IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i7p6051-d1112738.html
   My bibliography  Save this article

Evaluating the Urban-Rural Differences in the Environmental Factors Affecting Amphibian Roadkill

Author

Listed:
  • Jingxuan Zhao

    (School of Ecological Technology and Engineering, Shanghai Institute of Technology, Fengxian Campus, Shanghai 201418, China
    Center for Urban Road Ecological Engineering and Technology of Shanghai Municipality, Shanghai 201418, China)

  • Weiyu Yu

    (School of Ecological Technology and Engineering, Shanghai Institute of Technology, Fengxian Campus, Shanghai 201418, China
    Center for Urban Road Ecological Engineering and Technology of Shanghai Municipality, Shanghai 201418, China
    School of Geography and Environmental Science, University of Southampton, Building 44, Highfield, Southampton SO17 1BJ, UK)

  • Kun He

    (School of Ecological Technology and Engineering, Shanghai Institute of Technology, Fengxian Campus, Shanghai 201418, China)

  • Kun Zhao

    (School of Ecological Technology and Engineering, Shanghai Institute of Technology, Fengxian Campus, Shanghai 201418, China
    Center for Urban Road Ecological Engineering and Technology of Shanghai Municipality, Shanghai 201418, China)

  • Chunliang Zhou

    (School of Ecological Technology and Engineering, Shanghai Institute of Technology, Fengxian Campus, Shanghai 201418, China
    Center for Urban Road Ecological Engineering and Technology of Shanghai Municipality, Shanghai 201418, China)

  • Jim A. Wright

    (School of Geography and Environmental Science, University of Southampton, Building 44, Highfield, Southampton SO17 1BJ, UK)

  • Fayun Li

    (School of Ecological Technology and Engineering, Shanghai Institute of Technology, Fengxian Campus, Shanghai 201418, China
    Center for Urban Road Ecological Engineering and Technology of Shanghai Municipality, Shanghai 201418, China)

Abstract

Roads have major impacts on wildlife, and the most direct negative effect is through deadly collisions with vehicles, i.e., roadkill. Amphibians are the most frequently road-killed animal group. Due to the significant differences between urban and rural environments, the potential urban-rural differences in factors driving amphibian roadkill risks should be incorporated into the planning of mitigation measures. Drawing on a citizen-collected roadkill dataset from Taiwan island, we present a MaxEnt based modelling analysis to examine potential urban-rural differences in landscape features and environmental factors associated with amphibian road mortality. By incorporating with the Global Human Settlement Layer Settlement Model—an ancillary human settlement dataset divided by built-up area and population density—amphibian roadkill data were divided into urban and rural data sets, and then used to create separate models for urban and rural areas. Model diagnostics suggested good performance (all AUCs > 0.8) of both urban and rural models. Multiple variable importance evaluations revealed significant differences between urban and rural areas. The importance of environmental variables was evaluated based on percent contribution, permutation importance and the Jackknife test. According to the overall results, road density was found to be important in explaining the amphibian roadkill in rural areas, whilst precipitation of warmest quarter was found to best explain the amphibian roadkill in the urban context. The method and outputs illustrated in this study can be useful tools to better understand amphibian road mortality in urban and rural environments and to inform mitigation assessment and conservation planning.

