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The main objective of this work was to develop a viewer with web output, through which the changes experienced by the mangroves of the Gran Humedal del Norte de Ciego de Avila (GHNCA) can be evaluated from remote sensors, contributing to the understanding of the spatiotemporal variability of their vegetative dynamics. The achievement of this objective is supported by the use of open-source technologies such as MapStore, GeoServer and Django, as well as Google Earth Engine, which combine to offer a robust and technologically independent solution to the problem. In this context, it was decided to adopt an action model aimed at automating the workflow steps related to data preprocessing, downloading, and publishing. A visualizer with web output (Geospatial System for Monitoring Mangrove Ecosystems or SIGMEM) is developed for the first time, evaluating changes in an area of central Cuba from different vegetation indices. The evaluation of the machine learning classifiers Random Forest and Naive Bayes for the automated mapping of mangroves highlighted the ability of Random Forest to discriminate between areas occupied by mangroves and other coverages with an Overall Accuracy (OA) of 94.11%, surpassing the 89.85% of Naive Bayes. The estimated net change based on the year 2020 of the areas determined during the classification process showed a decrease of 5138.17 ha in the year 2023 and 2831.76 ha in the year 2022. This tool will be fundamental for researchers, decision makers, and students, contributing to new research proposals and sustainable management of mangroves in Cuba and the Caribbean. Full article

The China–Pakistan Economic Corridor (CPEC), as an important part of the Belt and Road Initiative, is of great significance for the promotion of sustainable development in the region through the study of land use change and the simulation of future multi-scenarios. Based on the multi-period land use data of the CPEC, this study firstly analyzed the spatial and temporal land use changes in the CPEC from 2000 to 2020 by using GIS technology, and, secondly, simulated the land use patterns of the CPEC under four scenarios, namely, natural development, investment priority, ecological protection, and harmonious development, in 2040 by using the Markov-FLUS model with comprehensive consideration of natural, socio-economic, and other driving factors. The results show the following: (1) The urban land, forest land, and grassland in the CPEC from 2000 to 2020 show an increasing trend, while the farmland, unutilized land, and water area categories show a decreasing trend. In terms of land use transfer changes, the most frequently transferred out is the conversion of unutilized land to grassland. (2) The FLUS model has high accuracy in simulating the land use pattern of the CPEC, and its applicability in the CPEC area is strong and can be used to simulate the future land use pattern of the CPEC. (3) Among the four different land use scenarios, the harmonious development scenario strikes a better balance between infrastructure construction, economic development, and ecological protection, and can provide a scientific basis for future land management in the CPEC, in order to highlight the importance of promoting economic growth and ecological protection and ultimately realize sustainable development. Full article

Regulation (EU) 2020/852 of the European Parliament, also known as the Taxonomy Regulation, facilitates environmentally sustainable investments. It is part of the concept of the European Green Deal and a ‘tool’ for financial institutions, enterprises, and investors, facilitating the assessment of the environmental impact of a particular project. The Regulation contains the criteria an activity must meet to be considered environmentally sustainable. The role of the Taxonomy Regulation is to enable the flow of public and private capital towards ecological and sustainable activities. The document does not need to be implemented into the legal order of individual EU member-states, which results in its direct application. The main financial instruments enabling the achievement of the goals of the Taxonomy Regulation may be green bonds and other forms of capital raising by entrepreneurs and forest ownership structures. The assumption of the Regulation is to achieve the principles of sustainable environmental activity when spending funds obtained from private investors. It is an issue of key significance to identify the areas of management and financial accounting in the operational activities of forest enterprises that can be qualified for the Taxonomy Regulation. Forestry activities, including the processes mentioned therein, the objectives of the New EU Forest Strategy, and the LULUCF Regulation, are to play an essential role in reducing greenhouse gas emissions. The role of forestry in the supply chain in its broad sense is also considered. Forestry and forest management can receive capital for sustainable development due to the threat resulting from exclusions that strengthen the protective function of the forest (the protection of biodiversity). These processes will occur at the expense of production and numerous social functions. Full article

