Spatial multi-criteria decision analysis for modelling suitable habitats of Ornithodoros soft ticks in the Western Palearctic region

Abstract

Ticks are economically and medically important ectoparasites due to the injuries inflicted through their bite, and their ability to transmit pathogens to humans, livestock, and wildlife. Whereas hard ticks have been intensively studied, little is known about soft ticks, even though they can also transmit pathogens, including African Swine Fever Virus (ASFV) affecting domestic and wild suids or Borrelia bacteria causing tick-borne relapsing fever (TBRF) in humans. We thus developed a regional model to identify suitable spatial areas for a community of nine Ornithodoros tick species (O. erraticus, O. sonrai, O. alactagalis, O. nereensis, O. tholozani, O. papillipes, O. tartakovskyi, O. asperus, O. verrucosus), which may be of medical and veterinary importance in the Western Palearctic region. Multi-Criteria Decision Analysis was used due to the relative scarcity of high-quality occurrence data. After an in-depth literature review on the ecological requirements of the selected tick community, five climate-related factors appeared critical for feeding activity and tick development: (i) a spring temperature exceeding 10°C to induce the end of winter soft tick quiescent period, (ii) a three-months summer temperature above 20°C to allow tick physiological activities, (iii) annual precipitation ranging from 60mm to 750mm and, in very arid areas, (iv) dry seasons interrupted by small rain showers to maintain minimum moisture inside their habitat along the year or (v) residual water provided by perennial rivers near habitats. We deliberately chose not to include biological factors such as host availability or vegetation patterns. A sensitivity analysis was done by performing multiple runs of the model altering the environmental variables, their suitability function, and their attributed weights. To validate the models, we used 355 occurrence data points, complemented by random points within sampled ecoregions. All models indicated suitable areas in the Mediterranean Basin and semi-desert areas in South-West and Central Asia. Most variability between models was observed along northern and southern edges of highly suitable areas. The predictions featured a relatively good accuracy with an average Area Under Curve (AUC) of 0.779. These first models provide a useful tool for estimating the global distribution of Ornithodoros ticks and targeting their surveillance in the Western Palearctic region.