Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Urbanization, climate and species traits shape mammal communities from local to continental scales

Nature Ecology & Evolution volume 7, pages 1654–1666 (2023)Cite this article

Subjects

An Author Correction to this article was published on 13 May 2024

This article has been updated

Abstract

Human-driven environmental changes shape ecological communities from local to global scales. Within cities, landscape-scale patterns and processes and species characteristics generally drive local-scale wildlife diversity. However, cities differ in their structure, species pools, geographies and histories, calling into question the extent to which these drivers of wildlife diversity are predictive at continental scales. In partnership with the Urban Wildlife Information Network, we used occurrence data from 725 sites located across 20 North American cities and a multi-city, multi-species occupancy modelling approach to evaluate the effects of ecoregional characteristics and mammal species traits on the urbanization–diversity relationship. Among 37 native terrestrial mammal species, regional environmental characteristics and species traits influenced within-city effects of urbanization on species occupancy and community composition. Species occupancy and diversity were most negatively related to urbanization in the warmer, less vegetated cities. Additionally, larger-bodied species were most negatively impacted by urbanization across North America. Our results suggest that shifting climate conditions could worsen the effects of urbanization on native wildlife communities, such that conservation strategies should seek to mitigate the combined effects of a warming and urbanizing world.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Study cities across North America and the study’s modelling approach.
Fig. 2: Influences of regional variation in vegetation greenness, temperature, urbanization and city age on mammal community trends across local urbanization gradients.
Fig. 3: Influences of species traits on within-city relationships between urbanization and mammal occupancy.

Similar content being viewed by others

Data availability

All data that support the findings of this study and that were used in the production of all figures are publicly available on Zenodo at https://doi.org/10.5281/zenodo.8083504.

Code availability

All code that supports the findings of this study and that was used in the production of all figures is publicly available on Zenodo at https://doi.org/10.5281/zenodo.8083504.

Change history

References

  1. Gaston, K. J. Global patterns in biodiversity. Nature 405, 220–227 (2000).

    Article  CAS  PubMed  Google Scholar 

  2. Seto, K. C., Güneralp, B. & Hutyra, L. R. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proc. Natl Acad. Sci. USA 109, 16083–16088 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Newbold, T., Oppenheimer, P., Etard, A. & Williams, J. J. Tropical and Mediterranean biodiversity is disproportionately sensitive to land-use and climate change. Nat. Ecol. Evol. 4, 1630–1638 (2020).

    Article  PubMed  Google Scholar 

  4. Leibold, M. A. et al. The metacommunity concept: a framework for multi-scale community ecology. Ecol. Lett. 7, 601–613 (2004).

    Article  Google Scholar 

  5. White, P. S. & Harrod, J. in Wildlife and Landscape Ecology: Effects of Pattern and Scale (ed. Bissonette, J. A.) 128–159 (Springer, 1997).

  6. Aronson, M. F. J. et al. Hierarchical filters determine community assembly of urban species pools. Ecology 97, 2952–2963 (2016).

    Article  PubMed  Google Scholar 

  7. Andrade, R. et al. Predicting the assembly of novel communities in urban ecosystems. Landsc. Ecol. 36, 1–15 (2020).

    Article  Google Scholar 

  8. Mazor, T. et al. Global mismatch of policy and research on drivers of biodiversity loss. Nat. Ecol. Evol. 2, 1071–1074 (2018).

    Article  PubMed  Google Scholar 

  9. Aronson, M. F. J. et al. A global analysis of the impacts of urbanization on bird and plant diversity reveals key anthropogenic drivers. Proc. R. Soc. B 281, 20133330 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Beninde, J., Veith, M. & Hochkirch, A. Biodiversity in cities needs space: a meta-analysis of factors determining intra-urban biodiversity variation. Ecol. Lett. 18, 581–592 (2015).

    Article  PubMed  Google Scholar 

  11. Norton, B. A., Evans, K. L. & Warren, P. H. Urban biodiversity and landscape ecology: patterns, processes and planning. Curr. Landsc. Ecol. Rep. 1, 178–192 (2016).

