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

Nature Ecology & Evolution (2026) Cite this article

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Abstract

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 such as iNaturalist offer a promising data source by providing extensive biodiversity data from urban areas, inferring causality remains challenging because of 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 the number of research-grade iNaturalist bee observations, used as a proxy for bee abundance, in Philadelphia, USA. The synthetic control estimated a 15.5–20.9% decline in bee observations in the 2 years post-event. By contrast, three conventional ecological analyses—an interrupted time-series regression, before–after comparison and a before–after control impact design—failed to detect this decline. Synthetic-control methods offer a powerful tool for estimating city-wide biodiversity responses to climate events and policy interventions, enhancing the utility of participatory science data for urban ecology.

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Fig. 1: Factors influencing biodiversity estimates from participatory science data.
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Fig. 2: Constructing synthetic controls for inference from participatory science data.
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Fig. 3: Effect of Hurricane Ida on iNaturalist bee observations in Philadelphia estimated using four methods, with only the synthetic-control method detecting a significant decline.
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Fig. 4: Placebo plot of all city outcomes compared with the synthetic-control outcome.
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Data availability

We use publicly available data and provide references to relevant data sources in Supplementary Information. Data to replicate these analyses are available via Open Science Framework (OSF) at https://osf.io/fv7cz (ref. 88) and via GitHub at https://github.com/asiakaiser/SyntheticControl-Repo (ref. 89).

Code availability

All code required to reproduce these analyses are available via GitHub at https://github.com/asiakaiser/SyntheticControl-Repo (ref. 89) and OSF at https://osf.io/fv7cz (ref. 88).

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Acknowledgements

We thank the members of the Resasco Laboratory for feedback on the paper. We also acknowledge the community of observers on iNaturalist for providing the biodiversity data used in this research. A.K. was supported by a USDA NIFA predoctoral fellowship (grant no. COLW-2023-11576), which provided stipend and training support during this work.

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  1. These authors jointly supervised this work: Julian Resasco, Laura E. Dee.

Authors and Affiliations

  1. Department of Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, CO, USA

    Asia Kaiser, Julian Resasco & Laura E. Dee

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Contributions

A.K., L.E.D. and J.R. conceived of the study and interpreted the results. A.K. prepared the materials and performed data extraction and analysis with input from L.E.D. and J.R. A.K. wrote the first draft of the paper. L.E.D. and J.R. reviewed and edited subsequent drafts.

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Correspondence to Asia Kaiser.

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Extended data

Extended Data Table 1 Balance table comparing Philadelphia, Synthetic Philadelphia, and the full donor pool
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Kaiser, A., Resasco, J. & Dee, L.E. Synthetic control methods enable stronger causal inference using participatory science data in cities. Nat Ecol Evol (2026). https://doi.org/10.1038/s41559-026-03084-4

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