Regime Transition Shapes the Composition, Assembly Processes and Co-Occurrence Pattern of Bacterioplankton Community in a Large Eutrophic Freshwater Lake

doi:10.21203/rs.3.rs-427021/v1

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Research Article

Regime Transition Shapes the Composition, Assembly Processes and Co-Occurrence Pattern of Bacterioplankton Community in a Large Eutrophic Freshwater Lake

https://doi.org/10.21203/rs.3.rs-427021/v1

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At certain nutrient concentrations, shallow freshwater lakes are generally characterized by two contrasting ecological regimes with disparate patterns of biodiversity and biogeochemical cycles: a macrophyte-dominated regime (MDR) and a phytoplankton-dominated regime (PDR).To reveal ecological mechanisms that affect bacterioplankton along the regime shift, Illumina MiSeq sequencing of the 16S rRNA gene combined with a novel network clustering tool (Manta) were used to identify patterns of bacterioplankton community composition across the regime shift in Taihu Lake, China. Marked divergence in the composition and ecological assembly processes of bacterioplankton community were observed under the regime shift. The alpha diversity of bacterioplankton community was observed to consistently and continuously decrease with the regime shift from MDR to PDR, while the beta diversity presents the opposite. Moreover, as the regime shifted from MDR to PDR, the contribution of deterministic processes first decreased and then increased again closer to the PDR, most likely as a consequence of differences in nutrient concentration. The topological properties of bacterioplankton co-occurrence networks along the regime shift differed, and the co-occurrences among species changed in structure and were significantly shaped by the environmental variables along the regime transition from MDR to PDR. The divergent environmental state of the regimes with diverse nutritional status may be the most important factor that contributes to the dissimilarity of bacterioplankton community composition along the regime shift and could be represented by phosphorus concentrations as well as several indicator species.

Applied & Industrial Microbiology

Freshwater lake

Regime shifts

Non-linear change

Bacterioplankton community

Network analysis

At certain eutrophic states, shallow lakes are generally characterized by two contrasting ecological regimes. These include a clear macrophyte-dominated regime (MDR) and a turbid phytoplankton-dominated regime (PDR) [1,2]. The shift between the two alternative states is related to nutrient concentrations, specifically phosphorus and nitrogen. These nutrients can have strong impacts on ecosystem structure and functioning and are generally introduced through human activities [3-5]. Once nutrient concentrations rise above a certain threshold, lakes may change from MDR to PDR and their ecosystems undergo drastic shifts. These changes include phytoplankton blooms, gradual or rapid disappearance of macrophytes and increased water turbidity [6-8]. These regimes may be alternative stable states, with presence of a specific regime determined by a lake’s history. Laboratory studies have found evidence of such alternative stable state dynamics in smaller mesocosms [9,10]. However, evidence of alternative stable states in natural ecosystems is scarce due to the high complexity, as well as the temporal and spatial scale of such systems [11].

Taihu Lake presents a unique opportunity to investigate the impact of nutrients on these alternative regimes. Although it is challenging to infer whether these are truly alternative stable states in the temporal sense, the physiochemical characteristics and macrophyte distribution of its subregions reflect either the MDR, the PDR, or a transition between these two regimes [12]. This regime transition from MDR to PDR has been linked to changes in the lake nutritional state before as the nutrients alter the top-down effects on lower trophic levels [5,13,14]. The structure of the participating bacterial communities may be different due to the contrasting decomposition products of algae and macrophytes in MDR and PDR, respectively [15]. To date, there have been studies establishing major differences in bacterioplankton community composition and diversity between these two regimes or across seasons [4,5,13,16]. Our previous study found similar contrasting patterns, with bacterial communities in the MDR having fewer functional genes but more microbial associations compared to the PDR regime [3]. Here, we analyze the dynamics of bacterioplankton communities and link them to changes in physicochemical parameters. More specifically, we address the question whether bacterioplankton community composition changes linearly or non-linearly along the regime transition between MDR and PDR.

