Within-Species Variation in Cognition in Cichlid Fishes: Influences of Social Status

Within-species Variation in Cognition in Cichlid Fishes: Influences of Social Status

and Personality

Dissertation

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy

in the Graduate School of The Ohio State University

Elizabeth Anne Hoskins

Graduate Program in Evolution, Ecology, and Organismal Biology

The Ohio State University

2018

Dissertation Committee

Ian M. Hamilton, Advisor

J. Andrew Roberts

Suzanne M. Gray

Dawn M. Kitchen

Copyrighted by

Elizabeth Anne Hoskins

2018

Abstract

Cognition, the processes involved in acquiring, storing, and the use of information from the environment, plays a critical role in the presentation of behavior. Cognition may influence the perception of risk, the success in which an individual obtains mating opportunities, or how an individual navigates through a complex habitat. While there are benefits to having some cognitive abilities, there is also variation within a species for many of these abilities. Differences in social status and behavioral type between individuals may drive the variation we observe in cognitive abilities. For my dissertation,

I examine this main question: How does individual variation in status and personality influence cognition? Specifically, I investigate how 1) dominance hierarchies influence the ability to recognize individuals, 2) dominance hierarchies influence use of socially acquired information, and 3) personality influences the rate of associative learning. I use an experimental approach using two Lake Tanganyikan fish species, Neolamprologus pulcher and Julidochromis ornatus. Both are species that live in long term groups with dominance hierarchies. In Chapter 2, I examine if mid-ranking individuals can recognize multiple individuals, and if they prioritize who they can individually identify based on potential fitness benefits. Using live and animated stimuli of N. pulcher, I found that mid- ranking individuals can discriminate between familiar and unfamiliar individuals. Even more, they can distinguish high-ranking (dominant) familiar and unfamiliar fish as well

ii as lower-ranking individuals. These results suggest that individuals benefit from recognizing multiple members of their group. In Chapter 3, I hypothesized that observers would use social information from individuals that would increase their fitness.

Specifically, I predicted that observers would use information from demonstrators more dominant (which in N. pulcher are older and larger) than themselves. I tested this is N. pulcher and I found that observers used social information from females that were larger than themselves, suggesting that social status does influence the use of social information. This could be either because they pay more attention to these individuals or because these individuals have been successful at foraging and avoiding predators In

Chapter 4, I tested the hypothesis that an interaction between personality and type of association task influences an individual’s learning rate. Specifically, I predicted that highly aggressive and exploratory individuals would learn novel associations quickly and reversal associations more slowly, compared to less aggressive and exploratory individuals. I tested this hypothesis with two food-location association tasks in J. ornatus.

I did not find that personality was associated with learning rate, thus failing to support my hypothesis. However, I did find that social environment (whether individuals had a mate) influenced learning task completion and environmental temperature influences learning rate. In an environment where food is limited, this suggests that motivation for a food reward influences learning. As a whole, this work suggests that the variation in an individual’s social environment does influence cognitive abilities, but I did not find evidence that variation in an individual’s personality influences the cognitive abilities that I explored.

iii

Acknowledgments

There are many people that have contributed to this dissertation and my training as a scientist. First and foremost, I would like to thank my advisor, Ian Hamilton. He is truly an outstanding mentor and I am grateful his patience, guidance, and support. I would also like to thank Libby Marschall, for she, along with Ian, invested a great amount of time teaching me how science works and how to be a scientist. I would like to thank my committee, Andy Roberts, Suzanne Gray, and Dawn Kitchen for their mentorship as well as their thoughtful contributions to this project. I owe thanks to Stu

Ludsin and the other members of the AEL for allowing me to take over one of their fish rooms for a summer, as well as for their guidance on the Julies. I am grateful to the

Animal Behavioral and Ecological Complex Systems (ABECS) Lab, both past and present for their advice and support.

I also want to thank my partner, Jeff Klenke, for without him I would have never been able to do this. Partners should receive honorary degrees. I would also like to thank my SACNAS family as well as ABD group for their support and their perspective on school and life.

Lastly, I want to acknowledge that completing this dissertation took a lot of hard work. It took the hard work of my Grandpa, who had to leave school before learning how to read so he could support his family. It took the hard work of my Grandma, who

iv worked three jobs to raise her children so that they could have a better life. It took hard work from my family in the Philippines, who did the hard work of manually planting rice so that my Mom could have a future. It took hard work from my Dad, who was the first person in our family to go to college and set the expectation that I would go to college. It took hard work from my Mom, who left home at an early age to support her family. She is and has always been the biggest advocate for my education. My entire family worked hard so that I could have this opportunity, and I am grateful.

Vita

May 2006 ...... Catholic Central High School, Springfield, OH 2006 to 2011 ...... B.S., Zoology; B.S., Psychology, The Ohio State University 2012 to present ...... Graduate Teaching Associate, Department of Evolution, Ecology, and Organismal Biology, The Ohio State University

Fields of Study

Major Field: Evolution, Ecology and Organizmal Biology

Table of Contents

Abstract ...... ii Acknowledgments...... iv Vita ...... vi List of Tables ...... ix List of Figures ...... x Chapter 1. Introduction ...... 1 Chapter 2. Using live animals and animations to test individual recognition in a highly social cichlid fish ...... 6 Abstract ...... 6 Introduction ...... 7 Methods...... 12 Results ...... 19 Discussion ...... 22 Chapter 3: Size and sex influence the use of social information in a social cichlid fish .. 39 Abstract: ...... 39 Introduction: ...... 40 Methods: ...... 45 Results: ...... 52 Discussion: ...... 53 Chapter 4: Temperature and social environment, not personality, influences learning rate in the cichlid fish Julidochromis ornatus ...... 63 Abstract ...... 63 Introduction ...... 64 Methods...... 68 Results ...... 74

vii

Discussion ...... 76 Chapter 5: Conclusion...... 86

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List of Tables

Table 1: Best fitting GEE for live individual recognition experiment. The following dependent variables are reported: a) Time tracking with time spent on side as covariate, b) Time tracking, c) Time spent near stimulus, d) Total behaviors towards stimulus with time spent near stimulus as a covariate, e) Total behavior towards stimulus. Significant p- values are in bold...... 29 Table 2: Best fitting GEE for animation individual recognition experiment. The following dependent variables are reported: a) Time tracking with time spent on side as covariate, b) Time tracking, c) Time spent near stimulus, d) Total behaviors towards stimulus with time spent near stimulus as a covariate, e) Total behavior towards stimulus. Significant p- values are in bold...... 32 Table 3: Linear Mixed Model output for sex and a) effect of food, b) effect of demonstrator, and c) effect of demonstrator + food. Significant p-values are in bold. .... 59 Table 4: Linear Mixed Model output for status and a) effect of food, b) effect of demonstrator, and c) effect of demonstrator + food. Significant p-values are in bold. .... 61

List of Figures

Figure 1: schematic of experimental set-up for live stimuli individual recognition test. The LSM or LSF focal fish was placed in the center tank where familiar and unfamiliar stimuli were placed on either side aquarium. The stimuli fish were restricted to the 15 cm of the tank adjacent to the focal fish’s tank to restrict movement. The grey panels are the 15 cm portions where time was recorded...... 28 Figure 2: Response to familiar/unfamiliar stimuli based on social status of live stimuli. A) Time spent tracking stimuli. Focal fish spent more time tracking familiar Dominant Female stimuli compared to unfamiliar Dominant Female stimuli. B) Behaviors toward stimuli. Focal fish performed more behaviors towards unfamiliar Small Subordinates compared to familiar Small Subordinates. C) Behaviors toward stimuli with time spent on side as a covariate. Focal fish performed more behaviors towards familiar Small Subordinates compared to unfamiliar Small Subordinates. Means are estimated means from the best-fitting GEE (Table 1). Error bars are 95% confidence intervals...... 35 Figure 3: Time spent tracking familiar/unfamiliar animated stimuli based on status of stimulus fish, with time spent on side as a covariate. Focal fish spent more time tracking familiar Dominant Male stimuli compared to unfamiliar Dominant Male stimuli. Focal fish also spent more time tracking unfamiliar Small Subordinates compared to familiar Small Subordinates. Means are estimated means from the best-fitting GEE (Table 2). Error bars are 95% confidence intervals...... 36 Figure 4: Following of familiar/unfamiliar stimuli based on social status of live stimuli, by sex. A) Male focal fish spend more time near familiar Dominant Females compared to unfamiliar Dominant Females. B) Male focal fish spend more time tracking familiar Dominant Females compared to unfamiliar Dominant Females. Means are estimated means from the best-fitting GEE (Table 1). Error bars are 95% confidence intervals. .... 37 Figure 5: Focal individuals time spent near stimulus based on treatment. Focal individuals, regardless of sex, did not change the time they spent near the stimulus when a novel stimulus was introduced at time period 6. Focal females overall spent more time near the stimulus compared to focal males ...... 38 Figure 6: a) schematic of home tank and food-location association learning. Each group is composed of four fish in a tank with a slate on the bottom left, a filter at the center top, and a heater on the right side. Each group had one neighbor and was fed on the side opposite of the neighbor for food-location association training (right side for focal group). b) Social information task. Observers were placed in the center of the tank that was similar to their home tank. Stimuli (in this figure, a neighbor feeding) is presented and removed after 2 minutes. Observer is released and allowed to swim freely for 10 minutes...... 58 x

Figure 7: The difference in time the observer spent on the demonstrator’s side when presented with a demonstrator feeding compared to a control based on the size difference between the demonstrator and observer. Positive x-axis values are when the demonstrator is larger than the observer, while negative x-axis values are when the observer is larger than the demonstrator. a) The interaction between demonstrator sex and the size difference between the demonstrator and observer, and b) the interaction between observer sex and size difference between the demonstrator and observer...... 60 Figure 8: Observer social status influences time spent on the demonstrator side when the demonstrator is feeding compared to only the presence of the demonstrator...... 62 Figure 9: Personality arena schematic. The 1/6 of the aquarium on the left was used to habituate J. ornatus after handling and between testing periods, with an opaque partition. This area was also used for the aggression assay, with a mirror replacing the opaque partition. The remaining 5/6ths of the aquarium was used for the exploration assay, which had 10 shelters laid out for the fish to explore...... 81 Figure 10: Learning trail arena schematic, view from above the tank. The learning arena consisted of a shelter where an individual started the task. On either corner, food was deposited during the training phase and either food or food scented water was deposited. A clear barrier was placed behind the shelter to prevent the individual from swimming away from the test area, and if the fish had a mate in the tank, he was placed behind the barrier...... 82 Figure 11: Scatter plot of number of days until an individual met learning criterion for the initial learning test plotted against a) our measure of aggressiveness (mean number of aggressive behaviors directed toward a mirror over a 10-minute period) and b) our measure of exploration (mean number of shelters visited during a 15-minute period) .... 83 Figure 12: Scatter plot of number of days until an individual met learning criterion for the reversal learning test plotted against a) our measure of aggressiveness (mean number of aggressive behaviors directed toward a mirror over a 10-minute period) and b) our measure of exploration (mean number of shelters visited during a 15-minute period) .... 84 Figure 13: Days to learning criterion for reversal learning task plotted against temperature. Individuals in the high (29°C) treatment met learning criterion in fewer days on average compared to individuals in the low (25°C) treatment...... 85

Chapter 1. Introduction

Cognition is the processes of acquiring, storing, and using information from the environment (Shettleworth 2010). Cognition can influence fitness by influencing an individual’s ability to obtain information about the environment, resulting in a behavioral response. Benefits of increased cognition include ability to assess a complex habitat

(Salvanes et al. 2013), assess the quality of a competitor (Grether 2011), or find a mate

(Higgie et al. 2000). Cognition may influence the perception of risk (Webster and Laland

2008), the success with which an individual obtains mating opportunities (Nowicki et al.

2002), and how an individual navigates through a complex habitat (Salvanes et al. 2013).

Within a species, there can be variation among individuals for a cognitive ability.

There are two potential reasons for this. First, individuals may have a cognitive ability

(e.g. ability to socially learn), but because of the costs and/or benefits, may not change their behavior in response to information gained using that ability. Second, some aspect of the individual (such as sex or social status) may or may not facilitate an ability (Injaian and Tibbetts 2014). This variation can have implications for both the individual as well as the group as a whole. Individual variation in cognition may influence how social groups and social hierarchies are formed and maintained (Yosida and Okanoya 2012), and how information flows between members of a group (Flack 2012).

For my dissertation, I used laboratory experiments to investigate how individual variation in social status and animal personality influences cognition. More specifically, I investigate the role of social status in recognizing familiar individuals (Chapter 2), how relative differences in social status between a demonstrator and observer influence the use of social information (Chapter 3), and how variation in personality correlates with variation in association and reversal learning (Chapter 4). I used two species of Lake

Tanganyika cichlids, Neolamprologus pulcher and Julidochromis ornatus. N. pulcher is a cooperatively breeding cichlid that has high reproductive skew favoring dominant individuals and subordinate individuals provide alloparental care to the offspring of others. This species also forms size-based dominance hierarchies and lives in long term groups embedded within a larger colony (Wong and Balshine 2011). J. ornatus is also a cooperatively breeding cichlid that lives in long term groups (Heg and Bachar 2006).

In Chapter 2, I examine how social status influences familiarity in N. pulcher.

Individual recognition has been shown in this species (Hert 1985; Balshine-Earn and

Lotem 1997; Kohda et al. 2015), but how social status influences this ability or if individuals can discriminate multiple group mates from strangers is unknown.

Recognizing familiar individuals is thought to be a costly, cognitively demanding ability

(Sheehan and Bergman 2015), and individuals may prioritize recognizing group mates that have a stronger effect on their fitness. To test this, I performed two experiments where mid-ranking individuals were presented with a familiar group mate and a size and sex matched stranger. These stimuli individuals were either higher- or lower-ranking than the focal individual. I predicted that if attention was limited, that mid-ranking

2 subordinates would show individual recognition towards high-ranking dominant individuals but not to low-ranking individuals. One experiment used live stimuli while the second used animated stimuli. I found that for both experiments, focal fish can distinguish between familiar and unfamiliar individuals regardless of rank relative to the focal individual. This suggests that for mid-ranking individuals, that there may be benefits for recognizing familiar high and low ranked individuals and that they can individually recognize multiple individuals despite the cost.

