Skip to main content
Article thumbnail
Location of Repository

Models for supporting forest management in a changing environment

By L. Fontes, J.-D. Bontemps, H. Bugmann, M. Van Oijen, C. Gracia, K. Kramer, M. Lindner, T. Rötzer and J.P. Skovsgaard


Forests are experiencing an environment that changes much faster than during the past several hundred years.\ud 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 focuses on three types of models that are used in forest management: empirical (EM), process-based (PBM) and\ud hybrid models. Recent approaches may 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. Twenty-five process-based models in use in Europe were analyzed in terms of their 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.\ud The strengths and weaknesses of the three model types differ and all are likely to remain in use. There is a\ud trade-off between how little data the models need for calibration and simulation purposes, and the variety of\ud input-output relationships that they can quantify. PBMs are the most versatile, with a wide range of environmental\ud conditions and output variables they can account for. However, PBMs require more data making them less\ud applicable whenever data for calibration are scarce. EMs, on the other hand, are easier to run as they require\ud much less prior information, but the aggregated representation of environmental effects makes them less\ud reliable in the context of environmental changes. The different disadvantages of PBMs and EMs suggest that\ud hybrid models may be a good compromise, but a more extensive testing of these models in practice is required

Topics: Ecology and Environment
Publisher: Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA)
Year: 2010
OAI identifier:

