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Winter temperatures predominate in spring phenological responses to warming


Research on woody plant species highlights three major cues that shape spring phenological events: chilling, forcing and photoperiod. Increasing research on the phenological impacts of climate change has led to debate over whether chilling and/or photoperiod cues have slowed phenological responses to warming in recent years. Here we use a global meta-analysis of all published experiments to test the relative effects of these cues. Almost all species show strong responses to all three cues, with chilling being the strongest and photoperiod the weakest. Forecasts from our findings for Central Europe suggest that spring phenology will continue to advance, as stalling effects of chilling generally appear above 4 °C warming in this region. Our results unify both sides of the debate over phenological cues: while all species may respond to all cues strongly in experimental conditions, in current environmental conditions the dominant signal of climate change is from increased forcing.

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Fig. 1: Short-term experiments on woody plant phenology manipulate photoperiod and temperature to estimate chilling, forcing and photoperiod cues.
Fig. 2: Estimated effects of chilling, forcing and photoperiod on budburst timing across 65 species.
Fig. 3: Estimates of budburst across a range of forcing temperatures and estimated chilling.
Fig. 4: Implications of warming on budburst timing varies across species and sites, depending strongly on prewarming climate conditions related to chilling for each site.

Data availability

The OSPREE budburst database used in this manuscript is publicly archived in the Knowledge Network for Biocomplexity30.

Code availability

The code for models used in this manuscript is publicly archived in the Knowledge Network for Biocomplexity30.


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We thank the many researchers who conducted the experiments synthesized in this manuscript. We thank B. Cook for help with climate data, E. Forrestel for assisting with data scraping and C. Zohner for sharing tables. We also thank J. Davies, S. Elmendorf and J. HilleRisLambers for helpful comments that improved the manuscript. The National Science Foundation (grant no. DBI 14-01854 to A.K.E.), NSERC Discovery Award (grant no. RGPIN-05038 to E.M.W.), Canada Research Chair in Temporal Ecology (E.M.W.) and Spanish Ministry for Science and Innovation (grant no. CGL2017-86926-P and PID2019/109711RJ-I00 to I.M.-C.) provided funding. Any opinion, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Author information




D.F.B.F., T.S. and E.M.W. conceived of the OSPREE database, which gave rise to this manuscript. All authors worked tirelessly to build the database and all contributed data analysis and/or code. C.J.C., D.M.B., E.M.W., I.M.-C. and A.K.E. created the figures. A.K.E. and E.M.W. wrote most of the manuscript, with substantial contributions from C.J.C., D.M.B. and I.M.-C. All authors reviewed and revised the manuscript.

Corresponding author

Correspondence to A. K. Ettinger.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Climate Change thanks Albert Phillimore, Constantin Zohner and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Extended data

Extended Data Fig. 1 Map of days to budburst experiments in the OSPREE database.

Legend shows each dataset included in the main OSPREE model with all species and treatments (Supplementary Tables 5, 6); symbols outlined in black represent datasets included in the main budburst model; triangles represent studies in which chilling was manipulated experimentally or through multiple field sample dates; circles represent studies that did not manipulate chilling. See Supplementary Table 1 for the reference associated with each dataset.

Extended Data Fig. 2 Map of maximum and minimum chilling, forcing, and photoperiod treatments, across all data included in our main model, and the locations where each experiment was conducted.

We did not find strong positive spatial autocorrelation—that is, indicating higher similarity in the treatments applied to experiments in nearby locations- in either minimum (A,C,E) or maximum (B,D,F) treatments, as measured by Moran’s I (shown here for European sites). Insets show relationship of each cue’s treatment level with the latitude of the experiment.

Extended Data Fig. 3 The diversity of study designs used in analyses.

Heatmaps show the range and commonness of different forcing (x-axis in all panels) by photoperiod (y-axis in all panels) combinations and with which chilling they were combined. In (A, top and bottom) we show our estimated chill units, which integrate across field (B, top and bottom) and experimental chilling (C, top and bottom). The top row shows all data included in the full model with 203 species, while the bottom row shows the data included in the main model with a subset of species well-represented across treatments and studies. Grey squares indicate a treatment was not applied (that is, the prevalence of grey squares in (C) highlights how few studies include experimental chilling). Field sample dates are counted as any reported sampling dates more than 14 days apart.

