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Coastal wetlands are large reservoirs of soil carbon (C). However, the annual C accumulation rates contributing to the C storage in these systems have yet to be spatially estimated on a large scale. We synthesized C accumulation rate (CAR) in tidal wetlands of the conterminous United States (US), upscaled the CAR to national scale, and predicted trends based on climate change scenarios. Here, we show that the mean CAR is 161.8 ± 6 g Cm −2  yr −1 , and the conterminous US tidal wetlands sequestrate 4.2–5.0 Tg C yr −1 . Relative sea level rise (RSLR) largely regulates the CAR. The tidal wetland CAR is projected to increase in this century and continue their C sequestration capacity in all climate change scenarios, suggesting a strong resilience to sea level rise. These results serve as a baseline assessment of C accumulation in tidal wetlands of US, and indicate a significant C sink throughout this century. It remains challenging to estimate carbon accumulation rates in tidal wetlands on a scale as large as the conterminous US. Here, the authors find that mean C accumulation rates vary greatly among watershed regions but not among vegetation types, and that tidal wetlands’ C sequestration capability will remain or increase by 2100, suggesting a resilience to sea level rise.

Coastal wetlands are sites of rapid carbon (C) sequestration and contain large soil C stocks. Thus, there is increasing interest in those ecosystems as sites for anthropogenic greenhouse gas emission offset projects (sometimes referred to as “Blue Carbon”), through preservation of existing C stocks or creation of new wetlands to increase future sequestration. Here we show that in the globally-widespread occurrence of diked, impounded, drained and tidally-restricted salt marshes, substantial methane (CH 4 ) and CO 2 emission reductions can be achieved through restoration of disconnected saline tidal flows. Modeled climatic forcing indicates that tidal restoration to reduce emissions has a much greater impact per unit area than wetland creation or conservation to enhance sequestration. Given that GHG emissions in tidally-restricted, degraded wetlands are caused by human activity, they are anthropogenic emissions, and reducing them will have an effect on climate that is equivalent to reduced emission of an equal quantity of fossil fuel GHG. Thus, as a landuse-based climate change intervention, reducing CH 4 emissions is an entirely distinct concept from biological C sequestration projects to enhance C storage in forest or wetland biomass or soil, and will not suffer from the non-permanence risk that stored C will be returned to the atmosphere.

Changes in plant phenology affect the carbon flux of terrestrial forest ecosystems due to the link between the growing season length and vegetation productivity. Digital camera imagery, which can be acquired frequently, has been used to monitor seasonal and annual changes in forest canopy phenology and track critical phenological events. However, quantitative assessment of the structural and biochemical controls of the phenological patterns in camera images has rarely been done. In this study, we used an NDVI (Normalized Difference Vegetation Index) camera to monitor daily variations of vegetation reflectance at visible and near-infrared (NIR) bands with high spatial and temporal resolutions, and found that the infrared camera based NDVI (camera-NDVI) agreed well with the leaf expansion process that was measured by independent manual observations at Harvard Forest, Massachusetts, USA. We also measured the seasonality of canopy structural (leaf area index, LAI) and biochemical properties (leaf chlorophyll and nitrogen content). We found significant linear relationships between camera-NDVI and leaf chlorophyll concentration, and between camera-NDVI and leaf nitrogen content, though weaker relationships between camera-NDVI and LAI. Therefore, we recommend ground-based camera-NDVI as a powerful tool for long-term, near surface observations to monitor canopy development and to estimate leaf chlorophyll, nitrogen status, and LAI.

Plant phenology research has gained increasing attention because of the sensitivity of phenology to climate change and its consequences for ecosystem function. Recent technological development has made it possible to gather invaluable data at a variety of spatial and ecological scales. Despite our ability to observe phenological change at multiple scales, the mechanistic basis of phenology is still not well understood. Integration of multiple disciplines, including ecology, evolutionary biology, climate science, and remote sensing, with long‐term monitoring data across multiple spatial scales is needed to advance understanding of phenology. We review the mechanisms and major drivers of plant phenology, including temperature, photoperiod, and winter chilling, as well as other factors such as competition, resource limitation, and genetics. Shifts in plant phenology have significant consequences on ecosystem productivity, carbon cycling, competition, food webs, and other ecosystem functions and services. We summarize recent advances in observation techniques across multiple spatial scales, including digital repeat photography, other complementary optical measurements, and solar‐induced fluorescence, to assess our capability to address the importance of these scale‐dependent drivers. Then, we review phenology models as an important component of earth system modeling. We find that the lack of species‐level knowledge and observation data leads to difficulties in the development of vegetation phenology models at ecosystem or community scales. Finally, we recommend further research to advance understanding of the mechanisms governing phenology and the standardization of phenology observation methods across networks. With the opportunity for “big data” collection for plant phenology, we envision a breakthrough in process‐based phenology modeling.

