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The Western Sichuan Plateau stands as a vital ecological barrier in China. In recent years, rapid urbanization and infrastructure development have led to increased ecosystem fragmentation in the region, compression of species habitats, and significant challenges to ecosystem service functions and biodiversity conservation. Constructing ecological corridors can help maintain landscape connectivity and preserve biodiversity by providing habitats for various species. Additionally, they can enhance population viability and species richness at both local and regional scales. Current research focuses on the construction of ecological networks and the potential impacts of corridors. Few studies consider corridor width as an object of study. This study addresses the width of corridor construction, aiming to support the practical implementation of corridor projects and explore the coordination between corridor costs and functionality at different widths. Based on an evaluation of ecosystem service functions and in conjunction with natural reserves, this study identified significant ecological source. Four primary resistance factors—land use type, elevation, slope, and Degree of relief—were selected to construct resistance surfaces. The MAXENT model is introduced to identify potential landslide-prone areas, and the resistance surfaces are accordingly adjusted. Potential corridors are identified utilising circuit theory. Subsequently, by employing the IEW-TOPSIS model, different ecological corridor widths are evaluated in terms of “cost-function-ecological benefits” to determine suitable corridor widths for the Western Sichuan Plateau. Results reveal significant variations in ecological corridor widths across different regions of the plateau. Taking topographical factors into account, 1400 m emerges as the most optimal width twice, while 800 m, 1700 m, and 2000 m each appear once. The corridor width evaluation system can provide decision support for ecological corridor construction in the Western Sichuan Plateau, contributing to the preservation of ecological security in the Qinghai-Tibet Plateau in China.

Urban progression influences water conservation by altering surface characteristics and local climate regimes, potentially posing significant risks to water sustainability and ecological integrity. Since 2015, the Chinese government has championed a new development philosophy aimed at promoting harmonious coexistence between human and nature. Based on the green connotation of the new development philosophy, this study simulated the spatial and temporal distribution of water conservation in five plateau lakeside cities (Plateau Lakeside City-PLC: Dianchi Lake Basin-DCB, Fuxian Lake Basin-FXB, Erhai Lake Basin-EHB, Qilu Lake Basin-QLB and Xingyun Lake Basin-XYB), established a comprehensive framework of indicators affecting water conservation, and quantified the changes in the drivers of water conservation. The results indicated that, except for DCB, the spatial distribution of water conservation in PLC exhibits similarity, but overall demonstrates a declining trend over time. The average importance of the factors in the PLC was weighed using Geodetector’s q value. The most critical factors affecting water conservation in PLC included climate change (precipitation: q_PRE=0.66; evaporation: q_ET=0.57) and land cover (q_LC=0.45). In addition, the topography (slope: q_SLOPE=0.39; elevation: q_DEM=0.43), vegetation coverage (q_NDVI=0.30), and soil (plant available water content: q_PAWC=0.38; root restricting layer depth: q_ROOT=0.18) were influential factors followed climate change and land cover. Among the social factors, population density (q_POP=0.37) has a small impact on water conservation, while economic activities and human-related land activities (q_CROP,q_NTL, and q_CITY are all less than 0.1) do not show a significant impact, due to the government’s implementation of the new development philosophy, which balances development and conservation.

Subterranean coal mining results in the occurrence of surface subsidence, soil degradation, and damage to agricultural lands, which is especially severe in regions with high groundwater levels. The collaborative reclamation scheme combining surface pre-reclamation and underground mining layout optimization can effectively control the problem of damage to farmlands. Hence, it is imperative to study the collaborative management technology. The research area for this study is Jiulishan Mine, located in coal-grain composite areas with a high groundwater table. Initially, a combined UAV and USV measuring system was utilized to acquire spatial data of the proposed reclamation area. Furthermore, we examined the evolution features of the subsidence ponding related to various mining methods using FLAC3D numerical modeling. Ultimately, the ArcGIS spatial analysis module was utilized to model and calculate the reclamation volume and rate for each plan. Our findings indicate that: (1) The extent of damage caused by shallow coal seam mining includes an area of 50.67 hm2 for farmland damaged area (FDA), 31.49 hm2 for seasonal subsidence ponding (SSP), and 23.51 hm2 for perennial subsidence ponding (PSP). Additionally, the boundary subsidence values for each damaged area are 0.8 m, 1.6 m, and 2.8 m, respectively. (2) Through the optimization of deep coal seam mining designs, the spatial configuration of the subsidence basin is successfully altered, creating the necessary condition for the development of a dynamic pre-reclamation plan. (3) The reclamation rate of the optimized pre-reclamation (PR) scheme is significantly higher, ranging from 40 % to 44 %, compared to the reclamation rate of 24 % of the traditional reclamation (TR). The choice of a suitable land reclamation and ecological restoration strategy was determined by optimizing mining plans and considering the characteristics of the subsidence area. This offers empirical evidence and specialized assistance for the ecological rehabilitation of coal-grain composite areas with high groundwater levels.

