Skip to main content
Log in

Fine-resolution assessment of potential refugia for a dominant fir species (Abies mariesii) of subalpine coniferous forests after climate change

  • Published:
Plant Ecology Aims and scope Submit manuscript

Abstract

The questions “Will the environment surrounding moorlands become refugia for a Japanese subalpine coniferous species, Abies mariesii Mast., after climate change?” and “How does the spatial resolution of a species distribution model affect the global warming predictions?” have been discussed in this study. This study was conducted at Hakkoda Mountains, the northern side of Honshu Island, Japan. We constructed 50-m mesh model using a climate variable, two topography variables and two variables relating to moorlands. We applied the model to eight global warming scenarios, including decreasing or non-decreasing scenarios of moorlands. We also constructed a coarse-resolution model at approximately 1-km resolution and compared the model predictions with the fine ones. The results showed that the coarse-resolution model tended to overestimate the range of suitable habitats for A. mariesii. On the other hand, some suitable habitats around moorlands could only be predicted by the fine-resolution model. The fine-resolution model indicated that the peripheries of the moorlands are the most important potential refugia for A. mariesii on Hakkoda Mountains. Although these suitable areas were notable in the +2°C scenario, all suitable habitats completely disappeared in the +4°C scenario. We concluded that it would be effective to conserve the A. mariesii populations around moorlands which are likely to persist after global warming, as well as moorlands themselves. This assessment could only be achieved by fine-resolution models that incorporate non-climatic variables including topography and moorland-related variables with climatic variables. In contrast, a coarse-resolution model overestimated the suitable habitats whilst underestimating potential local refugia. Thus, fine-resolution models are more effective for developing practical adaptation of conservation measures.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Abbreviations

AUC:

Area under curve

CT:

Classification tree

DEM:

Digital elevation model

DWS:

Deviance weighted scores

HAmap:

Hakkoda Abies mariesii map

PRS:

Summer (May–September) precipitation

PRW:

Winter (December–March) precipitation

ROC:

Receiver-operating characteristics

TN:

Terminal node

WI:

Warmth index

References

  • Araújo MB, Thuiller W, Williams PH, Reginster I (2005) Downscaling European species atlas distributions to a finer resolution: implications for conservation planning. Glob Ecol Biogeogr 14:17–30

    Article  Google Scholar 

  • Ashcroft MB, Chisholm LA, French KO (2009) Climate change at the landscape scale: predicting fine-grained spatial heterogeneity in warming and potential refugia for vegetation. Glob Change Biol 15:656–667

    Article  Google Scholar 

  • Beckage B, Osborne B, Gavin DG, Pucko C, Siccama T, Perkins T (2008) A rapid upward shift of a forest ecotone during 40 years of warming in the Green Mountains of Vermont. PNAS 105:4197–4202

    Article  PubMed  CAS  Google Scholar 

  • Charles SP, Bari MA, Kitsios A, Bates BC (2007) Effect of GCM bias on downscaled precipitation and runoff projections for the Serpentine catchment, Western Australia. Int J Climatol 27:1673–1690

    Article  Google Scholar 

  • Clark LA, Pregibon D (1992) Tree-based models. In: Chambers JM, Hastie TJ (eds) Statistical models in S. Wadsworth & Brooks/Cole Advanced Books & Software, Pacific Grove, pp 377–419

    Google Scholar 

  • De’ath G, Fabricius KE (2000) Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology 81:3178–3192

    Article  Google Scholar 

  • Efron B (1979) Bootstrap methods: another look at the jackknife. Ann Stat 7:1–26

    Article  Google Scholar 

  • Geographical Survey Institute (2000) Digital map 50 m grid (elevation). Geographical Survey Institute, Tsukuba

    Google Scholar 

  • Horikawa M, Tsuyama I, Matsui T, Kominami Y, Tanaka N (2009) Assessing the potential impacts of climate change on the alpine habitat suitability of Japanese stone pine (Pinus pumila). Landsc Ecol 24:115–128

    Article  Google Scholar 

  • Huntley B, Berry PM, Cramer W, McDonald AP (1995) Special paper: modelling present and potential future ranges of some European higher plants using climate response surfaces. J Biogeogr 22:967–1001

