ArticlePeerJ2019
The aridity index governs the variation of vegetation characteristics in alpine grassland, Northern Tibet Plateau.
Article in PeerJ, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 16 citations in OpenAlex.
- Distinguishing Stoichiometric Homeostasis of Soil Microbial Biomass in Alpine Grassland Ecosystems: Evidence From 5,000 km Belt Transect Across Qinghai-Tibet Plateau.Frontiers in plant science · 2021Article
- A rather dry subject; investigating the study of arid-associated microbial communities.Environmental microbiome · 2020Review
- Study of marsh wetland landscape pattern evolution on the Zoigê Plateau due to natural/human dual-effects.PeerJ · 2020Article
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The vegetation dynamic (e.g., community productivity) is an important index used to evaluate the ecosystem function of grassland ecosystem. However, the critical factors that affect vegetation biomass are disputed continuously, and most of the debates focus on mean annual precipitation (MAP) or temperature (MAT). This article integrated these two factors, used the aridity index (AI) to describe the dynamics of MAP and MAT, and tested the hypothesis that vegetation traits are influenced primarily by the AI. We sampled 275 plots at 55 sites (five plots at each site, including alpine steppe and meadow) across an alpine grassland of the northern Tibet Plateau, used correlation analysis and redundancy analysis (RDA) to explore which key factors determine the biomass dynamic, and explained the mechanism by which they affect the vegetation biomass in different vegetation types via structural equation modelling (SEM). The results supported our hypothesis, in all of the environmental factors collected, the AI made the greatest contribution to biomass variations in RDA , and the correlation between the AI and biomass was the largest (
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Registered trials
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