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RESEARCH ARTICLE
The effect of water temperature on routine swimming behaviour of
new born guppies (Poecilia reticulata)
Maud Kent* and Alfredo F. Ojanguren
ABSTRACT
Guppies have successfully established populations in places with
thermal regimes very different from the Tropical conditions in their
native range. This indicates a remarkable capacity for thermal
adaptation. Given their vulnerability to predation as juveniles, acute
changes in temperature, which can alter predator-prey relationships,
can impact juvenile survival and have amplified consequences at the
population level. To understand how temperature may impact juvenile
survival and gain insight into their success as an invasive species,
we researched the effect of acute temperature changes on the
routine swimming behaviour of juvenile guppies. Using a novel 3-
dimensional tracking technique, we calculated 4 routine swimming
parameters, speed, depth, and variation in speed or depth, at 6
different test temperatures (17, 20, 23, 26, 29, or 32˚C). These
temperatures cover their natural thermal range and also extended
past it in order to include upper and lower thermal limits. Using
model selection, we found that body length and temperature had
a significant positive relationship with speed. Variation in speed
decreased with rising temperatures and fish swam slightly closer to
the bottom at higher temperatures. All juveniles increased variation in
depth at higher temperatures, though larger individuals maintained
slightly more consistent depths. Our results indicate that guppies
have a large thermal range and show substantial plasticity in routine
swimming behaviours, which may account for their success as an
invasive species.
KEY WORDS: Poecilia reticulata, Routine swimming, Temperature,
Thermal range
INTRODUCTION
Temperature can affect every aspect of the physiology and
performance of organisms (Johnston and Bennett, 2008; Angilletta,
2009). In particular, the thermal conformity of ectotherms renders
them especially susceptible to changes in environmental temperatures
(Huey, 1982; Atkinson, 1994). Physiologically, temperature can
affect muscle fibre number (Wilkes et al., 2001) and muscle
performance (Putnam and Bennett, 1982), endurance (Ojanguren
and Bran˜ta, 2000), growth (Angilletta et al., 2004), metabolic rate
(Das and Das, 1982), heart rate (Richards, 1963), immune
functioning (Maniero and Carey, 1997; Le Morvan et al., 1998)
and size and age at maturity (Angilletta and Dunham, 2003).
Behaviourally, temperature can elicit thermoregulatory and
avoidance behaviours such as altered distribution patterns
(Walther et al., 2002), microhabitat use (Taylor, 1988; Adolph,
1990), foraging tactics (Persson, 1986; Fraser et al., 1993; Ayers
and Shine, 1997), and courtship behaviours (Hilder and Pankhurst,
2003; Denoe¨l et al., 2005).
The relationship between body temperature and measures of
performance is often modelled using thermal performance curves
(TPCs, Huey and Stevenson, 1979; Kingsolver et al., 2001;
Angilletta, 2006). For most ectotherms, TPCs take a similar shape
with performance gradually increasing from a minimum critical
temperature to an optimal temperature. After a peak or plateau at
optimal temperatures, performance tends to rapidly decline to a
critical thermal maximum where performance is zero (Huey and
Kingsolver, 1989). These minimum and maximum critical
temperatures represent the range of temperatures over which an
organism can perform a certain function. The shape and position
of TPCs can be influenced by an organism’s environment or
thermal experience, although there is competing evidence over
whether this is due to acclimation, involving phenotypic plasticity
(Leroi et al., 1994; Wilson and Franklin, 2002), or adaptation,
involving changes in gene frequencies (Zamudio et al., 1995). For
instance, Schaefer and Ryan (Schaefer and Ryan, 2006) found
that zebra fish reared in variable thermal environments had larger
tolerances than conspecifics reared in more stable thermal
environments as the result of developmental plasticity and non-
genetic adaptation. Despite this and other evidence that the shape
and position of TPCs can be moderated through plasticity during
an individual’s lifetime (Hamdoun et al., 2003; Kingsolver et al.,
2004), research also exists showing inheritance and correlations
to ancestral conditions (Morrison and Milkman, 1978; Huey and
Kingsolver, 1993; Wiens and Graham, 2005). Overall, TPCs are
useful tools for predicting an organism’s vulnerability to
environmental changes (Huey et al., 2012).
