IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0208266.html
   My bibliography  Save this article

Spatial variation in western corn rootworm (Coleoptera: Chrysomelidae) susceptibility to Cry3 toxins in Nebraska

Author

Listed:
  • Jordan D Reinders
  • Brianna D Hitt
  • Walter W Stroup
  • B Wade French
  • Lance J Meinke

Abstract

Repeated use of field corn (Zea mays L.) hybrids expressing the Cry3Bb1 and mCry3A traits in Nebraska has selected for field-evolved resistance in some western corn rootworm (WCR; Diabrotica virgifera virgifera LeConte) populations. Therefore, this study was conducted to characterize spatial variation in local WCR susceptibility to Cry3Bb1 and mCry3A traits in Keith and Buffalo counties, Nebraska, and determine the relationship between past management practices and current WCR susceptibility. Adult WCR populations were collected from sampling grids during 2015 and 2016 and single-plant larval bioassays conducted with F1 progeny documented significant variation in WCR susceptibility to Cry3Bb1 and mCry3A on different spatial scales in both sampling grids. At the local level, results revealed that neighboring cornfields may support WCR populations with very different susceptibility levels, indicating that gene flow of resistant alleles from high trait survival sites is not inundating large areas. A field history index, comprised of additive and weighted variables including past WCR management tactics and agronomic practices, was developed to quantify relative selection pressure in individual fields. The field history index-Cry3 trait survivorship relationship from year 1 data was highly predictive of year 2 Cry3 trait survivorship when year 2 field history indices were inserted into the year 1 base model. Sensitivity analyses indicated years of trait use and associated selection pressure at the local level were the key drivers of WCR susceptibility to Cry3 traits in this system. Retrospective case histories from this study will inform development of optimal resistance management programs and increase understanding of plant-insect interactions that may occur when transgenic corn is deployed in the landscape. Results from this study also support current recommendations to slow or mitigate the evolution of resistance by using a multi-tactic approach to manage WCR densities in individual fields within an integrated pest management framework.

Suggested Citation

  • Jordan D Reinders & Brianna D Hitt & Walter W Stroup & B Wade French & Lance J Meinke, 2018. "Spatial variation in western corn rootworm (Coleoptera: Chrysomelidae) susceptibility to Cry3 toxins in Nebraska," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-27, November.
    • Handle: RePEc:plo:pone00:0208266
    DOI: 10.1371/journal.pone.0208266
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0208266
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0208266&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0208266?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
    ---><---

