Jun 22, 2018 - the lab of Dr. Bob Curry at Villanova University. ...... Campus (hereafter Villanova) and Great Marsh, comprised resident Carolina ...... syndromes and animal personality in crustacean decapods: An imperfect map is better than ...
EXPLORATORY BEHAVIOR IN BLACK-CAPPED CHICKADEES, CAROLINA CHICKADEES, AND THEIR HYBRIDS
A Thesis presented to the faculty of the Department of Biology Villanova University In Partial Fulfillment of the Requirements for the Degree of Master of Science in Biology
by
Sarah E. Polekoff
Under the direction of Dr. Robert L. Curry, Mentor Dr. Vikram K. Iyengar Dr. John M. Olson
June 22, 2018
ACKNOWLEDGMENTS I thank Jessie DeAngelis, Paul Stathis, Breanna Bennett, Robert Driver, Holly Garrod, Becca Garlinger, Matt Dula, Taylor Heuermann, Kaitlyn Chan, and Gwen Saccocia for their help in the field. Extra special thanks to my box test partner Breanna Bennett, who not only recorded some of my tests when I needed a day off, but also let me use some of her own data in my analyses. Big thanks to Robert Driver and Emily Burton, who genotyped and sexed the majority of my birds. Thank you to my committee members, who had to read this very long paper, and thank you to Dr. Mike Russell and Dr. Joseph Pigeon for their statistical insight. I could not have finished this without guidance, insight, and assistance from my advisor Dr. Bob Curry. Thank you for believing in me! Thanks to the organizations allowing us to use their land for field sites: Pickering and French Creek Conservation Trust, Pennsylvania Department of Conservation and Natural Resources and Tuscarora State Park, and Hawk Mountain Sanctuary Association. Thank you to everyone who supported me during the long past three years: my fellow lab mates and grad students at Villanova, my friends, my family, my dozens of lizards, and my partner John Cavagnaro. And finally, thank you to all of the chickadees that behaved during my assays and only pooped on me a little bit.
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BIOGRAPHICAL SKETCH Sarah Polekoff grew up in Cherry Hill, New Jersey. She became interested in birdwatching as a teenager and attended Ursinus College to pursue studies in zoology and ecology. As an undergraduate, she took various classes on vertebrate zoology and participated in multiple research projects under the guidance of Drs. Kate Goddard, Robert Dawley, and Ellen Dawley. Her research projects included a mark-recapture study estimating the population density of white-footed mouse, and surveys of stream macroinvertebrate communities. In 2014 she graduated with a Bachelor’s of Science in Biology, with minors in Biostatistics and Spanish. Her honor’s thesis research was titled “Changes in the macroinvertebrate community of Darby Creek following the removal of a low head dam.” In 2013 she participated in an REU at the University of Kentucky with Dr. Dave Westneat. Her project was investigating the variety and possible function of different song types used by House Sparrows. This experience solidified her interest in bird behavior and encouraged her to look to graduate school as a future path. After graduating from Ursinus College, Sarah worked as a field technician for the Bird Conservancy of the Rockies surveying breeding waterbirds in North Dakota. The experience strengthened her love for fieldwork and long hours of driving, so she joined the lab of Dr. Bob Curry at Villanova University. Her Master’s thesis, presented here, explores personality traits in Black-capped and Carolina chickadees. Sarah will be pursuing a Ph.D. at Arizona State University and will be studying adaptations to urbanization in birds.
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TABLE OF CONTENTS
Acknowledgments............................................................................................................... ii Biographical sketch ............................................................................................................ iii List of Figures .................................................................................................................. viii List of Tables ..................................................................................................................... ix List of Appendices .............................................................................................................. x Abstract ............................................................................................................................... 1 Chapter I: Introduction ........................................................................................................ 2 Importance of Animal Personality for Ecology and Evolution .................................... 2 Carolina and Black-capped Chickadees: Why Are They Hybridizing? ....................... 4 Organization of the Thesis ............................................................................................ 6 Chapter II: Exploratory behavior as a personality trait across a chickadee hybrid zone .... 8 Introduction ................................................................................................................... 8 Personality and Species Interactions ....................................................................... 8 Personality, Fitness, and Divergence ...................................................................... 9 Exploratory Behavior ............................................................................................ 10 Chickadees as a System for Studying Personality Divergence ............................. 11 Hypotheses and Predictions .................................................................................. 13 Methods....................................................................................................................... 15 Study System ........................................................................................................ 15 Field procedures .................................................................................................... 15 Assaying Exploratory Behavior: “Box Assays” ................................................... 17 Animal Welfare ..................................................................................................... 20 Species and Sex Identification .............................................................................. 20 iv
Statistical Analyses ............................................................................................... 21 Results ......................................................................................................................... 24 Principal Components Analysis ............................................................................ 24 Predictors of Exploratory Behavior ...................................................................... 25 Repeatability of Exploratory Scores ..................................................................... 29 Assortative Mating ................................................................................................ 29 Discussion ................................................................................................................... 31 Exploratory Behavior as a Component of Chickadee Personality ........................ 31 Overlapping but Distinguishable Interspecific Variation ..................................... 32 Age and Sex Did Not Influence Personality ......................................................... 35 Other Correlates of Exploratory Behavior ............................................................ 36 No Evidence of Assortative Mating ...................................................................... 38 Broader Significance ............................................................................................. 40 Chapter III: Exploratory behavior in wild chickadees: contrasting two field methods .... 42 Introduction ................................................................................................................. 42 Chickadees as a System for Personality Research ................................................ 44 Hypotheses and Predictions .................................................................................. 45 Methods....................................................................................................................... 47 Study System ........................................................................................................ 47 Tent Assay ............................................................................................................ 48 Box Assay ............................................................................................................. 50 Animal Welfare ..................................................................................................... 51 Statistical Analyses ............................................................................................... 52
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Results ......................................................................................................................... 53 Principal Components Analysis ............................................................................ 53 Repeatability of Behavior ..................................................................................... 56 Correlation Between Box and Tent Assays .......................................................... 56 Tent Assay Analysis ............................................................................................. 57 Box Assay Analysis .............................................................................................. 60 Box Assay Optimal Test Duration ........................................................................ 63 Discussion ................................................................................................................... 65 Differences in Assay Design Result in Different Behaviors ................................ 65 Both Assays Represent Personality ...................................................................... 66 Behavior Differs Across Sex ................................................................................ 70 Behavior Differs Across Species .......................................................................... 71 Effects of External Environment on Behavior ...................................................... 73 Importance of Test Duration ................................................................................. 75 Broader Significance ............................................................................................. 76 Chapter IV: Conclusions ................................................................................................... 79 Evolution of Personality ............................................................................................. 79 The Role of Personality in Hybridization ................................................................... 80 Importance of Study Design ....................................................................................... 80 Future Directions ........................................................................................................ 81 Literature Cited ................................................................................................................. 84 Appendix ........................................................................................................................... 98 Appendix 1. Neophobia Assay ................................................................................... 98
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Introduction ........................................................................................................... 98 Methods............................................................................................................... 100 Results ................................................................................................................. 102 Conclusion .......................................................................................................... 103 Literature Cited ................................................................................................... 105 Appendix 2. All Mixed Models Created for Chapter 2 ............................................ 107
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LIST OF FIGURES Figure 1.1
Black-capped (left) and Carolina (right) chickadees side-by-side ................ 5
Figure 2.1
The four field sites used for this study ........................................................ 16
Figure 2.2
Mobile wooden box used to assay exploratory behavior ............................ 18
Figure 2.3
Exploratory behavior at different wing lengths (mm) for Black-capped (BCCH), Carolina (CACH), and hybrid (HYCH) chickadees .................... 26
Figure 2.4
Density distribution plot of exploratory behavior scores for Black-capped (BCCH), Carolina (CACH), and hybrid (HYCH) chickadees .................... 27
Figure 2.5
Effect of holding time (min) on exploratory score ..................................... 28
Figure 2.6
Relationship between the male and female exploratory scores of each social pair .............................................................................................................. 30
Figure 3.1
Mobile screen tent used to assay exploratory behavior .............................. 48
Figure 3.2
Behavioral responses during tent assay by sex (species combined) ............ 58
Figure 3.3
Behavioral responses during tent assay by species and season (sexes combined) ................................................................................................... 59
Figure 3.4
Association between tent assay scores and nesting order and test day ....... 60
Figure 3.5
Behavioral responses during box assay by species (sexes combined) ........ 61
Figure 3.6
Behavioral responses during box assay by sex (species combined) ............ 62
Figure 3.7
Association between box assay scores and nesting order and test day ....... 63
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LIST OF TABLES Table 2.1
Variables measured during exploratory behavior box assay ...................... 20
Table 2.2
Number of individuals per species per site ................................................. 21
Table 2.3
Fixed effect variables used in linear mixed models ..................................... 22
Table 2.4
PC1 eigenvectors for each variable ............................................................. 24
Table 2.5
Linear mixed models with ΔAIC < 2 .......................................................... 25
Table 3.1
Variables recorded or calculated during tent assays ................................... 49
Table 3.2
Variables recorded or calculated during box assays ................................... 51
Table 3.3
Percentage of variation explained by principal components 1 and 2 for each PCA model .................................................................................................. 55
Table 3.4
Definition of PCA components used for analyses ...................................... 56
Table 3.5
Test statistics and p-values for comparisons involving the five-, seven-, and ten-minute duration PC1s ........................................................................... 65
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LIST OF APPENDICES Appendix 1: Neophobia Assay ........................................................................................ 98 Appendix 2: All Mixed Models Created for Chapter 2 ................................................. 107
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ABSTRACT Behavioral differences between species can impact mate choice and dominance relationships, ultimately determining where species occur and whether they hybridize. I measured exploratory behavior in wild Carolina (Poecile carolinensis) and Black-capped (P. atricapillus) chickadees in the field using two different mobile assays: a screen tent and an illuminated box designed to reduce outside influences. I scored exploratory behavior by recording variables describing activity and latency to reach new areas, and then produced a composite score using principal components analysis. Individuals exhibited a wide range of exploratory scores. Carolina Chickadees were on average more exploratory than Black-capped Chickadees, with hybrids intermediate; however, most of the variation existed among individuals. The box and tent scores did not correlate, suggesting that these assays assessed different behaviors. Most measured behaviors were highly repeatable and might represent individual personality. Behavioral similarity may play a role in the species’ willingness to hybridize in the wild.
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CHAPTER I: INTRODUCTION Importance of Animal Personality for Ecology and Evolution Personality is a familiar concept: we understand that people and beloved pets can have unique personalities. In the context of behavioral ecology, personality is defined by two criteria: behavioral consistency within an individual across time or context, and differences in behavior across individuals (Canestrelli et al. 2016a). A wide range of animals display personality according to these criteria, including mammals (Weiss and Adams 2013), birds (Dingemanse et al. 2002, van Oers and Naguib 2013), reptiles (Carter et al. 2012), fish (Bell and Sih 2007, Bell et al. 2013), crustaceans (Gherardi et al. 2012), and even gastropods and echinoderms (Pruitt et al. 2012, Mather and Logue 2013). With personality so widespread in the animal kingdom, we cannot rely on average responses to understand how animals will react. To truly understand animal ecology, we need to understand the full range of behavioral responses and how they interact within the population. Personality is usually studied as differences among individuals of the same species (Gosling 2001) but personality may also play an important role in interspecies interactions (Canestrelli et al. 2016a). Invading individuals or those on the expanding edge of the species range may differ in personality from the average species personality type (Canestrelli et al. 2016a). If personality of individuals can affect interactions across species, then dominance interactions or hybridization events could play out differently depending on the personality type of the expanding individuals (Canestrelli et al. 2016b). For example, aggressive male Western Bluebirds (Sialia mexicana) disperse farther and therefore are the first to come into contact with Mountain Bluebirds (Sialia currucoides),
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which the aggressive Western Bluebirds are able to dominate (Duckworth et al. 2015). Even predator-prey interactions are affected by personality of individuals: more active ocher sea stars (Pisaster ochraceus) are better able to capture black turban snails (Chlorostoma funebralis) that exhibit low predator avoidance, while less active sea stars are better at capturing snails exhibiting high predator avoidance (Pruitt et al. 2012). Although behavior measured at the individual level is ideal and provides the most information, average species differences in behavior are much easier to collect and can still provide important context for species interactions. In birds, aggressive response to playback is often compared across species and used as an indicator of whether different bird species might behave aggressively towards each other in nature (Freeman and Montgomery 2015, Billerman and Carling 2016). Dominance of one species over another in general can dictate species ranges (Freeman and Montgomery 2015) and may even increase selection for trait divergence (Freshwater et al. 2014). Variation among individuals is the raw material for evolution, and personality traits fall under selection by influencing fitness through survival (Dingemanse et al. 2004), dominance (Fox et al. 2009), dispersal (Duckworth and Badyaev 2007), and offspring size (Schuett et al. 2011a). Logically, different species, or even different conspecific populations under different selection pressures, will express different personality phenotypes (Clarke and Lindburg 1993, Mettke-Hoffmann et al. 2002, Mettke-Hofmann et al. 2013, Kozlovsky et al. 2014). Contrasting species behaviors and tying those differences to ecology can help us learn how personality traits might be under selection.
