musculoskeletal biomechanics of the dual-function axial

Musculoskeletal Biomechanics of the Dual-Function Axial Musculature of Fishes in

Swimming and Suction Feeding

By

Yordano Eleazar Jimenez

B. S., Northern Arizona University, 2015

B. A., Northern Arizona University, 2015

A dissertation submitted in fulfillment of the requirements for the degree of Doctor of

Philosophy in the Department of Ecology and Evolutionary Biology at Brown University

Providence, RI

May 2021

iii

This dissertation by Yordano E. Jimenez is accepted in its present form by the

Department of Ecology and Evolutionary Biology as satisfying the dissertation

requirement for the degree of Doctor of Philosophy.

Date__________ _________________________ Elizabeth L. Brainerd, Advisor

Recommended to the Graduate Council

Date__________ _________________________ Richard L. Marsh, Reader Date__________ _________________________ Thomas J. Roberts, Reader Date__________ _________________________ Andrew A. Biewener, Reader

Approved by the Graduate Council

Date__________ _________________________ Andrew G. Campbell, Dean of the Graduate School

iv

YORDANO E. JIMENEZ, Ph.D. Curriculum Vitae

Research Interests

Vertebrate morphology, biomechanics, muscle function, and evolution

Education

Brown University, Providence, Rhode Island

2018 - current Ph.D. in Ecology and Evolutionary Biology*

*Advisor: Elizabeth L. Brainerd

Northern Arizona University, Flagstaff, Arizona

2015 B.S. in Biology cum laude

2015 B.A. in Philosophy

Honors

2017 National Science Foundation, Graduate Research Fellowship

2015 REU Friday Harbor Labs, Blinks Fellowship

2012 - 2014 Initiative for Maximizing Student Diversity Fellowship

2010 - 2014 Northern Arizona University Dean’s List Student

2010 - 2014 Arizona Board of Regent’s High Honors Tuition Scholarship

Grants

2020 Doctoral Dissertation Enhancement Grant, Brown University ($9,948)

Investigating biomechanical solutions to a universal constraint in

vertebrate axial musculature

2017 Society for Integrative and Comparative Biology Travel Award ($500)

v

2016 American Society of Ichthyology and Herpetology Raney Award

($1200)

2015 Northern Arizona University Student Travel Award ($500)

2014 - 2015 Undergraduate Research Mentoring Award ($2000)

2014 Hooper Undergraduate Research Award ($1000)

Publications *undergraduate collaborators

In prep Jimenez, YE, and Brainerd, EL. Motor control of the axial musculature

of bluegill sunfish in swimming and suction feeding.

In revision Jimenez, YE, Marsh, RL, and Brainerd, EL. A biomechanical paradox

in fish: swimming and suction feeding produce orthogonal

strain gradients in the axial musculature. Accepted pending

revisions in Scientific Reports.

2020 Lomax, JJ, *Martinson, TF, Jimenez, YE, and Brainerd, EL.

Bifunctional role of the sternohyoideus muscle during suction

feeding in striped surfperch, Embiotoca lateralis. Integrative

Organismal Biology, 2, 1-12.

2020 Jimenez, YE, and Brainerd, EL. 2020. Dual function of epaxial

musculature for swimming and suction feeding in largemouth

bass. Proceedings of the Royal Society B, 287, p.20192631

2018 Jimenez, YE, Camp, AL, *Grindall, JD, and Brainerd, EL. 2018. Axial

morphology and 3D neurocranial kinematics in suction-feeding

fishes. Biology Open, doi: 10.1242/bio.036335

vi

Work in Progress

Jimenez, YE, *Parsons, J, Brainerd, EL. Motor control of the hypaxial

and epaxial muscle during suction feeding in channel catfish

(Ictalurus punctatus)

Brainerd, EL, Jimenez, YE, Weller, HI. Impact of whole-muscle shear

and fascicle curvature on architectural gear ratio

Jimenez, YE, Coughlin, DJ, Brainerd, EL. Modelling the mechanical

effects of 3D muscle deformation in swimming and suction

feeding

Jimenez, YE, Brainerd, EL. Strain gradients in the skeletal muscle of

vertebrates.

Jimenez, YE, Brainerd, EL, Tytell, ED. Mechanical output of the C-

start in bluegill sunfish

Presentations

2021 Jimenez, YE. Musculoskeletal biomechanics of the dual-function axial

musculature of fishes in swimming and suction feeding. Brown

University. Providence, RI.Oral presentation. Dissertation

defense.

2021 Jimenez, YE, Marsh, RL, and Brainerd, EL. A biomechanical paradox

in the dual-function axial musculature of fish. Society for

Integrative and Comparative Biology. Virtual Meeting.

Oral presentation. Selected for student prize competition.

vii

2020 Jimenez, YE, and Brainerd, EL. Contributions of the epaxial

musculature to swimming and suction feeding in bluegill

sunfish. Society for Integrative and Comparative Biology.

Austin, TX.

Oral presentation.

2019 Jimenez, YE, and Brainerd, EL. Epaxial mechanics of swimming and

suction feeding in bluegill sunfish. Society for Integrative and

Comparative Biology Regional Meeting. Boston College.

Boston, MA.

Oral presentation.

2019 Jimenez, YE, Olsen, AM, and Brainerd, EL. Modelling the mechanical

effects of fiber architecture and muscle bulging on swimming

and suction feeding. International Congress of Vertebrate

Morphology. Prague, Czech Republic.

Poster presentation.

2018 Jimenez, YE, and Brainerd, EL. Comparison of epaxial muscle activity

during swimming and suction feeding. Society for Integrative

and Comparative Biology Regional Meeting. Brown University.

Providence, RI.

Oral presentation.

2017 Jimenez, YE, and Brainerd, EL. Is epaxial muscle activity in

largemouth bass regionalized during swimming and feeding?

viii

Society for Integrative and Comparative Biology Regional

Meeting. University of Massachusetts Lowell. Lowell, MA.

Oral presentation.

2017 Jimenez, YE, Summers, AP, and Brainerd, EL. Comparative

biomechanics of the defensive dorsal fin spines in filefishes and

triggerfishes. Society for Integrative Biology. New Orleans,

Louisiana.


2016 Jimenez, YE, Laurence-Chasen, JD, Grindall, JG, Camp, AL, and

Brainerd, EL. Where does the vertebral column bend during

suction feeding in fishes? International Congress of Vertebrate

Morphology. Washington DC.


2015 Jimenez, YE, and Gibb, AC. When is a C-start not a C-start? Escape

behavior in the English sole (Parophrys vetulus). Society for

Integrative and Comparative Biology. West Palm Beach,

Florida.


2014 Jimenez, YE, MacDonald I, and Gibb, AC. Do California halibut

(Paralichthys californicus) develop different escape responses

throughout their life history? Society for Integrative and

Comparative Biology. Austin, TX.


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2014 Jimenez, YE, MacDonald, I, and Gibb, AC. The fleeing flatfish: how

flatfish escape. URM & IMSD Symposium at Northern Arizona

University. Flagstaff, AZ.

Oral presentation.

Teaching

2020 Comparative Biology of Vertebrates

Teaching assistant. Designed, coordinated, and instructed the

dissection lab. Wrote and administered quizzes and lab practicals.

Assisted students with lecture portion. Transitioned lab portion of

course into online format during the Covid-19 global pandemic.

2017 - 2019 Human Anatomy I & II

Teaching assistant. Taught in the dissection lab, created practice

practical exams, administered practical exams, and lectured.

2017 Comparative Biology of Vertebrates

Teaching assistant. Assisted in the dissection lab, held review sessions,

and lectured on fish functional morphology.

2016 Evolutionary Biology

Teaching assistant. Facilitated weekly discussion groups, tutored

students individually, graded exams, administered exams, and gave a

lecture on vertebrate evolution.

x

2014 Introduction to Ethics

Teaching assistant. Taught students the fundamentals of analyzing

philosophical texts and ethical scenarios. Guided students in applying

philosophical analysis to their own lives. Administered and graded

coursework, quizzes, and exams.

2012 - 2013 Philosophies of the World

Teaching assistant. Engaged students in weekly discussion groups to

develop analytic skills and apply them to complex philosophical and

religious worldviews. Tutored students individually. Administered and

graded coursework, quizzes, and exams.

Synergistic Activities

2019 - current Translator (Spanish) for Integrative and Organismal Biology

Broadened access to scientific research for non-English speaking

countries by translating technical abstracts from a variety of

disciplines.

2019 - current Peer Reviewer for Zoology

2017 - 2019 Director/Co-director of the Marine Science Club

Designed hands-on projects to inspire and engage high school students

in an underserved community. Coordinated and led activities such as

fish dissections, live animal presentations, and video analysis.

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2016 Volunteer for Brown Junior Researchers Program

Taught a broad range of scientific topics to elementary school students

using hands-on experiments to foster interest and appreciation for

scientific discovery.

Professional Associations

American Society of Ichthyologists and Herpetologists

International Society of Vertebrate Morphology

Society for Integrative and Comparative Biology

Sigma Xi

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Preface and Acknowledgments

Over 500 million years ago, the first vertebrates used axial muscles for

swimming, but over time these muscles have been co-opted in most fish species for

suction feeding. As fish evolved cranial adaptations for suction feeding, the axial

musculature started to form an integral part of the feeding apparatus. Despite suction

feeding being the most pervasive mode of prey capture in vertebrates, relatively little is

known about how the axial musculature is involved. Suction feeding studies have focused

on cranial structures, and post-cranial structures have been considered almost exclusively

within a locomotor context. Recent studies have demonstrated that the axial musculature

is clearly used for generating power for suction feeding, challenging the long-held

implicit belief that the trunk musculature is exclusively a swimming structure. In my

dissertation work, I investigated form-function relationships between the axial

musculature and these two high-power behaviors and the potential constraints and

tradeoffs of a dual-function system.

Chapter 1 examines how the morphology of the axial skeleton affects

neurocranial elevation, a critical component of suction feeding. Chapter 2 examines

which parts of the epaxial musculature in largemouth bass contribute to swimming and

suction feeding. Chapter 3 quantifies bilateral motor control of the epaxial musculature in

a ‘suction specialist’, bluegill sunfish. Chapter 4 describes patterns of muscle strain in

swimming and suction feeding, and how these may create tradeoffs in performance.

My dissertation contributes to the broader field in laying the groundwork for

evaluating form-function relationships in a dual function context. Chapter 1 establishes

which regions of the axial skeleton bend during suction feeding using 3D in vivo

xiii

measurements of head movement to determine the axis of rotation for cranial elevation.

Prior work has usually inferred vertebral rotation from indirect measurements and

specimen manipulation. Together, Chapters 2 and 3 show that both largemouth bass and

bluegill sunfish can increase suction performance by activating epaxial muscle in a

dorsal-to-ventral and cranial-to-caudal manner. Prior work has assumed that only the

dorsal-most epaxial region near the head contributes to suction feeding. I also show that

the muscle regions activated for swimming and suction feeding overlap throughout most

of the body, and thus muscle function cannot be described as functionally regionalized in

fishes. I also infer from this finding that feeding uses a dorsal-to-ventral pattern of muscle

recruitment with the same neuroanatomy that modulates ventral-to-dorsal activation

during locomotion. Chapter 3 shows that bluegill sunfish can activate a greater

percentage of muscle volume than largemouth bass during suction feeding, which may

explain why bluegill sunfish have been shown in prior studies to produce substantially

higher mass-specific muscle power. This also suggests that recruitment of the axial

muscle may limit suction feeding performance across species. Chapters 4 determines that

swimming and feeding produce different patterns of muscle shortening in bluegill

sunfish, which may limit the ability of the muscle to produce high power in both

behaviors. Future work is needed to determine whether these different strain patterns

affect the ability of suction-feeding fishes to generate high muscle power outputs for both

biological functions.

I would like to thank my advisor Beth Brainerd for her support, guidance, and

encouragement, and whose intellectual rigor and depth of knowledge is something I

aspire to develop. Our stimulating conversations have helped me to grow in curiosity and

xiv

love of science. I am thankful to have had an advisor who showed tremendous confidence

in me and my intellectual interests while always encouraging me to develop as a

researcher. I am also grateful to my committee members, Tom Roberts, Rich Marsh, and

Andy Biewener, who guided the trajectory of my research and deepened my knowledge

and appreciation of muscle. I would also like to extend my gratitude to all who helped

make this dissertation possible, including Ariel Camp whose research inspired my work

and whose insights helped shape my dissertation, Dave Ellerby for sharing his wealth of

knowledge of fish muscle and helping me catch fish, Anabela Maia for stimulating

conversations about fish biomechanics, and Steve Gatesy for valuable discussions. I

would like to thank all the faculty and staff in the department of Ecology and

Evolutionary Biology and members of the Brainerd Lab at Brown University. I am

grateful for the opportunity to have worked with so many incredible people who

contributed to my professional and intellectual development. My work would not have

been possible without the financial support from the National Science Foundation, the

Bushnell Graduate Fund at Brown, and the American Society of Ichthyologists and

Herpetologists.

I would like to thank all my family and friends for their unwavering love, prayers,

and support at every step of this journey. I am deeply grateful to my wife for bringing so

much love and joy into my life, and for always being a willing spirit when it came to

reviewing my work. This dissertation would not have been possible without her.

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Table of Contents

Preface and Acknowledgments ....................................................................................... xiiii

List of Tables .................................................................................................................. xvii

List of Illustrations ......................................................................................................... xviii

Chapter 1. Axial morphology and 3D neurocranial kinematics in suction-feeding fishes 1

Abstract ................................................................................................................... 2

Introduction ............................................................................................................. 3

Methods................................................................................................................... 7

Results ................................................................................................................... 15

Discussion ............................................................................................................. 20

References ............................................................................................................. 29

Figures................................................................................................................... 34

Chapter 2. Dual function of epaxial musculature for swimming and suction feeding in

largemouth bass ................................................................................................................ 42

Abstract ................................................................................................................. 43

Introduction ........................................................................................................... 44

Methods................................................................................................................. 47

Results ................................................................................................................... 51

Discussion ............................................................................................................. 54

References ............................................................................................................. 62

Figures................................................................................................................... 67

xvi

Chapter 3. Motor control of the epaxial musculature of bluegill sunfish in swimming and

suction feeding .................................................................................................................. 76

Abstract ................................................................................................................. 77

Introduction ........................................................................................................... 78

Methods................................................................................................................. 82

Results ................................................................................................................... 88

Discussion ............................................................................................................. 93

References ........................................................................................................... 100

Figures................................................................................................................. 107

Chapter 4. A biomechanical paradox in fish: swimming and suction feeding produce

orthogonal strain gradients in the axial musculature ...................................................... 113

Abstract ............................................................................................................... 114

Introduction ......................................................................................................... 115

Methods............................................................................................................... 118

Results ................................................................................................................. 121

Discussion ........................................................................................................... 122

References ........................................................................................................... 130

Figures................................................................................................................. 136

xvii

List of Tables

Chapter 2

Table S1. Summary of linear regression analyses for figures 4 and S3 ................75

Chapter 4

Table 1. Summary of major axis regression analysis ..........................................143

xviii

List of Illustrations

Chapter 1

Figure 1. Axial morphology and neurocranial elevation ......................................34

Figure 2. Determining the AOR and kinematic trajectory of the neurocranium ..35

Figure 3. Quantification of body form and bone area percentage in the region just

caudal to the neurocranium (means±s.e.m.) ..............................................36

Figure 4. Comparative axial morphology for three study species ........................37

Figure 5. Kinematic profiles of the neurocranium, relative to the body plane,

during the expansive phase ........................................................................38

Figure 6. Normalized AOR position for each species ..........................................39

Figure 7. Comparison of 2D and 3D methods for locating the AOR, using

simulated planar and non-planar neurocranial motion ...............................40

Figure 8. Methods for VROMM videography ......................................................41

Chapter 2

Figure 1. Anatomy of the axial musculature and electrode placement ................ 67

Figure 2. Percent of trials with above-threshold muscle activity for different

behaviors across different epaxial regions .................................................68

Figure 3. Normalized muscle activation intensity in three dorsoventral regions at

0.35 BL ......................................................................................................69

Figure 4. Effects of muscle activation at 0.35 BL on (a) sprinting and (b) suction

feeding performance ..................................................................................70

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Figure 5. Diagrammatic representation of the intensities and spatial distributions

of muscle activity associated with different behaviors and performance ......

....................................................................................................................71

Figure S1. Effects of prey type on peak pressure .................................................72

Figure S2. Sampling distributions for each individual .........................................73

Figure S3. Effects of muscle activation at 0.50 BL on suction feeding

performance ...............................................................................................74

Chapter 3

Figure 1. Electrode and sonomicrometer placement in the epaxial musculature ......

..................................................................................................................107

Figure 2. Muscle activity and shortening for high-performance suction feeding

and swimming ..........................................................................................108

Figure 3. Percent of trials with above-threshold muscle activity in different

epaxial regions for different behaviors ....................................................109

Figure 4. Normalized muscle activation intensity for each individual, region, and

behavior....................................................................................................110

Figure 5. Active muscle volume used for different behaviors in sunfish and bass ...

..................................................................................................................111

Figure 6. Bilateral symmetry of activation intensity for feeding and locomotion ....

..................................................................................................................112

Chapter 4

Figure 1. Patterns of longitudinal strain in the epaxial musculature for swimming

and suction feeding .......................................................................... 136-137

xx

Figure 2. Sonomicrometer placement and in vivo muscle length dynamics .............

.......................................................................................................... 138-139

Figure 3. Feeding produces a strain gradient anatomically orthogonal to

swimming .................................................................................................140

Figure 4. Muscle length dynamics in burst swimming followed by mixed

swimming and feeding behavior ..............................................................141

Figure S1. Sample EMG traces showing epaxial muscle activity in feeding and

locomotion ...............................................................................................142

1

CHAPTER 1

Axial morphology and 3D neurocranial kinematics in suction-feeding fishes

Yordano E. Jimenez1,2, Ariel L. Camp1, Jonathan D. Grindall2,3, and Elizabeth L.

Brainerd1,2

1Department of Ecology and Evolutionary Biology, Brown University, 80 Waterman

Street, Providence RI, 02912, USA.

2Friday Harbor Laboratories, University of Washington, 620 University Road, Friday

Harbor WA, 98250, USA.

3 School of Aquatic and Fishery Sciences, University of Washington, 1122 Boat Street,

Seattle WA, 98105, USA.

2

Abstract

Many suction-feeding fishes use neurocranial elevation to expand the buccal

cavity for suction feeding, a motion necessarily accompanied by the dorsal flexion of

joints in the axial skeleton. How much dorsal flexion the axial skeleton accommodates

and where that dorsal flexion occurs may vary with axial skeletal morphology, body

shape, and the kinematics of neurocranial elevation. We measured three-dimensional

neurocranial kinematics in three species with distinct body forms: laterally compressed

Embiotoca lateralis, fusiform Micropterus salmoides, and dorsoventrally compressed

Leptocottus armatus. The area just caudal to the neurocranium occupied by bone was

42±1.5%, 36±1.8% and 22±5.5% (mean±s.e.m.; N=3, 6, 4) in the three species,

respectively, and the epaxial depth also decreased from E. lateralis to L. armatus.

Maximum neurocranial elevation for each species was 11, 24, and 37°, respectively,

consistent with a hypothesis that aspects of axial morphology and body shape may

constrain neurocranial elevation. Mean axis of rotation position for neurocranial elevation

in E. lateralis, M. salmoides, and L. armatus was near the first, third, and fifth

intervertebral joints, respectively, leading to the hypothesis of a similar relationship with

the number of intervertebral joints that flex. Although future work must test these

hypotheses, our results suggest the relationships merit further inquiry.

3

Introduction

For many suction-feeding fishes, neurocranial elevation is an important motion

for expanding the buccal cavity and generating suction (e.g., Schaeffer and Rosen, 1961;

Carroll and Wainwright, 2006; Camp and Brainerd, 2014; Van Wassenbergh et al.,

2015). The neurocranium elevates by rotating dorsally about a transverse axis relative to

the body. To produce this elevation, the vertebrae linked to the neurocranium must flex

dorsally across one or more intervertebral joints (IVJs; this study includes the

craniovertebral joint among the IVJs, unless otherwise noted). Therefore, it has been

hypothesized that vertebral morphology is modified for permitting high degrees of

neurocranial elevation in some fishes (Lesiuk and Lindsey, 1978; Lauder and Liem,

1981; Huet et al., 1999). Despite this interest in vertebral specializations, few studies

have explored how the axial skeleton as a whole may affect neurocranial motions—and

conversely, how the requirement of neurocranial elevation may influence body shape and

axial skeletal structures.

