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Crop Wild Relatives (CWR) are a valuable source of genetic diversity that can be transferred to commercial crops, so their conservation will become a priority in the face of climate change. Bizarrely, in situ conserved CWR populations and the traits one might wish to preserve in them are themselves vulnerable to climate change. In this study, we used a quantitative machine learning predictive approach to project the resistance of CWR populations of lentils to a common disease, lentil rust, caused by fungus Uromyces viciae-fabae. Resistance is measured through a proxy quantitative value, DSr (Disease Severity relative), quite complex and expensive to get. Therefore, machine learning is a convenient tool to predict this magnitude using a well-curated georeferenced calibration set. Previous works have provided a binary outcome (resistant vs. non-resistant), but that approach is not fine enough to answer three practical questions: which variables are key to predict rust resistance, which CWR populations are resistant to rust under current environmental conditions, and which of them are likely to keep this trait under different climate change scenarios. We first predict rust resistance in present time for crop wild relatives that grow up inside protected areas. Then, we use the same models under future climate IPCC (Intergovernmental Panel on Climate Change) scenarios to predict future DSr values. Populations that are rust-resistant by now and under future conditions are optimal candidates for further evaluation and in situ conservation of this valuable trait. We have found that rust-resistance variation as a result of climate change is not uniform across the geographic scope of the study (the Mediterranean basin), and that candidate populations share some interesting common environmental conditions. Peer reviewed

In the FAR-DWO DS1 oceanographic cruise we analyze the hydrographic and sedimentological variability of the Denmark Strait Overflow using water column and seafloor observation and sampling techniques. We also deploy multi-instrumented mooring lines at depths between 1000 m and 2000 m to record information of the dense current during a whole year. The FAR-DWO DS1 cruise will last from July 19th to August 12th 2023 onboard the oceanographic vessel Sarmiento de Gamboa (CSIC). We are a team of around thirty experts from the Universitat de Barcelona (UB), Universidad de Las Palmas de Gran Canaria (ULPGC), Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER), University of Iceland (UI), and the company specialized in satellite technology Lobelia Earth, and we count on the technical support of Unidad de Tecnología Marina (UTM-CSIC). Oceanographic data acquired during the FAR-DWO_DS1 Cruise (29SG20230719) on board the Research Vessel Sarmiento de Gamboa in 2023.

Characterization data of a Vicia faba germplams collection grown at Córdoba, Spain at three sites during 2022 and 2023.

The aim of the project is to develop an innovative palaeo-proxy approach to investigate the natural range of variability of the Hadley circulation during past episodes of extreme warmth and cold. The approach relies on the exploitation as climate proxy of an untapped but widespread material in marine sediments: windborne pyrogenic carbon (PyC) derived from savannah and grassland fires in the tropics. Through the geochemical and isotopic spatial characterization of PyC, along with the analysis of mineral dust in the modern tropical deep ocean, and a PyC biogeochemical model, we will build an interpretative framework of PyC deposition in deep-sea sediments. Its application in Pliocene-Pleistocene sequences from the Atlantic and the Pacific will allow the reconstruction of past meridional and zonal shifts in the Intertropical Convergence Zone and the Southern hemisphere westerlies, and provide new constraints on the natural variability of the Hadley circulation and associated hydroclimates. PYROWIND Cruise (29HE20220426) carried out on the Research Vessel Hespérides in 2022.

Oceanographic data acquired during the ANTARLOG_LEG2 Cruise (29SG20161203) on board the Research Vessel Sarmiento de Gamboa in 2016. Logistic voyage without any scientific work.

Oceanographic data acquired during theZEE-2021 Cruise (29HE20210325) on board the Research Vessel Hespérides in 2021 ZEE-2021: Study of the Spanish Economic Zone in the Canary Islands.

The MIAU-DCM campaign will study the biological characteristics of the deep chlorophyll maximum in the Northwest Mediterranean. To do this, the first 150-200 meters of the water column will be sampled with CTD along a transect between the towns of Blanes and a point near Cape Formentor, and from the continental shelf (depth 40-50 meters). Once the depth of the DCM has been defined, and its variability along the transect, the stations in which biological sampling will be carried out will be defined, which will consist of taking samples from approximately 100 m and the surface. In addition, station D (in the middle of the basin) will be characterized up to 2500 meters. Oceanographic data acquired during the MIAU-DCM Cruise (29GD20210601) on board the Research Vessel García del Cid in 2021.

Methodology: The genetic material comprise 57 populations (8 of Brutia pine and 49 of Aleppo pine) originating from 7 countries of the Mediterranean Basin (Italy, Tunisia, Spain, Turkey, France, Greece and Morocco). Trials were established in 1992 and assessments were carried out 1, 6, 9, 12, 14, 17 and 21 years after planting. Assessed variables are listed within the Excel file (Readme sheet). Access and reuse: This dataset is subject to a Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional License. This dataset compiles phenotypic information of height and diameter growth, survival and defoliation by the processionary moth (Thaumetopoea pityocampa) assessed at different ages (up to 22 years-old) in two provenance tests (Izarene and Chatba) of Pinus halepensis and Pinus brutia established in Morocco. This research was founded by the National Forest Research Centre budget, Morocco. RZ received support from the Grant FUTURPIN AGL2015-68274-C03-02R founded by MINECO/FEDER. No