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Tree-growth synchrony index, an effective indicator of historical climatic extremes

Ecological Processes volume 13, Article number: 55 (2024) Cite this article

Abstract

Background

Tree rings play an important role in reconstructing past climate. Growth differences among individual trees due to microclimatic conditions and local disturbances are averaged in developing tree-ring chronologies. Here, we addressed the problem of averaging by investigating growth synchrony in individual trees. We used tree-ring data of 1046 juniper trees from 32 sites on the Tibetan Plateau and 538 pine trees from 20 sites in the subtropical region of eastern China and calculated the tree-growth synchrony index (TGS).

Results

Our results showed that both the TGS index and tree-ring index could be indicators of interannual variation of climatic factors. The TGS index identified 20% more climatic extremes than tree-ring index over the last 50 years that high synchrony indicates extreme climate forcing in controlling forest growth.

Conclusions

The TGS index can identify extreme climatic events effectively than tree-ring index. This study provides a novel perspective for climate reconstruction, especially in the realm of tree growth response to extreme climate. Our findings contribute to understanding of the spatiotemporal dynamics and the causes of historical climate extremes and provide guidance for protecting trees from climate extremes in the future.

Introduction

Extreme climatic events are increasing in frequency, intensity, severity and duration, leading to ecosystem regime shifts and causing damage to the environment all over the world (Madrigal-González et al. 2020, 2023; Quesada-Román et al. 2020). Tracing the temporal and spatial dynamics of extreme climate events provides evidence for understanding the causes and occurrences of extremes and guides the mitigation of adverse effects.

Despite the utilization of dendrochronological analysis to reconstruct climate variations, the representations are not highly accurate due to the disregard for individual differences (Cohen et al. 2014; Zhang et al. 2015; Jiao et al. 2019; Liu et al. 2021). The growth of individual trees can be influenced by various factors, including external microclimatic conditions and internal resilience, leading to trees’ growth diversity in climate responses (Martin-Benito et al. 2018; Quesada-Román et al. 2022). However, the differences among individual trees are usually ignored in the process of establishing tree-ring width chronologies. Trees in forests exhibit coherent growth and synchronized responses when climatic influence is intensified (Moran 1953; Liebhold et al. 2004). The amplitude of tree growth synchrony (TGS) determines the key climate factors that control tree growth (Shestakova et al. 2016). We hypothesize that tree growth synchrony could be used to effectively identify extreme climatic events.

Frequent climate extremes have led to severe damage to forests across China, especially in areas sensitive to climate change over the past few decades (Shi et al. 2013; Liu et al. 2014; Liang et al. 2016). Studying the temporal and spatial dynamics of extreme climate events can provide valuable insights into their causes and occurrence, thus enabling strategies to mitigate their adverse effects. We used tree-ring data of juniper trees on the Tibetan Plateau and pine trees in the eastern China under different geographical locations and climatic conditions and tree-growth synchrony index (TGS index). By comparing the accuracy of the TGS index series and tree-ring index series (TRI) in reflecting climatic extremes, we evaluated the advantages of the TGS index in identifying extreme climate change.

Materials and methods

Study area

The study was performed on the Tibetan Plateau and Zhejiang, Anhui, Hunna, Jiangxi and Fujian provinces in the eastern China. On the Tibetan Plateau, the mean annual temperature ranged from − 10 to 6 °C, it’s about 10 °C lower than that warm temperate regions at the same latitude (Kuang and Jiao 2016; Chen et al. 2021); the total annual precipitation varied greatly by region, it ranges from 50 to 1000 mm (Cui and Graf 2009). The mean annual temperature and total annual precipitation ranged from 15 to 18 °C and 800 mm to 1600 mm in the eastern China, respectively (Zhang et al. 2009; Duan et al. 2012). We divided the study area into four zones (Zone-I, Zone-II, Zone-III and Zone-IV) based on geographic location and climatic conditions (Fig. 1 and Fig. S1).

Fig. 1
figure 1

Location of study sites

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Tree-ring data

We collected tree-ring data of 521 junipers (Juniperus prewalskii and Juniperus tibetica) at 14 sites in Zone-I, 525 junipers (Juniperus tibetica and Juniperus saltuaria) at 18 sites in Zone-II, 314 pines (Pinus massoniana) at 11 sites in Zone-III and 224 pines (Pinus massoniana) at 9 sites in Zone-IV (Supplementary Tables S1, S2). All tree-ring data used in the study were collected from our lab and previously described (Duan et al. 2013; Zhang et al. 2015; Jia et al. 2022). We used a 32-year cubic spline of 50% frequency–response cutoff to remove the low-frequency growth trends related to aging of each tree-ring sequence, 52 standard ring-width chronologies were generated by using Programme ARSTAN for each site, and four TRI series were obtained for each geographic zone.