To generate accurate TPCs, research looking into the direct
impacts of temperature on behaviour and physiology during
different life history stages is vital. Specifically, the impacts of
temperature on the early life stages of fish are significant since
any changes in survival subsequently affect recruitment and can
have amplified consequences at the population level (Houde,
1987). By influencing growth and development, temperature
affects vulnerability to predators. For instance, at colder
temperatures when growth rates often decline, fish remain
within the ‘‘window’’ of vulnerability for longer periods of time
(Cowan et al., 1996). Furthermore, since colder temperatures can
also reduce escape and cruising speeds (Johnston et al., 2001),
fish have a reduced likelihood of surviving predator encounters.
Warm temperatures also pose a problem as they often result in
Centre for Biological Diversity, Scottish Oceans Institute, University of St
Andrews, KY16 8LB, Scotland, UK.
*Author for correspondence (maudiakent@gmail.com)
This is an Open Access article distributed under the terms of the Creative Commons Attribution
License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution
and reproduction in any medium provided that the original work is properly attributed.
Received 8 August 2014; Accepted 27 November 2014
ß2015. Published by The Company of Biologists Ltd | Biology Open (2015) 000, 1–6 doi:10.1242/bio.20149829
1
Biology Open
faster cruising speeds and therefore increased predator encounter
rates (Fuiman and Cowan, 2003). These effects on larval
physiology and predator-prey interactions may render
temperature a key factor in determining juvenile fitness and
survival. Research into the shape and position of TPCs during
different life stages can yield valuable insight into this species’
capacity for thermal adaptation and survival in locations where
they are not endemic.
Guppies (Poecilia reticulata) are small freshwater fish native to
Trinidad and the North coast of South America although they have
successfully established populations in every continent except for
Antarctica (Deacon et al., 2011). The thermal regimes experienced
by many of these invasive populations differ greatly from the
tropical conditions they experience in their native range (Deacon
et al., 2011). This, in addition to the fact that temperatures in
Trinidad can fluctuate up to 7˚
C in a 24-h period (Reeve et al.,
2014), point at a remarkable capacity for both acute thermal
adaptation and long-term thermal adaptation. Looking into the
impacts of acute temperature changes on routine behaviour can
yield valuable insight into the potentially large impact of
temperature on survival and provide a better understanding of
the mechanisms enabling their success as an invasive species.
Here, we look at the effect of acute acclimation on routine
swimming in juvenile guppies. Using a novel 3D tracking
technique, the average depth, speed, and variation in speed and
depth were calculated for each individual. Speed refers to
distance travelled over time and depth was calculated as distance
of the fish from the bottom of the tank. Variation in speed or
depth refers to how consistently fish maintained a certain measure
of performance over the observation period. These four
parameters were used to characterize routine swimming and it
was expected that changes in temperature would alter these
routine swimming behaviours. By focusing on acute temperature
acclimation, this experiment demonstrates how even short-term
changes or fluctuations in environmental temperatures might
impact juvenile survival.
MATERIALS AND METHODS
Fish care and protocol
The guppies used in this experiment were descendants of fish taken from
two different Trinidadian streams: Tacarigua and Tunapuna. Habitat
differences between the upstream population of Tunapuna and the
downstream population of Lower Tacarigua have resulted in different life
history traits, such as size and number of offspring produced, which may
account for the population differences in juvenile guppy size used in this
experiment (Magurran, 2005).
In both streams, guppies naturally experience temperature fluctuations
of over 7˚
C in a 24-h period (Reeve et al., 2014). Generally, upstream
populations such as Tunapuna experience lower maximum temperatures
than downstream populations due to increased canopy coverage. The
experimental guppies were kept under controlled environmental
conditions at relatively constant temperatures ranging from 20˚
Cto26
˚
C.
