Are the Swimming Kinematics of Blind Cavefish Adapted for Active Flow-Sensing?

ARE THE SWIMMING KINEMATICS OF BLIND CAVEFISH ADAPTED FOR ACTIVE FLOW-SENSING?

Delfinn S. Tan

A Thesis

Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of

MASTER OF SCIENCE

August 2010

Committee:

Sheryl Lynn Coombs, Advisor

Moira van Staaden

Verner P. Bingman

ABSTRACT

Sheryl L. Coombs, Advisor

When visual cues are poor or unavailable, animals often rely on a form of active sensing in which they produce their own signal energy to probe various features of the environment (e.g. echolocation by bats). In a different and less-understood form of active sensing, blind cavefish use their burst and coast swimming motions to generate flow signals that can be detected by the lateral line. The coast phase of the swimming cycle produces a relatively stable, dipole-like flow signal that is distorted when fish swim by nearby obstacles. In this study, we test the hypotheses that (a) blind cavefish have evolved behavioral specializations for active flow-sensing compared to their nearest sighted relatives (a morph of the same species) and (b) flow signal production is regulated by lateral line sensory feedback. We compared the swimming kinematics of blind and sighted morphs in response to a novel, dark environment – both before (T1) and after (T2) a 24- hr familiarization period and with and without a functional lateral line. There were two major findings of this study. The first was that the majority of burst-coast kinematic parameters exhibited no significant differences as a function of morph, familiarity with environment or lateral line functionality. Noteable exceptions included an increase in coast duration, coupled with a decline in swim cycle frequency after T2 for both morphs. The second major finding was that blind morphs exhibited a significantly higher incidence of swim cycles that formed part of a straight swimming trajectory. Both lateral line deprivation and familiarization in the arena led to significant declines in this number for blind, but not sighted morphs. Taken together, these results suggest that both morphs have inherited common neuroethological strategies for regulating burst-coast swimming kinematics, but that blind morphs differ significantly from iii sighted morphs in their swimming trajectories and in lateral line-enabled abilities to link swim cycles into sequences that form straight trajectories. Differences in swim cycle sequences can best be understood in terms of the intermittent and short-range challenges of active flow-sensing by blind cavefish and suggest that these fish have evolved behavioral strategies for coping with these challenges. iv

ACKNOWLEDGMENTS

I would like to thank my advisor Dr Sheryl Coombs for her constant guidance and support in my research here in BGSU. I would also like to thank Dr Paul Patton for his help in technical support and advice on experimental design, Dr Shane Windsor for providing the initial matlab Dr Cordula Mora for advice on my thesis, the Center for Business Analytics and Prof

Nancy Boudreau for statistical help. And finally to my labmates who had also provided me with feedback, and especially Ashley Hammers and Roseanne Mauch for taking care of the fish for the majority of my research.

TABLE OF CONTENTS

Page

CHAPTER I. INTRODUCTION ...... 1

CHAPTER II. MATERIALS AND METHODS ...... 6

Brief Overview of Experimental Design ...... 6

Experimental Animals ...... 6

Experimental Setup ...... 7

Behavioral Procedures ...... 8

Pharmacological Blocking of the Lateral Line System ...... 9

Data Analysis ...... 9

Satatistical Analysis ...... 11

CHAPTER III. RESULTS ...... 13

Burst-coast swimming kinematics in the context of overall behavior ...... 13

Swimming kinematics of blind and sighted morphs during initial exploration ...... 15

RM ANOVA and the use of ‘N’ as a variable ...... 17

The effects of exposure time on swimming kinematics...... 18

The effects of blocking lateral line sensory feedback on swimming kinematics ...... 19

Differences between morphs in the effects of time and lateral-line inactivation ...... 19

CHAPTER V. DISCUSSION ...... 21

Intra- and interspecific comparisons of burst and coast swimming kinematics ...... 21

The functional significance of burst-and coast swimming gaits ...... 23

Temporal modulation of burst-coast swimming kinematics ...... 26

CHAPTER VI. SUMMARY AND CONCLUSIONS ...... 30 vi

FIGURE AND TABLE LEGENDS ...... 32

LITERATURE CITED ...... 34

APPENDIX A. TABLES AND FIGURES ...... 44 vii

LIST OF FIGURES/TABLES

List of Figures Page

1 Representative examples of burst-coast sequences ...... 44

2 Frequency distributions of burst (a) and coast (b) duration ...... 45

3 Frequency distributions of coast velocity (a) and distance (b)...... 46

4 Bar histograms showing the mean value of different swim cycle parameters ...... 47

5 The effects of exposure time on the number of swim cycles ...... 48

6 Frequency distributions of coast duration (a) and velocity (b)...... 49

List of Tables Page

1 Number of burst-coast cycles for all groups of fish ...... 50

2 Alternation frequency of control groups of fish ...... 51

3 Linear regression correlation coefficients (R2) and p values for swimming speed vs swim

cycle frequency for all fish groups ...... 52

4 Linear regression correlation coefficients (R2) and p values for swimming speed vscoast

duration for all fish groups ...... 53

5 Population means of various parameters of control fish groups ...... 54 1

INTRODUCTION

Animals use their senses to acquire information about their surrounding environment and to guide their behaviour with respect to other animals and landscape features in the environment.

While vision typically allows animals to gather spatial information from a distance and often, from a single vantage point, this sense is much less useful at night or under conditions of low- visibility. In these cases, some animals rely on non-visual, active sensing strategies in which they generate their own stimulus energy (e.g., sound) to probe the spatial features of the surrounding environment rather than passively relying on stimulus energy from other sources, such as sound energy from the environment (Nelson and MacIver, 2006).

One familiar example of active sensing is echolocation to detect stationary targets in total darkness (Griffin et al., 1958; Simmons, 1989). In this case, animals like dolphins and bats produce sound signals and then analyze the returning echoes from surrounding objects in the environment in order to detect their presence, distance and location (Griffin et al., 1958). Weakly electric fish use a different form of active sensing in which they generate a weak electric field around their body and detect the changes or distortions in this field caused by the difference in conductivity of objects and the surrounding water (Bullock et al., 1972; Davis and Hopkins,

1988; Knudsen, 1975; Nelson and Maciver, 1999; Watanabe and Takeda, 1963). Nocturnal rodents likewise use a form of active tactile sensing when they scan their surroundings with

‘whisking’ movements of their facial whiskers in contact with nearby surfaces (Berg and

Kleinfeld, 2003).

A much less appreciated and less understood form of active sensing is active flow- sensing by blind cavefish (Campenhausen et al., 1981; Hassan, 1985). In this case, flow signals are produced by the fish’s own swimming movements. As the fish moves forward, pressure is

2 built up in front of the fish and reduced in the rear, causing currents to flow from areas of high pressure to low pressure. Nearby stationary objects distort the self-generated flow field and the distortions can then be detected by the flow sensors of the lateral line system.

Compared to their nearest sighted relatives, sighted morphs of the same species (Avise and Selander, 1972), blind cavefish have several anatomical specializations of the lateral line system, including larger and more numerous superficial neuromasts (flow sensors) in the orbital region of the head (Schemmel, 1967) and elongated, hydrogel structures (cupulae) (Teyke, 1990) that couple the motions of the surrounding water to the underlying mechanosensory hair cells.

