Regional Environmental Change

, Volume 9, Issue 2, pp 101–119 | Cite as

Modelling bark beetle disturbances in a large scale forest scenario model to assess climate change impacts and evaluate adaptive management strategies

  • Rupert Seidl
  • Mart-Jan Schelhaas
  • Marcus Lindner
  • Manfred J. Lexer
Original Article

Abstract

To study potential consequences of climate-induced changes in the biotic disturbance regime at regional to national scale we integrated a model of Ips typographus (L. Scol. Col.) damages into the large-scale forest scenario model EFISCEN. A two-stage multivariate statistical meta-model was used to upscale stand level damages by bark beetles as simulated in the hybrid forest patch model PICUS v1.41. Comparing EFISCEN simulations including the new bark beetle disturbance module against a 15-year damage time series for Austria showed good agreement at province level (R² between 0.496 and 0.802). A scenario analysis of climate change impacts on bark beetle-induced damages in Austria’s Norway spruce [Picea abies (L.) Karst.] forests resulted in a strong increase in damages (from 1.33 Mm³ a−1, period 1990–2004, to 4.46 Mm³ a−1, period 2095–2099). Studying two adaptive management strategies (species change) revealed a considerable time-lag between the start of adaptation measures and a decrease in simulated damages by bark beetles.

