Search Results (87)

The distribution of Itea yunnanensis, a shrub species in the genus Itea of the family Iteaceae, is primarily concentrated in the Hengduan Mountains region of China, where it faces severe threats from global climate change. However, systematic research on the species’ distribution patterns, climatic response mechanisms, and future suitable habitat dynamics remains insufficient. This study aims to assess the spatiotemporal evolution and driving mechanisms of I. yunnanensis-suitable habitats under current and future climate change scenarios to reveal the migration patterns of its distribution centroid and ecological thresholds, and to enhance the reliability and interpretability of predictions through model optimization. For MaxEnt modeling, we utilized 142 georeferenced occurrence records of I. yunnanensis alongside environmental data under current conditions and three future Shared Socioeconomic Pathways (SSPs: SSP1-2.6, SSP2-4.5, SSP5-8.5). Model parameter optimization (Regularization Multiplier, Feature Combination) was performed using the R (v4.2.1) package ‘ENMeval’. The optimized model (RM = 3.0, FC = QHPT) significantly reduced overfitting risk (ΔAICc = 0) and achieved high prediction accuracy (AUC = 0.968). Under current climate conditions, the total area of potential high-suitability habitats for I. yunnanensis is approximately 94.88 × 10⁴ km², accounting for 9.88% of China’s land area, with core areas located around the Hengduan Mountains. Under future climate change, the suitable habitats show significant divergence, area fluctuation and contraction under the SSP1-2.6 scenario, and continuous expansion under the SSP5-8.5 scenario. Meanwhile, the species’ distribution centroid exhibits an overall trend of northwestward migration. This study not only provides key spatial decision-making support for the in situ and ex situ conservation of I. yunnanensis, but also offers an important methodological reference for the adaptive research on other ecologically vulnerable species facing climate change through its optimized modeling framework. Full article

Climate change intensifies hydrological cycles, leading to an increased variability in terrestrial water storage anomalies (TWSAs) and a heightened drought risk. Understanding the spatiotemporal dynamics of TWSAs and their driving factors is crucial for sustainable water management. While previous studies have primarily attributed TWSAs to regional factors, this study employs wavelet coherence, partial correlation analysis, and multiple linear regression to comprehensively analyze TWSA dynamics and their drivers in the Hengduan Mountains (HDM) region from 2003 to 2022, incorporating both regional and global influences. Additionally, dry–wet variations were quantified using the GRACE-based Drought Severity Index (GRACE-DSI). Key findings include the following: The annual mean TWSA showed a non-significant decreasing trend (−2.83 mm/y, p > 0.05), accompanied by increased interannual variability. Notably, approximately 36.22% of the pixels in the western HDM region exhibited a significantly decreasing trend. The Nujiang River Basin (NRB) (−17.17 mm/y, p < 0.01) and the Lancang (−17.17 mm/y, p < 0.01) River Basin experienced the most pronounced declines. Regional factors—particularly precipitation (PRE)—drove TWSA in 59% of the HDM region, followed by potential evapotranspiration (PET, 28%) and vegetation dynamics (13%). Among global factors, the North Atlantic Oscillation showed a weak correlation with TWSAs (r = −0.19), indirectly affecting it via winter PET (r = −0.56, p < 0.05). The decline in TWSAs corresponds to an elevated drought risk, notably in the NRB, which recorded the largest GRACE-DSI decline (slope = −0.011, p < 0.05). This study links TWSAs to climate drivers and drought risk, offering a framework for improving water resource management and drought preparedness in climate-sensitive mountain regions. Full article

