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The organization of societal conflicts by pavement ants (Tetramorium Caespitum)

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The organization of societal conflicts by pavement ants (Tetramorium Caespitum) Agent-based model of Amine mediated decision making
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Agent-based model of Amine mediated decision making
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Hoover, Kevin M. ( author )
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Ants -- Behavior ( lcsh )
Ants -- North America ( lcsh )
Ants ( fast )
Ants -- Behavior ( fast )
North America ( fast )
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bibliography ( marcgt )
theses ( marcgt )
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Ant colonies self-organize to solve complex problems despite the simplicity of an individual ant's brain. Pavement ant Tetramorium caespitum colonies must solve the problem of defending the territory that they patrol in search of energetically rich forage. When members of two colonies randomly interact at the territory boundary a decision to fight occurs when 1) there is a mismatch in nestmate recognition cues and 2) each ant has a recent history of high interaction rates with nestmate ants. Instead of fighting, some ants will decide to recruit more workers from the nest to the fighting location, and in this way a positive feedback mediates the development of colony wide wars. In ants, the monoamines serotonin (5-HT) and octopamine (OA) modulate many behaviors associated with colony organization and in particular behaviors associated with nestmate recognition and aggression. In this paper, we develop and explore an agent based model that conceptualizes how individual changes in brain concentrations of 5-HT and OA, paired with a simple threshold based decision rule can lead to the development of colony wide warfare. Model simulations do lead to the development of warfare with 91% of ants fighting at the end of 1 hour. When conducting a sensitivity analysis we determined that uncertainty in monoamine concentration signal decay influences the behavior of the model more than uncertainty in the decision making rule or density. We conclude that pavement ant behavior is consistent with the detection of interaction rate through a single timed interval rather than integration of multiple interactions.
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Thesis (M.S.)-University of Colorado Denver.
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Includes bibliographic references
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Full Text
THE ORGANIZATION OF SOCIETAL CONFLICTS BY PAVEMENT ANTS
(TETRAMORIUM CAESPITUM): AN AGENT-BASED MODEL OF AMINE
MEDIATED DECISION MAKING.
By
KEVIN M. HOOVER
B.S. Biomathematics, Florida Institute of Technology
A thesis submitted to the
Faculty of the Graduate School of the
University of Colorado in partial fulfillment
of the requirements for the degree of
Masters of Science
Biology
2016


ii


This thesis for the Master of Science degree by
Kevin M. Hoover
has been approved for the
Biology Program
by
Michael Greene, Chair
Michael Wunder
Douglas Shepherd
Loren Cobb


Hoover, Kevin M. (M.S., Biology)
The Organization of Societal Conflicts by Pavement Ants (Tetramorium caespitum)'. An
Agent-Based Model of Amine Mediated Decision Making.
Thesis directed by Associate Professor Michael Greene.
ABSTRACT
Ant colonies self-organize to solve complex problems despite the simplicity of an
individual ants brain. Pavement ant (Tetramorium caespitum) colonies must solve the
problem of defending the territory that they patrol in search of energetically rich forage.
When members of two colonies randomly interact at the territory boundary a decision to
fight occurs when 1) there is a mismatch in nestmate recognition cues and 2) each ant has
a recent history of high interaction rates with nestmate ants. Instead of fighting, some ants
will decide to recruit more workers from the nest to the fighting location, and in this way
a positive feedback mediates the development of colony wide wars. In ants, the
monoamines serotonin (5-HT) and octopamine (OA) modulate many behaviors
associated with colony organization and in particular behaviors associated with nestmate
recognition and aggression. In this paper, we develop and explore an agent based model
that conceptualizes how individual changes in brain concentrations of 5-HT and OA,
paired with a simple threshold based decision rule can lead to the development of colony
wide warfare. Model simulations do lead to the development of warfare with 91% of ants
fighting at the end of 1 hour. When conducting a sensitivity analysis we determined that
uncertainty in monoamine concentration signal decay influences the behavior of the
model more than uncertainty in the decision making rule or density.
We conclude that pavement ant behavior is consistent with the detection of interaction
rate through a single timed interval rather than integration of multiple interactions.


