MEASURING AND RECREATING HYDRODYNAMIC

ENVIRONMENTS AT BIOLOGICALLY RELEVANT SCALES

A DISSERTATION

SUBMITTED TO THE DEPARTMENT OF BIOLOGY

AND THE COMMITTEE ON GRADUATE STUDIES OF

STANFORD UNIVERSITY

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS

FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

Tom Hata

May 2015

http://creativecommons.org/licenses/by-nc/3.0/us/

This dissertation is online at: http://purl.stanford.edu/cd939ff4695

Includes supplemental files:

1. A barnacle (Tetraclita rubescens) feeding in an artificial 2m/s peak velocity wave, filmed at

250 fps, slowed 9x. (barnacle_feeding_slowed_9x.avi)

2. A barnacle (Tetraclita rubescens) feeding in an artificial 2m/s peak velocity wave, real time.

(barnacle_feeding_realtime.avi)

3. Coral (Isopora cuneata) larvae exposed to flow simulating back-reef conditions of Lizard

Island, Australia. (coral_larvae_in_flume.mp4)

© 2015 by Tom Hata. All Rights Reserved.

Re-distributed by Stanford University under license with the author.

This work is licensed under a Creative Commons Attribution-

Noncommercial 3.0 United States License.

ii

I certify that I have read this dissertation and that, in my opinion, it is fully adequate

in scope and quality as a dissertation for the degree of Doctor of Philosophy.

Mark Denny, Primary Adviser

I certify that I have read this dissertation and that, in my opinion, it is fully adequate

in scope and quality as a dissertation for the degree of Doctor of Philosophy.

Jeremy Goldbogen

I certify that I have read this dissertation and that, in my opinion, it is fully adequate

in scope and quality as a dissertation for the degree of Doctor of Philosophy.

George Somero

I certify that I have read this dissertation and that, in my opinion, it is fully adequate

in scope and quality as a dissertation for the degree of Doctor of Philosophy.

James Watanabe

Approved for the Stanford University Committee on Graduate Studies.

Patricia J. Gumport, Vice Provost for Graduate Education

This signature page was generated electronically upon submission of this dissertation in

electronic format. An original signed hard copy of the signature page is on file in

University Archives.

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Abstract

Marine communities are fundamentally shaped by water motion, which provides many

essential functions for a wide variety of marine organisms, such as gamete

fertilization, transportation, and food and nutrient delivery. However, it often remains

challenging to directly measure how individual organisms are affected by their

immediate flow environments. Water motion in marine habitats is often dynamic and

unpredictable, so average velocity measurements are unlikely to adequately capture

the environment at biologically relevant scales. Furthermore, many organisms (e.g.,

plankton) are small (≤1mm), which limits potentially available techniques. Finally,

the physical harshness of some environments, such as wave-swept shores, further

constrains the number of viable tools. To address these challenges, I have developed

several novel techniques to both measure and recreate environmental water motion at

very fine temporal and spatial scales. First, I designed and manufactured a field-

deployable flow sensor capable of measuring water velocities in the rocky intertidal

zone at scales relevant to settling spores and larvae. I found that high water velocities

(>2m s-1) can occur often (more than once per minute) even at heights just 0.250mm

above the substrate. A larva attached to the substrate may find shelter from these peak

velocities by hiding behind local topography or by settling in the right tidal conditions.

Second, I built a wave chamber capable of replicating the extreme flows found in the

intertidal zone and recorded adult barnacles feeding in these flows. I observed that

barnacles are able to feed in high water velocities (>1m s-1), and that their feeding

rates may potentially be independent of wave velocity. Finally, I measured flow

patterns on the Great Barrier Reef at scales relevant to settling coral larvae, and then

exposed coral larvae to a replication of these flow patterns in a lab setting. I found

that because coral larvae are weak swimmers compared to their ambient flow

environment, they are unable to affect their trajectories in even benign flows. Thus,

these coral larvae require turbulence to deposit them onto the substrate. The studies in

this thesis explore several ways in which marine organisms directly interact with their

hydrodynamic environments, and how their performances during these interactions

can potentially shape the distributions we observe in the field.

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Acknowledgements

Thank you to my mentors, colleagues, family, and friends for supporting me in my

endeavors as a scientist and growth as a person. Most of all, thank you to

Mark Denny for always leading by example.

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ix

TABLE OF CONTENTS

Abstract ........................................................................................................................... v

Acknowledgements ...................................................................................................... vii

Tables of Contents ......................................................................................................... ix

List of Tables ............................................................................................................... xiii

List of Figures ............................................................................................................... xv

General Introduction ....................................................................................................... 1

Chapter 1 ...................................................................................................................... 13

MEASURING WATER MOTION AT THE SCALE OF SETTLING ORGANISMS

IN THE ROCKY INTERTIDAL ZONE

1.1 Introduction ......................................................................................................... 13

1.2 Methods .............................................................................................................. 16

1.2.1 Triangular pressure block ............................................................................. 16

1.2.2 Flow sensor array ......................................................................................... 18

1.2.3 Field deployment .......................................................................................... 19

1.2.4 Distribution of peak velocities ..................................................................... 22

1.2.5 Return periods of high velocity events ......................................................... 22

1.3 Results ................................................................................................................. 24

1.3.1 Comparison of velocity data across treatments ............................................ 24

1.3.2 Return periods of high velocity events ......................................................... 25

1.4 Discussion ........................................................................................................... 27

1.4.1 Water velocity distributions ......................................................................... 27

1.4.2 Factors affecting return period ..................................................................... 29

1.4.3 Potential for settlement ................................................................................ 30

1.4.4 Difficulties in measuring dislodgement ....................................................... 32

1.5 Figures ................................................................................................................ 34

Chapter 2 ...................................................................................................................... 45

BARNACLE FEEDING BEHAVIORS IN EXTREME FLOW

2.1 Introduction ......................................................................................................... 45

x

2.2 Methods .............................................................................................................. 48

2.2.1 Field water velocity measurement ............................................................... 48

2.2.2 Wave chamber design .................................................................................. 51

2.2.3 Specimen collection ..................................................................................... 52

2.2.4 Recording feeding behavior ......................................................................... 52

2.2.5 Statistical analysis ........................................................................................ 55

2.3 Results ................................................................................................................. 56

2.3.1 Feeding time and potential flux .................................................................... 56

2.3.2 Maximum feeding velocity and buckling .................................................... 57

2.4 Discussion ........................................................................................................... 59

2.4.1 Feeding in high flows ................................................................................... 59

2.4.2 Morphological plasticity vs behavioral modification................................... 59

2.4.3 High flow tolerance in Tetraclita rubescens ................................................ 61

2.4.4 Flow environments in lab settings ................................................................ 61

2.4.5 Flux through cirral nets ................................................................................ 63

2.5 Tables .................................................................................................................. 66

2.6 Figures ................................................................................................................ 70

Chapter 3 ...................................................................................................................... 77

LARVAE OF THE BROODING CORAL ISOPORA CUNEATA CANNOT DIRECT

THEIR SETTLEMENT TOWARD THE SUBSTRATUM IN FLOW

ENVIRONMENTS SIMULATING THE REEF CREST

3.1 Introduction ......................................................................................................... 77

3.2 Methods .............................................................................................................. 81

3.2.1 Measuring water motion on the reef crest .................................................... 81

3.2.2 Analysis of PIV footage ............................................................................... 82

3.2.3 Assessing settlement behavior of Isopora cuneata larvae ........................... 83

3.2.4 Analysis of larval motion ............................................................................. 86

3.3 Results ................................................................................................................. 88

3.3.1 Larval swimming ......................................................................................... 88

3.3.2 Near-substrate flow environments ............................................................... 89

3.3.3 Larval contact with substrate ....................................................................... 89

3.4 Discussion ........................................................................................................... 90

3.4.1 Larval swimming ......................................................................................... 90

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3.4.2 Contact with substrate .................................................................................. 93

3.4.3 Turbulence and contact ................................................................................ 95

3.5 Tables .................................................................................................................. 97

3.6 Figures .............................................................................................................. 100

References .................................................................................................................. 113

xii

xiii

List of Tables

Table 2-1. ANOVA test of feeding time of Balanus glandula and Tetraclita rubescens

in wave chamber ............................................................................................... 66

Table 2-2. SNK multiple comparisons test of feeding time of B. glandula and T.

rubenscens ........................................................................................................ 67

Table 2-3. ANOVA test of potential flux filtered by B. glandula and T. rubenscens .. 68

Table 2-4. SNK multiple comparisons test of potential flux filtered by B. glandula and

T. rubenscens .................................................................................................... 69

Table 3-1. Levene’s test on variance of vertical velocity of Isopora cuneata larvae and

neutral particles in flume .................................................................................. 97

Table 3-2. Number of Isopora cuneata larvae tracked in each flume treatment .......... 98

Table 3-3. ANOVA test of I. cuneata larval speeds prior to contact with substrate .... 99

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List of Figures

Figure 1-1. Schematic of a Preston tube ....................................................................... 34

Figure 1-2. Schematic of the triangular pressure block ................................................ 35

Figure 1-3. Schematic of the field-deployed pressure block within housing ............... 36

Figure 1-4. Predicted velocities using static versus dynamic coefficients of drag ....... 37

Figure 1-5. Field deployment of velocity sensor array ................................................. 38

Figure 1-6. Peak water velocity as a function of significant wave height .................... 39

Figure 1-7. Sample free-stream and near-substrate water velocity data ...................... 40

Figure 1-8. Exceedance probabilities of water velocities ............................................. 41

Figure 1-9. Peak near-substrate velocities normalized by free-stream velocities ........ 42

Figure 1-10. Mean return periods of high-velocity events ........................................... 43

Figure 1-11. Exceedance probabilities of return periods .............................................. 44

Figure 2-1. Illustration of a feeding barnacle ............................................................... 70

Figure 2-2. Mean and fitted water speed profiles of normalized waves ...................... 71

Figure 2-3. Schematic of wave chamber ...................................................................... 72

Figure 2-4. Examples of barnacle feeding and non-feeding behaviors ........................ 73

Figure 2-5. Representative runs of scored barnacle footage ........................................ 74

Figure 2-6. Feeding times and potential fluxes filtered by barnacles ........................... 75

Figure 2-7. Maximum feeding velocities and mean buckling velocities of barnacles . 76

Figure 3-1. Rotational forces potentially experienced by a settling larva .................. 100

Figure 3-2. Coral reef substrate at Lizard Island, Australia ....................................... 101

Figure 3-3. Field particle image velocimetry (PIV) setup .......................................... 102

Figure 3-4. Schematic of oscillating flume ................................................................ 103

Figure 3-5. Flow conditions 1m above reef crest at Lizard Island ............................. 104

Figure 3-6. Velocities of Isopora cuneata larvae and neutral particles in flume ....... 105

Figure 3-7. Swimming and rotation rates of Isopora cuneata larvae in still water .... 106

Figure 3-8. Field PIV measurements of water velocities and bottom shears ............ 107

xvi

Figure 3-9. Theoretical contact rates of passive particles with substrate ................... 108

Figure 3-10. Composite image of larval trajectories in flume .................................... 109

Figure 3-11. Speed and rotation rates of larvae prior to contact with substrate ......... 110

Figure 3-12. Box plot of larval speeds before contact with substrate ........................ 111

Figure 3-13. Illustration of potential range suitable for passive substrate contact ..... 112

1

General Introduction

Environmental water motion fundamentally shapes virtually all marine

communities. Across a broad range of marine taxa, the effects of water motion on an

organism’s livelihood can be observed at almost any point of that individual’s life

history. To begin, broadcast spawning is a widely used reproductive strategy by

marine animals (e.g., cnidarians, fish, molluscs, crustaceans, and echinoderms), and

local turbulence in flow is primarily responsible for the high fertilization rates, and

thus viability, of this strategy (reviewed in Crimaldi 2012). Often, these fertilized

eggs and developing larvae, as well as other small organisms in the plankton (e.g.,

diatoms, copepods, and protists), are passively transported in the water column by

large-scale water motion (i.e., dispersal; reviewed in Eckman 1996). At the scale of a

planktonic individual, disturbances in water motion may provide hydrodynamic

signals of either predator or prey (e.g., Kiørboe & Visser 1999), or turbulent plumes

may transport chemical signals from a suitable settlement site (e.g., Hadfield & Koehl

2004). In the case of propagules (spores and larvae) of sessile organisms (e.g.,

barnacles, macroalgae, corals, and bryozoans) that must settle onto a substrate,

ambient water motion can either deposit these propagules to the substrate or remove

propagules that have attached (reviewed in Abelson & Denny 1997). Once developed,

these sessile organisms rely on environmental flow patterns to both transport food and

nutrients to them and to flush waste away from them. In these and many other ways,

the lives of marine organisms are dictated by the flows around them.

A difficult consideration is the vastly different temporal and spatial scales on

which these various processes operate. They can range in scale from short (<1s) and

small (<1mm) (e.g., fertilization, prey sensing) to long (>1day) and large (>1km) (e.g.,

dispersal). The goal of this dissertation is to better understand how water motion

directly affects marine life at the organismal level. To accomplish this, I have

developed several techniques to both measure and recreate environmental water

2

motion at very fine temporal (<1s) and spatial (0.25–10mm) scales. In the first two

chapters, I explore the extreme water velocities found in the rocky intertidal zone and

their ecological consequences. Chapter 1 is concerned with the development of a

field-deployable flow sensor to measure water velocities at a scale relevant to settling

propagules (0.25mm above the substrate). Chapter 2 seeks to recreate intertidal flows

at a larger (10mm) scale in a lab setting to observe the feeding patterns of adult

barnacles in extreme flow conditions (>1m s-1). Unlike the previous chapters, chapter

3 was conducted in the much calmer waters of the Great Barrier Reef of Australia.

The goal of this final chapter is to understand whether coral larvae can affect their

settlement patterns when exposed to complex flow patterns like those found on the

reef. In short, I seek to understand how organisms interact directly with their

hydrodynamic environments, and how their performances during these interactions

can potentially shape the distributions we observe in the field.

Marine life at larval scales

A vast majority of marine organisms spend at least part (if not all) of their life

cycle as plankton (e.g. fish, invertebrates, algae, and protists) suspended in the water

column. The trajectories of planktonic organisms are largely influenced by ambient

water motion (reviewed in Koehl & Hadfield, 2010), because they are small (typically

0.01–10mm in maximum length) and unable to maintain swimming speeds (typically

≤10mm s-1) greater than the flow patterns of their immediate environment. Due to the

small sizes and slow speeds of planktonic organisms, the flow conditions surrounding

these organisms, and the resultant hydrodynamic forces experienced by them, can be

accurately estimated using well-established equations borrowed from fluid mechanics.

Planktonic organisms operate at a low Reynolds number (Re), a dimensionless index

of inertial to viscous forces expressed by the equation: � = ��

3

where u is fluid velocity relative to the organism (m s-1), d is the characteristic length

of the organism (m) and is water’s kinematic viscosity (see Denny, 1993).

Planktonic organisms typically operate at Re<1, where viscous forces dominate.

Flows in these conditions are smooth, laminar, and predictable (e.g., copepods:

Kiørboe & Visser, 1999; diatoms: Miklasz & Denny, 2010).

Unfortunately, instantaneous hydrodynamic forces become more difficult to

calculate as flow rates relative to the organism, and thus Re, increase. The attachment

of propagules onto the substrate is a prime example where small, plankton-sized

organisms are exposed to high flow speeds. Once these organisms tether themselves

to the substrate, they cease to move along with ambient water motion. Thus, the

relative water velocity experienced by the larva instantly increases in magnitude from

near-zero to the value of water velocity relative to the substrate. Organisms in these

conditions are therefore operating at a higher Re once they contact the substrate. At

high Re (>1000), inertial, rather than viscous, forces tend to dominate. In these

conditions, the flow around an organism ceases to be laminar and becomes turbulent.

Turbulent flow is characterized by highly-variable instantaneous velocities even when

the average velocity of bulk water motion remains constant. Turbulent conditions are

therefore much more difficult, if not impossible, to accurately model without directly

measuring the flow environment first. Even in relatively benign flow conditions that

appear steady, instantaneous velocity peaks generated by local turbulence can exceed

average velocities by several orders of magnitude (Crimaldi et al. 2002). For a

propagule attempting to adhere to the substrate, its probability of successful settlement

depends on the frequency and intensity of these turbulent velocity peaks—and its

ability to resist the hydrodynamic forces generated by these peaks—rather than the

mean water velocity that the propagule experiences. Therefore, in order to make any

ecologically meaningful measurements of environmental water motion, the temporal

variability of turbulent flow at the scale relevant to the individual organism must be

captured.

To this end, the rocky intertidal zone is a useful model ecosystem in ecology and

biomechanics. The intertidal zone is an environment of physical extremes: resident

4

organisms must be able to tolerate daily desiccation, surface temperatures exceeding

30°C (Harley 2008), and water velocities exceeding 20m s-1 (Denny et al. 2003).

Despite these inhospitable conditions, organisms in this ecosystem face intense inter-

and intraspecific competition (e.g., Connell, 1961a, 1961b). This combination of

abiotic and biotic factors drives the striking patterns of intertidal zonation found on

surprisingly small scales. Even at a single point onshore, the water velocities

experienced by an individual organism are both random and highly variable by nature

(e.g., Helmuth & Denny 2003). Further confounding the accurate prediction of

onshore water velocities, hydrodynamic conditions can vary between sites just

centimeters apart and are heavily influenced by local topographical features

(O’Donnell & Denny 2008). Due to the large degree of temporal and spatial

variability, theoretical approaches to determine environmental water velocities can’t

be applied, and flow must be measured directly at scales relevant to resident

organisms.

Settlement by marine propagules

A wide variety of marine organisms are benthic, sessile broadcast spawners (e.g.,

mussels, barnacles, tunicates, and macroalgae). For these organisms, the successful

settlement (contact and attachment) of propagules onto suitable substrates is a

necessary step in maintaining existing populations and establishing new ones. The

transportation of propagules to intertidal and subtidal sites is an area of ongoing,

extensive study. Previous work incorporating oceanographic data (e.g., Alexander &

Roughgarden 1996, Connolly et al. 2001) shows that both large-scale (offshore) and

mesoscale (near-shore) water motion affect the transportation of propagules to

settlement sites. At the local level, the supply of larvae in the water column can

profoundly influence onshore recruitment rates (e.g., Bertness 1992). However, the

hydrodynamics involved in the final step of larval transportation, the contact and

attachment of propagules to the substrate, remains largely unmeasured. Without

environmental water velocity measurements at the scale of settling propagules, it is

5

difficult to determine how often propagules suspended in the water column actually

come into contact with the substrate and to predict their odds of successful settlement

once they reach the substrate. To address this issue, Reidenbach et al. (2009)

conducted one of the only experiments to map flow conditions at the scale of a

planktonic settler. They first measured water velocities in the field at sub-centimeter

scales, then they recreated these flow conditions in a flume containing an artificial

reef. Reidenbach et al. (2009) found that the heterogeneous topography of the reef,

coupled with wave-driven oscillations in flow, created a highly variable flow

environment where the magnitudes and frequencies of velocity peaks (and, as a result,

the probability of larval detachment from the substrate) varied greatly on the scale of

centimeters. However, these measurements were for relatively benign coral reef

flows. Flow measurements at such fine scales have not been conducted in high energy

environments such as the intertidal zone, because the physically harsh conditions limit

the techniques available with which to measure water velocities.

Flow measurement techniques

There are a number of techniques available to potentially measure near-shore

and intertidal water motion, each with its own strengths and limitations.