Nitrogen (N) and phosphorus (P) are essential nutrients for plant metabolism, and their availability often limits primary productivity. Whereas the effects of N availability on photosynthetic capacity are well established, we still know relatively little about the effects of P availability at a foliar level, especially in P‐limited tropical forests. We examined photosynthetic capacity, leaf mass per area (LMA) and foliar P fractions in five woody plant species after 6 years of N and P fertilization in a lowland tropical forest. Foliar N:P ratios indicated P limitation of the unfertilized plants; accordingly, photosynthetic P‐use efficiency (PPUE) and LMA decreased with P addition, and foliar N and P concentrations increased, whereas N addition had little effect on measured foliar traits. However, P addition enhanced photosynthetic capacity only in one species and not in other four species. We then assessed plant acclimation to low P availability by quantifying four fractions of foliar P representing different functional pools: structural P, metabolic P (including inorganic P), nucleic acid P, and residual P. We found that P addition enhanced the concentrations of metabolic, structural, and nucleic acid P fractions in all species, but the magnitude of the effect was species‐specific. Our findings indicate that tropical species acclimate to low P availability by altering allocation of foliar P to meet the demand of P for photosynthesis. Importantly, species typical of lowland tropical forests in East Asia maintained their photosynthetic rate under low P availability. We conclude that P limitation of leaf photosynthetic capacity may not be as common as previously assumed due to plant acclimation mechanisms in low‐P tropical forests. Species‐specific strategies to allocate P to different foliar fractions represent a potentially important adaptive mechanism for plants in P‐limited systems. 氮和磷是植物代谢过程中的重要养分也是限制生态系统初级生产力的主要因子。尽管我们对氮如何影响植物光合能力有了比较全面的认识，但在低磷的热带森林中，我们对土壤磷如何影响植物光合功能还缺乏系统的了解。本研究通过在热带森林的野外氮磷添加实验中测定不同植物的叶片光合能力和叶片功能性状以及叶片磷组分，系统的了解了这些植物在叶片尺度上的低磷环境的适应机制。叶片氮磷比的结果表明该森林是磷限制的生态系统，施磷降低了叶片磷利用效率和比叶重但增加了叶片磷和氮的浓度，而施氮对叶片性状无显著影响。施磷仅增加了一个广布种的叶片光合能力而对其他四个狭布种无显著影响。我们进一步了解了叶片磷组分的变化，发现施磷处理增加了所有物种的代谢磷、结构磷和核酸磷组分，但其增加的尺度因种而异。这些结果表明热带森林植物通过改变叶片中磷的分布来满足光合作用对磷的需求，并且东亚地区的热带森林的典型植物能够在低磷的土壤环境中维持相对稳定的光合速率。通过该项研究，我们认为由于热带植物进化的适应机制，低磷对热带森林植物的光合能力的限制比预想的要小的多。不同植物的叶片磷组分的分配策略是其重要的对低磷环境的适应机制。 A plain language summary is available for this article. Plain Language Summary

Coastal blue carbon refers to the carbon taken from atmospheric CO 2 ; fixed by advanced plants (including salt marsh, mangrove, and seagrass), phytoplankton, macroalgae, and marine calcifiers via the interaction of plants and microbes; and stored in nearshore sediments and soils; as well as the carbon transported from the coast to the ocean and ocean floor. The carbon sequestration capacity per unit area of coastal blue carbon is far greater than that of the terrestrial carbon pool. The mechanisms and controls of the carbon sink from salt marshes, mangroves, seagrasses, the aquaculture of shellfish and macroalgae, and the microbial carbon pump need to be further studied. The methods to quantify coastal blue carbon include carbon flux measurements, carbon pool measurements, manipulative experiments, and modeling. Restoring, conserving, and enhancing blue carbon will increase carbon sinks and produce carbon credits, which could be traded on the carbon market. The need to tackle climate change and implement China’s commitment to cut carbon emissions requires us to improve studies on coastal blue carbon science and policy. The knowledge learned from coastal blue carbon improves the conservation and restoration of salt marshes, mangroves, and seagrasses; enhances the function of the microbial carbon pump; and promotes sustainable aquaculture, such as ocean ranching.