Climate data

The monthly scPDSI (self-calibrated Palmer Drought Severity Index), monthly mean temperature and monthly total precipitation were sourced from https://climexp.knmi.nl/, the spatial resolution of the climate data is 0.5° × 0.5°. The climate conditions for each sampling site were averaged of the climate data from the four closest grid points. The climate conditions for each large region were averaged of the climate data from all sampling sites in each zone (Fig. S2).

Tree-growth synchrony index

There are three ways (increase, decrease or no change) in tree-ring widths varying from year to year. The relative change of tree-ring width is based on subtracting the previous year’s width from the current year’s width. The TGS index was calculated by subtracting the percentage of trees with a relative decrease from the percentage of trees with a relative increase in tree ring width (Visser 2015; Haneca et al. 2018; Jia et al. 2024).

$${\text{TGS}} = {\text{P}}_{{{\text{RI}}}} - {\text{P}}_{{{\text{RD}}}} ,$$

where PRI is the percentage of trees with a relative increase; PRD is the percentage of trees with a relative decrease in tree ring width.

To minimize the random change in the TGS index, we considered there was no change if the interannual variation was less than 0.01. The TGS index is in the range of − 1 to 1, the positive values indicate relative increase in tree growth, and the negative values indicate relative decrease in tree growth.

We calculated 52 TGS index series for 52 sampling sites. The interannual TGS index series for each geographic zone was calculated among all trees in this zone. Because of different ages of trees, we took the analysis interval with at least 15 and 100 sample replications for each year instead of the common interval for each sampling site and each zone, respectively.

Correlation analysis

Pearson correlation analysis was conducted between climatic factors and tree growth. For the correlation analysis, we selected the monthly mean temperature, monthly scPDSI and monthly total precipitation from October of previous year to September of current year, covering the period from 1963 to 2000.

Comparison of extremes

The pointer years were defined as those on the TGS index series and TRI series with values exceeding 1.65 times the standard deviation of the mean value, corresponding to the 90% confidence interval. To ensure the number of pointer years during the modern instrumental observation period (1963–2000 in our study), we selected the years with the minimum or maximum values on the TGS index series and TRI series to represent the pointer years. For Zone-I and Zone-II, we chose the 6 years with the lowest values for each series to refer to possible extreme drought events. For Zone-III and Zone-IV, we selected 4 years with the lowest values to refer to possible extreme low temperature events, and 4 years with the highest values to refer to possible extreme high temperature events for each series. Similarly, six dry extremes were selected in Zone-I and Zone-II, and four low temperature extremes and four high temperature extremes were selected in Zone-III and Zone-IV. The rates between climatic extremes and pointer years in the TGS index series and TRI series of each geographic zone were calculated. The rates in each zone were compared to assess which series could better identify climatic extremes.

Results

TGS index series and TRI series

The intervals of series for Zones I–IV were 1452 to 2001 (550 years), 1501 to 2001 (501 years), 1881 to 2001 (121 years) and 1884 to 2001 (118 years) with at least 100 samples for each year (Fig. 2). In Zones I–IV, 52, 44, 12, and 11 pointer years were identified in the TGS index series, and 45, 49, 12, and 9 pointer years were identified in the TRI series.

Fig. 2
figure 2

TGS index series, TRI series and number of samples in the four zones. The TGS index with positive values represents relative increase in tree growth and TGS index with negative values represents relative decrease in tree growth

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Correlation relationships between tree growth and climatic factors

On the monthly scale, the TGS chronologies and ring-width chronologies exhibited positive correlations with the monthly scPDSI in May and June of the current year at the sites on the Tibetan Plateau (p < 0.05) (Fig. 3a–d). On the seasonal scale, both the TGS chronologies and TRI series showed significant positive correlations with the monthly mean temperature from January to March at the sites in eastern China (p < 0.05) (Fig. 3e–h, Supplementary Figs. S3–S5).