Traditionally, intertidal water velocities have been calculated by measuring the

hydrodynamic force (drag) exerted on a regular shape (such as a sphere) mounted to a

force transducer and anchored to the bedrock as water moves over it (see Mach et al.

2011). This technique is useful for measuring flow at the scale of centimeters—a

scale relevant to relatively large organisms such as adult barnacles, predatory snails,

and limpets—but there is a lower limit to the size of the deployable shape (and thus

the resolution of measurement). The smallest sphere deployed was 4.7mm in diameter

(O’Donnell & Denny 2008), which provides flow measurements at the height of the

sphere’s center of area (2.35mm). Measurements at this scale are still an order of

magnitude larger than most propagules. Drag force scales with the square of a

sphere’s diameter, so a further decrease in the sphere’s size causes a much greater

6

decrease in the force signal. This diminished force signal, in turn, would be

increasingly difficult to detect accurately and reliably. In fact, measuring drag forces

on objects at sub-millimeter scales requires extremely specialized equipment that is far

from field-deployable (e.g., Doll et al. 2009). Thus, measuring drag force on larva-

sized objects to calculate water velocities is highly impractical.

In subtidal and lab settings, there are several tools and techniques capable of

measuring water motion at sub-centimeter and sub-millimeter scales. An Acoustic

Doppler velocimeter (ADV), a fundamental tool in coastal oceanography, measures

instantaneous water velocity in a small parcel of water (<0.5cm3) by transmitting an

acoustic signal to this parcel and measuring the Doppler shift of the return signal.

Although ADVs are designed to handle the rigors of the sub-tidal marine environment,

they remain relatively bulky and sensitive to damage by large hydrodynamic forces

and impacts. Furthermore, the accuracy of their velocity measurements decreases in

high-shear environments. For these reasons, the deployment of an ADV in the rocky

intertidal zone is not feasible.

Along these lines, a laser Doppler velocimeter (LDV) is another tool used to

measure fine-scale fluid velocities. A LDV measures the velocity at the point of

intersection between two laser beams (typically <0.1mm3, depending on laser beam

diameter), and, much like an ADV, measures the Doppler shift of the return signal.

Unfortunately, an LDV is even more sensitive to damage than an ADV, and its use is

limited to a lab setting.

Yet another method of flow measurement is particle image velocimetry (PIV),

where water-borne particles (usually illuminated by a plane of laser light) are filmed

as they move across the field of view of a video camera. Under the assumption that

the velocities of the particles match their surrounding fluid, the trajectories of these

particles are later tracked using software to calculate a velocity field. The advantage

of PIV is that it can measure a field of fluid velocities (spanning the field of view)

rather the velocity of a single point in space, as measured by an ADV or LDV.

However, much like the previous two instruments, a PIV setup would not be feasible

7

in the intertidal zone. The foamy water brought by waves breaking onshore would

make the detection of discrete particles nearly impossible. Furthermore, even if these

particles could be detected, they would be traveling so quickly past the camera’s field

of view that they would appear as velocity-dependent streaks, causing errant results.

For these reasons, none of these standard techniques in small-scale flow measurements

can be applied in the intertidal zone.

In chapter 1, I address these shortcomings in environmental flow measurement

by developing a field-deployable pressure block capable of measuring high flows

(>1m s-1) at very fine spatial (0.25mm above the substrate) and temporal scales

(0.001s). The sensor array I designed in this experiment is capable of simultaneously

measuring one set of free-stream velocities and two sets of near-substrate velocities.

Furthermore, the topography surrounding the near-substrate flow sensors can be

manipulated, allowing me to measure the effect of local topography (inside a barnacle

test, a bed of coralline algae, and an artificial mussel bed) on flow conditions. These

water velocity measurements, the first to be taken at sub-millimeter scales in the rocky

intertidal zone, provide a glimpse into the hydrodynamic environments faced by

settling larvae and spores.

I find that even at these small size scales, extremely high velocities (>2m s-1)

can often occur (more than once per minute). In exposed conditions, near-substrate

flow conditions do not greatly differ from free-stream flow. In contrast, local

topography can provide substantial shelter from hydrodynamic forces. Compared to

exposed conditions, peak water velocities within these hydrodynamic shelters are

much lower and occur much less frequently. Additionally, flow conditions in all cases

depend on the tidal height relative to the sensor. Peak velocities are greatest, and

occur most often, at an intermediate tidal height: when sea level is just high enough

for waves to break directly onto the sensor. Conversely, flow conditions are much

calmer at relatively low (waves break before reaching the sensors) and high (waves

break behind the sensors) tidal heights. A settling propagule would have the greatest

odds of remaining attached in these calmer conditions and if it found its way into a

hydrodynamic shelter. In summary, these measurements highlight the importance of

8

understanding how larger-scale environmental factors (surface topography, tidal

height) affect small-scale (<1mm) water motion, and, in turn, how small-scale water

motion may affect the odds of successful propagules settlement (and ultimately adult

distribution).

Benthic feeding in the intertidal zone

The livelihoods of sessile intertidal organisms are strongly tied to

environmental flow conditions even after settling propagules have developed into

juveniles and adults. Flow patterns that initially delivered these organisms to the

substrate now serve to transport a supply of nutrients and food particles to them. In

the intertidal zone, suspension feeding is a particularly effective feeding strategy used

by a diverse range of animals. During suspension feeding, food particles suspended in

water are captured as they are passed through an animal’s filtering elements (see

Labarbera 1984). A particularly iconic group of suspension feeders is the barnacle.

Barnacles have long been considered model study organisms in the fields of intertidal

ecology and population biology due to their ubiquitous distribution, high numerical

densities in near-shore and intertidal environments, and large economic impact

(biofouling of ship hulls and marine structures). Barnacles feed by extending their

bristled legs (cirri) into flow to capture particles. Members of a single species can

successfully feed in a wide variety of flow regimes due to their ability to adapt to

changes in their hydrodynamic environment both morphologically (changing the size

and shape of their cirral nets; e.g, Arsenault et al. 2001) and behaviorally (feeding at

certain velocity ranges; e.g., Sanford et al. 1994).

Despite this well-known relationship between barnacle feeding performance

and ambient water motion, few studies have attempted to quantify feeding behavior in

flow regimes representative of a barnacle’s actual environment. Traditionally, studies

have been conducted in unidirectional flows at relatively low water velocities

(u<60cm s-1) (e.g., Sanford et al. 1994). Generally, feeding behavior and food

particle capture rates by individuals declined at surprisingly low velocities (u<30cm

9

s-1; e.g., Nishizaki & Carrington 2014) when compared to potential water velocities

encountered in the field. Although the relatively gentle, unidirectional conditions used

in many past experiments may potentially provide adequate simulations of protected

environments, they are likely to be inappropriate for estimating the feeding

performances of barnacles residing in wave-exposed sites. Water velocities in the

wave-swept intertidal zone are characterized by brief (<1s) velocity peaks generated

by breaking waves that routinely exceed 2m s-1 (Miller 2007). If barnacle feeding

performance declines at velocities as slow as u=30cm s-1 (as suggested by lab

experiments), how do barnacles feed effectively in such extreme conditions? Miller

(2007) conducted the only experiment to record barnacles feeding on wave-swept

shores by filming the undersides of individuals that had settled onto a clear plate. He

found that barnacles extended their cirri into flows of up to 4m s-1. A limitation of this

technique, however, is that Miller (2007) was unable to determine whether barnacles

with extended cirri were successfully feeding. Large water velocities can overwhelm

a barnacle’s ability to maintain its feeding posture, causing the cirral net to buckle.

Without direct observation, it was impossible for Miller to tell whether or not a filmed

barnacle was successfully feeding. Lacking this information of feeding performance

in environmental flow conditions, it remains difficult to link a barnacle’s laboratory

feeding behavior to broader ecological contexts such as metabolic intake, growth, and,

ultimately, survival.

In chapter 2, I develop a wave chamber to recreate the extreme water velocities

found in the rocky intertidal zone to observe the feeding behaviors of three intertidal

species of acorn barnacles (Balanus glandula, Chthamalus fissus, and Tetraclita

rubescens)—the first direct measurements of barnacle feeding behavior in extreme

flows that resemble conditions on wave-swept shores. Contrary to findings of

previous lab studies, I find that all three species are indeed able to feed at high flow

velocities (>1m s-1). Many individuals regularly feed up to the point of buckling

(usually 1–2m s-1), so high velocities cause only temporary pauses in feeding

behavior. I also observe that as the peak velocity of artificial waves is increased,

barnacles feed for shorter periods of time. Surprisingly, this shortened feeding period

10

does not affect the barnacle’s potential food intake. When exposed to faster waves,

barnacles are sieving through water of greater average velocities. Thus, even with

reduced feeding times, barnacles sieve through similar volumes of water regardless of

wave velocity. For an organism that lives in an environment where flow conditions

are highly variable and unpredictable, this is a useful trait.

In summary, the unidirectional flow conditions of previous experiments likely

do not provide an accurate measure of barnacle feeding performance. This chapter

shows that barnacles are able to successfully feed in wave conditions where peak

velocities far exceed their mechanical limits. Therefore, it is essential to capture and

recreate the environmental variability that a barnacle experiences to accurately

measure its feeding performance in the real world.

Coral reefs at larval scales

Coral reefs are beautiful, diverse, and well-studied. Though not present in the

intertidal zone, corals themselves are another model group of benthic organisms that

rely on the dispersal of pelagic larvae to maintain their populations. As adult coral

colonies grow and become more bulky, they generally become more vulnerable to

death by hydrodynamic disturbances (Madin et al. 2014). Thus, the maintenance of

coral populations relies on the continual settlement of larvae onto the reef. Coral

larval settlement has been a subject of intense study by coral reef ecologists, but

experiments so far have been primarily limited to observing how larvae settle in still-

water lab settings (e.g., Heyward & Negri 1999) or studying larvae that have settled

onto tiles in the field (e.g., Raimondi & Morse 2000). However, there are currently no

direct observations of larval settlement in environmental flow conditions, so the

mechanism of their settlement in the field is largely unknown.

In still-water conditions, coral larvae exhibit responses to a suite of biotic and

abiotic signals, such as: hydrostatic pressure (Stake & Sammarco 2003), temperature

(Putnam et al. 2008), and chemical cues (Morse et al. 1988). The ability of larvae to

11

respond to these stimuli has led many to believe that larvae may use environmental

cues to navigate toward successful settlement sites. A potential issue regarding this

notion is that coral larvae are very poor swimmers (swimming speeds ≈0.5cm s-1; e.g.,

Gleason et al. 2009) compared to ambient water motion on the reef (1–10cm s-1 even

in relatively calm conditions; e.g., Koehl & Reidenbach 2007). Additionally,

turbulent water motion over the reef would cause larvae to frequently tumble end-

over-end as they approached the substrate. Thus, for a larva to be able to exert any

influence on its settlement site, it would need to be able to both swim against ambient

water motion and maintain its heading toward the substrate. So far, these behaviors

have not been documented in coral larvae, and thus, the navigational abilities of coral

larvae remain an open question.

In chapter 3, I measure the settlement performance (or lack thereof) of coral larvae

in environmental flow conditions. Although in situ measurements of larval attachment

in the field would be ideal, these measurements would be exceedingly difficult, if not

impossible, to obtain. I address this issue using the next-best alternative: by

measuring environmental water velocities at millimeter-scales above potential

settlement sites using PIV, and then exposing the larvae of Isopora cuneata (a

brooding coral) to flow conditions similar to these velocity measurements in a lab

setting. I find that although swimming behavior is detectable in still-water conditions,

swimming effort is no longer detectable in even modest flow conditions. In fact, the

settlement patterns of live larvae do not differ significantly from the settlement

patterns of euthanized larvae. These results strongly suggest that initial larval contact

with the substrate is primarily driven by the turbulence of ambient water motion.

Thus, in this particular case, the ability of I. cuneata larvae to swim and respond to

environmental stimuli may not correspond with its ability to navigate directly toward a

settlement site at all. Alternatively, a larva’s ability to swim may instead provide a

means for that larva to depart from unsuitable settlement sites or perhaps to navigate

the water column over the course of hours or days. In short, it is necessary to measure

a larva’s swimming performance relative to its environmental conditions to determine

the contexts in which a larva’s swimming behavior matters at all.

12

Summary

The work carried out in this dissertation illustrates the need to measure

environmental factors, such as flow, at scales relevant to the study organism at hand.

Each chapter contains at least one result that appears counterintuitive at face value

(such as dead larvae settling just as well as live larvae!), and these results would not

have become apparent without such fine-scale measurements. I hope to have

illustrated that the marine environment at larval scales provides an interesting and

challenging study system because of its dynamic and unpredictable nature. At these

scales, equations borrowed from physics often fail to describe the world that a larva

experiences from instant to instant, so direct measurements of this environment are

essential. Finally, I hope to convey that when the equipment to record these

measurements does not already exist, the solution can be engineered.

13

Chapter 1

Measuring water motion at the scale of settling

organisms in the rocky intertidal zone

1.1 Introduction

For many sessile organisms in the marine environment, the successful

attachment of planktonic propagules (e.g., spores and larvae) to benthic sites is a

crucial process in maintaining existing (and establishing new) populations. Water

motion influences propagule dispersal on several scales (reviewed in Palmer et al.

1996). Large scale (>>1m) flow patterns can determine regional recruitment by

transporting propagules to settlement sites (reviewed in Eckman 1996, Connolly et al.

2001, Schiel 2004). On a much smaller scale, the successful settlement of individual

propagules (e.g., invertebrate larvae, typically 0.01–1mm in size) are influenced by

their immediate hydrodynamic environment. Water motion relevant to larval scales

has the capacity to both directly deposit propagules to the substrate (reviewed in

Abelson & Denny 1997) and dislodge them (Jonsson et al. 2004), but instantaneous

velocities at this small scale usually do not correspond to measurements averaged over

larger size and time scales. Even in relatively benign flow conditions that appear

steady, instantaneous velocity peaks generated by local turbulence can exceed average

velocities by several orders of magnitude (Crimaldi et al. 2002). Furthermore, the

timing and intensity of these peaks are greatly influenced by oscillations in flows (e.g.,

waves) and mm to cm scale topographical features (Koehl et al. 2013), and these flow

patterns are a likely driver of the probability of successful settlement (Reidenbach et

al. 2009). As near-substrate water motion can greatly differ from bulk free-stream

14

flow, it is important, for a given environment, to characterize water motion at scales

relevant to propagules to mechanistically predict potential settlement patterns.

Capturing the temporal variability of water velocities near the substrate is also

essential because the attachment strength of many propagules changes (often

increasing) as a function of settlement stage (e.g., Eckman et al. 1990, Zardus et al.

2008, Larsson et al. 2010). For example, the colonization of barnacle cyprid larvae is

a well-studied phenomenon due to the ubiquitous distribution of adults in coastal

environments, the larva’s ability to swim and adhere in relatively high-flow

conditions, and their economic importance as fouling organisms. Cyprids initially

probe the suitability of a substrate by weakly attaching their antennules to the surface

and walking along the substrate by repositioning these antennules (reviewed in Crisp

et al. 1985). Larvae choose their settlement sites based on chemical and tactile cues

(Crisp et al. 1985). Permanent attachment is initiated by the secretion of an adhesive

proteinaceous cement by the larva, and the strength of attachment steadily increases as

the cement cures during the first few hours of attachment (e.g., 3 hours in the barnacle

Semibalanus balanoides) (reviewed in Eckman et al. 1990; see Walley & Rees 1969

for details on metamorphosis). Once cemented in place, the detachment risk of the

cemented barnacle is significantly diminished compared to an exploring cyprid in the

same flow environment (Larsson et al. 2010), and this adhesive tenacity is maintained

as the barnacle metamorphoses. Therefore, the biologically relevant time scales of

water motion, in terms of propagule settlement, ranges from seconds (initial contact

and temporary attachment) to minutes (exploration) and hours (metamorphosis).

Hydrodynamic environments at small size scales (mm–cm) have been recorded

in relatively benign conditions such as those found in harbors and coral reefs through

direct measurement and laboratory recreation using techniques such as acoustic

Doppler velocimetry (ADV), laser Doppler velocimetry (LDV), and particle image

velocimetry (PIV) (reviewed in Koehl & Hadfield 2010). In contrast, it is exceedingly

difficult to measure water velocities at these scales in a much more energetic

environment such as the intertidal zone of wave-swept shores. Peak free-stream

velocities measured over a coral reef in Kaneohe Bay, Hawaii (Koehl & Hadfield

15

2010) were ≈10cm s-1, approximately two orders of magnitude slower than peak

velocities measured in the rocky intertidal zone (>10m s-1) of Pacific Grove,

California (Denny et al. 2003). Although free-stream water velocities in the rocky

intertidal zone have been well studied on the size scale of centimeters (Gaylord 1999,

Denny et al. 2004, Mach et al. 2011)—a scale relevant to relatively large organisms

such as adult barnacles, predatory snails, and limpets—these extreme velocities

prevent the deployment of the sensitive instrumentation usually used to measure flows

at finer scales, such as an acoustic Doppler velocimeter (e.g., Hench & Rosman 2013).

I developed a field-deployable array of velocity sensors (based on the design

by Bocchiola et al., 2003) to measure near-substrate (250μm above substrate) water

velocities at spatial and temporal scales relevant to settling propagules. The purpose

of my field water velocity measurements was to determine similarities and differences

in the free-stream and near-substrate hydrodynamic environments, both in terms of

magnitude and timing of peak velocities. Additionally, I sought to determine the

effects of apparent hydrodynamic shelters on local flow patterns (e.g., rugose

structures created by ecosystem engineers such as algae, mussels, and barnacles).

Previous studies have shown that these structures may provide hydrodynamic shelter

at larger size scales or in calmer conditions (O’Donnell 2008, Koehl et al. 2013), but

there is a dearth of data for water motion within and around these structures at larval

scales in energetic environment such as the rocky intertidal zone. By investigating the

effects of small-scale topography as well as other physical conditions, such as tidal

height and significant wave height, on near-substrate flow conditions, we may be able

to more accurately predict conditions favorable to propagule settlement and when and

where ecosystem engineers can ameliorate the effects of dislodgement forces.

16

1.2 Methods

1.2.1 Triangular pressure block

Bocchiola et al. (2003) developed a novel method of measuring near-substrate

shear in high energy environments: a triangular pressure block based on the Preston

tube (Preston 1954) (Fig. 1-1). A Preston tube consists of a static port that faces

perpendicular to flow and an adjacent dynamic port that faces directly into flow. The

static port measures hydrostatic pressure, the ambient environmental pressure. The

dynamic port measures total pressure, the sum of the static and dynamic pressure (the

pressure generated by bringing a moving fluid to a halt). The relationship between

static, dynamic, and total pressure can be rewritten as a simplified version of

Bernoulli’s equation:

+ = (1-1)

where ps (Pa) is static pressure, q is dynamic pressure, and p0 is total pressure.

Dynamic pressure is defined as:

= . � (1-2)

where ρ (kg m3) is the density of the fluid and u (m s-1) is the flow velocity at the

center of the dynamic port. If the static and dynamic ports are near each other, it can

be assumed that the value of ps is equal between the ports, thus allowing calculation of

u:

= [ ] . = [ − � ] . (1-3)

The purpose of the Preston tube design is to measure wall shear stress in turbulent

flow, although the dynamic port is required to directly face incoming flow for accurate

measurement. Additionally, Winter (1977) reported that a Dexter yaw meter, an

equilateral triangular block with three total pressure ports, one on the center of each

face, could determine flow direction but not magnitude. Based on these two designs,

Bocchiola et al. designed a triangular pressure block with a static port through its

center and one total pressure port on each side, allowing measurement of both

17

magnitude and direction of local flow. More importantly, the dimensions of this

block, 7mm per side and 0.35mm in height, allowed measurement of water velocities

at sub-millimeter scales. Though the dimensions of the pressure block used in my

experiment were slightly different (see Fig 1-2), deployment of this device allowed me

to measure near-substrate water velocities in the field.

