北半球中高纬度地区近30年植被春季物候温度敏感性

doi:10.17521/cjpe.2021.0188

植物生态学报 ›› 2022, Vol. 46 ›› Issue (2): 125-135.DOI: 10.17521/cjpe.2021.0188

所属专题：生态遥感及应用

• 研究论文 • 下一篇

北半球中高纬度地区近30年植被春季物候温度敏感性

丛楠¹^,^*(), 张扬建¹^,²^,^*(), 朱军涛¹

¹中国科学院地理科学与资源研究所拉萨高原生态试验站, 北京 100101
²中国科学院地理科学与资源研究所生态网络观测与模拟重点实验室, 北京 100101

收稿日期:2021-05-17 接受日期:2021-07-14 出版日期:2022-02-20 发布日期:2021-08-06
通讯作者: 丛楠,张扬建
作者简介:张扬建, zhangyj@igsnrr.ac.cn
*丛楠, congnan@igsnrr.ac.cn;
基金资助:
国家杰出青年科学基金(41725003);国家重点研发计划(2018YFA0606101);国家重点研发计划(2019YFA0607302);国家自然科学基金(42071133)

Temperature sensitivity of vegetation phenology in spring in mid- to high-latitude regions of Northern Hemisphere during the recent three decades

CONG Nan¹^,^*(), ZHANG Yang-Jian¹^,²^,^*(), ZHU Jun-Tao¹

¹Lhasa Plateau Ecosystem Research Station, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
²Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

Received:2021-05-17 Accepted:2021-07-14 Online:2022-02-20 Published:2021-08-06
Contact: CONG Nan,ZHANG Yang-Jian
Supported by:
Outstanding Youth Scientist Program of NSFC(41725003);National Key R&D Program of China(2018YFA0606101);National Key R&D Program of China(2019YFA0607302);National Natural Science Foundation of China(42071133)

摘要/Abstract

摘要：

北半球气候变暖导致植被春季物候开始日期显著提前, 温度对春季物候的促进作用是一个过程事件而非瞬时事件, 且存在空间差异。该研究在以前研究的基础上, 进一步分析温度对植被物候的作用方式, 并探讨春季物候温度敏感性的空间特征及影响因素。利用GIMMS3g卫星植被指数产品, 采用5种方法提取1982-2009年植被春季物候, 并结合格网气象数据计算植被春季物候的温度敏感性, 着重分析自然植被春季物候温度敏感性与环境因素的关系。结果表明, 温度是北半球植被春季物候的主要制约因素, 54%的像元显示温度最大效应发生在物候开始当月和之前一个月。温度主导的春季物候的像元中, 91.3%的像元指示早春温度对物候开始的促进作用。植被春季物候的温度敏感性存在空间异质性, 随着区域环境因素的不同, 年际温度标准差、累积降水量和辐射对植被春季物候温度敏感性都具有各自或协同的调控作用。

关键词: 温度敏感性, 植被春季物候, 卫星遥感, 气候变化

Abstract:

Aims Under the current global warming, there are abundant evidence that the phenological events of vegetation in spring have advanced. Advancement of the phenological events in Northern Hemisphere under a gradual warming is considered a process of acclimation rather than an instantaneous feedback. Moreover, the occurrence of spring phenological advancement also varies across ecoregions. Following up on our previous studies, here we aim to determine the temproal scale that temperature has the most influential effect on changes in spring phenology. We further explore how the local spring temperature affects the temperature sensitivity of the spring phenology and the underlying mechanism.

Methods We extracted the dates for spring phenological events by five different methods derived from the GIMMS3g normalized difference vegetation index dataset during 1982-2009. We also employed gridded climatic datasets to calculate the temperature sensitivity of the spring phenology of vegetation, and analyzed the relationship between the temperature sensitivity of phenological events of natural vegetation and environmental variables.

Important findings The spring phenological events of vegetation were mainly regulated by the early spring temperature over the mid- to high-latitude regions in the Northern Hemisphere. Specifically, we found that the maximum temperature in the month of the green-up onset or in the preceding month played the dominant role in affecting the shifts in spring phenology over 54% of the pixels for the study regions; over 91.3% of the pixels displayed phenological shifts by early spring temperature. Interestingly, across the study regions, the standard deviation in interannual temperature, accumulative precipitation and short-wave radiation contrasted in their effects, and differentially or synergistically regulated the temperature sensitivity of spring vegetation phenology.

