Search Results (291)

This paper investigates how governance frameworks shape sustainable tourism outcomes in protected areas by comparing Tara National Park (Serbia) and Triglav National Park (Slovenia). Both parks, established in 1981 and classified under IUCN Category II, exhibit rich biodiversity and mountainous terrain but differ markedly in governance structures, institutional integration, and local community engagement. Using a qualitative, indicator-based methodology, this research evaluates ecological, economic, and social dimensions of sustainability. The findings reveal that Triglav NP demonstrates higher levels of participatory governance, tourism integration, and educational outreach, while Tara NP maintains stricter ecological protection with less inclusive management. Triglav’s zoning model, community council, and economic alignment with regional development policies contribute to stronger sustainability outcomes. Conversely, Tara NP’s centralized governance and infrastructural gaps constrain its potential despite its significant conservation value. This study highlights the importance of adaptive, inclusive governance in achieving the Sustainable Development Goals (SDGs) within protected areas. It concludes that hybrid approaches, combining legal rigor with participatory flexibility, can foster resilience and sustainability in ecologically sensitive regions. Full article

The transportation sector significantly contributes to greenhouse gas (GHG) emissions and remains a key research focus on emission quantification and mitigation. Although numerous models exist for estimating vehicle-based emissions, most lack accuracy at regional scales, particularly in remote or underdeveloped areas, including backcountry national parks and mountainous regions lacking basic infrastructure. This study addresses that gap by developing and applying a terrain-adjusted, segment-based methodology to estimate GHG emissions from tourist vehicles in Altai Tavan Bogd National Park, one of Mongolia’s most remote protected areas. The proposed method uses Tier 1 IPCC emission factors but incorporates field-segmented route analysis, vehicle categorization, and terrain-based fuel adjustments to achieve a spatially disaggregated Tier 1 approach. Results show that carbon dioxide (CO₂) emissions increased from 118.7 tons in 2018 to 2239 tons in 2024. Tourist vehicle entries increased from 712 in 2018 to 13,192 in 2024, with 99.1% of entries occurring between May and October. Over the same period, cumulative methane (CH₄) and nitrous oxide (N₂O) emissions were estimated at 300.9 kg and 45.75 kg, respectively. This modular approach is especially suitable for high-altitude, infrastructure-limited regions where real-time emissions monitoring is not feasible. By integrating localized travel patterns with global frameworks such as the IPCC 2006 Guidelines, this model enables more precise and context-sensitive GHG estimates from vehicles in national parks and similar environments. Full article

Forest biomass is closely related to carbon sequestration capacity and can reflect the level of forest management. This study utilizes four machine learning algorithms, namely Multivariate Stepwise Regression (MSR), K-Nearest Neighbors (k-NN), Artificial Neural Network (ANN), and Random Forest (RF), to estimate forest aboveground biomass (AGB) in Chenzhou City, Hunan Province, China. In addition, a canopy height model, constructed from a digital surface model (DSM) derived from Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) and an ICESat-2-corrected SRTM DEM, is incorporated to quantify its impact on the accuracy of AGB estimation. The results indicate the following: (1) The incorporation of multi-source remote sensing data significantly improves the accuracy of AGB estimation, among which the RF model performs the best (R² = 0.69, RMSE = 24.26 t·ha⁻¹) compared with the single-source model. (2) The canopy height model (CHM) obtained from InSAR-LiDAR effectively alleviates the signal saturation effect of optical and SAR data in high-biomass areas (>200 t·ha⁻¹). When FCH is added to the RF model combined with multi-source remote sensing data, the R² of the AGB estimation model is improved to 0.74. (3) In 2018, AGB in Chenzhou City shows clear spatial heterogeneity, with a mean of 51.87 t·ha⁻¹. Biomass increases from the western hilly part (32.15–68.43 t·ha⁻¹) to the eastern mountainous area (89.72–256.41 t·ha⁻¹), peaking in Dongjiang Lake National Forest Park (256.41 t·ha⁻¹). This study proposes a comprehensive feature integration framework that combines red-edge spectral indices for capturing vegetation physiological status, SAR-derived texture metrics for assessing canopy structural heterogeneity, and canopy height metrics to characterize forest three-dimensional structure. This integrated approach enables the robust and accurate monitoring of carbon storage in subtropical forests. Full article

