HEAD WIDTH INFLUENCES SENSING OF STEADY AND VORTICAL FLOWS BY THE LATERAL LINE CANAL IN FISHES

By

YUZO R. YANAGITSURU

A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF

FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2017

© 2017 Yuzo R. Yanagitsuru

To Maisie and Garya, my wonderful lungfish and gar, for always reminding me that I should take a breath of fresh air every now and then

4

ACKNOWLEDGMENTS

I first thank my mentors at Scripps Institution of Oceanography: Corey Jew,

Jeffrey Graham, Martin Tresguerres, Nick Wegner, Phil Hastings, HJ Walker, and

Natalya Gallo, who provided me with my first foray into fish research that ultimately lead

me to pursue graduate school. I thank Martin Tresguerres, in particular, for his endless

support and encouragement as well as being my primary role model as a research

scientist and educator. The inspiration that he has imparted on me through his

infectious enthusiasm for biological research is one that I hope to impart upon others in

the future. I also thank my advisor, Jimmy Liao, and my committee members: Larry

Page, Larry Ukeiley, and David Blackburn, for guiding me through my thesis project.

I am extremely grateful to Otar Akanyeti for advising on signal processing and

data analysis for my thesis project. I also thank Rob Robbins for the insightful

discussions about fish biology and the Florida Museum of Natural History Ichthyology

Collection for the preserved specimens that I collected morphometric data from. I

extend a special thank you to: David Simmons, Yuriy Bobkov, Nagayasu Nakanishi,

Naveen Wijesena, Elias Lunsford, and Jacy Hyde for their emotional support during my

time at University of Florida. I am also indebted to the regulars of the Super Science

Game Squad: David Anderson, Johanna Jantzen, Patrick Milligan, Ellen Humbel,

Stephanie Wheeler, Lucille Watkins, Pablo Moreno, Kin Lan Han, and George Tiley, for

taking me along on the many eccentric adventures (cooperative or not) to defeat Cthulu,

identify murderers, build fast food franchises, among many, many others.

5

TABLE OF CONTENTS page

ACKNOWLEDGMENTS .................................................................................................. 4

LIST OF FIGURES .......................................................................................................... 6

LIST OF ABBREVIATIONS ............................................................................................. 7

ABSTRACT ..................................................................................................................... 8

CHAPTER

1 INTRODUCTION ...................................................................................................... 9

2 METHODS .............................................................................................................. 12

Head Morphometrics............................................................................................... 12

Model Heads ........................................................................................................... 12 Pressure Sensors ................................................................................................... 13 Experimental Setup ................................................................................................ 13

Signal Processing ................................................................................................... 14 Hypothetical Modeling............................................................................................. 16

Statistical Analysis .................................................................................................. 17

3 RESULTS ............................................................................................................... 19

Pressure Difference Distribution (Steady flow) ....................................................... 19 Pressure Fluctuation Distribution (Vortical Flows) .................................................. 20 Determining Flow Parameters ................................................................................ 21

Vortex Shedding Frequency ............................................................................. 21 Flow Speed ...................................................................................................... 21

Cylinder Diameter ............................................................................................. 22 Signal-to-Noise Ratios ............................................................................................ 22

4 DISCUSSION ......................................................................................................... 33

Obstacle Avoidance ................................................................................................ 33

Prey Tracking .......................................................................................................... 33 Refuging ................................................................................................................. 34 Implications for Roboticists ..................................................................................... 36

LIST OF REFERENCES ............................................................................................... 39

BIOGRAPHICAL SKETCH ............................................................................................ 45

6

LIST OF FIGURES

Figure page 1-1 Table of fish head aspect ratios .......................................................................... 11

2-1 Experimental setup ............................................................................................. 18

3-1 Pressure sensitivity profiles in steady flows ........................................................ 24

3-2 Pressure fluctuation and sensitivity profiles in vortical flows ............................... 26

3-3 Detected vortex shedding frequencies by heads ................................................ 28

3-4 Calculated flow speeds and cylinder size ........................................................... 29

3-5 Signal-to-noise ratios across head position ........................................................ 30

3-6 Signal-to-noise ratios for different vortex sizes, flow speeds, and distances ...... 31

4-1 Model of signal-to-noise ratios across cylinder diameters .................................. 38

7

LIST OF ABBREVIATIONS

AR Aspect ratio. Dimensionless number that describes the relative width of a head. Calculated as the ratio of head width to head length

Cp Pressure coefficient. Dimensionless number that describes the relative pressures throughout a flow field.

D Cylinder diameter.

fexp Vortex shedding frequency. Frequency at which vortices are generated, expressed in Hz.

ρ Density of water at 20°C

SNR Signal-to-noise ratio. The ratio of the strength of a signa lto the noise, expressed in decibels

St Strouhal number. Dimensionless number describing oscillating flow mechanisms.

U0 Steady flow speed. Flow speed without interference of objects.

U(s) Local flow speed. Flow velocity just outside of the boundary layer at arc length, s, of an elliptical cylinder.

8

Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the

Requirements for the Degree of Master of Science

HEAD WIDTH INFLUENCES SENSING OF STEADY AND VORTICAL FLOWS BY THE LATERAL LINE CANAL IN FISHES

By

Yuzo R. Yanagitsuru

August 2017

Chair: James C. Liao Major: Zoology

Though there is considerable variation in head morphology, the cephalic

architecture of the flow-sensitive lateral line canal system remains highly conserved

between fish species. Because these canals ride upon a cranial platform, the sensory

input it receives is expected to change based on how flow interacts with the head. In

this study we explore how head width, a trait that varies between species and across

ontogeny, affects flow sensing. We inserted pressure sensors into 3D printed fish heads

of varying widths and placed these model heads in steady and vortical flows to measure

sensory performance. We find that skinnier heads had the highest sensitivities to

acceleration in both steady and vortical flows but sensitivity to both decreased rapidly

with distance from the snout. Conversely, wider heads had lower maximum sensitivities

that did not decrease as greatly with distance from the snout. We discovered that head

width passively optimizes the signal-to-noise ratios for different sized vortices,

suggesting that fish with skinnier heads could better detect smaller prey and those with

wider heads could better detect larger prey. Our results have implications for the

sensory ecology of fishes and the design of autonomous underwater vehicles.

9

CHAPTER 1 INTRODUCTION

The lateral line system is used to sense water motion (Dijkgraaf, 1963). This

mechanoreceptive organ is critical for performing tasks such as: detecting predators

and prey, navigating turbulent flows, and orienting to flows (Coombs et al., 2001; Liao et

al., 2003a,b; Montgomery et al., a1997; Pohlmann et al., 2001, 2004; Stewart et al.,

2014; Taguchi and Liao, 2011). The lateral line system is comprised of bundles of flow-

sensitive hair cells called neuromasts. The lateral line is composed of two types of

neuromasts: superficial, which lies directly on top of the skin of fish and canal

neuromasts, which are recessed within open-pored canal running just under the surface

of the body and head of fish. Superficial neuromasts are sensitive to velocity gradients

while canal neuromasts are sensitive to acceleration around the fish and, by extension

of Bernoulli’s principle, to the pressure differences between adjacent canal pores

(Coombs et al., 1988, 1996; Coombs and Janssen, 1990; Kroese and Schellart, 1992).

