Spatial heterogeneity of estuarine wetland ecosystem health influenced by complex natural and anthropogenic factors

A spatial machine learning approach to exploring the impacts of coal mining and ecological restoration on regional ecosystem health

Ecosystem health is an important approach to measuring urban and regional sustainability. In previous studies, the spatiotemporal changes of ecosystem health have been addressed using comprehensive assessment index system. However, the quantitative contribution of human activities and climate change to ecosystem health was less examined. In this study, Shuozhou City, a coal resource-based city, was chosen to explore the response of ecosystem health to human activities using the Geographically Weighted Artificial Neural Network (GWANN) model. The results showed a distinct improvement of ecosystem health in Shuozhou City from 1990 to 2020. The contribution of human activities increased during the study period, while the contribution of climate change decreased as a consequence of coal mining expanding. By 2020, human activities contributed 76% to ecosystem health compared with 24% of climate change. The direct impact of coal mining on ecosystem health occurred mainly in the surrounding areas within a radius of 6 km and 17 km under low and high mining intensity respectively. Ecosystem health will further decline by 2030 based on the scenario in which current coal mining is continued. However, only stopping mining activities in small coal mining areas for ecological restoration but keeping large coal mining areas in production, will realize 92.6% of restoration effects on ecosystem health as compared to ceasing all mining activities. This study examines the effects of coal mining on ecosystem health in resource-based cities, and underscores the importance of large coal mining sites in ecological restoration.

Simplification and simulation of evaluation process for low efficiency constructed wetlands based on principal component analysis and machine learning

The existing performance evaluation process of constructed wetlands (CWs) is complex, with shortcomings in both simplification of method and construction of simulation model, especially for low-efficiency CWs (LECWs, with an average close-degree calculated by the entropy weight method being <0.6). This study presents a case study of LECWs in the Ningxia region (comprising 13 subsurface flow constructed wetlands (SSF CWs) and 7 surface flow constructed wetlands (SF CWs)), employs the entropy weight method (EWM) to construct an evaluation of CW operational efficiency, simplifies evaluation indicators through principal component analysis (PCA), develops two random forest (RF) models to validate the rationality of the simplified indicators, and establishes simulation models by logistic regression (LR). The results demonstrate that the evaluation indicators of CWs can be simplified to chemical oxygen demand (COD) and total nitrogen (TN), with no significant difference observed between the evaluation results and the original model (P < 0.05), thereby indicating reliability. Moreover, the simulation model performs well with R2 values for fitting SSF CWs and SF CWs exceeding 0.8. According to the simulated results of the model, the operational efficiency of LECWs is more significantly affected by the COD removal rates compared to the TN removal rates. In comparison to influent with 0 < COD/TN < 3 and 5 < COD/TN < 8, the operational efficiency of SSF CWs and SF CWs is optimal when COD/TN is between 3 and 5. These research findings may provide valuable support for streamlining evaluation processes and daily management for LECWs.

Exploring the evolution of ecosystem health and sustainable zoning: A perspective based on the contributions of climate change and human activities

Maintaining ecosystem health (EH) in watersheds is crucial for building a national pattern of ecological security. However, a comprehensive diagnosis of watershed EH and an exploration of its driving mechanisms are still lacking. This study proposed an EH assessment model from a vitality-organization-resilience-service-environment (VORSE) perspective. Taking the Yellow River Basin of Shaanxi Province (YRBS), China, as a research object, the spatiotemporal evolution trend of EH from 2000 to 2020 was quantified. At the same time, we also quantified the respective contributions of climate change (CC) and human activities (HA) to the EH dynamics based on residual analysis. The results showed that EH in the YRBS increased by 11.80 % from 2000 to 2020, and the spatial distribution of the EH was higher in the southern region than in the northern part. At the pixel scale, areas with improving trends accounted for 90.57 % of the YRBS, while 9.43 % deteriorated, with the improving areas mainly in northern Shaanxi and the deteriorating areas in the Guanzhong region. The correlation between the EH and precipitation was primarily positive, while the correlation between the EH and temperature was mainly negative. The residual analysis showed that the contribution rate of CC to EH changes was 78.54 %, while that of HA was 21.46 %, indicating that CC was the dominant driver of EH changes in the YRBS. Specifically, 82.64 % of the improvement in EH was attributed to CC and 17.36 % to HA. Conversely, 65.30 % of the deterioration in EH was attributed to CC and 34.70 % to HA. Furthermore, CC, HA, and CC&HA dominated EH changes in 26.85 %, 3.77 %, and 69.38 % of the YRBS area, respectively. In addition, the Hurst exponent analysis identified six types of future EH development scenarios, each requiring different restoration strategies. This study provides valuable insights for future EH diagnosis, EH restoration efforts, and the formulation of sustainable development goals in other watersheds.

The past and future dynamics of ecological resilience and its spatial response analysis to natural and anthropogenic factors in Southwest China with typical Karst