The underlying mechanisms that induce the variations in bacterial community composition between sites are often summarized in two ways: niche-based selection and neutral processes [17,18]. Nevertheless, there is a controversy about the relative importance of these two processes in driving microbial communities [19]. To resolve such issues, Stegen et al. (2013) developed a procedure for the quantitative estimation of the influence of processes such as drift, selection and dispersal on bacterial community structure. This procedure demonstrated that both stochastic and deterministic processes appear to govern the assembly processes of bacterial communities [20,21]. However, a more complete understanding of community composition in the context of eutrophic lakes requires a thorough description of the abiotic drivers of co-occurrence patterns [22]. An inter-regime bacterioplankton co-occurrence network can demonstrate how clusters of organisms are driven by abiotic factors along regime shifts in freshwater lakes [23,24]. Moreover, clusters in the species' co-occurrence networks may be indicative of ecological processes governing community structure, such as niche filtering and habitat preference [25]. Additionally, microbial co-occurrence networks can predict hub species and potential interactions between species [26], thereby contributing to a deeper understanding of the underlying mechanisms that dominate the assembly process of bacterioplankton communities along the regime shift of freshwater lakes.

Taihu Lake is a typical large shallow eutrophic lake composed of several connected lake zones that maintain distinct-different nutrition loadings [27]. High spatial heterogeneity in the abundance of macrophytes and phytoplankton suggests variation in the responses to eutrophication within the lake, which may be a result of a specific distribution pattern of critical nutrient loads in Taihu Lake [28]. In such a large shallow lake, where water is completely mixed, it is unlikely that dispersal limitation and fast adaptation to local conditions could occur. Studies on bacterioplankton communities across (spatial) regime shifts from MDR to PDR in Taihu Lake are of great significance to provide insights into the mechanism of biodiversity and regime maintenance in the lake ecosystem from the microbiological point of view. In this study, Illumina MiSeq sequencing of the 16S rRNA genes was used to identify the patterns of bacterioplankton community composition across Taihu Lake, China. We addressed the following research questions (1) Whether the bacterioplankton community composition changes linearly or non-linearly with the regime transition from MDR to PDR? (2) Which process dominates the bacterioplankton community assembly along the regime transition? (3) Are there core factors or species that drive and maintain the changes in the bacterioplankton community composition along the regime transition?

Sample collection and high-throughput sequencing

The samples and sequencing data in this study came from our previous study [3]. Briefly, six sampling sites across Taihu Lake were selected along the transition from the macrophyte-dominated regime (MDR) to the phytoplankton-dominated regime (PDR) (MDR-Core1, MDR-Core2, MDR-Edge, PDR-Edge, PDR-Core2, PDR-Core1) (Fig. S1) in August 2013. Water sampling, large plankton removal and bacterioplankton collection were described in our previous study [3]. The physicochemical properties of the water samples, including the pH, dissolved oxygen (DO) and turbidity were measured in situ using a calibrated multifunction water quality sonde (YSI 6600, Yellow Springs, OH, USA). The concentrations of total nitrogen (TN), total phosphorus (TP), ammonium (NH₄⁺-N), nitrate (NO^-₃-N), nitrite (NO^-₂-N), orthophosphate (PO₄^3--P), dissolved total nitrogen (DTN) and dissolved total phosphorus (DTP), dissolved organic carbon (DOC) and concentrations of chlorophyll a (Chl a) were further determined [3].

The detailed information of DNA extraction, amplification, and high-throughput sequencing were described in our previous study [3]. In brief, microbial DNA was extracted, purified and the concentration and purity of the DNA was determined using the spectrophotometric method [29]. The bacterial 16S rRNA gene was PCR amplified and then sequenced using an Illumina MiSeq platform. The paired-end raw sequences were processed to control the quality by the Galaxy Pipeline (http://zhoulab5.rccc.ou.edu:8080/root/login?redirect=%2F) and then processed with QIIME v1.9.1 [29] as described in our previous study [3]. The raw reads were deposited into the NCBI Sequence Read Archive (SRA) database (BioProject: PRJNA511603).