In Chapter 3, I examined the role of social status and the use of socially acquired information in N. pulcher. Individuals may benefit from using social information from some demonstrators but not others. We predicted that observers will use information from demonstrators more highly ranked than themselves. We tested this prediction in N. pulcher by training observers to feed at a location in an aquarium and presenting them with conflicting social information. We found that mid-ranking fish use social information from females that were larger than themselves, but not larger males or smaller individuals. This supports our hypothesis that relative difference between demonstrators and observers influences the observer’s use of social information. This finding supports work in tetrapods that show that individuals use social information from dominant individuals (Nicol and Pope 1999; Krueger and Heinze 2008; Pongrácz et al.

2008; Kendal et al. 2015). We also tested whether local enhancement influenced the use of social information. Local enhancement was presented by having demonstrators present but not feeding, and thus not presenting any social information about the location of food.

We found that focal individuals did not change their foraging location when there was

3 local enhancement. In other words, observers only used social information when demonstrators showed that there was food at that location.

In Chapter 4, we tested Sih and Del Giudice’s (2012) hypothesis that associative learning rate is influenced by both personality and type of association task using J. ornatus. Sih and Del Giudice (2012) predict that individuals who are highly aggressive and exploratory should learn a novel association task faster than individuals who are less aggressive and exploratory. These highly aggressive and exploratory individuals should also learn tasks that involve an environmental change, such as reversal learning, slowly compared to less aggressive and exploratory individuals. Each fish was assayed twice in exploration and aggression tasks. Next, fish went through a novel learning task where fish had to associate a location in their aquarium with food. Individuals who learned the initial association task, were then presented with a reversal learning task where they had to associate a new location with food. We found that exploration and aggression assays each correlated, suggesting that these are personality traits in J. ornatus. However, we failed to find a correlation between personality and either association learning task.

However, this project was part of a larger project investigating how temperature influences physiology, personality, and behavior, and we had fish in two temperature treatments and fish were housed with another fish. We found that fish housed with another individual were more likely to complete the initial learning task compared to individuals who were housed alone. Additionally, we found that fish in the high temperature environment learned the reversal task faster than fish housed in the low temperature environment. Fish in this experiment were fed a set amount of food each day,

4 and previous work has shown that other fish in this larger project housed in the high temperature environment have higher metabolic rates compared to fish in low temperature environments. This suggests that motivation for a food reward may be driving association learning.

Chapter 2. Using live animals and animations to test group mate recognition in a highly social cichlid fish

Abstract

Distinguishing between familiar and unfamiliar individuals can allow an animal to adjust their behavior based on past interactions with particular social partners. Because interactions with some types of individuals, such as more dominant individuals, have greater fitness effects than others, familiarity may vary depending on the type of relationship. We predicted that individuals of Neolamprologus pulcher, a group living cichlid, will be more likely to discriminate among high-ranked individuals. We tested this by using a two-choice experiment using either live fish or animations as our stimuli as well as a habituation-dishabituation experiment. We presented individuals with an animation of a group mate for 10 minutes and either presented an animation of a new fish

(size and sex matched) or a second animation of the group mate and measured the behaviors towards the stimuli. For the two-choice tests, we found that mid-ranking fish could discriminate dominate familiar from unfamiliar individuals as well as lower- ranking familiar and unfamiliar individuals. This suggests that mid-ranking N. pulcher 1) have the cognitive ability to recognize familiar individuals at multiple ranks, and 2) can recognize multiple members within their group.

Introduction

The ability to discriminate between familiar and unfamiliar individuals can be seen across the animal kingdom, including fish (Bshary et al. 2002; Satoh et al. 2016), mammals (Pascalis and Bachevalier 1998; Pokorny and de Waal 2009; Townsend et al.

2011), and invertebrates (Karavanich and Atema 1998; Sheehan and Tibbetts 2011;

Injaian and Tibbetts 2014). For familiarity to occur, a signaler must have a signal that is different from others in the population, is consistent over time, and does not signal quality of that individual (Tibbetts and Dale 2007). In addition, the receiver of the familiarity signal must have able to process and remember the signal (Tibbetts and Dale

2007). Many signals of familiarity are visual (Pascalis and Bachevalier 1998; Bshary et al. 2002; Pokorny and de Waal 2009; Satoh et al. 2016), and many species are able to individually recognize conspecifics in addition to recognizing familiar individuals.

An underlying assumption of most social behavioral ecology research is that social species have cognitive abilities that facilitate social or cooperative behavior, including discriminating between members of their group (Byrne and Bates 2006). The ability to recognize and discriminate between different conspecifics can be beneficial to an individual. For example, individuals who can recognize group mates with established affiliative relationships can avoid the costs of aggression (Johnsson 1997; Frostman and

Sherman 2004; Jordan et al. 2010) or know whom to direct appeasement behaviors to avoid aggression or eviction (Bshary et al. 2002). Recognizing individuals and their competitiveness can also reduce the costs of fighting for contested resources (Höjesjö et al. 1996).

The ability to recognize familiar individuals can be found across taxa, including mammals (Pitcher et al. 2011; Townsend et al. 2012; Gilfillan et al. 2016), fish (Balshine-

Earn and Lotem 1998; Hotta et al. 2017; Madeira and Oliveira 2017), and invertebrates

(Vannini and Gherardi 1981; Sheehan and Tibbetts 2011; Rodrigues de Souza et al.

2014). Familiarity is seen in a range of social contexts (see Tibbetts 2007) and is thought that this cognitive ability facilitates the evolution of cooperation (Dugatkin 2002). Both models (Crowley et al. 1996) and empirical data (Bshary et al. 2002) show that individual recognition, a type of recognition, is involved in Iterated Prisoner’s Dilemma. Individual recognition, along with memory, allows for individuals to keep track of previous cooperators as well as defectors. Client fish of cleaner wrasse Labroides dimidiatus can recognize cheater cleaner wrasse and avoid those individuals (Tebbich et al. 2002;

Bshary et al. 2002).

In social species with dominance hierarchies, individuals could use cues of status, or status recognition to mediate potential costs of interactions. Status recognition is sometimes enough to maintain dominance hierarchies (Gherardi and Daniels 2003;

Gherardi et al. 2012). However, this recognition system may not be sufficient in cases where multiple groups live in close proximity to one another. For example in the group- living cichlid, Neolamprologus pulcher, multiple groups are located within a larger colony. While size or a size related phenotype may cue dominance status, there may be similar sized individuals within the colony for which relationships have not been established or for which the appropriate behaviors differ, and so recognizing familiar individuals may still be needed.

Individuals must allocate their attention to many tasks, such as finding mates, foraging, and/or predator avoidance, therefore attention may be limited. If attention is limited, individuals may be unable to distinguish among all individuals in a social group.

In humans, Dunbar’s number, defined as the number of individuals humans can keep track of, is thought to be limited to 150 (Dunbar, 1992). Other animals may also face similar constraints in monitoring individuals in their group. Fischer et al. (2014) found that in Neolamprologus pulcher, a cooperatively breeding cichlid, dominant fish would evict ‘unhelpful’ helpers in small groups, but not in large. This indicates that dominant individuals in large groups may not be able to associate individuals with their level of helping behavior.

If attention is limited, individuals may benefit from devoting more attention to conspecifics that have a greater effect on fitness. Different categories of individuals differ in their abilities to influence the group and the fitness of others (Flack et al., 2006;

Hamilton and Ligocki, 2012). For example, distinguishing among group mates might influence fitness more than distinguishing among neighbors. In groups with subordinate

‘helpers’ who provide alloparental care, dominant individuals are expected to recognize helpers since the number of helpers can influence female investment in eggs (Taborsky et al. 2007), and unhelpful individuals should be evicted. In turn, helpers should be able to identify more dominant individuals in their group in order to show helping and subordinate behavior.

In the group- and colony-living cichlid N. pulcher, it has been demonstrated that dominants can discriminate between group mates and strangers. This species are a

9 cooperatively breeding cichlid, where dominant group members are fathers and mothers of most offspring (Hellmann et al. 2016); smaller, subordinate individuals are ‘helpers’ that provide alloparental care. Dominants can discriminate between their own helpers and others (Hert 1985) and can discriminate between their mates and strangers (Balshine-

Earn and Lotem 1997). Dominant male N. pulcher can also differentiate between neighbors and strangers (Frostman and Sherman 2004). In addition, Jordan et al. (2009) showed subordinates preferred groups with familiar helpers. Further, Kohda and colleagues (2015) showed that N. pulcher responded to unfamiliar faces on a digital screen, indicating that individual recognition is likely mediated using visual cues.

Like many social species, N. pulcher can live in relatively large groups, with an average group size of 7-9 individuals but can be as large as 22 individuals (Balshine et al.

2001, Heg et al. 2005) and so costs or limits to recognizing all members of the group may be high. We hypothesized that, if attention is limited, more attention should be devoted to more influential group members compared to less influential group members. In N. pulcher, socially dominant individuals are highly influential, because they punish or evict helpers (Fischer et al. 2014) and are potential mates to subordinates (Stiver et al. 2009), so we predict that dominant group members will receive more attention than subordinates. Because the influence of dominant males and females might differ for male and female observers, we expect that sexes could differ in which dominant receives more attention. To test this hypothesis, we sought to test 1) if lower ranking individuals can discriminate between familiar and unfamiliar fish, 2) if individual recognition in N. pulcher differs with the status of the fish being observed, and 3) if morphological cues

10 alone are enough to discriminate between group mates and strangers. We performed three experiments. First, mid-ranking individuals were tested in a two-choice test where one stimulus was a live, familiar group mate and the other was a live, size- and sex-matched stranger. Second, we repeated this first experiment using animations of familiar and unfamiliar fish as stimuli to see the influence of morphology on individual recognition.

For these two experiments, we predicted that if a focal fish can discriminate between a familiar group mate and a stranger, there will be differential behavior towards the two stimuli (e.g. more aggression towards a stranger) or more time spent near one of the stimuli. If fish cannot individually recognize all members of their group, we expect to see differential behavior towards stimuli when the stimuli are of higher ranked individuals compared to the focal individual.

For the third experiment, we performed a habituation/dishabituation task to see if any differences in results between experiments 1 and 2 were a result of individuals paying attention to the animated stimuli. Fish went through a habituation task in which fish were exposed to a 20-minute animation. For half of the subjects, the identity of the fish in the animation changed halfway through to a different, size- and sex- matched individual. If the fish do pay attention to the stimulus fish and can distinguish individuals via their morphology, then I expect fish to perform behaviors and spend time near the stimulus when it is first presented and will decrease behaviors over time (Groves and

Thompson 1970; Borszcz et al. 1989). When a second stimulus is presented the fish should show activity similar to when the first stimulus was presented and decrease in activity over time (Groves and Thompson 1970; Borszcz et al. 1989).

Methods

Species

N. pulcher is a cooperatively breeding cichlid that lives in permanent-territorial groups that are composed of a dominant pair and 0-20 subordinate helpers and is endemic to Lake Tanganyika (Wong and Balshine 2011). Groups are characterized by sex- and size-based social hierarchies (Fischer et al. 2015), in which larger fish are socially dominant to smaller fish of the same sex. Individual recognition via visual signals and kin recognition via visual and chemical signals have been observed in N. pulcher

(Balshine-Earn and Lotem 1998, Frostman and Sherman 2004, Hert 1985, Kohda et al.

2015, le Vin et al. 2010).

N. pulcher for this experiment were obtained from an aquarium fish wholesaler

(Old World Exotic Fish, Homestead, FL, USA) and all individuals were F1 offspring captured at Kipili, Tanzania. Groups consisted of 6 individuals: dominant male (DM), dominant female (DF), large subordinate male (LSM), large subordinate female (LSF), and two small subordinates that were too small to sex. Groups were created at least 28 days prior to the beginning of each experiment to allow time for individuals to become familiar with their group mates.

Housing conditions

Groups were housed in 114 L aquariums with a sand substrate of an average depth of 40 mm. Each tank included two halves of a clay flower pot to serve as shelters and breeding substrate. Tanks were equipped with two PVC tubes (5 cm long) suspended near

12 the surface of the water on each side of the tank to serve as refuges for any fish receiving aggression. All tanks were maintained on a 12:12 hr light: dark cycle and under conditions reflecting those in Lake Tanganyika (temperature = 23-28 °C, pH = 7.8-8.4).

Every two weeks, levels of pH, ammonia, and nitrite were checked in each home tank.

During this time, tanks were also cleaned of algae on the glass and a 25% water change was performed.

Experiment 1: Recognition of live, familiar individuals

To test whether social status of observed fish influences familiarity, we created six groups, using the methods described above. The focal fish used to test individual recognition were the mid-ranking male and female (LSM and LSF), as we could test if higher and lower status individuals could be recognized by an individual. The experimental tanks used were three 114 L aquariums placed side to side (Figure 1). Three aquaria were used to ensure that any results were due to visual signals of individual recognition and not due to scent. The center tank, where the focal fish would be placed, was divided into left, center, and right sections, where the left and right sections were adjacent to the tanks where the stimuli fish were to be placed. For the tanks for the stimuli fish, opaque barriers were placed 15 cm away from the glass facing the focal tank to create a chamber that restricted the stimuli fish from swimming far away from the focal fish’s tank during the trial. Prior to the start of the trial, there were opaque barriers between the aquaria. Prior to and during trials, water conditions were kept to similar conditions to the home aquaria.

