Author response:
The following is the authors’ response to the original reviews
Public Reviews:
Reviewer #1 (Public review):
Summary:
Jiang et al. present a measure of phenological lag by quantifying the effects of abiotic constraints on the differences between observed and expected phenological changes, using a combination of previously published phenology change data for 980 species, and associated climate data for study sites. They found that, across all samples, observed phenological responses to climate warming were smaller than expected responses for both leafing and flowering spring events. They also show that data from experimental studies included in their analysis exhibited increased phenological lag compared to observational studies, possibly as a result of reduced sensitivity to climatic changes. Furthermore, the authors present compelling evidence that spatial trends in phenological responses to warming may differ from what would be expected from phenological sensitivity, due to the seasonal timing of when warming occurs. Thus, climate change may not result in geographic convergences of phenological responses. This study presents an interesting way to separate the individual effects of climate change and other abiotic changes on the phenological responses across sites and species.
Greater phenological lag with experimental studies results in reduced sensitivity to climatic changes, not other way around.
Strengths:
A clearly defined and straightforward mathematical definition of phenological lag allows for this method to be applied in different scientific contexts. Where data exists, other researchers can partition the effects of various abiotic forcings on phenological responses that differ from those expected from warming sensitivity alone.
Sensitivity does not tell the magnitude of phenological changes, nor does it provide indications of mechanisms responsible for changes in spring phenology. Because of uneven warming, the same average temperature change (annual or spring temperatures) can have greater (greater warming prior to budburst) or smaller (smaller warming prior to budburst) phenological change than that with even warming. When average temperature change is close to zero, uneven warming can lead to infinite sensitivity values, either advanced (warmer temperatures prior to budburst) or delayed (cooler temperatures prior to budburst) spring phenology.
It is not clear why sensitivity is so popularly used in phenological research.
Identifying phenological lag and associated contributing factors provides a method by which more nuanced predictions of phenological responses to climate change can be made. Thus, this study could improve ecological forecasting models.
Weaknesses:
The authors include very few data visualizations, and instead report results and model statistics in tables. This is difficult to interpret and may obscure underlying patterns in the data. Including visual representations of variable distributions and between-variable relationships, in addition to model statistics, provides stronger evidence than model statistics alone.
The use of stepwise, automated regression may be less suitable than a hypothesis-driven approach to model selection, combined with expanded data visualization. The use of stepwise regression may produce inappropriate models based on factors of the sample data that may preclude or require different variable selection.
We used two statistical methods, variance analysis to examine differential phenological responses (Figure 2) and regression analysis to determine the relative importance of forcing change, budburst temperature, and physiological lag, the drivers of changes in spring phenology (Table 2). Our objective was to understand why plants show differential responses by research approach, species origin, climatic region, and growth form identified in previous research. Variable selection may affect minor (altitude, latitude, MAT, and average spring temperature change) or insignificant (photoperiod and long-term precipitation) variables, but not those related to drivers of spring phenology. We are not sure how hypothesis-driven approach can help with our objective.
Reviewer #2 (Public review):
Summary:
This is a meta-analysis of the relative contributions of spring forcing temperature, winter chilling, photoperiod and environmental variables in explaining plant flowering and leafing phenology. The authors develop a new summary variable called phenology lag to describe why species might have different responses than predicted by spring temperature.
Strengths:
The summary statistic is used to make a variety of comparisons, such as between observational studies and experimental studies.
Weaknesses:
By combining winter chilling effects, photoperiod effects, and environmental stresses that might affect phenology, the authors create a new variable that is hard to interpret. The authors do not provide information in the abstract about new insights that this variable provides.
Phenological lag contains effects of all constraints that may include chilling effects, photoperiod effects, and environmental stresses and is, indeed, hard to interpret without investigation of individual constraints. In our synthesis, spring phenology (or photoperiod effect) is not significant across all studies complied. It is also unlikely that lack of winter chilling causes the systemic differences in phenological lag between observational and experimental studies or between native and exotic species (see discussion at lines 335-339). At individual study level, the contribution of different constraints to the overall lag effect can be specifically determined if moisture stresses, species chilling and photoperiod effects, or cold hardiness are known from on-site monitoring or previous research.
The meaning of phenological lag is described at lines 34-38 in the abstract.
Comments:
It would be useful to have a map showing the sites of the studies.
