10.5061/DRYAD.CNP5HQC38
Incorvaia, Darren
0000-0003-4443-951X
Michigan State University
Hintze, Arend
0000-0002-4872-1961
Dalarna University
Dyer, Fred
Michigan State University
Spatial allocation without spatial recruitment in bumblebees
Dryad
dataset
2020
Foraging
social insect
social behavior
2021-06-13T00:00:00Z
2021-06-13T00:00:00Z
en
https://doi.org/10.1093/beheco/araa125
2111288 bytes
3
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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: https://github.com/dcincorvaia/Spatial-Allocation.
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.