Search Results (22)

The rapid development of high-density cities has triggered severe ecological challenges, including habitat fragmentation, urban heat island (UHI) effects, and conflicting demands for public recreation. Traditional ecological networks (ENs) often focus only on “source” landscapes while neglecting degraded “sink” areas. This bias limits the ability of planners to resolve complex spatial conflicts. Therefore, the primary aim of this study is to develop a robust spatial planning framework that mitigates urban ecological conflicts and enhances regional resilience. To achieve this, we constructed a composite ecological network (CEN) for the high-density city of Guangzhou that harmonizes bird habitat conservation, thermal regulation, and cultural recreation. We combined the MaxEnt model, morphological spatial pattern analysis (MSPA), and circuit theory to identify functional “sources” and “sinks” across these three dimensions. Next, using complex network theory, we optimized the CEN and evaluated its structural robustness using low degree addition (LDA) and low betweenness addition (LBA) strategies. The results indicate the following: (1) The CEN effectively captured the complex mosaic landscape of the city. (2) Single-objective networks displayed distinct spatial differences—the recreational network formed a dispersed web of 242 corridors, while habitat and climate networks remained highly clustered. (3) The integrated CEN generated 1137 multi-layered corridors, creating a vital green skeleton to support species dispersal, mitigate UHI effects, and improve cultural access. (4) Optimization simulations verified that the LBA strategy provided the highest stability against targeted attacks by balancing network connectivity with local aggregation. Ultimately, this framework offers a highly adaptable planning tool for dense cities, providing precise spatial guidance to overcome ecological bottlenecks and harmonize urban growth with ecosystem resilience. Full article

As global climate change intensifies and urbanization accelerates, ecosystems face increasing pressure on carbon sequestration and biodiversity conservation. However, most studies focus on synergies between these functions while neglecting their complex trade-offs. Therefore, identifying integrated approaches that simultaneously enhance carbon sequestration and biodiversity conservation within multi-objective spatial optimization has become an urgent research priority. Using the Hangzhou metropolitan area as a case study, regional carbon sinks and bird habitats were quantified using the Carnegie–Ames–Stanford approach (CASA) and MaxEnt models. Core areas were identified using Morphological Spatial Pattern Analysis (MSPA) to extract carbon sink and bird habitat hubs, and resistance surfaces were constructed using the Analytic Hierarchy Process (AHP) and habitat suitability inversion. Circuit theory was then applied to construct carbon sink and bird habitat Green Infrastructure Networks (GINs), which were coupled to form a multi-functional carbon–bird habitat GIN. The network was evaluated for connectivity and robustness, and community detection was applied to explore its structure and inform ecological management. The results identified 133 carbon sequestration corridors and 171 bird habitat corridors. Coupling the two GINs produced a multi-functional network with dual carbon–bird habitat synergies, including 34 intersection points and 68 composite hubs. Unlike previous studies focused on single-function GINs, this study emphasizes synergy among multiple ecological functions. The findings indicate that the multi-functional GIN reduces uncertainty in multi-objective optimization, shifting GIN planning from single-objective management to coordinated multi-objective governance. This approach increases structural redundancy and functional resilience, thereby enhancing overall network robustness and connectivity. Full article

Promoting the agricultural recycling of biogas slurry (BS) is crucial for sustainable development, yet its long-term ecological impacts remain unclear. Through a multi-year field trial in a sugarcane system, this study examined the effects of BS application (0, 3, and 6 years) on the soil properties, bacterial communities, and functional genes for C, N, P, and S cycling. The results revealed distinct two-phase patterns of changes in soil properties, microbial communities, and functional genes. Short-term (3-year) application induced a “disturbance” phase, characterized by significant acidification (pH decreased by 17.91%), a surge in nitrate-N (increased by 757.27%), and a transient decline in bacterial richness. Long-term (6-year) application drove a “functional restructuring” phase, reversing acidification and significantly increasing soil organic matter (29.05%) and total nitrogen (TN) (20.81%). Bacterial richness recovered, and community composition distinctively restructured. Functional gene analysis revealed shifts in gene abundance that transitioned from high abundance in the short term to a new balance favoring processes like N fixation. Co-occurrence network analysis indicated that this functional shift was associated with core microbial modules (e.g., Firmicutes) and changes in soil pH and SOM. This study suggests that, although short-term application causes significant adjustments, sustained and appropriate BS application can ultimately enhance soil fertility and promote a functionally reorganized state by reshaping microbial interaction networks. It presents a microbial ecological basis for the safe and sustainable use of BS in circular agriculture. Full article