A map showing the sites of the studies was added as supplementary Figure S1.
The authors should provide a section in which the strengths and weaknesses of the approach are discussed. Is it possible that mixing different types of data, studies, sample sizes, number of years, experimental set-ups, and growth habits results in artifacts that influence the results?
Both strengths and weaknesses are discussed at various places throughout the paper. The weakness of our method, as indicated by the reviewer, is the inclusion of different constraints in the phenological lag and has been described at lines 34-38 in the abstract and lines 80-86 in the introduction of the concept. We have also expanded Conclusion section to discuss possible caveats at lines 369-393.
As in all data analyses, the results can change with addition of more/different data, especially when sample size is relatively small. Ideally, comparisons are made among levels of fixed effects while controlling variations of other conditions. In phenological studies, however, climatic, phenological, and biological conditions all vary. For example, observational and experimental studies differ not only in the nature of warming (natural climate change vs artificial warming), but also in levels of warming (greater warming with experimental studies) and climatic, phenological, and biological conditions (Table 1). All phenological syntheses (or meta-analyses) have to make do with this uncontrolled nature of phenological data.
Now that the authors have created this new variable, phenological lag, which of the components that contribute to it has the most influence on it? Or which components are most influential in which circumstances? For example, what are some examples where photoperiod causes a phenological lag?
Any of the phenological constraints identified can contribute alone or in combination with others to the overall effect of phenological lag. Across all studies with this synthesis, the lack of significance with spring phenology rules out photoperiod effect, while the association of longer phenological lags with longer accumulation of winter chilling does not suggest general chilling shortage with the current extent of climate change.
Although spring phenology is not significant across all studies, photoperiod effect can be influential at individual studies where changes in spring phenology are large. However, reported photoperiod effects in the literature are mostly confounding effects with temperatures, i.e., longer photoperiods are associated with longer hours of high daytime temperatures (see Chu et al., 2021). Other than European beech under an unlikely scenario of climate change (growth resumes at beginning of winter), there has been not clear evidence showing the effect of photoperiod in constraining spring phenology.
Another confounding effect with photoperiod is extra heating effect with artificial light sources in warming experiments. Some early studies have shown that leaf temperature can be several degrees above the ambient air, due to long-wave radiation with artificial light sources. It is hard to believe the constraining effect of photoperiod on spring phenology if phenological changes are within inter-annual variations (can be a few weeks), although photoperiod effect has been increasingly discussed recently.
Recommendations for the authors:
Reviewing Editor:
A key methodological concern is the inconsistent definition of growth temperature across observations. It is calculated over the interval between the baseline phenological date and the expected date under warming - a window that varies by species, site, and treatment. This variability limits comparability across observations and may introduce circularity, as growth temperature is derived from the same modelled expectation (i.e., the expected phenological advance) that it is later used to explain.
The term “growth temperature” has been replaced with “budburst temperature” to indicate temperatures at species events. Budburst temperature is the average temperature within the window of expected response with the warmer climate and, as indicated by the editor, varies by species, sites, and treatments. This species-specific temperature provides an opportunity to compare among species, sites, and treatments and helps explain differences in observed responses, as demonstrated in the discussion of results in this synthesis.
Forcing change, budburst temperature, and expected response are related. High budburst temperatures are associated with smaller expected responses, which helps explain smaller observed responses with late season species and areas of warm climates that have been often attributed to chilling or photoperiod effect.
Additionally, the use of degree days above 0 {degree sign}C as a universal metric for spring forcing oversimplifies species' temperature responses. This approach assumes not only a fixed base temperature but also a linear response to temperature accumulation, which overlooks well-established nonlinear or species-specific thermal response curves. To improve the robustness and interpretability of the phenological lag framework, we encourage the authors to consider these limitations and explore ways to test or justify these modelling assumptions more explicitly.
The use of 0 degree base temperature may not be the best choice for some species. Except for some early work, there has been few experimental research on physiological aspects of chilling and forcing processes. A popular alternative is modelling using assumed temperature response models. As variables influencing chilling and forcing processes are not controlled, the determined base temperatures and temperature response models may be OK with the species studied under particular conditions but would be inappropriate for applications beyond. It is hard to believe that species, in a study, all have different base temperature for accumulation of spring forcing and optimum temperature for winter chilling. Apparently, this is the result of model fitting, not actual dynamics of chilling and forcing processes.
