Decoding recognition cues in the pavement ant

Material Information

Decoding recognition cues in the pavement ant the relationship between cuticular hydrocarbon composition and nestmate recognition response in the ant tetramorium caespitum
Bannon, Nathanael D
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x, 71 leaves : ; 28 cm


Subjects / Keywords:
Pavement ant -- Nests ( lcsh )
Kin recognition in animals ( lcsh )
Hydrocarbons ( lcsh )
bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )


Includes bibliographical references (leaves 63-71).
General Note:
Department of Integrative Biology
Statement of Responsibility:
by Nathanael D. Bannon.

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|University of Colorado Denver
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Auraria Library
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Full Text
Nathanael D. Bannon
B.S., Biola University, 2007
A thesis submitted to the
University of Colorado Denver
in partial fulfillment of the
requirements for the degree of
Master of Science
Integrative Biology

This thesis for the Master of Science
degree by
Nathanael Bannon
has been approved
Diana Tomback, Ph.D.
THC M2 Date

Bannon, Nathanael D. (M.S. Biology, Department of Integrative Biology)
Decoding recognition cues in the pavement ant: The relationship between
hydrocarbons and nestmate recognition response in the ant T. caespitum
Thesis directed by Associate Professor Michael J. Greene
The Pavement Ant (Tetramorium caespitum) is a structural and agricultural pest
notorious for aggression towards conspecific non-nestmate and heterospecific
ants. Nestmate and species recognition in T. caespitum are informed by cues
present in cuticular hydrocarbons. I examined whether differences in relative
abundance of hydrocarbons in colony cuticular hydrocarbon profiles are
responsible for eliciting aggression in nestmate recognition responses. Nestmate
recognition is important in ants because it allows them to recognize self and
discriminate against foreigners in order to protect colony resources. Groups of 10
ants from each of 10 colonies were pitted against 10 ants from each of the other 9
colonies and aggression levels, corresponding to number of ants displaying
aggression, were assigned to each interaction. Through the use of multivariate and
univariate linear regression, I studied the correlation between aggression index
and differences in relative abundance of 16 hydrocarbon molecules. I found that
11-methyl pentacosane (t-value 2.001, p-value 0.06265) is the best predictors of
aggression. I then examined if certain hydrocarbon structural classes are more

important than others in eliciting a nestmate recognition response. This was tested
by pitting ants against nestmates supplemented with cuticular hydrocarbons
consisting of n-alkanes, methyl-branched alkanes, or n-alkenes. The data were
analyzed using ANOVA and the Tukey-Kramer method. The results indicate that
not all structural classes are equally important in nestmate recognition. A-alkanes
had a mean aggression index of 0, methyl-branched alkanes had a mean
aggression index of 5.5, and n-alkenes had a mean aggression index of 2.4 (F (3,
36) =61.239,p = 3.25 x 10"16). Thus, nestmate recognition cues are coded
primarily in cuticular methyl-branched alkanes with some information coded in
cuticular n-alkenes.
This abstract accurately represents the content of the candidates thesis. I
recommend its publication.
Michael J. Greene

It is with great pleasure that I recognize my primary advisor, Michael Greene
Ph.D., for his immeasurable guidance and encouragement in completing my
Additionally, I would like to thank Bradley Stith, Ph.D., Diana Tomback, Ph.D.,
and Michael Wunder, Ph.D. for their involvement.
Finally, I would like to thank my wife for her love and support despite the many
late hours spent collecting specimens.
Funding was provided by USDA CSREES #2007-02235, Arthropod and
Nematode Biology and Management Program Breaking the code of pavement ant
social recognition cues. PI: Mike Greene, Ph.D.

1. Introduction............................................1
1.1 Background..............................................1
1.1.1 Eusocial Insects........................................1
1.1.2 Recognition Systems...................................3
1.1.3 Cuticular Hydrocarbons..................................5
1.1.4 Tetramorium Caespitnm..................................15
1.2 Research Objective.....................................20
2. Research Methods.......................................21
2.1 Research Sites.........................................21
2.2 Chemical Methods.......................................22
2.2.1 Extraction of Cuticular Hydrocarbons...................22
2.2.2 Separation of Cuticular Hydrocarbon Structural Classes.22
2.2.3 Identification of Cuticular Hydrocarbons...............24
2.2.4 Reconstruction of -alkanes............................25
2.2.5 Measuring Relative Abundances..........................25

2.3 Quantification of Aggression............................29
2.4 Objective 1.............................................34
2.5 Objective 2.............................................38
3. Results................................................40
3.1 Results for Objective 1................................40
3.2 Results for Objective 2................................48
4. Discussion.............................................55
4.1 Conclusions............................................55
4.2 Objective 1 Discussion.................................55
4.3 Objective 2 Discussion.................................57
4.4 Future Research........................................60
References Cited......................................63

AGGRESSIVE INTERACTION.....................19
BEHAVIOR WHILE IN AN ARENA.................31
2.33 NESTMATE BEHAVIOR IN AN ARENA..............33

THE TREATMENT GROUPS.......................54

2.2 CUTICULAR Hydrocarbons corresponding
to the 16 peaks I examined..................................27
3.11 BOUT (colony pairing), Aggression Indexes (A.I.), and
Difference in Relative Abundances for 10 Ant Colonies
collected in and around Denver, CO............................42
3.12 MULTIVARIATE Regression Results: Predictors of Aggression.....46
3.13 UNIVARIATE Linear Regression
Results: Predictors of Aggression.............................47
3.21 MEAN aggression indexes for each of the supplementation
3.22 ANOVA results for supplementation experiment..................51
3.23 MEAN aggression indexes specifically for structural class
supplementation analysis......................................52
3.24 ANOVA results for structural class supplementation experiment.52

1.1 Background
1.1.1 Eusocial Insects
Eusocial insects have been evolutionarily successful. Eusociality is social
organization in which members of a population exhibit reproductive division of
labor, overlapping generations, and cooperative care of young (Wilson 1975). The
orders Hymenoptera, Isoptera, Hemiptera, and Thysanoptera considered the
social insects contain over 60,000 speices that are widely distributed on all
continents except Antarctica (Wilson & Holldobler 2005). Ants alone can
comprise up to 25% of the terrestrial animal biomass (Wilson & Holldobler
1990). Eusociality is presumed to be the chief cause of this success. Holldobler
and Wilson (1994) went as far as to posit that social organization has been among
the most consistently successful strategies in evolutionary history. Eusocial
colonies exhibit high relatedness among members and a high level of reproductive
Maintenance of a cohesive eusocial structure is only possible through the
use of recognition systems. For example, nestmate recognition systems have
evolved to prevent foreigners from entering the colony and reducing inclusive
fitness benefits (Holmes 2004, Tsutsui 2004). If outsiders exploit colony

resources, queens are less likely to survive, and subsequently the inclusive fitness
of sterile workers decreases.
In eusocial insect colonies, workers forage, maintain the nest, or take care
of the eggs and brood so that they incur maximum inclusive fitness benefits
through the reproductive success of the queen (Greene and Gordon 2003, Wilson
1975). Social insect colonies form such highly structure and cohesive units that
they are often referred to as superorganisms (Wheeler 1928). Viewing a colony as
a superorganism explicates the evolutionary stability of the eusocial structure. The
colony acts as a reproductive unit with individual members fulfilling a specific
role as would individual cells of an organism (Holldobler and Wilson 1990).
Therefore, the colony is the unit upon which natural selection acts since colonies
produce more colonies but individual ants do not.
Kin selection results in individuals discriminating against unrelated
conspecifics and heterospecifics which often results in benefits such as protection
of resources and protection against foreign diseases. By biasing preferential
behavior toward more closely related reproducing individuals and therefore
increasing the reproductive output of relatives, an individual gains fitness (Page &
Breed 1987). Fitness is gained by the individual by directing altruistic behavior
toward others whose phenotypes indicate shared genes (Sachs et al 2004).

