Search Results (92)

Urochloa brizantha (Brizantha) is cultivated under varying altitudinal and management conditions. Twelve full-sun (monoculture) plots and twelve shaded (silvopastoral) plots were established, proportionally distributed at 170, 503, 661, and 1110 masl. Evaluations were conducted 15, 30, 45, 60, and 75 days after establishment. The conservation and integration of trees in silvopastoral systems reflected a clear anthropogenic influence, evidenced by the preference for species of the Fabaceae family, likely due to their multipurpose nature. Although the altitudinal gradient did not show direct effects on soil properties, intermediate altitudes revealed a significant role of CaCO₃ in enhancing soil fertility. These edaphic conditions at mid-altitudes favored the leaf area development of Brizantha, particularly during the early growth stages, as indicated by significantly larger values (p < 0.05). However, at the harvest stage, no significant differences were observed in physiological or productive traits, nor in foliar chemical components, underscoring the species’ high hardiness and broad adaptation to both soil and altitude conditions. In Brizantha, a significant reduction (p < 0.05) in stomatal size and density was observed under shade in silvopastoral areas, where solar radiation and air temperature decreased, while relative humidity increased. Nonetheless, these microclimatic variations did not lead to significant changes in foliar chemistry, growth variables, or biomass production, suggesting a high degree of adaptive plasticity to microclimatic fluctuations. Foliar ash content exhibited an increasing trend with altitude, indicating greater efficiency of Brizantha in absorbing calcium, phosphorus, and potassium at higher altitudes, possibly linked to more favorable edaphoclimatic conditions for nutrient uptake. Finally, forage quality declined with plant age, as evidenced by reductions in protein, ash, and In Vitro Dry Matter Digestibility (IVDMD), alongside increases in fiber, Neutral Detergent Fiber (NDF), and Acid Detergent Fiber (ADF). These findings support the recommendation of cutting intervals between 30 and 45 days, during which Brizantha displays a more favorable nutritional profile, higher digestibility, and consequently, greater value for animal feeding. Full article

The Ichubamba Yasepan wetlands, in the Andean páramos of Ecuador, suffer heavy metal contamination due to anthropogenic activities and volcanic ash from Sangay, impacting biodiversity and ecosystem services. This quasi-experimental study evaluated the bioaccumulation and tolerance of metals in high Andean species through stratified random sampling and linear transects in two altitudinal ranges. Concentrations of Cr, Pb, Hg, As, and Fe in water and the tissues of eight dominant plant species were analyzed using atomic absorption spectrophotometry, calculating bioaccumulation indices (BAIs) and applying principal component analysis (PCA), clustering, and linear discriminant analysis (LDA). Twenty-five species from 14 families were identified, predominantly Poaceae and Cyperaceae, with Calamagrostis intermedia as the most relevant (IVI = 12.74). The water exceeded regulatory limits for As, Cr, Fe, and Pb, indicating severe contamination. Carex bonplandii showed a high BAI for Cr (47.8), Taraxacum officinale and Plantago australis for Pb, and Lachemilla orbiculata for Hg, while Fe was widely accumulated. The LDA highlighted differences based on As and Pb, suggesting physiological adaptations. Pollution threatens biodiversity and human health, but C. bonplandii and L. orbiculata have phytoremediation potential. Full article

The environmental heterogeneity caused by altitude can lead to trade-offs in nutrient utilization and allocation strategies among plant organs; however, there is still a lack of research on the nutrient variation in the “flower–leaf–branch–fine root–soil” systems of native shrubs along altitude gradients in China’s unique karst regions. Therefore, we analyzed the carbon (C), nitrogen (N), and phosphorus (P) contents and their ratios in flowers, leaves, branches, fine roots, and surface soil of Hypericum kouytchense shrubs across 2200–2700 m altitudinal range in southwestern China’s karst areas, where this species is widely distributed and grows well. The results show that H. kouytchense organs had higher N content than both global and Chinese plant averages. The order of C:N:P value across plant organs was branches > fine roots > flowers > leaves. Altitude significantly affected the nutrient dynamics in plant organs and soil. With increasing altitude, P content in plant organs exhibited a significant concave pattern, leading to unimodal trends in the C:P of plant organs, as well as the N:P of leaves and fine roots. Meanwhile, plant organs except branches displayed significant homeostasis coefficients in C:P and fine root P, indicating a shift in H. kouytchense’s P utilization strategy from acquisitive-type to conservative-type. Strong positive relationships between plant organs and soil P and available P revealed that P was the key driver of nutrient cycling in H. kouytchense shrubs, enhancing plant organ–soil coupling relationships. In conclusion, H. kouytchense demonstrates flexible adaptability, suggesting that future vegetation restoration and conservation management projects in karst ecosystems should consider the nutrient adaptation strategies of different species, paying particular attention to P utilization. Full article

