The Social Behavior of Pine Sawflies in the Genus Neodiprion

University of Kentucky UKnowledge

Theses and Dissertations--Biology Biology

2021

THE SOCIAL BEHAVIOR OF PINE SAWFLIES IN THE GENUS NEODIPRION

John W. Terbot II University of Kentucky, [email protected] Author ORCID Identifier: https://orcid.org/0000-0002-8067-0674 Digital Object Identifier: https://doi.org/10.13023/etd.2021.190

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Recommended Citation Terbot, John W. II, "THE SOCIAL BEHAVIOR OF PINE SAWFLIES IN THE GENUS NEODIPRION" (2021). Theses and Dissertations--Biology. 77. https://uknowledge.uky.edu/biology_etds/77

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The document mentioned above has been reviewed and accepted by the student’s advisor, on behalf of the advisory committee, and by the Director of Graduate Studies (DGS), on behalf of the program; we verify that this is the final, approved version of the student’s thesis including all changes required by the advisory committee. The undersigned agree to abide by the statements above.

John W. Terbot II, Student

Dr. Catherine R. Linnen, Major Professor

Dr. David W. Weisrock, Director of Graduate Studies

THE SOCIAL BEHAVIOR OF PINE SAWFLIES IN THE GENUS NEODIPRION

______

DISSERTATION ______

A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the College of Arts and Sciences at the University of Kentucky

By John William Terbot II Lexington, Kentucky Director: Dr. Catherine R. Linnen, Professor of Biology Lexington, Kentucky 2021

ABSTRACT OF DISSERTATION

THE SOCIAL BEHAVIOR OF PINE SAWFLIES IN THE GENUS NEODIPRION

Group living is found across the animal kingdom ranging from temporary mating aggregations to complex, eusocial lifestyles. A particularly common form of group living found among insects are larval or nymphal herds. This lifestyle consists of immature insects living together and results in several proposed costs and benefits. Benefits of this lifestyle include improved ability to regulate a group’s microenvironment, more efficient use of their host, and the ability to engage in collective predator defenses. Offsetting these benefits are costs resulting from living in close proximity to conspecifics which include increased competition, greater visibility to predators, and heightened disease risks. While evidence for these costs and benefits have been found in many species, they have not appeared consistently across all larval herding species. In this dissertation, we studied the larvae of a group of pine sawflies (the lecontei group of the genus Neodiprion) to investigate the causes and consequences of the larval herding lifestyle. Neodiprion sawflies are well suited to such study because there is natural variation across the genus in multiple traits including social behaviors and use of a common form of predator defense in larval herding organisms, aposematism. Aposematism is a combination of warning coloration and patterning alongside another form of defense, often of a chemical nature. For Neodiprion, this chemical defense results from the sequestration and regurgitation of resin from their host plants, pine trees (genus Pinus) and occurs in all species. We describe a quantification of social behavior, aggregative tendency, and used it alongside a more commonly used sociality trait, group size. We also measured a variety of other larval traits and features of their environment. We found that aggregative tendency and group size are distinct traits with their own adaptive roles. Group size was found to be highly related to the egg clutch size oviposited by mothers and, alongside the amount of chemical defense, larger group sizes cause increases in aposematic patterning. Aggregative tendency, however, was found to serve in a mediating role optimizing conditions of group-living given a particular group size. First, it appears to be related to ambient relative humidity serving as a means of water conservation. Second, it was found to be associated with immune activity in a manner implying it can alter group distance as a form of social immunity. These findings were robust for Neodiprion and we believe that they may be generalizable to other larval herding organisms.

KEYWORDS: Neodiprion, Social Behavior, Behavioral Ecology, Evolutionary Ecology

John William Terbot II (Name of Student)

05/06/2021 Date

THE SOCIAL BEHAVIOR OF PINE SAWFLIES IN THE GENUS NEODIPRION

By John William Terbot II

Catherine R. Linnen Director of Dissertation

David W. Weisrock Director of Graduate Studies

05/06/2021 Date

DEDICATION

To my mother, Cindy S. Terbot, and my father, John F Terbot II. For so many reasons, I literally would not be here without you.

ACKNOWLEDGMENTS

This dissertation would not have been possible without each member of the

Linnen Lab that I was fortunate enough to overlap with during my time at the University of Kentucky. Particularly, I wish to give explicit thanks to the undergraduate researchers whose time and effort contributed in a significant way to this dissertation. If you ever see this, please know you have my gratitude: Layne Gaynor for your aid with intraspecific aggregative tendency assays, Adam Douglas for your work analyzing a variety of photos and specimen collection, and Dylan P’Simer for your help with quantifying pine resin content. I also wish to thank the other undergraduate researchers I was lucky to supervise; my experience working with you is treasured and I hope that our research will help guide the Linnen Lab in future studies. I also want to thank Dr. Carita Lindstedt and Adam

Leonberger for instructing me in many of the phenotyping methods I used for this dissertation.

Next, I wish to thank the scholars who have provided feedback on my research and given their valuable insights. I am especially grateful to those scientists who responded to the unprompted questions from a random graduate student in Lexington,

Kentucky: Dr. Xoaquín Moreira, Dr. Mark Pagel, and Dr. John Endler. I also wish to thank my doctoral committee for their support. Additionally, I extend sincere appreciation to my doctoral advisor, Dr. Catherine Linnen, for her exhaustless patience and support during my time at the University of Kentucky. The widespread problems of graduate school would go some way to being solved if every student were fortunate to have an advisor like you.

iii

Finally, I extend my thanks to the others who supported my dissertation work

either directly or indirectly. The love and support of my parents, friends, and other loved

ones kept my head on my shoulder through even my worst times. I would also like to

thank Alexandra Elbakyan for perhaps the greatest gift to academia of the digital age;

may it never die. I also feel compelled to acknowledge that this work would have been

impossible without the entire scientific community and the scientists who came before us;

to appropriate from Peter Kropotkin, science being the collective work of humanity, the

product should be the collective property of humanity. I hope my work is found to be a worthwhile addition to that product. Lastly, I would like to extend my appreciation to my fellow workers at the University of Kentucky, particularly my union siblings in United

Campus Workers Kentucky (CWA Local UCW 3365), without your labor this university does not exist. While I will be leaving University of Kentucky, I wish you the best on the continued fight to ensure Kentucky universities are places where employees are respected and adequately compensated. Solidarity forever!

To all mentioned here, please know that my use of “we” in this dissertation is meant to convey that this dissertation was not the result of a solitary individual but only possible from our collective efforts.

TABLE OF CONTENTS

ACKNOWLEDGMENTS ...... iii

TABLE OF CONTENTS ...... v

LIST OF TABLES ...... viii

LIST OF FIGURES ...... ix

INTRODUCTION ...... 1

CHAPTER 1...... ANSWERING TINBERGEN’S FOUR QUESTIONS FOR AGGREGATIVE BEHAVIORS: A REVIEW OF CURRENT LITERATURE ...... 5 1.1 Introduction ...... 5 1.1.1 Defining Aggregation ...... 6 1.2 Proximate Causes ...... 7 1.2.1 The Cues Used in Aggregation ...... 7 1.2.2 The Genetics and Physiology of Aggregation ...... 11 1.2.2.1 Physiology of Aggregation; S. gregaria and L. migratoria 11 1.2.2.2 Genetics and Neurobiology of Aggregation; D. melanogaster and C. elegans 13 1.2.2.3 Conclusions on the Physiology and Genetics of Aggregation 14 1.2.3 The Dynamics of Aggregation ...... 16 1.2.4 Summary of Proximate Causes ...... 18 1.3 Ultimate Causes ...... 19 1.3.1 Fitness Benefits of Aggregation ...... 20 1.3.1.1 Optimal Microenvironment 20 1.3.1.2 Improved Foraging Ability 22 1.3.1.3 Improved Predator Defense 25 1.3.1.4 Mating Benefits 27 1.3.2 Fitness Costs of Aggregation ...... 28 1.3.3 The Interaction of Costs and Benefits of Aggregation ...... 31 1.3.4 Summary of Ultimate Causes ...... 32 1.4 Conclusions ...... 33

CHAPTER 2...... GREGARIOUSNESS DOES NOT VARY WITH GEOGRAPHY, DEVELOPMENTAL STAGE, OR GROUP RELATEDNESS IN FEEDING REDHEADED PINE SAWFLY LARVAE...... 36 2.1 Abstract ...... 36 2.2 Introduction ...... 37 2.3 Materials and Methods ...... 43 2.3.1 Collection and Rearing Information ...... 43 2.3.2 Video assays of larval aggregative tendency ...... 44 2.3.3 Generating a model of random dispersal ...... 46 2.3.4 Effect of developmental stage on aggregative tendency ...... 46 2.3.5 Effect of relatedness on aggregative tendency ...... 47

2.3.6 Intraspecific variation in aggregative tendency ...... 48 2.3.7 Interspecific variation in aggregative tendency ...... 49 2.4 Results ...... 49 2.4.1 Effect of relatedness on aggregative tendency ...... 51 2.4.2 Intraspecific variation in aggregative tendency ...... 51 2.4.3 Interspecific variation in aggregative tendency ...... 52 2.5 Discussion ...... 52 2.5.1 Assay limitations ...... 53 2.5.2 Effects of developmental stage and relatedness on larval aggregative tendency ...... 55 2.5.3 Intra- and interspecific variation in larval aggregative tendency ...... 57 2.5.4 Conclusions ...... 59 2.6 Tables and Figures ...... 61

CHAPTER 3...... MOTHER KNOWS BEST: MATERNAL CLUTCH SIZE PREDICTS LARVAL GROUP SIZE IN PINE SAWFLIES (GENUS NEODIPRION) ...... 68 3.1 Abstract ...... 68 3.2 Introduction ...... 69 3.3 Materials and Methods ...... 73 3.3.1 Characterizing variation in group-size traits ...... 73 3.3.2 Evaluating phylogenetic signal for group-size traits ...... 76 3.3.3 Evaluating relationships between group-size traits ...... 77 3.4 Results ...... 79 3.4.1 Variation in group-size traits ...... 79 3.4.2 Phylogenetic signal in group-size traits ...... 79 3.4.3 Relationships between group-size traits ...... 80 3.5 Discussion ...... 81 3.5.1 Potential limitations of this data set ...... 81 3.5.2 Explanations for lack of strong parent-offspring conflict ...... 84 3.5.3 Alternative explanations for variation in group-living ...... 85 3.5.4 Conclusions ...... 88 3.6 Tables And Figures ...... 89

CHAPTER 4...... SOCIAL DISTANCING AS AN IMMUNE ADAPTATION IN HERD- LIVING INSECTS...... 104 4.1 Abstract ...... 104 4.2 Introduction ...... 105 4.3 Materials and Methods ...... 108 4.3.1 Collection and Rearing of Specimens ...... 108 4.3.2 Aggregative Tendency ...... 110 4.3.3 Immune Assay ...... 110 4.3.4 Data Curation ...... 112 4.3.5 Intraspecific Analysis ...... 112 4.3.6 Phylogenetic Signal ...... 113 4.3.7 Interspecific Analysis without Phylogenetic Correction ...... 114 4.3.8 Interspecific Analysis with Phylogenetic Correction ...... 114 4.4 Results ...... 114

4.4.1 Data Curation ...... 114 4.4.2 Intraspecific Analysis ...... 115 4.4.3 Phylogenetic Signal ...... 115 4.4.4 Interspecific Analysis without Phylogenetic Correction ...... 115 4.4.5 Interspecific Analysis with Phylogenetic Correction ...... 116 4.5 Discussion ...... 116 4.6 Tables and Figures ...... 119

CHAPTER 5. ... ECOLOGICAL CAUSES OF SOCIALITY IN NEODIPRION LARVAE ...... 131 5.1 Abstract ...... 131 5.2 Introduction ...... 132 5.3 Materials and Methods ...... 135 5.3.1 Specimen Collection and Rearing ...... 135 5.3.2 Data Collection and Compilation ...... 136 5.3.3 Data Curation ...... 141 5.3.4 Phylogenetic Signal ...... 141 5.3.5 Abiotic Causes ...... 142 5.3.6 Biotic Causes ...... 142 5.4 Results ...... 144 5.4.1 Data Curation ...... 144 5.4.2 Phylogenetic Signal ...... 145 5.4.3 Abiotic Causes ...... 146 5.4.4 Biotic Causes ...... 147 5.5 Discussion ...... 148 5.6 Tables and Figures ...... 155

WORKS CITED ...... 177

VITA ...... 198

vii

LIST OF TABLES

Table 2.1 Collection information for sawflies used in Chapter 2 ...... 61 Table 3.1 Table of literature sources for colony size and egg clutch size data ...... 89 Table 3.2 Summary of sample sizes and sources of data for Chapter 3 ...... 90 Table 3.3 Collection information and summary of data points per site ...... 91 Table 3.4 Summary of combined colonies ...... 99 Table 3.5 Non-phylogenetic model comparisons of social traits ...... 100 Table 4.1 Collection information and sample sizes for wild caught colonies ...... 119 Table 4.2 Samples sizes for intraspecific data set ...... 123 Table 4.3 Sample sizes from combined colonies ...... 124 Table 4.4 Samples sizes for by-species data set ...... 125 Table 5.1 Collection information for wild-caught colonies ...... 155 Table 5.2 Sample sizes for colonies used in by-colony data set ...... 161 Table 5.3 Sample sizes for by-species data set ...... 164 Table 5.4 Details on Resin Sampling of Pines by Collection Site ...... 165 Table 5.5 Details on Resin Sampling of Pines by Species ...... 166 Table 5.6 Model comparisons of abiotic factors on aggregative tendency...... 167 Table 5.7 Model comparisons of abiotic factors on colony size ...... 168 Table 5.8 Model comparisons of biotic factors on aggregative tendency ...... 169 Table 5.9 Model comparisons of biotic factors on colony size ...... 170

viii

LIST OF FIGURES

Figure 2.1 Developmental variation in larval morphology in N. lecontei ...... 62 Figure 2.2 Gregarious assay test arena ...... 63 Figure 2.3 Impact of group size and developmental stage on aggregative tendency ...... 64 Figure 2.4 Impact of relatedness on aggregative tendency ...... 65 Figure 2.5 Intraspecific variation in aggregative tendency ...... 66 Figure 2.6 Interspecific variation in aggregative tendency ...... 67 Figure 3.1 Neodiprion species vary in multiple sociality traits ...... 101 Figure 3.2 Phylogenetic signals of sawfly sociality traits ...... 102 Figure 3.3 Correlations between sawfly sociality traits ...... 103 Figure 4.1 Immune response vs aggregative tendency within Neodiprion lecontei ...... 126 Figure 4.2 Immune response vs aggregative tendency within Neodiprion lecontei using a Mixed-Effects Model ...... 127 Figure 4.3 Immune response vs aggregative tendency across the genus Neodiprion ..... 128 Figure 4.4 Immune response vs aggregative tendency using species-level averages without phylogenetic correction ...... 129 Figure 4.5 Maximum likelihood estimates of models for Pagel’s λ from 0 to 1 ...... 130 Figure 5.1 Summary of biotic model naming convention ...... 171 Figure 5.2 Directed acyclic graphs of biotic models ...... 172 Figure 5.3 Correlogram of traits using by-colony data on aggregative tendency ...... 173 Figure 5.4 Correlogram of traits using by-colony data on colony size ...... 174 Figure 5.5 Correlogram of traits using by-species data ...... 175 Figure 5.6 Visual summary of the causes and consequences of sociality in Neodiprion 176

INTRODUCTION

This dissertation was motivated by a single question, “Why do organisms live in

groups?”. Numerous benefits to group living have been shown and the sheer variety and

number of group-living organisms inherently proves there must be some advantage. Still,

equally numerous costs have also been demonstrated and, at face value, a solitary

lifestyle devoid of a reliance on others seems to be most stable over evolutionary time; a

solitary lifestyle need not worry about invasion from “cheater” phenotypes that work on

altruism and cooperation still seeks to fully explain. To address this puzzle, we studied

the variation in social behavior across a genus of pine sawflies, Neodiprion. Social

behavior within Neodiprion is relatively simple consisting of larval herds and

(presumably) lacks many of the more complex social interactions seen in eusocial

organisms like bees, ants, and termites. While individual chapters can be read in

isolation, each chapter builds off findings from previous chapters.

In the first chapter, we use the framework of Tinbergen’s four questions to review

the causes of aggregation mainly focusing on invertebrate systems. However, there are

some references to vertebrate sociality, when necessary, to fill certain gaps in invertebrate

social behavior. This thorough review, therefore, provides a background on the ultimate

causes of social behavior via its adaptive role and adaptive function and proximate

causes, namely the genetics, physiology, and behavioral rules that have been found to

guide the formation of aggregations.

The second chapter introduces the Neodiprion system and the trait of aggregative tendency. Aggregative tendency attempts to quantify the degree to which larvae desire proximity to conspecifics. It was found to robustly be able to detect differences in sociability between feeding larvae and final instar larvae which are noted in many species 1

including Neodiprion sawflies for drastic changes in behavior as larvae seek out suitable

sites to undergo pupation. It should be stressed that unless otherwise noted the

“aggregative tendency” measure used in this dissertation is inversed, i.e., larger values of aggregative tendency correspond to less aggregative larvae.

Next, the social behaviors of Neodiprion and their relations to each other are

examined in the third chapter. To do this we use a combination of field observations,

literature sources, and lab experiments to determine species-level averages for three

social traits: egg clutch size, group size, and aggregative tendency. The main goal of

which was to determine if the aggregative tendency trait manifested in changes to larval

group size. To this end, the relationships between these traits were analyzed with and

without phylogenetic correction. It was determined that group size was only associated

with the maternally determined clutch size and aggregative tendency was not directly

influenced by either trait. This result implied that aggregative tendency may serve as a

mediating role to optimize conditions within a particular, maternally determined, group

size.

Via aggregative tendency, larvae may be able to control group densities allowing

for advantages of group living to be maintained while reducing risks such as disease

transmission. This potential was examined in the fourth chapter. A foreign object was

inserted into larval bodies for 24 hours and the amount of melanin deposited quantified.

Melanization is a general immune response of insects towards a variety of enemies

including viruses, bacteria, fungi, and parasitoids. We looked at the relationship between

aggregative tendency and immune response intraspecifically within the redheaded pine

sawfly, Neodiprion lecontei, and across the Neodiprion genus with and without

phylogenetic correction. The results were conclusive and consistent at all levels: less aggregative larvae had lower immune responses. This confirmed a potential role of aggregative tendency as a form of social immunity.

In the fifth and final chapter, ecological causes of social traits within Neodiprion were investigated. This study was done with both aggregative tendency and group size.

Starting with abiotic causes relating to environmental temperature and relative humidity, we found only a significant relationship between aggregative tendency and relative humidity implying that it may also serve a role in group microenvironment for water conservation. Next, we examined the relationships between biotic factors and social traits. Many species of Neodiprion sawflies have a combination of traits referred to as aposematism. This is a combination of some form of predator defense, like the resinous regurgitant Diprionid sawflies sequester from their pine hosts, with warning coloration and patterning. Aposematism is also commonly associated with group living. However, the causal connections between group-living, chemical defenses, and warning patterns are still unclear. To elucidate these connections, as well as a potential role of aggregation in overcoming host pine’s resinous defenses, a variety of causal models were tested with and without phylogenetic correction using confirmatory path analysis. The results conclusively showed warning patterning having a strong causal connection from the amount of chemical defense. Colony size was also found to have a relatively robust positive relationship with aposematic patterning, most likely being a second cause of aposematic patterning. Aggregative tendency, however, was not found to have robust links to amount of chemical defense or warning patterning, but these causal paths were still weakly supported and further study could further confirm or deny a connection.

Neither group size nor aggregative tendency were found to be directly related to host pine defenses.

Together, the results of this dissertation were used to construct a single model representing the current knowledge of social behavior within Neodiprion that could be generalized to apply to larval herding organisms more broadly. This model involves maternal choices determining initial conditions for larvae. The most important conditions to this study being egg clutch size which determines larval group size and host pine species which determines host resin content which subsequently determines the level of chemical defenses of a larva. Combined group size and amount of chemical defense cause the amount of warning signal (coloration or, from this study, patterning).

Meanwhile, aggregative tendency serves as a mediating role optimizing larval fitness in terms of immune risk and water conservation. From this, we have a tentative, and somewhat obvious answer, to our initial question. Group living emerges because it has advantages (in the case of Neodiprion, improved aposematic signaling) and costs (for

Neodiprion, disease transmission) can easily be accommodated via cooperation amongst group members. Moreover, these accommodating cooperative behaviors can be relatively simple such as the aggregative tendency of Neodiprion sawflies. In short, group living is worthwhile because cooperation can often overcome the risks of such a lifestyle while retaining the benefits of group living.

CHAPTER 1. ANSWERING TINBERGEN’S FOUR QUESTIONS FOR AGGREGATIVE BEHAVIORS: A REVIEW OF CURRENT LITERATURE

1.1 Introduction

Inspired by Aristotle’s four causes, Nikolaas Tinbergen summarized the key perspectives from which behavior can be analyzed: phylogenetic, ecological, developmental, and mechanical. Understanding behavior from these four directions can help answer the compelling question of “Why do these animals behave as they do?”

(Tinbergen 1963). While each direction is distinct and addresses important issues, it can be conceptually simpler to divide them into two categories: proximate and ultimate causes—mechanical and developmental; and phylogenetic and ecological, respectively.

Despite Tinbergen’s call over 50 years ago emphasizing that investigating behavior from all perspectives is important to a full conceptual understanding, relatively few systems have integrated these approaches in a single organism (Hofmann et al 2014). The most notable exception to this is bird song (Bateson and Laland 2013).

While almost any behavior and even other traits would benefit from the holistic approach Tinbergen proposed, the best traits to study in this way are those that are widespread and occur across multiple taxa. By studying a trait that occurs across multiple taxa, we can determine if the causes for the trait are similar in different groups or if there are hidden idiosyncrasies. Arguably, the best way to do this would be to study this trait extensively in a single taxon first. Perhaps one of the most intriguing behaviors yet to have a full integration across a single organism is aggregative behavior. Aggregation, the social behavior whereby individuals form a group or swarm, is well studied, and occurs in many taxa (Parrish and Edlestein-Keshet 1999). However, studies often focus on a

single driving mechanism for the behavior such as genetics or adaptive benefit; and they rarely incorporate factors from both proximate and ultimate causes. By understanding how work is done to address proximate and ultimate causes, we can determine the steps necessary to fully integrate these approaches in a focal organism.

In the following paper, we will summarize the existing research, first, on the proximate causes and, second, on the ultimate causes of aggregation. At the end, I will present the ways that studying this phenomenon from multiple levels will allow us to expand our knowledge in ways that are greater than studying a single direction on its own. We will mainly be focusing on insect systems but will introduce other invertebrate and vertebrate systems when appropriate. The justification for this is due to the authors’ familiarity with this taxa and the existing depth of research in insects for both proximate and ultimate causes. This bias is not meant to imply that other taxa are unimportant nor that those taxa necessarily must share all the characteristics or differ completely from aggregative insects.

1.1.1 Defining Aggregation

Before continuing it will be important to decide on how we will define key terminology. One important concern is that aggregation can have multiple primary activities. For example, organisms may aggregate in groups such as leks for mating purposes or may aggregate for feeding purposes. Moreover, aggregations can involve even more complex interactions such as those seen in eusocial species. For this paper, we will define aggregation as when multiple individuals non-randomly associate physically within their acceptable habitat. In other words, the clustering is not due to an underlying patchy habitat.

To demonstrate this distinction, we present two cases of potential feeding aggregations. Both narrow mouth toads, Microhyla carolinensis olivacea, and the sea urchin Strongyiocentrotus droebachiensis form aggregations on or near food sources, ant colonies and algal fields, respectively. However, the toad’s aggregation is based on the patchy distribution of ant colonies and will otherwise distribute throughout the environment (Carpenter 1954). The sea urchin will also only aggregate when in the presence of food, which at first seems to be a similar situation to the toads (Vadas et al

1986). However, these aggregations will only form when the algal food source is in a large field and will disperse if the algae is in isolated clumps itself (Lauzon-Guay and

Scheibling 2007). Ideally, an organism would aggregate in the absence of a food source; however, we should still consider organisms that aggregate only when food is present, so long as their aggregation is not a symptom of the food’s underlying distribution.

1.2 Proximate Causes

Having determined a working definition for “aggregation”, we can now begin to investigate some of the existing work describing the causes of aggregation. In this section, we will summarize the work done on describing the proximate mechanisms that drive aggregative behavior in organisms. This work can mainly be divided into three sub- categories; determining the cues organisms use to group together, uncovering the genetic, physiological, and neuronal pathways responsible for differences in aggregative behavior, and describing the dynamics of aggregation formation and maintenance.

1.2.1 The Cues Used in Aggregation

The cues an aggregating species uses to form or maintain cohesion of a group will obviously vary tremendously. However, these cues tend to fall into one of three categories, olfactory, vibrational, and tactile. An important note is that tactile cues can

contain aspects of the others via detection of contact pheromones on an individual’s body

as well as mechanical detection of another individual.

Of these, chemical cues are very well-studied, likely due to their importance in pest control. The origin of these cues can vary dramatically. For example, Japanese

beetles, Popillia japonica, are attracted to volatiles released by plants when previously

fed upon by other individuals but were not found to produce pheromones of their own

(Loughrin et al 1996). As well, the frass produced from feeding bark beetles in the genera

Dendroctonus and Ips have also been shown to contain aggregative pheromones (Vité

and Pitman 1968). Moreover, bark beetles, such as Ips paraconfusus, on the other hand

have been shown to use host plant chemicals as a precursor to the production of their own

aggregative volatiles (Byers et al 1979). However, the pheromone may also be produced

completely de novo without host compounds even in the same bark beetle species, I.

paraconfusus and close relatives such as I. pini (Seybold et al 1995). While volatile cues

tend to be useful for forming aggregations when individuals are dispersed, trail marking

pheromones are often used for maintaining aggregations as in the cactus moth,

Cactoblastic cactorum, and the red-headed pine sawfly, Neodiprion lecontei

(C. cactorum: Fitzgerald et al 2014 ; N. lecontei: Costa and Louque 2001; Flowers and

Costa). However, when conditions are appropriate, such as the relatively closed air

environment of the larval feeding chambers of the bark beetle Dendroctonus micans, a

volatile pheromone may produce a reliable enough gradient to be used for group cohesion

(Deneubourg et al 1990).

One important factor in discussing pheromone production is to determine where

the cue is produced. While the European earwig, Forficula auricularia, has been shown

to rely on both pheromone and thigmotactic cues, the source of the pheromone is an

unknown location (Lordan et al 2014). On the other hand, the trail marking cue of the

cactus moth larvae have been localized to mandibular salivary gland secretions

(Fitzgerald et al 2014). However, determining the source of a trail pheromone should not

be taken as necessarily easier; while the presence of a trail pheromone in red-headed pine

sawflies has been demonstrated, its source is as yet unknown (Flowers and Costa 2003).

Arguably, it is more important to determine the origin of trail pheromones over volatile

cues as there is more direct involvement by the individual in their placement compared to

the simple diffusion of volatile pheromones. A general trend in how chemical cues are used is that those used in the formation of groups tend to be volatile in nature, while those used for group cohesion tend to be deposited on to the substrate as a trail.

An equally important category of cues, especially given their participation in thigmotactic cues, are vibrational signals (Cocroft 2001). Vibrational signals tend to be mainly short range, similar to trail pheromones, and thus tend to be used to coordinate group movements and cohesion. Often, these are produced by striking the substrate with their body. In the eucalyptus-feeding sawfly, Perga affinis, individuals separated from the group will tap with their abdomens and respond by walking towards response taps from the group (Fletcher 2008). This use of vibrational signals to maintain group cohesion can also be found in the chrysmelid beetle, Polychalma multicava, and the tinged bug,

Corythucha hewitti (Cocroft 2001). Vibrational signals are also often found in coordinating the establishment of new feeding sites. The sawfly, P. affinis, has a separate cue of body contractions that is used to coordinate group movement from a feeding site

(Fletcher 2007). A membracid treehopper, Calloconophora pinguis, signals only when on

young leaves, the preferred food source, and orients towards the vibrations of other

individuals (Cocroft 2001). Similar use of vibrational cues to signal food sources are

found in the birch and alder feeding sawfly, Hemichroa crocea (Hoegraefe 1984).

Finally, cues that signal the relatedness between individuals may also be of importance in the formation of aggregations. In other words, do aggregations tend to form only between organisms with familial relationships? The recognition of kin or other familiar individuals can utilize multiple and complex cues. For example, even within

eusocial wasps there is evidence for olfactory cues as well as facial recognition (Costanzi

et al 2013; Sheehan et al 2014). While kinship is an important factor in some

aggregations such as over-wintering aggregations in southern flying squirrels, Glaucomys

volans (Thorington and Weigl 2011b), relatedness in many feeding aggregations tends to

be unimportant. This can be seen with kinship being an unimportant factor in group

formation in the larvae of the beetle Trypoxylus dichotomus (Kojima et al 2014), strain

odor having little effect when compared with presence of any other individual in

cockroaches (Ame et al 2004), and even different species of Ixodid ticks responding to

each other’s aggregative cues (Leahy et al 1983). The differences in the importance of

relatedness in these two aggregations may speak to differences in how costly the

increased competition for food is in these examples. In short, overwintering squirrels

have a limited supply of personally stored food caches, while food in these other cases is

more abundant (Thorington and Weigl 2011a).

Overall, detection of cues that signify other individuals, usually conspecifics

though not always, is key to the formation and maintenance of aggregations. These cues

can use almost any sensory modality, however chemical and vibrational cues are the most

common. Tactile cues are also extremely important and consist of chemical and tactile

components. The importance of kin-recognition in aggregative behavior seems to be related to how costly group formation could potentially be for individuals.

1.2.2 The Genetics and Physiology of Aggregation

Regardless of the cue used, for an aggregation to form, these cues must be perceived by individuals and integrated with other information to produce a behavioral response. Important to understanding how aggregations form based on an organism’s interactions with their environment is determining the internal mechanisms control this behavior. Much work has been done on determining the genes, neuronal pathways, and physiological properties responsible for aggregative differences. However much of this

work is done in model systems such as the plague locusts, Schistocera gregaria and

Locusta migratoria, the nematode worm, Caenorhabditis elegans, and the vinegar fly,

Drosophila melanogaster. Therefore, it will be useful to consider each as a case study in describing the molecular causes of aggregation.

1.2.2.1 Physiology of Aggregation; S. gregaria and L. migratoria

Two grasshopper species, the desert locust, S. gregaria, and the migratory locust,

L. migratoria, both have solitary and gregarious phases. Transitioning between the two

phases is not a guaranteed part of their life history; rather, switching between these

phases is induced by population density. Individuals of high density populations undergo

gregarization, in part due to the increase in being touched, and enter the gregarious phase

over time (Simpson et al 2001). The desert locust, S. gregaria, has been an instrumental model species in understanding the internal neurotransmitter and hormonal cues responsible for gregarization due to the presence of solitary and gregarious forms and knowledge of how to induce changes between the two with external cues. An interesting

difference between gregarious and solitary S. gregaria is the increased titer of

ecdysteroid of gregarious individuals during oogenesis and embryogenesis (Tawfik et al

1999). It is speculated that the increased ecdysteroid present in gregarious females may

prime offspring to become gregarious themselves.

An equally important factor is the induction of gregarious behavior when the

neurotransmitter serotonin is injected into solitary individuals or a reversal of

gregariousness when serotonin synthesis is inhibited (Rogers et al 2014). An important

distinction is that while a behavioral shift can be induced with serotonin, the

corresponding morphological changes are not induced (Tanaka and Nishide 2013).

Another neuropeptide known to affect these morphological changes, [His7]-corazonin,

was found to induce one of the gregarious behaviors, increased swaying, in L. migratoria,

but not in S. gregaria (Hoste et al 2002; Hoste et al 2003).

While much of the work in this system has focused on the physiological changes

that result or induce gregarious behavior, there has been some work in understanding the

underlying genetics as well. A study using expressed sequence tags and confirmed by q-

PCR looked at how gene expression varies between solitary and gregarious L. migratoria

found the importance of up-regulation of genes in the heads of gregarious individuals.

Many of these belonged to members of the JHPH superfamily which encompasses juvenile hormone-binding proteins, hexamerins, propheoloxidase, and hemocyanins.

They also determined an increase in genes involved with responses to external stimuli, which may be related to the importance of cues in forming and maintaining aggregations

(Kang et al 2004). Taken as a whole, this work illustrates the importance of

understanding the proximate mechanisms underlying aggregation both external to an organism and internal.

1.2.2.2 Genetics and Neurobiology of Aggregation; D. melanogaster and C. elegans

Larvae of D. melanogaster have two foraging variants; a “rover” strain that moves rapidly across food patches and avoids other larvae and a “sitter” strain that slows movement on food and tends to group with other larvae. Given that differences in neurotransmitters are partly responsible for the behavioral shifts in locust, it is not shocking that one of the most well documented genes involved in solitary versus social feeding in D. melanogaster are in a neurotransmitter receptor, namely the neuropeptide Y receptor family. Upregulation of this gene has been identified in the solitary “rover” behavior of D. melanogaster larvae; individuals with lower amounts of the gene or nonfunctional alleles have the social “sitter” phenotype (Wu et al 2003). The same gene family has also been implicated in strains of C. elegans that have comparable behavioral differences (de Bono and Bargmann 1998).

Work on these behavioral differences in C. elegans has been extremely fruitful in part due to its relatively simple nervous system comprised of only 302 neurons (Fujiwara et al 2002). Investigations into how upregulation of NPY genes in C. elegans lead to a reduction of aggregative behavior have allowed pinpointing its effect to inhibiting the action of the cyclic-GMP dependent channels tax-2 and tax-4. In the absence of NPY signaling, tax-2 and tax-4 work to promote signaling in the body cavity neurons AQR,

PQR, and URX leading to aggregative behavior (Coates and de Bono 2002). The potassium channel egl-2 was also found to inhibit the action of these neurons. It is

interesting to note that the action upon a body cavity sensory neuron emphasizes the

importance internal state can have on gregarious versus solitary behavior.

External sensory neurons have also been found to be important in controlling gregariousness in C. elegans. The nociceptive neurons ASH and ADL were found to induce gregarious behavior. The function of these neurons were reliant on the action of two TRP-related transduction channels, ocr-2 and osm-9, as well as two genes functioning to localize chemoreceptors to sensory cilia, odr-4 and odr-8. A loss of function in a gene involved in the development of 26 of the 60 sensory neurons in C. elegans, osm-3, was able to return social feeding implying that other sensory neurons function to inhibit social feeding (de Bono et al 2002). Indeed, mutations in the gene che-

2 which modulates sensory input in all 60 ciliated sensory neurons was found to halt the inhibition of a cyclic GMP-dependent protein kinase, leading to inhibition of roaming behavior and thus a phenotype similar to sitter morphs (Fujiwara et al 2002).

Interestingly, another cGMP-dependent protein kinase, dg2 or forage has been found to

have the opposite effect in both D. melanogaster (Osborne et al 1997). Increased action of this gene induces the “rover” phenotype in D. melanogaster larvae.

1.2.2.3 Conclusions on the Physiology and Genetics of Aggregation

Much of this work has taken a candidate gene approach to determining the underlying genes controlling behavioral differences. A candidate gene approach can be exceedingly productive. For example, the behavioral shift of honey bee workers, Apis mellifera, from in-hive duties to external foraging has been explained in part by looking at candidate genes. This aspect of honey bee’s temporal polyethism has been linked to increased presence of NPY-like peptides and an increase in a forage homolog (Ben-

Shahar et al 2002; Ament et al 2011). In both cases, this mirrors the behavioral

differences between the “rover” and “sitter” phenotypes of D. melanogaster.

One downside to a candidate gene approach is that discovery of novel candidate

genes requires the behavioral variation to exist in an already established model organism

such as C. elegans, or D. melanogaster (Fitzpatrick et al 2005). Even more problematic is that relying too heavily on candidate genes can lead to neglecting the search for genetic controls of the behavior in other areas of the genome. However, the advent of next generation sequencing will allow for RNA-seq experiments to investigate similar changes in gene regulation across an even wider range of taxa and across the entire transcriptome.

These studies have the potential to find numerous genes at the same time and can do so in disparate taxa. The results of these experiments are similar to the previously mentioned expression study done in L. migratoria, which identified transcriptional differences across the genome between solitary and gregarious phase grasshoppers.

