To clarify the alleviative effect of exogenous abscisic acid (ABA) on Medicago sativa under drought stress, we conducted an experiment with the drought-tolerant cultivar Dryland and drought-sensitive cultivar WL354HQ. With four treatments including water spray control, exogenous ABA treatment, drought treatment, and drought + ABA treatment, we analyzed the effects of exogenous ABA on the physiological characteristics of M. sativa seedlings with different drought tolerance. The results showed that drought stress significantly inhibited seedling growth, induced severe oxidative stress and osmotic adjustment responses, and suppressed photosynthetic capacity. Compared with drought treatment, the drought + ABA treatment significantly alleviated the inhibition of drought on seedling growth. Specifically, root length of drought-tolerant and drought-sensitive cultivars increased significantly by 34.6% and 9.8%, respectively. Meanwhile, membrane lipid peroxidation in plant cells was effectively alleviated. Malondialdehyde (MDA) content in leaves and roots of the drought-tolerant cultivar significantly decreased by 20.0% and 54.9%, respectively. Excessive proline accumulation was inhibited. Proline content in leaves and roots decreased by 19.8% and 64.0% in the drought-tolerant cultivar, and by 3.5% and 20.2% in the drought-sensitive cultivar. The photosynthetic stability of the drought-tolerant cultivar was superior to that of the drought-sensitive cultivar. Specifically, leaf transpiration rate was significantly decreased by 62.5% and 49.1%, and net photosynthetic rate was significantly reduced by 58.9% and 32.7% in drought-tolerant and drought-sensitive cultivars, respecti-vely. In conclusion, ABA mitigated drought stress in M. sativa by coordinating osmotic adjustment and oxidative defense systems, and the alleviatory effect was stronger in the drought-tolerant cultivar.
Estuarine marsh is one of the most sensitive ecosystems to global change and human activities. Estuarine marsh generally acts as a sink of exogenous nitrogen (N), with significant effects on N-cycling. Under the scenario of enhanced N and sulfur (S) loads in the Minjiang River estuary, it is of great significance to explore the responses of N distribution in plant-soil system. We conducted a field experiment with four treatments (T0, control; TN, high N load treatment, 98.0 g N·m-2·a-1; TS, high S load treatment, 216.0 g S·m-2·a-1; and TNS, high N and S load treatment, 98.0 g N·m-2·a-1 + 216.0 g S·m-2·a-1) in a typical Phragmites australis marsh in the Minjiang River estuary. We investigated nitrogen distribution patterns in the plant-soil system under elevated N and S inputs. Results showed that high N and S load significantly altered the contents of total nitrogen (TN), ammonium (NH4+-N) and nitrate (NO3--N) in marsh soils. Compared with the T0 treatment, the TN, NH4+-N and NO3--N contents in the TN treatment increased by 9.1%, 1.3% and 13.4%, the TN content in the TS treatment increased by 9.4%, while the NH4+-N and NO3--N contents decreased by 2.7% and 33.6%, respectively. The TN and NH4+-N contents in the TNS treatment decreased by 13.3% and 6.7%, respectively, while NO3--N content increased by 37.9%. Under different treatments, the TN contents were consistently higher in leaves and lower in roots of P. aus-tralis. Compared with the T0 treatment, root TN contents increased by 1.5% under the TN treatment but decreased by 7.0% and 15.4% under the TS and TNS treatments, while those in leaves in the TN, TS and TNS treatments increased by 10.4%, 1.9% and 7.9%, respectively. Among different treatments, the N stocks in the plant-soil system in the TN and TS treatments were much higher, while that in the TNS treatment was the lowest. Under different N and S loading conditions, P. australis adapted to environmental variations by altering its N uptake and translocation strategies. It adopted a "limited uptake and limited translocation" strategy under high N loading condition, a "massive uptake and massive translocation" strategy under high S loading condition, and a "limited uptake but massive translocation" strategy under high N and S loading condition.
