Brad Oberle, Piper Olivia` Cole, Garcia Frank, Alexandra Gates, Brittney Hall, Deric Harvey, Melody Elizabeth Scott, Cas Setterberg, Simon Peter Bustetter

Managing disturbed forests for climate mitigation and biodiversity requires monitoring the carbon (C) cycle consequences of replacing established exotic vegetation with native seedlings. Standard approaches rely on allometric growth equations with unexplored limitations for measuring C changes during restoration. Most plants lack species-specific allometric growth equations, which may perform poorly for different growth forms, especially when applied to both mature trees and seedlings. To address these limitations, we generated and compared allometric growth equations for four woody species with different biogeographic origins and growth forms, including two high impact invasive species, Cupaniopisis anacardioides and Schinus terebinthifolia. By borrowing strength from sampling across species to reduce estimation error within species, Bayesian multilevel models generated more accurate and precise estimates than either independent species-level models or generic equations. Because errors increased for smaller plants and species with unusual growth forms, allometric growth equations from custom multilevel models generated higher baseline aboveground biomass estimates and lower post-restoration estimates, which has important implications for monitoring C consequences of invasive tree management.