4.7 Article

The optimum temperature of soil microbial respiration: Patterns and controls

Journal

SOIL BIOLOGY & BIOCHEMISTRY
Volume 121, Issue -, Pages 35-42

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.soilbio.2018.02.019

Keywords

Soil respiration; Optimum temperature; Adaption; Substrate; Microbe; Forest

Categories

Funding

  1. Natural Science Foundation of China [31770655, 41571130043]
  2. National Key R&D Program of China [2016YFC0500202]
  3. program of Youth Innovation Research Team Project [LENOM2016Q0005]

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The temperature response of soil microbial respiration (R-h) is of significance, with the optimum temperature of R-h being the key parameter for accurately modeling how it responds to temperature change under climate warming scenarios. However, knowledge about T-opt in natural ecosystems remains limited, especially at large scales, which increases the uncertainty of climate projections. Here, we collected 25 soils from tropical to cold temperate forests in the northern hemisphere to quantify regional variation in T-opt and the controls underlying this variation. R-h was measured at high frequency using a novel system under the mode, with temperature gradually increasing from 5 to 50 degrees C. The results showed that T-opt ranged from 38.5 to 46.0 degrees C (mean: 42.4 degrees C). Of note, this study is the first to demonstrate that T-opt is far higher than the assumed value used in models (35 degrees C), varying greatly across different climatic zones and increasing with latitude from tropical to cold-temperate forest soils. To some extent, our results supported the substrate supply hypothesis, and contrast with the climate adaption hypothesis. In addition, climate, nutrient, and soil microorganisms jointly regulate regional variation in T-opt together explaining 53% of variation in T-opt. The higher T-opt in northern regions indicated that these regions have a greater potential to release more CO2 from soil, which might lead to a positive feedback to global warming. In conclusion, process-based models should incorporate the high variability of T-opt across regions to improve predictions of the carbon dynamics of terrestrial ecosystems under climate warming scenarios.

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