Fishes exhibit a large anatomical diversity in their lateral line canal; canal

diameters, pore spacing, neuromast shapes vary between species (Carton and

Montgomery, 2004; Coombs et al., 1988; Montgomery et al., 1994; van Netten, 2006;

Webb, 2013). Additionally, canals can possess features such as membranous pore

coverings, branching canal patterns, or localized constrictions within canals, all of which

can affect the lateral line’s sensitivity to hydrodynamic stimuli (Carton and Montgomery,

2004; Denton and Gray, 1983, 1988; Janssen, 1997, 2004; Montgomery et al., 1994;

Webb 1989, 2013). The structural organization of trunk canals can also vary based on

habitat preference (Bleckman and Münz, 1990; Coombs et al., 1988; Webb, 1989,

2013). For example, most fishes have a single, continuous trunk canal on their mid-

10

body. However, fishes that occupy habitats just under the surface have ventrally located

trunk canals and fishes that bury themselves in sediment have dorsally located trunk

canals (Webb, 2013).

Despite this tremendous diversity, the cephalic canal system remains highly

conserved across fishes; it usually consists of three rows: one located above the eye,

one below, and one along the lower mandible (Coombs et al., 1988; Webb, 1989,

2013). This conserved architecture becomes particularly interesting when considering

the large diversity of fish head morphologies (Alexander and Adams, 2004; Boglino et

al., 2013; Bouton et al., 2002; Cabuy et al., 1999; Clabaut et al., 2007; Geerinckx et al.,

2007; Kajiura, 2001; Lowry et al., 2007; Tedman, 1980; Wyckmans et al., 2007). When

fish swim, flow is induced across the head. How the flow interacts with the head

depends greatly on its shape and thus fish heads may receive different sensory inputs

depending on morphological variation. In this way, the alteration of flows induced by

head morphology may have important consequences for the ability of fish to sense in

their aquatic environment. To begin exploring how head morphology affects flow

detection, we focused initially on one common aspect that is known to vary across

species and ontogeny: head width (Table 1-1). In this study, we examine how head

width influences the ability of fishes to detect and characterize two types of flows that

fishes regularly encounter in their natural environment: steady and vortical flows.

11

Figure 1-1.Table of fish head aspect ratios. Ranges of head ARs (width/length) for several fish species from different families. Fish head ARs were measured at the Florida Natural History Museum Ichthyology Collection or obtained from cited literature. The number of individuals measured are shown in parentheses. Species are highlighted into different colors indicating ARs that are approximately those of the model skinny (cyan), intermediate (blue), and wide (purple) heads.

12

CHAPTER 2 METHODS

Head Morphometrics

Preserved whole fish specimens were acquired from the Florida Museum of

Natural History Ichthyology Collection at University of Florida. All morphometric data

was collected using dial calipers. Head length was measured as the distance from the

tip of the upper jaw to the posterior margin of the operculum. Head width was measured

at the point of maximum width across the head. Because head width can span a large

range based on the size of the fish, we use aspect ratios to normalize head width to fish

size. Aspect ratio (AR) was calculated as the head width divided by length. Therefore a

low AR head would be a relatively skinny head, whereas a high AR head would be a

relatively wide head.

Model Heads

We were interested in understanding the effects of head shape in the x-y axis

(Figure 2-1A). Approximating the curvature of fish heads in the x-y axis as half ellipses,

we fabricated head shapes as simple, half elliptical cylinders using Rhinoceros v5

(Robert McNeel & Associates). To represent different head widths for fish of similar

sizes, we made all heads with the same length (10 cm) but different widths (2, 6, 10

cm). The Ars of models fell within and spanned the naturally occurring range of fish

heads (Table 1-1). We call these models skinny, intermediate, and wide models from

here on. To simulate the lateral line canal, heads were designed with pores arranged in

a single horizontal line (Figure 2-1B). There was a single pore at the snout and 8 pores

along the sides (each pore was 3 mm diameter and spaced 1 cm apart from each

other). Each head was fabricated so that the posterior-most pores were located one cm

13

from the end. Model heads were then 3D printed with acrylonitrile butadiene styrene

(ABS) plastic using a Makerbot Replicator 2X (MakerBotR Industries LLC).

Pressure Sensors

We embedded seven surgical grade pressure sensors (Millar Instruments), one

in the snout pore and six others on the left side of model heads so that they were flush

with the surface (Figure 2-1B). Pressure sensors were calibrated one mm below the

surface of still water to estimate the value unit conversion to Pascals. Pressure was

recorded with a sampling rate of 1000 Hz.

Experimental Setup

Steady flows were generated using a 175 liter recirculating flow tank. A type of

vortical flow called a Kàrmàn vortex street was generated by placing a cylinder in a

steady flow (Figure 2-1C,ii; Blevins, 1990). The flow tank was fit with an 80/20 aluminum

frame (80/20 Inc.) custom designed to mount heads and cylinders in the working section

(25 x 26 x 87 cm; height x width x length). Heads were secured to the frame from above

with a precision support rod (Siskiyou Corporation) and cylinders were attached directly

to the frame. Both heads and cylinders rested against the bottom of the flow tank to

prevent self-oscillation.

For steady flow trials, heads were secured to the center of the working area with

no cylinder (Figure 2-1C,i). Pressure was then recorded prior to any flow to record mean

hydrostatic pressure for all seven sensors simultaneously. Steady flow was then

initiated after which pressure was recorded for 30 seconds. Hydrostatic pressure was

also recorded after flow was stopped to account for potential drift in our sensors. The

mean hydrostatic pressure was then subtracted from the steady flow pressure

recordings. Because the lateral line canal is sensitive to pressure differences, we

14

calculated the pressure difference between adjacent sensors for our analyses. To

assess sensory performance of each head for different sized vortices, we used three

different cylinder diameters (1.3, 2.5, 5 cm) to generate small, medium, and large

vortices, respectively. We additionally tested head performance for each vortex size in

four flow speeds (26, 52, 79, 105 cm/s) and seven positions from the cylinder (ranging

from 3-9 cylinder diameters downstream). Pressure was recorded for 60 seconds for all

vortical flow trials.