To clarify the distribution pattern of Quercus variabilis under the combined influences of climate change and habitat fragmentation, we used 87 occurrence records and 9 climatic variables, and applied the MaxEnt model and ArcGIS to simulate the current and future suitable habitats of Q. variabilis across China under SSP126 and SSP585 scenarios. The results showed that the MaxEnt model performed well (area of receiver operating characteristic curve was 0.884). The main climatic factors influencing the distribution of Q. variabilis were the minimum temperature of the coldest month, precipitation of the wettest quarter, maximum temperature of the warmest month, and mean temperature of the wettest quarter. During 1970-2000, the area of highly suitable habitats for Q. variabilis was approximately 104.92×104 km2, mainly located in central, eastern, southwestern, and southeastern China. Under both SSP126 and SSP585 scenarios, the total suitable habitat expanded during 2021-2100, but its structure shifted. The highly suitable area declined significantly (by 44.8%-50.5%), moderately suitable habitat also contracted (by 1.7%-34.4%) except for a slight increase under SSP126 during 2021-2040, and poorly suitable area increased (by 41.7%-104.5%). This trend suggested the increases in fragmentation and the decline of overall suitability of Q. variabilis habitats. The findings would provide scientific guidance for region-specific management and the long-term sustainable utilization of Q. variabilis resources.
Cable bacteria are a type of filamentous conductive microorganisms with the capacity of centimeter level long-range electron transfer (LDET). As a "biological cable" in the environment, they play a key role as geochemi-cal engineers in the rhizosphere microenvironment. They also drive a series of interconnected redox reactions by constructing a unique bioelectrochemical network that connects root oxygen secretion (ROL) with deep sulfide oxidation. We reviewed the colonization patterns of cable bacteria in the rhizosphere and their interactions with plants. LDET could efficiently drive the in-situ formation of sulfide detoxification and iron oxide barriers, deeply couple the sulfur iron phosphorus cycle, significantly reduce methane emissions from ecosystems such as rice paddies, and improve phosphorus fixation and retention efficiency. The interaction between cable bacteria and plants has great potential for applications in organic pollutant degradation, heavy metal stabilization, ecosystem restoration, and greenhouse gas emission reduction. This review would provide new research ideas and theoretical references for deepening the understanding of microbial-plant symbiosis and transforming it into ecological engineering strategies, such as rhizosphere remediation and greenhouse gas emission reduction.
Open-pit coal mines are widely distributed in China, and the ecological monitoring of which is susceptible to geographical heterogeneity in hydrothermal conditions, vegetation types, and soil characteristics. To achieve accurate cross-regional monitoring, it is essential to evaluate the applicability of ecological quality assessment indices. Based on Google Earth Engine and uniform Landsat 8 OLI data, we used the standardized remote sensing ecological index (RSEIs), land surface ecological status composition index (LSESCI), new remote sensing-based ecological index (RSEInew), and surface coal mine ecological index (SurMEI) to evaluate ecological quality of open-pit coal mines in four typical climatic-geomorphologic zones (arid Gobi, semi-arid grassland, semi-arid plateau, and karst plateau). Through comparative analysis of the evaluation results of four indices, combined with correlation analysis, spatial distribution identification, land cover response assessment, and validation with synchronous field measurement data from typical mining areas, we analyzed the adaptability and limitations of each index in open-pit coal mines across different climatic-geomorphologic zones. The results showed that the ecological quality assessment based on SurMEI exhibited the best performance (mean correlation coefficient of 0.810) in the four typical climatic-geomorphologic zones, demonstrating good adaptability in cross-regional evaluations. The performance disparities among the other three indices arose from mismatches between their index structures and regionally dominant ecological processes. RSEIs overestimated disturbances in humid areas due to excessive sensitivity to the normalized difference bare soil index. LSESCI exhibited misclassification in complex terrains, owing to instability of the brightness component derived from the tasseled cap transformation. RSEInew failed in arid zones because of the weak discriminative power of its added PM2.5 indicator. Furthermore, spatiotemporal analysis based on SurMEI revealed significant differences in ecological restoration potential across different climatic-geomorphologic zones, with the highest potential in the karst plateau, intermediate in the semi-arid region (including grassland and plateau), and the lowest in the arid Gobi, indicating that ecological restoration should follow the principle of "zonal management". This study would provide a theoretical basis for accurate monitoring of ecological quality and model optimization for open-pit coal mines across different climatic-geomorphologic zones.
