Discussion
Here, we provide the first quantitative synthesis of plasticity rates
(λ ) across taxa and apply a novel method to obtain standardized
and thus comparable measures of this plasticity parameter. For our focal
trait, acclimation of temperature tolerance to temperature change, the
shape of the plasticity responses was well described by an exponential
decay function (i.e. with the rate λE ). In other
words, the absolute rate of change in temperature tolerance when an
individual is shifted to a new temperature is proportional to the
deviance from the phenotype when completely acclimated to that
temperature. We thus validate an assumption that has previously been
made in theory describing the evolution of phenotypic plasticity (Lande
2014). In contrast, superior fit by piecewise linear regression was
primarily observed in experiments with poor temporal resolution, thus
demonstrating the importance of measuring phenotypes at multiple time
points in the new environment to determine the optimal model to use when
estimating the shape of the plasticity response.
Variation in estimated λE among species was
considerable. To put these values into perspective they can be
translated into half-times, or how long it takes for the initial
deviation from the fully acclimated phenotype to be reduced by 50%
after being shifted to a new environment, which is given as
ln(2)/λE . The mean observedλE of 0.0347 h-1 corresponds to
a half-time of 20.0 h, whereas the minimum and maximum species-specificλE , estimated to 0.0009 and 0.1892
h-1, correspond to half-times of 770.2 and 3.7 h,
respectively (based on data where SE λE< 0.01). Using model estimates (Table 2), half-times at 20 °C
when the final slope of the decay function is zero are 12.0, 16.9, 27.7,
36.5 and 38.5 h for amphibians, reptiles, insects, crustaceans and
fishes, respectively. Thus, our analyses demonstrate considerable
systematic variation in rates of phenotypic plasticity among these taxa.
This begs the question of why such variation has evolved. One might
speculate that due to the higher heat capacity of water compared to air,
fishes (all species) and crustaceans (5 out of 7 species in our data)
which inhabit aquatic environments experience higher temperature
stability than taxa inhabiting terrestrial environments. Furthermore,
aquatic habitats are often thermally stratified (lentic habitats such as
lakes and oceans) or provide cold-water plumes (rivers), which allows
for behavioural thermoregulation under periods of stressful temperatures
(Kurylyk et al., 2015; Freitas et al., 2016; Harrison et al., 2016).
This may reduce the strength of selection on rapid plasticity compared
to in the terrestrial environment occupied by reptiles and insects (all
species in our data). Although the amphibians also inhabit aquatic
environments (particularly during juvenile life stages), their
utilization of thermal refugia in deep or fast flowing water is likely
limited. Unfortunately, habitat use is confounded with phylogeny in this
data set, preventing direct analysis of the effect of habitat on rate of
plasticity in thermal tolerance. Thus, although we do demonstrate
evolutionary divergence in plasticity rates among these taxa, it remains
an open question as to whether this pattern results from evolutionary
adaptation to environmental conditions. However, this question could be
addressed in future work that targets populations or species that
experience known and contrasting patterns of environmental variability.
We observed a positive relationship between acclimation temperature and
rate of plasticity in temperature tolerance. This pattern may be
explained by the general relationship that exists between developmental
rate and body temperature in ectotherms, which is driven by the positive
effect of temperature on biochemical reactions and metabolic rate (Brown
et al., 2004). It may also explain the observation that within a
species, acclimation to high temperature is achieved faster than
acclimation to low temperature (Burton et al., 2020). A relationship
between metabolic rate and the rate of plasticity was previously
hypothesized and addressed by Rohr et al. (2018), but in a less direct
manner. Specifically, Rohr et al. (2018) argued that the effect of
metabolic rate on rates of thermal plasticity in ectotherms should be
evident as a negative relationship between body size and plasticity
rate, because smaller organisms tend to have a higher mass-specific
metabolic rate than larger ones. They did not however, calculate rates
of plasticity from experiments that were explicitly designed to do so.
Rather, they used data from experiments that measure the phenotype at
only two time points (z0 andz∞ in our terminology), and from this inferred
how the bias in acclimation capacity caused by insufficient acclimation
time was influenced by body size. Based on their results it was
concluded that rates of plasticity appeared to be higher for smaller
organisms. Using a more direct approach we failed to find support for a
general relationship between body size and rate of thermal plasticity.
Yet, our observation that the rate of plasticity in temperature
tolerance is positively related to acclimation temperature suggests a
role for metabolic rate in causing some of the variation in plasticity
rate across experiments.