Amidst global climate change and increased anthropogenic activities, the Qilian Mountains face critical ecological and environmental challenges, including vegetation degradation and declining carbon stocks. Therefore, effective mitigation strategies are critical to building China’s ecological security and enhancing carbon storage. This study constructed and optimized an ecological corridor in the Qilian Mountains using models, such as minimum spanning tree and carbon stock calculation. It combined geographic meta cellular automata and other methods to model ecological recovery in the Qilian Mountains following corridor construction and investigated the areas of particular concern. The results show that: (1) the distribution of the 118 extracted ecological source sites (2020) exhibited a pattern of “dense in the east and sparse in the west.” Compared with the results of the 2010 extraction, the source site area had shrunk by 0.87 % and number of sites had increased by 45.68 %. This implies that the shrinkage and fragmentation of source sites are still increasing, underscoring the urgent need for ecological corridors. (2) Compared to the pre-optimization period, the network node connectivity of the corridor network formed by the optimized 114 primary corridors and 131 secondary corridors has increased by 110 % and the network connectivity has increased by 112 %, which enhances the connectivity of the corridors. (3) From 2000 to 2020, the carbon stock in the Qilian Mountains decreased and then increased; with the construction of a corridor mountain range, the carbon stock can be further increased by 1.4748 million tons. Additionally, the Geographic Cellular Automata (GCA) model simulated that in 2030, the area of forest land in the Qilian Mountains, area of grassland, and carbon stock will increase by 71.75 km2, 313.87 km2, and 6.663 million tons, respectively. These results confirm that ecological protection and corridor construction promote ecological restoration and enhance carbon stocks in the Qilian Mountains, providing a scientific basis for future ecological projects and carbon stock analysis in the region.

Habitat fragmentation is an important factor affecting biodiversity conservation, and few studies have discussed the relationship between the overall pattern of wetlands and biological ecological functions. Based on this, this study proposed the construction of wetland biodiversity conservation Ecological Networks (ENs) from the perspective of connectivity, and proposed a comprehensive framework for the construction of wetland ENs. Firstly, the key species were identified, and the habitat patches suitable for egret migration were analyzed by Morphological Spatial Pattern Analysis (MSPA) and landscape connectivity index, and the ecological sources were determined based on the policy influence. The Minimum Cumulative Resistance (MCR) model was used to extract the wetland ENs, and the network evaluation index, gravity model and Hydrological analysis model were used to analyze the “network − corridor − node” respectively. The results showed that: (1) The wetland patches in Haikou City were relatively trivial, and the connectivity between and within the patches was insufficient. There were 13 potential sources suitable for egret habitat and 17 ecological sources for comprehensive extraction; (2) The wetland ENs tends to be east of Nandu River, the network tightness is 0.038, and the network connectivity is low; (3) There are 45 important corridors and 8 important ecological sources, mainly concentrated in the east of Nandu River; (4) Identify 115, 50 and 66 ecological pinpoints, obstacle points and break points of wetland network, and propose protection, optimization and restoration strategies respectively. Comprehensive identification of ecological sources and the progressive wetland ENs analysis model of “network − corridor − node” will promote the further systematization of wetland biological protection research.

Hills and mountains cover approximately 25 % of Earth’s landmass; therefore, investigating landscape ecological risk (LER) in these regions is imperative for ecosystem management. Hilly and mountainous regions susceptible to influences such as desertification and erosion include the Southern Hill and Mountainous Belt (SHMB) in southern China. Over the past 30 years, the SHMB has undergone significant changes, offering an excellent research case. A comprehensive assessment of the ecological status of this region is crucial for the protection and management of the ecosystems in the SHMB. This study assessed the LER of the SHMB, identified influencing factors over the past 30 years, and utilized a patch-generating land-use simulation model (PLUS) to project future land use and LER. Accounting for more than 90 % of the total area, cropland and forestland were the two most common land use types in the SHMB. From 1990 to 2020, impervious surface and water area saw obvious increases, rising by 154 % (from 1209.37 km2 to 3073.59 km2) and 21 % (from 1584.78 km2 to 1924.8 km2), respectively. Grasslands and shrublands decreased by 54 % and 34 %. It is anticipated that there will be little change in land-use types by 2030 compared to 2020. Additionally, the overall LER in the SHMB was low, with low- and medium–low-risk representing around 70 % of the overall risk. The general state of LER improved during the research period but did not converge. Specifically, the LER intensity did not decrease although the total LER showed a declining trend. In the western portion of the research region, the karst area displayed an increasing LER, and the severity of the high-risk area increased. Furthermore, natural factors promote the restoration of LER. For example, the abundance of hydrothermal conditions (i.e., increased temperature and precipitation) promotes the recovery of vegetation growth and reduces the LER. Human activities, such as the construction of stations, highways, and an increasing population, exacerbate LER, contributing to an increase in LER. This study offers helpful guidance for formulating ecological conservation strategies to maintain the ecological balance and sustainability of hilly and mountainous regions.

