Search Results (1,566)

Protected areas (PAs) are increasingly challenged by the need to reconcile biodiversity conservation with sustainable human use, particularly in landscapes containing underutilized or resting area (RA). This study evaluated the potential of resting forest and agricultural lands to enhance biodiversity and support sustainable land use within protected areas of Cesme, Türkiye. A Geographic Information System (GIS)-based multi-criteria evaluation approach was employed, integrating land cover data, soil group maps, topographic parameters, and protected area classifications to generate Plant Suitability Maps (PSMs). Eight thematic layers were developed, incorporating soil depth, slope, erosion risk, and land capability classes to identify suitable plant species and land-use options. The results indicate that the strategic use of resting agricultural lands could contribute up to 35.5% to ecological enhancement, while resting forest lands could contribute an additional 18%. The proposed plant assemblages include medicinal and aromatic species, erosion-control plants, and economically valuable perennial species that support ecosystem services such as pollination, beekeeping, and agro-tourism. Overall, the findings demonstrate that integrating RA management into conservation planning can simultaneously strengthen biodiversity, improve ecosystem services, and generate socio-economic benefits for local communities. The proposed GIS-based framework offered a transferable and scalable methodology for sustainable land management in Mediterranean landscapes and other protected regions worldwide. Also, in this research, the aim was to determine plant species using GIS-based suitability analyses of multi-spatial datato guide vegetation decisions in multi-criteria PA. Full article

Smart grid private communication networks need to support heterogeneous services with varying requirements for reliability, security, bandwidth, and controllability. In such networks, service function chains (SFCs) can provide customized network services by deploying virtual network functions (VNFs) over a shared substrate infrastructure. However, sharing physical servers among different service categories may conflict with the physical isolation requirement between critical grid services and common grid services. To address this problem, this paper investigates physical-isolation-aware SFC deployment for smart grid private communication networks. We first formulate an integer nonlinear programming (INLP) model that maximizes the network resource usage revenue while considering server resource constraints, link bandwidth constraints, flow conservation constraints, virtual link mapping constraints, server energy consumption, and physical isolation constraints. The nonlinear constraints are then linearized into an integer linear programming (ILP) model, which can be solved by an optimizer and used as a benchmark. To reduce the computational cost, we propose a private-network-oriented service function chain isolation deployment (PNO-SSID) algorithm. The proposed algorithm selects a revenue-aware subset of SFC requests, determines the service category to be preferentially processed, selects server nodes based on VNF-layer traffic cost, deploys VNFs using a matching-game-based method, and maps virtual links based on shortest paths. Simulation results show that PNO-SSID requires much less execution time than CPLEX while achieving close revenue in small-scale cases. Compared with online profit maximization (OLPM) variants using different request preprocessing strategies, PNO-SSID achieves higher network resource usage revenue and request acceptance ratio under physical isolation constraints. A prototype platform based on a fifth-generation non-standalone private network and the OAI platform further validates the feasibility of server-level isolated core network service chain deployment under the considered service-category separation requirement. Full article

In national park sections adjacent to large cities, protected wildlife habitats often intersect with roads, tourism, agriculture, forestry, and other community-use spaces. This overlap complicates the joint prioritization of multi-species conservation and potential human-wildlife conflict governance. Using species trace-point data from the Fourth National Giant Panda Survey, we developed 30 m MaxEnt distribution models for 12 mammal species in the Chengdu section of the Giant Panda National Park and integrated protected-species’ conservation priority with potential wild-boar-related conflict pressure. Test AUC values ranged from 0.702 to 0.897, and elevation was the dominant predictor for 11 species. The Top 15% weighted conservation priority area, based on protection status and rarity, covered 350.1 km². Potential wild boar conflict pressure was defined as wild boar suitability multiplied by human exposure, and the Top 15% risk area covered 348.3 km². Overlaying the two layers identified 61.6 km² of high-conservation-high-conflict areas. Functional-zone statistics showed that the core conservation zone concentrated higher multi-species conservation value, whereas the general control zone carried stronger potential wild boar conflict pressure. This framework provides a spatial basis for coordinating protected mammal monitoring, crop-damage warning, and community co-management. Full article

