Weekly variations of viruses and heterotrophic nanoflagellates and their potential impact on bacterioplankton in shallow waters of the central Red Sea

Abstract

Bacterioplankton play a pivotal role in marine ecosystems. However, their temporal dynamics and underlying control mechanisms are poorly understood in tropical regions such as the Red Sea. Here, we assessed the impact of bottom-up (resource availability) and top-down (viruses and heterotrophic nanoflagellates) controls on bacterioplankton abundances by weekly sampling a coastal central Red Sea site in 2017. We monitored microbial abundances by flow cytometry together with a set of environmental variables including temperature, salinity, dissolved organic and inorganic nutrients and chlorophyll a. We distinguished five groups of heterotrophic bacteria depending on their physiological properties relative nucleic acid content, membrane integrity and cell-specific respiratory activity, two groups of Synechococcus cyanobacteria and three groups of viruses. Viruses controlled heterotrophic bacteria for most of the year, as supported by a negative correlation between their respective abundances and a positive one between bacterial mortality rates and mean viral abundances. On the contrary, heterotrophic nanoflagellates abundance covaried with that of heterotrophic bacteria. Heterotrophic nanoflagellates showed preference for larger bacteria from both the high and low nucleic acid content groups. Our results demonstrate that top-down control is fundamental in keeping heterotrophic bacterioplankton abundances low (< 5 × 10 5 cells mL-1) in Red Sea coastal waters.

Keywords: Synechococcus; Red Sea; heterotrophic bacteria; heterotrophic nanoflagellate; top-down control; viruses.

Figure 1.

Weekly variability of environmental variables at KAUST Harbor during the sampled period. (A) temperature and salinity, (B) nitrate concentration, (C) Phosphate concentration, (D) DOC concentration, (F) DON concentration and (F) Total chlorophyll a concentration.

Figure 2.

Weekly variability of total abundance of (A), Synechococcus (LF Synechococcus + HL Synnechococcus) and (B), heterotrophic bacteria (HNA+LNA) at KAUST Harbor during the sampled period.

Figure 3.

Weekly contributions (%) to total cyanobacteria and heterotrophic bacterial abundance of (A), low fluorescence (LF) Synechococcus (relative to the sum of LF and HF groups), (B), Live bacteria (relative to the sum of Live and Dead bacteria), (C), HNA bacteria (relative to the sum of HNA and LNA bacteria) and (D), CTC+ cells (relative to the sum of HNA and LNA bacteria) at KAUST Harbor.

Figure 4.

Weekly variability of the abundance of (A), HNF (mean ± SE cells mL⁻¹), (B), Total viruses (V1+V2+V3) (mean ± SE particles mL⁻¹) and (C), Contribution to total numbers (%) of each of the three subgroups of viruses (V1, V2 and V3) according to their relative nucleic acid content (V1: low, V2: medium and V3: high) at KAUST Harbor station.

Figure 5.

(A), Relationship between the abundances of heterotrophic nanoflagellates and heterotrophic bacteria, compared with the empirical model by Gasol (1994). MAA is the maximum attainable abundance and MRA is mean realized abundance (see the text for details), (light color symbol is excluded from the regression) and (B), Relationship between the cell size of LNA and HNA and HNF abundance. All abundances are log₁₀ transformed. Fitted lines represent the ordinary least squares linear regressions model.

Figure 6.

(A), Estimated (mean ± SE) total heterotrophic bacteria (HNA+LNA) mortality rates caused by viruses obtained from 2016 experiments detailed in Silva et al. (2019) and (B), Relationship between the mortality rates of (A) and the log₁₀ transformed monthly averages of total viral abundance (V1+V2+V3) of this study (see Fig. 4 A for weekly data), (light color symbols are excluded from the regression).

Figure 7.

Relationship between the abundance of heterotrophic nanoflagellates and total viruses for the entire data set. Values are log₁₀ transformed. Fitted line represents the ordinary least squares linear regressions model.