VISUAL AND ELECTROSENSORY ECOLOGY OF BATOID ELASMOBRANCHS

by

Christine N. Bedore

A Dissertation Submitted to the Faculty of

The Charles E. Schmidt College of Science

in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

Florida Atlantic University

Boca Raton, FL

August 2013

Copyright by Christine N. Bedore 2013

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ACKNOWLEDGEMENTS

This work would not be possible without the advice, help, and support that I have received from so many people. To all of you: I am eternally grateful, thank you!

Several funding sources provided financial support of this work, including Florida

Atlantic University Department of Biological Sciences and Graduate College, American

Elasmobranch Society, Society for Integrative and Comparative Biology, Sigma Xi

Scientific Research Society, and American Museum of Natural History. The Bank of

Dad helped to financially supported my research before grant funds were secured, thanks Dad!

I would like to offer my sincere gratitude to my dissertation committee for their years of support, advice, and encouragement. John Baldwin, Tammy Frank, Bob

Hueter, and Mikki McComb have all significantly contributed to my scientific development, each in a unique way. A special thanks goes to Tammy Frank and Mikki

McComb who have each allowed me to borrow their ERG equipment and have spent countless hours combing over data, reviewing manuscripts, writing letters of recommendation, and troubleshooting with me. Another very special thanks goes to my committee Chair, Steve Kajiura. Steve taught me more than I ever expected to learn about biology, physics, and life. He stood by me through my successes and failures, offered me some amazing opportunities, allowed me the freedom to follow my crazy

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ideas and interests, and gave me free reign over his office. For that, I am forever indebted and thankful.

I am fortunate to have the absolute best lab mates I could ever ask for. I am lucky to have you as part of my extended family. Tricia Meredith, Laura Macesic-

Ekstrom, Dave McGowan, Shari Tellman, Kier Smith, Sara McCutcheon, Marianne

Porter, Kyle Newton, Jordan Snyder, Theresa Gunn, Eloise Cave, Avery Siciliano,

Nicole Blahut, Kim Denesha, Gabby Barbarite, and Joey Perez have endured collection trips, tank cleaning, animal food prep, fixing gillnets, have helped teach me electrophysiology, run my experiments, and edit grant proposals and manuscripts. I am especially thankful to Lindsay who has been my partner in crime for the past five years.

We have been through a lot together and she will always be my favorite Lamenologist!

Several other FAU people have offered advice and support: Mark Royer, Neal

Tempel, Jeanette Wyneken, Mike Salmon, Margueritte Koch, Tanja Godenschwege,

Jenny Govender, Michelle Cavallo, Jim Nichols, Kristen Ware, Elisa Gaucher, Dennis

Hanisak, Rod Murphey, and Gary Perry. Personnel at Gumbo Limbo and Mote Marine have provided experimental, technical, and husbandry assistance: Kirt Rusenko, Cody

Mott, Jack Morris, Carl Luer, Krystle Harvey, Jayne Gardiner, Jim DelBene, Andy

Stamper, and Lynne Byrd. Florida Fish and Wildlife Commission in Tequesta, FL,

Dynasty Marine, and Keys Marine Lab have helped provide fish for experiments. Rich

Brill, Nathan Hart, Kara Yopak, Dean Grubbs, Matt Kolmann, Alan Henningson, Robert

Fischer, Joe Bizzarro, Eric Noonburg, Sönke Johnsen, and Yakir Gagnon have provided invaluable advice on statistics, visual biology, and elasmobranch ecology. Ellis Loew graciously afforded me the opportunity to visit his lab and entertained me with his tales

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of his adventures in visual ecology. Many friends have been supportive in every aspect of my dissertation and they made the good days great and the bad days tolerable.

Finally, I need to give respectful appreciation to all of the rays that gave their lives for my research. They inspired me to ask questions and investigate their quirks as an answer to those questions, provided intellectual stimulation for my fellow graduate and undergraduate students, and provided entertainment and an opportunity for learning for thousands of Gumbo Limbo visitors over the years.

“You are capable of more than you know. Choose a goal that seems right

for you and strive to be the best, no matter however hard the path. Aim

high. Behave honorably. Prepare to be alone at times, and to endure

failure. Persist! The world needs all you can give.”

- E.O. Wilson

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ABSTRACT

Author: Christine N. Bedore

Title: Visual and electrosensory ecology of batoid elasmobranchs

Institution: Florida Atlantic University

Dissertation Advisor: Dr. Stephen M. Kajiura

Degree: Doctor of Philosophy

Year: 2013

The electrosensory and visual adaptations of elasmobranchs to the environment have been more studied than most other senses, however, work on these senses is mostly limited to descriptive analyses of sensitivity, morphology, and behavior. The goal of this work was to explore electrosensory and visual capabilities in a more ecological context. To gain an understanding of the content of bioelectric signals, the magnitude and frequency of these stimuli were recorded from a broad survey of elasmobranch prey items. Teleosts produced a greater voltage than invertebrates and elasmobranchs and there was no correlation between voltage strength with frequency, mass, or total length as assumed in previous studies. The DC bioelectric field was reproduced in a behavioral assay and the sensitivity of two batoid elasmobranchs, the cownose ray (Rhinoptera bonasus) and the yellow stingray (Urobatis jamaicensis) to these stimuli was quantified.

Cownose rays demonstrated a median sensitivity of 107nV cm-1,

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which was considerably less sensitive than yellow stingrays that demonstrated a sensitivity of 22nV cm-1. Cownose rays may have reduced sensitivity as a mechanism to prevent overstimulation of the electrosensory system by schooling conspecifics.

Although it is unlikely that cownose rays use their electrosensory systems to maintain position within a school as hypothesized, their visual adaptations suggest tracking of schoolmates may be primarily visual. Cownose rays had a faster temporal resolution than yellow stingrays, which would support this hypothesis. Color vision adaptations also correlated to the photic environment of each species; cownose rays inhabit turbid, green-dominated waters and had two cone visual pigments that maximize contrast of objects against the green background. Yellow stingrays were trichromatic and likely possess the ability to discriminate colors in their clear, reef and seagrass habitats, which are spectrally rich. Both species showed evidence of ultraviolet sensitivity, which may aid in predator and conspecific detection as an enhanced communication channel.

Future studies should investigate the integration of sensory input and sensory involvement in intraspecific communication to gain more insight into ecological adaptations.

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DEDICATION

For Dad, Katie, Layne, and Bruce. You have inspired me to achieve my dreams, no matter how outrageous they are, and to always reach for the stars, no matter how far away they are. Thank you for your endless support and encouragement.

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VISUAL AND ELECTROSENSORY ECOLOGY OF BATOID ELASMOBRANCHS

LIST OF TABLES ...... xiii

LIST OF FIGURES ...... xiv

CHAPTER 1: INTRODUCTION ...... 1

ELECTRORECEPTION ...... 2

VISION ...... 7

RESEARCH GOALS ...... 9

CHAPTER 2: BIOELECTRIC FIELDS OF MARINE ORGANSISMS: VOLTAGE

AND FREQUENCY CONTRIBUTIONS TO DETECTABILITY BY

ELECTRORECEPTIVE PREDATORS ...... 11

ABSTRACT ...... 11

INTRODUCTION ...... 12

MATERIALS AND METHODS ...... 15

Animal collection ...... 15

Experimental apparatus ...... 15

Electrophysiology protocol ...... 16

Bioelectric field generator electric potential ...... 19

RESULTS ...... 20

Bioelectric field stimulus generator electric potential ...... 22

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DISCUSSION ...... 23

Frequency measurements ...... 24

Voltage amplitude ...... 25

Voltage decay and elasmobranch responses to prey-simulating electric fields ..... 28

CHAPTER 3: ELECTROSENSORY ECOLOGY OF TWO BATOID

ELASMOBRANCHS ...... 44

ABSTRACT ...... 44

INTRODUCTION ...... 45

MATERIALS AND METHODS ...... 47

Electrosensory pore number and distribution ...... 47

Behavioral sensitivity ...... 48

Statistical analyses ...... 51

RESULTS ...... 52

Electrosensory pore number and distribution ...... 52

Behavioral sensitivity ...... 53

DISCUSSION ...... 54

Electrosensory pore number and distribution ...... 55

Behavioral sensitivity ...... 57

Behavioral ecology ...... 61

CHAPTER 4: COLOR VISION IN BATOID ELASMOBRANCHS ...... 70

ABSTRACT ...... 70

INTRODUCTION ...... 70

x

MATERIALS AND METHODS ...... 75

Animals ...... 75

Electrophysiology ...... 75

Microspectrophotometry ...... 78

Ocular Media Transmission ...... 79

Body color reflectance ...... 80

RESULTS ...... 80

Electrophysiology ...... 80

Microspectrophotometry ...... 81

Ocular media transmission ...... 81

Body color reflectance ...... 82

DISCUSSION ...... 82

Multiple visual pigments and color (hue) discrimination ...... 82

Ultraviolet transmission and sensitivity ...... 85

Visual ecology ...... 86

Conclusions ...... 89

CHAPTER 5: VISUAL TEMPORAL RESOLUTION IN ELASMOBRANCHS:

EFFECTS OF LIGHT, TEMPERATURE, AND ANESTHESIA ...... 97

ABSTRACT ...... 97

INTRODUCTION ...... 98

MATERIALS AND METHODS ...... 102

Animal collection and maintenance ...... 102

Experimental apparatus ...... 102 xi

Electroretinogram protocol ...... 103

Experimental conditions ...... 104

Data analysis ...... 105

RESULTS ...... 105

DISCUSSION ...... 106

Photic environment ...... 107

Thermal environment ...... 108

Anesthesia ...... 110

Conclusions ...... 112

CHAPTER 6: SYNTHESIS AND FUTURE DIRECTIONS ...... 118

ELECTROSENSORY ECOLOGY ...... 119

Bioelectric field characteristics ...... 119

Electrosensory morphology and behavior ...... 120

VISUAL ECOLOGY ...... 122

Color and ultraviolet vision ...... 122

Temporal resolution ...... 124

FUTURE DIRECTIONS ...... 125

CHAPTER 7: REFERENCES ...... 127

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LIST OF TABLES

Table 2.1 Median sensitivity, maximum detection distance, and preferred prey of

elasmobranchs to prey-simulating electric stimuli ...... 31

Table 2.3 Summary of regression analyses of voltage against mass (g) and total

length (cm) ...... 33

Table 2.4 Voltage produced by teleost and elasmobranch fishes and invertebrate

prey ...... 34

Table 2.5 Source voltage, electric field equations and theoretical elasmobranch

detection distance (cm) from electric potential decay measurements, with a

median sensitivity of 35nV cm-1 for elasmobranchs ...... 35

Table 2.6 Current-voltage relationships and resistance (Ω) calculated from the

bioelectric field generator ...... 36

Table 3.1 Electrosensory pore number, density, and pore coverage area ...... 63

Table 3.2 Summary of behavioral responses of two species of batoids to prey-

simulating electric fields ...... 64

Table 3.3 Behavioral electrosensitivity of batoid elasmobranchs ...... 65

Table 4.1 Ocular media transmission categories and vision likelihood predictions

for UV color vision in fishes...... 91

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LIST OF FIGURES

Figure 2.1 Waveform shape, amplitude, and frequency of electric potential

measured from elasmobranch prey items...... 37

Figure 2.2 Mean frequency (±SD) produced by invertebrate and vertebrate prey

items...... 38

Figure 2.3 Sample power spectrum and waveform recorded from the gills of a

barracuda (Sphyraenidae) ...... 39

Figure 2.4 Mean electric potential (µV) for each family ...... 40

Figure 2.5 Voltage plotted against mass for all tested species (mean ±SD) ...... 41

Figure 2.6 Sphyraenidae (A) and Ariidae (B) voltage and electric field decay...... 42

Figure 2.7 Voltage produced by the bioelectric field generator at a wide range of

biological current intensities at three temperature (20°C, 25°C, 30°C) and

salinity (0ppt, 15ppt, 35ppt) treatments ...... 43

Figure 3.1 Cephalic variation within the order Myliobatiformes ...... 66

Figure 3.2 Orientation measurements used to calculate sensitivity to dipole electric

fields...... 67

Figure 3.3 Electrosensory pore number and distribution of the cownose ray,

Rhinoptera bonasus, and yellow stingray, Urobatis jamaicensis...... 68

Figure 3.4 Percentage of orientations by sensitivity (voltage gradient) and detection

distance to prey-simulating electric fields ...... 69

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Figure 4.1 Spectral sensitivity curves from scotopic, photopic, and chromatic

adapted ERG...... 92

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CHAPTER 1: INTRODUCTION

The past few decades have seen a movement in ecology to understand environments from an animal’s eye view, rather than imposing our own perceptions upon the animals we study. This movement has stirred interest in the field of sensory ecology, which seeks to gain insight about how organisms obtain information from their environment

(Dusenbery, 1992; Dangles et al., 2009). An animal’s response to this information depends upon the amount and content of a signal; however, these components have not been well characterized for many types of signals. The physical properties of aquatic media can make signal propagation in water drastically different from propagation of the same signal in air. For example, low-frequency sound can travel much farther in water, so sound is an effective communication tool for great whales to signal conspecifics that are thousands of kilometers away (Mellinger and Clark, 2003). Aquatic environments have also enabled the use of sensory modalities that depend upon the water medium to conduct a signal, such as lateral line and electrosensory systems that detect changes in water flow and weak electric fields.

Biological signals are rarely perceived by a single sensory system. A prey item may simultaneously emit chemical, mechanical, visual, and electric stimuli, which require the integration of responses from multiple sensory modalities (Gardiner et al.,

2012). These different sensory modalities are typically tuned to maximize functionality within a certain ecological niche. For example, elasmobranch fishes, the sharks, skates,

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and rays, have traditionally been viewed as extraordinarily sensitive to olfactory stimuli; however, recent work suggests that they are no more sensitive to amino acid smells than marine teleosts (Meredith and Kajiura, 2010). Both groups of fishes have similar olfactory thresholds that are near the background concentration of amino acids dissolved in seawater (Hara, 1994; Meredith and Kajiura, 2010), thus highlighting the importance of environment, rather than phylogeny, as a determinant of sensory system function.

Other evidence of elasmobranch sensory tuning to their environment is in the ontogeny of sensory brain areas. Many species undergo ontogenetic shifts in ecology including changes in habitat and diet (Castro, 1993; Lowe et al., 1996; Grubbs, 2010).

These ecological shifts are reflected in the relative size of sensory brain areas with a larger emphasis on vision in juveniles and olfaction in adults (Lisney et al., 2007), suggesting a shift from visual foraging to olfactory mediated behaviors in adults, which may include identification of mates via pheromones (Johnson and Nelson, 1978; Kajiura et al., 2000; Chapman et al., 2003). The electrosensory and visual adaptations of elasmobranchs to the environment have been more studied than most other senses; however, work on these senses is still mostly limited to descriptive analyses of sensitivity, morphology, and behavior. Therefore, there is a need to investigate electrosensory and visual capabilities in a more ecological context to determine how elasmobranch fishes obtain and use information from physical, biological, and social environments.

ELECTRORECEPTION

Electroreception is the detection of electric fields in the environment of both biological and non-biological origin. Electroreception is typically a close-range sense limited to 2

detection of signals less than a meter away. Although an ancient sensory modality, the ability to detect and localize bioelectric fields persists in basal members of most vertebrate groups including teleost and chondrostean fishes, agnathans, monotreme mammals, and urodele amphibians (Bullock et al., 1983). Electroreception is most well studied in the most sensitive group, the elasmobranch fishes. The elasmobranch electrosensory system is composed of receptor cells that line the lumen of a bulbous ampulla, which leads to a canal filled with a conductive glycoprotein gel that terminates as a pore on the surface of the skin (Sisneros and Tricas, 2002). These pores are the interface between the seawater environment and the internal environment of the animal.

The receptors detect a voltage gradient between the pore and the reference potential at the receptor cell.

Bioelectric fields are created by physiological processes in all living organisms.

High frequency bioelectric fields are produced primarily by neuromuscular activity

(Kalmijn, 1972; Wilkens and Hofmann, 2005), whereas low-frequency signals are produced primarily by osmoregulatory and respiratory processes during active ventilation through the mouth and gills of aquatic animals. Standing direct current (DC) and low frequency modulated fields of <20Hz are most commonly used as signals by elasmobranchs, with greatest sensitivity at ≤2Hz (Tricas et al., 1995; Sisneros and

Tricas, 2000; Sisneros and Tricas, 2002). The ecological significance of electrosensory systems is evident in the system’s use for mate detection (Tricas et al., 1995; Sisneros and Tricas, 2000; Sisneros and Tricas, 2002), predator avoidance (Kempster et al.,

2012), prey detection (Kalmijn, 1972; Kalmijn, 1974; Kalmijn and Weinger, 1981;

Kajiura and Holland, 2002; Kajiura, 2003; Jordan et al., 2009 McGowan and Kajiura,

3

2009; Wueringer et al., 2012a), and has been hypothesized to coordinate navigation and orientation (Kalmijn, 1974; Kalmijn, 1982; Kalmijn, 2000; Montgomery and Walker,

2001).

Male and female round stingrays, Urolophus halleri, use bioelectric signals to detect and localize buried, ventilating conspecifics with a frequency of 0.25-2Hz (Tricas et al., 1995). Male Atlantic stingrays, Dasyatis sabina, undergo a seasonal increase in sensitivity to these stimuli, which corresponds to seasonal cycles in hormone levels

(Sisneros and Tricas, 2000), indicating that stingrays electrically tune themselves to their ecology. Similarly, egg-encapsulated skates, Raja eglanteria, (Sisneros et al., 1998) and bamboo sharks, Chiloscyllium punctatum, (Kempster et al., 2012) and neonate dogfish sharks, Scyliorhinus canicula (Peters and Evers, 1985), are known to respond to phasic stimuli of potential predators by ceasing ventilatory movements, and in the egg-bound species, also by ceasing rhythmic body movements to limit the stimuli available to predators for prey localization.

Elasmobranchs are most well known for their use of electroreception in prey detection. The sensitivity of several species to prey-simulating electric signals has been well described and maximal sensitivity for all species is <5nV cm-1, with median sensitivity typically in the range of 20-40nV cm-1 (Haine et al., 2001; Kajiura and

Holland, 2002; Kajiura, 2003; Jordan et al., 2009; Kajiura and Fitzgerald, 2009;

McGowan and Kajiura, 2009; Wueringer et al., 2012a). The number of electrosensory pores has not been correlated to sensitivity (Kajiura, 2001; Kajiura and Holland, 2002;

Kajiura, 2003; Jordan, 2008; Jordan et al., 2009;), but it has been hypothesized that pore distribution and density may correlate to some response behaviors (Raschi, 1986). For

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example, an increase in density of pores should result in an increase in spatial resolution, similar to increasing the resolution of a camera by increasing the number of pixels.

Jordan et al. (2009) described “overshooting” behavior in a batoid with low pore density, the pelagic stingray, Pteroplatytrygon violacea, in which rays initially passed by a prey-simulating stimulus before turning back to bite at a dipole. However, no empirical testing of a density-resolution correlation has been conducted and it is not known at present whether the behavior exhibited by pelagic stingrays was a result of low pore density or natural responses to a decrease in electric field strength. Additionally, most elasmobranchs have a similar maximum sensitivity to prey-simulating stimuli, but differences in median sensitivity have not been well correlated to any specific factor. It is possible that differences in diet, habitat, morphology, including pore distribution and density, and behavior give rise to differences in sensitivity, as well as other response characteristics like resolution.

Elasmobranch predator responses to phasic stimuli produced by prey are less- well known, in part due to a lack of information about these stimuli. Kalmijn (1972;

1974) conducted a broad survey of electric field production in marine organisms and reported that molluscs produce up to 10µV, crustaceans and elasmobranchs produce up to 50µV, and teleosts produce up to 500µV, with phasic modulation of that signal <20Hz in teleosts. Whereas these characteristics provide a general idea of the magnitude and frequency of electric fields produced by elasmobranch prey, there is no information regarding potentially ecologically significant variation of those signals within the scope of each group. The bioelectric field of a benthic flatfish or catfish may be very different in magnitude and frequency from more pelagic, mobile prey like a tuna or herring, for

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instance. Predators that are dietary specialists of one of these groups may use electric cues to localize prey differently than one that specializes on the other group.

Most work on electrosensory systems has been on sharks, but the batoids (skates and rays) show as much variation in behavior, morphology, lifestyle, habitat, and diet as sharks. More recent studies have elucidated some differences in response behaviors in batoids that correlate to some ecological factors. Atlantic stingrays have a wide geographical range on the east coast of the United States and have adapted to many types of habitats within that range, including a freshwater population in the St. Johns

River system in northeastern Florida (Snelson et al., 1988; Johnson and Snelson, 1996).

Freshwater is highly resistive to electric currents, and as a result, Atlantic stingrays in have a dramatically reduced sensitivity to prey-simulating stimuli freshwater when compared to seawater (McGowan and Kajiura, 2009). Shovelnose rays and sawfishes, on the other hand, are two groups of batoids with enlarged rostral regions compared to the typical batoid morphology and both groups have been known to use their rostrum in feeding behaviors (Wilga and Motta, 1998; Wueringer et al., 2012b). Electrosensory organs in both groups inundate the enlarged rostrum and help to orient the ray to the prey item for a strike and manipulation into the mouth (Wilga and Motta, 1998;

Wueringer and Tibbetts, 2008; Wueringer et al., 2011; Wueringer et al., 2012a;

Wueringer et al., 2012b;). Two species of Australian shovelnose rays which both reside in shallow, coastal habitats and have a similar rostral morphology have a four-fold difference in sensitivity to prey-simulating electric fields, which may correlate to differences in diet (Wueringer et al., 2012a); however, this hypothesis remains to be tested and emphasizes the need for future studies to investigate ecological correlates of

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sensory function. Elasmobranchs that exhibit differences in behavior, morphology, and diet will be suitable for such investigations.

VISION

While a few studies have investigated the effect of ecological factors, like predator and mate detection, on the functions of the electrosensory systems, such work is much more limited in studies of elasmobranch visual systems. Most ecological correlations have been made to the irradiance and spectral distribution of the photic environment. For example, deep-sea elasmobranchs show several visual characteristics that are adapted to low light and spectrally limited water; deep sea species typically have large eyes with large, immobile pupils, a well-developed tapetum lucidum, and rod photoreceptors that maximally absorb short wavelength, or blue, light (Lisney et al., 2012).

