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

Abstract

Integral projection models (IPMs) are a powerful tool for predicting structured population dynamics under global change. Inverse calibration approaches allow IPMs to be fit using widely available population density data increasing potential applications. Yet population density data alone may not provide sufficient information for IPMs to accurately identify the environmental factors driving demographic rates leading to poor predictions under variable future conditions. We construct a Bayesian dynamical IPM framework to integrate population density with individual growth data to better predict size-structured population dynamics. The framework pairs an IPM process model with data models that control for individual growth variability and a mismatch in the resolution and precision of population density and individual growth data. The model is applied to a combination of experimental and simulated forest population data to assess its ability to predict size-structured population density and estimate underlying demographic rates. Predictions of size-structured population density were similar regardless of whether the dynamical IPM was provided both population density and individual growth data (integrated model) or population density data alone (population model). The population model, however, did not identify annual growth effects driven by weather variables including vapor pressure deficit leading to poor estimates of mean size-structured growth. Simulation trials under which the integrated model was provided varying numbers of individual growth 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 variable environmental conditions, thereby improving the ability of IPMs to predict structured population 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.