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Climate change and mixed forests: how do altered survival probabilities impact economically desirable species proportions of Norway spruce and European beech?

  • Carola PaulEmail author
  • Susanne Brandl
  • Stefan Friedrich
  • Wolfgang Falk
  • Fabian Härtl
  • Thomas Knoke
Research Paper
Part of the following topical collections:
  1. Forest Adaptation and Restoration under Global Change

Abstract

Key message

Economic consequences of altered survival probabilities under climate change should be considered for regeneration planning in Southeast Germany. Findings suggest that species compositions of mixed stands obtained from continuous optimization may buffer but not completely mitigate economic consequences. Mixed stands of Norway spruce ( Picea abies L. Karst . ) and European beech ( Fagus sylvatica L.) (considering biophysical interactions between tree species) were found to be more robust, against both perturbations in survival probabilities and economic input variables, compared to block mixtures (excluding biophysical interactions).

Context

Climate change is expected to increase natural hazards in European forests. Uncertainty in expected tree mortality and resulting potential economic consequences complicate regeneration decisions.

Aims

This study aims to analyze the economic consequences of altered survival probabilities for mixing Norway spruce (Picea abies L. Karst.) and European beech (Fagus sylvatica L.) under different climate change scenarios. We investigate whether management strategies such as species selection and type of mixture (mixed stands vs. block mixture) could mitigate adverse financial effects of climate change.

Methods

The bio-economic modelling approach combines a parametric survival model with modern portfolio theory. We estimate the economically optimal species mix under climate change, accounting for the biophysical and economic effects of tree mixtures. The approach is demonstrated using an example from Southeast Germany.

Results

The optimal tree species mixtures under simulated climate change effects could buffer but not completely mitigate undesirable economic consequences. Even under optimally mixed forest stands, the risk-adjusted economic value decreased by 28%. Mixed stands economically outperform block mixtures for all climate scenarios.

Conclusion

Our results underline the importance of mixed stands to mitigate the economic consequences of climate change. Mechanistic bio-economic models help to understand consequences of uncertain input variables and to design purposeful adaptation strategies.

Keywords

Survival analysis Value at risk Climate change Species mixture Forest restoration Portfolio theory 

Notes

Acknowledgments

This work is part of the project “SURVIVAL-KW” funded by the Federal Ministry of Food and Agriculture of Germany (Waldklimafonds (FKZ: 28W-C-4-088-01). We would like to thank all members of the project for their contributions and helpful discussions on the topic. We would particularly like to thank Christian Kölling for his valuable ideas and support during project conceptualization. The assistance of Andreas Bender from Ludwig Maximilian University of Munich with statistical model development is gratefully acknowledged. We are furthermore grateful to all members and contributors of the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests for providing the valuable dataset used in our study. We also thank Michael Du for language editing the manuscript. We are most grateful to two anonymous reviewers for their valuable and constructive comments which helped to considerably improve the manuscript.

Funding

This work was funded by the Federal Ministry of Food and Agriculture of Germany (Waldklimafonds Project SURVIVAL-KW (FKZ: 28W-C-4-088). S.F. acknowledges funding by the Bavarian State Ministry for Food, Agriculture and Forestry (Project H10—“Climate induced risks for mixed stands of spruce and broad-leaved tree species”).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

13595_2018_793_MOESM1_ESM.pdf (914 kb)
ESM 1 (PDF 914 kb)