1.1.2 Recognition Systems
Recognition systems provide the basic framework by which social insects
evaluate and respond to social recognition cues. They are important to eusocial
insects as they allow benefits of colony labor to be conferred on related
individuals rather than on intruders (Holmes 2004, Tsutsui 2004).
Recognition systems involve at least two parties, a cue-bearer and an
evaluator, and consist of three components: 1) the expression component, 2) the
perception component, and 3) the action component (Mateo 2004). In an
interaction between cue-bearer and evaluator, the former expresses a phenotypic
cue to the evaluator. The evaluator must then perceive the cue and compare it to a
template. Upon assessment of the cue, the evaluator will act appropriate to the
cue-template match (Tsutsui 2004).
The expression component consists of the production and expression of
cues (Tsutsui 2004). Cues can be acquired, genetically determined, or both
(Liebert & Starks 2004, Mateo 2004). They can be encoded in many phenotypic
traits or behaviors: chemical odor, cell surface proteins, color pattern, song, or
stereotypic behavioral display (Payne et al 2004, Tsutsui 2004). In the context of
kin recognition systems, most cues are shared by the members of
a social group, and are not shared with members from other groups (Tsutsui

The process of detecting cues and comparing them to the recipients
template comprise the perception component (Mateo 2004). Detection of the cue
will always require a sensory organ. Templates, like cues, can be acquired
through learning or imprinting or they can be genetically hard-wired (Liebert &
Starks 2004, Mateo 2004). They are assumed to be stored memory in the brain
and can be either plastic or immutable. If a template is plastic and not hardwired,
the reference it is based upon is termed the referent (Tsutsui 2004).
Matching the cue-bearer to the template can either occur in the central
nervous system (Vandermeer et al 1989) or in the peripheral nervous system
(Couvillon et al 2007). An example of the latter can be seen in the carpenter ant
Camponotus japonicus. In this species, antennal sensillae habituate to recognition
chemicals and then recognize novel chemicals as foreign (Ozaki et al 2005). This
implies that ants may not specifically recognize kin, but rather, recognize foes
based on the presence of novel odors (Guerrieri et al 2009). This was
demonstrated in Camponotus herculeanus for which the presence of additional
novel hydrocarbons and not the absence of nestmate hydrocarbons draws
aggression (Guerrieri et al 2009).
Templates can be formed early in development or can be continually
updated to reflect changes in the evaluators own phenotype (Waldman et al 1988).
Ultimately the degree of similarity between cue-bearer and template is directly

related to probability of acceptance (Couvillon et al 2007).
The action component involves a response to the label: template match or
mismatch (Liebert & Starks 2004). The evaluators action is a function of the
perceived similarity between the cue and its template (Mateo 2004). The response
is often plastic and context-dependent (Lieber and starks 2004). The action
component of self recognition can be categorized as either acceptance or
rejection. Acceptance as kin can have many different nuances: refusal as a sexual
mate (i.e. to avoid inbreeding), altruistic behavior, acceptance into colony, etc.
(Couvillon et al 2007, Lieberman 2007, Waldman 1988, Page & Breed 1987).
Similarly, rejection has many manifestations ranging from acceptance as a sexual
mate to outright aggression (Buchwald & Breed, 2005, Breed et al. 2004). In
Formicidae, nestmate rejection responses involves agonistic behavior (e.g.
mandibular flaring, stinging, or biting) (Holldobler & Wilson, 1990).
1.1.3 Cuticular Hydrocarbons
The exoskeletons of arthropods are coated with surface lipids composed
mainly of hydrocarbons along with other lipid components such as waxy esters
(Nelson et al 2000, Howard and Blomquist 2005, Martin and Drijfhout 2009b).
These chemicals serve many functions: prevention of desiccation, providing
chemical defense and a barrier to microorganisms, acting as sex, epideictic,

territorial, alarm, recruitment, and thermoregulatory pheromones, acting as
kairomonal cues for parasites, and acting as recognition cues (Howard and
Blomquist 2005).
Hydrocarbons evolved a primary function of preventing desiccation
(Lockey 1988, Hadley 1984). In Drosophilla cuticular hydrocarbons play a vital
role in reducing transpiration of water through the cuticle (Gibbs 1998). Rates of
water loss depend largely on the cuticular hydrocarbons' physical properties:
chain length, number of unsaturations, locations of methyl-branching, etc.
(Gibbbs 1998). Out of three species of Antarctic mites, Alaskozetes antarcticus,
Hydrogamasellus antarcticus and Rhagidia gerlachei, A. antarcticus has 2-3 fold
the amount of cuticular hydrocarbons and has a subsequent 20-fold reduction in
net transpiration rate over the other two species (Benoit et al 2008). In the gall fly,
Eurosta solidaginis, larvae increase their cuticular hydrocarbons in the arid
autumn and winter in order to prevent dessication (Nelson & Lee 2004). Washing
off these cuticular lipids with chloroform resulted in increased water loss in
individuals (Nelson & Lee 2004)
The amounts of cuticular hydrocarbons in some species changes in
response to seasonal environmental variations (Hadley 1984). Furthermore,
insects found in hot, arid climates tend to have greater amount of cuticular

hydrocarbons than closely related species found in more moderate environments
(Hadley 1984).
Hydrocarbons commonly contain recognition cues: a function that is
common to the majority of arthropods (Howard and Blomquist 2005). Cues in
hydrocarbon profiles can provide information regarding species, colony, sex,
fertility status and task membership (Gamboa et al 1986, Crosland 1989, Akino et
al 2004, Bonavita-Cougourdan et al 1993, Greene and Gordon 2003, Sugeno et al
2006, Lacey et al 2008, Rutledge et al 2009, Lommelen et al 2010). They can also
convey information about queen identity and reproductive status. Hydrocarbons
great structural diversity (e.g. number of carbon atoms, number and positions of
unsaturation, number and positions of methyl branches, as well as combinations
of these features) provides a means of encoding recognition cues (Lucas et al
2005). In the pine engraver beetle (Ips spp.) the presence or absence of specific -
alkanes and certain methyl-branched alkanes allows discrimination of I.
paraconfusus, /. confitsus, and I. hoppingi (Page et al 1997). In Tsetse flies and
cerambycid beetles, each sex possesses cuticular hydrocarbons that differ from
the opposite sex (Howard and Blomquist 2005, Nelson et al 1988). The female
pine engraver beetle Ips lecontei expresses 8 more unique hydrocarbons than does
the male (Page et al 1997). Similarly, males and females of some Drosophila
species express the same hydrocarbons but in different relative abundances

(Bartlet et al 1986, Howard and Blomquist 2005). In contrast to the preceding
examples, other species of Drosophila have no distinction in hydrocarbon profiles
between males and females (Howard et al 2003). Additionally, in the case of
social insects, these chemicals can indicate kin, colony, nest, caste, and task-group
membership (Howard and Blomquist 2005, Greene and Gordon 2003, Bonavita-
Cougourdan et al 1991).
Seventy-eight ant species across 5 subfamilies express 1,000 distinct
cuticular hydrocarbon molecules (Martin and Drifjhout 2009). These
hydrocarbons belong to 187 distinct homologous series, groups whose members
differ only in the addition of carbon and hydrogen to the terminal end, and ten
hydrocarbon groups which are listed in descending order of abundance: -alkanes
> monomethylalkanes > dimethylalkanes > alkenes > dienes trimethylalkanes
methylalkenes > methylalkadienes > trienes > tetramethylalkanes (Martin and
Drifjhout 2009).
In insects, hydrocarbons are synthesized in abdominal oenocytes
associated with fat body or epidermal tissue (Lockey 1988, Howard and
Blomquist 2005). These hydrocarbons are then bound by lipophorin and
transported in the hemolymph to sites such as the post-pharyngeal gland, the
Dufours gland, or exocrine glands (Howard and Blomquist 2005). The

mechanism by which hydrocarbons are transported from hemolymph lipophorin
to the surface of the insect is unknown (Howard and Blomquist 2005).
Cuticular hydrocarbon profiles, the suite of hydrocarbons present on the
cuticle of an insect, are under genetic control and the influence of exogenous
cues (Gamboa et al 1986) including temperature and humidity (Wagner et al.,
2000), nest materials (Liang et al 2001), or diet (Liang et al 2001, Richard et al
2004). The relative contribution of genetic and exogenous cues to the recognition
profile is not known (Buczowski et al 2005). In the Argentine ant (Linepithema
humile) feeding discrete colonies common hydrocarbons masks subtle inherent
distinctions between the colonies (Buczowski et al 2005). In contrast, exposing
Argentine ant nest-mates to prey hydrocarbons can alter nestmate recognition
cues leading to aggression by nestmates (Liang and Silverman 2000, Silverman
and Liang 2001).
The variablility of hydrocarbon profile composition among members of a
colony necessitates the existence of a mechanism to maintain a uniform colony
odor. This uniform colony odor, referred to as gestalt odor, is conserved through
trophallaxis and allo-grooming (Crossland 1989, Boulay et al. 2000). Trophallaxis
and allo-grooming promote constant exchange of hydrocarbons among nestmates
resulting in the homogeneous gestalt odor. Trophallaxis, the stomodeal transfer of
foods or fluids and saliva, allows for postpharyngeal gland contents to be traded