We collected tree core samples from the dominant tree species Pinus armandii, Pinus tabuliformis, and Quercus aliena var. acuteserrata in the Dagou watershed of Maoxian County, Sichuan. Combined with long-term meteorological observation data from the Maoxian Mountain Ecosystem Positioning Research Station of the Chinese Academy of Sciences (CAS), we developed standard tree-ring width chronologies to investigate the radial growth responses of these species to climatic factors over the period of 1989-2021. There were significant increasing trends in both annual mean temperature and mean minimum temperature in the Dagou watershed over the past 40 years, with the most pronounced warming in spring. Annual precipitation exhibited a fluctuating decline from 1989 to 2006, and then it increased significantly at a rate of 28.26 mm·a-1, while winter precipitation decreased markedly. P. armandii and P. tabuliformis exhibited similar trends in basal area increment (BAI), increasing steadily before 2014 at average rates of 146.68 and 169.41 mm2·a-1, respectively, after which both species showed a decreasing trend. In contrast, the BAI of Q. aliena var. acuteserrata continued to increase rapidly, with a maximum average growth rate of 164.23 mm2·a-1 over the period 1998-2018. The radial growth of P. armandii was primarily influenced by temperature, showing significant positive correlations with the mean minimum temperature in July and August of the previous year, as well as with both the mean minimum temperature and mean temperature in March and April of current year. Additionally, its growth was significantly positively correlated with precipitation during the current growing season, specifically in June and September. The radial growth of P. tabuliformis showed a significant negative correlation with the mean maximum temperature in June of the current year and with precipitation during the previous growing season (June-October). The radial growth of Q. aliena var. acuteserrata was significantly positively correlated with mean temperature in spring and maximum temperature in summer, but negatively corre-lated with winter precipitation. In the context of continuously increasing temperatures, particularly the rising spring temperatures and declining winter precipitation, Q. aliena would exhibit stronger growth adaptability and competitive potential.
Based on data from 79 plots of Larix gmelinii secondary forests in the Greater Khingan Mountains, we classified the developmental stages of the stands by using affinity propagation clustering algorithm with thirteen eva-luation indicators from three aspects including tree species diversity, non-spatial structure, and spatial structure characteristics. We then compared the consistency of the full indicator set (13 indicators) and the minimum indicator set (five indicators) in evaluating stand structural complexity across different developmental stages by radar chart analysis. The main aim was to provide a theoretical basis for full-cycle multi-functional management of L. gmelinii secondary forests in the Greater Khingan Mountains. The results showed that the key indicators constraining stand structural complexity were mean diameter at breast height (Dg), dominant tree height (Ht), density degree (C), stand density (N), and stand volume (V), totaling five indicators. The affinity propagation clustering algorithm divided all plots into three developmental stages (Stage 1, Stage 2, and Stage 3). Among these, Dg and C showed a significant increasing trend with developmental stage, while N and Ht exhibited a significant decreasing trend. There was a significant difference of V between Stage 2 and Stage 3. The evaluation results based on the full indicator set showed that the stand structural complexity indices for the three developmental stages were 0.18, 0.22, and 0.31, respectively, while those based on the minimum indicator set were 0.10, 0.15, and 0.26. The indices obtained from the two methods were positively correlated (r>0.55). Using the minimum indicator set as the stan-dard, only one indicator performed well in Stage 1 (N) and Stage 2 (Ht), while three indicators (Dg, Ht, and V) reached the standard in Stage 3. Therefore, the structural complexity of L. gmelinii secondary forests gradually increased across developmental stages. The evaluation method based on the minimum indicator set showed high reliability and could provide targeted measures for forest management according to the key factors constraining structural development at each stage.
