Biology subjects

Hayman, D.

Publications and source records attributed to Hayman, D..

3 recordsLinked to original sources

bioRxiv2023

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 100 times over 100 years for 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.

ecology↗

bioRxiv2023

Mapping threatened Thai bovids provides opportunities for improved conservation outcomes in Asia

Wild bovids provide important ecosystem functions throughout their ranges. Five wild bovids remain in Thailand: gaur (Bos gaurus), banteng (Bos javanicus), wild water buffalo (Bubalus arnee), mainland serow (Capricornis sumatraensis) and Chinese goral (Naemorhedus griseus). However, their populations and habitats have declined substantially and become fragmented. Here, we identify potentially suitable habitat for these threatened bovids using ecological niche models and quantify how much suitable area remains within protected areas. We combined species occurrence data with environmental variables and used spatially-restricted Biotic-Abiotic-Mobility frameworks with species-specific and single large accessible areas. We used ensembles from eight algorithms for generating maps and out-of-sample predictions to validate model performance against new data. Gaur, banteng, and buffalo models performed well throughout the entire distribution ([≥]62%) and in Thailand ([≥]80%). Mainland serow and Chinese goral performed poorly for the entire distribution and in Thailand, though a 5 km movement buffer markedly improved model performance for serow. Particularly large suitable areas were in Thailand and India for gaur, Cambodia and Thailand for banteng, and India for buffalo. Over 50% of overall suitable habitat is located outside protected areas, with just 9% for buffalo in Thai protected areas, highlighting area for potential habitat management and conflict mitigation.

ecology↗

bioRxiv2019

Investigation of the validity of two Bayesian ancestral state reconstruction models for estimating Salmonella transmission during outbreaks

Ancestral state reconstruction models use genetic data to characterize a group of organisms common ancestor. These models have been applied to salmonellosis outbreaks to estimate the number of transmissions between different animal species that share similar geographical locations, with animal host as the state. However, as far as we are aware, no studies have validated these models for outbreak analysis. In this study, salmonellosis outbreaks were simulated using a stochastic Susceptible-Infected-Recovered model, and the host population and transmission parameters of these simulated outbreaks were estimated using Bayesian ancestral state reconstruction models (discrete trait analysis (DTA) and structured coalescent (SC)). These models were unable to accurately estimate the number of transmissions between the host populations or the amount of time spent in each host population. The DTA model was inaccurate because it assumed the number of isolates sampled from each host population was proportional to the number of individuals infected within each host population. The SC model was inaccurate possibly because it assumed that each host populations effective population size was constant over the course of the simulated outbreaks. This study highlights the need for phylodynamic models that can take into consideration factors that influence the characteristics and behavior of outbreaks, e.g. changing effective population sizes, variation in infectious periods, intra-population transmissions, and disproportionate sampling of infected individuals.

epidemiology↗