Keywords

Natural disturbances Climatic change Ips typographus Scaling Adaptation 

References

  1. Anonymous (1997) Ergebnisse der Österreichischen Waldinventur 1992 bis 1996. Mit Vergleichsdaten 1986 bis 1990. Forstliche Bundesversuchsanstalt—Waldforschungszentrum Wien. CD-ROMGoogle Scholar
  2. Anonymous (2002) Ergebnisse der Österreichischen Waldinventur 2000–2002. Bundesforschungs- und Ausbildungszentrum für Wald, Naturgefahren und Landschaft (BFW) Wien. http://web.bfw.ac.at/i7/oewi.oewi0002. 7 January 2006
  3. Ayres MP, Lombardero MJ (2000) Assessing the consequences of global change for forest disturbance from herbivores and pathogens. Sci Total Environ 262:263–286. doi:10.1016/S0048-9697(00)00528-3 CrossRefGoogle Scholar
  4. Baier P, Pennerstorfer J, Schopf A (2007) PHENIPS—a comprehensive phenology model for risk assessment of outbreaks of the European spruce bark beetle, Ips typographus (L.) (Col., Scolytidae). For Ecol Manage 249:171–186CrossRefGoogle Scholar
  5. Bale JS, Masters GJ, Hodkinson ID, Awmack C, Bezemer TM, Brown VK et al (2002) Herbivory in global climate change research: direct effects of rising temperature on insect herbivores. Glob Change Biol 8:1–16. doi:10.1046/j.1365-2486.2002.00451.x CrossRefGoogle Scholar
  6. Botkin DB (1993) Forest dynamics: an ecological model. Oxford University Press, Oxford, p 309Google Scholar
  7. Candau JN, Fleming RA (2005) Landscape-scale spatial distribution of spruce budworm defoliation in relation to bioclimatic conditions. Can J Res 35:2218–2232. doi:10.1139/x05-078 CrossRefGoogle Scholar
  8. Currie WS, Nadelhoffer JK, Aber JD (1999) Soil detrital processes controlling the movement of 15N tracers to forest vegetation. Ecol Appl 9:87–102Google Scholar
  9. Dale VH, Joyce LA, McNulty S, Neilson RP (2000) The interplay between climate change, forests, and disturbances. Sci Total Environ 262:201–204. doi:10.1016/S0048-9697(00)00522-2 CrossRefGoogle Scholar
  10. Donaubauer E, Krehan H, Tomiczek C (1995) Forstschadenssituation in Österreich. AFZ Der Wald 07(95):376–379Google Scholar
  11. Donaubauer E, Krehan H, Tomiczek C (1996) Forstschadenssituation in Österreich. AFZ Der Wald 07(96):386–388Google Scholar
  12. EEA (2006) How much bioenergy can Europe produce without harming the environment? European Environment Agency. EEA report 07/2006, Copenhagen. ISSN 1725-9177, pp 67Google Scholar
  13. Eriksson M, Pouttu A, Roininen H (2005) The influence of windthrow area and timber characteristics on colonization of wind-felled spruces by Ips typographus (L.). For Ecol Manage 216:105–116CrossRefGoogle Scholar
  14. Eriksson M, Neuvonen S, Roininen H (2007) Retention of wind-felled trees and the risk of consequential tree mortality by the European spruce bark beetle Ips typographus in Finland. Scand J For Res 22:516–523. doi:10.1080/02827580701800466 CrossRefGoogle Scholar
  15. Eriksson M, Neuvonen S, Roininen H (2008) Ips typographus (L.) attack on patches of felled trees: “Wind-felled” vs. cut trees and the risk of subsequent mortality. For Ecol Manage 255:1336–1341CrossRefGoogle Scholar
  16. Faraway JJ (2006) Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models. Chapman and Hall, Boca Raton. 301 ppGoogle Scholar
  17. Göthlin E, Schroeder LM, Lindelöw A (2000) Attacks by Ips typographus and Pityogenes chalcographus on windthrown spruces (Picea abies) during the two years following a storm felling. Scand J For Res 15:542–549. doi:10.1080/028275800750173492 CrossRefGoogle Scholar
  18. Harrington R, Fleming RA, Woiwood IP (2001) Climate change impacts on insect management and conservation in temperate regions: can they be predicted? Agric For Entomol 3:233–240. doi:10.1046/j.1461-9555.2001.00120.x CrossRefGoogle Scholar
  19. IPCC (2000) Emissions Scenarios 2000. Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, pp 570Google Scholar
  20. Jönsson AM, Harding S, Bärring L, Ravn HP (2007) Impact of climate change on the population dynamics of Ips typographus in southern Sweden. Agric Meteorol 146:70–81. doi:10.1016/j.agrformet.2007.05.006 CrossRefGoogle Scholar