Podophyllum hexandrum Royle (1834) (Himalayan Mayapple), a key Himalayan medicinal plant and source of podophyllotoxin for anticancer drugs, is declining due to overharvesting, habitat loss, and climate change. This study, conducted from May to September 2024 across nine populations in Swat, Pakistan, assessed its ethnobotanical importance and conservation status. A total of 331 participants (270 individual surveys + 61 group discussions) were included. Using ethnobotanical surveys, IUCN-CMP threat frameworks, and spatial analysis, results showed high cultural value (Use Value = 0.63–0.92) and strong consensus for rheumatism (ICF = 0.91) and fever (ICF = 0.89). Fidelity levels were 94% for rheumatism and 88% for fever. Only 35% of respondents demonstrated conservation awareness. Overharvesting was the main threat, followed by habitat degradation and climate change. The species showed restricted distribution (EOO = 4250 km²; AOO = 295 km²), high fragmentation (0.68), and a 35% population decline over 10 years. It is assessed as Endangered (EN B1ab (iii, v)). This study provides the first integrated ethnobotanical–GIS assessment of P. hexandrum in the Hindu Kush–Himalaya region of Pakistan, offering measurable conservation baselines and community perception data previously unavailable. Findings align with global medicinal plant decline trends and support integration with CBD, SDGs (3 and 15), and potential CITES listing. Urgent conservation actions are required, including community-based management, habitat restoration, sustainable harvesting, ex situ conservation, and policy enforcement. Full article

The development of digital photogrammetry techniques has revolutionized the study of marine ecosystems, enabling the generation of high-precision three-dimensional models from conventional imagery. Structure from Motion (SfM) algorithms have become effective tools for mapping and monitoring underwater habitats, offering a non-invasive and cost-effective alternative to traditional methods. This study presents a pilot methodological validation of SfM-based underwater photogrammetry for the non-invasive morphometric monitoring of vulnerable benthic species, using Pinna rudis. The research focused on refining photogrammetric methodologies for marine conservation, addressing technical challenges such as variations in light conditions, water turbidity, and image acquisition complexity. The study area, the Cabrera Archipelago Maritime-Terrestrial National Park, is a pristine marine environment in the western Mediterranean, hosting diverse benthic communities, including an abundant Pinna rudis population. Data acquisition comprises sampling by scuba diving techniques at depths ranging from 26 to 31 m, performed during the July 2022 field campaign within a permanent demographic plot established in 2013 and the methodology applied involved generating three-dimensional models using SfM, allowing for direct measurements of the seabed and extraction of morphometric parameters of sessile species. The characterization of the Pinna rudis aggregation was based on specimen density and size structure, determined using maximum shell width. The 3D model of the pilot plot covers 86.1 m², hosting 31 individuals. Morphometric measurements derived from SfM-based 3D models were validated against in situ diver measurements of maximum shell width. The results showed that the average maximum width obtained from 3D models (15.19 ± 3.23 cm) was consistent with in situ measurements (15.35 ± 3.48 cm). The mean difference between methods was −0.16 ± 0.82 cm, indicating a negligible systematic bias. The mean absolute error was 0.65 cm, corresponding to an average relative error of 4.34%, and a strong linear relationship was observed between both methods (r = 0.97). These results confirm that underwater photogrammetry is a reliable and non-invasive tool for monitoring vulnerable benthic species, providing high-resolution spatial and morphometric data to support conservation strategies in marine protected areas and allowing the collection of additional data compared to in situ surveys. Full article

This paper proposes a prototype-level hierarchical visual–sensor framework for monitoring the Zamioculcas houseplant in complex indoor environments and supporting adaptive care-mode selection. The proposed framework combines a two-level visual pipeline, consisting of YOLO-based target plant detection and MobileViT-S-based leaf-condition classification, with a Plant Health Index (PHI) and a rule-based decision-support module for integrating visual and IoT-derived indicators. For the detection task, YOLOv8, YOLO12, and YOLO26 were compared, with YOLO26 showing the most balanced performance among the evaluated implementations. To improve robustness in real indoor scenes, negative training samples were added; this reduced the image-level false alarm rate on an independent negative-scene test set from 50.7% to 10.0% and increased specificity from 49.3% to 90.0%. For the second visual level, MobileViT-S achieved an accuracy of 0.9857 and an F1-score of 0.9857 on the independent cropped leaf test subset. To reduce the dependence of this result on a single data split, an additional 5-fold cross-validation experiment was conducted on the full cropped leaf dataset of 847 images, resulting in an accuracy of 0.9858 ± 0.0068 and an F1-score of 0.9853 ± 0.0070. To further address plant-level generalization, an additional unseen-plant validation subset of 60 newly collected cropped leaf images was evaluated, and MobileViT-S achieved an accuracy of 0.9500 and an F1-score of 0.9499. These results support the stability of the leaf-condition classifier within the available data, although larger external validation with strict plant-level and session-level separation remains necessary. In addition, an Arduino-based module-level validation was conducted using a capacitive soil-moisture sensor to verify the proposed sensor-based and Vision–IoT decision rules. The experiment demonstrated that the rule-based layer can distinguish dry, normal, and wet soil states and select conservative care actions depending on both soil moisture and visual-condition input. A brief real-time camera–sensor communication test further confirmed that live camera input, Arduino-based soil-moisture sensing, PHI computation, and care-mode selection can be connected within one decision-support pipeline. The proposed PHI and care-mode selection module are therefore presented as a formalized decision-support layer rather than as a fully validated autonomous irrigation system. Further calibration, actuator integration, and closed-loop validation remain necessary before practical autonomous deployment. Full article