The pressure block was calibrated by measuring the dynamic pressures (q1, q2,

and q3, where q1 refers to the dynamic pressure of the upstream pressure port) of each

dynamic pressure port in known water velocities at a range of known yaw angles, θ

(°). Yaw is the angle of deviation of the upstream dynamic pressure port relative to

flow direction. In the particular case where θ=0°, the upstream pressure port is

directly facing flow. The pressure block would thus be acting as a Preston tube since

the dynamic pressure port would be oriented in the direction of water motion, and

water velocity could be directly calculated using q1 and Eqn. (1-3). For yaw angles up

to θ=60° (due to symmetry of the triangular block, a different dynamic port becomes

the upstream port above this angle), Bocchiola and colleagues found a relationship

between dynamic pressure and yaw angle:

,�= = − × − � − × − � + (1-4)

where q1 is the dynamic pressure at current angle θ and q1,θ=0 is the dynamic pressure

that would be observed if θ=0° [used to directly solve Eqn. (1-3)]. Thus, velocity

could be calculated by measuring θ and q1. θ would be difficult to directly measure in

a rapidly changing flow environment, but it can be indirectly calculated by comparing

the dynamic pressures among ports. Ratios of the dynamic pressures can be expressed

by the dimensionless value K, which is calculated as:

� = −� +� − ; ≤ � ≤ (1-5)

K was empirically found to have a monotonic relationship with yaw angle:

� = − � − � + (1-6)

18

In summary, measurements of dynamic pressures across the 3 dynamic pressure ports

(q1, q2, q3) allow us to determine the yaw angle θ via the dimensionless number K.

Values of q1 and θ are then used to solve for q1,θ=0, which is in turn used to calculate

velocity [Eqn. (1-3)].

1.2.2 Flow sensor array

Four pressure blocks (see Fig. 1-2 for dimensions) were manufactured from

Delrin® acetal sheets and fitted individually atop waterproof housings containing

pressure sensors. Each pressure port was connected via stainless steel hypodermic

tubing (1.8mm outer diameter) and plastic Tygon® tubing to a 0–300mmHg pressure

sensor (Honeywell International Inc., model 40PC006G2A) (see Fig. 1-3 for a

schematic of the apparatus) and filled with mineral oil (Sigma-Aldrich Co., M3516).

Free-stream water velocities were measured by a 2.54cm diameter roughened plastic

sphere attached to a 2-axis force transducer (Bokam Engineering Inc., model US-

06002). The signal of each axis was amplified 380 times by a differential amplifier

(Analog Devices, Inc., AD627). Free-stream velocities were converted from force

measurements on the sphere by using the equation:

= √ ��� �� (1-7)

where u is water velocity (m s-1), Fd is the measured drag force (N) exerted on the

transducer, A is the projected area of the sphere (m2), and Cd is the coefficient of drag

of the sphere. Cd across a range of Reynolds numbers (Re) can be calculated as: log �� = . log � − . log � + . log � − . (1-8)

(see Mach et al. 2011). Re is calculated by the equation:

� = �� (1-9)

where d is the sphere diameter (m) and is water’s kinematic viscosity (m2 s-1). Cd

was relatively insensitive to changes in u across most of the expected range (1–10m

19

s-1), so an average value of 0.45 was used for all velocity calculations (see Fig. 1-4 for

justification).

Pressure blocks were mounted one pair at a time to a rectangular PVC sheet

(45.7x20.3x1.9cm, LxWxH) (Fig. 1-5A) 22.9cm apart, as well as the force transducer,

mounted midway between the pressure blocks and 5.1cm upstream. The topography

immediately surrounding each pressure block could be manipulated by populating

PVC disks (17.8cm diameter) that were mounted immediately around each sensor.

This design allowed the simultaneous measurement of near-substrate water velocities

across a flat surface (flat-plate treatment) and a contrasting rugose surface, as well as

measurement of free-stream water velocities (free-stream treatment). Power and

signal were transmitted via cable to a power supply (Heath Co., model IP-2718) and

USB data acquisition card (National Instruments Corp., model NI USB-6211).

1.2.3 Field deployment

The sensor array was installed at the shoreward end of a wave-swept rocky

channel (Fig 1-5B) in the mid intertidal zone at a height of 1.0m above mean lower

low water at Mussel Point (36°37.302’N, 121°54.258’W) at Hopkins Marine Station,

Pacific Grove, California. The sensor array was secured to bolts cemented to the rock,

and the cable was secured to a series of eye bolts leading to a climate-sealed room

above high tide level.

The array was deployed for a three-week period spanning September 24 to

October 17, 2014. The pressure blocks were changed weekly for

maintenance. Velocities for three topographical treatments (around one of the

pressure blocks) were measured during the span of this experiment:

1. Within an empty barnacle test of Tetraclita sp. (3.4x2.8x1.8cm, LxWxH), Sept

24 to Oct 1 (barnacle treatment).

20

2. Within an artificial bed of epoxy-cast mussels (Mytilus californianus) (12cm

diameter, 5.5cm height, 0.18 individuals per cm2), Oct 1 to Oct 7 (mussel

treatment).

3. Within a bed of coralline algae (co-occurring beds of Corallina

vancouveriensis and Bossiella sp. were removed from the field and secured to

a plate with marine epoxy to create a circular bed approximately 9cm in

diameter), Oct 8 to Oct 17 (algae treatment).

Data were sampled at 20kHz via a custom LabVIEW (National Instruments Corp.)

script and continuously written to individual files, each containing 2 minutes of

measurements. For each file, data were filtered in Matlab (The Mathworks, Inc.) by a

5th order Butterworth low-pass filter with a cutoff frequency of 1kHz (the maximum

response frequency of the sensors), and drift for each channel was compensated by

subtracting the median (pressure sensors) or modal (force transducer) values from the

data. Because the vast majority of forces experienced by the transducer were fairly

small due to the high probability of instantaneous water velocities <1m s-1 (see Miller

2007), modal force values were used to account for drift in the force transducer data.

Drag force scales with velocity squared, so low water velocities generate

comparatively much smaller drag forces. Therefore, the most frequently occurring

value recorded by each axis of the force transducer in a given 2-minute span was close

to zero. Median pressure values were used to account for drift in the pressure sensors,

because pressures fluctuated in response to the instantaneous height of the water

column in addition to water velocity. Since pressure fluctuations were caused by these

two factors in concert, the modal pressure value proved to be an inappropriate estimate

of drift during initial analysis. Instead, median pressure was a better representative of

the pressure signal that would be measured in static conditions under some average

water column height for each run.

Pressure [Eqns. (1-1) through (1-6)] and force data [Eqn. (1-7)] were converted

to velocities. In instances where static pressure (ps) exceeded total pressure (p0),

velocity at that measurement was set to 0, since u, as determined by Eqn. (1-3), would

21

be undefined in cases where p0>ps. Likely causes of instances where p0>ps are

electrical noise and turbulence that was finer in spatial scale than the dimensions of

the pressure block. The minimum sensitivity of the pressure block sensors was u=1m

s-1, but 2m s-1 was taken as the signal floor to match the sensitivity of the force

transducer. Velocities were then anti-aliased to 1kHz measurements by the following

methods: the median of every 20 velocity-data points was used for the force

transducer measurements and the minimum of every 20 velocity-data points was used

for the pressure block measurements. The minima of the pressure block

measurements were used because laboratory calibration and initial field data revealed

that in low-flow environments (<1m s-1), small fluctuations in pressure across

individual ports due to electrical noise or turbulence could register a velocity signal up

to 2m s-1. Recording velocity as the minimum of every 20 velocity-data points

relieved this overestimation error, but, as a result, these measurements of u are a

conservative estimate. Although a single 1kHz measurement is still brief in absolute

terms (0.001s), a velocity peak would need to be present for 20 consecutive points in

the raw 20kHz data in order to be recorded. Even then, the recorded value of this peak

would be the lowest value rather than the median (e.g., force transducer

measurements). Furthermore, if a turbulent fluctuation during the high velocity event

caused an instantaneous measurement where p0>ps, then the reported velocity for that

20 point span would consequently be 0, as mentioned above.

For each of the deployments, the first 12 hours of data were excluded from

analysis to account for the time necessary for drift in the sensor signals to settle. Local

tide height (Ht) (NOAA; tide station Monterey) and offshore significant wave height

(Hs) (Datawell Directional Buoy, Coastal Data Information Program, Scripps

Institution of Oceanography; station 158: 36°37.58’N, 121°54.43’W, approximately

0.5km offshore from site) during the sampling period were obtained as measurements

of the larger, region-scale hydrodynamic environment. Significant wave height is the

average height of the highest one-third of waves.

22

1.2.4 Distribution of peak velocities

To characterize flow conditions, exceedance probability distributions of water

velocities for each treatment were calculated using all recorded velocity data.

Exceedance probability values are calculated as 1 – cumulative probability, or the

probability that a randomly sampled velocity will exceed a given value. As an

example, if a velocity of u=2m s-1 had an exceedance probability of 0.10, then the

probability of measuring a velocity value of 2m s-1 or greater during random sampling

is 0.10.

In addition, peak velocity data (upeak) were calculated by measuring the

maximum value of u per 8 second period (the average local wave period) for each

treatment. upeak values of each near-substrate treatment were then normalized by the

concurrent free-stream upeak value (upeak,freestream) to find the ratio of near-substrate

velocities to free-stream velocities. These normalized data were then binned by the

upeak,freestream value at which they occurred, into 0.1m s-1 increment bins, and mean

values (u̅peak,normalized) were calculated for each bin to represent the average reduction

(or amplification) in flow near the substrate compared to the free stream.

Significant offshore wave height (Hs) has been shown to correlate with onshore

wave forces in some cases but not others (Helmuth & Denny 2003). To determine

whether this relationship existed at my site, a linear regression of upeak,freestream data

with respect to Hs was performed. To remove tidal height as a potential confounding

factor, I limited the pool of velocity data to measurements taken when relative tidal

height (Ht,relative; tidal height – site height) was 0 to +0.25m.

1.2.5 Return periods of high velocity events

Return period (Treturn) data were generated for each sensor using all recorded

velocity data. Treturn was defined as the consecutive length of time that u remains

below a critical threshold velocity (uthresh). Treturn were calculated across a range of

2≤uthresh<9m s-1 in 0.2m s-1 increments, and periods shorter than 1s in duration were

excluded (n>3,000,000 over all measurements). For each Treturn, Ht and Hs were

23

recorded at the start of each return period, rounded to the nearest second. To examine

changes in Treturn over a tidal cycle for each treatment, mean Treturn values (T̅return)

binned by Ht,relative (-1≤Ht,relative<1m, 0.1m bins) and uthresh (bins same as above) were

calculated. Bins with n<10 were rejected, as they often did not provide reliable mean

data.

To examine the distribution of Treturn data within bins, exceedance probability

distributions of Treturn were calculated for three scenarios:

1. To determine the effect of Ht, I chose one value of uthresh (2m s-1) of the flat

plate treatment and analyzed Treturn across a range of Ht,relative:

a. -1≤ Ht,relative<-0.5m

b. -0.5≤ Ht,relative<0m

c. 0≤ Ht,relative<+0.5m

d. +0.5≤ Ht,relative<+1m

2. To determine the effect of uthresh, I chose one range of Ht,relative

(0≤Ht,relative<+0.5m) of the flat plate treatment and analyzed Treturn across a

range of uthresh:

a. uthresh=2m s-1

b. uthresh=3m s-1

c. uthresh=4m s-1

d. uthresh=5m s-1

3. To determine the effect of local substrate, I chose one value of uthresh (2m s-1)

and one range of Ht,relative (0≤ Ht,relative<+0.5m) and analyzed Treturn for all 5

treatments.

24

1.3 Results

1.3.1 Comparison of velocity data across treatments

Peak free-stream velocities exhibited a statistically significant but poor fit with

offshore significant wave height (Fig. 1-6), so Hs was excluded from further analysis.

Sample u data from the free-stream and flat-plate treatments (Fig. 1-7) taken during

high tide reveal that large water velocities (u>2m s-1) can occur in brief (lasting less

than 2s) but frequent intervals. Exceedance probability distributions of u data for each

treatment and substrate type (Fig. 1-8) reveal that u exceed the 2m s-1 sensitivity floor

<1% of the time for all treatments. Extremely high velocities (u>6m s-1) did not occur

in any of the near-substrate treatments, but the maximum velocity measured by the

free-stream treatment approached an astounding 20 m s-1. Although the exact value of

this extreme measurement may be overestimated due to the use of a static drag

coefficient, this value is in line with previous measurements of extreme water

velocities measured at sites near this location (Denny et al. 2003). This result shows

that at least some sheltering occurs from extremely rare, extremely high velocities

simply by virtue of being near the substrate. For near-substrate measurements, the

barnacle and algae treatments exhibited much lower exceedance probabilities than the

flat-plate treatment across virtually all values of u, as well as lower recorded maximal

velocities. For values of u≈4m s-1 and greater, the mussel treatment exhibited much

greater exceedance probabilities than the flat plate treatment, although the magnitudes

of these probabilities are both very small (p≈10-4 mussel and p≈10-5 flat plate, for

u=4m s-1).

For all substrate types, u̅peak,normalized steadily decreased with increasing

upeak,freestream (Fig. 1-9). At relatively low upeak,freestream values, substrate types exhibited

differing degrees of flow reduction (or lack of reduction in the mussel bed treatment).

u̅peak,normalized values (except mussel treatment) appear to steadily decrease toward a

value of approximately 0.5 for upeak,freestream>4.5m s-1. Even though peak flow

magnitudes were decreased in all near-substrate treatments relative to free-stream flow

(suggesting that flow at this height is located within the boundary layer), the absolute

25

magnitudes of these velocities were still very high. Small, sub-mm scale organisms

attached to the substrate can be exposed to water velocities >2m s-1at this

representative intertidal site.

1.3.2 Return periods of high velocity events

Overall patterns in mean return period as a function of threshold velocity and

tidal height (Fig. 1-10) can be roughly divided into two groups. The first group,

consisting of the free-stream, flat-plate, and mussel-bed treatments largely exhibit

T̅return values in the seconds-to-minutes range for values of uthresh<3m s-1. Return

period data were present in this group for relatively large values of uthresh, ranging

from 5.4 to 7m s-1 depending on the treatment. It is important to note that

qualitatively, T̅return values of the mussel-bed and flat-plate treatments were on average

longer than the corresponding T̅return values of the free-stream treatment, suggesting

that conditions near the substrate are somewhat sheltered from the frequent high

velocity events found in the free stream.

In contrast to the previously mentioned group, a second group (the algae and

barnacle treatments) exhibited much longer return periods overall, and these T̅return

data occurred at lower maximum values of uthresh. The structures in this group

therefore serve as hydrodynamic shelters that both damp flow velocity magnitudes and

increase the average amount of time available between high velocity events compared

to unsheltered conditions. At relative tidal heights <0m, T̅return values for virtually all

uthresh were in the span of minutes-to-hours. In fact, there were no return period data

for uthresh>4 m s-1 in this group because velocities did not exceed this value during the

course of each treatment.

Recall that at least ten intervals were needed to calculate T̅return, and the higher

the threshold velocity, the fewer intervals obtained. T̅return could be calculated for the

highest uthresh at an intermediate tidal height of Ht,relative≈+0.5m, suggesting that high

velocities occur most frequently around this tidal height. Additionally, for a given

26

uthresh, the values of T̅return were near or at their lowest values at this height. Therefore,

not only did higher velocities occur more frequently at this tidal height, but the

average amount of time between high velocity events (regardless of specific velocity)

was also shorter.

Exceedance probabilities of Treturn data (Fig. 1-11) are characterized by long-

tailed distributions, where extreme values of Treturn can exceed average values (T̅return)

by several orders of magnitude. For instance, T̅return values range from 10 to 100s for

the flat-plate treatment for uthresh=2m s-1 (see Fig. 1-10). In contrast to these relatively

low average values, Fig. 1-11A shows that an individual return period measured for

these conditions can exceed 104s (approximately 2.8 hours), although the probability

of a return period exceeding this length of time is extremely rare (p<10-3).

Additionally, Fig. 1-11A shows that the exceedance probability distribution is

dependent on relative tidal height. Return times are larger when still-water level is

below the site than when above. Distributions for negative Ht,relative values are similar

to each other but distinct from the distributions of positive Ht,relative, which are again

similar to each other. For example, the probability of Treturn exceeding 20s

for -1≤Ht,relative<-0.5m and -0.5≤ Ht,relative<0m (p=0.44 and p=0.40, respectively) are

approximately double the probability for 0≤Ht,relative<+0.5m and +0.5≤ Ht,relative<+1m

(p=0.19 and p=0.23, respectively).

The distribution of exceedance probabilities of Treturn also depends on the value

of uthresh used. An exceedance plot of flat-plate treatment Treturn data for the tidal range

0≤Ht,relative<+0.5m (Fig. 1-11B) shows that increasing uthresh uniformly increases the

probability of longer return periods.

Finally, the Treturn exceedance probability distribution for a tidal range of

0≤Ht,relative<+0.5m is also dependent on substrate type (Fig. 1-11C). For example, a

Treturn exceeding 100s is more than twice as likely to occur inside a barnacle test

(12.7%) than in any other condition (4.8% inside algal bed, 2.6% for flat plate).

Although not depicted, this effect is more pronounced at greater values of uthresh due to

growing differences in exceedance probabilities with increasing velocity.

27

1.4 Discussion

1.4.1 Water velocity distributions

The exceedance probability distribution for free-stream water velocities (Fig.

1-8) is in general agreement with a previous study conducted at Hopkins Marine

Station (Miller 2007). From a subset of 9 days of water velocity measurements at a

nearby site, Miller found that the probability of a randomly sampled water velocity

exceeding 2.3m s-1 was approximately 1% (0.3% for my data). In contrast, my results

differed from findings by Mach et al. (2011), also at Hopkins Marine Station. They

calculated a mean cumulative probability distribution of 399 days of water velocity

measurements normalized by each day’s maximal velocity. The maximal free-stream

velocity measured in this experiment was 19.76m s-1. Applying this maximal value to

the polynomial fit provided by Mach et al., it was predicted that the probability of a

randomly selected velocity exceeding 2.3m s-1 (25%) was much greater than my

calculated probability. However, it is important to note that the predicted data are

based on a mean distribution of many exceedances that qualitatively exhibited a large

variance. It is likely that my measurements fall within this variance, and therefore do

not disagree with the observations by Mach et al (2007). Additionally, these

predictions assume that the maximum free-stream velocity exceeds 19m s-1 daily,

which probably isn’t true. Finally, variations in flow environment were found to vary

greatly on meter (Denny et al. 2004) and even centimeter (O’Donnell & Denny 2008)

scales, so I do not expect the probability distributions of flows at one site to perfectly

match another, even one nearby, especially if measurements are taken years apart in

potentially distinct wave environments.

