The Interaction between Within-Group and Neighborhood-Level Social Behavior of

Cooperatively Breeding Organisms

THESIS

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University

By

Benjamin James Stucke

Graduate Program in Evolution, Ecology and Organismal Biology

The Ohio State University

2018

Master's Examination Committee:

Dr. Ian Hamilton, Advisor

Dr. Jacqueline Augustine

Dr. Elizabeth Marschall

Copyrighted by

Benjamin James Stucke

2018

Abstract

For group-living animals, individual fitness can be affected by within-group interactions.

However, groups often have nearby neighbors and in these cases, groups can interact among each other. These neighborhood-level interactions allow for added fitness benefits, such as mutual between-group cooperation, selfish investment in other groups, or exploitation.

However, the payoffs to these depend on the behavior of other groups, and further, these within- group and between-group interactions are not independent of one another. Interactions at one level may affect an individual’s ability or willingness to interact at another. In chapter 2, I built a game theoretical model of dyads in a simple neighborhood (2 groups) to examine how the potential for between-group interactions can affect the willingness of individuals to cooperate with partners within their group. I modeled several scenarios in which between-group interactions can be beneficial to group-living individuals. Our scenarios included 1) mutual cooperation in which the benefit to a group is additive when each group cooperates, 2) increased additive selfish benefit to a group that cooperates 3) a synergistic benefit to mutual cooperation, and 4) increased cost of between-group cooperation. For all scenarios, I found that, if within- group cooperation was necessary for between-group cooperation, cooperative efforts were high.

However, if only one individual in a group needed to cooperate within the group to yield between group cooperation, within-group cooperation was lower but still greater than zero. In

Chapter 3, I created pairs of groups of the cichlid fish Neolamprologus pulcher in the laboratory, and manipulated conflict in one group of each pair by either removing dominant females and immediately returning them in control treatments, or removing them for an extended period of

i time and then returning them in experimental treatments. I then exposed each pair of groups to a visual predatory stimulus, acting as an assay for between-group cooperation. I predicted that groups with higher within-group conflict would exhibit less between-group cooperative efforts, while neighbors in these treatments would compensate and exhibit a greater amount of defensive behavior toward than stimuli than in control treatments. I found that experimental groups were less active over time. This suggests that avoidance may be an alternative tactic to submission for mitigating conflict within the group. Additionally, I found that experimental groups increased aggression toward the predator, in contrast with our predictions. These results, collectively, are important as they show that how an individual interacts in a complex social environment can be dynamic. Our results suggest that changes at the neighborhood level can influence within-group dynamics and these changes in the group can feedback into the neighborhood.

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Dedicated to my mother, grandmother, and sister

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Acknowledgments

Thank you for the guidance and feedback from my advisor Dr. Ian Hamilton throughout this entire process. Thank you to Antonia Tribuzzo for aiding in experimental execution and data collection. Thank you to the undergraduate volunteers and graduate/post-doctoral lab-mates in the Hamilton lab. This research was funded through a grant awarded by the National Science

Foundation (award number: 1557836).

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Vita

2012...... Elyria High School

2016...... B.S. Wildlife Biology, Ohio University

Fields of Study

Major Field: Evolution, Ecology and Organismal Biology

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Table of Contents

Abstract ...... i

Acknowledgments...... iv

Vita ...... v

List of Tables ...... viii

List of Figures ...... ix

Chapter 1: Introduction ...... 1

Chapter 2: Effects of Potential for Between-Group Cooperation on Within-Group Dynamics ..... 7

Abstract ...... 7

Introduction ...... 8

Model Description ...... 12

Results ...... 19

Discussion ...... 20

Additional Materials ...... 33

Chapter 3: The Effect of Within-Group Conflict on Between-Group Interactions in

Neolamprologus pulcher ...... 52

Abstract ...... 52

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Introduction ...... 53

Methods ...... 56

Results ...... 63

Discussion ...... 74

Chapter 4: Conclusion...... 87

Works Cited ...... 91

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List of Tables

Table 2.1 Parameters for each scenario, *denotes the model being ran for all combinations of

variables with multiple values…………………………………………………………...17

Table 2.2: Each cell of the transition matrix aij gives the probability of a neighborhood state i

becoming state j in the next time step, as follows………………………………………..33

Table 3.1 ANOVA results for models a-d, significance denoted: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05

‘.’ 0.1 ‘ ’ 1………………………………………………………………………………..67

Table 3.2 ANOVA results for models e- f, significance denoted: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05

‘.’ 0.1 ‘ ’ 1………………………………………………………………………………..70

Table 3.3 ANOVA outputs for models g-i, Significance denoted: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’

0.05 ‘.’ 0.1 ‘ ’ 1…………………………………………………………………………..73

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List of Figures

Figure 2.1 The probability that an individual cooperates after receiving cooperation (p) based on

the between-group cooperative efforts for groups in state “cc” (P) and “cd” (R) across

low, intermediate, and high values of between group efforts for groups in state “dd” (Q)

for scenario 1, Base Mutual Cooperation. Other model parameters: k = 0.05…………..25

Figure 2.2 The probability that an individual cooperates after receiving defection (q) based on

the between-group cooperative efforts for groups in state “cc” (P) and “cd” (R) across

low, intermediate, and high values of between group efforts for groups in state “dd” (Q)

for scenario 1, Base Mutual Cooperation. Other model parameters: k = 0.05…………..26

Figure 2.3 The probability that an individual cooperates after receiving cooperation (p) based on

the between-group cooperative efforts for groups in state “cc” (P) and “cd” (R) across

low, intermediate, and high values of between group efforts for groups in state “dd” (Q)

for scenario 2, Increased Selfish Benefit. Other model parameters: k = 0.05…………...27

Figure 2.4: The probability that an individual cooperates after receiving defection (q) based on

the between-group cooperative efforts for groups in state “cc” (P) and “cd” (R) across

low, intermediate, and high values of between group efforts for groups in state “dd” (Q)

for scenario 2, Increased Selfish Benefit. Other model parameters: k = 0.05…………...28

Figure 2.5: The probability that an individual cooperates after receiving cooperation (p) based on

the between-group cooperative efforts for groups in state “cc” (P) and “cd” (R) across

low, intermediate, and high values of between group efforts for groups in state “dd” (Q)

for scenario 3, Increased Synergistic Benefit. Other model parameters: k = 0.05………29

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Figure 2.6: The probability that an individual cooperates after receiving defection (q) based on

the between-group cooperative efforts for groups in state “cc” (P) and “cd” (R) across

low, intermediate, and high values of between group efforts for groups in state “dd” (Q)

for scenario 3, Increased Synergistic Benefit. Other model parameters: k = 0.05………30

Figure 2.7: The probability that an individual cooperates after receiving cooperation (p) based on

the between-group cooperative efforts for groups in state “cc” (P) and “cd” (R) across

low, intermediate, and high values of between group efforts for groups in state “dd” (Q)

for scenario 4, Increased Between-Group Cost. Other model parameters: k = 0.05……31

Figure 2.8: The probability that an individual cooperates after receiving defection (q) based on

the between-group cooperative efforts for groups in state “cc” (P) and “cd” (R) across

low, intermediate, and high values of between group efforts for groups in state “dd” (Q)

for scenario 4, Increased Between-Group Cost. Other model parameters: k = 0.05…….32

Figure 2.9. The difference between probability that an individual cooperates after receiving

cooperation or defection (i.e., p – q) based on the between-group cooperative efforts for

groups in state “cc” (P) and “cd” (R) across low, intermediate, and high values of

between group efforts for groups in state “dd” (Q) for scenario 1, Base Mutual

Cooperation. Other model parameters: k = 0.05………………………………………..48

Figure 2.10: The difference between the probabilities that an individual cooperates after

receiving cooperation (p) and defection (q) based on the between-group cooperative

efforts for groups in state “cc” (P) and “cd” (R) across low, intermediate, and high values

of between group efforts for groups in state “dd” (Q) for scenario 2, Increased Selfish

Benefit. Other model parameters: k = 0.05……………………………………………49

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Figure 2.11: The difference between the probability that an individual cooperates after receiving

cooperation (p) ore defection (q) based on the between-group cooperative efforts for

groups in state “cc” (P) and “cd” (R) across low, intermediate, and high values of

between group efforts for groups in state “dd” (Q) for scenario 3, Increased Synergistic

Benefit. Other model parameters: k = 0.05……………………………………………..50

Figure 2.12: The difference between the probability that an individual cooperates after receiving

cooperation (p) ore defection (q) based on the between-group cooperative efforts for

groups in state “cc” (P) and “cd” (R) across low, intermediate, and high values of

between group efforts for groups in state “dd” (Q) for scenario 4, Increased Between-

Group Cost. Other model parameters: k = 0.05………………………………………….51

Figure 3.1 experimental treatment involving the 2 hour removal of a dominant female from a

focal group and a control treatment involving the sham removal of a dominant female

from a focal group………………………………………………………………………..79

Figure 3.2 A flowchart of the experimental design. The experiment was broken down into 4

rounds, each including 2 parts…………………………………………………………...80

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Figure 3.3 Least Square Means of Counts of aggressive behaviors from individuals (model a)

over 15 minute observation periods from focal individuals pre-removal, post-removal,

post-removal and post-predator assay across all treatments involving the long-term

removal and reintroduction of a dominant female (experimental) or the removal and

immediate return of a dominant female (control). Significant differences were found

between experimental treatments pre-removal and experimental treatments post-removal,

experimental treatments post-removal and post-predator assay, and control treatments

post-removal……………………………………………………………………………..81

Figure 3.4 Least-square means counts of aggression by actor status for individuals in focal

groups during all observation times: pre-removal, post-removal, post-removal and post-

predator assay across across all treatments involving the long-term removal and

reintroduction of a dominant female (experimental) or the removal and immediate return

of a dominant female(control). Actor status is abbreivated as dominant male (DM),

dominant female (DF), large subordinate male (LSM), large suboridnate female (LSF).82

Figure 3.5 least-square means counts of total group aggression (model b) over 15 minute

observations for each period (pre-removal, post-removal, post-removal and post-predator

assay) across treatment and actor location. Experimental treatments involved the long-

term removal and reintroduction of a dominant female (experimental) or the removal and

immediate return of a dominant femal (control)………………………………………83

Figure 3.6 Least Square Means counts of affiliative behaviors (model c) over 15 minute

observation periods for focal individuals pre-removal, post-removal, post-removal and

post-predator assay across all treatments involving the long-term removal and

reintroduction of a dominant female (experimental) or the removal and immediate return

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of a dominant femal (control). Significant differences (p < 0.05 in post hoc tests) were

found between experimental treatments pre-removal and experimental treatments post-

removal, experimental treatments psot-removal and post-predator assay, and control

treatments post-removal………………………………………………………………84

Figure 3.7 Total counts of aggression toward the predator for neighbor groups plotted

againsttotal counts of aggression toward the predator for focal groups………………85

Figure 3.8 Least Square Means total counts of aggression behaviors toward the predator (model

f) over a 15 minute observation periods for focal and neighbor groups across all

treatments involving the long-term removal and reintroduction of a dominant female

(experimental) or the removal and immediate return of a dominant female (control).

Significant negative effects were found for control treatments and neighboring groups in

both treatments………………………………………………………………………….86

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Chapter 1: Introduction

Living in groups can increase fitness benefits compared to solitary living (Bilde et al.

2007, Schradin et al. 2010, reviewed in rodents: Ebensperger 1999). For individuals that live in groups, fitness is not only influenced by an individual’s own behavior (Rhoades and Blumstein

2007), but also by the behavior of its group-mates (Warner 1995, Trillmich et al. 2004). The actions of an individual within a group can also be influenced by group membership (Conradt and Roper 2005), position in a group (spatial position: Blanco and Hirsch 2006, social rank:

Barta and Giraldeau 1998), or through the behaviors of other group members ( King et al. 2008,

Ostner et al. 2008). Interactions among group-mates can be cooperative (Eliassen and Joergenson

2014) as well as aggressive (Saito et al. 1998). Aggressive interactions within a group can be a result of conflict that has arisen within groups, such as during dominance hierarchy re- establishment (Wong and Balshine 2010, Wong et al. 2016). Within-group conflict can change the net benefits for individuals to cooperate with others within the group (Hannon et al. 1985,

Sheppard et al. 2018), and if escalated can result in the dissolution of the group altogether

(Aureli et al. 2002).