Biodiversity conservation plays a pivotal role in achieving sustainable development and fostering harmonious coexistence between humans and nature. This study identifies avian conservation priority areas across China by analyzing multi-dimensional biodiversity, incorporating species diversity, functional diversity, and phylogenetic diversity. Through systematic conservation planning using Zonation version 4 software, we delineated priority areas across these diversity dimensions. Our results demonstrate a distinct south-to-north diversity gradient in China’s avifauna, with functional and phylogenetic diversity hotspots concentrated in Yunnan Province, the Hengduan Mountains, Hainan Island, Taiwan Island, and southeastern coastal regions. The identified priority conservation areas cover 14.6% of China’s terrestrial territory, protecting 89.8% of the country’s bird species—including 93.5% of endemic species and 88.9% of critically endangered species. Notably, existing nature reserves encompass merely 8.1% of these priority areas, revealing substantial conservation gaps within the current protection framework. Building upon China’s 3C Zoning Framework (Cities and farms, Shared landscapes, and Large wild areas), we propose zone-specific conservation strategies, with particular emphasis on strengthening protected area networks in the eastern coastal regions and the middle-lower Yangtze River basin, where urbanization pressures are most acute. These findings highlight the critical importance of incorporating multi-dimensional diversity in conservation planning and offer novel perspectives for optimizing China’s protected area system. Full article

The Qinghai–Tibet Plateau is one of the most geologically active regions in the world, characterized by significant geomorphic variation and a wide range of geological hazards. The multifactorial coupling of tectonic movements, geomorphological evolution, climate variability, and lithological characteristics contributes to the pronounced spatial heterogeneity of the disaster-inducing environment. Identifying key controlling factors and their driving mechanisms is crucial for effective regional disaster prevention and mitigation. This study adopts a systematic framework based on regional disaster systems theory, integrating tectonic activity, engineering geology, topography, and precipitation to construct a multi-factor zoning system. Using the Random Forest model, we quantify factor contributions and delineate eight distinct disaster-inducing environment zones. Zones I–III (Himalayas–Hengduan Mountains–Qilian Mountains) are characterized by a dominant coupling mechanism of “tectonic fragmentation—topographic relief—precipitation erosion” and account for the majority of large-scale disasters. In contrast, Zones IV–VIII, primarily located in the central–western Plateau basins, are constrained by limited material sources, resulting in lower disaster densities. The findings indicate that geological structures and lithological fragmentation provide the material foundation for hazard occurrence, while topographic potential and hydrodynamic forces serve as critical triggering conditions. This nonlinear coupling of factors shapes a disaster geographic pattern characterized by “dense in the east and sparse in the west”. Based on these results, the targeted recommendations proposed offer valuable theoretical insights and methodological guidance for disaster mitigation and region-specific management across the Qinghai–Tibet Plateau. Full article

Amid accelerating global climate change, research quantifying the uncertainty of mountain ecosystems in relation to CMIP6 multi-model ensemble (MME) simulations remains limited. This study addresses this gap by evaluating future temperature and precipitation trends in the Hengduan Mountains and quantifying the uncertainty associated with CMIP6 MME outputs. Utilizing data from 11 CMIP6 climate models, bilinear interpolation was employed to standardize model resolution, while inverse distance weighting (IDW) interpolation was applied to assess spatial distribution patterns. To mitigate systematic biases, the multi-model ensemble mean approach was adopted. Through an equal-weight model selection strategy, EC-Earth3-Veg and MPI-ESM1-2-HR were identified as the optimal model combination for the region. Key findings include the following: (1) During the reference period (1985–2014), model simulations exhibited systematic biases, with temperatures underestimated by 0.46 ± 0.08 °C/month and precipitation overestimated by 2.07 ± 0.32 mm/month relative to observations. (2) In the future period (2031–2070), projected regional warming rates in typical years under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios are −0.294 ± 0.021 °C/decade, 0.081 ± 0.009 °C/decade, and 0.171 ± 0.012 °C/decade, respectively. (3) Precipitation is projected to decline overall, with the most pronounced decrease under the SSP5-8.5 scenario (−0.68 ± 0.07%). This study is the first to systematically quantify CMIP6 model uncertainty in the Hengduan Mountains, revealing regional climate change trajectories, providing a scientific basis for formulating adaptive strategies, and identifying critical pathways for enhancing regional climate modeling efforts. Full article