  21. Kilian W, Müller F, Starlinger F (1994) Die forstlichen Wuchsgebiete Österreichs. Eine Naturraumgliederung nach waldökologischen Gesichtspunkten. Bericht 82, Forstliche Bundesversuchsanstalt, Wien. ISSN 0374-9037. pp 60Google Scholar
  22. Krehan H (1993) Forstschadenssituation in Österreich. AFZ Der Wald 07/93:352–354Google Scholar
  23. Krehan H, Steyrer G (2004) Borkenkäferkalamität 2003. Forstschutz Aktuell 31, Bundesforschungs- und Ausbildungszentrum für Wald, Naturgefahren und Landschaft (BFW) Wien, ISSN 1815-5103. pp 10–12Google Scholar
  24. Krehan H, Steyrer G (2005) Borkenkäfer-Monitoring und Borkenkäfer-Kalamität 2004. Forstschutz Aktuell 33, Bundesforschungs- und Ausbildungszentrum für Wald, Naturgefahren und Landschaft (BFW) Wien, ISSN 1815–5103, pp 12–14Google Scholar
  25. Krehan H, Steyrer G (2006) Borkenkäfersituation und Borkenkäfer-Monitoring 2005. Forstschutz aktuell 35, Bundesforschungs- und Ausbildungszentrum für Wald, Naturgefahren und Landschaft (BFW), Wien. ISSN 1815–5103, pp 10–14Google Scholar
  26. Kurz WA, Stinson G, Rampley GJ, Dymond CC, Neilson ET (2008) Risk of natural disturbances makes future contribution of Canada’s forests to the global carbon cycle highly uncertain. Proc Natl Acad Sci USA 105:1551–1555. doi:10.1073/pnas.0708133105 CrossRefGoogle Scholar
  27. Landsberg JJ, Waring RH (1997) A generalized model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance and partitioning. For Ecol Manage 95:209–228CrossRefGoogle Scholar
  28. Leckebusch GC, Ulbrich U (2004) On the relationship between cyclones and extreme windstorm events over Europe under climate change. Global Planet Change 44:181–193. doi:10.1016/j.gloplacha.2004.06.011 CrossRefGoogle Scholar
  29. Leitgeb E, Englisch M (2006) Klimawandel- Standörtliche Rahmenbedingungen für die Forstwirtschaft. BFW Praxisinformation 10. Bundesforschungs- und Ausbildungszentrum für Wald, Naturgefahren und Landschaft (BFW), ISSN 1815-3895. Wien, pp 9–11Google Scholar
  30. Lexer MJ (1995) Beziehungen zwischen der Anfälligkeit von Fichtenbeständen (Picea abies (L.) Karst.) für Borkenkäferschäden und Standorts- und Bestandesmerkmalen unter besonderer Berücksichtigung der Wasserversorgung. Dissertation, University of Natural Resource Management and Applied Life Sciences (BOKU) Vienna, pp 210Google Scholar
  31. Lexer M, Hönninger K (1998) Estimating physical soil parameters for sample plots of large-scale forest inventories. For Ecol Manage 111:231–247CrossRefGoogle Scholar
  32. Lexer MJ, Hönninger K (2001) A modified 3D-patch model for spatially explicit simulation of vegetation composition in heterogeneous landscapes. For Ecol Manage 144:43–65CrossRefGoogle Scholar
  33. Lexer MJ, Hönninger K, Scheifinger H, Matulla C, Groll N, Kromp-Kolb H, Schadauer K, Starlinger F, Englisch M (2002) The sensitivity of Austrian forests to scenarios of climatic change: a large scale risk assessment based on a modified gap model and forest inventory data. For Ecol Manage 162:53–72CrossRefGoogle Scholar
  34. Lindner M (2000) Developing adaptive forest management strategies to cope with climate change. Tree Physiol 20:299–307Google Scholar
  35. Mäkelä A, Landsberg J, Ek AR, Burk TE, Ter-Mikaelian M, Agren GI et al (2000) Process-based models for forest ecosystem management: current state of the art and challenges for practical implementation. Tree Physiol 20:289–298Google Scholar
  36. Marschall J (1975) Hilfstafeln für die Forsteinrichtung. Österreichischer Agrarverlag, Wien. pp 199 Google Scholar
  37. Mitchell TD, Carter TR, Jones PD, Hulme M, New M (2004) A comprehensive set of high-resolution grids of monthly climate for Europe and the globe: the observed record (1901–2000) and 16 scenarios (2001–2100). Tyndall Centre Working Paper 55, pp 25Google Scholar
  38. Müller F (1994) Müssen wir waldbauliche Konzepte ändern? In: Geburek T, Müller F, Schultze U (eds) Klimaänderung in Österreich. Herausforderung an Forstgenetik und Waldbau. Forstliche Bundesversuchsanstalt Wien, Berichte 81. ISSN 1013-0713, pp 67–75Google Scholar