In addition, individual behavior and fitness can be influenced by the presence and behavior of other groups (Harris 2006, Cheney and Seyfarth 1982). The presence of other groups allows for between-group interactions, or neighborhood-level interactions (Cheney and Seyfarth

1987). These interactions can be cooperative (Krams et al. 2010, Gilby et al. 2012) or exploitative (Kitchen and Beehner 2007) and can increase the fitness of individuals who participate (reviewed by Clutton-Brock 2009). Cooperative interactions may occur because members of both groups receive a mutual benefit (Blumestein et al. 1997), such as joint defense of territories from predators (Micheletta 2012, Jungwirth et al. 2015). Exploitative interactions

1 with individuals in other groups may include opportunities for between-group mating; these interactions may be exploitative of same-sex members in neighboring group (Hughes et al. 2003,

Young et al. 2005).

There can be feedbacks among within-group and between-group levels. In other words, how an individual behaves within its group depends on the neighborhood context but the interactions of neighboring groups reflect, in part, the collective outcome of within-group behaviors. Tradeoffs between within- and between-group interactions can be present (Young et al. 2005), and can shift temporally with the strength of selection within- and between-groups for group beneficial traits (Dugatkin et al. 2003). Between-group interactions have been shown to be affected by within-group cooperation (Schradin and Pillay 2004) and conflict (Crofoot and Gilby

2012). Conversely, the presence of neighbors has shown to influence dynamics within a group

(Hellmann and Hamilton 2014), including whether individuals within the same group engage in aggressive conflict with one another (Hellmann and Hamilton 2018).

However, the interaction between the group-level social context and how the broader social context of between-group interactions emerges is poorly explored. It has previously been shown that the presence of neighbors can have an effect on the behavior of individuals

(Hellmann et al. 2014, Hellmann and Hamilton 2018, Maklakov et al. 2012). However, little work has been done taking into account the decisions of those neighbors and the effect that their behavior, instead of just their presence, has on the behavior of an individuals in both their direct social context (within-group) and broader social context (between groups). By using a game theoretical model, the payoffs for an individual in the model depend on their interactions with individuals both within- and between-groups, but also change based on how individuals in other groups interact with each other.

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Game theoretical models, first utilized in behavioral ecology by Smith and Price (1973) allow us to model the evolution of cooperation between individuals (or groups of individuals) based on costs and benefits. We then are able to utilize games previously established (e.g. in this model, prisoner’s dilemma and hawk-dove) to reflect the pay-offs and costs for players to cooperate with one another in a given situation. We used the prisoner’s dilemma as in the game natural selection favors defection (unless the game is repeated sufficiently many times), even though the highest payoff for all players is mutual cooperation (Dawes 1980). The Hawk-Dove game looks at the role of aggression in social interactions, where the best strategy is to play

“Hawk” or aggressiveness toward unaggressive players, but not towards others when they are also aggressive (Maynard-Smith and Price 1973). In all of our scenarios we look at how conflict within groups and cooperation within groups can be modulated by between-group cooperation, and also how within-group dynamics can be influenced by between-group cooperation.

Cooperatively breeding species, in which individuals aggregate into groups with alloparental care, are found in a multitude of different taxa (canines: Creel et al. 1997, primates:

Terborgh and Goldizen 1985, rodents: Young et al. 2006, fish: Balshine et al. 1998).

Cooperatively breeding species are useful systems for studying both within-group cooperation and conflict (Blumstein et al. 1997, Bergmueller et al. 2007, Wong et al. 2016, Hannon et al.

1985). There is also substantial support through both empirical and theoretical works for how individuals in these groups make decisions on cooperation, competition, and exploitation (King and Cowlishaw 2007, Stueckle and Zinner 2008, Arnott and Elwood 2008). Further, many breeding groups interact with neighbors (Yeager 1992, Cheney 1981). Within these groups, conflict among individuals is a common phenomenon (Aureli et al. 2002). Conflict may place new social or energetic impositions on individuals (Aureli and Schaffner 2007), creating

3 limitations on how an individual can interact with their immediate social group or with others in the broader social context. By looking at social interactions in the broader context and also taking into account the dynamics within a group, a more accurate analysis of the social environment can be taken. By having this better-rounded approach, I can garner a better understanding of the interactions between cooperation in the broader social context and an individual’s behavior, reproductive success, and survival.

Here, I examine the effects that within-group conflict and between-group cooperation have on one another. I use a game theoretical model, in which the pay-offs to an individual cooperating in their group depends on the state of the overall neighborhood, inherently making dynamics within a group dependent on the dynamics between groups to explore how within- group dynamics of different groups interact with one another. Additionally, I used a cooperatively breeding cichlid fish, Neolamprologus pulcher, to examine how within-group conflict influences between-group cooperative interactions. These two projects, in combination with one another, give a better insight to the inter-relationship between within-group dynamics and between-group dynamics.

In chapter 2, I built a game theoretical model that elucidates the role that the potential for between-group cooperation has on within-group cooperation. The model included two groups, each containing two players. Using an invasion analysis, I looked at how strategies of within- group cooperation or defection can arise under different scenarios of between-group cooperation by varying the relative pay-offs for individuals for different neighborhood states. The scenarios I looked at in the model include 1) the highest pay-off for an individual arose when both groups cooperated with one another 2) the highest pay-off for an individual arose when their group attempted to cooperate with another group (investing in another group) 3) the highest pay-off for

4 an individual arose when their group defected on another group that is investing in cooperation

(exploitation of another group).

I found that if within-group cooperation was necessary for between-group cooperation, cooperative efforts were high. However, if only one individual in a group needed to cooperate within the group to yield between group cooperation, within-group cooperation was lower but still greater than zero. Our model shows that the potential benefits of between-group interactions can have a strong influence on an individual’s behavior and that the benefits of interacting in a neighborhood-level can be a potential predictor of within-group dynamics.

In chapter 3, I examined how within-group conflict affects the between-group cooperative efforts of a group, using groups of N. pulcher in the laboratory. In nature, breeding groups of these fish occupy permanent territories (Taborksy 1984), which are clustered in close proximity to one another creating a neighborhood-level interactive environment for individuals

(Striver et al. 2004). Within this neighborhood, between-group cooperation in the form of joint defense against predators may arise (Jungwirth et al. 2015). I paired cooperatively breeding groups together in one tank and separated them with a transparent barrier. To look at how within- group conflict can affect between-group cooperation I exposed one group in each pair to either a control treatment or an experimental treatment with the aim to increase conflict within the group.

The experimental treatment was an extended removal of the dominant female from the breeding group causing a social perturbation to the dominance hierarchy, where as the control treatment involved the removal and immediate return of a dominant female from a breeding group. After receiving the treatment, each pair were exposed to a visual predatory stimulus to examine the between-group cooperative effort of each group.

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I found that all groups reduced activity after removal, but that experimental groups exposed to the longer removal had a longer overall suppression in within-group activity compared to control groups. I also found groups in experimental treatments were more aggressive toward the predator compared to control groups and compared to neighbors. These results show that avoidance may be an alternate tactic to mitigating conflict within-groups and that conflict within a group may prime that group to be more aggressive in other contexts, or present greater temporal opportunity for individuals in that group to act in other contexts. These results are important as they suggest that the within-group context can play either a direct role on the ability of an individual to participate in the broader social context in terms of available energy or time, or that within-group dynamics may shift the nature of an individual’s interactions in other contexts.

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Chapter 2: Effects of Potential for Between-Group Cooperation on Within-Group Dynamics

Benjamin J. Stucke and Dr. Ian M. Hamilton

Abstract

For group-living animals, individual fitness can be affected by within-group interactions.

However, groups often have nearby neighbors and in these cases, groups can interact among each other. These neighborhood-level interactions allow for the added fitness benefits, such as mutual between-group cooperation, selfish investment in other groups, or exploitation.

However, the payoffs to these depend on the behavior of other groups, and further, these within- group and between-group interactions are not independent of one another. Interactions at one level may affect an individual’s ability or willingness to interact at another. I built a game theoretical model of dyads in a simple neighborhood (2 groups) to examine how the potential for between-group interactions can affect the willingness of individuals to cooperate with partners within their group. I modeled several scenarios in which between-group interactions can be beneficial to group-living individuals. Our scenarios included 1) mutual cooperation in which the benefit to a group is additive when each group cooperates, 2) increased additive selfish benefit to a group that cooperates 3) a synergistic benefit to mutual cooperation, and 4) increased cost of between-group cooperation. For all scenarios, I found that, if within-group cooperation was necessary for between-group cooperation, cooperative efforts were high. However, if only one individual in a group needed to cooperate within the group to yield between group cooperation, within-group cooperation was lower but still greater than zero. Increasing the selfish or synergistic benefit to a group increased an individual’s willingness to cooperate within their group, while increasing the cost of between-group cooperation decreased an individual’s

7 willingness to cooperate within their group. Our model shows that the potential benefits of between-group interactions can have a strong influence on an individual’s behavior and that the benefits of interacting in a neighborhood-level can be a potential predictor of within-group dynamics.

Keywords: Game theory, Neighborhood, Intergroup Cooperation

Introduction

For group-living organisms, the social landscape can include nearby individuals outside of their immediate social group. This broader social context, or neighborhood, creates a population-level social environment which in turn can influence selection on behaviors at the individual level (Krause, Lusseau, and James 2009). The nature of between-group interactions can be exploitative (Young et al. 2007), aggressive (Saito et al. 1998, Schuerch et al. 2010), or cooperative (Eliassen and Joergenson 2014). Exploitative neighborhood level interactions include extra-group copulations (Lazaro-Perea 2001). Aggressive encounters include resource competition between groups (Dow 1977, Cheney 1992, Harris 2006) or territoriality (Scradin and

Pillay 2004, Lazaro-Perea 2001, Peres 1989). Cooperative neighborhood interactions include information transmission for foraging (Whiten et al. 2007), or mutually benefiting by-products such as increased cooperative vigilance (Campobello et al. 2012). Individuals that extend interactions to the broader social landscape can gain added fitness benefits compared with those that limit their interactions to exclusively within their group (Gilby et al. 2012).

Cooperation among neighboring groups can arise because of a mutual benefit to each cooperating group (Strassman 1989, reviewed by Clutton-Brock 2009). For example, in pied

8 flycatchers (Ficedula hypoleuca) mobbing behavior of individuals was influenced by the presence of other mobbing conspecifics, resulting in overall increased defense against nest predators (Krams et al 2009). Similarly, in Neolamprologus pulcher, a cooperatively breeding cichlid, groups in close proximity to one another mutually defend against predators. Each group individually puts forth less defense than if they were defending solitarily; however, the total amount of defensive effort was comparable to a single group defending on its own (Jungwirth et al. 2015). These cooperative neighborhood interactions can lower the energy expenditure of all individuals involved while still facilitating success of both groups (Jungwirth et al 2015).

Bonobos (Pan paniscus) demonstrate xenophilia or prosociality toward neighboring individuals, which is suggested to facilitate cooperation between groups for access to resources (Tan et al

2015). Tan et al. (2015) suggest that the benefit of forming between-group cooperative bonds is much greater than the costs in bonobos. Pro-social signaling is therefore a benefit awarded to a group of extending cooperation regardless of the actions of the receiving group.

The interaction between within-group dynamics and between-group cooperation is poorly explored and, where it has been studied, is highly variable. Tradeoffs or synergies between within- and between-group interactions can be present (Mares et al. 2012, Young et al. 2005).

Within-group cooperation affects between-group interactions in the context of between-group competition or territoriality (Schradin and Pillay 2004). In vervet monkeys, Chlorocebus pygerythrus, which individuals one directs affiliative behaviors toward within the group can vary greatly between individuals, but the group will act cohesively in competition against other groups (Cheney 1992). In meerkats, Suricata suricatta, grooming behavior has been shown to play a role in group cohesion with dominant individuals within a group grooming subordinate males longer than females; this is suggested to be linked to subordinate males playing a larger

9 role in territory defense from extra-group males (Kutsukake and Clutton-Brock 2010). Similarly, the presence of an out-group threat from conspecifics leads to an increase in within-group affiliation in N. pulcher (Bruintjes et al. 2015). In this case, mutual cooperation within a group might be necessary to prevent exploitation by members of other groups.

