Abstract
To assess the long-term impacts of forest management interventions under climate change, process based models, which allow to predict transient dynamics under environmental change, are arguably the most suitable tools available. A challenge for using these models for management decisions, however, is their higher parametric uncertainty, which propagates to predictions and thus into the decision ...
Abstract
To assess the long-term impacts of forest management interventions under climate change, process based models, which allow to predict transient dynamics under environmental change, are arguably the most suitable tools available. A challenge for using these models for management decisions, however, is their higher parametric uncertainty, which propagates to predictions and thus into the decision making process. Here, we demonstrate how this problem can be addressed through Bayesian inference. We first conduct a Bayesian calibration to generate an estimate of posterior parametric uncertainty for the process-based forest growth model 3-PG for Fagus sylvatica. The calibration uses data from twelve sites in Germany, together with a robust (Student's t) error model. We then propagate the estimated uncertainty together with economic uncertainty to forest productivity and Land Expectation Value (LEV), allowing us to evaluate alternative management regimes under climate change. Our results demonstrate that parametric and economic uncertainty have strong impacts on the variation of predicted forest productivity and profitability. Management regimes with increased thinning intensity were overall most robust to economic, climate change and parametric model uncertainty. We conclude that estimating and propagating economic and model uncertainty is crucial for developing robust adaptive management strategies for forests under climate change. (C) 2017 Elsevier B.V. All rights reserved.
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