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Carolina and Black-capped Chickadees: Why Are They Hybridizing? Carolina (Poecile carolinensis) and Black-capped (P. atricapillus) chickadees are parapatric sister species that hybridize extensively along their contact zone (Bronson et al. 2003, Driver 2017). Extreme morphological similarity likely plays a large role in their tendency to see each other as mates (Fig. 1.1), but behavioral similarity may increase hybridization as well. Some animals consider personality in addition to physical traits when choosing a mate (Schuett et al. 2010), so behavioral similarity could facilitate hybridization. Carolina and Black-capped chickadees both choose social mates during the nonbreeding season, while in flocks (Mostrom et al. 2002, Smith et al. 2010). How a species behaves in a flock setting (foraging style, social interactions, etc.) could signal mate compatibility and therefore act as a prezygotic mating barrier. Carolina Chickadees are replacing Black-capped Chickadees as the hybrid zone moves north. The northward movement of Carolina Chickadees is closely correlated with warming of winter minimum temperatures, suggesting that a changing climate is allowing Carolina Chickadees to live farther north now than in the past (McQuillan and Rice 2015). Black-capped Chickadees should be able to survive farther south than they actually occur, which suggests that Carolina Chickadees are excluding Black-capped Chickadees from suitable habitat through behavioral interactions (McQuillan and Rice 2015). In the hybrid zone, it seems that Black-capped Chickadees are not being
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Figure 1.1. Black-capped (left) and Carolina (right) chickadees side-by-side. Photo: R. Curry. outcompeted for nest sites or otherwise excluded physically, but instead broadly hybridizing with Carolina Chickadees until no pure Black-capped individuals remain. As new Carolina Chickadee immigrants move into the population, the proportion of hybrids increases, and Black-capped Chickadee genes gradually disappear from the population (Driver 2017). Female chickadees of both species may prefer to mate with Carolina males. Females preferred to associate with dominant males (all of which were Carolina Chickadees) after observing a dyad of males in captivity (Bronson et al. 2003). Wild females preferred Carolina males as extrapair partners, and were more likely to have extrapair offspring in the nest if the social male was Black-capped (Reudink et al. 2006). If personality plays a role in mate choice, then it may help explain why male Carolina Chickadees are successfully outcompeting Black-capped Chickadees for mating opportunities.
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Organization of the Thesis This thesis contains four chapters. Following the present general introduction are two data chapters. I conclude with a chapter summarizing my overall conclusions. The first data chapter focuses on comparing the behavior of Carolina and Blackcapped chickadees and their hybrids, as well as investigating the possibility of assortative mating. This first data chapter uses exploratory behavior to better understand the hybrid zone and why hybridization might be occurring. The second data chapter focuses on the development of methods to measure exploratory behavior: components include contrasting the preliminary and final methods, finding optimal test length, and measuring the effects of season and weather on test results. This second chapter is addressed to other field ecologists who may want to develop their own method to measure exploratory behavior, and to future Curry Lab students who may modify my methods for future studies involving chickadee personality. Reading these chapters together will provide a fuller understanding of my project, but each can stand alone and serves a separate purpose for a separate audience. In my conclusion chapter I discuss how I was able to develop a novel method capable of discerning chickadee behavioral differences. I reflect on what my project has revealed about chickadees and also about the nature of personality assays. The thesis also includes a “mini-chapter” as Appendix 1. This appendix describes a short experiment I conducted in 2016 to measure neophobia, another personality trait. My original thesis proposal involved studying both exploratory behavior and neophobia, and also testing whether the two traits correlated as part of a behavioral syndrome. However, I ran into some challenges that led to me dropping the neophobia aspect from
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my thesis. First, it was impractical for me to carry out both tests at once during the breeding season because they both needed to be done on the same birds during the same timeframe. Second, both the male and female of a pair were often present during the neophobia assay, meaning that each score represented a pair rather than a single bird. Finally, because of the timing difficulties, many birds were unbanded during the neophobia assay; therefore, even if I wanted to analyze each bird’s response separately, I could not tell the two members of a pair apart during the assay. I include the results of my rather incomplete neophobia study as Appendix 1, so that future studies may consider my methods (both pros and cons).
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CHAPTER II: EXPLORATORY BEHAVIOR AS A PERSONALITY TRAIT ACROSS A CHICKADEE HYBRID ZONE Introduction Personality and Species Interactions Animal personality, or behavioral polymorphisms present in a population, can impact both dispersal and species interactions (Canestrelli et al. 2016a, 2016b). A diverse range of personality types might let a species cover a larger range or help cope with changing environments, increasing the likelihood of coming into contact with other species. Certain personality types may be more likely to interact with another species because of differences in dispersal or habitat preferences. Finally, the outcome of an interaction between two species may depend on the personality types of the individuals involved (Pruitt et al. 2012, Duckworth et al. 2015). The interaction between Western Bluebirds (Sialia mexicana) and Mountain Bluebirds (S. currucoides) represents an excellent case study that illustrates how personality type impacts the outcome of a species interaction. Male Western Bluebirds that are more aggressive when defending against territorial intrusions also disperse farther than non-aggressive males (Duckworth and Badyaev 2007) and therefore are more likely to come into contact with Mountain Bluebirds, a fellow cavity-nester that is the first to return to recently burned areas (Duckworth et al. 2015). Increased aggressiveness may also help these far-dispersing male Western Bluebirds take over territories of Mountain Bluebirds (Duckworth et al. 2015). After initial succession, the Western Bluebird population is dominated by aggressive personality types, but over time, more non-aggressive birds move into the habitat (Duckworth et al. 2015). Thus, in this system,
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personality influences patterns of dispersal and succession because of its impact on crossspecies dominance relationships. Personality can influence hybridization dynamics in two main ways: in addition to the concept of a “dispersal phenotype” that brings dispersal-inclined individuals into contact with another species, personality may also influence mating choices (Canestrelli et al. 2016b). No studies have directly investigated the role that personality plays in cross-species mate selection, though a few studies have found a link between dominance and directional hybridization (Bronson et al. 2003, MacGregor et al. 2016). In both chickadees and wall lizards, females of one species prefer to mate with males of the opposite species because they are dominant in cross-species pairwise interactions. Personality might drive differences in dominance between species, or act as a sexually selected trait itself. Additionally, assortative mating in a contact zone could occasionally lead to hybrid pairs if the two species display overlap in personality phenotypes used for mate selection. Overall, the current body of work strongly suggests that personality of individuals can influence population-level dynamics and interactions between closely related species. Therefore, study of population-level interactions, such as a moving hybrid zone, will be incomplete without considering how the behavior of individuals might be driving the phenomenon. Personality, Fitness, and Divergence Within species, personality has been directly linked to fitness measures such as winter survival (Dingemanse et al. 2004) and reproductive output (Schuett et al. 2011a). Personality may also be intricately linked to both reproductive success and survival
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through its impact on social dynamics. At a basic level, position within a dominance hierarchy is associated with personality type (Verbeek et al. 1999, Fox et al. 2009). However, recent studies are beginning to unravel more complex connections. In Great Tits, the association between “popularity” (how central a bird is within a network of birds) and foraging initiation depends on the birds’ personality types (Evans et al. 2018). If personality has fitness consequences, then we might expect personality traits to evolve in response to different selection pressures, whether social or environmental. Differences in average personality type exist across separate populations within species (Kozlovsky et al. 2014), and personality evolution may even be induced by the introduction of predators (Bell and Sih 2007). Average personality traits differ across species as well; across parrots, differences in behavior are associated with differences in natural diet and habitat (Mettke-Hoffmann et al. 2002). Closely related species provide an excellent system to study which selective pressures might have contributed to divergence in personality traits over a long time-span, or how variation in personality traits can be maintained despite selection. Exploratory Behavior Exploratory behavior is a personality trait that can be defined as how quickly a bird explores a novel space. Exploratory behavior correlates with fitness-associated traits such as survivorship (Dingemanse et al. 2004), immune system (Jacques-Hamilton et al. 2017), dominance (Dingemanse and De Goede 2004, Fox et al. 2009), mate choice (Schuett et al. 2011b), reproductive success (Schuett et al. 2011a), cognition (Guillette et al. 2011, 2015), and foraging choices (Evans et al. 2018) in various bird species. Most studies on exploratory behavior have focused on parids, especially Great Tits. One
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hypothesis suggests that exploratory behavior falls on the proactive-reactive axis, a syndrome or suite of personality traits that correlate with each other and which can be described in proactive or reactive terms (Sih et al. 2004); for example, birds may be bold or shy, or they may be fast explorers or slow explorers. Exploratory behavior is commonly used in personality research because it is relatively easy to measure in individuals in a standardized way and might represent a larger suite of behaviors included in the proactive-reactive axis. Chickadees as a System for Studying Personality Divergence Carolina Chickadees (Poecile carolinensis) and Black-capped Chickadees (P. atricapillus) are sister taxa (Harris et al. 2014) in the family Paridae that occur parapatrically and hybridize in a narrow contact zone. Both species hold territories during the breeding season and then form flocks during the nonbreeding season, often participating in mixed-species flocks (Mostrom et al. 2002, Smith et al. 2010). The species are similar in ecological niche; range differences are largely associated with differences in cold tolerance (Taylor et al. 2014, McQuillan and Rice 2015). These extremely similar species represent an excellent study system for investigating divergence and the formation of prezygotic barriers to hybridization. Hybridization between Carolina and Black-capped chickadees occurs frequently and extensively in the hybrid zone (Driver 2017), representing the breakdown of prezygotic barriers. Black-capped and Carolina chickadees associate with each other in the same winter flocks, rather than segregating (Roche 2016, Dula 2018); therefore, the species are mingling at the time when chickadees form mated pairs (Mostrom et al. 2002, Smith et al. 2010). Genetically pure Carolina Chickadees are capable of learning Black-
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capped Chickadee song (Kroodsma et al. 1995, Wright Nelson 2016), and hybrids are often bilingual (Curry et al. 2007, Szot 2015, Monroe 2018), which could lead to false imprinting and accelerate the rate of hybridization. Chickadees sing year-round, but they sing much more frequently during territory formation and while females are laying eggs, suggesting that song may play a larger role attracting extrapair copulations rather than initial pair formation. Hybrid pairs might form because of a combination of misimprinting and general similarity between the species leading to the failure of species recognition (Gee 2005). Similarity in personality may reflect another confusing signal that leads these species to accidentally hybridize. Introgression is highly directional in this species pair, and evidence suggests that females of both species might prefer Carolina Chickadee males. In a lab setting, male Carolina Chickadees dominated Black-capped Chickadees in pairwise interactions, and the female observers preferred to associate with the dominant bird after witnessing the confrontation (Bronson et al. 2003). In wild hybrid chickadees, females were more likely to have extrapair offspring (EPO) if paired with a Black-capped-like male, and the EPO were more likely to have been sired by a Carolina-like male (Reudink et al. 2006). Dominance is often associated with body size in birds; however, Carolina Chickadees are smaller and yet still dominate Black-capped Chickadees. Personality type is associated with dominance in other parid species (Verbeek et al. 1999, Fox et al. 2009), though exploration in a small cage may not be linked to dominance in Black-capped Chickadees (Devost et al. 2016). Personality could potentially influence mate selection directly, as in zebra finches (Schuett et al. 2011b), or could impact hybrid pair formation by influencing perception of social dominance.
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Hypotheses and Predictions Hypothesis 1: When sister species diverge in allopatry to occupy different ecological niches, their genetically based personalities should also undergo divergent selection because behavior that confers high fitness is likely to differ between environments. This hypothesis is predicated on the assumption that personality variation will exist in each population for the same general reasons as in other similar taxa. Using the chickadee study system, the hypothesis leads to several testable predictions: 1) Behavior will be repeatable within individuals, even across seasons, and populations will have variety of behavioral types, suggesting personality. This prediction derives from the assumption that chickadees display personality. 2) Carolina and Black-capped chickadees will differ in exploratory behavior, with Carolina chickadees more exploratory on average. Faster exploration will evolve when fast individuals have an advantage, whether through environmental exploration or social dominance. Two lines of evidence support this prediction. Studies in Mountain Chickadees suggest that birds living in harsher environments will be slower explorers (Kozlovsky et al. 2014); Black-capped Chickadees live farther north, and thus experience harsher winters. Additionally, the dominant bird in pairwise interactions between Great Tits is usually the faster explorer (Verbeek et al. 1999), and Carolina Chickadees were dominant over Black-capped Chickadees in pairwise interactions in one study (Bronson et al. 2003).
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3) Hybrids between Carolina and Black-capped chickadees will have intermediate exploratory behavior. This prediction derived from the hypothesis that personality has a genetic component. Hypothesis 2: Personality is tied closely to social dominance in birds, whether by cause or effect, such that socially dominant birds are more likely to be of one personality type than socially subordinate birds. Studies supporting this hypothesis find different patterns for exploratory behavior across species (Verbeek et al. 1999, Fox et al. 2009) so there may not be any universal pattern (for example, that fast explorers are always dominant across all species). While data directly relating to social dominance are lacking for the chickadee study system, we know that chickadees tend to choose mates according to their dominance rank. Therefore, I can test the following prediction based on this hypothesis: 4) Pure species pairs will mate assortatively, such that fast males pair with fast females and slow males pair with slow females. Hypothesis 3: Personality itself is used during mate selection in birds in order to improve the compatibility of the parents and increase reproductive output. This hypothesis is an alternative to hypothesis 2. In my chickadee system I can make the following predictions deriving from this hypothesis: 5) Both pure species and hybrid pairs will mate assortatively. 6) Pairs of highly similar birds will have higher reproductive output than dissimilar pairs, measured as clutch size and clutch initiation date (as late season nesting attempts are more likely to fail).
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Methods Study System I studied chickadees in 2016 and 2017 at four sites in southeastern Pennsylvania (Fig. 2.1) (Taylor et al. 2014). The two southernmost sites, Villanova University’s West Campus (hereafter Villanova) and Great Marsh, comprised resident Carolina Chickadees. Irrupting Black-capped Chickadees also used these sites during the 2016-2017 nonbreeding season. Tuscarora State Park (hereafter Tuscarora) comprised mostly pure Black-capped Chickadees, though a few phenotypic hybrids were discovered breeding there in 2017. Hawk Mountain Sanctuary (hereafter Hawk Mountain) is at the center of the hybrid zone: 96% of birds used in this study from Hawk Mountain are hybrids (details in the Species and Sex Identification section). Field procedures At Great Marsh, Tuscarora, and Hawk Mountain, chickadees breed in nest tubes, which are ~ 2 m tall, 8-10 cm in diameter, and constructed out of PVC pipes (based on Grubb and Bronson 1995). During my study, there were 100 nest tubes at Great Marsh,
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Figure 2.1. Locations of the four field sites used for this study. The sites are, from south to north: Villanova (Villanova, Delaware Co.), Great Marsh (East Nantmeal Twp., Chester Co.), Hawk Mountain (Kempton, Berks Co., and Drehersville, Schuylkill Co.), and Tuscarora (south of Barnesville, Schuylkill Co.). 60 at Tuscarora, and 196 at Hawk Mountain. I used the Villanova University site only during the nonbreeding season. I and other members of the Curry laboratory (hereafter, “we”) monitored chickadee nest progression, recording locations of active nests, laying date, clutch size, fledge date, and number of chicks fledged. At Great Marsh, there were 23 active nests (which reached the chick stage at minimum, or female otherwise stuck around) in 2016 and 25 active nests in 2017. At Tuscarora, there were 8 active nests in 2016, and 10 active nests in 2017. We tested Hawk Mountain birds only in 2016, when there were 22 active nests.