For each trial, the focal fish was placed in the center tank, and stimulus fish were placed in the side tanks. One stimulus fish was a familiar group mate to the focal fish, and the other stimulus fish was sex and size matched (within 5% SL) to the familiar fish, but that the focal fish had never seen since the beginning of the experiment. Fish were given 5 minutes to adjust to the experimental apparatus. During this time, we looked for signs of stress, such as resting at the bottom of the tank and increased respiration rate.

After 5 minutes, the opaque barriers between the tanks were removed and the focal fish was allowed to swim freely for 20 minutes. During the 20-minute trial period, we measured the time the focal fish spent within 15 cm of the left and right sides of the tank, time focal fish spent tracking one of the stimulus fish (swimming along the glass adjacent to the stimulus), and the count of all behaviors towards each stimulus fish (Sopinka et al.

2009). Fish were then returned to their home aquarium and monitored for aggression and stress.

Each focal fish had three types of stimuli: a familiar DM and a size and sex matched stranger, a familiar DF and a size and sex matched stranger, and a SS and size and sex matched stranger. Since we had a small sample size for each sex, each focal fish could be exposed to up to 2 rounds of the above stimuli combinations, using different unfamiliar fish. Also, for each combination of familiar and unfamiliar pair, focal fish were tested with this combination twice, with each test having the focal fish on the right or left side to reduce side bias.

Experiment 2: Recognition of animated, familiar individuals

In experiment 2, we repeated experiment 1 with the following changes: Since we had a larger sample size for this experiment, each fish went through a maximum of six trials of the treatment combinations above. In experiment 1, we found that activity declined after 10 minutes, so for experiment 2 we reduced the trial time from 20 minutes to 10 minutes. Focal fish were also kept in a transparent tube at the center of the experimental arena until the start of the trial to ensure a neutral starting area. Lastly, we used animations in place of live fish as stimuli. All other conditions were similar to experiment 1.

Animation creation

The methods to create the animations are similar to Balzarini et al. (2017). To create the animations, we photographed all of the fish with a Canon EOS Rebel T3i using a 18-55 mm lens. A white balance card was in every photo to ensure that color was consistent in every animation, and photographs were taken in RAW format. To photograph fish, we removed each fish out of their tank and transferred them to a bucket with aquarium water until we were ready to photograph them. When taking the fish’s photographs, they were placed on their side on a plastic white background. Three to four photographs were taken and then the fish was turned over onto its other side for another

3-4 photographs. The fish was then placed back in the bucket. The fish was out of the water for photographing for less than 60 seconds to minimize stress and any color change that would occur due to the stress. To process the photographs for the animation, photographs were imported into Photoshop (Adobe, Creative Cloud). The white

15 background as well as any shadows from the fish were removed so that the final image was only the fish. The new images were then saved as JPEG.

The new images were then used to create an animation in PowerPoint. The animations were created to be played on an ASUS ZenPad 3S 10, which has a 10” screen at a resolution of 2048 x 1536 pixels. Similar to Balzarini and colleagues (2017), animations were on a light green background, and familiar fish were scaled so that they would appear on the screen as the size they were in real life. Stranger fish were sized to match the familiar fish. For the 10-minute animation, the fish image crossed the screen in a straight line until it disappeared on the other side. This animation took 30 seconds to complete. After this first animation, the image was mirrored so that it was facing the opposite direction and crossed the screen from the other direction. These animations were repeated for the entire 10-minute duration of the animation.

Experiment 3: Habituation

Trial

Similar to the individual recognition trial, the focal fish was placed in a transparent tube in the center of the 114 L aquarium. Fish were given 5 minutes to adjust from being handled prior to the beginning of the trial. The tablet was placed on one side of the tank.

At the beginning of the trial, the tablet animation was played and the fish was removed from the tube and allowed to swim freely. Time spent within 15 cm of the side of the tank with the stimulus, counts of behaviors towards the stimulus, and time spent tracking the stimulus were measured. The 20-minute trail time was partitioned into 2-minute bins to measure how behavior towards the stimulus changed over time.

Animation creation

Animations used in this experiment were similar to familiarity animation, except that animations were 20 minutes and, in one half of the animations, the identity of the fish changed partway through. For the control, the same familiar fish was present for all 20 minutes. For treatment animations, the familiar fish was used for the first 10-minutes of the animation. After 10 minutes, the familiar fish was replaced with an image of a size and sex matched fish.

Animal welfare

The Ohio State University Institutional Animal Care and Use Committee (Protocol

2008A0095).

Statistical Analysis

Experiments 1 and 2 – two-choice test (live and animated)

All statistical analyses were done on IBM SPSS, version 24. For both the live stimulus and video stimulus experiments, we used a series of generalized estimating equations (GEE). For all models, we started with a full model and removed interactions if the reduced candidate model had a QICc value >2 compared to the model with the interaction retained. Interactions that directly addressed our hypothesis (for example, the interaction between sex and treatment) were always retained.

The full model for the live IR data had focal fish and unfamiliar fish ID as subjects (for a given focal fish, the familiar fish ID for a particular status would always be the same), with social status of stimuli, side familiar stimuli presented, trial ID, and familiar vs unfamiliar sides of tank (hereafter, familiarity) as nested within-subject variables. Focal fish sex, status of stimuli fish, side familiar stimuli presented, familiarity, and all possible interactions were factors. For the full model with the tablet IR data is similar to the live data, but without unfamiliar fish ID as a subject.

For experiments 1 and 2, there were 5 dependent variables of interest: time spent following the stimuli (with and without the covariate of time spent near stimulus), time spent near stimulus, and total behaviors towards stimulus (with and without the covariate of time spent near stimulus). Models using time spent near a stimulus were used because time tracking and total counts of behaviors could be influenced by the time available to perform these behaviors. Models with time as the dependent variable fit a gamma distribution and a gamma distribution with a log link was used in the GEE. To use the gamma distribution, we added one to all of the values of time following and time spent near stimulus to remove zeros (which cannot be in a gamma distribution). For total behaviors towards stimulus, we used a negative binomial distribution with a log link.

Experiment 3

To look at the effect of sex and treatment (whether or not a focal fish was exposed to a stranger fish) on time spent near the stimulus and time spent following the stimulus, we performed two-way repeated measures ANOVA. To look at the response to the second stimulus introduced at time period 6, we ran a series of Wilcoxon sign rank tests

18 before and after the new stimulus was introduced periods 5 and 6 to see if focal fish sex or treatment influenced time near stimulus and/or time tracking stimulus.

Results

Experiments 1 and 2

Experiment 1 involved 157 trials by 13 focal fish (7 female, 6 male). In three trials in experiment 1, the fish started on one end of the aquarium and never moved.

These trials were removed from all analyses. The best fitting models for Experiment 1 are reported in Table 1. Experiment 2 involved 149 trails by 24 individuals (12 female, 12 male). The best fitting models are reported in Table 2.

Effects of status on familiarity

To test if status influenced individual recognition, we tested for the effect of the interaction between social status of the stimuli and familiarity. For both the live stimuli as well as the animated stimuli, there were significant effects of the interaction between status of stimuli and familiarity.

In experiment 1, there was a significant effect of this interaction for time tracking

(Wald χ2=7.137, df=2, p=.028) and total count of behavior (Wald χ2=11.090, df=2, p=.004) towards stimulus (Figure 2). Focal fish spent more time tracking a familiar DF stimulus compared to an unfamiliar DF stimulus (pairwise contrast: p=.019) but did not differ in time spent tracking familiar vs. unfamiliar individuals for other statuses. When presented with DF stimuli, focal fish performed more behaviors towards the familiar stimulus compared to the unfamiliar stimulus (pairwise contrast, p=.033). Focal fish also

19 performed more behaviors towards unfamiliar SS stimuli compared to familiar stimuli

(pairwise contrast, p=.018). In a model controlling for time spent near a familiar or unfamiliar individual by incorporating time on the side as covariate, we again found an interaction between status and familiarity on counts of behavior (Wald χ2=8.911, df=2, p=.012), with focal fish performing more behaviors toward familiar SS stimuli, (pairwise contrast p=.019). In a model with time on side as a covariate, we did not find a significant effect of the interaction between status and familiarity on tracking (Wald

χ2=2.506, df=2, p=.286).

In experiment 1, there were a significant effect of the three-way interaction among focal fish sex, status, and familiarity on time spent on a side (Figure 4a, Wald

χ2=6.955, df=2, p=.031) and on time tracking (Figure 4b, Wald χ2=6.348, df=2, p=.042).

Males spent more time near familiar DF stimuli compared to unfamiliar DF stimuli

(pairwise contrast, p=.007) and spent more time tracking familiar DF compared to unfamiliar DF stimuli (pairwise contrast, p=.027).

In experiment 2, there was a significant effect of the interaction between status of stimuli and familiarity on time spent tracking stimuli when controlling for time spent near stimuli (Figure 3, Wald χ2=8.650, df=2, p=.013). When the stimuli were DM, focal fish spent more time following the familiar DM compared to the unfamiliar DM (pairwise contrast, p=.047). We also found that focal fish spent more time tracking unfamiliar SS compared to familiar SS (pairwise contrast, p=.021).

Other effects

In experiment 1, we found a significant effect of sex on total count of behaviors, with females performing more behaviors towards all stimuli compared to males (Wald

χ2=10.441, df=1, p=.001). We found similar results with behavior when controlling for time spent near a stimulus (Wald χ2=9.904 df=1, p=.002).

In experiment 2, we found a significant effect of status of stimuli on time tracking the stimuli. Fish spent more time tracking when the stimuli were SS compared to DM

(Wald χ2=6.538, df=2, p=.038, pairwise contrast, p=.007).

For both experiments 1 and 2, we found that there was a side bias within the experimental arena. For experiment 2, we found that focal fish spent more time following on the right side of the tank (Wald χ2=8.445, df=1, p=.004) and performed more behaviors towards the left stimulus (Wald χ2=8.187, df=1, p=.004). Behaviors towards a stimulus also showed a left side bias (Wald χ2=7.394, df=1, p=.007). This side bias was present regardless of the familiarity of the stimulus.

For experiment 1, we found that side bias interacted with familiarity for tracking

(Wald χ2=5.922, df=1, p=.015) and tracking controlling for time spent on a side (Wald

χ2=18.023, df=1, p<.001). Specifically, we found that fish follow the familiar individual more when the familiar stimulus was on the right side of the tank compared to the left

(tracking: pairwise contrast, p=.019). Observers spent more time following on the left side when the stimulus was familiar and spent more time following on the right side when the stimulus was unfamiliar. We also found a significant effect of the three-way

21 interaction between focal fish sex, familiarity, and side on behaviors towards a stimulus controlling for time spent on a side (Wald χ2=6.521, df=1, p<.011)

Experiment 3

There was not a significant effect of treatment on time spent near stimulus (Figure

5, F1, 30= .022, p=.882) or tracking stimulus (F1, 30= .021, p=.885). However, there was a significant effect of sex on time spent near stimulus (F1, 30= 8.426, p=.007) and tracking stimulus (F1, 30= 9.796, p=.004).

To see the effect of the introduction of a novel stimulus between time periods 5 and 6, we performed Wilcoxon Sign Rank tests, looking at sex of focal fish and the change in the dependent variable between time periods 5 and 6. We did not find significant effects of treatment on either females (Control: Z=13, N=8, p=.866,

Treatment: Z=7, N=9, p=.208) or males (Control: Z=21, N=9, p=.859, Treatment: Z=36,

N=9, p=.214) for time spent near stimulus. We did not find significant effects of treatment on either female (Control: Z=14, N=8, p=1, Treatment: Z=7, N=8, p=.123) or males (Control: Z=14, N=9, p=1, Treatment: Z=36, N=9, p=.110) for time spent tracking stimulus.

Discussion

For this study, we hypothesized that if attention is limited, then focal fish should prioritize recognizing high status individuals that could potentially provide a fitness benefit. We expected that if a focal fish can discriminate between a familiar group mate and an unfamiliar stranger, then the focal fish should differ in their behavior or time spent

22 towards each stimulus. We did find support that mid-ranking individuals can recognize familiar individuals using visual cues. This supports other work done in N. pulcher

(Jordan et al. 2010) and paper wasps (Injaian and Tibbetts 2014; Tibbetts et al. 2018).

Injainan and Tibbetts (2014) propose two potential reasons why non-dominant individuals would have this cognitive ability. First, familiarity in non-dominant individuals could be a byproduct of selection for this ability in dominants. Second, there may be benefits for non-dominant or mid-ranking individuals to be capable of recognizing familiar individuals. Both of these hypotheses may fit what we have found in this study.

Further, considering the results of experiment 1 and 2 together, we found that large subordinates use visual cues to distinguish between familiar group mates and unfamiliar strangers for individuals ranked both above and below them. This suggests that at least for individuals in the middle of the dominance hierarchy, attention towards other group mates may not be may not be so limited as to prevent recognition of multiple group members and individuals can recognize multiple group members, regardless of their status. In fact, there may be selection for the ability to recognize multiple individuals, despite the cost. Since dominance position is not permanent and individuals are likely to ascend in the social hierarchy, N. pulcher may benefit from recognizing multiple individuals regardless of their social rank, as the fitness effect of those other individuals might change over time.

As with many cognitive abilities, familiarity is a cognitively costly trait (Sheehan and Bergman 2015), and Dunbar’s number predicts that there is a cap in the number of

23 individuals one can know (Dunbar 1992). However, the benefit of recognizing multiple individuals may be beneficial despite the cost. Zeus and colleagues (2018) tested this prediction in the group living bat, Myotis nattereri, and found surprisingly, that individuals could maintain long term, stable relationships with almost 80 roost mates.

Our experiment showed that mid-ranking individuals can recognize multiple members, and while groups of 6 are large in the lab, N. pulcher can live in groups with up to 20 group members (Wong and Balshine 2011). Future work should look at the limit of individuals a focal fish can recognize at a time.

