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Evolutionary BiologyEvolutionary Biology ISSN 0071-3260 Evol BiolDOI 10.1007/s11692-011-9130-y

Sympatric Divergence and PerformanceTrade-Offs of Bluegill Ecomorphs

David J. Ellerby & Shannon P. Gerry

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RESEARCH ARTICLE

Sympatric Divergence and Performance Trade-Offs of BluegillEcomorphs

David J. Ellerby • Shannon P. Gerry

Received: 2 May 2011 / Accepted: 13 July 2011

� Springer Science+Business Media, LLC 2011

Abstract Phenotypic plasticity in response to environ-

mental cues can create distinct morphological types within

populations. This variation in form, and potentially func-

tion, may be a factor in initiating population divergence

and the formation of new species. Here we show the

translation of sympatric, habitat-specific morphological

divergence into performance differences in energy econ-

omy, maneuverability and steady-state locomotion. Littoral

and pelagic bluegill sunfish ecomorphs show differences in

performance that appear adaptive within their respective

habitats: greater maneuverability in the heavily vegetated

littoral; greater steady-state swimming speed and economy

in the open-water pelagic. This represents a trade-off in

unsteady versus steady swimming performance, likely

because morphological features associated with maximiz-

ing maneuverability are incompatible with enhancing

steady-swimming performance. This may constrain the

direction of adaptive change, maintaining the divergence

created by phenotypic plasticity. The combination of hab-

itat specific sympatric adaptation and constraints imposed

by performance trade-offs may be an important factor

underlying the high rate of speciation in freshwater fishes

from post-glacial lakes.

Keywords Polyphenism � Adaptation � Swimming �Bluegill sunfish � Lepomis macrochirus

Introduction

The diversification of organisms is a central focus of

evolutionary biology (Darwin 1859; Mayr 1942). Resource

polyphenism, the emergence of divergent, environmentally

triggered, resource-use phenotypes within a population, has

been identified as a potential factor in this process, possibly

representing incipient speciation (reviewed in Skulason

and Smith 1995; Pfennig et al. 2010). Resource polyphe-

nisms have been recorded in many taxa (e.g. Papaj and

Prokopy 1989; Pfennig 1990; Meyer 1993; Wund et al.

2008). Polyphenisms should promote and sustain diver-

gence if they translate into performance differences

between distinct phenotypes, performance trade-offs pre-

vent optimization of multiple functions, and selection

pressures favor different aspects of performance in differ-

ent environments. The potential for diversifying selection

within populations is of particular interest as it represents

the earliest stage of divergence, preceding reproductive

isolation and speciation.

Many fish species have polyphenic populations where

morphological divergence has been ascribed to diversify-

ing selection constrained by performance trade-offs

(Schluter 1995; Robinson et al. 1996; Robinson 2000;

Svanback and Eklov 2003; Bolnick and Lau 2008). Sunfish

(Lepomis) populations consistently diverge into littoral and

pelagic ecomorphs that differ in foraging behavior, diet and

external morphology (Mittelbach 1981; Ehlinger and Wil-

son 1988; Ehlinger 1990; Yonekura et al. 2002, 2007;

Robinson et al. 1993; Jastrebski and Robinson 2004; Par-

sons and Robinson 2006, 2007; Gerry et al. 2011). Com-

pared to pelagic fish, littoral forms typically have deeper

body shapes and fins located further from their center of

mass (Gerry et al. 2011). Biomechanical modeling and

inter-specific comparisons suggest that deep body shapes

D. J. Ellerby (&)

Department of Biological Sciences, Wellesley College,

106 Central Street, Wellesley, MA 02481, USA

e-mail: dellerby@wellesley.edu

S. P. Gerry

Department of Biological Sciences, Fairfield University,

1073 North Benson Rd, Fairfield, CT 06824, USA

123

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DOI 10.1007/s11692-011-9130-y

Author's personal copy

may enhance maneuverability, while a more fusiform body

should favor economical steady swimming (Webb 1984;

Walker 1997; Domenici 2003; Gerry et al. 2011). This

implies a locomotor performance trade-off: phenotypic

features associated with economy and drag reduction are

incompatible with high maneuverability and vice versa.

Locomotor performance is linked to fitness through its

roles in foraging, predator avoidance, reproductive behav-

ior and as a major component cost in organismal energy

budgets (Huey and Stevenson 1979; Arnold 1983; Arnold

and Bennett 1988; Norberg 1995; Garland and Losos

1994). Despite their potentially important role as a factor in

divergence, no data are available concerning trade-offs in

locomotor performance between ecomorphs within a pop-

ulation. The examination of intraspecific swimming per-

formance trade-offs has been restricted to comparisons

between separate populations (Taylor and McPhail 1986;

Langerhans 2009).

