Examining the influence of sociodemographics, residential segregation, and historical redlining on eBird and iNaturalist data disparities in three U.S. cities

Abstract

Ecologists often leverage contributory science, also referred to as citizen science, to answer large-scale spatial and temporal biodiversity questions. Contributory science platforms, such as eBird and iNaturalist, provide researchers with incredibly fine-scale data to track biodiversity. However, data generated by these platforms are spatially biased. Research has shown that factors like income, race, and historical redlining can influence spatial patterns of reported eBird and iNaturalist data. However, the role of contemporary residential segregation remains unclear. Additionally, we do not understand how these variables potentially relate to certain Census tracts having more or less biodiversity data than you would expect based on size or population density. To further understand the social factors that may contribute to spatial biases in eBird and iNaturalist data, we focused on three cities within the USA (Oakland, California; St. Louis, Missouri; and Baltimore, Maryland). We specifically investigated how income, race, segregation, and redlining via Home Owners’ Loan Corporation grades (grades A = best, B, C, and D = hazardous and “redlined”) are associated with the difference between reported and expected observations based on area and human population density. We find that census tracts with higher income and more White people generally have more observations than expected. We only find segregation to influence differences in reported and expected observations in Baltimore, with more segregated Census tracts having more observations than expected. Lastly, we find that grades C and D consistently have fewer data than expected compared with grades A and B for both platforms in each city. Our results show that although each city has distinct societal and ecological features, societal inequity permeates each city to shape the uptake of data for two of the largest sources of biodiversity data.

Keywords

• biases
• citizen science
• ebird
• inaturalist
• redlining
• segregation
• socioeconomics