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. 2021 Sep 24;11:19073. doi: 10.1038/s41598-021-98584-7

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© The Author(s) 2021

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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A conceptual figure depicting the methods used in our analysis. We used the residual from the relationship between the number of log10-transformed eBird observations (i.e., semi-structured citizen science observations) and log10-transformed iNaturalist observations (i.e., unstructured citizen science observations) to quantify the over- or under-representation of a species in unstructured citizen science data. We predicted that species which were over-represented in unstructured iNaturalist data would be larger in size, occur more frequently in large flocks, be brighter in color, and be categorized as Least Concern IUCN status (a proxy for commonness).