|
| |
|
|
|
|
||||
| Home Help Feedback Subscriptions Archive Search Table of Contents | ||||
First published online June 29, 2007
Journal of Experimental Biology 210, 2436-2443 (2007)
Published by The Company of Biologists 2007
doi: 10.1242/jeb.004275
Morphological predictors of swimming speed: a case study of pre-settlement juvenile coral reef fishes
Biological Sciences, University of Windsor, 401 Sunset Avenue, Windsor, Ontario, N9B3P4, Canada
* Author for correspondence (e-mail: rebecca_fisher76{at}yahoo.com.au)
Accepted 25 April 2007
 |
Summary |
|---|
 TOP Summary IntroductionMaterials and methodsResultsDiscussionReferences |
|---|
Key words: swimming, morphology, dispersal, behaviour, Ucrit
|
Introduction |
|---|
|
TOPSummaryIntroductionMaterials and methodsResultsDiscussionReferences |
|---|
), to successful
migration (Fisher and Bellwood,
2003), ultimately influencing Darwinian fitness
(Plaut, 2001;
Reidy et al., 2000).
Consequently, swimming performance has long been a topic of considerable
interest to ecologists. Perhaps the earliest published measurements of fish
swimming speed are from 1893, where Regnard measured the maximum swimming
speed of small fishes in an annular, rotating channel
(Regnard, 1893). Since then,
many researchers have measured the swimming performance of fishes
(Bainbridge, 1958;
Beamish, 1970;
Brett, 1964;
Fry and Hart, 1948;
Magnan, 1930), and such
studies have been viewed as a prelude to, or put in the context of, the
functional design, physiology and ecology of fish swimming
(Blake, 2004).
While much work has been done measuring swimming performance of fishes in
the last century, data have been biased toward particular taxa or groups, and
are most abundant for temperate fishes. Excellent data are available on the
swimming performance of the cod-like fishes
(Claireaux et al., 1995;
Lough and Potter, 1993;
Reidy et al., 2000), as well
as the Salmonidae such as salmon and trout (e.g.
Brett, 1964;
Greenland and Thomas, 1972;
Paulik and DeLacy, 1957;
Small and Randall, 1989;
Taylor and McPhail, 1985) and
the Scombridae such as tunas, mackerel (e.g.
Blake et al., 2005;
Dewar and Graham, 1994;
Korsmeyer et al., 1996). For
these groups, differences in overall body shape, along with other
physiological differences such as muscle type, can account for differences in
their swimming performance, with faster swimming taxa showing a generally more
streamlined body form (Wardle,
1977).
Tropical ecosystems are characteristically much more diverse than their
temperate counterparts, and coral reefs in particular often house thousands of
fish species that include both perciform families, and less derived teleost
orders (Sale, 1991). As such,
coral reef fish fauna represent the most diverse group of fishes in the world
(Sale, 1991). Examination of
the swimming abilities of coral reef fish species is comparatively recent,
with an explosion of research occurring over the last decade. Much of this
research has focused on the late-larval/early-juvenile stages of coral reef
associated fishes (Fisher and Bellwood,
2003; Fisher et al.,
2000; Fisher et al.,
2005; Fisher and Wilson,
2004; Hogan et al.,
2007; Leis and Carson-Ewart,
1997; Stobutzki,
1997; Stobutzki and Bellwood,
1994), and the potential for active swimming behaviour to be used
to modify dispersal distances (Fisher,
2005; Stobutzki and Bellwood,
1997). Several studies have also shown the importance of swimming
performance in structuring adult distributions on coral reefs (see
Fulton et al., 2001;
Fulton et al., 2005;
Wainwright et al., 2002).
The swimming performance of different species and taxonomic groups of
fishes within the coral reef ecosystem are known to vary widely, even when
fishes are examined at the same developmental stage. For example, the maximum
swimming speeds of pre-settlement juvenile coral reef fishes range from 5 to
100 cm s–1 (Fisher et
al., 2005). Although hydrodynamic theory and bio-energetics
indicate that the optimal swimming speeds of fish should be dependent on their
body length (Ware, 1978),
variation in swimming performance among taxonomic groups are often only
explained weakly by total length
(Bainbridge, 1960;
Blake, 2004;
Fisher et al., 2005;
Stobutzki and Bellwood, 1997),
and it is clear that other morphological and physiological factors must be
important.
