2022 ESA Annual Meeting (August 14 - 19)

COS 106-5 CANCELLED - Causal drivers of climate-mediated coral reef regime shifts

4:30 PM-4:45 PM
513D
Suchinta Arif, Dalhousie University;Aaron MacNeil,Dalhousie University;Nicholas Graham,Lancaster University;Shaun K. Wilson,University of Western Australia;
Background/Question/Methods

Climate-induced coral bleaching events are a leading threat to coral reef ecosystems and can result in coral-macroalgal regime shifts that are difficult to reverse. In Seychelles, a 1998 widespread bleaching event led to approximately half of the coral reef sites recovering, and the other half regime shifting to macroalgae-dominated ecosystems, providing a unique ecosystem to understand how different factors may influence regime shift trajectory. Here, we apply the structural causal model (SCM) framework to understand which factors influenced recovery vs. regime shift trajectory across Seychelles, using ecological surveys collected from 1994 to 2014. SCM is a novel causal inference framework that can be used across observational studies. It uses directed acyclic graphs (DAGs) to represent the causal structure of a system under study, which in turn guides controls required to answer causal questions.

Results/Conclusions

Our causal models reveal additional causal drivers of regime shifts, including initial macroalgae cover, wave exposure, and branching coral cover. We also find that reduced depth and structural complexity and increased nutrients increase the likelihood of regime shifting. Further, we use a DAG-informed predictive model to show how recovering reefs are expected to change after a recent 2016 bleaching event, suggesting that three out of twelve recovering reefs are expected to regime shift given their pre-disturbance conditions. Collectively, our results provide the first causally-grounded analysis of how different factors influence post-bleaching regime shift vs recovery potential on coral reefs. More broadly, SCM stands apart from previous observational analysis and provides a strong framework for causal inference across other observational ecological studies.