A stage-based life cycle implementation for individual-based population viability analyses of grey wolves (Canis lupus) in Europe

Population viability analyses (PVA) are important tools for decision-making and planning of adaptive wildlife management actions. While earlier approaches on individual based PVAs have often been age-based, analyses of species with strong social structure might benefit from a stage-based model approach. In this study, we designed an individual-based and stage-based PVA within the software Vortex. As a case study, we applied our model to the German part of the European wolf population, making use of comprehensive data sets originating from the German monitoring regime including individual genotypes. Genetic diversity and inbreeding were important considerations in our analysis, as they could greatly impact population dynamics. We aimed to assess the population's trajectory, extinction risk, and genetic integrity under different scenarios while analyzing factors that could affect its survival. We found that mortality rates at different life cycle stages had varying effects on population growth. Higher mortality rates among pups and dispersers led to negative growth and increased the risk of extinction. In comparison, higher mortality rates among yearlings and subdominant wolves still resulted in positive growth but at a slower rate. Mortality among the breeding individuals within the pack (territorials) had the highest impact on population trajectory. Although the German wolves represent a rather recently founded population, our simulations predicted generally good genetic diversity as long as the population was not held at artificially low numbers.