Invasive Emerald Ash Borer (EAB) damage pose significant challenges for sustainable forest management, necessitating accurate mapping of damaged ash trees. Traditional change detection methods, using time-series imagery, are essential for monitoring forest disturbances but complicated by abnormal fluctuations in original time-series features. Tree phenology also complicates this process by masking the reflectance characteristics indicative of EAB infestation. To address these challenges, we propose an improved change detection method integrating patterns from host tree phenology and EAB life history. This improved method includes: (1) select the indices with time stability to enhance detection reliability by partial least squares method (PLS); (2) correction on negative change values before and positive change values after the phenological peak based on known patterns of tree phenology and EAB life history. Result confirms that this method effectively reflects the seasonal growth and decline dynamics of ash trees, revealing the impacts of phenology and EAB infestation. EAB-damaged trees exhibited slower growth in May and premature decline in July compared with healthy tree, with the damage severity influencing the rate of leaf decline. This proposed method achieved an overall accuracy of 53.4%-76.7% across different months for ash trees with health, light and severe damage. This study highlights the capabilities of integrating pest life history and phenology in change detection method and provide a new method to monitor individual tree health across large areas by high-resolution satellite imagery.

The heat island effect has far-reaching impacts on human settlements and natural ecosystems. Current research predominantly focuses on urban areas and overlooks the broader natural ecosystems beyond urban boundaries. Therefore, there’s an increasing urgency to reassess and study the heat island effect from a regional spatial perspective. This study introduces the concept of Land Heat Island (LHI) and quantifies it through Land Heat Island Intensity (LHII). Utilizing multivariate data and statistical analyses, this study investigates the spatiotemporal evolution of LHII in the Beibu Gulf urban agglomeration from 2012 to 2021. Redundancy analysis (RDA) and spatial bivariate correlation analysis were used to understand the relationships and evolution of factors influencing LHII. The results showed: (1) From 2012 to 2021, the heat island effect within the Beibu Gulf urban agglomeration gradually intensified. LHII was higher in the coastal areas compared to the inland regions, more developed economic areas exhibited higher LHII than less developed ones, and areas characterized by concentrated human activities had higher LHII than ecological agglomeration areas. (2) The primary zones of LHII growth in the Beibu Gulf urban agglomeration were slightly hot island areas and weakly hot island areas. The northern part of the area where forestland was clustered had a greater influence on the cold island pattern than on the heat island pattern; the lower latitude area had a greater influence on the heat island pattern than on the cold island pattern; and the eastern part of the area where the economy was well developed had an increasing influence on the heat island pattern year by year. (3) The enhancement of LHII in the Beibu Gulf urban agglomeration is primarily driven by urban construction and socio-economic factors. Specifically, Carbon Emissions and the Nighttime Light Index (NLI), which representing the strength of urban construction, contribute 11.4% and 8.3% respectively. Additionally, the Population Size (PS), representing the strength of socio-economic factors, contributes 12%. (4) The weakening of LHII was primarily affected by natural ecological factors, with the top two contributors being the average Digital Elevation Model (DEM) of cultivated land and average Normalized Difference Vegetation Index (NDVI), accounting for 32.9% and 14% respectively. This study enhances our understanding of the spatiotemporal evolution of the heat island effect and aims to mitigate it in the Bay Area urban agglomeration.

The understanding of the interplay between the environment, ecosystem structure, and function is crucial for effectively managing ecosystems in the face of climate change and environmental disturbances. Numerous observations and simulation experiments relying on correlation analysis have yielded the conclusion that environmental factors can directly or indirectly affect ecosystem community structure and function. However, the limitations of traditional ecological approaches lie in their inadequate control over confounding factors, thus hindering our comprehensive understanding of the true causal relationships. Our study aims to address these limitations and obtain a more accurate picture of causality about environment, ecosystem structure and function within a long-term time series data of an ecosystem. Here, utilizing a 31-year dataset of typical temperate grassland ecosystems, we developed an advanced causal discovery model and further enhanced it to effectively identify the causal network within the ecosystem, while also quantifying the intensity of causal pathways connecting the environment, ecosystem structure, and function. We found that the primary driver for changes in ecosystem structure and function was the temperature during the growing season, with soil water content being the secondary factor. However, there was only a weak causal relationship between ecosystem structure and gross primary productivity (GPP), which was mediated by eurytopic xerophyte plants. These findings challenge the conventional notion that precipitation is a key driver of changes in ecosystem carbon fluxes, highlighting the importance of plant-available water in regulating ecosystem structure and function. Our results suggest that the effects of global warming and severe droughts on plant growth and carbon sequestration capacity necessitate significant attention and further mitigation measures in arid and semi-arid regions.

Understanding the spatiotemporal dynamics of carbon storage in regional ecosystem and its underlying mechanisms is essential for environmental science. This study presents an integrated geographical analysis framework to identify and analyze the primary factors influencing carbon storage across various spatial scales. Initially, a global-scale Geographic Detector identified key factors affecting carbon storage, refined later using an Ordinary Least Squares (OLS) model to mitigate multicollinearity and redundancy. Subsequently, a multi-scale geographic weighted regression model (MGWR) explored spatial heterogeneity and nuanced impacts of these factors across various spatial scales. Our findings reveal a consistent downward trend in carbon storage in Fujian Province, China, from 1995 to 2020, accompanied by significant spatial autocorrelation. Elevation, slope, annual precipitation, and distance to the expressway emerged as primary determinants, synergistically enhancing global-scale spatial variance in carbon storage. Slope and distance to the expressway have a broad influence, while the added value of the primary industry and annual precipitation show localized effects. Collectively, these factors positively impact carbon storage, although with considerable variability in spatial orientation and magnitude of influence. This framework highlights the scale-specific and heterogeneous nature of carbon storage drivers, offering fresh insights into the mechanisms governing its spatiotemporal patterns in regional ecosystems.

