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Quantification of soil respiration in forest ecosystems across China
https://orcid.org/0000-0002-9752-6185
Abstract We collected 139 estimates of the annual forest soil CO2 flux and 173 estimates of the Q 10 value (the temperature sensitivity) assembled from 90 published studies across Chinese forest ecosystems. We analyzed the annual soil respiration (Rs) rates and the temperature sensitivities of seven forest ecosystems, including evergreen broadleaf forests (EBF), deciduous broadleaf forests (DBF), broadleaf and needleleaf mixed forests (BNMF), evergreen needleleaf forests (ENF), deciduous needleleaf forests (DNF), bamboo forests (BF) and shrubs (SF). The results showed that the mean annual Rs rate was 33.65 t CO2 ha−1 year−1 across Chinese forest ecosystems. Rs rates were significantly different (P < 0.001) among the seven forest types, and were significantly and positively influenced by mean annual temperature (MAT), mean annual precipitation (MAP), and actual evapotranspiration (AET); but negatively affected by latitude and elevation. The mean Q 10 value of 1.28 was lower than the world average (1.4–2.0). The Q 10 values derived from the soil temperature at a depth of 5 cm varied among forest ecosystems by an average of 2.46 and significantly decreased with the MAT but increased with elevation and latitude. Moreover, our results suggested that an artificial neural network (ANN) model can effectively predict Rs across Chinese forest ecosystems. This study contributes to better understanding of Rs across Chinese forest ecosystems and their possible responses to global warming.
Highlights Mean annual soil respiration rate was 33.65 t CO2 ha−1 year−1 across Chinese forest ecosystems. Mean Q 10 value of 1.28 was lower than the world average (1.4–2.0). Artificial neural network model may effectively predict Rs across Chinese forest ecosystems. Q 10 values derived from the soil temperature significantly increased with elevation and latitude.
Quantification of soil respiration in forest ecosystems across China
https://orcid.org/0000-0002-9752-6185
Abstract We collected 139 estimates of the annual forest soil CO2 flux and 173 estimates of the Q 10 value (the temperature sensitivity) assembled from 90 published studies across Chinese forest ecosystems. We analyzed the annual soil respiration (Rs) rates and the temperature sensitivities of seven forest ecosystems, including evergreen broadleaf forests (EBF), deciduous broadleaf forests (DBF), broadleaf and needleleaf mixed forests (BNMF), evergreen needleleaf forests (ENF), deciduous needleleaf forests (DNF), bamboo forests (BF) and shrubs (SF). The results showed that the mean annual Rs rate was 33.65 t CO2 ha−1 year−1 across Chinese forest ecosystems. Rs rates were significantly different (P < 0.001) among the seven forest types, and were significantly and positively influenced by mean annual temperature (MAT), mean annual precipitation (MAP), and actual evapotranspiration (AET); but negatively affected by latitude and elevation. The mean Q 10 value of 1.28 was lower than the world average (1.4–2.0). The Q 10 values derived from the soil temperature at a depth of 5 cm varied among forest ecosystems by an average of 2.46 and significantly decreased with the MAT but increased with elevation and latitude. Moreover, our results suggested that an artificial neural network (ANN) model can effectively predict Rs across Chinese forest ecosystems. This study contributes to better understanding of Rs across Chinese forest ecosystems and their possible responses to global warming.
Highlights Mean annual soil respiration rate was 33.65 t CO2 ha−1 year−1 across Chinese forest ecosystems. Mean Q 10 value of 1.28 was lower than the world average (1.4–2.0). Artificial neural network model may effectively predict Rs across Chinese forest ecosystems. Q 10 values derived from the soil temperature significantly increased with elevation and latitude.
Quantification of soil respiration in forest ecosystems across China
https://orcid.org/0000-0002-9752-6185
Abstract We collected 139 estimates of the annual forest soil CO2 flux and 173 estimates of the Q 10 value (the temperature sensitivity) assembled from 90 published studies across Chinese forest ecosystems. We analyzed the annual soil respiration (Rs) rates and the temperature sensitivities of seven forest ecosystems, including evergreen broadleaf forests (EBF), deciduous broadleaf forests (DBF), broadleaf and needleleaf mixed forests (BNMF), evergreen needleleaf forests (ENF), deciduous needleleaf forests (DNF), bamboo forests (BF) and shrubs (SF). The results showed that the mean annual Rs rate was 33.65 t CO2 ha−1 year−1 across Chinese forest ecosystems. Rs rates were significantly different (P < 0.001) among the seven forest types, and were significantly and positively influenced by mean annual temperature (MAT), mean annual precipitation (MAP), and actual evapotranspiration (AET); but negatively affected by latitude and elevation. The mean Q 10 value of 1.28 was lower than the world average (1.4–2.0). The Q 10 values derived from the soil temperature at a depth of 5 cm varied among forest ecosystems by an average of 2.46 and significantly decreased with the MAT but increased with elevation and latitude. Moreover, our results suggested that an artificial neural network (ANN) model can effectively predict Rs across Chinese forest ecosystems. This study contributes to better understanding of Rs across Chinese forest ecosystems and their possible responses to global warming.
Highlights Mean annual soil respiration rate was 33.65 t CO2 ha−1 year−1 across Chinese forest ecosystems. Mean Q 10 value of 1.28 was lower than the world average (1.4–2.0). Artificial neural network model may effectively predict Rs across Chinese forest ecosystems. Q 10 values derived from the soil temperature significantly increased with elevation and latitude.
Quantification of soil respiration in forest ecosystems across China
Song, Xinzhang (author) / Peng, Changhui (author) / Zhao, Zhengyong (author) / Zhang, Zhiting (author) / Guo, Baohua (author) / Wang, Weifeng (author) / Jiang, Hong (author) / Zhu, Qiuan (author)
Atmospheric Environment ; 94 ; 546-551
2014-05-26
6 pages
Article (Journal)
Electronic Resource
English
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