Advertisement

Regional adaptation of European beech (Fagus sylvatica) to drought in Central European conditions considering environmental suitability and economic implications

  • Lukas BaumbachEmail author
  • Aidin Niamir
  • Thomas Hickler
  • Rasoul Yousefpour
Original Article

Abstract

European beech (Fagus sylvatica) is a widespread deciduous tree in Europe, but may face significant distribution shifts due to expected increasing drought frequency under climate change. An alternative adaptation strategy for beech forests to improve drought tolerance and economic outcome consists in the admixtion of silver fir (Abies alba). To explore potentially suitable areas for mixing under future climate conditions, species distribution models (SDMs) represent a useful tool, but should be accompanied by economic analyses and uncertainty evaluations to serve as a solid decision basis for forest management. Therefore, in this study, we apply state-of-the-art SDMs, review uncertainties resulting from different modeling approaches, estimate the economic value of pure and mixed beech and fir stands, and discuss managerial implications of the results in Germany. Our model results projected widespread beech declines for Germany, while silver fir distributions remained largely constant. The degree of decline varied significantly between the investigated climate scenarios and resulted in associated economic losses between − 180 and − 4000 billion euros. With regard to the uncertain magnitude of climate change and the risk of high economic losses, we recommend an adaptation of beech forests in its projected hot spots of decline and find silver fir to be an environmentally suitable mixing species. The combination of ecological, economic, and uncertainty analyses used here represents a promising set of tools to evaluate climate change effects and assist in the regional adaptation of forests.

Keywords

Climate change Species distribution modeling Species mixing Adaptive management 

Notes

Funding information

This work has been financially supported by the Federal Ministry of Food and Agriculture (BMEL) as part of the project “BuTaKli: Beech–Silver Fir Mixed Forests as an Adaptation of Commercial Forests to Climate Change Extreme Events.”

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Acevedo P, Jiménez-Valverde A, Lobo JM, Real R (2012) Delimiting the geographical background in species distribution modelling. J Biogeogr 39(8):1383–1390.  https://doi.org/10.1111/j.1365-2699.2012.02713.x Google Scholar
  2. Acevedo P, Jiménez-Valverde A, Aragón P, Niamir A (2016) New Development in the Study of Species Distribution. In: Mateo R, Arroyo B, Garcia JT (eds) Current trends in wildlife research. Springer, Cham, pp 151–175.  https://doi.org/10.1007/978-3-319-27912-1_7 Google Scholar
  3. Albrecht A, Fortin M, Kohnle U, Ningre F (2013) Ein Sturmschadensmodul für BWinProBW: Konzept und erste Ergebnisse waldbaulicher Variantenstudien. In: DVFFA. Sektion Ertragskunde: Beiträge zur Jahrestagung, pp 75–83Google Scholar
  4. Allen CD, Macalady AK, Chenchouni H, Bachelet D, McDowell N, Vennetier M, Kitzberger T, Rigling A, Breshears DD, Hogg EH, Gonzalez P, Fensham R, Zhang Z, Castro J, Demidova N, Lim JH, Allard G, Running SW, Semerci A, Cobb N (2010) A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For Ecol Manag 259(4):660–684.  https://doi.org/10.1016/j.foreco.2009.09.001 Google Scholar
