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Spatial allocation without spatial recruitment in bumblebees

Cite this dataset

Incorvaia, Darren; Hintze, Arend; Dyer, Fred (2021). Spatial allocation without spatial recruitment in bumblebees [Dataset]. Dryad.


Any foraging animal is expected to allocate its efforts among resource patches that vary in quality across time and space. For social insects, this problem is shifted to the colony level: the task of allocating foraging workers to the best patches currently available. To deal with this task, honeybees rely upon differential recruitment via the dance language, while some ants use differential recruitment on odor trails. Bumblebees, close relatives of honeybees, should also benefit from optimizing spatial allocation, but lack any targeted recruitment system. How bumblebees solve this problem is thus of immense interest to evolutionary biologists studying collective behavior. It has been thought that bumblebees could solve the spatial allocation problem by relying on the summed individual decisions of foragers, who occasionally sample and shift to alternative resources. We use field experiments to test the hypothesis that bumblebees augment individual exploration with social information. Specifically, we provide behavioral evidence that when higher-concentration sucrose arrives at the nest, employed foragers abandon their patches to begin searching for the better option; they are more likely to accept novel resources if they match the quality of the sucrose solution experienced in the nest. We explored this strategy further by building an agent-based model of bumblebee foraging. This model supports the hypothesis that using social information to inform search decisions is advantageous over individual search alone. Our results show that bumblebees use a collective foraging strategy built on social modulation of individual decisions, providing further insight into the evolution of collective behavior.


This experimental data was collected from colonies of the bumblebee Bombus impatiens. We video recorded bees at artificial feeders, and then later extracted behavioral data and recorded it manually in notebooks. Our behavioral measures here are latency to feed from the constant feeder and latency to discover the novel feeder. We then transferred data from the notebooks into an Excel spreadsheet. Please see the full manuscript for more information.

Simulation data was collected by running replicates of an agent-based model, designed to simulate a colony of foraging bumblebees. This model was written in Python. Data from resulting files were extracted and analyzed using Python. Please see the manuscript for more information about the model. The data presented here are the raw data files; code to process the files (as well as the model code itself) can be found here:

Usage notes

The "Notes" column includes details on individual datapoints that anyone wishing to use this data may want to take into consideration. There is an "Exclude" column as well, with the reason for the exclusion usually mentioned in the notes. There is one data point excluded in the "Spatial Allocation Latency" data that is exluded with no note; this was excluded because the bee in question hadn't experienced the treatment; the treatment was injected into the nest, and this bee didn't return to the nest before this measurement was taken.