Search Results (145)

Quantifying sex-specific dietary differences in small mammals reveals the internal resource allocation mechanisms within a species and provides new insights for ecosystem management and conservation practices. The plateau pika (Ochotona curzoniae) and plateau zokor (Eospalax baileyi) are dominant small mammals that exhibit distinct lifestyles and social structures on the Qinghai-Tibetan Plateau. Despite the fact that the diets of both species have been extensively studied, sex-specific dietary differences have rarely been investigated. This study employed DNA metabarcoding combined with a self-constructed plant DNA barcode database to analyze the diet composition and trophic niche of male and female plateau pika and plateau zokor during the growing season. The results showed that male and female plateau pika consumed 39 and 37 plant species, respectively, and male and female plateau zokor consumed 38 and 39 plant species, respectively. With respect to the plateau pika, males showed a significantly higher intake of Phlomoides umbrosa than females (p < 0.05), whereas females consumed a significantly greater proportion of tuberous plants (p < 0.05). Females also exhibited a significantly greater dietary diversity and trophic niche breadth than males. But there was no significant difference in dietary diversity and trophic niche breadth between the sexes in the plateau zokor. In conclusion, our results show that dietary differences between males and females depend on each species’ lifestyle. Social, surface-living pikas show apparent sex-based differences, while solitary, underground-living zokors do not. Full article

Ecological restoration of mine wastelands is central to biodiversity conservation and ecosystem recovery worldwide. However, the long-term ecological consequences of active restoration versus natural regeneration remain debated, particularly in mountainous karst landscapes. Using a space-for-time substitution, we established a five-stage chronosequence—recently abandoned, 10 years, 20 years, 30 years, and a late-successional forest (>35 years)—in a typical underground coal-mine wasteland in eastern Yunnan, southwest China. Each age class contained paired active restoration and natural regeneration sites; the late-successional forest served as a reference. We surveyed nested vegetation plots (20 × 20 m with shrub and herb subplots) in summer and autumn, recorded vertebrate species with camera traps, and quantified α-diversity (species richness, Shannon–Wiener diversity, Simpson’s diversity, Pielou’s evenness) and β-diversity (Bray–Curtis dissimilarity, non-metric multidimensional scaling). Overall plant α-diversity was highest in natural regeneration and lowest in active restoration, whereas tree-layer diversity was highest in active restoration and shrub and herb layers were richer under natural regeneration. Preliminary data from our camera traps suggested that animal species richness ranked late-successional forest > natural regeneration > active restoration, but evenness peaked in active restoration, suggesting early-stage homogenization. Plant β-diversity indicated stronger compositional divergence among active restoration sites and greater similarity between natural regeneration and the reference forest; both modes converged toward the reference forest over time but followed distinct patterns. These findings suggest that active restoration accelerates structural development yet increases between-site heterogeneity, whereas natural regeneration maintains higher overall diversity and compositional similarity to reference communities. Our results provide preliminary empirical guidance for selecting restoration strategies in similar karst coal-mine landscapes. Full article

Endophytic bacteria play an important role in the growth, stress tolerance, and metabolic function of salt-tolerant peanuts, yet their community assembly across different saline–alkali soils and plant organs remains poorly characterized. In this study, the V3–V4 variable region of the endophytic bacteria 16S rRNA gene in three organs (roots, leaves, and pods) of high-oleic-acid peanut variety Huayu9118 from three saline–alkali locations (Xinjiang, Jilin, and Shandong, China) was analyzed by high-throughput sequencing. A total of 1,360,313 effective sequences yielded 19,449 amplicon sequence variants (ASVs), with Proteobacteria (45.86–84.62%), Bacteroidota (6.52–13.90%), and Actinobacteriota (3.97–10.87%) dominating all samples. Niche strongly influenced microbial diversity: the roots exhibited the highest level of richness (Chao 1/ACE indices), while the leaves showed the greatest diversity (Shannon/Simpson indices) in XJ samples. Significant compositional differences were observed between aerial (leaves) and underground (roots/pods) organs. Geographic location also markedly shaped endophytic communities, with stronger effects in roots and pods than in leaves—a pattern supported by PCoA combined with ANOSIM (R (roots) = 1, R (pods) = 0.874, R (leaves) = 0.336, respectively, p < 0.001). Saline–alkali adaptation led to a marked enrichment of Novosphingobium in roots and pods and of Halomonas in leaves compared to non-saline–alkali-grown peanuts. Furthermore, the endophytic communities within the same organ type varied significantly across the three saline–alkali sites. Redundancy analysis (RDA) identified the key environmental factors shaping bacterial community composition in the root samples from each location: available phosphorus (AP) and sulfate (SO₄²⁻) were the strongest predictors in XJ; available potassium (AK) and chloride (Cl⁻) in DY; and hydrolyzed nitrogen (HN), pH, soil organic matter (SOM), and bicarbonate (HCO₃⁻) in JL. These findings demonstrate that niches and geographical conditions determined the composition and relative abundance of endophytic bacteria in salt-tolerant peanuts, providing new insights into microbial ecological adaptation in saline–alkali ecosystems. Full article