More recently, attempts have been made to describe the potential for color vision, visual fields, and retinal topography in an ecological context. After the discovery of cone photoreceptors in the eye of the lemon shark (Gruber et al., 1963), cones have been found in several species of elasmobranchs (Gruber et al., 1975; Gruber et al., 1991; Hart et al., 2004; Theiss et al., 2007; Hart et al., 2011; Schieber et al., 2012), with multiple types of cones present in some batoids. Color vision is possible with the presence of multiple types of visual pigments and the ability of the nervous system to compare the output of the photoreceptors, and recent behavioral evidence supports the hypothesis that multiple cone types in elasmobranchs does lend the ability to discriminate color (Hart et al., 2006; Van-Eyk et al., 2011; Lisney et al., 2012). The peak absorbance of photoreceptor visual pigments is typically similar to that of the dominant spectra in the environment with species that have multiple types of cones 7

being spectrally matched to and/or offset from the background light (Hart et al., 2004;

Theiss et al., 2007). The spectral sensitivities of three coastal sharks have peak sensitivities that are matched to the spectral distribution of their habitat during twilight, a period of heightened foraging (McComb et al., 2010). Although sensitivity to ultraviolet wavelengths has yet to be studied in any elasmobranch, the potential does exist for some species to be UV sensitive in order to enhance contrast for prey, predator, and conspecific detection due to their presence in high UV environments.

Temporal properties are also dependent on the light environment, with faster working eyes during photopic (light-adapted, daytime) conditions than scotopic (dark- adapted, nighttime) conditions in three coastal sharks (McComb et al., 2010). The bonnethead shark, Sphyrna tiburo, which inhabits bright water over seagrass beds, has a faster temporal resolution than the blacknose shark, Carcharhinus acronotus, which inhabits demersal, turbid, coastal waters (McComb et al., 2010). Additionally, the sandbar shark, Carcharhinus plumbeus, which can inhabit both dim and bright environments demonstrates a faster temporal resolution when collected from clear water compared to those collected from turbid water (Litherland, 2009).

The visual field of elasmobranch fishes is also adapted for ecological factors, which primarily reflect a species depth profile, lifestyle, and eye position (Lisney et al.,

2012). High densities of photoreceptors that improve resolution and image formation can occur in several places and in different arrangements in the retina. These areas of high concentration can occur in horizontal streaks or in concentric increasing concentrations called areae. Streaks and areae are positioned in the plane in which the plane of sight typically occurs for a species. Benthic species have dorsally positioned

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horizontal streaks with eyes canted ventrally that allow the animal to scan the benthos, whereas pelagic predators of marine turtles and birds have ventrally positioned horizontal streaks in dorsally canted eyes to scan the water surface for prey (Bozzano and Collin, 2000; Lisney and Collin, 2008). Physiological visual field work in batoids shows differences in horizontal and vertical visual fields that result from differences in eye position and relate to differences in behavior (McComb and Kajiura, 2008). For example, the benthic species with dorsally positioned eyes had expansive horizontal visual fields, which allows them to see in front of and behind their head. However, their vertical visual field was occluded by their disc, so visual detection of objects below their body, such as food, is not possible. In contrast, a benthopelagic species with laterally positioned eyes, the cownose ray, Rhinoptera bonasus, has a vertical visual field that is expanded to a full 360°, with a blind spot behind the head in the horizontal visual field.

Cownose rays group together in large schools, and the expanded visual field has been hypothesized to coordinate an individual’s position within the school (McComb and

Kajiura, 2008).

RESEARCH GOALS

Despite advances in elasmobranch electrosensory and visual capabilities, many studies still remain restricted to descriptions of basic capabilities, like sensitivity and morphology. The goals of this study were to describe the electrosensory and visual capabilities of two batoid elasmobranchs, the cownose ray, Rhinoptera bonasus, and the yellow stingray, Urobatis jamaicensis, in an ecological context. Cownose rays are schooling, benthopelagic batoids in turbid coastal and estuarine habitats (Smith and

Merriner, 1987; Neer and Thompson, 2005; Collins et al., 2007a; Collins et al., 2008). 9

Cownose rays feed primarily on benthic, immobile or slow-moving invertebrates like bivalve molluscs, gastropods, and echinoderms (Smith and Merriner, 1985; Collins et al., 2007b; Ajemian and Powers, 2012). Yellow stingrays are strictly benthic batoids in bright, clear reef and seagrass associated habitats and are dietary opportunists on small invertebrates, including crustaceans and polychaetes (Yanez-Arancibia and Amezcua-

Linares, 1979; Fahy, 2004; Ward-Paige et al., 2011). These differences in habitat, diet, and lifestyle are evident in their electrosensory and visual adaptations. The specific goals of this study were to:

1. Characterize the voltage and frequency properties of electric fields

produced by an array of elasmobranch prey items

2. Correlate differences in electrosensory pore distribution and density, diet,

morphology, and behavior of cownose rays and yellow stingrays to

differences in behavioral responses to prey-simulating electric fields

3. Using an integrative approach, quantify the sensitivity of cownose rays

and yellow stingrays to chromatic stimuli and determine the potential for

color and ultraviolet vision

4. Quantify changes in temporal resolution in both species in response to

fluctuations in photic environment and ambient temperature, and

correlate those changes to differences between the ecological

characteristics of the two species.

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CHAPTER 2: BIOELECTRIC FIELDS OF MARINE ORGANSISMS: VOLTAGE AND FREQUENCY CONTRIBUTIONS TO DETECTABILITY BY ELECTRORECEPTIVE PREDATORS1 ABSTRACT

Behavioral responses of elasmobranch fishes to weak electric fields have been well studied. These studies typically employ a stimulator that produces a dipole electric field intended to simulate the natural electric field of prey items. However, the characteristics of bioelectric fields have not been well described. The magnitude and frequency of the electric field produced by 11 families of marine organisms were quantified in this study.

Invertebrate electric potentials ranged from 14-28µV and did not differ from elasmobranchs, which ranged from 18-30µV. Invertebrates and elasmobranchs produced smaller electric potentials than teleost fishes, which ranged from 39-319µV.

All species produced electric fields within the frequency range that is detectable by elasmobranch predators (<16Hz), with the highest frequencies produced by the penaeids

(10.3Hz) and gerreids (4.6Hz). Although voltage differed by family, there was no relationship between voltage with mass and length of prey. Differences in prey voltage may be related to osmoregulatory strategies; invertebrates and elasmobranchs are osmoconformers and have less ion exchange with the surrounding seawater than teleosts species, which are hyposmotic. Voltage production was greatest at the mucous

1 Originally published in Physiological and Biochemical Zoology (Bedore, C.N. and S.M. Kajiura. 2013. Physiol Biochem Zool 86(3): 298-311). Reproduced with permission by University of Chicago Press. 11

membrane-lined mouth and gills as predicted, which are sites of direct ion exchange with the environment.

INTRODUCTION

Sensory stimuli are influenced by the medium in which they are produced and transmitted. For example, aquatic environments allow low frequency sound waves to be broadcast over longer distances and more quickly than in air (Urick 1975). The aquatic environment has also facilitated the evolution of sensory systems unique to that environment, such as electrosensory and lateral line systems. Electroreception has independently evolved several times in the vertebrate lineage and persists in all elasmobranch, chondrostean, and sarcopterygian fishes, all monotreme mammals, and some teleost fishes and amphibians (Bullock et al. 1983). Electroreception was also recently described in the Guiana dolphin (Czech-Damal et al. 2012). Electroreception is most well-studied in the elasmobranch fishes, the sharks, skates, and rays, and is used for prey detection (Kalmijn 1966; Kalmijn and Weinger 1981), conspecific recognition

(Tricas et al. 1995), predator detection (Sisneros et al. 1998; Kempster et al. 2012), and has been hypothesized to play a role in navigation and orientation (Kalmijn 1982;

Montgomery and Walker 2001).

All organisms produce minute, yet dynamic, electric fields that typically consist of direct current (DC) and alternating current (AC) components. Although the specific details regarding bioelectric fields are poorly understood, it is thought that standing DC fields surround an organism and result from ion leakage across mucous membranes, including the mouth, gills, cloaca, and siphons (Wilkens and Hofmann 2005). The 12

standing DC field can also be modulated by the opening and closing of the mouth and pharynx during ventilation or from rhythmic limb movement, thus imparting a frequency component (Wilkens and Hofmann 2005). High frequency AC fields

(>20Hz) arise from muscle contraction action potentials along the body of an animal

(Kalmijn 1972; Kalmijn 1974; Wilkens and Hofmann 2005), but are typically outside the detection range of electroreceptive marine organisms. Therefore, the standing and modulated DC electric fields are used by predators to detect and localize prey items in a highly conductive seawater environment (Kalmijn 1971; Kalmijn 1974; Eeuwes et al.

2008; Kimber et al. 2011).

The magnitude and frequency of bioelectric fields has been recorded for prey items of paddlefish, Polydon spathula, (Wojtenek et al. 2001), teleosts (Patullo and

MacMillan 2004; Eeuwes et al. 2008), elasmobranchs (Kalmijn 1972; Kalmijn 1974;

Haine et al. 2001), and the platypus, Ornithorhynchus anatinus (Taylor et al. 1992).

However, there is limited information regarding variation in frequency, magnitude of the electric field along the body, and voltage decay with distance from the source. Kalmijn

(1972; 1974) recorded voltage, or bioelectric potential, produced from a number of diverse elasmobranch prey items and reported that molluscs produce up to 10µV, crustaceans and elasmobranchs produce up to 50µV, and teleosts produce up to 500µV.

In a subsequent study, Haine et al. (2001) quantified the voltage and frequency produced by three species of teleosts and five species of invertebrates and reported that the voltage was greater at the mucous membranes, especially on the head, than the rest of the body.

These studies provided some information regarding the magnitude and frequency of the electric field, however, specific details are lacking.

13

Although there is a paucity of information regarding the characteristics of bioelectric fields of elasmobranch prey, the behavioral sensitivity of elasmobranchs to prey-simulating electric fields has been described for several species (Table 2.1).

Bioelectric fields are complex and multipolar in nature (Kalmijn 2000). Since higher order fields (quadrapole, octopole) decay extremely rapidly with distance from the source, bioelectric fields are best approximated as a dipole. A bioelectric field stimulus generator, that creates a dipole electric field in seawater, has been used in behavioral studies to quantify the sensitivity of elasmobranch fishes to weak electric fields (Kajiura and Holland 2002; Kajiura 2003; Jordan et al. 2009; McGowan and Kajiura 2009). The stimulus generator produces an electric field magnitude intended to replicate prey bioelectric fields based on measurements by Kalmijn (1972; 1974). Although these studies described a range of bioelectric field intensities for groups of marine organisms, a lack of detail makes it difficult to adequately simulate a target prey item’s electric field.

The goals of this study were to 1) quantify the bioelectric potential from a variety of invertebrate and vertebrate elasmobranch prey items, 2) characterize the voltage at different locations along the body of fish and with increasing distance, 3) quantify the frequency at which the electric field is modulated as a result of ventilation and 4) quantify the voltage produced by the bioelectric field stimulus generator previously employed to investigate behavioral responses of elasmobranchs to prey- simulating electric fields.

14

MATERIALS AND METHODS

Animal collection

Four species of invertebrates and 11 species of vertebrates from 11 families and five classes were chosen as representative elasmobranch prey items for this study (Table

2.1). All specimens were collected from South Florida waters and were acclimated to the laboratory conditions in a flow-through seawater system (35ppt salinity and 25-

27°C) for a minimum of 24 hours before experiments began. Mass (g) and length data

(cm) were collected from all individuals. Shell length (distance between lateral shell margins) was recorded for bivalves, total body length was recorded for arthropods, and total lengths were recorded for all fishes (Table 2.2). Bivalve body mass was determined by subtracting the weight of the empty shell from the shelled animal. All experiments were conducted in accordance with Florida Atlantic University IACUC protocol #A09-20.

Experimental apparatus

An electrophysiological technique was employed to quantify the voltage produced by selected elasmobranch prey items. Individual prey items were secured in an acrylic experimental tank (89x43x21cm) equipped with flow-through seawater. A non- polarizable Ag-AgCl electrode (E45P-M15NH, Warner Instruments, Hamden, CT,

USA) was fitted with a seawater/agar-filled glass capillary tube that terminated in a

100µm tip. The tip of the recording electrode was positioned <1mm from the source

(mouth, gills or body of prey item as specified below) and a similar reference electrode was positioned in the far corner of the experimental tank. The output from the two electrodes was differentially amplified (DP-304, Warner Instruments, Hamden, 15

Connecticut) at 1,000-10,000x, filtered (0.1 Hz – 0.1 kHz, 60 Hz notch; DP-304, Warner

Instruments & Hum Bug, Quest Scientific, North Vancouver, British Columbia), digitized at 1 kHz using a Power Lab® 16/30 model ML 880 (AD Instruments,

Colorado Springs, CO, USA) and recorded using Chart™ Software (v.5, AD

Instruments). All experiments were conducted at temperatures from 24°C-27°C.

Electrophysiology protocol

Voltage measurements were recorded from the most electrogenic (greatest electric signal) tissue in invertebrates; the incurrent siphon of the bivalves, the swimmerets of the penaeids and the book gills of the horseshoe crabs. Recordings from fishes were made along the body to determine what body region is the most electrogenic. All frequency measurements were recorded from the gills, except the penaeids, in which the frequency at the swimmerets was reported. Voltage and frequency measurements were averaged from three recordings at each location on an individual prey item. The mean voltage and frequency were reported and used in statistical analyses. Power spectrum analysis confirmed the fundamental frequencies of the background noise and bioelectric signals from the gills of each individual, which were averaged to determine the mean fundamental frequency for each family.

Bivalves. To measure the electric field from bivalves, an individual hard clam,

Mercenaria mercenaria, was positioned with the siphons towards the water surface.

The recording electrode was positioned at the opening of the incurrent siphon to record the direct current (DC) field produced by ion exchange from the gill lamellae. The voltage difference between the siphon and baseline electrical activity in the tank was determined to quantify DC electric potential.

16

Arthropods. Shrimp, Penaeus setiferus and P. duorarum, were secured to a 2cm square acrylic block, approximately the length of the cephalothorax, with superglue applied to the dorsal surface of the carapace. The recording electrode was placed at the swimmerets on the abdomen of an individual shrimp for voltage and frequency recordings. Horseshoe crabs, Limulus polyphemus, were secured with Velcro straps to a plastic mesh support on the dorsal carapace. The recording electrode was placed at the book gills of the horseshoe crab to record the magnitude and frequency of the bioelectric potential.

Teleost fish. Fish were lightly anesthetized with MS-222 (1:12,000-1:20,000 wt:vol) to a level that allowed ventilation and slight movements of the fins, but eliminated whole body movements. Individual fish were then restrained with Velcro straps on a submerged plastic mesh frame positioned on the lateral side of the fish, opposite of the recording electrode. Voltage was recorded at four positions along the length of the fish: mouth, gills, midway between the gills and tail, and caudal peduncle. To quantify the decay of the electric field over distance, the recording electrode was placed at the mouth and the fish was moved away from the electrode in 1cm increments using a computer- controlled linear translation track (eTrack-300 Linear Stage, Newmark Systems, Inc.,

Rancho Santa Margarita, CA). Fish were displaced until the signal was no longer greater than the background level of electrical noise in the tank. The mean of three voltage and frequency measurements was calculated for each individual from each location and distance increment.

To determine the distance at which an elasmobranch can electrically detect prey, the electric field (i.e., voltage gradient) was calculated as the first order derivative of the

17

recorded voltage. This equation was applied to literature values of elasmobranch electrosensitivity (Kajiura and Holland 2002; Kajiura 2003; Jordan et al. 2009;

McGowan and Kajiura 2009) to predict the distance at which each teleost fish can be detected by elasmobranch predators.

Sharks. Juvenile bonnethead sharks, Sphyrna tiburo, were anesthetized and secured in the tank as described for teleost fishes. Sphyrnid sharks are obligate ram ventilators, therefore the anesthetized sharks required supplemental ventilation with a submerged pump to move water over the gills between recordings. Because these sharks do not rhythmically pump water over their gills, the temporal component (frequency) was not measured. The mean of three voltage measurements for each location was calculated for each individual.

Stingrays. Yellow stingrays, Urobatis jamaicensis, were anesthetized as described for teleosts and were secured with Velcro straps to a submerged, rigid mesh platform.

Electric signals were recorded from the spiracle with rays in their natural orientation, and signals were recorded from the mouth, gills, middle of the ventral surface of the body and the base of the tail with rays oriented ventral side up. The mean of three voltage and frequency measurements was calculated for each individual.

Statistical analyses. One-way ANOVAs with α= 0.05 determined significant differences in mean voltage magnitude and frequency among families of elasmobranch prey and between locations on fish within a family. Data conformed to normality assumptions.

Regression analyses tested for relationships between voltage magnitude with length and mass. All analyses were performed with JMP statistical software (v.9.0.2, SAS Institute,

Cary, NC).

18

Bioelectric field generator electric potential

To verify the suitability of the bioelectric field stimulus generator (cf Kajiura and

Holland 2002) as a source of simulated bioelectric fields in behavioral assays, the voltage produced by the apparatus was measured at a range of current intensities at three temperatures (20°C, 25°C, ad 30°C) and three salinities (0ppt, 15ppt, and 35ppt).

Because the resistive properties of water change with salinity and temperature, the range of applied currents in each combination was different. Current intensities were applied in 1µA increments for freshwater (0ppt) and 10µA increments for brackish (15ppt) and saltwater (35ppt) treatments.

The stimulus generator was connected to an underwater cable that terminated in a pair of gold-plated stainless steel electrodes (Impulse Enterprise, San Diego, CA).

Electrodes were connected to a pair of water filled polyethylene tubes that were fitted to the underside of an acrylic plate that interfaced with the tank water through two 1mm holes with a 1cm separation distance that simulates a prey item. The plate was contained within an acrylic experimental tank (89x43x21cm) and the water temperature was controlled with an aquarium chiller/heater unit (SeaChill TR5, Teco, S.r.l, Ravenna,

Italy). The stimulus generator was powered by a 9V battery with a multimeter connected in series to monitor applied current. A recording electrode was placed in the center of the 1cm dipole and the reference electrode was placed in the far corner of the experimental tank. Voltage produced from the stimulus generator was recorded in the same manner as reported for prey measurements. The voltage produced at each applied current was recorded twice and averaged. Resistance (Ω) at each temperature and salinity was calculated according to Ohm’s Law.

19

The decay of a 52µV prey-simulating stimulus produced by the bioelectric field generator was quantified to determine if the experimental tank constrained the measured voltage decay of prey items. A pair of seawater-filled polyethylene tubes connected to the electrode output of the stimulus generator was positioned within the tank in a manner to mimic the position of fish during decay measurements. The tubing was secured to the mesh frame with a dipole separation distance of 1cm and the open ends were oriented toward the tip of the recording electrode, in the same orientation as the fish mouths. The stimulus was moved away from the electrode in 1cm increments until the stimulus was no longer larger than the ambient noise in the tank. The electric field was calculated as the derivative of voltage decay.

RESULTS

Bioelectric potentials (voltage) and frequencies differed dramatically among three families of invertebrates and eight families of fishes (Figure 2.1). The amplitude and shape of the recorded waveform varied among species and also at different locations along the body within a species. The ventilatory frequency ranged from 1.1 to 10.3Hz

(Figure 2.2). The frequency recorded from the swimmeret movement of the penaeids was 10.3Hz and was significantly faster than the frequencies produced by ventilation by all other families (ANOVA; F8,45= 35.01, p<0.0001). The ventilatory frequency of the book gills in the horseshoe crab, Limulidae (2.4Hz), was not different from the ventilatory frequencies of the vertebrates. Gerreidae frequency (4.6Hz) was faster than all other fishes, but did not differ significantly from Sphyraenidae (2Hz). A Fourier transform power spectrum analysis revealed that the fundamental frequencies from all teleost and elasmobranch species were within 0.1Hz of cyclic frequency measurements. 20

Among the invertebrates, Penaeidae had a fundamental frequency of 9.5Hz, compared to a cyclic frequency of 10.3Hz, and Limulidae had a fundamental frequency of 1.9Hz in the power spectrum, compared to a cyclic frequency of 2.4Hz. The dominant frequency of the background electrical noise was 0.06Hz and had very little power compared to the prey signal power (Figure 2.3).

Invertebrates and elasmobranchs produced significantly smaller electric potentials than most of the teleost fishes, except the catfish and mojarra (ANOVA;

F10,54= 9.27, p<0.0001) (Figure 2.4). Invertebrates produced a mean voltage of 17µV, the elasmobranchs produced a mean voltage of 25µV, and teleosts produced a mean voltage of 164µV, approximately eight times greater than the invertebrates and elasmobranchs.

Significant differences were also seen among the teleosts with the puffers

(Diodontidae), snapper (Lutjanidae), and barracuda (Sphyraenidae) producing the greatest voltage. The catfish (Ariidae) produced the smallest mean voltage of 39µV, approximately one order of magnitude smaller than that of the snapper mean voltage of

319µV. There was no relationship between electric potential and total length or mass within or among families (Table 2.3). Although there was a difference in mass between the two elasmobranch species, the voltage did not differ (Figure 2.5). Also, within the teleosts, the catfish and snapper were similar in mass, but differed greatly in voltage.

The magnitude of the voltage was greater at the head (mouth and gills) of fishes than the trunk and tail in all families, although they were not significantly different than the trunk and tail in some families (Table 2.4). The largest signal was recorded from the lutjanids, which produced a mean potential of 319µV at the gills.

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Voltage produced from the invertebrates and elasmobranchs was too weak to be recorded away from the source, therefore, voltage decay was recorded for teleosts only.

The electric potential was measured up to 15cm away from the mouth of fish and decreased dramatically within the first few centimeters as an inverse power function

(Figure 2.6). The electric field (i.e., voltage gradient) was derived from voltage decay measurements to determine at what distance from the source an electric signal remained within detection range for elasmobranch predators (Table 2.5). A 35nV cm-1 mean sensitivity for elasmobranchs was determined from literature values (Kajiura and

Holland 2002; Kajiura 2003; Jordan et al. 2009; McGowan and Kajiura 2009; Jordan et al. 2011) and based on this sensitivity, the detection distance for teleost fishes ranged from 32-75cm (Figure 2.6).

Bioelectric field stimulus generator electric potential

The bioelectric field stimulus generator sufficiently reproduced a wide range of biologically relevant electric stimuli that represented both invertebrate and vertebrate bioelectric fields (Figure 2.7). Resistance was inversely related to both salinity and temperature (Table 2.6). The stimulus generator produced 665-1615µV at applied currents from 3-7µA in freshwater, 111-1317µV at applied currents from 10-100µA in brackish water, and 60-707µV at applied currents from 10-100µA in saltwater. Voltage produced by the stimulus generator was recorded up to 5cm away from the source. The voltage decayed as an inverse square, while the electric field decayed as an inverse cube with distance from the source (Table 2.5).