Signal Processing

Steady Flows. To validate our empirical measurements, we compared our

measured pressure differences to theoretical predictions. To do this, we first converted

mean pressure differences into pressure coefficients (Cp) using Bernoulli’s Law:

Cp =

P

0.5ρU0 2

(2-1)

where P is mean pressure difference (Pa), ρ is density of water at 20°C (998.2

kg/m3), and U0 is steady flow speed (cm/s).

To calculate theoretical Cp around heads, we first approximated the local flow

speeds just outside the boundary layer around heads using the potential flow solution

for an elliptical cylinder (Khan et al., 2005):

U(s) =

U0(1 + ϵ)sinθ

√1 − e2 cos2 θ (2-2)

where U is local flow speed (cm/s), s is arc length of head (cm), ϵ is AR of head,

θ is the angle measured from the snout, and e is eccentricity = √1 − ϵ2. The calculated

local flow speed was then used to calculate Cp along the head using Bernoulli’s Law:

Cp =

P

0.5ρU0 2

= 1 − (U(s)

U0

)

2

(2-3)

15

The Cp difference was then calculated at one centimeter increments

corresponding to the location of sensors on our model heads to yield the Cp differences.

To determine if heads were able to determine flow speeds using pressure

differences, we calculated flow speeds with Bernoulli’s Law using both the theoretical Cp

and our measured pressure differences.

U0 = √P

0.5ρCp (2-4)

To examine how well heads could detect acceleration, we calculated sensitivity.

We define sensitivity as the change in pressure difference induced by a unit change in

flow speed. Thus, the larger the change in pressure difference, the higher the

sensitivity. Pressure differences at each head position increased linearly with flow

speeds. We calculated sensitivity at each head position as the slope of the linear fit for

mean pressure differences at different flow speeds. Because sensitivities varied across

head positions, we fit an exponential curve for the sensitivities of each head to model a

continuous distribution of sensitivities across heads.

Vortical Flows. Pressure differences in vortical flows fluctuated over time due to

the presence of vortices. To account for this temporal component, we measured

fluctuations of each pressure difference. We first removed offset in the data by

subtracting the mean of the pressure signal from itself (Figure 2-1D,ii). Pressure

fluctuation was then calculated as the standard deviation of pressure difference over

time and thus represents a value proportional to the amplitude of the fluctuating signal.

Pressure fluctuations were converted to Cp using equation 3. We fit an

exponential curve to the Cp fluctuations for each head to model a continuous distribution

around heads. Using the Cp from the exponential fit and measured pressure differences,

16

we calculated flow speeds for vortical flows using equation 4. Sensitivities to

acceleration were calculated in the same way as for steady flows and we fit an

exponential curve to model a continuous distribution of sensitivities across heads.

Signal-to-noise ratio. We evaluated the ability of a head to detect vortical flows by

using signal-to-noise ratios (SNRs). We first obtained a frequency spectrum for

pressure differences via fast-Fourier transformation to identify the dominant detected

frequency and its amplitude. We then calculated SNR in decibels using the ratio of the

amplitude of the detected signal and noise in the frequency spectrum. Sensor

performance between heads was analyzed using only the maximum SNR observed on

heads.

We additionally compared the detected frequency with the expected vortex

shedding frequency, which we calculated as:

fexp =

StU

D (2-5)

where fexp is the expected vortex shedding frequency (Hz), St is the Strouhal

number (0.2) appropriate for the Reynolds numbers of our experiments (3,300-53,000)

(Blevins, 1990), U is steady flow speed (cm/s), and D is cylinder diameter (cm).

Hypothetical Modeling

We generated a hypothetical model to predict SNRs for vortex sizes that we were

unable to test due to limitations of flow tank size. The model was generated by fitting a

second-degree polynomial to the SNR of each head width for all cylinder diameters.

SNR was assumed to be zero when the cylinder diameter was zero as this would

indicate a steady flow. SNR was then normalized to the maximum SNR of each model.

17

Statistical Analysis

A four-way ANOVA and subsequent Tukey’s multiple comparison post hoc test

was used to determine the effect of head width, vortex size, flow speed, and cylinder

position on SNR. The four-way ANOVA was performed using MATlab R2013a (Matlab,

Mathworks) at an α level of 0.05.

18

Figure 2-1. Experimental setup. A) Top-down view of experimental models. White circles indicate sensor positions along the head. B) Diagram of lateral view of head model with pressure sensors threaded through. Gray lines indicate the pressure sensor cord, gray circles on the top of the head are insertion points and gray circles on the sides indicate where pressure sensors were placed. C) Model heads were tested in i) steady and ii) vortical flows. Vortical flows were generated by placing a cylinder upstream of a steady flow. D) 5 second sample of pressure difference between sensors 1 and 2 in i) steady flow and in a ii) vortical flow (sampling rate = 1000 Hz).

19

CHAPTER 3 RESULTS

Pressure Difference Distribution (Steady flow)

The largest pressure differences were concentrated towards the snout of all

heads (Figure 3-1A). This characteristic pressure profile was most pronounced in skinny

and intermediate heads where the pressure maximum was detected at the snout-most

pair of sensors (Figure 3-1A,i-ii). This pressure profile was less pronounced in wide

heads (Figure 3-1A,iii), where the pressure difference maximum was detected at the

second pair of sensors.

Skinnier heads experience higher maximum pressure differences than wider

heads for a given flow speed but this large maximum pressure difference is restricted to

a narrower region at the front of the head (Figure 3-1AB). For example, skinny heads

experience a substantial drop from their pressure maxima and reach their pressure

minima after the first 1.5 cm along the head (Figure 3-1AB,i), while intermediate and

wide heads reached their pressure minima around 2.5 and 5.5 cm (Figure 3-1AB,ii-iii),

respectively.

Cp distribution across heads closely matched theoretical predictions (Figure 3-

1B). For skinny heads, theoretical predictions were nearly identical to its measured Cp

distribution. But for intermediate and wide heads, theoretical predictions were larger

than the measured Cp for the mid-region of heads. Furthermore, Cp did not vary

between flow speeds for skinny and intermediate heads but there was some variation

for wide heads.

The sensitivity to acceleration closely reflect the pressure profiles for heads

(Figure 3-1C). We first find that the highest sensitivities were concentrated towards the

20

snout of all heads and skinnier heads had higher sensitivity maxima than wider heads.

We also find that the sensitivity of skinnier heads decreased much more rapidly with

distance from the snout than in wider heads. To relate the sensitivity of these heads to a

real fish, we used an estimate of a lateral line canal neuromast detection threshold (1

mPa) reported in van Netten (2006) for the ruffe (Gymnocephalus cernuus). For steady

flows, this detection threshold indicates the minimum pressure difference necessary to

detect a one mm/s change in flow speed. We modified this value to fit our model head

canal pore spacing (1 cm). We find that regardless of head width, the snout region of

heads had sensitivities larger than the detection threshold and thus all heads are

capable of detecting a change in flow speed of one mm/s or lower. Due to the more

rapid decrease in sensitivity, we find that skinnier heads had narrower regions where

their sensitivities were greater than the detection threshold compared to wider heads;

the sensitivity of skinny, intermediate, and wide heads were projected to drop below the

detection threshold at: 0.7, 2.3, and 5.0 cm along the head, respectively.