Dioryctria abietella Denis Schiffermuller (Lepidoptera: Pyralidae) is an oligophagous pest that mainly damages Pinaceae plants. Here, we investigated the effects of the Bacillus thuringiensis 2913 strain (Bt 2913), which carries the Cry1Ac, Cry2Ab, and Vip3Aa genes, on the D. abietella midgut transcriptome at 6, 12, and 24 h after infection. In total, 7497 differentially expressed genes (DEGs) were identified from the midgut transcriptome of D. abietella larvae infected with Bt 2913. Among these DEGs, we identified genes possibly involved in Bt 2913-induced perforation of the larval midgut. For example, the DEGs included 67 genes encoding midgut proteases involved in Cry/Vip toxin activation, 74 genes encoding potential receptor proteins that bind to insecticidal proteins, and 19 genes encoding receptor NADH dehydrogenases that may bind to Cry1Ac. Among the three transcriptomes, 88 genes related to metabolic detoxification and 98 genes related to immune defense against Bt 2913 infection were identified. Interestingly, 145 genes related to the 60S ribosomal protein were among the DEGs identified in the three transcriptomes. Furthermore, we performed bioinformatic analysis of zonadhesin, GST, CYP450, and CarE in the D. abietella midgut to determine their possible associations with Bt 2913. On the basis of the results of this analysis, we speculated that trypsin and other serine proteases in the D. abietella larval midgut began to activate Cry/Vip prototoxin at 6 h to 12 h after Bt 2913 ingestion. At 12 h after Bt 2913 ingestion, chymotrypsin was potentially involved in degrading the active core fragment of Vip3Aa toxin, and the detoxification enzymes in the larvae contributed to the metabolic detoxification of the Bt toxin. The ABC transporter and several other receptor-protein-related genes were also downregulated to increase resistance to Bt 2913. However, the upregulation of 60S ribosomal protein and heat shock protein expression weakened the resistance of larvae to Bt 2913, thereby enhancing the expression of NADH dehydrogenase and other receptor proteins that are highly expressed in the larval midgut and bind to activating toxins, including Cry1Ac. At 24 h after Bt 2913 ingestion, many activated toxins were bound to receptor proteins such as APN in the larval midgut, resulting in membrane perforation. Here, we clarified the mechanism of Bt 2913 infection in D. abietella larvae, as well as the larval immune defense response to Bt 2913, which provides a theoretical basis for the subsequent control of D. abietella using B. thuringiensis. Full article

This study was designed to develop a 30 m resolution land cover dataset to improve the performance of regional weather forecasting models in East China. A 10-class land cover mapping scheme was established, reflecting East China’s diverse landscape characteristics and incorporating a new category for plastic greenhouses. Plastic greenhouses are key to understanding surface heterogeneity in agricultural regions, as they can significantly impact local climate conditions, such as heat flux and evapotranspiration, yet they are often not represented in conventional land cover classifications. This is mainly due to the lack of high-resolution datasets capable of detecting these small yet impactful features. For the six-province study area, we selected and processed Landsat 8 imagery from 2015–2018, filtering for cloud cover. Complementary datasets, such as digital elevation models (DEM) and nighttime lighting data, were integrated to enrich the inputs for the Random Forest classification. A comprehensive training dataset was compiled to support Random Forest training and classification accuracy. We developed an automated workflow to manage the data processing, including satellite image selection, preprocessing, classification, and image mosaicking, thereby ensuring the system’s practicality and facilitating future updates. We included three Weather Research and Forecasting (WRF) model experiments in this study to highlight the impact of our land cover maps on daytime and nighttime temperature predictions. The resulting regional land cover dataset achieved an overall accuracy of 83.2% and a Kappa coefficient of 0.81. These accuracy statistics are higher than existing national and global datasets. The model results suggest that the newly developed land cover, combined with a mosaic option in the Unified Noah scheme in WRF, provided the best overall performance for both daytime and nighttime temperature predictions. In addition to supporting the WRF model, our land cover map products, with a planned 3–5-year update schedule, could serve as a valuable data source for ecological assessments in the East China region, informing environmental policy and promoting sustainability. Full article