The form and content of this abstract are approved. I recommend its publication.
Approved: Michael Greene
v


ACKNOWLEDGMENTS
The research was supported, in part, by a Center for Brain and Behavioral Research
(CBBRe) Pilot grant to KJR, University of South Dakota, and NSF grant # IOS 1256898
to JGS. The authors thank Michael Wunder for statistical advice. We also thank Jennifer
Larmore, William Schumann, Harper Jocque, and Allison Pierce for their insights in
reviewing early drafts of this manuscript.
vi


TABLE OF CONTENTS
CHAPTER
I INTRODUCTION...............................................................1
Monoamines and Their Role in Ant Physiology............................3
II MATERIALS AM) METHODS......................................................5
Collection of Ants.....................................................5
Worker Density Trials..................................................5
Sample Preparation and Dissection......................................6
Quantification of Monoamines...........................................6
Agent-Based Model......................................................7
Purpose.............................................................7
Entities, State Variables, and Scales...............................7
Process Overview and Scheduling.....................................8
Design Concepts.....................................................9
Basic Principles.................................................9
Emergence........................................................9
Sensing..........................................................9
Interaction......................................................9
Stochasticity...................................................10
Observation.....................................................10
Initialization.....................................................10
Submodels..........................................................10
Monoamine Signal Decay..........................................10
vii


Fighting Decision Rule.....................................11
Sensitivity Analysis..........................................11
III RESULTS.............................................................12
Worker Density and Monoamines....................................12
Agent-Based Model................................................12
IV DISCUS SION.........................................................14
Future Directions................................................16
V TABLES AND FIGURES..................................................18
Table 1..........................................................18
Table 1....................................................18
Figure 1.........................................................19
Figure 1...................................................19
Figure 2.........................................................20
Figure 2...................................................20
Figure 3.........................................................21
Figure 3...................................................21
Figure 4.........................................................22
Figure 4...................................................23
REFERENCES..............................................................24
CONTRIBUTIONS...........................................................28
viii


CHAPTER I
INTRODUCTION*
Despite their miniaturized and simple brains, ants are able to solve complex problems
when organized at the colony level. Ant colonies are regulated as non-hierarchical
distributed systems (Gordon, 2010; Collignon and Detrain, 2009; Camazine et al., 2001;
Robinson et al., 2014). Without an authority to direct the actions of workers, it is
necessary for each individual to assess local information cues, integrate information in
those cues, compare them to an inherent set of rules, and make decisions to change their
behavior (Arganda et al., 2012; Couzin et al., 2005; Greene and Gordon, 2003). Colony
behavior changes collectively because of the cascade of individual decisions made by
workers. Examples of collective decision making include choosing a new nest, allocating
the proper number of workers to perform jobs to support colony homeostasis, forming
foraging trails, and even tending gardens (Gordon, 1986; Collignon and Detrain, 2009;
Frederickson et al., 2005; Sumpter and Pratt, 2009). Thus, we witness the aggregation of
simple deterministic components leading to a nuanced, variable system that natural
selection can act on; the superorganism of the ant colony (Detrain and Deneubourg, 2006;
Gordon, 2010).
A key component in the collective decision making of ants is the effect of interaction rate
on behavior. Interaction rate is often used by insects as a proximate measurement of local
density (). It has been shown that in Red Haverster ants (Pogonomyrmex barbatus)
* Portions of this chapter were previously published in Hoover et al. 2016 Current Zoology 2016 and are
included with the permission of the copyright holder.
1


