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Background Ticks are the primary vectors of numerous zoonotic pathogens, transmitting more pathogens than any other blood-feeding arthropod. In the northern hemisphere, tick-borne disease cases in humans, such as Lyme borreliosis and tick-borne encephalitis, have risen in recent years, and are a significant burden on public healthcare systems. The spread of these diseases is further reinforced by climate change, which leads to expanding tick habitats. Switzerland is among the countries in which tick-borne diseases are a major public health concern, with increasing incidence rates reported in recent years. Methods In response to these challenges, the “Tick Prevention” app was developed by the Zurich University of Applied Sciences and operated by A&K Strategy Ltd. in Switzerland. The app allows for the collection of large amounts of data on tick attachment to humans through a citizen science approach. In this study, citizen science data were utilized to map tick attachment to humans in Switzerland at a 100 m spatial resolution, on a monthly basis, for the years 2015 to 2021. The maps were created using a state-of-the-art modeling approach with the software extension spatialMaxent, which accounts for spatial autocorrelation when creating Maxent models. Results Our results consist of 84 maps displaying the risk of tick attachments to humans in Switzerland, with the model showing good overall performance, with median AUC ROC values ranging from 0.82 in 2018 to 0.92 in 2017 and 2021 and convincing spatial distribution, verified by tick experts for Switzerland. Our study reveals that tick attachment to humans is particularly high at the edges of settlement areas, especially in sparsely built-up suburban regions with green spaces, while it is lower in densely urbanized areas. Additionally, forested areas near cities also show increased risk levels. Conclusions This mapping aims to guide public health interventions to reduce human exposure to ticks and to inform the resource planning of healthcare facilities. Our findings suggest that citizen science data can be valuable for modeling and mapping tick attachment risk, indicating the potential of citizen science data for use in epidemiological surveillance and public healthcare planning. Graphical Abstract

In response to the pressing challenges of the ongoing biodiversity crisis, the protection of endangered species and their habitats, as well as the monitoring of invasive species are crucial. Habitat suitability modeling (HSM) is often treated as the silver bullet to address these challenges, commonly relying on generic variables sourced from widely available datasets. However, for species with high habitat requirements, or for modeling the suitability of habitats within the geographic range of a species, variables at a coarse level of detail may fall short. Consequently, there is potential value in considering the incorporation of more targeted data, which may extend beyond readily available land cover and climate datasets. In this study, we investigate the impact of incorporating targeted land cover variables (specifically tree species composition) and vertical structure information (derived from LiDAR data) on HSM outcomes for three forest specialist bat species (Barbastella barbastellus, Myotis bechsteinii, and Plecotus auritus) in Rhineland‐Palatinate, Germany, compared to commonly utilized environmental variables, such as generic land‐cover classifications (e.g., Corine Land Cover) and climate variables (e.g., Bioclim). The integration of targeted variables enhanced the performance of habitat suitability models for all three bat species. Furthermore, our results showed a high difference in the distribution maps that resulted from using different levels of detail in environmental variables. This underscores the importance of making the effort to generate the appropriate variables, rather than simply relying on commonly used ones, and the necessity of exercising caution when using habitat models as a tool to inform conservation strategies and spatial planning efforts. Traditional habitat suitability models, relying on variables with coarse level of detail, may inadequately represent the complexities of specialized species. This study shows that habitat suitability modeling for forest specialist bat species can be improved by integrating more targeted data, such as tree species classification map and LiDAR‐derived vertical structure information. Furthermore, the results show a high difference in resulting distribution maps when using different levels of detail in environmental predictors, which underscores the importance of exercising caution when using habitat models as a tool to inform conservation strategies and spatial planning efforts.