Estimating disease vector population size from citizen science data

J R Soc Interface. 2021 Nov;18(184):20210610. doi: 10.1098/rsif.2021.0610. Epub 2021 Nov 24.


Citizen science projects have the potential to address hypotheses requiring extremely large datasets that cannot be collected with the financial and labour constraints of most scientific projects. Data collection by the general public could expand the scope of scientific enquiry if these data accurately capture the system under study. However, data collection inconsistencies by the untrained public may result in biased datasets that do not accurately represent the natural world. In this paper, we harness the availability of scientific and public datasets of the Lyme disease tick vector to identify and account for biases in citizen science tick collections. Estimates of tick abundance from the citizen science dataset correspond moderately with estimates from direct surveillance but exhibit consistent biases. These biases can be mitigated by including factors that may impact collector participation or effort in statistical models, which, in turn, result in more accurate estimates of tick population sizes. Accounting for collection biases within large-scale, public participation datasets could update species abundance maps and facilitate using the wealth of citizen science data to answer scientific questions at scales that are not feasible with traditional datasets.

Keywords: Lyme disease vector; citizen science; community science; population trends.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Citizen Science*
  • Disease Vectors
  • Population Density

Associated data

  • figshare/10.6084/m9.figshare.c.5712952
  • Dryad/10.5061/dryad.v9s4mw6vq