Transcriptome based experiments like RNA-seq do have their issues. First, they require identifying and isolating the correct tissue that transcriptional differences will occur, and, second, that the behavioral differences be related to changes in gene expression. An alternative approach that can identify genomic regions of interest without relying on transcriptional differences is QTL mapping using crosses between individuals with known behavioral differences. While this has yet to be done to address aggregative behavior, it has proved to be an influential tool for other behaviors. It has already aided in finding potential genetic mechanics for complex behaviors such as burrow formation in

Peromyscus mice (Weber et al 2013). As well, it has been useful in locating influential

linkage groups for personality traits such as feeding activity, risk-taking, and exploration

in nine-spined sticklebacks, Pungitius pungitius (Laine et al 2014).

1.2.3 The Dynamics of Aggregation

Knowledge about the cues and the underlying molecular causes manifest as observable patterns in the formation of aggregations that can be modeled and described as decision rules. Discussions on the dynamics of aggregative behavior can be as simple as determining their existence and where they occur, such as work with the Western spotted cucumber beetle, Diabrotica undecimpunctata undecimpunctata, concluding that aggregations occur at corn-bean borders in agricultural fields (Luna and Xue 2009).

However, the best examples use a combination of observation and modeling to determine the decision rules used in aggregation formation, such as in the cockroach, Blatella germanica, and the gregarious parasitoid wasps, Anaphes listronoti (Boivin and Van

Baaren 2000; Ame et al 2004). Theoretical treatments have found that a tendency to stop or slow movement when encountering other individuals can be a simple decision rule that leads to the formation of aggregations. The predictions of these models fit empirical observations of tests done with cockroaches, B. germanica (Ame et al 2004; Jeanson et al

2005; Lihoreau et al 2010). Despite relying only on simple rules and local cues, cockroaches have been shown to have collective foraging without active recruitment or knowledge of overall group structure. Interestingly, this behavioral rule fits well with descriptions of the “rover” and “sitter” phenotypes of C. elegans and D. melanogaster

larvae.

Another important set of decision rules are those dictating when and how the

aggregation moves. Rather than groups moving en masse to, for example, a new feeding

site, movement tends to be spurred by single individuals moving first, such as in the 16

forest tent caterpillar, Malacosoma disstria, and red-headed pine sawfly, N. lecontei

(Costa and Louque 2001; McClure et al 2011b). In red-headed pine sawflies movement tended to occur when the current branch had been defoliated. Thus, the decision to leave the group seems to be motivated by the underlying energetic state of the individual with hungry individuals being more likely to lead group migration as is seen in the forest tent caterpillar (McClure et al 2011b). Unsurprisingly, personality has been shown to vary based off environmental conditions. In mustard leaf beetles, Phaedon cochleariae, individuals reared on low quality food were bolder and less active than those from high quality food sources (Tremmel and Muller 2013). An important exception to individuals with higher energetic demands being more likely to leave a feeding group is in irradiated fruit flies, Ceratitis capitata (Galun et al 1985). In this case, irradiation halted the reproductive development and would have decreased the energetic requirements of individuals. Irradiated flies were observed less frequently aggregating on food sources; however the radiation could have caused other changes leading to this difference in behavior.

Finally, these decision rules may change based on other internal or developmental states. These can be a simple change in aggregative tendency on a cyclical basis, such as the increase in aggregation at night seen in the Consperse stink bug, Euschistus conspersus (Krupke et al 2006). A starker change is in the attraction of gregarious desert locusts, S. gregaria, by life stage to volatile cues produced by other life stages. In this species, nymphs are only attracted to nymph volatile blends while both young (4 to 8 days post-ecdysis) adults and older adults only respond to pheromones produced by older adults (Obeng-Ofori et al 1993). Additionally, these differences can also be linked to

potentially interesting functional purposes. For example, mated female bed bugs, Cimex

lectularius, are insensitive to aggregative pheromones and do not produce an aggregation

pheromone themselves (Seybold et al 1995, Pfiester et al 2009). This sex difference in

aggregative tendency could relate to the foundation of new colonies by single females.

Finally, these differences can inform us as to the role of the aggregation itself.

Differences in pheromone production between Ips and Dendroctonus bark beetles implies

that the aggregation serves different roles to different beetles. While both groups induce

pheromone production by feeding and the pheromone can be found in their frass, Ips

continues to do so after feeding sites are established while Dendroctonus ceases to

produce pheromones and stores frass after the feeding site is established. Moreover, Ips

was found to produce more attractants above a girdled tree, while the reverse was true for

Dendroctonus. It was proposed that this variation speaks to Ips using aggregations to

locate and colonize scattered and temporary habitats, while Dendroctonus uses aggregations primarily to overcome host defenses (Vité and Pitman 1968). Understanding these decision rules and both when and how they may change can be extremely important to understanding the adaptive role of aggregative behavior.

1.2.4 Summary of Proximate Causes

Aggregation is the non-random physical clustering of individuals in an area that is not due to an underlying patchy habitat. Signaling cues are used for both formation and maintaining feeding aggregations. The most common cues used are chemical and vibrational cues; tactile cues may have aspects of both. Chemical cues tend to be either volatile, when forming a group, or trail depositing, when maintaining a group.

Vibrational cues are mainly used for group cohesion and movement. Cues involved in kin-recognition seem to only be important when the costs of the aggregation may be high. 18

The underlying genetic mechanisms emphasize the importance of external and internal

stimuli in controlling aggregative behavior. Much work has been done on candidate

genes in model organisms. From this, two genes have repeatedly been implicating in

controlling this behavior have been determined, the family of NPY receptors and the

cGMP-dependent protein kinase forage. However, future transcriptomic and QTL studies

could be extremely fruitful and allow for the determination of more genes in a wide

variety of taxa. Aggregations can form based on simple rules involving slowed or stopped

movement when encountering a group. The internal status of individuals in a group,

namely their energy requirements dictate when a group will move. Changes in these

dynamics and decision rules can inform why feeding aggregations are adaptive.

1.3 Ultimate Causes

Having discussed the proximate factors influencing aggregative behavior, the next step will be to consider the ultimate causes behind aggregation. Namely this entails describing and determining the ecological purpose and evolutionary history of aggregative behavior. This involves establishing how aggregative behavior has been shaped from a phylogenetic perspective or, more commonly, determining what fitness benefit or adaptive role it serves. The simplest studies look for the presence of a fitness advantage of group living in gregarious species (rather than identifying the specific benefit) via manipulating group size and finding better survival, growth rate, or faster development. One example of these studies are observations of natural variation in aggregation size in the pine sawfly N. swainei wherein larger groups had greater survival

(Lyons 1962). A second using the larvae of butterfly, Euselasia chrysippe, found a positive relationship between group size and survival again using natural variation, but also found a positive relation between group size, and larval growth rate and adult weight 19

when group size was artificially manipulated in the lab (Allen 2010). Finally, a

comprehensive review of published survival curves of gregarious and solitary species of

Lepidoptera and Symphyta found that overall gregarious species had higher larval

survival than solitary species (Hunter 2000). Clearly, under certain conditions

aggregative living can confer a fitness benefit, however, to truly get at this we should

consider the potential benefits and costs of group living.

1.3.1 Fitness Benefits of Aggregation

We will first investigate some of the proposed benefits to group living. These advantages tend to be detected by manipulating group size and one other variable and looking for improved survival between life stages, faster growth or development, or greater overall growth. Of these benefits there are four main classes of potential benefits.

The first is the production of an optimal microenvironment either through thermal regulation or improved water retention. The second is an improved foraging ability which will manifest as overcoming host defenses, improved speed at locating better feeding sites and improved prey catching in group hunting organisms. Third, groups may provide better defense against predation either through enhanced detection (many eyes), predator dilution or satiation, or coordinated defenses. Finally, it is assumed that mating aggregations provide some form of mating advantage to either one sex or both. It is important to note that these benefits are not mutually exclusive. For example, a thermoregulatory or feeding benefit that decreases development time would reduce the amount of time an organism would spend in earlier life stages which may be more vulnerable to predation.

1.3.1.1 Optimal Microenvironment

Given that heat and water are common products of basic metabolic processes,

when organisms group together they can form their own microenvironments that vary in

temperature and relative humidity from ambient conditions. One proposed advantage of

group living is in the creation of these microenvironments. Given a sufficient size,

aggregations can even have a large enough effect on group temperature that ambient

conditions become insignificant to determining group temperature. Aggregations of

carrion-feeding blow flies once at a threshold size can maintain an optimal temperature

for growth of 30°C to 35°C during ambient temperatures as low as 14.6°C to as high as

37.2°C (Slone and Gruner 2007). Aggregations with an apparent thermoregulatory role

may occasionally be related to natural environmental variations such as in Hyla regilla

tadpoles, which are found aggregating in the warmest parts of a pool. However, tadpoles

can also aggregate to produce greater group temperatures as in Hyla crucier or aggregate

mainly during colder periods such as night as in the California yellow-legged frog, Rana

boylei (Brattstrom 1962). These differences in temperature can have a direct impact on

fitness as larvae of the sawfly P. affinis found in groups had both faster developmental times and greater than ambient temperatures (Fletcher 2009). Finally, the cooperatively

built tents of Eastern tent caterpillars, Malacosoma americanum, alongside

environmental factors such as time of day and direction of sunlight, and behavioral

factors such as aggregating create an extremely heterogeneous microenvironment that

allow larvae to occupy a space at their optimal temperature (Joos et al 1988).

Outside of thermoregulation, retention of water is also an important factor,

especially with small organisms such as arthropods given their greater surface area to

volume ratio. In the woodlice, Porcellio scaber, for example, individual water loss had a

profound reduction by grouping behavior with isolated individuals losing twice as much

when compared to groups with forty or more members. Moreover, to the extent that was

possible aggregations of woodlice approached that of a spherical shape which has the

lowest surface area to volume ration (Broly et al 2014). However, the importance of this can vary by life stage as the benefit to coping with lower humidity was seen in larval bush ticks, Haemaphysalis longicornis, but not in nymphs or adults (Tsunoda 2008).

The importance of microenvironments, and how that importance varies across an organism’s development, is perhaps best illustrated by the larvae of the emperor moth caterpillar, Imbrassia belina. Larvae of I. belina have three gregarious and two solitary instars with consistent predation across their lifespan and no evidence of inducible defenses by their host plants. However, both temperature and water retention were found to be improved by group living in the second and third instars. Larvae in groups had both less water loss than they would individually and maintained a body temperature equivalent to the solitary fourth and fifth instars. Because these larvae exhibit this behavioral switch to a solitary lifestyle, the likelihood that microenvironment is an important advantage of group living in I. belina is very high (Klok and Chown 1999).

1.3.1.2 Improved Foraging Ability

Another benefit to aggregations is improved ability to handle or find food. In simplest terms, this would mean that aggregated individuals should be able to consume more food at a greater rate. For example, first instar groups of the nymphalid, Chlosyne poecile, which feed on shrubs in the genus Razisea, were found to have a greater per- capita feeding rate with larger group sizes (Inouye and Johnson 2005). A proposed

advantage groups may have in foraging is that they may more easily overcome host

defenses. The increased growth of larvae of the bark beetle D. micans in groups is 22

attributed to overcoming plant defenses since mortality is greatly increased when additional resin, a major component of the host plant’s defense, is added to feeding chambers (Storer et al 1997). A more rigorous way to test this hypothesis is to find group living related to some fitness benefit, for example faster growth rate, and determine if that benefit holds true if the organisms are exposed to food that has been pre-handled. For example, larvae of the neotropical nymphalid, Chlosyne janais, had increased weight gain in larger groups then smaller. However, when the leaves were cut before larvae were allowed to feed, this difference disappeared (Denno and Benrey 1997). A similar trend was observed in the related species, C. lacinia, where larvae had faster development when reared in groups, but this difference disappeared when larvae were provided an artificial diet or a diet consisting of pre-chewed leaves (Clark and Faeth 1997). If overcoming host defenses is the selective pressure for aggregation, then the host plant the larvae are feeding on can affect whether a difference is observed. Developmental time was faster for saddleback caterpillars, Acharia stimulea, when reared in groups on oak or beech. However, only groups on oak had an increased growth rate as well (Fiorentino et al 2014). A similar variation was observed with larvae in different sized groups on different host plants in the Lepidopteran, Doratifera casta (Reader and Hochuli 2003).

Another advantage group foraging may provide is improved efficiency in finding new or better feeding sites. In a simple form this may be related to group feeding allowing for greater consumption overall. Models show that if consumption of a growing plant is concentrated to a single area, there is a greater amount consumed per individual than if consumption is distributed throughout (Tsubaki and Shiotsu 1982). If feeding is concentrated in one area of a plant, the other areas will be able to keep growing

effectively providing more total food when compared to a plant that has consumption and

reduced growth across the entire plant. However, group living may also allow for better

and faster choices in food selection. Much of this work has been done on the forest tent

caterpillar, Malacasoma disstria. When silk trails were pre-established, larvae were

quicker to find food and spent less time spinning silk themselves. Combined with the

faster development of second instar larvae when in groups, this implies that larger groups

that are more able to divide the labor of laying trails and finding new food sources

(Despland and Le Huu 2007). This has been more directly tested with M. disstria with

whether they will exhibit compensatory feeding when reared on low carbohydrate or low

protein diets. The caterpillars were not shown to independently regulate their carbohydrate and protein consumption (Despland and Noseworthy 2006). However,

when deprived of both nutrients, they were more likely to leave an unsuitable host to

search and find an ideal host. Moreover, when given a choice between an unsuitable host

and a suitable host, larger groups were quicker to locate and remain on the suitable host

(McClure et al 2013). Together these do provide evidence that group living can produce more efficient foraging in at least one species.

A related benefit is in the improved foraging success that some carnivorous organisms have when hunting as groups. While mostly associated of with vertebrate

hunters, such as the doubling of success rate when more than one lion hunts and that

hyenas often failed when hunting alone but succeeded in groups (Schaller 1972; Kruuk

1975; Kruuk 1979), similar trends can be seen in arthropods as well. For example,

African weaver ants, Oecophylla longinoda, can be found taking down prey much larger

as a group than as single individuals, and do so through a coordinated attack. Moreover,

the success rate of these attacks is much greater when they form a highly aggregated

hunting column than when engaging in normal foraging behavior (Wojtusiak et al 1995).

Similarly, the capture rate during inactive periods of the social spider, Anelosimus

eximius, was higher in larger colonies then smaller ones (Pasquet and Krafft 1992).

Overall, there seems to be an advantage in both capture success and size of prey for

group hunting organisms; these advantages mirror those of herbivorous group foragers in

overcoming host defenses and improved ability to locate optimal foraging locations.

1.3.1.3 Improved Predator Defense

Despite being more apparent to predators, it may be possible that group living

allows for an improvement in overall predator defense when compared to individuals.

The simplest form of this is that by living in a group an individual has a lower chance of

being eaten if found then it would if it were alone. This advantage is conferred only if the

predator is unable to eat the entire group through a large handling time or it becomes

satiated before consuming each individual. This exact trade-off of increased encounter by

predators, but decreased individual risk was observed in the group pupating Trichopteran,

Rhyacophila vao, against the planarian predator, Polyselic coronate (Wrona and Dixon

1991). Predator dilution should be expected to occur when the predator exhibits this type

II functional response, wherein predation rate plateaus at high prey densities. This is exactly the case in the bark beetle Ips pini (Aukema and Raffa 2004). While these previous examples relied on predator satiation, predator dilution can also be a part of a collective defense, such as the scattering response of the aggregated flotillas of the marine water stider, Halobates robustus. This aggregation also has the added benefit of preventing individuals from being swept out into open water (a possible microenvironmental advantage) (Foster and Reherne 1980). 25

Another group advantage against predation is the ability to participate in

cooperative defense. Here a group of organisms may be better able to fend off predators

than an individual would. Models based on Caribbean spiny lobsters, Panulirus argus, show that even small advantages in group benefit are sufficient to make aggregations advantageous (Dolan and Butler 2006). Related to this is the idea that groups are more

able to spot predators than individuals. A combination of “many-eyes” and cooperative

defense is thought to explain why whirligig beetles, Dineutes assimilis, group together at

low temperatures when well-fed. The reasoning being that when cold they have a slower

reaction time to spotting predators and benefit from the quicker spotting a group affords.

These beetles also engage in rapid whirling as a form of collective defense serving to

confuse predators and produce an excreted defense chemical, gyrinidal, which may be

more effective when in a group (Romey and Rossman 1995).

In fact, chemically defended species are often found aggregating. These organisms (and some with physical defenses such as spines) also tend to have bright, warning coloration. This aposematic coloration was found to be more effective in deterring predation by American bullfrogs, Rana catesbeiana, when prey were grouped then when alone (Hatle and Salazar 2001). The order of evolution of these three traits; aposematism, defense (chemical or physical), and group living; has been extensively researched and there are many competing hypotheses. By far the most supported order is that chemical defense arose first, followed by either group living or aposematism.

However, it is interesting to note that R. A. Fisher proposed that gregariousness should arise first due to some other group benefit followed by defense and then aposematism

(Fisher 1930; Ruxton and Sherratt 2006). The order of evolution of these traits can be

tested in a phylogenetic context which can allow us to learn about why these aggregations may have been selected for in the first place.

Most phylogenies show larval gregariousness evolving in lineages with likely solitary, but aposematic and unpalatable ancestors such as in Heliconius butterflies

(Beltrán et al 2007). When using broader taxonomic sampling and considering three families of butterfly, Nymphalidae, Pieridae, and Papilionidae, gregariousness was found to have independently evolved 23 times. In five of those cases group living evolved in a cryptic species and in fifteen it is inferred to have occurred in an ancestor with aposematic larvae. In three of the cases, the evolution of warning coloration and gregariousness were unable to be differentiated (Sillén-Tullberg 1998). A later study used a similar data set consisting of over 800 forest-dwelling Lepidoptera species and used phylogenetic independent contrasts to determine if aggregation did evolve more frequently in lineages with repellant defenses or warning coloration. Gregariousness was found to have evolved in warning colored lineages in ten out of twelve contrasts compared with only two of the twelve having more cryptic ancestors showing evolution of gregariousness. While only nine independent contrasts were available to test repellently defended lineages versus those without, the results, however, were stark with all nine having more frequent evolution of group living in defended lineages.

Unfortunately, because warning coloration and repellent defenses are so closely linked, the order of their evolution has, as yet, been undetermined (Tullberg and Hunter 1996).

1.3.1.4 Mating Benefits

A final advantage to aggregating is that it may increase the number or likelihood of mating events. While mating is itself often a somewhat aggregative process involving two individuals, it is not uncommon for aggregations to be found of multiple individuals 27

ostensibly for the purpose of mating. Were this connection true, one would predict that

the chances of mating success should increase with aggregation size. This relationship

was found in the adults of the stink bug, Megacopta punctissimum, which aggregates on

the bush-clover, Lespedeza crytobotria, but neither feed nor oviposit on that plant

(Hibino and Itô 1983). While the aggregations of M. punctissimum have an

approximately equal sex ratio, mating aggregations can also be biased towards one sex or

the other. For there to be a mating advantage in these groups, we would expect that larger

aggregations have more frequent visits from the other sex and more matings overall. In

the mating swarms of female dance-flies, Empis borealis, larger groups had more males

visit and the mating rate was greater for both males and females (Svensson and Petersson

1992). Thus, the increased competition for mates in these aggregations appears to be

offset by the overall increase in number of potential mates.

1.3.2 Fitness Costs of Aggregation

Group living, while often beneficial, is not without its own set of costs. In fact,

organisms operating in close proximity necessarily incur some cost to their fitness. These

costs include increased competition for limited resources, increased apparency to

predators, a potentially maladaptive micro-environment, and increased exposure and risk to disease and pathogens.

The most obvious of these is that there will be competition within the group for any limited resource; be this food in feeding aggregations or mates in mating aggregations. Evidence that this competition is indeed costly can be seen in the tendency for gregarious larvae to disperse at later instars (Klok and Chown 1999, Inouye and

Johnson 2005). For example, the per-capita feeding rate C. poecile was inversely related to group size in all but the first instars, though only significantly so in the second instar. 28

In wild populations the distribution of larvae became random among available leaves by the fourth instar (Inouye and Johnson 2005). In these cases, the costs of the aggregation begin to outweigh the benefits, and thus the group begins to dissolve. This has been directly tested to demonstrate that competition is a common force selecting for smaller aggregations. For example, meadow spittlebugs, Philaenus spumarius, have a positive relationship between mortality and group size when forced to aggregate on a single plant and movement to a new plant is restricted. However, aggregations were commonly recorded in the field; if a plant had more than one nymph they tended to be grouped together. The researchers hypothesized that the competitive cost seen in the experimental aggregations is avoided by the ability of natural populations to move to a new plant once a resource is depleted (Wise et al 2006). On a similar note, the larger and less numerous eggs of the summer generation and more southerly populations of the pine processionary moth, Thaumetopoea pityocampa, when compared to the winter generation and more northern populations is hypothesized to be explained by the increased competition under warmer conditions due to faster development (Pimentel et al 2012).

Despite improved predator defense being a possible benefit to group living, aggregated groups will often be more apparent to potential predators. Even purported random-search predators have been found to have increased detection of larger groups

(Wrona and Dixon 1991). Moreover, large groups of 50 individuals of the pine sawfly, N. sertifer, were found to have increased predation rates when compared to groups of only

10 individuals Lindstedt et al 2011).

Similarly, the alterations to microenvironment aggregations provide, may also become costly. While usually seen as a benefit, one can imagine a case where the

increased temperature an aggregation produces may go beyond the optimal temperature for that organism. Moreover, the supposed benefits of the altered microenvironment may be due to another selective benefit of aggregation being greater than initial costs of the environmental differences; followed by a subsequent adaptation to the different conditions. An example of a costly microenvironment can be seen in the depletion of oxygen in the center of groups of schooling fish (McFarland and Moss 1967; Brierley and

Cox 2010).

A final cost for group living is that aggregated organisms increase their potential exposure to and risk from disease and pathogens. This can include the increased risk of exposure to an infected individual, the aggregation providing better conditions for diseases to grow, and that the necessary increase in immune function to combat these risks inherently diverts resources away from other metabolic processes. For example, P. affinis was shown to have increased heat when aggregated and warmed hemolymph was shown to allow for greater growth of Escherichia coli when compared to cold hemolyph

(Fletcher 2009). Increased susceptibility to disease would therefore be expected to manifest in immunological differences between solitary and gregarious species.

Gregarious species would be expected to have increased resistance to diseases, such as viral infections, as they age given the longer they remain in a group the more likely they will become exposed to a disease. If this increased immune function is costly, solitary species would be expected to have a flat resistance given their risk is consistent throughout their lifespans given the inherent costs. This expectation was confirmed in a comparative study of temperate Leptidopteran species, providing evidence that gregarious species do have greater immune function and that this enhanced immunity is

indeed costly (Hochberg 1991). Finally, these shifts can be detected in species that have differences in gregariousness within lifespans or generations such as S. gregaria. A transcriptional study found evidence for increased presence of immunologically relevant genes, specifically those involved with a generalized immune response (e.g. melanization) rather than those for specific pathogens (Wang et al 2013). Because these immunological resistances are not seen in solitary individuals, it is again assumed there must be some production cost in addition to the general increased risk of disease gregarious individuals face.

1.3.3 The Interaction of Costs and Benefits of Aggregation

There is a natural play between the costs and benefits of group living. These were previously discussed as occurring within a generation wherein aggregating individuals begin to disperse when the benefits no longer outweigh the costs. These trade-offs have been well documented in M. disstria. Three of the potential benefits of aggregation have been proposed for this species, thermoregulation, improved foraging, and predator defense. Moreover, evidence for the benefits of group living has been found for each of these factors (Despland and Le Huu 2007; Mcclure et al 2011a; McClure and Despland

2011). However, in all three cases the benefit of group living was found to decrease or disappear entirely as the larvae got larger. Larger instars were able to thermoregulate themselves alone as well as they did in groups, and larger individuals were better able to defend against predation even when alone (Mcclure et al 2011a; McClure and Despland

2011). The most impressive shift was that larger groups of fourth instar larvae had slower growth and less per-capita consumption rate than individuals, while the reverse was true for second instars (Despland and Le Huu 2007). From this focal species, the advantages of group living can shift over time even for an individual organism. 31

However, this balancing of costs and benefits can also occur across multiple

generations. Aggregation may not be immediately beneficial to the individuals

aggregating, but may otherwise be advantageous to the preceding generation (i.e. the

mothers). In the walnut infesting fly, Rhagoletis juglandis, there was no benefit to

survival by group size to the larvae. However, eggs are often found clustered together and

there may even be multiple clusters from different females in the same walnut. One

proposed explanation for this is that clustering eggs reduces the time of ovipositioning

and wear on the ovipositor for the female and may allow access to more impenetrable

fruit (Nufio and Papaj 2011). In this case, the group living larvae are not conferred a

benefit directly by group-living, but the mother has improved her fitness through

optimized ovipositioning. A similar case is seen in the membracid, Publilia modesta,

which had a negative relationship between percent of nymphs completing development to

adulthood and group size. However, large aggregations of hundreds of nymphs can be found. Again, it is concluded that the mother improves her fitness by being able to care for nymphs (defending nymphs and eggs) and she can only care for all her young if they occur on the same plant (Reithel and Campbell 2008).

1.3.4 Summary of Ultimate Causes

Aggregation has inherent costs associated with it including increased apparency to predators and increased risk of exposure to disease or pathogens. Therefore, there should be some benefit conferred to group living organisms when compared to solitary individuals. Typically these are found to manifest as greater growth rate, faster development time, or increased survival. These benefits are thought to arise from some combination of microenvironmental alterations, improved foraging ability, improved predator defense, and benefits to mating success. Most of these are also further dividable 32

into sub-categories. Microenvironmental advantages are often through the ability to create optimal temperatures or improved water retention and improved foraging ability emerge through better ability to deal with host defenses, faster and improved foraging choices, or greater success in capturing prey. The anti-predator benefits range from the relatively simple phenomenon of predator dilution to coordinated defense. Our understanding of the fitness benefits and costs can be improved by looking at how aggregation evolves alongside other traits from a phylogenetic perspective. For example, much work has been done on the complex interaction of traits such as warning coloration, repellant defenses, and group living. The end result of these studies, is that chemically defended and aposematic species were more likely to evolve aggregative behavior then the reverse direction. As with many traits, the evolution of aggregation is dictated by the trade-offs between its benefits and costs. In some cases, we can observe the benefits becoming offset by the costs in a single generation, while other cases are best explained by looking for a costly aggregation being beneficial due to reduction in costs during the previous generation.

1.4 Conclusions

Having summarized the proximate and ultimate causes of aggregative behavior, the lack of a single system that traverses all corners is readily apparent. While some systems, such as S. gregaria do have some knowledge in both proximate and ultimate causes and can set an example for how best to proceed; the best studied systems of each avenue are comparatively empty from the other direction. While the proximate mechanisms for aggregation in C. elegans and D. melanogaster are well documented, very little is known about their adaptive role aside from some work showing C. elegans aggregations maintaining hypoxic conditions (Gray and Chang 2004). Conversely, M. 33

disstria is well studied for the adaptive role aggregation plays, but aside from some

knowledge on the movement of aggregations being dictated by the hungriest individuals

relatively little is known about the underlying proximate mechanisms, especially the

physiological and genetic controls. Either a new system should be approached comprehensively from all directions or more work should be done in the well-studied organisms from their less examined direction.

A comprehensive understanding of aggregative behavior is more than simply four answers to Tinbergen’s original questions. Rather, as Tinbergen knew, the ability to combine across levels allows for even deeper understanding of biological phenomena.

For example, we can investigate whether the same proximate mechanisms are responsible for gregarious behavior regardless of its adaptive role. Alternatively, we could find that different adaptive functions of aggregative behavior tend to alter different parts of the underlying genetics. Similarly, if we understood the nature of genetic changes that occur between these two strategies we may learn something about the directionality this trait evolves. If switching in one direction involves loss of function mutations, we may then expect a greater prevalence in that direction with few reversions to the ancestral state.

Trends like this have already been found in transitions between pollinator syndromes in flowering plants (Wessinger and Rausher 2014). Finally, by understanding the physiological and genetic changes that occur in species which exhibit both solitary and gregarious phases, we can investigate if similar differences are observed in closely or, even, distantly related species which vary in their social behavior.

These examples are only meant to illustrate the power that can arise from a truly comprehensive understanding of behavior and are obviously not meant to be exhaustive.

Yet, there remain few behaviors with a truly comprehensive understanding, whether its aggregative behavior or others. With current advances in molecular methods, there is no reason behavioral ecologists should not be seeking to understand the underlying genetics more. And molecular biologists should be empowered to use the amazing evolutionary tools available. In this modern era, we should be able to rapidly attain comprehensive understanding of aggregative behavior as well as many more. I propose that aggregation may be one of the best behaviors to grasp in this manner.

Because aggregation is such a taxonomically widespread phenomenon, any findings could have implications across the entire animal kingdom. Moreover, given the complex interplay of benefits and costs already found, the likelihood for interesting idiosyncrasies is rather high. The ideal system for this approach would be one which consists of a group of closely related species that vary in their aggregative behavior. This would allow studies to approach the system from multiple evolutionary standpoints and allow for any molecular resources developed to be usable across a set of solitary and gregarious species.

CHAPTER 2. GREGARIOUSNESS DOES NOT VARY WITH GEOGRAPHY, DEVELOPMENTAL STAGE, OR GROUP RELATEDNESS IN FEEDING REDHEADED PINE SAWFLY LARVAE

This was previously published as: Terbot II, J. W., Gaynor, R. L. & Linnen, C. R. 2017 Gregariousness does not vary with geography, developmental stage, or group relatedness in feeding redheaded pine sawfly larvae. Ecol. Evol. 7, 3689–3702. (doi:10.1002/ece3.2952)

2.1 Abstract

Aggregations are widespread across the animal kingdom, yet the underlying

proximate and ultimate causes are still largely unknown. An ideal system to investigate

this simple, social behavior is the pine sawfly genus Neodiprion, which is experimentally

tractable and exhibits interspecific variation in larval gregariousness. To assess

intraspecific variation in this trait, we characterized aggregative tendency within a single

widespread species, the red-headed pine sawfly (N. lecontei). To do so, we developed a

quantitative assay in which we measured inter-individual distances over a 90-minute video. This assay revealed minimal behavioral differences: (1) between early-feeding and late-feeding larval instars, (2) among larvae derived from different latitudes, and (3) between groups composed of kin and those composed of non-kin. Together, these results

suggest that, during the larval feeding period, the benefits individuals derive from

aggregating outweigh the costs and that this cost-to-benefit ratio does not vary dramatically across space (geography) or ontogeny (developmental stage). In contrast to the feeding larvae, our assay revealed a striking reduction in gregariousness following the final larval molt in N. lecontei. We also found some intriguing interspecific variation: while N. lecontei and N. maurus feeding larvae exhibit significant aggregative tendencies, feeding N. compar larvae do not aggregate at all. These results set the stage for future

work investigating the proximate and ultimate mechanisms underlying developmental

and interspecific variation in larval gregariousness across Neodiprion.

2.2 Introduction

Aggregations, or spatial groupings of organisms, are widespread in nature and

occur across diverse taxa (Parrish and Edelstein-Keshet 1999; Prokopy and Roitberg

2001; Krause and Ruxton 2002). While some aggregations are passive, arising as a consequence of features of the landscape that lead to clumped distributions (e.g.

Carpenter 1954; Schartel and Schauber 2016), many aggregations stem from individuals actively seeking out and maintaining contact with conspecifics (e.g., Schmuck 1987;

Costa and Loque 2001; Jeanson et al 2005). To understand these aggregative behaviors, we must investigate both their proximate (developmental, physiological, and molecular mechanisms) and ultimate (adaptive function and evolutionary history) causes (Tinbergen

1963). Integration of these distinct perspectives is most easily accomplished with (1) experimentally tractable organisms (i.e., can be reared, crossed, and manipulated in the lab) with interesting behavioral variation, and (2) simple and reliable assays for quantifying those behaviors (e.g., Osborne et al 1997; Sokolowski et al 1997; de Bono and Bargmann 1998; Fujiwara et al 2002; Wu et al 2003; Ame et al 2004; Jeanson et al

2003; Jeanson et al 2005; Broly et al 2012). In this study, we introduce a potentially powerful system for investigating both the proximate and ultimate causes of behavioral variation and describe an assay for quantifying one variable behavior involved in aggregation, larval aggregative tendency.

Neodiprion (Hymenoptera: Diprionidae) is a Holarctic genus of ~50 sawfly species, all of which specialize on host plants in the family Pinaceae (Wallace and

Cunningham 1995; Linnen and Smith 2012). Because many species in the genus are 37

forestry pests, Neodiprion life histories have been studied in great detail. These studies

have revealed a remarkable amount of inter- and intraspecific variation in a wide range of

traits, including: host preference, oviposition pattern, larval color, overwintering stage,

and larval gregariousness (Atwood 1962; Coppel and Benjamin 1965; Baker 1972;

Knerer 1984; Knerer 1993; Larsson et al 1993). In addition to harboring variation in many interesting traits, Neodiprion are experimentally tractable. They can be manipulated in the lab and in the field, and many different interspecific crosses are possible (Ross

1961; Kraemer and Coppel 1983; Linnen and Farrell 2007; personal observation).

Moreover, a molecular phylogeny is available for the genus (Linnen and Farrell 2008a,

Linnen and Farrell 2008b), and there are a growing number of genomic resources, including an assembled and annotated genome for the red-headed pine sawfly (N.

lecontei; Vertacnik et al 2016), a linkage map, and genome assemblies for all 20 species

in the eastern North American “Lecontei” clade (unpublished data). Together, the well

described natural history, extensive variation, and growing set of genetic and genomic

tools will facilitate investigations into the proximate and ultimate causes of many

different types of traits.

Importantly, Neodiprion larvae exhibit intriguing developmental and interspecific

variation in their tendency to aggregate. While larvae of many Neodiprion species have

been categorized as “gregarious” and form conspicuous feeding aggregations in the field,

larvae of several species that do not form large aggregations are categorized as “solitary”

or “intermediate” (Larsson et al 1993). Moreover, the tendency to aggregate appears to change over the course of development. For example, all Neodiprion species have a morphologically and behaviorally distinct final, non-feeding instar (Figure 2.1; Hetrick

1959; Ghent 1960; Smith 1993). During this stage, any aggregative tendency disappears

as the larva wanders from the group to find an appropriate site to spin a cocoon in which

to pupate. Additionally, in at least some Neodiprion species (e.g., N. tsugae, N. abietis,

and N. abbotii), larval aggregative tendencies appear to decline in late-feeding instars

(Hopping and Leach 1936; Furniss and Dowden 1941; Hetrick 1956; Rose and Lindquist

1994; Anstey et al 2002).

To understand why some Neodiprion species and life stages tend to aggregate and

others do not, we must consider the costs and benefits of aggregating. Costs of

gregariousness in pine sawfly larvae (and other externally feeding folivores) include

increased disease risk (Bird 1955; Mohamed et al 1985; Young and Yearian 1987;

Hochberg 1991; Fletcher 2009), increased predation risk (Bertram 1978; Vulinec 1990;

Lindstedt et al 2011), and competition for resources (Prokopy et al 1984; Pimentel 2012).

Proposed benefits of gregariousness in pine sawfly larvae include thermoregulation

(Seymour 1974; Joos et al1988; Codella and Raffa 1993; Klok and Chown 1999; Fletcher

2009; McClure et al 2011a), enhancement of group defense (Tostowaryk 1972; Bertram

1978; Puliam and Caraco 1984; Codella and Raffa 1993; McClure and Despland 2011),

and increased foraging efficiency/improved ability to overcome plant defenses (Young

and Moffet 1979; Tsubaki and Shiotsu 1982; Stamp and Bowers 1990; Codella and Raffa

1993; Despland and Le Huu 2007; McClure et al 2013). If there is heritable variation in

gregariousness and the costs and benefits of aggregating vary among populations and

species, natural selection is expected to produce intra- and interspecific variation in

aggregation behavior. Additionally, whenever aggregation costs outweigh its benefits,

natural selection should favor a complete loss of gregariousness. Testing these

predictions requires objective methods for quantifying aggregative behaviors and for

distinguishing between gregarious and non-gregarious behavior.