Under the backdrop of global climate change, the frequent occurrence of combined disasters of high temperature and drought poses severe challenges to food security, ecological environment, and sustainable socio-economic development. Based on the meteorological observation data from 1971 to 2024, we constructed an intensity index by combining the nested Copula model with the random forest algorithm, and analyzed the spatiotemporal variations, recurrence interval characteristics and intensity evolution law of compound high-temperature and drought disasters in Liaoning Province by coupling GIS technology. The results showed that the occurrence frequency of compound high-temperature and drought disasters presented a pattern of being high in the west and low in the east. The western region was a continuously expanding and intensifying high-frequency agglomeration area, while the eastern and coastal areas remained a stable low-frequency area for a long time. Compound high-temperature and drought disasters in Liaoning Province were dominated by short recurrence interval (0-2 years) events, which featured with high occurrence frequency and strong spatial agglomeration. The western region as the core high-incidence area. With the extension of the return period, the occurrence scope of disasters shrank sharply and the frequency decreased, and long recurrence interval events were only sporadically distributed in the western region. The intensity of single events experienced a phased evolution of weak occurrence-initial increase-rapid increase-maintenance-attenuation, peaking in the 1990s. The cumulative intensity gradually evolved from a pattern of single low-value agglomeration in the western region in the 1970s to a dual high-intensity agglomeration pattern in the western and central regions in the 2010s. The intensity of the core western region reached its peak at the end of the study period. In summary, the western region of Liaoning Province was the core affected area of compound high-temperature and drought disasters, and the disaster intensity showed an increasing trend. This study could provide a scientific basis for the formulation of disaster prevention and mitigation strategies and risk management in Liaoning Province.
Carrying out risk division of carbon sink losses caused by heat damage in tea plantation is of great signi-ficance for regional carbon balance and the sustainable development of tea industry. With tea plantations in Fuding City of Fujian Province as an example, we retrieved distribution information of tea plantations based on GF-7 sate-llite images, and integrated carbon sink data, temperature, elevation, and socioeconomic data to build a risk indicator system. The system covered the exposure of the disaster environment, the vulnerability of carbon sink, the risk of the disaster factor, and the disaster prevention and mitigation ability. By combining the entropy weight method, the analytic hierarchy process method and the comprehensive weighted method, we constructed a heat damage risk assessment model to achieve a refined division of carbon sink loss risk for tea plantation under heat damage. The results showed that the accuracy of tea plantation distribution information extracted from GF-7 satellite images was relatively high, with a producer's accuracy of 76.4% and a user's accuracy of 78.6%. The fitting degree between the remote sensing estimated tea plantation area and the records of statistical yearbook reached a significant level (R2=0.89). In the heat damage risk assessment model, the risk of the disaster factor had the maximum weight value (0.62), followed by the exposure of the disaster environment (0.21) and the vulnerability of carbon sink (0.16), while the disaster prevention and mitigation ability had the minimum weight value (0.01). Areas where the risk disaster factor reached severe level and above mainly distributed in the central, northern, southeastern and some southern parts of the study area. Those of exposure of the disaster environment were mainly located in the central, southeastern, eastern and some northern parts, and those of the vulnerability of carbon sink and extremely strong disaster prevention and mitigation ability were mainly in the central and southeastern regions. The risk area of carbon sink losses for tea plantation under heat damage could be divided into four levels, with low, moderate, high, and extremely high accounting for 18.4%, 27.0%, 29.8%, and 24.8% of the total tea plantation area, respectively. The areas with low and moderate risks were mainly distributed in the eastern coastal and high-altitude inland regions, while areas with high and extremely high risks were mostly located in low-altitude inland regions. The study could help relevant departments accurately identify the potential risks of carbon sink losses and thus provide a scientific basis for carbon sink management in tea plantations.
Accurate estimation of evaporation from small water bodies is critical for addressing water resource losses induced by global climate change. Based on hydrogen and oxygen stable isotope data and meteorological observations collected from a fish pond in Anhui Province during 2020-2022, we calculated the monthly evaporation from the pond using a stable isotope mass balance model and the eddy covariance (EC) method. With the EC observations as a reference, we evaluated the applicability of different parameterization schemes for kinetic fractionation. The results showed that monthly evaporation from the fish pond exhibited seasonal variability: summer (99.6±37.3 mm) > spring (89.4±26.5 mm) > autumn (58.7±21.7 mm) > winter (20.6±4.5 mm). The results of the isotope mass conservation model based on different isotopes and parameterization schemes were generally consistent with the observations. The applicability of deuterium (2H) was better than that of oxygen-18 (18O) in summer and autumn, while 18O performed better in spring and winter. Overall, 2H showed better performance. Among different parameterization schemes, the ocean-based scheme was generally superior to the lake-based scheme. The localized parameterization scheme based on 18O and developed using pond observation data (Pearson correlation coefficient=0.85, root mean square error=18 mm) outperformed both the traditional lake-based and ocean-based parameterization schemes. These findings provide important theoretical support and methodological references for enhancing the accuracy and reliability of stable isotope techniques in calculating evaporation from small water bodies.
