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Background  Knowledge of  Ixodes ricinus  tick distribution is critical for surveillance and risk management of transmissible 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  distribution (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, temperature, 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 addressing 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. Further development of tools to better assess uncertainties in the models and predictions are necessary to allow more informed interpretation of distribution maps.

Using imperfect data in predictive mapping of vectors: a regional example of Ixodes ricinus distribution