Synthetic control methods enable stronger causal inference using participatory science data in cities

As urban populations grow, conserving biodiversity within cities is increasingly vital and of global policy interest. However, urban environments pose unique challenges for understanding drivers of biodiversity change, as fragmented land ownership makes traditional biodiversity monitoring and randomized experiments logistically difficult. While participatory science platforms like iNaturalist offer a promising data source by providing extensive biodiversity data from urban areas, inferring causality remains challenging due to confounding factors in observational data. To leverage these data advances, we offer a framework that combines records from iNaturalist with synthetic control methods, a quasi-experimental approach. We demonstrate this approach in a case study assessing the impact of Hurricane Ida (2021) on bee biodiversity in Philadelphia, USA. The synthetic control estimated a 9.4% decline in bee abundance two years post-event. In contrast, three conventional ecological analyses—an interrupted time series regression, before-after comparison, and a before-after control impact (BACI) design—failed to detect this decline, with the before-after approach naively detecting an increase due to unaccounted temporal trends. Synthetic control methods offer a powerful tool for estimating citywide biodiversity responses to climate events and policy interventions, enhancing the utility of participatory science data for urban ecology.