    Article  Google Scholar 

  12. McGill, B. J., Dornelas, M., Gotelli, N. J. & Magurran, A. E. Fifteen forms of biodiversity trend in the Anthropocene. Trends Ecol. Evol. 30, 104–113 (2015).

    Article  PubMed  Google Scholar 

  13. Lerman, S. B. et al. Residential yard management and landscape cover affect urban bird community diversity across the continental USA. Ecol. Appl. 31, e02455 (2021).

    Article  PubMed  Google Scholar 

  14. Rega-Brodsky, C. C. et al. Urban biodiversity: state of the science and future directions. Urban Ecosyst. 25, 1083–1096 (2022).

    Article  Google Scholar 

  15. Magle, S. B. et al. Wealth and urbanization shape medium and large terrestrial mammal communities. Glob. Change Biol. 27, 5446–5459 (2021).

    Article  CAS  Google Scholar 

  16. Fidino, M. et al. Landscape-scale differences among cities alter common species’ responses to urbanization. Ecol. Appl. 31, e02253 (2020).

    Article  PubMed  Google Scholar 

  17. McDonnell, M. J. & Pickett, S. T. A. Ecosystem structure and function along urban–rural gradients: an unexploited opportunity for ecology. Ecology 71, 1232–1237 (1990).

    Article  Google Scholar 

  18. Magle, S. B. et al. Advancing urban wildlife research through a multi-city collaboration. Front. Ecol. Environ. 17, 232–239 (2019).

    Article  Google Scholar 

  19. Faeth, S. H., Bang, C. & Saari, S. Urban biodiversity: patterns and mechanisms. Ann. N. Y. Acad. Sci. 1223, 69–81 (2011).

    Article  PubMed  Google Scholar 

  20. Shochat, E. et al. Invasion, competition, and biodiversity loss in urban ecosystems. BioScience 60, 199–208 (2010).

    Article  Google Scholar 

  21. Avolio, M. L., Swan, C., Pataki, D. E. & Jenerette, G. D. Incorporating human behaviors into theories of urban community assembly and species coexistence. Oikos 130, 1849–1864 (2021).

    Article  Google Scholar 

  22. Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES, 2019); https://doi.org/10.5281/ZENODO.3553579

  23. Ferenc, M. et al. Are cities different? Patterns of species richness and beta diversity of urban bird communities and regional species assemblages in Europe. Glob. Ecol. Biogeogr. 23, 479–489 (2014).

    Article  Google Scholar 

  24. Uchida, K. et al. Urban biodiversity and the importance of scale. Trends Ecol. Evol. 36, 123–131 (2021).

    Article  PubMed  Google Scholar 

  25. Crooks, K. R. & Soulé, M. E. Mesopredator release and avifaunal extinctions in a fragmented system. Nature 400, 563–566 (1999).

    Article  CAS  Google Scholar 

  26. McKinney, M. L. Urbanization, biodiversity, and conservation. BioScience 52, 883–890 (2002).

    Article  Google Scholar 

  27. Rega-Brodsky, C. C. et al. Mammalian functional diversity and trait responses to anthropogenic and environmental factors across the contiguous USA. Urban Ecosyst. 26, 309–322 (2023).

    Article  Google Scholar 

  28. Santini, L. et al. One strategy does not fit all: determinants of urban adaptation in mammals. Ecol. Lett. 22, 365–376 (2019).

    Article  PubMed  Google Scholar 

  29. Suraci, J. P. et al. Disturbance type and species life history predict mammal responses to humans. Glob. Change Biol. 27, 3718–3731 (2021).

    Article  CAS  Google Scholar 

  30. Bateman, P. W. & Fleming, P. A. Big city life: carnivores in urban environments. J. Zool. 287, 1–23 (2012).

    Article  Google Scholar 

  31. Urban Carnivores: Ecology, Conflict, and Conservation (eds Gehrt S. D. et al.)(John Hopkins Univ. Press, 2010).

  32. Turrini, T. & Knop, E. A landscape ecology approach identifies important drivers of urban biodiversity. Glob. Change Biol. 21, 1652–1667 (2015).