With the global land use/land cover (LULC) and climate change, the ecological resilience (ER) in typical Karst areas has become the focus of attention. Its future development trend and its spatial response to natural and anthropogenic factors are crucial for understanding the changes of ecologically fragile areas to human behavior. However, there is still a lack of relevant quantitative research. The study systematically analyzed the characteristics of LULC changes in Southwest China with typical Karst over the past 20 years. Drawing on the landscape ecology research paradigm, a potential-elasticity-stability ER assessment model was constructed. Revealing the characteristics and heterogeneity of the spatial distribution, annual evolution, and development trend of ER in the past and under different scenarios of shared socioeconomic pathways and representative concentration pathways (SSP-RCP) in the future. In addition, the spatial econometric model was utilized to reveal the spatial effect response mechanism of ER, and adaptive development strategies were proposed to promote the sustainable development of Southwest China. The study found that : (1) In the past 20 years, the LULC in Southwest China showed an accelerated change trend, the ER decreased declined in general, and there was significant spatial heterogeneity, showing the spatial distribution pattern of "west is larger than east, south is larger than north, and reduction in the west was slower than that in the east." (2) Under the same SSP scenario, with the increase of RCP emission concentration, the area of the lowest-resilience increased significantly, and the area of the highest-resilience decreased. (3) The woodland was the largest contributor to ER per unit area in the Southwest China, and grassland was the main LULC type, which had a prominent impact on the ER of the study area. (4) The average precipitation and the normalized difference vegetation index (NDVI) were significant natural drivers of ER in the study area, and the economic growth, innovation, and optimization of industrial structure contributed to the ER of Southwest China. Overall, the integration of quantitative assessment and multi-scenario-based modeling not only provides new perspectives for understanding the pattern of change and response mechanisms, but also provides valuable references for other typical Karst regions around the world to achieve sustainable development.

Pollution levels and potential ecological risks of trace elements in relation to bacterial community in surface water of shallow lakes in northern China before and after ecological water replenishment

Ecological water replenishment is a crucial and effective measure to improve the water quality and ecological function of lakes. However, the effects of ecological water replenishment on the pollution characteristics and ecological risks of trace elements and bacterial communities in lake surface water are still kept unclear. We investigated the pollution levels and potential ecological risks for trace elements, as well as variation of the bacterial community in surface water in the BYD lake before and after ecological water replenishment. Our results revealed that higher levels and pollution indexes (Igeo) of trace metals (e.g., As, Cd, Co, Cu and Ni; p < 0.05) after ecological water replenishment were observed than before ecological water replenishment and their total potential ecological risk (∑RI) were increased. In contrast, the network complexity of these trace elements, including nodes, edges, average diameter, modularity, clustering coefficient and average pathlength showed a decrease after ecological water replenishment than before. The diversity (community richness, community diversity and phylogenetic diversity decreased) and community structure of the bacterial community in the surface water (p < 0.05) were greatly changed after ecological water replenishment than before, with the increase in heavy metal-resistant phylum (e.g., Acidobacteriota). Moreover, the concentration of trace elements and ∑RI were significantly correlated with the alpha diversity of bacterial community, as well as dissolved organic carbon (DOC) and ORP, after ecological water replenishment. The findings indicate that it is very necessary to continuously monitor trace metal pollution levels and heavy metal-resistant phylum and identify their potential pollution sources for water environment control and lake ecosystem health.

Mapping and assessment of ecosystem health in the Vilnius functional zone (Lithuania)

Urban expansion is a global phenomenon that impacts biodiversity loss and climate change. Soil sealing increases land degradation and the ecosystem services supply. This degradation also negatively affects ecosystem health, essential to make cities more sustainable and liveable. This work aims to study the ecosystem health spatiotemporal evolution (1990, 2000, 2006, 2012 and 2018) in the Vilnius (Lithuania) functional zone, using the vigour, organisation and resilience (VOR) method. The results showed that ecosystem health model validation was acceptable (r = -0.761; p < 0.01). Between 1990 and 2018, an increase (18.37 %) in ecosystem vigour was observed. The values were significantly higher in 2006, 2012 and 2018 than in 1990 and 2000. We identified a decrease between 1990 and 2018 regarding ecosystem organisation (7.15 %) and resilience (9.92 %). However, no significant differences between the years were identified. Ecosystem health decreased (11.49 %) between 1990 and 2018, mainly between 2012 and 2018. Ecosystem health values in 2018 were significantly lower than those identified in the previous years. The lowest values of ecosystem vigour, organisation and resilience were identified in the Vilnius city centre, while the highest was observed in the Vilnius functional zone. From 1990 to 2018, ecosystem vigour increased in some elderships located on the fringe of the studied area due to land abandonment and forest plantations. Simultaneously, a decrease in ecosystem organisation and resilience in the elderships located in Vilnius city centre was observed due to urban sprawl and the consequent landscape fragmentation. This negatively impacted ecosystem health, overshadowing the positive trend observed in ecosystem vigour. Different processes (e.g., urban sprawl, land abandonment, forest plantations) occurred in the Vilnius functional zone. It is essential to halt urban expansion and its adverse impacts on ecosystem health, city sustainability and liveability.

Wetland health, water quality, and resident perceptions of declining ecosystem services: a case study of Mount Abu, Rajasthan, India

Ecosystem services provided by wetlands are essential for communities living near wetlands, especially in an underdeveloped semi-arid landscape. The land use land cover changes and ecosystem degradation and water quality change over the past few decades have had immense effects on declining wetland ecosystem services. With the degradation, it is exerting superfluous effects on wetland communities including loss of livelihood, and decline in other wetland services like fishing, aquaculture, fuelwood, fodder, and many more. The present study attempts to assess the changing nature of wetland health, water quality, and declining ecosystem services of Mount Abu wetlands in Rajasthan, India. For assessing the change of wetland extent, we have used the remote sensing-based data for preparation of land use land cover change from 1992 to 2020. The water samples have been collected from the wetland, and different biophysical parameters of the water have been tested in the laboratory. A questionnaire-based household survey has been conducted to understand the perception of the wetland communities on the loss of ecosystem services over three decades. Further, a correlation and cluster assessment has been conducted to understand the degradation of wetland health in the selected wetlands. The study results indicated deteriorating conditions of wetland health and declining ecosystem services in the study area over the time periods. The land use land cover change analysis indicated a decrease in the spatial extent of the wetlands in the study area. Wetland communities are being affected due to the degradation of wetland health. The study recommended executing a wetland management plan for long-term conservation and livelihood management for the Mount Abu wetlands and communities.

