Simulated genotype data from: Effective population size estimation in large marine populations: Considering current challenges and opportunities when simulating large datasets with high-density genomic information

Next-generation sequencing has broadened perspectives regarding the
estimation of the effective population size (Ne) by providing high-density
genomic information. These technologies have expanded data collection and
analytical tools in population genetics, increasing understanding of
populations with high abundance, such as marine species with high
commercial or conservation priority. Several common methods for estimating
Ne are based on allele frequency spectra or linkage disequilibrium between
loci. However, their specific constraints make it difficult to apply them
to large populations, especially with confounding factors such as
migration rates, complex sampling schemes, or non-independence between
loci. Computer simulations have long represented invaluable tools to
explore the influence of biological or logistical factors on Ne estimation
and to assess the robustness of dedicated methods. Here, we outline
several Ne estimation methods and their foundational principles,
requirements, and likely caveats regarding application to populations of
high abundance. Thereafter, we present a simulation framework built upon
recent computational genomic tools that combine the possibility to
generate biologically realistic datasets with realistic patterns of
long-term neutral genetic diversity. This framework aims at reproducing
and tracking the main critical features of data derived from a large
natural population when running a simulation-based population genetics
study, e.g., evaluating the strengths and limitations of various Ne
estimation methods. We illustrate this framework by generating genotype
datasets with varying sample sizes and locus numbers and analyzing them
with three software tools (NeEstimator2, GONE, and GADMA). Detailed and
annotated simulation scripts are provided to ensure reproducibility and to
support future research on Ne estimation. These resources can support
method comparisons and validations, particularly for nonspecialists, such
as conservation practitioners and students.