    References listed on IDEAS

    as
    1. Silvia Ferrari & Francisco Cribari-Neto, 2004. "Beta Regression for Modelling Rates and Proportions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(7), pages 799-815.
    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. Matthias Schmid & Florian Wickler & Kelly O Maloney & Richard Mitchell & Nora Fenske & Andreas Mayr, 2013. "Boosted Beta Regression," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-15, April.
    2. Paulus, Anne & Hagemann, Nina & Baaken, Marieke C. & Roilo, Stephanie & Alarcón-Segura, Viviana & Cord, Anna F. & Beckmann, Michael, 2022. "Landscape context and farm characteristics are key to farmers' adoption of agri-environmental schemes," Land Use Policy, Elsevier, vol. 121(C).
    3. Domenico Piccolo & Rosaria Simone, 2019. "The class of cub models: statistical foundations, inferential issues and empirical evidence," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 389-435, September.
    4. Jorge I. Figueroa-Zúñiga & Cristian L. Bayes & Víctor Leiva & Shuangzhe Liu, 2022. "Robust beta regression modeling with errors-in-variables: a Bayesian approach and numerical applications," Statistical Papers, Springer, vol. 63(3), pages 919-942, June.
    5. Yayan Hernuryadin & Koji Kotani & Tatsuyoshi Saijo, 2020. "Time Preferences of Food Producers: Does “Cultivate and Grow” Matter?," Land Economics, University of Wisconsin Press, vol. 96(1), pages 132-148.
    6. Mhamed Ben Salah & Cédric Chambru & Maleke Fourati, 2022. "The colonial legacy of education: evidence from of Tunisia," ECON - Working Papers 411, Department of Economics - University of Zurich, revised Sep 2024.
    7. Muhammad Suhail Rizwan & Asifa Obaid & Dawood Ashraf, 2017. "The Impact of Corporate Social Responsibility on Default Risk: Empirical evidence from US Firms," Business & Economic Review, Institute of Management Sciences, Peshawar, Pakistan, vol. 9(3), pages 36-70, September.
    8. Korkeamäki, Timo & Virk, Nader & Wang, Haizhi & Wang, Peng, 2018. "Learning Chinese? The changing investment behavior of foreign institutions in the Chinese stock market," BOFIT Discussion Papers 19/2018, Bank of Finland Institute for Emerging Economies (BOFIT).
    9. Ameztegui, Aitor & Coll, Lluís & Messier, Christian, 2015. "Modelling the effect of climate-induced changes in recruitment and juvenile growth on mixed-forest dynamics: The case of montane–subalpine Pyrenean ecotones," Ecological Modelling, Elsevier, vol. 313(C), pages 84-93.
    10. Takeshima, Hiroyuki & Liverpool-Tasie, Lenis Saweda O., 2015. "Fertilizer subsidies, political influence and local food prices in sub-Saharan Africa: Evidence from Nigeria," Food Policy, Elsevier, vol. 54(C), pages 11-24.
    11. Mustafa Ç. Korkmaz & Emrah Altun & Morad Alizadeh & M. El-Morshedy, 2021. "The Log Exponential-Power Distribution: Properties, Estimations and Quantile Regression Model," Mathematics, MDPI, vol. 9(21), pages 1-19, October.
    12. Silvia Balia, 2007. "Reporting expected longevity and smoking: evidence from the SHARE," Health, Econometrics and Data Group (HEDG) Working Papers 07/10, HEDG, c/o Department of Economics, University of York.
    13. Maria V. Sokolova, 2016. "Trade Re(Im)Balanced: The Role of Regional Trade Agreements," IHEID Working Papers 06-2016, Economics Section, The Graduate Institute of International Studies.
    14. Sokolova, Maria V., 2016. "Exchange Rates, International Trade and Growth: Re-Evaluation of Undervaluation," Conference papers 332790, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    15. Baccini, Leonardo & Urpelainen, Johannes, 2012. "Legislative fractionalization and partisan shifts to the left increase the volatility of public energy R&D expenditures," LSE Research Online Documents on Economics 45571, London School of Economics and Political Science, LSE Library.
    16. Grün, Bettina & Kosmidis, Ioannis & Zeileis, Achim, 2012. "Extended Beta Regression in R: Shaken, Stirred, Mixed, and Partitioned," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i11).
    17. Dries P.J. Kuijper & Jakub W. Bubnicki & Marcin Churski & Bjorn Mols & Pim van Hooft, 2015. "Context dependence of risk effects: wolves and tree logs create patches of fear in an old-growth forest," Behavioral Ecology, International Society for Behavioral Ecology, vol. 26(6), pages 1558-1568.
    18. Guillermo Martínez-Flórez & Artur J. Lemonte & Germán Moreno-Arenas & Roger Tovar-Falón, 2022. "The Bivariate Unit-Sinh-Normal Distribution and Its Related Regression Model," Mathematics, MDPI, vol. 10(17), pages 1-26, August.
    19. Albert Esteve & Coro Chasco & Antonio López-Gay, 2022. "Modeling Local Variations in Intermarriage," Mathematics, MDPI, vol. 10(7), pages 1-18, March.
    20. Lucio Masserini & Matilde Bini & Monica Pratesi, 2017. "Effectiveness of non-selective evaluation test scores for predicting first-year performance in university career: a zero-inflated beta regression approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(2), pages 693-708, March.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0208266. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.