Body shape and the morphology of the axial skeleton have typically been studied

in the context of lateral bending of the body for swimming (e.g., Gray, 1933; Hebrank et

al., 1990; Jayne and Lauder, 1995; Westneat and Wainwright, 2001; Long et al., 2002;

Porter et al., 2009; Nowroozi et al., 2012; Nowroozi and Brainerd, 2012, 2014; Moran et

al., 2016). It is reasonable to expect that they are also relevant to dorsal bending of the

body during suction feeding but few studies have examined them in this context. Without

data on both 3D neurocranial kinematics and axial morphology, it is difficult to

generate—let alone test—hypotheses of how axial and body morphology relate to

neurocranial elevation. In this study, we examine some aspects of axial skeletal

4

morphology (Fig. 1) and overall body shape together with 3D motions of the

neurocranium. While ligaments, tendons, and axial musculature also play important roles

in vertebral column flexion, the present study focuses on the axial skeleton and overall

body shape.

Axial morphology may be related to where along the vertebral column dorsal

flexion is likely to occur during neurocranial elevation. This location can be described by

the axis of rotation (AOR) between the neurocranium and the body of the fish. The AOR

is the pivot point between the neurocranium and the body at which neurocranial elevation

can be described as pure rotation without translation (Fig. 2A). As such, the AOR is the

center of rotation, with dorsal flexion of the IVJs likely distributed on either side of it

(except in rare cases of a single IVJ specialized for dorsal flexion, see below). Several

studies have estimated the position of the AOR using morphological observation,

specimen manipulation, and 2D kinematic analysis. Based on the manipulation of

specimens, Gregory (1933) suggested that the AOR is located near the post-temporal-

supracleithral joint, which Carroll et al. (2004) used as the AOR for calculating the

mechanical advantage of epaxial musculature in centrarchid fishes. Others have

suggested that the AOR is located at the craniovertebral joint based on its specialized

morphology (Lauder and Liem, 1981), or in the case of Rhaphiodon vulpinus, Lesiuk and

Lindsey (1978) inferred that the AOR must be located in the IVJ just posterior to the

Weberian apparatus, since the apparatus is relatively immobile. In other instances, the

AOR has been determined in largemouth bass and pipefish based on modified versions of

the Reuleaux method, which estimates the AOR from a 2D kinematic analysis of the

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position of the neurocranium at rest and at maximum elevation (Reuleaux, 1876; Van

Wassenbergh et al., 2008, 2015).

One aspect of axial morphology proposed to relate to neurocranial elevation is the

position and shape of the supraneurals, neural spines, and pterygiophores (Fig. 1). As part

of a larger study of epaxial muscle activity during suction feeding in largemouth bass,

Thys (1997) studied the supraneurals, neural spines, and pterygiophores located just

caudal to the neurocranium. X-ray images of largemouth bass postmortem showed these

bones moving closer together during moderate cranial elevation of just 8°, and Thys

hypothesized that these bones could become crowded together in a way that would limit

cranial elevation. Lesiuk and Lindsey (1978) also reported that neighboring neural spines

restrict ex vivo dorsal flexion in Rhaphiodon vulpinus. Hence, if the supraneurals, neural

spines, and pterygiophores together occupy a greater area of the region immediately

caudal to the neurocranium in some species relative to others, this "densely-packed"

configuration might be associated with lower magnitudes of neurocranial elevation

during suction feeding.

The morphology of the vertebrae and intervertebral joints may also influence the

magnitude and the location of dorsal body flexion, as suggested by studies of fishes with

extremely high (>45°) magnitudes of neurocranial elevation (Lesiuk and Lindsey, 1978;

Lauder and Liem, 1981; Huet et al., 1999). Studies of R. vulpinus (Lesiuk and Lindsey,

1978), Luciocephalus pulcher (Lauder and Liem, 1981), and Uranoscopus scaber (Huet

et al., 1999) each noted that some vertebrae appear to be specialized for dorsal flexion. In

particular, hypertrophied zygapophyses have been suggested to limit torsional (long-axis

rolling) motions of the neurocranium to protect the spinal cord from injury during

6

extreme dorsal flexion (Lauder and Liem, 1981). This suggests species with larger and

more closely articulated zygapophyses may show less torsion (roll) of the neurocranium

(Holmes, 1989), but neurocranial roll has so far been directly measured in only a single

species, largemouth bass (Camp and Brainerd, 2014).

The potential effects of body shape are less clear. Large neurocranial elevations

are possible across a range of body shapes as demonstrated by the laterally compressed R.

vulpinus, the fusiform L. pulcher, and the dorsoventrally compressed U. scaber. While

body width and length could potentially relate to neurocranial kinematics, body height

seems most likely to be related to neurocranial elevation. A tall body, with a tall

supraoccipital crest and tall epaxial musculature, has been hypothesized to enhance the

subambient pressures generated during suction feeding by increasing the moment arm

and physiological cross-sectional area for epaxial muscles (Carroll et al., 2004). But, for

the same amount of neurocranial rotation, the dorsal tip of a taller supraoccipital crest

will translate further caudally than that of a shorter crest. And more caudal motion of the

supraoccipital crest may be limited or prevented by the supraneurals, neural spines, and

pterygiophores (Fig. 1). Furthermore, the magnitude of cranial elevation in a deep-bodied

fish may be limited by the overall bulk of tissue located just caudal to the head. Thus, the

height of the body—and particularly the height of the epaxial region dorsal to the

vertebral column—could theoretically be related to the magnitude of neurocranial

elevation.

Our study presents a synthesis of 3D kinematics of the neurocranium, 3D

measurement of the AOR, and comparative morphology of three distantly related species.

Our goal is to generate more informed hypotheses about the relationships between axial

7

skeletal morphology, body shape, and neurocranial kinematics rather than to provide

definitive tests of those hypotheses. We began with descriptions of the body shape and

axial morphology of three species of suction feeders with a range of body shapes:

laterally compressed and tall-bodied, Embiotoca lateralis (striped surfperch); fusiform,

Micropterus salmoides (largemouth bass); and dorsoventrally compressed, Leptocottus

armatus (Pacific staghorn sculpin). We then recorded 3D motion of the neurocranium

and a body plane using XROMM (Brainerd et al., 2010) and VROMM (X-ray and Video

Reconstruction of Moving Morphology, respectively). Using a body plane as a frame of

reference, we measured 3D kinematics of the neurocranium and identified the position of

the AOR for neurocranial elevation. Finally, we quantified and compared the accuracy of

our 3D method with that of the 2D Reuleaux method, which has been previously used for

locating the AOR.

Methods

Animals

We collected Leptocottus armatus (Girard, 1854) and Embiotoca lateralis

(Agassiz, 1854) in the San Juan Islands, Washington, USA. Fish were housed at

University of Washington, Friday Harbor Labs in a flow-through seawater system. Fish

were fed shrimp and squid daily, which were also used in feeding trials. We collected

VROMM data for E. lateralis (N=4; 197, 201, 210, and 213 mm SL) and L. armatus

(N=4; 125, 210, 292, and 316 mm SL), and re-analyzed XROMM animations of

Micropterus salmoides (Lacépède, 1802; N=3; 201, 228, and 233 mm SL) originally

published in Camp and Brainerd, 2014. The University of Washington’s Institutional

8

Animal Care and Use Committee (IACUC) approved all collections, husbandry, and

experimental procedures.

Morphology

We CT (computed tomography) scanned 3-6 individuals from each species. In

addition to the three M. salmoides used for XROMM, we scanned three more individuals

(200, 248, and 255 mm SL) for morphological examination only. We scanned M.

salmoides at 0.625 mm slice thickness [Philips Medical System, Best, The Netherlands].

Scans for one E. lateralis and one L. armatus were made at 0.185 mm slice thickness or

lower [Animage FIDEX, Pleasanton, CA, USA]. Scans for the other three E. lateralis and

L. armatus were made at 0.142 mm slice thickness or lower [Microphotonics Skyscan

1173, Allentown, PA, USA]. Using Horos CT-imaging software (version 1.1; 64-bit;

www.horosproject.org), we rendered and examined the morphology of the axial skeleton,

including the shape, orientation, and spacing of bones. We were unable to find a CT

window width and height that showed the thin lamina of bone that fills the supraoccipital

crest of E. lateralis while also providing suitable contrast for the rest of the skeleton.

Therefore, we applied different contrast to the supraoccipital crest in this species to

reflect correctly the presence of bone in this area. To examine vertebral morphology

closely, we dissected out the first five vertebrae of one individual from each species. We

CT scanned the disarticulated vertebrae for higher resolution models of the morphology

on the Animage FIDEX scanner at 0.162 mm slice thickness or lower.

From CT cross-sections we measured the height and width of the body and the height and

width of the epaxial region. Longitudinal body position was standardized by selecting a

9

slice at the anteroposterior position of the second rib. For the epaxial region, we

measured the height of the body dorsal to the vertebral centrum and width across the

body at the dorsoventral level of the centrum.

From the CT scans we also measured the percentage of area occupied by bone in

the epaxial region just caudal to the head (area outlined in Fig. 1). The MIP (maximum

intensity projection) tool in Horos was used to create a sagittal thick slice spanning the

mediolateral width of the vertebrae and view the MIP of the slice from the lateral side.

We excised an image of the area bounded by the caudal aspect of the neurocranium, the

vertebral column, the caudal aspect of the first pterygiophore and the dorsum of the fish.

In ImageJ (1.51, Wayne Rasband, NIH), we thresholded the image to include all bone

within the threshold area and measured the percent of the area of the whole image

occupied by bone (i.e., neural spines, supraneurals and first pterygiophore). To test

reproducibility of this method we repeated the whole process three times on the same CT

scan for each species, starting with a new MIP in Horos through to measurement in

ImageJ, and found a maximum variation of ±1% of area in the measurements (e.g., bone

occupying 25±1% of 2D projected area behind the head).

VROMM

For VROMM, we anesthetized specimens with MS-222 and sutured white plastic

beads (1.8 mm diameter) onto the skin of the neurocranium (three to four beads) and

body (four to six beads). Markers were placed as far as possible from each other in a non-

linear arrangement (Fig. 8A), since 3D motion cannot be captured—i.e., one degree of

freedom is lost—when markers are arranged in a straight line, and wider marker

10

distributions can more accurately capture its motion. After marker attachment, we

returned the fish to its tank and allowed it to recover fully prior to starting feeding trials.

Three 1024 PCI Photron high-speed cameras (Photron USA Inc., San Diego, CA, USA)

recorded feeding events from three different perspectives (Fig. 8B,C). In some trials for

E. lateralis we used video from just two cameras, which is sufficient for 3D

reconstruction as long as the markers are visible in both cameras (Brainerd et al., 2010).

We filmed all strikes at 500 Hz with the exception of three trials for L. armatus, which

were filmed at 250 Hz. Before and after each set of feeding trials, we recorded images of

a calibration object—made from Lego bricks with precise 3D locations—inside the tank.

Using XMALab software (version 1.3.1; available from bitbucket.org/xromm/xmalab),

we calibrated the tank space with images of the 3D calibration object and tracked the

external markers to generate XYZ motion coordinates. The precision of tracking external

markers suffers from non-circular marker projections (Knörlein et al., 2016) and from

distortion produced by refraction when filming through multiple media (i.e., water and an

aquarium; Henrion et al., 2015). Nonetheless, the mean standard deviation of intermarker

distances for beads rigidly attached to the neurocranium for this VROMM study was

0.122 mm (based on the standard deviations for 150 pairwise distances from 31 strikes;

0.104 mm for E. lateralis and 0.182 mm for L. armatus) compared with 0.1 mm or less

for XROMM (Brainerd et al., 2010; Knörlein et al., 2016).

To generate 3D motion data from individual markers, we created rigid bodies in

XMALab, which uses the tracked XYZ coordinates of three or more markers to calculate

their motion as a single rigid body with six degrees of freedom. Two rigid bodies were

made, one for the neurocranium and one for a plane defining the position of the body,

11

based on the four to six beads attached to the body (Fig. 8A). Body beads are attached to

soft tissue and are expected to move slightly relative to each other and do not form a true

rigid body. When we calculate "rigid body" motion from 4-6 bead positions, the result is

a 3D pose that averages across the positions of all of the beads (Camp and Brainerd,

2014). In this study, during any given strike, the pairwise distances between the body

beads typically varied less than 0.2 mm and at most up to 1 mm.

Rigid body transformations (translations and rotations) were imported into

Autodesk Maya 2014 (San Rafael, CA, USA) to animate a polygonal mesh model of the

neurocranium and a polygon plane object for the body plane (Fig. 8C). To generate a

mesh model of the neurocranium for VROMM, we CT scanned the specimens with the

markers still attached (see “Morphology” section above for scan details). We created

mesh models of the neurocrania of all individuals in Horos with additional mesh

refinement in MeshLab (ISTI-CNR, Pisa, Italy). We also made mesh models of the first

five vertebrae for anatomical study, although none of the vertebral structures were

animated.

Given that our study includes data from separate XROMM and VROMM

projects, different prey types, sizes, and exclusion criteria were used. Prey were typically

delivered directly in front of the fish, but the position of the food item relative to the

mouth was highly variable (above, below, to a side), as it was often determined by how

the fish approached the prey. For M. salmoides, individuals were recorded feeding on live

goldfish (Camp and Brainerd, 2014). During most feeding trials for L. armatus and E.

lateralis, individuals were recorded performing a single successful strike on a piece of

shrimp or squid. However, in one trial, L. armatus was fed a small live shrimp, and in

12

another trial, the same individual missed on the first strike and performed two additional

strikes—only the first attempt was analyzed since it was the fastest of the three and

involved substantial cranial expansion. We excluded slow strikes with minimal

movement of the cranial elements (e.g., neurocranium, hyoid, and lower jaw). Both E.

lateralis and M. salmoides used neurocranial elevation for every strike. Conversely, L.

armatus sometimes showed no neurocranial elevation or even neurocranial depression,

particularly when taking food from the bottom of the aquarium. We only included strikes

in which L. armatus showed neurocranial elevation, including some bottom strikes,

resulting in seven useable strikes from one individual and just one acceptable strike per

individual from the other three.

Quantifying the AOR

To find the axis of rotation (AOR) we used joint coordinate systems (JCSs),

implemented in Autodesk Maya with the XROMM Maya Tools (version 2.1.1; available

from xromm.org/software). The XROMM and VROMM animations contained two rigid

bodies: the neurocranium and the body plane (Fig. 8A). The body plane provided a fish-

based frame of reference that moved with the fish but did not move as a part of cranial

expansion. To identify the AOR during neurocranial elevation, we created JCSs and

aligned them with the long axis of the fish as defined by the neurocranium and body

plane. JCSs consist of proximal and distal anatomical coordinate systems (ACS). The

proximal ACS was fixed to the body plane and the distal ACS followed the

neurocranium, allowing the JCS to measure neurocranial motion as rotations and

translations relative to the body plane.

13

JCSs were oriented so that Z-axis rotation represents neurocranial elevation (i.e.,

dorsal rotation), Y-axis rotation corresponds to cranial yaw (i.e., lateral rotation to the left

or right), and X-axis rotation to cranial roll (i.e., long-axis rotation). All rotations and

translations refer to motion of the neurocranium relative to the body plane, with zero

rotation defined as the pose of the fish before the onset of neurocranial elevation in each

strike, and zero time as the frame before the onset of neurocranial elevation.

Each JCS at a different position generates different translation values, so we located the

AOR by placing numerous JCSs at different anteroposterior and dorsoventral positions of

the fish in 1-mm increments. This method is based on the principle that even a purely

rotational motion can be measured as having translation, depending on the position of the

JCS relative to the AOR (Fig. 2B). For example, if a JCS is placed anterior to the AOR,

then the magnitude of Y-axis translation will increase in proportion to its distance from

the AOR. Similarly, if the JCS is placed dorsal to the AOR, then X-axis translation will

increase.

To find the anteroposterior position of the AOR for neurocranial elevation, we

placed multiple JCSs in the animation space for each strike until a clear trend in Y-axis

translation values revealed its position (Fig. 2B). We repeated this procedure for finding

the dorsoventral position, but instead of sampling anteroposterior positions and

minimizing Y-translation, we sampled dorsoventral positions and minimized X-

translation. We quantified the position of the AOR as x- and y- coordinates of the

distance (cm) from the occiput. These distances were then normalized to vertebral

centrum lengths (using V5 of each individual) to compare AOR positions among

individuals and across species.

14

Comparison of 2D and 3D methods

We compared the accuracies of 2D (Reuleaux, 1876) and 3D methods using

simulated rotations about AORs placed three distances posterior to the occiput: 0, 10, and

20 mm (Fig. 7). To simulate neurocranial motion during feeding, we created an animation

in Maya containing a neurocranium (with anatomical landmarks) and a body plane. We

applied known rotations to the neurocranium using Maya’s keyframe tool, which allowed

us to set the neurocranium’s initial and final position. The intervening positions were

interpolated by the software to produce a smooth motion from rest to peak neurocranial

elevation. We performed two trials for each of the three AOR positions: one with planar

motion of the neurocranium and one with non-planar 3D motion. Starting from a resting

position, in the first trial (planar motion), the neurocranium was rotated only about a

mediolateral axis, resulting in a total of 15° of neurocranial elevation. In the second trial

(non-planar motion), the neurocranium again reached 15° of elevation from the same

resting position but was also rotated with 7° of yaw (about a dorsoventral axis) and 3° of

roll (about an anteroposterior axis).

We analyzed the simulated data using both the 2D Reuleaux method (Reuleaux,

1876; Chen and Katona, 1999) and the 3D JCS method used in this paper (Fig. 7). The

Reuleaux method locates the AOR by tracking at least two landmarks with 2D kinematics

(Fig. 7A). We tracked six landmarks that were arbitrarily placed on the neurocranium.

We connected the starting (rest) and final (peak neurocranial elevation) positions of each

landmark with a straight line. Finally, we drew perpendicular lines that bisected each

straight line. The point of intersection between the bisecting lines is the estimated

position of the AOR (Fig. 7A). The JCS method locates the AOR by quantifying relative

15

motion between rigid bodies (Figs 2, 7B). For the Reuleaux method, error was measured

as the mean (±s.e.m.) linear distance between the estimated and known AORs.

Statistical analysis

Means and s.e.m. were calculated by taking the mean for each individual and then

the mean of means, with sample size being the number of individuals. To test for inter-

individual and interspecific differences in neurocranial rotations, we performed nested

ANOVAs, with individuals nested within species. Means were calculated from the peak

magnitudes attained during each strike for each of the three neurocranial rotations (i.e.,

elevation, yaw, and roll). For yaw and roll, we ignored directionality (positive and

negative) and instead calculated their means from their absolute values. Then we

conducted Tukey post-hoc tests of pairwise species comparisons to test for significant

differences between species. Statistical significance was declared at the 0.05 probability

level, and all tests were performed using JMP Pro 12.2.0 (SAS Institute Inc., Cary, NC).

Results

Axial morphology

The overall body shape of E. lateralis is tall and laterally compressed, with a

height to width ratio of 2.5±0.17 (mean±s.e.m., N=3) for the whole body and 1.0±0.11 for

the epaxial region (Fig. 3). The axial skeleton in the region just caudal to the head is

comprised of long neural spines, supraneurals and pterygiophores (Fig. 4A). The first two

supraneurals lie adjacent to the anterior aspects of the neural spines of V1 and V2,

respectively, and the third supraneural is positioned posterior to the neural spine of V2.

