Logistical and preference bias in participatory science butterfly data

Author:

Goldstein Benjamin R12,Stoudt Sara3,Lewthwaite Jayme MM4,Shirey Vaughn45,Mendoza Eros4,Guzman Laura Melissa4

Affiliation:

1. Department of Forestry and Environmental Resources North Carolina State University Raleigh NC

2. Department of Environmental Science, Policy, and Management University of California–Berkeley Berkeley CA

3. Department of Mathematics Bucknell University Lewisburg PA

4. Marine and Environmental Biology Section, Department of Biology University of Southern California Los Angeles CA

5. Department of Biology Georgetown University Washington DC

Abstract

The volume of and interest in unstructured participatory science data has increased dramatically in recent years. However, unstructured participatory science data contain taxonomic biases—encounters with some species are more likely to be reported than encounters with others. Taxonomic biases are driven by human preferences for different species and by logistical factors that make observing certain species challenging. We investigated taxonomic bias in reports of butterflies by characterizing differences between a dedicated participatory semi‐structured dataset, eButterfly, and a popular unstructured dataset, iNaturalist, in spatiotemporally explicit models. Across 194 butterfly species, we found that 53 species were overreported and 34 species were underreported in opportunistic data. Ease of identification and feature diversity were significantly associated with overreporting in opportunistic sampling, and strong patterns in overreporting by family were also detected. Quantifying taxonomic biases not only helps us understand how humans engage with nature but also is necessary to generate robust inference from unstructured participatory data.

Publisher

Wiley

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