The double-edged sword of inducible defences: costs and benefits of maladaptive switching from the individual to the community level

In our simulations for the autotrophs, we varied two of the three trade-off properties (level of defence, plasticity costs and defence costs; see Fig. 1b) at a time and kept the third one constant. This results in three constellations reflecting three different trade-offs between these properties (Table 1):

• parallel: trade-off between defence and plasticity costs;

• crossing: trade-off between defence costs and plasticity costs;

• angle: trade-off between defence and defence costs.

In all three constellations, the autotrophic species B spanned the entire defence range, i.e. it had a completely undefended phenotype B_u and a maximally defended phenotype B_d. A either had a more limited defence range (in constellations parallel and angle) or spanned the entire range as well (in constellation crossing), representing three distinct ways that the trade-off between defence, growth rate, and plasticity range may play out. For each constellation, we varied the maximum switching rate χ_max over 5 orders of magnitude to investigate the effect of plasticity (Table 1, middle row). This parameter determines how rapidly a species can switch between phenotypes (see “Methods”, “Exchange rates”); higher values indicate faster adaptation. These results were also compared with a non-plastic baseline scenario where both phenotypes of each species are presented but χ_max = 0 (Table 1, upper row), as well as a rigid scenario where the species have only a single phenotype (Table 1, bottom row). All parameters and their values can be found in Supplementary Table S1.

In the following, we give a detailed description of the results for constellation parallel, where the autotroph species A and B have the same defence costs resulting in parallel trade-off lines between defence and growth rate, while varying the level of defence for A and varying the plasticity costs for B (Table 1, left column). We start with examining patterns for the phenotype biomasses, coexistence and community stability in the non-plastic baseline scenario “parallel 0”, and then compare the corresponding scenarios with a low exchange rate (“parallel 0.01”) and a high exchange rate (“parallel 1”). We next discuss the other two constellations (crossing and angle, Table 1) more briefly. Finally, we generalize across all scenarios and focus on the coexistence, the degree of maladaptive switching, and the consumer and total autotroph biomasses.

Non-plastic baseline dynamics: scenario parallel 0

In this scenario, four single phenotypes unconnected by exchange compete with each other. Thus, species coexistence here depends entirely on phenotype coexistence: the trade-offs have to be such that for each species, at least one phenotype is a good enough competitor to survive. Which phenotypes survive depend on the two trade-off parameters, defence of the defended phenotype of species A (d_Au) and plasticity costs for species B (pc_B), which thus determine whether coexistence is possible.

The defence costs were kept constant at an intermediate value of 0.3 for both species, resulting in parallel trade-off lines (Table 1, scenario “parallel 0”). The undefended phenotype of A, A_u, is a growth-specialist with the highest growth rate of all phenotypes. The defended phenotype of the same species, A_d, has a defence between 0 and 0.9 and a relatively high growth rate, and can be viewed as a generalist. Species B has variable plasticity costs that lower the growth rate of both phenotypes. The defended phenotype of species B, B_d, has the lowest growth rate of all phenotypes but is very well-defended, and thus a defence-specialist. Its undefended phenotype, B_u, is as undefended as A_u but has a lower growth rate; it is thus always an inferior competitor and inevitably goes extinct (Fig. 2c).

As B_u never survives, coexistence of the autotroph species requires the survival of defence-specialist B_d. B_d can only survive if A_d is not too defended, because A_d has a higher growth rate than B_d and will outcompete B_d in the “defended” niche otherwise (region Ib; Fig. 2d,h). A second criterion is that the plasticity costs for B must not be too high, because then the benefits of the defence of B_d no longer outweigh the costs, and it will go extinct even if there are no other highly defended phenotypes around (region Ia; Fig. 2d,g). In the regions where B_d goes extinct, species coexistence is not possible (Fig. 2e). The generalist A_d either survives by itself (region Ia in Fig. 2b,g) if its defence is low to intermediate, or together with the growth-specialist A_u if its defence is high (region Ib in Fig. 2a,b,h). In the regions II and III where B_d survives, it never survives on its own, but always together with one of the phenotypes of A. It coexists with the growth-specialist A_u if the plasticity costs are very low (region II in Fig. 2,i), and together with A_d if they are low to intermediate (region III in Fig. 2,j). These two regions do support species coexistence (Fig. 2e).

In three of the four regions (Ib, II and III in Fig. 2f), consumer biomass is low, because the final community always contains a well-defended phenotype (A_d in region Ib, and B_d in regions II and III); the overall level of defence of the community is relatively high in these regions (Supplementary Figure S1). Conversely, consumer biomass is relatively high in region Ia, because the only surviving autotroph phenotype is relatively fast-growing and fairly undefended (Fig. 2f,g). The regions where a well-defended phenotype survives often show antiphase cycles (Ib, II and III in Fig. 2e). These cycles do not occur in the region where only A_d survives (Ia in Fig. 2e); but regular quarter-lag predator–prey cycles can be found here if A_d is almost entirely undefended.

