Abstract
Acute exposure to warming temperatures increases minimum energetic
requirements in ectotherms. However, over and within multiple
generations, increased temperatures may cause plastic and evolved
changes that modify the temperature sensitivity of energy demand and
alter individual behaviours. Here, we aimed to test whether populations
recently exposed to geothermally elevated temperatures express an
altered temperature sensitivity of metabolism and behaviour. We expected
that long-term exposure to warming would moderate metabolic rate,
reducing the temperature sensitivity of metabolism, with concomitant
reductions in boldness and activity. We compared the temperature
sensitivity of metabolic rate (acclimation at 20 versus 30°C) and
allometric slopes of routine, standard, and maximum metabolic rates, in
addition to boldness and activity behaviours, across eight recently
divergent populations of a widespread fish species (Gambusia
affinis ). Our data reveal that
warm-source populations express a reduced temperature sensitivity of
metabolism, with relatively high metabolic rates at cool acclimation
temperatures and relatively low metabolic rates at warm acclimation
temperatures. Allometric scaling of metabolism did not differ with
thermal history. Across individuals from all populations combined,
higher metabolic rates were associated with higher boldness and
activity. However, warm-source populations displayed relatively more
bold behaviour at both acclimation temperatures, despite their
relatively low metabolic rates at warm acclimation
temperatures. Overall, our data
suggest that in response to warming, multigenerational processes may not
direct trait change along a simple ”pace-of-life syndrome” axis, instead
causing relative decreases in metabolism and increases in boldness.
Ultimately, our data suggest that multigenerational warming may produce
a novel combination of physiological and behavioural traits, with
consequences for animal performance in a warming world.
Keywords: Mosquitofish, Gambusia , metabolism,
temperature, pace-of-life, thermal
history.
Introduction
Warming is expected to increase minimum energetic requirements and thus
metabolic rate (Gillooly, Brown, West, Savage, & Charnov, 2001; Brown,
Gillooly, Allen, Savage, & West, 2004), potentially influencing
ecologically important behaviours and the strength of top-down effects
(Angilletta & Dunham, 2003; Gardner, Peters, Kearney, Joseph, &
Heinsohn, 2011; Sibly, Brown, & Kodric‐Brown, 2012; Holt & Jorgensen,
2015; Norin, Malte, & Clark, 2016). The effects of thermal change may
be particularly pronounced in ectothermic species, where environmental
temperature regulates body temperature. Within species, populations may
respond differently to warming depending on their history of temperature
exposure. For example, populations chronically exposed to elevated
temperatures may exhibit altered metabolic and behavioural traits
(Crozier & Hutchings, 2014). When challenged with warming environmental
temperatures, these trait differences can arise quickly due to
plasticity (i.e., within generation plasticity, developmental
plasticity) and evolutionary adaptation, potentially mediating overall
trait responses to warming (Fryxell et al., 2020; Pilakouta et al.,
2020).
Population differences in the temperature dependence of metabolic rates
are often studied using species distributed over altitudinal or
latitudinal gradients, where adaptive change may occur over long periods
(White, Alton, & Frappell, 2012; McKenzie, Estivales, Svendsen,
Steffensen, & Agnèse, 2013; Gaitán-Espitia & Nespolo, 2014). However,
it is less clear that these differences in metabolism can arise over
short timescales, as could be the case under current warming. Laboratory
experiments demonstrate that metabolic traits can respond quickly to
temperature (Alton, Condon, White, & Angilletta, 2017; Mallard, Nolte,
Tobler, Kapun, & Schlötterer, 2018; Morgan, Finnøen, Jensen, Pélabon,
& Jutfelt, 2020). Alternatively, geothermally heated habitats can offer
valuable natural experiments that overcome the limitations of other
natural thermal gradients and experimental approaches. For example, the
use of geothermal or artificially heated waterways has recently
demonstrated that long-term exposure (e.g., 1000’s of years) to
increased temperatures may reduce the temperature sensitivity of
metabolism in freshwater fishes (Bruneaux et al., 2014; Pilakouta et
al., 2020). Similar reductions in metabolic temperature sensitivity may
occur over shorter time scales (e.g., 10’s to 100’s of years) congruent
with current environmental warming (Sandblom et al., 2016; Moffett,
Fryxell, Palkovacs, Kinnison, & Simon, 2018; White & Wahl, 2020). Such
moderations in metabolic rate may be associated with changes to other
ecologically significant traits, such as animal behaviour, but these
connections are largely unknown.
