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The effect of urbanization on plant phenology depends on regional temperature

Abstract

Plant and animal phenology is shifting in response to urbanization, with most hypotheses focusing on the ‘urban heat island’ (UHI) effect as the driver. However, generalities regarding the direction and magnitude of phenological response to urbanization have not yet emerged because most studies have focused on remote-sensed vegetative phenologies or at local scales with relatively few species. Furthermore, how urbanization interacts with broad-scale climate gradients remains an unknown but important component of anthropogenically driven phenological change. Here, we used a database with >22 million in situ plant phenological observations from the United States and Europe to study the joint influence of varying human population density, which serves as an urbanization measure, and of regional temperature on median flowering and leaf-out dates across a wide plant phylogenetic spectrum. Separately, increasing population density and warmer regional temperature both advanced plant flowering and leaf-out. However, the influence of human population density on plant flowering and leaf-out depends on the regional temperature: high population density advanced plant phenology in cold areas but this effect disappeared or even reversed in warm areas. UHI effects (as measured by daily land surface temperature) alone cannot explain the overall influence of urbanization on plant phenology, suggesting that urbanization also affects plant phenology via other mechanisms. Shorter plants with large specific leaf areas and early flower or leaf-out dates were most affected by urbanization and temperature changes. Our study provides strong empirical evidence that the influence of urbanization on plant phenology varies with regional temperature. Therefore, robust understanding and accurate prediction of phenological changes must take this interaction into account.

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Fig. 1: Locations of plant phenology observations used in the analyses.
Fig. 2: Population density interacts with regional temperature to affect plant phenology.

flower and leaf silhouettes reproduced from PhyloPic under a Creative Commons licence CC0 1.0

Data availability

All data used in this study are available online at https://www.plantphenology.org. See Methods for details about data sources.

Code availability

Key R codes used in this study are available at Github (https://github.com/daijiang/Phenology_NEE) and are permanently archived at Zenodo (https://zenodo.org/badge/latestdoi/202783130).

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Acknowledgements

We thank A. R. Ives and E. G. Denny for helpful comments on the manuscript. Data were provided by USA-NPN and the many participants who contribute to its Nature’s Notebook programme, the Pan European Phenology Dataset and the National Ecological Observatory Network. B.B. was supported by an NSF-ABI Award (no. 1458034). A grant from the US Geological Survey under Grant/Cooperative Agreement no. G16AC00268 to R.P.G., and support for J.D. from an NSF-SAVI Award (no. 1321595), were critical in development of PPO and associated data-processing tools upon which this contribution is built.

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Contributions

D.L., B.J.S., B.B. and R.P.G designed the study. B.J.S. J.D. and R.P.G. compiled the database. D.L. analysed the data and all authors discussed the results. D.L. wrote the manuscript with substantial input from R.P.G., B.J.S. and B.B. All authors commented on the manuscript and approved the final version of the manuscript.

Corresponding author

Correspondence to Daijiang Li.

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The authors declare no competing interests.

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Extended data

Extended Data Fig. 1

Human population density of 10 km by 10 km grid cells of the USA (the 2010s) and Europe (2011).

Extended Data Fig. 2

Long-term average temperature from November to May of the USA and Europe based on three commonly used datasets. See Methods for details about each dataset.

Extended Data Fig. 3 Phylogeny, estimated coefficients (conditional modes) based on random terms of linear mixed models, and functional traits for all 136 plant species.

Note that only a fraction of these species has both flowering and leaf-out data. The intercepts can be used as approximates for plant phenology (day of year), while the slopes (Pop., Temp., and Temp.:Pop.) were estimates of phenological shifts.

Extended Data Fig. 4 Relationships between shifts in plant flowering and functional traits.

Median flowering times were estimated as the conditional modes of random term intercept of the best linear mixed model in Table 1 and used as a functional trait. Shifts in flowering time were estimated as conditional modes of random slopes of the best linear mixed model in Table 1. Each point presents a species. Regression lines in red indicate significant relationships.

Extended Data Fig. 5 Relationships between shifts in plant leaf-out and functional traits.

Median leaf-out times were estimated as the conditional modes of random term intercept of the best linear mixed model in Table 1 and used as a functional trait. Shifts in leaf-out time were estimated as conditional modes of random slopes of the best linear mixed model in Table 1. Each point presents a species. Regression lines in red indicate significant relationships.

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Li, D., Stucky, B.J., Deck, J. et al. The effect of urbanization on plant phenology depends on regional temperature. Nat Ecol Evol 3, 1661–1667 (2019). https://doi.org/10.1038/s41559-019-1004-1

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