Changing conditions can promote evolutionary change in various ways (Botero et al., 2015; Tufto, 2015). One commonly envisioned mode of evolution is the continuous change of trait means as result of changing mean conditions (Darwin, 1859). Yet, although trait changes in response to novel conditions are widely observed (e.g., due to climate change, Piao et al., 2019), they frequently result from phenotypic plasticity (Boutin and Lane, 2014), i.e., changes of the phenotype in response to an environmental cue. Phenotypic plasticity may provide a short-term relief from changing conditions (Charmantier et al., 2008; Chevin et al., 2010), but also shield a genotype from selection and thereby prevent evolution (Oostra et al., 2018), or it may facilitate evolution via genetic accommodation (Kelly, 2019). In any case, phenotypic plasticity is a pervasive evolutionary strategy, and considered a major factor in a rapidly changing climate (Fox et al., 2019).

The time scale of phenotypic change depends on the time scale of environmental fluctuation (Rando and Verstrepen, 2007; Stomp et al., 2008). Fluctuations over very rapid timescales can be addressed by reversible plasticity, which includes, for example, the induction of plant defense when herbivores are present (Green and Ryan, 1972). Gradual long-term changes, on the other hand, are addressed by genetic adaptation. Between those extremes lie environmental fluctuations that are roughly on the scale of one life span. When environments change over the course of an organism’s development, they can be tackled by irreversible developmental plasticity, i.e., plastic adjustment of developmental pathways that lead to alternative phenotypes (Botero et al., 2015). For example, some Daphnia can produce protective phenotypes when chemical cues from predators are sensed during development (Krueger and Dodson, 1981). When environments are constant throughout an organism’s life time but change from one generation to the next, phenotypic change can be induced in the offspring generation. These are referred to as anticipatory parental effects (Burgess and Marshall, 2014) or intergenerational inheritance (Perez and Lehner, 2019). For example, aphids that live under crowded conditions may produce winged offspring that can leave the colony and avoid high predation pressure or plant deterioration (Braendle et al., 2006). Lastly, when environmental fluctuations last for several generations, epigenetic modifications may be integrated into the germ line and affect multiple succeeding generations. This is referred to as transgenerational plasticity or non-genetic inheritance (Perez and Lehner, 2019; Adrian-Kalchhauser et al., 2020). For the remainder of the article we will refer to all these irreversible changes simply as phenotypic plasticity, ignoring the potential physiological constrains that may limit their evolution. They all have in common that there is a long delay between information sensing and phenotype induction.

Although often assumed, phenotypic plasticity does not need to be adaptive (Ghalambor et al., 2007; Arnold et al., 2019). Plasticity requires some environmental cue on which the induction of phenotypic change is based, and uncertainty around the future environmental state may turn plasticity maladaptive (Burgess and Marshall, 2014; Donelson et al., 2018). Such unpredictable conditions instead favor bet-hedging, which refers to the reduction of fitness variance (Cohen, 1966; Seger and Brockmann, 1987; Starrfelt and Kokko, 2012). Bet-hedging can be achieved by avoiding risky investments (conservative bet-hedging), or by spreading the risk among one’s offspring (diversified bet-hedging), i.e., producing offspring with varying phenotypes (Seger and Brockmann, 1987; Starrfelt and Kokko, 2012). Although empirical evidence is difficult to obtain (Simons, 2011), bet-hedging is a likely explanation for high trait variance or unexpected trait means in many systems, such as the seed dormancy of desert annuals (Cohen, 1966), diapausing strategies of insects (Hopper, 1999) and annual killifish (Furness et al., 2015), wing dimorphisms (Grantham et al., 2016), facultative sexual reproduction (Gerber and Kokko, 2018), dispersal and partial migration (Goossens et al., 2020).

