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A Guide for Developing Demo-Genetic Models to Simulate Genetic Rescue.

Julian E Beaman¹, Katie Gates², Frédérik Saltré^1,3,4

¹Global Ecology | Partuyarta Ngadluku Wardli Kuu, College of Science and Engineering Flinders University Adelaide South Australia Australia.

Evolutionary Applications

|May 15, 2025

Summary

Genetic rescue can help threatened populations by modeling demo-genetic feedback, which combines genetic and demographic factors to predict extinction risk. This strategy aids conservation efforts for vulnerable species.

Keywords:

SLiM conservation genetics demography density feedback inbreeding depression marsupials software

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Area of Science:

Conservation genetics
Population dynamics
Ecological modeling

Background:

Small, isolated populations face extinction risks from genetic drift, inbreeding, and demographic stochasticity.
Genetic rescue aims to mitigate these risks but its success is influenced by complex interactions.
Understanding demo-genetic feedback is crucial for effective conservation strategies.

Purpose of the Study:

To explain the interplay between genetic drift, inbreeding, and demographic stochasticity.
To present methods for modeling these demo-genetic feedback processes.
To evaluate the utility of genetic rescue simulations for conservation planning.

Main Methods:

Developing predictive models incorporating deleterious mutations and density-dependent demographic rates.
Comparing capabilities of open-source software for individual-based simulations.
Creating heuristic and case-study models using genetic data from Australian marsupials.

Main Results:

Demo-genetic feedback amplifies extinction risk in small populations.
Simulated genetic rescue successfully delayed extinction in virtual populations.
Published genetic data are applicable for model parameterization, calibration, and validation.

Conclusions:

Predictive modeling of demo-genetic feedback is essential for successful genetic rescue.
Individual-based simulations offer a flexible platform for evaluating conservation scenarios.
Model-based decision-making, informed by parameter sensitivity analysis, can optimize genetic rescue strategies.