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Wu Z, Hou Q, Yang Z, Yu T, Li D, Lin K, Ma X. Identification of factors driving the spatial distribution of molybdenum (Mo) in topsoil in the Longitudinal Range-Gorge Region of Southwestern China using the Geodetector model. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 271:115846. [PMID: 38242045 DOI: 10.1016/j.ecoenv.2023.115846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 12/13/2023] [Accepted: 12/14/2023] [Indexed: 01/21/2024]
Abstract
As a key component of plant nitrogen-fixing enzymes and a variety of human coenzyme factors, molybdenum (Mo) plays an essential role in supporting both plant growth and human health. Soil is a key medium for the cycling of Mo in the biosphere. However, the driving anthropogenic and natural factors governing the spatial distribution of Mo in soil and their interactions are not well understood. To determine the factors that affect the spatial patterns of Mo in topsoil, 6980 samples were collected from the Longitudinal Range-Gorge Region (Linshui County, Sichuan Province, China). In this area, tall mountains are adjacent to deep valleys. Topsoil with enriched Mo is mostly distributed in mountainous areas. The most important factors influencing Mo in topsoil are soil parent materials (q = 0.482), altitude (q = 0.256), and soil type (q = 0.259). There are synergistic effects among the various driving factors [q(X1 ∩ X2) > Max[q(X1), q(X2)]]. The Geodetector model was used to validate the magnitude of the interaction effects. The contribution to interacting factors is nonlinearly enhanced when the contribution of a single factor was low (any two factors of aspect, road distance, land use type, and S). The contribution to interacting factors is enhanced bidirectionally when the contribution of a single factor was high (any two factors of altitude, soil type, soil parent material, OM, and TFe2O3). When the contribution of one factor is high and the other is low, the contributing to interacting factors is mostly enhanced bidirectionally and a few are nonlinearly enhanced.
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Affiliation(s)
- Zhiliang Wu
- School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China
| | - Qingye Hou
- School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China.
| | - Zhongfang Yang
- School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China
| | - Tao Yu
- School of Science, China University of Geosciences, Beijing 100083, China
| | - Dapeng Li
- School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China
| | - Kun Lin
- School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China
| | - Xudong Ma
- School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China
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Yue Y, Wang F, Pan J, Chen XP, Tang Y, Yang Z, Ma J, Li M, Yang M. Spatiotemporal dynamics, community assembly and functional potential of sedimentary archaea in reservoirs: coaction of stochasticity and nutrient load. FEMS Microbiol Ecol 2022; 98:6701916. [PMID: 36111740 DOI: 10.1093/femsec/fiac109] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 08/16/2022] [Accepted: 09/14/2022] [Indexed: 01/21/2023] Open
Abstract
Archaea participate in biogeochemical cycles in aquatic ecosystems, and deciphering their community dynamics and assembly mechanisms is key to understanding their ecological functions. Here, sediments from 12 selected reservoirs from the Wujiang and Pearl River basins in southwest China were investigated using 16S rRNA Illumina sequencing and quantitative PCR for archaeal abundance and richness in all seasons. Generally, archaeal abundance and α-diversity were significantly correlated with temperature; however, β-diversity analysis showed that community structures varied greatly among locations rather than seasons, indicating a distance-decay pattern with geographical variation. The null model revealed the major contribution of stochasticity to archaeal community assembly, which was further confirmed by the neutral community model that could explain 71.7% and 90.2% of the variance in archaeal assembly in the Wujiang and Pearl River basins, respectively. Moreover, sediment total nitrogen and organic carbon levels were significantly correlated with archaeal abundance and α-diversity. Interestingly, these nutrient levels were positively and negatively correlated, respectively, with the abundance of methanogenic and ammonia-oxidized archaea: the dominant sedimentary archaea in these reservoirs. Taken together, this work systematically characterized archaeal community profiles in reservoir sediments and demonstrated the combined action of stochastic processes and nutrient load in shaping archaeal communities in reservoir ecosystems.
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Affiliation(s)
- Yihong Yue
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai, 200444, China
| | - Fushun Wang
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai, 200444, China
| | - Jie Pan
- Archaeal Biology Center, Institute for Advanced Study, Shenzhen University, Shenzhen, Guangdong, 518060, China.,Shenzhen Key Laboratory of Marine Microbiome Engineering, Institute for Advanced Study, Shenzhen University, Shenzhen, Guangdong, 518060, China
| | - Xue-Ping Chen
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai, 200444, China
| | - Yi Tang
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai, 200444, China
| | - Zhihong Yang
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai, 200444, China
| | - Jing Ma
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai, 200444, China
| | - Meng Li
- Archaeal Biology Center, Institute for Advanced Study, Shenzhen University, Shenzhen, Guangdong, 518060, China.,Shenzhen Key Laboratory of Marine Microbiome Engineering, Institute for Advanced Study, Shenzhen University, Shenzhen, Guangdong, 518060, China
| | - Ming Yang
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai, 200444, China
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