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.