the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Ecosystem physiophenology revealed using circular statistics
Daniel E. PabonMoreno
Talie Musavi
Mirco Migliavacca
Markus Reichstein
Christine Römermann
Miguel D. Mahecha
Quantifying how vegetation phenology responds to climate variability is a key prerequisite to predicting how ecosystem dynamics will shift with climate change. So far, many studies have focused on responses of classical phenological events (e.g., budburst or flowering) to climatic variability for individual species. Comparatively little is known on the dynamics of physiophenological events such as the timing of maximum gross primary production (DOY_{GPPmax}), i.e., quantities that are relevant for understanding terrestrial carbon cycle responses to climate variability and change. In this study, we aim to understand how DOY_{GPPmax} depends on climate drivers across 52 eddy covariance (EC) sites in the FLUXNET network for different regions of the world. Most phenological studies rely on linear methods that cannot be generalized across both hemispheres and therefore do not allow for deriving general rules that can be applied for future predictions. One solution could be a new class of circular–linear (here called circular) regression approaches. Circular regression allows circular variables (in our case phenological events) to be related to linear predictor variables as climate conditions. As a proof of concept, we compare the performance of linear and circular regression to recover original coefficients of a predefined circular model for artificial data. We then quantify the sensitivity of DOY_{GPPmax} across FLUXNET sites to air temperature, shortwave incoming radiation, precipitation, and vapor pressure deficit. Finally, we evaluate the predictive power of the circular regression model for different vegetation types. Our results show that the joint effects of radiation, temperature, and vapor pressure deficit are the most relevant controlling factor of DOY_{GPPmax} across sites. Woody savannas are an exception, where the most important factor is precipitation. Although the sensitivity of the DOY_{GPPmax} to climate drivers is sitespecific, it is possible to generalize the circular regression models across specific vegetation types. From a methodological point of view, our results reveal that circular regression is a robust alternative to conventional phenological analytic frameworks. The analysis of phenological events at the global scale can benefit from the use of circular statistics. Such an approach yields substantially more robust results for analyzing phenological dynamics in regions characterized by two growing seasons per year or when the phenological event under scrutiny occurs between 2 years (i.e., DOY_{GPPmax} in the Southern Hemisphere).
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Phenology is the study of the timing of biological events that can be observed at either the organismic level or the ecosystem scale (Lieth, 1974). For the latter, phenology is the study of some integral behavior across phenological states of the integrated canopy reflectance captured by remote sensing (Richardson et al., 2009; Zhang et al., 2003) or vegetationdriven ecosystem–atmosphere CO_{2} exchange fluxes (Richardson et al., 2010). Ecosystemscale physiophenological processes of this kind are relevant quantities in global biogeochemical cycles and integrate both the seasonal dynamics of biophysical states (e.g., reflected in the canopy development) and the observed photosynthesis at the stand level (i.e., gross primary production). Here we are particularly interested in the timing when ecosystems reach their maximum CO_{2} uptake within a growing season. Ecosystem physiophenology is influenced by climate conditions but simultaneously contributes to the regulation of different micro and macrometeorological patterns. Physiophenological cycles determine the temporal dynamics of land–atmosphere water and energy exchange fluxes. Likewise, the terrestrial carbon cycle is affected by phenological controls on CO_{2} uptake and release (Peñuelas et al., 2009).
The eddy covariance (EC) technique allows us to continuously measure the exchange of energy and matter between ecosystems and the atmosphere (Aubinet et al., 2012). The FLUXNET network collects EC data for most ecosystems of the world along with other meteorological variables, i.e., radiation, temperature, precipitation, atmospheric humidity, and often soil moisture (Baldocchi et al., 2001; Baldocchi, 2020). Particularly relevant to phenological studies is the seasonal trajectory of gross primary production (GPP), which allows us to derive phenological transition dates such as start and end of the growing season (e.g., Luo et al., 2018) as well as the timing of the maximum gross primary production, hereafter as referred to as DOY_{GPPmax} (Zhou et al., 2016; Peichl et al., 2018; Wang and Wu, 2019).