As an important component of inland waters, shallow lakes are hotspots for CO2 emissions. Due to the influence of eutrophication and aquatic macrophyte, CO2 fluxes at the water-air interface of shallow lakes exhibit complex variability, posing challenges for high-accuracy simulation. To compare the performance of different machine learning models in simulating CO2 fluxes in shallow lakes, we focused on a floating-leaved vegetation zone in eastern Lake Taihu. Based on CO2 flux observations from an eddy covariance system, combined with meteorological, water quality, and vegetation variables, we developed four machine learning models, random forest (RF), support vector machine (SVM), backpropagation neural network (BPNN), and long short-term memory network (LSTM). Then, we evaluated the performance under three modeling scenarios, including growing season, non-growing season, and whole-season. Among the three modeling scenarios, the whole-season modeling approach achieved the best overall performance, with test-set metrics consistently outperforming those of the seasonal models. The RF model exhibited the highest accuracy and robustness under all the three scenarios. In the whole-season mode-ling scenario, the RF model achieved a coefficient of determination (R2) of 0.72 and a root mean square error (RMSE) of 0.57 μmol·m-2·s-1. For the growing-season model, the RF performance yielded an R2 of 0.64 and an RMSE of 0.88 μmol·m-2·s-1, while in the non-growing-season model, the R2 and RMSE were 0.61 and 0.43 μmol·m-2·s-1, respectively. The SVM and BPNN models showed comparable but inferior performance, whereas the LSTM model performed relatively poorly. Furthermore, we used recursive feature elimination (RFE) to identify the optimal combination of driving factors for the RF model under the whole-season scenario. The selected feature set included: surface water temperature (Tw_20), sediment temperature (Ts), dissolved oxygen (DO), air tempera-ture (Ta), incoming shortwave radiation (Rs_in), wind speed (WS), total nitrogen (TN), water pH, friction velocity (u*), and normalized difference vegetation index (NDVI). This feature set further improved simulation accuracy (R2=0.76, RMSE=0.55 μmol·m-2·s-1) and effectively reduced model complexity. The SHAP analysis showed the significant influences of water temperature, radiation, dissolved oxygen, and vegetation index on CO2 fluxes. The results would provide a useful methodological reference for CO2 flux modeling and carbon cycle studies in shallow lakes.
Biofilms play a crucial role in regulating the behavior of microplastics (MPs) in aquatic environments, yet their dynamic mechanisms have often been overlooked in traditional models. We reviewed the formation mechanism of biofilm and its impact on the sedimentation behavior of MPs. Upon entering water, MPs are rapidly colonized by microorganisms, forming biofilm structures composed of extracellular polymeric substances (EPS) and microbial communities. This process is influenced by exposure time, environmental conditions, and the intrinsic properties of MPs. Biofilms significantly affect MPs sedimentation and vertical distribution by increasing their effective density, promoting aggregation, and altering surface properties, challenging the applicability of prediction models based solely on physical attributes. Microorganisms (particularly microalgae) and their EPS secretions are key factors driving sedimentation differences. We further summarized current research progress on biofilm-MPs interactions, their applications, and limitations. Future research should focus on the following areas. Mechanistically, we should develop multiscale models that integrate biofilm dynamics with hydraulic conditions. Methodologically, we should advance in-situ observation techniques to quantitatively characterize biofilm properties such as EPS composition and community function. From an application perspective, we should explore bioremediation strategies that use functional microorganisms, such as specific algae or bacteria, to control MPs sedimentation.
