Autoregressive Modeling of Forest Dynamics

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 b...

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Published inForests Vol. 10; no. 12; p. 1074
Main Authors Rumyantseva, Olga, Sarantsev, Andrey, Strigul, Nikolay
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.12.2019
Subjects
Autoregressive models
Autoregressive processes
Bayesian analysis
Biomass
Dynamics
Empirical analysis
Forest biomass
Forest ecosystems
Forests
Inventory
Markov analysis
Markov chains
Mathematical models
Modelling
Negative feedback
Normality
Partial differential equations
Random walk
Securities markets
Statistical analysis
Stochasticity
Stock exchanges
Canada
United States > US
Quebec Canada
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Summary: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 performed a time series statistical analysis of forest biomass and basal areas recorded in Quebec provincial forest inventories from 1970 to 2007. The geometric random walk model adequately describes the yearly average dynamics. For individual patches, we fit an autoregressive process (AR) of order 1 capable to model negative feedback (mean-reversion). Overall, the best fit also turned out to be geometric random walk; however, the normality tests for residuals failed. In contrast, yearly means were adequately described by normal fluctuations, with annual growth on average of 2.3%, but with a standard deviation of order of 40%. We used a Bayesian analysis to account for the uneven number of observations per year. This work demonstrates that autoregressive models represent a valuable tool for the modeling of forest dynamics. In particular, they quantify the stochastic effects of environmental disturbances and develop predictive empirical models on short and intermediate temporal scales.
ISSN:1999-4907
1999-4907
DOI:10.3390/f10121074