IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i10p3444-d172352.html
   My bibliography  Save this article

Optimization of the Fuzzy Matter Element Method for Predicting Species Suitability Distribution Based on Environmental Data

Author

Listed:
  • Quanzhong Zhang

    (School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China
    National Engineering Laboratory for Resource Development of Endangered Crude Drugs in Northwest of China, Shaanxi Normal University, Xi’an 710119, China)

  • Haiyan Wei

    (School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China)

  • Zefang Zhao

    (School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China
    National Engineering Laboratory for Resource Development of Endangered Crude Drugs in Northwest of China, Shaanxi Normal University, Xi’an 710119, China
    The Key Laboratory of Medicinal Resources and Natural Pharmaceutical Chemistry, the Ministry of Education, Shaanxi Normal University, Xi’an 710119, China)

  • Jing Liu

    (School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China
    National Engineering Laboratory for Resource Development of Endangered Crude Drugs in Northwest of China, Shaanxi Normal University, Xi’an 710119, China)

  • Qiao Ran

    (School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China
    National Engineering Laboratory for Resource Development of Endangered Crude Drugs in Northwest of China, Shaanxi Normal University, Xi’an 710119, China
    The Key Laboratory of Medicinal Resources and Natural Pharmaceutical Chemistry, the Ministry of Education, Shaanxi Normal University, Xi’an 710119, China)

  • Junhong Yu

    (School of Mathematics and Information Science, Shaanxi Normal University, Xi’an 710119, China)

  • Wei Gu

    (National Engineering Laboratory for Resource Development of Endangered Crude Drugs in Northwest of China, Shaanxi Normal University, Xi’an 710119, China
    The Key Laboratory of Medicinal Resources and Natural Pharmaceutical Chemistry, the Ministry of Education, Shaanxi Normal University, Xi’an 710119, China
    College of Life Sciences, Shaanxi Normal University, Xi’an 710119, China)

Abstract

Over the years, with the efforts of many researchers, the field of species distribution model (SDM) has been well explored. The model of fuzzy matter elements (FME), which, combined with GIS to predict species distribution, has received extensive attention since its emergence. Based on previous studies, this paper improved FME, extended the scope of the membership degree and habitat suitability index, and explored the unsuitable areas of species. We have enhanced the limitation effect of key variables on species habitats, making the operation of FME more consistent with biological laws. By optimizing the FME, it could avoid the accumulation of predicted errors with multi-variables, and make the predicted results more reasonable. In this study, Gynostemma pentaphyllum (Thunb.) Makino was used as an example. The experimental process used several major environmental variables (climate, soil, and terrain variables) to predict the habitat suitability distribution of G. pentaphyllum in China for its current and future period, which includes the period of 2050s (average for 2041–2060) and 2070s (average for 2061–2080) under representative concentration pathways 4.5 (RCP4.5). The results of the analysis showed that the model performed well with a high accuracy by reducing the redundancy of the environmental data. The study could relieve the reliance on a large database of environmental information and propose a new approach for protecting the G. pentaphyllum in unsuitable areas under climate change.