The ecological sustainability of arid and semi-arid areas is under increasing threat due to global climate change, and well-established ecological networks are essential for improving landscape connectivity and conserving biodiversity. This study takes the Tacheng-Emin Basin in China as an example. Based on the “source-resistance-corridor” research paradigm and sustainable development theory, the “contribution-organization-vitality-resilience” and circuit theory of the ecosystem are incorporated into the construction of the ecological networks. The results show that (1) the ecosystem service value of the Tacheng-Emin basin is 5.85 × 1010 Yuan, the ecosystem of the basin is of medium health level, and the ecological security hierarchy has low level and uncoordinated problems. (2) The total integrated resistance surface in the basin is of medium level, dominated by natural resistance, and characterized by evident fragmentation. (3) 25 ecological sources, 58 ecological corridors, 32 ecological stepping stones, 39 obstacle points, and 46 ecological fracture points were extracted at the threshold of 10,000 in the circuit model. (4) The reconstruction cost of the ecological network is 1,007.41 × 104 Yuan, and the reconstructed ecological network have increased 2 ecological sources, 1 stepping stone, and reduced 3 obstacle points, while the closure of the ecological networks structure has been significantly improved and the ecological networks has developed toward a stable multi-grid structure. (5) The ecological zoning protection and restoration scheme of “one ring, one belt, one sink, multiple zones, and multiple corridors” is conducive to the sustainable ecological development of the basin. The results of this study can serve as a reference for ecological management and sustainable development in arid and semi-arid regions of Central Asia.

In the past 40 years, the surface water temperature of lakes worldwide has generally shown an upward trend, with a significant spatial heterogeneity. Previous studies generally attributed the change in lake surface water temperature to direct impact of climate change. However, few studies have explored the potential impact of additional anthropogenic factors, resulting in an incomplete understanding of the anthropogenic influence on lake surface water temperatures. We thus propose a new method to quantify the anthropogenic impact on the surface water temperature of lakes. We selected 11 lakes characterized by significant variations in the intensity of watershed urbanization development on the Yunnan-Guizhou Plateau in China for an empirical study, and discussed the trend of LSWT (lake surface water temperature) changes under the anthropogenic impact, as well as the potential link between these changes and human social policies. The research results show that (1) The mean annual rate of change in LSWT due to anthropogenic impact fluctuates of 0.06℃ per year. (2) LSWT is sensitive to changes in anthropogenic activities; a 1℃ increase in LSWT due to anthropogenic factors typically results in a mean temperature variation of 0.24℃. (3) During the three years of the COVID-19 pandemic, LSWT on the Yunnan-Guizhou Plateau was significantly affected by anthropogenic activities.

Water temperature is an important physical indicator of rivers because it impacts many other physical and biogeochemical processes and controls the metabolism of aquatic species in rivers. Having a good knowledge of river thermal dynamics is of great importance. In this study, an advanced machine learning based model that is fast, accurate and easy to use, namely the nonlinear autoregressive network with exogenous inputs (NARX) neural network, was coupled with Bayesian Optimization (BO) algorithm for optimizing the number of NARX hidden nodes and lagged input/target values and the Bayesian Regularization (BR) backpropagation algorithm for the NARX training, to forecast daily river water temperatures (RWT). Long-term observed data from 18 rivers of the Vistula River Basin, one of the largest rivers in Europe, were used for model testing, and model performance was compared with the air2stream model. The results showed that the NARX-based model performs significantly better than the air2stream model in the calibration and validation stages, and can better capture the seasonal pattern and peak values of RWT. Input combinations impact the performance of the NARX-based model in RWT modeling, and air temperature and the day of the year (DOY) are the major inputs, while streamflow and rainfall play a minor role on modeling RWT at the Vistula River Basin. Considering that future times series of air temperatures are easily accessible from climate models and DOY is easy to be considered in the model, the NARX-based model can serve as a promising tool to investigate the impact of climate change on river thermal dynamics.

Climate change significantly impacts ecological resistance and ecological corridors. However, the difficulty in evaluating this impact lies in two crucial issues: (i) the uncertainty arising from spatial heterogeneity, and (ii) the diversity of impacts arising from this uncertainty. A set pair analysis (SPA) model is designed to assess the impact of climate change on ecological resistance and ecological corridors for addressing the aforementioned problems. First, the ecological resistance and corridors under different climate change scenarios are simulated. Then, the five-element connection number is used to reflect changes in ecological resistance and corridors influenced by climate change and uncertainty. Finally, the influence indices are constructed to quantify the three distinct impacts of climate change on them. The evaluation results for Jiangxi Province are as follows. (i) The province’s UR index values are 0.085, 0.127, 0.351, 0.501, and 0.468 under five climate change scenarios, respectively. The impact of climate change on ecological resistance is rapidly deteriorating, and the central region is the high-value center for future ecological resistance. (ii) The values of UC in the central and southern regions range from − 0.568 to − 0.320 and from − 0.816 to − 0.446, respectively. In the two regions, the assessments of the impact of climate change on ecological corridors are negative, which results in a reduction in connectivity. (iii) The crucial meteorological risks with the greatest impact on ecological resistance and corridors are floods and high-temperature disasters. (iv) The proposals for Jiangxi Province to address climate change are as follows: enhancing flood prevention, improving high-temperature monitoring, and establishing an ecological management system. (v) The SPA model can effectively assess the different impacts of climate change with spatial uncertainty on ecological resistance and corridors.

