搜索结果：Ecological Modeling

This study investigates the spatiotemporal dynamics of Land Use/Land Cover (LULC) in Saharsa District, Bihar, India, over 21 years (2002-2023), using multi-temporal Landsat satellite imagery (Landsat TM and Landsat OLI/TIRS) and geospatial analysis. The novel scientific contribution of this work lies in the integrated application of a Potential Ecological Vulnerability Index (PEVI) framework alongside LULC change detection to identify ecological stress hotspots and assess the cumulative environmental consequences of land transformation in a flood-prone Megafan environment, a combination not previously applied to the ecologically sensitive Kosi Basin. Satellite images from four time points (2002, 2009, 2016, and 2023) were subjected to radiometric, atmospheric, and geometric pre-processing, including cross-sensor radiometric calibration to ensure spectral consistency between Landsat 5 TM and Landsat 8 OLI/TIRS sensors, and classified using the supervised Maximum Likelihood Classifier (MLC) in ERDAS IMAGINE. Key quantitative findings reveal a 100.60 km² (6.04%) increase in built-up area and a 77.42 km² (4.66%) decline in forest cover over the study period. PEVI mapping identified a progressive shift from moderate to poor and very poor ecological quality, particularly in the central and southeastern zones of the district, corresponding to areas of accelerated deforestation and urban expansion. These results underscore the ecological vulnerability of the district and highlight the urgent need for sustainable land management policies, reforestation programmes, and flood-resilient urban planning. Future research should incorporate higher-resolution imagery (e.g., Sentinel-2), machine learning classifiers (Random Forest, SVM), socio-economic variables, and hydrological modelling for more comprehensive land-use policy formulation.

The circular economy (CE) has emerged as a prominent framework for addressing environmental challenges while promoting collective well-being. By aiming to "close the loop" of product life cycles through reuse and recycling, the CE encourages entrepreneurial and economic activities oriented toward ecological transition and sustainable urban development. However, CE implementation remains predominantly technology-centric and industry-driven, privileging innovation and system optimization - approaches increasingly seen as insufficient to support transformative socio-ecological change. Consequently, CE policies often remain confined to waste management, overlooking preventive strategies and the potential of social innovation. This study investigates the contribution of grassroots initiatives to circular transitions in the metropolitan city of Bologna (Italy) and examines whether gaps in the supporting ecosystem and infrastructure hinder their implementation. The analysis focuses on the participatory project R-innovare l'Economia Circolare, developed within the NRRP ECOSISTER program and involving 17 organizations engaged in reuse, repair, sharing, and regeneration practices. Using participatory methodologies - including contextual mapping and facilitated co-design workshops - the study identifies actors, circular practices, and business models, as well as implementation barriers emerging at the organizational, community and institutional levels. Rather than focusing on technological optimization or recycling, findings reveal a heterogeneous set of circular practices centered on waste prevention, community engagement, and social equity, which diverge from mainstream CE models. These practices point to emerging forms of collaboration and service-based approaches that generate socio-environmental value by integrating ecological care with community well-being. At the same time, identified challenges include limited access to spaces and resources, insufficient incentives for non-technological innovation and widespread reuse practices, and a fragmented governance landscape marked by conflicting policy domains. Drawing on the Bologna case, the study shows how grassroots actors reinterpret circularity and broaden the transformative potential of the CE beyond recycling and the economic sphere, reframing it as a process rooted in care, solidarity, and territorial well-being. The analysis also highlights gaps within policy and research infrastructures that must be addressed to advance a more just and place-based socio-ecological transition.

