ABSTRACT
Coastal ecosystems are considered buffer zones for the discharge of land-derived nutrients without accounting for potential negative side effects. Hence, there is an urgent need to better understand the ecological assembly and dynamics of the microorganisms that are involved in nitrogen (N) cycling in such systems. Here, we employed two complementary methodological approaches (i.e., shotgun metagenomics and quantitative PCR) to examine the distribution and abundance of selected microbial genes involved in N transformations. We used soil samples collected along a well-established pristine salt marsh soil chronosequence that spans over a century of ecosystem development at the island of Schiermonnikoog, The Netherlands. Across the examined soil successional stages, the structure of the populations of genes involved in N cycling processes was strongly related to (shifts in the) soil nitrogen levels (i.e., [Formula: see text], [Formula: see text]), salinity and pH (explaining 73.8% of the total variation, R (2) = 0.71). Quantification of the genes used as proxies for N fixation, nitrification and denitrification revealed clear successional signatures that corroborated the taxonomic assignments obtained by metagenomics. Notably, we found strong evidence for niche partitioning, as revealed by the abundance and distribution of marker genes for nitrification (ammonia-oxidizing bacteria and archaea) and denitrification (nitrite reductase nirK, nirS and nitrous oxide reductase nosZ clades I and II). This was supported by a distinct correlation between these genes and soil physico-chemical properties, such as soil physical structure, pH, salinity, organic matter, total N, [Formula: see text], [Formula: see text] and [Formula: see text], across four seasonal samplings. Overall, this study sheds light on the successional trajectories of microbial N cycle genes along a naturally developing salt marsh ecosystem. The data obtained serve as a foundation to guide the formulation of ecological models that aim to effectively monitor and manage pristine and impacted salt marsh areas. Such models should account for the ecology as well as the historical contingency of N cycling communities.