Integrating nature-based solutions into sustainable urban design has become increasingly important in response to rapid urbanization and climate-related environmental challenges. As part of these solutions, green walls not only enhance the thermal and acoustic performance of buildings but also contribute to urban ecosystem health by supporting biodiversity. In this context, the careful selection of plant species is essential to ensure ecological efficiency, resilience, and low maintenance. This study presents a model for selecting plant species suitable for natural green walls in Mediterranean cities, with a focus on habitats protected under Directive 92/43/EEC. The selection followed a multi-phase process applied to the native flora of Italy, using criteria such as chorological type, life form, ecological indicator values, altitudinal range, and habitat type. Alien and invasive species were excluded, favoring only native Mediterranean species adapted to local pedoclimatic conditions and capable of providing ecosystem, esthetic, and functional benefits. The outcome of this rigorous screening led to the identification of a pool of species suitable for green wall systems in Mediterranean urban settings. These selections offer a practical contribution to mitigating the urban heat island effect, improving air quality, and enhancing biodiversity, thus providing a valuable tool for designing more sustainable and climate-adaptive buildings. Full article

The Caucasus region, characterized by its complex topography and diverse climatic regimes, exhibits pronounced spatial variability in temperature and precipitation patterns. This study investigates the seasonal behavior of air temperature, precipitation, vertical temperature gradients, and inversion phenomena across distinct landscape types using observational data from 63 meteorological stations for 1950–2022. Temperature trends were analyzed using linear regression, while vertical lapse rates and inversion layers were assessed based on seasonal temperature–elevation relationships. Precipitation regimes were evaluated through Mann-Kendall trend tests and Sen’s slope estimators. Results reveal that temperature regimes are strongly modulated by landscape type and elevation, with higher thermal variability in montane and subalpine zones. Seasonal temperature inversions are most frequent in spring and winter, especially in western lowlands and enclosed valleys. Precipitation patterns vary markedly across landscapes: humid lowlands show autumn–winter maxima, while arid and semi-arid zones peak in spring or late autumn. Some landscapes exhibit secondary maxima and minima, influenced by Mediterranean cyclones and regional atmospheric stability. Statistically significant trends include increasing cool-season precipitation in humid regions and decreasing spring rainfall in arid areas. These findings highlight the critical role of topography and landscape structure in shaping regional climate patterns and provide a foundation for improved climate modeling, ecological planning, and adaptation strategies in the Caucasus. Full article

Rumen microbiota and metabolites play important roles in energy metabolism and immune regulation in the host. However, the underlying mechanisms of their interaction with the host to regulate yak plateau adaptation remain unknown. In this study, the effects of altitude on the rumen microbiome, metabolome, and fermentation parameters of yaks were analyzed. The fiber content of pasture grasses increased with altitude, while crude protein content was significantly higher at an altitude of 2800 m (T2800) compared to an altitude of 4500 m (T4500) (p < 0.05). The acetic acid, propionic acid, and volatile fatty acids of yaks in the T4500 group were significantly higher than in the T2800 group (p < 0.05). Simpson’s index of rumen microorganisms in the T4500 group of yaks was significantly higher than in T2800 and T3500 yaks. The relative abundance of Rikenellaceae_RC9_gut_group and Succiniclasticum was significantly higher in T4500 than in T2800, while Prevotella and Streptococcus were more abundant in T2800 than in T4500. Rumen metabolomics analyses revealed that yak rumen metabolites at different altitudes were influenced by forage and altitude, mainly affecting energy metabolism and fatty acid biosynthesis (such as purine and glycerophospholipid metabolism). In summary, altitude may influence rumen microbes and metabolites through pasture nutrient composition. Full article