Given the general patterns in rate of plasticity observed here, further
efforts in studying this plasticity parameter may be fruitful and
provide a better foundation for understanding how plasticity evolves in
response to environmental variation. From an empirical perspective,
including a temporal-dimension in experiments that study plasticity may
be included without large costs. In this respect, we make two
recommendations. First, a proper choice of model (linear vs. exponential
decay) for estimating lambda requires multiple measurements of the
phenotype as it responds to the new environment. Our analyses indicate
that five or more measurements may be required to adequately establish
the shape of the plasticity response (Fig. S6). Superficially, this
requirement might appear to substantially increase the workload of such
studies in comparison to studies that only estimate the capacity for
plastic phenotypic change in a trait. However, once the model that best
describes the shape of the plastic response to the new environment is
established, a single measurement zt after timet (which must be prior to achievement of full acclimation) in
addition to those typically measured (z0 in
non-acclimated individuals and z∞ after the full
acclimation response has been obtained) is sufficient to accurately
estimate Dt , which in turn can be used to
calculate the rate of plasticity (λE =
ln(Dt )/t for exponential decay orλL = (1-Dt )/t for
linear decay). Thus, the workload in such experiments can be greatly
reduced by performing a pilot experiment with sufficient temporal
resolution (in terms of measurement time points) that provides a precise
description of the shape of plasticity response to the new environment
before performing more replicates at a lower temporal resolution to
obtain the desired estimates of λ . It should be noted thatλE and λL are not directly
comparable, because the initial approach towards the fully adjusted
phenotype is more rapid under exponential decay. Thus, the relative
support for these two types of plasticity responses should be reported.
As a second recommendation, experimenters should strive to ensure that
complete acclimation to the new environment is achieved prior to
measuring z∞ . Our analyses show that failing to
do so can, and does, lead to bias in estimation ofλE (Fig. S2, S7). Ideally this is achieved by
rearing individuals in all the alternative environments for the whole
duration of the experiment (i.e. both prior to and after some of the
individuals are transferred into new environments). As pointed out by
Burton et al. (2022), this has rarely been done in studies of rates of
plasticity. Rather, the majority of studies first acclimate the animals
to a single initial environment before shifting them to a new
environment and then performing repeated measures of phenotype in this
new environment for what typically appears to be a pre-determined (and
potentially insufficient) duration.
Natural next steps in research on evolution of plasticity would be to
test for links between environmental variation and the evolution of
rates of plasticity, and to provide theoretical models that address the
co-evolution of plasticity rates and capacity (see Introduction).
Although this is beyond the scope of the current paper, our work
provides both methodology and novel insights that should stimulate
future work along these lines. We also re-emphasize a point made
previously (Burton et al., 2022) - that selection on the rate of
plasticity might be stronger than selection on the capacity for
plasticity. Evolutionary theory posits a central role for phenotypic
plasticity in mitigating the fitness impact of environmental variation,
but that possessing the potential for such a response is associated with
a fitness cost in stable environments (Lande, 2009). Fitness costs of
plasticity can be categorized into costs of maintenance and costs of
production. Costs of maintenance represent the investment of resources
into maintaining the machinery required for detecting and responding to
a change in the environment and will be paid at a constant rate
independent of environmental conditions (Auld et al., 2010). In
contrast, production costs are only paid when the plastic response is
triggered and are compensated by the fitness benefits associated with
changing the phenotype. If one assumes that the capacity for plasticity
can be increased by operating the ‘machinery’ required to change a trait
for a longer duration, this will increase production costs but not
maintenance costs. Populations living in less variable environments may
therefore pay a small price for maintaining their capacity for
plasticity (as shown by Van Buskirk & Steiner, 2009), and adaptation of
this parameter of plasticity to levels of environmental fluctuations may
therefore be relatively modest in magnitude. In contrast, increasing the
rate of change in the same trait would require increasing the size or
output of that ‘machinery’, with corresponding increases in maintenance
costs. Populations living in less variable environments should therefore
experience strong selection against maintaining rapid plasticity due to
higher maintenance costs, and adaptative evolution across populations
may then be expected to be more pronounced for the rate of plasticity.
This line of reasoning is also consistent with theoretical results
showing that maintenance costs shape the evolution of plasticity to a
greater extent than production costs (Sultan & Spencer, 2002). Given
these considerations, and the results presented in the current study, it
seems prudent to address the hypothesis that adaptation to environmental
variation may be more pronounced in terms of rates of plasticity rather
than capacity of plasticity. By providing clear evidence that rates of
plasticity have diverged among ectotherm classes we show that it is a
trait that evolves, and that increased empirical and theoretical focus
on the rate parameter is likely to provide a way forward for a more
comprehensive understanding of phenotypic plasticity.