For this experiment, juvenile fish were collected from stock tanks
containing both males and females as well as from maternity tanks
containing single pregnant females. In order to standardize age at testing,
juveniles were taken from isolated pregnant female tanks as often as
possible, which were inspected on a daily basis. Each day, three juveniles
from each population were placed in floating tanks within temperature
controlled water baths. Three juveniles from each population were tested
at each test temperature for each of the three replicates. Juveniles ranged
from one day old to three weeks old and were between 6 mm and 14 mm
standard length (hereafter SL, SL6s.d., Tunapuna: 8.061.7 mm, Lower
Tacarigua: 7.561.6 mm). To measure the fish after the swimming trials,
we placed each juvenile into a petri dish with a small amount of water
and took a vertical picture. The pictures were measured using image
analysis software (Image J, National Institutes of Health, USA). Fish
were introduced into the water bath when the temperature had reached
typical temperatures experienced within the stock tanks or maternity
tanks (e.g. between 23˚
C and 24˚
C). Over a period of 5–7 h, temperatures
were gradually changed to the target test temperatures, which were either
17, 20, 23, 26, 29, or 32˚
C. Juveniles were given at least 18 h to acclimate
to test temperatures before experiments began the next day. Given
research showing that guppies can experience daily fluctuations up to
10˚
C in their native habitats (Reeve et al., 2014), 18 h was deemed to be
an appropriate time scale upon which to study the affects of acute daily
fluctuations considering that the maximum temperature change in this
experiment was 9˚
C. We had no mortality as a result of temperature
manipulations and fish seemed comfortably acclimated to all test
temperatures, as demonstrated by normal swimming and feeding
behaviours the morning of the experiment. The average6s.d. (range)
temperatures maintained during the 18-h acclimatization period for each
target temperature were 17.160.2˚
C (17.0–17.4), 19.760.3˚
C (19.4–
19.9), 23.060.1˚
C (23.3–23.4), 25.360.3˚
C (25.0–25.6), 29.460.3˚
C
(29.2–29.7), and 32.260.2˚
C (32.0–32.4).
Every day at least an hour before testing began, the fish were fed flake
food ad libitum to avoid differences in satiation rate that could affect
swimming behaviour during video recording. Using water from the water
bath to ensure that testing occurred at the appropriate test temperature,
fish were placed in 10610610 cm glass observation tanks filled to a
depth of about 9 mm, with a mirror positioned at 45˚overhead. To
prevent drastic temperature changes during the filming period, four sides
of the glass observation tanks were insulated with polystyrene (Fig. 1).
After allowing the fish at least 3 min to get used to the conditions inside
the chamber, they were filmed for 10 min with a video camera located
approximately 1 m from the observation tank at 30 frames per second.
After each trial, fish were photographed and measured for standard length
(mm), then placed in stock juvenile tanks.
To analyse routine swimming behaviour, we subsampled each video
by taking 1-minute segments from the beginning, middle and end (0–
1 min, 5–6 min, and 9–10 min). Each subsampled video was converted
into a stack of 60 images (1 frame per second) and imported into ImageJ.
Calibrations were then determined for each image stack by dividing a
known distance, such as the side of the tank (10 cm), by the number of
pixels. The Manual Tracking plugin in ImageJ was then used to obtain X
and Y coordinates of fish movement between frames from both the head-
on and the overhead views. These coordinates were combined to generate
3D (X, Y and Z) coordinates, which allowed us to calculate average
speed (i.e. distance over time) (mm s
21
), variation in speed, average
depth (Z coordinate), and variation in depth.
Fig. 1. Illustration of experimental setup and apparatus used. A camera
was placed 1 m away from a glass tank (10610610 cm) placed in a 3-sided
Styrofoam insulation chamber with a mirror at 45˚overhead.
