Benthic and pelagic viral decay experiments: a model-based analysis and its applicability

Abstract

The viral decay in sediments, that is, the decrease in benthic viral concentration over time, was recorded after inhibiting the production of new viruses. Assuming that the viral abundance in an aquatic system remains constant and that viruses from lysed bacterial cells replace viruses lost by decay, the decay of viral particles can be used as a measure of viral production. Decay experiments showed that this approach is a useful tool to assess benthic viral production. However, the time course pattern of the decay experiments makes their interpretation difficult, regardless of whether viral decay is determined in the water column or in sediments. Different curve-fitting approaches (logarithmic function, power function, and linear regression) to describe the time course of decay experiments found in the literature are used in the present study and compared to a proposed "exponential decay" model based on the assumption that at any moment the decay is proportional to the amount of viruses present. Thus, an equation of the form dVA/dt = -k x VA leading to the time-integrated form VAt = VA0 x e(-k x t) was used, where k represents the viral decay rate (h(-1)), VAt is the viral abundance (viral particles ml(-1)) at time t (h), and VA0 is the initial viral abundance (viral particles ml(-1)). This approach represents the best solution for an accurate curve fitting based on a mathematical model for a realistic description of viral decay occurring in aquatic systems. Decay rates ranged from 0.0282 to 0.0696 h(-1) (mean, 0.0464 h(-1)). Additionally, a mathematical model is presented that enables the quantification of the viral control of bacterial production. The viral impact on bacteria based on decay rates calculated from the different mathematical approaches varied widely within one and the same decay experiment. A comparison of the viral control of bacterial production in different aquatic environments is, therefore, improper when different mathematical formulas are used to interpret viral decay experiments.

FIG. 1.

Changes in benthic bacterial and viral counts as a function of time after inhibiting production of new viruses by the addition of KCN and in untreated samples (control).

FIG. 2.

Changes in benthic viral counts as a function of time after inhibiting production of new viruses by the addition of KCN.

FIG. 3.

Viral control of benthic BSP. Calculation is based on VDR determined by using different mathematical approaches and different time periods of the decay experiments as follows: by linear regression and data from the period 0 to 3 h (D5), 0 to 5 h (D1, D2, D3, and D4), and 0 to 9 h (D6) (all, Lin 0-3/5/9 h); by linear regression and data from the period 3 to 24 h (D5), 5 to 24 h (D1, D2, D3, and D4), and 9 to 24 h (D6) (all, Lin 3/5/9-24 h); by power function and data from the entire period (Pow 0-24 h); by logarithmic function and data from the initial 9-h period (Log 0-9 h) and data from the entire period (Log 0-24 h); and by exponential decay function and data from the initial 9-h period (Exp 0-9 h), data from the entire period (Exp 0-24 h), and data from the period 9 to 24 h (Exp 9-24 h). Error bars indicate the range of values calculated by using a burst size of 25 and 40.