Petroleum hydrocarbon pollution is a serious environmental and human health problem. In recent decades, the impact of this substance has been profound and persistent, affecting the balance of aquatic and terrestrial ecosystems and leading to significant physical and psychosocial effects among the population. Natural sources (crude oil, natural gas, forest fires, and volcanic eruptions) and anthropogenic (road traffic, smoking, pesticide use, oil drilling, underground water leaks, improper oil spills, industrial and mining waste water washing, etc.), the molar weight of the hydrocarbon, and the physicochemical properties are important factors in determining the degree of pollution. The effects of pollution on the environment consist of altering the fundamental structures for sustaining life (infertile lands, climate change, and loss of biodiversity). In terms of human health, diseases of the following systems occur: respiratory (asthma, bronchitis), cardiovascular (stroke, heart attack), pulmonary (infections, cancer), and premature death. To reduce contamination, sustainable intervention must be carried out in the early stages of the pollution-control process. These include physical techniques (isolation, soil vapor extraction, solvent extraction, soil washing), chemical techniques (dispersants–surfactants, chemical oxidation, solidification/stabilization, thermal desorption), biological techniques (bioremediation, phytoremediation), and indigenous absorbents (peat, straw, wood sawdust, natural zeolites, clays, hemp fibers, granular slag, Adabline II OS). Due to the significant environmental consequences, decisions regarding the treatment of contaminated sites should be made by environmental experts, who must consider factors such as treatment costs, environmental protection regulations, resource recovery, and social implications. Public awareness is also crucial, as citizens need to understand the severity of the issue. They must address the sources of pollution to develop sustainable solutions for ecosystem decontamination. By protecting the environment, we are also safeguarding human nature. Full article

Bats are ecologically vital mammals, serving as pollinators, seed dispersers, and bioindicators of ecosystem health. Many species inhabit natural caves, which offer optimal conditions for survival but present challenges for direct ecological monitoring due to their dark, complex, and inaccessible environments. Traditional monitoring methods, such as mist-netting, are invasive and limited in scope, highlighting the need for non-intrusive alternatives. In this work, we present a portable multisensor platform designed to operate in underground habitats. The system captures multimodal data, including near-infrared (NIR) imagery, ultrasonic audio, 3D structural data, and RGB video. Focusing on NIR imagery, we evaluate the effectiveness of the YOLO object detection framework for automated bat detection and counting. Experiments were conducted using a dataset of NIR images collected in natural shelters. Three YOLO variants (v10, v11, and v12) were trained and tested on this dataset. The models achieved high detection accuracy, with YOLO v12m reaching a mean average precision (mAP) of 0.981. These results demonstrate that combining NIR imaging with deep learning enables accurate and non-invasive monitoring of bats in challenging environments. The proposed approach offers a scalable tool for ecological research and conservation, supporting population assessment and behavioral studies without disturbing bat colonies. Full article

Linpan ecosystems, distinct to western Sichuan, China, are integral to regional biodiversity and carbon cycling. However, comprehensive biomass estimation for these systems has not been thoroughly investigated. This study seeks to fill this gap by enhancing the accuracy and precision of biomass estimation in these ecologically vital landscapes through the application of multi-source remote sensing techniques, specifically by integrating the strengths of optical and radar remote sensing data. The focus of this research is on the forest biomass of Linpan, encompassing the tree layer, which includes the trunk, branches, leaves, and underground roots. Specifically, the research focused on the Linpan ecosystems in the Wenjiang District of western Sichuan, utilizing an integration of Sentinel-1 SAR, Sentinel-2 multispectral, and GF-2 high-resolution data for multi-source remote sensing-based biomass estimation. Through the preprocessing of these data, Pearson correlation analysis was conducted to identify variables significantly correlated with the forest biomass as determined by field surveys. Ultimately, 19 key modeling factors were selected, including band information, vegetation indices, texture features, and phenological characteristics. Subsequently, three algorithms—multiple stepwise regression (MSR), support vector machine (SVM), and random forest (RF)—were employed to model biomass across mixed-type, deciduous broadleaved, evergreen broadleaved, and bamboo Linpan. The key findings include the following: (1) Sentinel-2 spectral data and Sentinel-1 VH backscatter coefficients during the summer, combined with vegetation indices and texture features, were critical predictors, while phenological indices exhibited unique correlations with biomass. (2) Biomass displayed a marked north–south gradient, characterized by higher values in the south and lower values in the north, with a mean value of 161.97 t ha⁻¹, driven by dominant tree species distribution and management intensity. (3) The RF model demonstrated optimal performance in mixed-type Linpan (R² = 0.768), whereas the SVM was more suitable for bamboo Linpan (R² = 0.892). The research suggests that integrating multi-source remote sensing data significantly enhances Linpan biomass estimation accuracy, offering a robust framework to improve estimation precision. Full article

