Coral reefs are one of the most biologically diverse ecosystems on Earth, but their regression has largely increased in the last decades due to local human pressures and global warming. This project aims to use numerical models and simulations to expand our understanding of coral reefs' dynamics and improve marine conservation strategies. I will follow an interdisciplinary approach, using complex systems and non-linear physics techniques combined with ecological knowledge and field data of coral reef systems. My results will be the foundation to study a large variety of open questions in coral reef science using an innovative and ground-breaking point of view. In this project, I will focus on two applications of the models: i) the study of the underlying mechanisms causing the emergence of halo-like patterns around reef islands (grazing halos) and ii) the construction of a protocol to optimize and reduce costs in coral restoration programs. I expect my results to have a high impact on the scientific community and contribute to making better and more informed decisions about the sustainable management of coastal zones, in line with the new EU Biodiversity Strategy for 2030 - a comprehensive, ambitious, long-term plan for protecting nature and reversing the degradation of ecosystems aiming to put Europe's biodiversity on a path to recovery by 2030 with benefits for people, the climate and the planet.

In 2016, the Laser Interferometer Gravitational-Wave Observatory (LIGO) collaboration announced its first ground-breaking detections of gravitational waves sourced by binary black hole mergers. These observations herald the beginning of a new era in astronomy: gravitational waves are expected to shed light on many unsolved astrophysical and theoretical problems, such as finding neutron stars' equation of state, modelling the formation and evolution of compact objects and testing alternative theories of gravity. With LIGO's next observational run starting in late 2016 and the prospect of seeing the space-based interferometer eLISA fly in the near future, gravitational wave physics is set to be one of the most active and exciting fields in contemporary science. Black hole binaries are going to be key targets both for LIGO and eLISA: it is thus crucial to perfect the modelling of these systems, as this will enable us to extract the rich information encoded in the gravitational wave signals that will be detected in the years to come. This project proposes the development of a state-of-the-art code to study extreme mass-ratio inspirals into Kerr black holes, including the full gravitational self-force (i.e. both conservative and dissipative effects). The project will contribute to the construction of a template bank for eLISA. Furthermore, it will inform the calibration of effective-one-body (EOB) and phenomenological waveform models which form the basis of LIGO-Virgo data analysis.

C4 plants are leading grain (maize, sorghum), sugar (sugarcane), and biofuel (miscanthus) producers. Their higher productivity potential arises from the operation of a Carbon Concentrating Mechanism (CCM), which is an effective ‘turbocharger’ of the assimilatory machinery. In recent years there has been a considerable drive towards engineering a CCM into C3 crops as a possible strategy to boost agricultural productivity. This emerged as an alternative strategy to the traditional breeding, which seem to be inadequate to ensure complete food and nutrient security in the face of global warming, population growth, and decreasing arable land availability. Advanced breeding of C4 plants is currently impinged on negatively by lack of knowledge of fundamental C4 physiology. This lack of fundamental knowledge calls for a deeper understanding of the biochemical underpinnings of C4 photosynthesis and quantitative predictions of the effect of genetic manipulation. For C4 photosynthesis to operate, a substantial flow of metabolites is continuously exchanged between two partially isolated compartments in the leaf parenchyma (mesophyll and bundle sheath). This project (DILIPHO) hypothesizes that under low turgor the exchange of metabolites slows down, thus jamming the C4 machinery. DILIPHO consists of three phases. Firstly, the applicant Chandra Bellasio will learn concepts of advanced Mathematics and Biophysics, and develop a mechanistic model to study metabolite transport at leaf level, DILIMOD. Secondly, the hypothesis will be experimentally tested. In the hypothesis will be experimentally tested. Thirdly, the model will be interrogated to mechanistically explain the dataset and to answer fundamental questions in C4 ecology and physiology. The findings and the theoretical tools that will be developed in DILIPHO are urgently needed, and have a notable potential to benefit advanced breeding, the economy and the society as a whole.

Hazardous weather events affecting populated coastal are among the most devastating natural disasters in terms of mortality and economical losses due to their low predictability. Currently, the generation of useful predictions, reliable and anticipated of hazardous weather events affecting populated coastal regions remains an ambitious challenge for the scientific community. Deficiencies in the accurate prediction of such events are tightly related with the initial value problem, which states that better the state of the atmosphere is estimated, the more accurate the forecasts. This problem is addressed by using advanced Data Assimilation (DA) techniques, which play an important role in current numerical weather prediction and is currently at the forefront of atmospheric and oceanic sciences research. However, although using the most current sophisticated DA algorithms, the estimation of the atmosphere is not accurate enough to improve the predictability of hazardous weather events, mainly because their linear and Gaussian underlying assumptions. The main aim of the present project is to go beyond the state of the art in DA by developing and implementing a novel and advanced DA technique that takes nonlinearities and non-Gaussianities into account, enabling us to to improve high-impact weather forecasts. The new DA will be tested in real cases in combination with a high-resolution atmospheric model to improve the predictability of several poorly forecasted Mediterranean Hurricanes. This novel technique will significantly improve global, regional, and climate forecasts. The applicant’s strong mathematical and theoretical skills in DA together with his broad experience running numerical weather models using HPC facilities will facilitate the achievement of the key goals of this proposal. This project will also expand the applicant’s experience, research competencies and professional networks, enhancing the development of his career as an independent researcher.

Harmful Algal Blooms (HABs) are naturally occurring seasonal algal blooms which can have major negative impacts on human activities and health in coastal areas. While the factors triggering these high biomass blooms has received much attention in the last decades, far less is known about what causes blooms to terminate. Parasitoid infection of dinoflagellates may play a major role in the termination of HABs along the Balearic and Catalan coasts; however, very little is known about the biological or the biophysical mechanisms controlling parasitoid infection in dinoflagellates, nor how prevalent parasitoids are in natural dinoflagellate populations. The focus of this project is to unravel the interactions between parasitoids and dinoflagellates and the cellular and population levels to determine how parasitoids might be used to control HABs. I will accomplish this by: (1) studying the abundance of parasitoids and dinoflagellates of natural phytoplankton assemblages during bloom and non-bloom conditions in Cala Santanyí (Mallorca); (2) conducting a series of microfluidic experiments to study the biophysics, behaviors of and interactions between individual parasitoids, host cells, and healthy dinoflagellates; (3) modeling the interactions between dinoflagellates and parasitoids to determine the concentration of parasitoids necessary to control HABs along; and, (4) validating the model results using a series of microcosm experiments. By studying the biophysical and behavioral interactions between dinoflagellates and parasitoids at both individual- and population-based scales, the long-term goal of this project is to exploit these interactions as an effective, environmentally-friendly method to control HABs near the Balearic Islands and the Catalan coast.