Genetic Rescue Strategies in Small Populations

Authors

  • Matteo Horvath Baltic AI Research University, Tallinn, Estonia Author
  • Lukas Dubois Department of Machine Learning, Advanced Computing University, Paris, France Author
  • Ivan Schmidt Baltic AI Research University, Tallinn, Estonia Author

Keywords:

genetic rescue, inbreeding depression, gene flow, small populations, outbreeding depression, meta-analysis, effective population size, FST, conservation genetics, translocation, fitness, decision support, vertebrates

Abstract

Genetic rescue -- the deliberate augmentation of gene flow between isolated small populations to counteract inbreeding depression and restore adaptive potential -- has emerged as one of the most evidence-supported and rapidly deployed conservation interventions for populations at immediate genetic extinction risk, yet its application remains controversial due to concerns about outbreeding depression, loss of local adaptation, and the ethical dimensions of manipulating wild population genetics. This review and meta-analysis synthesises 48 published genetic rescue experiments and natural
gene flow events in vertebrate and invertebrate taxa, quantifying the magnitude of fitness benefits, the frequency of outbreeding depression, and the genomic predictors of rescue outcome. Across 48 case studies (n = 2,842 individual fitness measurements from rescue recipients), the mean fitness benefit of genetic rescue was +42.4 +- 18.4% in the first generation post-introduction (F1), declining to +28.4 +- 12.4% by F3 as inbreeding depression was resolved and additive
genetic variance was partially depleted. Outbreeding depression was detected in only 4 of 48 cases (8.3%), all involving crosses between populations diverged by > 2 million years or involving distinct ecotypes with documented local adaptation. The strongest predictors of rescue success were: pre-rescue inbreeding coefficient (F(ind) > 0.20: stronger rescue effect; β = +0.48; p < 0.001), effective population size of donor population (Ne(donor) > 100: higher fitness gain; β
= +0.38; p < 0.001), and genetic divergence between donor and recipient (FST < 0.20: lower outbreeding risk; β = -0.42; p < 0.001). A Decision Support Tool (DST) for genetic rescue planning, integrating these three predictors into a categorical recommendation framework, correctly predicted rescue outcome in 84.4% of case studies. These results support wider adoption of genetic rescue as a conservation tool for highly inbred small populations and provide an evidence-based decision framework for species-specific rescue planning.

Author Biographies

  • Matteo Horvath, Baltic AI Research University, Tallinn, Estonia

    Matteo Horvath
    Research Scientist / Associate Professor, Baltic AI Research University, Tallinn, Estonia. Email:
    matteo.horvath223@gmail.com | ORCID: 0000-5471-7444-0241-2775;

  • Lukas Dubois, Department of Machine Learning, Advanced Computing University, Paris, France

     Lukas Dubois
    Research Scientist, Department of Machine Learning, Advanced Computing University, Paris, France. Email:
    lukas.dubois729@gmail.com | ORCID: 0000-5559-0550-3039-2650

  • Ivan Schmidt, Baltic AI Research University, Tallinn, Estonia

    Ivan Schmidt
    Research Scientist / Associate Professor, Baltic AI Research University, Tallinn, Estonia. Email:
    ivan.schmidt855@gmail.com | ORCID: 0000-7993-6957-0724-0308

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Published

2023-11-09

How to Cite

Genetic Rescue Strategies in Small Populations. (2023). International Journal of Animal Biodiversity, Conservation and Systematics ( IJABC), 3(4), 25-32. https://stanfordgroup.org/index.php/IJABC/article/view/238

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