  5. Araújo MB, New M (2007) Ensemble forecasting of species distributions. Trends Ecol Evol 22(1):42–47.  https://doi.org/10.1016/j.tree.2006.09.010 Google Scholar
  6. Araújo MB, Pearson RG (2005) Equilibrium of species’ distributions with climate. Ecography 28:693–695.  https://doi.org/10.1111/j.2005.0906-7590.04253.x Google Scholar
  7. Araújo MB, Guisan A (2006) Five (or so) challenges for species distribution modelling. J Biogeography 33(10):1677–1688.  https://doi.org/10.1111/j.1365-2699.2006.01584.x
  8. Barbet-Massin M, Jiguet F, Albert CH, Thuiller W (2012) Selecting pseudo-absences for species distribution models: how, where and how many? Methods Ecol Evol 3(2):327–338.  https://doi.org/10.1111/j.2041-210x.2011.00172.x Google Scholar
  9. Baumbach L, Siegmund JF, Mittermeier M, Donner RV (2017) Impacts of temperature extremes on European vegetation during the growing season. Biogeosciences 14:4891–4903.  https://doi.org/10.5194/bg-14-4891-2017 Google Scholar
  10. BMEL (2014) The forests in Germany - selected results of the third national forest inventory. Federal Ministry of Food and Agriculture (BMEL), BerlinGoogle Scholar
  11. Bolte A, Ammer C, Löf M, Madsen P, Nabuurs GJ, 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(6):473–482.  https://doi.org/10.1080/02827580903418224 Google Scholar
  12. Bolte A, Kämpf F, Hilbrig L (2013) Space sequestration below ground in old-growth spruce-beech forests – signs for facilitation? Front Plant Sci 4:322.  https://doi.org/10.3389/fpls.2013.00322 Google Scholar
  13. Bonan GB (2008) Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320(5882):1444–1449.  https://doi.org/10.1126/science.1155121 Google Scholar
  14. Cheaib A, Badeau V, Boe J, Chuine I, Delire C, Dufrêne E, François C, Gritti ES, Legay M, Pagé C, Thuiller W, Viovy N, Leadley P (2012) Climate change impacts on tree ranges: model intercomparison facilitates understanding and quantification of uncertainty. Ecol Lett 15:533–544.  https://doi.org/10.1111/j.1461-0248.2012.01764.x Google Scholar
  15. Coumou D, Rahmstorf S (2012) A decade of weather extremes. Nat Clim Chang 2(7):491–496.  https://doi.org/10.1038/nclimate1452 Google Scholar
  16. Czajkowski T, Bolte A (2006) Unterschiedliche Reaktion deutscher und polnischer Herkünfte der Buche (Fagus sylvatica L.) auf Trockenheit. Allgemeine Jagd- und Forstzeitung 177(2):30–40Google Scholar
  17. Dannenmann M, Bimüller C, Gschwendtner S, Leberecht M, Tejedor J, Bilela S, Gasche R, Hanewinkel M, Baltensweiler A, Kögel-Knabner I, Polle A, Schloter M, Simon J, Rennenberg H (2016) Climate change impairs nitrogen cycling in European beech forests. PLoS One 11(7):e0158823.  https://doi.org/10.1371/journal.pone.0158823 Google Scholar
  18. Deser C, Phillips A, Bourdette V, Teng H (2012) Uncertainty in climate change projections: the role of internal variability. Clim Dyn 38(3–4):527–546.  https://doi.org/10.1007/s00382-010-0977-x Google Scholar
  19. Devi S, Angrish R, Madaan S, Toky OP, Arya SS (2016) Sinker root system in trees with emphasis on soil profile. In: Cloudhary DK, Varma A, Tuteja N (eds) Plant-microbe interaction: an approach to sustainable agriculture. Springer, Singapore, pp 463–474.  https://doi.org/10.1007/978-981-10-2854-0_21 Google Scholar
  20. Dieter M (2001) Land expectation values for spruce and beech calculated with Monte Carlo modelling techniques. Forest Policy Econ 2(2):157–166.  https://doi.org/10.1016/s1389-9341(01)00045-4 Google Scholar
