Introduction
Phenotypic plasticity, or the expression of different phenotypes across
environments by a single genotype, is an important process by which
organisms can minimize environmental impacts on fitness (Gabriel, 2005;
Gabriel et al. 2005; Padilla & Adolph, 1996; Siljestam & Östman,
2017). Such plasticity can be described by two parameters. First, thecapacity for plasticity determines the amount by which an
individual can actively change its phenotype following a shift in the
environment. The term “actively change” is used here to separate
between passive and active phenotypic plasticity. An example of passive
plasticity is the increase in respiration that typically occurs in
response to an increase in temperature, which organisms may counteract
over time by active plasticity responses (Kielland et al., 2017). Thus,
it is active plasticity that is of interest when studying the responses
that organisms have evolved to minimize the effect of environmental
change on fitness. This parameter can be measured as the change in the
slope of the relationship between trait value and environment (i.e. the
slope of the reaction norm) as plasticity proceeds, from acute exposure
until the full plastic response has been achieved (see Einum et al.,
2019 for arguments why it is this change in the slope, and not slopeper se , that describes capacity for plasticity ).
The second parameter of active phenotypic plasticity is the rate
of plasticity , which represents how quickly the change in phenotype
(and hence the change in the reaction norm slope) occurs following a
change in the environment (Fig. 1). Whereas the capacity for
plasticity has received considerable theoretical (e.g. Lande, 2014) and
empirical interest (e.g. Seebacher et al., 2015; Pottier et al. 2022)
from ecologists and evolutionary biologists, empirical support for
certain predictions regarding the evolution of this plasticity parameter
remain equivocal. For example, while it has been proposed that organisms
inhabiting more variable environments should evolve greatercapacity for plasticity , this is rarely supported by empirical
data (Gunderson & Stillman, 2015; Kelly et al., 2012; MacLean et al.,
2019; Pereira et al., 2017; Phillips et al., 2016; Sgro et al., 2010;
van Heerwaarden et al., 2016; van Heerwaarden et al., 2014). Recently,
Burton et al. (2022) suggested that this discrepancy between theoretical
expectations and empirical data gives reason for pause, and that greater
considerations of the second parameter, the rate of plasticity ,
which addresses the timescale over which plastic phenotypic change
occurs, might aid in bringing this field of research forward.
If the plasticity of a trait is an adaptive response, the fitness cost
that an organism incurs following a change in its environment should be
minimized once the phenotype becomes fully adjusted to the new
environment. Hence, the rate at which the phenotype approaches this
state should determine how long the individual expresses a sub-optimal
phenotype, and in part, determine the magnitude of the fitness cost
associated with that change in the environment. Given that organisms are
unlikely to be able to predict changes in all of the relevant
environmental variables they are exposed to, it seems plausible that
individuals may actually spend a considerable proportion of their time
having a phenotype that is not fully adjusted to their current
environment. This mismatch between environment and phenotype, and
associated cumulative fitness costs, will be exacerbated if the
adjustment of the phenotype is slow relative to the timescale of
environmental change. Furthermore, as pointed out by Burton et al.
(2022), the rate of plasticity might even influence how thecapacity for plasticity evolves, because the evolution of
capacity depends on the predictability of the environment. Organisms
that can rapidly implement their phenotypic response to a new
environment can postpone the onset of this process closer to the time of
selection in that environment than organisms that do so at a slower
rate. Thus, in a temporally autocorrelated environment, a fasterrate of plasticity might effectively increase predictability in
the environment, which in turn should favour the evolution of greatercapacity for plasticity (Lande, 2014).
Presently, a quantitative synthesis of data on the rate of
plasticity is lacking, and consideration of how this parameter of
phenotypic plasticity might be expected to evolve in response to
environmental change is absent from current theoretical models (Lande,
2014; Siljestam & Östman, 2017). Although a substantial number of
empirical studies document how phenotypes change over time when
introduced into new environments, these studies remain largely
descriptive, fail to address evolutionary hypotheses, and very rarely
(four out of 166 studies surveyed by Burton et al., 2022) attempt to
provide any formal statistical quantification of the time course of
plasticity. Thus, advancing our understanding of the evolution of
phenotypic plasticity might arguably benefit from a shift in focus fromcapacity for plasticity to rates of plasticity . To
stimulate such a shift, we provide the first comparative analysis of
published data describing rates of plasticity . In doing so, we
follow recent suggestions (Burton et al., 2022) regarding the estimation
of plasticity rates in a (i) standardized way, which is (ii) consistent
with theory and (iii) directly comparable across taxa and traits.
We draw upon published data from studies of acclimation to temperature
among ectotherms. Temperature is an environmental variable that affects
all organisms, varies substantially in space and time, and which has
particularly pervasive effects on biochemical, physiological and
ecological processes in this group of animals (Daufresne et al., 2009).
We focus our synthesis on traits describing temperature tolerance. We
first determine the shape of how temperature tolerance changes over time
(exponential vs. linear decay) in response to a shift in ambient
temperature, as this is the first step required when calculating therate of plasticity . After calculating rates of plasticity for
each published dataset, we then investigate relationships betweenrates of plasticity and taxonomic class, body size, and
acclimation temperature. By providing clear evidence that rates of
plasticity have diverged among ectotherm classes we show how this rate
can, and does, evolve, and that increased empirical and theoretical
focus on the rate parameter is likely to provide a way forward in
understanding evolution of phenotypic plasticity.