interaction rate is associated with colonial task allocation in addition to the availability
and dedication of reserve workers to
foraging (Pinter-Woilman et al., 2013). In Temnothorax albipennis interaction rate is
shown to modulate the process of nest emigration (Pratt, 2004).Interaction rate could be
measured by a spectrum of methods with two extremes 1) integration of all encounters in
a given time or 2) response based only on the interval before 1st encounter (Pratt, 2004).
Indeed, there are examples of both occurring in insects. With integration over multiple
interactions clearly observed during locust (Schistocerca gregaria) aggregation, and nest
construction in Polybia occidentalis as a result of single interval measurement (refs).
Pavement ant (Tetramorium caespitum) workers perform random walks to search colony
territory for foods high in sugar and fat content (Collignon and Detrain, 2009;
Countryman et al., 2015). In order to secure territory, pavement ant workers organize
wars against neighboring colonies (Bubak et al., unpublished; Plowes, 2012). This
organization occurs after individuals from neighboring colonies meet during the course of
a random walk, touch antennae to the others cuticle, and assess nestmate recognition
cues coded in cuticular hydrocarbon profiles (Sano et al., in review). A decision rule to
fight a non-nestmate ant is satisfied when two conditions are met: 1) there is a mismatch
in the information coded in the cues (Sano et al., in review; Martin and Drijfhout, 2009);
and, 2) the ant had a recent history of interactions with nestmate ants (Bubak et. al.,
unpublished). The probability that a pavement ant worker will fight a non-nestmate
increases with the density of nestmate ants as assessed by the interaction rate (Bubak et
al., unpublished). Fighting among conspecifics involves a ritualized pushing of mass
between dyads that are locked together by mandibles and grasping appendages (Plowes,
2


2012). The fights last for many hours during which few, if any, ants die (Plowes, 2012).
However, the dedication of the vast majority of available foragers to this behavior
constitutes an opportunity cost as foraging efforts cease during the course of the war
(Plowes 2012). Some workers do not fight, but instead recruit more nestmates to the war
in a positive feedback loop (Plowes, 2012). Collectively, the decisions to fight by many
workers aggregated through feedback in recruitment lead to wars between neighboring
colonies.
Monoamines and Their Role in Ant Physiology
The monoamines serotonin (5-HT), and octopamine (OA), modulate many behaviors
important to ant colony function, including: colony formation, reproductive dominance,
division of labor, behavioral development, trophallaxis, predatory aggression, and
nestmate recognition (Kamhi and Traniello 2013; Boulay et al., 2000; Wada-Katsumata
et al., 2011, Aonuma and Watanabe, 2012, Szczuka et al., 2013, Koyama et al., 2015;
Kamhi et al.,2015). In pavement ants, brain concentrations of 5-HT and OA change
according to social context (Bubak et al. unpublished). Brain concentration of both 5-HT
and OA increase after interactions with nestmate ants and rapidly return to baseline levels
at or before 3 minutes. This presents a potential physiological mechanism for the fighting
decision rule in pavements ants; we hypothesis that high 5-HT and OA brain
concentrations correlate to recent interactions with nestmates and may prime the ant to
fight (Bubak et al., unpublished). A mismatch in nestmate recognition cues preceded by
elevated brain concentrations of 5-HT and OA, thus satisfies the proposed decision rule
to initiate a fight.
3


To test the feasibility of this mechanism, we leverage the bottom-up approach of agent-
based modelling. This modelling system was developed as a way to explore how system
level characteristics emerge from simple, rule-based and stochastically interacting agents
(Bankes 2002; Hare and Deadman 2004, Grimm et al. 2006, Holcombe et al. 2012). Use
of the bottom-up approach took off rapidly in the field of eusocial insect behavior
(Sumpter 2006) and has been used to explore the self-organization of the complex
behaviors of ants including: aggregation (Morale et al., 2004), nest choice (Pratt et al.,
2005), foraging (Robinson et al., 2008), task allocation (Momen, 2013) and intra-specific
battles (Martelloni et al., 2015). This modelling method integrates two key sources of
variation that are often ignored in analytic analyses: distributed spatial structures and
individual behavioral heterogeneity (Parunak, et al., 1998).
Here, we build an agent based model to conceptualize how changes in individual
monoamine brain concentrations and the application of simple decision rules lead to the
development of the observed colony behavior of fighting in pavement ant wars. In order
to understand the organization of social insects, we must undertake an exploration of both
the physiological mechanisms and contextual stimuli behind individual decision-making,
and how these decisions lead to system level patterns of behavioral organization in
colonies.
4