There are numerous aspects of the body morphology of fishes that have been
identified as important in defining swimming performance, including aspect
ratio (Sambilay, 1990),
fineness ratio (Bainbridge,
1960), caudal peduncle depth factor
(Webb and Weihs, 1986) and
propulsive area (Fisher et al.,
2000). However, with the exception of some work on pectoral fin
locomotion (Wainwright et al.,
2002) and a brief correlative study
(Fisher et al., 2005), no
attempt has been made to use the diverse range of reef fish families available
to examine the extent to which swimming capabilities correlate with, and can
be predicted by, external body morphology. The large amount of data available
on the swimming speeds of pre-settlement juvenile coral reef fishes provides
an excellent opportunity to examine the relationship between form and function
in fish swimming, and to determine how this varies among taxonomic groups,
different swimming modes, among different coral reef regions, and with
habitat. Although perhaps not as diverse as their adult counterparts, these
pre-settlement juvenile fishes still represent a wide range of body forms and
swimming modes, allowing an examination of factors influencing swimming
performance across a broad range of body types.
Importantly, it is not possible to measure the swimming performance of many fish species because such data are difficult and time consuming to collect, requiring specialized equipment. Additionally, some taxa can only be caught in the field using methods that injure or kill individuals (such as towed nets), or are caught so rarely that good swimming data are unlikely to ever be collected. If morphological characteristics can be reliably used to predict fish swimming performance, this could provide an invaluable means of estimating the abilities of unstudied taxa, and allow the incorporation of essential swimming parameters into ecological studies.
Despite its potential utility, no studies have developed a general model for predicting the swimming ability of coral reef fishes. Here we explore the relationship between swimming ability and external body morphology across a broad range of coral reef fish taxa, using existing data on Ucrit swimming speeds of early-juvenile-stage coral reef fishes. Ucrit speeds represent the maximum sustainable speeds of fishes, and at these speeds fish predominantly utilize a body–caudal fin swimming mode. The best model for estimating swimming performance is determined, and the fit of the model is examined in relation to a variety of ecological characteristics of each family, including its taxonomic order, preferred swimming mode at cruising speed, adult habitat (pelagic/demersal) and geographic region.
|
Materials and methods |
|---|
|
TOPSummaryIntroductionMaterials and methodsResultsDiscussionReferences |
|---|
;
Fisher et al., 2005;
Hogan et al., 2007),
representing 100 species of fishes from 26 different families. These data were
collected from fish in the Great Barrier Reef in the Pacific and from both the
Turks and Caicos Islands and Turneffe Atoll off Belize in the Atlantic
(Caribbean). The experimental protocol adopted at all locations was identical,
using similarly designed and calibrated swimming flumes. All swimming
experiments were conducted at ambient seawater temperatures, which ranged
between 28 and 30°C at both locations. The majority of larvae were swum
within 6 h of capture and all were swum within 12 h, using the swimming flumes
described by Stobutzki and Bellwood and others identical in design to them
(Stobutzki and Bellwood, 1994;
Stobutzki and Bellwood, 1997).
Ucrit was measured by placing larvae in the swimming flume
and increasing the speed incrementally over time until the larva could no
longer maintain position for the full time interval, and then calculated
following Brett (Brett, 1964)
as:
 |
), the most common values used were a speed increment of 3
body lengths (BL) s–1 and a time interval of 2 min.
There is no evidence that variation in either speed or time interval
influences Ucrit speed estimates over the range used (see
Fisher et al., 2005;
Hogan et al., 2007).
Samples were obtained for these studies predominantly using light traps,
although some were also captured using crest nets. Both light traps and crest
nests capture coral reef fishes at the end of their pelagic phase, and most
specimens swum are best described as pre-settlement juveniles. For most
families, this stage represents the transition from the pelagic to the benthic
habitat, and most families have largely developed into the adult body form.
The size of the different families of fishes at this developmental stage
varied widely, from an average of 6.37 mm for the Ogcocephalidae up to 37.27
mm for the Holocentridae. The average size across all families was 19.30 mm.
