Benefits and Drawbacks of Citizen Science to Complement Traditional Data Gathering Approaches for Medically Important Hard Ticks (Acari: Ixodidae) in the United States

Eisen, Lars; Eisen, Rebecca J.

Benefits and Drawbacks of Citizen Science to Complement Traditional Data Gathering Approaches for Medically Important Hard Ticks (Acari: Ixodidae) in the United States

2020

Eisen, Lars | Eisen, Rebecca J.

Tick-borne diseases are increasing in North America. Knowledge of which tick species and associated human pathogens are present locally can inform the public and medical community about the acarological risk for tick bites and tick-borne infections. Citizen science (also called community-based monitoring, volunteer monitoring, or participatory science) is emerging as a potential approach to complement traditional tick record data gathering where all aspects of the work is done by researchers or public health professionals. One key question is how citizen science can best be used to generate high-quality data to fill knowledge gaps that are difficult to address using traditional data gathering approaches. Citizen science is particularly useful to generate information on human–tick encounters and may also contribute to geographical tick records to help define species distributions across large areas. Previous citizen science projects have utilized three distinct tick record data gathering methods including submission of: 1) physical tick specimens for identification by professional entomologists, 2) digital images of ticks for identification by professional entomologists, and 3) data where the tick species and life stage were identified by the citizen scientist. We explore the benefits and drawbacks of citizen science, relative to the traditional scientific approach, to generate data on tick records, with special emphasis on data quality for species identification and tick encounter locations. We recognize the value of citizen science to tick research but caution that the generated information must be interpreted cautiously with data quality limitations firmly in mind to avoid misleading conclusions.

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