16

The neural spines from V2 and V3 form an open wedge, inside which the first

pterygiophore and the ventral process from the third supraneural bone are positioned,

effectively filling the space (Fig. 4A). The second pterygiophore lies adjacent to the

posterior aspect of the V3 neural spine, and the third and all subsequent pterygiophores

nearly touch the anterior aspects of the neural spines. The first three vertebrae also have

pre- and post-zygapophyses, with the exception of V1 which lacks pre-zygapophyses

because it is fused with the occiput (Fig. 4D). Post-zygapophyses are robust and protrude

from the lateral body of the centrum, crossing the joint and articulating inferior to the pre-

zygapophysis of the subsequent vertebra. Posterior to V4, the vertebral centra are spool

shaped and lack zygapophyses, with V4 being transitional between the vertebrae with and

without zygapophyses (Fig. 4D).

The overall body shape of M. salmoides is fusiform, with a height to width ratio

of 1.8±0.10 (mean±s.e.m., N=6) for the whole body and 0.66±0.01 for the epaxial region

(Fig. 3). The fusiform body of M. salmoides has shorter neural spines and less overlap

between the second and third neural spines and the supraneurals than in E. lateralis (Fig.

4B). The neural spines of V1 and V2 and the first two supraneurals lie in close

apposition, but the remaining neural spines are more widely spread apart from each other.

The first supraneural lies adjacent to the anterior aspect of the neural spine of V1, a

pattern that is repeated for the two subsequent supraneurals (Fig. 4B). No supraneurals or

pterygiophores fill the space between the neural spines of V3 and V4. The first and

second pterygiophores fill the space between the neural spines of V4 and V5, just as the

third and fourth pterygiophores fill the space between the neural spines of V5 and V6.

Starting with the fifth pterygiophore, only one pterygiophore inserts between each pair of

17

neural spines. Like E. lateralis, the zygapophysial joints start at V1 and begin to decrease

in size around V4 (Fig. 4E).

The body of L. armatus is dorsoventrally compressed with a height to width ratio

of 0.79±0.07 (mean±s.e.m., N=4) for the whole body and 0.34±0.03 for the epaxial

region (Fig. 3). Unlike the other two species, it lacks supraneural bones entirely, resulting

in a gap between the neurocranium and the first pterygiophore (Fig. 4C). Neural spines

and pterygiophores overlap and contact each other in a similar manner to M. salmoides.

The first and second pterygiophores surround the anterior and posterior aspects of the

neural spine of V3, whereas the remaining pterygiophores are evenly distributed between

neural spines. In L. armatus, zygapophysial joints are present from V1 to V5 (Fig. 4F),

though these vertebrae are not associated with any distinct supraneural arrangement like

E. lateralis and M. salmoides. V5 is transitional for vertebrae with articulating

zygapophyses and vertebrae with non-articulating zygapophyses. Vertebrae with non-

articulating zygapophyses have relatively larger intervertebral spaces (spaces between the

vertebral centra) than the more cranial joints (Fig. 4C).

The bones in the area just caudal to the supraoccipital crest of the three species

occupy different proportions of the available space (Fig. 3C). From the images, the neural

spines, supraneurals and pterygiophores appear to be most “densely packed” in E.

lateralis (Fig. 4A), followed by M. salmoides with substantial spaces between the

supraoccipital crest and the first supraneural and between the third supraneural and first

pterygiophore (Fig. 4B). Supraneurals are absent in L. armatus, making this species

appear to have the most open space behind its head (Fig. 4C). To quantify these

observations, we measured the percent area occupied by bone and found a mean (±s.e.m.)

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of 42±1.5% in E. lateralis, 36±0.8% in M. salmoides and 22±5.5% in L. armatus (Fig.

3C). One-way ANOVA with species as the effect showed a significant overall effect of

species on percentage of bone (P<0.0001) and Tukey pairwise post-hoc tests showed that

all three species were significantly different from each other (p<0.05). These quantitative

results confirm that the neural spines, supraneurals and pterygiophores are most densely

packed in E. lateralis, followed by M. salmoides and then L. armatus.

3D cranial kinematics and the AOR

All three species used neurocranial elevation as part of their suction strikes (Fig.

5). As mentioned above, L. armatus sometimes showed no neurocranial elevation or even

neurocranial depression; only strikes that used neurocranial elevation were included for

L. armatus. Although 3D neurocranial kinematics of the M. salmoides strikes have been

reported previously (Camp and Brainerd, 2014), in that study they were measured using a

JCS positioned at the craniovertebral joint. In this study, we report the 3D neurocranial

kinematics of these strikes as measured by a JCS placed at the AOR, in order to make

them directly comparable to those of L. armatus and E. lateralis.

Neurocranial kinematics were measured with a JCS placed at the AOR during the

expansive phase of each strike—from the onset to the peak of neurocranial elevation.

Means (±s.e.m.) for maximal neurocranial elevation were 7.9±0.7° for E. lateralis (N=4

individuals), 14.4±2.2° for M. salmoides (N=3 individuals), and 11.2±2.8° for L. armatus

(N=4 individuals). Nested ANOVA, with individual nested within species, yielded a

significant overall model (P<0.0001) with significant effects of both individual

(P=0.015) and species (P=0.00046). Tukey pairwise post-hoc tests showed that maximal

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neurocranial elevation was statistically significantly higher in M. salmoides than in E.

lateralis (p<0.05), although L. armatus was not significantly different from either of the

other two species (p>0.05). Mean (±s.e.m.) duration of the expansive phase was 37.9±3.3

ms for E. lateralis, 63.0±9.9 ms for M. salmoides, and 125.4±13.3 ms for L. armatus. The

maximum observed neurocranial elevation across all strikes for each species was 11.1°

for E. lateralis, 23.8° for M. salmoides, and 36.5° for L. armatus.

In addition to elevating, the neurocranium in all three species yawed and rolled

relative to the body plane (Fig. 5). Cranial yaw denotes the magnitude and direction that

the fish turned its head to the left or right relative to the body during the strike (positive is

to the fish’s left, negative to the right). Means for the magnitude (absolute value) of

maximal yaw were 1.7±0.2° for E. lateralis, 5.0±1.1° for M. salmoides, and 6.3±1.2° for

L. armatus. Nested ANOVA yielded a significant overall model (P=0.0025) with a

significant effect of species (P=0.0025) but not individual (P=0.44). Tukey post-hoc tests

showed that maximal yaw in M. salmoides and L. armatus was not statistically

significantly different (P>0.05), although both had significantly higher yaw than E.

lateralis (P<0.05).

Cranial roll denotes the magnitude and direction that the fish rotated its head

about its long axis (Fig. 5). Means for the absolute magnitude of maximal roll were

0.9±0.1° for E. lateralis, 2.1±0.3° for M. salmoides, and 3.5±0.9° for L. armatus. Nested

ANOVA yielded a significant overall model (P<0.0001) with significant effects of

species (P<0.0001) and individual (P=0.05). Tukey post-hoc tests showed that maximal

roll was significantly different between all three pairs of species (P<0.05).

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The mean AOR for neurocranial elevation was located closest to the occiput

(craniovertebral joint) in E. lateralis, more caudally in M. salmoides, and most caudally

in L. armatus (Fig. 6). The AORs in all three species were distributed across a wide range

of positions (Fig. 6). In E. lateralis, most AORs were clustered around the occiput,

though two strikes had AORs in positions far dorsal to the vertebral column. Close

examination and re-estimation of AORs for these two outliers reaffirmed the measured

position and yielded no methodological explanation for the far dorsal position. The AORs

in M. salmoides were clustered between the second and fourth IVJs—posterior to those of

E. lateralis—and were almost always at the dorsoventral level of the vertebral column.

Finally, AORs in L. armatus were widely and sparsely distributed across the

anteroposterior and dorsoventral axes.

Discussion

We found significant differences in the percent area of bone occupying the space

behind the head in our three species (Fig. 3C), but our expectation that less bone area and

shorter epaxial height would correlate with more neurocranial elevation was only

partially borne out. Mean neurocranial elevation was higher in M. salmoides than in E.

lateralis, as expected, but L. armatus produced a highly variable amount of neurocranial

elevation, such that the mean magnitude was not significantly different from either of the

other two species (Fig. 5). However, if we consider the maximum magnitude of cranial

elevation observed for each species across all strikes, the pattern does match our

expectations with bone area decreasing in the order E. lateralis, M. salmoides, L. armatus

(Fig. 3) and maximum observed neurocranial elevation increasing in the order E. lateralis

21

(11.1°), M. salmoides (23.8°) and L. armatus (36.5°). The large magnitude of

neurocranial elevation in L. armatus was accompanied by the absence of supraneurals

and the enlarged intervertebral spacing posterior to V5 (Fig. 4C). Conversely, E. lateralis

had the tallest body and epaxial ratio (Fig. 3A-B) and an axial skeleton with long

interdigitating bones (Fig. 4A), along with the least amount of neurocranial elevation.

These data suggest that axial skeleton morphology may indeed be related to the

magnitude and location of dorsal flexion of the vertebral column. However, the data

presented in this study are correlative, and in vivo measurements of the kinematics of the

axial skeleton are needed to test this hypothesis. Furthermore, claims regarding maximal

performance must be made cautiously, as Astley et al. (2013) have previously pointed

out, these claims are more easily falsified than verified. Despite our attempts to elicit

maximal performance, it is possible that such strikes did not occur in this study and that

our study species can produce greater amounts of neurocranial elevation than we

observed.

The anteroposterior position of the AOR for neurocranial elevation varied among

our study species with the mean AOR located closest to the craniovertebral joint in E.

lateralis, farther back in M. salmoides, and most caudal in L. armatus (Fig. 6). In some

instances, E. lateralis and L. armatus had AORs outside the body. Similarly, Van

Wassenbergh et al. (2008) found that when producing neurocranial elevation, pipefish

typically have AORs located outside the body. The mean AORs in E. lateralis and M.

salmoides were located close to the area occupied by supraneurals; very few strikes had

AORs in the region with interdigitating neural spines and pterygiophores. Conversely,

only some AORs in L. armatus were found in the space where supraneurals are absent,

22

and the majority occurred in the region with neural spines and pterygiophores.

Interestingly, that is the region where the spacing of the intervertebral joints (IVJ)

appears to be relatively larger in L. armatus than in the other species (Fig. 4C). These

patterns suggest there could be a relationship between vertebral flexion and the

distribution of the IVJs, neural spines, supraneurals, and pterygiophores.

Despite producing similar mean amounts of neurocranial elevation, M. salmoides

and L. armatus had very different AOR positions. This shows that kinematic similarities

(i.e., magnitude of neurocranial elevation) do not necessarily indicate similar patterns of

dorsal flexion in the IVJs. Conversely, M. salmoides and E. lateralis may be flexing a

similar number of IVJs, but it is likely that M. salmoides is flexing them to a greater

extent to produce more neurocranial elevation than E. lateralis. The more posterior AORs

in L. armatus suggest that they typically distribute dorsal flexion across more IVJs than

E. lateralis and M. salmoides.

In some fishes, neurocranial elevation has been thought to be primarily the

product of flexion at a single joint, based on the specialized appearance of the

surrounding vertebrae (Lesiuk and Lindsey, 1978; Lauder and Liem, 1981). Others have

suggested that multiple joints flex to produce a smooth curve during dorsal flexion in

stargazers (Huet et al., 1999), and still others surmised that the first four to five vertebrae

are involved (Liem, 1978). While our study does not definitively resolve this issue, there

is good reason to think that the species we studied exhibit the latter condition. With the

exception of E. lateralis, nearly all strikes have AORs posterior to the craniovertebral

joint position, and we suggest that flexion is distributed across multiple joints, especially

in strikes that have more caudal AORs. The alternative is that all flexion occurs at a

23

single joint. However, this scenario is biologically implausible since it would require

neurocranial elevation—which often exceeds 10°—to be the sole product of dorsal

flexion at a single IVJ. The degree of IVJ dorsal flexion during feeding has not been

quantified, although Nowroozi and Brainerd (2013) used marker-based XROMM to

quantify axial skeletal motion during the startle response of Morone saxatilis. They found

that dorsal flexion of the IVJs is usually under 5°, and that lateral flexion rarely exceeds

10° in the cervical region, so it seems unlikely that dorsal flexion can exceed 10°. Finally,

even if a single joint could produce all the dorsiflexion for neurocranial elevation, some

mechanism would still be needed to restrict the motion of the surrounding joints that are

otherwise mobile.

Yaw and roll

Rotation about the Y-axis of our JCS (green axis in Fig. 7B) measures yaw of the

head relative to the body and can result from lateral flexion of the body in the region of

the body plane, as well as yaw at the IVJs near the cranium. All three species sometimes

exhibited substantial amounts of yaw, with maximum values exceeding 50% of those for

neurocranial elevation of the strike (Fig. 5). Neurocranial yaw has a more variable

kinematic profile than neurocranial elevation because it is directional (i.e., fish can use

left and right yaw). This directionality reflects how yaw may be used for repositioning

the mouth during prey capture, although this was not measured in our study. Being able

to yaw may allow predators to adjust their strike and place elusive prey within the suction

flow field or directly inside the mouth (Muller et al., 1982; Ferry-Graham et al., 2003;

Day et al., 2005; Higham et al., 2006). While it is possible that differences in the ability

24

to yaw and aim place constraints on feeding behavior and performance, the contributions

of lateral flexion to suction feeding are likely only of secondary importance—unlike the

clear role of lateral flexion in locomotion (Jayne and Lauder, 1995; Westneat and

Wainwright, 2001).

Rotation about the X-axis of our JCS (red axis in Fig. 7B) measures roll of the

head about its long axis, relative to the body. Because neurocranial roll does not appear to

confer any advantage to suction feeding, we were surprised by the amount of cranial roll

detected—although of the three cranial rotations measured, it still had the lowest

magnitudes for each species (Fig. 5). Cranial roll is possibly a byproduct of simultaneous

neurocranial elevation and yaw, which may destabilize the body and produce superfluous

motion in the form of axial torsion. This destabilizing effect may explain why roll in L.

armatus, which produces the most motion across the most joints, is so high. We predicted

that more well-developed zygapophyses may act to prevent roll but we found the

opposite correlation in our three study species. We observed the most roll in L. armatus,

which had distinct zygapophyses on the first five vertebrae (Fig. 4F), less roll in M.

salmoides with zygapophyses on the first four vertebrae (Fig. 4E), and the least roll in the

species with zygapophyses on just the first three vertebrae, E. lateralis (Fig. 4D). It

appears that other features, such as overall body form, may influence the amount of

cranial roll more than the number of zygapophyses.

Advantages and limitations of VROMM

VROMM shows promise as an alternative to XROMM for measuring 3D skeletal

kinematics without X-ray imaging, but only for situations where external markers can be

25

rigidly attached to the bones, as is the case in the dermal bones of ray-finned fishes with

tightly attached skin. The overall marker tracking precision for this study was 0.122 mm,

compared with 0.1 mm or better for XROMM (Knörlein et al., 2016). Several factors

account for the lower precision of VROMM, including refraction, external marker

motion, and calibration object design. Because VROMM uses standard (not X-ray)

cameras, images can be distorted by the refraction that occurs at the air-tank-water

interface. This is especially true when cameras are positioned more obliquely to the tank,

since higher angles of incidence increase refraction and may reduce precision (Henrion et

al., 2015).

We found lower precision for L. armatus than E. lateralis (0.182 versus 0.104

mm), which may have been caused by greater effects of refraction and by greater marker

motion. The effect of refraction is likely to be greater when the animal moves across the

tank during a strike, perhaps explaining why trials were less precise for L. armatus—

which tended to use more body ram—than for E. lateralis. It is also possible that skin

deformations and whole-body movements caused the external markers to move,

especially in L. armatus which had looser skin.

Accuracy of 2D and 3D methods for measuring AOR

The JCS method was generally more accurate than the Reuleaux method when

analyzing non-planar motions. Optimizing the accuracy of the Reuleaux method requires

that certain conditions be met. For example, two landmarks should never form—or come

close to forming—a straight line with the AOR (Fig 7A). This can result in high errors,

since near-parallel lines must extend for longer distances before intersecting. It follows

26

that landmark placement relative to the AOR requires careful consideration when using

the 2D method, but this can only be accomplished if the AOR position is assumed a

priori. Therefore, obtaining high errors with this method becomes more likely when we

consider the high variability of AOR positions among and within species (Fig. 6).

The Reuleaux method also requires a high level of confidence that the motion is

planar, since non-planar motions increase error (Fig. 7C). One way this issue is

commonly avoided is by limiting analysis to footage where the camera is aimed

perpendicular to the animal. But this should be done cautiously, as there can be instances

when motion is non-planar even when perpendicular views are acquired. While tradeoffs

exist between the Reuleaux and JCS methods (e.g., between ease of use and accuracy),

they share the assumption that there is a single AOR. This assumption is reasonable,

especially for the original purpose of the Reuleaux method, which was to describe the

kinematics of machines consisting of rigid parts with constrained motions (Reuleaux,

1876). However, since biological motion is not always so uniform and constrained, the

AOR may change dynamically throughout a behavior. For the purposes of this study,

finding the overall AOR is useful for approximating where dorsal flexion may occur

along the vertebral column. Finally, the JCS method doubled as a tool for both locating

the AOR and measuring 3D neurocranial motion, the latter of which the Reuleaux

method cannot do.

Concluding remarks

Correlating kinematics and the AOR for neurocranial elevation with axial skeletal

morphology is valuable for exploring the role of the axial skeleton during suction

27

feeding. The magnitude of neurocranial motion can approximate the degree of dorsal

flexion the axial skeleton must accommodate, and the AOR can approximate where that

flexion is likely to occur. A more extensive comparison of diverse morphologies and a

more quantitative analysis of in vivo skeletal motion may uncover the relationship

between body shape, axial morphology, and neurocranial kinematics.

Our data show that neurocranial motions during suction feeding are not only 3D,

but that interspecific variation can exist in all dimensions. Neurocranial motions are made

possible by flexion in the cervical region, making their motions neck-like. Since yaw and

roll were sensitive to directionality (Fig. 5), these values likely represent only half the

total flexibility of the vertebral column, such that a mean absolute value of 5° yaw

indicates a total range of motion of 10° (i.e., from left to right). The cervical vertebrae in

Morone saxatilis undergo substantial amounts of lateral flexion during some C-starts

(Nowroozi and Brainerd, 2013), further supporting the idea that fish cervical vertebrae

are not only distinct in their morphology and mechanical properties, but also in their

motions. Given this added complexity, it is unclear how the axial morphology may be

optimized for both feeding and swimming motions and whether functional tradeoffs are

made. This study underscores the importance of studying post-cranial morphology to

understand suction feeding fully.

28

Acknowledgements

We thank Adam Summers and Ben Knörlein for their technical expertise,

J. D. Laurence-Chasen and Thomas Martinson for their assistance with collecting and

processing data, Florian Witzmann for his insights, as well as the staff of University of

Washington, Friday Harbor Labs. This research was supported by the National Science

Foundation [1661129] to E.L.B., and [1655756] to E.L.B and A.L.C, a BBSRC Future

Leader Fellowship to A.L.C., the Blinks Research Fellowship to Y.E.J. and the Bushnell

Research and Education Fund to Y.E.J.

Data availability

Data have been deposited and opened for public use in the XMAPortal (xmaportal. org),

in the studies ‘Largemouth Bass Feeding’, with permanent ID BROWN6 and ‘VROMM

Suction Feeding in Fishes’ with permanent ID BROWN50. Video data stored in

accordance with best practices for video data management in organismal biology

(Brainerd et al., 2017).

29

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34

Figures

Figure 1. Axial morphology and neurocranial elevation. Lateral view of the axial

skeleton in the region hypothesized to undergo dorsal flexion during neurocranial

elevation. Shaded in green is the region used for taking bone area measurements.

Illustration is based on the morphology of Micropterus salmoides in a resting position.

BP, body plane; NC, neurocranium; NS, neural spine; Occ, occiput; Pty, pterygiophore;

SN, supraneural; SOC, supraoccipital crest; V, Vertebra.

35

Figure 2. Determining the AOR and kinematic trajectory of the neurocranium. (A)

The starting and final positions of neurocranial elevation. The intersection of the lines

through the neurocranium marks the position of the axis of rotation (AOR). (B)

Hypothetical JCS positions for measuring neurocranial motion. As shown by points 1

(red) and 2 (orange), dorsoventral and anteroposterior placement of a JCS affects

measured X- and Y-translation values, respectively. Translation is reduced only when the

JCS is positioned closer to the actual AOR and translation is zero at the AOR where

motion is captured as a pure rotation. (C) Three examples of neurocranial elevation, with

final neurocranium positions and AORs in corresponding colors, showing the kinematic

significance of AOR position.

36

Figure 3. Quantification of body form and bone area percentage in the region just

caudal to the neurocranium (means±s.e.m.). (A) Body depth measured as dorsoventral

body height divided by width. (B) Depth of the epaxial area. (C) Area percentage

occupied by bone in a 2D X-ray projection of the epaxial area between the neurocranium

and the first dorsal pterygiophore.

37

Figure 4. Comparative axial morphology for three study species. (A-C) Lateral view,

CT volume renderings of (A) E. lateralis, (B) M. salmoides and (C) L. armatus. Images

are maximum intensity projections with inverted gray levels to make X-ray positive

images. (D-F) Mesh models of the first five vertebrae of (D) E. lateralis, (E) M.

salmoides and (F) L. armatus. Mesh models were made from dissected and rearticulated

vertebrae, and are shown in lateral view with cranial to the left. For A, a different contrast

was applied to the supraoccipital crest, relative to the rest of the skeleton, to accurately

show the bone in this area. Occ, occiput; PostZyg, post-zygapophysis; PreZyg, pre-

zygapophysis; V1-V5, vertebrae 1 to 5.

38

Figure 5. Kinematic profiles of the neurocranium, relative to the body plane, during

the expansive phase. In each graph, each line represents rotation during a single strike.

Lines are color coded by strike, so each strike has the same color for all rotations.

Positive roll indicates clockwise rotation of the head (from an anterior view). Positive

yaw indicates the head turning to the left (from a dorsal view). Positive neurocranial

elevation denotes upward head lifting. The Y-axis of each graph is scaled the same

throughout, whereas the X-axis is scaled differently for each species, with time 0

indicating the frame before the onset of neurocranial elevation.

39

Figure 6. Normalized AOR position

for each species. The position of the

AOR for each strike (open circles) is

plotted as the dorsoventral and

anteroposterior distances (normalized

to vertebral centrum length) of the

AOR from the occiput. (A) E. lateralis,

(B) M. salmoides, and (C) L. armatus.

The occiput is the origin (0, 0), with

positive X-and Y-coordinates denoting

caudal and dorsal positions,

respectively. For each species, the

average AOR position is shown as a

large, filled circle. AOR was measured

with the 3D JCS method (Fig. 7B).

40

Figure 7. Comparison of 2D and 3D methods for locating the AOR, using simulated

planar and non-planar neurocranial motion. (A) The 2D Reuleaux method uses

changes in landmark position (red and orange circles) to estimate the true AOR (gray

circle). Each landmark is connected by a line (black dashed arrows), which is then

bisected by a perpendicular line (black solid lines). The intersection of any two

perpendicular lines is the estimated AOR position. Pairwise combinations of landmarks

generate a total of 15 AOR estimates (green circles). (B) The 3D method creates multiple

joint coordinate systems (JCSs) to identify the position that minimizes translation values.

(C) Both methods were used to calculate the AOR from simulations of both planar (black

triangles) and non-planar (gray triangles) neurocranial elevation at three distances

posterior to the occiput. Error for the Reuleaux method was calculated as the mean

(±s.e.m.) linear distance between the 15 estimated AORs and the actual AOR. Maximum

error of the 3D method is equal to the JCS sampling density, 1 mm for these simulations.

41

Figure 8. Methods for VROMM videography. All images are from E. lateralis. (A)

Lateral view, generalized marker attachment sites for the neurocranium (NC) and body

plane (BP). (B) Dorsal view, camera positions used for feeding trials. Blue shading shows

each camera view, with the darkest area showing where the fish is visible from all three

cameras (though only two camera views are needed). (C) High-speed video frames at

mid-strike from all three camera views with VROMM-animated models of the

neurocranium and body plane superimposed. Markers on the pectoral girdle and hypaxial

region are visible but were not analyzed in this study.

42

CHAPTER 2

Dual function of epaxial musculature for swimming and suction feeding in largemouth bass

Yordano E. Jimenez1 and Elizabeth L. Brainerd1

1Department of Ecology and Evolutionary Biology, Brown University, Providence, RI,

USA.

43

Abstract

The axial musculature of many fishes generates the power for both swimming and

suction feeding. In the case of the epaxial musculature, unilateral activation bends the

body laterally for swimming, and bilateral activation bends the body dorsally to elevate

the neurocranium for suction feeding. But how does a single muscle group effectively

power these two distinct behaviors? Prior electromyographic (EMG) studies have

identified fishes’ ability to activate dorsal and ventral epaxial regions independently, but

no studies have directly compared the intensity and spatial activation patterns between

swimming and feeding. We measured EMG activity throughout the epaxial musculature

during swimming (turning, sprinting, and fast-starts) and suction feeding (goldfish and

pellet strikes) in largemouth bass (Micropterus salmoides). We found that swimming

involved obligate activation of ventral epaxial regions whereas suction feeding involved

obligate activation of dorsal epaxial regions, suggesting regional specialization of the

epaxial musculature. However, during fast-starts and suction feeding on live prey, bass

routinely activated the whole epaxial musculature, demonstrating the dual function of this

musculature in the highest performance behaviors. Activation intensities in suction

feeding were substantially lower than fast-starts which, in conjunction with suboptimal

shortening velocities, suggests that bass maximize axial muscle performance during

locomotion and underutilize it for suction feeding.

44

Introduction

For over 500 million years, axial musculature has been integral to the undulatory

locomotion of fishes. After 200 million years of functioning as a locomotor structure

(Fletcher et al., 2014; Webb, 1982), many fishes co-opted the axial musculature for high-

performance suction feeding (Lauder, 1982; Longo et al., 2016; Schaeffer and Rosen,

1961) and integrated the cranial feeding apparatus with the post-cranial musculature.

Although outsourcing power to the trunk muscles enabled fishes to generate powerful

cranial expansion, it placed new mechanical demands on the axial musculature since,

unlike locomotion, which involves lateral flexion of the trunk and tail, suction feeding

involves dorsiflexion of the head and caudoventral rotation of the pectoral girdle. So how

do fish modulate contraction patterns of the axial musculature to produce both swimming

and suction feeding?

One possibility is that each of the two behaviors uses different regions within the

epaxial and/or hypaxial musculature. In this study we focus in on the epaxial

musculature, but a parallel study of hypaxial musculature, particularly in a species that

relies heavily on hypaxial musculature for suction feeding (e.g. catfishes; Van

Wassenbergh et al., 2005) would also be interesting. For epaxial regionalization, a

common view has been that the epaxial musculature just behind the head generates power

for suction feeding (Coughlin and Carroll, 2006; Lauder, 1980; Liem, 1978), while the

remainder of axial musculature is used primarily for locomotion (Ellerby and Altringham,

2001; Franklin and Johnston, 1997; Shadwick et al., 1998). It was only recently

discovered that the axial musculature in some fishes generates over 95% of the power for

suction feeding (Camp et al., 2015; Camp et al., 2018). This expansion power originates

45

from muscle shortening in the cranial two-thirds of the body, which comprises over 70%

of the white axial muscle mass(Camp and Brainerd, 2014). Consequently, swimming and

suction feeding appear to use much of the same muscle mass to generate power,

challenging the notion of a craniocaudal division of labor.

We reviewed prior studies on the activation of the epaxial musculature in

swimming and suction feeding in largemouth bass (Jayne and Lauder, 1995; Thys, 1997)

and synthesized their findings to develop a new hypothesis. We propose that fishes

modulate muscle function by selectively activating ventral regions of the epaxial

musculature for swimming and dorsal regions for suction feeding. We developed this

hypothesis based on the following electromyographic (EMG) evidence. In bass, suction

feeding always activated muscle in the dorsal epaxial regions between 0.15 and 0.25

body lengths (BL), while the ventral regions remained inactive (Thys, 1997). In contrast,

burst swimming (i.e., locomotor behaviors that activate the white musculature) always

activated muscle in the ventral epaxial regions near the tail at ~0.75 BL (Jayne and

Lauder, 1995). Activation of dorsal epaxial regions seemed to depend on the type of burst

swimming (Jayne and Lauder, 1995). For example, fishes consistently activated all

epaxial regions when performing rapid escape and predatory maneuvers (i.e., fast-starts);

however, they activated only the ventral epaxial regions for sprinting, leaving the dorsal

regions inactive (Jayne and Lauder, 1995; also see Ellerby and Altringham, 2001 for

similar results in trout). These studies speculated that differences between sprints and

fast-starts produced these recruitment patterns, but they did not quantify the relationship

between performance and muscle activation patterns.

46

These studies provide a glimpse into the potential contributions of motor control

to the dual function of the epaxial musculature, but several factors preclude our ability to

draw conclusions from the existing data with confidence. Firstly, the swimming studies

did not measure how frequently muscle activity was observed in different regions.

Secondly, Thys (1997) did not measure suction feeding performance, and the swimming

studies did not measure and compare performance within and between locomotor

behaviors. Finally, methodological differences among these studies prevent quantitative

comparisons of the intensity and spatial patterns of activation between swimming and

suction feeding (e.g., electrode construction, different animals, different sampling

distributions).

The goal of this study is to test our functional regionalization hypothesis by

quantifying and comparing the regional contributions of the axial musculature to burst

swimming and suction feeding in largemouth bass. Using electromyography, we

measured electrical activity in the epaxial musculature to identify which regions are

active during low- and high-performance locomotion and feeding. Electrodes were

implanted in the dorsal and middle positions of the epaxial musculature in three

longitudinal positions known to generate power for swimming and/or suction feeding. An

additional electrode was placed in the ventralmost epaxial region of the cranialmost

position (figure 1a). We also quantified suction feeding performance with an intraoral

pressure transducer and evaluated swimming performance with high-speed video.

47

Methods

Animals

Bass were purchased from Hickling’s Fish Farm located in Edmeston, New York.

Fish were kept at Brown University and fed a mixed diet of goldfish and carnivore

pellets. Standard lengths for bass 01, 02, and 04 were 245 mm, 247 mm, and, 259 mm

respectively. All husbandry and experimental procedures complied with Brown

University IACUC protocol #1509000157.

Experimental Design

To detect disparities in muscle activity related to function and performance, we

recorded EMGs during swimming and suction feeding in largemouth bass at different

performance intensities. To elicit variable feeding performance, we alternated between

feeding pellet food and live goldfish and quantified intraoral pressure changes to measure

the effects of food type on performance. We elicited three types of burst swimming—

turns, sprints, and fasts-starts. All three burst swimming behaviors use white muscle

fibers, but they differ in their mechanics and intensities. We categorized turning and

sprinting as lower-performance behaviors and fast-starting as a high-performance

behavior.

Trials were filmed from a dorsal view with a GoPro camera at either 60 or 100

frames per second. To synchronize video with EMG and pressure data, a computer

monitor displaying real-time EMG and pressure recordings was positioned in the

camera’s field of view. Video, EMG, and pressure data for this publication have been

deposited in the ZMAPortal (zmaportal.org) in the study ‘Largemouth bass feeding and

48

swimming EMG’ with permanent ID ZMA17. Video data are stored in accordance with

best practices for video data management in organismal biology (Brainerd et al., 2017).

Bass were generally fed goldfish and pellets in alternating order to ensure that

differential performance was associated with food type and not with satiation. To ensure

that muscle activity during suction feeding was not confounded with locomotor activity,

we used video footage to exclude trials in which any ipsilateral flexion (relative to the

electrodes) of the body is concurrent with the strike. We recorded tail beat frequencies

comparable to the maximum frequencies recorded in other fish species (Bainbridge,

1958; Wardle, 1975) similar in size to our bass (245 mm, 247 mm, and, 259 mm),

suggesting that we indeed elicited strong sprinting performance.

Intraoral Pressure Recordings

To measure intraoral pressure, we surgically implanted a cannula onto the buccal

cavity under anesthesia with buffered tricaine methanesulfonate (Norton and Brainerd,

1993). All fish were given a minimum one-week recovery period after surgery. On the

day of experimentation, the pressure transducer was threaded into to the cannula until the

sensor was partially exposed to the oral cavity.

Prior studies and our pressure data justify the classification of pellet and goldfish

strikes as low- and high-performance behaviors, respectively. Pellet and goldfish strikes

in our study produced significantly different peak pressures, as has been previously

reported in multiple studies (Lauder, 1980; Norton and Brainerd, 1993). Two-way

ANOVA revealed no significant effect of individual on peak pressure and pooled data

from all three individuals showed bass produce significantly greater (P < 0.05) pressure

49

differentials for goldfish than for pellets (figure S1). Furthermore, we elicited peak

pressures comparable to those measured in bass in at least two other studies (Carroll and

Wainwright, 2006; Norton and Brainerd, 1993), suggesting that we elicited maximal or

near-maximal suction-feeding performance.

Electromyography

Bipolar electrodes were constructed from 0.1 mm diameter, enamel-insulated

silver wire. We glued together the last 10 cm of each wire pair along their length with

cyanoacrylate and removed approximately 1 mm of insulation from the tip of each wire at

the glued end. At the exposed end, we separated the last 3 mm of wire into a “V” shape

and bent them back to form recurved anchoring hooks. Each bipolar electrode was placed

inside a 23-gauge hypodermic needle, with the hooked end resting on the beveled

opening of the needle. Electrodes were percutaneously implanted through the soft tissue

between the scales to a depth halfway between the skin and the mid-sagittal plane. Once

all the electrodes had been implanted, they were glued together to form a common cable.

The common cable was secured onto the skin with suture in two locations to relieve any

tension from the electrodes and to prevent them from dislodging from the animal.

Connector pins were soldered onto the exposed ends of the wire once the electrodes had

been implanted and the fish had been returned to its tank.

Electrodes were implanted in a total of nine positions in each bass. The

cranialmost of the longitudinal positions (0.35 0.50, and 0.70 BL) was implanted with

three electrodes corresponding to the different dorsoventral regions of the epaxial

musculature—the dorsal-pointing arm (DPA), the posterior-pointing cone (PPC), and the

50

anterior-pointing cone (APC; figure 1). The data from all body regions include values

from all three bass, with the exception of both the DPA and PPC of the midbody, for

which we collected data from only two out of three individuals (see figure S2 for

electrode sampling distribution). Immediately after collecting data, bass were CT scanned

with an in vivo veterinary CT scanner to confirm electrode placement (Animage Fidex

scanner; isovolumetric voxels 0.15 mm).

Signal Filtering and Processing

Electromyograms were amplified by 10,000 (A-M systems, differential AC

amplifier, model 1700, Everett, WA, USA) with low- and high-pass hardware filters set

to 10 kHz and 100Hz, respectively. A 60 Hz hardware notch filter was also used to

reduce noise from ambient AC circuits. Analog to digital conversion was done using

PowerLab data acquisition hardware at a sampling frequency of 10 kHz, and EMGs were

recorded in LabChart (AD Instruments, Sydney, Australia). Raw EMG signals were

rectified and software-filtered using the biosignalEMG package for R-studio (Guerrero

and Macias-Diaz, 2018). Raw data were processed with a butterworth filter with low- and

high-pass settings of 100 Hz and 1 kHz, respectively. Using the envelope function in

biosignalEMG, we created an envelope around the filtered data using a moving average

with a window size set to 15 points. For each channel, we selected a raw voltage value

for considering whether a muscle was active or inactive; muscles that exceeded this

voltage value were considered to exhibit above-threshold muscle activity (ATMA). To

minimize bias, threshold voltage was selected by visually comparing several (but not all)

trials with different levels of muscle activity. We then selected a voltage value that

51

seemed to represent the minimum threshold for active muscle and applied that value

consistently, including trials that we did not visually examine for setting the threshold

values. We calculated activation intensity by normalizing voltages for all behaviors to

maximum voltage measured by each electrode across all behaviors or within each

behavior (feeding or swimming; as noted in figure legends). The highest intensities

always occurred during fast-starts, so intensities within each electrode were normalized to

the highest fast-start value, except when normalizing within the feeding behaviors.

Statistics

Statistical analyses, including linear regressions, ANOVAs, Tukey post-hoc tests,

and t-tests were performed in R using its native functions. ANOVA’s with significant

results were followed up with Tukey’s HSD post-hoc tests. For all tests, a P-value < 0.05

was considered statistically significant.

Results

Variation of Spatial Activation Patterns

The presence of above-threshold muscle activity (ATMA) in the epaxial

musculature at 0.35 and 0.50 body lengths (BL) varied with food type (figure 2a). The

dorsal-pointing arm (DPA; the dorsal epaxial region) at the cranial position (0.35 BL)

was active for 100% of goldfish and pellet strikes, indicating that muscle activity in the

cranial DPA, in contrast to the midbody and caudal DPA, is obligate during suction

feeding. Furthermore, the cranial and midbody anterior-pointing cones (APC; the ventral

epaxial region) were active for goldfish strikes significantly more often than for pellet

52

strikes (cranial: 79% versus 42%; midbody: 63% versus 35%; paired t-test: P < 0.05).

ATMA percentages for the cranial and midbody posterior-pointing cone (PPC; the

middle epaxial region) were intermediate to those of the DPA and the APC, and though

not statistically different, two out of three bass recruited the cranial PPC and all

individuals recruited the midbody PPC more often for goldfish strikes. Despite variation

across individuals, each bass—with the exception of bass02’s cranial PPC where ATMA

percentages were 100% for both food types—recruited ventral muscle regions (PPC and

APC) more frequently for goldfish strikes compared to pellet strikes.

In contrast to suction feeding, the relationship between burst swimming

performance and regional activation was spatially inverted along the dorsoventral axis

(figure 2b). In all three bass, burst swimming elicited muscle activity from the cranial and

midbody APC for all performance levels and behavior types. Compared to the APC, the

cranial, midbody, and caudal PPC had slightly lower ATMA percentages—though still

above 75%—for turns, sprints, and stage-2 fast-starts. In the cranial, midbody, and caudal

DPA, ATMA percentages for turns and sprints fell between 40 and 65% but remained at

100% for both stages of fast-starts.

Muscle activation varied with longitudinal position for feeding, but not for burst

swimming (figure 2). ATMA percentages for suction feeding generally decreased

craniocaudally, while ATMA percentages for burst swimming behaviors remained

relatively similar. For example, goldfish strikes were similar in the cranial and midbody

DPA (> 90%), but decreased significantly in the caudal DPA (paired t-test: P < 0.05).

ATMA percentages for high-performance swimming were always 100%, indicating that

the epaxial muscle was active in all longitudinal and dorsoventral positions for stage-1

53

and -2 fast-starts. The remainder of the results section addresses data exclusively from the

cranial region at 0.35 BL.

Effects of Activation Intensity on Performance

We found a relationship between behavior and peak muscle activation intensity,

calculated as percent of the peak fast-start voltage (the highest voltages) measured from

each electrode (figure 3). Mean activation intensities in the DPA were generally lowest

for sprints and turning maneuvers (5 to 13%; range of means across individuals),

intermediate for pellet and goldfish strikes (6 to 58%), slightly higher in stage-2 fast-

starts (17 to 65%), and highest in stage-1 fast-starts (56 to 63%). In both the PPC and

APC, feeding strikes had substantially lower mean activation intensities than stage-1 (45

to 86%) and stage-2 (21 to 56%) fast-start intensities. Mean activation intensities in the

PPC and APC were lowest in strikes (0 to 17%), intermediate in sprints and turning

maneuvers (8 to 30%), and highest in stage-1 and -2 fast-starts (45 to 86%).

We performed a multifactorial ANOVA and found that individual, muscle region,

behavior, and interactions between these factors all had a significant effect on activation

intensity—with the exception of muscle region, which considered alone did not have a

significant effect. Within each individual, food type had no significant effect on

activation intensity. Muscle activation intensity was variable between individuals, but

showed a general trend of very high recruitment for fast-starts relative to other behaviors

(figure 3). In all three muscle regions for all individuals, stage-1 fast-starts used

significantly higher activation intensities than sprints, turns, and suction feeding, with the

exception of the DPA in bass02 where goldfish strikes and stage-1 fast-starts were not

54

statistically different. Suction feeding (both food types) always used activation intensities

statistically similar to or greater than sprints and turns.