The families captured were generally reef associated (occur in the vicinity of
reefs), however several are distinctly more `pelagic' (including the
Sphyraenidae, Carangidae, Clupeidae and Nomeidae). Predominantly demersal
families consisted of archetypical reef fish families, closely associated with
coral reef habitat see (Bellwood,
1996), as well as more generalist families associated with a range
of benthic habitats. The families swum also represented a range of swimming
modes, including pectoral fin, pectoral-caudal fin, caudal fin and
dorso-ventral fin locomotors. In all, families from five different orders of
fishes are represented, including six sub-orders of the highly diverse
Perciformes.
Morphological measurements
Data for morphological measurements were obtained from digital images of
the fish taken immediately after the swimming trials. These images were
obtained from the same individuals that comprised the swimming data used in
analysis (Fisher et al., 2005;
Hogan et al., 2007). Variables
used for analysis are shown and defined in
Fig. 1. All measurements were
taken to the nearest 0.1 mm, using the image analysis software, ImageTool
(UTHSCSA 2002). Body width (BW, measured at the widest region,
usually the head) was also measured to the nearest 0.1 mm using vernier
calipers.
|
Developing the morphological model
The shape of different families of pre-settlement juveniles was
characterised using several morphological variables and five morphometric
ratios that were identified from the literature as being potentially important
for describing swimming abilities in fishes
(Table 1). Because all of the
raw morphological variables were highly correlated with length, rather than
using them directly, they were used to calculate various morphometric ratios
obtained from the literature, which were known to be important in swimming.
Although residual analysis provides an effective and alternative means of
removing co-variance with length in morphometric studies, this approach was
not adopted here because of the potential confounding effect between size and
the magnitude of the residuals: as size increases the magnitude of the
residual values increase, even when the actual body shape remains the same.
Initial analysis indicated that models incorporating residual values were
highly unstable outside the observed range of data.
|
Morphometric ratios considered in the analysis included: muscle ratio
(MR, the ratio of the muscle area of the fish relative to the total
body area), propulsive ratio (PR, the ratio of the propulsive area to
total body area), fineness ratio [FR, length divided by the average
between the body width and body depth
(Bainbridge, 1960)], aspect
ratio [AR, caudal fin height divided by the square root of caudal fin
area (Sambilay, 1990)] and
caudal peduncle depth factor [CPDF, caudal peduncle depth divided by
body depth (Webb and Weihs,
1986)] (Table 1B).
Initially a correlation matrix and a PCA were used to examine the
relationships among the different ratios, and the spread of the different
families among the different body shapes.
A best-subsets multiple regression approach was used to develop a
morphological model that could be used to predict Ucrit
swimming speeds across families. A forward stepwise approach was not adopted
because of moderately high levels of multi-collinearity in the data, which can
cause problems when testing for the significance (and therefore
inclusion/exclusion) of predictor variables
(Graham, 2003). The regsubsets
function in the leaps package contributed to R [written by Thomas Lumley using
Fortran code by Alan Miller (see Miller,
1990)] was used for best sub-sets regression analyses. Although
generally weak, at the family level Ucrit is positively
related to size for pre-settlement juvenile coral reef fishes
(Fisher et al., 2005). To
account for the overall influence of size on swimming performance, total
length was forced as the first variable in the best-subsets multiple
regressions. Because shape is not necessarily linearly related to swimming
speed, the squares of all variables were also entered, allowing the
best-subsets regression to fit both linear or quadratic functions to obtain
the best-overall model for predicting swimming performance. Including the
scope for quadratic functions allows for the likely possibility that shape
measurements and/or morphometric ratios will have an optimal-response
relationship with swimming speed, where swimming speed increases up to some
optimal value, after which point there is no longer an increase (and perhaps
even a decrease) in swimming performance.
Best-fit predictive equations were obtained for sequentially greater
numbers of variables included in the model. Observed values of
Ucrit were plotted against predicted values and
R2 (both regular and adjusted) (Zar, 1999) were calculated
to evaluate the performance of the predictive relationships. We used a
second-order Akaike information criterion for small sample sizes (AICc)
(Burnham and Anderson, 2002) to
select the best model that contained the least number of predictor variables,
evaluating the relative fit of each model using Akaike weights
(
i) (Burnham and
Anderson, 2002).
All regressions were performed on the means for each species using the statistical programming language R. The mean for each species was weighted according to their percentage contribution to each family (such that the sum of the weights of all species in a family were equal to 1), to ensure that all families were equally represented in the analysis (so the analysis would not be biased towards the specious families).
