CrowdCurio: an online crowdsourcing platform to facilitate climate change studies using herbarium specimens. Issue 1 (10th April 2017)

• Record Type: Journal Article
• Authors: Willis, Charles G.; Law, Edith; Williams, Alex C.; Franzone, Brian F.; Bernardos, Rebecca; Bruno, Lian; Hopkins, Claire; Schorn, Christian; Weber, Ella; Park, Daniel S.; Davis, Charles C.
• Is Part Of: New phytologist. Volume 215:Issue 1(2017)
• Page Start: 479
• Page End: 488
• Publication Date: 2017-04-10
• Subjects: citizen science -- flowering -- fruiting -- phenological sensitivity -- phenology -- phenophase; Botany -- Periodicals; 580
• DOI: 10.1111/nph.14535
• Languages: English
• ISSNs: 0028-646X

Abstract

Summary: Phenology is a key aspect of plant success. Recent research has demonstrated that herbarium specimens can provide important information on plant phenology. Massive digitization efforts have the potential to greatly expand herbarium‐based phenological research, but also pose a serious challenge regarding efficient data collection. Here, we introduce CrowdCurio, a crowdsourcing tool for the collection of phenological data from herbarium specimens. We test its utility by having workers collect phenological data (number of flower buds, open flowers and fruits) from specimens of two common New England (USA) species: Chelidonium majus and Vaccinium angustifolium . We assess the reliability of using nonexpert workers (i.e. Amazon Mechanical Turk) against expert workers. We also use these data to estimate the phenological sensitivity to temperature for both species across multiple phenophases. We found no difference in the data quality of nonexperts and experts. Nonexperts, however, were a more efficient way of collecting more data at lower cost. We also found that phenological sensitivity varied across both species and phenophases. Our study demonstrates the utility of CrowdCurio as a crowdsourcing tool for the collection of phenological data from herbarium specimens. Furthermore, our results highlight the insight gained from collecting large amounts of phenological data to estimate multiple phenophases.