Different phosphorus (P)-acquisition strategies may be relevant for species coexistence and plant performance in terrestrial communities on P-deficient soils. However, how interspecific P facilitation functions in natural systems is largely unknown.We investigated the root physiological activities for P mobilization across 19 coexisting plant species in steppe vegetation, and then grew plants with various abilities to mobilize sorbed P in a microcosm in a glasshouse.We show that P facilitation mediated by rhizosphere processes of P-mobilizing species promoted growth and increased P content of neighbors in a species-specific manner. When roots interacted with a facilitating neighbor, Cleistogenes squarrosa and Bromus inermis tended to show greater plasticity of root proliferation or rhizosheath acid phosphatase activity compared with other non-P-mobilizing species. Greater variation in these root traits was strongly correlated with increased performance in the presence of a facilitator. The results also show, for the first time, that P facilitation was an important mechanism underlying a positive complementarity effect.Our study highlights that interspecific P-acquisition facilitation requires that facilitated neighbors exhibit a better match of root traits with a facilitating species. It provides a better understanding of species coexistence in P-limited communities.

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Flooding contributes to tremendous hazards every year; more accurate forecasting may significantly mitigate the damages and loss caused by flood disasters. Current hydrological models are either purely knowledge-based or data-driven. A combination of data-driven method (artificial neural networks in this paper) and knowledge-based method (traditional hydrological model) may booster simulation accuracy. In this study, we proposed a new back-propagation (BP) neural network algorithm and applied it in the semi-distributed Xinanjiang (XAJ) model. The improved hydrological model is capable of updating the flow forecasting error without losing the leading time. The proposed method was tested in a real case study for both single period corrections and real-time corrections. The results reveal that the proposed method could significantly increase the accuracy of flood forecasting and indicate that the global correction effect is superior to the second-order autoregressive correction method in real-time correction.