Journal of Environmental Management最新文献

Coastal landscape transformation can degrade ecosystem-service functions and constrain the capacity of coastal systems to sustain resource provision, environmental regulation, habitat support, and recreational use. Existing studies have advanced coastal carrying capacity assessment, but many rely on aggregate indices or individual landscape metrics, making it difficult to identify which landscape dimensions drive specific functional changes and how such evidence can support targeted environmental management. To address this gap, this study develops an environmental-management-oriented diagnostic framework linking landscape patterns to coastal carrying capacity. Using the Bohai Rim of China as an empirical case, we construct a four-dimensional carrying capacity framework from an ecosystem service supply-demand perspective and integrate SHAP-XGBoost, bivariate spatial autocorrelation, and geographically and temporally weighted regression to identify key landscape drivers, spatial coupling patterns, and spatiotemporal non-stationarity. The results show that landscape morphology mainly constrains resource supply capacity by reducing the continuity of production landscapes; landscape composition has context-dependent effects on environmental regulation and habitat support, depending on whether diversity results from natural habitat enrichment or artificial land-cover intrusion; and fragmentation was predominantly associated with lower travel and leisure carrying capacity by weakening visual continuity and ecological integrity. Spatial coupling and GTWR results reveal increasing polarization between development-intensive coastlines and conservation-dominated ecological patches. Based on these findings, we propose a zoning-dimension-carrying capacity framework to help guide decisions for managing carrying capacity degradation in multifunctional coastal environmental systems.

Topographic position modulates soil moisture distribution and seasonal dynamics in alpine ecosystems, thereby shaping vegetation water-use patterns. However, whether dominant woody species occupying different hillslope habitats differ in the stability of their water-use strategies remains poorly understood at the hillslope scale. We investigated two dominant conifers, Picea crassifolia and Juniperus przewalskii, and a widespread alpine shrub, Dasiphora fruticosa across upslope, midslope, and footslope positions during the pre-rainy, rainy, and late-rainy seasons in an alpine catchment of the upper Heihe River Basin. By integrating δ²H and δ¹⁸O of soil and xylem water with a MixSIAR Bayesian mixing model, together with leaf δ¹³C, we quantified water sources for each species and evaluated water-use stability across hillslope positions and seasonal stages. Despite differences in dominant uptake depth, both conifers maintained relatively stable water-use strategies across slope positions and seasons. J. przewalskii persistently relied on deep soil water, which contributed 47% to 53% of total uptake, whereas P. crassifolia predominantly used shallow soil water, accounting for 44% to 65%. In contrast, D. fruticosa showed strong plasticity, with deep water uptake increasing from 16.2% to 41.0%, although this flexibility was constrained at upslope positions where available water was largely confined to shallow soil layers. Variability in leaf δ¹³C further indicated stronger physiological stability in the two conifer species within their respective hillslope habitats than in the shrub species, particularly at upper slope positions under greater evaporative demand. These findings suggest that water-use stability was associated with the sustained dominance of reliable soil water sources.

Data science (DS) methods have attracted growing attention in ecosystem-services (ES) research, yet their bibliographic visibility across service categories and the implications of this pattern for environmental management remain insufficiently understood. This study combines bibliometric analysis, CICES-based classification, association-rule analysis, and decision-oriented synthesis to examine how DS methods are represented across ES domains. Bibliographic records were retrieved from the Web of Science Core Collection for 2015-2025. After LLM-assisted relevance screening, CICES-based classification, and manual validation, 53,227 ES publications were retained. We analyze temporal publication trends, geographical and institutional patterns, collaboration networks, service-domain distributions, and the bibliographic visibility of DS terms in titles, abstracts, author keywords, and Keywords Plus. Association-rule analysis was used to identify recurring ES theme-DS method associations using appearance, lift, and confidence. The results show sustained growth in ES publications and geographically uneven research output. DS-related terms were identified in 9.56% of publications under the broad bibliographic-field indicator and in 3.17% under the keyword-field indicator. Their visibility increased over time but remained concentrated in established methods. Random forest and principal component analysis were prominent among the highest-scoring theme-method associations, whereas deep learning, generative models, and explainable artificial intelligence appeared less prominently. The results suggest that established DS can support prediction, classification, and dimensionality reduction, but effective decision support requires validation, uncertainty characterization, interpretability, representativeness assessment, and accountability safeguards. The findings highlight the need to align ES questions, analytical tasks, data structures, validation strategies, management scales, and intended uses when designing DS applications for environmental management.

