Ecological Indicators最新文献

Actively addressing the negative effects of global climate change on vegetation has always been a hot topic of academic concern. How to accurately and comprehensively assess the vegetation risk due to climate change has been rarely reported. By comprehensively considering three dimensions—species structure (species richness), carbon sequestration function (Net Primary Production, NPP), and physiological processes (transpiration)—and based on the prediction of the future distribution characteristics of 3,370 plant species in China, this study quantified the vegetation risk and its driving mechanisms under different Shared Socioeconomic Pathways. The composite index of vegetation risk decreased to the west of the Hu Huanyong Line (NT and QTP regions) but increased to the east (NE and ST regions), with the magnitude of increase growing with the intensification of emission scenarios. In the 2070 s, the proportion of high risk and extremely high-risk areas in the east increased from 14.5 % under SSP126 to 50.0 % under SSP585. NPP and transpiration generally show an increasing trend, and species richness changes similarly to vegetation risk. In the 2070 s under SSP585, 39.2 % of QTP areas see a species richness increase over 50 %, while 33.0 % of ST areas experience a decrease over 30 %. The increase in vegetation risk in the NE region is driven by increased soil moisture, while in the ST region, it is mainly due to decreased runoff and SPEI. Therefore, China should actively respond to the risk of vegetation degradation in the east due to future climate change.

Increasing carbon sequestration (CS) in soils and biomass is an important land-based solution in mitigating global warming. Ecosystems provide a wide range of ecosystem services (ESs). The necessity to augment CS may engender alterations in the interrelationships among ESs, thereby heightening the probability of ecosystem destabilization. This study developed a framework that integrates machine learning and interpretable predictions to evaluate the destabilization risk resulting from alterations in ecosystem service relationships dominated by CS. We selected Northeastern China as study area to estimate six ESs and identified areas of destabilization risk among the three services most relevant to CS, including food production (FP), soil retention (SR), and habitat quality (HQ). Subsequently, we compared three machine learning models (random forest, extreme gradient boosting, and support vector machine) and introduced the Shapley additive interpretation (SHAP) method for driving mechanism analysis. The results showed that: (1) CS-FP had 30.28% of its area at destabilization risk and is the most significant ecosystem service pair; (2) Heilongjiang Province was the region with the highest destabilization risk of CS, with CS-FP and CS-SR accounting for 44.76% and 52.89% of all regions, respectively; (3) a non-linear relationship and the presence of threshold features between socio-ecological factors and the prediction of destabilization risk. The study has potential practical value for destabilization risks prevention, while also providing a scientific basis for formulating comprehensive carbon management policies and maintaining ecosystem stability.

Land rocky desertification (RD) is one of the most serious environmental disasters in karst landforms. Identifying the rocky desertification severity level (RDSL) is a key task in the prevention and control projects of rocky desertification in karst areas. How to efficiently and accurately identify the RDSL is an urgent issue. It requires higher accuracy and more advanced techniques. Currently, machine learning-based remote sensing technology (RST) faces challenges in identifying the RDSL, including insufficient dataset features, low accuracy of identification models, and incomplete exploration of rocky desertification driving factors. To address these issues, this study leverages multi-source remote sensing satellite data and related product data to construct a multidimensional dataset with feature factors. By combining convolutional neural networks (CNN) and graph neural networks (GNN), a graph convolutional network segmentation model based on deep learning image segmentation is proposed for the automatic identification of RDSL. In addition, the study has investigated the spatiotemporal changes of RD in Guizhou Province in recent years and explored the impacts of various natural driving factors on the RDSLs. The experimental results indicate that the multidimensional feature dataset (Dataset-2) contributes to enhancing the identification accuracy of the model. The proposed model has capabilities such as composite representation in non-Euclidean space, deep extraction of image semantics, and multiscale segmentation and fusion. The model achieves an Mean Intersection over Union (MIoU) of 84.724, which outperforms other mainstream image segmentation methods. Although rocky desertification from 2015 to 2022 in Guizhou Province is significantly distributed, there is a trend toward mitigation. This study provides effective technical tools and data support for exploring the evolution process of desertification in subtropical karst areas, as well as for the implementation of projects related to environmental protection, afforestation, soil and water conservation, and land monitoring.

Assessing the sustainability of socio-ecological system (SES) is the basis for ensuring human well-being and achieving Sustainable Development Goals (SDG) worldwide, especially in arid regions. However, the oasis as a typical socio-ecological system is still lacking a proper approach to examine its evolutionary direction and the sustainability of suitable scale. This study proposes an adaptive cycle framework assessing the sustainability of oasis socio-ecological system to quantify the relationships between the evolution of oasis socio-ecological system and its scale suitability. The framework is constructed by coordination degree, order degree, and oasis suitability metrics of the oasis socio-ecological system to apply in the oases of Tarim basin, Northwest China. The results show that the adaptive cycle evolution of various oases subsystems in study area does not go through four stages successively, but proceeds in a hopping way. The most of them are in the conservation (K) −recognition (α), or recognition (α) − conservation (K) stage. The information entropy of oasis socio-ecological system is inversely proportional to the order degree. The overall oasis information entropy is high ranging from 0.64 to 1.06, but the order degree is at a low level ranging from 0.04 to 0.41. The higher the degree of coordination between subsystems, the higher the degree of oasis suitability. The overall oasis suitability in study area shows a barely appropriate- appropriate-barely appropriate fluctuation state. The main factors affecting the oasis evolution of socio-economic, eco-environment and water resources subsystem are the distribution of industrial structure, green ratio, water consumption per unit grain output, and per capita daily living water consumption, respectively. This study provides support for guiding the sustainable evolution of desert oasis system to adapt to the development scale and the management of oasis socio-ecological system.