    Article  Google Scholar 

  • IPCC (2007) In: Parry ML, Canziani OF, Palutikof JP, Linden PJ, Hanson CE (eds) Climate Change 2007: impacts, adaptation and vulnerability. Contribution of Working Group II to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, p 976

  • Iverson LR, Prasad AM (1998) Predicting abundance of 80 tree species following climate change in the eastern United States. Ecol Monogr 68:465–485

    Article  Google Scholar 

  • Japan Map Center (1998) Numerical map user guide, 2nd version. Japan Map Center, Tokyo

    Google Scholar 

  • Japan Meteorological Agency (1996) Climate normals for Japan. Japan Meteorological Agency, Tokyo

    Google Scholar 

  • Kaji M (1982) Studies on the ecological geography of subalpine conifers: distribution pattern of Abies mariesii in relation to the effect of climate in the postglacial warm period. Bull Tokyo Univ For 72:31–120

    Google Scholar 

  • Kira T (1977) A climatological interpretation of Japanese vegetation zone. In: Miyawaki A, Tuexen R (eds) Vegetation science and environmental protection. Maruzen, Tokyo, pp 21–30

    Google Scholar 

  • Koike K, Toshikazu T, Chinzei K, Miyagi T (2005) Regional geomorphology of Japanese Islands, geomorphology of Tohoku region, vol 3. University of Tokyo Press, Tokyo

    Google Scholar 

  • Leitinger G, Höller P, Tasser E, Walde J, Tappeiner U (2008) Development and validation of a spatial snow-glide model. Ecol Model 211:363–374

    Article  Google Scholar 

  • Lenoir J, Gegout JC, Marquet PA, de Ruffray P, Brisse H (2008) A significant upward shift in plant species optimum elevation during the 20th century. Science 320:1768–1771

    Article  PubMed  CAS  Google Scholar 

  • Lobo JM, Jiménez-Valverde A, Real R (2008) AUC: a misleading measure of the performance of predictive distribution models. Global Ecol Biogeogr 17:145–151

    Article  Google Scholar 

  • Marschner H (1991) Mechanisms of adaptation of plants to acid soils. Plant Soil 134:1–20

    CAS  Google Scholar 

  • Matsui T, Nakaya T, Yagihashi T, Taoda H, Tanaka N (2004a) Comparing the accuracy of predictive distribution models for Fagus crenata forests in Japan. Jpn J For Environ 46:93–102

    Google Scholar 

  • Matsui T, Yagihashi T, Nakaya T, Taoda H, Yoshinaga S, Daimaru H, Tanaka N (2004b) Probability distributions, vulnerability and sensitivity in Fagus crenata forests following predicted climate changes in Japan. J Veg Sci 15:605–614

    Google Scholar 

  • Matsui T, Takahashi K, Tanaka N, Hijioka Y, Horikawa M, Yagihashi T, Harasawa H (2009) Evaluation of habitat sustainability and vulnerability for beech (Fagus crenata) forests under 110 hypothetical climatic change scenarios in Japan. Appl Veg Sci 12:328–339

    Article  Google Scholar 

  • Mearns LO, Rosenzweig C, Goldberg R (1997) Mean and variance change in climate scenarios: methods, agricultural applications, and measures of uncertainty. Clim Change 35:367–396

    Article  Google Scholar 

  • Metz CE (1978) Basic principles of ROC analysis. Semin Nucl Med 8:283–298

    Article  PubMed  CAS  Google Scholar 

  • 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

    Article  PubMed  Google Scholar 

  • Morita Y (1985) The vegetational history of the subalpine zone in northeast Japan II. The Hachimantai mountains. Jpn J Ecol 35:411–420

    Google Scholar 

  • Murach D, Ulrich B (1988) Destabilization of forest ecosystems by acid deposition. GeoJournal 17:253–259

    Article  Google Scholar 

  • Nogami M (1994) Thermal condition of the forest vegetation zones and their potential distribution under different climates in Japan. J Geogr 103:886–897

    Article  Google Scholar 

  • Nogami M, Ohba H (1991) Japanese vegetation seen from warmth index. Kagaku 61:39–49

