AtlantECO-KER-IM-3

The ocean hosts a vast diversity of life, from microscopic plankton to large marine mammals, with their distribution shaped by environmental factors and species-specific traits. While ocean currents largely determine the passive transport of plankton, some species can regulate their buoyancy or actively migrate, such as diel vertical migrators (DVM). Larger, more mobile species, like fish and marine mammals, navigate their environment with stronger swimming abilities. Understanding how marine species move and interact with their environment is crucial for predicting ecosystem shifts, especially as climate change alters ocean conditions. This thesis investigates how species traits and environmental factors influence the transport and connectivity of marine organisms using Lagrangian flow modeling. Three case studies explore the dispersal of different marine species: vertically migrating zooplankton, thermally constrained plankton, and cold-stunned Kemp’s ridley turtles. The first study (Chapter 2) examines DVM zooplankton in the Benguela upwelling system, comparing their movement to that of floating and sinking materials such as phytoplankton, nutrients, and biogenic matter. Results show that DVM zooplankton separate more rapidly from other materials, especially during the upwelling season. However, coherent ocean features like eddies and filaments trap different particle types together, potentially increasing interactions between zooplankton, their prey, and pollutants. Chapter 3 applies network theory to Lagrangian modeling to assess minimum time connectivity pathways in the Atlantic Ocean. Passive plankton can theoretically connect all Atlantic locations within three years, but connectivity time increases with depth. When thermal constraints are introduced, connectivity across the basin is reduced, emphasizing the role of environmental preferences in species dispersal. Chapter 4 investigates Kemp’s ridley turtle strandings in the Netherlands, using Lagrangian modeling to trace their drift pathways. Findings suggest that juvenile turtles become cold-stunned in the southern North Sea and experience temperatures below 12°C for up to a month. These results highlight the need for targeted rehabilitation and conservation strategies for this critically endangered species. Together, these studies demonstrate that both species traits and environmental constraints shape marine organism dispersal, influencing ecological interactions and habitat connectivity. As climate change alters ocean temperatures and circulation, species distributions will shift, affecting marine food webs and conservation planning. Future research should refine species-specific behavioral models and integrate dispersal with ecosystem dynamics to better inform marine conservation strategies.

As the volume of accessible marine pelagic observations increases exponentially, incorporating diverse data types such as metagenomics and quantitative imaging, the need for standardized modelling frameworks becomes critical to predict biogeographic patterns in space and time and across the diverse range of emergent sampling methods. In response, we introduce CEPHALOPOD (Comprehensive Ensemble Pipeline for Habitat modelling Across Large-scale Ocean Pelagic Observation Datasets), a standardized, highly automated and flexible framework designed to integrate and analyse heterogeneous marine data for multi-species habitat modelling following best practices in the field. CEPHALOPOD is built on observational data from federating initiatives such as AtlantECO, OBIS, GBIF, associated with already existing statistical and machine learning methods that enable to extract and model information from heterogeneous, scarce and biased field observations. It is highly automated and follows explicit quality checks informing the user of the predictive accuracy and interpretability of the results. Here, we document our statistical ensemble modelling approach and then assess its strengths and limitations with a virtual ecologist approach. We show how our framework performs in reproducing a range of distributions from biased field samples. Our modelling framework serves as a foundation for the consistent generation of Essential Biodiversity and Ocean Variables (EBVs and EOVs) and carries the potential to significantly advance our comprehension of biodiversity and marine ecosystem functioning. Finally, it provides an unprecedented opportunity to foster collaborations in the field of marine science, sustainable ecological practices, and ultimately contribute to the preservation of global marine biodiversity.

Every few years, juvenile Kemp’s ridley turtles (Lepidochelys kempii) are stranded on the Dutch coasts. The main population distribution of this critically endangered species primarily inhabits the Gulf of Mexico and the east coast of the United States. This study focuses on five reports from the Netherlands between 2007 and 2022, where juvenile turtles were reported to strand alive during the winter, albeit in a hypothermic state. At ambient ocean temperatures between 10°C and 13°C, Kemp’s ridley turtles begin to show an inability to actively swim and remain afloat on the ocean’s surface, a condition termed ‘cold stunning’. Understanding their transport in cold-stunned state can help improve the rehabilitation process of stranded turtles. Cold-stunned turtles are back-tracked as passive, virtual particles from their stranding location using Lagrangian flow modelling. This study investigates when and where these juvenile turtles cross the threshold temperatures between 10° C and 14° C before stranding by tracking the temperature along the trajectories. As expected, the simulations show the transport of the cold-stunned turtles via the English Channel. More surprisingly, the analysis suggests they likely experience cold-stunning in the southern North Sea region and encounter temperatures below 10°C for only a few days to up to three weeks, and below 12°C for up to a month before stranding. The estimate of cold-stunned drift duration of the turtles provides additional knowledge about their health status at the time of stranding. Adherence to rehabilitation protocols for Kemp’s ridley and post-release monitoring are recommended to improve their long-term survival.

