PhD students

PhD students within the research area Observation Technology.

Aurelia GabelliniAurelia Pereira Gabellini


Title of PhD project

Marine Connectivity across the Atlantic Ocean—past, present and future

Supervisors

Patrizio Mariani and Asbjørn Christensen

Background of project

Interactions between ocean currents and life history traits can regulate fundamental processes in marine ecosystems including spatial segregation, speciation and meta-population structures. These processes can act across several temporal and spatial scales altering the response of ecosystems to multiple pressures including climate change. It is therefore relevant to develop methods to assess the marine connectivity across distant biogeographic regions to support the identification of management strategies for the sustainable exploitation of ocean resources.

About the project

My PhD project aims to better understand dispersion and connectivity patterns across biogeographic regions in the Atlantic Ocean, by combining trait-based modeling description of marine organisms to high resolution ocean circulation models. General circulation models for the Atlantic Ocean will be coupled to a Lagrangian particle tracking algorithm simulating dispersion of numerical particles with properties defined by specific traits. The analyses will include a wide range of movement strategies from passive transport to more directed movements (e.g. migrations). The resulting connectivity matrices will be investigated to assess the importance of specific traits and the importance of transport across specific regions. The model will be used to assess past and present conditions as well as to provide scenarios of future connectivity patterns in the Atlantic Ocean. 

Perspective

This thesis is part of Mission Atlantic project which aim is to map and assess the present and future status of Atlantic marine ecosystems. The results are expected to contribute to further our knowledge about connectivity in the Atlantic Ocean and possible consequences in the recruitment of some selected groups due to climate change.

Anshul ChauhanAnshul Chauhan


Title of PhD project

Resolving marine ecosystem dynamics in time and space with machine learning approaches 

Supervisors

Patrizio Mariani, Michael St. John and Filipe Rodrigues

Background of project

Understanding ocean dynamics is vital to interpreting marine ecosystems functioning and determining key processes affecting global climate and biodiversity. Changes in chemical composition, warming of the ocean, loss of biodiversity, and several climate interactions alter the dynamic equilibrium between ocean, land, atmosphere, and between biotic and abiotic components in the Earth System. Such interactions can operate across multiple temporal and spatial scales and generate extreme conditions that significantly alter ocean dynamics and ecosystem functioning. The complexity of these processes is high and many uncertainties still exist on physical, biological, and chemical mechanisms regulating them.

About the project

The aim of my PhD is to focus on advancing state-of-the-art processing and interpretation of big ocean data introducing deep learning methods and hybrid-modeling approaches (statistical and process based) for understanding marine ecosystems. This research project is primarily concerned with critical oceanic variables like sea surface temperature (SST), sea surface salinity (SSS), ocean currents, and phytoplankton groups as well as other ocean variables valuable in assessing present and future ecosystem states.

Perspective

With this PhD, we expect to develop indicators for ecosystem state, understand the correlation between extreme events, detect abrupt transitions in ecological states across regions, and simulate possible future outcomes in the spatio-temporal domain.

Philip Alexander Hedlund SmithPhilip Alexander Hedlund Smith 


Title of PhD project

Big data analytics to support ecosystem-based risk management of marine ecosystems

Supervisors

Patrizio Mariani, Asbjørn Christensen and Michael St. John

Background of project

Ocean dynamics are essential for the functioning of the Earth system with important effects on climate regulation and global biodiversity. Regional and global processes driving storage and transport of heat, carbon, nutrients, and marine organisms are crucial for providing many ecosystems’ goods and services that enable life on Earth. These processes are driven by mechanisms interacting and operating over wide ranges of spatial and temporal scales, and inherently involve both horizontal and vertical dimensions, making them exceedingly difficult to monitor and to understand fully.

About the project

The general objective is to determine and understand spatio-temporal dependencies, relations, and mutual effects in the abundant climate and biogeochemical data. The goal is to understand these relationships as well as constructing frameworks for predicting future behavior. Moreover, to establish systems where ocean and ecosystem dynamics are learned and can be emulated for different initial state values. Neural networks and deep learning approaches in particular display major advantages in exploiting spatio-temporal data and capturing nonlinear relations in data compared to classical approaches.

Perspective

Generating deep learning frameworks to combine remotely sensed and in situ observations may improve estimates and models of subsurface ocean state variables, which presently can be difficult to monitor due to the scarcity of local measurements. Furthermore, predictive data-driven models that accurately reproduce simulation data may facilitate comprehensive risk analyses and assessments, as changes in simulation data for varying driver inputs may be considerably less time consuming.

Previous PhD students (since 2020)

Søren Lorenzen Post

Blue whiting (Micromesistius poutassou): behaviour and distribution in Greenland waters
Go to DTU Orbit to download thesis