In the absence of performance data the inference of

functional divergence and performance trade-offs from

phenotype may be problematic for a number of reasons. (1)

Diversifying selection is likely to act on whole-organism

measures of performance, not morphology per se (Garland

and Losos 1994; Elstrott and Irschick 2004; Garland and

Kelly 2006). (2) Many interacting morphological and

physiological factors influence performance (Arnold 1983;

Ghalambor et al. 2003; Walker 2007), and in complex

biological systems with many components, different mor-

phologies can produce similar functional outcomes (Alfaro

et al. 2004; Pettersson and Hedenstrom 2000). (3) Where

morphological divergence is subtle (Rouleau et al. 2009;

Gerry et al. 2011), it is possible that within population

divergence is insufficiently marked to produce significant

performance differences (Vanhooydonck et al. 2001). (4)

Where potential trade-offs have been predicted from mor-

phological and biomechanical analyses, these may (Bennett

et al. 1984; Losos et al. 1993; Calsbeek and Irschick 2007)

or may not (Garland et al. 1990; Garland and Else 1987;

Sorci et al. 1995) be confirmed by subsequent measures of

performance. (5) The links between phenotype and per-

formance are potentially obscured by behavioral factors.

For example, kinematic differences may contribute to

performance variation between ecomorphs (Taylor and

McPhail 1986), and perceptual and behavioral asymmetries

are associated with variation in fish fast-start performance

(Dadda et al. 2010). It is clear that trade-offs and the

potential for diversifying selection cannot be reliably pre-

dicted from morphological analyses alone and direct

measures of performance are therefore required in order to

assess the functional consequences of polyphenisms within

populations.

Our primary goal is to quantify multiple aspects of

steady and unsteady swimming performance in littoral and

pelagic ecomorphs drawn from a single population of

bluegill sunfish. We predict that as particular phenotypic

features influence both steady and unsteady swimming

performance in different ways, optimization of both modes

will be prevented and there will be a performance trade-off

between these two aspects of swimming performance.

Littoral fish maneuver slowly through a structurally com-

plex environment to glean invertebrates (Mittelbach 1981;

Ehlinger and Wilson 1988; Ehlinger 1989, 1990). The

pelagic form alternates rapid movements between dis-

persed food patches with bouts of hovering while feeding

(Mittelbach 1981; Ehlinger and Wilson 1988; Ehlinger

1989, 1990). Maneuverability, steady swimming perfor-

mance and energy economy are therefore likely to be

linked to fitness through their influence on foraging success

and costs, and subject to habitat-related differential selec-

tion. We quantified maneuverability, as indicated by the

ability to transit an obstacle course; steady swimming

performance as indicated by sustainable swimming speeds

and kinematics; and the energy economy of steady swim-

ming, as indicated by the aerobic cost of transport (COT).

We hypothesize that littoral bluegill will be better suited to

swimming through a complex, vegetated environment as

indicated by increased maneuvering performance through

the obstacle course. In contrast, pelagic bluegill are

expected to be better adapted to steady swimming as

indicated by higher maximal speeds and lower COT than

the littoral form.

Materials and Methods

Study Species

A total of eleven littoral (102.1 ± 7.1 g) and twelve

pelagic (119.5 ± 13.0 g) Bluegill sunfish (Lepomis mac-

rochirus) were collected by hook-and-line from Lake

Waban, Massachussetts, USA from June to August 2010.

Littoral collecting sites were characterized by the presence

of macrophytes and were\1 m in depth. Pelagic collecting

sites were 5–10 m depth and free of macrophytes. Analyses

of stomach contents (SP Gerry, unpublished data), gut flora

(Yonekura et al. 2007) and parasite loads (Wilson et al.

1996) suggest high fidelity of bluegill to their given habitat

type. Pairs of bluegill were housed in 75 l tanks with a

mesh divider separating the fish at a temperature of 22�C

on a 12:12 light:dark cycle. The fish were fed earthworms,

frozen bloodworms and frozen shrimp daily. Although the

aim was to collect multiple performance measures for each

individual, this was not possible in all cases due to loss of

condition before all the necessary data could be collected,

or refusal to swim consistently in the flume or manoeuvring

course. Table 1 shows the data collected from each fish to

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indicate the overlap of study groups for each performance

experiment.

Steady Swimming Experiments

Steady swimming experiments were carried out at 22�C in

a sealable, recirculating flume (Model 90; Loligo Systems,

Hobro, Denmark) capable of generating flow velocities

from 5 to 150 cm s-1. The flume was 88.6 L in volume,

with a working section of 20 9 20 9 70 cm. Fish were

fasted for 24 h and acclimated in the flume overnight

before swimming.

Ten littoral fish (98.5 ± 7.5 g, mean body mass ± SEM)

and ten pelagic fish (119.8 ± 8.4 g) were subjected to step

increases in speed, starting at 0.5 body lengths (L) s-1, and

increasing in 0.2 L s-1 increments at 15 min intervals until

steady swimming could no longer be maintained. This

coincided with the onset of burst and coast swimming

behavior and high amplitude lateral body undulation,

probably indicative of the recruitment of anaerobic muscle.