whereas allo-grooming causes transposition of cuticular hydrocarbons (Howard
and Blomquist 2005). In Camponotus fellah, complete isolation with no physical
interaction resulted in divergence of hydrocarbon profiles whereas isolation with
limited physical contact produces colony fragments with identical profiles
(Katazav-Gozansky et al 2004).
Hydrocarbon profiles are plastic, exhibiting seasonal variation (Ichinose
1991) and changing throughout the life of the colony (Hadley 1984, Vander Meer
et al 1989, Buczowski et al 2005). The rate at which the gestalt odor can change is
evidenced by experiments with the ant Camponotus fellah, for which twenty days
of social isolation resulted in workers being attacked by non-isolated nestmates
(Boulay 2000). Chemical analysis confirmed quantitative differences in cuticular
hydrocarbon profiles (Boulay 2000). In contrast to the increased aggression seen
from non-isolated nestmates, isolated nestmates exhibited low to no aggression
toward nestmates as well as non-nestmate conspecifics (Boulay and Lenoir 2001).
Aggression toward heterospecifics was not affected (Boulay and Lenoir 2001).
Another example of the dynamic nature of hydrocarbon profiles can be seen in the
harvester ant Pogonomyrex barbatus. Workers housed in the laboratory were
shown to have more than twice the amount of cuticular hydrocarbons than their
wild nestmates (Tissot et al 2001).
Evidence also suggests that colonies may have more clearly defined

gestalt odor in the spring as a result of increased trophallactic exchange during
hibernation (Katzerke et al 2006). There are seasonal variations in aggression
responses. In Formicidae exsecta and Plagiolepis pygmaea, aggression towards
conspecific foreigners is high in the spring and often nonexistent in the autumn
(Katzerke et al 2006, Thum and Aron 2008). It is also posited that this could
reflect a context-dependent adaptive behavior in response to changing
environmental conditions (Thum and Aron 2008).
Hydrocarbon profiles are important in multiple ant recognition systems:
species (Greene and Gordon 2007bb, Howard and Blomquist 2005), task (Greene
and Gordon 2003), caste (Bonavita-Cougourdan et al 1993, Boulay et al 2007),
fertility status (Liebig et al 2000) and nestmate (Holldobler 1995, Wagner et al
2000, Lucas et al., 2005).
Species-specific cues exist in cuticular hydrocarbon profiles because
cuticular hydrocarbons are qualitatively unique to a species (Howard and
Blomquist 2005). In Linepithema humile and Aphaenogaster cockerelli, a
combination of at least two heterospecific hydrocarbon structural classes (i.e. n-
alkanes, methyl-branched alkanes and H-alkenes) is required to elicit aggression
(Greene and Gordon 2007b). Moreover, in this species no single structural class is
more important than another (Greene and Gordon 2007b). This is not the case
with Tetramorium bicarinatum. T. bicarinatum discriminate against allospecific

Myrmica rubra due solely to their possession of cuticular methyl-branched
alkanes: a structural class novel to T. bicarinatum (Astruc et al 2001).
Pachycondyla subversa is yet another species that only responds to methyl-
branched alkanes (Lucas et al 2005). Unlike T. bicarinatum, this species does
innately express methyl-branched alkanes (Lucas et al 2005).
Task-specific cues have been shown to exist in the components of the
dufours gland, the postpharyngeal gland, and cuticular hydrocarbon profiles
(Bonavita-Cougourdan et al 1993, Boulay edt al 2007, Wagner et al 1998). In the
ant Camponotus vagus a clear quantitative distinction is present between the
thoracic cuticular hydrocarbon profiles of brood-tenders and foragers (Bonavita-
Cougourdan et al 1993).
As stated earlier, environmental conditions play a role in cuticular
hydrocarbon expression. Therefore, ants that are consistently exposed to a certain
environment should have qualitatively different hydrocarbon profiles from ants
that are exposed to a different climate. This concept underlies task-specific
cuticular hydrocarbon profiles. In the harvester ant Pogonomyrmex barbatus
foragers have significantly higher relative abundance of H-alkanes than do nest-
maintenance workers (Wagner et al 1998). Experimentation demonstrated that
exposure to hot, arid conditions increased -alkane production (Wagner et al
2001). This adaptation is reasonable since rc-alkanes are more effective than n-

alkenes and methyl-branched alkanes at preventing desiccation. Additionally,
behavioral bioassays show that ants respond according to the task-specific
hydrocarbons they encounter. In Pogonomyrmex barbatus foraging levels
increase when patroller hydrocarbon-coated beads are dropped down nest
entrances (Greene and Gordon 2003). This response is not elicited when beads
coated with a colony-specific hydrocarbon blend are used (Greene and Gordon
In the family Formicidae, cuticular hydrocarbons serve as direct mediators
of nestmate recognition (Vander Meer and Morel 1998, Liang and Silverman
2000). Other semiochemicals are present on the cuticle and may play a role in
recognition (Vander Meer and Morel 1998), but evidence supports the conclusion
that hydrocarbons are the primary source of recognition cues in ants (Howard and
Blomquist 2005).
When immobilized and exposed to non-nestmate hydrocarbon-coated
paper, Pachycondyla subversa retract their antennae and flare their mandibles
(Lucas et al 2005). In contrast this same species displays no aggressive reaction
when exposed to paper treated with non-hydrocarbon cuticular components
(Lucas et al 2005). Cataglyphis niger and Pogonomyrmex barbatus respond in the
same way when exposed to these stimuli (Lahav et al 1999, Wagner et al 2000).
The relative abundance of foreign cuticular hydrocarbons expressed

appears to be directly proportionate to the level of aggression reciprocated. This is
the case for at least one species of ant, the Argentine ant Linepithema humile
(Torres et al 2007). L. humile show no aggression, however, to nestmates
supplemented with excess nestmate hydrocarbons (Torres et al 2007).
How nestmate recognition cues are encoded in cuticular hydrocarbon
profiles is a burgeoning area of research. Mechanisms seem to be species-specific.
The Camponotus vagus and Camponotus herculeanus colony signatures appear to
be primarily encrypted in the relative proportions of dimethylalkanes (Bonavita-
Courgourdan et al 1993, Guerrieri et al 2009). In contrast, Formica exsecta use
only the (Z)-9-alkene component of the cuticular hydrocarbon profile as a
nestmate cue (Martin and Drijfhout 2009). (Z)-9-alkenes are also used to
designate nest-membership in Formica japonica. However, (Z)-9-alkenes alone
are not sufficient to elicit aggression. This species relies on colony-specific ratios
of five rc-alkanes as well as five (Z)-9-alkenes (Akino et al 2004). Combinations
and relative abundances seem to play a major role in most species nestmate
recognition systems. When Linepithema humile are presented with -alkane
cuticular hydrocarbons alone, no aggression is educed (Greene and Gordon
2007b). However when the same species is presented with a nestmate profile
supplemented with very high levels of n-alkanes, high levels of aggression are
elicited (Greene and Gordon 2007b). The preponderance of data on nestmate

recognition mechanisms suggests that nestmate cues are encoded in a mixture of
hydrocarbons in the profile rather than in individual components. Tsutsui et al
(2010) found that in L. humile both quantitative and qualitative changes to
cuticular hydrocarbon profiles trigger aggression among nestmates.
1.1.5 Tetramorium caespitum
Tetramorium caespitum, commonly known as the pavement ant, is the
focal organism in this study. This species is well-known for its large-scale ant
wars that occur in the summer and spring in which thousands of workers fight in
groups. Workers also readily fight in the laboratory setting. These qualities allow
for straightforward behavioral bioassay results.
Tetramorium caespitum is an invasive tramp species whose habitat
extends from Eurasia to North America (Holldobler and Wilson, 1990).
Introduced to North America as early as the 1700s, T. caespitum has since been a
pest thriving in human habitat. Belonging to the family Formicidae, subfamily
Myrmicinae, pavement ants are small in body size, ranging from 1.6 2.5 mm in
length. They have a dark or dark brown body, pale-colored legs and antennae, and
a series of grooves on the head and thorax (Fig 1.1 & 1.2).The dorsal thorax has
two spines projecting upward and the petiole has two nodes. Their antennae

consist of 12 segments with the 3 apical flagellomeres forming a club (Fig 1.1 &
Tetramorium caespitum can form large colonies of up to tens of thousands
of workers owing to their polygynous colony structure (Brian, et al., 1967; Steiner
et al., 2003).
Their diet consists of a variety of foods including, grease, meat, insects,
seeds, fruit, sweets, and nuts. Tetramorium caespitum's fondness of nuts has led
to significant losses in Californias almond production. Tetramorium caespitum
are also a structural pest with a propensity to nest under building foundations,
sidewalks, stones, pavement, and in crevices of housing structures (McGlynn,
Tetramorium caespitum are notorious for their aggression towards
conspecific non-nestmate and heterospecific ants (Holldobler and Wilson, 1990).
They usually fight in groups occasionally consisting of thousands of ants (Fig
1.4). This figure depicts a battle between 2 colonies fighting for a resource-rich
territory. Common formations seen in T. caespitum fight include dyads and
triads: two or three ants locked in combat, respectively. Using their mandibles,
ants grip, and potentially tear, their opponents legs, antennae, or mandibles.
Although aggressive, T. caespitum fights are usually not lethal. Most of the
participants in an ant war will walk away after territorial boundaries have been

established. In the field it appears that T. caespitum usually avoid confrontation
when unaccompanied by nestmates.
Nestmate and species recognition in T. caespitum is informed by cues
present in cuticular hydrocarbons (Sano and Greene, unpublished data)

Grooves One Pair of spines
Two nodes


1.2 Research Objectives
The central goal of my research is to synthesize information obtained from
behavioral bioassays, chemical analytical methods (GC), and manipulation of
cuticular hydrocarbon profiles in order to determine how variation in cuticular
hydrocarbon compounds elicits aggressive nestmate recognition responses in the
ant Tetramorium caespitum. In other words, it is my aim to de-code the nestmate
recognition cues of this species.
My specific objectives are:
1. To test the hypothesis: Differences in relative abundance of
hydrocarbons in colony cuticular hydrocarbon profiles are responsible
for eliciting aggression in nestmate recognition responses.
2. To test the hypothesis: Certain hydrocarbon structural classes on the
cuticle of T. caespitum are more important than others in eliciting a
nestmate recognition response.

