Abstract
Carbon partition among plant parts has a vital influence not only on the growth of individual plants but also on decomposition, carbon and nitrogen sequestration, and plant–atmosphere water exchange. Although many studies have tried to reveal plant growth mechanisms using observational living biomass or the biomass ratio among different organs, knowledge and understanding about carbon partition is still scarce and exists much uncertainty. In this work, a dataset about 1,089 sample plots of natural forests downloaded from the Chinese Ecosystem Research Network (CERN) was used to explore the dependences of net primary production (NPP) partition among foliage, stem and branch, and root on forest age, and mean annual temperature (MAT). The results found that (a) for all forest types, NPP partition had a significant relationship with forest age (p < 0.0001), that is, younger plants usually allocated a higher proportion of the NPP to stems, branches, and roots. As plants aged, an increasing proportion of the NPP was allocated to foliage; (b) MAT was negatively correlated with the proportions of the NPP allocated to foliage (F leaf; %) and roots (F root; %), while proportions of the NPP allocated to stems and branches (F stbr; %) were positively dependent on MAT; (c) independent effect analysis demonstrated that forest age had a larger direct influence on F leaf and F root, while MAT was relatively important for F stbr; and (d) forest age and MAT had a stronger combined effect on NPP allocation for broad‐leaved forests, while for needled‐leaved forests, the influences of forest age and MAT existed large differences among different forest types. This work not only is important for understanding the contribution of climatic factor and forest age on forest NPP partition, but also provides valuable ideas for developing ecological models.
Highlights
The process through which plants allocate carbon among dif‐ ferent organs is important for plant growth (Shvidenko, Schepaschenko, Nilsson et al, 2007b; Shvidenko, Schepaschenko, McCallum et al, 2007a) and for forest carbon cycling rates and plant–atmosphere water exchange (Aber, Melillo, Nadelhoffer, Pastor, & Boone, 1991)
Many improvements have been achieved in understanding carbon allocation of individual plant, for example, some have described the distribution of biomass in different parts of individual plants (e.g., Poorter et al, 2012; Reich et al, 2014; Dube & Mutanga, 2015; Zhang, Song, et al, 2015), while others have ex‐ plored the relationships between biomass allocation and forest age (Litton, Raich, & Ryan, 2007; Yuan & Chen, 2010; Zhang, Song, et al, 2015; Zhao et al, 2014), climate or environmental factors (Chen et al, 2015; Luo, Wang, Zhang, Booth, & Lu, 2012; Poorter et al, 2012; Reich et al, 2014; Wang, Fang, & Zhu, 2008; Zhang, Song, et al, 2015)
Reich et al (2014) demonstrated that temperature was a better predictor of biomass allocation than mois‐ ture availability because the distribution of biomass fraction to roots or foliage was unrelated to aridity, while Chu et al (2016) used three different approaches to analyze the same dataset, and confirmed that both temperature and precipitation were critical to carbon allocation
Summary
China has approximately 175 million ha of forest, which cover approx‐ imately 18.21% of the country’s land area (Fu et al, 2010), and the for‐ est types range from boreal needle‐leaved and broad‐leaved forests to temperate deciduous broad‐leaved forests and subtropical evergreen broad‐leaved forests to tropical rainforests (Fang et al, 2001) Such terrestrial ecosystems provide a vital carbon sink (Fang et al, 2001; Piao et al, 2009). This point is very useful to evaluate and develop ecological models because most of the cur‐ rent ecological models describe individual growth by NPP partition This dataset includes relatively large number of observa‐ tional data, covering various forest types over China, so this work is an important supplement to related research, and provides some vital clues for ecology, and for evaluations and developments of ecological models (e.g., Dynamic Global Vegetation Models (DGVMs))
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