Forest fires are one of the significant factors affecting forest ecosystems globally, with their impacts on soil microbial community structure and function drawing considerable attention. This study focuses on the short-term effects of different fire intensities on soil bacterial community structure and function in Abies (Pinus densata) forests within the Birishen Mountain National Forest Park in southeastern Tibet. High-throughput sequencing technology was employed to analyze soil bacterial community variations under unburned (C), low-intensity burn (L), moderate-intensity burn (M), and high-intensity burn (S) conditions. The results revealed that with increasing fire severity, the dominant phylum Actinobacteriota significantly increased, while Proteobacteria and Acidobacteriota markedly decreased. At the genus level, the relative abundance of Bradyrhizobium declined significantly with higher fire severity, whereas Arthrobacter exhibited a notable increase. Additionally, soil environmental factors such as available phosphorus (AP), dissolved organic carbon (DOC), C/N ratio, and C/P ratio displayed distinct trends: AP content increased with fire severity, while DOC, C/N ratio, and C/P ratio showed decreasing trends. Non-metric Multidimensional Scaling (NMDS) analysis indicated significant differences in soil bacterial community structures across fire intensities. Diversity analysis demonstrated that Shannon and Simpson indices exhibited regular fluctuations correlated with fire severity and were significantly associated with soil C/N ratios. Functional predictions revealed a significant increase in nitrate reduction-related bacterial functions with fire severity, while nitrogen-fixing bacteria declined markedly. These findings suggest that forest fire severity profoundly influences soil bacterial community structure and function, potentially exerting long-term effects on nutrient cycling and ecosystem recovery in forest ecosystems. Full article

The microbial community is one of the key indicators for evaluating the health of alpine marsh wetlands, and understanding the composition and health of alpine wetland communities provides a scientific rationale for conservation and restoration efforts. Taking the alpine marsh wetlands in Qilian Mountain National Park, Qinghai Province, as the research object, 27 soil samples (0–30 cm depth) were collected in July 2024 from three types of wetlands: non-degraded (ND), low-level degraded (LD), and heavily degraded (HD). Using high-throughput sequencing, PICRUSt2 functional prediction, nonmetric multidimensional scaling (NMDS), and redundancy analysis (RDA), we analyzed the bacterial community structure and functional characteristics as well as the soil physicochemical properties across different degradation levels and soil depths. Pearson correlation analysis and RDA were used to identify key soil indicators influencing microbial community characteristics. The results showed that (1) compared to ND, the relative abundance of Acidobacteriota increased from 12.3% to 23.7%, and that of Pseudomonadota increased from 28.5% to 35.1% in HD wetlands. Meanwhile, the Shannon index rose from 5.31 in ND to 6.52 in HD, indicating significantly increased microbial community diversity and complexity with wetland degradation (p < 0.05). (2) Vertically, the six major primary metabolic functions gradually weakened with increasing soil depth in all three types of wetlands, the relative abundance of Proteobacteria decreased from 0 to 30 cm, and the α-diversity indices of soil bacteria also declined with depth. (3) Compared to ND, LD and HD showed significantly lower soil moisture content, organic matter, and total organic carbon (p < 0.05), while total potassium and pH increased significantly (p < 0.05). With increasing depth, total nitrogen significantly decreased across all degradation types (p < 0.05). Bacterial diversity, as measured by the Shannon and Simpson indices, showed a significant correlation with several soil properties (moisture, organic matter, total nitrogen, total potassium, cation exchange capacity, and total organic carbon; p < 0.05). Furthermore, pH emerged as a primary environmental driver shaping microbial community structure across different soil depths. These findings offer technical guidance and a theoretical framework for comprehending the degradation and restoration dynamics of alpine marsh wetland ecosystems in the Qilian Mountains. Full article

The Mediterranean trout (currently referred to as Salmo ghigii for Corsican and Italian-native populations) is listed as Endangered in the IUCN Red List, due to fragmented distribution and declining populations across its whole range, and is included in Annex II of the European Habitat Directive. The widespread genome introgression from the invasive Atlantic trout (Salmo trutta), overexploitation, and habitat alterations represent major threats to the persistence of native populations. The LIFE18NAT/IT/000931 STREAMS project aims to enhance conservation status of Mediterranean trout in 6 Italian pilot areas (Maiella, Sibillini Mountains, Casentino Forests and Pollino National Parks, Montemarcello-Magra-Vara Regional Park, and Sardinia with five sites of the Natura 2000 Network), and in 19 transferability areas covering almost the whole Italian species range. To achieve this, the following conservation strategies were implemented: (i) the identification of residual native populations; (ii) eradication of entirely Atlantic-exotic populations and removal of hybrids in admixed populations; (iii) restocking/reintroduction of native populations; (iv) monitoring/improving the Mediterranean trout habitats quality; (v) production of the “Guidelines for the conservation and management of native Mediterranean trout and its habitat”; and (vi) the prevention of illegal stocking. Here, we present the project rationale, major outcomes on demographic and genetic characterization of wild populations, and summary results from conservation actions. Full article

