Climatic Change

, Volume 105, Issue 1–2, pp 313–328 | Cite as

A process-based approach to estimate lodgepole pine (Pinus contorta Dougl.) distribution in the Pacific Northwest under climate change

Article

Abstract

Lodgepole pine (Pinus contorta Dougl.) is a widely distributed species in the Pacific Northwest of North America. The extent that the current distribution of this species may be altered under a changing climate is an important question for managers of wood supply as well as those interested in conservation of subalpine ecosystems. In this paper, we address the question, how much might the current range of the species shift under a changing climate? We first assessed the extent that suboptimal temperature, frost, drought, and humidity deficits affect photosynthesis and growth of the species across the Pacific Northwest with a process-based model (3-PG). We then entered the same set of climatic variables into a decision-tree model, which creates a suite of rules that differentially rank the variables, to provide a basis for predicting presence or absence of the species under current climatic conditions. The derived decision-tree model successfully predicted weighted presence and absence recorded on 12,660 field survey plots with an accuracy of ~70%. The analysis indicated that sites with significant spring frost, summer temperatures averaging <15°C and soils that fully recharged from snowmelt were most likely to support lodgepole pine. Based on these criteria, we projected climatic conditions through the twenty-first century as they might develop without additional efforts to reduce carbon emissions using the Canadian Climate Centre model (CGCM2). In the 30-year period centered around 2020, the area suitable for lodgepole pine in the Pacific Northwest was projected to be reduced only slightly (8%). Thereafter, however, the projected climatic conditions appear to progressively favor other species, so that by the last 30 years of twenty-first century, lodgepole pine could be nearly absent from much of its current range. We conclude that process-based models, because they are highly sensitive to seasonal variation in solar radiation, are well adapted to identify the importance of different climatic variables on photosynthesis and growth. These same variables, once indentified, and run through a decision-tree model, provide a reasonable approach to predict current and future patterns in a species’ distribution.