Evaluating the model
A bootstrap analysis was carried out to examine the stability of the size
of the best selected model as well as to determine the relative usefulness of
the different morphometric ratios for predicting swimming ability. The full
analysis was carried out using 10 000 bootstrap iterations, each based on a
randomly selected set of families (with replacement). From these, the mean
Akaike weight and the proportion of iterations in which each variable was
included in the best-fit model of each model size was calculated.
An independent test of the model was carried out by using the best models to predict swimming ability of steelhead trout (Oncorhynchus mykiss, total length=18.4 cm), and comparing the predicted value to empirical data obtained from specimens swum at the National Marine Fisheries Service Laboratory in Santa Cruz, CA, USA. The average size of the individual steelhead was 179 mm (fork length, FL). (Data are courtesy of A. Ammann, unpublished.)
An ANOVA based on the residuals from the best-fit model was used to examine how well a purely morphological model was able to predict swimming speed across families caught in different oceans (Caribbean and Great Barrier Reef). A similar analysis was also carried out for families from different orders of fishes, different swimming modes and different adult habitat characteristics (demersal or pelagic).
|
Results |
|---|
|
TOPSummaryIntroductionMaterials and methodsResultsDiscussionReferences |
|---|
|
|
i) identify the 5-parameter model (which includes three
variables) as the most likely best model
(Fig. 3B). As the number of
parameters increases beyond 5, i dips and then drops off
steadily, despite a gradual increase in R2
(Fig. 3). According to the best sub-sets regression on the raw data, the best 3-variable model included TL, AR and CPDF2 (five parameters, including the intercept and error terms; Table 2).
|
Evaluating the model
Although the raw data indicated that the 5-parameter model was probably the
best, the selection of this sized model was relatively unstable based on the
boot-strap analysis, which indicated that, on average, the 7-parameter model
had a higher average Akaike weighting. Despite some instability in selection
of the best model size, the bootstrap analysis showed consistency in the
variables included in the best model. For smaller sized models, AR
and CPDF were almost always included
(Fig. 3C), and the bootstrap
did confirm that a model including TL, AR and
CPDF2 was most frequently chosen as the best 3-variable
model (Table 2). As the number
of parameters increases, either PR or FR were included in
the best model, with similar frequency
(Fig. 3C). The best 7-parameter
model included four variables: TL, AR, CPDF and PR, with
linear and squared terms for PR, and was consistent with that
obtained using the raw data (Table
2).
Although the bootstrap analysis indicated that a 7-parameter model may be preferable, an independent test of the model using data for Californian steelhead trout (Oncorhynchus mykiss, mean FL=179 mm, mean Ucrit=82.3±2.17 cm s–1) indicated that the larger sized models were unstable when used to predict swimming speeds for fishes outside the observed data range (Table 2). While the 3-variable model was able to reliably predict the swimming speeds of this species, the larger model substantially underestimated their swimming speed (Table 2). For this reason, the 3-variable model was used for further analysis.
Overall, the best-fit model fitted equally well to the different reef fish families, and was able to explain 69% of the variation in swimming speeds among species from both regions (Fig. 4). The fastest swimming species belonged to the Holocentridae, followed by the Carangidae, Siganidae and Acanthuridae (Fig. 4). The slowest swimmers were the Ogcocephalidae and Antennariidae (Fig. 4).
|
|
|
Discussion |
|---|
|
TOPSummaryIntroductionMaterials and methodsResultsDiscussionReferences |
|---|
).
Aspect ratio is believed to have a strong influence on the swimming
abilities of fishes (e.g. Fisher et al.,
2005; Sambilay,
1990; Webb and Weihs,
1986), and this variable was consistently included in the
best-subset regression models, regardless of how many other variables were
added. A high aspect ratio is thought to be characteristic of pelagic marine
fish that have enhanced cruising speeds, enabling them to travel over wide
areas in search of food and breeding grounds
(Webb, 1994).
Caudal peduncle depth (expressed as a ratio of body depth) was also
strongly related to swimming speed across families, confirming the importance
of this variable to swimming ability in fishes. A deep caudal peduncle is
found in fish that are described as `accelerators'
(Webb, 1994). These hover in
the water column and ambush prey but have poor sustained swimming speeds
(Webb, 1994). On the other
hand a narrow caudal peduncle is characteristic of thunniform fish, which use
an axially oscillating caudal fin to generate thrust for cruising at high
speed (Webb, 1984;
Webb and Weihs, 1986).
