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We assess the identification accuracy of ‘research grade’ observations of lichens posted on the online platform iNaturalist. Our results show that these observations are frequently misidentified or lack the necessary chemical and (or) microscopic information for accurate identification. Lichens are a taxonomically difficult group, but they are ubiquitous and eye-catching and are regularly the subject of observations posted on iNaturalist. Therefore, we provide best practice recommendations for posting lichen observations and commenting on observations. Data from iNaturalist are a valuable tool for understanding and managing biodiversity, particularly at this crucial time when large scale biodiversity decline is occurring globally. However, the data must be accurate for them to effectively support biodiversity conservation efforts. Our recommendations are also applicable to other taxonomically difficult taxa.
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Assessing Identification Accuracy of Research Grade iNaturalist Observations in Lichens and other Taxonomically Difficult Organisms
Community science-generated biodiversity data can provide essential information for understanding species distributions, behaviors and conservation statuses. However, their utility can be limited due to high uncertainty and variability in quality, especially for small taxonomically difficult organisms like fungi and insects. One important set of community-generated data that are increasingly used by scientists are Research Grade (RG) iNaturalist observations. These observations are aggregated into the Global Biodiversity Information Facility database. Here we assessed the accuracy of RG lichen observations in iNaturalist. Lichens are mutualistic symbioses formed between fungi and a photosynthetic partner, either algae or cyanobacteria that occur in every terrestrial ecosystem on the planet (Brodo et al. 2001). They are sensitive indicators of environmental health, especially air quality, and provide essental food and nesting material for animals, along with performing many other ecosystem services (Allen and Lendemer 2021, Brodo et al. 2001, Nimis et al. 2002). We examined hundreds of observations and determined if the identification was correct, if it was not possible to identify the observation given the data provided, or if the identification was incorrect. Identification accuracy of selected species varied widely, from zero observations with enough information for correct identification (e.g., Rhizocarpon geographicum and Cladonia chlorophaea ) to 100% correct identifications (e.g., Cetradonia linearis and Physconia subpallida , McMullin and Allen 2022). Most frequently, species that require microscopic examination or chemical tests for accurate identification were unable to be verified versus those that require only macromorphology. We provide a series of suggestions for best practices to improve the quality of RG observations and thus the utility of community-generated observation data for taxonomically difficult organisms.
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- Award ID(s):
- 2115191
- PAR ID:
- 10436322
- Date Published:
- Journal Name:
- Biodiversity Information Science and Standards
- Volume:
- 6
- ISSN:
- 2535-0897
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3897/biss.6.95689
