Abstract

In the context of global warming, seagrass species exhibit various adaptive responses to rising temperatures, including the migration to deeper (cooler) areas. However, moving to deeper habitats does not only result in reaching lower temperatures, but also to be exposed other critical environmental factors that vary with depth. For instance, light availability decreases with depth, which can impact the metabolism of primary producers, and constrain their depth distribution. Previous research has modelled seagrass distribution based on lethal thermal limits under optimal light conditions. Other studies have projected a future vertical habitat contraction of seagrass meadows assuming similar thermal tolerance across water depth. Nevertheless, the interaction between light and temperature requires further investigation, as they may interact and impact the thermal performance of the organism, including their thermal thresholds, and consequently, their vertical distribution. To investigate this interaction, we conduced mesocosms experiments using the three seagrass species (Cymodocea nodosa, Posidonia oceanica and Zostera noltei) present in the Mediterranean Sea. Thermal performance curves were used to assess the seagrass growth, survival and metabolic rates at varying light intensity, which was used as a proxy for depth. Understanding the interaction between environmental factors, such as light intensity and temperature, on performance and thermal thresholds is crucial for enhancing predictions regarding vertical distribution of species and developing effective conservation strategies for marine ecosystems under climate change scenarios.

Abstract

Subsurface uncertainty poses a serious difficulty in deploying geo-energy applications, owing to its complexity and our limited access to it. Reducing such uncertainty is essential to enhance the reliability of simulation results that define safe operating conditions. Ground deformation analysis is capable of contributing to reducing subsurface uncertainty. For example, a double-lobe ground deformation shape revealed a vertical fault zone at depth in the CO2 storage project at In Salah, Algeria. The aim of this work is to outline a process for reducing subsurface uncertainty by correlating subsurface characteristics with ground deformation data. The workflow begins with training of a supervised gradient boosting-based machine learning regression model that predicts ground deformation caused by reservoir pressurization.  We utilize a verified analytical solution (Wu, Rutqvist, and Vilarrasa, 2024) to assess ground displacement in response to pressurization of a reservoir intersected by either an impermeable or permeable fault to train the machine learning model. The instantaneous solution provided by the analytical solution enables us to generate an extensive dataset for training the model, encompassing fault and reservoir geometry as well as mechanical properties and operation conditions, i.e., reservoir pressurization. Simultaneously, principal component analysis and a simplified parametric space analysis are also performed. The results indicate that the pore pressure buildup and reservoir depth have the most significant impact on ground displacement. This study highlights that an appropriately trained machine learning model can effectively predict ground deformation and provide valuable information about the corresponding subsurface characteristics.

Abstract

In the Western Mediterranean Sea, vast extensions of rhodolith beds hold significant ecological value. These habitats are composed of free-living species of red coralline algae which are ecosystem engineers. The calcareous composition of rhodolith species makes them susceptible to climate change impacts, mainly to seawater acidification. Mediterranean rhodolith beds are multispecific habitats found from 60 up to 100 m below the surface, a deeper distribution that likely provides a more stable environment with fewer fluctuations in warming. However, there are gaps of knowledge on the responses of this multi-specific habitat to different disturbances. In this regard, we have focused on untangle the performance of two genera of rhodoliths along a thermal gradient, specifically of Lithothamium spp. and Phymatholiton spp. Measurements of dissolved oxygen and total alkalinity during the experiment allowed the calculation of metabolic rates. The thermal patterns and optimal temperatures for metabolic rates displayed by both genera were similar. These genera inhabit areas with mean yearly temperatures of 15°C and little temperature variation. However, both genera seem to be acclimated to a warmer range of temperature. Our findings suggest that Mediterranean rhodoliths genera are likely to cope with ocean warming.

Abstract

Ocean pollution is becoming a bigger and bigger problem with time.  To mitigate, manage and reduce this, it is important to understand the paths different types of pollution will follow in the Ocean.   Understanding ocean dynamics, namely ocean currents, is thus very important to determine the pathways of different pollutants.  The world's ocean currents have the potential to transport material like plastic over global scales, connecting continents, but the sources and sinks of that pollution have also been found to be sometimes very local.  Different oceanic scales affect these pathways, and thanks to new data we can start to understand how finer-scale process are affecting larger-scale ones. In this seminar, I will present the work I carried out during my previous postdoc as part of the parcels team at Utrecht University (The Netherlands), where we use ocean physics to understand different aspects of marine pollution in very different regions from the Netherlands to different areas of the Pacific Ocean like Japan.  The main tool we use for our Lagrangian ocean analysis simulations is the OceanParcels tool, which is open software. I will close by highlighting the importance of Open Science and the role this has in our research.

On Wednesday, March 26, the IMEDEA will host the European Space Science Committee (ESSC) Earth Science Panel meeting, with the participation of Marta Marcos. The ESSC plays a key role in shaping European space science policies by setting scientific priorities. This in-person working meeting will bring together six participants at IMEDEA, plus four joining online.

El miércoles 5 de marzo, a las 11:00 h, únete a nuestro directo en YouTube en el que reflexionaremos sobre el artículo "Ten simple rules for a mom-friendly Academia".  

Conectaremos con las autoras del artículo, quienes compartirán datos y experiencias para analizar afirmaciones clave sobre los retos que enfrentan las madres en la academia, de una manera dinámica e interactiva a través de un juego de afirmaciones verdaderas o falsas.