CHAPTER II
MATERIALS AND METHODS*
Collection of Ants
Pavement ants were collected along foraging trails in urban and suburban areas of Denver
and Aurora, Colorado. U.S.A. Ants were collected by aspiration on baited foraging trails.
Collected ants were brought to the laboratory for experimental manipulations that were
conducted between 24 and 36 hours after collection. Ants were temporarily housed at
room temperature (25 C) in plastic containers and allowed to drink ad libitum from glass
tubes filled with water and plugged with cotton
Worker Density Trials
In order to study the effects of increasing interaction rate on brain levels of biogenic
amines, ants were placed in Petri dishes with worker densities of 5, 20, or 100 ants per
petri dish (area = 56.5 cm2). This allowed us to infer if ants detect interaction rate through
integration of multiple interactions, or if they measure only a single time interval. There
were 11 replicates of each density created using ants from 6 separate colonies. Petri
dishes were all treated with the non-stick coating Insect-A-Slip (Bioquip) to prevent ants
from escaping. Ants were allowed to interact for 10 minutes after which individuals were
removed and their brains were dissected for measurement of brain monoamine levels.
* Portions of this chapter were previously published in Hoover et al. 2016 Current Zoology 2016 and are
included with the permission of the copyright holder.
5


Sample Preparation and Dissection
Brain removal and preparation was done following previously published methods (Bubak
et al., 2013). Briefly, ants were rapidly decapitated under a dissection microscope
immediately following behavioral trials using micro-scissors. A small medial-lateral
incision was made directly behind the mandibles avoiding the disruption of brain tissue.
Exposed brains were then removed with tweezers and submerged in 60 pL of ice-cold
acetate buffer containing the internal standard, alpha-methyl DA. Samples were frozen
immediately on dry ice and stored at -80 C until monoamine quantification. Each
sample contained two brains with each dissection time averaging less than one minute
Quantification of Monoamines
We quantified brain concentrations of the monoamines, octopamine (OA), and serotonin
(5-HT), using a high performance liquid chromatography with electrochemical detection
methods as described in Bubak et al. (2013) with slight modifications. Each sample was
thawed, briefly sonicated, rapidly refrozen on dry ice, and allowed to thaw again before
being centrifuged at 17,200 rpms. The supernatant (60 pL) was extracted from the
samples, 50 pL was injected into a Waters Alliance e2695 separations module, and a Ci8
4 pm NOVA-PAK radial compression column (Waters Associates, Inc. Milford, MA)
was used for monoamine separation. The initial mobile phase (pH 4.1) was prepared
using 8.6 g sodium acetate, 250 mg EDTA, 14 g citric acid, 130 mg octylsulfonic acid,
and 160 mL methanol in 1 L of distilled water (chemicals were purchased through
Sigma-Aldridge, St. Louis, MO). Electrochemical detection of amines was accomplished
using an LC 4 potentiostat and glassy carbon electrode (Bioanalytical Systems, West
Lafayette, IN) set at a sensitivity of 0.5 nA/V with an applied potential of+1.0 V versus
6


an Ag/AgCl reference electrode. After dissolving the tissue pellet in 100 pL of 0.4 N
NaOH, protein content was analyzed using the Bradford method (1976). A CSW32 data
program (DataApex Ltd., Czech Republic) was used to determine OA, and 5-HT
concentrations using peak heights calculated from standards. Corrections were made for
injection versus preparation volumes and monoamine concentrations were normalized by
sample protein content (pg amine/pg protein).
Agent-Based Model
This model description follows the Overview, Design concepts, Details (ODD) protocol
for describing individual- and agent-based models (Grimm et al. 2006, 2010). The model
was coded in Python 2.7.6 and is available in the SI.
Purpose
The purpose of this model is to determine if the proposed individual decision rule for
fighting, based on changes in brain concentrations of 5-HT and OA, can consistently lead
to the development of wars between neighboring colonies, as observed in pavement ants.
Additionally, we performed a sensitivity analysis to determine which parameters, if any,
stand out as key forcing parameters for this behavior.
Entities, State Variables, and Scales
This model is built in two scales: individual ants and the arena. Individuals are described
by the following state variables: colony, position (x,y), 5-HT concentration, OA
concentration, 5-HT decay and OA decay rates where decay rates represent time for the
respective monoamines to return to baseline values, and decision rule. The arena is
described by the following state variables: width, length, maximum number of ants per
7