The current global decline in biodiversity is a matter of pressing concern, necessitating the conservation of diverse ecosystems across various spatial scales. Regions such as the tropical Andes face the imminent threat of biotic homogenization due to intensive livestock grazing, posing a significant risk to biodiversity. This study is focused on the sub-humid grasslands of northwestern Bolivia, within the the National Park Apolobamba. We surveyed a total of 105 plots distributed across seven sites, representing a natural gradient of grazing intensity. Within each site, the plots were organized into five clusters to explore the impact of environmental factors on plant diversity within and among communities. Our research reveals that local plant diversity, quantified by species richness and the inverse Simpson index, is predominantly shaped by soil pH. Notably, more acidic soil is associated with diminished diversity. Furthermore, our findings highlight that the dissimilarity in species composition among local communities may be linked to grazing intensity. This suggests that intensified grazing may have the potential to homogenize plant communities across the landscape. A concerning implication is the likelihood of communities becoming dominated by acquisitive species, leaving them more susceptible to the impacts of climate variability. The study underlines the necessity to analyze multiple facets of diversity for a comprehensive understanding of the environmental factors regulating and therefore to address potential drivers of diversity loss. To mitigate these threats, managers may consider adjusting livestock quantities and the spatial range used by grazers, aiming to sustain multiple aspects of plant diversity and prevent homogenization and degradation of grasslands in a changing world.

The universal contamination of arable land with potentially toxic elements (PTEs) poses a severe threat to food security and jeopardizes worldwide efforts to meet the United Nations Sustainable Development Goals (SDGs). How to obtain more reliable concentrations of PTEs in regional agricultural soils is a priority problem to be solved. Multispectral satellite remote sensing, with its advantages of high spatial and temporal resolution, broad coverage, and low cost, offers the potential to acquire spatial distribution of PTEs in agricultural soils over large areas. However, owing to the complexity of soil environments and the insufficiency of spectral information, the mechanism for retrieving concentrations of PTEs in agricultural soils via multispectral satellites is not yet clear, and the accuracy needs to be improved. In this study, we aimed to assess whether employing a fusion of spectral information and environmental covariates, results in more accurate retrievals of PTEs, specifically chromium (Cr) and mercury (Hg), in croplands than does employing spectral information alone. Three machine learning algorithms—kernel-based support vector machine (SVM), neural network-based back propagation neural network (BPNN), and tree-based extreme gradient boosting (XGBoost)—were developed to retrieve Cr and Hg concentrations in agricultural soils. The results showed that the fusion of spectral information and environmental covariates combined with the XGBoost model performed best in retrieving both Cr and Hg concentrations in agricultural soils with coefficient of determination (R²) values of 0.73 and 0.74, respectively. Environmental covariates were important variables for determining Cr and Hg concentrations in agricultural soils, but the ability to retrieve these element concentrations by utilizing spectral information alone was limited. High Cr concentrations occurred in central towns and southern hilly mountains. High Hg concentrations were detected in the northwestern region and southern hilly mountains. The potential of fusing spectral information and environmental covariates to precisely retrieve PTE concentrations in agricultural soils can serve as a reference for agricultural soil health information monitoring worldwide.

Forest burned area (FBA) detection using remote sensing (RS) data is critical for timely forest management and recovery attempts after wildfires. This study introduces a dual-path attention residual-based U-Net (DPAttResU-Net), a novel end-to-end deep learning (DL) model tailored for FBA detection using dual-source post-fire Sentinel-1 (S1) and Sentinel-2 (S2) satellite RS imagery. To better distinguish FBAs from other land cover types, DPAttResU-Net incorporates a dual-pathway structure to exploit complementary geometrical/physical and spectral features from S1 and S2, respectively. An integral component in the proposed architecture is the channel-spatial attention residual (CSAttRes) block, which emphasizes salient features through the channel and spatial attention modules, thus improving the burned area feature representation. To compare DPAttResU-Net to state-of-the-art DL models, experiments were conducted on benchmark FBA datasets collected over 12 areas, where ten datasets were used as training data and two datasets were used to test the trained DL models. The experimental results demonstrate the high proficiency of the proposed deep model in meticulously delineating FBAs. In further detail, DPAttResU-Net, with a $P_{FN}$ of 17.96 (%) in the first case and an IoU of 89.31 (%) in the second case, outperformed the existing U-Net-based models. Accordingly, through dual-path integration and attention mechanisms, DPAttResU-Net contributes to accurately identifying FBAs by preserving their geometrical details, making it a promising tool for post-wildfire forest management.