The time maintained at the final speed interval (T, in min)

was recorded. Umax (in L s-1) = Ufin ? 0.2(T/Tint), where

Ufin (in L s-1) was the speed of the final interval at which

steady swimming could be maintained and Tint was the time

interval between speed increments. This test was not

equivalent to a critical swimming speed (Ucrit) test, as these

typically involve exercising the fish until complete

exhaustion, indicated by an inability to move from the mesh

at the rear of the flume working section (Brett 1964). Our

aim was to determine maximal aerobically supported per-

formance in a similar manner to Claireaux et al. (2006).

Speeds approaching Ucrit involve the recruitment of anaer-

obic muscle (Burgetz et al. 1998; Lurman et al. 2007), ele-

vating performance above sustainable, aerobic levels. The

fish were video recorded using a Sony HDR HC-3 cam-

corder (Sony, New York, NY, USA) at a frame rate of

30 Hz. A mirror mounted above the flume at a 45� angle

allowed simultaneous recording of lateral and dorsal views

of the fish. Pectoral and caudal fin beat frequencies were

determined from sequences of steady swimming in the

center of the flume working section. Frequencies were cal-

culated from sequences of at least 10 fin beats (average, 32).

Sunfish change gait from pectoral fin powered, labriform

swimming, to body and caudal fin powered undulatory

swimming at approximately 50% of their maximal sustain-

able swimming speed (Kendall et al. 2007). The maximal

labriform swimming speed, marking the gait transition

(Utrans), the maximal sustainable swimming speed (Umax)

and the stride lengths (distance traveled per fin beat) at these

speeds were used for kinematic comparisons.

Respirometry

Oxygen consumption data were obtained from six littoral

(96.9 ± 7.0 g) and six pelagic fish (122.0 ± 15.0 g)

swimming at Utrans and Umax in the recirculating flume at

22�C. These were a subset of the fish, chosen at random,

from which steady swimming kinematic data were previ-

ously obtained. Fish were fasted for 24 h and acclimated in

the flume overnight before oxygen consumption measure-

ments. Oxygen consumption was measured using a proto-

col that interspersed Utrans and Umax swimming with

low-velocity rest periods (0.5 L s-1) in the order: rest,

Utrans, rest, Umax, rest. Each period lasted 30 min. The rate

of oxygen consumption ( _MO2) was calculated from the rate

of decline of oxygen concentration in the sealed flume.

Readings were taken with a polarographic oxygen probe

(MI-730, Microelectrodes, Inc., Bedford, NH, USA)

inserted through a port in the flume, and transferred to a PC

via a PowerLab A to D converter (PowerLab, ADInstru-

ments Inc., CO, USA) linked to a computer. The data were

recorded on the computer in LabChart (Version 5.5.5,

ADInstruments Inc., CO, USA).

Initial oxygen concentration measurements were taken in

an empty flume to determine the rate of oxygen consumption

Table 1 Types of data collected for individual littoral and pelagic

bluegill sunfish

Steady swimming Maneuvering _MO2

Littoral 1 x x x

2 x x x

3 x x x

4 x x x

5 x x x

6 x x x

7 x x

8 x x

9 x x

10 x

11 x

Pelagic 1 x x x

2 x x x

3 x x x

4 x x x

5 x x

6 x x

7 x x

8 x x

9 x x

10 x x

11 x

12 x

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by the oxygen electrode and microorganisms in the flume.

These readings were repeated after obtaining data for each

fish and were subsequently subtracted from all fish _MO2

values. Flume volume was corrected for the volume of water

displaced by the fish (calculated from body mass, assuming

an average density equal to water). Mass-specific _MO2¼

R Vflume � Vfish

� �=M

� �mg kg-1 h-1, where R is the mea-

sured rate of oxygen decline in the sealed flume in mg l-1

h-1, Vflume is the flume volume in liters, Vfish is the volume

displaced by the fish, and M is the body mass of the fish in kg._MO2

was converted to energy units using an oxycaloric value

of 13.54 J mg-1 (Brett and Groves 1979).

Maneuvering Experiments

Maneuverability data were obtained from ten littoral

(102.1 ± 7.1 g) and nine pelagic fish (119.5 ± 13.0 g) at

22�C. Maneuverability has previously been assessed in

three ways: based on radius of curvature of turns (Blake

et al. 1995); from angular velocities during fast starts

(Dadda et al. 2010); or from performance maneuvering

through slits or tubes (Webb et al. 1996; Schrank and Webb

1998; Schrank et al. 1999). We have adopted an alternative

approach for a number of reasons. Zero turn radii can be

achieved by many fish by employing their median and

paired fins (Walker 2000), so turn radius alone has limited

value as an indicator of maneuverability. Turning rates

during fast starts are different to those executed during

routine maneuvers (Dadda et al. 2010), and movements of

this type are associated with development of high accel-

erations and velocities, rather than the need to move around

obstacles. Slits and tubes have limited ecological relevance

for bluegill sunfish as they do not resemble their normal

structural environment, with this in mind, we constructed

an obstacle course that approximated the arrangement of a

bed of vertical macrophyte stems (Fig. 1). The vertical

barriers consisted of 15 cm lengths of 2.54 cm outer

diameter, white PVC water pipe. The course consisted of

11 barriers arranged in a random fashion so that a fish

would be required to execute at least one turn regardless of

the path taken.