Tibbetts (2004) suggests that to have a signal for familiarity recognition, there must be variation in the signal to discriminate at the level of the individual. Our study, as well as other work on recognition suggest that in N. pulcher the signal likely utilizes a visual modality (Kohda et al. 2015). Further, since focal individuals could discriminate between familiar and unfamiliar individuals at multiple social ranks, a visual signal is likely present for all sexually mature individuals.

We found similar results between experiments 1 and 2 even though the stimuli differed in several ways. By using the animation in experiment 2, we removed the influences of behavior from the stimulus fish, as well as potential ultraviolet (UV) signals, both of which could have potentially influenced familiarity in experiment 1. In both experiments, fish interacted more with unfamiliar small subordinates than familiar small subordinates (counts of all behaviors in experiment 1, time tracking in experiment

2). However, we also found differences between experiments 1 and 2. In experiment 1, using live stimuli, we found that familiarity influenced the time focal fish spent tracking

24 dominant females, whereas in experiment 2, using animated stimuli, we found that familiarity influenced time tracking dominant males. Previous work on UV signaling in

N. pulcher showed contradictory results to experiment 2; responses to familiar dominant males differed when the focal individual could not see a UV signal compared to when they could (Sabol et al. 2017). These differences in results between experiments 1 and 2, and between experiment 2 and Sabol and colleagues (2017) could potentially be due to small sample sizes. They could also be due to differences in the signal of individuality between individuals of different statuses. Signals of individual recognition are often multimodal (Tibbetts and Dale 2007), so even if focal individuals use morphology, such as facial markings, the lack of behavior may not be enough of a signal for a focal individual to discriminate between familiar and unfamiliar individuals. A UV signal in addition to other visual signals may be necessary to discriminate between familiar and unfamiliar dominant individuals.

The results of our habituation study (Experiment 3) did not show a typical habituation pattern where behavior decreases over time until a novel stimulus is shown, nor did it show a spike in activity when an unfamiliar stimulus appeared on the screen as we predicted. We have three potential reasons for these results. First, we may not have seen an increase in behavior when an unfamiliar stimulus was presented because focal fish may not have habituated within the 10 minutes prior to the introduction of the unfamiliar stimulus. As a focal individual learns that a stimulus is not harmful or relevant, it is expected that less attention is paid to that stimulus. When a new stimulus is presented and the focal individual has yet to learn whether the new stimulus is harmful or

25 relevant and they should not ignore this novel stimulus (Groves and Thompson 1970).

Because we did not find an initial decrease in activity toward the first stimulus, we may not have observed a change in behavior even if the fish did notice the new stimulus.

Second, we used dominant males as our stimuli for this experiment and the focal fish may not habituate to that stimulus because it is socially relevant. Dominant males can be aggressive towards subordinates (Hellmann and Hamilton 2018), and subordinates may benefit from paying attention to dominant males. We also found that female focal fish spent more time near the stimulus and tracked the stimulus compared to males. This could be because dominant males are potential mates for females.

Third, we may not have observed a difference in behavior between the familiar and unfamiliar stimulus because a signal for individual recognition is missing when we present fish using a screen. While this reason potentially explains our findings for

Experiment 3, we did find that fish discriminated between animations of familiar and unfamiliar dominant males in Experiment 2. Future work on habituation in N. pulcher should test the habituation response with non-social stimuli to further understand how individuals respond to animations.

In summary, we found that mid-ranking N. pulcher can discriminate between unfamiliar and familiar individuals both above and below themselves in the dominance hierarchy. This not only suggests that mid-ranking subordinates do not have as limited attention for recognizing individuals as previously thought, but that they may benefit from being able to distinguish among different individuals across dominance ranks. Mid- ranking large subordinates may benefit from recognizing more dominant individuals

26 because dominant individuals can either be potential mates or be a source of aggression to a large subordinate. Similar to dominants influencing behavior on large subordinates, lower ranking small subordinates’ behavior in turn can be modified or enforced by large subordinates if those individuals can recognize the small subordinates and remember previous interactions with them. Based on the results of Experiment 2, we find that behavior and UV signals may not be necessary signals for recognition in N. pulcher.

This paper focuses on the ability of a recipient of a signal of individuality; however, familiarity recognition is a signaling system in which there must be both benefits of distinguishing among individuals as well as sufficient phenotypic variation among individuals as a basis for recognition (Tibbetts and Dale 2007). Our results suggest that the former of these conditions is met at least for mid-ranking fish and the latter of these conditions is met across ranks. Future work should look at the signals of individual recognition. Kohda and colleagues (2015) narrowed down the signal for recognition to the facial region; but future work should investigate if there is sufficient variation and if UV is involved and if familiarity recognition uses other modalities to create a multimodal signal. Future work should explore the interaction between status signals and recognition signals. Lastly, work should be done to see if there are selective benefits of providing identity signals.

a Time following with time spent on side as covariate p- Source Wald Chi-Square df value (Intercept) 1226.157 1 0.000 Time near stimulus 809.311 1 0.000 Focal fish sex 0.032 1 0.857 Side familiar stimulus 3.199 1 0.074 Status of stimulus 2.252 2 0.324 Familiarity 2.457 1 0.117 Focal fish sex * Side familiar stimulus 0.023 1 0.880 Focal fish sex * Status of stimulus 0.661 2 0.718 Focal fish sex * Familiarity 2.056 1 0.152 Side familiar stimulus * Status of stimulus 0.386 2 0.825 Side familiar stimulus * Familiarity 18.023 1 0.000 Status of stimulus * Familiarity 2.506 2 0.286 Focal fish sex * Side familiar stimulus * Familiarity 0.198 1 0.656 Focal fish sex * Status of stimulus * Familiarity 3.383 2 0.184

Time following p- Wald Chi-Square df b Source value (Intercept) 8432.659 1 0.000 Focal fish sex 0.015 1 0.904 Side familiar stimulus 0.395 1 0.530 Status of stimulus 4.765 2 0.092 Familiarity 0.111 1 0.739 Focal fish sex * Side familiar stimulus 2.299 1 0.129 Focal fish sex * Status of stimulus 0.000 2 1.000 Focal fish sex * Familiarity 0.649 1 0.421 Side familiar stimulus * Status of stimulus 2.305 2 0.316 Side familiar stimulus * Familiarity 5.922 1 0.015 Status of stimulus * Familiarity 7.137 2 0.028 Focal fish sex * Status of stimulus * Familiarity 6.348 2 0.042 Side familiar stimulus * Status of stimulus * Familiarity 4.218 2 0.121

Continued

Table 1 Continued

c Time spent near stimulus p- Source Wald Chi-Square df value (Intercept) 263434.909 1 0.000 Focal fish sex 0.282 1 0.595 Side familiar stimulus 0.309 1 0.578 Status of stimulus 2.967 2 0.227 Familiarity 0.032 1 0.859 Focal fish sex * Side familiar stimulus 0.044 1 0.835 Focal fish sex * Status of stimulus 1.829 2 0.401 Focal fish sex * Familiarity 0.233 1 0.629 Side familiar stimulus * Status of stimulus 2.545 2 0.280 Side familiar stimulus * Familiarity 1.662 1 0.197 Status of stimulus * Familiarity 3.616 2 0.164 Focal fish sex * Side familiar stimulus * Familiarity 0.454 1 0.500 Focal fish sex * Status of stimulus * Familiarity 6.955 2 0.031

d Total behaviors towards stimulus with time spent following as a covariate p- Source Wald Chi-Square df value (Intercept) 88.509 1 0.000 Time near stimulus 51.280 1 0.000 Focal fish sex 9.904 1 0.002 Side familiar stimulus 0.687 1 0.407 Status of stimulus 1.888 2 0.389 Familiarity 0.029 1 0.865 Focal fish sex * Side familiar stimulus 0.138 1 0.710 Focal fish sex * Status of stimulus 3.730 2 0.155 Focal fish sex * Familiarity 1.199 1 0.273 Side familiar stimulus * Status of stimulus 0.248 2 0.883 Side familiar stimulus * Familiarity 0.796 1 0.372 Status of stimulus * Familiarity 8.911 2 0.012 Focal fish sex * Status of stimulus * Familiarity 0.566 2 0.753 Focal fish sex * Side familiar stimulus * Familiarity 6.521 1 0.011

Continued 30

Table 1 continued e Total behavior towards stimulus p- Source Wald Chi-Square df value (Intercept) 549.227 1 0.000 Focal fish sex 10.441 1 0.001 Side familiar stimulus 1.109 1 0.292 Status of stimulus 1.896 2 0.388 Familiarity 0.000 1 0.999 Focal fish sex * Side familiar stimulus 0.163 1 0.687 Focal fish sex * Status of stimulus 3.314 2 0.191 Focal fish sex * Familiarity 0.878 1 0.349 Side familiar stimulus * Status of stimulus 0.025 2 0.987 Side familiar stimulus * Familiarity 0.051 1 0.821 Status of stimulus * Familiarity 11.090 2 0.004 Focal fish sex * Status of stimulus * Familiarity 0.862 2 0.650 Focal fish sex * Side familiar stimulus * Familiarity 2.914 1 0.000

Table 2: Best fitting GEE for animation individual recognition experiment. The following dependent variables are reported: a) Time tracking with time spent on side as covariate, b) Time tracking, c) Time spent near stimulus, d) Total behaviors towards stimulus with time spent near stimulus as a covariate, e) Total behavior towards stimulus. Significant p- values are in bold.

a Time following with time spent on side as covariate p- Source Wald Chi-Square df value (Intercept) 382.110 1 0.000 Time near stimulus 2403.829 1 0.000 Focal fish sex 0.267 1 0.605 Side Familiar on 1.689 1 0.194 Treatment 3.619 2 0.164 Familiarity 1.003 1 0.317 Focal fish sex * Side Familiar on 0.146 1 0.703 Focal fish sex * Treatment 2.100 2 0.350 Focal fish sex * Familiarity 0.957 1 0.328 Side Familiar on * Treatment 0.828 2 0.661 Side Familiar on * Familiarity 1.030 1 0.310 Treatment * Familiarity 8.650 2 0.013 Focal fish sex * Side Familiar on * Treatment 5.310 2 0.070 Focal fish sex * Side Familiar on * Familiarity 1.766 1 0.184 Focal fish sex * Treatment * Familiarity 1.009 2 0.604

b Time Following p- Source Wald Chi-Square df value (Intercept) 1891.339 1 0.000 Focal fish sex 0.363 1 0.547 Side Familiar on 8.445 1 0.004 Treatment 6.538 2 0.038 Familiarity 0.022 1 0.883 Focal fish sex * Side Familiar on 0.388 1 0.533 Focal fish sex * Treatment 0.863 2 0.650 Focal fish sex * Familiarity 0.172 1 0.678 Side Familiar on * Treatment 0.800 2 0.670 Side Familiar on * Familiarity 3.758 1 0.053 Treatment * Familiarity 1.024 2 0.599 Focal fish sex * Side Familiar on * Treatment 4.454 2 0.108 Focal fish sex * Side Familiar on * Familiarity 1.003 1 0.317 Side Familiar on * Treatment * Familiarity 1.916 2 0.384

Continued

Table 2 continued c Time spent near stimulus

p- Source Wald Chi-Square df value (Intercept) 35941.141 1 0.000 Focal fish sex 0.518 1 0.472 Side Familiar on 3.187 1 0.074 Treatment 5.526 2 0.063 Familiarity 0.010 1 0.921 Focal fish sex * Side Familiar on 1.516 1 0.218 Focal fish sex * Treatment 2.843 2 0.241 Focal fish sex * Familiarity 0.160 1 0.689 Side Familiar on * Treatment 3.036 2 0.219 Side Familiar on * Familiarity 0.451 1 0.502 Treatment * Familiarity 0.978 2 0.613 Focal fish sex * Side Familiar on * Familiarity 0.018 1 0.894 Focal fish sex * Treatment * Familiarity 0.908 2 0.635

d Total behaviors towards stimulus with time spent near stimulus as a covariate p- Source Wald Chi-Square df value (Intercept) 28.782 1 0.000 Focal fish sex 1.064 1 0.302 Side Familiar on 7.394 1 0.007 Treatment 0.947 2 0.623 Familiarity 1.430 1 0.232 Focal fish sex * Side Familiar on 0.191 1 0.662 Focal fish sex * Treatment 0.981 2 0.612 Focal fish sex * Familiarity 1.007 1 0.316 Side Familiar on * Treatment 0.664 2 0.718 Side Familiar on * Familiarity 0.074 1 0.785 Treatment * Familiarity 2.173 2 0.337 Time near stimulus 110.610 1 0.000 Focal fish sex * Treatment * Familiarity 3.004 2 0.223 Focal fish sex * Side Familiar on * Familiarity 0.486 1 0.486

Continued 33

Table 2 continued

e Total behavior towards stimulus p- Source Wald Chi-Square df value (Intercept) 0.103 1 0.748 Focal fish sex 1.663 1 0.197 Side Familiar on 8.187 1 0.004 Treatment 3.833 2 0.147 Familiarity 1.327 1 0.249 Focal fish sex * Side Familiar on 0.056 1 0.812 Focal fish sex * Treatment 2.641 2 0.267 Focal fish sex * Familiarity 0.322 1 0.570 Side Familiar on * Treatment 0.802 2 0.670 Side Familiar on * Familiarity 0.024 1 0.878 Treatment * Familiarity 3.979 2 0.137 Focal fish sex * Side Familiar on * Familiarity 0.116 1 0.733 Focal fish sex * Treatment * Familiarity 2.963 2 0.227

C) Figure 2: Response to familiar/unfamiliar stimuli based on social status of live stimuli (Experiment 1). A) Time spent tracking stimuli. Focal fish spent more time tracking familiar Dominant Female stimuli compared to unfamiliar Dominant Female stimuli. B) Behaviors toward stimuli. Focal fish performed more behaviors towards unfamiliar Small Subordinates compared to familiar Small Subordinates. C) Behaviors toward stimuli with time spent on side as a covariate. Focal fish performed more behaviors towards familiar Small Subordinates compared to unfamiliar Small Subordinates. Means are estimated means from the best-fitting GEE (Table 1). Error bars are 95% confidence intervals. 35

Figure 3: Time spent tracking familiar/unfamiliar animated stimuli based on status of stimulus fish (Experiment 2), with time spent on side as a covariate. Focal fish spent more time tracking familiar Dominant Male stimuli compared to unfamiliar Dominant Male stimuli. Focal fish also spent more time tracking unfamiliar Small Subordinates compared to familiar Small Subordinates. Means are estimated means from the best- fitting GEE (Table 2). Error bars are 95% confidence intervals.