Suggested Citation

  • Quanzhong Zhang & Haiyan Wei & Zefang Zhao & Jing Liu & Qiao Ran & Junhong Yu & Wei Gu, 2018. "Optimization of the Fuzzy Matter Element Method for Predicting Species Suitability Distribution Based on Environmental Data," Sustainability, MDPI, vol. 10(10), pages 1-16, September.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:10:p:3444-:d:172352
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/10/3444/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/10/10/3444/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lu, Chun Yan & Gu, Wei & Dai, Ai Hua & Wei, Hai Yan, 2012. "Assessing habitat suitability based on geographic information system (GIS) and fuzzy: A case study of Schisandra sphenanthera Rehd. et Wils. in Qinling Mountains, China," Ecological Modelling, Elsevier, vol. 242(C), pages 105-115.
    2. Freeman, Elizabeth A. & Moisen, Gretchen G. & Frescino, Tracey S., 2012. "Evaluating effectiveness of down-sampling for stratified designs and unbalanced prevalence in Random Forest models of tree species distributions in Nevada," Ecological Modelling, Elsevier, vol. 233(C), pages 1-10.
    3. Zefang Zhao & Yanlong Guo & Haiyan Wei & Qiao Ran & Wei Gu, 2017. "Predictions of the Potential Geographical Distribution and Quality of a Gynostemma pentaphyllum Base on the Fuzzy Matter Element Model in China," Sustainability, MDPI, vol. 9(7), pages 1-15, July.
    4. Trevor H. Booth, 2017. "Assessing species climatic requirements beyond the realized niche: some lessons mainly from tree species distribution modelling," Climatic Change, Springer, vol. 145(3), pages 259-271, December.
    5. Lei Zhang & Shirong Liu & Pengsen Sun & Tongli Wang & Guangyu Wang & Xudong Zhang & Linlin Wang, 2015. "Consensus Forecasting of Species Distributions: The Effects of Niche Model Performance and Niche Properties," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-18, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhengping Liu & Wang Zhang & Hongxian Liu & Guohe Huang & Jiliang Zhen & Xin Qi, 2019. "Characterization of Renewable Energy Utilization Mode for Air-Environmental Quality Improvement through an Inexact Factorial Optimization Approach," Sustainability, MDPI, vol. 11(8), pages 1-19, April.
    2. Maurizio Marchi & Iztok Sinjur & Michele Bozzano & Marjana Westergren, 2019. "Evaluating WorldClim Version 1 (1961–1990) as the Baseline for Sustainable Use of Forest and Environmental Resources in a Changing Climate," Sustainability, MDPI, vol. 11(11), pages 1-14, May.
    3. Wei Xu & Yuqi Miao & Shuaimeng Zhu & Jimin Cheng & Jingwei Jin, 2023. "Modelling the Geographical Distribution Pattern of Apple Trees on the Loess Plateau, China," Agriculture, MDPI, vol. 13(2), pages 1-14, January.
    4. Chenyang Xue & Chaofeng Shao & Sihan Chen, 2020. "SDGs-Based River Health Assessment for Small- and Medium-Sized Watersheds," Sustainability, MDPI, vol. 12(5), pages 1-20, March.

