IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v484y2023ics0304380023001898.html
   My bibliography  Save this article

Addition of finer scale data and uncertainty analysis increases precision of geospatial suitability model for non-native plants in the US

Author

Listed:
  • Kim, Seokmin
  • Koop, Anthony
  • Fowler, Glenn
  • Israel, Kimberly
  • Takeuchi, Yu
  • Lieurance, Deah

Abstract

“Proto3” is a geospatial model used by the United States Department of Agriculture (USDA) Plant Protection and Quarantine to predict the potential distribution of non-native weed species in the continental U.S. as part of routine weed risk assessments (WRA). While performing as well as other methods, this tool has the benefit of being simple to produce, expanding accessibility and reproducibility. However, it has the tendency to overestimate potential distributions. To address this shortcoming, this paper introduces the “Proto4” model and compares it with the established and mechanistically similar “Proto3” model currently used. Both models overlay Plant Hardiness Zones, precipitation, and Köppen-Geiger climate classes with global distribution of a plant species and rely on semi-qualitative assessments of a plant's affinity for each of the climate categories. However, Proto4 uses more detailed layers of the Plant Hardiness Zones and Köppen-Geiger climate classes, adds elevation as a fourth predictive variable to increase the precision of predictive maps. Additionally, we incorporate uncertainty to spatially distinguish regions of different potential suitability. We compared the performance of both models by estimating the predicted distributions of 30 broadly distributed, invasive plants in the U.S. with Proto3 and Proto4. We found that on average, the Proto4 model produces predicted distributions that are nearly 780,000 square kilometers (an area larger than the state of Texas) smaller than the Proto3, while only failing to capture a median of fewer than 0.5% more georeferenced points. Furthermore, the inclusion of uncertainty classes adds to the utility of Proto4 by distinguishing areas with greater and lesser degrees of evidence that a particular area is suitable for an invasive species, providing more information to help select invasive species prevention and management prioritization strategies.

