Predicting the dynamics of establishing tree populations: A framework for statistical inference and lessons for data collection

Abstract

Under global change, there is an urgent need to forecast the dynamics of establishing tree populations. However, tree population dynamics are slow and historical data on these dynamics are rare. This raises the question whether tree population dynamics can be reconstructed from data collected at a single time point. Doing so poses challenges for modelling, data collection and model-data integration. We present a Bayesian framework that uses multiple data types to parametrize an individual-based model (IBM) for the growth of establishing tree populations. The framework combines likelihood-based Bayesian inference and approximate Bayesian computation (ABC). Using this framework, we assess the information content of three data types (recruitment data, dendrochronological data describing individual growth and molecular markers characterizing within-population pedigrees) by comparing the bias and uncertainty of parameter estimates and model forecasts obtained under different simulated scenarios of data availability. The combination of all data types leads to accurate forecasts of the future state of tree populations, despite large uncertainties in some parameter estimates. Dendrochronological data were the most informative of the examined data types. Combining data types improved forecasts of population state. Nevertheless, for a given parameter related to a given process, combining data types did not improve estimates compared to using only the data type most closely related to the process. The presented Bayesian framework allows to infer the dynamics of establishing tree populations from data collected at a single time point. It helps to optimally allocate limited resources for data collection in order to rapidly improve the understanding and forecasting of tree population dynamics.