Modeling Inbreeding in Endangered Populations With Simulation

ISEF Category: Animal Sciences

Subcategory: Genetics  ·  Difficulty: Advanced  ·  Setup: Home Setup  ·  Time: 1 to 2 Months

The Hook

A small population can lose genetic diversity fast. Think of it like shuffling a tiny deck of cards, the same cards keep coming back, and hidden problems get copied again and again. That matters for endangered animals, because inbreeding can raise the chance of weak offspring and lower survival. Your project can test when a rescue move helps, and when it does not.

What Is It?

Inbreeding depression means related animals are more likely to pass on the same harmful variants, which can lower survival, fertility, or disease resistance. In a tiny population, chance matters more, so genetic diversity drops faster and the bad effects can build up.

A simple way to picture it is a game with too few cards. If you keep drawing from the same small pile, you see the same patterns over and over. In a simulation like SLiM or simuPOP, you build a digital version of that population, set the starting size, mating rules, migration, and fitness effects, then watch how the population changes across generations.

Why This Is a Good Topic

This is a strong science fair topic because you can change one variable at a time and measure clear outputs like heterozygosity, inbreeding coefficient, survival, and extinction risk. It connects directly to a real conservation problem, so your results have a real-world purpose. You also get to learn population genetics, simulation design, and data analysis without needing a wet lab.

Research Questions

• How does starting population size change the rate of heterozygosity loss in a Florida panther model?
• What is the effect of different migration rates on the inbreeding coefficient over time?
• Does adding one unrelated migrant every few generations reduce simulated extinction risk?
• To what extent do different survival-rate assumptions change the predicted strength of inbreeding depression?
• Which census-data inputs have the largest effect on the model's final population size?
• How does the initial level of genetic diversity change how quickly harmful alleles rise?

Basic Materials

• Laptop or desktop computer with at least 8 GB of RAM.
• Free copy of SLiM or simuPOP.
• R with RStudio or Python with Jupyter Notebook.
• Spreadsheet software for tracking scenarios and outputs.
• Published census and life-history data for the population you choose.
• Notebook or digital log for recording assumptions and model settings.

Advanced Materials

• High-performance workstation or university cluster access.
• SLiM with tree-sequence recording support.
• R with the tidyverse, ggplot2, and lme4 packages, or Python with pandas, NumPy, and SciPy.
• Version control with Git and GitHub for tracking model versions.
• Published genomic estimates for harmful-variant load, survival, or fertility effects.
• Access to full conservation reports, pedigree data, or recovery-plan appendices.

Software & Tools

• SLiM: Runs forward-time population genetics simulations for selection, drift, migration, and inbreeding.
• simuPOP: Builds individual-based genetic models when you want flexible mating and pedigree rules.
• R: Analyzes simulation output, compares scenarios, and makes clear graphs.
• Python: Helps clean census data, automate batch runs, and format input files.
• GitHub: Tracks versions of your scripts and keeps scenario changes organized.

Experiment Steps

1. Define the conservation question you want to answer and the output you will track, such as heterozygosity, inbreeding coefficient, or extinction risk.
2. Choose one real endangered population and gather published census and life-history data that set your starting assumptions.
3. Build a simple baseline model first, then add selection, migration, or rescue only after the baseline behaves as expected.
4. Set up comparison scenarios so each change isolates one cause, not several at once.
5. Plan how you will repeat runs, summarize variation, and graph the results with confidence bands or boxplots.
6. Decide which result will count as evidence for or against a rescue strategy before you start running the full set.

Common Pitfalls

• Treating census size as effective population size, which makes the inbreeding rate look more precise than the biology supports.
• Running only one simulation per scenario, which lets random chance look like a real trend.
• Using fitness penalties without citing the source, which leaves your assumptions unsupported.
• Forgetting to include immigration or rescue in a scenario meant to test conservation action, which makes the model unrealistically harsh.
• Comparing scenarios with different starting allele pools, which mixes the effect of population size with the effect of initial diversity.

What Makes This Competitive

A stronger version does more than run one model. It tests several parameter sets, reports how stable the result stays when you change mutation load, mating structure, or migration, and compares the outputs with published census data. If you add uncertainty bands, sensitivity analysis, and a clear reason for each assumption, your project starts to look like real conservation genetics work.

Project Variations

• Model another endangered carnivore, such as the Florida black bear or Iberian lynx, and compare how the risk curve changes with the same migration rule.
• Switch from whole-population collapse to one trait, like juvenile survival, and test how different inbreeding loads change that trait across generations.
• Compare isolated populations with a genetic rescue scenario that adds one migrant every few generations, then measure how much diversity returns.

Learn More

• SLiM Manual: Official documentation for forward-time population genetics simulation, found on the SLiM project website.
• simuPOP Documentation: Guides and examples for individual-based genetic simulation, found on the simuPOP project site.
• US Fish and Wildlife Service Florida Panther resources: Recovery reports and census background, found by searching the USFWS site.
• NCBI Bookshelf: Free chapters on population genetics, drift, and inbreeding, found by searching NCBI Bookshelf.
• PubMed: Review articles on inbreeding depression and genetic rescue, found by searching PubMed.
• NIH NHGRI Education Pages: Plain-language genetics background, found on the NHGRI site.