Ecosystem surface plays an important role in maintaining ecological balance and supporting economic development. Accurate and effective identification of ecosystem surface with different ecological uses allows for better categorization and management and contributes to the orderly restoration of ecosystems. In this study, the Human Footprint Index (HFI) is constructed by comprehensively considering the factors of biodiversity, human activity impacts, and geometric features, and combined with the multi-factor knowledge constraints such as changes in land cover. We carry out accurate and effective identification and analysis of ecosystem surfaces, using Changzhou City’s tree cover as a test area. The results show that: (1) The degree of tree cover affected by human activities in Changzhou City varies greatly, and Gross Domestic Product (GDP) and elevation are the main factors affecting the level of the HFI. (2) The distribution of native ecosystem surface and artificial ecosystem surface are uneven, with more than 95% of native ecosystem surfaces distributed in the peripheral areas of the city with higher elevations and farther away from the city center. Artificial ecosystem surfaces are more likely to be found in areas with high GDP and frequent human activities, mainly in urban centers with a large proportion of buildings and along roads. (3) The overall identification accuracy of the ecosystem surface is 83.20%, which is higher in the urban center area with a large proportion of buildings and slightly lower in the area with a large proportion of agricultural production land. The results of the study provide a method for the objective, accurate and effective identification of ecosystem surface, which can improve the enthusiasm of local governments in fulfilling their ecological protection duties and contribute to the efficient achievement of the goal of ecological civilization construction.

The pine wood nematode (PWN) is one of the most threatening pests of pine forests in China. An essential management response involves felling and removing the infested trees before the PWN can spread over large areas, but this requires early detection. In this study, we analyzed the temporal changes in the spectral signatures of healthy and infested trees throughout the entire growing season (May to October) using unmanned aerial vehicle (UAV) hyperspectral images (HSI). To explore the potential of these seasonal spectral changes in detecting PWN, we investigated the separability of healthy and infested trees using both absolute values and changes in values. A random forest (RF) classifier was applied to separate healthy and infested trees based on reflectances, derivatives, vegetation indices (VIs), and their seasonal change values. We obtained the following results: (1) the seasonal changes of tree crown spectra following a PWN infestation were similar to those observed using absolute spectral values, though the latter performed better in early detection; (2) in the early stage of infestation (June and July), the green and red-edge regions contained the most sensitive bands, whereas the near-infrared portion played a more important role in later stages; (3) derivatives achieved higher overall accuracy (OA) than the reflectance in the early stage of PWN infestation (June and July); (4) in June, the Pigment Specific Simple Ratio (PSSR) had the highest separability (OA = 0.77) for PWN infestation, followed by the Plant Senescence Reflectance Index (PSRI) and the Normalized Difference Red-edge Index (NDRE), both with a OA of 0.70. In July, PSRI, Red Edge Normalized Difference Vegetation Index (RENDVI), and the Modified Red Edge Simple Ratio (MRESR) had the highest OA of 0.77. These VIs were calculated based on the red-edge regions. In August, RENDVI achieved the best accuracy (0.87), followed by the Normalized Difference Vegetation Index (NDVI, OA = 0.83) and the Plant Stress Index (PSI, OA = 0.80). In September and October, PSRI, PSI, the Greenness Index (GI), and RENDVI showed the strongest separability (0.87 to 0.90). Similar results were obtained using change values of VIs. In June, RENDVI, PSI, and NDVI showed the highest accuracy (OA = 0.73). In July, MRESR had the best accuracy (0.77), followed by RENDVI (0.73) and PSI (0.73). In the late stage of PWN infestation, NDVI, PSRI, PSI, RENDVI, GI, and PSSR obtained the highest accuracies (0.83 to 0.93), surpassing the accuracy achieved using absolute spectra; (5) in RF classification model, seasonal variation values derived from reflectances, derivatives, and VIs performed well in the late stages of the PWN infestation (September and October) but were not as good as absolute values in the early and middle stages (June to August). Our study explored the potential of seasonal changes in UAV spectra for early detection of forest pests.

