| Abstract |
iNaturalist and other technology-enabled biodiversity recording applications allow individuals to easily capture and share biodiversity data. Built in machine-learning algorithms facilitate initial identification of the observed taxon, which is subsequently refined, corrected, or validated by the community of users. With hundreds of millions of records in iNaturalist alone, there is enormous potential to use this data for understanding where species occur in space and time. Insects (including species that can act as pests) are a commonly observed taxon within these databases. Numerous end-users are finding ways to effectively use this data to study and better understand pest insects. Here, we share three case studies that demonstrate the power of community-science data from iNaturalist in advancing science. We argue that in order to maintain a sustainable system of community science to serve these purposes, the following conditions must be met: excellent user-experience of the technology must be upheld, a welcoming and supportive atmosphere is maintained within the community of users, and that the efforts of contributors are formally recognized.
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© The Authors 2024
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