Incorporating drivers of reproductive success improves population viability analysis

Population viability analysis (PVA) utilizes simulation models to project the genetic and demographic trajectories of populations over time. One benefit to using PVA to assist in ex situ population planning is the ability to compare outcomes from multiple management scenarios. Reproductive viability analysis (RVA), used to identify biological and reproductive characteristics of animals in breeding pairs that correlate with successful reproduction, is also beneficial, informing population managers on best practices for population planning. Our objectives were to: (1) compare genetic and demographic predictions for the managed North American ex situ fennec fox population from two PVA programs: Vortex and ZooRisk; (2) show how RVA results can be incorporated into PVAs in Vortex and how that affects PVA projections; and (3) perform a sensitivity analysis to investigate how uncertainty in the influence of factors on the reproductive success of fennec fox pairs affects variation in population projections. In this study, the relative and average genetic and demographic predictions were generally the same across both software programs when given similar inputs. The inclusion of factors that influence breeding success (RVA) in the PVA models resulted in a lower probability of extinction, less inbreeding accumulation, and slower loss of genetic diversity, due to an overall higher rate of reproductive success. The uncertainty in the negative effects of increasing female and male age, and the positive effect of female parity on reproductive success had the greatest effect on projected genetic diversity and inbreeding. Our study demonstrates that Vortex is capable of projecting the viability of ex situ populations with flexibility, and has the ability to incorporate complexity and uncertainty into population parameters. It also reveals that adjusting ex situ population management, more specifically the consideration of factors affecting reproductive success to create breeding pairs with a higher probability of success, will improve population sustainability.

Automated summary

Population viability analysis (PVA) uses simulation models to predict genetic and demographic changes in populations, while reproductive viability analysis (RVA) improves breeding practices. Comparisons between two PVA programs and incorporating RVA results showed improved genetic diversity and reduced extinction risk. Adjusting ex situ population management to consider factors affecting reproductive success enhances population sustainability.