In this study we focus on understanding how climate variability affects the time when ecosystems reach their maximum potential for CO_{2} absorption. In order to reach this “optimum state”, several preconditions must be met during the preceding part of the growing season. So far several studies have focused on studying the variability of maximum GPP during the growing season (GPPmax). For instance, Zhou et al. (2017) studied how the variability of annual GPP is influenced by GPPmax and the start and the end of the growing season. The authors found that GPPmax is a better explanatory parameter for the interannual variability of annual GPP than the start and end days of the growing season. Bauerle et al. (2012) studied how photoperiod and temperature influence plants' photosynthetic capacity for 23 tree species in temperate deciduous hardwoods, reporting that the photoperiod explains the variability of photosynthetic capacity better than temperature. So far, to the best of our knowledge, only one study has focused on understanding the temporal variability of GPPmax; Wang and Wu (2019) used a combination of satellite remotesensing and eddy covariance data to explore how DOY_{GPPmax} is controlled by climatic conditions. The authors reported that higher temperatures advance DOY_{GPPmax}, while the influence of precipitation and radiation were biomedependent. This study had a geographical focus on China; a global approach considering several ecosystems across the whole latitudinal gradient is still lacking.
The challenge of understanding phenology is generally to characterize a discrete event that repeats periodically. Classically, phenological analyses have been performed using linear regression models (MorenteLópez et al., 2018; Zhou et al., 2016). Most of these studies analyze ecosystems characterized by one growing season (e.g., temperate or boreal forests) and when the summer is centered around the middle of the calendar year. The existing methods are, however, not sufficiently generic to describe (i) ecosystems in the Southern Hemisphere and (ii) ecosystems with multiple growing seasons per year as is often observed in, for example, semiarid regions.
Figure 1 illustrates the problem of northern vs. southern hemispheric summers from a conceptual point of view, assuming that some discrete event recurs annually, but the timing varies according to some external drivers. We would then need to find a predictive model explaining the interannual variability of phenology, i.e., the probability of this recurrent event in the course of the annual cycle. Figure 1 shows that linear regression models would be inappropriate to predict the day of the year (DOY) of some phenological event in the Southern Hemisphere as the actual target values to predict may alternate between $\mathit{\u2a86}\frac{\mathrm{3}\mathit{\pi}}{\mathrm{2}}$ and $\mathit{\u2a85}\frac{\mathit{\pi}}{\mathrm{2}}$.
In recent years, circular statistics have gained some attention as they offer a solution to problems of this kind (Morellato et al., 2010; Beyene et al., 2018). Unlike classical statistics, the predicted variables are expressed in terms of angular directions (degrees or radians) across a circumference (Fisher, 1995), allowing us to perform statistical analysis where the data space is not Euclidean. In this framework, point events can be described as a von Mises distribution (Von Mises, 1918), the equivalent to the normal distribution in circular statistics. The von Mises distribution is described by two parameters: the mean angular direction (μ) and the concentration parameter (κ). Circular–linear regressions (in the following simply named circular regression) allow us to predict circular responses (e.g., the timing of phenological events) from other linear variables (Morellato et al., 2010). Given that any phenological event can be interpreted as an angular direction and should be modeled as such, we assume that these circular regressions are well suited in this context. Despite this evident suitability, circular statistics have not yet been extensively applied in the study of phenology and will therefore be presented here as an alternative to conventional linear techniques.
In this paper, we aim to identify the factors controlling the timing of the maximal seasonal GPP (DOY_{GPPmax}). The questions we want to answer are as follows: first, can circular statistics describe and predict DOY_{GPPmax} per vegetation type? This aspect requires testing the methodological advantages and caveats of circular statistics across hemispheres in comparison with linear methods. Second, can DOY_{GPPmax} be explained using cumulative climate conditions? This question needs to consider different possibilities for generating temporally integrating features. And third, how is DOY_{GPPmax} affected by the climatic conditions during the growing season? The last question requires a global crosssite analysis. Based on the findings of these three questions, we then discuss the potential of circular regressions beyond this specific application case in related phenological problems and outline future applications.
