The northern tamarisk beetle (Diorhabda carinulata) was released in 2001 as a biocontrol agent for Tamarix spp., an invasive tree that dominates riparian ecosystems throughout the southwestern United States. The factors that influence its effectiveness at controlling Tamarix, and the effects of control on plant communities, are not well known. Here we report patterns of Tamarix dieback, mortality, and vegetation composition at ten of the early D. carinulata release sites in western Colorado. Across the ten release sites, 265 permanently marked Tamarix trees were measured over a six year period (2008-2014). Vegetation composition and woody debris adjacent to each of these trees were measured annually for four years (2010-2014). We examined relationships between site factors (soil properties, hydrology, and land use history), Tamarix dieback, and vegetation composition. Tamarix mortality was observed at seven of ten sites, where it ranged from 15-56% after six years. Overall, Tamarix crown cover decreased by more than half (54%) while crown volume decreased by 63% in the first two years of the study. Neither total plant cover nor fallen woody debris increased under Tamarix trees over the last four years of the study. Combined cover of classified noxious weeds and other non-native species was greater than native plant cover at eight of ten sites. D. carinulata proved to be effective in controlling the Tamarix invasion locally. However, the high cover of noxious weeds will continue to be a management problem, with or without Tamarix control by the northern tamarisk beetle.

Scientists use deterministic models to study and forecast the behavior of complex environmental processes, with increasing emphasis on incorporating data to inform model input parameters and accounting for parameter uncertainty. We work with a deterministic, individual‐based model (IBM) of tree growth and mortality, which is under development to explore forest dynamics. Some values of IBM input parameters cause premature virtual tree mortality relative to the actual mortality status of an observed tree. This discordance in mortality causes dimension changes in the state of a stochastic implementation of IBM outputs and leads us to address trans‐dimensional moves among states with a novel formulation of reversible jump Markov chain Monte Carlo (RJMCMC). In particular, we present an RJMCMC algorithm that uses a continuously supported, multidimensional index—the IBM input parameter—instead of a discrete index typical of model determination applications. We use both synthetic data and data from the Forest Inventory and Analysis database representing two tree species. We compare results for each dataset and species between our reversible jump (RJ) specification and an alternative, non‐RJ specification. The RJ formulation compares favorably to the non‐RJ formulation with regard to achieving convergence and yielding biologically realistic IBM input parameter estimates. Copyright © 2013 John Wiley & Sons, Ltd.