On the other hand, all behaviors have costs and benefits associated with them, which can vary with differing social contexts (Parker and Stuart 1976) and the ability to participate in each of these behaviors may be limited as different types of behaviors can be differentially costly

(Grantner and Taborsky 1998). Therefore, it is expected that there can be tradeoffs between investing in within-group interactions and investment in between-group interactions. There is some evidence for such tradeoffs. The reforming of social ties within groups after conflict can cause limitations on a group’s ability to cooperate with others (Crofoot and Gilby 2012).

Finally, between-group cooperation might have little to do with within-group interactions. In other words, how groups of individuals interact with other groups can be independent of how individuals within groups interact with each other. For example, in the Arabian babbler

(Turdoides squamiceps), subordinate individuals participate in predator-directed mobbing behavior cooperatively between groups to advertise their quality for the formation of dispersal coalitions and not for the benefit of the group in which they are currently a member (Maklakov

2002).

Previous theoretical models have shown that neighbors play an important role in influencing within-group dynamics (Hellmann and Hamilton 2018a, Cant and Johnstone 2009).

Similarly, previous empirical studies have also shown the importance of neighbors on within- group conflict (Hamilton and Hellman 2018b), predator defense (Jungwirth et al. 2015,

Hellmann et al. 2014), and group membership (Hellmann et al. 2015, Bergmueller et al. 2005).

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However, these previous approaches that have shown that neighbors play a key role on within- group dynamics rarely consider that neighbors themselves are also making within-group decisions. Our model expands on these previous works by incorporating neighboring group dynamics into our model. By incorporating neighboring group dynamics into our model, they too can influence the state of the neighborhood allowing us to garner a more realistic representation how groups of individuals might all have important but different influences in the broader cooperative social network.

The purpose of this model is to examine how the behavior of an individual within a group is influenced by the fitness effects of neighboring groups and the effects of within-group interactions on between-group interactions. Specifically, I test four scenarios of cooperation. In the first scenario, individuals receive the same benefit if either their group or the other group cooperates; if both cooperate, they receive twice the benefit. In the second scenario, individuals receive a greater benefit from their group cooperating than from their neighbor cooperating.

Again, if both cooperate, benefits are additive. In the third scenario, individuals receive a synergistic benefit if both groups cooperate. Finally, in the fourth scenario, there is again an additive benefit to mutual cooperation, but if only one group cooperates, individuals gain more from their neighbor cooperating than if their group cooperates. To test these four scenarios, I extended models of iterated pair-wise cooperation to incorporate conflict or cooperation between groups as well as cooperation or defection within groups. I built a model of a simple neighborhood (two groups, each containing two individuals and with symmetrical payoffs). This model was used to ask questions about the inter-play between inter-group and within-group cooperation of group-living animals. As both between-group and within-group cooperation can yield fitness benefits to an individual understanding when either form of cooperation is most

11 beneficial to an individual can help predict when either form of cooperation will arise in the social landscape.

Model Description

The model consists of two groups G = {F,N}, where F is the focal group and N is its neighbor. Each group in the model is comprised of two individuals for a total of four individuals in the game B = {F1, F2, N1, N2}. The strategy profile of a player consists of some probability of cooperating if its partner cooperated in a previous round ∈ (pi) and some probability of cooperating if its partner defected (qi). At any time, t, a pair, , consisting of individual i

and its partner where j ≠ i and gi =gj can be in one of four states,∈ individual i cooperates and individual j cooperates ∈ (mutual cooperation, “cc”), individual i cooperates and individual j defects (“cd”), individual i defects and individual j cooperates (“dc”), or mutual defection (“dd").

There are also four states that a neighborhood can be in at any given time, t: both F and N invest in cooperation (“CC”), F invests in cooperation and N defects (“CD”), F defects and N invests in cooperation (“DC”), or mutual defection (“DD”). The probability that a neighborhood will be in each state depends on the current state of both pairs. A pair in state “cc” attempts to cooperate with another group at a probability (P), a pair of individuals in states “cd” or “dc” attempts to cooperate with another group at a probability (R), a pair of individuals in a state “dd” attempts to cooperate with another group at a probability (Q). In each model run, P, Q, and R are fixed, so the neighborhood state distribution can be determined from the states of pairs F and N.

I combined the state of both groups in the neighborhood into a single term to determine the probability of a neighborhood being in some state, such as a neighborhood where both groups are

12 in state “cc” the neighborhood is in state “cccc”, and when one group is in state “cd” and one is in state “cc” the neighborhood is in state “cdcc”, etc.

To determine the expected frequency that the neighborhood is in some state (e.g., cccc) after many repeated interactions, I used a discrete-time first-order Markov chain. This process for determining a stable distribution of states has been used in evolutionary modeling previously by Nowak (2004, 2006). The transition probability from a state at time t (e.g., cccc at time t) to a state at time t+1 (e.g., cccc at time t+1) was calculated based on the values of p and q (the probability an individual cooperates within the group upon receiving cooperation or defection from their partner, respectively) for all individuals {F1, F2, N1, N2}. For example, the transition from a neighborhood state of cccc at time t to state cccd at time t+1 is

, while the transition from cccc at time t to cdcc at time t+ 1 is 푝퐹 ∗ 푝퐹 ∗ 푝푁 ∗ −

(see푝푁 Additional Materials for full transition matrix). I used these to 푝construct퐹 ∗ − a 푝transition퐹 ∗ 푝푁 matrix,∗ 푝푁

A. The stable state distribution (y) is given by the normalized dominant left Eigenvector of A

(See Additional Materials). After finding the stable state distribution, I found expected fitness as the inner product of the vector of state distributions and a vector of fitnesses for each state (w; see below).

Fitness was determined from the payoffs of between group interactions and the individual costs of behaviors (see Table 1 for scenario-specific parameters). Each state of the neighborhood had a fixed group-level payoff (V) associated with it that was then divided evenly amongst an individual and its partner. This group-level pay-off (V) was derived from the selfish benefit of investing in another group (h), the benefit of receiving cooperation for that group (b), the synergistic benefit of investing in between-group interaction (d), the cost of investing in between-group interactions (f), and the benefit of a group when neither group cooperated with

13 each other (α), multiplied by the probability that gi will invest (Ci). I assume that Ci is either 1 if the group invests in between-group interactions or 0 if not. (equation 1).

(1)

From = ℎ (1) +I can define+ 푑 the −payoffs + to 훼 a group for each neighborhood state:

(2)

= ℎ + + 푑 − + 훼 (3)

= ℎ − + 훼 (4)

= + 훼 (5)

= 훼 An individual that cooperates with its partner pays some cost of cooperation (k). I then found the fitness of an individual when incorporating between group interactions (w) for each state of within and between group interactions (see below).

(6) 푉 푤 = / − + ∗ − ∗ ( − ) + − / −

푤 = ∗ /−+∗−∗ / − + − ∗ ∗ / (7) −

+ − ∗ − ∗ / −

푤 = ∗ /−+∗−∗ / − + − ∗ ∗ / (8) −

+ − ∗ − ∗ / −

푤 = ∗ ∗ /−+∗−∗ / − + − ∗ ∗ (9)

/ − + − ∗ − ∗ / −

푤 = ∗ /−+∗−∗ / − + − ∗ ∗ / (10) −

+ − ∗ − ∗ / − 푉 푤 = / − + ∗ − ∗ ( − ) + − ∗ / − (11)

14

푉 푤 = / − + ∗ − ∗ ( − ) + − ∗ / − (12)

푤 = ∗ /−+∗−∗ / − + − ∗ ∗ (13)

/ − + − ∗ − ∗ / −

푤 = ∗ / + ∗ − ∗ / + − ∗ ∗ / + (14)

− ∗ − ∗ / (15) 푉 푤 = / + ∗ − ∗ ( ) + − ∗ /

(16) 푉 푤 = / + ∗ − ∗ ( ) + − ∗ /

푤 = ∗ ∗ / + ∗ − ∗ / + − ∗ ∗ / + (17)

− ∗ − ∗ /

푤 = ∗ ∗ / + ∗ − ∗ / + − ∗ ∗ / + (18)

− ∗ − ∗ /

푤 = ∗ ∗ / + ∗ − ∗ / + − ∗ ∗ / + (19)

− ∗ − ∗ /

푤 = ∗ ∗ / + ∗ − ∗ / + − ∗ ∗ / + (20)

− ∗ − ∗ / (21) 푉 푤 = ∗ / + ∗ − ∗ ( ) + − ∗ / To test the effects of model parameters on results, I start with a ‘mutual defense’ scenario in which h = b and d and f are 0. Under this scenario the effects of between-group cooperation are additive. If only one group defends, the benefits for a group defending are the same

15 compared to if they only let their partner defend. Then, I increased each parameter in equation 1 in turn by 0.1, with the exception of b.

16

Table 2.1 Parameters for each scenario, *denotes the model being run for all combinations of variables with multiple values

Scenario h b d f α P* R* Q* K*

1 0.5 0.5 0 0 0.1 0.05, 0.05, 0.05, 0.05,

Base 0.50, 0.50, 0.50, 0.50,

Mutual 0.95 0.95 0.95 0.95

Cooperation

2 0.6 0.5 0 0 0.1 0.05, 0.05, 0.05, 0.05,

Increase in 0.50, 0.50, 0.50, 0.50,

Selfish 0.95 0.95 0.95 0.95

benefit

3 0.5 0.5 0.1 0 0.1 0.05, 0.05, 0.05, 0.05,

Increase in 0.50, 0.50, 0.50, 0.50,

Synergistic 0.95 0.95 0.95 0.95

Benefit

4 0.5 0.5 0 0.1 0.1 0.05, 0.05, 0.05, 0.05,

Increase in 0.50, 0.50, 0.50, 0.50,

cost 0.95 0.95 0.95 0.95

17

To model strategy profile evolution, I assumed that most individuals play a ‘resident’ strategy r={pr,qr); however, there exists a mutant strategy m= {pm,qm}, that plays p or q at a slightly different frequency For a given time step, m differs slightly (±.001) from r in terms of the value of p or q. Whether m and r differed in terms of p or q alternated between generations. I limited each tactic to be between 0.001 and 0.999 so that A was always irreducible. I assumed the frequency of the mutant in the population was sufficiently low that the probability that a mutant appears in a group with another mutant is ≈ 0. In each generation, I found the vector of fitnesses for individual F1, given its neighborhood state, (W). To find expected fitness, I multiplied W by the frequency of the different states for both resident and mutant strategies in the current generation (our dominant left eigenvector of matrix A) for p and q. This process gave us fitness pay-off of an individual playing either strategy (U). For both p and q I multiplied U for all combinations m or R from all individuals, to get the total fitness pay-off for both mutant and resident strategies of p and q (see below).

1. (22)

푚 2. = ∑ ∑ 푚 (23)

푟 3. = ∑ ∑ (24)

푚 4. = ∑ ∑ 푚 (25)

푟 = ∑ ∑ If Hm>Hr, the mutant strategy was adopted in the next time-step as the resident strategy. I stopped our simulation after p and q both either stabilized at a fixed point using a fixed number of generations. This was determined through visual inspection of final values for p and q in each iteration of the model using a varied number of generations to determine at what point the final value became fixed.

18

Results

Scenario 1: Base Mutual Cooperation

The probability of cooperating upon receiving cooperation, p increased as the between- group cooperative effort of groups with mutual within-group cooperation P increased (Figure

2.1). The value of p also increased as the cooperative effort of groups with mutual within-group defection, R, increased. The probability of cooperation after receiving cooperation, p, decreased as the between-group efforts of groups with mutual defection, Q, increased (Figure 2.1). The probability of cooperating upon receiving defection, q, mirrored the same trends and interactions as p (Figure 2.2) with similar values (Additional Materials: Figure 2.9). The probability of cooperating upon receiving cooperation or defection both decreased as within-group cost (k) increased.

Scenario 2: Increase in Selfish Benefit

The probability of cooperation upon receiving cooperation (p) followed the same trends as observed in scenario 1 but with overall higher values. There was also non-zero cooperation over a wider range of parameters. The probability of cooperation upon receiving cooperation close to 1 occurs at lower values of P (when R is low), and was non-zero at lower values of R than seen in scenario 1 (Figure 2.3). These trends were also followed for the probability of cooperating upon receiving defection (q) for all combinations (Figure 2.4), with similar values

(Additional Materials: Figure 2.10).