To date, most descriptions of larval gregariousness in Neodiprion have been

qualitative, assigning species to different behavioral categories (i.e., “gregarious,”

“intermediate,” and “solitary”) on the basis of the size of typical larval aggregations

encountered in the field (Larsson et al 1993). One problem with this approach is that

colony size depends not only on the behavior of aggregating larvae, but also on the

behavior of ovipositing females. For example, while females of some species tend to lay all of their eggs on a single branch terminus, others distribute their eggs across multiple hosts (Atwood 1962; Coppel and Benjamin 1965; Baker 1972; Knerer 1984; Knerer

1993; Larsson et al 1993). Because female oviposition behavior may be shaped by selection pressures that are distinct from those shaping larval behavior (Scheirs et al

2000; Scheirs et al 2005; Nufio and Papaj 2012), it is important that we disentangle the contributions of adult and larval behaviors to larval aggregation size. Additionally, because qualitative categories may miss ecologically relevant behavioral variation, it is essential that we quantify these behaviors. To these ends, we describe here a simple quantitative assay of larval aggregative tendency under artificial, but highly repeatable, conditions.

To evaluate our assay, we focused on the redheaded pine sawfly, N. lecontei.

Although our ultimate goal is to assay larval behavior across the genus Neodiprion, we chose to focus on this species first because its life history and highly gregarious behavior are especially well described in the literature (Benjamin 1955; Wilson et al 1992; Codella and Raffa 1993, 1995a, 1995b; Costa and Louque 2001; Flowers and Costa 2003). N.

lecontei is widely distributed in eastern North America, where it occurs on multiple pine

species (Wilson et al 1992; Linnen and Farrell 2010). After mating, adult females use

their saw-like ovipositors to embed their eggs into the host plant needles. Usually, an

individual female will lay her entire complement of ~100-150 eggs in adjacent needles in

a single branch terminus (Benjamin 1955; Wilson et al 1992). Upon hatching, larvae

form aggregations and feed in groups until they molt into the final, non-feeding instar

(Figure 2.1). When a branch is defoliated, larvae migrate in small groups to a new

feeding site, where they re-coalesce. Colony migration appears to be mediated both by

chemical cues deposited by the migrating larvae (which serve to orient larvae to the new

feeding site) and by tactile cues from the larvae themselves (which reinforce feeding site

selection) (Costa and Louque 2001; Flowers and Costa 2003). Additionally, isolated N.

lecontei larvae become highly agitated and exhibit increased wandering behavior,

presumably in search of a feeding aggregation to join (Kalin and Knerer 1977). Thus,

while initial colony size may be attributable to the behavior of ovipositing females

(Codella and Raffa 1995a), detection of and response to larval cues maintain colony

cohesion over the course of development (Costa and Louque 2001; Flowers and Costa

2003).

Together, these previously published accounts of N. lecontei behavior provide us with testable predictions that we can evaluate with a quantitative assay. First, we asked how larval aggregative tendency changes over the course of the larval feeding period. In

diprionid sawflies, early-instar larvae may experience difficulty establishing feeding

incisions on tough pine foliage; in aggregations, so long as some individuals are able to

make a feeding incision, the group can benefit (Ghent 1960; but see Kalin and Knerer

1977). However, older larvae have no difficulty feeding; thus, if there are not additional benefits to group-living, its costs may favor colony splitting (Coppel and Benjamin 1965;

Codella and Raffa 1993). Based on existing natural history literature and our own experience, we predicted that all feeding instars would aggregate. However, if there is a large reduction in the net benefit of aggregating over the course of larval development, we expected to see a corresponding decrease in larval aggregative tendency. Second, we asked how larval aggregative tendency changed between feeding and non-feeding instars.

Because non-feeding instars disperse to spin cocoons, we expected a complete loss of aggregative tendency in the final, non-feeding instars.

Third, we asked how relatedness among group members impacts larval aggregative tendency. If aggregating is costly to individual sawfly larvae (e.g, Bird 1955;

Mohamed et al 1985; Young and Yearian 1987; Lindstedt et al 2011), kin selection theory predicts that kin groups will have elevated aggregative tendency compared to non- kin groups. Alternatively, we would expect aggregative tendency to be unaffected by the relatedness of group members if: the direct benefits of aggregating outweigh its costs; individual larvae are unable to distinguish between kin and non-kin; or the costs of kin- based discrimination are too high.

Finally, after exploring how N. lecontei behavior changes with development and group composition, we apply our assay to multiple N. lecontei populations and two additional Neodiprion species (one “gregarious” species and one “solitary” species) to gain a first glimpse into levels of inter-population and interspecific variation in larval behavior in the genus. Together, our results lay the groundwork for future studies, while

also providing insights into both the proximate and ultimate mechanisms underlying

larval aggregative tendency.

2.3 Materials and Methods

2.3.1 Collection and Rearing Information

Sawfly larvae used in our experiments were either wild-caught or derived from colonies that we reared for no more than two generations in the lab using our standard lab protocols (described in more detail in Harper et al 2016; Bagley et al 2017). Briefly, we

transported wild-caught larval colonies to the lab in brown paper bags. Upon arrival, we

transferred each larval colony to a plastic box with a mesh top (32.4 cm x 17.8 cm x 15.2

cm) and fed them clipped pine foliage from their natal host species as needed until they

had spun cocoons. We stored cocoons individually in size “0” gelatin capsules, and

checked daily for emergence. We stored emerged adults at 4°C until needed. To produce

the next generation of larvae, we released adult females (either mated or unmated, see

below) into large mesh cages (35.6 x 35.6cm x 61cm) containing Pinus banksiana

seedlings. Once eggs had hatched and larvae had consumed the seedling foliage, we

transferred them to plastic boxes and reared them on clipped P. banksiana foliage as

described above.

Like all hymenopterans, Neodiprion have haplodiploid sex determination; mated

females produce diploid daughters and haploid sons and unmated females produce

haploid males only (Heimpel and de Boer 2008; Harper et al 2016). For our experiments,

we used larvae derived from both unmated and mated females. Whenever possible, we

used the haploid male offspring of unmated females to minimize possible noise stemming

from sex-based differences in behavior. However, for some experiments, families from

unmated females were not available. Because we cannot easily differentiate between

female and male larvae, families derived from mated females likely contained a mixture of both sexes. We provide more detailed information on the source and rearing history of larvae for each experiment below and in Table 2.1.

2.3.2 Video assays of larval aggregative tendency

To measure larval aggregative tendency, we developed a video assay. Prior to the start of each video, we spaced larvae equidistantly along the perimeter of a 14.5 cm petri dish (Figure 2.2). The number of larvae per video varied from 2-8, depending on the experiment, and no larvae were used in more than one video. For our first set of assays, we used mixed-sex larvae derived from mated mothers from Grayling, MI (from RB261,

Table 2.1). We recorded each group of larvae for 90 minutes on either a Logitech or

Microsoft webcam connected to a Lenovo Ideapad laptop. We recorded all videos in an environmental room at 22°C and 70% relative humidity. For each video, we then used the program Video Image Master Pro (A4Video 2016) to extract one frame every fifteen seconds, for a total of 360 frames per video. Based on preliminary analyses of different intervals ranging from 15 s to 1800 s, we further reduced the sampling for each video to one frame every 180 s (30 frames per video). We chose this sampling frequency because it reduced data processing time, while yielding results indistinguishable from shorter intervals.

For each video frame, we manually selected the position of each larval head capsule and calculated all pairwise distances using a custom Java application. Although video scoring was not blind with respect to our treatments, the objective nature of our data collection (clicking on the physical position of head capsules) provides minimal opportunity for observer bias to influence our results. After videos were scored, we used a combination of Microsoft Excel 2013 (Microsoft 2012) and a custom Perl script to 44

calculate the mean pairwise distances for the entire video as well as subsets of the video.

Analysis of the full video yielded 30 pairwise distances per video.

Differences in larval mobility could influence pairwise larval distances, and, thus, affect our measurement of larval aggregative tendency. We therefore used two approaches to minimize the impact of larval mobility. First, we visually examined each video to ensure all larvae were in good condition and moving freely during the recording.

Second, because frames at the start of the video reflected experimental spacing rather than larval behavior and because handled larvae sometimes become agitated (Costa and

Louque 2001), we examined a large number of videos to see how pairwise distances changed over time and to determine how long it takes for pairwise distances to stabilize.

Based on these preliminary analyses, we discarded the first 12 frames from every video as an acclimation period. We then averaged the remaining 18 frames to produce a single summary statistic for each video, which we refer to as the mean pairwise distance.

Overall, videos with smaller mean pairwise distances indicate that larvae tended to remain closer to each other and thus can be described as having a higher aggregative tendency. We log-transformed (natural log) pairwise distances prior to statistical analysis

to reduce the impact of outliers and to satisfy the assumptions of the statistical tests used.

The host-free petri dish environment used in our assays is obviously very different from conditions under which larvae aggregate in nature. Nevertheless, our assay will measure—under these simple conditions—what we refer to as “larval aggregative tendency”. Specifically, the aggregative tendency of a particular group of larvae

(quantified as the average mean pairwise distance of the larvae following the acclimation period) will reflect a combination of: (1) the tendency of the larvae to form an

aggregation in the first place, and (2) the cohesiveness of the aggregation once formed.

The primary benefits of this assay are that it is fast, simple, and can be applied in a consistent manner to any group of larvae, facilitating comparisons among different populations and species. In the discussion, we consider possible limitations and extensions of our assay.

2.3.3 Generating a model of random dispersal

To generate the expected distribution of pairwise distances under the null hypothesis that larvae distribute themselves randomly in the petri dish (i.e., do not actively aggregate or disperse), we used a custom Java application to perform a series of simulations that mimicked our sampling procedure. For each of the experimental group sizes that we used (2, 5, 6, and 8 larvae), we simulated 100 videos by randomly placing the corresponding number of points (2, 5, 6, or 8) in a virtual 14.5 cm circular arena. To mimic our subsampling, we repeated this process 30 times (“frames”) per “video” and calculated mean pairwise distance across all frames. We then calculated a 95% confidence interval from the 100 simulated mean pairwise distances.

2.3.4 Effect of developmental stage on aggregative tendency

To determine the impact of developmental stage on the aggregative tendency of

N. lecontei larvae and to assess optimal group size for our assays, we recorded videos for all possible combinations of three developmental stages (early-feeding instars, late- feeding instars, and non-feeding instars) and three group sizes (2, 5, and 8 larvae). N. lecontei males have 5 feeding instars, while N. lecontei females have 6; both sexes have a single non-feeding instar. We determined developmental stage based on reliable changes in size and color that accompany larval development (Wilson et al 1992). In our analysis, we considered 2nd and 3rd instars to be “early-feeding instars”. These larvae had head

capsules ≤ 1.05 mm (0.44 to 1.03 mm) and had not yet developed mature coloration, which consists of a reddish orange head capsule with a black ring around each eye and up to four paired rows of black spots (Wilson et al 1992) (Figure 2.1a). We considered 4th thru 6th instars to be “late-feeding instars”. These larvae had fully developed color patterns and head capsules ≥ 1.5 mm (1.5 to 1.86 mm) (Figure 2.1b). “Non-feeding instars” were easily identifiable due to their distinct coloration pattern (pale cream to yellow body color and head capsule, distinct spotting pattern; Hetrick 1959; Ghent 1960;

Smith 1993) (Figure 2.1c). For these experiments, we used mixed-sex larvae produced by mated females from the Grayling, MI lab colony (from RB261; Table 2.1).

For each combination of group-size and developmental-stage, we recorded 5 videos, for a total of 45 videos. We processed these videos as described above (one frame every 180 s, with the first 2160 s—or 12 frames—discarded as an acclimation period).

We then averaged all pairwise distances from each video and log-transformed (natural log) this value to obtain the video’s log mean pairwise distance. We analyzed these values with an ANOVA, followed by post-hoc t-tests to determine which life stages differed significantly. Finally, we compared each life stage and group-size combination to the randomly generated distribution described above. Because distances recorded from different larval group sizes are not directly comparable (i.e., the maximum possible pairwise distance declines as group size increases), we analyzed each group size separately. We performed all ANOVA and Tukey HSD tests in JMP10 (SAS Institute

2012).

2.3.5 Effect of relatedness on aggregative tendency

To determine whether the relatedness of larvae impacts their tendency to aggregate, we videotaped larval behavior under three treatments: (1) all larvae derived 47

from the same mother (brothers), (2) an equal mix of larvae from two mothers from the

same population (non-siblings, but possibly related), (3) an equal mix of larvae from two

mothers from different populations (non-relatives). For these assays, we used haploid

male larvae produced by virgin mothers derived from two populations; one near Bitely,

Michigan (from RB380, RB381, RB383, RB384; Table 2.1) and another near Necedah,

Wisconsin (from RB397, RB398, RB399, RB400; Table 2.1). For each treatment, we

recorded 14-17 videos of 6 late-feeding instar larvae, for a total of 46 videos (N = 17, 14, and 15 videos for same mother, different mother/same population, different mother/different population, respectively). We used an ANOVA to determine if relatedness had an effect on log-transformed mean pairwise distance (natural log). Again, we compared each treatment to our random, null distribution using Tukey HSD.

2.3.6 Intraspecific variation in aggregative tendency

To assess the extent to which different N. lecontei populations vary in their aggregative tendency, we recorded videos of larvae from five different locations (Table

2.1): Bitely, MI (from RB244; N = 29 videos); Grayling, MI (from RB261; N = 32 videos), Orange Springs, FL (from RB316; N = 19 videos); Lexington, KY (from RB335;

N = 18 videos); and Piscataway, NJ (from LL031; N = 21 videos). These populations were chosen because they provide a representative sample of the geographic range and genetic diversity of N. lecontei. In particular, all three major genetic clusters identified via a population genomic analysis are represented in our sample (Bagley et al 2017). To obtain larvae for video analyses, we reared the haploid male offspring from 15-18 virgin females per population. For these assays, we used 8 late-feeding instars per video. We log-transformed (natural log) pairwise distances and used ANOVAs to determine whether aggregative tendency differed among: populations, genetic clusters, or by latitude. 48

Populations, both separately and combined by genetic cluster, were also compared to the random, null model using Tukey HSD.

2.3.7 Interspecific variation in aggregative tendency

To determine how aggregative tendency of N. lecontei larvae compares with other

Neodiprion species, we recorded videos of two other Neodiprion species. One of these

species (N. maurus) has been categorized as “gregarious,” while the other species (N. compar) has been categorized as “solitary” (Wilson 1977, Larsson et al 1993). For these assays, we used 5 late-feeding instar larvae per video, all of which were wild-caught. Our sample sizes for each species were as follows: N = 7 for N. maurus (from NS037; Table

1); N = 8 for N. lecontei (from NS043; Table 2.1); N = 4 for N. compar (from multiple colonies, Table 1). We note that because N. compar is rarely found in groups in nature, we had to combine individuals from multiple sites and our sample sizes were limited compared to other species. We used ANOVAs to compare log-transformed (natural log) mean pairwise differences among species, followed by post-hoc, pairwise Tukey HSD tests. To further evaluate previous designations of “gregarious” and “solitary,” we compared data from each of these species to our simulated random distribution using a

Tukey HSD test.

2.4 Results

Effect of developmental stage and number of larvae on aggregative tendency

In total, we recorded 45 videos of various combinations of larval group size and developmental stage. Before analyzing these data, we first confirmed that our 2160 s acclimation period was sufficient for larval behavior to stabilize. As illustrated in Figure

2.3a, 2.3c, and 2.3e, pairwise distances tend to stabilize by approximately 1200-1800 seconds for all treatments.

When we condensed each video down to a single log-transformed (natural log)

mean pairwise distance (for post-acclimation period only), we found that developmental stage had a pronounced impact on aggregative tendency, but that its effects were partially dependent on the number of larvae in the assay (Figure 2.3b, 2.3d, 3f). We found that developmental stage significantly impacted aggregative tendency in the 5- and 8-larvae videos (5-larvae videos: ANOVA, F2, 12 = 8.4885, P = 0.0050; 8-larvae videos: ANOVA,

F2, 12 = 11.9256, P = 0.0014). In both cases, this difference was attributable to a decrease

in aggregative tendency (i.e., an increase in average pairwise distance) that occurred in

the final, non-feeding instar (5-larvae videos Tukey HSD: early-feeding vs. late-feeding,

P = 0.6979; early-feeding vs. non-feeding, P = 0.0237; late-feeding vs. non-feeding, P =

0.0055; 8-larvae videos Tukey HSD: early-feeding vs. late-feeding, P = 0.8119; early- feeding vs. non-feeding, P = 0.0019; late-feeding vs. non-feeding, P = 0.0057). By contrast, the impact of developmental stage on aggregative tendency was not significant in the 2-larvae videos (ANOVA, F2, 12 = 3.4549, P = 0.0653). We note, however, that the

overall patterns are the same (non-feeding instar is less gregarious than feeding instars) in

the 2-larvae videos. Our observed lack of significance for the 2-larvae treatment likely

stems from a greater inter-video variation (Figure 2.3e), suggesting that larger group sizes

(e.g., 5 or more larvae) may yield more reliable results than smaller group sizes.

We also compared our observed pairwise distances to those expected under the

null hypothesis that larvae distribute themselves randomly throughout the arena. For all

group sizes, the early-feeding and late-feeding instars had significantly smaller pairwise

differences than the random model (2-larvae videos Tukey HSD: early-feeding vs.

random P < 0.0001; late-feeding vs. random P < 0.0001; 5-larvae videos Tukey HSD:

early-feeding vs. random P < 0.0001; late-feeding vs. random P < 0.0001; 8-larvae videos Tukey HSD: early-feeding vs. random P < 0.0001; late-feeding vs. random P <

0.0001). Together, these results confirm that N. lecontei feeding instars are gregarious.

Additionally, for the 2- and 8-larvae videos, non-feeding instars did not differ significantly from the random model (2- larvae Tukey HSD: P = 0.3498; 8-larvae Tukey

HSD: P = 0.4972). By contrast, non-feeding instars from the 5-larvae videos appeared to have greater pairwise distances than expected under the random model (Tukey HSD: P =

0.0001). Together, these results suggest that while N. lecontei feeding instars have a strong behavioral tendency to aggregate, non-feeding instars either ignore or actively avoid one another.

2.4.1 Effect of relatedness on aggregative tendency

In our experiments, relatedness had no detectable impact on the aggregative

tendency of larvae (Figure 2.4, ANOVA, F2, 43 = 0.3045; P = 0.7391). Moreover, all three

treatments were significantly more aggregative than the random model (Tukey HSD: P <

0.0001 for all comparisons).

2.4.2 Intraspecific variation in aggregative tendency

Examination of aggregative tendency of late-feeding instars sampled from diverse

N. lecontei populations revealed substantial within-population variation, but very little

variation among populations (Figure 2.5). Population of origin did not significantly affect

aggregative tendency (ANOVA, F4, 114 = 0.2662, P = 0.8991). We also did not detect any differences when we lumped populations according to their membership in one of three genetic clusters (ANOVA, F2, 116 = 0.1014; P = 0.9037), nor did we detect any

relationship between population latitude and aggregative tendency (ANOVA, F1, 117 =

0.1644; P = 0.6859). Finally, each of the five populations and three genetic clusters

differed significantly from the random model (Tukey HSD: P < 0.0001 for all comparisons).

2.4.3 Interspecific variation in aggregative tendency

The three Neodiprion species we assayed differed significantly in their aggregative tendency (ANOVA, F2, 16 = 10.6675; P = 0.0011). As expected, the two species that have previously been described as “gregarious” (N. lecontei and N. maurus) had significantly lower average pairwise distances than the “solitary” species, N. compar

(Figure 2.6, Tukey HSD: N. lecontei vs. N. compar, P = 0.0085; N. maurus vs. N. compar, P = 0.0009). In contrast, the two gregarious species did not differ significantly from one another (Tukey HSD, N. lecontei vs. N. maurus, P = 0.3445). Consistent with these results, we found that the aggregative tendency of N. compar larvae was indistinguishable from the random model (Tukey HSD, P = 0.7534), while N. lecontei and N. maurus both exhibited a significant aggregative tendency (N. lecontei vs. random

Tukey HSD: P<0.0001; N. maurus vs random Tukey HSD: P < 0.0001).

2.5 Discussion

Pine sawflies are a promising group of organisms for investigating both the proximate and ultimate causes of phenotypic variation. To facilitate future comparative and functional studies of one variable trait—larval gregariousness—we developed a quantitative assay of larval aggregative tendency. Using this assay, we tested several predictions regarding larval behavior in the highly gregarious pest species, Neodiprion lecontei. First, we found that while early- and late-feeding instars do not differ appreciatively in their aggregative tendency, there is a pronounced shift in behavior in the last, non-feeding instar. Second, we found that larval groups composed of kin do not have more cohesive aggregations than groups containing non-kin. Third, we found that

variation in aggregative tendency among N. lecontei populations sampled across a wide geographical range is minimal compared to within-population variation. Fourth, we found that our assay can be used to distinguish between species with “gregarious” larvae and those with “solitary” larvae. After discussing limitations of our assay, we discuss each of these findings and their implications for the causes and consequences of larval gregariousness.

2.5.1 Assay limitations

Although our assay provides an objective and repeatable way to discriminate

between gregarious and solitary larval phenotypes, there was also a great deal of variation

among groups sampled from a particular developmental stage and population (Figure 2.3

and Figure 2.5). While this apparent “noise” may reflect true variation in aggregative

tendency at the level of the individual or the group, it may also stem from developmental

or sex-based differences in behavior that we did not properly account for in our assays.

First, in grouping the 5-6 feeding instars into two developmental stages (early-feeding

and late-feeding), we may have lumped together behaviorally distinct instars. Second,

behavioral differences in aggregative tendency between the sexes could have contributed

to variation in our mixed-sex assays (Figures 2.3 and 2.6). We note, however, that

among-group variation is also evident in single instar assays (final instars; Figure 2.3)

and in male-only assays (Figures 2.4 and 2.5). Third, behavioral variation may change

over the course of a single instar. For example, larvae may exhibit differences in

aggregative tendency as they prepare to molt or based on their physiological state such as

hunger (Ribiero 1989; Costa and Louque 2001; McClure et al 2011b; Tremmel and

Muller 2012; Fletcher 2015). Future assays can account for some of these potential

sources of variation via more precise developmental staging of larvae and because larvae 53

are difficult to sex, by using male-only colonies. Other potential sources of variation in

our assays include fluctuations in light, temperature, and olfactory environment (e.g.,

stemming from presence of other larval colonies in the environmental room in which we

recorded our videos) among videos. Regarding possible temporal effects, although we did

not control for time of day in our assays, we note that previous work on N. lecontei larval

activity patterns indicates that they lack a clear circadian pattern to their group foraging

dynamics—instead, they feed continuously throughout the day and night (Flowers and

Costa 2003). Nevertheless, failure to account for potential sources of variation in larval

aggregative tendency may have hampered our ability to detect small, but biologically

meaningful, differences in larval behavior.

Another limitation of our assay is that larval aggregative behavior in our artificial

assay environment (petri dish, no host, small groups) may not fully recapitulate behavior

in the wild. First, our artificial, bounded arena may induce wall-following behavior

similar to that seen in cockroaches, Blatella germanica (Jeanson et al 2003; Jeanson et al

2005). Second, a lack of host material in the test arena also deviates from the natural

conditions under which larvae aggregate. Our rationale for excluding host material was that we wanted to observe how larval interactions alone shape aggregative tendency.

Because feeding larvae are attracted to host foliage, the presence of host material in a test

arena might have caused otherwise solitary larvae to appear highly aggregative (Ghent

1960; Coppel and Benjamin 1965). Also, we note that host-independent aggregations

have been documented in nature—for example, N. lecontei larvae have been observed to migrate en masse up to 19 feet in search of a new host plant (Benjamin 1955). Third, our experimental groups were much smaller than the N. lecontei groups that are typically

encountered in the field (Benjamin 1955; Wilson 1977; Costa and Louque 2001; personal

observation). If differences in gregariousness manifest as differences in preferred group

size, we would not have detected these differences with our assay. Although additional work is needed to determine how different features of the environment or larval colony influence larval gregariousness, our results clearly indicate that even under our admittedly artificial assay conditions, we can reliably distinguish between aggregative and non-aggregative larvae (Figures 2.3 and 2.6).

Finally, because we assayed groups rather than individuals, we could not assess

variation at the level of the individual. Our decision to assay behavior in groups was a

practical one. Although we could isolate larvae and measure response to particular

aggregation cues, we have not yet identified the pertinent cues. Moreover, isolated N.

lecontei larvae become very agitated and exhibit increased wandering (Kalin and Knerer

1977). Another alternative would have been to track individual larvae within a group.

However, because behaviors of individual larvae in a test arena are not independent, the

appropriate unit of replication is therefore the group (Costa and Loque 2001).

2.5.2 Effects of developmental stage and relatedness on larval aggregative tendency

Despite the limitations of our assay, we found clear evidence that, following the

final molt, larvae shift from a “gregarious” feeding mode to a “solitary”, non-feeding mode. While these findings confirm previous natural history accounts, we introduce for the first time an objective criterion for categorizing larval behavior (i.e., via comparison to a random distribution). Similar behavioral shifts have been reported in many other insect taxa and are thought to facilitate dispersal to a suitable location for completing development (Nijhout and Williams 1974; Dominick and Truman 1983; Reimann 1986;

Jones et al 1992; Sedlacek et al 1996; Li 2016). There are two distinct mechanisms by

which this developmental shift could occur: (1) final instar larvae may simply lose their

attraction to conspecifics; or (2) final instar larvae may switch their response to

conspecifics from attraction to repulsion. Intriguingly, our 5-larvae assays indicated that

final instar larvae maintain greater inter-larvae distances than expected by chance,

suggesting that they may be actively avoiding other larvae. Although the 8-larvae final

instar data trended in the same direction, the departure from random expectations was not

significant. One explanation for this apparent discrepancy is that, within a test arena of

fixed size, larger groups of larvae cannot spread out enough to distinguish between a

random distribution and an over-dispersed one.

Our data also indicate that, in contrast to late-feeding instars of some Neodiprion

species (Hopping and Leach 1936; Furniss and Dowden 1941; Hetrick 1956; Rose and

Lindquist 1994; Anstey et al 2002), late-feeding instars of N. lecontei do not exhibit a pronounced reduction or loss of gregariousness. These results imply that larval aggregations remain beneficial throughout the larval feeding period of N. lecontei. Costs

and benefits of larval aggregations are also relevant to whether or not cooperative

behaviors should be directed preferentially to kin. If behaviors are costly to the

individuals, kin-based behavioral preferences are more likely to evolve (Hamilton 1964).

In contrast to these predictions, we did not observe any detectable reduction or loss of

gregariousness in larval groups that contained non-kin. Similarly, fusion of unrelated

Neodiprion colonies—and even different Neodiprion species—appears to be relatively

common in nature, especially at high population densities (Tostowaryk 1972; Codella and

Raffa 1993, 1995a; Costa and Loque 2001; personal observation). These observations

suggest that, when it comes to forming and maintaining larval aggregations, larvae do not

discriminate between kin and non-kin (Figure 2.4). These findings cannot be explained

by a lack of capacity for kin recognition in this species because previous work has shown

that adults do discriminate between related and unrelated mates (Harper et al 2016). One

possible implication of our results is that individuals derive sufficient direct benefits from

aggregating that kin selection need not be invoked to explain the evolution and

maintenance of larval gregariousness.

That said, there could be opportunities for larvae to discriminate against non-kin

in ways that we would not have detected in our assays. For example, in small colonies, N.

lecontei larvae continually cycle between exposed and protected feeding positions

(Codella and Raffa 1993). Also, colony defense is enhanced by simultaneous

regurgitation of host resin—both by startling would-be predators (Sillen-Tullberg 1990)

and coating one another with sticky regurgitant that increases handling time (Tostowaryk

1972; Eisner et al 1974). Because assuming exposed positions and depletion of larval

defenses can be costly (Higginson et al 2011; Miettinen 2015), unrelated individuals may

be less willing to incur these costs. Thus, analysis of the effect of relatedness on position

cycling and defensive regurgitation may reveal these subtler forms of kin discrimination

in feeding N. lecontei larvae.

2.5.3 Intra- and interspecific variation in larval aggregative tendency

One approach that has been used to investigate adaptive function of particular traits is to see how phenotypic variation within species correlates with environmental variables (Pulliam and Caraco 1984; Harvey and Pagel 1991; Reeve and Sherman 1993;

Garland and Adolph 1994). For example, latitudinal variation in color and diapause characteristics—both of which are important adaptations to different temperature 57

regimes—are widespread in nature (Masaki 1999; Alho et al 2010; Chahal and Dev 2013;

Parsons and Joern 2014; Lehmann et al 2015). Likewise, there is empirical evidence from several organisms that feeding aggregations can improve thermoregulation (Seymour

1974; Joos et al 1988; Codella and Raffa 1993; Klok and Chown 1999; Fletcher 2009). If aggregations serve a thermoregulatory function in N. lecontei, there may be clinal variation in aggregative tendency. Although we have surveyed only a small number of N. lecontei populations, the populations we did sample were distributed across a broad latitudinal gradient—if there was clinal variation, we would expect to see differences among the latitudinal extremes (e.g., FL vs. MI). In contrast to these predictions, we found that larvae from all populations aggregated significantly more than expected under the random model, and that variation among groups within populations exceeded variation among populations (Figure 2.5).

Nevertheless, with these data alone, we cannot rule out a thermoregulatory function for larval aggregations. For example, it is possible that these populations differ in their plastic responses to temperature (e.g., perhaps northern populations show increased aggregation at low temperatures)—such a difference would not have been detected in our assays, which took place at a single temperature. Thus, to definitively assess latitudinal variation in aggregative behavior, these assays should be repeated at different temperatures.

In contrast to the lack of variation among populations, we observed pronounced differences among species: whereas N. lecontei and N. maurus are decidedly gregarious,

N. compar larvae are not. The pronounced difference between N. lecontei and N. compar is especially intriguing because these two species have a very similar geographical

distribution, share many of the same hosts, and contend with many of the same predators and parasites (Linnen and Farrell 2010). The most obvious difference between these two species is in their larval coloration: whereas N. lecontei larvae are conspicuously colored

(white to bright yellow body with several rows of spots), N. compar larvae are cryptically colored (green body covered by longitudinal green stripes). One possible explanation for this association between gregariousness and conspicuous coloration is that larval aggregations amplify aposematic signals, thereby enhancing avoidance learning and reducing predation (Riipi et al 2001). Consistent with this hypothesis, phylogenetic analyses of folivorous lepidopterans that suggest that aposematic coloration often evolves before gregariousness (Sillen-Tullberg 1988; Tullberg and Hunter 1996; Beltrán et al

2007). While our results suggest that a similar trend may also be true for pine sawflies, evaluating this hypothesis will require comparable data from more Neodiprion species and a formal phylogenetic comparative analysis.

2.5.4 Conclusions

Although much work remains, our results have several implications for the proximate and ultimate mechanisms underlying larval aggregations in Neodiprion. From a proximate perspective, we have shown that larval grouping occurs even in the absence of host plant material, highlighting the importance of cues from the larvae themselves. Nevertheless, in terms of the maintenance of these aggregations, there does not appear to be any sort of kin discrimination in the feeding larvae. We also describe how gregarious behavior changes over the course of N. lecontei larval development. From an ultimate perspective, our observation that larvae remain gregarious throughout the feeding period and that larvae do not discriminate against non-kin suggest that, in N. lecontei, the benefits of aggregating to the individual consistently outweigh the costs. Moreover, as we have 59

demonstrated here, this assay can be applied to any Neodiprion species. Future work will examine the costs and benefits of aggregations over different developmental stages and multiple species using both experimental and comparative approaches. Together, these data will provide a comprehensive understanding of aggregative behavior.

2.6 Tables and Figures

Table 2.1 Collection information for sawflies used in Chapter 2 1Each colony ID corresponds to a unique larval colony (or individual) collected in the field. When multiple colonies were collected at the same location at the same time, multiple colony IDs are given. Because N. compar videos combined larvae from different locations, two IDs are given. The first ID refers to how larvae were grouped into one of 4 videos (CN001-CN004); the second, in parentheses, refers to the original collection ID (NSxxx) Colony ID1 Species Date of Collection Nearest City, State Host Plant Latitude, Longitude RB261 N. lecontei 7/17/2013 Grayling, MI P. banksiana 44.65689, -84.6958 LL031 N. lecontei 8/14/2013 Piscataway, NJ P. sylvestris 40.54955, -74.4308 RB244 N. lecontei 7/16/2013 Bitely, MI P. banksiana 43.79322, -85.74 RB316 N. lecontei 8/7/2013 Orange Springs, FL P. palustris 29.50772, -81.8598 RB335 N. lecontei 8/22/2013 Lexington, KY P. elliottii 38.014, -84.504 RB380, RB381, RB383, RB384 N. lecontei 7/15/2015 Bitely, MI P. banksiana 43.7675, -85.7403 61

RB397, RB398, RB399, RB400 N. lecontei 7/17/2015 Necedah, WI P. banksiana 44.15611, -90.1322 NS037 N. maurus 6/17/2014 Rhinelander, WI P. banksiana 45.66427, -89.4919 NS043 N. lecontei 7/2/2014 Spooner, WI P. banksiana 45.82233, -91.8884 CN001 (NS174) N. compar 8/15/2015 Hawk Junction, ON P. banksiana 48.04558, -84.5494 CN001 (NS182) N. compar 8/17/2015 Gurney, WI P. banksiana 46.50895, -90.5027 CN002 (NS175) N. compar 8/15/2015 Hawk Junction, ON P. banksiana 48.02968, -84.6513 CN002 (NS184) N. compar 8/17/2015 Glidden, WI P. banksiana 46.11489, -90.5511 CN003 (NS176) N. compar 8/15/2015 White River, ON P. banksiana 48.54371, -85.1911 CN003 (NS178) N. compar 8/16/2015 Mokomon, ON P. banksiana 48.41605, -89.6412 CN003 (NS168) N. compar 8/13/2015 Petawawa, ON P. banksiana 45.92631, -77.3254 CN004 (NS169) N. compar 8/13/2015 Petawawa, ON P. banksiana 45.93154, -77.3333 CN004 (NS170) N. compar 8/14/2015 Onaping, ON P. banksiana 46.62311, -81.4552 CN004 (NS172) N. compar 8/14/2015 Gogama, ON P. banksiana 47.46476, -81.8467 CN004 (NS174) N. compar 8/15/2015 Hawk Junction, ON P. banksiana 48.04558, -84.5494

Figure 2.1 Developmental variation in larval morphology in N. lecontei Representative photographs of early-feeding (a), late-feeding (b), and non-feeding (c) instars. Photos by R. Bagley.

Figure 2.2 Gregarious assay test arena a) Equidistant placement of larvae at the start of the video. (b) Image taken from the middle of a larval video. Circles indicate the location of head capsules and lines indicate pairwise distances.

Figure 2.3 Impact of group size and developmental stage on aggregative tendency Log-transformed pairwise distances (natural log) estimated from videos using eight (a, b), five (c, d), or two (e, f) larvae. In a, c, and e, points are the mean (+/- SEM) average pairwise distances computed from a single time point (video frame) for a particular developmental stage: early-feeding instars (open squares), late-feeding instars (black triangles), non-feeding instars (light grey circles). Note that pairwise distances tend to stabilize by ~2000s. In b, d, e, each point represents the log-transformed mean pairwise distance (natural log) calculated from a single video, following a 2160 s acclimation period. Horizontal bars represent the overall average for each life stage. The light gray bar in B, D, and E represents the 95% confidence interval for mean pairwise distance estimated via simulation under a model of random larval distribution for the respective number of larvae. Whereas early- and late-feeding instars aggregate significantly more than the null model, final instars do not aggregate.

Figure 2.4 Impact of relatedness on aggregative tendency Each dark-grey circle represents the log-transformed (natural log) mean pairwise distances estimated from a single video of six late- feeding instar larvae (following a 2160 s acclimation period), black bars represent the overall mean for each treatment. The light-grey bar .

Figure 2.5 Intraspecific variation in aggregative tendency Each dark-grey circle represents the log-transformed (natural log) mean pairwise distances estimated from a single video of eight late-feeding instar, male larvae (following a 2160 s acclimation period), black bars represent the overall mean for each population. The light-grey bar represents the 95% confidence interval for the mean pairwise distance between 8 larvae estimated via simulation under a model of random larval distribution. Compared to within-population variation, between-population variation was minimal.

Figure 2.6 Interspecific variation in aggregative tendency Each dark-grey circle represents the log-transformed (natural log) mean pairwise distances estimated from a single video of five late-feeding instar larvae (following a 2160 s acclimation period), black bars represent the overall mean for each species (N. lecontei, N. maurus, and N. compar). The light-grey bar represents the 95% confidence interval for the mean pairwise distance between 5 larvae estimated via simulation under a model of random larval distribution. Larvae of the two gregarious species, N. lecontei and N. maurus, were significantly more aggregative than the random model and larvae of the solitary species, N. compar.