Soil organic carbon (SOC) represents a crucial factor in agricultural production, and its accumulation is influenced by soil microbial community and microbial metabolism. Straw returning combined with decomposing agents is recognized practice to enhance SOC. On the other hand, the impacts of controlled-release nitrogen fertilizer (CR) on the function of the decomposing agent in degrading straw are underexplored. In this study, an incubation experiment with ¹³C labeled straw in three nitrogen fertilizer treatments (CK, no nitrogen applied; UR, urea applied; CR, controlled-release fertilizer applied) was carried out to elucidate how CR regulates the straw decomposition agent and bacterial community to influence the SOC sequestration, based on field experiments. And we examined the changes in soil organic carbon and the stability of the bacterial networks by combining co-occurrence networks and a structural equation model. In the incubation experiment, the results demonstrated that CR increased the relative abundance of straw decomposition agent and straw-derived SOC (SO¹³C). Additionally, CR enhanced the stability of soil bacterial networks, compared with UR, by strengthening the interactions within the soil bacterial community. Pearson correlations confirmed that straw decomposition agent was positively associated with SO¹³C. Moreover, the straw decomposition agent was positively correlated with the activities of the nitrogen-cycling enzyme (urease, N-acetyl-β-glucosaminidase) and carbon-degrading enzyme (β-1,4-glucosidase, cellulase). Furthermore, structural equation modeling indicated that soil inorganic nitrogen played the most direct role in changes in the straw decomposition agent and then indirectly stimulated the activity of cellulase, ultimately increasing straw-derived carbon in the soil. This study elaborates the mechanism of straw returning combined with straw decomposition agent and controlled-release fertilizers to enhance the SOC of coastal saline–alkali soil from the perspective of underground biology. Collectively, the results of this research might improve the management of straw returning and sustainable utilization of fertility in saline–alkali soil. It provides a new perspective on fertilization for increasing soil carbon sequestration in future farmland ecosystems. Full article

Cultural customs often condition solid waste management, especially in developing countries. The decomposition of solid waste depends on climatic conditions and is related to geomorphology and anthropogenic practices. Leachate generated in landfills can migrate superficially and underground, contaminating soils and aquifers. Knowing the level of contaminant load in leachate is important for proper solid waste management. However, in the Andean regions of Peru, there is scarce data on the polluting potential of leachates. This research aimed to determine the Leachate Pollution Index (LPI) according to the sub-indexes of organic, inorganic, and heavy metals from landfills in the high Andean regions of Peru. Physical, chemical, and microbiological parameters were evaluated in fresh and retained leachate samples, in both dry and rainy seasons, from two landfills located at around 3000 m of altitude. The results showed high contamination levels, particularly in BOD₅, COD, NH₃-N, and total coliforms, with high organic and inorganic sub-indexes that affect the LPI, indicating high levels of contamination and posing a potential risk to surrounding ecosystems. It was also found that the high Andean landfills studied have a good-to-high biodegradability. This research contributes essential baseline information for environmental monitoring and supports the need for improved leachate management in high-altitude landfills in Peru and similar Andean contexts. Full article