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References

  1. Almeida AC, Landsberg JJ, Sands PJ (2004) Parameterisation of 3-PG model for fast-growing Eucalyptus grandis plantations. Ecol Manag 193:179–195CrossRefGoogle Scholar
  2. Austin MP (1985) Continuum concept, ordination methods and niche theory. Ann Rev Ecolog Syst 16:39–61CrossRefGoogle Scholar
  3. Barrow EM, Yu G (2005) Climate scenarios for Alberta. A report prepared for the Prairie Adaptation Research Collaborative (PARC) in co-operation with Alberta Environment, University of Regina, Saskatchewan. http://www.parc.ca/research_pub_scenarios.htm
  4. Bechtold WA, Patterson PL (2005) The enhanced forest inventory and analysis program—national sampling design and estimation procedures. General Technical Report SRS-80, US Department of Agriculture, Forest Service, Southern Research Station, Ashville, NC, USGoogle Scholar
  5. Berry PM, Dawson TP, Harrison PA, Pearson RG (2002) Modelling potential impacts of climate change on the bioclimatic envelop e of species in Britain and Ireland. Glob Ecol Biogeogr 11:453–462CrossRefGoogle Scholar
  6. Breiman L, Friedman JH, Olshen RA, Stone CG (1984) Classification and regression trees. Wadsworth International Group, BelmontGoogle Scholar
  7. Cochran PH, Bersten CM (1973) Tolerance of lodgepole and ponderosa pine to low night temperatures. Science 19:272–280Google Scholar
  8. Coops NC, Waring RH, Landsberg JJ (1998) Assessing forest productivity in Australia and New Zealand using a physiologically-based model driven with averaged monthly weather data and satellite derived estimates of canopy photosynthetic capacity. Ecol Manag 104:113–127CrossRefGoogle Scholar
  9. Coops NC, Waring RH, Schroeder T (2009) Combining a generic process-based productivity model and a statistical classification method to predict presence and absence of tree species in the Pacific Northwest, U.S.A. Ecol Model 220:1787–1796CrossRefGoogle Scholar
  10. Critchfield WB, Little EL Jr (1966) Geographic distribution of the pines of the world’ US Department of Agriculture Miscellaneous Publication 991, pp 1–97Google Scholar
  11. De’ath G (2002) Multivariate regression trees: a new technique for modeling species–environment relationships. Ecology 83:1105–1117Google Scholar
  12. Dye PJ, Jacobs D, Drew D (2004) Verification of 3-PG growth and water-use prediction in twelve Eucalyptus plantation stands in Zululand, South Africa. Ecol Manag 193:197–218CrossRefGoogle Scholar
  13. Eamus D, Jarvis PJ (1989) The direct effects of increases in global atmospheric CO2 concentration on natural and commercial temperate trees and forests. Adv Ecol Res 19:2–56Google Scholar
  14. Easterling DR, Meehl GA, Parmesan C, Changnon SA, Karl TR, Mearns LO (2000) Climate extremes: observations, modeling, and impacts. Science 289:2068–2074CrossRefGoogle Scholar
  15. Elith J, Graham CH, Anderson RP (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29:129–151CrossRefGoogle Scholar
  16. Flannigan MD, Logan KA, Amiro BD, Skinner WR, Stocks BJ (2005) Future area burned in Canada. Clim Change 72:1–16CrossRefGoogle Scholar
  17. Flato G, Boer GJ, Lee WL, McFarlane NA, Ramsden D, Reader MC, Weaver AJ (2000) The Canadian centre for climate modelling and analysis global coupled model and its climate. Clim Dyn 16:451–467CrossRefGoogle Scholar
  18. Green RN, Marshall PL, Klinka K (1989) Estimating site index of Douglas-fir (Pseudotsuga menziesii [Mirb.] Franco) from ecological variables in southwestern British Columbia. Science 35:50–63Google Scholar
  19. Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecol Model 135:147–186CrossRefGoogle Scholar
  20. Hall RJ, Price DT, Raulier F, Arsenault E, Bernier PY, Case BS, Guo X (2006) Integrating remote sensing and climate data with process-based models to map forest productivity within west-central Alberta’s boreal forest: Ecoleap-West. For Chron 82:159–176Google Scholar
  21. Hamann A, Wang T (2005) Models of climatic normals for genecology and climate change studies in British Columbia. Agric Meteorol 128:211–221CrossRefGoogle Scholar
  22. Hijmans R, Graham CH (2006) The ability of climate envelope models to predict the effect of climate change on species distributions. Glob Chang Biol 12:2272–2281CrossRefGoogle Scholar
  23. Hu J, Moore DJP, Burns SP, Monson RK (2009) Longer growing seasons lead to less carbon sequestration by a subalpine forest. Glob Chang Biol 16:771–783. http://www3.interscience.wiley.com/journal/122377223/abstract?CRETRY=1&SRETRY=0 CrossRefGoogle Scholar
  24. IPCC (2001) Intergovernmental panel on climate change. Cambridge University Press, Cambridge. http://www.ipcc.ch/pub/online.htm Google Scholar
  25. Iverson LR, Prasad AM (1998) Predicting abundance of 80 tree species following climate change in the eastern United States. Ecol Monogr 68:465–485CrossRefGoogle Scholar
  26. Iverson LR, Prasad AM (2001) Potential changes in tree species richness and forest community types follow climate change. Ecosystems 4:186–199CrossRefGoogle Scholar
  27. Kimball JS, Running SW, Nemani R (1997) An improved method for estimating surface humidity from daily minimum temperature. Agric Meteorol 85:87–98CrossRefGoogle Scholar
  28. Knowles N, Dettinger MD, Cayan DR (2006) Trends in snowfall versus rainfall in the western United States. J Climate 19:4545–4559CrossRefGoogle Scholar
  29. Kurz WA, Dymond CC, Stinson G, Rampley GJ, Neilson ET, Carroll AL, Ebata T, Safranyik L (2008) Mountain pine beetle and forest carbon feedback to climate change. Nature 452:987–990CrossRefGoogle Scholar
  30. Landsberg JJ, Waring H, Coops NC (2003) Performance of the forest productivity model 3-PG applied to a wide range of forest types. Ecol Manag 172:199–214CrossRefGoogle Scholar