Ucrit measures the prolonged, high speed swimming
capabilities of fishes (Plaut,
2001) and appears to be closely correlated with their fastest
sustainable speed (over 24 h) (Fisher and
Wilson, 2004). As such, it closely represents `cruising' behavior,
as opposed to rapid acceleration, although there may be some component of
maneuverability that is important when measuring swimming performance using
flumes.
Propulsive ratio and fineness ratio both appear to produce similarly viable
4-parameter models when combined with the best 3-parameter model. Both of
these variables are related to the muscle mass of the fish, with PR
measuring the proportion of the fish's body able to be used in swimming, and
FR estimating the combined total drag of the body due to frictional
resistance and form drag, with an optimal value of 2.5 thought to allow the
greatest amount of muscle mass (Bainbridge,
1960). However, the inclusion of a fourth variable in the model
added little in terms of explained variance, and appeared to make the model's
predictive ability outside the observed range of data unstable
(Table 2). The fact that
TL, CPDF2 and AR are able to explain nearly 70%
of the variability in Ucrit is remarkable, given the wide
range of body shapes exhibited by the different families. Although not
necessarily the only factor of importance in fish swimming (physiological
factors such as muscle type must also be considered), body morphology is
clearly highly correlated with, and therefore can be used to predict swimming
performance.
Pectoral fins have not been considered in this study, despite being
valuable predictors of swimming abilities of adult fishes, especially among
those that use pectoral fin locomotion
(Fulton et al., 2005;
Wainwright et al., 2002), and
being well developed even at this pre-settlement juvenile stage. For
pre-settlement juvenile fishes, pectoral fins are very small and transparent,
making them impossible to view on whole photographs and difficult to dissect
and pin. For these reasons they were not considered in this study, although
they could prove useful for predicting endurance swimming (which may be
affected by swimming mode) and are strongly correlated with gait transition
speed in adult fishes (Fulton et al.,
2005). Given that body and caudal fin undulation replace median
and paired fin undulation at higher speeds
(Webb, 1994), body and caudal
fin morphometrics may provide a more useful predictor of
Ucrit performance than pectoral fin morphology. This is
confirmed by the fact that there was little difference in the predictive
ability of the model among families from different swimming modes, and it
seems unlikely that our predictive power would be greatly improved with the
inclusion of pectoral fin morphology, at least for the purposes of predicting
maximum swimming speeds.
Fishes that occupy different habitats or environments might be expected to
have evolved to thrive in their particular habitat. Given that different
swimming modes and gaits occur for fishes among different habitats and with
different feeding modes (Webb,
1984; Webb, 1994),
we might expect to see systematic differences in the fit of our morphological
model for fishes that live in different habitats. In the present study we
included fishes of which the adult and juvenile forms are relatively sedentary
and associated with the benthic, coral reef habitat, as well as fishes that
are more mobile and associated with pelagic habitat. Despite the potential for
differences in morphology relating to these different habitat specialties, we
found that the fit of the model was unaffected by adult habitat
characteristics. This may reflect the fact that these fishes are all
pre-settlement, and potentially exhibit pelagic behavioural and physiological
traits. We used measurements of fishes that can be best described as
pre-settlement early-juvenile fishes, although for most families these
individuals have largely developed into the adult body form. They are at the
transition point from a pelagic to a benthic existence, and it is possible
that the relationship between body morphology and swimming ability of these
juvenile fishes may differ to that of their adult counterparts. Previous
studies have reported a drop in swimming performance following settlement
(Bellwood and Fisher, 2001;
Stobutzki and Bellwood, 1994)
and this could be due to both physiological and behavioral changes over this
transition period.
