Divergent contributions of spring and autumn photosynthetic phenology to seasonal carbon uptake of subtropical vegetation in China - Peng, Ying; Li, Peng; Zhou, Xiaolu; Luo, Yunpeng; Zhang, Cicheng; Wang, Longjun; Li, Tong; Peng, Changhui (Journal of Geographical Sciences, 2024)

Headwater stream ecosystem: an important source of greenhouse gases to the atmosphere - Li, Mingxu; Peng, Changhui; Zhang, Kerou; Xu, Li; Wang, Jianming; Yang, Yan; Li, Peng; Liu, Zelin; He, Nianpeng (Water Research, 2021)

Satellite monitoring reveals short-term cumulative and time-lag effect of drought and heat on autumn photosynthetic phenology in subtropical vegetation - Ren, Peixin; Li, Peng; Tang, Jiayi; Li, Tong; Liu, Zelin; Zhou, Xiaolu; Peng, Changhui (Environmental Research, 2023)

Grazing alters vegetation phenology by regulating regional environmental factors on the Tibetan Plateau - Wang, Longjun; Li, Peng; Li, Tong; Zhou, Xiaolu; Liu, Zelin; Zou, Ziying; Zhu, Qiuan; Peng, Changhui (Agriculture, Ecosystems & Environment, 2023)

Combined control of multiple extreme climate stressors on autumn vegetation phenology on the Tibetan Plateau under past and future climate change - Li, Peng; Liu, Zelin; Zhou, Xiaolu; Xie, Binggeng; Li, Zhongwu; Luo, Yunpeng; Zhu, Qiuan; Peng, Changhui (Agricultural and Forest Meteorology, 2021)

Strong controls of daily minimum temperature on the autumn photosynthetic phenology of subtropical vegetation in China - Ren, Peixin; Liu, Zelin; Zhou, Xiaolu; Peng, Changhui; Xiao, Jingfeng; Wang, Songhan; Li, Xing; Li, Peng (Forest Ecosystems, 2021)
Abstract Background Vegetation phenology research has largely focused on temperate deciduous forests, thus limiting our understanding of the response of evergreen vegetation to climate change in tropical and subtropical regions. Results Using satellite solar-induced chlorophyll fluorescence (SIF) and MODIS enhanced vegetation index (EVI) data, we applied two methods to evaluate temporal and spatial patterns of the end of the growing season (EGS) in subtropical vegetation in China, and analyze the dependence of EGS on preseason maximum and minimum temperatures as well as cumulative precipitation. Our results indicated that the averaged EGS derived from the SIF and EVI based on the two methods (dynamic threshold method and derivative method) was later than that derived from gross primary productivity (GPP) based on the eddy covariance technique, and the time-lag for EGS sif and EGS evi was approximately 2 weeks and 4 weeks, respectively. We found that EGS was positively correlated with preseason minimum temperature and cumulative precipitation (accounting for more than 73% and 62% of the study areas, respectively), but negatively correlated with preseason maximum temperature (accounting for more than 59% of the study areas). In addition, EGS was more sensitive to the changes in the preseason minimum temperature than to other climatic factors, and an increase in the preseason minimum temperature significantly delayed the EGS in evergreen forests, shrub and grassland. Conclusions Our results indicated that the SIF outperformed traditional vegetation indices in capturing the autumn photosynthetic phenology of evergreen forest in the subtropical region of China. We found that minimum temperature plays a significant role in determining autumn photosynthetic phenology in the study region. These findings contribute to improving our understanding of the response of the EGS to climate change in subtropical vegetation of China, and provide a new perspective for accurately evaluating the role played by evergreen vegetation in the regional carbon budget.

