Models for supporting forest management in a changing environment

  • Luis Fontes
  • Jean-Daniel Bontemps
  • Harald Bugmann
  • Marcel Van Oijen
  • Carlos Gracia
  • Koen Kramer
  • Marcus Lindner
  • Thomas Rötzer
  • Jens Peter Skovsgaard
Keywords: process-based models, empirical models, environmental change, hybrid models, forest management,


Forests are experiencing an environment that changes much faster than in at least the past several hundred years. In addition, the abiotic factors determining forest dynamics vary depending on its location. Forest modeling thus faces the new challenge of supporting forest management in the context of environmental change. This review has focused on three types of models that are used in forest management: empirical, process-based and hybrid models. Recent approaches might lead to the applicability of empirical models under changing environmental conditions, such as (i) the dynamic state-space approach, or (ii) the development of productivity-environment relationships. 25 process-based models in use in Europe were analyzed in terms of its structure, inputs and outputs having in mind a forest management perspective. Two paths for hybrid modeling were distinguished: (i) coupling of EMs and PBMs by developing signal-transfer environment-productivity functions; (ii) hybrid models with causal structure including both empirical and mechanistic components. Several gaps of knowledge were identified for the three types of models reviewed. The strengths and weaknesses of the three model types differ and all are likely to remain in use. There is a trade-off between how little data the models need for calibration and simulation purposes, and the variety of input-output relationships that they can quantify. PBMs are the most versatile, with a wide range of environmental conditions and output variables they can account for. However, PBMs require more data making them less applicable whenever data for calibration are scarce. The EMs, on the other hand, are easier to run as they require much less prior information, but their simplicity makes them less reliable in the context of environmental changes. These different deficiencies of PBMs and EMs suggest that hybrid models may be a good compromise, but a more extensive testing of these models is required in practice.
Articulos originales