Identifying the most suitable areas for developing forest health care in Hainan Tropical Rainforest National Park (HTRNP) is of great significance to its ecological protection and development. This study selected 107 health care points in HTRNP as research objects to monitor environmental factors, a forest health care evaluation system was constructed based on those environmental factors, and the health care resource points were rated. Kernel density analysis and buffer zone analysis were used to analyze other factors such as roads, villages, and water inside and outside of the national park. Multi-factor superposition analysis of the first-level health care points with other impact factors was performed to obtain a map of the distribution of health care potential in different sub-areas of HTRNP. A total of 67 first-level health care points were selected through the forest health care evaluation system. Through superposition analysis, it was found that, among the seven sub-areas of HTRNP, there are 42 first-level health care points within the 5 km buffer zone for roads and waterways, including 11 in Diaoluo Mountain, 10 in Limu Mountain, 6 in Yingge Ridge, 5 in Jianfeng Ridge, 4 in Bawang Ridge, 4 in Maorui, and 2 in Wuzhi Mountain. There are nine first-level health care points located in the area with a village kernel density greater than 3000, including three in Diaoluo Mountain, two in Limu Mountain, two in Yingge Ridge, and two in Maorui. At the same time, to meet the above two conditions of the first level of health care points, there are six, including three in Diaoluo Mountain, two in Maorui, and one in Yingge Ridge. Through the results analysis, Diaoluo Mountain is considered to be the area with the greatest potential for developing forest health care in HTRNP. In addition, the comprehensive performance of Limu Mountain is second only to Diaoluo Mountain, and Limu Mountain, Maorui, and Yingge Ridge are listed as areas with great potential for developing forest health care. Full article

Mountain watersheds play a crucial role in sustaining freshwater resources, yet they are highly vulnerable to climate change. In this study, we investigated the summer water temperature of 35 alpine springs in the highest part of the Retezat Mountains, Southern Carpathians, between 2020 and 2023. During the four-year monitoring period, water temperatures across all springs ranged from 1.2 °C to 10.5 °C. Springs emerging from rock glaciers had the lowest average temperature (2.37 °C), while those on cirque and valley floors were the warmest (6.20 °C), followed closely by springs from meadow-covered slopes (6.20 °C) and those from scree and talus slopes (4.70 °C). However, only four springs recorded summer temperatures below 2 °C, suggesting a direct interaction with ground ice. The majority of springs exhibited temperatures between 2 and 4 °C, exceeding conventional thresholds for permafrost presence. This challenges the applicability of traditional thermal indicators in marginal periglacial environments, where reduced ground ice content within rock glaciers and talus slopes can lead to spring water temperatures ranging from 2 °C to 4 °C during summer. Additionally, cold springs emerging from rock glaciers displayed minimal daily and seasonal temperature fluctuations, highlighting their thermal stability and decoupling from atmospheric conditions. These findings underscore the critical role of rock glaciers in maintaining alpine spring temperatures and acting as refugia for cold-adapted organisms. As climate change accelerates permafrost degradation, these ecosystems face increasing threats, with potential consequences for biodiversity and hydrological stability. This study emphasizes the need for long-term monitoring and expanded investigations into water chemistry and discharge dynamics to improve our understanding of high-altitude hydrological systems. Furthermore, it provides valuable insights for the sustainable management of water resources in Retezat National Park, advocating for conservation strategies to mitigate the impacts of climate change on mountain hydrology and biodiversity. Full article