Statistical analyses

The pipeline-based subsampled OTU table was used for the following statistical analyses. The OTU richness, Simpson evenness and Faith's phylogenetic diversity were calculated with the QIIME scripts 'alpha_diversity.py' [30,31]. Weighted UniFrac [32] was calculated to quantify the phylogenetic beta diversity of bacterioplankton communities [21]. A principal coordinates analysis (PCoA) plot was constructed using the Bray-Curtis dissimilarity index and weighted UniFrac matrices with the 'vegan' package (version 2.5-6) [33] in R software (version 3.6.1). Akaike information criterion values for linear-regression model and quadratic-regression of correlations between bacterial phylum and total phosphorus were calculated to select the proper models (Table S1). The bacterioplankton community composition along the regime transition was compared using analysis of similarity (ANOSIM) with the 'vegan' package (version 2.5-6) [34]. The standardized transformation of all environmental variables was performed, prior to calculating Euclidean distances of environmental variables with the 'vegdist' command in the 'vegan' (version 2.5-6) package in R software (version 3.6.1) after standardized transformation. Mantel and partial Mantel tests were performed to determine if the differences in bacterioplankton community composition between the samples were correlated with environmental parameters using the 'vegan' package (version 2.5-6) in R [35].

Network analysis

The pipeline-based OTU table was further used for network analysis. The samples were divided into six groups along the transition from macrophyte-dominated regime (MDR) to the phytoplankton-dominated regime (MDR-Core1, MDR-Core2, MDR-Edge, PDR-Edge, PDR-Core2, PDR-Core1) (Fig. S1). To avoid unreliable network inference on zero-rich taxa, only the most commonly abundant OTUs (those detected in at least 50% of samples in each regime, that is, 5 samples in one sampling site) were considered. In order to avoid the taxon number biases, we further trimmed each community dataset of sampling sites into the same taxon number, finally keeping the 527 most-abundant OTUs. CoNet was used to individually construct networks for the six sampling sites following the protocols described previously [41,42] with minor modifications. In brief, the distribution of all pair-wise scores was calculated for each of five similarity measures (Bray-Curtis dissimilarity, Kullback-Leibler dissimilarity, Pearson and Spearman correlation, and Mutual Information), and the top 1000 positive and 1000 negative edges supported by at least two measures were retained initially. For each measurement and edge, 100 permutations (with renormalization for correlation measures) and bootstrap scores were generated, following the ReBoot routine. The measure-specific P-value was then computed, merged using Brown's method and discarded applying Benjamini-Hochberg's false discovery rate correction (edges with P ≥ 0.05 were removed) [42]. Edges with scores outside the 95% confidence interval defined by the bootstrap distribution or not supported by at least two measures were discarded as well [42]. Network topologies were calculated in R software using 'igraph' packages (version 1.2.4.1) [43], and networks were visualized using Cytoscape software (version 3.6.1) [44]. Environmental variables were further integrated into the networks to reveal the relationship between nodes and environmental variables. Only correlations between environmental variables and species that were significant (P < 0.05, Benjamini-Hochberg adjust) and strong (r ≥ 0.75 or r ≤ -0.75) were considered. A novel microbial association network clustering algorithm (Manta) (version 1.0.1), which determines the optimal cluster number automatically, was used to separate the networks into clusters with default settings [22] after calculating the global network based on all 54 samples across the regime shift from MDR to PDR. Indexes, including modularity, clustering coefficient, average path length, network diameter, average degree and graph density were calculated using the 'igraph' packages in R to describe the attributes of a network [45]. One thousand random networks were generated using the 'igraph' packages in R, and all of the indexes of the random networks were calculated individually. A statistical Z-test was used to verify if the network indexes between the observed and random networks were significantly different.

