IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i2p462-d475527.html
   My bibliography  Save this article

Evaluation of Climate Change Impacts on the Potential Distribution of Styrax sumatrana in North Sumatra, Indonesia

Author

Listed:
  • Muhammad Hadi Saputra

    (Environmental and Forestry Research and Development Agency of Aek Nauli, Sibaganding, Simalungun 21174, Indonesia)

  • Han Soo Lee

    (Transdisciplinary Science and Engineering Program, Graduate School of Advanced Science and Engineering, Hiroshima University, Higashi-Hiroshima 739-8529, Japan)

Abstract

This study aims to assess the impact of climate change on the distribution of Styrax sumatrana in North Sumatra by applying the maximum entropy (MaxEnt) model with biophysical factors (elevation, slope, aspect, and soil), climatic factors (19 bioclimate data sets for 2050 and 2070), and anthropogenic factors (land use land cover (LULC) changes in 2050 and 2070). The future climate data retrieved and used are the output of four climate models from Coupled Model Intercomparison Project Phase 5 (CMIP5), namely, the CCSM4, CNRM-CM5, MIROC5, and MRI-CGCM3 models, under the Representative Concentration Pathways (RCPs) 4.5 and 8.5 scenarios. The MaxEnt modelling results showed the importance of the mean temperature of the coldest quarter and the LULC variables. Styrax sumatrana rely on environmental conditions with air temperatures ranging from 13 to 19 °C. The potentially suitable land types for Styrax sumatrana are shrubs, gardens, and forests. The future predictions show that the suitable habitat for Styrax sumatrana is predicted to decrease to 3.87% in 2050 and to 3.54% in 2070 under the RCP4.5 scenario. Under the RCP8.5 scenario, the suitable area is predicted to decrease to 3.04% in 2050 and to 1.36% in 2070, respectively. The degradation of the suitable area is mainly due to increasing temperature and deforestation in future predictions. The modelling results illustrate that the suitable habitats of Styrax sumatrana are likely to be reduced under future climate change scenarios or lost in 2070 under the RCP8.5 scenario. The potential future extinction of this species should alert authorities to formulate conservation strategies. Results also demonstrated key variables that should be used for formulating ex situ conservation strategies.