As ecologically sensitive zones within natural ecosystems, national parks demand more precise evaluation models for ecological sensitivity assessment. This study takes Shennongjia Forestry District, a pioneer among China’s first-batch national parks, as an study object to optimize the ecological sensitivity evaluation framework. In this study, we developed an integrated methodology incorporating high-precision ASTER GDEM elevation data, Landsat8 TM vegetation density inversion, SWAT-based flash flood simulation, and SVM-LSM landslide prediction while introducing dynamic protection elements including species migration corridors and human activity risks. The results demonstrate that the refined data structure enhances terrain coupling accuracy by transitioning from “Vegetation Type—Runoff Coefficient” to “Vegetation Density—Runoff Coefficient” conversions, with the optimized model exhibiting superior sensitivity in spatial element identification. This approach provides scientifically grounded technical support for balancing ecological conservation and visitor management in protected areas. Full article

An ecological restoration assessment aims to evaluate whether ecological restoration projects (ERPs) have achieved predefined ecological objectives, such as improving fractional vegetation cover (FVC) and enhancing ecosystem services (ESs), as well as to optimize restoration strategies based on assessment outcomes. Despite recent advancements, current studies still fall short of fully capturing the trade-offs among ESs and identifying the underlying drivers of different vegetation trends. To address these challenges, we applied the Theil–Sen method to delineate vegetation change zones in the Qilian Mountain National Park (QLMNP) between 2000 and 2020, employed bivariate Moran’s I statistics to analyze the trade-offs and synergies among four ESs within these zones, including carbon sequestration (CS), soil conservation (SC), water conservation (WC), and biodiversity maintenance (BIO), and utilized a spatial random forest (SRF) model to explore the main socio-ecological driving factors of vegetation trends and their spatial distribution. Our results revealed significant vegetation recovery in the QLMNP between 2000 and 2020, particularly in regions with initially low FVC. Positive trends in the CS, SC, and BIO highlighted the success of restoration efforts, primarily driven by land conversion to forests and increased precipitation. However, 8.82% of the QLMNP exhibited stagnation or degradation due to rising temperatures and overgrazing, leading to declines in the SC and BIO. Notably, vegetation restoration introduced trade-offs among the ESs, especially in the high FVC areas, where a strong trade-off emerged between FVC and WC. These findings highlight the need for refining restoration strategies to balance water resource allocation. Finally, we integrated vegetation trends, ES relationships, and driving factors to propose grid-based zonal governance plans for the QLMNP, prioritizing WC and FVC enhancement as critical components of future ecological planning. This study serves as a foundation for optimizing restoration strategies in the QLMNP, maintaining and enhancing ESs, while offering actionable insights for fine-grained restoration evaluation and sustainable development planning in other regions. Full article

Bayanaul State National Nature Park (BSNNP), which was established in 1985 and is one of the biggest natural parks in the Republic of Kazakhstan, conserves and rehabilitates the natural flora and fauna of the Bayanaul mountain range. This article expands the floristic inventory of BSNNP and identifies the ecological and ethnobotanical importance of the park. The literature revealed that 681 plant species inhabited the BSNNP region but it was hypothesized that the park’s plant diversity was greater than the documented 681 plant species. Following our expedition travels to BSNNP, we extended the flora summary with an addition of 81 new plant species. Now, according to this study, the flora of BSNNP comprises 762 plant species belonging to 335 genera and 81 families. The leading families are Asteraceae Dumort., Poaceae Barnhart, Brassicaceae Burnett, Fabaceae Lindl, Rosaceae Juss., Caryophyllaceae Juss, Lamiaceae Lindl., Apiaceae Lindl., and Scrophulariaceae Juss. They comprise 57.7% of the total plant species in the national park and 58.5% of the total genera. The largest genera are wormwood, sedge, onion, cinquefoil, speedwell, and astragalus, based on which these genera can be considered polymorphic. Moreover, 16 species of endemic plants belonging to 14 genera and 7 families were also reported. The flora is characterized by high biological diversity with the participation of boreal relicts. The largest group among useful species is medicinal plants, represented by 186 species (24.4%) belonging to 83 genera, and 39 families. Our findings enhance the scientific understanding of plant diversity in BSNNP and provide the groundwork for future conservation research. Full article

