The effect of resource history on the functioning of soil microbial communities is maintained across time
Abstract. Historical resource conditions appear to influence microbial community function. With time, historical influences might diminish as populations respond to the contemporary environment. Alternatively, they may persist given factors such as contrasting genetic potentials for adaptation to a new environment. Using experimental microcosms, we test competing hypotheses that function of distinct soil microbial communities in common environments (H1a) converge or (H1b) remain dissimilar over time. Using a 6 × 2 (soil community inoculum × litter environment) full-factorial design, we compare decomposition rates in experimental microcosms containing grass or hardwood litter environments. After 100 days, communities that develop are inoculated into fresh litters and decomposition followed for another 100 days. We repeat this for a third, 100-day period. In each successive, 100-day period, we find higher decomposition rates (i.e. functioning) suggesting communities function better when they have an experimental history of the contemporary environment. Despite these functional gains, differences in decomposition rates among initially distinct communities persist, supporting the hypothesis that dissimilarity is maintained across time. In contrast to function, community composition is more similar following a common, experimental history. We also find that "specialization" on one experimental environment incurs a cost, with loss of function in the alternate environment. For example, experimental history of a grass-litter environment reduced decomposition when communities were inoculated into a hardwood-litter environment. Our work demonstrates experimentally that despite expectations of fast growth rates, physiological flexibility and rapid evolution, initial functional differences between microbial communities are maintained across time. These findings question whether microbial dynamics can be omitted from models of ecosystem processes if we are to predict reliably global change effects on biogeochemical cycles.