Extended Data Fig. 4 Chilling accumulates differently in experiments with constant temperatures versus natural systems, in which temperature is more strongly correlated with chilling.

See ‘Estimating chilling’ in Methods for a detailed description of ‘Field’ climate data, for which we use historical climate data from Europe. Fig. 3 uses ‘Field’ relationships (that is, climate data and relationships from field chilling conditions to convert chill temperature to total chilling), whereas Supplementary Fig. 2 uses ‘Constant temperature’ conditions (analogous to most experimental conditions) to estimate total chilling.

Extended Data Fig. 5 Estimates for effects of chilling exceeded estimates for forcing, photoperiod, provenance latitude, and the interaction between latitude and photoperiod, for most species, in the latitude budburst model.

Using Utah units for chilling (Supplementary Table 10) and standardized predictor variables, which allow comparisons across cues, we show that, as with the main budburst model (Fig. 2), most species (smaller symbols) are responsive to most cues. Chilling is the strongest cue when considering overall estimates across species (larger, dark blue circles).

Extended Data Fig. 6 Forecasted changes in chilling and spring phenology vary with amount of warming across European locations included in the PEP725 database.

Changes in chilling (top panel) and budburst for Betula pendula (bottom panel) are calculated relative to the mean chilling and leafout dates during a prewarming time period (1951-1960) for each location. Arrows indicate sites shown in Fig. 4a and Supplementary Fig. 4A (latitude = 46.8N, longitude = 12.8E, 659 m above sea level) and Fig. 4b and Supplementary Fig. 4B (latitude = 48.3N, longitude = 15.8E, 210 m above sea level).

Extended Data Fig. 7 Budburst is affected by climate change induced shifts in photoperiod, especially at high latitudes, although effects vary by site and are minor compared to effects of warming.

We show forecasted effects of varying levels of warming on Fagus sylvatica, the most photoperiod-sensitive species in our database, across three latitudes within its range, as predicted by the latitude model. The low latitude site (A) is located at 46.8N, 15.7E; the mid-latitude site (B) is located at 47.7N, 16.3E; and the high-latitude (C) site is located at 48.8N, 15.4E.

Extended Data Fig. 8 Declining sensitivities observed in long-term European data for a suite of common trees may be explained by a statistical artefact.

We compared the sensitivity estimated from linear regressions of day of leafout versus mean spring temperature (estimated thus as days/°C) from PEP725 data for Betula pendula from 45 sites (‘European data’) with estimated declines using simulations where the cues were held constant but spring temperatures warmed by 1-4°C (‘Simulations’) and found the estimated temperature sensitivity measured as days/°C declined even though the underlying cues had not changed. See Potential statistical artefacts in declines of temperature sensitivity in observational long-term data in the Supplementary Information for further details.

Extended Data Fig. 9 Day of leafout varies with chilling, growing degree-days, and mean spring temperature.

These relationships are shown prewarming (left panels, 1951-1960) and post-warming (right panels, 2000-2010) for PEP725 sites in Germany where Betula pendula phenology has been monitored for decades.

Extended Data Fig. 10 Growing degree days (GDD) versus chill units at the time of budburst from the OSPREE database for common species in the PEP725 long-term phenological database.

The black line shows the range of chilling (10-90% quantiles) accumulated from 1 September to 1 March for 45 sites for Betula pendula (see also Potential statistical artefacts in declines of temperature sensitivity in observational long-term data in the Supplementary Information). We calculated GDD here as the average daily forcing temperature multiplied by days to budburst.

Supplementary information

Supplementary Information

Supplementary text, Figs. 1–5, Tables 1–14 and references.

Supplementary Appendix 1

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Ettinger, A.K., Chamberlain, C.J., Morales-Castilla, I. et al. Winter temperatures predominate in spring phenological responses to warming. Nat. Clim. Chang. 10, 1137–1142 (2020).

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