RESEARCH ARTICLE Biology Open (2015) 000, 1–6 doi:10.1242/bio.20149829
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Biology Open
Fish care and handling complied with institutional and national animal
welfare laws, guidelines and policies.
Statistical analysis
Multiple linear regression models were used to describe the variability of
routine swimming speed, depth, and variation in depth and speed based
on the potential effects of temperature (T), standard length, Temperature
squared (T
2
), and the interaction between T and Length. T
2
was included
in the models to account for a non-linear relationship (SAS Institute Inc.,
1999; Montgomery et al., 2012), such as a bell-shaped thermal
performance curve (Huey and Stevenson, 1979). Population and
replicate, originally included as random effects in this analysis, were
removed since there was very minimal variance between groups
(replicate variance51.7610
214
, population variance51.7610
22
)
(Starkweather, 2010). To correct for departures from normality,
velocity and variation in velocity were square root transformed and
depth and variation in depth were log transformed.
Models were then compared using an information theoretic approach:
Akaike’s information criterion for finite samples (hereafter AICc). Both
delta AIC (D
i
) values, a measure of each model relative to the best model,
and model weights (w
i
), a measure of the evidence supporting a specific
model, were used for model comparison and selection. When there were
multiple models with D
i
,2, selection was based on the difference in
parameters present in each model, reduction in deviance and log-
likelihood values (Burnham and Anderson, 2002).
RESULTS
A series of six models were tested for their effects on speed,
variation in speed, depth, and variation in depth. The variables
tested within the set of models included temperature, temperature
squared (T
2
), standard length, and an interaction between
temperature and length. When testing these models against speed
(Table 1), model 2 had the lowest Akaike’s information criterion
for finite samples (AICc) and received a weight of 0.49. The other
top model, model 1, had a D
i
value of 1.39 and weight of 0.25. Both
model 1 and 2 include length and temperature, though model 1 also
allows for a non-linear relationship through the inclusion of T
2
.
Given that both models have essentially the same log-likelihood
value (2116.9 vs. 2116.5), the additional variable, T
2
, adds little
to the top model and can be considered an uninformative parameter
(Burnham and Anderson, 2008; Arnold, 2010). Within the top
model, both length and temperature are significant (F
(2,110)
521.04,
p-value,0.001). Increases in both these variables resulted in
corresponding increases in swimming speed (Fig. 2). Fig. 2 shows
average speeds from each population across both replicates against
acclimation temperature. Although T
2
did not add to the best
model, the polynomial trendlines on this graph suggest that had
higher temperatures been tested, there may have been a drop in
performance and more conformity to a typical TPC.
For variation in speed, there was substantial support (D
i
,2) for
models including temperature. Model 5 received the lowest AICc
of 477 and a model weight of 0.33. Models 3, 2 and 1 were all
within 2 D
i
units of model 5 with AICc scores and model weights
of 478 and 0.21 for model 3, 479 and 0.16 for model 2 and 479
and 0.15 for model 1. The two top models, model 5 and 3,
included temperature as a predictor of variation in speed. Model 5
only differed through the inclusion of T
2
. Within model 5,
however, T
2
was insignificant indicating that the relationship
between variation in speed and temperature is still linear (T
2
:
F
(2,110)
55.48, p50.09; Temperature: F
(2,110)
55.48, p50.05). The
3
rd
and 4
th
best models, models 2 and 1, both included
temperature and length. The model including only the affect of
length (model 4), however, received the highest AICc score and
had the least support. No models containing the interaction
between temperature and length ranked among the top models.
Overall, temperature had a consistent impact on variation in
speed throughout all the top models. As temperatures increased,
variation in speed decreased. The amount of variance explained
by the top models, however, was generally low (R
2
50.08).
