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Responses of vegetation green-up date to temperature variation in alpine grassland on the Tibetan Plateau
Ecological Indicators 104 (2019) 390–397
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Original Articles
Responses of vegetation green-up date to temperature variation in alpine grassland on the Tibetan Plateau
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Xiaoting Lia,b, Wei Guoa,b, , Ji Chenc,d, Xiangnan Nia,b, Xiaoyi Weia,b a
Department of Earth and Environmental Sciences, Xi’an Jiaotong University, Xi’an 710049, China Institute of Global Environmental Change, Xi’an Jiaotong University, Xi’an 710049, China c Research Center for Eco-Environmental Sciences, Northwestern Polytechnical University, Xi’an 710049, China d Aarhus University Centre for Circular Bioeconomy, Department of Agroecology, Aarhus University, Blichers Allé, 20, 8830 Tjele, Denmark b
A R T I C LE I N FO
A B S T R A C T
Keywords: Green-up date Temperature Alpine grassland Tibetan Plateau Kobresia humilis
Temperature increases in spring can advance vegetation green-up date (GUD) due to the increased heat accumulations. However, temperature increases in winter will delay the GUD due to the postponed fulfilment of chilling requirements. Such contrasting impacts of temperature changes in spring and winter on the GUD have been documented from many ecosystems in the Northern Hemisphere. However, the combined effects of temperature variations in winter and spring on the GUD remained unclear, especially in the cold Tibetan Plateau. To advance our understanding of how alpine grassland GUD responds to climate warming, this study evaluated 14 years of field GUD observations of sedge species Kobresia humilis on the Tibetan Plateau. The results showed that January and March-April were two critical periods in determining the long-term GUD variations. The minimum temperatures (Tmin) during these two critical periods played the dominant roles in controlling the GUD. Increases in the Tmin significantly shifting the GUD by −3.9 days °C−1 and 1.7 days °C−1 in the spring and winter, respectively. Moreover, Tmin in March-April played a more important role in determining the GUD than did Tmin in January. In addition, the difference in minimum temperature (ΔTmin) between these two critical periods of January and March-April might be a novel indicator for vegetation GUD. This study provides novel insights into the differential impacts of minimum temperatures in winter and spring on the GUD and how they should be explicitly considered to better understand the effects of climate change on vegetation phenology.
1. Introduction Spring green-up date (GUD) provides ecological cues that reveal continuous patterns in timing of vegetation spring onset. Understanding such patterns is crucial to uncovering past and future climate change (Chuine and Régnière, 2017; Richardson et al., 2013; Tang et al., 2016). Moreover, shifts in GUD play important roles in regulating feedbacks to the climate system (Richardson et al., 2013). For example, changes in GUD had cascading impacts on ecosystem structure and function, canopy conductance, albedo and even carbon, water and energy fluxes (Liu et al., 2017; Richardson et al., 2013; Tang et al., 2016; Wu et al., 2015; Zhang et al., 2011). Extensive research has been conducted to elucidate the GUD patterns, but no consensus has been reached until now (Ahas et al., 2002; Ge et al., 2015; Guo et al., 2018; Jeong et al., 2011; Ren et al., 2018; Sherry et al., 2007). This is because the underlying mechanisms driving the long-term changes in GUD are still
unclear. Temperature increases in spring and winter can influence the GUD through direct and indirect pathways (Lin et al., 2018; Martínez-lüscher et al., 2017; Pope et al., 2014; Shi et al., 2017). Specifically, temperature increases in spring can directly advance seed germination and leaf unfolding as heat accumulation can be met in advance due to the increased temperature (Harrington and Gould, 2015; Shi et al., 2017). Meanwhile, there is also evidence that temperature increases in the winter can delay the GUD by decreasing the chilling requirement, which is necessary for breaking bud dormancy in cool conditions (Luedeling et al., 2012; Martínez-lüscher et al., 2017; Vitasse et al., 2014). Hence, whether the GUD is delayed or advanced will to an extent rely on the trade-off between these two opposing mechanisms. However, the relative importance of winter chilling and spring forcing in determining the GUD is still unclear. During recent decades, responses of GUD to temperature variations in the alpine ecosystem
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Corresponding author at: Department of Earth and Environmental Sciences, Xi’an Jiaotong University, Xi’an 710049, China. E-mail addresses: [email protected] (X. Li), [email protected] (W. Guo), [email protected] (J. Chen), [email protected] (X. Ni), [email protected] (X. Wei). https://doi.org/10.1016/j.ecolind.2019.05.003 Received 4 August 2018; Received in revised form 19 February 2019; Accepted 2 May 2019 Available online 15 May 2019 1470-160X/ © 2019 Elsevier Ltd. All rights reserved.
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used to simulate the trend of vegetation GUD from 1997 to 2010. The following simple linear regression analysis model was applied to the research:
have been intensively studied, but no consensus has been reached on the responses of GUD of alpine plants to climate warming (Cui et al., 2017; Dorji et al., 2013; Mohandass et al., 2015). Some studies showed continuous advancement of GUD in response to climate warming according to a range of satellite-based GUD detections (Thompson and Paull, 2017; Zhang et al., 2013), long-term field observations (Zheng et al., 2016) and experimental manipulation (Suonan et al., 2017). However, Yu et al. (2010) and Zhang et al. (2018) suggested a delayed GUD under winter and spring warming. In addition, a number of other studies reported no significant temporal trend in the GUD in spite of continued warming on the Tibetan Plateau (Liu, 2016; Zhou et al., 2016a). One critical reason for the lack of consensus is that underlying mechanisms, particularly the combined effects of temperature increases in winter and spring on the GUD, are not well understood. Further research is therefore needed to clarify the mechanism of alpine plants’ GUD in response to temperature changes in winter and spring. The Tibetan Plateau, known as the ‘Earth’s third pole’, is a pilot region of climatic fluctuation that is extremely sensitive to global climate change (Ding et al., 2013; Yang et al., 2017; Zhang et al., 2014; Zheng et al., 2016). The alpine meadow grassland is one of the most widespread vegetation types on the Plateau, covering an area of ∼1.2 × 106 km2 and accounting for about half of its land area (Chen et al., 2016a; Yang et al., 2018), and is of considerable importance in animal husbandry and the local ecological environment. However, the underlying mechanism of alpine grassland GUD in response to temperature increases in winter and spring is not well understood, mainly being constrained by the lack of long-term records of grass phenology from ground observations (Yu et al., 2010; Zhang et al., 2013, 2018). To this end, based on 14 years of field GUD observations of the dominant perennial sedge species (i.e., Kobresia humilis) on the Tibetan Plateau, we investigated the effects of long-term air temperature variations on alpine grassland GUDs. In the present study, we aimed to clarify the following questions: (1) What are the critical periods for GUD variations? (2) How does GUD respond to temperature increases in winter and spring separately? and (3) How can the combined effects of temperature variations in both winter and spring be quantified?
n
Slope =
n
n
n (∑i = 1
i)
n × ∑i = 1 GUDi − ∑i = 1 i ∑i = 1 GUDi n×
n ∑i = 1 i 2
−
2
(1)
where n is the number of studied years; GUDi is the vegetation GUD of year i; Slope > 0 means the changing tendency of vegetation green-up among n years is delayed, and on the contrary, it is advanced (Wang et al., 2016). Meteorological data were obtained from the Haibei meteorological station, including the daily maximum temperature (Tmax) and minimum temperature (Tmin), precipitation, evaporation and the duration of the solar radiation from 1997 to 2010. Mean Tmax and Tmin were 10.0 °C and −5.7 °C, respectively. Mean annual total precipitation and evaporation were 400.1 mm and 1441.3 mm, respectively, and the mean annual total sunshine duration was 2934.2 h. All daily temperatures were averaged to an 15-day running mean temperature for more recognizable temperature response patterns of GUDs in subsequent statistical analyses (Luedeling and Gassner, 2012). 2.3. Partial Least Squares analysis Partial Least Squares (PLS) regression was used to identify the relevant periods when temperature had a strong impact on the GUDs of K. humilis. As a commonly used multivariate analysis method, PLS is reliable in the case that the independent variables are highly autocorrelated or the number of independent variables over-exceeds the number of dependent variables (Abdi, 2010; Luedeling and Gassner, 2012; Martínez-lüscher et al., 2017). Such cases can be encountered in analyzing the response of plant phenology to temperature records at high temporal resolution, and recent work has shown that PLS regression is useful for identifying the specific periods that are critical for plant green-up in this context (Luedeling and Gassner, 2012; Martínezlüscher et al., 2017). Equations are clarified in detail with reference to Abdi (2010). The two main outputs of PLS regression are the variable importance in the projection (VIP) and the standardized model coefficients. The VIP value reflects the importance of the independent variables to interpret the dependent variables, and the commonly held threshold is 0.8 (Guo et al., 2013; Luedeling and Gassner, 2012). Model coefficients indicate the direction and strength of each variable on the dependent variable. For example, high and positive model coefficients indicate that the GUD is delayed when temperature increases during these relevant periods, while negative coefficients reflect that the warmer temperature triggers an earlier GUD. The VIP values and the standardized model coefficients were output at a daily scale over the study period. Critical periods were determined with VIP values greater than 0.8 and high absolute model coefficients. In addition, the root-mean-square error (RMSE) [Eq. (2)] was calculated to measure the applicability of the model.