This study advances understanding of teacher wellbeing by examining how pre- and in-service English Language Teaching (ELT) student teachers in Iran's private sector experience flourishing amid systemic precarity. Drawing on Bronfenbrenner's Ecological Systems Theory and using interpretative phenomenological analysis of semi-structured interviews with ten Iranian ELT student teachers, wellbeing is traced across microsystem, mesosystem, exosystem, macrosystem, and chronosystem layers. Findings show that immediate classroom interactions, pedagogical confidence, student rapport, and skill cultivation, co-construct hedonic joy and eudaimonic purpose, while mentorship, familial support, institutional autonomy, and resource equity function as key resilience resources at the meso- and exosystem levels. At the macro- and chronosystem levels, socio-economic instability, COVID-19 disruptions, and AI-related anxieties intensify vulnerabilities to classroom monotony, work-life spillover, and market-driven employment uncertainty, particularly for novice teachers. Theoretically, the study extends ecological and wellbeing frameworks by demonstrating how hedonic and eudaimonic dimensions are jointly produced across nested systems in a commercialized ELT context, thereby contributing to contemporary positive psychology. Practically, it identifies multilevel leverage points, such as diversified practicum assignments, structured cross-institute mentorship, fair contractual and policy arrangements, and public campaigns that revalue teaching, that can support more sustainable wellbeing trajectories for ELT student teachers in private-sector settings.

To investigate whether established synthetic data quality metrics predict when deep generative augmentation improves performance under simulated external validation in radiomics. Three conditional generators (WGAN-GP, CVAE, TabDDPM) were trained on 50 public binary-classification radiomic datasets from radMLBench. For each dataset-generator pair (n&#x2009;=&#x2009;150), five quality metrics and &#x394;AUC (change in simulated external AUC under domain shift) were recorded across 10 repetitions; undefined AUCs (single-class test folds) were excluded a priori (30/2500 external, 1.2%). Six analyses tested quality-&#x394;AUC associations: rank correlations with FDR correction, ROC discrimination, subgroup random-effects meta-analysis, composite scoring, quality-guided generator selection, and SMOTE comparison. All experiments were replicated with a random forest classifier. Pooled quality metrics correlated significantly with &#x394;AUC (|&#x3c1;|&#x2009;= 0.30-0.36, all adjusted p&#x2009;<&#x2009;0.001); within each generator, all correlations were non-significant (all p&#x2009;>&#x2009;0.05), revealing an ecological fallacy. After Benjamini-Hochberg correction, 5 of 20 correlations survived: the 4 pooled associations and DDPM MMD (unexpected direction). ROC-AUC ranged from 0.42 to 0.61 (near-chance). Quality-guided selection yielded a significantly negative pooled &#x394; (-0.0051; 95% CI: -0.0083 to -0.0019) and improved external AUC in only 17/50 (34%) datasets versus 34/50 (68%) for an oracle. Random forest replication rendered pooled correlations non-significant (all p&#x2009;>&#x2009;0.09). A pre-specified sensitivity analysis on the 29/50 datasets statistically distinguishable from chance produced identical conclusions. Aggregate quality-performance correlations are driven by between-generator differences, not within-generator variation that could guide practical decisions. Quality metrics are insufficient proxies for clinical utility; task-specific external validation remains indispensable. Question Can established quality metrics of synthetic radiomics data identify when generative augmentation actually improves the performance of prediction models under simulated external validation? Findings Across 50 public datasets, aggregate quality-performance correlations vanished within individual generators (ecological fallacy), and quality-guided generator selection underperformed real-only training. Clinical relevance Quality metrics commonly used to validate synthetic radiomics data cannot identify when augmentation improves clinical model performance under simulated domain shift, underscoring the irreplaceable role of task-specific external validation before clinical deployment.

Stabilizing grain production in double-cropping rice areas depends largely on early rice; however, its production is often considered a reserve due to high-temperature stress during the grain filling stage, leading to poor quality and low market acceptance. High-quality late rice early planting-continuous cropping (HLREP-CC) model was proposed to improve grain quality in early-season rice under limited high-quality early rice varieties. Two high-quality late indica rice varieties were selected as test materials to explore the superiority of the HLREP-CC model. Meanwhile, we compared the differences in rice yield and quality between the early and late seasons across three ecological planting areas in China: central Jiangxi (C-Jx), south-central Jiangxi (SC-Jx), and southern Jiangxi (S-Jx). The HLREP-CC model significantly increased yield cooking and eating quality in the early season; although processing and appearance quality decreased in the early-season rice, it is recommended to select varieties with an environmentally quality-insensitive variety. As latitude decreased, rice processing and appearance, and nutritional quality enhanced; the cooking and eating quality depended on the rice variety. There are significant differences in the performance of this model among different ecological zones. The SC-Jx had the highest yield and best comprehensive performance, as it can balance yield and maintain good rice quality, making it the ideal core promotion area. The S-Jx region exhibited superior processing and appearance quality but slightly lower yield, requiring careful variety selection. The C-Jx showed limited potential due to significantly lower early-season yield and quality. &#xa9; 2026 Society of Chemical Industry.