While it is clear that blind cavefish have evolved anatomical specializations for enhanced lateral line sensing, much less is known about behavioural specializations for active flow-sensing

- namely the fine-scale swimming kinematics which may influence both the production and reception of flow signals. Blind cave fish swim in a burst-coast fashion (Teyke, 1985; Teyke and

Schaerer, 1994; Windsor et al., 2008) in which they use body and caudal fin propulsion (1 or more tail beats) to accelerate during the burst phase and then passively coast as drag forces cause them to slowly decelerate. Theoretically, active flow-sensing of object-generated distortions is thought to occur during the coast phase, when the body is held relatively straight and the flow field is more or less stable and predictable. In contrast, the the burst phase involves body and caudal fin undulations, which cause unsteady flows and shed vortices in the wake of the fish

(Hanke et al., 2000; Hanke and Bleckmann, 2004; Wolfgang et al., 1999).

There are several lines of experimental evidence in support of the importance of coasts to active flow-sensing by the lateral line. One is that blind cavefish avoid collisions with obstacles more frequently when they are gliding than when they are bursting (Teyke, 1985; Windsor et al.,

2008). Furthermore, the collision rate goes up when the lateral line is blocked (Windsor et al.,

2008). Finally, when fish are actively swimming, the sensitivity of lateral line receptors is known to be reduced by inhibitory inputs from the octavolateralis efferent system in brainstem motor regions (Roberts and Meredith, 1989; Russell and Roberts, 1972).

Because blind cavefish have evolved to live in caves in total darkness (Espinasa and

Borowsky, 2001; Jeffery et al., 2003; Romero and Paulson, 2001), active flow-sensing provides a critical means by which these fish can compensate for the absence of vision. Indeed, there is substantial evidence that blind cavefish use active flow-sensing for a variety of spatial tasks, including obstacle avoidance (Teyke, 1985; Windsor et al., 2008), discrimination of spatial features that differ in their orientation (Campenhausen et al., 1981) or spacing (Hassan, 1986), and exploration of novel environments, presumably to learn and remember spatial features of the environment (Braithwaite and De Perera, 2006; Burt de Perera and Braithwaite, 2005; Burt de

Perera, 2004; Hassan, 1986; Teyke, 1985; Teyke, 1989).

A number of studies have investigated the exploratory behaviour of blind cavefish in novel environments (Campenhausen et al., 1981; Sharma et al., 2009; Teyke, 1985) or in response to a novel feature in an otherwise familiar environment (Teyke, 1985; Teyke, 1989). In general, these studies reveal that blind cavefish exhibit two types of behavioural responses to novel features – one is an increase in swimming activity and speed and the other is a wall- following behaviour in which fish swim close to and nearly parallel to boundary features of the novel environment.

Although it has been assumed that wall-following behaviours and increased swimming speeds serve an exploratory function to enhance information-gathering abilities in novel environments (Burt de Perera, 2004; Creed and Miller, 1990; Jeanson et al., 2003; Kallai et al.,

2007; Teyke, 1989), proper controls to rule out other explanations (i.e. behavioural responses to

4 fear or stress, rather than novelty) have rarely been done. Nevertheless, increased swimming speeds are consistent with an exploratory hypothesis. According to mathematical hydrodynamic models (Hassan, 1985; Hassan, 1993), an increase in swimming speed results in an increase in amplitude of the self-generated flow signal, suggesting that blind cavefish may be increasing the gain of their flow signal for active sensing purposes.

In short, there are numerous studies in support of the idea that blind cavefish use active flow-sensing for behavioural tasks that require spatial knowledge of the environment. Although it is well-known that blind cavefish have evolved several anatomical specializations of the lateral line that presumably enhance this ability, little is known about behavioural specializations for the production and reception of flow signals during active flow-sensing. One significant difference between active flow sensing by blind cavefish and other kinds of active sensing is that signal generation involves the fish’s swimming movements, which simultaneously must serve a locomotor function – e.g., to get the fish from point A to point B. In contrast, bats can theoretically modulate the amplitude, duration and frequency of their outgoing echolocation signals to maximize sensory performance without being directly constrained by locomotor needs.

Indeed, adaptive control of the outgoing signal is one of the chief advantages of active sensing

(Nelson and MacIver, 2006). For example, bats decrease the duration and increase the frequency of their echolocation cries as they near their targets to reduce the overlap between the outgoing call and the incoming echo (Schnitzler, 1973). Likewise, weakly electric fish can adjust the frequency of their electric organ discharges to minimize jamming of their own signals by those of nearby fish (Bullock et al., 1972; Watanabe and Takeda, 1963).

Unfortunately, the degree to which blind cavefish can control and manipulate flow signal production to enhance sensory performance is unclear – especially in light of potential

5 constraints posed by locomotor requirements. For example, if fish were to swim faster in order to increase the amplitude of the flow signal for enhanced signal detection, they would also reduce the amount of time available for reacting to and avoiding collisions with an impending obstacle.

Thus, there may be an optimum range of swimming speeds – neither too fast nor too slow - for active flow-sensing purposes. This raises the interesting question of whether or by how much swimming kinematics can be adjusted for active flow-sensing purposes without compromising locomotor function. There is also the question of how or whether lateral line sensory feedback modulates centrally-controlled pattern generators for the adaptive control of swimming and flow- signal generation. Until recently, the long-standing dogma has been that lateral line sensory feedback plays no role in swimming kinematics (Dijkgraaf, 1963). Recent work on how fish adapt their swimming gaits to altered flow regimens, however, suggest that the lateral line, as well as vision play important, if subtle roles in modulating swimming behaviour (Liao et al.,

2003; Liao, 2007). In this study, the swimming kinematics of blind and sighted Astyanax are compared under identical conditions to determine if blind cavefish have evolved behavioural specializations for spatial exploration and active flow-sensing of novel environments.

METHODS

Brief Overview of Experimental Design

The swimming behaviours of blind and sighted Astyanax morphs were video-recorded during two, 16-min time periods: immediately after fish were first introduced into a novel, enclosed environment (T1), and after fish had 24 hours to become familiarized with the environment (T2).

Individual swim cycles (one burst plus one coast) were characterised in terms of (a) burst-coast parameters (e.g., the duration, travel distance, and acceleration/deceleration of the burst and coast phases of the cycle), (b) overall swimming speed, (c) heading direction, and (d) whether fish were in central or peripheral regions of the arena. Within-fish comparisons of swimming kinematics in unbounded (central) and near-boundary (peripheral) regions of the test arena were made to determine how or if swimming kinematics were altered by the presence of boundary features. Likewise, within-fish comparisons of swimming behaviours between T1 and T2 recording periods were used to determine how or if swimming kinematics changed after fish became more familiar with their environment. Finally, between-fish comparisons of swimming kinematics were made to determine how or if swimming kinematics differed between groups of blind and sighted individuals and between groups of the same morph with or without lateral line sensory feedback.

Experimental Animals

Naïve blind cavefish and sighted Mexican tetra (Astyanax mexicanus) of comparable standard lengths (5.16 ± 0.13 cm and 5.25 ± 0.11 cm, respectively, ±SE) were used for these experiments. Body depths were likewise comparable (~1.6 cm) to yield similar fineness ratios

(body length/maximum body diameter) of ~ 3.0. However, body widths of blind cavefish (0.98 ±

0.05 cm) were significantly greater than those of sighted morphs (0.76 ±0.02 cm) (t-test, unequal variances, p = 0.001). Although we did not directly measure the mass of experimental fish, post- hoc measurements of groups of similarly sized fish revealed an average mass of 3.68 g for blind cavefish and 2.77g for sighted morphs. Blind cavefish were obtained from local aquarium suppliers, whereas sighted morphs were provided by Dr. Timothy Bonner at Texas State

University, San Marcos, Texas. All fish were maintained in 75.8L aquariums at 20-25°C. To simulate the natural conditions of these fish, sighted fish were maintained at 12:12 light:dark cycles , while blind fish were maintained in complete darkness. Protocols for the maintenance, care and experimental use of animals followed the Guidelines for the Care and Use of

Laboratory Animals and were approved by Bowling Green State University Institutional Animal

Care and Use Committee.

