New locality and host records of mites and ticks (Chelicerata: Acari) parasitizing lizards of Morocco

Abstract

The aim of this study was to investigate the parasitic relationships of mites and ticks infesting lizards in Morocco and their determinant factors. A total of 1498 lizards representing 27 species, 8 families and 18 genera were randomly captured and examined in their natural habitats. The overall prevalence of ectoparasite infestation was 22.4%, with a mean intensity of 14.5 (95% CI: 12.2 – 16.8). Four families of Acari (Chelicerata) were identified: Ixodidae, Macronyssidae, Pterygosomatidae, and Trombiculidae, with eleven new host records and new data on geographical distributions. Trombiculid mites were the most prevalent (17.2%), followed by the Pterygosomatidae (4.1%), the Ixodidae (1.5%), and the Macronyssidae (0.4%). Nevertheless, Macronyssids showed the highest mean intensity 31.7 (14.3 – 54.2), followed by the Pterygosomatidae 22.0 (16.2 – 30.5), the Trombiculidae 12.7 (10.9 – 14.8), and the Ixodidae 1.7 (1.3 – 2.2). In total, nine species of parasites were found, and co-infestations were recorded on three lizard species. Ixodid prevalence and intensity were significantly related to altitude and host species, whereas Macronyssid prevalence and intensity were associated with host age and altitude. Ectoparasite intensity was associated with lizard sex for all mite families, with males carrying heavier loads than females, while prevalence was related to sex only for Pterygosomatid and Trombiculid mites. Season was important for Macronyssid and Trombiculid infestation intensity. Finally, host characteristics (species, size, age class), along with altitude, were determinant factors for infestation prevalence and intensities in Pterygosomatid and Trombiculid mites. The Ixodidae and Macronyssidae were mainly attached to the ventral side of hosts, whereas the Trombiculidae and Pterygosomatidae were attached to different parts of the host body with aggregations in skin folds. This updated information should prove useful for modeling biodiversity and predicting zoonotic disease outbreaks.