2. Materials and methods 2.1. Study region This study was conducted at the Haibei Grassland Ecological Monitoring Station (Haibei station, 36°57′N, 100°51′E) in the northeastern Tibetan Plateau. With a typical plateau continental climate, this region is characterized by low air temperature, a large temperature difference between day and night, limited precipitation and strong solar radiation. Based on the meteorological data from 1997 to 2010, the mean annual air temperature was 1.5 °C, and the mean annual precipitation was 400.1 mm, with 85% of the rainfall concentrated in the growing season (i.e., from May to September). The soil is sandy loam, which is classified as mountain brown soil according to the Chinese soil classification (Chen et al., 2016b). The dominant species at the study site is K. humilis (Cyperaceae), which is a perennial sedge species with a short rhizome. K. humilis is an excellent forage grass for livestock, and it is ecologically and economically important in the long-term adaption to cold conditions and resistance to human grazing on the Tibetan Plateau. More detailed and longer-term information about the study site can be found in Chen et al. (2015) and Chen et al. (2016b).
n
RMSE =
∑i = 1 (obsi − prei )2 n
(2)
where obsi is the observed GUD in year i; prei is the simulated GUD in year i; n is the sample size (Chen et al., 2017b). To determine the critical periods, the GUDs were set as the dependent variables, and the daily Tmin (or Tmax) (subjected to an 15-day running mean) for the 12 months leading up to the GUD was set as the independent variables. PLS regression was implemented in the R programming language (R Core Team, 2015), using the “chillR” package (Luedeling, 2018) and the “pls” package (Mevik et al., 2018).
2.2. Phenological and meteorological data In situ phenological data of K. humilis during 1997 to 2010 were collected from the Haibei Station, which is a main international alpine terrestrial ecosystem research base of the China Meteorological Administration. Julian days were used to indicate the GUD of K. humilis on a particular day-of-year (DOY). Simple linear regression analysis was
2.4. Correlation analysis between temperature and GUDs Partial correlation analysis was applied to assess the relative 391
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Fig. 1. Correlation between the green-up date (GUD) and Tmax, Tmin during the non-growing season (Tmax,NGS , Tmin,NGS ), and inter-annual variation in the GUD and Tmax,NGS , Tmin,NGS from 1997 to 2010. GUD was negatively correlated with Tmax,NGS , Tmin,NGS with correlation coefficients (r, marked in red color) of −0.143 (P > 0.05), −0.205 (P > 0.05), respectively. The landscape and weather station of the study site are also shown in the inset pictures.
GUD. The VIP values were larger than 0.8 during January as well as during March to April. But increases in Tmin had contrasting effects on the GUD during these two periods. Specifically, the model coefficients were positive during January, which indicated that the increased Tmin in January postponed the GUD. In contrast, the model coefficients were negative during March to April, which suggested that the earlier occurrence of the spring green-up was related to increased Tmin during March to April (Fig. 2). For the analysis of Tmax, January was also a critical period when the temperature had significant positive correlation with GUDs. However, increased Tmax during March to April had neither consistently advancing nor delaying effect on the GUD (Fig. 3).
importance of Tmax and Tmin in the variation of vegetation GUDs in relevant periods. The correlations between GUD and Tmin (Tmax) were calculated, setting Tmax or Tmin as the controlling variables. For variables whose partial correlation coefficients (r) were significant (p < 0.05), the absolute value of r between GUD and Tmin was compared with that between GUD and Tmax to examine which factor was more dominant in influencing vegetation phenology. In addition, to assess the response of GUD to Tmin (Tmax) variations, we determined the temperature sensitivity as the slope of the linear regression between the time-series of GUD and Tmin (Tmax) in critical periods from 1997 to 2010, which illustrated a time shift of the spring green-up in response to a unit shift in temperature.
3.2. Correlation between temperature (Tmin, Tmax) and the GUDs 3. Results Our data analysis on the partial correlation analysis clearly demonstrated that the partial coefficients between Tmin and GUD were higher than those between Tmax and GUD in both critical periods (Table 1). The results suggested that changes in Tmin had a greater impact on the GUD than Tmax in both January and March-April. Furthermore, the temperature sensitivity of the GUD also showed that Tmin had a significantly stronger impact on the GUD than Tmax. Tmin increases in January postponed the GUD at a rate of 1.66 days °C−1 (R2 = 0.286, p < 0.05) and Tmin increases in March-April induced an earlier GUD at a rate of 3.93 days °C−1 (R2 = 0.581, p < 0.01); however, an increase in Tmax had no significant effect on the GUD in either January and March-April. In addition, when analyzed in different months, the GUD was more associated with Tmin variation in MarchApril (r = 0.70, p < 0.01) than in January (r = 0.36, p > 0.05) (Table 1). Therefore, the present results suggested that alpine plants’ green-up was more sensitive to Tmin variation than Tmax, and Tmin in March-April plays a more important role than that of January on the spring green-up on the Tibetan Plateau.
3.1. Critical periods for the GUDs of K. humilis From 1997 to 2010, asymmetric increases in Tmax and Tmin during the non-growing season (i.e., from October to April) were found at a rate of 0.35 °C/decade (R2 = 0.069, p > 0.05) and 0.88 °C/decade (R2 = 0.390, p < 0.05), respectively (Fig. 1). Moreover, we found no significant relationship between GUD and Tmax (or Tmin) during the non-growing season, and we further conducted PLS regression at a daily scale. Based on the 14 years of field observations, the average GUD of K. humilis was DOY 108 (i.e., April 18th), and there was no significant relation between the GUD and year from 1997 to 2010 (0.37 days/ decade, R2 = 0.001, p > 0.05; Fig. 1a). The daily Tmax and Tmin from previous May to April were used as independent variables, while the GUDs were used as the dependent variables. The RMSEs were 1.07 (Tmax) and 1.09 (Tmin) days from the PLS regression, indicating that this method was applicable to the data. Based on the VIP and the standardized model coefficients, relevant periods were determined when the temperature was significantly correlated with the GUD of K. humilis. To examine the influence of Tmin on the GUD, two periods in winter and spring were identified when Tmin had significant impacts on the
3.3. Mechanism of spring green-up in response to minimum temperature An increase of 1 °C in Tmin in March-April (Tmin, 392
Mar.-Apr.)
and in
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Table 1 Impacts of Tmin and Tmax in relevant periods on vegetation green-up date (GUD).
Partial coefficient (r) between GUD and temperature Temperature sensitivity of GUD (day °C
January March-April January March-April
Tmin
Tmax
0.36 −0.70*** 1.66** −3.93***
0.20 0.16 1.66 −1.68
Asterisks indicate significance of factors; ***p < 0.01, **p < 0.05. Correlation and sensitivity without an asterisk are not significant (p > 0.05).