For depth, 3 models received D
i
values under 2. The two top
models, model 0 and model 6, both included temperature, length,
and the interaction between temperature and length. Model 0 only
differed through the inclusion of T
2
. Given that T
2
is not
significant in model 0, however, there is reason to select the more
parsimonious linear model 6. Furthermore, both these top models
had similar weights (0.33 vs. 0.31) and D
i
values (0 vs. 0.2). The
third best model, model 2, had much less support with a weight of
0.15 and a D
i
value of 1.6. Temperature had a significant positive
correlation to depth with fish swimming slightly deeper as
temperatures increased, although there was a large amount of
variation in the data (R
2
50.10) (F
(3,109)
54.6, p50.04). Length
was insignificant (F
(3,109)
54.6, p50.11), while interaction
between length and temperature was slightly insignificant
(F
(3,109)
54.6, p50.06).
As with Depth, the top models fitted against variation in depth
included the interaction between temperature and length. Model 6
ranked highest with an AICc score of 210.65, a weight of 0.48
and R
2
50.10. Model 0 only just ranked within 2 D
i
units of model
6 (1.6), did not improve upon the R
2
value (0.09) or the fit of the
model (log-likelihood 0.32 lower than top model) and had an
Table 1. Each model as tested against velocity
Model Variables Df R
2
AICc D
i
w
i
M2 SL + T 4 0.27 242.2 0.0 0.49
M1 SL + T + T
2
5 0.26 243.6 1.4 0.25
M6 SL + T + (T*SL) 5 0.26 244.3 2.1 0.17
M0 SL + T + T
2
+ (T*SL) 6 0.25 245.8 3.6 0.08
M5 T + T
2
4 0.20 251.1 8.9 0.01
M3 T 3 0.19 252.1 9.9 0.00
M4 SL 3 0.09 265.2 23.0 0.00
The variables included in each model, the degrees of freedom (Df), Akaike’s
Information Criteria for finite samples (AICc), Delta AIC (D
i
) and model
weights (w
i
) are listed. Variables tested within each model include standard
length (SL), acclimation temperature (T) and temperature squared (T
2
).
Shaded rows represent the models with substantial support (D,2).
Fig. 2. Graph of average swimming speed (mm s
21
) across all
replicates against acclimation temperature by population. The graph
shows 2
nd
order polynomial trendlines fitted to mean speeds
RESEARCH ARTICLE Biology Open (2015) 000, 1–6 doi:10.1242/bio.20149829
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Biology Open
AICc score of 29.06 and weight of 0.22. Model 6 and model 0
only differ through the inclusion of T
2
, which was insignificant
within model 0, meaning there is justification for selecting model
6 as our best model. Both length and temperature significantly
affected variation in depth (length: F
(3,109)
55.23, p50.0;
temperature: F
(3,109)
55.23, p50.01). The interaction between
length and temperature was also significant (F
(3,109)
55.32,
p50.02). While larger individuals varied their swimming depth
less than smaller individuals, all juveniles increased variation as
temperatures increased.
DISCUSSION
The results of this study indicate that changes in water
temperature, even over a short period of time, can affect the
routine swimming activity of juvenile guppies. As test
temperatures were increased, average swimming speeds and
depths increased while variation in speed decreased, meaning that
fish swam progressively closer to the bottom at faster, more
consistent speeds as acclimation temperatures increased. The
effect of temperature on variation in depth was also found to vary
with length, where larger individuals swam slightly shallower
than smaller individuals and maintained more consistent depths.
The overall impact of temperature on routine swimming activity
may be due in large part to the physiological impacts of
temperature, although behavioural reasons to modify routine
swimming also exist.
