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\begin{document}

\title{WIN proposal:~ Heterogeneity in collectives}



\author[1]{Jacob Davidson}%
\author[1]{medhavi.vishwakarma}%
\author[1]{smithleemichael}%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 10, 2024}


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\par\null\par\null\par\null\par\null\par\null\par\null

My previous application for Zukunftskolleg (Konstanz) on movement
patterns in honey bee colonies:~

\par\null

\textbf{TITLE:}

Do the right thing, in the right place, at the right time: how
individual and collective motion patterns predict task allocation and
developmental state in honey bee colonies

\textbf{~}

\textbf{~}

\textbf{BACKGROUND:}

How social insect colonies organize tasks among individuals both
fascinates and facilitates their ecological dominance (Wilson 1971;
Oster \& Wilson 1978; H\selectlanguage{ngerman}ölldobler \& Wilson 1990). Some species, such as
leaf-cutter ants, rear specialized individuals to complete tasks, but
most social insects rely on age polyethism, where workers complete
different tasks as they age. In honey bee colonies, for example, young
workers care for brood, middle-aged workers process nectar and maintain
the nest, and elderly bees collect forage (Seeley 1995). These tasks are
performed in well-defined areas of the nest: brood care in the center;
nectar processing at the periphery; hive maintenance throughout; and
foraging outside (Fig1, Seeley 1982). While we know where workers
perform tasks, and at what age, we have no understanding of how movement
patterns get workers to the ``right place at the right time.''

The ``right thing to do,'' depends on the developmental state of an
organism, and the environmental conditions (Bonner 1974). This also
applies to a superorganism social insect colony, but now it is
cooperating individuals, not cells, that must adaptively invest
resources between survival, growth, and reproduction (Starr 2006;
Hölldobler \& Wilson 2008). The investment strategies, however, depend
on the colony's developmental state. For example, honey bees only begin
to invest in reproduction once the colony has surpassed a threshold
number of workers (Smith et al. 2014). The environment can also
radically change investment strategies. Nectar availability, for
example, is stochastic, so colonies must capitalize on collecting nectar
for winter survival on the few days when nectar flows heavily (Seeley
1995). Therefore, a colony's investment priorities change quickly, and
workers must detect and react to these changes. This relies on workers
being in the ``right place at the right time'' but also doing the
``right thing,'' depending on colony state. The research proposed here
will elucidate how individuals change their movement patterns to gather
information as they age, and how individuals use local information to
detect global state.

To link local information with global state, and vice versa, we require
methods that cover spatial and temporal scales at both the level of the
individual bee, and the whole colony. This requires continuous tracking
of multiple individuals. Previously, continuous tracking was nearly
impossible. Lindauer (1952) heroically hand-tracked a single bee for
7.5+ hours each day for her entire 24-day life, but such methods are
unsuitable for tracking even a few bees. Fortunately, recent advances in
computer vision and tracking software has made automated tracking of
individuals in social insect colonies feasible (Mersch et al. 2013;
Crall et al. 2015; Wario et al. 2015). These advances allow us to
address novel questions in a complex system that were previously
considered inconceivable.

~

~

\textbf{INVESTIGATION 1: Patterns of motion in individual bees from
birth until death}

~

~

\textbf{QUESTIONS 1:}

-~~~~~~ How do patterns of motion change as bees age and switch task
repertoires?

-~~~~~~ How does the information from an individual's past environment
predict its future task allocation?

~

~

\textbf{METHODS AND ANALYSIS 1:}

This investigation will obtain continuous data on the movements of an
age-marked cohort of bees as they age and progress through task
repertoires. We will monitor the nest environment, to infer tasks being
completed, and to define the local environment and relevant information
that individuals experience. We will repeat the following experiments,
in series, on three colonies during the first summer.

A one-sided 4-frame observation hive will be installed with 2500
mixed-age workers, a queen, and combs with known contents (honey stores,
broodnest, empty comb, and empty space, as in Smith et al. 2017). Comb
contents will be re-evaluated every five days (as in Smith et al. 2016).
On day 0, we will tag 200 newborn workers, 50 newborn drones, and the
queen, with BEEtag (Crall et al. 2015). Starting on day 0, three cameras
will simultaneously record the observation hive, at 10 frames per
second. Image conditions will be optimized beforehand using pilot data
(e.g., black and white threshold values, noise level, lighting
conditions). Colonies will be kept under infrared light, to leave the
bees undisturbed. Recording will continue until all marked workers and
drones have died, ca. 30 days.