Suggested articles


  1. (1999). A combined simulation model of Scots pine, Norway spruce and Silver birch ecosystems in the European boreal zone. Forest Ecology and Management doi
  2. (2009). A comparison of four process-based models and a statistical regression model to predict growth of Eucalyptus globulus plantations. doi
  3. (2009). A general quantitative theory of forest structure and dynamics. doi
  4. (1997). A generalised model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance and partitioning.
  5. (2003). A growth model for eucalypt
  6. (2002). A model for individual tree development based on physiological processes.
  7. (1996). A model of even-aged beech stands productivity with process-based interpretations.
  8. (2003). a model of growth and cycling of elements in boreal forest ecosystems.
  9. (2001). A modified 3D-patch model for spatially explicit simulation of vegetation composition in heterogeneous landscapes.
  10. (2004). A process-based model of forest ecosystems driven by meteorology. doi
  11. (1998). A re-assessment of high elevation treeline positions and their explanation.
  12. (2001). A review of forest gap models. doi
  13. (2009). A Review of Forest Succession Models and Their Suitability for Forest Management Planning.
  14. (1996). A simplified forest model to study species composition along climate gradients. doi
  15. (2007). A state-space approach to stand growth modelling of European beech. doi
  16. (1991). A Transport-Resistance Model of Forest Growth and Partitioning.
  17. (2002). Addressing multi-use issues in sustainable forest management with signal-transfer modeling. Forest Ecology and Management
  18. (2006). Analyzing the carbon dynamics of central European forests: comparison of Biome-BGC simulations with measurements. doi
  19. (2006). Autecology of sessile oak (Quercus petraea) in the north-west Iberian Peninsula.
  20. (2005). Bayesian calibration of process-based forest models: bridging the gap between models and data. doi
  21. (2005). BGC-model parameters for tree species growing in central European forests. doi
  22. (2007). Biometrical models as tools for forest ecosystem management. An European review and perspective. Pma doi
  23. (2005). Bridging process-based and empirical approaches to modeling tree growth.
  24. (2008). Bridging the gap between ecophysiological and genetic knowledge to assess the adaptive potential of European beech.
  25. (2006). Calibration and testing of a generalized process-based model for use in Portuguese eucalyptus plantations.
  26. (2002). CAPSIS : Computer-Aided Projection for Strategies In Silviculture : Advantages of a shared forest-modelling platform. In Modelling forest systems. Edited by Amaro Ana, Reed David, and Soares Paula.
  27. (2005). Carbon flux and growth in mature deciduous forest trees exposed to elevated CO2.
  28. (2009). change impacts, adaptive capacity, and vulnerability of European forest ecosystems. Forest Ecology and Management, in revision.
  29. (2010). Climate-sensitive modelling of site-productivity relationships for Norway spruce (Picea abies (L.) Karst.) and common beech (Fagus sylvatica L.). Forest Ecology and
  30. (2006). Comparative analysis of IKONOS, SPOT, and ETM+ data for leaf area index estimation in temperate coniferous and deciduous forest stands. Remote Sensing of Environment
  31. (2003). Comparison of a physiological model and a statistical model for prediction of growth and yield in boreal forests.
  32. (1998). Description of a numerical simulation model for predicting the dynamics of energy, water, carbon, and nitrogen in a terrestrial ecosystem.
  33. (2010). Does nitrogen deposition increase forest production? The role of phosphorus.
  34. (1999). Ecophysiological models of forest growth: uses and limitations. In Empirical and process-based models for forest tree and stand growth simulation.
  35. (2010). Enhancing Forest Value Productivity through Fiber Quality.
  36. (2007). Environmental science - Nitrogen impacts on forest carbon.
  37. (2008). Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser. Remote Sensing of Environment doi
  38. (2003). Evaluating forest models in a sustainable forest management context. doi
  39. (2005). Evaluating the accuracy and generality of a hybrid patch model.
  40. (2008). Evaluation of past and future changes in European forest growth by means of four process-based models.
  41. (2009). Extensions and evaluations of a general quantitative theory of forest structure and dynamics.
  42. (2009). Forest Dynamics, Growth And Yield. doi
  43. (2008). Forest Eco-physiological Models and Carbon Sequestration. In Managing Forest Ecosystems: The Challenge of Climate Change. Edited by
  44. (2004). Functional responses of plants to elevated atmospheric CO2 - do photosynthetic and productivity data from FACE experiments support early predictions? doi
  45. (2009). Gaining local accuracy while not losing generality - extending the range of gap model applications.
  46. (2003). Generating 3D sawlogs with a process-based growth model.
  47. (1996). Growth Trends in European Forests.
  48. (2003). How forest models are connected to reality: evaluation criteria for their use in decision support.
  49. (2001). How much physiology is needed in forest gap models for simulating long-term vegetation response to global change? Challenges, limitations, and potentials.
  50. (1995). How Physics and Biology Matter in Forest Gap Models. doi
  51. (2008). Impact de différentes stratégies sylvicoles sur la fonction puits de carbone des peuplements forestiers. Forêt Wallonne(95):
  52. (2004). Improving the formulation of tree growth and succession in a spatially explicit landscape model. doi
  53. (2010). in press. Population- and individually-based approaches of forest genetic modeling. Forest Systems,
  54. (2010). in press. Recent approaches to model the risk of storm and fire to European forests and their integration into simulation and decision support tools. Forest Systems,
  55. (2002). Influence of edaphic factors and tree nutritive status on the productivity of Pinus radiata D. Don plantations in northwestern Spain. Forest Ecology and Management doi
  56. (2005). Introducing effects of temperature and CO2 elevation on tree growth into a statistical growth and yield model.
  57. (2008). Is the spatial distribution of European beech (Fagus sylvatica L.) limited by its potential height growth?
  58. (2001). Linking growth and yield and process models to estimate impact of environmental changes on growth of loblolly pine.
  59. (2009). Long-Term Changes in Forest Productivity: A Consistent Assessment in Even-Aged Stands.
