Using the moving window incorporated neural network to forecast the population behavior of Nostocales spp. in the River Darling, Australia

The paper demonstrates the nonstationarity of algal population behaviors by analyzing the historical populations of Nostocales spp. in the River Darling, Australia. Freshwater ecosystems are more likely to be nonstationary, instead of stationary. Nonstationarity implies that only the near past behaviors could forecast the near future for the system. However, nonstionarity was not considered seriously in previous research efforts for modeling and predicting algal population behaviors. Therefore the moving window technique was incorporated with radial basis function neural network (RBFNN) approach to deal with nonstationarity when modeling and forecasting the population behaviors of Nostocales spp. in the River Darling. The results showed that the RBFNN model could predict the timing and magnitude of algal blooms of Nostocales spp. with high accuracy. Moreover, a combined model based on individual RBFNN models was implemented, which showed superiority over the individual RBFNN models. Hence, the combined model was recommended for the modeling and forecasting of the phytoplankton populations, especially for the forecasting.

Modeling phytoplankton dynamics in the River Darling (Australia) using the radial basis function neural network

A radial basis function neural network was employed to model the abundance of cyanobacteria. The trained network could predict the populations of two bloom forming algal taxa with high accuracy, Nostocales spp. and Anabaena spp., in the River Darling, Australia. To elucidate the population dynamics for both Nostocales spp. and Anabaena spp., sensitivity analysis was performed with the following results. Total Kjeldahl nitrogen had a very strong influence on the abundance of the two algal taxa, electrical conductivity had a very strong negative relationship with the population of the two algal species, and flow was identified as one dominant factor influencing algal blooms after a scatter plot revealed that high flow could significantly reduce the algal biomass for both Nostocales spp. and Anabaena spp. Other variables such as turbidity, color, and pH were less important in determining the abundance and succession of the algal blooms.

Global negative vegetation feedback to climate warming responses of leaf litter decomposition rates in cold biomes

Whether climate change will turn cold biomes from large long-term carbon sinks into sources is hotly debated because of the great potential for ecosystem-mediated feedbacks to global climate. Critical are the direction, magnitude and generality of climate responses of plant litter decomposition. Here, we present the first quantitative analysis of the major climate-change-related drivers of litter decomposition rates in cold northern biomes worldwide. Leaf litters collected from the predominant species in 33 global change manipulation experiments in circum-arctic-alpine ecosystems were incubated simultaneously in two contrasting arctic life zones. We demonstrate that longer-term, large-scale changes to leaf litter decomposition will be driven primarily by both direct warming effects and concomitant shifts in plant growth form composition, with a much smaller role for changes in litter quality within species. Specifically, the ongoing warming-induced expansion of shrubs with recalcitrant leaf litter across cold biomes would constitute a negative feedback to global warming. Depending on the strength of other (previously reported) positive feedbacks of shrub expansion on soil carbon turnover, this may partly counteract direct warming enhancement of litter decomposition.