Research Method
2.1 Research Sites
My research was performed 1) at field sites in urban areas of Denver,
Colorado and 2) in Dr. Greenes laboratory at University of Colorado Denver.
Field sites were generally sidewalks adjacent to grass. The two primary sidewalk
sites were at an apartment complex and at an elementary school, typical urban
habitat for this tramp species. Nests, at least 1 meter apart, were selected at
random. In order to verify that each nest was truly distinct from the others,
preliminary bioassays, described in section 2.3, were performed. If aggression
resulted, nests were assumed to be discrete.
Ants were collected between dusk and midnight during their heaviest
foraging period. Collection for Objective 1 took place in May, 2008, whereas
collection for Objective 2 occurred first in June, 2008, and then second in August,
2009. Ants were baited with honey nut cream cheese and then aspirated for
collection as foragers were recruited to the bait.
Behavioral bioassays were always initiated within an hour of capturing the
ants. Behavioral research for Objective 1 was conducted in the field so as to test
non-nestmate aggression under the proper behavioral and ecological context.
Behavioral research for Objective 2 occurred in the Greene lab.

2.2 Chemical Methods
2.2.1 Extraction of Cuticular Hydrocarbons:
The cuticular hydrocarbon component of the surface lipids were extracted
and isolated using techniques reviewed by Nelson and Blomquist (1995) that are
already established in the Greene laboratory (Greene and Gordon 2003, 2007b).
Ants were killed by freezing (-20C). I immersed 100 frozen T. caespitum in 1.0
ml of pentane for 10 minutes with occasional mild agitation. I created a gravity
chromatography column by placing a 4 centimeter column of silica gel (Sigma-
Aldrich, Grade 60, 70-230 mesh, 60A) in a 5 % inch Pasteur pipette plugged with
filter paper. The silica gel was then activated with pentane. The pentane/surface
lipid solution was added to the column. Because of silica gel's polarity, non-polar
components, which include hydrocarbons, elute before more polar ones. Using
pentane, I eluted the non-polar cuticular hydrocarbons from the column separating
them from the polar surface lipid components. Upon completion of the isolation
process I was left with a yield of about 1.5 micrograms of cuticular hydrocarbon
per ant.
2.2.2 Separation of cuticular hydrocarbon structural classes:
Separation of -alkanes, methyl branched-alkanes, and alkenes was
conducted according to methods detailed by Nelson and Blomquist (1985) and

Greene and Gordon (2007). To separate saturated cuticular hydrocarbons from
unsaturated -alkanes, I dissolved 2.0 g of Sigma silver nitrate in 10 ml of
acetonitrile/water solution (10:1, v:v). I then mixed 6.0 ml of the 20% (m:v) silver
nitrate with 4.8 g of Sigma Grade 60, 70-230 mesh, 60A silica gel. I allowed the
mixture to dry and then created a gravity chromatography column using a Pasteur
pipette, filter paper, and a 4 centimeter column of the silver nitrate/silica gel
mixture. The column was covered with aluminum foil to avoid exposure of silver
nitrate to light. I activated the column with pentane and then added the cuticular
hydrocarbon sample isolated from the surface lipids. I eluted saturated
hydrocarbons (i.e. n-alkanes and methyl-branched alkanes) with 3.0 ml of
pentane. Unsaturated hydrocarbons (i.e. n-alkenes) were eluted with 3.0 ml of a
10% ethyl acetate/90% pentane solution..
To separate n-alkanes from methyl-branched alkanes I used 5 A molecular
sieves (Sigma, M-0258; 1/16th inch pellets, nominal pore diameter 5A). n-alkanes
become trapped in the pores which the bulkier methyl-branched alkanes are too
large to enter (Greene and Gordon 2007b). Prior to separation, I desiccated the
sieves by baking them in a 170 C oven for two days in order to ensure that
moisture was not blocking the pores. Sieves were washed with isooctane twice
and then stored in an Erlenmeyer flask in isooctane. The saturated hydrocarbons
were dried, re-solvated with isooctane and placed in the Erlenmeyer flask with the

5A sieves. I sealed the Erlenmeyer flask and placed it on a heater block set at 75
C. Following two days of constant heat and occasional mild agitation, the
Erlenmeyer flask was opened and its contents were separated. The pellets were
washed with isooctane to remove remaining methyl-branched hydrocarbons. The
pellets which contained the rc-alkanes in their pores were discarded. I allowed the
isooctane to evaporate and I re-solvated methyl-branched hydrocarbon fraction in
pentane. I ran this solution through a silica gel column to remove contaminants.
After each separation step, I analyzed an aliquot of the sample using the
Greene Lab gas chromatograph to confirm accurate separation. Gas
chromatography was performed using a DB-1 fused silica capillary column (30-m,
0.25 i.d., 0.25-_m film thickness; J&W Scientific, Folsom, CA, USA). The oven
temperature was held at 170C for the first 5 minutes and then raised to 220C at
a rate of 25C per minute. It was then raised to 310C at a rate of 3C per minute
with a final 5 minute hold (Greene and Gordon 2007b).
2.2.3 Identification of cuticular hydrocarbons:
The hydrocarbon profiles were analyzed using gas chromatography. In
order to identify the molecules, the peaks were matched up with data provided by
Clair Chen using retention times (figure 2.21) (Chen unpublished data).

2.2.4 Reconstruction of n-alkanes:
Because the structural class separation process results in n-alkanes being retained
in the molecular sieves, it was necessary to reconstruct n-alkanes for Objective 2.
I dissolved 5.0 milligrams of pentadecane (Fluka), 3.4 milligrams of tricosane
(Aldrich), 4.3 milligrams of tetracosane (Fluka), 2.7 milligrams of pentacosane
(Fluka), 5 milligrams of hexacosane (Supelco), 3.1 milligrams of heptacosane
(Aldrich), and 3.1 milligrams of henitriacontane (Aldrich) in 40 milliliters of
pentane. This was equally distributed into 10 test tubes and allowed to dry.
2.2.5 Measuring Relative Abundances:
1 measured the relative abundance of 16 major hydrocarbon peaks on gas
chromatograms by dividing the area of each peak by the sum of all 16 peak areas
(figure 2.22). Only 16 peaks were measured because the other 8 were not
common to all of the colony-specific chromatograms I measured relative
abundance rather than absolute abundance because differences in relative
abundance rather than differences in overall abundance of cuticular hydrocarbon
are necessary to elicit aggression.

I. 3-methylheneicosane
2 9-tricosene
3. rc-tricosane
4 9-, 11-raethvkricosane
5 3-meth>itrico$ane
6 k-tetracosane
13-, 11-methyitetracosane
S 4-methyttetracosane
9 n -pentacosane
10. 11-methylpentacosane
II. 3-methvlpentacosane
12. 12-, 10-, $-, 3-methvlhexacosane
13 -methvlhexacosane
4-niethyihexacosane, $, 10,14-trimethyIhexacosane
13 -methylheptacosane
3-methylheptacosane, 5,10 dime th>iheptac o s ane
14 -. 5 -metliylo c t ac o s ane
6,12.21 -trimethyloctacosane

Table 2.2 CUTICULAR Hydrocarbons corresponding to the 16 peaks I examined
Peak # Molecule Name
1 n-tricosane
2 9-; 11-methyltricosane
3 3 -methyltricosane
4 n-tetracosane
5 11-; 13-methyltetracosane
6 -pentacosane
7 11-methylpentacosane
8 3 -methy lpentacosane
9 12-; 10-; 8-; 3-methylhexacosane
10 13-methylhexacosane
11 4-methylhexacosane/ 8,10,14-trimethylhexacosane
12 -heptacosane
13 13-methylheptacosane
14 3 -methylheptacosane/ 5,10-dimethylheptacosane
15 3,11-dimethylheptacosane
16 n-nonacosane

Relative Abundance
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COLONY. Asterisks denote the 16 peaks used for statistical analysis.