The linear relationship found in this experiment between
temperature and swimming speed does not conform to the
thermal performance curves described in other papers (Randall
and Brauner, 1991; Ojanguren and Bran˜ta, 2000; Koumoundouros
et al., 2002). Counter to what would be expected in typical TPCs,
average speeds did not show any decline at the highest
temperatures tested. This could, however, be a reflection of the
fact that the upper thermal limits of guppies were not included
within the range of test temperatures. Furthermore, although T
2
,
the parameter included to account for a non-linear relationship, was
not included in the top model against speed, the slight plateau at the
highest temperatures may indicate that these were part of the
optimal thermal range of guppies and performance would have
decreased after this point had temperatures come closer to their
upper thermal limits (Fig. 2). Ultimately, the fact that the large
range of temperatures tested did not contain the full thermal range
of juvenile guppies is indicative of the fact that this species has
adapted a wide thermal range that enables them to be effective
thermal adaptors and may account for their success as an invasive
species. This study also found a remarkable behavioural plasticity
in average swimming speeds over a wide range of temperatures,
which further demonstrates how guppies can survive in habitats
characterized by thermal regimes very different from those
experienced in the tropics, or indeed those experienced in their
maternal tanks.
Thermal conditions have large physiological impacts on fish and
often act as a determining factor in swimming speed (Beamish,
1978; O’Steen and Bennett, 2003). At higher temperatures, studies
have shown that fish can maintain faster swimming speeds
(Dickson et al., 2002). Increases in maximum velocities after
acute acclimation have been attributed to increases in active
metabolic rates (Claireaux et al., 2006), cardiac output or oxygen
consumption (Beamish, 1970; Clark et al., 2008). Conversely,
lower temperatures often result in slower swimming speeds since
cold muscle cannot generate the same force as warm muscle
(Rome et al., 1990; Green and Fisher, 2004). Importantly, Johnston
et al. (Johnston et al., 1990) showed that extended exposure to
lower temperatures can result in compensatory mechanisms that
allow fully acclimatized fish to out-perform acutely exposed fish.
While this study looked into the effect of acute temperature
changes, future research could investigate the effect of long-term
acclimatization on routine swimming speeds.
Ultimately, alterations in swimming speed could function to
behaviourally mitigate the impacts of temperature. Increases in
swimming speed in warmer waters, although often physiologically
induced, can promote behavioural thermoregulation and enable
fish to exploit more optimal thermal niches. For instance, lotic
ecosystems, such as those that guppies occupy, are thermally
heterogeneous environments that vary both vertically and
horizontally. Armstrong et al. (Armstrong et al., 2013) found that
juvenile salmon sometimes travel from colder water where they
forage into warmer waters where metabolic rates accelerated,
promoting faster growth and increased survival potential. In a
study by O’Steen and Bennett (O’Steen and Bennett, 2003), in
which they found that River barbels reduce activity when
temperatures dip below preferred levels, they discuss the
potential benefits of reducing activities as a way of conserving
energy to perform necessary survival behaviours. At lower
temperatures, where maximum capacities are reduced, decreasing
speed and swimming less constantly could potentially save energy
for escape responses. The behavioural plasticity found in this study
could therefore serve to increase juvenile survival rates in the wild
and provide a potential advantage to guppies as an invasive species
that may experience temperatures radically different to those of
their native habitat.
In this study, we also found that swimming speed was affected
by juvenile length. We found that larger individuals tended to
swim faster than smaller individuals. This positive correlation
may be due to the larger propulsive systems (Fisher et al., 2000)
or increased anaerobic efficiency (Webb et al., 1984).
Temperature also affected variation in speed. At higher
temperatures, juvenile guppies swam at more consistent,
higher average speeds. This finding could either indicate a
switch to burst-and-coast swimming at lower temperatures or
extended periods of inactivity and intermittent movement
characterized by a wide range of speeds. Smith and Koenst
(Smith and Koenst, 1975) found that juvenile walleye subjected
to acute reductions in acclimation temperatures showed decreased
swimming activity and periods of idleness. Other studies have
observed switches to burst-and-coast swimming at lower
velocities when reduced power output reduces performance
sustainability and renders burst-and-coast swimming more
advantageous (Rome et al., 1984; Rome et al., 1990). This
finding, however, has been countered by other studies and the
energetics of burst-and-coast swimming are still debated (Kramer
and McLaughlin, 2001).