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DISCUSSION

This study surveyed three families of invertebrate and eight families of fish prey that are consumed by elasmobranchs (Table 2.1). The magnitude and frequency of bioelectric fields differed among families, as well as among individuals. These results expand previous knowledge of the characteristics of bioelectric field production in elasmobranch prey.

Bioelectric fields in seawater are created by the release of charged ions from a biological source into a conductive medium (Kalmijn 1972; Wilkens and Hofmann

2005). The conductive properties of seawater enable propagation of these signals away from the source and they can act as signals for predators to detect prey (Kalmijn 1966), conspecifics to find mates (Tricas et al. 1995), and for prey to avoid predators (Sisneros et al. 1998). Marine animals release ions during their normal physiological processes of osmoregulation that take place at the gills, gastrointestinal tract, and renal glands

(Foskett et al. 1983). Therefore, mucous membrane-lined openings associated with these organs are active sites of ion exchange with the external environment and were expected to produce higher magnitude electric fields than the skin, which is relatively impermeable to ion and water loss (Foskett et al. 1983). Invertebrates and elasmobranchs maintain internal environments that are nearly isosmotic with their environment (Robertson 1953; Ballantyne 1997), and therefore, were expected to produce smaller electric potentials than teleosts that are hyposmotic and lose more ions to the environment (Foskett et al. 1983). Osmoregulatory strategies likely explained much of the difference in bioelectric potential between the large potential, hyposmotic teleosts and the small potential, isosmotic invertebrates and elasmobranchs. 23

Frequency measurements

The oropharyngeal cavity of fish is the major site of ion exchange that results from osmoregulation. The rhythmic expansion and contraction of the buccal and pharyngeal cavities during respiration alternately exposes and encloses the mucous membranes of the oropharynx and results in a modulation of the standing DC electric field (Kalmijn 1974; Wilkens and Hofmann 2005). For most families, the ventilatory frequency was ≤2Hz, which corresponds to the peak sensitivity frequency of elasmobranch electroreceptors. Only two families produced frequencies outside this range; the Gerreidae (4.6Hz) and the Penaeidae (10.3Hz) both had higher frequencies than the other families. The gerreids were the smallest of the fish studied and their higher ventilatory frequency may reflect the metabolic demands of smaller organisms, which require greater oxygen consumption per unit mass than larger organisms (von

Bertalanffy 1957). The frequency recorded from the penaeids reflects the collective movement of all pairs of the swimmerets rather than ventilatory movements.

Ventilatory frequency has not been previously measured from crustaceans, in part because the gills are protected beneath the carapace (Bauer 1999), which inhibits propagation of electric fields away from the animal. Therefore, the electric field produced by the rhythmic movement of the swimmerets was considered to be the most biologically relevant source that is most likely to be detected by elasmobranchs. Higher frequency stimuli which are outside the peak frequency range of elasmobranch electroreceptors, like the penaeids and gerreids, may possess sufficient power in their harmonics that are near the maximum sensitivity of the predator to stimulate electroreceptor cells. However, in the case of penaeids and gerreids, harmonics near the

24

peak sensitivity (i.e. 1-2Hz) were rare, and were ≤1% of the power of the dominant frequency when present (Figure 2.3). Although the fundamental frequencies of the elasmobranchs and teleosts were within 0.1Hz of the cyclic measurements, the fundamental frequencies of the invertebrates were 0.8Hz (penaeids) and 0.5Hz

(limulids) lower than the cyclic measurements, which likely reflect the variation in frequency within an individual that was not seen in the vertebrates.

Frequency was not recorded from the venerids or sphyrnids because they both had a constant water flow over the gills, rather than the rhythmic water flow created by pumping water over the gills. Voltage oscillations for all other groups correlated to respiratory movements, or swimmeret movements in the penaeids. These data are similar to those reported by Haine et al. (2001) for three teleost species, Pomacentrus amboinensis, Sillago sihama, and Gerres filamentosus, with electric field frequencies of approximately 1.5Hz. Kalmijn (1972) reported low frequency AC fluctuations (< 20Hz) in teleosts and that as frequency increased in an individual, electric potential increased accordingly. No such trend between voltage magnitude and frequency was seen in this study.

Voltage amplitude

The signal location considered to be the most likely to be detected by elasmobranch predators was used for inter-family comparisons. For teleost fishes and bonnethead sharks, the gills were used as the most biologically relevant signal, whereas the spiracle was used for stingrays. Only one location was measured from invertebrates and was used in comparisons of voltage magnitude among families. The mouth and gills of fish produced potentials that were 1.6-22.6 times greater than the trunk and tail (Table 2.4).

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The greatest voltage by any family was 319µV at the gills of Lutjanidae (Table 2.4,

Figures 2.4, 2.5). Similarly, Haine et al. (2001) recorded potentials from 40-100µV at the head of teleost fish and 1-2µV at the caudal peduncle, although details that describe interspecific variation were not provided.

The electric potential was greater in teleosts than in invertebrates and elasmobranchs, as reported in previous studies (Kalmijn 1972; Kalmijn 1974; Haine et al. 2001), although there was some overlap in voltage production among the groups

(Figures 2.4, 2.5). The invertebrates did not differ from the elasmobranchs and some teleost fishes, including the ariids, gerreids, and haemulids, even though the gerreids and haemulids produced voltage that was nearly double that of the invertebrates and elasmobranchs (Figure 2.5). Although most potentials from elasmobranchs were similar to those reported by Kalmijn (<50µV; 1972), the gills of the urolophids produced an average potential of 82µV. One individual produced 128µV at the gills, which was equal to the average voltage for the gerreids and greater than that of the haemulids.

Potentials greater than 2mV were recorded from an individual puffer and an individual mullet, both of which were excluded from statistical analyses. Although these individuals were assumed to be outliers, they may be indicative of extremes of natural variation in electric fields in teleosts and potentials of this magnitude were not previously reported, even for wounded individuals.

The cause of the wide range of variation within the teleosts remains speculative.

Although all bony fishes in this study are neopterygiians, the ariids belong to the basal teleost group, the ostariophysii, whereas all other fishes are among the most highly derived of the teleosts (Moyle and Cech 2004). Perhaps more important than the

26

phylogenetic relationships among species is variation in gill surface area with greater gill surface facilitating a greater exchange of ions leading to greater magnitude electric potentials (Gonzalez and McDonald 1994; Nilsson 2007). Although gill surface area has not been strongly correlated to body size, it has been correlated to activity with more active species possessing a greater area to accommodate higher oxygen demands (Gray

1954; Hughes 1966). In this study the benthically associated ariids were considered the most inactive fish (according to criteria described by Gray 1954) and also had the smallest potential. The Lutjanidae was considered the most active family in this study and produced the greatest potential. The diodonts, gerreids, haemulids, and sphyraenids are moderately active when compared to the ariids and lutjanids and produced intermediate voltages. Future studies should investigate the relationship between fish activity, gill surface area, and voltage production to verify this hypothesis.

In addition to variation in electric potential among families of teleosts, there was also variation among individuals within a family. Some of the individual variation may be attributed to minor differences in electrode placement between individuals (Wilkens and Hofmann 2005), as well as stress of restraint and anesthesia throughout experiments, which may have affected individuals differently. The individual variation in electric potential may also be attributed to unknown physiological states that differed among individuals. These differences likely reflect physiological responses that fish undergo as they encounter changes in oxygen, temperature, pH, and salinity in their environment.

The present study failed to find a relationship between electric potential with mass or length within or among families (Table 2.3). For example, the two

27

elasmobranchs differed greatly in mass, but produced similar bioelectric potentials

(Figure 2.5). In contrast, the ariids and the lutjanids were similar in mass, but the lutjanids produced a significantly greater voltage than the ariids. Although the R2 values obtained in regression analyses were too low to draw definitive conclusions, these results suggest that the previously reported size-related differences in potential may have been due to small sample sizes that did not adequately account for the variation inherent in bioelectric field production, or that size-dependent electric fields may be limited to invertebrates (Haine et al. 2001; Patullo and Macmillan 2004; Kimber et al. 2011).

Voltage decay and elasmobranch responses to prey-simulating electric fields

Electric signals were recorded up to 15cm from the mouth of teleost species with larger magnitude signals being detected farther away from the source than small signals. This indicates that species with larger potentials, like the lutjanids, can be detected from a greater distance than those with small signals, like the ariids (Figure 2.6). Voltage decayed over distance as an inverse power function (Table 2.5). The first order derivative of the power function yielded the electric field (µV cm-1) and was calculated to determine the distance at which elasmobranchs would be able to detect teleost prey.

An average sensitivity of 35nV cm-1 obtained from literature values of elasmobranch electrosensitivity (Table 2.1) was used to calculate the distance at which elasmobranch predators can detect their prey based on electric signals (Kajiura and Holland 2002;

Kajiura 2003; Jordan et al. 2009; McGowan and Kajiura 2009). Electric fields were reduced to 35nV cm-1 between 32cm (gerreids) and 75cm (sphyraenidae) (Figure 2.6).

The 35nV cm-1 value was based on median sensitivity from behavioral assays, and although most species responded to electric fields <1nV cm-1 in these studies, the

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percentage of responses to electric fields of this magnitude is small (Kajiura and Holland

2002; Kajiura 2003; Jordan et al. 2009; McGowan and Kajiura 2009). Therefore, theoretical detection distances based on median sensitivity represent a conservative estimate. If the <1nV cm-1 sensitivity is applied to the detection distance equation, the theoretical detection distance for the barracuda (Sphyraenidae) would be 4.4m from the source, which is likely an overestimation due to difficulty in extrapolating data beyond the range recorded in the laboratory from electric fields that may have been artificially constrained by the limits of the tank. Additionally, limited electrical noise within the experimental tank compared to that present in natural environments complicate extrapolation to the natural environment at greater distances. Caution was taken to minimize these constraints by measuring electric fields in mid-water and by placing the plastic mesh frame and acrylic block used to support study organisms on only one the side of the organism, opposite of the recording electrode. The bioelectric field stimulus generator demonstrated voltage decay as an inverse square function, and is indicative that the generator produced a near ideal dipole under the experimental conditions.

Although we expected prey stimuli to decay at the same rate as the prey-simulating dipole, all fish voltages decayed at a slower rate than an ideal dipole. Bioelectric fields are more complex and are composed of not only a dipole electric field, but also contain quadrapole, octopole, etc. components, which may account for the differences in signal decay between the prey-simulating stimulus and actual prey stimuli.

Behavior studies presented electric stimuli produced by the bioelectric field generator in the range of 39-64µV to elasmobranchs to quantify sensitivity to weak electric fields. The magnitude of these stimuli is equivalent to the voltage produced by

29

the mouth and gills of ariids and haemulids in the present study. Elasmobranchs detect and orient to these simulated stimuli from 22-40cm from the source (Table 2.1), similar to predicted detection distances calculated in this study (Table 2.5). The stimulus generator was designed to produce dipole electric fields that simulate those prey items measured by Kalmijn (1972). Voltage produced by the stimulus generator was recorded at temperatures and salinities (Figure 2.7), which reflect the physical properties of environments that are inhabited by elasmobranch fishes and that influence how electrosensory systems are used. We show that the stimulus generator does adequately simulate bioelectric fields of prey. Equations for the relationship between salinity, temperature, and voltage are provided (Table 2.6) to facilitate selection of an appropriate prey-simulating stimulus for use in studies investigating electrosensitivity.

Additionally, the stimulus generator produced a standing DC field only. With the frequency information described in this study, a new stimulus generator could be constructed to more accurately reproduce the modulation in this field that results from ventilatory activities of prey.

Bioelectric fields remain poorly understood and future studies should further investigate sources of AC and DC potentials, behavioral responses of elasmobranch predators to different components of electric fields, sources of variation within and among groups, as well as size-related differences in potential by recording voltage from a wider range of sizes within species to better understand how electroreceptive fishes detect and localize prey using electric signals.

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TableTable 2.1 2Median.1 Median Sensitivity, maximumsensitivity, detection distance,maximum and prey preferencesdetection of elasmobranch distance, predators and to preferredprey- prey of elasmobranchssimulating electric stimuli. to prey-simulating electric stimuli Species Common name Sensitivity Detection distance Preferred prey Reference

(nV cm-1) (cm)

Squalus acanthias Spiny dogfish 14 30 Teleosts, crustaceans1 Jordan et al. 2011

Mustelus canis Dusky smoothound 29 26 Crustaceans2 Jordan et al. 2011

Urobatis halleri Round stingray 29 40 Crustaceans3 Jordan et al. 2009

Myliobatis californica California bat ray 48 40 Bivalves, crustaceans4 Jordan et al. 2009

Pteroplatytrygon violacia Pelagic stingray 40 30 Squid, teleosts5 Jordan et al. 2009

Dasyatis sabina Atlantic stingray 5 44 Crustaceans6 McGowan and Kajiura 2009

Sphyrna tiburo Bonnethead shark 47 22 Crustaceans7 Kajiura 2003

Carcharhinus plumbeus Sandbar shark 30 32 Crustaceans, teleosts8 Kajiura and Holland 2002

Sphyrna lewini Scalloped hammerhead 25 31 Crustaceans, teleosts, Kajiura and Holland 2002

elasmobranchs9,10

Note: To facilitate comparison, all studies referenced here used the same bioelectric field generator and behavioral Note: To facilitate comparison,1 all studies references2 here used the3 same bioelectric field generator and analysis. References for diet composition: Jones and Geen 1977, Gelsleichter1 et al. 1999, Valadez-Gonzalez et 2al. behavioral2001, 4Gray et analysis.al. 1992, 5Wil sonReferences and Beckett 1970, for diet6Cook composition:1994, 7Cortes et al. 1996,Jones 8McElroy and Greenet al. 2006, 1977, 9Clarke Gelsleichter et al. 1999, 3 10 4 5 6 7 Valadez1971, Bush-Gonza 2003 lez et al. 2001, Gray et al. 1992, Wilson and Beckett 1970, Cook 1994, Cortes et al. 1996, 8McElroy et al. 2006, 9Clarke 1971, 10Bush 2003

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Table 2.2 Summary of species, total length (TL), mass, and sample size for elasmobranch prey measured in this Table 2.2 Summary of species, total length (TL), mass, and sample size for study. elasmobranch prey measured in this study

Class Order Family Species n TL range (cm) Mass range (g)

Bivalvia Veneroidea Veneridae (clam) Mercenaria mercenaria 6 8 57-76

Malacostraca Decapoda Penaeidae (shrimp) Penaeus setiferus, P. duorarum 6 7-8 2-3

Merostomata Xiphosurida Limulidae (horseshoe crab) Limulus polyphemus 6 13-16 33-49

Actinopterygii Perciformes Gerreidae (mojarra) Diapterus auratus 5 11-14 21-42

Haemulidae (grunt) Haemulon flavolineatum 2 19-21 161-165

Haemulon plumierii 1 14 52

Haemulon purra 7 27-30 325-500

Lutjanidae (snapper) Lutjanus griseus 6 24-37 230-800

Sphyraenidae (barracuda) Sphyraena barracuda 4 18-43 27-500

Siluriformes Ariidae (catfish) Ariopsis felis 3 35-37 397-410

Tetradontiformes Diodontidae (puffer) Chilomycterus schoepfi 5 7-20 21-215

Diodon holocanthus 4 18-19 172-298

Elasmobranchii Carcharhiniformes Sphyrnidae (bonnethead shark) Sphyrna tiburo 5 21-34 42-157

Myliobatiformes Urolophidae (yellow stingray) Urobatis jamaicensis 5 17-22 342-600

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Table 2.3 Summary of regression analyses of voltage against mass (g) and total length (cm) Mass Total length

Family Regression equation R2 p Regression equation R2 p

Sphyrnidae y= 17.141 + 0.031x 0.46 0.21 y= 11.729 + 0.302x 0.52 0.17

Urolophidae y= 54.637 - 0.050x 0.24 0.40 y= 25.248 + 0.278x 0.00 0.92

Ariidae*

Diodontidae y= 76.896 + 0.745x 0.14 0.33 y= 23.885 + 11.022x 0.07 0.49

Gerreidae y= 207.813 - 2.494x 0.36 0.29 y= 331.863 - 16.457x 0.24 0.41

Haeumulidae y= 77.278 - 0.018x 0.00 0.85 y= 77.210 - 0.230x 0.00 0.93

Lutjanidae y= 541.730 - 0.521x 0.61 0.07 y= 935.643 - 20.958x 0.52 0.11

Sphyraenidae y= 172.099 + 0.187x 0.24 0.51 y= 181.834 + 1.050x 0.02 0.85

Limulidae y= -25.778 + 1.320x 0.36 0.21 y= -90.079 + 8.070x 0.32 0.24

Penaeidae y= 1.724 + 2.366x 0.13 0.48 y= -4.310 + 1.691x 0.09 0.56

Veneridae y= 9.414 + 0.075x 0.02 0.80 y= 26.984 - 1.462x 0.01 0.86 Among all families y= 39.948 + 3.461x 0.06 0.07 y= 72.126 + 0.152x 0.05 0.07 * Mass and TL data not available for all specimens, so regression could not be performed on Ariidae.

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Table 2.4 Voltage produced by teleost and elasmobranch fishes and invertebrate prey

Family Mouth Gills Spiracle Trunk Tail F,p

Sphyrnidae 18.2±2.8A 18.5±2.6A 10.6±2.2B 11.2±2.6B 21.7, <.0001

Urolophidae 26.5±13.7A 83.1±32.4B 30.6±10.1A 11.9±6.7A 8.6±3.5A 16.0, <.0001

Ariidae 23.1±3.3AB 39.0±14.4A 8.8±4.0B 14.1±7.9B 7.1, 0.01

Diodontidae 113.1±91.9A 204.5±173.7A 17.3±9.6A 11.5±4.5A 3.2, 0.04

Gerreidae 99.4±55.3A 121.9±34.2A 9.0±1.7B 7.2±2.4B 16.9, <.0001

Haemulidae 40.9±19.7A 71.4±39.8B 12.5±7.3C 11.0±5.8C 15.8, <.0001

Lutjanidae 299.0±147.8A 319.1±138.4A 37.8±37.4B 14.8±4.7B 15.2, <.0001

Sphyraenidae 114.6±81.3AB 215.7±74.4A 17.8±19.2B 16.0±15.6B 11.3, 0.0008

Invertebrates Voltage

Limulidae 28.3±17.3

Penaeidae 7.5±1.8

Veneridae 14.6±3.8

Note: Voltage is reported as mean±SD. Locations within a family that share the same letter were not different from each other. Locations (shaded) deemed the most biologically relevant for elasmobranch detection were used for comparisons among families. Only one location was recorded in invertebrates and was used in comparisons.

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Table 2.5 Source voltage, electric field equations and theoretical elasmobranch detection distance (cm) from electric potential decay measurements, with a median sensitivity of 35nV cm-1 for elasmobranchs.

Family Point source voltage Electric field Detection distance

Ariidae y=15.98x-0.48 y=7.62x-1.48 38

Diodontidae y=76.55x-1.19 y=90.72x-2.19 36

Gerreidae y=74.39x-1.27 y=94.77x-2.27 32

Haemulidae y=32.65x-0.67 y=21.78x-1.67 47

Lutjanidae y=127.74x-0.97 y=124.30x-1.97 63

Sphyraenidae y=197.58x-1.02 y=201.33x-2.02 73

Prey-simulating y=118.59x-1.95 y=231.49x-2.95 20

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Table 2.6 Current-voltage relationships and resistance (Ω) calculated from the bioelectric field generator.

Temperature 20°C 25°C 30°C Salinity

0ppt y=251.05x+39.03 y=218.35x+70.61 y=211.03x+26.64

R=260.1 R=233.8 R=216.9

15ppt y=13.15x+2.24 y=12.07x+2.84 y=11.00x-1.40

R=13.2 R=12.2 R=11.0

35ppt y=7.05x+0.43 y=6.41x+0.08 y=5.82x+1.70

R=7.1 R=6.4 R=5.9

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Figure 2.1 Waveform shape, amplitude, and frequency of electric potential measured from elasmobranch prey items. Electric field characteristics were recorded from 11 families of elasmobranch prey. Representative waveforms from an individual of each family are shown for each location measured. Prey are scaled to the mean total length (cm) for each family, waveforms are scaled to the mean amplitude (µV) and frequency (Hz) for each family. All prey illustrations were reproduced with permission when necessary (Sphyrnidae, Ariidae, Gerreidae, Haemulidae, Lutjanidae, and Sphyraenidae are © Diane Rome Peebles; Urolophidae © Gillian Harris; Penaeidae permission by Florida Department of Agriculture and Consumer Services; Diodontidae permission by Encyclopaedia Britannica).

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Figure 2.2 Mean frequency (±SD) produced by invertebrate and vertebrate prey items. Frequency was recorded from the swimmerets of the penaeids (shrimp) and gills from all other prey. All families produced frequencies within the range detectable by elasmobranchs (<16Hz), and all families except Penaeidae and Gerreidae produced frequencies within the maximum range of sensitivity for elasmobranchs (≤2Hz). Bars that share the same letter are not different. White bar= invertebrate, gray= elasmobranch , and black= teleost.

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Figure 2.3 Sample power spectrum and waveform recorded from the gills of a barracuda (Sphyraenidae). The modulated prey signals (black) were dominated by a narrow-band fundamental frequency from 1.8-2.1Hz, which correlated with periodicity of the waveform. Dominant noise frequencies (grey) were broad-band and present from 0.06-1.7Hz at ≤0.15% power of the prey signal. Power spectrum data are plotted on the primary axes and waveform data are on the secondary axes.

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Figure 2.4 Mean electric potential (µV) for each family. The mean electric potential (±SD) differed among families. Invertebrates (16.8±13.1) and elasmobranchs (25.2±9.0) produced smaller potentials in general than teleosts fishes (163.8±137.9). Puffers (Diodontidae), snapper (Lutjanidae), and barracuda (Sphyraenidae) produced the greatest potential. Bars that share the same letter are not significantly different. White bar= invertebrate, gray= elasmobranch , and black= teleost.

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Figure 2.5 Voltage plotted against mass for all tested species (mean ±SD). There was no relationship between voltage and mass. The invertebrates had the smallest mass and the smallest potential. Although the bonnethead shark (Sphyrnidae) was smaller in mass than the yellow stingray (Urolophidae), they produced similar voltage. Teleosts showed the greatest variation in voltage and mass. The snapper (Lutjanidae) and catfish (Ariidae) were similar in mass, but the snapper produced nearly 300µV greater than the catfish. White= invertebrates, gray= elasmobranch, and black= teleost.