Across semi-arid and environmentally vulnerable regions, intensifying climate pressures, land degradation, and resource scarcity are placing growing demands on institutions, communities, and land users. However, the knowledge and technical skills required to respond effectively remain uneven and often poorly aligned with local needs. This study presents a comparative skills audit in Kimberley, Upington, and Rietfontein in the Northern Cape, identifying capacity gaps, stakeholder-specific training priorities, and structural barriers in natural resource and sustainable land management. Using questionnaires, semi-structured interviews, participatory site visits, and multi-stakeholder consultations, competencies were assessed across GIS and remote sensing, climate resilience, soil and land restoration, water conservation, sustainable agriculture, and policy literacy. Results show significant disparities in skills proficiency. GIS and remote sensing (0.8) and climate resilience strategies (1.0) were weakest, while policy literacy (1.5) and soil management (2.0) were also limited. Sustainable agriculture (4.0) and water conservation (2.8) showed relatively stronger capacity. Training needs varied by stakeholder, with government prioritizing geospatial tools and governance, and farmers emphasizing climate adaptation and resource management. Key barriers include limited digital infrastructure (83%), insufficient government support (80%), high training costs (78%), and contextual mismatches (50%). Integrated, place-based capacity development is essential to strengthen adaptive governance and long-term resilience. Full article

Powdery mildew fungi are major obligate biotrophic plant pathogens, that cause widespread disease in agricultural and natural ecosystems worldwide, but a comparative structural view of their secretomes across multiple genera has remained limited. Here, we performed computational structure prediction and comparative analysis of 7545 secretome candidates from 26 isolates representing five genera (Blumeria, Erysiphe, Golovinomyces, Parauncinula, and Podosphaera) using AlphaFold2-based structure prediction, structural annotation against CATH, SCOPe, and PDB, Foldseek-based clustering and network analysis, structure-based grouping of RALPH (RNase-like proteins associated with haustoria) candidates, and comparison with defined fungal effector structural families. The predicted secretomes showed comparable model confidence across isolates and revealed a conserved structural core composed of recurrent microbial ribonuclease, immunoglobulin/fibronectin-like, glycosidase-related, and other enzyme-associated folds, with MoHrip2-like representing the most prominent shared fold among defined fungal effector structural families. Structural clustering and network analysis identified a prominent RALPH-centered component with additional conserved and lineage-enriched communities. RALPH candidates formed a structurally diverse repertoire that could be partitioned into 15 topology-defined groups, several linked to previously characterized powdery mildew effectors. Blumeria was structurally distinct, showing expansion of RALPH-associated components and the absence of multiple fold/domain categories retained in dicot-associated genera. Together, these results establish a comparative structural landscape of powdery mildew secretomes and provide a framework for future functional, evolutionary, and genomics-driven studies of conserved and lineage-associated secretome candidate. Full article

The management of European mountain landscapes is increasingly threatened by rural abandonment and escalating environmental risks. This study investigates an innovative Stewardship–Renewable Energy Communities model for the Central Apennines, exploring how post-seismic public reconstruction can serve as a financial engine for territorial maintenance. Utilizing Open Data Sisma administrative records and Photovoltaic Geographical Information System irradiation metrics, this research assesses the solar potential of 18 municipalities within the Sibillini seismic crater. To ensure a reliable baseline, a Building Suitability Coefficient was introduced as a conservative proxy for the public reconstruction sector. Results indicate that the implementation of a distributed network of 6.5 MWp across 325 public nodes, with a specific yield of 1390 kWh/kWp on the entire area, could generate 9 GWh/year. This translates to approximately EUR 1.08 million in annual revenue from energy incentives and sharing. This economic surplus provides a Stewardship Capacity sufficient to fund the active maintenance of 789.77 hectares per year through Nature-Based Solutions, based on a regional rate of 1200 EUR/ha. The novelty of this study lies in bridging post-disaster energy policy with landscape resilience, demonstrating that distributed rooftop solar portfolios represent a non-invasive, self-funding mechanism. By leveraging the reconstructed public stock, mountain territories can transition from passive neglect to active, energy-backed stewardship, offering a reproducible template for high-value cultural landscapes. Full article

