IDEAS home Printed from https://ideas.repec.org/a/spr/climat/v178y2025i6d10.1007_s10584-025-03947-y.html
   My bibliography  Save this article

Climatic determinants of the Carpathian treeline and its projected upward shifts in response to climate change

Author

Listed:
  • Alexander Mkrtchian

    (Leibniz Institute of Agricultural Development in Transition Economies (IAMO))

  • Daniel Mueller

    (Leibniz Institute of Agricultural Development in Transition Economies (IAMO)
    Humboldt-Universität zu Berlin)

Abstract

Treelines represent a significant ecological boundary in mountainous regions. Changes in temperature and precipitation regimes due to climate change affect the location of treelines, contingent on fine-scale variations in orographic and climatic conditions. Using high-resolution satellite imagery, we identify the climatic treeline — the potential upper limit of forests determined by climatic conditions — in the Carpathian Mountains, one of Europe’s largest contiguous forest ecosystems. We downscale climate variables to a 30-m resolution by applying a polynomial approximation to the regression residuals, incorporating terrain attributes. We then correlate climatic variables with the location of the climatic treeline. The mean temperature of the warmest quarter demonstrates the strongest correlation with treeline location. We find a total area of 1,370 km2 above the current climatic treeline in the Carpathians, which constitutes the climatic envelope for alpine ecosystems. Depending on future climate projections, this area will decrease to 410–515 km2 by 2040, 100–320 km2 by 2060, and 15–290 km2 by 2080. The anticipated upward shift of the treeline jeopardizes the region's rare and endemic alpine species and has substantial ramifications for ecosystems, water balance, and the carbon cycle in the Carpathian Mountains. Our analysis highlights the importance of understanding how climate affects treeline locations for effective ecosystem management and conservation planning in a changing climate.