For velocities <3m s-1, the exceedance distributions between the free-stream,

flat-plate, and mussel treatments did not greatly differ (the probability of exceeding

u=3m s-1 was p≈10-4). Thus, sub-millimeter scale organisms are not sheltered from

free-stream velocities within this velocity range, because flows above 3m s-1 are

equally likely to occur in all 3 cases. In fact, the maximal velocity of the mussel

treatment (6.15m s-1) was greater than the maximal velocity of the flat plate treatment

28

(4.67m s-1), suggesting topographical amplification. These findings are contrary to

previous measurements that found reduced wave forces in patches surrounded by

artificial mussel beds compared to bare patches (O’Donnell 2008). This may be due to

the fact that O’Donnell’s measurements were taken in patches surrounded by mussel

beds rather than directly inside a bed. Laboratory measurements of near-substrate

water velocities in an artificial clam bed by Crimaldi et al. (2002) showed that

instantaneous shear values along the substrate in between individual clams could be up

to several orders of magnitude greater than mean shear values, and that this

amplification increased as spacing between clams decreased. Thus, the tight spacing

of the mussels in my experiment may have driven local flow amplification relative to

the flat plate. This phenomenon is highlighted by the mussel treatment’s u̅peak,normalized

profile (see Fig. 1-9). For 2≤upeak,freestream≤2.2m s-1, u̅peak,normalized is >1, meaning that

on average, peak velocities of the mussel treatment for this range exceeded concurrent

free-stream peak velocities. Additionally, u̅peak,normalized values of the mussel treatment

were substantially greater than u̅peak,normalized values of all other treatments across the

range of upeak,freestream. However, these findings are still potentially at odds with

pressure measurements by Denny (1987) on mussel beds at Tatoosh Island,

Washington, which showed that mussel beds experience a substantial lift force. In this

case, lift is generated by the water over the mussel bed moving appreciably faster than

inside the mussel bed. This may be due to the model mussel bed’s lack of a byssal

thread network (reviewed in Carrington 2002), which not only anchors the mussels to

the substrate and each other, but also traps sediment and, as a result, greatly reduces

flow within the bed. Additionally, the mussel “bed” used in this experiment was much

smaller in area (≈0.01m2) than the acres of mussel bed at Tatoosh Island (Denny, pers.

comm.), so this tiny artificial bed may not be capable of retarding water velocities as

much larger beds can.

In contrast to the artificial mussel bed, the barnacle test and algal beds appear

to successfully act as hydrodynamic shelters. The probability of a sampled water

velocity exceeding u=3m s-1 was approximately an order of magnitude smaller

(p≈10-5) for the algae and barnacle treatments than the others. Additionally the

29

maximal velocities measured in the barnacle and algae treatments (3.45m s-1) are

approximately 74% of the maximal flat plate velocity. The data therefore suggest that

these topographical features can not only reduce the probability of high velocity

events, but can also damp the maximal velocities experienced.

1.4.2 Factors affecting return period

As previously stated, high-velocity events occurred more frequently and at

higher velocities at intermediate tidal heights (Ht,relative≈0.5m). Mean return periods

for all treatments were briefest around this tidal height, and T̅return data were calculated

for the greatest uthresh values in this range. This is likely due to the fact that waves

were breaking directly onto the sensor array at this tidal height. For Ht,relative<+0.5m,

waves had a tendency to break before reaching the treatment, leading to a premature

dissipation of energy. Conversely, for Ht,relative>+0.5m, the water level was high

enough so that waves no longer broke directly on the sensor array. Instead, waves

rolled past the sensors and broke upshore of them. For example, the probability of

Treturn exceeding 100 seconds for a threshold velocity of 2m s-1 in low tide conditions

above a flat plate (Fig. 1-11A; -1≤ Ht,relative<-0.5m; p=0.13) was approximately 4 times

greater than in intermediate tidal heights (0≤ Ht,relative<+0.5m; p=0.03). Additionally,

T̅return values were calculated for higher maximum uthresh values at intermediate tidal

heights (uthresh=5.5m s-1) than at low tide (uthresh=4.5m s-1), which shows that at

intermediate tidal heights, greater velocities occurred frequently enough to be detected

for return-period calculation. It is therefore unlikely that a sizeable fraction of

successful settlement of larvae and spores can occur when the tide is in this

intermediate Ht,relative range unless the settling organism is extremely tenacious or

secures its attachment very quickly.

An organism’s resistance to dislodgement would greatly influence its windows

of opportunities for potential settlement (Crimaldi et al. 2002). Figure 1-11B shows

that for a given set of conditions, Treturn distribution depends on the value of uthresh. As

an example, for an exceedance probability of p=0.2, the difference in Treturn between

30

uthresh=2ms-1 (Treturn≈10s) and uthresh=5ms-1 (Treturn≈104s) is approximately 3 orders of

magnitude. As a result, potential settlers that are more tenacious have a greater chance

of encountering longer periods of time where they can potentially explore the substrate

and initiate adhesion. Likewise, local substrate affects the return period distribution

(see Fig. 1-11C), so a settler’s odds of successful settlement likely depends on the

substrate on which it lands. Not only were T̅return values greatest inside the barnacle

test and algal bed, the magnitudes of flow experienced within these treatments were

lower than those of the flat plate.

The long-tailed distribution of Treturn may explain why successful settlement

can occur in the rocky intertidal zone despite mean return periods being fairly short for

much of the measured conditions. In many cases, the maximal Treturn for a given

combination of threshold velocity and relative tide height is several orders of

magnitude greater than the mean, reaching several hours in certain cases. It is perhaps

only a small fraction of planktonic larvae and spores that return to shore that not only

contact the substrate but encounter conditions favorable to successful adhesion.

1.4.3 Potential for settlement

Figure 1-9 shows that an organism 250μm tall in the rocky intertidal zone

would likely be within the boundary layer, but the flow magnitudes experienced are

still very large at this size scale. Aside from the mussel treatment, the normalized

peak velocities of each near-substrate treatment was u̅peak,normalized≈0.5 for

upeak,freestream>4.5m s-1. Although a 50% reduction in near-substrate peak velocities

compared to free-stream is substantial, organisms still experience water velocities in

excess of u=2m s-1 during free-stream high velocity events.

Based on direct measurements of attachment strength for cyprid larvae of the

barnacle B. balanoides by Crisp et al. (1985), I estimate the water velocity required to

detach a larva of this species from the substrate to be between 0.79m s-1 and 1.8m s-1

during initial contact and exploration. The required detachment velocity greatly

31

increases to a range between 4.3m s-1 and 9.7m s-1 once it secretes its primary cement.

Detachment velocities were calculated from attachment strength data using Eqn. (1-7).

Cyprids are ≈1000μm long and wide, and ≈500μm tall. Therefore, the average height

of a larva attached to a substrate would coincidentally be the height of the

measurements recorded in this experiment, 250μm. Cyprids are roughly ellipsoid in

shape, so their projected surface area would be A=1.6*10-6m2. At water velocities

near u=2m s-1, a larva would be operating at a high Reynolds number (Re≈1000). At

this Re, the drag coefficient (Cd) of the cyprid is likely somewhere between 0.2 (a

streamlined body) and 1 (a blunt body). The maximum force required to detach an

exploring cyprid was 5x10-4N. Thus, the predicted detachment velocities using these

parameters were 0.79m s-1 for a blunt body and 1.8m s-1 for a streamlined body,

comparable to the minimum velocity measurements of this study.

The force required to detach a cyprid increases by over an order of magnitude

almost immediately once the larva secretes its primary cement (1.5x10-2N). As a

result, a cyprid that has undergone primary fixation is predicted to detach only at much

higher velocities (4.3–9.7m s-1) than an exploring cyprid. The probability of near-

substrate water velocity exceeding 4 m s-1 on a flat substrate or in a mussel bed was

extremely low (≈10-4) (Fig. 1-8) and near-substrate velocities did not exceed 7 m s-1

under any treatment. In fact, velocities did not exceed 4 m s-1 in the barnacle and

algae treatments. Therefore a newly attached barnacle within a hydrodynamic shelter,

such as those treatments, would essentially be completely safe from detachment by

wave forces. In summary, the settling cyprid larvae of B. balanoides are likely able to

withstand the extreme water velocities present on wave-swept shores once they deploy

their primary cement. Prior to this point, exploration of the substrate by cyprids may

be limited by the hydrodynamic environment, as drag forces capable of detaching

exploring cyprids occur frequently, on a time scale of seconds to minutes.

32

1.4.4 Difficulties in measuring dislodgement

It is important to note that barnacle cyprids likely represent the upper end of

adhesive capability by settling larvae, and that the findings above may not hold true

for spores and larvae of less robust organisms on wave-swept shores. It is for this

reason that the adhesive capabilities of organisms must be directly measured on a

case-by-case basis. Unfortunately, there are very few direct measurements of larval or

spore adhesive force, likely due to the small sizes of these organisms and the difficulty

of measuring such minuscule forces. Many experiments measure adhesive

performance of settling propagules (e.g., Qian et al. 1999, 2000, Zardus et al. 2008) by

observing dislodgement rates of larvae exposed to flow for a period of time. In many

cases, these conditions are turbulent, and therefore vary through time and space

(within the test chamber), often in ways that are unaccounted for. For example, a

well-accepted method to report detachment strength is to measure the shear stress

required to dislodge the larva, even though the primary dislodgement force on a larva

operating at Re>100 would be drag due to water motion occurring at the larva’s mean

height. Shear stress is determined by the velocity gradient immediately above the

substrate, so it can potentially be used as a proxy for velocity, assuming a linear

velocity gradient, at the larva’s mean height. However, a linear velocity gradient only

applies to conditions where water motion is laminar (Re<1). In turbulent conditions,

as is the case for many experiments, shear can be concentrated in the viscous sub-layer

of the boundary layer that can be only ≈50μm thick, meaning that larvae that are

>100μm tall are primarily exposed to conditions that are potentially very different

from the assumed conditions that would arise from homogenous shear. It is for this

reason that the most appropriate measure for adhesion in settling propagules is either

the instantaneous water velocity at the height of the organism required to cause

detachment or a direct measurement of detachment force (e.g., Yule & Crisp 1983,

Crisp et al. 1985). Pairing the adhesive performance of a particular organism with

direct measurements of its local hydrodynamic environment (such as the

measurements of this study) will expand our mechanistic understanding of the patterns

of larval settlement observed at local and regional scales. In this context, the flow

33

sensor array used in this study is valuable because it allows us to quantify the

hydrodynamic environment at temporal and spatial scales relevant to settling larvae

and spores, a previously unmeasured scale in such a high energy environment.

34

1.5 Figures

Figure 1-1. Schematic of a Preston tube. A Preston tube measures fluid velocity in

the direction the dynamic port is facing. The pressure difference between the dynamic

port and static port is the dynamic pressure, which is used to calculate fluid velocity

[Eqn. (1-3)]. The height at which this velocity occurs is the average height of the

dynamic port, or 0.5h from the substrate.

35

Figure 1-2. Top (left) and side (right) view of the triangular pressure block.

36

Figure 1-3. Schematic of the field-deployed pressure block within a waterproof

housing.

37

Figure 1-4. (A) Free-stream velocity was calculated by measuring drag on a sphere

attached to a force transducer. The velocity predicted by a particular value of drag

depended on the drag coefficient (Cd) used [Eqn. (1-7)]. Two methods were

compared: 1. Cd was allowed to vary depending on Reynolds number, and 2. Cd was a

constant value of 0.45. Predicted velocities diverged at low (<1N) and high (>5N)

forces, but they were in general agreement for force values in between these extremes,

which spanned the predicted range of velocities in the field (≈1–10m s-1). (B) The

relative error of using a constant Cd across this span ranged from approx. -3% to 22%.

The minimum velocity used in analysis, 2m s-1, corresponded with a force of 0.47N

(by variable Cd). The relative error in predicted velocity at force=0.47N was 4%. The

low levels of relative error at this value of force (and greater) showed that a constant

Cd value of 0.45 was a reasonable approximation for this velocity range.

A B

38

Figure 1-5. (A) Water velocity sensors mounted to plate in the field. On the plate

are: a pressure block on a flat surface (right), a pressure block surrounded by a bed of

corraline algae (left), and a roughened sphere attached to a force transducer (upper

middle). (B) The sensor array was deployed in the mid intertidal zone at the end of a

wave-swept rocky channel at Hopkins Marine Station.

A

B

39

Figure 1-6. Free-stream peak velocities exhibit a statistically significant yet poor

correlation (dashed line) as a function of significant wave height (Hs).

[peak velocity] = 2.435 + 0.3329*[Hs]; R2=0.015, p<0.001.

40

Figure 1-7. 60 seconds of sample water velocity data (1kHz) taken at high tide. Free-

stream (top) and near-substrate (bottom) measurements on a flat surface exhibit peaks

of similar intensity and timing. Dashed line indicates the 2m s-1 signal floor.

41

Figure 1-8. Exceedance probabilities of water velocities for each treatment and

substrate type. For all treatments, approximately 90% of recorded velocities were

below the 2m s-1 noise floor.

42

Figure 1-9. For all near-substrate measurements, mean peak velocity data normalized

by free-stream peak velocity data (u̅peak,normalized) decreased in response to increasing

upeak,freestream (n>10). For all values of upeak,freestream, u̅peak,normalized of the mussel

treatment exceeded all u̅peak,normalized data of all other treatments.

43

Figure 1-10. Log-scale color plot of mean return period data (T̅return) for each

treatment type binned by uthresh and Ht,relative (n>10 for each cell). White cells

represent insufficient measurements at those values.

44

Figure 1-11. Exceedance probabilities of return period (Treturn) are characterized by

long-tail distributions. Log probability plots are on the right. (A) The distribution of

Treturn depends on relative tidal height for a given substrate type and uthresh. (B) The

distribution of Treturn depends on threshold velocity (uthresh) for a given substrate type

and relative tidal height (Ht,relative). (C) The distribution of Treturn depends on local

substrate.

C

A

B

45

Chapter 2

Barnacle feeding behaviors in extreme flow

2.1 Introduction

Acorn barnacles have been model study organisms in the field of intertidal

ecology due to their great abundance and dense concentrations on rocky shores, lack

of mobility, and striking patterns of zonation within their environment (Connell

1961a). A long history of intensive study has shown that the spatial and size

distributions and abundance of a particular barnacle population depend on many biotic

and abiotic factors including: predation (Connell 1961a, b), intra- and interspecific

competition (Connell 1961a, b), temperature (Bertness et al. 1999), tidal level (Barnes

& Powell 1953), food concentration (Sanford et al. 1994), and water velocity

(Bertness et al. 1991). Particularly, because barnacles are sessile suspension feeders,

food capture (and as a consequence, growth and potentially fecundity and

survivability) in barnacles is inextricably tied to their ability to effectively feed in their

local flow environments (e.g., Crisp & Stubbings 1957, Trager et al. 1990, Nishizaki

& Carrington 2014).

Barnacles are suspension feeders that capture food particles by using their

feeding legs (known as cirri) as filters (see Fig. 2-1). Particle capture using bristled

appendages such as these involves unique mechanical constraints. The spacing

between filtering elements (setae), the diameter of these setae, and the ambient flow

velocity (u) determine the filtering efficiency of the appendage by determining the

amount of fluid that is directed through the filtering elements rather than around the

appendage, that is, the filter’s “leakiness” (Cheer & Koehl 1987). An effective

feeding appendage has a high degree of leakiness, allowing most or all of the fluid to

46

flow through the filtering elements. The relationship between these factors determines

the overall effectiveness of the feeding appendage. For example, finer spacing

between setae would allow the capture of smaller food particles, but could potentially

cause a decrease in filtering efficiency by reducing leakiness or increasing drag on the

appendage to the point where the appendage could not be effectively held in flow.

Compounding the complexity of this issue, barnacles reside in a broad range of flow

regimes (Anderson & Southward 1987), and they have successfully addressed the

potential problems of effectively feeding through several means: variety in setal

morphology (Chan et al. 2008), phenotypic plasticity of cirri with respect to ambient

water velocity (Arsenault et al. 2001, Marchinko 2003, 2007, Marchinko & Palmer

2003, Li & Denny 2004, Chan & Hung 2005), and changes in feeding behavior at

different velocities (Crisp & Southward 1961, Anderson & Southward 1987, Miller

2007).

Despite the strong relationship between barnacle feeding performance and

ambient water motion, few studies have attempted to quantify feeding behavior in

flow regimes representative of a barnacle’s actual environment (exceptions: Trager et

al. 1990, Miller 2007). Traditionally, studies have been conducted in unidirectional

flows at relatively low water velocities (u<60cm s-1) (e.g., Sanford et al. 1994,

Geierman & Emlet 2009). Generally, feeding behavior (Marchinko 2007, Nishizaki &

Carrington 2014) and food particle capture rates (Trager et al. 1994, Nishizaki &

Carrington 2014) by individuals declined at surprisingly low velocities (u<30cm s-1)

when compared to potential water velocities encountered in the field. However,

notable exceptions have been observed (e.g., feeding at u=1.4m s-1 by the intertidal

barnacle Tetraclita squamosa (Hunt & Alexander 1991)). Although the relatively

gentle, unidirectional conditions used in many past experiments may potentially

provide adequate simulations of protected environments, they are likely to be

inappropriate for estimating the feeding performances of barnacles residing in wave-

exposed sites. Water velocities in the wave-swept intertidal zone are characterized by

brief (<1s) velocity peaks generated by breaking waves that can routinely exceed 2m

s-1, followed by a slower backwash as the wave recedes (Miller 2007). If barnacle

47

feeding performance declines at velocities as slow as u=30cm s-1 (as suggested by lab

experiments), how do barnacles feed effectively on wave-washed shores?

The aim of this study is to observe the feeding behaviors of three species of

acorn barnacles that reside in the mid to high intertidal zone (Balanus glandula,

Tetraclita rubescens, and Cthamalus fissus) when exposed to realistic flow conditions

in a laboratory setting. Although these large water velocities would be considered

extreme in the context of laboratory recreations, they are comparatively average from

the point of view of a barnacle on a wave-exposed shore. Direct measurements of

feeding behavior in realistic flow regimes are imperative in order to link an acorn

barnacle’s laboratory feeding behavior to broader ecological contexts such as

metabolic intake, growth, and ultimately, survival.

48

2.2 Methods

2.2.1 Field water velocity measurement

Water velocities (u) of onshore waves were measured in the intertidal zone to

calculate an average water velocity profile to be replicated in the lab setting.

Velocities were sampled at the end of a wave-swept rocky channel in the mid

intertidal zone at a height of 1.0m above mean lower low water at Mussel Point

(36°37.302’N, 121°54.258’W) at Hopkins Marine Station, Pacific Grove, California

on October 10 and 11, 2011, a site typical of moderately exposed open shores.

Water velocities were measured by recording force on a vertically-oriented

roughened plastic cylinder (1.27cm diameter, 3cm height) attached to a 2-axis force

transducer (Bokam Engineering Inc., model US-06002). Velocities were calculated

from force measurements on the cylinder by using the equation:

= √ ��� �� (2-1)

where u is water velocity (m s-1), Fd is the drag force (N) exerted on the transducer, ρ

is the density of the fluid (kg m-3), A is the projected area of the cylinder (m2), and Cd

is the coefficient of drag of the cylinder. The Cd of an object is dependent on the

object’s shape and roughness as well as the Reynolds number (Re) in which it is

operating. Re is calculated by the equation:

� = �� (2-2)

where d is the characteristic length of the object ([diameter x height]0.5; m) and is

kinematic viscosity (m2 s-1). For an expected u range of 0.75–10m s-1, Re would span

49

approximately 104–105. Values of Cd are relatively insensitive to changes in Re in

this range (see Gudmestad & Geir, 1996), so Cd was estimated as 1.2 for all velocity

calculations.