The southwestern region of China is tectonically situated within the Tethyan tectonic domain, with the eastern part comprising the Upper Yangtze Block, while the western orogenic belt forms the main part of the Tibetan Plateau. This belt was formed by the subduction of the Paleo-Tethys Ocean and subsequent arc-continent collision, and was later further modified by the India-Asia collision, resulting in complex geological structures such as the Hengduan Mountains. The lithostratigraphy in this region can be divided into six independent units. In terms of mineralization, the area encompasses two first-order metallogenic domains: the Tethyan-Himalayan and the Circum-Pacific. This study synthesizes extensive previous research to systematically investigate representative rare earth element (REE) deposits (e.g., Muchuan and Maoniuping in Sichuan; the Xinhua deposit in Guizhou; the Lincang deposit in Yunnan). Through comparative analysis of regional tectonic-metallogenic settings, we demonstrate that REE distribution in Southwest China is fundamentally controlled by Tethyan tectonic evolution: sedimentary-weathered types dominate in the east, while orogenic magmatism-related types prevail in the west. These findings reveal critical metallogenic patterns, establishing a foundation for cross-regional resource assessment and exploration targeting. The region hosts 32 identified REE occurrences, predominantly light REE (LREE)-enriched, genetically classified as endogenic, exogenic, and metamorphic deposit types. Metallogenic epochs include Precambrian, Paleozoic, and Mesozoic-Cenozoic periods, with the latter being most REE-relevant. Six prospective exploration areas are delineated: Mianning-Dechang, Weining-Zhijin, Long’an, Simao Adebo, Shuiqiao, and the eastern Yunnan-western Guizhou sedimentary-type district. Notably, the discovery of paleo-weathering crust-sedimentary-clay type REE deposits in eastern Yunnan-western Guizhou significantly expands regional exploration potential, opening new avenues for future resource development. Full article

Subalpine shrubland is an important vegetation type in the Hengduan Mountains region of China, and its distribution has been substantially influenced by global warming. In this research, four subalpine shrub communities in the Hengduan Mountains were selected: Rhododendron heliolepis Franch. scrub, Rhododendron flavidum Franch. scrub, Quercus monimotricha (Hand.-Mazz.) Hand.-Mazz. scrub, and Pinus yunnanensis var. pygmaea (Hsueh ex C. Y. Cheng, W. C. Cheng & L. K. Fu) Hsueh scrub. A MaxEnt model was used to assess the suitable habitats and their primary drivers of four subalpine shrublands in China under different climate scenarios. Our results indicate the following: (1) The suitable habitat areas of the four subalpine shrublands exhibit a predominant distribution within the Hengduan Mountains region, with small populations in the Himalayas and Wumeng Mountain. Temperature and precipitation are identified as the primary drivers influencing the suitable habitat areas of the four subalpine shrublands, and the temperature factor is more influential than the precipitation factor. Furthermore, the contribution rate of slope to Quercus monimotricha scrub is 19.2%, which cannot be disregarded. (2) Under future climate scenarios, the total suitable habitats of the four subalpine shrublands show an expanding trend. However, the highly suitable areas of three shrublands (Rhododendron flavidum scrub, Quercus monimotricha scrub, and Pinus yunnanensis var. pygmaea scrub) show a contracting trend under the high-carbon-emission scenario (SSP585). (3) Driven by global warming, the suitable habitat areas of Rhododendron heliolepis scrub, Rhododendron flavidum scrub, and Pinus yunnanensis var. pygmaea scrub shift toward higher elevations in the northwest, while the distribution of Quercus monimotricha scrub varies under different carbon emission scenarios, with a much smaller shift range than the other three scrubs. Our study contributes valuable insights into the spatiotemporal dynamics of subalpine shrublands in China under climate change, providing scientific guidance for biodiversity conservation and ecosystem restoration. Full article

