Abstract
Trees provide an important ecosystem service, modulating climate by impacting biogeochemical cycling as well as carbon sequestration. Despite their key role in the Earth System, patterns and drivers of forest carbon accumulation are still not well understood. Inference about carbon accumulation is limited by data availability and resolution, as well as by the quantitative approaches used to estimate accumulation. In this study, we use tree-rings and diameter data to estimate above ground biomass and biomass increment for sample sites across the northeastern United States. This geographic coverage of the sites makes it possible to address questions about the variability in tree growth response to climate, and the impacts of regional versus local drivers of these growth patterns. Biomass was estimated using a Bayesian hierarchical model that integrated multiple data types and quantified uncertainty in data and processes. First, we estimated annual tree growth with uncertainty. Then, biomass accumulation was estimated with uncertainty using allometric equations. We inferred biomass accumulation patterns for these six sites that span the northeastern United States. Tree ring and diameter data for these sites had already been collected. Using PRISM climate data we made comparisons and explored how climate affects tree-growth. We found that at a specific site tree-growth responds to climate with variability between species response.
Despite increasing research, predicting biomass responses under changing climatic conditions remains complex. This study underscores the importance of species-specific responses and local environmental conditions in shaping forest carbon dynamics. The integration of tree-ring data and Bayesian modeling offers valuable insights for understanding and predicting forest carbon sequestration, emphasizing the need for spatially explicit and temporally resolved data. These findings contribute to improving forest management strategies aimed at mitigating climate change impacts.