Discordant Temporal Turnovers of Sediment Bacterial and Eukaryotic Communities in Response to Dredging: Nonresilience and Functional Changes

Abstract

To study the stability and succession of sediment microbial and macrobenthic communities in response to anthropogenic disturbance, a time-series sampling was conducted before, during, and 1 year after dredging in the Guan River in Changzhou, China, which was performed with cutter suction dredgers from 10 April to 20 May 2014. The microbial communities were analyzed by sequencing bacterial 16S rRNA and eukaryotic 18S rRNA gene amplicons with Illumina MiSeq, and the macrobenthic community was identified using a morphological approach simultaneously. The results indicated that dredging disturbance significantly altered the composition and structures of sediment communities. The succession rates of communities were estimated by comparing the slopes of time-decay relationships. The temporal turnover of microeukaryotes (w ϭ 0.3251, P Ͻ 0.001 [where w is a measure of the rate of log(species turnover) across log(time)]) was the highest, followed by that of bacteria (w ϭ 0.2450, P Ͻ 0.001), and then macrobenthos (w ϭ 0.1273, P Ͻ 0.001). During dredging, the alpha diversities of both bacterial and microeukaryotic communities were more resistant, but their beta diversities were less resistant than that of macrobenthos. After recovery for 1 year, all three sediment communities were not resilient and had reached an alternative state. The alterations in sediment community structure and stability resulted in functional changes in nitrogen and carbon cycling in sediments. Sediment pH, dissolved oxygen, redox potential, and temperature were the most important factors influencing the stability of sediment communities and ecosystem multifunctionality. This study suggests that discordant temporal turnovers and nonresilience of sediment communities under dredging resulted in functional changes, which are important for predicting sediment ecosystem functions under anthropogenic disturbances.IMPORTANCE Understanding the temporal turnover and stability of biotic communities is crucial for predicting the responses of sediment ecosystems to dredging disturbance. Most studies to date focused on the bacterial or macrobenthic community, only at two discontinuous time points, before and after dredging, and hence, it was difficult to analyze the community succession. This study first compared the stabilities and temporal changes of sediment bacterial, microeukaryotic, and macrobenthic communities at a continuous time course. The results showed that discordant responses of the three communities are mainly related to their different biological inherent attributes, and sensitivities to sediment geochemical variables change with dredging, resulting in changes in sediment ecosystem multifunctionality.

Freshwater Sediment Microbial Communities Are Not Resilient to Disturbance From Agricultural Land Runoff

“…However, the diversity of microbial communities within sediment samples from highly impacted locations was significantly higher ( p < 0.05) 4 months post-disturbance compared to their pre-disturbance state ( Supplementary Figures S1C,D , Branch 2 and Downstream sites). Other studies in coastal ( Galand et al, 2016 ) and freshwater sediments ( Zhang et al, 2017 ) have also found that disturbance increases diversity in sediment microbial communities. Microbial communities from less or non-impacted sites were relatively stable over the course of the sampling regimen.…”

“…Additionally, these results are supported by significant increases in the abundance of taxa associated with nutrients or pollutants from agricultural runoff at highly impacted locations (see Figure 4 above). Other studies have shown that microbial communities may take several years to exhibit resilience to disturbance ( Enwall et al, 2007 ; Allison and Martiny, 2008 ; Zhang et al, 2017 ). In highly impacted areas of chronic contamination such as the Kewaunee River watershed, microbial communities may never return to a “pre-disturbance” state as they are continually exposed to multiple stressors from agricultural land runoff (microbial pathogens, heavy metals, nutrients, antibiotics, etc.).…”

“…Resistance and resilience indices at the alpha-diversity level were calculated using estimators of sample diversity, richness, and evenness including Chao1, ACE, Shannon, and Inverse Simpson diversity indices. Resistance and resilience at the beta-diversity level were estimated by calculating the difference in beta-diversity between pre- and post-disturbance microbial communities using both PERMANOVA (analysis of variance) and ANOSIM (analysis of similarity) tests, as in Shade et al (2012b) and Zhang et al (2017) .…”

16S and 18S rRNA Gene Metabarcoding Provide Congruent Information on the Responses of Sediment Communities to Eutrophication

“…In the context of aquaculture-induced organic enrichment, this work has focused on foraminifera (Pawlowski et al, 2014(Pawlowski et al, , 2016He et al, 2019) and ciliates (Forster et al, 2018), as well as general analyses of eukaryotic (Chariton et al, 2015) and bacterial communities (Dowle et al, 2015;Fodelianakis et al, 2015;Stoeck et al, 2018;Verhoeven et al, 2018;Moncada et al, 2019). The use of metabarcoding as a monitoring tool has also been explored with reference to localized disturbances caused by dredging (Zhang et al, 2017) and off-shore drilling (Lanzén et al, 2016;Laroche et al, 2017;Frontalini et al, 2020). These studies have shown that bacterial and eukaryotic metabarcoding analyses can serve as sensitive methods to detect anthropogenic impacts on sediment habitats, including both short-term responses and longterm effects on community resilience and stability.…”

“…Although several studies have correlated water column and/or sediment geochemical variables with metabarcoding data (Pawlowski et al, 2014;Chariton et al, 2015;Dowle et al, 2015;Fodelianakis et al, 2015;Lanzén et al, 2016;Forster et al, 2018;He et al, 2019;Moncada et al, 2019), there is a lack of research on this topic involving cross-comparisons of prokaryotic and eukaryotic communities. In cases where both types of metabarcoding analyses have been employed, shifts in community structure have either not been correlated with geochemical data or only a limited set of measurements has been used (La Rosa et al, 2001;Zhang et al, 2017;Keeley et al, 2018;Stoeck et al, 2018). Addressing this absence of information, therefore, is essential to establishing a full understanding of how metabarcoding can be best employed as a tool for marine ecosystem monitoring.…”