Unintended pregnancy (UIP) remains a persistent public health concern among young women in sub-Saharan Africa, including Tanzania. This study examined Sexual Health Literacy for Pregnancy Prevention (SHLPP) and engagement in Sexual Behaviours potentially leading to UIP (referred to in this study as Risky Sexual Behaviours [RSB]) among female university students using a Social Ecological Model (SEM). Specifically, it assessed (i) levels of SHLPP and RSB, (ii) differences across individual, interpersonal, and contextual factors, and (iii) the predictive effects of these factors on SHLPP and RSB, including the influence of SHLPP on RSB. A cross-sectional study was conducted involving 255 female university students at Sokoine University of Agriculture, Tanzania. Data were collected using a structured questionnaire and analysed using descriptive statistics, factorial ANOVA, and hierarchical multiple regression. Most participants (75.6%) exhibited low-to-moderate SHLPP, while 63.7% reported moderate-to-high RSB. Upbringing environment significantly influenced both RSB (F(1,165)&#x2009;=&#x2009;17.817, p&#x2009;<&#x2009;.001, &#x3b7;2&#x2009;=&#x2009;.097) and SHLPP (F(1,218)&#x2009;=&#x2009;4.124, p&#x2009;<&#x2009;.05, &#x3b7;2&#x2009;=&#x2009;.019), with urban students reporting higher levels of both. Year of study showed a small, albeit significant effect on RSB (F(2,165)&#x2009;=&#x2009;3.246, p&#x2009;<&#x2009;.05, &#x3b7;2&#x2009;=&#x2009;.038) only. Parental education and accommodation type did not have significant independent effects on SHLPP and RSB. However, they exerted significant interaction effects between: (i) parental education and upbringing environment, and between parental education and accommodation type on RSB and (ii) upbringing environment and accommodation on SHLPP. Parent-daughter communication emerged as the strongest predictor of both SHLPP (&#x3b2;&#x2009;=&#x2009;.318, p&#x2009;<&#x2009;.001) and RSB (&#x3b2;&#x2009;=&#x2009;.402, p&#x2009;<&#x2009;.001). Notably, SHLPP did not significantly predict RSB (&#x3b2;&#x2009;=&#x2009;.088, p&#x2009;=&#x2009;.221), indicating a disconnect between knowledge and behavior. CONCLUSION&#xa0;AND IMPLICATIONS: Sexual behaviour among FUS is more strongly shaped by interpersonal and contextual factors than by individual knowledge alone. These findings underscore universities' need to enhance parent-child communication, deliver targeted sexual health education, and consider student backgrounds when allocating housing.

A comprehensive understanding of land use carbon metabolism characteristics from the production-living-ecological space (PLES) perspective is crucial for formulating carbon reduction strategies. As the core economic zone of northern China, the Beijing-Tianjin-Hebei (BTH) region faces severe carbon emission pressures due to rapid urbanization and intensive land use transformation. However, focusing solely on carbon metabolism calculation without considering future changes and optimization effects may prevent achieving carbon emission reduction targets. This study assessed carbon emissions and sequestration based on different land use types in PLES, constructed a multi-objective carbon reduction scenario utilizing the Dinamica-EGO model, nondominated sorting genetic algorithm II, and entropy weight-TOPSIS model, and simulated 2035 carbon reduction characteristics by coupling PLES changes. Taking the BTH region as a case study, a methodological framework and corresponding models were established. The results show that from 2000 to 2020, the total carbon emissions in the BTH region increased significantly, presenting a spatial pattern of high emissions in the southeast and low emissions in the northwest. In contrast, the overall carbon sequestration capacity showed a decreasing trend, with stronger capacity in the northwest and weaker capacity in the southeast. The multi-variable 2035 carbon emission reduction prediction model achieved an accuracy of 82.24%. The 2035 carbon reduction plan developed based on this framework outperformed the original land use plan: economic benefits, emission reduction efficiency, spatial compactness, and accessibility are projected to increase by 15.8%, 7.9%, 2.5%, and 8.3%, respectively, while carbon emissions are expected to decrease by 19.04%. The proposed PLES-based framework for carbon metabolism measurement and emission reduction simulation exhibits good applicability in regional spatial emission reduction. These findings contribute to exploring regional carbon dynamics and provide references for governments to formulate carbon reduction policies.

