RT Journal Article
SR Electronic
T1 Data-intensive multidimensional modeling of forest dynamics
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 005009
DO 10.1101/005009
A1 Jean F. Liénard
A1 Dominique Gravel
A1 Matthew V. Talluto
A1 Nikolay S. Strigul
YR 2014
UL http://biorxiv.org/content/early/2014/05/10/005009.abstract
AB 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.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 longer-term increases in the average age of trees, biodiversity, and shade intolerant species.Synthesis. 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. This method can be applied in other contexts such as conservation and silviculture, and can be delivered as an efficient tool for sustainable forest management.