pixel, the identities and number of ants, the identities and number of fighting ants (Table
1). The arena is a discrete lattice with each pixel corresponding to the maximum speed at
which an ant can traverse during the simulated time step (~4.5mm on a side).
Process Overview and Scheduling
This model walks through discrete time at thirds of a second for a total of 1 hour or
10800 time steps. During each time step 3 processes take place: movement, interactions,
and fighting (Fig.l.) In the movement process, each ant moves into one of the eight
adjacent pixels with equal probability, provided the pixel is not at capacity. Also during
movement, each ants monoamines decay representing the lack of interaction during
movement. In the interaction phase, the arena checks each pixel and selects those with 2
or more ants inside. The ants in each pixel update their monoamine levels
simultaneously. If interacting with nestmates, OA and 5-HT spike to their physiological
maximal concentration. If interacting with non-nestmates, just OA spikes because of
detection of non-nestmate cuticular hydrocarbons (Bubak et al., 2015). Because an ant
will not fight unless primed over threshold levels of OA and 5-HT, non-nestmate
cuticular hydrocarbon cues increase OA. Finally during the fighting process, each pixel
that had interacting non-nestmate ants in the previous process gets updated. Each ant in
the pixel uses its associated decision rule to discern its willingness to fight. If both ants
are willing to fight, they initiate a fight and will no longer update for the rest of the
simulation. Otherwise, they move as normal in the next time step.
8


Design Concepts
Basic Principles. This model is based on the principle of stochastic interactions
between pavement ant foragers during the course of a random walk. Using the results
from our density trials on brain concentrations of the monoamines OA and 5-HT, we
propose a new decision rule for individual ants consistent with single interval
measurement. Here, we assume that levels of OA and 5-HT increase to the physiological
maximum observed in samples taken from interacting ants, after one interaction with a
nestmate or non-nestmate ant. Then, brain levels of OA and 5-HT decay with time. The
higher the rate of interaction with other ants, the lower the time interval between
interactions, and therefore less time for decay rate to affect the brain state.
Emergence. This model will determine if the proposed individual decision rule
can give rise to the emergence of the observed system level behavior of warfare between
neighboring colonies of pavement ants.
Sensing. The ants in this model make decisions based on their position, colony,
and brain concentrations of 5-HT and OA, and their willingness to fight. They also
perfectly detect which colony an ant they interact with is from and whether or not an
adjacent pixel is at maximum capacity.
Interaction. As specified in the process overview, ants interact with all other ants
that share their pixel. If the pair are nestmates, then both ants experience a spike in brain
concentrations of 5-HT and OA. If ants are from different colonies, they both experience
a spike in brain concentrations of OA and then check their willingness to fight. If both
ants are willing to fight, they do so and do not update for the rest of the model.
9


Stochasticity. Stochasticity is utilized for the process of ant movement. Ants can
move to any of the 8 adjacent pixels with uniform probability provided the pixel is not at
capacity (Arena Parameters Table 1).
Observation. The output from the model is the proportion of ants fighting over
the course of the simulation. The proportion of ants fighting at the end of the time series
is recorded and compared for the analyses in this paper.
Initialization
The model is initialized as a 70 x 105 pixel lattice which corresponds to the 305 mm x
457 mm arena used in other laboratory experiments, ants from colony 1 are distributed
randomly along the left edge of the arena, and ants from colony 2 on the right edge. Pixel
capacity is initialized to 2 ants per pixel which corresponds with the observed
organization of aggressive dyads in the war (Plowes, 2012) Ants are generated with OA
and 5-HT brain concentrations set at zero (Table 1). Unless otherwise stated, 50 ants are
generated from each colony with a decision rule with rate parameter k=3 (Fig. 2)and a
linear full decay of monoamine concentration in 540 time steps corresponding to the 3
minute return to baseline concentrations observed in unpublished data from this lab.
Submodels
Monoamine Signal Decay. 5-HT and OA brain concentrations are modeled to
return linearly to baseline concentrations within 3 min. This is based off of the work done
by Bubak et al. (unpublished) which presents 3 minutes as the finest time resolution
available currently. For this reason, we model decay completion at 3 minutes as the most
conservative estimate in keeping with observed data.
10