  21. Döbbeler H, Albert M, Schmidt M, Nagel J, Schröder J (2011) BWINPro. Programm zur Bestandesanalyse und Prognose. Handbuch zur gemeinsamen Version von BWINPro und BWINPro-S. Version 6.3. Nordwestdeutsche Forstliche Versuchsanstalt und TU DresdenGoogle Scholar
  22. Dormann CF (2007) Promising the future? Global change projections of species distributions. Basic Appl Ecol 8(5):387–397.  https://doi.org/10.1016/j.baae.2006.11.001 Google Scholar
  23. Dormann CF, Schymanski SJ, Cabral J, Chuine I, Graham C, Hartig F, Kearney M, Morin X, Römermann C, Schröder B, Singer A (2012) Correlation and process in species distribution models: bridging a dichotomy. J Biogeogr 39:2119–2131.  https://doi.org/10.1111/j.1365-2699.2011.02659.x Google Scholar
  24. Drake JM, Randin C, Guisan A (2006) Modelling ecological niches with support vector machines. J Appl Ecol 43(3):424–432.  https://doi.org/10.1111/j.1365-2664.2006.01141.x Google Scholar
  25. Duan RY, Kong XQ, Huang MY, Fan WY, Wang ZG (2014) The predictive performance and stability of six species distribution models. PLoS One 9(11):e112764.  https://doi.org/10.1371/journal.pone.0112764 Google Scholar
  26. Dyderski MK, Paź S, Frelich LE, Jagodziński AM (2018) How much does climate change threaten European forest tree species distributions? Glob Chang Biol 24:1150–1163.  https://doi.org/10.1111/gcb.13925 Google Scholar
  27. Elith J, Graham CH, Anderson RP, Dudík M, Ferrier S, Guisan A, Hijmans RJ, Huettmann F, Leathwick JR, Lehmann A, Li J, Lohmann LG, Loiselle BA, Manion G, Moritz C, Nakamura M, Nakazawa Y, Overton JM, Townsend Peterson A, Phillips SJ, Richardson K, Scachetti-Pereira R, Schapire RE, Soberón J, Williams S, Wisz MS, Zimmermann NE (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29(2):129–151.  https://doi.org/10.1111/j.2006.0906-7590.04596.x Google Scholar
  28. Ellenberg H (1988) Vegetation ecology of Central Europe. Cambridge University Press, Cambridge, New York, New Rochelle, Melbourne, SydneyGoogle Scholar
  29. Elling W, Dittmar C, Pfaffelmoser K, Rötzer T (2009) Dendroecological assessment of the complex causes of decline and recovery of the growth of silver fir (Abies alba Mill.) in Southern Germany. For Ecol Manag 257(4):1175–1187.  https://doi.org/10.1016/j.foreco.2008.10.014 Google Scholar
  30. ESRI (2011) ArcGIS Desktop: Release 10. Environmental Systems Research Institute (ESRI), RedlandsGoogle Scholar
  31. Fang J, Lechowicz MJ (2006) Climatic limits for the present distribution of beech (Fagus L.) species in the world. J Biogeogr 33(10):1804–1819.  https://doi.org/10.1111/j.1365-2699.2006.01533.x Google Scholar
  32. Faustmann M (1849) Calculation of the value which forest land and immature stands possess for forestry. Reprinted in: Journal of Forest Economics 1(1):7–44Google Scholar
  33. Fitzpatrick MC, Hargrove WW (2009) The projection of species distribution models and the problem of non-analog climate. Biodivers Conserv 18(8):2255–2261.  https://doi.org/10.1007/s10531-009-9584-8 Google Scholar
  34. Forrester DI (2014) The spatial and temporal dynamics of species interactions in mixed-species forests: from pattern to process. For Ecol Manag 312:282–292.  https://doi.org/10.1016/j.foreco.2013.10.003 Google Scholar
  35. Geßler A, Keitel C, Kreuzwieser J, Matyssek R, Seiler W, Rennenberg H (2007) Potential risks for European beech (Fagus sylvatica L.) in a changing climate. Trees 21(1):1–11.  https://doi.org/10.1007/s00468-006-0107-x Google Scholar