Illegal mining operations induce cascading ecosystem degradation by causing extensive ground subsidence, necessitating accurate underground goaf localization for effectively induced-hazard mitigation. The conventional locating method applied the synthetic aperture radar interferometry (InSAR) technique to obtain ground deformation to estimate underground goaf parameters, and the locating accuracy was crucially contingent upon the appropriateness of nonlinear deformation function models selection and the precision of geological parameters acquisition. However, conventional model-driven underground goaf locating frameworks often fail to sufficiently integrate prior geological information during the model selection process, potentially leading to increased positioning errors. In order to enhance the operational efficiency and locating accuracy of underground goaf, deformation model selection must be aligned with site-specific geological conditions under varying cases of prior information. To address these challenges, this study categorizes prior geological information into three different hierarchical levels (detailed, moderate, and limited) to systematically investigate the correlations between model selection and prior information. Subsequently, field validation was carried out by applying two different non-linear deformation function models, Probability Integral Model (PIM) and Okada Dislocation Model (ODM), with three different prior geological information conditions. The quantitative performance results indicate that, (1) under a detailed prior information condition, PIM achieves enhanced dimensional parameter estimation accuracy with 6.9% reduction in maximum relative error; (2) in a moderate prior information condition, both models demonstrate comparable estimation performance; and (3) for a limited prior information condition, ODM exhibits superior parameter estimation capability showing 3.4% decrease in maximum relative error. Furthermore, this investigation discusses the influence of deformation spatial resolution, the impacts of azimuth determination methodologies, and performance comparisons between non-hybrid and hybrid optimization algorithms. This study demonstrates that aligning the selection of deformation models with different types of prior geological information significantly improves the accuracy of underground goaf detection. The findings offer practical guidelines for selecting optimal models based on varying information scenarios, thereby enhancing the reliability of disaster evaluation and mitigation strategies related to illegal mining. Full article

The digital transformation of the Architecture, Engineering and Construction sector is accelerating the adoption of digital platforms as critical enablers of data integration, stakeholder collaboration and process optimization. This paper presents a systematic review of 125 peer-reviewed journal articles (2015–2025), selected through a PRISMA-guided search using the Scopus database, with inclusion criteria focused on English-language academic literature on platform-enabled digitalization in the built environment. Studies were grouped into six thematic domains, i.e., artificial intelligence in construction, digital twin integration, lifecycle cost management, BIM-GIS for underground utilities, energy systems and public administration, based on a combination of literature precedent and domain relevance. Unlike existing reviews focused on single technologies or sectors, this work offers a cross-sectoral synthesis, highlighting shared challenges and opportunities across disciplines and lifecycle stages. It identifies the functional roles, enabling technologies and systemic barriers affecting digital platform adoption, such as fragmented data sources, limited interoperability between systems and siloed organizational processes. These barriers hinder the development of integrated and adaptive digital ecosystems capable of supporting real-time decision-making, participatory planning and sustainable infrastructure management. The study advocates for modular, human-centered platforms underpinned by standardized ontologies, explainable AI and participatory governance models. It also highlights the importance of emerging technologies, including large language models and federated learning, as well as context-specific platform strategies, especially for applications in the Global South. Full article

The implementation of a decentralized blue–green infrastructure (BGI) is a key strategy in climate adaptation and stormwater management. However, the integration of urban trees into the multifunctional infrastructure remains insufficiently addressed, particularly regarding rooting space in dense urban environments. Addressing this gap, the BoRSiS project developed the soil-pipe system (SPS), which repurposes the existing underground pipe trenches and roadway space to provide trees with significantly larger root zones without competing for additional urban space. This enhances tree-related ecosystem services, such as cooling, air purification, and runoff reduction. The SPS serves as a stormwater retention system by capturing excess rainwater during heavy precipitation events of up to 180 min, reducing the pressure on drainage systems. System evaluations show that, on average, each SPS module (20 m trench length) can store 1028–1285 L of water, enabling a moisture supply to trees for 3.4 to 25.7 days depending on the species and site conditions. This capacity allows the system to buffer short-term drought periods, which, according to climate data, recur with frequencies of 9 (7-day) and 2 (14-day) events per year. Geotechnical and economic assessments confirm the system stability and cost-efficiency. These findings position the SPS as a scalable, multifunctional solution for urban climate adaptation, tree vitality, and a resilient infrastructure. Full article

Climate change, represented by global warming, significantly affects the structure and function of alpine wetland ecosystems. Investigating the response strategies of alpine wetland plants to temperature changes is fundamental to understanding how alpine wetlands cope with global warming. This study, conducted at the typical alpine wetland Napahai, uses the latest predictions from the Intergovernmental Panel on Climate Change (IPCC) and employs open–top chamber warming experiments (OTCs) to study the responses of typical alpine wetland plants, Zizania caduciflor and Sparganium stoloniferum, to simulated warming. The results indicate that simulated warming significantly reduced the photosynthetic capacity of Z. caduciflor, and obviously decreased the biomass accumulation of both Z. caduciflor and S. stoloniferum (p < 0.05). The mean annual temperature (MAT) and annual maximum temperature (max) are the primary temperature factors affecting the photosynthetic and biomass parameters. Specifically, the net photosynthetic rate, stomatal conductance, transpiration rate, the aboveground, underground, and total biomasses, and the nitrogen contents of aboveground and underground buds of Z. caduciflor all showed significant negative correlations with MAT and max (p < 0.05). The parameters of S. stoloniferum mainly showed significant correlations with max, with its underground biomass, total biomass, and root nitrogen content all showing significant negative correlations with max, while its fibrous root carbon content and underground bud phosphorus content showed significant positive correlations with max (p < 0.05). The results are consistent with previous studies in high–altitude regions, indicating that warming reduces the photosynthetic capacity and biomass accumulation of alpine wetland plants, a trend that is widespread and will lead to a decline in the productivity of alpine wetlands and changes in vegetation composition. The study can provide a case for understanding the response strategies of alpine wetlands in the context of climate change. Full article