The force transducer was mounted centrally on a PVC sheet (45.7x20.x1.3cm,

LxWxH) which in turn was secured to bolts cemented into the bedrock. The signal of

each axis was amplified 380 times by a differential amplifier (Analog Devices, Inc.,

AD627). Power and signal were transmitted via cable from a power supply (Heath

Co., model IP-2718) and to an USB data acquisition card (National Instruments Corp.,

model NI USB-6211).

Data were sampled at 20 kHz via a custom LabVIEW (National Instruments

Corp.) script and continuously written to individual files, each containing 2 minutes of

measurements. For each file, data were filtered in Matlab (The Mathworks, Inc.) by a

5th order Butterworth low-pass filter with a cutoff frequency of 10 Hz to filter out high

frequency turbulence, and drift for each channel was compensated for by subtracting

the modal value of each channel from the data in each 2-minute file. Force magnitude

was measured for each time point by calculating the vector sum of the two

axes. 20kHz force data were converted to velocities using Eqn. (2-1), then anti-

aliased to 1kHz velocity measurements by taking the median of every 20 velocity-data

points. The minimum sensitivity of the sensor was u=0.75m s-1. Additionally, u data

were associated with concurrent data of local tide height (NOAA; tide station

Monterey).

Waves were sampled from this set of continuous water velocity data to create

an average wave profile. Individual waves were identified by locating local velocity

peaks. Peaks were required to be ≥2m s-1 to ensure adequate sampling above the

transducer’s noise floor and to occur >7s after the previous peak (approximately the

minimum average offshore wave period during the sampling period) to prevent

multiple samples of the same wave. Wave data were obtained from a Datawell

Directional Buoy (Coastal Data Information Program, Scripps Institution of

Oceanography; station 158: 36°37.58’N, 121°54.43’W) approximately 0.5km offshore

50

of the site. The water speed (velocity magnitude regardless of direction) profile of a

wave was defined as the 10s time period of water speed measurements spanning from

2s before the speed peak through 8s after the peak. The 10s period corresponded to

the average offshore wave period during data collection. Waves that occurred at tide

heights <0.5m above the sensor height were excluded to ensure that the sensor was

submerged during each wave. Speed profiles for each wave were then normalized by

the peak speed of each wave, and a mean normalized speed profile of all waves

(n=1050) was calculated (Fig. 2-2). It is important to note that directionality is not

present in the data, but it can generally be inferred by separating the speed profile into

two phases, the shoreward upsurge and the seaward backwash, since flow direction

must reverse between these two phases.

A set of equations were fit to the mean wave speed profile to allow replication

in a lab setting. Normalized speeds (unorm) below ≈0.25 were considered below

minimum transducer sensitivity and therefore unable to be discerned. The equations

were therefore designed to match the magnitudes and shapes of the two speed peaks

generated by the wave’s upsurge and backwash with the assumption that bulk water

speed reaches zero and reverse directions between the two phases. The two local

peaks were unorm=1 at t=2s and unorm≈0.33 at t≈8s. The equations used to describe

these peaks were:

= − √|sin [ − ]| ; ≤ ≤ (2-3)

= − 9 − − . ; < ≤ (2-4)

where t is time in seconds. Eqn. (2-3) approximated the steep, symmetrical rise and

fall of water speed about the apex of the upsurge. Eqn. (2-4) is in the form of a beta

distribution, which is normally expressed as:

�, , = � ∙ � − − � − ; ≤ � ≤ (2-5)

where k, α, and β are constants. α and β determine the shape of the function, while the

value of k is adjusted to determine the function’s integrated value (1 in the case of a

51

probability distribution). The values of the constants used in Eqn. (2-4) satisfied three

criteria:

1. Peak unorm,backwash value occurred at t=8s.

2. Peak unorm,backwash=0.33.

3. The volumes of water transported in each phase (the integrated values of

each equation) were equal (see below).

Regarding this final point, it was assumed that an equal volume of water, or flux, was

transported during the upsurge and backwash of a single wave. It was reasonable to

assume that on average, water wasn’t substantially piling on one side or the other of

the sensor over the course of a single wave, especially when the data were averaged

over an entire tidal cycle. The units for flux were m3/(m2 of area projected into flow),

or m, and were equal to 0.651m for both Eqns. (2-3) and (2-4).

2.2.2 Wave chamber design

High-velocity water jets were generated in a saltwater aquarium

(76.2x45.7x30.5cm, LxWxH) to recreate the previously calculated water-speed profile

(see Fig. 2-3). The temperature of the aquarium was held between 16 and 19°C, using

three thermoelectric chillers (Nova Tec, model IPAC-50W), as temperature has been

shown to affect feeding performance in barnacles (Nishizaki & Carrington 2014).

Water was driven through a two-way pneumatic piston (6.35cm bore; McMaster-Carr,

model 6498K493) by a hydraulic arm (Parker Electrohydraulics; PLA series) through

a series of one-way check valves and polyethylene tubing (1.27cm inner diameter) to

two opposing circular openings (1.27cm diameter) 7.62cm apart. This design allowed

for bidirectional control of flow patterns. The position of the hydraulic arm was

controlled by an analog voltage signal transmitted at 1kHz by a data acquisition card

(National Instruments Corp., model NI USB-6211) and custom Matlab script.

Positional information for the hydraulic arm was calculated by integrating Eqns. (2-3)

and (2-4). Velocity data derived from Eqn. (2-4) were multiplied by -1 due to the

52

reversal of flow direction during the backwash. Positional data were then multiplied

by the ratio of the outflow opening’s area to the piston’s bore area to account for the

outflow’s smaller aperture. Maximum velocity of a particular simulated wave was

controlled by multiplying the positional data by the desired maximum velocity. A

1x1cm metal square attached to a force transducer and mounted between the two

outflow jets was used to calibrate flow in the wave chamber. Similarly to the field

flow sensor, known drag force, projected area, and drag coefficient (Cd=1.2; Hoerner

1965) were used to calculate water velocity [see Eqn. (2-1)].

2.2.3 Specimen collection

Between 12 and 15 specimens each of three intertidal barnacle species were

collected in October 2014: Balanus glandula, Cthamalus fissus, and Tetraclita

rubescens. B. glandula and T. rubsecens specimens were collected by locating

individuals settled on mussel shells. Individuals and the underlying shell to which

they were attached were excised using wire cutters. C. fissus primarily inhabited

regions above the mussel line. For these barnacles, individuals and their underlying

substrate were chipped directly off the rock. All specimens were kept in a flow-

through seawater table and tested within two weeks of collection. Specimens that

molted in captivity were not used, as molting appeared to hinder feeding behavior.

2.2.4 Recording feeding behavior

Barnacles were individually placed in the wave chamber by attaching them to

acrylic plates with modeling clay and securing this plate to a stand situated between

the outflow jets. Feeding behaviors in simulated waves were recorded at 250 frames

per second using a high-speed camera (Photron USA, Inc., model Fastcam-512PCI

32K; see supplemental video online). B. glandula and T. rubescens were tested across

two sets of parameters:

53

1. Orientation of the barnacle test (see Fig. 2-1) relative to the upsurge flow.

a. 0° – Anterior (rostrum) facing upsurge.

b. 90° – Anterior perpendicular to upsurge and backwash.

2. Maximal flow velocity (wave velocity).

a. 2m s-1 – Slow

b. 3m s-1 – Medium

c. 4m s-1 – Fast

Barnacles were allowed to acclimate to each combination of orientation and flow

velocity for at least five minutes. Behavior was not recorded until consistent attempts

at feeding were exhibited by the individual. The tank was seeded with Artemia cysts

to provide food and encourage feeding. A single run was defined by the feeding

behavior exhibited by an individual during one wave period. For each individual, six

consecutive runs were recorded for each combination of orientation and velocity.

Runs for a particular orientation and velocity were discarded and re-recorded if at any

point during recording the individual fully retracted its cirri and closed its operculum,

as this meant that the individual, at least temporarily, either had no interest in feeding

or lacked the capability. Individuals were not counted if sufficient runs at all

combinations of orientation and velocity were not measured. Measurements of six

individuals each of B. glandula and T. rubscens were recorded.

Feeding behavior of C. fissus was much less consistent than that of T.

rubsecens and B. glandula. Data from five individuals were recorded in the 90°

orientation. Very few individuals would feed at 0° for prolonged periods. Of the

three species, individuals of C. fissus appeared to be the least tolerant of high

velocities, so a very slow 1m s-1 wave-velocity trial was added for this species, and

individuals were tested at increasing velocities until they stopped exhibiting feeding

behavior.

54

Behavior was divided into three categories:

1. Successfully feeding in flow – The individual’s cirral net was fully

extended and facing the direction of flow. Its legs were in an organized

pattern capable of catching particles.

2. Buckled – Buckling occurred when large water velocities overwhelmed an

individual’s ability to maintain a feeding posture. The cirral net was blown

backward and the legs became splayed and disorganized. An additional

criterion is the inability of an individual to directly recover from this

position to a feeding posture, requiring the individual to retract its legs

before a subsequent attempt to feed.

3. Miscellaneous – All behaviors not described above. This category contains

a suite of behaviors including but not limited to: partial retraction of the

cirral legs with the operculum remaining open, transition between

successful feeding and other behaviors, respiratory pumping, feeding

attempts that do not appear successful or effective (e.g., unable to directly

face flow or fully extend cirri), and scanning during periods of no or little

flow by lateral rotation of a fully extended cirral net.

Each frame of each run was scored with one of these three categories (see Fig. 2-4 for

examples of behavior, Fig. 2-5 for scoring of typical example runs). Behavioral data

were used to calculate four parameters:

1. Feeding time – The amount of time an individual spent successfully

feeding in either the upsurge or backwash, expressed as a fraction of total

time available in each wave phase.

2. Potential flux (m) – A tentative measurement of the potential water volume

filtered per unit of projected surface area by an individual’s cirral net.

Potential flux was calculated by multiplying the water velocities occurring

during frames of successful feeding during an individual run by the

duration of each frame (0.004s), and summing the resultant values of each

10s run.

55

3. Buckling velocity (m s-1) – The initial velocity at which buckling occurred

for an individual.

4. Maximum feeding velocity (m s-1) – The maximum speed at which feeding

behavior was observed.

In addition, each run was divided into two sections for calculations of feeding time

and potential flux: upsurge and backwash. For each combination of velocity and

orientation, the average values across all six runs were reported for each individual for

these four parameters.

2.2.5 Statistical analysis

Variance in feeding time and potential flux data of B. glandula and T.

rubescens were normalized by transforming these data using arcsin(x0.5) and log(x+1)

transforms, respectively. Separately, these data sets were analyzed by 4-factor fixed-

effect ANOVAs. The four factors were: 1. species, 2. orientation of test, 3. flow

direction, and 4. maximum wave velocity. Significant factors were further tested by

Student–Newman–Keuls (SNK) post-hoc tests. C. fissus data were omitted from

statistical analysis due to limited sampling compared to the other two species.

Variance in maximum feeding velocity data could not be normalized through

transformation and was therefore omitted from statistical analysis.

56

2.3 Results

2.3.1 Feeding time and potential flux

Feeding time generally declined as wave velocity increased (Fig. 2-6A,B,C).

Differences in feeding time with respect to wave velocity were significantly different

(p=0.031, Table 2-1), but an SNK post-hoc test failed to detect any significant

difference between feeding times grouped by wave velocity. Therefore, the detected

difference was likely driven by the overall decline in feeding time rather than any

substantial difference in feeding time between two specific wave velocities. Wave

velocity did not interact with the other three factors tested (species, orientation, and

flow direction), which suggests that the magnitude of change in feeding time as a

response to change in wave velocity was not affected significantly by these factors.

Independent of wave velocity, feeding times also exhibited a significant

difference due to a 3-way interaction (p<0.001) between species, orientation, and flow

direction. Response in feeding time was dependent on specific combinations of these

three factors, or, in other words, when one parameter (such as orientation) is changed,

the expected change in feeding time would be dependent on the states of the other two

factors (species and flow direction). SNK test results (Table 2-2) revealed that feeding

time was significantly shorter for barnacles feeding in the backwash (except T.

rubescens oriented perpendicular to flow). In particular, B. glandula at 0° orientation

could not completely turn their cirral nets backwards to feed in the backwash,

although they did attempt to do so (see Fig. 2-4E). Backwash feeding time for this

group was thus 0 in all cases, and the low mean value for this specific combination of

species, orientation, and flow direction is likely the primary driver of the significant 3-

way interaction.

Potential flux, in contrast, was not significantly affected by wave velocity

(p=0.466, Table 2-3). This finding suggests that individual barnacles were potentially

filtering approximately equal volumes of water per wave, regardless of wave velocity

(see Fig. 2-6D,E,F). As shown above, barnacles spent less time feeding in faster

waves, so it is likely that feeding in higher average velocities compensated for shorter

57

feeding time. The 3-way interaction between species, orientation, and flow direction

did not significantly affect potential flux (p=0.062), unlike the findings for feeding

time. However, this interaction’s p-value was very near the p=0.05 cutoff, so an SNK

post-hoc test was conducted on these data as well (Table 2-4). Results of this test

showed that T. rubescens sieved through the greatest volume of fluid (per unit

projected area) in almost all combinations of orientation and flow direction compared

to B. glandula. This was likely due to the ability of T. rubescens to feed in higher

flow speeds than B. glandula (Fig. 2-7A,D). The exceptional case was the relatively

lower potential flux experienced by T. rubescens when individuals faced upstream and

fed in the backwash. Although individuals of this species were able to completely

rotate their cirral nets backwards to face backwash flows, it did take them additional

time to do so (supported by the relatively low feeding time for T. rubescens in this

combination of orientation and flow direction, Table 2-2), and feeding in this

configuration anecdotally appeared to be less stable than in others. Finally, B.

glandula could not rotate their nets backwards into the backwash while facing

upstream, so flux, like feeding time in this group, was 0, significantly lower than all

other groups. Although C. fissus data were not analyzed statistically, the data were

included in Figure 2-6C,F.

2.3.2 Maximum feeding velocity and buckling

Maximum feeding velocity varied greatly between species and between

individuals of the same species (Fig. 2-7A,B,C). One particular T. rubescens

individual was able to feed at or near the maximum velocity of each wave, while

others did not feed at velocities exceeding 1m s-1 in certain conditions. In general, T.

rubescens fed at higher maximum velocities than both B. glandula and C. fissus.

Orientation did not appear to have a large effect on maximum feeding velocity, except

for the 3m s-1 wave velocity trials, where a greater number of both T. rubescens and B.

glandula individuals were able to feed at velocities in excess of 2m s-1 at 0°

orientation than at 90°. Potentially, a test orientation facing flow could allow greater

58

structural support to the extended limbs than a perpendicular orientation where the

animal was required to contort its body 90° to feed. Individuals oriented toward

incoming flow appeared to be able to brace themselves on the rigid back of their

aperture. In contrast, individuals that were oriented perpendicular to flow were

pressed against their more flexible opercular plate and side of their aperture, which

may not have provided as much support. This effect of orientation on maximum

feeding velocity was less apparent for measurements during 4m s-1 waves. This is a

likely consequence of most individuals avoiding the extreme peak velocities in this

wave condition, as evidenced by the overall decrease in maximal feeding velocity for

4m s-1 waves compared to 3m s-1 waves.

Buckling unexpectedly occurred at relatively low velocities (approx. 1–2m s-1)

(Fig. 2-7B,E), well below the velocity peaks of 3m s-1 and 4m s-1 waves, and even

occurred during the backwash for some individuals of B. glandula and C. fissus (e.g.,

Fig. 2-5). Additionally, more B. glandula individuals exhibited buckling and buckled

at lower velocities in the 90° orientation than the 0° orientation. This observation is in

agreement with the previous notion that, due to increased structural support, an

individual barnacle may be able to maintain an effective feeding posture at greater

velocities if it is oriented toward incoming flow.

In Figure 2-7C,F, average buckling velocities of individuals were subtracted

from their average maximum feeding velocities. A negative value signifies that on

average, the maximum feeding velocity of an individual was less than the velocity at

which it buckled. A majority of these data points are near 0, falling within a range of

+/-0.4m s-1, which suggests that these individuals were attempting to feed near or at

the mechanical limits of their cirral nets. In contrast, two instances were recorded

where maximum feeding velocity exceeded buckling velocity by >0.5m s-1. In other

words, buckling had likely occurred at a velocity much lower than the mechanical

limit of the individual’s cirral net. Anecdotally, barnacles sometimes appeared to

buckle at otherwise manageable water velocities if they attempted to extend their cirral

nets at poorly timed moments during the course of a wave (e.g., high fluid

acceleration), which may explain these data.

59

2.4 Discussion

2.4.1 Feeding in high flows

Barnacles can successfully feed at water velocities of 0.5–4m s-1, a much

greater range and magnitude than previously explored by laboratory experiments.

Feeding is limited by buckling at higher velocities, and maximum velocity for feeding

can be affected by orientation. These data are in accord with the sole field

measurement of barnacle feeding on wave-swept shores (see Miller 2007). In my

experiment, the maximum feeding velocity of C. fissus failed to exceed 2m s-1,

supporting Miller’s observation that the fractional feeding times of C. fissus in the

field were quite large (>50% of the time) at low velocities (<1.3m s-1), but steadily

decreased as water velocities increased beyond 2m s-1. Feeding behavior essentially

ceased at velocities above 4m s-1. Because C. fissus lack a calcareous basal plate,

Miller (2007) was able to track body movements of individuals by filming the

undersides of specimens that had settled on a clear acrylic plate. Body movement

away from the plate was correlated with extension of the cirral net, implying feeding

behavior. These methods, however, were a proximal measurement of successful

feeding in the field. My video records showed that cirral net extension did not

necessarily indicate an effective feeding posture (see Fig. 2-3E,F) if the barnacle could

not successfully turn toward the direction of flow and maintain an appropriate feeding

posture. To further improve the accuracy of feeding time measurements in barnacle

individuals, field observations using the methods established by Miller (2007) (which,

unfortunately, can only be performed on species that have a membranous basal plate)

could be paired with laboratory measurements of feeding performance of these same

individuals with respect to flow angle and velocity.

2.4.2 Morphological plasticity vs behavioral modification

Barnacles are able to finely tune aspects of their morphology with respect to

local flow conditions (Cirral legs: López et al. 2010; Penis: Neufeld & Palmer 2008).

60

Within a species, individuals in areas of greater wave exposure had shorter, stouter

cirri than those in more protected areas (Arsenault et al. 2001, Marchinko & Palmer

2003). Marchinko and Palmer (2003) proposed that decreased leg length and increased

leg thickness in high flow conditions would provide greater mechanical stiffness to

potentially prevent damage from high water velocities, while longer legs in calmer

conditions would provide greater feeding area.

For at least one barnacle species, there is an apparent limit to the

morphological plasticity of the cirral net with respect to flow environment. Li and

Denny (2004) observed that the average cirral leg length of B. glandula did not scale

with water velocity in environments where maximum water velocities exceeded ≈2m

s-1. This velocity range corresponds with decreased feeding time in C. fissus as water

velocity rose above 2m s-1. Miller (2007) proposed that in environments where water

velocities exceed 2m s-1, behavioral modification (i.e., the retraction of cirri to avoid

large, potentially damaging water velocities) by individuals could potentially

compensate for the lack of observed phenotypic plasticity in order to maintain feeding

performance. The range of buckling velocities observed in C. fissus and B. glandula

during this study, approximately 1–2m s-1 (Fig. 2-7B,E), coincides with the critical

velocities observed by Miller (2007) and by Li and Denny (2004). For these

individuals (collected from a wave-swept environment), the drag forces experienced at

water velocities ≈2m s-1 appear to be the mechanical limits of these individuals’ cirral

nets. Indeed, buckling velocities and maximum feeding velocities (in individuals that

buckled) were similar in magnitude (Fig. 2-7C,F) in most cases (+/-0.4m s-1),

suggesting that barnacles were regularly attempting to feed at their mechanical limits.