CHAPTER 3. MOTHER KNOWS BEST: MATERNAL CLUTCH SIZE PREDICTS LARVAL GROUP SIZE IN PINE SAWFLIES (GENUS NEODIPRION)

3.1 Abstract

Evolutionary conflicts are pervasive in nature and have the potential to drive antagonistic coevolution of conflict-related traits. However, when such conflicts are weak or idiosyncratic, phenotypic signatures of coevolutionary arms races may be absent. Here, we ask whether variation in group-living traits among pine-sawfly species in the genus Neodiprion is consistent with a history of parent-offspring conflict. To address this question, we compile data on adult female clutch size, larval aggregation behavior, and larval group size for a monophyletic group of 19 eastern North

American Neodiprion species from field observations, laboratory assays, and published descriptions. We then evaluate the extent to which each trait exhibits phylogenetic signal and, based on these results, examine correlations between group-size traits both with and without phylogenetic correction. Although female oviposition behavior and larval grouping behavior varies among species and variation in these traits is decoupled from phylogeny, we find no evidence of antagonistic coevolution between these traits.

Furthermore, while larvae are physically capable of dispersal, female clutch size is a strong predictor of larval colony size, indicating that larvae do not substantially alter initial group size after hatching. Thus, although theoretical work demonstrates the potential for parent-offspring conflict over group size in animals that lack parental care, our data suggest that this type of conflict is not likely to be a long-term driver of

phenotypic evolution.

3.2 Introduction

Evolutionary theory predicts that conflict—scenarios in which interacting entities

have divergent fitness optima for some shared trait of interest—is widespread in nature

(Queller and Strassmann 2008). Conflict manifests in a variety of contexts, including

between males and females (sexual conflict; (Chapman et al 2003; Arnqvist and Rowe

2005), parents and their offspring (parent-offspring conflict; (Trivers 1974; Godfray

1995; Crespi and Semeniuk 2004)), and within the genome of a single individual

(intragenomic conflict; (Burt and Trivers 2009; Garder and Úbeda 2017)). Regardless of

the context, evolutionary conflicts have the potential to fuel antagonistic coevolution, in

which there is reciprocal evolutionary change in the genes and traits that mediate the

conflict (Rice 1996; O’Neill et al 2007; Carmona et al 2015; Lindholm et al 2016;

Dougherty et al 2017). When this coevolutionary arms race plays out over the course of lineage diversification, it can give rise to correlated evolution of conflict-related traits

(Briskie et al 1994; Arnqvist and Rowe 2002; Lloyd and Martin 2003; Koene and

Schulenburg 2005; Kölliker et al 2005; Ronn et al 2007) and elevated rates of diversification (Arnqvist et al 2000; Zen and Zen 2000; Kraaijeveld et al 2011; Crespi and Nosil 2013). Thus, a comparative approach is a potentially powerful tool for making inferences about the strength and long-term evolutionary consequences of conflict.

One type of conflict that has received theoretical attention, but minimal comparative study, is conflict between parents and offspring over offspring group size.

Whenever siblings compete for limited resources, a female’s optimal clutch size can differ from that of her offspring (Godfray 1987; Parker and Mock 1987; Godfray and Ives

1988; Damman 1991; Godfray et al 1991; Desouhand et al 2000). This type of parent-

offspring conflict could occur even in the absence of any parental care (Costa 2018). For

example, siblicide among parasitoid larvae may constrain females to produce clutches

smaller than would be otherwise optimal (Godfray 1987; Parker and Mock 1987;

Rosenheim 1993). Similarly, competition among herbivorous, gregarious insect larvae

may favor a reduction in clutch size of ovipositing females (Godfray and Parker 1992).

Conversely, when larvae disperse readily—as is the case in many grazing insects—egg

clustering or dumping may be favored (Roitberg and Mangel 1993). Under this scenario,

there can be parent-offspring conflict over larval dispersal tendencies (Roitberg and

Mangel 1993; Sjerps and Haccou 1994). Thus, across a wide range of invertebrate life

histories, parent-offspring conflict has the potential to drive coevolution between female

oviposition behavior (clutch size) and offspring behaviors that mediate group dynamics

(e.g., siblicide, non-lethal aggression, and dispersal tendency).

Following Arnqvist and Rowe (2002), who described coevolutionary dynamics

between traits that mediate sexual conflict, we can make three predictions regarding the

evolution of traits that mediate group-size conflict. First, because antagonistic

coevolution can produce rapid evolutionary change, parent-offspring conflict over group size should generate extensive interspecific variation in group-living traits. Second, because coevolutionary arms races are expected to have bouts of escalation and de- escalation (Parker 1983; Härdling 1999), trait evolution should be decoupled from phylogenetic history (minimal “phylogenetic inertia”, (Losos 1999; Arnqvist and Rowe

2002)). Third, assuming female optimum clutch size consistently exceeds the offspring optimum clutch size (Godfray 1987; Parker and Mock 1987; Godfray and Parker 1992;

Roitberg and Mangel 1993; Godfray 1995), increases in female clutch size should favor

offspring behaviors that either increase competitive ability or decrease colony size (e.g., via dispersal) and vice versa, generating a negative correlation between these traits among species. Although analogous predictions have been confirmed in lineages experiencing sexual conflict (Arnqvist and Rowe 2002), they remain untested in the context of group-size conflict (but see: Mayhew 1998).

To test these predictions, we take advantage of a group of herbivorous insects for which extensive ecological data are available. The sawfly genus Neodiprion (order:

Hymenoptera; family: Diprionidae) is a Holarctic group of approximately 50 species specialized on host plants in the family Pinaceae. Within this genus, the “Lecontei clade” is a monophyletic lineage of 21 eastern North American species well studied from taxonomic (Ross 1955; Ross 1961; Linnen and Smith 2012), phylogenetic (Linnen and

Farrell 2007; Linnen and Farrell 2008a; Linnen and Farrell 2008b; Linnen and Farrell

2010), and life history and behavioral perspectives (Coppel and Benjamin 1965; Knerer and Atwood 1973; Knerer 1993; Costa and Louque 2001; Fowers and Costa 2003; Terbot

II et al 2017). Importantly, eastern North American Neodiprion have documented variation in female egg-laying behavior (Table 3.1) and larval grouping behavior (Terbot

II et al 2017, Table 3.1).

To provide further context for our comparative analysis of Neodiprion clutch-size traits, a review of relevant life history details is needed (further reviewed in Coppel and

Benjamin 1965; Knerer and Atwood 1973; Wilson et al 1992; Knerer 1993). Adult females emerge from cocoons with a full complement of mature eggs, find a suitable host, and attract males via a powerful pheromone. Shortly after mating, females use their saw-like ovipositors to embed their eggs within pine needles. While females of some

species tend to lay their full complement of eggs on a single branch terminus, females of

other species seek out multiple branches or trees for oviposition. Overall, female

oviposition behavior is highly species-specific and, in some cases, diagnostic (Ghent

1959). Adult Neodiprion are non-feeding and short-lived (~2-4 days), dying soon after

mating and oviposition. After hatching from eggs, Neodiprion larvae of many species

form feeding aggregations that remain intact to varying degrees across 4-7 feeding

instars, depending on the sex and the species. As larvae defoliate pine branches, they

migrate to new branches and sometimes to new host trees (Benjamin 1955, Smirnoff

1960). During these migrations, colonies may undergo fission and fusion events (Codella

and Raffa 1993, Codella and Raffa 1995, Costa and Loque 2001). Thus, while initial

colony size corresponds closely to clutch size, larvae are highly mobile and their

dispersal behavior has the potential to substantially alter colony size (Codella and Raffa

1995).

Beyond having a variable and well-documented natural history, Neodiprion

provides an excellent test case for examining coevolution of female egg-laying and larval grouping behaviors because, as is likely the case for many insects, feeding in groups could confer both costs and benefits to the larvae (Codella and Raffa 1995, Heitland and

Pschorn-Walcher 1993). On the one hand, larvae living in large groups compete for access to pine needles (Prokopy et al 1984). Also, because large colonies quickly defoliate branches, larvae must expend energy and risk exposure to predators to reach new branches or trees (Benjamin 1955, Smirnoff 1960, Teras 1982, Costa and Louque

2001, Flowers and Costa 2003). Compared to small colonies, large colonies may be more

conspicuous to predators and parasitoids (Lyons 1962, Olofsson 1986) and more

susceptible to viral infection (Mohamed et al 1985; Young and Yearian 1990). On the other hand, grouping is hypothesized to confer numerous benefits to larval colonies. For example, in early instars, grouping is thought to be essential to establishing feeding sites on tough pine needles (Ghent 1960, but see Kalin and Knerer 1977). Also, living in large groups may decrease per capita predation risk (Hamilton 1971; Codella and Raffa 1995) or enhance aposematic signalling (Tostowaryk 1972, Sillen-Tullberg and Leimar 1988,

Linnen et al 2018).

If parent-offspring conflict over group size has shaped the evolution of clutch-size

traits, trait variation should match the three predictions outlined above: extensive

interspecific variation, minimal phylogenetic signal, and a negative correlation between

female clutch size and larval grouping behavior among species. To evaluate these

predictions, we combined our own observations from the field and the lab with published

descriptions of female and larval behavior from 19 of the 21 eastern North American

Neodiprion species. To gain additional insight into our data, we also examined

correlations between female and larval behavior and larval colony size. If larval

behaviors tend to modify initial clutch size, we expect positive correlations between

larval grouping behaviors and larval colony size. Conversely, if there is minimal post-

hatching modification of the initial clutch size, we would expect a stronger correlation

between female clutch size and larval colony size.

3.3 Materials and Methods

3.3.1 Characterizing variation in group-size traits

We compiled data on female clutch size, larval grouping behavior (aggregative

tendency), and larval colony size for 19 of 21 eastern North American Neodiprion species

(Linnen and Farrell 2008a, b). The only two species for which we were unable to obtain

data were N. cubensis and N. insularis, both endemic to Cuba. Here, we briefly describe how we compiled data for each trait. Additional details are provided in Tables 3.1, 3.2,

3.3, and 3.4.

To estimate clutch size for each species, we combined field observations, laboratory assays, and published descriptions. Field observations took place in 2002,

2015, and 2016 and consisted of counts of egg scars on needles found in close proximity to early instar larvae that were collected and reared to later instars or adults for identification. Clutch sizes were estimated from the number of egg scars found on individual branch termini. To supplement our field observations, we used laboratory assays to obtain clutch-size estimates for several species. In each assay, we released a lab-reared, virgin female into a mesh cage (35.6 x 35.6cm x 61cm) containing four Pinus seedlings. To obtain an average clutch size for each female, we recorded the number of eggs laid in each discrete clutch (i.e., a single branch terminus on a single seedling). For these assays, females were reared via methods described elsewhere (Harper et al 2016) and provided seedlings from the Pinus species on which they (or their wild-caught progenitors) were originally collected. Finally, to supplement our own clutch-size data, we surveyed the literature and included published data from which we could infer mean clutch size. In total, we obtained clutch-size data for 16 of the 19 Neodiprion species included in our study (Tables 3.1 and 3.2).

To measure larval aggregative tendency, we used a behavioral assay described in

Terbot II et al 2017. Briefly, we spaced five larvae of a particular species equidistantly along the perimeter of a 14.5 cm diameter petri dish and recorded their behavior for 90 minutes using a Lenovo Ideapad laptop and either a Logitech or Microsoft webcam. We

then used the program Video Image Master Pro (A4Video 2016) to extract individual

video frames and a custom Java application to extract one image still per every 3 minutes of video (Terbot II et al 2017). For each extracted image, we measured all pairwise distances between each of the five larval heads. To provide an acclimation period for the larvae to adjust to the petri dish arena, we excluded the first 12 images (36 minutes) of each video (Terbot II et al 2017) using Microsoft Excel 2013 (Microsoft 2012) and a custom Perl script. This processing pipeline produced a single mean pairwise larval distance measure for each video. Larger mean pairwise distances correspond to a greater average distance between larvae and therefore a lower “larval aggregative tendency” (i.e., a greater tendency to leave the group); smaller values correspond to a smaller average distance and a higher “larval aggregative tendency”. All larvae used in these assays were collected in the field, then reared in the lab on their natal host species via standard rearing protocols until they reached a suitable size for videotaping. To reduce developmental noise in aggregative behavior, only late-instar, feeding larvae were assayed (Terbot II et al 2017). Collection information and other details regarding the aggregative tendency assays are included in Tables 3.3 and 3.4. In total, we measured larval aggregative tendency in 19 eastern North American Neodiprion species.

To obtain estimates of larval colony size for each species, we combined our own field observations with published descriptions of Neodiprion colony size. Field observations, which were recorded between 2002-2004 and 2013-2016, consisted of approximate counts of the number of larvae found in each collected colony. Colonies were located in the field via a combination of visual searching and the use of beating sheets (i.e., tapping on branches and catching dislodged larvae on a large, square piece of

fabric). With this combination of methods, we were able to collect both large,

conspicuous colonies and isolated individuals. We considered sawflies to be in the same

colony if they formed a distinct cluster of larvae (e.g., on the same branch tip) that was

spatially isolated from other such groups. We also noted when larvae were collected as

isolated individuals (i.e., colony size = 1). To supplement these field data, we included

observations from published papers. In total, we obtained group-size data for 19

Neodiprion species.

To ensure that our data were normally distributed prior to statistical analysis, we

applied a van der Waerden rank-based inverse normal transformation to all three datasets.

We also multiplied each value in the aggregative tendency dataset by -1 to ensure that for all three datasets, higher values always corresponded to “large group size” phenotypes

(larger clutches, higher aggregative tendency, larger larval colony sizes). We used

Microsoft Excel 2013 for all transformations. To determine whether species differed in

trait values after accounting for variation within species, we used JMP 12 (SAS Institute

2016) to perform an analysis of variance (ANOVA) on each of the three datasets. Finally,

for use in regression analyses, we calculated mean values for each species and trait.

3.3.2 Evaluating phylogenetic signal for group-size traits

To evaluate the extent to which each group-size trait exhibits phylogenetic signal,

we estimated Pagel’s λ for each trait using BayesTraits v 3.0 (Meade and Pagel 2017).

Pagel’s λ is a scaling parameter that describes how well phylogenetic relationships

predict trait covariance among species, with λ=1 corresponding to a model in which a trait evolves via Brownian motion along a phylogeny (i.e., strong phylogenetic signal) and λ=0 corresponding to a star-phylogeny model in which a trait evolves independently for each species (Pagel 1999). For each trait, we used BayesTraits to estimate the 76

likelihood of our data under the “Continuous: Random Walk (Model A)” and three

different λ values (λ=0, λ=1, estimated λ). Then, to evaluate the significance of the

observed phylogenetic signal, we performed likelihood ratio tests (LRTs; LR = -2∆lnL;

LR is χ2-distributed with 1 df) in which we compared the likelihood of models in which

Pagel’s λ was fixed (λ=1 or λ=0) to more complex models in which λ was estimated from

the data. To account for phylogenetic uncertainty, we performed LRTs for each tree in

sample of 15,000 trees from the posterior distribution of a Bayesian analysis described in

Linnen and Farrell 2008a. Prior to analysis, branch lengths on each tree were scaled to

time using r8s version 1.71 (Sanderson 2003), as described in Linnen and Farrell 2010.

To account for variation in sample size among traits (N = 16 for clutch size, N = 19 for aggregative tendency and larval colony size), we repeated this analysis on reduced (N =

16) aggregative-tendency and larval-group-size datasets.

3.3.3 Evaluating relationships between group-size traits

Because evidence for phylogenetic signal was mixed (see results), we evaluated correlations between group-size traits both with and without phylogenetic correction. For uncorrected analyses, we performed correlation analyses and linear regressions in JMP

12. To test the prediction that conflict generates correlated evolution between parent and

offspring traits, we estimated Pearson’s correlation coefficient I and evaluated its

significance via comparison to Student’s t-distribution. To determine which traits

contribute to larval colony size, we used linear regression to model the relationship

between larval colony size (dependent variable) and different combinations of

explanatory variables (clutch size, larval aggregative tendency, and their interaction). To

compare these models, we calculated the Aikaike information criteria (AICC) for all

possible models. We then calculated the AICC difference between each model and the

model with the lowest AICC score. We also calculated Aikaike weights and evidence

ratios for each model and for the subset of models that had AICC differences less than or

equal to 5. We used JMP 12 to calculate the AICC scores, generate model parameter

values, evaluate significance of specific model parameters, and calculate model

significance compared to a null model. To calculate AICC differences, Aikaike weights,

and evidence ratios, we used Microsoft Excel 2013.

To account for possible phylogenetic constraints on trait evolution, we repeated correlation and regression analyses in BayesTraits v3.0. First, to evaluate the relationship between clutch size and aggregative tendency, we computed the likelihood of the data

under the “Continuous: Random Walk (Model A)” model with correlation either assumed

or set to zero. To evaluate the significance of the correlation, we used a LRT to compare

the models with and without the correlation. Second, to evaluate the contribution of

clutch size and aggregative tendency to larval colony size, we used the “Continuous:

Regression” model (Organ et al 2007), with colony size as the dependent variable and

clutch size, larval aggregative tendency, and clutch size + larval aggregative tendency as the explanatory variables. To evaluate the significance of the regression models, we performed LRTs that compared models with and without the explanatory variables. For all correlation and regression analyses, we set λ=1. As described above, we accounted for phylogenetic uncertainty by performing all analyses on a set of 15,000 ultrametric trees sampled from the posterior distribution of a previous MCMC analysis (Linnen and

Farrell, 2008a; Linnen and Farrell 2010).

3.4 Results

3.4.1 Variation in group-size traits

Clutch size, larval aggregative tendency, and larval colony size varied both between and within species (Figure 3.1). Although there was substantial intraspecific variation in some traits/species, ANOVAs revealed significant interspecific variation in all three traits (clutch size: F15, 59 = 4.8850, p < 0.0001; larval aggregative tendency: F18,

377 = 5.3260, p < 0.0001; larval colony size: F18, 360 = 17.5712, p < 0.0001;)

3.4.2 Phylogenetic signal in group-size traits

Overall, we found strong evidence of phylogenetic signal for larval colony size,

weak evidence for clutch size, and no evidence for larval aggregative tendency. For larval

colony size, the mean estimated λ across 15,000 trees was close to 1 (λ=0.96 ± 0.05 s.d.;

Figure 3.2). Moreover, while 58% of the trees rejected the model in which λ=0 (at

α=0.05), only 0.03% of the trees rejected the λ=1 model. Although λ estimates for clutch size were also high (mean estimated λ=0.83 ± 0.37 s.d.; Figure 3.2]), none of the trees rejected any of the fixed- λ models (0% for both λ=1 and λ=0). This difference may be attributable, in part, to the smaller sample size for clutch size (N = 16). In support of this interpretation, we also failed to reject fixed- λ models when we repeated the larval colony size analysis using only those taxa present in the female clutch size analysis (estimated

λ=0.84 ± 0.10 s.d. , 0% reject λ=1, 0.17% reject λ=0). In contrast, aggregative tendency consistently rejected models in which λ=1 (100%), but not λ=0 (0%), regardless of sample size. For this trait, the mean estimated λ was 0 (± 0.00 s.d.; Figure 3.2]).

3.4.3 Relationships between group-size traits

Regardless of whether or not we corrected for phylogeny, we found no evidence

of a correlation between clutch size and aggregative tendency (non-phylogenetic: r =

0.0255, p = 0.5547, Figure 3.3A; phylogenetic: estimated r = -0.0733 ± 0.0495 s.d., 0%

reject null, Figure 3.3D). Likewise, we found no evidence that larval aggregative

2 tendency predicted larval colony size across species (non-phylogenetic: F1,17 = 0.0216, R

= 0.0013, p = 0.8850, Figure 3.3C; phylogenetic: estimated R2 =0.0870 ± 0.0289, 0.005%

reject null, Figure 3.3F). In contrast, our regression analyses consistently indicated a

relationship between clutch size and larval colony size (non-phylogenetic: F1,14 =

13.8751, R2 = 0.4978, p = 0.0023, Figure 3.3B; phylogenetic: estimated R2 =0.4997 ±

0.0356, 100% reject null, Figure 3.3E). Importantly, these relationships were in the predicted direction: larger clutches consistently produced larger larval groups.

Of the eight possible statistical models incorporating some combination of the explanatory traits, clutch size and aggregative tendency, and their interaction; the best fitting model with the lowest AICC score was clutch size alone (AICC = 23.77864; p =

0.0023 compared to a null model). The next most likely model used clutch size and the

interaction between clutch size and aggregative tendency as explanatory variables (AICC

= 25.92652, p = 0.0062 compared to a null model). The third most likely model used

clutch size and aggregative tendency as explanatory variables (AICC = 27.41347, p =

0.0114 compared to a null model). All other statistical models had Δ AICC scores greater

than 5. The p-values for comparisons to a null model, AICC scores, Δ AICC scores,

Aikaike weights and evidence ratios for all statistical models are available in Table 3.5.

3.5 Discussion

Comparative data across diverse systems suggest that antagonistic coevolution

stemming from evolutionary conflicts can fuel rapid diversification of traits and species

(Crespi and Nosil 2013; Carmona et al 2015; Queller and Strassmann 2018). Such dynamics have been confirmed in the context of parent-offspring conflict in organisms providing substantial parental care (Briskie et al 1994; Lloyd and Martin 2003). Although conflicts are also expected even in the absence of parental care (Godfray and Parker

1987; Parker and Mock 1987; Godfray and Parkre 1992; Roitbeerg and Mangel 1993;

Rosenheim 1993; Sjerps and Haccou 1994), comparative studies examining such systems are lacking (but see Mayhew 1998). In this study, we have taken advantage of a genus of herbivorous insects with well-documented interspecific variation in traits that mediate group size to ask whether evolutionary dynamics are consistent with parent-offspring

conflict. While we find extensive interspecific variation in all three traits and some

decoupling from phylogeny, there is no evidence of coevolution between adult female

oviposition behavior and larval grouping behavior. Furthermore, while larvae have the

physical capacity to disperse (Benjamin 1955; Smirnoff 1960; Codella and Raffa 1995),

the close correspondence between clutch size and larval colony size suggests that variable

aggregative behavior in the larvae does not substantially alter the initial clutch size. Here,

we first discuss possible limitations of our data set and then consider possible biological

explanations for the apparent lack of strong parent-offspring conflict over group size and

propose alternative explanations for interspecific variation in group-living traits.

3.5.1 Potential limitations of this data set

Although the main findings of our study are robust to analysis method and

phylogenetic uncertainty, there are limitations of the three phenotypic datasets that should

be revisited with additional laboratory assays and field studies. First, our clutch size

dataset had more missing data than the other datasets, which reduced our power to detect

phylogenetic signal and correlated evolution with other traits. Nevertheless, we did detect

a strong relationship between clutch size and larval grouping behavior (Figure 3.3B,

Table 3.5). The clutch data also relied more heavily on literature records than other datasets (Tables 3.1 and 3.2). By and large, these literature records contain detailed descriptions of oviposition behavior and conform well to our own observations. Also, where possible, we supplemented published clutch size data with our own field observations and lab studies. For field observations, we assumed that eggs found on a single branch terminus were laid by a single female. In support of this assumption, ovipositing females of some species appear to produce an oviposition deterrent that may prevent multiple-female clutches (Tisdale and Wagner 1991). That said, “contagious oviposition” at the level of host trees has been reported for some Neodiprion species

(Wilson 1975; Codella and Raffa 1995) and there are anecdotal reports of multiple females ovipositing in a single branch terminus as well (Warren and Coyne 1958;

Codella and Raffa 1995). Thus, it is possible that some of our field clutch observations

over-estimated female clutch size. Our laboratory measurements may have also over-

estimated clutch size due to a small test arena and limited availability of branch termini.

Second, in contrast to the clutch data, all aggregative tendency data were obtained

from laboratory assays. While the use of a uniform quantitative assay likely reduced

noise in our behavioral data, there are several limitations of this assay (see Terbot II et al

2017). Most relevant for this study, the small, host-free test arena used in this assay may have simultaneously increased larval tendency to disperse (e.g., in search of host foliage)

and reduced our power to detect differences in larval aggregative tendency (e.g., due to

the inability to leave the small arena). Despite these limitations, a previous study

demonstrated that this assay is sufficient to detect behavioral differences between different Neodiprion species and between different developmental stages (feeding versus wandering larvae) of a single species (Terbot II et al 2017). Beyond assay design, another

limitation of our data is that we did not control for sex, which is difficult to determine

from larval morphology (Wilkinson 1971). Although it is currently unknown whether

sexes differ in their larval aggregative tendency, sex-based differences–which have been

documented for other larval behaviors in pine sawflies (Lindstedt et al 2018) – could

potentially introduce noise into our data. Finally, there are additional larval behaviors that

we have not measured that could also impact colony size, such as trail following during

migration between feeding sites (Costa and Louque 2001, Flowers and Costa 2003).

Third, the majority of our colony size data points came from direct field observations obtained from all developmental stages (different feeding larval instars).

Because developmental variation in gregarious behaviors is well documented in pine sawflies and other insects (Damman 1991, McClure and Despland 2011, Klok and

Chown 1999, Furniss and Dowden 1941, Hetrick 1956, Terbot et al 2017), our inclusion of different instars likely introduced noise into our larval colony size dataset.

Unfortunately, we did not have sufficient field data to compare larval colony size results from different developmental stages. Nevertheless, the strong correlation we observed between larval colony size and clutch size (Figure 3.3B, Table 3.5) suggests that group size is relatively stable across development. Another potential issue with our field observations stems from detection bias. Because larger groups of larvae are easier to see,

they may be overrepresented in our data and bias our colony-size estimates. However,

this potential source of bias is mitigated to some extent by our use of beating sheets to

locate smaller colonies and solitary individuals, both of which were well represented.

Moving forward, the relationship between clutch size, larval grouping behaviors, and

larval group size could be further clarified by carefully tracking individual clutches from

hatching to cocooning (e.g., as with N. lecontei in Costa and Louque 2001 and Flowers

and Costa 2003) in many different Neodiprion species.

Although our analysis of grouping behavior variation would clearly benefit from additional phenotypic measurements, the existing dataset produces robust results that are unlikely to be explained by biases in our data collection. Specifically, despite considerable interspecific variation in larval aggregative behaviors, our data suggests that this variation is decoupled from both maternal oviposition behavior and larval group size.

3.5.2 Explanations for lack of strong parent-offspring conflict

One potential explanation for the apparent lack of correlated evolution between female clutch-size and larval grouping behavior is that populations often lack heritable variation in grouping traits that mediate conflicts over group size. However, this explanation seems unlikely because we have detected considerable interspecific variation in both oviposition traits and larval aggregative tendency, even when measured under uniform laboratory conditions (Figure 3.1). Although non-genetic sources (e.g., plasticity) may explain some of this variation, experimental crosses have confirmed that some of this behavioral variation is attributable to genetic differences between populations and species (Knerer and Atwood 1972; Bendall et al 2017). That said, even with genetic variation for adult and larval behaviors, physiological limits on some traits could constrain trait evolution in response to parent-offspring conflict over group size. 84

For example, sawfly females typically emerge from cocoons with their full complements of eggs (Coppel and Benjamin 1965), placing an upper limit on maximum clutch size.

This upper limit could prevent clutch sizes from becoming large enough to generate strong selection on dispersal tendencies, thereby limiting the potential for repeated bouts of reciprocal evolutionary change.

Assuming populations could respond to selection stemming from parent-offspring conflict, another explanation for a lack of correlated evolution is that conflict is too weak or idiosyncratic to produce such a pattern. For example, many theoretical studies on group-size conflict assume that larval fitness is optimized at a group size of n = 1 (Parker and Mock 1987; Godfray and Parker 1992; Roitberg and Mangel 1993; but see Godfray and Ives 1988), ignoring possible advantages to larval grouping (e.g., an Allee effect

(Courchamp et al 1999)). By contrast, potential benefits of group-living are well documented (Seymour 1974; Bertram 1978; Young and Moffett 1979; Tsubaki and

Shiotsu 1982; Pulliam and Caraco 1984; Joos et al 1988; Stamp and Bowers 1990;

Codella and Raffa 1993; Klok and Chown 1999; Despland and Le Huu 2007; Fletcher

2009; McClure et al 2011a; McClure et al 2011b; McClure et al 2013; Campbell and

Stastny 2015). In Neodiprion, it has been hypothesized that group-living helps larvae overcome host defenses (Ghent 1960, but see Kalin and Knerer 1977). If benefits of group-living consistently outweigh costs in Neodiprion, parent-offspring conflict over group size could be minimal.

3.5.3 Alternative explanations for variation in group-living

Overall, our data suggest that parent-offspring conflict is not the predominant force shaping interspecific variation in female clutch size and larval grouping behavior.

Another potential explanation for among-species variation in these traits is that it 85

accumulated via genetic drift as species diverged from common ancestors. Under this scenario, however, we would expect a strong phylogenetic signal for grouping traits. By contrast, our data indicate that traits are at least partially (female clutch-size) or completely (larval aggregative tendency) decoupled from phylogeny. Therefore, we now consider other possible selection pressures that may shape intra- and interspecific variation in these traits. Ultimately, testing these hypotheses will require a combination of experiments that verify targets and agents of selection and comparative analyses that evaluate the contribution of different selective mechanisms to genus-wide variation.

For an adult female, optimal clutch size depends on the distribution and quality of available host plants, the physiological condition of the female, and the relationship between clutch size and offspring survival (Skinner 1985; Mangel 1987; Tostowaryk

1972). Variation in any of these factors could favor different oviposition strategies in different species. For example, Neodiprion species vary in their preferred oviposition hosts (Knerer and Atwood 1972; Knerer and Atwood 1973; Linnen and Farrell 2010;

Bendall et al 2017). If preferred hosts differ in their patchiness, different oviposition strategies may be favored. Interspecific variation in clutch size may also arise if the relationship between clutch size and larval survival varies among species. For example, some Neodiprion species have aposematic coloration (Tostwaryk 1972; Linnen et al

2018). When aposematic signals are used, group-living can enhance the efficacy of those signals (Gamberale and Tullberg 1998; Hatle and Salazar 2001; Riipi et al 2001).

Anecdotally, Neodiprion with brighter coloration (e.g., Neodiprion lecontei) tend to have larger colony sizes than more cryptically colored species (e.g., N. compar). However,

additional comparative work is needed to determine whether group-living correlates with

conspicuousness in Neodiprion.

For larvae, although aggregative tendency does not appear to impact larval group size, it may nevertheless play an important role in larval survival. By controlling the density of larvae within groups, variation in aggregative tendency can impact disease transmission rates, microclimate, and host physical and chemical defenses. First, because disease transmission occurs primarily through physical contact, reduced disease transmission is one possible advantage of having a diffuse larval group (i.e., low aggregative tendency). Notably, nucleopolyhedrovirus and other infectious diseases can be a major source of mortality for sawfly larvae and gregarious larvae of ecologically similar species (Bird 1955; Mohamed et al 1985; Young and Yearian 1987; Hochbereg

1991; Fletcher 2009). Second, through managing the larval group’s density, larval aggregative tendency could also be a mechanism through which group-living species optimize their micro-environment (Seymour 1974; Joos et al 1988; Codella and Raffa

1993; Klok and Chown 1999; Fletcher 2009; McClure et al 2011a). For example,

compared to warm and wet environments, cold or dry environments may favor tighter

clustering of larval groups. Third, variation in pine needle morphology and defensive

chemistry among pine species may favor different larval densities. When feeding in

groups, sawfly larvae form a characteristic “ring” around a needle near the tip and, as a

group, consume the needle tissue down to the fascicle (Ghent 1960). However, the ability

to form a stable feeding ring may decrease on thin- and long-needled hosts. Reduced

aggregative tendency may prevent larvae from overloading thin needles unable to bear

their weight. By contrast, thick and resinous needles may favor increased aggregative

tendency to ensure a sufficient number of larvae to establish feeding sites and distribute

host resins (Ghent 1960).

3.5.4 Conclusions

Comparative analysis is a powerful tool for testing and honing intuitions derived

from evolutionary theory. For example, although some theoretical work suggests that

small, gregarious broods of parasitoids are evolutionary unstable and transitions from

solitary to gregarious states should be rare (Godfray 1987), clutch-size data from

parasitoid wasps suggests that such transitions have occurred many times independently

(Mayhew 1998). Analogously, although theory demonstrates the potential for parent-

offspring conflict over group size in plant-feeding insects (Roitberg and Mangel 1993),

our data suggest that this conflict is not sufficiently strong or consistent to drive

correlated evolution of adult and larval grouping traits in pine sawflies. To be clear, these

findings do not rule out parent-offspring conflict entirely. Rather, they suggest that when parental care is minimal, parent-offspring conflict is not a major driver of long-term patterns of phenotypic change. Although these trends are intriguing, more comparative data are needed to evaluate whether some forms of conflict are more likely than others to leave a detectable phenotypic footprint in phylogenetic comparative analyses.

