Simulation tools are key to designing and optimising breeding programs that are multi-year, high-effort endeavours. Tools that operate on real genotypes and integrate easily with other analysis software can guide users towards crossing decisions that best balance genetic gains and genetic diversity required to maintain gains in the future. Here we present genomicSimulation, a fast and flexible tool for the stochastic simulation of crossing and selection based on real genotypes. It is fully written in C for high execution speeds, has minimal dependencies, and is available as an R package for integration with R’s broad range of analysis and visualisation tools. Comparisons of a simulated recreation of a breeding program to a real data set demonstrates the simulated offspring from the tool correctly show key population features, such as genomic relationships and approximate linkage disequilibrium patterns. Both versions of genomicSimulation are freely available on GitHub: The R package version at https://github.com/vllrs/genomicSimulation/ and the C library version at https://github.com/vllrs/genomicSimulationC/.

Effective management of protected species requires information on appropriate evolutionary and geographic population boundaries and knowledge of how the physical environment and life‐history traits combine to shape the population structure and connectivity. Saltwater crocodiles (Crocodylus porosus) are the largest and most widely distributed of living crocodilians, extending from Sri Lanka to Southeast Asia and down to northern Australia. Given the long‐distance movement capabilities reported for C. porosus, management units are hypothesised to be highly connected by migration. However, the magnitude, scale, and consistency of connection across managed populations are not fully understood. Here we used an efficient genotyping method that combines DArTseq and sequence capture to survey ≈3000 high‐quality genome‐wide single nucleotide polymorphisms from 1176 C. porosus sampled across nearly the entire range of the species in Queensland, Australia. We investigated historical and present‐day connectivity patterns using fixation and diversity indices coupled with clustering methods and the spatial distribution of kin pairs. We inferred kinship using forward simulation coupled with a kinship estimation method that is robust to unspecified population structure. The results demonstrated that the C. porosus population has substantial genetic structure with six broad populations correlated with geographical location. The rate of gene flow was highly correlated with spatial distance, with greater differentiation along the east coast compared to the west. Kinship analyses revealed evidence of reproductive philopatry and limited dispersal, with approximately 90% of reported first and second‐degree relatives showing a pairwise distance of <50 km between sampling locations. Given the limited dispersal, lack of suitable habitat, low densities of crocodiles and the high proportion of immature animals in the population, future management and conservation interventions should be considered at regional and state‐wide scales.

Simulation tools are key to designing and optimising breeding programs that are many-year, high-effort endeavours. Tools that operate on real genotypes and integrate easily with other analysis software are needed for users to integrate simulated data into their analysis and decision-making processes. This paper presents genomicSimulation, a fast and flexible tool for the stochastic simulation of crossing and selection on real genotypes. It is fully written in C for high execution speeds, has minimal dependencies, and is available as an R package for integration with R’s broad range of analysis and visualisation tools. Comparisons of a simulated recreation of a breeding program to the real data shows that the tool’s simulated offspring correctly show key population features. Both versions of genomicSimulation are freely available on GitHub: The R package version at https://github.com/vllrs/genomicSimulation/ and the C library version at https://github.com/vllrs/genomicSimulationC