This technique creates a data storage problem. At 10 frames per second,
we will generate 25,920,000 images per camera over 30 days. At 93750 kb
per image, that is 7290 TB of data. Using real-time GPU accelerated
compression, however, we can reduce storage by 100x. Instead of
permanently storing the data, we will cycle through four 8 TB hard
drives in a disk station: one drive recording data, one drive having its
data processed at the High Performance Computing Cluster at University
Konstanz, and two drives ``on call'' for when the first drive is full.

This experimental method will generate movement patterns for each
individual from birth until death, interaction rates between
individuals, and the nest environment wherein the individual moved. We
will use the R packages move and CTMM for visuals and to generate
predictive models (Kranstauber \& Smolla 2016; Fleming \& Calabrese
2016). We will first compare movements of workers in three broad age
classes: young, middle-aged, and elderly. For each age class, we will
define home range, likelihood of remaining in each nest environment
(e.g. broodnest versus food storage area), and the time it takes to
cover given areas. We will then conduct the inverse analysis, taking a
set nest environment, and analyzing the individuals that enter that
environment, and how their movement changes while in that environment.

We will then focus on individuals as they transition from one task
repertoire to the next. For example, individuals engaged in brood care
in the center of the nest that transition to food storage at the
periphery of the nest. We will test for differences in prior movement
patterns, propensity to explore outside the defined home range,
interaction rates with individuals that have already transitioned, and
nest coverage area. Workers that transition from one task repertoire to
another particularly early, or late, are of special interest. We will
use these workers to determine how past movement patterns and
interaction rates predict future task repertoire.

We will also compare movement patterns of bees doing specific tasks,
such as: queen retinue bees (defined as workers within 1 cm of the queen
for \textgreater{}60 s); workers tending drones; foragers that collect
nectar versus foragers that collect nectar and pollen (foragers
collecting pollen must deposit pollen themselves, so they are easily
identified); and patrol bees (those that cover large areas of the nest).

\textbf{~}

\textbf{ORIGINALITY, IMPACT, AND EXPECTED CONTRIBUTION 1:}

The proposed research is largely exploratory, describing the patterns of
movement that characterize bees of different ages and task repertoires.
Even if we found that all bees move in an identical motion pattern, with
similar kinematic profiles, this result would be interesting, because
these data are the foundation needed for future experimental work (see
Investigation 2). Most likely, the movement patterns of individuals will
change with age and task repertoire, which raises the question of how
and why these movement patterns change.

This research will be the first to examine honey bee movement throughout
the lives of individuals in a realistic setting, for both workers and
drones. Previous work, while valuable, has used nest areas and colonies
that are too small to describe worker movement throughout diverse
environments in the nest (e.g. Wario et al. 2015). Furthermore, to
determine if and how information from the nest environment influences
future task repertoire, one must record an individual throughout its
life. These novel analyses will link key research at different levels of
colony organization, including age polyethism, task allocation, and
information processing.

\textbf{~}

\textbf{INVESTIGATION 2: How collective motion changes with colony
state}

\textbf{~}

\textbf{QUESTIONS 2:}

-~~~~~~ What are the patterns of collective motion in a honey bee
colony?

-~~~~~~ How does collective motion change when a colony: Invests heavily
in \textbf{survival}? Invests heavily in colony \textbf{growth}? Becomes
capable of \textbf{reproduction}?

-~~~~~~ How do individuals relay information about the colony's global
state?

~

\textbf{METHODS 2:}

The goal of this investigation is to first define a pattern of
collective movement in the colony (using methods in Vicsek \& Zafeiris
2012), and then see how that pattern changes when the colony invests in
survival, growth, or reproduction. The method for this investigation
will be the same as in Investigation 1, except that now multiple age
cohorts will be marked. We will introduce 100 newborn bees marked with
BEEtag every other day for 30 days prior to the start of the experiment,
and throughout the experiment. The whole colony will contain 2500
individuals (1500 of which are marked). The observation hive and nest
environment will be setup and recorded as described above, and repeated
on three colonies. Each experiment will begin with a 5-day baseline
recording, to define a pattern of collective motion before the following
experimental manipulations:

~

\textbf{Survival}- During a nectar dearth, we will train foragers from
the observation hive to collect nectar from an \emph{ad libitum} feeder
(as in Griffin et al. 2012). This 2.5 M sucrose feeder will provide a
bonanza food source to the colony, thus mimicking a heavy nectar flow
that allows a colony to invest in winter survival. The feeder will be
recorded during foraging hours to identify foragers and rate of
foraging. Over three days, we will provide food at the feeder, both
continuously and stochastically. We will then remove the feeder, and
take a post-treatment baseline measurement.