As the volume of accessible marine pelagic observations increases exponentially, incorporating diverse data types such as metagenomics and quantitative imaging, the need for standardized modelling frameworks becomes critical to predict biogeographic patterns in space and time and across the diverse range of emergent sampling methods. In response, we introduce CEPHALOPOD (Comprehensive Ensemble Pipeline for Habitat modelling Across Large-scale Ocean Pelagic Observation Datasets), a standardized, highly automated and flexible framework designed to integrate and analyse heterogeneous marine data for multi-species habitat modelling following best practices in the field. CEPHALOPOD is built on observational data from federating initiatives such as AtlantECO, OBIS, GBIF, associated with already existing statistical and machine learning methods that enable to extract and model information from heterogeneous, scarce and biased field observations. It is highly automated and follows explicit quality checks informing the user of the predictive accuracy and interpretability of the results. Here, we document our statistical ensemble modelling approach and then assess its strengths and limitations with a virtual ecologist approach. We show how our framework performs in reproducing a range of distributions from biased field samples. Our modelling framework serves as a foundation for the consistent generation of Essential Biodiversity and Ocean Variables (EBVs and EOVs) and carries the potential to significantly advance our comprehension of biodiversity and marine ecosystem functioning. Finally, it provides an unprecedented opportunity to foster collaborations in the field of marine science, sustainable ecological practices, and ultimately contribute to the preservation of global marine biodiversity.

Marine planktonic communities carry out almost half of the net primary production on our planet, keep atmospheric CO2 levels at roughly half of what they would be otherwise, form the base of food webs and produce more than half of the oxygen we breathe. Considering the wealth and economic importance of ecosystem services and climate regulation provided by our oceans fuelled from the very base of its food web, impacts of climate change on marine plankton are still poorly investigated. Within this project we aim to overcome disciplinary boundaries that contribute to impeding exchanges of scientific results between the research community and policy makers. We aim to build an interactive web tool for policy makers to help visualise future projections of climate change impact on global plankton ecosystems as a function of societal decisions. We continue the effort made by the ETH Environmental Physics (UP) Group (Zürich, Switzerland) that has mapped the biogeography of hundreds of plankton species and use this knowledge to define biomes for the surface ocean and identify hotspots of plankton diversity changes under global warming. We aim to include three fully coupled Earth System Models from the Coupled Model Intercomparison Project Phase CMIP5 using three different representative concentration pathways (RCP 2.6, RCP 4.5, RCP 8.5) covering the period from 2012 until 2100. We will translate projections into quantitative global impact metrics targeted at policy makers and characterize ecosystem impacts as a function of carbon emissions and global warming. International decision-makers are informed on potential future changes in global marine plankton and can start addressing challenges to marine conservation.

The AtlantECO GeoNode is based on the open-source GeoNode project, a geospatial content management system (CMS) and a spatial data infrastructure (SDI) platform designed to enable sharing and collaboration of geospatial data and maps. The project aims to promote the use of geospatial data and make it more accessible to people all over the world. It does this by building on state-of-the-art, open-source geospatial web technologies and standards, supported by the Open Source Geospatial Foundation (OSGEO) and the Open Geospatial Consortium (OGC). As such, it can be customized to meet the needs of the AtlantECO project. The AtlantECO GeoNode was launched on April 2022 and is available at https://atlanteco-geonode.eu/. The web platform was customized to match the look and feel of the AtlantECO project website4. After some tests with the AtlantECO maps NetCDF files, data population of the AtlantECO GeoNode has kicked off in the spring of 2023 and will continue throughout the lifetime of the project, as new versions and datasets become available. The AtlantECO GeoNode aims to promote the research outputs of the project by: ● Providing a web platform that allows to search for, visualise, download and share the AtlantECO MAPSboth within the project as well as to other stakeholders. ● Providing standard OGC web services that allow to search for, visualise and download AtlantECO MAPS within common Geospatial Information System (GIS) tools (e.g. QGIS, ArcGIS) as well as scripting languages (e.g. R, Python) ● Enabling wider dissemination of AtlantECO MAPS by inclusion in other web map projects like the European Atlas of the Seas5 that support the OGC standards.