Fish were transferred from their holding tanks to the

open area at one end of the maneuvering tank. With few

exceptions, rapid movements of a net were needed to

encourage the fish to swim through the course. Swims were

recorded using a high-speed digital video camera (AOS

Technologies AG, Baden Daettwil, Switzerland) mounted

above the tank. Video sequences were captured at a frame

rate of 250 Hz and resolution of 400 9 800 pixels, 1 pixel

being equal to 0.11 cm. These were downloaded to a PC

using AOS Digital Imaging software (Baden Daettwil,

Switzerland). The snout and center of mass (COM) of the

fish were tracked as they moved through the course using

Image-J. The COM of bluegill sunfish is located approxi-

mately 40% of total body length from the snout (Tytell and

Lauder 2008; Gerry et al. 2011). Fish were tracked from

when the snout passed the first barrier until the COM

passed the final barrier of the course. Swims where the fish

did not complete the course or collided head-on with a

barrier were excluded from the analysis. Position data were

smoothed using a smoothing spline interpolation in the

application Igor Pro (Wavemetrics, Lake Oswego, OR,

USA). This method is similar to the cubic spline algorithm

recommended by Walker (1998) for calculating velocities

and accelerations from position data. The level of

smoothing is dictated by the standard deviation of the data.

Smoothed COM position data were differentiated to obtain

COM velocity, and velocity differentiated to obtain COM

acceleration. The COM and snout position data were used

to calculate the heading of the fish. The body axis between

the COM and snout is inflexible, and the vector between

these two points indicates fish heading. The coordinate

scheme was arranged such that movements from start to

finish areas were in the y-direction, and those perpendicular

to this were in the x-direction. The heading angle of the fish

relative to the y-direction (h�) was calculated as h� ¼tan�1 dx

�dy

� �360=2pð Þ; where dx and dy are the distances

between the COM and snout in the x and y directions.

Angle data were smoothed using a spline interpolation, as

for the position data, and differentiated to give a turning

Fig. 1 Vertical view of a fish traversing the obstacle course. Fish

were tracked using high speed video at 250 Hz

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rate in �s-1, this was further differentiated to give angular

acceleration in �s-2.

No more than three runs were recorded per individual

per day to avoid fatiguing the fish. On average, 8 runs were

recorded per individual. The following variables were used

as a basis for comparing maneuvering performance

between the two ecomorphs: duration, path length, center

of mass velocity (mean, minimum, maximum), center of

mass acceleration (mean, minimum, maximum), angular

velocity (mean, minimum, maximum), peak angular

acceleration and peak angular deceleration while traversing

the course. As there are multiple paths through the course,

an individual may vary its level of effort dependent on the

path chosen. We therefore compared both mean perfor-

mances across all trials and the best performances for each

individual.

Statistics

Data were tested for normality using a Kolmogorov–

Smirnov test (P \ 0.05) and Levene’s equality of error

variances test (P \ 0.05). All data were log-transformed to

achieve normality. Negative values (representing deceler-

ation) were adjusted by absolute value transformation

before log transformation. Means for untransformed data

are presented in tables and figures. Analysis of covariance

(ANCOVA) was conducted using the general linear model

function in SPSS (SPSS v. 17, SPSS Inc., Chicago, IL) to

test for differences in speed, stride length and cost of

transport (COT) between littoral and pelagic fish at both

the maximal labriform speed (Utrans) and maximal swim-

ming speed (Umax). Ecomorph type and sex were used as

fixed factors in the model. Body length was used as a

covariate for speed and stride length analyses, and body

mass as a covariate for COT analyses. Where no sex dif-

ferences in performance were found, data were pooled and

comparisons made between ecomorphs using a one-way

ANCOVA. ANOVA was used to test for differences in

maneuvering performance variables between the two eco-

morphs. Where data for all maneuvering trials were ana-

lyzed an individual identifier for each fish was included as

a factor in the model in addition to ecomorph and sex.

Again, where no sex difference was found, sex was

removed as a factor in the model. To account for the use of

multiple comparisons the experiment-wise error rate was

adjusted using a sequentially rejective multiple test pro-

cedure applying Ryan’s Q (Ryan 1960). Cohen’s d (Cohen

1988) was calculated as an indicator of effect size.

d = (ml - mp)/rpooled where ml and mp were the mean

values for the littoral and pelagic groups respectively and

rpooled was the root mean square of their standard

deviations.