    Article  Google Scholar 

  33. Marzluff, J. M. in Avian Ecology and Conservation in an Urbanizing World (eds Marzluff, J. M. et al.) 19–47 (Springer, 2001).

  34. McKinney, M. L. & Lockwood, J. L. Biotic homogenization: a few winners replacing many losers in the next mass extinction. Trends Ecol. Evol. 14, 450–453 (1999).

    Article  CAS  PubMed  Google Scholar 

  35. Boron, V. et al. Richness, diversity, and factors influencing occupancy of mammal communities across human-modified landscapes in Colombia. Biol. Conserv. 232, 108–116 (2019).

    Article  Google Scholar 

  36. Catterall, C. P. in Ecology of Cities and Towns: A Comparative Approach (eds McDonnell, M. J. et al.) 129–155 (Cambridge Univ. Press, 2009).

  37. Sutherland, C., Brambilla, M., Pedrini, P. & Tenan, S. A multiregion community model for inference about geographic variation in species richness. Methods Ecol. Evol. 7, 783–791 (2016).

    Article  Google Scholar 

  38. Haight, J. D. et al. Data and code for ‘Urbanization, climate, and species traits shape mammal communities from local to continental scales’. Zenodo https://doi.org/10.5281/zenodo.8083504 (2023).

  39. Padilla, B. J. & Sutherland, C. A framework for transparent quantification of urban landscape gradients. Landsc. Ecol. 34, 1219–1229 (2019).

    Article  Google Scholar 

  40. Wu, J., Jenerette, G. D., Buyantuyev, A. & Redman, C. L. Quantifying spatiotemporal patterns of urbanization: the case of the two fastest growing metropolitan regions in the United States. Ecol. Complex. 8, 1–8 (2011).

    Article  Google Scholar 

  41. Moll, R. J. et al. What does urbanization actually mean? A framework for urban metrics in wildlife research. J. Appl. Ecol. 56, 1289–1300 (2019).

    Article  Google Scholar 

  42. Forman, R. T. T. Urban Ecology: Science of Cities 462 (Cambridge Univ. Press, 2014).

  43. Regolin, A. L. et al. Spatial heterogeneity and habitat configuration overcome habitat composition influences on alpha and beta mammal diversity. Biotropica 52, 969–980 (2020).

    Article  Google Scholar 

  44. Fahrig, L. Why do several small patches hold more species than few large patches? Glob. Ecol. Biogeogr. 29, 615–628 (2020).

    Article  Google Scholar 

  45. Martin, C. A., Proulx, R., Vellend, M. & Fahrig, L. How the relationship between vegetation cover and land-cover variance constrains biodiversity in a human dominated world. Landsc. Ecol. 36, 3097–3104 (2021).

    Article  Google Scholar 

  46. Fahrig, L. Effects of habitat fragmentation on biodiversity. Annu. Rev. Ecol. Evol. Syst. 34, 487–515 (2003).

    Article  Google Scholar 

  47. Lewis, J. S. et al. Human activity influences wildlife populations and activity patterns: implications for spatial and temporal refuges. Ecosphere 12, e03487 (2021).

    Article  Google Scholar 

  48. Hall, S. J. et al. Convergence of microclimate in residential landscapes across diverse cities in the United States. Landsc. Ecol. 31, 101–117 (2016).

    Article  Google Scholar 

  49. Hantak, M. M., McLean, B. S., Li, D. & Guralnick, R. P. Mammalian body size is determined by interactions between climate, urbanization, and ecological traits. Commun. Biol. 4, 972 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Ouyang, J. Q. et al. A new framework for urban ecology: an integration of proximate and ultimate responses to anthropogenic change. Integr. Comp. Biol. 58, 915–928 (2018).

    PubMed  PubMed Central  Google Scholar 

  51. Bateman, H. L. et al. Novel water sources restore plant and animal communities along an urban river. Ecohydrology 8, 792–811 (2015).

    Article  Google Scholar 

  52. Schell, C. J. et al. The ecological and evolutionary consequences of systemic racism in urban environments. Science 369, eaay4497 (2020).

