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Historical redlining is associated with increasing geographical disparities in bird biodiversity sampling in the United States

Nature Human Behaviour volume 7, pages 1869–1877 (2023)Cite this article

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Abstract

Historic segregation and inequality are critical to understanding modern environmental conditions. Race-based zoning policies, such as redlining in the United States during the 1930s, are associated with racial inequity and adverse multigenerational socioeconomic levels in income and education, and disparate environmental characteristics including tree canopy cover across urban neighbourhoods. Here we quantify the association between redlining and bird biodiversity sampling density and completeness—two critical metrics of biodiversity knowledge—across 195 cities in the United States. We show that historically redlined neighbourhoods remain the most undersampled urban areas for bird biodiversity today, potentially impacting conservation priorities and propagating urban environmental inequities. The disparity in sampling across redlined neighbourhood grades increased by 35.6% over the past 20 years. We identify specific urban areas in need of increased bird biodiversity sampling and discuss possible strategies for reducing uncertainty and increasing equity of sampling of biodiversity in urban areas. Our findings highlight how human behaviour and past social, economic and political conditions not just segregate our built environment but may also leave a lasting mark on the digital information we have about urban biodiversity.

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Fig. 1: Map of 195 selected cities included in the analyses, spanning a range of social and ecological conditions.
Fig. 2: Patterns of biodiversity across redlined areas.
Fig. 3: Predictions of sampling density and completeness.
Fig. 4: Temporal trends in cumulative sampling density of bird observations in km2 across HOLC grades from 2000 to 2020.

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Data availability

Data used in this analysis are publicly available on Zenodo (https://doi.org/10.5281/zenodo.8052525) and were queried from GBIF as a derived dataset (https://doi.org/10.15468/dd.ha9ksv). Climatic predictors used in our analysis are publicly available through the Chelsa Climatology platform (https://chelsa-climate.org/), remote-sensing data are available on the Google Earth Engine (https://earthengine.google.com) and the PAD-US dataset can be downloaded from the United States Geological Survey website (https://www.usgs.gov).

Code availability

The codes used to download biodiversity data and environmental data, as well as for necessary analysis and creation of figures to recreate our results are available on Zenodo (https://doi.org/10.5281/zenodo.8052525).

References

  1. Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).

    Article  PubMed  Google Scholar 

  2. Hooper, D. U. et al. A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature 486, 105–108 (2012).

    Article  PubMed  Google Scholar 

  3. Update of the Zero Draft of the Post-2020 Global Biodiversity Framework (UNEP, 2020).

  4. Díaz-Reviriego, I., Turnhout, E. & Beck, S. Participation and inclusiveness in the Intergovernmental Science–Policy Platform on Biodiversity and Ecosystem Services. Nat. Sustain. 2, 457–464 (2019).

    Article  Google Scholar 

  5. Jetz, W. et al. Essential biodiversity variables for mapping and monitoring species populations. Nat. Ecol. Evol. 3, 539–551 (2019).

    Article  PubMed  Google Scholar 

  6. Amano, T., Lamming, J. D. L. & Sutherland, W. J. Spatial gaps in global biodiversity information and the role of citizen science. Bioscience 66, 393–400 (2016).

    Article  Google Scholar 

  7. Bird, T. J. et al. Statistical solutions for error and bias in global citizen science datasets. Biol. Conserv. 173, 144–154 (2014).

    Article  Google Scholar 

  8. Callaghan, C. T., Rowley, J. J. L., Cornwell, W. K., Poore, A. G. B. & Major, R. E. Improving big citizen science data: moving beyond haphazard sampling. PLoS Biol. 17, e3000357 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. La Sorte, F. A. & Somveille, M. Survey completeness of a global citizen-science database of bird occurrence. Ecography 43, 34–43 (2020).

    Article  Google Scholar 

  10. Meyer, C., Jetz, W., Guralnick, R. P., Fritz, S. A. & Kreft, H. Range geometry and socio-economics dominate species-level biases in occurrence information. Glob. Ecol. Biogeogr. 25, 1181–1193 (2016).