3.6 Tables And Figures

Table 3.1 Table of literature sources for colony size and egg clutch size data

Citation Species Colony Size Records ClutchEgg Size Records Atwood, 1961 N. compar 1 Atwood and Peck, 1943 N. abbotii 1 Atwood and Peck, 1943 N. compar 1 Baker, 1972 N. lecontei 1 Baker, 1972 N. taedae 1 Becker, 1965 N. rugifrons 1 Becker, 1965 N. dubiosus 2 2 Becker, 1965 N. nigroscutum 1 Becker and Benjamin, 1964 N. swainei 1 Becker and Benjamin, 1967 N. nigroscutum 2 1 Codella and Raffa, 2002 N. lecontei 1 Ghent, 1955 N. pratti 1 Ghent and Wallace, 1958 N. swainei 1 Hetrick, 1941 N. taedae 1 Hetrick, 1956 N. abbotii 1 Hetrick, 1956 N. hetricki 1 Jansons, 1966 N. compar 1 Knerer, 1984 N. pratti 1 Knerer, 1990 N. maurus 1 Lyons, 1964 N. swainei 4 Middleton, 1921 N. lecontei 1 Rauf and Benjamin, 1980 N. maurus 1 Rauf and Benjamin, 1980 N. pinetum 1 Schedl, 1938 N. dubiosus 1 Smirnoff, 1960 N. swainei 2 Tostowaryk, 1972 N. pratti 1 Tostowaryk, 1972 N. swainei 1 Wilkinson, 1961 N. rugifrons 3 1 Wilkinson, 1965 N. merkeli 1 Wilkinson, 1971 N. merkeli 1 Wilkinson, 1978 N. pratti 1 Wilkinson, Becker, and Benjamin, 1966 N. rugifrons 1 1 Wilson, 1971 N. pratti 1 1 Wilson, 1977 N. nigroscutum 1 Wilson, 1977 N. abbotii 1

Table 3.2 Summary of sample sizes and sources of data for Chapter 3 Aggregative Colony Size Egg Clutch Size Tendency Species Field Collection Literature Field Lab Literature Total Total Lab Behavior Observations Logs References Observations Behavior References N. abbotii 7 2 0 9 0 0 3 3 19 N. compar 32 30 2 64 1 0 1 2 8 N. dubiosus 3 31 1 35 9 0 3 12 27 N. excitans 8 3 0 11 0 0 0 0 36 N. fabricii 14 3 0 17 0 2 0 2 12 N. hetricki 16 0 0 16 0 0 1 1 35 N. knereri 4 1 0 5 0 0 0 0 8 N. lecontei 26 11 1 38 1 0 3 4 26

90 N. maurus 19 0 0 19 0 0 2 2 22 N. merkeli 6 0 0 6 0 0 2 2 10 N. nigroscutum 12 2 4 18 1 0 2 3 8 N. pinetum 7 2 0 9 0 13 1 14 22 N. pinusrigidae 7 2 0 9 7 0 0 7 42 N. pratti 44 2 1 47 0 3 5 8 46 N. rugifrons 2 30 4 36 1 0 3 4 16 N. swainei 6 10 2 18 0 0 7 7 11 N. taedae 19 0 0 19 0 0 2 2 23 N. virginiana 1 0 0 1 0 2 0 2 19 N. warreni 2 0 0 2 0 0 0 0 6

Table 3.3 Collection information and summary of data points per site Asterisks (*) indicate videos that were the result of combined colonies (see Table 3.4)

Lab -

Observations Collection identifier Collection Species Size of Colony Number Observations Egg of Field Number Clutch of Aggregative Number Videos Tendency Number of In Cages Oviposition Collection of Date Latitude Longitude 001-04 N. abbotii 1 04/23/04 30.2908 -82.0749 NS013 N. abbotii 1 09/01/13 35.1310 -85.4000 NS014 N. abbotii 1 09/01/13 35.5670 -84.4630 NS021 N. abbotii 1 1 03/19/14 29.7797 -84.7011 NS027 N. abbotii 1 04/28/14 35.4506 -80.5947 122-04B N. abbotii 1 07/20/14 35.2588 -81.3600 NS072 N. abbotii 2 08/18/14 45.1316 -91.9247 NS086 N. abbotii 1* 09/07/14 38.1722 -83.5570 NS088 N. abbotii 1* 09/07/14 38.1039 -83.5113 NS114.03 N. abbotii 1 1 04/25/15 36.1791 -85.9456 NS117.03 N. abbotii 4 04/25/15 35.9419 -86.5278 NS141 N. abbotii 3 05/10/15 37.7862 -80.3004 NS162.01 N. abbotii 1 5 07/25/15 44.5511 -68.3945 147-02 N. compar 1 08/08/02 45.9264 -77.3256 148-02 N. compar 1 08/08/02 45.9264 -77.3256 149-02 N. compar 1 08/08/02 45.9264 -77.3256 154-02 N. compar 1 08/08/02 45.9316 -77.3332 158-02 N. compar 1 08/08/02 45.9316 -77.3332 159-02 N. compar 1 08/08/02 45.9673 -77.3715 194-02 N. compar 1 08/11/02 46.1334 -80.6873 197-02 N. compar 2 08/11/02 46.6240 -81.4544 203-02 N. compar 1 08/11/02 46.6240 -81.4544 224-02 N. compar 1 08/13/02 48.5500 -89.7115 234-02 N. compar 1 08/14/02 49.7247 -94.2481 241-02 N. compar 1 08/14/02 49.7247 -94.2481 255-02 N. compar 1 08/15/02 48.6885 -93.0019 263-02 N. compar 1 08/16/02 48.6315 -85.3619 265-02 N. compar 1 08/16/02 48.5423 -85.1882 266-02 N. compar 1 08/16/02 48.0295 -84.6518 289-02 N. compar 1 08/17/02 48.2203 -82.0736 312-02 N. compar 1 08/18/02 47.4835 -81.8459 321-02 N. compar 1 08/18/02 47.4849 -81.4034

Table 3.3 (continued) 326-02 N. compar 1 08/18/02 47.6471 -80.9150 371-02 N. compar 1 09/25/02 41.8828 -70.6385 378-02 N. compar 1 09/25/02 41.8658 -70.6576 380-02 N. compar 1 09/25/02 41.8658 -70.6576 111-04A N. compar 1 07/19/04 36.1370 -85.5031 114-04 N. compar 1 07/19/04 36.0484 -84.9640 122-04A N. compar 1 07/20/04 35.2588 -81.3600 128-04 N. compar 1 07/22/04 37.1854 -81.1285 163-04 N. compar 1 07/29/04 45.4906 -70.2360 182-04 N. compar 1 08/14/04 44.5709 -91.6352 214-04 N. compar 1 08/17/04 46.5007 -90.4621 NS029 N. compar 1 05/04/14 37.1015 -77.5426 NS148 N. compar 1 06/14/15 33.9282 -83.3761 NS168.01 N. compar 1* 08/13/15 45.9263 -77.3254 NS169.01 N. compar 1* 08/13/15 45.9315 -77.3333 NS170.01 N. compar 1* 08/14/15 46.6231 -81.4552 NS172.01 N. compar 1 1* 08/14/15 47.4648 -81.8467 NS174.01 N. compar 1 2* 08/15/15 48.0456 -84.5494 NS175 N. compar 1* 08/15/15 48.0297 -84.6513 NS176 N. compar 1 1* 08/15/15 48.5437 -85.1911 NS178.01 N. compar 1 1* 08/16/15 48.4161 -89.6412 NS182.01 N. compar 1 1* 08/17/15 46.5089 -90.5027 NS183 N. compar 1 08/17/15 46.5006 -90.4626 NS184.01 N. compar 1* 08/17/15 46.1149 -90.5511 NS217 N. compar 6 1 08/15/16 48.0302 -84.6490 NS218 N. compar 11 2+1* 08/15/16 48.0459 -84.5507 NS220 N. compar 5 1* 08/15/16 48.6312 -85.3626 NS221 N. compar 2 1* 08/15/16 48.7935 -87.0895 NS217 N. dubiosus 2 08/15/16 48.0302 -84.6490 203-02 N. dubiosus 1 08/11/02 46.6240 -81.4544 210-02 N. dubiosus 1 08/13/02 48.3780 -89.5799 216-02 N. dubiosus 1 08/13/02 48.3763 -89.5844 217-02 N. dubiosus 1 08/13/02 48.4156 -89.6378 226-02 N. dubiosus 1 08/13/02 48.5500 -89.7115 245-02 N. dubiosus 1 08/14/02 49.7247 -94.2481 252-02 N. dubiosus 1 08/15/02 48.6885 -93.0019 276-02 N. dubiosus 1 08/16/02 47.9763 -84.5318 278-02 N. dubiosus 1 08/16/02 47.9763 -84.5318 280-02 N. dubiosus 1 08/16/02 47.9584 -84.2683 281-02 N. dubiosus 1 08/17/02 47.9337 -83.106 282-02 N. dubiosus 1 08/17/02 48.0934 -82.6727 284-02 N. dubiosus 1 08/17/02 48.0934 -82.6727 92

Table 3.3 (continued) 285-02 N. dubiosus 1 08/17/02 48.0934 -82.6727 286-02 N. dubiosus 1 08/17/02 48.1851 -82.2276 288-02 N. dubiosus 1 08/17/02 48.2203 -82.0736 290-02 N. dubiosus 1 08/17/02 48.2203 -82.0736 293-02 N. dubiosus 1 08/17/02 48.2203 -82.0736 295-02 N. dubiosus 1 08/17/02 48.3052 -81.7574 297-02 N. dubiosus 1 08/17/02 48.3651 -81.5758 300-02 N. dubiosus 1 08/17/02 47.8084 -81.5917 306-02 N. dubiosus 1 08/17/02 47.8084 -81.5917 309-02 N. dubiosus 1 08/18/02 47.7059 -81.7576 311-02 N. dubiosus 1 08/18/02 47.4835 -81.8459 323-02 N. dubiosus 1 08/18/02 47.4849 -81.4034 327-02 N. dubiosus 1 08/18/02 47.6471 -80.915 329-02 N. dubiosus 1 08/18/02 47.6471 -80.915 330-02 N. dubiosus 1 08/18/02 47.6471 -80.915 331-02 N. dubiosus 1 08/18/02 47.6651 -80.6184 334-02 N. dubiosus 1 08/18/02 47.6651 -80.6184 338-02 N. dubiosus 1 08/19/02 47.4254 -79.7427 161-04 N. dubiosus 1 07/29/04 45.4872 -70.2462 NS057 N. dubiosus 2 08/03/14 46.6231 -81.4591 NS059 N. dubiosus 2 08/04/14 48.1717 -80.2546 NS060 N. dubiosus 4 08/04/14 47.7313 -80.3358 NS061.01 N. dubiosus 6 08/04/14 47.6537 -81.0517 NS062 N. dubiosus 4 08/04/14 47.4836 -81.8459 NS064 N. dubiosus 4 08/05/14 48.2414 -82.3542 NS066 N. dubiosus 4 08/05/14 47.8744 -83.2854 NS151.01 N. dubiosus 2 06/27/15 48.2205 -82.0732 NS151.03 N. dubiosus 1 06/27/15 48.2205 -82.0732 NS151.04 N. dubiosus 1 06/27/15 48.2205 -82.0732 NS151.05 N. dubiosus 1 06/27/15 48.2205 -82.0732 NS151.06 N. dubiosus 1 06/27/15 48.2205 -82.0732 NS151.07 N. dubiosus 1 06/27/15 48.2205 -82.0732 NS151.08 N. dubiosus 1 06/27/15 48.2205 -82.0732 NS152.01 N. dubiosus 1 06/27/15 47.4838 -81.8460 NS152.02 N. dubiosus 1 06/27/15 47.4838 -81.8460 NS154.01 N. dubiosus 1 06/27/15 46.3111 -81.6557 NS219 N. dubiosus 1 08/15/16 48.5437 -85.1910 175-03 N. excitans 1 11/23/03 29.6798 -83.2567 179-03 N. excitans 1 11/25/03 28.7869 -81.9818 099-04 N. excitans 1 07/14/04 31.4978 -84.5933 NS094 N. excitans 1 2 10/18/14 29.7173 -82.4570 NS095 N. excitans 1 1 10/18/14 29.7184 -82.4562 93

Table 3.3 (continued) NS097 N. excitans 1 10/18/14 29.7351 -82.4455 NS098 N. excitans 1 10/18/14 29.7309 -82.4520 NS100.01 N. excitans 4 10/19/14 29.7074 -82.4522 NS100.02 N. excitans 5 10/19/14 29.7074 -82.4522 NS104 N. excitans 7 11/09/14 29.7076 -82.4523 NS108 N. excitans 1 9 11/10/14 28.7870 -81.9818 NS133.02 N. excitans 1 4 05/09/15 36.4248 -77.6326 NS134 N. excitans 1 05/09/15 36.4647 -77.8229 NS135.02 N. excitans 1 4 05/09/15 36.4379 -78.0875 109-04 N. fabricii 1 07/18/04 35.9332 -86.5323 111-04B N. fabricii 1 07/19/04 36.1370 -85.5031 115-04 N. fabricii 1 07/19/04 36.0222 -85.0438 NS023.02 N. fabricii 1 04/27/14 35.9292 -86.5233 NS128 N. fabricii 1 05/08/15 35.4745 -80.2625 NS133.01 N. fabricii 2 2 05/09/15 36.4248 -77.6226 NS135.01 N. fabricii 1 2 05/09/15 36.4378 -78.0875 NS135.03 N. fabricii 2 3 05/09/15 36.4378 -78.0875 NS138.02 N. fabricii 1 05/10/15 37.2094 -77.4743 NS142 N. fabricii 1 05/25/15 38.4545 -76.0661 NS143 N. fabricii 2 4 05/25/15 38.0627 -76.5344 NS144 N. fabricii 3 2 05/25/15 37.5005 -76.3014 NS023.01 N. hetricki 5 04/27/14 35.9292 -86.5233 NS031 N. hetricki 2 05/05/14 37.6843 -77.0825 NS113.02 N. hetricki 3 8 04/25/15 36.1397 -85.8066 NS114.02 N. hetricki 5 7 04/25/15 35.8104 -86.3984 NS120.01 N. hetricki 5 05/09/15 36.5533 -78.1801 NS136.01 N. hetricki 1 3 05/09/15 36.5533 -78.1801 NS136.02 N. hetricki 1 1 05/09/15 36.5533 -78.1801 NS138.01 N. hetricki 1 3 05/10/15 37.2094 -77.4743 NS138.02 N. hetricki 1 3 05/10/15 37.2094 -77.4743 NS138.03 N. hetricki 1 05/10/15 37.2094 -77.4743 NS140.03 N. hetricki 2 05/10/15 37.1959 -77.5244 082-04 N. knereri 1 07/11/04 29.0046 -81.7574 NS101 N. knereri 1 10/20/14 29.0046 -81.7581 NS102 N. knereri 1 3 10/20/14 29.0340 -81.6404 NS214 N. knereri 2 5 07/11/16 29.0040 -81.7577 171-02 N. lecontei 1 08/10/02 44.7305 -79.1686 174-02 N. lecontei 1 08/10/02 44.7305 -79.1686 175-02 N. lecontei 1 08/10/02 44.7305 -79.1686 345-02 N. lecontei 1 08/19/02 46.3949 -79.2442 372-02 N. lecontei 1 09/25/02 41.8741 -70.6524 075-04 N. lecontei 1 07/10/04 28.0955 -81.2752

Table 3.3 (continued) 076-04 N. lecontei 1 07/10/04 28.0955 -81.2752 087-04 N. lecontei 1 07/13/04 29.7476 82.4775 106-04 N. lecontei 1 07/17/04 32.0738 -83.7606 188-04 N. lecontei 1 08/15/04 44.7589 -91.4570 207-04 N. lecontei 1 08/17/04 45.9753 -90.4964 NS043.03 N. lecontei 8 07/02/14 45.8223 -91.8884 NS050 N. lecontei 1 07/04/14 44.8629 -89.6366 NL006 N. lecontei 1 11/08/14 30.5931 -84.3652 NL007 N. lecontei 1 11/08/14 30.2906 -84.3514 NS145.01 N. lecontei 2 06/13/15 35.9803 -85.0152 NS145.03 N. lecontei 2 06/13/15 35.9803 -85.0152 NS145.04 N. lecontei 1 06/13/15 35.9803 -85.0152 NS145.05 N. lecontei 1 06/13/15 35.9803 -85.0152 NS158 N. lecontei 2 07/09/15 38.1003 -83.5035 NS160.01 N. lecontei 1 07/23/15 40.3681 -74.3026 NS184.02 N. lecontei 1 08/17/15 46.1149 -90.5511 NS186.01 N. lecontei 2 4 08/23/15 35.9803 -85.0152 NS186.02 N. lecontei 1 08/23/15 35.9803 -85.0152 NS186.03 N. lecontei 1 08/23/15 35.9803 -85.0152 NS204 N. lecontei 1 06/28/16 45.8223 -91.8884 NS212.01 N. lecontei 5 6 07/10/16 29.0978 -82.1861 NS216 N. lecontei 3 8 07/11/16 29.3207 -81.7267 NS217 N. lecontei 1 08/15/16 48.0302 -84.6490 NS037 N. maurus 3 7 06/17/14 45.6643 -89.4919 NMr002 N. maurus 4 6 07/02/14 45.9975 -91.6183 NMr003 N. maurus 1 1 07/03/14 45.6019 -89.3511 NS203 N. maurus 3 06/28/16 45.5792 -91.759 NS205.02 N. maurus 1 3 06/28/16 45.8428 -91.8877 NS206.02 N. maurus 3 06/29/16 46.5544 -91.3228 NS208 N. maurus 1 3 06/29/16 46.1150 -90.5513 NS210.01 N. maurus 1 06/29/16 46.0538 -89.4857 NS210.02 N. maurus 2 2 06/29/16 46.0538 -89.4857 NS105 N. merkeli 1 3 11/10/14 29.9238 -81.5317 NS106 N. merkeli 1 2 11/10/14 26.9260 -81.5177 NS107 N. merkeli 1 11/10/14 26.9214 -81.5086 NS109 N. merkeli 1 3 11/10/14 28.7870 -81.9818 NS212.02 N. merkeli 1 07/10/16 29.0978 -82.1861 NS212.03 N. merkeli 1 1 07/10/16 29.0978 -82.1861 NS213 N. merkeli 1 07/10/16 28.7864 -81.9816 214-02 N. nigroscutum 1 08/13/02 48.3763 -89.5844 209-04 N. nigroscutum 1 08/17/04 46.0865 -90.5507 NS047 N. nigroscutum 1* 07/04/14 45.1145 -88.3591

Table 3.3 (continued) NS051 N. nigroscutum 1 1* 07/05/14 44.8440 -89.6906 NS070.03 N. nigroscutum 2 08/18/14 45.1248 -92.3836 NS205.01 N. nigroscutum 4 1 06/28/16 45.8428 -91.8877 NS206.01 N. nigroscutum 4 1+1* 06/29/16 46.5544 -91.3228 NS211 N. nigroscutum 3 1 2+1* 06/30/16 44.8440 -89.6905 NP003 N. pinetum 16 07/2313 36.0023 -85.0438 NP034 N. pinetum 2 08/15/15 38.0324 -84.5655 NP036 N. pinetum 2 08/19/15 38.0324 -84.5655 NP039 N. pinetum 7 08/20/15 37.9706 -84.4976 NP041 N. pinetum 1 08/20/15 37.9729 -84.5004 377-02 N. pinetum 1 09/25/02 41.8459 -70.6798 113-04 N. pinetum 1 07/19/04 35.98 -85.0152 NP047 N. pinetum 6 09/06/15 39.6188 -83.6037 NP048 N. pinetum 1 09/06/15 39.6188 -83.6037 NP049 N. pinetum 1 09/06/15 39.6188 -83.6037 NP050 N. pinetum 2 09/06/15 39.6188 -83.6037 NP051 N. pinetum 1 09/06/15 39.6188 -83.6037 NP052 N. pinetum 1 4 09/06/15 39.6188 -83.6037 NP053 N. pinetum 1 09/06/15 39.6188 -83.6037 373-02 N. pinusrigidae 1 09/25/02 41.8459 -70.6776 375-02 N. pinusrigidae 1 09/25/02 41.8459 -70.6776 NS188.02 N. pinusrigidae 1 1 10 09/13/15 39.8864 -74.5099 NS188.03 N. pinusrigidae 1 1 09/13/15 39.8864 -74.5099 NS188.04 N. pinusrigidae 1 1 12 09/13/15 39.8864 -74.5099 NS189.01 N. pinusrigidae 1 1 09/13/15 39.8860 -74.5061 NS189.02 N. pinusrigidae 1 1 4 09/13/15 39.8860 -74.5061 NS189.03 N. pinusrigidae 1 1 12 09/13/15 39.8860 -74.5061 NS189.04 N. pinusrigidae 1 1 4 09/13/15 39.8860 -74.5061 076-02 N. pratti 1 06/29/02 46.3113 -81.6553 103-02 N. pratti 1 06/30/02 45.9298 -77.3318 NS022 N. pratti 6 6 3 04/13/14 38.0847 -83.5098 NS034 N. pratti 1 4 06/16/14 46.1113 -89.6693 NS110 N. pratti 2 04/24/15 36.8544 -84.8457 NS112 N. pratti 1 1 04/24/15 36.5753 -85.1380 NS117.01 N. pratti 6 6 04/25/15 35.9419 -86.5278 NS118.02 N. pratti 1 1 04/25/15 35.9419 -86.5278 NS118.03 N. pratti 1 04/25/15 35.9419 -86.5278 NS122 N. pratti 2 1 04/26/15 34.5275 -84.9465 NS123.01 N. pratti 3 05/06/15 38.0860 -83.5117 NS123.02 N. pratti 1 05/06/15 38.0860 -83.5117 NS123.03 N. pratti 3 4 05/06/15 38.0860 -83.5117 NS124 N. pratti 4 05/06/15 38.0860 -83.5117 96

Table 3.3 (continued) NS125 N. pratti 4 05/06/15 38.0843 -83.5098 NS140.01 N. pratti 1 1 05/06/15 37.1959 -77.5244 NS138.04 N. pratti 1 05/10/15 37.2094 -77.4743 NS150 N. pratti 1 06/14/15 47.9385 -83.0621 NS153 N. pratti 1 06/27/15 46.6237 -81.4547 NS154.02 N. pratti 4 4 06/27/15 46.3111 -81.6557 NS155 N. pratti 2 1 06/28/15 46.3023 -79.3835 NS163 N. pratti 2 6 07/25/15 44.4801 -67.5931 NS164 N. pratti 1 07/25/15 44.7799 -67.5930 NS165 N. pratti 1 3 07/25/15 44.4774 -67.5908 NS166 N. pratti 1 07/25/15 44.4775 -67.5906 NS207 N. pratti 2 06/29/16 46.5086 -90.5015 155-02 N. rugifrons 1 08/08/02 45.9316 -77.3332 156-02 N. rugifrons 1 08/08/02 45.9316 -77.3332 157-02 N. rugifrons 1 08/08/02 45.9316 -77.3332 181-02 N. rugifrons 1 08/10/02 45.7987 -80.5352 182-02 N. rugifrons 1 08/10/02 45.7987 -80.5352 183-02 N. rugifrons 1 08/10/02 45.7987 -80.5352 184-02 N. rugifrons 1 08/10/02 45.7987 -80.5352 190-02 N. rugifrons 1 08/11/02 45.9718 -80.5760 204-02 N. rugifrons 1 08/11/02 46.6240 -81.4544 208-02 N. rugifrons 1 08/11/02 46.6240 -81.4544 242-02 N. rugifrons 1 08/14/02 49.7247 -94.2481 243-02 N. rugifrons 1 08/14/02 49.7247 -94.2481 267-02 N. rugifrons 1 08/16/02 48.0295 -84.6518 268-02 N. rugifrons 1 08/16/02 48.0295 -84.6518 273-02 N. rugifrons 1 08/16/02 48.0458 -84.5503 274-02 N. rugifrons 1 08/16/02 48.0458 -84.5503 277-02 N. rugifrons 1 08/16/02 47.9763 -84.5318 283-02 N. rugifrons 1 08/17/02 48.0934 -82.6727 287-02 N. rugifrons 1 08/17/02 48.1851 -82.2276 292-02 N. rugifrons 1 08/17/02 48.2203 -82.0736 298-02 N. rugifrons 1 08/17/02 48.3651 -81.5758 299-02 N. rugifrons 1 08/17/02 47.8084 -81.5917 304-02 N. rugifrons 1 08/17/02 47.8084 -81.5917 310-02 N. rugifrons 1 08/18/02 47.4835 -81.8459 313-02 N. rugifrons 1 08/18/02 47.4835 -81.8459 314-02 N. rugifrons 1 08/18/02 47.4771 -81.5260 318-02 N. rugifrons 1 08/18/02 47.4771 -81.5260 319-02 N. rugifrons 1 08/18/02 47.4849 -81.4034 324-02 N. rugifrons 1 08/18/02 47.4849 -81.4034 333-02 N. rugifrons 1 08/18/02 47.6651 -80.6184 97

Table 3.3 (continued) NS044 N. rugifrons 6 07/02/14 45.8396 -91.9139 NS052 N. rugifrons 1 4 07/20/14 45.8396 -91.9139 NS070.01 N. rugifrons 1 08/18/14 45.1248 -92.3836 NS172.04 N. rugifrons 3 08/14/15 47.4648 -81.8467 NS200 N. rugifrons 1 1 2 06/28/16 44.5711 -91.6363 193-02 N. swainei 1 08/11/02 46.0580 -80.6252 195-02 N. swainei 1 08/11/02 46.1334 -80.6873 254-02 N. swainei 1 08/15/02 48.6885 -93.0019 301-02 N. swainei 1 08/17/02 47.8084 -81.5917 307-02 N. swainei 1 08/17/02 47.8084 -81.5917 336-02 N. swainei 1 08/18/02 47.6387 -79.9771 337-02 N. swainei 1 08/18/02 47.6387 -79.9771 179-04 N. swainei 1 08/14/04 44.5814 -91.5290 185-04 N. swainei 1 08/14/04 44.5709 -91.6352 211-04b N. swainei 1 08/17/04 46.1195 -90.5544 NS053.01 N. swainei 5 07/20/13 46.1143 -90.5513 NS056.02 N. swainei 1 07/20/14 44.8440 -89.6906 NS061.02 N. swainei 5 08/04/14 47.6537 -81.0517 NS091 N. swainei 1 09/13/14 45.9974 -91.6181 NS169.02 N. swainei 1 08/13/15 45.9315 -77.3333 NS184.03 N. swainei 3 08/17/15 46.1149 -90.5511 NS184.04 N. swainei 1 08/17/15 46.1149 -90.5511 NTL003 N. taedae 1 05/23/14 36.0095 -87.3781 NTL001 N. taedae 2 05/18/14 36.3177 -94.7105 NTL004 N. taedae 4 06/09/14 37.7271 -92.7033 NS113.01 N. taedae 3 04/25/15 36.1397 -85.8066 NS114.01 N. taedae 8 3 04/25/15 36.1791 -85.9456 NS115.03 N. taedae 5 04/25/15 36.4716 -86.6816 NS117.02 N. taedae 5 8 04/25/15 35.9419 -86.5278 NS118.01 N. taedae 1 04/25/15 35.9419 -86.5278 NS119 N. taedae 2 04/25/15 35.9333 -86.5322 LL185 N. virginiana 1 07/12/15 37.3585 -77.9611 LL186 N. virginiana 4 07/12/15 37.3586 -77.9609 LL187 N. virginiana 3 07/12/15 37.3586 -77.9609 LL188 N. virginiana 3 07/12/15 37.3586 -77.9609 LL189 N. virginiana 5 07/12/15 37.3585 -77.9611 LL192 N. virginiana 3 07/12/15 37.9863 -84.4171 NS159 N. virginiana 1 2 07/22/15 39.7042 -78.3288 NS099 N. warreni 1 3 10/19/14 29.7093 -82.4542 NS103 N. warreni 1 3 11/09/14 29.7091 -82.4541

Table 3.4 Summary of combined colonies Some colonies were combined from multiple collection sites in order to obtain enough larvae for aggregative tendency videos Comb. Colony Species Colonies Used NS086_088 N. abbotii NS086, NS088 CN001 N. compar NS174.01, NS182.01 CN002 N. compar NS175, NS184.01 CN003 N. compar NS168.01, NS176, NS178.01 CN004 N. compar NS169.01, NS170.01, NS172.01, NS174.01 CN005 N. compar NS168.01, NS174.01, NS182.01 (Non-Video Only) CN006 N. compar NS218, NS220, NS221 NS051.01.C N. nigroscutum NS047, NS051.01 NS206.01_211 N. nigroscutum NS206.01, NS211 (Video Only)

Table 3.5 Non-phylogenetic model comparisons of social traits Results for non-phylogenetically corrected model comparisons. In the “Model” column, “C” refers to “colony size”, “E” to “(Egg) Clutch size”, and “A” to “aggregative tendency”. The columns contain the p-values for each model compared to the null model (colony size alone), AICc are the Akaike information critera corrected for sample size, ΔAICc are the differences between AIC scores from the best model (C ~ E), wi are the Akaike weights for each model, and ERi are the evidence ratios of each model compared to the best model (C ~ E). Clutch size is a better predictor of larval colony size than larval aggregative tendency. Model p-value AICc ΔAICc wi ERi C ~ E + A + E*A 0.0196 30.097 6.318 0.027 23.552 C ~ E + A 0.0114 27.413 3.635 0.103 6.156 C ~ E + E*A 0.0062 25.927 2.148 0.216 2.927 C ~ A + E*A 0.4044 36.205 12.427 0.001 499.326 C ~ E*A 0.1707 32.575 8.797 0.008 81.315 C ~ E 0.0023 23.779 0 0.631 1 C ~ A 0.6593 34.567 10.789 0.003 220.151 C . 31.721 7.942 0.012 53.036

100

Figure 3.1 Neodiprion species vary in multiple sociality traits Phylogenetic relationships for 19 eastern North American Neodiprion are from (Linnen and Farrell 2008a; Linnen and Farrell 2010). Box plots depict intraspecific variation for clutch size, larval aggregative tendency, and larval colony size. For all three traits, there is significant interspecific variation (ANOVAs, P<0.001).

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Figure 3.2 Phylogenetic signals of sawfly sociality traits Histograms of estimated lambda values across 15,000 trees sampled from the posterior distribution of a Bayesian analysis (Linnen and Farrell 2008a; Linnen and Farrell 2010) for the three traits of interest: egg-clutch size, aggregative tendency, and colony size. Noted on each histogram is the percentage of trees that rejected the fixed lambda models of λ=0 and λ=1. Egg-clutch size has a weak phylogenetic signal; aggregative tendency has no evidence of a phylogenetic signal; and larval colony size has a strong phylogenetic signal.

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Figure 3.3 Correlations between sawfly sociality traits Panels A-C represent analyses lacking phylogenetic correction: egg-clutch size vs. larval aggregative tendency (correlation test, A), egg-clutch size vs. larval colony size (regression analysis, B), and aggregative tendency vs. larval colony size (regression analysis, C). Panels D-F are histograms of phylogenetically corrected correlation coefficients between traits estimated for 15,000 trees from the posterior distribution of a Bayesian analysis (Linnen and Farrell 2008a; Linnen and Farrell 2010): egg-clutch size vs. larval aggregative tendency (correlation test, D), egg-clutch size vs. larval colony size (regression analysis, E), and aggregative tendency vs. larval colony size (regression analysis, F). Larval aggregative tendency is not correlated with egg-clutch size and does not influence larval colony size; however, egg-clutch size predicts larval colony size. ), egg-clutch size vs. larval colony size (regression analysis, E), and aggregative tendency vs. larval colony size (regression analysis, F). Larval aggregative tendency is not correlated with egg-clutch size and does not influence larval colony size; however, egg- clutch size predicts larval colony size.

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CHAPTER 4. SOCIAL DISTANCING AS AN IMMUNE ADAPTATION IN HERD- LIVING INSECTS

4.1 Abstract

Living in groups can be a highly advantageous lifestyle; yet proximity and

frequently high relatedness of groups leads to the risk of disease and pathogens to be

raised compared to solitary organisms. To adapt to this increased risk, organisms use a

variety of adaptations from coordinated behaviors benefiting the entire group, known as

social immunity, to more individualistic changes such as increased immune activity in

response to increased densities, i.e., density-dependent prophylaxis. Social immunity is

more often associated with more advanced social systems such as eusocial insects and

phenomenon like density-dependent prophylaxis associated with more simple social

systems. However, this is not strict dichotomy. In this study, we look at the relationship

between a social behavior and immune activity in a herding larvae system, pine sawflies

(genus Neodiprion). We analyzed this relationship both within a highly social species (N.

lecontei) as well as across a monophyletic group of 19 species found across Eastern

North America. We found a consistent and significant relationship between aggregative

tendency of larvae and their immune responsiveness; more aggregative larvae were found

to have greater immune responses. Because the behavior we used was not an estimate or

alteration of larval colony density directly, but rather a measurement of larvae’s

preference for proximity with other larvae, we believe this provides evidence for a simple

form of social immunity found within a larval herding organism. Larvae have evolved

behavioral preferences for group densities that allow for the advantages of group-living while minimizing the risk of diseases and the necessity to invest in greater immune

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responses. This simple form of social distancing among larval herds may also explain the

inconsistent presence of density-dependent prophylaxis in other ecologically similar organisms.

4.2 Introduction

Group-living is a widespread and common behavior. However, group-living is

incredibly diverse from relatively simple aggregations to highly advanced eusocial

organisms. The aggregations of larval insects into “larval herds” are particularly common

(Costa 2018). A “larval herd” lifestyle has been shown to provide many benefits

including anti-predator defenses (Tostowaryk 1972; Bertram 1978; Codella and Raffa

1993), improved foraging efficiency (Tsubaki and Shiotsu 1982; Codella and Raffa 1993;

Despland and Le Huu 2007; McClure et al 2013), and managing microenvironments

(Codella and Raffa 1993; Fletcher 2009). However, living in proximity also increases the risk of disease due to increased risk of transmission and, often, a high amount of relatedness between group members (Cremer et al 2007; Shakhar 2019).

Fortunately, the cost of increased disease risk can be offset via collective behaviors and traits including those classified as social immunity. Social immunity refers to immunity traits that are the result, at least in part, from selection on the benefit to other members of the group (Van Meyel et al 2018). The most remarkable of these behaviors are unsurprisingly found in eusocial societies that engage in coordinated behaviors like allogrooming, undertaking, and cannibalism (Cremer 2007; Cremer 2019; Liu et al 2019;

Cini et al 2020). Some of these behaviors, such as group-induced fever in bees, are effective only if the group size is large enough highlighting the collective nature of these behaviors (Bonoan et al 2020). While social immunity is most noticeable in eusocial organisms, it can be found in many group-living organisms (Van Meyel et al 2018; 105

Nuotclà et al 2019; Trienens and Rohlfs 2020). In fact, immune adaptations are likely an

essential step in the evolution of group-living (Pull and McMahon 2020). While social

immunity behaviors are some of the most impressive solutions to the increased disease

risk of group-living, emergent properties from individuals’ immune responses to group

living can also address this cost. Good examples of non-social immunity adaptations are

herd immunity (John and Samuel 2000; Cremer et al 2007; Masri and Cremer 2014) and

density-dependent prophylaxis (Van Meyel et al 2018).

Density-dependent prophylaxis, or the increase in physiological immune defenses

in response to high density groups, has been well documented within insects (Reeson et

al 1998; Barnes and Siva-Jothy 2000; Wilson et al 2001; Wilson et al 2002; Wang et al

2013). However, an increased physiological immune defense is not cost free and will

result in trade-offs of its own (Kraaieveld et al 2002; Schmid-Hempel 2005). Moreover, density-dependent prophylaxis has not been found in every group-living organism studied

(Wilson et al 2003; Fletcher 2009). One potential explanation for this inconsistency may be due to the immune challenges of group living being mitigated through social immunity behaviors. While much of the work on density-dependent prophylaxis has been in non- eusocial systems, it can still be found in some eusocial groups (Ruiz-González et al

2009), but not all (Pie et al 2005). It has been suggested that eusocial groups tend to evolve social immunity over density-dependent prophylaxis due to their obligate social life histories (Pie et al 2005). This may explain why most of the work on social immunity has been in eusocial groups or social systems beyond the complexity of simple larval herds. However, social immunity includes behaviors that reduce physical encounters and alter the overall social network dynamics (Stroeymeyt et al 2018; Liu et al 2019;

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Malagocka et al 2019). While such behaviors can involve complex sorting of group

members into specific areas of a colony, a relatively uncomplicated behavior like social

avoidance could feasibly occur in systems like larval herds and achieve similar reductions

in transmission risk. Evidence of this simple social immunity behavior in a larval herding organism could help explain inconsistencies in the evidence for density-dependent

prophylaxis from previous studies.

To test the relationship between larval density and immune activity, we studied

the larvae of pine sawflies in the genus Neodiprion. Within Neodiprion there is a

monophyletic group of 19 species endemic to Eastern North America (lecontei group)

which contains a large diversity in group-living from solitary species to those that form

the large feeding aggregations typical of larval herds (Chapter 3). Pine sawflies also

benefit from being experimentally tractable and well-studied (Terbot II et al 2017). We

have also developed reliable methods for quantifying larval aggregative tendency and

shown that this trait is distinct from larval colony size (Terbot II et al 2017; Chapter 3).

Like all insects, pine sawflies use a melanization pathway as a core part of their

immune system that can be measured to quantify immune response. The melanization

process involves recognizing foreign bodies and coating them with hemocytes and

melanin to form a capsule (Reed et al 2007; Moreno-Garcia et al 2013; Dudzic et al 2015;

Nakleh et al 2017). Capsule formation is a non-specific immune response that is known

to be lethal to parasitoids, bacteria, fungi, and viruses (Nakleh et al 2017). The

encapsulation process can be measured via multiple techniques (Moreno-Garcia et al

2013) and, despite being a common immune response to insects, the encapsulation

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response is known to vary within species and between closely related species (Russo et al

1995; Reed et al 2007; Bugila et al 2014).

Many previous studies have experimentally manipulated group sizes and

examined the impact on immune response (i.e., density-dependent prophylaxis). This

study, however, will focus on examining the natural variation in a larval social behavior

and in immune activity. Rather than examine the impact of overall group size on immune

activity, we will be assessing the relationship between immune activity and the

propensity of larvae to seek out and remain in proximity to other larvae (larval

aggregative tendency). We will also examine this within a particularly social species (N.

lecontei) as well as across the genus with and without accounting for their shared

evolutionary history. We predicted that there will be an inverse correlation between

immune activity and larval aggregative tendency. This would indicate that more socially

oriented larvae are selected to increase their immune activity to account for the increased

risk of diseases, that larvae have evolved behaviors to decrease density and lower their

disease risk without incurring costs of higher immune activity, or a combination of these

two. Notably, evidence for a possible social distancing behavior in pine sawflies could be

the first social immunity behavior found in a larval-herding organism (Meunier 2015;

Van Meyel et al 2018).

4.3 Materials and Methods

4.3.1 Collection and Rearing of Specimens

Sawflies were reared according to standard lab protocols (described in more detail

Harper et al 2016). Briefly, we transported wild-caught larval colonies to the lab in brown

paper bags. Upon arrival, we placed colonies into a paper towel-lined, plastic box with mesh lid (32.4 cm x 17.8 cm x 15.2 cm). Using clippings from their natal host species

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kept in floral water picks, we fed larvae ad libitum until larvae spun cocoons. We then

stored cocoons in size “0” gelatin capsules until adults emerged. Upon adult emergence,

we stored adult sawflies at 4°C until needed. Adults were then released into a large mesh

cage (35.6 cm x 35.6 cm x 61 cm) with Pinus banksiana seedlings. Released adults could

include mated females, unmated females with males, or unmated females depending on

the intended purpose of the cage. Due to Neodiprion’s haplodiploid sex determination,

mated females lay eggs that will develop into diploid daughters and haploid males;

however, unmated females lay eggs that produce haploid males only (Heimpel and de

Boer 2008; Harper et al 2016). Once hatched, larvae were allowed to consume seedling

foliage until transferred to the plastic boxes and reared on clipped P. banksiana foliage as

described above.

