Skip to main content

Advertisement

SpringerLink
Log in

Modelling Betula utilis distribution in response to climate-warming scenarios in Hindu-Kush Himalaya using random forest

  • Original Paper
  • Published:
Biodiversity and Conservation Aims and scope Submit manuscript

Abstract

Globally, the increase in the climatic variability has led to adverse effects on the treeline species in the high-elevation mountain landscapes. Identifying the geographical space that supports the treeline species survival over time is essential for conservation biogeography. Increase in the global warming and snowmelt has made available the treeline species favourable niches in the higher elevations. Random Forest algorithm assuming non-parametric distribution was employed to predict the potential distribution of Betula utilis niche in the Hindu-Kush Himalayan (HKH) region. The potential distributions were simulated in the Last Inter-Glaciation (LIG), present (the year 1970–2000) and future (the year 2061–2080) environmental conditions. The actual distribution of the species in the current time was modelled and evaluated. The model sensitivity with reference to independent evaluation dataset for highly suitable B. utilis niche was 0.78. The model statistics of the current time was further applied to both the LIG and future (2061–2080) scenarios in order to get a fundamental niche of B. utilis. The treeline species, B. utilis was projected to become vulnerable to 21st century climate changes. The high suitability of B. utilis occurrence in the LIG, current and the future scenario were more likely in the elevation ranges 2601–2800 m, 3801–4000 m, and 4201–4400 m, respectively. The magnitude of advancement was relatively more along elevation and longitude, compared to the latitudinal gradient. The present study provides scientific evidence to conclude that the treeline species potential distribution in HKH is climate driven.

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

Access this article

Log in via an institution

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Allouche O, Tsoar A, Kadmon R (2006) Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistics (TSS). J Appl Ecol 43:1223–1232

    Article  Google Scholar 

  • Baek HJ, Lee J, Lee HS et al (2013) Climate change in the 21st century simulated by HadGEM2-AO under representative concentration pathways. Asia-Pacific J Atmos Sci 49(5):603–618

    Article  Google Scholar 

  • Barbet-Massin M, Jiguet F, Albert CH, Thuiller W (2012) Selecting pseudo-absences for species distribution models: how, where and how many? Methods Ecol Evol 3:327–338

    Article  Google Scholar 

  • Barry S, Elith J (2006) Error and uncertainty in habitat models. J Appl Ecol 43:413–423

    Article  Google Scholar 

  • Bobrowski M, Gerlitz L, Schickhoff U (2017) Modelling the potential distribution of Betula utilis in the Himalaya. Glob Ecol Conserv 11:69–83

    Article  Google Scholar 

  • Breiman L (2001a) Statistical modeling: the two cultures. Stat Sci 16(3):199–231

    Article  Google Scholar 

  • Breiman L (2001b) Random forests. Mach Learn 45:5–32

    Article  Google Scholar 

  • Broennimann O, Cola VD, Guisan A (2018) ecospat: spatial ecology miscellaneous methods. R package version 3.0. https://CRAN.R-project.org/package=ecospat

  • Danby RK, Hik DS (2007) Variability, contingency and rapid change in recent subarctic alpine tree line dynamics. J Ecol 95:352–363

    Article  Google Scholar 

  • Dormann CF, Elith J, Bacher S et al (2013) Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36:27–46

    Article  Google Scholar 

  • Evans JS, Murphy MA, Holden ZA, Cushman SA (2011) Modeling species distribution and change using random forest. In: Drew CA, Wiersma YF, Huettmann F (eds) Predictive species and habitat modeling in landscape ecology: concepts and applications. Springer, New York, pp 139–159

    Chapter  Google Scholar 

  • Fick SE, Hijmans RJ (2017) Worldclim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol 37:4302–4315

    Article  Google Scholar 

  • Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24(1):38–49

    Article  Google Scholar 

  • Franklin J, Miller JA (2010) Mapping species distributions: spatial inference and prediction. Cambridge University Press, UK

    Book  Google Scholar 

  • Gaire NP, Koirala M, Bhuju DR, Borgaonkar HP (2014) Treeline dynamics with climate change at the central Nepal Himalaya. Clim Past 10:1277–1290