Key words: temperature sensitivity, vegetation spring phenology, remote sensing, climate change

丛楠, 张扬建, 朱军涛. 北半球中高纬度地区近30年植被春季物候温度敏感性. 植物生态学报, 2022, 46(2): 125-135. DOI: 10.17521/cjpe.2021.0188

CONG Nan, ZHANG Yang-Jian, ZHU Jun-Tao. Temperature sensitivity of vegetation phenology in spring in mid- to high-latitude regions of Northern Hemisphere during the recent three decades. Chinese Journal of Plant Ecology, 2022, 46(2): 125-135. DOI: 10.17521/cjpe.2021.0188

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2021.0188

https://www.plant-ecology.com/CN/Y2022/V46/I2/125

图/表 8

表1 植被物候提取方法核心计算公式

Table 1 Core formulas for extraction of vegetation spring phenology

方法名称 Name of method	滤波核心公式 Core formula for filtering	阈值确定方式 Threshold algorithm
Gauss-Midpoint	$NDVI(t)=a+b\times {{\text{e}}^{-{{(({{x}_{t}}-c)/d)}^{2}}}}$	\[NDV{{I}_{\text{ratio}}}(t)=\frac{NDV{{I}_{t}}-NDV{{I}_{\min }}}{NDV{{I}_{\max }}-NDV{{I}_{\min }}}\]
Spline-Midpoint	$NDVI(t)={{a}_{t}}{{t}^{3}}+{{b}_{t}}{{t}^{2}}+{{c}_{t}}t+{{d}_{t}}$	\[NDV{{I}_{\text{ratio}}}(t)=\frac{NDV{{I}_{t}}-NDV{{I}_{\min }}}{NDV{{I}_{\max }}-NDV{{I}_{\min }}}\]
HANTS-Maximum	$NDVI(t)={{a}_{0}}+\underset{i=1}{\overset{n}{\mathop \sum }}\,{{a}_{i}}\cos ({{\omega }_{i}}t-{{\varphi }_{i}})$	\[NDV{{I}_{\text{ratio}}}(t)=\frac{NDVI(t+1)-NDVI(t)}{NDVI(t)}\]
Polyfit-Maximum	$NDVI(t)={{a}_{0}}+{{a}_{1}}t+{{a}_{2}}{{t}^{2}}+{{a}_{3}}{{t}^{3}}+\cdots {{a}_{n}}{{t}^{n}}$	\[NDV{{I}_{\text{ratio}}}(t)=\frac{NDVI(t+1)-NDVI(t)}{NDVI(t)}\]
Timesat-SG	$NDVI(t)=\frac{\sum\limits_{i=-m}^{i=m}{{{C}_{i}}NDV{{I}_{j+i}}}}{N}$	\[\text{Threshold}=20%\times (NDV{{I}_{\max }}-NDV{{I}_{\min }})\]

图1 北半球中高纬度地区植被春季物候开始日期与不同时间段温度和降水量的相关系数(平均值±标准差)。从平均生长日期所在的5月份为基础, 以月为单位, 向前推算。例如, 1表示物候发生当月5月, 2表示物候发生前1个月即4月份, 依次类推, 至前一年11月份(7)。

Fig. 1 Correlation coefficients of spring green-up dates with temperature and precipitation for different vegetation types in different periods in mid- to high-latitude regions of Northern Hemisphere (mean ± SD). The calculation was made backward by one-month starting from May as the average start season. E.g., the value 1 on the x-axis indicates the start month of phenological event (May in this study), and 2 the preceding month (April in this study). All way backward to November of the previous year (7).

图2 北半球中高纬度地区植被春季物候的温度“滞后”效应统计。时间段数字表示植被春季物候所在月份的前(n-1)个月的时间段, 例如, 1表示春季物候发生的当月, 2表示植被物候发生前1个月, 即当月和物候发生前1个月时间段的月平均温度。

Fig. 2 The “lag effect” of mean temperature on spring green-up onset in mid- to high-latitude regions of Northern Hemisphere. The values for period show the month(s) precede the average start of season. E.g. value 1 represents the month of start of spring, and 2 the preceding month before the start of month, calculated as the average temperature between the month of phenological event and the preceding month.

图3 北半球中高纬度地区植被春季物候开始日期与温度(T)、降水(P)和辐射(R)的相关关系。

Fig. 3 Correlations of green-up with temperature (T), precipitation (P) and radiation (R) in mid- to high-latitude regions of Northern Hemisphere.