Larvae used for intraspecific studies within N. lecontei were reared from virgin

females from four colonies originally collected between July 16, 2013 and August 22,

2013. We kept Larvae from a single female together as single families during study.

Larvae used for interspecific comparisons were originally wild caught larvae collected

between March 19, 2014 and August 15, 2016 and were used for data collection prior to

spinning cocoons. We combined larvae of the same species from each collection site into

single colonies for analyses. Occasionally, we combined larvae from different collection sites due to larval number requirements for our aggregative tendency assay (described below). Details on the collection sites for the original intraspecific colonies and the interspecific colonies can be found in Table 2.1 and details regarding combined colonies

can be found in Table 3.4.

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4.3.2 Aggregative Tendency

We used a behavioral assay described in Terbot II et al 2017 to measure larval

aggregative tendency. We placed eight larvae from a single family for our intraspecific

data set or five larvae from a single colony for our interspecific data set. Larvae were

equally spaced along the perimeter of a 14.5 cm diameter petri dish. Using a Lenovo

Ideapad laptop and either a Logitech or Microsoft webcam, we recorded larval behavior

for 90 minutes. We then used the program Video Image Master Pro (A4Video 2016) and a custom Java application to extract one image still per every 3 minutes of video (Terbot

II et al 2017). For each extracted image, we measured all pairwise distances between the five larval heads. We excluded the first 12 images (36 minutes) of each video (Terbot II et al 2017) using Microsoft Excel for Microsoft 365 (Microsoft 2020) and a custom Perl script to allow for an acclimation period. This produced a single mean pairwise larval distance measure for each video which was then log-transformed and averaged between families, colonies, and species. Larger mean pairwise distances correspond to a greater average distance between larvae and, therefore, a lower “larval aggregative tendency”

(i.e., a greater tendency to leave the group and lower colony density); smaller values correspond to a smaller average distance and a higher “larval aggregative tendency” (i.e., a greater tendency to seek out other larvae and a larger colony density). Total sample sizes for each family, colony, and species are available in Table 4.2, Table 4.3, and Table

4.4 respectively.

4.3.3 Immune Assay

The immune assay was performed alongside other phenotypic measurements. We first isolated individual larva into 10 cm diameter petri dish lined with paper towel and provided a small clipping of the larval host foliage. We then left larvae in their rearing

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chamber for at least one hour to acclimate to the dish. We then removed larvae from the petri dish and subjectively assessed defensive response and body coloration. Following this, we collected defensive regurgitant into 5 µl capillary tube and measured body length and head capsule width. We placed regurgitant filled capillary tubes into 1.5 mL microcentrifuge tubes, added 150 µL of hexane, and stored tubes in a -20°C freezer. We returned larvae to their petri dishes again for a minimum of one hour. We then removed larvae and anesthetized them using a CO2 diffused through a mesh platform. Once larvae were non-responsive, we photographed larvae and took spectrophotometric measurements of their bodies. While still anesthetized, we created a small hole in the larval cuticle above their second true leg on their right side using a sterile needle. From this hole, we extracted 5 µL of hemolymph, mixed with 5 µL of 1 x PBS buffer, and stored on ice until final storage at -80°C. After hemolymph was collected, we inserted a small nylon monofilament implant (Zebco Omniflex 4-Pound Test Fishing Line) into the body of the larvae allowing for a small amount to hang out for later retrieval. We returned larvae to their petri-dishes and returned to the rearing chamber for approximately 24 hours. After 24 hours, the implant was removed and stored in a 1.5 ml microcentrifuge tube at room temperature. We then placed larvae into ethanol-filled tubes stored at 4°C or -20°C.

Later, we removed the implant from the tube and photographed from 3 angles using a microscope before being returned to the microcentrifuge tube. The use of three photographs of each implant was done to account for potential heterogeneity in melanin deposition (Moreno-Garcia et al 2013). Implant photographs were then analyzed using

Adobe Photoshop and a custom Perl script to calculate the difference in the mean gray

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value between the implant and the white background of the microscope stage alongside

the area of the implant inserted into the larval body for each photo. Mean gray value

differences of zero indicating the implant was as or more white than the background were

dropped from analysis. Using Microsoft Excel for Microsoft 365 (Microsoft 2020), we

multiplied the mean gray difference by the inserted area, scaled by 1/1000, and averaged

the three values for each implant to generate a single immune response value for each

larvae. We then calculated the average value for each family (intraspecific), colony

(interspecific), or species (interspecific) and then used a log-transformation. The larger this value is the greater amount of capsule formation occurred on the inserted portion of the implant. Total sample sizes for each family, colony, and species are available in Table

4.2, Table 4.3, and Table 4.4 respectively.

4.3.4 Data Curation

After collecting our two data sets (aggregative tendency and immune response), we first checked that there was sufficient between group variation compared to within group variation. For the intraspecific data set, we compared between families, and for the interspecific data set, we compared between species. We conducted our statistical analysis using R (R Core Team 2020) to perform ANOVA testing and repeatability analysis using the “aov” function of the “stats” package and “rpt” function of the “rptR” package respectively (R Core Team 2020; Stoffel et al 2017).

4.3.5 Intraspecific Analysis

We examined the relationship between aggregative tendency and immune response within our intraspecific data set using R. First, we built a general linear model with the “glm” function of the “stats” package (R Core Team 2020) using aggregative tendency as the predictor variable for immune response. Second, we built a linear mixed-

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effects model with the “lme” function of the “nlme” package (Pinheiro et al 2020) of the same relationship with the original source colony as a random effect. The general linear model was compared to a null model using an F-test with the “anova.glm” function of the

“stats” package (R Core Team 2020) and a likelihood ratio test with the “lrtest” function of the “lmtest” package (Zeileis and Hothorn 2002). The linear mixed-effects model was compared to a null model using a Likelihood Ratio test with the same function and package.

4.3.6 Phylogenetic Signal

Before analyzing our interspecific data set, we first determined the extent of phylogenetic signal within the immune response and aggregative tendency traits. For this and other analyses accounting for shared evolutionary history, we used the best estimated species tree based on three nuclear gene regions determined in previous studies (Linnen and Farrell 2008a; Linnen and Farrell 2010). Using the “phylosig” function in the

“phytools” package (Revell 2012) of R, we estimated the Pagel’s λ value for both traits and compared using a likelihood ratio test to a model that fixed λ to 0. The values used in this analysis were the species means for both traits based on all collected data. When using comparative analyses between closely related species, it is considered best practices to account for phylogeny regardless of the strength and significance of the phylogenetic signals. This is due to the ability for many phylogenetic comparative analyses to include models without underlying phylogenetic structures (e.g. when Pagel’s λ is set to 0) and the possibility that the residuals of the relationships analyzed may themselves have phylogenetic signals that one or more underlying traits lack (Symonds and Blomberg

2014). Therefore, we analyzed our interspecific data set with and without phylogenetic correction. 113

4.3.7 Interspecific Analysis without Phylogenetic Correction

We analyzed our interspecific data set using a by-colony data set composed of

mean values for both traits for each colony that had measurements for both traits and with

a by-species data set composed of overall species means for both traits. Both by-colony

and by-species data sets were modeled with a general linear model. General linear models

were once again compared to null models using an F-test and likelihood ratio test.

4.3.8 Interspecific Analysis with Phylogenetic Correction

Our final analysis examined the relationship between immune response and

aggregative tendency within the genus Neodiprion while accounting for phylogeny. We

used the “pgls.SEy” function of the “phytools” (Revell 2012) package which performs a

modified phylogenetic generalized least squares model based off of Ives (2007). This

method accounts for intraspecific variation in the predicted value (immune response)

based on the species standard error of that trait (calculated from species variances).

Models were constructed for a range of values of Pagel’s λ from 0 to 1 and compared to

null models using the “anova.gls” function of the “nlme” package (Pinheiro et al 2020).

4.4 Results

4.4.1 Data Curation

The variation between families was sufficient compared to within-family variation for both intraspecific aggregative tendency and immune response. This was true when assessed with repeatability analysis (Immune response, R = 0.3238, P < 0.0001;

Aggregative tendency, R = 0.2705 P = 0.0066) and using ANOVA testing (Immune response, P < 0.0001; Aggregative tendency, P = 0.0008). The same was also true for the interspecific data set when assessed using repeatability (Immune response, R = 0.1160, P

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<0.0001; Aggregative tendency, R = 0.1986, P < 0.0001) and ANOVA testing (Immune

response, P <0.0001; Aggregative tendency, P <0.0001).

4.4.2 Intraspecific Analysis

The general linear model predicting immune response based on aggregative tendency within N. lecontei (Figure 4.1) approached significance when analyzed using an

F-Test (F=3.9250, P = 0.0557) and significant when analyzed using a likelihood ratio test

(Log Likelihood of Null Model = -22.7362, Log Likelihood of Linear Model = -20.7697,

P = 0.0473). The mixed-effects model of the same relationship with source population included as a random effect (Figure 4.2) was significant when analyzed with a likelihood ratio test (Log Likelihood of Null Model = -21.6028, Log Likelihood of Linear Mixed-

Effects Model = -18.6249, P = 0.0147).

4.4.3 Phylogenetic Signal

There was no evidence of a strong phylogenetic correlation for either immune response (λ = 0.0072, P = 0.9873) or aggregative tendency (λ < 0.0001, P = 1). However, these are closely related species and the relationship between these two traits may themselves have a phylogenetic correlation. Considering the results of the analysis with and without phylogenetic correlation will be necessary.

4.4.4 Interspecific Analysis without Phylogenetic Correction

The interspecific analysis conducted with colony-level data using a general linear model (Figure 4.3) approached significance both with an F-Test (F = 3.8038, P = 0.0539) and a likelihood ratio test (Log Likelihood of Null Model = -78.2790, Log Likelihood of

Linear Model = -76.3751, P = 0.0510). However, the general linear model using species- level data (Figure 4.4) was significant both when analyzed with an F-Test (F= 11.2020, P

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= 0.0038) and a likelihood ratio test (Log Likelihood of Null Model = -2.7404, Log

Likelihood of Linear Model = 2.0683, P = 0.0019).

4.4.5 Interspecific Analysis with Phylogenetic Correction

The relationship between immune response and aggregative tendency when accounting for phylogeny was better than a null model for all values of Pagel’s λ (Figure

4.5). This improvement was significant when comparing the null model’s optimal value for λ (0) to the full model’s optimal value for λ (1) with a likelihood ratio test (Log

Likelihood of Null Model = -2.1564, Log Likelihood of Linear Model = 3.8743, P =

0.0005).

4.5 Discussion

Overall, there was a robust relationship between immune response and aggregative tendency with less aggregative larvae having less of an immune response to a foreign object than more aggregative larvae. This trend was significant intraspecifically and interspecifically. These results are consistent with two potential explanations. The first, density-dependent prophylaxis, would predict more sociable families and species would have more dense colonies and, thus, a greater immune response. The second explanation of this trend is larvae have been selected to have a simple form of social immunity, a reduction of their aggregative tendency resulting in a reduction of colony density without altering overall colony size. This social immunity adaptation would allow for a reduction of investment into immune defenses and, thus, the observed relationship between larval aggregative tendency and immune response. While both explanations are possible and not entirely mutually exclusive, we believe this study alongside previous work more strongly supports variation in aggregative tendency as a social immunity adaptation.

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While this relationship appears to be entirely consistent with the density- dependent prophylaxis observed previously across social insects (Reeson et al 1998;

Barnes and Siva-Jothy 2000; Wilson et al 2001; Wilson et al 2002; Wang et al 2013), we

have studied this relationship from a unique angle that suggests the observed relationship

is distinct from density-dependent prophylaxis. Most studies examining this phenomenon

quantify or manipulate sociality using group sizes, though some have specifically sought

to look at the impacts of both overall size and density (Trienens and Rohlfs 2020). In this study, social behavior was quantified using a larval behavior to determine their preferred larval density (aggregative tendency). As well, previous work in this system has demonstrated that larval colony size is most related to maternal egg clutching behavior rather than larval disposition to other larvae (Chapter 3).

Because larval aggregative tendency is decoupled from larval colony size, variation in this trait could be a result of selection on larvae to alter their colony density to control the risk of pathogen spread. Rather than increasing immune response due to increased larval density, the increased selection from pathogens and the investment costs of immune defenses within a maternally determined colony-size could select for larval control over density via changes in larval aggregative tendencies. Larval evolution may be carefully balancing the benefits of group living with costs through slight differences in social behavior that impact colony density.

This may seem like a minor distinction from density-dependent prophylaxis, but it is a rather significant difference. Selection for larval behavior controlling colony density would be more akin to the social network-modification mainly observed in eusocial insects (Stroeymeyt et al 2018; Liu et al 2019; Malagocka et al 2019). Unlike density-

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dependent prophylaxis, behavioral control of larval colony density would better fit the

criteria for social immunity. To be effective, this behavior would need to be shared by all

colony members and would afford protection to the entire group rather than being simply

the result of individuals responding to a particular social environment. This distinction

between density-dependent prophylaxis and larval control of colony density may also explain cases wherein density-dependent prophylaxis would be expected but was not found such as in some studies of other larval herding organisms (Wilson et al 2003;

Fletcher 2009).

While we believe our results are best explained by larval behavioral control of colony density rather than density-dependent prophylaxis, further study on the relationship between immune defenses and social behavior in Neodiprion will be necessary to make a definitive conclusion. Manipulating larval colony sizes and determining the impact on larval immune defenses, the typical study design for determining density-dependent prophylaxis, would be necessary to fully determine the presence or absence of density-dependent prophylaxis in Neodiprion. While we are unable to make a firm conclusion as to the reason for the relationship between social behavior and immune investment in Neodiprion, this study is a robust example of this relationship. By demonstrating this relationship both within a single species, N. lecontei, and across a diverse genus within a phylogenetic context, it supports the universality of this relationship and provides a potential example of social immunity behavior within a larval herding social system.

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4.6 Tables and Figures

Table 4.1 Collection information and sample sizes for wild caught colonies “Agg. T.” refers to the number of videos recorded (5 larvae each) from each colony; “Implants” refers to the number of individual larvae from which immune response data was collected. Asterisks (*) indicate data collected from a combined colony that used larvae from that collection site. Further information on these combined colonies can be found in Tables 3.4 and 4.3. Collection ID Species Date Latitude Longitude Agg. T. Implants NS021 N. abbotii 3/19/2014 29.7887 -84.7665 1 NS072 N. abbotii 8/18/2014 45.1316 -91.9247 2 4 NS086 N. abbotii 9/7/2014 38.1722 -83.5570 1* 2* NS088 N. abbotii 9/7/2014 38.1039 -83.5113 1* 2* NS117.03 N. abbotii 4/25/2015 35.9419 -86.5278 4 13 NS141 N. abbotii 5/10/2015 37.7862 -80.3004 3 11 NS146 N. abbotii 6/13/2015 36.1791 -85.9455 5 11 NS147 N. abbotii 6/13/2015 35.8118 -86.3994 1 3 NS162.01 N. abbotii 7/25/2015 44.5511 -68.3945 5 6 NS188.01 N. abbotii 9/13/2015 39.8864 -74.5099 1 3 NS174.01 N. compar 8/15/2015 48.0456 -84.5494 2* 12* NS182.01 N. compar 8/17/2015 46.5089 -90.5027 1* 9* NS175 N. compar 8/15/2015 48.0297 -84.6513 1* 5* NS184.01 N. compar 8/17/2015 46.1149 -90.5511 1* 5* NS168.01 N. compar 8/13/2015 45.9263 -77.3254 1* 9* NS176 N. compar 8/15/2015 48.5437 -85.1911 1* 5* NS178.01 N. compar 8/16/2015 48.4161 -89.6412 1* 5* NS169.01 N. compar 8/13/2015 45.9315 -77.3333 1* 3* NS170.01 N. compar 8/14/2015 46.6231 -81.4552 1* 3* NS172.01 N. compar 8/14/2015 47.4648 -81.8467 1* 3* NS218 N. compar 8/15/2016 48.0459 -84.5507 2+1* 1+1* NS220 N. compar 8/15/2016 48.6312 -85.3626 1* 1* NS221 N. compar 8/15/2016 48.7935 -87.0895 1* 1* NS217 N. compar 8/15/2016 48.0302 -84.6490 1 NS069 N. compar 8/6/2014 48.7014 -85.5435 4 NS057.01 N. dubiosus 8/3/2014 46.6231 -81.4591 2 5 NS059 N. dubiosus 8/4/2014 48.1717 -80.2546 2 3 NS060 N. dubiosus 8/4/2014 47.7313 -80.3358 4 6 NS061.01 N. dubiosus 8/4/2014 47.6537 -81.0517 6 6 NS062 N. dubiosus 8/4/2014 47.4836 -81.8459 4 6 NS064 N. dubiosus 8/5/2014 48.2414 -82.3542 4 8 NS066 N. dubiosus 8/5/2014 47.8744 -83.2854 4 5 NS151.01 N. dubiosus 6/27/2015 48.2205 -82.0732 3 18 NS045.01 N. dubiosus 7/2/2014 46.1283 -90.5689 2

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Table 4.1 (continued) NS094 N. excitans 10/18/2014 29.7173 -82.4570 2 7 NS095 N. excitans 10/18/2014 29.7184 -82.4562 1 3 NS100.01 N. excitans 10/19/2014 29.7074 -82.4522 4 5 NS100.02 N. excitans 10/19/2014 29.7074 -82.4522 5 5 NS104 N. excitans 11/9/2014 29.7076 -82.4523 7 9 NS108 N. excitans 11/10/2014 28.7870 -81.9818 9 13 NS133.02 N. excitans 5/9/2015 36.4248 -77.6326 4 5 NS135.02 N. excitans 5/9/2015 36.4379 -78.0875 4 4 NS023.02 N. fabricii 4/27/2014 35.9292 -86.5233 1 1 NS083 N. fabricii 8/14/2014 35.9332 -86.5323 1 3 NS114.03 N. fabricii 4/25/2015 36.1791 -85.9456 1 7 NS121 N. fabricii 4/26/2015 34.5636 -84.9446 1 4 NS135.01 N. fabricii 5/9/2015 36.4379 -78.0875 5 23 NS138.02 N. fabricii 5/10/2015 37.2094 -77.4743 3 5 NS143 N. fabricii 5/25/2015 38.0627 -76.5344 4 6 NS144 N. fabricii 5/25/2015 37.5005 -76.3014 2 7 NS129 N. fabricii 5/9/2015 35.3942 -77.1439 3 NS023.01 N. hetricki 4/27/2014 35.9292 -86.5233 5 7 NS113.02 N. hetricki 4/25/2015 36.1397 -85.8066 8 5 NS114.02 N. hetricki 4/25/2015 36.1791 -85.9456 7 6 NS120.01 N. hetricki 4/25/2015 35.8104 -86.3984 5 12 NS136.01 N. hetricki 5/9/2015 36.5533 -78.1801 4 6 NS138.01 N. hetricki 5/10/2015 37.2094 -77.4743 3 4 RB367.02 N. hetricki 5/23/2014 36.0095 -87.3781 1 NS102 N. knereri 10/20/2014 29.0340 -81.6404 3 15 NS214 N. knereri 7/11/2016 29.0040 -81.7577 5 4 NS043.03 N. lecontei 7/2/2014 45.8223 -91.8884 8 8 NS186.01 N. lecontei 8/23/2015 35.9803 -85.0152 4 7 NS212.01 N. lecontei 7/10/2016 29.0978 -82.1861 6 7 NS216 N. lecontei 7/11/2016 29.3207 -81.7267 8 9 NMr002 N. maurus 7/2/2014 45.9975 -91.6183 6 6 NMr003 N. maurus 7/3/2014 45.6019 -89.3511 1 3 NS037 N. maurus 6/17/2014 45.6643 -89.4919 7 8 NS205.02 N. maurus 6/28/2016 45.8428 -91.8877 3 2 NS208 N. maurus 6/29/2016 46.1150 -90.5513 3 2 NS210.02 N. maurus 6/29/2016 46.0538 -89.4857 2 3 NS105 N. merkeli 11/10/2014 29.9238 -81.5317 3 8 NS106 N. merkeli 11/10/2014 26.9260 -81.5177 2 8 NS107 N. merkeli 11/10/2014 26.9214 -81.5086 1 5 NS109 N. merkeli 11/10/2014 28.7870 -81.9818 3 11 NS212.03 N. merkeli 7/10/2016 29.0978 -82.1861 1 3 NS047 N. nigroscutum 7/4/2014 45.1145 -88.3591 1* 5*

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Table 4.1 (continued) NS051.01 N. nigroscutum 7/5/2014 44.8440 -89.6906 1* 5* NS070.03 N. nigroscutum 8/18/2014 45.1248 -92.3836 2 8 NS205.01 N. nigroscutum 6/28/2016 45.8428 -91.8877 1 2 NS206.01 N. nigroscutum 6/29/2016 46.5544 -91.3228 1 1* NS211 N. nigroscutum 6/30/2016 44.8440 -89.6905 2 3+1* NP034 N. pinetum 8/15/2015 38.0324 -84.5655 2 2 NP036 N. pinetum 8/19/2015 38.0324 -84.5655 2 NP039 N. pinetum 8/20/2015 37.9706 -84.4976 7 4 NP041 N. pinetum 8/20/2015 37.9729 -84.5004 1 NP047 N. pinetum 8/31/2014 39.0084 -84.6499 6 3 NP052 N. pinetum 9/6/2015 39.6188 -83.6037 4 7 NS188.02 N. pinusrigidae 9/13/2015 39.8864 -74.5099 22 19 NS189.03 N. pinusrigidae 9/13/2015 39.8860 -74.5061 20 25 NS022 N. pratti 4/13/2014 38.0847 -83.5098 6 10 NS034 N. pratti 6/16/2014 46.1113 -89.6693 4 7 NS112 N. pratti 4/24/2015 36.5753 -85.1380 1 3 NS117.01 N. pratti 4/25/2015 35.9419 -86.5278 6 8 NS118.02 N. pratti 4/25/2015 35.9419 -86.5278 1 2 NS122 N. pratti 4/26/2015 34.5275 -84.9465 1 4 NS123.03 N. pratti 5/6/2015 38.0860 -83.5117 4 5 NS124 N. pratti 5/6/2015 38.0860 -83.5117 4 4 NS125 N. pratti 5/6/2015 38.0843 -83.5098 4 NS140.01 N. pratti 5/6/2015 37.1959 -77.5244 1 4 NS154.02 N. pratti 6/27/2015 46.3111 -81.6557 4 15 NS155 N. pratti 6/28/2015 46.3023 -79.3835 1 4 NS163 N. pratti 7/25/2015 44.4801 -67.5931 6 13 NS165 N. pratti 7/25/2015 44.4774 -67.5908 3 8 Can089 N. rugifrons 8/17/2014 47.7039 -83.3188 1 NS044 N. rugifrons 7/2/2014 45.8396 -91.9139 6 7 NS052 N. rugifrons 7/20/2014 45.8396 -91.9139 4 7 NS070.01 N. rugifrons 8/18/2014 45.1248 -92.3836 1 2 NS172.04 N. rugifrons 8/14/2015 47.4648 -81.8467 3 3 NS200 N. rugifrons 6/28/2016 44.5711 -91.6363 2 4 Can058 N. swainei 8/1/2014 44.1217 -85.4708 2 5 NS053.01 N. swainei 7/20/2014 46.1143 -90.5513 5 7 NS056.02 N. swainei 7/20/2014 44.8440 -89.6906 1 2 NS061.02 N. swainei 8/4/2014 47.6537 -81.0517 5 8 NS114.01 N. taedae 4/25/2015 36.1791 -85.9456 3 9 NS115.03 N. taedae 4/25/2015 36.4716 -86.6816 5 9 NS117.02 N. taedae 4/25/2015 35.9419 -86.5278 8 8 NTL001 N. taedae 5/8/2014 36.3177 -94.7105 2 4 NTL003 N. taedae 5/23/2014 36.0095 -87.3781 1 3

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Table 4.1 (continued) NTL004 N. taedae 6/9/2014 37.7271 -92.7033 4 7 LL189 N. virginiana 7/12/2015 37.3585 -77.9611 6 12 LL186 N. virginiana 7/12/2015 37.3586 -77.9609 10 31 LL192 N. virginiana 7/30/2015 37.9836 -84.4175 3 14 NS089.02 N. virginiana 9/7/2014 38.1006 -83.5048 1 NS099 N. warreni 10/19/2014 29.7093 -82.4542 3 10 NS103 N. warreni 11/9/2014 29.7091 -82.4541 3 12

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Table 4.2 Samples sizes for intraspecific data set Sample sizes and source colony of families used for the intraspecific data set. Data is arranged in two columns for formatting reasons only. “Agg. Tend.” refers to the number of videos (8 larvae in each video) recorded from each family and “Implants” refers to the number of individual larvae from which immune response data was collected. Information on the collection location of source colonies can be found in Table 2.1. Source Agg. Source Agg. Family ID Implants Family ID Implants Colony Tend Colony Tend LL031.06B LL031 1 1 RB261.01B RB261 5 5 LL031.10D LL031 2 2 RB261.03A RB261 3 3 LL031.12B LL031 1 1 RB261.04B RB261 5 5 LL031.18D LL031 3 3 RB261.05C RB261 3 3 LL031.20C LL031 4 4 RB261.06C RB261 5 5 LL031.22B LL031 1 1 RB261.07A RB261 5 5 LL031.26B LL031 4 4 RB261.11A RB261 3 3 LL031.27A LL031 2 2 RB261.13A RB261 3 3 LL031.28AB LL031 1 1 RB316.07CB RB316 2 2 RB244.01B RB244 3 3 RB316.09B RB316 4 4 RB244.02D RB244 1 1 RB316.12A RB316 5 5 RB244.06B RB244 5 5 RB316.14CB RB316 1 1 RB244.09D RB244 3 3 RB316.15D RB316 3 3 RB244.10D RB244 1 1 RB316.18C RB316 4 4 RB244.11A RB244 4 4 RB335.01A RB335 3 3 RB244.13A RB244 5 5 RB335.05C RB335 5 5 RB244.14C RB244 4 4 RB335.08AB RB335 5 5 RB244.15C RB244 3 3 RB335.10B RB335 5 5

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Table 4.3 Sample sizes from combined colonies Number of videos recorded (“Agg. Tend.”) and individuals used for immune response data (“Implants”) from each combined colony. Agg. Collection ID Species Tend. Implants NS086_088 N. abbotii 1 2 CN001 N. compar 1 5 CN002 N. compar 1 5 CN003 N. compar 1 5 CN004 N. compar 1 3 CN005 N. compar 0 4 CN006 N. compar 1 1 NS051.01.C N. nigroscutum 1 5 NS206.01+NS211 N. nigroscutum 1 0

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Table 4.4 Samples sizes for by-species data set Number of videos recorded (5 larvae each; “Agg. Tend.”) and individual larvae that underwent the immune assay (“Implants”) for each species. Agg. Species Tend. Implants N. abbotii 23 53 N. compar 8 28 N. dubiosus 29 59 N. excitans 36 51 N. fabricii 18 59 N. hetricki 32 41 N. knereri 8 19 N. lecontei 26 31 N. maurus 22 24 N. merkeli 10 35 N. nigroscutum 8 18 N. pinetum 22 16 N. pinusrigidae 42 44 N. pratti 46 87 N. rugifrons 17 23 N. swainei 13 22 N. taedae 23 40 N. virginiana 19 58 N. warreni 6 22

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Figure 4.1 Immune response vs aggregative tendency within Neodiprion lecontei Note that larger values of aggregative tendency mean larger distances between larvae and less aggregative larvae. Values are unitless due to log transformation. Relationship between immune response (amount of melanin deposited onto implant) and aggregative tendency (mean distance between larvae during videos) within N. lecontei was found to be significant when compared to a null model using a likelihood ratio test (P = 0.0473). More aggregative larvae had a greater immune response.

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Figure 4.2 Immune response vs aggregative tendency within Neodiprion lecontei using a Mixed-Effects Model From top to bottom, lines represent the relationships within samples from RB316 (Blue), RB244 (Yellow), LL031 (Red), all collected (Black), RB261 (Green), and RB335 (Pink). Again, values are unitless due to log transformations and larger values of aggregative tendency mean less aggregative larvae. The relationship between immune response and aggregative tendency within N. lecontei while accounting for source population as a random effect was also found to be significant with a likelihood ratio test (P = 0.0147).

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Figure 4.3 Immune response vs aggregative tendency across the genus Neodiprion The interspecific relationship between immune response and aggregative tendency using colony-level data and no phylogenetic correction. Individual points represent average values for each colony used and the overall trendline is depicted with the black line. This relationship approached significance when analyzed with a likelihood ratio test versus a null model (P = 0.0510).

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Figure 4.4 Immune response vs aggregative tendency using species-level averages without phylogenetic correction Each species is represented by a single point and the overall trend is shown with the black line. This relationship was found to be significant versus a null model both via an F test (P = 0.0038) and likelihood ratio test (P = 0.0019).

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Figure 4.5 Maximum likelihood estimates of models for Pagel’s λ from 0 to 1 Maximum Likelihood estimates for both the null model (red, lower) and model predicting immune response based on aggregative tendency (green, upper) for values of Pagel’s λ from 0 to 1 while accounting for intraspecific variation in immune response. The maximum likelihood of the model is greater than that of the null model for all values of Pagel’s λ indicating it better represents the data. This improvement is significant when comparing the models using their optimal values for λ (indicated by the greatest maximum likelihood score; P = 0.0005).

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CHAPTER 5. ECOLOGICAL CAUSES OF SOCIALITY IN NEODIPRION LARVAE

5.1 Abstract

One of the more common social lifestyles found among insects are larval herds.

Specific benefits of this lifestyle vary between species but include improved management of microenvironment, increased feeding efficiency, and enhanced predator defenses. One taxonomic group that contains both species with solitary larvae and these larval herds are pine sawflies in the genus Neodiprion. We used data from nineteen species of Neodiprion and a variety of phylogenetic and non-phylogenetic methods, including confirmatory path analysis, to test for the presence of these different adaptive drivers of larval herding using two sociality traits, colony size and aggregative tendency. We found that these two social traits were most strongly related to two different adaptive benefits. Colony size was found to be a causal parent of aposematic patterning along with the amount of chemical defense. Aggregative tendency, however, was found to be most related to environmental relative humidity with possible relationships of unclear direction to chemical defenses and aposematic patterning. Finally, we combined the findings from this study along with previous work in this group to construct and propose a holistic model of Neodiprion larval social behavior’s causes, benefits, and costs.

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5.2 Introduction

Intraspecific cooperation and group-living are extremely common and successful lifestyles found across the animal kingdom (Kropotkin 1902; Parrish and Edlestein-

Keshet 1999). While these lifestyles can be very complicated, one of the simple forms of group-living are the larval herds found in many insect species (Costa 2018). It is not surprising that this lifestyle is so common when it has been found to provide multiple benefits. These benefits can vary tremendously from improved ability to regulate microclimatic factors like temperature and relative humidity (Klok and Chown 1999;

Lockwood and Story 1986) to enhanced foraging through coordinated behaviors to overcome host defenses and determine optimal foraging sites (Tsubaki and Shiotsu 1982;

Despland and Le Huu 2007; McClure et al 2013). Group-living can also afford protections from predation through dilution effects, greater ability to spot potential predators through many-eyes effects, or coordinated defenses (Tostowaryk 1972; Bertram

1978; Codella and Raffa 1993; Cisternas et al 2020).

One of the more conspicuous defenses found among group-living organisms are the correlations between group-living, chemical defenses, and warning coloration. While much work has been done to determine how these three traits relate to each other, results are still inconclusive. The link between chemical defenses and warning coloration occurs so frequently they collectively are referred to by the term aposematism (Tullberg and

Hunter 1996). They have even been used as completely synonymous characters in studies looking at their relationship with group-living (Sillén-Tullberg 1998; Wang et al 2021).

However, this choice has been critiqued and the importance of whether unpalatability or warning coloration arises first or if both arise simultaneous in determining selective pressures have been discussed previously (Mallet and Singer 1987; Guilford 1988). 132

Efficacy of aposematic coloration has consistently been found to be enhanced by the overall size of the signal either via larger group sizes (Gagliardo and Guilford 1993;

Gamberale and Tullberg 1996; Gamerale and Tullberg 1998) or for larger body sizes in non-group-living organisms (Hagman and Forsman 2003). It has even been proposed that large groups could themselves be a form of warning signal (Tullberg and Hunter 1996), but this has not been found in every species studied (Lindstedt et al 2011). Still, the question of if group-living drives aposematism or vice-versa is unresolved. For some

Lepidopteran larvae, analysis indicates a solitary, aposematic larvae as the most likely ancestral state prior to transitions to group living (Sillén-Tullberg 1998), a prior study found that either chemical defense or warning coloration alone tended to precede the evolution of group-living (Tullberg and Hunter 1996), and aposematism has been shown to be advantageous as a predator defense on an individual level (Sillén-Tullberg 1985).

However, other experiments have concluded that evolutionarily novel aposematic signals would be most effective for a chemically defended, group-living organism (Alatalo and

Mappes 1996; Riipi et al 2001).

Overall, it is still unclear which of these group-living benefits are the most common drivers of that lifestyle among larval herding species. Collection of data from a group of related species that vary in these traits of interest would be ideal to test the association of group-living with these benefits. One group that meets these criteria are pine sawfly species in the genus Neodiprion. Specifically, a monophyletic group of 19 species found in Eastern North America (lecontei group) is well suited for these comparisons. First, pine sawfly larvae across the family Diprionidae are noted for their ability to sequester the resin of their pine hosts to use as a defensive regurgitant against

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natural enemies (Tostowaryk 1972; Eisner et al 1974; Lindstedt et al 2018). Second,

larvae of species within the lecontei group vary in social traits, preferred pine host, larval

color and pattern, and the time and location of their emergence (i.e. natural variation in

the ambient temperature and relative humidity of larval environments) (Atwood 1962;

Coppel and Benjamin 1965; Baker 1972; Knerer 1984; Knerer 1993; Larsson et al 1993).

Third, this group has had extensive recent study documenting and assessing these

variations (Terbot II et al 2017, Chapter 3, Chapter 4, Linnen et al 2018) and their

phylogenetic relationships (Linnen and Farrell 2008a; Linnen and Farrell 2010). Of

particular interest is the presence of two social traits, aggregative tendency and colony

size, that previous work has shown to be independent (Chapters 3 and 4). While colony

size seems to be determined by maternal egg clutching behavior (Chapter 3), aggregative

tendency is independent of both colony size and egg clutching but is related to larval immune activity (Chapter 4). Therefore, it may be possible that these two traits are associated with different benefits of group living or a relationship between them may be mediated through the costs and benefits of group living.

In this study, we looked at potential abiotic and biotic causes of sociality for both traits. To do this we used both non-phylogenetic methods using a data set comprised of colony-level data across the Neodiprion genus and phylogenetic methods using a data set of species-level averages. Abiotic factors were examined using traditional model testing methods. Biotic factors were analyzed using both phylogenetic and non-phylogenetic confirmatory path analysis. Path analysis utilizes the assumption that observed correlations are the result of underlying causal links between variables. Phylogenetic path analysis has recently been used to determine the causes and consequences of urbanization

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and human activity (Olalla-Tárraga et al 2015; Sol et al 2018; Santini et al 2019) as well

as a variety of social traits from monogamy and cooperation to social organization

(Cornwallis et al 2017; Szemán et al 2021). However, most of these studies have

examined vertebrate taxa and seldom, if ever, have these methods been applied to

invertebrate taxa. From our confirmatory path analysis, we sought to gain support for

possible causal structures between social behaviors, aposematism, and chemical defense

in pine sawflies. Finally, we combined the results of this study alongside prior work to

develop a proposed model for the causes of social behaviors within the Neodiprion

sawflies.