Two base temperatures are commonly used, 0 and 5 oC, although choice is not generally justified. It is known for long time that temperatures above 0oC contribute to spring forcing. My personal experience at tree nursery suggests that seedlings will flush after winter cold storage, even at forcing temperatures ≤ 5 oC in the dark. The use of 5 oC is rather the choice of tradition (5 oC is commonly used to define growing season) than scientific justification. The use of high base temperatures may not make much difference at high temperatures due to short forcing duration but will underestimate forcing at low temperatures due to long forcing duration and large proportions of forcing between 0 and base temperatures. We are not aware of any experimental studies that demonstrate non-zero base temperatures.
Within the dominant range of spring temperatures (e.g., between 5 and 25 oC), the forcing responses to temperatures can be approximated with linear models. Again, we are not aware of any non-linear forcing models that can be safely applied beyond the species studied under particular conditions.
Regardless, the uses of different base temperatures or forcing models would not affect the partitioning of phenological changes, simply because temperature response models reflect physiological aspects of chilling and forcing processes and would not change with climate warming.
The authors introduce a new metric, phenological lag, to assess how phenological constraints influence spring phenology, offering new insights into phenological research. However, there are several concerns. First, the research question and the study's aim are not clearly presented. The authors primarily analyzed phenological lag and simply compared it across different groups, but additional analyses are needed to adequately address the research question. In addition, the broader importance of this study is not clearly explained - why this research is necessary and what it contributes to the field should be explicitly stated.
The research question is outlined at lines 92-108. We added “Our objective was to determine how phenological responses differ among different groups and how differential responses are related to drivers of spring phenology, i.e., forcing change, budburst temperature, and phenological lag” at lines 106-108.
(1) Abstract: The methodological improvements and more key results should be included.
Growth temperature has been replaced with “budburst temperature” to indicate temperatures at time of budburst. More results are added at lines 40-48.
(2) Line 32: Terms such as "sensitivity analysis" and "phenological lag" need clearer definitions.
We added at lines 32-33 to define sensitivity analysis “that is based on rates of phenological changes, not on drivers of spring phenology”. Phenological lag is defined at lines 34-38.
(3) Lines 38-47: Further results and the urgency or importance of the study should be conveyed.
More results are added at lines 40-48. The importance of this study is described at lines 48-50.
(4) Line 57-58: This sentence is unclear - please clarify.
The sentence is modified to “difficult using sensitivity analysis that is based on rates of phenological changes, not on drivers of spring phenology".
(5) Line 60: break "endodormancy".
Breaking dormancy would mean endodormancy.
(6) Line 67: What does "growth temperature" refer to?
Growth temperature has been replaced with “budburst temperature” to indicate temperatures at time of budburst. It is calculated as the average temperature within the window of expected response with the warmer climate.
(7) Lines 87-94: The specific purpose of the study is vague. Why is this method needed, and how will it serve future research?
We have modified the paragraph at lines 92-108 to provide justification and objective of the study.
(8) Lines 163-164: The rationale for exploring differences in observed responses and phenological lag needs to be better justified.
We added explanations at lines 179-182 why observed responses and phenological lag were chosen in the analysis.
(9) Lines 178-183: Tables and figures should be properly cited within the text.
Table S3 was added at line 197.
(10) Lines 195-198: Clarify whether variables were scaled before model analysis.
We clarified at line 192 “variables were not standardized prior to regression analysis”.
(11) Line 206-207: The observed response is presented as the number of advanced days, while temperature sensitivity refers to the response of spring phenology to temperature - these are different variables and should not be conflated.
The two variables are related but show different aspects of phenological changes. Observed response divided by average temperature change gives temperature sensitivity. Observed response is the total changes in number of days observed, while temperature sensitivity is the change in number of days per unit change in average temperature (oC). Sensitivity may reflects rates of phenological change with temperature (see responses to reviewer 1).
(12) In the discussion section, the authors compared phenological responses among different groups separately. This section requires substantial improvement to more clearly answer the research question.
These discussions are related to our objective “how phenological responses differ among different groups identified in previous research (i.e., research approach, species origin, climatic region, and growth form) and how these differential responses are related to drivers of spring phenology, i.e., forcing change, budburst temperature, and phenological lag”.