图4 北半球中高纬度地区植被春季物候开始日期的温度敏感性空间分布图。

Fig. 4 Spatial pattern of temperature sensitivity for dates of vegetation spring green-up in mid- to high-latitude regions of Northern Hemisphere.

图5 北半球中高纬度地区植被春季物候开始日期的温度敏感性(平均值±标准差)沿多年温度标准差梯度的分布。

Fig. 5 Distribution of temperature sensitivity (mean ± SD) in vegetation spring phenology along gradient of standard deviation for temperature in mid- to high-latitude regions of Northern Hemisphere.

图6 北半球中高纬度地区植被温度敏感性沿降水量梯度分布特征(平均值±标准差)。

Fig. 6 Distribution of temperature sensitivity of vegetation phenology along precipitation gradient in mid- to high-latitude regions of Northern Hemisphere (mean ± SD).

图7 北半球中高纬度地区植被春季物候温度敏感性沿辐射梯度分布特征(平均值±标准差)。

Fig. 7 Distribution of temperature sensitivity of vegetation phenology along radiation gradient in mid- to high-latitude regions of Northern Hemisphere (mean ± SD).

参考文献 43

[1]	Bigler C, Vitasse Y (2019). Daily maximum temperatures induce lagged effects on leaf unfolding in temperate woody species across large elevational gradients. Frontiers in Plant Science, 10, 398-410.
[2]	Cong N, Huang K, Zhang YJ (2021). Unsynchronized driving mechanisms of spring and autumn phenology over Northern Hemisphere grasslands. Frontiers in Forests and Global Change, 3, 610612. DOI: 10.3389/ffgc.2020.610162. DOI
[3]	Cong N, Piao SL, Chen AP, Wang XH, Lin X, Chen SP, Han SJ, Zhou GS, Zhang XP (2012). Spring vegetation green- up date in China inferred from SPOT NDVI data: a multiple model analysis. Agricultural and Forest Meteorology, 165, 104-113. DOI URL
[4]	Cong N, Shen MG (2016). Variation of satellite-based spring vegetation phenology and the relationship with climate in the Northern Hemisphere over 1982 to 2009. Chinese Journal of Applied Ecology, 27, 2737-2746. DOI PMID
	[ 丛楠, 沈妙根 (2016). 1982-2009年基于卫星数据的北半球中高纬地区植被春季物候动态及其与气候的关系. 应用生态学报, 27, 2737-2746.] PMID
[5]	Cong N, Shen MG, Piao SL, Chen XQ, An S, Yang W, Fu YH, Meng FD, Wang T (2017). Little change in heat requirement for vegetation green-up on the Tibetan Plateau over the warming period of 1998-2012. Agricultural and Forest Meteorology, 232, 650-658.
[6]	Cong N, Shen MG, Zu JX, Zhang YJ (2019). Spatial sampling inconsistency leads to differences in phenological sensitivity to warming between natural and experiment sites. Science Bulletin, 64, 961-963. DOI URL
[7]	Cong N, Wang T, Nan HJ, Ma YC, Wang XH, Myneni RB, Piao SL (2013). Changes in satellite-derived spring vegetation green-up date and its linkage to climate in China from 1982 to 2010: a multimethod analysis. Global Change Biology, 19, 881-891. DOI PMID
[8]	Fu YH, Campioli M, van Oijen M, Deckmyn G, Janssens IA (2012). Bayesian comparison of six different temperature- based budburst models for four temperate tree species. Ecological Modelling, 230, 92-100. DOI URL
[9]	Fu YH, Liu YJ, de Boeck HJ, Menzel A, Nijs I, Peaucelle M, Peñuelas J, Piao SL, Janssens IA (2016). Three times greater weight of daytime than of night-time temperature on leaf unfolding phenology in temperate trees. New Phytologist, 212, 590-597. DOI URL
[10]	Fu YH, Piao S, Delpierre N, Hao F, Hänninen H, Liu Y, Sun W, Janssens IA, Campioli M (2018). Larger temperature response of autumn leaf senescence than spring leaf-out phenology. Global Change Biology, 24, 2159-2168. DOI URL
[11]	Fu YH, Piao S, Vitasse Y, Zhao H, de Boeck HJ, Liu Q, Yang H, Weber U, Hänninen H, Janssens IA (2015a). Increased heat requirement for leaf flushing in temperate woody species over 1980-2012: effects of chilling, precipitation and insolation. Global Change Biology, 21, 2687-2697. DOI URL