Experimental Set up

Fish were tested in a small rectangular novel arena (30 cm x 40 cm), housed inside a larger rectangular tank resting on top of a vibration–isolation table. A video camera (Sony

Handicam DCR-HC 42) mounted ~1 m above the testing arena recorded the swimming behaviour of fish. The entire set-up was enclosed in a light-tight enclosure made with thick black curtains to prevent any visible light from entering the testing arena. A 115 x 77 cm matrix of infrared (IR) LED diodes (~10 Amp, 20 V, λ< 970 nm) placed underneath the test arena provided an upwelling source of IR illumination for the dark conditions of these experiments. A white plastic sheet above the light sources served as a diffuser to evenly distribute the light across the

8 floor of the experimental arena. To minimize depth of field errors due to vertical excursions of the fish, water depth in the experimental arena was kept shallow (5 cm).

Behavioural Procedures

Fish were transferred from their home tank to the experimental arena via a transport bucket and using a plastic-lined, water-filled net to minimize mechanical damage to superficial neuromasts of the lateral line system. Fish were introduced into a small holding ring (~15 cm in diameter) in the centre of the rectangular test arena. The dark room curtains were then sealed shut, the outside room lights turned off, and the fish allowed to acclimatize for one hour before the IR illumination was turned on and fish were released from the holding ring at the start of video recording. The one hour acclimation period also allowed sighted morphs to become dark- adapted before they were released into the novel environment. After the initial release from the holding ring, the swimming behaviour of the fish was digitally recorded in a sequential series of four, 4-min time segments interspersed by short time intervals (~1- 2 min) during which each video file was saved. File size limitations due to high rates of video frame acquisition prevented data acquisition in a single, uninterrupted session. After the initial recording period, the IR illumination was turned off and the fish was left in the test arena for another 24 hours to become familiarized with the test arena. After 24 hours, the IR illumination was turned on and video recording resumed for another 16 minutes (four, 4-min time segments as before). Water temperature was measured before and after the 24 hr familiarization period.

Pharmacological Blocking of the Lateral Line System

An aminoglycoside antibiotic, gentamicin, was used to block the receptor cells (hair cells) of the lateral line (Song et al., 1995). Fish were removed from their home tank and placed into individual 15 L tanks containing home tank water (control/sham treatment group) or 0.001% solutions of gentamicin in home tank water (drug treatment group) for 24 hours. Recent experiments in our lab and others have revealed that this low-level concentration and exposure duration are sufficient to block the hair cell transduction channels of lateral line hair cells in both canal and superficial neuromasts of blind cavefish and other species (Van Trump et al., 2010).

This pharmacological method of blocking the lateral line was preferable to other methods (e.g., immersion in solutions of CoCl2) because the low doses and short exposure durations minimize the potential for global toxic effects on general behaviour. To minimize stress factors associated with differences in water quality (water temperature, conductivity, pH etc), home tank water was used in both treatment tanks and experimental arenas. Treatment tanks were likewise maintained at the same temperature as home tanks and experimental arenas were aerated during the 24 hour treatment period. After the 24 hour sham or drug treatment period, fish were removed from the treatment tanks and transported to the lab for behavioural experiments. Six fish were treated with gentamicin and six fish were given the control (sham treatment) for each group of morphs, to have a total of 12 blind cavefish and 12 sighted fish. All fish were tested in dark conditions.

Data Analysis

Video Capture Software (Winnov, Version 3.2.4185) was used to view and capture digital images of the fish’s swimming behaviour at the rate of 30 frames per second. The frame- by-frame position of the fish with respect to the walls of the experimental tank, as well as the

10 position of the caudal (tail) fin with respect to the midline of the fish body was automatically tracked and analyzed with a commercially-available image analysis system (Image Pro) (Version

6.0 Media Cybernetics) and custom-written MATLAB scripts modified after Windsor et al

(2008). The software has an automatic image detection and tracking feature that first fits an ellipse to the outline of the fish’s main body (from the tip of the snout to the base of the caudal peduncle), and then determines the centroid of the ellipse, as well as the midline of the ellipse, extended through the caudal peduncle and fin of the fish.

For each video frame, the software determined the position of the centroid in Cartesian co-ordinates relative to a fixed reference point on the video screen and the major and minor axis of the ellipse. Custom MATLAB subroutines subsequently computed the ellipse’s orientation to and distance from the walls of the arena, as well as distance moved from one frame to the next in order to derive a characteristic swimming velocity for each frame pair. Distance to the wall was computed as the shortest distance from the centroid of the ellipse to the wall. Orientation to the wall was defined by the direction in which the snout-end of the ellipse was pointing with respect to the wall. The ‘snout-end’ of the ellipse was distinguished from the ‘tail-end’ by determining the direction of movement from one frame to the next, with the assumption that the fish moved forwards rather than sideways or backwards.

Swim cycle activity was characterized using three parameters – bending angle of the tail

(deg) (Fig. 1a, b), velocity of the fish (cm/s) (Fig. 1c, d), and angular velocity of the tail (deg/s).

The burst (acceleration) phase of the swim cycle (red lines, Fig. 1a - d ) was identified using multiple criteria, including critical values of angular tail velocity (±50 - 100°/s) and angular deviation of the tail (>±5 - 8°) (dashed lines, Fig. 1a, b) and a net increase in fish velocity coincident with these events (Fig. 1c, d). The coast (deceleration) phase of the swim cycle (green

11 lines, Fig. 1a - d) was identified by its own criteria, including the absence of such events and a net decrease in velocity.

Three additional criteria were used to select swim cycles for the data analysis, in order to minimize the effects of other, potentially confounding factors. One, the orientation of the fish (as shown in Fig. 1e, f) could not change by more than 15º every six frames (~200 ms). Two, each cycle had to be part of 3 or more sequential swim cycles that also met the orientation and other criteria. Three, fish could not maintain snout contact with wall while moving. If the swim cycles met all three of these criteria, they were included in the analysis.

Potential sources of measurement errors include pixel resolution (<1 mm/pixel) and the goodness of fit between the ellipse and the fish’s body. Errors in estimating centroid distance and fish orientation depend largely on the bending angle of the fish. When the fish’s body is straight, the ellipse fitting procedure gives a reliable estimate of both body orientation and centroid position. If the fish is involved in a sharp turn and the bending angle is large, the fit is such that the centroid of the ellipse is shifted a few mm relative to the true centroid of the fish. The orientation of the ellipse is also shifted relative to the true orientation of the fish. However, measurement errors due to large bending angles are assumed to be infrequent, small and randomly distributed over time and between populations and treatments. Moreover, swim cycle analysis was restricted to cases in which the orientation of the fish did not vary by more than 15º every six video frames (see selection criteria above). For each of the two time periods (T1, T2), measurements were made of the fish’s overall swimming speed, distance from the nearest wall, and various kinematic parameters, including coast frequency, duration, slope (deceleration) and velocity. Swimming behaviour near the arena walls (<1 body lengths away from the wall) was classified separately from that occurring in more central and open regions of the tank (> 1 body

12 lengths away from the wall). Overall swimming speed was characterized from mean cycle velocities, computed by averaging the frame-to-frame swimming velocities of fish throughout each burst-coast cycle. Likewise, mean burst or coast velocities were similarly based on averaged swimming velocities through each burst or coast phase of single swimming cycles.

