Ribosomal Database Project (RDP; http://rdp.cme.msu.edu/) provides the research community with aligned and annotated rRNA gene sequence data, along with tools to allow researchers to analyze their own rRNA gene sequences in the RDP framework. RDP data and tools are utilized in fields as diverse as human health, microbial ecology, environmental microbiology, nucleic acid chemistry, taxonomy and phylogenetics. In addition to aligned and annotated collections of bacterial and archaeal small subunit rRNA genes, RDP now includes a collection of fungal large subunit rRNA genes. RDP tools, including Classifier and Aligner, have been updated to work with this new fungal collection. The use of high-throughput sequencing to characterize environmental microbial populations has exploded in the past several years, and as sequence technologies have improved, the sizes of environmental datasets have increased. With release 11, RDP is providing an expanded set of tools to facilitate analysis of high-throughput data, including both single-stranded and paired-end reads. In addition, most tools are now available as open source packages for download and local use by researchers with high-volume needs or who would like to develop custom analysis pipelines.

Endophytes are microorganisms that live within plant tissues without causing symptoms of disease. They are important components of plant microbiomes. Endophytes interact with, and overlap in function with, other core microbial groups that colonize plant tissues, e.g., mycorrhizal fungi, pathogens, epiphytes, and saprotrophs. Some fungal endophytes affect plant growth and plant responses to pathogens, herbivores, and environmental change; others produce useful or interesting secondary metabolites. Here, we focus on new techniques and approaches that can provide an integrative understanding of the role of fungal endophytes in the plant microbiome. Clavicipitaceous endophytes of grasses are not considered because they have unique properties distinct from other endophytes. Hidden from view and often overlooked, endophytes are emerging as their diversity, importance for plant growth and survival, and interactions with other organisms are revealed.

Summary: Aridland ecosystems cover about one‐third of terrestrial environments globally, yet the extent to which models of carbon (C) and nitrogen (N) cycling, developed largely from studies of mesic ecosystems, apply to aridland systems remains unclear. Within aridland ecosystems, C and N dynamics are often described by a pulse‐reserve model in which episodic precipitation events stimulate biological activity that generate reserves of biomass, propagules and organic matter that prime the ecosystem to respond rapidly to subsequent precipitation events. The role of microbial C and N processing within the pulse‐reserve paradigm has not received much study. We present evidence suggesting that fungi play a critical and underappreciated role in aridland soils, including efficient decomposition of recalcitrant C compounds, N‐transformations such as nitrification, and nutrient storage and translocation of C and N between plants and biotic soil crusts. While fungi may perform some of these functions in other ecosystems, this ‘fungal loop’ assumes particular importance in the N cycle in aridlands because water availability imposes even greater restrictions on bacterial activity and physicochemical processes limit accumulation of soil organic matter (SOM). We incorporate these findings into a Threshold‐Delay Nutrient Dynamics (TDND) model for aridland ecosystems in which plant responses to pulsed precipitation events are mediated by a fungal loop that links C and N cycling, net primary production (NPP) and decomposition in aridland soils. Synthesis. Arid ecosystems are highly sensitive to global environmental change including N deposition and altered precipitation patterns; yet, models from mesic ecosystems do not adequately apply to aridland environments. Our ‘fungal loop’ N cycle model integrates spatial structure with pulse dynamics and extends the pulse‐reserve paradigm to include the key role of microbial processes in aridland ecosystem dynamics.

The broad distribution and high colonization rates of plant roots by a variety of endophytic fungi suggest that these symbionts have an important role in the function of ecosystems. Semiarid and arid lands cover more than one-third of the terrestrial ecosystems on Earth. However, a limited number of studies have been conducted to characterize root-associated fungal communities in semiarid grasslands. We conducted a study of the fungal community associated with the roots of a dominant grass, Bouteloua gracilis , at the Sevilleta National Wildlife Refuge in New Mexico. Internal transcribed spacer ribosomal DNA sequences from roots collected in May 2005, October 2005, and January 2006 were amplified using fungal-specific primers, and a total of 630 sequences were obtained, 69% of which were novel (less than 97% similarity with respect to sequences in the NCBI database). B. gracilis roots were colonized by at least 10 different orders, including endophytic, coprophilous, mycorrhizal, saprophytic, and plant pathogenic fungi. A total of 51 operational taxonomic units (OTUs) were found, and diversity estimators did not show saturation. Despite the high diversity found within B. gracilis roots, the root-associated fungal community is dominated by a novel group of dark septate fungi (DSF) within the order Pleosporales. Microscopic analysis confirmed that B. gracilis roots are highly colonized by DSF. Other common orders colonizing the roots included Sordariales, Xylariales, and Agaricales. By contributing to drought tolerance and nutrient acquisition, DSF may be integral to the function of arid ecosystems.

Fungi are key organisms in many ecological processes and communities. Rapid and low cost surveys of the fungal members of a community can be undertaken by isolating and sequencing a taxonomically informative genomic region, such as the ITS (internal transcribed spacer), from DNA extracted from a metagenomic sample, and then classifying these sequences to determine which organisms are present. This paper announces the availability of the Warcup ITS training set and shows how it can be used with the Ribosomal Database Project (RDP) Bayesian Classifier to rapidly and accurately identify fungi using ITS sequences. The classifications can be down to species level and use conventional literature-based mycological nomenclature and taxonomic assignments.

Taxonomic and phylogenetic fingerprinting based on sequence analysis of gene fragments from the large-subunit rRNA (LSU) gene or the internal transcribed spacer (ITS) region is becoming an integral part of fungal classification. The lack of an accurate and robust classification tool trained by a validated sequence database for taxonomic placement of fungal LSU genes is a severe limitation in taxonomic analysis of fungal isolates or large data sets obtained from environmental surveys. Using a hand-curated set of 8,506 fungal LSU gene fragments, we determined the performance characteristics of a naïve Bayesian classifier across multiple taxonomic levels and compared the classifier performance to that of a sequence similarity-based (BLASTN) approach. The naïve Bayesian classifier was computationally more rapid (>460-fold with our system) than the BLASTN approach, and it provided equal or superior classification accuracy. Classifier accuracies were compared using sequence fragments of 100 bp and 400 bp and two different PCR primer anchor points to mimic sequence read lengths commonly obtained using current high-throughput sequencing technologies. Accuracy was higher with 400-bp sequence reads than with 100-bp reads. It was also significantly affected by sequence location across the 1,400-bp test region. The highest accuracy was obtained across either the D1 or D2 variable region. The naïve Bayesian classifier provides an effective and rapid means to classify fungal LSU sequences from large environmental surveys. The training set and tool are publicly available through the Ribosomal Database Project ( http://rdp.cme.msu.edu/classifier/classifier.jsp ).