January (Tmin, Jan.) significantly shifted the GUD by −3.93 days °C−1 and 1.66 days °C−1, respectively. Moreover, we found that Tmin, Jan. and Tmin, Mar.-Apr. were significantly negatively correlated, with a correlation coefficient of −0.57 (p < 0.05) (Fig. 4). Thus, when Tmin increased in January, it decreased later in March-April, whereas when Tmin decreased in January, it was followed by a warmer March-April before the spring green-up. Consequently, shifts in Tmin, Jan. and Tmin, Mar.-Apr. intensified the response of the green-up from the same direction. That is, an increased Tmin, Jan. and following decreased Tmin, Mar.-Apr. had the same delaying effects on the GUD and consequently postponed the onset of vegetation green-up. In contrast, a decreased Tmin, Jan. and increased Tmin, Mar.-Apr. exerted the same influence on advanced GUD. Furthermore, we introduced a temperature difference (ΔTmin = Tmin, Mar.-Apr. − Tmin, Jan.) between January and March-April to reflect the combined effects of Tmin variations on the GUD. These relationships further demonstrated that a larger ΔTmin would significantly advance the onset of the vegetation growth at a rate of 1.51 days °C−1 (R2 = 0.476, p < 0.01) (Fig. 5).
Fig. 2. The results of the Partial Least Squares (PLS) regression correlating GUD for K. humilis with an 11-day average daily minimum temperature from May to April. (a) Variable importance in the projection (VIP) values: the blue color indicates a VIP value greater than 0.8; (b) standardized model coefficients: the red color indicates the model coefficients are negative, while the green color indicates the model coefficients are positive between GUD and temperature; (c) variation in daily minimum temperature: the black line indicates the minimum temperature and the grey, green and red areas represent the standard deviation of the daily minimum temperature for each day.
4. Discussion Alpine grassland has been suggested to be susceptible to future climate change and is of considerable interest in terms of regional and global climate change (Chen et al., 2015). As the dominant species in the typical alpine meadow on the Tibetan Plateau, K. humilis is important not only for animal husbandry but also for local ecological environment (Chen et al., 2015; Chen et al., 2016b). A better understanding of the GUD in response to temperature variation is important for projecting phenology dynamics, evaluating ecosystem productivity and further optimizing grassland management (Chang et al., 2017; Keenan et al., 2014; Richardson et al., 2010; Zhou et al., 2016b). Therefore, based on long-term field observations, we elucidated the critical periods when temperature was strongly correlated with alpine grassland GUD and investigated how the GUD responded to temperature variations in those periods. 4.1. The critical periods for vegetation green-up Temperature increases in winter and spring result in opposite impacts on vegetation green-up. Climate warming has initiated a general advance in GUD in most regions of the Northern Hemisphere since the 1980s (Cleland et al., 2012; Korner and Basler, 2010; Linkosalo et al., 2009; Yaacoubi et al., 2014; Gordo and Sanz, 2009), suggesting a dominantly advancing effect of a warmer spring. However, over recent years, a delayed GUD has been revealed in the alpine ecosystem, which suggested that the delaying effect of a warm winter is expected to become more pronounced (Fu et al., 2013; Yu et al., 2010). Thus, the differential impacts of temperature in winter and spring on the GUD should be further investigated especially in alpine regions such as the Tibetan Plateau. In previous studies, PLS regression has been able to identify the days that are most important for the timing of trees’ green-up (Guo et al., 2013; Luedeling and Gassner, 2012). For example, Luedeling and Gassner (2013) studied the leaf emergence of the walnut cultivars in
Fig. 3. The results of the Partial Least Squares (PLS) regression correlating GUD for K. humilis with an 11-day average daily maximum temperature from May to April. (a) Variable importance in the projection (VIP) values: the blue color indicates a VIP value greater than 0.8; (b) standardized model coefficients: the red color indicates the model coefficients are negative, while the green color indicates the model coefficients are positive between GUD and temperature; (c) variation in daily maximum temperature: the black line indicates the maximum temperature and the grey, green and red areas represent the standard deviation of the daily maximum temperature for each day.
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Fig. 4. The inter-annual variation of minimum temperature (Tmin) in January and from March to April from 1997 to 2010.
significant than that to Tmax (−1.68 days °C−1, p > 0. 05) in MarchApril. Namely, a greater time shift of the GUD in response to a unit shift in Tmin. However, although temperature sensitivity remained the same for both Tmin and Tmax in January, the GUD response was significantly correlated to Tmin (1.66 days °C−1, p < 0. 05), while it was not significantly correlated to Tmax (1.66 days °C−1, p > 0. 05) (Table 1). These relationships suggest that Tmin played the dominant role in controlling alpine grassland GUD. It is possible that in winter, Tmin contributed more to the chilling requirement. As buds gradually become more sensitive to freezing temperatures during leaf emergence in spring (Vitasse et al., 2014), increases in Tmin could favor freezing resistance and heat accumulation even with just a minor increase in low temperatures (Shi et al., 2017). Another possible explanation might be that a higher Tmin could provide more available soil water from snow and ice that is typically constrained by the low soil temperature (Yang, 2013; Yi et al., 2013). Once the soil moisture increases, plant roots absorb more water to prepare for the leaf unfolding. In contrast, a previous study found that satellitederived GUD was determined mainly by Tmax in the Northern Hemisphere between 1982 and 2011 (Piao et al., 2015). This discrepancy may be primarily due to considerable variation in climate and landscape between Europe, parts of the US and the Tibetan Plateau. The Tibetan Plateau has a typical plateau continental climate and is characterized by low air temperature that is much lower than that of the temperate climate in most middle- and high-latitude areas in the Northern Hemisphere (Chen et al., 2016b; Yang et al., 2018). Low temperatures are the primary factor limiting plant growth, which suggests that alpine grassland is likely to be more sensitive to minimum temperature variations. Overall, the present results suggest that Tmin is an effective indicator for green-up of alpine grassland. Furthermore, we found that shifts in the GUD were mainly controlled by Tmin, Mar.-Apr. rather than Tmin, Jan. (Table 1), suggesting a stronger effect of the spring minimum temperature than winter minimum temperature on the GUD. This finding is consistent with previous studies which showed that temperature occurring 1–3 months before spring vegetation phenology plays the dominant role of controlling the variation of phenology (Guo et al., 2013; Güsewell et al., 2017; Matsumoto et al., 2003; Menzel, 2003; Ren et al., 2018). For example, a previous study of deciduous trees species revealed that temperature variations in March and April were critical for leaf unfolding, and the spring forcing was of great essence in advancing leaf unfolding (Juknys et al., 2016). Those results, together with our findings, suggest that minimum temperature in spring still is the dominant factor in impacting the vegetation green-up.
Fig. 5. The relationship between the green-up date of K. humilis and the minimum temperature difference (ΔTmin) between March-April and January from 1997 to 2010. The regression equation and correlation coefficient are given in the figure. ΔTmin = Tmin, Mar.-Apr. − Tmin, Jan..
California, and suggested intervals between November to mid-January and mid-January to March as the effective chilling and forcing accumulation periods for cultivar Payne, respectively. Similarly, by using PLS regression, we identified January and March-April as two critical periods for alpine grassland green-up. Furthermore, high temperatures in January delayed the GUD whereas warm conditions in March-April advanced the GUD (Table 1). These might be primarily related to the warming-induced direct and indirect impacts on the vegetation dormancy (Lin et al., 2018; Martínez-lüscher et al., 2017; Shi et al., 2017). That is, temperature increases in spring could directly advance the GUD due to the increased heat accumulation, whereas temperature increases in winter prolongs the fulfilment of the chilling requirements (Schwartz and Hanes, 2010; Vitasse et al., 2014). Therefore, our findings further revealed and confirmed the differential effects of temperature increases in winter and spring in determining the GUD.