Understanding ecological niche evolution patterns is crucial for elucidating biogeographic history and guiding biodiversity conservation. Taxus is a Tertiary relict gymnosperm with 11 lineages mainly distributed across East Asia, spanning from tropical to subarctic regions. However, the spatiotemporal dynamics of its ecological niche evolution and the roles of ecological and geographical factors in lineage diversification, remain unclear. Using occurrence records, environmental data, and reconstructed phylogenies, we employed ensemble ecological niche models (eENMs), environmental principle components analysis (PCA-env), and phyloclimatic modeling to analyze niche similarity and evolution among 11 Taxus lineages. Based on reconstructed Bayesian trees and geographical distribution characteristics, we classified the eleven lineages into four clades: Northern (T. cuspidata), Central (T. chinensis, T. qinlingensis, and the Emei type), Western (T. wallichiana, T. florinii, and T. contorta), and Southern (T. calcicola, T. phytonii, T. mairei, and the Huangshan type). Orogenic activities and climate changes in the Tibetan Plateau since the Late Miocene likely facilitated the local adaptation of ancestral populations in Central China, the Hengduan Mountains, and the Yunnan–Guizhou Plateau, driving their expansion and diversification towards the west and south. Key environmental variables, including extreme temperature, temperature and precipitation variability, light, and altitude, were identified as major drivers of current niche divergence. Both niche conservatism and divergence were observed, with early conservatism followed by recent divergence. The Southern clade exhibits high heat and moisture tolerance, suggesting an adaptive shift, while the Central and Western clades retain ancestral drought and cold tolerance, displaying significant phylogenetic niche conservatism (PNC). We recommend prioritizing the conservation of T. qinlingensis, which exhibits the highest PNC level, particularly in the Qinling, Daba, and Taihang Mountains, which are highly degraded and vulnerable to future climate fluctuations. Full article

Climate and land use directly influence species’ spatial distribution, which can alter species’ distribution and lead to significant changes in biodiversity spatial patterns. There are few reports on how climate and land use changes affect plant biodiversity spatial distribution patterns. This study focuses on Chinese endemic tree plants, analyzing the changes in hotspots under current and future conditions (2050 SSP1–2.6 and SSP5–8.5 climate and land use scenarios). Using spatial distribution data of endemic tree plants in China, the Biomod2-integrated species distribution model, and the “top 5% diversity” hotspot identification method, we examine species richness (SR), functional diversity (FD), and phylogenetic diversity (PD). The results indicate that with changes in climate and land use: (1) significant shifts occur in the spatial distribution patterns of hotspots. Although the number of hotspots identified by different diversity indices varies, fragmentation increases across all scenarios. (2) Hotspots tend to concentrate in low-latitude and high-altitude regions. In future scenarios, the longitudinal position of hotspots is significantly lower, and their elevation is significantly higher compared to the current scenario. (3) The spatial patterns of plant diversity in hotspots also change significantly. The SR and PD patterns show similar distribution trends across different scenarios. Under current conditions, the highest values of SR and PD are found in the eastern mountainous regions, such as the Wuyi Mountains and Nanling Mountains, while in future scenarios, they shift to central and western mountainous areas like the Qinling Mountains and Hengduan Mountains. The FD distribution pattern differs, with its highest values consistently found in southeastern Tibet and the Hengduan Mountains across all scenarios. Thus, climate and land use changes not only alter the spatial distribution of hotspots but also change plant diversity within them. This study provides scientific evidence for regional-scale biodiversity conservation under global change. Full article