Continuous performance variables for swimming and suction feeding were

significantly correlated with weighted activation intensity, which estimates the number of

active muscle fibers (figure 4 and table S1). Weighted activation intensity was strongly

correlated with the tail beat frequency of sprinting (figure 4a; bass01: R2 = 0.68; bass02;

R2 = 0.67; bass04; R2 = 0.66), and somewhat less strongly correlated with peak pressure

for all strikes—including failed attempts (figure 4b; bass01: R2 = 0.19 bass02; R2 = 0.43;

bass04; R2 = 0.50; see figure S3 for midbody feeding data). Weighted activation intensity

takes the activation intensity for each electrode and multiplies it by the cross-sectional

area of its corresponding region. The sum of the values was then divided by the total

cross-sectional area of the epaxial muscle to generate an estimate of the percentage of

muscle fibers active within the entire cross-sectional area.

Discussion

Functional Regionalization

Bass modulated function and performance by adjusting the regional distribution

of muscle recruitment while also varying muscle activation intensity (figure 5). Bass

recruited distinct epaxial regions depending on whether they were swimming or suction

feeding (figure 5a, c), yet they remained fully capable of recruiting all three dorsoventral

regions for high-performance behaviors (figure 5b, d). Bass also used a wide range of

activation intensities within and among behaviors, reflecting the different mechanical

demands of each behavior and the range of intensities at which they were performed

55

(figure 3). This was evident in sprinting and suction feeding behaviors where continuous

performance variables correlated strongly with weighted activation intensities (figure 4,

S3). Suction feeding required contributions from more muscle fibers to produce greater

peak pressures during buccal expansion (Carroll and Wainwright, 2006), and sprinting

required contributions from more muscle fibers to increase tail beat frequency and swim

more quickly (Ampatzis et al., 2013). We surmise that the dual function of the axial

musculature for both swimming and suction feeding depends on the ability to produce

these distinct activation patterns.

The dual function of the epaxial musculature was produced primarily through

dorsoventral regionalization, and these regionalized activation patterns are consistent

with the neuroanatomy of this musculature. Axial muscle fibers in fishes are innervated

by both primary and secondary motoneurons (Westerfield et al., 1986). Primary

motoneurons innervate large territories of muscle fibers and receive input from Mauthner

neurons which can produce complete activation of the axial musculature (Ampatzis et al.,

2013; Bello-Rojas et al., 2019; Liu and Westerfield, 1988; Tytell and Lauder, 2002). In

comparison, secondary motoneurons innervate smaller, overlapping territories of muscle

fibers that can produce graded activation of the axial musculature (Ampatzis et al., 2013;

Bello-Rojas et al., 2019; Liu and Westerfield, 1988; McLean et al., 2007). This dual

innervation allows primary motoneurons to activate the entire axial musculature at higher

intensities during fast-starts, while still permitting secondary motoneurons to activate the

dorsal and ventral epaxial regions independently at lower intensities during non-fast-start

behaviors. Interestingly, prior studies suggest that regional activation of the the hypaxial

musclulature may mirror epaxial motor patterns. Hypaxial motoneuron distributions are

56

similar to those of the epaxial musculature (Westerfield et al., 1986), and experimental

data shows decoupled activity of different regions in the post-anal hypaxials of

largemouth bass during locomotion (Jayne and Lauder, 1995). Although these studies

documented the independent activation of myomeric regions (Ellerby and Altringham,

2001; Jayne and Lauder, 1995; Thys, 1997), here we tested and confirmed their

functional significance for both swimming and suction feeding.

Epaxial Contributions to Suction Feeding

Our data indicate that both dorsal and ventral epaxial regions in the cranial two-

thirds of the body can contribute to high-performance suction feeding. Dorsal regions

were nearly always active and used higher intensities than ventral regions (figures 2, 3).

The ventralmost region in the cranial and midbody positions was active for 70% of

goldfish strikes and—despite having significantly lower activation intensities—could still

contribute to power output owing to the relatively large cross-sectional area of the APC

(figures 4, S3). In terms of mass and cross-sectional area, the dorsal-pointing arm (DPA)

was the smallest while the anterior-pointing cone (APC) was the largest—having a cross-

sectional area over twice as large as the DPA. Owing to their size and/or activation

intensities, both dorsal and ventral regions appeared to contribute to high suction power

during buccal expansion.

Our finding of full epaxial activation offers insight into epaxial mechanics in

high-performance suction feeding. Epaxial mechanics have previously been modelled as

a lever system wherein forces from the epaxial musculature act on the in-lever and forces

within the buccal cavity act on the out-lever (Carroll et al., 2004). This model assumed

57

that the fulcrum of this lever system was the post-temporal supra-cleithral (PTSC) joint,

such that only force generated dorsal to the fulcrum would contribute to neurocranial

elevation and force generated ventral to the fulcrum would resist neurocranial elevation.

However, a more recent study found that the axis of rotation (i.e., the fulcrum) for cranial

elevation is positioned at the level of the vertebral column—ventral to the PTSC joint and

ventral to most of the epaxial muscle in bass (Jimenez et al., 2018). Therefore, muscle

positioned dorsal to the fulcrum, including the middle and ventral regions (PPC and APC

and sloping portion between them), may contribute power to neurocranial elevation.

Indeed, bass activated the ventralmost epaxial region (APC) significantly more often for

high-performance strikes than for low-performance strikes in both the cranial and

midbody positions (figure 2a-b), suggesting that APC recruitment contributes to suction

performance. Furthermore, the correlation between weighted activation intensity and

peak pressure in both the cranial (figure 4b) and midbody (figure S3) regions shows that

greater areas of muscle—including the APC—are recruited in order to produce observed

changes in suction performance. However, APC muscle fibers positioned very close to

the vertebral column may shorten very little and potentially contribute more toward joint

stabilization than power production.

We suspect that bass maximize power output of the axial musculature for

swimming (i.e., fast-starts) but underutilize it for suction feeding. Maximizing power

output from muscle, though determined by many other factors (Gabaldón et al., 2008;

Marsh, 1999; Marsh and Bennett, 1986), generally requires meeting two conditions: all

fibers within the muscle must be activated and must shorten at an approximate rate of

one-third of their maximum shortening velocity (Marsh and Bennett, 1986; Rome, 2005).

58

Fast-starts met the first of these conditions by activating all epaxial regions at high

activation intensities (figures 2, 3), and though muscle shortening in swimming has not

been measured in bass, other species have been shown to use optimal shortening

velocities during fast-starts (Franklin and Johnston, 1997; James and Johnston, 1998). In

contrast, suction feeding met neither of these strain-activation conditions, as bass used

suboptimal muscle shortening velocities (Camp et al., 2015) and low activation

intensities (figure 3). Consequently, the epaxial musculature generated submaximal

power for most of their strikes. Interestingly, non-Mauthner dependent behaviors like

sprinting and turning often produced higher activation intensities than those used for

feeding, indicating that bass could—but did not—produce more power from the axial

musculature for feeding (figure 3).

Turning

The bass in our study performed turns characterized by slow, high-amplitude

body flexion where the head and tail nearly touched. Because bass often used this

behavior for repositioning themselves and reversing their direction, with little translation

of the center of mass, we were surprised to find that white muscle was always active

during these relatively slow maneuvers (figure2a), often at intensities comparable to

those of sprinting (figure 3). We suspect that using white muscle for a relatively slow

behavior like turning may be due to the position of the muscle fibers relative to the

vertebral column rather than their contractile properties as fast-twitch muscles. Although

we cannot comment on the activity or inactivity of red muscle fibers during these turns,

we speculate that the large distance between the superficial red muscle and the neutral

59

axis of bending (the vertebral column) would require extreme shortening to produce

considerable lateral flexion (Rome and Sosnicki, 1991). However, deeper white muscle

fibers, being positioned closer to the neutral axis and oriented oblique to the long-axis of

the fish, have a higher gear ratio and may be better for flexing the body with relatively

less muscle fiber shortening (Alexander, 1969; Brainerd and Azizi, 2005).

Evolutionary Considerations of a Dual-Function System

The evolutionary history of the axial musculature poses a challenge: how do we

best evaluate form-function relationships in this dual-function structure? The

conventional approach has been to examine this muscle group from either a swimming or

feeding perspective. However, adopting this strategy poses the risk of misidentifying

form-function relationships, as structures that appear to underperform might actually be

dual-function structures acting within the context of their secondary function. Perhaps a

more appropriate framework would be one that identifies primary and secondary

functions by comparing muscle performance during swimming and suction feeding. If

only one function fully exerts the muscle, it is reasonable to hypothesize that selection

pressure for that function may have been more important in shaping its morphology and

contractile properties (Rome, 1998).

Although many fishes have co-opted the axial musculature for suction feeding, we

are not aware of any studies that have examined whether this co-opting has created

tradeoffs in muscle function and performance. It is reasonable to hypothesize that

tradeoffs exist between high-performance undulatory locomotion and feeding, as both

behaviors recruit all regions of the epaxial musculature yet require disparate types of

60

axial flexion. These different axial motions create distinct strain gradients throughout the

muscle mass—lateral flexion in locomotion (Wakeling and Johnston, 1999) and

dorsoventral flexion in feeding are likely to produce mediolateral and dorsoventral strain

gradients, respectively. Such non-uniform strain of the muscle would likely negatively

affect performance, as muscle fibers would operate at different points on their force-

length and power-velocity curves, thereby decreasing power output. Prior studies have

hypothesized that the complex fiber architecture of the axial musculature functions as a

gearing system that homogenizes muscle fiber strain to allow for high power production

in locomotion (Alexander, 1969; Rome and Sosnicki, 1991; Van Leeuwen et al., 2008;

Wakeling and Johnston, 1999). However, since the muscle strain gradient for suction

feeding occurs on a different axis, feeding would require a different gearing system.

Thus, performance tradeoffs may exist at this muscle-architecture level. Given such

considerations, we suggest that a dual-function framework is a strong conceptual tool for

examining and comparing form-function relationships of the axial musculature in fishes

with diverse locomotor and feeding strategies.

61

Acknowledgments

We are grateful to Erika Tavares for research administrative support, to Thomas Roberts

and Stephen Gatesy for general advice on this project, and Richard Marsh for his

assistance with analyzing EMG signals and his guidance throughout the course of the

experiments. We also thank Ariel Camp, David Ellerby, and Anabela Maia for their

insights and thoughtful discussions. This research was supported by National Science

Foundation (NSF) grants 1655756 and 1661129 to E.L.B. and by the American Society

of Ichthyologists and Herpetologists’ Raney Award, the Bushnell Graduate Education

and Research Fund, and an NSF Graduate Research Fellowship to Y.E.J.

Data availability

Video, EMG, and pressure data used in this study are available on the Zoological Motion

Analysis Portal upon request (zmaportal.org, Study Identifier ZMA17).

62

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Figures

Figure 1. Anatomy of the axial musculature and electrode placement. (a) Lateral

view of a largemouth bass showing the serially repeating segments of W-shaped

myomeres of the axial musculature. Blue dots represent electrode placement in three

longitudinal positions: cranial (0.35 BL), midbody (0.50 BL), and caudal (0.70 BL). (b)

Cross-sectional view of the axial musculature, showing the relative proportions and

distributions of red and white muscle fibers. (c) Lateral view of a myomere showing the

three subregions of the dorsal epaxial region considered in this study. Abbreviations:

APC, anterior-pointing cone; DPA, dorsal-pointing arm; PPC, posterior-pointing cone.

68

Figure 2. Percent of trials with above-threshold muscle activity for different

behaviors across different epaxial regions. Each plot shows a different anatomical

region where the top row is dorsal and the left column is cranial. Each bar is the mean ±

s.e.m. of the percentage values from all three individuals (except for swimming behaviors

in the midbody region where N = 2). For feeding behaviors, data from successful and

unsuccessful strike attempts are both included in this analysis. (a) Cranial region at 0.35

BL (b) Midbody region at 0.50 BL and (c) caudal region at 0.70 BL. Abbreviations:

APC, anterior-pointing cone; DPA, dorsal-pointing arm; PPC, posterior-pointing cone;

S1, stage 1; S2, stage 2.

69

Figure 3. Normalized muscle activation intensity in three dorsoventral regions at

0.35 BL. Activation intensities were normalized to the peak fast-start voltage (the highest

voltages) recorded from each electrode. Dorsoventral regions are separated by row and

different individuals are separated by column. For feeding behaviors, only data from

successful strikes are included in this analysis. Each colored box includes a horizontal

line (median value) that separates the second (top) and third (bottom) quartiles, while the

top and bottom error bars are the first and fourth quartiles, respectively, and

individual points are outliers. Abbreviations: APC, anterior-pointing cone; DPA, dorsal-

pointing arm; PPC, posterior-pointing cone; S1, stage 1; S2, stage 2.

70

Figure 4. Effects of muscle activation at 0.35 BL on (a) sprinting and (b) suction

feeding performance. Weighted activation intensity was calculated by normalizing each

electrode to the max voltage measured for the corresponding behavior (feeding or

sprinting), weighing the normalized intensity from each dorsoventral region by its

percentage of epaxial cross-sectional area and adding all the weighted values. Data from

successful and unsuccessful strike attempts are both included in (b). Black circle, bass01;

red triangle, bass02; blue square, bass04. Regression equations, R2 values, and p-values

in Table S1.

71

Figure 5. Diagrammatic representation of the intensities and spatial distributions of

muscle activity associated with different behaviors and performance. Lower-

performance feeding (a) uses only the dorsalmost region at moderate intensities. Higher-

performance feeding (b) recruits the dorsal and ventral regions at high and moderate

intensities, respectively. Lower-performance burst swimming (c; sprinting) typically uses

only the ventralmost region at moderate intensities (20 to 40% full activation). High-

performance burst swimming (d; fast-starts) maximally recruits the entire epaxial muscle

mass. Feeding activation is expected to be bilateral (although this study used only

unilateral electrode sampling), whereas swimming activation is primarily unilateral.

72

Figure S1. Effects of prey type on peak pressure. Data are pooled from all three bass.

Mean ± s.e.m. peak pressures for successful strikes were 1.79 ± 0.09 kPa for pellets (N =

31) and 5.32 ± 0.81 kPa for goldfish (N = 25).

73

Figure S2. Sampling distributions for each individual. Locomotion and feeding data

were collected from all marked sites, except for those marked with an ‘f’.

74

Figure S3. Effects of muscle activation at 0.50 BL on suction feeding

performance. Weighted activation intensity was calculated by normalizing each

midbody electrode to the max voltage measured for feeding, weighing the normalized

intensity from each dorsoventral region by its percentage of epaxial cross-sectional area

and adding all the weighted values. Successful and unsuccessful strike attempts are both

included in this analysis. Black circle, bass01; red triangle, bass02; blue square, bass04.

Regression equations, R2 values, and p-values in Table S1.

75

Table S1. Summary of linear regression analyses for figures 4 and S3. In all cases,

there is a positive and significant relationship between performance and weighted

activation intensity. As noted in the methods, we did not analyze midbody data for

sprinting behaviors due to incomplete sampling in that region.

76

CHAPTER 3

Motor control of the epaxial musculature of bluegill sunfish in swimming and suction feeding

Yordano E. Jimenez1, and Elizabeth L. Brainerd1


Street, Providence, RI 02912, USA

77

Abstract

Fishes possess an impressive repertoire of swimming and feeding behaviors that

in a majority of cases rely on the same power source: the axial musculature. Since

feeding and locomotion actuate this musculoskeletal system in different ways, integrating

these systems would likely require modular motor control. To characterize bilateral motor

control of the epaxial muscle in feeding and locomotion, we measured muscle activity in

four bluegill sunfish (Lepomis macrochirus) using electromyography. We also compared

these results with our recent findings on largemouth bass (Micropterus salmoides), a

closely related species. We found that bluegill sunfish, like bass, recruit epaxial regions

in a dorsal-to-ventral manner to increase feeding performance, such that high-

performance feeding activates all muscle dorsal to the vertebral column. Unlike bass,

sunfish did not seem to use a ventral-to-dorsal recruitment strategy to increase locomotor

performance. Among all behaviors, fast-starts activated the highest muscle volumes but

were in many cases statistically similar to the muscle volumed activated for suction

feeding on live prey. Muscle activity was present in both sides of the body in nearly all

feeding and fast-start behaviors. However, the active muscle volume was bilaterally

symmetrical in feeding and bilaterally asymmetrical in locomotion, with higher regional

volumes active on the side of muscle shortening. Finally, bluegill sunfish activated

epaxial volumes during high-performance suction feeding that were on par with most

fast-starts, whereas this was not the case for bass. Our findings suggest that centrarchid

fishes use similar motor control strategies for feeding, but interspecific differences in

peak suction feeding performance are due to differences in peak active muscle volume.

78

Introduction

Motor control makes muscle functionally versatile, enabling animals to match

their movements to a wide range of physical and environmental conditions. Variable

muscle recruitment can adjust the speed, force, and direction of movement, and

altogether change what type of behavior is produced (Biewener and Corning, 2001;

Ellerby et al., 2001; Jimenez and Brainerd, 2020). For example, monitor lizards vary

activity of red and white muscle fibers to maintain similar locomotor speeds despite

temperature changes expected to have detrimental effects on locomotor performance

(Jayne et al., 1990). Golden-colored manakins alter the activation patterns of flight

muscles during courtship displays to produce audible snaps with their wings—a behavior

markedly different from powered flight (Fuxjager et al., 2017). Thus, comparing the

activation patterns of muscle under various conditions and during different behaviors can

elucidate the functional versatility, specialization, limitations, and evolution of muscle.

In fishes, the axial musculature is equipped with a neural circuitry that enables

most species to switch seamlessly between distinct mechanical tasks: axial locomotion

and suction feeding (Jimenez and Brainerd, 2020). Axial locomotion involves lateral

body bending produced by alternating contractions of the left and right sides of the axial

musculature. In contrast, suction feeding involves rapid cranial expansion that is

typically, through numerous musculoskeletal linkages in the skull, driven by neurocranial

elevation from the pull of the epaxials, and/or pectoral girdle retraction from the pull of

hypaxials (Camp and Brainerd, 2014; Camp et al., 2018; Camp et al., 2020; Van

Wassenbergh et al., 2005). This configuration enables the axial muscles to routinely

generate over 90% of suction power (Camp et al., 2015; Camp et al., 2018; Jimenez and

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Brainerd, 2020). These starkly different behaviors involving different musculoskeletal

regions, the head and the body, are united by a common power source, and the emerging

challenge is to determine the mechanical and evolutionary implications of such a dual-

function muscle. A critical first step in this effort is to quantify the motor control patterns

that have integrated these two musculoskeletal systems. To do so, it is important to first

review motor control of axial locomotion and feeding in fishes.

Fishes use several recruitment strategies to adjust performance of axial

locomotion. The first strategy recruits muscle in order of fiber type, where the

progression of muscle activation starts with slow-twitch muscle (small red fibers) and

ends with fast-twitch muscle (large white fibers), a phenomenon known as the size

principle (Boddeke et al., 1959; Henneman, 1957; Henneman et al., 1965). Consequently,

fish activate only red muscle fibers at low swimming speeds but activate an increasing

number of white muscle fibers with increasing tailbeat frequency (Jayne and Lauder,

1994; Jimenez and Brainerd, 2020; Johnston et al., 1977). This pattern is also observed in

fish species with pink muscle fibers, with an intermediate stage of pink fiber activation

between the red and white fiber activation (Coughlin and Rome, 1996; Johnston et al.,

1977). Once white muscle fibers are recruited, subsequent increases in performance are

produced not by activating different fiber types, but by activating a greater number of

white fibers (Bello-Rojas et al., 2019; Jimenez and Brainerd, 2020; McLean et al., 2007),

which comprise 75 – 100% of the muscle’s cross-sectional area in most bony fishes

(Boddeke et al., 1959; Greer‐Walker and Pull, 1975). For example, largemouth bass

(Micropterus salmoides) vary locomotor performance by activating muscle in a ventral-

to-dorsal progression, where low-frequency sprinting recruits only the ventral epaxial

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regions and high-frequency sprinting activates both ventral and dorsal epaxial regions

(Jimenez and Brainerd, 2020). At the peak of locomotor performance, evasive and

predatory fast-starts, fishes can recruit the totality of fibers in all regions of the axial

musculature (Ellerby and Altringham, 2001; Jimenez and Brainerd, 2020; Lauder, 1980;

Liu and Hale, 2014; Tytell and Lauder, 2002; Westneat et al., 1998). Underlying these

complex activation patterns is a motor pool comprised of spatially diverse motor units

that, depending on the behavior, can independently activate different muscle regions and

different numbers of muscle fibers within those regions (Bello-Rojas et al., 2019;

McLean et al., 2007).