Quantifying the spatial nonstationary response of influencing factors on ecosystem health based on the geographical weighted regression (GWR) model: an example in Inner Mongolia, China, from 1995 to 2020

The identification of ecosystem health and its influencing factors is crucial to the sustainable management of ecosystems and ecosystem restoration. Although numerous studies on ecosystem health have been carried out from different perspectives, few studies have systematically investigated the spatiotemporal heterogeneity between ecosystem health and its influencing factors. Considering this gap, the spatial relationships between ecosystem health and its factors concerning climate, socioeconomic, and natural resource endowment at the county level were estimated based on a geographically weighted regression (GWR) model. The spatiotemporal distribution pattern and driving mechanism of ecosystem health were systematically analysed. The results showed the following: (1) the ecosystem health level in Inner Mongolia spatially increases from northwest to southeast, displaying notable global spatial autocorrelation and local spatial aggregation. (2) The factors influencing ecosystem health exhibit significant spatial heterogeneity. Annual average precipitation (AMP) and biodiversity (BI) are positively correlated with ecosystem health, and annual average temperature (AMT) and land use intensity (LUI) are estimated to be negatively correlated with ecosystem health. (3) Annual average precipitation (AMP) significantly improves ecosystem health, whereas annual average temperature (AMT) significantly worsens eco-health in the eastern and northern regions. LUI negatively impacts ecosystem health in western counties (such as Alxa, Ordos, and Baynnur). This study contributes to extending our understanding of ecosystem health depending on spatial scale and can inform decision-makers about how to control various influencing factors to improve the local ecology under local conditions. Finally, this study also proposes some relevant policy suggestions and provides effective ecosystem preservation and management support in Inner Mongolia.

Estimating Soil Salinity with Different Levels of Vegetation Cover by Using Hyperspectral and Non-Negative Matrix Factorization Algorithm

Hyperspectral technology has proven to be an effective method for monitoring soil salt content (SSC). However, hyperspectral estimation capabilities are limited when the soil surface is partially vegetated. This work aimed to (1) quantify the influences of different fraction vegetation coverage (FVC) on SSC estimation by hyperspectra and (2) explore the potential for a non-negative matrix factorization algorithm (NMF) to reduce the influence of various FVCs. Nine levels of mixed hyperspectra were measured from simulated mixed scenes, which were performed by strictly controlling SSC and FVC in the laboratory. NMF was implemented to extract soil spectral signals from mixed hyperspectra. The NMF-extracted soil spectra were used to estimate SSC using partial least squares regression. Results indicate that SSC could be estimated based on the original mixed spectra within a 25.76% FVC (R2cv = 0.68, RMSEcv = 5.18 g·kg-1, RPD = 1.43). Compared with the mixed spectra, NMF extraction of soil spectrum improved the estimation accuracy. The NMF-extracted soil spectra from FVC below 63.55% of the mixed spectra provided acceptable estimation accuracies for SSC with the lowest results of determination of the estimation R2cv = 0.69, RMSEcv = 4.15 g·kg-1, and RPD = 1.8. Additionally, we proposed a strategy for the model performance investigation that combines spearman correlation analysis and model variable importance projection analysis. The NMF-extracted soil spectra retained the sensitive wavelengths that were significantly correlated with SSC and participated in the operation as important variables of the model.

Causes & effects of upstream-downstream flow regime alteration over Catchment-Estuary-Coastal systems

The construction of large dams along rivers has significantly changed the natural flow regime, reducing the inflow into many lakes and terminal wetlands. However, the question of the impact of dam operation on downstream estuarine wetlands has less been taken into account. Spatio-temporal flow regime alteration in the Mond River shows the complexity of drivers affecting the estuary-coastal system named the Mond-Protected Area in southern Iran. To this end, we applied river impact (RI) and Indicator of hydrological alteration (IHA) methods on monthly and daily river flow data across the basin. Based on the river impact method, a "drastic" impact below two in-operation (Tangab and Salman Farsi) dams, with RI values of 0.02 and 0.08, diminish to a 'severe' impact with RI value of 0.35 at the last gauge (Ghantareh) on the main corridor of the Mond river due to the addition of flow from a large mid-basin (about 20,254 km²). Furthermore, the degree of hydrological alteration (daily flow analysis) at mid-stream (e.g., Dehram gauges) was similar to the unregulated upstream tributaries (e.g., Hanifaghan gauges). The remote sensing analysis in the Mond Protected Area showed the prevailing impact of sea-level rise in the Persian Gulf with the inundation of the coastal area and a shift of vegetation in a landward direction which complied with standardized precipitation index (SPI) values as a meteorological drought indicator. Thus, the consequence of climate change (e.g., sea-level rise, draught) has a higher impact on the protected area than the upstream river regulation and land-use change in the Mond basin. The holistic approach and the catchment-level study allowed us to see the complexity of the drivers influencing the estuary-coastal system.

Evolution of habitat quality and analysis of influencing factors in the Yellow River Delta Wetland from 1986 to 2020

