Role of biochar in raising blue carbon stock capacity of salt marshes

Salt marshes are an important blue carbon ecosystem, with surprisingly fast carbon accumulation rates that are 40 times higher than those of terrestrial forests. In recent decades, salt marshes have suffered great degradation and loss all over the world. The idea to enhance carbon stock in salt marshes(so-called blue carbon) using biochar (so-called black carbon) has recently been proposed. Although experiments and observations remain limited, significant enhancements in soil organic carbon and plant growth have been documented in most case studies. However, due to the limited number of observations and their relatively short time window ranging from months to less than one year, there still exists a knowledge gap regarding the process, mechanism, and effect of biochar in enhancing carbon stock in salt marshes. Future research is urgently needed in the following perspectives:1) exploring the relationship between carbon stock enhancement efficiency and biochar properties, 2) optimizing the physical and chemical properties of biochar to boost its efficiency, and 3)studying the in-situ responses of complex carbon pools to biochar addition, especially under tidal conditions and over a longer period of time.

Algal community structure prediction by machine learning

The algal community structure is vital for aquatic management.However,the complicated environmental and biological processes make modeling challenging.To cope with this difficulty,we investigated using random forests(RF)to predict phytoplankton community shifting based on multi-source environmental factors(including physicochemical,hydrological,and meteorological variables).The RF models robustly predicted the algal communities composed by 13 major classes(Bray-Curtis dissimilarity=9.2±7.0%,validation NRMSE mostly<10%),with accurate simulations to the total biomass(validation R^(2)>0.74)in Norway's largest lake,Lake Mjosa.The importance analysis showed that the hydro-meteorological variables(Standardized MSE and Node Purity mostly>0.5)were the most influential factors in regulating the phytoplankton.Furthermore,an in-depth ecological interpretation uncovered the interactive stress-response effect on the algal community learned by the RF models.The interpretation results disclosed that the environmental drivers(i.e.,temperature,lake inflow,and nutrients)can jointly pose strong influence on the algal community shifts.This study highlighted the power of machine learning in predicting complex algal community structures and provided insights into the model interpretability.