Scenario 3: Increase in Synergistic Benefit

The probability of cooperation upon receiving cooperation (p) followed the same trends as in scenario 1, but with overall higher values, similar to scenario 2 (Figure 2.5). These trends

19 were also followed for the probability of cooperating upon receiving defection (q) for all combinations (Figure 2.6), with similar values (Additional Materials: Figure 2.11).

Scenario 4: Increase in Cost

The probability of cooperation upon receiving cooperation (p) followed the same trends as seen in scenario 1, with overall lower values than observed in scenario 1, 2, or 3 (Figure 2.7).

The probability of cooperating upon receiving defection (q) mirrored these similar trends (Figure

2.8) with similar values to p (Additional Materials: Figure 2.12).

Discussion

For all scenarios, cooperating with other members of the group occurred only if the cost incurred for an individual to cooperate with their partner was very low. Even at a cost of 0.5, I did not find within-group cooperation. Cost of cooperation has been previously determined to play an important role in the probability of cooperation (Smith and Price 1973, Broom, Koenig, and Borries 2009). In our model, even the intermediate values of cost tested were sufficiently large, compared to even the highest pay-off of between-group cooperation in each scenario, to favor within-group defection. Although not modeled here, it has previously been shown that costs of group-living can outweigh the benefits (Schoepf and Schradin 2012) and in these instances, group dissolution can occur (Aureli et al. 2002). Further exploration of the threshold costs at which cooperation or group living can arise is needed.

Not all combinations of between-group efforts for groups in different states (P, R, Q) could create situations that are likely to be biologically realistic. For example, it is difficult to see a situation in which the between group efforts of one, but not two individuals would lead to a benefit for both members of a group (high between-group efforts for a group in “cd”, R, and low between-group efforts for groups with either mutual cooperation or defection, P and Q

20 respectively). One biologically realistic combination of between-group efforts is high between- group efforts for groups in states “cc” and “cd” (P and R) and low between-group efforts for groups in state “dd” (Q). One example of a system in which these combinations might apply is groups who participate in joint anti-predator behavior. It has been shown that groups who participate in joint defense against predators can reduce the costs of predator defense if the other group cooperates (Jungwirth et al. 2015). Our model predicts that there should be high investment in within-group cooperation if within-group cooperation is necessary for engaging in joint defense (i.e., Q is low). One way this could occur is if defection within groups makes the group weaker and less cohesive overall during between-group interactions (Crofoot and Gilby

2012) or if individuals must resolve within-group conflict in order to engage in joint defense

(e.g., Chapter 2). There is some evidence that lower within-group conflict is associated with between-group cooperation. In white-faced capuchins (Cebus capucinus), pro-social individuals in a group received less aggression from others therefore paying a lower cost to group membership and as result were also more likely to interact with individuals between groups

(Crofoot et al. 2011).

Scenario 2 modeled an increase in the additive selfish benefit to between-group cooperation. An example of such an effect could be pro-social signaling. In bonobos, Pan paniscus, individuals will invest in forming ties between groups independent of whether the other group immediately reciprocates, suggesting a benefit to initiating group interactions that is greater than the cost of doing so, (Tan et al. 2015). In the cooperatively breeding cichlid, N. pulcher, Hellmann et al. (2014) found that individuals increase defensive efforts in the presence of neighbors. They suggest that increased help could be acting as a signal to neighboring individuals that facilitates between-group movement or other future between-group interactions.

21

If so, the helper would gain a direct, future benefit from its help. Our model predicts that there should be high investment in within-group cooperation if it is needed to facilitate between-group efforts. Hellmann and Hamilton (2014) found that helping increased more when neighbors are unfamiliar, and argued that this was because there was a greater benefit of signaling to unfamiliar neighbors.

In scenario 3, the synergistic pay-off to between-group cooperation is increased. An example of such an effect could be groups who participate in joint defense that either increases as the number of groups who defend increase or is proportionally larger than expected for the cost incurred for a single group. For example, groups of N. pulcher can decrease cost and put forth less individual effort defending against a predator, without the overall defensive effort against the predator decreasing, if neighboring groups also participate in joint defense (Jungwirth et al. 2015). Our model predicts high investment in within-group efforts if they are needed to facilitate synergistic between-group interactions.

Our scenario 4 involved increasing the cost of between-group interactions (f). Increasing the cost effectively decreased the between-group pay-off for any neighborhood state in which a group cooperates, and overall decreased an individual’s willingness to cooperate when they had received cooperation or defection (p and q, respectively). Although the values of p and q were lower, specifically when the model predicts p < 1, the general effect that P, R, and Q each had on p and q were similar to the other scenarios.

Increasing risk of injury during defense would increase the cost of between-group cooperation. In this case, our model predicts that as the risk of injury from a threat increases, within-group cooperation should decrease. If the outside threat is risky, it may be more beneficial to avoid both the within- and between-group costs of cooperation by allowing

22 neighboring groups to defend. Therefore I predict that higher danger to defenders will be associated with lower within-group cooperation or higher within-group conflict. However, in N. pulcher, individuals in unstable groups had higher rates of aggression and submission within the group after being introduced to a predator, showing that predation risk can potentially influence a group to re-stabilize for cooperation (Tribuzzo, Stucke, and Hamilton, unpublished data).

There is some evidence that within-group cooperation increases with increasing outside threats, even in colonial species. In N. pulcher, out-group threat increased within-group affiliation (Bruintjes et al. 2015) and increased predation risk increased within-group helping

(Zottl et al. 2013). Much of the previous research in this area has not been explicitly in a between-group setting. Most situations in which there would be a higher between-group cost might also have a higher benefit as interacting between groups often adds fitness benefits to individuals who participate. Therefore, dissecting cost and benefits will be important in any test.

Our model makes several testable predictions regarding the effects of different between- group benefits and costs on within group cooperation. For example, as described above, changes to the risk of injury from defense could alter the cost of between-group cooperation (f). I would predict that as f increases, the willingness of an individual to invest in their group will decrease if the between-group efforts for a group in which one or both individuals (R and P) are also high.

Changing the distance between groups could alter the selfish benefit (h) of prosocial signaling.

For example, h may vary depending on whether it is more beneficial to signal to cooperate with a group who is unfamiliar (h might increase with unfamiliar groups if it increases their likelihood to reciprocate or to accept individuals that migrate; Hellmann & Hamilton 2014). I would predict that when the selfish benefit to a group (h) increases, the willingness of an individual to cooperate within their group would increase if the between-group efforts for a group in which

23 one or both individuals (R and P) are also high. Alternatively, I can look at specific within-group tasks by varying cost of within-group cooperation (k). For example, in groups in which there is variation in role (i.e. cooperative breeding groups with a dominance hierarchy in which within- group aggression toward subordinates can fluctuate), I can determine if the cost of maintaining membership in that group based on the tasks will cause an individual to stay or disperse, as subordinates can prospect nearby territories to their current group as potential options to join. I would expect that as the cost of cooperating within the group (k) increases the willingness of an individual to cooperate within the group could decrease.

For group-living animals who arrange into neighborhoods, between-group interactions can be a strong influence on the fitness of individuals who interact at the broader social landscape (reviewed by Clutton-Brock 2009). Although our model derives pay-offs to an individual from the potential for between-group interactions, in reality these are not the only interactions that affect an individual’s fitness (e.g. within-group interactions can increase fitness,

Ebensperger et al. 2012). It is common for group-living species to have more than two individuals (Kokko et al. 2001), and the within-group benefits to an individual can vary with varying group-size (King and Cowlishaw 2007, Roberts 1996, Stacey and Ligon 1991).

Additionally, these behaviors of an individual have also been shown to be affected by increasing neighboring group density (Hellmann et al. 2014). I propose that future studies expand on this current model to include large group and neighborhood sizes.

24

Figure 2.1 The probability that an individual cooperates after receiving cooperation (p) based on the between-group cooperative efforts for groups in state “cc” (P) and “cd” (R) across low, intermediate, and high values of between group efforts for groups in state “dd” (Q) for scenario

1, Base Mutual Cooperation. Other model parameters: k = 0.05

25

Figure 2.2 The probability that an individual cooperates after receiving defection (q) based on the between-group cooperative efforts for groups in state “cc” (P) and “cd” (R) across low, intermediate, and high values of between group efforts for groups in state “dd” (Q) for scenario

1, Base Mutual Cooperation. Other model parameters: k = 0.05

26

Figure 2.3 The probability that an individual cooperates after receiving cooperation (p) based on the between-group cooperative efforts for groups in state “cc” (P) and “cd” (R) across low, intermediate, and high values of between group efforts for groups in state “dd” (Q) for scenario

2, Increased Selfish Benefit. Other model parameters: k = 0.05.

27

Figure 2.4: The probability that an individual cooperates after receiving defection (q) based on the between-group cooperative efforts for groups in state “cc” (P) and “cd” (R) across low, intermediate, and high values of between group efforts for groups in state “dd” (Q) for scenario

2, Increased Selfish Benefit. Other model parameters: k = 0.05.

28

Figure 2.5: The probability that an individual cooperates after receiving cooperation (p) based on the between-group cooperative efforts for groups in state “cc” (P) and “cd” (R) across low, intermediate, and high values of between group efforts for groups in state “dd” (Q) for scenario

3, Increased Synergistic Benefit. Other model parameters: k = 0.05.

29

Figure 2.6: The probability that an individual cooperates after receiving defection (q) based on the between-group cooperative efforts for groups in state “cc” (P) and “cd” (R) across low, intermediate, and high values of between group efforts for groups in state “dd” (Q) for scenario

3, Increased Synergistic Benefit. Other model parameters: k = 0.05.

30

Figure 2.7: The probability that an individual cooperates after receiving cooperation (p) based on the between-group cooperative efforts for groups in state “cc” (P) and “cd” (R) across low, intermediate, and high values of between group efforts for groups in state “dd” (Q) for scenario

4, Increased Between-Group Cost. Other model parameters: k = 0.05.

31

Figure 2.8: The probability that an individual cooperates after receiving defection (q) based on the between-group cooperative efforts for groups in state “cc” (P) and “cd” (R) across low, intermediate, and high values of between group efforts for groups in state “dd” (Q) for scenario

4, Increased Between-Group Cost. Other model parameters: k = 0.05.

32

Additional Materials

Transition Matrix (A) of a neighborhood with four individuals.

Table 2.2: Each cell of the transition matrix aij gives the probability of a neighborhood state i becoming state j in the next time step, as follows

cccc ccdc cccd ccdd cdcc cddc cdcd cddd dccc dcdc dccd dcdd ddcc dddc ddcd dddd cccc a1,1* a1,2 a1,3 a1,4 a1,5 a1,6 a1,7 a1,8 a1,9 a1,10 a1,11 a1,12 a1,13 a1,14 a1,15 a1,16 ccdc a2,1 a2,2 a2,3 a2,4 a2,5 a2,6 a2,7 a2,8 a2,9 a2,10 a2,11 a2,12 a2,13 a2,14 a2,15 a2,16 cccd a3,1 a3,2 a3,3 a3,4 a3,5 a3,6 a3,7 a3,8 a3,9 a3,10 a3,11 a3,12 a3,13 a3,14 a3,15 a3,16 ccdd a4,1 a4,2 a4,3 a4,4 a4,5 a4,6 a4,7 a4,8 a4,9 a4,10 a4,11 a4,12 a4,13 a4,14 a4,15 a4,16 cdcc a5,1 a5,2 a5,3 a5,4 a5,5 a5,6 a5,7 a5,8 a5,9 a5,10 a5,11 a5,12 a5,13 a5,14 a5,15 a5,16 cddc a6,1 a6,2 a6,3 a6,4 a6,5 a6,6 a6,7 a6,8 a6,9 a6,10 a6,11 a6,12 a6,13 a6,14 a6,15 a6,16 cdcd a7,1 a7,2 a7,3 a7,4 a7,5 a7,6 a7,7 a7,8 a7,9 a7,10 a7,11 a7,12 a7,13 a7,14 a7,15 a7,16 cddd a8,1 a8,2 a8,3 a8,4 a8,5 a8,6 a8,7 a8,8 a8,9 a8,10 a8,11 a8,12 a8,13 a8,14 a8,15 a8,16 dccc a9,1 a9,2 a9,3 a9,4 a9,5 a9,6 a9,7 a9,8 a9,9 a9,10 a9,11 a9,12 a9,13 a9,14 a9,15 a9,16 dcdc a10,1 a10,2 a10,3 a10,4 a10,5 a10,6 a10,7 a10,8 a10,9 a10,10 a10,11 a10,12 a10,13 a10,14 a10,15 a10,16 dccd a11,1 a11,2 a11,3 a11,4 a11,5 a11,6 a11,7 a11,8 a11,9 a11,10 a11,11 a11,12 a11,13 a11,14 a11,15 a11,16 dcdd a12,1 a12,2 a12,3 a12,4 a12,5 a12,6 a12,7 a12,8 a12,9 a12,10 a12,11 a12,12 a12,13 a12,14 a12,15 a12,16

