Publisher
BMC
Abstract
Background: Knowledge of Ixodes ricinus tick distribution is critical for surveillance and risk management of trans-
missible tick-borne diseases such as Lyme borreliosis. However, as the ecology of I. ricinus is complex, and robust long-
term geographically extensive distribution tick data are limited, mapping often relies on datasets collected for other
purposes. We compared the modelled distributions derived from three datasets with information on I. ricinus distribu-
tion (quantitative I. ricinus count data from scientific surveys; I. ricinus presence-only data from public submissions;
and a combined I. ricinus dataset from multiple sources) to assess which could be reliably used to inform Public Health
strategy. The outputs also illustrate the strengths and limitations of these three types of data, which are commonly
used in mapping tick distributions.
Methods: Using the Integrated Nested Laplace algorithm we predicted I. ricinus abundance and presence-absence
in Scotland and tested the robustness of the predictions, accounting for errors and uncertainty.
Results: All models fitted the data well and the covariate predictors for I. ricinus distribution, i.e. deer presence, tem-
perature, habitat, index of vegetation, were as expected. Differences in the spatial trend of I. ricinus distribution were
evident between the three predictive maps. Uncertainties in the spatial models resulted from inherent characteristics
of the datasets, particularly the number of data points, and coverage over the covariate range used in making the
predictions.
Conclusions: Quantitative I. ricinus data from scientific surveys are usually considered to be gold standard data and
we recommend their use where high data coverage can be achieved. However in this study their value was limited by
poor data coverage. Combined datasets with I. ricinus distribution data from multiple sources are valuable in address-
ing issues of low coverage and this dataset produced the most appropriate map for national scale decision-making in
Scotland. When mapping vector distributions for public-health decision making, model uncertainties and limitations
of extrapolation need to be considered; these are often not included in published vector distribution maps. Fur-
ther development of tools to better assess uncertainties in the models and predictions are necessary to allow more
informed interpretation of distribution maps.
Year
2019
Category
Refereed journal