Fig. 3
figure 3

Correlations between tree growth and climatic factors at the site scale. a–d Relationships between TGS and TRI with monthly scPDSI in Zone-I and Zone-II, e–h relationships between TGS and TRI with monthly mean temperature in Zone-III and Zone-IV. p10, p11 and p12 indicate October, November and December of the previous year, Pq4 represents the fourth quarter (October to December) of previous year, Cq1, Cq2 and Cq3 represent the first quarter (January to March), the second quarter (April to June) and the third quarter (July to September) of current year; the boxes represent the 25th and 75th percentiles, the lines and squares in the boxes represent the medians and mean values, the circles represent the maximum or minimum values, and “*” represents significant correlations between tree growth and climatic factors at more than 50% of the sampling sites; the correlation coefficients were calculated in the interval between 1963 and 2000

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At the regional level, the TGS chronologies and TRI series were positively correlated with the mean monthly scPDSI in May to June in Zone-I and Zone-II (p < 0.05), and were positively correlated with the monthly mean temperature from January to March in Zone-III and Zone-IV (p < 0.05) (Fig. 4).

Fig. 4
figure 4

Relationships between tree growth and climatic factors at the regional level. scPDSI5–6 represents the mean monthly scPDSI in May and June, Tmn1–3 represents the mean temperature from January to March, white up-pointing triangle represents interannual differences, the correlation coefficients were calculated in the interval between 1963 and 2000

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Extreme years in the TGS index series and TRI series

In Zone-I, four out of the six extremely dry years corresponded to the pointer years in the TGS index series, while three out of the six extreme years corresponded to the pointer years in the TRI series. Five extremely dry years were reflected in the TGS index series and four out of the six extreme years were reflected in the TRI series as pointer years in Zone-II. Similarly, four out of the eight climatic extremes in Zone-III corresponded to the pointer years in the TGS index series, and two out of the eight corresponded to the pointer years in the TRI series. In Zone-IV, five and three out of the eight climatic extremes were reflected in the TGS index series and TRI series as pointer years (Fig. 5).

Fig. 5
figure 5

Climatic extremes and pointer years in the TGS index series and TRI series. TGS represents tree-growth synchrony, TRI represents the tree-ring index. The orange boxes indicate climatic extremes, and the stars indicate the pointer years in the TGS index series and TRI series

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Discussion

Tree-growth synchrony index

Asynchronous growth between individual trees is ubiquitous and affects forest vulnerability to climate change. However, this ubiquitous growth asynchronous was often ignored in dendroclimatic studies and a singular chronology with averaged tree-ring series was used in climate reconstruction. Both the TGS chronologies and TRI series were positively correlated with the climatic factors, and the TRI series exhibited higher correlations with climate variables than the TGS chronologies in most zones. The site TRI series were developed by averaging tree-ring series from all individuals to minimize signals of individual differences (Fritts 1995). Asynchronous growth among individual trees affected our accurate identification of the extreme interference signals. We only retained individual trees having the same direction of growth change by eliminating the individuals with asynchronous growth year by year. Although this method made the values in TGS chronologies more discrete and resulting in lower correlations with climate variables, we retained the common tree-growth response signals of climatic drivers for the year to respond to extreme climate signals. A high amplitude of tree-growth synchrony reflects strong external forcing signals, and we could effectively identify climatic extremes from these extreme values without noise signals in the TGS index series.

Tree growth synchrony is also acknowledged as an indicator of forest sensitivity (Boden et al. 2014; Shestakova et al. 2016). Forests more sensitive to external climate factors show higher growth synchrony and increased tree vulnerability to climate change (Lindner et al. 2010; Millar and Stephenson 2015; Engen and Sæther 2016). High growth synchrony in forests was interpreted as an indicator of higher drought susceptibility, and growth synchrony was higher in dry sites than that in wet sites due to the greater sensitivity of tree growth to drought (Camarero et al. 2015; Anderegg and HilleRisLambers 2019; Gazol et al. 2020). Latte et al. (2015) found that the increased forest sensitivity to climate response to more frequent warming-induced droughts led to an increase in growth synchrony. Increasing tree growth synchrony could decrease population stability and reduce forest resistance and resilience (Clark et al. 2012; Tejedor et al. 2020), leading to the vulnerability of forests to decline or death in a warming climate (Allen et al. 2010; Anderegg et al. 2020; Astigarraga et al. 2020).