Although microbial communities in rice agroecosystems regulate nitrogen transformations, methane dynamics, crop residue decomposition, and pathogen suppression, their integration into agronomic decision-making remains limited. Existing rice microbiome reviews largely describe taxonomic diversity without critically linking microbial processes to management trade-offs, greenhouse gas mitigation, and productivity outcomes. This review synthesizes current knowledge through a process-based and management-oriented framework, emphasizing how water and crop residue management, fertilization, tillage, and genotype selection shape microbial functionality rather than merely community composition. Advances in stable isotope probing (SIP), metatranscriptomics, and multi-omics have improved functional inference, yet a persistent gap remains between genetic potential and in situ process rates. By integrating microbiome science within a One Health perspective, we propose a conceptual framework linking microbial network structure to interconnected dimensions of ecosystem, plant, and human health. This framework addresses not only agronomic outcomes but also food safety concerns, including mycotoxin contamination by fungal pathogens, microbial contributions to nutritional quality, and pathways through which soil and plant microbiomes influence human health via the food chain. We critically examine how microbiome management can simultaneously target productivity, environmental sustainability, and health risk mitigation. We identify priority research needs in predictive microbial ecology, activity-based validation, and microbiome-informed management strategies. Rather than framing microbiomes as a universal solution to global food security, this review critically examines their realistic and context-dependent contribution to improving sustainability, resilience, and resource-use efficiency in rice production under climatic and environmental constraints, while safeguarding food safety and public health. Full article

The microbial community surrounding plant roots plays a vital role in plant growth, nutrient uptake, stress resilience and other potential functions. This review synthesizes available evidence that plant growth-promoting microorganisms (PGPMs) not only directly benefit the plant but also modulate the rhizospheric microbiome by mediating metabolic reprogramming of the host plant. PGPMs modify the composition of root exudates through the regulation of phytohormone signaling and transcriptional networks, thereby promoting beneficial microbes and suppressing disease. Key mechanisms involve the jasmonate, ethylene, and strigolactones signaling pathways. Transcription factors MYB72, ERF1 regulate biosynthesis and secretion of metabolites like organic acids and coumarins. The exudates serve as specific signals for microbial community assembly and as enhancers of feedback loops that reinforce plant-microbe mutualism. We examine the ecological and agricultural significance of PGPM-induced metabolic reprogramming of the host due to PGPMs that enhances disease suppression, abiotic stress tolerance, and nutrient use efficiency. Lastly, we address advanced methods and strategies for transferring these biological pathways to the agricultural realm and on to a more sustainable agricultural practice with emphasis on the need to integrate multi-omics (whole genomics, transcriptomics, and metabolomics), synthetic microbial communities and plant genetic engineering for microbiome-assisted agriculture. This synthesis reveals that PGPM-induced metabolic reprogramming operates through an integrated cross-scale framework linking microbial perception, phytohormone signaling, transcriptional regulators, and transporter-mediated exudate efflux, with root exudates functioning as plant-controlled ecological filters that selectively shape the rhizosphere microbiome. We further identify key translational challenges, including context-dependent efficacy and the lab-to-field gap, and propose a roadmap combining multi-omics, synthetic communities, and genome editing to realize the potential of microbiome-assisted sustainable agriculture. Full article