Given the increasing prevalence of suicide risk among young adults, identifying proximal risk factors remains a key challenge. Traditional approaches focusing on distal predictors provide limited insight into short-term fluctuations in risk. The present study examined whether daily variations in sleep, physical activity, and physiological arousal, in interaction with positive and negative life events, were associated with short-term changes in suicide risk. A sample of 104 university students completed an 8-day ecological momentary assessment protocol with four daily prompts assessing suicide risk and affect. Sleep and physical activity were continuously monitored via wrist actigraphy, and heart rate was assessed using a wearable device. Linear mixed-effects models were used to examine within- and between-person associations between total sleep time, daytime acceleration, and heart rate and next-day suicide risk, as well as their interactions with daily positive and negative life events. Within-person decreases in total sleep time and increases in heart rate predicted higher next-day suicide risk. Within-person increases in daytime acceleration were also associated with higher next-day suicide risk, although this effect was weaker in robust sensitivity analyses and should be interpreted cautiously. Daily negative life events moderated the associations between both physical activity and heart rate and suicide risk, such that these relationships were stronger on days characterized by higher levels of negative events. In contrast, positive life events showed limited buffering effects.These findings suggest that suicide risk in university students is linked to day-to-day changes in sleep, physical activity, and physiological arousal, especially in the presence of daily stressors.

Hyperuricemia (HUA) is increasingly recognized as a systemic inflammatory-metabolic disorder that contributes to immune-associated renal injury. Kynurenic acid (KYNA), a tryptophan-derived metabolite with reported immunomodulatory properties, has emerged as a potential mediator linking gut metabolism and distal organ inflammation. In this study, we investigated the protective effects of astilbin (ASB) against HUA and hyperuricemia-associated renal injury using an adenine-induced goose model, which is translationally relevant because geese naturally lack functional uricase, together with monosodium urate (MSU)-challenged primary renal tubular epithelial cells. By integrating multi-omics analyses, molecular docking, and molecular biology approaches, we found that ASB reduced serum uric acid, improved renal function, and attenuated inflammatory and fibrotic changes in vivo. ASB also suppressed circulating pro-inflammatory cytokines, restored intestinal barrier-related markers, and partially corrected gut microbial dysbiosis. Untargeted metabolomics revealed that KYNA was markedly reduced under HUA conditions and was restored by ASB treatment in both intestinal contents and serum. In parallel, ASB increased IDO2 expression in intestinal and hepatic tissues. In vitro, both ASB and KYNA attenuated MSU-induced inflammatory responses, restored urate transporter expression, and suppressed TGF-&#x3b2;/Smad-associated profibrotic signaling in primary renal cells. Collectively, these findings support a working model in which ASB ameliorates hyperuricemia-associated sterile inflammation and renal injury partly in association with gut-linked KYNA immunometabolic remodeling, accompanied by increased IDO2 expression. Because IDO1, kynurenine, KAT activity, and receptor/pathway inhibition were not assessed, the proposed mechanism should be regarded as hypothesis-supporting rather than definitively proven.