At fluctuations of intermediate time scales where there is a delay between information sensing and phenotype induction, both phenotypic plasticity (e.g., Baker et al., 2019) and bet-hedging (e.g., Venable, 2007) may be expected to evolve. Various theoretical studies have clarified the conditions that may lead to one or the other (Botero et al., 2015; Tufto, 2015), but although occurring potentially simultaneously, bet-hedging and plasticity are nevertheless often treated independently (Donelson et al., 2018). Moreover, when diversified bet-hedging and plasticity are considered jointly, there is no clear consensus about their exact relationship. Adaptive offspring variance that is needed for diversified bet-hedging might be either established by developmental instability (Simons and Johnston, 1997; Kærn et al., 2005; Veening et al., 2008; Woods, 2014; Dueck et al., 2016; Perrin, 2016) or by overly relying on cues with little predictive power (“microplasticity,” Simons and Johnston, 2006; “hyperplasticity,” Scheiner and Holt, 2012). With this article we aim to clarify the relationship between bet-hedging and plasticity, with special attention to readers that are familiar with plasticity but less familiar with bet-hedging theory. We will first use one simple numerical example (insect diapause) to explain the relationship of diversified bet-hedging, conservative bet-hedging and arithmetic mean maximization in detail. We will then extend the consideration to a range of environments whose state is partially predictable, thereby adding the potential for phenotypic plasticity. Lastly, we generalize from our example and describe a method to quantify phenotypic plasticity and bet-hedging based on reaction norm shapes.

Common examples of bet-hedging are transgenerational biphenisms, i.e., the parent decides among two possible physiological states of the offspring in the face of uncertainty (e.g., Cohen, 1966; Grantham et al., 2016; Maxwell and Magwene, 2017; see Simons, 2011 for further examples). One of these examples is the timing of insect diapause (Halkett et al., 2004; Pélisson et al., 2013), which we will use to illustrate the theory throughout this article.

Multivoltine insects benefit from exponential population growth throughout the growing season, but need to produce an overwintering (diapausing) generation before the onset of cold weather (Kivelä et al., 2016). Aphids, for example, reproduce by parthenogenesis during summer, which enables particularly quick population growth; in autumn they invest in sexual offspring that produce diapausing eggs, as frost kills the soft-bodied insects and only eggs survive (Simon et al., 2002). The struggle to keep the growing season long on one hand and to avoid death on the other hand puts diapause timing under intense selection pressure. If the onset of frost would be invariant, day length could be used as reliable cue of impeding winter, so plasticity in response to day length is expected to evolve. However, if just one generation faces early frosts, all offspring may simultaneously die and the genotype is driven to extinction, regardless of their otherwise high growth rates. Under unpredictable or only partially predictable conditions, bet-hedging strategies may therefore be expected to evolve (Halkett et al., 2004).

For the remainder of this article we will use examples that are loosely based on aphid overwintering. We will assume that parthenogenetic offspring (P₁) may produce four offspring when environmental conditions are mild, but face a 90% mortality rate when conditions change. In contrast, diapausing offspring (P₂) only replace themselves with 1 offspring in either environment. Hence we assign phenotype P₁ a fitness value of 4 in E₁ (summer), but only 0.1 in E₂ (winter), whereas phenotype P₂ achieves 1 fitness in either environment. We assume that the evolution of these growth rates is constrained, so only the proportion of each phenotype may evolve.

We wish to explain the bet-hedging concept in detail with a few numerical examples. We first consider an entirely unpredictable environment, in which an aphid mother cannot collect any information about the potential environment of their offspring, i.e., there is a 50% chance that the offspring will face beneficial summer conditions (E₁), but also a 50% chance for harsh winter conditions (E₂). A genotype that invests exclusively in parthenogenesis (P₁) achieves on average 2.05 fitness (Table 1), while increasing the proportion of diapausing offspring (P₂) lowers arithmetic mean fitness. Nevertheless, a genotype that invests exclusively in diapause (P₂) is more successful on the long term, because the parthenogenetic genotype nearly dies out every two years. For example, a parthenogenetic population would decline to 16% of its original size over four years (4 ^∗ 0.1 ^∗ 4 ^∗ 0.1), while the population size of the diapausing genotype would remain constant. The arithmetic mean obviously fails here as predictor of long-term population growth.