Pressure Fluctuation Distribution (Vortical Flows)

The largest pressure fluctuations were concentrated towards the snout of all

heads (Figure 3-2A). This was true regardless of flow speed or vortex size. Similar to

the pressure difference profile in steady flow, skinnier heads experienced larger

maximum pressure fluctuations compared to wider heads. Pressure fluctuations

increased with the size of vortices for skinny and intermediate heads. However, these

increases were small relative to the increases observed with flow speeds (Figure 3-2C).

Cp fluctuations across skinny and intermediate heads for the lowest flow speed deviated

from those at the other flow speeds. Cp fluctuations across wide heads did not vary with

flow speeds.

21

Sensitivity to acceleration in vortical flows closely reflect the pressure fluctuation

profile for all heads (Figure 3-2D). Similar to steady flows, the highest sensitivities were

concentrated towards the snout of all heads and skinnier heads had higher sensitivity

maxima than wider heads. The sensitivity of skinnier heads decreased more rapidly with

distance from the snout than in wider heads. We found that heads were 1000-fold less

sensitive to acceleration in vortical flows compared to steady flows. To reflect this, the

detection threshold in vortical flows indicates the minimum pressure difference

necessary to detect a one m/s change in flow speed. Similar again to steady flows,

skinnier heads had narrower regions than wider heads where sensitivities were greater

than the detection threshold; the sensitivity of skinny, intermediate, and wide heads

were projected to drop below the detection threshold at: 1.8, 2.2, and 5.5 cm along the

head, respectively.

Determining Flow Parameters

Vortex Shedding Frequency

All heads detected a high amplitude signal for a specific frequency in vortical

flows (Figure 3-3A). Because each head position had its own unique pressure

difference/fluctuation, every head position could detect vortex shedding frequencies

independently from one another. But the most accurate frequency was determined by

averaging the detected frequencies at all head positions. The average detected

frequencies were close to but generally lower than the calculated vortex shedding

frequencies (Figure 3-3B).

Flow Speed

Flow speeds could be calculated using pressure differences and pressure

fluctuations for all heads in steady and vortical flows, respectively (Figure 3-4). Every

22

head position could calculate flow speed independently from one another. Skinny heads

were able to most accurately determine steady flow speed by using its snout-most pair

of sensors. Intermediate and wide heads, on the other hand, were able to most

accurately determine steady flow speeds by averaging the flow speeds calculated at

each head position. All heads could accurately determine vortical flow speeds by

averaging the calculated flow speeds at all head positions.

Cylinder Diameter

By determining both vortex shedding frequency and flow speed, cylinder

diameters could be determined for vortical flows using equation 5. Cylinder diameters

were calculated using the vortex shedding frequency and flow speeds calculated using

the method that yielded the most accurate result. Calculated cylinder diameters were

inaccurate at the lowest flow speed for all heads but could be accurately calculated for

all other flow speeds (Figure 3-4C).

Signal-to-Noise Ratios

On average, SNR was optimal at the snout of all heads for all vortex sizes and

flow speeds (Figure 3-5). To evaluate the ability of heads to detect vortical flows, we

used only the optimal SNR at the snout for our analyses.

SNRs varied for each head for a given vortex size (Figure 3-6A). For small

vortices, the skinny head (15.10±0.24 Db) had the highest SNR, followed by the

intermediate (13.8±0.24 Db), and then wide head (11.1±0.23 Db). For medium vortices,

skinny (20.7±0.30 Db) and intermediate heads (20.4±0.21 Db) both had higher SNRs

than the wide head (14.68±0.24 Db). For large vortices, intermediate heads (21.57±0.19

Db) had the highest SNR, followed by the skinny (19.68±0.41 Db), and then wide head

(17.78±0.34 Db). For all heads for all vortex sizes, SNR was highest for intermediate

23

flow speeds (Figure 3-6B). SNR varied greatly with distance but did not follow any

predictable pattern with our tested distances (Figure 3-6C).

24

Figure 3-1. Pressure sensitivity profiles in steady flows. A) Pressure difference around the perimeter of i) skinny, ii) intermediate, and iii) wide heads at different flow speeds. All heads had a characteristic pressure profile for a given flow speed. Pressure differences increased with flow speed across the entirety of the head for intermediate and wide heads. The increase in pressure difference with flow speed was restricted to the snout-most section of the skinny head. B) Pressure coefficients (Cp) around the perimeter of i) skinny ii) intermediate, and iii) wide heads at different flow speeds. Pressure coefficients remained

25

similar across all flow speeds for skinny and intermediate heads but not for wide heads. Bernoulli-predicted pressure coefficients closely match measured pressure coefficients for skinny heads but deviate for intermediate and wide heads. C) Sensitivities to a change in flow speed of one mm/s at different points along the head. Exponential curves represent a continuous distribution of sensitivities across each head. Dashed grey line indicates the estimated threshold (1mPa/1mm/s) at which a lateral line canal neuromast can detect a one mm/s change in flow speed. This value was modified from (Van Netten, 2006), which estimated the detection threshold for the ruffe (Gymnocephalus ceruus). Sensitivity was greatest at the snout-most section of all heads. Skinnier heads yield the highest sensitivity to flowspeeds but this high sensitivity was restricted to the snout-most region of the head and sensitivity falls below the detection threshold at sensor positions well before that of wider heads.

26

Figure 3-2. Pressure fluctuation and sensitivity profiles in vortical flows A) Pressure fluctuations across i) skinny, ii) intermediate, and iii) wide heads averaged for all vortex sizes. Each head had a characteristic pressure fluctuation profile and experienced larger magnitude pressure fluctuations at higher flow speeds. B) Pressure coefficient fluctuations across i) skinny, ii) intermediate, and iii) wide heads averaged for all vortex sizes. Pressure coefficient

27

fluctuations for lowest flow speeds varied from that of other flow speeds for skinny and intermediate heads but not for wide heads. C) Pressure fluctuations at the snout-most pair of sensors for i) skinny, ii) intermediate, and iii) wide heads for different cylinder diameters at all flow speeds. Vortices from larger cylinders induce larger pressure fluctuations in skinny and intermediate but not in wide heads. However, the change in pressure fluctuation from different flow speeds are much greater than those induced by vortex size. D) Sensitivities to changes in flow speed of one m/s at different points along the head. Dashed grey line indicates detection threshold (1mPa/m/s) at which a lateral line canal neuromast can detect a one m/s change in flow speed. Sensitivity was greatest at the snout-most section of all heads. Skinnier heads yield the highest sensitivity to flow speeds but this high sensitivity was restricted to the snout-most region of the head and sensitivity falls below the detection threshold at sensor positions well before that of wider heads.