Results

The two ecomorphs exhibited clear differences in loco-

motor performance. Pelagic ecomorphs had a signifi-

cantly faster maximum labriform swimming speed

(Utrans, Table 2). Utrans was also significantly faster in

males than females (ANOVA, F = 5.51,20, P = 0.03).

There was no significant interaction between ecomorph

type and sex. Stride length was also significantly greater

in littoral than pelagic ecomorphs (Table 2). We also

detected significant differences in energy economy at

Utrans, with the pelagic form showing a lower cost of

transport (Table 2). In contrast, no significant differences

between ecomorphs were detected in Umax, the stride

length at Umax, or the COT at Umax (Table 2). Cohen’s d

indicates a small effect size based on the differences in

maximal steady swimming kinematics and cost between

ecomorphs.

Table 2 A comparison of steady swimming performance and energetic cost for each ecomorph

Littoral Pelagic df F statistic P Cohen’s d

Maximum labriform speed (Utrans, L s-1) 0.80 ± 0.04 $ 1.03 ± 0.05 $ 1 13.0 0.002 -0.6 (-1.8)

0.94 ± 0.07 # 1.22 ± 0.06 #

Maximum sustainable speed (Umax, L s-1) 2.07 ± 0.11 2.04 ± 0.21 1 0.03 0.856 -0.15 (-0.1)

Stride length at Utrans, L 0.29 ± 0.02 0.37 ± 0.02 1 13.2 0.002 -0.7 (-1.4)

Stride length at Umax, L 0.56 ± 0.06 0.57 ± 0.08 1 0. 5 0.510 -0.14 (-0.04)

Cost of transport at Utrans, J kg-1 m-1 5.46 ± 0.37 3.56 ± 0.25 1 9.9 0.010 0.3 (1.9)

Cost of transport at Umax, J kg-1 m-1 7.16 ± 0.62 6.27 ± 0.54 1 0.3 0.629 0.1 (0.5)

N = 10 littoral, 11 pelagic (kinematics), N = 6 littoral, 6 pelagic (cost of transport). Littoral and pelagic values show means ± S.E.M. for non-

transformed data. For easier comparison to other values in the literature, mean speed and stride length values are expressed relative to fish body

length and COTs as body mass specific costs. F statistics and P values correspond to those obtained from ANCOVA with log transformed

absolute values and body length or mass as covariates. Bold indicates significance based on Ryan’s Q. Cohen’s d is an indicator of effect size

based on the difference between means. Cohen’s d was calculated based on both absolute, untransformed values, and in parentheses, the size

specific values reported in the table. Cohen (1988) classified effect sizes as small (0.2), medium (0.5) and large (0.8)

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Table 3 shows mean performance during the unsteady

swimming maneuvers created by the obstacle course based

on compiled data for all trials from each individual. Littoral

ecomorphs sustained a greater average velocity than the

pelagic ecomorphs while transiting the course and their

minimum velocity was also greater than that of the pelagic

ecomorphs. This resulted in a significantly shorter littoral

transit duration. In addition to these variables, a large effect

size (Cohen’s d C 0.8) is associated with the differences

between means for path length (shorter in littoral), peak

velocity (greater in littoral), peak deceleration (greater in

pelagic), average acceleration (greater overall deceleration

in pelagic) and peak acceleration (greater in pelagic).

Based on a comparison of best performances only, lit-

toral ecomorphs sustained a greater average velocity than

the pelagic ecomorphs (Table 4). A large effect size

(Cohen’s d C 0.8) is associated with the differences

between means for transit duration (shorter in littoral),

average velocity (faster in littoral), angular velocity

(greater in littoral) and angular deceleration (greater in

littoral). With the exception of peak acceleration the nature

of the differences associated with large effect sizes is

consistent with greater maneuverability in the littoral than

the pelagic form.

There were significant correlations between the average

velocity maintained through the obstacle course and both

stride length during maximal labriform swimming (Fig. 2a)

and COT during maximal labriform swimming (Fig. 2b).

Higher maneuvering performance was associated with a

low labriform stride length (Fig. 2a) and higher labriform

Table 3 A comparison of overall ecomorph maneuvering performance for all trials

Littoral Pelagic df F statistic P Cohen’s D

Duration (s) 1.28 ± 0.09 2.44 ± 0.28 1 17.0 <0.001* -1.8

Path length (m) 0.280 ± 0.002 0.292 ± 0.006 1 4.8 0.030 -0.9

Minimum velocity (L s-1) 0.83 ± 0.09 0.42 ± 0.08 1 19.1 <0.001* 1.6

Average velocity (L s-1) 1.78 ± 0.12 1.17 ± 0.15 1 24.5 <0.001* 1.5

Peak velocity (L s-1) 3.72 ± 0.22 2.87 ± 0.27 1 7.1 0.008 1.1

Peak deceleration (N) -0.80 ± 0.11 -5.25 ± 1.61 1 5.9 0.017 1.3

Average acceleration (N) -0.047 ± 0.008 -0.17 ± 0.06 1 1.7 0.191 1.0

Peak acceleration (N) 0.71 ± 0.11 4.57 ± 1.65 1 2.0 0.158 -1.1

Angular velocity (�s-1) 640 ± 52 580 ± 46 1 0.3 0.580 0.4

Angular deceleration (�s-2) -47400 ± 5500 -41800 ± 5200 1 0.7 0.410 -0.3

Angular acceleration (�s-2) 48700 ± 6000 42200 ± 5700 1 1.0 0.319 0.4

N = 10 littoral, 9 pelagic. Littoral and pelagic values show means ± S.E.M. for non-transformed data. F statistics and P values correspond to log