Alpine wetlands, critical ecosystems in high-altitude mountain areas, play essential roles in water conservation, biodiversity protection, and carbon (C) sequestration. These ecosystems are particularly sensitive to climate change, with temperature and precipitation variations significantly impacting their structure and functional processes, such as litter decomposition, a key mechanism for C stabilization. This study focused on the Zoige wetland, a representative alpine wetland located on the eastern edge of the Qinghai-Tibet plateau. An incubation experiment was conducted with soil samples under simulated wet and dry moisture conditions to evaluate the environmental impacts on litter decomposition within this ecosystem. In this study, we employed the Tea Bag Index method, utilizing standardized litter, alongside native litter bags to compare the decomposition processes. This comparison between the uniform composition of standardized litter and the chemically diverse native plant litter aims to provide a comprehensive understanding of litter decomposition dynamics in alpine wetland ecosystems. Our finding showed that both standardized and native litter decomposition significantly decreased under dry conditions, in contrast to wetter condition. The components of both standardized and native litter exhibited a decrease over time, with the easily decomposable fractions breaking down swiftly, in contrast to the slower decomposition of the more resistant components. Furthermore, soil exo-enzyme activities varied significantly with environmental conditions. Wet conditions were observed to enhance soil microbial activity, whereas dry conditions resulted in shifts in microbial biomass C and nitrogen, indicative of drought resilience. The correlation analysis revealed that the composition of native litter is the primary factor influencing its decomposition. In contrast, the decomposition of standardized litter was influenced by both its composition and soil microbial activity. Thus, the distinction between the influences on native and standardized litter decomposition highlights the necessity of considering both litter quality and microbial interaction in ecological studies. This approach offers critical insight into the advantages and limitations of each decomposition methodology within alpine wetland ecosystems.

As the fourth pole of China's economic growth, the Chengdu-Chongqing urban agglomeration plays a significant role in reinforcing the ecological barrier in the upper reaches of the Yangtze River, and is crucial for ecological and environmental protection strategies. In this paper, Google Earth Engine (GEE) and the MODIS images from 2000, 2005, 2010, 2015, and 2022 were utilized to construct the Improved Remote Sensing Ecological Index (IRSEI) to characterize the ecological quality more accurately than RSEI. Additionally, combined with nighttime light remote sensing data, land use data and socio-economic data, the GDP sub-industry spatialization model was utilized to analyze the urbanization process in depth. To dynamically monitor and evaluate the interaction between urbanization and ecological environment quality, a coupling coordination model incorporating the above methods was developed. The results show that (1) The effective information of the IRSEI in ecological quality analysis increased by 3.26% compared to RSEI, and the correlation with each ecological index was higher; (2) The ecological environment quality of the Chengdu-Chongqing urban agglomeration peaked in 2005 and has been declining since then, with the rate of decline gradually slowing down from 2005 to 2022; (3) The GDP sub-industry spatialization model is more suitable for characterizing the scattered villages, and can effectively exhibit the urbanization process. From 2000 to 2022, urbanization was rapidly developed, and the level of core cities such as Chengdu and Chongqing far exceeded those of neighboring cities; (4) The coupling coordination level between IRSEI and GDP spatialization model generally increased, indicating ongoing improvements in the synergy between ecological environment and urbanization in the Chengdu-Chongqing urban agglomeration. This study developed a method for quickly monitoring and assessing the relationship between ecological environment quality and urbanization by using IRSEI and GDP sub-industry spatialization model, and provides scientific analytical methods and data support for monitoring and governing the ecological environment in emerging urban agglomerations.

The coordinated development of urban competitiveness (UC) and green total factor productivity (GTFP) is paramount for achieving global sustainable development objectives, especially for regional sustainability in China. This study utilizes panel data from 2011 to 2019 encompassing 273 Chinese cities to evaluate the urban competitiveness index employing the projection pursuit method. The research categorizes Chinese cities into four regions—East, Central, West, and Northeast—for comparative analysis purposes. The findings unveil: (1) a discernible rise in both overall and regional coordination between urban competitiveness and green total factor productivity in China; (2) throughout the study period, except for fluctuations observed in Northeastern China, coordination in other regions sustained relative stability, manifesting a trend of divergence-convergence; (3) the coordination between urban competitiveness and green total factor productivity across all regions demonstrated β-convergence, with Central China exhibiting the swiftest convergence and Northeastern China the slowest; (4) taking into account factors such as government intervention, industrial structure, and human capital, there is a likelihood that the growth rate of coordination between urban competitiveness and green total factor productivity in diverse regions will converge towards a synergistic trend. Therefore, as Chinese cities strive to enhance competitiveness and green total factor productivity, governmental attention should be directed towards discerning the developmental trends and influencing factors affecting their coordination. Such an approach will facilitate the realization of regional sustainable development objectives while propelling the synchronized advancement of urban competitiveness and green total factor productivity.

