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Journal of Mountain Science

, Volume 13, Issue 8, pp 1411–1422 | Cite as

Effects of climate change on potential habitats of the cold temperate coniferous forest in Yunnan province, southwestern China

  • Wang-jun Li
  • Ming-chun Peng
  • Motoki Higa
  • Nobuyuki Tanaka
  • Tetsuya Matsui
  • Cindy Q. Tang
  • Xiao-kun Ou
  • Rui-wu Zhou
  • Chong-yun Wang
  • Hai-zhong Yan
Article

Abstract

We built a classification tree (CT) model to estimate climatic factors controlling the cold temperate coniferous forest (CTCF) distributions in Yunnan province and to predict its potential habitats under the current and future climates, using seven climate change scenarios, projected over the years of 2070-2099. The accurate CT model on CTCFs showed that minimum temperature of coldest month (TMW) was the overwhelmingly potent factor among the six climate variables. The areas of TMW<-4.05 were suitable habitats of CTCF, and the areas of -1.35 < TMW were non-habitats, where temperate conifer and broad-leaved mixed forests (TCBLFs) were distribute in lower elevation, bordering on the CTCF. Dominant species of Abies, Picea, and Larix in the CTCFs, are more tolerant to winter coldness than Tsuga and broad-leaved trees including deciduous broad-leaved Acer and Betula, evergreen broad-leaved Cyclobalanopsis and Lithocarpus in TCBLFs. Winter coldness may actually limit the cool-side distributions of TCBLFs in the areas between -1.35°C and -4.05°C, and the warm-side distributions of CTCFs may be controlled by competition to the species of TCBLFs. Under future climate scenarios, the vulnerable area, where current potential (suitable + marginal) habitats (80,749 km2) shift to non-habitats, was predicted to decrease to 55.91% (45,053 km2) of the current area. Inferring from the current vegetation distribution pattern, TCBLFs will replace declining CTCFs. Vulnerable areas predicted by models are important in determining priority of ecosystem conservation.

Keywords

Classification tree Climate scenarios Vulnerable area Abies Picea Larix Evergreen broad-leaved tree ALOS remote-sensing images 