Individual differences in baseline metabolic requirements may lead to
consistent behavioural differences (Biro & Stamps, 2010). For example,
individuals with high standard metabolic rates may also express high
boldness, exploration, and activity (Biro & Stamps, 2010; Biro,
O’Connor, Pedini, & Gribben, 2013; Bartolini, Butail, & Porfiri,
2015). As such, increased metabolic demand with rising temperature may
be associated with an increased frequency of risk-taking behaviours to
maximise energy intake (Mathot & Dingemanse, 2015). However, selective
environments may modify these plastic responses to warming. For example,
if warming selects for a ”fast” pace-of-life syndrome, individuals may
evolve faster metabolic rates, faster maturation, and bolder behaviours.
Alternatively, if warming selects for a ”slow” pace-of-life syndrome
(countergradient selection), then metabolism and boldness may be
reduced, counteracting effects of thermal plasticity alone (Sih, Bell,
& Johnson, 2004; Réale et al., 2010). Individual traits may also
respond in different ways to increased temperature. For example,
metabolic rate may decrease while boldness increases, indicating no
pace-of-life syndrome trait change (Royauté, Berdal, Garrison, &
Dochtermann, 2018; Morgan et al., 2020). Ultimately, our ability to
predict the ecological consequences of warming hinges on understanding
responses of a suite of ecologically relevant traits, including
metabolism and behaviour.
Here, we use populations of a
globally distributed freshwater fish, Gambusia affinis (hereafterGambusia ), to test how metabolism and behaviour are affected by
multiple generations of recent (~100 years) exposure to
elevated temperature in natural ecosystems (Fig. 1). Gambusiashow inter-individual and inter-population variation in behavioural
traits (Cote, Fogarty, Weinersmith, Brodin, & Sih, 2010; Polverino,
Santostefano, Díaz-Gil, & Mehner, 2018) and make an ideal model
organism as they have recently invaded geothermal habitats of various
temperatures (Table 1) (Fryxell & Palkovacs, 2017; Moffett et al.,
2018). Previous work examining in situ metabolic rates ofGambusia affinis populations across a geothermal gradient showed
that the temperature sensitivity of metabolism was about seven times
less than the expectation of metabolic theory (Moffett et al., 2018).
This pattern suggests that (1) Gambusia has a low inherent
temperature sensitivity of metabolism or (2) that multigenerational
exposure to increased temperatures in warmer-source populations has
favoured reduced metabolic rates. Here, we use laboratory acclimation of
geothermal and nongeothermal populations of Gambusia to test the
hypothesis that geothermal populations exhibit a relatively low
metabolic rate at high temperatures, with a concomitant reduction in
boldness and activity. This result would suggest that multigenerational
processes cause countergradient trait change along the pace-of-life
syndrome axis (Conover, Duffy, & Hice, 2009). Alternatively, if fish
from geothermal populations show relatively low metabolism but high
behavioural rates, this result would suggest that multigenerational
processes (i.e., selection on metabolic rate) act to modify trait
relationships, giving rise to novel trait combinations in response to
warming.
Materials and methods
Fish populations and
collection
Gambusia were introduced to New Zealand in the 1930s and have
spread throughout its North Island, including into geothermal streams
(McDowall, 1978). Assuming two generations per year (Pyke, 2008), there
have been approximately 170 generations since Gambusiaintroduction to New Zealand. We collected Gambusia in January
2016 from eight populations in the North Island of New Zealand that
differ in thermal histories (Table 1). Four sites had geothermal
influence and therefore had temperatures exceeding air temperature
(’warm-source’), and four sites followed changes in air temperature
(’ambient-source’). Both geothermal and ambient sites experienced daily
and seasonal temperature variation. We did not have long-term continuous
temperature profiles of all sites (Fig. 1 & S1), but at the time of
fish collection, site temperature was, on average, 11°C higher for
warm-source populations compared to ambient-source populations.
