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Cited by 58Species Distribution and Climate ChangeResearch Data Management PracticesGeographic Information Systems Studies

A framework for contextualizing social‐ecological biases in contributory science data

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Carlen, Elizabeth J., Estien, Cesar O., Caspi, Tal, Perkins, Deja, Goldstein, Benjamin R., Kreling, Samantha E. S., Hentati, Yasmine, Williams, Tyus D. +6 more·2024·People and Nature
DOI 10.1002/pan3.10592
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Abstract

Contributory science—including citizen and community science—allows scientists to leverage participant‐generated data while providing an opportunity for engaging with local community members. Data yielded by participant‐generated biodiversity platforms allow professional scientists to answer ecological and evolutionary questions across both geographic and temporal scales, which is incredibly valuable for conservation efforts. The data reported to contributory biodiversity platforms, such as eBird and iNaturalist, can be driven by social and ecological variables, leading to biased data. Though empirical work has highlighted the biases in contributory data, little work has articulated how biases arise in contributory data and the societal consequences of these biases. We present a conceptual framework illustrating how social and ecological variables create bias in contributory science data. In this framework, we present four filters— participation , detectability , sampling and preference —that ultimately shape the type and location of contributory biodiversity data. We leverage this framework to examine data from the largest contributory science platforms—eBird and iNaturalist—in St. Louis, Missouri, the United States, and discuss the potential consequences of biased data. Lastly, we conclude by providing several recommendations for researchers and institutions to move towards a more inclusive field. With these recommendations, we provide opportunities to ameliorate biases in contributory data and an opportunity to practice equitable biodiversity conservation. Read the free Plain Language Summary for this article on the Journal blog.

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  • Individuals may be more inclined to report individuals with charismatic coloration compared to those of regular coloration when contributing to community-gathered data.

    “However, we caution the use of community-gathered data as individuals may be more inclined to report individuals with charismatic coloration compared to those of regular coloration (Husby, 2017;Zbyryt et al., 2021;Carlen et al. 2024).”

    From Of Rarity and Symbolism: Understanding Human Perceptions of Charismatic Color Morphs (2024) · cites this paper as Carlen et al. 2024

    The Figure 1 discussion says a preference filter favours colourful, charismatic species, and the St. Louis case study compares reports of colourful cardinals with reports of pigeons (page numbers unavailable). These passages support a possible reporting bias based on appearance, but they compare species, not individuals with charismatic versus regular coloration within a species. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • Biases in community-gathered data could lead to misinterpretations of the data.

    “certainly has value, biases in these observations could lead to misinterpretations of the data (Dickman, 2010;Carlen et al. 2024).”

    From Of Rarity and Symbolism: Understanding Human Perceptions of Charismatic Color Morphs (2024) · cites this paper as Carlen et al. 2024

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Authors
Carlen, E. J., Estien, C. O., Caspi, T., Perkins, D., Goldstein, B. R., Kreling, S. E. S., Hentati, Y., Williams, T. D., Stanton, L. A., Des Roches, S., Johnson, R. F., Young, A. N., Cooper, C. B., & Schell, C. J.
Affiliations
  • Washington University in St. Louis (US)
  • University of California, Berkeley (US)
  • University of California, Davis (US)
  • North Carolina State University (US)
  • University of Washington (US)
  • School of Aquatic and Fishery Sciences University of Washington Seattle Washington USA
  • California Academy of Sciences (US)
Corresponding author
  • Carlen, Elizabeth J.
  • Estien, Cesar O.
ORCID
Venue
People and Nature
Publisher
Wiley
Published
2024-03-03
Volume
6
Issue
2
Pages
377-390
ISSN
2575-8314 (ISSN-L)
DOI
10.1002/pan3.10592
OpenAlex
W4392372060
Cited by
57
References
113
Citations by year
2026: 21 · 2025: 26 · 2024: 9 · 2023: 1
FWCI
52.86
Type
journal article
Language
en
Open access
Licence
Locations
  • People and Nature (publishedVersion, cc-by-nc)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Science Foundation (DBI‐2109587, DGE‐1752814, DGE‐2146752, DGE‐2140004, DRL‐1713562)
  • University of California
Topics
  • Physical Sciences › Environmental Science › Ecological Modeling › Species Distribution and Climate Change
  • Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
  • Social Sciences › Geography, Planning and Development › Geographic Information Systems Studies
Keywords
citizen science, data bias, biodiversity data, eBird, iNaturalist, participation bias, sampling bias, preference bias, biodiversity conservation, equity in conservation, inclusive science

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