4.2. The dominant role of Tmin on vegetation green-up During the past few decades, warming on the Tibetan Plateau is believed to have been higher than that for the rest of the world (Trenberth, 2007), in particular with a trend that daily Tmin increased significantly faster than Tmax (Dong et al., 2012; Liu et al., 2006). In the present study, we found the same trend in Tmin and Tmax increases (Fig. 1). It is therefore crucial to investigate the differential responses of GUD to the increases in Tmin and Tmax. Our data analysis revealed that the GUD response to Tmin (−3.93 days °C−1, p < 0. 01) was more 394
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fourth quadrant from Fig. 7) was triggered by a decreased Tmin, Jan. and an increased Tmin, Mar.-Apr.. We further defined a minimum temperature difference (ΔTmin) between January and March-April to reflect the GUD. A larger ΔTmin was significantly associated with an advanced GUD (Fig. 5), which thus suggested that a favorable pattern (decreased Tmin, Jan. and increased Tmin, Mar.-Apr.) could be coupled with the advanced spring green-up. These relationships were supported by the underlying mechanisms that temperature increases in winter and spring had contrasting impacts on the GUD. Thus, when the temperature changes were negative correlated, the GUD was influenced by the intensive effect of Tmin because the effects of two key variables (Tmin, Jan. and Tmin, Mar.-Apr.) compensate each other. Indeed, very few studies and experiments have been conducted that look into the intensive effect of preseason temperature changes on vegetation green-up on the Tibetan Plateau, probably due to the lack of long-term phenological records or the negligence of winter and spring thermal correlations. Therefore, the present study provides new insight that ΔTmin may be a novel indicator for the GUD as it ideally combines Tmin variations from both winter and spring. However, it needs to be further examined that to what extent the temperature difference between winter and spring can lead to an advanced GUD.
Fig. 6. A conceptual explanation of the impact of Tmin on the GUD. Tmin would intensify the response of GUD when Tmin increases (decreases) in winter and decreases (increases) in spring. Specifically, Tmin increases in winter and decreases in spring (blue lines) would postpone the winter chilling and spring forcing, and consequently result in a delayed GUD. Tmin decreases in winter and increases in spring (red lines) would fulfill the winter chilling and spring forcing in advance, and it would thus shift the GUD to occur earlier.
4.3. Impact of Tmin difference on vegetation green-up 4.4. Implications Whether green-up is advanced or not depends on the combined effects of winter and spring temperatures. Some researchers suggested that the trade-off between temperature in winter and spring had offset effects on the GUD (Shi et al., 2017; Zhang et al., 2018), because reverse effects of temperature increases during both winter and spring counteract each other. However, in the present study, the significant negative correlation between winter and spring minimum temperature (Fig. 4) shifted the reverse effects to intensive effects on the GUD. Namely, a Tmin, Jan. increase and a Tmin, Mar.-Apr. decrease (blue line from Fig. 6) postponed both the chilling requirements and heat accumulation of the plants, which suggested a same delaying effect on spring greenup. In contrast, a Tmin, Jan. decrease and a Tmin, Mar.-Apr. increase (red line from Fig. 6) fulfilled both the chilling requirements and heat accumulation in advance. Thus, the combined contribution of the Tmin variation represented a same advancing effect on the GUD. We further quantified the combined effects of Tmin, Jan. and Tmin, Mar.-Apr. on the GUD in each year, which explicitly explained that the GUD changes were mainly related to the negatively correlated Tmin in winter and spring. More specifically, a delayed GUD (blue dots in the second quadrant from Fig. 7) was triggered by an increased Tmin, Jan. and a decreased Tmin, Mar.-Apr.; however, an advanced GUD (red dots in the
Alpine grassland has wide potential in livestock feed, water, soil conservation and ecological protection in Mainland China (Chen et al., 2018). Accurate knowledge of green-up dates in response to temperature variations could be helpful in predicting ecological changes in alpine ecosystems under projected climate change (Richardson et al., 2013; Tang et al., 2016). Our study is important as it provides valuable evidence of how plants respond to rapid and asymmetric temperature changes in an alpine ecosystem. We found that the GUD was strongly controlled by Tmin rather than Tmax, which suggests a more precise temperature indicator for alpine grassland GUD and provides critical information for improving existing phenology models at high altitude. Furthermore, Tmin increases in March-April significantly advanced the GUD whereas Tmin increases in January postponed the GUD. These relationships are in line with previous findings that vegetation green-up responds differently to warming in winter and spring (Asse et al., 2018; Chen et al., 2017b; Shi et al., 2017), which implies that further studies should consider separately the differential effects of temperature increases on the GUD in winter and spring. Moreover, the stronger effect of Tmin in March-April implies that Fig. 7. Combined effects of Tmin in January and March-April on the vegetation green-up date (GUD). Var.TJan. means Tmin, Jan. of the current year minus Tmin, Jan. of the prior year, and Var.TMar.-Apr. means Tmin, Mar.-Apr. of the current year minus Tmin, Mar.-Apr. of the prior year. Variation in GUD: red dots indicate an advanced GUD in the following year and blue dots represent a delayed GUD in the next year. See Table S1 for description of the GUD in response to a Tmin variation in Jan. and Mar.-Apr. from 1997 to 2010.
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Chinese Academy of Sciences.
temperature changes in spring rather than in winter trigger the shifts in the GUD of alpine grassland. The projected warming on the Tibetan Plateau is higher than the global average (IPCC, 2014), and if this projected warming occurs, it will render the spring onset of alpine grassland to occur earlier. Such an earlier occurrence of GUD could be expected to enhance ecosystem carbon uptake (Keenan et al., 2014; Richardson et al., 2010), which suggests a possible future enhancement of grassland carbon uptake on the Tibetan Plateau (Chen et al., 2017a). Furthermore, with respect to grassland management in the future, this advanced onset trend in the GUD may be closely related to increased grassland productivity, extended grazing season and greater grazing capacity (Chang et al., 2017; Keenan et al., 2014; Richardson et al., 2010; Zhou et al., 2016). Finally, the present results imply Tmin between spring and winter may be a novel indicator for spring onset, with a larger ΔTmin (lower Tmin in January and higher Tmin in March-April) triggering an earlier GUD. However, there are several questions remain to be answered. For instance, how will the temperature (i.e., Tmean, Tmin, and Tmax) change in the future? Will the increasing temperature difference (ΔTmin) between winter and early spring lead to more significant phenological shifts of alpine grassland? Is there an optimal Tmin threshold that results in the advanced GUD? In addition, besides temperature, other climatic cues, such as precipitation, photoperiod and snow melting dates, may potentially drive the plant spring green-up in cold climates (Keller and Körner, 2003; Sedlacek et al., 2015). However, in the present study, we mainly focused on the impacts of temperature on alpine grassland; thus, further research on vegetation response to other climatic factors as well as the interactive effects of climate change are needed.
Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ecolind.2019.05.003. References Abdi, H., 2010. Partial least squares regression and projection on latent structure regression (PLS Regression). doi: 10.1002/wics.051. Ahas, R., Aasa, R., Menzel, A., Fedotova, V.G., Scheifinger, H., 2002. Changes in European spring phenology. Int. J. Climatol. https://doi.org/10.1002/joc.818. Asse, D., Chuine, I., Vitasse, Y., Yoccoz, N.G., Delpierre, N., Badeau, V., Delestrade, A., Randin, C.F., 2018. Warmer winters reduce the advance of tree spring phenology induced by warmer springs in the Alps. Agric. For. Meteorol. 252, 220–230. https:// doi.org/10.1016/j.agrformet.2018.01.030. Chang, J., Ciais, P., Viovy, N., Soussana, J.F., Klumpp, K., Sultan, B., 2017. Future productivity and phenology changes in European grasslands for different warming levels : implications for grassland management and carbon balance. 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5. Conclusion Long-term field observations of vegetation green-up are urgently needed in order to make accurate predictions regarding the effects of climate change on terrestrial ecosystems. In particular, phenological observations of alpine herbs need more attention due to serious weaknesses in data collections and determination of grass phenological phases. Moreover, partial least squares regression is a useful statistical analysis tool for understanding the phenological responses to climate factors at a daily scale. In the present study, January and March-April were identified as two critical periods in winter and spring for vegetation green-up. Tmin played a dominant role in controlling the spring onset in both seasons. However, Tmin in March-April played a more important role than that in January on the GUD, which suggested an advanced green-up on the Tibetan Plateau under projected climate warming scenarios. More importantly, a difference in Tmin between winter and spring may be a novel indicator for GUD, and a larger ΔTmin was found to trigger an earlier spring green-up in alpine grassland. These results highlight that further investigation is required to consider the differential as well as the combined effects of temperature variations in winter and spring for future vegetation phenology dynamics. Acknowledgments Funding information. Contributions from the Dr. Guo were financially supported by the National Natural Science Foundation of China (41301007), the Fundamental Research Funds for the Central Universities (xzy012019008), the State Key Laboratory of Loess and Quaternary Geology (SKLLQG1809), the National Key Technology R&D Program (2012BAH31B03). Contributions from the Dr. Chen were financially supported by the National Natural Science Foundation of China (41701292), China Postdoctoral Science Foundation (2017M610647, 2018T111091), the Natural Science Basic Research Plan in Shaanxi Province (2017JQ3041), the State Key Laboratory of Loess and Quaternary Geology (SKLLQG1602), the Key Laboratory of Aerosol Chemistry and Physics (KLACP-17-02), Institute of Earth Environment, 396
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Original Articles
Responses of vegetation green-up date to temperature variation in alpine grassland on the Tibetan Plateau
T
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Xiaoting Lia,b, Wei Guoa,b, , Ji Chenc,d, Xiangnan Nia,b, Xiaoyi Weia,b a
Department of Earth and Environmental Sciences, Xi’an Jiaotong University, Xi’an 710049, China Institute of Global Environmental Change, Xi’an Jiaotong University, Xi’an 710049, China c Research Center for Eco-Environmental Sciences, Northwestern Polytechnical University, Xi’an 710049, China d Aarhus University Centre for Circular Bioeconomy, Department of Agroecology, Aarhus University, Blichers Allé, 20, 8830 Tjele, Denmark b
A R T I C LE I N FO
A B S T R A C T
Keywords: Green-up date Temperature Alpine grassland Tibetan Plateau Kobresia humilis
Temperature increases in spring can advance vegetation green-up date (GUD) due to the increased heat accumulations. However, temperature increases in winter will delay the GUD due to the postponed fulfilment of chilling requirements. Such contrasting impacts of temperature changes in spring and winter on the GUD have been documented from many ecosystems in the Northern Hemisphere. However, the combined effects of temperature variations in winter and spring on the GUD remained unclear, especially in the cold Tibetan Plateau. To advance our understanding of how alpine grassland GUD responds to climate warming, this study evaluated 14 years of field GUD observations of sedge species Kobresia humilis on the Tibetan Plateau. The results showed that January and March-April were two critical periods in determining the long-term GUD variations. The minimum temperatures (Tmin) during these two critical periods played the dominant roles in controlling the GUD. Increases in the Tmin significantly shifting the GUD by −3.9 days °C−1 and 1.7 days °C−1 in the spring and winter, respectively. Moreover, Tmin in March-April played a more important role in determining the GUD than did Tmin in January. In addition, the difference in minimum temperature (ΔTmin) between these two critical periods of January and March-April might be a novel indicator for vegetation GUD. This study provides novel insights into the differential impacts of minimum temperatures in winter and spring on the GUD and how they should be explicitly considered to better understand the effects of climate change on vegetation phenology.
1. Introduction Spring green-up date (GUD) provides ecological cues that reveal continuous patterns in timing of vegetation spring onset. Understanding such patterns is crucial to uncovering past and future climate change (Chuine and Régnière, 2017; Richardson et al., 2013; Tang et al., 2016). Moreover, shifts in GUD play important roles in regulating feedbacks to the climate system (Richardson et al., 2013). For example, changes in GUD had cascading impacts on ecosystem structure and function, canopy conductance, albedo and even carbon, water and energy fluxes (Liu et al., 2017; Richardson et al., 2013; Tang et al., 2016; Wu et al., 2015; Zhang et al., 2011). Extensive research has been conducted to elucidate the GUD patterns, but no consensus has been reached until now (Ahas et al., 2002; Ge et al., 2015; Guo et al., 2018; Jeong et al., 2011; Ren et al., 2018; Sherry et al., 2007). This is because the underlying mechanisms driving the long-term changes in GUD are still
unclear. Temperature increases in spring and winter can influence the GUD through direct and indirect pathways (Lin et al., 2018; Martínez-lüscher et al., 2017; Pope et al., 2014; Shi et al., 2017). Specifically, temperature increases in spring can directly advance seed germination and leaf unfolding as heat accumulation can be met in advance due to the increased temperature (Harrington and Gould, 2015; Shi et al., 2017). Meanwhile, there is also evidence that temperature increases in the winter can delay the GUD by decreasing the chilling requirement, which is necessary for breaking bud dormancy in cool conditions (Luedeling et al., 2012; Martínez-lüscher et al., 2017; Vitasse et al., 2014). Hence, whether the GUD is delayed or advanced will to an extent rely on the trade-off between these two opposing mechanisms. However, the relative importance of winter chilling and spring forcing in determining the GUD is still unclear. During recent decades, responses of GUD to temperature variations in the alpine ecosystem
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Corresponding author at: Department of Earth and Environmental Sciences, Xi’an Jiaotong University, Xi’an 710049, China. E-mail addresses: [email protected] (X. Li), [email protected] (W. Guo), [email protected] (J. Chen), [email protected] (X. Ni), [email protected] (X. Wei). https://doi.org/10.1016/j.ecolind.2019.05.003 Received 4 August 2018; Received in revised form 19 February 2019; Accepted 2 May 2019 Available online 15 May 2019 1470-160X/ © 2019 Elsevier Ltd. All rights reserved.
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used to simulate the trend of vegetation GUD from 1997 to 2010. The following simple linear regression analysis model was applied to the research:
have been intensively studied, but no consensus has been reached on the responses of GUD of alpine plants to climate warming (Cui et al., 2017; Dorji et al., 2013; Mohandass et al., 2015). Some studies showed continuous advancement of GUD in response to climate warming according to a range of satellite-based GUD detections (Thompson and Paull, 2017; Zhang et al., 2013), long-term field observations (Zheng et al., 2016) and experimental manipulation (Suonan et al., 2017). However, Yu et al. (2010) and Zhang et al. (2018) suggested a delayed GUD under winter and spring warming. In addition, a number of other studies reported no significant temporal trend in the GUD in spite of continued warming on the Tibetan Plateau (Liu, 2016; Zhou et al., 2016a). One critical reason for the lack of consensus is that underlying mechanisms, particularly the combined effects of temperature increases in winter and spring on the GUD, are not well understood. Further research is therefore needed to clarify the mechanism of alpine plants’ GUD in response to temperature changes in winter and spring. The Tibetan Plateau, known as the ‘Earth’s third pole’, is a pilot region of climatic fluctuation that is extremely sensitive to global climate change (Ding et al., 2013; Yang et al., 2017; Zhang et al., 2014; Zheng et al., 2016). The alpine meadow grassland is one of the most widespread vegetation types on the Plateau, covering an area of ∼1.2 × 106 km2 and accounting for about half of its land area (Chen et al., 2016a; Yang et al., 2018), and is of considerable importance in animal husbandry and the local ecological environment. However, the underlying mechanism of alpine grassland GUD in response to temperature increases in winter and spring is not well understood, mainly being constrained by the lack of long-term records of grass phenology from ground observations (Yu et al., 2010; Zhang et al., 2013, 2018). To this end, based on 14 years of field GUD observations of the dominant perennial sedge species (i.e., Kobresia humilis) on the Tibetan Plateau, we investigated the effects of long-term air temperature variations on alpine grassland GUDs. In the present study, we aimed to clarify the following questions: (1) What are the critical periods for GUD variations? (2) How does GUD respond to temperature increases in winter and spring separately? and (3) How can the combined effects of temperature variations in both winter and spring be quantified?