33 ddcc a13,1 a13,2 a13,3 a13,4 a13,5 a13,6 a13,7 a13,8 a13,9 a13,10 a13,11 a13,12 a13,13 a13,14 a13,15 a13,16 dddc a14,1 a14,2 a14,3 a14,4 a14,5 a14,6 a14,7 a14,8 a14,9 a14,10 a14,11 a14,12 a14,13 a14,14 a14,15 a14,16 ddcd a15,1 a15,2 a15,3 a15,4 a15,5 a15,6 a15,7 a15,8 a15,9 a15,10 a15,11 a15,12 a15,13 a15,14 a15,15 a15,16 dddd a16,1 a16,2 a16,3 a16,4 a16,5 a16,6 a16,7 a16,8 a16,9 a16,10 a16,11 a16,12 a16,13 a16,14 a16,15 a16,16

34

*Formulae for each cell are presented below: :

, F F N N = p ∗ p ∗ p ∗ p

, F F N N = p ∗ p ∗ − p ∗ p

, F F N N = p ∗ p ∗ p ∗ − p

, F F N N = p ∗ p ∗ − p ∗ − p

, F F N N = p ∗ − p ∗ p ∗ p

, F F N N = p ∗ − p ∗ − p ∗ p

, F F N N = p ∗ − p ∗ p ∗ − p

, F F N N = p ∗ − p ∗ − p ∗ − p

, F F = − p ∗ p ∗ p ∗ p

, F F N N = − p ∗ p ∗ − p ∗ p

, F F N N = − p ∗ p ∗ p ∗ − p

, F F N N = − p ∗ p ∗ − p ∗ − p

, F F = − p ∗ − p ∗ p ∗ p

, F F N N = − p ∗ − p ∗ − p ∗ p

, F F N N = − p ∗ − p ∗ p ∗ − p

, F F N N = − p ∗ − p ∗ − p ∗ − p

, F F N N = p ∗ p ∗ p ∗ q

, F F N N = p ∗ p ∗ − p ∗ q

, F F N N = p ∗ p ∗ p ∗ − q

, = pF ∗ pF ∗ − pN ∗ − qN 35

, F F N N = p ∗ − p ∗ p ∗ q

, F F N N = p ∗ − p ∗ − p ∗ q

, F F N N = p ∗ − p ∗ p ∗ − q

, F F N N = p ∗ − p ∗ − p ∗ − q

, F F = − p ∗ p ∗ p ∗ q

, F F N N = − p ∗ p ∗ − p ∗ q

, F F N N = − p ∗ p ∗ p ∗ − q

, F F N N = − p ∗ p ∗ − p ∗ − q

, F F = − p ∗ − p ∗ p ∗ q

, F F N N = − p ∗ − p ∗ − p ∗ q

, F F N N = − p ∗ − p ∗ p ∗ − q

, F F N N = − p ∗ − p ∗ − p ∗ − q

, F F N N = p ∗ p ∗ q ∗ p

, F F N N = p ∗ p ∗ − q ∗ p

, F F N N = p ∗ p ∗ q ∗ − p

, F F N N = p ∗ p ∗ − q ∗ − p

, F F N N = p ∗ − p ∗ q ∗ p

, F F N N = p ∗ − p ∗ − q ∗ p

, F F N N = p ∗ − p ∗ q ∗ − p

, F F N N = p ∗ − p ∗ − q ∗ − p

, F F N N = − p ∗ p ∗ q ∗ p 36

, F F N N = − p ∗ p ∗ − q ∗ p

, F F N N = − p ∗ p ∗ q ∗ − p

, F F N N = − p ∗ p ∗ − q ∗ − p

, F F N N = − p ∗ − p ∗ q ∗ p

, F F N N = − p ∗ − p ∗ − q ∗ p

, F F N N = − p ∗ − p ∗ q ∗ − p

, F F N N = − p ∗ − p ∗ − q ∗ − p

, F F N N = p ∗ p ∗ q ∗ q

, F F N N = p ∗ p ∗ − q ∗ q

, F F N N = p ∗ p ∗ q ∗ − q

, F F N N = p ∗ p ∗ − q ∗ − q

, F F N N = p ∗ − p ∗ q ∗ q

, F F N N = p ∗ − p ∗ − q ∗ q

, F F N N = p ∗ − p ∗ q ∗ − q

, F F N N = p ∗ − p ∗ − q ∗ − q

, F F N N = − p ∗ p ∗ q ∗ q

, F F N N = − p ∗ p ∗ − q ∗ q

, F F N N = − p ∗ p ∗ q ∗ − q

, F F N N = − p ∗ p ∗ − q ∗ − q

, F F N N = − p ∗ − p ∗ q ∗ q

, F F N N = − p ∗ − p ∗ − q ∗37 q

, F F N N = − p ∗ − p ∗ q ∗ − q

, F F N N = − p ∗ − p ∗ − q ∗ − q

, F F N N = q ∗ p ∗ p ∗ p

, F F N N = q ∗ p ∗ − p ∗ p

, F F N N = q ∗ p ∗ p ∗ − p

, F F N N = q ∗ p ∗ − p ∗ − p

, F F N N = q ∗ − p ∗ p ∗ p

, F F N N = q ∗ − p ∗ − p ∗ p

, F F N N = q ∗ − p ∗ p ∗ − p

, F F N N = q ∗ − p ∗ − p ∗ − p

, F F = − q ∗ p ∗ p ∗ p

, F F N N = − q ∗ p ∗ − p ∗ p

, F F N N = − q ∗ p ∗ p ∗ − p

, F F N N = − q ∗ p ∗ − p ∗ − p

, F F = − q ∗ − p ∗ p ∗ p

, F F N N = − q ∗ − p ∗ − p ∗ p

, F F N N = − q ∗ − p ∗ p ∗ − p

, F F N N = − q ∗ − p ∗ − p ∗ − p

, F F N N = q ∗ p ∗ p ∗ q

, F F N N = q ∗ p ∗ − p ∗ q

, F F N N = q ∗ p ∗ p ∗ − q 38

, F F N N = q ∗ p ∗ − p ∗ − q

, F F N N = q ∗ − p ∗ p ∗ q

, F F N N = q ∗ − p ∗ − p ∗ q

, F F N N = q ∗ − p ∗ p ∗ − q

, F F N N = q ∗ − p ∗ − p ∗ − q

, F F = − q ∗ p ∗ p ∗ q

, F F N N = − q ∗ p ∗ − p ∗ q

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, F F N N = − q ∗ − q ∗ − q ∗ − q

47

Figure 2.9. The difference between probability that an individual cooperates after receiving cooperation or defection (i.e., p – q) based on the between-group cooperative efforts for groups in state “cc” (P) and “cd” (R) across low, intermediate, and high values of between group efforts for groups in state “dd” (Q) for scenario 1, Base Mutual

Cooperation. Other model parameters: k = 0.05

48

Figure 2.10: The difference between the probabilities that an individual cooperates after receiving cooperation (p) and defection (q) based on the between-group cooperative efforts for groups in state “cc” (P) and “cd” (R) across low, intermediate, and high values of between group efforts for groups in state “dd” (Q) for scenario 2, Increased Selfish

Benefit. Other model parameters: k = 0.05.

49

Figure 2.11: The difference between the probability that an individual cooperates after receiving cooperation (p) ore defection (q) based on the between-group cooperative efforts for groups in state “cc” (P) and “cd” (R) across low, intermediate, and high values of between group efforts for groups in state “dd” (Q) for scenario 3, Increased Synergistic

Benefit. Other model parameters: k = 0.05.

50

Figure 2.12: The difference between the probability that an individual cooperates after receiving cooperation (p) ore defection (q) based on the between-group cooperative efforts for groups in state “cc” (P) and “cd” (R) across low, intermediate, and high values of between group efforts for groups in state “dd” (Q) for scenario 4, Increased Between-

Group Cost. Other model parameters: k = 0.05.

51

Chapter 3: The Effect of Within-Group Conflict on Between-Group Interactions in

Neolamprologus pulcher

Benjamin J. Stucke and Ian M. Hamilton

Abstract

Within-group conflict is a common occurrence for group-living animals and may cause long-lasting effects on the dynamics of the group. Though conflict is prevalent, individuals will cooperate in groups to potentially increase direct and indirect fitness benefits. Group-living animals often cluster near other groups, allowing cooperative interactions between social groups. Cooperative interactions between groups may require energy and time and therefore also affect the ability of individuals to cooperate within their group, and vice versa. I tested whether conflict within a group would also affect an individual’s ability to cooperate with individuals outside of their social group. I created pairs of groups of the cichlid fish Neolamprologus pulcher in the laboratory, and manipulated conflict in one group of each pair by either removing dominant females and immediately returning them in control treatments, or removing them for an extended period of time and then returning them in experimental treatments. I then exposed each pair of groups to a visual predatory stimulus, acting as an assay for between-group cooperation. I predicted that groups with higher within-group conflict would exhibit less between-group cooperative efforts, while neighbors in these treatments would compensate and exhibit a greater amount of defensive behavior toward the stimuli than in control treatments. I found that experimental groups were less active over time. This

52 suggests that avoidance may be an alternative tactic to submission for mitigating conflict within the group. Additionally, I found that experimental groups increased aggression toward the predator, in contrast with our predictions. I suggest that groups with high conflict may also be primed to be more aggressive in other settings. Alternatively, individuals avoiding other individuals within the group might have more opportunity to interact outside of the group.

Keywords: Neolamprologus pulcher, social behavior, within-group, joint territoriality, conflict

Introduction

Group-living can result in fitness benefits to group members (Molvar and Bowyer

1994, Baker et al. 1998, Baglione et al. 2002, Van Horn et al. 2004, Bilde et al. 2007). In many group-living species, territories are spatially distributed in close proximity of one another, resulting in clusters of territories (here after referred to as a neighborhood)

(Clarke and Fitzgerald 1994, Stiver et al. 2007, Branch 1993). The benefits of settling in these neighborhoods can include learning cues for anti-predator behavior (Uetz et al.

2002) or creating opportunity for greater mating success for individuals (McDonald 2007,

Say and Pontier 2004). Groups clustering in close proximity can lead to intergroup behavioral interactions (Sanchez-Villagra et al 1998, Bisther 2002) and individuals can also benefit when inter-group cooperation, or mutually coordinated beneficial activities between groups, is present (Blumstein et al. 1997, Gilby 2012). Cooperating with others in the neighborhood can result in benefits such as sharing defense of resources or

53 territories from predation (Micheletta et al. 2012, Hellman and Hamilton 2014). For example, in the cichlid fish, Neolamprologus pulcher, groups will jointly defend against a predator when both groups are threatened by a predator. In this case, members of both groups benefit from lowering their investment in defense if in conjunction with another group, but the total defensive effort against the predator is similar to that of a single group without a partner (Jungwirth et al. 2015).

Conflict within groups is a common consequence to group-living (Curry 1988,

Hannon et al. 1985, Wong et al. 2016, Sheppard et al. 2018). Conflict between group members can reduce the benefits afforded by group-living and if escalated can outweigh any benefit of group living altogether (Schoepf and Schradin 2012). Potential consequences of within-group conflict to an individual include eviction (Stephens et al.

2004), reduced access to resources (Cords 1992), and in extreme cases, dissolution of their social group (Aurelia, Cords, and van Schaik 2002).

There can be a tradeoff between within- and between-group social interactions

(Enquist and Leimar 1993, Young et al. 2005). For example, in meerkats (Surricata suricata) there is a tradeoff between warding off prospecting males from a territory and feeding pups within the territory (Mares et al. 2012). These tradeoffs can shift temporally with the strength of within- and between-group selection for group-beneficial traits

(Dugatkin et al. 2003), which can be affected by conflict. Specifically, conflict within groups might limit between-group cooperation (Crofoot and Gilby 2012, Aureli et al.