Gamasid mites (Acari: Mesostigmata) are ecologically diverse arthropods, many of which act as vectors for zoonotic diseases such as rickettsial pox and hemorrhagic fever with renal syndrome. This study investigates the faunal and ecological patterns of gamasid mites across five zoogeographic microregions in Yunnan Province, China, a biodiversity hotspot with complex topography. From 1990 to 2022, 18,063 small mammal hosts (primarily rodents) were surveyed, yielding 167 mite species (141,501 specimens). The key findings include the following: (1) Low host specificity: most mite species parasitized >10 host species, with Laelaps nuttalli, L. echidninus, Dipolaelaps anourosorecis, L. guizhouensis, L. turkestanicus, and L. chini dominating (>76.59% abundance). (2) Environmental heterogeneity: mountainous and outdoor habitats exhibited higher mite diversity than flatland/indoor environments. (3) Zoonotic risks: thirteen vector species with low host specificity were identified, potentially amplifying disease transmission. (4) Ecological niche dynamics: high niche overlaps (e.g., Laelaps guizhouensis vs. L. xingyiensis: O_ik = 0.997) and positive interspecific correlations (e.g., L. echidninus vs. L. nuttalli: R = 0.97, p < 0.01) suggest co-occurrence trends on shared hosts. (5) Biogeographic patterns: mite communities were clustered distinctly by microregion, with the highest similarity being obtained between western/southern plateaus (IV and V) and unique diversity in the Hengduan Mountains (I). (6) Chao 1 estimation predicted 203 total mite species in Yunnan, 36 of which were undetected in the current sampling. These results highlight the interplay of biogeography, host ecology, and environmental factors in shaping mite distributions, with implications for zoonotic disease surveillance in biodiverse regions. Full article

The Hengduan Mountains region (HMR) is vulnerable to flash flood disasters, which account for the largest proportion of flood-related fatalities in China. Flash flood regionalization, which divides a region into homogeneous subdivisions based on flash flood-inducing factors, provides insights for the spatial distribution patterns of flash flood risk, especially in ungauged areas. However, existing methods for flash flood regionalization have not fully reflected the spatial topology structure of the inputted geographical data. To address this issue, this study proposed a novel framework combining a state-of-the-art unsupervised Graph Neural Network (GNN) method, Dink-Net, and Shapley Additive exPlanations (SHAP) for flash flood regionalization in the HMR. A comprehensive dataset of flash flood inducing factors was first established, covering geomorphology, climate, meteorology, hydrology, and surface conditions. The performances of two classic machine learning methods (K-means and Self-organizing feature map) and three GNN methods (Deep Graph Infomax (DGI), Deep Modularity Networks (DMoN), and Dilation shrink Network (Dink-Net)) were compared for flash-flood regionalization, and the Dink-Net model outperformed the others. The SHAP model was then applied to quantify the impact of all the inducing factors on the regionalization results by Dink-Net. The newly developed framework captured the spatial interactions of the inducing factors and characterized the spatial distribution patterns of the factors. The unsupervised Dink-Net model allowed the framework to be independent from historical flash flood data, which would facilitate its application in ungauged mountainous areas. The impact analysis highlights the significant positive influence of extreme rainfall on flash floods across the entire HMR. The pronounced positive impact of soil moisture and saturated hydraulic conductivity in the areas with a concentration of historical flash flood events, together with the positive impact of topography (elevation) in the transition zone from the Qinghai–Tibet Plateau to the Sichuan Basin, have also been revealed. The results of this study provide technical support and a scientific basis for flood control and disaster reduction measures in mountain areas according to local inducing conditions. Full article

The Qinghai–Tibet Plateau is recognized as a biodiversity hotspot, with a wide variety of grasshopper species, including several endemic to the region, which play significant roles in both agricultural and forestry ecosystems. The purpose of this study was to analyze the species diversity and distribution pattern of grasshoppers on the Qinghai–Tibet Plateau. A comprehensive database comprising 390 grasshopper species was established through specimen collection, a literature review, and a geographical distribution data analysis. Diversity analysis showed that the diversity of species under the five vegetation types was relatively average. However, the alpine cold vegetation of Qinghai–Tibet and subtropical evergreen broad-leaved forest still showed a relatively high Shannon index and Simpson index. Grasshopper species are mainly concentrated in the eastern and southern parts of the Qinghai–Tibet Plateau. The richness pattern showed that grasshopper species diversity was particularly high in certain mountain areas, with Bayankala Mountain and Hengduan Mountain being endemic hotspots. The MaxEnt models were used to assess the potential habitats for four dominant genera of grasshoppers under projected climate change scenarios for 2050 and 2070. Altitude was the factor affecting the distribution of Locusta, Chorthippus, and Kingdonella, while precipitation and temperature were the factors affecting the distribution of Leuconemacris. These findings improve our understanding of the distribution patterns of different grasshopper species across various habitat types on the Qinghai–Tibet Plateau and provide valuable insights for developing targeted ecological protection strategies in response to environmental changes. Full article