In addition, it is necessary to consider the effects of tree size or tree age to assess synchronous growth under climate change (Heiland et al. 2022). Our study found that the TGS index of trees about 200 years old in Zone-III and Zone-IV rises faster than TGS index of trees over 500 years old in Zone-I and Zone-II, even though we could not get the exact age because of the hollow or decentered of the large trees in mature forests in the Tibetan Plateau and eastern China (Fig. 6). Ruiz-Benito et al. (2016) considered that the growth resistance and resilience of large trees or old trees to climatic disturbances are enhanced due to the larger carbohydrate reserves. Therefore, large trees or old trees tended to respond more asynchronously to climate change than trees with small or young (Ruiz-Benito et al. 2016; Sabatini et al. 2018). There is necessity to group trees according to tree size or age when calculating tree growth synchrony in future research, even though this factor has not affected the results in the present research.

Fig. 6
figure 6

Trends of tree growth synchrony in the four zones during 1900–2001. CTGS represent the yearly cumulative value of tree growth synchrony index, the red lines are 20-year smoothing average curves of CTGS

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The study of tree growth synchrony is critical because it provides insights into several ecological processes and dynamics. Shestakova et al. (2016) showed that growth synchrony patterns stored in tree rings can be early signals of climate change impacts on forests. By understanding the dynamics of tree growth synchrony, scientists can better understand of the resilience and response of forest ecosystems to environmental change.

Climatic signals reflected in the TGS index series

The TGS series were positively correlated with the moisture or temperature in the four zones (p < 0.05). Similar to the TRI index, the TGS index can be used in climate reconstruction research, especially in identifying extreme climate changes.

In Zone-I, the TGS index series and ring-width chronologies were positively correlated with the scPDSI from May to June (p < 0.05) (Figs. 4 and 5). Such correlations were reported by Zhang et al. (2015). They reconstructed the variation of scPDSI in May–June and selected the top 10 extremely dry years in the past five and a half centuries in this region (Zhang et al. 2015). 7 of the 10 extremely dry years are reflected in the TGS index series, while only 40% of the extremely dry years are reflected in the TRI series (Supplementary Table S3). In Zone-II, tree growth also showed positive correlations with the scPDSI in May and June (p < 0.05) (Figs. 4 and 5). Similarly, compared with the top 9 extremely dry years identified based on Zhang’s research between 1501 and 2001 for this area, 67% and only 44% of the extremely dry years are reflected in the TGS index series and TRI series, respectively (Supplementary Table S3). On the Tibetan Plateau, the years with extreme values in the TGS index series correspond better to extreme climate change than those in the TRI series.

The TGS index could be an indicator of interannual variation, especially the extreme changes in scPDSI from May to June on the Tibetan Plateau. The juniper trees’ growth is limited by soil moisture during the growing season in most parts of the northern Tibetan Plateau found in other research (Gou et al. 2013; Liu et al. 2021). Zeng et al. (2019) also reported that the soil moisture dominated the stem growth of conifer trees in the semiarid region of China. Different from the northern part of Tibetan Plateau, the growth of juniper trees was positively correlated with the monthly total precipitation from May to June and negatively correlated with monthly mean temperature in Zone-II (p < 0.05) (Supplementary Figs. S3–S5). Moisture during the growing season had a great contribution to the growth of juniper on the Tibetan Plateau. In May and June, high temperature-induced water limitations will affect the normal physiological functions of trees, and cause the stomatal closure and inhibit photosynthesis of trees (Sardans and Peñuelas 2013; Anderegg et al. 2013), these will limit the growth of trees. On the one hand, drought reduces the availability of soil nutrients, limits the activity of soil microorganisms, and reduces the nutrients needed for tree growth. Adequate water supply reduces summer drought stress and positively affects radial growth (Deslauriers et al. 2016).

In Zone-III, tree growth showed positive correlations with the mean temperature from January to March (p < 0.05). Compared with the 6 extreme low-temperature years and 6 extreme high-temperature years selected in other studies (Wen and Chen 2006; Duan et al. 2013) (Supplementary Table S3), climatic extremes reflected in the TGS index series are greater than those reflected in the TRI series (50% > 42%). In Zone-IV, 4 of the 6 extreme low-temperature years (1945, 1969, 1974 and 1984) and 3 of the 6 extreme high-temperature years (1898, 1981 and 2000) corresponded to the pointer years in the TGS index series. However, only 33% of the climatic extremes were reflected in the TRI series, and less than 58% of the climatic extremes were reflected in the TGS series. In the eastern China, the TGS index can also better identify extreme climate change.

