- Reference : FEM-SAS-2022-320
- Position type: PhD position
- Duration: 36 month
- Localisation: Boulogne-sur-Mer (62) - Ifremer
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This thesis will be part of the task “Uncertainty related to ecosystem models” of the NESTORE project, a collaborative project coordinated by France Energies Marines and the University of Caen. This task will be lead by the Institut Français de Recherche pour l’Exploitation de la Mer (Ifremer) and the Université du Littoral Côte d’Opale (ULCO). The PhD student will work in a team, in particular within the unit Halieutique Manche Mer du Nord at Ifremer in Boulogne-sur-Mer (France) and in direct interaction with the team dedicated to the Environmental Integration of offshore renewable energies of France Energies Marines and the Laboratory of Oceanology and Geosciences (LOG) at ULCO (France).
The main objective of this thesis is to provide predictions of potential impacts of offshore windfarms with an interval of confidence under contrasted climate change and fishing pressure scenarios.
Understanding the effects of human impacts on marine food webs is essential to set effective management plans and ensure a sustainable use of marine resources. In complex systems, the assessment of cumulative impacts is challenging and requires an ecosystem-based approach to achieve ecological, economic and social objectives. Such an approach is growing in popularity in parallel with the development of ecosystem models (Pikitch et al., 2004). These models attempt to incorporate multiple process (e.g., predation, mortalities, distribution, movement, growth, reproduction, etc.) in a single framework (Geary et al., 2020). However, increasing model complexity and realism may increase the uncertainty around the predictions. Recent studies highlighted the need to understand and evaluate the different sources of uncertainties for management decisions to provide credible advice (Link et al., 2012). The main sources of uncertainty mentioned in ecosystem models are described as natural variability (or process uncertainty), observation error (or estimation uncertainty) and structural complexity (or model uncertainty) and the scenario uncertainty (IPBES, 2016; Lehuta et al., 2016; Link et al., 2012).
In the English Channel, an extensive sensitivity analysis was applied to an Atlantis ecosystem model (Bracis et al., 2020; Girardin et al.) using the Morris screening method growth, mortality, and reproduction parameters (Morris, 1991).
Similar method will be used to explore with OSMOSE English Channel (Travers-Trolet et al., 2019) to identify which parameters are more or less influential. A multi-model approach will be applied to better capture uncertainty in predictions and improve the robustness of ecosystem models making them operational. Within the framework of the project NESTORE, this thesis aims to develop a multi-model approach to characterize and quantify the uncertainty related to the effects of Offshore Renewable Energies in the Eastern English Channel ecosystem in a changing environment. Different ecosystem models have already been implemented in the study area: i/ Individual-based models, OSMOSE (Travers-Trolet et al., 2019) ii/ End-to-End model, Atlantis (Girardin et al., 2016), and iii/ mass-balanced model, EwE (Araignous et al., in prep). These models will be used to address three main sources of uncertainty: natural variability (or process uncertainty), observation error (or estimation uncertainty) and structural complexity (or model uncertainty). A sensitivity analysis will be performed to identify which parameters are more or less influential under different forcing conditions. A set of exploratory and target-seeking scenarios will be simulated to investigate the impacts of fishing and climate considering the multiple dimensions of uncertainty.
To this end, various scientific objectives will be addressed:
- Review of potential ecosystem impacts of offshore windfarms
- Update of the current OSMOSE and Atlantis ecosystem models developed for the Eastern English Channel
- Investigate how the different sources of uncertainty drive models’ responses using a formal framework
- Evaluation of potential offshore windfarms effects considering the different sources of uncertainty
- Assessment of cumulative impacts of renewable marine energies, climate change and fishing
MSc in marine ecology, quantitative ecology or equivalent
• Good knowledge of general statistics, spatial statistics and cartography
• Capacity to manage large datasets
• R programming skills (R Studio, R Markdown)
• Knowledge of fish biology, marine ecosystems, food webs
• Good communication skills in English
• Knowledge/experience with Linux OS
• Background in sampling design and/or sensitivity analysis
• Experience with ecosystem models
• Ability to work in a team
• Good writing and communication skills in English
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Job : PhD thesis on dealing with ecosystem models uncertainty when assessing potential windfarms impacts – Warning: please send your application directly and only by e-mail to email@example.com
Reference : FEM-SAS-2022-320