Ecosystem vulnerability assessment is paramount for local environmental stability and lasting economic progress. This study selects Hubei Province as the research area, applying multi-source spatiotemporal datasets spanning the period 2000–2023. A pressure–state–response (PSR) framework, incorporating 14 distinct indicators, was developed. The selection criteria for these indicators adhered to principles of scientific rigor, all-encompassing scope, statistical representativeness, and practical applicability. The chosen indicators effectively encompass natural, anthropogenic, and socio-economic drivers, aligning with the specific ecological attributes and key vulnerability factors pertinent to Hubei Province. The analytic network process (ANP) method and entropy weighting (EW) method were integrated to ascertain comprehensive weights, thereby computing the ecological vulnerability index (EVI). In the meantime, we analyzed temporal and spatial EVI shifts. Spatial autocorrelation analysis, the geodetic detector, the Theil–Sen median, the Mann–Kendall trend test, and the Grey–Markov model were employed to elucidate spatial distribution, driving factors, and future trends. Results indicate that Hubei Province exhibited mild ecological vulnerability from 2000 to 2023, but with a notable deteriorating trend: extreme vulnerability areas expanded from 0.34% to 0.94%, while moderate and severe vulnerability zones also increased. Eastern regions demonstrate elevated vulnerability, but they were lower in the west, correlating with human activity intensity. The global Moran’s I index ranged from 0.8579 to 0.8725, signifying a significant positive spatial correlation of ecological vulnerability, with the highly vulnerable areas concentrated in regions with intense human activities, while the less vulnerable areas are located in ecologically intact areas. Habitat quality index and carbon sinks emerged as key drivers, possibly stemming from the forest–wetland composite ecosystem’s high dependence on water conservation, biodiversity maintenance, and carbon storage functions. Future projections based on Grey–Markov models indicate that ecological fragility in Hubei Province will exhibit an upward trend, with ecological conservation pressures continuing to intensify. This research offers a preliminary reference basis of grounds for ecological zoning, as well as sustainable regional development in Hubei Province, while also providing a theoretical and practical framework for constructing an ecological security pattern within the Yangtze River Economic Belt (YREB) and facilitating ecological governance in analogous river basins globally, thereby contributing to regional sustainable development goals. Full article

As a critical aquatic–terrestrial ecological transition zone, the lake littoral zone exhibits steep biogeochemical gradients and plays a vital role in regulating submerged microbial communities and their functions. This study aims to reveal how multi-scale environmental gradients influence microbial succession processes along desert lake littoral zones, as well as the distribution patterns of functional genes involved in carbon (C), nitrogen (N), and sulfur (S) cycling. The results demonstrated that microbial alpha-diversity in the vadose zone exhibited significant individual variability horizontally, while showing pronounced inter-group differences vertically. Horizontally, a distinct functional succession was observed from the shore to the water’s edge, with microbial potential shifting progressively from aerobic oxidative types toward anaerobic reductive types. Vertically, the root-intensive layer fostered more complex co-occurrence networks through enhanced interspecific interactions, suggesting higher functional resilience compared to other layers. Further analysis identified soil moisture as the primary environmental filter driving microbial composition, explaining 27.7% of the variation. Structural equation modeling (SEM) further elucidated that pH and Total Organic Carbon (TOC) were the key regulators of carbon fixation and sulfur oxidation genes, while Total Nitrogen (TN) dominated the distribution patterns of nitrogen cycling genes. These findings deepen the mechanistic understanding of microbial-mediated element cycling in desert lakeshore zones and provide a theoretical basis and data support for maintaining the functions of these fragile ecosystems. Full article

