This paper describes a project that involved the public collecting data for use in scientific investigations. The study found that citizen science projects with few barriers to entry can gather viable datasets for scoping broad trends, providing that the projects are carefully designed and analysed to ensure data quality.
Citizen science projects can gather datasets with observation counts and spatio-temporal coverage far in excess of what can easily be achieved using only professional scientists. However, there exists a potential trade-off between the number of participants and the quality of data gathered. The Bugs Count survey, a citizen science project run by the Open Air Laboratories (OPAL) network, had thousands of participants because of its few barriers to taking part, allowing participation by anyone in England with access to any area of outdoor space. It was designed to scope for both the effects of variation in local habitat and urbanisation on broad taxonomic groups of invertebrates, and the responses of six target ‘Species Quest’ species (Adalia bipunctata, Ocypus olens, Aglais urticae, Palomena prasina, Limax maximus, and Bombus hypnorum) to urbanisation. Participants were asked to search for invertebrates in three areas: ‘soft ground surfaces’, ‘human-made hard surfaces’, and ‘plants’ for 15 min per search. Participants recorded counts of taxa found and a range of environmental information about the survey area. Data samples were weighted according to identification experience and participant age and analysed using canonical correspondence analysis, and tests of observation homogeneity.
Species Quest species showed species-specific relationships with urbanisation, but broad taxonomic groups did not show significant relationships with urbanisation. The latter were instead influenced by habitat type and microhabitat availability. Citizen science projects with few barriers to entry are therefore a suitable approach to gather viable datasets for scoping broad trends, providing that the projects are carefully designed and analysed to ensure data quality.
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