ROCKS PARADOX – Dissecting the paradox of stasis in evolutionary biology
Linking microevolutionary theory with evolutionary change observed across millions of years (macroevolution) remains a challenge for evolutionary biology.
Natural populations often experience strong directional selection and harbour substantial additive genetic variance in most quantitative traits. Accordingly, on short timescales, populations typically show rapid evolutionary responses – just as predicted by theory. Yet, over long timescales the very same quantitative traits often evolve exceedingly slowly: periods of low or no net evolution over millions of years – morphological stasis – is a dominating feature of life on earth according to current interpretations of the fossil record.
Why do we observe so little evolution on long timescales when we observe so much evolution on shorter timescales?
This “paradox of stasis” remains a fundamental challenge that needs to be solved if we are to obtain a cohesive understanding of phenotypic evolution across timescales.
Researchers studying phenotypic evolution at opposite ends of the timescale continuum have largely adapted different perspectives and tools in their work, hampering the progress towards resolving the paradox of stasis. The ROCKS-PARADOX project will remedy this by merging theory and data across paleontology and evolutionary biology.
The ambitious and overarching goal of the ROCKS-PARADOX project is to dissect the paradox of stasis by providing a fundamental and deeper understanding of the patterns of evolutionary trait dynamics across long and short timescales, and the processes that govern them.
How will we reach these ambitious goals?
A large database of morphological time series – explicit measures of traits across time within a lineage – spanning generational to million-year timescales will be analyzed using a newly developed statistical modeling framework. The goal is to explore evolutionary dynamics on timescales in-between micro- and macroevolution, a time-scale that is understudied in an evolutionary context.
We will also capitalize on the clonal nature of bryozoans and obtain estimates of key quantitative genetic parameters from samples of fossil and recent bryozoan specimens using machine-learning algorithms. We will estimate temporal broad-sense genetic variances (and covariances) from several independent lineages spanning a few thousands to a few million years and investigate the effects of evolvability and genetic constraints on morphological evolution.