References

  1. Albert M, Nagel R-V, Nuske R, Sutmöller J, Spellmann H (2017) Tree species selection in the face of drought risk—uncertainty in forest planning. Forests 8:363.  https://doi.org/10.3390/f8100363 CrossRefGoogle Scholar
  2. BayStMELF (2017) Waldbesitzer bewältigen Sturm und Borkenkäfer: Bayerisches Staatsministerium für Ernährung, Landwirtschaft und Forsten. ForstinfoGoogle Scholar
  3. Beinhofer BT (2009) Zur Anwendung der Portfoliotheorie in der Forstwissenschaft – Finanzielle Optimierungsansätze zur Bewertung von Diversifikationseffekten: [Applying the portfolio theory in forest science—financial optimisation approaches for evaluating diversification effects], Technische Universität MünchenGoogle Scholar
  4. Beinhofer B, Knoke T (2010) Finanziell vorteilhafte Douglasienanteile im Baumartenportfolio: [Financially advantageous proportion of Douglas fir in a tree species portfolio]. Forstarchiv 81:255–265Google Scholar
  5. Benneter A, Forrester DI, Bouriaud O, Dormann CF, Bauhus J (2018) Tree species diversity does not compromise stem quality in major European forest types. For Ecol Manag 422:323–337.  https://doi.org/10.1016/j.foreco.2018.04.030 CrossRefGoogle Scholar
  6. Blennow K, Sallnäs O (2002) Risk perception among non-industrial private forest owners. Scand J For Res 17:472–479.  https://doi.org/10.1080/028275802320435487 CrossRefGoogle Scholar
  7. Bolte A, Ammer C, Löf M, Madsen P, Nabuurs G-J, Schall P, Spathelf P, Rock J (2009) Adaptive forest management in central Europe: climate change impacts, strategies and integrative concept. Scand J For Res 24:473–482CrossRefGoogle Scholar
  8. Bright G, Price C (2000) Valuing forest land under hazards to crop survival. Forestry 73:361–370.  https://doi.org/10.1093/forestry/73.4.361 CrossRefGoogle Scholar
  9. Broström G (2015) Event history analysis with R. Chapman & Hall/The R Series. CRC Press, Boca RatonGoogle Scholar
  10. Brunette M, Dragicevic A, Lenglet J, Niedzwiedz A, Badeau V, Dupouey J-L (2017) Biotechnical portfolio management of mixed-species forests. J Bioecon 19:223–245.  https://doi.org/10.1007/s10818-017-9247-x CrossRefGoogle Scholar
  11. Burkhardt T, Möhring B, Gerst J (2014) Modeling natural risks in forest decision models by means of survival functions. In: Kant S, Alavalapati J (eds) Handbook of forest resource economics. RoutledgeGoogle Scholar
  12. Clasen C (2015) Der Verlust von Baumarten in Mischbeständen durch Schalenwildverbiss: [Losing admixed tree species by ungulate browsing: a new approach to value financial consequences under different site conditions]. Dissertation, Technische Universität MünchenGoogle Scholar
  13. Clasen C, Griess VC, Knoke T (2011) Financial consequences of losing admixed tree species: a new approach to value increased financial risks by ungulate browsing. Forest Policy Econ 13:503–511.  https://doi.org/10.1016/j.forpol.2011.05.005 CrossRefGoogle Scholar
  14. R Core Team (2017) R: a language and environment for statistical computing. https://www.R-project.org/
  15. Couture S, Cros M-J, Sabbadin R (2016) Risk aversion and optimal management of an uneven-aged forest under risk of windthrow: a Markov decision process approach. J For Econ 25:94–114.  https://doi.org/10.1016/j.jfe.2016.08.002 CrossRefGoogle Scholar
  16. Cubbage F, Mac Donagh P, Sawinski Júnior J, Rubilar R, Donoso P, Ferreira A, Hoeflich V, Olmos V, Ferreira G, Balmelli G, Siry J, Báez M, Alvarez J (2007) Timber investment returns for selected plantations and native forests in South America and the Southern United States. New For 33:237–255.  https://doi.org/10.1007/s11056-006-9025-4 CrossRefGoogle Scholar