Gazania is a genus of herbaceous plants from the Asteraceae (daisy) family. Native to southern Africa, several species of this genus have been introduced to different countries as ornamental garden plants due to their beautiful flowers. In the wild, Gazania species have been observed with flowers of different shades of pink, red, yellow, orange and combination of these colours. Some species of Gazania have escaped the gardens and become highly invasive weeds in their introduced range. Invasive, drought-tolerant and prolific seed-producing Gazania plants are found in Australia, New Zealand, Algeria, Egypt, Europe and California. In particular, two perennial species, Gazania linearis and Gazania rigens, commonly known as gazania, have become a major problem in Australia. They have naturalized and are widespread in a range of environments, such as roadsides, pasture/grassland systems, coastal sand dunes, and natural and managed ecosystems. Their seeds and underground reproductive structures are carried along roadsides by slashers, machinery, wind and water, and spread into native vegetation, pastures, horticultural crops and broadacre agronomic crop production systems. Gazania causes significant environmental, production and economic losses in the infested ecosystems. While limited research has been conducted on their biology and invasion ecology, anecdotal evidence suggests that the ability of gazania plants to produce a large number of seeds form thick, dense populations, and tolerate harsh environments, including drought, heat and sub-optimal soil pH, making them persistent, problematic weed species. In addition, perennial growth habit, high genetic diversity and allelopathic potential have also been suggested to facilitate their invasion success, but no research has been conducted on these aspects. Gazania is very difficult to manage, and currently, there are no effective control options available, including chemical herbicides. The lack of knowledge on their biology, invasion pathways and management is hindering the effective management of gazanias. This review compiles and synthesizes currently available information on the distribution, biology, ecology and management of weedy gazania species, with a particular focus on Australia. We also highlight the key knowledge gaps for future research. We believe this information provides researchers and practitioners with an up-to-date account on the weedy aspects of these popular ornamental plants and will help improve management efforts. Full article

This study aims to enhance natural heritage through detailed monitoring aimed at evaluating ongoing environmental dynamics and anthropic impacts on fragile coastal ecosystems, with particular attention to dune ecosystems and back-dune ponds in Southern Italy. The integration of remote sensing technologies, such as thermal cameras and geospatial data, has made it possible to identify underground water sources that are useful for characterizing and monitoring the water regime of the targeted area. Through modelling software, different methods of assessing the environmental state, aimed at identifying the best sustainable practices that can be implemented in these fragile ecosystems, are also proposed. The presented multidisciplinary approach demonstrates how science and technology can support the sustainable management of protected areas, with positive implications for environmental protection and local development, and the adoption of best practices, inspired by international models, that can promote the conservation of biodiversity and the valorization of historical heritage. Full article

This paper examines two major artificial wetlands in Shanghai—Shanghai Fish and Dishui Lake—as case studies to explore the biomass, carbon content, carbon density, and carbon sequestration functions of wetland plants in urban ecosystems. Through field sampling and elemental analysis of 20 common wetland plant species, this study investigated the differences in aboveground and underground biomass and carbon storage capacity across different plant types. The results indicated that emergent plants have the highest carbon storage capacities, with species such as Cyperus involucratus, Arundo donax, Phragmites australis, and Nelumbo sp. exhibiting higher carbon densities, while floating plants demonstrated relatively weaker carbon storage capacity. The carbon content varied significantly between different parts and species of plants, while soil carbon density was much higher than that of the plant portions, highlighting the crucial role of soil in wetland carbon sequestration. Additionally, an inversion model for wetland plant carbon density was established, and remote sensing data were used to assess the vegetation distribution characteristics and carbon density variations in the two artificial wetlands. This distribution pattern reflects the influence of wetland vegetation and water level (which affect water availability and nutrient distribution) on carbon density. The results showed a significant increase in carbon density from 2018 to 2023, particularly in lakeshore areas, suggesting that wetland ecological restoration and management measures have achieved positive outcomes, including a measurable increase in carbon density and enhanced vegetation coverage. The findings are significant for understanding and enhancing the carbon sequestration potential of artificial wetlands in urban ecosystems. Full article