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References

  1. Berry PM, Rounsevell DA, Harrison PA, et al. (2006) Assessing the vulnerability of agricultural land use and species to climate change and the role of policy in facilitating adaptation. Environmental Science & Policy 9(2): 189–204. DOI: 10.1016/j.envsci.2005.11.004CrossRefGoogle Scholar
  2. Clark LA, Pregibon D (1992) Tree-based models. In:Chambers JM, Hastie TJ. Statistical models in S. Wadsworth & Brooks/Cole advanced books & soft-ware. Pacific Grove, California. pp 377–419.Google Scholar
  3. Efron B (1979) Bootstrap methods: another look at the jackknife. The Annals of Statistics 7(1): 1–26. DOI: 10.1214/aos/1176344552CrossRefGoogle Scholar
  4. Euskirchen EA, Mc Guire F, Chapin IS, et al. (2009) Changes in vegetation in northern Alaskaunder scenarios of climate change, 2003-2100: implications for climate feedbacks. EcologicalApplications 19: 1022–1043. DOI: 10.1890/08-0806.1Google Scholar
  5. Higa M, Nakao K, Tsuyama I, et al. (2013a) Indicator plant species selection for monitoring the impact of climate change based on prediction uncertainty. Ecological Indicators 29: 307–315. DOI: 10.1016/j.ecolind.2013.01.010CrossRefGoogle Scholar
  6. Higa M, Tsuyama I, Nakao K, et al. (2013b) Influence of nonclimatic factors on the habitat prediction of tree species and an assessment of the impact of climate change. Landscape and Ecological Engineering 9: 111–120. DOI: 10.1007/s11355-011-0183-yCrossRefGoogle Scholar
  7. Hijmans RJ, Cameron SE, Parra JL, et al. (2005) Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965–1978.CrossRefGoogle Scholar
  8. Horikawa M, Tsuyama I, Matsui T, et al. (2009) Assessing the potential impacts of climate change on the alpine habitat suitability of Japanese stone pine (Pinus pumila). Landscape Ecology 24: 115–128. DOI: 10.1007/s10980-008-9289-5CrossRefGoogle Scholar
  9. Iverson LR, Prasad AM, Schwartz MW (2005) Predicting potential changes in suitable habitat and distribution by 2100 for tree species of the eastern United States. Journal of Agricultural Meteorology 61: 29–37.CrossRefGoogle Scholar
  10. Leathwick JR, Whitehead D, McLeod M (1996) Predicting changes in the composition of New Zealand’s indigenous forests in response to global warming: a modelling approach. Environmental Software 11: 81–90. DOI: 10.1016/S0266-9838 (96)00045-7CrossRefGoogle Scholar
  11. Li WJ, Peng MC, Pan HZ, et al. (2013) Landscape pattern in Honghe River bas in based on moving window method. Journal of Anhui Agriculture Science 41(10): 4455–4457. (In Chinese).Google Scholar
  12. Liu Q, Wu N, Cheng QH (2000) Exploration of non-equilibrium of subalpine conifer forests ecosystem in western China. World Science-technology R&D 22: 58–63. (In Chinese).Google Scholar
  13. McCarthy JJ, Canziani OF, Leary NA, et al. (2001) Climate change 2001: Impacts, adaptation, and vulnerability. Cambridge University Press, New York, NY, USA.Google Scholar
  14. Matsui T, Nakaya T, Yagihashi T, et al. (2004a) Comparing the accuracy of predictive distribution models for Fagas crenata forest in Japan. Japanese JournalFor Environment 46: 93–102.Google Scholar
  15. Matsui T, Yagihashi T, Nakaya T, et al. (2004b) Climatic controls on distribution of Fagus crenata forests in Japan. Journal of Vegetation Science 15: 57–66. DOI: 10.1111/j.1654-1103.2004.tb02237.xGoogle Scholar
  16. Matsui T, Yagihashi T, Nakaya T, et al. (2004c) Probability distributions, vulnerability and sensitivity in Fagus crenata forests following predicted climate changes in Japan. Journal of Vegetation Science 15: 605–614. DOI: 10.1111/j.1654-1103.2004.tb02302.xGoogle Scholar
  17. Metz CE (1978) Basic principles of ROC analysis. Seminars in nuclear medicine 8: 283–298. DOI: 10.1016/S0001-2998(78) 80014-2CrossRefGoogle Scholar
  18. Nakao K, Higa M, Tsuyama I, et al. (2013) Spatial conservation planning under climate change: Using species distribution modeling to assess priority for adaptive management of Fagus crenata in Japan. Journal for Nature Conservation 21: 406–413.CrossRefGoogle Scholar
  19. Nakao K, Matsui T, Tanaka N, et al. (2009) Climatic controls of the distribution and abundance of two evergreen Quercus species in Japan. Japanese Journal For Environment 51(1): 27–37.Google Scholar
  20. Ohsawa M (1990) An interpretation of latitudinal patterns of forest limits in South and East Asian mountains. Journal of Ecology 78: 326–339.CrossRefGoogle Scholar
  21. Ohsawa M (1993) Latitudinal pattern of mountain vegetation zonation in southern and eastern Asia. Journal of Vegetation Science 4: 13–18.CrossRefGoogle Scholar
  22. Parmesan C, Yohe G (2003) A globally coherent fingerprint of climate change impacts across natural systems. Nature 421: 37–42.CrossRefGoogle Scholar
  23. Pearson RG, Dawson TP, Liu C (2008) Modelling species distributions in Britain: a hierarchical integration of climate and land cover data. Ecography 27: 285–298. DOI: 10.1111/j.0906-7590.2004.03740.xCrossRefGoogle Scholar