Suction-feeding fishes use the same axial motor pool for both locomotion and

feeding and can modulate performance with variable white muscle activity. Largemouth

bass increase suction feeding performance via incremental recruitment of different

epaxial regions (Jimenez and Brainerd, 2020) and increased activation intensity (Carroll

and Wainwright, 2006; Jimenez and Brainerd, 2020). An important difference is that the

progression of muscle recruitment is inverted in suction feeding, such that muscle activity

starts dorsally and progresses ventrally. For example, low-motivation strikes on pellet

food recruit the dorsal region partially, but high-motivation strikes on live prey are more

likely to activate all epaxial regions. Unlike locomotion, suction feeding exhibits a

rostrocaudal recruitment pattern where low-performance strikes activate rostral muscle,

but high-performance strikes activate rostral, midbody, and caudal muscle (Jimenez and

Brainerd, 2020; Thys, 1997). Until now, axial motor control as a dual-function

phenomenon has only been studied directly in one species so far, largemouth bass

(Jimenez and Brainerd, 2020). Neuroanatomical similarities across taxa would suggest

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fish share the capacity to modulate behavior and performance with similar motor control

strategies, but good reasons exist to expect variability.

Fishes exhibit a wide range of variability in the ability to suction feed (Carroll et

al., 2004; Longo et al., 2016; Oufiero et al., 2012) and the mechanical contributions of

the axial musculature to suction feeding can vary by species (Camp et al., 2020). Thus,

some species may be capable of generating high muscle power outputs for suction

feeding, whereas others are not. For example, largemouth bass use very low activation

intensities (Jimenez and Brainerd, 2020) and generate only a fraction of the muscle’s

theoretical maximum power in suction feeding (Camp et al., 2015; Carroll et al., 2009).

In contrast, bluegill sunfish routinely generate very high power outputs, up to 438 Wkg-1

(Camp et al., 2018). The varying mechanical contributions of the axial musculature to

suction feeding raises the question—what is responsible for interspecific and intraspecific

variation in suction feeding power?

Research Objectives

The objectives of this study were threefold. (1) Determine whether the dual-

function recruitment patterns found in largemouth bass also exist in a closely-related

species with a different body shape, ecological niche, and feeding strategy, such as

bluegill sunfish (Higham, 2007; Jimenez and Brainerd, 2020; Norton and Brainerd,

1993). Is there evidence that the observed recruitment patterns in suction feeding are

conserved? (2) Assess muscle recruitment in a species that produces high muscle power

for suction feeding relative to bass. Can a species that is more of a ‘suction specialist’

recruit a greater cross-sectional area of the musculature to generate greater power

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outputs? (3) Quantify and compare the bilateral muscle activity between locomotor and

feeding behaviors in the same set of experimental animals. Is muscle activity bilaterally

symmetrical for suction feeding and bilaterally asymmetrical for swimming?

Methods

Animals

Bluegill sunfish (Lepomis macrochirus) were caught at Morses Pond in

Wellesley, Massachusetts. Fish (standard length 176, 156, 142, and 149 mm for Lm01,

Lm02, Lm03, Lm04, respectively) were housed at Brown University in tanks at room

temperature. We acclimated the fish for a minimum of six weeks, during which time they

were trained to feed on carnivore pellets and live prey, such as goldfish (Carassius

auratus) and rosies (Pimephales promelas). Two weeks prior to surgery and

experimentation, we fed the fish less frequently (and eventually not at all) to increase

their appetite. All procedures were approved by the Institutional Animal Care and Use

Committee (IACUC) of Brown University and followed guidelines and policies set forth

by the IACUC of Brown University.

Experimental Design

To detect disparities in muscle activity related to function and performance, we

recorded EMGs during swimming and feeding in bluegill sunfish at a range of

performance intensities. To elicit variable feeding performance, we alternated between

feeding bass pellet food and live goldfish and quantified intraoral pressure changes to

measure the effects of prey type on performance. We elicited three types of burst

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swimming—turns, sprints, and fasts-starts. All three burst swimming behaviors use white

muscle fibers, but they differ in their mechanics and intensities. We categorized turning

and sprinting as lower-performance behaviors and fast-starting as a high-performance

behavior. Trials were filmed from a lateral view and a dorsal or anterior view with a

GoPro camera at a minimum of 60 frames per second. We synchronized EMG recordings

with sonomicrometry recordings by sending a 1 Hz signal from the LabChart PowerLab

(Model PL3516) to the sonomicrometry acquisition software. To synchronize data and

video, this 1 Hz signal was also sent to a flashing LED light in the field of view of the

cameras. Video, EMG, and sonomicrometry data for this publication have been deposited

in the ZMAPortal (zmaportal.org) in the study ‘Sunfish EMG and Sonomicrometry in

Feeding and Locomotion’ with permanent ID ZMA27. Video data are stored in

accordance with best practices for video data management in organismal biology

(Brainerd et al., 2017).

Sunfish were fed pellets and live prey (goldfish and rosies) in haphazard order to

ensure that differential performance was associated with food type and not with satiation

during data collection. Data from successful and unsuccessful suction strikes are both

included in this analysis. To ensure that muscle activity during suction feeding was not

confounded with locomotor activity, we used video footage and sonomicrometry to

exclude trials with substantial lateral body movements during the strike. We recorded a

range of tail beat frequencies (3 to 22 Hz, based on the duration of a half wave of muscle

shortening) comparable to the range of frequencies recorded in largemouth bass (Jimenez

and Brainerd, 2020) and other fish species similar in length to our sunfish, suggesting that

we indeed elicited strong sprinting performance.

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Electrode construction

Bipolar electrodes were constructed from 0.1 mm diameter, Teflon-insulated

stainless-steel wire. We braided together the two wires and added a tighter braid to the

last ~3 cm on the recording end, where we offset the tips by 3 mm. After offsetting the

wires, we removed approximately 1 mm of insulation from the tip of the recording end

for each wire. Connector pins were soldered onto the connector ends of the wire. The

recording end of the bipolar electrode was placed inside the beveled opening of the

needle and back to form a hook, allowing the leads with connector pins to hang on the

outside of the needle.

Surgical procedures

Prior to each surgery, we anesthetized and CT scanned each individual in vivo and

took measurements of the intended implantation sites. Fish were anesthetized via

immersion in 0.12 g/L buffered MS-222 (Tricaine methanesulfonate). We then placed the

fish in a surgical tray with a flow of anesthetic solution and intubated the mouth to flow

oxygenated water over the gills. For each sonomicrometer (4 total per individual), we

removed scales from the implantation site, made a small dermal incision (1 mm), and

used a 16-gauge needle with a blunted tip to make a path for the sonomicrometer-holder

unit. We then sutured the external arms of the holder onto the skin. Electrodes (six to

eight per individual) were then percutaneously implanted through the soft tissue between

the scales to a depth halfway between the skin and the midline. The electrode and

sonomicrometer wires exiting from the muscle were glued together (E600 flexible craft

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adhesive) to form a common cable, which we then sutured onto the region above the head

to prevent the sonomicrometers from dislodging. The common cable was secured onto

the skin with suture in two locations to relieve any tension from the wires and to prevent

them from dislodging from the animal.

Implantation sites

Electrodes were implanted in a total of six positions in each sunfish at ~35% SL

(Fig. 1A). Three electrodes were implanted on each side of the body, each location

corresponding to the different dorsoventral regions of the epaxial musculature—the

dorsal-pointing arm (DPA), the posterior-pointing cone (PPC), and the anterior-pointing

cone (APC; Fig. 1), anatomical regions which also approximate the territories of distinct

motorneurons (Bello-Rojas et al., 2019). Sonomicrometers were positioned to measure

strain of the musculature at different distances from the neutral axes of bending, not to

measure strain within a single myomere. Each sonomicrometer transducer was mounted

on a custom-made stainless steel holder with three arms (Olson and Marsh, 1998) that

allowed the transducer to be positioned at the desired muscle depth. We implanted two

pairs of transducers (1 mm diameter) in the epaxial musculature on the left side of the

body. Each pair was approximately 15 mm apart, with each unit on either side of the

electrodes. Each pair was also oriented parallel to the long-axis of the animal, defined as

a line going from the snout tip to the notch in the caudal fin. One pair was implanted

dorsomedially at approximately 14 mm dorsal to the vertebral column and 3 mm lateral

to midline. The other pair was implanted ventrolaterally at approximately 8 mm dorsal to

the vertebral column and 5 mm lateral to the midline (Fig. 1A). After each experiment,

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sunfish were CT scanned with an in vivo veterinary CT scanner to confirm electrode and

sonomicrometer positions (Animage Fidex scanner; isovolumetric voxels 0.15 mm).

Data collection and filtering

Electromyograms were amplified by 1,000 or 10,000 (A-M systems, differential

AC amplifier, model 1700, Everett, WA, USA), depending on signal strength, with low-

and high-pass hardware filters set to 10 kHz and 100 Hz, respectively. A 60 Hz hardware

notch filter was also used to reduce noise from ambient AC circuits. Analog to digital

conversion was done using PowerLab data acquisition hardware at a sampling frequency

of 4 kHz, and EMGs were recorded in LabChart (AD Instruments, Sydney, Australia).

Raw EMG signals were rectified and software-filtered using the biosignalEMG package

for R-studio. Raw data were processed with a butterworth filter with low- and high-pass

settings of 100 Hz and 1 kHz, respectively.

Muscle length (L) data were recorded at a sampling rate of 1041 Hz in SonoLab

software (version 3.4.81) using a Sonometrics system (Model TR-USB Series 8). We

measured water temperature to get the muscle temperature in our ectothermic fish, and

inputted the appropriate speed of sound at the beginning of each experiment to account

for any temperature changes (Marsh, 2016). Post-processing of the sonomicrometer

signals was done in Igor Pro (Wavemetrics) and muscle length data were smoothed using

the smooth.spline function in the stats package in before and after calculating strain (R

Development Core Team, 2018). Initial muscle length (Li) was defined as the muscle

length prior to the onset of the behavior and muscle shortening. Strain was calculated as

(L-Li)/Li.

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Data Analysis

For each channel, we selected a raw voltage value for considering whether a

muscle was active or inactive; channels with activity exceeding this voltage value were

considered active. Using the envelope function in biosignalEMG, we created an envelope

of the filtered data using a moving average with a window size set to 5 frames or 1.25

milliseconds. We calculated activation intensity (AI) by normalizing the voltage

envelopes (VE) of all trials to the maximum voltage envelope (VEmax) within a given

individual and electrode (AI = VE/VEmax). We estimated active muscle volume by

multiplying activation intensity of each electrode by its relative cross-sectional area, and

adding this value for all three regions. The relative cross-sectional area for each epaxial

region was estimated by dividing the cross-section into dorsoventral thirds, consisting of

a dorsal, middle, and ventral third, and dividing the area of each region by the total

epaxial area. Unless otherwise noted, the activation intensities and active muscle volumes

for feeding behaviors were calculated from the side with the highest value (left or right of

a given region). Activation intensities and active muscle volumes for locomotor

behaviors calculated from the ipsilateral side, the side of the body undergoing muscle

shortening.

Statistics

Statistical analyses, including linear regressions, eta-squared test of variance,

ANOVAs, and Tukey post-hoc tests were performed in R using its native functions.

ANOVA’s with significant results were followed up with Tukey’s HSD post-hoc tests. A

P-value < 0.05 was considered statistically significant for all tests.

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Results

Muscle activation and strain

Epaxial muscle actively shortened in both feeding and locomotor behaviors (Fig.

2). In both behaviors, muscle activated prior to the onset of muscle shortening. An

example suction strike on a live goldfish (Fig. 2A) shows the onset of simultaneous

bilateral muscle activation in all epaxial regions promptly followed by rapid muscle

shortening. We observed pre-lengthening in some strikes, but this was not present in all

trials. The example fast-start (Fig. 2B) shows bilateral asymmetry of muscle activity,

where stage-1 muscle activity is prominent ipsilaterally (the side of the body undergoing

muscle shortening), followed by prominent contralateral muscle activity that begins just

prior to stage 2. Muscle strain and video data were used to determine the side of active

muscle shortening (i.e., the direction of body bending during swimming behaviors). For

example, strain data aided in determining the which electrodes were ipsilateral and

contralateral in swimming behaviors. For example, muscle activity recorded in the

electrodes on the left side of the body is considered ipsilateral activity only if the fish is

bending to the left. However, we refrain from further analysis of strain data in this study,

as strain data were analyzed in a separate study (Jimenez et al., in review).

Regional Muscle Activation

The presence of muscle activity varied with epaxial region and feeding behavior

(Fig. 3A). On average, the percent of trials with above-threshold muscle activity was

highest in the dorsalmost region (dorsal-pointing arm, DPA), intermediate in the middle

region (posterior-pointing cone, PPC), and lowest in the ventralmost region (anterior-

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pointing cone, APC). Mean frequencies decreased from dorsal to ventral for all feeding

behaviors on both sides of the body. Furthermore, average frequencies were consistently

highest in goldfish strikes, intermediate in pellet strikes, and lowest in chews. Each

individual activated the DPA more frequently for suction feeding (goldfish and pellet

strikes) than for chewing, except for the left DPA in Lm03. Ventral muscle regions (PPC

and APC) were also activated more frequently for goldfish strikes than for pellet strikes

and chews. Across locomotor behaviors, the frequency of ipsilateral muscle activity

varied with behavior but not by region (Fig. 3B). Stage-1 and -2 fast-starts activated all

six epaxial muscle regions in over 95% of trials. In contrast, ipsilateral muscle activity

was present for over 65% of sprints and over 90% of turning maneuvers in each of the

three regions. Activity in the contralateral muscle during sprints and turns was present in

0-100% of trials, depending on the individual and the muscle region.

Activation Intensity

Muscle activation intensity was significantly affected by behavior, accounting for

58% of the variance in activation intensity (Fig. 4; factorial ANOVA, P < 0.01; eta-

squared test of variance). Other significant effects included individual, and behavior-

region, behavior-individual, and region-individual interactions (P < 0.01, interaction

terms separated by dashes), but combined accounted for less than 3% of the variance

(eta-squared test of variance). Region alone and region-individual-behavior interactions

had no statistically significant effect on activation intensity. Mean ± s.d. normalized

activation intensity for pooled data grouped only by behavior were from highest to

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lowest: stage-1 fast-starts (54 ±27%), stage-2 fast-starts (33 ± 25%), goldfish strikes (17

± 20%), sprints (3 ± 5%), turns (4 ± 5%), pellet strikes (2 ± 2%), chews (1 ± 2%).

We performed a tukey post-hoc test to determine which interactions between

individual, region, and behavior were statistically significant. Goldfish strikes had higher

mean activation intensities than pellet strikes and chews in all three epaxial regions for all

individuals, including Lm02 where the average difference was small. However, these

differences were only statistically significant in two cases: the goldfish strike-chew

comparison in the DPA of Lm01 and Lm04. Pellet strikes and chews used statistically

similar intensities across all epaxial regions and individuals. Goldfish strikes and stage-1

fast-starts used statistically similar activation intensities in 5 of 12 cases—the DPA and

PPC of Lm01 and Lm03, and the APC of Lm03. In the other 7 cases, stage-1 fast-starts

used statistically higher activation intensities than goldfish strikes. Activation intensities

were also statistically similar in goldfish strikes and stage-2 fast-starts in 10 of 12 cases,

except for the PPC and APC of Lm02, where activation intensities were statistically

greater for stage-2 fast-starts. Goldfish strikes were in all cases statistically similar to

sprints and turns. Within each region of each individual, stage-1 fast-starts used

statistically higher activation intensities than both sprints and turns, and stage-2 fast-starts

used statistically higher activation intensities than sprints. Stage-2 fast-starts used

statistically higher activation intensities than turns in 10 of 12 cases, those statistically

non-significant being the DPA and APC of Lm04. We found no statistical differences

between sprint and turn activation intensities for any region of any individual.

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Active Muscle Volume

We estimated the percentage of active fibers within the whole cross-sectional area

of the epaxials at 35% standard length (SL) in bluegill sunfish and largemouth bass

(Jimenez and Brainerd, 2020). Behavior had a statistically significant and primary effect

on the active muscle volume in both species, accounting for 73% of variance in bass and

59% of variance in sunfish (factorial ANOVA, P < 0.01; eta-squared). Within each

species, all other significant and non-significant effects combined accounted for less than

4% of variation. Furthermore, we suspected that a substantial portion of intraspecific

variation in active muscle volume was due to motivation and performance. Thus, we

pooled all data to statistically test species differences in total activation intensity (Fig. 5).

Behavior, species, and behavior-species interactions all had statistically significant

effects (factorial ANOVA, P <0.01) but behavior accounted for 60% of the variance

(60%) as compared to 2% explained by the other two variables (eta-squared test). Sunfish

and bass used similar activation intensities for each behavior except turns (tukey post-hoc

test, P < 0.01). In both species, stage-1 fast-starts activated significantly more muscle

volume than all other behaviors, including stage-2 fast-starts. Stage-2 fast-starts activated

significantly more muscle volume than all other behaviors, except stage-1 fast-starts.

Bilateral Symmetry of Activation Intensity

Bilateral activation intensity was symmetrical in feeding (similar intensities on the

left and right sides) but asymmetrical in axial locomotion (higher intensities ipsilaterally).

Feeding behaviors used bilaterally symmetrical activation intensities at a statistically

greater frequency than swimming (Fig. 6). Bilateral symmetry was significantly affected

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by function—feeding or locomotion—in all three muscle regions (factorial ANOVA

followed by tukey post-hoc tests, P < 0.001). Although the other interactions also had a

statistically significant effect on bilateral symmetry (function-region, function-individual,

function-region-individual), these interactions combined explained only 7% of variance,

whereas function alone explained 86% of variance (eta-squared test). As a result, we

plotted the pooled data from all muscle regions of all four individuals (Fig. 6). The

percentage of trials with bilateral symmetry were then taken for each electrode of each

individual for each behavior. Activation intensity was bilaterally symmetrical in more

than 75% of feeding trials for 30 of 36 points (4 fish x 3 bilateral electrode pairs per fish

x 3 feeding behaviors). Conversely, activation intensity was bilaterally symmetrical for

less than 25% of swimming trials for 43 of 48 points.

Bilaterally symmetrical activation intensities were defined differently for feeding

and locomotion, given inherent differences in body movement: axial locomotion uses

lateral body flexion and feeding uses dorsiflexion (Jimenez et al., in review; see methods

for explanation of statistical analysis). For feeding, we tested for similarity of activation

intensity between electrode pairs in the left and right sides while ignoring which side had

the higher value. For swimming, we considered only whether ipsilateral activity (the side

producing muscle shortening and positive mechanical work) was greater than

contralateral activity. Given these a priori expectations based on the mechanics of each

behavior, the threshold of similarity (feeding) or difference (swimming) for each

behavior was based on the highest standard deviation measured for a behavior in any

given electrode of the individual. If muscle activation intensities of the left and right sides

were within 20% for a goldfish strike or within 2% for a chew (absolute percentage), then

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that specific trial was counted as symmetrical. For swimming, stage-1 ipsilateral

activation intensity had to be 27% higher than contralateral activity during stage 1 to be

counted as asymmetrical or ipsilateral sprint activity needed an activation intensity that

was 5% higher than contralateral activity.