The assessment of habitat quality plays an important role in the effective conservation of wetland biodiversity. The Yellow River Delta Wetland is located in the intertwining zone of sea, terrestrial, and river ecosystems, increasing human activities and climate change posed a great threat to wetland biodiversity. This study first analyzed the spatial and temporal evolution characteristics of habitat quality in the Yellow River Delta Wetland under the evolution of the shoreline after runoff-sediment variability (1986–2020) using the InVEST-habitat quality model and then identified the dominant influence factors on habitat quality based on Geographical Detector. Finally, elasticity index was introduced to analyze the impacts of different reclamation activities on habitat quality. Results showed that the habitat quality decreased from 0.4798 in 1986 to 0.4078 in 2020, with high values of habitat quality concentrated in mudflat wetlands and low values of habitat quality concentrated in construction land and salt pans. The results of the Geographical Detector analysis showed that the influence of human activities, especially reclamation activities, had stronger effects on habitat quality than climatic factors. The elasticity index analysis showed that the elasticity of all three types of reclamation activities, namely, culture ponds, construction land, and salt pans, were negative from 1986 to 2005, 2005 to 2020, and 1986 to 2020. The reclamation activities had a negative impact on habitat quality. The habitat quality of the Yellow River Delta Wetland was most sensitive to the change in reclamation intensity of construction land from 1986 to 2020, and the sensitivity of the change of habitat quality to the change of reclamation intensity of culture ponds and salt pans was strengthening. This study explicitly revealed the effect of climate change and human activities on the habitat quality of the Yellow River Delta Wetland and proposed to analyze the response intensity of habitat quality to different reclamation activities by using the elasticity index, thus providing a scientific basis for mitigating the tradeoff between biodiversity conservation and rapid social development in the Yellow River Delta Wetland in the future.

Temporal-spatial change of China's coastal ecosystems health and driving factors analysis

Understanding the spatial distribution pattern change and driving factors behind ecosystem health is essential to ecosystem management and restoration. However, in the research of regional ecosystem health, there is little research on ecosystem health in coastal regions, and there is little exploration of its temporal and spatial pattern change and its driving factors. In this study, we use the Vigor-Organization-Resilience-Services (VORS) model and marine ecosystem health index to diagnose the ecosystem health of the whole coastal area of China over the last 20 years, and find the main contributing factors affecting ecosystem health with the help of geographic detectors and geographic weighted regression analysis. Our results show that: (1) the ecosystem health level in the south of the coastal region is higher than that in the north, mainly with 30° north latitude as the main dividing line. (2) The regions with high change rate are mainly concentrated in Bohai Bay, the Yangtze River Estuary, Hangzhou Bay and the Pearl River Estuary, and the change is mainly negative. (3) Both natural and human factors have an impact on ecosystem health, and the influencing factors are different on different scales. The interaction between different factors is greater than the impact of a single factor on ecosystem health. The study puts forward a new evaluation framework for the study of ecosystem health in coastal areas, which can be applied to other coastal areas with similar conditions, and can help the sustainable and healthy development of coastal areas.

Endangered plant species under differing anthropogenic interventions: how to preserve Pterygopleurum neurophyllum in Wondong wetland?

Endangered wetland plants are important as the potential keystone species and mediators for plant-soil interactions. Establishing conservation strategies for endangered plants is also prioritized because of the elevating extinction risk by human-induced wetland disturbances. The present study examined the factors controlling the incidence of Pterygopleurum neurophyllum, the endangered wetland plant experiencing severe habitat loss throughout Northeast Asia. Here, P. neurophyllum populations and their surrounding environments were addressed in the last natural Korean habitat to assess the possible influential factors (vegetation coverage, species richness, exotic plant species, coarse rock content, soil bulk density, and soil electroconductivity and pH) under anthropogenic wetland interventions (with or without soil disturbance). Our results showed that P. neurophyllum occurred 6 out of 32 plots in the study area. All P. neurophyllum were found in Miscanthus-dominated area, but preferred microhabitats featuring reduced vegetation coverage, increased species richness, and undisturbed soils under vegetation removal. Multimodel inference also indicated that vegetation coverage (relative importance = 1.00) and coarse rock content (relative importance = 0.70) were the major influential factors for P. neurophyllum population size, and the surviving P. neurophyllum were strictly limited to where both of them were kept lowered. Furthermore, the wetland intervention with soil disturbance had a negative effect on P. neurophyllum by creating the rocky and compacted soil surface as a result of land reclamation treatments. Conversely, the wetland intervention without soil disturbance enhanced the P. neurophyllum incidence by decreasing vegetation coverage of the overcrowding competitive plants. Overall findings reflect that the strategies to counteract habitat loss and manage the overly dense competitive plants should be necessary for conserving P. neurophyllum, as well as other wetland plants threatened by the human-induced disturbances and excessive competition intensities.

Mapping the Spatial Heterogeneity of Anthropogenic Soil Nitrogen Net Replenishment Based on Soil Loss: A Coastal Case in the Yellow River Delta, China

To explore the spatial heterogeneity of nitrogen supply from human activities to soil in coastal areas, we established a soil nitrogen net replenishment index (A-SNNRI). We applied the Revised Universal Soil Loss Equation (RUSLE) model for soil loss risk calculation and geostatistical analysis for process simulation. A case study in the Yellow River Delta (YRD) showed that the A-SNNRI worked well. During the summer crop-growing season, population and land use presented significant influences on the soil total nitrogen (STN) status. Urban villages and arable land both had the largest summary STN and variety. There was a negative correlation between STN change and soil loss. The east coast held both the largest A-SNNRIs and soil loss risks. There were significant positive correlations between A-SNNRIs and population and GDP. Therefore, to control and reduce soil-source nitrogen exports in the YRD, we need to reduce nitrogen emissions from urban villages, agriculture, industry, and aquaculture and determine the main risk locations along the east coast and in the main city.