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Figure 2.6 Sphyraenidae (A) and Ariidae (B) voltage and electric field decay. Voltage was recorded up to 15cm away from the mouth of teleost fishes. For all teleosts, the magnitude of the voltage decreased as a power function (solid black line). The electric field (dashed blue line) was calculated as the derivative of the voltage. Elasmobranch median sensitivity of 35nV cm-1 was used to determine the detection distance for elasmobranch predators. Shaded regions under the electric field line represent the range of distances that each fish is detectable by elasmobranch predators. Fish illustrations © Diane Rome Peebles.

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Figure 2.7 Voltage produced by the bioelectric field generator at a wide range of biological current intensities at three temperature (20°C, 25°C, 30°C) and salinity (0ppt, 15ppt, 35ppt) treatments. Squares= 20°C, diamonds= 25°C, circles= 30°C; black= freshwater (0ppt), gray= brackish (15ppt), white= seawater (35ppt). Equations and resistance values are provided in Table 5.

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CHAPTER 3: ELECTROSENSORY ECOLOGY OF TWO BATOID

ELASMOBRANCHS

ABSTRACT

Electrosensory pore number, distribution, and sensitivity to prey-simulating electric fields have been described for many shark species. Electrosensory systems in batoids have received much less attention. Pore number and distribution have yet to be correlated to differences in sensitivity. However, pore number, pore distribution and sensitivity have been linked to behavior, diet, and morphology and follow species- specific trends. I report here that cownose rays have a greater number of pores than the yellow stingray, most of which are concentrated on the anterior ventral surface for both species. However, yellow stingrays have a broader arrangement of pores on both their dorsal and ventral surfaces than the cownose rays. Yellow stingrays demonstrated a median behavioral sensitivity to weak electric fields of 22nV cm-1 and were among the most highly sensitive batoids studied to date. Cownose rays were less sensitive than all other elasmobranch species with a median sensitivity of 107nV cm-1. As reported in previous studies, a higher pore number did not result in greater sensitivity. Cownose rays are benthopelagic schooling rays and may benefit from reduced sensitivity to bioelectric fields when they are surrounded by electrogenic conspecifics. Yellow stingrays, on the other hand, are typically solitary and bury in the substrate. A greater number of pores on

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their dorsal surface might improve detection of predators above them. Also, increased sensitivity and a broader distribution of pores is beneficial as small prey items move past a buried ray.

INTRODUCTION

The ability to detect weak bioelectric fields has evolved independently in several vertebrate lineages (Bullock et al. 1983; Czech-Damal et al. 2012). The most renowned and most sensitive group known to use electroreception is the elasmobranch fishes, the sharks, skates, and rays (Bullock et al. 1983). The elasmobranch electrosensory system is composed of receptor cells that line the lumen of a bulbous ampulla, which leads to a canal filled with a conductive glycoprotein gel that terminates as a pore on the surface of the skin (Hueter et al. 2004). These pores are the interface between the seawater environment and the internal environment of the animal. The receptors detect a voltage gradient between the pore and the reference potential at the receptor cell. The pores are distributed in species-specific arrangements on the head of sharks and on the dorsal and ventral body surfaces of skates and rays (Raschi 1986; Kajiura et al. 2010).

Pore distribution has been correlated with habitat, diet, morphology, and lifestyle

(Raschi 1986; Raschi and Mackanos 1987; Kajiura 2001; Jordan 2008; Wueringer and

Tibbets 2008; Kajiura et al. 2011; Wueringer et al. 2011), but neither pore distribution nor pore number have been correlated to sensitivity. (Kajiura 2001; Kajiura and Holland

2002; Jordan et al 2009). Nonetheless, pore number and distribution may still contribute to behavioral responses to bioelectric fields, perhaps by providing an increase in spatial resolution. For example, Raschi (1986) correlated high pore densities in skates with diets composed of immobile infaunal invertebrates and hypothesized that high pore 45

densities increase spatial resolution during prey localization. Additionally, broad distributions of pores across dorsal and ventral surfaces of skates were correlated with generalized diets of mobile infaunal and epibenthic invertebrates, as well as with mobile pelagic prey like squid and teleosts (Raschi 1986). These broad distributions are thought to expand the electrosensory search area so that prey farther from the mouth can be more easily detected.

The batoid order Myliobatiformes exhibits a wide range of morphologies, lifestyles, habitats, and diets (Figure 1), which may influence both pore distribution and the capability to detect and localize prey electrically. Yellow stingrays (Urobatis jamaicensis) are basal myliobatids with a strictly benthic lifestyle and generalized diet

(Yáñez-Arancibia and Amézcua-Linares 1979) and morphology. Cownose rays

(Rhinoptera bonasus) are among the most highly derived myliobatid rays and have adopted a benthopelagic lifestyle and specialized diet composed primarily of weakly electrogenic immobile or slow moving benthic prey (Smith and Merriner 1985; Collins et. al 2007; Ajemian and Powers 2012; Bedore and Kajiura 2013). The cownose rays possess a head morphology with paired extensions from the ventral surface (cephalic lobes) that are unique to the genus (Figure 3.1). The cephalic lobes are used to excavate prey from the substrate and are thought to contain a high density of electrosensory pores

(Chu and Wen 1979). However, pore number has not been quantified and detailed descriptions of pore distribution in this species are lacking.

Differences in pore number, distribution and density that are correlated to diet, morphology, and lifestyle may be reflected in the behavioral responses of batoids to pre- simulating electric stimuli. In this study, pore number, distribution and density, and

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behavioral sensitivity in cownose rays and yellow stingrays were quantified. Due to pelagic-associated schooling behavior and preferences by cownose rays for electrically cryptic invertebrate prey, it was predicted that cownose rays would demonstrate a greater density, but a more narrow distribution of electrosensory pores than yellow stingrays. It was also expected that cownose rays would be more sensitive to prey- simulating electric stimuli than yellow stingrays.

MATERIALS AND METHODS

Electrosensory pore number and distribution

To determine the influence of electrosensory pore distribution and density on behavior, the number of pores was quantified from five cownose rays, Rhinoptera bonasus (disc width (DW) 36-57 cm; 3 female, 2 male), and five yellow stingrays,

Urobatis jamaicensis (DW 16-21 cm; 3 female, 2 male), obtained from incidental mortalities in other studies or from the Florida Museum of Natural History Ichthyology

Collection. Individual pores were counted under a magnifying lens and each pore was marked to prevent recounting. To minimize bias of a single observer, the left and right sides of each specimen were counted by different observers. The two counts were summed to provide dorsal, ventral, and total pore number.

High-resolution digital photographs of one specimen of each species were used to construct a digital representation of the electrosensory pore distribution on the bodies of the cownose ray and yellow stingray. The digital pore map was then used to quantify dorsal and ventral pore coverage areas and the density of pores within the respective dorsal and ventral pore fields. The pore fields and body surface areas were calculated by placing landmarks on the pore map to accurately represent the outlines of the 47

respective surface areas (adapted from Jordan, 2008). Body surface landmarks were: 1. most anterior point on the rostrum (yellow stingrays) or cephalic lobe (cownose rays), 2. most lateral point of the pectoral fin margin, 3. pectoral-pelvic fin intersection, and 4. most posterior point of the cloaca. Dorsal pore field landmarks were: 1. most anterior pore on the rostrum or cephalic lobe, 2. most lateral pore on the disc-pectoral fin margin,

3. and most posterior pore on the head. Ventral pore field landmarks were: 1. most anterior pore on the rostrum or cephalic lobe, 2. most lateral pore on the pectoral fin, 3. most posterior pore on the disc, and 4. most posterior pore on the branchial basket. The surface area (cm2) contained within the landmarks for body surface and pore coverage area was quantified using ImageJ (NIH). Percent pore coverage was taken as the proportion of the body surface that contained the pore field. Density was calculated as total number of pores contained within the pore coverage area (pores cm-2). To minimize the effects of differences in density due to body size and shape, the pore map of both species was scaled to the same disc length (20cm).

Behavioral sensitivity

Animal collection and maintenance

Behavioral electrosensitivity for 12 juvenile cownose rays (DW 35-53 cm; 6 female, 6 male) and eight adult yellow stingrays (DW 17-20 cm; 6 female, 2 male) was quantified. Cownose rays were collected by gillnet and cast net from Sarasota Bay,

Florida, U.S.A. Animals were maintained for 3-7 nights in a holding tank at Mote

Marine Laboratory in Sarasota prior to their transport to the Florida Atlantic University

(FAU) Marine Science Laboratory at Gumbo Limbo (GL) Environmental Complex in

Boca Raton. Yellow stingrays were collected by hand-net from patch reefs near Long

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Key, Florida. Rays were held in holding tanks at Keys Marine Laboratory in Layton for

1-3 nights before transport to the FAU GL facility.

All rays were held in large indoor tanks with flow-through seawater at temperatures 25-29°C and salinity of 35 ppt prior to and during behavior trials. Cownose rays were held in a 4.9 m diameter round tank and yellow stingrays in a 1.2 x 2.4 x 1.2 m rectangular tank. Rays were fed to satiation daily for a minimum of one month before experiments. All rays were individually tagged with colored plastic tags for identification during the experimental period. All procedures with live animals were approved by Florida Atlantic University Institutional Animal Care and Use Committee

(#A08-34).

Experimental apparatus and protocol

A behavioral assay was employed to quantify the sensitivity of cownose rays and yellow stingrays to prey-simulating electric fields. Cownose rays were tested within the

4.9 m holding tank. A barrier constructed from PVC pipe and plastic mesh was used to isolate the experimental animals from the other rays in the tank. Yellow stingrays were tested in a rectangular tank, identical in its dimensions to that of the holding tank. Single cownose rays were unresponsive to electric stimuli, so two rays were tested simultaneously in all trials.

A 1 x 1 m acrylic plate was placed on the bottom of the experimental arena in each tank. For cownose rays, graded acrylic ramps were secured to the acrylic plate to provide a smooth transition from the tank bottom onto the acrylic plate. For yellow stingrays, the acrylic plate was placed on the sand and the margins buried to blend the plate with the substrate. Four electric dipoles, each with a separation distance of 1 cm,

49

were equally spaced on the plate. Seawater filled polyethylene aquarium tubing was fitted on the underside of the plate at each dipole to provide a salt bridge. The tubing was connected to a pair of gold-plated stainless steel electrodes joined to an underwater cable (Impulse Enterprise, San Diego, CA, USA). The four electrodes were connected to an electric stimulator (cf Kajiura and Holland, 2002) powered by a 9V battery with a multimeter connected in series to monitor applied current. A 20 cm diameter reference circle surrounded each dipole to provide a calibration measurement for subsequent video analysis (Figure 2).

Food was withheld for 24 hours (cownose rays) or 48 hours (yellow stingrays) prior to experimentation to ensure rays were motivated to behaviorally respond to prey- simulating stimuli. Two rays were placed into the experimental arena or tank and allowed them to acclimate to the dipole array for a period of 30-60 minutes. Upon acclimation, a food odor (squid or scallop rinse) was delivered to the tank through an odor delivery tube mounted flush to the center of the array. These odors initiated prey- searching behavior. When the ray began to forage near the electrode array, the odor delivery was stopped and a 12µA prey-simulating electric stimulus (Bedore and Kajiura

2013) was applied randomly to one of the four electrode pairs, with the three inactive dipoles acting as controls. Once a ray bit at an active dipole, the electrode pair was immediately switched off and another dipole was randomly activated. Trials lasted a maximum of one hour and each ray was tested a maximum of three times. Responses were recorded with a Sony HDR-CX260 digital video camera (30 frames s-1; Tokyo,

Japan) mounted above the center of the array, 1.5m above the water surface.

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Video analysis

Video footage was imported into a computer, edited with iMovie (v 7.1.4, Apple

Computer, Inc.) and analyzed using ImageJ (NIH). The frame in which an orientation was initiated was exported as a still image and the distance (r) and angle (θ) to the dipole axis at the point of orientation was measured (Figure 3.2). To ensure rays did not influence responses of one another, frames in which both rays were present were excluded from analysis. The electric field strength (E) at which a ray initiated a response to the electric stimulus (sensitivity) was calculated for all orientation responses using the following ideal dipole equation (Kalmijn 1982 and Kajiura and Holland 2002):

� = !"# ���� !!!

(1) where ρ= resistivity of seawater (18.0-19.2 mΩ cm), I= applied current (12.0 µA), d= dipole separation distance (1 cm), r= orientation distance (measured to the nearest hyoid electroreceptor cluster; cm), and θ= orientation angle (with respect to the dipole axis; degrees) (Figure 3.2).

Statistical analyses

Electrosensory pore number

ANOVA was used to test for significant differences in dorsal, ventral, and total pore counts between species and paired t-tests to determine if there were significant differences between dorsal and ventral pore counts within a species. Regression analysis was used to determine if pore number varied significantly with disc width.

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Behavioral sensitivity

Bite responses that included a change in swimming trajectory as the dipole was approached (orientation) were used to calculate the strength of the electric field at the point of orientation as a measure of behavioral sensitivity to prey-simulating electric fields. Responses >1µV cm-1 were omitted from analysis (10% for cownose rays and 0% for yellow stingrays) because they occurred too close to the dipole to be measured accurately. Non-parametric analyses were used to determine if there were significant differences between species in orientation distance (cm) and sensitivity (nV cm-1).

Generalized linear models (GLM) were used to determine if there was an effect of sex or disc width on orientation distance and sensitivity within each species. Orientation distance and sensitivity data were log-transformed to achieve normality. Because behavioral responses are typically right-tailed, median values were used to compare sensitivity and detection distance between the two species (Sokal and Rohlf 1994;

Kajiura and Holland 2002; Kajiura 2003; Jordan et al. 2009; McGowan et al. 2009). All statistical analyses were performed using JMP statistical software (v.9.0.2, SAS

Institute, Cary, NC) and α=0.05.

RESULTS

Electrosensory pore number and distribution

The number of electrosensory pores for each species is reported in Table 3.1.

Cownose rays had significantly fewer dorsal pores than the yellow stingray (ANOVA:

F1,13 = 26.29, P <0.001), but a significantly greater number of ventral and total pores

(F1,13 = 185.29 , P <0.001 and F1,13 = 118.79, P <0.001, respectively; Figure 3.3). Dorsal pore number was significantly lower than ventral pair number in both species (paired t- 52

test: cownose ray: t4 = 21.04, P <0.001; yellow stingray: t4 = 50.34, P <0.001). Pore number did not vary with disc width for either species (regression analysis; cownose

2 2 ray: R = 0.48, F1,3 = 2.75, P = 0.20; yellow stingray: R = 0.21, F1,3 = 0.82, P = 0.43).

Cownose rays had smaller dorsal and ventral pore coverage areas than yellow stingrays (Table 3.1; Figure 3.3). Pores on the dorsal surface of the cownose ray were limited to the medial anterior portion of the body and extended just posterior and lateral to the branchial basket. Pores on the dorsal surface of the yellow stingray extended from the most anterior margin of the body to the most posterior margin of the disc. Pores extended laterally to approximately halfway between the midline and lateral margin of both pectoral fins. Pores on the ventral surface of both species occurred primarily on the anterior portion of the body, with most pores located anterior to the fifth gill slit and a high concentration of pores around the mouth. Ventral pores of yellow stingrays, however, extended along the lateral margin of the disk and had a lower density within the ventral pore field than cownose rays (Table 3.1). Ventral pores of the cownose ray were more concentrated anteriorly than yellow stingrays, with the greatest concentration of pores around the mouth and on the margins of the cephalic lobes.

Behavioral sensitivity

Cownose rays and yellow stingrays demonstrated 625 and 295 bite responses, respectively, to prey-simulating dipole electric fields (Table 3.2). Of those, cownose rays demonstrated 347 orientations to the dipole and yellow stingrays demonstrated 141 orientations. Cownose rays initiated orientations to dipoles from significantly closer distances and hence greater electric field strengths than yellow stingrays (Kruskal-

Wallis: H1 = 63.73, P <0.001 and H1 =65.09, P <0.001, respectively). For cownose rays

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the median orientation distance was 7.5cm and the maximum orientation distance was

26.8 cm (Figure 3.4). These values yielded a median sensitivity of 106.5 nV cm-1 for cownose rays and a minimum electric field detected, or best response, of 0.3 nV cm-1.

For yellow stingrays the median orientation distance was 11.1 cm and the maximum orientation distance was 30.8 cm. These values yielded a median sensitivity of 22.6 nV cm-1 and a minimum electric field detected of 0.2 nV cm-1. Cownose rays demonstrated

43.5% of their responses to electric fields < 100nV cm-1, whereas yellow stingrays demonstrated 85.8% of responses < 100nV cm-1. Sex and disc width did not have an effect on orientation distance (GLM: cownose rays: F2,310 = 0.33, P = 0.72; yellow stingrays: F2,138 = 1.60, P = 0.21) or sensitivity (cownose rays: F2,310 = 0.24, P = 0.79; yellow stingrays: F2,138 = 0.36, P = 0.70). Both rays failed to respond to the active dipole a small number of times, however, neither species bit at an inactive (control) dipole.

Both rays also occasionally failed to bite at the target and instead bit elsewhere near the target. Sixty-eight of the total number of bites (10.9%) for cownose rays were ≥1 cm outside of the target, whereas 112 bites (38.0% of the total number of bites) were inaccurate for yellow stingrays. Inaccurate cownose bites were 4.9 ± 2.2 cm (mean ±

SD) from the dipole center and inaccurate yellow stingray bites were 3.4 ± 1.6 cm from the dipole center.

DISCUSSION

The peripheral morphology and behavioral sensitivity of the electrosensory system in two batoids were quantified to compare potential differences in sensitivity that may result from morphology, diet, or behavior. Whereas the hypothesis that the cownose ray will possess a greater number, but more limited distribution of pores, was supported, 54

the hypothesis that the cownose ray will demonstrate greater sensitivity to prey- simulating electric fields than the yellow stingray was rejected.

Electrosensory pore number and distribution

The number of electrosensory pores for the two batoids in this study, the cownose ray, Rhinoptera bonasus, and the yellow stingray, Urobatis jamaicensis, were within the ranges of those reported for other batoids (Raschi 1986; Jordan 2008;

Wueringer and Tibbets 2008; Wueringer et al. 2011; Kempster et al. 2012). As predicted, the yellow stingray had a significantly greater number of dorsal pores than the cownose ray; the yellow stingray is strictly benthic and these pores are likely an advantage to benthic rays for electrical detection of signals emitted from predators that swim overhead. In contrast, the cownose ray is benthopelagic and is typically higher in the water column than most electrical signals the rays would encounter.

Both species feed on benthic prey and therefore, both benefit from a large number of ventral pores. Although both species possess more pores on the ventral surface than the dorsal surface, the cownose ray had a significantly greater number of pores on the ventral surface than the yellow stingray. The distribution of both dorsal and ventral pores was more widespread for the yellow stingray. Pores on the cownose ray were limited to the head and anterior wing margin, whereas pores on the yellow stingray extended from anterior to posterior and out to the lateral disc margins. On the ventral surface of the cownose ray, pores were highly concentrated around the mouth and anterior margins of the head. The limited distribution and high density of pores around the mouth is thought to increase resolution for detection of immobile buried and cryptic prey items (Raschi 1986), like bivalve molluscs and echinoderms, which are major prey

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types of cownose rays (Smith and Merriner 1985; Collins et al. 2007; Ajemian and

Powers 2012). The more widespread distribution in the yellow stingray is likely to increase the electroreceptive surface area on the body, which confers a greater search area to detect their mobile infaunal prey, like polychaetes and small crustaceans (Raschi,

1986; Yáñez-Arancibia and Amézcua-Linares 1979).

Pore distribution can also be influenced by other factors, including swimming style and body morphology. Cownose rays swim with large oscillatory flapping movements of the wings (Rosenberger 2001; Schaefer and Summers 2005), and pores located at the wing tips would experience significant electrical noise due to their movement through the seawater. This likely constrains pore distribution to the immobile portions of the body with lateral extension of pores terminating medial to the axis of bending in the pectoral fin (Schaefer and Summers 2005). Conversely, the yellow stingrays swim by small undulations of the lateral fin margin (Rosenberger 2001;

Schaefer and Summers 2005), so pores can be positioned closer to the lateral disc margin without compromising the electrical signal that they receive.

The body shape of the yellow stingray is typical of that of basal Myliobatiformes

(Figure 3.1) and the pore distribution is similar to that of the closely related round stingray and Atlantic stingray (Jordan 2008, McEachran and Aschliman 2004). The cownose ray is similar in body shape to the more derived Myliobatiformes (Figure 3.1) with laterally expanded pectoral fins and increasing degrees of cephalization. For example, the eagle rays and bat rays have a single, enlarged rostrum (Karl and Obrebski

1976; Sasko et al. 2006; Schluessel et al. 2010; Ajemian et al. 2012). Cephalization is more pronounced in the most derived Myliobatiformes, the cownose rays, mantas, and

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mobulas, which have paired extensions of the anterior pectoral fins, called cephalic lobes, on the anterior margin of the head (González-Isáis 2003; McEachren and

Aschliman 2004; Sasko et al. 2006). In all species with some degree of cephalization, the rostrum or cephalic lobes are involved in feeding and can function in prey manipulation and excavation, as well as detection and localization (Karl and Obrebski

1976; González-Isáis 2003; Sasko et al. 2006; Schluessel et al. 2010; Adnet et al. 2012;

Ajemian et al. 2012). The cephalic lobes of cownose rays are more flexible than other

Myliobatiformes (González-Isáis 2003; Sasko et al. 2006) and are inundated with electrosensory pores, unlike the mobulas and mantas, which lack electrosensory structures on the cephalic lobes (Chu and Wen 1979). Electrosensory pores on the cephalic lobes of cownose rays likely contribute to high resolution during feeding when the lobes are extended and a ray is searching for prey along the substrate. Although it has been suggested that high pore densities result in increased resolution, this hypothesis has yet to be empirically tested.

Behavioral sensitivity

Both yellow stingrays and cownose rays detected electric fields <0.5nV cm-1, similar to other batoid and shark species studied with the same technique (Table 3.3).

However, their median sensitivities differed with the yellow stingray demonstrating much greater sensitivity than the cownose ray. The yellow stingray initiated 85.8% of responses to stimuli at <100nV cm-1 whereas the cownose ray initiated only half that number (43.5%) of responses to voltage gradients <100nV cm-1. Although the electric stimuli were identical for both species, differences in physiology and behavior between

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the two rays required variations in the experimental design that could have affected responses.