Furthermore, the smaller size of these fishes relative to their adult forms
may influence the relationship between body shape and swimming speed. Reynolds
number Re is related to the viscosity of the water around a body; at
low Reynolds numbers viscous forces act on the body, at high Reynolds numbers
inertial forces take over (Vogel,
1994), and Re has been closely linked to changes in
swimming modes of larval fishes during development
(Weihs, 1980). The Reynolds
values that correspond to the shift between viscous forces and inertial forces
are believed to be between 200 and 400
(Fuiman and Batty, 1997;
Weihs, 1980). With the
exception of the very slow swimming Ogcocephalidae, all of our fish species
swam in an environment above a Reynolds number of 1000 and so should
experience inertial forces whilst swimming, similar to that expected for adult
forms. Large herring larvae (18.2 mm TL), experiencing Reynolds
environments of 100–500 had similar locomotor mechanics to juvenile and
adult fishes (Fuiman and Batty,
1997). Therefore, Reynolds number at least is unlikely to
influence the observed relationships, and barring physiological and
behavioural differences, the developed model may work equally well for
adults.
There was no evidence that the model fit differed among taxonomic groups,
despite the inclusion of five different orders of fishes. This suggests that
body morphology alone appears sufficient to explain the bulk of the
differences in swimming performance among taxonomic groups and that
phylogenetic constraint of body morphology may limit maximum sustainable
swimming speeds. The fastest groups are the Beryciformes (Holocentrids),
followed by the Siganidae and the Acanthuridae, both members of the
Perciformes. These groups appear to have evolved similar streamlined body
forms, not unlike that of the tunas (also from the order Perciformes),
optimized for fast steady swimming (Blake,
2004). These morphological adaptations have evolved independently
in a number of phylogenetically distant groups
(Blake, 2004). A handful of
species (notably those from the orders Tetraodontiformes and Lophiiformes),
exhibit exceptionally slow swimming speeds, having body morphologies clearly
incapable of sustained swimming at high speed. Such species must have quite
unique ecological characteristics that allow their slow swimming existence,
and it is perhaps not surprising that at least some of their representatives
exhibit factors such as chemical or cryptic defenses from predation. The
majority of fishes swim at intermediate speeds, with most families swimming
between 20 and 50 cm s–1. It is at these medium range speeds
that the largest variation in body form is found, and may reflect the fact
that a diversity of body morphology is probably adequate for producing a
reasonable level of swimming proficiency. For medium pace swimmers, design
factors beneficial to other swimming skills, such as acceleration or
maneuverability, may result in a diversity of morphological shape.
We have examined only marine tropical fishes in this study, which may bias
model predictions if salinity and temperature affect swimming performance.
Although seawater has a moderately higher density than freshwater, this slight
change should have little effect on their relative viscosities. It is
therefore likely that model predictions should hold for freshwater fishes
providing there are no other physiological differences between marine and
freshwater fishes. Temperature, on the other hand, may be expected to have a
large effect on the swimming performance of fishes. Although temperature
effects on the viscosity of seawater should also have little effect on
swimming performance of fishes of this size, colder water is expected to have
a considerable physiological effect on swimming performance by causing lower
tail-beat frequencies and slower swimming speeds
(Fuiman and Batty, 1997). As a
result, it is likely that the presented model may over estimate swimming
performance in temperate and possibly sub-tropical fishes, although the
independent out-group test using the salmonid Oncorhynchus mykiss
suggests that this may not be the case, as these fish were swum at a
temperature of around 12°C. Further comparisons are required using a
larger range of temperate species to determine the extent to which the model
can be used for predicting swimming speeds of fishes in colder regions.
Here we clearly show the utility of using a small number of easily measured external morphological parameters to predict maximum (Ucrit) swimming speeds in coral reef fishes. The model appears to work equally well for fishes in the Great Barrier Reef and the Caribbean, and for families with different adult habitat characteristics and swimming modes. This model provides an invaluable means of predicting swimming abilities of fishes that are unable to be reared in the laboratory, do not perform well in swimming flumes or are unable to be captured live in the field.
|
Acknowledgments |
|---|
|
References |
|---|
|
TOPSummaryIntroductionMaterials and methodsResultsDiscussionReferences |
|---|
Bainbridge, R. (1958). The speed of swimming of
fish as related to the size and to the frequency and amplitude of the tail
beat. J. Exp. Biol. 35,109
-133.
Bainbridge, R. (1960). Speed and stamina in three fish. J. Exp. Biol. 370,129 -153.
Beamish, F. (1970). Oxygen consumption of largemouth bass, Micropterus salmoides, in relation to swimming speed and temperature. Can. J. Zool. 48,1221 -1228.[Medline]
Bellwood, D. (1996). The Eocene fishes of Monte Bolka: the earliest coral reef fish assemblage. Coral Reefs 15,1 -9.