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We captured birds at active nests (May – Jun) or at feeders (Oct – Mar) using mist nets. During the breeding season, we captured breeding birds only when the chicks were older than 6 days, when they were sufficiently capable of thermoregulation such that the absence of a brooding parent would not impact the chicks’ development; due to the additional threat of nest takeover by House Wrens (Doherty and Grubb 2002) at Great Marsh especially, we typically only captured breeding birds at that site when the chicks were 8 days of age or older. We banded all new birds with unique combinations using a numbered aluminum USGS band and either 2-3 plastic color bands or Passive Integrated Transponder (PIT) tags embedded in a large plastic band, which were used for a separate study. For every bird captured, we took a ~ 30 μL blood sample and recorded age, sex (when discernable, during nesting season), and measured mass, wing chord, and tarsus length. I incorporated these measurements into my statistical models because body size (Dingemanse et al. 2002), sex (Šíchová et al. 2014, Amy et al. 2017), and age (Verbeek et al. 1999) may influence behavior during personality assays in some species. Assaying Exploratory Behavior: “Box Assays” To quantify exploratory behavior, I used a portable wooden observation box modeled roughly after the rooms used by Dingemanse et al. (2002) (Fig. 2.2). The box was 1.8 x 1.2 x 1.2 m and rested on a trailer so that we could tow it from site to site. The box was entirely enclosed and contained 20 wall pegs as well as two wooden trees placed 60 cm apart with four pegs each. The floor was divided into six zones (60 x 60 cm each).
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B
A
C
Figure 2.2. Mobile wooden box used to assay exploratory behavior. (A) Box exterior. The box was 1.8 x 1.2 x 1.2 m and rested on a trailer so that it could be towed from site to site. The observer viewed the bird through a 61 x 45 cm slanted, smoked plexiglass window. A bird cage cover over the back of the observer reduced light entering the box, so that the bird was unable to see the observer. The bird was allowed into the box through one of two sliding doors (white panels), so that the bird avoided human contact immediately preceding the test. (B) Box interior. The box was entirely enclosed and contained 20 wall pegs as well as two wooden trees placed 60 cm apart with four pegs each. The floor was divided into six zones (60 x 60 cm each). The box was internally lit with six remote-controlled LED strips that attach to the ceiling with Velcro. (C) Holding cage (38.1 x 22.8 x 38.3 cm), where the bird was stored pre-test, attached to hooks on the right side of the box. Six remote-controlled LED light strips that attach to the ceiling illuminated the interior during tests. The observer viewed the bird through a 61 x 45 cm slanted, smoked plexiglass window. A bird cage cover over the back of the observer reduced light entering the box, so that the bird was unable to see the observer. Before the test, the bird was held for ≥ 30 min (mean = 43.9 min, median = 38 min) in a covered bird cage hung on the outside of the box. The bird was allowed into the box through one of two sliding doors, so that the bird avoided human contact immediately preceding the test. 18
At the start of the assay, we allowed the bird up to 5 min to enter the box unprompted; after 5 min, we scared the bird into the box by tapping on the outside of the cage (63 out of 240 tests required tapping). I recorded latency to perch on the entrance and then to enter the box. Similarly, I allowed the bird 5 min to leave the box and re-enter the cage after the conclusion of the test. We encouraged movement in and out of the box using light: at the start of the test, the box was lit and the cage was dark, while after the conclusion of the test, I shut off the internal lights and uncovered the cage. During the 10-min assay, I recorded each of the bird’s actions using a voice recorder, using a clicking noise as a timestamp. I transcribed these actions and timestamps into an Excel worksheet which calculated nine variables including number of actions, time spent flying, total number of places reached, and latency to reach zones inside the box (Table 2.1). After the test, I released the subject directly from the cage (if its territory were < 300 m away from the cage) or released it close to its territory (if > 300 m away). We banded most new birds after the test because in some early tests, obsessive pecking at bands interfered with the assay. Analyses that follow incorporate data from 251 box assays invovling 179 individuals. I performed 212 assays on 146 individuals; Breanna Bennett conducted the remainder of the assays, all at Hawk Mountain.
19
Table 2.1 Variables measured during exploratory behavior box assay Variable
Description
Actions
Number of actions taken by the bird during the test period
Places
Number of independent places reached by the bird during the test period
Flight Time
Time (s) spent flying, rather than perching or hopping
Zones
Number of zones pegs reached by the bird during the test period (max: 6 zones)
Percent Upper
Percent time the bird spent in the upper half of the exploratory chamber, including hanging from walls or perching on pegs
Percent Floor
Percent time the bird spent on the floor of the exploratory chamber
Latency to 3rd Zone
Time (s) taken by the bird to reach three total independent zones
Latency to 4th Zone
Time (s) taken by the bird to reach four total independent zones
Latency to 5th Zone
Time (s) taken by the bird to reach five total independent zones
Animal Welfare My methods were designed to minimize impact on the birds in accordance with authoritative guidelines (Fair et al. 2010). I minimized time that birds were held, especially those with nestlings to feed, and kept them separated from each other to prevent fighting. Species and Sex Identification We identified species by a combination of morphological, geographical, and genetic methods. We assumed that all breeding birds at Great Marsh were Carolina Chickadees (Table 2.2). We classified birds captured at Great Marsh and Villanova during the nonbreeding season as either Carolina or Black-capped (irrupting individuals)
20
Table 2.2 Number of individuals per species per site Site
CACH
HYCH
BCCH
Villanova
13
0
2
Great Marsh
73
0
3
Hawk Mountain
0
24
1
Tuscarora
0
5
48
based on morphology. Markers include mass, tail-to-leg ratio, degree of white on coverts and retrices, and bib shape. All birds at Hawk Mountain were genotyped by fellow students Robert Driver and Emily Burton using eight SNP markers (Driver 2017, McQuillan et al. 2017). Some birds at Tuscarora were genotyped, but most were classified as Black-capped or hybrids based on morphology alone. Methods for determining each bird’s sex varied by season. Chickadees can be reliably sexed morphologically during the breeding season based on presence of brood patch (females) or cloacal protuberance (males). To determine the sex of birds captured only during the nonbreeding season, I used the sex-linked CHD gene (Griffiths et al. 1998). Statistical Analyses I used principal components analysis (PCA) to produce a single composite “exploratory score” from the original nine variables (Table 2.1), using only the first test for each individual so that including repeat tests did not skew the score calculation (because repeat tests tend to be faster). Then, I used the formula for calculating the first principal component (PC1) to calculate scores for repeat tests. Further analyses used PC1 to represent each bird’s exploratory score. I used all individuals to calculate PC1, but I 21
then excluded birds missing data from the variables listed in Table 2.3 from linear models.
Table 2.3 Fixed effect variables used in linear mixed models Variable
Type
Description
Species Categorical Species label: CACH, BCCH, or hybrid (HYCH). Sex Categorical Sex label: Male, female, or unknown. Age Categorical Age label: Young bird (hatch year or second year), old bird, or unknown. Wing Length Continuous Length of wing (mm) when pressed flat against ruler. Used to represent body size. Season Categorical Breeding season or nonbreeding season. Weather Categorical Bright (when tested under sunny conditions) or shade (tested in shade or under cloudy weather). Sequence Continuous Test sequence, such as first test, second test, etc. The highest value was 4. Interval Continuous Log-transformed interval (in days) between current test and last test. First tests set to 730 days. Holding Time Continuous Time (min) the bird spent in the covered holding cage before the start of the test. Species * Sequence Interaction Season * Sequence Interaction Interval * Sequence Interaction Age * Sex Interaction Sex * Wing Length Interaction Species * Weather Interaction Age * Wing Length Interaction Species * Wing Interaction Length
22
I produced linear mixed models (LMMs) to model exploratory score using fixed explanatory variables (Table 2.3) and with individual ID as a random effect. I included season, interval, and sequence because these variables affected exploratory score in Great Tits (Dingemanse et al. 2002). I used wing length to represent body size because I had wing size measurements for the largest number of birds. Wing length correlated with tarsus length, a more common proxy for body size, in my sample (r = 0.56). I ranked models using Akaike’s Information Criterion (AIC) and then calculated ΔAIC by subtracting the best AIC score from each model’s AIC score; models receiving a ΔAIC score < 2 contain variables which accurately explain behavior scores (Freeman 2015). I tested whether fixed effect coefficients differed from zero using Type II Wald Chi Square Tests. To determine repeatability of individuals’ behavior across tests, I calculated the intraclass correlation coefficient (ICC) by using variance associated with the best LMM. I divided the variance associated with the random effect by the total variance associated with the model (Lessells and Boag 1987). I investigated whether a social pair’s exploratory scores were correlated and whether more similar pairs were more successful. I used a combination of simple linear models (when data were distributed normally), Spearman’s rank-order correlation tests (nonparametric correlative test which gives the correlation coefficient ρ, “rho”) when residuals from linear models were not normally distributed, and the Kruskal-Wallis test (another nonparametric test) to compare the means of each species. Personality may be involved in mate choice such that pairs are assortative (Groothuis and Carere 2005) or disassortative (Formica et al. 2004), and more similar pairs may make better parents (Schuett et al. 2011a). I compared pair score similarity (as the absolute value of the
23
difference between male and female first test scores) across species, and tested correlation with clutch size and clutch initiation date (the difference in days between actual clutch initiation date and the earliest clutch initiation date for each site, such that the earliest nesting pair at each site receives a 0). I used pairs from 2016 and 2017 breeding seasons. A few birds formed new pairs in the 2017 season: for these cases, I reused the bird’s first test and compared it with the new mate’s first test, generating a unique pair score similarity measure. I produced the PCA using the program JMP v13 (SAS Institute Inc. 2016). I used the program R, v3.4.3 (R Core Team 2017) for all other statistical analyses, and the lme4 package (Bates et al. 2014) to produce LMMs. Results Principal Components Analysis PCA of nine variables (Table 2.1) produced a PC1 explaining 62.6% of the variation observed in the data. Variables describing activity and latencies loaded highly, Table 2.4 PC1 eigenvectors for each variable Variable
PC1 Eigenvector
Actions
0.370
Places
0.381
Flight Time
0.249
Zones
0.370
Percent Upper
-0.190
Percent Floor
0.164
rd
-0.371
th
Latency to 4 Zone
-0.400
Latency to 5th Zone
-0.399
Latency to 3 Zone
24
Table 2.5 Linear mixed models with ΔAIC < 2 ΔAIC
Model
Df
AIC
PC1 ~ Sequence + Holding Time + Species * Wing Length + Interval + (1|ID)
11
1014.7
0
PC1 ~ Sequence * Season + Holding Time + Species * Wing Length + Interval + (1|ID)
13
1015.1
0.4
PC1 ~ Sequence * Season + Holding Time + Species * Wing Length + Weather + Interval + (1|ID)
15
1015.4
0.7
PC1 ~ Sequence + Season + Holding Time + Species * Wing Length + Interval + (1|ID)
12
1016
1.3
PC1 ~ Sequence * Interval + Holding Time + Species * Wing Length + (1|ID)
12
1016.5
1.8
except flight time (Table 2.4). More exploratory birds received higher scores: highscoring tests involved a higher number of actions and shorter latencies to new zones. Percent upper, percent floor, and flight time loaded more highly onto PC2 (explaining 18.2% of variation); however, percent upper and percent floor did not correlate with flight time, so interpretation of PC2 was not clear. Predictors of Exploratory Behavior I created 61 different linear mixed models (Appendix 2) containing combinations of the nine fixed effects and seven interactions listed in Table 2.3, as well as individual ID as a random effect. In order to create models with equal sample sizes, I removed birds missing holding time or wing length data, for a new total of 240 test samples and 171 individuals. Five models received a ΔAIC < 2, indicating that they are as good as the best model (Table 2.5).