2.3 Quantification of aggression
Many behavioral bioassays performed on ants result in interactions that
are out of ecological context. It is questionable if these interactions are a true
indicator of natural behaviors. These include immobilizing ants by tethering,
chilling, or even pinning (Roulston et al 2003). In order to ensure minimal human
interference, I developed a method similar to one used by Chen and Nonacs
(2000) on Argentine ants. I created an arena by placing a bottomless Fluon-
coated polystyrene Petri dish (Fisherbrand, 60 mm x 15 mm) on a flat surface. I
placed putty around the outside of the arena in order to completely isolate it from
its surroundings. I then placed 10 ants from the focal colony inside the arena and
allowed them 1 minute to acclimate. I then added 10 non-nestmates from a single
In order to record aggression I took a still picture 30 seconds into the
interaction and then subsequently at 1 minute. I counted the number of ants
involved in aggressive behavior in each photo and used the average of the two
values as the final aggression index for the interaction. Aggressive behavior was
limited to mandibular flaring, biting, including formation of dyads and triads, and
stinging (figure 2.31). I paired groups of ants rather than individual ants because
preliminary tests suggested that T. caespitum displays minimal aggression when
fighting in small numbers. Furthermore, this behavioral bioassay mimics inter-

colonial fights in the wild, albeit in a smaller, more easily quantifiable manner.

WHILE IN AN ARENA. Mandibular flaring, biting, and grappling are circled.
The three circles to the right show dyads: 2 ants locked in combat.

Figure 2.32 NESTMATE INTERACTIONS IN AN ARENA. Aggregating and
antennating were commonly seen when ants were placed in an arena with

Figure 2.33 NESTMATE BEHAVIOR IN AN ARENA. Allogrooming among

2.4 Objective 1: Testing the hypothesis: differences in relative abundance
of hydrocarbons in colony cuticular hydrocarbon profiles are responsible for
eliciting aggression in nestmate recognition responses.
I froze 100 ants from each of the 10 colonies at -20 C and then extracted
their cuticular hydrocarbons according to the methods detailed above in the
Chemical Methods (2.2.1). A portion of the extract was analyzed with the Greene
laboratorys gas chromatograph to measure the relative abundance of cuticular
hydrocarbon peaks (2.2.5).
At the same 10 focal colonies I tested for aggression against the following
stimuli: 1.) 10 nestmates and 2.) 10.) 10 non-nestmates from each of the other 9
To accomplish this, 1 lured ants from the focal colony out with a cream
cheese bait (2.1). After they had been allowed to forage for 10 minutes I created
an arena adjacent to the foraging site (2.3). This was intended to simulate an
invasion of resource rich territory. In each bout, 10 ants from the focal colony
were placed in the arena and allowed 1 minute to adjust. Following acclimation,
one of the above stimuli was introduced (figure 2.31 and figure 2.32). An
aggression index was calculated using the methods outlined above under
Quantification of Aggression (2.3). Following each bout ants from the arena were
freed at least 10 yards from the arena site. I repeated this until the focal colony

was tested against every colony including itself.
My data (Table 3.11) met the assumptions of normality, independence,
linearity and homoscedasticity for linear regression analysis. Using the
multivariate and univariate linear regression features of R Statistical Computing
software, available at I determined how differences in
hydrocarbon levels between colonies related to aggression between colonies. The
dependent variable was the aggression index of the interaction in question. There
were 16 independent variables corresponding to the absolute value of the
difference in relative abundance of each of 16 measured hydrocarbon peaks
between ant colonies involved in the bout. Multivariate linear regression was used
because it examines the linear correlation between each hydrocarbon peak and
aggression index in the context of the averages of the other 15 peaks. Univariate
linear regression was used to examine the correlation between each peak and
aggression index independently of the other 15 peaks. Using Microsoft Excel, T-
tests were performed to determine whether the slope of the regression line
differed significantly from zero, indicating a significant relationship between the
independent variables and the dependent variable.

*' i



is visible to the right of the arena.

SECONDS AFTER THE ANTS WERE ADDED. Aggressive interactions are

2.5 Objective 2: Testing the hypothesis that certain hydrocarbon structural
classes on the cuticle of T. caespitum are more important than others in
eliciting a nestmate recognition response.
Experimentation for Objective 2 was conducted early in the summer of
2010.1 collected approximately 200 workers from each of 10 T. caespitum
colonies. I partitioned off 6 sets of 20 ants from each of the colonies and housed
each set in a discrete petri dish. The remaining 80 ants were kept separatly to use
as the mother colony. In each of the petri dishes I placed a damp cotton ball and
approximately 0.5 grams of cream cheese for food. One group of 20 ants was left
in their petri dish with no disturbance for three days.
The other 5 groups from each colony were vortexed for 1 minute in a test
tube containing either 1) evaporated pentane (blank control), 2) 400 ant-
equivalents of non-nestmate hydrocarbons, 3) 400 ant-equivalents of synthetic n-
alkanes, 4) 400 ant-equivalents of non-nestmate methyl-alkanes, or 5) 400 ant-
equivalents of non-nestmate H-alkenes (Torres et al 2007). Following vortexing,
groups were placed back in their respective petri dish. The same vortexing
process was performed for the two subsequent mornings. This vortexing process
is a means of guaranteeing supplementation of cuticular hydrocarbons with the
contents of the test tubes with minimal or no noticeable damage to the ants. Upon
completion of the supplementation process each of the 10 mother colonies was

tested against the following stimuli for aggression.
1. ) 10 nestmates that had been isolated from the mother colony for 3 days
2. ) 10 nestmates vortexed in dry test tube previously treated with pentane
(blank control)
3. ) 10 nestmate supplemented with non-nestmate cuticular hydrocarbons
4. ) 10 nestmate supplemented with rc-alkanes
5. ) 10 nestmate supplemented with methyl-alkane
6. ) 10 nestmate supplemented with n-alkenes
I assigned an aggression score to each interaction according to the method
outlined above (2.3). I tested two replicates for each of the stimuli. I compared the
aggression indices in order determine which structural group is primarily
responsible for eliciting aggression. In order to determine the relative significance
of each of the structural classes on eliciting an aggressive nestmate recognition
response, the data (figure 3.21 & 3.22), having met the requirements of normality,
linearity, and homoscedasticity, were analyzed using the one-way ANOVA
feature of R Statistical Computing software, available at

3.1 Results for Objective 1: Testing the hypothesis: differences in relative
abundance of hydrocarbons in colony cuticular hydrocarbon profiles are
responsible for eliciting aggression in nestmate recognition responses.
The data (Table 3.11) analyzed for this experiment include the aggression
index, number of ants fighting for each pairing of colonies, and the absolute value
of the difference in relative abundance of each of 16 hydrocarbon peaks.
Multivariate linear regression was used to evaluate aggression relative to the
independent variables, hydrocarbon relative abundances as measured from gas
chromatograms, in the context of the averages of the other 15 independent
variables. These results (Table 3.12) indicate that peak 16, nonacosane, (t-value -
2.542, p-value 0.02177) is the best predictor of aggression. Interestingly,
nonacosane has a negative slope. This means that the more similar ants are in
their expression of this molecule, the more aggression there will be. From a
biological standpoint this is unlikely. Using this model if ants are identical in
expression, as in sisters, one would expect to see high levels of aggression. Peaks
7, 11-methylpentacosane, (t-value 2.001, p-value 0.06265) and 8, 3-
methylpentacosane, (t-value -1.788, p-value 0.09265) are the only peaks with t-
values and p-values that suggest a possible role in nestmate recognition. Peak 8

has a negative slope which is most likely indicative of a false relationship. The p-
value for our model was 0.4182 suggesting little significance. The multiple R-
squared value is low, indicating that only 52.62% of the variance is explained by
our model. This value is also inflated due to the large number of variables. The
standard errors are at least one third the value of the mean. The range is even
larger for peak 14 which has a standard error more than ten times as large as the
mean value. These standard errors imply that any linear relationship found
between the 16 peaks and aggression may only have statistical significance but no
biological significance.
In order to determine if there is a correlation between differences in
relative abundance of any specific hydrocarbon molecule and aggression levels,
the data were analyzed using univariate linear regression. The results (Table 3.13)
imply that the difference in relative abundance of no gas chromatogram peak has
a linear relationship to aggression. The peak that yields the highest t-value and
lowest p-value is peak 12, heptacosane, (t-value 2.719, p-value 0.109). Peaks 16,
noncosane, (t-value 2.015, p-value 0.166) and 6, pentacosane, (t-value 1.834, p-
value 0.185) are the only other molecules with a p-value below 0.35. The R-
squared values are also extremely low (<0.062) indicating that close to none of
the variance is explained by any model.