Agroecology is increasingly shaped by the convergence of traditional knowledge, farmers’ lived experiences, and scientific research, fostering a plural dialog that embraces the ecological and socio-political complexity of agricultural systems. Within this framework, soil biodiversity is essential for maintaining ecosystem functions, with soil microbiology, and particularly arbuscular mycorrhizal fungi (AMF), playing a pivotal role in enhancing soil fertility, plant health, and agroecosystem resilience. This review explores the synergy between agroecological practices and AMF by examining their ecological, economic, epistemic, and territorial contributions to sustainable agriculture. Drawing on recent scientific findings and Latin American case studies, it highlights how practices such as reduced tillage, crop diversification, and organic matter inputs foster diverse and functional AMF communities and differentially affect their composition and ecological roles. Beyond their biological efficacy, AMF are framed as relational and socio-ecological agents—integral to networks that connect soil regeneration, food quality, local autonomy, and multi-species care. By bridging ecological science with political ecology and justice in science-based knowledge, this review offers a transdisciplinary lens on AMF and proposes pathways for agroecological transitions rooted in biodiversity, cognitive justice, and territorial sustainability. Full article

Habitat fragmentation due to anthropogenic pressures threats functional connectivity across landscapes for flying mammals. Tadarida brasiliensis depends on nocturnal movement corridors linking refuge and foraging areas, yet these pathways are increasingly constrained in semi-arid regions of northern Mexico. This study developed and analyzed the potential ecological corridors connecting the main colony of T. brasiliensis located in Santa Eulalia with the Irrigation District 005 Delicias, in Chihuahua, Mexico. We integrated multi-source geospatial data within a geographic information system, including wind speed, terrain slope, normalized difference vegetation index, land surface temperature, distance to rivers, landscape aggregation, nighttime lighting, and distance to roads, power lines, and human settlements. Landscape resistance to movement was assessed using a combined framework based on the Analytic Hierarchy Process, the InVEST-Habitat Quality model, and Least Cost Path analysis, generating composite resistance. Five potential corridors were identified, with ranges of lengths and CWD:EucD ratios of 6.8–34.0 km and 20.4–51.3, respectively, reflecting variable cumulative resistance along pathways. Nighttime lighting and proximity to urban areas were major contributors to high resistance, particularly within urban and agricultural environments. The identified corridor network provides a spatial representation of potential routes and supports landscape-level conservation planning to mitigate anthropogenic pressures and maintain functional connectivity. Full article

The oral cavity contains several microbial niches, including saliva, dental plaque and tongue coating, each shaped by distinct local environments and host factors. This study compared the ecological and functional characteristics of the microbiomes of these three oral sites within the same individuals and examined host conditions associated with their variation. Saliva, supragingival plaque and tongue coating samples were collected simultaneously from 31 adults without clinical oral lesions. The bacterial 16S rRNA gene (V3–V4 region) was sequenced using the Illumina MiSeq platform, and analyses included α and β diversity, Mantel correlations, differential abundance tests, network analysis and functional prediction. The three sites displayed a clear ecological gradient. Saliva and tongue coating were taxonomically similar but were influenced by different host factors, whereas plaque maintained a distinct, biofilm-like structure with limited systemic influence. Functional divergence was most pronounced on the tongue coating despite its taxonomic similarity to saliva, whereas functional differences between saliva and plaque were modest despite larger taxonomic separation. These findings indicate that microbial composition and function vary independently across oral niches and support the need for multi-site sampling to more accurately characterize oral microbial ecology. Full article