n
Slope =
n
n
n (∑i = 1
i)
n × ∑i = 1 GUDi − ∑i = 1 i ∑i = 1 GUDi n×
n ∑i = 1 i 2
−
2
(1)
where n is the number of studied years; GUDi is the vegetation GUD of year i; Slope > 0 means the changing tendency of vegetation green-up among n years is delayed, and on the contrary, it is advanced (Wang et al., 2016). Meteorological data were obtained from the Haibei meteorological station, including the daily maximum temperature (Tmax) and minimum temperature (Tmin), precipitation, evaporation and the duration of the solar radiation from 1997 to 2010. Mean Tmax and Tmin were 10.0 °C and −5.7 °C, respectively. Mean annual total precipitation and evaporation were 400.1 mm and 1441.3 mm, respectively, and the mean annual total sunshine duration was 2934.2 h. All daily temperatures were averaged to an 15-day running mean temperature for more recognizable temperature response patterns of GUDs in subsequent statistical analyses (Luedeling and Gassner, 2012). 2.3. Partial Least Squares analysis Partial Least Squares (PLS) regression was used to identify the relevant periods when temperature had a strong impact on the GUDs of K. humilis. As a commonly used multivariate analysis method, PLS is reliable in the case that the independent variables are highly autocorrelated or the number of independent variables over-exceeds the number of dependent variables (Abdi, 2010; Luedeling and Gassner, 2012; Martínez-lüscher et al., 2017). Such cases can be encountered in analyzing the response of plant phenology to temperature records at high temporal resolution, and recent work has shown that PLS regression is useful for identifying the specific periods that are critical for plant green-up in this context (Luedeling and Gassner, 2012; Martínezlüscher et al., 2017). Equations are clarified in detail with reference to Abdi (2010). The two main outputs of PLS regression are the variable importance in the projection (VIP) and the standardized model coefficients. The VIP value reflects the importance of the independent variables to interpret the dependent variables, and the commonly held threshold is 0.8 (Guo et al., 2013; Luedeling and Gassner, 2012). Model coefficients indicate the direction and strength of each variable on the dependent variable. For example, high and positive model coefficients indicate that the GUD is delayed when temperature increases during these relevant periods, while negative coefficients reflect that the warmer temperature triggers an earlier GUD. The VIP values and the standardized model coefficients were output at a daily scale over the study period. Critical periods were determined with VIP values greater than 0.8 and high absolute model coefficients. In addition, the root-mean-square error (RMSE) [Eq. (2)] was calculated to measure the applicability of the model.
2. Materials and methods 2.1. Study region This study was conducted at the Haibei Grassland Ecological Monitoring Station (Haibei station, 36°57′N, 100°51′E) in the northeastern Tibetan Plateau. With a typical plateau continental climate, this region is characterized by low air temperature, a large temperature difference between day and night, limited precipitation and strong solar radiation. Based on the meteorological data from 1997 to 2010, the mean annual air temperature was 1.5 °C, and the mean annual precipitation was 400.1 mm, with 85% of the rainfall concentrated in the growing season (i.e., from May to September). The soil is sandy loam, which is classified as mountain brown soil according to the Chinese soil classification (Chen et al., 2016b). The dominant species at the study site is K. humilis (Cyperaceae), which is a perennial sedge species with a short rhizome. K. humilis is an excellent forage grass for livestock, and it is ecologically and economically important in the long-term adaption to cold conditions and resistance to human grazing on the Tibetan Plateau. More detailed and longer-term information about the study site can be found in Chen et al. (2015) and Chen et al. (2016b).
n
RMSE =
∑i = 1 (obsi − prei )2 n
(2)
where obsi is the observed GUD in year i; prei is the simulated GUD in year i; n is the sample size (Chen et al., 2017b). To determine the critical periods, the GUDs were set as the dependent variables, and the daily Tmin (or Tmax) (subjected to an 15-day running mean) for the 12 months leading up to the GUD was set as the independent variables. PLS regression was implemented in the R programming language (R Core Team, 2015), using the “chillR” package (Luedeling, 2018) and the “pls” package (Mevik et al., 2018).
2.2. Phenological and meteorological data In situ phenological data of K. humilis during 1997 to 2010 were collected from the Haibei Station, which is a main international alpine terrestrial ecosystem research base of the China Meteorological Administration. Julian days were used to indicate the GUD of K. humilis on a particular day-of-year (DOY). Simple linear regression analysis was
2.4. Correlation analysis between temperature and GUDs Partial correlation analysis was applied to assess the relative 391
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Fig. 1. Correlation between the green-up date (GUD) and Tmax, Tmin during the non-growing season (Tmax,NGS , Tmin,NGS ), and inter-annual variation in the GUD and Tmax,NGS , Tmin,NGS from 1997 to 2010. GUD was negatively correlated with Tmax,NGS , Tmin,NGS with correlation coefficients (r, marked in red color) of −0.143 (P > 0.05), −0.205 (P > 0.05), respectively. The landscape and weather station of the study site are also shown in the inset pictures.
GUD. The VIP values were larger than 0.8 during January as well as during March to April. But increases in Tmin had contrasting effects on the GUD during these two periods. Specifically, the model coefficients were positive during January, which indicated that the increased Tmin in January postponed the GUD. In contrast, the model coefficients were negative during March to April, which suggested that the earlier occurrence of the spring green-up was related to increased Tmin during March to April (Fig. 2). For the analysis of Tmax, January was also a critical period when the temperature had significant positive correlation with GUDs. However, increased Tmax during March to April had neither consistently advancing nor delaying effect on the GUD (Fig. 3).
importance of Tmax and Tmin in the variation of vegetation GUDs in relevant periods. The correlations between GUD and Tmin (Tmax) were calculated, setting Tmax or Tmin as the controlling variables. For variables whose partial correlation coefficients (r) were significant (p < 0.05), the absolute value of r between GUD and Tmin was compared with that between GUD and Tmax to examine which factor was more dominant in influencing vegetation phenology. In addition, to assess the response of GUD to Tmin (Tmax) variations, we determined the temperature sensitivity as the slope of the linear regression between the time-series of GUD and Tmin (Tmax) in critical periods from 1997 to 2010, which illustrated a time shift of the spring green-up in response to a unit shift in temperature.
3.2. Correlation between temperature (Tmin, Tmax) and the GUDs 3. Results Our data analysis on the partial correlation analysis clearly demonstrated that the partial coefficients between Tmin and GUD were higher than those between Tmax and GUD in both critical periods (Table 1). The results suggested that changes in Tmin had a greater impact on the GUD than Tmax in both January and March-April. Furthermore, the temperature sensitivity of the GUD also showed that Tmin had a significantly stronger impact on the GUD than Tmax. Tmin increases in January postponed the GUD at a rate of 1.66 days °C−1 (R2 = 0.286, p < 0.05) and Tmin increases in March-April induced an earlier GUD at a rate of 3.93 days °C−1 (R2 = 0.581, p < 0.01); however, an increase in Tmax had no significant effect on the GUD in either January and March-April. In addition, when analyzed in different months, the GUD was more associated with Tmin variation in MarchApril (r = 0.70, p < 0.01) than in January (r = 0.36, p > 0.05) (Table 1). Therefore, the present results suggested that alpine plants’ green-up was more sensitive to Tmin variation than Tmax, and Tmin in March-April plays a more important role than that of January on the spring green-up on the Tibetan Plateau.
3.1. Critical periods for the GUDs of K. humilis From 1997 to 2010, asymmetric increases in Tmax and Tmin during the non-growing season (i.e., from October to April) were found at a rate of 0.35 °C/decade (R2 = 0.069, p > 0.05) and 0.88 °C/decade (R2 = 0.390, p < 0.05), respectively (Fig. 1). Moreover, we found no significant relationship between GUD and Tmax (or Tmin) during the non-growing season, and we further conducted PLS regression at a daily scale. Based on the 14 years of field observations, the average GUD of K. humilis was DOY 108 (i.e., April 18th), and there was no significant relation between the GUD and year from 1997 to 2010 (0.37 days/ decade, R2 = 0.001, p > 0.05; Fig. 1a). The daily Tmax and Tmin from previous May to April were used as independent variables, while the GUDs were used as the dependent variables. The RMSEs were 1.07 (Tmax) and 1.09 (Tmin) days from the PLS regression, indicating that this method was applicable to the data. Based on the VIP and the standardized model coefficients, relevant periods were determined when the temperature was significantly correlated with the GUD of K. humilis. To examine the influence of Tmin on the GUD, two periods in winter and spring were identified when Tmin had significant impacts on the
3.3. Mechanism of spring green-up in response to minimum temperature An increase of 1 °C in Tmin in March-April (Tmin, 392
Mar.-Apr.)
and in
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Table 1 Impacts of Tmin and Tmax in relevant periods on vegetation green-up date (GUD).
Partial coefficient (r) between GUD and temperature Temperature sensitivity of GUD (day °C
January March-April January March-April
Tmin
Tmax
0.36 −0.70*** 1.66** −3.93***
0.20 0.16 1.66 −1.68
Asterisks indicate significance of factors; ***p < 0.01, **p < 0.05. Correlation and sensitivity without an asterisk are not significant (p > 0.05).
