SPECIAL BRAG will be held this Thursday 09/03 at 1 pm via Zoom/GP-Y801.
What’s a SPECIAL BRAG? In these sessions, presenters will have 10-15 minutes to discuss their research, with a focus on work that is close or already published. Following the presentation, an external reviewer/assessor/panelist/colleague (the discussant) will have an additional 10-15 minutes to summarize the research and highlight relevant discussion points and questions. What makes SPECIAL BRAG unique is that the discussant will not be an expert in the presenter’s field, offering fresh perspectives and an opportunity for both the presenter and discussant to develop their skills in scholarly communication.
This week we will have a presentation by @Carolyne Chercham with @James Hogg as the discussant.
Zoom Link: https://qut.zoom.us/j/86982060024?pwd=dHFHdm44R2NjYWNEbWhwTmFWQ09PZz09
Password: brag@QUT (if prompted)
Carolyne’s Talk
Title: Hybrid modelling for the prediction of regional seagrass dynamics
Abstract: Seagrasses are flowering plants that form meadows in marine environments worldwide. They are ecosystem engineers and interact with their physical environment through bio-physical feedback loops. Changes in the density of a meadow impact its physical environment. At the same time, a change in the physical environment may impact the seagrass meadow’s dynamics.
After a strong loss, both in extent and density, of seagrass in the Arcachon Basin (SW France) in the past decades, we aim to predict the future trend of these meadows under climate change. In a first step, we want to simulate the past regional seagrass dynamics in a hindcast. To be able to reproduce the effects of the bio-physical feedback loop on the time scale of decades, we use a hybrid modelling strategy. Our hybrid modelling strategy relies on adapting and coupling pre-existing deterministic and probabilistic models.
The deterministic model is a regional physical ocean model (MARS) that includes seagrass as a physical obstacle (Lazure & Dumas, 2008, Kombiadou et al., 2014). In the MARS model domain, N=2089 grid cells are suitable for seagrass to grow in. For each of these N grid cells, MARS outputs the evidence for environmental conditions every month.
The probabilistic model is a Dynamic Bayesian Network (DBN) for seagrass dynamics (Wu et al., 2017, Hatum et al., 2022). The DBN computes the monthly change in shoot density based on environmental conditions. In order to better resolve the meadows spatially, we take N=2089 individual DBNs, one for each grid cell in MARS that has potential for seagrass.
The coupled model is still a work in progress. Challenges are, amongst others, the inclusion of spatial connectivity between the individual DBNs, the translation (output/input) between MARS and DBN, the handling of uncertainty and the scarcity of observational data.
Thanks,
Jamie & Scott
BRAG Co-Chairs
Bayesian Research & Applications Group 