5.3 Materials and Methods

5.3.1 Specimen Collection and Rearing

Except as noted, we collected all data from wild caught larvae obtained between

March 19, 2014 and August 15, 2016. Details of collections sites can be found in Table

5.1. We performed collections as noted previously in Chapters 2 through 4. In summary, larvae were clipped as a colony or obtained through beating sheets and placed with host

foliage into brown paper bags. Upon our return to the lab, we transferred larvae into

plastic boxes with mesh lids (32.4 cm x 17.8 cm x 15.2 cm) lined with a paper towel.

Larvae of a single species from a collection site were pooled into single colonies for

analysis. Species with solitary lifestyles or that were found in small groups were occasionally combined into single colonies for use in our aggregative tendency assay

(described below). Details on these combinations of larvae from multiple collection sites

can be found in Table 3.4. Prior to being measured, we fed larvae ad libitum using

clippings of their natal host species that were placed into floral picks with water. We only

used larvae for data collection prior to them molting into their final, wandering instar.

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5.3.2 Data Collection and Compilation

We constructed two data sets to use in our statistical analyses. The first data set is of all colonies that contained data for each of the traits under study (by-colony data set) and the second was the mean trait values (and variances) for each trait for each species

(by-species data set). We used the following traits in this study: mean temperature at collection site (temperature), mean relative humidity at collection site (relative humidity), total non-volatile resin content of host pine species (host resin content), regurgitant volume of larvae (regurgitant volume), transition aspect ratio of larval pattern

(aposematic patterning), aggregative tendency of larvae (aggregative tendency) and number of larvae in each colony (colony size). The by-colony data set has two further divisions, one using colonies with aggregative tendency data along other non-colony size measurements and another with colonies for which colony size data was available alongside other non-aggregative tendency measurements. A summary of the sample sizes for each trait in each data set can be found in Table 5.2 for the by-colony data set and

Table 5.3 for the by-species data set.

For each collection site, the geographic location, host species, and colony size were recorded. Using R (R Core Team 2020), the package ‘nasapower’ (Sparks 2018) and NASA’s POWER database, we obtained the mean temperature and relative humidity for a 31-day period centered around the collection date (day and year) and based on the

GPS coordinates of their collection site. These values were used for each colony in the by-colony data set. For colonies that were the result of combined collections, we took the mean value of temperature and relative humidity for all collection sites that contributed to the combined colonies. For the by-species data set, we calculated each species temperature and relative humidity values based on the mean values for each colony of 136

that species collected. We calculated the colony size trait for the by-colony data set

similarly using the log-transformed, noted colony size from collection when a single

colony was collected at a given collection site or the mean colony size for collection sites

with multiple colonies or colonies that were the result of combining multiple collection sites. For the by-species data set, we took the mean value of all colony size information of a species that had been recorded. This also included colony size data from literature sources or prior collections as in Chapter 3.

For 11 species of pine native to Eastern North America, we collected 10 pine clippings from 3 to 5 sites twice in a year from May 7, 2017 to May 21, 2017 and from

August 2, 2017 to August 15, 2017 and resin samples were taken from each pine clipping. On occasion, sample extractions failed; total number of successful resin samples per species and per collection site alongside specific collection information can be found in Tables 5.3 and 5.4. Resin content was determined gravimetrically based on methods detailed in Moreira et al 2012 and Moreira et al 2014. Pine clippings were placed individually in plastic bags and kept on ice until returned to the lab. Once in the lab, we kept clippings at 4°C until used. We began by weighing approximately 3 grams of needles excluding the area below the fascicle sheath. We then sliced the weighed samples using a clean razor blade and placed them into a pre-weighed test tube. To this tube, we added 6 mL of 95 to 99% purity hexane and placed them into an ultrasonic bath for 15 minutes at room temperature. We then capped samples and left them for 24 hours. After waiting, we then transferred the extract through a GF/D or GF/F glass fiber filter

(Whatman Int. Ltd, Maidstone, Kent, UK) into a second pre-weighed test tube. We returned vegetative matter to the original test tube, added 6 mL of 95 to 99% purity

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hexane, placed it into an ultrasonic bath for 10 minutes at room temperature, capped the

tube, and left it for an additional 24 hours. We then transferred the extract to a third pre-

weighed test tube and repeated this extraction another time. We then left extractions

covered by a disposable wiping cloth (Kimwipes, Kimberly-Clark Corp, Irving, TX,

USA) under a fume hood until any volatile compounds (including the hexane) had

evaporated and then weighed. Vegetative matter was dried in an oven at 40°C until the

mass no longer changed and then weighed. Total non-volatile resin content for each

sample was calculated by dividing the combined extracted resin mass by the dry needle

mass. All weights for this were obtained using a precision scale (0.0001 g). Finally, we

calculated the mean resin content for each pine species using each sample’s total non-

volatile resin content. During larval specimen collection, we noted the host species each

colony was found on and the calculated resin content for that host species was assigned to

that colony. For the by-species data set, we took the mean of the assigned resin content of

each colony collected of a particular species.

Prior to collecting other larval phenotypes using wild-caught larvae, we collected

data on larval aggregative tendency. This behavioral assay is well described in Terbot II et al 2017. Briefly, this assay involves placing 5 larvae equally distanced along the perimeter of a 14.5 cm petri dish and recording their behavior for 90 minutes. Frames from video recordings were extracted every 3 minutes using Video Image Master Pro

(A4Video 2016) and a custom Java application. For each frame, we calculated all pairwise distances between larval heads. We excluded the first 12 frames (36 minutes) of data to allow larvae to acclimate to the petri dish and calculated the mean pairwise distance of each video. We then log-transformed this value and averaged among all

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videos from a single colony for the by-colony data set or among all videos of a single

species for the by-species data set. For this trait, it is worth noting that larger values

indicate a greater mean distance between larvae and, therefore, less aggregative larvae

while smaller values indicate more aggregative larvae.

No sooner than one day after any larvae had been videotaped for aggregative

tendency measurements, individual larvae were isolated into small petri-dishes (10 cm diameter) lined with paper towels alongside a small clipping of host species. Larvae were then left for at least one hour to acclimate before visually assessed for body color and presence of body patterning. At this time, defensive responses (twitching, rearing back, or production of regurgitation in response to motion or light prodding) of larvae were also noted. We then used a microcapillary tube (5 µl) to collect larval defensive regurgitant.

We measured regurgitant volume as the length (mm) in the capillary tube the regurgitant occupied. We noted occasions when the regurgitant collection caused injury to a larva and excluded those values from analysis. At this time, we also measured the body length and head capsule width of each larva. We then returned larvae to their individual petri dishes for a minimum of one hour. During this period, we placed regurgitant-filled

microcapillary tubes into 1.5mL microcentrifuge tubes, added 150 µL of hexane, and

stored tubes in a -20°C freezer. Next, we removed larvae individually from their petri- dishes and placed them onto a mesh bed with a stream of CO2 diffused through it to

anesthetize the larvae. We then photographed larvae from four angles (dorsal, both lateral

sides, and ventral) which were analyzed later for color and pattern (described below). At

this time, we also took spectrophotometric measurements, collected hemolymph samples,

and began an immune assay described in Chapter 4. We averaged the regurgitant volumes

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(mm) of larvae from a single colony for the by-colony data set or averaged the

regurgitant volumes for all larvae of a single species for the by-species data set.

We performed the analysis of photographs of individual larvae using methods suggested in Endler 2012. First, using Adobe Photoshop (Adobe 2015) each photograph’s white balance was adjusted based on a color standard present in each photograph. Then the larval body excluding legs and head capsule was manually cut from the rest of the photograph and placed onto a separate photographic layer. The remaining parts of the photograph were given transparency values before the entire photo was saved as a single

PNG file. We then processed this picture file using a custom Java application. First, the picture was rotated to a common orientation with the head on the left and the anus on the right. Then, each pixel of the larval body was assigned to a color using k-means centering; for the data used in this study k was set to 2; pixels of the background image were assigned a third value to signify their exclusion from analysis. Then the entire photo was broken into a blocked grid with each block being a 0.1 mm square; the color value of each block was assigned based on the color value of the center pixel. The number of transitions within larval bodies between color values was then calculated for grid rows

(along the body axis) and grid columns (across the body axis) and the corresponding transition densities were calculated. The transition aspect ratio was then calculated for each photo by dividing the row transition density by the column transition density. Thus, values close to 1 correspond to spotted patterns (a disruptive and aposematic pattern for pine sawflies), values above 1 correspond to striping patterns across the body axis (also a disruptive and aposematic pattern for pine sawflies), and values below 1 correspond to a striping pattern along the body axis (a cryptic pattern for pine sawflies). For each larva,

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we calculated the mean transition aspect ratio based on lateral and dorsal photos. We

excluded larvae without a dorsal photo and at least one lateral photo from analysis. Then,

we averaged values from larvae of a single colony for the by-colony data set or larvae of

a single species for the by-species data set.

5.3.3 Data Curation

To ensure our traits varied between species, we tested that the between-species variation was large enough compared to within-species variation to make our species- level analyses meaningful. To do so, we used ANOVA testing and repeatability analysis using R and the “aov” function of the “stats” package and “rpt” function of the “rptR” package respectively (Stoffel et al 2017; R Core Team 2020). We also created correlograms of our traits using the aggregative tendency and colony size by-colony data sets and the by-species data set. Correlograms were created using the ‘GGally’ package

(Schloerke et al 2021) with the Pearson correlation coefficient, density plot, and a scatterplot with a smoothed line estimate. While conclusions were not directly drawn from these statistics and plots, they were used to help guide model construction and interpret conclusions from later data analyses.

5.3.4 Phylogenetic Signal

Our final step before our statistical analysis of our models was to determine the extent of phylogenetic signal present in each of our traits. For this, we used the best estimated species tree based on three nuclear gene regions from previous studies (Linnen and Farrell 2008a; Linnen and Farrell 2010). Phylogenetic signal was assessed using estimates of Pagel’s λ and comparing those models to one that fixes λ to 0. We performed these tests using the “phylosig” function in the “phytools” package (Revell 2012) of R.

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5.3.5 Abiotic Causes

We tested the impact of two abiotic factors, temperature and relative humidity, on

our sociality traits, aggregative tendency and colony size. This was done using standard

model comparison methods of models with one predictor trait, temperature or relative

humidity, both predictor traits, and null models with no predictor traits. For the by-colony

data set, we used generalized least squares to generate these models with the “gls” function of the “nlme” package (Pinheiro et al 2020). For the by-species data set, we used the “pgls.SEy” function of the “phytools” (Revell 2012) package. This method uses a modified phylogenetic generalized least squares method based off Ives 2007 which allows for intraspecific variation in the predicted value to be accounted for using the species standard errors of that trait. Models with aggregative tendency as their predicted trait used the species variances to determine standard errors, while models with colony size as the predicted trait used standard errors based off of pooled variances using a methods from Garamszegi 2014. Models using phylogenetic information were built using values of Pagel’s λ from 0 to 1 and the optimal value for λ was chosen via maximum likelihoods. All model comparisons, both GLS and PGLS, were conducted using the

“lrtest” function of the “lmtest” package (Zeileis and Hothorn 2002).

5.3.6 Biotic Causes

The impact of biotic factors; host resin content, regurgitant volume, and aposematic patterning; on our sociality traits was assessed using confirmatory path analysis methods described in Gonzalez-Voyer and von Hardenberg 2014 alongside the phylogenetic confirmatory path analysis methods described in Gonzalez-Voyer and von

Hardenberg 2014 and Hardenberg and Gonzalez-Voyer 2013. Confirmatory path analysis relies on the construction of directed, acyclic graphs (DAGs) that represent underlying

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causal connections between different values. Specifically, we used the d-separation method which uses the connections not contained within these graphs, conditional independencies, to calculate a summary statistic (C). This summary statistic follows a chi-square distribution with degrees of freedom equal to the number of conditional independencies tested. Therefore, a chi-square test was used to first exclude all models that did not sufficiently account for underlying causalities indicated by P-values less than

0.05. Next, we calculated the CICc statistic which can be used to compare supported models within an information theory framework.

In total, we tested 32 different causative models for both of our sociality traits, and with and without accounting for phylogeny. These models represented possible relationships between our values that could be tested with the d-separation method; in other words, fully saturated models (models with connections between all possible pairs of values) or potentially cyclic models were not considered. These models all shared a common causal link from host resin content to regurgitant volume and otherwise had 0 to

4 other causal links. The justification for the shared common causal link is that sawfly larvae feeding on a more resinous host will have to either process, digest, and excrete additional resin consumed or sequester that resin for use as their regurgitant defense; moreover, a larva’s ability to sequester resin will obviously be limited by the amount of resin consumed within their host food. We will refer to each of these models using a four- character name (“XXXX”) to represent these causal links. The first character represents a causal link from aposematic patterning to sociality traits (“A”), the opposite direction

(“B”), or no link (“N”). The second character represents a causal link from regurgitant volume to sociality traits (“A”), the opposite direction (“B”) or no link (“N”). The last

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two characters in each model name are the presence or absence (“Y” or “N”) of causal

links; first from regurgitant volume to aposematic patterning and second from host resin

content to sociality traits. A summary of this naming convention can also be found in

Figure 5.1. All possible permutations of these links were tested except for “ABXX”

models due to potentially cyclic graphs being generated. Visual diagrams of all tested models can be found in Figure 5.2. As discussions of these models can become confusing either due to relying solely on their assigned name or due to repetitive summaries of their causal paths; we recommend keeping Figures 5.1 and 5.2 on hand to facilitate understanding. Conditional independencies for each model were determined with the help of the ‘phylopath’ package in R (van der Bijl 2018). Causal models were first tested with

the by-colony data set without accounting for phylogeny; conditional independencies

were modeled using generalized least squares methods using the “gls” function of the

“nlme” package (Pinheiro et al 2020). Next, we tested these causal networks using the

by-species data set while accounting for phylogeny using the “pgls.SEy” function of the

“phytools” (Revell 2012) package. Standard errors for conditional independencies with

aposematic pattering, regurgitant, or aggregative tendency as their predicted variable

were calculated using species variances; conditional independencies with colony size as

the predicted variable used adjusted species variances calculated using pooled variances

and methods described in Garamszegi 2014.

5.4 Results

5.4.1 Data Curation

Variation between species for all traits was found to be sufficient compared to

intraspecific variation. We found this was true for both repeatability analysis

(Temperature, R = 0.4533, P < 0.0001; Relative Humidity, R = 0.3445, P < 0.0001; Host

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Resin Content, R = 0.6417, P < 0.0001; Regurgitant Volume, R = 0.2078, P < 0.0001;

Aposematic Patterning, R = 0.5215, P < 0.0001; Aggregative Tendency, R = 0.1986, P <

0.0001; Colony Size, R = 0.3493, P < 0.0001) and ANOVA testing (Temperature, F18,201

= 8.5997, R = 0.4533, P < 0.0001; Relative Humidity, F18,201 = 6.0717, R = 0.3445, P <

0.0001; Host Resin Content, F18,201 = 17.2903, R = 0.6417, P < 0.0001; Regurgitant

Volume, F18,744 = 11.7541, R = 0.2078, P < 0.0001; Aposematic Patterning, F18,808 =

44.9393, R = 0.5215, P < 0.0001; Aggregative Tendency, F18,389 = 6.0258, P < 0.0001;

Colony Size, F18,486 = 19.2255, P < 0.0001). Correlograms generated showed multiple

significant and otherwise potentially interesting relationships between traits; many of

these were consistent regardless of data set used. Correlograms for the two by-colony

data sets and the by-species data set can be found in Figures 5.3, 5.4, and 5.5.

5.4.2 Phylogenetic Signal

We found only a single trait, colony size, had significant evidence of a

phylogenetic signal (λ = 0.9999, P = 0.0310). A second trait, aposematic patterning, had

some evidence of a phylogenetic signal, but it was not significantly better than the model

that fixed λ to 0 (λ = 0.3156, P = 0.6496). All other traits were found to have optimal

values of λ less than 0.0001 and were not significant improvements over λ being fixed to

0 (P = 1.0000). Because these are closely related species; some of our models contain

colony size, aposematic patterning or both; and our methods include testing models

wherein λ is fixed to 0 (effectively the same as non-phylogenetic regressions), it will be

appropriate to use methods that account for phylogeny and non-phylogenetic methods to

make conclusions as appropriate.

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5.4.3 Abiotic Causes

Using our by-colony data and non-phylogenetic methods, we found the model

containing relative humidity to have a significant correlation with aggregative tendency

compared to a null model (LLNull = -69.2473, LL~Relative Humidity = -66.9643, P = 0.03261).

The model using temperature as a predictor variable was not significantly better than the

null model (LL~Temperature = -67.3796, P = 0.0533) and the model with both predictor

variables was not an improvement over the null model nor relative humidity alone (LLFull

= -66.4503, PFull vs Null = 0.0610, PFull vs ~RH = 0.3106). However, for colony size no model

was found to be better than the null model (LLNull = -93.2604, LL~Relative Humidity = -

92.3456, LL~Temperature = -92.9486, LLFull = -92.3414; P~RH vs Null = 0.1762, P~T vs Null =

0.4297, PFull vs Null = 0.3989). It is worth noting that the model with relative humidity

alone was superior to temperature alone.

When we accounted for phylogeny and used our by-species data set, however, no

model was a significant improvement over the null model. This was true for both

aggregative tendency (LLNull = -2.3979, LL~Relative Humidity = -2.3911, LL~Temperature = -

2.2423, LLFull = -2.0776; P~RH vs Null = 0.5769, P~T vs Null = 0.9075, PFull vs Null = 0.7260) and

for colony size (LLNull = -18.9657, LL~Relative Humidity = -18.7225, LL~Temperature = -18.9320,

LLFull = -18.1394; P~RH vs Null = 0.4855, P~T vs Null = 0.7951, PFull vs Null = 0.4377). Again, the model using relative humidity alone was superior to temperature alone but should be rejected regardless based on comparisons to the null model. Additional statistical details on these model comparisons can be found in Table 5.6 for models predicting aggregative tendency and Table 5.7 for models predicting colony size. Statistics for model comparisons with and without accounting for phylogeny can be found in these tables.

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5.4.4 Biotic Causes

In comparing our models, we first excluded all models whose P-value from chi- square tests was greater than 0.05. Next, we used the recommended cut off of ΔCICc scores less than 2 compared to the best model. However, our results tables will present all models up to ΔCICc less than 5. Without accounting for phylogeny and using our by- colony data sets, the best model for the aggregative tendency trait was “NBYN”; this corresponds to a models where aggregative tendency causes regurgitant volume which then causes aposematic patterning. The other likely models (ΔCICc < 2 from the best model) are “NAYN”, “BBYN”, “BAYN”, “AAYN”, “NNYN”, and “ANYN” (Figures

5.4 and 5.5 should be referenced to understand the differences between these models).

Table 5.8 contains the full information for these models including their specific CICc scores, relative likelihoods, and weights. The best non-phylogenetic model for colony size was “NAYY” and “NBYY”; the structure of these models results in testing the same conditional independencies, therefore the methods used are not able to distinguish between the direction of causality between regurgitant volume and colony size when both are causally linked to host resin content. Other likely models that met the thresholds are

“NANY”, “NBYN”, “NAYN”, and “NBNY” ” (Figures 5.4 and 5.5 should be referenced to understand the differences between these models. The specific statistics for these and other models can be found in Table 5.9.

When phylogeny was accounted for during model testing and the by-species data set used, the best model including aggregative tendency was “NNYN” (only a causal link between chemical defense and aposematic patterning). All other models in this set had

ΔCICc exceeding 2. Specific details for this and other models with ΔCICc less than 5 can be found in Table 5.8. For colony size, the best model was also “NNYN”; however, it 147

was followed extremely closely by the “BNYN” model. The only other well supported

model for colony size was “ANYN” (models with an additional link from colon size to

aposematic patterning or vice versa respectively). Specific model statistics for all colony-

size models that accounted for phylogeny are reported in Table 5.9.

5.5 Discussion

In interpreting our results, we should be careful of cases where analyses that

accounted for phylogeny conflict with those that did not account for phylogeny. This is

particularly true regarding models involving colony size. This is for two reasons; first,

colony size was the only trait found in these data sets with a significant phylogenetic

signal. Second, the by-colony data set for colony size, however, was missing data from

two species (N. swainei and N. virginiana) and had eight species with fewer than 3 sampled colonies (N. dubiosus, N. knereri, N. nigroscutum, N. pinetum, N. pinusrigidae,

N. rugifrons, N. taedae, and N. warreni). Therefore, while we have presented the non- phylogenetic analyses of our by-colony colony size data set, we do not feel it is

appropriate to draw conclusions regarding its relationships to other traits from those

analyses. On the other hand, our by-colony data set for aggregative tendency contained

colony data from each of the 19 species and only three species had fewer than two

colonies with data (N. knereri, N. pinusrigidae, and N. warreni) and aggregative tendency

was not found to have a significant phylogenetic signal. Accounting for phylogeny may

still be appropriate even in cases where traits do not have strong phylogenetic signals on

their own. So, we will still be drawing conclusions based on our analyses that accounted

for phylogeny but relying more heavily on analyses that did not account for phylogeny.

Our most consistent result was that amount of chemical defense (regurgitant volume) causes aposematic patterning. This causal link was found in the best model of all 148

four analyses done of biotic causes (both aggregative tendency and colony size analyzed with and without accounting for phylogeny). Moreover, only two models that surpassed our threshold criteria lacked this causal link and were from our least robust analysis

(analyzing colony size without accounting for phylogeny). We conclude that our findings support existing work showing correlations between chemical defense to aposematic warnings (Speed and Ruxton 2006). Specifically, we found a causal link from chemical defenses to aposematic patterning in Neodiprion sawflies. This is not a particularly surprising finding as the presence of aposematic warnings without defensive mechanisms

(or evidence of Batesian mimicry) would be extremely costly to individuals without another cause to develop bright coloration such as sexual selection.

Our results were less clear regarding traits causing or caused by social behaviors.

We found no evidence that colony size was linked with either abiotic factor examined

(temperature and relative humidity). Aggregative tendency’s relationship to these causes depended on whether phylogeny was accounted for. Without phylogeny, relative humidity had a clear relationship with aggregative tendency and the direction of this relationship was as predicted from previous literature (Figures 5.3 and 5.5; Klok and

Chown 1999; Broly et al 2014). Larvae from environments with greater relative humidity were found to be less aggregative. However, when phylogeny was accounted for the significance of this relationship disappeared, though relative humidity was still a superior predictor of aggregative tendency than temperature. Per the previously given reasons, we believe the non-phylogenetic analyses still provide reliable evidence for a relationship between relative humidity and aggregative tendency. Future studies directly manipulating

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ambient relative humidity and measuring the impact on aggregative tendency for multiple

species would further confirm or reject this link.

Despite the optimal model for colony size when analyzed using phylogenetic

methods only including the link between chemical defense and aposematism, we believe

there is sufficient evidence for a link between colony size and aposematism. While it was

the second-best supported model, the “BNYN” model (causative links from both colony

size and regurgitant volume to aposematic patterning) had a ΔCICc of only 0.0262 and had a relative likelihood of 0.9870 making it nearly as likely as the “NNYN” (causal link only from regurgitant volume to aposematic patterning). Combined with the “ANYN” model (the same as “BNYN” but with the causal direction between colony size and aposematic patterning reversed) being the only other well supported model and itself having a relative likelihood of 0.7166 compared to the “NNYN” model, the presence of some link between these traits seems more probably than its absence. Because the

“BNYN” model was still superior to the “ANYN” model, we conclude that a causal direction from colony size to aposematic patterning to be supported by our data.

When phylogeny is accounted for our analyses of aggregative tendency clearly reject any link between it and the biotic factors we studied. The only model that was supported based off our threshold criteria was “NNYN”. However, as stated above, we believe aggregative tendency’s lack of a phylogenetic signal and the depth of sampling for its by-colony data set make it appropriate to use our non-phylogenetic analyses to make some conclusions. From this, we can currently reject the possibility of host resin content impacting larval aggregative tendency. However, the presence, absence, and direction of links between chemical defenses or aposematism and aggregative tendency

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are less definitive. The most supported model, “NBYN” includes a link from aggregative tendency to regurgitant volume; yet, it is closely followed by the “NAYN” model

(relative likelihood of 0.8194) which includes a link in the reverse direction. Still, some form of link between these two traits is included in the top six of the eight models that surpassed our threshold criteria. Absent from these top models is the “ABYN” model; however, this model could not be included in our analyses due to the cyclical nature of its causal structure. Approximately half as likely as these models are ones which include links between aggregative tendency and aposematic patterning with the direction from aggregative tendency to aposematism again being slightly favored. From this, we conclude a link between aggregative tendency to chemical defenses being most supported with the link occurring in the opposite direction as being possible but less supported. As well, further study may reveal an additional link between aggregative tendency and aposematism, but our current study does not highly support this. Interestingly, both relationships with aggregative tendency occur in the opposite direction as may be expected. Less aggregative larvae appear to have greater chemical defenses and greater aposematic patterning. One explanation may be that as larvae become more diffuse, individual larvae having a complete defense package of chemical defense and aposematic signaling becomes more important. This may also help explain why the link between aggregative tendency and regurgitant volume is greater than that between aggregative tendency and aposematism. Solitary species such as N. compar may gain advantages with greater chemical defenses but would still incur a cost if it were also aposematic. Other species which live in groups while being less aggregative may also benefit from greater individual chemical defenses and group aposematic signals. Thus, the link between

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aggregative tendency and regurgitant volume is consistent across colony sizes, but the link between aggregative tendency and aposematism is inconsistent. Another explanation may simply be that an apparent link between aggregative tendency and aposematism is the result of their shared links to chemical defenses and that path analysis has appropriately penalized models with this additional, spurious link.

From the results of this study alongside our previous work and other literature, we have developed a single causative model to represent our current knowledge of

Neodiprion larvae social behavior (Figure 5.6). Beginning with a “black box” of maternal ovipositioning behavior, decisions are made that determine the egg clutch size and host plant from which larvae will hatch. The host plant then determines the amount of chemical defense a larva can sequester which in turn causes aposematic patterning to be favored. At the same time, egg clutch size determines the colony size of the larvae which itself contributes to greater aposematic patterning. Larger colonies incur greater risks of disease due to the increased risk of transmission and the high relatedness among members (Cremer et al 2007; Shakhar 2019). This immune pressure is then related in some manner to the aggregative tendency of the larvae perhaps using changes in this behavior to reduce contact with conspecifics while maintaining a group-living lifestyle as a form of social immunity (Chapter 4). Changes in aggregative tendency are also likely to be caused by changes in their environment’s relative humidity and itself may cause changes in the amount of chemical defense sequestered (or, possibly, the reverse). The final possible link is between aggregative tendency and aposematic patterning which has some support from this current study.

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Future studies are recommended to test predictions from this model to determine its rigor. Specifically, greater sampling within Neodiprion or of a greater number of taxa would allow this model to be directly tested. Currently we are limited by our sampling in the complexity of models we can test. First, uneven sampling between Neodiprion species in our by-colony data set prevented us from being able to robustly include species as a fixed or random factor in our non-phylogenetic analyses. Deeper sampling within

Neodiprion would allow us to include species as a factor or allow us to robustly use phylogenetic methods that allow multiple measurements within each species in a phylogenetic context instead of the methods used here that only account for intraspecific variation via the variation in the predicted trait. A sampling from a greater number of taxa would allow us to use the methods from this study for more complicated models.

Phylogenetic confirmatory path analysis penalizes models via CICc based on the number of estimated traits and links in comparison to the number of species analyzed

(Hardenberg and Gonzalez-Voyer 2013; Gonzalez-Voyer and von Hardenberg 2014).

Our proposed model (Figure 5.6) would include eight to nine traits (depending on the inclusion or exclusion of temperature) and seven to ten causal links; with only nineteen species, we either cannot analyze these models or can predict that such models would be penalized so greatly that the model with the fewest causal links would be resolved as the best fit regardless of any actual underlying causal structures. The inclusion of a greater number of species across Neodiprion and, possibly, the entire Diprionid family would increase our species sample size and allow the analysis of these more complex, composite models. This broader data set would also allow conclusions regarding the causes of social behaviors to be applied more generally to larval herding species.

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We believe we have presented a robust summary of the causes and consequences of social living for larvae within the genus Neodiprion. While caution should be taken in applying these conclusions more broadly, we believe that specific predictions regarding the connection between colony size, warning coloration, and chemical defense can be made based off our findings from Neodiprion. This study demonstrates quite clearly that chemical defenses should arise prior to aposematic patterning and warning coloration in systems without other selective benefits (e.g., sexual selection) of bright coloration. This study also emphasizes the relationship between colony sizes and warning coloration and predicts that larger colonies (or body sizes for large, non-social organisms (Hagman and

Forsman 2003)) drives selection for greater aposematism rather than the reverse

(aposematism selecting for larger groups or body sizes). Moreover, our current model predicts that correlations between colony sizes and chemical defenses are due to their shared causal child of aposematism. One result of this is that for a given amount of warning coloration and patterning we predict an inverse relationship between colony size or body size and strength or amount of chemical defense to be found. In other words, a species that forms large colonies and is only weakly chemically defended would be expected to have similar amounts of warning coloration and patterning to a solitary species with a strong chemical defense. Testing this prediction in other systems could also allow the model proposed here to more robustly be generalized to other systems.

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5.6 Tables and Figures

Table 5.1 Collection information for wild-caught colonies Information on the date of collection, geographic location and host species for each colony used in this study. At time of collection, colony size data was also recorded. Asterisks (*) indicate colonies which had larvae placed into combined colonies with larvae collected elsewhere. Further details on these combinations can be found in Table 3.4. Double asterisks (**) indicate two colonies of the same species that were collected from the same site on the same day that would ordinarily have been combined into a single colony for data analysis. However, these two colonies consisted of larvae from two different developmental stages (early instar and late instar) and were analyzed as distinct. Collection ID Species Date Latitude Longitude Host Species Note NS013 N. abbotii 9/1/2013 35.1310 -85.4000 P. virginiana NS014 N. abbotii 9/1/2013 35.5670 -84.4630 P. taeda NS021 N. abbotii 3/19/2014 29.7887 -84.7665 P. clausa NS024 N. abbotii 4/27/2014 35.5638 -84.9443 P. taeda NS027 N. abbotii 5/3/2014 35.4506 -80.5947 P. virginiana NS072 N. abbotii 8/18/2014 45.1316 -91.9247 P. resinosa NS086 N. abbotii 9/7/2014 38.1722 -83.5570 P. virginiana * NS088 N. abbotii 9/7/2014 38.1039 -83.5113 P. virginiana * NS113.03 N. abbotii 4/25/2015 36.1397 -85.8066 P. taeda NS117.03 N. abbotii 4/25/2015 35.9419 -86.5278 P. taeda NS141 N. abbotii 5/10/2015 37.7862 -80.3004 P. virginiana NS146 N. abbotii 6/13/2015 36.1791 -85.9455 P. taeda NS147 N. abbotii 6/13/2015 35.8118 -86.3994 P. taeda NS162.01 N. abbotii 7/25/2015 44.5511 -68.3945 P. resinosa NS188.01 N. abbotii 9/13/2015 39.8864 -74.5099 P. rigida NS029 N. compar 5/4/2014 37.1015 -77.5426 P. taeda NS069 N. compar 8/6/2014 48.7014 -85.5435 P. banksiana NS073 N. compar 8/18/2014 44.9117 -91.2837 P. banksiana NS148 N. compar 6/14/2015 33.9282 -83.3761 P. taeda NS168.01 N. compar 8/13/2015 45.9263 -77.3254 P. banksiana * NS169.01 N. compar 8/13/2015 45.9315 -77.3333 P. banksiana * NS170.01 N. compar 8/14/2015 46.6231 -81.4552 P. banksiana NS172.01 N. compar 8/14/2015 47.4648 -81.8467 P. banksiana * NS174.01 N. compar 8/15/2015 48.0456 -84.5494 P. banksiana * NS175 N. compar 8/15/2015 48.0297 -84.6513 P. banksiana * NS176 N. compar 8/15/2015 48.5437 -85.1911 P. banksiana * NS178.01 N. compar 8/16/2015 48.4161 -89.6412 P. banksiana * NS182.01 N. compar 8/17/2015 46.5089 -90.5027 P. banksiana * NS183 N. compar 8/17/2015 46.5006 -90.4626 P. banksiana NS184.01 N. compar 8/17/2015 46.1149 -90.5511 P. banksiana * NS217 N. compar 8/15/2016 48.0302 -84.6490 P. banksiana 155

Table 5.1 (continued) NS218 N. compar 8/15/2016 48.0459 -84.5507 P. banksiana * NS220 N. compar 8/15/2016 48.6312 -85.3626 P. banksiana * NS221 N. compar 8/15/2016 48.7935 -87.0895 P. banksiana * NS045.01 N. dubiosus 7/2/2014 46.1283 -90.5689 P. banksiana NS054 N. dubiosus 7/21/2014 45.9419 -88.2880 P. banksiana NS057.01 N. dubiosus 8/3/2014 46.6231 -81.4591 P. banksiana NS058 N. dubiosus 8/3/2014 47.3006 -81.7591 P. banksiana NS059 N. dubiosus 8/4/2014 48.1717 -80.2546 P. banksiana NS060 N. dubiosus 8/4/2014 47.7313 -80.3358 P. banksiana NS061.01 N. dubiosus 8/4/2014 47.6537 -81.0517 P. banksiana NS062 N. dubiosus 8/4/2014 47.4836 -81.8459 P. banksiana NS063 N. dubiosus 8/5/2014 48.3282 -81.7215 P. banksiana NS064 N. dubiosus 8/5/2014 48.2414 -82.3542 P. banksiana NS066 N. dubiosus 8/5/2014 47.8744 -83.2854 P. banksiana NS067 N. dubiosus 8/6/2014 48.4004 -89.6054 P. banksiana NS068 N. dubiosus 8/6/2014 48.6920 -85.9239 P. banksiana NS151.01 N. dubiosus 6/27/2015 48.2205 -82.0732 P. banksiana NS152.01 N. dubiosus 6/27/2015 47.4838 -81.8460 P. banksiana NS154.01 N. dubiosus 6/27/2015 46.3111 -81.6557 P. banksiana NS172.03 N. dubiosus 8/14/2015 47.4648 -81.8467 P. banksiana NS173.02 N. dubiosus 8/14/2015 47.4838 -81.8462 P. banksiana NS174.02 N. dubiosus 8/15/2015 48.0456 -84.5494 P. banksiana NS217.02 N. dubiosus 8/15/2016 48.0302 -84.6490 P. banksiana NS219 N. dubiosus 8/15/2016 48.5437 -85.1910 P. banksiana NS092 N. excitans 10/17/2014 31.4993 -84.5934 P. glabra NS094 N. excitans 10/18/2014 29.7173 -82.4570 P. glabra NS095 N. excitans 10/18/2014 29.7184 -82.4562 P. glabra NS097 N. excitans 10/18/2014 29.7351 -82.4455 P. glabra NS098 N. excitans 10/18/2014 29.7309 -82.4520 P. glabra NS100.01 N. excitans 10/19/2014 29.7074 -82.4522 P. glabra NS100.02 N. excitans 10/19/2014 29.7074 -82.4522 P. glabra NS104 N. excitans 11/9/2014 29.7076 -82.4523 P. glabra NS108 N. excitans 11/10/2014 28.7870 -81.9818 P. elliottii NS128.01 N. excitans 5/8/2015 35.4745 -80.2625 P. echinata NS133.02 N. excitans 5/9/2015 36.4248 -77.6326 P. taeda NS134 N. excitans 5/9/2015 36.4647 -77.8229 P. taeda NS135.02 N. excitans 5/9/2015 36.4379 -78.0875 P. taeda NS023.02 N. fabricii 4/27/2014 35.9292 -86.5233 P. taeda NS024.02 N. fabricii 4/27/2014 35.5638 -84.9443 P. taeda NS083 N. fabricii 8/14/2014 35.9332 -86.5323 P. taeda NS093 N. fabricii 10/17/2014 31.4993 -84.5934 P. glabra NS114.03 N. fabricii 4/25/2015 36.1791 -85.9456 P. taeda

