Species composition, distribution, and ecological preferences of the ticks of grazing sheep in north-central Spain

Abstract

The distribution and ecological preferences of tick (Acari: Ixodidae) parasites of grazing sheep in the region of Aragón (north-central Spain) were surveyed on flocks four times a year and mapped into a 5 x 5 km grid. Nine tick species were found. These were species of the Rhipicephalus sanguineus group (about 95% of them Rhipicephalus turanicus Pomerantsev, in 91% of cells of the grid), Rhipicephalus bursa Canestrini & Fanzago (79% of cells), Dermacentor marginatus (Sulzer) (58% of cells), Haemaphysalis punctata Canestrini & Fanzago (74% of cells) and Ixodes ricinus (Linnaeus) 14% of cells. Other species weakly represented in the surveys were Dermacentor reticulatus (Fabricius), Haemaphysalis sulcata Canestrini & Fanzago and Hyalomma m. marginatum Koch. Data on temperature, Normalized Difference Vegetation index (NDVI), topography, vegetation categories and plant productivity were used to build models of distribution and abundance of D. marginatus, H. punctata, R. bursa and R. turanicus. The occurrence models largely incorporated climatic variables and had good discrimination ability (P < 0.0001 for every modelled species, correct classification rate or sensitivity within 0.89 and 0.99), whereas the abundance models had a lower explanatory power. These models are relevant in the understanding of the variables composing the main distribution patterns, but they are unable adequately to predict the density. Abundance models produce good predictions in cells with low tick density, whereas poor correlation is observed in sites with high tick abundance. Several causes may be responsible for this low predictive power of the abundance models. Model output might be sensible to host density, to local farming practices, or to the size of the grid used to refer the results of the survey. In the latter case, small patches may support locally important populations of ticks, influencing largely the results of the survey. These patches of particular abiotic conditions, or supporting large host densities, may have been undetected at the resolution of the survey, thus obscuring the impact of the predictive variables.