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Abstract Exploring why species of different plant growth forms can coexist in the same forest is critical for understanding the long-term community stability, but is poorly studied from root ecological strategies. The aim of this study was to explore the variation of root functional traits among different growth forms and their distribution patterns in root economics space to clarify how plant growth forms affect the root resource acquisition strategies of co-occurring species in a forest community. We sampled 115 co-occurring species with five growth forms (i.e., trees, shrubs, lianas, herbs and ferns) from a mega-plot (>50 ha) in temperate forest and measured seven root functional traits, including root morphological, anatomical and chemical traits, that are closely associated with root resource foraging and conservation strategies. We found that root specific length (SRL) and tissue density (RTD) showed wider variations than other traits among the five growth forms. Moreover, compared with clade and mycorrhizal type, variations of SRL and RTD were largely attributed to growth forms. Importantly, 115 co-occurring species were separately aggregated by growth forms along the trade-off dimension of SRL and RTD in root economics space, suggesting the diversity in root resource acquisition strategies at a local forest community is linked to plant growth forms. In particular, herbs were concentrated towards the side of high SRL and RN, by contrast, trees, shrubs and ferns were positioned at the side of high RTD and carbon/nitrogen, and lianas were located towards the middle. Diverse root resource acquisition strategies in plant growth forms allow them to occupy specific belowground ecological niches, thereby relieving the competition for the common resource. These findings advance our understanding of the mechanism for maintaining community species coexistence from a below-ground perspective. 

Abstract Many agricultural regions in China are likely to become appreciably wetter or drier as the global climate warming increases. However, the impact of these climate change patterns on the intensity of soil greenhouse gas (GHG) emissions (GHGI, GHG emissions per unit of crop yield) has not yet been rigorously assessed. By integrating an improved agricultural ecosystem model and a meta‐analysis of multiple field studies, we found that climate change is expected to cause a 20.0% crop yield loss, while stimulating soil GHG emissions by 12.2% between 2061 and 2090 in China's agricultural regions. A wetter‐warmer (WW) climate would adversely impact crop yield on an equal basis and lead to a 1.8‐fold‐ increase in GHG emissions relative to those in a drier‐warmer (DW) climate. Without water limitation/excess, extreme heat (an increase of more than 1.5°C in average temperature) during the growing season would amplify 15.7% more yield while simultaneously elevating GHG emissions by 42.5% compared to an increase of below 1.5°C. However, when coupled with extreme drought, it would aggravate crop yield loss by 61.8% without reducing the corresponding GHG emissions. Furthermore, the emission intensity in an extreme WW climate would increase by 22.6% compared to an extreme DW climate. Under this intense WW climate, the use of nitrogen fertilizer would lead to a 37.9% increase in soil GHG emissions without necessarily gaining a corresponding yield advantage compared to a DW climate. These findings suggest that the threat of a wetter‐warmer world to efforts to reduce GHG emissions intensity may be as great as or even greater than that of a drier‐warmer world. 

DNA is an incredibly dense storage medium for digital data. However, computing on the stored information is expensive and slow, requiring rounds of sequencing, in silico computation, and DNA synthesis. Prior work on accessing and modifying data using DNA hybridization or enzymatic reactions had limited computation capabilities. Inspired by the computational power of “DNA strand displacement,” we augment DNA storage with “in-memory” molecular computation using strand displacement reactions to algorithmically modify data in a parallel manner. We show programs for binary counting and Turing universal cellular automaton Rule 110, the latter of which is, in principle, capable of implementing any computer algorithm. Information is stored in the nicks of DNA, and a secondary sequence-level encoding allows high-throughput sequencing-based readout. We conducted multiple rounds of computation on 4-bit data registers, as well as random access of data (selective access and erasure). We demonstrate that large strand displacement cascades with 244 distinct strand exchanges (sequential and in parallel) can use naturally occurring DNA sequence from M13 bacteriophage without stringent sequence design, which has the potential to improve the scale of computation and decrease cost. Our work merges DNA storage and DNA computing, setting the foundation of entirely molecular algorithms for parallel manipulation of digital information preserved in DNA.< 

Abstract Lentic systems (lakes and reservoirs) are emission hotpots of nitrous oxide (N₂O), a potent greenhouse gas; however, this has not been well quantified yet. Here we examine how multiple environmental forcings have affected N₂O emissions from global lentic systems since the pre-industrial period. Our results show that global lentic systems emitted 64.6 ± 12.1 Gg N₂O-N yr⁻¹in the 2010s, increased by 126% since the 1850s. The significance of small lentic systems on mitigating N₂O emissions is highlighted due to their substantial emission rates and response to terrestrial environmental changes. Incorporated with riverine emissions, this study indicates that N₂O emissions from global inland waters in the 2010s was 319.6 ± 58.2 Gg N yr⁻¹. This suggests a global emission factor of 0.051% for inland water N₂O emissions relative to agricultural nitrogen applications and provides the country-level emission factors (ranging from 0 to 0.341%) for improving the methodology for national greenhouse gas emission inventories.