Models for supporting forest management in a changing environment

Luis Fontes,Harald Bugmann,Marcel Van Oijen,Koen Kramer,Carlos Gracia,Jens Peter Skovsgaard,Thomas Rötzer,Marcus Lindner,Jean-Daniel Bontemps

doi:10.5424/fs/201019s-9315

Abstract

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.

Highlights

The need for a new forest modeling paradigmIn the 21st century, forests are experiencing an abiotic environment that changes much faster than during the past several hundred years
This coupling relies on the development of environment-productivity signal-transfer functions from simulations of the process-based model, that are incorporated into the empirical model; (ii) the development of hybrid models sensu stricto, based on concepts from both empirical and process-based modeling that are embodied within the same piece of computer code
Forest ecosystem responses to environmental conditions are widely considered species-specific, but intra-specific ecotypic responses may restrict the domain of application of most current models (Kramer et al, 2008; Kramer et al, 2010)

Summary

Introduction

In the 21st century, forests are experiencing an abiotic environment that changes much faster than during the past several hundred years. Because EMs remain an accurate tool for yield prediction, there has been a renewed interest for uncovering the environmental determinants of site index (Albert and Schmidt, 2010; Diaz-Maroto et al, 2006; Seynave et al, 2005), based on regression models of site index against soil, climate and other environmental indicators Such productivity-environment relationships have to be designed at scales much larger than individual stands, to cover a wide range of environmental conditions, ranging from regional (Sánchez-Rodríguez et al, 2002; Szwaluk and Strong, 2003) to national (Seynave et al, 2005; Tyler et al, 1996). Because empirical growth models are principally well suited for the investigation of a wide range of management alternatives, their coupling with productivity-environment models based on large-scale ecological gradients (Dhôte, 1996; Seynave et al, 2008) provides a cost-effective and accurate alternative approach for the prediction of timber production in the context of environmental change. Latitude (to determine sun angle), soil texture and soil depth are the most commonly required site and soil data inputs

Region to Hour or continent half-hour

Findings

Discussion and conclusions