Research progress, challenges, and prospects of PM_2.5 concentration estimation using satellite data - Zhu, Shoutao; Tang, Jiayi; Zhou, Xiaolu; Li, Peng; Liu, Zelin; Zhang, Cicheng; Zou, Ziying; Li, Tong; Peng, Changhui (Environmental Reviews, 2023)
Satellite data are vital for understanding the large-scale spatial distribution of particulate matter (PM 2.5 ) due to their low cost, wide coverage, and all-weather capability. Estimation of PM 2.5 using satellite aerosol optical depth (AOD) products is a popular method. In this paper, we review the PM 2.5 estimation process based on satellite AOD data in terms of data sources (i.e., inversion algorithms, data sets, and interpolation methods), estimation models (i.e., statistical regression, chemical transport models, machine learning, and combinatorial analysis), and modeling validation (i.e., four types of cross-validation (CV) methods). We found that the accuracy of time-based CV is lower than others. We found significant differences in modeling accuracy between different seasons ( p < 0.01) and different spatial resolutions ( p < 0.01). We explain these phenomena in this article. Finally, we summarize the research process, present challenges, and future directions in this field. We opine that low-cost mobile devices combined with transfer learning or hybrid modeling offer research opportunities in areas with limited PM 2.5 monitoring stations and historical PM 2.5 estimation. These methods can be a good choice for air pollution estimation in developing countries. The purpose of this study is to provide a basic framework for future researchers to conduct relevant research, enabling them to understand current research progress and future research directions.

Spatial patterns of leaf δ^13 C and its relationship with plant functional groups and environmental factors in China: Factors Dominate Plant Traits - Li, Mingxu; Peng, Changhui; Wang, Meng; Yang, Yanzheng; Zhang, Kerou; Li, Peng; Yang, Yan; Ni, Jian; Zhu, Qiuan (Journal of Geophysical Research: Biogeosciences, 2017)

Quantification and uncertainty of global upland soil methane sinks: Processes, controls, model limitations, and improvements - Song, Hanxiong; Peng, Changhui; Zhu, Qiuan; Chen, Zhi; Blanchet, Jean-Pierre; Liu, Qiuyu; Li, Tong; Li, Peng; Liu, Zelin (Earth-Science Reviews, 2024)

Phenolic compounds weaken the impact of drought on soil enzyme activity in global wetlands - Li, Tong; Ge, Leming; Zhao, Ruotong; Peng, Changhui; Zhou, Xiaolu; Li, Peng; Liu, Zelin; Song, Hanxiong; Tang, Jiayi; Zhang, Cicheng; Li, Quan; Wang, Meng; et al. (Frontiers in Microbiology, 2024)
Soil enzymes play a central role in carbon and nutrient cycling, and their activities can be affected by drought-induced oxygen exposure. However, a systematic global estimate of enzyme sensitivity to drought in wetlands is still lacking. Through a meta-analysis of 55 studies comprising 761 paired observations, this study found that phosphorus-related enzyme activity increased by 38% as result of drought in wetlands, while the majority of other soil enzyme activities remained stable. The expansion of vascular plants under long-term drought significantly promoted the accumulation of phenolic compounds. Using a 2-week incubation experiment with phenol supplementation, we found that phosphorus-related enzyme could tolerate higher biotoxicity of phenolic compounds than other enzymes. Moreover, a long-term (35 years) drainage experiment in a northern peatland in China confirmed that the increased phenolic concentration in surface layer resulting from a shift in vegetation composition inhibited the increase in enzyme activities caused by rising oxygen availability, except for phosphorus-related enzyme. Overall, these results demonstrate the complex and resilient nature of wetland ecosystems, with soil enzymes showing a high degree of adaptation to drought conditions. These new insights could help evaluate the impact of drought on future wetland ecosystem services and provide a theoretical foundation for the remediation of degraded wetlands.