Cownose rays are physically larger and are active swimmers, so they required a greater absolute search area in behavior trials. Although a larger search area alone should decrease responsiveness in cownose rays by decreasing the chances of interacting with the dipole, their activity level offset the differences in experimental arena size.

Cownose rays spent considerably more time searching for prey, which resulted in an increased number of responses by each ray compared to yellow stingrays. The decreased sensitivity in the cownose ray may have been an artifact of decreased motivational state when compared to the yellow stingrays, although this is unlikely. Yellow stingrays were fasted 48 hours before each trial and cownose rays 24 hours before each trial. The metabolic physiology of these rays necessitated these differences in fasting periods; yellow stingrays are similar to other benthic batoids that rest buried in the substrate for most of the day, and therefore, have a slow metabolism (Di Santo and Bennett 2011).

Cownose rays experience a two-fold increase in metabolic rate at the same temperature as a benthic batoid (Neer et al. 2006; Di Santo and Bennett 2011), so fasting for 48 hours would have had detrimental effects on the health of cownose rays. Differences in dietary composition between the two species may have contributed to differences in responsiveness with the prey-simulating stimuli being more attractive to foraging cownose rays than yellow stingrays. Although the diets of yellow stingrays have considerable overlap with cownose rays in southwest Florida (Yáñez-Arancibia and

Amézcua-Linares 1979; Collins et al. 2007), cownose ray diets typically have a greater proportion of stationary or slow-moving invertebrates than yellow stingrays. Our prey-

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simulating stimulus was strictly DC, more similar to a bivalve with a constant flow of water through the gills than other invertebrates which have an additional, modulated component associated with rhythmic gill movements (Kalmijn 1974; Bauer 1999;

Bedore and Kajiura 2013). Both species readily searched for food when the prey odor was introduced, and both species vigorously bit at the dipole and consumed food during trials, so the stimulus was likely appropriate for replicating a range of invertebrate prey items for both species.

Life history characteristics and feeding behavior are more likely to describe the differences in sensitivity between the species than variations in experimental design.

The diet of cownose rays and yellow stingrays may contribute to differences in feeding behavior between the two species. The combined electric and mechanical (lateral line) stimuli produced by mobile prey items provides yellow stingrays with the required information to localize a prey item in the absence of visual cues during prey striking behavior with their ventrally positioned mouth (Maruska 2001; Gardiner et al. 2012). A large part of the juvenile cownose ray diet consists of weakly electrogenic (Bedore and

Kajiura 2013) immobile or slow-moving infauna (Smith and Merriner 1985; Collins et al. 2007; Ajemian and Powers 2012). The high density of pores surrounding the mouth and on the cephalic lobes may increase resolution and accuracy in localization of a stationary, buried prey item in the absence of lateral line and visual stimuli (Raschi

1986; Jordan et al 2009). During behavior trials, yellow stingrays failed to accurately locate the center of the prey-simulating dipole in 38% of bite responses, whereas cownose rays failed to localize the target only 10% of the time, thus supporting the hypothesis that increased pore density leads to increased spatial resolution.

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The schooling behavior of cownose rays may also affect behavioral sensitivity.

Schooling rays are perpetually surrounded by conspecifics, each with their own bioelectric field. A large proportion of dorsal pores aids in detection of conspecifics of benthic batoids (Tricas et al. 1995; Sisneros et al. 1998; Sisneros and Tricas 2002) and could benefit cownose rays in maintaining position within a school. However, rays may experience overstimulation of the electrosensory system while schooling due to exposure to the additive voltage component of electric fields by schoolmates, as well as from asynchronous wing movements of different frequencies and periodicity by each neighbor (personal observation). Also, the electrical noise created by schooling conspecifics likely exceeds the electric fields of their prey, which can be as small as 10-

30µV (Kalmijn 1974; Haine et al. 2001; Bedore and Kajiura 2013). The small number of dorsally positioned pores, small dorsal pore coverage area, and reduced electrosensitivity may decrease the processing demand on the central nervous system.

Although pelagic-associated elasmobranchs tend to have smaller cerebellar-like areas for discrimination of electrosensory information than their benthic counterparts (Kajiura et al. 2010; Yopak et al. 2010; Yopak et al. 2012), central integration and neuronal convergence from the ampullary neurons to the CNS in pelagic and schooling species need to be considered in future studies to determine differences in information processing that may be related to schooling.

Although cownose rays may experience a decrease in behavioral electrosensitivity, they likely benefit from enhanced visual systems to enable maintenance of position in a school and facilitate prey detection. Cownose rays have laterally positioned eyes, rather than dorsally positioned as in most batoids, which

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affords an expanded vertical field of view, and enables them to visualize objects both above and below their heads (McComb and Kajiura 2008). The expanded visual field may aid in schooling behaviors and tracking of conspecifics. Although the initial cues used in prey detection by cownose rays are currently unknown, it is interesting to note that rays performed poorly in behavior trials when tested individually and demonstrated a dramatic improvement in prey searching behavior and responsiveness when tested in pairs. Potentially these rays use a cooperative feeding strategy and rely in part on visual conspecific cues to initiate feeding behaviors (Galef and Giraldeau 2001).

Behavioral ecology

Electrosensory pore number and distribution are poor predictors of behavioral sensitivity to prey-simulating electric fields. For example, hammerhead sharks benefit from a greater number of pores spread across a larger surface on their laterally expanded cephalofoil, which increases electrosensory search area but not sensitivity (Kajiura

2001; Kajiura and Holland 2002). In this study, the cownose ray had a greater number of pores with a higher pore density on the anterior ventral surface of the head, but was less sensitive to prey-simulating electric fields than the yellow stingray. The difference in behavioral sensitivity is likely a reflection of differences in behavior, diet preferences, and body morphology rather than differences in electrosensory pore number. Cownose rays are among the most highly derived batoids and their pore distribution has evolved to best fit their benthopelagic schooling behavior and diet preferences. The cownose ray benefits from a high density of pores surrounding the mouth and on the cephalic lobes to better enable accurate localization of a weakly electric, stationary prey source.

Conversely, the yellow stingray was more sensitive to prey-simulating electric fields,

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but resolution is sacrificed for a wider distribution of pores that allow a greater electrosensory search area to detect mobile prey. Future studies should empirically test the hypothesis that higher densities of pores do result in greater resolution. The role of electroreception in schooling elasmobranchs should also be examined in more detail to determine if electrosensory cues are used for maintenance of position within a school.

Investigations of multimodal integration in schooling species will also shed light on specific cues used in prey detection while schooling. If sensitivity is decreased because of schooling behavior, cownose rays may rely more heavily on olfactory cues, as well as visual cues from other members within the school to indicate the source of buried invertebrate prey. This study illustrates the complexity of sensory ecology and that species may vary greatly in their sensory capabilities due to a wide range of factors, which should be considered in detail when investigating the significance of sensory system function.

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Table 3.1 Electrosensory pore number, density, and pore coverage area.

Cownose ray Yellow stingray

Rhinoptera bonasus Urobatis jamaicensis

Dorsal Ventral Dorsal Ventral

Pore number 134.4 ± 8.1 1096.4 ± 107.5 187.4 ± 14.3 627.2 ± 25.9

(± SD)

Coverage area 11.7 19.7 23.6 46.7

(%)

Density 1.7 8.3 2.5 4.2

(pores cm-2)

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Table 3.2 Summary of behavioral responses of two species of batoids to prey- simulating electric fields.

Response Cownose ray Yellow stingray

Total number of bites 625 295

Bites with orientation 313 141

% orientations <1000nV cm-1 90.2 100.0

% orientations <100nV cm-1 43.5 85.8

Median detection distance (cm) 7.5 11.2

Median sensitivity (nV cm-1) 107 22

Maximum orientation distance 26.8 30.8

Best response (nV cm-1) 0.31 0.23

% Inaccurate bites 10.9 38.0

Note: The best response and maximum orientation distance were calculated from an individual of each species. Median sensitivity and distance were calculated from all responses <1000nV cm-1 for each species. % inaccurate bites were calculated as a percentage of all bites.

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Table 3.3 Behavioral electrosensitivity of batoid elasmobranchs. Table 3.3 Behavioral electrosensitivity of batoid elasmobranchs.

Species Common name Minimum Median Lifestyle Reference

(nV cm-1) (nV cm-1)

Rhinoptera bonasus Cownose ray 0.3 107 Benthopelagic Present study

Urobatis jamaicensis Yellow stingray 0.2 22 Benthic Present study

Myliobatis californica Bat ray 0.1 48 Benthopelagic Jordan et al. 2009

Urobatis halleri Round stingray 0.3 29 Benthic Jordan et al. 2009

Pteroplatytrygon violacea Pelagic stingray 0.3 40 Pelagic Jordan et al. 2009

Dasyatis sabina Atlantic stingray 0.6 5 Benthic McGowan and Kajiura 2009

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Myliobatiformes

Urotrygonidae Dasyatidae Myliobatidae

Urobatis Dasyatis Myliobatis Aetobatus Rhinoptera Manta

Body Morphology

Ventral head morphology

Figure 3.1 Cephalic variation within the order Myliobatiformes. Basal members (Dasyatis and Urobatis) lack cephalic specialization whereas derived members (Myliobatis, Aetobatus, Rhinoptera, and Manta) display increasing complexity in cephalic lobes that correlate with specific derivation (adapted from Sasko et al. 2006). Like colors represent like lifestyle and feeding (red- benthic, green- benthopelagic, blue- pelagic), shading represents cephalic specialization or lobes (green- contains electroreceptors, blue- lacks electroreceptors).

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90º

r

Ө 0º

20cm

Figure 3.2 Orientation measurements used to calculate sensitivity to dipole electric fields. Orientation distance (r) and angle with respect to the dipole axis (θ) were used to calculate the voltage gradient at the point where a ray initiated a turn toward the dipole source. The electric field (red) intensity decreases with increasing distance from the center of the dipole. The electric field also decreases as a cosine function and is strongest along the dipole axis (0°) while no detectable field is produced perpendicular to the axis (90°). A 20cm diameter circle on the electrode array provided a calibration to quantify orientation distance.

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Dorsal Ventral

Cownose ray Rhinoptera bonasus

Yellow stingray Urobatis jamaicensis

Figure 3.3 Electrosensory pore number and distribution of the cownose ray, Rhinoptera bonasus, and yellow stingray, Urobatis jamaicensis. Both species had fewer numbers of dorsal pores than ventral pores and these pores were more limited in distribution on the cownose ray where the pores occurred only on the head and near the gills. Ventral pore distribution was also more widespread on the yellow stingray and nearly extended to the lateral disc margins. Ventral pores on the cownose were again limited to the head. The highest concentrations of pores in both species was around the mouth, however, pores were more highly concentrated around the mouth and on the cephalic lobes on the cownose ray than the yellow stingray.

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100 100 a U. jamaicensis c 90 510152025 90 80 60 40 20 10 80 80

70 70

60 60

50 50

% orientations % 40 * orientations % 40 30 30

20 20 *

10 10

0 0 0 5101520253035 0 10 20 30 40 50 60 70 80 90 100 >100 Detecon distance (cm) Electric field gradient (nV cm-¹)

100 100 b R. bonasus 90 d 90

80 80

70 70 60 60 * * 50 50

% orientations % 40

% orientations % 40

30 30

20 20

10 10

0 0 0 51015202530350 10 20 30 40 50 60 70 80 90 100 >100 Detecon distance (cm) Electric field gradient (nV cm-¹)

Figure 3.4 Percentage of orientations by sensitivity (voltage gradient) and detection distance to prey-simulating electric fields. Yellow stingrays oriented to weaker electric fields (A) and at greater distances (C) than cownose rays (B and D respectively). Orientations to weaker electric fields and at greater detection distances indicate greater sensitivity to bioelectric fields because the electric field decreases in intensity with distance from the source (inset). 43.5% of cownose ray responses were <100nV cm-1, while 85.8% of yellow stingray responses were <100nV cm-1. * indicates median sensitivity or detection distance.

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CHAPTER 4: COLOR VISION IN BATOID ELASMOBRANCHS

ABSTRACT

The potential for color vision in elasmobranchs has been studied in detail, however, a high degree of variation exists among the group. Evidence for ultraviolet vision is lacking, despite the presence of UV vision in every other vertebrate class. An integrative physiological approach was used to investigate color and ultraviolet vision in cownose rays and yellow stingrays, two batoids that inhabit different spectral environments. Both species had peaks in UV, short, medium, and long-wavelength spectral regions in dark, light, and chromatic-adapted electroretinograms. Although no

UV cones were found with microspectrophotometric analysis, both rays had multiple cone visual pigments with λmax at 470nm and 551nm in cownose rays (Rhinoptera bonasus) and 475nm, 533nm, and 562nm in yellow stingrays (Urobatis jamaicensis).

That same analysis demonstrated that both species had rod λmax at 500nm and 499nm respectively. The lens and cornea of cownose rays maximally transmitted wavelengths greater than 350nm and greater than 376 nm in yellow stingrays. These results support the potential for color vision in these species and future investigations should reveal the extent to which color discrimination is significant in a behavioral context.

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INTRODUCTION

Vision in the elasmobranch fishes, the sharks, skates, and rays, has been presumed to be relatively poor and adapted for dim-light (scotopic) conditions due to previously thought possession of all-rod retinas, tapeta lucida, and high sensitivity to light in (Walls 1942;

Ripps and Dowling 1991; Lisney et al. 2012). Recent techniques employed to study the visual systems of these fishes have revealed much more diversity in visual capabilities than previously though. For example, many species, including the lemon shark,

Negaprion brevirostris, and Atlantic guitarfish, Rhinobatos lentiginosus, possess duplex retinas composed of both rods and cones (Gruber et al. 1963; Gruber et al. 1991).

Although these two species and most sharks that have been studied are limited to one class of cone visual pigment, most batoids studied to date possess multiple cone visual pigments, and thus, have the potential for true color vision (Hart et al. 2004; Theiss et al.

2007; Van-Eyk et al. 2011). Color vision is most commonly achieved by the presence of at least two spectrally distinct classes of photoreceptors and a neural network, either retinal or central, that can compare the photoreceptor outputs and derive a wavelength- specific signal for further analysis (Jacobs 1981).

Ultraviolet (UV) vision, as defined here, is an axis of color space and is considered adaptive due to the presence of UV wavelengths in the environment. An animal can utilize the UV signals as long as the preretinal media allow for transmission of UV wavelengths and there are photoreceptors that are UV sensitive, either by

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possessing a UV visual pigment or through β-band absorption of UV wavelengths by another visual pigment (Losey et al. 1999; Siebeck and Marshall 2001; Lisney et al.

2012). UV sensitivity in coral reef fishes is thought to enhance the contrast of predators, prey, and conspecifics in UV-rich environments against background illumination (Losey et al. 1999; Siebeck and Marshall 2001; Siebeck 2004; Partridge and Cuthill 2010;

Siebeck et al. 2010) although studies to support these hypotheses are still limited. Thus far, no evidence has been found to support UV vision in any elasmobranch, despite the similarities in ecology of some species to those of teleosts that are known to possess UV cones.

The potential for color vision in elasmobranchs has typically been based on visual pigment identification and absorbance characteristics of visual pigments in individual photoreceptors in situ (ie. microspectrophotometry [MSP]) and supported by histology (light and electron microscopy) or whole retina physiology (ie. the electroretinogram [ERG]). These techniques can provide data to support the potential for color vision, or lack thereof; however, only behavioral testing can confirm that a particular animal actually possesses color vision. MSP characterization is a powerful tool that allows classification of visual pigments based on their distinct spectral absorbance characteristics, and consequently, confirms the presence or lack of multiple visual pigments, the basis for true color vision. However, these techniques do not necessarily describe the actual spectral sensitivity of the whole photoreceptor (i.e. the eye) that can be influenced by factors such as preretinal filtering, nor do they explain how the output from photoreceptor cells is analyzed. To a point, the ERG allows quantification of photoreceptor contributions to the overall response of the eye, and

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incorporates contributions of spectral filters and interactions between cells in the retina.

However, ERG alone cannot always ascribe peaks in spectral sensitivity to a particular mechanism. Therefore, integration of MSP and ERG permits a better estimation of the physiological basis for color vision that may underlie behavioral functional significance.

Like many other characteristics of the visual system, the potential for color vision in all organisms should be correlated to their ecology. Species that inhabit brightly lit and spectrally rich environments tend to possess more visual pigments than those in dim or spectrally limited environments (Marshall and Vorobyev 2003). Also, many species that inhabit spectrally rich environments, like coral reefs, commonly incorporate bright coloration or patterning onto their body surface to exploit conspecific vision for mate detection and territory displays, while they also deceive predator visual systems and function in camouflage and mimicry (Hazlett 1979; Chiao et al. 2000;

Siebeck et al. 2008; Cheney et al. 2009). On the other hand, species that inhabit dim or spectrally limited environments tend to be less colorful and have fewer visual pigments than their colorful counterparts (Marshall and Vorobyev 2003).

The potential for color vision has been investigated in only a handful of the >800 species of elasmobranch fishes and has been supported in only a subset of those (see

Lisney et al. 2012 for review). Because elasmobranch fishes are diverse in terms of ecology, the number of visual pigments is highly variable throughout the group, ranging from zero to three cone pigments (Gruber et al. 1991; Ripps and Dowling 1991; Hart et al. 2004; Theiss et al. 2007; Hart et al. 2011). The goals of this study were to assess the potential for color and ultraviolet vision in two batoids that differ in their ecology. The cownose ray, Rhinoptera bonasus, is a benthopelagic, estuarine/coastal inhabitant that

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experiences variable photic conditions as it transitions from schooling at the water surface to foraging in the benthic zone (Smith and Merriner 1987; McEachren and de

Carvahlo 2002; Neer and Thompson 2005; Collins et al. 2007a; Collins 2008). The yellow stingray, Urobatis jamaicensis, is a strictly benthic ray in bright, spectrally rich reef and seagrass habitats (McEachren and de Carvahlo 2002; Fahy 2004). Yellow stingrays are also elaborately patterned, likely to provide camouflage and potentially also a visual cue for conspecific recognition. Both of these species may use vision for intraspecific communication to maintain position within a school (cownose rays) or to recognize camouflaged conspecifics and predators (yellow stingrays) as has been suggested by their respective visual fields (McComb and Kajiura 2008). Additionally, both species inhabit UV-rich waters, either while swimming at the water surface or foraging in shallow water (cownose ray), or by living in reef and seagrass associated habitats in shallow, clear water (yellow stingray). I hypothesize that the yellow stingray will be more likely to utilize color and incorporate UV sensitivity into their color vision system than the cownose ray, due to its bright spectrally rich habitat and elaborate body patterning. To test these hypotheses, I: 1) electrophysiologically quantified the spectral sensitivities of cownose rays and yellow stingrays under different photic conditions

(dark adapted, white light adapted, and chromatically adapted) 2) determined the spectral classes of photoreceptors present in the retinas using MSP, 3) quantified the transmission of light through the ocular elements, and 4) quantified the spectral reflectances of the bodies of both species.

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MATERIALS AND METHODS

Animals

Juvenile and adult cownose rays, Rhinoptera bonasus (n=8; disc width (DW): 39-77cm), and adult yellow stingrays, Urobatis jamaicensis (n=7; DW: 18-23cm), were collected with gillnet and hand net, respectively, from south Florida waters. Rays were housed in indoor tanks at the Florida Atlantic University Marine Science Facility at Gumbo Limbo

Environmental Complex in Boca Raton, FL or at the Marine Experimental Research

Facility at Mote Marine Laboratory in Sarasota, FL. Rays were held under a 12h:12h light:dark cycle under fluorescent lighting until they were used for experiments. All experiments were conducted in accordance with Institutional Animal Care and Use

Committee (IACUC) approved protocols from Florida Atlantic University (A09-25,

A12-33) and Mote Marine Laboratory (12-09-SK1).

Electrophysiology

Experimental apparatus

An electroretinogram (ERG) was used to quantify the in vivo extracellular spectral sensitivities of R. bonasus (n=6) and U. jamaicensis (n=5) photoreceptors. Experiments were conducted within an acrylic experimental tank (89 X 43 X 21cm) with electrically grounded seawater. Summed photoreceptor responses to monochromatic light flashes that bathed the whole eye in light were recorded with a Ag-AgCl 100µm tip glass microelectrode filled with potassium chloride (E45P-M15NH, Warner Instruments,

Hamden, CT, USA) that was positioned within the vitreal component of one eye just below the surface of the water. An identical reference electrode was positioned nearby on the skin. The output of the two electrodes was differentially amplified (DP-304, 75

Warner Instruments) at 1000–10,000x, filtered (0.1 Hz–100 Hz bandpass, 50/60 Hz notch filter) (DP-304, Warner Instruments and HumBug, Quest Scientific, North

Vancouver, BC, Canada), digitized at 1 kHz (Power Lab® 16/30 model ML 880, AD

Instruments, Colorado Springs, CO, USA) and recorded using LabChart® 7 Software

(v7.2.5 AD Instruments).

Following McComb et al. (2010), white light from a fiber optic light source was passed through interference bandpass filters (full width at half maximum (FWHM) = 10 nm) from 350-620nm (19 filters with individual peak transmission at 350, 360, 370,

380, 390, 400, 410, 430, 450, 470, 490, 500, 510, 520, 540, 550, 560, 590, and 620nm) with irradiance controlled by neutral density filters. Monochromatic light was passed through one branch of a bifurcated liquid light guide and was presented to the eye through the common end, which uniformly illuminated the entire corneal surface. For chromatic adaptation, the adapting light from a halogen lamp (LS-1, Ocean Optics, Inc.,

Dunedin, FL, USA) fitted with a 550nm longpass filter was passed through the second branch of the light guide to superimpose the stimulus on the adapting light. The common end of the light guide was fitted with a UV-transparent glass diffusor (Edmund

Optics) to mix the input of the two branches. The irradiances of monochromatic test stimuli were calibrated with an optometer (United Detector Technology Model S370,

Gamma Scientific, San Diego, CA, USA) fitted with a calibrated radiometric probe.

Experimental protocol

An individual R. bonasus or U. jamaicensis was dark adapted for one hour and then was anesthetized with tricaine methanesulfonate (1:10,000 wt:vol) until ventilation ceased.

Set up and adjustments to the apparatus in the dark were made under dim red light to

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limit light-adaptation of the eye. The ray was then transferred to the experimental tank, secured to a platform with Velcro straps, and artificially ventilated with oxygenated seawater (dissolved oxygen >6mg L-1 at 0.5-2.0 L min-1) through a tube inserted in the mouth. Rays were immobilized with an intramuscular (IM) injection of the neuromuscular blocking agent pancuronium bromide (0.3-0.4mg kg-1) and provided with a maintenance dose of tricaine (1:12,000-1:15,000 wt:vol) throughout the experiment.

