A time-inhomogeneous Markov chain approach to predict forest biomass under dynamic disturbance regimes
Forecasting forest dynamics under evolving environmental conditions is a challenging problem, as it involves considering together two interdependent components, climatic regimes and forested ecosystems. We propose here a novel method to model progressive changes in forest biomass resulting from variations in natural disturbance regimes and land-use practices using inhomogeneous Markov Chain models. The first step of the methodology is the estimation of biomass transition matrices based on forest inventories using a Bayesian approach. The second step is the incorporation of growth and disturbance terms with the inclusion of dynamic coefficients within the transitions, reflecting the changing environmental conditions. The third and final step is the simulation of transient dynamics and stationary states.
We present an application of this method to investigate the consequences of global warming scenarios, which predict changes in fire rate in Quebec hardwood forests as well as possible growth enhancements due to increasing CO2 and temperature. We report that none of the considered scenarios was able to counterbalance the currently observed trend of increasing biomass, in the next 30 to 40 years. The developed approach is capable of unifying predictions from mechanistic models of disturbance and growth, and can readily be transposed to other datasets. Overall, the developed method can be broadly employed to incorporate inhomogeneous effects in different areas of ecology, such as demography, life-cycle analysis and stage-structured population dynamics, where matrix transition models are traditionally used.