Bellwood, D. R. and Fisher, R. (2001). Relative swimming speeds in reef fish larvae. Mar. Ecol. Prog. Ser. 211,299 -303.
Blake, R. W. (2004). Fish functional design and swimming performance. J. Fish Biol. 65, 119.
Blake, R., Chan, K. and Kwok, E. (2005). Finlets and the steady swimming performance of Thunnus albacares.J. Fish Biol. 67,1434 -1445.[CrossRef]
Brett, J. R. (1964). The respiratory metabolism and swimming performance of young sockeye salmon. J. Fish. Res. Board Can. 21,1183 -1226.
Burnham, K. P. and Anderson, D. R. (2002).Model Selection and Multimodel Inference; A Practical Information-Theoretic Approach (2nd edn) . New York: Springer.
Claireaux, G., Webber, D. M., Kerr, S. R. and Boutilier, R. G. (1995). Physiology and behaviour of free-swimming Atlantic cod (Gadus morhua) facing fluctuating temperature conditions. J. Exp. Biol. 198,49 -60.[Medline]
Dewar, H. and Graham, J. (1994). Studies of tropical tuna swimming performance in a large water tunnel – kinematics. J. Exp. Biol. 192,45 -59.[Abstract]
Fisher, R. (2005). Swimming speeds of larval coral reef fishes: impacts on self-recruitment and dispersal. Mar. Ecol. Prog. Ser. 285,223 -232.
Fisher, R. and Bellwood, D. R. (2003). Undisturbed swimming behaviour and nocturnal activity of coral reef fish larvae. Mar. Ecol. Prog. Ser. 263,177 -188.
Fisher, R. and Wilson, S. K. (2004). Maximum sustainable swimming speeds of nine species of late stage larval reef fishes. J. Exp. Mar. Biol. Ecol. 312,171 -186.[CrossRef]
Fisher, R., Bellwood, D. R. and Job, S. D. (2000). The development of swimming abilities in reef fish larvae. Mar. Ecol. Prog. Ser. 202,163 -173.
Fisher, R., Leis, J. M., Clark, D. J. and Wilson, S. K. (2005). Critical swimming speeds of late-stage coral reef fish larvae: variation within species, among species and between locations. Mar. Biol. 147,1201 -1212.[CrossRef]
Fry, F. E. J. and Hart, J. S. (1948). Cruising speed of goldfish in relation to water temperature. J. Fish. Res. Board Can. 7,169 -175.
Fuiman, L. A. and Batty, R. S. (1997). What a drag it is getting cold: partitioning the physical and physiological effects of temperature on fish swimming. J. Exp. Biol. 200,1745 -1755.[Abstract]
Fulton, C. J., Bellwood, D. R. and Wainwright, P. C. (2001). The relationship between swimming ability and habitat use in wrasses (Labridae). Mar. Biol. 139, 25-33.[CrossRef]
Fulton, C. J., Bellwood, D. R. and Wainwright, P. C. (2005). Wave energy and swimming performance shape coral reef fish assemblages. Proc. R. Soc. Lond. B Biol. Sci. 272,827 -832.[Medline]
Graham, M. H. (2003). Confronting multicollinearity in ecological multiple regression. Ecology 84,2809 -2815.[CrossRef]
Greenland, D. C. and Thomas, A. E. (1972). Swimming speed of fall Chinook Salmon (Oncorhynchus tshawytscha) fry. Trans. Am. Fish. Soc. 1972,696 -700.
Hogan, J. D., Fisher, R. and Nolan, C. (2007). Critical swimming abilities of late-stage coral reef fish larvae from the Caribbean: a methodological and intra-specific comparison. Bull. Mar. Sci. 80,219 -232.
Korsmeyer, K. E., Dewar, H., Lai, N. C. and Graham, J. B. (1996). Tuna aerobic swimming performance: physiological and environmental limits based on oxygen supply and demand. Comp. Biochem. Physiol. 113B,45 -56.[CrossRef]
Leis, J. M. and Carson-Ewart, B. M. (1997). In situ swimming speeds of the late pelagic larvae of some indo-pacific coral reef fishes. Mar. Ecol. Prog. Ser. 159,165 -174.
