IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0197589.html
   My bibliography  Save this article

Using within-day hive weight changes to measure environmental effects on honey bee colonies

Author

Listed:
  • William G Meikle
  • Niels Holst
  • Théotime Colin
  • Milagra Weiss
  • Mark J Carroll
  • Quinn S McFrederick
  • Andrew B Barron

Abstract

Patterns in within-day hive weight data from two independent datasets in Arizona and California were modeled using piecewise regression, and analyzed with respect to honey bee colony behavior and landscape effects. The regression analysis yielded information on the start and finish of a colony’s daily activity cycle, hive weight change at night, hive weight loss due to departing foragers and weight gain due to returning foragers. Assumptions about the meaning of the timing and size of the morning weight changes were tested in a third study by delaying the forager departure times from one to three hours using screen entrance gates. A regression of planned vs. observed departure delays showed that the initial hive weight loss around dawn was largely due to foragers. In a similar experiment in Australia, hive weight loss due to departing foragers in the morning was correlated with net bee traffic (difference between the number of departing bees and the number of arriving bees) and from those data the payload of the arriving bees was estimated to be 0.02 g. The piecewise regression approach was then used to analyze a fifth study involving hives with and without access to natural forage. The analysis showed that, during a commercial pollination event, hives with previous access to forage had a significantly higher rate of weight gain as the foragers returned in the afternoon, and, in the weeks after the pollination event, a significantly higher rate of weight loss in the morning, as foragers departed. This combination of continuous weight data and piecewise regression proved effective in detecting treatment differences in foraging activity that other methods failed to detect.

Suggested Citation

  • William G Meikle & Niels Holst & Théotime Colin & Milagra Weiss & Mark J Carroll & Quinn S McFrederick & Andrew B Barron, 2018. "Using within-day hive weight changes to measure environmental effects on honey bee colonies," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-21, May.
  • Handle: RePEc:plo:pone00:0197589
    DOI: 10.1371/journal.pone.0197589
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0197589
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0197589&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0197589?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. Simon N. Wood, 2001. "Minimizing Model Fitting Objectives That Contain Spurious Local Minima by Bootstrap Restarting," Biometrics, The International Biometric Society, vol. 57(1), pages 240-244, 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. William G Meikle & Vanessa Corby-Harris & Mark J Carroll & Milagra Weiss & Lucy A Snyder & Charlotte A D Meador & Eli Beren & Nicholas Brown, 2019. "Exposure to sublethal concentrations of methoxyfenozide disrupts honey bee colony activity and thermoregulation," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-21, March.
    2. Coby van Dooremalen & Frank van Langevelde, 2021. "Can Colony Size of Honeybees ( Apis mellifera ) Be Used as Predictor for Colony Losses Due to Varroa destructor during Winter?," Agriculture, MDPI, vol. 11(6), pages 1-11, June.

    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. Daniel Rojas-Diaz & Alexandra Catano-Lopez & Carlos M. Vélez & Santiago Ortiz & Henry Laniado, 2024. "Confidence sub-contour box: an alternative to traditional confidence intervals," Computational Statistics, Springer, vol. 39(5), pages 2821-2858, July.
    2. Emmanuel Kasongo Yakusu & Joris Van Acker & Hans Van de Vyver & Nils Bourland & José Mbifo Ndiapo & Théophile Besango Likwela & Michel Lokonda Wa Kipifo & Amand Mbuya Kankolongo & Jan Van den Bulcke &, 2023. "Ground-based climate data show evidence of warming and intensification of the seasonal rainfall cycle during the 1960–2020 period in Yangambi, central Congo Basin," Climatic Change, Springer, vol. 176(10), pages 1-28, October.
    3. Andrews, Jeffrey L., 2018. "Addressing overfitting and underfitting in Gaussian model-based clustering," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 160-171.
    4. Nedorezov, Lev V. & Sadykova, Dinara L., 2008. "Green oak leaf roller moth dynamics: An application of discrete time mathematical models," Ecological Modelling, Elsevier, vol. 212(1), pages 162-170.
    5. Steffen Rebennack & Vitaliy Krasko, 2020. "Piecewise Linear Function Fitting via Mixed-Integer Linear Programming," INFORMS Journal on Computing, INFORMS, vol. 32(2), pages 507-530, April.
    6. Sun, Yan & Wan, Chuang & Zhang, Wenyang & Zhong, Wei, 2024. "A Multi-Kink quantile regression model with common structure for panel data analysis," Journal of Econometrics, Elsevier, vol. 239(2).
    7. William G Meikle & Vanessa Corby-Harris & Mark J Carroll & Milagra Weiss & Lucy A Snyder & Charlotte A D Meador & Eli Beren & Nicholas Brown, 2019. "Exposure to sublethal concentrations of methoxyfenozide disrupts honey bee colony activity and thermoregulation," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-21, March.
    8. Spada, Matteo & Paraschiv, Florentina & Burgherr, Peter, 2018. "A comparison of risk measures for accidents in the energy sector and their implications on decision-making strategies," Energy, Elsevier, vol. 154(C), pages 277-288.

    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:pone00:0197589. 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.