Developing allometric equations to estimate forest biomass for tree species categories based on phylogenetic relationships - Yang, Mingxia; Zhou, Xiaolu; Peng, Changhui; Li, Tong; Chen, Kexin; Liu, Zelin; Li, Peng; Zhang, Cicheng; Tang, Jiayi; Zou, Ziying (Forest Ecosystems, 2023)

Global response of terrestrial gross primary productivity to climate extremes - Yuan, Minshu; Zhu, Qiuan; Zhang, Jiang; Liu, Jinxun; Chen, Huai; Peng, Changhui; Li, Peng; Li, Mingxu; Wang, Meng; Zhao, Pengxiang (Science of The Total Environment, 2021)

Responses of autumn vegetation phenology to climate change and urbanization at northern middle and high latitudes - Du, Hui; Wang, Mei; Liu, Yuxin; Guo, Mengjiao; Peng, Changhui; Li, Peng (International Journal of Applied Earth Observation and Geoinformation, 2022)

Divergent response of vegetation phenology to extreme temperatures and precipitation of different intensities on the Tibetan Plateau - Sun, Mai; Li, Peng; Ren, Peixin; Tang, Jiayi; Zhang, Cicheng; Zhou, Xiaolu; Peng, Changhui (Science China Earth Sciences, 2023)

Divergent responses of autumn vegetation phenology to climate extremes over northern middle and high latitudes - Wang, Mei; Li, Peng; Peng, Changhui; Xiao, Jingfeng; Zhou, Xiaolu; Luo, Yunpeng; Zhang, Cicheng (Global Ecology and Biogeography, 2022)
Abstract Aim Compared with gradual climate change, extreme climatic events have more direct and dramatic impacts on vegetation growth. However, the influence of climate extremes on important phenological periods, such as the end of the growing season (EOS), remains unclear. Here, we investigate the temporal trends of EOS across different biomes and quantify the response of EOS to multiple climate extreme indices (CEIs). Location Northern middle and high latitudes. Time period 2000–2020. Major taxa studied Plants. Methods Three phenology extraction methods were used to compute EOS from satellite, FLUXNET and Pan European Phenology Project PEP725 phenological datasets. Different stress states of cold, hot, dry and wet extremes were represented by 12 CEIs. Partial correlation and ridge regression analysis were used to quantify the response of EOS to climate extremes across latitudinal and biome scales. Results Our study showed a delayed EOS in boreal biomes, but a significantly advanced EOS in temperate biomes. The advanced EOS induced by cold stress was observed for c . 80% of the vegetated pixels. The warm‐related CEIs delayed the EOS in high latitudes, and the delayed effect weakened or even reversed with decreasing latitude. In contrast, EOS exhibited opposite response patterns to dry days and wet‐related CEIs. Overall, EOS exhibited higher sensitivity to extreme temperature in boreal biomes than in temperate biomes. Specifically, continuous drought and high heat stress induced an earlier EOS in some temperate forest biomes, whereas moderate heat stress delayed the EOS in most study biomes. In contrast, EOS was not sensitive to extreme drought in water‐restricted biomes. Main conclusions EOS exhibited divergent responses to various climate extremes with different intensities and frequencies. Moreover, the response of EOS to extreme climate stress was dependent on the biome and latitude. These findings emphasize the importance of incorporating the divergent extreme climate effects into vegetation phenological models and Earth system models.

A Review of General Methods for Quantifying and Estimating Urban Trees and Biomass - Yang, Mingxia; Zhou, Xiaolu; Liu, Zelin; Li, Peng; Tang, Jiayi; Xie, Binggeng; Peng, Changhui (Forests, 2022)
Understanding the biomass, characteristics, and carbon sequestration of urban forests is crucial for maintaining and improving the quality of life and ensuring sustainable urban planning. Approaches to urban forest management have been incorporated into interdisciplinary, multifunctional, and technical efforts. In this review, we evaluate recent developments in urban forest research methods, compare the accuracy and efficiency of different methods, and identify emerging themes in urban forest assessment. This review focuses on urban forest biomass estimation and individual tree feature detection, showing that the rapid development of remote sensing technology and applications in recent years has greatly benefited the study of forest dynamics. Included in the review are light detection and ranging-based techniques for estimating urban forest biomass, deep learning algorithms that can extract tree crowns and identify tree species, methods for measuring large canopies using unmanned aerial vehicles to estimate forest structure, and approaches for capturing street tree information using street view images. Conventional methods based on field measurements are highly beneficial for accurately recording species-specific characteristics. There is an urgent need to combine multi-scale and spatiotemporal methods to improve urban forest detection at different scales.

