| Abstract Detail
Biodiversity Informatics & Herbarium Digitization Ackerfield, Jennifer [1], Lichtenberger, Taran [2], Davis, Jeny [3], Paradiso, Lydia [4]. EcoFloras provide valuable insight into the use of iNaturalist data. In a time of unprecedented global biodiversity crisis, it is now more critical than ever to understand which species are most at risk of extirpation or extinction. To assess these risks, scientists must first have a baseline of biodiversity – the species present in a given area. Two types of data can be used to assess biodiversity – observational (e.g., a photograph with accompanying georeferenced locality information) or a physical specimen collected and stored in a museum or herbarium. One of the most common groups of organisms observed on iNaturalist are plants. Herbarium specimens of plants are also one of the most numerous types of preserved collections. Therefore, a wealth of plant observational and specimen data exists for biodiversity analyses. EcoFloras are an innovative model for connecting people to plant collections, nature, and urban biodiversity in meaningful ways. EcoFloras combine existing knowledge from herbaria and libraries with contemporary observations of plants and biotic interactions. Implementation of EcoFlora projects by botanical gardens in cities across the U.S. allowed us to observe several benefits and pitfalls of using iNaturalist data. We will examine the power, or lack thereof, of community scientists by comparing iNaturalist observations of different plant species to herbarium specimen records for each urban area. By sharing these examples, we hope to provide valuable insight for anyone seeking to use iNaturalist data in their own research.
1 - Denver Botanic Gardens, 909 York Street, Denver, CO, 80206, USA
2 - Chicago Botanic Garden, Budburst
3 - Desert Botanical Garden, Phoenix, AZ
4 - New York Botanical Garden, 2900 Southern Blvd, Bronx, NY, 10458, USA
Keywords:
iNaturalist
community engagement
Citizen Science
Botanic gardens.
Presentation Type: Oral Paper
Number: BI&HD II001
Abstract ID:147
Candidate for Awards:None |
