European Journal of Forest Research

, Volume 134, Issue 4, pp 609–621 | Cite as

Site suitability for tree species: Is there a positive relation between a tree species’ occurrence and its growth?

Original Paper

Abstract

In order to preserve forest ecosystem services under climate change, site suitability for tree species has to be re-assessed and management strategies have to be developed to adapt species composition. Thereby, it is reasonable to consider information on both site suitability estimations based on current species distribution and also stand productivity determined by tree growth. Currently, models for species distribution (SDMs) and tree growth are used to investigate the response of tree species to climate. However, both approaches were only applied separately so far. In this study, SDMs and growth models for Picea abies, Fagus sylvatica, Abies alba and Pinus sylvestris were calculated based on the German national forest inventories. We asked whether there is a positive relation between a tree species’ occurrence and its growth and what can be learned by their joint interpretation. The two approaches resulted in different patterns with respect to the considered environmental variables. Tree growth and occurrence probabilities were not positively correlated. This may be explained by the influence of forest pathogens and competition on species distribution by means of an increase in mortality. We concluded that the consideration of demographic processes as drivers of species distribution improves the reliability of estimates for site suitability and additionally provides information on productivity.

Keywords

Environmental niche modelling Habitat modelling Tree growth Model comparison Site suitability General additive model 

Supplementary material

10342_2015_876_MOESM1_ESM.docx (845 kb)
Supplementary material 1 (DOCX 844 kb)