The traditional view of forest dynamics originated by Kira and Shidei [Kira T, Shidei T (1967) Jap J Ecol 17:70–87] and Odum [Odum EP (1969) Science 164(3877):262–270] suggests a decline in net primary productivity (NPP) in aging forests due to stabilized gross primary productivity (GPP) and continuously increased autotrophic respiration (R ₐ). The validity of these trends in GPP and R ₐ is, however, very difficult to test because of the lack of long-term ecosystem-scale field observations of both GPP and R ₐ. Ryan and colleagues [Ryan MG, Binkley D, Fownes JH (1997) Ad Ecol Res 27:213–262] have proposed an alternative hypothesis drawn from site-specific results that aboveground respiration and belowground allocation decreased in aging forests. Here, we analyzed data from a recently assembled global database of carbon fluxes and show that the classical view of the mechanisms underlying the age-driven decline in forest NPP is incorrect and thus support Ryan’s alternative hypothesis. Our results substantiate the age-driven decline in NPP, but in contrast to the traditional view, both GPP and R ₐ decline in aging boreal and temperate forests. We find that the decline in NPP in aging forests is primarily driven by GPP, which decreases more rapidly with increasing age than R ₐ does, but the ratio of NPP/GPP remains approximately constant within a biome. Our analytical models describing forest succession suggest that dynamic forest ecosystem models that follow the traditional paradigm need to be revisited.

A sustainable future depends on increasing agricultural carbon (C) and nitrogen (N) sequestration. Winter rapeseeds are facing severe yield loss after waterlogging due to the effects of extreme rainfall, especially in the seedling stage, where rainfall is most sensitive. Uncertainty exists over the farming greenhouse gas (GHG) release of rapeseed seedlings following the onset of waterlogging. The effect of waterlogging on GHG release and leaf gas exchange in winter rapeseed was examined in a pot experiment. The experiment included waterlogging treatments lasting 7-day and 21-day and normal irrigation as a control treatment. According to our findings, (1) The ecosystem of rapeseed seedlings released methane (CH 4 ) and nitrous oxide (N 2 O) in a clear up change that was impacted by ongoing waterlogging. Among them, N 2 O release had a transient rise during the early stages under the effect of seedling fertilizer. (2) The net photosynthetic rate, transpiration rate, stomatal conductance, plant height, soil moisture, and soil oxidation–reduction potential of rapeseed all significantly decreased due to the ongoing waterlogging. However, rapeseed leaves showed a significant increase in intercellular carbon dioxide (CO 2 ) concentration and leaf chlorophyll content values after waterlogging. Additionally, the findings demonstrated an extremely significant increase in the sustained-flux global warming potential of the sum CO 2 -eq of CH 4 and N 2 O throughout the entire waterlogging stress period. Therefore, continuous waterlogging can increase C and N release from rapeseed seedlings ecosystem and decrease yield. Therefore, we suggest increasing drainage techniques to decrease the release of agricultural GHGs and promote sustainable crop production.

Background An acceleration of model-data synthesis activities has leveraged many terrestrial carbon datasets, but utilization of soil respiration (RS) data has not kept pace. Scope We identify three major challenges in interpreting RS data, and opportunities to utilize it more extensively and creatively: (1) When RS is compared to ecosystem respiration (RECO) measured from EC towers, it is not uncommon to find RS > RECO. We argue this is most likely due to difficulties in calculating RECO, which provides an opportunity to utilize RS for EC quality control. (2) RS integrates belowground heterotrophic and autotrophic activity, but many models include only an explicit heterotrophic output. Opportunities exist to use the total RS flux for data assimilation and model benchmarking methods rather than less-certain partitioned fluxes. (3) RS is generally measured at a very different resolution than that needed for comparison to EC or ecosystem- to global-scale models. Downscaling EC fluxes to match the scale of RS, and improvement of RS upscaling techniques will improve resolution challenges. Conclusions RS data can bring a range of benefits to model development, particularly with larger databases and improved data sharing protocols to make RS data more robust and broadly available to the research community.

As the development of the Internet of Things (IoT) continues, Federated Learning (FL) is gaining popularity as a distributed machine learning framework that does not compromise the data privacy of each participant. However, the data held by enterprises and factories in the IoT often have different distribution properties (Non-IID), leading to poor results in their federated learning. This problem causes clients to forget about global knowledge during their local training phase and then tends to slow convergence and degrades accuracy. In this work, we propose a method named FedRAD, which is based on relational knowledge distillation that further enhances the mining of high-quality global knowledge by local models from a higher-dimensional perspective during their local training phase to better retain global knowledge and avoid forgetting. At the same time, we devise an entropy-wise adaptive weights module (EWAW) to better regulate the proportion of loss in single-sample knowledge distillation versus relational knowledge distillation so that students can weigh losses based on predicted entropy and learn global knowledge more effectively. A series of experiments on CIFAR10 and CIFAR100 show that FedRAD has better performance in terms of convergence speed and classification accuracy compared to other advanced FL methods.