Impact of infectious diseases on wild bovidae populations in Thailand: Insights from population modelling and disease dynamics

The wildlife and livestock interface is vital for wildlife conservation and habitat management. Infectious diseases maintained by domestic species may impact threatened species such as Asian bovids, as they share natural resources and habitats. To predict the population impact of infectious diseases with different traits, we used stochastic mathematical models to simulate the population dynamics over 100 years for 100 times a model gaur (Bos gaurus) population with and without disease. We simulated repeated introductions from a reservoir, such as domestic cattle. We selected six bovine infectious diseases; anthrax, bovine tuberculosis, hemorrhagic septicaemia, lumpy skin disease, foot and mouth disease and brucellosis, all of which have caused outbreaks in wildlife populations. From a starting population of 300, the disease-free population increased by an average of 228% over 100 years. Brucellosis with frequency-dependent transmission showed the highest average population declines (-97%), with population extinction occurring 16% of the time. Foot and mouth disease with frequency-dependent transmission showed the lowest impact, with an average population increase of 200%. Overall, acute infections with very high or low fatality had the lowest impact, whereas chronic infections produced the greatest population decline. These results may help disease management and surveillance strategies support wildlife conservation.

The R codes were developed to simulate the population dynamics of the gaur with and without disease infection. The disease and animal biological parameters were collected from peer-reviewed literature. We built the model structures from basic SIR models, with compartments varying based on disease traits. In the scripts, we used the Poisson distribution to calculate the probability of events and then calculated the average population changes (see equations 4 and 5 in the manuscript) to identify which diseases have the most impact on population changes. The population change results can be found in the supplementary file (ndiff_mean_all.csv).