Synergistic effects of water temperature and dissolved nutrients on litter decomposition and associated fungi

In woodland streams, the decomposition of allochthonous organic matter constitutes a fundamental ecosystem process, where aquatic hyphomycetes play a pivotal role. It is therefore greatly affected by water temperature and nutrient concentrations. The individual effects of these factors on the decomposition of litter have been studied previously. However, in the climate warming scenario predicted for this century, water temperature and nutrient concentrations are expected to increase simultaneously, and their combined effects on litter decomposition and associated biological activity remains unevaluated. In this study, we addressed the individual and combined effects of water temperature (three levels) and nutrient concentrations (two levels) on the decomposition of alder leaves and associated aquatic hyphomycetes in microcosms. Decomposition rates across treatments varied between 0.0041 day^?1 at 5 °C and low nutrient level and 0.0100 day^?1 at 15 °C and high nutrient level. The stimulation of biological variables at high nutrients and temperatures indicates that nutrient enrichment of streams might have a higher stimulatory effect on fungal performance and decomposition rates under a warming scenario than at present. The stimulation of fungal biomass and sporulation with increasing temperature at both nutrient levels shows that increases in water temperature might enhance fungal growth and reproduction in both oligotrophic and eutrophic streams. The stimulation of fungal respiration and litter decomposition with increasing temperature at high nutrients indicates that stimulation of carbon mineralization will probably occur at eutrophied streams, while oligotrophic conditions seem to be ‘protected’ from warming. All biological variables were stimulated when both factors increased, as a result of synergistic interactions between factors. Increased water temperature and nutrient level also affected the structure of aquatic hyphomycete assemblages. It is plausible that if water quality of presently eutrophied streams is improved, the potential stimulatory effects of future increases in water temperature on aquatic biota and processes might be mitigated.     

Mass loss and nutrient release during the decomposition of sixteen types of plant litter with contrasting quality under three precipitation regimes

Mass loss and nutrient release during litter decomposition drive biogeochemical cycling in terrestrial ecosystems. However, the relationship between the litter decomposition process and the decomposition stage, precipitation, and litter quality has rarely been addressed, precluding our understanding of how litter decomposition regulates nutrient cycling in various ecosystems and their responses to climate change. In this study, we measured mass loss as well as carbon and nutrient releases during the decomposition of 16 types of leaf litter under three precipitation treatments over 12 months in a common garden experiment (i.e., using standardized soil and climatic conditions). Sixteen types of leaves were divided into three functional groups (evergreen, deciduous, and herbaceous). The objectives were to understand the effects of decomposition stages and precipitation regimes on litter decomposition and to examine the relationship between this effect and chemical properties. The mass loss and release of nitrogen and potassium were significantly higher in the 6‐ to 12‐month stage of decomposition (high temperature and humidity) than in the 0‐ to 6‐month stage. Phosphorus was relatively enriched in evergreen leaves after 6 months of decomposition. The rates of mass loss and nutrient release were significantly greater in herbaceous than in deciduous and evergreen leaves. Increasing precipitation from 400 to 800 mm accelerated mass loss and potassium release but decreased phosphorus release in the 0‐ to 6‐month stage of decomposition. These results highlighted the contribution to and complexity of litter chemical properties in litter decomposition.     

Drought conditions alter litter decomposition and nutrient release of litter types in an agroforestry system of China

Evaluating how decomposition rates and litter nutrient release of different litter types respond to changes in water conditions is crucial for understanding global carbon and nutrient cycling. However, it is unclear how decreasing water affects litter mixture interactions for the maize–poplar system in arid regions. Here, the responses of the litter decomposition process and litter mixture interactions in the agroforestry system to changes in water conditions (control, light drought, and moderate drought) were tested. Moderate drought significantly decreased the decomposition rate for poplar leaf and mixed litters, and decomposition rate was significantly reduced for maize straw litter in light and moderate drought stress. The mass loss rates of maize straw and mixed litters were significantly higher than that of the poplar leaf litter under drought conditions, but there was no significant difference among the three litter types in the control. There was no interaction between mass loss of the mixed litter in the control and light drought conditions, and the litter mixture interaction showed nonadditive synergistic interactions under moderate drought. In terms of nutrient release, there was also no interaction between litter mixture with nitrogen and carbon, but there was antagonistic interaction with potassium release under the light drought condition. Our results demonstrate that drought conditions can lead to decreasing decomposition rate and strong changes in the litter mixture interactions from additive effects to nonadditive synergistic effects in moderate drought. Moreover, light drought changed the mixture interaction from an additive effect to an antagonistic interaction for potassium release.     

Microbial carbon limitation: The need for integrating microorganisms into our understanding of ecosystem carbon cycling

Numerous studies have demonstrated that fertilization with nutrients such as nitrogen, phosphorus, and potassium increases plant productivity in both natural and managed ecosystems, demonstrating that primary productivity is nutrient limited in most terrestrial ecosystems. In contrast, it has been demonstrated that heterotrophic microbial communities in soil are primarily limited by organic carbon or energy. While this concept of contrasting limitations, that is, microbial carbon and plant nutrient limitation, is based on strong evidence that we review in this paper, it is often ignored in discussions of ecosystem response to global environment changes. The plant‐centric perspective has equated plant nutrient limitations with those of whole ecosystems, thereby ignoring the important role of the heterotrophs responsible for soil decomposition in driving ecosystem carbon storage. To truly integrate carbon and nutrient cycles in ecosystem science, we must account for the fact that while plant productivity may be nutrient limited, the secondary productivity by heterotrophic communities is inherently carbon limited. Ecosystem carbon cycling integrates the independent physiological responses of its individual components, as well as tightly coupled exchanges between autotrophs and heterotrophs. To the extent that the interacting autotrophic and heterotrophic processes are controlled by organisms that are limited by nutrient versus carbon accessibility, respectively, we propose that ecosystems by definition cannot be ‘limited’ by nutrients or carbon alone. Here, we outline how models aimed at predicting non‐steady state ecosystem responses over time can benefit from dissecting ecosystems into the organismal components and their inherent limitations to better represent plant–microbe interactions in coupled carbon and nutrient models.     

Traits mediate drought effects on wood carbon fluxes

CO₂ fluxes from wood decomposition represent an important source of carbon from forest ecosystems to the atmosphere, which are determined by both wood traits and climate influencing the metabolic rates of decomposers. Previous studies have quantified the effects of moisture and temperature on wood decomposition, but these effects were not separated from the potential influence of wood traits. Indeed, it is not well understood how traits and climate interact to influence wood CO₂ fluxes. Here, we examined the responses of CO₂ fluxes from dead wood with different traits (angiosperm and gymnosperm) to 0%, 35%, and 70% rainfall reduction across seasonal temperature gradients. Our results showed that drought significantly decreased wood CO₂ fluxes, but its effects varied with both taxonomical group and drought intensity. Drought‐induced reduction in wood CO₂ fluxes was larger in angiosperms than gymnosperms for the 35% rainfall reduction treatment, but there was no significant difference between these groups for the 70% reduction treatment. This is because wood nitrogen density and carbon quality were significantly higher in angiosperms than gymnosperms, yielding a higher moisture sensitivity of wood decomposition. These findings were demonstrated by a significant positive interaction effect between wood nitrogen and moisture on CO₂ fluxes in a structural equation model. Additionally, we ascertained that a constant temperature sensitivity of CO₂ fluxes was independent of wood traits and consistent with previous estimates for extracellular enzyme kinetics. Our results highlight the key role of wood traits in regulating drought responses of wood carbon fluxes. Given that both climate and forest management might extensively modify taxonomic compositions in the future, it is critical for carbon cycle models to account for such interactions between wood traits and climate in driving dynamics of wood decomposition.     

Boosting Superior Lithium Storage Performance of Alloy‐Based Anode Materials via Ultraconformal Sb Coating–Derived Favorable Solid‐Electrolyte Interphase

Alloy materials such as Si and Ge are attractive as high‐capacity anodes for rechargeable batteries, but such anodes undergo severe capacity degradation during discharge–charge processes. Compared to the over‐emphasized efforts on the electrode structure design to mitigate the volume changes, understanding and engineering of the solid‐electrolyte interphase (SEI) are significantly lacking. This work demonstrates that modifying the surface of alloy‐based anode materials by building an ultraconformal layer of Sb can significantly enhance their structural and interfacial stability during cycling. Combined experimental and theoretical studies consistently reveal that the ultraconformal Sb layer is dynamically converted to Li₃Sb during cycling, which can selectively adsorb and catalytically decompose electrolyte additives to form a robust, thin, and dense LiF‐dominated SEI, and simultaneously restrain the decomposition of electrolyte solvents. Hence, the Sb‐coated porous Ge electrode delivers much higher initial Coulombic efficiency of 85% and higher reversible capacity of 1046 mAh g^?1 after 200 cycles at 500 mA g^?1, compared to only 72% and 170 mAh g^?1 for bare porous Ge. The present finding has indicated that tailoring surface structures of electrode materials is an appealing approach to construct a robust SEI and achieve long‐term cycling stability for alloy‐based anode materials.     

Modeling of dynamical systems through deep learning

This review presents a modern perspective on dynamical systems in the context of current goals and open challenges. In particular, our review focuses on the key challenges of discovering dynamics from data and finding data-driven representations that make nonlinear systems amenable to linear analysis. We explore various challenges in modern dynamical systems, along with emerging techniques in data science and machine learning to tackle them. The two chief challenges are (1) nonlinear dynamics and (2) unknown or partially known dynamics. Machine learning is providing new and powerful techniques for both challenges. Dimensionality reduction methods are used for projecting dynamical methods in reduced form, and these methods perform computational efficiency on real-world data. Data-driven models drive to discover the governing equations and give laws of physics. The identification of dynamical systems through deep learning techniques succeeds in inferring physical systems. Machine learning provides advanced new and powerful algorithms for nonlinear dynamics. Advanced deep learning methods like autoencoders, recurrent neural networks, convolutional neural networks, and reinforcement learning are used in modeling of dynamical systems.     

Meiofauna promotes litter decomposition in stream ecosystems depending on leaf species

Litter decomposition, a fundamental process of nutrient cycling and energy flow in freshwater ecosystems, is driven by a diverse array of decomposers. As an important component of the heterotrophic food web, meiofauna can provide a trophic link between leaf‐associated microbes (i.e., bacteria and fungi)/plant detritus and macroinvertebrates, though their contribution to litter decomposition is not well understood. To investigate the role of different decomposer communities in litter decomposition, especially meiofauna, we compared the litter decomposition of three leaf species with different lignin to nitrogen ratios in litter bags with different mesh sizes (0.05, 0.25, and 2 mm) in a forested stream, in China for 78 days. The meiofauna significantly enhanced the decomposition of leaves of high‐and medium‐ quality, while decreasing (negative effect) or increasing (positive effect) the fungal biomass and diversity. Macrofauna and meiofauna together contributed to the decomposition of low‐quality leaf species. The presence of meiofauna and macrofauna triggered different aspects of the microbial community, with their effects on litter decomposition varying as a function of leaf quality. This study reveals that the meiofauna increased the trophic complexity and modulated their interactions with microbes, highlighting the important yet underestimated role of meiofauna in detritus‐based ecosystems.