After electrode placement, rays were dark-adapted for an additional 1-2h until the amplitude of the response to control flashes were consistently within 10% of one another for five consecutive flashes. One-second flashes of monochromatic light were presented to the eye with a computer-controlled shutter and the irradiance of the flash was adjusted until a criterion response (20-50µV above the level of background noise) was met for each wavelength. Control flashes were presented between every wavelength setting to ensure that dark-adaptation was maintained throughout the experiment. Once responses were recorded for all 19 wavelengths, rays were white light-adapted for 20-30 minutes using a white LED within the light-tight compartment and the protocol was repeated.

The irradiance of the white light was adjusted such that the irradiance required to elicit the criterion response at the peak sensitivity was ≥1 log unit brighter than in the dark- adapted state. The protocol was also conducted after rays were adapted to the long wavelength light, the irradiance of which was adjusted so the stimulus required to elicit the criterion response was ≥1 log units brighter than light-adapted peak response, but was dim enough so that criterion responses could still be obtained at UV wavelengths.

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Analysis

Spectral sensitivity curves for dark, light, and chromatic adaptation experiments were generated by plotting the normalized inverse of the irradiance (photons cm-2 s-1) that produced the criterion response against wavelength for all individuals.

To determine if the sensitivity shifts in the spectral sensitivity curves were under different light conditions were significant, differences in spectral sensitivities among dark-, light-, and chromatic-adapted eyes were quantified by comparing the ratios of responses from spectral sensitivity curves. The mean of the normalized inverse of the irradiance required to produce the criterion response in four regions of the spectral sensitivity curves was calculated: ultraviolet (UV; 360-380nm), short wavelength (blue;

430-470nm), medium wavelength (green; 470-520nm), and long wavelength (red;

520nm and 550nm- 560nm) (cf Frank et al. 2009). The ratios of the mean normalized responses for each region were compared among dark, light, and chromatic adapted eyes with a one-way ANOVA. All data met normality and heterogeneity assumptions for

ANOVA.

Microspectrophotometry

The absorbance spectra of visual pigments in individual photoreceptors were obtained using a microspectrophotometer (MSP). Two individuals of each species were transported from Boca Raton, FL to Ithaca, NY and were held in light-tight, aerated tanks with artificial seawater for 2-4 days. Rays were sacrificed with an overdose of

MS-222 (>1:5000 wt:vol), followed by severing of the spinal cord. Three eyes from both species were enucleated, hemisected, and stored in artificial seawater in light-tight containers and were used within 24h (all but one were used within 8 hours). The retina

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was dissected away from the eye under infrared light and stored in Sorenson’s buffer solution (pH= 7.2) with 6% sucrose. A small wedge of the retina was macerated with a razor blade to free photoreceptors from the retinal tissue and was sealed between two glass coverslips. Individual photoreceptors were selected under infrared light and measured using a computer-controlled, single beam MSP as previously described

(Loew 1994). Briefly, a baseline scan through a photoreceptor-free region of the retina was performed at 1 nm increments from 750 – 350 nm and back. An outer segment of a single photoreceptor, which contained the visual pigment, was then moved over the measuring beam and the transmission measured. These data were converted to absorbance, and absorbance spectra that satisfied selection criteria were fitted to a visual pigment template to determine λmax (for details see Loew 1994; Losey et al. 2003).

Ocular Media Transmission

One eye each from both species was obtained from rays used in the MSP analysis. The eye was enucleated and hemisected and the ocular elements (cornea and lens) were placed on a 200µm light measurement probe with the cornea facing down. A 12V

100W quartz halogen broad-spectrum lamp was positioned above the eye to allow even transmission of light through the ocular elements. Transmission from 350-750nm was measured with an Ocean Optics S2000 spectrometer (Ocean Optics, Dunedin, FL, USA) and analyzed with OOIBase 32 software (Ocean Optics). A 100% transmission standard was obtained by recording along the measurement path, which was used to calculate transmittance. The resulting data were plotted against wavelength. The T0.5, the wavelength at which the transmission has decreased to 50% of the maximum, was used to determine the short-wavelength absorbing properties of the eye (following Losey et

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al. 2003). The T0.5 and slope of both curves were categorized according to Douglas and

McGuigan (1989) and Losey et al. (2003) to determine a vision likelihood category

(Losey et al. 2003) that describes their likelihood for utilizing UV vision.

Body color reflectance

Spectral reflectance measurements were recorded from the head, gills, pectoral fin, and pelvic fins on the dorsal body surface on fresh frozen specimens of both species (n= 4 each). Reflectance (percent and intensity counts) was measured with a 400µm UV-VIS reflectance probe (Ocean Optics) connected to an Ocean Optics S2000 spectrometer and analyzed with SpectraSuite software (Ocean Optics). Reflection was recorded at wavelengths from 350-750nm and each location was measured twice and averaged. The normalized mean reflectance from all individuals was plotted against wavelength to produce reflectance curves.

RESULTS

Electrophysiology

Four spectral sensitivity peaks were present under dark-, light-, and chromatic- adapted

ERGs in both species (Fig. 4.1). Dark-adapted cownose rays were maximally sensitive to 500nm and yellow stingrays were maximally sensitive to 490nm. Under light- adaptation, cownose ray maximum sensitivity shifted to 450nm, while yellow stingrays remained maximally sensitive to 490nm. However, under red chromatic-adaptation, yellow stingray maximum sensitivity shifted to 550nm while the largest cownose ray peak remained at 450nm. Shifts in spectral sensitivity peaks under light and chromatic adaptation were not significant in comparisons of mean response ratios in the visible portion of the spectrum (ANOVA; cownose rays: blue:green p= 0.18, blue:red p= 0.47, 80

green:red p= 0.71; yellow stingrays: blue:green p= 0.21, blue:red p= 0.10, green:red p=

0.07). The magnitude of the UV response relative to the blue, green, and red peaks did not differ among dark, light, and chromatic adaptation in either species (ANOVA; cownose rays: UV:blue p = 0.25; UV:green:, p = 0.78; UV:red: p = 0.94; yellow stingrays: UV:blue p= 0.20, UV:green p= 0.08, UV:red p= 0.21). Although the ratios were not significantly different due to small sample size and large standard error, mean

UV responses were greater under chromatic adaptation than light adaptation for all yellow stingray individuals.

Microspectrophotometry

Based on morphology, rods and cones were found in the retina of both species.

Absorbance spectra that met criteria outlined by Loew (1994) were used for further analysis (Fig. 4.2). All spectra were best fit with A1-based visual pigment templates, suggesting that retinal is the only chromophore type present in cownose ray and yellow stingray visual pigments. Both species had a rod pigment with a λmax near 500nm (Fig.

4.3). The cownose ray had two cone pigments with λmax (mean±SD) at short

(470n±1nm) and long wavelength (551±2nm) regions of the spectrum. The yellow stingray had three cone pigments with λmax at short (475±2nm), medium (533±4nm), and long wavelength (562±3nm) regions.

Ocular media transmission

Cownose rays and yellow stingrays both transmitted light to 350nm (Fig. 4.4). The cownose ray had a type I lens with T0.5=350nm and the yellow stingray had a type II-IIa lens with T0.5=376nm (Douglas and McGuigan 1989; Losey et al. 2003). The slope of both species’ transmission curves was categorized as class II (Douglas and McGuigan

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1989; Siebeck and Marshall 2001; Losey et al. 2003) with a gradual slope of >30nm between 0-100%. Cownose rays were determined to have a UV vision likelihood of 1 and yellow stingrays had a UV vision likelihood of 2 (Table 4.1).

Body color reflectance

The dorsal surface of the cownose ray was uniformly brown in color, as expected, with a broad-band maximum reflectance from 600-725nm in each location measured (Fig 4.5).

The yellow stingray demonstrated two reflectance peaks, the primary peak near 610nm

(brown) and a secondary peak near 560nm (yellow). The peaks correlate with the dark brown spots on a yellow background of the body of the yellow stingray. The yellow reflectance peak was near the long-wavelength cone λmax of 562nm.

DISCUSSION

The results presented here support the potential for color vision in two species of batoid elasmobranch, and are suggestive of a mechanism of UV sensitivity in yellow stingrays.

Although behavioral tests are required to determine the functional significance of polychromatic vision in these species, recent work of another trichromatic batoid, the giant shovelnose ray, Glaucostegus typus, suggests that polychromacy in batoid elasmobranchs does result in color vision (Hart et al. 2004; Van-Eyk et al. 2011).

Multiple visual pigments and color (hue) discrimination

The ability of teleost fishes to discriminate colors based on the presence of multiple visual pigments is well known, and in fact, almost all teleosts possess more than one spectral class of cone (Marshall and Vorobyev 2003; Losey et al. 2003). Color discrimination abilities in elasmobranch fishes, however, remain widely speculative and are primarily based on morphological and histological evidence (Gruber et al. 1963; 82

Hamasaki and Gruber 1965; Stell 1972), with more emphasis in recent studies elucidating the nature of photoreceptor classes and spectral sensitivities in the group

(Hart et al. 2004; Theiss et al. 2007; McComb et al. 2010; Hart et al. 2011).

Rod visual pigments of coastal elasmobranchs are typically maximally sensitive in the green region of the visible light spectrum, around 500nm. Cownose rays,

Rhinoptera bonasus, and yellow stingrays, Urobatis jamaicensis, were no exception with a rod λmax of 499nm and 501nm, respectively (Fig. 4.3). MSP results showed that cownose rays were potentially dichromatic with two visual pigments: one with a λmax of

470nm and the other peaking at 551nm. Yellow stingrays were potentially trichromatic with three pigments that had peak absorbance at 475nm, 533nm, and 562nm.

The dark-adapted and white light-adapted spectral sensitivity curves for the cownose ray showed peaks in the visible spectrum at 470nm, 490-500nm and 540- 550 nm, which match the presence of cones containing visual pigments with λmax of 470 and

550 nm, and a rod with a 499 λmax visual pigment. The yellow stingray spectral sensitivity curves were less conclusive with visible spectrum peaks at 450nm, 490nm,

510nm, and 550nm in the dark-adapted eyes with peaks slightly shifted under light- adaptation (450nm, 490nm, 520nm, and 560nm). Although it is unclear if the small dips in the curves may be a result of low resolution in the curve (responses were only tested every 10-30nm) or noise, the curve loosely matched the MSP λmax of cone visual pigments at 475nm, 533nm, and 562nm and rod λmax at 499nm. Low sampling resolution or retinal filtering mechanisms may be responsible for the discrepancy between the 450nm ERG peak and the MSP 475nm λmax, although this hypothesis has not been verified.

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Typically in vertebrates, rod photoreceptors function in dim light (scotopic) and are more sensitive than cones, which function in bright light (photopic) conditions and provide the basis for color vision (Schultze 1866). According to the duplicity theory, one would expect primarily rod contribution to the scotopic ERG and cone contribution to the photopic ERG (von Kries 1894). Early work on lemon sharks, Negaprion brevirostris, described rods and cones in the retina (Gruber et al. 1963) and rhodopsin with a λmax of 501nm was extracted (Bridges 1965). Subsequent investigation with ERG

(O’Gower and Mathewson 1967; Cohen and Gruber 1977) and ganglion cell recordings

(Cohen and Gruber 1985) found a mismatch between spectral sensitivity and the λmax of extracted retinal pigment and the authors hypothesized that rods and cones both contribute to the scotopic ERG. The mismatch was later attributed to an ontogenetic shift in the λmax of the visual pigment (Cohen et al. 1991). More recent work on dark- adapted spectral sensitivities in sharks found two peaks in scotopic curves, presumably from a rod and a cone; however, the photoreceptor classes of those species have yet to be identified (McComb et al. 2010). The present study found rod and cone contribution to the scotopic and photopic ERG in cownose rays and yellow stingrays and supports the hypothesis that elasmobranchs do not possess distinct scotopic and photopic visual mechanisms. The functional significance of this finding is not yet understood due to the paucity of behavioral vision studies and inconclusive results in early studies where elasmobranchs may have been discriminating stimuli based on perceived brightness, rather than wavelength (Clark 1963; Tester and Kato 1966; Gruber 1975). However, behavioral work with marine mammals (Wartzok and McCormick 1978; Griebel and

Schmid 1992; Griebel and Schmid 2002) and red-monochromatic primates (Jacobs et al.

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1993) that possess only two visual pigments, a rod and one cone, are able to discriminate wavelengths with a rudimentary color vision system that is at least functional under mesopic conditions. Therefore, comparison of rod and cone input in elasmobranchs may serve to broaden the spectrum of light visible to elasmobranchs, or to enhance sensitivity to light under scotopic conditions (Hart et al. 2011).

Ultraviolet transmission and sensitivity

The basic requirements for a functional ultraviolet visual mechanism are presence of UV wavelengths in the environment, transmission of those wavelengths to the retina, and a

UV-sensitive retinal mechanism subserved either by UV-sensitive cones or by β-band absorption by other photoreceptors (reviewed by Losey et al. 1999). Cownose rays and yellow stingrays are both frequently found in UV-rich habitats at depths <20m (Table

4.1), both species transmitted UV wavelengths to the retina, indicated by ocular media transmission (Fig. 4.4), and both species demonstrated peaks in the UV region of dark, light, and chromatic-adapted spectral sensitivity curves (Fig. 4.1). Failure to find a particular class of photoreceptor when performing MSP, like the results presented here, does not necessarily mean that those photoreceptors are not present in the retina. This could occur if the cells were sparse or present in localized regions that were not present in the retinal pieces selected. For example, in the goldfish, Carassius auratus, several early attempts at finding UV cones with MSP failed, even though ERG results indicated the presence of a UV cone (Bowmaker et al. 1991). However, ERG data did not support the presence of a UV cone mechanism in cownose rays. The small peaks in the UV region of both species’ spectral sensitivity curves were not significantly increased relative to the 550nm peaks under the presence of the 550nm adapting light and suggest

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the peaks were a result of β-band absorption. However, all yellow stingrays did demonstrate an increased sensitivity to UV wavelengths in the chromatic condition relative to the light-adapted trials. Although these results must be interpreted with caution, they warrant further investigation into the potential use of UV light for prey detection, conspecific recognition, and predator avoidance behaviors in yellow stingrays.

Visual ecology

Photoreceptors of aquatic organisms tend to be spectrally tuned to the depth and water quality in which they are found (Munz and McFarland 1973; Munz and

McFarland 1977; Loew and Lythgoe 1978; Hart et al. 2011; Lisney et al. 2012). For example, pelagic species that inhabit the open ocean usually have blue-shifted sensitivity presumably to detect contrast of objects against their blue upwelling light background (Loew and Lythgoe 1978; Munz and McFarland 1977; Bowmaker et al.

1994; Lythgoe et al. 1994; Hart et al. 2011). Coastal and estuarine species are typically green-yellow shifted compared to deep sea or pelagic species because scattering due to increased turbulence and absorption by dissolved organics (‘Gelbstoff’) decrease the contribution of short wavelengths to the overall irradiance. In species with multiple visual pigments, the best detection of objects is accomplished with a photoreceptor with maximum absorbance that matches the background and one that is offset from the background, which provides high contrast of objects against the background (Lythgoe

1972; Jerlov 1976; Levine and MacNichol 1982).

Most batoids in which the absorbance of photoreceptors has been studied in detail have multiple types of cones, which provide them the potential to possess color

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vision (Hart et al. 2004, Theiss et al. 2007, Van-Eyk et al. 2011). While both of the species in our study also had multiple types of cones, their ability to discriminate color across the spectrum differs. The yellow stingray, being a trichromat, had λmax of cones similar to that of the blue-spotted maskray, Neotrygon kuhlii, which also inhabits tropical reef-associated spectrally rich waters (Last and Stevens 1994; Last and

Compagno 1999; Fahy 2004; Theiss et al. 2007; Ward-Paige et al. 2011). The cownose ray on the other hand was a dichromat and is an estuarine and inshore benthopelagic ray

(Smith and Merriner 1987; Neer and Thompson 2005; Collins et al. 2007a; Collins et al.

2008; Ajemian and Powers 2012), which typically experiences a more spectrally limited and variable irradiance habitat than the yellow stingray. Because of the limitation of light available, trichromacy would not be much of a benefit to cownose rays. The two cone pigments of the cownose ray had peaks in the blue and orange regions of the spectrum, and with the abundance of longer wavelength light present in the environment, these cones are similar to (orange) and offset from (blue) the background coloration.

Trichromacy should be highly beneficial to the yellow stingray, however.

Teleosts and invertebrates that live on spectrally complex reefs utilize color vision and color signals as an enhanced communication channel for conspecific recognition, mating and territory displays, and for camouflage from predators (Hazlett BA 1979; Collin and

Trezise 2004; Hart et al. 2004; Kelber and Roth 2006; Siebeck et al. 2008). The peak of the background color of the yellow stingray body (561nm) was closely matched to the

λmax of the long wavelength cone (562nm). Therefore it likely that yellow stingrays are able to utilize color vision at least for conspecific recognition within a colorful

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environment. The longer-wavelength dark spots at 610nm on the yellow stingray help to provide camouflage and are probably similar to the rock structure in the sandy areas around reefs where yellow stingrays tend to bury (personal observation; Fahy 2004;

Ward-Paige et al. 2011).

Ultraviolet vision as an axis of color vision in teleost fishes and invertebrates is typically mediated by UV-sensitive visual pigments and can be involved in intraspecific communication, detection of UV-reflecting planktonic prey, and enhanced contrast detection in UV-rich environments (Marshall and Vorobyev 2003; Siebeck 2004;

Partridge and Cuthill 2010; Siebeck et al. 2010). Criteria based on a study of ocular media transmission and MSP of nearly 200 species of reef fishes outlined by Losey et al.

(2003) assign a vision likelihood category that predicts UV sensitivity is likely to highly likely in both of the species in the present study (Table 4.1). Cownose rays primarily forage on benthic and buried immobile invertebrates, therefore, they would not benefit from UV sensitivity for prey detection and did not show evidence of functional UV sensitivity in this study. Yellow stingrays, however, feed on epifaunal crustaceans and polychaetes, including stomatopod crustaceans (D. Fahy, pers. comm). Some stomatopods have UV-reflecting meral spots on their pedipalps that aid in mating and territory displays (Hazett 1979; Marshall et al. 1996; Chiao et al. 2000), and could be an enhanced visual signal to foraging stingrays as well. Enhanced contrast capabilities could be beneficial to both species for detection of predators against their UV-rich background, however, the potential for a UV mechanism is only suggested for the yellow stingray based on the ocular media and ERG results presented here. Although functional significance of the UV peak in spectral sensitivity curves is purely

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speculative at the present time, filter-feeding species, like whale sharks, Rhincodon typus, and the mobulid rays may benefit from UV sensitivity to localize potentially UV- reflective prey.

Conclusions

Sensory systems of elasmobranchs have been relatively poorly studied compared to most other vertebrate classes, however, it is known that sensory capabilities are often tuned to a species ecology. There is also generally a lack of knowledge regarding the general ecology and behavior of many elasmobranchs, such as diet composition, range and movement, mating and courtship behaviors, habitat preferences, and activity patterns. Without this information, it is difficult to assess the functionality of sensory systems with respect to ecology.

Additionally, the function of a combined scotopic and photopic visual system remains hypothetical at this time and the neuronal circuitry of the retina remains widely unstudied, but elasmobranchs may share characteristics with teleost fishes that have combined scotopic and photopic visual systems (Burkhardt 1966; Witkovsky 1968;

Cohen et al. 1977; Allen and Fernald 1985; Barlow 1985; Dearry and Barlow 1987).

Future studies should investigate the synaptic connections between photoreceptors, horizontal cells, bipolar cells, and convergence onto retinal ganglion cells, as well as employ behavioral testing of elasmobranchs with different classes of photoreceptors in varying photic conditions. Behavioral tests will also elucidate whether these species have behaviorally significant UV sensitivity, as well as the potential ecological significance of UV color vision for elasmobranchs.

Despite the lack of information regarding life history and rod and cone

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interactions, the conclusion can be made that vision is likely an important sensory modality for both species, as indicated by the presence of multiple classes of cones and extensive vertical (cownose ray) and horizontal (yellow stingray) visual fields (McComb and Kajiura 2008). Schooling behavior, prey detection, mate detection, and predator avoidance are all likely to be mediated by vision and future studies should consider visually guided behaviors of elasmobranchs.

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Table 4.1 Ocular media transmission categories and vision likelihood predictions for UV color vision in fishes.

T.05 Slope of transmission curve Diurnal habits Vision likelihood

Type λ cutoff Class Shape Category Light exposure Category T.05, Diurnal Prediction

(nm) habit

I <355 I Steep; <30nm 1 Diurnal; full 1 I UV color vision

from 0-100% ambient daylight 1 or 2 highly likely

transmission

I ≤355 II Gradual; >30nm 2 Nocturnal; full 2 IIa UV color vision

from 0-100% ambient daylight 1 or 2 likely

transmission

II 355< T.05<405 III Intermediate 3 Hidden from 3 IIb Violet vision

maxima daylight 1 or 2 likely (not true

UV)

IIa 355< T.05≤380 IV ≥97% 4 I, IIa, or IIb Short λ vision

transmission 3 (not UV)

from 300-700nm

IIb 380< T.05≤405 5 III No short λ

1, 2, or 3 vision

III >405

Cownose ray I 350 II >30nm slope 1 Diurnal; shallow 1 β-band UV

estuary, coastal absorption

Yellow stingray IIa 376 II >30nm slope 2 Nocturnal; reef, 2 β-band UV

seagrass absorption

Note: Categories and predictions based on studies by aDouglas and McGuigan 1989 (50 species), bSiebeck and Marshall 2001 (211 species), cLosey et al. 2003 (195 species).

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Figure 4.1 Spectral sensitivity curves from scotopic, photopic, and chromatic adapted ERG. Cownose rays and yellow stingrays demonstrated four peaks in each curve in the regions from 360-390nm, 450-470nm, 490-520nm, and 550-560nm. Light adaptation decreased the contribution of the rod (500nm) relative to the blue (470nm) and orange (550nm) peaks and chromatic adaptation decreased the contribution of the long- wavelength cone (550nm) in the cownose ray. The 450nm peak was decreased under light adaptation in the yellow stingray, while the 510nm peak was decreased in magnitude and shifted to 520nm, which suggests single pigments may be contributing to multiple peaks.

