the functional composition of plant community for aquatic ecosystem productivity and nutrient cycles will be explored. This project will be based on the model of plant communities from the ponds of the Kerguelen Islands.

CHANGE aims to improve our understanding of how past environmental changes have influenced the tempo of species and phenotypic diversification. Phylogenetic models currently used to test the association between environmental factors and macroevolutionary rates have major limitations, such as testing the effect of one environmental variable at a time, not accounting for variations in rates linked to unobserved variables, and not accounting for species-specific responses to environmental changes. We will address these limitations by using innovative modelling and statistical techniques. Next, we will use large datasets, including a unique high-quality 3D scan database of more than 2200 tetrapods, to conduct the most ambitious analysis to date of the effect of past environmental changes on species and phenotypic diversification. Our results will move the field from a qualitative to a quantitative assessment of the effects of past environmental changes on the evolution of biodiversity.

Despite ambitious targets at reducing biodiversity loss, assessing these changes remains an epic challenge. A better monitoring of ecosystems remains the only solution. Acoustic passive monitoring is getting popular to sample different groups of vocal wildlife. However these tools remain limited as hardware is expensive and does not give a clear view of species at a large scale. This is due to: lack of accuracy in sound source positioning and tracking ; impossibility to improve it and to cover large areas by distributing sound antennas on several sensors ; lack of global ultra-precise synchronization of the sensor network. The storage and power limitations, high sampling frequency recordings are necessary for species detection, are crucial. Therefore, we propose from advanced AI to low cost embedded AI advances and precise localisation to allow relevant target recording and supply longer autonomy and accuracy of biodiversity surveys. In SylvanIA (SYnchronized Low power Versatile Acoustic Network with embedded AI), we will develop a novel biodiversity sensing network paradigm for monitoring and tracking accurately biodiversity species on large survey areas. For that, we will join the forces of acoustic researchers and engineers, hardware and software development, and quantitative ecologists and conservation biologists, including a collaboration with the MFFP department of the ministry of Canada. We aim to build a low cost, low power and precisely auto-synchronized (innovation 1, by radio and acoustic emissions from each node), distributed intelligent sensor network (innovation 2). Each node will have a large frequency band and versatile intelligent triggers (innovation 3) by joint research between IM2NP, LIS, and LEHNA and MFFP. SylvaniA innovate AI solutions for such network. The recent deep learning advances there has been an exponential growth in the use of Deep Networks (DNs) on various time-series, with some promising results in bioacoustical transients (Ferrari Glotin 2021). However, the vast majority of DNs do not directly observe the time-series data but instead a handcrafted representation : vast majority of state-of-the-art methods combine DNs with some variant of a Time-Frequency Representation (TFR) an image representation of a time series, such as wavelets or localized complex sinusoids. It is for example common to employ wavelet transforms on biological signals and spectrogram on voices. These different TFR have different precisions. Hence, the choice of TFR has the potential to dim, or amplify, the precise bioacoustical content. In SylvaniA will take advantage of synchronous observation to adapt by Wigner Ville learnable decomposition in order to discriminate the different sources in time and space and frequency, and thus optimize the information content of the AI representations of the target species (innovation 4). Then TRL 6 pilot studies will be conducted with novel protocols (innovation 5), deployed in the Alpine area for studying specific bird species very sensitive to climate change, on borders of agricultural areas, humid zones, and on the Québec Biodiversity monitoring Network recently initiated to measure changes across ecosystems and communities in view of climate change. A spatial and temporal map of distribution of acoustic diversity is expected as a result of this study, allowing to investigate movement and phenology strategies at both large and small spatio-temporal scales. Detection will be included into stochastic and imperfect detection models for better understanding how climate change and management practices can transform the sound landscape. Integrated in Québec and the Alpin monitoring network, proper result dissemination and knowledge transfer toward landscape managers and locales policies will be ensured.

Due to growing urbanization, industrialization and agriculture, natural water is increasingly threatened by the release of synthetic chemicals into the environment. Both diffuse pollution (e.g., agricultural activities) and point pollution (e.g., domestic and industrial activities) are of high concern because they alter water ecosystem functions and constitute risks for human health (e.g., contaminated food and drinking water), welfare and economy (e.g., access restriction to recreational waters or prohibition of commercial fishing). This project aims at tackling the environmental problem of domestic and industrial point sources of pollution by monitoring critical chemicals in effluents and by providing decision-makers with timely actionable knowledge in order to reduce the release of these compounds in the environment. While conventional laboratory-based methods usually have good analytical performances, they are poorly adapted to the provision of timely and representative information. They rely on sophisticated sampling strategy (expensive and time-consuming sample transfer to offsite laboratories and sample preparation), thus needing time to provide results. In situ and online analytical techniques are thus urgently needed. To overcome these limitations, we will validate a new concept of measure based on time-resolved fluorescence for continuous in-situ monitoring of critical chemicals such as glyphosate, phosphonates, sulphonates, chelating surfactants and PFASs. This project will be based on an earlier patented method on the dosage of limestone and corrosion inhibitors via TRF technique. The technique will be supported by using a standard addition approach and artificial intelligence training (AI-TRF). The quantification procedure will be optimized to monitor a large variety of contaminants commonly present in effluents by both laboratory and mesocosm assays feedbacks. The performances of AI-TRF prototype will be compared to those of conventional laboratory-based techniques (e.g., LC-MSMS, ICP-MS). In a second phase, we will deploy AI-TRF in real effluents to acquire information on the release of contaminants in sewage networks (signaling of exceedance of limit concentrations and production of information on the types of potential sources). In this study, we will focus on (pre)treated wastewaters because they have a less complex matrix than raw wastewaters, but the ultimate goal is to provide a tool that can be deploy at any site of a sewage network to obtain the maximal amount of relevant information. With this project, we would like to prove that first AI-TRF should be an alternative technology for timeliness quantifications of pollutants comparing to conventional analytical methods currently used by environmental agencies. Secondly, the in-situ TRF installations could drastically increase the spatial coverage of monitoring campaign, thus generating deeper knowledge on critical pollutants, facilitating the development of policies concerning chemical production and consumption as well as on wastewater treatment strategies; thus sustainably preserving the environment. Third, artificial intelligence could support, not only the hard-working tasks normally done continuatively by technical staff, but also the implementation of new generation of state-of-the-art devices.