Fighting Decision Rule. The decision rule for fighting (Fig 2) is modeled on the
unit square of 5-HT and OA. Concentration axes are unit-less and bounded between 0
and 1, where 0 represents basal levels associated with isolated ants and 1 represents the
physiological maximum observed in samples taken from interacting ants. The functional
form of the decision rule is an exponential decay curve: OA=exp(-k *5-HT). This
equational form was in keeping with the data obtained in our preliminary research.
Specifically, pharmacologically induced increases in brain 5-HT and OA (resulting in
coordinates above the unit square represented here) in isolated ants led to fighting
behavior upon introduction of a non-nestmate, but increases in OA concentrations
induced by exposure to non-nestmate cuticular hydrocarbons on glass beads (resulting in
coordinates on the y-axis of the unit square) where insufficient for development of
fighting.
Sensitivity Analysis
To better understand the key parameters underlying this model a sensitivity analysis was
performed over the 3 explanatory parameters: worker density, decision making rule, and
monoamine concentration signal decay rate. To measure the effects of uncertainty in
these parameters, we compared the proportion of ants fighting at the end of the model
simulation runs under 5 parameter conditions A) No uncertainty B) Uncertainty in all
parameters C) Uncertainty in monoamine concentration signal decay rate. D) Uncertainty
in density parameter E) Uncertainty in decision-making rule. Uncertainty was introduced
by allowing the uncertain parameters to be drawn from a uniform distribution before each
run between 50 % and 150 % of the parameter estimates in the model free of uncertainty
(Table 1).
11


CHAPTER III
RESULTS*
Worker Density and Monoamines
Concentrations of brain 5-HT recorded between density groups ranged from 4.2 and 14.9
pg/pg protein with a mean of 10.5 2.1(SEM, N = 38). Concentrations of OA ranged
between 3.3 and 17.1 pg/pg protein with a mean of 8.2 3.8(SEM, N= 27). Worker
density did not account for the observed variability in monoamines with ANOVA failing
to support significant changes in variance: (5-HT ANOVA, p = 0.97 and OA ANOVA, p
= 0.23) (Fig.3).
Agent-Based Model
Simulated agents self-organized into wars, with a majority of the available workers
engaged in fighting (Mean =0.907; 90% Credible Interval = (.70, 1.0), Fig. 4A). When
uncertainty was added to the 3 key parameters of the model the distribution of proportion
fighting at the end of each simulation shifted. (Mean =0.794; 90% Credible Interval =
(0.65, 0.90), Fig. 4B). The contributions to this shift were decomposed into the
contributions of uncertainty in the parameter estimates of the rates of monoamine signal
decay (Mean =0.817; 90% Credible Interval = (0.75, 0.90), Fig. 4C), density
(Mean=0.924; 90% Credible Interval = (0.85, 0.95), Fig. 4D), and decision rule (Mean
=0.955; 90% Credible Interval = (0.90, 1.0), Fig. 4E). While uncertainty introduced to the
estimates for density and decision-making only moderately shifted the distribution,
* Portions of this chapter were previously published in Hoover et al. 2016 Current Zoology 2016 and are
included with the permission of the copyright holder.
12


uncertainty in the monoamine signal decay rate has a profound effect on the spread of the
distribution.
13


CHAPTER IV
DISCUSSION*
Pavement ant colonies self-organize to accomplish complex and nuanced tasks despite
the relatively simple brains of the individual ants. The aggregation of simple
deterministic rules in response to external stimuli can lead to subtle and wide-ranging
societal behavioral changes at the colony level. Here, we use a model to demonstrate how
changes in brain concentrations of the monoamines 5-HT and OA after interactions with
nestmate ants could cause a brain state that leads to the engagement of fighting behavior
of conspecific non-nestmate ants.
In particular, we examined the behaviors associated with nestmate recognition and the
ritualized, aggressive exclusion of conspecific non-nestmates at the boundary between
territories. While it is known that pavement ants only engage in ritualized combat when
they had sufficiently interacted with other nestmates (Bubak et al., unpublished), it was
assumed that the rate or abundance of interactions with nestmates over time was being
integrated by the worker ants (Bubak et al., unpublished). However, increased density
and, therefore increased interaction rate, did not result in changes in brain monoamine
concentrations (Fig. 3; Bubak et al., unpublished). Based on this result, we hypothesize
that the ants decision making observes a hysteretic effect of their most recent
interactions based on the return of brain monoamines to baseline concentrations and thus
determine interaction rate through a single interval measurement instead of integration
over multiple interactions.
* Portions of this chapter were previously published in Hoover et al. 2016 Current Zoology 2016 and are
included with the permission of the copyright holder.
14


