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.
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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.
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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.
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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.
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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.
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Supplementary Tables 1 and 2.
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).
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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
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DOI: https://doi.org/10.1038/s41559-023-02166-x
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