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by claude@2026-07, 2026-07-04
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The paper studied how two site frequency spectrum–based demographic inference methods, Stairway Plot 2 and Epos, perform under conservation-like conditions, using forward-time simulations in SLiM to generate 609 population trajectories across scenarios of decline, expansion, stability, and bottleneck while varying sample size (20–200 individuals) and SNP density (1,000–50,000). Across the simulated data, the authors assessed computational performance, trajectory reconstruction accuracy, temporal reliability, and effective population size (Nₑ) estimation error. They found that both methods reliably detected sustained declines and expansions with >80% overall correct trajectory reconstruction, with Epos faster and better at stable trajectories and Stairway Plot 2 more accurate for Nₑ estimation and bottleneck detection, but both inflated Nₑ in the most recent ~15 generations and bottlenecks were consistently hard to reconstruct. The work is explicitly a preprint and notes that SFS-based demographic inference should not be relied upon for contemporary Nₑ estimation or short-lived bottleneck detection, despite recommending sample-size prioritization over marker density. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.
Abstract
Genomic methods for inferring historical effective population size (Nₑ) trajectories offer valuable tools for conservation, yet their reliability under conditions typical of conservation datasets—small sample sizes, reduced-representation SNP data, and recent demographic change—remains poorly characterised. We evaluated the performance of two widely used site frequency spectrum (SFS)–based methods, Stairway Plot 2 and Epos, for reconstructing recent demographic histories relevant to conservation management. Using forward-time simulations in SLiM, we generated 609 unique population trajectories across four demographic scenarios (decline, expansion, stability, and bottleneck) with 20 replicates each, varying sample sizes (20–200 individuals) and number of loci (1,000–50,000 SNPs). Simulated genetic data were provided to both inference methods, and outputs were assessed for computational performance, trajectory reconstruction accuracy, temporal reliability, and Nₑ estimation error. Both methods reliably detected sustained declines and expansions, with correct trajectory reconstruction exceeding 80% overall. Epos was computationally faster and better at identifying stable trajectories, while Stairway Plot 2 was more accurate for Nₑ estimation and bottleneck detection. Both methods produced inflated Nₑ estimates in the most recent ~15 generations, and bottleneck scenarios were consistently difficult to reconstruct. Increasing sample size improved inference more than increasing SNP density. SFS-based demographic inference can effectively identify directional population trends under realistic conservation conditions but should not be relied upon for contemporary Nₑ estimation or short-lived bottleneck detection. We recommend prioritising individual sampling over marker density, trimming recent estimates, and integrating SFS-based results with complementary methods such as linkage disequilibrium–based estimators for robust conservation decision-making.
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This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.
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Genomic methods for inferring historical effective population size (Nₑ) trajectories offer valuable tools for conservation, yet their reliability under conditions typical of conservation datasets—small sample sizes, reduced-representation SNP data, and recent demographic change—remains poorly characterised. We evaluated the performance of two widely used site frequency spectrum (SFS)–based methods, Stairway Plot 2 and Epos, for reconstructing recent demographic histories relevant to conservation management. Using forward-time simulations in SLiM, we generated 609 unique population trajectories across four demographic scenarios (decline, expansion, stability, and bottleneck) with 20 replicates each, varying sample sizes (20–200 individuals) and number of loci (1,000–50,000 SNPs). Simulated genetic data were provided to both inference methods, and outputs were assessed for computational performance, trajectory reconstruction accuracy, temporal reliability, and Nₑ estimation error. Both methods reliably detected sustained declines and expansions, with correct trajectory reconstruction exceeding 80% overall. Epos was computationally faster and better at identifying stable trajectories, while Stairway Plot 2 was more accurate for Nₑ estimation and bottleneck detection. Both methods produced inflated Nₑ estimates in the most recent ~15 generations, and bottleneck scenarios were consistently difficult to reconstruct. Increasing sample size improved inference more than increasing SNP density. SFS-based demographic inference can effectively identify directional population trends under realistic conservation conditions but should not be relied upon for contemporary Nₑ estimation or short-lived bottleneck detection. We recommend prioritising individual sampling over marker density, trimming recent estimates, and integrating SFS-based results with complementary methods such as linkage disequilibrium–based estimators for robust conservation decision-making.
https://doi.org/10.32942/X2VD41
Evolution, Genomics
Published: 2026-03-15 21:26
Last Updated: 2026-03-15 21:26
CC BY Attribution 4.0 International
Conflict of interest statement:
The authors declare no conflicts of interest associated with this research
Language:
English
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