Review Quantification of soil respiration in forest ecosystems across China Xinzhang Song a, * , Changhui Peng b, c, ** , Zhengyong Zhao b , Zhiting Zhang d , Baohua Guo e , Weifeng Wang f , Hong Jiang a , Qiuan Zhu c a The Nurturing Station for the State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Lin'an 311300, China b Institute of Environment Sciences, Department of Biology Sciences, University of Quebec at Montreal, Case Postale 8888, Succursale Centre-Ville, Montreal H3C 3P8, Canada c Laboratory for Ecological Forecasting and Global Change, College of Forestry, Northwest Agriculture and Forest University, Yangling 712100, China d Hebei North College, Zhangjiakou 075000, China e International Centre for Bamboo and Rattan, Beijing 100102, China f Department of Geography, McGill University, Montreal H3A 0B9, Canada highlights  Mean annual soil respiration rate was 33.65 t CO 2 ha 1 year 1 across Chinese forest ecosystems.  Mean Q 10 value of 1.28 was lower than the world average (1.4e2.0).  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. article info Article history: Received 23 April 2014 Received in revised form 25 May 2014 Accepted 26 May 2014 Available online 27 May 2014 Keywords: Soil CO 2 flux Temperature sensitivity Artificial neural network model Climate change Carbon cycle abstract We collected 139 estimates of the annual forest soil CO 2 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 eco- systems, 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 CO 2 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 (1.4e2.0). 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. © 2014 Elsevier Ltd. All rights reserved. 1. Introduction Soil respiration is an important flux of carbon in forest ecosys- tems and globally (Schlesinger and Bernhardt, 2013). With a total area of 195.45 million hectares, Chinese forests cover 20% of the surface of China (State Forestry Administration, 2011), representing a variety of different conditions across 50 degrees of latitude be- tween 53  N and 3  N. This paper analyzes the available data for soil respiration in Chinese forests, to elucidate patterns with climate, soils, and location, which have proven to be important in other * Corresponding author. ** Corresponding author. Institute of Environment Sciences, Department of Biology Sciences, University of Quebec at Montreal, Case Postale 8888, Succursale Centre-Ville, Montreal H3C 3P8, Canada. E-mail addresses: songxinzhang@gmail.com (X. Song), peng.changhui@uqam.ca (C. Peng). Contents lists available at ScienceDirect Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv http://dx.doi.org/10.1016/j.atmosenv.2014.05.071 1352-2310/© 2014 Elsevier Ltd. All rights reserved. Atmospheric Environment 94 (2014) 546e551