Increases in atmospheric CO2 have resulted in significant changes in global climate and ecosystem processes. It is well known that the terrestrial biosphere fixes large quantities of carbon from the atmosphere. Still, less certain is whether soil and terrestrial vegetation can mediate the increase in CO2 and for how long. Carbon storage largely depends on water constraints, and climate models predict drier future conditions, particularly in the Mediterranean. Although droughts are associated with reductions in photosynthetic rates, their effect on the role of forests in climate change mitigation is still unclear. The Drought Impact on the Climate Benefit of Carbon Sequestration project aims to advance understanding of the impact of soil moisture on forests-avoided warming and for how long it occurs by achieving three objectives: i) simulate the effect of drought on the global terrestrial biosphere C cycle under global warming scenarios, ii) develop a mechanistic model based on experimental observations to represent the carbon cycle dynamics under drought conditions in a Mediterranean forest, and iii) compare the warming produced by CO2 emissions and that avoided by Mediterranean forests under drought scenarios. These objectives will permit answering whether the climate benefit of C sequestration is sensitive to drought conditions on a global and local (Mediterranean) scale and whether Mediterranean forests mantain their offset CO2 emissions under these conditions. The above will be carried out by linking photosynthesis, storage and respiration processes through the combination of Transit Time, Carbon Sequestration, and the Climate Benefit of Sequestration concepts with measurements from a unique long-term drought experiment, the compartmental system approach and global carbon cycle models. This project will result in relevant scientific contributions and a valuable and comprehensible tool to enhance policy-oriented discussion on nature-based climate mitigation.

The evolutionary relevance of behavioral traits has been largely debated among biologists. Behavioral changes often constitute the first response to changing environmental conditions. This suggests that behavioral variation among individuals within a population potentially represents the raw material for natural selection, ultimately determining the ecological and evolutionary responses to new selective pressures. However, the genetic basis of behavioral traits remains largely unknown. This uncertainty limits our understanding of the role of behavior in shaping the adaptive potential of natural populations. In this project I aim to uncover the molecular underpinnings of risk-taking behavior, a key trait associated with survival in the lizard Anolis sagrei under new predation regimes. First, using restriction site-associated DNA sequencing (RADseq) data, I will characterize the heritability of risk-taking behavior by estimating its additive genetic variance based on multigenerational pedigrees of lizards that have undergone behavioral assessments. I will then search for genomic regions and variants associated with risk-taking behavior by FST outlier scans, selective sweep scans and genome-wide association study (GWAS) with the use of RADseq and whole-genome sequencing (WGS) data. Finally, using whole-genome bisulfite sequencing (WGBS) data, I will explore the impact of DNA methylation on the variation in risk-taking behavior to shed light on the role of epigenetic mechanisms in controlling animal behavior. By characterizing the genetic and epigenetic architecture of risk-taking behavior, riskADAPT will decisively advance our understanding of the role of ecologically-relevant behaviors in the evolutionary adaptation to rapid environmental changes.

The overall objective of the project is to develop a virtual environment equipped with FAIR multi-disciplinary data and services to support marine and freshwater scientists and stakeholders restoring healthy oceans, seas, coastal and inland waters. The AquaINFRA virtual environment will enable the target stakeholders to store, share, access, analyse and process research data and other research digital objects from their own discipline, across research infrastructures, disciplines and national borders leveraging on EOSC and the other existing operational dataspaces. Besides supporting the ongoing development of the EOSC as an overarching research infrastructure, AquaINFRA is addressing the specific need for enabling researchers from the marine and freshwater communities to work and collaborate across those two domains. A specific goal of AquaINFRA will be to develop an EOSC based research infrastructure combining the marine and freshwater domains, which will include the development of a cross domain and cross-country search and discovery mechanism as well as building services for spatio-temporal analysis and modelling through Virtual Research Environments. A set of strategic use cases including a Pan-European use case as well as more focused use cases in the Baltic Sea and the North Sea will provide the setting for co-designing and testing services in the targeted research communities. The AquaINFRA project results are expected to contribute to the utilisation of EOSC as an overarching research infrastructure enabling collaboration across the domains of marine and freshwater scientists and stakeholders working on restoring of healthy oceans, seas, coastal and inland waters.

The feedback between climate and the land carbon (C) cycle poses one of the largest uncertainties in climate change projections. FIBER targets the unresolved challenge for Dynamic Global Vegetation Models (DGVM) to simulate effects of soil fertility and nutrient deposition on biomass productivity (BP) and the land C balance. Accumulating evidence documents how plants adjust their growth strategies and C allocation under multiple limiting resources. Current DGVMs lag behind these new insights, produce widely diverging results for C cycling and nutrient limitation under future scenarios and fail to explain the observed land C sink. This work will provide a new global modelling approach to simulating flexible plant C allocation following optimality principles. A better understanding of the controls on BP is crucial for assessing climate change impacts on ecosystem services and to reduce uncertainty in C cycle and climate change projections. I will develop a new type of plant growth model to predict increased root growth and export of labile C to soil biota on infertile soils and under low N inputs, consistent with powerful data from forest inventories and ecosystem manipulation experiments. By accounting for trade-offs between different growth strategies and a C cost of nutrient uptake, I will simulate the plant C economy under optimality constraints – a powerful approach, supported by observations but not exploited for DGVMs. The project is conceived to combine the relevant expertise and exploit the pioneering science of leading European researchers with my integrating role and demonstrated model development skills. Collaboration with two secondment hosts will facilitate the mining of their large data resources and fusing data into model predictions using Bayesian statistical tools. This project will integrate new model components developed at my current host institute and will be a crucial step on the way to building the next generation of vegetation models.