  In this work, we employ autoregressive models developed in financial
engineering for modeling of forest dynamics. Autoregressive models have some
theoretical advantage over currently employed forest modeling approaches such
as Markov chains and individual-based models, as autoregressive models are both
analytically tractable and operate with continuous state space. We perform time
series statistical analysis of forest biomass and basal area recorded in Quebec
provincial forest inventories in 1970-2007. The geometric random walk model
adequately describes the yearly average dynamics. For individual patches, we
fit an AR(1) process capable to model negative feedback (mean-reversion).
Overall, the best fit also turns out to be geometric random walk, however, the
normality tests for residuals fail. In contrast, yearly means are adequately
described by normal fluctuations, with annual growth, on average, 2.3%, but
with standard deviation of order 40%. We use Bayesian analysis to account for
uneven number of observations per year. This work demonstrates that
autoregressive models represent a valuable tool for modeling of forest
dynamics. In particular, they quantify stochastic effects of environmental
disturbances and develop predictive empirical models on short and intermediate
temporal scales.