January (Tmin, Jan.) significantly shifted the GUD by −3.93 days °C−1 and 1.66 days °C−1, respectively. Moreover, we found that Tmin, Jan. and Tmin, Mar.-Apr. were significantly negatively correlated, with a correlation coefficient of −0.57 (p < 0.05) (Fig. 4). Thus, when Tmin increased in January, it decreased later in March-April, whereas when Tmin decreased in January, it was followed by a warmer March-April before the spring green-up. Consequently, shifts in Tmin, Jan. and Tmin, Mar.-Apr. intensified the response of the green-up from the same direction. That is, an increased Tmin, Jan. and following decreased Tmin, Mar.-Apr. had the same delaying effects on the GUD and consequently postponed the onset of vegetation green-up. In contrast, a decreased Tmin, Jan. and increased Tmin, Mar.-Apr. exerted the same influence on advanced GUD. Furthermore, we introduced a temperature difference (ΔTmin = Tmin, Mar.-Apr. − Tmin, Jan.) between January and March-April to reflect the combined effects of Tmin variations on the GUD. These relationships further demonstrated that a larger ΔTmin would significantly advance the onset of the vegetation growth at a rate of 1.51 days °C−1 (R2 = 0.476, p < 0.01) (Fig. 5).
Fig. 2. The results of the Partial Least Squares (PLS) regression correlating GUD for K. humilis with an 11-day average daily minimum temperature from May to April. (a) Variable importance in the projection (VIP) values: the blue color indicates a VIP value greater than 0.8; (b) standardized model coefficients: the red color indicates the model coefficients are negative, while the green color indicates the model coefficients are positive between GUD and temperature; (c) variation in daily minimum temperature: the black line indicates the minimum temperature and the grey, green and red areas represent the standard deviation of the daily minimum temperature for each day.
4. Discussion Alpine grassland has been suggested to be susceptible to future climate change and is of considerable interest in terms of regional and global climate change (Chen et al., 2015). As the dominant species in the typical alpine meadow on the Tibetan Plateau, K. humilis is important not only for animal husbandry but also for local ecological environment (Chen et al., 2015; Chen et al., 2016b). A better understanding of the GUD in response to temperature variation is important for projecting phenology dynamics, evaluating ecosystem productivity and further optimizing grassland management (Chang et al., 2017; Keenan et al., 2014; Richardson et al., 2010; Zhou et al., 2016b). Therefore, based on long-term field observations, we elucidated the critical periods when temperature was strongly correlated with alpine grassland GUD and investigated how the GUD responded to temperature variations in those periods. 4.1. The critical periods for vegetation green-up Temperature increases in winter and spring result in opposite impacts on vegetation green-up. Climate warming has initiated a general advance in GUD in most regions of the Northern Hemisphere since the 1980s (Cleland et al., 2012; Korner and Basler, 2010; Linkosalo et al., 2009; Yaacoubi et al., 2014; Gordo and Sanz, 2009), suggesting a dominantly advancing effect of a warmer spring. However, over recent years, a delayed GUD has been revealed in the alpine ecosystem, which suggested that the delaying effect of a warm winter is expected to become more pronounced (Fu et al., 2013; Yu et al., 2010). Thus, the differential impacts of temperature in winter and spring on the GUD should be further investigated especially in alpine regions such as the Tibetan Plateau. In previous studies, PLS regression has been able to identify the days that are most important for the timing of trees’ green-up (Guo et al., 2013; Luedeling and Gassner, 2012). For example, Luedeling and Gassner (2013) studied the leaf emergence of the walnut cultivars in
Fig. 3. The results of the Partial Least Squares (PLS) regression correlating GUD for K. humilis with an 11-day average daily maximum temperature from May to April. (a) Variable importance in the projection (VIP) values: the blue color indicates a VIP value greater than 0.8; (b) standardized model coefficients: the red color indicates the model coefficients are negative, while the green color indicates the model coefficients are positive between GUD and temperature; (c) variation in daily maximum temperature: the black line indicates the maximum temperature and the grey, green and red areas represent the standard deviation of the daily maximum temperature for each day.
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Fig. 4. The inter-annual variation of minimum temperature (Tmin) in January and from March to April from 1997 to 2010.
significant than that to Tmax (−1.68 days °C−1, p > 0. 05) in MarchApril. Namely, a greater time shift of the GUD in response to a unit shift in Tmin. However, although temperature sensitivity remained the same for both Tmin and Tmax in January, the GUD response was significantly correlated to Tmin (1.66 days °C−1, p < 0. 05), while it was not significantly correlated to Tmax (1.66 days °C−1, p > 0. 05) (Table 1). These relationships suggest that Tmin played the dominant role in controlling alpine grassland GUD. It is possible that in winter, Tmin contributed more to the chilling requirement. As buds gradually become more sensitive to freezing temperatures during leaf emergence in spring (Vitasse et al., 2014), increases in Tmin could favor freezing resistance and heat accumulation even with just a minor increase in low temperatures (Shi et al., 2017). Another possible explanation might be that a higher Tmin could provide more available soil water from snow and ice that is typically constrained by the low soil temperature (Yang, 2013; Yi et al., 2013). Once the soil moisture increases, plant roots absorb more water to prepare for the leaf unfolding. In contrast, a previous study found that satellitederived GUD was determined mainly by Tmax in the Northern Hemisphere between 1982 and 2011 (Piao et al., 2015). This discrepancy may be primarily due to considerable variation in climate and landscape between Europe, parts of the US and the Tibetan Plateau. The Tibetan Plateau has a typical plateau continental climate and is characterized by low air temperature that is much lower than that of the temperate climate in most middle- and high-latitude areas in the Northern Hemisphere (Chen et al., 2016b; Yang et al., 2018). Low temperatures are the primary factor limiting plant growth, which suggests that alpine grassland is likely to be more sensitive to minimum temperature variations. Overall, the present results suggest that Tmin is an effective indicator for green-up of alpine grassland. Furthermore, we found that shifts in the GUD were mainly controlled by Tmin, Mar.-Apr. rather than Tmin, Jan. (Table 1), suggesting a stronger effect of the spring minimum temperature than winter minimum temperature on the GUD. This finding is consistent with previous studies which showed that temperature occurring 1–3 months before spring vegetation phenology plays the dominant role of controlling the variation of phenology (Guo et al., 2013; Güsewell et al., 2017; Matsumoto et al., 2003; Menzel, 2003; Ren et al., 2018). For example, a previous study of deciduous trees species revealed that temperature variations in March and April were critical for leaf unfolding, and the spring forcing was of great essence in advancing leaf unfolding (Juknys et al., 2016). Those results, together with our findings, suggest that minimum temperature in spring still is the dominant factor in impacting the vegetation green-up.
Fig. 5. The relationship between the green-up date of K. humilis and the minimum temperature difference (ΔTmin) between March-April and January from 1997 to 2010. The regression equation and correlation coefficient are given in the figure. ΔTmin = Tmin, Mar.-Apr. − Tmin, Jan..
California, and suggested intervals between November to mid-January and mid-January to March as the effective chilling and forcing accumulation periods for cultivar Payne, respectively. Similarly, by using PLS regression, we identified January and March-April as two critical periods for alpine grassland green-up. Furthermore, high temperatures in January delayed the GUD whereas warm conditions in March-April advanced the GUD (Table 1). These might be primarily related to the warming-induced direct and indirect impacts on the vegetation dormancy (Lin et al., 2018; Martínez-lüscher et al., 2017; Shi et al., 2017). That is, temperature increases in spring could directly advance the GUD due to the increased heat accumulation, whereas temperature increases in winter prolongs the fulfilment of the chilling requirements (Schwartz and Hanes, 2010; Vitasse et al., 2014). Therefore, our findings further revealed and confirmed the differential effects of temperature increases in winter and spring in determining the GUD.
