Summary: Variation and tradeoffs within and among plant traits are increasingly being harnessed by empiricists and modelers to understand and predict ecosystem processes under changing environmental conditions. While fine roots play an important role in ecosystem functioning, fine‐root traits are underrepresented in global trait databases. This has hindered efforts to analyze fine‐root trait variation and link it with plant function and environmental conditions at a global scale. This Viewpoint addresses the need for a centralized fine‐root trait database, and introduces the Fine‐Root Ecology Database (FRED, http://roots.ornl.gov) which so far includes > 70 000 observations encompassing a broad range of root traits and also includes associated environmental data. FRED represents a critical step toward improving our understanding of below‐ground plant ecology. For example, FRED facilitates the quantification of variation in fine‐root traits across root orders, species, biomes, and environmental gradients while also providing a platform for assessments of covariation among root, leaf, and wood traits, the role of fine roots in ecosystem functioning, and the representation of fine roots in terrestrial biosphere models. Continued input of observations into FRED to fill gaps in trait coverage will improve our understanding of changes in fine‐root traits across space and time.

Summary: Ecosystem functioning relies heavily on below‐ground processes, which are largely regulated by plant fine‐roots and their functional traits. However, our knowledge of fine‐root trait distribution relies to date on local‐ and regional‐scale studies with limited numbers of species, growth forms and environmental variation. We compiled a world‐wide fine‐root trait dataset, featuring 1115 species from contrasting climatic areas, phylogeny and growth forms to test a series of hypotheses pertaining to the influence of plant functional types, soil and climate variables, and the degree of manipulation of plant growing conditions on species fine‐root trait variation. Most particularly, we tested the competing hypotheses that fine‐root traits typical of faster return on investment would be most strongly associated with conditions of limiting versus favourable soil resource availability. We accounted for both data source and species phylogenetic relatedness. We demonstrate that: (i) Climate conditions promoting soil fertility relate negatively to fine‐root traits favouring fast soil resource acquisition, with a particularly strong positive effect of temperature on fine‐root diameter and negative effect on specific root length (SRL), and a negative effect of rainfall on root nitrogen concentration; (ii) Soil bulk density strongly influences species fine‐root morphology, by favouring thicker, denser fine‐roots; (iii) Fine‐roots from herbaceous species are on average finer and have higher SRL than those of woody species, and N₂‐fixing capacity positively relates to root nitrogen; and (iv) Plants growing in pots have higher SRL than those grown in the field. Synthesis. This study reveals both the large variation in fine‐root traits encountered globally and the relevance of several key plant functional types and soil and climate variables for explaining a substantial part of this variation. Climate, particularly temperature, and plant functional types were the two strongest predictors of fine‐root trait variation. High trait variation occurred at local scales, suggesting that wide‐ranging below‐ground resource economics strategies are viable within most climatic areas and soil conditions.

DNA extraction bias is a frequently cited but poorly understood limitation of molecular characterizations of environmental microbial communities. To assess the bias of a commonly used soil DNA extraction kit, we varied the cell lysis protocol and conducted multiple extractions on subsamples of clay, sand, and organic soils. DNA, as well as bacterial and fungal ribosomal gene copies as measured by quantitative PCR, continued to be isolated in successive extractions. When terminal restriction fragment length polymorphism was used, a significant shift in community composition due to extraction bias was detected for bacteria but not for fungi. Pyrosequencing indicated that the relative abundances of sequences from rarely cultivated groups such asAcidobacteria,Gemmatimonades, andVerrucomicrobiawere higher in the first extraction than in the sixth but that the reverse was true forProteobacteriaandActinobacteria. This suggests that the well-known phylum-level bacterial cultivation bias may be partially exaggerated by DNA extraction bias. We conclude that bias can be adequately reduced in many situations by pooling three successive extractions, and additional measures should be considered when divergent soil types are compared or when comprehensive community analysis is necessary.

Terminal restriction fragment length polymorphism (T-RFLP) is a culture-independent method of obtaining a genetic fingerprint of the composition of a microbial community. Comparisons of the utility of different methods of (i) including peaks, (ii) computing the difference (or distance) between profiles, and (iii) performing statistical analysis were made by using replicated profiles of eubacterial communities. These samples included soil collected from three regions of the United States, soil fractions derived from three agronomic field treatments, soil samples taken from within one meter of each other in an alfalfa field, and replicate laboratory bioreactors. Cluster analysis by Ward's method and by the unweighted-pair group method using arithmetic averages (UPGMA) were compared. Ward's method was more effective at differentiating major groups within sets of profiles; UPGMA had a slightly reduced error rate in clustering of replicate profiles and was more sensitive to outliers. Most replicate profiles were clustered together when relative peak height or Hellinger-transformed peak height was used, in contrast to raw peak height. Redundancy analysis was more effective than cluster analysis at detecting differences between similar samples. Redundancy analysis using Hellinger distance was more sensitive than that using Euclidean distance between relative peak height profiles. Analysis of Jaccard distance between profiles, which considers only the presence or absence of a terminal restriction fragment, was the most sensitive in redundancy analysis, and was equally sensitive in cluster analysis, if all profiles had cumulative peak heights greater than 10,000 fluorescence units. It is concluded that T-RFLP is a sensitive method of differentiating between microbial communities when the optimal statistical method is used for the situation at hand. It is recommended that hypothesis testing be performed by redundancy analysis of Hellinger-transformed data and that exploratory data analysis be performed by cluster analysis using Ward's method to find natural groups or by UPGMA to identify potential outliers. Analyses can also be based on Jaccard distance if all profiles have cumulative peak heights greater than 10,000 fluorescence units.