Combining ab initio computations, neural networks, and diffusion Monte Carlo: An efficient method to treat weakly bound molecules

We describe a new method to calculate the vibrational ground state properties of weakly bound molecular systems and apply it to (HF)2 and HF-HC1. A Bayesian Inference neural network is used to fit an analytic function to a set of ab initia data points, which may then be employed by the quantum diffusion Monte Carlo method to produce ground state vibrational wave functions and properties. The method is general and relatively simple to implement and will be attractive for calculations on systems for which no analytic potential energy surface exists. © 1996 American Institute of Physics

Contrasting soil fungal communities in Mediterranean pine forests subjected to different wildfire frequencies

Mediterranean forest ecosystems are characterized by various vascular plant groups with their associated mycorrhizae and free living soil fungi with various ecological functions. Fire plays a major role in Mediterranean ecosystem dynamics and impacts both above- and below-ground community structure and functioning. However, studies on the effects induced by altered disturbance regimes (associated with recent land use and climate extremes) on fire ecology and especially on its below-ground impacts are few. The objectives of this study were to evaluate the effects of different wildfire regimes on soil fungal community structure using two different molecular methods. We investigated the long-term effects of wildfire on soil fungal communities associated with Pinus pinaster forests in central Portugal, by comparing the results of denaturing gradient gel electrophoresis (DGGE)-based profiling with those obtained with 454 pyrosequencing. Four forest stands with differing fire history and fire return interval, and vegetation cover (mature forest, early successional stage of pine regeneration, and forest converted to scrubland) were sampled 6 years after the last fire event. The pyrosequencing-based approach indicated ca. eight-fold higher numbers of taxa than DGGE. However, fungal community fingerprinting data obtained for the different study stands with DGGE were congruent with those obtained with pyrosequencing. Both short (7.6 years) and long (24 years) fire return intervals (indicated by the presence of ericaceous shrubs in the understorey) induced a decrease in the abundance ratio between basidiomycetes and ascomycetes and appeared to reduce the frequency of ectomycorrhizal fungal species and saprophytes. Wildfire significantly reduced the frequency of late stage successional taxa (e.g. Atheliaceae and Cantharellales) and known or putative saprophytes belonging to the Clavulinaceae and the Archaeorhizomycetaceae. Conversely, early successional fungal species belonging to the Thelephoraceae were favoured by both fire return intervals, while the abundance of Cortinarius and Hebeloma, which include several Cistus-specific species, increased with short wildfire return intervals. This last finding highlights the relationship between post-fire vegetation composition and cover (vegetation successional stage), and fungal symbionts. We hypothesise that these changes could, in the long term, exhaust the resilience of Mediterranean pine forest vegetation and associated soil fungal communities by preventing pine regeneration. © 2014, Mushroom Research Foundation