To assess whether estimated dietary microplastic (MP) intake is linked to age-standardized lip and oral cavity cancer rates at the country level, independent of smoking, alcohol, socioeconomic status, and urbanization, we conducted a cross-sectional ecological study across 106 countries (2020 anchor year). While testing for effect modification by smoking, dietary MP intake showed a non-linear association with lip-oral cavity cancer incidence in unadjusted spline models. Smoking prevalence and urbanization were also positive predictors, although the evidence was inconsistent. Placebo/permutation testing, spatial residual diagnostics, and dietary-structure sensitivity analysis using fish/seafood supply as a proxy supported the stability and specificity of the association. We additionally performed a pre-specified spatial econometric sensitivity analysis: residual Moran's I on the refitted OLS model (n&#x2009;=&#x2009;79) was 0.099 (p_sim&#x2009;=&#x2009;0.082); Anselin's robust LM-lag test was significant (p&#x2009;=&#x2009;0.003), identifying a Spatial Lag Model (SLM) as the appropriate primary spatial specification (&#x3c1;&#x2009;=&#x2009;0.35; post-fit Moran's I = - 0.006, p_sim&#x2009;=&#x2009;0.466 autocorrelation absorbed). Under SLM, the MP p90-vs-p10 exposure contrast was - 0.41 (p&#x2009;=&#x2009;0.85); the pre-specified decision rule thereby classifies this result as null/hypothesis-generating (Path C). These findings generate hypotheses and do not establish causality, but encourage further individual-level exposure assessments and mechanistic studies to better understand the potential role of microplastics in oral carcinogenesis.

Dengue outbreaks in Kenya are concentrated along the coast despite widespread ecological suitability for Aedes aegypti inland, suggesting that transmission risk is shaped by more than climate alone. Population-level variation in mosquito life-history traits-interacting with vector competence, climate-driven abundance, and socio-ecological factors-may contribute to this heterogeneity, yet direct comparative data of differences in life history traits across Kenyan populations remain scarce. Using a common-garden experiment with minimally colonized populations, we compared early survival and developmental timing among Ae. aegypti from coastal Mombasa, inland Kisumu, and a laboratory-adapted Mexico colony under identical insectary conditions. Substantial colony-level differences persisted: Mombasa mosquitoes showed higher early-stage success and faster development, Kisumu mosquitoes exhibited reduced early performance and slower development, and the Mexico colony maintained consistently high survival. Although derived from single field collections, these findings demonstrate that meaningful variation in key life-history traits can persist among Ae. aegypti colonies when evaluated under standardized conditions. Such differences may influence local population growth dynamics, but do not alone determine transmission risk. Incorporating population-specific mosquito biology into surveillance and modeling frameworks-alongside measures of vector competence, adult behavior, and human exposure-may improve spatial risk prediction and support more context-appropriate vector control strategies.

Natural habitats are globally threatened and fragmented, posing challenges for dynamic ecosystems dependent on ecological processes and intact habitat networks. Our study, therefore, examines the implementation of the European Union's Habitats Directive in safeguarding the habitat type "Alpine rivers with their ligneous vegetation with Myricaria germanica". Using Bavarian and Austrian mapping guidelines, alongside simple and habitat suitability models, we identified disparities and limitations in current habitat delineation methods. Our findings reveal substantial inconsistencies between the Bavarian and Austrian mapping guidelines and the species' potential habitat predicted by the habitat suitability model. The Bavarian method overestimates the extent of the habitat type by also classifying areas in advanced successional stages as such, covering 88% of the study area. Habitat suitability modeling shows this exceeds the suitable habitat by up to 74%. Compared to the more selective Austrian method, the Bavarian method maps 14 times more area. The Austrian method focuses on the current habitat occupancy of M. germanica, a crucial factor in detecting changes in distribution and habitat quality in dynamic river systems. However, monitoring pioneer, pre- and sub-successional habitats alongside existing populations is essential to fully protect metapopulation dynamics. Habitat Suitability Modeling offers an opportunity to complement field surveys for this purpose. Our findings further highlight the need for more consistent cross-border mapping and standardized assessment criteria to accurately track habitat changes, supporting effective implementation of instruments such as the Nature Restoration Regulation and ensuring long-term conservation and restoration of dynamic ecosystems.

