Understanding the relationships between wildlife biodiversity and zoonotic infectious diseases in a changing climate is a challenging issue that scientists must address to support further policy actions. Our project aims at tackling this challenge by focusing on rodent-borne diseases in European temperate forests and large urban green spaces. Rodents are important reservoirs of zoonotic agents; forests and green spaces are environments where rodents are abundant, human/domestic-wildlife interactions are plausible to occur, and efforts are undertaken to preserve biodiversity. The originality of this project is to extend previous research into four promising research directions: i) impact of coinfections on epidemiology, ii) interactions between gut microbiome and host susceptibility to infectious agents, iii) influence of socio-economic contexts on human exposure to wildlife and iv) temporal variability of biodiversity/health relationships. Using rodent sampling and large investigation of zoonotic agents and microbiome, we will establish an up-to-date, open database and maps of rodent-borne pathogens circulating in western-central European countries. We will apply eco-epidemiological approaches to enhance our understanding of the processes that influence zoonotic pathogen transmission in rodent populations. Mathematical models will be developed to analyse the influence of spatiotemporal scales and within-host interactions on the relationships between biodiversity (rodents and microbiome) and zoonotic diseases. Landscape features will be included in this modelling. Lastly, we will evaluate the impact of climatic change scenarios on zoonotic disease risk and rodent-microbiome biodiversity in forests and urban green spaces. Sociologists will be at the core of the project to help partners develop effective knowledge exchanges, what will enable transdisciplinary collaborations among scientists and with relevant stakeholders. A first circle of stakeholders will integrate collaborators from public health, biodiversity management and NGOs representing public at risk of rodent-borne zoonoses. These stakeholders will be strongly engaged throughout the project, as they will be informed, consulted and involved in project activities and dissemination. A second, larger, circle of stakeholders will be engaged mostly through knowledge exchanges, to guarantee that all organizations and public interested in, affected by rodent-borne diseases or involved in nature management are informed of our project. Overall, we aim to provide proof-of-concept that joint strategies between public health and conservation biology programs can help to prevent emergence of zoonotic pathogens from wildlife. In addition to protocols, maps and lists of zoonotic pathogens, an important outcome will be the improvement of zoonose prevention policies through dissemination of adapted surveillance, training and awareness campaigns designed with the active participation of stakeholders.

This project aims at buidlgin biodiversity scenarios for savannas. Savannas are an important ecosystem worldwide (20% of land surfaces), are currently under threat and pose many difficulties in their modelling. Savannas are under the control of climate, but also of fire regime and grazing, which are sometimes more important drivers than climate in shaping ecosystem structure, function and dynamics. The consortium of this project groups ecologists and socio-economists from 5 continents, spanning all the diversity of savanna systems worldwide. The project will perform the following tasks: 1. Synthesis of savanna models as a generic conceptual framework for savanna ecosystems. 2. Synthesis of current knowledge on savanna biodiversity. 3. Synthesis of current knowledge on interactions between socio-economic activities and savanna dynamics in order to select relevant scenarios. 4. Analysis of the decision process in environmental problems. 5. Generation of biodiversity scenarios. 6. Identification of gaps requiring further research. This project is a unique opportunity to group such an expertise on savanna ecosystems and on interaction between science and decision-making.

The goal of COCOBOTS is to develop conversational assistants and cobots capable of interacting with human coworkers in sophisticated ways. One crucial such way is through the development of a natural language programming toolkit that will allow human users to teach new actions to conversational cobots and construct joint actions with them through natural conversation in an interactive way. Programming through conversation would allow a human user without sophisticated programming skills or access to massive amounts of training data to program an assistant or cobot on the spot in the way that we teach other humans, without having to rely on an expert programmer to intervene. One could try out an idea with the robot and then modify it just as one would do with another human when teaching or developing a joint action. Such a toolkit would open up a wide range of new markets for companies, such as LINAGORA, who specialize in the development of conversational assistants or cobots that need to perform actions such as alerting workers on an assembly line to malfunctioning equipment or physically intervening to fix that equipment. It would also bring increased value to companies, such as Airbus, that seek to boost their manufacturing output by adding cobots to assembly lines or maintenance tasks. For the moment, the utility of conversational assistants or cobots is limited to carrying out commands and performing actions that are pre-defined via hard-coding or, in the case of robots, learned through demonstration or manual manipulation. A natural language programming toolkit would give a user without programming expertise the power to adapt their assistant to their needs via on the spot training. To demonstrate the efficacy of our toolkit, COCOBOTS will develop a proof of concept featuring a simulated assembly cobot that is able to learn new concepts associated with manufacturing, such as a torx, and new actions, such as how to build a certain kind of bridge, by stringing together atomic actions as instructed by a human user. Bringing together conversational models with the capacity of cobots to physically interact in their environments will be crucial for testing our approach, as we think that the capacity to understand situated conversation (and in fact, any conversation at all) is greatly enhanced by physical interaction with the outside world. Observing a robot's interaction with objects in a physical environment or its ability to string together primitive actions based on multimodal, conversational instructions, will also provide clear criteria to evaluate our approach and show that following a program specified via natural language is more effective than hard coding, demonstration, or manual manipulation of a robot. To develop the model of multimodal dialogue needed to make cobots truly conversational, we will build on a solid foundation of expertise of COCOBOTS members in semantic grounding (ANITI/CerCo, Airbus), dialogue models (University of Potsdam, LINAGORA, ANITI/IRIT), conversational assistants (LINAGORA) and robotics (ANITI/LAAS, Airbus). The novelty of our approach will lie in bringing together work on semantic and conversational grounding, which is generally pursued by separate communities, to develop a hybrid model that exploits the way that these processes influence each other. Our approach will require us to overcome three major challenges. First, we will need to bring the compositionality of referential meaning to bear on the semantic grounding of complex expressions using a hybrid AI approach. Second, we will need to account for the different ways that the nonlinguistic environment can ground and contributed to discourse meaning. Third, we will need to develop a model of situation discourse that provides a symbolic skeleton that we can then flesh out with subsymbolic pairing of nonlinguistic content with linguistic expressions.

This project aims at developing a physics-based tool to forecast scenarios for the location and time of a fissure eruption following magma propagation below the surface. Often magma avoids the central conduit and propagates through tortuous pathways, eventually opening a new fissure on the volcano flanks or within a caldera. Such eruptive fissures are found in many areas now densely populated. The related hazard has so-far been estimated purely based on the spatial distribution of previous events. In the proposed research, we will take advantage of the knowledge accumulated in decades of magma propagation research, that is now mature enough for the creation of a forecasting tool based on mechanical principles. We will taylor our approach on three well-monitored cases: Campi Flegrei (Italy) is extremely high-risk and motivates development of near-real time forecasting methods; Etna, Italy, and Piton de la Fournaise, La Reunion have had frequent fissure eruptions and offer data-rich environment to test our models. Established deterministic models will be combined in order to retrieve simultaneously trajectory and timing information. We will use a Monte Carlo approach to invert for the current mechanical state of the volcano based on past eruptive patterns and data assimilation techniques to update forecasts based on data recorded during magma propagation. Synthetic data from analog laboratory experiments and numerical scenario simulations will be added to the data pool allowing us flexible testing and application to Campi Flegrei. The outcomes of this project will be: 1) A better understanding of how the state of stress of volcanoes of different shapes evolve with time and how the edifice history controls the migration of surface volcanism, 2) A long-term forecasting tool for the future distribution of eruptive vents, useful for land planning, 3) A short-term tool to to update the forecast scenarios according to intrusion parameters progressively determined by assimilating monitoring data.