2002). Within-group conflict could result in the allocation of time and energy toward conflict management (Aureli and Schaffner 2007, Cools et al. 2008), towards reforming

54 ties in their own group (Cords 1988, Schino 1998), mate-guarding (Nichols et al. 2010), or toward winning the conflict (Hannon et al. 1984). These allocations might limit ability to invest, or change the net benefits of investing, in between-group cooperation.

Therefore, conflict can change the benefit to an individual of cooperating with others between- and within-groups and can therefore change the investment of an individual to cooperate with others in each context (Bergmueller et al. 2005a, Bergmueller et al.

2005b). The effects of within-group conflict might then cascade into the broader social landscape, which itself, can be a strong influence on the fitness of an individual

(reviewed by Clutton-Brock 2009).

Here, I investigate how within-group conflict influences an individual’s participation in between-group cooperation. I hypothesize that within-group conflict will limit the ability of individuals in that group to participate in between-group cooperation.

When groups are in a state of high conflict it will be more costly to maintain cohesion and cooperation among individuals in the immediate social group (Cords 1992).

Therefore individuals in groups experiencing high conflict may be unable (such as if the behavior becomes too energetically costly) or unwilling (such as if other behaviors are more beneficial currently) to extend cooperation to individuals outside of the immediate social group. It has previously been shown within-group conflict can increase anti- predator behavior (Cameron and du Toit 2005) so the impacts on between-group cooperation in the context of shared defense may be impacted differently than between- group cooperation in other contexts.

Using breeding groups of Neolamprologus pulcher, a cooperatively breeding

55 cichlid fish, I compared between-group cooperative efforts of individuals that have experienced a social perturbation designed to increase conflict in their own group with those of individuals from control groups, as well as that of neighboring groups that were not directly perturbed. I perturbed groups by temporarily removing the dominant female in the breeding group; such temporary removals have increased within-group aggression in other experiments on this species (Ligocki et al. 2015, Hellmann et al. 2015). I then exposed groups to a predatory stimulus presented directly between the two territories.

The predator, Altolamprologus compressiceps, is a small fish predator and unlikely to be an immediate threat to adults, but is a threat to fry (Sturmbauer et al. 2008). I used this predator because, as a fry predator, this species would act as a measure of parental and alloparental effort. I predicted that 1) perturbed groups will display increased within- group conflict with more frequent aggressive behaviors, more frequent submissive behaviors, and less frequent affiliative behaviors, 2) perturbed groups will reduce defensive efforts against the predator, and 3) neighbors of perturbed groups would increase defense against predators (Jungwirth et al. 2015).

Methods

Study Species:

Neolamprologus pulcher is a cichlid fish endemic to Lake Tanganyika in East

Africa. N. pulcher forms cooperatively breeding groups containing a dominant breeding pair and multiple smaller subordinate males and females (Taborsky 1984). These groups maintain breeding territories which are defended against other conspecifics as well as predators (Frostman and Sherman 2004). Both dominant individuals (breeders) and

56 subordinate individuals (helpers) participate in territory defense (Taborsky and

Limberger 1981). Social status has been shown to play a role in individuals’ behavior towards conspecifics, with subordinates often showing the most aggression toward out- group individuals (Ligocki et al. 2015). Groups cluster into larger colonies (or neighborhoods) and there is substantial mating (Hellmann et al. 2016) and movement

(Bergmueller et al. 2005, Hellmann et al. 2016) among groups. These colonies are thought to form due to high predation pressure (Jungwirth et al. 2015). The presence of neighbors has shown to influence the behavior of subordinates toward predators

(Hellmann and Hamilton 2014), and the intrusion of groups by neighboring individuals increases within-group affiliations among individuals (Bruintjes et al. 2015). Relatedness is highly variable within (Dierkes et al. 2005) and among groups (Hellmann 2015).

Group Structure and Housing

In each round of the experiment, 4 pairs of groups, each group consisting of 6 fish, were created: rounds were repeated 4 times to gain a total sample size of 15 focal groups (one group was omitted from treatment as the dominant female died during observations). Fish were sourced from an F1 wild population whose parent population was originally sourced from the Kipili-Luagala region of Lake Tanganyika in Tanzania,

Africa. I held fish in the lab environment for approximately eight weeks before they were used for observations. I used the same eight groups for the first and second round of our experiment, and a new set of 8 groups for the third and fourth round. On round two and four of our experiment, I recombined pairs of groups, placed both groups into an unfamiliar tank, and switched the position of the group in the tank (see fig 2). In other

57 words, any group that was first used as a neighboring group that did not undergo a treatment became a focal group and vice versa (fig 1).

Groups were housed in four 208-liter tanks divided into two sections with a

Plexiglas barrier. This 208-liter tank was filled with 5cm of CaribSea Instant Aquarium

Substrate (CaribSea, Fort Pierce, FL) and was maintained with water conditions similar to conditions in Lake Tanganyika. Each group was comprised of a dominant male (the largest male, 50-70 mm SL), dominant female (the largest female, 48-70 mm SL), a large subordinate female (second largest female, 43-49 mm SL), large subordinate male

(second largest male, 42-53 mm SL), and one to two smaller subordinates (too small to sex, 32-41 mm SL). Group formation occurred at least twenty-one days prior to testing to establish group dynamics.

Experimental Design

For each tank, one of the groups was designated the ‘focal’ group and the other the ‘neighbor’ group. Neighbor groups were not directly perturbed. To perturb within- group social relationships, I removed and then reintroduced dominant female fish from the focal group (Figure 3.2). In one half of the tanks, dominant female fish were removed from the focal group for two hours. This time was chosen because Ligocki et al. (2015) found that social hierarchies begin to breakdown within fifteen minutes, indicated by rising hormone levels within group members in response to new social opportunities, while in the field, Hellmann et al. (2015) found that removing subordinates for four hours increased conflict upon return. While removed, I placed dominant females in an isolation

58 tank with similar water conditions and aesthetic appearance to the home tank (same sand, same placement of shelter, etc.) to minimize stress of a new environment.

As a control for comparison with the perturbation, a sham removal was performed on the focal group in the other half of the tanks. At the beginning of the two hour period,

I briefly ran a net through the focal side of the tank, and then toward the end of the two- hour period I removed the dominant female and then immediately returned her. Again, neighbor groups were left undisturbed. To equalize the amount of net exposure within the two treatments, immediately before I returned a dominant female in the experimental treatment, I ran an empty net briefly through the focal side of the tank and then reintroduced the dominant female. Therefore there are 3 total net exposures in each treatment, and dominant females were captured in each treatment. Treatments differed in the duration of removal of the dominant female (two hours in experimental treatment, and very minimal in the control treatment.) After 7 days, treatments were switched (so that tanks that received sham removals experience experimental removals and vice versa) and the procedure was repeated.

Predator Assay

Groups from the control (sham removal) treatment and experimental (removal) treatments both underwent the same predator exposure assay for between-group cooperation. First, I conducted a 15-minute post-perturbation recording. Then, fish were shown a 15 minute animation (created using Microsoft Powerpoint 2007) of a predator

(Altolamprologus compressiceps) moving back and forth on a Galaxy Tab Pro tablet

(Samsung, Seoul, South Korea) which was placed directly perpendicular to the Plexiglas

59 barrier and behind the tank (Figure 3.2), so that the tablet was equidistant to both groups.

Images of A. compressiceps used for the animations were taken of 8 A. compressiceps individuals housed in lab, using a Nikon DSLR3500 camera (Nikon, Tokyo, Japan). I then resized each image uniformly using Adobe Photoshop (version cs6, San Jose, CA).

Each video exposure involved a unique A. compressiceps individual, unseen before by all groups in the experiment (a unique image used during each half of each round with four rounds in total). Each image was controlled for size and all matched the snout to tail length of 15 cm SL as described by Smith (1998). Video presentations have been used to expose cichlid fish to other individuals of the same species (Balshine-Earn and Lotem

1998, Baldauf et al. 2009); our preliminary data suggest that there is a difference in an individual’s reaction to the tablet when a blank screen is shown compared to when a video of a predator is shown (BJ Stucke, unpublished data).

Behavioral Observations

Groups were filmed using a HERO3 Silver GoPro (Gopro, San Mateo, CA). The fish were filmed in 30fps at 1080px. Videos were scored for all individuals using the behavioral scoring application Jwatcher (v0.9, Jwatcher Development Team 2006), using the N. pulcher ethogram created by Sopinka et al. (2009). All individuals in both focal and neighbor groups were observed. I scored for all aggressive, submissive, and affiliative behaviors from every individual to all others as well as toward the predator stimulus.

Groups were recorded for 15 minutes the day before being perturbed (Day 1) to gather baseline behavior data for groups. On Day 2, I performed three separate 15 minute

60 recordings: one immediately following the removal or sham removal, one during the predator assay, and one immediately following the predator assay as well. I then allowed groups to sit for seven days (15-minute videos were recorded on days 3 and 5 for another study). On day 7 I recorded groups again for 15 minutes to re-gather baseline data and on day 8, I exposed each focal group to the opposite treatment they were originally given (if a sham removal occurred on day 2, that same group would experience a 2 hour removal of the dominant female on day 8 and vice versa.) I recorded groups on Day 8 using the same methods as on Day 2.

Statistical Analyses

Using the package “lme4” in R (R Core Development Team 2013) I used

Generalized Linear Mixed Models with a Poisson distribution and log-link to test for differences in between-group cooperation. Response variables for the models were a) the counts of aggressive behaviors from focal individuals toward conspecifics pre-removal, post-removal, post-removal and post-predator assay b) total aggression directed toward conspecifics summed for all individuals from focal groups pre-removal (Day 1), post- removal (Day 2, before the predator assay), and post-removal and post-predator assay

(Day 3), c) counts of affiliative behavior from focal individuals toward conspecifics pre- removal, post-removal, post-removal and post-predator assay, d) counts of submissive behaviors toward conspecifics from focal individuals pre-removal, post-removal, post- removal and post-predator assay, e) counts of aggressive behaviors toward the predator by individuals in focal group and neighboring groups, f) total aggression received by the predator from focal and neighboring groups, g) counts of aggressive behavior toward

61 other members of the same group during the predator assay, h) counts of affiliative behavior toward other members of the same group during the predator assay, and i) counts of submissive behavior toward group members during the predator assay.

Treatment, order (i.e. whether sham or actual removal occurred first), and actor status were included as fixed effects. For tests a-d, period (i.e., whether pre-removal, post- removal or post-removal and post-predator assay) was also included as a fixed effect. For tests e and f, location (focal or neighbor) was included as a fixed effect. Random effects within the model were individual and group identity for all models except b and f, which only included group identity as a random effect.

I used models a-d to test our prediction 1, models e-i to test prediction 2 and 3.

Each response variable was tested with a different model and each model went through a selection process based on ΔAICc. Predictors were dropped from the model if doing so resulted in ΔAICc > 2. However, explanatory terms relevant to our predictions were always included (e.g. for model a, actor location was not dropped from our final model even if doing so resulted in ΔAICc > 2). For models that incorporated multiple observation times, such as models a-c, I included the day of observation as a predictor.

Significant difference between observation days was assumed to be due to treatment differences between the two times. I used Tukey Post-hoc tests to determine where significant differences arose in factors with more than two levels. I also used Spearman’s

Correlations to look at the relationship between the total amount of aggression exhibited toward the predator by focal and neighbor groups in relation to each other for prediction

4.

62

Results

Comparison of social interactions before and after removal

Model a: Counts of Aggressive Behaviors from Focal Individuals Toward Conspecifics

I found a significant effect of treatment (Figure 3.3) on aggressive behavior toward conspecifics in pre- and post-removal observation periods for focal groups in experimental treatments (Table 3.1). Experimental groups were significantly more aggressive than control groups (Figure 3.3, Table 3.1). I also found a significant effect of period on aggressive behaviors (Table 3.1) with significantly less aggression in the immediate post-removal compared to pre-removal (p<0.05 in post-hoc tests). There was a significant interaction between treatment and period (Figure 3.3, Table 3.1) with significantly less aggression in post-removal periods and post-removal and post-predator assay periods than in the pre-removal periods for experimental treatments (p<0.05 for all post-hoc tests).

