Examining the Effect of Beech Bark Disease on Radial Growth in American Beech

Abstract

Beech bark disease (BBD) is a significant threat to forests of North America and the impact of BBD on radial growth in the American beech is substantial. We developed a novel hierarchical Bayesian (HB) model to simultaneously model disease dynamics, tree growth, and the interaction of the two. Our model can be adapted to both emerging and more mature forest–pathogen systems to aid in ecosystem loss predictions. Long-term data from a single site minimized potential confounding variables such as climate change, precipitation, land use history, and soil conditions that may influence radial growth. Here, 206 beech trees were monitored over 15 years at an 85-acre site in southwestern Vermont, measuring diameter at breast height (DBH) and progression of BBD. Our model allows us to accurately estimate error rates in disease severity estimation and DBH measurements, and estimate the true state based on environmental variables. As disease poses significant threats to many tree species around the world, researchers can obtain more value and information from their datasets utilizing an adapted HB model.