Figure 4: Following of familiar/unfamiliar stimuli based on social status of live stimuli, by sex (Experiment 1). A) Male focal fish spend more time near familiar Dominant Females compared to unfamiliar Dominant Females. B) Male focal fish spend more time tracking familiar Dominant Females compared to unfamiliar Dominant Females. Means are estimated means from the best-fitting GEE (Table 1). Error bars are 95% confidence intervals.

Figure 5: Focal individuals time spent near stimulus based on treatment for Experiment 3. The control was a familiar stimulus for the entire presentation period where the treatment was a presentation of a familiar stimulus for time periods 1-5 and an unfamiliar stimulus for time periods 6-10. Focal individuals, regardless of sex, did not change the time they spent near the stimulus when a novel stimulus was introduced at time period 6. Focal females overall spent more time near the stimulus compared to focal males

Chapter 3: Size and sex influence the use of social information in a social cichlid fish

Abstract:

Group living poses a challenge for social learners: individuals are faced with multiple, possible sources of information, each with potentially different information. Learning from sources with inaccurate or irrelevant information may be costly. In species with social hierarchies, status may influence the value of social information. Previous work on birds and mammals shows that individuals use social information from more dominant individuals and subordinate individuals use social information more compared to dominant observers. Observers may use information from high status individuals because these demonstrators have higher quality information or because observers pay more attention to higher ranked individuals over others. Alternatively, observers may use information from individuals similar in status to themselves, as these individuals might display similar preferences and experience similar risks. We tested these hypotheses in the social cichlid, Neolamprologus pulcher, which form size-based hierarchies. We trained observers to associate a location with food and then presented a clear tube in another location containing a demonstrator feeding, a demonstrator not feeding, or no demonstrator. We measured the time observers spent at the demonstrators’ previous location, after the demonstrators were removed. We found effects of sex and relative size

39 on foraging location; fish that observed female demonstrators larger than themselves spent more time at the location of a feeding demonstrator than a non-feeding demonstrator or empty tube. Our results show that the relative relationship between a demonstrator and observer, as well as the sex of the demonstrator, can influence information transfer. To our knowledge, this is the first example to show social status influencing transmission of social information in fish, suggesting that a wide variety of taxa consider social status when using social information.

Introduction:

In changing environments, it is important for individuals to be able to update information about the location of resources, such as food, safety, and mates, as an animal’s current information can quickly become outdated (Boyd and Richerson 1985;

Boyd and Richerson 1988). Individuals can face a trade-off between obtaining accurate information through costly personal learning and reducing costs by acquiring less reliable information socially (Boyd and Richerson 1985). When an observer is presented with information that conflicts with their own, utilizing the social information often provides a higher likelihood of making a correct decision than ignoring it (Bikhchandani et al.

1992). When social learners are frequent in a population, social information can be unreliable as individuals are no longer sampling the environment. Under these circumstances, observers may benefit from obtaining or using social information selectively from reliable or higher quality sources (Coussi-Korbel and Fragaszy 1995;

Laland 2004). Further, the quality of social information from others is not equal, and

40 therefore observers could gain increased benefits from using social information from particular individuals (Kendal et al. 2015). Traits of demonstrators, such as their age

(Taborsky et al. 2012), sex (Nicol and Pope 1999), social status (Nicol and Pope 1994), or familiarity (Lachlan et al. 1998) can influence the quality of the information for the observer. In addition, observers likely allocate attention differentially among demonstrators, resulting in unequal use of social information from demonstrators. These two, non-mutually exclusive explanations could influence the transmission of information between individuals.

Information gained from older individuals may be more valuable compared to information from younger individuals because these individuals have acquired patterns of behaviour that have allowed them to avoid predation, find food, and reproduce successfully (Galef and Laland 2005). The presence of and interactions with older conspecifics in early life may induce a young learner to continually modify their social behaviour (Arnold and Taborsky 2010; Taborsky et al. 2012). For example, Taborsky and colleagues (2012) found that juveniles of the cichlid fish Neolamprologus pulcher that were raised in an environment with older conspecifics displayed appropriate social behaviour in various situations and that this social competency lead to fitness benefits, such as toleration by the dominant breeding pair. Further, the age of the observer may influence the propensity to use social information. Older individuals may avoid using information from younger individuals because of their inexperience (Brown and Laland

2003; Chalmeau and Gallo 1993), while younger individuals may be more likely to use social information (Thorton and Malapert 2009; Allen et al. 2013; and Aplin et al. 2015).

Demonstrators and observers of the same sex may experience similar costs and benefits from the demonstrated resource, so the value of information from these sources is high. For example, Nicol and Pope (1998) found that domestic hens, Gallus gallus domesticus, performed more key pecks in a foraging task after observing hens than they did after observing cockerels, most likely because hens have the same nutritional needs.

Additionally, familiarity of the demonstrator may impact an observer’s decision to partake in social learning. In the wild, guppies often forage in small, organized shoals, and the act of shoaling often transmits information, such as the location of food. There is evidence that familiar fish form more cohesive shoals faster (Chivers et al. 1995) and that this familiarity may augment social learning opportunities (Lachlan et al. 1998).

Alternatively, the acquisition and use of social information from demonstrators may vary because of differential attention paid by the observer, rather than only because of differences in the quality of the information. If an individual spends more time or devotes greater cognitive resources to some individuals over others, then opportunities for information transmission will differ among demonstrators. For example, female zebra finches prefer learning food preferences from male demonstrators (Benskin et al. 2002;

Katz and Lachlan 2003). Katz and Lachlan (2003) suggest that females pay more attention to males, in general, and male feeding, specifically, to assess the qualities of potential mates. Attentional bias towards particular individuals may not be at the cost of utilizing demonstrators with quality information for an observer, as an observer may pay more attention to high quality demonstrators than low-quality ones.

In dominance structured societies, relative dominance status of observers and demonstrators may influence acquisition and use of information through both of these mechanisms. Observers may use information differentially with regards to demonstrator status due to differences in value, and observers may have different opportunities to acquire information depending on demonstrator status. There are at least two possible patterns of social information use with regards to dominance status. First, individuals may be more likely to gain or use information from more dominant demonstrators.

Higher-ranking individuals are often older or larger (Wong and Balshine 2011) and have been successful at surviving and obtaining resources (Nicol and Pope 1994; Broom et al.

2009). Social hierarchies are often established and maintained through aggression or threat of aggression (Dierkes et al. 1999; Drea and Wallen 1999; Skubic et al. 2004), and subordinate individuals pay attention to dominant demonstrators in order to avoid aggressive behaviors. By observing dominant individuals, the subordinate may obtain new information as a byproduct (Nicol and Pope 1994).

Second, individuals may be more likely to gain or use information from demonstrators of similar social rank. Subordinate individuals are often more neophilic than their dominant conspecifics and are more willing to sample new environments to find new resources (Boogert et al. 2006; Seok An et al. 2011; Krueger et al. 2014).

Moreover, information given by a demonstrator of similar rank as the observer may be a more valuable source of information because it is more applicable to the observer

(Cambefort 1981). Finally, aggressive interactions are frequent among similarly ranked

43 individuals (Balshine-Earn et al. 1998; Field and Cant 2009), and so variation in attention may favor transmission of information among individuals of similar rank.

Past work has shown that dominance does play a role in social learning. Often, subordinates are more likely to use social information compared to dominants, and observers use social information from dominants (Nicol and Pope 1994; Krueger and

Heinze 2008; Kendal et al. 2015; Jones and Monfils 2016, though see Awazu and Fujita

2000 and Kar et al. 2017). Dominance status has been shown to influence social learning predominantly in mammals (Krueger et al. 2008; Pongrácz et al. 2008; Kendal et al.

2015; Awazu et al. 2000; Jones and Monfils 2016) and some birds (Nicol and Pope 1994;

1998). However, to our knowledge, this has not been investigated in fish. Some species of teleost fish, particularly cichlids, live in long-term groups with linear dominance hierarchies, cooperative breeding, and complex social interactions. Although similar to the cooperative breeding and other dominance-structured social systems of tetrapods, these are likely independently evolved (Heg and Bachar 2006). By looking at how fish use social information, we can see if patterns are similar across taxa.

We used dominance-structured groups of cooperatively breeding cichlid fish,

Neolamprologus pulcher, to explore how social status of observers and demonstrators influence transmission of information about the location of food. We trained N. pulcher individuals to associate food with a patch, and then provided them with conflicting social information about the location of food. We then allowed them to observer a demonstrator from a familiar, neighboring group on a different patch and measured subsequent changes in the observers’ patch use. Because size relative to that of other members of the same

44 sex is strongly related to social status in the species, we considered differences in size between observer and demonstrator to be a measure of relative status. We also directly tested the effects of social status by considering the status of the observer and demonstrator in their respective groups. We tested two predictions: 1) observers change patch use more when observing demonstrators more dominant than themselves, and 2) observers change patch use more when observing demonstrators similar in rank to themselves. In addition, we investigated the influence of a conspecific on social information use by presenting demonstrators alone compared to demonstrators feeding

(Coolen et al. 2003). That is, were observers paying attention to the behaviour of demonstrators or were they simply attracted to the presence of a conspecific alone.

Methods:

Study Species

N. pulcher is a cooperatively breeding cichlid endemic to Lake Tanganyika

(Wong and Balshine 2011). The diet of N. pulcher consists mainly of zooplankton (Hori

1983; Gashagaza and Nagoshi 1986). N. pulcher lives in groups that are composed of a dominant pair and 0-20 subordinate helpers of both sexes (Wong and Balshine 2011).

Groups are characterized by sex- and size-based social hierarchies (Fischer et al. 2015), in which larger fish are socially dominant to smaller fish of the same sex. Individual recognition and kin recognition via visual and chemical signals have been observed in N. pulcher (Balshine-Earn and Lotem 1997; Frostman and Sherman 2004; Hert 1985; Kohda et al. 2015; le Vin et al. 2010; le Vin et al. 2011). This suggests that individuals may be

45 able to identify the conspecifics they are watching, which may also influence the use of social information.

Housing Conditions

Groups were housed in 114 L aquariums with a sand substrate of an average depth of 40 mm (Figure 6a). Each tank included two halves of a clay flower pot to serve as shelters and breeding substrate. Tanks were equipped with two PVC tubes (5 cm long) suspended near the surface of the water on each side of the tank to serve as refuges for any fish receiving aggression. Tanks were also equipped with a slate (9.8 x 9.8 cm) that was placed in the sand on one side (always to the experimenter’s left when facing the front of the tank; in 10 tanks this was the east side of the tank and in 6 tanks, this was the west side of the tank) to serve as a landmark so fish could differentiate between the two sides of the tank. Opaque barriers were placed between every other tank, which allowed for each group to only have one visible neighboring group to interact with. All tanks were maintained on a 12:12 hr light: dark cycle and under conditions reflecting those in Lake

Tanganyika (temperature = 23-28 °C, pH = 7.8-8.4). Every two weeks, levels of pH, ammonia, nitrate, and nitrite were checked in each home tank. During this time, tanks were also cleaned of algae and a 25% water change was performed.

Group Formation

N. pulcher for this experiment were obtained from an aquarium fish wholesaler

(Old World Exotic Fish, Homestead, FL, USA) and all individuals were F1 offspring captured at Kipili, Tanzania. We formed 16 groups of four individuals composed of, a

46 dominant male (DM, SL=63-73 mm), a dominant female (DF, SL= 48-72 mm), a large subordinate male (LSM, SL=38-57 mm), and a small subordinate (SS, SL=27-40 mm), which were too small to sex. We waited 28 days before testing to allow groups and neighbors to establish familiarity.

Association Test

To provide conflicting social information, we first trained fish to associate one side of their tank (side with or without slate) with food for seven days before the start of the social information trials. The fish were fed a mix of frozen bloodworms, brine shrimp, and daphnia at least once daily on the assigned side throughout the entirety of the experiment. All fish in the tank were fed together so that individuals could learn about food location via their own exploration or through social learning. Thus, personal information in this experiment was not necessarily asocially acquired.

After the first seven days of feeding in one location, food association tests were performed to determine whether the fish had learned to associate one side of the tank with food prior to provision of any conflicting social information. All trials were conducted in an experimental tank that was identical to home tanks, in terms of the location of potential landmarks, except that, to reduce variation resulting in time fish spent hiding in or exploring potential breeding shelters, there were no clay pots for fish to use as shelters (Figure 6b). During a food association test, the focal fish was first placed in a transparent “observation” tube (10.16 cm in diameter) in the center of a testing tank and allowed 2 minutes for habituation. Afterwards, a small amount of frozen food

(bloodworms, brine shrimp, and daphnia, approximately 5 mL) was deposited on each side of the tank simultaneously and the focal fish was released from the tube and allowed to swim freely. The time the focal fish spent on each side of the tank was timed for 10 minutes. Changing sides was defined as when half of the fish’s body had moved to the other side of the tank. When the trial was over, the focal fish was placed back into its respective home tank and monitored for stress.

The individual had to spend at least six out of 10 minutes on the trained side during the association trial to move on to the social information trial, which occurred the next day. If the individual spent less than six minutes on the trained side, the individual was returned to their home tank and performed the association test four days later. If an individual failed nine consecutive food association tests (at least 30 days of training), it was removed from the experiment altogether. In the interim, all fish continued to be fed on the same side of the tank as before.