Combining Different Transformations of Ground Hyperspectral Data with Unmanned Aerial Vehicle (UAV) Images for Anthocyanin Estimation in Tree Peony Leaves

To explore rapid anthocyanin (Anth) detection technology based on remote sensing (RS) in tree peony leaves, we considered 30 species of tree peonies located in Shaanxi Province, China. We used an SVC HR~1024i portable ground object spectrometer and mini-unmanned aerial vehicle (UAV)-borne RS systems to obtain hyperspectral (HS) reflectance and images of canopy leaves. First, we performed principal component analysis (PCA), first-order differential (FD), and continuum removal (CR) transformations on the original ground-based spectra; commonly used spectral parameters were implemented to estimate Anth content using multiple stepwise regression (MSR), partial least squares (PLS), back-propagation neural network (BPNN), and random forest (RF) models. The spectral transformation highlighted the characteristics of spectral curves and improved the relationship between spectral reflectance and Anth, and the RF model based on the FD spectrum portrayed the best estimation accuracy (R2c = 0.91; R2v = 0.51). Then, the RGB (red-green-blue) gray vegetation index (VI) and the texture parameters were constructed using UAV images, and an Anth estimation model was constructed using UAV parameters. Finally, the UAV image was fused with the ground spectral data, and a multisource RS model of Anth estimation was constructed, based on PCA + UAV, FD + UAV, and CR + UAV, using MSR, PLS, BPNN, and RF methods. The RF model based on FD+UAV portrayed the best modeling and verification effect (R2c = 0.93; R2v = 0.76); compared with the FD-RF model, R2c increased only slightly, but R2v increased greatly from 0.51 to 0.76, indicating improved modeling and testing accuracy. The optimal spectral transformation for the Anth estimation of tree peony leaves was obtained, and a high-precision Anth multisource RS model was constructed. Our results can be used for the selection of ground-based HS transformation in future plant Anth estimation, and as a theoretical basis for plant growth monitoring based on ground and UAV multisource RS.

Predicting forested wetland soil carbon using quantitative color sensor measurements in the region of northern Virginia, USA

Forested wetland soils within the Piedmont and Coastal Plain physiographic provinces of Northern Virginia (NOVA) were investigated to determine the utility of a handheld colorimeter, the Nix Pro Color Sensor ("Nix"), for predicting carbon contents (TC) and stocks (TC stocks) from on-site color measurements. Both the color variables recorded with each Nix scan ("Nix color variables"; n = 15) and carbon contents significantly differed between sites, with redder soils (higher a and h) at Piedmont sites, and higher TC at sites with darker soils (lower values of L, or lightness; p < 0.05). Nix-carbon correlation analysis revealed strong relationships between L (lightness), X (a virtual spectral variable), R (additive red), and K_K (black) and log-transformed TC (Ln[TC]; |r| = 0.70; p < 0.01 for all). Simple linear regressions were conducted to identify how well these four final Nix variables could predict soil carbon. Using all color measurements, about 50% of Ln(TC) variability could be explained by L, X, R, or K_K (p < 0.01), yet with higher predictive power obtained for Coastal Plain soils (0.55 < R² < 0.65; p < 0.01). Regression model strength was maximized between Ln(TC) and the four final Nix variables using simple linear regressions when color measurements observed at a specific depth were first averaged (0.66 < R² < 0.70; p < 0.01). While further study is warranted to investigate Nix applicability within various soil settings, these results demonstrate potential for the Nix and its soil color measurements to assist with rapid field-based assessments of soil carbon in forested wetlands.

Spatiotemporal Evolution of Ecosystem Health of China's Provinces Based on SDGs

In the context of increasing ecological scarcity, maintaining the balance between natural and artificial capital has become a popular research topic in the field of ecosystem health. From the perspective of coordinating natural and artificial capital and maintaining the balance between human systems and the Earth's ecosystem, the Ecosystem Health Index (EHI) was developed on the basis of the Sustainable Development Goals (SDGs). The EHI consists of the Social Progress Index (SPI), Economic Development Index (EDI), Natural Environment Index (NEI), and a pressure adjustment coefficient. Comprehensive indicator assessment models were used to analyze the spatial and temporal evolution of the EHIs in 30 of China's provinces from 2013 to 2019. A three-dimensional judgment matrix was used to classify the 30 provinces into four basic types. The results show the following: (1) From 2013 to 2019, the EHIs of all provinces improved to different degrees, with 19 provinces achieving a healthy state. (2) Spatially, the EHI showed some regional aggregation in 2013. Provinces with high EHIs were concentrated in the west, followed by those in the east, and those in the central provinces had the lowest EHIs. However, the differences between regions had narrowed by 2019. (3) The spatial distribution patterns of the NEI and the EDI varied widely, and most provinces did not reach a high level of coordination between natural and artificial capital. (4) The environmental pressure in all provinces, except Liaoning, decreased over time. In some cases, excessive pressure decreased the pressure-adjusted EHI, regardless of the EHI value. (5) According to the results of the ecosystem health classification in each province, the factors that hinder ecosystem health vary from place to place.

Macro analysis of spatiotemporal variations in ecosystems from 1996 to 2016 in Xishuangbanna in Southwest China

This study used remote sensing images from 1996 to 2016 as the main data source, and selected the average annual ecosystem type net change rate, ecosystem type transfer matrix, and comprehensive index of land development degree, to analyze the macro change of the ecosystem pattern in Xishuangbanna Dai Autonomous Prefecture in the past 20 years. Quantitative analysis was performed on amplitude, rate, type of transition, and degree of disturbance of human activities. The results reveal the spatial and temporal changes of the Xishuangbanna ecosystem and their regional differentiation. The results showed that (1) from 1996 to 2016, Xishuangbanna as a whole was dominated by forest ecosystems and rubber ecosystems, followed by tea, farmland, built-up area, and water ecosystems. (2) During 1996-2016, the ecosystem in Xishuangbanna accounted for more than 99% of the total area has not changed. From 1996 to 2003, the transfer of ecosystem types in Xishuangbanna was mainly between forest and rubber ecosystem. (3) The extent of land development and utilization in Xishuangbanna in the past 20 years is relatively low, slightly lower than the national average, and the overall level of land use is at a medium level of utilization, and over time, the degree of disturbance of human activities has shown an increasing trend.