Second-hand trading platforms have become an important channel for advancing circular economy practices in China. Yet prior research has paid more attention to the benefits of platform use than to the burdens that may undermine continued participation. Drawing on conservation of resources (COR) theory, this study examines how transaction reliability (TR), economic benefits (EB), community interactivity (CI), and reverse logistics convenience (RLC) shape perceived value (PV), and how platform fatigue (PF) weakens the relationship between PV and usage intention (IU). Using survey data from 297 users of major Chinese second-hand trading platforms, we test the proposed model with PLS-SEM. The results show that TR, EB, and RLC significantly enhance PV, whereas CI does not have a significant effect. PV, in turn, showed a substantial positive association with IU. PF attenuates the conversion of PV into IU. In addition, multi-group analysis based on circular economy identity (CEI) shows that the positive effect of PV on IU is stronger among users with higher CEI, whereas the negative moderating effect of PF is weaker in this group. These findings suggest that usage intention toward circular economy platforms is shaped by the joint operation of resource gain and resource depletion, and that this process varies across users with different levels of CE identity. Full article

To explore the distribution patterns of plant communities in southwestern Xizang and their relationships to environmental factors, this study focused on providing a theoretical basis for the conservation of biodiversity and ecological restoration of plant communities in the study area. Based on survey data from 87 sample plots in southwestern Xizang, in this study, two-way indicator species analysis (TWINSPAN) and canonical correspondence analysis (CCA) were employed for quantitative classification and ordination purposes, respectively. Additionally, the diversity of the classified community types obtained was analyzed, along with the factors influencing them. The results indicated that: a total of 295 species of vascular plants belonging to 171 genera and 61 families were recorded in the 87 sample plots; according to TWINSPAN classification, the plants in southwestern Xizang were divided into 17 associations, with the vegetation types being dominated by tussock-forming grass alpine steppes and tussock-forming Kobresia alpine meadows; CCA ordination revealed that the annual average temperature, annual precipitation, and altitude exhibited significant explanatory power; both the α- and β-diversity indices of the coniferous forest community type were the highest, indicating notable community stability; and annual average temperature and annual precipitation significantly affected plant diversity, while the altitude was negatively correlated with the above diversity indices. In summary, the temperature and precipitation were the main environmental factors influencing the composition and distribution of plant communities in southwestern Xizang. The research results could provide a theoretical basis for further investigation and conservation of plant diversity as well as ecological restoration in southwestern Xizang. Full article

Controller Area Network (CAN) remains a key in-vehicle communication protocol for distributed automotive control systems, where predictable communication timing is essential for coordinated operation of electronic control units (ECUs). This paper presents a cross-validated framework for timing analysis of automotive CAN networks by combining Deterministic and Stochastic Petri net (DSPN) modeling with worst-case response-time (WCRT) analysis. A DSPN model is developed to represent CAN message generation, priority-based arbitration, bus access, and non-preemptive frame transmission. The model is implemented in TimeNet to evaluate bus utilization, queue occupancy, and access-delay behavior under representative automotive traffic. In parallel, analytical WCRT equations are used to derive conservative latency bounds for each message class. The proposed framework links stochastic performance observations from DSPN simulation with deterministic schedulability guarantees from WCRT analysis, enabling consistency checks between average-case and worst-case timing results. A case study based on a 500 kbit/s automotive CAN configuration with six priority classes is presented. The results show that the network operates at approximately 35.9% bus utilization and that all message classes satisfy their timing requirements with a substantial margin, with the maximum worst-case response time remaining below 2 ms. The study further discusses the modeling assumptions, abstraction limits, and sensitivity of timing behavior to frame length and traffic configuration. The proposed framework provides a practical methodology for timing-oriented design and early-stage validation of automotive CAN communication systems. Full article