[12]	Fu YH, Piao S, Zhao H, Jeong SJ, Wang X, Vitasse Y, Ciais P, Janssens IA (2014). Unexpected role of winter precipitation in determining heat requirement for spring vegetation green-up at northern middle and high latitudes. Global Change Biology, 20, 3743-3755. DOI URL
[13]	Fu YH, Zhao H, Piao S, Peaucelle M, Peng S, Zhou G, Ciais P, Huang M, Menzel A, Peñuelas J, Song Y, Vitasse Y, Zeng Z, Janssens IA (2015b). Declining global warming effects on the phenology of spring leaf unfolding. Nature, 526, 104-107. DOI URL
[14]	Ge QS, Dai JH, Zheng JY (2010). The progress of phenology studies and challenges to modern phenology research in China. Bulletin of Chinese Academy of Sciences, 25, 310- 316.
	[ 葛全胜, 戴君虎, 郑景云 (2010). 物候学研究进展及中国现代物候学面临的挑战. 中国科学院院刊, 25, 310-316.]
[15]	Jeong SJ, Ho CH, Gim HJ, Brown ME (2011). Phenology shifts at start vs. end of growing season in temperate vegetation over the Northern Hemisphere for the period 1982-2008. Global Change Biology, 17, 2385-2399.
[16]	Kariyeva J, Woodhouse CA (2012). Impacts of climate gradients on the vegetation phenology of major land use types in Central Asia (1981-2008). Frontiers of Earth Science, 6, 206-225. DOI URL
[17]	Kramer K, Leinonen I, Loustau D (2000). The importance of phenology for the evaluation of impact of climate change on growth of boreal, temperate and Mediterranean forests ecosystems: an overview. International Journal of Biometeorology, 44, 67-75. PMID
[18]	Menzel A, Sparks TH, Estrella N, Koch E, Aasa A, Ahas R, Alm-Kübler K, Bissolli P, Braslavská O, Briede A, Chmielewski FM, Crepinsek Z, Curnel Y, Dahl Å, Defila C, et al. (2006). European phenological response to climate change matches the warming pattern. Global Change Biology, 12, 1969-1976. DOI URL
[19]	Misra G, Asam S, Menzel A (2021). Ground and satellite phenology in alpine forests are becoming more heterogeneous across higher elevations with warming. Agricultural and Forest Meteorology, 303, 108383. DOI: 10.1016/j.agrformet.2021.108383. DOI URL
[20]	Myneni RB, Keeling CD, Tucker CJ, Asrar G, Nemani RR (1997). Increased plant growth in the northern high latitudes from 1981 to 1991. Nature, 386, 698-702. DOI URL
[21]	Nezlin NP, Kostianoy AG, Li B (2005). Inter-annual variability and interaction of remote-sensed vegetation index and atmospheric precipitation in the Aral Sea region. Journal of Arid Environments, 62, 677-700. DOI URL
[22]	Pan RZ, Dong YD (1999). Plant Physiology. Higher Education Press, Beijing.
	[ 潘瑞炽, 董愚得 (1999). 植物生理学. 高等教育出版社, 北京.]
[23]	Piao S, Ciais P, Friedlingstein P, Peylin P, Reichstein M, Luyssaert S, Margolis H, Fang J, Barr A, Chen A, Grelle A, Hollinger DY, Laurila T, Lindroth A, Richardson AD, Vesala T (2008). Net carbon dioxide losses of northern ecosystems in response to autumn warming. Nature, 451, 49-53. DOI URL
[24]	Piao S, Cui M, Chen A, Wang X, Ciais P, Jie L, Tang Y (2011). Altitude and temperature dependence of change in the spring vegetation green-up date from 1982 to 2006 in the Qinghai-Xizang Plateau. Agricultural and Forest Meteorology, 151, 1599-1608. DOI URL
[25]	Piao S, Fang J, Zhou L, Ciais P, Zhu B (2006). Variations in satellite-derived phenology in China’s temperate vegetation. Global Change Biology, 12, 672-685. DOI URL
[26]	Piao S, Liu Q, Chen A, Janssens IA, Fu Y, Dai J, Liu L, Lian X, Shen M, Zhu X (2019). Plant phenology and global climate change: current progresses and challenges. Global Change Biology, 25, 1922-1940. DOI URL
[27]	Piao S, Liu Z, Wang T, Peng S, Ciais P, Huang M, Ahlstrom A, Burkhart JF, Chevallier F, Janssens IA, Jeong SJ, Lin X, Mao J, Miller J, Mohammat A, et al. (2017). Weakening temperature control on the interannual variations of spring carbon uptake across northern lands. Nature Climate Change, 7, 359-365. DOI URL