This conceptualization of decision-making via a monoamine clock timed by the rate of
return of monoamines to baseline concentrations reconciles the apparent requirements of
integrating complex information with the relatively simple organization of an individual
ant brain. A key implication of this finding is that the density sensing apparatus of the
pavement ant worker observes the mathematical forgetfulness or memoryless property
(Leemis and McQueston, 2008), that is, there is no difference between an ants 1st
interaction and its 20th, the probability of having concentration levels above the decision
threshold at sometime (t) after each interaction is the same. This model, consistent with
the proposed single interval measurement, demonstrates a way in which interaction rate
would not lead to differences in monoamine concentrations but still affect decision
making in ants. It further implies that the decision making processes attributed to rate of
interaction is more accurately attributed to the amount of time a worker spends primed
for a decision following an increase in monoamine concentrations.
The proposed decision mechanism is further supported by our agent-based model as
uncertainty in density had minimal effects on the system behavior compared to changes
due to uncertainty in the rate of monoamine concentration decay. In the terms of a
monoamine clock this would indicate that the rate at which the clock ticks (decay rate)
is more important than how often the clock is reset (interaction rate). To date, the
dynamics for monoamine concentrations decay in the ant brain are unknown, and
therefore estimates of this key parameter for decision making are by necessity highly
uncertain.
In order to advance our understanding of how individual, simple, and physiologically
driven decision making can lead to the development of complex, nuanced and colonial
15


responses, a better understanding of both the time-scale and function of this monoamine
clock must be established.
Future Directions
Due to the small size of ant brains, HPLC analysis requires an aggregation of two brains
per a sample. This course grain data is unable to resolve enough information to fully
propose a mechanistic explanation of aggressive behavior in ants. To that end future
research should look at specific aminergic pathways within the ant brain and their
dynamics under the different social context that pavement ant workers experience in their
life.
Having at length explored the necessary conditions for the engagement of aggressive
conflicts between neighboring Pavement ant colonies, we look forwards to studying the
processes that sustain and ultimately conclude this behavior. While this paper explores
the interactions of ants along the territorial border, it is not well known why some ants
decide to engage in active recruitment of sisters by returning to the nest instead of
fighting. This critical positive feedback is a necessary component of the observed
escalation in wars starting from dozens of ants to many thousand. Additionally it has
been suggested that in societal conflicts group size performs a similar function as body
size does in conflicts between solitary organisms. In this way recruitment of a colonies
workforce is an important indicator of strength, and recruitment rate becomes a reliable
indicator of victory in an engagement between colonies.
Pavement ant wars last over 10 hours but then disengage in under 30 minutes. The source
of this synchronized withdrawal is not well understood. Preliminary data demonstrates
16


that dyads of non-nestmate ants engaged in ritualized fighting experience a marked
increase in Dopamine both 3 minutes and 2 hours after the start of the fight (Bubak et al.,
unpublished). By extending the logic from this paper we propose that the length of
fighting behavior is modulated by the rate at which this elevated concentration decreases
to basal levels. We suspect that a super-threshold level of Dopamine sustains fighting and
the drop to sub-threshold levels will signal a cessation of hostilities.
Finally, while this behavior has been attributed to territory defense there are at this time
no studies that empirically study the cost and benefit of this behavior in the terms of
colony fitness. Colonies will often return to sites of previous wars within 24 hours and a
new war will begin in approximately the same location with no clearly demarked change
in territory size. This patter can last for 3-5 days and it is not clear what benefit this
prolonged dedication of colony resources achieves.
17


CHAPTER V
TABLES AND FIGURES*
Table 1
Table 1
Entities, processes and parameters of the agent based model, with default values. With
the exception of the sensitivity analysis these values are used in model initialization.
During the sensitivity analysis the targeted parameters are pulled from a uniform
distribution between 50% of the default value before each simulation.
Description Default
Ant
Colony Identity 1 or 2
Position Uniformly distributed on left or right column
5-HT Concentration 0
OA Concentration 0
Decision rule (rate parameter) 3
Willing to fight False
Is fighting False
Ant Processes
Movement Uniformly distributed movement across 8 adjacent pixels.
Nestmate Interaction Set 5-HT to 1; Set OA to 1
Non-nestmate Interaction Set OA to 1, if both ants are willing to fight set Is fighting to True
Check Willingness Compare (5-HT, OA) coordinate to decision rule. If (5-HT, OA) > then decision slope, then set willing to fight to True.
5-HT decay rate -1/540 per time step (equivalent to full decay in 3 min)
OA decay rate -1/540 per time step(equivalent to full decay in 3 min)
Arena
Time steps 10800 (equivalent to 1 hour)
* Portions of this chapter were previously published in Hoover et al. 2016 Current Zoology 2016 and are
included with the permission of the copyright holder.
18