25
The best model received an AIC of 1014.7 and contained five fixed variables (species, wing length, sequence, interval, and holding time) as well as one interaction (species * wing length) and individual ID as a random factor. All factors were significant predictors of exploratory score. Among the fixed effects, sequence had the highest sum of squares at 47.15, followed by species at 31.46. Age, sex, season, and weather (sunny or not) did not appear in the best model and were not good predictors of exploratory behavior. Season did appear in the second, third, and fourth best models (ΔAIC = 0.4, 0.7, and 1.3, respectively) but was not a significant
Figure 2.3. Exploratory behavior at different wing lengths (mm) for Black-capped (BCCH), Carolina (CACH), and hybrid (HYCH) chickadees. Wing length serves as a proxy for overall body size. The interaction between wing length and species was significant (χ22 = 16.3, p < 0.001), and each effect was significant on its own as well (species: χ22 = 26.1, p < 0.001; wing length: χ21 = 8.5, p = 0.003). Larger Black-capped Chickadees are more exploratory (slope = 0.586), but there was no association between wing length and exploratory score in Carolina Chickadees (slope = 0.013), or hybrid (slope = 0.005) chickadees. Slope values and other statistics were derived from the best fit model. 26
predictor in any of those models, nor was the season * sequence interaction term. No other interaction terms were significant predictors, either. Weather appeared in the third best model (ΔAIC = 1.3) but was not a significant predictor of exploratory behavior. Birds with larger body size (represented by wing length) received higher exploratory scores, but this trend was only strong in Black-capped Chickadees (Fig. 2.3). In Carolina and hybrid chickadees, the slope for the wing length effect was near zero. Species differed in their mean exploratory behavior, but all phenotypes were present in all populations (Fig. 2.4). Despite the body size trend present in Black-capped chickadees, Carolina Chickadees (the smaller species) were faster than Black-capped Chickadees on average. The fast explorer phenotype made up a larger percentage of the Carolina Chickadee sample, and the slowest birds were outliers in the population. The
Figure 2.4. Density distribution plot of exploratory behavior scores for Black-capped (BCCH), Carolina (CACH), and hybrid (HYCH) chickadees. The y-axis represents frequency of occurrence of the exploratory behavior score indicated on the x-axis. The lines were fitted using kernel smoothing, using a bandwidth of 0.6. Scores ranged from -5.02 to 3.93. 27
Black-capped Chickadee sample included a more normal distribution of phenotypes, with slow explorer phenotypes more prevalent. The hybrid chickadee distribution is intermediate between the two parent species, but the average exploratory behavior was closer to the average of Carolina Chickadees so that the two were not statistically different from each other. Birds in holding for longer periods of time were faster explorers during their box test (Fig. 2.5). Extremely fast phenotypes (scoring over 3.6 in PC1) only begin to appear when holding times exceed 40 min, and extremely slow phenotypes (around -5 in PC1)
Figure 2.5. Effect of holding time (min) on exploratory score. The fitted line is a simple linear model, with gray area illustrating standard error of the slope (slightly different from the slope estimated by the linear mixed model). Birds held for longer periods of time scored higher during the exploratory test (χ21 = 14.7, p < 0.001; slope = 0.027). 28
also disappear with around 40 min of holding time. However, the effect is not strong, and 93.75% of the full range of scores did appear in birds held for under 40 min. Sequence influenced test scores such that on average, birds scored faster on retests (2nd, 3rd, and 4th tests). The same pattern appears when looking at an individual level: individual birds usually scored faster on their second test than on their first test (t52 = 5.6, p < 0.001; slope = 0.59). Second test scores were on average 0.97 points higher than first test scores. The interval between tests, or number of days, weakly affected exploratory score such that birds with short intervals scored higher on their retests. In other words, birds retested shortly after their first test scored higher than birds that were retested months later. However, interval was not significant in a model including only retests (71 tests, 56 individuals). Despite the retest model’s smaller sample size, other variables with weak coefficients still show signal: wing length and sequence, which have similar coefficients, are near significance (0.05 < p < 0.06), and holding time, with a much lower coefficient (0.017) is still significant at α = 0.05. Confounding factors may either be creating or clouding the relationship between test interval and exploratory behavior. Repeatability of Exploratory Scores A large amount of the variation in test scores was explained by individual ID as a random factor (ICC r = 0.62), and therefore exploratory behavior was highly repeatable within individuals. Assortative Mating Chickadee pairs across all three sites did not pair assortatively or disassortatively: scores of paired males and females were uncorrelated (ρ51 = 0.15, p = 0.28; Fig. 2.6).
29
Figure 2.6. Relationship between the male and female exploratory scores of each social pair. Exploratory scores of the male and female in each pair were uncorrelated. There was also no association between the similarity of a pair’s exploratory behavior scores (as the absolute value of the difference between male and female scores) and their clutch size (ρ51 = 0.08, p = 0.56), clutch initiation date (ρ51 = 0.09, p = 0.54), or species (χ22 = 0.52, p = 0.77). Variance in exploratory scores did not differ among the taxonomic groups (Bartlett’s K22 = 2.4431, p = 0.29). Pair exploratory score showed some correlation with clutch size (such that slower explorers had larger clutches), but only in Black-capped Chickadee pairs (BCCH: t11 = 3.0, p = 0.01, r = -0.67; CACH: ρ20 = -0.07, p = 0.76; HYCH: t14 = -1.5, p = 0.14). Looking at males and females of pure Black-capped pairs separately, only male 30
exploratory score correlated with clutch size (t11 = 33.8, p = 0.01; r = -0.70), though the relationship between female exploratory score and clutch size was near significance (t12 = -1.9, p = 0.08; r = -0.50). Exploratory score did not correlate with clutch initiation date (CID). In a linear model with species, CID, and the interaction between them, none of the effects predicted pair average exploratory score (CID: F1,45 = 0.02, p = 0.88; Species: F2,45 = 1.30, p = 0.28; F2,45 = 1.67, p = 0.20). Discussion These chickadees exhibited personality-type variation in exploratory behavior; Carolina Chickadees on average tended to exhibit more exploratory behavior than did Black-capped Chickadees; and hybrids were intermediate to the parent species in their exploratory behavior. Among Black-capped Chickadees only, larger birds were more exploratory. Behavior did not differ across sex and age class. I found no evidence for assortative mating, nor any breeding benefits for assortative or disassortative pairs. However, I did find that less exploratory pairs, especially with less exploratory males, laid had larger clutch sizes. Below, I discuss these findings in turn. Exploratory Behavior as a Component of Chickadee Personality Exploratory score was highly repeatable in Carolina Chickadees, Black-capped Chickadees, and their hybrids, suggesting that this trait represents personality. Behavior was repeatable even across seasons. The intraclass correlation r = 0.62 is on the high end of repeatability reported by similar exploratory behavior studies in birds; the studies I reviewed report repeatability estimates ranging from 0.23 to 0.66, with an average of 0.38 (Dingemanse et al. 2002, Schuett et al. 2011a, Edwards et al. 2015, Hall et al. 2015,
31
Devost et al. 2016, Thys et al. 2017, Dubuc-Messier et al. 2017). This is the first study to demonstrate repeatability of exploratory behavior in Carolina Chickadees and the second in Black-capped Chickadees (Devost et al. 2016), but repeatability of other personality traits has been demonstrated previously in Carolina Chickadees (Harvey and Freeberg 2007, Eldredge 2015). Overlapping but Distinguishable Interspecific Variation Black-capped and Carolina chickadees differed in average exploratory behavior scores, but also displayed complete overlap in the range of personality types: extremely slow and extremely fast explorers were present in both species. Looking at only first tests, both the lowest and highest scores were Carolina Chickadees. This wide variety of behavioral phenotypes might be maintained in these populations for a wide variety of reasons, such as fluctuating selection or density-dependent fitness (reviewed in Schuett et al. 2010). However, because the distribution shapes are different between the species, selectional forces might be acting differently on each species. For example, the slow explorer phenotype might only have high fitness in Carolina Chickadees when in very low frequencies, an example of density-dependent fitness. Unraveling the effects of selection and how personality relates to fitness would involve detailed studies on individuals that also measure survivorship, nestling mass, dominance, paternity, social interactions, etc. in both species. Some excellent work has been done to quantify fitness across environmental conditions in association with personality types in Great Tits (Dingemanse et al. 2004) that might serve as a blueprint for future studies across other parid species.
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Hybrid chickadees were intermediate in personality: they had an intermediate distribution of phenotypes, with a mean indistinguishable from Carolina Chickadees. The hybrids included in this study were genetically more “Carolina-like” (mean proportion Carolina alleles = 0.67), which may explain why their mean exploratory score was closer to that of Carolina Chickadees than Black-capped Chickadees. Body size (represented by wing length) correlated with exploratory behavior in Black-capped Chickadees but not Carolina Chickadees, such that Black-capped Chickadees with longer wings were more exploratory. Body size is associated with dominance in many species of bird, including Black-capped Chickadees (Funghi et al. 2015, Devost et al. 2016) and wing length specifically is associated with dominance in Great Tits (Farine et al. 2012). If dominance is associated with exploratory score in Black-capped Chickadees, then that may explain the association with body size. Dominance is associated with exploratory behavior in both Great Tits and Mountain Chickadees (Verbeek et al. 1999, Fox et al. 2009). A similar study on Black-capped Chickadees reported no association between exploratory behavior and dominance (Devost et al. 2016), but used a smaller cage as the novel space, which may evoke a different behavioral response than my box novel space (Chapter 3). Even more interesting are the lack of a pattern between body size and personality in Carolina Chickadees, and the lack of an overall effect of size on behavior across species. Black-capped Chickadees are larger than Carolina Chickadees (mean difference: 2.2 mm), and yet they are less exploratory on average. Additionally, Carolina Chickadees are dominant over Black-capped Chickadees (Bronson et al. 2003). Dominance is the missing piece to the puzzle, but may not explain the patterns between body size and
33
personality across species. Different social environments could lead to a correlation between dominance and personality in one species but not the other. Alternatively, other factors such as mate choice, predator pressure, foraging style, behavioral syndromes, or energetics might explain the differences in pattern between these two species. The effect of social environment on personality evolution across species is understudied but potentially powerful. Differences in social group sizes and dominance systems could lead to differences in the frequency of personality types. Both Carolina and Black-capped chickadees participate in mixed-species flocks during the winter, but the number of chickadee individuals per flock may differ. Black-capped Chickadees typically form flocks of 6-10 individuals (Lemmon et al. 1997, Schubert et al. 2008), while Carolina Chickadees may form smaller flocks of only 3-5 birds (Freeberg and Harvey 2008). Although not well studied, different flock sizes could be associated with differences in flock cohesiveness or the strength or stability of dominance hierarchies. More exploratory birds might have higher fitness in a less cohesive flock or a weaker hierarchy, for example. Correlates of dominance (including body size and personality) might also depend on dominance patterns such as the stability of a hierarchy. Dominance is strongly related to fitness in Black-capped Chickadees (Schubert et al. 2008), so if personality were related to dominance, it would be under strong selective pressure. The dominance hierarchies of Carolina Chickadees are understudied by comparison; typically, they are assumed to function the same as Black-capped Chickadee hierarchies, but we have no studies of wild Carolina Chickadee flocks to support this assumption. A caveat is that the majority of my Carolina and Black-capped Chickadees come from single, separate populations, so differences between species might instead represent
34
differences between populations. Populations within the same species have been shown to differ in other parids (Dingemanse et al. 2012, Kozlovsky et al. 2014, Riyahi et al. 2016). Of the 90 Carolina Chickadees included in this study, 14 come from Villanova’s campus, while the rest come from my primary Carolina Chickadee site, Great Marsh. The species hybridize extensively when co-occurring (24 of 25 birds from the hybrid zone site, Hawk Mountain, are hybrids) so the only way to control for population differences would be to sample Carolina and Black-capped chickadees from other sites, preferably in different parts of their ranges and with different habitats. Considering the field effort required to obtain a large enough sample size, collaboration with other labs may be the best answer to this complication. Age and Sex Did Not Influence Personality Exploratory behavior did not differ between first year and older birds. This suggests that personality may remain consistent as chickadees age, although across-year repeat samples would better support this hypothesis. Cross-age consistency has been observed in Superb Fairy-Wrens; Hall et al. (2015) report that exploratory behavior remains consistent as birds grow from juveniles (1-6 months old) to young adults (6 months to 2 years old). There is also evidence that personality can change from young juveniles ( 300 m away). I performed a total of 94 tent assays on 79 individuals.
49
Box Assay From May 2016 through July 2017, I used portable observation box for exploratory behavior tests, modeled after the rooms used by Dingemanse et al. (2002). Chapter 2 presents details about the box (Fig. 2.2). Before each test, I held the bird for ≥ 30 min (mean: 44.8 min) in a covered (and thus dark) cage attached to the outside of the box. At the start of the assay, I allowed the bird 5 min to enter the box spontaneously (with the interior lights of the box illuminated); after 5 min, we scared the bird into the box by tapping on the cage. I recorded latency to perch on the entrance and then to enter the box. Similarly, after the 10-min test period I allowed the bird 5 min to leave the box (with interior lights extinguished) and re-enter the (uncovered) cage. During the 10-min assay, I recorded each of the bird’s actions using a voice recorder, using the clicking noise from a handheld counter as a timestamp. I transcribed these actions and timestamps and then calculated variables including number of actions, time spent flying, total number of pegs reached, vocalizations, and latency to reach zones inside the box (Table 3.2). A bird performed an “action” when it changed location, such as hopping from one peg to another, flying from a location, landing at a new location from flight, or hopping between zones on the floor. After the test, I released the subject as for tent tests. I performed a total of 199 box assays on 145 individuals.
50
Table 3.2 Variables recorded or calculated during box assays Variable
Description
Enter
Latency (s) to enter the box before the test start
Entrance
Latency (s) to land on the entrance to the box before the test start
Leave
Latency (s) to leave the box after test conclusion
Actions
Total number of actions (defined as a change in location) taken
Places
Number of independent places reached
Flights
Number of flights
Flight Time
Time (s) spent flying, rather than perching or hopping
Wall Pegs
Number of independent wall pegs reached
Tree Pegs
Number of independent tree pegs reached
Total Pegs
Total number of all pegs reached
Zones
Number of zones pegs reached (max: 6 zones)
Proportion Upper
Proportion of time in the upper half of the chamber, including hanging from walls or perching on pegs
Proportion Floor
Proportion of time on chamber floor
Latency to 1st Zone
Time (s) to land in first zone
Latency to 2nd – 6th Zone (5 variables)
Time (s) to reach two-six total independent zones
Animal Welfare My methods (approved by Villanova’s IACUC) were designed to minimize impact on the birds in accordance with authoritative guidelines (Fair et al. 2010). I minimized time that birds were held, especially those with nestlings to feed, and kept them separated from each other to prevent fighting.
51
Statistical Analyses I used principal components analysis (PCA) to produce composite “exploratory scores” for each assay using only the first sample for each bird. To calculate exploratory scores for each bird’s second test, I applied the formula used to calculate the principal component to the set of raw measurements for that bird’s second test. I measured repeatability of behavior by calculating the Pearson correlation coefficient between the scores of the first and second tests of each bird. In cases where variables were not normally distributed, I used the Spearman correlation coefficient ρ (rho). Similarly, I calculated correlation coefficients to determine whether birds’ behaviors across the two different assays (tent vs. box) were correlated. I examined the effect of species, sex, season of test (breeding or nonbreeding), and a species-season interaction effect on behavior within the tent assay using linear models. Principal components from the box assay were not normally distributed, so I instead used Mann-Whitney U tests to examine each variable independently, and the Scheirer-Ray-Hare test to examine a potential interaction effect between species and season. For the box assay, I also used a Kruskal-Wallis test to determine the effect of weather (“sunny” or “not sunny”) on behavioral scores. I also examined whether testing date or nesting order during the breeding season were associated with behaviors: for the tent assay, I used linear models, and for the box assay, I calculated Spearman’s correlation coefficients. I standardized nesting order by ranking all breeders by order of clutch initiation date per site, and then assigned each bird a number according to order (giving ties the same number). Day of testing was standardized across sites by using the days since first test at each site.