28

Figure 3-3. Detected vortex shedding frequencies by heads. All heads could accurately identify vortex shedding frequencies. A) Representative frequency spectrum for head in vortical flow. Frequency spectrum for the pressure difference between the snout-most pair of sensors for an intermediate head exposed to medium vortices at a flow speed of 52 cm/s. Grey line indicates the detected signal frequency. Red line indicates the calculated vortex shedding frequency. B) Detected signal frequencies averaged across all head positions for each head plotted against calculated vortex shedding frequencies. Black line indicates where detected and calculated frequencies match. Detected signal frequencies were close to but were generally lower than the calculated vortex shedding frequencies.

29

Figure 3-4. Calculated flow speeds and cylinder size. All heads were able to calculate flow speeds and cylinder diameters using pressure differences and pressure fluctuations. A) Flow speeds calculated using theoretically derived pressure coefficients and measured pressure difference data for heads in steady flows. Intermediate and wide head flow speeds are the average of all calculated flow speeds at all head positions. Flow speeds calculated as the average across all head positions for the skinny head deviated greatly from the actual flow speed and the flow speed calculated from the snout-most pair of sensors, which was consistent with the actual flow speed, is shown instead. B) Flow speeds calculated using the pressure coefficients from the exponential fit of pressure coefficient fluctuations and measured pressure fluctuations for heads in vortical flows. All flow speeds were calculated as the average across all head positions. C) Cylinder diameters calculated using the calculated flow speeds and measured vortex shedding frequencies for all heads. Though calculated cylinder diameters for the lowest flow speed were highly anomalous (not shown), cylinder diameters were calculated within 20% error for all other flow speeds.

30

Figure 3-5. Signal-to-noise ratios across head position. SNR for all head sensors for A) small, B) medium, and C) large vortices. All heads yielded the highest SNR at the snout-most region of the head. SNR subsequently decreased with distance from the snout for all heads.

31

Figure 3-6. Signal-to-noise ratios for different vortex sizes, flow speeds, and distances. Different head widths yield different signal-to-noise ratios (SNR) for different vortex sizes A) Average optimal SNR for all heads for different vortex sizes. SNRs within a vortex size varied with head width. For small vortices, skinny heads yielded higher SNRs than both intermediate and wide heads and intermediate heads yielded higher SNR than wide heads. For medium vortices, both skinny and intermediate heads yielded higher SNRs than wide heads. For large vortices, intermediate heads yielded higher SNRs than the

32

other heads and skinny heads yielded higher SNR than wide heads. B) SNR for all heads at different flow speeds for i) small, ii) medium, and iii) large vortices. In general, heads had the highest SNR at the intermediate flow speeds. C) SNR for all heads at different distances downstream of cylinder for i) small, ii) medium, and iii) large vortices. SNR varied but there was no discernible pattern within our tested distances.

33

CHAPTER 4 DISCUSSION

Obstacle Avoidance

Sensitivity to acceleration was highest at the snout for all of our model heads.

This could suggest that the snout is specialized for detecting flow disturbances, which

may be induced by predators, prey, or obstacles. Evidence for this is shown in the blind

Mexican cavefish (Astyanax fasciatus), which is able to detect and avoid obstacles

using its lateral line (Windsor et al., 2008). When approaching a wall, either head-on or

while gliding parallel to one, the snout of the cavefish experiences the largest change in

flow (Windsor et al., 2010a,b). This indicates that the largest stimuli are present at the

snout during obstacle avoidance regardless of the approach angle. Although our models

were stationary in flows and most closely represent a gliding fish, our findings may

remain relevant for actively swimming fish. Head movements characteristic of

undulatory swimming may be expected to alter pressure around the head of fish.

However, steady swimming fish couple head yaw and heave motions in order to

minimize self-induced pressure disturbances (Akanyeti et al., 2016). This results in a

minimally disturbed pressure profile where the snout remains the most sensitive to

external stimuli (Akanyeti et al., 2016).

Prey Tracking

Undulating aquatic organisms such as fish generate vortex wakes that the lateral

line can sense (Gardiner and Atema, 2007, Hanke and Bleckman, 2004; Müller et al.,

1997; Pohlmann et al., 2001, 2004). These vortex wakes contain information about an

organism’s swimming speed and size (Blickhan et al., 1992; Hanke et al., 2000).

Sensitivity to changes in vortical flow speed was low for all heads and thus pressure

34

profiles would likely prove ineffective at detecting changes in prey swimming speed.

Alternatively, heads could detect changes in vortex shedding frequency, which is

directly proportional to prey swimming speed (Blickhan et al., 1992). By determining the

swimming speed of prey from their hydrodynamic “footprints”, it may be possible for fish

to more effectively pursue prey. Likewise, fish preferentially capture prey of a specific

size to maximize their energy gain (Gill, 2003; Kislalioglu and Gibson, 1976; Prejs et al.,

1990; Scharf et al., 2000; Scott, 1987; Wankowski, 1979; Werner, 1974). By detecting

the size of the prey, fish could further optimize their hunting strategy.

Refuging

Fish hold station behind objects to take refuge from predators and extreme flow

conditions (Johansen et al., 2008; Krause et al., 1998; Sutterlin and Waddy, 1975).

Additionally, some fish taking refuge behind the vortical flows of cylinders will adjust

their swimming gait to slalom between and exploit vortices (Kármán gait), allowing them

to reduce the cost of locomotion (Liao et al., 2003a, b; Taguchi and Liao, 2011). Trout

without vision or lateral line sense will still entrain behind a cylinder but are less likely to

exhibit the classic slaloming Kármán gaiting behavior (Liao, 2006). Thus it is probable

that refuging behind vortical flows is mediated, in part, by the lateral line.

Preference to Kármán gait within a vortical flow is influenced by the size of the

fish relative to the cylinder (Liao et al., 2003a; Liao, 2006). Therefore, detecting vortical

flows that correspond to the optimal cylinder for Kármán gaiting would benefit fish

seeking refuge. Pressure differences can be utilized to determine vortical flow

parameters such as: flow speed and vortex shedding frequency, both of which can then

be used to calculate cylinder diameter (Chambers et al., 2014; Ćurčić-Blake and van

Netten, 2006; Franosch et al., 2009; Klein and Bleckman, 2011; McConney et al., 2009;

35

Pandya et al., 2006; Ren and Mohseni, 2012; Yang et al. 2006, 2010). While

determining cylinder size using this method would definitively allow fish to seek refuge

behind an optimal cylinder, we also show that head width passively optimizes the

detection of vortices from certain sized cylinders (Figure 4-1). We suggest that this may

simplify seeking optimal refuge by allowing them to best detect vortical flows in which

they can Kármán gait. While the ratio of head width to body length varies between

species, it typically varies little with ontogeny (Ceas and Page, 1996; Randall and Page,

2012; Wright and Page, 2008). Thus as fish capable of Kármán gaiting grow, the head

could continue to passively optimize detection of vortical flows that they can refuge

within.