&#x3b2;-carboline alkaloids such as harmine and harmaline are prominent specialized metabolites in Peganum multisectum and exert potent inhibitory effects on seedling growth of treated plants, highlighting their potential for development as novel herbicides. However, the enzymatic logic underlying their biosynthesis remains unresolved. Here, we identified two O-methyltransferases, PmOMT1 and PmOMT2, that catalyze the 7-O-methylation of harmol and harmolol, respectively, to form harmine and harmaline. Heterologous expression, biochemical assays, and substrate-feeding experiments in Nicotiana benthamiana confirm their catalytic activities. Intriguingly, phylogenetic analysis and ancestral sequence reconstruction revealed that these enzymes evolved from broadly distributed caffeic acid O-methyltransferases (COMTs). COMT enzymes, which function in S-lignin and melatonin biosynthesis, also have weak &#x3b2;-carboline 7-O-methylation activity and provide a latent foundation for subsequent specialization. Structural modeling and site-directed mutagenesis further revealed key residues and pocket remodeling events that underlie the shift in substrate recognition in PmOMT1 and PmOMT2 relative to canonical COMTs. More importantly, phytotoxicity assays showed that harmine and harmaline exhibit stronger inhibitory effects on Amaranthus tricolor seedling root growth than harmol and harmolol, pointing to possible ecological roles for &#x3b2;-carboline methylation in Peganum. Together, our findings uncover the O-methylation step in &#x3b2;-carboline biosynthesis and illuminate how pre-existing catalytic potential in COMTs can be co-opted to generate lineage-specific alkaloid chemistry in Peganum.

While social bees are a primary focus of research and monitoring efforts among pollinators, more than 80% of bee species are solitary. For solitary bees, foraging behavior and efficiency directly impact the reproductive outcomes of individuals (fitness), as the consequences of foraging performance are not buffered by a colony. Cavity-nesting bees (e.g., Osmia spp. and Megachile spp. (Megachilidae)) progressively provision pollen in cells within hollow stems and other cavities, with each cell generally containing one offspring, making their reproductive output easily quantifiable. Cavity-nesting bees thus represent an ideal system to study fitness effects of individual variation in foraging niche, including differences in foraging activity and responses to environmental conditions. Understanding the links between individual performance and fitness can improve our understanding of solitary bee behavior, physiology, and ecology, particularly in response to environmental change. However, quantifying both foraging activity and provisioning rates within nests is prohibitively labor-intensive in many cases, especially for the extended time periods (i.e., weeks or months) over which nests are provisioned and under naturalistic ecological conditions. While recent work has established computer vision tools for automated monitoring of foraging activity at solitary bee nests, combining these approaches with in-nest monitoring could help better link foraging to fitness in solitary bees. Here we introduce Osmia Camera Activity Monitoring (osmiaCAM), a low-cost, open-source, automated monitoring system for foraging and nest-provisioning behavior of cavity-nesting bees, including key pollinator and research model organisms, such as Osmia spp. and Megachile spp. We demonstrate the potential of this system in a small validation experiment in Central California, where we quantified foraging transits and nest provisioning rates of Osmia spp. under variable weather conditions. This validation experiment yielded accurate, near-continuous monitoring for several weeks continuously. The osmiaCAM system has broad applications across cavity-nesting solitary bee species, facilitating research on their behavior, ecology, and responses to rapid environmental change. The development of autonomous monitoring systems and associated data analysis pipelines provide an opportunity for open-source ecological methods that would be applicable across a multitude of systems and disciplines.

Dissolved organic carbon (DOC) is a major and dynamic carbon pool regulating carbon cycling in freshwater systems. Over the past two decades, research on freshwater DOC has moved beyond simple concentration monitoring to examine its sources, molecular composition, degradation potential, and interactions with climate change. Lake studies increasingly focus on DOC processing, long-term storage, and its role in greenhouse gas production, while river studies emphasize DOC mobilization, transport, and connectivity between terrestrial landscapes, inland waters, and downstream coastal environments. Long-term observations reveal increases in DOC concentrations in lakes and rivers, with mean growth rates of 0.042 and 0.015 mg L-1 per year, respectively. These trends are driven by multiple factors, including recovery from acid deposition, climate warming, extreme precipitation, and intensified human activities such as land-use change and wastewater discharge. Rising DOC concentrations lead to water browning, degraded water quality, biodiversity loss, and increased greenhouse gas emissions, with consequent socioeconomic impacts, particularly in fisheries and tourism. Addressing these challenges requires integrated strategies combining source control, long-term monitoring, and ecosystem restoration. Future research should prioritize global spatiotemporal dynamics of DOC concentration and composition by integrating field observations, remote sensing, and modeling to better understand and mitigate its ecological and socioeconomic impacts.