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Figure 4.2 Frequency distribution of the λmax of individual photoreceptors used in MSP analysis. Although not quantitatively analyzed, retinas of both species appeared to be rod dominated. Bars are color-coded to correspond to specific photoreceptor types: rod= gray, SWS (short wavelength cone)= blue, MWS (medium wavelength cone)= green, LWS (long wavelength cone)= red.

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Figure 4.3 Normalized mean MSP absorbance spectra for cownose ray and yellow stingray visual pigments. Visual pigment templates (solid lines) were fitted to normalized mean absorbance curves for each pigment type (dotted line). Each point in the absorbance curves represents the mean of all photoreceptors at that wavelength. Cownose rays were dichromatic, with rod λmax= 500±2nm (mean±SD), SWS λmax= 470±1nm, and LWS λmax= 551±2nm. Yellow stingrays were trichromatic with rod λmax= 499±2nm, SWS λmax= 475±2nm, MWS λmax= 533±4nm, and LWS λmax= 562±3nm. 94

Figure 4.4 Transmission of white light through the ocular elements (cornea, lens, vitreous humor). Short wavelength filtering of white light occurs as it passes through the cornea (A), lens (B), and vitreous (C) as a protective mechanism against UV damage to the retina (D). Both species showed minimal filtering of UV wavelengths by the ocular media and transmitted wavelengths down to 350nm. However in yellow stingrays, wavelengths less than 376nm transmitted less than 50% (T0.5, horizontal dashed line), indicating the presence of some pigmentation.

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Figure 4.5 Dorsal body color reflectance spectra and reflectance data measurement locations. Each location is denoted by a corresponding color in both the ray illustration and the reflectance plots. Boxes on ray illustrations outline the region that each measurement was recorded from. Each point in the reflectance curve represents the mean reflectance from all individuals at the corresponding wavelength. Head= blue, branchial= red, pectoral fin= green, and pelvic fin= gray. a Cownose rays were uniform in color with a broad-band peak that reflected maximally from approximately 600- 725nm (red-brown) from all locations. b Yellow stingrays had two reflectance peaks that were consistent across locations. The lighter background color was 561nm (yellow) and the darker spots were 610nm (orange-brown).

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CHAPTER 5: VISUAL TEMPORAL RESOLUTION IN ELASMOBRANCHS:

EFFECTS OF LIGHT, TEMPERATURE, AND ANESTHESIA

ABSTRACT

Visual temporal capabilities in fishes have been correlated to ecological factors such as the relative irradiance of the environment and ambient temperature; however, the additive effects of these factors have not yet been studied. Additionally, mode of immobilization of experimental animals differs across studies, yet the specific effects of anesthesia on temporal resolution have not been investigated. The maximum critical flicker fusion frequency (CFFmax) of two elasmobranchs in response to changes in the photic and thermal environments and choice of anesthetics was quantified. The photopic and scotopic CFFmax of the cownose ray, Rhinoptera bonasus, were 30 Hz and

15 Hz and were faster than that of the yellow stingray, Urobatis jamaicensis, with a photopic CFFmax of 26 Hz and scotopic CFFmax of 11Hz. Both species demonstrated significant reductions in temporal resolution when water temperature was decreased by

10°C and when tricaine was used as the anesthetic agent. The fast photopic and slow scotopic CFFmax in both species is suggestive of fast and slow photoreceptors, which function in daytime and light-limited conditions. The faster scotopic CFFmax in cownose rays may be an adaptation to enable motion perception while rays are actively schooling in a broad-range of environmental irradiances.

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INTRODUCTION

Visual capabilities can be assessed in terms of spatial and temporal properties, utility of color vision, and other visual adaptations like ultraviolet and polarized vision. Temporal properties, including temporal resolution, have received less attention than spatial properties in many organisms, including elasmobranch fishes (McComb et al., 2010;

Lisney et al., 2012). Temporal resolution is the ability of an organism to visually track moving objects and is defined by each species’ photoreceptor response integration time.

Temporal resolution can be measured in several ways, but the most common method is flicker fusion frequency (FFF), which is the ability of photoreceptors to respond to individual flashes of a flickering stimulus. Stimuli that flicker at a faster frequency than an eye’s FFF appear as a constant, rather than flickering, light source. Because FFF increases with increasing brightness of the stimulus (Crozier et al. 1938), a more specific measure of temporal resolution, maximum critical flicker fusion frequency (CFFmax), can be used to assess temporal resolution across individuals, preparations, species, and treatment groups. CFFmax is the maximum rate of flicker that an eye can follow at any irradiance.

Temporal properties can be linked to ecological factors including the photic environment, ambient temperature, and activity level and lifestyle of an organism

(Autrum, 1958; Hanyu and Ali 1963; Hanyu and Ali 1964; Frank 2003; McComb et al.

2010). Marine photic environments vary greatly with depth, amount of dissolved and suspended particulate matter, and photoperiod. As a result, organisms living within these different environments have been exposed to different selective pressures acting on their visual systems (Munz, 1977; Levine and MacNichol, 1979). As ambient 98

irradiance increases, FFF increases with the availability of photons in the environment

(Warrant, 1999). In light-limited environments, sensitivity to light increases and retinal responses must be slowed to allow processing of more photons in order to create an image (Warrant, 1999). For example, shallow water decapod crustaceans have a FFF that is ≥20Hz faster than decapod crustaceans in the light-limited mesopelagic zone at depths greater than 200m (Bröcker, 1935; Frank, 1999; Frank, 2000). Temperature- sensitive physiological and biochemical processes in ectotherms, like those of the nervous system, can also be modulated by environmental conditions. Changes in water temperature confers a corresponding change in FFF as demonstrated in the swordfish,

Xiphius gladus, which shows a decline in FFF from 40Hz to 5Hz when water temperature is lowered from 20°C to 10°C (Fritsches et al., 2005). Finally, activity level and lifestyle also influence FFF based on the needs of an organism to track quickly moving prey, conspecifics, and predators (Autrum, 1958). Leatherback sea turtles,

Dermochelys coracea, consume primarily slow-moving prey like jellyfishes and have a slower FFF than loggerhead sea turtles, Caretta caretta, which consume more mobile prey like zooplankton and crustaceans (Horch and Salmon, 2009).

Although ecological factors have been considered in previous studies of temporal resolution in fishes, these factors may be additive and there is a lack of examination of the compounding effects of these factors on visual function. Temporal resolution in fishes in particular is highly variable due to the group’s adaptations to most aquatic environments and niches ranging from Antarctic light-limited, near-freezing temperatures to bright, clear tropical reef environments (Moyle and Cech 2004).

Although studies on FFF in fishes are limited, teleosts tend to have faster working eyes

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than elasmobranchs. The upper limit of FFF in teleosts occurs in the range of 40-80Hz

(Hanyu and Ali, 1963; Hanyu and Ali, 1964; Brill et al. 2005; Fritsches et al. 2005) while the upper limit for elasmobranchs is typically 20-45Hz (O'Gower and Mathewson,

1967; Green and Siegel, 1975; Bullock et al., 1991; McComb et al. 2010) with the fastest response from an individual sandbar shark, Carcharhinus plumbeus, at 54Hz

(Litherland, 2009). However, data for elasmobranchs are sparse compared to those available for teleosts, so with increased interest in visual adaptations of elasmobranch fishes, future studies may find that elasmobranch FFF is within the same range as teleosts.

Comparisons of FFF across studies can be difficult due to variability in methodology (Fritsches et al., 2005; Horch and Salmon, 2009). Variation arising from differences in procedures for immobilization and anesthesia can be particularly problematic. Immobilization is required throughout many of these physiological experiments to prevent movements that arise from skeletal musculature and ventilation that can negatively impact the preparation. Anesthesia is also required for these procedures to limit distress of prolonged restraint and potential pain caused by invasive procedures (Ross and Ross, 1999; Neiffer and Stamper, 2009). The most common method of immobilization and anesthesia is immersion in seawater containing tricaine methanesulfonate, or MS-222, which provides anesthetic effects by inhibition of sensory perception and depression of the central nervous system (Miller et al., 2005; Neiffer and

Stamper, 2009; Carter et al. 2011). Exposure to tricaine causes inhibition of the lateral line, auditory, and electrosensory neurophysiology in teleost and elasmobranch fishes

(Hensel et al., 1975; Späth and Schweickert, 1977; Palmer and Mensinger, 2004). Much

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less attention has been paid to visual impairments induced by tricaine, but it is known to inhibit dark-adaptation in amphibians (Hoffman and Basinger, 1977; La Touche and

Kimeldorf, 1978; Rapp and Basinger, 1982; Bernstein, 1986). Neuromuscular blockers like Flaxedil (gallamine triethiodide) and Pavulon (pancuronium bromide) do not alter sensory physiology, but also do not have any anesthetic effects (Cordova and Braun,

2007; Neiffer and Stamper, 2009). Therefore, further assessment of anesthetic effects on visual responses is necessary for proper interpretation of data with different modes of immobilization. This is especially true for fishes, as there are no studies of the effects of tricaine on fish visual systems even though it remains an anesthetic of choice for many researchers.

The physiological temporal resolution in two species of batoid elasmobranch fishes that differ in habitat and lifestyle were examined here. The Atlantic cownose ray,

Rhinoptera bonasus, is a eurythermal, benthopelagic, actively schooling ray in coastal and estuarine waters throughout temperate to subtropical habitats along the eastern

United States and Gulf of Mexico (Smith and Merriner, 1987; Neer and Thompson,

2005; Collins et al., 2007a; Collins et al., 2008). The yellow stingray, Urobatis jamaicensis, is a stenothermal, benthic, solitary, fairly inactive batoid that is common in reef and seagrass habitats in subtropical to tropical regions around South Florida, the

Florida Keys, and the Caribbean Sea (McEachren and de Carvahlo 2002; Fahy, 2004). I addressed four general questions regarding temporal resolution using these two species as model elasmobranchs: 1. Are differences in their lifestyles and habitats reflected in their visual temporal resolutions? 2. Does light adaptation affect temporal resolution in these species? 3. Does temperature affect temporal resolution in elasmobranchs? 4.

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Does tricaine anesthesia have an effect on temporal resolution in elasmobranchs? I predicted that yellow stingrays, which inhabit warm, clear waters throughout their geographical range and across seasons, to have faster working eyes than cownose rays, which inhabit environments that vary in temperature and irradiance across regions and seasons.

MATERIALS AND METHODS

Animal collection and maintenance

Visual temporal resolution was quantified in two species of batoid elasmobranchs, the yellow stingray, Urobatis jamaicensis (Cuvier 1816), and the Atlantic cownose ray,

Rhinoptera bonasus (Mitchill 1815). Urobatis jamaicensis (disc width (DW): 18-23cm) were collected by hand net from South Florida reefs and transported in oxygenated, buffered water to either the Florida Atlantic University Marine Science Complex (FAU) in Boca Raton, FL or the Marine Experimental Research Facility at Mote Marine

Laboratory (MML) in Sarasota, FL. Rhinoptera bonasus (DW: 70-77cm) were collected via gillnet from Tampa Bay and transported to MML. All animals at MML were maintained in a closed-seawater system, whereas animals at FAU were maintained in tanks equipped with a flow-through seawater system. All animals were held under

12h:12h light:dark cycles and fed >20% of their body weight/week. All experiments were conducted in compliance with FAU and MML Institutional Animal Care and Use

Committee protocols (FAU: A09-25 and A12-33, Mote: 12-09-SK1).

Experimental apparatus

The electroretinogram (ERG) was used to quantify the temporal resolution of U. jamaicensis and R. bonasus as previously described (McComb et al., 2010). Briefly, an 102

Ag-AgCl 100µm tip microelectrode filled with potassium chloride (Warner Instruments,

Hamden, CT) was positioned in the vitreal component of one eye and an identical reference electrode was placed on the skin. The output from the electrodes was differentially amplified (DP-304, Warner Instruments) at 1000–10,000x, filtered (0.1

Hz–100 Hz, 50/60 Hz) (DP-304, Warner Instruments and HumBug, Quest Scientific,

North Vancouver, BC, Canada), digitized at 1 kHz (Power Lab® 16/30 model ML 880,

AD Instruments, Colorado Springs, CO, USA) and recorded using LabChart® 7

Software (v7.2.5 AD Instruments).

Computer-controlled 2s trains of sinusoidal white light flashes (50:50 light:dark) from a white LED were presented to the eye through a liquid light guide (PN 38476,

Dymax Corporation, Torrington, CT, USA) positioned to evenly illuminate the corneal surface. Stimulus irradiance was controlled by a combination of individual neutral density filters (1-3 log units each) and a circular stepped neutral density filter wheel with seven log units of intensity.

Electroretinogram protocol

Rays were dark-adapted for one hour and then anesthetized with tricaine (1:10,000 wt:vol) until ventilation ceased. Rays were immobilized with an intramuscular (IM) injection of the paralytic pancuronium bromide (0.4 mg kg-1) and anesthetized with either a maintenance dose of tricaine (1:12,000-1:15,000 wt:vol) or ketamine hydrochloride (IM; 12-15 mg kg-1). Anesthetized rays were moved to an 89 X 43 X 21 cm acrylic experimental tank, secured to a submerged platform with Velcro straps and ventilated with oxygenated seawater (dissolved oxygen >6 mg L-1 at 0.5-2.0 L min-1) through a tube inserted in the mouth throughout the experiment.

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Temporal resolution was evaluated by quantifying flicker fusion frequency, or the ability of the eye to electrically respond to individual flashes of a flickering light stimulus at a given irradiance. Flicker fusion frequency increases with a corresponding increase in irradiance until it reaches a maximum frequency. This maximum critical flicker fusion frequency (CFFmax) is the fastest frequency of flashes that an eye can follow at any irradiance. Increasing frequencies of 2s trains of white light flashes were presented to the eye at increasing irradiance until the electrical response of the eye

(ERG) no longer remained in sync with the stimulus. When the irradiance was increased by two log units without a corresponding increase in the frequency response of the eye, the eye was considered to have reached CFFmax. Stimulus trains were presented once every 2m at the lowest irradiances and every 3-5m at the highest irradiances. A

250ms control flash was presented between each change in irradiance to ensure that dark or light adaptation was maintained and that the eye was not experiencing fatigue.

Experimental conditions

To evaluate the effect of light, temperature, and anesthesia on temporal resolution,

CFFmax was quantified in scotopic (dark-adapted) and photopic (light-adapted) conditions, at 20°C and 30°C, and with two anesthetics, tricaine and ketamine. After anesthesia was administered and electrodes placed, rays were dark-adapted for a minimum of one hour for scotopic trials and 20 minutes for photopic trials.

Temperature was controlled with aquarium heaters and an aquarium heater/chiller unit

(SeaChill TR5, Teco, Ravenna, Italy) and was maintained within ±1°C of the target temperature. Ambient light for photopic trials was provided by a white LED array

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mounted above the tank. Irradiance was adjusted so that photosensitivity was reduced by two log units relative to scotopic responses.

Data analysis

CFFmax was quantified for all combinations of light adaptation, temperature, and drug effects within each species (n=3-6/treatment; see Table 1 for summary). Effects of species, light adaptation, temperature and drugs were tested for significance with generalized linear models (GLM). All data conformed to assumptions for parametric statistics.

The proportional differences between CFFmax under scotopic vs. photopic conditions, 20°C vs. 30°C, and tricaine vs. ketamine were compared using a series of calculated coefficients (Qlight, Q10, and Qdrug) as follows:

!""!"# !"#$#%&" Q!"#$% (1) !""!"# !"#$#!%&

!""!"# °! �!" = (2) !""!"# °!

!""!"# !"#$%#&' �!"#$ = (3) !""!"# !"#$%&'"

RESULTS

Upon inspection of the data, it was noticed that the photopic ketamine trials for the cownose rays fell into two distinct groups with largely different means. The groups reflect the length of time in captivity; group 1 rays were tested approximately 8-10 weeks after they were introduced to the lab and had a photopic CFFmax that was nearly 105

half that of the group 2 rays, which were tested within five days of collection. It is possible that long term captivity had an effect on the visual physiology of these rays that limited the maximum photoreceptor response, e.g. by targeting fast conducting potassium channels. Group 1 photopic cownose rays were significantly slower than group 2 rays (ANOVA: F1,5= 24.38, P= 0.0078), but there was not a significant difference in scotopic CFFmax between groups 1 and 2 (ANOVA: F1,5= 0.05, P= 0.84), so group 1 photopic trials were excluded from analysis.

Cownose ray, Rhinoptera bonasus, scotopic and photopic CFFmax were 14.7±0.5

(mean±s.e.m.) and 30±5 Hz, respectively (Table 1; see Table 2 for summary of statistics) and were faster than the yellow stingray, Urobatis jamaicensis, (scotopic:

10.6±0.4 Hz; photopic: 25.5±1.3 Hz). Photopic CFFmax was faster than scotopic in both species. Dark-adapted CFFmax was 51% slower than light-adapted in R. bonasus and was 58% slower in U. jamaicensis (Table 3).

A decrease in ambient water temperature of 10°C decreased scotopic and photopic CFFmax in both species (Table 1). Dark-adapted R. bonasus CFFmax decreased significantly (Table 2) with a Q10 of 2.2 (Table 3). Although light-adapted R. bonasus

CFFmax decreased 32% with a 10°C decrease in temperature, small sample size prohibited statistical analyses of the photopic data. Scotopic and photopic U. jamaicensis CFFmax decreased significantly with a decrease in temperature (Table 2) with scotopic Q10= 1.8 and photopic Q10= 2.0 (Table 3).

Flicker fusion frequency was slower in tricaine treatments than ketamine treatments for both species (Table 1). DISCUSSION

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Flicker fusion frequencies (FFF) measured in the cownose ray, Rhinoptera bonasus, and the yellow stingray, Urobatis jamaicensis, were within the range of other elasmobranchs studied thus far (Table 4). Although comparisons of absolute flicker fusion frequency among studies with different methodology is difficult (Fritsches et al., 2005; Horch and

Salmon, 2009), generalized trends of the effects of ecology on visual function are suitable for comparison. In this study, photopic (light-adapted) maximum critical flicker fusion frequency, CFFmax, was always faster than scotopic (dark-adapted), 30°C was always faster than 20°C, and CFFmax from ketamine-sedated rays was always faster than tricaine for both species (Table 1). When the effects of dark-adaptation, temperature decrease, and tricaine were considered collectively, both species demonstrated a >80% reduction in temporal resolution.

Photic environment

Cownose rays and yellow stingrays had a faster photopic temporal resolution than scotopic, suggesting that they possess both fast and slow conducting photoreceptors that support visual function under dark and light conditions (Laughlin and Weckström,

1993). Fast-conducting channels are metabolically expensive to maintain (Niven et al.,

2007) and they are not commonly found in species whose visual ecology does not require, or whose photic environment does not permit, fast temporal resolution

(Laughlin and Weckström, 1993; Frank, 2003). For example, deep-sea euphausiids that live in a completely dark habitat lack fast cells and do not demonstrate an increase in temporal resolution when light adapted (Frank, 2003). Cownose rays that were maintained in captivity >8 weeks before data collection showed a up to a 45-55% decrease in mean photopic CFFmax relative to group 2 rays that were tested within five

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days of collection and addition to the lab. It is probable that the physiology of the fast photoreceptors in the group 1 cownose rays was affected by the lab conditions, decreasing the proportion of energy devoted to fast photoreceptors.

Cownose rays had a faster scotopic and photopic CFFmax than yellow stingrays, although the photopic CFFmax was not significantly greater due to small sample size for cownose rays (Table 2). It was predicted that yellow stingrays would have faster working eyes due to their bright, clear water habitat and diet of quickly moving epifaunal invertebrates (McEachren and de Carvahlo, 2002; Fahy, 2004), whereas the cownose rays encounter much more variability in water clarity (Smith and Merriner

1987; Collins et al. 2007a; Collins et al. 2008) and feed primarily on slow moving or immobile prey that are often buried in the substrate (Smith and Merriner 1985; Collins et al. 2007b; Ajemian and Powers, 2012) so visual cues are not useful for prey detection.

However, cownose rays are often found in large schools, so tracking of conspecifics may take precedence over prey detection as a primary force driving the visual ecology of the species.

Thermal environment

Metabolic processes in ectotherms generally double their rate with an increase of 10°C

(Schmidt-Nielson, 1997), and vertebrate photoreceptors, including fishes, typically show a Q10 near 2.0 (Hanyu and Ali, 1963; Hanyu and Ali, 1964; Baylor et al., 1983; Lamb,

1984; Fritsches et al., 2005) , indicating that visual biochemical reactions are similarly temperature sensitive to other physiological processes. Therefore an approximate doubling of whole eye flicker response over a 10°C in temperature was expected.

Yellow stingrays had a photopic and scotopic visual Q10 of 2.0 (Table 3) with a

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reduction of 43% and 51% in dark and light-adapted states, respectively, which mirrored that of metabolic Q10 measured from other benthic elasmobranchs (Di Santo and

Bennett, 2011), indicating that visual and metabolic physiologies are similarly temperature sensitive as seen in other fishes (Fritsches et al., 2005).

Cownose rays showed a statistically significant 52% decrease in scotopic CFFmax with a 10°C decrease in temperature, whereas the photopic CFFmax decreased less dramatically (32%) and not significantly. An increased sample size will likely yield a statistically significant difference in photopic CFFmax with the change in temperature.

Cownose ray metabolic Q10 measured from quiescent rays is 2.4 (Neer and Thompson,

2005), similar to that of the scotopic visual Q10 measured here (Table 3). However, the photopic Q10 was 1.5, considerably lower than that of the scotopic and metabolic Q10.

Although the reason for this discrepancy is purely speculative at the current time, blowfly, Calliphora vicina, dark-adapted photoreceptors are more temperature sensitive than light-adapted photoreceptors (Tatler et al. 2000). The Q10 of the time between the stimulus presentation and the peak of the response was the same as the cownose ray

CFFmax Q10 (Table 3), with a scotopic Q10 of 2.3 and photopic Q10 of 1.5 (Tatler et al.