As an inevitable issue in the contemporary world, global change imposes a profound and complex influence on the Earth’s ecosystem. This study begins by addressing the adaptation to risks associated with global change and focuses on the concept of land ecological security, thereby exploring its related connotations and interactions with global climate change. Through the use of econometric analysis methods along with induction and summarization techniques, a comprehensive analysis is conducted of the evolutionary stage, research hotspots, dynamic trends, and main contents of land ecological security assessment studies in both the Web of Science and China National Knowledge Infrastructure databases from 2004 to 2024. The findings provide crucial insights into the evaluation of land ecological security under conditions of global change. Evaluation efforts have focused primarily on the entropy weight method, ecosystem services, index system, matter–element model, early warning and climate change. Moreover, synergistic, additive, and antagonistic relationships exist between climate change and land ecological security. However, existing evaluation methods, index systems, spatiotemporal scales, evaluation levels, and criteria all exhibit certain limitations that should be optimized further through leveraging information technology and big data. Future research on development under global change faces numerous pressures and challenges. It is important to enhance the integration of diverse data sources, facilitate technological innovation and promote interdisciplinary collaboration. Moreover, incorporating the spillover effect of ecosystems into the evaluation index system, emphasizing process simulation and dynamic assessment, tracking the impact of uncertainties such as climate change and human activities on land ecosystems, and strengthening the analysis of influencing factors are essential. The integration of evaluation, monitoring, regulation, management, and protection serves as a pivotal strategy in addressing global change in land ecological security research.

This paper investigates the response of mid-latitude montane peatlands to climate warming, focusing on changes occurring in a montane peat bog during a drought period. Unmanned Aerial Systems (UAS) equipped with multispectral and thermal sensors were used for high-resolution monitoring to analyze qualitative changes within the peat bog and their spatial distribution. The study was conducted in the Rokytka mountain peat bog in Šumava National Park, Czech Republic, which is one of the largest mountain peat bog complexes in Central Europe. Monitoring took place during the 2019 vegetation season, coinciding with the peak of the 2015–2019 drought. The recurrent UAS imaging campaigns were complemented by continuous hydrological and hydropedological monitoring and in-situ calibration measurements. The findings revealed diverging responses of montane peatlands to climate change across different functional zones of the peat bog. UAS thermal mapping identified distinct land surface temperature variations across various vegetation categories under different conditions. Notably, ponds and waterlogged areas displayed a stabilizing effect on land surface temperature variability, though they exhibited different absolute temperatures. In contrast, shallow waterlogged areas exhibited surface temperatures akin to dry open peat areas. Multispectral UAS monitoring demonstrated significant transitions among the peat bog zones in response to heat and drought propagation. The most pronounced changes occurred in shallow waterlogged areas, which shrank notably from 22.8% to 4.5%, while bare peat expanded from 26.8% to 45.5% during the 2019 drought season. High-resolution thermal and multispectral monitoring has revealed the scope and magnitude of the intra-peatland responses to drought and heat waves and serves as a sensible indicator of environmental changes of peatlands. It has disclosed a large cumulative effect of change in an environment composed of highly heterogeneous and subtle structures. The results highlighted the effectiveness of UAS monitoring in understanding the extent of change in montane peatlands as a fragile environment exposed to the effects of climate change.

Although the pollution of trace elements has been extensively studied, there is still a lack of comprehensive understanding regarding accumulation of these elements in lake ecosystems within the Yellow River basin, specifically the effects of lacustrine groundwater discharge (LGD) on trace elements. This study investigated the distribution, sources, and toxicity risks associated with 20 target trace elements (i.e., Li, Sc, Ti, V, Mn, Cr, Co, Ni, Cu, Zn, Rb, Y, Mo, Sb, Ba, W, Tl, Pb, U and Sr) in lake water (LW), groundwater (GW), and river water (RW) influenced by the LGD process in lake Ulansuhai and lake Daihai. The natural source is the primary contributor of trace elements in both LW and GW within the two lake basins, while industrial discharge plays a secondary role. The average LGD fluxes in lake Ulansuhai and Daihai basins were 6.39 × 10⁵ m³/d and 1.07 × 10⁵ m³/d, respectively. Additionally, the trace element fluxes from LGD in lake Ulansuhai (mean of 106,659.37 g/d) exceeded those observed in lake Daihai (mean of 3236.57 g/d). The overall toxicity risk was found to be higher in lake Ulansuhai (LW (17.36) > RW (16.81) > GW (12.76)) compared to lake Daihai (GW (10.43) > LW (9.10) > RW (4.63)). As well as the toxicity levels of trace elements in lake Ulansuhai were elevated compared to Daihai, which can be attributed to the influenced of ion compositions and water nutrients. Among them, Ca²⁺, Mg²⁺, and SO₄²⁺ were identified as key ions impacting the toxicity of trace elements in lake Ulansuhai. These findings provide crucial insights for targeted monitoring and mitigation of trace element pollution in the Yellow River basin’s lake ecosystems.