If there are multiple decisions to make and the outcome is multiplicative, the geometric mean is a much better predictor for long-term growth, because it is sensitive to variance among years (Cohen, 1966; Seger and Brockmann, 1987; Starrfelt and Kokko, 2012). In the above example of population growth over multiple years, the lower arithmetic mean fitness was more than compensated by the reduction in fitness variance, therefore the risk-averse strategy achieved higher geometric mean fitness than the arithmetic mean maximization (AMM) strategy. This risk-aversive strategy of investing in lower fitness fluctuation at the cost of arithmetic mean fitness is called conservative bet-hedging (CBH), akin to investing in gold when stock markets fluctuate. The risky strategy of maximizing arithmetic mean fitness (AMM), on the other hand, is superior when fluctuations are low, and an analogy in economics would be the investment in a highly profitable product that is not insured against loss (“unhedged”).

Now let us consider a genotype with high developmental instability, i.e., whose offspring phenotype is randomly determined (Table 1). This means that the arithmetic mean fitness is not reduced as strongly as that of the risk-aversive phenotype (100% P₂), but the fitness fluctuation between E₁ and E₂ is also not as great as that of the arithmetic mean maximizer (100% P₁). This genotype will increase in population size over four years by the factor 1.89 (2.5 ^∗ 0.55 ^∗ 2.5 ^∗ 0.55), so in this example it is clearly superior to both CBH and AMM. Investing equally in both phenotypes (P₁ and P₂) breaks down the fitness correlation among the offspring, as half of the offspring takes a risk, while the other half plays it safe (Starrfelt and Kokko, 2012). This strategy is similar to investing in a portfolio of stocks rather than a single stock and is called diversified bet-hedging (DBH).

The geometric mean can be calculated for any phenotype proportion p (proportion of P₂) between 0 and 100% (Figure 1A, solid blue line), showing that actually neither of the three strategies (AMM, CBH, DBH) is optimal. Instead, p = 0.61, i.e., a mix of CBH and DBH, yields the highest geometric mean fitness (Table 1). Starrfelt and Kokko (2012) explored the relationship among AMM, CBH and DBH in great detail, and explained fitness optimization as a three-way trade-off between maximizing the arithmetic mean, reducing fitness variance, and reducing fitness correlation among the offspring. However, as outlined in our example, this three-way relationship breaks down to a simple linear gradient when there are exactly two phenotypes to choose from.

The same principles also apply when the two environments do not occur with equal frequency, e.g., when the probability of E₂ (winter) is reduced to 20%. In this case the arithmetic mean fitness of P₁ and P₂ needs to be weighted by the frequencies of E₁ and E₂. Nevertheless, arithmetic mean fitness is still a linear function of the phenotype proportion p (Figure 1A, dashed orange line), and increasing the proportion of P₂ constitutes a change from AMM towards DBH or CBH. In this example with only occasionally adverse conditions, the optimum lies at p = 0.17 (solid orange line), i.e., much closer to an AMM strategy. If the frequency of E₂ is raised to 70%, on the other hand, the optimal strategy moves with p = 0.90 close to pure CBH (not shown). The optimal strategy thus strongly depends on the environmental frequency.

We wish to complete this description of fitness maximization in a single environment with two last special cases. First, we consider the production of two specialist phenotypes, in which P₁ achieves a fitness of 4 in E₁, but none in E₂, while P₂ achieves 0 fitness in E₁ but 4 fitness in E₂ (thus deviating from the aphid example). With these parameters geometric mean fitness peaks at p = 0.5 (Figure 1B, blue solid line), so a strategy that maximizes developmental instability is optimal. Yet, the mixed production of offspring does not constitute DBH, because the diversification does not come at the cost of arithmetic mean fitness (i.e., the dashed blue line is flat). If, however, the growth rates of the two phenotypes are slightly uneven, e.g., reduced to 3.9 for P₂ in E₂, the same investment in P₂ would lower arithmetic mean fitness (dotted gray lines), and hence technically classify as a diversified bet-hedging strategy. This borderline example shows that the classification of bet-hedging strategies is not only a question of whether arithmetic mean fitness is reduced, but rather by how much. The second special case concerns very high probabilities of adverse conditions. When the frequency of E₂ is raised to 0.9, it carries so much weight that the arithmetic mean fitness does not decrease, but increase with the proportion of P₂ (Figure 1B, dashed orange line). The strategy that avoids variance is hence also the one which maximizes arithmetic mean fitness, so increasing geometric mean fitness (solid orange line) does not come at the cost of arithmetic mean fitness and CBH becomes impossible. In general, the linear gradient from AMM over DBH to CBH (and, in fact, the occurrence of bet-hedging) breaks down, when there is no conflict between arithmetic mean maximization and reduction of fitness variance. We will avoid these special situations in the remainder of the article.