Wider heads may not detect smaller vortices as well as skinnier heads because

of vortex rebound (Orlandi, 1990). Vortex rebound occurs when vortices contact a wall

and then bounces off. For a vortex travelling directly at a wall, the vortex would bounce

away from the wall in the direction that the vortex originally travelled from. In the case of

a sequence of vortices like a Kármán vortex street, a rebounded vortex would interact

with the subsequent vortex travelling towards the wall and would likely create further

noise in the vortex signal. Because wider heads will more closely approximate a wall for

smaller vortices, we suggest that vortex rebound may be responsible for reducing SNR

for wider heads and smaller vortices. On the other hand, larger vortices will likely more

approximate steady flows for skinnier heads, leading to a reduction in the vortex signal.

All heads detected vortices best at intermediate flow speeds, which correlate with

the speeds that rainbow trout are most likely to Kármán gait (Akanyeti and Liao, 2013).

Because Kármán gaiting is a largely passive mode of locomotion (Liao 2003a,b), it is

36

unlikely that a heightened advantage to detect vortices would allow a trout to actively

enhance its ability to Kármán gait. Rather, this apparent increase in SNR and likelihood

to Kármán gait at intermediate flow speeds could be explained by weaker vortices at

lower flow speeds and the introduction of additional turbulence at higher flow speeds

(Blevins, 1990). Regardless, this phenomenon remains interesting for refuging because

the larger SNR at intermediate flow speeds could optimize localization of optimal

vortical flows in which fish can Kármán gait.

Implications for Roboticists

Hydrodynamic sensors are crucial for the development of autonomous

underwater vehicles. Artificial lateral lines and mathematical models have shown that a

linear array of pressure sensors can be used to accurately determine environmental

flow speeds, characterize vortex wakes, and localize objects (Chambers et al., 2014;

Ćurčić-Blake and van Netten, 2006; Franosch et al., 2009; Klein and Bleckman, 2011;

McConney et al., 2009; Pandya et al., 2006; Ren and Mohseni, 2012; Yang et al. 2006,

2010). In this current study we show that by using the measured pressure differences

and the theoretical Cp around a head, all heads are able to determine steady flow

speeds. While head width had minimal influence on the accuracy of the calculated flow

speeds, the method of determining flow speed differed between heads. Skinny heads

were only able to accurately determine flow speeds by using the pressure difference

from the snout-most pair of sensors. The intermediate and wide heads, on the other

hand, could most accurately determine flow speeds by averaging all of the calculated

flow speeds at each head section. This could suggest that skinnier heads may rely on

fewer sensors to detect flow velocity compared to wider heads. Coupled with the

37

relatively larger sensitivity to acceleration towards the snout of skinnier heads, a skinny

sensor platform may be optimal for AUVs.

We additionally demonstrate that head width has minimal effect on accuracy at

determining vortex shedding frequency, flow speed, and cylinder size within a Kármán

vortex street. In this case, all three parameters were an average of values calculated at

each head section for all head widths. Thus, it is possible to characterize a Kármán

vortex street using pressure differences but it is necessary to have multiple sensors to

calculate these parameters accurately regardless of head width.

Our study demonstrates for the first time that head width influences detection of

steady and vortical flows. Here, we explore the effect of one two-dimensional aspect of

a fish’s head on flow sensing. However, fish heads are unsteady, complex three-

dimensional structures that yaw and heave. To fully understand how its morphology

affects flow sensing, it is necessary to explore it in motion and in three-dimensions.

While we suggest that fish swimming behavior may minimize any effect on the findings

we observe for our stationary models, it is crucial to empirically test this. A good start for

the further exploration of head morphology on flow sensing would be explore additional

aspects of general head morphology such as: height or snout curvature. We also

suggest that one particularly rich area of investigation is the influence of surface

topography on flow sensing. Microscopic depressions on heads called epidermal pits

have been suggested to increase SNRs by 10-30 Db when detecting predators or prey

(Herzog et al., 2017). This becomes particularly interesting when considering the large

diversity of fish scale morphologies that exist (Agassiz, 1833; Roberts, 1993).

38

Figure 4-1. Model of signal-to-noise ratios across cylinder diameters. Each head width has a specific cylinder diameter at which vortical flows are detected best. SNR normalized to the maximum SNR predicted for all heads varied for vortices of a given cylinder diameter. This model suggests that each head has a unique cylinder diameter at which vortices are detected optimally. The model also suggests that wider heads may have a larger range of vortex sizes that they are able to detect. Solid = within experimentally tested range of vortex sizes. Dashed = projected SNRs for untested vortex sizes.

39

LIST OF REFERENCES

Agassiz, L. (1833). Recherches sur les poissons fossiles: Tome 2 (Vol. 2). Petitpierre. Akanyeti, O. and Liao, J.C. (2013). The effect of flow speed and body size on Kármán gait kinematics in rainbow trout. J. Exp. Biol. 216, 3442-3449. Akanyeti, O., Thornycroft, P. J. M., Lauder, G. V., Yanagitsuru, Y. R., Peterson, A. N., & Liao, J. C. (2016). Fish optimize sensing and respiration during undulatory swimming. Nat. commun. 7. Alexander, G.D. and Adams, C.E. (2004). Exposure to a common environment erodes

inherited between‐population trophic morphology differences in Arctic charr. J. Fish Biol. 64, 253-257. Bleckmann, H. and Münz, H. (1990). Physiology of lateral-line mechanoreceptors in a teleost with highly branched, multiple lateral lines. Brain Behav. Evol. 35, 240- 250. Blevins, R. D. (1990). Flow Induced Vibration, 2nd edition. Malabar, Florida: Krieger Publishing Company. Blickhan, R., Krick, C., Zehren, D., Nachtigall, W. and Breithaupt, T. (1992). Generation of a vortex chain in the wake of a Suhundulatory swimmer. Naturwissenschaften, 79, 220-221. Boglino, A., Wishkerman, A., Darias, M.J., Andree, K.B., De la Iglesia, P., Estévez, A. and Gisbert, E. (2013). High dietary arachidonic acid levels affect the process of eye migration and head shape in pseudoalbino Senegalese sole Solea senegalensis early juveniles. J. Fish Biol. 83, 1302-1320. Bouton, N., Visser, J.D. and Barel, C.D. (2002). Correlating head shape with ecological

variables in rock‐dwelling haplochromines (Teleostei: Cichlidae) from Lake Victoria. Biol. J. Linnean Soc. 76, 39-48. Cabuy, E., Adriaens, D., Verraes, W. and Teugels, G.G. (1999). Comparative study on the cranial morphology of Gymnallabes typus (Siluriformes: Clariidae) and their less anguilliform relatives, Clariallabes melas and Clarias gariepinus. J. Morphol. 240, 169-194. Carton, A.G. and Montgomery, J.C. (2004). A comparison of lateral line morphology of blue cod and torrentfish: two sandperches of the family Pinguipedidae. Env. Biol. Fish, 70, 123-131. Ceas, P.A. and Page, L.M. (1996). Chaetostoma yurubiense (Teleostei: Siluriformes), a new species of loricariid catfish from the Aroa, Urama, and Yaracuy river systems in Venezuela. Copeia. 671-677.