  17. Deegen P, Matolepszy K (2015) Economic balancing of forest management under storm risk, the case of the Ore Mountains (Germany). J For Econ 21:1–13.  https://doi.org/10.1016/j.jfe.2014.10.005 CrossRefGoogle Scholar
  18. Díaz-Yáñez O, Mola-Yudego B, González-Olabarria JR, Pukkala T (2017) How does forest composition and structure affect the stability against wind and snow? For Ecol Manag 401:215–222.  https://doi.org/10.1016/j.foreco.2017.06.054 CrossRefGoogle Scholar
  19. Dieter M, Moog M, Borchert H (2001) Considering serious hazards in forest management decision-making. In: von Gadow K (ed) Risk analysis in forest management. Springer Netherlands, Dordrecht, pp 201–232CrossRefGoogle Scholar
  20. Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carré G, Marquéz JRG, Gruber B, Lafourcade B, Leitão PJ, Münkemüller T, McClean C, Osborne PE, Reineking B, Schröder B, Skidmore AK, Zurell D, Lautenbach S (2013) Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36:27–46.  https://doi.org/10.1111/j.1600-0587.2012.07348.x CrossRefGoogle Scholar
  21. Dragicevic A, Lobianco A, Leblois A (2016) Forest planning and productivity-risk trade-off through the Markowitz mean-variance model. For Pol Econ 64:25–34.  https://doi.org/10.1016/j.forpol.2015.12.010 CrossRefGoogle Scholar
  22. Dyderski MK, Paź S, Frelich LE, Jagodziński AM (2017) How much does climate change threaten European forest tree species distributions? Glob Chang Biol 24:1150–1163.  https://doi.org/10.1111/gcb.13925 CrossRefPubMedPubMedCentralGoogle Scholar
  23. Eichhorn J, Roskams P, Potocic N, Timmermann V, Ferretti M, Mues V, Szepesi A, Durrant D, Seletkovic I, Schroeck H-W, Bussotti F, Garcia P, Wulff S (2016) Part IV. Visual assessment of crown condition and damaging agents. In: UNECE ICP Forests (ed) Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Thünen Institute of Forest Ecosystems, EberswaldeGoogle Scholar
  24. Elton EJ, Gruber MJ, Brown SJ, Goetzmann WN (2014) Modern portfolio theory and investment analysis, 9th edn. Wiley, HobokenGoogle Scholar
  25. Eriksson L (2014) Risk perception and responses among private forest owners in Sweden. Small Scale For 13:483–500.  https://doi.org/10.1007/s11842-014-9266-6 CrossRefGoogle Scholar
  26. Estrada F, Gay C, Conde C (2011) A methodology for the risk assessment of climate variability and change under uncertainty. A case study: coffee production in Veracruz, Mexico. Clim Chang 113:455–479.  https://doi.org/10.1007/s10584-011-0353-9 CrossRefGoogle Scholar
  27. Fasen V, Klüppelberg C, Menzel A (2014) Quantifying extreme risks. In: Klüppelberg C, Straub D, Welpe IM (eds) Risk—a multidisciplinary introduction. Imprint. Springer, Cham, pp 151–181CrossRefGoogle Scholar
  28. Faustmann M (1849) Berechnung des Werthes, welchen Waldboden, sowie noch nicht haubare Holzbestande fur die Waldwirthschaft besitzen [Calculation of the value which forest land and immature stands possess for forestry]. Allg Forst- u J-Ztg 25:441–455Google Scholar
  29. Gardiner B, Blennow K, Carnus JM, Fleischer P, Ingemarson F, Landmann G, Lindner M, Marzano M, Nicoll B, Orazio C, Peyron JL, Reviron MP, Schelhaas MJ, Schuck A, Spielmann M, Usbeck T (2011) Destructive storms in European forests: past and forthcoming impacts: Final report to European Commission - DG Environment. European Forest Institute. Available online http://mfkp.org/INRMM/article/13942333. Accessed 20 Feb 2018
