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Abstract Global rivers and streams are important carbon transport pathways from land to the ocean. However, few studies have quantified terrigenous carbon dynamics in river ecosystems and its variations due to climate change and anthropogenic perturbations. Therefore, our study analysed fluvial particulate organic carbon (POC) and developed a processed‐based model (TRIPLEX‐HYDRA) to simulate the production, transport and removal (i.e., deposition, degradation and dam retention) processes of fluvial POC along the land–ocean aquatic continuum (LOAC). Based on our results, approximately 0.29 Pg of POC is exported from land to the ocean through rivers each year. More specifically, we found that rivers at low latitudes (30°S–30°N, 0.18 Pg yr −1 ) and high northern latitudes (60°N–90°N, 0.05 Pg yr −1 ) had higher POC fluxes compared to rivers in other regions. This high POC flux is related to strong erosion rates and high soil organic carbon storage. Additionally, our model simulation revealed that total POC flux from global river has not significantly changed from 1983 to 2015 but displays markedly decreased or increased trend at regional scale. These regional variations in POC export are affected by climate warming and dam construction. Moreover, approximately 0.46 Pg of POC is deposited or trapped by dams along the LOAC system, which plays a vital role in the global river carbon budget. Although some limitations and uncertainties remain, this study establishes a theoretical and methodological basis for quantifying riverine POC dynamics in the LOAC system.

Numerous empirical studies have demonstrated that street trees not only reduce dust pollution and absorb particulate matter (PM) but also improve microclimates, providing both ecological functions and aesthetic value. However, recent research has revealed that street tree canopy cover can impede the dispersion of atmospheric PM within street canyons, leading to the accumulation of street pollutants. Although many studies have investigated the impact of street trees on air pollutant dispersion within street canyons, the extent of their influence remains unclear and uncertain. Pollutant accumulation corresponds to the specific characteristics of individual street canyons, coupled with meteorological factors and pollution source strength. Notably, the characteristics of street tree canopy cover also exert a significant influence. There is still a quantitative research gap on street tree cover impacts with respect to pollution and dust reduction control measures within street spaces. To improve urban traffic environments, policymakers have mainly focused on scientifically based street vegetation deployment initiatives in building ecological garden cities and improving the living environment. To address uncertainties regarding the influence of street trees on the dispersion of atmospheric PM in urban streets, this study reviews dispersion mechanisms and key atmospheric PM factors in urban streets, summarizes the research approaches used to conceptualize atmospheric PM dispersion in urban street canyons, and examines urban plant efficiency in reducing atmospheric PM. Furthermore, we also address current challenges and future directions in this field to provide a more comprehensive understanding of atmospheric PM dispersion in urban streets and the role that street trees play in mitigating air pollution.

Many hypotheses have been proposed to explain elevational species richness patterns; however, evaluating their importance remains a challenge, as mountains that are nested within different biogeographic regions have different environmental attributes. Here, we conducted a comparative study for trees, shrubs, herbs, and ferns along the same elevational gradient for 22 mountains worldwide, examining the performance of hypotheses of energy, tolerance, climatic variability, and spatial area to explain the elevational species richness patterns for each plant group. Results show that for trees and shrubs, energy-related factors exhibit greater explanatory power than other factors, whereas the factors that are associated with climatic variability performed better in explaining the elevational species richness patterns of herbs and ferns. For colder mountains, energy-related factors emerged as the main drivers of woody species diversity, whereas in hotter and wetter ecosystems, temperature and precipitation were the most important predictors of species richness along elevational gradients. For herbs and ferns, the variation in species richness was less than that of woody species. These findings provide important evidence concerning the generality of the energy theory for explaining the elevational species richness pattern of plants, highlighting that the underlying mechanisms may change among different growth form groups and regions within which mountains are nested.

Many hypotheses have been proposed to explain elevational species richness patterns; however, evaluating their importance remains a challenge, as mountains that are nested within different biogeographic regions have different environmental attributes. Here, we conducted a comparative study for trees, shrubs, herbs, and ferns along the same elevational gradient for 22 mountains worldwide, examining the performance of hypotheses of energy, tolerance, climatic variability, and spatial area to explain the elevational species richness patterns for each plant group. Results show that for trees and shrubs, energy-related factors exhibit greater explanatory power than other factors, whereas the factors that are associated with climatic variability performed better in explaining the elevational species richness patterns of herbs and ferns. For colder mountains, energy-related factors emerged as the main drivers of woody species diversity, whereas in hotter and wetter ecosystems, temperature and precipitation were the most important predictors of species richness along elevational gradients. For herbs and ferns, the variation in species richness was less than that of woody species. These findings provide important evidence concerning the generality of the energy theory for explaining the elevational species richness pattern of plants, highlighting that the underlying mechanisms may change among different growth form groups and regions within which mountains are nested.

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.

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.

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.

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.

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.

Abstract Digital leaf physiognomy (DLP) is considered as one of the most promising methods for estimating past climate. However, current models built using the DLP data set still lack precision, especially for mean annual precipitation (MAP). To improve predictive power, we developed five machine learning (ML) models for mean annual temperature (MAT) and MAP respectively, and then tested the precision of these models and some of their averaging compared with that obtained from other models. The precision of all models was assessed using a repeated stratified 10‐fold cross‐validation. For MAT, three combinations of models ( R 2 = .77) presented moderate improvements in precision over the multiple linear regression (MLR) model ( R 2 = .68). For log e (MAP), the averaging of the support vector machine (SVM) and boosting models improved the R 2 from .19 to .63 compared with that of the MLR model. For MAP, the R 2 of this model combination was 0.49, which was much better than that of the artificial neural network (ANN) model ( R 2 = .21). Even the bagging model, which had the lowest R 2 (.37) for log e (MAP), demonstrated better precision ( R 2 = .27) for MAP. Our palaeoclimate estimates for nine fossil floras were also more accurate, because they were in better agreement with independent paleoclimate evidence. Our study confirms that our ML models and their averaging can improve paleoclimatic reconstructions, providing a better understanding of the relationship between climate and leaf physiognomy.

Paleobotanists have long built leaf climate models based on site mean of leaf physiognomic characteristics of woody dicotyledons species (WDS) for estimating past climate. To explore the potential of the order Ericales in estimating paleoclimate, we developed two linear models for each climatic factor. One is based on WDS, and the other is based on both WDS and leaf physiognomic characters of the order Ericales (WDS-E). We found that, compared with WDS models, WDS-E models improved greatly in mean annual precipitation (MAP), growing season precipitation (GSP) and mean annual range in temperature (MART). When the minimum species number of the order Ericales is three per site, the WDS-E models improved the r2 from 0.64 to 0.78 for MART, from 0.23 to 0.61 for ln(MAP), and from 0.37 to 0.64 for ln(GSP) compared with the WDS models. For mean annual temperature (MAT), the WDS-E model (r2 = 0.86) also exhibited a moderate improvement in precision over the WDS model (r2 = 0.82). This study demonstrates that other patterns, such as those of the order Ericales, can contribute additional information towards building more precise paleoclimate models.