The main causes of habitat conversion, degradation, and fragmentation—all of which add to the loss in biodiversity—are human activities, such as urbanization and farmland reclamation. In order to inform scientific land management and biodiversity conservation strategies and, therefore, advance sustainable development, it is imperative to evaluate the effects of land-use changes on biodiversity, especially in areas with high biodiversity. Using data from five future land-use scenarios under various Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs), this study systematically assesses the characteristics of land-use and landscape pattern changes in southwest China by 2050. This study builds a comprehensive biodiversity index and forecasts trends in species richness and habitat quality using models like Fragstats and InVEST to evaluate the overall effects of future land-use changes on biodiversity. The research yielded the subsequent conclusions: (1) Grasslands and woods will continue to be the primary land uses in southwest China in the future. But the amount of grassland is expected to decrease by 11,521 to 102,832 km², and the amounts of wasteland and urban area are expected to increase by 8130 to 16,293 km² and 4028 to 19,677 km², respectively. Furthermore, it is anticipated that metropolitan areas will see an increase in landscape fragmentation and shape complexity, whereas forests and wastelands will see a decrease in these aspects. (2) In southwest China, there is a synergistic relationship between species richness and habitat quality, and both are still at relatively high levels. In terms of species richness and habitat quality, the percentage of regions categorized as outstanding and good range from 71.63% to 74.33% and 70.13% to 75.83%, respectively. The environmental circumstances for species survival and habitat quality are expected to worsen in comparison to 2020, notwithstanding these high levels. Western Sichuan, southern Guizhou, and western Yunnan are home to most of the high-habitat-quality and species-richness areas, while the western plateau is home to the majority of the lower scoring areas. (3) The majority of areas (89.84% to 94.29%) are forecast to undergo little change in the spatial distribution of biodiversity in southwest China, and the general quality of the ecological environment is predicted to stay favorable. Except in the SSP1-RCP2.6 scenario, however, it is expected that the region with declining biodiversity will exceed those with increasing biodiversity. In comparison to 2020, there is a projected decline of 1.0562% to 5.2491% in the comprehensive biodiversity index. These results underscore the major obstacles to the conservation of biodiversity in the area, highlighting the need to fortify macro-level land-use management, put into practice efficient regional conservation plans, and incorporate traditional knowledge in order to save biodiversity. Full article

Biodiversity monitoring is key for understanding the delivery of ecosystem functions and services. Mediterranean forests and woodlands harbor many characteristic species of the Mediterranean vascular flora, and hence, they are a good surrogate for detecting changes in biodiversity linked to global change. In this work, we present a database resulting from the study of vascular plant diversity in multi-scale plots of 0.1 ha, measured around the first decade of this century and located in Mediterranean forest environments. Diversity profiles are calculated from Hill numbers (0, 1 and 2) for local (α) and regional (ϒ) diversity, as well as a multiplicative calculation of differential diversity (β). The main Mediterranean forests sampled had a medium coverage of 51% and stand dominant height of 10.6 m, and they were monospecific in two-thirds of cases. Local diversity reaches its highest values (around 78 species per 0.1 ha) in Holm oak dehesas, with values below 50 species for the most productive forest stands dominated by species of the genus Pinus. As regards the contribution to regional diversity, broadleaf formations contribute the most, with stone pine forests and dehesas in an intermediate position, and pine forests contributing the lowest in species richness terms. Full article

Sustainable forest management faces challenges from various biotic and abiotic stresses. The Asian longhorned beetle (ALB) and drought stress both induce water shortages in poplar trees, but require different management strategies. In northwestern China, ALB and drought stress caused massive mortality in poplar shelterbelts, which seriously affected the ecological functions of poplars. Developing a large-scale detection method for discriminating them is crucial for applying targeted management. This study integrated UAV-hyperspectral and LiDAR data to distinguish between ALB and drought stress in poplars of China’s Three-North Shelterbelt. These data were analyzed using a Partial Least Squares-Support Vector Machine (PLS-SVM). The results showed that the LiDAR metric (elev_sqrt_mean_sq) was key in detecting drought, while the hyperspectral band (R970) was key in ALB detection, underscoring the necessity of integrating both sensors. Detection of ALB in poplars improved when the poplars were well watered. The classification accuracy was 94.85% for distinguishing well-watered from water-deficient trees, and 80.81% for detecting ALB damage. Overall classification accuracy was 78.79% when classifying four stress types: healthy, only ALB affected, only drought affected, and combined stress of ALB and drought. The results demonstrate the effectiveness of UAV-hyperspectral and LiDAR data in distinguishing ALB and drought stress in poplar forests, which contribute to apply targeted treatments based on the specific stress in poplars in northwest China. Full article

