Metabolite cycling indicated by long-range correlation in a sediment bioreactor mixed microbial community

Geophysical surveys add value to biogeochemical studies because of their ability to characterize systems remotely, and their precise time resolution. One limitation, however, is their lack of biogeochemical process specificity. Here, electrochemical time series from an oxic-anoxic cyclical bioreactor experiment were reanalyzed with detrended fluctuation analysis (DFA) to distinguish dominant biogeochemical processes. Measurements of EH, pH, dissolved oxygen (DO) were recorded every 20 minutes for 74 days. The time series were divided by geochemical environment (aerobic respiration, NO3- reduction, mixed Fe(III), Mn(IV), SO42- reduction, and a...  more

Geophysical surveys add value to biogeochemical studies because of their ability to characterize systems remotely, and their precise time resolution. One limitation, however, is their lack of biogeochemical process specificity. Here, electrochemical time series from an oxic-anoxic cyclical bioreactor experiment were reanalyzed with detrended fluctuation analysis (DFA) to distinguish dominant biogeochemical processes. Measurements of EH, pH, dissolved oxygen (DO) were recorded every 20 minutes for 74 days. The time series were divided by geochemical environment (aerobic respiration, NO3- reduction, mixed Fe(III), Mn(IV), SO42- reduction, and anoxic-oxic transition), and analyzed for correlation strength using DFA. Correlation strength varied systematically by environment over five oxic-anoxic cycles. This repetition makes it clear that electrode fluctuations are not random, nor are they noise. In fact, electrode fluctuations are a system-specific measurement of dominant geochemical conditions. The results of this study in a well-constrained environment with a complex microbial community support the potential to use galvanic and electrochemical approaches to remediation as a viable, cost-effective, and simple long-term monitoring strategy. The information provided by time series analysis requires no special sensors or additional data collection, just a short computational analysis for new, valuable information about ongoing geochemical reactions. This approach could be valuable in any application where remote, long-term monitoring of ongoing biogeochemical processes is desirable, such as agriculture, bioreactors, or in long-term remediation and monitoring programs where inexpensive, consistent data sets could provide valuable insight into degradation and environmental stability.