Social Information Trial

To test the effects of status on social information use, we performed a series of social information trials. During these trials, the focal fish from the association test

(hereafter, the “observer”) was first placed in a transparent observation tube in the center of the testing tank and allowed 2 minutes for habituation (Figure 6b). Next, a

“demonstration tube” that was sealed at the bottom (10.16 cm in diameter) was filled with aquarium water, and, for all treatments except for the control, a demonstrator was placed into the demonstration tube. All demonstrators, regardless of their status, were 48 from the observer fish’s neighboring tank, which could be seen from their home tank.

The demonstration tube was sealed at the bottom to prevent food or water flow between the demonstrator and the observer, thus food scents or chemical cues from the demonstrator could not influence an observer’s decision to spend time on the demonstrator’s side of the tank. The demonstration tube was then placed on the opposite side of the tank that the observer had learned to associate with food. For all treatments, the demonstration tube was presented to the focal fish for three minutes, removed and one minute was allowed to pass before the focal fish was released from the observation tube. Next, a small amount of food was delivered to each side of the tank and the observer was released and allowed to swim freely around the tank. The amount of time the observer fish spent on each side of the tank, as defined above, was timed for 10 minutes. When the trial was over, the observer and demonstrator fish were placed back into their respective home tanks.

Observers were presented with three types of stimuli: demonstrator feeding in the demonstration tube (“demonstrator feeding”), demonstrator present in the demonstration tube but not feeding (“demonstrator alone”), and an empty demonstration tube (“empty tube”) as a control. For treatments involving demonstrators, observers were exposed to demonstrators of each status within the observer’s neighboring group (DM, DF, LSM, and SS). With each combination of demonstrator status and treatment, observers could perform up to nine social information trials. The order of stimulus trials was randomized for each observer, and the order for all of the observers tested in a day was randomized.

After at least four days of retraining, observer fish went through another food- location association test to determine if they still associated the side on which they were trained with food. If they passed using the same criterion as before (6/10 minutes), they moved on to another social test that included a different demonstrator/stimulus. This was repeated until observer fish completed all nine social tests or until they failed out of the experiment.

Data Analysis

We used a series of Linear Mixed Models (LMM) to test the effects of each of our treatments on patch use. For each analysis, we built two LMMs. In the first, the sex of the observer (male, female or unsexed), sex of the demonstrator, and relative size

(demonstrator SL – observer SL), and all interactions among these were included as fixed effects in the model. Because size is strongly related to dominance in this species, relative size is a proxy for relative status, had these individuals been in the same group.

For the second model, the status of the observer (DM, DF, LM, SS) in its home group, the status of the demonstrator in its group, and the interaction between these were included as fixed effects. For both models, the observer’s home tank and observer identity were included as random effects.

To test the effects of status, given the presence of a conspecific alone, our dependent variable was the difference in the time spent on the demonstrator side between the demonstrator alone and empty tube treatment, which we will refer to as “effect of demonstrator”. This controls for any effects of side preferences unrelated to the task. To

50 test the effects of status when the demonstrator is providing information about food, our dependent variable was the difference in the time spent on the demonstrator side between the demonstrator feeding and demonstrator alone treatments, which we will refer to as

“effect of food”. Finally, to test whether the social status of feeding demonstrators influenced patch use overall, our dependent variable was the difference in time spent on the difference in the time spent on the demonstrator side between the demonstrator feeding and empty tube treatments, which we will refer to as “effect of demonstrator + food”. Post hoc pairwise comparison for significant effects were corrected for multiple comparisons using a Bonferroni correction. All statistical models were run in IBM SPSS

Statistics 24 (SPSS, Inc., Chicago, IL, U.S.A.).

Animal Welfare

Groups were observed for signs of aggression and eviction, and if observed, either the individual receiving the aggression or the one that was being the aggressor was temporarily isolated to reduce aggression in the group. Tests were conducted between the hours of 0800 and 2000 from 12 May-16 October 2016. Trials were conducted by six different people, all of whom followed the same procedural protocol that was approved by The Ohio State University Institutional Animal Care and Use Committee (Protocol

2008A0095).

Results:

Effects of Relative Size

We did not find a significant effect of demonstrator or observer sex, relative size, or any of the interactions among these on the effect of the demonstrator alone (Table 3).

We found a significant effect of relative size (F1,45.871= 5.513, p=.023), and the interaction of

demonstrator sex and relative size on effect of food (Table 3a, Figure 7a) (F2, 35.21= 5.94, p=.006). When demonstrators had been observed feeding, observers increased use of the demonstrators’ side with increased relative size of the demonstrator. However, this was

only the case when demonstrators were female (M vs. F: F1, 25.423= 11.803, p=.002). When female demonstrators were larger than the observers, observers typically spent more time on the demonstrator side when the demonstrator had been feeding than when she had not.

When the female demonstrator was smaller than the observer, the observer typically spent less time on the demonstrator side when the demonstrator had been feeding than when she had not. We also found a significant effect of the interaction between observer

sex and difference in demonstrator and observer length on the effect of food (F2, 44.531=

4.221, p=.021, M vs. F: F1, 27.022= 7.324, p=.012, F vs. U: F1, 24.871= 4.297, p=.049) (Table 3a,

Figure 7b). Results were similar for the overall effect of demonstrator + food; we found

significant effects of relative size (F1, 48.416= 9.320, p=.004), the interaction of demonstrator

sex and relative size (F2, 35.571= 6.838, p=.003, M vs. F: F1, 32.342= 13.291, p=.001), and the

interaction of observer sex and relative size (F2, 50.865= 3.962, p=.025, M vs.: F1, 37.960= 6.662,

p=.014, F vs. U: F1, 19.039= 6.520, p=.019) (Table 7c) on this measure.

Effects of Status in Home Tanks

We did not find a significant effect of demonstrator or observer status, or the interaction on the effect of the demonstrator or on the effect of demonstrator + food

(Table 4b, c). We found a significant effect of observer status on the effect of food (F3,

44.846= .668, p = .038) (Table 4a). This effect was greater for small subordinate observers than for dominant male observers. (Figure 8) (Estimated Marginal Means, p=.030), regardless of demonstrator status.

Discussion:

We found effects of the relative size of both observers and demonstrators on observers’ patch use after observing demonstrators that had fed relative to a demonstrator that had not fed or no demonstrator at all. These results are consistent with the general hypothesis that observer and demonstrator social rank influences an observer’s use of social information. N. pulcher have a size-based dominance hierarchy (Wong and

Balshine 2011) and therefore may be using size as a proxy for dominance. The effects were strongest when demonstrators were female (Fig 2) and when observers were of low social status (Fig 3), consistent with the hypothesis that individuals better acquire and/or are more likely to use social information from highly ranked individuals. Our findings are similar to other work that showed that more subordinate individuals used social information from dominants (birds: Nicol and Pope 1994; mammals: Pongrácz et al.

2008; Krueger et al. 2008; Kendal et al. 2015; Jones and Monfils 2016). Previous work suggests that dominant demonstrators display information better, observers find

53 information from dominant demonstrators more salient, or are used for social information due to attentional bias by the observer. In our study, all demonstrators fed for the feeding treatment, therefore it is unlikely that demonstrators displayed information about food on that patch differentially to observers. While information is equal from all demonstrators

(feeding or not), dominant demonstrators information may be more salient to observers due to their dominance status, size or age. In addition, the observers in this experiment may be using social information from dominant individuals differently than from similarly ranked or subordinate individuals due to attentional bias. Using social network analysis, dominant N. pulcher within their groups are shown to be more connected (Dey et al. 2013), and dominant demonstrators could be potential mates (Stiver et al. 2009;

Hellmann et al. 2016), so our results may be a byproduct of observers paying attention to demonstrators.

Observers, regardless of their sex and status, showed more of an increase in the use of demonstrated patch after observing large female demonstrators compared to similar and smaller females and male demonstrators. N. pulcher show male biased dispersal, and therefore large females are often the individuals that have been on a territory the longest (Stiver et al. 2004) and may have high quality information regarding that location. In addition, preliminary work shows that subordinates can discriminate between familiar and unfamiliar large dominant females but not lower ranked subordinates (Author unpublished), suggesting that large female individuals may be highly salient to an observer. For both males and females, size is not only related to dominance, but also strongly related to age (Dierkes et al. 2005) and safety in this species

(Jordan et al. 2009) and is related to foraging success in other fishes (Ryer 1988). A large fish that has been successful at foraging and avoiding predators could be a good source of information for a foraging related task. Furthermore, in female fish, size correlates with fecundity (Hislop 1988; Heinimaa and Heinimaa 2004). This would make large dominant females potential mates for male observers and potential competitors for female observers, which may result in observers paying more attention to large females compared to other conspecifics.

Small, unsexed individuals spent more time on the demonstrator side, regardless of demonstrator sex and status, when the demonstrator was larger and higher ranked. This is similar to what was found in horses (Kruger and Heinze 2008; Kruger et al. 2014) and chimps (Watson et al. 2017; Kendal et al. 2015) (although Nicol and Pope 1999 found the opposite in hens--a species where subordinate individuals are often exploited as producers) and is consistent with the hypothesis that individuals use information from dominant individuals or individuals who are more dominant than themselves. These unsexed individuals are also likely the youngest, and these individuals may have the least reliable personal information through experience.

For the most part, we did not find an effect of social status per se (i.e., whether the individual was dominant or subordinate in its home tank) in our analyses. The demonstrators used in this study were neighboring fish and knowing the dominance rank of neighboring groups may be difficult to assess or unimportant information for observers. While all fish in our experiment had an opportunity to view and observer their neighbors, it could be size, rather than status, that is more useful information. Should two

55 individuals ever be in the same group, their relative statuses would be determined mainly by size (Reddon et al. 2011).

We did not find a significant effect of demonstrator status, observer status, demonstrator or observer sex, or size on the use of information in N. pulcher when comparing the presence of the demonstrator alone compared with an empty tube. This suggests that local enhancement or learning the location of food via the attraction of other foraging individuals (Thorpe 1963; Valone 1989; Valone and Templeton 2002; Coolen et al. 2003), was not enough to influence an observer’s decision to change foraging locations. In contrast, wild Trinidadian guppies (Poecilia reticulata) preferred entering a feeder that contained an artificial shoal of conspecifics over a feeder that did not. Even when the shoal was removed, the guppies still preferred to enter the feeder where the shoal was once found (Reader et al. 2003). In our experiment, observers may have needed more information about the patch before choosing to forage on the demonstrator side. Alternatively, observers may not have been paying attention to the demonstrator when the demonstrator was not foraging. In addition, there may be a trade-off between the benefits of foraging and the costs of entering a patch with a neighbor via aggression or as a competitor for food. Observers may be less willing accrue a potential cost of aggression from a neighbor that does not demonstrate the presence of food on a patch.

In summary, this study shows that the social status of both the demonstrator and observer influences the observer's use of social information use in N. pulcher. To our knowledge, this is the first example to show social status influencing social information use in fish, suggesting that the social information use rules are common across taxa. We

56 also found support for two of the heuristics Kendal and colleagues (2015) find in chimps: copy dominant individuals and copy when subordinate. In addition, we found an interaction with relative size difference and demonstrator sex, indicating that social status is not the only factor to influence the decision for observers to obtain and use social information. Future studies should seek to understand how sex and other biases influence use of social information, as well as how attention and value of social information contribute to social learning.

a Neighbour Focal

tan

Figure 6: a) schematic of home tank and food-location association learning. Each group is composed of four fish in a tank with a slate on the bottom left, a filter at the center top, and a heater on the right side. Each group had one neighbor and was fed on the side opposite of the neighbor for food-location association training (right side for focal group). b) Social information task. Observers were placed in the center of the tank that was similar to their home tank. Stimuli (in this figure, a neighbor feeding) is presented and removed after 2 minutes. Observer is released and allowed to swim freely for 10 minutes.

Table 3: Linear Mixed Model output for sex and a) effect of food, b) effect of demonstrator, and c) effect of demonstrator + food. Significant p-values are in bold. a Effect of food

dfnum dfdenom F p Intercept 1 44.261 0.553 0.461 Observer sex 2 35.953 0.125 0.883 Demonstrator sex 2 43.651 2.003 0.147 Relative size difference 1 45.871 5.513 0.023 Observer sex * Demonstrator sex 4 40.784 1.847 0.138 Observer sex * Relative size difference 2 44.531 4.221 0.021 Demonstrator sex * Relative size difference 2 35.213 5.941 0.006 Observer sex * Demonstrator sex * Relative 4 39.643 1.038 0.400 size difference b Effect of demonstrator alone

dfnum dfdenom F p Intercept 1 55.566 0.557 0.459 Observer sex 2 49.477 0.501 0.609 Demonstrator sex 2 43.850 0.058 0.943 Relative size difference 1 48.754 1.006 0.321 Observer sex * Demonstrator sex 4 51.036 0.327 0.859 Observer sex * Relative size difference 2 50.035 0.710 0.497 Demonstrator sex * Relative size difference 2 36.626 0.254 0.777 Observer sex * Demonstrator sex * Relative 4 39.246 0.056 0.994 size difference

c Effect of demonstrator + food

dfnum dfdenom F p Intercept 1 54.046 1.161 0.286 Observer sex 2 45.023 1.847 0.169 Demonstrator sex 2 43.622 2.208 0.122 Relative size difference 1 48.416 9.320 0.004 Observer sex * Demonstrator sex 4 51.192 2.045 0.102 Observer sex * Relative size difference 2 50.865 3.962 0.025 Demonstrator sex * Relative size difference 2 35.571 6.838 0.003 Observer sex * Demonstrator sex * Relative 4 43.354 1.221 0.316 size difference

Table 4: Linear Mixed Model output for status and a) effect of food, b) effect of demonstrator, and c) effect of demonstrator + food. Significant p-values are in bold.

a Effect of food dfnum dfdenom F p Intercept 1 67.143 1.810 0.183 Observer Status 3 68.965 2.958 0.038 Demonstrator Status 3 44.846 0.668 0.576 Observer Status * Demonstrator 45.280 1.746 0.106 Status 9 b Effect of demonstrator alone Source dfnum dfdenom F p Intercept 1 91.413 2.780 0.099 Observer Status 3 90.753 0.466 0.707 Demonstrator Status 3 48.182 1.158 0.335 Observer Status * Demonstrator 9 53.057 0.404 0.928 Status

c Effect of demonstrator + food dfnum dfdenom F p Intercept 1 85.220 4.815 0.031 Observer Status 3 85.044 0.687 0.562 Demonstrator Status 3 51.548 0.469 0.705 Observer Status * Demonstrator 9 52.675 0.360 0.949 Status

Figure 8: Box and whisker plots showing observer social status influences time spent on the demonstrator side when the demonstrator is feeding compared to only the presence of the demonstrator.