    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. Zhang, Quanzhong & Wei, Haiyan & Liu, Jing & Zhao, Zefang & Ran, Qiao & Gu, Wei, 2021. "A Bayesian network with fuzzy mathematics for species habitat suitability analysis: A case with limited Angelica sinensis (Oliv.) Diels data," Ecological Modelling, Elsevier, vol. 450(C).
    2. Xuhui Zhang & Haiyan Wei & Zefang Zhao & Jing Liu & Quanzhong Zhang & Xiaoyan Zhang & Wei Gu, 2020. "The Global Potential Distribution of Invasive Plants: Anredera cordifolia under Climate Change and Human Activity Based on Random Forest Models," Sustainability, MDPI, vol. 12(4), pages 1-18, February.
    3. Pecchi, Matteo & Marchi, Maurizio & Burton, Vanessa & Giannetti, Francesca & Moriondo, Marco & Bernetti, Iacopo & Bindi, Marco & Chirici, Gherardo, 2019. "Species distribution modelling to support forest management. A literature review," Ecological Modelling, Elsevier, vol. 411(C).
    4. Floris M. Beest & Efrén López-Blanco & Lars H. Hansen & Niels M. Schmidt, 2023. "Extreme shifts in habitat suitability under contemporary climate change for a high-Arctic herbivore," Climatic Change, Springer, vol. 176(4), pages 1-14, April.
    5. Jingwei Song & Xinyuan Wang & Ying Liao & Jing Zhen & Natarajan Ishwaran & Huadong Guo & Ruixia Yang & Chuansheng Liu & Chun Chang & Xin Zong, 2014. "An Improved Neural Network for Regional Giant Panda Habitat Suitability Mapping: A Case Study in Ya’an Prefecture," Sustainability, MDPI, vol. 6(7), pages 1-18, June.
    6. Diederik Strubbe & Laura Jiménez & A. Márcia Barbosa & Amy J. S. Davis & Luc Lens & Carsten Rahbek, 2023. "Mechanistic models project bird invasions with accuracy," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    7. Jeong Soo Park & Donghui Choi & Youngha Kim, 2020. "Potential Distribution of Goldenrod ( Solidago altissima L.) during Climate Change in South Korea," Sustainability, MDPI, vol. 12(17), pages 1-11, August.
    8. Perennes, Marie & Diekötter, Tim & Groß, Jens & Burkhard, Benjamin, 2021. "A hierarchical framework for mapping pollination ecosystem service potential at the local scale," Ecological Modelling, Elsevier, vol. 444(C).
    9. Trevor H. Booth & Paul R. Muir, 2020. "Climate change impacts on Australia's eucalypt and coral species: Comparing and sharing knowledge across disciplines," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 11(5), September.
    10. Maurizio Marchi & Iztok Sinjur & Michele Bozzano & Marjana Westergren, 2019. "Evaluating WorldClim Version 1 (1961–1990) as the Baseline for Sustainable Use of Forest and Environmental Resources in a Changing Climate," Sustainability, MDPI, vol. 11(11), pages 1-14, May.
    11. Yuncheng Zhao & Mingyue Zhao & Lei Zhang & Chunyi Wang & Yinlong Xu, 2021. "Predicting Possible Distribution of Tea ( Camellia sinensis L.) under Climate Change Scenarios Using MaxEnt Model in China," Agriculture, MDPI, vol. 11(11), pages 1-18, November.
    12. Lucas Kruger, 2018. "Population Estimates of Trindade Petrel (Pterodroma arminjoniana) by Ensemble Nesting Habitat Modelling," International Journal of Environmental Sciences & Natural Resources, Juniper Publishers Inc., vol. 10(4), pages 145-157, April.
    13. Yanlin Tian & Zongming Wang & Dehua Mao & Lin Li & Mingyue Liu & Mingming Jia & Weidong Man & Chunyan Lu, 2019. "Remote Observation in Habitat Suitability Changes for Waterbirds in the West Songnen Plain, China," Sustainability, MDPI, vol. 11(6), pages 1-21, March.
    14. Loehle, Craig, 2018. "Disequilibrium and relaxation times for species responses to climate change," Ecological Modelling, Elsevier, vol. 384(C), pages 23-29.
    15. José-Silva, Leandro & dos Santos, Reginaldo Carvalho & de Lima, Bruna Martins & Lima, Mendelson & de Oliveira-Júnior, José Francisco & Teodoro, Paulo Eduardo & Eisenlohr, Pedro V. & da Silva Junior, C, 2018. "Improving the validation of ecological niche models with remote sensing analysis," Ecological Modelling, Elsevier, vol. 380(C), pages 22-30.
    16. Jiménez, L. & Soberón, J., 2022. "Estimating the fundamental niche: Accounting for the uneven availability of existing climates in the calibration area," Ecological Modelling, Elsevier, vol. 464(C).
    17. Yumin Wang & Weijian Ran, 2019. "Comprehensive Eutrophication Assessment Based on Fuzzy Matter Element Model and Monte Carlo-Triangular Fuzzy Numbers Approach," IJERPH, MDPI, vol. 16(10), pages 1-17, May.
    18. Xingtao Wei & Oliver Valentine Eboy & Lu Xu & Di Yu, 2023. "Ecological Sensitivity of Urban Agglomeration in the Guanzhong Plain, China," Sustainability, MDPI, vol. 15(6), pages 1-21, March.
    19. Benkendorf, Donald J. & Schwartz, Samuel D. & Cutler, D. Richard & Hawkins, Charles P., 2023. "Correcting for the effects of class imbalance improves the performance of machine-learning based species distribution models," Ecological Modelling, Elsevier, vol. 483(C).
    20. Lewis A. Jones & Philip D. Mannion & Alexander Farnsworth & Fran Bragg & Daniel J. Lunt, 2022. "Climatic and tectonic drivers shaped the tropical distribution of coral reefs," Nature Communications, Nature, vol. 13(1), pages 1-10, 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:gam:jsusta:v:10:y:2018:i:10:p:3444-:d:172352. 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.