  24. Peng MC, Ou GL, Wang CY, et al. (2013) Research on the vegetation landscape pattern of the core area of the Three Pa rallel rivers Belt in Gongshan county of Yunnan province. Guangxi Agricultural Sciences 24: 141–147. (In Chinese)Google Scholar
  25. Ranjitkar S, Sujakhu MN, Yang L, et al. (2016) Climate modelling for agro forestry species selection in Yunnan Province, China. Environmental Modelling and Software 75: 263–272. DOI: 10.1016/j.envsoft.2015.10.027CrossRefGoogle Scholar
  26. Rouget M, Richardson DM, Lavorel S, et al. (2001) Determinants of distribution of six Pinus species inCatalonia, Spain. Journal of Vegetation Science 12: 491–502. DOI: 10.2307/3237001CrossRefGoogle Scholar
  27. Sakai A (1975) Freezing resistance of evergreen and deciduous broad-leaf trees in Japan with specialreference to their distributions. Japanese Journal of Ecology 25: 101–111.Google Scholar
  28. Sakai A and Malla B (1981) Winter hardiness of tree species at high altitude in the East Himalaya, Nepal. Ecology 62: 1288–1298.CrossRefGoogle Scholar
  29. Scinocca JN, McFarlane MA, Lazare M, et al. (2008) Technical Note: The CCCma third generation AGCM and its extension into the middle atmosphere. Atmospheric Chemistry and Physics 8: 7055–7074. DOI: 10.5194/acp-8-7055-2008CrossRefGoogle Scholar
  30. Shafer S L, Bartlein P, Tompson R (2001) Potential changes in the distributions of western north america tree and shrub taxa under future climate scenarios. Ecosystems 4(3): 200–215. DOI: 10.1007/s10021-001-0004-5CrossRefGoogle Scholar
  31. Swets J A (1988) Measuring the accuracy of diagnostic systems. Science 240: 1285–1293. DOI: 10.1126/science.3287615CrossRefGoogle Scholar
  32. Tanaka N, Nakao K, Tsuyama I, et al. (2012) Predicting the impact of climate change on potentialhabitats of fir (Abies) species in Japan and on the East Asian continent. Procedia EnvironmentalSciences 13: 455–466.Google Scholar
  33. Tanaka N, Matsui T, Yagihashi T, et al. (2006) Climatic controls on natural forest distribution andpredicting the impact of climate warming:Especially referring to Buna (Fagus crenata) forests. Global Environmental Research 10(2): 151–160.Google Scholar
  34. Tanaka N, Nakazono E, Tsuyama I, et al. (2009) Assessing impact of climate warming on potential habitats of ten conifer species in Japan. Tikyu-kankyo 14: 153–164 (In Japanese).Google Scholar
  35. Tang CQ (2015) The subtropical vegetation of southwestern China: plant distribution, diversity and ecology. Plants and Vegetation, Vol. 11. Springer, Dordrecht. DOI: 10.1007/978-94-017-9741-2CrossRefGoogle Scholar
  36. Venables WN, Ripley BD (1999) Modern applied statistics with S-Plus. Springer, New York, NY, USA.CrossRefGoogle Scholar
  37. Tsuyama I, Nakao K, Matsui T, et al. (2011) Climatic controls of a keystone understory species, Sasamorpha borealis, and an impact assessment of climate change in Japan. Annals of Forest Science 68: 689–699. DOI: 10.1007/s13595-011-0086-yCrossRefGoogle Scholar
  38. Walker BH, Steffen WL, Canadell J, et al. (1999) The terrestrial biosphere and global change: Implications for natural and managed ecosystems. Cambridge University Press, Cambridge, UK. p 142.Google Scholar
  39. Wang J (1990) Statistical analysis of temperatures of both the upper and lower bounds of sub-alpine dark conifer forests in China.Scientia Geographica Sinica 10(2): 142–149. (In Chinese)Google Scholar
  40. Wu ZY (2010) Flora Republicae Popularis Sinicae. Science Press, Beijing, China. pp 498–501. (In Chinese)Google Scholar
  41. Wu ZY, Zhu YC, Jiang HQ (1987) Yunnan vegetation. Science Press, Beijing, China. pp 472–473. (In Chinese)Google Scholar
  42. Woodward FI (1987) Climate and plant distribution. Cambridge University Press, New York, NY, USA. pp 52–54.Google Scholar
  43. Zhang MG, Zhou ZK, Chen WY, et al. (2012) Using species distribution modeling to improve conservation and land use planning of Yunnan, China. Biological Conservation 153: 257–264. DOI:10.1016/j.biocon.2012.04.023CrossRefGoogle Scholar
  44. Zomer RJ, Xu J, Wang MC, et al. (2015) Projected impact of climate change on the effectiveness of the existing protected area network for biodiversity conservation within Yunnan province, China. Biological Conservation 184: 335–345. DOI: 10.1016/j.biocon.2015.01.031CrossRefGoogle Scholar

Copyright information

© Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Wang-jun Li
    • 1
  • Ming-chun Peng
    • 1
  • Motoki Higa
    • 2
  • Nobuyuki Tanaka
    • 3
    • 4
  • Tetsuya Matsui
    • 3
  • Cindy Q. Tang
    • 1
  • Xiao-kun Ou
    • 1
  • Rui-wu Zhou
    • 1
  • Chong-yun Wang
    • 1
  • Hai-zhong Yan
    • 1
  1. 1.Institute of Ecology and GeobotanyYunnan UniversityKunmingChina
  2. 2.Kochi University, Kochi-shiKochiJapan
  3. 3.Forestry and Forest Products Research InstituteIbarakiJapan
  4. 4.Tokyo University of AgricultureTokyoJapan

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