Geothermal sites reached warmer temperatures and had warmer minimum
temperatures (measured bi-monthly) than ambient sites (Table 1). Fish
were collected by hand netting and transported to the laboratory in 20 L
insulated buckets with water collected on-site and a portable aerator.
At the time of fish collection, we measured dissolved oxygen, pH,
conductivity, and temperature using hand-held meters (YSI Professional
Plus; YSI ProODO).
Temperature Acclimation
Fish were acclimated in 20L tanks in the laboratory, with each tank
containing fish from a single population. The eight populations were
randomly assigned to tanks, and two tanks were established for each
population (16 tanks total). We randomly allocated ~12
fish from a population to a tank (n = 198 fish total; details in S1). In
each tank, we separated males and females using dividers to minimise
sexually antagonistic interactions that can affect survival; however,
mosquitofish females store sperm, so most females were pregnant during
the time of trait measurements, as they would be in nature. Each
population was acclimated to two experimental temperatures (20 ± 0.5 and
30 ± 0.5°C) over four months. Tank temperatures were initially set to
the collection temperature for a given population and then adjusted by
increasing or decreasing the set temperature of aquarium heaters by a
maximum of 1°C every two days until the target temperature was reached.
We started with water from the appropriate field site in each aquarium
combined with treated tap water to remove chlorine (API Stress Coat) and
progressively replaced it with treated water over two weeks. We fed fish
twice daily by hand to satiation with freeze-dried Daphnia and
Nutrafin MAX small tropical fish micro-granules and maintained a light
cycle of 12:12 throughout the experiment. Each aquarium had artificial
macrophytes and stones to provide refuge. Water was continuously
filtered using sponge air filters, which we cleaned every second day.
Fish mortality was low in most of the populations (see Table S1). We
fasted individuals for 24 hours before measuring behavioural and
metabolic traits to control for food digestion.
Metabolic Rate
We measured metabolism as maximum metabolic rate (MMR), standard
metabolic rate (SMR), and routine metabolic rate (RMR). MMR is the
maximum metabolic rate of an individual and sets the upper limit on
organismal metabolic performance (Fry, 1971). In contrast, SMR is the
minimum metabolic rate, measured after rest, with no digestion cost, on
non-stressed fish and sets the lower requirement of an animal to sustain
life. RMR was measured under similar conditions as SMR but allowed for
some activity and sits between SMR and MMR. As RMR incorporates
variation in activity between individuals, it may closely relate to
behavioural traits (Mathot & Dingemanse, 2015).
We measured RMR and MMR using static respirometry and SMR using
intermittent flow-through respirometry at each fish’s acclimation
temperature (Steffensen, 1989; Clark, Sandblom, & Jutfelt, 2013). We
used respirometers comprising 40 mL acrylic chambers with magnetic stir
bars in the chamber base to ensure water mixing throughout our oxygen
measures in all assays. We measured metabolic rate as oxygen consumption
(MO2) using a FireSting four-channel oxygen logger with
optical oxygen sensors (PyroScience, Germany). Respirometers were placed
into 80 L aquaria, filled with treated tap water, fitted with a UV
filtration system, an aerator, and a 100W aquarium heater.
