WeChat Mini Program
Old Version Features

Shifts in Soil C Stabilization Mechanisms Are Linked to Reindeer-Induced Changes in Plant Communities and Associated Fungi in Subarctic Tundra

SOIL BIOLOGY & BIOCHEMISTRY(2024)

Nat Resources Inst Finland Luke

Cited 1|Views21
Abstract
Arctic tundra ecosystems store a significant proportion of the global soil organic carbon (C). However, warming-induced shrub encroachment and reindeer (Rangifer tarandus L.) grazing regimes promoting graminoid vegetation may strongly influence tundra soil C stability. Here, we studied how reindeer grazing intensity and experimental warming affect soil C stabilization in a tundra ecosystem. We hypothesized that under light grazing, persistent complexes formed by fungal necromass (FNM) and condensed tannins (CT) from shrub roots stabilize the soil C, whereas, under heavy grazing, the soil C stabilization is affected by glomalin-related soil proteins (GRSP) produced by arbuscular mycorrhizal fungi of graminoids. In addition, we expect warming to mediate grazing effects, diminishing the potential for C stabilization.Our results show no effect of grazing on stable C concentration, however, under light grazing the labile C concentration was higher. We found higher concentrations of chitin and tannins under light grazing, indicative of soil C stabilization potential through FNM-CT complexes. By contrast, we found more root ergosterol under heavy grazing, suggesting a high abundance of endophytes, usually melanized, and a slightly higher GRSP concentration. Warming did not cause changes in stable C concentration but was associated with changes in the soil chemical quality, pointing to a decrease of lignin, polypeptides, and polysaccharides.We conclude that different soil C stabilization mechanisms operate under light and heavy grazing pressures and that these mechanisms are closely linked to changes in the vegetation and the fungi typically associated with them.
More
Translated text
Key words
Vegetation Change
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined