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Desert riparian plants experience high variability in water availability due to hydrological fluctuations. How riparian plants can survive with low water availability has been well studied, however, little is known about the effects of high water availability on plant community structuring. We conducted a mesocosm experiment to test whether seedling competition under simulated high groundwater availability can explain the shift of co-dominance of Populus euphratica and Tamarix ramosissima in early communities to P. euphratica dominance in mature ones along the Tarim River in northwestern China. Seedlings of these two plant species were grown in monoculture and mixture pools with high groundwater availability. Results indicated that the above-ground biomass and relative yield of T. ramosissima were higher than those of P. euphratica. The competitive advantages of T. ramosissima included its rapid response in growth to groundwater enrichment and its water spender strategy, as evidenced by the increased leaf biomass proportion and the inert stomatal response to leaf-to-air vapor pressure deficit (VPD). In comparison, P. euphratica showed a conservative strategy in water use, with a sensitive response to leaf-to-air VPD. Result of the short-term competition was inconsistent with the long-term competition in fields, suggesting that competition exclusion is not the mechanism structuring the desert riparian plant communities. Thus, our research highlights the importance of mediation by environmental fluctuations (such as lessening competition induced by disturbance) in structuring plant communities along the Tarim riparian zones.

A projection pursuit cluster (PPC) model was used to analyze the regional partitioning of agricultural non-point source pollution in China. The environmental factors impacting the agricultural non-point source pollution were compiled into a projection index to set up the projection index function. A novel optimization algorithm called Free search (FS) was introduced to optimize the projection direction of the PPC model. By making the appropriate improvements as we explored the use of the algorithm, it became simpler, and developed better exploration abilities. Thus, the multi-factor problem was converted into a single-factor cluster, according to the projection, which suc- cessfully avoided subjective disturbance and produced objective results. The cluster results of the PPC model mirror the actual regional partitioning of the agricultural non-point source pollution in China, indicating that the PPC model is a powerful tool in multi-factor cluster analysis, and could be a new method for the regional partitioning of agricultural non-point source pollution.