Community- and values-based approaches to the conservation of architectural heritage are increasingly emphasised. Yet empirical evidence on how local communities assess the potential of interventions for built heritage remains limited. Hence, when examining heritage interventions based on prevailing theories, visual scenario testing is considered a significant communication tool. Therefore, this study investigates local community preferences for different intervention levels in Koya City’s urban heritage using Artificial Intelligence (AI)-assisted visualisations that span a continuum of interventions. The visualisations serve as the basis for the survey, which was used to explore local preferences for the various heritage intervention scenarios. The correspondence between the AI-assisted visuals and the theoretical interventions was assessed by experts before the survey. The findings suggest that low- to moderate-intensity conservation strategies that preserve architectural character are consistently preferred by the survey community. In contrast, interventions that involved considerable physical change were markedly less favoured. Results from the expert validation test indicate that low- to moderate-intervention levels were reliably visualised using AI-assisted visualisations, while the higher-intervention levels were considered less representative. The supplementary calibration further highlights the importance of visual granularity in participatory heritage evaluation. However, the study remains exploratory and limited by the use of AI-assisted visualisations, a single case-study context, and the difficulty of translating nuanced conservation doctrines into visually discrete categories. This study makes a dual contribution by providing empirical evidence of local preferences across a continuum of conservation interventions, and by proposing an AI-assisted visual methodology to bridge expert conservation theory with public understanding. Full article

Fortress conservation models, such as Protected Areas (PAs), are often critically examined as frontiers of land control driven by state or external actors, leading to dispossession and marginalisation of local communities. However, such analyses tend to reduce land control to its coercive outcomes, overlooking the processes and social relations through which it operates and produces differentiated impacts. This study addresses this gap by analysing how multiple sources of land control—including conservation practices, politically mediated development and welfare interventions, and local agrarian power relations—interact to shape marginalisation. Using qualitative methods—semi-structured interviews, focus group discussions, and oral histories—this research examines land control dynamics in the Amrabad Tiger Reserve, Telangana, India, a Protected Area embedded within a regional agrarian economy. The study employs an expansive concept of land control that incorporates historically rooted and shaped agrarian class and caste relations alongside conservation practices. The findings show that these interactions reproduce interconnected relations of oppression among caste, class, and land control. The historically discriminated against and oppressed groups amongst the generally considered marginalised communities—Dalit Madigas and Adivasi Chenchus—emerge as the most marginalised, even in spaces that previously enabled partial escape from such conjugated oppression. By demonstrating how PA-based conservation reshapes class differentiation, the study argues that marginalisation under protection is contextual, historically contingent, and differentiated, contributing to debates on equitable conservation and the agrarian political economy of conservation enclosures. Full article

As coastal communities face escalating climate risks driven by climate change and biodiversity loss, integrating mangrove ecosystems into sustainability-oriented governance frameworks spanning ecological conservation, climate adaptation, and natural capital accounting has become a global priority. However, quantifying their protection values based on spatiotemporal shoreline dynamics under extreme disturbance remains challenging. Focusing on Dongzhai Harbor (China), this study integrates multi-temporal remote sensing (2010–2021), shoreline evolution analysis, and the Replacement Cost Method to assess ecosystem resilience against Super Typhoon Rammasun in 2014. Results show mangroves exhibited substantial post-disturbance resilience, with only 6.10% area loss following Typhoon Rammasun and 46% natural recovery within six years. Bootstrap confidence intervals for the mangrove-shoreline association overlapped zero across all three temporal periods, indicating that the observational data do not support a statistically confirmed causal protection effect at the landscape scale. This finding underscores that spatially co-occurring ecosystem services do not automatically imply causation, reinforcing the need for empirically grounded valuation in sustainable land-use planning. Because mangroves naturally establish in sheltered environments, the observed spatial overlap between mangroves and the shoreline cannot be interpreted as direct evidence of causal shoreline stabilization. Based on this framework, the potential protection value reached 907.65 × 10⁴ CNY yr⁻¹ across 32.57 km of weighted coastline aligned with mangroves. Notably, erosional segments contributed 50.5% of this value despite comprising only 27.3% of the length, indicating that the replacement-cost estimate is concentrated in erosional segments under the assumed parameters. While acknowledging the need for local biophysical validation and uncertainty analysis in scaling, these findings support integrating dynamic nature-based solutions into territorial planning and Gross Ecosystem Product accounting. The resulting valuation framework offers a replicable pathway for advancing multi-dimensional sustainability encompassing climate-adaptive coastal governance, natural capital integration, and evidence-based coastal spatial planning. Full article