  30. Gerds TA, Schumacher M (2006) Consistent estimation of the expected brier score in general survival models with right-censored event times. Biom J 48:1029–1040.  https://doi.org/10.1002/bimj.200610301 CrossRefPubMedGoogle Scholar
  31. Gray LK, Hamann A (2011) Strategies for reforestation under uncertain future climates: guidelines for Alberta, Canada. PLoS One 6:e22977.  https://doi.org/10.1371/journal.pone.0022977 CrossRefPubMedPubMedCentralGoogle Scholar
  32. Griess V, Knoke T (2013) Bioeconomic modeling of mixed Norway spruce—European beech stands: economic consequences of considering ecological effects. Eur J For Res 132:511–522.  https://doi.org/10.1007/s10342-013-0692-3 CrossRefGoogle Scholar
  33. Griess VC, Acevedo R, Härtl F, Staupendahl K, Knoke T (2012) Does mixing tree species enhance stand resistance against natural hazards? A case study for spruce. For Ecol Manag 267:284–296.  https://doi.org/10.1016/j.foreco.2011.11.035 CrossRefGoogle Scholar
  34. Gutsch M, Lasch-Born P, Suckow F, Reyer CPO (2016) Evaluating the productivity of four main tree species in Germany under climate change with static reduced models. Ann For Sci 73:401–410.  https://doi.org/10.1007/s13595-015-0532-3 CrossRefGoogle Scholar
  35. Hahn WA, Härtl F, Irland LC, Kohler C, Moshammer R, Knoke T (2014) Financially optimized management planning under risk aversion results in even-flow sustained timber yield. Forest Policy Econ 42:30–41.  https://doi.org/10.1016/j.forpol.2014.02.002 CrossRefGoogle Scholar
  36. Hanewinkel M, Hummel S, Cullmann DA (2010) Modelling and economic evaluation of forest biome shifts under climate change in Southwest Germany. For Ecol Manag 259:710–719.  https://doi.org/10.1016/j.foreco.2009.08.021 CrossRefGoogle Scholar
  37. Hanewinkel M, Hummel S, Albrecht A (2011) Assessing natural hazards in forestry for risk management: a review. Eur J For Res 130:329–351.  https://doi.org/10.1007/s10342-010-0392-1 CrossRefGoogle Scholar
  38. Härtl F, Hahn A, Knoke T (2013) Risk-sensitive planning support for forest enterprises: the YAFO model. Comput Electron Agric 94:58–70.  https://doi.org/10.1016/j.compag.2013.03.004 CrossRefGoogle Scholar
  39. Härtl FH, Barka I, Hahn WA, Hlásny T, Irauschek F, Knoke T, Lexer MJ, Griess VC (2016) Multifunctionality in European mountain forests—an optimization under changing climatic conditions. Can J For Res 46:163–171.  https://doi.org/10.1139/cjfr-2015-0264 CrossRefGoogle Scholar
  40. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978.  https://doi.org/10.1002/joc.1276 CrossRefGoogle Scholar
  41. ICP Forests (2018) ICP Forests online database. International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests. www.icp-forest.net. Accessed 6 August 2018
  42. Jandl R, Bauhus J, Bolte A, Schindlbacher A, Schüler S (2015) Effect of climate-adapted forest management on carbon pools and greenhouse gas emissions. Curr For Rep 1:1–7.  https://doi.org/10.1007/s40725-015-0006-8 CrossRefGoogle Scholar
  43. Jorion P (2009) Value at risk: the new benchmark for managing financial risk, 3rd edn. McGraw-Hill, New YorkGoogle Scholar
  44. Kataoka S (1963) A stochastic programming model. Econometrica 31:181–196.  https://doi.org/10.2307/1910956 CrossRefGoogle Scholar
  45. Knoke T, Seifert T (2008) Integrating selected ecological effects of mixed European beech—Norway spruce stands in bioeconomic modelling. Ecol Model 210:487–498.  https://doi.org/10.1016/j.ecolmodel.2007.08.011 CrossRefGoogle Scholar