Continued population loss in the context of urban shrinkage has led to the transformation and reconfiguration of urban land use space, which in turn has had a major impact on regional habitats and ecosystem services. To explore the status of habitat quality (HQ) in shrinking cities, based on land use data from 2010 to 2020, this study uses the InVEST model to analyze the spatiotemporal characteristics of HQ in shrinking cities. Then, we reveal the shrinkage effect of HQ in shrinking cities from the perspective of population change rate, and detect the dominant influencing factors of HQ in different types of shrinking cities with the help of geographical detector. The results show that:(1) From 2010 to 2020, the primary land use types in both shrinking and non-shrinking cities of the three northeastern provinces were forestland and cropland. However, there were significant differences in the area of construction land between the two categories of cities. (2) The HQ in both shrinking and non-shrinking cities showed a declining trend, yet the HQ index of shrinking cities was higher than that of non-shrinking cities. (3) The distribution of HQ in shrinking cities has a significant shrinkage effect, and HQ index gradually strengthens as urban shrinkage intensifies. Low-quality habitats have a strong distributional advantage in slightly shrinking cities, while severely shrinking cities tend to be dominated by high-quality habitats. (4) Population profile, economic development, industrial structure and built environment are important factors influencing the spatial heterogeneity of HQ in shrinking cities. However, the spatial determinants of HQ vary significantly among different types of shrinking cities in terms of dimensions and indicators, with two-factor interactions exerting a stronger influence than single-factor impacts. Based on the findings of this study, the government can formulate differentiated habitat protection measures, dynamically adjust, and optimize them according to the distribution characteristics of HQ grades in shrinking cities.

The evolvement of urbanization has caused artificial light at night (ALAN) to be one of the major pollution sources in major cities, resulting in numerous environmental concerns. In view of the increasing intensity and range of ALAN, this study aims to evaluate the influence of ALAN on the physiology and behavior of the Pacific abalone Haliotis discus hannai, a typical nocturnal marine shellfish. Street lighting at night (SL6, lights on from 18:00 until 00:00; SL12, lights on from 18:00 until 06:00 the next morning) and car lighting (CL, lights were turned once every hour for 30 s and then turned off) were simulated indoors, with the natural light cycle group (lights on from 06:00 to 18:00) treated as the control group (CTR). The significantly higher cumulative distance covered along with the duration of abalone movement was observed in Group SL12 upon comparison with other groups (P

Conducting risk and early warning assessments of heavy metals (HMs) pollution in agricultural soils holds significant importance for pollution prevention and sustainable agricultural development. Early warning studies on heavy metal contamination of agricultural soils in oases in arid zones are rare in previous studies, therefore, this study carried out spatial and temporal simulation and early warning studies on the environmental capacity of agricultural soils in the Hexi Corridor based on field monitoring data and remote sensing technology and other methods. The results revealed that agricultural soils have been affected by human activities, leaving HMs at a moderate pollution level. The mean environmental capacity indices for Cr, Ni, Cu, Zn, As and Pb showed that they all belonged to the medium-capacity state, and 7.01 % of the sampling points being overloaded and in the low capacity for As. Cr, Ni, Zn and As were found in the moderate and high capacity zones located in the study area's western part, and the spatial distribution was basically the same. HMs in the middle parts area were dominated by low to medium capacity status, which may be related to agricultural activities such as application of fertilizers and pesticides and sewage irrigation. Based on the soil dynamic capacity model, it was found that in the next 20 years, HMs showed a decrease of 70–80 %, and the soil environment dynamic capacity was smaller than both the static and existing capacity, and it showed a decreasing trend with the change of time, which indicated that the soil's capacity for pollutants in the study area would be reduced if no pollution control measures were taken. Risk warning results showed that the overall spatial distribution pattern of the probability of pollution in the study area during the next 10 and 20 years was lower in the west and the northern part of Wuwei City, and higher in the middle part. This study suggests constructing a management system of “pollution evaluation - source treatment - risk early warning” for heavy metal contaminated sites, formulating corresponding policies and early warning measures, strengthening the research and development of monitoring, remediation, and treatment technologies, and realizing the prevention and control of agricultural soil pollution and sustainable development in the study area.

Accurately estimating the above-ground biomass (AGB) of high-density tropical rainforests is a challenging issue. In this study, airborne multi-baseline PolInSAR data were used to estimate the tropical rainforest AGB in Gabon, Africa. The most suitable baseline combination of the PolInSAR data was initially determined through baseline selection, and the PolInSAR parameters related to forest height were obtained based on the forest canopy height estimation theory and microwave penetration depth theory. The height parameter, baseline parameter, and observation geometry parameter were then used as independent variables to construct the AGB regression model. Support vector regression (SVR) was chosen as the AGB estimation model, and the global best particle swarm algorithm (GLB-PSO) was used to optimize the SVR model’s parameters. The results show that the RFECV variable selection method is superior to the Pearson method. The GLB-PSO algorithm can also further improve the saturation point of the SVR model—the estimation results show that the saturation point of AGB estimation of PolInSAR multidimensional features combined with the SVR machine learning algorithm is up to 500 Mg/ha, while this saturation point can be increased to 650 Mg/ha when using the GLB-PSO-SVR algorithm.