Swimming velocities and kinematic parameters associated with each Astyanax population (blind cavefish or sighted morphs) and treatment regimen (lateral line enabled or disabled) were first plotted as frequency distributions according to each time period and tank region in order to visually compare distribution ranges and shapes.

Statistical analysis

Repeated-measures analysis of variance (RM ANOVA) was used to determine the effects of within (time and tank region) and between- (lateral line enabled vs. disabled, blind vs. sighted morphs) fish factors on swim cycle parameters. Linear regressions tested whether swim cycle frequency, duration and coast distance were correlated with swimming velocity.

RESULTS

Results are organized into several sections in order to describe (1) some of the basic characteristics of burst-coast swim cycles in blind and sighted morphs, (2) fundamental similarities and differences between morphs during exploration of novel environments, (3) the repeated measures analysis of within and between-fish differences and (4) the effects of morph, time, and lateral line deprivation on swimming characteristics.

Burst-coast swimming kinematics in the context of overall behavior

Blind and sighted morphs in this study exhibited some of the same differences in swimming behaviours as observed in previous studies (Sharma et al., 2009). For example, when swimming near the walls of the novel test arena, sighted morphs tended to angle their nose inward towards the wall while touching it and swimming past it in a sweeping motion – first in one direction and then in the opposite direction. Blind morphs, on the other hand, tended to swim in one direction at a time with the long axis of their body more parallel to the wall, while maintaining a narrow range of distances from the wall. Sighted morphs also appeared to turn more frequently than blind morphs – even when swimming in central regions of the tank and they also sometimes remained nearly motionless, whereas blind morphs were always in constant motion. To minimize the confounding effects of gross behavioural differences like these

(whether within or between morphs), the analysis of fine-scale swimming kinematics was restricted to swim cycles that were part of a continuous, straight motion unimpeded by walls (see methods for further details on selection criteria). Table 1 summarizes the number of cases

(individual burst-coast cycles) that met the selection criteria for blind (a,b) and sighted (b,d)

14 morphs in control (a,c) and lateral-line inactiviated (b,d) groups and for different time periods

(T1 vs. T2) and tank locations (centre vs. wall).

Representative time waveforms of burst-coast sequences meeting the selection criteria are shown in Fig. 1 for one blind (BCF) (a,c and e) and one sighted (SF) (b,d and f) morph swimming in the central regions of the tank. In both cases, the samples were taken from control fish (lateral line intact) during the first 16-min time period (T1) after exposure to the novel environment. As has been shown previously by Windsor et al (2008), the burst phase of the swimming cycle (red lines in Fig. 1a - d) could easily be differentiated from the coast phase

(green lines in Fig. 1a - d) based on several criteria, including when the bending angle of the tail exceeded a threshold criterion (horizontal dashed lines (Fig. 1a, b). In addition, peak swimming velocity (black dots, Fig. 1c, d) marked the transition point between the burst phase of the cycle during which swimming velocity increased and the coast phase during which swimming velocity decreased. As these examples illustrate, each burst phase of the swimming cycle consisted of either a one-sided flick of the tail to one side of the fish (Fig. 1b) or two-sided, multiple flicks

(Fig.1a). For the blind morph example, there were two-sided tail flicks (one complete tail-beat) almost every burst (Fig. 1a) to maintain a relatively straight swimming trajectory (Fig. 1e) for several seconds. In contrast, tail flicks associated with consecutive swim cycles in the sighted morph example were all one-sided (Fig. 1b), resulting in small, step-wise changes in the overall swimming direction (Fig. 1f). The frequency with which the tail alternated sides was characterized by counting the total number of side reversals (left-to-right or right-to-left) within and between consecutive swim cycles and dividing that number by the number of swim cycles

(Table 2). On average, blind morphs had slightly higher alternation frequencies (0.87 ± 0.098 and 0.78 ± 0.16 alternations/cycle, T1 and T2) than sighted morphs (0.63 ± 0.062 and 0.45 ±

0.06, respectively)(Table 2). Despite the small and relatively brief orientation changes associated with each burst-phase of the swim cycle, the coast portion of each cycle was relatively straight for both morphs (Fig. 1e,f).

The examples illustrated in Fig. 1 also indicate that the blind morph, swimming at a higher mean velocity than the sighted morph, had a correspondingly higher burst and coast frequency (and shorter coasts) than the sighted morph, suggesting that overall swimming velocity may depend on swim cycle frequency. A linear regression analysis indicated that in the majority of all experimental conditions and individuals, the correlation between these two variables was insignificant (regression line slope not significantly greater than zero at p = 0.05 level), relatively weak (R2 < 0.3) or both (Table 3, black entries). In only three out of 96 possible cases was the correlation both strong (R2 > 0.3) and significant (Table 3, lateral line blocked individuals, red entries). Thus, swimming speed was largely independent of swim cycle frequency over the range of swimming speeds observed in these studies.

Because a fish’s momentum (mass x speed) increases with burst speed, the question arises as to whether the subsequent coast phase also relies, to a certain extent, on the initial momentum of the fish. A linear regression analysis of coast duration against swimming speed

(Table 4) indicated that in the majority of experimental conditions, correlations were rarely both significant and strong (3 out of 96 cases). (Table 4, bolded, in red). In contrast, coast distance was significantly correlated with burst speed (P << 0.001 in all but one case), with average correlation coefficients of 0.39 ± 0.023 and 0.27 ± 0.053 for blind cavefish at T1 and T2 respectively, and 0.40 ± 0.077 and 0.35 ± 0.058 for sighted morphs.

Swimming kinematics of blind and sighted morphs during initial exploration of a novel environment

Table 5 summarizes the mean values of various swim-cycle parameters measured from control

(lateral line enabled) populations of blind (BCF) and sighted (SF) morphs before (T1) and after

(T2) 24 hours of exposure to the novel tank. Because there were no significant effects of tank region on these parameters, data were pooled across central and peripheral regions of the tank.

The burst phase of the swim cycle was found to be remarkably similar between blind and sighted morphs, with burst durations falling within a very narrow range of nearly identical and overlapping frequency distributions (Fig.2a). The mean burst duration of blind and sighted morphs was approximately 1/3rd the mean duration of the coast portion of the swim cycle (Table

5a,b). As a consequence, the mean duty cycles (% of swim cycle taken up by the coast) of blind and sighted morphs were also quite similar - 68 ± 0.02 and 72 ± 0.02%, respectively (Table 5c).

The burst phases of the swim cycle in each population likewise had similarly steep slopes

(change in velocity/time) that were on average at least 2.5 times steeper than the mean coast slope (Table 5a and b). In contrast to the burst phase of the swim cycle, the coast phase was more variable in duration (Fig. 2b). Unlike the frequency distributions for burst durations, those for coast durations showed less overlap and blind morph distributions (red function, Fig. 2b) were shifted slightly downward towards lower durations relative to those for sighted morphs (blue function, Fig. 2b)(as is also illustrated by the time waveform examples in Fig. 1). Swim cycle velocities (both burst and coast) were also shifted slightly upward for blind compared to sighted morphs (Fig. 3a, coast velocity only) and these differences are reflected in the mean burst and coast velocities for blind (6.6 ± 0.6 and 6.4 ± 0.6 cm/s) compared to sighted (4.7 ± 0.4 and 4.6 ±

0.4 cm/s) morphs (Table 3a,b). Coast velocities of blind morphs were almost, but not quite

17 significantly greater than those of sighted morphs at T1 (RM ANOVA, P=0.0684) and coast durations were likewise, almost, but not quite significantly shorter (RM ANOVA, P=0.0926). If blind morphs were gliding for slightly less time, but at slightly higher speeds than sighted morphs, the question arises as to whether both morphs were covering approximately the same distance. The answer to this question appears to be yes, as both morphs covered an average distance of 3 cm (2.9 ± 0.3 and 3.1 ± 0.2cm for blind and sighted morphs respectively) or approximately 0.6 of a body length during the coast (Table 3b; Fig. 3B). Despite the slight trends noted here for differences between morphs in a few coast parameters, there were no significant effects of fish type on any swim cycle parameter (see statistical details below).