Immediately following behavioural trials (see below), we measured RMR by
placing individuals into chambers and measuring oxygen consumption over
15 minutes. Chambers were then connected to a recirculating pump and
slowly flushed with oxygenated water for five minutes before beginning
SMR measurements. Oxygen consumption measurements for SMR were taken
overnight over an approximately 18-hour period. A computer-controlled
aquarium pump intermittently flushed chambers for five minutes to ensure
a complete turnover of water inside the chambers, then an oxygen
measurement period of 15 minutes began after a 30 second wait period. We
controlled oxygen flow and data logging through a PC using the software
’AquaResp’ (Svendsen, 2017). Following SMR measurements, we measured MMR
using an exhaustive chase protocol to induce maximum oxygen consumption
(Clark et al., 2013; Norin & Clark, 2016). Fish were removed from
chambers one by one and placed into a circular tank; in this tank, we
used an aquarium net to chase the fish until exhaustion (defined as the
lack of ability for burst swimming) (Norin & Clark, 2016). Fish were
then immediately placed into a static respirometer, and oxygen
consumption was measured for 5 minutes. We chose to measure MMR after
SMR measurement to ensure our SMR measurement accuracy as metabolic
rates may remain elevated for long periods after exhaustive exercise. We
immediately euthanised the fish following the measurement of MMR using
clove oil. Fish were then measured for mass, length, sex, and volume,
then dried at 60°C for 48 hours and re-weighed for dry mass.
We controlled for microbial oxygen consumption in our metabolism assay
water by subtracting the oxygen consumption in blanks (respirometers
with water only), which were run before and after every trial. We
assumed a linear increase in microbial oxygen consumption between
measurements in blanks.
We calculated each SMR, MMR, and RMR as;
\(\text{MO}_{2}=\left(V_{r}-V_{f}\right)\times\frac{{\Delta C}_{wO2}}{\text{Δt}}\)
Where: MO2 is oxygen consumption rate,
Vr is respirometer volume, Vf is
fish volume, ΔCwO2 is the change in oxygen
concentration, Δt is the change in time.
We calculated SMR using the mean of the lowest 10 % of all
measurements, excluding any outliers (± two standard deviations [SD]
from the mean), aerobic scope as MMR-SMR, and factorial aerobic scope as
MMR/SMR (Clark et al., 2013; Chabot, Steffensen, & Farrell, 2016).
Behaviour
Immediately before measuring metabolism, we conducted behavioural assays
on individuals in a 60 L aquarium with a water depth of 20 cm and
temperature set to the acclimation temperature. We fit the aquarium with
an air pump and a UV filtration system to maintain high oxygen
saturation and control microbial respiration. We measured individual’
boldness’ as latency to exit a refuge and individual ’activity’ as time
spent exploring a novel environment (Cote et al., 2010; Wilson, Godin,
& Ward, 2010). For these behavioural measures, we placed individuals
into a small enclosed and darkened area
(’refuge,’ 10cm × 30cm) at one end
of the 60 L aquarium. The aquarium was covered on all but one side to
allow for observation. In the refuge, we provided artificial macrophytes
and river stones. Fish were left in the refuge for 10 minutes before a 4
× 4 cm door was opened remotely, allowing fish to exit and explore the
remainder of the tank (’open area’). In the open area, we placed
macrophytes opposite the refuge opening as a visual cue for exploration.
We measured boldness using a stopwatch as the time it took the fish to
leave the refuge. Fish that did not leave were assigned a maximum
latency time of 600 seconds and were not measured for activity as forced
tests may measure anxiety or fear traits (Brown, Burgess, &
Braithwaite, 2007). Once the fish began exploring, we video-recorded
their movement and later measured activity as time spent moving (versus
remaining stationary) over five minutes following their emergence from
the refuge.
Statistical analysis
Overview
We first constructed a full model incorporating all predictors (mass,
acclimation temperature, and thermal history as geothermal or ambient)
and their interactions to analyse the influence of our predictors on
metabolism and behaviour. We included a random effect for population
identity. We used the Akaike Information Criterion (AIC) to reduce these
models (Säfken, Rügamer, Kneib, & Greven, 2018; Mazerolle, 2019). We
ranked models by conditional Akaike information criterion (AICc) values
and averaged candidate models with ΔAICc <4 using the R
package ’MuMin’ v.1.43.17 and removed models with interaction terms that
were not significant (Burnham & Anderson, 2001; Barton, 2020) (Table S2
& S3). Second, for each metric of metabolism, we used linear regression
on subsets of the data to calculate metabolic parameters b and Ea
(described below), as is standard practice for analysing metabolism data
(Gillooly et al., 2001; Tattersall et al., 2012). Third, we tested the
relationship between metabolism and behaviour across individuals from
all populations combined. To do so, we used censored regression models
to relate mass corrected metabolic rate (i.e., per unit mass) to
boldness and Poisson-lognormal generalised linear mixed-effects models
to relate mass corrected metabolic rate to activity. We expressed
metabolic rate per unit mass for models comparing metabolic rate to
behaviour to avoid collinearity between metabolic rate and mass in the
full model.