We so far discussed the optimal phenotype proportion in a single, isolated environment. However, the benefit of diapause lies in adapting to a continually changing environment. Like in many other insects, aphid diapause is mainly governed by night length. Aphids exclusively reproduce by parthenogenesis under long-day (short night) conditions, but transition to the production of sexual forms under long-night conditions (Marcovitch, 1923). The diapause decision can hence be visualized as a biphenic reaction norm, in which the x-axis represents a continuous night length and the y-axis represents a probability (or, from the mother’s perspective, a proportion) of diapause induction between 0 and 100%. This reaction norm to night length generally follows a logit-curve that ranges from a probability of zero under short nights to a probability of 1 under long nights, and the inflection point at which half of the offspring are diapausing forms is called critical day length (Danilevskii, 1965). The night length response is additionally modulated by temperature (warm temperatures delay diapause), but we ignore the additional plasticity in response to temperature in our considerations.

We will now use the diapause example to illustrate how to calculate optimal reaction norm shapes. Imagine an environment in which winter onsets over many years always occur at 14 h night length. Obviously night length would be a reliable cue and plasticity in response to night length can be expected to evolve. Conversely, night length is useless as cue for a plastic response if winter onset fluctuates randomly. Between those extremes lies an only partially reliable cue, i.e., there is between-years variation in the relationship of night length and winter onset. For example, winter onset may in some years coincide with a night length of 14 h, but fall in other years on an earlier (13.8 h) or later (14.5 h) date, which can be described by a normal distribution with a mean of 14 h and some standard deviation. We now use three different scenarios of how environmental conditions (winter onset) may vary: 1) Winter onset fluctuates according to a normal distribution N₁(14, 1) with a mean cue value of 14 h and standard deviation 1; 2) Winter onset follows a normal distribution N₂(14, 4) with a mean cue value of 14 h and standard deviation 4, thus simulating lower predictability by night length; 3) Winter onset fluctuates according to a normal distribution N₃(14, 2) with standard deviation 2, but half of the winters are mild enough that offspring of type P₁ (e.g., parthenogenetic offspring) can survive.

The cumulative distribution function of N describes the probability that winter will occur at a night length of c or lower (Figure 2A). If, for example, an aphid lives in an environment of exactly 14 hours night length, it can expect that the offspring will experience winter conditions with a 50% probability (the optimal phenotype proportion is then 0.61, see Table 1). At 13 h night length winter onset is less probable (18%) for environment N₁ (blue line) than for N₂ (41%, orange line), because winter onset variability is lower. In N₃ the probability distribution must be multiplied by 0.5, i.e., with the chance that winter is mild (green line). This reduces the probability of winter onset at c = 13 h to 16%. Given these environmental frequencies and the fitness values introduced earlier (parthenogenesis: 4/0.1; diapause: 1/1; in summer/winter conditions, respectively), one can now calculate the optimal proportion p as described in section “Arithmetic Mean Maximization, Diversified Bet-Hedging and Conservative Bet-Hedging.” This proportion is 0.47 (nearly pure DBH) in scenario 1, as there is considerable risk of unfavorable conditions, but in scenarios 2 and 3 the ratios drop to 0.12 and 0.11, respectively. Thus, DBH is favored over pure AMM with increasing probability of winter conditions. The same calculations can be performed along the whole range of c, so the complete optimal reaction norm can be calculated if mean and standard deviation of the environment-cue relationship are known (Figures 2B,C).

