Data and code from: Quantifying feedback among traits in coevolutionary models

Phenotypic traits rarely evolve in isolation. Instead, multiple traits
typically interact to influence fitness, resulting in complex
coevolutionary dynamics. Such dynamics can be predicted using mathematical
frameworks such as adaptive dynamics and quantitative genetics. Selection
gradients play a crucial role in these frameworks, describing the
direction and strength of selection and thus predicting evolutionary
trajectories and potential endpoints. Current theory focuses mainly on
analysing how traits change in response to selection, which changes over
time as traits evolve. However, the extent to which changes in each trait
contribute to changes in the selection environment remains unquantified,
leaving much of our understanding of trait coevolution reliant on verbal
reasoning. To advance a more comprehensive and quantitative understanding
of coevolutionary dynamics, we develop a general framework that examines
how trait changes feed back to influence the selection environment. This
framework enables a fine-grained and systematic investigation of
coevolutionary feedback between traits and selection gradients by
quantifying the pathways through which they influence one another. Our
framework can be applied both to adaptive-dynamic models and to
quantitative-genetic models under the weak selection limit. We illustrate
our approach with three examples that showcase its potential to deepen our
understanding of established models.