Discussion
Here, we provide the first 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 were 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 numbers 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 0.0342 h-1 corresponds to
a half-time of 20.3 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 (only including data used in the analyses, i.e. with SEλE < 0.01). Using model estimates
(Table 2), half-times at 20 °C when the final slope of the decay
function is zero is 10.7, 15.8, 30.1, 43.3 and 40.8 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 and z∞ 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. S5). 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. S1, S6). 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.