  36. Goberville E, Hautekèete NC, Kirby RR, Piquot Y, Luczak C, Beaugrand G (2016) Climate change and the ash dieback crisis. Sci Rep 6:35303.  https://doi.org/10.1007/s00468-006-0107-x Google Scholar
  37. 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 Google Scholar
  38. Guisan A, Thuiller W (2005) Predicting species distribution: offering more than simple habitat models. Ecol Lett 8(9):993–1009.  https://doi.org/10.1111/j.1461-0248.2005.00792.x Google Scholar
  39. Hair JF, Anderson RE, Tatham RL, Black WC (1995) Multivariate data analysis with readings, 3rd edn. MacMillan, New YorkGoogle Scholar
  40. Hanewinkel M, Hummel S, Cullmann DA (2009) Modelling and economic evaluation of forest biome shifts under climate change in Southwest Germany. For Ecol Manag 259(4):710–719.  https://doi.org/10.1016/j.foreco.2009.08.021 Google Scholar
  41. Hanewinkel M, Cullmann DA, Schelhaas MJ, Nabuurs GJ, Zimmermann NE (2013) Climate change may cause severe loss in the economic value of European forest land. Nat Clim Chang 3(3):203–207.  https://doi.org/10.1038/nclimate1687 Google Scholar
  42. Häsler H, Senn J (2012) Ungulate browsing on European silver fir Abies alba: the role of occasions, food shortage and diet preferences. Wildl Biol 18(1):67–42.  https://doi.org/10.2981/09-013 Google Scholar
  43. Hengl T, de Jesus JM, Heuvelink GB, Gonzalez MR, Kilibarda M, Blagotić A, Shangguan W, Wright MN, Geng X, Bauer-Marschallinger B, Guevara MA, Vargas R, MacMillan RA, Batjes NH, Leenaars JGB, Ribeiro E, Wheeler I, Mantel S, Kempen B (2017) SoilGrids250m: global gridded soil information based on machine learning. PLoS One 12(2):e0169748.  https://doi.org/10.1371/journal.pone.0169748 Google Scholar
  44. Hickler T, Vohland K, Feehan J, Miller PA, Smith B, Costa L, Giesecke T, Fronzek S, Carter TR, Cramer W, Kühn I, Sykes MT (2012) Projecting the future distribution of European potential natural vegetation zones with a generalized, tree-species based dynamic vegetation model. Glob Ecol Biogeogr 21:50–63.  https://doi.org/10.1111/j.1466-8238.2010.00613.x Google Scholar
  45. Hickler T, Rammig A, Werner C (2015) Modelling CO2 impacts on forest productivity. Curr Forest Rep 1(2):69–80.  https://doi.org/10.1007/s40725-015-0014-8 Google Scholar
  46. Hijmans RJ (2016) Raster: geographic data analysis and modeling. R package version 2.5–8Google Scholar
  47. 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(15):1965–1978.  https://doi.org/10.1002/joc.1276 Google Scholar
  48. Houston Durrant T, de Rigo D, Caudullo G (2016) Fagus sylvatica and other beeches in Europe: distribution, habitat, usage and threats. In: San-Miguel-Ayanz J, de Rigo D, Caudullo G, Houston Durrant T, Mauri A (eds) European atlas of Forest tree species. Publ, Off. EU, pp 94–95Google Scholar
  49. Jimenez-Valverde A, Lobo JM, Hortal J (2009) The effect of prevalence and its interaction with sample size on the reliability of species distribution models. Commun Ecol 10(2):196–205.  https://doi.org/10.1556/comec.10.2009.2.9 Google Scholar
  50. Jump AS, Hunt JM, Penuelas J (2006) Rapid climate change-related growth decline at the southern range edge of Fagus sylvatica. Glob Chang Biol 12(11):2163–2174.  https://doi.org/10.1111/j.1365-2486.2006.01250.x Google Scholar
