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Research Project: Determining the Impacts of Pesticide- and Nutrition-Induced Stress on Honey Bee Colony Growth and Survival

Location: Honey Bee Research

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

Author
item Meikle, William
item Holst, Niels - Aarhus University
item Colin, Theotime - Macquarie University
item Weiss, Milagra
item Carroll, Mark
item Mcfrederick, Quinn - University Of California
item Barron, Andrew - Macquarie University

Submitted to: PLoS One
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/6/2018
Publication Date: 5/23/2018
Citation: Meikle, W.G., Holst, N., Colin, T., Weiss, M., Carroll, M.J., Mcfrederick, Q., Barron, A. 2018. Using within-day hive weight changes to measure environmental effects on honey bee colonies. PLoS One. 13(5):e0197589. https://doi.org/10.1371/journal.pone.0197589.
DOI: https://doi.org/10.1371/journal.pone.0197589

Interpretive Summary: The use of hive scales is increasing, and scales are being used to monitor not just hive weight gain due to honey, but bee colony growth and activity. The challenge for those using hive scales is to extract as much useful information as possible from weight data. Here we obtained raw within-day weight changes for many hives over several different experiments. Hive weights change during the day for many reasons, such as forager departure and return, water gain and loss, and nectar collection (and consumption). We fit a kind of “broken line” regression to the data, which was essentially a line with several segments. We found that a line with 5 segments had the best fit over time and across experiments. Such lines tell us when the bees leave in the morning, when foragers start coming back in large numbers, when the bees finish foraging for the day, and how much colony weight changes at night. These are all useful response variables in field experiments. We applied this method to an experiment in which we had not been able to detect colony-level effects of malnutrition using other methods. This new method proved to be more sensitive, and we were able to detect treatment differences.

Technical 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. The piecewise regression approach was then used to analyze a fourth 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.