  39. Nabuurs GJ, Paivinen R, Pussinen A, Schelhaas MJ (2003) Development of European Forests until 2050. European Forest Institute Research Report 15. Brill, Leiden, Boston, pp 242Google Scholar
  40. Nabuurs GJ, Pussinen A, van Brusselen J, Schelhaas MJ (2007) Future harvesting pressure on European forests. Eur J For Res 126:391–400. doi:10.1007/s10342-006-0158-y Google Scholar
  41. Nabuurs GJ, Schelhaas MJ, Pussinen A (2000) Validation of the European Forest Information Scenario Model (EFISCEN) and a Projection of Finish forests. Silva Fenn 34:167–179Google Scholar
  42. Netherer S, Nopp-Mayr U (2005) Predisposition assessment systems (PAS) as supportive tools in forest management—rating of site and stand-related hazards of bark beetle infestation in the High Tatra Mountains as an example for system application and verification. For Ecol Manage 207:99–107CrossRefGoogle Scholar
  43. Netherer S, Pennerstorfer J (2001) Parameters relevant for modelling the potential development of Ips typographus (Coleoptera: Scolytidae). Integr Pest Manage Rev 6:177–184. doi:10.1023/A:1025719301446 CrossRefGoogle Scholar
  44. Okland B, Bjornstad ON (2003) Synchrony and geographical variation of the spruce bark beetle (Ips typographus) during a non-epidemic period. Popul Ecol 45:213–219. doi:10.1007/s10144-003-0157-5 CrossRefGoogle Scholar
  45. Parmesan C, Ryrholm N, Stefanescu C, Hillk JK, Thomas CD, Descimon H et al (1999) Poleward shifts in geographical ranges of butterfly species associated with regional warming. Nature 399:579–583. doi:10.1038/21181 CrossRefGoogle Scholar
  46. Pussinen A, Schelhaas MJ, Verkaik E, Heikkinen E, Paivinen R, Nabuurs GJ (2001) Manual for the European Forest Information Scenario Model (EFISCEN); version 2.0. EFI Internal report 5. European Forest Institute. Joensuu, Finland, pp 49Google Scholar
  47. Pussinen A, Meyer J, Zudin S, Lindner M (2005) European mitigation potential. In: Kellomäki S, Leinonen S (eds) Management of European forests under changing climatic conditions. Research Note 163, University of Joensuu. ISBN 952-458-652-5, pp 383–400Google Scholar
  48. R Development Core Team (2006) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. Available at: http://www.R-project.org. 17 January 2007
  49. Rouault G, Candau JN, Lieutier F, Nageleisen LM, Martin JC, Warzée N (2006) Effects of drought and heat on forest insect populations in relation to the 2003 drought in Western Europe. Ann Sci 63:613–624. doi:10.1051/forest:2006044 CrossRefGoogle Scholar
  50. Sallnäs O (1990) A matrix growth model of the Swedish forest. Stud For Suec 183:23Google Scholar
  51. Schadauer K (1999) Oberhöhenbonität und Standort der Fichte nach Daten der Österreichischen Forstinventur. Mitteilungen der Forstliche Bundesversuchsanstalt Wien 171. ISBN:3-901347-18-6, pp 135Google Scholar
  52. Schelhaas MJ, Nabuurs GJ, Sonntag M, Pussinen A (2002) Adding natural disturbances to a large-scale forest scenario model and a case study for Switzerland. For Ecol Manage 167:13–26CrossRefGoogle Scholar
  53. Schelhaas MJ, Nabuurs GJ, Schuck A (2003) Natural disturbances in the European forests in the 19th and 20th centuries. Glob Change Biol 9:1620–1633. doi:10.1046/j.1365-2486.2003.00684.x CrossRefGoogle Scholar
  54. Schelhaas MJ, van Brusselen J, Pussinen A, Pesonen E, Schuck A, Nabuurs GJ, et al (2006) Outlook for the development of European forest resources. Geneva timber and forest discussion papers 41, United Nations Economic Commission for Europe, Food and Agriculture Organization of the United Nations. ISSN 1020-7228, pp 118Google Scholar
  55. Schelhaas MJ, Eggers J, Lindner M, Nabuurs GJ, Pussinen A, Päivinen R, et al (2007) Model documentation for the European Forest Information Scenario model (EFISCEN 3.1). Wageningen, Alterra, Alterra report 1559, EFI Technical Report 26, Joensuu, Finland, p 118Google Scholar
  56. Schroeder LM (2001) Tree mortality by the bark beetle Ips typographus (L.) in storm-disturbed stands. Integr Pest Manage Rev 6:169–175. doi:10.1023/A:1025771318285 CrossRefGoogle Scholar
  57. Schröter D, Acosta-Michlik L, Arnell AW, Araújo MB, Badeck F, Bakker M, et al (2004) ATEAM final report 2004. Advanced Terrestrial Ecosystem Analysis and Modelling. Potsdam Institute for Climate Impact Research (PIK), pp 139Google Scholar