The topological roles of certain nodes were determined by within-cluster connectivity (Zi) and connectivity among clusters (Pi), which reﬂect the connection between a node to nodes within its cluster and those in other clusters [45,46]. Zi and Pi are calculated according to Guimerà and Amaral (2005). The nodes in a network are divided into four subcategories according to the simpliﬁed classiﬁcation: peripheral nodes, connectors, cluster hubs and network hubs [46,47]. The topological properties of nodes in a co-occurrence network can reveal species that correlate in their responses to the environment and may therefore be indicators of these responses. The topological role of each OTU was determined according to the scatter plot of within-cluster connectivity (Zi) and among-cluster connectivity (Pi) [48]; including: Peripheral nodes (Zi ≤ 2.5, Pi ≤ 0.62) that have only a few links and seldom connect to other clusters; connectors (Zi ≤ 2.5, Pi > 0.62) that are highly linked to several clusters; cluster hubs (Zi > 2.5, Pi ≤ 0.62) that are highly connected to nodes in their own clusters; network hubs (Zi > 2.5, Pi > 0.62) that act as both cluster hubs and connectors.

Environmental heterogeneity in the sampling sites across the regime transition

The geographic distance was significantly correlated with Euclidean distance of environmental parameters (Fig. S2, P < 0.001), indicating that more distant samples were more dissimilar in terms of their environmental parameters. TP concentrations were significantly different along the regime transition from the macrophyte-dominated regime (MDR) to the phytoplankton-dominated regime (PDR) (Fig. S3). Furthermore, almost all of the environmental parameters at six sampling sites across the transition from MDR to PDR were significantly correlated with TP (Fig. S4). The concentrations of TN, NO₃^--N, TP, DTP, PO₄^3--P, DOC, and Chl a continuously increased with the regime shift from MDR to PDR, while the concentrations of DTN and NO₂^--N significantly decreased with the regime shift (Fig. S4).

Community composition and assembly processes of bacterioplankton altered along the regime shift from MDR to PDR

In total, we obtained 1,877,021 high-quality sequences from the two regimes. The total OTU richness was 18,655 at a 97% similarity level for all samples. Indices of the OTU richness, phylogenetic diversity and Pielou's evenness of the bacterioplankton community significantly decreased along the regime shift from MDR to PDR (Fig. 1a). The NMDS plot revealed that based on both of the taxonomy and phylogeny, the bacterioplankton community clustered together for each site, along the regime transition from MDR to PDR (Fig. 1b). As the regime shifted from MDR to PDR, the beta diversity of bacterioplankton community increased and reached its peak at the PDR-Edge site (Fig. S5 and S6). The average relative abundance of bacterial phyla and genera changed along the regime shift from MDR to PDR (Fig. 2 and Fig. S7). Bacteroidetes, Betaproteobacteria and Actinobacteria dominated the bacterioplankton community at the MDR-Core1 site (Fig. 2a). As opposed to a gradual change of communities along the environmental gradient of regime transition from MDR to PDR, the results demonstrate that the relative abundance of all taxa changed with increased TP concentration linearly or non-linearly (Fig. 2b, P < 0.001 in all cases). Moreover, the non-linear model fit better for most taxa (Table S1). Typically, as the regime shifted from MDR to PDR, the relative abundance of Betaproteobacteria decreased non-linearly and gradually, whereas abundance of Actinobacteria increased non-linearly and reached its peak at the MDR-Edge site. Firmicutes and Gammaproteobacteria became abundant at the PDR-Edge site, and respectively dominated the PDR-Core sites with a linear and non-linear tendency, respectively.