  51. Karger DN, Conrad O, Böhner J, Kawohl T, Kreft H, Soria-Auza RW, Zimmermann NE, Linder HP, Kessler M (2017) Climatologies at high resolution for the earth’s land surface areas. Sci Data 4:170122.  https://doi.org/10.1038/sdata.2017.122 Google Scholar
  52. Keenan RJ (2015) Climate change impacts and adaptation in forest management: a review. Ann For Sci 72(2):145–167.  https://doi.org/10.1007/s13595-014-0446-5 Google Scholar
  53. Kelty MJ (2006) The role of species mixtures in plantation forestry. For Ecol Manag 233(2):195–204.  https://doi.org/10.1016/j.foreco.2006.05.011 Google Scholar
  54. Kölling C, Falk W, Walentowski H (2011) Standörtliche Anbaumöglichkeiten der Tanne (Abies alba und Abies grandis) in Bayern. LWF Wissen 66:11–19Google Scholar
  55. Körner C, Asshoff R, Bignucolo O, Hattenschwiler S, Keel SG, Pelaez-Riedl S, Pepin S, Siegwolf RTW, Zotz G (2005) Carbon flux and growth in mature deciduous forest trees exposed to elevated CO2. Science 309(5739):1360–1362.  https://doi.org/10.1126/science.1113977 Google Scholar
  56. Kramer K, Degen B, Buschbom J, Hickler T, Thuiller W, Sykes MT, de Winter W (2010) Modelling exploration of the future of European beech (Fagus sylvatica L.) under climate change: range, abundance, genetic diversity and adaptive response. For Ecol Manag 259(11):2213–2222.  https://doi.org/10.1016/j.foreco.2009.12.023 Google Scholar
  57. Lebourgeois F, Gomez N, Pinto P, Mérian P (2013) Mixed stands reduce Abies alba treering sensitivity to summer drought in the Vosges mountains, Western Europe. For Ecol Manag 303:61–71.  https://doi.org/10.1016/j.foreco.2013.04.003 Google Scholar
  58. Leuzinger S, Körner C (2007) Water savings in mature deciduous forest trees under elevated CO2. Glob Chang Biol 13:2498–2508.  https://doi.org/10.1111/j.1365-2486.2007.01467.x Google Scholar
  59. Lindner M, Maroschek M, Netherer S, Kremer A, Barbati A, Garcia-Gonzalo J, Seidl R, Delzon S, Corona P, Kolström M, Lexer MJ, Marchetti M (2010) Climate change impacts, adaptive capacity, and vulnerability of European forest ecosystems. For Ecol Manag 259(4):698–709.  https://doi.org/10.1016/j.foreco.2009.09.023 Google Scholar
  60. Linkevicius E (2014) Single tree level simulator for Lithuanian pine forests. Dissertation, Technische Universität Dresden, Institute of Forest Growth and Forest Computer SciencesGoogle Scholar
  61. Lobo JM, Jiménez-Valverde A, Hortal J (2010) The uncertain nature of absences and their importance in species distribution modelling. Ecography 33(1):103–114.  https://doi.org/10.1111/j.1600-0587.2009.06039.x Google Scholar
  62. Magh RK, Grün M, Knothe VE, Stubenazy T, Tejedor J, Dannenmann M, Rennenberg H (2017) Silver-fir (Abies alba MILL.) neighbors improve water relations of European beech (Fagus sylvatica L.), but do not affect N nutrition. Trees 32(1):337–348.  https://doi.org/10.1007/s00468-017-1557-z Google Scholar
  63. Mauri A, de Rigo D, Caudullo G (2016) Abies alba in Europe: distribution, habitat, usage and threats. In: San-Miguel-Ayanz J, de Rigo D, Caudullo G, Houston Durrant T, Mauri A (eds) European atlas of Forest tree species. Publ. Off. EU, pp 48–49Google Scholar
  64. Mauri A, Strona G, San-Miguel-Ayanz J (2017) EU-Forest, a high-resolution tree occurrence dataset for Europe. Sci Data 4:160123.  https://doi.org/10.1038/sdata.2016.123 Google Scholar
  65. Menard S (1995) Applied logistic regression analysis: Sage university series on quantitative applications in the social sciences. Sage, Thousand OaksGoogle Scholar
