NeOGen

NeOGen estimates genetic effective population size (N_e) and models demography for species with overlapping generations to support design and validation of molecular N_e studies.

Key Features:

• Precision and Accuracy Forecasting: Predicts precision and accuracy of molecular N_e estimates to optimize study design and required sampling effort.
• Power Analysis Framework: Implements population simulations and genetic power analyses that simulate demographics, genetic composition, and N_e from species-specific life-history parameters, mortality rates, population size, and genetic priors.
• Sampling and Locus Requirement Estimation: Determines necessary sample sizes and numbers of microsatellite or SNP loci required to achieve target precision for N_e estimates under age-structured sampling.
• Validation Against Demography: Compares empirical N_e estimates with population demographic and life-history properties to validate and interpret genetic estimates.
• Applicability to Iteroparous Taxa: Applies to a broad range of iteroparous species including mammals, birds, reptiles, and long-lived fishes.
• Empirical Demonstration: Demonstrated using empirical data from the Australian east coast zebra shark (Stegostoma fasciatum).

Scientific Applications:

• Genetic N_e Estimation: Estimating precise and accurate genetic N_e in species with overlapping generations such as mammals, birds, reptiles, and long-lived fishes.
• Sampling and Locus Planning: Planning sampling regimes and selecting numbers of microsatellite or SNP loci to meet precision and power targets.
• Validation and Interpretation: Validating empirical N_e estimates by corroborating genetic results with demographic and life-history data, including age-structured sampling strategies.
• Empirical Case Studies: Applying simulation-based inference to empirical datasets such as Stegostoma fasciatum to demonstrate real-world performance.

Methodology:

Integrates species-specific life-history parameters, mortality rates, population size, and genetic priors into an age-structured population simulation to predict genetic outcomes (N_e) under varying sampling schemes and produces graphical results.