We constructed models using ’lme4’ v.1.1.23 and calculated p values
using ’LmerTest’ v.3.1.2 package in R with Satterthwaite’s degrees of
freedom method (Bates, Mächler, Bolker, & Walker, 2015; Kuznetsova,
Brockhoff, & Christensen, 2017). We performed all analyses using R
version 4.0.0 and determined results to be statistically significant at
the cut-off value α = 0.05 (R Development Core Team, 2020).
Metabolism
To understand the relationship between metabolic traits (SMR, RMR, MMR,
AS) and acclimation temperature or thermal history, we used a linear
mixed effect model (LMM) with mass, thermal history, and acclimation
temperature included as predictor variables. We chose to exclude the
factor sex in preliminary analyses because sex was not significant in
determining four of the five models used for metabolic traits. Further,
sex is confounded by body size in this species (females are larger,
Pyke, 2008), and by excluding sex, we increased our models’ statistical
power to detect other effects (Table S6).
We calculated allometric scaling coefficients (slope, b) using
least-squares linear regression models of log10metabolic rate (µg O2 min-1) data
against log10 mass (mg) data, separately for each
thermal history × acclimation temperature combination (n=4). The
activation energy (Ea) of metabolism was calculated from Arrhenius plots
of mass-normalised metabolic rates (MO2 ×
M-b ), against acclimation temperature as an
inverse function (1/kT) where T is the respirometry temperature (same as
acclimation temperature) in degrees Kelvin, k is the Boltzmann
constant (8.62 × 10-5 eV K-1), and M
is mass as in Gillooly et al. (2001).
Behaviour
Because our data are censored (fish had a maximum latency time of 600
seconds), we used a mixed-effects binomial logistic model to understand
if thermal history, mass, and acclimation temperature influenced
boldness. Then, taking individuals that left the refuge, we used a
Poisson-lognormal generalised linear mixed-effects model to understand
if thermal history and acclimation temperature influenced activity. Like
in our metabolism models, we chose to exclude sex to avoid confounding
trends between sex and body size and increase statistical power.
To understand if boldness was associated with mass corrected SMR, MMR,
or RMR, we used censored regression models using ’censReg’ v. 0.5.30
(Henningsen 2019). Similarly, to understand if behaviour, as activity,
was related to SMR, MMR, or RMR, we used a Poisson-lognormal generalised
linear mixed-effects model with source population as a random effect. We
constructed separate models for each acclimation temperature to avoid
confounding between metabolic rate and acclimation temperature, though
this limited our ability to detect any interactions between metabolic
rate and acclimation temperature.
Results
Based on model selection criteria,
our best models for SMR (model 5), RMR (model 5), and MMR (models 4, 1,
and 5) included history × acclimation temperature and mass × acclimation
temperature interactions. For AS, the best model (1) included only main
effects (see Table 2 for candidate models).
Allometric scaling of
metabolism
The relationship between metabolic rate and mass depended on acclimation
temperature (mass × acclimation temperature, p < 0.05, Fig. 2,
Table S4). In particular, metabolism rose less with increasing mass at
30°C than at 20°C (Fig 1). The metabolic rates of smaller individuals
were most sensitive to increasing acclimation temperature, as metabolic
rates converged between acclimation temperatures for larger fish (Fig
1). Allometric slopes were similar between ambient- and warm-source fish
at each of the acclimation treatment temperatures. Across all metabolic
rate measurements, scaling exponents varied from 0.178 to 0.556 and were
lowest for MMR (Table S7). Aerobic scope increased with mass and was
higher when fish were acclimated at 30°C compared to 20°C (Table S4).