  46. Knoke T, Wurm J (2006) Mixed forests and a flexible harvest policy: a problem for conventional risk analysis? Eur J For Res 125:303–315.  https://doi.org/10.1007/s10342-006-0119-5 CrossRefGoogle Scholar
  47. Knoke T, Messerer K, Paul C (2017) The role of economic diversification in forest ecosystem management. Curr For Rep 3:93–106.  https://doi.org/10.1007/s40725-017-0054-3 CrossRefGoogle Scholar
  48. Littell JS, McKenzie D, Kerns BK, Cushman S, Shaw CG (2011) Managing uncertainty in climate-driven ecological models to inform adaptation to climate change. Ecosphere 2:art102.  https://doi.org/10.1890/ES11-00114.1 CrossRefGoogle Scholar
  49. Macmillan WD (1992) Risk and agricultural land use: a reformulation of the portfolio-theoretic approach to the analysis of a von Thünen economy. Geogr Anal 24:142–158.  https://doi.org/10.1111/j.1538-4632.1992.tb00257.x CrossRefGoogle Scholar
  50. Markowitz H (1952) Portfolio selection. J Financ 7:77–91.  https://doi.org/10.1111/j.1540-6261.1952.tb01525.x CrossRefGoogle Scholar
  51. Markowitz HM (2010) Portfolio theory: as I still see it. Annu Rev Fin Econ 2:1–23.  https://doi.org/10.1146/annurev-financial-011110-134602 CrossRefGoogle Scholar
  52. Markowitz H, Blay K (2014) Risk-return analysis: the theory and practice of rational investing. McGraw-Hill Education, New YorkGoogle Scholar
  53. Messerer K, Pretzsch H, Knoke T (2017) A non-stochastic portfolio model for optimizing the transformation of an even-aged forest stand to continuous cover forestry when information about return fluctuation is incomplete. Ann For Sci 74:45.  https://doi.org/10.1007/s13595-017-0643-0 CrossRefGoogle Scholar
  54. Metz J, Annighöfer P, Schall P, Zimmermann J, Kahl T, Schulze E-D, Ammer C (2016) Site-adapted admixed tree species reduce drought susceptibility of mature European beech. Glob Chang Biol 22:903–920.  https://doi.org/10.1111/gcb.13113 CrossRefPubMedPubMedCentralGoogle Scholar
  55. Möllmann TB, Möhring B (2017) A practical way to integrate risk in forest management decisions. Ann For Sci 74:75.  https://doi.org/10.1007/s13595-017-0670-x CrossRefGoogle Scholar
  56. Moore DF (2016) Applied survival analysis using R. Use R! Springer, SwitzerlandCrossRefGoogle Scholar
  57. Neumann M, Mues V, Moreno A, Hasenauer H, Seidl R (2017) Climate variability drives recent tree mortality in Europe. Glob Chang Biol 23:4788–4797.  https://doi.org/10.1111/gcb.13724 CrossRefPubMedPubMedCentralGoogle Scholar
  58. Neuner S, Knoke T (2017) Economic consequences of altered survival of mixed or pure Norway spruce under a dryer and warmer climate. Clim Chang 140:519–531.  https://doi.org/10.1007/s10584-016-1891-y CrossRefGoogle Scholar
  59. Neuner S, Albrecht A, Cullmann D, Engels F, Griess VC, Hahn WA, Hanewinkel M, Härtl F, Kölling C, Staupendahl K, Knoke T (2015) Survival of Norway spruce remains higher in mixed stands under a dryer and warmer climate. Glob Chang Biol 21:935–946.  https://doi.org/10.1111/gcb.12751 CrossRefPubMedPubMedCentralGoogle Scholar
  60. Nothdurft A (2013) Spatio-temporal prediction of tree mortality based on long-term sample plots, climate change scenarios and parametric frailty modeling. For Ecol Manag 291:43–54.  https://doi.org/10.1016/j.foreco.2012.11.028 CrossRefGoogle Scholar