Table 3.11: BOUT (colony pairing), Aggression Indexes (A.I.), and Difference in
Relative Abundances for 10 Ant Colonies collected in and around Denver, CO
Bout A.I. Dif peak 1 Dif peak 2 Dif peak 3 Dif peak 4
1 v2 18 0.00871 0.00431 0.00165 0.00047
1 v 4 15.5 0.01822 0.0405 0.0343 0.03864
1 v 5 15 0.01965 0.052 0.04452 0.04585
1 v 6 15 0.02107 0.04352 0.01711 0.04155
1 v7 16.5 0.03437 0.03829 0.02139 0.03816
1 v 8 18 0.02027 0.04483 0.03607 0.04142
1 v9 12 0.04133 0.04482 0.03782 0.01748
1 v 10 5.5 0.00268 0.00046 0.00243 0.00552
2 v 4 6.5 0.02693 0.03619 0.03265 0.03817
2 v 5 13.5 0.02836 0.04769 0.04287 0.04538
2 v 6 15.5 0.02978 0.03921 0.01547 0.04108
2 v 8 1 0.02898 0.04051 0.03442 0.04095
2 v 9 17 0.05004 0.04051 0.03617 0.017
2 v 10 12 0.01139 0.00478 0.00078 0.00504
4 v 5 13.5 0.00143 0.0115 0.01022 0.00721
4 v 6 8 0.00285 0.00302 0.01718 0.00291
4 v 8 14 0.00205 0.00433 0.00177 0.00278
4 v 9 5 0.02311 0.00432 0.00352 0.02117
4 v 10 6 0.01554 0.04096 0.03187 0.03313
5 v 6 8 0.00142 0.00848 0.02741 0.0043
5 v 8 13 0.00062 0.00718 0.00845 0.00443
5 v 9 3 0.02168 0.00718 0.00671 0.02837
5 v 10 16 0.01697 0.05247 0.04209 0.04033
6 v 7 16.5 0.0133 0.00523 0.00428 0.0034
6 v 8 16.5 0.0008 0.00131 0.01895 0.00013
6 v 9 16.5 0.02026 0.0013 0.0207 0.02408
6v 10 3 0.01839 0.04398 0.01468 0.03604
7 v 8 17.5 0.0141 0.00653 0.01468 0.00327
7 v 9 15.5 0.00696 0.00653 0.01643 0.02068
7 v 10 10.5 0.03169 0.03876 0.01896 0.03264
8 v 9 16 0.02106 5.77E-06 0.00175 0.02395
8 v 10 16.5 0.01759 0.04529 0.03364 0.03591
9 v 10 16.5 0.03865 0.04528 0.03539 0.01196

Table 3.11 (Cont.): BOUT (colony pairing), Aggression Indexes, and Difference
in Relative Abundances for 10 Ant Colonies collected in and around Denver, CO
Bout A.I. Dif peak 5 Dif peak 6 Dif peak 7 Dif peak 8
1 v2 18 0.02591 0.0012 0.01327 0.00186
1 v 4 15.5 0.04644 0.05899 0.09099 0.03523
1 v 5 15 0.06872 0.06906 0.07409 0.02837
1 v 6 15 0.0606 0.04316 0.14179 0.08971
1 v7 16.5 0.06082 0.0191 0.11539 0.09712
1 v 8 18 0.06346 0.0397 0.10773 0.05483
1 v 9 12 0.06534 0.02905 0.00155 0.08946
1 v 10 5.5 0.03195 0.00477 0.00222 0.00088
2 v 4 6.5 0.02053 0.05778 0.07773 0.03709
2 v 5 13.5 0.04281 0.06786 0.06082 0.03023
2 v 6 15.5 0.03469 0.04196 0.12852 0.09157
2 v 8 1 0.03755 0.03849 0.09446 0.05669
2 v 9 17 0.03943 0.03026 0.01482 0.09132
2 v 10 12 0.00604 0.00597 0.01549 0.00098
4 v 5 13.5 0.02228 0.01007 0.01691 0.00686
4 v 6 8 0.01416 0.01583 0.05079 0.05448
4 v 8 14 0.01702 0.01929 0.01673 0.0196
4 v 9 5 0.01889 0.08804 0.09254 0.05423
4 v 10 6 0.0145 0.06375 0.09321 0.03612
5 v 6 8 0.00812 0.0259 0.0677 0.06134
5 v 8 13 0.00526 0.02937 0.03364 0.02646
5 v 9 3 0.00339 0.09811 0.07563 0.06109
5 v 10 16 0.03678 0.07383 0.07631 0.02925
6 v 7 16.5 0.00022 0.02406 0.0264 0.00741
6 v 8 16.5 0.00286 0.00346 0.03406 0.03488
6 v 9 16.5 0.00473 0.07221 0.14334 0.00025
6v 10 3 0.02866 0.04792 0.14401 0.09059
7 v 8 17.5 0.00264 0.0206 0.00766 0.04229
7 v 9 15.5 0.00452 0.04815 0.11694 0.00766
7 v 10 10.5 0.02887 0.02387 0.11761 0.09801
8 v 9 16 0.00188 0.06875 0.10928 0.03463
8 v 10 16.5 0.03151 0.04446 0.10995 0.05572
9 v 10 16.5 0.03339 0.02429 0.00067 0.09034

Table 3.11 (Cont.): BOUT, Aggression Indexes, and Difference in Relative
Abundances for 10 Ant Colonies collected in and around Denver, CO
Bout A.I. Dif peak 9 Dif peak 10 Dif peak 11 Dif peak 12
1 v2 18 0.00448 0.0025 0.00107 0.00341
1 v 4 15.5 0.01724 0.0332 0.0214 0.13741
1 v5 15 0.01567 0.03285 0.01942 0.11119
1 v 6 15 0.02139 0.03104 0.02215 0.05392
1 v 7 16.5 0.00989 0.02069 0.01065 0.0303
1 v 8 18 0.01321 0.03128 0.02261 0.09465
1 v 9 12 0.011311 0.03828 0.00168 0.03324
1 v 10 5.5 0.00149 0.00261 0.00327 0.00309
2 v 4 6.5 0.02172 0.0357 0.02247 0.13399
2 v 5 13.5 0.02015 0.03535 0.02049 0.10778
2 v 6 15.5 0.02587 0.03354 0.02322 0.0505
2 v 8 1 0.01769 0.03378 0.02368 0.09123
2 v 9 17 0.10863 0.03579 0.00275 0.03665
2 v 10 12 0.00299 0.00011 0.0022 0.00032
4 v 5 13.5 0.00157 0.00035 0.00197 0.02621
4 v 6 8 0.00416 0.00216 0.00075 0.08349
4 v 8 14 0.00402 0.00192 0.00121 0.04276
4 v 9 5 0.13034 0.07148 0.01971 0.17065
4 v 10 6 0.01873 0.03581 0.02467 0.13432
5 v 6 8 0.00572 0.00181 0.00273 0.05728
5 v 8 13 0.00246 0.00157 0.00318 0.01655
5 v 9 3 0.12878 0.07113 0.01774 0.14443
5 v 10 16 0.01716 0.03546 0.02269 0.10811
6 v 7 16.5 0.01151 0.01035 0.0115 0.02361
6 v 8 16.5 0.00818 0.00024 0.00046 0.04073
6 v 9 16.5 0.1345 0.06932 0.02046 0.08715
6v 10 3 0.02288 0.03365 0.02542 0.05083
7 v 8 17.5 0.00333 0.01059 0.01196 0.06434
7 v 9 15.5 0.12299 0.05897 0.00896 0.06354
7 v 10 10.5 0.01138 0.0233 0.01392 0.02721
8 v 9 16 0.12632 0.06957 0.02092 0.12789
8 v 10 16.5 0.0147 0.03389 0.02588 0.09156
9 v 10 16.5 0.11162 0.03567 0.00496 0.03633

