Abstract

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.

Highlights

  • The rise of public participation in data collection [1] provides unprecedented opportunities for scientific research

  • The number of ticks collected by the public in each county in New York State (NYS) is strongly correlated with the size of the tick population as determined by active surveillance

  • 0 0 10 20 30 40 50 rank from citizen science estimated tick total estimated tick total year 11 estimated tick total estimated tick total largest numbers of ticks were submitted by citizens of the counties with the largest tick populations, and few ticks were submitted by citizens of counties with smaller tick populations

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Summary

Introduction

The rise of public participation in data collection [1] provides unprecedented opportunities for scientific research. Citizen science data have monitored weather patterns and bird populations across North America for over a century [3,4], discovered new planets [5], classified galaxies [6] and crowd-sourced biodiversity observations [7,8]. These and many similar projects were made possible by the immense volumes of data collected by millions of participants [1,9]. The inability to identify and resolve citizen science data collection inconsistencies may result in inaccurate representations of the system being studied [2,10,11]

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