Evolvability and trait function predict phenotypic divergence of plant populations.

Understanding the causes and limits of population divergence in phenotypic traits is a fundamental aim of evolutionary biology, with the potential to yield predictions of adaptation to environmental change. Reciprocal transplant experiments and the evaluation of optimality models suggest that local adaptation is common but not universal, and some studies suggest that trait divergence is highly constrained by genetic variances and covariances of complex phenotypes. We analyze a large database of population divergence in plants and evaluate whether evolutionary divergence scales positively with standing genetic variation within populations (evolvability), as expected if genetic constraints are evolutionarily important. We further evaluate differences in divergence and evolvability-divergence relationships between reproductive and vegetative traits and between selfing, mixed-mating, and outcrossing species, as these factors are expected to influence both patterns of selection and evolutionary potentials. Evolutionary divergence scaled positively with evolvability. Furthermore, trait divergence was greater for vegetative traits than for floral (reproductive) traits, but largely independent of the mating system. Jointly, these factors explained ~40% of the variance in evolutionary divergence. The consistency of the evolvability-divergence relationships across diverse species suggests substantial predictability of trait divergence. The results are also consistent with genetic constraints playing a role in evolutionary divergence.

The Paradox of Predictability Provides a Bridge Between Micro- and Macroevolution.

The relationship between the evolutionary dynamics observed in contemporary populations (microevolution) and evolution on timescales of millions of years (macroevolution) has been a topic of considerable debate. Historically, this debate centers on inconsistencies between microevolutionary processes and macroevolutionary patterns. Here, we characterize a striking exception: emerging evidence indicates that standing variation in contemporary populations and macroevolutionary rates of phenotypic divergence are often positively correlated. This apparent consistency between micro- and macroevolution is paradoxical because it contradicts our previous understanding of phenotypic evolution and is so far unexplained. Here, we explore the prospects for bridging evolutionary timescales through an examination of this "paradox of predictability." We begin by explaining why the divergence-variance correlation is a paradox, followed by data analysis to show that the correlation is a general phenomenon across a broad range of temporal scales, from a few generations to tens of millions of years. Then we review complementary approaches from quantitative-genetics, comparative morphology, evo-devo, and paleontology to argue that they can help to address the paradox from the shared vantage point of recent work on evolvability. In conclusion, we recommend a methodological orientation that combines different kinds of short-term and long-term data using multiple analytical frameworks in an interdisciplinary research program. Such a program will increase our general understanding about how evolution works within and across timescales.

On the importance of scale in evolutionary quantitative genetics.

The informed use of scales and units in evolutionary quantitative genetics is often neglected, and naïve standardizations can cause misinterpretations of empirical results. A potentially influential example of such neglect can be found in the recent book by Arnold (2023. Evolutionary quantitative genetics. Oxford University Press). There, Arnold championed the use of heritability over mean-scaled genetic variance as a measure of evolutionary potential arguing that mean-scaled genetic variances are correlated with trait means while heritabilities are not. Here, we show that Arnold's empirical result is an artifact of ignoring the units in which traits are measured. More importantly, Arnold's argument mistakenly assumes that the goal of mean scaling is to remove the relationship between mean and variance. In our view, the purpose of mean scaling is to put traits with different units on a common scale that makes evolutionary changes, or their potential, readily interpretable and comparable in terms of proportions of the mean.

Evolvability predicts macroevolution under fluctuating selection.

Heritable variation is a prerequisite for evolutionary change, but the relevance of genetic constraints on macroevolutionary timescales is debated. By using two datasets on fossil and contemporary taxa, we show that evolutionary divergence among populations, and to a lesser extent among species, increases with microevolutionary evolvability. We evaluate and reject several hypotheses to explain this relationship and propose that an effect of evolvability on population and species divergence can be explained by the influence of genetic constraints on the ability of populations to track rapid, stationary environmental fluctuations.