52
I compared the efficiency of different test lengths by producing a PC1 score at three different test durations: 5, 7, and 10 minutes. The variables I used were number of actions, pegs reached, places reached, zones reached, and total time spent flying. Then, I compared the three scores by running the same analyses as I had earlier, investigating the effect of species, sex, season, weather, and holding time on exploratory score. I conducted statistical analyses using JMP v.13 (SAS Institute) and program R v3.4.3 (R Core Team 2017). For testing hypotheses, I used α = 0.05. Results Principal Components Analysis In both the tent and box assays, birds moved around their environments in an exploratory manner. Number of flights or hops, as well as latencies to new walls or zones, varied greatly across individuals; for example, total number of hops in the tent assay varied from 0 to 119, and latency (sec) to wall three varied from 6 to never (38% of individuals never touched the third wall). I used Principal Components Analysis (PCA) to produce two variables for each test that described as much of the variation I observed as possible. Across multiple PCAs using different sets of variables, the first two components of tent assay PCAs always explained less of the variation in data compared to the first two components of box assay PCAs (Table 3.3). The biggest difference is the drop in amount explained by PC1. PC1 for both tests included measures of activity and latencies to reach new areas. The difference in amount of variation explained between the box and tent assay, especially in PC1, suggests that the tent assay produced noisier data, especially regarding how active birds were during the assay.
53
Tent assay PCAs explained less variation overall compared to box PCAs and produced two components that were close in amount explained: PC1 ranged from 23 to 32%, while PC2 ranged from 15.1 to 18.7% explained. All tent PC1s correlated tightly (|r| ranging from 0.87 to 0.99). Variables describing flight behavior and mid-test latencies (latencies to the 2nd and 3rd walls) loaded highly onto all tent PC1s. Therefore, PC1 described activity or “exploratory behavior” for this test. The PC2s correlated less tightly: specifically, the full model PC2 differed greatly from the other two (ρ = 0.65 and -0.56), but the totals-only and reduced PC2s did correlate tightly (ρ = -0.86). Variables describing use of vertical space as well as the first and last wall latencies loaded highly onto PC2. Birds that spent a lot of time in the upper half of the tent also had a shorter latency to reach all four walls as well as a shorter latency to reach their first wall (in other words, less time spent flying around at the very beginning). Box assay PCAs produced one primary principal component (PC1) which explained a large percent of variation in data (43.4 – 60.8% explained by PC1 in different PCA models) compared to the other components (e.g., PC2, 7.9 – 13.3%). PC1s across the different PCA models correlated very tightly (|ρ| ranging from 0.97 to 0.99). Variables measuring actions and latencies always loaded highly on PC1, with more active birds having lower latencies; therefore, PC1 best reflects “exploratory behavior.” PC2 scores also correlated but not very tightly (|ρ| ranging from 0.35 to 0.48) and usually
54
Table 3.3 Percentage of variation explained by principal components 1 and 2 for each PCA model Box Assay (n = 144) PCA Model
PC1
PC2
Total
Tent Assay (n = 78) PC1
PC2
(PC1 + PC2)
Total (PC1 + PC2)
Full1
58.0%
7.9%
65.9%
23.9%
15.1%
39.0%
Totals only2
43.4%
11.1%
54.5%
26.0%
16.0%
42.0%
Reduced3
59.3%
13.3%
72.6%
32.4%
18.7%
51.1%
1
Full PCA includes mid-test counts. Tent assay included 34 variables, while box assay included 39 variables.
2
The totals-only box PCA’s PC3 (explaining 9.4%) that described variables represented by PC2 in the other two models.
3
Reduced PCA includes only 13 variables in the box assay (latency to zones 2-6, flight time, actions, places, wall pegs, tree pegs, zones, % upper, and % floor) and 7 variables (latency to walls 2-4, flight time, flights, hops, and % upper) in the tent assay.
included high-loading variables describing flight behavior as well as vertical position in the box (birds spending more time up high also spent more time flying). An exception is the totals-only model: in this model, the highest-loading variables for PC2 were the latencies to enter the box before the test had begun, and vertical use of space was instead captured in PC3. The box PC2s from different models also correlated with this PC3 (full model: ρ = -0.17, reduced model: ρ = 0.74). For the following sections, I used the “reduced” PCA for both the box and tent assay, which exclude uninformative variables to simplify interpretation of the principal components (Table 3.4).
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Repeatability of Behavior Behavior in the box assay was repeatable between the first and second tests (n = 46 birds) for both PC1 (ρ = 0.54, p < 0.001) and PC2 (ρ = 0.57, p < 0.001). Some behavior in the tent assay was repeatable across first and second tests (n = 14 birds). Tent PC2 was repeatable (r = 0.65, p = 0.01) but tent PC1 was not (p = 0.13). Considering the highest loading variables in PC1 separately, flight behavior was repeatable (number of flights: r = 0.82, p < 0.001; flight time: ρ = 0.70, p = 0.006) but latencies were not repeatable (p > 0.05). Correlation Between Box and Tent Assays None of the principal components from the box assay correlated with any of the components from the tent assay (n = 25 birds, 80 comparisons, all p > 0.05). No single variable from the box assay correlated with any single variable from the tent assay (n = 25 birds, 41 comparisons, all p > 0.05). Therefore, a bird’s behavior in one assay had no relation to its score in the other assay.
Table 3.4 Definition of PCA components used for analyses Component1
Descriptor
High-loading variables Box
1
Tent
PC1
Exploratory behavior
Activity, mid latency Flight behavior, mid latency
PC2
Manner of exploration
Vertical position, flight behavior
Components from the “reduced model” (Table 3.3)
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Vertical position, late latency
Tent Assay Analysis I modeled PC1, PC2, and the flights variable using linear models including species, sex, season, and a species-season interaction term. Tent PC1 exploratory score and number of flights were both different between sexes (PC1: F2,72 = 3.65, p = 0.03; Flights: F2,72 = 3.35, p = 0.04), with males being more “exploratory” and taking more flights (Fig. 3.2). PC1 scores did not differ between species or across seasons (Fig. 3.3). For tent PC2 scores, species’ response differed by season (F1,72 = 5.49, p = 0.02): in winter, the species behaved similarly, but in spring, Carolina Chickadees received higher scores (associated with more time in the upper half of the tent and longer latency to reach all four walls) (Fig. 3.3). There was a trend for PC2 scores to differ by sex (with males scoring lower) but it was not significant (F2,72 = 2.94, p = 0.059) (Fig. 3.2). I ran separate analyses including only breeding season birds to assess whether behavior differed depending on nesting order (ordinal measure describing the order in which the birds initiated clutches) or date of testing, creating separate models for each species. I used linear models where possible, including the measure of order (nesting order or test day) for both PC1 and PC2, and sex for PC1 only. PC1 differed across both test day (F1,21 = 7.46, p = 0.01) and nest order (ρ = 0.46, p = 0.02) in Carolina Chickadees, but there was no trend for Black-capped Chickadees (nesting order: F1,21 = 0.002, p = 0.96; date of testing: p = 0.45) (Fig. 3.4). Carolina Chickadees that were tested earlier in the season were faster explorers in the tent, receiving higher PC1 scores. PC2 scores were not related to nesting order or test day during the breeding season in either species (Fig. 3.4). The trend between Black-capped Chickadee PC2 score and test day was near significance (F1,12 = 3.74, p = 0.08). There was a trend for Carolina Chickadee
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Figure 3.2. Behavioral responses during tent assay by sex (species combined). (A) Flights. (B) PC1 score. (C) PC2 score. In each panel, boxes denote 25th and 75th percentiles, with a line marking the median; whiskers denote 150% of the interquartile range. Outliers are plotted individually. Lower-case lettering denotes groups which differed significantly. U represents birds of unknown sex. Males took more flights and had higher PC1 scores than females. There was a nonsignificant trend for females to score higher in PC2 (spending more time up high). 58
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Figure 3.3. Behavioral responses during tent assay by species and season (sexes combined). (A) Flights. (B) PC1 score. (C) PC2 score. See Fig. 3.2 for explanation of figure format. Flights and PC1 scores did not differ across species or season. I found a significant interaction effect between species and season for PC2: spring CACH scores were higher than BCCH or winter CACH scores. 59
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Figure 3.4. Association between tent assay scores and nesting order and test day. (A, C) Nesting order. (B, D) Test day. Nesting order is an ordinal measure such that the birds of the first pair to initiate a clutch are assigned 0, the second assigned 1, etc. (allowing for ties if birds initiated a clutch on the same date) and unique for each field site. Test day is the number of days since the first test at that field site. Fitted lines show simple linear models; gray areas denote standard error for the slope estimates. clutch initiation date (CID) to correlate within individuals across 2015-2016 (ρ = 0.69, p = 0.09, n = 7 birds) but not across 2014-2015 (linear model: p = 0.15, n = 10 birds). Black-capped Chickadee CID was not correlated across the 2015-2016 seasons (linear model: p = 0.43, n = 5 birds). Box Assay Analysis Because box assay residuals were not normally distributed, I used Mann-Whitney U and Kruskal-Wallis tests to compare PC1 and PC2 box assay scores by species, sex, weather, and season. PC1 exploratory scores differed by species (Fig. 3.5), with Carolina 60
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Figure 3.5. Behavioral responses during box assay by species (sexes combined). (A) PC1 score. (B) PC2 score. See Fig. 3.2 for explanation of figure format. Lower-case lettering denotes groups which differed significantly. CACH received higher (faster) PC1 scores. Chickadees receiving “faster” scores (U140 = 1627, p = 0.002). PC2 scores differed by sex (U81 = 619, p = 0.04): males spent more time lower in the box, on lower pegs or the floor, and less time flying (Fig. 3.6). Season and weather had no effect on either score. Using Scheirer-Ray-Hare tests, I did not find any interaction effect between season and species on either principal component. Using Spearman’s correlations, I did not find any association between behavior and holding time, or between behavior and testing date or order of nesting during the breeding season.
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Figure 3.6. Behavioral responses during box assay by sex (species combined). (A) PC1 score. (B) PC2 score.See Fig. 3.2 for explanation of figure format. Outliers are plotted individually. Lower-case lettering denotes groups which differed significantly. Males received lower PC2 scores, associated with more time spent low in the box. During the breeding season, PC1 correlated with nesting order and testing day in Carolina Chickadees only, while PC2 did not correlate with nesting order and testing day in either species (Fig. 3.7). Carolina Chickadees which nested or were tested earlier in the breeding season were faster explorers than those later in the season. Carolina Chickadee clutch initiation date (CID) was correlated within individuals across 2016-2017 (r = 0.90, p = 0.01, n = 6 birds) but not across 2015-2016 (ρ = 0, p = 1, n = 8 birds). Black-capped
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Chickadee CID was not correlated across seasons (Spearman’s correlation: p = 0.37, n = 4 birds), but sample size was incredibly small. Box Assay Optimal Test Duration To determine if shorter test durations were sufficient, I ran three PCAs on equivalent variables at three different test durations: five minutes, seven minutes, and ten minutes. Each test included five variables: number of actions, pegs reached, places
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Figure 3.7. Association between box assay scores and nesting order and test day. (A, C) Nesting order. (B, D) Test day. Nesting order is an ordinal measure such that the birds of the first pair to initiate a clutch are assigned 0, the second assigned 1, etc. (allowing for ties if birds initiated a clutch on the same date) and unique for each field site. Test day is the number of days since the first test at that field site. The fitted lines are simple linear models, with gray area illustrating standard error. I ran separate models for each species (CACH n = 44, BCCH n = 30), CACH 2016 season only, BCCH 2016 and 2017 seasons to boost sample size. In CACH, PC1 differed across both nesting order (F1,42 = 7.16, p = 0.01) and test day (F1,42 = 5.03, p = 0.03), but neither corelated within BCCH (nesting order: F1,28 = 0.17, p = 0.68; date of testing: F1,28 = 0.08, p = 0.78). 63
reached, zones reached, and also total time spent flying. Each test produced a PC1 explaining over 72% of the variation in the data, with all variables loading highly. More active and exploratory birds received higher scores. The amount explained increased with test duration, so that the ten-minute duration PC1 explained the most, at 77.1%. All PC1s correlated tightly (r > 0.92 for all). Test durations closer in length correlated more tightly, so that the five-minute and seven-minute duration scores were most similar across birds (r = 0.97). All three duration scores differ by species (with Carolina Chickadees receiving higher exploratory scores) but not by sex, weather, season, or holding time (Table 3.5). Therefore, these component scores, produced with fewer variables, show the same patterns as the component score PC1 used earlier, regardless of assay time duration. I produced linear models for 5-min and 7-min scores including species, sex, weather, holding time, season, and a species-season interaction as factors; none of these except species significantly predicted exploratory score (Table 3.5). In both models, tests conducted during sunny weather were nearly (but not significantly) different from the other weather conditions (5-min: t130 = -1.8, p = 0.074, 7-min: t131 = -1.7, p = 0.090). The 10-min residuals were not normally distributed, so I instead ran Mann-Whitney U tests, a Scheirer-Ray-Hare test for the interaction, and calculated a Spearman’s correlation coefficient for holding time. The 10-min duration scores agreed with the other durations: species influenced score, but sex, weather, season, and holding time did not, nor did I find a species-season interaction effect through a Scheirer-Ray-Hare test.