  58. Seidl R, Lexer MJ, Jäger D, Hönninger K (2005) Evaluating the accuracy and generality of a hybrid forest patch model. Tree Physiol 25:939–951Google Scholar
  59. Seidl R, Rammer W, Baier P, Schopf A, Lexer MJ (2007a) Modelling tree mortality by bark beetle infestation in Norway spruce forests. Ecol Modell 206:383–399. doi:10.1016/j.ecolmodel.2007.04.002 CrossRefGoogle Scholar
  60. Seidl R, Rammer W, Jäger D, Currie WS, Lexer MJ (2007b) Assessing trade-offs between carbon sequestration and timber production within a framework of multi-purpose forestry in Austria. For Ecol Manage 248:64–79CrossRefGoogle Scholar
  61. Seidl R, Rammer W, Jäger D, Lexer MJ (2008) Impact of bark beetle (Ips typographus L.) disturbance on timber production and carbon sequestration in different management strategies under climate change. For Ecol Manage 256:209–220CrossRefGoogle Scholar
  62. Shugart HH (1984) A theory of forest dynamics: the ecological implications of forest succession models. Springer, New York, p 278Google Scholar
  63. Spiecker H (2003) Silvicultural management in maintaining biodiversity and resistance of forests in Europe—temperate zone. J Environ Manage 67:55–65. doi:10.1016/S0301-4797(02)00188-3 CrossRefGoogle Scholar
  64. Spiecker H, Hansen J, Kilmo E, Skovsgaard JP, Sterba H, von Teuffel K (2004) Norway spruce conversion—options and consequences. EFI Research Report 18. Brill, Leiden, p 320Google Scholar
  65. Sturtevant BR, Gustafson EJ, Li W, He HS (2004) Modelling biological disturbances in LANDIS: a module description and demonstration using spruce budworm. Ecol Modell 180:153–174. doi:10.1016/j.ecolmodel.2004.01.021 CrossRefGoogle Scholar
  66. Thürig E, Schelhaas MJ (2006) Evaluation of a large-scale forest scenario model in heterogeneous forests: a case study for Switzerland. Can J Res 36:671–683. doi:10.1139/x05-283 CrossRefGoogle Scholar
  67. Tomiczek C, Krehan H, Cech T, Donaubauer E (1997) Forstschadenssituation in Österreich. AFZ Der Wald 07/97:387–389Google Scholar
  68. Tomiczek C, Cech T, Krehan H, Perny B, Steyrer G (2005) Forstschutzsituation 2004 in Österreich. AFZ Der Wald 07(05):377–379Google Scholar
  69. Urban DL, Acevedo MF, Garman SL (1999) Scaling fine-scale processes to large-scale patterns using models derived from models: meta-models. In: Mladenoff DJ, Baker WL (eds) Spatial modelling of forest landscape change: approaches and applications. Cambridge University Press, Cambridge, pp 70–98Google Scholar
  70. Weiss P, Schieler K, Schadauer K, Radunsky K, Englisch M (2000) Die Kohlenstoffbilanz des Österreichischen Waldes und Betrachtungen zum Kyoto-Protokoll. Monographien M-106, Umweltbundesamt Wien, p 94. ISBN 3-85457-454-1Google Scholar
  71. Wermelinger B (2004) Ecology and management of the spruce bark beetle Ips typographus—a review of recent research. For Ecol Manage 202:67–82CrossRefGoogle Scholar
  72. Wermelinger B, Seifert M (1999) Temperature-dependent reproduction of the spruce bark beetle Ips typographus, and analysis of the potential population growth. Ecol Entomol 24:103–110. doi:10.1046/j.1365-2311.1999.00175.x CrossRefGoogle Scholar
  73. Williams DW, Liebhold AM (2002) Climate change and the outbreak ranges of two North American bark beetles. Agric For Entomol 4:87–99. doi:10.1046/j.1461-9563.2002.00124.x CrossRefGoogle Scholar
  74. Woltjer M, Rammer W, Brauner M, Seidl R, Mohren GMJ, Lexer MJ (2008) Coupling a 3D patch model and a rockfall module to assess rockfall protection in mountain forests. J Environ Manage 87:373–388. doi:10.1016/j.jenvman.2007.01.031 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Rupert Seidl
    • 1
    • 2
  • Mart-Jan Schelhaas
    • 3
  • Marcus Lindner
    • 1
  • Manfred J. Lexer
    • 2
  1. 1.European Forest InstituteJoensuuFinland
  2. 2.Department of Forest and Soil Sciences, Institute of SilvicultureUniversity of Natural Resources and Applied Life Sciences (BOKU) ViennaViennaAustria
  3. 3.Alterra WageningenWageningenThe Netherlands

Personalised recommendations