Replanting of broadleaved trees alters internal nutrient cycles of native and exotic pines in subtropical plantations of China

Background:The replanting of broadleaved trees in pure coniferous plantations is widely implemented,as mixed plantations are generally more stable and functional.However,the effect of interspecific interactions between broadleaved and coniferous trees on internal nutrient cycles of conifers remains unclear.Methods:We selected pure coniferous plantations of a native(Pinus massoniana)and an exotic(P.elliottii)pine species and their corresponding mixed plantations with broadleaved trees(Schima superba)in subtropical China,and measured the nitrogen(N)and phosphorus(P)contents in the rhizosphere soils,fine roots,twigs,needles and needle litter of pines.We calculated the root capture,needle resorption and translocation of N and P by pines to determine the mobility of nutrients in trees.Results:Although the N and P in the rhizosphere soils increased due to the replanting of broadleaved trees,the N and P contents in the aboveground tissues of the two pine species did not increase in mixed plantations.Mixed planting had a negative effect on the N and P capture of native pine and a positive effect on that of exotic pine.The N and P resorption efficiencies increased in native pine but were unchanged in exotic pine after the replanting of S.superba.Native pine preferentially employed an aboveground nutrient resorption strategy,whereas exotic pine tended to adopt a belowground nutrient capture strategy after replanting.Translocation of N and P in trees was detected,which reflected the trade-offs between root nutrient capture and needle nutrient resorption.Conclusions:The effect of mixed planting varied between the species of native and exotic pines,and the internal nutrient cycles of both pine species might be dominated by interspecific interaction effects on nutrients rather than soil nutrients.Our study highlights the importance of selecting suitable broadleaved species for replanting in coniferous plantations.

Exploiting Pre-Trained Network Embeddings for Recommendations in Social Networks

Recommender systems as one of the most efficient information filtering techniques have been widely studied in recent years. However, traditional recommender systems only utilize user-item rating matrix for recommendations, and the social connections and item sequential patterns are ignored. But in our real life, we always turn to our friends for recommendations, and often select the items that have similar sequential patterns. In order to overcome these challenges, many studies have taken social connections and sequential information into account to enhance recommender systems. Although these existing studies have achieved good results, most of them regard social influence and sequential information as regularization terms, and the deep structure hidden in social networks and rating patterns has not been fully explored. On the other hand, neural network based embedding methods have shown their power in many recommendation tasks with their ability to extract high-level representations from raw data. Motivated by the above observations, we take the advantage of network embedding techniques and propose an embedding-based recommendation method, which is composed of the embedding model and the collaborative filtering model. Specifically, to exploit the deep structure hidden in social networks and rating patterns, a neural network based embedding model is first pre-trained, where the external user and item representations are extracted. Then, we incorporate these extracted factors into a collaborative filtering model by fusing them with latent factors linearly, where our method not only can leverage the external information to enhance recommendation, but also can exploit the advantage of collaborative filtering techniques. Experimental results on two real-world datasets demonstrate the effectiveness of our proposed method and the importance of these external extracted factors.