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Table 3.5 Test statistics and p-values for comparisons involving the five-, seven-, and tenminute duration PC1s Assay Duration Variable
5 minutes
7 minutes
10 minutes
Species
F1,130 = 17.1, p < 0.001 ***
F1,131 = 16.6, p < 0.001 ***
U140 = 1413, p < 0.0001 ***
Sex
F2,130 = 0.96, p = 0.39
F2,131 = 0.70, p = 0.50
U81 = 702, p = 0.21
Weather
F3,130 = 1.73, p = 0.16
F3,131 = 1.89, p = 0.14
χ23 = 5.07, p = 0.17
Season
F1,130 = 0.04, p = 0.84
F1,131 = 0.002, p = 0.97
U140 = 2027, p = 0.067
Holding Time
F1,130 = 1.65, p = 0.20
F1,131 = 1.13, p = 0.29
ρ140 = 0.075, p = 0.38
Species*Season Interaction
F1,130 = 0.74, p = 0.39
F1,131 = 0.44, p = 0.51
H1,138 = 2.03, p = 0.15
Discussion Differences in Assay Design Result in Different Behaviors I set out to compare the efficiency of two similar field assays designed to measure exploratory behavior. Instead, I discovered that bird behavior in the two assays is not correlated in any way, suggesting that these tests do not measure the same “exploratory behavior”. The biggest difference between the designs is the opacity of the walls: birds can see outside of the tent, but they cannot see outside of the box. I suspect wall opacity is the primary reason for the difference in behavior during these tests. The tests also differed in size of enclosure, holding period before test, handling by humans before test, and the availability and distribution of perches; these could influence behavior during the 65
assays as well. The exploratory behavior literature includes assay designs with both opaque (Verbeek et al. 1994) and transparent (Kluen et al. 2012) walls, as well as novel spaces of dramatically different sizes; these design differences may be relevant to the behaviors recorded. In a similar study, Arvidsson and colleagues (2017) tested the traditional exploratory behavior assay (Dingemanse et al. 2002) for correlation with a maze-like assay which aimed to measure exploration in a different manner. Exploratory behavior in the maze was scored as the number of chambers reached within the maze. The two tests did not correlate at all within birds (Arvidsson et al. 2017). The lack of correlation is surprising because it suggests that exploratory behaviors in one context cannot predict behavior in another exploratory context, but the designs of the two exploratory assays were more different than my two assays. My study confirms Arvidsson et al. (2017)’s findings and adds that even extremely similar assay designs may still represent different contexts. Both Assays Represent Personality Both of my assays provided some repeatable results, suggesting that at least some of a bird’s response to these tests represents personality. Notably, however, tent assay latencies were not repeatable, causing tent PC1 to also not be repeatable. Latency in the tent most likely is influenced by extrinsic factors such as the environment surrounding the testing area. Presence of branches, for example, could influence where birds land and how quickly they approach new walls. If a good branch is on one side of tent, bird may spend the whole test trying to fly to it, rather than exploring the tent. Other exploratory
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assays used opaque walls to solve this issue (Huang et al. 2015, Dubuc-Messier et al. 2017), and I designed the box assay to remove this complication as well. Behavior during the box assay seems to represent exploratory behavior: birds often hopped around the box slowly, taking pauses to look around and investigate pegs or holes. My birds seem to behave similarly to the Great Tits in the larger exploratory room used by Dingemanse et al. (2002) and Verbeek et al. (1994) (on which I based my box assay’s design and protocol), but the chickadees seem to hop more and fly less, possibly due to the chamber’s smaller size (R. L. Curry, pers. comm.). Another field adaptation of the classic exploratory chamber design is very similar to mine, small with many pegs (Dubuc-Messier et al. 2017). Smaller designs may also be more effective in quantifying behavior of small birds such as chickadees and Blue Tits, which fly around erratically in a larger space (R. L. Curry, pers. comm.). Behavior in the tent may represent escape behavior rather than exploratory behavior. Huang et al. (2015) covered their exploratory chamber with white sheets because with only mesh walls, they noticed birds exhibiting “frantic escape behavior” rather than exploratory behavior. During our tent assays, I noticed birds spent most of their time climbing mesh walls or flying back and forth between two walls in a corner; both behaviors seem to reflect an intent to escape. Additionally, only 8 of 79 birds ever landed on one of the two “trees” in the center of the tent, and therefore most birds did not explore the full space. By comparison, 140 of 147 birds landed on the interior “trees” during the box assay. Other escape-related personality traits have been measured in birds before, such as ability to escape a cage with an open door (Kluen et al. 2012), effort when attempting to avoid a human hand within a cage (Amy et al. 2017), and flight initiation
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distance in the wild (Blumstein et al. 2016). These may or may not be related to the behaviors that I observed inside the tent. Other authors have used open “novel spaces” to measure exploratory behavior, in which birds can see outside of the novel space. The most common design is a small wire cage that is much smaller than the screen tent that I used (Kluen et al. 2012, Kluen and Brommer 2013, Devost et al. 2016). Blue Tit activity in a small wire cage is repeatable across seasons, but individuals tend to be less active in winter (Kluen and Brommer 2013). I found that chickadee behavior is repeatable across seasons as well, but only Carolina Chickadees behaved differently in winter; also, I did not find any differences in activity level (as number of flights or hops), but did find differences in use of vertical space. Devost et al. (2016) used a small wire cage based on the design of Kluen and Brommer (2013) to test Black-capped Chickadees, but their results differ greatly from my tent assay. Latencies were repeatable in their small cage, while number of actions was weakly repeatable (Devost et al. 2016); in contrast, when I analyzed latencies and actions separately, I found that the number of flights was repeatable, but that latencies were not repeatable in the tent assay. Additionally, activity and latency measured by Devost et al. (2016) loaded onto different principal components, while my measures of activity and latency both loaded strongly onto the same principal component. Even though our novel space designs are both transparent, birds may respond differently because of the difference in area of the novel space to be explored. I also analyzed PC2 for both assays, a score representing behaviors other than exploratory behavior exhibited during the assay, but found interpretation of PC2 difficult. Use of vertical space always loaded highly on this component, but other variables loaded
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as well, such as time spent flying erratically and latency to the last wall of the tent assay. Principal component analysis serves to capture as much variation as possible in the early components, so any variation not correlated with PC1 would be pooled together in PC2. Therefore, PC2 will naturally vary greatly depending on which variables are included in the model, and which variables vary the most in that particular sample of birds. PC2 is not as easy to interpret as PC1, so future studies might consider just analyzing vertical space use by itself. In the tent assay, PC2 appears to best describe a behavior dichotomy: highscoring birds spent a lot of time at one wall or corner, climbing through short hops, and spent a lot of time as high up as possible, while low-scoring birds were less transfixed on climbing to the top and instead used lower areas more frequently, and also flew between walls more frequently and were more likely to reach all four walls. This dichotomy might represent different escape strategies or differences in level of fear, but further tests are needed to draw a connection. There was no strongly apparent set of behaviors associated with PC2 in the box assay. Use of vertical space might be a meaningful trait to measure in a box context because it is repeatable within individuals. It is an easily measured trait if already recording behavior inside a novel space, but rarely reported in the literature. The few studies measuring use of floor space during an exploratory test on birds found that the measure did not correlate with exploratory behavior, but the authors did not measure repeatability (Atwell et al. 2012, Funghi et al. 2015, Perals et al. 2017). Atwell et al. (2012) measured both use of floor and use of trees and reported a PC2 (where PC1 represented exploration) which described use of trees vs. floor, similar to my PC2.
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Additionally, Perals et al. (2017) found that floor space was not associated with other personality traits either, including activity, shyness, motivation, and neophobia; this suggests that use of vertical space is an independent behavior. Other studies on use of vertical space have been done in the wild, where space use is associated with environment (Hogstad 2015, Harrison et al. 2015). The vertical position of birds in a flock can depend on dominance: in willow tits, dominant birds forage higher up in tree canopies, where they are safer from predation, while subordinate juveniles forage in the lower half of the tree (Hogstad 2015). Behavior in the exploratory box could potentially reflect foraging preferences, dominance-related assortment, or where birds feel safer from threats. Behavior Differs Across Sex Both the tent and box assays recorded behaviors which differed on average between the sexes, something that is rarely reported in birds (Amy et al. 2017). Tent assay activity levels and flight behavior were higher in males than in females, with males taking on average 19.5 more flights in 5 min (average total flights per 5 min: 57.2). Use of vertical space was associated with sex in both tests (although not significantly different in the tent assay), with females spending more time up high than males in both assays. In the tent assay, the sex difference falls just short of significance, possibly due to smaller sample size compared to the box assay or the cruder manner of measure: I only recorded the bird’s vertical location every 15 seconds, rather than at every action as I measured it in the box assay. Sex differences in personality might be associated with differences in dominance or foraging location. Male chickadees are dominant over females, but the two sexes have
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separate linear hierarchies (Lemmon et al. 1997). Sex is either ignored (by using only males) or non-explanatory in other studies of parid exploratory behavior (Verbeek et al. 1994, Fox et al. 2009, Kozlovsky et al. 2014, Devost et al. 2016), but patterns of association between personality, dominance, and fitness differ across the sexes in Great Tits (Dingemanse and De Goede 2004). Male Black-capped Chickadees tend to spend more time foraging lower in the canopy than females (Desrochers 1989); this agrees with my finding that females spend more time up high during exploratory assays. The sexes also differ in body size, but body size was not an important predictor variable of exploratory behavior in linear mixed models using my expanded dataset, nor was there a significant interaction between body size and sex (Chapter 2).
Behavior Differs Across Species Species also differed in behavior during both assays, but not in the same behaviors. Carolina Chickadees were more exploratory in the box assay, taking more actions and reaching new areas more quickly. In the tent assay, Carolina Chickadees spent more time up high, but only during the breeding season; during winter, the average Carolina Chickadee spent less time up high, comparable to Black-capped Chickadees. The differences between these species are very small, with lots of overlap between populations. Populations of Mountain Chickadees living in different habitats differ in exploratory behavior as well (Kozlovsky et al. 2014), so it is not surprising that different species of chickadee separated geographically also differ in behavior. However, the differences are small, likely because Carolina and Black-capped chickadees still occupy very similar niches.
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The behavioral differences between Carolina and Black-capped chickadees may be due to several potential causes, most of which I cannot test with current data. Differences in social environment (e.g. winter flock size) (Lemmon et al. 1997, Freeberg and Harvey 2008), foraging behavior (Mostrom et al. 2002, Smith et al. 2010), body size, metabolism (Olson et al. 2010), or potential behavioral syndromes could explain the slight differences in behavior that I observed. I addressed these potential influences on box-style exploratory behavior in Chapter 2 of this thesis. The only one I tested specifically was body size, and I found an effect only in Black-capped Chickadees: longer-winged individuals were more exploratory. Therefore, body size may not correlate with personality differences across species. I suspect that social environment, especially patterns of social dominance, may influence the evolution of personality, but more studies are needed to address this hypothesis. Use of vertical space, represented in my assays by PC2, might be closely tied to foraging behavior in the wild, but this behavior is also highly environment-specific. Black-capped Chickadees differ from other sympatric chickadee species in their foraging height (Sturman 1968, Vassallo and Rice 1982, Hill and Lein 1988), in their use of branches vs. trunks, and species of tree used (Sturman 1968, Hill and Lein 1988). However, one study suggests that Black-capped and Carolina chickadees may not differ in foraging height or use of branches (Brewer 1963). Carolina Chickadees may have a slight preference for foraging on thin twigs, but foraging behavior of the two species has not been widely contrasted (Mostrom et al. 2002, Smith et al. 2010). Instead, differences in foraging style, especially use of weeds on the ground, may be best explained by
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differences in habitat rather than by species (Brewer 1963). I compared two populations of chickadees inhabiting different habitats (Great Marsh is primarily broadleaf, while Tuscarora is a broadleaf-conifer mix), so it is possible that the behavioral differences I observed were associated with local habitat rather than species, as in Mountain Chickadees (Kozlovsky et al. 2014). However, personality has not yet been tied to foraging behavior in any wild birds. A comparison of behavior in Black-capped and Carolina chickadees inhabiting similar habitats, along with a study on individual foraging patterns, would address this hypothesis. Effects of External Environment on Behavior Use of vertical space (represented by PC2) differed across seasons for Carolina Chickadees in the tent assay, but not for either species in the box assay. The open design of the tent may have caused this difference, as birds can see outside the tent during the assay but not the box. However, it is also possible that other seasonal factors impact the behaviors expressed during these tests differently, so that the vertical position changes seasonally in the tent but not in the box. Hormone levels change seasonally and could potentially lead to differences in behavior, but external factors such as temperature and social environment could also potentially impact behavior. It’s also possible that the pattern is false and due to the small sample size of Black-capped Chickadees, or nonrandom samples. Because I only used first tests, my analysis actually comparing two groups of birds captured for the first time during different seasons. It is possible that in this behavior alone (but not the other behaviors that I measured), breeding birds differ from birds that are unable to find a mate.