The Eastern Tibetan Plateau (ETP), characterized by its intricate topography and pronounced altitudinal gradient, presents significant challenges for climate model simulations. This study assesses precipitation over the ETP using high-resolution (HR) and low-resolution (LR) models from CMIP6 HighResMIP. Both HR and LR models successfully reproduce the spatial distribution of annual precipitation, capturing the northwest-to-southeast increasing gradient. However, HR models significantly outperform LR models, reducing the annual mean precipitation bias from 1.09 mm/day to 1.00 mm/day (9% reduction, p < 0.05, two-tailed Student’s t-test) and decreasing RMSE by 12% (p < 0.05) in the ETP for the 1985–2014 period. Furthermore, HR models exhibit superior skill in simulating extreme precipitation events, particularly over the Sichuan Basin. For the 1985–2014 period, HR models show markedly smaller biases in representing extreme precipitation and accurately reflect observed trends. Projections for the future suggest a pronounced intensification of extreme precipitation events across the region. Process-based scaling diagnostics attribute these changes predominantly to dynamical components, which account for approximately 85% of the total scaling change in HR models and 89% in LR models. These findings underscore the pivotal role of dynamical processes in shaping extreme precipitation and highlight the advantages of HR models in enhancing simulation fidelity. This study provides critical insights into climate model performance, offering robust information to inform climate mitigation and adaptation strategies tailored for the ETP. Full article

Accurate remote sensing identification of river ice not only provides scientific evidence for climate change but also offers early warning information for disasters such as ice jams. Currently, many researchers have used remote sensing index-based methods to identify river ice in alpine regions. However, in high-altitude areas, these index-based methods face limitations in recognizing river ice and distinguishing ice-snow mixtures. With the rapid advancement of machine learning techniques, some scholars have begun to use machine learning methods to extract river ice in northern latitudes. However, there is still a lack of systematic studies on the ability of machine learning to enhance river ice identification in high-altitude, complex terrains. The study evaluates the performance of machine learning methods and the RDRI index method across six aspects: river type, altitude, river width, ice periods, satellite data, and snow cover interference. The results show that machine learning, particularly the RF method, demonstrates superior generalization ability and higher recognition accuracy for river ice in the complex high-altitude terrain of the Tibetan Plateau by leveraging a variety of input data, including spectral and topographical information. The RF model performs best under all types of test conditions, with an average Kappa coefficient of 0.9088, outperforming other machine learning methods and significantly outperforming the traditional exponential method, demonstrating stronger recognition capabilities. Machine learning methods are adaptable to different types of river ice, showing particularly improved recognition of river ice in braided river systems. RF and SVM exhibit more accurate river ice recognition across different altitudinal gradients, with RF and SVM significantly improving the identification accuracy of river ice (0–90 m) on the plateau. RF and SVM methods offer more precise boundary recognition when identifying river ice across different ice periods. Additionally, RF demonstrates better generalization in the transfer of multisource satellite data. RF’s performance is outstanding under different snow cover conditions, overcoming the limitations of traditional methods in identifying river ice under thick snow. Machine learning methods, which are well suited for large sample learning and have strong generalization capabilities, show significant potential for application in river ice identification within high-altitude, complex terrains. Full article

Vegetation water-use efficiency (WUE), which represents the trade-off between carbon assimilation and water consumption, is a key indicator of ecosystem adaptation to environmental change. While previous studies have addressed the climatic controls on WUE in alpine ecosystems, the quantitative response mechanisms along elevation gradients remain insufficiently explored. This study investigated the growing season WUE patterns of alpine grasslands across elevation zones on the Qinghai–Tibetan Plateau by integrating partial correlation analysis and structural equation modeling (SEM). The findings revealed a clear triphasic pattern in WUE variation: a modest increase below 3000 m, a pronounced peak near 3700 m, and a steady decline at higher elevations. The dominant hydrothermal drivers shift with elevation. At lower altitudes, WUE was primarily influenced by the vapor pressure deficit (VPD), whereas soil temperature (ST) and VPD jointly govern WUE at mid-to-high altitudes. The SEM results indicated that the total effect of temperature on WUE increased from 0.51 at low elevations to 0.95 at high elevations, while the total effect of precipitation rose from −0.36 to −0.18. ST and VPD mediate the effects of temperature and precipitation on WUE, reflecting indirect and nonlinear regulatory pathways. Moreover, contribution rate analysis showed an elevation-dependent shift in WUE control: evapotranspiration (ET) exerted a dominant influence at low elevations (contribution rate: −82.50%), while net primary productivity (NPP) became the primary driver at high elevations (contribution rate: 54.71%). These findings demonstrate that alpine vegetation’s carbon–water coupling exhibits threshold-like behavior along altitudinal gradients, governed by differentiated hydrothermal constraints, offering new insights into ecosystem resilience under climate change. Full article