Chapter 4: Temperature and social environment, not personality, influences learning rate in the cichlid fish Julidochromis ornatus

Abstract

Learning can be beneficial, but there is often variation in learning rate. Variation in personality may contribute to this. It has been hypothesized that the speed at which individuals learn an association task is related to both their personality and type of association task. Specifically, more aggressive and exploratory individuals are predicted to learn a novel association more quickly due to their propensity to move around in their environment compared to less exploratory and aggressive individuals. Less aggressive and exploratory individuals are predicted to learn associations based on environmental changes, such as reversal learning tasks, faster than highly aggressive and exploratory individuals. We tested these predictions in the cichlid, Julidochromis ornatus. Each fish was assayed twice for aggression and exploration and performed a novel and reversal associative learning task. While we found that both exploration and aggression were repeatable, we found no significant relationship between personality and the learning rate for either task, and thus, did not find support for our hypothesis. Fish used in this experiment had been acclimated to two different temperature treatments and were housed either singly or in pairs, and we used this variation opportunistically to also explore the roles of pairing status and temperature on learning rate. Fish housed with another

63 individual were more likely to complete the initial learning task and fish in the high temperature treatment completed the reversal learning task more quickly. We suggest that these conditions may have influenced motivation to perform the food-related learning task.

Introduction

Learning, defined as the change in behavior over time as a result of experience, can influence fitness by influencing an individual’s ability to obtain information about the environment, such as the location or characteristics of mates or a food source. In particular, learning may be advantageous in a changing environment (Robinson and

Dukas 1999). While there are many benefits to learning in a changing environment, there is often variation in learning rate or ability. This potential variation could be related to several factors, such as sex (Titulaer et al. 2012), age (Krueger et al. 2014), and environmental rearing (Amiel and Shine 2012; Clark et al. 2014).

Another potential source of variation in learning rate may be an individual’s personality, or its consistency of behaviors across contexts and time. Personality may alter an individual’s perception of the environment, which in turn may influence their performance on learning tasks (Réale et al. 2007). For example, individuals who are highly exploratory likely personally encounter more information about the environment than do individuals who are less exploratory. In turn, these exploratory individuals may learn about the environment compared to other individuals.

Many personality traits, such as aggression, exploration, and boldness, fall along a proactive-reactive gradient (Koolhaas et al. 1999). Proactive individuals score high in 64 aggression, boldness, and exploration assays, while reactive individuals score low in these assays. Studies show that individuals who are proactive or reactive show different behaviors, utilize their environment differently, and have different physiological reactions to stressors (Koolhaas et al. 1999). In addition, these differences in personality could also result in differences in learning rate. Previous work on personality and learning has proposed that personality correlates with a speed-accuracy preference in sampling the environment (Sih et al. 2004). The hypothesis proposes that proactive individuals, because they are more exploratory and bold, are more likely to sample their environment quickly and approach novel objects more frequently than reactive individuals, resulting in increased sampling events compared to reactive individuals. This increased sampling is suggested to allow for proactive individuals to learn associations, such as where food is located, faster than reactive individuals. However, support for this hypothesis has been mixed (Griffin et al. 2015).

While work investigating learning and personality has shown conflicting results, this may be due to differences among associative learning tasks, particularly for studies in which an association task is paired with a reversal learning task. The abilities needed to master these tasks may differ. For example, compared to an initial association, a reversal learning task requires an individual to inhibit the previously learned association and create a new association (Dias et al. 1997). Sih and Del Giudice (2012) hypothesize that the speed at which proactive and reactive individuals learn is based on whether or not the task is novel. Proactive individuals are hypothesized to learn novel associative learning tasks faster than reactive individuals, while reactive individuals are hypothesized to learn

65 associative learning tasks involving environmental change, such as reversal learning tasks, faster than proactive individuals. Proactive individuals often form routines where their behavior does not change in response to a change in environmental stimuli

(Koolhaas et al. 1999; Groothuis and Carere 2005). Reactive individuals are less likely to approach novel objects, but because they are less likely to form routines, they can detect changes in their environment faster; therefore, reactive individuals might learn about changes in their environment faster than proactive individuals (Koolhaas et al. 1999;

Groothuis and Carere 2005).

Studies testing this hypothesis have only appeared recently, and results have been mixed. For example, Mazza and colleagues (2018) found that bolder bank voles learned an association task quickly but then took longer to reach learning criterion for the reversal task. Several experiments show partial support or show that different variables influence learning. For example, some studies show support for the hypothesis for one personality measurement, but not others (Guenther et al. 2014). In other studies, there is support that proactive individuals perform better on initial associative learning tasks, but do not find support that reactive individuals perform better at reversal learning tasks (Guenther et al.

2014a). Also, there are studies that do not show a relationship between personality and initial association learning but show that personality correlates with reversal learning rate

(Guillette et al. 2009; Guido et al. 2017). Further, additional traits may also have an interactive effect with personality and learning rate. Titulaer and colleagues (2012) found different results based on sex; highly exploratory (proactive) males learned the reversal

66 task faster, while slowly exploring females (reactive) learned the reversal task faster than highly exploratory females.

We investigated the relationship between exploration, aggression and associative learning in a common Lake Tanganyikan cichlid fish, Julidiochromis ornatus. We took assayed personality scores of 21 female adult J. ornatus and measured their performance on two associative learning tasks. Each fish was tested in an initial learning task, which we defined as the novel learning task. Upon successful completion of the initial learning task, fish were tested in a reversal learning task, which we defined as the environmental learning task. In accordance with Sih and Del Guidance’s (2012) hypothesis, we predicted that proactive (scoring high on exploration and aggression) individuals will learn the novel association task faster than reactive (scoring low on exploration, boldness, and aggression) individuals. We also predicted that reactive individuals will learn the reversal task faster than proactive individuals. While our central question investigated how personality relates to learning speed, fish from this experiment were used concurrently in a larger project investigating how increased temperature influences physiology and behavior of Julidochromis ornatus. In addition to personality, there were two temperature treatments, and some fish were with a group mate while others were solitary. While we did not have specific predictions, we used this variation opportunistically to explore the role of temperature and social environment on learning rate.

Methods

Study species and housing conditions

Julidochromis ornatus is a cooperatively breeding cichlid endemic to Lake

Tanganyika (Heg and Bachar 2006). In Lake Tanganyika, groups of unrelated J. ornatus can be found in areas with rocky crevices which are used for breeding. Groups are composed of a dominant breeding pair and 0-5 subordinates that provide help through territory defense and brood (offspring) care (Heg and Bachar 2006). Subordinate helpers are typically males, who may participate in reproduction (Awata et al. 2005).

This study was part of a larger experiment that investigated the effects of water temperature on the physiology, personality and behavior of J. ornatus. Fish were acquired from an aquarium fish wholesaler (Old World Exotic Fish, Homestead, FL,

USA) and were housed in 56-liter aquaria with a slate breeding shelter. Fish used in this study were housed in one of two treatments: a low temperature treatment (25°C) and a high temperature treatment (29°C). The low temperature treatment reflected current water temperatures in Lake Tanganyika. The high temperature reflects the predicted future temperature of Lake Tanganyika (Tierney et al. 2010). To limit confounding influences of sex, only females housed as singles or mated pairs were used (N=21). With the potential exception of temperature, water conditions were kept to similar levels as Lake

Tanganyika. Fish were kept on a 12:12 h light: dark photoperiod and were fed two pellets daily prior to the start of the learning experiment (Hikari Sinking Cichlid Gold Mini pellets).

Experimental timeline

This study consists of 3 phases: personality assay 1, personality assay 2, and the learning tests. J. ornatus were initially housed in 25°C for the first 18 months of the experiment.

During this time, the first exploration and aggression assay was performed on most of the fish (at 12 months). At 18 months, half of the fish were exposed to the higher temperature environment, 29°C, while the other half of the fish were maintained at 25°C. The second personality assay was done at month 27, with some fish performing both sets of personality assays during this time. The learning trials took place at month 28-29 of the experiment.

Personality testing

Experimental arena set-up

Prior to the learning task, each fish was tested twice on measures of aggression and exploration. Methods used in this study are similar to the methods used in Schürch and Heg (2010). A 76-liter aquarium was split into 2 areas, one for the exploration assay and a second that acted as a habituation area and was the location for aggression assay

(Figure 1). The exploration area consisted of 5/6 of the aquarium area and had 10 slate shelters that were similar to the shelters in each fish’s home aquarium. The habituation area was 1/6 of the aquarium volume. These two areas were partitioned using a removable opaque barrier. We tested exploration then aggression for each fish in that order. This was to reduce the number of times the fish had to be handled and reduce the effect of aggression assay influencing the exploration assay.

Water for the personality aquarium came from a reservoir that was also used for each fish’s home aquaria to ensure that fish were tested in the temperature that they were housed in. When we were not performing personality tests, the aquarium was equipped with an aerator, heater and filter to maintain water quality. Prior to testing, the aerator, heater, and filter were removed and two LED light strips were turned on to provide sufficient lighting for video recording. Water chemistry and temperature was also recorded and adjusted as necessary to reflect the water chemistry in the fish’s home aquarium.

Exploration

We defined exploration as the propensity for an individual to move around in their environment. To test exploration, each fish was moved from their home aquarium to the small habituation area of the personality aquarium (see Figure 9). The habituation area was partitioned from the rest of the personality aquarium with an opaque barrier. An opaque barrier was used so the fish could not see the other testing areas. The rest of the aquarium was the exploration area, which contained ten slate shelters in two rows. After a ten-minute habituation period, the opaque barrier was removed and the fish was allowed to freely swim throughout the entire aquaria. The fish was video recorded from above the aquaria for 15 minutes. We recorded the total number of shelters visited.

Aggression

We defined aggression as the number of restrained and direct attacks made on a mirror image. After a 10-minute habituation period, the opaque barrier was replaced with a mirror. In closely related Lake Tanganyika fish, a mirror has been shown to act as a

70 proxy as a competitive conspecific to measure aggression (Balzarini et al. 2014). The fish was then filmed for 10 minutes and the films were scored for aggressive behaviors towards the mirror. Behaviors included overt aggression, such as bites and rams towards the mirror, and restraint aggression, such as fin raises and operculum spreads. At the end of the aggression task, we returned the fish to their home aquaria. If another personality test followed for another fish, we performed a 50% water change to reduce any chemicals emitted from the previous fish.

Learning task

Set-up

The purpose of the learning task was to test how quickly fish associated a location of their aquaria (one of the front corners) with food (Figure 10), and to test if learning was correlated with personality. The learning task was performed in the fish’s home aquarium, described above. Testing was performed during the hours of 0800-1600. Prior to any testing, a clear barrier was placed halfway back in the aquaria to limit the fish’s movement to the testing area of the aquarium, but to allow the fish to see their group mate if they had one. If the fish was housed with a mate, the mate was placed behind the barrier. When aggression was observed between the pair, the mate was placed in a floating net cage behind the barrier to reduce aggression during testing. There were 3 phases to the learning task: training, initial side learning, and reversal side learning.

Training

The purpose of the training period was to allow fish to acclimate to a new food for this study (New Life Spectrum Small Fish Formula), and to allow fish to acclimate to

71 feeding from a spot feeder, an elongated tube used to target feed bottom organisms, in a new location of their home aquarium. Prior to training, each fish was fed 2 medium pellets that were deposited in the center of the aquarium. To allow fish to learn the new food (New Life Spectrum Small Fish Formula), and to habituate to the feeding apparatus, fish were trained to feed on the smaller sinking pellets and with the spot feeders three times a day over 10 days. For days 1-3, food was released from the spot feeders at both corners at the top of the water column. For days 4-6, food was presented to the fish with spot feeders halfway down the water column. On days 7-10, food was presented from the spot feeders 2.5-5 cm from the bottom of the water column. The purpose of this gradual training was to ensure that the fish fed each day of training.

Initial side testing

Initial side testing began the day after the end of the training period. All fish were tested to see if they could associate one corner of their home aquaria with food. Each fish was randomly assigned a side that food was to be presented on. For each day of testing, fish performed 10 trials. For each trial, fish began in their shelter, which was placed ~1/3 back from the front of the aquaria at the center. Once the fish was in the shelter, 2 spot feeders that were covered with opaque tape were placed in each corner 2.5-5 cm from the bottom of the water column and its contents were released. One spot feeder had water with a small amount of food and the second spot feeder had only water. The water from both spot feeders was from a container with food to ensure that both spot feeders had the same food odor. The spot feeders were taped so that the fish could not choose a corner based on where they saw the food in the spot feeder. A trial was scored as a success if the

72 fish swam to the area where the food was released. A trial was scored as a failure if the fish swam to the area where only water was released. If the fish did not swim to either of these areas within 2 minutes, then the trial was scored as ‘did not choose’. During the trials, fish were allowed to swim freely, and another trail could begin once the fish was in its shelter. This was repeated until the fish had performed 10 trials. We defined a learning criterion (LC), a level at which an individual has been defined to have learned the task, as having at least seven successes per day, two days in a row. Fish were tested daily until they had reached LC or had reached a maximum of 10 days.

