Detecting and quantifying introgression in hybridized populations: simplifying assumptions yield overconfidence and...

Area of Science:

• Conservation genetics
• Population genetics
• Molecular ecology

Background:

• Genetic introgression from non-native species poses a significant threat to global biodiversity.
• Advancements in molecular genetics provide more species-diagnostic markers, but efficient sampling and interpretation require robust uncertainty estimates.

Purpose of the Study:

• To develop and utilize a simulation model to assess the accuracy of detecting and quantifying genetic introgression in native populations.
• To evaluate the impact of common simplifying assumptions on introgression estimates and uncertainty.

Main Methods:

• Developed an age-structured population simulation model tracking non-native alleles across generations.
• Simulated stochastic mating, survival, and marker transmission in virtual fish populations.
• Sampled varying numbers of individuals and diagnostic markers across diverse hybridization scenarios.

Main Results:

• Commonly used simplifying assumptions often lead to overconfidence in detecting non-native alleles.
• These assumptions can result in unrealistically narrow confidence intervals for introgression estimates.
• Underestimation of detection failure and uncertainty can be by orders of magnitude.

Conclusions:

• Overconfidence in detecting and quantifying genetic introgression can negatively impact conservation and management decisions.
• Accurate estimation of uncertainty is crucial for effective strategies against species introgression.
• Simulation modeling provides a framework for evaluating assumptions in conservation genetics studies.