    Article  Google Scholar 

  • Gardelle J, Arnaud Y, Berthier E (2011) Contrasted evolution of glacial lakes along the Hindu Kush Himalaya mountain range between 1990 and 2009. Glob Planet Change 75:47–55

    Article  Google Scholar 

  • Gardner AS, Moholdt G, Cogley G et al (2013) A reconciled estimate of glacier contributions to sea level rise: 2003 to 2009. Science 340:852–857

    Article  CAS  Google Scholar 

  • GBIF (Global Biodiversity Information Facility) (2018) GBIF occurrence download. https://doi.org/10.15468/dl.gp3fox. Accessed 05 Nov 2018

  • Gottfried M, Pauli H, Futschik A et al (2012) Continent-wide response of mountain vegetation to climate change. Nat Clim Change 2:111–115

    Article  Google Scholar 

  • Grabherr G, Gottfried M, Pauli H (1994) Climate effects on mountain plants. Nature 369:448

    Article  CAS  Google Scholar 

  • Guillera-Arroita G, Lahoz-Monfort JJ, Elith J et al (2015) Is my species distribution model fit for purpose? Matching data and models to applications. Glob Ecol Biogeogr 24:276–292

    Article  Google Scholar 

  • Guisan A, Thuiller W, Zimmermann NE, Cola VD, Georges D, Psomas A (2017) Habitat suitability and distribution models. Cambridge University Press, UK. https://doi.org/10.1017/9781139028271

    Book  Google Scholar 

  • Hamid M, Khuroo AA, Charles B, Ahmad R, Singh CP, Aravind NA (2018) Impact of climate change on the distribution range and niche dynamics of Himalayan birch, a typical treeline species in Himalayas. Biodivers Conserv. https://doi.org/10.1007/s10531-018-1641-8

    Article  Google Scholar 

  • Hansen JE, Sato M (2012) Paleoclimate implications for human-made climate change. In: Berger A, Mesinger F, Šijački D (eds) Climate change: inferences from paleoclimate and regional aspects. Springer, Wien, pp 21–47

    Chapter  Google Scholar 

  • Hewitt K (2011) Glacier change, concentration, and elevation effects in the Karakoram Himalaya, upper Indus basin. Mt Res Dev 31(3):188–200

    Article  Google Scholar 

  • Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978

    Article  Google Scholar 

  • Hirzel AH, Lay GL, Helfer V, Randin C, Guisan A (2006) Evaluating the ability of habitat suitability models to predict species presences. Ecol Model 199:142–152

    Article  Google Scholar 

  • Hutchinson GE (1991) Population studies: animal ecology and demography. Bull Math Biol 53:193–213

    Article  Google Scholar 

  • [ICIMOD] International Centre for Integrated Mountain Development (2018a) Outline boundary of Hindu Kush Himalayan (HKH) region. Kathmandu, Nepal: ICIMOD. http://www.rds.icimod.org. Accessed 05 Jan 2018

  • [ICIMOD] International Centre for Integrated Mountain Development (2018b) Strategy and results framework 2017. Kathmandu: ICIMOD. ISBN: 978-92-9115-597-2

  • [IPCC] Intergovernmental Panel on Climate Change (2013) Climate Change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. In: Stocker TF, Qin D, Plattner GK, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds), Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp 1535

  • [IPCC] Intergovernmental Panel on Climate Change (2014) Climate Change 2014: impacts, adaptation, and vulnerability. Part A: global and sectoral aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. In: Field CB, Barros VR, Dokken DJ, Mach KJ, Mastrandrea MD, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR, and White LL (eds), Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp 1132

  • Körner C (2012) Alpine treelines: functional ecology of the global high elevation tree limits. Springer, Basel

    Book  Google Scholar 

  • Kukla GJ, Bender ML, de Beaulieu JL et al (2002) Last interglacial climates. Q Res 58(1):2–13

    Article  Google Scholar 

  • Liu C, Newell G, White M (2016) On the selection of thresholds for predicting species occurrence with presence-only data. Ecol Evol 6(1):337–348

    Article  Google Scholar 

  • Mackenzie DI, Royle JA (2005) Designing occupancy studies: general advice and allocating survey effort. J Appl Ecol 42:1105–1114