Width
Length
Pixel Capacity
Number of ants
Arena Processes
Move ants
Check pixel capacity
Interactions
Proportion fighting
70
105
2
100 (split evenly between colony 1 and 2)
Move all ants
Return true is pixel is at or above the capacity set by
Pixel Capacity
Checks all pixels for those with occupancy >1. Each
ant in those pixels will execute the appropriate
interactions
Returns the proportion of total ants fighting at time t
Figure 1
Figure 1
Process scheduling in agent based model of pavement ant fighting. During each time step
all non-fighting ants move, check for interactions, and then either begin fighting or
prepare to move in the next timestep.
19


Figure 2
Decision Map
X
1
-3
-5
Figure 2
Physiological Decision Map. If an ant has a brain state that places it above some decision
threshold, it will decide to fight. Lines represent the decision rule used in our agent-
based model OA=exp(- X 5-HT), where X is the rate parameter of interest.
20


Figure 3
A
15i
c
t-
=F
tft
&J-Ii i
S 20
Ants/56 cm2
100
B
15i
T
20
Ants/56 cm2
100
Figure 3
Increasing density of ants is not sufficient to explain variation in brain levels of
monoamines in laboratory density test. Means with SEM. A) Serotonin: no treatment is
significantly different, ANOVA p = 0.97. Ns=14, N2o=13, Nioo=l 1. B) Octopamine: no
treatment is significantly different, ANOVA p = 0.23. Ns=9, N2o=9, Nioo=9.
21


Number of Simulations/20,000
Figure 4
9000
6000
3000
0
9000
6000
3000
0
9000
6000
3000
0
9000
6000
3000
0
9000
6000
3000
0
Sensitivity Analysis for Agent Based Model
Ei.....ia.......s.......s.......0
i i i i i i i
0.60 0.65 0.70 0.75 0.80 0.85 0.90
Proportion of Ants Fighting
0.95
1.00
Uncertainty
None
Global
Decay
-I- Density
a- Decision
22


Figure 4
Sensitivity analysis on agent based model. A) No uncertainty B) Uncertainty in all
parameters C) Uncertainty in monoamine concentrations decay rate. D) Uncertainty in
density parameter E) Uncertainty in decision making rule. It should be noted that
uncertainty in concentration decay rate is the greatest contributor to the distributional
shift associated with global uncertainty. This indicates that signal decay is the most
sensitive parameter in the model.
23


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27


CONTRIBUTIONS
This this thesis was a collaboratory work and published in whole as:
Hoover, K. M., Bubak, A. N., Law, I. J., Yaeger, J. D., Renner, K. J., Swallow, J. G., & Greene, M.
J. (2016). The organization of societal conflicts by pavement ants Tetramorium caespitum: an
agent-based model of amine mediated decision making. Current Zoology, zow041.
As the primary author I took the major role of organizing the research performed by my
colleagues and organizing it into its manuscript form. I structured and wrote the
introduction found in Chapter 1 of this thesis. In Chapter 2,1 helped design and conduct
the collection of ants and the worker density trials, acting as the leading mentor of Isaac
Law, and undergraduate student. I am also sloley responsible for the design and
implementation of the Agent-Based Model. In Chapter 3 I am responsible for the results
pertaining to the Agent based-model, and the writing of the whole chapter. In Chapter 4 I
once again acted as primary author and lead the discussion. Finally in the 5th chapter I am
responsible for Table 1 as well as Figures 1,2 &4.
I am grateful to my collegauges contributions and know that this thesis is stronger
because of the interdisciplinary approach that was fostered under the Senior Author Dr.
Michael Greene.
28


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