Land surface temperature (LST) is a critical parameter in agricultural drought monitoring, crop growth analysis, and climate change research. However, the challenge of acquiring high-resolution LST data with both fine spatial and temporal scales remains a significant obstacle in remote sensing applications. Despite the high temporal resolution afforded by daily MODIS LST observations, the coarse (1 km) spatial scale of these data restricts their applicability for studies demanding finer spatial resolution. To address this challenge, a novel deep learning-based approach is proposed for LST downscaling: the spatial resolution downscaling attention network (SRDA-Net). The model is designed to upscale the resolution of MODIS LST from 1000 m to 250 m, overcoming the shortcomings of traditional interpolation techniques in reconstructing spatial details, as well as reducing the reliance on linear models and multi-source high-temporal LST data typical of conventional fusion approaches. SRDA-Net captures the feature interaction between MODIS LST and auxiliary data through global resolution attention to address spatial heterogeneity. It further enhances the feature representation ability under heterogeneous surface conditions by optimizing multi-source features to handle heterogeneous data. Additionally, it strengthens the model of spatial dependency relationships through a multi-level feature refinement module. Moreover, this study constructs a composite loss function system that integrates physical mechanisms and data characteristics, ensuring the improvement of reconstruction details while maintaining numerical accuracy and model interpret-ability through a triple collaborative constraint mechanism. Experimental results show that the proposed model performs excellently in the simulation experiment (from 2000 m to 1000 m), with an MAE of 0.928 K and an R² of 0.95. In farmland areas, the model performs particularly well (MAE = 0.615 K, R² = 0.96, RMSE = 0.823 K), effectively supporting irrigation scheduling and crop health monitoring. It also maintains good vegetation heterogeneity expression ability in grassland areas, making it suitable for drought monitoring tasks. In the target downscaling experiment (from 1000 m to 500 m and 250 m), the model achieved an RMSE of 1.804 K, an MAE of 1.587 K, and an R² of 0.915, confirming its stable generalization ability across multiple scales. This study supports agricultural drought warning and precise irrigation and provides data support for interdisciplinary applications such as climate change research and ecological monitoring, while offering a new approach to generating high spatio-temporal resolution LST. Full article

Urban–rural mountainous regions face persistent challenges in reconciling ecological conservation, cultural heritage preservation, and recreational demands, all of which are vital to advancing social sustainability. This study develops an integrated corridor framework for the Qinling–Daba region that couples ecological, heritage, and recreational networks within a socially sustainable planning perspective. Ecological sources were identified using Morphological Spatial Pattern Analysis (MSPA) combined with connectivity indices (IIC, PC, dPC). Heritage and recreation resources were inventoried through field surveys and prioritized using the Analytic Hierarchy Process (AHP). Function-specific corridors were modelled with a Minimum Cumulative Resistance (MCR) approach, and the three networks were synthesized through GIS overlay and hotspot analysis. The results indicate that there are 19 ecological sources and 28 corridors, 34 heritage nodes and 41 corridors, and 29 recreation nodes and 50 corridors. The composite network comprises 69 key nodes and 141 segments, classified into four node categories and three corridor types. Derived planning directives include graded buffer zones, continuity of riparian and forest belts, remediation of breakpoints with wildlife-friendly crossings, and universal accessibility standards for high-demand sites. By aligning ecological integrity, cultural values, and equitable access, the proposed framework offers a reproducible pathway to integrate people and places through multifunctional corridors. Beyond regional application, this research provides transferable insights for socially sustainable governance of urban–rural built environments in mountainous territories, supporting the achievement of Sustainable Development Goal 11. Full article

The concentration characteristics of SO₂, NO₂, O₃, and CO in the marine atmosphere are of great significance for understanding air–sea interactions and regional atmospheric chemical processes. However, due to the challenging conditions of marine monitoring, long-term continuous observational data remain scarce. To address this gap, this study proposes a Transfer Learning–Convolutional Neural Network (TL-CNN) model that integrates ERA5 meteorological data, EAC4 atmospheric composition reanalysis data, and ground-based observations through multi-source data fusion. During data preprocessing, the Data Interpolating Empirical Orthogonal Function (DINEOF), inverse distance weighting (IDW) spatial interpolation, and Gaussian filtering methods were employed to improve data continuity and consistency. Using ERA5 meteorological variables as inputs and EAC4 pollutant concentrations as training targets, a CNN-based inversion framework was constructed. Results show that the CNN model achieved an average coefficient of determination (R²) exceeding 0.80 on the pretraining test set, significantly outperforming random forest and deep neural networks, particularly in reproducing nearshore gradients and regional spatial distributions. After incorporating transfer learning and fine-tuning with station observations, the model inversion results reached an average R² of 0.72 against site measurements, effectively correcting systematic biases in the reanalysis data. Among the pollutants, the inversion of SO₂ performed relatively poorly, mainly because emission reduction trends from anthropogenic sources were not sufficiently represented in the reanalysis dataset. Overall, the TL-CNN model provides more accurate pollutant concentration fields for offshore regions with limited observations, offering strong support for marine atmospheric environment studies and assessments of marine ecological effects. It also demonstrates the potential of combining deep learning and transfer learning in atmospheric chemistry research. Full article