Discussion

Comparison of Bluegill Sunfish and Largemouth Bass

Muscle recruitment patterns of bluegill sunfish show important similarities and

differences to those of largemouth bass (Jimenez and Brainerd, 2020). Similarities

between these species include (1) dorsal-to-ventral activation of the epaxial musculature

associated with increased feeding performance, (2) activation of all epaxial regions in

fast-starts, and (3) substantially higher activation intensities for fast-starts than for all

other behaviors. Differences between these species include (1) bluegill sunfish did not

show a strong dorsoventral regionalization of muscle activity associated with swimming

performance, whereas largemouth bass were less likely to use the dorsal muscle regions

for low-performance swimming, (2) bluegill sunfish used activation intensities for

goldfish strikes that were substantially higher than pellet strikes, whereas these were very

similar in largemouth bass, (3) bluegill used activation intensities for the highest

performance goldfish strikes that were similar to the highest activity in fast-starts.

Overall, the recruitment patterns observed in largemouth bass and bluegill sunfish are

consistent with a neuroanatomy, described in zebrafish (Danio rerio) and goldfish

(Cyprinus carpio), which consists of motoneurons that innervate overlapping

dorsoventral epaxial regions (Bello-Rojas et al., 2019; Fetcho, 1987; Westerfield et al.,

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1986). Our observations and those made in prior anatomical and experimental studies

suggest that teleost fishes possess the pre-requisite neuroanatomy necessary for

modulating suction feeding performance through regionalized and graded recruitment of

axial muscle.

Differences between largemouth bass and bluegill sunfish also suggest that

muscle activation intensity can limit muscle power output during suction feeding. Active

muscle volume for sunfish and bass overlap considerably (Fig. 5), coinciding with the

overlapping mass-specific power outputs generated by the epaxial muscle of these

species (Camp et al., 2018). Yet, at the highest levels of suction feeding performance,

bass and sunfish produce considerably different muscle power outputs, and as we find

here, the highest recorded active muscle volumes are considerably greater in sunfish as

compared to the highest values for bass (Fig. 5). For example, the highest recorded mass-

specific power of the axial muscle for sunfish (438 Wkg-1) is over three times greater

than that recorded for bass (141 Wkg−1; Camp et al., 2018), and here we showed sunfish

routinely used active muscle volumes two to three times greater than the highest value

recorded in bass (Fig. 5). These complementary findings suggest that although epaxial

muscle is critical to suction feeding in these species, only some species may maximize

the mechanical potential of the muscle for suction feeding. Sunfish generate very high

suction powers using epaxial activation intensities on par with fast-starts, whereas bass

do not.

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Comparing Bilateral Mechanics

Analyzing the bilateral symmetry of activation intensity of the epaxial

musculature revealed that feeding and locomotion use distinct motor control strategies

(Fig. 6) and, consequently, different mechanical strategies (see Westneat et al., 1998 and

Tytell and Lauder, 2002 for bilateral asymmetry along the body). Feeding and

locomotion involve different muscle synergies, as each behavior requires the axial muscle

to flex in a different plane (Jimenez et al., in review). In swimming, the left and right

sides of the body are antagonistic: active shortening on one side (ipsilateral: positive

work) tends to produce lengthening on the other side (contralateral: negative work). In

suction feeding, the left and right sides of the body are synergistic, as muscle on both

sides generate positive work to elevate the neurocranium. Thus, if positive work

production is unilateral for swimming but bilateral for feeding, it is plausible that high-

performance suction feeding can produce higher net muscle power outputs than fast-

starts. Although axial muscle in opposite sides of the body is necessarily antagonistic in

locomotion, this arrangement may still provide performance advantages.

It has been speculated that the preparatory phase of the fast start (stage one) can

enhance performance in the propulsive phase (stage two) (Franklin and Johnston, 1997).

Higher muscle power outputs, higher mechanical outputs, velocities, and accelerations in

stage-2 fast-starts provide evidence of enhancement (Altringham and Johnston, 1990;

Frith and Blake, 1995; James and Johnston, 1998; Tytell and Lauder, 2008), although the

specific mechanisms of performance enhancement remain unclear. Since active

lengthening of muscle enhances force production, active lengthening of contralateral

muscle during stage one increases muscle force and power in stage two (James and

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Johnston, 1998). However, only a relatively small percentage of muscle fibers actively

lengthen in swimming, and any mechanical enhancement due to active lengthening would

be limited to that smaller group of fibers. Thus, a hypothesis of stage two mechanical

enhancement would have to account for active lengthening in a few fibers and passive

lengthening in most fibers. An alternative hypothesis suggests that intramuscular

pressures in the contralateral muscle enhances fast-start performance through elastic

energy storage (Westneat et al., 1998), and not necessarily enhanced muscle

performance. Finally, competing hypotheses exist for explaining contralateral muscle

activity and higher stage-2 performance, including body stiffening, force transmission,

and escape trajectory (Westneat et al., 1998), in which contralateral activity is not

primarily explained in terms of mechanical enhancement. Future studies are needed to

more precisely establish or rule out any of these hypothesized mechanical functions,

which may vary across species with different body shapes and swimming modes (R

Development Core Team, 2018).

Muscle Activation in Fast-starts

Bluegill sunfish activated epaxial muscle bilaterally for fast-starts, but with higher

activation intensities on the side of muscle shortening (Fig. 2,6). Other fish species have

also been shown to activate the axial muscle bilaterally for fast-starts, although muscle

recruitment patterns for fast-starts can vary intra- and interspecifically (Czuwala et al.,

1999; Ellerby and Altringham, 2001; Hale, 2002; Tytell and Lauder, 2002; Westneat et

al., 1998). One example of intraspecific variation includes the muskellunge (Esox

masquinongy), which activate muscle unilaterally along the whole body during stage-1 of

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C-starts, but activate muscle bilaterally in posterior regions during stage-1 of S-starts

(Hale et al., 2002). Interspecific variation of bilateral muscle activity during C-starts

ranges from some species using bilateral activity for some stages, unilateral activity for

other stages, and in some cases, no activity whatsoever in stage-2 fast-starts (Hale et al.,

2002). In comparison, we show both qualitatively (Fig. 2B) and quantitatively (Fig. 3B)

that bluegill sunfish use bilateral muscle activity in both stages of the fast-start. Two

factors may account for these interspecific differences, one biological and the other

methodological. The biological source of variation could be the diversity of neural

circuits that drive fast-starts (Liu and Hale, 2014). Although it is plausible that these

differences are due to interspecific differences in muscle activation patterns, we suspect

the more important factor is methodological. We used a thresholding method to

determine whether the muscle was active or inactive, whereas other studies used different

methods. In some cases, EMG bursts with very low voltages seem to be reasonably ruled

out as inactive, but in our analysis may have been included as active. Although these

methodological and biological factors may have important interpretive implications, it is

worth noting that all are in agreement that muscle on the side of muscle shortening uses

higher levels of muscle activity than muscle on the side of muscle lengthening for fast-

starts (Fig. 6).

Comparing Muscle Function Across Behaviors

Species from various vertebrate taxa have been shown to modulate muscle

activity in response to variable conditions and for different behaviors (Azizi et al., 2008;

Biewener and Gillis, 1999; Carr et al., 2011; Gillis and Biewener, 2000; Roberts et al.,

98

2007). In fishes, motor control of the axial musculature has been studied primarily in

relation to locomotor performance, corresponding to a tradition of interpreting

musculoskeletal anatomy, physiology, and mechanics primarily through the lens of axial

locomotion. Considering that locomotion is the ancestral function of the axial

musculoskeletal system (Fletcher et al., 2014; Webb, 1982), such an approach has yielded

insights into the form-function relationships of fish locomotion. Yet, the emerging

multifunctional perspective for fish axial musculature cautions against automatically

assuming musculoskeletal traits are locomotor adaptations. The evidence suggests that

most suction feeding fish can activate all epaxial regions (Camp et al., 2015; Camp et al.,

2018; Jimenez and Brainerd, 2020), and that even a fish species with a relatively common

morphology (bluegill sunfish) may be capable of activating the entire muscle volume at

35% standard length in high-performance suction feeding. How does this affect our

understanding of and approach to the axial musculature of fishes?

In bass, high muscle recruitment in high-performance swimming and low muscle

recruitment in suction feeding provide evidence that the axial musculature is primarily

suited for swimming. If all fish used the axial muscle like bass, then it would strongly

suggest that this is primarily a swimming muscle, which has been the historical

perspective. The similar levels of muscle recruitment in high-performance swimming and

high-performance suction feeding in bluegill sunfish provides a counter example to the

historical ‘locomotor’ perspective. The fact that axial muscle can be recruited to similar

degrees for swimming and suction feeding suggests that either could be the primary

function of the muscle. Elucidating the functional specialization of this muscle may

require exploration into its architecture and physiology.

99

Acknowledgments


and Andrew Biewener for valuable comments and discussion on the manuscript, to David

Ellerby for help with collecting animals, and to Jake Parsons, Jarrod Petersen, and Amy

Rutter for assistance with surgeries. This research was supported by National Science

Foundation (NSF) grant nos. 1655756 and 1661129 to E.L.B., a Doctoral Dissertation

Enhancement Grant from the Bushnell Graduate Education and Research Fund to Y.E.J.

and an NSF Graduate Research Fellowship to Y.E.J.

100

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Figures

Figure 1. Electrode and sonomicrometer placement in the epaxial musculature. (A)

Lateral view of a bluegill sunfish showing the serially repeating segments of W-shaped

myomeres of the axial musculature. Red circles show approximate electrode positions in

the three epaxial regions at 35% standard length considered in this study. Blue and green

circles show positions of the two sonomicrometers pairs from which muscle shortening

was measured. (B) Lateral view of an isolated myomere showing the three epaxial

regions examine in this study. (C) Cross-sectional view of electrode (bilateral) and

sonomicrometer (left side only) positions. Only one sonomicrometer from each pair is

shown. Longitudinal muscle strain was measured in the dorsomedial region (blue pair)

and ventrolateral region (green pair). APC, anterior-pointing cone; DPA, dorsal-pointing

arm; PPC, posterior-pointing cone.

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Figure 2. Muscle activity and shortening for high-performance suction feeding and

swimming. Columns show EMG traces from different sides of the body and rows show

different epaxial regions, where the top row is the dorsalmost region and bottom row is

the ventralmost region. (A) Muscle activity during a suction strike on live prey in the left

and right sides of the body with muscle strain measured with sonomicrometers positioned

in the dorsomedial muscle on the left side of the body. (B) Muscle activity during a fast-

start in the ipsilateral and contralateral sides of the body with muscle strain measured

with sonomicrometers positioned in the ventrolateral muscle on the left side of the body.

Ipsilateral denotes the side with muscle shortening and contralateral denotes the side with

muscle lengthening. Scale bars to the right of each trace are 1 millivolt. APC, anterior-

pointing cone; DPA, dorsal-pointing arm; PPC, posterior-pointing cone; S1, stage 1; S2,

stage 2.

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Figure 3. Percent of trials with above-threshold muscle activity in different epaxial

regions for different behaviors. (A) Feeding behaviors (B) Swimming behaviors. Each

plot shows a different anatomical region where each column is a different side of the

body and the top row is the dorsalmost region. Each bar is the mean ± s.d. of the

percentage values from all individuals. Individuals Lm01-04 are denoted by triangle,

square, circle, inverted triangle, respectively. APC, anterior-pointing cone; DPA, dorsal-

pointing arm; PPC, posterior-pointing cone; S1, stage 1; S2, stage 2.

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Figure 4. Normalized muscle activation intensity for each individual, region, and

behavior. Activation intensities were normalized to the peak fast-start voltage (the

highest voltages) recorded from each electrode. Columns show different individuals and

rows show different dorsoventral regions. For swimming, activation intensity is always

for the side of muscle shortening (e.g., left electrodes for a left body bend). For feeding,

the side with the highest activation intensity is used. For feeding behaviors, data from

both successful and unsuccessful (n = 6 goldfish) suction feeding strikes are included in

this analysis. Each colored box includes a horizontal line (median value) that separates

the second (top) and third (bottom) quartiles, while the top and bottom error bars are the

first and fourth quartiles, respectively, and individual points are outliers (defined as

points exceeding or deceeding the interquartile range by 1.5). APC, anterior-pointing

cone; DPA, dorsal-pointing arm; PPC, posterior-pointing cone; S1, stage 1; S2, stage 2.

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Figure 5. Active muscle volume used for different behaviors in sunfish and bass.

Active muscle volume at 35% SL was estimated by multiplying activation intensity of

each electrode by its relative cross-sectional area and adding this value for all three

epaxial regions. Active muscle volume was calculated from three electrodes for each

behavior. For feeding, the side with the higher activation intensity for a given epaxial

region was used. For swimming, the side with active muscle shortening for locomotion

was used. Bass data from Jimenez and Brainerd, 2020 were analyzed for this study.

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Figure 6. Bilateral symmetry of activation intensity for feeding and locomotion.

Behaviors are grouped by the direction of their movements—dorsiflexion for feeding and

lateral flexion for locomotion—which are expected to be symmetrical and asymmetrical,

respectively. Individuals Lm01-04 are denoted by triangle, square, circle, inverted

triangle, respectively. Epaxial regions are not distinguished by color or symbol.

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CHAPTER 4

A biomechanical paradox in fish: swimming and suction feeding produce orthogonal strain gradients in the axial musculature

Yordano E. Jimenez1, Richard L. Marsh1, and Elizabeth L. Brainerd1


Street, Providence, RI 02912, USA

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Abstract

The axial musculature of fishes has historically been characterized as the

powerhouse for explosive swimming behaviors. However, recent studies show that some

fish also use their ‘swimming’ muscles to generate over 90% of the power for suction

feeding. Can the axial musculature achieve high power output for these two mechanically

distinct behaviors? Muscle power output is enhanced when all of the fibers within a

muscle shorten at optimal velocity. Yet, axial locomotion produces a mediolateral

gradient of muscle strain that should force some fibers to shorten too slowly and others

too fast. This mechanical problem prompted research into the gearing of fish axial muscle

and led to the discovery of helical fiber orientations that homogenize fiber velocities

during swimming, but does such a strain gradient also exist and pose a problem for

suction feeding? We measured muscle strain in bluegill sunfish, Lepomis macrochirus,

and found that suction feeding produces a gradient of longitudinal strain that, unlike the

mediolateral gradient for locomotion, occurs along the dorsoventral axis. A dorsoventral

strain gradient within a muscle with fiber architecture shown to counteract a mediolateral

gradient suggests that bluegill sunfish should not be able to generate high power outputs

from the axial muscle during suction feeding—yet prior work shows that they do, up to

438 Wkg-1. Solving this biomechanical paradox may be critical to understanding how

many fishes have co-opted ‘swimming’ muscles into a suction feeding powerhouse.

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Introduction

Whether an animal is swimming, jumping, running or flying, the mechanical

output of muscle is one of the key drivers of performance. Hence, studying the conditions

in which muscle contractions produce maximal force, work, and power is critical to

understanding how animals execute the behaviors that enable them to survive (Askew

and Marsh, 1998; Azizi and Roberts, 2010; Peplowski and Marsh, 1997; Roberts et al.,

2019; Rome et al., 1988). When chasing prey or evading predators, fish accelerate

through the water using their axial musculature to flex the body from side to side (Frith

and Blake, 1995; Johnston et al., 1995; Tytell and Lauder, 2002; Wakeling and Johnston,

1998). As the axial muscles contract and bend the body, lateral body flexion produces a

mediolateral gradient of strain within the axial muscle mass, with the greatest shortening

occurring near the skin and the least shortening near the backbone (Fig. 1a)(Alexander,

1969; Azizi and Brainerd, 2007; Coughlin et al., 1996; Wakeling and Johnston, 1999).

Beam-like bending of an axial musculature with longitudinal muscle fiber orientations

(i.e., the null morphology) would produce heterogeneous fiber strains and velocities, and

given the power-velocity properties of muscle (Coughlin and Akhtar, 2015; Young and

Rome, 2001), only a thin band of fibers would shorten at velocities that generate high

powers (Fig. 1b). As a result, this gradient is thought to severely limit muscle power

output. The detrimental mechanical implications of this null morphology prompted

investigations into the mechanical role of the helical fiber orientations observed in fish

and shark axial muscle. Multiple studies have shown that these complex muscle fiber

orientations form a sophisticated gearing system that makes muscle fiber strain and

shortening velocity more homogeneous than predicted by beam-like bending of the null

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morphology (Alexander, 1969; Azizi and Brainerd, 2007; Ellerby and Altringham, 2001;

Gemballa and Vogel, 2002; Rome and Sosnicki, 1991; Van Leeuwen et al., 2008). This

strain-homogenizing fiber architecture is thought to enable high power outputs from

fibers throughout the whole muscle mass, independent of their distance from the vertebral

column (i.e., the neutral axis). This hypothesis is supported by numerous studies showing

that fish generate very high power from the white axial musculature during fast-starts,

rapid evasive and predatory swimming maneuvers (Frith and Blake, 1995; James and

Johnston, 1998; Johnston et al., 1995; Wakeling and Johnston, 1998).

In addition to locomotion, many fishes have co-opted the axial musculature for

suction feeding (Camp and Brainerd, 2014; Carroll and Wainwright, 2006; Jimenez and

Brainerd, 2020; Lauder, 1982). Species like largemouth bass (Micropterus salmoides)

and bluegill sunfish (Lepomis macrochirus) generate over 90% of their suction power by

actively shortening most of the ‘locomotor’ muscle mass (Camp and Brainerd, 2014;

Camp et al., 2015; Camp et al., 2018; Jimenez and Brainerd, 2020). Suction feeding

studies have typically modelled epaxial mechanics with the assumption that the epaxial

muscle mass functions like muscle belly actuating a lever system (Fig. 1c)(Carroll et al.,

2004; Day et al., 2015; Holzman et al., 2008; Lauder and Liem, 1981; Lesiuk and

Lindsey, 1978; Wainwright et al., 2007). This lever model has yielded valuable insights

for comparative studies of suction feeding performance across species (Carroll et al.,

2004; Day et al., 2015; Holzman et al., 2008; Wainwright et al., 2007). The lever model

infers from specimen manipulation that the fulcrum is at the level of a joint within the

pectoral girdle and makes the simplifying assumption that the input muscle force acts on

a single point of the neurocranium, implying that epaxial muscle shortens uniformly

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(Carroll et al., 2004). The lever model is challenged by the finding that neurocranial

elevation usually involves dorsal flexion of multiple intervertebral joints, suggesting

smooth bending rather than hinge-like rotation occurs (Jimenez et al., 2018). Hence, we

hypothesize that suction feeding is powered by beam-like bending that produces a

dorsoventral gradient of longitudinal strain in the axial musculature, where muscle strain

decreases from dorsal to ventral during any given bout of muscle shortening (Fig. 1d).

A dorsoventral gradient of longitudinal strain in the epaxial muscle mass

(hereafter also referred to as muscle strain) during feeding would be anatomically

orthogonal to the mediolateral gradient in swimming. If muscle fiber architecture is

indeed specialized to homogenize fiber strain within the musculature as it undergoes

heterogeneous longitudinal strain during locomotion, high muscle power outputs are

unlikely during suction feeding because the muscle fibers would be oriented to counteract

a mediolateral gradient, not a dorsoventral gradient. Conversely, if the architecture were

specialized for equalizing fiber strain along a dorsoventral gradient, this would likely

impede locomotor power production. Thus, behaviors with motions that produce

anatomically orthogonal strain gradients in the muscle might have gearing solutions that

conflict with each other, preventing the muscle from generating maximum power output

for both swimming and suction feeding, and perhaps limiting peak muscle performance

to only one of these vital behaviors. Here, we measured muscle shortening using

sonomicrometry to determine whether the dorsal half of the axial musculature, the

epaxial musculature, of bluegill sunfish exhibits a dorsoventral gradient of longitudinal

strain in suction feeding and a mediolateral gradient of longitudinal strain in locomotion.

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Methods

Animals and training protocol

Bluegill sunfish (Lepomis macrochirus) were caught at Morses Pond in

Wellesley, Massachusetts. Fish (standard length 176, 156, and 149 mm for Lm01, Lm02,

and Lm04, respectively) were housed at Brown University in tanks at room temperature.

