Elevation is important for determining the nutrient biogeochemical cycle in forest ecosystems. Changes in the ecological stoichiometry of nutrients along an elevation gradient can be used to predict how an element cycle responds in the midst of global climate change. We investigated changes in concentrations of and relationships between nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) in the leaves and roots of the dominant tree species, along an elevation gradient (from 500 to 1,000 m above mean sea level) in a subtropical natural forest in China. We analyzed correlations between 's above-ground biomass and stoichiometry with environmental factors. We also analyzed the soil and plant stoichiometry of this population. Our results showed that leaf N decreased while leaf K and Ca increased at higher elevations. Meanwhile, leaf P showed no relationship with elevation. The leaf N:P indicated that was limited by N. Elevation gradients contributed 46.40% of the total variance of ecological stoichiometry when assessing environmental factors. Our research may provide a theoretical basis for the biogeochemical cycle along with better forest management and fertilization for this population.
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http://dx.doi.org/10.7717/peerj.11553 | DOI Listing |
Zool Res
January 2025
Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China. E-mail:
Animal adaptation to environmental challenges is a complex process involving intricate interactions between the host genotype and gut microbiome composition. The gut microbiome, highly responsive to external environmental factors, plays a crucial role in host adaptability and may facilitate local adaptation within species. Concurrently, the genetic background of host populations influences gut microbiome composition, highlighting the bidirectional relationship between host and microbiome.
View Article and Find Full Text PDFFront Microbiol
January 2025
Xinjiang Key Laboratory of Desert Plant Roots Ecology and Vegetation Restoration, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China.
Soil fungi are essential to ecosystem processes, yet their elevational distribution patterns and the ecological mechanisms shaping their communities remain poorly understood and actively debated, particularly in arid regions. Here, we investigated the diversity patterns and underlying mechanisms shaping soil fungal communities along an elevational gradient (1,707-3,548 m) on the northern slope of the Central Kunlun Mountains in northwest China. Results indicated that the dominant phyla identified across the seven elevational gradients were and , displaying a unimodal pattern and a U-shaped pattern in relative abundance, respectively.
View Article and Find Full Text PDFFront Cell Infect Microbiol
January 2025
Department of Clinical Pharmacy, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
Background: Sepsis is a major cause of mortality in intensive care units (ICUs) and continues to pose a significant global health challenge, with sepsis-related deaths contributing substantially to the overall burden on healthcare systems worldwide. The primary objective was to construct and evaluate a machine learning (ML) model for forecasting 28-day all-cause mortality among ICU sepsis patients.
Methods: Data for the study was sourced from the eICU Collaborative Research Database (eICU-CRD) (version 2.
PeerJ
January 2025
Department of Environmental Engineering, Konkuk University, Seoul, Republic of Korea.
Approximately 64% of the Republic of Korea comprises mountainous areas, which as cold and high-altitude regions are gravely affected by climate change. Within the mountainous and the alpine-subalpine ecosystems, microbial communities play a pivotal role in biogeochemical cycling and partly regulate climate change through such cycles. We investigated the composition and function of microbial communities, with a focus on fungal communities, in Republic of Korea's second tallest mountain, Mt.
View Article and Find Full Text PDFJMIR Form Res
January 2025
1, Department of Health Administration, College of Software and Digital Healthcare Convergence, Yonsei University, Changjogwan, Yonseidae-gil 1, Wonju, 26493, Republic of Korea, +82 (0) 33-760-2257.
Background: Diabetes is prevalent in older adults, and machine learning algorithms could help predict diabetes in this population.
Objective: This study determined diabetes risk factors among older adults aged ≥60 years using machine learning algorithms and selected an optimized prediction model.
Methods: This cross-sectional study was conducted on 3084 older adults aged ≥60 years in Seoul from January to November 2023.
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