Mountain ecosystems offer valuable opportunities to study species distribution and diversity along altitudinal gradients, particularly for amphibians. This research examined amphibian species distribution, diversity, and conservation across an elevational gradient in the Tangjiahe National Nature Reserve, part of the Hengduan Mountains in southwestern China. A total of 25 amphibian species, encompassing 8 families and 2 orders, were documented, including three newly recorded species: Fejervarya kawamurai, Polypedates braueri, and Boulenophrys minor. Among these, eight species were designated as “threatened” under IUCN criteria and are listed on China’s Biodiversity Red List. Caijiaba exhibited the highest species diversity, whereas Shuichiping had the lowest. Fourteen species were found in terrestrial-farmland and aquatic-lotic habitats, which supported the greatest species richness. The distribution pattern along the altitudinal gradient showed peaks in species richness at 900–1100 m and 1900–2100 m elevation bands, with higher elevations displaying reduced richness. These findings highlighted the spatial characteristics of amphibian distribution and diversity across altitudinal ranges in the Tangjiahe National Nature Reserve and provide insights for formulating conservation policies and adaptive habitat management strategies. Full article

Exploring the changes in plant leaf functional traits in response to altitude across various altitudinal gradients of climax communities in karst regions can elucidate the characteristics of survival strategy adaptations among plant communities. This understanding may also reveal the growth dynamics and driving factors of climax communities in unique habitats. In this study, we examined nine climax communities located in the karst region of Southwest China, categorizing them into three distinct altitude gradients: low-, middle-, and high-altitude communities. By integrating species characteristics and community structure, we analyzed the patterns of change in leaf functional traits among plant communities at different altitudinal gradients and the relationships between these functional traits and environmental factors across the varying altitudes. The results indicated the following: (1) There was a significant difference in the specific leaf area (SLA) of the community as altitude increased, with a gradual decrease observed. The traits exhibiting higher coefficients of variation (CVs) in the leaves of the karst vertex community included the leaf carbon-to-nitrogen ratio (LCN), leaf area (LA), and leaf dry matter content (LDMC). Additionally, the environmental factors with higher CVs included soil organic carbon (SOC), soil phosphorus content (SPC), and the soil carbon-to-phosphorus ratio (SCP). (2) Soil organic carbon content (SOC), total nitrogen content (SNC), carbon-to-phosphorus ratio (SCP), and nitrogen-to-phosphorus ratio (SNP) demonstrated significant differences with increasing altitude. (3) The primary environmental factors influencing plant communities in karst areas included soil nitrogen content (SNC), mean annual temperature (NJW), soil organic carbon content (SOC), soil phosphorus content (SPC), soil water content (SWC), and mean annual precipitation (NJS). Our results indicated that the variation in leaf functional traits with altitude in karst climax communities was inconsistent. Among these traits, the specific leaf area (SLA) showed the most significant variation, and karst climax communities appeared to adapt to environmental changes by regulating traits such as leaf area (LA), leaf dry matter content (LDMC), and leaf carbon-to-nitrogen ratio (LCN). Soil organic carbon (SOC) and soil phosphorus content (SPC) are key factors contributing to habitat heterogeneity in the karst region. The karst climax communities are influenced by both soil and climatic factors along the altitudinal gradient. As altitude increases, these communities tend to adopt a life strategy. Furthermore, high-altitude terminal communities in karst areas are more susceptible to environmental filtering, while low-altitude areas are more affected by limitations in similarity. Full article