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I found no effect of season on either assay’s PC1 (exploratory behavior), but season sometimes affected the reduced-duration PC1 scores depending on the analysis. Linear models of 5-min and 7-min scores found no effect of season, even when season was the only variable included in the model. However, factor ANOVAs on 5-min and 7min scores suggest that season does have a significant effect, but no species*season interaction. When I ran separate ANOVAs for each species, season was only significant in Carolina Chickadees but not Black-capped Chickadees. The same trend is present in the Black-capped Chickadee (more exploratory scores in the nonbreeding season) but not strong enough to be statistically significant, leading to a lack of an interaction effect. A linear mixed model including more data (more individuals as well as repeat tests for some birds) suggests no effect of season (Chapter 2), so this pattern is likely a false positive. However, it may be worth including season in future models to control for any potential effects. Within the breeding season, Carolina Chickadees tested earlier in the season scored higher in the exploratory component PC1 within both tent and box assays. In both assays, Black-capped Chickadees showed no trend, and also PC2 (manner of exploration, including use of vertical space) showed no association with testing date. The cause for this correlation is unclear: fast explorers may be more likely to nest early in the season, or alternatively behavior of all birds may shift to a “slower” phenotype through time in association with internal or external factors. I did recapture some individuals during the nonbreeding season or in the following breeding season, but not enough to distinguish between the two possible causes. Great Tit average exploratory behavior also changes throughout the breeding season, (Dingemanse et al. 2002), but the pattern also persists
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into the nonbreeding season. Lines of Great Tits selectively bred as fast explorers or slow explorers show an associated change in laying date: fast explorers lay earlier in the season, while slow explorers lay later (Groothuis et al. 2008). I found that clutch initiation date is somewhat repeatable within individual Carolina Chickadees in some years, but not Black-capped Chickadees. However, I had very few assayed birds breed across multiple consecutive years (my highest sample size was 10 Carolina Chickadees across 2015-2016). Consistency of clutch initiation timing would support the hypothesis that certain personality types begin clutches earlier in the season. Weather did not impact box assay scores, suggesting that the box successfully excluded external variables as intended. It is possible that bright light entering the box during sunny weather had a weak effect that might be detected with a larger sample size, but weather was not a useful variable in linear mixed models including larger sample size either (Chapter 2). If testing in a forested environment, an easy solution is to always perform tests in the shade so that bright light cannot enter the box. I found no effect of holding time on box assay scores in the tests I performed in this chapter, but a more detailed analysis does reveal a weak effect of holding time (Chapter 2). Despite weak effects, standardizing external influences such as lighting and length of pre-test holding may strengthen patterns in the data. Importance of Test Duration A shorter assay duration with fewer variables is sufficient to find differences in behavior across Carolina and black-capped chickadees. In an attempt to capture as much variation as possible, I recorded a great number of variables across a ten-minute time period. However, the meaningful variation was present even when I only used activity in
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the first five minutes. Latencies correlated very strongly with activity variables, suggesting that it is not necessary to measure both. Nuances such as which pegs the birds use, late-test latencies, and time spent flying may be more relevant to other questions about personality, but were not necessary to detect differences between species and do not seem to represent exploratory behavior. Broader Significance Defining “exploratory behavior” is difficult if slight differences in assay design can lead to completely different behavioral responses. My results suggest that room size and transparency are important factors, but other changes to assay design used in the literature, such as introduction of food, novel objects, or extra chambers could also fundamentally change how animals respond. Future studies should use caution when comparing results across studies with differences in methodology, as they might not represent the same behavior. Is exploratory behavior still worth measuring? I believe exploratory assays are still a powerful tool, but that we should be cautious. Exploratory behavior as measured in assays represents a real component of personality, so any correlations with fitness, mate choice, dominance, or other life history traits are still meaningful. Exploratory behavior is easy to measure in a standardized way, reducing noise while also decreasing amount of effort required to obtain high sample sizes. Validating exploratory chamber measures with exploration during cognition tests can improve credibility of the exploratory behavior measure as a personality trait (Perals et al. 2017). However, a major flaw is that it is unclear what natural behaviors are represented by “exploratory behavior” as measured in an artificial chamber. A few recent studies have successfully tied exploratory
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behavior assays to natural exploratory movements (van Overveld and Matthysen 2013, Herborn et al. 2014, Arvidsson and Matthysen 2016, Portugal et al. 2017), but more such studies are needed, especially if different assay methodologies represent different behaviors. Do differences in personality measures between species reflect differences in ecology? There are numerous studies on how personality traits might be linked to fitness within one species, but fewer studies address the evolution of personality traits across species as an adaptation to environment. Notable exceptions include the many studies on neophobia across species, especially by Greenberg and Mettke-Hoffmann (Greenberg 1983, Mettke-Hoffmann et al. 2002, Mettke-Hofmann et al. 2013), and work by Blumstein on the evolution and correlates of flight initiation distance across taxa, which can represent boldness (Blumstein 2006, Blumstein et al. 2016). These studies do suggest that personality traits associate with habitat use, foraging, or migratory behavior across species, but many more studies need to be done before we can draw broad conclusions about the role of personality in ecological adaptation. Measuring personality is laborious and time-consuming work and often requires taking animals into captivity, factors that likely limit the number of cross-species comparisons. Hopefully, the rise of mobile protocol might help increase the number of species assayed, and adoption of similarly designed assays could allow for more cross-species comparisons. Personality is important in an ecological context. The study of personality can help us better understand species interactions, including competition and hybridization, and also the distribution of species through habitat preference or dispersive tendencies (Canestrelli et al. 2016a). Behavioral ecologists should continue to consider behaviors at
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an individual level because all members of a population will not behave the same way, and the intraspecific variation may contribute meaningfully to the outcome of environmental change or species interactions. Hopefully, by considering individual differences, we might discover new patterns shaping the ecology and evolution of species.
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CHAPTER IV: CONCLUSIONS Evolution of Personality Personality is the maintenance of standing phenotypic variation within a population, and therefore personality traits may play a role in local adaptation or evolution. I found that a variety of behaviors including exploration, activity, and vertical space use represent personality in both Carolina and Black-capped chickadees. For the most part, the same personality phenotypes exist within both of these sister species. However, the species do differ in some personality traits by population average and by relative abundance of the different phenotypes. These differences could be evolutionarily significant; by studying the fitness consequences of personality traits such as exploratory behavior in both species, we might be able to learn how behavior is shaped by environment. I reported a few specific differences between species. “Fast explorer” personality types are more abundant within Carolina Chickadees than in Black-capped Chickadees, and conversely, “slow explorer” types are more abundant within Black-capped Chickadees. The distribution of personality types in hybrids is intermediate, consistent with a genetic basis for exploratory behavior. I also reported that the species differed in their use of vertical space within the screen tent assay, when they could see their surroundings: in spring only, Carolina Chickadees spent more time in the upper half of the tent on average, while the two species behaved similarly during the nonbreeding season. The seasonal effect on this personality trait suggests a potential impact of hormones on behavior.
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I also reported as some potential associations between personality and fitness. Carolina Chickadees that breed earlier in the season tend to be faster explorers (in box assay) and have higher activity (in tent assay). However, this pattern was not evident in all years; it’s possible that the traits correlate only under certain conditions, or that random nesting disruptions may have clouded the pattern in some years. I also found that in Black-capped Chickadees, females paired to slow explorer males lay larger clutch sizes. This pattern could represent a tradeoff in investment strategy. The Role of Personality in Hybridization Personality could impact hybridization dynamics in a number of ways, most of which I did not address in this study. One straightforward impact would be assortative mating: if chickadees mate assortatively by personality, and the species overlap in personality, then some chickadees may pair with a heterospecific. However, I did not find any evidence for assortative mating, nor any evidence that assortatively mated pairs have higher fitness. I did find that slow explorer pairs of Black-capped Chickadees lay larger clutch sizes, but that pattern seems to be driven by male exploratory behavior alone, rather than by the two parents as a pair. However, personality could still play another role in mate choice. Personality could be associated with ability to hold a territory, territory quality, dominance, plumage brightness, or number of EPO, to name a few possibilities. I was not able to investigate these traits, but a future study on the role of personality in hybridization should certainly investigate some or all of these. Importance of Study Design Through the process of designing my study I discovered that assay design impacts behavioral response in ways that I did not anticipate. Even simple measures of activity
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did not correlate across the box and tent assays: the two assays elicited totally different behavioral responses. My finding has implications for cross-study comparisons. Even if two assays are somewhat similar, differences in space transparency and size might render them incomparable. One of my initial goals was to add to the exploratory literature in parids so that my results could be directly contrasted with other exploratory assays, but now I do not believe that is possible. Instead, my study is a warning to others that it might be better to standardize tests across species if we hope to compare the results or make generalizations about exploratory behavior. On a positive note, both mobile assays that I used were successful. I observed highly repeatable behavior suggestive of personality in both assays, with minimal disturbance to the birds. I was able to measure personality in wild birds without having to take them into captivity, and I was even able to assay birds during the breeding season without impacting their nesting success. There are now a few other published studies using similar mobile exploratory assays on wild birds as well (Edwards et al. 2015, Dubuc-Messier et al. 2017). It might be easier to accurately measure fitness in the wild if the birds can be assayed for personality on-site and then immediately released. Future Directions Tying personality to fitness is an important next step in understanding the evolution of personality. With two similar model species, fitness associations could be compared across species to better understand how different species adapt and how they are impacted by different selection pressures. Mortality is an obvious choice to represent fitness, and has been linked with exploratory behavior in other birds (Dingemanse et al. 2004). We would need a few years of data collection to study mortality, given that the
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sampled birds must die, but a mortality study could potentially be done without any extra effort by the Curry Lab. We already collect sighting, breeding, and feeder use data; in a few years, perhaps we can correlate these with personality types in the same subset of birds that I used for this thesis. Reproductive output can also represent fitness, but we would need to collect more standardized data such as nestling mass at a set age, or record visitation rates to estimate parental effort. We would also need to increase the number of samples per season to help control for potential variation in fitness across seasons due to other factors, so we may need to focus on a single site rather than stretching sampling across three sites as we did for my thesis. Another key role of personality might be its effect on mate choice. It is nearly impossible to observe wild birds in the process of forming pair bonds, but it is still possible to study EPCs in the wild. By genotyping the offspring and identifying fathers, we could compare the personality of the social father to the extrapair male to see whether personality plays a role in EPCs. Some personality types could invest more heavily in EPCs as opposed to parental investment, which could tie in with a study on breeding biology as I described above. This dichotomy has been described in White-throated Sparrows (Tuttle 2003), but not in chickadees. The proximate causes of personality are still relatively unknown in birds. We know that hormones, genetics, and the developmental environment can all impact adult personality in many animals, but almost nothing is known about how these things impact exploratory behavior in birds. Future studies could address some of these causes through hormonal implant studies, cross fostering, or whole genome analysis.
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Personality studies are still new to behavioral ecology, so there are numerous possibilities for continuing studies on the exploratory behavior of Black-capped and Carolina chickadees. This study system of sister and hybridizing species represents an excellent model for understanding both the evolution of personality and the role that personality plays in hybridization. I hope that future studies will build on the work that I have done for my thesis.
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APPENDIX Appendix 1. Neophobia Assay Introduction Neophobia is a personality trait which is commonly used for animal personality research. Neophobia (or, conversely, neophilia) can be defined as the latency to approach a novel object (An et al. 2011); this measure is sometimes termed boldness (Carere and Van Oers 2004), though it may not necessarily overlap with boldness under risk of predation. To measure neophobia, most species need to be encouraged to approach the object, so objects are usually placed near food or a location of interest. However, in the case of neophilia, some individuals may approach a novel object unprompted (MettkeHoffmann et al. 2002). Neophobia can vary across populations such that the “front line” of invasion is less neophobic, as is the case in House Sparrows (Passer domesticus) (Martin and Fitzgerald 2005). When two or more personality traits are correlated, they are considered a behavioral syndrome (Sih et al. 2004a). The two different traits (for example aggressiveness and boldness) may have the same proximate causes, whether genetic or developmental. Behavioral syndromes have been observed in a variety of animals including spiders (Riechert and Hedrick 1990), fish (Bell 2005), and birds (Verbeek et al. 1994). Behavioral syndromes may differ across populations and species because of different selection pressures such as predation (Riechert and Hedrick 1990, Bell 2005). The constraints of behavioral syndromes may help maintain intra-individual consistency (Sih et al. 2004b); however, other mechanisms besides constraints have also been proposed such as the benefits of predictability (Dall et al. 2004), positive feedback loops
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between state and behavior (Dall et al. 2004, Sih et al. 2004b), and sexual selection (Schuett et al. 2010). Carolina Chickadees are cavity nesters which will also readily use nest boxes or other artificial nests. During the breeding season, chickadees visit their nest cavities frequently, and therefore are presumably very familiar with the appearance of their cavity and the outside snag. This system offers an opportunity to study neophobia in wild birds without needing to habituate the birds to a feeder: a novel object can easily be placed in an environment in which a bird is already familiar, and birds return to their nests frequently enough for an observer to record an encounter with a novel object. Hypothesis 1: Neophobia represents a personality trait in Carolina Chickadees. In order to best support this hypothesis, I would need to observe both variation in behavior across individuals and repeatability of behavior within individuals. Unfortunately, because of limited time, I was only able to test the first prediction: that Carolina Chickadee individuals will vary in their response to a novel object. Hypothesis 2: In Carolina Chickadees, neophobia and exploratory behavior represent a behavioral syndrome and are associated with the same proximate cause. Based on this hypothesis, I predicted that exploratory behavior (as measured in Chapter 2) and neophobia would correlate such that more exploratory birds are less neophobic.