156

Table 5.1 (continued) NS121 N. fabricii 4/26/2015 34.5636 -84.9446 P. taeda NS128.02 N. fabricii 5/8/2015 35.4745 -80.2625 P. echinata NS129 N. fabricii 5/9/2015 35.3942 -77.1439 P. taeda NS133.01 N. fabricii 5/9/2015 36.4248 -77.6226 P. taeda NS135.01 N. fabricii 5/9/2015 36.4379 -78.0875 P. taeda NS138.02 N. fabricii 5/10/2015 37.2094 -77.4743 P. taeda NS142 N. fabricii 5/25/2015 38.4545 -76.0661 P. taeda NS143 N. fabricii 5/25/2015 38.0627 -76.5344 P. taeda NS144 N. fabricii 5/25/2015 37.5005 -76.3014 P. taeda NS023.01 N. hetricki 4/27/2014 35.9292 -86.5233 P. taeda NS113.02 N. hetricki 4/25/2015 36.1397 -85.8066 P. taeda NS114.02 N. hetricki 4/25/2015 36.1791 -85.9456 P. taeda NS115.01 N. hetricki 4/25/2015 36.4716 -86.6816 P. taeda NS120.01 N. hetricki 4/25/2015 35.8104 -86.3984 P. taeda NS136.01 N. hetricki 5/9/2015 36.5533 -78.1801 P. taeda NS138.01 N. hetricki 5/10/2015 37.2094 -77.4743 P. taeda NS140.03 N. hetricki 5/10/2015 37.1959 -77.5244 P. taeda RB367.02 N. hetricki 5/23/2014 36.0095 -87.3781 P. taeda NS101 N. knereri 10/20/2014 29.0046 -81.7581 P. clausa NS102 N. knereri 10/20/2014 29.0340 -81.6404 P. clausa NS214 N. knereri 7/11/2016 29.0040 -81.7577 P. clausa NL006 N. lecontei 11/8/2014 30.5931 -84.3652 P. elliottii NL007 N. lecontei 11/8/2014 30.2906 -84.3514 P. elliottii NL008 N. lecontei 11/10/2014 26.8760 -81.4168 P. elliottii NS043.03 N. lecontei 7/2/2014 45.8223 -91.8884 P. banksiana NS050 N. lecontei 7/4/2014 44.8629 -89.6366 P. banksiana NS053.03 N. lecontei 7/20/2014 46.1143 -90.5513 P. banksiana NS053.04 N. lecontei 7/20/2014 46.1143 -90.5513 P. banksiana NS087.01 N. lecontei 9/7/2014 38.1232 -83.5241 P. virginiana NS087.02 N. lecontei 9/7/2014 38.1039 -83.5113 P. virginiana NS089.01 N. lecontei 9/7/2014 38.1006 -83.5048 P. virginiana NS145.01 N. lecontei 6/13/2015 35.9803 -85.0152 P. virginiana NS158 N. lecontei 7/9/2015 38.1003 -83.5035 P. virginiana NS160.01 N. lecontei 7/23/2015 40.3681 -74.3026 P. rigida NS168.02 N. lecontei 8/13/2015 45.9263 -77.3254 P. banksiana NS182.02 N. lecontei 8/17/2015 46.5089 -90.5027 P. banksiana NS184.02 N. lecontei 8/17/2015 46.1149 -90.5511 P. banksiana NS186.01 N. lecontei 8/23/2015 35.9803 -85.0152 P. virginiana NS204 N. lecontei 6/28/2016 45.8223 -91.8884 P. banksiana NS212.01 N. lecontei 7/10/2016 29.0978 -82.1861 P. palustris NS216 N. lecontei 7/11/2016 29.3207 -81.7267 P. palustris NMr002 N. maurus 7/2/2014 45.9975 -91.6183 P. banksiana

157

Table 5.1 (continued) NMr003 N. maurus 7/3/2014 45.6019 -89.3511 P. banksiana NS037 N. maurus 6/17/2014 45.6643 -89.4919 P. banksiana NS045.02 N. maurus 7/2/2014 46.1283 -90.5689 P. banksiana NS051.02 N. maurus 7/5/2014 44.8440 -89.6906 P. banksiana NS203 N. maurus 6/28/2016 45.5792 -91.7590 P. banksiana NS205.02 N. maurus 6/28/2016 45.8428 -91.8877 P. banksiana NS206.02 N. maurus 6/29/2016 46.5544 -91.3228 P. banksiana NS208 N. maurus 6/29/2016 46.1150 -90.5513 P. banksiana NS210.01 N. maurus 6/29/2016 46.0538 -89.4857 P. banksiana ** NS210.02 N. maurus 6/29/2016 46.0538 -89.4857 P. banksiana ** NS105 N. merkeli 11/10/2014 29.9238 -81.5317 P. elliottii NS106 N. merkeli 11/10/2014 26.9260 -81.5177 P. elliottii NS107 N. merkeli 11/10/2014 26.9214 -81.5086 P. elliottii NS109 N. merkeli 11/10/2014 28.7870 -81.9818 P. elliottii NS212.02 N. merkeli 7/10/2016 29.0978 -82.1861 P. palustris NS212.03 N. merkeli 7/10/2016 29.0978 -82.1861 P. palustris NS213 N. merkeli 7/10/2016 28.7864 -81.9816 P. elliottii NS047 N. nigroscutum 7/4/2014 45.1145 -88.3591 P. banksiana * NS047 N. nigroscutum 7/4/2014 45.1145 -88.3591 P. banksiana NS051.01 N. nigroscutum 7/5/2014 44.8440 -89.6906 P. banksiana * NS057.02 N. nigroscutum 8/3/2014 46.6231 -81.4591 P. banksiana NS070.03 N. nigroscutum 8/18/2014 45.1248 -92.3836 P. banksiana NS181.01 N. nigroscutum 8/17/2015 48.6885 -93.0022 P. banksiana NS205.01 N. nigroscutum 6/28/2016 45.8428 -91.8877 P. banksiana NS206.01 N. nigroscutum 6/29/2016 46.5544 -91.3228 P. banksiana * NS211 N. nigroscutum 6/30/2016 44.8440 -89.6905 P. banksiana * NP034 N. pinetum 8/15/2015 38.0324 -84.5655 P. strobus NP036 N. pinetum 8/19/2015 38.0324 -84.5655 P. strobus NP039 N. pinetum 8/20/2015 37.9706 -84.4976 P. strobus NP041 N. pinetum 8/20/2015 37.9729 -84.5004 P. strobus NP047 N. pinetum 8/31/2014 39.0084 -84.6499 P. strobus NP052 N. pinetum 9/6/2015 39.6188 -83.6037 P. strobus NS161 N. pinusrigidae 7/24/2015 41.7809 -70.6108 P. rigida NS188.02 N. pinusrigidae 9/13/2015 39.8864 -74.5099 P. rigida NS189.03 N. pinusrigidae 9/13/2015 39.8860 -74.5061 P. rigida NS022 N. pratti 4/13/2014 38.0847 -83.5098 P. virginiana NS034 N. pratti 6/16/2014 46.1113 -89.6693 P. banksiana NS110 N. pratti 4/24/2015 36.8544 -84.8457 P. taeda NS112 N. pratti 4/24/2015 36.5753 -85.1380 P. taeda NS117.01 N. pratti 4/25/2015 35.9419 -86.5278 P. taeda NS118.02 N. pratti 4/25/2015 35.9419 -86.5278 P. virginiana NS122 N. pratti 4/26/2015 34.5275 -84.9465 P. taeda

158

Table 5.1 (continued) NS123.03 N. pratti 5/6/2015 38.0860 -83.5117 P. virginiana NS124 N. pratti 5/6/2015 38.0860 -83.5117 P. virginiana NS125 N. pratti 5/6/2015 38.0843 -83.5098 P. virginiana NS126 N. pratti 5/6/2015 38.0843 -83.5098 P. virginiana NS132 N. pratti 5/9/2015 36.4047 -77.6465 P. taeda NS137 N. pratti 5/10/2015 37.2591 77.4003 P. taeda NS138.04 N. pratti 5/10/2015 37.2094 -77.4743 P. taeda NS140.01 N. pratti 5/6/2015 37.1959 -77.5244 P. taeda NS150 N. pratti 6/26/2015 47.9385 -83.0621 P. banksiana NS153 N. pratti 6/27/2015 46.6237 -81.4547 P. banksiana NS154.02 N. pratti 6/27/2015 46.3111 -81.6557 P. banksiana NS155 N. pratti 6/28/2015 46.3023 -79.3835 P. banksiana NS163 N. pratti 7/25/2015 44.4801 -67.5931 P. banksiana NS164 N. pratti 7/25/2015 44.7799 -67.5930 P. banksiana NS165 N. pratti 7/25/2015 44.4774 -67.5908 P. banksiana NS166 N. pratti 7/25/2015 44.4775 -67.5906 P. banksiana NS207 N. pratti 6/29/2016 46.5086 -90.5015 P. banksiana Can089 N. rugifrons 8/17/2014 47.7039 -83.3188 P. banksiana NS044 N. rugifrons 7/2/2014 45.8396 -91.9139 P. banksiana NS052 N. rugifrons 7/20/2014 45.8396 -91.9139 P. banksiana NS070.01 N. rugifrons 8/18/2014 45.1248 -92.3836 P. banksiana NS172.04 N. rugifrons 8/14/2015 47.4648 -81.8467 P. banksiana NS173.01 N. rugifrons 8/14/2015 47.4838 -81.8462 P. banksiana NS177 N. rugifrons 8/15/2015 48.7021 -85.5433 P. banksiana NS200 N. rugifrons 6/28/2016 44.5711 -91.6363 P. banksiana Can058 N. swainei 8/1/2014 44.1217 -85.4708 P. banksiana NS053.01 N. swainei 7/20/2014 46.1143 -90.5513 P. banksiana NS053.02 N. swainei 7/20/2014 46.1143 -90.5513 P. banksiana NS056.02 N. swainei 7/20/2014 44.8440 -89.6906 P. banksiana NS061.02 N. swainei 8/4/2014 47.6537 -81.0517 P. banksiana NS070.02 N. swainei 8/18/2014 45.1248 -92.3836 P. banksiana NS071 N. swainei 8/18/2014 45.1364 -92.1923 P. banksiana NS074.01 N. swainei 8/19/2014 44.8586 -89.6911 P. banksiana NS077 N. swainei 8/19/2014 45.4710 -89.7110 P. banksiana NS091 N. swainei 9/13/2014 45.9974 -91.6181 P. banksiana NS169.02 N. swainei 8/13/2015 45.9315 -77.3333 P. banksiana NS178.02 N. swainei 8/16/2015 48.4161 -89.6412 P. banksiana NS181.02 N. swainei 8/17/2015 48.6885 -93.0022 P. banksiana NS184.03 N. swainei 8/17/2015 46.1149 -90.5511 P. banksiana NS113.01 N. taedae 4/25/2015 36.1397 -85.8066 P. taeda NS114.01 N. taedae 4/25/2015 36.1791 -85.9456 P. taeda NS115.03 N. taedae 4/25/2015 36.4716 -86.6816 P. taeda

159

Table 5.1 (continued) NS117.02 N. taedae 4/25/2015 35.9419 -86.5278 P. taeda NS118.01 N. taedae 4/25/2015 35.9419 -86.5278 P. virginiana NS119 N. taedae 4/25/2015 35.9333 -86.5322 P. taeda NTL001 N. taedae 5/8/2014 36.3177 -94.7105 P. taeda NTL002 N. taedae 5/19/2015 36.4723 -86.6813 P. taeda NTL003 N. taedae 5/23/2014 36.0095 -87.3781 P. taeda NTL004 N. taedae 6/9/2014 37.7271 -92.7033 P. taeda LL186 N. virginiana 7/12/2015 37.3586 -77.9609 P. virginiana LL189 N. virginiana 7/12/2015 37.3585 -77.9611 P. virginiana LL192 N. virginiana 7/30/2015 37.9836 -84.4175 P. virginiana NS089.02 N. virginiana 9/7/2014 38.1006 -83.5048 P. virginiana NS159 N. virginiana 7/22/2015 39.7042 -78.3288 P. virginiana NS099 N. warreni 10/19/2014 29.7093 -82.4542 P. glabra NS103 N. warreni 11/9/2014 29.7091 -82.4541 P. glabra

160

Table 5.2 Sample sizes for colonies used in by-colony data set Sample sizes of each colony for all traits with N > 1. Temperature, relative humidity, and host resin content were based off collection information. Thus, they all have sample sizes of 1. For combined colonies (last column), the mean values of temperature and relative humidity from their source colonies was taken and assigned to them. Combined colonies were only created using larvae of the same species found on the same host. “Regurgitant” refers to the number of larvae we collected regurgitant from without injury, “Aposematic Pattering” refers to the number of larvae from each colony with available photos of their dorsal and at least one lateral side, “Aggregative Tendency” refers to the number of videos (5 larvae in each) recorded using larvae from only that colony, and “Colony Size” refers to the number of colony sizes recorded from each site at time of collection. Note that for the by-colony analyses, only colonies which had data on all traits were used; colonies with aggregative tendency data, but not colony size data, were included in by- colony analyses of aggregative tendency and vice-versa for colony size. Collection Aposematic Aggregative Colony Combined Species Regurgitant ID Patterning Tendency Size Colony NS072 N. abbotii 7 7 2 No NS086_088 N. abbotii 2 2 1 Yes NS117.03 N. abbotii 12 14 4 No NS141 N. abbotii 11 11 3 1 No NS146 N. abbotii 10 12 5 3 No NS147 N. abbotii 4 3 1 1 No NS162.01 N. abbotii 8 8 5 1 No NS188.01 N. abbotii 3 3 1 1 No CN001 N. compar 5 5 1 1 Yes CN002 N. compar 5 5 1 Yes CN003 N. compar 5 5 1 1 Yes CN004 N. compar 3 3 1 1 Yes CN006 N. compar 1 1 1 1 Yes NS069 N. compar 3 4 3 No NS218 N. compar 1 1 2 11 No NS045.01 N. dubiosus 2 2 1 No NS057.01 N. dubiosus 5 6 2 No NS059 N. dubiosus 4 4 2 No NS060 N. dubiosus 6 6 4 No NS061.01 N. dubiosus 4 6 6 No NS062 N. dubiosus 5 6 4 No NS064 N. dubiosus 5 8 4 No NS066 N. dubiosus 6 5 4 No NS151.01 N. dubiosus 26 23 3 3 No NS094 N. excitans 11 12 2 1 No NS095 N. excitans 3 3 1 1 No NS100.01 N. excitans 5 6 4 No NS100.02 N. excitans 10 10 5 No

161

Table 5.2 (continued) NS104 N. excitans 8 9 7 No NS108 N. excitans 15 15 9 1 No NS133.02 N. excitans 5 5 4 1 No NS135.02 N. excitans 8 9 4 1 No NS023.02 N. fabricii 1 1 1 No NS083 N. fabricii 3 3 1 No NS114.03 N. fabricii 7 7 1 1 No NS121 N. fabricii 4 5 1 3 No NS135.01 N. fabricii 25 25 5 3 No NS138.02 N. fabricii 11 11 3 1 No NS143 N. fabricii 7 7 4 2 No NS144 N. fabricii 7 6 2 3 No NS023.01 N. hetricki 6 7 5 No NS113.02 N. hetricki 6 7 8 3 No NS114.02 N. hetricki 4 6 7 5 No NS120.01 N. hetricki 12 13 5 No NS136.01 N. hetricki 7 7 4 2 No NS138.01 N. hetricki 3 4 3 2 No NS102 N. knereri 11 15 3 1 No NS214 N. knereri 4 4 5 2 No NS043.03 N. lecontei 6 8 8 No NS186.01 N. lecontei 8 7 4 4 No NS212.01 N. lecontei 8 8 6 5 No NS216 N. lecontei 7 9 8 3 No NMr002 N. maurus 6 7 6 4 No NMr003 N. maurus 3 3 1 1 No NS037 N. maurus 6 8 7 3 No NS205.02 N. maurus 3 3 3 1 No NS208 N. maurus 3 3 3 1 No NS210.02 N. maurus 3 3 2 2 No NS105 N. merkeli 12 15 3 2 No NS106 N. merkeli 7 8 2 1 No NS107 N. merkeli 4 5 1 2 No NS109 N. merkeli 11 11 3 1 No NS212.03 N. merkeli 3 3 1 1 No NS051.01.C N. nigroscutum 6 7 1 Yes NS070.03 N. nigroscutum 10 10 2 No NS205.01 N. nigroscutum 3 3 1 4 No NS211 N. nigroscutum 4 4 2 3 No NP034 N. pinetum 2 2 2 No NP039 N. pinetum 4 4 7 No NP047 N. pinetum 2 3 6 No 162

Table 5.2 (continued) NP052 N. pinetum 7 8 4 7 No NS188.02 N. pinusrigidae 22 23 22 3 No NS189.03 N. pinusrigidae 25 28 20 4 No NS022 N. pratti 7 10 6 9 No NS034 N. pratti 3 7 4 1 No NS112 N. pratti 4 3 1 2 No NS117.01 N. pratti 11 11 6 No NS118.02 N. pratti 1 2 1 2 No NS122 N. pratti 3 4 1 2 No NS123.03 N. pratti 9 7 4 7 No NS124 N. pratti 7 9 4 1 No NS140.01 N. pratti 5 5 1 1 No NS154.02 N. pratti 16 18 4 4 No NS155 N. pratti 4 4 1 2 No NS163 N. pratti 14 14 6 2 No NS165 N. pratti 9 9 3 1 No NS044 N. rugifrons 8 8 6 No NS052 N. rugifrons 5 7 4 1 No NS070.01 N. rugifrons 2 2 1 No NS172.04 N. rugifrons 3 3 3 No NS200 N. rugifrons 4 5 2 1 No Can058 N. swainei 5 5 2 No NS053.01 N. swainei 7 7 5 No NS056.02 N. swainei 3 3 1 No NS061.02 N. swainei 8 8 5 No NS114.01 N. taedae 8 9 3 8 No NS115.03 N. taedae 8 9 5 No NS117.02 N. taedae 8 8 8 No NTL001 N. taedae 4 4 2 No NTL003 N. taedae 3 3 1 No NTL004 N. taedae 5 7 4 No LL186 N. virginiana 28 32 10 No LL189 N. virginiana 12 14 6 No LL192 N. virginiana 15 15 3 No NS099 N. warreni 10 10 3 4 No NS103 N. warreni 11 12 3 4 No

163

Table 5.3 Sample sizes for by-species data set Sample sizes for each trait for each species. “Apo. Pattern.” is the number of individual larvae of each species with photographs of their dorsal and at least one lateral side, “Agg. Tendency” is the number of videos (5 larvae in each) recorded using larvae of that species. Colony size data was supplemented beyond the colonies detailed in Table 5.1 with previous measures from collection logs and literature sources. Information on these additional collection logs and literature sources can be found in Tables 3.3 and 3.1 respectively. Prior collection log data will have collection dates prior to the year 2013. Relative Resin Apo. Agg. Colony Species Temperature Humidity Content Regurgitant Pattern. Tendency Size N. abbotii 15 15 15 49 67 23 14 N. compar 19 19 19 29 29 8 70 N. dubiosus 21 21 21 63 66 29 43 N. excitans 13 13 13 65 69 36 12 N. fabricii 14 14 14 76 51 18 20 N. hetricki 9 9 9 39 56 32 14 N. knereri 3 3 3 15 19 8 5 N. lecontei 20 20 20 29 32 26 42 N. maurus 11 11 11 24 27 22 21 N. merkeli 7 7 7 37 42 10 9 N. nigroscutum 8 8 8 24 25 8 21 N. pinetum 6 6 6 15 17 22 9 N. pinusrigidae 3 3 3 47 52 42 10 N. pratti 24 24 24 93 103 46 49 N. rugifrons 8 8 8 22 25 17 37 N. swainei 14 14 14 23 23 13 21 N. taedae 10 10 10 36 40 23 14 N. virginiana 5 5 5 56 62 19 1 N. warreni 2 2 2 21 22 6 8

Table 5.4 Details on Resin Sampling of Pines by Collection Site

tude Pine Species Lati Longitude Collection Date 1 Collection Date 2 Resin Samples 1 Collection Resin Samples Collection P. taeda 34.5631 -84.9444 5/7/2017 8/2/2017 10 10 P. taeda 34.4122 -81.7060 5/7/2017 8/2/2017 10 10 P. palustris 33.0505 -83.7188 5/8/2017 8/3/2017 10 10 P. echinata 32.5077 -83.4532 5/8/2017 8/3/2017 9 10 P. elliottii 31.6347 -83.5843 5/8/2017 8/3/2017 10 10 P. palustris 30.2835 -82.4757 5/8/2017 8/3/2017 9 10 P. elliottii 30.2835 -82.4757 5/8/2017 8/3/2017 9 10 P. clausa 29.5002 -81.8474 5/9/2017 8/4/2017 8 10 P. clausa 29.2503 -81.7272 5/9/2017 8/4/2017 10 10 P. clausa 29.1193 -81.5760 5/9/2017 8/4/2017 9 10 P. clausa 29.0046 -81.7574 5/9/2017 8/4/2017 10 10 P. glabra 29.7080 -82.4524 5/9/2017 8/5/2017 10 10 P. elliottii 30.0987 -83.4698 5/9/2017 8/5/2017 9 10 P. glabra 30.1033 -83.5585 5/9/2017 8/5/2017 10 10 P. palustris 30.1033 -83.5585 5/9/2017 8/5/2017 10 10 P. glabra 30.4731 -84.9331 5/10/2017 8/5/2017 8 10 P. taeda 30.5727 -84.7048 5/10/2017 8/5/2017 10 10 P. glabra 31.4993 -84.5932 5/10/2017 8/5/2017 10 10 P. palustris 31.0980 -86.5550 5/10/2017 8/5/2017 10 10 P. elliottii 31.1019 -86.5582 5/10/2017 8/5/2017 9 10 P. echinata 34.3532 -87.5086 5/10/2017 8/6/2017 10 10 P. taeda 35.9419 -86.5271 5/10/2017 8/6/2017 10 10 P. virginiana 35.9419 -86.5271 5/10/2017 8/6/2017 8 10 P. virginiana 38.1674 -83.5905 5/15/2017 8/10/2017 10 10 P. strobus 38.4239 -82.3230 5/15/2017 8/10/2017 10 10 P. echinata 35.4746 -80.2620 5/15/2017 8/10/2017 10 10 P. taeda 34.8056 -77.1554 5/16/2017 8/11/2017 10 10 P. virginiana 37.1133 -78.0271 5/16/2017 8/11/2017 10 10 P. rigida 38.7283 -79.4632 5/17/2017 8/11/2017 9 10 P. virginiana 38.7283 -79.4632 5/17/2017 8/11/2017 10 10 P. strobus 39.4588 -77.9859 5/17/2017 8/11/2017 10 10 P. rigida 39.8856 -74.5062 5/17/2017 8/12/2017 9 10 P. rigida 41.8465 -70.6793 5/18/2017 8/12/2017 10 10 P. strobus 41.8465 -70.6793 5/18/2017 8/12/2017 10 10 P. resinosa 44.6381 -84.6327 5/20/2017 8/14/2017 10 10 P. banksiana 44.6381 -84.6327 5/20/2017 8/14/2017 10 10 P. resinosa 46.0956 -85.3916 5/20/2017 8/14/2017 10 10 P. resinosa 44.8440 -88.4499 5/20/2017 8/14/2017 10 10 P. banksiana 45.6021 -89.3522 5/20/2017 8/14/2017 10 10 P. banksiana 46.5006 -90.4625 5/21/2017 8/15/2017 10 10 P. strobus 46.1053 -90.9878 5/21/2017 8/15/2017 10 10 P. resinosa 45.1402 -91.9352 5/21/2017 8/15/2017 10 10 P. banksiana 44.2266 -90.7075 5/21/2017 8/15/2017 10 10 P. rigida 37.2622 -84.9634 5/21/2017 8/6/2017 10 10

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Table 5.5 Details on Resin Sampling of Pines by Species

Species # of Collection Sites Resin Total Samples P. taeda 5 100 P. palustris 4 79 P. echinata 3 59 P. elliottii 4 77 P. clausa 4 77 P. glabra 4 78 P. virginiana 4 78 P. strobus 4 80 P. rigida 4 78 P. resinosa 4 80 P. banksiana 4 80

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Table 5.6 Model comparisons of abiotic factors on aggregative tendency Comparisons of models predicting aggregative tendency on abiotic factors were done without accounting for phylogeny (top, GLS) or while accounting for phylogeny (bottom, PGLS.SEy). Log likelihoods of each model are reported alongside the estimated coefficients (β) for each abiotic factor (Temperature and Relative Humidity), P-values from likelihood ratio tests comparing each model to the corresponding null model, and P- values from likelihood ratio tests comparing each model to the preceding model as applicable. GLS: Aggregative Tendency ~… … Log Likelihood β Temp β RH P vs Null P vs Previous Null -69.2473 NA NA NA NA Temperature -67.3796 -0.0264 NA 0.0533 0.0533 Relative Humidity -66.9643 NA 0.0205 0.0326 0.0000 Temp + RH -66.4503 -0.0157 0.0149 0.0610 0.3106

PGLS.SEy: Aggregative Tendency ~… … Log Likelihood β Temp β RH P vs Null P vs Previous Null -2.3979 NA NA NA NA Temperature -2.3912 -0.0030 NA 0.9075 0.9075 Relative Humidity -2.2423 NA 0.0098 0.5769 0.0000 Temp + RH -2.0778 0.0250 0.0236 0.7260 0.5662

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Table 5.7 Model comparisons of abiotic factors on colony size Comparisons of models predicting colony size on abiotic factors were done without accounting for phylogeny (top, GLS) or while accounting for phylogeny (bottom, PGLS.SEy). Log likelihoods of each model are reported alongside the estimated coefficients (β) for each abiotic factor (Temperature and Relative Humidity), P-values from likelihood ratio tests comparing each model to the corresponding null model, and P- values from likelihood ratio tests comparing each model to the preceding model as applicable. GLS: Colony Size ~… … Log Likelihood β Temp β RH P vs Null P vs Previous Null -93.2604 NA NA NA NA Temperature -92.9486 0.0275 NA 0.4297 0.4297 Relative Humidity -92.3456 NA -0.0340 0.1762 0.0000 Temp + RH -92.3414 0.0037 -0.0326 0.3989 0.9272

PGLS.SEy: Colony Size ~… … Log Likelihood β Temp β RH P vs Null P vs Previous Null -18.9657 NA NA NA NA Temperature -18.9320 0.0151 NA 0.7951 0.7951 Relative Humidity -18.7225 NA 0.0276 0.4855 0.0000 Temp + RH -18.1394 0.0862 0.0689 0.4377 0.2802

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Table 5.8 Model comparisons of biotic factors on aggregative tendency Statistics of biotic models using aggregative tendency as the social trait obtained from confirmatory path analysis for all models with ΔCICc < 5 from the model with the lowest CICc score. C is the summary statistic obtained from partial regressions of conditional independencies in each model, k is the number of conditional independencies in each model, q is the number of parameters estimated in each model (number of traits plus number of edges), CICc is an information criteria calculated based off of C and penalized by q and sample size, ℓ is the relative likelihood of each model compared to the best model, and is the weight or probability of a model given the data and model set. In this case, was only calculated based off models which met the cut-off threshold of ΔCICc <2. Results𝓌𝓌 from models that did not account for phylogeny using GLS are on top while results𝓌𝓌 from models that did account for phylogeny, PGLS.SEy, are on bottom. Models Evaluated Using GLS Model Name C k q P CICc ΔCICc ℓ NBYN 1.7903 3 7 0.9379 16.9570 0.0000 1.0000 0.2409 𝓌𝓌 NAYN 2.1886 3 7 0.9016 17.3553 0.3983 0.8194 0.1974 BBYN 0.5813 2 8 0.9651 18.0971 1.1401 0.5655 0.1363 BAYN 0.9796 2 8 0.9129 18.4954 1.5385 0.4634 0.1116 AAYN 0.9822 2 8 0.9125 18.4980 1.5410 0.4628 0.1115 NNYN 5.7270 4 6 0.6778 18.5930 1.6360 0.4413 0.1063 ANYN 3.6332 3 7 0.7262 18.7999 1.8429 0.3979 0.0959 NAYY 1.7480 2 8 0.7820 19.2638 2.3069 0.3155 NBYY 1.7480 2 8 0.7820 19.2638 2.3069 0.3155 BNYN 4.5181 3 7 0.6069 19.6847 2.7278 0.2557 AAYY 0.4985 1 9 0.7794 20.4134 3.4565 0.1776 BAYY 0.5280 1 9 0.7680 20.4429 3.4859 0.1750 BBYY 0.5280 1 9 0.7680 20.4429 3.4859 0.1750 NNYY 5.6848 3 7 0.4594 20.8514 3.8945 0.1427 ANYY 3.5464 2 8 0.4709 21.0622 4.1052 0.1284

Models Evaluated using PGLS.SEy Model Name C k q P CICc ΔCICc ℓ NNYN 5.1364 4 6 0.7429 24.1364 0.0000 1.0000 1.0000 𝓌𝓌 BNYN 3.1633 3 7 0.7881 27.3452 3.2088 0.2010 NNYY 4.0786 3 7 0.6660 28.2604 4.1241 0.1272 ANYN 4.4013 3 7 0.6225 28.5831 4.4467 0.1082 NNNN 14.0377 5 5 0.1713 28.6530 4.5167 0.1045 NBYN 4.8032 3 7 0.5693 28.9850 4.8486 0.0885 NAYN 4.8794 3 7 0.5594 29.0612 4.9248 0.0852

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Table 5.9 Model comparisons of biotic factors on colony size Statistics of biotic models using aggregative tendency as the social trait obtained from confirmatory path analysis for all models with ΔCICc < 5 from the model with the lowest CICc score. Statistics reported are the same as in Table 5.6. Results from models that did not account for phylogeny using GLS are on top while results from models that did account for phylogeny, PGLS.SEy, are on bottom. Models Evaluated Using GLS Model Name C k q P CICc ΔCICc ℓ NAYY 1.0105 2 8 0.9082 19.5368 0.0000 1.0000 0.2711 𝓌𝓌 NBYY 1.0105 2 8 0.9082 19.5368 0.0000 1.0000 0.2711 NANY 5.1424 3 7 0.5257 21.0734 1.5366 0.4638 0.1257 NBYN 5.1456 3 7 0.5253 21.0766 1.5398 0.4631 0.1255 NAYN 5.4740 3 7 0.4846 21.4050 1.8682 0.3929 0.1065 NBNY 5.6014 3 7 0.4693 21.5324 1.9956 0.3687 0.1000 AAYY 0.7102 1 9 0.7011 21.9245 2.3877 0.3031 BAYY 0.7634 1 9 0.6827 21.9777 2.4409 0.2951 BBYY 0.7634 1 9 0.6827 21.9777 2.4409 0.2951 NANN 9.6058 4 6 0.2938 23.0296 3.4928 0.1744 BANY 4.5690 2 8 0.3344 23.0954 3.5585 0.1688 BBNY 4.5690 2 8 0.3344 23.0954 3.5585 0.1688 NBNN 9.6784 4 6 0.2883 23.1021 3.5653 0.1682 AANY 4.8421 2 8 0.3039 23.3684 3.8316 0.1472 BBYN 5.0431 2 8 0.2829 23.5694 4.0326 0.1331 AAYN 5.2916 2 8 0.2587 23.8180 4.2812 0.1176 BAYN 5.3714 2 8 0.2513 23.8978 4.3609 0.1130

Models Evaluated using PGLS.SEy Model Name C k q P CICc ΔCICc ℓ NNYN 6.8189 4 6 0.5563 25.8189 0.0000 1.0000 0.3699 𝓌𝓌 BNYN 1.6633 3 7 0.9479 25.8451 0.0262 0.9870 0.3651 ANYN 2.3035 3 7 0.8898 26.4853 0.6664 0.7166 0.2651 BNNN 10.0915 4 6 0.2587 29.0915 3.2726 0.1947 NNNN 14.7403 5 5 0.1418 29.3557 3.5368 0.1706 ANNN 10.7092 4 6 0.2187 29.7092 3.8903 0.1430 NBYN 5.6111 3 7 0.4681 29.7929 3.9740 0.1371 NAYN 5.9231 3 7 0.4319 30.1049 4.2860 0.1173 NNYY 6.3849 3 7 0.3815 30.5667 4.7478 0.0931

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Figure 5.1 Summary of biotic model naming convention

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Figure 5.2 Directed acyclic graphs of biotic models Visual diagrams of the 32 biotic models analyzed using directed acyclic graphs (DAGs). Names of models were assigned according to the conventions in Figure 5.1. “Apo” refers to the aposematic patterning of larvae estimated using transition aspect ratios, “chD” is the amount of chemical defense of larvae estimated using regurgitant volume produced, “Soc” is the social trait being analyzed (either aggregative tendency or colony size), and “hoD” is the amount of host defense of a pine tree estimated using their total non-volatile resin content.

Figure 5.3 Correlogram of traits using by-colony data on aggregative tendency

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Figure 5.4 Correlogram of traits using by-colony data on colony size

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Figure 5.5 Correlogram of traits using by-species data

Figure 5.6 Visual summary of the causes and consequences of sociality in Neodiprion Summary diagram of our proposed model of sawfly sociality based on the data and analyses currently available. Dotted arrows represent causal connections resulting from behaviors not quantified here that impact traits studied (i.e., maternal choices regarding host selection and clutch size). Dashed arrows represent causal connections assumed to be true based off logical assumptions, prior studies, and literature. Solid arrows represent causal connections currently supported; arrows with two heads represent cases where the causal direction is still uncertain. The weight of each arrow corresponds to the strength of support for that connection alongside the presence of a “?” note. The “+” and “-” notes refer to positive and negative relationships, respectively.

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VITA EDUCATION Ph.D. (expected), Biology, University of Kentucky 2013 – 2021 B.S. with Honors, Entomology & Evolution/Ecology, The Ohio State University 2008 – 2012

PROFESSIONAL EXPERIENCE Teaching Assistant, General Biology, University of Kentucky Jan, 2021 to May, 2021 Teaching Assistant, Intro to Evolution, University of Kentucky Aug, 2020 to Dec, 2020 Teaching Assistant, Intro to Microbiology, University of Kentucky Aug, 2019 to May, 2020 Teaching Assistant, Intro to Biology, University of Kentucky Jan, 2018 to May, 2019 Teaching Assistant, Intro to Evolution, University of Kentucky Aug, 2017 to Dec, 2017 Teaching Assistant, Intro to Biology, University of Kentucky Aug, 2016 to May, 2017 Graduate Research Assistant, University of Kentucky Jan, 2016 to May, 2016 Teaching Assistant, Intro to Microbiology, University of Kentucky Jan, 2015 to May, 2015 Research Assistant, The Ohio State University Apr, 2010 to May, 2012

PUBLICATIONS Terbot II, J. W., Gaynor, R. L. & Linnen, C. R. 2017 Gregariousness does not vary with geography, developmental stage, or group relatedness in feeding redheaded pine sawfly larvae. Ecology and Evolution. 7, 3689–3702. (doi:10.1002/ece3.2952) Nikbakhtzadeh, M. R., Terbot II, J. W. & Foster, W. A. 2016 Survival Value and Sugar Access of Four East African Plant Species Attractive to a Laboratory Strain of Sympatric Anopheles gambiae (Diptera: Culicidae). Journal of Medical Entomology. 53, 1105–1111. (doi:10.1093/jme/tjw067) Terbot II, J. W., Nikbakhtzadeh, M. R. & Foster, W. A. 2015 Evaluation of Bacillus thuringiensis israelensis as a Control Agent for Adult Anopheles gambiae. Journal of American Mosquito Control Association. 31, 258–261. (doi:10.2987/moco-31-03-258- 261.1) Nikbakhtzadeh, M. R., Terbot II, J. W., Otienoburu, P. E. & Foster, W. A. 2014 Olfactory basis of floral preference of the malaria vector Anopheles gambiae (Diptera: Culicidae) among common African plants . Journal Vector Ecology. 39, 372–383. (doi:10.1111/jvec.12113)

GRANTS, FELLOWSHIPS, AND AWARDS University of Kentucky Ribble Mini-Grant, University of Kentucky ($700.00) 2017 Travel Award, NSF and Society of Systematic Biologists ($600.00) 2016 Graduate Student Research Award, Society of Systematic Biologists ($1500.00) 2015 Academic Year Fellowship (Fall Semester), University of Kentucky 2015 Ribble Mini-Grant, University of Kentucky ($300.00) 2015 SysEB Student Research Travel Award, Entomological Society of America ($1491.00) 2015 Academic Year Fellowship (Fall Semester), University of Kentucky 2014 Daniel R. Reedy Quality Achievement Award, University of Kentucky ($3000.00) 2013 Academic Year Fellowship, University of Kentucky 2013 Flora Ribble Research Fellowship, University of Kentucky ($2000.00) 2013 The Ohio State University Dean’s List: Autumn 2008, Winter 2009, Spring 2009, Spring 2011, Spring 2012 Ohio State Honors Scholarship

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