transformed data. Bold indicates significance based on Ryan’s Q. Data from all trials were included in the analysis. Cohen’s d is an indicator of

effect size based on the difference between means. Effect sizes classified as large are marked in bold (C0.8, Cohen 1988)

Table 4 A comparison of the best maneuvering performances for each ecomorph

Littoral Pelagic df F statistic P Cohen’s D

Duration (s) 0.55 ± 0.06 0.90 ± 0.17 1 2.7 0.115 -0.9

Path length (m) 0.261 ± 0.002 0.259 ± 0.014 1 0.1 0.735 0.1

Minimum velocity (L s-1) 0.084 ± 0.021 0.030 ± 0.013 1 2.6 0.129 0.7

Average velocity (L s-1) 1.84 ± 0.19 1.16 ± 0.12 1 15.9 <0.001* 1.4

Peak velocity (L s-1) 6.44 ± 0.52 6.23 ± 0.66 1 0.2 0.699 0.1

Peak deceleration (N) -2.31 ± 0.51 -10.60 ± 5.60 1 0.4 0.562 0.7

Average acceleration (N) -0.002 ± 0.008 -0.021 ± 0.011 1 0.4 0.544 0.6

Peak acceleration (N) 2.31 ± 0.53 10.92 ± 6.07 1 0.8 0.375 -0.7

Angular velocity (� s-1) 1100 ± 200 710 ± 100 1 2.1 0.168 0.8

Angular deceleration (� s-2) -133000 ± 13000 -90700 ± 14700 1 5.4 0.030 -1.0

Angular acceleration (� s-2) 132000 ± 22000 101000 ± 16800 1 4.9 0.041 0.5

N = 10 littoral, 9 pelagic. Littoral and pelagic values show means ± S.E.M. for non-transformed data. F statistics and P values correspond to log

transformed data. Bold indicates significance based on Ryan’s Q. Only the maximal values for each individual were included in the analysis.

Cohen’s d is an indicator of effect size based on the difference between means. Effect sizes classified as large are marked in bold (C0.8, Cohen

1988)

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cost of transport (Fig. 2b). In contrast, no significant rela-

tionships were detected between maneuvering performance

and both stride length and COT at the maximal sustained

swimming speed (Fig. 2c, d).

Discussion

Performance Trade-offs

Bluegill sunfish ecomorphs exhibit habitat-specific varia-

tion in swimming performance. The littoral form is more

maneuverable than the pelagic form, and pelagic fish sus-

tain a higher labriform swimming speed with greater

energy economy than in the littoral form (Tables 2, 3, 4;

Fig. 2). Our data, at least in part, support the classic view

of trade-offs in steady versus unsteady swimming perfor-

mance. As far as we are aware, these are the first direct

measurements of locomotor performance trade-offs in

ecomorphs within a fish population.

Higher velocities through the obstacle course are asso-

ciated with relatively ineffective labriform swimming,

indicated by a low stride length (Fig. 2a; Table 2) and

higher labriform cost of transport (Fig. 2b; Table 2). This

trade-off may arise because there are phenotypic features

or functional complexes of features that influence both of

these aspects of performance. Although many features

contribute to both labriform swimming and maneuvering

performance, pectoral fin and body shape have been

identified as being particularly important in both behaviors

(Webb 1984; Domenici 2003). Pectoral fin aspect ratio and

body depth cross load in a principal components analysis of

littoral and pelagic bluegill external morphology (Gerry

et al. 2011), and are negatively correlated across both

ecomorphs (Fig. 3). In labriform swimmers, high aspect

ratio pectoral fins are associated with effective and efficient

thrust production and high-speed swimming (Walker and

Westneat 2000, 2002), and low aspect ratios with maneu-

verability (Gerstner 1999) and low efficiency of thrust

production (Walker 2004). The deeper body form of littoral

fish places the fins that execute turning maneuvers further

from the body center of mass, increasing their moment

arms and effectiveness at creating turning moments

(Standen and Lauder 2005). While potentially increasing

maneuverability, for a given body mass and volume a

deeper body shape will have a greater wetted surface area,

which is proportional to drag (Webb 1992). This should

add to the mechanical power requirements of labriform

6

5

4

3

2

1

Man

euve

ring

spee

d (L

s-1

)

6543

Labriform COT (J kg-1

m-1

)