2.1 Data
We use 52 EC sites (with at least 7 years of data) located throughout the latitudinal gradient of the globe from the FLUXNET2015 database (Table A1; http://fluxnet.fluxdata.org/, last access: 11 July 2019 Pastorello et al., 2020). Each FLUXNET site is identified with an abbreviation of the country and the name of the place, e.g., the EC tower AUHow, means that it is located in Howard Springs, Australia. From the dataset we use the GPP data that were derived using the nighttime partitioning method and considering the threshold of the variable u^{∗} to discriminate values of insufficient turbulence (Reichstein et al., 2005). In order to identify maximum daily GPP, we compute the quantile 0.9 for each day based on the halfhourly flux observations. As potential explanatory variables for DOY_{GPPmax} we use the daily air temperature (Tair), shortwave incoming radiation (SWin), precipitation (Precip), and vapor pressure deficit (VPD).
Given that the past climate conditions affect the CO_{2} exchange between the atmosphere and the ecosystems (ecological memory; Liu et al., 2019; Ryan et al., 2015), we assume that an aggregated form of these climatic variables needs to be considered in the prediction of the phenological responses. We aggregate the original time series of the Tair, SWin, Precip, and VPD for each DOY_{GPPmax} using a halflife decay function (Eq. 1),
for estimating an exponentially weighted mean of the observation vector ${\mathbf{x}}_{t}=({x}_{t},{x}_{t\mathrm{1}},\mathrm{\dots},{x}_{t\mathit{\tau}}{)}^{T}$ at time step t. The symbol 〈…〉 denotes the weighted average; i indicates the number of days before t, going back to τ=365 days. The weight decay is represented by
The decay function gives the instantaneous value a weight of 1 (w_{0}=1), and all preceding values receive an exponentially reduced weight as determined by the halftime parameter t_{1∕2}. Finally, we make these variables comparable via centering standardization to unit variance. We perform a sensitivity analysis, evaluating the effect of the halftime parameter, and identify the optimum as the value when the variance explained by the circular regression model is at a maximum. The results are presented in Supplement 1.
Due to the high colinearity between the exponential weighted variables of Tair, SWin, and VPD, we perform a principal component analysis (PCA) on the matrix of variables and FLUXNET sites and retain the leading principal component of these variables as well as precipitation as input for the circular statistics model (Hastie et al., 2009). The results of the PCA are presented in Supplement 2.
2.2 Circular statistics
Since units of the circular response variable must be in radians or degrees, we transform the days of the year to radians using Eq. (3). For leap years we remove the last day.
where DOY means day of the year.
A basic circular regression model was proposed by Fisher and Lee (1992) as follows:
where y is the target variable (i.e., DOY_{GPPmax}) in radians, μ is the mean angular direction of the target variable, x_{i} represents the values for the variable i, and β_{i} is the regression coefficient. The parameters μ and β are fitted via the maximum likelihood method using the reweighted least squares algorithm as proposed by Green (1984).
Relevant interpretations of fitted circular regression models are (1) the sign of the β coefficients, (2) the statistical significance of the coefficients, and (3) the accuracy of the prediction. Regarding the first point, a negative sign of the coefficient would mean that an increasing value of the predictor would lead to an earlier DOY_{GPPmax} compared to the mean angular direction. The inverse would happen when the coefficient is positive. Figure 2 conceptually illustrates how the coefficients affect the predictions. Regarding the second aspect, we can state that if a coefficient is not significant, then its contribution would not be relevant to explaining the phenological observation. In our case we define the coefficient to be significant if the median of the distribution of p values is less than 0.05. Finally, we can estimate the accuracy of the prediction using the Jammalamadaka–Sarma (JS) correlation coefficient (Jammalamadaka and Sarma, 1988). As in any other regression framework, this approach helps us to quantify the effect of each climate variable on the interannual variability of DOY_{GPPmax}.