    Google Scholar 

  • Pearce J, Ferrier S (2000) Evaluating the predictive performance of habitat models developed using logistic regression. Ecol Model 133:225–245

    Article  Google Scholar 

  • Pearson RG, Dawson TP, Liu C (2004) Modelling species distributions in Britain: a hierarchical integration of climate and land-cover data. Ecography 27:285–298

    Article  Google Scholar 

  • R Development Core Team (2008) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna

    Google Scholar 

  • Shidei T (1956) A view on the cause of the lack of coniferous forest zone in subalpine area on some mountains in the Japan sea side. J Jpn For Soc 38:356–358

    Google Scholar 

  • Smith W, Germino M, Johnson D, Reinhardt K (2009) The altitude of alpine treeline: a bellwether of climate change effects. Bot Rev 75:163–190

    Article  Google Scholar 

  • Sugita H (1990) Consideration on the history of the development of the Abies mariesii forest during postglacial time based on its distributional character. Jpn J Hist Bot 6:31–37

    Google Scholar 

  • Sugita H (1992) Ecological geography of the range of the Abies mariesii forest in northeast Honshu, Japan, with special reference to the physiographic conditions. Ecol Res 7:119–132

    Article  Google Scholar 

  • Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–1293

    Article  PubMed  CAS  Google Scholar 

  • Tanaka N, Nakazono E, Tsuyama I, Matsui T (2009) Assessing impact of climate warming on potential habitats of ten conifer species in Japan. Glob Environ Res 14:153–164

    Google Scholar 

  • Thuiller W, Araújo MB, Lavorel S (2003) Generalized models vs. classification tree analysis: predicting spatial distributions of plant species at different scales. J Veg Sci 14:669–680

    Article  Google Scholar 

  • Thuiller W, Lavorel S, Araújo MB, Sykes MT, Prentice IC (2005) Climate change threats to plant diversity in Europe. PNAS 102:8245–8250

    Article  PubMed  CAS  Google Scholar 

  • Trivedi MR, Berry PM, Morecroft MD, Dawson TP (2008) Spatial scale affects bioclimate model projections of climate change impacts on mountain plants. Glob Change Biol 14:1089–1103

    Article  Google Scholar 

  • Tsuyama I, Nakao K, Matsui T, Higa M, Horikawa M, Kominami Y, Tanaka N (2011) Climatic controls of a keystone understory species, Sasamorpha borealis, and an impact assessment of climate change in Japan. Ann For Sci 68:689–699

    Article  Google Scholar 

  • Yamanaka M, Sugawara K, Ishikawa S (1988) A historical study of the Abies mariesii forest found in the montane zone in the south Hakkoda mountains, northeast Japan. Jpn J Ecol 38:147–157

    Google Scholar 

  • Yonekura K, Kajita T (2003) BG Platns wamei-gakumei (Japanese–Latin) index (YList). http://bean.bio.chiba-u.jp/bgplants/index.html (in Japanese). Accessed 28 July 2011

  • Zimmermann NE, Kienast F (1999) Predictive mapping of alpine grasslands in Switzerland: species versus community approach. J Veg Sci 10:469–482

    Article  Google Scholar 

  • Zweig MH, Campbell G (1993) Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem 39:561–577

    PubMed  CAS  Google Scholar 

Download references

Acknowledgments

The authors are grateful to K. Yonekura for advice about species identification as well as K. Hikosaka, T. Sasaki, C. Kamiyama, A. Yoshida, H. Daimaru and M. Yasuda for their valuable suggestions. This study was supported by the Global Environment Research Fund (grant Nos. F-092, S-4 and S-8) of the Ministry of the Environment, Japan.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ikutaro Tsuyama.

Additional information

Nomenclature: Yonekura and Kajita (2003).

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 316 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shimazaki, M., Tsuyama, I., Nakazono, E. et al. Fine-resolution assessment of potential refugia for a dominant fir species (Abies mariesii) of subalpine coniferous forests after climate change. Plant Ecol 213, 603–612 (2012). https://doi.org/10.1007/s11258-012-0025-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11258-012-0025-5

Keywords

Navigation