References

  1. Albert M, Schmidt M (2010) Climate-sensitive modelling of site-productivity relationships for Norway spruce (Picea abies (L.) Karst.) and common beech (Fagus sylvatica L.). For Ecol Manag 259:739–749. doi:10.1016/j.foreco.2009.04.039 CrossRefGoogle Scholar
  2. Albert M, Schmidt M (2012) Standort-Leistungs Modelle für die Entwicklung von waldbaulichen Anpassungsstrategien unter Klimawandel. Archiv f Forstwesen u Landschökol 46:57–71Google Scholar
  3. Aussenac G (2002) Ecology and ecophysiology of circum-Mediterranean firs in the context of climate change. Ann For Sci 59:823–832. doi:10.1051/forest:2002080 CrossRefGoogle Scholar
  4. Benito-Garzón M, Ruiz-Benito P, Zavala MA (2013) Interspecific differences in tree growth and mortality responses to environmental drivers determine potential species distributional limits in Iberian forests: including tree growth and mortality into species distribution. Glob Ecol Biogeogr 22:1141–1151. doi:10.1111/geb.12075 CrossRefGoogle Scholar
  5. Brus DJ, Hengeveld GM, Walvoort DJJ et al (2012) Statistical mapping of tree species over Europe. Eur J For Res 131:145–157. doi:10.1007/s10342-011-0513-5 CrossRefGoogle Scholar
  6. Carcaillet C, Muller SD (2005) Holocene tree-limit and distribution of Abies alba in the inner French Alps: anthropogenic or climatic changes? Boreas 34:468–476. doi:10.1080/03009480500231377 CrossRefGoogle Scholar
  7. Charru M, Seynave I, Morneau F, Bontemps J-D (2010) Recent changes in forest productivity: an analysis of national forest inventory data for common beech (Fagus sylvatica L.) in north-eastern France. For Ecol Manag 260:864–874. doi:10.1016/j.foreco.2010.06.005 CrossRefGoogle Scholar
  8. Dobbertin M (2005) Tree growth as indicator of tree vitality and of tree reaction to environmental stress: a review. Eur J For Res 124:319–333. doi:10.1007/s10342-005-0085-3 CrossRefGoogle Scholar
  9. European Commission, Institute for Environment and Sustainability (European Commission. Joint Research Centre), European Soil Bureau, European Communities (2005) Soil atlas of Europe. European Communities, LuxembourgGoogle Scholar
  10. Falk W, Mellert KH (2011) Species distribution models as a tool for forest management planning under climate change: risk evaluation of Abies alba in Bavaria. J Veg Sci 22:621–634. doi:10.1111/j.1654-1103.2011.01294.x CrossRefGoogle Scholar
  11. Gómez-Aparicio L, García-Valdés R, Ruiz-Benito P, Zavala MA (2011) Disentangling the relative importance of climate, size and competition on tree growth in Iberian forests: implications for forest management under global change. Glob Change Biol 17:2400–2414. doi:10.1111/j.1365-2486.2011.02421.x CrossRefGoogle Scholar
  12. Guisan A, Thuiller W (2005) Predicting species distribution: offering more than simple habitat models. Ecol Lett 8:993–1009. doi:10.1111/j.1461-0248.2005.00792.x CrossRefGoogle Scholar
  13. Guisan A, Edwards TC, Hastie T (2002) Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecol Model 157:89–100. doi:10.1016/S0304-3800(02)00204-1 CrossRefGoogle Scholar
  14. Hanewinkel M, Hummel S, Albrecht A (2011) Assessing natural hazards in forestry for risk management: a review. Eur J For Res 130:329–351. doi:10.1007/s10342-010-0392-1 CrossRefGoogle Scholar
  15. Hanewinkel M, Cullmann DA, Schelhaas M-J et al (2012) Climate change may cause severe loss in the economic value of European forest land. Nat Clim Change 3:203–207. doi:10.1038/nclimate1687 CrossRefGoogle Scholar
  16. Hastie T (1992) Generalized additive models. In: Chambers JM, Hastie T (eds) Statistical models in S. Chapman & Hall/CRC. Fla, Boca RatonGoogle Scholar
  17. Hastie T (2014) Gam: generalized additive models. R package version 1.09.1. http://CRAN.R-project.org/package=gam
  18. Hiederer R (2013) European Commission, Joint Research Centre, Institute for Environment and Sustainability Mapping soil properties for Europe spatial representation of soil database attributes. Publications Office, LuxembourgGoogle Scholar
  19. Hijmans RJ (2014) raster: geographic data analysis and modeling. R package version 2.3-0. http://CRAN.R-project.org/package=raster
  20. Hijmans RJ, Cameron SE, Parra JL et al (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978. doi:10.1002/joc.1276 CrossRefGoogle Scholar
  21. Hunger W (1986) Soil condition, nutrition and growth performance of Norway spruce (Picea abies [L.] Karst.) in fast-growing plantations. Fertil Res 10:243–250. doi:10.1007/BF01049354 CrossRefGoogle Scholar
  22. Ilisson T, Metslaid M, Vodde F et al (2005) Storm disturbance in forest ecosystems in Estonia. Scand J For Res 20:88–93. doi:10.1080/14004080510041020 CrossRefGoogle Scholar
  23. Iverson LR, Prasad AM (1998) Predicting abundance of 80 tree species following climate change in the eastern United States. Ecol Monogr 68:465. doi:10.2307/2657150 CrossRefGoogle Scholar
  24. James JC, Grace J, Hoad SP (1994) Growth and photosynthesis of Pinus sylvestris at its altitudinal limit in Scotland. J Ecol 82:297. doi:10.2307/2261297 CrossRefGoogle Scholar
  25. Jansen M (2008) Anpassungsstrategien für eine nachhaltige Waldbewirtschaftung unter sich wandelnden Klimabedingungen—Entwicklung eines Entscheidungsunterstützungssystems “Wald und Klimawandel” (DSS-WuK) Adaptation strategies for a sustainable forest management under climate change: development of a Decision Support System Forest and Climate Change (DSS-WuK). Forstarchiv. doi:10.4432/0300-4112-79-131 Google Scholar
  26. Johnstone D, Moore G, Tausz M, Nicolas M (2013) The measurement of plant vitality in landscape trees. Arboric J 35:18–27. doi:10.1080/03071375.2013.783746 CrossRefGoogle Scholar