  61. Pretzsch H, Biber P, Ďurský J (2002) The single tree-based stand simulator SILVA: construction, application and evaluation. For Ecol Manag 162:3–21.  https://doi.org/10.1016/S0378-1127(02)00047-6 CrossRefGoogle Scholar
  62. Pretzsch H, Block J, Dieler J, Dong PH, Kohnle U, Nagel J, Spellmann H, Zingg A (2010) Comparison between the productivity of pure and mixed stands of Norway spruce and European beech along an ecological gradient. Ann For Sci 67:712.  https://doi.org/10.1051/forest/2010037 CrossRefGoogle Scholar
  63. Pretzsch H, Schütze G, Uhl E (2013) Resistance of European tree species to drought stress in mixed versus pure forests: evidence of stress release by inter-specific facilitation. Plant Biol (Stuttg) 15:483–495.  https://doi.org/10.1111/j.1438-8677.2012.00670.x CrossRefGoogle Scholar
  64. Pukkala T (2018) Effect of species composition on ecosystem services in European boreal forest. J For Res 29:261–272.  https://doi.org/10.1007/s11676-017-0576-3 CrossRefGoogle Scholar
  65. Roessiger J, Griess VC, Härtl F, Clasen C, Knoke T (2013) How economic performance of a stand increases due to decreased failure risk associated with the admixing of species. Ecol Model 255:58–69.  https://doi.org/10.1016/j.ecolmodel.2013.01.019 CrossRefGoogle Scholar
  66. Schou E, Jacobsen JB, Kristensen KL (2012) An economic evaluation of strategies for transforming even-aged into near-natural forestry in a conifer-dominated forest in Denmark. Forest Policy Econ 20:89–98.  https://doi.org/10.1016/j.forpol.2012.02.010 CrossRefGoogle Scholar
  67. Schou E, Thorsen BJ, Jacobsen JB (2015) Regeneration decisions in forestry under climate change related uncertainties and risks: effects of three different aspects of uncertainty. Forest Policy Econ 50:11–19.  https://doi.org/10.1016/j.forpol.2014.09.006 CrossRefGoogle Scholar
  68. Seidl R, Schelhaas M-J, Rammer W, Verkerk PJ (2014) Increasing forest disturbances in Europe and their impact on carbon storage. Nat Clim Chang 4:806–810CrossRefPubMedCentralGoogle Scholar
  69. Seidl R, Aggestam F, Rammer W, Blennow K, Wolfslehner B (2016) The sensitivity of current and future forest managers to climate-induced changes in ecological processes. Ambio 45:430–441.  https://doi.org/10.1007/s13280-015-0737-6 CrossRefPubMedPubMedCentralGoogle Scholar
  70. Seidl R, Thom D, Kautz M, Martin-Benito D, Peltoniemi M, Vacchiano G, Wild J, Ascoli D, Petr M, Honkaniemi J, Lexer MJ, Trotsiuk V, Mairota P, Svoboda M, Fabrika M, Nagel TA, Reyer CPO (2017) Forest disturbances under climate change. Nat Clim Chang 7:395–402.  https://doi.org/10.1038/nclimate3303 CrossRefPubMedPubMedCentralGoogle Scholar
  71. Staupendahl K (2011) Modellierung der Überlebenswahrscheinlichkeit von Waldbeständen mithilfe der neu parametrisierten Weibull-Funktion: [Modelling the survival probability of forest stands using the parameterised Weibull function]. Forstarchiv 82:10–19Google Scholar
  72. Staupendahl K, Möhring B (2011) Integrating natural risks into silvicultural decision models: a survival function approach. Forest Policy Econ 13:496–502.  https://doi.org/10.1016/j.forpol.2011.05.007 CrossRefGoogle Scholar
  73. Staupendahl K, Zucchini W (2011) Schätzung von Überlebensfunktionen der Hauptbaumarten auf der Basis von Zeitreihendaten der Rheinland-Pfälzischen Waldzustandserhebung. Allg Forst- u J-Ztg 182:129–145Google Scholar
  74. Teuffel K, Baumgarten M, Hanewinkel M, Konold W, Sauter UH, Spiecker H, Wilpert K (2005) Waldumbau: Für eine zukunftsorientierte Waldwirtschaft. Springer-VerlagGoogle Scholar