The karst region of Southwest China is characterized by extensive bedrock exposure and limited soil cover, forming a dual structure of soil and water. Such unique geological background makes epikarst water a critical water source for plants, while bedrock lithology is the key factor driving the development and water storage capacity of epikarst zone. Under the combined impacts of extreme climate events and frequent karstic drought, the sustainability of vegetation restoration faces severe challenges. It is crucial to clarify the influences of lithology-regulated water supply on plant adaptation strategies. We summarized research progress in the regulation of karst bedrock lithology on plant water source, transport, and utilization strategies. Lithology determines the water storage capacity of bedrock. Limestone features well-developed fractures and fissures that could store abundant water sources, while dolomite' dense structure hinders bedrock fissure development, resulting in weaker water storage capacity. Rock moisture serves as a crucial water source for karst plants. Dominant species in limestone habitats typically possess deep root systems that capable of accessing karst aquifer water. Their hydraulic regulation tends to be "anisohydric strategy", wherein plants maintain stomatal opening under drought stress to gain carbon benefits. Dolomite hillslopes are dominated by shallow-rooted herbaceous plants and shrubs relying primarily on recent precipitation or shallow soil water sources. Their water utilization exhibits a "short water age" pattern, where water remains for a short duration during storage and transport in the root zone. Karst plants cope with drought stress through water-conserving strategies, including switching water source depth, separating hydrological niches, and enhancing water use efficiency. Compared to plants on limestone-derived landscapes, shallow-rooted deciduous shrubs growing on dolomite-developed hillslopes face heavier drought mortality risks. Future research should quantitatively characterize the coupling processes and feedback mechanisms among lithology, water availability, and plant adaptation. This could provide scientific basis for assessing vegetation dynamics and achieving high-quality vegetation restoration in ecologically fragile karst ecosystem.
Accurate quantification of the value of water conservation function is crucial for assessing the effectiveness of ecological restoration and promoting the realization of ecological product value. Shanxi Province, characteri-zed by fragile ecosystems, has been a key implementation area for national ecological projects. However, precise economic valuation of the ecological restoration outcomes in Shanxi Province remains limited. To address this, we integrated the InVEST model, Monte Carlo model, and a shadow project approach incorporating geomorphic zoning-based differential pricing to analyze the spatiotemporal variations of water conservation capacity and its economic value in Shanxi Province from 2005 to 2023. The driving mechanisms were further investigated using Geodetector and principal component analysis. The results showed that the total water conservation capacity in the province showed an overall increasing trend from 2005 to 2023, with the increase in its economic value (66.8%) being much higher than the increase in water conservation capacity (8.7%). Water conservation capacity exhibited a spatial pattern of higher in the south and lower in the north, whereas the growth rate of water conservation value pre-sented characteristics of higher in the north and lower in the south, rising in the west and slow in the east. The Lyuliang mountainous area in western Shanxi and the northern region showed particularly significant growth, with the strongly eroded loess ridge-hill area recording the highest value increase of 91.9%. The dominant factors and interactive mechanisms driving spatial variations of water conservation value demonstrated clear geomorphic dependence. In transitional geomorphic zones with intensive human activities, the synergistic effect between normalized difference vegetation index, and construction land played a dominant role. In ecologically vulnerable loess zones and the arid areas of northern Shanxi, the climate-vegetation synergy served as the key regulatory mechanism. Artificial ecological projects had generated significant marginal value returns in ecologically fragile zones. Future ecological management should shift towards a "zonal governance, precision enhancement" strategy. The high-value-return areas such as western and northern Shanxi should focus on improving vegetation cover and grassland quality. The high-capacity areas such as southeastern Shanxi should strengthen ecological space protection and structural optimization. At the provincial scale, the ecological space baseline must be strictly maintained to systematically enhance water conservation value.