A Novel Hybrid Method for Landslide Susceptibility Mapping-Based GeoDetector and Machine Learning Cluster: A Case of Xiaojin County, China

Landslide susceptibility mapping (LSM) could be an effective way to prevent landslide hazards and mitigate losses. The choice of conditional factors is crucial to the results of LSM, and the selection of models also plays an important role. In this study, a hybrid method including GeoDetector and machine learning cluster was developed to provide a new perspective on how to address these two issues. We defined redundant factors by quantitatively analyzing the single impact and interactive impact of the factors, which was analyzed by GeoDetector, the effect of this step was examined using mean absolute error (MAE). The machine learning cluster contains four models (artificial neural network (ANN), Bayesian network (BN), logistic regression (LR), and support vector machines (SVM)) and automatically selects the best one for generating LSM. The receiver operating characteristic (ROC) curve, prediction accuracy, and the seed cell area index (SCAI) methods were used to evaluate these methods. The results show that the SVM model had the best performance in the machine learning cluster with the area under the ROC curve of 0.928 and with an accuracy of 83.86%. Therefore, SVM was chosen as the assessment model to map the landslide susceptibility of the study area. The landslide susceptibility map demonstrated fit with landslide inventory, indicated the hybrid method is effective in screening landslide influences and assessing landslide susceptibility.

Spatiotemporal differentiation of urban-rural income disparity and its driving force in the Yangtze River Economic Belt during 2000-2017

The income imbalance between urban and rural areas has seriously affected social fairness and justice and has become a key factor restricting the sustainable development of the economy and society. The analysis of the spatiotemporal laws and causes of urban-rural income disparity is of great significance to realizing the coordinated and integrated development of regional urban and rural areas. In this study, the coefficient of variation, Theil decomposition index, spatial autocorrelation method and GeoDetector model were used to analyze the spatiotemporal characteristics of the urban-rural income gap and its driving force in the Yangtze River Economic Belt from 2000 to 2017. The results show that the per capita disposable income of urban and rural residents in the study area shows a trend of rapid growth from 2000 to 2017. The urban-rural income gap in the study area showed an inverted "U"-shaped development process as a whole, and the relative difference showed an increasing trend. Regarding the spatial pattern, the study area showed a significant east-west differentiation pattern. The spatial distribution of the urban-rural income gap in the study area has an obvious positive spatial correlation, that is, the phenomena of high-value agglomeration and low-value agglomeration were significant. The economic development level, the industrial structure, the regional development policy, transportation, topographical conditions and resource endowments can strongly explain the spatial differentiation pattern of the urban-rural income gap in the study area. The spatial differentiation pattern of the urban-rural income gap is affected by both natural factors and socioeconomic factors. Among them, socioeconomic factors are the dominant factors, followed by natural factors. There is a significant interaction between natural factors and socio-economic factors, and the combination of socio-economic factors and adverse natural factors can significantly affect the regional urban-rural income gap.

Spatiotemporal Characteristics of Vegetation Net Primary Productivity on an Intensively-Used Estuarine Alluvial Island

Net Primary Productivity (NPP) can effectively reflect the characteristics and strength of the response to external disturbances on estuarine alluvial island ecosystems, which can provide evidence for regulating human development and utilization activities and improving blue carbon capacity. However, there are a few studies on NPP of estuarine alluvial islands. We established a model based on a Carnegie–Ames–Stanford Approach (CASA) to estimate NPP on Chongming Island, a typical estuarine alluvial island, by considering the actual ecological characteristics of the island. The NPP of different land-cover types and protected areas in different years and seasons were estimated using Remote Sensing and Geographic Information System as the main tools. Correlations between NPP and Remote Sensing-based spatially heterogeneous factors were then conducted. In the last 30 years, the mean NPP of Chongming Island initially increased and then slowly decreased, while total NPP gradually increased. In 2016–2017, Chongming Island total NPP was 422.32 Gg C·a−1, and mean NPP was 287.84 g C·m−2·a−1, showing significant seasonal differences. NPP showed obvious spatial differentiation in both land-cover and protected area types, resulting from joint influences of natural and human activities. Chongming Island vegetation growth status and cover were the main factors that positively affected NPP. Soil surface humidity increased NPP, while soil salinity, surface temperature, and surface aridity were important NPP limiting factors.

Assessing hydrodynamic effects of ecological restoration scenarios for a tidal-dominated wetland in Liaodong Bay (China)

Estuarine wetlands have experienced a variety of ecological and environmental problems caused by natural and anthropogenic factors. China has proposed a series of measures and made great efforts to control coastal degradation; however, decision makers still urgently need to know which measures to implement and how they will influence the estuarine environment and functions. This study used field observations, a hydrodynamic model, and statistical methods to investigate the effects of potential restoration scenarios on hydrodynamic conditions in the tidal-influenced estuarine wetland system, Liaodong Bay (China). Results reveal that the average total phosphorus, organic carbon, available phosphorus, pH, total nitrogen content, and moisture content in the soil and sediment environment were 0.04 ± 0.003%, 0.84 ± 0.25%, 16.3 ± 4.7 mg/kg, 8.3 ± 0.1, 0.07 ± 0.02%, and 44 ± 2%, respectively, exhibiting an overall trend of degradation. A series of restoration scenarios in regards to hydrodynamic regulation and tidal inputs were used to preserve the ecological value of the estuarine wetland. Model simulations indicate that the significant improvement of hydrodynamic fields (inundation depth and flow velocity) is more likely to occur when the tidal amplitudes reach around 2 m, while relatively weak responses can be observed when the tidal levels are lower than 1 m. Additionally, the construction of floodgates may play a key role in determining the tidal inputs and flowpaths across the wetland. The modifications in micro-topography of the wetland may play a complementary role in enhancing the connectivity condition via increased creek depth of 0.5 m and width up to around 20 m. This work represents a first attempt in exploring hydrodynamic effects of restoration scenarios for a tidal-dominated wetland. An improved understanding of the estuarine system also highlights that the design and implementation of wetland restoration projects should use more comprehensive measures to achieve long-term landscape management, connectivity planning, and ecological sustainability.