While the community defence (i.e. mean defence of the autotroph community) depends strongly on the coexisting phenotypes, the community growth rate is roughly constant because over the entire trait space, at least one phenotype with a high growth rate always survives (Supplementary Figure S1). The standing variance of the community defence was high when two phenotypes coexist as they occupy different niches along the defence axis (Fig. 2h–j). In contrast, the variance of the community growth rate was very low and almost constant across all regions.

Effect of phenotypic plasticity

Even a little bit of plasticity in the scenario parallel 0.01 (χ_max = 0.01) can change the above patterns for coexistence, stability, and average consumer biomass (Fig. 3a–d). While the autotrophs are intuitively expected to benefit from being plastic, the effect of plasticity on consumer biomass always turned out to be positive (Fig. 3a). This may be explained by the fact that switching was always, on average, maladaptive (Fig. 3c,d), measured by the adaptation index Φ (see Eqs. (11–13) in “Methods”). This index combines information on the net “flow” of individuals due to switching (i.e. whether more undefended individuals switch to defended or vice versa) with the fitness difference between the two phenotypes, and thus measures whether overall, more individuals switch from a low-fitness to a high-fitness phenotype (adaptive) or the reverse (maladaptive). This index can approach zero, but is always negative at equilibrium (see Appendix B), indicating maladaptive switching.

The most striking effect of plasticity was on coexistence, which was affected both positively and negatively by plasticity in different regions of the parameter space (Fig. 3b, Supplementary Figure S4a–d). A negative effect on coexistence is seen in region II, where the autotroph species previously coexisted (Fig. 2e), while with plasticity, B outcompeted A (Fig. 3b). Without plasticity, coexistence was possible in this region because A_u and B_d survived; importantly, A_u outcompeted B_u due to its higher growth rate, even though the difference between their growth rates is very small in this region (Fig. 2i). Plasticity reverses the competitive exclusion pattern between the two undefended phenotypes: B_u receives a constant flow of biomass from the well-defended B_d, which compensates for its slightly lower growth rate and allows it to outcompete A_u. Thus, coexistence is reduced as a direct consequence of maladaptive switching.

Plasticity can also promote coexistence, as the coexistence region now extends into former region Ib where the generalist A_d is highly defended (Fig. 2b, Supplementary Figure S1a). This is also an effect of maladaptive switching, though in this case the effect is indirect, mediated through the effect of plasticity on consumer biomass. Without plasticity, coexistence was impossible in region Ib because B_d was always outcompeted by A_d: even though the latter had a slightly lower level of defence, this was outweighed by its higher growth rate, making A_d the superior competitor over B_d. However, plasticity changes this because maladaptive switching increases the consumer biomass, which in turn alters the cost/ benefit balance of defence: B_d derives a stronger benefit from its high level of defence, which now outweighs the cost and allows it to survive. Coexistence through this mechanism is not possible when the plasticity costs for B are too high or when A_d is too well-defended, explaining the narrowing of the coexistence “tail” for high defence of A_d (Fig. 3b).

While the patterns of coexistence changed when allowing for plasticity, the patterns in the trait values were nearly indistinguishable from the previous scenario (Supplementary Figure S1, S2). Finally, plasticity had a strong impact on the community dynamics, as most of the antiphase cycles were stabilized (Ib, II, III in Fig. 3b). Their area decreased sharply as these cycles were characterized by asynchronous dynamics between the two prey phenotypes, which were reduced by plasticity. In contrast, the area of the quarter-lag predator–prey cycles remained unaffected by plasticity.

All the above patterns were found to a far stronger degree with a higher amount of plasticity (χ_max = 1; Fig. 3e–h, Supplementary Figure S4e–h). Consumer biomass increased strongly everywhere (cf. Fig. 3a,e), reflecting the strong increase in the degree of maladaptive switching (cf. Fig. 3c,d,g,h). The higher exchange rates led to more synchronization between the phenotypes, extinguishing the antiphase cycles completely (Fig. 3f). It also decreased the biomass of both defended phenotypes (cf. Supplementary Figure S4b,d,f,h). This in turn led to a lower community defence and a higher community growth rate (Supplementary Figure S3) both contributing to a higher consumer biomass. Finally, there was a sharp decrease in the coexistence region for high plasticity (Fig. 3e). Region II, where B outcompetes A through maladaptive switching, doubled in size due to the much higher degree of maladaptive switching (Fig. 3g,h). Region I, where A outcompetes B, now also increased, when the level of defence of A_d is relatively low (Fig. 3e). This is again an indirect effect of maladaptive switching causing a strong increase in consumer biomass, affecting the cost/ benefit balance of defence: while B_d derives a strong benefit from its high level of defence, B_u is completely undefended, and is at an extra disadvantage because of its low growth rate. Thus, while B_d would have been able to survive by itself, the high exchange rate causes a strong source-sink dynamic that drives B extinct.

