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 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.

Of Rarity and Symbolism: Understanding Human Perceptions of Charismatic Color Morphs

“…Finally, with a recent uptick in research in wildlife coloration (Leveau, 2021;Cosentino & Gibbs, 2022;Kreling, 2023), participatory science platforms, such as iNaturalist, may become critical in understanding the spatial distribution, abundance, and presence of these charismatic color morphs. 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). While community-gathered data wildlife.…”

“…Yet, we see in multiple cases wildlife with charismatic coloration is not treated as pests but celebrated and often memorialized. certainly has value, biases in these observations could lead to misinterpretations of the data (Dickman, 2010;Carlen et al 2024). Therefore, greater understanding of the reasons why people perceive and respond to novel colorations in wildlife facilitate human-wildlife coexistence and foster stewardship for the animals we collectively share space with.…”

Data coverage, biases, and trends in a global citizen‐science resource for monitoring avian diversity

“…The revolution in information technology and the growth of 'big data' resources (Chen et al, 2014) and citizen science initiatives focused on the acquisition of ecological data has the potential to bridge this information gap for some taxa (Chandler et al, 2017;Devictor et al, 2010;Pocock et al, 2018). However, citizen science initiatives typically compile observations opportunistically and sporadically across space and time and the data often contains social, ecological, and taxonomic biases (Carlen et al, 2024;Di Cecco et al, 2021). Citizen science data can be useful for quantifying species' ecological niches and geographical distributions but the opportunistic nature of the data and associated biases can make it difficult to apply the data towards biodiversity monitoring (Johnston et al, 2023).…”