Phytoremediation is a cost-effective and environmentally friendly remediation technology that uses plants to treat contaminated sites, thereby restoring ecosystems and reclaiming degraded land. Plant remediation efficiency is influenced by multiple factors, often leading to inconsistent outcomes. This study was conducted on Artemisia lavandulaefolia, a pioneer plant with remediation potential. A space-for-time substitution approach was employed to investigate its adaptive strategies to contaminated environments, heavy metal remediation potential, and the underlying driving mechanisms during natural succession in an abandoned Pb-Zn mining area. It was observed that A. lavandulaefolia transitioned from a stress-tolerant pioneer to an active rhizosphere regulator as succession progressed. Its cadmium (Cd) enrichment coefficient was found to peak at 20.86 under intense interspecific competition during the late successional stage. Partial least squares path modeling (GoF&#x202f;=&#x202f;0.60) revealed that soil properties significantly influenced the niche parameters (path coefficient&#x202f;=&#x202f;0.84, R2&#x202f;=&#x202f;0.71), functional traits, and tissue elemental composition of A. lavandulaefolia (path coefficient&#x202f;=&#x202f;0.98, R2&#x202f;=&#x202f;0.95), thereby shaping the rhizosphere soil environment (path coefficients&#x202f;=&#x202f;0.46 and 0.57, R2&#x202f;=&#x202f;0.96). The resulting rhizosphere effects were found to largely determine the heavy metal enrichment and transfer coefficients (path coefficient&#x202f;=&#x202f;1.13, R2&#x202f;=&#x202f;0.96). At the 25-year site, the rhizosphere effects of &#x3b2;-glucosidase (802.98%), available manganese (613.29%), available phosphorus (1243.66%), and available cadmium (1123.63%) were observed to peak simultaneously. Through combined random forest and Spearman correlation analyses, it was demonstrated that rhizosphere &#x3b2;-glucosidase activity, available manganese, and available phosphorus were key factors driving the Cd enrichment coefficient of A. lavandulaefolia. Our findings identify key rhizosphere regulators (phosphorus, manganese, and &#x3b2;-glucosidase activity) for maximizing cadmium enrichment in A. lavandulaefolia, offering a targeted strategy for precision ecological restoration in mining areas.

Solar salterns are environmentally stable yet biologically extreme ecosystems that serve as vital models for understanding and managing hypersaline environments, including industrial saline effluents. Despite their ecological and biotechnological significance, Indian solar salterns remain functionally underexplored. In this study, we integrated culture-dependent isolation with whole-metagenome sequencing to investigate microbial community assembly, functional specialization, and eco-technological potential across four geographically distinct Indian salterns.Physicochemical analyses revealed pronounced spatial variation in salinity, pH, and electrical conductivity, which together strongly structured microbial communities. Metagenomic sequencing generated between 4.84 and 8.68 Gb of raw data across individual site, yielding between 429,420 and 669,991 predicted genes in high-salinity locations. Taxonomic reconstruction demonstrated archaeal dominance at extreme salinity, particularly among Euryarchaeota, whereas comparatively moderate salinity sites supported more balanced bacterial-archaeal assemblages. Alpha diversity patterns indicated higher richness in Tamil Nadu and Rajasthan, while Gujarat exhibited reduced evenness consistent with environmental filtering.Culture-dependent approaches recovered 42 halophilic and polyextremophilic isolates, primarily affiliated with Halobacteriaceae and Bacillaceae, complementing the broad taxonomic detection of these lineages inferred from metagenomic data. Functional annotation revealed extensive enrichment of genes involved in ion transport, energy production, osmoprotectant biosynthesis, and DNA repair, reflecting an adaptive mechanism critical for survival in high-salinity industrial processes. Amino acid metabolism genes exceeded 25,000 hits in selected sites, and replication and repair genes reached 32,554 in Gujarat, indicating heightened stress-response activity. Secondary metabolite biosynthetic gene clusters, including pathways for novel antimicrobial peptides, terpene, ribosomally synthesized and post-translationally modified peptide-like, and type III polyketide synthase pathways, were widely distributed, offering new biological control mechanisms for environments impaired by stress. Antimicrobial resistance signatures were limited and unevenly distributed across sites.These findings demonstrate that salinity acts as a dominant ecological filter driving both taxonomic composition and functional specialization in Indian solar salterns. By linking environmental gradients to adaptive genomic traits, this study establishes a functional baseline for hypersaline ecosystems.