This study selected an apex predator-Amur tiger (Panthera tigris altaica), and a typical herbivore-red deer (Cervus elaphus) as the focal species to construct ecological corridors in the Northeast China Tiger and Leopard National Park (TLNP). The aim was to build ecological corridors based on the predator-prey relationship, enhance habitat connectivity, promote species dispersal and gene exchange, and mitigate the threat of habitat fragmentation to biodiversity. The resistance surface was constructed based on the habitat preferences of the focal species. Ecological sources were identified by combining Morphological Spatial Pattern Analysis (MSPA) with the dispersal abilities of the species. Finally, ecological corridors and key nodes were identified based on Circuit Theory. The results revealed that (1) There were 9 ecological sources for both the Amur tiger and red deer respectively, which were mostly located near the border in the southeastern and northwestern areas of the TLNP. The Amur tiger and the red deer each had a total of 15 corridors, more than half of which required crossing roads. (2) For the Amur tiger and the red deer, 4 and 32 pinch points were identified, along with 6 and 7 barrier points, respectively. The barrier points of the red deer were primarily found where roads and ecological corridors met; (3) Wildlife passages should be constructed in sections of S502, S318, and G331 with high ecological corridors density. This study can provide a scientific basis for the construction of ecological corridors in the TLNP and hold significant practical value for guiding wildlife passage site selection, mitigating human-wildlife conflict, and promoting transboundary population exchange.

Assessing Cd behavior in agricultural soils requires consideration of total soil Cd, its operational partitioning, and crop accumulation. A 122-day greenhouse pot experiment evaluated coal gangue, air-dried cattle manure, and their mixtures in two contrasting composite soils: an acidic lower-Cd soil (LS) and a neutral-to-alkaline higher-Cd soil (HS). After cultivation, total soil Cd averaged 0.648 and 1.825 mg kg^-1 in LS and HS, respectively, whereas operationally labile Cd concentrations were similar (0.195 and 0.207 mg kg^-1). Residual Cd accounted for 19.97% of fraction-sum Cd in LS and 57.50% in HS, while mean root-associated, stem, and leaf Cd concentrations were consistently higher in LS. Among grain-bearing amended pots, mixed coal gangue-manure treatments had lower grain Cd concentrations than manure-only treatments in both soils. Mean grain Cd mass decreased from 8.94 to 2.49 μg pot^-1 in LS and from 6.92 to 1.50 μg pot^-1 in HS for manure-only and mixed treatments, respectively. Grain dry mass was comparable between these treatment groups in LS and slightly lower under mixed treatments in HS, indicating that the lower grain Cd was not attributable solely to biomass dilution. Ten of 18 manure-only grain samples, but none of the 29 mixed-treatment samples, exceeded the 0.10 mg kg^-1 reference threshold. Because the unamended planted controls produced no grain at harvest, grain-based comparisons were restricted to grain-bearing amended pots. The contrasting soil and plant responses demonstrate that total soil Cd alone does not adequately represent crop Cd behavior. Integrating soil Cd partitioning with grain Cd concentration and Cd mass provides a more informative basis for evaluating amendment responses in soil-maize systems.

With agricultural activities widely cited as drivers of biodiversity loss globally, it is important to distinguish between agricultural practices that have negative impacts and those with positive impacts on biodiversity. To measure on-farm biodiversity outcomes, the direction and magnitude of impacts need to be assessed. Outcomes can be measured using indicators. However, there is currently a multitude of biodiversity indicators available. This abundance makes monitoring biodiversity outcomes challenging, as farmers lack clarity on what to measure. This study presents a shortlist of biodiversity indicators selected via an expert panel as part of a modified Delphi process. The modified Delphi implemented two scoring approaches across four rounds, exploring the decision-making behind indicator selection. Thirty-nine experts scored 58 indicators based on their appropriateness for evaluating on-farm biodiversity and the practicality of data collection by non-specialists. The variation in scoring approaches helped combat respondent fatigue and build robustness into the indicator selection process. At the end of the study, nine indicators were chosen for monitoring on-farm biodiversity: area of habitat types, a connectivity index, a diversity index (landscape), both a crop and livestock diversity index, in-field crop temporal heterogeneity, indicator species, species richness, and species rarity. A final step considering each indicator's ability to inform on-farm decision-making is likely to have increased the efficacy of the chosen indicators in driving practice change. This would ensure the selected indicators are multifunctional, providing an evaluation of impact while also highlighting the desired direction of travel for the farmer or landowner.

Urine represents a critical water resource for closing the water and nitrogen loop in life support systems during manned space exploration. This work focuses on the impacts of pretreatment agents on urine progression and dissolved organic matter (DOM) transformation during long-term storage, with a perspective towards technology innovation for nitrogen-water synergistic recovery from urine in space. Urea hydrolysis, driven by microbial activity, was identified as the primary driver of urine variation, initiating rapid changes within 8 h of storage. The time point of significant DOM variation was prior to urea hydrolysis, and fresh humic-like substance from recent biological activity, which were dominated the variations of DOM. Meanwhile, the increasing NH₄⁺-N and pH can resulted in precipitation, such as MgNH₄PO₄, which showed potential risks to the urine transport and treatment devices. Both H₂SO₄ and CrO₃ showed excellent urine stabilization performance with urea hydrolysis rates of 0.10∼0.17 within 336 h. However, there have divergent DOM transformation pathways and sediment components: H₂SO₄ influenced the fresh DOM by microbial inhibition through acidic environment, and uric acid and CaSO₄ were the main sediments; CrO₃ could enhance the degradation of protein-like substances by strong oxidation, and generate Cr-containing dense sediments. Therefore, the selection criteria for technologies and pretreatment agents on urine recovery are proposed, which encourage the integration of membrane separation and biosynthesis. This work reveals the dynamic variation and the mechanism of pretreatment agents during urine storage, which offers the basis for innovation in urine recovery for deep space exploration.