  60. (2009). MEPHYSTO: Combining population dynamics and drought related ecophysiology in the regional forest model TreeMig.
  61. (2005). Model-based analysis of management alternatives at stand and regional level in Brandenburg (Germany). Forest Ecology and Management
  62. (2005). Modeling annual production and carbon fluxes of a large managed temperate forest using forest inventories, satellite data and field measurements.
  63. (1994). Modeling Climate-Change Effects with Provenance Test Data. doi
  64. (1999). Modélisation des effets du bilan hydrique sur la production primaire et la croissance d‟un couvert de pin maritime (Pinus pinaster Ait.) en lande humide.
  65. (2009). Modelling above and below ground carbon dynamics in a mixed beech and spruce stand influenced by climate.
  66. (2004). Modelling at the interface between scientific knowledge and management issues. Towards the Sustainable Use of Europe's Forests -Forest Ecosystem and Landscape Research: Scientific Challenges and Opportunities(49):
  67. (2004). Modelling biogeochemical cycles in forests: State of the art and perspectives.
  68. (2005). Modelling carbon and water cycles in a beech forest Part I: Model description and uncertainty analysis on modelled NEE.
  69. (2010). Modelling exploration of the future of European beech (Fagus sylvatica L.) under climate change-Range, abundance, genetic diversity and adaptive response.
  70. (2003). Modelling forest ecosystems: state of the art, challenges, and future directions.
  71. (1994). Modelling forest growth and yield: applications to mixed tropical forests.
  72. (1997). Modelling the dynamics of the forest ecosystem for climate change studies in the boreal conditions. Ecological Modelling 97(1-2): 121-140.
  73. (2006). Modelling the response of tree growth to temperature and CO2 elevation as related to the fertility and current temperature sum of a site. Ecological Modelling
  74. (2008). Modelling vegetation dynamics in heterogeneous pasture-woodland landscapes.
  75. (2008). Models for forest ecosystem management: A European perspective.
  76. (1996). Models to predict the General Yield Class of Douglas fir, Japanese larch and Scots pine on better quality land in Scotland. doi
  77. (2003). Near-surface soil characteristics and understory plants as predictors of Pinus contorta site index in southwestern Alberta, Canada. Forest Ecology and Management
  78. (2004). Needs and opportunities for using a process-based productivity model as a practical tool in Eucalyptus plantations.
  79. (1992). Nitrogen Relations in a Forest Plantation -Soil Organic-Matter Ecosystem Model.
  80. (1990). On scale problems in modelling: an example from soil ecology. In Theoretical Production Ecology: Reflections and Prospects. Edited by
  81. (2010). On the relative magnitudes of photosynthesis, respiration, growth and carbon storage in vegetation.
  82. (2006). Parameter sensitivity and uncertainty of the forest carbon flux model FORUG: a Monte Carlo analysis. doi
  83. (2005). Picea abies site index prediction by environmental factors and understorey vegetation: a two-scale approach based on survey databases.
  84. (2006). Plant CO2 responses: an issue of definition, time and resource supply.
  85. (2008). Potential recovery of industrial wood and energy wood raw material in different cutting and climate scenarios for Finland.
  86. (1999). Predicting effects of different harvesting intensities with a model of nitrogen limited forest growth. doi
  87. (2001). Process models as tools in forestry research and management.
  88. (1996). Process versus empirical models: Which approach for forest ecosystem management?
  89. (1998). Process-based forest productivity models and their application in forest management.
  90. (2003). Process-based modelling of tree and stand growth: towards a hierarchical treatment of multiscale processes.
  91. (2000). Process-based models for forest ecosystem management: current state of the art and challenges for practical implementation. Tree Physiology
  92. (2006). Quantitative validation and comparison of a range of forest growth model types. doi
  93. (1995). Review of sixteen forest-soil-atmosphere models. doi
  94. (2000). Scaling issues in forest succession modelling. doi
  95. (2008). Sensitivity and uncertainty analysis from a coupled 3-PG and soil organic matter decomposition model. doi
  96. (2000). Signal-transfer modeling for regional assessment of forest responses to environmental changes in the southeastern United States. Environmental Modeling &
  97. (2010). Simulating stand climate, phenology, and photosynthesis of a forest stand with a process-based growth model
  98. (2009). Site-specific height growth models for six common tree species in Denmark. doi
  99. (2006). Spatial interactions between ungulate herbivory and forest management.
  100. (1966). Strategy of Model Building in Population Biology.
  101. (2010). Succession modeling shows that CO2 fertilization effect in forests is offset by reduced tree longevity. Oecologia: under revision. doi
  102. (2000). The consequences of hierarchy for modeling in forest ecosystems. doi
  103. (2006). The dependence of respiration on photosynthetic substrate supply and temperature: integrating leaf, soil and ecosystem measurements.
  104. (2003). The interacting effects of ungulates and fire on forest dynamics: an analysis using the model FORSPACE. Forest Ecology and Management
  105. (1970). The Principles of Forest Yield Studies.
  106. (2007). The role of sensitivity analysis in ecological modelling. doi
  107. (1994). The state-space approach in growth modelling.
  108. (2003). The U-approach to forest modeling. doi
  109. (2001). The use of multiscale remote sensing imagery to derive regional estimates of forest growth capacity using 3-PGS. Remote Sensing of Environment doi
  110. (1998). Theoretical Ecosystem Ecology: Understanding Element Cycles.
  111. (2006). TreeMig: A forest-landscape model for simulating spatio-temporal patterns from stand to landscape scale. Ecological Modelling
  112. (2002). Trembling aspen site index in relation to environmental measures of site quality at two spatial scales. doi
  113. (2008). Use of 3-PG and 3-PGS to simulate forest growth dynamics of Australian tropical rainforests - II. An integrated system for modelling forest growth and scenario assessment within the wet tropics bioregion. doi
  114. (2007). Yield-SAFE: A parameter-sparse, process-based dynamic model for predicting resource capture, growth, and production in agroforestry systems. doi

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.