    Article  CAS  PubMed  Google Scholar 

  53. Apfelbeck, B. et al. Designing wildlife-inclusive cities that support human–animal co-existence. Landsc. Urban Plan. 200, 103817 (2020).

    Article  Google Scholar 

  54. Nilon, C. H. et al. Planning for the future of urban biodiversity: a global review of city-scale initiatives. BioScience 67, 332–342 (2017).

    Article  Google Scholar 

  55. Gese, E. M., Morey, P. S. & Gehrt, S. D. Influence of the urban matrix on space use of coyotes in the Chicago metropolitan area. J. Ethol. 30, 413–425 (2012).

    Article  Google Scholar 

  56. Kelt, D. A. & Van Vuren, D. Energetic constraints and the relationship between body size and home range area in mammals. Ecology 80, 337–340 (1999).

    Article  Google Scholar 

  57. De Bondi, N., White, J. G., Stevens, M. & Cooke, R. A comparison of the effectiveness of camera trapping and live trapping for sampling terrestrial small-mammal communities. Wildl. Res. 37, 456–465 (2010).

    Article  Google Scholar 

  58. Meek, P. D. et al. Recommended guiding principles for reporting on camera trapping research. Biodivers. Conserv 23, 2321–2343 (2014).

    Article  Google Scholar 

  59. de Albuquerque, F. S., Bateman, H. L., Boehme, C., Allen, D. C. & Cayuela, L. Variation in temperature, precipitation, and vegetation greenness drive changes in seasonal variation of avian diversity in an urban desert landscape. Land 10, 480 (2021).

    Article  Google Scholar 

  60. Kanda, L. L., Fuller, T. K., Sievert, P. R. & Kellogg, R. L. Seasonal source–sink dynamics at the edge of a species’ range. Ecology 90, 1574–1585 (2009).

    Article  PubMed  Google Scholar 

  61. Wilmers, C. C., Nisi, A. C. & Ranc, N. COVID-19 suppression of human mobility releases mountain lions from a landscape of fear. Curr. Biol. 31, 3952–3955.e1-e3 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Anderson, A. K., Waller, J. S. & Thornton, D. H. Partial COVID-19 closure of a national park reveals negative influence of low-impact recreation on wildlife spatiotemporal ecology. Sci. Rep. 13, 687 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Bates, A. E. et al. Global COVID-19 lockdown highlights humans as both threats and custodians of the environment. Biol. Conserv. 263, 109175 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Ramalho, C. E. & Hobbs, R. J. Time for a change: dynamic urban ecology. Trends Ecol. Evol. 27, 179–188 (2012).

    Article  PubMed  Google Scholar 

  65. Huang, K., Li, X., Liu, X. & Seto, K. C. Projecting global urban land expansion and heat island intensification through 2050. Environ. Res. Lett. 14, 114037–114037 (2019).

    Article  Google Scholar 

  66. Santos, M. J. et al. The role of land use and land cover change in climate change vulnerability assessments of biodiversity: a systematic review. Landsc. Ecol. 36, 3367–3382 (2021).

    Article  Google Scholar 

  67. IPCC Climate Change 2022: Impacts, Adaptation and Vulnerability (eds Pörtner, H.-O. et al.) (Cambridge Univ. Press, 2022).

  68. Iwaniec, D. M. et al. in Resilient Urban Futures (eds Hamstead, Z. A. et al.) 1–9 (Springer International, 2021); https://doi.org/10.1007/978-3-030-63131-4_1

  69. Fischer, J. D., Schneider, S. C., Ahlers, A. A. & Miller, J. R. Categorizing wildlife responses to urbanization and conservation implications of terminology. Conserv. Biol. 29, 1246–1248 (2015).

    Article  PubMed  Google Scholar 

  70. Parris, K. M. et al. The seven lamps of planning for biodiversity in the city. Cities 83, 44–53 (2018).

    Article  Google Scholar 

  71. Nassauer, J. I. & Opdam, P. Design in science: extending the landscape ecology paradigm. Landsc. Ecol. 23, 633–644 (2008).