Suggested Citation

  • Jingxuan Zhao & Weiyu Yu & Kun He & Kun Zhao & Chunliang Zhou & Jim A. Wright & Fayun Li, 2023. "Evaluating the Urban-Rural Differences in the Environmental Factors Affecting Amphibian Roadkill," Sustainability, MDPI, vol. 15(7), pages 1-16, March.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:6051-:d:1112738
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/7/6051/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/7/6051/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    2. Shwiff, Stephanie A. & Smith, Henry T. & Engeman, Richard M. & Barry, Robert M. & Rossmanith, Robin J. & Nelson, Mark, 2007. "Bioeconomic analysis of herpetofauna road-kills in a Florida state park," Ecological Economics, Elsevier, vol. 64(1), pages 181-185, October.
    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. Mansoor, Umer & Jamal, Arshad & Su, Junbiao & Sze, N.N. & Chen, Anthony, 2023. "Investigating the risk factors of motorcycle crash injury severity in Pakistan: Insights and policy recommendations," Transport Policy, Elsevier, vol. 139(C), pages 21-38.
    2. Bissan Ghaddar & Ignacio Gómez-Casares & Julio González-Díaz & Brais González-Rodríguez & Beatriz Pateiro-López & Sofía Rodríguez-Ballesteros, 2023. "Learning for Spatial Branching: An Algorithm Selection Approach," INFORMS Journal on Computing, INFORMS, vol. 35(5), pages 1024-1043, September.
    3. Akash Malhotra, 2018. "A hybrid econometric-machine learning approach for relative importance analysis: Prioritizing food policy," Papers 1806.04517, arXiv.org, revised Aug 2020.
    4. Nahushananda Chakravarthy H G & Karthik M Seenappa & Sujay Raghavendra Naganna & Dayananda Pruthviraja, 2023. "Machine Learning Models for the Prediction of the Compressive Strength of Self-Compacting Concrete Incorporating Incinerated Bio-Medical Waste Ash," Sustainability, MDPI, vol. 15(18), pages 1-22, September.
    5. Tim Voigt & Martin Kohlhase & Oliver Nelles, 2021. "Incremental DoE and Modeling Methodology with Gaussian Process Regression: An Industrially Applicable Approach to Incorporate Expert Knowledge," Mathematics, MDPI, vol. 9(19), pages 1-26, October.
    6. Wen, Shaoting & Buyukada, Musa & Evrendilek, Fatih & Liu, Jingyong, 2020. "Uncertainty and sensitivity analyses of co-combustion/pyrolysis of textile dyeing sludge and incense sticks: Regression and machine-learning models," Renewable Energy, Elsevier, vol. 151(C), pages 463-474.
    7. Zhu, Haibin & Bai, Lu & He, Lidan & Liu, Zhi, 2023. "Forecasting realized volatility with machine learning: Panel data perspective," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 251-271.
    8. Spiliotis, Evangelos & Makridakis, Spyros & Kaltsounis, Anastasios & Assimakopoulos, Vassilios, 2021. "Product sales probabilistic forecasting: An empirical evaluation using the M5 competition data," International Journal of Production Economics, Elsevier, vol. 240(C).
    9. Zhang, Ning & Li, Zhiying & Zou, Xun & Quiring, Steven M., 2019. "Comparison of three short-term load forecast models in Southern California," Energy, Elsevier, vol. 189(C).
    10. Smyl, Slawek & Hua, N. Grace, 2019. "Machine learning methods for GEFCom2017 probabilistic load forecasting," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1424-1431.
    11. Barzin,Samira & Avner,Paolo & Maruyama Rentschler,Jun Erik & O’Clery,Neave, 2022. "Where Are All the Jobs ? A Machine Learning Approach for High Resolution Urban Employment Prediction inDeveloping Countries," Policy Research Working Paper Series 9979, The World Bank.
    12. Eike Emrich & Christian Pierdzioch, 2016. "Volunteering, Match Quality, and Internet Use," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 136(2), pages 199-226.
    13. Kusiak, Andrew & Zheng, Haiyang & Song, Zhe, 2009. "On-line monitoring of power curves," Renewable Energy, Elsevier, vol. 34(6), pages 1487-1493.
    14. Zhu, Siying & Zhu, Feng, 2019. "Cycling comfort evaluation with instrumented probe bicycle," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 217-231.
    15. Catherine Ikae & Jacques Savoy, 2022. "Gender identification on Twitter," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(1), pages 58-69, January.
    16. Barkan, Oren & Benchimol, Jonathan & Caspi, Itamar & Cohen, Eliya & Hammer, Allon & Koenigstein, Noam, 2023. "Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1145-1162.
    17. Martijn Kagie & Michiel Van Wezel, 2007. "Hedonic price models and indices based on boosting applied to the Dutch housing market," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 15(3‐4), pages 85-106, July.
    18. Matthias Bogaert & Michel Ballings & Dirk Van den Poel, 2018. "Evaluating the importance of different communication types in romantic tie prediction on social media," Annals of Operations Research, Springer, vol. 263(1), pages 501-527, April.
    19. Dursun Delen & Hamed M. Zolbanin & Durand Crosby & David Wright, 2021. "To imprison or not to imprison: an analytics model for drug courts," Annals of Operations Research, Springer, vol. 303(1), pages 101-124, August.
    20. Doruk Cengiz & Arindrajit Dube & Attila S. Lindner & David Zentler-Munro, 2021. "Seeing Beyond the Trees: Using Machine Learning to Estimate the Impact of Minimum Wages on Labor Market Outcomes," NBER Working Papers 28399, National Bureau of Economic Research, Inc.

    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:15:y:2023:i:7:p:6051-:d:1112738. 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.