Evaluation system of coastal wetland ecological vulnerability under the synergetic influence of land and sea: A case study in the Yellow River Delta, China

A comprehensive evaluation system and model of Coastal Wetland Ecological Vulnerability (CWEV) was constructed and applied to reveal spatial heterogeneity of the ecological vulnerability of the Yellow River Delta Wetland (YRDW). The results showed that the score of the ecological vulnerability (EVS) of the YRDW was 0.49, which was generally at a medium vulnerability level. The wetland area of high vulnerability was up to 943km², accounting for 35.2% of the total area, followed by the medium vulnerable area with an area of 750km², accounting for 28.1% of the total area. From the coastline perpendicularly to the land, the "seaward" gradient effect gradually decreased, the vulnerability-increasing "hydrologic connectivity" effect increased with the distance from the river channel, and the "land source influence" effect gradually decayed along with the vulnerability of population and economy gathering areas.

Spatiotemporal Characteristics and Driving Force of Ecosystem Health in an Important Ecological Function Region in China

Quantitative assessment can scientifically determine the health status of a regional ecosystem, identify regional eco-environmental problems, and assist in promoting regional sustainable development and environmental management. Taking China's important ecological function region, the Yellow River affected area as an example, this study constructed an extended evaluation index system based on the pressure-state-response framework, and remote sensing and GIS techniques were used to dynamically evaluate the spatial and temporal characteristics of ecosystem health in the study area. Furthermore, influencing factors on ecosystem health in the study area were extensively analyzed using the GeoDetector model. The results show that the ecosystem health level in the study area shows significant spatial heterogeneity from 1995-2015, and showed a fluctuating change process. Areas with large fluctuations in health level were mainly distributed in extreme climate areas, ecologically fragile areas, on plains and in hilly areas. Spatial differences of ecosystem health were well explained by using the biological abundance index, relief degree of land surface, soil type, annual average precipitation, elevation, annual average temperature, and population density. Influencing factors have significant interactive effects on ecosystem health.

Planktonic Bacterial and Archaeal Communities in an Artificially Irrigated Estuarine Wetland: Diversity, Distribution, and Responses to Environmental Parameters

Bacterial and archaeal communities play important roles in wetland ecosystems. Although the microbial communities in the soils and sediments of wetlands have been studied extensively, the comprehensive distributions of planktonic bacterial and archaeal communities and their responses to environmental variables in wetlands remain poorly understood. The present study investigated the spatiotemporal characteristics of the bacterial and archaeal communities in the water of an artificially irrigated estuarine wetland of the Liaohe River, China, explored whether the wetland effluent changed the bacterial and archaeal communities in the Liaohe River, and evaluated the driving environmental factors. Within the study, 16S rRNA quantitative PCR methods and MiSeq high-throughput sequencing were used. The bacterial and archaeal 16S rRNA gene abundances showed significant temporal variation. Meanwhile, the bacterial and archaeal structures showed temporal but not spatial variation in the wetland and did not change in the Liaohe River after wetland drainage. Moreover, the bacterial communities tended to have higher diversity in the wetland water in summer and in the scarce zone, while a relatively higher diversity of archaeal communities was found in autumn and in the intensive zone. DO, pH and PO₄-P were proven to be the essential environmental parameters shaping the planktonic bacterial and archaeal community structures in the Liaohe River estuarine wetland (LEW). The LEW had a high potential for methanogenesis, which could be reflected by the composition of the microbial communities.

Freshwater release into estuarine wetlands changes the structure of benthic invertebrate assemblages: A case study from the Yellow River Delta

Freshwater releases to wetlands degraded by excessive soil salinity in estuarine areas have been widely used to restore vegetation and maintain biodiversity of the wetland ecosystems. However, freshwater release also alters the physical and chemical properties of the water body, having a profound impact on the ecology of restored wetland ecosystems. In this study, physical and chemical parameters of water quality were compared between restored and non-restored tidal wetlands after 10 years of freshwater release in the Yellow River estuary. In addition, benthic invertebrates were used as bioindicators to reveal the effects of freshwater release on estuarine wetland ecology. The results showed that there was a significant difference in salinity between restored and non-restored wetlands (P < 0.05), which led to differences in the composition of benthic invertebrate communities (ANOSIM P < 0.001). The primary components of benthic invertebrate community were insects in restored wetlands, and in non-restored tidal wetlands the primary components were annelids, crustaceans, and gastropods. More long-term monitoring research of the impacts of freshwater introduction on degraded estuarine wetland ecosystems is needed to fully assess consequences.

Identifying spatial heterogeneity of groundwater and its response to anthropogenic activities

With the rapid development of economy and society, the quality of groundwater is deteriorating under the dual influence of natural factors and anthropogenic factors, and it seriously threatens the safety of human drinking water. Identifying and analyzing the impact of anthropogenic activities is the key to solving this problem. Based on the groundwater problem in Changle County, Shandong Province, P.R. China, 43 groundwater samples were taken and tested. The statistical characteristics of the monitoring data, the groundwater chemical types, spatial distribution of groundwater, and influencing factors were analyzed by using enrichment factor, Mahalanobis distance, grey water footprint, and so on. The analysis results show that the overall water quality of Changle County is poor, and the main over-standard ions are Cl- , SO2- 4, and NO- 3. There is obvious spatial heterogeneity in the groundwater quality. The spatial variation of NO- 3 is affected by structural factors (topography, hydrology, etc.) and random factors (industrial, agricultural, etc.), and the spatial variability of NO- 3 is the most significant. Other water quality indicators are mainly affected by structural factors. The mass concentration of most ions decreases gradually from north to south, and the overall water quality in the southern region is better than that in the northern region. Thus, the supervision of chemical fertilizers and pesticides should be strengthened. The advanced treatment and reuse of wastewater from industrial parks should be promoted to improve the quality of groundwater and ensure the safety of human drinking water.