[28]	Piao S, Tan J, Chen A, Fu Y, Ciais P, Liu Q, Janssens IA, Vicca S, Zeng Z, Jeong SJ, Li Y, Myneni RB, Peng S, Shen M, Peñuelas J (2015). Leaf onset in the northern hemisphere triggered by daytime temperature. Nature Communications, 113, 6911. DOI: 10.1038/ncomms7911. DOI
[29]	Richardson AD, Black TA, Ciais P, Delbart N, Friedl MA, Gobron N, Hollinger DY, Kutsch WL, Longdoz B, Luyssaert S, Migliavacca M, Montagnani L, Munger JW, Moors E, Piao SL, et al. (2010). Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philosophical Transactions of the Royal Society, 365, 3227-3246. DOI URL
[30]	Richardson AD, Keenan TF, Migliavacca M, Ryu Y, Sonnentag O, Toomey M (2013). Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agricultural and Forest Meteorology, 169, 156- 173. DOI URL
[31]	Schwartz MD (1998). Green-wave phenology. Nature, 394, 839-840.
[32]	Stucky BJ, Guralnick R, Deck J, Denny EG, Bolmgren K, Walls R (2018). The plant phenology ontology: a new informatics resource for large-scale integration of plant phenology data. Frontiers in Plant Science, 9, 517. DOI: 10.3389/fpls.2018.00517. DOI PMID
[33]	Sulla-Menashe D, Gray JM, Abercrombie SP, Friedl MA (2019). Hierarchical mapping of annual global land cover 2001 to present: the MODIS Collection 6 Land Cover product. Remote Sensing of Environment, 222, 183-194. DOI
[34]	Tang J, Körner C, Muraoka H, Piao SL, Shen MG, Thackeray SJ, Yang X (2016). Emerging opportunities and challenges in phenology: a review. Ecosphere, 7, e01436. DOI: 10.1002/ecs2.1436. DOI
[35]	Tucker CJ, Pinzon JE, Brown ME, Slayback DA, Pak EW, Mahoney R, Vermote EF, el Saleous N, (2005). An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. International Journal of Remote Sensing, 26, 4485-4498. DOI URL
[36]	Tucker CJ, Townshend JR, Goff TE (1985). African land-cover classification using satellite data. Science, 227, 369-375. PMID
[37]	Visser ME (2016). Phenology: interactions of climate change and species. Nature, 535, 236-237. DOI URL
[38]	Wang T, Ottlé C, Peng S, Janssens IA, Lin X, Poulter B, Yue C, Ciais P (2014). The influence of local spring temperature variance on temperature sensitivity of spring phenology. Global Change Biology, 20, 1473-1480. DOI PMID
[39]	Wang XH, Piao SL, Xu X, Ciais P, MacBean N, Myneni RB, Li L (2015). Has the advancing onset of spring vegetation greenup slowed down or changed abruptly over the last three decades? Global Ecology and Biogeography, 24, 621-631. DOI URL
[40]	White MA, de Beurs KM, Didan K, Inouye DW, Richardson AD, Jensen OP, O’keefe J, Zhang G, Nemani RR, Brown JF, de Wit A, Schepman M, Lin X, Dettinger M, et al. (2009). Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982-2006. Global Change Biology, 15, 2335-2359.
[41]	Wolkovich EM, Cook BI, Allen JM, Crimmins TM, Betancourt JL, Travers SE, Pau S, Regetz J, Davies TJ, Kraft NJB, Ault TR, Bolmgren K, Mazer SJ, McCabe GJ, McGill BJ, et al. (2012). Warming experiments underpredict plant phenological responsed to climate change. Nature, 485, 494-497. DOI URL
[42]	Xie Y, Wang X, Silander Jr JA (2015). Deciduous forest responses to temperature, precipitation, and drought imply complex climate change impacts. Proceedings of the National Academy of Sciences of the United States of America, 112, 13585-13590.
[43]	Zhu WQ, Jiang N, Chen GS, Zhang DH, Zheng ZT, Fan DQ (2017). Divergent shifts and responses of plant autumn phenology to climate change on the Qinghai-Tibetan Plateau. Agriculture and Forest Meteorology, 239, 166-175. DOI URL