The three measures of metabolic rate were related (linear regression,
r2 = 0.596 - 0.829; Figure S2). Overall, MMR was 1.6
and 1.8 times greater than SMR for fish at 30°C and 20°C, respectively.
RMR was 1.3-and 1.4 times greater than SMR for fish at 30 and 20°C,
respectively (Fig. S2). Across all individual fish, factorial aerobic
scope values ranged from 1.4 to 9.1.
Temperature sensitivity of metabolism
The effect of thermal history on all metabolic rate measures (SMR, RMR,
and MMR) depended on acclimation temperature (i.e., significant thermal
history × acclimation temperature interactions, p < 0.001,
Table S4). Individuals from warm-source populations had lower metabolic
rates than individuals from ambient-source populations at 30°C, but the
reverse was true at 20°C (Fig 1A, B, C). We found no relationship
between aerobic scope and thermal history (p = 0.871).
Similarly, temperature sensitivity (as activation energy, Ea) varied
with population and acclimation temperature, where metabolic rates of
individuals from warm-source populations had lower Ea compared to
individuals from ambient populations (Fig. 3). Activation energies
ranged from -1.655 to -0.998 eV for ambient-source fish and from -1.351
to -0.775 eV for warm-source fish.
Behaviour
Our best-supported model for boldness was model 1, which included only
main effects (Table 2). Fewer individuals from ambient-source
populations left the refuge compared to warm-source populations (n = 29
and 76 respectively) (Fig. 4). Individuals were bolder when they were
smaller (z = -0.972, p = 0.044), originated from a warm-source
population (z = 0.718, p = 0.044), and were acclimated to 30°C (z =
0.061, p = 0.046) (Table S5).
The best-supported models for activity were models 2, 5, and 4, which
included interactions among all factors (Table 2). We found that the
effect of thermal history on activity depended on acclimation
temperature (z = 2.144, p = 0.032), where fish from warm-source
populations were less active when acclimated at 20°C compared to fish
from ambient-source populations. We also found an interaction between
mass, thermal history, and acclimation temperature on activity (z =
2.321, p = 0.020). As such, we show that smaller individuals acclimated
at 30°C were more active than larger individuals acclimated at 20°C and
that activity was higher for cool-source fish at 20°C than warm-source
fish at 20°C.
Boldness and activity were both related to mass-corrected metabolic
rate, but this effect was stronger when fish were acclimated at 20°C
(Table S8 & S9). When acclimated at 20°C, individuals with higher SMRs
(z = -2.216, p = 0.027) and RMRs (z = -2.149, p = 0.032) were bolder,
but this behaviour was not related to MMR (z = -1.464, p = 0.143).
Similarly, after acclimation at 20°C, individuals with higher SMRs (z =
6.187, p <0.0001) and RMRs (z =2.436, p = 0.015) were more
active, but there was no effect of MMR (z = 0.474, p = 0.635). When
acclimated at 30°C no measure of metabolic rate was related to boldness
(p > 0.05). In contrast, when acclimated to 30°C increased
activity was related to higher RMRs (z =2.027, p = 0.043) and MMRs (z
=2.249, p = 0.025), but SMR was not related to activity (z = 1.857, p =
0.063).
Discussion
Multigenerational exposure to increased temperatures may alter the
temperature dependence of physiological and behavioural traits; however,
the temperature dependence of physiological traits is not often examined
over multiple generations (West, Brown, & Enquist, 1997; Persson,
Leonardsson, de Roos, Gyllenberg, & Christensen, 1998; Cheung et al.,
2012; Holt & Jorgensen, 2015). Here,
our results demonstrate that
populations with a recent history of multigenerational exposure to
warmer temperatures (i.e., geothermal source populations) display a
significantly reduced temperature sensitivity of metabolism.
Moreover, at warmer acclimation
temperatures, populations with a warmer thermal history show lower
metabolic rates than populations from ambient conditions, suggesting
that multigenerational processes (e.g., plasticity, adaptation) may
counteract the metabolic consequences of temperature rise (Sandblom et
al., 2016; Jutfelt, 2020).