Effect of plasticity in constellations crossing and angle

In constellation crossing the trade-off lines of both species cross in the trait space, as the level of defence is the same for both defended phenotypes; species B has a lower growth rate for its undefended phenotype than species A due to plasticity costs, while its defence costs are low and thus the growth rate of its defended phenotype is higher than for species A (Table 1, Supplementary Figure S5). Without plasticity the crossing trade-off lines lead to coexistence of both species in all simulations as A_u and B_d were always the only survivors, mostly showing antiphase oscillations (Supplementary Figure S5).

Allowing for phenotypic plasticity has the same results as were observed for constellation parallel: consumer biomass sharply increases (Fig. 4a,e); antiphase cycles are dampened or absent; and the area of coexistence decreases (Fig. 4b,f). All these changes are more pronounced for higher exchange rates (cf. Fig. 4a,b,e,f). Again, the biomass of the defended phenotypes decreased for high exchange rates (Supplementary Figure S6). Switching was always maladaptive for high exchange rates (Fig. 4g,h), and mostly maladaptive for low exchange rates (Fig. 4c,d). As was seen for constellation parallel, maladaptive switching was the reason for the decrease in coexistence. B can outcompete A when B has low plasticity costs. B_d has a much higher growth rate than A_d, while the undefended phenotypes have similar growth rates. The direction of competitive exclusion between A_u and B_u is thus easily reversed by B_d donating biomass to the sink B_u, allowing B to occupy both niches and outcompete A (region II in Fig. 4b,f). The same mechanism happens in reverse for high plasticity and defence costs of B: the differences in growth rate for the undefended phenotypes are high, while the defended phenotypes have very similar growth rates. A_u can support A_d, and A outcompetes B (region III in Fig. 4b,f).

In constellation angle there are no plasticity costs, and thus the undefended phenotypes A_u and B_u have identical growth rates. The defended phenotypes take the same places in trait space as in the parallel constellation: A_d is a generalist, with a lower level of defence and a relatively high growth rate due to low defence costs, whereas B_d is a defence-specialist with a high level of defence but a low growth rate. This leads to the trade-off lines forming an angle (see Table 1). Without phenotypic plasticity, the coexistence patterns are the same as in constellation parallel, except that no competitive exclusion occurs between the undefended phenotypes; instead, they (neutrally) coexist in regions Ib, II and III (Supplementary Figure S7; cf. Fig. 2).

With plasticity, neutral coexistence vanished: the defended phenotype that survived (A_d in region Ib, B_d in region III) could support the undefended phenotype of its own species, driving the other species extinct (Fig. 5b,f). As in the other constellations, the area of coexistence and the biomasses of the defended phenotypes decreased and antiphase cycles vanished with increasing χ_max (Fig. 5b,f, Supplementary Figure S8), while maladaptive switching and the consumer biomass increased (Fig. 5).

General results

As plasticity had very similar effects across all three constellations, we here generalize our results: we compare the three constellations for exchange rates over 5 orders of magnitude, as well as the non-plastic scenario and the rigid scenario (Table 1). That is, all simulations from one scenario (e.g. parallel 0) were summarized into one bar respective point in Fig. 6.

For all constellations, the fraction of simulation runs leading to coexistence was highest in the non-plastic scenario and decreased with increasing χ_max (Fig. 6a–c). In constellation parallel the share of coexistence for increasing χ_max continuously decreased from 51 to 3% (Fig. 6a). In crossing, the share decreased from full to no coexistence (Fig. 6b). In angle, the share of coexistence was 88% in the non-plastic scenario when taking also neutral coexistence into account (Fig. 6c). Its share decreased to 9% for a χ_max of 10 and increased again to 25% for the rigid scenario. Maladaptive switching increased for both species and all constellations for increasing χ_max (Fig. 6d–f). The increased plasticity led to a lower total autotroph biomass and a lower share of defended phenotypes (Fig. 6g–i), which resulted in higher consumer biomass (Fig. 6g–i).

Interestingly, and counterintuitively, the above patterns show that increasing the speed of plasticity (by increasing χ_max) makes the system behave more like the rigid system. The coexistence patterns in scenarios with high χ_max approach those of the rigid scenarios in two of the constellations (Fig. 6a,b). Similarly, the total autotroph and consumer biomasses approach the ones in the rigid scenarios (Fig. 6g–i). Thus, we found the higher χ_max make the autotrophs not more adaptive, but behave more like non-adaptive species.

Source: Ecology - nature.com