The optimization of ecological networks based on the coupling of natural and human systems is of great significance for mitigating the fragmentation of ecological spaces and the disconnection of human landscape spaces in old industrial cities. Taking Shenyang City as a case, we identified ecological sources using morphological spatial pattern analysis and landscape connectivity analysis. We integrated historical landscapes and industrial heritage as human sources, applied the circuit theory to construct three types of spatial networks: ecological, historical landscape, and industrial heritage networks. We further employed topological structure analysis and the coupling coordination degree model to analyze the structural characteristics and spatial coupling relationships among these three networks and propose optimization strategies. The results showed that the ecological network structure of Shenyang City was relatively fragile, with network closure (α), connectivity (β), and connection rate (γ) indices being 0.30, 1.48, and 0.54, respectively. A total of 16 ecological sources were identified, with a corridor density of 0.07 km·km-2. There were 38 ecological key points and 115 ecological interference points. The historical landscape network structure was intermediate, with the three indices being 0.35, 1.66, and 0.57, respectively, exhibiting a polycentric radial pattern spatially. The industrial heritage network structure was relatively stable, with the three indices being 0.38, 1.70, and 0.59, respectively, showing a high concentration of industrial heritage in Tiexi District. The coordination degree of the three networks decreased from the center to the periphery. Interaction hotspot areas were all located within the Third Ring Road of Shenyang City, where the ecological-historical landscape hotspot areas highly overlapped with the composite hotspot areas of the three networks. After optimization, the number of ecological sources increased to 33, and the area increased 144.31 km2. Corridor density increased to 0.15 km·km-2, while the α, β and γ indices improved by 37.7%, 19.1%, and 11.2%, respectively, compared to those of the pre-optimization ecological network. This study validated the feasibility of integrating historical landscape conservation and ecological restoration of industrial brownfields into ecological networks, which would provide a quantitative scientific basis for old industrial cities to implement "multi-plan integration" and achieve systematic governance and collaborative optimization of ecological, historical, and industrial spaces within territorial spatial planning.
Estuarine wetlands play a crucial role in maintaining ecosystem services through carbon and nitrogen cycling. However, escalating nitrogen inputs from human activities, coupled with global environmental change, are altering carbon uptake, turnover, and storage. We synthesized current knowledge on the biogeochemical responses of estuarine wetlands to increasing nitrogen loading, with emphasis on the interactive pathways linking soil/sediment, vegetation, and atmosphere. Based on the understanding of key coupling processes, we discussed potential implementation pathways from a nature-based solutions (NbS) perspective to enhance nitrogen reduction and carbon sequestration, aiming to provide a scientific reference for mitigating coastal eutrophication and improving the carbon-sink function of estuarine wetlands.
Suzhou urban water network is a typical plain river-lake composite system regulated by sluices. To clarify the spatiotemporal variations and driving mechanisms of phytoplankton functional groups (PFGs) under intensive anthropogenic interference, we conducted a systematic investigation across 102 sampling sites, including backbone rivers, urban rivers, and lakes, during flood and non-flood seasons from 2022 to 2023. Results showed that a total of 198 species belonging to 8 phyla were identified, with Chlorophyta (42.4%), Bacillariophyta (25.2%), and Cyanophyta (13.1%) as the dominant phyla. Fourteen dominant functional groups were identified, including eutrophic mesothermal (M), mixed eutrophic (P), clear-water diatom (J), stagnant-water planktonic (T), stagnant-water mixotrophic (Y), turbidity-tolerant (D), and mesotrophic (B). The average density and biomass were 6.87×105 cells·L-1 and 0.09 mg·L-1, respectively. Phytoplankton biomass in the Suzhou water network exhibited significant spatiotemporal heterogeneity, being generally higher in the flood season than in the non-flood season. Y, D, T, P, and B were the core dominant groups across the entire region. The seasonal succession was driven by the dual effects of natural rhythms and artificial regulation. The flood season was dominated by the high-nutrient-tolerant groups M and Lo, while the non-flood season shifted toward T and B, which were adapted to mesothermal and stable habitats. The extreme heat event in 2022 significantly enhanced the year-round dominance of thermophilic groups Y and T. Results of structural equation modeling (SEM) indicated that phytoplankton biomass in lakes was primarily driven by water temperature and total phosphorus, whereas electrical conductivity exerted a more significant influence on rivers. This study elucidated the response mechanisms of phytoplankton functional groups to multiple environmental stressors in subtropical urban river networks, providing a theoretical basis for the ecological management of sluice-controlled river-lake systems.
