Geography of race and income shape spatial data gaps in two national participatory science projects

Abstract Context and Need : Participatory projects where people contribute geo‐referenced biodiversity data, like eBird and iNaturalist, are commonly used tools to enhance the data collection capacity for research, management, and environmental learning. Despite their utility, demographic disparities in participation, demographic patterns of residential segregation, and individual choice of participation may skew data, influencing research outcomes, management decisions, and the distribution of project benefits. Approach and Methods : We used hurdle models to analyse the occurrence and abundance of volunteer observations from 2018 to eBird and iNaturalist across the United States ( n = 90 million) in relation to the racial composition and median household income levels of Census Tracts (approximately 1200–8000 residents per tract) in 2018. We ran models separately for urban and rural areas to account for different patterns of residential demographic segregation and the geographic concentration of racial and income groups relative to tract size. Main Results : The geography of race and income was a significant predictor of eBird and iNaturalist locations, respectively. In urban areas, the eBird locations were significantly less likely to occur and less abundant in tracts that were predominantly Black Indigenous and People of Color (BIPOC). In urban and rural areas, iNaturalist locations were significantly less likely to occur and less abundant in tracts that were predominantly low‐income residents. Synthesis and Applications : Our analysis explicitly examined whether the geography of people aligns with the geography of contributory science participation, highlighting how socio‐spatial processes shape both demographic patterns and data coverage. This unevenness can distort scientific inferences, limit the effectiveness of participatory science in addressing local ecological and environmental management needs and lead to the inequitable distribution of benefits. In addition, areas with less data may have lower attraction for participants, and/or project structures may (inadvertently) exclude people of colour and those with lower income. If these patterns occur in similar participatory projects, which number in the thousands in the US, then contributory projects may inadvertently be creating data‐rich and data‐poor areas. We recommend future studies disaggregate participation data by specific racial communities because solutions may not be one‐size‐fits‐all. Read the free Plain Language Summary for this article on the Journal blog.