Temporal correlation

We firstly examined the degree of synchrony of FLD and FFD, in relation to the WWS temporally. The phenological data were selected from the Pan European Phenological Database (PEP725)27. The daily gridded climate data, with a spatial resolution of 0.25°, were obtained from the European Climate Assessment & Dataset project (E-OBS)28. The degree of phenological synchrony, i.e., the degree of timing-convergence, was assessed by the standard deviation (SD) of the FLD and FFD among all individual plants within an area coincident with the geographical grids defined in the climate data1. A low SD indicates a high degree of synchrony. The WWS for the FLD and FFD was calculated as the linear slope of the daily mean temperature against the days of the year during spring (see Method).

During the period from 1951 to 2011, the average FLD and FFD for all individual plants in the PEP725 dataset advanced significantly and the long-term advancement was closely associated with a distinct increase in the long-term temperature in the spring season (supplementary Figure 1). This is consistent with many previous claims that recent global warming has resulted in significant phenological shifts worldwide11,13,29,30,31. The SDs of the FLD and FFD among individual plants within each grid, however, exhibited large year-to-year fluctuations without a distinct long-term trend (Fig. 2a,c), indicating that the long-term temperature increase, i.e., the change in the temperature magnitude alone had no significant effect on the annual synchrony of the FLD or FFD.

Figure 2 The standard deviation (SD) of spring phenological dates in relation to the within-spring warming speed (WWS, °C/day) during the period 1951–2011. (a,c) show the annual variations in the SD of the first leafing day (FLD) and the first flowering day (FFD). (b,d) show the WWS during spring for FLD and FFD. For each year, the SDs of the FLD or FFD were obtained for all the individual plants located within each grid. The bottom and the top of the box denote the 25th and 75th percentiles, respectively and the line within the box represents the 50th percentile (the median). The whiskers extends to the maximum and the minimum SD excluding the outliers, which are the SD >(q3 + 1.5(q3 − q1)) or SD< (q1 − 1.5(q3 − q1)), where q1 and q3 are the 25th and 75th percentiles, respectively. (e,f) are scatterplots of the annual mean SD in relation to the annual mean WWS for the FLD (r = −0.75, P < 0.01) and FFD (r = −0.55, P < 0.01). The shaded region represents the 95% confidence interval of the regression line. All of the regressions have P-values < 0.01. Full size image

We then examined the relationship between the annual SDs of the two spring events in relation to the within-spring warming speed. Visually, year-to-year fluctuations of WWS presented a rough inverse pattern with that of SDs (Fig. 2b,d). Decrease in SD was associated with increase in WWS. In addition, a simple linear regression analysis showed that the annual SDs of the FLD and FFD were both significantly higher in the years with lower annual WWSs (Fig. 2e,f). To reduce the bias caused by a small number of observational data in a grid, we also conducted the regression analysis after excluding grids containing fewer than 10 individuals and the results were consistent (supplementary Figure 2). Further partial correlation analysis confirmed that the annual SDs showed no significant partial correlation with the annual averaged spring temperature and total spring precipitation (Supplementary Figure 3). These results indicate that the rate of temperature increase but not the magnitude of temperature within the spring season is probably responsible for the annual fluctuation in the synchrony of the spring phenological events.

Spatial correlation

The within-spring warming rate is also expected to change spatially. The WWS calculated from the multi-year mean daily temperature for each geographical grid was higher in inland areas than in coastal areas (Fig. 3). We investigated whether spatial changes in the WWS affected the synchrony of local plants. The results showed that the SDs of the multi-year mean FLD and FFD were lower in the inland grids than in the coastal grids, especially for the FLD (Fig. 4a,b). As a result, the SDs decreased significantly with the increase of the local WWS, with a correlation coefficient of −0.48 for the FLD and −0.23 for the FFD (Fig. 4c,d). Moreover, this correlation pattern was consistent for both individual plants grouped by each species and for species averages per grid (supplementary Figures 4 and 5).

Figure 3 The geographical pattern of the within-spring warming speed (WWS, °C/day). The map was created using MATLAB 8.0 (http://cn.mathworks.com/). Full size image

Figure 4 The spatial patterns of the standard deviation (SD) of spring phenological dates, the temperature sensitivity and their association with the within-spring warming speed (WWS, °C/day). (a,b) show the SDs of the FLD and FFD; (e,f) show the SDs of the temperature sensitivity of the FLD and FFD, respectively. Each coloured point indicates the SD of the multi-year averaged phenological dates or their temperature sensitivity for all plants within a grid. The scatterplots show the SDs in relation to the WWS, with each data point denoting one grid. c and d show the SDs of the FLD and FFD associated with their WWS; (g,h) show the SDs of the FLD and FFD temperature sensitivity associated with their WWS, respectively. Grids with individual plant numbers <=10 are indicated by white colour and were not included in the correlation analysis. The line is the linear regression line and the shaded region represents the 95% confidence interval. All of the regressions have P-values < 0.01. (a,b,e,f) were created using MATLAB 8.0 (http://cn.mathworks.com/). Full size image

Some factors may affect the correlations between the SDs and WWS. To evaluate whether any of the data processes introduced bias into the results obtained above, we performed the following procedures. First, to reduce the bias caused by a small number of observational data in a grid, we excluded grids containing fewer than 20 individuals (supplementary Figure 6). Second, to cope better with the phenological data, we examined synchrony by deeming each phenology station as the minimum unit instead of using the grid (supplementary Figure 7). Third, we examined the interquartile range (i.e., the upper quartile minus the lower quartile) instead of the SD to quantify synchrony (supplementary Figure 8). After the above procedures, we found that all of the results were consistent with our original findings. Finally, the results were also consistent with the initial results after controlling for other potential factors including the altitudinal variance, the spatial aggregation of the phenology stations, species diversity of the collected data within a grid as well as the mean spring temperature and total spring precipitation (supplementary method, supplementary Figure 9).

Temperature sensitivity

The phenological trait, temperature sensitivity which characters the response of phenology to temperature, receives a lot of concerns in the context of global warming18,19,26. The synchrony of the temperature sensitivity as well has important ecological and evolutionary consequences27. We thus further examined whether the synchrony of the temperature sensitivity is also correlated to the within-spring warming speed. Herein, the temperature sensitivity was evaluated as a linear slope of the event dates with respect to an effective temperature (see method, days/°C). We conducted all the analysis performed above and found that in the areas with a slower WWS, the FLD and FFD show larger SDs of temperature sensitivity among individual plants, among individual plants grouped by each species or for species averages per grid (Fig. 4g,h; supplementary Figures 4–10). It is in line with the synchrony of the timing of phenological events.