With these considerations we explained the reaction norm shape as a series of binary decisions. In each of these decisions, phenotype proportions may range from AMM to CBH, with DBH in between. The overall degree of bet-hedging is hence defined by the reaction norm shape, and in our specific examples mostly correlates with the reaction norm slope (Figure 2B, orange and blue lines) and range (green line). However, as indicated by the skew in the orange line towards the lower range of c (AMM is discouraged even under low risk) in Figure 2C, more complex shapes are also possible and the relative contribution of each strategy is difficult to quantify. Furthermore, our examples are based on cumulative densities of normal distributions, but depending on the environmental cue, other shapes (e.g., bimodal, sinusoid) are possible. We hence require summary statistics that adequately describe the reaction norm shape.

In this section we will describe some typical reaction norm shapes and discuss useful summary statistics to describe the overall degree of plasticity, arithmetic mean maximization, conservative bet-hedging and diversified bet-hedging. First, let us assume a “plastic” reaction norm (Figure 3A, dark blue line). A step function describes a sudden switch from one phenotype (AMM) to the other (CBH), and the number of environments in which a mix of phenotypes is produced is minimized. This function maximizes the standard deviation of phenotype proportions p across environments. We refer to the variance of p as σamong2. The opposite of a step function is one in which the mother’s decision is entirely independent of the environmental cue, i.e., left to developmental instability, and both phenotypes are produced in equal measure (DBH; Figure 3A, light blue line). While σamong2 is zero, there is variance in phenotypes within each environment (σwithin2). The trait choice is a Bernoulli draw and the variance of each p is calculated as p ^∗ (1 – p), so we define σwithin2 across environments as the mean Bernoulli variance. The two variance components (among and within environments) complement each other, and we define their sum s=σamong2+σwithin2 as the phenotypic variance of the genotype. It is not possible to maximize both σamong2 (steep slope, high range) and σwithin2 (minimal departure from 50%) at once, but intermediate reaction norms with mixed contributions of σamong2 and σwithin2 are possible (solid and dashed medium blue lines). The trade-off between σamong2 and σwithin2 can be described by the ratio r = σamong2σwithin2. r thus describes the degree of developmental (in)stability across environments.

The variance composition is not the only parameter in which reaction norms may vary. Reaction norms may, for example, be flat (r = 0), but the proportion of P₂ (p) might be zero (Figure 3B, light orange line), 0.8 (dark orange) or 1 (darkest line) in all environments. These reaction norms differ in the mean frequency of phenotype P₂ across environments, which we denote as f. A frequency of zero indicates a pure AMM strategy, while f = 1 is a pure CBH strategy. A mean frequency of 0.5 indicates a reaction norm with maximal phenotypic variance (s), enabling the aforementioned gradient from phenotypic plasticity to DBH (Figure 3A, solid lines). As with Figure 3A, intermediate reaction norm shapes are also possible: a reaction norm may, for example, range from p = 0 to p = 0.3 or from p = 0.7 to p = 1 (Figure 3B, dashed lines). Reaction norms can thus vary from complete canalization to high phenotypic variance, and we express their shape by mean frequency of phenotype P₂ and by the variance composition. A canalized reaction norm may be only expressing risk-aversive phenotypes, or only expressing arithmetic mean optimizers, whereas high phenotypic variance may indicate steep plastic reaction norms or DBH.

The two shape parameters f and r reflect the reaction norm shape to a reasonable extent, but as summarizing statistics they cannot sufficiently describe all its features. For example, the reaction norms in Figure 3C both share the same mean frequency (0.5) and variance composition (0.47), but the strategies under environments that correspond to a low cue c differ considerably. In our aphid example these two strategies differ in the mean timing of diapause induction, which is an important consideration when the onset of seasons is under directional change (IPCC, 2014). This mean timing can be assessed by calculating the inflection point (called critical day length for diapause reaction norms), but for non-logistic reaction norms or more complicated reaction norm shapes a different approach, e.g., based on autocorrelation patterns, is required.

