Search Results (108)

Elevational gradients in temperature, moisture, and vegetation strongly influence soil nutrient content and stoichiometry in mountainous regions. However, exactly how total, microbial, and enzymatic carbon (C), nitrogen (N), and phosphorus (P) stoichiometry vary with elevation in karst peak-cluster depressions remains poorly understood. To address this, we studied soil total, microbial, and enzymatic C:N:P stoichiometry in seasonal rainforests within karst peak-cluster depressions in southwestern China at different elevations (200, 300, 400, and 500 m asl) and depths (0–20 and 20–40 cm). We found that soil organic carbon (SOC), total nitrogen (TN), and the C:P and N:P ratios increased significantly with elevation, whereas total phosphorus (TP) decreased. Microbial phosphorus (MBP) also declined with elevation, while the microbial N:P ratio rose. Activities of nitrogen- (β-N-acetylglucosaminidase and L-leucine aminopeptidase combined) and phosphorus-related enzymes (alkaline phosphatase) increased markedly with elevation, suggesting potential phosphorus limitation for plant growth at higher elevations. Our results suggest that total, microbial, and enzymatic soil stoichiometry are collectively shaped by topography and soil physicochemical properties, with elevation, pH, and exchangeable calcium (ECa) acting as the key drivers. Microbial stoichiometry exhibited positive interactions with soil stoichiometry, while enzymatic stoichiometry did not fully conform to the expectations of resource allocation theory, likely due to the functional specificity of phosphatase. Overall, these findings enhance our understanding of C–N–P biogeochemical coupling in karst ecosystems, highlight potential nutrient limitations, and provide a scientific basis for sustainable forest management in tropical karst regions. Full article

Uncaria tomentosa (Ut) is a Rubiaceae widely used in Peru’s traditional medicine. It is mainly known by the vernacular name of Cat’s claw due to its morphological aspects and is found in tropical low mountain forests of Central and South America. A decoction of Ut bark, root and leaves is used traditionally for different health problems, including arthritis, weakness, viral infections, skin disorders, abscesses, allergies, asthma, cancer, fevers, gastric ulcers, haemorrhages, inflammations, menstrual irregularity, rheumatism, urinary tract inflammation and wounds, among others, which gave rise to scientific and commercial interest. The present paper reviews research progress relating to the ethnobotany, phytochemistry and pharmacology of Ut, and some promising research routes are also discussed. We highlight the centrality of its different biological activities, such as antioxidant, anti-inflammatory, antiproliferative, antiviral, and antinociceptive, among others. Recently, studies of the health effects of this plant suggest that novel nutraceuticals can be obtained from it and applied as a preventive or prophylaxis strategy before the start of conventional drug therapy, especially for patients who are not prone to conventional pharmacological approaches to diseases. The present work emphasizes the current pharmacological properties of Uncaria tomentosa, evidencing its therapeutic benefits and encouraging further research on this medicinal plant. Full article

The Palearctic region is characterised by high endemism in the west and east, and a low endemism centre. The endemic lineages occurring at the two ends are largely distinct, and eastern endemics are typically associated with humid climates and forests, representing the start of a continuum from temperate to tropical forest groups and leading to Indo-Malay endemics. In contrast, western Palearctic endemics are typically associated with arid or seasonally dry (Mediterranean) climates and vegetation. Those lineages occurring in the central Palearctic are typically of western origin. Here, we use phylogenetic age (older than 34 million years (My)) to define a list of tetrapod and vascular plant lineages endemic to the western and central Palearctic, map their distributions at the ecoregion scale, and combine these maps to illustrate and understand lineage richness and endemism patterns. Sixty-three ancient lineages were recovered, approximately half of them reptiles, with several herbaceous and shrubby angiosperms, amphibians, and rodents, and single lineages of woody conifers, insectivores, and birds. Overall, we show high lineage richness in the western Mediterranean, eastern Mediterranean, and Iran, with the highest endemism values recorded in the western Mediterranean (southern Iberian Peninsula, southern France). This paints a picture of ancient lineage survival in areas of consistently dry climate since the Eocene, but also in association with persistent water availability (amphibians in the western Mediterranean). The almost complete absence of ancient endemic bird lineages is unusual and perhaps unique among the world’s biogeographic regions. The factors accounting for these patterns include climate since the end of the Eocene, micro-habitats and micro-climates (of mountain terrain), refugia, and patchiness and isolation (of forests). Despite their aridity adaptations, some of the lineages listed here may be tested under anthropogenic climatic change, although some may extend into the eastern Palearctic. We recommend using these lineages as flagships for conservation in the study region, where their uniqueness and antiquity deserve greater recognition. 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

