MaxTemp: A Method to Maximise Precision of the Temporal Method for Estimating N_e in Genetic Monitoring Programs

Area of Science:

• Population Genetics
• Conservation Biology
• Bioinformatics

Background:

• Genetic monitoring programs are crucial for tracking global biodiversity changes.
• Estimating effective population size (N_e) is key for evaluating population health and management impacts.
• Traditional temporal methods for N_e estimation have limitations in precision.

Purpose of the Study:

• Introduce MaxTemp, a novel software to improve N_e estimation precision using the temporal method.
• Enhance the accuracy of single-generation N_e estimates by leveraging multigenerational genetic drift data.
• Provide a tool for more reliable assessment of population dynamics in conservation.

Main Methods:

• Developed the MaxTemp software incorporating a new method to utilize multigenerational temporal estimates.
• Analyzed how incorporating data from adjacent generations reduces variance in N_e estimates.
• Evaluated MaxTemp's performance using simulated data and a real-world case study (California delta smelt).

Main Results:

• MaxTemp increases the precision of temporal N_e estimates, reducing standard deviation by up to 50%.
• Improved precision tightens confidence intervals and minimizes extreme or infinite N_e estimates.
• The software demonstrated practical utility in a long-term genetic monitoring program.

Conclusions:

• MaxTemp offers a significant advancement in estimating effective population size for genetic monitoring.
• The software enhances the reliability of biodiversity tracking and informs conservation management strategies.
• MaxTemp provides a valuable tool for scientists and managers assessing population dynamics.