DOI: 10.5061/dryad.cnp5hqc38
title: Spatial allocation without spatial recruitment in bumblebees

description: 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.
description: This experimental data was collected from colonies of the
 bumblebee <em>Bombus impatiens</em>. 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.
description: 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.