Human activities have led to an increased influx of carbon into lakes due to changes in land use that result in higher erosion rates, eutrophication, and the introduction of organic matter. This, in turn, causes greater carbon exports and carbon accumulation in sediments. In our study, we estimated the fluxes of total particulate carbon (F_TPC), particulate organic carbon (F_POC), and particulate inorganic carbon (F_PIC) in three lakes with different trophic statuses. Two lakes, one eutrophic (Bosque Azul) and one mesotrophic (San José), are in the anthropically impacted zone of the plateau. In contrast, an oligotrophic lake (Tziscao) is in the mountainous, pristine area of “Lagunas de Montebello” National Park, a tropical karst lake district in Chiapas, Mexico. Our findings revealed that the highest F_POC values were observed in the eutrophic lake (0.47 ± 0.2 g m⁻² d⁻¹), while the highest F_PIC were observed in the mesotrophic lake (1.11 ± 0.8 g m⁻² d⁻¹). In contrast, the oligotrophic lake exhibited the lowest fluxes. Eutrophication increased the levels of F_POC, while deforestation and erosion contributed to the rise in F_PIC. Eutrophication and erosion in the lakes of LMNP led to five-, two-, and sixteen-fold increases in the F_TPC, F_POC, and F_PIC, respectively. Full article

Landslides affecting soil layers up to 1–2 m deep pose a significant hazard in mountainous and hilly regions, particularly in the Mediterranean, where intense precipitation is increasing. Identifying landslide-prone areas is crucial for risk assessment and mitigation, as landslides can severely impact land surfaces, infrastructure, and inhabited areas. Forest cover and management play a fundamental role in stabilizing soil and reducing landslide susceptibility. This study focuses on landslide forecasting models, which integrate geological, climatic, and topographic data to predict landslide probability and severity. Specifically, we compare the predictive accuracy of the 4SLIDE model with the established SHALSTAB model in a forested mountain catchment within Sila National Park, Southern Italy, using GIS-based analysis. The results demonstrate that both models effectively identify high-risk areas, with ROC analysis confirming the superior predictive capability of the 4SLIDE model. Our findings underscore the critical importance of early landslide identification, supporting timely interventions and the implementation of forest engineering and Civil Protection measures to mitigate the impact of landslides on communities and infrastructure. Full article

eDNA appears well positioned to play a significant role in the future of biomonitoring, and the need to assess the efficacy of eDNA-based surveys in a variety of habitats is increasing. We conducted an eDNA metabarcoding-based survey of fish communities in the Great Smoky Mountains National Park (GSMNP), located in eastern Tennessee and western North Carolina. The GSMNP, widely recognized as a biodiversity hotspot, encompasses 211,419 hectares of the Southern Appalachian Mountains with elevations up to 2205 meters and is home to approximately 73 species of fish, including 12 families and three species classified as endangered or threatened. We collected 50 water samples in first to sixth order streams at elevations of 336 to 1462 meters, including all major watersheds found in the park. eDNA was amplified utilizing two primer sets which each target differing regions of the 12S mitochondrial gene and generate amplicons of varying size (97 and 225 bp, respectively), and sequencing was conducted to an expected read depth of 400,000 reads per sample per marker. We detected a total of 40 fish species; of these, 36 were detected with the primer set which produces a 97 bp amplicon, and 12 of these 36 were detected only by this primer set. Species assemblages varied between stream orders, and species richness decreased with increasing elevation and increased with increasing stream order. Significant correlations were observed between biomass data from electrofishing monitoring (1984–2023) and eDNA metabarcoding read counts in five of seven species examined, including all salmonids. eDNA metabarcoding was demonstrated to be effective in assessing fish communities in high-elevation lotic systems in the Southern Appalachians, and our results suggest that primers targeting shorter amplicons may exhibit greater efficacy in these ecosystems. Full article

Aboveground biomass (AGB) is crucial in forest ecosystems and is intricately linked to the carbon cycle and global climate change dynamics. This study investigates the efficacy of synthetic aperture radar (SAR) data from the X, C, and L bands, combined with Sentinel-2 optical imagery, vegetation indices, gray-level co-occurrence matrix (GLCM) texture metrics, and topographical variables in estimating AGB in the Küre Mountains National Park, Türkiye. Four machine-learning regression models were employed: partial least squares (PLS), least absolute shrinkage and selection operator (LASSO), multivariate linear, and ridge regression. Among these, the PLS regression (PLSR) model demonstrated the highest accuracy in AGB estimation, achieving an R² of 0.74, a mean absolute error (MAE) of 28.22 t/ha, and a root mean square error (RMSE) of 30.77 t/ha. An analysis across twelve models revealed that integrating ALOS-2 PALSAR-2 and SAOCOM L-band satellite data, particularly the SAOCOM HV and ALOS-2 PALSAR-2 HH polarizations with optical imagery, significantly enhances the precision and reliability of AGB estimations. Full article