    Article  Google Scholar 

  11. Troudet, J., Grandcolas, P., Blin, A., Vignes-Lebbe, R. & Legendre, F. Taxonomic bias in biodiversity data and societal preferences. Sci. Rep. 7, 9132 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Raja, N. B. et al. Colonial history and global economics distort our understanding of deep-time biodiversity. Nat. Ecol. Evol. 6, 145–154 (2022).

    Article  PubMed  Google Scholar 

  13. Wang, Q., Phillips, N. E., Small, M. L. & Sampson, R. J. Urban mobility and neighborhood isolation in America’s 50 largest cities. Proc. Natl Acad. Sci. USA 115, 7735–7740 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Sullivan, B. L. et al. Using open access observational data for conservation action: a case study for birds. Biol. Conserv. 208, 5–14 (2017).

    Article  Google Scholar 

  15. Soteropoulos, D. L., De Bellis, C. R. & Witsell, T. Citizen science contributions to address biodiversity loss and conservation planning in a rapidly developing region. Diversity 13, 255 (2021).

  16. Schuster, R. et al. Optimizing the conservation of migratory species over their full annual cycle. Nat. Commun. 10, 1754 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Ruiz-Gutierrez, V. et al. A pathway for citizen science data to inform policy: a case study using eBird data for defining low-risk collision areas for wind energy development. J. Appl. Ecol. 58, 1104–1111 (2021).

    Article  Google Scholar 

  18. Perkins, D. M. Blind Spots in Citizen Science Data: Implications of Volunteer Bias in eBird Data. MSc thesis, North Carolina State Univ. (2020).

  19. Locke, D. H. et al. Residential housing segregation and urban tree canopy in 37 US Cities. npj Urban Sustain. 1, 15 (2021).

    Article  Google Scholar 

  20. Browning, M. H. E. M. et al. Curated dataset of accessible and recreational parks inthe U.S.: comparison to greenspace metrics and sociodemographics. Preprint at EcoEvoRxiv https://doi.org/10.32942/osf.io/pydqe (2022).

    Article  Google Scholar 

  21. Athey, S., Ferguson, B., Gentzkow, M. & Schmidt, T. Estimating experienced racial segregation in US cities using large-scale GPS data. Proc. Natl Acad. Sci. USA 118, e2026160118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Aaronson, D., Hartley, D. & Mazumder, B. The effects of the 1930s HOLC ‘Redlining’ maps. Am. Econ. J. Econ. Policy 13, 355–392 (2021).

    Article  Google Scholar 

  23. Appel, I. & Nickerson, J. Pockets of poverty: the long-term effects of redlining. SSRN https://ssrn.com/abstract=2852856 (2016).

  24. Rutan, D. Q. & Glass, M. R. The lingering effects of neighborhood appraisal: evaluating redlining’s legacy in Pittsburgh. Prof. Geogr. 70, 339–349 (2018).

    Article  Google Scholar 

  25. Gee, G. C. A multilevel analysis of the relationship between institutional and individual racial discrimination and health status. Am. J. Public Health 92, 615–623 (2002).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Massey, D. S. American apartheid: segregation and the making of the underclass. Am. J. Sociol. 96, 329–357 (1990).

    Article  Google Scholar 

  27. Nardone, A., Rudolph, K. E., Morello-Frosch, R. & Casey, J. A. Redlines and greenspace: the relationship between historical redlining and 2010 greenspace across the United States. Environ. Health Perspect. 129, 17006 (2021).

    Article  PubMed  Google Scholar 

  28. Hoffman, J. S., Shandas, V. & Pendleton, N. The effects of historical housing policies on resident exposure to intra-urban heat: a study of 108 US urban areas. Climate 8, 12 (2020).

  29. Burghardt, K. T. et al. Current street tree communities reflect race-based housing policy and modern attempts to remedy environmental injustice. Ecology 104, e3881 (2022).

  30. Roman, L. A. et al. Human and biophysical legacies shape contemporary urban forests: a literature synthesis. Urban For. Urban Green. 31, 157–168 (2018).

