The Temporal Scaling of Bacterioplankton Composition: High Turnover and Predictability during Shrimp Cultivation

Abstract

The spatial distribution of microbial communities has recently been reliably documented in the form of a distance-similarity decay relationship. In contrast, temporal scaling, the pattern defined by the microbial similarity-time relationships (STRs), has received far less attention. As a result, it is unclear whether the spatial and temporal variations of microbial communities share a similar power law. In this study, we applied the 454 pyrosequencing technique to investigate temporal scaling in patterns of bacterioplankton community dynamics during the process of shrimp culture. Our results showed that the similarities decreased significantly (P = 0.002) with time during the period over which the bacterioplankton community was monitored, with a scaling exponent of w = 0.400. However, the diversities did not change dramatically. The community dynamics followed a gradual process of succession relative to the parent communities, with greater similarities between samples from consecutive sampling points. In particular, the variations of the bacterial communities from different ponds shared similar successional trajectories, suggesting that bacterial temporal dynamics are predictable to a certain extent. Changes in bacterial community structure were significantly correlated with the combination of Chl a, TN, PO4 (3-), and the C/N ratio. In this study, we identified predictable patterns in the temporal dynamics of bacterioplankton community structure, demonstrating that the STR of the bacterial community mirrors the spatial distance-similarity decay model.

Response of Bacterioplankton Communities to Cadmium Exposure in Coastal Water Microcosms with High Temporal Variability

“…Total phosphorus (TP), PO 4 3Ϫ -P, NO 3 Ϫ -N, NH 4 ϩ -N, and NO 2 Ϫ -N were analyzed following the standard methods (26). Chlorophyll a (Chl-a) was measured as described previously (21).…”

“…Bacterial communities in surface seawater were shown to be highly diverse and dynamic on a temporal scale (19). However, several recent studies demonstrate repeatable and predictable patterns rather than random distributions in BCCs across different temporal scales (19)(20)(21)(22). In other words, variation in BCC could reflect multiple underlying environmental changes.…”

“…The whole bacterioplankton community may change rapidly to maintain the completed ecological function in highly variable environments (23). For example, high turnover and predictability of bacterioplankton community succession were observed in seawater ponds during a 77-day shrimp cultivation using the similarity-time decay model (21).…”

Development of a prototype 16S rRNA gene-based microarray for monitoring planktonic actinobacteria in shrimp ponds

“…Of the 42 families recognized in actinobacteria, the probes targeted ten families (Acidimicrobiaceae, Iamiaceae, Mycobacteriaceae, Cellulomonadaceae, Intrasporangiaceae, Microbacteriaceae, Micrococcaceae, Nocardioidaceae, Propionibacteriaceae, and Nitriliruptoraceae) shown to be dominant in shrimp ponds in the previous studies (Xiong et al 2014a). Three probes targeted the same family, complementing several unique regions of the 16S rRNA gene (Loy et al 2002).…”

“…The V1-V3 hyper variable region of bacterial 16S rRNA genes was amplified using the primer set 27F and 519R (Xiong et al 2014a). The bacterial 16S rRNA gene products were sequencing using a Roche FLX 454 pyrosequencing machine (Roche Diagnostics Corporation, Branford, CT, USA).…”