Actor status had a significant effect on aggressive behavior (Table 3.1), with large subordinate females being significantly less aggressive compared to dominant males and large subordinate males (p<0.05 in post-hoc tests). An interaction between actor status, treatment, and period (pre-removal, post-removal, or post-removal and post-predator assay) also significantly affected aggressive behavior (Table 3.1, Figure 3.4) with a significant difference between dominant males in experimental treatments pre-removal and large subordinate females in control treatments pre-removal, post-removal, post- removal and post-predator assay, and between dominant females in control treatments post-removal, and post-removal and post-predator assay (p<0.05 for all post-hoc tests).

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Large subordinate females in experimental treatments during post-removal were significantly less aggressive than dominant males and large subordinate males in experimental treatments pre-removal (p<0.05 for all post-hoc tests). Dominant males in control treatments post-removal and predator assay were significantly more aggressive than large subordinate females in control treatments post-removal (p<0.05 in post-hoc tests.)

Model b: Aggression Directed Toward Conspecifics Summed for All Individuals from

Focal Groups

There was a significant effect of period (pre- removal, post-removal, or post- removal and post-predator assay) on the total frequency of aggression by a group (Table

3.2) though there was no significant difference between levels in post-hoc tests. There was also a significant effect of treatment on aggression; aggression in control groups was lower than in experimental groups (Table 3.2). Neighbors showed significantly higher total aggression than focal groups (Table 3.2). An interaction between period and location showed a significant difference between neighbors on day 1 and focal groups on day 2. An interaction between treatment and period had a significant effect on total aggression (Table 3.2); although there were no significant comparisons in post-hoc tests.

An interaction between period, actor location, and treatment also had a significant effect on total aggression (Figure 3.5). There was a significant interaction between treatment and location (Table 3.2), with significantly more aggression for neighbors in experimental treatments compared to focal groups in control treatments (p<0.05 in post-

64 hoc tests.) There was a significant effect of first treatment (Table 3.2); if the first treatment was a control, there was a significant negative effect on total aggression.

Model c: Affiliation from Focal Individuals Before and After Removal

Period (pre-removal, immediately post-removal, and post-predator assay), actor status, first treatment, and an interaction between treatment and period were all significant predictors of affiliative behavior from individuals in focal groups as well

(Table 3.3). An interaction between treatment and period had a significant effect on affiliation (Table 3.3) with significant differences between control groups on day 2 and experimental groups on day 1, and between control groups on day 2 and day 3 (Figure

3.6, p<0.05 for all post-hoc tests). There were significant difference in affiliation by actor status with higher affiliation from dominant males and large subordinate males compared to dominant females, significantly less affiliative behavior from large subordinate females compared to dominant males and compared to large subordinate males, and significantly less affiliation from small subordinates compared to all other statuses

(p<0.05 for all post-hoc tests.) there was also significantly less affiliation within groups during the post-removal period compared to the pre-removal period (p<0.05).

Model d: Submissive Behavior from Focal Individuals Before and After Removal

There were significant effects of treatment, period, and an interaction between treatment and first treatment on counts of submissive behavior (Table 3.4). Period had significant effect on submission (Table 3.4); though in post-hoc tests there were no significant differences among pairs. An interaction between treatment and period also had a significant effect on submission (Table 3.4) with significantly less submission in

65 control groups post-removal compared to experimental groups pre-removal, and significantly less submission in experimental groups post-removal and post-predator assay compared to experimental groups pre-removal (p<0.05 in post-hoc tests.)

66

Table 3.1 ANOVA results for models a-d, significance denoted: 0 ‘***’ 0.001 ‘**’ 0.01

‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model a: Aggression in Focal individuals Pre-removal, Post-removal and Post-assay:

Χ2 d.f. p Effect Treatment 9.1398 1 0.0025012 ** First Treatment 1.6918 1 0.1933630 Actor Status 15.9202 3 0.0011775 ** Period 84.8249 2 < 2.2e-16 *** Treatment x First Treatment 7.1070 1 0.0076782 ** Treatment x Actor Status 20.5577 3 0.0001301 *** Treatment x Period 22.9292 2 1.05e-05 *** Actor status x Day 10.8856 6 0.0919779 . Treatment x Actor Status x Day 22.3440 6 0.0010488 **

Model b: Total group aggression pre-removal, post-removal, and post-assay: Χ2 d.f. p Effect Period 110.4361 2 < 2.2e-16 *** Actor Location 162.9033 1 < 2.2e-16 *** Treatment 11.2607 1 0.0007917 *** First treatment 6.9585 1 0.0083420 ** Period x Actor Location 2.9504 2 0.2287309 Period x Treatment 12.9267 2 0.0015595 ** Actor Location x Treatment 3.1762 1 0.0747198 . Period x Actor Location x Treatment 23.5434 2 7.72e-06 ***

Model c: Affiliation in focal groups pre-removal, post-removal, post-assay: Χ2 d.f. p Effect Treatment 2.5166 1 0.112657 First Treatment 8.6759 1 0.003224 ** Period 22.6674 2 1.196e-05 *** Actor Status 27.4783 5 4.602e-05 *** Treatment x First Treatment 0.4598 1 0.497700 Period x Treatment 35.0306 2 2.473e-08 ***

Model d: Submission in focal groups pre-removal, post-removal, post-assay: Χ2 d.f. p Effect Treatment 1.0634 1 0.302452 First Treatment 2.3807 1 0.122842 67

Period 18.4695 2 9.759e-05 *** Treatment x First Treatment 8.5754 1 0.003407 ** Period x Treatment 8.7947 2 0.012310

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Aggression toward Predator

Model e: Individual aggression toward predator

Aggression toward the predatory stimulus significantly differed based on status of the actor (Table 5), with significantly more aggression by large subordinate males and dominant males compared to large subordinate females (p<0.05 in post-hoc tests.)

Treatment did not have a significant effect at the individual level (Table 5). There was not a significant effect of the interaction between treatment and group location. Pre- removal aggression had a weakly significant effect on aggression toward the predator

(Table 5). I did not find a significant correlation between the total amount of aggression towards the predator by neighbor and focal groups (Ρ=-0.048, p>0.05, Figure 3.7).

Model f: Total aggression toward predator

Treatment, first treatment, and an interaction between treatment and first treatment all had a significant effect on the total aggression (Table 3.7). In post-hoc tests there was no significant difference between any pairs of first treatment by treatment combinations. Fish in control treatments showed significantly lower frequencies of aggression than did those in experimental treatments (Table 3.7, Figure 3.8). A group’s aggression pre-removal also had a significant effect on total aggression toward the predator (Table 7).

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Table 3.2 ANOVA results for models e- f, significance denoted: 0 ‘***’ 0.001 ‘**’ 0.01

‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model e: Individual ANOVA Table for Aggression toward predator:

Χ2 d.f. p Effect Actor Status 14.5289 3 0.002267 ** Actor Location 27.9531 1 1.243e-07 *** Treatment 0.5705 1 0.450063 Part 4.0311 1 0.044669 * Individual Aggression Pre-Removal 3.5092 1 0.061030 . Actor Status x Treatment 1.0375 3 0.792176 Actor Location x Treatment 0.3752 1 0.540189

Model f: Grouped Aggression toward Predator:

Χ2 d.f. p Effect Actor Location 4.4685 1 0.0345254 * Treatment 8.4094 1 0.0037329 ** First Treatment 1.8862 1 0.1696354 Group Aggression Pre-Removal 15.1235 1 0.0001007 *** Actor Location x Treatment 0.6696 1 0.4131752 Actor Location x First Treatment 1.7921 1 0.1806677 Treatment x First Treatment 15.5850 1 7.888e-05 ***

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Group Dynamics during Predator Assay

Model g: Counts of Aggressive Behavior Toward other Members of Same Group During

Predator Assay

Treatment had a significant effect on aggressive behaviors, with individuals in control groups showing less aggression than experimental groups (Table 6). During the predator assay, aggressive behavior from focal individuals toward others (both within and between groups) significantly differed based on actor status, with large subordinate females being significantly less aggressive than dominant males, and small subordinates showing significantly less aggression compared to all other statuses (p<0.05 in post-hoc tests). There was a significant effect of the interaction between actor status and treatment on aggression. Dominant males in experimental treatments were significantly higher in aggression compared to large subordinate females in control treatments (p<0.05 in post- hoc tests.) There was a significant effect on aggressive behavior from an interaction between actor location and treatment (Table 6); with neighbors in control treatments being more aggressive than both neighbors in experimental treatments and focal groups in control treatments (p<0.05 in post-hoc tests.) There were significant differences based on an interaction between actor status, actor location (focal or neighbor), and treatment

(Table 6).

Model h: Affiliative Behavior Toward Other Members of the Same Group During the

Predator Assay

Fish in the control treatment showed significantly lower frequencies of affiliative behaviors than did those that experienced a 2 hr removal (Table 7.) Groups significantly

71 differed in affiliative behavior based on treatment, actor status, first treatment, and actor location (Table 7.) Dominant females were significantly lower in affiliation than large subordinate males, and large subordinate females were also significantly lower in affiliation compared to large subordinate males (p<0.05 in post-hoc tests.)

Model i: Submissive Behavior Toward Other Members of the Same Group During the

Predator Assay

Submissive behavior was significantly different by treatment, with significantly less frequent submissive behavior in control treatments than in experimental treatments

(Table 8). Submission also differed with actor status (Table 3.8) Large subordinates

(both sexes) had higher submission compared to dominant males (p<0.05 for all in post- hoc tests.)

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Table 3.3 ANOVA outputs for models g-i, Significance denoted: 0 ‘***’ 0.001 ‘**’ 0.01

‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model g: Aggression from individuals toward others during Predator Assay:

Χ2 d.f. p Effect Actor Status 68.9521 4 3.777e-14 *** Actor Location 1.3040 1 0.2534877 Treatment 34.5235 1 4.211e-09 *** Part 2.5006 1 0.1138041 Actor Status x Actor Location 9.8832 4 0.0424424 * Actor Status x Treatment 28.9242 4 8.099e-06 *** Actor Location x Treatment 27.8110 1 1.338e-07 *** Actor Status x Actor Location x Treatment 23.5024 4 0.0001005 ***

Model h: Affiliation from individuals toward others during Predator Assay

Χ2 d.f. p Effect Actor Status 22.2192 4 0.0001813 *** Actor Location 4.3346 1 0.0373441 * Treatment 9.5046 1 0.0020495 ** First treatment 28.1127 1 1.145e-07 *** Actor Status x Actor Location 2.5437 4 0.6368276 Actor Status x Treatment 0.4419 4 0.9789042 Actor Location x Treatment 0.7700 1 0.3802124 Actor Status x Actor Location x Treatment 9.2931 4 0.0541756 .

Model i: Submission from individuals toward others during Predator Assay

Χ2 d.f. p Effect Actor Status 27.3859 4 1.661e-05 *** Actor Location 0.0438 1 0.8342172 Treatment 12.3443 1 0.0004423 *** First treatment 1.2818 1 0.2575708 Actor Status x Actor Location 6.8885 4 0.1418996

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Discussion

I found higher levels of total aggression from individuals in experimental treatments compared to control treatments (Figure 3.3). This is consistent with previous studies in N. pulcher that have shown higher aggression within groups after removing individuals (Balshine-Earn et al. 1998, Wong and Balshine 2010). However, I also found an overall reduction in aggression post-perturbation across all treatments when compared to the pre-removal state. I found similar patterns for affiliation (Figure 3.6). This suggests that the disturbance reduced overall activity of all groups. However, by day 3, this lower activity only remains in the groups that had experienced the 2 hr removal

(Figure 3.3). This suggests that removal of the dominant female has lasting effects on overall social activity. Our experimental treatment may still reflect increased within group conflict if reduced activity is an indicator of avoidance. Using this species, Wong and Balshine (2010) induced conflict over social ascension in the group and also found that the overall activity of groups was less than the pre-perturbed state. Hellmann et al.

(2015) found that avoidance (in the form of hiding) and submission were negatively correlated, suggesting that avoidance was an alternative tactic to receiving aggression than submission and the use of hiding appeared to be dependent on social context.

