Uneven biodiversity sampling across redlined urban areas in the United States

Abstract

Citizen science data has rapidly gained influence in urban ecology and conservation planning, but with limited understanding of how such data reflects social, economic, and political conditions and legacies. Understanding patterns of sampling bias across socioeconomic gradients is critical to accurately map and understand biodiversity patterns, and to generating representative and just environmental knowledge. In this study we explore how historic racially-explicit zoning policies (redlining) relate to biodiversity data collection across and within 195 metropolitan areas in the United States covering >30 million people across 38 states. We specifically look at birds, as they are the most widely studied group of animals, and hundreds of thousands of citizen scientists collect biodiversity data each year. We consistently find uneven bird observation sampling density across redlined areas, with so-called ‘desirable’ areas (i.e. historically white areas) having more than twice the density than areas redlined as ‘hazardous’. We further estimate the degree to which historically redlined areas are surveyed sufficiently and identify regions across all metro areas in need of enhanced bird surveying. After accounting for differences in vegetation, open space, and climate, we find significantly lower sampling density and sampling completeness in these redlined neighborhoods. Our results shed light on the importance of considering socio-political conditions in the interpretation of urban biodiversity estimates. We conclude by discussing specific policy implementations– such as the Justice 40 Initiative and propose a new EPA indicator – and opportunities in collaborating with bottom-up community and social justice organizations for a more representative understanding of biodiversity in urban areas.

Evaluating the use of semi-structured crowdsourced data to quantify inequitable access to urban biodiversity: A case study with eBird

“…Since green space coverage is greatest in wealthier areas of Buffalo, the biases in sample selection create a similar set of outcomes, i.e., that bird communities are under sampled in lower income portions of the city. Ongoing work by Ellis Soto and colleagues [ 14 ] finds biased sampling with respect to historic redlining in data from the Global Biodiversity Information Facility, a source that includes eBird records. Redlining refers to the racialized zones that the Home Owners Loan Corporation developed to guide lending practices in the early 20th century [ 16 ].…”

“…by trained experts) are likely to plague volunteer, user-gathered data as well [7]. This may yield socioeconomic, gender, and racial biases in who collects the data and uses the platforms [9][10][11], where users go to collect data [10,12,13], and consequently the representativeness of these datasets, particularly for historically marginalized communities [8,13,14]. The extent of sample selection bias in large, user-submitted data platforms remains largely unexplored (but see [13,14]), but may pose particular challenges for open, semi-structured data platforms.…”

“…This may yield socioeconomic, gender, and racial biases in who collects the data and uses the platforms [9][10][11], where users go to collect data [10,12,13], and consequently the representativeness of these datasets, particularly for historically marginalized communities [8,13,14]. The extent of sample selection bias in large, user-submitted data platforms remains largely unexplored (but see [13,14]), but may pose particular challenges for open, semi-structured data platforms. This paper aims to assess how sample selection bias may affect studies using eBird, one of the largest crowdsourced platforms for biodiversity sampling [11], to analyze disparities in access to urban biodiversity.…”

“…A few recent studies point to potential problems of sample selection bias in eBird and other SSCS platforms [ 12 – 14 , 35 ]. Perkins [ 13 ] found a consistent pattern of geographic bias in the distribution of eBird sampling locations; neighborhoods in lower income groups were generally underrepresented among sites with checklists.…”

“…Note that our approach is distinct from related work that focuses on the racial and demographic composition of CS users [ 38 , 39 ]; instead, we ask whether the spatial coverage of information available from SSCS datasets may be systematically biased toward neighborhoods of particular racial and socioeconomic composition. Second, much of the extant literature comprises case studies within single cities, leaving unclear whether results generalize across locales (but see [ 14 ]). We study two cities that are embedded in different ecoregions (temperate forest and arid desert, respectively) and have distinct urban geographies, finding similar results for both.…”