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Knowledge update in adaptive management of forest resources under climate change: a Bayesian simulation approach



We develop a modelling concept that updates knowledge and beliefs about future climate changes, to model a decision-maker’s choice of forest management alternatives, the outcomes of which depend on the climate condition.


Applying Bayes’ updating, we show that while the true climate trajectory is initially unknown, it will eventually be revealed as novel information become available. How fast the decision-maker will form firm beliefs about future climate depends on the divergence among climate trajectories, the long-term speed of change, and the short-term climate variability.


We simplify climate change outcomes to three possible trajectories of low, medium and high changes. We solve a hypothetical decision-making problem of tree species choice aiming at maximising the land expectation value (LEV) and based on the updated beliefs at each time step.


The economic value of an adaptive approach would be positive and higher than a non-adaptive approach if a large change in climate state occurs and may influence forest decisions.


Updating knowledge to handle climate change uncertainty is a valuable addition to the study of adaptive forest management in general and the analysis of forest decision-making, in particular for irreversible or costly decisions of long-term impact.

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  1. Allen M, Stott P, Mitchell J, Schnur R, Delworth T (2000) Quantifying the uncertainty in forecasts of anthropogenic climate change. Nature 407:617–620

    CAS  PubMed  Article  Google Scholar 

  2. Armstrong D, Castro I, Griffiths R (2007) Using adaptive management to determine requirements of re-introduced populations: the case of the New Zealand hihi. J Appl Ecol 44:953–962

    Article  Google Scholar 

  3. Bayes T, Price M (1763) An essay towards solving a problem in the doctrine of chances. Philos Trans R Soc London 53:370–418

    Article  Google Scholar 

  4. Bolte A, Ammer C, Loef M, Madsen P, Nabuurs G, Schall P, Spathelf P, Rock J (2009) Adaptive forest management in central Europe: climate change impacts, strategies and integrative concept. Scand J Forest Res 24:473–482

    Article  Google Scholar 

  5. Crome F, Thomas M, Moore LA (1996) A novel Bayesian approach to assessing impacts of rain forests logging. Ecol Appl 6:1104–1123

    Article  Google Scholar 

  6. Eriksson LO (2006) Planning under uncertainty at the forest level: a systems approach. Scand J Forest Res 21:111–117

    Article  Google Scholar 

  7. Hanewinkel M, Hummel S, Albrecht A (2011) Assessing natural hazards in forestry for risk management: a review. Eur J Forest Res 130:329–351

    Article  Google Scholar 

  8. Hauser C, Possingham H (2008) Experimental or precautionary? Adaptive management over a range of time horizons. J Appl Ecol 45:72–81

    Article  Google Scholar 

  9. Hildebrandt P, Knoeke T (2011) Investment decisions under uncertainty—a methodological review on forest science studies. Forest Policy Econ 13:1–15

    Article  Google Scholar 

  10. Iverson T, Perrings C (2012) Precaution and proportionality in the management of global environmental change. Global Environ Chang 22:161–177

    Article  Google Scholar 

  11. Jacobsen JB, Thorsen BJ (2003) A Danish example of optimal thinning strategies in mixed-species forest under changing growth conditions caused by climate change. Forest Ecol Manag 180:375–388

    Article  Google Scholar 

  12. Kangas J, Store R, Leskinen P, Mehtaetalo L (2000) Improving the quality of landscape ecological forest planning by utilizing advanced decision-support tools. Forest Ecol Manag 132:157–171

    Article  Google Scholar 

  13. McDonald-Madden E, Probert W, Hauser C, Runge M, Possingham H, Jones ME, Moore JL, Rout TM, Vesk PA, Wintle BA (2010) Active adaptive conservation of threatened species in the face of uncertainty. Ecol Appl 20:1476–1489

    PubMed  Article  Google Scholar 

  14. Neuner S, Beinhofer B, Knoke T (2013) The optimal tree species composition for a private forest enterprise—applying the theory of portfolio selection. Scand J For Res 28:28–48

    Article  Google Scholar 

  15. Prato T (2000) Multiple attributes Bayesian analysis of adaptive ecosystem management. Ecol Model 133:181–193

    Article  Google Scholar 

  16. Probert W, Hauser C, McDonald-Madden E, Michael C, Baxter P, Possingham H (2010) Managing and learning with multiple models: objectives and optimization algorithms. Biol Conserv 144:1237–1245

    Article  Google Scholar 

  17. Pukkala T, Kellomäki S (2012) Anticipatory vs adaptive optimization of stand management when tree growth and timber prices are stochastic. Forestry 85:463–472

    Article  Google Scholar 

  18. Scherrer S, Appenzeller C, Liniger M, Schär C (2005) European temperature distribution changes in observations and climate change scenarios. Geophys Res Lett 32, L19750. doi:10.1029/2005GL024108

    Article  Google Scholar 

  19. Schou E (2013) Transformation to near-natural forest management, Climate change and uncertainty. Dissertation, University of Copenhagen

  20. Spiecker H (2003) Silvicultural management in maintaining biodiversity and resistance of forests in Europe-temperate zone. J Environ Manage 67:55–65

    PubMed  Article  Google Scholar 

  21. Sykes M, Prentice C (1996) Climate change, tree species distributions and forest dynamics. A case study in the mixed conifer/Northern hardwoods zone of Northern Europe. Clim Change 34:161–177

    Article  Google Scholar 

  22. Ticehurst J, Curtis A, Merritt W (2011) Using Bayesian networks to complement conventional analyses to explore landholder management of native vegetation. Environ Model Softw 26:52–65

    Article  Google Scholar 

  23. Tomar AS, Ranade DH (2002) Predicting cumulative rainfall deficits by Gompertz Growth Model. Ind J Soil Conserv 30:101–103

    Google Scholar 

  24. Yousefpour R, Hanewinkel M (2009) Modelling of forest conversion planning with an adaptive simulation-optimization approach and simultaneous consideration of the values of timber, carbon and biodiversity. Ecol Econ 68:1711–1722

    Article  Google Scholar 

  25. Yousefpour R, Jacobsen JB, Thorsen BJ, Meilby H, Hanewinkel M, Oehler K (2012) A review of decision-making approaches to handle uncertainty and risk in adaptive forest management under climate change. Ann For Sci 69:1–15

    Article  Google Scholar 

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Acknowledgments and funding

This study was conducted as part of the project MOTIVE ‘MOdels for adapTIVE forest management’ funded by the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 226544. JBJ and BJT further acknowledge the support of the Danish National Science foundation.

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Corresponding author

Correspondence to Rasoul Yousefpour.

Additional information

Handling Editor: Marc Hanewinkel

Contribution of the co-authors

Rasoul Yousefpour: Corresponding Author, Developing the conceptual model, Analysis of the example, Providing tables and figures, Writing the manuscript, Supervising preparation of the manuscript according to the comments of all co-authors, Setting up the MS according to the format of AFS Jette Bredahl Jacobsen, Henrik Meilby, and Bo Jellesmark Thorsen: Developing the conceptual model and analysis, and writing the manuscript.

Appendix A

Appendix A

Table 5.

Table 5 Sensitivity analysis of beliefs update to observation variances

Appendix B

Table 6.

Table 6 Land expectation value (LEV) of optimal decisions based on Bayesian updating

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Yousefpour, R., Jacobsen, J.B., Meilby, H. et al. Knowledge update in adaptive management of forest resources under climate change: a Bayesian simulation approach. Annals of Forest Science 71, 301–312 (2014).

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  • Subjective risk
  • Belief update
  • Adaptive forest management
  • Monte Carlo simulation
  • Species selection