Poster

Modelling the effects of site constancy in bumble bees

eSMB2020 eSMB2020 Follow 2:30 - 3:30pm EDT, Monday - Wednesday
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Sarah MacQueen

"Modelling the effects of site constancy in bumble bees"
Foraging site constancy, or repeated return to the same location forage, is an important aspect of bumble bee behaviour, and should therefore be an important consideration when using modelling to predict the pollination services provided by bumble bees. However, it is unknown exactly how bumble bees select their foraging site, and most modelling studies do not account for this uncertainty. We used an individual based model to explore how predictions of pollination services and bee fitness change under different foraging site selection methods. Pollination services are measured as the percent of fields and number of flowers visited, and bee fitness is measured as the amount of different resource types collected and using behavioural budgets. We tested two different site-reconnaissance or searching methods (random and realistic exploration behaviour) and four different site- selection methods (random and optimizing based on distance from the nest, local wildflower density, or net rate of energy return), as well as comparing results on landscapes with different total amounts of resource and proportional amounts of crop. We found that site- selection methods have a greater impact on crop pollination services and bee fitness than do site-reconnaissance or landscape characteristics, indicating that the site-selection method is an important consideration when modelling bumble bee pollination services. In general, site-selection based on optimizing for the net rate of energy return leads to both the highest crop pollination services and the longest foraging trips. The percent of crop fields visited, amount of time spent foraging, number of foraging sites located in crops, and the number of flowers visited may be used to make hypotheses about how real bees select their foraging sites.
eSMB2020
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Virtual conference of the Society for Mathematical Biology, 2020.