Data-intensive multidimensional modeling of forest dynamics

Background/Question/Methods

Forest dynamics are highly dimensional phenomena that are poorly understood theoretically. Modeling these dynamics is data-intensive and requires repeated measurements taken with a consistent methodology. Forest inventory datasets offer unprecedented opportunities to model these dynamics, but they are analytically challenging due to high dimensionality and sampling irregularities across years. We develop a methodology for predicting forest stand dynamics using such datasets. Our methodology involves the following steps: 1) computing stand level characteristics from individual tree measurements, 2) reducing the characteristic dimensionality through analyses of their correlations, 3) parameterizing transition matrices for each independent dimension using Gibbs sampling, and 4) deriving predictions of forest developments at different timescales. Model development and validation are illustrated using the Quebec provincial forest inventory data. This inventory is one of the extended forest inventories that have been established in North America, among others led by the Canadian provincial governments and the USDA Forest Inventories and Analysis program in the USA.

Results/Conclusions

We applied this approach to the Quebec forest inventory dataset and validated the model using two independent subsets of data. Applying our methodology to the Quebec forest inventory database, we discovered that four independent dimensions were required to describe the stand structure. We were able to successfully estimate transition matrices for each of these dimensions. The model predicted substantial short-term increases in biomass and small longer-term increases in the average age of trees, biodiversity, and shade intolerant species. Using highly dimensional and irregularly sampled forest inventory data, our original data-intensive methodology provides both descriptions of the short-term dynamics as well as predictions of forest development on a longer timescale. Therefore, the framework will be useful both in applied contexts (e.g., conservation, silviculture) as well as in developing our conceptual understanding of how forested ecosystems are organized.