2000). During flight in direct sunlight, blowflies can experience an internal temperature up to 6°C greater than that of the environment (Stavenga et al. 1994), and behavioral thermoregulation is thought to increase the temporal resolution during flight-associated behaviors like navigation and foraging (Tatler et al. 2000). Although a neither a physiological or behavioral basis for endothermy has been proposed in cownose rays, cranial endothermy in fishes has independently evolved in several lineages, including some species of Mobula and Manta (Schweitzer and Notarbartolo Di Sciara, 1986;

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Alexander, 1996), which are in the same family as cownose rays and share similar cranial morphology, behaviors, and habitat with cownose rays. Also, a greater proportion of cranial red muscle (RM) has been noticed in cownose rays than benthic rays, including yellow stingrays (pers obs; M. Kolmann, pers. comm). RM has a higher metabolic rate than white muscle (Bernal et al., 2005) and in cownose rays, much of this

RM lies adjacent to the eye and associated nervous tissue (pers obs; Kolmann, 2012), and may help to buffer these tissues against environmental temperature changes

(Tubbesing and Block, 2000). Additionally, cownose rays have a large amount of adipose tissue that surrounds the eye and encases the distal portion of the optic nerve, similar to that seen in the cranially endothermic mako shark, Isurus oxyrhincus (pers. obs.), which could aid in insulating tissues. Orbital temperature measurements in free- swimming rays would provide an estimate of the contribution of metabolic heat retention to visual function and a more realistic estimate of temperature sensitivity, and thus temporal resolution, could be calculated.

Anesthesia

Tricaine significantly decreased CFFmax in both species and in all photic and thermal treatments, except the yellow stingray scotopic 20°C, which was already greatly reduced compared to the photopic and warmer conditions (Tables 1,2). CFFmax was reduced by

46% with the use of tricaine, which was similar to the reduction in temporal resolution due to dark-adaptation (45%) and slightly less than the effect of temperature (57%).

Although the mode of action of tricaine on sensory systems is not well understood, studies of olfactory, auditory, and lateral line systems in teleosts and amphibians

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describe decreases in sensitivity to stimuli when animals are exposed to sedative and anesthetic doses of the drug (Hensel et al., 1975; Späth and Schweickert, 1977; Lewis et al., 1985; Quinn et al., 1988; Palmer and Messinger, 2004; Cordova and Braun, 2007).

The effect of tricaine on the visual system has received less attention, however, it is known to inhibit rod function in frogs by formation of a Schiff-base with retinal, preventing regeneration of rhodopsin until tricaine is removed from the system

(Hoffman and Basinger, 1977; Rapp and Basinger, 1982; Bernstein et al., 1986) .

Tricaine further inhibits dark adaptation by binding to sodium channels on the rod outer segment, which limits passage of sodium out of the cell during the dark current (Fraziera and Narahashia, 1975). Reversible retinotoxicity has also been reported in an ichthyologist who experienced increased photosensitivity and decreased visual function after chronic exposure to tricaine (Bernstein et al., 1997).

The use of tricaine in sensory experiments presents an ethical dilemma (Palmer and Messinger, 2004); chemical restraint without anesthesia during physiological procedures may cause distress due to prolonged unalleviated restraint (Ross and Ross

1999; Nieffer and Stamper, 2009; Carter et al., 2011). However, the most widely accessible and commonly used anesthetic is tricaine. Ketamine hydrochloride has been accepted as an appropriate anesthetic for ERG and is easily tolerated by elasmobranchs at an anesthetic dose (Sasovetz, 1978; Ross and Ross, 1999; Litherland, 2009; Nieffer and Stamper, 2009). In preliminary trials, there was no reduction in yellow stingray

CFFmax with the use of ketamine when compared to immobilization with pancuronium without anesthesia (unpublished data). Although a ketamine-pancuronium regimen should be considered over tricaine in studies on sensory function, selection of

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anesthetics should be made carefully. Ketamine is a dissociative-type anesthetic and does not provide adequate immobilization when administered alone, so use of a neuromuscular blocking agent is also necessary. However, the effects of pancuronium are not reversible in elasmobranch fishes, and these experiments are terminal as a result.

The use of other anesthetics, such as Propofol, which have a short half-life, are reversible, and provide appropriate immobilization may be preferred in some studies

(Miller et al., 2005; Neiffer and Stamper, 2009).

Conclusions

Elasmobranch fishes are found in nearly every marine habitat, and therefore, demonstrate a wide diversity in adaptations to the photic environment in terms of temporal and spatial resolution, spectral sensitivity, light sensitivity, and retinal topography (see Lisney et al., 2012 for review). Both species in our study had a temporal resolution that was intermediate among elasmobranchs studied with similar methodology, and temporal resolution was greatly reduced by the anesthetic, MS-222

(Table 4). Generally, species that inhabit variable or light limited waters tend to have slower eyes than those in bright environments however, there is considerable overlap among species that is not solely correlated to the photic environment and may be attributed to diet, behavior, or temperature. This study illustrates that these other ecological correlates may be better indicators of visual function, however, little information is available regarding the general ecology of most elasmobranch species.

The cownose ray is a rare example where descriptive analyses of diet, movement patterns, and habitat preferences are detailed, due to the potential role of schooling rays that forage on beds of commercially important bivalves (Smith and Merriner, 1985;

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Collins et al., 2007b; Collins et al., 2008; Ajemian and Powers, 2012). However, there is still a lack of information regarding diel activity and schooling and foraging behaviors, which are key in understanding sensory ecology. Also, anthropogenic activities that alter water clarity and temperature are likely to impact visual function of these fishes, although such effects are difficult to predict due to a lack of basic biological information. Coastal pollution that continues to increase turbidity in the habitats of both species may become a critical factor in the ability of rays and other marine organisms to visually discriminate conspecifics and predators, track prey, and navigate in their environment. Consequences of habitat change on sensory systems may be far more reaching than direct impacts of the sensitivity of these systems, so increased awareness of sensory interactions with the environment, such as this study, will be critical to understanding how environmental changes affect whole organism biology.

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Table 5.1 Summary of mean±s.e.m. maximum critical flicker fusion frequency (CFFmax).

Species Drug Temperature Scotopic FFF n Photopic FFF n (Hz) (Hz) R. bonasus Ketamine 20°C 6.7 ± 0.8 6 20.5 ± 2.5 2 30°C 14.7 ± 0.5 6 30.0 ± 5.0 2 Tricaine 20°C 4.0 ± 0.5 5 5.6 ± 0.5 4 30°C 9.8 ± 1.2 5 13.0 ± 2.0 4 U.jamaicensis Ketamine 20°C 6.0 ± 1.4 4 12.5 ± 0.9 4 30°C 10.6 ± 0.4 5 25.5 ± 1.3 4 Tricaine 20°C 4.7 ± 0.3 3 7.0 ± 0.8 4 30°C 8.5 ± 0.5 4 11.0 ± 2.7 4

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Table 5.2 Summary of sample size and results from GLM analyses (F factor and p- value) of CFFmax across treatments.

1Species Photic condition F P Scotopic F1,10=38.54 0.002 Photopic F1,5=1.52 0.29 2Light adaptation Species F P R. bonasus F1,7=36.91 0.0009 U. jamaicensis F1,8=142.71 <0.0001 3Temperature Species Light-adapted status F P R. bonasus DA F1,11=72.00 <.0001 LA F1,3=2.89 0.23 U. jamaicensis DA F1,8=13.06 0.01 LA F1,7=67.60 0.0002 4Anesthesia Species Light-adapted status Temperature F P R. bonasus DA 20°C F1,10=7.48 0.02 DA 30°C F1,10=15.25 0.004 LA 20°C F1,6=89.59 0.0002 LA 30°C F1,5=15.41 0.02 U. jamaicensis DA 20°C F1,8=0.67 0.45 DA 30°C F1,6=11.06 0.01 LA 20°C F1,7=21.35 0.004 LA 30°C F1,7=22.73 0.003

Note: Individual GLMs with α=0.05 were used to test for significant differences in 1 2 photopic and scotopic CFFmax between species and for differences in light adaptation, 3temperature and 4anesthetic within species. DA= dark-adapted, LA= light-adapted

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Table 5.3 Fractional differences (± s.e.m.) of CFFmax between treatment conditions.

Ecological Qfactor Species Scotopic Photopic Qlight R. bonasus 2.0 U. jamaicensis 2.4 Q10 R. bonasus 2.2 1.5 U. jamaicensis 1.8 2.0 Experimental Qfactor Species Scotopic Photopic 20°C 30°C 20°C 30°C Qdrug R. bonasus 1.7 1.5 1.7 1.5 U. jamaicensis 1.3 1.2 1.3 1.2

Note: Ecological Qfactors are those that represent differences in temporal resolution between species (Qspecies), photic environment (Qlight), and thermal environment (Q10). Experimental Qfactor represents the difference in temporal resolution between anesthetics (Qdrug).

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Table 5.4 Summary of elasmobranch physiological flicker fusion frequency (FFF) and experimental conditions.

Species Drug Temperature Scotopic FFF Photopic FFF Reference (Hz) (Hz) Cownose ray Rhinoptera bonasus Ketamine 30°C 14.7 ± 0.5 25.0 ± 5.8 present study Yellow stingray Urobatis jamaicensis Ketamine 30°C 10.6 ± 0.4 25.5 ± 1.3 present study Scalloped hammerhead shark Tricaine 24-25°C 25.1 ± 2.53 27.3 ± 3.15 McComb et al. 2010 Sphyrna lewini Bonnethead shark Sphyrna tiburo Tricaine 24-25°C 25.6 ± 2.30 31.0 ± 2.89 McComb et al. 2010 Blacknose shark Tricaine 24-25°C 16.0 ± 1.0 18.0 ± 0.85 McComb et al. 2010 Carcharhinus acronotus Lemon shark * * 38 45 O’Gower and Mathewson 1967 Negaprion brevirostris Tiger shark Ketamine 16 21 Litherland 2009 Galeocerdo cuvier Sandbar shark Carcharhinus plumbeus Ketamine 21 23 Litherland 2009 Skate None+ * 5 30 Green and Siegel 1975 Raja spp.

Note: FFF is presented as mean ± s.e.m. *Not reported. +Study used isolated eyecups rather than live animal preps.

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CHAPTER 6: SYNTHESIS AND FUTURE DIRECTIONS

Ecologists often attempt to characterize adaptations of organisms to their specific niches. In sensory ecology, questions are often directed towards sensory tuning and how, what, and why certain information is obtained from physical, biological, and social components of an environment. Elasmobranch fishes, the sharks, skates, and rays, in particular use an exquisite suite of sensory modalities to interact with their environments. Some sensory capabilities of elasmobranchs have been noted to be extraordinarily sensitive, such as olfaction, despite a lack of evidence to support such a claim (Meredith and Kajiura, 2010; Gardiner et al., 2012). Vision and electroreception are perhaps the most well-studied modalities in this group of fishes; however, critical information regarding the functionality of these systems is still unknown, in part due to a lack of data detailing the basic biology of the nearly 1000 species. As an added complication, the characteristics of the stimuli themselves have been poorly described.

Understanding the biology of a species and the characteristics of signals are essential to appreciating how the signals are received and processed. The goals of this dissertation were to characterize the properties of some of these signals and to explore sensory adaptations of elasmobranchs in an ecological context using the cownose ray,

Rhinoptera bonasus, and the yellow stingray, Urobatis jamaicensis, as model elasmobranchs.

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ELECTROSENSORY ECOLOGY

Much attention has been paid to the sensitivity of the electrosensory system of shark species to prey-simulating stimuli (Kalmijn, 1982; Haine et al., 2001; Kajiura and

Holland, 2002; Kajiura, 2003; Kajiura and Fitzgerald, 2009;) with more recent consideration of batoids (Jordan et al., 2009; McGowan and Kajiura, 2009; Wueringer et al., 2012). Despite the interest in electrosensory capabilities in elasmobranch fishes, the physical characteristics of electric signals and contribution of electrosensory pore placement to sensitivity have not been well categorized.

Bioelectric field characteristics

The conductive nature of seawater permits propagation of electric signals. Some attributes of prey electric signals have been quantified previously (Kalmijn, 1972;

Kalmijn, 1974; Haine et al., 2001), however, spatial and temporal aspects of the signals have not been quantified in sufficient detail. Rhythmic expansion of the buccopharyngeal cavity during ventilation in fishes creates a modulated electric field in the range of 1-2Hz, that match the range of frequencies best detected by elasmobranch predators (Tricas et al., 1995; Sisneros et al., 1998). Generally, invertebrate and elasmobranch electric potential was significantly smaller than teleosts, although there was overlap among the groups. There was no relationship between voltage with frequency, mass, or total length, although trends of increasing voltage with increasing frequency and body size have been reported previously (Kalmijn, 1972; Haine et al.,

2001). In this study, osmoregulatory strategies and activity level were better predictors of voltage. The smallest electric potentials were seen in the invertebrates and elasmobranchs, which are osmoconformers and have less ion exchange with seawater 119

than the hyposmotic teleosts, which actively pump ions out of the body through the gills and mucous membranes (Robertson, 1953; Foskett et al., 1983; Evans et al., 2005).

Among the teleosts, the species with the smallest voltage production was the catfish, a benthic species that was considered to be the most inactive among the species studied

(Gray, 1954). Alternatively, snapper were considered to be the most active and produced the greatest voltage of any species. Electric potential may be greater in more active species due to an increased gill surface area to maximize oxygen uptake (Gray,

1954; Hughes, 1966), and thus a larger surface area for ion exchange to occur. Larger electric potentials also lend to farther signal propagation. Snapper can be detected by an elasmobranch predator that is approximately 60cm away if the predator has a sensitivity of 35nV cm-1. The small electric potential of the catfish could be detected by the same predator at a distance of 38cm. Detection distance measured in behavioral assays of elasmobranch sensitivity is in the range of 20-40cm to prey-simulating electric fields similar in strength to the catfish. An ideal dipole electric field decays as an inverse cube function. The decay of the electric field of teleosts occurred in the range of an inverse square to an inverse cube for all species, illustrating that electric fields occurring in nature are far more complex than a simple dipole. The contribution of higher order, i.e. quadrapole and octopole, and modulated electric fields should be considered in future studies of elasmobranch electroreception.

Electrosensory morphology and behavior

Correlations of behavioral sensitivity to prey-simulating electric fields with electrosensory pore number have not been successful (Kajiura, 2001; Kajiura and

Holland, 2002; Kajiura, 2003; Jordan et al., 2009;). However, hypotheses have been

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predicted that rather than increasing sensitivity, variations in pore distribution and density across species serve to increase the electrosensory search area of a predator

(Kajiura, 2001) and increase spatial resolution for localization of weakly electric prey

(Raschi, 1986; Jordan et al., 2009). Trends have been noted in batoids where there is an increase in the density of ventral pores in species that feed on immobile infaunal and epifaunal prey and that could benefit from an increase in spatial resolution for prey detection. However, the hypotheses of density correlations to enhanced spatial resolution have yet to be empirically tested.

In the present study, cownose rays had a greater number and higher density of pores on the ventral surface of the head than yellow stingrays. Cownose rays are benthopelagic, schooling predators that feed primarily on immobile or slow-moving benthic prey such as clams, gastropods, and echinoderms (Smith and Merriner, 1985;

Collins et al., 2007a; Ajemian and Powers, 2012). Yellow stingrays are benthic opportunistic feeders on small mobile crustaceans and polychaetes (Yáñez-Arancibia and Amézcua-Linares 1979). As expected, the greater number of pores in the cownose ray did not contribute to increased sensitivity to electric fields. Yellow stingrays were significantly more sensitive to prey-simulating electric fields than cownose rays, but at the expense of resolution. Yellow stingrays failed to direct a strike at the target dipole in a behavioral assay in 38% of approaches to the stimulus. In contrast, cownose rays accurately localized the dipole center in 89% of approaches. Accurate localization of stationary, buried prey items is more of a benefit to cownose rays than high sensitivity.

Schooling rays may rely more heavily on visual cues of schoolmates and olfaction to begin foraging and switch to electroreception and tactile input once they have descended

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to an area of the benthos with high prey density. The differences in response behaviors and sensitivity between the two species reveal the dynamic nature of sensory systems and that a wide range of factors must be considered when describing capabilities of a particular sensory modality.

VISUAL ECOLOGY

Biologists have been interested in the visual biology of elasmobranch fishes for nearly a century; however, the breadth of information gained by these studies has remained fairly limited to histological and morphological examination of the eyes. A recent renewed interest in elasmobranch vision has elucidated the vast variation in visual capabilities within the group. Most species are now known to possess duplex retinae composed of both rod and cone photoreceptors. However, all sharks studied to date are reported to be monochromatic, or to lack cones all together, whereas most batoids are dichromatic or trichromatic. Although the reason for this discrepancy is unknown, photoreceptor sensitivity for both sharks and rays is spectrally tuned to the depth and water quality in which they are found (Munz and McFarland, 1973; Munz and McFarland, 1977; Loew and Lythgoe, 1978; Hart et al., 2011; Lisney et al., 2012). For example, pelagic species that inhabit the open ocean usually have blue-shifted sensitivity due to attenuation of long wavelengths with depth (Munz and McFarland, 1977; Loew and Lythgoe, 1978;

Bowmaker et al., 1994; Lythgoe, 1994; Hart et al., 2011). Incredible diversity exists among elasmobranchs in terms of visual function that can be correlated to spectral, thermal, and photic habitat, behavior, and diet.

Color and ultraviolet vision

Awareness of the potential for elasmobranchs to utilize color vision began in the early 122

1960’s with the discovery of cones in the lemon shark, Negaprion brevirostris (Gruber et al., 1963). Since then, cones have been discovered in many species, with multiple cone types present in some batoids (Hart et al., 2004; Theiss et al., 2007). Both the cownose ray and yellow stingray possessed an A1-based rhodopsin with maximal absorbance (λmax) of approximately 500nm. Both rays also possessed multiple types of cone visual pigments; the cownose ray was a dichromat with short-wavelength

λmax=470nm and long-wavelength λmax=551nm. The yellow stingray was a trichromat with short, medium, and long-wavelength λmax= 475nm, 533nm, and 561nm, respectively. Multiple cone types with separate and distinct spectral sensitivities provide the basis for color vision if higher order nervous system structures have the ability to compare the spectral output from the photoreceptors (Lisney et al., 2012).

Electroretinogram (ERG) data further support that cownose rays and yellow stingrays are likely to have the ability to discriminate color. Both species had multiple peaks in scotopic, photopic, and chromatic adapted spectral sensitivity curves that correlated to the λmax of the photoreceptors. Yellow stingrays also had a spectral sensitivity peak in the UV region at 360-370nm that was enhanced relative to the long-wavelength peak under chromatic adapted conditions, suggesting a UV-sensitive cone (UVS) may also be present. Although no UVS were found with microspectrophotometry (MSP), cone size typically decreases with wavelength, so UVS cones may have been too small to find with MSP or may have been too few in number, as seen with goldfish, Carassius auratus (Burkhardt, 1966), or both. Teleosts and invertebrates use color vision, including ultraviolet wavelengths, for an enhanced communication channel to recognize conspecifics, predators, and prey (Hazlett, 1979; Collin and Trezise, 2004; Hart et al.,

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2004; Kelber and Roth, 2006; Siebeck et al., 2008). The significance of multiple cone types and UV sensitivity in the species studied here need to be evaluated with behavioral analyses to determine what functionality is provided by the physiological responses.

Temporal resolution

Environmental factors, such as photoperiod and temperature, can radically influence behaviors and physiological processes in ectotherms. For example, surface-dwelling pelagic tunas have an increased temporal resolution compared to vertically migrating pelagic tunas that spend significant periods of time in deep, light-limited water (Brill et al., 2005; Fritsches et al., 2005). Cownose rays and yellow stingrays had maximum critical flicker fusion frequencies, CFFmax that was in the range of other elasmobranchs

(O'Gower and Mathewson, 1967; Green and Siegel, 1975; Litherland, 2009; McComb et al., 2010). There was a relationship between CFFmax and both photic and thermal environmental factors. Cownose rays had a significantly faster temporal resolution than yellow stingrays by 4-5Hz. Scotopic temporal resolution was decreased by 51%,

(cownose rays) and 58% (yellow stingrays) relative to photopic temporal resolution. A decrease in water temperature of 10°C decreased cownose ray CFFmax by 32-55% and decreased yellow stingray CFFmax by 43-51%.

The results presented here reflect adaptations to each species’ ecological niche, especially in terms of the photic environment. Cownose rays frequently inhabit variable light environments (Smith and Merriner, 1987; Neer and Thompson, 2005; Collins et al.,

2007b; Collins et al., 2008) and their eyes are able to maintain faster temporal resolution in both light-limited and bright environments than yellow stingrays, which are most often found in clear, bright reef and seagrass habitats (McEachren and de Carvahlo,

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2002; Fahy, 2004). Similarly, cownose rays experience a broad range of temperatures both regionally and seasonally, whereas yellow stingrays experience little seasonal or regional fluctuation in temperature. Although yellow stingrays had 50% reduction in

CFFmax with a decrease in temperature, the rays are unlikely to experience such a dramatic decrease in performance in their natural environment. Cownose rays, on the hand, may experience a reduction in temporal resolution when cooler waters are encountered. A potential contribution of metabolic heat retention and insulation of the eye should be considered. Cranial red muscle and orbital fat may maintain consistent eye temperatures when low and high temperature extremes are present. Additionally, the contribution of retinomotor movements with changes in environmental irradiance, as well as the degree of spatial and temporal summation should be investigated to determine the tradeoffs of photosensitivity and temporal resolution of both species.

FUTURE DIRECTIONS

A significant conclusion of this dissertation was that such basic information about the biology of most elasmobranch species is lacking, including diet composition, movement patterns, intraspecific communication, and schooling behaviors, to name a few.

Responses to all of these ecological factors are dependent upon reception, perception, and processing of environmental signals. To better understand the ecology of elasmobranch fishes, we must continue to detail aspects of their biology, characterization of habitats and associated stimuli, and behavioral, physiological, and morphological adaptations of sensory systems that enable the success of this group.

It is well documented that anthropogenic activities have profound effects on the environment. These changes are likely to have both direct and indirect consequences on 125

sensory function that interrupt normal patterns of signal detection, and thus alter the ways in which elasmobranch interact with each other and their environment. For example, anecdotal evidence suggests that elasmobranchs utilize pheromones for mate detection and courtship behaviors (Johnson and Nelson, 1978; Kajiura et al., 2000;

Chapman et al., 2003). Chemical pollutants in many aquatic habitats may bind to olfactory receptor neurons and disrupt the olfactory pathway necessary for mate discrimination in teleost fishes (Hubbard et al., 2003; Fisher et al., 2006). Additionally, some of these compounds, like endocrine-disrupting compounds, have caused feminization of the gonads in some male shark species (Gelsleichter et al., 2005;

Gelsleichter et al., 2007), which could eliminate pheromone production, and thus, inhibit mate detection. Unfortunately, virtually nothing is known about elasmobranch mating behaviors, and even less is known about the potential role of pheromones in achieving successful mating events (Meredith and Kajiura, 2010). Continued interest in elasmobranch fishes, their biological habits and requirements, and sensory interactions with the environment will be key to understanding how habitat change may affect the survival of the group.

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