Dissecting the paradox of stasis in evolutionary biology
There is something deeply disconcerting about the current state of knowledge on rates of morphological evolution across different timescales: Why do most species in the fossil record exhibit negligible morphological change when co...
There is something deeply disconcerting about the current state of knowledge on rates of morphological evolution across different timescales: Why do most species in the fossil record exhibit negligible morphological change when contemporary populations often respond rapidly to selection? The ROCKS-PARADOX project will address this fundamental question – known as the paradox of stasis – along mutually reinforcing lines of enquiry, by merging theory and data across paleontology and evolutionary biology. The prevalence of stasis and other patterns of change are hard to evaluate without knowledge of evolution on timescales unattainable by studies of contemporary populations (microevolution) and comparative species-data (macroevolution). The ROCKS-PARADOX project will address this by analyzing the world’s largest collection of data on within-lineage evolution – spanning decadal to million-year timescales – using a statistical framework (developed by the project) where new and already established mathematical models of evolution are implemented. The ROCKS-PARADOX project also will conduct an unprecedented assessment of the effects of genetic constraints and evolvability on evolution beyond microevolutionary timescales. To do this, we will break new ground by estimating quantitative genetic parameters from fossil samples using machine-learning algorithms on a collection of 150,000 fossil clonal organisms (bryozoans) from a rich and highly-resolved stratigraphic section spanning 2.3 million years. The ROCKS-PARADOX project will bridge our current understanding of phenotypic evolution across timescales into a single cohesive theoretical framework, and open up new avenues for how fossil data can be collected and analyzed to inform questions within evolutionary biology. The project will develop new methodology with broad applications, including long-awaited tools for high-throughput phenotyping.ver más
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