Furthermore, each set of runs of an individual barnacle at a given wave velocity was

recorded consecutively, so these average buckling velocities were often observations

of individuals experiencing mechanical failure for at least six consecutive waves.

Anecdotally, barnacles were able to retract their buckled cirral nets during high flows

by rolling their splayed cirri to one side of their test and retracting them while their

legs were pressed closely to the outside of the test. These observations suggest that

barnacles may not necessarily avoid high water velocities because these velocities are

61

potentially damaging, but rather because the barnacles are unable to maintain an

effective feeding posture.

2.4.3 High flow tolerance in Tetraclita rubescens

Compared to the other species, T. rubescens fed at higher maximum velocities

and exhibited the fewest incidences of buckling. T. rubescens also had the greatest

inter-individual variation in performance. All incidences of buckling in T. rubescens

occurred in a single individual, while another individual was able to feed at 4m s-1.

Additionally, T. rubescens was the only species to demonstrate a full 180° rotation of

its cirral net to feed in flows opposing the orientation of its test, further establishing

the robustness of its feeding capability. Feeding behavior in Cthamalus and especially

Balanus have been well studied in comparison to Tetraclita (e.g., Hunt & Alexander

1991), but perhaps, in light of these results, T. rubescens may be considered a prime

species for the study of feeding in extremely high flows.

2.4.4 Flow environments in lab settings

Acorn barnacles exhibit two distinct feeding strategies: 1. active rhythmic

sweeps of their cirri through the water to capture particles, and 2. prolonged, passive

extension of their cirral nets into ambient flow (Crisp & Southward 1961). Active

feeding is primarily exhibited in low velocity and no-flow conditions, while passive

feeding is exhibited in the presence of sufficient ambient current (reviewed in Trager

et al. 1994). Traditionally, observations of barnacle feeding have been conducted at

relatively low velocities (<60 cm s-1) (Trager et al. 1992, Sanford et al. 1994,

Geierman & Emlet 2009, Nishizaki & Carrington 2014) compared to the range of

water velocities present on wave-exposed shores (>2m s-1) (Miller 2007, Mach et al.

2011). Additionally, virtually all feeding observations have been made in constant,

unidirectional flow, which may not be a suitable representation of natural flow

environments. Thus, mimicking accurately the temporal variability of environmental

62

water velocities is a necessary parameter in order to accurately observe and compare

feeding performances of barnacles in a laboratory setting.

For example, Trager et al. (1990, 1992) observed that barnacles exposed to

gently oscillating flow conditions (maximum velocity range of approx. +/-10cm s-1 at

frequencies 0.16–0.65Hz) responded to accelerations and decelerations in water

motion. Individuals exhibited gradual increases in feeding time during prolonged

exposure to a particular flow condition by reversing the orientation of their cirri prior

to actual flow reversal. Trager et al. suggested this as evidence of flow prediction by

barnacles. Individuals in my experiment did not exhibit rotation prior to flow reversal,

perhaps due to the much more extreme conditions or the temporal asymmetry between

the upsurge (4s) and backwash (6s) phases. However, individuals were consistently

able to extend their cirri into oncoming flow well before the large fluid accelerations

that preceded peak water velocities. The predictive capability of barnacles in these

high flow conditions may be tested by altering wave periods experienced by barnacles

on a wave-by-wave basis. Preliminary observations in conditions where individuals

were exposed to 2m s-1 waves that cycled through one wave each of a 6s, 10s, and then

14s period wave indicated that barnacles had little difficulty feeding effectively in

shifting flow periods. More rigorous sets of observations (e.g., randomized wave

patterns or sudden disruptions in flow after periods of acclimation to one setting) are

needed to understand whether barnacles retain their predictive capabilities in these

extreme flow environments.

Barnacles fed in both the upsurge and backwash of waves, contrary to Li and

Denny’s (2004) proposal that acorn barnacles on wave-exposed shores may be limited

to primarily feeding in the slower backwash due to their inability to withstand the peak

upsurge velocities. However, feeding performance did depend on the wave phase.

Both feeding time (as a fraction of available time) and potential flux sieved by

individuals differed significantly between the two wave phases (Table 2-1, 2-3), even

though equivalent volumes of water were transported over the barnacles during each

phase. For B. glandula oriented perpendicular to flow, individuals fed significantly

longer in the upsurge (19% of available time) than in the backwash (13.9%) (Table

63

2-2), and they exhibited buckling during velocity peaks of both wave phases. It is

worth noting that the difference in fractional feeding time between the two wave

phases is only 0.05, which, though significant, may not necessarily be biologically

meaningful. Furthermore, the feeding time of a given barnacle is very likely to be

sensitive to the exact velocity profile experienced by the individual, and the estimated

profile used in this experiment does not capture the flow environment of a wave with

complete accuracy. However, these results still suggest that feeding during a wave’s

upsurge phase provides a substantial contribution to an individual’s overall feeding

effort, even in waves that greatly exceed a barnacle’s mechanical limit.

2.4.5 Flux through cirral nets

Water velocity did not significantly affect potential flux sieved by barnacles,

even though feeding time significantly decreased with increasing velocity. This

suggests that a barnacle’s ability to acquire food is broadly independent of a given

wave’s peak velocity—an advantageous trait in a highly variable and unpredictable

environment. However, due to two concerns, potential flux should be considered a

relative estimate of performance rather than an absolute measure:

1. Potential flux per unit projected area of an individual’s cirral net assumes

that the projected area of the cirral net remains constant throughout all

frames scored as successful feeding for an individual. This is far from the

case, as the cirral nets of individuals tended to deform in high flows (also

see Marchinko 2007) by becoming hyperextended or splayed, sometimes

very dramatically, which would decrease the projected area.

2. Potential flux assumes that flow is entirely directed through an individual’s

cirral net at free-stream velocity, that is, that none of the flow is redirected

around the net (see below).

Barnacle cirri are populated with bristles, or setae, which are used to sieve food

particles from water passing through their interstices. When exposed to ambient water

64

motion, the amount of fluid that moves through the sieve relative to the amount

directed around the entire bristled structure is dependent on setal spacing, the diameter

of the setae, and ambient water velocity, which determine the Reynolds number [see

Eqn. (2-2)] (Cheer & Koehl 1987). At low Reynolds numbers (Re<0.1), the viscosity

of the fluid would significantly retard water motion through the setal interstices,

leading to poor filtration performance by the cirrus. In contrast a cirrus operating at a

high Re would be relatively “leaky”, allowing a significant amount of water to flow

between the setae, and thus functioning as an effective filter. For a medium sized B.

glandula, the average width of its longest seta is 0.012mm and the spacing between

setae is approximately 0.013mm (Geierman & Emlet 2009). In a nominal flow of 1m

s-1, this cirrus would operate at Re≈12, which is a sufficient value for near or complete

leakiness (Cheer & Koehl 1987). These simplified calculations, however, do not take

into account the complex three-dimensional structure of a complete cirral net. When

cirri are extended in flow, the velocity gradient that forms around each cirral leg could

potentially have large effects on retarding the flow experienced by the setae on each

leg as well as those on neighboring legs. In addition, an increase in cirral leg

curvature could decrease the spacing between setae. Interdigitation of setae between

legs could further decrease setal spacing, potentially causing a substantial reduction in

the overall leakiness of the cirral net. Therefore, it may not be appropriate to assume

that the flux through a complicated structure such as the cirrus can be calculated

accurately without empirical measurement.

Water velocity through an actual cirral net would be difficult to measure due to

the cirrus’ small size, so the use of an enlarged, dynamically-scaled model (see

Loudon et al. 1994, Koehl 2004) may be useful to obtain measurements of flow

through a cirral net for a range of environmental water velocities. A dynamically-

scaled model operates at the same Re as the object of interest [Eqn. (2-2)], and the

ratios of the velocities and the forces at corresponding points are the same between the

model and original object. For models larger than the original object (increased d), Re

is matched by either decreasing the fluid velocity (u) or increasing viscosity of the

fluid surrounding the model (increased ). In this case, the leakiness of a real cirral

65

net would be matched by the model when operating at the same Re. By coupling these

measurements of flow through cirral nets with flow-dependent behavioral

measurements (such as those found in this study) and environmental measurements of

local flow patterns, we can develop a model for flux through the appendages of

barnacles feeding in the field. Furthermore, this flux model could be combined with

measurements of food particle density (assuming a uniform density of these particles

are carried through the cirral net along with the fluid) and size distribution (assuming

that particle capture size is determined by the spacing between setae) in the water to

generate a rudimentary model for metabolic intake by barnacles in the field. Finally,

this metabolic model, combined with other physiological data such as temperature and

desiccation tolerance, may aid in understanding the striking patterns of zonation

expressed by intertidal barnacle populations.

66

2.5 Tables

Table 2-1. ANOVA results for feeding time of Balanus glandula and Tetraclita

rubescens. Data were arcsin(x0.5) transformed to normalize variance (Cochran’s C

test, C = 0.129, p = 0.158).

Source of variation df MS F p

Species 1 0.399 18.057 <0.001

Orientation 1 0.326 14.779 <0.001

Wave velocity 2 0.079 3.594 0.031

Flow direction 1 1.796 81.372 <0.001

Species*Orientation 1 0.060 2.725 0.101

Species*Wave velocity 2 0.019 0.859 0.426

Species*Flow direction 1 0.277 12.571 0.001

Orientation*Wave velocity 2 0.014 0.612 0.544

Orientation*Flow direction 1 0.786 35.600 <0.001

Wave velocity*Flow direction 2 0.002 0.069 0.933

Species*Orientation*Wave velocity 2 0.008 0.353 0.703

Species*Orientation*Flow direction 1 0.258 11.673 0.001

Species*Wave velocity*Flow direction 2 0.003 0.114 0.892

Orientation*Wave velocity*Flow direction 2 0.000 0.003 0.997

Species*Orientation*Wave velocity*Flow direction 2 0.027 1.204 0.303

Error 120 0.022

67

Table 2-2. Summary of Student-Newman-Keuls multiple comparisons test (p=0.05) of

feeding time to examine the significant 3-way interaction between species (B = B.

glandula, T = T. rubescens), test orientation (0° = facing upsurge, 90°=

perpendicular), and flow direction (u = upsurge, b = backwash). Lines indicate groups

that are not significantly different.

Group B0°,u : T0°,u : T90°,u : B90°,u : T90°,b > T0°,b : B90°,b > B0°,b

Feeding time

(frac) 0.273 0.261 0.256 0.190 0.188 0.140 0.139 0

68

Table 2-3. ANOVA results for potential flux filtered by Balanus glandula and

Tetraclita rubescens. Data were log(x+1) transformed to normalize variance

(Cochran’s C test, C = 0.122, p = 0.228).

Source of variation d.f. MS F p

Species 1 0.313 24.438 <0.001

Orientation 1 0.087 6.771 0.010

Wave velocity 2 0.010 0.767 0.466

Flow direction 1 0.151 11.759 0.001

Species*Orientation 1 0.001 0.108 0.743

Species*Wave velocity 2 0.014 1.100 0.336

Species*Flow direction 1 0.022 1.719 0.192

Orientation*Wave velocity 2 0.008 0.604 0.548

Orientation*Flow direction 1 0.214 16.728 <0.001

Wave velocity*Flow direction 2 0.001 0.078 0.925

Species*Orientation*Wave velocity 2 0.007 0.513 0.600

Species*Orientation*Flow direction 1 0.046 3.557 0.062

Species*Wave velocity*Flow direction 2 0.003 0.223 0.800

Orientation*Wave velocity*Flow direction 2 0.000 0.028 0.972

Species*Orientation*Wave velocity*Flow direction 2 0.007 0.581 0.561

Error 120 0.013

69

Table 2-4. Summary of Student-Newman-Keuls multiple comparisons test (p=0.05) of

potential flux to examine the near-significant (p=0.062) 3-way interaction between

species (B = B. glandula, T = T. rubescens), test orientation (0° = facing upsurge,

90°= perpendicular), and flow direction (u = upsurge, b = backwash). Lines indicate

groups that are not significantly different.

Group T90°,b : T90°,u : T0°,u > B0°,u > B90°,b : T0°,b > B90°,u > B0°,b

Potential

flux (m) 0.837 0.830 0.822 0.617 0.543 0.504 0.403 0

70

2.6 Figures

Figure 2-1. Barnacle extending its cirral net to capture suspended food particles. The

barnacle’s test consists of overlapping calcareous plates. The anterior plate is the

rostrum and the posterior plate is the carina. Orientation of a barnacle is defined by

the angle of its rostro-carinal axis relative to water motion.

71

Figure 2-2. Mean and fitted water-speed profiles of waves (with 95% confidence

intervals), normalized by peak water speed (peak is set to t=2s). Waves are

characterized by a brief spike in water speed during the upsurge toward shore (0–4s),

followed by a longer, slower (peak speed ≈ 0.33) backwash period directed toward the

ocean (6–10s).

72

Figure 2-3. Design of wave chamber to observe barnacle feeding behavior (not to

scale). Arrows indicate the direction of water motion during the down stroke of the

hydraulic arm. During the upstroke, water is drawn in and expelled on the right side

of the chamber.

73

Figure 2-4. Examples of scored barnacle behavior. All scale bars represent 5mm.

The top images are successful feeding postures by (A) Tetracilta rubescens, (B)

Balanus glandula, and (C) Cthamalus fissus during the upsurge phase. (D) In

conditions where tests were oriented toward the upsurge, T. rubescens were the only

barnacles capable of turning their cirral nets completely around to successfully feed in

the backwash. (E) B. glandula (and C. fissus, not pictured) facing the upsurge did

attempt to feed in the backwash, but they were unable to turn completely around. This

was scored as a miscellaneous behavior, as it did not appear to be an effective feeding

posture. (F) B. glandula buckling.

74

Figure 2-5. Representative runs of scored barnacle footage. Barnacles are oriented

perpendicular to flow and wave velocity is 3m s-1. T = Tetraclita rubescens, B =

Balanus glandula, C = Cthamalus fissus. The scale on the right axis pertains only to

flow velocity of the sample run.

75

Figure 2-6. Individual barnacle mean data (+/-1SE) of feeding time (A–C) as a

fraction of total time and potential flux filtered (D–F) during feeding per unit cirral net

projected area.

C A B

F D E

76

Figure 2-7. Mean data of individual barnacle maximum feeding velocity (A,D), and

buckling velocity (B,E). The dashed line is the maximum possible feeding velocity at

each wave velocity. (C,F) For individuals that demonstrated buckling, average

buckling velocity was subtracted from average maximum feeding velocity.

C A B

F D E

77

Chapter 3

Larvae of the brooding coral Isopora cuneata cannot

direct their settlement toward the substratum in flow

environments simulating the reef crest

3.1 Introduction

For many benthic marine organisms with planktonic larvae, colonization of a

new surface occurs in four distinct phases: development and dispersal, testing of

habitat, settlement, and finally metamorphosis (Keough & Downes 1982). In cases

where settlement rate is a primary driver of overall recruitment rate, successful

settlement (contact and attachment to a surface) can affect benthic community

structure: e.g., temporal and spatial distributions of adults, population structures, and

interactions among species (reviewed in Eckman 1996, Schiel 2004). Early studies

assumed that larval settlement in marine invertebrates was essentially a random, low

probability process due to the massive egg production observed in many species

(summarized in Hadfield et al. 2014). Subsequent studies have revealed that larval

settlement in a broad range of marine invertebrates can be mediated by environmental

cues (e.g., light, gravity, temperature, salinity, and chemical signals) (reviewed in

Pawlik 1992). Planula larvae of Scleractinian corals are a model example of larval

response to environmental stimulus. Coral larvae respond to a suite of biotic and

abiotic factors in ways that may ultimately influence their settlement location

(reviewed in Ritson-Williams et al. 2009, Gleason & Hofmann 2011), including light

(Mundy & Babcock 1998), hydrostatic pressure (Stake & Sammarco 2003),

sedimentation (Babcock & Davies 1991), temperature (Putnam et al. 2008), and

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chemical signals (Morse et al. 1988, Negri et al. 2001, Baird & Morse 2004). Once a

larva has contacted the substrate, it may also respond to tactile cues corresponding

with surface microtopography (Whalan et al. 2015).

In particular, the effects of chemical cues on larval settlement and

metamorphosis have become a topic of intense interest. Chemical extracts from

crustose coralline algae (CCA) are potent inducers of settlement and metamorphosis in

many coral species (e.g., Morse et al. 1988, Heyward & Negri 1999). The magnitude

of the response, however, is highly dependent on both the coral and algal species

(Baird & Morse 2004, Diaz-Pulido et al. 2010, Ritson-Williams et al. 2010). Several

distinct compounds extracted from CCA have been successfully identified as

metamorphosis inducers (Morse et al. 1988, 1994, Kitamura et al. 2007), although the

exact chemical stimulus required for metamorphosis has not yet been identified.

Furthermore, bacterial biofilms on the surface of CCA appear to provide additional

chemical cues for larval metamorphosis (Negri et al. 2001, Webster et al. 2004).

Negri et al. (2001) found that a substantial fraction of larvae exposed to a particular

strain of bacteria would metamorphose in the water column without attaching

themselves to the substrate. The fraction of larvae that attached themselves to the

substrate prior to metamorphosis increased with the addition of inert coral skeleton

chips, which suggests that settlement and metamorphosis are potentially decoupled

processes that can be separately induced by distinct chemical signals. A common

feature among these compounds is that they are primarily bound to the CCA cell wall

or surrounding biofilm, suggesting that larvae may not be able to detect these signals

unless extremely close to or in direct contact with the substrate (Gleason & Hofmann

2011).

Coral larvae also respond to waterborne cues. Chemicals released by

macroalgae and cyanobacteria can act as positive (Birrell et al. 2008) or negative

(Kuffner et al. 2006, Birrell et al. 2008) settlement cues. Gleason et al. (2009)

observed increased downward swimming and benthic exploration in larvae exposed to

water collected 1m above shallow reefs as opposed to water collected from the open

ocean. Although this experiment was conducted in a beaker with still water and the

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chemical cue was unknown, these results suggest that larvae may be able to use

waterborne cues to navigate toward the substrate when they reach a potentially

habitable area.