To estimate the relative sensitivity of DOY_{GPPmax} to the leading principal component representing Tair, SWin, and VPD as well as to Precip, we use the implementation of Eq. (4) in the R package “circular” (Agostinelli and Lund, 2017). To increase the robustness of the method we implemented a block bootstrapping per growing season, generating a model parameter average based on 1000 iterations. In each analysis, we estimate the accuracy of the model using the JS correlation coefficient.
2.3 Circular vs. linear regression
To assess the performance of linear versus circular regressions we perform an experiment with simulated data in which we evaluate the accuracy and precision of both approaches to recover original regression coefficients in a circular setting (Eq. 4). We add noise generated with a random von Mises distribution with the parameters n=100 and κ=30 to the model to ensure that the result follows a normal distribution. We predefined a range of values for two regression coefficients (${\mathit{\beta}}_{\mathrm{1}}=\langle \mathrm{0.01},\mathrm{\dots},\mathrm{3}\rangle $, ${\mathit{\beta}}_{\mathrm{2}}=\langle \mathrm{0.01},\mathrm{\dots},\mathrm{3}\rangle $). We simulate the variables x_{1} and x_{2} as normal distributions with n=100; a mean of 10 and 15, respectively; and standard deviations (SDs) of 1 and 2. We evaluate all possible combinations for the regression coefficients 100 times, simulating different x_{1} and x_{2}. In each iteration we generate y using the setup previously described, and we recover the original regression coefficients using y as a response variable and x_{1} and x_{2} as predictors. Finally, we analyze two scenarios: (1) when the target timing occurs at the beginning of the year (μ=0) and (2) when the target timing happens midyear (μ=π). The parameters for the entire setup generate realistic data, where the standard deviation of y is not higher than 0.3 rad. An SD of 0.5 rad would be equivalent to having phenological observations across half a year, which would not be realistic.
To quantify the accuracy of each model per coefficient we estimate the mean absolute error per model and coefficient (Eq. 5). To compare the accuracy between models by coefficient we test the mean absolute errors between models (Eq. 6). To generate a single measure that allows us to compare both coefficients and models we estimate the mean difference accuracy (Eq. 7). The results can be understood as follows: if the difference is higher than 0, the circular model has a higher mean accuracy compared to the linear model and vice versa. To quantify which model has higher precision we estimate the difference between the SD of the mean absolute errors per model for each coefficient (Eq. 8). Finally, we estimate the mean differences of precision between the regression coefficients (Eq. 9), where again if the value is higher than 0, the circular model has a higher mean accuracy than the linear model; the inverse is true if the value is lower than 0.
We estimate regression coefficients for the bootstrap sample $i\in \mathit{\{}\mathrm{1},\mathrm{\dots},m\mathit{\}}$ (m=100) for the regression coefficient β_{j}, $j\in \mathit{\{}\mathrm{1},\mathrm{2}\mathit{\}}$, and the model $M\in \mathit{\{}l,c\mathit{\}}$ (denoted as ${\widehat{\mathit{\beta}}}_{j,i}^{M}$). The model accuracy can then be estimated as the mean absolute error of the estimated regression parameter ${\widehat{\mathit{\beta}}}_{j}^{M}$, $j\in \mathit{\{}\mathrm{1},\mathrm{2}\mathit{\}}$ for the linear model M=l and the circular model M=c:
The difference in accuracy for the coefficient j between the circular and the linear model is shown in
Finally, the mean difference in accuracy between the linear and the circular model is given by
The difference in precision for the coefficient j between the linear (l) and the circular model (c) is shown in
The mean difference in precision between the linear and the circular model is given by
where s_{M,j} is the sample SD of the vector $({\widehat{\mathit{\beta}}}_{j,i}^{M}{)}_{i}$, $M\in \mathit{\{}l,c\mathit{\}}$.