    Article  Google Scholar 

  31. Bonney, R. Expanding the impact of citizen science. Bioscience 71, 448–451 (2021).

    Article  Google Scholar 

  32. Schell, C. J. et al. The ecological and evolutionary consequences of systemic racism in urban environments. Science 4497, eaay4497 (2020).

    Article  Google Scholar 

  33. Schmidt, C. & Garroway, C. J. Systemic racism alters wildlife genetic diversity. Proc. Natl Acad. Sci. USA 119, e2102860119 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Callaghan, C. T. et al. Quantifying effort needed to estimate species diversity from citizen science data. Ecosphere 13, e3966 (2022).

    Article  Google Scholar 

  35. Hughes, A. C. et al. Sampling biases shape our view of the natural world. Ecography 44, 1259–1269 (2021).

    Article  Google Scholar 

  36. Harris, N. C. et al. Socio-ecological gap analysis to forecast species range contractions for conservation. Proc. Natl Acad. Sci. USA 120, e2201942119 (2022).

    Article  PubMed  Google Scholar 

  37. Miriti, M. N., Bowser, G., Cid, C. R. & Harris, N. C. Overcoming blind spots to promote environmental justice research. Trends Ecol. Evol. 36, 269–273 (2021).

    Article  PubMed  Google Scholar 

  38. Blake, C., Rhanor, A. & Pajic, C. The demographics of citizen science participation and its implications for data quality and environmental justice. Citiz. Sci. 5, 21 (2020).

    Google Scholar 

  39. Rutter, J. D. et al. Racial, ethnic, and social patterns in the recreation specialization of birdwatchers: an analysis of United States eBird registrants. J. Outdoor Recreat. Tour. 35, 100400 (2021).

    Article  Google Scholar 

  40. Rosenblatt, C. J. et al. Highly specialized recreationists contribute the most to the citizen science project eBird. Ornithol. Appl. 124, duac008 (2022).

    Google Scholar 

  41. Cooper, C. B. et al. Inclusion in citizen science: the conundrum of rebranding. Science 372, 1386–1388 (2021).

    Article  CAS  Google Scholar 

  42. Jordan, R. C., Gray, S. A., Howe, D. V., Brooks, W. R. & Ehrenfeld, J. G. Knowledge gain and behavioral change in citizen-science programs. Conserv. Biol. 25, 1148–1154 (2011).

    Article  PubMed  Google Scholar 

  43. Brewer, C. Outreach and partnership programs for conservation education where endangered species conservation and research occur. Conserv. Biol. 16, 4–6 (2002).

    Article  PubMed  Google Scholar 

  44. Remme, R. P. et al. An ecosystem service perspective on urban nature, physical activity, and health. Proc. Natl Acad. Sci. USA 118, e2018472118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Gonzalez, P. A. et al. Community-based participatory research and policy advocacy to reduce diesel exposure in West Oakland, California. Am. J. Public Health 101, S166–S175 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Dernoga, M. A., Wilson, S., Jiang, C. & Tutman, F. Environmental justice disparities in Maryland’s watershed restoration programs. Environ. Sci. Policy 45, 67–78 (2015).

    Article  Google Scholar 

  47. Tiago, P., Gouveia, M. J., Capinha, C., Santos-Reis, M. & Pereira, H. M. The influence of motivational factors on the frequency of participation in citizen science activities. Nat. Conserv. 18, 61–78 (2017).

    Article  Google Scholar 

  48. Sustainable Development Goal 11: Sustainable Cities and Communities (United Nations, 2021).

  49. Marshall, K. A. & Gonzalez-Meler, M. A. Can ecosystem services be part of the solution to environmental justice? Ecosyst. Serv. 22, 202–203 (2016).

    Article  Google Scholar 

  50. Prévot, A.-C., Cheval, H., Raymond, R. & Cosquer, A. Routine experiences of nature in cities can increase personal commitment toward biodiversity conservation. Biol. Conserv. 226, 1–8 (2018).