Despite the wealth of knowledge on biotic and abiotic cues capable of

influencing larval dispersal and settlement, few studies have addressed the degree and

scale to which larvae themselves can influence their ultimate settlement location on a

coral reef, which is both topographically and hydrodynamically complex (e.g., Hench

& Rosman 2013). Larval dispersal over long distances is largely influenced by

currents (e.g., Roberts 1997), but navigation at the reef scale is less clear. Coral larvae

are weak swimmers, with speeds that are slow (<0.5cm s-1; e.g., Gleason et al. 2009)

compared to the water velocities in their typical ambient flow environment (see

Gleason & Hofmann 2011). Even in protected conditions, over the course of a wave,

water velocities just above a coral reef can oscillate between peaks of up to 20cm s-1 in

each direction of the primary axis of water movement (e.g., Reidenbach et al. 2009,

Koehl & Hadfield 2010), almost 40 times greater than observed larval swimming

speeds. Larvae would likely have little to no control over their trajectories during

velocity peaks. Additionally, for larvae to successfully maintain a heading (constant

yaw angle, see Fig. 3-1), they would need the ability to resist torque imparted on them

by hydrodynamic shear stress (e.g., Durham et al. 2009). Shear stress (τ; Pa) is force

per area acting on a surface aligned parallel to the direction of fluid motion (reviewed

in Koehl 2007) and is described by the equation:

� = � ��� (3-1)

where is the dynamic viscosity of the fluid (Pa s) and dudz (s-1) is the velocity gradient,

or shear strain rate. Due to the presence of a boundary layer, a larva would experience

the steepest velocity gradient, and consequently the greatest shear stress, near the

substrate (Koehl 2007). There are so far no published studies of the ability of coral

larvae to resist torque-induced rotation, so it is unclear whether they would have the

capacity to maintain their orientation as they approach (or are carried to) potential

settlement sites. Some negatively buoyant larvae of other taxa (e.g., Phystilla

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sebogae: Hadfield & Koehl 2004) are able to descend onto suitable substrates by

passively sinking once waterborne cues are detected, thereby circumventing the

problem of active navigation toward the substrate. In contrast, many species of coral

larvae are positively buoyant upon release (e.g., Villanueva et al. 2011), so this

strategy of passive descent onto potential settlement sites would not be available to

them, at least initially.

Oscillations in flow patterns create additional complications in potential

settlement patterns when compared to unidirectional water motion. In a flume

experiment, Reidenbach et al. (2009) observed that the superimposition of wave-

driven oscillations onto a constant unidirectional current increased momentum

transport deeper into an artificial reef compared to conditions with only unidirectional

current. This suggests that oscillatory water motion may increase the passive transport

of larvae as well as other suspended materials and molecules to the reef. In contrast,

peak dislodgement forces and shears experienced by hypothetical larvae were

significantly higher in oscillatory conditions, suggesting a diminished probability of

successful navigation and attachment by larvae when exposed to these conditions.

However, intense peaks in velocity and shear in oscillating conditions are often brief,

which may provide larvae with windows of opportunity to navigate during the lulls

between the peaks. Additionally, centimeter-scale topographical rugosity can reduce

or amplify local flow environments (Koehl et al. 2013), and features on the substrate

such as protrusions and depressions may promote larval contact on these features

(reviewed in Abelson & Denny 1997). In summary, small-scale variation in flow

pattern and topography may have large effects on larval settlement in the field, and it

is therefore important to replicate these sources of variation when measuring

settlement performance in a lab setting.

In light of the current paucity of direct observations of coral larval settlement

in complex topographical and hydrodynamic environments, my study aimed to

measure the flow environments experienced by coral larvae at potential settlement

sites in the field at size and time scales relevant to individual larvae. Additionally, I

exposed larvae to flow environments simulating these measurements to determine

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whether coral larvae can influence successful settlement through their behavior. My

direct observation of larval settlement in realistic flow conditions across complex

topographical features can provide a crucial bridge between laboratory observations of

larvae in still water and actual distribution patterns found in the field.

3.2 Methods

3.2.1 Measuring water motion on the reef crest

Water motion over the reef crest was measured on the fringing reef of Lizard

Island, Australia, between South and Palfrey Islands (14.700°S, 145.449°E) (see

Madin et al. 2014). An acoustic Doppler velocimeter (ADV) (Nortek Inc.; Vectrino)

was deployed on the reef crest from November 16 to 24, 2013, taking samples at 8Hz

in a burst of 2,048 samples once every 20 minutes at a height approximately 1m from

the sea floor. Data were summarized by recording the mean velocity components for

each burst in the toward-shore (u), along-shore (v) and vertical (w) directions. The

power spectra of individual bursts were examined to determine the dominant period of

oscillation.

The substrate along the reef crest of Lizard Island (Fig. 3-2) is typical of many

other coral reef crests and is characterized by patches of hard substratum raised above

a sandy bottom. Larval settlement is inhibited on heavily sedimented hard surfaces,

and larvae are unable to settle on sand (reviewed in Babcock & Davies 1991).

Therefore, I chose to measure fine-scale water motion above raised substrates as

representatives of potential settlement sites. Water velocities were measured using

particle image velocimetry (PIV) (see Whitman and Reidenbach 2012 for details). A

vertical plane of water parallel to water motion was illuminated by a laser sheet

(Laserglow Technologies; 300mW, 532nm) (Fig. 3-3). Waterborne particles within

the laser sheet were filmed at 30 frames per second (fps) using a digital video camera

(Sony, model HDR-CX160) in an underwater housing with a 532nm band pass filter.

Both laser and camera were attached to an adjustable aluminum tripod frame (80/20®

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Inc.) 40x40x30cm (LxWxH) in size, and a black felt curtain was extended ≈60cm

behind the laser sheet to reduce background light. At each site, 2 minutes of video

were recorded in an 8x5 cm (width x height) field of view (FOV) over a relatively flat

area of the site, approximately central to the shoreward and seaward edges of the

raised feature. The FOV was directly above the substrate, including the upper 0.5 to

1cm of the substrate. Sites were chosen based on their potential for larval recruitment;

dead table coral skeletons low in algal cover were targeted. Eight sites were chosen

along a transect perpendicular to the reef crest and in line with the ADV (+/-2m to

either side along-shore), from the crest to 50m towards shore.

3.2.2 Analysis of PIV footage

Video footage was stabilized using the Deshaker software (Thalin 2013) in

VirtualDub (Lee 2013), and 30 seconds of each 2 minute clip were then analyzed

using PIVlab (Thielicke & Stamhuis 2014), in Matlab (The MathWorks, Inc.). Due to

the difficult nature of obtaining usable field PIV footage, data from only two sites

(1.0m and 3.2m behind the reef crest) were deemed of adequate quality to be analyzed.

The velocity measurements (u,w) at each site were recorded as a single vertical

velocity profile located centrally in the FOV for each frame, starting approximately

1mm from the substrate and increasing in 1.3mm height increments. For each site,

mean toward-shore velocity (u) as a function of distance from substrate (h) was

calculated across all processed frames. Additionally, bottom shear for each frame was

calculated using Eqn. (3-1). The velocity gradient was assumed to be linear between

the substrate (where u=0) and the closest velocity measurement to the substrate, a

conservative estimate.

Each data set was then seeded with virtual particles to determine potential

encounter rates of purely passive particles with the substrate. For each frame, virtual

particles were placed at each height for which there was a velocity measurement.

With each frame advancement, each virtual particle was displaced in two dimensions

(x,z) by following each particle along a Lagrangian track. Displacement was

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calculated as the horizontal and vertical velocities at the particle’s current distance

from substrate (to the nearest recorded measurement) multiplied by the elapsed time

between frames (0.033 seconds). Velocities for height values of h beyond the FOV

were set to equal the velocities measured at the height furthest from the substrate,

because it was assumed that conditions at this height approached free-stream

conditions. The fraction of virtual particles that contacted the substrate for each

starting height was recorded. The results of particles placed in the last 90 frames were

discarded due to insufficient remaining time to contact the substrate. For particles that

did contact the substrate, the average total horizontal distance travelled before contact

was calculated as a function of starting height. If the average horizontal travel

distance were much larger in scale (>1m) than the FOV (8cm), an actual particle in the

field would likely be transported to a different microenvironment with its own distinct

flow conditions. In this case, this method of analysis would be inappropriate.

Conversely, a small travel distance would potentially decrease the odds of a particle in

the real environment traveling far enough to encounter flow environments radically

different from the velocity data used from a single site. In this case, this method of

analysis provides a reasonable estimate of a passive particle’s contact rate with the

substrate.

3.2.3 Assessing settlement behavior of Isopora cuneata larvae

The study organism, Isopora cuneata (family Acroporidae), a brooding coral,

was chosen due to the abundance of colonies on the reef flat and crest at Lizard Island,

as well as its remarkably large (length>1mm) larvae. Isoporid corals are major reef

builders in shallow-water coral reef communities throughout the Indo-Pacific (see

Kojis 1986). As opposed to the eggs of broadcast spawning corals that are released

into the water column, the eggs of brooding corals are fertilized internally and

embryos develop into motile planula larvae within the polyp, which are then released

(see Baird et al. 2009). Branches of I.cuneata colonies were collected in the field and

were placed in an outdoor flow-through seawater tank. Flow was suspended overnight

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and larvae were collected in the morning by pipette. This was repeated once more,

and approximately 250 larvae were collected. Larvae were kept in two plastic

containers containing approximately 0.5L of 0.2μm filtered seawater (FSW) each.

To assess larval settlement behavior in flow conditions similar to those found

on coral reefs, I. cuneata larvae were placed in a recirculating flume capable of

generating oscillating water motion (see Fig. 3-4 for details). Larval swimming

behavior was initially measured in still water in the presence of substrates containing

settlement cue. Cue-laden substrates were prepared in 2 ways:

1. Slide treatment: CCA chips and attached coral matrix collected from the

field were dried, pulverized, and then secured to a glass microscope slide

with silicone adhesive. The slide was allowed to cure for 12 hours before

being placed on the floor of the flume working section.

2. Tile treatment: A rectangular fragment of a brick settlement tile (deployed

on the reef for ≈3 months and collected immediately prior to the

experiment) containing live CCA and other algae (7x2.5x1cm, LxWxH)

was deposited directly onto the floor of the flume working section.

Larvae were exposed to each substrate separately and were not reused. For each

treatment, the flume was filled with FSW, and water was allowed to stabilize for ≈10

minutes. Twelve I. cuneata larvae were inserted into this chamber via pipette, and

water motion was allowed to stabilize for 1 minute. Larvae were filmed for 10 minutes

at 30 fps across a 4x2cm (WxH) FOV (Sony, model HDR-CX160). Kinematic data

(position, velocity, orientation, and rotation) of individual larvae were tracked from

recorded footage using a custom Matlab script. Footage of larvae that did not remain

in the illuminated midsection of the camera’s FOV were excluded from analysis.

Flow velocities and oscillation periods were initially calculated by manually

tracking 10 particles each in field PIV footage recorded near the reef crest (3m behind

ADV) and on the back reef (27m behind ADV), taken within 2 hours of each other on

November 17, from 9:30am to 11:30am. Particles approximately 2cm above the

substrate were tracked using the MTtrackJ plugin (Meijering et al. 2012) in ImageJ

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(NIH). Near the crest, water motion oscillated between 0 and 11cm s-1 while flows on

the back reef oscillated between 0 and 5cm s-1. These two velocity ranges simulated

high-flow (crest) and low-flow (back reef) flume conditions, with a 3s oscillation

period (the dominant period of oscillation at the crest, see Fig. 3-5) for both

conditions. Flow patterns were calibrated by manually tracking video footage of

neutrally buoyant Artemia cysts (serving as passive particles) and adjusting the analog

voltage signal to the servomotor until the velocity ranges matched the above values.

Initial trials showed that larvae exposed to the high-flow regime had essentially no

chance of successful attachment. Furthermore, larvae were much more frequently

destroyed by the rotating propeller in the high-flow condition than in the low-flow

condition. As a result, only low-flow conditions were used in all trials of this

experiment.

Larvae were filmed in oscillating low-flow conditions for three surface

topography treatments (see supplemental video online):

1. Slide treatment. FOV: Midsection of the glass slide, including the

substrate.

2. Tile treatment. FOV: Two-thirds downstream of the tile’s leading edge, to

avoid capturing leading-edge vortices.

3. Block treatment: A small, rectangular section (1x2.5x1cm, LxWxH) of the

same settlement tile from treatment 2 was mounted on a glass slide with

silicone adhesive. FOV: The entire block surface.

The three treatments represent conditions of increasing local turbulence within the

FOV as a result of increasing topographical complexity, which was predicted to

increase larval contact with the substrate (see introduction). Before each treatment,

the flume was emptied and rinsed of larvae and particles, then filled with fresh FSW.

For each treatment, ≈60 I. cuneata larvae were introduced into the flume via pipette

while water was in motion. The experimental setup was allowed to stabilize for 2

minutes, then the larvae in the still water of the working section of the flume were

filmed for ≈1 hour. Subsequently, ≈100 neutrally buoyant Artemia cysts (passive

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particles) were then introduced by pipette and filmed for approximately 10 minutes to

characterize flow conditions. Successful adhesion (attachment to the substrate for at

least 5 minutes after initial contact) was observed solely in the block treatment. A

subsequent trial was conducted with the block treatment using euthanized larvae to

examine whether successful attachment was a result of larval behavior or a

consequence of the hydrodynamic environment. Larvae were euthanized in 10%

formalin solution for one hour prior to the trial. The kinematic data of larvae were

tracked using a custom Matlab script, and incidences of contact with the substrate

(either followed by successful attachment or immediate detachment) were recorded.

3.2.4 Analysis of larval motion

Mean swimming speeds (the magnitude of velocity regardless of direction) of

larvae were recorded in still water. Additionally, the rotation rate of a lone, tortuously

swimming larva about its transverse axis was measured to estimate turning

performance and potential resistance to shear. For a spherical object in simple shear,

the equilibrium velocity of rotation (φ; radians per second) is described by the

equation (Jeffery 1922, as summarized by Ghosh & Ramberg 1976):

� = . ��� (3-2)

where dudz is the velocity gradient, or strain rate [see Eqn. (3-1)]. For a larva to

maintain a constant heading while exposed to a velocity gradient, it would need to be

able rotate at a rate equal to (but in the opposite direction of) the shear-induced

rotation rate imparted by the fluid [Eqn. (3-2)]. I set φ equal to the maximum turning

rate observed by the swimming larva to estimate its potential maximum resistance to

fluid strain rate ��� . Maximum resistance to shear (τcrit) was then calculated by

applying this strain rate value to Eqn. (3-1).

Vertical velocities (w) of larvae and neutral particles were compared in the

slide and tile treatments. Horizontal swimming behavior was difficult to discern for

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larvae because water velocities in the flume (simulating those in the field) were up to

an order of magnitude faster than larval swimming speeds (see results). It was

assumed that larvae can only swim in roughly the direction they are facing, so w of

larvae were split into two groups: vertical (oriented 80–90° relative to horizontal) and

horizontal (0–10° relative to horizontal). Data of larvae in other orientations were

discarded. The third group was w of neutral particles. The variances of w were

expected to be similar between passive particles and horizontally oriented larvae,

because a horizontally oriented larva would not be capable of propelling itself

vertically. Variance of w in vertically oriented larvae was expected to be greater than

the previous two groups if the larvae were able to exhibit appreciable swimming

behavior. Although orientation of the anterior-posterior axis relative to horizontal

could be discerned in the footage, it was not possible to consistently determine which

end was anterior. For each substrate treatment, a Levene’s test was applied to w of the

three groups to compare equality of variances. A potential confounding factor for this

analysis would be if w were affected by the phase of flow oscillation; e.g., if vertical

accelerations in flow accompanied horizontal accelerations as a result of experimental

design. Horizontal velocities (u) of larvae and particles are necessarily dependent on

oscillation phase (Fig. 3-6A), as this is the flow parameter being directly manipulated.

To this end, u can be treated as a proxy for phase. By visual inspection, w appears to

be independent of phase (Fig. 3-6A), and poor correlation between u and w of larvae

and neutral particles in both treatments suggest w’s phase-independence (Fig. 3-6B,C).

In contrast, w was dependent on both spatial and temporal factors for the block

treatment, so this analysis was not conducted on those data.

For dead and live larvae that did contact the substrate during the block

treatment, larval speeds immediately before impact were compared by two-way

ANOVA (factors: 1. Living, whether larva was dead or alive, and 2. Attachment,

whether larva successfully remained attached to the substrate after initial contact).

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3.3 Results

3.3.1 Larval swimming

In still water, larvae exhibited a distinct downward trajectory toward the

settlement cue-laden substrate, potentially a stimulus response, after which they either

remained stationary or crawled along the substrate. The kinematic data of 7 larvae

were recorded between the two tests. Of the trajectories recorded, 6 were linear,

vertical trajectories. A single larva swam in a tortuous path down to the substrate. Its

kinematic data were recorded for points in time where it was moving parallel to the

FOV. Horizontal swimming was completely absent in the footage. Mean larval

swimming speeds for straight trajectories ranged from 0.24cm s-1 to 0.55cm s-1 (Fig.

3-7), with a total range of 0 to 0.58 cm s-1 and a mean between all larvae of 0.40cm s-1.

The average speed of the tortuously swimming larva was slightly lower at 0.19cm s-1.

The turning larva’s mean rotation rate (yaw) while swimming in a plane parallel to the

FOV was 18.9° s-1 (range 0.3–62.1° s-1). The maximum observed turning rate of

62.1°s-1 was used as an estimate for a larva’s maximum ability to withstand torque-

induced rotation about its transverse axis. From Eqn. (3-2), the predicted maximum

strain that the larva could withstand to maintain its heading would be ���=2.17s-1.

Using this value in Eqn. (3-1), I calculated the maximal larval resistance to shear to be

τcrit=2.34mPa. That is, if τ>2.34mPa, larvae would necessarily rotate.

In oscillating flow over a flat surface (glass-slide treatment), the variance of

instantaneous vertical velocities (v) differed significantly (p<0.001) between coral

larvae (grouped by horizontal or vertical orientation) and neutral particles (Table

3-1A). Variance of w for vertically oriented larvae (0.026cm2 s-2) was over two times

greater than the variances of v for horizontally oriented larvae (0.012 cm2 s-2) and

neutral particles (0.0081cm2 s-2), which were comparatively similar to each other.

Variance of w for all 3 groups was substantially greater in the tile treatment than in the

glass-slide treatment (Table 3-1B). Additionally, differences in variance between the

3 groups were no longer significant in the tile treatment (p=0.8589). It is important to

note that the sample sizes used in the two Levene’s tests varied greatly, both between

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groups within a treatment and between the two treatments overall. The sensitivity of

Levene’s test increases with larger sample sizes, so there was concern that the

significant differences in variances found in the glass-slide treatment (pooled n=3365)

but not in the flat-plate treatment (pooled n=997) may have been an effect of differing

sample sizes. Random subsampling of the data sets such that n=95 for each group for

each test produced similar results, ruling out potential sample size bias.

3.3.2 Near-substrate flow environments

Mean velocities even near the substrate (h<2mm) exceeded larval swimming

speeds at both sites (Fig. 3-8). As expected in boundary-layer conditions, the velocity

gradient was steepest near the substrate. Bottom shear routinely exceeded expected

larval resistance to shear (τcrit=2.34mPa). In fact, conditions favorable to directed

larval motion near the substrate (u<0.6cm s-1 and τ<2.34mPa) occurred less than 10%

of the time (2.67% at the 3.2m site, 9.79% at the 1.0m site). Contact rates of passive

virtual particles (Fig. 3-9) were extremely high at starting points near the substrate

(>50% up to 8mm away from the substrate at 1.0m site), but rates decreased

drastically with increased starting height (<10% at both sites for h>1.5cm). Small

average horizontal travel distances for particles near the substrate prior to contact

(<10cm travelled at h<1cm) suggest that particles that do contact the substrate are

unlikely to be transported far enough away from the site to experience radically

different flow conditions.