As unique geographical entities, island cities boast abundant ecological resources and profound cultural values, serving as critical hubs for maintaining ecosystem services in coastal transition zones. Ensuring the stability of ecosystem services is strategically significant for sustainable urban development, while the construction of Ecosystem Service Networks (ESNs) has emerged as a core strategy to enhance ecological functionality and mitigate systemic risks. Based on current research gaps, this study focuses on three key questions: (1) How to construct a Composite Ecosystem Service Index (CESI) for island cities? (2) How to identify the Ecosystem Service Networks (ESNs) of island-type cities? (3) How to optimize the ecosystem service networks of island cities? This study selects Pingtan Island as a representative case, innovatively integrating the InVEST and MaxEnt models to conduct a comprehensive assessment of ecological and cultural services. By employing Principal Component Analysis (PCA), a Composite Ecosystem Service Index (CESI) was established. The research follows a systematic technical approach to construct and optimize the ESN: landscape connectivity indices were applied to identify ecological source areas based on CESI outcomes; multidimensional resistance factors were integrated into the Minimum Cumulative Resistance (MCR) model to develop the foundational ecological network; gradient buffer zone analysis and circuit theory were sequentially employed to refine the network structure and evaluate ecological efficacy. Key findings reveal: (1) Landscape connectivity analysis scientifically delineated 20 ecologically valuable source areas; (2) The coupled MCR model and circuit theory established a hierarchical ESN comprising 45 corridors (12 Level-1, 14 Level-2, and 19 Level-3), identifying 5.75 km² of ecological pinch points, 7.17 km² of ecological barriers, and 84 critical nodes—primarily concentrated in cultivated areas; (3) Buffer zone gradient analysis confirmed 30 m as the optimal corridor width for multi-scale planning; (4) Circuit theory optimization significantly enhanced network current density (1.653→8.224), demonstrating a leapfrog improvement in ecological service efficiency. The proposed “assessment–construction–optimization” integrated methodology establishes an innovative paradigm for deep integration of ecosystem services with urban spatial planning. These findings provide practical spatial guidance for island city planning, supporting corridor design, conservation prioritization, and targeted restoration, thereby enhancing ecosystem service efficiency, biodiversity protection, and resilience against coastal ecosystem fragmentation. Full article

Gut microbial dysbiosis, especially bacteriome, has been implicated in Alzheimer’s disease (AD). However, nonbacterial members of the gut microbiome in AD, such as the mycobiome, archaeome, and virome, are unexplored. Here, we perform higher-resolution shotgun metagenomic sequencing on fecal samples collected longitudinally from a mouse model of AD to investigate longitudinal and multi-kingdom gut microbiome profiling. Shotgun metagenomic sequencing of fecal samples from AD mice and healthy mice returns 41,222 bacterial, 414 fungal, 1836 archaeal, and 1916 viral species across all time points. The ecological network pattern of the gut microbiome in AD mice is characterized by more complex bacterial–bacterial interactions and fungal–fungal interactions, as well as simpler archaeal–archaeal interactions and viral–viral interactions. The development of AD is accompanied by multi-kingdom shifts in the gut microbiome composition, as evidenced by the identification of 1177 differential bacterial, 84 differential fungal, 59 differential archaeal, and 10 differential viral species between healthy and AD mice across all time points. In addition, the functional potential of the gut microbiome is partially altered in the development of AD. Collectively, our findings uncover longitudinal and multi-kingdom gut microbiome alterations in AD and provide a motivation for considering microbiome-based therapeutics during the prevention and treatment of AD. Full article