Further, we show that boldness and
activity were positively related to
metabolic rates at the individual
level. However, when comparing populations, fish from warmer source
populations showed relatively high boldness at both acclimation
temperatures despite relatively low metabolic rates at the warmer
acclimation temperature. Together, these results suggest that
multigenerational warming will cause a reduction in metabolic rate and
an increase in boldness and activity, but that multigenerational
processes may not act to direct these trait changes neatly along a
”pace-of-life syndrome” axis.
Allometric scaling and temperature sensitivity of metabolism
In Gambusia , allometric slopes changed with acclimation
temperature, and this change was similar between thermal histories (Fig.
2). Allometric slopes were shallower at the warm acclimation
temperature, with small fish showing the largest difference in metabolic
rates between acclimation temperatures. This difference in allometric
slopes indicates that increased temperature may influence smaller
individuals to a greater extent than larger individuals. The
temperature-size rule demonstrates a reduction in body size with warming
in ectotherms, which Gambusia show (Gardner et al. 2011;
Moffett et al. 2018; Fryxell et al. 2020). As such, our
data suggest that the effect of warming on Gambusia metabolism
will be the most pronounced at smaller body sizes. This increased
temperature sensitivity of metabolism of small individuals with warming
may have significant consequences for population size-structure (e.g.,
mortality/ reproduction) and the strength of top-down effects (e.g.,
consumption) as energetic demand increases parallel to declines in body
size (Biro, Post, & Booth, 2007; Fryxell et al., 2020).
Our data suggest that multigenerational exposure to warm temperatures
reduced the minimum energetic requirements of metabolism (SMR) at the
warm acclimation temperature (Fig. 2). Similarly, sticklebacks
(Gasterosteus aculeatus) and European perch (Perca
fluviatilis , L.) show a reduction in SMR with a warm thermal history,
indicating that such changes may be a common consequence of elevated
thermal histories (Pilakouta et al., 2020; Sandblom et al., 2016). While
metabolic change in sticklebacks occurred over a long period, change in
the European perch was rapid, occurring over three decades. Here, change
in metabolic rate in Gambusia occurred rapidly over
~100 years or ~170 generations (NLNZ,
1928; Pyke, 2008). Previous work with Gambusia in the wild
demonstrated that metabolic temperature sensitivity was about seven
times less than predicted by metabolic theory (Moffett et al., 2018).
Here, our data suggest this discrepancy may be explained by
multigenerational processes acting to reduce the metabolic rate of
warm-exposed populations over multiple generations. Similarly, the
metabolic rates of coral reef fishes originating from high latitudes
were less sensitive to warming than those from low latitudes, suggesting
such patterns may be widespread (Munday, McCormick, & Nilsson, 2012).
Further, we found that metabolic rates were relatively low for
warm-source individuals when measured at 30°C but were relatively high
at 20°C, demonstrating that thermal history can modify plastic responses
to temperature itself. In contrast, metabolic rates in warm-source
Stickleback were consistently lower than cool-source regardless of assay
temperature (Pilakouta et al., 2020). In this study, the differences in
metabolic rates with acclimation temperature may indicate a trade-off
associated with warm adaptation. For example, if warm-source fish
evolved down-regulation of enzymatic and mitochondrial density to save
energy at warmer temperatures, this downregulation in mitochondrial
density may lead to a lower ability to cold acclimate and to function
well at cooler temperatures (Salin, Auer, Rey, Selman, & Metcalfe,
2015).