Recent advances in carbon footprint studies of urban ecosystems: overview, application, and future challenges - Chen, Kexin; Yang, Mingxia; Zhou, Xiaolu; Liu, Zelin; Li, Peng; Tang, Jiayi; Peng, Changhui (Environmental Reviews, 2022)
Urban ecosystems are complex systems with anthropogenic features that generate considerable CO 2 emissions, which contributes to global climate change. Quantitative estimates of the carbon footprint of urban ecosystems are crucial for developing low-carbon development policies to mitigate climate change. Herein, we reviewed more than 195 urban carbon footprint and carbon footprint related studies, collated the recent progress in carbon footprint calculation methods and research applications of the urban ecosystem carbon footprint, analyzed the research applications of the carbon footprint of different cities, and focused on the need to study the urban ecosystem carbon footprint from a holistic perspective. Specifically, we aimed to: (i) compare the strengths and weaknesses of five existing carbon footprint calculation methods [life cycle assessment, input–output analysis, hybrid life cycle assessment, carbon footprint calculator, and Intergovernmental Panel on Climate Change (IPCC)]; (ii) analyze the status of current research on the carbon footprint of different urban subregions based on different features; and (iii) highlight new methods and areas of research on the carbon footprint of future urban ecosystems. Not all carbon footprint accounting methods are applicable to the carbon footprint determination of urban ecosystems; although the IPCC method is more widely used than the others, the hybrid life cycle assessment method is more accurate. With the emergence of new science and technology, quantitative methods to calculate the carbon footprint of urban ecosystems have evolved, becoming more accurate. Further development of new technologies, such as big data and artificial intelligence, to assess the carbon footprint of urban ecosystems is anticipated to help address the emerging challenges in urban ecosystem research effectively to achieve carbon neutrality and urban sustainability under global change.

Response of Vegetation Photosynthetic Phenology to Urbanization in Dongting Lake Basin, China - Li, Peng; Sun, Mai; Liu, Yuxin; Ren, Peixin; Peng, Changhui; Zhou, Xiaolu; Tang, Jiayi (Remote Sensing, 2021)
Urbanization can induce environmental changes such as the urban heat island effect, which in turn influence the terrestrial ecosystem. However, the effect of urbanization on the phenology of subtropical vegetation remains relatively unexplored. This study analyzed the changing trend of vegetation photosynthetic phenology in Dongting Lake basin, China, and its response to urbanization using nighttime light and chlorophyll fluorescence datasets. Our results indicated the start of the growing season (SOS) of vegetation in the study area was significantly advanced by 0.70 days per year, whereas the end of the growing season (EOS) was delayed by 0.24 days per year during 2000–2017. We found that urbanization promoted the SOS advance and EOS delay. With increasing urbanization intensity, the sensitivity of SOS to urbanization firstly increased then decreased, while the sensitivity of EOS to urbanization decreased with urbanization intensity. The climate sensitivity of vegetation phenology varied with urbanization intensity; urbanization induced an earlier SOS by increasing preseason minimum temperatures and a later EOS by increasing preseason precipitation. These findings improve our understanding of the vegetation phenology response to urbanization in subtropical regions and highlight the need to integrate human activities into future vegetation phenology models.