  31. Law BE, Goldstein AH, Anthoni PM, Unsworth MH, Panek JA, Bauer MR, Fracheboud JM, Hultman N (2001) Carbon dioxide and water vapor exchange by young and old ponderosa pine ecosystems during a dry summer. Tree Physiol 21:299–308Google Scholar
  32. Little EL Jr (1971) Atlas of United States trees, volume 1, conifers and important hardwoods U.S. Department of Agriculture Miscellaneous Publication 1146, 9 ppGoogle Scholar
  33. McKenney DW, Pedlar J, Hutchinson M, Lawrence K, Campbell K (2007) Potential impacts of climate change on the distribution of North American trees. Bioscience 57:939–948CrossRefGoogle Scholar
  34. McKenzie D, Peterson DW, Peterson DL, Thornton PE (2003) Climatic and biophysical controls on conifer species distributions in mountain forests of Washington State, USA. J Biogeogr 30:1093–1108CrossRefGoogle Scholar
  35. Marshall JD, Monserud RA (1996) Homeostatic gas-exchange parameters inferred from 13C/12C in tree rings of conifers. Oecologia 105:13–21CrossRefGoogle Scholar
  36. Melendez KV, Jones DL, Feng AS (2006) Classification of communication signals of the little brown bat. J Acoust Soc Am 120:1095–1102CrossRefGoogle Scholar
  37. Monserud RA, Yang Y, Huang S, Tchebakova N (2008) Potential change in lodgepole pine site index and distribution under climate change in Alberta. Can J For Res 38:343–352CrossRefGoogle Scholar
  38. Mote PW, Hamlet AF, Clark MP, Lettenmaier DP (2005a) Declining mountain snowpack in western North America. Am Meteorol Soc 86:1–39CrossRefGoogle Scholar
  39. Mote P, Salathé E, Peacock C (2005b) Scenarios of future climate for the Pacific Northwest. Climate Impacts Group, University of Washington, October 2005. http://cses.washington.edu/db/pdf/kc05scenarios462.pdf. Accessed 3 October 2009
  40. Nightingale JM, Coops NC, Waring RH, Hargrove WW (2007) Comparison of MODIS gross primary production estimates for forests across the U.S.A. with those generated by a simple process model, 3-PG. Remote Sens Environ 109:500–509CrossRefGoogle Scholar
  41. Parmesan C, Yohe G (2003) A globally coherent fingerprint of climate change impacts across natural systems. Nature 421:37–42CrossRefGoogle Scholar
  42. Pearson RG, Dawson TP, Berry PM, Harrison PA (2002) Species: a spatial evaluation of climate impact on the envelope of species. Ecol Model 154:289–300CrossRefGoogle Scholar
  43. Rehfeldt GE, Ying CC, Spittlehouse DL, Hamilton DA Jr (1999) Genetic responses to climate in Pinus contorta: niche breadth, climate change and reforestation. Ecol Monogr 69:375–07Google Scholar
  44. Rodriguez R, Espinosa M, Real P, Inzunza J (2002) Analysis of productivity of radiata pine plantations under different silvicultural regimes using the 3-PG process-based model. Aust For 65:165–172Google Scholar
  45. Root TL, Price JT, Hall KR (2003) Fingerprints’ of global warming on animals and plants. Nature 421:57–60CrossRefGoogle Scholar
  46. Sands PJ, Battaglia M, Mummery D (2000) Application of process-based models to forest management: experience with ProMod, a simple plantation productivity model. Tree Physiol 20:383–392Google Scholar
  47. Schwalm CR, Black TA, Amiro BD, Arain MA, Barr AG, Bourque CP, Dunn AL, Flanagan LB, Giasson MA, Lafleur PM, Margolis HA, McCaughey JH, Orchansky AL, Wofsy SC (2006) Photosynthetic light use efficiency of three biomes across and east-west continental-scale transect in Canada. Agric Meteorol 140:260–286Google Scholar
  48. Schroeder TA, Hember R, Coops NC, Liang S (2009) Validation of incoming shortwave solar radiation surfaces for use in forest productivity models. J Appl Meteorol Climatol 48:2331–2458CrossRefGoogle Scholar
  49. Schroeder TA, Hamann A, Coops NC, Wang T (2010) Occurrence and dominance of six Pacific Northwest conifer species. J Veg Sci 23(3):586–596CrossRefGoogle Scholar
  50. Stape JL, Ryan MG, Binkley D (2004) Testing the utility of the 3-PG model for growth of Eucalyptus grandis xurophylla with natural and manipulated supplies of water and nutrients. Ecol Manag 193:219–234CrossRefGoogle Scholar
  51. Swenson JJ, Waring RH, Fan W, Coops NC (2005) Predicting site index with a physiologically based growth model across Oregon, USA. Can J For Res 35:1697–1707CrossRefGoogle Scholar
  52. Sykes MT (2001) Modelling the potential distribution and community dynamics of lodgepole pine (Pinus contorta Dougl. ex. Loud.) in Scandinavia. Ecol Manag 141:69–84CrossRefGoogle Scholar
  53. Thuiller W, Albert C, Araujo MB, Berry PM, Cabeza M, Guisan A, Hickler A, Midgley GF, Paterson J, Schurr FM, Sykes, Zimmermann NE (2008) Predicting global change impacts on plant species’ distributions: future challenges. Perspect Plant Ecol Evol Syst 9:137–152CrossRefGoogle Scholar
  54. Waring RH (2000) A process model analysis of environmental limitations on growth of Sitka spruce plantations in Great Britain. Forestry 73:65–79CrossRefGoogle Scholar
  55. Wang G, Huang S, Monserud RA, Klos RJ (2004) Lodgepole pine site index in relation to synoptic measures of climate, soil moisture and soil nutrients. For Chron 80:678–686Google Scholar
  56. Whitehead RJ, Russo GL (2005) Beetle-proofed lodge pole pine stands in interior British Columbia have less damage from mountain pine beetle. Natural Resources Canada. Canadian Forest Service. Victoria BC Information Report BC-X-402Google Scholar
  57. Williams JW, Jackson ST, Kutzbach JE (2007) Projected distributions of novel and disappearing climates by 2100 AD. Proc Natl Acad Sci U S A 104:5738–5742CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Department of Forest Resource ManagementUniversity of British ColumbiaVancouverCanada
  2. 2.College of ForestryOregon State UniversityCorvallisUSA

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