4.2. The dominant role of Tmin on vegetation green-up During the past few decades, warming on the Tibetan Plateau is believed to have been higher than that for the rest of the world (Trenberth, 2007), in particular with a trend that daily Tmin increased significantly faster than Tmax (Dong et al., 2012; Liu et al., 2006). In the present study, we found the same trend in Tmin and Tmax increases (Fig. 1). It is therefore crucial to investigate the differential responses of GUD to the increases in Tmin and Tmax. Our data analysis revealed that the GUD response to Tmin (−3.93 days °C−1, p < 0. 01) was more 394
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fourth quadrant from Fig. 7) was triggered by a decreased Tmin, Jan. and an increased Tmin, Mar.-Apr.. We further defined a minimum temperature difference (ΔTmin) between January and March-April to reflect the GUD. A larger ΔTmin was significantly associated with an advanced GUD (Fig. 5), which thus suggested that a favorable pattern (decreased Tmin, Jan. and increased Tmin, Mar.-Apr.) could be coupled with the advanced spring green-up. These relationships were supported by the underlying mechanisms that temperature increases in winter and spring had contrasting impacts on the GUD. Thus, when the temperature changes were negative correlated, the GUD was influenced by the intensive effect of Tmin because the effects of two key variables (Tmin, Jan. and Tmin, Mar.-Apr.) compensate each other. Indeed, very few studies and experiments have been conducted that look into the intensive effect of preseason temperature changes on vegetation green-up on the Tibetan Plateau, probably due to the lack of long-term phenological records or the negligence of winter and spring thermal correlations. Therefore, the present study provides new insight that ΔTmin may be a novel indicator for the GUD as it ideally combines Tmin variations from both winter and spring. However, it needs to be further examined that to what extent the temperature difference between winter and spring can lead to an advanced GUD.
Fig. 6. A conceptual explanation of the impact of Tmin on the GUD. Tmin would intensify the response of GUD when Tmin increases (decreases) in winter and decreases (increases) in spring. Specifically, Tmin increases in winter and decreases in spring (blue lines) would postpone the winter chilling and spring forcing, and consequently result in a delayed GUD. Tmin decreases in winter and increases in spring (red lines) would fulfill the winter chilling and spring forcing in advance, and it would thus shift the GUD to occur earlier.
4.3. Impact of Tmin difference on vegetation green-up 4.4. Implications Whether green-up is advanced or not depends on the combined effects of winter and spring temperatures. Some researchers suggested that the trade-off between temperature in winter and spring had offset effects on the GUD (Shi et al., 2017; Zhang et al., 2018), because reverse effects of temperature increases during both winter and spring counteract each other. However, in the present study, the significant negative correlation between winter and spring minimum temperature (Fig. 4) shifted the reverse effects to intensive effects on the GUD. Namely, a Tmin, Jan. increase and a Tmin, Mar.-Apr. decrease (blue line from Fig. 6) postponed both the chilling requirements and heat accumulation of the plants, which suggested a same delaying effect on spring greenup. In contrast, a Tmin, Jan. decrease and a Tmin, Mar.-Apr. increase (red line from Fig. 6) fulfilled both the chilling requirements and heat accumulation in advance. Thus, the combined contribution of the Tmin variation represented a same advancing effect on the GUD. We further quantified the combined effects of Tmin, Jan. and Tmin, Mar.-Apr. on the GUD in each year, which explicitly explained that the GUD changes were mainly related to the negatively correlated Tmin in winter and spring. More specifically, a delayed GUD (blue dots in the second quadrant from Fig. 7) was triggered by an increased Tmin, Jan. and a decreased Tmin, Mar.-Apr.; however, an advanced GUD (red dots in the
Alpine grassland has wide potential in livestock feed, water, soil conservation and ecological protection in Mainland China (Chen et al., 2018). Accurate knowledge of green-up dates in response to temperature variations could be helpful in predicting ecological changes in alpine ecosystems under projected climate change (Richardson et al., 2013; Tang et al., 2016). Our study is important as it provides valuable evidence of how plants respond to rapid and asymmetric temperature changes in an alpine ecosystem. We found that the GUD was strongly controlled by Tmin rather than Tmax, which suggests a more precise temperature indicator for alpine grassland GUD and provides critical information for improving existing phenology models at high altitude. Furthermore, Tmin increases in March-April significantly advanced the GUD whereas Tmin increases in January postponed the GUD. These relationships are in line with previous findings that vegetation green-up responds differently to warming in winter and spring (Asse et al., 2018; Chen et al., 2017b; Shi et al., 2017), which implies that further studies should consider separately the differential effects of temperature increases on the GUD in winter and spring. Moreover, the stronger effect of Tmin in March-April implies that Fig. 7. Combined effects of Tmin in January and March-April on the vegetation green-up date (GUD). Var.TJan. means Tmin, Jan. of the current year minus Tmin, Jan. of the prior year, and Var.TMar.-Apr. means Tmin, Mar.-Apr. of the current year minus Tmin, Mar.-Apr. of the prior year. Variation in GUD: red dots indicate an advanced GUD in the following year and blue dots represent a delayed GUD in the next year. See Table S1 for description of the GUD in response to a Tmin variation in Jan. and Mar.-Apr. from 1997 to 2010.
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temperature changes in spring rather than in winter trigger the shifts in the GUD of alpine grassland. The projected warming on the Tibetan Plateau is higher than the global average (IPCC, 2014), and if this projected warming occurs, it will render the spring onset of alpine grassland to occur earlier. Such an earlier occurrence of GUD could be expected to enhance ecosystem carbon uptake (Keenan et al., 2014; Richardson et al., 2010), which suggests a possible future enhancement of grassland carbon uptake on the Tibetan Plateau (Chen et al., 2017a). Furthermore, with respect to grassland management in the future, this advanced onset trend in the GUD may be closely related to increased grassland productivity, extended grazing season and greater grazing capacity (Chang et al., 2017; Keenan et al., 2014; Richardson et al., 2010; Zhou et al., 2016). Finally, the present results imply Tmin between spring and winter may be a novel indicator for spring onset, with a larger ΔTmin (lower Tmin in January and higher Tmin in March-April) triggering an earlier GUD. However, there are several questions remain to be answered. For instance, how will the temperature (i.e., Tmean, Tmin, and Tmax) change in the future? Will the increasing temperature difference (ΔTmin) between winter and early spring lead to more significant phenological shifts of alpine grassland? Is there an optimal Tmin threshold that results in the advanced GUD? In addition, besides temperature, other climatic cues, such as precipitation, photoperiod and snow melting dates, may potentially drive the plant spring green-up in cold climates (Keller and Körner, 2003; Sedlacek et al., 2015). However, in the present study, we mainly focused on the impacts of temperature on alpine grassland; thus, further research on vegetation response to other climatic factors as well as the interactive effects of climate change are needed.
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5. Conclusion Long-term field observations of vegetation green-up are urgently needed in order to make accurate predictions regarding the effects of climate change on terrestrial ecosystems. In particular, phenological observations of alpine herbs need more attention due to serious weaknesses in data collections and determination of grass phenological phases. Moreover, partial least squares regression is a useful statistical analysis tool for understanding the phenological responses to climate factors at a daily scale. In the present study, January and March-April were identified as two critical periods in winter and spring for vegetation green-up. Tmin played a dominant role in controlling the spring onset in both seasons. However, Tmin in March-April played a more important role than that in January on the GUD, which suggested an advanced green-up on the Tibetan Plateau under projected climate warming scenarios. More importantly, a difference in Tmin between winter and spring may be a novel indicator for GUD, and a larger ΔTmin was found to trigger an earlier spring green-up in alpine grassland. These results highlight that further investigation is required to consider the differential as well as the combined effects of temperature variations in winter and spring for future vegetation phenology dynamics. Acknowledgments Funding information. Contributions from the Dr. Guo were financially supported by the National Natural Science Foundation of China (41301007), the Fundamental Research Funds for the Central Universities (xzy012019008), the State Key Laboratory of Loess and Quaternary Geology (SKLLQG1809), the National Key Technology R&D Program (2012BAH31B03). Contributions from the Dr. Chen were financially supported by the National Natural Science Foundation of China (41701292), China Postdoctoral Science Foundation (2017M610647, 2018T111091), the Natural Science Basic Research Plan in Shaanxi Province (2017JQ3041), the State Key Laboratory of Loess and Quaternary Geology (SKLLQG1602), the Key Laboratory of Aerosol Chemistry and Physics (KLACP-17-02), Institute of Earth Environment, 396
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