Repeated measures analysis of variance and the use of ‘N’ as a dependent variable

These experiments involved two sets of independent variables - two within-subject factors (exposure time, T1 vs. T2 and tank region, central vs. peripheral) and two between- subject factors (fish type, blind vs. sighted morph and sensory feedback, lateral line enabled vs. disabled). One difficulty with this design and the sheer number of dependent variables it entails, is that it requires a high number of observations to reach statistical significance for relatively small effects (e.g., the slight differences noted above for coast parameters). As Table 1 shows, however, there were several cases where individual fish under certain test conditions behaved in such a manner as to produce very few to zero swim cycles that met the selection criteria for inclusion into the analysis. This was particularly true for sighted individuals and T2 test conditions. For tests of between-subject effects, for example, the number of possible observations (12) from the two experimental groups (6 fish in each group) was effectively reduced to 8 because of failures of swim cycles to meet selection criteria in one or more test

18 condition. This effect substantially reduced the statistical power of the repeated measures

MANOVA, which showed that there were no significant differences in swim cycle parameters due to any of the within- or between-fish factors. Although an increase in the number of individuals tested may have increased the statistical power somewhat, it is largely the behavioural response of fish to different experimental conditions, rather than the number of fish tested, that dictates the number of useable observations. Indeed, there were obvious and consistent between-factor differences in the number of swim cycles meeting the selection criteria

(e.g., compare mean number of cases for blind morphs at T1 vs. T2 in Table 1a). For this reason, this number was treated as a valid parameter in its own right for estimating the frequency with which fish execute continuous swim cycles without abrupt stops or turns. Using this single variable as a parameter, all 24 possible observations were used in a repeated measures ANOVA to examine all 4 (within- and between-fish) factors. As with the previous analysis of swim cycle parameters, this analysis revealed that there were no significant effects due to tank region.

However, there was a significant 3-way interaction between time, sense and fish type (p = 0.01) for the ‘N’ parameter. The nature of time, sense and fish-dependent effects on both swimming kinematics and the ‘N’ parameter is described in the next three sections.

The effects of exposure time on swimming kinematics

Although time of exposure to the novel environment was found to have no significant effects on the majority of burst or coast parameters, several points are worth noting. One is that relative to most other parameters, duty cycle is remarkably similar across time and additionally, between morphs (Fig. 4). Secondly, there is significant trend for both burst (RM ANOVA,

P=0.0148) and coast durations (RM ANOVA, P=0.0057) to increase from T1 to T2 in both

19 morphs (Fig. 4), and also a corresponding significant decrease in cycle frequency (RM ANOVA,

P=0.0297) (Fig.4). Thirdly, despite previous reports of declines in the overall swimming velocity of blind cavefish after many hours of exposure to a novel environment (Teyke 1988), there was very little indication of a trend in this direction from either burst (Table 3a) or coast velocities

(Table 3b, Fig. 4). Furthermore, additional tests on momentary swim velocities (distance moved from one video frame to the next divided by the time interval, 0.033 s) over the entire 16-min time period likewise revealed no significant differences in swim velocity as a function of time

(T1 vs. T2) for either morph. Thus the absence of an effect was not due to selection criteria for culling the data.

Despite non-significant differences in most swim cycle parameters, blind cavefish exhibited large and significant differences (paired sample t-test, p = 0.0107) in the number of swim cycles classified as part of a continuous and uninterrupted sequence. The mean number of swim cycles for control blind cavefish decreased from 232 at T1 to 84 at T2 (Table 1a, Fig. 5, red solid line). In contrast, there were no significant effects of time on this parameter for sighted morphs, for which the mean numbers of swim cycles at both times (98 at T1 and 75 at T2) were in the same ball park as that for blind morphs at T2 (Table 1c, Fig. 5, blue solid line).

The effects of blocking lateral line sensory feedback on swimming kinematics

As already noted, inactivation of the lateral line had no effect on any burst or coast parameters in either morph. Indeed, there was not even a ‘hint’ of an effect, as illustrated by the overlapping and nearly identical frequency distributions of coast durations (Fig. 6a) and velocities (Fig.6b) for lateral line enabled and disabled blind cavefish. Nevertheless, lateral line inactivation did have significant effects on the frequency with which swim cycles continued

20 uninterrupted in blind, but not sighted morphs. That is, during the initial exploratory period of the novel environment (T1), the number of swim cycles in blind morphs declined significantly

(from a mean of 232 to 108, t-test, unequal variance, p = 0.01) in lateral line-inactivated individuals (Table 3b, Fig. 5). As a consequence, the time-dependence of the number of swim cycles noted for control cavefish was absent in lateral line-deprived individuals (Fig. 5, compare red solid with red-dashed line functions). By contrast, sighted morphs exhibited no significant decline in this parameter at T1 after lateral line inactivation and neither morph exhibited significant differences in this parameter at T2 due to lateral line inactivation.

Differences between blind and sighted morphs in the effects of time and lateral-line inactivation

Whereas blind morphs with functioning lateral line systems showed significant differences in the number of swim cycles between T1 and T2, sighted morphs did not (Fig. 5).

This difference is essentially due to the fact that blind morphs had significantly greater number of swim cycles at T1 than sighted morphs (t-test, unequal variances, p=0.023). Lateral-line inactivation also caused a significant reduction in the number of swim cycles in blind, but not sighted morphs, at T1, but not T2.

DISCUSSION

Intra- and interspecific comparisons of burst and coast swimming kinematics

When exploring a novel dark environment, blind and sighted Astyanax exhibit many similar, if not identical burst-coast swimming kinematics, including similar duty cycles, coast distances and burst slopes and durations (Table 6). With but one exception, parameter values for blind cavefish fell well within the range of values reported previously for these fish by Windsor et al (2008), despite somewhat different experimental conditions. The one exception - burst duration - was somewhat longer (0.21 ± 0.015s) in this study compared to the values reported in the Windsor et al (2008) study (0.15 – 0.16 s). Given that the sampling rates of the two studies were 30 and 50 frames/s, duration measurements were accurate to within only 0.02 – 0.03 s or approximately half of the 0.06 s difference between the durations measured in the two studies.

Although there are very few data on the burst-coast swimming parameters of other species, some interspecific comparisons are worth noting. Compared to other sighted species, the duty cycles (% of swim cycle taken up by the coast) of blind cavefish (68%) were somewhat less than those reported for larval (92%) and adult (86%) zebrafish of smaller size (SL < 3 cm)

(Muller et al., 2000), but within the range of values (60 – 74%) reported for koi carp of comparable size (SL = 5.5 cm) (Wu et al., 2007). Likewise, burst durations of Astyanax morphs

(~250ms) were longer than those of larval (80 ms) and adult (140 ms) zebrafish (Muller et al.,

2000), but similar in duration to those of koi carp (200 – 300 ms) (Wu et al., 2007). Thus, it would appear that temporal features of burst-coast cycles are scaled to fish body length, most likely because of size-dependent differences in momentum (speed x mass) during the burst phase and/or drag during the coast phase (McHenry and Lauder, 2005; McHenry and Lauder, 2005;

Muller et al., 2000).