Analysis and Prediction of Expansion of Central Cities Based on Nighttime Light Data in Hunan Province, China - Liu, Yuxin; He, Tian; Wang, Yi; Peng, Changhui; Du, Hui; Yuan, Shuai; Li, Peng (Sustainability, 2021)
Quantifying the characteristics of urban expansion as well as influencing factors is essential for the simulation and prediction of urban expansion. In this study, we extracted the built-up regions of 14 central cities in the Hunan province using the DMSP-OLS night light remote sensing datasets from 1992 to 2018, and evaluated the spatial and temporal characteristics of the built-up regions in terms of the area, expansion speed, and main expansion direction. The backpropagation (BP) neural network and autoregressive integrated moving average (ARIMA) model were used to predict the area of the built-up regions from 2019 to 2026. The model predictions were based on the GDP, ratio of the secondary industry output to the GDP, ratio of the tertiary industry output to the GDP, year-end urban population, and urban road area. The results demonstrated that the built-up area and expansion speed of the central cities in the eastern part of the Hunan province were significantly higher than those in the western part. The main expansion directions of the 14 central cities were east and south. The urban road area, year-end urban population, and GDP were the main driving factors of the expansion. The urban expansion model based on the BP neural network provided a high prediction accuracy (R = 0.966). It was estimated that the total area of urban built-up regions in the Hunan province will reach 2463.80 km2 by 2026. These findings provide a new perspective for predicting urban areas rapidly and simply, and it also provides a useful reference for studying the spatial expansion characteristics of central cities and formulating a sustainable urban development strategy during the 14th Five-Year Plan of China.

Soil CH4 and CO2 dynamics and nitrogen transformations with incubation in mountain forest and meadow ecosystems - Zhang, Junjun; Peng, Changhui; Xue, Wei; Yang, Zhenan; Yang, Bin; Li, Peng; Zhu, Qiuan; Wang, Meng (CATENA, 2018)

Application of climate change scenarios in the simulation of forest ecosystems: an overview - Liu, Haoyun; Li, Peng; Peng, Changhui; Liu, Cong; Zhou, Xiaolu; Deng, Zhengmiao; Zhang, Cicheng; Liu, Zelin (Environmental Reviews, 2023)
Climate change scenarios established by the Intergovernmental Panel on Climate Change have developed a significant tool for analyzing, modeling, and predicting future climate change impacts in different research fields after more than 30 years of development and refinement. In the wake of future climate change, the changes in forest structure and functions have become a frontier and focal area of global change research. This study mainly reviews and synthesizes climate change scenarios and their applications in forest ecosystem research over the past decade. These applications include changes in (1) forest structure and spatial vegetation distribution, (2) ecosystem structure, (3) ecosystem services, and (4) ecosystem stability. Although climate change scenarios are useful for predicting future climate change impacts on forest ecosystems, the accuracy of model simulations needs to be further improved. Based on existing studies, climate change scenarios are used in future simulation applications to construct a biomonitoring network platform integrating observations and predictions for better conservation of species diversity.

Change in Autumn Vegetation Phenology and the Climate Controls From 1982 to 2012 on the Qinghai–Tibet Plateau - Li, Peng; Zhu, Qiuan; Peng, Changhui; Zhang, Jing; Wang, Meng; Zhang, Junjun; Ding, Juhua; Zhou, Xiaolu (Frontiers in Plant Science, 2020)

Variation in Soil Methane Fluxes and Comparison between Two Forests in China - Wei, Hua; Peng, Changhui; Liu, Shirong; Liu, Xiaojing; Li, Peng; Song, Hanxiong; Yuan, Minshu; Wang, Meng (Forests, 2018)
Methane (CH4) is a vital greenhouse gas with a 28-fold higher global warming potential than carbon dioxide when considering a molar basis for the time horizon of 100 years. Here, we investigated the variation of soil CH4 fluxes, soil physiochemical properties, and CH4-related bacteria community composition of two forests in China. We measured CH4 fluxes using static chambers and analyzed soil bacterial communities using next-generation high-throughput sequencing in a temperate broad-leaved deciduous forest at Baotianman Nature Reserve (TBDF-BTM) and a tropical rainforest at Jianfengling National Natural Reserve (TRF-JFL). Our results showed that the soils from both sites were CH4 sinks. Significant variation in soil CH4 fluxes was found at TBDF-BTM exclusively, while no seasonal variation in the CH4 uptake was observed at TRF-JFL. The CH4 fluxes at TBDF-BTM were substantially higher than those at TRF-JFL during all seasons. One genus of methanotrophs and three genera of methylotrophs were detected at both sites, though they had no direct relationship with soil CH4 fluxes. Water-filled pore space and soil total carbon content are the main factors controlling the soil CH4 fluxes at TBDF-BTM. At TRF-JFL, the soil CH4 fluxes showed no significant correlations with any of the soil properties. This study improves our understanding of soil CH4 fluxes and their influencing factors in forests in different climatic zones and provides a reference for future investigation of forest soil CH4 fluxes, the forest ecosystem carbon cycle, and the forest CH4 model.