    Article  Google Scholar 

  72. Road Networks: Single Line Street Network (City of Edmonton’s Open Data Portal, 2019); https://data.edmonton.ca/

  73. Building Footprint (City of Edmonton’s Open Data Portal, 2018); https://data.edmonton.ca/

  74. Wickham, J., Stehman, S. V., Sorenson, D. G., Gass, L. & Dewitz, J. A. Thematic accuracy assessment of the NLCD 2016 land cover for the conterminous United States. Remote Sens. Environ. 257, 112357 (2021).

    Article  Google Scholar 

  75. R Core Team. R: A Language and Environment for Statistical Computing v.4.0.1 (R Foundation for Statistical Computing, 2020).

  76. 2015 Land Cover of North America at 30 Meters (Commission for Environmental Cooperation, 2020); http://www.cec.org/nalcms

  77. Homer, C. et al. Developing a new North American land cover product at 30m resolution: methods, results and future plans. In American Geophysical Union, Fall Meeting 2017 Abstr. GC52C-01 (American Geophysical Union, 2017).

  78. Hesselbarth, M. H. K., Sciaini, M., With, K. A., Wiegand, K. & Nowosad, J. landscapemetrics: an open-source R tool to calculate landscape metrics. Ecography 42, 1648–1657 (2019).

    Article  Google Scholar 

  79. Aguinis, H., Gottfredson, R. K. & Culpepper, S. A. Best-practice recommendations for estimating cross-level interaction effects using multilevel modeling. J. Manage. 39, 1490–1528 (2013).

    Google Scholar 

  80. Milliren, C. E., Evans, C. R., Richmond, T. K. & Dunn, E. C. Does an uneven sample size distribution across settings matter in cross-classified multilevel modeling? Results of a simulation study. Health Place 52, 121–126 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  81. Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).

    Article  Google Scholar 

  82. Wang, T., Hamann, A., Spittlehouse, D. & Carroll, C. Locally downscaled and spatially customizable climate data for historical and future periods for North America. PLoS ONE 11, e0156720–e0156720 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  83. Wilman, H. et al. EltonTraits 1.0: species-level foraging attributes of the world’s birds and mammals. Figshare https://doi.org/10.6084/m9.figshare.c.3306933.v1 (2016).

  84. Jones, K. E. et al. PanTHERIA: a species-level database of life history, ecology, and geography of extant and recently extinct mammals. Ecology 90, 2648–2648 (2009).

    Article  Google Scholar 

  85. Tenan, S., Brambilla, M., Pedrini, P. & Sutherland, C. Quantifying spatial variation in the size and structure of ecologically stratified communities. Methods Ecol. Evol. 8, 976–984 (2017).

    Article  Google Scholar 

  86. Kays, R. W. & Wilson, D. E. Mammals of North America 58 (Princeton Univ. Press, 2009).

  87. McKerrow, A. U.S. Geological Survey—Gap Analysis Project Species Range Maps CONUS_2001 (USGS, 2018); https://doi.org/10.5066/F7Q81B3R

  88. Plummer, M. JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling v.4.3.0 (2003).

  89. Gelman, A. et al. Bayesian Data Analysis (Chapman & Hall/CRC, 2014).

  90. Chao, A. et al. Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies. Ecol. Monogr. 84, 45–67 (2014).

    Article  Google Scholar 

  91. Kéry, M. & Royle, J. A. in Applied Hierarchical Modeling in Ecology Vol. 11, 631–728 (Elsevier, 2016).

  92. Broms, K. M., Hooten, M. B. & Fitzpatrick, R. M. Accounting for imperfect detection in Hill numbers for biodiversity studies. Methods Ecol. Evol. 6, 99–108 (2015).

    Article  Google Scholar 

  93. Greenland, S. Principles of multilevel modelling. Int. J. Epidemiol. 29, 158–167 (2000).

    Article  CAS  PubMed  Google Scholar 

  94. Arnold, T. W. Uninformative parameters and model selection using Akaike’s information criterion. J. Wildl. Manage. 74, 1175–1178 (2010).