99
Methods At our field site in East Nantmeal, Chester Co., Pennsylvania, Carolina Chickadees readily nest in artificial nest snags constructed of PVC, which closely match the diameter, height, and color of natural tree snags. About 100 such snags exist across the field site. The snags are filled with sawdust so that chickadees can perform natural excavation behavior. I measured neophobia in Carolina Chickadees using a field assay based on the protocol of Dr. Mark Stanback. The assays took place during the breeding season, when chicks were at least 7 days old. I conducted a total of 10 ducky assays from May 25 to June 8, 2016. I first approached a nest snag and checked for live chicks inside, then waited for the parents to enter the vicinity. Once at least one adult chickadee was present, I set the audio recorder (Zoom H2n Handy Recorder) at the base of the snag and covered it with a camouflage cloth. Next, I initiated the control trial: I touched the top of the snag as if placing something, said “start” to mark the start of the trial on the audio recorder, and then retreated to a hiding spot where I could observe the snag without agitating the adult chickadees. If the chickadees never stopped giving alarm calls during the control trial or followed me, I restarted the trial and chose a different hiding spot. Hiding spots were approximately 6 to 12 m from the snag. I conducted the trial for 15 min or until a parent entered the nest. I recorded latency to cease alarm calling and latency to enter nest snag. Immediately after the control trial concluded, I returned to the snag to initiate the ducky trial. I placed a novel object (a Munchkin “white hot” yellow rubber ducky; Fig. 5.1) on top of the snag, said “start,” and then retreated to the same hiding spot. This trial
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Figure 5.1. The novel object used during neophobia assays. It is a Munchkin “white hot” rubber ducky. also lasted 15 min. By hand in the field, I recorded the following variables: latencies to approach ducky (within 5 m, 3 m, and 1 m approximated areas), number of physical attacks, time of each attack, latency to cease alarm calling, and latency for second bird to appear. Only one bird entered the nest cavity during the ducky trial, and it was as a heavy rain began. I used the audio recordings to count alarm calls given during minutes 2-4 of the assay (3 min total; I discarded the first minute as I was not yet hidden). I classified each alarm call (as “chick-a-dee” calls or other) and also counted the number of dees given in chick-a-dee calls. From these data, I generated the following variables: number of calls (including non-chick-a-dee calls) given in first 120 sec and in first 180 sec; average dees per dee call in first 120 sec, last 60 sec only, and over full 180 sec period. For the behavioral syndrome analysis, I used the exploratory scores generated in Chapter 2. I performed simple correlation tests (Pearson’s or Spearman’s, depending on whether the data were normally distributed) between the 8 quantitative variables measured in the ducky test and the exploratory scores as well as chick age (in days) and number of chicks present at the time of the test. The 4 exploratory scores I used were male’s score, female’s score, pair average score, and difference between pair scores (absolute value of the male score minus the female score). All the ducky assay scores
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represent pair scores because I was unable to score the birds separately. I performed 48 correlations, and so I used the adjusted α = 0.001 (0.05 / 48 as per Bonferroni Correction). I also tested whether birds who attacked the ducky differed in average score from those who did not attack using Wilcoxon Signed-Rank tests. Results I observed a large amount of variation in pairs’ responses to the ducky stimulus (Table 5.1). Chickadees generally responded to the ducky stimulus by giving alarm and chick-a-dee calls and moving rapidly through the trees. Most birds approached the ducky, but 5 pairs never approached within 1 m. In 4 of the 10 tests, the second bird never appeared during the assay. Only in 4 trials did a bird physically attack the ducky. Chickadees almost always gave more calls in the first 120 sec than in the last 60 sec. I found no evidence of a behavioral syndrome: none of the ducky assay variables correlated with any of the exploratory behavior variables (48 tests, all p > 0.001). One Table 5.1 Ducky assay results Chicks Age Count (Days) 15 6
Number of Calls 120 180 sec sec 27 59
Average dees per call 120 Last 180 sec 60 sec sec 9.1 5.7 7.5
Latency to Approach 5 3m 1m m 175 175 900
Date
Time
May 25
13:06
May 25
13:58
9
7
60
94
3.6
3
3.4
900
900
900
0
May 25
15:00
6
5
38
55
6.4
6.2
6.4
433
454
900
0
May 25
16:34
7
7
43
79
9.1
7.7
8.6
81
198
900
0
May 27
8:12
10
1
51
89
4.4
4.3
4.4
182
520
750
3
May 27
11:45
9
7
0
16
0
6.1
6.1
31
47
445
1
May 29
11:14
13
5
52
82
5.8
4.8
5.4
174
490
540
0
May 29
11:50
10
7
1
1
8
0
8
62
184
900
0
Jun 01
10:14
11
5
41
64
5.1
4.4
5
52
52
477
2
Jun 08
10:53
7
5
47
75
5.3
5.4
5.3
69
87
87
1
102
No. of Attacks 0
correlation received p < 0.05, between the male’s exploratory score and latency to approach within 5 m of the ducky. However, this was not significant after the Bonferroni Correction. The average scores of those that attacked the ducky did not differ from birds that did not attack. Surprisingly, response to the ducky assay was also not associated with chick age or number: none of the eight variables correlated with either. Conclusion I found no associations whatsoever between exploratory behavior and neophobia, or between neophobia and chick age or number. There are two primary reasons why I suspect I found nothing. First, I only had a sample size of ten. As my other chapters have demonstrated, studying behavior of wild animals requires a large sample size. Nuisance variables are inevitable in the field, and I would need high power to detect subtle differences in behavior. Second, I sampled birds as pairs, and in most cases, could not identify individuals even if only one bird of the pair was reacting. My earlier chapters also demonstrated that pairs do not necessarily behave alike, at least in their exploration, and so pair scores may not be informative. These major limitations could be improved in a future study, but I did not have the time to improve them myself. I suspected that nestling age would be a large factor in the aggressive response to the ducky that the parents exhibited, but instead I found no correlation. I suspect that this is largely due to sample size and the way that I coded my results. For example, in my opinion, the parents of the oldest nestlings did react very aggressively. The birds postured and gave threatening calls that I had never heard before. However, they did not attack the ducky physically. Some responses, such as latency to approach within 1 m and attacking, might differ more greatly across individuals than number of dees given. If my nests
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varied in both parental aggressiveness and chick age, I might not be able to detect either pattern with a sample size of just ten. Environment surrounding the nest (such as presence of branches close to nest), my position, weather, time of day, and date may also have impacted results. I simply could not control for all of them with so few tests. Finally, I doubt that my test really represented neophobia. Instead, because birds behaved very aggressively, I believe that my test represented nest defense behavior or boldness. Another fellow labmate, Taylor Heuermann, designed a boldness assay using a moving woodpecker. The birds’ responses are identical to both stimuli, although they respond more vigorously when the stimulus is moving and emitting sound. Future neophobia studies might want to use food to lure birds into interacting with the stimulus instead, to separate these two personality traits.
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Literature Cited An, Y. S., B. Kriengwatana, A. E. Newman, E. A. MacDougall-Shackleton, and S. A. MacDougall-Shackleton. 2011. Social rank, neophobia and observational learning in Black-capped Chickadees. Behaviour 148:55–69. Bell, A. M. 2005. Behavioural differences between individuals and two populations of stickleback (Gasterosteus aculeatus). Journal of Evolutionary Biology 18:464–73. Carere, C., and K. Van Oers. 2004. Shy and bold Great Tits (Parus major): body temperature and breath rate in response to handling stress. Physiology and Behavior 82:905–12. Dall, S. R. X., A. I. Houston, and J. M. McNamara. 2004. The behavioural ecology of personality: consistent individual differences from an adaptive perspective. Ecology Letters 7:734–739. Martin, L. B., and L. Fitzgerald. 2005. A taste for novelty in invading House Sparrows, Passer domesticus. Behavioral Ecology 16:702–707. Mettke-Hoffmann, C., H. Winkler, and B. Leisler. 2002. The significance of ecological factors for exploration and neophobia in parrots. Ethology 108:249–272. Riechert, S. E., and A. V. Hedrick. 1990. Levels of predation and genetically based antipredator behaviour in the spider, Agelenopsis aperta. Animal Behaviour 40:679– 687. Schuett, W., T. Tregenza, and S. R. Dall. 2010. Sexual selection and animal personality. Biological Reviews 85:217–46. Sih, A., A. Bell, and J. C. Johnson. 2004a. Behavioral syndromes: an ecological and evolutionary overview. Trends in Ecology & Evolution 19:372–378.
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Sih, A., A. Bell, and J. C. Johnson. 2004b. Behavioral syndromes: an integrative overview. Quarterly Review of Biology 79:241–277. Verbeek, M. E. M., P. J. Drent, and P. R. Wiepkema. 1994. Consistent individual differences in early exploratory behaviour of male Great Tits. Animal Behaviour 48:1113–1121.
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Appendix 2. All Mixed Models Created for Chapter 2 ΔAIC
Model
Df
AIC
PC1 ~ Sequence + Holding Time + Species * Wing Length + Interval + (1|ID) PC1 ~ Sequence * Season + Holding Time + Species * Wing Length + Interval + (1|ID) PC1 ~ Sequence * Season + Holding Time + Species * Wing Length + Weather + Interval + (1|ID) PC1 ~ Sequence + Season + Holding Time + Species * Wing Length + Interval + (1|ID) PC1 ~ Sequence * Interval + Holding Time + Species * Wing Length + (1|ID) PC1 ~ Holding Time + Species*Wing Lenth + Interval + (1|ID) PC1 ~ Season + Holding Time + Species*Wing Lenth + Interval + (1|ID) PC1 ~ Sequence*Interval + Holding Time + Species*Wing Length + Season + (1|ID) PC1 ~ Sequence*Season + Holding Time + Species*Wing Length + (1|ID) PC1 ~ Sequence*Season + Holding Time + Species*Wing Lenth + Sex + Interval + (1|ID) PC1 ~ Sequence*Season + Holding Time + Species*Wing Lenth + Age + Interval + (1|ID) PC1 ~ Species*Wing Length + Hold + Age + Sequence*Season + (1|ID) PC1 ~ Species*Wing Length + Hold + Age*Wing Length + Sequence*Season + (1|ID) PC1 ~ Species*Wing Length + Hold + Age*Wing Length + Sequence*Season + Weather + (1|ID) PC1 ~ Species*Wing Length + Hold + Age*Wing Length + Sequence + Season + (1|ID) PC1 ~ Species*Wing Length + Hold + Age*Wing Length + Sequence*Season + Sex + (1|ID) PC1 ~ Sequence*Season + Species*Wing Length + Interval + (1|ID) PC1 ~ Sequence*Season + Holding Time + Species + Wing Lenth + Interval + (1|ID) PC1 ~ Species*Wing Length + Hold + Age*Wing Length + Sequence*Season + Sex + Weather + (1|ID) PC1 ~ Species*Wing Lenth + Interval + (1|ID)
11
1014.7
0
13
1015.1
0.4
15
1015.4
0.7
12
1016
1.3
12
1016.5
1.8
10
1017.4
2.7
11
1017.8
3.1
13
1017.8
3.1
12
1018.5
3.8
15
1019
4.3
15
1019.1
4.4
14
1022.2
7.5
16
1022.8
8.1
18
1024.9
10.2
15
1025
10.3
18
1026.7
12
12
1027.2
12.5
11
1028.5
13.8
20
1028.8
14.1
9
1028.9
14.2
107
PC1 ~ Sequence*Interval + Holding Time + Species + Wing Length + (1|ID) PC1 ~ Sequence + Season + Holding Time + Species + Wing Length + Interval + (1|ID) PC1 ~ Species + Holding Time + Age*Wing Length + Sequence*Season + (1|ID) PC1 ~ Species + Holding Time + Age*Wing Length + Sequence + Season + (1|ID) PC1 ~ Species + Holding Time + Age*Wing Length + Sequence*Season + Weather + (1|ID) PC1 ~ Sequence*Season + Holding Time + Species + Interval + (1|ID) PC1 ~ Species*Sequence + Holding Time + Wing Length + (1|ID) PC1 ~ Species + Holding Time + Age*Wing Length + Sex + Season*Sequence + Weather + Interval + (1|ID) PC1 ~ Species + Holding Time + Age*Wing Length + Sequence*Season + Sex + (1|ID) PC1 ~ Species*Sequence + Holding Time + Season*Sequence + (1|ID) PC1 ~ Species*Sequence + Holding Time + Sex + (1|ID) PC1 ~ Species*Sequence + Holding Time + (1|ID) PC1 ~ Species + Holding Time + Age*Wing Length + Sequence*Season + Age*Sex + (1|ID) PC1 ~ Species*Sequence + Holding Time + Weather + (1|ID) PC1 ~ Species*Sequence + Holding Time + Age*Wing Length + Sex*Wing Length + (1|ID) PC1 ~ Species*Sequence + Holding Time + Age + (1|ID) PC1 ~ Species*Sequence + Holding Time + Age + Season*Sequence + (1|ID) PC1 ~ Sequence + Species + Interval + (1|ID) PC1 ~ Species + Holding Time + Age + Species*Weather + Sequence*Season + (1|ID) PC1 ~ Species*Sequence + Holding Time + Age + Sex*Wing Length + (1|ID) PC1 ~ Sequence*Season + Holding Time + Wing Length + Interval + (1|ID) PC1 ~ Holding Time + Interval + (1|ID) PC1 ~ Sequence*Season + Holding Time + (1|ID) PC1 ~ Sequence*Season + Species + (1|ID)
108
10
1028.9
14.2
10
1029
14.3
14
1031.5
16.8
13
1033.5
18.8
16
1033.9
19.2
10
1034.1
19.4
10
1034.4
19.7
19
1034.8
20.1
16
1035.3
20.6
11
1039.8
25.1
11 9 20
1039.9 1040 1040
25.2 25.3 25.3
11
1042.6
27.9
18
1042.8
28.1
11 13
1043.8 1043.8
29.1 29.1
7 17
1044.5 1045.6
29.8 30.9
16
1045.7
31
9
1046.6
31.9
5 7 8
1047.3 1048.2 1048.3
32.6 33.5 33.6
PC1 ~ Species*Sequence + Holding Time + Age + Season + Weather + (1|ID) PC1 ~ Species*Sequence + Holding Time + Age*Sex + (1|ID) PC1 ~ Interval + Sequence + (1|ID) PC1 ~ Sequence*Season + Interval + (1|ID) PC1 ~ Sequence*Season + Interval + (1|ID) PC1 ~ Interval + (1|ID) PC1 ~ Sequence*Season + (1|ID) PC1 ~ Sequence*Season + Age*Wing Length + (1|ID) PC1 ~ Sequence*Season + Wing Length + (1|ID) PC1 ~ Species + Age*Wing Length + (1|ID) PC1 ~ Species + (1|ID) PC1 ~ Hold + Age*Wing Length + (1|ID) PC1 ~ Holding Time + (1|ID) PC1 ~ Age*Wing Length + (1|ID) PC1 ~ Sex + (1|ID) PC1 ~ 1 + (1|ID) PC1 ~ Wing Length + (1|ID) PC1 ~ Season + (1|ID) PC1 ~ Age + (1|ID) PC1 ~ Weather + (1|ID)
109
14
1048.4
33.7
17
1048.5
33.8
5 7 7 4 6 11 7 10 5 9 4 8 5 3 4 4 5 5
1054.2 1055.3 1055.3 1056.5 1058.4 1058.4 1059.3 1059.4 1067.4 1069.8 1070.2 1076.7 1077.6 1078.3 1079.1 1079.4 1080.8 1082.2
39.5 40.6 40.6 41.8 43.7 43.7 44.6 44.7 52.7 55.1 55.5 62 62.9 63.6 64.4 64.7 66.1 67.5