Reverse side testing

Fish who had reached LC were tested to see if they could associate the opposite corner of the aquarium with food, referred to as reversal learning. The procedure was the same as the initial side testing, except that the assigned side that food was presented and side with only the food-scented water were reversed for each individual. Fish were tested daily, beginning the day after they reached LC for the initial side learning until they met the LC for the reversal learning or after a maximum of 10 days. LC for the reversal learning was 7 successes for two days in a row, the same as the LC for the initial side learning.

Data analysis

To test if aggression and exploration were repeatable, we performed 1-tailed

Spearman Rank Correlations for total shelters visited (exploration) and total aggressive behaviors.

To test if successfully reaching learning criterion was associated with exploration or aggression, we performed Mann-Whitney U tests using days until learning criterion was met (for both the initial and reversal learning test) and the mean personality measure

(total aggression, total shelters visited). To test if exploration or aggression were associated with learning rate we performed 2-tailed Spearman Rank Correlations using the mean personality measure (total aggression, total shelters visited) and days until learning criterion was met for the fish that successfully learned the task.

In addition to the analyses to test our predictions, we ran analyses to test if being paired/solitary and environmental temperature influenced learning completion or rate. To test if temperature influenced learning completion for the initial learning task, we used

Fisher’s Exact tests. To test if temperature influenced learning rate, we ran Mann-

Whitney U tests, days until learning criterion was met and temperature treatment as variables. To test whether social environment influences learning, we ran Fisher’s Exact tests to see if social environment influences learning task completion, and Mann-Whitney

U tests to see if social environment influences learning rate.

Animal Welfare

Throughout the experiment, animals were observed for signs of stress and received aggression. If signs of received aggression or stress were observed, fish were temporarily isolated. This study followed a protocol that was approved by The Ohio State

University IACUC (protocol #2012A00000112).

Results

Repeatability of exploration and aggression

We found that when using total shelters visited for exploration and total aggressive behaviors, both exploration and aggression were each significantly correlated between measures 1 and 2 (exploration: rs = .398, p=.041, N = 21; aggression: rs = .427, p=.027, N

= 21), indicating that both measures are repeatable in J. ornatus. However, we did not find a correlation between aggression and exploration (rs=.117, p=.307, N=21), as we would expect for a proactive-reactive behavioral syndrome.

Initial side learning

Out of 21 individuals, 13 completed the initial learning task. We found that there was no significant relationship between aggression and completion of the learning task

(U=46, p=.664, N=21) or exploration and completion of the learning task (U=42, p=.469,

N=21). For individuals who completed the initial learning task, there was not a significant relationship between mean aggression and completion rate (rs=.242, p=.426,

N=13) or exploration and completion rate (rs=-.542, p=.056, N=13) (Figure 11).

We also did not find a significant relationship between environmental temperature and initial learning rate completion (Fisher’s Exact Test p=.659) or learning rate (U=14, p=.269, N=21). We did not find a significant relationship between social environment

(single housed vs. paired) and initial learning rate (U=14.5, p=.929, N=13), but we found that more paired individuals completed the initial learning task compared to singly- housed individuals (Fisher’s Exact Test p=.008).

Reversal learning

Of the 13 individuals who participated in the reversal learning task, 11 reached learning criterion. We found that there was not a significant relationship between aggression and completing the learning task (U=5, p=.236) nor was there a significant relationship between exploration and completing the learning task (U=10, p=.844)

(Figure 12). For individuals who completed the initial learning task, there was not a significant relationship between completion rate and aggression (rs=-.419, p=.199) or exploration (rs=-.066, p=.847).

Individuals that were housed in the high temperature treatment completed the learning task faster than did fish housed in the low temperature treatment (U=2, p=.019)

(Figure 13). We did not find a significant relationship between social environment and reversal learning rate (U=11, p=.833).

Discussion

In this study, we tested Sih and Del Giudice’s hypothesis that personality influences learning rate. While both aggression and exploration were repeatable and the correlation between exploration and initial side learning rate approached significance, we did not find a relationship between personality and association learning, and thus failed to support our hypothesis. However, we did find that individuals paired with mates were more likely to complete the initial association task. Further, individuals housed in the high temperature environment completed the reversal learning in fewer days on average compared to fish housed in the low temperature environment. The mates could be competitors for food, and fish in higher temperature environments may have a higher

76 food need (Brodnik 2015), so motivation for food may drive association learning in this study.

Sih and Del Giudice (2012) hypothesized that exploratory and aggressive individuals will learn an association task faster compared to less aggressive and exploratory individuals. While the correlation between exploration and initial learning rate was almost significant, we did not find support for this hypothesis. These results are in contradiction to some studies (Guenther et al. 2014) but align with others (e.g. Cole and Quinn 2012; Bousquet et al. 2015). One reason we may not have found support may be due to the maximum days to meet learning criterion. With a capped time period for testing associative learning, less exploratory individuals who were able to learn the task, but needed a longer period, may have been selected out of the study. This is concerning, since we only had 13 of our original 21 individuals pass the initial learning task. Morton et al (2013) found that in brown capuchin monkeys, certain personality types were selected out of a learning task. However, we did not find that there was a relationship between exploration and completion of the learning task, and therefore we likely did not select out less exploratory individuals during the initial learning task.

We did not find a relationship between learning rate and aggression for either learning task. Similar to exploration, support for this hypothesis regarding has been mixed (Guenther et al. 2014, Brust and Guenther 2017) The lack of support may be due to the type of associative learning task used in this study. J. ornatus feed on phytoplankton in the water column and therefore may not be competing for food in the

77 wild. Therefore, we may not expect to see a relationship between an association task with a food reward and aggression.

Surprisingly, there have been few studies that have looked at aggression and learning. This may be due to other personality traits being more easily associated with learning. For example, more exploratory or bold individuals may increase their sampling rate, but highly aggressive individuals may not. Proactive individuals—individuals who score highly on exploration, boldness, and aggression assays—are predicted to learn novel associations quickly and form routines (Sih and Del Giudice 2012). Our study did not find highly aggressive individuals to also be highly exploratory, thus the proactive- reactive behavioral syndrome may not be applicable to this population.

We found that more individuals who were housed with a mate completed the initial learning task than solitary individuals. As part of another experiment, fish were on a set diet per tank, and thus, their mates could also be competitors for food. While individuals were tested alone for the association task, individuals who could learn the association between location and food faster may be more competitive and receive a higher proportion of the pair’s food. Work on pigeons showed that the presence of a conspecific influences foraging choices (Plowright and Redmond 1996; Plowright and

Landry 2000). In addition, individuals who were not solitary may have been more motivated to learn the food-location association task to reduce their search time at a later foraging date.

For the reversal learning task, we found that fish housed in the high temperature treatment completed the task faster than individuals housed in the low temperature

78 treatment. Similar to paired individuals, fish in the high temperature treatment may be more motivated to learn the association task for the food reward. Metabolic rate is a function of body temperature. Poikilotherms in higher environmental temperatures may have a higher metabolic rate unless they acclimatize (Scholander et al. 1953). Brodnik

(2015) found that male J ornatus in housed in a high temperature environment had a higher standard metabolic rate compared to individuals in a low temperature environment. Fish housed in the higher temperature treatment may have a higher metabolic rate and therefore may have a higher demand for food, leading them to be more motivated to learn a task with a food reward. Studies in reptiles have also shown that individuals reared in high temperature environments perform better on learning tasks

(Amiel and Shine 2012; Amiel et al. 2014; Clark et al. 2014).

In summary, we did not find support that personality was associated with learning rate in either an initial association task or a reversal learning task. However, we did find an effect of both temperature and social environment in association learning, suggesting motivation for a food reward may be a strong influence on learning in J. ornatus. Future work on causes of variation in learning should consider how variation in motivation may interact with personality to influence task learning. While we did not feed the individuals prior to the trials that day to try to standardize hunger levels and encourage participation in the task, differences in metabolic need or competition may drive the willingness to participate and learn a task with a food reward. If energetic state or other influences on motivation to forage is an important influence on these results, future tests of the Sih-Del

Giudice’s (2012) hypothesis should consider using other, non-food-based rewards or punishments in learning assays.

Figure 9: Personality arena schematic. The 1/6 of the aquarium on the left was used to habituate J. ornatus after handling and between testing periods, with an opaque partition. This area was also used for the aggression assay, with a mirror replacing the opaque partition. The remaining 5/6ths of the aquarium was used for the exploration assay, which had 10 shelters laid out for the fish to explore.

Figure 10: Learning trail arena schematic, view from above the tank. The learning arena consisted of a shelter where an individual started the task. On either corner, food was deposited during the training phase and either food or food scented water was deposited. A clear barrier was placed behind the shelter to prevent the individual from swimming away from the test area, and if the fish had a mate in the tank, he was placed behind the barrier.

Figure 11: Scatter plot of number of days until an individual met learning criterion for the initial learning test plotted against a) our measure of aggressiveness (mean number of aggressive behaviors directed toward a mirror over a 10-minute period) and b) our measure of exploration (mean number of shelters visited during a 15-minute period)

Figure 12: Scatter plot of number of days until an individual met learning criterion for the reversal learning test plotted against a) our measure of aggressiveness (mean number of aggressive behaviors directed toward a mirror over a 10-minute period) and b) our measure of exploration (mean number of shelters visited during a 15-minute period)

Figure 13: Days to learning criterion for reversal learning task plotted against temperature. Individuals in the high (29°C) treatment met learning criterion in fewer days on average compared to individuals in the low (25°C) treatment

Chapter 5: Conclusion

For my dissertation, I investigated the role of individual variation in the variation of cognitive abilities. Specifically, I examined how social status influences individual recognition (Chapter 2) and use of social information (Chapter 3) in Neolamprologus pulcher, and how variation in personality influences variation in associative learning rate in Julidiochromis ornatus (Chapter 3). This variation in social status and personality may result in differences in the benefits of having a cognitive ability or learning. My hypothesis was that these measures in individual variation would influence variation in cognition.

In Chapter 2, I found that for both animated and live stimuli, mid-ranking fish could discriminate between unfamiliar and familiar fish, both of both higher and lower status than themselves. I also found that fish can also recognize more than one individual in their group, which has not been found before in this species. Fish also took part in a habituation/dishabituation task to see if lack of attention towards an animated stimulus could influence our results with the two-choice experiment with animated stimuli. In the habituation/dishabituation experiment, I found differences in time spent near the stimulus as well as time spent tracking the stimulus between sex, I did not find activity towards the stimulus changing over time, or when a novel stimulus was presented. Our results suggest that mid-ranking individuals can identify multiple group mates and likely benefit from 86 individually recognizing lower-ranking fish. Future work should look more closely at the costs and benefits of recognizing fish of different statuses, as well as to the potential maximum number of fish that an individual can recognize. Further, our work focuses on the recipient’s ability, but future work should investigate what visual signals may be underlying individual recognition and the costs and benefits of displaying signals of individual recognition.

In Chapter 3, I hypothesized that differential use of social information could result from the difference in status between the observer and the demonstrator. I found that mid-ranking individuals used social information about where to forage from larger female demonstrators, who are higher ranking. I did not find similar results with larger males, indicating that both social status and sex influence the use of social information in a foraging context. I also investigated the role of local enhancement vs. public information in the use of social information by observers. I found that local enhancement, or just the presence of a social stimulus, was not enough for observers to change foraging locations. Observers needed to see demonstrators feeding before changing foraging locations, and even then, the feeding demonstrators had to be dominant females. To our knowledge, this is the first time that social status has been shown to influence transmission of information in fish. The use of social information from more dominant females may influence the rate of information diffusion within a group, as well as cultural evolution. Novel information introduced by males or subordinate individuals may be less likely to transmit throughout the group, so that behavioral variation among females may

87 have a stronger influence on group-level variation. Future work should test how these patterns of social information use scale to group level effects.

In Chapter 4, I found that both exploration and aggression were repeatable in J. ornatus, suggesting that these are personality traits in this species. However, there was no correlation between either personality measurement and learning rate in either association learning task, and thus I failed to support a hypothesis presented by Sih and Del Giudice

(2012). I also found that fish housed with a mate were more likely to complete the initial learning task and fish housed in the high temperature treatment had a faster learning rate for the reversal learning task compared to fish in the low temperature treatment. Fish in this experiment were given a food reward for learning the task, and I suggest motivation for this reward influenced learning rate and completion. Future work should explore the role of rewards associated with learning tasks. Future work should also carefully consider potential differences in motivation when designing learning experiments.

Future directions

As a whole, I did not find that personality influenced cognitive abilities, but variation in social status and social environment did. The work done for this dissertation has focused at the level of the individual, but many species, including those used in these studies, form groups, and this variation likely influences group dynamics. A potential avenue for future work could be investigating how heterogeneity in social status, personality, or cognition interact to influence group dynamics. In particular, the diffusion of information may be influenced by interindividual heterogeneity within groups.

Diffusion of social information is thought to occur along a social network (Coussi-Korbel

88 and Fragaszy 1995). While I did not find significant effects on personality and associative learning, personality may be influenced by social networks (Krause et al. 2010), which in turn may influence information transmission. Further, if novel information is transmitted among a group, who discovers the novel information may drive the rate at which information spreads, or whether or not it spreads among the population at all. For example, certain personality traits may be more likely to discover novel information.

Similarly, social status may influence the discovery and diffusion of information among a group. My results from Chapter 3 suggest that in N. pulcher, novel information discovered by dominant females might be more likely to be transmitted. Work on social networks find that connections are formed with familiar individuals (Swaney et al. 2001).

Individual recognition may influence the development of familiarity between sets of individuals. Further, our work supports two heuristics that Kendal and colleagues (2015) found in chimps: use information if you are subordinate and use information from more dominant individuals. This could have implications for cultural evolution. Watson and colleagues (2017) suggests that while individuals will use social information from dominant individuals, it is often subordinate individuals that bring novel knowledge to the group. Investigating how social status interacts with the discovery novel information and its transmission to influence behavioral variation among groups should be further explored.

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