  75. Therneau TM, Grambsch PM (2001) Modeling survival data: extending the Cox model, Statistics for biology and health, 2nd edn. Springer, New YorkGoogle Scholar
  76. Thiele JC, Nuske RS, Ahrends B, Panferov O, Albert M, Staupendahl K, Junghans U, Jansen M, Saborowski J (2017) Climate change impact assessment—a simulation experiment with Norway spruce for a forest district in Central Europe. Ecol Model 346:30–47.  https://doi.org/10.1016/j.ecolmodel.2016.11.013 CrossRefGoogle Scholar
  77. UNECE ICP Forests (ed) (2016) Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. UNECE ICP Forests Programme Co-ordinating Centre. Thünen Institute of Forest Ecosystems, EberswaldeGoogle Scholar
  78. Wan Y, Clutter ML, Mei B, Siry JP (2015) Assessing the role of U.S. timberland assets in a mixed portfolio under the mean-conditional value at risk framework. Forest Policy Econ 50:118–126.  https://doi.org/10.1016/j.forpol.2014.06.002 CrossRefGoogle Scholar
  79. Wellbrock N, Eickenscheidt N, Hilbrig L, Dühnelt P-E, Holzhausen M, Bauer A, Dammann I, Strich S, Engels F, Wauer A (2018) Leitfaden und Dokumentation zur Waldzustandserhebung in Deutschland. Thünen Working Paper, vol 84. Thünen-Institut für Waldökosysteme, EberswaldeGoogle Scholar
  80. WorldClim (2018a) WorldClim—global climate data—free climate data for ecological modeling and GIS: MPI-ESM-LR model representing the period 2061–2080. http://www.worldclim.org/cmip5_30s. Accessed 20 February 2018
  81. WorldClim (2018b) WorldClim—global climate data—free climate data for ecological modeling and GIS: current climate version 1.4. www.worldclim.org/current. Accessed 20 February 2018
  82. Yemshanov D, McCarney GR, Hauer G, Luckert MK, Unterschultz J, McKenney DW (2015) A real options-net present value approach to assessing land use change: a case study of afforestation in Canada. Forest Policy Econ 50:327–336.  https://doi.org/10.1016/j.forpol.2014.09.016 CrossRefGoogle Scholar
  83. Yousefpour R, Hanewinkel M (2016) Climate change and decision-making under uncertainty. Curr For Rep 2:143–149.  https://doi.org/10.1007/s40725-016-0035-y CrossRefGoogle Scholar
  84. Yousefpour R, Jacobsen JB, Meilby H, Thorsen BJ (2014) Knowledge update in adaptive management of forest resources under climate change: a Bayesian simulation approach. Ann For Sci 71:301–312.  https://doi.org/10.1007/s13595-013-0320-x CrossRefGoogle Scholar
  85. Yousefpour R, Temperli C, Jacobsen JB, Thorsen BJ, Meilby H, Lexer MJ, Lindner M, Bugmann H, Borges JG, Palma JHN, Ray D, Zimmermann NE, Delzon S, Kremer A, Kramer K, Reyer CPO, Lasch-Born P, Garcia-Gonzalo J, Hanewinkel M (2017) A framework for modeling adaptive forest management and decision making under climate change. E S 22.  https://doi.org/10.5751/ES-09614-220440
  86. Zubizarreta-Gerendiain A, Garcia-Gonzalo J, Strandman H, Jylhä K, Peltola H (2016) Regional effects of alternative climate change and management scenarios on timber production, economic profitability, and carbon stocks in Norway spruce forests in Finland. Can J For Res 46:274–283.  https://doi.org/10.1139/cjfr-2015-0218 CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Institute of Forest Management, TUM School of Life Sciences WeihenstephanTechnische Universität MünchenFreisingGermany
  2. 2.Department of Forest Economics and Sustainable Land-use PlanningGeorg-August Universität GöttingenGöttingenGermany
  3. 3.Bavarian State Institute of Forestry (LWF)FreisingGermany

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