In this regard, it is interesting to note that for a given swim speed and standard length, blind cavefish should have a higher momentum than sighted morphs because they have wider bodies and thus, greater mass. In principle, the increased momentum should carry them for greater distances during the coast phase of the swim cycle (McHenry and Lauder, 2005).

However, coast distances were remarkably similar between blind and sighted morphs – on average, ~ 3 cm or 0.6 SL, despite the fact that mean burst and coast speeds were slightly higher in blind compared to sighted morphs (Table 6). Nevertheless, coast distance was significantly correlated with burst speed in both morphs and thus, varied with burst momentum. One possible explanation for the absence of predicted differences in coast distances is that the momentum

‘advantage’ theoretically enjoyed by the more massive blind cavefish may be counteracted by increased pressure drag (due to the greater body width), resulting in a more rapid loss in momentum during the coast/deceleration phase of the swim cycle. Indeed, deceleration rates for blind cavefish were higher, on average, than those for sighted morphs (7.2 ± 1.1 vs. 4.7 ± 0.6 cm/s2, Table 5) – a finding that is consistent with coast durations that were on average ~ 200 ms shorter in blind morphs (Table 5).

In summary, the limited comparative data suggest that blind cavefish are not particularly unusual or different in the temporal characteristics of their burst and coast swimming kinematics when compared to conspecific sighted morphs or other sighted species of comparable size in distant taxa. Since there was insufficient spatial resolution to examine other fine-scale parameters like tail beat amplitude or pectoral fin movements, it is possible that there may have been differences in these or other parameters (e.g., body tilt) observed by Windsor (2008) in blind cavefish . Nevertheless, blind morphs differed from sighted morphs in a few interesting, if subtle ways, which included a significantly higher number of burst-coast cycles contributing to

23 straight swimming trajectories and slight trends towards higher burst and coast velocities, as well as steeper and longer coasts. The possible significance of these differences is discussed below in terms of the adaptive significance of burst-coast swimming gaits.

The functional significance of burst-and coast swimming gaits

Many fish exhibit burst-and-coast swimming gaits that involve cyclic bursts of caudal body and fin propulsion followed by passive coasting (Blake, 2004; Domenici and Blake, 2000). As observed for Asytanax morphs in this study and in other species (McHenry and Lauder, 2005;

Muller et al., 2000; Videler and Weihs, 1982; Windsor et al., 2008), the body is held relatively motionless and straight during the coast phase of the swim cycle. This results in a substantial reduction in drag and energy-cost savings compared to steady swimming in which the drag on lateral body undulations is thought to be several times higher (Videler and Weihs, 1982; Weihs,

1974; reviewed in Blake, 2000). For fish like cod and saithe, burst and coast gaits are used at low speeds during foraging, presumably to conserve energy during this time-consuming activity

(Videler and Weihs, 1982). Burst-coast swimming might also confer an energetic advantage during foraging by blind and sighted Astyanax. However, the foraging strategies of these two morphs in their natural environments are dramatically different. Sighted morphs typically rely on visual cues for diurnal foraging in the water column, whereas blind cavefish search for food on the bottom of lightless caves, adopting a head-down posture to take advantage of ventrally- located taste buds and lateral line neuromasts, which are more numerous in blind than sighted morphs (Huppop, 1987; Schemmel, 1967). Whether or not both morphs have retained some or all of the same adaptive advantages in the context of foraging behavior remains to be seen.

Sighted fish also move much less often in novel environments under well-lit compared to dark

24 conditions (Sharma et al., 2009), which lends credence to a different foraging, and possibly predation avoidance tactic, as movement increases the likelihood of being detected by a predator.

In contrast, blind cavefish have no known natural predators (except the young, which are preyed upon by larger conspecifics) and therefore can afford to move more frequently for exploratory or foraging purposes.

In addition to energy savings, the burst-coast swimming gait and other forms of intermittent swimming are likely to have significant perceptual advantages (Kramer and

McLaughlin, 2001). In fact, recent field studies on the foraging behavior of brook char in still- water pools suggest that perceptual advantages of intermittent burst and coast swimming are likely to be far more important than any energetic advantages (McLaughlin and Grant, 2001).

Both the optic (for sighted morphs) and lateral line flow fields created by the fish’s movements will be considerably more stable during linear translations of the body (the coast phase) than during lateral undulations of the body (burst phase or steady swimming). Body and caudal fin undulations additionally create vortex structures, which roll along the body surface before they are shed in the wake of the fish (Hanke et al., 2000; Hanke and Bleckmann, 2004; Muller et al.,

2000; Wolfgang et al., 1999; Wu et al., 2007). That blind cavefish have a clear perceptual advantage during the coast phase is demonstrated by the increased frequency with which they avoid head on collisions with walls during the coast compared to the burst phase of the swim cycle (John, 1957; Windsor et al., 2008).

In this regard, it is interesting to note that the swimming behaviors of blind cavefish differ from sighted morphs in ways that are likely to promote steadier flows during exploration of novel environments. That is, the frequency with which burst-coast cycles are generated in continuous sequences devoid of turns or stops is far greater for blind than sighted morphs (Fig

5). Since 1-sided (Fig. 1B), but not 2-sided tail beats produce turning behaviors (Fig. 1a)

(Windsor et al., 2008; Wu et al., 2007), this finding suggests that blind cavefish are using the double, 2-sided tail beat strategy more frequently than sighted morphs. Double-sided tail beats could theoretically enhance flow field stability by minimizing head turning during bursts, but also by minimizing lateral displacements of the body during coasts. Lateral body displacements are produced from asymmetrical forces when vortices are shed on one side of the body during single-sided tail beats (Wu et al., 2007). During 2-sided tail beats, lateral displacements are minimized due to counterbalanced forces from vortices being shed on alternate sides of the body

(Wu et al., 2007). In fact, Windsor et al (2008) report a higher percentage of double tail beats when blind cavefish were swimming parallel to the wall than when swimming in central regions of the tank. Given that fish maintain a straight course when swimming parallel to the wall, it is not surprising that they used a 2-sided, double tail- beat strategy to accomplish this feat (Fig. 1a, e).

Interestingly, Wu et al (2007) also found that in koi carp, multiple tail beats produced slightly longer and faster burst phases than single tail beats, presumably due to increases in tail beat frequency, as has been demonstrated for steady swimming (Bainbridge, 1958; Steinhausen et al., 2005). Windsor et al (2008) reported similar trends in blind cavefish. Thus, 2-sided tail beating could be a motor strategy for increasing flow signal amplitudes. The finding that the mean burst and coast velocities were slightly higher in blind compared to sighted morphs (Table

5) is consistent with the notion that blind morphs execute 2-sided tail beats more frequently than sighted morphs. Moreover, because blind cavefish are approximately 1.3x wider than sighted morphs of comparable SLs, the flow amplitude generated by a blind morph will be greater than that of a sighted morph swimming at the same speed. Crude estimates based on flow field

26 equations for a dipole source velocity of 5 cm/s and source diameters equivalent to the mean body widths of blind (9.8 mm) and sighted (7.6 mm) morphs predict that the flow amplitudes produced by blind cavefish will be twice those of sighted morphs. Thus, blind cavefish may have evolved both morphological and motor strategies for increasing flow signal amplitudes.

Interestingly, Teyke (1988) reports that there is a high negative correlation between preferred swim velocity and the cross-sectional body area of blind cavefish, suggesting that smaller blind cavefish compensate for a low-strength flow field by swimming faster.