Bamboo forest ecosystems are an important component of the Earth’s terrestrial ecosystems and play an important role in addressing the global timber crisis as well as climate change. Bamboo is a typical shallow-rooted, fast-growing clonal plant species whose developed rhizome system and high canopy closure play an important role in soil and water conservation. The function of soil and water conservation services of bamboo forests can intuitively reflect the regional regulation of precipitation, the redistribution function of precipitation, and the function of soil fixation, which is one of the crucial ecological service functions in regional ecosystems. Bamboo forests are divided into monopodial bamboo forests, sympodial bamboo forests, and mixed bamboo forests, which are mainly distributed in tropical and subtropical mountainous areas. The region’s variable climate, abundant precipitation, and high potential risk of soil erosion, in conjunction with the frequent operation of bamboo forests and frequent occurrence of extreme weather events, have the potential to adversely affect the ecosystem function of bamboo forests. Presently, bamboo forests are primarily managed through the cultivation of bamboo, with the objective of enhancing productivity. Extensive research has been conducted on the long-term maintenance of bamboo forest productivity. However, there is a paucity of research on the mechanisms of management measures for ecosystem stability and the development of adaptive management technology systems suitable for soil and water conservation, carbon sequestration and sink enhancement, and biodiversity conservation. This paper is predicated on the biological characteristics of bamboo and, thus, aims to compile the extant research progress on the following subjects: the role of rainfall redistribution in bamboo forest canopies, the role of deadfall interception, and the mechanism of soil fixation mechanics of the root system. It also synthesizes the current status of research on the impact of traditional management measures on the soil and water conservation function of bamboo forests. Finally, it discusses the problems of current research and the direction of future development. Full article

Forest biomass, as a carrier of carbon, is an important indicator for judging forest productivity, stability and sustainable development capacity. Using the survey data of sample plots in eight forest areas in central Hainan, the biomass distribution of tropical mountain rainforests in National Park of Hainan Tropical Rainforest in different community sizes, diameter classes, altitudes and spaces was measured to explore the relationship between forest biomass and environmental factors. The results show that (1) the total area of tropical montane rainforests in National Park of Hainan Tropical Rainforest was about 983.70 km², distributed within an altitude range of 700–1300 m; the total aboveground biomass was about 25.208 million tons, which decreased first and then increased with increasing altitude, with an average aboveground biomass per unit area of 236.00 t·hm⁻²; (2) the primary forest accounted for 83.23% of the total aboveground biomass of the tropical mountain rainforest with only 29.84% of the total area, and the aboveground biomass per unit area was generally higher than that of the secondary forest; and (3) medium- and large-diameter trees were the main carriers of aboveground biomass in tropical mountain rain forests. More than 83.73% of the aboveground biomass was concentrated in large-diameter trees. The results of this study provide a reference for others aiming to perform measurement and evaluation of the carbon sink and the capacity for carbon neutrality in tropical rainforest ecosystems or to maintain regional biodiversity. Full article

The increasing frequency of both singular and compound extreme climate events driven by global warming has profoundly impacted terrestrial ecosystems. Using machine learning-based Random Forest algorithms and moving correlation analysis, this study quantifies the impacts of extreme climate indices (ECIs) on two ecological indicators (EIs), the NDVI and GPP, from 1982 to 2019. The results reveal that singular extreme climate events exert a more pronounced influence on ecosystems across Southwestern China (SWC) than compound ones. Specifically, the NDVI and GPP exhibited strong correlations with summer days (SU) and diurnal temperature range (DTR), with SU contributing positively (weight = 0.275 for the GPP and 0.238 for the NDVI) and DTR negatively (weight = 0.107 for the GPP and 0.130 for the NDVI). Regional analyses highlighted distinct spatial patterns: in mid–high-altitude areas (>1 km), including the Hengduan Mountains (HDMs) and Yunnan–Guizhou Plateau (YGP), extreme temperatures and precipitation significantly promoted vegetation growth, with rainfall day index (RDI), frost days (FD), extreme temperature index (ETI), SU, and DTR all having a strong influence (>0.1) on the GPP and NDVI. These areas showed strong adaptability to extreme climate, benefiting overall vegetation health. In contrast, ecosystems in low-altitude areas (<1 km) showed more variable responses. The Guangxi Basin (GXB) exhibited strong resistance to ECIs, with vegetation being almost unaffected by extreme precipitation and benefiting from continuous warming. Only consecutive wet days (CWD) and FD were significantly negatively correlated with EIs (p < 0.05), and their correlation weights were low (weights = 0.043 and 0.013). However, the vegetation in the Sichuan Basin (SCB) is more susceptible to climate extremes, which have particularly strong effects on the NDVI. SU, tropical nights (TR), ETI, and growing season length (GSL), which have positive effects on EIs in mid–high-altitude areas, show extremely significant negative correlations in the SCB (p < 0.001), and their weights account for one-third of the total (weights = 0.15, 0.11, 0.061 and 0.012, respectively). These findings underscore the heterogeneous responses of ecosystems to ECIs and emphasize the need for region-specific strategies in ecosystem management and disaster prevention amid climate change. Full article

Hopea chinensis is a representative tree species in evergreen monsoon forests in the northern tropics, but it is currently in a critically endangered state due to destruction by human activities and habitat loss. In this study, we measured and analyzed the number of regenerating seedlings and habitat factors in wild populations of H. chinensis by combining field surveys with laboratory analysis. The aim of this study was to clarify the spatial distribution of H. chinensis seedlings and related factors to provide a scientific basis for conserving its germplasm resources and population restoration. In six populations, most size-class seedlings had aggregated distributions at three scales, and the intensity of aggregation decreased as the sample plot scale increased for most size-class seedlings. In the northern foothills of the Shiwandashan Mountains, size class I seedlings tended to be distributed in habitats with a higher rock bareness rate, whereas size class II and III seedlings tended to be distributed in habitats with a higher canopy density, thicker humus layers, and higher soil moisture content. In the southern foothills of the Shiwandashan Mountains, size class I and II seedlings tended to be distributed in habitats with higher available nitrogen contents, and size class III seedlings tended to be distributed in habitats with higher available nitrogen and soil moisture contents. Therefore, in the southern foothills of the Shiwandashan Mountains, the survival rate of H. chinensis seedlings can be improved by artificially adding soil to increase the thickness of the soil layer in stone crevices and grooves, regularly watering the seedlings during the dry season, and appropriately reducing the coverage of the shrub layer. In the northern foothills, the survival rate of H. chinensis seedlings can be enhanced by regularly applying nitrogen fertilizer and watering to increase the available nitrogen and soil moisture contents. Full article

For tropical rainforest regions with dense vegetation cover, the development of effective large-scale soil mapping methods is crucial to improve soil management practices to replace the time-consuming and laborious conventional approaches. While machine learning (ML) algorithms demonstrate superior predictability of soil properties over linear models, their practical and automated application for predicting soil properties using remote sensing data requires further assessment. Therefore, this study aims to integrate Unmanned Aerial Vehicles (UAVs)-based hyperspectral images and Light Detection and Ranging (LiDAR) points to predict the soil properties indirectly in two tropical rainforest mountains (Diaoluo and Limu) in Hainan Province, China. A total of 175 features, including texture features, vegetation indices, and forest parameters, were extracted from two study sites. Six ML models, Partial Least Squares Regression (PLSR), Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient Boosting Decision Trees (GBDT), Extreme Gradient Boosting (XGBoost), and Multilayer Perceptron (MLP), were constructed to predict soil properties, including soil acidity (pH), total nitrogen (TN), soil organic carbon (SOC), and total phosphorus (TP). To enhance model performance, a Bayesian optimization algorithm (BOA) was introduced to obtain optimal model hyperparameters. The results showed that compared with the default parameter tuning method, BOA always improved models’ performances in predicting soil properties, achieving average R² improvements of 202.93%, 121.48%, 8.90%, and 38.41% for soil pH, SOC, TN, and TP, respectively. In general, BOA effectively determined the complex interactions between hyperparameters and prediction features, leading to an improved model performance of ML methods compared to default parameter tuning models. The GBDT model generally outperformed other ML methods in predicting the soil pH and TN, while the XGBoost model achieved the highest prediction accuracy for SOC and TP. The fusion of hyperspectral images and LiDAR data resulted in better prediction of soil properties compared to using each single data source. The models utilizing the integration of features derived from hyperspectral images and LiDAR data outperformed those relying on one single data source. In summary, this study highlights the promising combination of UAV-based hyperspectral images with LiDAR data points to advance digital soil property mapping in forested areas, achieving large-scale soil management and monitoring. Full article