Estimates and Predictions of Methane Emissions from Wastewater in China from 2000 to 2020 - Du, Mingxi; Zhu, Qiuan; Wang, Xiaoge; Li, Peng; Yang, Bin; Chen, Huai; Wang, Meng; Zhou, Xiaolu; Peng, Changhui (Earth's Future, 2018)
Methane accounts for 20% of the global warming caused by greenhouse gases, and wastewater is a major anthropogenic source of methane. Based on the Intergovernmental Panel on Climate Change greenhouse gas inventory guidelines and current research findings, we calculated the amount of methane emissions from 2000 to 2014 that originated from wastewater from different provinces in China. Methane emissions from wastewater increased from 1349.01 to 3430.03 Gg from 2000 to 2014, and the mean annual increase was 167.69 Gg. The methane emissions from industrial wastewater treated by wastewater treatment plants ( E It ) accounted for the highest proportion of emissions. We also estimated the future trend of industrial wastewater methane emissions using the artificial neural network model. A comparison of the emissions for the years 2020, 2010, and 2000 showed an increasing trend in methane emissions in China and a spatial transition of industrial wastewater emissions from eastern and southern regions to central and southwestern regions and from coastal regions to inland regions. These changes were caused by changes in economics, demographics, and relevant policies. , Key Points Methane emission from wastewater from 2000 to 2014 was calculated to increase from 1349.01 Gg to 3430.03 Gg. Methane emission from wastewater from 2015 to 2020 was estimated to increase from 3875.30 Gg to 5212.75 Gg. A spatial transition of methane emission from wastewater was found and discussed in the present study.

Detecting and mapping tree crowns based on convolutional neural network and Google Earth images - Yang, Mingxia; Mou, Yuling; Liu, Shan; Meng, Yanrong; Liu, Zelin; Li, Peng; Xiang, Wenhua; Zhou, Xiaolu; Peng, Changhui (International Journal of Applied Earth Observation and Geoinformation, 2022)

Model prediction of biome‐specific global soil respiration from 1960 to 2012 - Zhao, Zhengyong; Peng, Changhui; Yang, Qi; Meng, Fan‐Rui; Song, Xinzhang; Chen, Shutao; Epule, Terence Epule; Li, Peng; Zhu, Qiuan (Earth's Future, 2017)
Abstract Biome‐specific soil respiration (Rs) has important yet different roles in both the carbon cycle and climate change from regional to global scales. To date, no comparable studies related to global biome‐specific Rs have been conducted applying comprehensive global Rs databases. The goal of this study was to develop artificial neural network ( ANN ) models capable of spatially estimating global Rs and to evaluate the effects of interannual climate variations on 10 major biomes. We used 1976 annual Rs field records extracted from global Rs literature to train and test the ANN models. We determined that the best ANN model for predicting biome‐specific global annual Rs was the one that applied mean annual temperature ( MAT ), mean annual precipitation ( MAP ), and biome type as inputs ( r 2 = 0.60). The ANN models reported an average global Rs of 93.3 ± 6.1 Pg C yr −1 from 1960 to 2012 and an increasing trend in average global annual Rs of 0.04 Pg C yr −1 . Estimated annual Rs increased with increases in MAT and MAP in cropland, boreal forest, grassland, shrubland, and wetland biomes. Additionally, estimated annual Rs decreased with increases in MAT and increased with increases in MAP in desert and tundra biomes, and only significantly decreased with increases in MAT ( r 2 = 0.87) in the savannah biome. The developed biome‐specific global Rs database for global land and soil carbon models will aid in understanding the mechanisms underlying variations in soil carbon dynamics and in quantifying uncertainty in the global soil carbon cycle. , Key Points Predict biome‐specific global soil respiration from 1960 to 2012 using an artificial neural network model Prediction determined an average global soil respiration of 93.3 ± 6.1 Pg C yr −1 and an increasing trend of 0.04 Pg C yr −1 The 10 biome‐specific soil respiration estimates made it possible to trace different responses to global climate change in each biome