Temporal modulation of burst-coast swimming kinematics

In addition to the known ability of fish to modulate swim velocity by adjusting tail beat frequency (Bainbridge, 1958; Steinhausen et al., 2005), it appears that fish can also modulate temporal characteristics of burst and coast swimming. Both blind and sighted Astyanax morphs exhibited a high degree of variability in coast durations (Fig. 2b, Table 5), as has been reported for other species (McHenry and Lauder, 2005; Wu et al., 2007). The reason for this variability is unclear, but the data selection criteria of this study (see methods) make it highly unlikely that durations at the shorter end of the variations were caused by interruptions to the coast, as might be expected if fish turned abruptly to avoid collision with an impending wall. Since coast duration was independent of burst speed, it would appear that this particular parameter is free to vary over a range of swimming speeds and that fish may modulate coast durations for locomotor and/or sensing purposes. Indeed, recent field studies indicate that the swimming movements of brook char while foraging are highly intermittent with periods of hovering (not moving) and passive coasting interspersed with periods of active swimming (McLaughlin and Grant, 2001).

The intermittent nature of these movements was furthermore correlated with challenges

27 associated with prey detection and capture. The sensing challenges faced by visually-deprived fish during spatial exploration of a novel dark environment are likely to be quite different from those faced by highly visual fish like brook char for capturing prey in well-lit environments.

Nevertheless, this example illustrates that fish do indeed manipulate swim gaits for perceptual advantages. Moreover, it suggests that an ability to modulate swim gaits for visually-modulated foraging could easily have been inherited by blind cavefish from their sighted ancestor and subsequently co-opted for non-visual purposes in similar (e.g., foraging) or different (e.g., spatial exploration) behavioral contexts.

At the heart of vertebrate rhythmic behaviours like swimming and flying, are central pattern generators that reside in the brainstem and spinal cord and which are controlled by higher-order motor command systems (Grillner et al., 1995; Grillner, 2003; Pearson, 1993). In this regard, it is likely that many of the burst-coast swimming features shared by blind and sighted Astyanax morphs are controlled by a suite of central control mechanisms inherited from their common sighted ancestor. Given that various burst-coast parameters, especially coast duration, can be modulated by fish, this raises the fundamental question of how higher order brain centers control (a) the timing (activation) of pattern-generating circuits and (b) the transitions from one phase of motor activity (e.g., tail beats during the burst) to another (e.g., pausing during the coast). In other words, how do fish ‘decide’ when to terminate each coast and begin the next burst and when to terminate the burst and start the coast? Here, sensory inputs are believed to play a pivotal role in the initiation of a particular phase of motor activity – i.e. when to begin the burst (Grillner, 2003). Sensory feedback on the animal’s own movements and the consequences of those movements is also important - especially when corrections are needed to

28 e.g., avoid an impending obstacle or to stabilize flying or swimming movements in turbulent flows (Grillner, 2003).

In principle, fish could use several different strategies to regulate the timing of burst- coast swim cycles. They could monitor coast speed and determine when it declines to some threshold level - e.g., the initial burst speed before initiating the next burst. They could also monitor the time that has elapsed from the end of the burst phase in order to restart the burst after a given time interval specified by an internal clock mechanism. Finally, they could monitor the distance travelled during the coast and begin the next burst after some fixed distance - e.g., some fraction of a body length. Given that coast duration is highly variable and uncorrelated with burst speed, it is unlikely that fish are using a fixed time interval for ending the coast and beginning the next burst. Rather, it is more likely that they monitor speed or distance. Given that burst- coast cycles often start and end at similar, if not identical velocities (Fig. 1C,D), speed seems more likely – especially under circumstances in which it is important to maintain the same average speed, as is likely for active flow sensing. In theory, fish could use the lateral line system to compute flow speed past the body surface (Chagnaud et al., 2008). The fact that pharmacological blocking of the lateral line has no effects on burst-coast parameters or swim speed argues against the possibility that fish use their lateral line to monitor flow speeds for this purpose. Whereas the current study found no effects of lateral line inactivation on swim speed, previous studies report either an increase (Hassan et al., 1992; Janssen, 2000; Windsor et al.,

2008) or decrease (Abdel-Latif et al., 1990) in swimming speed. While it is difficult to reconcile these conflicting results, one possible explanation is that some of the various techniques to block the lateral line may have caused unintended, global (e.g., toxic) effects on overall behavior,

29 rather than specific effects on lateral line functionality. The low dose and short-duration regimen for blocking the transduction sites of lateral line hair cells in this study minimizes this potential.

Despite the absence of lateral-line effects on swimming kinematics and swim speed, the drastic reduction in the number of analyzable swim cycles in lateral line-deprived blind, but not sighted morphs suggests that loss of lateral line information does indeed have an effect on swimming performance. That is, it reduces the probability that any given swim cycle will be part of a straight swimming trajectory with 3 or more cycles in a row. This result can perhaps best be understood in terms of both the intermittent and short-range nature of active-flow sensing.

Unlike sighted fish, which normally have continuous visual information available to them during both burst and coast phases of the swim cycle, blind cavefish must rely primarily, if not exclusively on the coast phase for lateral line sensory updates about their spatial surroundings.

As such, decisions about e.g., the direction and speed of the next swim cycle must be made during the coast phase of the previous cycle in an intermittent rather than continuous fashion.

Seen in this light, it is easy to see how an absence of lateral line information might disrupt swim cycle continuity in blind cavefish that normally rely on this information. In addition, sighted fish can determine the spatial relationship of distant landmark features from a single vantage point, whereas blind cavefish must reconstruct spatial relationships from sequential encounters of different features at close range. Thus, swimming more often in a straight trajectory would not only produce a more steady flow field against which perturbations from nearby obstacles could be more readily detected and interpreted, but it might also facilitate the acquisition of information about the spatial relationships of sequentially encountered features in novel environments. The fact that blind, but not sighted morphs exhibit a high frequency of swim cycles in straight trajectories during the initial exploration of a novel environment (T1), but not

30 after 24 hours of familiarization (T2), indicates that blind cavefish may use this strategy for enhanced active-flow sensing and/or exploration for spatial knowledge.

At odds with the active-flow sensing hypothesis is the absence of any evidence in this study for a reduction in swim speeds in either morph from T1 to T2. These results are in stark contrast to previous reports of declines in the overall activity levels and swimming velocities of blind cavefish after an equivalent number of hours of exposure to a novel environment (Teyke,

1988; Teyke, 1989). It is reasonable to expect that blind cavefish might increase their swim velocities to enhance active flow-sensing during exploration of novel environments or features, as has been reported in several studies by Burt de Perera (2004, 2005). The increased swim speeds would not only increase flow signal amplitudes, but they should also increase the encounter rate of novel features for the purpose of gaining spatial knowledge. However, the activity levels and swim speeds of fish depend on numerous factors, including temperature and oxygen levels (Schurmann and Steffensen, 1994). Unfortunately, Teyke (1988) did not report whether or not temperature and oxygen levels were monitored; thus, it is difficult to know if the reported declines in swim speeds and activity levels were due to these or other factors, including familiarization with the environment. Nevertheless, the current study provided some evidence of decreased activity levels from T1 to T2. That is, both burst and coast durations were significantly higher, resulting in significantly lower swim cycle frequencies for both blind and sighted morphs. In other words, the swim cycle activity decreased after 24 hours.