  66. Merow C, Smith MJ, Edwards TC, Guisan A, McMahon SM, Normand S, Thuiller W, Wüest RO, Zimmermann NE, Elith J (2014) What do we gain from simplicity versus complexity in species distribution models? Ecography 37:1267–1281.  https://doi.org/10.1111/ecog.00845 Google Scholar
  67. Metz J, Annighöfer P, Schall P, Zimmermann J, Kahl T, Schulze ED, Ammer C (2016) Site-adapted admixed tree species reduce drought susceptibility of mature European beech. Glob Chang Biol 22(2):903–920.  https://doi.org/10.1111/gcb.13113 Google Scholar
  68. Miller ME, Hui SL, Tierney WM (1991) Validation techniques for logistic regression models. Stat Med 10(8):1213–1226.  https://doi.org/10.1002/sim.4780100805 Google Scholar
  69. Mina M, Del Río M, Huber MO, Thürig E, Rohner B (2018) The symmetry of competitive interactions in mixed spruce, silver fir and European beech forests. J Veg Sci 29:775–787.  https://doi.org/10.1111/jvs.12664 Google Scholar
  70. Mölder I, Leuschner C (2014) European beech grows better and is less drought sensitive in mixed than in pure stands: tree neighbourhood effects on radial increment. Trees 28:777–792.  https://doi.org/10.1007/s00468-014-0991-4 Google Scholar
  71. Nagel J (1999) Konzeptionelle Überlegungen zum schrittweisen Aufbau eines waldwachstumskundlichen Simulationssystems für Nordwestdeutschland. Schriften aus der Forstlichen Fakultät der Universität Göttingen und der Nieders. Forstl. Versuchsanstalt, Band 128. J.D. Sauerländer's Verlag, Frankfurt a.M.Google Scholar
  72. Naimi B, Araújo MB (2016) sdm: a reproducible and extensible R platform for species distribution modelling. Ecography 39:368–375.  https://doi.org/10.1111/ecog.01881 Google Scholar
  73. Neumann RB, Cardon ZG (2012) The magnitude of hydraulic redistribution by plant roots: a review and synthesis of empirical and modeling studies. New Phytol 194:337–352.  https://doi.org/10.1111/j.1469-8137.2012.04088.x Google Scholar
  74. Nocentini S (2009) Structure and management of beech (Fagus salvatica L.) forests in Italy. iForest 2:105–113.  https://doi.org/10.3832/ifor0499-002 Google Scholar
  75. Phillips SJ, Dudík M, Elith J, Graham CH, Lehmann A, Leathwick J, Ferrier S (2009) Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol Appl 19(1):181–197.  https://doi.org/10.1890/07-2153.1 Google Scholar
  76. Pretzsch H, Schütze G, Uhl E (2013a) Resistance of European tree species to drought stress in mixed versus pure forests: evidence of stress release by inter-specific facilitation. Plant Biol 15(3):483–495.  https://doi.org/10.1111/j.1438-8677.2012.00670.x Google Scholar
  77. Pretzsch H, Bielak K, Block J, Bruchwald A, Dieler J, Ehrhart HP, Kohnle U, Nagel J, Spellmann H, Zasada M, Zingg A (2013b) Productivity of mixed versus pure stands of oak (Quercus petraea (Matt.) Liebl. and Quercus robur L.) and European beech (Fagus sylvatica L.) along an ecological gradient. Eur J For Res 132:263–280.  https://doi.org/10.1007/s10342-012-0673-y Google Scholar
  78. R Core Team (2017) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  79. Ramsfield TD, Bentz BJ, Faccoli M, Jactel H, Brockerhoff EG (2016) Forest health in a changing world: effects of globalization and climate change on forest insect and pathogen impacts. Forestry 89:245–252.  https://doi.org/10.1093/forestry/cpw018 Google Scholar
  80. Reyer C (2013) The cascade of uncertainty in modeling forest ecosystem responses to environmental change and the challenge of sustainable resource management. Dissertation, Humboldt University BerlinGoogle Scholar
