A framework for contextualizing social‐ecological biases in contributory science data
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 answe...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-04-01
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| Series: | People and Nature |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/pan3.10592 |
| _version_ | 1797226559688409088 |
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| author | Elizabeth J. Carlen Cesar O. Estien Tal Caspi Deja Perkins Benjamin R. Goldstein Samantha E. S. Kreling Yasmine Hentati Tyus D. Williams Lauren A. Stanton Simone Des Roches Rebecca F. Johnson Alison N. Young Caren B. Cooper Christopher J. Schell |
| author_facet | Elizabeth J. Carlen Cesar O. Estien Tal Caspi Deja Perkins Benjamin R. Goldstein Samantha E. S. Kreling Yasmine Hentati Tyus D. Williams Lauren A. Stanton Simone Des Roches Rebecca F. Johnson Alison N. Young Caren B. Cooper Christopher J. Schell |
| author_sort | Elizabeth J. Carlen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T14:26:50Z |
| format | Article |
| id | doaj.art-7de92bd2945d4b7db3cdee6ab14ad02c |
| institution | Directory Open Access Journal |
| issn | 2575-8314 |
| language | English |
| last_indexed | 2024-04-24T14:26:50Z |
| publishDate | 2024-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | People and Nature |
| spelling | doaj.art-7de92bd2945d4b7db3cdee6ab14ad02c2024-04-03T04:30:39ZengWileyPeople and Nature2575-83142024-04-016237739010.1002/pan3.10592A framework for contextualizing social‐ecological biases in contributory science dataElizabeth J. Carlen0Cesar O. Estien1Tal Caspi2Deja Perkins3Benjamin R. Goldstein4Samantha E. S. Kreling5Yasmine Hentati6Tyus D. Williams7Lauren A. Stanton8Simone Des Roches9Rebecca F. Johnson10Alison N. Young11Caren B. Cooper12Christopher J. Schell13Living Earth Collaborative Washington University in St. Louis St. Louis Missouri USADepartment of Environmental Science, Policy, and Management University of California–Berkeley Berkeley California USADepartment of Environmental Science and Policy University of California–Davis Davis California USADepartment of Forestry & Environmental Resources, Center for Geospatial Analytics North Carolina State University Raleigh North Carolina USADepartment of Environmental Science, Policy, and Management University of California–Berkeley Berkeley California USASchool of Environment and Forest Sciences University of Washington Seattle Washington USASchool of Environment and Forest Sciences University of Washington Seattle Washington USADepartment of Environmental Science, Policy, and Management University of California–Berkeley Berkeley California USADepartment of Environmental Science, Policy, and Management University of California–Berkeley Berkeley California USASchool of Aquatic and Fishery Sciences University of Washington Seattle Washington USACenter for Biodiversity and Community Science California Academy of Sciences San Francisco California USACenter for Biodiversity and Community Science California Academy of Sciences San Francisco California USADepartment of Forestry & Environmental Resources, Center for Geospatial Analytics North Carolina State University Raleigh North Carolina USADepartment of Environmental Science, Policy, and Management University of California–Berkeley Berkeley California USAAbstract 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.https://doi.org/10.1002/pan3.10592biasesbiodiversitycitizen sciencecommunity scienceeBirdiNaturalist |
| spellingShingle | Elizabeth J. Carlen Cesar O. Estien Tal Caspi Deja Perkins Benjamin R. Goldstein Samantha E. S. Kreling Yasmine Hentati Tyus D. Williams Lauren A. Stanton Simone Des Roches Rebecca F. Johnson Alison N. Young Caren B. Cooper Christopher J. Schell A framework for contextualizing social‐ecological biases in contributory science data People and Nature biases biodiversity citizen science community science eBird iNaturalist |
| title | A framework for contextualizing social‐ecological biases in contributory science data |
| title_full | A framework for contextualizing social‐ecological biases in contributory science data |
| title_fullStr | A framework for contextualizing social‐ecological biases in contributory science data |
| title_full_unstemmed | A framework for contextualizing social‐ecological biases in contributory science data |
| title_short | A framework for contextualizing social‐ecological biases in contributory science data |
| title_sort | framework for contextualizing social ecological biases in contributory science data |
| topic | biases biodiversity citizen science community science eBird iNaturalist |
| url | https://doi.org/10.1002/pan3.10592 |
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