The probability of evolutionary rescue: towards a quantitative comparison between theory and evolution experiments.

Area of Science:

• Evolutionary biology
• Population genetics
• Quantitative biology

Background:

• Evolutionary rescue is crucial for population persistence in stressful environments.
• Forecasting evolutionary rescue, including drug resistance, needs validated quantitative predictions.
• Existing models often lack the complexity to capture real-world population dynamics.

Purpose of the Study:

• To develop general analytical predictions for evolutionary rescue probability using diffusion approximations.
• To extend existing models to incorporate broader demographic and genetic factors.
• To provide a framework for estimating biologically relevant quantities from empirical data.

Main Methods:

• Developed analytical predictions based on diffusion approximations.
• Extended the Orr & Unckless (2008) rescue model to include varied mutation effects, overlapping generations, and bottlenecks.
• Validated predictions against experimental data from Saccharomyces cerevisiae and Pseudomonas fluorescens.

Main Results:

• The model demonstrates qualitative agreement with experimental patterns of evolutionary rescue.
• Biologically relevant quantities, such as the per capita rate of rescue, can be estimated.
• The study provides a foundation for more quantitative tests of evolutionary rescue theory.

Conclusions:

• The proposed model offers a powerful tool for predicting evolutionary rescue probability.
• The findings have implications for understanding and managing drug resistance and other adaptation processes.
• Further research can refine the model for more precise quantitative predictions.