Mountain grassland ecosystems around the globe are highly sensitive to seasonal extreme climate events, which thus highlights the critical importance of understanding how such events have affected vegetation dynamics over recent decades. However, research on the time-lag of the effects of seasonal extreme climate events on vegetation has been sparse. This study focuses on Tajikistan, which is characterized by a typical alpine meadow–steppe ecosystem, as the research area. The net primary productivity (NPP) values of Tajikistan’s grasslands from 2001 to 2022 were estimated using the Carnegie–Ames–Stanford Approach (CASA) model. In addition, 20 extreme climate indices (including 11 extreme temperature indices and 9 extreme precipitation indices) were calculated. The spatiotemporal distribution characteristics of the grassland NPP and these extreme climate indices were further analyzed. Using geographic detector methods, the impact factors of extreme climate indices on grassland NPP were identified along a gradient of different altitudinal bands in Tajikistan. Additionally, a time-lag analysis was conducted to reveal the lag time of the effects of extreme climate indices on grassland NPP across different elevation levels. The results revealed that grassland NPP in Tajikistan exhibited a slight upward trend of 0.01 gC/(m²·a) from 2001 to 2022. During this period, extreme temperature indices generally showed an increasing trend, while extreme precipitation indices displayed a declining trend. Notably, extreme precipitation indices had a significant impact on grassland NPP, with the interaction between Precipitation anomaly (PA) and Max Tmax (TXx) exerting the most pronounced influence on the spatial variation of grassland NPP (q = 0.53). Additionally, it was found that the effect of extreme climate events on grassland NPP had no time-lag at altitudes below 500 m. In contrast, in mid-altitude regions (1000–3000 m), the effect of PA on grassland NPP had a significant time-lag of two months (p < 0.05). Knowing the lag times until the effects of seasonal extreme climate events on grassland NPP will appear in Tajikistan provides valuable insight for those developing adaptive management and restoration strategies under current seasonal extreme climate conditions. Full article

Phacelia secunda J.F. Gmel. (Boraginaceae) is a widely distributed insect-pollinated perennial herb. In central Chile (33° S), it occurs from the sea level up to 3600 m in the Andes, exhibiting broad morphological variation. In this study, we developed and characterized novel polymorphic microsatellites for this species, using an Illimina MiSeq sequencing platform. Nineteen polymorphic loci were obtained, with alleles numbers ranging from 3 to 13 per locus (mean = 5.84). Observed (H_O) and expected heterozygosities (H_E) ranged from 0.050 to 0.900 and from 0.049 to 0.825, respectively. These markers were applied to assess the genetic diversity and population structure along an altitudinal spanning from 1600 to 3600 m. The highest elevation population exhibited significantly lower within-population genetic diversity compared to lower-elevation populations. Significant population differentiation was observed along the gradient. Gene flow estimates support a stepping-stone like mode of migration, with greater exchange between adjacent elevations. These new microsatellites provide a valuable tool for elucidating the influence of altitude on genetic diversity and structure, and for evaluating the roles of local adaptation and phenotypic plasticity in shaping population variation. Full article

Exploring the diversity and community structure of darkling beetles (Tenebrionidae) and the associated environmental factors on an alluvial fan provides useful insights into the ecology of these landscape features. This study investigated Chaqikou in the Helan Mountains, which features unique alluvial fan landforms. Sample plots (200 × 200 m) were established at three positions: the fan top, fan middle, and fan edge. From May to October 2023, pitfall traps were used to survey beetle community composition and its relationship with environmental factors. Significant variations were observed in species composition and diversity indices across different months and sample plots. Strongly xerophilous species exhibited broader ecological niche breadth, while moderately xerophilous species tended to distribute in the mid-to-upper segments of alluvial fans. Non-metric multidimensional scaling analysis revealed temporal shifts in community composition, with beta diversity analysis showing that species nestedness dominated from June to August, while species replacement was prominent in May, September, and October. Redundancy analysis indicated that environmental factors affecting species distribution varied by plot. On the landscape scale, altitude was the primary factor affecting beetle distribution. Variance partitioning analysis showed that topographic, soil, and vegetation factors explained 51.7%, 20.2%, and 9.4% of the variation in the beetle community, respectively. It is evident that altitudinal gradients shape ecological filtering pressures by creating multidimensional heterogeneity in topography, soil properties, and vegetation coverage. The adaptive matching between Tenebrionid species’ biological traits and environmental factors ultimately governs the spatial distribution patterns of darkling beetle diversity in alluvial fan desert grasslands. Full article

High-altitude regions offer outstanding opportunities for investigating the impacts of combined abiotic stresses on plant physiological processes given their significant differences in terms of the ecological environment in high-elevation areas, low anthropogenic disturbance, and obvious distribution characteristics of plants along altitudinal gradients. Therefore, plants in high-altitude areas can be used as good targets for exploring plant adaptations to abiotic stress under extreme conditions. Plants that thrive in high-altitude environments such as the Qinghai-Tibet Plateau endure extreme abiotic stresses, including low temperatures, high UV radiation, and nutrient-poor soils. This study explores their adaptation mechanisms via phenotypic variation analyses and multiomics approaches. Key findings highlight traits such as increased photosynthetic efficiency, robust antioxidant systems, and morphological modifications tailored to high-altitude conditions. These insights advance our understanding of plant evolution in harsh environments and inform strategies to increase stress resistance in crops. Full article