IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v481y2023ics0304380023001138.html
   My bibliography  Save this article

Modeling potential natural vegetation: A new light on an old concept to guide nature conservation in fragmented and degraded landscapes

Author

Listed:
  • Bourdouxhe, Axel
  • Wibail, Lionel
  • Claessens, Hugues
  • Dufrêne, Marc

Abstract

Modeling biotope distributions is of paramount importance to monitor species habitats and guide conservation and restoration actions to decrease population extinction rates. However, modeling biotopes as independent landscape units, as is current practice, has some limitations. Vegetation communities that define biotopes evolve through different stages and associations until they reach an equilibrium. To consider these temporal dynamics, we developed a modeling approach based on potential natural vegetation (PNV) corresponding to ecological contexts supporting vegetation succession. The assumption made is that modeling PNV better distinguishes biotope ecological niches, improving prediction accuracy. Results of the final prediction map were excellent, with an overall accuracy of 0.95 and a kappa coefficient of 0.91. The proposed method was also compared with a classic biotope model and our approach showed 29% mean improvement in accuracy. Our results produced a good distinction between the different ecological niches of potential natural vegetation. However, some areas of confusion were identified but these are mainly explained by imprecision and incompleteness of the reference biotope dataset and long-term human management. Using potential natural vegetation is therefore recommended for further studies on biotope mapping.

Suggested Citation

  • Bourdouxhe, Axel & Wibail, Lionel & Claessens, Hugues & Dufrêne, Marc, 2023. "Modeling potential natural vegetation: A new light on an old concept to guide nature conservation in fragmented and degraded landscapes," Ecological Modelling, Elsevier, vol. 481(C).
  • Handle: RePEc:eee:ecomod:v:481:y:2023:i:c:s0304380023001138
    DOI: 10.1016/j.ecolmodel.2023.110382
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380023001138
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2023.110382?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    2. Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
    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. Hou, Lei & Elsworth, Derek & Zhang, Fengshou & Wang, Zhiyuan & Zhang, Jianbo, 2023. "Evaluation of proppant injection based on a data-driven approach integrating numerical and ensemble learning models," Energy, Elsevier, vol. 264(C).
    2. Ma, Zhikai & Huo, Qian & Wang, Wei & Zhang, Tao, 2023. "Voltage-temperature aware thermal runaway alarming framework for electric vehicles via deep learning with attention mechanism in time-frequency domain," Energy, Elsevier, vol. 278(C).
    3. Patrick Krennmair & Timo Schmid, 2022. "Flexible domain prediction using mixed effects random forests," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1865-1894, November.
    4. Manuel J. García Rodríguez & Vicente Rodríguez Montequín & Francisco Ortega Fernández & Joaquín M. Villanueva Balsera, 2019. "Public Procurement Announcements in Spain: Regulations, Data Analysis, and Award Price Estimator Using Machine Learning," Complexity, Hindawi, vol. 2019, pages 1-20, November.
    5. Sachin Kumar & Zairu Nisha & Jagvinder Singh & Anuj Kumar Sharma, 2022. "Sensor network driven novel hybrid model based on feature selection and SVR to predict indoor temperature for energy consumption optimisation in smart buildings," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(6), pages 3048-3061, December.
    6. Escribano, Álvaro & Wang, Dandan, 2021. "Mixed random forest, cointegration, and forecasting gasoline prices," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1442-1462.
    7. Yigit Aydede & Jan Ditzen, 2022. "Identifying the regional drivers of influenza-like illness in Nova Scotia with dominance analysis," Papers 2212.06684, arXiv.org.
    8. Siyoon Kwon & Hyoseob Noh & Il Won Seo & Sung Hyun Jung & Donghae Baek, 2021. "Identification Framework of Contaminant Spill in Rivers Using Machine Learning with Breakthrough Curve Analysis," IJERPH, MDPI, vol. 18(3), pages 1-26, January.
    9. Karim Zkik & Anass Sebbar & Oumaima Fadi & Sachin Kamble & Amine Belhadi, 2024. "Securing blockchain-based crowdfunding platforms: an integrated graph neural networks and machine learning approach," Electronic Commerce Research, Springer, vol. 24(1), pages 497-533, March.
    10. Yan, Ran & Wang, Shuaian & Du, Yuquan, 2020. "Development of a two-stage ship fuel consumption prediction and reduction model for a dry bulk ship," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 138(C).
    11. Yi Cao & Xue Li, 2022. "Multi-Model Attention Fusion Multilayer Perceptron Prediction Method for Subway OD Passenger Flow under COVID-19," Sustainability, MDPI, vol. 14(21), pages 1-16, November.
    12. Filmer,Deon P. & Nahata,Vatsal & Sabarwal,Shwetlena, 2021. "Preparation, Practice, and Beliefs : A Machine Learning Approach to Understanding Teacher Effectiveness," Policy Research Working Paper Series 9847, The World Bank.
    13. Jonas Botz & Diego Valderrama & Jannis Guski & Holger Fröhlich, 2024. "A dynamic ensemble model for short-term forecasting in pandemic situations," PLOS Global Public Health, Public Library of Science, vol. 4(8), pages 1-18, August.
    14. Daniel Boller & Michael Lechner & Gabriel Okasa, 2025. "The effect of sport in online dating: evidence from causal machine learning," Palgrave Communications, Palgrave Macmillan, vol. 12(1), pages 1-13, December.
    15. Zhenchao Zhang & Weixin Luan & Chuang Tian & Min Su, 2025. "Impact of Urban Expansion on School Quality in Compulsory Education: A Spatio-Temporal Study of Dalian, China," Land, MDPI, vol. 14(2), pages 1-20, January.
    16. Jorge Antunes & Peter Wanke & Thiago Fonseca & Yong Tan, 2023. "Do ESG Risk Scores Influence Financial Distress? Evidence from a Dynamic NDEA Approach," Sustainability, MDPI, vol. 15(9), pages 1-32, May.
    17. Lyudmyla Kirichenko & Tamara Radivilova & Vitalii Bulakh, 2018. "Machine Learning in Classification Time Series with Fractal Properties," Data, MDPI, vol. 4(1), pages 1-13, December.
    18. Cini, Federico & Ferrari, Annalisa, 2025. "Towards the estimation of ESG ratings: A machine learning approach using balance sheet ratios," Research in International Business and Finance, Elsevier, vol. 73(PB).
    19. Ivan Brandić & Lato Pezo & Nikola Bilandžija & Anamarija Peter & Jona Šurić & Neven Voća, 2023. "Comparison of Different Machine Learning Models for Modelling the Higher Heating Value of Biomass," Mathematics, MDPI, vol. 11(9), pages 1-14, April.
    20. Jianghong Xu & Wei Lu & Weixin Wang, 2024. "From “fragile smallholders” to “resilient smallholders”: measuring rural household resilience in China," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-14, December.

    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:eee:ecomod:v:481:y:2023:i:c:s0304380023001138. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    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.