Simulation tools for decision support to adaptive forest management in Europe

In forest management there is a tendency towards measuring less and simulating more. In this context the development of reliable, user friendly forest simulators has become economically relevant. The objective of this perspective paper is to highlight the recent trends in forest simulation and to identify the remaining challenges to make forest simulation a reliable tool for forest policy and management. Experiences with forest simulators for various purposes in different geographical contexts illustrate how the important challenges of forest decision support can be addressed through flexible customization for different end-user categories, offering spatially explicit approaches at the landscape scale, and integrating empirical and mechanistic models in hybrid and bayesian simulation approaches. Recent development trends in forest simulation for decision support are mainly related to the ever increasing calculation speed and capacity of computers, facilitating the development of robust tools with comfortable user interface and realistic functions and options. Another trend is the combination of simulation tools with optimization and choice algorithms fading away the difference between simulators and decision support systems. The remaining challenges are basically in the high expectations of stakeholders concerning the ability of simulators to predict a range of outcomes in terms of ecosystem services and sustainability indicators, as well as the quality of their outcome in terms of output credibility to stakeholders. Need for accepted and realistic model validation and verification methods preferably using empirical data is crucial in this matter.

[1]  Jennie Popp,et al.  Sustainability indices with multiple objectives , 2001 .

[2]  Oleg Chertov,et al.  ROMUL — a model of forest soil organic matter dynamics as a substantial tool for forest ecosystem modeling , 2001 .

[3]  Timo Pukkala,et al.  Introduction to Multi-Objective Forest Planning , 2002 .

[4]  Ron Smith,et al.  Bayesian calibration of process-based forest models: bridging the gap between models and data. , 2005, Tree physiology.

[5]  Kevin McGarigal,et al.  Landscape Metrics, Scales of Resolution , 2008 .

[6]  Euan G. Mason,et al.  Quantitative validation and comparison of a range of forest growth model types , 2006 .

[7]  Bart Muys,et al.  Multipurpose conversion management of Scots pine towards mixed oak–birch stands—A long-term simulation approach , 2009 .

[8]  Daniel L. Schmoldt,et al.  Computer-aided decision making , 2006 .

[9]  H. M. Rauscher.  Ecosystem management decision support for federal forests in the United States: a review , 1999 .

[10]  Joseph A. C. Delaney.  Sensitivity analysis , 2018, The African Continental Free Trade Area: Economic and Distributional Effects.

[11]  M. J. Kraak,et al.  Visualization of spatial data , 1996 .

[12]  Hannu Hökkä,et al.  Models for predicting stand development in MELA System , 2002 .

[13]  M. Kirschbaum,et al.  CenW, a forest growth model with linked carbon, energy, nutrient and water cycles , 1999 .

[14]  José G. Borges,et al.  HEURISTICS IN MULTI-OBJECTIVE FOREST MANAGEMENT , 2002 .

[15]  B. Muys,et al.  Sustainable Forest Management Worldwide: A Comparative Assessment of Standards , 2004 .

[16]  H. Kros,et al.  Evaluation of biogeochemical models at local and regional scale , 2002 .

[17]  D. C. van der Werf,et al.  Model documentation for the European Forest Information Scenario model (EFISCEN 3.1.3) , 2007 .

[18]  H. Peltola,et al.  Does climate change affect optimal planning solutions for multi-objective forest management? , 2010 .

[19]  Harald Vacik,et al.  A decision support tool to improve forestry extension services for small private landowners in southern Austria , 2005 .

[20]  Stef Proost,et al.  An integrated decision support framework for the prediction and evaluation of efficiency, environmental impact and total social cost of domestic and international forestry projects for greenhouse gas mitigation: description and case studies. , 2005 .

[21]  Jerome K. Vanclay,et al.  Evaluating a growth model for forest management using continuous forest inventory data , 1995 .

[22]  Bo Song,et al.  Visualization with spatial data , 2006 .

[23]  Edward J. Rykiel,et al.  Testing ecological models: the meaning of validation , 1996 .

[24]  D J Spiegelhalter,et al.  Computer aided decision making in medicine. , 1984, British medical journal.

[25]  Manfred J. Lexer,et al.  Addressing biodiversity in a stakeholder-driven climate change vulnerability assessment of forest management , 2009 .

[26]  M. Kurttila.  The spatial structure of forests in the optimization calculations of forest planning — a landscape ecological perspective , 2001 .

[27]  Patrick G. Cunningham,et al.  Estimating variation in a landscape simulation of forest structure , 2006 .

[28]  Rupert Seidl,et al.  Evaluating the accuracy and generality of a hybrid patch model. , 2005, Tree physiology.

[29]  Paul J. Valdes,et al.  Dynamics of a global-scale vegetation model , 2006 .

[30]  Margarida Tomé,et al.  Modelling non-wood forest products in Europe: a review. , 2011 .

[31]  Marek Fabrika,et al.  Implementing Tree Growth Models in Slovakia , 2006 .

[32]  Hans Pretzsch,et al.  Recommendations for Standardized Documentation and Further Development of Forest Growth Simulators , 2002, Forstwissenschaftliches Centralblatt vereinigt mit Tharandter forstliches Jahrbuch.