北半球中高纬度地区近30年植被春季物候温度敏感性

Temperature sensitivity of vegetation phenology in spring in mid- to high-latitude regions of Northern Hemisphere during the recent three decades

全文

PDF (PC)

可视化

被引次数

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献 43

相关文章 15

编辑推荐

Metrics

本文评价

[1]	陈以恒玉素甫江·如素力阿卜杜热合曼·吾斯曼. 2001-2020年天山新疆段草地植被覆盖度时空变化及驱动因素分析[J]. 植物生态学报, 2024, 48(5): 561-576.
[2]	王袼, 胡姝娅, 李阳, 陈晓鹏, 李红玉, 董宽虎, 何念鹏, 王常慧. 不同类型草原土壤净氮矿化速率的温度敏感性[J]. 植物生态学报, 2024, 48(4): 523-533.
[3]	张计深, 史新杰, 刘宇诺, 吴阳, 彭守璋. 气候变化下中国潜在自然植被生态系统碳储量动态[J]. 植物生态学报, 2024, 48(4): 428-444.
[4]	臧妙涵, 王传宽, 梁逸娴, 刘逸潇, 上官虹玉, 全先奎. 基于纬度移栽的落叶松叶、枝、根生态化学计量特征对气候变暖的响应[J]. 植物生态学报, 2024, 48(4): 469-482.
[5]	梁逸娴, 王传宽, 臧妙涵, 上官虹玉, 刘逸潇, 全先奎. 落叶松径向生长和生物量分配对气候变暖的响应[J]. 植物生态学报, 2024, 48(4): 459-468.
[6]	吴茹茹, 刘美珍, 谷仙, 常馨月, 郭立月, 蒋高明, 祁如意. 气候变化对巨柏适宜生境分布的潜在影响和预测[J]. 植物生态学报, 2024, 48(4): 445-458.
[7]	杨宇萌, 来全, 刘心怡. 气候变化和人类活动对内蒙古植被总初级生产力的定量影响[J]. 植物生态学报, 2024, 48(3): 306-316.
[8]	张启, 程雪寒, 王树芝. 北京西山老龄树记载的森林干扰历史[J]. 植物生态学报, 2024, 48(3): 341-348.
[9]	任培鑫, 李鹏, 彭长辉, 周晓路, 杨铭霞. 洞庭湖流域植被光合物候的时空变化及其对气候变化的响应[J]. 植物生态学报, 2023, 47(3): 319-330.
[10]	夏璟钰, 张扬建, 郑周涛, 赵广, 赵然, 朱艺旋, 高洁, 沈若楠, 李文宇, 郑家禾, 张雨雪, 朱军涛, 孙建新. 青藏高原那曲高山嵩草草甸植物物候对增温的异步响应[J]. 植物生态学报, 2023, 47(2): 183-194.
[11]	李杰, 郝珉辉, 范春雨, 张春雨, 赵秀海. 东北温带森林树种和功能多样性对生态系统多功能性的影响[J]. 植物生态学报, 2023, 47(11): 1507-1522.
[12]	魏瑶, 马志远, 周佳颖, 张振华. 模拟增温改变青藏高原植物繁殖物候及植株高度[J]. 植物生态学报, 2022, 46(9): 995-1004.
[13]	党宏忠, 张学利, 韩辉, 石长春, 葛玉祥, 马全林, 陈帅, 刘春颖. 樟子松固沙林林水关系研究进展及对营林实践的指导[J]. 植物生态学报, 2022, 46(9): 971-983.
[14]	李肖, PIALUANG Bounthong, 康文辉, 冀晓东, 张海江, 薛治国, 张志强. 近几十年来冀西北山地白桦次生林径向生长对气候变化的响应[J]. 植物生态学报, 2022, 46(8): 919-931.
[15]	苏启陶, 杜志喧, 周兵, 廖永辉, 王呈呈, 肖宜安. 牯岭凤仙花及其传粉昆虫在中国的潜在分布区域分析[J]. 植物生态学报, 2022, 46(7): 785-796.