Bacteria are vital for maintaining ecosystem function in the rapidly changing environment, but limited data was reported regarding the characteristics of bacterial communities along the regional-scale in aquatic ecosystems on the Fildes Peninsula (Antarctica), where vulnerable aquatic habitats have been affected by the dramatic retreat of the Collins glacier. This study revealed differences and hidden connections of bacterial communities in two aquatic ecosystems (periglacial lakes and coves) near Collins glacier using amplicon sequencing. The results showed periglacial lakes and coves harbored 568 and 388 unique OTUs, respectively, but also shared 199 common OTUs, which meant there existed some linkages connections between the two ecosystems. Compared to periglacial lakes, communities in coves exhibited decreased richness and Shannon index (P

The distribution of species is mostly influenced by climate synergistic effects and land use. The prediction of endangered species is dependent on fine-scale environmental features, especially in forests. The capture of fine-scale suitable habitats is bounded by low spatial resolution and coarse categorization. In this study, we improved the land use information and forest spatial detail through high forest thematic resolution land use data. To understand the relative influence of environmental factors, suitable habitat models for Chinese endangered tree species (Firmiana danxiaensis) under three climate and high forest thematic resolution land use conditions was constructed. We also assess F. danxiaensis 's response to climate and land use, and differential performance in land use on the suitable habitat. The area of suitable habitat for F. danxiaensis grows slowly under the SSP1-2.6 (shared social pathway scenario) scenario, decreases by 21.33% from the present to 2090 under the SSP2-4.5 scenario, and expands significantly under the SSP5-8.5 scenario. We conclude that high forest thematic resolution land use is beneficial in capturing species' requirements for specific habitats and especially necessary in predicting endangered species. At the same time, the distribution of suitable habitats for species is primarily driven by climate, and limited their development by land use. The study shows that the synthetic effects of climate and land use change on F. danxiaensis are positive, with a marked trend toward the northeast. As a consequence, some endangered species are able to benefit from future SSPs scenarios, in particular they respond more strongly to the SSP5-8.5 scenario than to the other scenarios. Assessing the role of changes in the climate and high forest thematic resolution land use on F. danxiaensis will help promote sustainable land use management and contribute to the conservation policy for Chinese endangered species.

The balance between the supply and demand of ecosystem services (ESSD) is important for sustainable regional development. The hilly regions of south China have rich natural resources and are rapidly being urbanized, which significantly impacts sustainable development in the region. Therefore, how to analyze ESSD from the perspective of social ecology is imperative. Ganzhou region, which is a typical hilly region in southern China, as an example, this paper creatively integrated ESSD into the social-ecological system (SES) framework, analyzed the spatial and temporal changes of ESSD in 2005, 2010, 2015, and 2020, and constructed a system of indicators from the SES, and analyzed the driving factors of ESSD, so as to provide a reference for balancing ESSD. Based on the classical SES framework proposed by Ostrom, this study first analyses the social-ecological system profile of the Ganzhou region at the macro-scale and constructs an SES indicator system. The innovation of combining the SES framework and ecosystem services lies in integrating the social and ecosystem perspectives and merging these two concepts to understand better and manage the relationship between humans and the natural environment. It provides a comprehensive analytical approach and decision support tool for achieving sustainable development. Our results demonstrate that: (1) From 2005 to 2020, ecosystem services in the study area were highly valued, lower areas had homogeneous distribution characteristics, total demand displayed spatial differentiation, there were high-value areas in the midwest, and low-value areas were concentrated in the region’s boundary. (2) There are obvious supply and demand deficits in the southeast and east of the study area, while supply–demand is equal in the north and supply exceeds demand in other regions. From 2005 to 2020, the overall coupling coordination degree of ESSD in the study area was lower than 0.5, indicating a mild to moderate imbalance. (3) The supply of ecosystem services (ESS) was primarily driven by carbon storage, habitat quality, and water yield, while the demand was primarily driven by forest and grassland areas. Factors related to the ecosystem, resource unit, resource system, social system, governance system, and actor all have experienced two-factor interactions, which can explain up to 99% of ESSD. Based on the SES framework, this paper studies the relationship between ESSD in typical areas of the southern hills to provide a reference for research on ESSD in relevant areas and provide a scientific basis for the Chinese government to evaluate ecological protections and improve compensation policies, which is important for ensuring China's ecological security.

A simpler, unbiased, and comprehensive random forest (RF) model is needed to improve the accuracy of aboveground biomass (AGB) estimation. In this study, data were obtained from 128 sample plots of Pinus yunnanensis forest located in Chuxiong prefecture, Yunnan province, China. Sentinel-2 imagery data were applied to extract the important predictors of forest AGB, which were screened using the Boruta algorithm. We compared the fitting performance of two modified random forest models − regularized random forest (RRF) and quantile random forest (QRF) − with the random forest model. Moreover, we combined the smallest mean error of each quantile model as the best QRF (QRFb). The result showed: (1) Window sizes of 3 × 3 pixels and 5 × 5 pixels demonstrated greater sensitivity and suitability for estimating AGB than the 7 × 7 pixels window size. Enhanced vegetation indices derived from Red Edge 1 (B5) and Near-Infrared bands (B8A) were strongly correlated with AGB, indicating the heightened sensitivity of B5 and B8A bands to biomass and their potential in AGB estimation. (2) The RRF model outperformed both the standard RF and QRF in fitting performance, with an R2 of 0.56 and RMSE 57.14 Mg/ha. (3) The QRFb model exhibited the highest R2 of 0.88 and lowest RMSE of 29.56 Mg/ha, significantly reducing overestimation and underestimation issues. The modified RF regression supplies new insights into improving forest AGB estimation, which will be helpful for future research addressing carbon cycling.