    Article  Google Scholar 

  • Maher SP, Randin CF, Guisan A, Drake JM (2014) Pattern-recognition ecological niche models fit to presence-only and presence-absence data. Methods Ecol Evol 5:761–770

    Article  Google Scholar 

  • Mayor JR, Sanders NJ, Classen AT et al (2017) Elevation alters ecosystem properties across temperate treelines globally. Nature 542:91–95

    Article  CAS  Google Scholar 

  • Merow C, Smith MJ, Silander JA Jr (2013) A practical guide to MaxEnt for modelling species’ distributions: what it does, and why inputs and settings matter. Ecography 36:1058–1069

    Article  Google Scholar 

  • Naimi B, Hamm NAS, Groen TA, Skidmore AK, Toxopeus AG (2014) Where is positional uncertainty a problem for species distribution modelling? Ecography 37:191–203

    Article  Google Scholar 

  • Olden JD, Lawler JJ, Poff NL (2008) Machine learning methods without tears: a primer for ecologists. Q Rev of Biol 83(2):171–193

    Article  Google Scholar 

  • Otto-Bliesner BL, Marshall SJ, Overpeck JT, Miller GH, Hu A, CAPE Last Interglacial Project members (2006) Simulating arctic climate warmth and icefield retreat in the last interglaciation. Science 311:1751–1753

    Article  CAS  Google Scholar 

  • Paulsen J, Körner C (2014) A climate-based model to predict potential treeline position around the globe. Alpine Bot 124:1–12

    Article  Google Scholar 

  • Phillips SJ, Elith J (2010) POC plots: calibrating species distribution models with presence-only data. Ecology 91(8):2476–2484

    Article  Google Scholar 

  • R Core Team (2018) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/. Accessed 20 July 2018

  • Ranhotra PS, Bhattacharyya A, Kotlia BS (2007) Vegetation and climatic changes around Lamayuru, Trans-Himalaya during the last 35 kyr B.P. Palaeobot 56:117–126

    Google Scholar 

  • Riahi K, Rao S, Krey V et al (2011) RCP 8.5—a scenario of comparatively high greenhouse gas emissions. Clim Change 109:33–57

    Article  CAS  Google Scholar 

  • Schickhoff U (2005) The upper timberline in the Himalayas, Hindu Kush and Karakorum: a review of geographical and ecological aspects. In: Broll G, Keplin B (eds) Mountain ecosystems: studies in treeline ecology. Springer, Heidelberg, pp 275–354

    Chapter  Google Scholar 

  • Schickhoff U, Bobrowski M, Böhner J et al (2015) Do Himalayan treelines respond to recent climate change? An evaluation of sensitivity indicators. Earth Syst Dyn 6:245–265

    Article  Google Scholar 

  • Shi P, Körner C, Hoch G (2008) A test of the growth-limitation theory for alpine tree line formation in evergreen and deciduous taxa of the eastern Himalayas. Funct Ecol 22:213–220

    Article  Google Scholar 

  • Singh CP (2015) Long-term monitoring of alpines of the Himalaya. ENVIS Newsl Himal Ecol 12(2):1–3

    CAS  Google Scholar 

  • Singh CP, Panigrahy S, Parihar JS (2011) Alpine vegetation ecotone dynamics in Gangotri catchment using remote sensing techniques. ISPRS Archives XXXVIII- 8/W20. In: Workshop Proceedings: Earth Observation for Terrestrial Ecosystems, pp 162–169

  • Singh CP, Panigrahy S, Thapliyal A, Kimothi MM, Soni P, Parihar JS (2012) Monitoring the alpine treeline shift in parts of the Indian Himalayas using remote sensing. Curr Sci 102(4):559–562

    Google Scholar 

  • Singh CP, Panigrahy S, Parihar JS, Dharaiya N (2013) Modelling environmental niche of Himalayan Birch and remote sensing based vicarious validation. Trop Ecol 54(3):321–329

    Google Scholar 

  • Singh CP, Mohapatra J, Pandya HA, Gajmer B, Sharma N, Shrestha DG (2018) Evaluating changes in treeline position and land surface phenology in Sikkim Himalaya. Geocarto Int. https://doi.org/10.1080/10106049.2018.1524513