Land use and cover change (LUCC) is directly linked to the sustainability of ecosystems and the long-term well-being of human society. The Helong Region in the Loess Plateau has become one of the areas most severely affected by soil and water erosion in China due to its unique geographical location and ecological environment. The long-term construction of terraces and orchards is one of the important measures for this region to combat soil erosion. Despite the important role that terraces and orchards play in this region, current studies on their extraction and understanding remain limited. For this reason, this study designed a land use classification system, including terraces and orchards, to reveal the patterns of LUCC and the effectiveness of ecological restoration projects in the area. Based on this system, this study utilized the Random Forest classification algorithm to create an annual land use and cover (LUC) dataset for the Helong Region that covers eight periods from 1986 to 2020, with a spatial resolution of 30 m. The validation results showed that the maps achieved an average overall accuracy of 87.54% and an average Kappa coefficient of 76.94%. This demonstrates the feasibility of the proposed design and land coverage mapping method in the study area. This study found that, from 1986 to 2020, there was a continuous increase in forest and grassland areas, a significant reduction in cropland and bare land areas, and a notable rise in impervious surface areas. We emphasized that the continuous growth of terraces and orchards was an important LUCC trend in the region. This growth was primarily attributed to the conversion of grasslands, croplands, and forests. This transformation not only reduced soil erosion but also enhanced economic efficiency. The products and insights provided in this study help us better understand the complexities of ecological recovery and land management. Full article

Assessing vegetation changes in alpine arid and fragile ecosystems is imperative for informed ecological restoration initiatives and adaptive ecosystem management. Previous studies primarily employed the Normalized Difference Vegetation Index (NDVI) to reveal vegetation dynamics, ignoring the spatial heterogeneity alterations caused by bare soil. In this study, we used a comprehensive analysis of NDVI and its spatial heterogeneity to examine the vegetation changes across the Three-River Headwaters Region (TRHR) over the past two decades. A random forest model was used to elucidate the underlying causes of these changes. We found that between 2000 and 2022, 9.4% of the regions exhibited significant changes in both NDVI and its spatial heterogeneity. These regions were categorized into six distinct types of vegetation change: improving conditions (62.1%), regrowing conditions (11.0%), slight degradation (16.2%), medium degradation (8.4%), severe degradation (2.0%), and desertification (0.3%). In comparison with steppe regions, meadows showed a greater proportion of improved conditions and medium degradation, whereas steppes had more instances of regrowth and slight degradation. Climate variables are the dominant factors that caused vegetation changes, with contributions to NDVI and spatial heterogeneity reaching 68.9% and 73.2%, respectively. Temperature is the primary driver of vegetation dynamics across the different types of change, with a more pronounced impact in meadows. In severely degraded steppe and meadow regions, grazing intensity emerged as the predominant driver of NDVI change, with an importance value exceeding 0.50. Notably, as degradation progressed from slight to severe, the significance of this factor correspondingly increased. Our findings can provide effective information for guiding the implementation of ecological restoration projects and the sustainable management of alpine arid ecosystems. Full article

The scarcity of hardwoods from tropical forests makes the search for alternative species necessary for commercialization. This study aimed to establish groups of timber species from the Amazon Forest with potential for logging purposes through the assessment of their physical-mechanical properties, aiming to identify alternative species that can meet the market demands. We utilized data from the Forest Products Laboratory (LPF) (containing information on basic density and other wood mechanical properties) and the Timberflow platform, as well. We applied a multivariate cluster analysis technique with the aim of grouping species based on the technological characteristics of their wood and evaluating similarity among them to obtain homogeneous groups in terms of economic potential and utilization. The results indicated four homogeneous groups: Cluster 1 (40.72% of species, basic density-db: 690 kg m⁻³), Cluster 2 (13.92%, db: 260 and 520 kg m⁻³), Cluster 3 (27.32%, db: 550 and 830 kg m⁻³), and Cluster 4 (18.04%, db: 830 kg m⁻³). Most of the 20 listed species are classified as more commercially viable (70%), with high wood density. Species identified as alternatives include Dialium guianense and Zollernia paraensis for Dipteryx odorata, Terminalia argentea for Dinizia excelsa, Terminalia amazonia and Buchenavia grandis for Goupia glabra, and Protium altissimum and Maclura tinctoria for Hymenaea courbaril. The analysis highlighted the overexploitation of a restricted group of species and the need to find alternatives to ensure the sustainability of forest management. This study contributed to identifying species that can serve as alternatives to commercial ones, promoting a more balanced and sustainable forest management. Full article