[33]  Annikki Mäkelä,et al.  Simulating wood quality in forest management models , 2011 .

[34]  Keith M. Reynolds,et al.  Integrating the DPSIR approach and the analytic network process for the assessment of forest management strategies. , 2007 .

[35]  Hojka Kraigher,et al.  Using the process-based stand model ANAFORE including Bayesian optimisation to predict wood quality and quantity and their uncertainty in Slovenian beech , 2009 .

[36]  David B. Lindenmayer,et al.  Adaptive risk management for certifiably sustainable forestry , 2008 .

[37]  Markus Erhard,et al.  Analyzing the Ecosystem Carbon Dynamics of Four European Coniferous Forests Using a Biogeochemistry Model , 2003, Ecosystems.

[38]  A. Lehmann,et al.  Improving generalized regression analysis for the spatial prediction of forest communities , 2006 .

[39]  B. Reineking,et al.  Models for forest ecosystem management: a European perspective. , 2007, Annals of botany.

[40]  H. Kros,et al.  Modelling the afforested system: the forest/tree model , 2007 .

[41]  G. Heil,et al.  Effects of Light and N Availability in Forests on Plant Species Diversity in the Field Layer: A Plant Ecological and Modelling Approach , 2007 .

[42]  A. Kozak,et al.  Does cross validation provide additional information in the evaluation of regression models , 2003 .

[43]  Hans Pretzsch,et al.  The single tree-based stand simulator SILVA: construction, application and evaluation , 2002 .

[44]  George Harper,et al.  Lodgepole pine growth as a function of competition and canopy light environment within aspen dominated mixedwoods of central interior British Columbia , 2009 .

[45]  François Gillet,et al.  Modelling vegetation dynamics in heterogeneous pasture-woodland landscapes , 2008 .

[46]  Jyrki Kangas,et al.  Multiple criteria decision support methods in forest management: an overview and comparative analyses. , 2002 .

[47]  H. Kros,et al.  AFFOREST sDSS: a metamodel based spatial decision support system for afforestation of agricultural land , 2005, New Forests.

[48]  J. Pennanen,et al.  A spatial simulation approach to natural forest landscape dynamics in boreal Fennoscandia , 2002 .

[49]  Kathy Steppe,et al.  ANAFORE: A stand-scale process-based forest model that includes wood tissue development and labile carbon storage in trees , 2008 .

[50]  Harold E. Burkhart,et al.  Modeling production and decay of coarse woody debris in loblolly pine plantations. , 2009 .

[51]  Marc Palahí Lozano,et al.  Herramientas de soporte a la decisión en la planificación y gestión forestal: presentación del sistema de planificación forestal MONTES , 2004 .

[52]  Walter D. Potter,et al.  NED-2: A decision support system for integrated forest ecosystem management , 2005 .

[53]  Risto Sievänen,et al.  Introducing effects of temperature and CO2 elevation on tree growth into a statistical growth and yield model , 2005 .

[54]  H. Kros,et al.  Metafore: The Afforest Deposition-Soil-Water-Vegetation Metamodel , 2007 .

[55]  D. C. van der Werf,et al.  Bridging the gap between ecophysiological and genetic knowledge to assess the adaptive potential of European beech , 2008 .

[56]  Stefan Seifert.  Modellierung und Visualisierung des Waldwachstums auf Landschaftsebene , 2010 .

[57]  Changhui Peng,et al.  Forest simulation models , 2006 .

[58]  Gert-Jan Nabuurs,et al.  Future harvesting pressure on European forests , 2007, European Journal of Forest Research.

[59]  Hubert Hasenauer,et al.  Testing mechanistic modeling to assess impacts of biomass removal , 2005 .

[60]  Jos Van Orshoven,et al.  Demand for afforestation management in North-Western Europe , 2007 .

[61]  M. Etienne,et al.  Western European Silvopastoral Systems , 1996 .

[62]  H. Pretzscha,et al.  The single tree-based stand simulator SILVA : construction , application and evaluation , 2002 .

[63]  J. McAdam,et al.  Agroforestry in Europe : current status and future prospects , 2009 .

[64]  Juho Matala,et al.  Modelling the response of tree growth to temperature and CO2 elevation as related to the fertility and current temperature sum of a site , 2006 .

[65]  Jean Paul Metzger,et al.  Modeling landscape dynamics in an Atlantic Rainforest region: Implications for conservation , 2009 .

[66]  Annika Kangas,et al.  Multiple Criteria Decision Support Methods in Forest Management , 2002 .

[67]  Randall K. Kolka,et al.  Carbon pools and productivity in a 1-km2 heterogeneous forest and peatland mosaic in Minnesota, USA , 2009 .

[68]  Gert Jan Reinds,et al.  Modelling soil carbon sequestration of intensively monitored forest plots in Europe by three different approaches. , 2009 .

[69]  Annikki Mäkelä,et al.  Bridging process-based and empirical approaches to modeling tree growth. , 2005, Tree physiology.

[70]  M. Stone.  Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .