Leveraging the Bayesian Stochastic Antecedent Model to Quantify Long-Term Drought Effects on Forest

2523 

Contributed Abstract 

Poster 

Ashlan Simpson (1)

(1) N/A, N/A

Ashlan Simpson  
N/A

Ashlan Simpson  
N/A

Statistical methods are essential in ecology, helping researchers navigate complex, noisy data to understand environmental change and predict ecosystem responses. One critical application is assessing how forests, which play a key role in carbon capture, are impacted by increasing drought frequency-a threat that may have long-term consequences overlooked by traditional models. This study addresses that gap by applying the Bayesian Stochastic Antecedent Model, a statistical approach designed to quantify how past drought severity and duration influence future growth. This methodology will be applied to B4WarmED, a new and rare long-term experimental dataset that uniquely combines temporal depth, a controlled causal setup, and the ability to extract signals from a low signal-to-noise ratio, making it ideal for comprehensive understanding. This methodology, combined with the depth and design of B4WarmED, enables nuanced, data-driven insights into the complex, long-term effects of drought, allowing for stronger causal claims about how past conditions shape future tree growth. Refining predictions of forest carbon dynamics will improve climate models and inform conservation strategies.

ecology|time-series|antecedent events|causal inference|tree growth|

Section on Statistics and the Environment

Spatio-temporal statistics