40

Chambers, L.D., Akanyeti, O., Venturelli, R., Ježov, J., Brown, J., Kruusmaa, M., Fiorini, P. and Megill, W.M. (2014). A fish perspective: detecting flow features while moving using an artificial lateral line in steady and unsteady flow. J. R. Soc. Interface. 11, 20140467. Ciccotto, P.J. and Page, L.M. (2016). From 12 to One Species: Variation in Lobocheilos rhabdoura (Fowler, 1934)(Cyprinidae: Labeonini). Copeia. 104, 879-889. Clabaut, C., Bunje, P.M., Salzburger, W. and Meyer, A. (2007). Geometric morphometric analyses provide evidence for the adaptive character of the Tanganyikan cichlid fish radiations. Evolution. 61, 560-578. Coombs, S., Janssen, J. and Webb, J.F. (1988). Diversity of lateral line systems: evolutionary and functional considerations. In Sensory biology of aquatic animals, pp. 553-593, Springer New York. Coombs, S. and Janssen, J. (1990). Behavioral and neurophysiological assessment of lateral line sensitivity in the mottled sculpin, Cottus bairdi. J. Comp. Physiol. A. 167, 557-567. Coombs, S., Hastings, M. and Finneran, J. (1996). Modeling and measuring lateral line excitation patterns to changing dipole source locations. J. Comp. Physiol. A. 178, 359-371. Coombs, S., Braun, C.B. and Donovan, B. (2001). The orienting response of Lake Michigan mottled sculpin is mediated by canal neuromasts. J. Exp. Biol. 204, 337-348. Ćurčić-Blake, B. and van Netten, S.M. (2006). Source location encoding in the fish lateral line canal. J. Exp. Biol. 209, 1548-1559. Denton, E.J. and Gray, J. (1983). Mechanical factors in the excitation of clupeid lateral lines. Proc. R. Soc. Lond. B Biol. Sci. 218, 1-26.

Dijkgraaf, S. (1963). The functioning and significance of the lateral‐line organs. Biol. Rev. Camb. Philos. Soc. 38, 51-105. Franosch, J. M. P., Hagedorn, H. J., Goulet, J., Engelmann, J., & van Hemmen, J. L. (2009). Wake tracking and the detection of vortex rings by the canal lateral line of fish. Phys. Rev. Lett. 103, 078102. Gardiner, J.M. and Atema, J. (2007). Sharks need the lateral line to locate odor sources: rheotaxis and eddy chemotaxis. J. Exp. Biol. 210, 1925-1934.

41

Geerinckx, T., Brunain, M., Herrel, A., Aerts, P. and Adriaens, D. (2007). A head with a suckermouth: a functional-morphological study of the head of the suckermouth armoured catfish Ancistrus cf. triradiatus (Loricariidae, Siluriformes). Belg. J. Zool. 137, 47. Gill, A.B. (2003). The dynamics of prey choice in fish: the importance of prey size and satiation. J. Fish Biol. 63, 105-116. Hanke, W., Brucker, C. and Bleckmann, H. (2000). The ageing of the low-frequency water disturbances caused by swimming goldfish and its possible relevance to prey detection. J. Exp. Biol. 203, 1193-1200. Hanke, W. and Bleckmann, H. (2004). The hydrodynamic trails of Lepomis gibbosus (Centrarchidae), Colomesus psittacus (Tetraodontidae) and Thysochromis ansorgii (Cichlidae) investigated with scanning particle image velocimetry. J. Exp. Biol. 207, 1585-1596. Herzog, H., Klein, B. and Ziegler, A. (2017). Form and function of the teleost lateral line revealed using three-dimensional imaging and computational fluid dynamics. J. R. Soc. Interface. 14, 20160898. Janssen, J. (1997). Comparison of response distance to prey via the lateral line in the ruffe and yellow perch. J. Fish Biol. 51, 921-930. Janssen, J. (2004). Lateral line sensory ecology. In The Senses of Fish, pp. 231-264, Springer Netherlands. Johansen, J.L., Bellwood, D.R. and Fulton, C.J. (2008). Coral reef fishes exploit flow refuges in high-flow habitats. Mar. Ecol. Prog. Ser. 360, 219-226. Kajiura, S.M. (2001). Head morphology and electrosensory pore distribution of carcharhinid and sphyrnid sharks. Env. Biol. Fish., 61, 125-133. Khan, W.A., Culham, R.J. and Yovanovich, M.M. (2005). Fluid flow around and heat transfer from elliptical cylinders: analytical approach. J. Thermophys. Heat Transfer. 19. 178-185. Kislalioglu, M. and Gibson, R.N. (1976). Prey ‘handling time’and its importance in food selection by the 15-spined stickleback, Spinachia spinachia (L.). J. Exp. Mar. Biol. Ecol. 25, 151-158. Klein, A. and Bleckmann, H. (2011). Determination of object position, vortex shedding frequency and flow velocity using artificial lateral line canals. Beilstein J. Nanotechnol. 2, 276-283.