  27. Jump AS, Hunt JM, Peñuelas J (2006) Rapid climate change-related growth decline at the southern range edge of Fagus sylvatica. Glob Change Biol 12:2163–2174. doi:10.1111/j.1365-2486.2006.01250.x CrossRefGoogle Scholar
  28. Kölling C (2007) Klimahüllen für 27 Waldbaumarten. AFZ-DerWald 23:1242–1245Google Scholar
  29. Kunstler G, Albert CH, Courbaud B et al (2011) Effects of competition on tree radial-growth vary in importance but not in intensity along climatic gradients. J Ecol 99:300–312. doi:10.1111/j.1365-2745.2010.01751.x CrossRefGoogle Scholar
  30. Lobo JM, Jiménez-Valverde A, Hortal J (2010) The uncertain nature of absences and their importance in species distribution modelling. Ecography 33:103–114. doi:10.1111/j.1600-0587.2009.06039.x CrossRefGoogle Scholar
  31. Mette T, Dolos K, Meinardus C, et al (2013) Climatic turning point for beech and oak under climate change in Central Europe. Ecosphere 4: art145. doi: 10.1890/ES13-00115.1
  32. Millar CI, Stephenson NL, Stephens SL (2007) Climate change and forests of the future: managing in the face of uncertainty. Ecol Appl 17:2145–2151. doi:10.1890/06-1715.1 CrossRefPubMedGoogle Scholar
  33. Morin X, Thuiller W (2009) Comparing niche- and process-based models to reduce prediction uncertainty in species range shifts under climate change. Ecology 90:1301–1313. doi:10.1890/08-0134.1 CrossRefPubMedGoogle Scholar
  34. Nicoll BC, Gardiner BA, Rayner B, Peace AJ (2006) Anchorage of coniferous trees in relation to species, soil type, and rooting depth. Can J For Res 36:1871–1883. doi:10.1139/x06-072 CrossRefGoogle Scholar
  35. Panagos P, Van Liedekerke M, Jones A, Montanarella L (2012) European Soil Data Centre: response to European policy support and public data requirements. Land Use Policy 29:329–338. doi:10.1016/j.landusepol.2011.07.003 CrossRefGoogle Scholar
  36. Peng C (2000) From static biogeographical model to dynamic global vegetation model: a global perspective on modelling vegetation dynamics. Ecol Model 135:33–54. doi:10.1016/S0304-3800(00)00348-3 CrossRefGoogle Scholar
  37. Polley H, Schmitz F, Hennig P, Kroiher F (2010) Germany. In: Tomppo E, Gschwantner T, Lawrence M, McRoberts RE (eds) National Forest Inventories—Pathways for Common Reporting. Springer, 233 Spring Street, New York, Ny 10013, United States, New YorkGoogle Scholar
  38. Pretzsch H, Dursky J (2002) Growth reaction of Norway spruce (Picea abies (L.) Karst.) and European beech (Fagus silvatica L.) to possible climatic changes in Germany. A sensitivity study. Forstwiss Cent 121:145–154Google Scholar
  39. Purves DW (2009) The demography of range boundaries versus range cores in eastern US tree species. Proc R Soc B Biol Sci 276:1477–1484. doi:10.1098/rspb.2008.1241 CrossRefGoogle Scholar
  40. R Development Core Team (2012) R: A language and environment for statistical computing, reference index version 2.15.2. R Foundation for Statistical Computing, Vienna and AustriaGoogle Scholar
  41. Reidl K (2013) Landesanstalt für Umwelt, Messungen und Naturschutz Baden-Württemberg, Referat Boden, Altlasten, Forstliche Versuchs- und Forschungsanstalt Baden-Württemberg Potentielle Natürliche Vegetation von Baden-Württemberg. Verl. Regionalkultur, Ubstadt-WeiherGoogle Scholar
  42. Rötzer T, Grote R, Pretzsch H (2005) Effects of environmental changes on the vitality of forest stands. Eur J For Res 124:349–362. doi:10.1007/s10342-005-0086-2 CrossRefGoogle Scholar
  43. Scharnweber T, Manthey M, Criegee C et al (2011) Drought matters—declining precipitation influences growth of Fagus sylvatica L. and Quercus robur L. in north-eastern Germany. For Ecol Manag 262:947–961. doi:10.1016/j.foreco.2011.05.026 CrossRefGoogle Scholar
  44. Svenning J-C, Skov F (2004) Limited filling of the potential range in European tree species: limited range filling in European trees. Ecol Lett 7:565–573. doi:10.1111/j.1461-0248.2004.00614.x CrossRefGoogle Scholar
  45. Sykes M, Prentice I, Cramer W (1996) A bioclimatic model for the potential distributions of north European tree species under present and future climates. J Biogeogr 23:203–233Google Scholar
  46. Thomas CD, Cameron A, Green RE et al (2004) Extinction risk from climate change. Nature 427:145–148. doi:10.1038/nature02121 CrossRefPubMedGoogle Scholar
  47. Thuiller W, Münkemüller T, Schiffers KH, et al (2014) Does probability of occurrence relate to population dynamics? Ecography n/a–n/a. doi: 10.1111/ecog.00836
  48. Tinner W, Colombaroli D, Heiri O et al (2013) The past ecology of Abies alba provides new perspectives on future responses of silver fir forests to global warming. Ecol Monogr 83:419–439. doi:10.1890/12-2231.1 CrossRefGoogle Scholar
  49. Tomppo EO, Schadauer K (2012) Harmonization of national forest inventories in Europe: advances under COST action E43. For Sci 58:191–200. doi:10.5849/forsci.10-091 Google Scholar
  50. Veloz SD, Williams JW, Blois JL et al (2012) No-analog climates and shifting realized niches during the late quaternary: implications for 21st-century predictions by species distribution models. Glob Change Biol 18:1698–1713. doi:10.1111/j.1365-2486.2011.02635.x CrossRefGoogle Scholar
  51. Wermelinger B (2004) Ecology and management of the spruce bark beetle Ips typographus—a review of recent research. For Ecol Manag 202:67–82CrossRefGoogle Scholar
  52. Wood SN (2011) Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J R Stat Soc Ser B Stat Methodol 73:3–36. doi:10.1111/j.1467-9868.2010.00749.x CrossRefGoogle Scholar
  53. Wunder J, Reineking B, Bigler C, Bugmann H (2008) Predicting tree mortality from growth data: how virtual ecologists can help real ecologists. J Ecol 96:174–187Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Institute for Geography and GeoecologyKarlsruhe Institute of TechnologyKarlsruheGermany
  2. 2.University of BayreuthBayreuthGermany

Personalised recommendations