Morphological and micro-morphological traits of characteristics serve as the cornerstone for species identification and taxonomy, and they also ensure the adaptive responses of species to specific environmental conditions. Allium prattii C.H. Wright is mainly distributed in the mountains of southwestern China (MSC) and adjacent regions, and exhibits pronounced variations in leaf morphology and micro-morphology across different growth environments, making it an ideal taxa to study species adaptation to diverse conditions. In this study, we conducted extensive field surveys, sample collections, and morphological experiments, amassing data on leaf morphological and micro-morphological traits from 45 populations of A. prattii. Specifically, we explored the differences in leaf morphology among populations and the patterns of geographical distribution. Consequently, we examined the correlation between seven climatic factors, longitude, latitude, and leaf morphological traits, and simulated the changes in the A. prattii distribution area during different historical periods. Our results indicate that all populations of A. prattii can be categorized into four distinct lineages, characterized by significant leaf morphological divergence and distinct geographical distribution patterns. Populations located in the Hengduan Mountains and neighboring regions demonstrated elevated coefficients of variation (CV) in leaf morphology. The correlation analysis between morphological traits and climatic factors highlighted substantial links between the density of stomata on the upper epidermis and environmental variables, as well as significant correlations between leaf length/width and geographical distribution (latitude and longitude). Simulations of the distribution area revealed that the distribution ranges of A. prattii underwent a significant fluctuation from the Last Interglacial Period (LIG) to the Last Glacial Maximum (LGM), the Mid-Holocene (MH), and the current period, accompanied by expansion of its potential distribution area in the future. These results underscore that the leaf morphology of A. prattii has significantly varied in response to climatic environmental factors across different regions, with a decrease in leaf width and an increase in stomatal density on the upper epidermis. The heterogeneous environment of the southwestern mountain region, characterized by variations in altitude, temperature, and precipitation, is the primary driver of morphological variation and geographical distribution patterns in A. prattii leaves. Our findings hold substantial scientific significance, shedding light on the evolutionary adaptation of species in the MSC and adjacent areas. 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

Understanding the drivers of species richness patterns is a major goal of ecology and evolutionary biology, and the drivers vary across regions and taxa. Here, we assessed the influence of environmental factors and evolutionary history on the pattern of species richness in the genus Sorbus (110 species). We mapped the global species richness pattern of Sorbus at a spatial resolution of 200 × 200 km, using 10,652 specimen records. We used stepwise regression to assess the relationship between 23 environmental predictors and species richness and estimated the diversification rate of Sorbus based on chloroplast genome data. The effects of environmental factors were explained by adjusted R², and evolutionary factors were inferred based on differences in diversification rates. We found that the species richness of Sorbus was highest in the Hengduan Mountains (HDM), which is probably the center of diversity. Among the selected environmental predictors, the integrated model including all environmental predictors had the largest explanatory power for species richness. The determinants of species richness show regional differences. On the global and continental scale, energy and water availability become the main driving factors. In contrast, climate seasonality is the primary factor in the HDM. The diversification rate results showed no significant differences between HDM and non-HDM, suggesting that evolutionary history may have limited impact on the pattern of Sorbus species richness. We conclude that environmental factors play an important role in shaping the global pattern of Sorbus species richness, while diversification rates have a lesser impact. Full article