Revealing the globally multiscale controls of environmental factors on carbon use efficiency

• DOI: 10.1016/j.scitotenv.2023.164634
• Publication Date: 2023-06-03T01:13:17Z
• Authors: Biao Wang; Wei Hu; Jianming Xue; Yaodong Jing; Hongfen Zhu; Haoxi Ding
• Keywords: Carbon fibers

The carbon use efficiency (CUE), which is the ratio of net primary production to gross primary production, is an essential element for detecting the terrestrial carbon cycle and ecosystem function. The spatial variation of CUE is controlled by environmental factors independently or interactively with different intensity. However, previous studies have mainly focused on the effect of climate on the local CUE at the sampling scale, while neglecting the effects of topography or soil on the global CUE, and even its spatially predictive model. In the study, the relative contributions of potentially influencing factors (i.e., climatic, topographic, and edaphic factors), and their interactions on the global CUE were analyzed using the combined methods of curvelet transform and geographical detector model, and the spatial model of CUE were established based on its relationships with influencing factors. The results showed that CUE values at the sampling scale were generally greater in the mid- and high-latitude regions than those in the low-latitude region, which was characterized by its spatial pattern at the large scale. Climate had the greater effects on CUE variations at the large scale, while topography was the main factor controlling CUE at the small or medium scale. However, the explanatory power of the interaction among factors on CUE was greater than any single factor, among which the interaction between climatic and topographical factors was the strongest at all scales. The CUE predication based on scale-dependent effects was more accurate than that based on the sampling scale especially in the high-latitude, and temperature and elevation was the main predictors. Based on the model, the spatial patterns of CUE under future scenarios with any climatic changes could be extracted. This study can further advance our understanding on spatial variation of CUE, and provide a unique insight for CUE prediction responding to climate changes.