    Article  Google Scholar 

  51. Pollock, N. B. et al. Personal BioBlitz: a new way to encourage biodiversity discovery and knowledge in K–99 education and outreach. Bioscience 65, 1154–1164 (2015).

    Article  Google Scholar 

  52. Nelson, R. K., Winling, L., Marciano, R. & Connolly, N. Mapping Inequality: Redlining in New Deal America (American Panorama, 2018).

  53. Pebesma, E. Simple features for R: standardized support for spatial vector data. R J. https://doi.org/10.32614/rj-2018-009 (2018).

  54. R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).

  55. Walker, K. Tigris: an R package to access and work with geographic data from the US Census Bureau. R J. https://doi.org/10.32614/rj-2016-043 (2016).

  56. Goodchild, M. F., Anselin, L. & Deichmann, U. A framework for the areal interpolation of socioeconomic data. Environ. Plan. A 25, 383–397 (1993).

    Article  Google Scholar 

  57. Sullivan, B. et al. eBird: a citizen-based observation network in the biological sciences. Biol. Conserv. 142, 2282–2292 (2009).

    Article  Google Scholar 

  58. Chamberlain, S. Package ‘rgbif’: interface to the Global ‘Biodiversity’ Information Facility API (CRAN, 2019).

  59. La Sorte, F. A., Aronson, M. F. J., Lepczyk, C. A. & Horton, K. G. Area is the primary correlate of annual and seasonal patterns of avian species richness in urban green spaces. Landsc. Urban Plan. 203, 103892 (2020).

    Article  Google Scholar 

  60. Callaghan, C. T. et al. Using citizen science data to define and track restoration targets in urban areas. J. Appl. Ecol. 56, 1998–2006 (2019).

    Article  Google Scholar 

  61. Lobo, J. M. et al. KnowBR: an application to map the geographical variation of survey effort and identify well-surveyed areas from biodiversity databases. Ecol. Indic. 91, 241–248 (2018).

    Article  Google Scholar 

  62. Protected Areas Database of the United States (PAD-US) version 2.1 (USGS, 2020).

  63. La Sorte, F. A. et al. Documenting stewardship responsibilities across the annual cycle for birds on U.S. public lands. Ecol. Appl. 25, 39–51 (2015).

    Article  PubMed  Google Scholar 

  64. Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Bates, D., Mächler, M., Bolker & Walter, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).

    Article  Google Scholar 

  66. Aguinis, H., Gottfredson, R. K. & Joo, H. Best-practice recommendations for defining, identifying, and handling outliers. Organ. Res. Methods 16, 270–301 (2013).

    Article  Google Scholar 

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Acknowledgements

We thank T. Engel, C. Schmidt, S. Yanco, R. Oliver and J. M. Grove for helpful feedback on this paper. D.E.-S. acknowledges support from the Yale Institute for Biospheric Studies, and the Yale Race, Indigeneity and Transnational Migration Institute. The findings and conclusions in this paper are those of the author(s) and should not be construed to represent any official USDA or US Government determination or policy. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA

    Diego Ellis-Soto

  2. Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, USA

    Melissa Chapman

  3. Baltimore Field Station, Northern Research Station, USDA Forest Service, Baltimore, MD, USA

    Dexter H. Locke

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  1. Diego Ellis-Soto
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  2. Melissa Chapman
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Contributions

D.E.-S., M.C. and D.H.L. designed the study and performed research. D.E.-S. led the writing of the paper with substantial contributions from M.C. and D.H.L.

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Correspondence to Diego Ellis-Soto.

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Nature Human Behaviour thanks Ashley Dayer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Figs. 1 and 2 and Tables 1–4.

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Supplementary Data 1

Collection of bird biodiversity coldspots across 195 cities in the United States.

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Ellis-Soto, D., Chapman, M. & Locke, D.H. Historical redlining is associated with increasing geographical disparities in bird biodiversity sampling in the United States. Nat Hum Behav 7, 1869–1877 (2023). https://doi.org/10.1038/s41562-023-01688-5

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