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Ecological Predictors of Zoonotic Vector Status Among Dermacentor Ticks (Acari: Ixodidae): A Trait-Based Approach - PubMed

  • ️Sat Jan 01 2022

Ecological Predictors of Zoonotic Vector Status Among Dermacentor Ticks (Acari: Ixodidae): A Trait-Based Approach

Jessica T Martin et al. J Med Entomol. 2022.

Abstract

Increasing incidence of tick-borne human diseases and geographic range expansion of tick vectors elevates the importance of research on characteristics of tick species that transmit pathogens. Despite their global distribution and role as vectors of pathogens such as Rickettsia spp., ticks in the genus Dermacentor Koch, 1844 (Acari: Ixodidae) have recently received less attention than ticks in the genus Ixodes Latreille, 1795 (Acari: Ixodidae). To address this knowledge gap, we compiled an extensive database of Dermacentor tick traits, including morphological characteristics, host range, and geographic distribution. Zoonotic vector status was determined by compiling information about zoonotic pathogens found in Dermacentor species derived from primary literature and data repositories. We trained a machine learning algorithm on this data set to assess which traits were the most important predictors of zoonotic vector status. Our model successfully classified vector species with ~84% accuracy (mean AUC) and identified two additional Dermacentor species as potential zoonotic vectors. Our results suggest that Dermacentor species that are most likely to be zoonotic vectors are broad ranging, both in terms of the range of hosts they infest and the range of ecoregions across which they are found, and also tend to have large hypostomes and be small-bodied as immature ticks. Beyond the patterns we observed, high spatial and species-level resolution of this new, synthetic dataset has the potential to support future analyses of public health relevance, including species distribution modeling and predictive analytics, to draw attention to emerging or newly identified Dermacentor species that warrant closer monitoring for zoonotic pathogens.

Keywords: Dermacentor; host range; machine learning; tick-borne disease.

© The Author(s) 2022. Published by Oxford University Press on behalf of Entomological Society of America.

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Figures

Fig. 1.
Fig. 1.

Mean relative influence of the top predictor variables. Relative influence indicates the importance of each variable in reducing prediction error. Error lines represent ± 1.5 × IQR, where IQR is the interquartile range between the first and third quartiles, generated from 50 bootstrap runs of the generalized boosted regression model. Morphological measurements are given in millimeters. Predictor variables are defined in Supp. Table S1 (online only).

Fig. 2.
Fig. 2.

Partial dependence plots for a selection of top predictor variables from the generalized boosted regression model used to predict the vector status of Dermacentor ticks. The black line represents the average marginal effect (y-axis, left) of a given trait (x-axis) on vector status after accounting for the average effect of all other predictor variables in the model. Gray bands represent 95% CI. The histograms show the relative frequency (y-axis, right) of tick species with a given value of each trait. Morphological measurements are given in millimeters.

Fig. 3.
Fig. 3.

Global distribution of Dermacentor tick species.

Fig. 4.
Fig. 4.

Species richness of Dermacentor ticks in North America (A), Europe (B), and Asia (C). Species richness ranges from a single species (lighter colors) to eight species (darker colors).

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