6

5

4

3

2

1

Man

euve

ring

spee

d (L

s-1

)0.450.400.350.30

Labriform SL (L)LittoralPelagic

6

5

4

3

2

1

Man

euve

ring

spee

d (L

s-1

)

0.650.600.550.500.450.40

Maximum Speed SL (L)

6

5

4

3

2

1

Man

euve

ring

spee

d (L

s-1

)9876

Maximum speed COT (J kg-1

m-1

)

A B

C D

Fig. 2 Relationships between

the average speed maintained

through the obstacle course and

a Stride length at the maximal

labriform speed b Cost of

transport at the maximal

labriform speed c Stride length

at the maximal undulatory speed

d Cost of transport at the

maximal undulatory speed. For

a and b there was a significant

correlation between the two

variables (Spearman’s q,

P \ 0.05. q = -0.68 and 0.70

for a and b respectively). For

b and c, no significant

correlation was detected

between the two variables

(Spearman’s q, P [ 0.05.

q = 0.15 and 0.20 for c and

d respectively)

Evol Biol

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swimming, increasing the cost of transport in deep bodied,

littoral fish (Fig. 2b).

At maximal steady-state speeds no differences in per-

formance were detected between ecomorphs, and maximal

performance showed no apparent trade-off with maneu-

verability (Table 2). There are a number of possible factors

underlying this observation. First, given the relatively

small sample size, a lack of statistical power may have

resulted in a Type II statistical error. With the exception of

mass specific COT at Umax, the effect sizes associated with

these variables fall below the d-value benchmark of 0.2

indicating a ‘small’ effect size (Cohen 1988). Inspection of

the mean values, and the d-values suggests that for Umax

and the stride length at this speed (Table 2), that any dif-

ferences, if present, would not be substantial. Collection of

further COT data at the maximal speed may however be

merited to confirm whether a cost difference is present.

Second, accurate measurement of maximal performance is

sensitive to the number of trials performed per individual

(Adolph and Pickering 2008), and our steady-swimming

performance data are based on single replicates for each

individual. This can lead to underestimates of performance,

particularly during transient behaviors such as escape

responses (Adolph and Pickering 2008). However, for

measures of maximal aerobic swimming performance in

fish intra-individual variation is relatively low (Reidy et al.

2000; Marras et al. 2010) and the use of single replicates

should not have significantly biased our data. Third, the

absence of a trade-off between maximal swimming per-

formance and maneuverability (Fig. 2c, d) may reflect a

partial functional separation of the phenotypic features that

determine these different aspects of performance. The

maximum sustainable swimming speed should be closely

related to the maximal aerobic capacity of the fish. This is

determined by the linked capacities of the respiratory,

cardiovascular and skeletal muscle systems at to exchange,

deliver and consume nutrients at multiple levels from the

respiratory exchange surface to the muscle mitochondria.

These factors should have limited influence on short-lived

maneuvers. Other features, such as body and fin shape are

however likely to influence both aspects of performance

(Standen and Lauder 2005; Tytell and Lauder 2008; Tytell

et al. 2008). Fourth, in comparison to other aspects of

performance, the habitat-specific selection pressures acting

on this aspect of performance may be relatively weak. High

speed swimming may be a feature of predator avoidance,

or mating behavior (Gross and MacMillan 1981), activities

that are less strongly exposed to habitat-specific selection

pressures than those associated with foraging. Finally, even

in the presence of habitat-specific selection pressures, traits

that influence maximal swimming performance, such as

aerobic capacity, may be constrained by developmental

factors that limit functional changes in those traits irre-

spective of selection (Maynard Smith et al. 1985; Futuyma

2010).

The combination of resource polyphenism and trade-

off constraints has the potential to drive diversification

(Langerhans 2009). Freshwater fishes in postglacial lakes

display a wide diversity of recently evolved phenotypic

variation (Schluter 1996; Bernatchez and Wilson 1998).

The extent and rate of emergence of this variation is par-

ticularly high within the Lepomis clade (sunfishes; Collar

et al. 2005). Once divergent forms emerge, the locomotor

performance trade-offs identified in the present study, and

previously identified trade-offs in foraging efficiency

(Robinson 2000) could constrain the trajectory of adaptive

change for each ecomorph, potentially maintaining the

population divergence initiated by phenotypic plasticity.

This may underlie the high rate of speciation within this

group and be a feature of other speciose taxa (Pfennig et al.

2010).

