An Individual-Based Model of the Population Dynamics of the Arctic Fox (Vulpes lagopus semenovi) on Mednyi Island, Commander Islands, North Pacific

Abstract

We have developed a spatially explicit individual-based model that imitates the population dynamics of the Arctic fox on Mednyi Island. In the Red Data Book of the Russian Federation, the Mednyi Arctic fox is listed as an endangered species. The model developed is based on data collected over 19 years of fieldwork. Annual marking of cubs in the study area allowed us to identify up to 80% of animals individually and to collect their life-history data. As a result, we identified the mortality rates of males and females of all age groups, the probabilities of breeding, litter sizes, the sex ratios of animals in different age groups, social structures, dispersion distances, and the patterns of making decisions in the selection of social partners and habitat patches. The model is spatially explicit, i.e., the heterogeneity of the habitat patches is defined in explicit form. The model works with a time step of one year. In simulations, demographic parameters such as population dynamics, population age structure, sex ratio in different age groups, and the structure and size of families conformed to the empirical data. An analysis of the model sensitivity to variations in mortality rates in different age groups showed that the sensitivity to shifts in cub mortality is much higher compared to adults of all age groups. Through increasing the cub mortality rate to 95% over a period of one to five years, we simulated the effect of the otodectic mange epizootic, which was observed in a real population of the Mednyi Arctic fox. The population recovery time after the end of the impact in the simulation was significantly longer compared to field data. We suggest that, in reality, with a low population size, the productivity of the population increases. This feedback that accelerates population recovery has not yet been introduced into the model. In conclusion, we discuss advantages of the individual-based modeling based on long-term field studies.