The challenge of studying the ecology of biomass-degrading communities has always been the ability to accurately determine a representative picture of the true diversity of the mi­crobial consortia, and the biochemical processes. Advances in molecular biology have led to development of culture-independent methods for describing microbial communities based on analysis of DNA extracted directly from natural populations thereby circumventing the need to isolate and culture bacteria for phylogenetic analysis. The recent surge of research in molecular microbial ecology has provided evidence for the existence of many novel types of microorganisms in the environment, in numbers, and varieties far greater than culti­vated in the laboratory, which probably comprise less than 1% of all microorganisms (33). Additional corroboration comes from estimates of DNA complexity and the discovery of many unique bacterial 16S rRNA gene sequences from numerous environmental sources (2, 35, 83). One approach to classifying complex communities is to use “marker” genes as phylogenetic anchors for identification of source microorganisms. Ribosomal RNAs are highly conserved and are commonly used to determine phylogenic relationships between organisms. Other marker genes can be used including recA and rpoB genes, however, the largest available database is for 16S rRNA. Another approach is to use gene-centric analysis of large datasets that focus on the identification and taxonomic characterization of genes important to the overall community function.

DNA-based fingerprinting techniques developed to characterize and compare whole genomes of organisms include amplified fragment length polymorphism (84), terminal restriction fragment length polymorphism (68), denaturing gradient gel electrophoresis (70), amplified rRNA gene restriction analysis (85), restriction landmark genome scan­ning (86), and automated ribosomal intergenic spacer analysis (87). Dominate members of biomass-degrading communities can be determined using methods such as terminal restriction fragment length polymorphism (T-RFLP) and denaturing gradient gel elec­trophoresis (DGGE).