Basal metabolic rates (BMRs) increase with temperature and body mass. Environmental temperatures rapidly change in tropical mountains due to elevation (macro scale) and vegetation structure (micro scale). Thus, tropical mountains are good settings for testing the effects of temperature on BMRs. We measured the BMRs at four temperature ranges on six territorial and closely related species of Rubyspot damselflies (Hetaerina, Calopterygidae), which also share very similar behavior and morphology and are segregated by habitat and elevation across the Western Colombian Andes. We analyzed the effects of body mass, habitat, elevation, temperature, and sex on their BMRs, using a phylogenetic framework. We found that the main factors regulating their niche partition seemed to be environmental temperature, body size, and BMR. We found differences in their BMRs related to elevation when the temperatures were close to those experienced by the damselflies at their elevational range. As predicted, the larger species associated with colder habitats, forests, and highlands had higher BMRs. However, at high stressful temperatures, only the body mass was positively related to the BMR, showing that smaller individuals can keep their BMRs lower under high temperatures compared to bigger ones. Habitat use was not associated with changes in the BMR. Finally, phylogenetic reconstruction showed all species clustered in three clades. Each clade in the phylogenetic tree shares similar habitat preferences, pointing to a mixture of evolutionary history, thermal adaptations, and body mass differences as a possible explanation for the great diversity of these damselflies in a small area. Under the global warming scenario, we expect Rubyspots with smaller body sizes to be favored since they will tolerate higher temperatures, which would ultimately lead to populations with smaller body sizes overall, which could negatively affect their fitness. Full article

The forests and grasslands in the U.S. are vulnerable to global warming and extreme weather events. Current satellites do not provide historical vegetation density images over the long term (more than 50 years), which has restricted the documentation of key ecological processes and their resultant responses over decades due to the absence of large-scale and long-term monitoring studies. We performed point-by-point regression and collected data from 391 tree-ring plots to reconstruct the annual normalized difference vegetation index (NDVI) time-series maps for the contiguous U.S. from 1850 to 2010. Among three machine learning approaches for regressions—Support Vector Machine (SVM), General Regression Neural Network (GRNN), and Random Forest (RF)—we chose GRNN regression to simulate the annual NDVI with lowest Root Mean Square Error (RMSE) and highest adjusted R2. From the Little Ice Age to the present, the NDVI increased by 6.73% across the contiguous U.S., except during some extreme events such as the Dust Bowl drought, during which the averaged NDVI decreased, particularly in New Mexico. The NDVI trend was positive in the Northern Forest, Tropical Humid Forest, Northern West Forest Mountains, Marin West Coast Forests, and Mediterranean California, while other ecoregions showed a negative trend. At the state level, Washington and Louisiana had significantly positive correlations with temperature (p < 0.05). Washington had a significantly negative correlation with precipitation (p < 0.05), whereas Oklahoma had a significantly positive correlation (p < 0.05) with precipitation. This study provides insights into the spatial distribution of paleo-vegetation and its climate drivers. This study is the first to attempt a national-scale reconstruction of the NDVI over such a long period (151 years) using tree rings and machine learning. Full article