2.4 Analysis setup
The target variable DOY_{GPPmax} is the day of the year when GPP reaches its maximum during the growing season. Given that different ecosystems present more than one growing season per year (e.g., semiarid ecosystems), it is necessary to identify the number of growing seasons per year. To identify the number of growing seasons we apply a fast Fourier transformation (FFT; Cooley and Tukey, 1965) to the mean seasonal cycle of the GPP time series. The number of growing seasons is equal to the maximum absolute value of the first four FFT coefficients (excluding the first one). For each FLUXNET site, we reconstruct the GPP time series, taking the real numbers of the inverse FFT. We use these reconstructed time series to calculate the expected mean timing of DOY_{GPPmax} and use this value as a template. To recover the real DOY_{GPPmax} from the original time series, we define a window around the template of length inversely proportional to the number of cycles (180 d∕number of growing seasons). To increase the robustness of the analysis we identify the days with the 10 highest GPP values. These days are used in the block bootstrapping mentioned above. Finally, since most of the sites are located in the Northern Hemisphere we expect that, in most cases, DOY_{GPPmax} will be reached by the middle of the calendar year.
To quantify the contribution of each climate variable, we count the number of sites per vegetation type where the regression coefficient is statistically significant. We perform a leaveoneout crossvalidation per vegetation type to evaluate the predictive power of the circular regression using climate conditions. We only consider vegetation types with more than five sites. In this case the standardization of the climate variables is not applied. Finally, we use the mean of the optimum halftime parameter per vegetation type to weigh the climate conditions.
Here, we first report results from simulated data to describe the performance of the circular regression approach compared to a linear model. Second, we compare the performance of circular and linear regression using empirical data. Third, we analyze the sensitivity of DOY_{GPPmax} across vegetation types and climate classes. Finally, we show the results of the predictive power of circular regression per vegetation type.
3.1 Circular vs. linear regression
Figure 3a and c show that for μ=0 (DOY_{GPPmax} at the beginning of the year), circular regression has a higher accuracy and precision compared to the linear regression for the entire space of regression coefficient values, with a maximum difference of the order of 0.1 in terms of accuracy and of the order of 1 for precision. For μ=π (DOY_{GPPmax} midyear) the linear model has a higher accuracy in most of the evaluated space, with a maximum difference of the order of 0.001 compared with the circular regression, while circular regression has a higher precision for most of the regression coefficients of the order of 0.001. These results show that circular regression has a higher precision in recovering the original regression coefficients than linear regression no matter the moment of the year. On the other hand, circular regression has a higher accuracy than the linear model at the beginning of the year. While linear is better midyear, the differences are of the order of 0.001.
To illustrate the method in practice, we compare the circular and linear models using data from two sites: USHa1 (Northern Hemisphere, deciduous broadleaf forest) and AUHow (Southern Hemisphere, woody savanna). We relate the climate variables with DOY_{GPPmax} (see Methods) and reconstruct the DOY_{GPPmax} using the linear and circular regression models. We compare observed and predicted DOY_{GPPmax} using JS correlation for the circular model and the Pearson product moment for the linear model. For USHa1 both methods show similar performance in predicting DOY_{GPPmax} (Fig. 4), while for AUHow, the circular model retrieves the original data better than the linear model, explaining 30 % more of the variance. In the event that the DOY_{GPPmax} is reached at the beginning of the year, linear methods produce a strong bias that predicts the timing across the entire year (Fig. 4b).
