Adaptive Peak Tracking as Explanation of Sparse Fossil Data Across Fluctuating Ancient Environments

Area of Science:

• Evolutionary Biology
• Paleontology
• Quantitative Genetics

Background:

• Species persistence relies on tracking adaptive landscape optima, which are influenced by environmental factors like sea temperature.
• Phenotypic evolution over geological timescales is recorded in the fossil record, offering insights into past environmental tracking.
• Understanding how traits evolve in response to environmental change is crucial for predicting future evolutionary trajectories.

Purpose of the Study:

• To develop and test a methodology for reconstructing phenotype-environment relationships from sparse fossil data.
• To simulate evolutionary tracking systems using both non-plastic and reaction norm models.
• To apply the methodology to a 2.3-million-year fossil record of *Microporella agonistes* bryozoans.

Main Methods:

• Simulations of univariate intercept-slope reaction norm models and non-plastic selection.
• Application of weighted least squares (WLS) method to analyze fossil phenotypic data and environmental proxies (e.g., oxygen isotopes for sea temperature).
• Model performance evaluation using leave-one-out cross-validation and Akaike Information Criterion (AIC) compared to random walk models.

Main Results:

• The methodology successfully identified linear mean phenotype-environment functions from as few as nine fossil samples.
• Predicted phenotypic values closely matched observed values, with prediction errors within or near standard errors.
• The WLS model demonstrated superior performance over alternative random walk models based on AIC.

Conclusions:

• Sparse fossil data, when analyzed with appropriate methods, can reveal significant evolutionary dynamics and phenotype-environment relationships.
• This approach provides a powerful tool for reconstructing past evolutionary responses to environmental change.
• Further research is needed to validate predictions over longer time scales and in more complex scenarios.