Both taxonomic and phylogenetic dissimilarity of bacterioplankton community were signiﬁcantly correlated with environmental distance along the regime shift (Fig. S8, P < 0.05). Concentrations of TP along the MDR and PDR showed significant negative correlations with the alpha diversity of the bacterioplankton community (Fig. 1a), indicating that the alpha diversity decreased with increasing nutrient concentration under the regime shift. Significant positive correlations between the TP dissimilarity and beta diversity of bacterioplankton community were also observed (Fig. 1c), indicating that the differences of TP concentration between the samples contribute to the heterogeneity of bacterioplankton communities along the regime shift from MDR to PDR. Based on the Mantel test and partial Mantel test results (Table S2), the bacterioplankton community was signiﬁcantly correlated with most of the environmental parameters along the regime shift (P < 0.05), especially for TP (R = 0.7645 and 0.5377 for Mantel test and partial Mantel test, respectively). The PCoA plot further highlighted that the community composition aligned well to environmental parameters (Fig. 3).

Almost all of the betaNTI values were considerably less than -2 (Fig. S9a). This suggests that deterministic processes dominate in shaping the bacterioplankton communities along the two regimes [40]. Moreover, as the regime shifted from MDR to PDR, the contribution of deterministic processes first decreased in the edge sites and then increased again closer to the PDR. Furthermore, the assembly analysis revealed the quantitative importance of variable selection (selection under heterogeneous abiotic and biotic environmental conditions leading to more dissimilar structures among communities; also called variable selection), homogeneous selection (selection under homogeneous abiotic and biotic environmental conditions leading to more similar structures among communities), dispersal limitation, homogeneous dispersal, and other processes in shaping the community patterns of the bacterioplankton community along the two regimes (Fig. S9b). At a relative importance of more than 60%, variable selection contributed the most to the assembly processes of bacterioplankton communities in these six sites. At 28.58%, homogenizing dispersal was the second-most important assembly process of the bacterioplankton community.

Co-occurrence patterns of the bacterioplankton community along the regime shift

We constructed six co-occurrence networks from the six sampling sites spaced out along the regime transition. These sampling sites, located along the regime transition from MDR to PDR, were labelled MDR-Core1, MDR-Core2, MDR-Edge, PDR-Edge, PDR-Core2 and PDR-Core1. The derived co-occurrence networks featured environment-specific species co-occurrence relationships (Fig. 4 and Fig. S10). Subnetworks inferred from datasets trimmed into uniform number of OTUs contained distinct co-occurrence patterns in terms of species composition (Fig. 4). Moreover, these regime-specific co-occurrence relationships displayed network characteristics that were significantly different compared to random networks (Table 1). The Z-test results comparing a variety of indexes of the observed correlation-based network with those of random networks, including Modularity, Transitivity and Network diameter, indicated that the networks among species along the regime shift from PDR and MDR were all nonrandom (P < 0.001 in all cases, Table 1). Based on the size and taxonomic information, the bacterioplankton community network changed along the regime transition from MDR to PDR (Table 1 and Fig. S10). Bacteroidetes, Betaproteobacteria and Actinobacteria dominated and clustered in the MDR sites, whereas Firmicutes and Gammaproteobacteria dominated and clustered in the PDR sites.

Phosphorus concentrations as an indicator of regime transition

This and previous studies showed that the lake water displays significant environmental differences along the regime transition from the macrophyte-dominated regime (MDR) to the phytoplankton-dominated regime (PDR) [12,49,50]. Certain environmental parameters have been suggested to be responsible for the observed differences in bacterioplankton community, such as pH [51] and nutrient enrichment [5], as well as the occurrence of phytoplankton [3,52], which are frequently associated with eutrophication. The mechanisms of the regime shift can involve multiple drivers as well as interactions of macrophytes, phytoplankton, nutrients, and herbivorous waterfowl [53]. Here, we focused on the phosphorus concentration because it is commonly targeted to mitigate eutrophication and it is proposed in previous studies that regime shifts from MDR to PDR in shallow lakes are mostly induced by increasing nutrient concentrations, especially phosphorus [2,52,53]. In this study we found that almost all of the environmental parameters at six sampling sites across the transition from MDR to PDR were significantly linearly correlated with TP, especially turbidity (Fig. S4). Turbidity is a measure of water transparency, which is mainly influenced by concentrations of phytoplankton and re-suspended sediment particles [13,14]. The wind-driven process of sediment re-suspension is much stronger in PDR-Core sites than in MDR-Core sites in Taihu Lake because the submersed macrophyte communities in MDR have a calming effect on the water column and sediment turbulence [8,54], which is consistent with the results found in this study.

