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.
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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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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
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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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