We acclimated the fish for a minimum of six weeks, during which time they were trained

to feed on carnivore pellets and live prey, such as goldfish (Carassius auratus) and rosies

(Pimephales promelas). All procedures were approved by the Institutional Animal Care

and Use Committee (IACUC) of Brown University and followed guidelines and policies

set forth by the IACUC of Brown University. Reporting of methods and results followed

ARRIVE guidelines (du Sert et al., 2020).

Sonomicrometer positions

We anesthetized and CT scanned each individual in vivo to measure the

mediolateral and dorsoventral distances from the vertebral column at the intended

implantation sites. Sonomicrometers were positioned to measure strain at different

distances from the neutral axes of bending, not to measure strain within a single

myomere. Each sonomicrometer transducer was mounted on a custom-made stainless

steel holder with three arms (Olson and Marsh, 1998) that allowed the transducer to be

positioned at the desired muscle depth.. We implanted two pairs of transducers (1 mm

diameter) in the epaxial musculature on the left side of the body at approximately 35%

standard length (SL; Fig. 2a). Each pair was approximately 15 mm apart and parallel to

the long-axis of the animal, defined as a line going from the snout tip to the notch in the

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caudal fin. One pair was implanted dorsomedially at approximately 14 mm dorsal to the

vertebral column and 3 mm lateral to midline. The other pair was implanted

ventrolaterally at approximately 8 mm dorsal to the vertebral column and 5 mm lateral to

the midline. After each experiment, we anesthetized and CT-scanned each individual to

confirm sonomicrometer positions.

Surgical procedures

Fish were anesthetized via immersion in 0.12 g/L buffered MS-222 (Tricaine

methanesulfonate). We then placed the fish in a surgical tray with a flow of anesthetic

solution, and intubated the mouth to flow oxygenated water over the gills. For each

sonomicrometer (4 total per individual), we removed scales from the region of

implantation, made a small dermal incision (1 mm), and used a 16-gauge needle with a

blunted tip to make a path for the sonomicrometer-holder unit. We then sutured the

external arms of the holder onto the skin. As a part of a related project, we also implanted

three electrodes in the epaxial muscle on each side of the body at approximately 35% SL.

The electrode and sonomicrometers wires exiting from the muscle were glued together

(E600 flexible craft adhesive) to form a common cable, which we then sutured onto the

region above the head to prevent the sonomicrometers from dislodging.

Data collection

Muscle length (L) data were recorded at a sampling rate of 1041 Hz in SonoLab

software (version 3.4.81) using a Sonometrics system (Model TR-USB Series 8). We

measured water temperature to get the muscle temperature in our ectothermic fish, and

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input the appropriate speed of sound at the beginning of each experiment to account for

any temperature changes(Marsh, 2016). We synchronized strain recordings and light

video by using a LabChart PowerLab (Model PL3516) to send a 1 Hz signal to the

sonomicrometry acquisition software and a flashing LED light in the field of view of the

cameras. We elicited various swimming (turns [i.e., non-fast start body bends], sprints

and fast-starts) and feeding behaviors (suction feeding on live prey and pellets, coughs,

and chews) that require varying degrees of axial flexion. We used synchronized

recordings to classify behaviors and to exclude trials in which the fish moved in more

than one plane (e.g., suction feeding strikes with concomitant lateral flexion). Post-

processing of the sonomicrometer signals was done in Igor Pro (Wavemetrics) and

recordings were smoothed using the smooth.spline function in the stats package in (R

Development Core Team, 2018). Initial muscle length (Li) was defined as the muscle

length prior to the onset of the behavior and muscle shortening. Strain was calculated as

(L-Li)/Li.

Statistical analysis

Each trial consists of a full or half wavelength of lengthening or shortening,

depending on the behavior. Each data point is the absolute value of peak strain measured

at both positions during the behavior. As neither the dorsomedial nor ventrolateral

position is an independent variable, we performed a model 2 major axis linear regression

using the lmodel2 package in R (Legendre and Oksanen, 2018). A major axis regression

is appropriate for data because both the dorsomedial and ventrolateral positions are

expected to have similar measurement errors, and the relationship between X and Y is

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symmetric (Smith, 2009). Regression results and confidence intervals can be found in

Table 1.

Results

We found that locomotor behaviors produced a mediolateral gradient of

longitudinal strain in the epaxial muscle, while feeding behaviors produced a

dorsoventral gradient of longitudinal strain (Figs 2 and 3; see supplementary Fig. S1 for

sample EMG traces). If longitudinal muscle strain were homogeneous, strain would be

the same at the dorsoventral position (‘A’) and mediolateral position (‘B’), independent

of their different distances from the neutral axes of bending. In this case, the slopes of the

regressions in Figure 3 would be equal to one. However, linear regressions showed that

all three individuals had slopes statistically significantly greater than one during

locomotion, indicating that longitudinal strain was larger in the lateral muscle region.

Conversely, linear regressions showed that all three individuals had slopes statistically

significantly less than one during feeding, indicating that longitudinal strain was larger in

the dorsal muscle region (Fig. 3 and Table 1; significance based on the 95% confidence

intervals not overlapping with a slope of one). Finally, although we excluded non-planar

feeding behaviors from our formal analysis, we found that side strikes (feeding behaviors

with simultaneous dorsiflexion and lateral flexion) produced muscle length dynamics

consistent with the finding of anatomically orthogonal strain gradients (Fig. 4).

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Discussion

The Paradox

Our discovery of a dorsoventral strain gradient in feeding presents a paradox:

bluegill sunfish should not be able to attain high power outputs from their epaxial muscle

in suction feeding, but they do. Bluegill sunfish can generate exceptionally high epaxial

muscle power, up to 438 Wkg -1, in the most powerful strikes (Camp et al., 2018). What

makes this a paradox? Within the current paradigm, muscle fiber architecture is

specialized to equalize fiber strain mediolaterally for axial locomotion. Thus, this same

architecture is not expected to be able to equalize fiber strain dorsoventrally in feeding.

Yet, the seeming contradiction (i.e., the paradox) is that bluegill sunfish do generate very

high axial muscle power despite the expectation that a dorsoventral gradient should

prevent them from doing so.

The current paradigm of fish axial locomotion and white muscle mechanics has

argued and shown to varying degrees that (1) lateral body flexion causes the whole

musculature to deform heterogeneously along a mediolateral gradient, (2) muscle fiber

orientations in the axial musculature are specialized to allow for homogeneous fiber

strain within a whole muscle undergoing heterogeneous strain during locomotion because

(3) heterogeneous fiber strain is mechanically detrimental. We regard point one as

uncontroversial, since it has been demonstrated repeatedly across different species,

including here in bluegill. We regard points two and three as assumptions that, if rejected

or refined, can resolve the paradox in bluegill sunfish and provide a new biomechanical

insight. Point two could be rejected if epaxial fiber gearing in bluegill sunfish were

specialized for suction feeding, not swimming. Point two could also be rejected if epaxial

123

fiber gearing were specialized for both suction feeding and swimming with a

sophisticated, albeit unknown, gearing system that can homogenize strain for

anatomically orthogonal gradients. Finally, point three could be rejected if bluegill

sunfish were capable of generating peak power for both behaviors without any

specialized fiber gearing. Here we consider each of these solutions to the paradox briefly.

The first possibility is that, in bluegill sunfish, gearing of the epaxial muscles is

specialized for power production during feeding and cannot produce high muscle power

output during swimming. Very high muscle power is routinely observed in fishes during

fast-starts (Franklin and Johnston, 1997; Frith and Blake, 1995; Johnston et al., 1995;

Wakeling and Johnston, 1998), but fast-start power has not been measured in bluegill

sunfish. Bluegill sunfish may have responded to selective pressures favoring a muscle

fiber architecture that homogenizes fiber strain along the dorsoventral axis to maximize

muscle power output for prey capture. This scenario would challenge the prevailing and

well-supported view that most fish maximize axial muscle power output during fast-starts

(Frith and Blake, 1995; Johnston et al., 1995; Wakeling and Johnston, 1998), and future

studies would be needed to determine whether bluegill sunfish actually do achieve high

mass-specific powers during fast-starts. The second possibility is that bluegill sunfish do

indeed maximize muscle power for both behaviors, but require a sophisticated gearing

system that can homogenize fiber strain for orthogonal gradients. Since gearing is

determined by fiber angulation and three-dimensional muscle deformation (Azizi et al.,

2008; Brainerd and Azizi, 2005; Holt et al., 2016), the former of which cannot change in

a given organism, the only possible way to produce orthogonal gearing gradients would

be if the bulging or shearing within the muscle mass was different for each behavior.

124

Both of the aforementioned solutions assume that having a gradient of fiber strain

(i.e., fiber strain heterogeneity) is as problematic as previously suggested (Alexander,

1969; Van Leeuwen et al., 2008). The problem of heterogeneous fiber strain has often

relied on assessing muscle performance based on experimentally derived isotonic muscle

properties. However, such assessments of muscle performance must be made with

caution. Even operating under this assumption, power-velocity curves can have a broad

plateau where muscle fibers not shortening at velocities that generate peak power (Vopt)

can still generate high power (Askew and Marsh, 1998). In addition to isotonic

properties, Vopt can vary based on the frequency, strain trajectory, and activation patterns

of the behavior, among many other factors (Askew and Marsh, 1998; Askew and Marsh,

2002). Due to these complex interactions, the sensitivity of in vivo power production to

fiber strain heterogeneity is unclear.

Implications for Suction Feeding Muscle Mechanics

Most suction feeding studies have assumed or implied uniform muscle strain

when modelling epaxial muscle mechanics (Fig. 1c)(Carroll et al., 2004; Day et al., 2015;

Holzman et al., 2008). Contrary to this assumption, we show that suction feeding

produces a dorsoventral gradient of longitudinal strain in the epaxial musculature of

bluegill sunfish (Fig. 1d), where muscle strain varies along the dorsoventral axis of the

cross-section of muscle (Figs 2 and 3). Yet much of our current understanding of epaxial

mechanics is based on muscle strain measurements that we now know do not represent

the entire muscle. If strain had been measured in a ventral epaxial region, longitudinal

muscle strain would be lower in magnitude and muscle function would have been

125

interpreted differently, as active shortening would imply positive power production (i.e.,

a motor), but active contractions with minimal shortening would imply isometric force

production (i.e., a strut or stabilizer). Of course, all of this assumes a null morphology of

longitudinal muscle fibers, which is certainly not the case in the axial muscle of fishes

(Alexander, 1969; Gemballa and Vogel, 2002; Van Leeuwen et al., 2008). Hence,

conclusions about in vivo epaxial mechanics should be made cautiously when using

longitudinal strain. Determining in vivo muscle mechanics in suction feeding may require

quantifying the variables that have been shown to influence the gearing of muscle—fiber

orientations and in vivo muscle deformation (Azizi et al., 2008; Brainerd and Azizi,

2005).

Side Strikes: Mixed Swimming and Feeding Behaviors

Bluegill sunfish occasionally bent their heads to the side while attacking live prey

that were not located directly in front of the mouth. These side strikes were characterized

by dorsiflexion with simultaneous lateral flexion (Fig. 4). Although we excluded such

feeding trials from our formal analysis in order to analyze lateral and dorsal bending

separately, they illustrate the complexity of muscle length dynamics during behaviors

involving simultaneous motion in two planes. These muscle length dynamics are both

implied by the orthogonal gradients in planar feeding and planar swimming (Fig. 3) and

evident in otherwise anomalous longitudinal strain data (Fig. 4). Suction strikes with

simultaneous dorsal and lateral flexion are distinguished from sequences of behaviors

with relatively discrete motions, such as lateral flexion for acceleration followed by

dorsiflexion for suction feeding followed by lateral flexion for deceleration (Higham,

126

2007). Among many interesting points, how exactly do side strikes affect power

production? Does the concave side of the body (where muscle shortens) contribute

positive power to cranial expansion? Conversely, does the convex side generate negative

power, thereby resisting cranial expansion? While these questions are difficult to answer,

comparing performance of planar strikes and side strikes may be a feasible way of doing

so. Considering the complexity of the physical environments that many fish species

inhabit, side strikes may be, along with planar strikes, an important part of the food

capture repertoire of fishes in the wild.

Methodological Considerations

We suspect that longitudinal muscle strain for suction feeding is a linear function

of distance from the neutral axis, although we could not determine the linearity of this

relationship with the spatial distribution of our sampling. Determining linearity would

require measuring longitudinal strain in at least three positions at different distances from

the neutral axis of interest and ideally at the same distances from the neutral axis of the

other behavior—such that all strain recordings are equally affected by non-planar

motions. For example, a robust configuration for detecting a linear strain gradient in

suction feeding would involve measuring longitudinal strain at a dorsal, middle, and

ventral position, with each instrument positioned close to the vertical septum where

longitudinal muscle strain is low for swimming. A related issue is whether the

measurements of midline curvature in conjunction with beam theory can be used to

calculate longitudinal muscle strain. This concept has been successfully implemented in

locomotion studies but requires a dorsal or ventral view of fish swimming in order to

127

calculate midline curvature from a digitized outline of the body (Coughlin et al., 1996;

Katz and Shadwick, 1998; Shadwick et al., 1998; Van Leeuwen et al., 1990). Of critical

importance to this technique is the observation that the midline, calculated using the left

and right edges of the body, is an accurate estimate of the vertebral column’s position.

Such methodology is not feasible for dorsiflexion during feeding as the dorsal and ventral

edges of the body are not equidistant from the vertebral column, and so a calculated

midline curvature would not accurately estimate vertebral curvature. Although it is not

feasible to estimate longitudinal strain with a lateral view of feeding, techniques such as

fluoroscopy, rotoscoping, and XROMM, could be used to test the relationship between

vertebral flexion and longitudinal epaxial strain. Even so, this relationship may not be

simple because the epaxial muscles are physically connected with the hypaxial muscles,

the ventral half of the axial musculature that plays a complementary but distinct

kinematic role in suction feeding—actuation of the hyo-pectoral interface (Camp, 2019).

Finally, quantitatively associating the neutral axis of axial dorsiflexion with a discrete

anatomical structure is complicated by the kinematic variability of suction feeding

behaviors at both the individual and species levels, although the available evidence

suggests that the neutral axis of bending is approximately at the level of the vertebral

column (Jimenez et al., 2018).

Concluding Remarks

Past studies have only examined the problem of heterogeneous fiber strain in the

axial musculature of fishes within the context of locomotion. Our findings suggest that

we must broaden our investigation of strain heterogeneity to include suction feeding.

128

Strain heterogeneity may be particularly problematic for species that require high mass-

specific power outputs from the axial musculature, such as bluegill sunfish (Camp et al.,

2018). In contrast, strain heterogeneity may be less problematic for species that activate

only a small cross-sectional area of the epaxial muscle during suction feeding

(largemouth bass)(Jimenez and Brainerd, 2020), and those that generate relatively low

mass-specific power from axial muscle during suction feeding (largemouth bass and

channel catfish)(Camp et al., 2018; Camp et al., 2020). Interspecific variation of suction

feeding and locomotor performance is well-known in fishes, but here we show how the

muscle mechanics for high-performance swimming and high-performance suction

feeding may be at odds. Indeed, orthogonal strain gradients may create mechanical

constraints and tradeoffs between axial locomotion and suction feeding in fishes.

Investigating the dual functionality of this muscle will elucidate how many fishes have

successfully co-opted, and perhaps in some cases mechanically specialized, the

‘locomotor’ muscle for generating powerful feeding behaviors that are crucial for

survival.

129

Acknowledgments


and Andrew Biewener for general advice on this project, to David Ellerby for help with

collecting animals, and to Jake Parsons, Jarrod Petersen, and Amy Rutter for assistance

with surgeries. This research was supported by National Science Foundation (NSF) grant

nos. 1655756 and 1661129 to E.L.B., a Doctoral Dissertation Enhancement Grant from

the Bushnell Graduate Education and Research Fund to Y.E.J. and an NSF Graduate

Research Fellowship to Y.E.J.

130

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136

Figures

Figure 1. Patterns of longitudinal strain in the epaxial musculature for swimming

and suction feeding. (a) Axial locomotion: lateral body flexion produces a mediolateral

gradient of longitudinal strain in the axial muscle mass. The neutral axis, the vertebral

column, undergoes neither shortening nor lengthening. If the muscle fibers were oriented

longitudinally, they would also experience this strain gradient. (b) A diagrammatic

power-velocity curve, illustrating how a gradient of fiber strain rate would affect power

production. Muscle shortening velocities in some regions would be faster (circle) or

slower (triangle) than those needed for generating maximal power (Vopt), and only some

regions (star) would shorten at Vopt. (c,d) Alternative models of epaxial mechanics in

suction feeding. (c) The lever model of neurocranial elevation for suction feeding 30.

137

Epaxial forces acting on the in-lever generate neurocranial elevation, intraoral forces

acting on the out-lever resist neurocranial elevation, and the fulcrum occurs at the level of

the S-PT joint in the pectoral girdle. Epaxial muscle dorsal to this joint shortens

uniformly to produce neurocranial elevation, and therefore could potentially all contract

at Vopt, but muscle ventral to this joint (dark grey) cannot contribute to cranial elevation.

(d) An alternative, the beam model of neurocranial elevation for suction feeding, where

neurocranial elevation is produced by the dorsiflexion of multiple intervertebral joints.

All muscle dorsal to the vertebral column can shorten to produce neurocranial elevation,

but the amount of longitudinal shortening is largest in the dorsal region and approaches

zero at vertebral column. We test this hypothesis of a dorsoventral gradient in suction

feeding in this study. Abbreviation: S-PT, supracleithral and post-temporal bones.

138

Figure 2. Sonomicrometer placement and in vivo muscle length dynamics. (a) Lateral

view of the implantation sites for each sonomicrometer pair. (b) Epaxial length dynamics

during an evasive fast-start alongside a transverse view of sonomicrometer positions

relative to the neutral axis of bending (solid red line). In lateral flexion, longitudinal

strain is expected to vary as a function of mediolateral distance from the neutral axis, and

thus the dorsoventral position of the instrument can be ignored. (c) Epaxial length

dynamics during a suction feeding on live prey alongside a transverse view of

sonomicrometer positions. In dorsiflexion, longitudinal strain is expected to vary as a

function of dorsoventral distance from the neutral axis (solid red line), and thus the

mediolateral position of the instrument can be ignored. Lengthening before shortening, as

139

shown here, occurred in some but not most feeding strikes. Thick lines indicate the

duration of muscle activity (see supplementary Fig. S1 for sample EMG traces). Data

shown are from individual Lm01.

140

Figure 3. Feeding produces a strain gradient anatomically orthogonal to swimming.

Longitudinal strain measured at the ventrolateral location (‘B’) as a function of strain

measured at the dorsomedial location (‘A’) for three individual L. macrochirus (Lm01,

02 and 04). Locomotion: open blue circles. Feeding: closed red circles. In the absence of

either mediolateral or dorsoventral strain gradients, the strains at the two locations are

predicted to be equal as is indicated by the dashed lines with a slope of 1.0. Regression

lines are shown with 95% confidence intervals. Regression coefficients, equations, and

statistics for linear regressions can be found in Table 1. Inset shows cross-sectional view

of positions ‘A’ and ‘B’. Abbreviations: ML, mediolateral; DV, dorsoventral.

141

Figure 4. Muscle length dynamics in burst swimming followed by mixed swimming

and feeding behavior. Plot shows a sprint (grey area) followed by a strike with body

bending to the right (orange area). The sprint sequence shows the expected cycles of

muscle lengthening and shortening, and more importantly, the higher magnitudes of

longitudinal strain the lateral sonomicrometer (green) relative to the medial

sonomicrometer pair. The sprint sequence is then followed by a strike on pellet food with

simultaneous lateral flexion to the right, which shows the more complex yet expected

pattern of high muscle shortening in the dorsal region (blue) with muscle lengthening in

the lateral region (green). Data shown are from individual Lm01.

142

Figure S1. Sample EMG traces showing epaxial muscle activity in feeding and

locomotion. Timing of muscle length changes are indicated by red arrows. Data were

sampled at 4000 Hz and EMG signals were filtered with a bandwidth of 100 – 1,000 Hz.

Traces correspond to data shown in Fig. 2.

143

Table 1. Summary of major axis regression analysis.

musculoskeletal biomechanics of the dual-function axial

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