Compositional dissimilarity of bacterioplankton communities differed along regime transition

Our results indicate that the bacterioplankton community was signiﬁcantly correlated with environmental factors at the regime transition, especially TP, which is regarded as a limiting nutrient in the eutrophic freshwater lakes that contribute to regime shift [55-57]. A significant difference between bacterioplankton community in lake ecosystems with disparate environmental variables has also been widely observed [13,58,59]. The alpha diversity of bacterioplankton communities along the regime transition from MDR to PDR was observed to decrease and linearly correlate with TP signiﬁcantly, which is consistent with the observation that bacterial diversity was negatively affected by phytoplankton blooms [60]. In general, when the evenness of bacterioplankton community decreases with the regime shift, the diversity does so too, indicating that a particular species becomes more dominant with the regime transition [61]. Also, several taxonomic groups displayed substantial changes in abundance as the regime shifted (Fig. 2 and Fig. S7). Regime shifts provide a mechanism for selection, as abundant OTUs become rare or even more abundant due to their sensitivity to changes in the environmental conditions [1,3]. Intriguingly, these changes were occasionally non-linear, suggesting that taxa had different optima along the MDR-PDR gradient. For a number of taxa, e.g., Acidobacteria and Cyanobacteria, the relationship to TP is well described by quadratic functions, which means that the taxon has an optimal value for TP and declines in abundance below and above that value, as opposed to a gradual change of bacterioplankton communities along the environmental gradient along the regime shift from MDR to PDR. It has been demonstrated that Betaproteobacteria tends to be the dominant group in relatively oligotrophic lakes [12,13], while Firmicutes, which appears to be involved in DOC degradation [62,63], often become dominant in eutrophic lakes alongside blooms of Cyanobacteria [64]. Our results, that Betaproteobacteria and Firmicutes were negative and positive linearly correlated with TP, are consistent with these findings. Therefore, those consistently variable OTUs might be used as discriminant taxa for the different regimes.

Changes in assembly processes of bacterioplankton communities were attributed to regime transition

Although the deterministic processes were observed to dominate in shaping the bacterioplankton communities in both the MDR-Core and PDR-Core sites, the deterministic process in MDR-Core sites were observed to be stronger than in PDR-Core sites. According to other experimental studies, competition among species could be reduced in more productive environments, hence weakening selection and strengthening the stochasticity of bacterial communities [5,65]. Previous work has also shown that the degree of determinism increases in extreme or low resource environments for the assembly process of microbial community [66-69]. The stronger deterministic process in MDR-Core sites may be due to lower nutrient concentrations compared to PDR-Core-sites, leading to relatively stronger selection due to increased competition for resources and less diverse resources [49]. As to the decreased contribution of deterministic processes in the edge sites of MDR and PDR, there could be two reasons. On the one hand, edge sites could provide a more favorable living environment because of the higher nutrient concentrations compared to MDR-Core sites and lower algal toxin pressure compared to PDR-Core sites [64]. On the other hand, water disturbance driven by strong wind in the edge sites of the two regimes in Lake Taihu could also have resulted in an increase of the disturbance of environmental conditions and the passive dispersal of species, hence increasing the stochasticity of bacterioplankton community [65,70]. While the comparably strong deterministic process in PDR-Core sites may be due to the algal toxin pressure that comes from Cyanobacteria bloom [62], but weakened by the extremely abundant resources [65]. Of the investigated ecological processes, variable selection contributed the most to the assembly of bacterioplankton community (Fig. S9b). In freshwater lakes, both community structure and the assembly processes of bacterioplankton have been shown to be intensively inﬂuenced by nutrient loading [5,49] as well as submerged macrophytes [8,12]. The heterogeneous pattern of the bacterioplankton community assembly processes in this study could therefore be attributed to differences in nutrient loading along the regime shift from MDR to PDR (Fig. 5).

