A Picture is Worth 930 Words: The Delmar Divide

Trivers, Ian; Rosenthal, Joanna

doi:10.1111/foge.12065

Cited by 4 publications

(2 citation statements)

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“…Moreover, the human population density is relatively even throughout the city, meaning no region is vastly more populated (Figure 2). Notably, St. Louis is highly segregated along a north–south axis, with poor, Black communities in the north and wealthy, white communities in the south (Benton, 2018; Trivers & Rosenthal, 2015). As a result of segregation, environmental conditions vary, with more vacant properties, pollution, and illegal trash dumping in the northern, Black portion of the city (Interdisciplinary Environmental Clinic, 2019).…”

Section: Applying the Framework: A Case Study In The United Statesmentioning

confidence: 99%

“…Burr et al., 2016; Mallinak, 2019; Moreno, 2018). Uneven participation in St. Louis is likely a consequence of variables, such as race, income and/or contemporary politics, which differ between the northern and southern parts of the city (Benton, 2018; Trivers & Rosenthal, 2015). Particularly, the legacy of land‐use policies, specifically racial segregation, may be driving where data are spatially reported in St. Louis.…”

Section: Applying the Framework: A Case Study In The United Statesmentioning

confidence: 99%

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

Carlen,

Estien,

Caspi

et al. 2024

People and Nature

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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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Section: Applying the Framework: A Case Study In The United Statesmentioning

confidence: 99%

Section: Applying the Framework: A Case Study In The United Statesmentioning

confidence: 99%