In summary we discussed three important parameters that describe a reaction norm shape: The frequency f, the variance composition r (among:within environments), and (for logistic reaction norms) the inflection points. These three parameters are partially interdependent of one another, and can be drawn as three perpendicular axes (Figure 3D; see also Supplementary Figure S1 for an alternative representation). The resulting parameter space has three distinct ends which conform to maximum plasticity (i.e., a step-function, dark blue dot), CBH (dark orange), and AMM (light orange). Parameters outside these bounds are not possible, e.g., DBH and plasticity cannot occur in canalized reaction norms, and on the other hand mean frequencies of 0.5 necessarily imply phenotypic variance by DBH or plasticity.

So far we described optimal strategies in a single environment (Section “Arithmetic Mean Maximization, Diversified Bet-Hedging and Conservative Bet-Hedging”), calculated optimal reaction norm shapes (Section “Calculating Optimal Reaction Norm Shapes”), and explored which reaction norm shapes are generally possible (Section “Classification of Reaction Norm Shapes”). We now return to our aphid diapause example to illustrate how optimal reaction norms change when environmental conditions and fitness functions are altered. We will cover cases with more frost-resistant parthenogenetic forms (i.e., higher fitness of P₁ in E₂), harsher summer environments (lower fitness of P₁ in E₁), and three forms of change in the environment that are directly relevant for aphid biology: first, mean winter onset may vary with latitude, with earlier winter onset at high latitudes (Danilevskii, 1965). Secondly, winter onset dates may vary among years, which is the condition that should lead to bet-hedging in diapause timing (Halkett et al., 2004). Lastly, aphid populations in warmer climates frequently lost the ability to produce sexual forms and reproduce by parthenogenesis throughout the year (anholocyclic life cycles, Simon et al., 2002). The preparation for winter makes only sense if there is sufficient change in environmental conditions, so this kind of canalization (obligate development) is expected at southern latitudes.

We start with environments that vary in among-years predictability. Using night length (in hours) as a cue c, we consider scenarios where winter onset is normally distributed with a mean cue c of 14 and standard deviations ranging from 0 to 10. In our standard example with growth rates of 4/0.1 (parthenogenetic) and 1/1 (diapausing), the optimal mean frequency f of risk-aversive (diapausing) phenotypes increases with environmental variance (Figure 4A, blue solid line), while the variance ratio r (among : within environments) decreases (Figure 4C, blue solid line). Thus, a greater tendency towards DBH and CBH is expected to evolve across environments in unpredictable conditions (see also Figure 4B, blue lines). With decreasing growth rate of P₁ in E₁(parthenogenesis in summer) the optimal ratio decreases less sharply and the frequency of P₂ (diapause) increases more strongly (solid orange and green lines in Figure 4A, green lines in Figure 4B). Here the riskier strategy pays off less, and the balance is shifted towards CBH. When the growth rate of P₁ in E₂ (winter) is raised to 0.33 (frost tolerance) both r and f change less steeply with environmental unpredictability (dashed lines), i.e., the optimal reaction norms tend towards AMM. Increasing the growth rate in E₂ further to 0.66 (dash-dotted lines) leads to a strategy that ignores environmental risk, except when the chance of mild (summer) conditions is very low. The range of environments that feature a sufficiently low chance of P₁ decreases with increasing environmental variance, causing a drop of both f and r as a sign of canalization to AMM (Figure 4D). Overall, both CBH and DBH can be expected under unpredictable conditions, but their relative benefits vary depending on the arithmetic mean fitness of risk-aversive and risk-prone phenotypes.

We now simulate global changes in the probability of events, for instance increased or decreased probabilities of severe winters. For the latter, we multiply the normal distribution by 0.5, overall halving the probability of being in the harsh environment E₂ (see also Figure 2A). This discourages risk-aversion and, for example, having all offspring diapausing is no longer beneficial (Figure 5). When the growth rate of P₁ is either 4 (summer) or 0.1 (winter), the frequency f stagnates at 0.2 to 0.25, while the ratio r decreases from 0.47 to 0.17 (Figures 5A,C, solid blue line). This is because the reaction norm range is constrained (Figure 5B). A lower growth rate of P₁ in E₁ restores phenotypic variance (Figures 5A,C, orange and green lines), as it reduces its arithmetic mean fitness and makes the alternative phenotype again more profitable (Figure 5B, green lines). Lowering the environmental risk further increases the benefit of arithmetic mean maximization (dashed lines) and eventually leads to AMM under all environmental conditions (dash-dotted lines). Overall, Figure 5 shows that a global reduction of the probability for E₂ may discourage CBH, and instead favor AMM. For example, a lower risk of freezing in winter may explain the existence of anholocyclic lines.