    Google Scholar 

Download references

Acknowledgements

We acknowledge the dedicated work of all UWIN members and all those who have supported the ongoing research, education and outreach missions of the network. We specifically recognize R. Mueller from Northwest Trek Wildlife Park and Z. Hawn of Point Defiance Zoo and Aquarium for their data contributions to this project. This research was partially supported by the National Science Foundation through the Central Arizona-Phoenix Long-Term Ecological Research Program grant no. DEB-1832016. Funding for M.F., E.W.L. and S.B.M. was provided by the Abra Prentice-Wilkin Foundation and the EJK Foundation.

Author information

Authors and Affiliations

  1. School of Life Sciences, Arizona State University, Tempe, AZ, USA

    Jeffrey D. Haight & Sharon J. Hall

  2. Urban Wildlife Institute, Lincoln Park Zoo, Chicago, IL, USA

    Mason Fidino, Cria A. M. Kay, Elizabeth W. Lehrer, Seth B. Magle & Maureen Murray

  3. Washington University in St. Louis, St. Louis, MO, USA

    Solny A. Adalsteinsson & Elizabeth Biro

  4. Kansas State University, Manhattan, KS, USA

    Adam A. Ahlers

  5. Butler University, Indianapolis, IN, USA

    Julia Angstmann, Travis Ryan & Carmen Salsbury

  6. University of Health Sciences and Pharmacy, St. Louis, MO, USA

    Whitney J. B. Anthonysamy

  7. University of Maryland, College Park, MD, USA

    Merri K. Collins & Travis Gallo

  8. St. Edward’s University, Austin, TX, USA

    Barbara Dugelby

  9. School of Biological Sciences, University of Utah, Salt Lake City, UT, USA

    Austin M. Green & Çagan H. Șekercioğlu

  10. University of Colorado Denver, Denver, CO, USA

    Laura Hartley

  11. Seattle University, Seattle, WA, USA

    Mark J. Jordan

  12. Woodland Park Zoo, Seattle, WA, USA

    Robert A. Long & Katie R. Remine

  13. University of Iowa, Iowa City, IA, USA

    Brandon MacDougall & Heather A. Sander

  14. Conservation Society of California, Oakland, CA, USA

    Darren E. Minier

  15. Atlanta Coyote Project, Atlanta, GA, USA

    Chris Mowry & Larry Wilson

  16. Central Florida Zoo and Botanic Gardens, Sanford, FL, USA

    Kristina Nininger

  17. Sageland Collaborative, Salt Lake City, UT, USA

    Mary E. Pendergast

  18. University of California, Berkeley, Berkeley, CA, USA

    Christopher J. Schell

  19. Department of Molecular Biology and Genetics, Koç University, Istanbul, Türkiye

    Çagan H. Șekercioğlu

  20. City of Edmonton, Edmonton, AB, Canada

    Catherine J. Shier

  21. Texas Parks and Wildlife Department, Austin, TX, USA

    Kelly C. Simon

  22. University of Alberta, Edmonton, AB, Canada

    Colleen C. St. Clair & Cassondra J. Stevenson

  23. California State University, Long Beach, Long Beach, CA, USA

    Theodore Stankowich

  24. San Francisco Public Utilities Commission, San Francisco, CA, USA

    Lisa Wayne

  25. Seneca Park Zoo, Rochester, NY, USA

    Dave Will

  26. Wildlife Habitat Council, Bethesda, MD, USA

    Jacque Williamson

  27. Occidental College, Los Angeles, CA, USA

    Amanda J. Zellmer

  28. Arroyos & Foothills Conservancy, Pasadena, CA, USA

    Amanda J. Zellmer

  29. College of Integrative Sciences and Arts, Arizona State University, Mesa, AZ, USA

    Jesse S. Lewis

Authors
  1. Jeffrey D. Haight
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sharon J. Hall
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mason Fidino
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Solny A. Adalsteinsson
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Adam A. Ahlers
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Julia Angstmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Whitney J. B. Anthonysamy
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Elizabeth Biro
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Merri K. Collins
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Barbara Dugelby
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Travis Gallo
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Austin M. Green
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Laura Hartley
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Mark J. Jordan
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Cria A. M. Kay
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Elizabeth W. Lehrer
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Robert A. Long
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Brandon MacDougall