3.2 Sensitivity of DOY_{GPPmax} to climate variables
From the 52 sites analyzed in this study, only one site (ESLJu) shows bimodal growing seasons (see Supplement 1.2). As expected, in most cases DOY_{GPPmax} occurs in the middle of the calendar year (Fig. S6 in the Supplement), reflecting the uneven site distribution in FLUXNET (Schimel et al., 2015). However, some ecosystems in the Northern Hemisphere do reach DOY_{GPPmax} at the beginning of the year; these are Mediterranean sites such as USVar and ESLJu. In general terms, most of the sites have an SD between 10 d and 40 d. The maximal SD is 46.9 d for the AUTum site. A detailed table with the mean angular direction and SD of DOY_{GPPmax} of each site is presented in Sect. S1.2.
For half of the sites, the JS correlation coefficients are between 0.70 and 0.97 (Supplement 1, Fig. S5), showing that the interannual variability of DOY_{GPPmax} is mainly explained by the cumulative effect of the climate variables. Nineteen sites have a JS coefficient of less than 0.7 (DKSor, FIHyy, USMMS, DKZaH, FRPue, USUMB, AUTum, USTon, FRLBr, USMe2, ITLav, ATNeu, DEGri, ITMBo, ITRo2, USWkg, BRSa1, FRFon, CZwet). For ESLJu the JS coefficient is 0.77 for the first growing season and 0.78 for the second one (Table S2 in the Supplement).
We find that air temperature, shortwave incoming radiation, and vapor pressure deficit appear as the dominant drivers worldwide at 43 of the total sites (84 %; Supplement 3). Precipitation is the main driver for five sites (AUHow USTon ZAKru USSRM USWkg; Supplement 3). Interestingly, precipitation was the most important factor for all the woody savanna sites (Supplement 3). For three sites (DEGri, ITRo2, BRSa1), any climatic variable is significant. In terms of the sign of the coefficients, all the variables are predominantly negative (Table 1). This means that higher values of radiation, air temperature, VPD, and precipitation lead to an earlier DOY_{GPPmax}. Individual sensitivities per site are shown in Supplement 3.
The PCA between shortwave incoming radiation, air temperature, and vapor pressure deficit has the highest frequency of significant correlation coefficients by number of sites for all the vegetation types with the exception of woody savannas (WSAs), where precipitation is shown to be more important for most sites than the dimensionality reduction between Tair, SWin, and VPD (Fig. 5). For closed shrublands (CSHs) and savannas (SAVs), both drivers have the same number of sites where the coefficients are statistically significant.
A special case for understanding the sensitivity of DOY_{GPPmax} to climate variables is the site “Llano de los Juanes” (ESLJu), an open shrubland ecosystem in Spain. It is the only clearly bimodal ecosystem in our study (Fig. 6). In this case precipitation is not statistically significant, while the combination of Tair, SWin, and VPD is significant for both seasons. Furthermore, in both growing seasons Tair, SWin, and VPD have a negative coefficient.
The leaveoneout crossvalidation for several vegetation types shows that the predictive power of the model for grassland (GRA) and evergreen broadleaf forest (EBF) is −0.3 and −0.31, respectively. For deciduous broadleaf forest (DBF) it is 0.46, and for evergreen needleleaf forest (ENF) it is 0.4, while for mixed forest (MF) the predictive power of the model is 0.88 (Fig. 7).
4.1 Circular vs. linear regression
We explored whether circular regression is a suitable tool for analyzing phenological events. Our results suggest that circular regressions can recover predefined coefficients in a set of simulations with higher accuracy and precision than linear regressions. Hence, we would generally suggest that circular regressions may be advantageous when the aim is analyzing the effect of climatic variables on phenological events. We also found cases where the classical linear regression may be either more robust or equally suitable, e.g., when phenological events are reached close to midyear. In the overall view, however, we consider that circular regressions are to be preferred over linear regression for their conceptual capacity to analyze the physiophenology of ecosystems regardless of the day of the year when an event of interest occurs. This allows us to analyze phenological studies at the global scale regardless of geographic location or the distribution of the observations during the year.
