Abstract
Interactions between individuals from different species are highly relevant in the transmission of pathogens in multi-host systems. In this regard, we can distinguish between direct interactions, which involve contact, and indirect interactions, mediated by the environment or vectors. Various technologies have been employed to study animal interactions, such as proximity loggers, GPS tracking devices and/or camera traps. However, the subsequent analysis of these data to estimate risk transmission is often reduced to interaction rates and the probability of transmission given interaction. Thereby, a continuous spatio-temporal process is simplified and discretized, overlooking the influence of interaction characteristics and the pathogen's relationship with the host and the environment.
In this study, we applied a novel movement-based model that considers the interaction duration -by means of the number of GPS locations where the interaction occurs and the time interval between them-, the temporal window between consecutive locations of different individuals, a continuous pathogen decay in the environment until its survival period in the environment is fulfilled, and the excretion and acquisition rates of each host species. Therefore, each interaction has unique characteristics, and consequently, a unique transmission risk. Using animal tuberculosis (Mycobacterium tuberculosis complex) as disease example, we analyzed GPS data from 54 collared wild (15 red deer [Cervus elaphus], 7 fallow deer [Dama dama] and 12 wild boar [Sus scrofa]) and domestic animals (8 pig and 12 cattle) in two management systems in Spain where this disease is prevalent: a national park and an area with extensive free-ranging pig and cattle farms. We considered an interaction to occur within the GPS error margin (12 or 26 meters, depending on the GPS device) and a pathogen survival period in the environment of 4 to 12 days, depending on the area and season. We compared the results with those obtained for the same data in previous studies based just on the quantification of interaction rates. As a result, wildlife species identified as most involved in pathogen transmission to livestock differed between the two approaches, particularly in the national park. Among the three considered wild species, fallow deer had the highest interaction rate with livestock according to the traditional approach, but with the new approach, wild boar took the lead as posing the highest transmission risk to livestock. These results indicate that failing in considering the specific conditions of each interaction can imply a misidentification of key species in pathogen transmission. In addition, we found that wildlife poses a greater risk to livestock than livestock to wildlife in both studied systems, emphasizing that disease control measures should be implemented in wild populations in addition to those already established for livestock.