3.3.3 Larval contact with substrate

For all treatments in oscillating flow, larval contact with the substrate was

relatively rare or non-existent (see Table 3-2). All incidences of attachment and

virtually all incidences of contact occurred solely in the block treatment. Figure 3-10

is a composite image of multiple trajectories of living larvae in the block treatment,

which were similar to the trajectories of dead larvae (pers. obs.). Generally, larvae

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were accelerated as water motion pushed them up and over the upper upstream (left)

edge of the block, leading to larval contact with the substrate in some cases. A

relatively stagnant patch of water developed in front of the lower half of the upstream

face of the block (i.e., the dark patch that does not contain larval images). Larvae

were rarely transported to this region, but many larvae entering this region did contact

the substrate. On the downstream (right) end of the block, larvae were frequently

trapped in a turbulent eddy that potentially brought larvae into close proximity to the

substrate (and therefore contact) before being pushed downstream. During this

treatment, both live and dead larvae exhibited successful attachment (attachment of a

larva to the substrate for at least 5min after initial contact) during some incidences of

contact. For both live and dead larvae, successful attachment did not occur at larval

speeds greater than 2.1cm s-1 (Fig. 3-11). Larval speeds were significantly higher in

incidences of immediate detachment than in incidences leading to prolonged

attachment (p=0.005) (Table 3-3, Fig. 3-12). There was no significant difference in

speeds between live and dead larvae (p=0.134), which suggests that for this species in

these flow conditions, swimming efforts by live larvae to increase successful contact is

not detectable. Although live larvae successfully settled at flow speeds comparable to

their swimming speeds, dead larvae also successfully settled at these speeds. This

result shows that in certain low-flow conditions, settlement effort by living larvae may

not be required for successful attachment to the substrate. Furthermore, rotation rates

of larvae prior to contact exceeded the maximum larval turning rate observed in still

water in all observations of contact except one. In these conditions, it would be either

difficult or impossible for larvae to maintain a heading toward the substrate.

3.4 Discussion

3.4.1 Larval swimming

Measurements of I. cuneata swimming speeds (Fig. 3-7) are in agreement with

previous observations of larvae from other brooding coral species (see Gleason et al.

2009). Vertical swimming by larvae was detectable in low-turbulence (glass-slide)

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conditions (Table 3-1A). Surprisingly, mean swimming velocity was biased slightly

upward (+0.032cm s-1), away from the substrate. However, this small upward bias in

velocity was much smaller than one standard deviation of these data (variance =

0.026cm2 s-2, therefore one standard deviation = 0.16032cm s-1), which suggests that

vertical swimming direction was largely unbiased. In contrast, larvae in still water

overwhelmingly swam downward toward cue-laden substrate. Perhaps if larvae are

moving too quickly over a chemical cue, they may not be able to detect the signal

unless brought into direct contact (especially since the cue present in crushed CCA

and coral matrix is primarily water insoluble; see Morse et al. 1988). Alternatively,

detection of the cue may elicit swimming behavior in the direction that the larva is

currently facing, but the larva may not be able to detect the source’s direction.

The larvae’s slow swimming speeds relative to ambient water may mean that any

swimming behavior would not appreciably affect its trajectory on small (0.1–10cm)

distance scales. Swimming behavior quickly became indiscernible in conditions of

increased turbulence (e.g., tile treatment, see Table 3-1B). Furthermore, the flow

conditions used in this experiment were based on low-flow, back-reef conditions

measured on a calm day. Larvae appeared to have no ability to successfully attach to

the substrate, let alone control their trajectories, when exposed to high-flow, reef crest

water velocities. Calm, laminar flows over completely flat surfaces (i.e., the glass

slide experiment) are likely a rare occurrence on actual reefs. Therefore, larvae being

transported over the reef are unlikely to be able to control their movements near the

substrate.

Because of their large size compared to other species, I. cuneata larvae likely

represent an upper end of swimming performance by coral larvae. The inability to

detect swimming effort by I. cuneata larvae in all but the most modest conditions

suggests that environmental flow patterns would dictate the trajectories of larvae of

other species as well. The larvae of brooding corals, such as I. cuneata, are usually

much larger than their broadcast spawning counterparts (e.g., Whalan et al. 2015),

sometimes by an order of magnitude. A reasonable assumption is that larger larvae

would be able to swim at greater absolute speeds than smaller larvae, and therefore the

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larvae of brooders are likely faster than the larvae of spawners. An unpublished meta-

analysis of coral larval swimming data by Andrew Baird and Vivian Cumbo (James

Cook University, Australia) reported a mean swimming speed range of 0.157 to

0.479cm s-1 for the larvae of nine coral species (primarily brooders), and the mean

speed I observed was 0.40cm s-1. However, unpublished data by Danielle Dixson (see

Dixson et al. 2014) claim that three species of broadcast spawning corals (Acropora

millepora, A. nasuta, and A. tenuis) may have larvae that can swim much faster than

0.4cm s-1. In contrast to these findings, I anecdotally observed the larvae of a

broadcast spawning coral Pocillopora eydouxi (length≈0.2mm) in still-water

conditions using the same setup as this experiment, and I was unable to detect

swimming behavior by these larvae. If the larvae were swimming, their motion was

not significantly greater than the slow, ambient water motion driven by convection in

the chamber. Additionally, individuals were difficult to track due to their small sizes.

If the larvae of certain broadcast spawning corals are able to swim as quickly as

claimed by Dixson et al. (2014), it may be important to design experimental setups

sensitive enough to measure swimming performance by much smaller larvae, such as

Acropora and P. eydouxi, because a large fraction of Scleractinian corals are broadcast

spawners (see Baird et al. 2009).

Additional measurements in larval swimming performances (with respect to

environmental conditions) across a broad range of species (both brooding and

broadcast spawning) may reveal differential settlement performance between species,

ultimately leading to distinct adult distributions. Within a given environment, robustly

swimming larvae may potentially be able to influence their trajectories, and therefore

their settlement sites, if they can swim at rates comparable to ambient water motion.

In contrast, the settlement patterns of weakly swimming larvae would be largely

dictated by the environment. Additionally, measurements of swimming performance

must include a larva’s ability to maintain a heading in addition to its swimming speed.

Although far from rigorous due to a sample size of one, this study was the first

recorded attempt to quantify turning capabilities in coral larvae. To understand the

93

extent of a larva’s navigation capabilities (and thus site selection), more work must be

done to measure individuals’ ability to maintain their heading in turbulent conditions.

3.4.2 Contact with substrate

Successful attachment of both dead and live larvae occurred at low speeds and

rotation rates, although unsuccessful attachment occurs at a much broader range of

speeds (Fig. 3-11,12). These results suggest that attachment or detachment after initial

contact can be primarily attributed to water motion rather than swimming, especially

since dead larvae were able to passively attach to the substrate in conditions similar to

live larvae. Additionally, in all but one incident, larval rotation rates prior to contact

exceeded the maximum turning speed observed by a larva in still water (62.1° s-1),

sometimes by an order of magnitude. It is highly unlikely that live larvae were able to

exert appreciable control of their heading in these conditions. Furthermore, field

measurements of bottom shear (Fig. 3-8B) regularly exceeded τcrit, a larva’s maximum

rotational resistance to shear. Bottom shears exceeded τcrit over 90% of the time,

sometimes by an order of magnitude. Compounding this problem, the average flow

velocity at each site was greater than larval swimming speeds at all heights above the

substrate. Thus, it is unlikely, at least at these potential settlement sites, that larvae

could successfully direct themselves toward the substrate even if they were

millimeters away.

Although these results indicate that larvae may not be able to direct themselves

toward the substrate in any meaningful way, passive larvae may still maintain an

appreciable chance of contact with the substrate if they are suspended at the right

height in the water column. At my two field sites, passive particles that started at

heights up to 2mm away from the substrate had a >60% chance of contacting the

substrate due to ambient water motion. Even at a starting height of 1cm above the

substrate (at the site 1m shoreward of the reef crest), the contact rate of virtual

particles remained substantial (40%). Furthermore, these contact rates take into

account only one region of upward-facing surface on the entire rocky protrusion.

Coral larvae can also settle on the vertical faces and undersides of hard substrate

94

(Babcock & Davies 1991), thereby increasing the suitable depth range for passive

contact with the substrate (Fig. 3-13).

The results of this study indicate that environmental flow pattern, rather than larval

response to stimuli, is likely the primary determinant for initial contact between larvae

and potential settlement sites. This brings into question why larvae would exhibit the

complex suite of stimulus responses they possess. Perhaps swimming by larvae is

used to position themselves at appropriate heights in the water column (>1m

displacement over the course of hours or days) rather than to swim directly toward a

suitable substrate. As mentioned earlier, larvae could potentially face high

probabilities of contact with the substrate, even while floating passively, if they were

at the correct depth range. At larger, reef-sized scales, the depth distribution of

Agaricia humilis (a brooding coral) colonies corresponds with depth-dependent

swimming behavior by its larvae (Raimondi & Morse 2000).

Additionally, larvae could use swimming to exit stagnant, unsuitable patches,

resuspending themselves into the ambient current. During the block treatment of the

flume experiments, there was a stagnant patch of water near the bottom corner of the

upstream face of the block, illustrated by the lack of larvae near the block’s bottom-

left corner in Figure 3-10. Dead larvae that drifted into this corner remained there for

the rest of the treatment. Even if they were not firmly attached to the substrate, they

were not ejected from this region due to local stagnation of water movement. On

several occasions, live larvae were also deposited in this same region. Unlike the dead

larvae, these live larvae eventually become dislodged and returned to circulation.

Since dead larvae were not ejected from the stagnant area in the same way, it is

possible that the live larvae actively chose to swim out of this region, although their

reasons for doing so are unclear. When examining the behaviors and movements of

such small organisms, it is imperative to consider the magnitude of their capabilities in

relation to the magnitude of forces exerted on them by the environment. Although the

function of swimming in a planktonic larva, whose ultimate goal must be the

successful attachment to a site suitable for metamorphosis and growth, would appear

95

to be straightforward, the actual application of this function may be nuanced due to

environmental constraints.

3.4.3 Turbulence and contact

In the lab flume experiments, turbulence generated by heterogeneous

topography was necessary for larvae to contact the substratum. This result, coupled

with the larvae’s weak swimming abilities, suggests that larvae are reliant primarily on

environmental turbulence to deposit them directly onto the substrate. Healthy coral

reefs are incredibly topographically “rough” or complex, and the collective bottom

roughness of a reef generates large frictional forces along the floor as water passes

over it (reviewed by Monismith 2007). Greater bottom frictional forces correspond

with increased bottom shear and turbulent mixing (Monismith 2007). Reidenbach et

al. (2006) found that bottom shear was 3–5 times greater in coral reefs than nearby

sandy flats. As a result, there was a 2-fold increase in the rate of turbulent mixing on

reefs compared to flats. This is a cause for concern regarding degraded reefs. As a

reef becomes degraded, its architectural complexity correspondingly declines

(Alvarez-Filip et al. 2009). Many species of reef residents depend on reef complexity

for functions such as predator refuge (e.g., Almany 2004) and increased nutrient flux

(Genin et al. 2009). The performance of these functions would be compromised on

depleted, flattened reefs. Additionally, algal cover exhibits a negative correlation with

reef complexity (see Graham & Nash 2013); turf and macroalgae are direct space

competitors with corals (reviewed in Hoey & Bellwood 2011). Coral populations on

degraded reefs may face a three-pronged recruitment problem:

1. Fewer available larvae supplied by fewer live, healthy corals.

2. Fewer patches suitable for settlement that are not occupied by algae.

3. Less frequent contact of dispersed larvae with the substrate due to

decreased turbulence over the reef.

96

Mechanistic insight into the successful settlement of coral larvae may aid in efforts to

maintain and restore reefs. Direct, quantitative observations of larval settlement in

realistic flow conditions may aid in bridging the gap between two traditional

disciplines of coral reef ecology—still-water lab experiments and field ecology—in

order to better understand and predict community structure.

The results of this study show that I. cuneata larvae are modest swimmers that

are likely unable to influence their settlement rates by direct navigation to potential

settlement sites. Water velocities regularly overwhelm larval swimming speeds even

near the substrate, where boundary-layer flows can be much slower than free-stream

velocities. In addition, I. cuneata larvae are unlikely to be able to resist torque-

induced rotation near the substrate, rendering them unable to keep a constant heading

toward a desired destination. In conclusion, measurements of both an organism’s

performance and its environmental conditions are important to understand the

situations where an organism’s response to stimulus has the ability to affect its current

state and when its response is simply overwhelmed by environmental forces. For

settling coral larvae, this provides a greater understanding of when larvae can be

modeled as passive particles and when they should be modeled as sensing, probing

organisms. This line of study could potentially increase the ability to predict

settlement patterns in benthic invertebrates, and by extension future adult

distributions, across hydrodynamically and topographically complex environments

such as coral reefs.

97

3.5 Tables

Table 3-1. Levene’s test on variance of vertical velocity (w) of vertically and

horizontally oriented larvae and of neutral particles for glass-slide (A) and flat-tile (B)

treatments. Positive velocities indicate movement away from the substrate.

Group n Mean w (cm s-1) Variance (cm2 s-2)

A. GLASS-SLIDE TREATMENT

Larvae (vertical orientation) 998 0.032 0.026

Larvae (horizontal orientation) 600 0.028 0.012

Neutral particles 1767 0.003 0.0081

Pooled 3365 0.016 0.014

Levene’s statistic = 147.57

d.f. = 2; 3362

p < 0.001

B. FLAT-TILE TREATMENT

Larvae (vertical orientation) 95 0.031 0.096

Larvae (horizontal orientation) 208 0.012 0.11

Neutral particles 694 0.056 0.10

Pooled 997 0.044 0.10

Levene’s statistic = 0.152

d.f. = 2; 994

p = 0.8589

98

Table 3-2. Number of Isopora cuneata larvae tracked in each treatment of oscillating

flow. Attachment by larvae was defined as continued adhesion to the substrate for at

least 5 minutes following initial contact.

Treatment Larvae tracked Contact Attachment

Glass slide 60 0 0

Tile 35 1 0

Raised block (live larvae) 36 11 5

Raised block (dead larvae) 64 7 3

99

Table 3-3. 2-Way ANOVA on Isopora cuneata larval speeds immediately prior to

contact with substrate. Factors: Living = alive or dead; Attachment = immediately

detached after contact or remained attached. Cochran’s C test for equality of variance,

C = 0.425, p = 0.515.

Source d.f. MS F P

Living 1 6.25 2.53 0.134

Attachment 1 27.6 11.2 0.005

Living*Attachment 1 6.42 2.6 .129

Error 14 2.47

100

3.6 Figures

Figure 3-1. (A) Changes in the heading of a larva, or the direction that its anterior end

is facing, occurs by rotation (yaw) about its transverse axis perpendicular to its

anterior-posterior axis. (B) When a larva is exposed to a velocity gradient, such as one

present near the substrate, it experiences rotational forces due to shear. In order to

maintain its heading, a larva’s ability to turn about its transverse axis must exceed

these rotational forces.

A B

101

Figure 3-2. The substrate along the reef crest of a coral reef (e.g., Lizard Island,

pictured) is typically characterized by patches of hard substratum (potentially suitable

for settlement) raised above a sandy bottom (not suitable for settlement).

102

Figure 3-3. Field particle image velocimetry (PIV) setup.

103

Figure 3-4. Schematic of oscillating flume. The flume was composed of ABS pipe

(7.6cm outer diameter; 45x30cm, LxH) with a clear, rectangular plexiglass working

section (15x2.8x5cm inner LxWxH). Flow recirculated in a closed vertical loop,

driven by a propeller located in the vertical arm of the flume downstream of the

working section. The propeller was attached to a servomotor. Rotation rate of the

servomotor was controlled by an amplified analog voltage signal output by a custom

Matlab script and transduced by a data acquisition card (National Instruments, model

NI USB-6211). Flow-straightening grids were placed on either end of the working

section. The middle of the working section was illuminated from above by light from

an LED source (LED Lenser®, model P14) passed through a narrow slit (3mm) sitting

atop the working section that spanned the length of the chamber across the middle of

the working section.

104

Figure 3-5. Flow conditions 1m above the reef crest at Lizard Island, measured by an

acoustic Doppler velocimeter (ADV).

105

Figure 3-6. (A) Horizontal velocities (u) of neutral particles are dependent on the

phase of flow oscillation, while vertical velocities (w) are independent of phase. This

pattern holds true for larvae as well, in both glass-slide and tile treatments. u and w of

Isopora cuneata larvae and artemia cysts in the tile treatment (B) and glass-slide

treatment (C) exhibit poor correlation. Each data set exhibited poor linear correlation

between u and w (r2<.02 in all cases). Lack of correlation confirms that analysis of w

is not confounded by vertical fluid accelerations attributable to oscillation phase.

C

A B

106

Figure 3-7. Box plots of swimming speeds and rotation rates of Isopora cuneata

larvae measured in still water (median and interquartile range, ±1.5 interquartile range,

and outliers). All larvae swam vertically in straight trajectories (as evidenced by

rotation rate ≈0), except larva 7, which swam downward in a tortuous path. The mean

swimming speed of all larvae (excluding larva 7) was 0.40cm s-1.

107

Figure 3-8. (A) Mean water velocities measured above the substrate using PIV

exceed the mean swimming speed of Isopora cuneata larvae (0.4cm s-1), which

suggests that larvae would have difficulty swimming against ambient water motion.

(B) Box plot of instantaneous bottom shears at each site (median and interquartile

range, ±1.5 interquartile range, and outliers). Instantaneous shear values exceeded the

potential resistance to shear by larvae (τcrit=2.34mPa) >90% of the time at both sites,

so larvae would additionally have difficulty maintaining a constant heading toward

potential settlement sites.

τcrit

A B

108

Figure 3-9. (A) Probability of passive virtual particles in PIV-measured flow fields

encountering the substrate as a function of starting height from substrate. At both

sites, a substantial fraction (>15%) of virtual particles encountered the substrate at

starting heights up to 1cm away from the substrate, rapidly approaching 0 beyond this

distance. (B) The average horizontal distance travelled by virtual particles before

contacting the substrate is small (<10cm) at short starting distances from the

substratum (<1cm).

A B

109

Figure 3-10. Composite image of trajectories of dead Isopora cuneata larvae over a

raised block. The direction of average water motion was from left to right.

110

Figure 3-11. Incidents of larval contact with substrate with respect to speeds and

rotation rates of larvae immediately prior to contact. Attach = successful attachment

of larva to the substrate for at least 5 minutes after initial contact. Detach =

detachment from substrate after contact. The regression line shows that rotation rates

generally increased with speed. All observations except one occurred with larvae

rotating at rates greater than the maximum observed rate in still water. Successful

attachment of both dead and live larvae did not occur at speeds greater than 2.2cm s-1.

However, contact and subsequent detachment occurred at a range of velocities

between 0.1–7cm s-1. The solid line indicates a positive correlation between rotation

rate and speed (rotation rate = 69.6*[velocity] + 153.4; R2=0.313; p=0.014). The

dotted line indicates maximum turning speed observed in larva in still water (≈60° s-1).

111

Figure 3-12. Box plot of speeds of larvae immediately prior to contact, grouped by

whether larvae were alive (alive/dead) and if they had successfully remained attached

to substrate (attached/detached) (median and interquartile range, ±1.5 interquartile

range, and outliers). Larval speeds before contact were significantly higher (see Table

3-3) in larvae that immediately detached from the substrate than larvae that remained

attached. There was no significant difference between dead and live larval speeds.

112

Figure 3-13. Illustration of potential range for passive larval contact with the

substrate. For larvae suspended in the water column and pushed across the reef by

ambient current, the probability of passive contact with hard substratum (potential

settlement sites) would be substantial if they were able to position themselves at the

appropriate height in the water column (e.g., by swimming).

113

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