Table 3.11: BOUT (colony pairing), Aggression Indexes (A.I.), and Difference in
Relative Abundances for 10 Ant Colonies collected in and around Denver, CO
Bout A.I. Dif peak 13 Dif peak 14 Dif peak 15 Dif peak 16
1 v 2 18 0.00416 0.00598 0.01552 0.00495
1 v 4 15.5 0.00648 0.00509 0.03696 0.02415
1 v5 15 0.01356 0.02799 0.01248 0.01308
1 v 6 15 0.01717 0.00608 0.03103 0.02802
1 v 7 16.5 0.01085 0.0153 0.00781 0.0243
1 v 8 18 0.00063 0.01955 0.02205 0.002062
1 v 9 12 0.07187 0.02461 0.04087 0.02507
1 v 10 5.5 0.00633 0.00889 0.0116 0.01271
2 v 4 6.5 0.01064 0.01107 0.02144 0.0291
2 v 5 13.5 0.0094 0.02201 0.00303 0.01803
2 v 6 15.5 0.02133 0.0001 0.01551 0.03296
2 v 8 1 0.00479 0.01357 0.00653 0.02557
2 v 9 17 0.06771 0.03059 0.02535 0.02012
2 v 10 12 0.00217 0.00291 0.02711 0.00776
4 v 5 13.5 0.02004 0.03308 0.02447 0.01107
4 v 6 8 0.01069 0.01118 0.00593 0.00386
4 v 8 14 0.00585 0.02464 0.0149 0.00353
4 v 9 5 0.07835 0.01952 0.00391 0.04923
4 v 10 6 0.01281 0.01398 0.04855 0.03686
5 v 6 8 0.03073 0.02191 0.01854 0.01493
5 v 8 13 0.01419 0.00844 0.00957 0.00754
5 v 9 3 0.05831 0.0526 0.02839 0.03816
5 v 10 16 0.00723 0.0191 0.02408 0.02579
6 v 7 16.5 0.00632 0.00922 0.02321 0.00371
6 v 8 16.5 0.01654 0.01347 0.00897 0.0074
6 v 9 16.5 0.08904 0.03069 0.00985 0.05309
6 v 10 3 0.0235 0.00281 0.04262 0.04072
7 v 8 17.5 0.01022 0.00425 0.01424 0.00368
7 v 9 15.5 0.08272 0.03991 0.03306 0.04938
7 v 10 10.5 0.01718 0.00641 0.01941 0.03701
8 v 9 16 0.07249 0.04416 0.01882 0.04560
8 v 10 16.5 0.00696 0.01066 0.03365 0.03333
9 v 10 16.5 0.06554 0.0335 0.05247 0.01237

Table 3.12: MULTIVARIATE Regression Results: Predictors of Aggression
Coefficients: Estimate Std. Error p-value
(Intercept) 15.998 5.433 0.00952
w-tricosane 190.973 239.986 0.43781
9-, 11 -methyltricosane 265.654 306.028 0.39819
3-methyltricosane -46.273 193.673 0.8142
11 13-methyltetracosane -518.376 443.704 0.2598
n-tetracosane 14.049 105.906 0.89612
rt-pentacosane -115.69 136.897 0.41052
11 -methylpentacosane 145.749 72.836 0.06265
3-methylpentacosane -111.82 62.522 0.09265
12-, 10-,8-,3-methylhexacosane -341.125 295.696 0.26559
13-methylhexacosane 1006.785 717.358 0.17959
4-methylhexacosane & 8,10,14-trimethylhexacosane -446.436 528.586 0.41079
n-heptacosane -32.661 61.99 0.60551
13-methylheptacosane 230.808 230.746 0.33207
3-methylheptacosane & 5,10-dimethylheptacosane 13.239 140.376 0.92603
3,11-dimethylheptacosane 34.154 135.663 0.80444
H-nonacosane -678.785 267.06 0.02177

Table 3.13: UNIVARIATE Linear Regression Results: Predictors of Aggression
Dif in Peak Slope Estimate Std. Error p-value
fl-tricosane 1.07584 3.56822 0.765
9-,l 1-methyltricosane 0.62139 2.27065 0.786
3-methyltricosane 1.92337 3.19543 0.552
11-,13-methyltetracosane -1.51695 2.79329 0.591
n-tetracosane 1.84894 2.14774 0.396
rc-pentacosane -46.502 34.333 0.185
11 -methylpentacosane -0.6547 0.95591 0.498
3-methylpentacosane -0.9748 1.39379 0.49
12-, 10-,8-,3-methylhexacosane -0.08137 0.93035 0.93
13-methylhexacosane -1.4044 2.0176 0.492
4-methylhexacosane & 8,10,14-trimethylhexacosane -4.45341 4.73891 0.355
n-heptacosane -1.54095 0.93455 0.109
13-methylheptacosane 0.05382 1.61882 0.974
3-methylheptacosane & 5,10-dimethylheptacosane 0.60324 3.48189 0.864
3,11-dimethylheptacosane 0.17291 3.49991 0.96
H-nonacosane -4.1701 2.93787 0.166

3.2 Results for Objective Two: Testing the hypothesis that the three
Cuticular hydrocarbon structural classes on the cuticle of T. caespitum have
differing degrees of consequence in nestmate recognition.
In order to determine the relative significance of each of the structural classes on
eliciting an aggressive nestmate recognition response, the data (figure 3.21 &
3.22) were analyzed using one-way ANOVA. Effects of hydrocarbon
supplementation on nestmate recognition differed significantly across the six
hydrocarbon groups (F(5, 54) = 83.28,p = 2.2 x 10'16) (Table 3.22). The results
indicate that methyl-branched alkanes and, to a lesser degree, n-alkenes are more
important in nestmate recognition than are n-alkanes (figure 3.23).
Supplementation with methyl-branched alkanes elicited aggression from a mean
of 5.5 ants out of 20 (lower limit 3.715, upper limit 7.285), supplementation with
n-alkenes elicited aggression from a mean of 2.4 ants (lower limit 0.822, upper
limit 3.978), and -alkanes elicited aggression from 0 ants (lower limit of 0, upper
limit of 0) (figure 3.23). Data (Table 3.23) pertaining specifically to structural
classes were analyzed using one-way ANOVA. Effects of supplementation on
nestmate recognition differed significantly across the three structural classes and
the control group (F (3, 36) =61.239,/? = 3.25 x 10'16) (Table 3.24). Tukeys Post
Hoc Test showed significant differences between the means of n-alkenes, n-
alkanes, methyl-branched alkanes and the control. Furthermore, comparisons

between the means of methyl-branched alkanes and the means of -alkanes, n-
alkenes, and the control are statistically significant (figure 3.23).

n-alkanes n-alkeres methyl-
a colcry 1
i colcrv 2
a colcrv 3
a colcrv 4
a colcrv 5
d colcrv 6
a colcrv 7
colcrv 8
0 colcrv 9
ss colcrv 10
bars represent data from each colony. The control group consisted of colony
fragments left in isolation during the duration of the supplementation process. The
vortex group consisted of colony fragments vortexed in pentane coated tubes.

Table 3.21: MEAN aggression indexes for each of the supplementation
Mean Aggression Index against nestmates treated with:
Colony Control Vortex Pooled CHCs n- alkanes m- branched alkanes n-alkenes
1 0 0 9 0 3 6.5
2 0 0 10 0 1.5 3
3 0 0 9.5 0 1.5 3.5
4 0 0 9 0 7.5 7.5
5 0 0 10 0 3.5 4
6 0 0 9.5 0 3.5 5.5
7 0 0.5 9 0 9 5
8 0 2 8.5 0 7 2
Table 3.22: ANOVA results for supplementation experiment
Sum of Squares Degrees of Freedom Mean Square F Value Pr (>F)
Between Groups 498.13 5 99.627 83.28 < 2.2xl0'16
Within Groups 64.60 54 1.196
Total 562.73 59

Table 3.23: MEAN aggression indexes specifically for structural class
supplementation analysis
Mean Aggression Index against nestmates treated with:
Colony Vortex (Control) n-alkanes m-branched alkanes n-alkenes
1 0 0 3 6.5
2 0 0 1.5 3
3 0 0 1.5 3.5
4 0 0 7.5 7.5
5 0 0 3.5 4
6 0 0 3.5 5.5
7 0.5 0 9 5
8 2 0 7 2
Table 3.24 ANOVA results for structural class supplementation experiment
Sum of Squares Degrees of Freedom Mean Square F Value Pr (>F)
Between Groups Within Groups Total 196.47 38.50 3 36 65.492 1.069 61.239 3.25 xlO'16

Means Aggression Index from Encounter cetween Focal Untreated Control Group
and Hydro car con-Class-Treated Sisters
c ranched
SUPPLEMENTATION EXPERIMENTS. Letters indicate groups whose means
have statistically significant differences.