Lough, R. G. and Potter, D. C. (1993). Vertical distribution patterns and diel migrations of larval and juvenile haddock Melanogrammus aeglefinus and Atlantic cod Gadus morhua on Georges Bank. Fish. Bull. 9, 281.
Magnan, A. (1930). Les caractérisitiques géométriques et physiques des poisons. Ann. Sci. Nat. 13,355 -489.
Miller, A. J. (1990). Subset Selection in Regression. London: Chapman & Hall.
Paulik, G. J. and DeLacy, A. C. (1957). Swimming abilities of upstream migrant silver salmon, sockeye salmon, and steelhead at several water velocities. Univ. Wash. Sch. Fish. Tech. Rep. 44,1 -40.
Plaut, I. (2001). Critical swimming speed: its ecological relevance. Comp. Biochem. Physiol. 131A,41 -50.
Regnard, P. (1893). Sur un dispositif qui permet de mesurer la vitesse de translation d'un poisson se mouvant dans l'eau. C. R. Soc. Biol. Paris Ser. 9 5, 81-83.
Reidy, S. P., Kerr, S. R. and Nelson, J. A. (2000). Aerobic and anaerobic swimming performance of individual Atlantic cod. J. Exp. Biol. 203,347 -357.[Abstract]
Sale, P. (1991). Introduction. In The Ecology of Fishes on Coral Reefs (ed. P. Sale), pp. 1-15. San Diego: Academic Press.
Sambilay, V. C. (1990). Interrelationshps between swimming speed, caudal fin aspect ratio and body length of fishes. Fishbyte 8,16 -20.
Small, S. A. and Randall, D. J. (1989). Effects of triploidy on the swimming performance of coho salmon (Oncorhynchus kisutch). Can. J. Fish. Aquat. Sci. 46,243 -245.
Stobutzki, I. C. (1997). Swimming abilities and orientation behaviour of pre-settlement coral reef fishes. PhD thesis, James Cook University, Townsville, Australia.
Stobutzki, I. C. and Bellwood, D. R. (1994). An analysis of the sustained swimming abilities of pre- and post-settlement coral reef fishes. J. Exp. Biol. Ecol. 175,275 -286.[CrossRef]
Stobutzki, I. C. and Bellwood, D. R. (1997). Sustained swimming abilities of the late pelagic stages of coral reef fishes. Mar. Ecol. Prog. Ser. 149, 35-41.
Taylor, E. B. and McPhail, J. D. (1985). Variation in burst and prolonged swimming performance among British Columbia populations of coho salmon, Oncorhynchus kisutch. Can. J. Fish. Aquat. Sci. 42,2029 -2033.
Videler, J. J. (1993). Fish Swimming. London: Chapman & Hall.
Vogel, S. (1994). Life in Moving Fluids. Princeton: Princeton University Press.
Wainwright, P. C., Bellwood, D. R. and Westneat, M. W. (2002). Ecomorphology of locomotion in labrid fishes. Environ. Biol. Fishes 65, 47-62.[CrossRef]
Wardle, C. S. (1977). Effects of size on the swimming speeds of fish. In Scale Effects in Animal Locomotion (ed. T. Pedley), pp. 299-313. London: Academic Press.
Ware, D. M. (1978). Bioenergetics of pelagic fish: theoretical change in swimming speed and ration with body size. J. Fish. Res. Board Can. 35,220 -228.
Webb, P. W. (1984). Body form, locomotion and foraging in aquatic vertebrates. Am. Zool. 24,107 -120.
Webb, P. W. (1994). The biology of fish swimming. In Mechanics and Physiology of Animal Swimming (ed. L. Maddock, Q. Bone and J. M. V. Rayner), pp.45 -62. Cambridge: Cambridge University Press.
Webb, P. W. and Weihs, D. (1986). Functional locomotor morphology of early life history stages of fishes. Trans. Am. Fish. Soc. 115,115 -127.[CrossRef]
Weihs, D. (1980). Energetic signifiance of changes in swimming modes during
Related articles in JEB:
This article has been cited by other articles:
|
|
 |
|
  S. Vogel Modes and scaling in aquatic locomotion Integr. Comp. Biol., April 11, 2008; (2008) icn014v1. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 L. Blackburn MODEL SWIMMERS J. Exp. Biol., July 15, 2007; 210(14): iii - iii. [Full Text] [PDF]
|
|
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
