BEESCOUT: A model of bee scouting behaviour and a software tool for characterizing nectar/pollen landscapes for BEEHAVE.
Becher et al 2016 Ecol Model BEESCOUT.pdf (1.321Mb) mmc2.pdf (693.3Kb) mmc3.pdf (988.6Kb) mmc4.xlsx (66.81Kb) mmc5.pdf (87.43Kb) mmc6.gif (4.501Kb) mmc7.gif (15.46Kb) mmc8.gif (25.73Kb) mmc9.gif (4.164Kb) mmc10.pdf (214.5Kb) mmc10.pdf (214.5Kb) mmc11.pdf (193.3Kb) mmc12.pdf (319.0Kb) mmc13.pdf (321.0Kb) S1_EcolMod_Beescout_model.nlogo (129.6Kb)Show MoreShow Less
© 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CCBY license (http://creativecommons.org/licenses/by/4.0/).
Social bees are central place foragers collecting floral resources from the surrounding landscape, but little is known about the probability of a scouting bee finding a particular flower patch. We therefore developed a software tool, BEESCOUT, to theoretically examine how bees might explore a landscape and distribute their scouting activities over time and space. An image file can be imported, which is interpreted by the model as a "forage map" with certain colours representing certain crops or habitat types as specified by the user. BEESCOUT calculates the size and location of these potential food sources in that landscape relative to a bee colony. An individual-based model then determines the detection probabilities of the food patches by bees, based on parameter values gathered from the flight patterns of radar-tracked honeybees and bumblebees. Various "search modes" describe hypothetical search strategies for the long-range exploration of scouting bees. The resulting detection probabilities of forage patches can be used as input for the recently developed honeybee model BEEHAVE, to explore realistic scenarios of colony growth and death in response to different stressors. In example simulations, we find that detection probabilities for food sources close to the colony fit empirical data reasonably well. However, for food sources further away no empirical data are available to validate model output. The simulated detection probabilities depend largely on the bees' search mode, and whether they exchange information about food source locations. Nevertheless, we show that landscape structure and connectivity of food sources can have a strong impact on the results. We believe that BEESCOUT is a valuable tool to better understand how landscape configurations and searching behaviour of bees affect detection probabilities of food sources. It can also guide the collection of relevant data and the design of experiments to close knowledge gaps, and provides a useful extension to the BEEHAVE honeybee model, enabling future users to explore how landscape structure and food availability affect the foraging decisions and patch visitation rates of the bees and, in consequence, to predict colony development and survival.
We thank Peter Kennedy and Emma Wright for their contributions to the development of BEESCOUT and its publication. This work was funded by the Biotechnology and Biological Sciences Research Council of the UK [BB/J014915/1].
This is the final version of the article. Available from Elsevier via the DOI in this record.
Vol. 340, 24 November 2016, pp. 126 - 133
Place of publication