For the people by the people: Citizen science web interface for real‐time monitoring of tick risk areas in Finland

• Published in: Ecological Solutions and Evidence
• Main Authors: Jani J. Sormunen, Niko Kulha, Theophilus Y. Alale, Tero Klemola, Ilari E. Sääksjärvi, Eero J. Vesterinen
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2023
• Subjects: citizen science; crowdsourcing; public health; sampling bias; tick risk areas; tick‐borne diseases; Environmental sciences; GE1-350; Ecology; QH540-549.5; Northern Finland
• Online Access: https://doi.org/10.1002/2688-8319.12294; https://doaj.org/article/5302ae92aaeb4d7da7aab67ae966142a

Abstract Ticks and tick‐borne diseases (TBDs) form a significant and growing threat to human health and well‐being in Europe, with increasing numbers of tick‐borne encephalitis (TBE) and Lyme borreliosis cases being reported during the past few decades. Increasing knowledge of tick risk areas and seasonal activity remains the primary method for preventing TBDs. Crowdsourcing provides the best alternative for rapidly obtaining data on tick occurrence on a national level. In order to produce and share up‐to‐date data of tick risk areas in Finland, an online platform, Punkkilive (www.punkkilive.fi/en), was launched April 2021. On the website, users can submit and browse tick observations, report tick numbers and hosts, and upload pictures of ticks. Here, we looked at trends in the crowdsourced data from 2021, assessed the effect of local tick species on seasonality of observations and examined sampling bias in the data. The high number of tick observations (n = 78,837) highlights that there was demand for such a service. Approximately 97% of 5573 uploaded pictures represented ticks. Seasonal patterns of tick observations varied across Finland, highlighting variability in the risk associated with the two human‐biting tick species Ixodes ricinus and I. persulcatus, the latter having a shorter, unimodal activity peak in late spring–early summer. Tick numbers were low and the proportion of new sightings high in northern Finland, as may be expected near the latitudinal distribution limits of both species. While the number of inhabitants generally explained the number of tick observations well, geographically weighted regression models also identified areas that deviated from this general pattern. This study offers a prime example of how crowdsourcing can be applied to track vectors of zoonotic diseases, to the benefit of both researchers and the public. Areas with more or less observations than predicted based on number of inhabitants were revealed, wherein more specific analyses may reveal factors contributing to lower ...