Dwarf bamboo (Fargesia denudata) is a crucial food source for the giant pandas. With its shallow root system and rapid growth, dwarf bamboo is highly sensitive to drought stress and nitrogen deposition, both major concerns of global climate change affecting plant growth and rhizosphere environments. However, few reports address the response mechanisms of the dwarf bamboo rhizosphere environment to these two factors. Therefore, this study investigated the effects of drought stress and nitrogen deposition on the physicochemical properties and microbial community composition of the arrow bamboo rhizosphere soil, using metagenomic sequencing to analyze functional genes involved in carbon and nitrogen cycles. Both drought stress and nitrogen deposition significantly altered the soil nutrient content, but their combination had no significant impact on these indicators. Nitrogen deposition increased the relative abundance of the microbial functional gene nrfA, while decreasing the abundances of nirK, nosZ, norB, and nifH. Drought stress inhibited the functional genes of key microbial enzymes involved in starch and sucrose metabolism, but promoted those involved in galactose metabolism, inositol phosphate metabolism, and hemicellulose degradation. NO₃⁻-N showed the highest correlation with N-cycling functional genes (p < 0.01). Total C and total N had the greatest impact on the relative abundance of key enzyme functional genes involved in carbon degradation. This research provides theoretical and technical references for the sustainable management and conservation of dwarf bamboo forests in giant panda habitats under global climate change. Full article

This study explores how machine learning can optimize financial risk management for non-profit organizations by evaluating various algorithms aimed at mitigating loan default risks. The findings indicate that ensemble learning models, such as random forest and LightGBM, significantly improve prediction accuracy, thereby enabling non-profits to better manage financial risk. In the context of the 2008 subprime mortgage crisis, which underscored the volatility of financial markets, this research assesses a range of risks—credit, operational, liquidity, and market risks—while exploring both traditional machine learning and advanced ensemble techniques, with a particular focus on stacking fusion to enhance model performance. Emphasizing the importance of privacy and adaptive methods, this study advocates for interdisciplinary approaches to overcome limitations such as stress testing, data analysis rule formulation, and regulatory collaboration. The research underscores machine learning’s crucial role in financial risk control and calls on regulatory authorities to reassess existing frameworks to accommodate evolving risks. Additionally, it highlights the need for accurate data type identification and the potential for machine learning to strengthen financial risk management amid uncertainty, promoting interdisciplinary efforts that address broader issues like environmental sustainability and economic development. Full article

Clarifying the complex relationships among ecosystem services (ESs) and their driving mechanisms is essential for effective ecosystem management and enhancing human welfare. Nonetheless, the current research on these issues still remains limited; therefore, further theoretical exploration is required. This study aims to quantitatively illustrate the trade-off strength of ESs and investigate the spatiotemporal heterogeneity connections between these relationships and various anthropogenic and natural factors in Baiyangdian basin, China, integrating InVEST, RMSE, geographical detector and MGWR methods. From 2000 to 2020, the total water yield (WY) and nutrient export (NE) increased, while the total carbon storage (CS) and habitat quality (HQ) decreased slightly. The trade-offs of ESs showed spatiotemporal heterogeneity. The most serious trade-off occurred between regulating services (CS and NE) and supporting services (HQ) in 2000, which was mainly distributed in the densely forested and grassed western and northern regions of the basin. The trade-off intensities of half of the pairwise ESs in 2020 increased, with the strengthened areas mainly located in the southeast of the watershed where built-up lands are concentrated. Various factors dominated the trade-offs among ESs, with the interactive effects of multiple drivers being more significant than those of individual factors. Land use type, vegetation cover and precipitation have the most pronounced effect on the trade-offs among ESs. The findings of this study may suggest and advocate for spatial ecological strategies to enhance the integrated and holistic advancement of various ESs and also serve as a reference for regional ecosystem governance and the attainment of sustainable growth. Full article