95% family-wise confidence level
Control ( i ) 1
m-branched alkeres 1 ( )
n-a kaoes-alkenes ( * 4- j 1
pooled alkenes - f t i
Vertex alkenes ~ ;i
rn-bidiicned Control ; ( t 1
ii dlkd.ries < Control ~ i 1 i
pooled 'Control j r )
Voitex Control i
a;ones m brarohed - f. 1 i
pooled m-brarcnod - t-j >
Vole:-: m-brar:hod -\ ( ) i i
pooled n-alkancs 1 | ( * i
Vortex i i* )
Vclex pooled -* i i i i |
-10 -o 0 0 13
Lined! I U'iClKir:

4.1 Conclusions
1. Nestmate recognition responses by Tetramorium caespitum
workers are not informed by the relative abundance of any one
specific molecule. Regression analysis showed that no single
cuticular hydrocarbon molecule serves as an accurate predictor for
the degree of intercolonial aggression between two colonies.
2. The relative abundances of methyl-branched alkanes code
nestmate recognition cues and are the most important structural
class in nestmate recognition responses by T. caespitum workers.
3. The relative abundances of n-alkenes also code for nestmate
recognition cues for T. caespitum workers, but elicit less
aggression than methyl-branched alkanes.
4. The relative abundance of /i-alkanes does not code for nestmate
recognition recognition cues in T. caespitum.
4.2 Objective One Discussion
In order elucidate the relationship between differences in relative

abundance and degree of aggression in T. caespitum, I used a novel observational
approach. Employing univariate and multivariate linear regression, I examined the
relationship between observed intercolonial aggression and differences in relative
abundance of specific cuticular hydrocarbons. This approach was necessary to
establish preliminary data since I had no a priori knowledge regarding
recognition cues in T. caespitum. Being purely observational with no
experimental basis, these data can only offer correlative evidence to suggest
possible recognition cues.
I found no linear relationship between the differences in the relative
abundances of hydrocarbon molecules and levels of intercolonial aggression for
the ant Tetramorium caespitum. Through using multivariate and univariate linear
regression I determined that no clear linear relationship exists. Statistically n-
nonacosane is the best predictor for aggression. Unfortunately, the relationship
between differences in n-nonacosane and aggression has a negative slope. This
means that dissimilar ants should display no aggression towards each other and
similar ants should exhibit the highest levels of aggression. If this were true,
colonies would not be able to thrive due to constant intracolonial aggression. Data
did suggest that 11-methylpentacosane may be a predictor of aggression. This
correlation was not a statistically significant trend (p-value 0.06265). It has been
demonstrated that mono-methylalkanes serve as recognition cues in Formica

sanguinea (Martin et al 2008).
Evidence suggests that the magnitude of differences in relative abundance
of foreign cuticular hydrocarbons expressed is directly proportionate to the level
of aggression reciprocated. This is the case for at least one species of ant, the
Argentine ant Linepithema humile (Torres et al 2007). L. humile show no
aggression, however, to nestmates supplemented with excess endogenous
hydrocarbons (Torres et al 2007). This indicates that greater differences in relative
abundance of cuticular hydrocarbons result in greater degrees of aggression.
4.3 Objective Two Discussion
Based on my findings, I propose that interactions between Tetramorium
caespitum proceed as follows. First cue-bearer ants, expressing cues encoded in
the relative abundance of cuticular methyl-branched alkanes and n-alkenes,
approach receiver ants. Receiver ants perceive these cues and compare them to an
internal template defining the receivers colony's scent. If the cues match the
template the cue-bearers are accepted as self. If, however, the cues do not match
the template, rejection occurs and usually aggression, in the form of mandibular
flaring, biting, and stinging ensues. Aggression does not occur if there are only
two participants: one cue-bearer and one receiver. This suggests that fight

organization is a rate dependent process.
I suggest the above mechanism because I found that supplementation with
exogenous methyl-branched alkanes or n-alkenes elicited aggression from
untreated sisters. Supplementation with methyl-branched alkanes elicited higher
levels of aggression than did supplementation with n-akenes. A-alkanes had no
visible influence on the nestmate recognition response. This suggests that methyl-
branched alkanes are the primary structural class involved in nestmate
These results are consistent with the preponderance of published
entomological data relating to nestmate, species, and fertility recognition cues. It
has been found that many species encode these group identification signals in
methyl-branched alkanes and -alkenes. In a closely related species, Tetramorium
bicarinatum, colonies differed only in methyl-branched alkanes, not in -alkenes
or n-alkanes. By default, this implies it is only methyl-branched alkanes that serve
as recognition cues (Astruc et al 2001). Likewise, I found that methyl-branched
alkanes are the most important structural class in nestmate recognition in
Tetramorium caespitum. Guerrieri et al (2009) demonstrated that supplementing
the ant Camponotus herculeanus with 3,11-dimethylheptacosane was enough to
disrupt colony and species signatures. Interestingly, this molecule was present in
the mixture of methyl-branched alkanes used in my supplementation experiment.

This mixture, which disrupted colony dynamics, also included 5,10-dimethyl
heptacosane and 5,12- and 5,7-dimethylnonacosane. Martin et al found that
proportions of dimethylalkanes, with chain lengths ranging from 27 to 35, are
putative species signals for Formica uralensis, Formica lugubris, Formica
aquilonia, Formica pratensis, Formica polyctena, Formica rufa, and Formica
truncorum (2008). Similarly, methyl-branched alkanes encode fertility cues in
many insect species: Callidiellum rufipenne, Neoclytus acuminatns, Gastrophysa
atrocyanea, and Gnamptogenys striatula (Rutledge et al 2009, Lacey et al 2008,
Sugeno et al 2006, Lommelen et al 2010). I suggest a similar mechanism for
nestmate recognition is used by Tetramorium caespitum.
Not all ants use methyl-branched alkanes as recognition cues, Formica
exsecta and Formica lemani use only the (Z)-9-alkene component of the cuticular
hydrocarbon profde as species and nestmate cues (Martin and Drijfhout 2009).
(Z)-9-alkenes are also used to designate nest-membership in Formica japonica.
However, (Z)-9-alkenes alone are not sufficient to elicit aggression. This species
relies on colony-specific ratios of five -alkanes as well as five (Z)-9-alkenes
(Akino et al 2004). An analogous mechanism could be used by T. caespitum. At
least one (Z)-9-alkene, (Z)-9-tricosene, was present in the mixture of alkenes
capable of disrupting colony-identification in my supplementation experiment.
Supplementing T. caespitum with n-alkanes did not induce aggression

with untreated nestmates. This suggests that -alkanes are not involved in the
nestmate recognition response. A-alkanes are ubiquitous and readily fluctuate in
response to environmental stimuli (Martin & Drijfhout 2009a). These qualities
make them excellent candidates for conveying task membership as has been
demonstrated in the harvester ant Pogonomyrmex barbatus (Greene and Gordon
2003). These same qualities make n-alkanes a poor choice to use in species and
colony recognition. This explains why Tetramorium caespitum do not respond
with aggression to nestmates supplemented with n-alkanes. This is consistent with
behavior in honeybees which attack nestmates when their n-alkene profile is
changed but ignore nestmates when their n-alkane profile is altered (Dani et al.
2005). In the ant Linepihithema humile supplementation with large amounts of n-
alkanes, however, can result in aggressive nestmate recognition responses
(Greene and Gordon 2007). This response requires increasing the relative
abundance of n-alkanes beyond the magnitude found in nature.
4.4 Future Research
Although pleased with the outcome of my research, I do have a couple of
recommendations to further test my hypotheses. The first set of experiments
investigating if differences in relative abundance of specific hydrocarbons in
colony cuticular hydrocarbon profiles are responsible for eliciting aggression in

nestmate recognition responses was somewhat limited by chemical analysis. I
would like to replicate these experiments using more precise chemical
identification techniques. This would result in more accurate differences in
relative abundances among each specific hydrocarbon molecule among colonies
and, hopefully, linear regression results that are more conclusive.
Additionally, data did suggest that 11-methylpentacosane may be a
predictor of aggression. Unfortunately, this correlation was not statistically
significant (p-value 0.06265). This finding and patterns of recognition cues in
other species justify future research into the possibility of 11-methylpentacosanes
involvement in nestmate recognition.
Other molecules that deserve future investigation are (Z)-9-alkenes and
dimethylalkanes. These are both expressed by T. caespitum and have been shown
to be involved in recognition in other species.
Further study of the dynamics of inter-colony aggression is also
warranted. An early behavioral bioassays involved dropping one ant into a foreign
colony and measuring the proportion of aggressive to non-aggressive interactions
(Wallis 1962). With T. caespitum almost every interaction under these conditions
involved aggressive behavior from members of the whole colony. Consequently,
it was impossible to note any difference in aggression levels between different
colony pairs. Other behavioral bioassays involve only two ants and are performed

by immobilizing the ants by tethering, chilling, or even pinning (Roulston et al
2003). Heinze et al conducted behavioral bioassays by placing one live ant from
two discrete colonies in a neutral arena (1996). Similar methods were not
effective for T. caespitum, because ants of this species avoid aggressive
interactions when they are not accompanied by colony-mates. The behavioral
bioassay that yielded the best results was similar to Nonacs and Chens (2000)
procedure which consisted of placing 20 ants from each of 2 distinct colonies
together and counting the number of ants involved in aggressive behavior. This
produced repeatable results which varied consistently by colony pairing.
Interestingly, it was only when ants where accompanied by colony-mates that
they exhibited aggressive behavior. This could suggest rate-dependent behavior as
is observed in foraging in the harvester ant Pogonomyrmex barbatus (Greene and
Gordon 2007a). Greene and Gordon found that harvester ants base the amount of
foraging in which they engage on the rate at which patrollers enter the colony. It
may be that T. caespitum only exhibit aggressive behavior when the ratio of
exposure to foreign cuticular hydrocarbons to exposure to nestmate hydrocarbons
does not exceed a set point. This possibility is an area of future study for the
Greene Lab for T caespitum.

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