A statistical estimator for determining the limits of contemporary and historic phenology

Abstract

Climate change affects not just where species are found, but also when species’ key life-history events occur—their phenology. Measuring such changes in timing is often hampered by a reliance on biased survey data: surveys identify that an event has taken place (for example, the flower is in bloom), but not when that event happened (for example, the flower bloomed yesterday). Here, we show that this problem can be circumvented using statistical estimators, which can provide accurate and unbiased estimates from sparsely sampled observations. We demonstrate that such methods can resolve an ongoing debate about the relative timings of the onset and cessation of flowering, and allow us to place modern observations reliably within the context of the vast wealth of historical data that reside in herbaria, museum collections, and written records. We also analyse large-scale citizen science data from the United States National Phenology Network and reveal not just earlier but also potentially more variable flowering in recent years. Evidence for greater variability through time is important because increases in variation are characteristic of systems approaching a state change.

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Fig. 1: Example demonstration of the difference between our method and taking first observations at face value.
Fig. 2: Rates of change of the onset, bulk and cessation of flowering through time are tightly correlated in the Rocky Mountain dataset.
Fig. 3: Reconciling flowering phenology in two historic datasets.
Fig. 4: Annual variation in flowering phenology throughout North America in the NPN data.

Change history

  • 09 February 2019

    In the version of this Article originally published, the rate of change plotted in Figure 2 was incorrect because of a coding error. The corrected figure is shown below. In the original Figure 2 legend, the onset of flowering slope was given as ‘0.99, 95% CI: 0.90–1.08’, the cessation of flowering slope was given as ‘1.02, 95% CI: 0.91–1.13’, and the r2adjusted for each model was given as greater than 74%’. The correct values are ‘1.04, 95% CI: 0.97–1.12’, ‘1.25, 95% CI: 0.87–1.62’, and ‘60%’, respectively. The main text and the conclusion that the slopes of these relationships are statistically indistinguishable from 1.00 are unchanged. These errors have now been corrected in the PDF and HTML versions of the article. The authors are grateful to A. Iler, who drew attention to this issue.

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Acknowledgements

We thank the volunteers of the NPN for data collection. W.D.P. and T.J.D. were funded by Fonds de Recherche Nature et Technologies grant number 168004. The Colorado data collection was supported by National Science Foundation grants DEB 75-15422, DEB 78-07784, BSR 81-08387, DEB 94-08382, IBN 98-14509, DEB 02-38331 and DEB 09-22080 to D.W.I. We are grateful to D. Roberts and A. Solow for helping to calculate the s.e. of estimates of timing, and E. L. Wolkovich and B. G. Waring for comments on the paper. We also thank the photographers whose images we used in Fig. 3.

Author information

W.D.P., T.J.D. and C.C.D. conceived of the study. W.D.P. analysed the data. All authors wrote the manuscript and interpreted the results.

Correspondence to William D. Pearse or T. Jonathan Davies.

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Supplementary Information

Supplementary Figures 1–4, Supplementary Tables 1–8, Supplementary Reference

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R code

R code to calculate limits. This file was missing when the Article was fisrt published.

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Pearse, W.D., Davis, C.C., Inouye, D.W. et al. A statistical estimator for determining the limits of contemporary and historic phenology. Nat Ecol Evol 1, 1876–1882 (2017). https://doi.org/10.1038/s41559-017-0350-0

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