Profound global transformations in the Anthropocene epoch are hastening shifts in species ranges, with natural mountain treeline migration playing a crucial role in this overarching species movement. The varied reactions of mountain treelines to climatic conditions across diverse climatic zones, when compounded by local disturbances, result in distinct migration patterns. Usually, warming encourages mountain treelines to migrate to higher elevations. Nevertheless, in a period of rapid warming, it remains unclear whether the natural mountain treeline in global thermal climatic zones and subclimatic zones has expedited its upward movement. Here, we employed remote sensing observations and the random forest algorithm to investigate the natural treeline dynamics across 24 major mountain ranges worldwide amidst a period of rapid warming (1990–2020). Our research shows substantial disparities in the migration patterns of natural mountain treelines across the global thermal zone. The natural mountain treeline in tropical and subtropical zones descends by an average of 1.1 and 0.8 m per year, respectively. Only 18.8 and 35.5% of the natural mountain treelines in these regions had undergone upward migration, respectively. The average migration rates of natural mountain treelines in temperate and boreal zones were 0.7 m per year. Correspondingly, 47 and 33.2% of the natural mountain treelines in these zones had already shifted to higher elevations. The highest average migration rate of natural mountain treelines occurs in temperate continental climates (1.7 m per year). The loss or degradation of alpine species habitats, a direct consequence of the upward movement of the treeline, highlights the necessity for increased monitoring and protection of alpine species in temperate and boreal zones in the future. Full article

Expeditionary studies of the functioning of landscapes of mid-mountain monsoon (including fog) forests have been being conducted within the landscape and ecological station in the territory of the Bidoup-Nui Ba National Park and the adjacent Hon Giao since 2018 and are currently underway. One of the research objectives is to clarify the biogeochemical migrations of the material composition of soils in the “leaf debris–soil” system. We have consistently studied natural objects for their material composition as well as the intensity and rate of involvement of chemical elements in physicochemical migration processes in the “leaf debris–soil” system. Our findings indicate an active influx of a select group of examined elements (Se, Pd, Ag, Cd, Sn, Bi), particularly Bi, Pd, Se, and Cd, through the leaf debris and the detachment of aboveground plant organs, warranting their integration into organogenic soil horizons. Subsequently, lateral migration (Pd, Cd, Se) ensues. Slope processes within subordinate landscape facets, in addition to soil moisture and aeration processes, contribute to the subsequent redistribution of elemental volumes introduced into organogenic soil horizons. Full article

Protecting biodiversity and keeping the Earth’s temperature stable are both very important jobs performed by tropical forests. In the last few decades, remote sensing has given us new tools and ways to track changes in land cover. To understand what causes changes in forest cover, it is important to look at the things that affect those changes. However, there is not enough research that uses a logistic regression model (LRM) and compares the results with machine learning (ML) techniques to investigate the specific factors that cause forest cover change in remote mountainous areas like Thailand’s Mae Hong Son and Chiang Mai Provinces. Following a comparison of an LRM, a random forest, and an SVM, this study of the causes of changes in forest cover in Mae Hong Son found six important factors: soil series, rock types, slope, the NDVI, the NDWI, and the distances to city areas. Compared to the LRM, both the RF and SVM machine learning algorithms had higher values for the kappa coefficient, sensitivity, specificity, accuracy, positive and negative predictions, and sensitivity, especially the RF. Following what was found in Mae Hong Son, when the important factors were examined in Chiang Mai, the RF came out on top. It is believed that these results can be used in more situations to help make plans for restoring ecosystems and to promote long-lasting methods of managing land use. Full article

In mountain tropical forests, understory herbs have received little attention compared to trees, and their commonness and rarity are virtually unknown. We studied ground herbs to explore how they are assembled in a full one-hectare plot and to test the influence of light intensity (LI) and topographic habitats in species composition. The plot is a humid montane forest located in the Pasochoa Volcano, at 3300 m. We found 43 genera and 50 perennial species (30 angiosperms in 17 families, and 20 ferns). Interestingly, herbs are 64% richer in species than trees in the same study plot (50 vs. 32). Herbs were mostly obligately terrestrial (70% of the species), while 30% were fallen climbers and epiphytes rooted in the ground. Across the forest, there were 31,119 individuals that covered 8.5% of the ground. We concluded that both LI and topography shaped the species distribution, the floristic composition, and the community structure of ground herbs. For instance, 12% of the species were exclusively found in places with high LI; the rest of species grew in medium- to low-LI environments. Concerning rarity, we found that 39% of the species are rare (judging by botanical collections; <100), which stresses the need of conservation strategies for this group of plants. Full article