In the subtropical region of eastern China, the TGS index series of pine trees are sensitive to temperature changes during the cold season (January–March) (Figure S5). The temperature in the cold season limits tree growth in temperate and subtropical regions found in many other tree-ring studies (Chen and Zhou 2012; Cai et al. 2016). On the one hand, the low temperature in the cold season will cause the plant tissue to freeze and cavitate, damaging the plant tissue; on the other hand, the low temperature freezes the soil and reduces the effective water needed by the plant, delaying the onset of radial tree growth (Montwé et al. 2018). In contrast, the higher winter temperatures reduce frost damage to trees, ensure normal metabolic activities of trees, and can also effectively promote photosynthesis and cambium activities of trees, which is beneficial to tree growth. Different from Zone-IV while similar to Zone-II, the GS index chronologies in Zone-III also showed negative correlations with the monthly temperature in the second quarter (p < 0.05). High temperatures reduced the efficiency of water use for tree growth by increasing transpiration and soil water loss, which would limit tree growth (Walker and Johnstone 2014; Sanchez-Salguero et al. 2018; Zhu et al. 2018).

Conclusion

The TGS index is an effective indicator in showing climate variations and identifying extreme climate events. The climatic extremes identified by the TGS index are 20% more than those identified by the tree-ring index over the last 50 years. The study provides evidence for understanding the spatiotemporal dynamics and the causes of historical climate extremes, and has a positive significance for protecting trees from climate extremes in the future.

Availability of data and materials

The tree-ring data supporting the results of this study are available from the corresponding author upon reasonable request. The gridded climate data supporting the results of this study are available from the Climatic Research Unit at https://climexp.knmi.nl/.

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Acknowledgements

We extend our gratitude to Professor Qi-Bin Zhang for his supervision and idea on the TGS index and his meticulous revision of the manuscript, as well as to the members of his team who provided invaluable assistance in tree-ring sampling and crossdating.

Funding

This work was supported by National Natural Science Foundation of China (NSFC) (Grant Nos. 32271886 and 32271672), the Second Tibetan Plateau Scientific Expedition and Research Program (STEP) (Grant No. 2019QZKK0301), and the Qinghai Provincial Forestry and Grassland Administration (project code QHXH-2021-022/Package 2).

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Authors and Affiliations

  1. State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China

    Hengfeng Jia, Jiacheng Zheng, Jing Yang, Lixin Lyu, Yuntao Dong & Ouya Fang

  2. University of the Chinese Academy of Sciences, Beijing, 101408, China

    Hengfeng Jia, Jiacheng Zheng, Jing Yang & Yuntao Dong

  3. China National Botanical Garden, Beijing, 100093, China

    Hengfeng Jia, Jiacheng Zheng, Jing Yang, Lixin Lyu, Yuntao Dong & Ouya Fang

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  1. Hengfeng Jia
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  2. Jiacheng Zheng
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  3. Jing Yang
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  4. Lixin Lyu
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  5. Yuntao Dong
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Contributions

Hengfeng Jia: conceptualization, investigation, formal analysis, writing—original draft. Jiacheng Zheng: formal analysis, writing—original draft. Jing Yang, Lixin Lyu and Yuntao Dong: discussion. Ouya Fang: conceptualization, writing—review and editing.

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Correspondence to Ouya Fang.

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Supplementary Information

13717_2024_536_MOESM1_ESM.docx

Supplementary Material 1: Table S1. Information of the studied sites and tree rings on the Tibetan Plateau. Table S2. Information of the studied sites and tree rings in eastern China. Table S3. Climatic extremes in four zones based on other studies. Figure S1. Comparison of climate conditions in Zone-III and Zone-IV. Figure S2. The diagram to illustrate the climate data collection process. Figure S3. Pearson correlations between TGS index chronologies and monthly climatic factors. Figure S4. Pearson correlations between tree-ring index chronologies and monthly climatic factors. Figure S5. Pearson correlations between TGS index chronologies, tree-ring index chronologies and seasonal climatic factors.

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Jia, H., Zheng, J., Yang, J. et al. Tree-growth synchrony index, an effective indicator of historical climatic extremes. Ecol Process 13, 55 (2024). https://doi.org/10.1186/s13717-024-00536-2

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