Different phenological models have been developed, ranging from empirical approaches (Richardson et al., 2013) to process models (Asse et al., 2020) over the last decades. As we demonstrate here, circular statistics open new opportunities to increase the robustness of phenological models, allowing us to analyze ecosystems across hemispheres within the same consistent framework. In fact, the results of the phenological sensitivity of DOY_{GPPmax} indicate the complexity of ecosystem responses to climate variability. Our approach provides motivation to integrate circular regressions into more complex statistical techniques like regression trees, Gaussian processes, or artificial neural networks, targeting a circular response variable.
4.2 Sensitivity of DOY_{GPPmax} to climate variables
The geographical location of the FLUXNET2015 sites represents an advantage when capturing the DOY_{GPPmax} variability at the global scale (Supplement 1, Fig. S6). Most of the analyzed sites (47) are located in the Northern Hemisphere. Two sites (GFGuy and BRSa1) are located in the tropical region, and three sites (ZAKru, AUHow, AUTum) are in the Southern Hemisphere. However, because of the low number of sites reported in the tropical and southern region with more than 7 years of data, our understanding of the DOY_{GPPmax} variability in these regions is still limited. Increasing the number of tropical and Southern Hemisphere sites should be considered a high priority in the near future to complement our knowledge about the physiophenological ecosystem state.
The high values of the JS correlation coefficients for most of the sites demonstrate that the interannual variability of DOY_{GPPmax} can be explained as the cumulative effect of the climate variables during the growing season. Sites where it was not possible to explain the variations in DOY_{GPPmax} with enough confidence (JS correlation <0.7) might require the incorporation of biotic variables (e.g., species composition; Peichl et al., 2018) or soil property information that can improve the predictive power of the model.
Our results suggest that there is no pattern between the DOY_{GPPmax} sensitivity across vegetation types and climate classes (Sect. Fig. S1.7). In other words, the DOY_{GPPmax} sensitivity is sitespecific, probably produced by the unique combination of biotic (e.g., species composition, species phenology, species interaction, and phenotypic plasticity) factors that are not evaluated in our study. Several studies that focused on ecosystem phenology suggest that species composition plays a fundamental role in ecosystem physiophenology of the CO_{2} uptake (Gonsamo et al., 2017; Peichl et al., 2018).
While there is no clear relationship between the DOY_{GPPmax} sensitivity and the vegetation type, we find a predominant role of the combined effects of shortwave incoming radiation (SWin), air temperature (Tair), and vapor pressure deficit (VPD) at the global scale on the DOY_{GPPmax} interannual variability, where for most of the sites these variables have a negative regression coefficient. This means that if the SWin, Tair, and VPD increase during the growing season, the DOY_{GPPmax} will be reached earlier. This effect can be a consequence of DOY_{GPPmax} being reached when SWin and Tair are at a maximum.
On a global scale, our analysis shows that the combination of air temperature, shortwave incoming radiation, and vapor pressure deficit as well as precipitation has a negative sign. This means that if these variables increase during the growing season, the GPPmax will be reached earlier. Our results are similar to those obtained by Wang and Wu (2019), who were the authors to conclude that an increase in the temperature produces an earlier DOY_{GPPmax}. This phenomenon is likely explained by the leafout advancing during spring. Nevertheless, there is still no consensus on whether the increase in temperature will produce an earlier end of the growing season. Several studies have demonstrated for different vegetation types that when temperature increases, spring onset is earlier, and autumn senescence is later (Stocker et al., 2013; Linkosalo et al., 2009; Migliavacca et al., 2012; Morin et al., 2010; Post and Forchhammer, 2008), increasing the length of the growing season and the amount of CO_{2} that is taken up by ecosystems (Richardson et al., 2013).
Ecosystems with two growing seasons per year represent a very interesting case of the effect of climate drivers on DOY_{GPPmax} across different growing seasons. In Llano de los Juanes, Spain (ESLJu; Fig. 6), DOY_{GPPmax} is reached in the first growing season, when the rainy season is finishing, while in the second growing season DOY_{GPPmax} is reached in the middle of the rainy season (data not shown). The effect of shortwave incoming radiation, temperature, and vapor pressure deficit for both growing seasons is negative, suggesting that if we increase these variables during the period before, the DOY_{GPPmax} will happen earlier.