SUMMARY AND CONCLUSIONS

In summary, the limited comparative data suggest that blind cavefish are not particularly unusual or different in the characteristics of their burst-coast swimming kinematics when compared to conspecific sighted morphs or other sighted species of comparable size in distant taxa. Given that both blind and sighted morphs have a common sighted ancestor, it is likely that the burst-coast swim gait and its central control mechanisms have been inherited from their common ancestor and may have similar or different functional advantages. However, blind cavefish differ from sighted morphs in their proclivity to link individual swim cycles into a continuous, unidirectional sequence. Furthermore, this proclivity declines in blind, but not sighed morphs after 24 hrs of exposure to a novel environment and after blocking the lateral line. Taken together, these combined effects indicate that the lateral line is of greater importance to blind than sighted morphs during exploration of a novel environment and that lateral line feedback during this time assists congenitally blind morphs in maintaining the moment-to-moment swim cycle continuity necessary for short-range and intermittent flow-sensing of the environment. This ability is apparently reduced or entirely absent in sighted morphs when they find themselves in dark novel environments.

FIGURE AND TABLE LEGENDS

Figure 1: Representative examples of burst-coast sequences meeting the selection criteria as illustrated by corresponding time waveforms of tail angle bending (a, b), swimming velocity (c, d) and relative fish heading (e, f) for a blind (BCF) (a, c, e) and sighted (SF) (b, d, f) morph swimming in the center of the tank and under control conditions (lateral line intact). Magenta dots represent peak angles of the tail, red lines the burst-portion of the burst-coast cycle, and green lines the coast portion. Grey vertical lines mark the beginning of each burst-coast cycle.

Representative data were taken from center-swimming fish.

Figure 2: Frequency distributions of burst (a) and coast (b) duration. Distributions are based on the binned means of normalized individual frequency distributions (6 fish in each) from blind

(red) and sighted (blue) populations during initial exploration of a novel environment (T1).

Figure 3: Frequency distributions of coast velocity (a) and distance covered during the coast (b).

Frequency distributions are based on the binned means of normalized individual frequency distributions (6 fish in each) in blind (red) and sighted (blue) populations during initial exploration of a novel environment (T1).

Fig. 4. Bar histograms showing the mean duration and standard deviation (T-bars) for each population both before (T1) and after (T2) 24 hour exposure to the novel environment.

Figure 5: The effects of exposure time (before, T1 or after, T2, 24 hours of familiarization) on the number of swim cycles meeting the selection criteria for continuous, un-interrupted motion.

Solid-line functions with filled symbols depict results from blind (red diamonds) and sighted

(blue circles) morphs with lateral line intact (LL+), whereas dashed line functions with open symbols depict results when the lateral line is inactivated (LL-)

Fig. 6. Frequency distributions of coast duration (a) and velocity (b). Distributions are based on the binned means of normalized individual frequency distributions (6 fish in each) from lateral line inactivated (red) and enabled (blue) populations of blind cavefish during initial exploration of a novel environment (T1).

Table 1: Number of burst-coast cycles that met the analysis criteria for each blind (BCF1-

BCF12) (a, b) and sighted (SF1-SF12) (c, d) morph in control (lateral line intact, LL+) (a, c) and lateral line-inactivated (LL-) (b, d) groups. Number of cases are shown for two, 16-min time periods: time 1 (T1) after initial introduction of the fish into the novel arena and time 2 (T2) after a 24-hr familiarization period. The total number of cases at each time period are also divided up into those obtained when fish were swimming in the center of the tank vs. near the wall.

Table 2: Tail polarity changes – the number of times the tail polarity changes per burst coast, as well as the number of burst-coasts before a kick-polarity change occurs. BCF 2, BCF 4 and BCF

8 (Indicated with *) have larger polarity changes then number of burst-coasts, indicating that there were more than one kick-polarity change (ie tail moved back and forth before a coast occurred).

Table 3: Linear regression correlation coefficients (R2) and p values for swimming speed vs. swim cycle frequency for blind (a,b) and sighted (c,d) morphs in different experimental conditions (see Table 1). Blank cells indicate that the number of cases (Table 1) was too low for the regression analysis.

Table 4: Linear regression correlation coefficients (R2) and p values for swimming speed vs glide duration for blind (a,b) and sighted (c,d) morphs in different experimental conditions (see Table

1). Blank cells indicate that the number of cases (Table 1) was insufficient for a regression analysis. Red indicates when correlation is significant (P<0.05). Only 3 R2 values are more than

0.50.

Table 5. Population means of various burst (a), coast (b) and swim cycle (c) parameters measured in control (lateral line enabled) fish for blind (BCF) and sighted (SF) morphs before

(T1) and after (T2) 24 hour exposure to a novel environment.

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50 45

Figure 2: Frequency distributions of burst (a) and coast (b) duration. Distributions are based on the binned means of normalized individual frequency distributions (6 fish in each) from blind cavefish (BCF, red) and sighted fish (SF, blue) populations during initial exploration of a novel environment (T1).

51 46

Figure 3: Frequency distributions of coast velocity (a) and distance (b). Frequency distributions are based on the binned means of normalized individual frequency distributions (6 fish in each) from blind cavefish (BCF, red) and sighted fish (SF, blue) populations during initial exploration of a novel environment (T1).

52 47

Fig. 4. Bar histograms showing the mean value of different swim cycle parameters and standard errors (T-bars) for each blind (BCF) and sighted (SF) morph population before (T1) and after (T2) 24 hour exposure to the novel environment.

53 48

Figure 5: The effects of exposure time (before, T1 or after, T2, 24 hours of familiarization) on the number of swim cycles meeting the selection criteria for continuous, un-interrupted motion. Solid-line functions with filled symbols depict results from blind (red diamonds) and sighted (blue circles) morphs with lateral line intact (LL+), whereas dashed line functions with open symbols depict results from the different morph populations when the lateral line is inactivated (LL-)

54 49

55 50 Table 1: Number of burst-coast cycles for all groups of fish

Number of burst-coast cycles that met the analysis criteria for each blind (BCF1- BCF12)(a,b) and sighted (SF1-SF12)(c,d) morph in control (lateral line intact, LL+)(a,c) and lateral line-inactivated (LL-)(b,d) groups. Number of cases are shown for two, 16- min time periods: time 1 (T1) after initial introduction of the fish into the novel arena and time 2 (T2) after a 24-hr familiarization period. Number of cases are also shown for two tank locations: in the center of the tank or near the tank walls.

57 51 Table 2: Alternation frequency of control groups of fish

Alternation frequency – the number of times the tail polarity changes per burst coast. BCF 2 and BCF 4 (Indicated with *) have an alternation frequency larger then 1, indicating that there were more than one tail-alternation change (ie tail moved back and forth on either side of the body before a coast occurred).

58 52

Table 3: Linear regression correlation coefficients (R2) and p values for swimming speed vs swim cycle frequency for all fish groups

Linear regression correlation coefficients (R2) and p values for swimming speed vs swim cycle frequency for blind (a,b) and sighted (c,d) morphs in different experimental conditions (see Table 1). Blank cells indicate that the number of cases (Table 1) was insufficient for a regression analysis.

59 53 Table 4: : Linear regression correlation coefficients (R2) and p values for swimming speed vs coast duration for all fish groups

Linear regression correlation coefficients (R2) and p values for swimming speed vs coast duration for blind (a,b) and sighted (c,d) morphs in different experimental conditions (see Table 1). Blank cells indicate that the number of cases (Table 1) was insufficient for a regression analysis. Red indicates when correlation is significant (P<0.05). Only 3 R2 values are more than 0.50.

60 54 Table 5: Population means of various burst (a), coast (b) and swim cycle (c) parameters of control fish groups

Population means of various burst (a), coast (b) and swim cycle (c) parameters measured in control (lateral line enabled) fish for blind (BCF) and sighted (SF) morphs before (T1) and after (T2) 24 hour exposure to a novel environment. ± SE