Integration of individual and population data to improve predictions of size-structured forest dynamics

Integral projection models (IPMs) are a powerful tool for predicting structured forest dynamics under global change. Inverse calibration approaches allow IPMs to be fit using widely available forest inventory data increasing potential applications. Yet inventory data may not provide sufficient information for IPMs to accurately identify underlying demographic rates leading to poor predictions. We construct a Bayesian dynamical IPM framework to integrate forest inventory (population density) with tree ring (individual growth) data to better predict size-structured forest dynamics. The framework pairs an IPM process model with data models that control for individual growth variability and a mismatch in the scale of forest inventory and tree ring data. The model is applied to a combination of experimental forest and simulated data to assess its ability to predict size-structured population density and estimate underlying demographic rates. We focus on the ability of the model to make inference about high-frequency variables associated with weather extremes and disturbance given their importance for predicting forest dynamics under global change. Predictions of size-structured population density were similar regardless of whether the dynamical IPM was provided both forest inventory and tree ring data (integrated model) or forest inventory data alone (population model). The population model, however, did not identify annual growth effects driven by high-frequency weather variables leading to poor estimates of population growth rate. Simulation trials under which the integrated model was provided varying numbers of tree ring records indicated that 10 records were sufficient for the model to estimate annual growth effects with near equivalent inference when 30 or more records were applied. Results highlight the potential for inversely calibrated IPMs to correctly predict structured population dynamics while incorrectly estimating underlying demographic rates. Integrating individual demographic data resolves this issue allowing for inference on growth responses to high-frequency weather and disturbance variables, thereby improving the ability of IPMs to predict structured forest dynamics under global change. While individual demographic rate data is often limited, simulation results indicate that only a small number of individual records are needed for valid inference.