Suggested Citation

  • Kim, Seokmin & Koop, Anthony & Fowler, Glenn & Israel, Kimberly & Takeuchi, Yu & Lieurance, Deah, 2023. "Addition of finer scale data and uncertainty analysis increases precision of geospatial suitability model for non-native plants in the US," Ecological Modelling, Elsevier, vol. 484(C).
    • Handle: RePEc:eee:ecomod:v:484:y:2023:i:c:s0304380023001898
    DOI: 10.1016/j.ecolmodel.2023.110458
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380023001898
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2023.110458?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. Gastón, Aitor & García-Viñas, Juan I., 2013. "Evaluating the predictive performance of stacked species distribution models applied to plant species selection in ecological restoration," Ecological Modelling, Elsevier, vol. 263(C), pages 103-108.
    2. Ariel Dinar & Jessica Bradford & Edgar Castelan & Jorge Gavino & Jacquelyn González & Adam Jantz & Yang Li & Fortino Morales III & Michael Parmer, 2022. "Climate Change Policy," World Scientific Book Chapters, in: Lecture Notes in Global-Local Policy Interactions, chapter 7, pages 117-131, World Scientific Publishing Co. Pte. Ltd..
    3. Lars T. Ruig & Toon Haer & Hans Moel & Samuel D. Brody & W. J. Wouter Botzen & Jeffrey Czajkowski & Jeroen C. J. H. Aerts, 2022. "Climate-proofing the National Flood Insurance Program," Nature Climate Change, Nature, vol. 12(11), pages 975-976, November.
    4. Christine Ro, 2022. "Marching in the streets for climate-crisis action," Nature, Nature, vol. 603(7900), pages 349-351, March.
    5. Chengcheng J. Fei & Bruce A. McCarl & Yingqian Yang & Essayas Kaba Ayana & Raghavan Srinivasan & Yuhong Lei & Lingyi Li & Bingru Sheng & Xinxin Fan, 2022. "Impacts of climate change on water management," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 44(3), pages 1448-1464, September.
    6. Thomas F. Coleman & Nicole S. Dumont & Wanqi Li & Wenbin Liu & Alexey Rubtsov, 2022. "Optimal Pricing of Climate Risk," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 1101-1134, October.
    7. Richard S. J. Tol, 2023. "Costs And Benefits Of The Paris Climate Targets," Climate Change Economics (CCE), World Scientific Publishing Co. Pte. Ltd., vol. 14(04), pages 1-18, November.
    8. Oecd, 2022. "Teaching for climate action," Teaching in Focus 44, OECD Publishing.
    9. Barney P. Caton & Anthony L. Koop & Larry Fowler & Leslie Newton & Lisa Kohl, 2018. "Quantitative Uncertainty Analysis for a Weed Risk Assessment System," Risk Analysis, John Wiley & Sons, vol. 38(9), pages 1972-1987, September.
    10. Ocde, 2022. "L'éducation à l'action climatique," L'enseignement à la loupe 44, OECD Publishing.
    11. Katherine R. H. Wagner, 2022. "Designing insurance for climate change," Nature Climate Change, Nature, vol. 12(12), pages 1070-1072, December.
    12. Oke, Oluwatobi A. & Thompson, Ken A., 2015. "Distribution models for mountain plant species: The value of elevation," Ecological Modelling, Elsevier, vol. 301(C), pages 72-77.
    13. ., 2022. "Climate change," Chapters, in: Rethinking Agricultural and Food Policy, chapter 7, pages 119-136, Edward Elgar Publishing.
    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. Pata, Ugur Korkut & Kartal, Mustafa Tevfik & Erdogan, Sinan & Sarkodie, Samuel Asumadu, 2023. "The role of renewable and nuclear energy R&D expenditures and income on environmental quality in Germany: Scrutinizing the EKC and LCC hypotheses with smooth structural changes," Applied Energy, Elsevier, vol. 342(C).
    2. Albà, C.G. & Alkhatib, I.I.I. & Llovell, F. & Vega, L.F., 2023. "Hunting sustainable refrigerants fulfilling technical, environmental, safety and economic requirements," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    3. Cheng, Louis T.W. & Shen, Jianfu & Wojewodzki, Michal, 2023. "A cross-country analysis of corporate carbon performance: An international investment perspective," Research in International Business and Finance, Elsevier, vol. 64(C).
    4. Keskin, Burcu B. & Griffin, Emily C. & Prell, Jonathan O. & Dilkina, Bistra & Ferber, Aaron & MacDonald, John & Hilend, Rowan & Griffis, Stanley & Gore, Meredith L., 2023. "Quantitative Investigation of Wildlife Trafficking Supply Chains: A Review," Omega, Elsevier, vol. 115(C).
    5. Chiang, Chia-Chun & Niehaus, Greg, 2024. "Market discipline and policy loans," Journal of Banking & Finance, Elsevier, vol. 159(C).
    6. Brinkhoff, James & Houborg, Rasmus & Dunn, Brian W., 2022. "Rice ponding date detection in Australia using Sentinel-2 and Planet Fusion imagery," Agricultural Water Management, Elsevier, vol. 273(C).
    7. Coppens, Léo & Venmans, Frank, 2025. "The welfare properties of climate targets," LSE Research Online Documents on Economics 125996, London School of Economics and Political Science, LSE Library.
    8. Sangui Yi & Jihua Zhou & Liming Lai & Qinglin Sun & Xin Liu & Benben Liu & Jiaojiao Guo & Yuanrun Zheng, 2021. "Different Causal Factors Occur between Land Use/Cover and Vegetation Classification Systems but Not between Vegetation Classification Levels in the Highly Disturbed Jing-Jin-Ji Region of China," Sustainability, MDPI, vol. 13(8), pages 1-23, April.
    9. Malay Pramanik & Atul Kumar Diwakar & Poli Dash & Sylvia Szabo & Indrajit Pal, 2021. "Conservation planning of cash crops species (Garcinia gummi-gutta) under current and future climate in the Western Ghats, India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(4), pages 5345-5370, April.
    10. Ji-Zhong Wan & Chun-Jing Wang & Fei-Hai Yu, 2017. "Spatial conservation prioritization for dominant tree species of Chinese forest communities under climate change," Climatic Change, Springer, vol. 144(2), pages 303-316, September.
    11. Kosicki, Jakub Z., 2017. "Should topographic metrics be considered when predicting species density of birds on a large geographical scale? A case of Random Forest approach," Ecological Modelling, Elsevier, vol. 349(C), pages 76-85.
    12. Nick Kirsop-Taylor & Duncan Russel & Anne Jensen, 2023. "A typology of the climate activist," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-7, December.
    13. Kuehnlenz, Sophia & Orsi, Bianca & Kaltenbrunner, Annina, 2023. "Central bank digital currencies and the international payment system: The demise of the US dollar?," Research in International Business and Finance, Elsevier, vol. 64(C).
    14. L. Lombardo & G. Fubelli & G. Amato & M. Bonasera, 2016. "Presence-only approach to assess landslide triggering-thickness susceptibility: a test for the Mili catchment (north-eastern Sicily, Italy)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(1), pages 565-588, October.
    15. Zabihinia Gerdroodbari, Yasin & Khorasany, Mohsen & Razzaghi, Reza & Heidari, Rahmat, 2024. "Management of prosumers using dynamic export limits and shared Community Energy Storage," Applied Energy, Elsevier, vol. 355(C).

    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:484:y:2023:i:c:s0304380023001898. 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.