Algal bloom prediction remains challenging in data-scarce lake systems where continuous in-situ bloom observations and long-term external loading records are limited. This study developed an integrated multi-scale framework for predicting algal bloom occurrence in Lake Burdur over the 1987-2024 period by combining satellite-based deep learning (DL), catchment-scale hydrological and water-quality simulation, machine learning, and explainable artificial intelligence. Long-term Landsat and Sentinel-2 imagery was used with U-Net DL models to detect algal blooms, achieving test accuracies of 94.91% and 91.53%, respectively. The Soil and Water Assessment Tool (SWAT) was implemented to simulate catchment-derived hydrological and water-quality inputs entering the lake, including point and non-point source contributions. These SWAT-derived variables, together with meteorological parameters, were used to train Random Forest, Support Vector Machine (SVM), eXtreme Gradient Boosting, and Categorical Boosting models under three temporal lag scenarios, namely short-term (t-1 to t-3), medium-term (t-1 to t-6), and long-term (t-1 to t-12). The short-term lag scenario provided the most favorable overall balance between predictive performance, stability, and generalization, with the SVM model achieving an overall accuracy of 89.74% and a balanced accuracy of 90.42%. SHapley Additive exPlanations analysis indicated that short-term bloom prediction was mainly associated with meteorological predictors, particularly Logarithmic Solar Radiation-Wind Speed Index and relative humidity, whereas SWAT-derived external Total Nitrogen to Total Phosphorus loading ratio became more influential under the medium-term scenario. These findings indicate that bloom prediction in Lake Burdur was associated with interacting recent atmospheric conditions and antecedent catchment-derived nutrient inputs. The proposed framework provides an interpretable basis for algal bloom prediction and supports future development toward early-warning applications in data-scarce lake systems.

Cattle grazing is a known source of water pollution. Identifying areas within grazinglands where cattle activity contributes most to downstream water quality impairment is important for effective management. This study presents a framework to detect these runoff pollutant hotspots. The framework is based on Pearson correlation coefficients between cattle presence across the catchment and water quality at the outlet. Cattle locations were recorded at 5-60 min time intervals using Virtual Fencing (VF) technology in three experimental catchments located in north-central Oklahoma, USA, two under rotational grazing and one continuously grazed. Escherichia coli (E. coli), total Kjeldahl nitrogen (TKN), and total phosphorus (TP) were measured during runoff events between March 2022, and June 2024. The spatial and temporal scales at which runoff quality was most affected by cattle presence were determined through a sensitivity analysis of grid cell size (GCS), time interval (ΔT) between consecutive cattle Global Positioning System (GPS) records, and pollutant half-life (HL) representing manure decay. The strongest grid cell-scale correlations between water quality parameters and cattle presence ranged from 0.48 to 0.83 for E. coli, 0.70 to 0.86 for TKN, and 0.67 to 0.90 for TP. These hotspots were detected using grid cell sizes ranging from 70 m × 70 m to 100 m × 100 m, and half-life values ranging from 3 to 12 days for E. coli, 3-23 days for TKN, and 3-20 days for TP. Compared to the continuously grazed catchment, hotspots in rotationally grazed, VF-managed catchments with cattle exclusion from riparian buffers were fewer, diffused, and located away from streams. Significant positive spatial autocorrelation was observed in five hotspot maps. Hotspot identification was influenced by pollutant persistence and grazing management, with rotational grazing showing fewer and more diffuse hotspots than continuous grazing.

Enhancing forest resilience to climate change requires operationalizing resilience assessments and engaging stakeholders to support effective decision-making. Here, we assessed stakeholder perceptions of resilience indicators (i.e., predictors and system variables) and their relevance for promoting the resilience of European forests across ecological and socioeconomic dimensions. Stakeholder profiles and regional systems were considered as complementary contextual dimensions to explore variability in resilience perceptions across different forest social-ecological contexts. Through bipartite networks and ranking analyses, we assessed whether stakeholders perceived modular relationships between resilience predictors and systems variables associated with forest ecosystem services and forest-based value chains, identified the predictors most broadly relevant for promoting resilience, and assessed their perceived operational applicability. Results revealed consistent modules linking biodiversity-related variables with key resilience predictors associated with forest management, conservation and forest structural attributes across stakeholder profiles and regional contexts. By contrast, productivity-related variables were associated with predictors related to harvesting intensity, land use, and forest economics, particularly within the socioeconomic dimension, among value chain actors, and in a central and northern Europe contexts. Forest management and forest structural attributes consistently emerged as the most broadly connected predictors across dimensions, whereas some predictors, such as deadwood, were perceived as having more specialized relationships with biodiversity-related variables. Perceived practicability was moderately correlated with explanatory strength and measurability, suggesting a gap between perceived relevance and operational implementation. Overall, our results highlight the value of perception-based network approaches for identifying resilience priorities and supporting resilience-oriented governance and decision-making under climate change.