Phenology in Mediterranean ecosystems is mainly controlled by water availability (Kramer et al., 2000; Luo et al., 2018; Peñuelas et al., 2009). However, our results suggest that DOY_{GPPmax} is mainly sensitive to SWin, Tair, and VPD. These results agree with the analysis performed by Gordo and Sanz (2005), who were the authors to evaluate the phenological sensitivity of Mediterranean ecosystems to temperature and precipitation. They concluded that temperature was the most important driver. Although water is a limiting factor in Mediterranean ecosystems, its influence on plant physiology and plant phenology can be completely different. In terms of physiology, the GPPmax value can decrease, but in terms of phenology, DOY_{GPPmax} can still be the same.
Complex interactions between climate variables and phenological response and the interspecificity of the sensitivity at the site level explain in part the poor predictive power of the model for grasslands, evergreen broadleaf forest, evergreen needleleaf forest, and deciduous broadleaf forests in the crossvalidation analysis (Fig. 7). However, the predictive power for mixed forest is high, even when the distribution of the latitudinal gradient is not the same for all the sites. These results reflect the fact that the circular regression model can be extrapolated from different sites to predict the DOY_{GPPmax} interannual variability. This advantage could be a way to solve the common criticism that phenological models cannot be extrapolated by only generating ad hoc hypotheses (Richardson et al., 2013).
In this study we explored the potential of “circular regressions” to explain the physiophenology of maximal CO_{2} uptake rates. We conclude that (1) shortwave incoming radiation, temperature, and vapor pressure deficit are the main drivers of the timing of maximal CO_{2} uptake at the global scale (precipitation only plays a secondary role, with the exception of woody savannas, where the most important variable is precipitation), and (2) although the sensitivity of the DOY_{GPPmax} to the climate drivers is sitespecific, it is possible to extrapolate the circular regression model for different sites with the same vegetation type and similar latitudes. Finally, we used simulated and empirical data to demonstrate that circular regression produces more accurate results than linear regression, in particular in cases when data need to be explored across hemispheres.
Code is available under GPL3 license at https://github.com/dpabon/ecosystemphysiophenologyrepo, last access: 29 July 2020 (https://doi.org/10.5281/zenodo.3921892 PabonMoreno et al., 2020).
FLUXNET database is available online at https://fluxnet.fluxdata.org/, last access: 11 July 2019 (https://doi.org/10.1038/s4159702005343 Pastorello et al., 2020).
The supplement related to this article is available online at: https://doi.org/10.5194/bg1739912020supplement.
DEPM, TM, MM, and MDM designed the study in collaboration with MR and CR. DEPM conducted the analysis and wrote the manuscript, with substantial contributions from all coauthors.
The authors declare that they have no conflict of interest.
We thank the reviewers for their helpful suggestions and Guido Kraemer for his help with the mathematical notation. This project has received funding from the European Union's Horizon 2020 research and innovation program via the TRuStEE project under the Marie SkłodowskaCurie grant agreement no. 721995. This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, FluxnetCanada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFluxTERN, TCOSSiberia, and USCCC. The ERAInterim reanalysis data are provided by ECMWF and processed by LSCE. The FLUXNET eddy covariance data processing and harmonization were carried out by the European Fluxes Database Cluster, AmeriFlux Management Project, and the Fluxdata project of FLUXNET, with the support of CDIAC and ICOS Ecosystem Thematic Center, as well as the OzFlux, ChinaFlux, and AsiaFlux offices.
This research has been supported by the H2020 Marie SkłodowskaCurie Actions (TRuStEE grant no. 721995).
The article processing charges for this openaccess
publication were covered by the Max Planck Society.
This paper was edited by David Bowling and reviewed by two anonymous referees.
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