Effects of increasing aerosol optical depth on the gross primary productivity in China during 2000–2014 - Zhang, Jing; Ding, Juhua; Zhang, Jiang; Yuan, Minshu; Li, Peng; Xiao, Zhongyong; Peng, Changhui; Chen, Huai; Wang, Meng; Zhu, Qiuan (Ecological Indicators, 2020)

Temporal and spatial variation of vegetation photosynthetic phenology in Dongting Lake basin and its response to climate change - Ren, Pei-Xin; Li, Peng; Peng, Chang-Hui; Zhou, Xiao-Lu; Yang, Ming-Xia; 1College of Geographic Sciences, Hunan Normal University, Changsha 410081, China; 2Department of Biology Sciences, Institute of Environment Sciences, University of Quebec at Montreal, Montreal H3C 3P8, Canada (Chinese Journal of Plant Ecology, 2023)

Dynamic carbon allocation trade‐off: A robust approach to model tree biomass allometry - Yang, Mingxia; Zhou, Xiaolu; Liu, Zelin; Li, Peng; Liu, Caixia; Huang, Huabing; Tang, Jiayi; Zhang, Cicheng; Zou, Ziying; Xie, Binggeng; Peng, Changhui (Methods in Ecology and Evolution, 2024)
Abstract Forest above‐ground biomass (AGB) is often estimated by converting the observed tree size using allometric scaling between the dry weight and size of an organism. However, the variations in biomass allocation and scaling between tree crowns and stems due to survival competition during a tree's lifecycle remain unclear. This knowledge gap can improve the understanding of modelling tree biomass allometry because traditional allometries ignore the dynamics of allocation. Herein, we characterised allometric scaling using the dynamic ratio ( r ) of the stem biomass (SB) to AGB and a dynamic exponent. The allometric models were biologically parameterised by the r values for initial, intermediate and final ages rather than only a regression result. The scaling was tested using field measurements of 421 species and 2213 different‐sized trees in pantropical regions worldwide. We found that the scaling fluctuated with tree size, and this fluctuation was driven by the trade‐off relationship of biomass allocation between the tree crown and stem depending on the dynamic crown trait. The allometric scaling between SB and AGB varied from 0.8 to 1.0 for a tree during its entire lifecycle. The fluctuations presented a general law for the allometric scaling of the pantropical tree biomass and size. Our model quantified the trade‐off and explained 94.1% of the allometric relationship between the SB and AGB (93.8% of which between D 2 H and AGB) for pantropical forests, which resulted in a better fit than that of the traditional model. Considering the effects of the trade‐off on modelling, the actual biomass of large trees could be substantially greater than conventional estimates. These results highlight the importance of coupling growth mechanisms in modelling allometry and provide a theoretical foundation for better describing and predicting forest carbon accumulation.

Vegetation browning: global drivers, impacts, and feedbacks - Liu, Qiuyu; Peng, Changhui; Schneider, Robert; Cyr, Dominic; Liu, Zelin; Zhou, Xiaolu; Du, Mingxi; Li, Peng; Jiang, Zihan; McDowell, Nate G.; Kneeshaw, Daniel (Trends in Plant Science, 2023)