Computer simulations are a very useful and widely used tool in conservation biology as they are able to provide useful reference points for decision-making in population management. This is why we developed an individual-based stochastic model of lynx population development, aimed to predict the long-term viability of the population in Dinaric Mountains and South-eastern Alps under different management scenarios. The emphasis of the model is on genetics, since genetic erosion is the main threat to survival of the Dinaric lynx population, which is suffering from high inbreeding and loss of genetic diversity due to the small number of founder animals and long-term isolation.
The translocations done within the LIFE Lynx project are having a great impact on genetic parameters and seem to be considerably increasing the probability of the population’s survival for at least 55 more years. However, in absence of connection with other populations, continued genetic management is crucial. Based on modelling results, we proposed six possible viable translocation strategies that vary in the intervals between translocations from 3 to 25 years. All these strategies have different pros and cons from the ecological, genetic, management, and cost-effectiveness points of view.
In general, 10-20-year intervals between translocations of 5-10 animals per action demonstrate better outcomes than shorter or longer intervals, and such a timing allows us to detect the response of the recipient population to the reintroductions via monitoring while keeping the genetic parameters at an acceptable level. However, short (3-5 year) intervals allow minimization of inbreeding coefficient fluctuations, and regular translocations of 1-3 animals may be easier to organise. We would like to stress that any future management actions should be accompanied by continuous monitoring of genetic and demographic status, which would allow modifying the models and bringing them closer to reality, contributing to long-term survival of the Dinaric-SE Alpine lynx population.