Regime transition revealed by network clusters and hub species

In ecological systems, coexistence is potentially supported by niche processes like environmental ﬁltering, as the different filters lead to establishment of different communities [17,40]. Species that share similar ecological niches may compete or cooperate to resist environmental pressure when resources are scarce or under environmental stress [71,72]. Compared to the MDR-core sites, the Cyanobacteria bloom caused by high concentration of TP in PDR-Core sites strengthen selection due to its algal toxin [60,62]. The modularity of the site-specific co-occurrence network varied along the regime shift (Table 1). Previous studies have shown the existence of environmentally driven modules [73,74]. In this study we also found that closely related taxa tended to be positively interconnected and clustered together (Fig. 4). Moreover, we found several hub species in the three network clusters, which are likely indicators of the effect of the regime shift on the bacterioplankton community. The first and third cluster contain taxa that dominate the MDR or PDR regime, respectively. These are also the taxa that consistently increase or decrease across the environmental gradient (Fig. 2). For instance, Firmicutes dominating cluster 3 and the PDR regime increase with TP while Betaproteobacteria dominating cluster 1 and the MDR regime decrease with TP. The taxa in the second cluster show more subtle patterns. Actinobacteria first increase and then decrease with TP, thus differing in their response from the Firmicutes, whereas Gammaproteobacteria increase with TP but then saturate, in contrast to both the Firmicutes and the Actinobacteria. Thus, cluster 2 groups taxa which benefit from increased TP, but only up to a point. Given that clusters in microbial co-occurrence networks may represent different niches [23], the TP-correlated clusters indicate high similarity in species' co-occurrence patterns along the regime shift and may be summarized by the hub species in these clusters (Fig. 5).

All results suggest that the ecological regimes drive a marked divergence in the community structure of bacterioplankton communities (Fig. 5). The alpha diversity and evenness index linearly decreased with the regime shift, indicating that particular species become more dominant with the regime transition. The hub species that could reflect the regime conditions belonged to the phyla Betaproteobacteria, and Actinobacteria in MDR-Core sites, phyla Betaproteobacteria, Gammaproteobacteria and Actinobacteria in edge sites, phyla Cyanobacteria in PDR-Core sites (Fig. 5). We demonstrated that the contribution of deterministic process decreased at the edge sites when the regime shifted from MDR to PDR, most likely as a consequence of differences in nutrient concentration (especially TP) and the presence of macrophytes. The bacterioplankton co-occurrence networks were clearly linked to environmental variables and were divided into three clusters that reflected the regime shift. The diverse nutrient status may be the most important factor that contributes to the dissimilarity of bacterioplankton community composition along the regime shift. Our results provide a better understanding of the interplay between nutrients and taxonomic composition in macrophyte- and phytoplankton-dominated freshwater regimes.

Funding

This work was supported by the National Natural Science Foundation of China (41871096); the Natural Science Foundation of Jiangsu Province, China (BK20181311); the Fundamental Research Funds for the Central Universities (2018B43414; B200203051); and the China Scholarship Council (No. 201906710009).

Conflicts of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Availability of data and material

The raw reads were deposited into the NCBI Sequence Read Archive (SRA) database (BioProject: PRJNA511603).

Code availability

Not applicable.