In other species, group-members often increase affiliative behaviors after a conflict (macaques: De Marco et al. 2010, baboons: Judge and Mullen 2005, chimpanzees: Fraser and Aureli 2008), unless the perturbation is not threatening to a group’s stability and therefore no increase in affiliation among group members would

74 result (Schaffner et al. 2005, van den Bos 1998). Specifically, in N. pulcher, the presence of neighbors has been shown to affect within-group conflict differently based on who the conflict was between (Hellmann and Hamilton 2018) and out-group threat has been shown to increase within-group affiliation (Bruintjes et al. 2015). I found a significant rebound in affiliation in control groups on day 3, but not in experimental groups (Figure

3.3). It has also been shown that the need to re-establish a hierarchy is a strong determinant of aggression within a group (Wong and Balshine 2010). Our results show an increase in aggression on day 3 in control groups and no change in aggression between days 2 and 3 in experimental groups. These results of increased aggression and rebounding affiliation in control groups on day 3 might suggest that the re-establishment the dominance hierarchy may have occurred earlier in control groups with a less extreme conflict, whereas in experimental groups depressed activity is ongoing.

Our prediction that between-group cooperative efforts would be reduced in perturbed groups was not supported. This might be a result of using defense against a predator as our measure of between-group cooperation. It has been previously shown that anti-predator behavior increases with increased within-group conflict (Cameron and du

Toit 2005) and instability (Monclues and Roedel 2008). Our finding that experimental focal groups exhibited more aggression toward the predator than control groups is consistent with these previous studies (e.g., Hellmann et al. 2015) if the longer-lasting depression in overall activity in experimental groups is indicative of higher conflict within the group. As well as fish in experimental treatments had significantly higher levels of aggression to conspecifics prior to exposure to the predator, this heightened 75 aggression might have carried over to aggression in other contexts, such as toward predators. Our results showing that aggression to conspecifics pre-removal had a significant effect on total aggression toward the predator from a group supports this as well. Redirection of aggression toward third parties after conflict has been shown in several systems (Waal and Yoshihara 1983; Watts 1995; Csermely and Wood-Gush

2009); the increased aggression toward the predator could result from such redirection.

Alternatively, the reduction in within-group activity in experimental treatments, possibly due to avoidance, may allow individuals more opportunity to interact with potential predator.

I did not find an interaction between actor location (focal group or neighbor) and treatment and I did not find a significant negative correlation between neighbor and focal aggression toward the predator. These results do not support our prediction that when perturbed groups reduced defensive efforts, neighbors would increase defense against predators. This finding is not consistent with previous findings by Jungwirth et al. (2015) who found that groups decrease their defensive effort when it is shared. As our study incorporates within-group conflict, this shift in within-group social dynamic could cause groups to act differently in the neighborhood than I would expect if they were stable such as what Jungwirth et al. (2015) observed. Jungwirth et al. (2015) also observed a compensatory effect, meaning that neighboring groups increased defensive efforts when focal groups decreased their defense so the neighborhood put forth the same amount of defense overall felt by the predator. I did not find a significant correlation between focal and neighbor group aggression toward the predator which does not support such a 76 compensatory effect among our study. Interestingly, I did find that neighboring groups had lower predator defense than focal groups (which all experienced some disturbance) across all treatments. This result suggests that neighboring groups might be sensitive to disturbance that increases the aggressiveness of neighbors and modulate their defensive efforts to be effective but less costly, without necessarily fine-tuning their response to the exact change in aggressiveness of their neighbor.

First-time neighbors in control treatments had the highest frequency of predator defense. I also found that control groups that had not previously undergone any other treatment had significantly higher levels of aggression toward the predatory stimulus, followed by experimental treatments when they occurred first (fig 6). There may be lasting effects of the predator assay on a group, or a familiarization with the predatory stimulus occurring before groups entered the second treatment. Herczeg et al. (2015) found that when groups of fish were previously exposed to predatory stimuli, they become more hesitant to act toward predators than groups who were predator naïve.

Wright et al. (2017) found similar results of slower and reduced predatory defense when previously exposed to predatory cues in spider colonies.

Our results suggest that disturbance to a group can have effects that are directly correlated to the magnitude of the disturbance. This is shown by our results that focal groups in experimental treatments lowered their activity overall after the removal and continued to stay lower on day 3 compared to control treatments that rebounded in both aggression and affiliation, and also that neighbors which experience no removal have the

77 highest activity (Figure 3.3, Figure 3.6). As focal groups in experimental treatments also exhibited greater aggression toward the predator assay, within-group conflict may have broader-reaching effects into inter-group interactions by either allowing individuals more opportunities for inter-group interaction by decreasing within-group interaction as shown by Hellmann et al. 2015, or through potentially redirecting, or ‘priming’, individuals to be more aggressive in other contexts, as seen by Waal and Yoshihara 1983.

As our results show that disturbance within a group can having long-lasting and multi-faceted effects on a group, the underlying mechanisms of such effects are not known. With evidence for disturbance potentially priming individuals within the group to act more aggressively, I suggest future studies look at the underlying effects of disturbance on behavior via hormonal changes. There is strong support for the modulation of cooperative behavior within groups (Maruska et al. 2010, reviewed by

Soarese tal. 2010) as well as between groups (Dreu et al. 2012) via hormonal mechanisms. Looking at hormonal mechanisms might help clarify if increased aggression towards predators is redirected aggression from group members or simply a result of an increase in energetic and temporal opportunity if within-group interactions are reduced due to avoidance. This can better round our understanding of how individuals of varying social rank within a dominance hierarchy navigate within-group conflict while in the presence of competing interests to cooperate.

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Figure 3.1 Experimental treatment involving the 2 hour removal of a dominant female from a focal group and a control treatment involving the sham removal of a dominant female from a focal group

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Figure 3.2 A flowchart of the experimental design. The experiment was broken down into 4 rounds, each including 2 parts.

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Figure 3.3 Least Square Means of Counts of aggressive behaviors from individuals (model a) over 15 minute observation periods from focal individuals pre-removal, post- removal, post-removal and post-predator assay across all treatments involving the long- term removal and reintroduction of a dominant female (experimental) or the removal and immediate return of a dominant female (control). Significant differences were found between experimental treatments pre-removal and experimental treatments post-removal, experimental treatments post-removal and post-predator assay, and control treatments post-removal.

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Figure 3.4 Least Square Means counts of aggression by actor status for individuals in focal groups during all observation times: pre-removal, post-removal, post-removal and post-predator assay across across all treatments involving the long-term removal and reintroduction of a dominant female (experimental) or the removal and immediate return of a dominant female(control). Actor status is abbreivated as dominant male (DM), dominant female (DF), large subordinate male (LSM), large suboridnate female (LSF).

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Figure 3.5 Least Square Means counts of total group aggression (model b) over 15 minute observations for each period (pre-removal, post-removal, post-removal and post- predator assay) across treatment and actor location. Experimental treatments involved the long-term removal and reintroduction of a dominant female (experimental) or the removal and immediate return of a dominant femal (control).

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Figure 3.6 Least Square Means counts of affiliative behaviors (model c) over 15 minute observation periods for focal individuals pre-removal, post-removal, post-removal and post-predator assay across all treatments involving the long-term removal and reintroduction of a dominant female (experimental) or the removal and immediate return of a dominant femal (control). Significant differences (p < 0.05 in post hoc tests) were found between experimental treatments pre-removal and experimental treatments post- removal, experimental treatments psot-removal and post-predator assay, and control treatments post-removal.

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Figure 3.7 Total counts of aggression toward the predator for neighbor groups plotted againsttotal counts of aggression toward the predator for focal groups.

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Figure 3.8 Least Square Means total counts of aggression behaviors toward the predator (model f) over a 15 minute observation periods for focal and neighbor groups across all treatments involving the long-term removal and reintroduction of a dominant female (experimental) or the removal and immediate return of a dominant female (control). Significant negative effects were found for control treatments and neighboring groups in both treatments.

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Chapter 4: Conclusion

For group-living animals, aggregation of groups into a neighborhood can allow opportunities for individuals to interact with others outside of their immediate social group. These interactions in the neighborhood may offer fitness benefits to an individual

(Bilde et al. 2007, Schradin et al. 2010) in addition to the benefits gained from interacting within the group. Further, interactions both at the within-group level and neighborhood- level are not independent of one another. The presence of neighbors can affect an individual’s behavior within a group (Hellmann and Hamilton 2018) and, those within- group dynamics can change with an increase in the density of neighbors (Hellmann et al.

2014). However, both time and energy are limited, there may be a tradeoff with whom an individual invests time and energy interacting (Young et al. 2005, Mares et al. 2012).

Several previous studies have included the presence of neighbors to look at their effects on the dynamics within-groups (Hellmann et al. 2014, Hellmann et al. 2015,

Hellmann and Hamilton 2018, Harrison 2010, Cheney and Seyfarth 1987). I have expanded on these previous works by exploring the bidirectional influence between neighborhood-level and group-level dynamics. In other words, how do the dynamics within a group affect how individuals interact with others in the neighborhood, but outside of the group and conversely, how do the neighborhood-level interactions affect how an individual interacts with its immediate social group?

In Chapter 2, I built a game theoretical model in which the pay-off for an individual was derived from the potential for between-group interactions. I modeled a situation of mutual cooperation that could be likened to joint defense against intruders by

87 two neighboring groups (e.g., Jungwirth et al. 2015). Within this model, I varied different components of such a pay-off including the selfish benefit to a group that cooperates, the synergistic pay-off for when both groups cooperate, and the cost of cooperating between- groups. I found that individuals would cooperate within their group if there were high selfish or synergistic benefits to between-group cooperation, and conversely, high costs of between-group cooperation could inhibit the cooperation within the group.

In Chapter 3, I examined how within-group conflict affects the between-group cooperative efforts of a group, using groups of the cooperatively breeding cichlid fish,

Neolamprologus pulcher in the laboratory. In nature, breeding groups of these fish occupy permanent territories (Taborksy 1984), which are clustered in close proximity to one another creating a neighborhood-level interactive environment for individuals

(Striver et al. 2004). Within this neighborhood between-group cooperation in the form of joint defense against predators may arise (Jungwirth et al. 2015). To look at how within- group conflict can affect between-group cooperation I exposed one group in each pair to either a control treatment or an experimental treatment with the aim to increase conflict within the group. The experimental treatment was an extended removal of the dominant female from the breeding group causing a social perturbation to the dominance hierarchy, whereas the control treatment involved the removal and immediate return of a dominant female from a breeding group. I found that all groups reduced activity after removal, but that experimental groups exposed to the longer removal had a longer overall suppression in within-group activity compared to control groups. I also found groups in experimental treatments were more aggressive toward the predator compared to control groups and

88 compared to neighbors. These results suggest that the within-group context can play either a direct role on the ability of an individual to participate in the broader social context in terms of available energy or time, or that within-group dynamics may shift the nature of an individual’s interactions in other contexts.

Looking at the results of both chapters, I have shown a bi-directional influence between within-group and neighborhood-level dynamics. The results of the game theoretical model give a greater insight that the broader social landscape can affect an individual’s interactions within their group (see also Hellmann and Hamilton 2014), and even further, the actions of neighbors instead of just their presence can play a role in the affecting within-group dynamics. Conversely, the results of the experimental chapter show that within-group dynamics can create opportunity, either temporal or energetic, that influences how individuals interact in the broader social landscape. In other words, the ability of an individual to interact between groups is affected by the dynamics within a group.

These results, collectively, are important as they show that how an individual interacts in a complex social environment can be dynamic. Cooperatively and communally breeding species, in which individuals aggregate into groups with alloparental care, are found in a multitude of taxa (canines: Creel et al. 1997, primates:

Terborgh and Goldizen 1985, rodents: Young et al. 2006, birds: Maklakov 2002, fish:

Balshine et al. 1998), and further many such groups interact repeatedly with neighbors

(primates: Lazaro-Perea 2001, French et al. 1995, cichlid fishes: Jungwirth et al. 2015,

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Bergmueller et al. 2005). Therefore the trends I have described can potentially be seen broadly across many taxa.

Changes in the physical environment, such as fragmentation or disruption can have cascading effects into the social environment. Behavior is often highly plastic, and thus can be responsive to perturbation. Perturbations in the social environment can affect individual behavior, and by changing an individual’s behavior their interactions with others is also subject to change. Our results suggest that changes at the neighborhood level can influence within-group dynamics and these changes in the group can feedback into the neighborhood. Although not explicitly shown here, these changes may affect an individual’s reproductive success, which, by changing density could again influence neighborhood level effects. With ever increasing anthropogenic disturbances, understanding how interactions among levels of grouping can influence behavior and reproductive success could help facilitate appropriate conservation measures. There can be positive or negative feedbacks among different levels of an individual’s social environment, as suggest by our study. As the effects of such an inter-play can be either positive or negative, further study is warranted to determine the nature of such effects.

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