The moderating effects of thermal history on the temperature sensitivity
of metabolism were consistent across all three measurements of metabolic
rate. However, measurements of individual maximum metabolic rate showed
the least variation between acclimation temperatures. Accordingly, our
data suggest that minimum energy requirements may be more plastic than
maximum energy requirements. Thermal tolerance may often evolve
asymmetrically; for example, a species may show greater variation in
their ability to adapt to cold temperatures than warm temperaturesvia greater thermal compensation in SMR compared to MMR (e.g.,
Addo-Bediako, Chown, & Gaston, 2000; Araújo et al., 2013; Sandblom et
al., 2016; Bennett et al., 2021). Here, factorial aerobic scope (MMR /
SMR) values in our study were somewhat low, ranging from 1.4 to 9.1,
whereas in teleost fishes, values ranged from 1.8 to 12.4 (Killen et
al., 2016). Lower factorial aerobic scope values may suggest that the
exhaustive chase protocol used to measure MMR underestimated actual MMR
(Andersson, Sundberg, & Eklöv, 2020). Nevertheless, any asymmetry in
metabolic measures (SMR, RMR, MMR) could limit performance under warming
if adaptive change is slow (Sterner & George, 2000).
4.2. Behaviour, metabolism,
and temperature
Fish with higher metabolic rates were bolder and more active. Studies
that have assessed the link between metabolism and behaviour have found
mixed support for a relationship between these factors (Biro & Stamps,
2010; Niemela & Dingemanse, 2018; Royauté et al., 2018). Here, our data
indicate that rising metabolic rate may play a significant role in
regulating behaviour, where as metabolic rate increases, boldness and
activity time also increase. For example, individuals with higher
metabolic rates were more active and displayed less variability in
activity (Fig. 4). Across all metabolic rate measurements, individual
behaviour was most consistently related to RMR, suggesting that
physiological measures may be best related to behaviour when they allow
for, rather than control against, variation in behaviour (Careau,
Thomas, Humphries, & Réale, 2008). Here, RMR measurement likely
included some recovery from handling stress and any effect of being in a
new environment. As such, the relatively strong correlation between RMR
and behaviour may have resulted from muscle oxygen consumption, as
activity was likely a significant component of RMR measurements.
Overall, our data suggest that increasing metabolic demand increases
risk-taking and minimal behavioural activity as temperatures warm.
Despite the positive relationships among acclimation temperature,
metabolic rate, and behaviour across all fish combined, we found these
relationships to differ based on thermal history. If multigenerational
processes had caused trait change along a pace-of-life syndrome axis,
populations with relatively low metabolic rates should have shown low
boldness and activity (Réale et al., 2010). However, warm-source
populations displayed relatively bold behaviour at both acclimation
temperatures, despite their relatively low metabolic rates at warm
acclimation temperatures (Fig. 4). Patterns in activity were less clear,
as warm-source individuals were less active than ambient-source
individuals when measured at 20°C, while at 30°C, patterns were similar.
Such patterns in activity with different thermal histories may be
further evidence of a significant trade-off with warm adaptation, which
may not only affect metabolic performance at lower temperatures (see
above) but also behaviour. Thus,
warmer environments seem to favour
risk-taking behaviour while reducing routine activity, perhaps to
conserve energy when resources are not perceived to be readily
available. Other studies typically show an increase in boldness and
activity with increased environmental or acclimation temperature (Careau
et al., 2008; Biro, Beckmann, & Stamps, 2010; Forsatkar, Nematollahi,
Biro, & Beckmann, 2016), but they rarely account for population
differences. From our study, it
appears that multigenerational processes may not direct trait change
along a simple pace-of-life syndrome axis, instead producing a novel
combination of physiological and behavioural traits.
Our results suggest that multiple generations of exposure to warmed
temperatures result in significant changes to animal physiology and
behaviour. Several processes may have generated the trait changes
discovered here, including developmental plasticity, transgenerational
plasticity, and evolution. However, ultimately, it is the net effect of
these processes that will determine the outcomes of warming for
individuals and populations. Importantly, our results show that the
combination of these processes over several generations tends to
counteract the increase in metabolism caused by acclimation to warm
temperatures while intensifying acclimatory changes in some behavioural
traits. This result calls to question the notion that warming will cause
trait changes along a neat pace-of-life syndrome axis. Clearly, we must
understand the novel trait combinations arising over multiple
generations of exposure to warming to better predict outcomes for the
ecology of individuals, populations, and ecosystems.