A third axis of environmental variation concerns changes in mean environments. Moving the distribution of environments to a mean c of 9 h simulates the change of winter onset with latitude, as well as the effects of a changing climate. Although highly relevant for the optimization of fitness, the changes in optimal reaction norm shapes are trivial to describe. We refer to Supplementary Material S2 for further exploration.

In general, we find that r and f evolve with changes in environmental predictability (Figure 4, solid lines), leading to CBH and DBH in unpredictable environments. Changes in the fitness function (growth rates in our example) may, however, affect the balance of AMM and CBH, and very low rewards for CBH instead lead to the evolution of risky strategies that seek to maximize the arithmetic mean (Figure 4, dash-dotted lines). When the probability of adverse conditions is globally lowered across the range of environments (e.g., mild winters), the reaction norm range can become constricted, which further affects the balance of the fitness maximization strategies. Lastly, f additionally depends strongly on the mean environment (e.g., winter onset, Supp. S2), but within reasonable limits the general shape of the reaction norms is not affected.

Phenotypic plasticity can help organisms adapt to changing conditions (Fox et al., 2019), but this requires a predictable cue (Bonamour et al., 2019). Especially for transgenerational plasticity cues are not entirely predictable (Burgess and Marshall, 2014; Donelson et al., 2018), which, at least under some conditions, favors bet-hedging instead (Botero et al., 2015; Tufto, 2015). Nevertheless, the value of bet-hedging strategies as alternatives to plasticity is frequently overlooked.

Starrfelt and Kokko (2012) have explained bet-hedging, including its mathematical foundation, in great detail. The main finding was that arithmetic mean fitness maximization, diversified bet-hedging and conservative bet-hedging form a three-way trade-off of conflicting strategies. However, it was difficult to see how these strategies play out in practice (Haaland et al., 2020). We provided a simple, detailed calculation of fitness based on insect diapause as example. Based on this system with only two possible phenotypes (biphenisms) we described how a conflict between arithmetic and geometric mean optimization can result in bet-hedging (Figures 1A,B). We explained that the three strategies form a gradient, in which arithmetic mean maximization (AMM) and conservative bet-hedging (CBH) are represented by distinct phenotypes, and diversified bet-hedging (DBH) by a mixture of the two extremes. We also extended the concept by adding a cue the organisms can respond to, thereby incorporating reaction norms and the potential for phenotypic plasticity. We identified the mean phenotype frequency f and the variance composition r as two summary statistics of reaction norms that allow distinguishing between AMM, CBH, DBH and plasticity, and the sum s of the variance components as a measure of phenotypic variance. Moreover, for logistic reaction norm shapes we discuss the inflection point as a third useful summary statistic.

In this review we rephrased reaction norm evolution as a complex trade-off among four strategies. It is increasingly recognized that changes in climate extremes and in predictability are as important as changes in means (IPCC, 2014; Donelson et al., 2018) – focusing only on strategies to match the mean is hence not fruitful. For example, failure to shift mean phenology with climate change (Gienapp et al., 2013) is not problematic per se – it could be mitigated by concurrent changes in phenotypic variance. Similarly, the lack of both phenotypic plasticity and mean change may not have severe fitness consequences, if the lack of plasticity is mitigated by diversified bet-hedging. It is the combination along all three axes that defines fitness in a given environment.

All code necessary to produce the figures is included in the Supplementary Material S3.

JJ and DB developed the theory and contributed to the final version of the manuscript. JJ wrote the first draft. DB supervised the work. Both authors contributed to the article and approved the submitted version.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.