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In this work, we employ autoregressive models developed in financialengineering for modeling of forest dynamics. Autoregressive models have sometheoretical advantage over currently employed forest modeling approaches suchas Markov chains and individual-based models, as autoregressive models are bothanalytically tractable and operate with continuous state space. We perform timeseries statistical analysis of forest biomass and basal area recorded in Quebecprovincial forest inventories in 1970-2007. The geometric random walk modeladequately describes the yearly average dynamics. For individual patches, wefit an AR(1) process capable to model negative feedback (mean-reversion).Overall, the best fit also turns out to be geometric random walk, however, thenormality tests for residuals fail. In contrast, yearly means are adequatelydescribed by normal fluctuations, with annual growth, on average, 2.3%, butwith standard deviation of order 40%. We use Bayesian analysis to account foruneven number of observations per year. This work demonstrates thatautoregressive models represent a valuable tool for modeling of forestdynamics. In particular, they quantify stochastic effects of environmentaldisturbances and develop predictive empirical models on short and intermediatetemporal scales.

Autoregressive Modeling of Forest Dynamics