Chitin is one the most abundant polymers in nature and interacts with both carbon and nitrogen cycles. Processes controlling chitin degradation are summarized in reviews published some 20 years ago, but the recent use of culture-independent molecular methods has led to a revised understanding of the ecology and biochemistry of this process and the organisms involved. This review summarizes different mechanisms and the principal steps involved in chitin degradation at a molecular level while also discussing the coupling of community composition to measured chitin hydrolysis activities and substrate uptake. Ecological consequences are then highlighted and discussed with a focus on the cross feeding associated with the different habitats that arise because of the need for extracellular hydrolysis of the chitin polymer prior to metabolic use. Principal environmental drivers of chitin degradation are identified which are likely to influence both community composition of chitin degrading bacteria and measured chitin hydrolysis activities.

Recent meta-analyses suggest that ecosystem functioning increases with biodiversity, but contradictory results have been presented for some microbial functions. Moreover, observations of only one function underestimate the functional role of diversity because of species-specific trade-offs in the ability to carry out different functions. We examined multiple functions in batch cultures of natural freshwater bacterial communities with different richness, achieved by a dilutionto-extinction approach. Community composition was assessed by molecular fingerprinting of 16S rRNA and chitinase genes, representing the total community and a trait characteristic for a functional group, respectively. Richness was positively related to abundance and biomass, negatively correlated to cell volumes and unrelated to maximum intrinsic growth rate. The response of chitin and cellulose degradation rates depended on the presence of a single phylotype. We suggest that species identity and community composition rather than richness matters for specific microbial processes.

The genome encodes the metabolic and functional capabilities of an organism and should be a major determinant of its ecological niche. Yet, it is unknown if the niche can be predicted directly from the genome. Here, we conduct metagenomic binning on 123 water samples spanning major environmental gradients of the Baltic Sea. The resulting 1961 metagenomeassembled genomes represent 352 species-level clusters that correspond to 1/3 of the metagenome sequences of the prokaryotic size-fraction. By using machine-learning, the placement of a genome cluster along various niche gradients (salinity level, depth, sizefraction) could be predicted based solely on its functional genes. The same approach predicted the genomes' placement in a virtual niche-space that captures the highest variation in distribution patterns. The predictions generally outperformed those inferred from phylogenetic information. Our study demonstrates a strong link between genome and ecological niche and provides a conceptual framework for predictive ecology based on genomic data.