Suggested Citation

  • Alexander Mkrtchian & Daniel Mueller, 2025. "Climatic determinants of the Carpathian treeline and its projected upward shifts in response to climate change," Climatic Change, Springer, vol. 178(6), pages 1-23, June.
  • Handle: RePEc:spr:climat:v:178:y:2025:i:6:d:10.1007_s10584-025-03947-y
    DOI: 10.1007/s10584-025-03947-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10584-025-03947-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10584-025-03947-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Emma L. Davis & Robert Brown & Lori Daniels & Trudy Kavanagh & Ze’ev Gedalof, 2020. "Regional variability in the response of alpine treelines to climate change," Climatic Change, Springer, vol. 162(3), pages 1365-1384, October.
    2. Emma L. Davis & Robert Brown & Lori Daniels & Trudy Kavanagh & Ze’ev Gedalof, 2020. "Correction to: Regional variability in the response of alpine treelines to climate change," Climatic Change, Springer, vol. 163(2), pages 1105-1105, November.
    3. Steven I. Higgins & Simon Scheiter, 2012. "Atmospheric CO2 forces abrupt vegetation shifts locally, but not globally," Nature, Nature, vol. 488(7410), pages 209-212, August.
    4. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Prabal Das & D. A. Sachindra & Kironmala Chanda, 2022. "Machine Learning-Based Rainfall Forecasting with Multiple Non-Linear Feature Selection Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 6043-6071, December.
    2. Jie Zhao & Ji Chen & Damien Beillouin & Hans Lambers & Yadong Yang & Pete Smith & Zhaohai Zeng & Jørgen E. Olesen & Huadong Zang, 2022. "Global systematic review with meta-analysis reveals yield advantage of legume-based rotations and its drivers," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    3. Piaopiao Chen & Agnès H. Michel & Jianzhi Zhang, 2022. "Transposon insertional mutagenesis of diverse yeast strains suggests coordinated gene essentiality polymorphisms," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    4. Paulo Infante & Gonçalo Jacinto & Anabela Afonso & Leonor Rego & Pedro Nogueira & Marcelo Silva & Vitor Nogueira & José Saias & Paulo Quaresma & Daniel Santos & Patrícia Góis & Paulo Rebelo Manuel, 2023. "Factors That Influence the Type of Road Traffic Accidents: A Case Study in a District of Portugal," Sustainability, MDPI, vol. 15(3), pages 1-16, January.
    5. Li, Li & Li, Han & Panagiotelis, Anastasios, 2025. "Boosting domain-specific models with shrinkage: An application in mortality forecasting," International Journal of Forecasting, Elsevier, vol. 41(1), pages 191-207.
    6. Ephrem Habyarimana & Faheem S Baloch, 2021. "Machine learning models based on remote and proximal sensing as potential methods for in-season biomass yields prediction in commercial sorghum fields," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-23, March.
    7. Banks, Jonathan & Rabbani, Arif & Nadkarni, Kabir & Renaud, Evan, 2020. "Estimating parasitic loads related to brine production from a hot sedimentary aquifer geothermal project: A case study from the Clarke Lake gas field, British Columbia," Renewable Energy, Elsevier, vol. 153(C), pages 539-552.
    8. Crespo, Cristian, 2020. "Two become one: improving the targeting of conditional cash transfers with a predictive model of school dropout," LSE Research Online Documents on Economics 123139, London School of Economics and Political Science, LSE Library.
    9. María Adelaida Gómez & Ashton Trey Belew & Deninson Alejandro Vargas & Lina Giraldo-Parra & Neal Alexander & David E. Rebellón-Sánchez & Theresa A. Alexander & Najib M. El-Sayed, 2025. "Innate biosignature of treatment failure in human cutaneous leishmaniasis," Nature Communications, Nature, vol. 16(1), pages 1-14, December.
    10. Alexander Wettstein & Gabriel Jenni & Ida Schneider & Fabienne Kühne & Martin grosse Holtforth & Roberto La Marca, 2023. "Predictors of Psychological Strain and Allostatic Load in Teachers: Examining the Long-Term Effects of Biopsychosocial Risk and Protective Factors Using a LASSO Regression Approach," IJERPH, MDPI, vol. 20(10), pages 1-20, May.
    11. Tang, Kayu & Parsons, David J. & Jude, Simon, 2019. "Comparison of automatic and guided learning for Bayesian networks to analyse pipe failures in the water distribution system," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 24-36.
    12. Daifeng Xiang & Gangsheng Wang & Jing Tian & Wanyu Li, 2023. "Global patterns and edaphic-climatic controls of soil carbon decomposition kinetics predicted from incubation experiments," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    13. Joel Podgorski & Oliver Kracht & Luis Araguas-Araguas & Stefan Terzer-Wassmuth & Jodie Miller & Ralf Straub & Rolf Kipfer & Michael Berg, 2024. "Groundwater vulnerability to pollution in Africa’s Sahel region," Nature Sustainability, Nature, vol. 7(5), pages 558-567, May.
    14. Grimm, Volker & Berger, Uta, 2016. "Robustness analysis: Deconstructing computational models for ecological theory and applications," Ecological Modelling, Elsevier, vol. 326(C), pages 162-167.
    15. Bellotti, Anthony & Brigo, Damiano & Gambetti, Paolo & Vrins, Frédéric, 2021. "Forecasting recovery rates on non-performing loans with machine learning," International Journal of Forecasting, Elsevier, vol. 37(1), pages 428-444.
    16. Tranos, Emmanouil & Incera, Andre Carrascal & Willis, George, 2022. "Using the web to predict regional trade flows: data extraction, modelling, and validation," OSF Preprints 9bu5z, Center for Open Science.
    17. Štefan Lyócsa & Petra Vašaničová & Branka Hadji Misheva & Marko Dávid Vateha, 2022. "Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-21, December.
    18. Arjan S. Gosal & Janine A. McMahon & Katharine M. Bowgen & Catherine H. Hoppe & Guy Ziv, 2021. "Identifying and Mapping Groups of Protected Area Visitors by Environmental Awareness," Land, MDPI, vol. 10(6), pages 1-14, May.
    19. repec:plo:pone00:0185380 is not listed on IDEAS
    20. Marcos Rodrigues & Fermín Alcasena & Pere Gelabert & Cristina Vega‐García, 2020. "Geospatial Modeling of Containment Probability for Escaped Wildfires in a Mediterranean Region," Risk Analysis, John Wiley & Sons, vol. 40(9), pages 1762-1779, September.
    21. Pinki Mondal & Sonali Shukla McDermid, 2021. "Editorial for Special Issue: “Global Vegetation and Land Surface Dynamics in a Changing Climate”," Land, MDPI, vol. 10(1), pages 1-4, January.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:climat:v:178:y:2025:i:6:d:10.1007_s10584-025-03947-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.