~

\textbf{Growth}- Honey bee workers only build new comb when there is
incoming nectar and no empty comb in which to store it (Pratt 1999). For
this experiment, the empty comb frame in the observation hive will be
replaced with a capped honey frame, so there is little space to store
incoming nectar. After recording a baseline measurement, we will turn on
comb building by providing a feeder to the colony. We will record the
colony while nectar is incoming but comb has not yet been built (ca. 2
days), while comb is being built (5 days), and then turn off the feeder
and record the colony as comb building stops (5 days). The new comb area
will be measured daily on a plastic sheet, as will the cluster of bees
that tend to form around the comb building area. This area will define
which bees are potential comb builders when we overlay the movement
trajectories atop the nest environment data.

~

\textbf{Reproduction}- \textbf{} Honey bee colonies begin to invest in
reproduction once they've surpassed a threshold number of workers (4000
workers, Smith et al. 2014). In this experiment, we will increase the
number of workers in the colony to 5000 individuals (3000 of which are
marked with BEEtag; 200 individually marked newborns per day, for 30
days before the start of the experiment). After taking a 5-day baseline
measurement of collective motion at the increased colony size, we will
induce comb building to confirm that this colony is above the
reproductive threshold (i.e. the colony will build large-celled drone
comb, which is used for rearing reproductive males, drones). We will
induce comb building and monitor the comb built by workers as described
above.

~

\textbf{ORIGINALITY, IMPACT, AND EXPECTED CONTRIBUTION 2:}

The first step in Investigation 2 will be to robustly define the
patterns of ``collective motion'' that characterize the baseline
measurements. The direction of this analysis will depend on the results
from Investigation 1, but will likely include analyses such as:
interaction rates between age-marked individuals (defined as individuals
less than 1 cm apart with their heads oriented at each other), heat maps
to identify areas of high activity, physical forces within and between
age cohorts (e.g. attraction, repulsion, and alignment, as in Couzin et
al. 2002), and transit rates in key nest environments (e.g. how long
does it take for 90\% of individuals to pass through the honey storage
area? How long do individuals remain in a given environment?). We will
then use these patterns to determine how the experimental manipulations
on colony investment altered collective motion.

Each experimental manipulation relies on a robust colony-level
phenomenon that has never been studied in the context of collective
motion with marked individuals. For example, the survival manipulation
will help us answer the long-standing question of why there are so many
seemingly inactive individuals in social insect colonies (Charbonneau \&
Dornhaus 2015). If these inactive individuals spring into action to
handle the sudden flow of incoming nectar, this will provide strong
evidence that inactive individuals are a buffer for workload variation.
The growth manipulation will be the first to investigate how individuals
sample their environment to detect the need for colony growth
\emph{before} activating their wax glands to build comb. Furthermore, as
group size increases, the proportion of individuals needed to guide the
group is predicted to decrease (Couzin et al. 2005), so comb building in
larger colonies should require fewer bees to initiate colony growth, a
prediction our data will explicitly test. Finally, the reproduction
manipulation will be the first to characterize how colony size changes
collective motion, and will further expand our understanding of how
individuals sample their environment to assess whether the colony can
invest in reproduction. We will use these data to test proposed
hypotheses of how individuals detect colony size, such as interaction
rate (Gordon \& Mehdiabadi 1999; Smith et al. 2017), and activity cycles
(Boi et al. 1999). These results will also help test hypotheses of
scaling properties, similar to vibrant cities, where social interactions
scale exponentially (Bettencourt 2013).

The research proposed in Investigation 2 is rooted in understanding how
individuals detect and respond to information about colony state,
whether survival, growth, or reproduction. For example, when we provide
a food bonanza to increase investment in colony survival, we will know
which individual was the first to discover the food source, how many
individuals she advertised the food source to (by waggle dances), and
how those individuals relayed local information from the first forager
to shift the global state of the colony. These changes are fundamental
shifts that all organisms make, but at a level of coordinating
individuals, not cells. Unlike multicellular organisms, whose units are
mostly fixed to a location, a honey bee colony is comprised of movable
units that interact with each other and with their nest environment to
gather both local and global information.

\textbf{~}

\par\null

\textbf{REFERENCES:}

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Collective memory and spatial sorting in animal groups. Journal of
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~

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