Abstract. Soil heterotrophic respiration (RH) is one of the largest and most uncertain components of the terrestrial carbon cycle, directly reflecting carbon loss from soils to the atmosphere. However, high variations and uncertainties of RH existing in global carbon cycling models require RH estimates from different angles, e.g., a data-driven angle. To fill this knowledge gap, this study applied a Random Forest (RF) algorithm (a machine learning approach) to (1) develop a globally gridded RH dataset and (2) investigate its spatial and temporal patterns from 1980 to 2016 at the global scale by linking field observations from the Global Soil Respiration Database and global environmental drivers (temperature, precipitation, soil water content, etc.). Finally, a globally gridded RH dataset was developed covering from 1980 to 2016 with a spatial resolution of half a degree and a temporal resolution of 1 year. Globally, the average annual RH was 57.2±0.6 Pg C a−1 from 1980 to 2016, with a significantly increasing trend of 0.036±0.007 Pg C a−2. However, the temporal trend of the carbon loss from RH varied in climate zones, and RH showed a significant and increasing trend in boreal and temperate areas. In contrast, such a trend was absent in tropical regions. Temperature-driven RH dominated 39 % of global land and was primarily distributed at high-latitude areas. The areas dominated by precipitation and soil water content were mainly semiarid and tropical areas, accounting for 36 % and 25 % of global land area, respectively, suggesting variations in the dominance of environmental controls on the spatial patterns of RH. The developed globally gridded RH dataset will further aid in the understanding of the mechanisms of global soil carbon dynamics, serving as a benchmark to constrain terrestrial biogeochemical models. The dataset is publicly available at https://doi.org/10.6084/m9.figshare.8882567 (Tang et al., 2019a).

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Abstract. Soil heterotrophic respiration (RH) is one of the largest and most uncertain components of the terrestrial carbon cycle, directly reflecting carbon loss from soil to the atmosphere. However, high variations and uncertainties of RH existing in global carbon cycling models require an urgent development of data-derived RH dataset. To fill this knowledge gap, this study applied Random Forest (RF) algorithm – a machine learning approach, to (1) develop a globally gridded RH dataset and (2) investigate its spatial- and temporal-patterns from 1980 to 2016 at the global scale by linking field observations from the Global Soil Respiration Database and global environmental drivers – temperature, precipitation, soil water content, etc. Finally, a globally gridded RH dataset was developed covering from 1980 to 2016 with a spatial resolution of half degree and a temporal resolution of one year. Globally, the average annual RH was 57.2 ± 0.6 Pg C a−1 from 1980 to 2016, with a significantly increasing trend of 0.036 ± 0.007 Pg C a−2. However, the temporal trend of the carbon loss from RH varied with climate zones that RH showed significant increasing trends in boreal and temperate areas, in contrast, such trend was absent in tropical regions. Temperature driven RH dominated 39 % of global land and was mainly distributed at a high latitude. While the areas dominated by precipitation and soil water content were mainly semi-arid and tropical areas, accounting for 36 % and 25 % of the global land, respectively, suggesting variations in the dominance of environmental controls on the spatial patterns of RH. The developed globally gridded RH dataset will further aid in understanding of the mechanisms of global soil carbon dynamics, serving as a benchmark to constrain global vegetation models. The dataset is publicly available at https://doi.org/10.6084/m9.figshare.8882567 (Tang et al., 2019a).

Moso bamboo (Phyllostachys heterocycla (Carr.) Mitford cv. Pubescens) is an important timber substitute in China. Site specific stand management requires an accurate estimate of soil organic carbon (SOC) stock for maintaining stand productivity and understanding global carbon cycling. This study compared ordinary kriging (OK) and inverse distance weighting (IDW) approaches to study the spatial distribution of SOC stock within 0–60 cm using 111 soil samples in Moso bamboo forests in subtropical China. Similar spatial patterns but different spatial distribution ranges of SOC stock from OK and IDW highlighted the necessity to apply different approaches to obtain accurate and consistent results of SOC stock distribution. Different spatial patterns of SOC stock suggested the use of different fertilization treatments in Moso bamboo forests across the study area. SOC pool within 0–60 cm was 6.46 and 6.22 Tg for OK and IDW; results which were lower than that of conventional approach (CA, 7.41 Tg). CA is not recommended unless coordinates of the sampling locations are missing and the spatial patterns of SOC stock are not required. OK is recommended for the uneven distribution of sampling locations. Our results can improve methodology selection for investigating spatial distribution of SOC stock in Moso bamboo forests.

Cytoplasmic effects (CEs) have been discovered to influence a diverse array of agronomic traits in crops, and understanding the underlying mechanisms can help accelerate breeding programs. Seed oil content (SOC) is of great agricultural, nutritional, and economic importance. However, the genetic basis of CEs on SOC (CE-SOC) remains enigmatic. In this study, we use an optimized approach to sequence the cytoplasmic (plastid and mitochondrial) genomes of allotetraploid oilseed rape (Brassica napus) cultivars, 51218 and 56366, that bear contrasting CE-SOC. By combining comparative genomics and genome-wide transcriptome analysis, we identify mitochondria-encoded orf188 as a potential CE-SOC determinant gene. Functional analyses in the model system Arabidopsis thaliana and rapeseed demonstrated that orf188 governs CE-SOC and could significantly increase SOC, strikingly, through promoting the yield of ATP. Consistent with this finding, transcriptional profiling with microarray and RNA sequencing revealed that orf188 affects transcriptional reprogramming of mitochondrial energy metabolism to facilitate ATP production. Intriguingly, orf188 is a previously uncharacterized chimeric gene, and the presence of this genetic novelty endows rapeseed with positive CE-SOC. Our results shed light on the molecular basis of CEs on a key quantitative trait in polyploid crops and enrich the theory of maternal control of oil content, providing new scientific guidance for breeding high-oil rapeseed germplasms.

DNA methylation is a process through which methyl groups are added to the DNA molecule, thereby modifying the activity of a DNA segment without changing the sequence. Increasing evidence has shown that DNA methylation is involved in various aspects of plant growth and development via a number of key processes including genomic imprinting and repression of transposable elements. DNA methylase and demethylase are two crucial enzymes that play significant roles in dynamically maintaining genome DNA methylation status in plants. In this work, 22 DNA methylase genes and six DNA demethylase genes were identified in rapeseed (Brassica napus L.) genome. These DNA methylase and DNA demethylase genes can be classified into four (BnaCMTs, BnaMET1s, BnaDRMs and BnaDNMT2s) and three (BnaDMEs, BnaDML3s and BnaROS1s) subfamilies, respectively. Further analysis of gene structure and conserved domains showed that each sub-class is highly conserved between rapeseed and Arabidopsis. Expression analysis conducted by RNA-seq as well as qRT-PCR suggested that these DNA methylation/demethylation-related genes may be involved in the heat/salt stress responses in rapeseed. Taken together, our findings may provide valuable information for future functional characterization of these two types of epigenetic regulatory enzymes in polyploid species such as rapeseed, as well as for analyzing their evolutionary relationships within the plant kingdom.