  81. Rösch M (2015) Nationalpark – Natur – Weißtanne – Fichte. Sechs Jahrtausende Wald und Mensch im Nordschwarzwald. Denkmalpflege Baden-Württemberg 44(3):154–159Google Scholar
  82. Saltré F, Duputié A, Gaucherel C, Chuine I (2015) How climate, migration ability and habitat fragmentation affect the projected future distribution of European beech. Glob Chang Biol 21(2):897–910.  https://doi.org/10.1111/gcb.12771 Google Scholar
  83. Sardans J, Peñuelas J (2014) Hydraulic redistribution by plants and nutrient stoichioimetry: shifts under global change. Ecohydrology 7:1–20.  https://doi.org/10.1002/eco.1459 Google Scholar
  84. Schröder J, Röhle H, Eisenhauer D, Brand S (2006) Zum Jugendwachstum der Eiche unter Kiefernschirm in Sachsen. Forstarchiv 77(6):195–202Google Scholar
  85. Schröder J, Röhle H, Gerold D, Münder K (2007) Modeling individual-tree growth in stands under forest conversion in East Germany. Eur J For Res 126(3):459–472.  https://doi.org/10.1007/s10342-006-0167-x Google Scholar
  86. Serra-Diaz JM, Ninyerola M, Lloret F (2012) Coexistence of Abies alba (Mill.) - Fagus sylvatica (L.) and climate change impact in the Iberian Peninsula: a climatic-niche perspective approach. Flora 207(1):10–18.  https://doi.org/10.1016/j.flora.2011.10.002 Google Scholar
  87. Sinclair SJ, White MD, Newell GR (2010) How useful are species distribution models for managing biodiversity under future climates? Ecol Soc 15(1):8.  https://doi.org/10.5751/es-03089-150108 Google Scholar
  88. Spellmann H, Meesenburg H, Schmidt M, Nagel RV, Sutmöller J, Albert M (2015) Klimaanapassung ist Vorsorge für den Wald. ProWald November 2015:4–10Google Scholar
  89. Staatsbetrieb Sachsenforst (2016) Walderneuerung und Erstaufforstung: Hinweise für Waldbesitzer. Staatsbetrieb Sachsenforst, PirnaGoogle Scholar
  90. Straka TJ, Bullard SH (1996) Land expectation value calculation in timberland valuation. Appraisal J 64:399–405Google Scholar
  91. Strona G, Mauri A, San-Miguel-Ayanz J (2016) A high-resolution pan-European tree occurrence dataset. figshare.  https://doi.org/10.6084/m9.figshare.c.3288407
  92. Thünen-Institut (2012) Third national forest inventory database. https://bwi.info. Accessed 27 July 2017
  93. Tinner W, Colombaroli D, Heiri O, Henne PD, Steinacher M, Untenecker J, Vescovi E, Allen JRM, Carraro G, Conedera M, Joos F, Lotter AF, Luterbacher J, Samartin S, Valsecchi V (2013) The past ecology of Abies alba provides new perspectives on future responses of silver fir forests to global warming. Ecol Monogr 83(4):419–439.  https://doi.org/10.1890/12-2231.1 Google Scholar
  94. Tinner W, Conedera M, Bugmann H, Colombaroli D, Gobet E, Vescovi E, Heiri O, Joos F, Luterbacher J, La Mantia T, Pasta S, Untenecker J, Henne PD (2016) Europäische Wälder unter wärmeren Klimabedingungen - Neue Erkenntnisse aus Paläoökologie und dynamischer Vegetationsmodellierung. AFZ-DerWald 71(18):45–49Google Scholar
  95. US Geological Service (1996) GTOPO30: Global 30 arc-seconds digital elevation model. https://lta.cr.usgs.gov/GTOPO30. Accessed 21 February 2017
  96. Van der Wal J, Shoo LP, Graham C, Williams SE (2009) Selecting pseudo-absence data for presence-only distribution modeling: how far should you stray from what you know? Ecol Model 220:589–594.  https://doi.org/10.1016/j.ecolmodel.2008.11.010 Google Scholar
