@article{carlen_estien_caspi_perkins_goldstein_kreling_hentati_williams_stanton_des roches_et al._2024, title={A framework for contextualizing social-ecological biases in contributory science data}, volume={3}, ISSN={["2575-8314"]}, DOI={10.1002/pan3.10592}, abstractNote={


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.}, journal={PEOPLE AND NATURE}, author={Carlen, Elizabeth J. and Estien, Cesar O. and Caspi, Tal and Perkins, Deja and Goldstein, Benjamin R. and Kreling, Samantha E. S. and Hentati, Yasmine and Williams, Tyus D. and Stanton, Lauren A. and Des Roches, Simone and et al.}, year={2024}, month={Mar} }
 @article{grade_chan_gajbhiye_perkins_warren_2022, title={Evaluating the use of semi-structured crowdsourced data to quantify inequitable access to urban biodiversity: A case study with eBird}, volume={17}, ISSN={["1932-6203"]}, DOI={10.1371/journal.pone.0277223}, abstractNote={Credibly estimating social-ecological relationships requires data with broad coverage and fine geographic resolutions that are not typically available from standard ecological surveys. Open and unstructured data from crowdsourced platforms offer an opportunity for collecting large quantities of user-submitted ecological data. However, the representativeness of the areas sampled by these data portals is not well known. We investigate how data availability in eBird, one of the largest and most popular crowdsourced science platforms, correlates with race and income of census tracts in two cities: Boston, MA and Phoenix, AZ. We find that checklist submissions vary greatly across census tracts, with similar patterns within both metropolitan regions. In particular, census tracts with high income and high proportions of white residents are most likely to be represented in the data in both cities, which indicates selection bias in eBird coverage. Our results illustrate the non-representativeness of eBird data, and they also raise deeper questions about the validity of statistical inferences regarding disparities that can be drawn from such datasets. We discuss these challenges and illustrate how sample selection problems in unstructured or semi-structured crowdsourced data can lead to spurious conclusions regarding the relationships between race, income, and access to urban bird biodiversity. While crowdsourced data are indispensable and complementary to more traditional approaches for collecting ecological data, we conclude that unstructured or semi-structured data may not be well-suited for all lines of inquiry, particularly those requiring consistent data coverage, and should thus be handled with appropriate care.}, number={11}, journal={PLOS ONE}, author={Grade, Aaron M. and Chan, Nathan W. and Gajbhiye, Prashikdivya and Perkins, Deja J. and Warren, Paige S.}, year={2022}, month={Nov} }
 @article{terpin_perkins_richter_leavey_snell_pierson_2019, title={A scientific note on the effect of oxalic acid on honey bee larvae}, volume={50}, ISSN={["1297-9678"]}, DOI={10.1007/s13592-019-00650-7}, abstractNote={The approval of oxalic acid as a treatment for Varroa destructor infestation of honey bee hives gives beekeepers an additional option for controlling this devastating parasite and disease vector, but the effects of oxalic acid on developing bees are not completely understood. In this study, we find that doses of oxalic acid not reported to be toxic to adult bees are toxic to larval bees. While it has been recommended that oxalic acid only be used during broodless periods because it does not penetrate cappings and is only effective in killing phoretic mites, it is tempting to use it at other times of the year because of the dearth of effective treatment options. Knowing whether oxalic acid is toxic to larvae and at what doses is important for beekeepers as they manage their colony population throughout the year.}, number={3}, journal={APIDOLOGIE}, author={Terpin, Bethany and Perkins, Deja and Richter, Stephanie and Leavey, Jennifer Kraft and Snell, Terry W. and Pierson, John A.}, year={2019}, month={Jul}, pages={363–368} }