Accurately identifying "hot-spot pathways" in fishery science and technology (S&T) innovation is critical for food security, economic development, and ecological sustainability. Traditional technology foresight methods struggle to capture complex, dynamic evolutionary patterns in S&T innovation networks. Drawing on Dosi's conceptual framework of technological trajectories as domain-inspired design heuristics-whereby Dosi's qualitative concepts provide structural guidance for model design rather than formal axioms that exhaustively capture the theoretical framework-this study proposes DTH-GNN (Documents-based Temporal Heterogeneous Graph Neural Network), integrating Graph Neural Networks with dynamic evolutionary analysis to identify potential hot-spot pathways. We construct a dynamic heterogeneous knowledge graph from multi-source data (2010-2024) encompassing 32,847 publications, 8,956 patents, and 1,856 projects. DTH-GNN combines an R-GCN-based heterogeneous encoder with a GRU-based temporal evolution module, achieving AUC = 0.934 and AP = 0.928 (after rigorous leakage assessment), significantly outperforming GCN, R-GCN, and EvolveGCN baselines. Information-theoretic analysis indicates that temporal features account for a substantial share of mutual information in link prediction (31.8%, 95% CI: [28.4%, 35.1%]), comparable to structural features (29.1%) and higher than attribute features (19.7%). Three high-potential pathways are identified and validated through expert evaluation (Krippendorff&#x15b; &#x3b1; = 0.804): Smart Aquaculture, Green Seed Industry, and Ecological Fisheries. These findings provide data-driven scientific support for S&T investment prioritization in the fishery sector.

Nickel (Ni) remobilization in eutrophic lake sediments is controlled by interactions between iron-manganese (Fe-Mn) redox processes and dissolved organic matter (DOM), but the mechanisms under different ecological regimes remain unclear. Here, we employed an in situ high-resolution approach integrating HR-Peeper sampling and multi-analytical techniques to simultaneously characterize Ni, Fe, Mn, and DOM in sediment pore water, and applied it to algal- (MLW) and macrophyte-dominated (DTH) zones of Taihu Lake over one year. Dissolved Ni concentrations in overlying water exhibited a sharp, synchronous peak during March-April 2021, exceeding the WHO drinking-water limit (70&#x202f;&#x3bc;g&#x202f;L-1) by up to 1.4 times. In MLW, this Ni pulse was associated with humic-like DOM-mediated Mn(IV) reduction, evidenced by concurrent increases in Ni, Mn, and humic-like DOM, with Mn identified as the dominant predictor in random forest analysis. Partial least squares path modeling (PLS-PM) further indicated that DOM acted as an electron shuttle, accelerating Mn-oxide reduction (&#x3bb; = 0.971, p&#x202f;<&#x202f;0.001) and releasing adsorbed Ni. In contrast, Ni mobilization at the DTH site was primarily governed by DOM complexation. Visual MINTEQ simulations showed that more than 55% of dissolved Ni occurred as Ni-DOM complexes. Consistently, fluorescence titration demonstrated strong coordination between Ni and both protein-like and humic-like DOM components (log Km > 3.682; r&#x202f;>&#x202f;0.939). Fourier transform infrared spectroscopy combined with two-dimensional correlation analysis further confirmed that Ni primarily interacted with aromatic C-H, alkene CC, aliphatic -COOH, alcoholic C-O, and aliphatic C-H functional groups. These findings provide new mechanistic insight into redox-DOM coupling and its control on Ni cycling in algal- and macrophyte-dominated sediments.