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Prediction-based approach for quantifying phenological mismatch across landscapes under climate change

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Abstract

Context

Climate change is driving phenological shifts across landscapes, but uncoordinated shifts might cause a potential “phenological mismatch.” There has been little consensus on the existence and magnitude of such a mismatch. The lack of agreement among studies can be attributed to the wide variety of definitions for the term “phenological mismatch,” as well as the methods used to measure it. The lack of comparability among measures of phenological mismatch creates a challenge for conservation.

Objectives

We proposed a novel theoretical framework to generalize existing measures of phenological mismatch and an approach to quantify the decoupling between phenology and the environment using the loss in predictive skill over time. We aimed to estimate the magnitude of phenological mismatch on large spatial scales and test the proposed predictive approach’s ability to detect multiple types of phenological mismatch.

Methods

We modeled historical climate-phenology coupling and quantified phenological mismatch as the deviation between observed and predicted phenology under climate change. First, we used two large empirical spatiotemporal datasets to estimate phenological mismatch in plant flowering phenology in the eastern United States and bird reproductive phenology in Finland. Historical climate-phenology coupling was modeled with spatial linear regression. Second, we conducted four simulation experiments representing different types of mismatch during climate change. We recovered simulated phenological mismatch by fitting a data-driven nonlinear model (Gaussian Process Empirical Dynamic Modeling) and predicting phenology.

Results

In the eastern US, we found that advancing plant flowering phenology generally matched spring warming from 1895 to 2015, with seven out of the 19 species studied having significant phenological mismatches, with observed flowering time earlier than predictions even considering warming. A similar phenological mismatch was found in birds in Finland from 1975 to 2017, with the bird breeding season advancing more than expected in 21 out of the 36 species studied. In four simulation experiments, we were able to accurately recover the simulated phenological mismatches in the timing of events, pace of development, and intensity of activities, although with greater challenges in quantifying a mismatch in life history.

Conclusions

Overall, these case studies show that our prediction-based measure effectively quantifies multiple types of phenological mismatch, providing a more generalizable and comparable measure of phenological mismatch across study systems and scales. This study will enable the investigation of phenological mismatch at large scales, improving understanding of the patterns and consequences of climate-change-induced phenological changes.

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Data availability

We used published and publicly available raw data in all case studies. Plant reproductive phenology data from herbarium specimens in the eastern continental United States (Park et al. 2018) are available from the Harvard Forest Data Archive (HF309): https://harvardforest1.fas.harvard.edu/exist/apps/datasets/showData.html?id=HF309. Bird nestling ringing data in Finland (Hällfors et al. 2020) are available from Dryad: https://doi.org/10.5061/dryad.wstqjq2ht. TerraClimate data (Abatzoglou et al. 2018) are available from the data catalog of the Climatology Lab: https://www.climatologylab.org/terraclimate.html. The fully reproducible workflow, including code and data, is available on Zenodo (https://doi.org/10.5281/zenodo.7553656).

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Acknowledgements

Y. Song was supported by the UCSC Hammett Fellowship and Microsoft Azure AI for Earth grant. K. Zhu was supported by the UCSC Committee on Research Faculty Research Grant and NSF Awards 1926438, 2306198 (CAREER).

Funding

Y. Song was supported by the UCSC Hammett Fellowship and Microsoft Azure AI for Earth grant. K. Zhu was supported by the UCSC Committee on Research Faculty Research Grant and NSF Awards 1926438, 2306198 (CAREER).

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Authors and Affiliations

  1. Department of Environmental Studies, University of California, Santa Cruz, CA, USA

    Yiluan Song & Kai Zhu

  2. Department of Applied Mathematics, University of California, Santa Cruz, CA, USA

    Stephan B. Munch

  3. Institute for Global Change Biology and School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, USA

    Kai Zhu

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  1. Yiluan Song
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  2. Stephan B. Munch
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  3. Kai Zhu
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Contributions

All authors contributed to the study conception and design. Data analyses were performed by YS. Methods of Gaussian Process empirical dynamic modeling was developed by SBM. The draft of the manuscript was written by YS and all authors commented and edited. All authors read and approved the final manuscript.

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Correspondence to Kai Zhu.

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Song, Y., Munch, S.B. & Zhu, K. Prediction-based approach for quantifying phenological mismatch across landscapes under climate change. Landsc Ecol 38, 821–845 (2023). https://doi.org/10.1007/s10980-023-01595-0

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