Authors' contributions

Conceptualization: Dayong Zhao, Xinyi Cao; Methodology: Lisa Röttjers, Karoline Faust, Xinyi Cao; Formal analysis and investigation: Xinyi Cao, Lisa Röttjers, Karoline Faust; Writing - original draft preparation: Xinyi Cao, Hongjie Zhang; Writing - review and editing: Xinyi Cao, Lisa Röttjers, Karoline Faust, Dayong Zhao, Hongjie Zhang; Funding acquisition: Dayong Zhao; Supervision: Dayong Zhao.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Consent to participate

Not applicable.

Consent for publication

The publisher has the authors' permission to publish the content presented herein.

Acknowledgements

We are especially grateful to Qinglong Wu for his experiment design of this study, Yujing Wang for her assistance in the sample collection and the measurement of physicochemical parameters. This work was supported by the National Natural Science Foundation of China (41871096); the Natural Science Foundation of Jiangsu Province, China (BK20181311); the Fundamental Research Funds for the Central Universities (2018B43414; B200203051); and the China Scholarship Council (No. 201906710009).

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Table 1 Topological properties of the empirical network and associated random network for bacterioplankton community along the regime transition from macrophyte-dominated regime (MDR) to phytoplankton-dominated regime (PDR) in Lake Taihu.

	Empirical network										Random network^b
Sites	Nodes	Edges				Modularity	Transitivity	Network diameter	Average degree	Modularity (SD)		Transitivity (SD)	Network diameter (SD)
Global network	614	Overall	positive		2150	0.464^a	0.269^a	9^a	12.65		0.243 (0.004)	0.021 (0.001)	4.427 (0.495)
			negative		1734
		Cluster1		positive	697
				negative	7
		Cluster2		positive	691
				negative	15
		Cluster3		positive	422
				negative	0
MDR-Core1	301	positive			353	0.791^b	0.360^a	13^a	3.535		0.542 (0.008)	0.011 (0.004)	9.933 (0.807)
		negative			179
MDR-Core2	517	positive			241	0.454^b	0.291^a	19^a	1.624		0.850 (0.013)	0.003 (0.003)	25.63 (3.385)
		negative			179
MDR-Edge	429	positive			286	0.671^b	0.301^a	21^a	2.275		0.721 (0.010)	0.005 (0.003)	16.472 (1.617)
		negative			202
PDR-Edge	431	positive			1439	0.878^b	0.453^a	21^a	7.360		0.337 (0.005)	0.017 (0.002)	6.003 (0.308)
		negative			147
PDR-Core2	381	positive			1143	0.964^b	0.411^a	11^a	8.661		0.303 (0.005)	0.023 (0.002)	5.134 (0.341)
		negative			507
PDR-Core1	269	positive			419	0.691^b	0.425^a	21^a	3.777		0.516 (0.009)	0.014 (0.005)		9.231 (0.739)
		negative			89

^aSignificant difference between the empirical network and the random network (P < 0.001, Z-test).

^bRandom networks were generated by rewiring all of the links with the same numbers of nodes and edges to the corresponding empirical network. The numbers in parentheses indicate the standard deviation (SD) of topological properties of 1000 random networks.

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Regime Transition Shapes the Composition, Assembly Processes and Co-Occurrence Pattern of Bacterioplankton Community in a Large Eutrophic Freshwater Lake

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Abstract

Figures

Introduction

Methods

Sample collection and high-throughput sequencing

Statistical analyses

Network analysis

Results

Environmental heterogeneity in the sampling sites across the regime transition

Community composition and assembly processes of bacterioplankton altered along the regime shift from MDR to PDR

Co-occurrence patterns of the bacterioplankton community along the regime shift

Discussion

Phosphorus concentrations as an indicator of regime transition

Compositional dissimilarity of bacterioplankton communities differed along regime transition

Changes in assembly processes of bacterioplankton communities were attributed to regime transition

Regime transition revealed by network clusters and hub species

Conclusions

Declarations

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Conflicts of interest

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Authors' contributions

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Consent for publication

References

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Supplementary Files

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