Stable isotope bioarchaeology is an important area of bioarchaeological research, which can provide direct evidence for reconstructing the trophic positions and dietary sources of ancient humans and animals. Compared with bulk bone collagen isotope analysis, compound-specific isotope analysis of amino acids (CSIA-AA) can effectively overcome the inherent limitations of the traditional method in terms of isotopic baseline variation, fertilization effects, and physiological stress, ensuring more precise determination of the trophic positions and food resource utilization of ancient humans and animals. This method has become a research frontier in international bioarchaeology, but its application remains underdeveloped in China. To address this gap, we introduced the fundamental concepts and analytical principles of CSIA-AA, illustrated its unique advantages in paleodietary reconstruction through representative case studies, and reviewed advances in areas such as the optimization of trophic position estimation parameters, machine learning assisted dietary discrimination, and the reconstruction of individual life histories. Finally, we provided a prospective outlook on the application of CSIA-AA in archaeological research in China.
Identifying the primary agrometeorological disasters affecting single-season rice in the middle and lower reaches of the Yangtze River, as well as their occurrence patterns and responses to large-scale circulation indices, can provide scientific basis for ensuring secure rice production. We used bibliometric analysis to determine the main types of agrometeorological disasters, critical growth stages, and identification methods for single-season rice in the region. With observational data from 274 meteorological stations and circulation indices from 1990 to 2020, we comparatively analyzed the occurrence characteristics of major disasters during key growth periods and their relationships with circulation factors. Results showed that heat stress, chilling damage, flood, and drought were the dominant agrometeorological threats to single-season rice in the area. Among these factors, heat stress events occurred most frequently, with an average incidence of 1.04 events per year. Spatially, the distribution patterns of severe heat stress and drought zones were consistent, primarily concentrated in the southeastern and southwestern parts of the study area. In these regions, heat stress intensity frequently exceeded 34 °C·d, while drought intensity surpassed 1.94 with chilling damage and flood incidents being less severe. Interannual trend analysis revealed that 92% of stations experienced increased frequency of heat stress events, with magnitude of change exceeding that of the other three disaster types. Chilling damage and drought events showed significant decreasing trends in northeastern areas, with reduction rates generally exceeding 0.25 events per decade. The number of stations reporting increased versus decreased flood occurrences was nearly equivalent. Among the four disaster types, heat stress occurrences had the strongest correlation with the Western Pacific subtropical high intensity, showing significant positive correlations at 52.6% of stations. Chilling damage events generally exhibited negative correlation with high intensity, though only 7.7% of stations reached statistical significance. Responses of flood and drought to this circulation index displayed contrasting patterns between southern and northern regions. These findings would provide valuable insights for guiding single-season rice production strategies in the middle and lower Yangtze River basin.
Tropical forest soil is a significant source of N2O emissions, exerting a notable impact on global climate change. Due to the complex environmental conditions (high temperature, high humidity, intense precipitation, and strong leaching) in tropical regions, coupled with strong spatial heterogeneity and insufficient observational data, there remains significant uncertainty regarding the N2O emission flux from tropical forest soil and microbial regulation mechanisms. We reviewed the characteristics of N2O emissions from tropical forest soil, the mechanisms by which soil microorganisms produce N2O, influencing factors, as well as the advancements in the application of stable isotope techniques for N2O source apportionment and process quantification. N2O emissions from tropical forest soil exhibit notable spatiotemporal heterogeneity. The production processes primarily involve autotrophic/heterotrophic nitrification, denitrification, and their coupled processes, and are synergistically regulated by factors such as soil moisture, temperature, pH, carbon and nitrogen substrate availability, and microbial community structure. Stable isotope tracing techniques (15N natural abundance method, 15N labeling method, isotopic isotopomer method) can effectively distinguish the contributions of different microbial processes to N2O production, evolving from qualitative identification to precise quantitative analysis, providing crucial support for refining the nitrogen cycle theory in tropical forests. Currently, most studies focus on short-term and small-scale experiments. There are uncertainties regarding isotope fractionation parameters and model applicability, limiting their application in assessing N2O flux at the regional scale and in global tropical regions. In the future, it is essential to strengthen the integration of multi-temporal and multi-spatial scale in situ observations with stable isotope techniques, integrating molecular biology, machine learning, and process modeling methods to explore the mechanisms of different microbial processes. This would enhance our understanding of the mechanisms underlying N2O emission from tropical forest soil and its response to global change, providing a scientific basis for precise reduction of greenhouse gas emissions in the tropics.