Suggested Citation

  • Muhammad Hadi Saputra & Han Soo Lee, 2021. "Evaluation of Climate Change Impacts on the Potential Distribution of Styrax sumatrana in North Sumatra, Indonesia," Sustainability, MDPI, vol. 13(2), pages 1-22, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:2:p:462-:d:475527
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/2/462/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/2/462/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kirsten L. Findell & Alexis Berg & Pierre Gentine & John P. Krasting & Benjamin R. Lintner & Sergey Malyshev & Joseph A. Santanello & Elena Shevliakova, 2017. "The impact of anthropogenic land use and land cover change on regional climate extremes," Nature Communications, Nature, vol. 8(1), pages 1-10, December.
    2. Austin, Mike, 2007. "Species distribution models and ecological theory: A critical assessment and some possible new approaches," Ecological Modelling, Elsevier, vol. 200(1), pages 1-19.
    3. Muhammad Hadi Saputra & Han Soo Lee, 2019. "Prediction of Land Use and Land Cover Changes for North Sumatra, Indonesia, Using an Artificial-Neural-Network-Based Cellular Automaton," Sustainability, MDPI, vol. 11(11), pages 1-16, May.
    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. Hyeseon Choi & Nash Jett DG. Reyes & Minsu Jeon & Lee-Hyung Kim, 2021. "Constructed Wetlands in South Korea: Current Status and Performance Assessment," Sustainability, MDPI, vol. 13(18), pages 1-19, September.
    2. Václavík, Tomáš & Meentemeyer, Ross K., 2009. "Invasive species distribution modeling (iSDM): Are absence data and dispersal constraints needed to predict actual distributions?," Ecological Modelling, Elsevier, vol. 220(23), pages 3248-3258.
    3. Muñoz-Mas, Rafael & Vezza, Paolo & Alcaraz-Hernández, Juan Diego & Martínez-Capel, Francisco, 2016. "Risk of invasion predicted with support vector machines: A case study on northern pike (Esox Lucius, L.) and bleak (Alburnus alburnus, L.)," Ecological Modelling, Elsevier, vol. 342(C), pages 123-134.
    4. Meineri, Eric & Dahlberg, C. Johan & Hylander, Kristoffer, 2015. "Using Gaussian Bayesian Networks to disentangle direct and indirect associations between landscape physiography, environmental variables and species distribution," Ecological Modelling, Elsevier, vol. 313(C), pages 127-136.
    5. Marmion, Mathieu & Luoto, Miska & Heikkinen, Risto K. & Thuiller, Wilfried, 2009. "The performance of state-of-the-art modelling techniques depends on geographical distribution of species," Ecological Modelling, Elsevier, vol. 220(24), pages 3512-3520.
    6. Kaiping Wang & Weiqi Wang & Niyi Zha & Yue Feng & Chenlan Qiu & Yunlu Zhang & Jia Ma & Rui Zhang, 2022. "Spatially Heterogeneity Response of Critical Ecosystem Service Capacity to Address Regional Development Risks to Rapid Urbanization: The Case of Beijing-Tianjin-Hebei Urban Agglomeration in China," Sustainability, MDPI, vol. 14(12), pages 1-21, June.
    7. Milad Asadi & Amir Oshnooei-Nooshabadi & Samira-Sadat Saleh & Fattaneh Habibnezhad & Sonia Sarafraz-Asbagh & John Lodewijk Van Genderen, 2022. "Urban Sprawl Simulation Mapping of Urmia (Iran) by Comparison of Cellular Automata–Markov Chain and Artificial Neural Network (ANN) Modeling Approach," Sustainability, MDPI, vol. 14(23), pages 1-16, November.
    8. Sellami, Mohamed Habib & Sifaoui, Mohamed Salah, 2008. "Modelling of heat and mass transfer inside a traditional oasis: Experimental validation," Ecological Modelling, Elsevier, vol. 210(1), pages 144-154.
    9. Di Traglia, Mario & Attorre, Fabio & Francesconi, Fabio & Valenti, Roberto & Vitale, Marcello, 2011. "Is cellular automata algorithm able to predict the future dynamical shifts of tree species in Italy under climate change scenarios? A methodological approach," Ecological Modelling, Elsevier, vol. 222(4), pages 925-934.
    10. Mouton, Ans M. & De Baets, Bernard & Goethals, Peter L.M., 2010. "Ecological relevance of performance criteria for species distribution models," Ecological Modelling, Elsevier, vol. 221(16), pages 1995-2002.
    11. Xiaolong Jin & Penghui Jiang & Haoyang Du & Dengshuai Chen & Manchun Li, 2021. "Response of local temperature variation to land cover and land use intensity changes in China over the last 30 years," Climatic Change, Springer, vol. 164(3), pages 1-20, February.
    12. Aertsen, Wim & Kint, Vincent & van Orshoven, Jos & Özkan, Kürşad & Muys, Bart, 2010. "Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests," Ecological Modelling, Elsevier, vol. 221(8), pages 1119-1130.
    13. Lyndsie S Wszola & Victoria L Simonsen & Erica F Stuber & Caitlyn R Gillespie & Lindsey N Messinger & Karie L Decker & Jeffrey J Lusk & Christopher F Jorgensen & Andrew A Bishop & Joseph J Fontaine, 2017. "Translating statistical species-habitat models to interactive decision support tools," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-13, December.
    14. Basille, Mathieu & Calenge, Clément & Marboutin, Éric & Andersen, Reidar & Gaillard, Jean-Michel, 2008. "Assessing habitat selection using multivariate statistics: Some refinements of the ecological-niche factor analysis," Ecological Modelling, Elsevier, vol. 211(1), pages 233-240.
    15. Rufino, Marta M. & Albouy, Camille & Brind'Amour, Anik, 2021. "Which spatial interpolators I should use? A case study applying to marine species," Ecological Modelling, Elsevier, vol. 449(C).
    16. Mouton, Ans M. & De Baets, Bernard & Van Broekhoven, Ester & Goethals, Peter L.M., 2009. "Prevalence-adjusted optimisation of fuzzy models for species distribution," Ecological Modelling, Elsevier, vol. 220(15), pages 1776-1786.
    17. Stoklosa, Jakub & Huang, Yih-Huei & Furlan, Elise & Hwang, Wen-Han, 2016. "On quadratic logistic regression models when predictor variables are subject to measurement error," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 109-121.
    18. Suárez-Seoane, Susana & García de la Morena, Eladio L. & Morales Prieto, Manuel B. & Osborne, Patrick E. & de Juana, Eduardo, 2008. "Maximum entropy niche-based modelling of seasonal changes in little bustard (Tetrax tetrax) distribution," Ecological Modelling, Elsevier, vol. 219(1), pages 17-29.
    19. Hopkins, Robert L. & Burr, Brooks M., 2009. "Modeling freshwater fish distributions using multiscale landscape data: A case study of six narrow range endemics," Ecological Modelling, Elsevier, vol. 220(17), pages 2024-2034.
    20. Caitlyn E. Sutherlin & Nathaniel A. Brunsell & Gabriel de Oliveira & Timothy E. Crews & Lee R. DeHaan & Giulia Vico, 2019. "Contrasting Physiological and Environmental Controls of Evapotranspiration over Kernza Perennial Crop, Annual Crops, and C 4 and Mixed C 3 /C 4 Grasslands," Sustainability, MDPI, vol. 11(6), pages 1-14, March.

    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:gam:jsusta:v:13:y:2021:i:2:p:462-:d:475527. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.