Exploring the regional differences of ecosystem health and its driving factors in China

A better understanding of regional differences in ecosystem health and its driving factors is conducive to ecosystem management and restoration. Although various studies on ecosystem health have been carried out in different regions, few studies have been devoted to the insightful exploration of the spatial heterogeneity of ecosystem health and its driving forces at a national scale. In this study, we used an evaluation framework in terms of vigor, organization, resilience, and ecosystem service functions to assess the ecosystem health level in China from 2000 to 2015. Then, spatial agglomeration and regional differences in ecosystem health were examined using the spatial autocorrelation method and K-means clustering analysis, and the factors driving the regional differences of ecosystem health were explored based on the geographical detector model. Our results showed the following: (1) the ecosystem health level in China spatially increases from the northwest to the southeast, exhibiting significant global spatial autocorrelation and local spatial agglomeration; (2) eleven zones with three types were identified to indicate the regional differences of ecosystem health; (3) In terms of the driving factors, the moisture index and land use intensity contributed 24.5% and 20.7% to the variation in ecosystem health at the national scale. The ecosystem health changes were influenced by the interaction of meteorological and socio-economic factors in most regions with high ecosystem health types. Socio-economic factors act as a bridge that linked and reinforced the other factors in most regions with low and medium ecosystem health types. Ecologically protected factors were found to exert a remarkable impact in the southwestern region and the Loess Plateau region. Our findings can provide more effective and detailed decision-making support for ecosystem conservation and management in China.

An evaluating system for wetland ecological health: Case study on nineteen major wetlands in Beijing-Tianjin-Hebei region, China

Wetland is one of the three major ecosystems on the earth and has fundamental ecological functions and plays an irreplaceable role in serving biological survival and human development. Considering the characteristics of five types of wetlands, this study constructed a wetland ecological health evaluation indicator system using a wide variety of data from statistical report, field sampling, remote sensing, and questionnaire survey. In this study, we have selected 13 indicators (related to water, soil, biological, landscape and social factors) for ecological health evaluation of 19 wetlands in Beijing-Tianjin-Hebei region of China which have national importance. The detailed analysis shows a comprehensive health index of 5.53. There was significant spatial heterogeneity in the health status of the 19 wetlands in this region. The evaluation results and analysis provides scientific services for developing reasonable and targeted wetland protection and utilization policies of wetlands.

Coastal Vulnerability to Erosion Using a Multi-Criteria Index: A Case Study of the Xiamen Coast

The assessment of coastal vulnerability to erosion is urgently needed due to increasing coastal erosion globally. Based on the coastal characteristics of the Xiamen artificial coastline, which accounts for more than 80% of the coastline in this area, this study provides an integrated approach based on a multi-criteria index. The evaluation index system of the local coastal vulnerability to the erosion of Xiamen includes 12 indexes based on natural (coastal characteristics, coastal forcing), and socio-economic factors (coastal infrastructure, disaster reduction). The spatial differentiation characteristics of the coastal vulnerability to erosion along the Xiamen coast (2018) have been quantitatively assessed with the aid of GIS (Geographic Information System) and RS (Remote Sensing) technology. The results show that the very high vulnerability, high vulnerability, medium vulnerability, low vulnerability and very low vulnerability areas of coastal erosion accounted for 4.6%, 30.5%, 51.6%, 12.5% and 0.8% of the Xiamen coast, respectively. The coastal vulnerability to erosion classes of artificial coasts is significantly higher than those of natural coasts. This difference is mainly controlled by the coastal slope and coastal buffer ability. The results of the evaluation are basically consistent with the present situation. The rationality of the index system and the applicability of the theoretical method are well explained. The evaluation model constructed in this study can be extended to other areas with high ratios of artificial coasts.

An Optimization Model for a Wetland Restoration Project under Uncertainty

Restoring natural wetlands with conservation projects is an urgent task for human well-being. This paper introduces the Interval linear programming (ILP) method in wetland restoration projects for the first time and builds an optimization model. The purpose of the optimization model is to find an optimal restoration measures allocation pattern that can minimize the total investment in wetland restoration projects and obtain additional ecological environment and socio-economic benefits. The optimization model can also decrease the influence of interval uncertainty in the system by expressing the executed solution as interval numbers with an upper bound and a lower bound. The result of the optimization model for the wetland restoration project indicated a range of 6.84%–15.43% reduction on comparison with the original scheme which verified the effectiveness and validity of this optimization model. Our findings indicate that higher ecological and social benefits of wetland restoration projects can be achieved with lower restoration investment on the application of the reasonable and optimal restoration measures allocation pattern by the optimization model. The results of interval solutions can provide guidance for project managers to select a satisfactory decision-making plan by adjusting the decision variables in the interval solutions according to the practical situation. It can be seen that reeds were suggested to be planted over 46.75 km², with the same lower bound and higher bound. Meanwhile, populus euphratica, and dryland willow were recommended to be planted in a mixed forest pattern within the interval of 30.54 km² to 37.25 km², and so forth. With the optimal solutions obtained from the model, the total project investment would be in the range of 2193.14 (10⁴ CNY) to 2416.01 (10⁴ CNY). Future improvements of our optimization model in wetland restoration projects should consider other kinds of uncertainties in the system such as stochastic uncertainties, fuzzy uncertainties, and integrated uncertainties.