    Article  Google Scholar 

  • Thuiller W, Georges D, Engler R, Breiner F (2016) biomod2: Ensemble platform for species distribution modeling. R package version 3.3-7. https://CRAN.R-project.org/package=biomod2

  • [USGS] United States Geological Survey (2006) Shuttle Radar Topography Mission, 30 Arc Second, Global Land Cover Facility, University of Maryland, College Park, Maryland, February 2000

  • Van Vuuren DP, Edmonds J, Kainuma M et al (2011) The representative concentration pathways: an overview. Clim Change 109:5–31

    Article  Google Scholar 

  • Zhang JW, Wang JT, Chen WL, Li BS, Zhao KY (1988) Tibetan vegetation. Science Press, Beijing

    Google Scholar 

Download references

Acknowledgements

The authors gratefully acknowledge Shri. D.K. Das, Director, Space Applications Centre and Dr Raj Kumar, Deputy Director, Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area, Indian Space Research Organisation (ISRO), Ahmedabad for their support and encouragement. Authors are thankful to Dr B.K. Bhattacharya, Head, Agriculture and Land Ecosystem Division and Dr R.P. Singh, Project Director (PRACRITI-II) and Head, Land Hydrology Division for their guidance. The authors thank Mr Ankit Singh, High Altitude Plant Physiology Research Centre, Hemvati Nandan Bahuguna Garhwal University, Srinagar, Garhwal, Uttarakhand for his contribution to this study. The project has been carried out under ‘Alpine Ecosystem Dynamics and Impact of Climate Change in Indian Himalaya’ under PRACRITI-II program of ISRO. The outline boundary of Hindu-Kush Himalayan (HKH) region provided by ICIMOD, Kathmandu, Nepal is duly acknowledged. The authors would also like to thank the anonymous reviewers for their insightful and critical comments, which make this paper be improved largely.

Author information

Authors and Affiliations

  1. Agriculture and Land Ecosystem Division (AED), Biological and Planetary Sciences and Applications Group (BPSG), Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area (EPSA), Space Applications Centre (SAC), ISRO, Ahmedabad, Gujarat, 380015, India

    Jakesh Mohapatra & Chandra Prakash Singh

  2. Centre for Biodiversity and Taxonomy, Department of Botany, University of Kashmir, Srinagar, Jammu and Kashmir, 190006, India

    Maroof Hamid & Anzar A. Khuroo

  3. Council of Scientific and Industrial Research (CSIR)-Institute of Himalayan Bioresource Technology (IHBT), Palampur, Himachal Pradesh, 176061, India

    Anirudh Verma & Amit Kumar

  4. High Altitude Plant Physiology Research Centre (HAPPRC), Hemvati Nandan Bahuguna Garhwal University, Srinagar, Garhwal, Uttarakhand, 246174, India

    Sudeep Chandra Semwal & Mohan C. Nautiyal

  5. Sikkim State Council of Science and Technology, Deorali, Gangtok, Sikkim, 737102, India

    Bandan Gajmer & Narpati Sharma

  6. Department of Botany, Bioinformatics, and Climate Change Impacts Management, University School of Sciences, Gujarat University, Ahmedabad, Gujarat, 380009, India

    Jakesh Mohapatra & Himanshu A. Pandya

Authors
  1. Jakesh Mohapatra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chandra Prakash Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maroof Hamid
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anirudh Verma
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sudeep Chandra Semwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Bandan Gajmer
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Anzar A. Khuroo
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Amit Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Mohan C. Nautiyal
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Narpati Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Himanshu A. Pandya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jakesh Mohapatra.

Additional information

Communicated by M.D. Behera, S.K. Behera and S. Sharma.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 11146 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mohapatra, J., Singh, C.P., Hamid, M. et al. Modelling Betula utilis distribution in response to climate-warming scenarios in Hindu-Kush Himalaya using random forest. Biodivers Conserv 28, 2295–2317 (2019). https://doi.org/10.1007/s10531-019-01731-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10531-019-01731-w

Keywords

Search

Navigation