  97. Wicht-Lückge G, Biewald G, Göckel C, Jacob A, Kilian M, Kohnle U, Michiels HG, Nagel J, Schabel A, Schmalfuß N (2014) Richtlinie landesweiter Waldentwicklungstypen. Landesbetrieb Forst Baden-Württemberg, Ministerium für Ländlichen Raum und Verbraucherschutz Baden-Württemberg, StuttgartGoogle Scholar
  98. Wonsack D (2016) Überführung von gleichaltrigen in ungleichaltrige Fichtenwälder im Mathislewald. Master’s thesis, Albert-Ludwigs-Universität-Freiburg, Professur für Forstökonomie und ForstplanungGoogle Scholar
  99. Yousefpour R, Hanewinkel M, Le Moguédec G (2010) Evaluating the suitability of management strategies of pure Norway spruce forests in the Black Forest area of Southwest Germany for adaptation to or mitigation of climate change. Environ Manag 45(2):387–402.  https://doi.org/10.1007/s00267-009-9409-2 Google Scholar
  100. 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):1–15.  https://doi.org/10.1007/s13595-011-0153-4 Google Scholar
  101. Yousefpour R, Augustynczik AL, Hanewinkel M (2017) Pertinence of reactive, active, and robust adaptation strategies in forest management under climate change. Ann For Sci 74(2):40.  https://doi.org/10.1007/s13595-017-0640-3 Google Scholar
  102. Zang C, Hartl-Meier C, Dittmar C, Rothe A, Menzel A (2014) Patterns of drought tolerance in major European temperate forest trees: climatic drivers and levels of variability. Glob Chang Biol 20(12):3767–3779.  https://doi.org/10.1111/gcb.12637 Google Scholar
  103. Zapater M, Hossann C, Bréda N, Bréchet C, Bonal D, Granier A (2011) Evidence of hydraulic lift in a young beech and oak mixed forest using 18O soil water labelling. Trees 25:885–894.  https://doi.org/10.1007/s00468-011-0563-9 Google Scholar
  104. Zebisch M, Grothmann T, Schröter D, Hasse C, Fritsch U, Cramer W (2005) Climate change in Germany. In: Federal Environmental Agency Germany/Umweltbundesamt (ed.) vulnerability and adaptation of climate sensitive sectors / Klimawandel in Deutschland–Vulnerabilität und Anpassungsstrategien klimasensitiver Systeme, report 201. Federal Environment Agency Germany, Dessau, pp 41–253Google Scholar
  105. Zimmermann NE, Yoccoz NG, Edwards Jr TC, Meier ES, Thuiller W, Guisan A, Schmatz DR, Pearman PB (2009) Climatic extremes improve predictions of spatial patterns of tree species. PNAS 106(Supplement 2):19723–19728.  https://doi.org/10.1073/pnas.0901643106
  106. Zimmermann J, Hauck M, Dulamsuren C, Leuschner C (2015) Climate warming-related growth decline affects Fagus sylvatica, but not other broad-leaved tree species in Central European mixed forests. Ecosystems 18(4):560–572.  https://doi.org/10.1007/s10021-015-9849-x Google Scholar
  107. Zimmermann NE, Normand S, Psomas A (2016) PorTree final report. A project funded by the BAFU-WSL program on “forests and climate change” in Switzerland. Swiss Federal Research Institute (WSL), Birmensdorf.  https://doi.org/10.3929/ethz-a-010689681 Google Scholar
  108. Zurell D, Thuiller W, Pagel J, Cabral JS, Münkemüller T, Gravel D, Dullinger S, Normand S, Schiffers KH, Moore KA, Zimmermann NE (2016) Benchmarking novel approaches for modelling species range dynamics. Glob Chang Biol 22:2651–2664.  https://doi.org/10.1111/gcb.13251 Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute of Forestry Economics and Forest PlanningAlbert-Ludwigs-Universität FreiburgFreiburg im BreisgauGermany
  2. 2.Senckenberg Biodiversity and Climate Research Centre (BiK-F)Frankfurt am MainGermany

Personalised recommendations