Regardless of whether or not our results point to a switch in
swimming mode or periods of inactivity, possible advantages to
any type of intermittent locomotion exist. For instance, as muscles
fatigue, periods of reduced swimming speed and pauses in active
swimming may allow for partial recovery (Kramer and
McLaughlin, 2001). Additionally, pauses in active swimming
could act to enhance sensory awareness by stabilizing the visual
field, reducing motion blur, and allowing time for animals to
receive and process all relevant stimuli from their environment
(Land, 1999). Behaviourally, varying speed may be advantageous
if it reduces conspicuousness of juveniles to predators or increases
unpredictability (Humphries and Driver, 1970), especially when
RESEARCH ARTICLE Biology Open (2015) 000, 1–6 doi:10.1242/bio.20149829
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Biology Open
maximum swimming capabilities are compromised at lower
temperatures. While our results showed an increase in variation
in speed at lower temperatures, future research could investigate
whether or not burst-and-coast swimming is energetically
advantageous in juvenile guppies and therefore a viable reason
why intermittent locomotion increased at lower temperatures.
The average swimming depths of juvenile guppies were also
found to alter slightly with acclimation temperature. As acute
acclimation temperatures increased, juvenile guppies swam
slightly closer to the bottom of the tank. In their natural
environment, swimming deeper in the water column when
thermal conditions exceed optimum temperatures could aid in
thermoregulation. In fact, there are many studies (McCullough,
1999; Dunham et al., 2003; Buisson et al., 2008) showing that
temperature can have a large impact on the local distribution of
fish. For instance, a common response to suboptimal thermal
conditions is relocation to thermal refuges, such as areas made
shady by undercut banks or protruding vegetation, or areas with
cooler water such as side-channels, lateral seeps or groundwater
seeps (Bell, 2006; Dallas, 2008). Groundwater outflows in
particular provide critical microhabitat through provision of
alternative flow regimes, thermal regimes, oxygen and nutrient
levels as well as water quality (Heggenes et al., 2010). Bunt et al.
(Bunt et al., 2013) found that juvenile Black Redhorse exploit
groundwater seepages as thermal refuges and improved water
quality. Furthermore, the increased variation in depth found at
higher temperatures could suggest that juvenile guppies in this
experiment were more active about seeking out potential thermal
refuges as temperatures increased. This study may indicate
thermoregulatory behaviours in juvenile guppies that would
confer survival advantages if employed in their natural habitats.
Overall, this study found that temperature impacts the routine
swimming behaviours (such as speed, variation in speed, and depth
and variation in depth) of juvenile guppies. As test temperatures
were acutely increased, fish swam slightly deeper at faster, more
consistent speeds. Our test temperatures, however, did not contain
the full thermal range of juvenile guppies, which indicates a wide
thermal tolerance. This, in addition to their substantial plasticity in
routine swimming behaviours, may be a factor in their success as
an invasive species. This study, which investigated the impacts of
temperature on routine behaviour, could help expand our
understanding of the effect of temperature on juvenile survival
and future research could investigate the direct survival
implications of the altered behaviours found in this study.
List of abbreviations
TPC: thermal performance curve; SL: standard length; s.d.:
standard deviation; AIC: Akaike’s information criterion; T
2
:
temperature squared; D
i
values: delta values, a measure of each
model relative to the best model; w
i
values: a measure of the
evidence supporting a specific model.
Acknowledgements
We thank Al Reeve, Jennifer House, and the Fish Lunch discussion group for
helpful discussions during early stages of this research. Stephen McKelvie and
Caya Sievers edited earlier versions of the manuscript.
Competing interests
The authors declare no competing or financial interests.
Author contributions
Both A. Ojanguren and M. Kent designed the experiment. Data was collected and
analysed by M. Kent with contributions from A. Ojanguren. The manuscript was
written by M. Kent and edited by A. Ojanguren.
Funding
Funding for this research came from The Buckland Foundation, The Scottish
Fisheries Museum Trust Ltd. Additional costs and supplies were provided by the
School of Biology of The University of St Andrews.
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