Seminar by Paula Sobenko Hatum, postdoctoral researcher at Dyneco/Dhysed

The next DYNECO seminar will be presented by  Paula Sobenko Hatum, postdoctoral researcher at DYNECO/DHYSED on: Dynamic Bayesian Networks for Understanding Ecosystem Resilience to Extreme Climate Events – A Seagrass Study

Monday 9 décember 2024 at 10a.m. in the DYNECO meeting room (Ifremer Plouzané, Building 216).

Abstract:

This study focuses on the critical role of seagrass as a primary producer habitat, essential for biodiversity and ecosystem services, and its vulnerability to climate change, particularly the increasing severity and frequency of marine heatwaves (MHWs). Using a Dynamic Bayesian Network (DBN) approach, this study models the nonlinear, dynamic processes affecting seagrass meadows under various stressors in locations such as Arcachon Bay, France, and Leschenault Estuary and Gladstone Harbour, Australia, focusing on key species like Zostera noltei, Zostera muelleri, and Halophila ovalis. The thesis comprises four studies:

1. A framework for adapting DBN models to new sites with limited data was developed, addressing a key challenge in seagrass management globally. It provided guidelines for model adaptation and validation using expert knowledge and local data, demonstrated with Zostera noltei in Arcachon Bay.

2. A DBN model was extended to assess resilience to cumulative MHW events for Halophila ovalis in Western Australia. Advantages of this modelling approach include the ability to simulate repeated MHW occurrences with different intervals to estimate resilience.

3. A methodological approach was developed for integrating DBN models with climate projections to predict seagrass responses at local sites, exemplified with Zostera muelleri in Gladstone Harbour. Using an approach based on Monte Carlo simulations and novel resilience metrics, the framework enhances transparency by explicitly characterizing uncertainties, emphasizing the importance of adaptive management and uncertainty-driven risk assessment.

4. An interactive application, SeagrassWatcher, was developed to make complex models such as DBN models more accessible to ecosystem managers, facilitating informed decision-making.

Overall, this research underscores the importance of understanding seagrass responses to climate-induced stressors and the need for adaptable models to support conservationists and policymakers in different ecosystems and scenarios.