Linking form, function and fitness

Overall, the observed performance differences fit with

theoretical expectations of the relationship of body form

and fin shape to performance (Webb 1984; Domenici 2003;

Langerhans and Reznick 2010). The surprising aspect is the

magnitude of the performance difference when compared

to the degree of morphological divergence. Yet, selection

on individual components of a functional unit may be

relaxed while the overall selection on performance is

strong (Schwenk 2001). The morphological variation on

which our predictions of performance differences were

based is subtle. For example, although habitat related dif-

ferences in relative body depth between ecomorphs are

statistically significant, there is only a 3% difference

0.42

0.40

0.38

0.36Rel

ativ

e bo

dy d

epth

(L)

3.53.02.52.01.51.0Pectoral fin aspect ratio

LittoralPelagic

Fig. 3 The relationship between relative body depth and pectoral fin

aspect ratio. Data are replotted from Gerry et al. 2011. N = 33

littoral, 17 pelagic. There was a significant correlation between the

two variables (Spearman’s q, P \ 0.05. q = -0.42)

Evol Biol

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between the mean values (Gerry et al. 2011). Despite the

fine morphological differences, the relative performance

differences can be quite marked. For example, the mean

gait transition speed is 31% higher in the pelagic than the

littoral form in the present study. In this case, morphology

is an indicator of the nature, but not the degree of the

performance difference.

Performance levels arise from the linked properties of

many structures and systems (Schwenk 2001; Ghalambor

et al. 2003; Walker 2007). No single phenotypic feature is

likely to be a reliable predictor of any given aspect of

performance. The many-to-one, and many-to-many map-

ping of phenotypic features to performance has been

identified in a number of systems (Alfaro et al. 2004). The

challenge lies in discerning the patterns of functional

association within these complex interrelationships of form

and function.

Matrix-based, correlational analyses provide an analyt-

ical framework for potentially revealing the interactions

between multiple traits and performance (Walker 2007).

Given sufficient data concerning morphology, physiology

and performance, functional groups of traits that combine

to enhance particular aspects of performance can be iden-

tified. Trade-offs are also revealed where different pheno-

typic characters, or functional units, are associated with

opposite effects on performance. This analytical approach

has been applied in relatively few systems (Calsbeek and

Irschick 2007). At present, there are insufficient data con-

cerning the study population to attempt this type of anal-

ysis. A meaningful analysis will require the integration of

multiple types of phenotypic, behavioral and performance

data. These will need to include not only the typically

quantified external morphological features like body shape,

but more complex functionally relevant features such as the

moment arm of fins in relation to the fish center of mass

(Gerry et al. 2011). While past studies have focused pri-

marily on external features; greater attention will need to

be paid to internal morphology and physiological data. The

turning moment exerted by a fin, for example, is not only

dictated by its area and location, but by the capacity of the

fin muscles to exert force. This could be estimated mor-

phologically from the physiological cross sectional area

(Thomason 1991), or measured directly using in vitro

techniques (Kendall et al. 2007; Jones et al. 2007, 2008).

For sustained locomotor performance, particularly at high

levels of effort, the capacities of the respiratory and car-

diovascular systems to deliver nutrients, and the aerobic

capacities of the active tissues are likely to be important

factors. Proxies for these such as gill area, blood hemato-

crit, and the activity of respiratory enzymes in muscle

tissue should therefore be included in future analyses.

Ultimately, although adaptation can be inferred from

morphology and performance, only direct measures of

fitness can unambiguously indicate habitat-specific adap-

tion in pelagic and littoral ecomorphs. Selection is only

divergent if intermediate phenotypes have reduced fitness

relative to the more extreme littoral and pelagic pheno-

types. Directly measuring reproductive success or survival

rates in large, aquatic populations is challenging. Potential

proxies for fitness include growth rates estimated from

scale annuli (Ehlinger 1991; Robinson et al. 1996) or

biochemical indicators (Bolnick and Lau 2008), condition

factors based on lipid reserves (Robinson et al. 1996), or

comparisons between measured mass and overall mass-

length relationships for a given species or population

(Bolger and Connolly 1989). Direct measures of growth

rates have been confined to laboratory experiments, artifi-

cial habitats or enclosures placed in natural habitats

(Schluter 1993, 1995). Fitness proxies have yet to be

examined in relation to habitat-related differences in

locomotor performance.

Conclusions

Trade-offs in steady and unsteady locomotor performance

have been demonstrated within a population of bluegill

sunfish. Maneuverability around obstacles is associated

with reduced performance and economy during steady

labriform swimming. Littoral fish have greater maneuver-

ability and poorer labriform performance than pelagic fish.

This is suggestive of habitat-specific adaptation as the lit-

toral habitat is structurally complex, while pelagic fish

inhabit open water. The performance trade-off may arise as

there are phenotypic features influencing both aspects of

performance that cannot be optimized for both functions.

Future work should integrate multiple measures of external

and internal morphology, behavioral and physiological data

with measures of both steady and unsteady locomotor

performance in order to fully reveal the functional associ-

ations of phenotypic features. Measurements of fitness, or

proxies for fitness in relation to performance will be

required to fully assess the adaptive significance of fish

polyphenisms.

Acknowledgments We would like to thank Kara Feilich, Cataia

Ives and Jessica Liao for their assistance with these experiments.

Funding for this project was provided by a Wellesley College

Brachman-Hoffman grant to DJE and by NSF 0715937 to DJE.

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