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Seth B. Magle
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Darren E. Minier
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Chris Mowry
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Maureen Murray
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Kristina Nininger
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Mary E. Pendergast
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Katie R. Remine
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Travis Ryan
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Carmen Salsbury
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Heather A. Sander
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Christopher J. Schell
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. Çagan H. Șekercioğlu
    View author publications

    You can also search for this author in PubMed Google Scholar

  31. Catherine J. Shier
    View author publications

    You can also search for this author in PubMed Google Scholar

  32. Kelly C. Simon
    View author publications

    You can also search for this author in PubMed Google Scholar

  33. Colleen C. St. Clair
    View author publications

    You can also search for this author in PubMed Google Scholar

  34. Theodore Stankowich
    View author publications

    You can also search for this author in PubMed Google Scholar

  35. Cassondra J. Stevenson
    View author publications

    You can also search for this author in PubMed Google Scholar

  36. Lisa Wayne
    View author publications

    You can also search for this author in PubMed Google Scholar

  37. Dave Will
    View author publications

    You can also search for this author in PubMed Google Scholar

  38. Jacque Williamson
    View author publications

    You can also search for this author in PubMed Google Scholar

  39. Larry Wilson
    View author publications

    You can also search for this author in PubMed Google Scholar

  40. Amanda J. Zellmer
    View author publications

    You can also search for this author in PubMed Google Scholar

  41. Jesse S. Lewis
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

J.D.H., J.S.L., S.J.H. and M.F. conceptualized the manuscript and wrote the initial draft. J.D.H. and M.F. designed and conducted all the statistical analyses. All authors (J.D.H., J.S.L., S.J.H., M.F., S.A.A., A.A.A., J.A., W.J.B.A., E.B., M.K.C., B.D., T.G., A.M.G., L.H., M.J.J., C.A.M.K., E.W.L., R.A.L., B.M., S.B.M., D.E.M., C.M., M.M., K.N., M.E.P., K.R.R., T.R., C.S., C. J. Schell, Ç.H.Ș., C. J. Shier, K.C.S., C.C.S.C., T.S., C. J. Stevenson, L. Wayne, D.W., J.W., L. Wilson and A.J.Z.) contributed data and assisted with drafting and editing the manuscript.

Corresponding author

Correspondence to Jeffrey D. Haight.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Ecology & Evolution thanks Rachel Blakey, Jiajia Liu and Ben Hirsch for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Community-average occupancy probability, species richness, and species diversity across gradients of local environmental predictors.

a-c, urbanization. d-f, natural patch density. g-i, agricultural footprint. Across 725 sites in 20 North American cities, we used a Bayesian multi-city community occupancy model and accompanying community composition meta-analysis models to estimate the local, within-city response variables community-average occupancy (a,d,g), species richness (b,e,h) and species diversity (c,f,i). These three response variables respectively refer to the average probability of site use among the mammal community, the total number of species at each site (Hill Number 0), and the number of species at each site weighted by species evenness (the exponentiated Shannon index; Hill Number 1). Based on modeled effects of within-city variables on occupancy (Table 1, Effect Type 1) and on richness and diversity (Table 2, Effect Type 1), we predicted occupancy, richness, and diversity values across hypothetical ranges of each within-city variable, with all other variables held constant at their mean values; we then represented the median and 95% Bayesian credible interval of these predicted values using the trendlines and their corresponding shaded regions. The points and bars correspond to the mean and 95% CRI of 10,000 posterior estimates of richness and diversity at each camera site, based on actual ranges of within-city variables.