42

Krause, J., Loader, S.P., McDermott, J. and Ruxton, G.D. (1998). Refuge use by fish as a function of body length–related metabolic expenditure and predation risks. Proc. R. Soc. Lond. B. 265, 2373- 2379. Kroese, A.B. and Schellart, N.A. (1992). Velocity-and acceleration-sensitive units in the trunk lateral line of the trout. J. Neurophysiol. 68, 2212-2221. Liao, J.C., Beal, D.N., Lauder, G.V. and Triantafyllou, M.S. (2003a). Fish exploiting vortices decrease muscle activity. Science. 302, 1566-1569. Liao, J.C., Beal, D.N., Lauder, G.V. and Triantafyllou, M.S. (2003b). The Karman gait: novel body kinematics of rainbow trout swimming in a vortex street. J. Exp. Biol. 206, 1059-1073. Liao, J.C. (2006). The role of the lateral line and vision on body kinematics and hydrodynamic preference of rainbow trout in turbulent flow. J. Exp. Biol. 209, 4077-4090. Lowry, D., Motta, P.J. and Hueter, R.E. (2007). The ontogeny of feeding behavior and cranial morphology in the leopard shark Triakis semifasciata (Girard 1854): a longitudinal perspective. J. Exp. Mar. Biol. Ecol. 341, 153-167. McConney, M.E., Chen, N., Lu, D., Hu, H.A., Coombs, S., Liu, C. and Tsukruk, V.V. (2009). Biologically inspired design of hydrogel-capped hair sensors for enhanced underwater flow detection. Soft Matter. 5, 292-295. Montgomery, J., Coombs, S. and Janssen, J. (1994). Form and function relationships in lateral line systems: comparative data from six species of Antarctic notothenioid fish. Brain Behav. Evol. 44, 299-306. Montgomery, J.C., Baker, C.F. and Carton, A.G. (1997). The lateral line can mediate rheotaxis in fish. Nature. 389, 960-963. Müller, U.K., Van Den Heuvel, B.L.E., Stamhuis, E.J. and Videler, J.J. (1997). Fish foot prints: morphology and energetics of the wake behind a continuously swimming mullet (Chelon labrosus Risso). J. Exp. Biol. 200, 2893-2906. Orlandi, P. (1990). Vortex dipole rebound from a wall. Phys. Fluids A. 2, 1429-1436. Pandya, S., Yang, Y., Jones, D.L., Engel, J. and Liu, C. (2006). Multisensor processing algorithms for underwater dipole localization and tracking using MEMS artificial lateral-line sensors. EURASIP J. App. Signal Process. 2006, 199-199. Prejs, A., Lewandowski, K. and Stańczykowska-Piotrowska, A. (1990). Size-selective predation by roach (Rutilus rutilus) on zebra mussel (Dreissena polymorpha): field studies. Oecologia. 83, 378-384.

43

Pohlmann, K., Grasso, F.W. and Breithaupt, T. (2001). Tracking wakes: the nocturnal predatory strategy of piscivorous catfish. Proc. Natl. Acad. Sci. 98, 7371-7374. Pohlmann, K., Atema, J. and Breithaupt, T. (2004). The importance of the lateral line in nocturnal predation of piscivorous catfish. J. Exp. Biol. 207, 2971-2978. Randall, Z.S. and Page, L.M. (2012). Resurrection of the genus Homalopteroides (Teleostei: Balitoridae) with a redescription of H. modestus (Vinciguerra 1890). Zootaxa. 3586, 329-346. Ren, Z. and Mohseni, K. (2012). A model of the lateral line of fish for vortex sensing. Bioinspir. Biomim. 7, 036016. Roberts, C.D. (1993). Comparative morphology of spined scales and their phylogenetic significance in the Teleostei. Bull. Mar. Sci. 52, 60-113. Scharf, F.S., Juanes, F. and Rountree, R.A. (2000). Predator size-prey size relationships of marine fish predators: interspecific variation and effects of ontogeny and body size on trophic-niche breadth. Mar. Ecol. Prog. Ser. 208, 229-248. Scott, A. (1987). Prey selection by juvenile cyprinids from running water. Freshwater Biol. 17, 129-142. Stewart, W.J., Nair, A., Jiang, H. and McHenry, M.J. (2014). Prey fish escape by sensing the bow wave of a predator. J. Exp. Biol. 217, 4328-4336. Sutterlin, A.M. and Waddy, S. (1975). Possible role of the posterior lateral line in obstacle entrainment by brook trout (Salvelinus fontinalis). J. Fish. Res. Bd. Can. 32, 2441-2446. Taguchi, M. and Liao, J.C. (2011). Rainbow trout consume less oxygen in turbulence: the energetics of swimming behaviors at different speeds. J. Exp. Biol. 214, 1428-1436. Tedman, R.A. (1980). Comparative study of the cranial morphology of the labrids Choeroden venustus and Labroides dimidiatus and the scarid Scarus fasciatus (Pisces: Perciformes) II. Cranial myology and feeding mechanisms. Mar. Freshwater Res. 31, 351-372. Van Netten, S.M. (2006). Hydrodynamic detection by cupulae in a lateral line canal: functional relations between physics and physiology. Biol. Cybern. 94, 67-85. Wankowski, J.W.J. (1979). Morphological limitations, prey size selectivity, and growth response of juvenile Atlantic salmon, Salmo salar. J. Fish Biol. 14, 89-100.

44

Webb, J.F. (1989). Gross Morphology and Evolution of the Mechanoreceptive Lateral- Line System in Teleost Fishes (Part 1 of 2). Brain Behav Evol. 33, 34-43. Webb, J.F. (2013). Morphological diversity, development, and evolution of the mechanosensory lateral line system. In The lateral line system, pp. 17-72, Springer New York. Werner, E.E. (1974). The fish size, prey size, handling time relation in several sunfishes and some implications. J. Fish. Res. Board Can. 31, 1531-1536. Windsor, S.P., Tan, D. and Montgomery, J.C. (2008). Swimming kinematics and hydrodynamic imaging in the blind Mexican cave fish (Astyanax fasciatus). J. Exp. Biol. 211, 2950-2959. Windsor, S.P., Norris, S.E., Cameron, S.M., Mallinson, G.D. and Montgomery, J.C. (2010). The flow fields involved in hydrodynamic imaging by blind Mexican cave fish (Astyanax fasciatus). Part I: open water and heading towards a wall. J. Exp. Biol. 213, 3819-3831. Wright, J.J. and Page, L.M. (2008). A new species of Synodontis (Siluriformes: Mochokidae) from tributaries of the Kasai River in Northern Angola. Copeia. 2008, 294-300. Wyckmans, M., Van Wassenbergh, S., Adriaens, D., Van Damme, R. and Herrel, A.

(2007). Size‐related changes in cranial morphology affect diet in the catfish Clariallabes longicauda. Biol. J. Linnean Soc. 92, 323-334. Yang, Y., Chen, J., Engel, J., Pandya, S., Chen, N., Tucker, C., Coombs, S., Jones, D.L. and Liu, C. (2006). Distant touch hydrodynamic imaging with an artificial lateral line. Proc. Natl. Aca. Sci. 103, 18891-18895. Yang, Y., Nguyen, N., Chen, N., Lockwood, M., Tucker, C., Hu, H., Bleckmann, H., Liu, C. and Jones, D.L. (2010). Artificial lateral line with biomimetic neuromasts to emulate fish sensing. Bioinspir. Biomim. 5, 016001.

45

BIOGRAPHICAL SKETCH

Yuzo R. Yanagitsuru was born and raised in San Mateo, California, U.S.A. in

1992. In 2010 he was admitted to University of California, San Diego where he doubled

majored in ecology, behavior, and evolution, and earth science and received a Bachelor

of Science degree in 2014.