Figure 2. Graphical hypotheses for the relationship between
environmental properties and benefits of plasticity for adaptive
evolution. Panel A [i-iii] describes the benefit of plasticity for
adaptive evolution across an increasing rate of environmental change,
Panel B [i-iii] describes this dynamic across increasing
environmental variance, and Panel C [i-ii] across increasing
temporal autocorrelation.
Hypothesis A[i]: The benefit of plasticity increases with increase
in the rate of environmental change, eventually plateauing. Selection is
weak when the mean environmental change is slow, and phenotypic lag is
small. Population growth is consequently high, and heritability of
fitness-related traits is high. In this scenario, plasticity adds little
to adaptive tracking, and thus the costs of plasticity outweigh the
benefits in decreasing the phenotypic lag. Conversely, when the mean
environment changes too fast for adaptive evolution to track, and
phenotypic lag is high, plasticity helps to ‘catch up’ with the moving
optimum by allowing for the population to increase in size, and thus
maintain the genetic diversity.
Hypothesis A[ii]: The benefit of plasticity decreases with increases
in the rate of environmental change. Contrary to Hypothesis A[i],
when selection is weak, lag load can increase because the population
evolves more slowly. In this scenario, plasticity can bring the
population phenotypic mean close to the selection peak at a low rate of
environmental change. Conversely, as rate of mean environmental change
increases, the limits of plasticity set by its costs (physiological toll
and erosion of genetic diversity) may result in a limited role of
plasticity for adaptive tracking. The population size may be small at
high rates of environmental change, and plasticity may increase the
chance of extinction due to drift by shifting the phenotypic average and
thus shading the genetic variation from selection. Moreover, a high rate
of environmental change can limit the efficacy of plasticity given the
low predictability of the future environment.
Hypothesis A[iii]: The benefit of plasticity is maximised at an
intermediate rate of environmental change, above (following A[i])
and below (following A[ii]) which its benefit decreases. Following
moving optimum theory, there is an intermediate rate of environmental
change at which the balance between selection strength and population
persistence is optimal.
Hypothesis B[i]: The benefit of plasticity to adaptive evolution
increases with increasing environmental variation. As the environment
becomes more variable, plastic responses in physiology, life history,
phenology, and/or behaviour can dampen the detrimental effects of
unpredictable fluctuations, thereby preventing extinction. This
buffering would afford the population more time to reach its adaptive
peak via adaptive evolution. This benefit would eventually cross a point
of diminishing returns, as when the environment becomes too variable,
the costs involved in plastic responses may outweigh their benefits in
part due to the lack of predictability in the temporal environment.
Moreover, at a highly variable environment with a stationary mean,
evolution may serve to be nonadaptive, and plasticity can allow the
genotypic mean to remain near the environmental mean amidst the
environmental variability.
Hypothesis B[ii]: The benefit of plasticity to adaptive evolution
decreases with increasing environmental variation. In an environment
with a small amount of variation, plasticity works together with
evolution to fix traits helpful in the new environment. As the
environment becomes more variable, phenotypic plastic responses may
drive a disconnect between phenotypic selection and genotypic selection,
ultimately making the genetic variation in the population maladapted to
future environmental conditions. In other words, plasticity might help a
population that is stuck in a valley or a local peak to find a global
peak on a fitness landscape when the environment is moderately variable;
if the environment is too variable, peak searching might be disrupted
too much—and peaks themselves would be shifting on the landscape.
Hypothesis B[iii]: The benefit of plasticity to evolution is highest
at low and high amounts of environmental variability. The ability of the
trait mean in the population to reach the peak of fitness landscapes via
adaptive evolution may be optimal at an intermediate level of
environmental variance. In this case, the facilitative role of
plasticity would be low at an intermediate level of environmental
variance if it masks genetic variance of the population from selection,
or shifts the phenotypic average.
Hypothesis C[i]: The benefit of plasticity to adaptive evolution
increases with increasing temporal autocorrelation. Higher
autocorrelation in the environment corresponds to higher reliability of
temporal cues and thus higher predictability of future environmental
states. Therefore, plastic responses can more accurately track moving
selection targets, and aid adaptive tracking. In addition, adaptive
evolution may be less likely to occur in isolation in highly
autocorrelated environments.
Hypothesis C[ii]: The benefit of plasticity to adaptive evolution
decreases with increasing temporal autocorrelation. Autocorrelation at
temporal lags that are not in resonance (similar lengths of time) with
paces of life history can increase extinction risk. This could lead to a
population existing in an unfavourable environment for long periods
(reducing temporal refugia), and plasticity could decrease the genetic
variation upon which selection can act. Thus, the ability for plasticity
to help adaptively track moving optima decreases.