Extended Data Fig. 2 Regional species richness in relation to regional environmental covariates.

a-d, vegetation greenness (a); temperature (b); urbanization (c); and city age (d). Estimates of regional species richness γr were calculated as the sum of predicted species presence values within each of 20 cities (\({\gamma }_{r}\,={\sum }_{s}^{S}{\omega }_{s,r}\)), using the probability of regional species presence Ωr to correct observed species richness for the region-wide imperfect detection of species. Points correspond to each city’s mean value of γr across 60,000 Bayesian posterior estimates. Trendline and shaded region respectively depict the median and 95% Bayesian credible interval of γr predicted across hypothetical ranges of among-city covariate values, where all other covariates were held constant at their mean. We represented regional vegetation greenness using the Enhanced Vegetation Index (EVI), regional temperature corresponds to mean annual temperature (in °C), regional urbanization was estimated as the city’s overall percentage of urban land cover types, and we measured city age as the approximate number of years since Euroamerican colonization of the metropolitan region.

Extended Data Fig. 3 Influences of species traits on site-level mammal occupancy.

a, body mass, represented by the log-transformed mean body mass of each species (in kg). b, carnivory, calculated as the percentage of vertebrate prey in each species’ diet. Each point and bar respectively represent the mean and 95% Bayesian credible interval (CRI) of estimated occupancy probabilities for 29 mammal species commonly detected across 725 camera sites in 20 North American cities (excluding eight species detected in fewer than 10 total trap-days). Trendline and shaded region depict the median and 95% CRI of response variables predicted across a hypothetical range of trait values. Results primarily demonstrate that more carnivorous species are generally rarer.

Extended Data Fig. 4 Collinearity between pairs of local (within-city) covariates.

Within-city covariates include local urbanization, natural patch density, and agricultural intensity across 725 sites in 20 North American cities. The diagonal cells of the figure depict the frequency distribution of values for each covariate, upper-right half of the figure depicts the Pearson correlation between each pair of covariates, and the lower-left half visualizes each correlation in the form of a scatterplot.

Extended Data Fig. 5 Collinearity between pairs of regional variables of 20 North American cities.

The diagonal cells of the figure depict the frequency distribution of values for each variable, upper-right half of the figure depicts the Pearson correlation between each pair of variables, and the lower-left half visualizes each correlation in the form of a scatterplot. We selected four variables to include in our final analysis as among-city covariates: vegetation greenness, mean annual temperature, regional urbanization, and city age. EVI = Enhanced Vegetation Index; PET = potential evapotranspiration; MAT = mean annual temperature; MAP = mean annual precipitation; MST = mean summer temperature; MSP = mean summer precipitation; CMD = climatic moisture deficit; URB = urban land cover (regional urbanization); AGR = agricultural land cover (regional agricultural area); NAT = natural land cover; FOR = woody vegetation (forest, shrubland) cover; PD = natural patch density; AGE = city age (years since colonization); LAT = latitude of city center; LON = longitude of city center.

Extended Data Table 1 Summary characteristics of 20 study cities included in the analysis of mammal presence and community composition. Species observation data from each city were collected via motion-triggered camera traps during the same 35-day summer period within different sampling years within different study areas. We represented sampling effort of each city using the total number of sites sampled and the across-site sum of its camera trap-days, the number of days in which each site was functional and collecting data. Total sampling effort was 20,176 camera trap-days across all 725 sites. We used four regional environmental variables to differences in among-city environment in our analysis: regional vegetation greenness (Enhanced Vegetation Index; EVI); regional temperature (mean annual temperature, in °C; MAT); regional urbanization (% urban land cover; URB); regional city age (years since colonization; AGE)
Full size table

Supplementary information

Supplementary Information

Supplementary Tables 1 and 2.

Reporting Summary

Peer Review File

Supplementary Data 1

Full lists of modelled species-level and community-level effect parameters and species trait information. The effect parameters were estimated using a Bayesian multi-region community occupancy model and meta-analysis models (species richness and diversity).

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Haight, J.D., Hall, S.J., Fidino, M. et al. Urbanization, climate and species traits shape mammal communities from local to continental scales. Nat Ecol Evol 7, 1654–1666 (2023). https://doi.org/10.1038/s41559-023-02166-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41559-023-02166-x

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing