Received: 5 May 2020 | Revised: 26 April 2021 | Accepted: 5 May 2021 DOI: 10.1111/fwb.13735

ORIGINAL ARTICLE

Habitats and seasons differentiate the assembly of bacterial communities along a trophic gradient of freshwater lakes

Congcong Jiao1 | Dayong Zhao1 | Rui Huang1 | Fei He2 | Zhongbo Yu1

1Joint International Research Laboratory of Global Change and Water Cycle, State Key Abstract Laboratory of Hydrology-­Water Resources 1. Freshwater lakes are subject to variable degrees of eutrophication. Within lakes, and Hydraulic Engineering, Hohai University, Nanjing, China the planktonic bacterial community (PBC) and sediment bacterial community 2Nanjing Institute of Environmental (SBC) are both significant participants in biogeochemical processes of lake eco- Sciences, Ministry of Ecology and systems. However, how the assembly patterns of bacterial communities vary sea- Environment, Nanjing, China sonally along a trophic gradient in freshwater lakes is poorly understood. Correspondence 2. Here, we collected and analysed water and sediment samples from 13 shallow Dayong Zhao, State Key Laboratory of Hydrology-­Water Resources and Hydraulic lakes located in an urban region of China during summer and winter, the trophic Engineering, Hohai University, 1 Xikang states of which ranged from mesotrophic to middle eutrophic in summer and Road, Nanjing 210098, China. Email: [email protected] oligo-­mesotrophic to light eutrophic in winter. High-­throughput sequencing of 16S ribosomal RNA genes was used to determine the diversity and composition of Funding information National Key R&D Program of China, Grant/ bacterial communities. Award Number: 2016YFC0402710; National 3. Our results indicated that bacterial communities derived from different habitats Natural Science Foundation of China, Grant/ Award Number: 41671078 and 41871096; and seasons did not exhibit a uniform response to lake trophic states. Linear and Natural Science Foundation of Jiangsu nonlinear mixed effect models suggested that the -­diversity of PBC and SBC, Province, China, Grant/Award Number: α BK20181311; Fundamental Research Funds respectively, showed a unimodal and monotonically decreasing trend with in- for the Central Universities, Grant/Award creasing eutrophication in summer, whereas that of PBC and SBC, respectively, Number: B210202009 and B200203051; Postgraduate Research & Practice exhibited no obvious trend or an increased pattern along the trophic gradient in Innovation Program of Jiangsu Province, winter. In addition, the taxonomic compositional dissimilarity of the PBC was most Grant/Award Number: KYCX20_0465 significantly related to lake trophic differences in summer. Phylogenetic structure analysis revealed that mostly environmental selection regulated the SBC and PBC in both seasons. Moreover, dispersal limitation and homogenising dispersal contributed more to the assembly of SBC and PBC in both seasons, respectively. Water temperature, associated with seasonal variability, was the most important variable driving the PBC assembly, while sediment pH overwhelmed nutrients in regulating the seasonal patterns of SBC assemblages. 4. Overall, we highlighted that the water and sediments, as well as the seasons, dif- ferentiated the diversity patterns and assembly processes of bacterial commu- nities along a trophic gradient of freshwater lakes. Our findings provide novel information for understanding the ecological responses of lacustrine bacterial communities to trophic gradients and seasonal variations. This study also contrib- utes an important reference for predicting the changes of microbial community biodiversity under future scenarios of eutrophication.

Freshwater Biology. 2021;00:1–15. wileyonlinelibrary.com/journal/fwb © 2021 John Wiley & Sons Ltd. | 1 2 | JIAO et al.

KEYWORDS biodiversity, community dissimilarity, environmental selection, lake trophic states, seasonal variation

1 | INTRODUCTION remains largely unknown (Zeng, Jiao, et al., 2019). Nonetheless, seasonal differences in bacterial diversity patterns along a trophic The ubiquitous bacterial communities are of environmental signifi- gradient might be expected to occur, as lake environmental char- cance in freshwater lake ecosystems, where they play a prominent acteristics typically change seasonally, thereby possibly affecting role in driving biogeochemical cycling processes (e.g. carbon and ni- lake trophic status and species diversity in parallel (Zwirglmaier trogen; Prosser et al., 2007). Lacustrine habitat types are categorised et al., 2015). into water and sediments, each marked by differing intrinsic environ- Understanding the processes and mechanisms underlying the mental characteristics (Yang et al., 2016). Habitat differences gen- biodiversity patterns across space (e.g. along environmental gra- erally discriminate bacterial communities (Thompson et al., 2017). dients) and time (e.g. between different seasons) is a main goal Related studies have shown that water and sediments are char- in microbial community ecology (Dini-­Andreote et al., 2015; Xu acterised by different bacterial assemblages (Han et al., 2019; Liu et al., 2020; Zhou et al., 2014). Recent studies have generally ac- et al., 2018). Nevertheless, both the sediment bacterial community cepted that microbial communities are governed by both deter- (SBC) and planktonic bacterial community (PBC) are highly vul- ministic and stochastic assembly processes (Cordovez et al., 2019; nerable to changes in their aquatic environments (Liu et al., 2018; Ladau & Eloe-­Fadrosh, 2019). Based on the phylogenetic turnover Zeglin, 2015; Zhang, Lei, et al., 2019). There is an increasing aware- (i.e. the phylogenetic dissimilarity between two microbial communi- ness of the effects of anthropogenic activities on lake systems; how- ties; Stegen et al., 2013), deterministic assembly processes are parti- ever, knowledge gaps remain in terms of understanding the holistic tioned into homogeneous selection (i.e. selection under similar biotic responses of bacterial communities within water and sediments to and abiotic environmental conditions) and heterogeneous selection changes in environmental conditions, particularly along a given tro- (i.e. selection under variable biotic and abiotic environmental condi- phic gradient (Zeng, Jiao, et al., 2019). tions); stochastic components are embedded in homogenising dis- Previous studies have manipulated nutrient enrichment in persal, dispersal limitation, and ecological drift (Stegen et al., 2013; aquatic microcosms or mesocosms to identify bacterial community see also Supporting Information). However, given that community patterns across trophic gradients (Haukka et al., 2006; Horner-­ assembly is a dynamic and context-­dependent process, we lack a Devine et al., 2003; Ren et al., 2017; Wang et al., 2016). However, clear understanding of the most important assembly mechanisms they may provide limited insight due to differences in microbial as- under differing conditions, such as two unique lake habitats in dif- semblages between manipulated experimental systems and actual ferent seasons (Langenheder & Lindström, 2019). It is therefore vital freshwater ecosystems (Shade et al., 2012). Furthermore, most stud- to fill this knowledge gap and determine how ecological processes ies have focused on either PBC or SBC, but not both. As well, sea- vary between habitat types (for example water and sediments) and sonal variations in bacterial communities are known to occur in lakes seasons. In fact, seasonal variations in aquatic environmental condi- of various trophic states (Dai et al., 2016; Van Der Gucht et al., 2001; tions could trigger a shift in the relative importance of environmen- Zwirglmaier et al., 2015). However, little information has been ob- tal selection (Li et al., 2019; Zeng, Lin, et al., 2019). Additionally, the tained regarding the response of PBC and SBC along a trophic gradi- range of environmental conditions in which a species can exist (i.e. ent of lakes during different seasons. niche breadth) plays a crucial role in influencing the relative impor- The classical ecological productivity–­diversity relationships tance of stochasticity and determinism (Wu et al., 2018); and aquatic (PDRs; Willig, 2011) hold that species diversity varies with pro- bacterial niche breadth varies seasonally (Wang et al., 2020). We ductivity. Among the previously discovered macrobiological PDRs, thus expect that there may be seasonal variability in the community a unimodal PDR is the most commonly reported pattern (Fraser assembly of PBC and SBC. et al., 2015; Mittelbach et al., 2001; Pärtel et al., 2007), despite In this study, we simultaneously collected water and sedi- the generality of this pattern remaining controversial (Cusens ment samples during winter and summer from a series of shallow et al., 2012; Gillman & Wright, 2006; Whittaker & Heegaard, 2003). freshwater lakes with different trophic status (ranging from oligo-­ Accumulating evidence shows that microorganisms also obey the mesotrophic to light eutrophic in winter, and mesotrophic to mid- key macroecological principles (Prosser et al., 2007; Smith, 2007; dle eutrophic in summer) located in Nanjing city, China. We aimed Soininen & Teittinen, 2019); however, unlike other principles in to investigate the patterns of bacterial communities between the macroecology, PDRs have not yet been tested effectively for mi- two lake habitats during different seasons and identify the under- crobial communities (Geyer & Barrett, 2019). Productivity is gener- lying ecological assembly mechanisms. As well, the PDRs were ally related closely to nutrient loading in lakes (Auer & Arndt, 2001; studied to understand the variation in bacterial species diversity Schindler, 1978). Whether species diversity in PBC and SBC produces along the lake productivity gradient. High-­throughput sequencing a unimodal pattern along a lake trophic gradient in different seasons of 16S ribosomal RNA (rRNA) genes and phylogenetic structure JIAO et al. | 3 analysis were applied to determine the bacterial diversity and plexiglass water sampler (LCD-­280S, Shanghai BIO Environmental community composition and quantify the ecological assembly Science and Technology Co., Ltd.) at a depth of 0.5 m below the processes, respectively. In the absence of direct measurements of water surface. The three replicate water samples were mixed to- productivity, lake trophy assessed by the comprehensive trophic gether for making a sample for each station and stored in a sterile state index (TSIc) was used as our surrogate for lake primary pro- plastic bottle (volume 1.5 L). We also obtained three replicate sur- ductivity (Auer & Arndt, 2001; Carlson & Simpson, 1996; Kuehl face sediment cores (0–­1 cm depth) at each station using a Kajak & Troelstrup, 2013). In the present study, we hypothesised that: sediment corer (KC Denmark A/S). The triplicate sediment samples (1) planktonic and sediment bacterial communities exhibit PDRs were then also mixed together as one sample for each station and along the trophic gradient, but the patterns would be seasonally transferred into a 50-­ml round-­bottom sterile tube. A total of 156 variable; and (2) the relative contributions of stochastic and de- environmental samples (78 water samples and 78 sediment samples) terministic processes to the assembly of PBC and SBC along the were obtained over the two seasons. All the collected water and sur- trophic gradient may vary by seasons. face sediment samples were deposited in a portable icebox (around 4°C) until transported to the laboratory (within 12 hr). Afterwards, the water samples were immediately filtered onto 0.22-μ­ m pore size 2 | METHODS polycarbonate membrane filters (47 mm diameter; Millipore) to col- lect planktonic bacterial biomass. Filters were stored at −80°C until 2.1 | Study lakes and sample collection DNA extraction.

We undertook two field sampling campaigns in 2018, one in Januar y (winter, air temperature during sampling: c. −4 to 1°C) and the other 2.2 | Physicochemical analyses of water and in July (summer, air temperature during sampling: 35–­38°C). We sediment samples sampled three stations in each lake (Table S1). Both water and sedi- ment samples were collected from 13 shallow freshwater lakes (or Water temperature, dissolved oxygen, and pH were determined in lake zones; a total of 39 sampling sites) in the Nanjing region of the field using a multiparameter water quality monitor (YSI 6600, China (Figure S1) located in the lower reaches of the Yangtze River. Yellow Springs Instruments). Water depth of each sampling station As reported by our previous study (Jiao et al., 2018), these shal- was measured with a handheld depth sounder (SM-­5, Speedtech low lakes (maximum measured water depth: 6 m; Table S1) were Instruments). Water transparency and turbidity were determined subject to different levels of anthropogenic disturbances (mainly by a Secchi disk (circular plate, 20 cm in diameter) and a portable sewage discharge and tourism activities). Additionally, these in- turbidity meter (WZB-­170, Shanghai INESA Scientific Instrument vestigated lakes are located in the subtropical monsoon climate Co., Ltd.), respectively. The concentrations of total nitrogen (TN), region and their trophic states (ranging from oligo-­mesotrophic nitrate nitrogen (NO3-­N), nitrite nitrogen (NO2-N),­ ammonia nitro- to light eutrophic in winter, and mesotrophic to middle eutrophic gen (NH3-­N), total phosphorus (TP), dissolved organic carbon, and in summer) were detailed in Table S1. Some of the sampled lakes chlorophyll a (Chla) in water samples were measured following the are relatively close geographically (Figure S1). Most of the sampled methods in previous studies (Zeng, Jiao, et al., 2019; Zhao, Cao, lakes (with the exception of Lake Xuanwu and Lake Yueya) are not et al., 2017). connected to rivers (or ditches). Lake Xuanwu is connected to the Prior to analysing sediment physicochemical properties, samples Yangtze River through the Jinchuan River and the Qinhuai River. were vacuum freeze-­dried (Labconco FreeZone 4.5), further ground The southern part of Lake Yueya recharges the Mingyu River via a and homogenised using a mortar and pestle. Measurements of sed- sluice gate. Moreover, there is water exchange between the three iment TN, NO3-­N, NO2-­N, NH3-­N, TP, pH, and loss on ignition (LOI) lake zones within the Lake Xuanwu, while the other 10 lakes are were conducted according to previous publications (Jiao et al., 2020; physically separated from each other (Figure S1). Sampling stations Zeng et al., 2016). In the absence of direct measurement of surface on most lakes (with the exception of Lake Meihua and Lake Yueya) sediment temperature in the field, we here used water temperature are not covered with macrophytes. In addition, sampling stations as a proxy for surface sediment temperature (Liu & Yang, 2020). on some lakes (i.e. Lake Guanlian, Lake Pipa, Lake Xuanwu, and To investigate whether there was a significant nutrient-­exchange Lake Zhongshan3) are free of macrophytes but there are macro- association between surface sediments and overlying water, we phytes in the lake littoral zones (non-­sampling areas). More infor- correlated the concentrations of nitrogen and phosphorus in the mation about these freshwater lakes can be found in the captions sediments with those in the water using Spearman's correlation of Figure S1. Locations (latitude and longitude) recorded with a analysis. portable global positioning system and limnological characteristics The normal distribution of environmental data was tested with a of lake sampling sites were summarised in Table S1. Shapiro–­Wilk test in SPSS (v20.0; IBM Corp). Statistical differences Following the sample collection method as we have reported in the measured environmental factors between seasons within hab- previously (Zeng, Jiao, et al., 2019), at each station, we collected itats were compared with two-­tailed Mann–­Whitney U tests in SPSS. three replicate water samples (0.5 L per replicate sample) using a To investigate the variation in environmental factors between winter 4 | JIAO et al. and summer samples, we used principal component analysis (PCA) 2.5 | Sequencing data processing with package vegan (v2.5-­6; Oksanen et al., 2018) in R (R Core Team, 2019). Here, PCA presented the first two principal components (i.e. There were 20,476,893 raw reads generated after Illumina paired-­ PC1 and PC2). For PCA, environmental data were standardised with end sequencing. All the raw reads were trimmed and filtered using the function scale in the R package vegan. Trimmomatic (v0.36) software (Bolger et al., 2014). These trimmed paired-­end reads were then merged using FLASH (fast length ad- justment of short reads) software (v1.2.11) with a minimum overlap 2.3 | Assessment of lake trophic status of 10 bp and a maximum overlap of 200 bp to obtain the spliced sequences (raw tags; Magoč & Salzberg, 2011). Chimeric sequences To assess the trophic status of the lakes, the classical Carlson were detected and deleted with UCHIME de novo (Edgar et al., 2011) trophic state index (TSI; Carlson, 1977) was calculated from using VSEARCH (v2.9.0; Rognes et al., 2016). Similar sequences three water environmental variables (i.e. Chla (μg/L), Secchi were assigned to operational taxonomic units (OTUs) defined at the disk transparency (SDT, m), and TP (μg/L)). Given that the as- 97% similarity level using the UCLUST algorithm (Edgar, 2010) in sessment of lake trophic status is generally a multivariate com- QIIME (v1.9.1; Caporaso et al., 2010). An approximately maximum-­ prehensive decision-­making process, a comprehensive TSI (TSIc) likelihood phylogenetic tree was built using the FastTree (v2.1.10; is recommended to evaluate the trophic status of the lakes (Cai Price et al., 2009) implemented in QIIME (script make_phylogeny. et al., 2002). The TSIc is the sum of the three weighted TSI values py). Taxonomic classification information of sequences was obtained (i.e. TSI(Chla), TSI(SDT), and TSI(TP)) according to the comprehen- using the SILVA 16S rRNA database (v132; Quast et al., 2013). sive assessment model proposed by Cai et al. (2002). The compu- Archaea, chloroplast, mitochondria, eukaryota, unknown, unclas- tational forms of the equations for these indices were detailed in sified, and singleton sequences (i.e. sequence appearing only one the Supporting Information. In this study, lake trophy assessed by time in the entire data set) were filtered out by running the QIIME TSIc was taken as a proxy for lake primary productivity (Auer & script filter_taxa_from_otu_table.py. To address uneven sequencing Arndt, 2001; Carlson & Simpson, 1996; Kuehl & Troelstrup, 2013). depth across samples, all samples were rarefied to the lowest num- The TSIc values rank the sampled lakes on a numerical scale from ber of sequences per sample (Weiss et al., 2017) using the QIIME 0 to 100, where a larger TSIc value corresponds to a greater lake script multiple_rarefactions_even_depth.py. After that, we obtained trophic level. The calculated lake TSIc was used for both the water a total of 3,334,050 sequences derived from 150 DNA samples and column and surface sediments in the subsequent analyses (Zeng, 8,994 OTUs clustered at a 3% dissimilarity level. the URLs of the Jiao, et al., 2019). software and tools used in this study Are detailed in the Supporting Information.

2.4 | DNA extraction, polymerase chain reaction amplification, and Illumina HiSeq sequencing 2.6 | Statistical analyses of diversity and community composition of bacterial communities Planktonic and sediment bacterial DNA were extracted from water filter membranes (0.22-μ­ m pore size) and 0.25 g (dry weight) of To uncover the taxonomic composition of PBC and SBC at the homogenised sediments, respectively. Detailed methods for DNA phylum level (class level for Proteobacteria) along the lake trophic extraction and purification of PBC and SBC have been described status, stacked bar graphs were drawn using SigmaPlot (v12.5) soft- previously (Zeng, Jiao, et al., 2019). The primers 515F and 806R were ware (SigmaPlot Software Inc.). Bacterial α-diversity­ (i.e. observed chosen for polymerase chain reaction (PCR) amplification of the V4 OTUs and Faith's phylogenetic diversity (Faith's PD, Faith, 1992)) region of the bacterial 16S rRNA gene (Caporaso et al., 2012). The and β-diversity­ (i.e. Bray–­Curtis dissimilarity) were calculated using PCR mixture and thermocycling conditions for PCR amplification the commands alpha_diversity.py and beta_diversity.py in QIIME, were in line with a previous study (Jiao et al., 2018). High-­throughput respectively. Principal coordinate analysis was performed using the sequencing was performed using the strategies of PE250 (paired-­ function cmdscale in the R package vegan to visualise the community end 250 bp) on an Illumina HiSeq platform (Illumina, San Diego, CA, dissimilarity of diverse sample groups (i.e. winter water, winter sedi- USA). Due to the absence of extracted DNA for six of our samples, ment, summer water, and summer sediment). To test for the compo- we obtained successful sequencing for 150 DNA samples (Table S2). sitional differences of bacterial communities between habitats and The obtained raw sequencing data were submitted to the Sequence between seasons within habitats, a permutational multivariate anal- Read Archive database under the NCBI BioProject number ysis of variance (PERMANOVA) was run with 999 permutations using PRJNA552955 (accession numbers SRR9899571 to SRR9899720). the function adonis in the R package vegan (Anderson, 2017). A one-­ It should be noted that the same sequencing data have been used way analysis of variance (ANOVA) and a Turkey HSD post hoc test to investigate the seasonal co-­occurrence patterns and ecologi- in SPSS were used to compare the bacterial community dissimilarity cal stochasticity of PBC and SBC in our previous publication (Jiao within and between habitats. Given that samples were collected in et al., 2020). triplicate for each combination of season and habitat, Bray–­Curtis JIAO et al. | 5 dissimilarity was initially calculated separately for each combination standardised with the function scale and then taken to calculate of samples collected from the same habitat type and season, and av- multivariate Euclidean distances with the function vegdist in the R erage Bray–­Curtis dissimilarities were then calculated from the dis- package vegan. Phylogenetic Mantel correlograms exhibited signifi- similarities for samples belonging to each pairwise combination of cant positive correlations between OTU niche distances and phylo- lakes. In addition, for each season, bacterial community dissimilarity genetic distances across relatively short phylogenetic distances for was also compared between each pair of sites within each lake and different sample groups (p < 0.05, Figure S2), indicating that there visualised using Prism (v8.0.2; GraphPad Software Inc.). were phylogenetic signals in OTU environmental niches. Thus, it was appropriate to carry out the analysis of phylogenetic structure among the closest relatives (Stegen et al., 2013; Wang et al., 2013; 2.7 | Linear and nonlinear mixed effect models Zeng, Jiao, et al., 2019).

Linear and nonlinear regressions with mixed effects were used to investigate the effect of lake trophic status (TSIc) on bacterial α-­ 2.8.2 | Phylogenetic turnover and the diversity metrics and on the relative abundance of bacterial phyla quantification of various ecological processes within each sample group. The more appropriate polynomial fit (e.g. linear and quadratic model fits) was determined on the basis To measure phylogenetic turnover in community composition be- of a lower value of the corrected Akaike information criterion (AIC; tween pairwise samples, the β-­nearest taxon index (βNTI; Stegen Hurvich & Tsai, 1989). AIC can reflect both parsimony and goodness et al., 2013) was calculated using the function bNTI.p with 1,000 of fit of the models. We implemented linear mixed effect models randomisations in the R package ieggr (v2.9; Ning & Escalas, 2017). (LMEM) with the α-­diversity metrics and the relative abundance of The βNTI is a standardised effect size metric that indicates the bacterial phyla as dependent variables, TSIc as a fixed independ- difference between the observed between-­community mean-­ ent variable and study site as a random effect. When a nonlinear nearest-­taxon-­distance and the mean of the null distribution relationship (e.g. unimodal pattern) was observed, a nonlinear in units of standard deviation (of the null distribution; Stegen mixed effect model (NLMEM) was used. We ran both LMEM and et al., 2013). The ecological processes governing bacterial com- NLMEM with the R package nlme (v3.1-­141; Pinheiro et al., 2019). munity assembly include homogeneous selection (i.e. selection The R package MuMIn (v1.43.17; Bartoń, 2019) was used in LMEM under similar biotic and abiotic environmental conditions), het- to determine the variation explained by fixed effect variables. It has erogeneous selection (i.e. selection under variable biotic and abi- been reported that the coefficient of determination (r2) is an inap- otic environmental conditions), homogenising dispersal, dispersal propriate goodness-­of-­fitting test for nonlinear models (Spiess & limitation, and undominated processes (Stegen et al., 2013, 2015, Neumeyer, 2010). We therefore used AIC scores to evaluate the see also Supporting Information). To estimate the relative contri- performance of NLMEM. The significance of the fixed effect vari- butions of various ecological processes, a null-model-­ ­based quan- ables in LMEM and NLMEM was assessed using F-­statistics (function titative framework proposed by Stegen et al. (2013) was used in ANOVA in R). this study, in which null-­model-­based phylogenetic and taxonomic β-diversity­ metrics (i.e. βNTI and the modified Bray–­Curtis-­based

Raup–­Crick, RCBray; Stegen et al., 2013) were calculated. Here, the 2.8 | Analysis of phylogenetic structure null model analysis was run separately for each habitat type and

season. We calculated phylogenetic βNTI and taxonomic RCBray 2.8.1 | Phylogenetic signals in OTU separately for each combination of samples collected from the environmental niches same habitat type and season, and then calculated average β-­ diversity values from the dissimilarities for samples belonging to To determine the appropriate metrics, phylogenetic signals in OTU each pairwise combination of lakes. environmental niches (i.e. closely related OTUs tended to be less The fraction of pairwise comparisons with βNTI values of >+2 dissimilar in their ecological preferences than expected) need to be or <−2 estimates the influence of heterogeneous selection or ho- first evaluated (Stegen et al., 2012). To test for phylogenetic signals mogeneous selection on the assembly of bacterial communities, across a range of phylogenetic distances, the relationship between respectively; βNTI values between −2 and +2 denote the effect ecological niche distances and OTUs’ phylogenetic distances was of stochastic assembly processes structuring the bacterial com- determined using a Mantel correlogram in the R package vegan munities. Subsequently, RCBray (R function RC.p; package ieggr; (Stegen et al., 2012). OTUs’ phylogenetic distances were measured 1,000 randomisations) is used to partition the pairwise compari- as the total phylogenetic branch length between OTUs and gener- sons that are not assigned to selection (i.e. |βNTI| < 2). The frac- ated with the function pdist.p in the R package picante (v1.8; Kembel tion of pairwise comparisons with absolute βNTI values of <2 and et al., 2010). An ecological niche value for each OTU was estimated RCBray values of >+0.95 or <−0.95 suggests that bacterial com- for each measured environmental factor as in Stegen et al. (2012). munity turnover is driven by dispersal limitation or homogenis- OTU niche estimates for all measured environmental factors were ing dispersal, respectively (Stegen et al., 2013). The fraction of 6 | JIAO et al.

pairwise comparisons with |βNTI| < 2 and |RCBray| < 0.95 is es- communities were distinguished significantly between habitat types timated as the impact of undominated processes (Stegen et al., (PERMANOVA: F = 71.01, p < 0.001 in winter; F = 73.70, p < 0.001 2015). A two-­step procedure evaluating the influences of eco- in summer) and between seasons within habitats (PERMANOVA: logical processes is also illustrated graphically in the Supporting F = 42.08, p < 0.001 for PBC; F = 4.711, p < 0.001 for SBC; Information (Figure S3). Figure 1b). The average Bray–­Curtis dissimilarity (mean ± standard deviation) of SBC (winter: 0.476 ± 0.085; summer: 0.487 ± 0.110) was significantly lower than that of PBC (winter: 0.516 ± 0.096; sum- 2.8.3 | Linking environmental variables with mer: 0.555 ± 0.097) in both seasons (one-­way ANOVA with Turkey phylogenetic turnover HSD test, p < 0.05), and there was significantly lower dissimilarity in taxonomic community compositions within habitats than between To explore which measured environmental variables drive the seasonal habitats (winter: 0.843 ± 0.104; summer: 0.899 ± 0.033) in both assembly of bacterial assemblages, a partial Mantel test was used to seasons (p < 0.05; Figure S5a). These results were also supported assess the Spearman's correlation between the Euclidean distance by the comparison of the Bray–­Curtis dissimilarity within each lake, matrix of each environmental variable and βNTI after controlling for except for a few lakes (Figure S5b). Additionally, the compositional other environmental variables. Euclidean distance matrix of each en- dissimilarity of PBC in summer showed a significant downward trend 2 vironmental variable denoted the difference in a single standardised with increasing eutrophication (r = 0.384, p = 0.024), whereas no variable between two samples, which was calculated using R function significant relationship was observed for the other sample groups vegdist. Partial Mantel tests (999 permutations) were performed using (p > 0.05; Figure S6). We further observed that the compositional function mantel.partial in the R package vegan. Prior to doing partial turnover of PBC in summer was most strongly associated with dif- 2 mantel tests, the multicollinearity among the explanatory variables ferences in lake trophic states (r = 0.123, p = 0.002) relative to was checked by calculating variation inflation factors using function other bacterial communities (Figure S7). vif in the R package car (v3.0-­3; Fox & Weisberg, 2018). The variation Along the trophic gradient in freshwater lakes, the taxonomic inflation factors were then used in combination with a backward selec- compositions of PBC and SBC varied markedly at the phylum level tion method to select environmental variables in water and sediments (class level for Proteobacteria; Figure S8). In both seasons, the for partial Mantel test analysis. To evaluate the variations in phyloge- most abundant phylum/class (average relative abundance-­based) netic turnover along the trophic gradient, βNTI was regressed against in PBC and SBC was Betaproteobacteria and Chloroflexi, respec- the Euclidean distance matrix of lake TSIc. tively (Figure S8). Nevertheless, we observed that the relative abundance of planktonic cyanobacteria increased linearly along the trophic gradient in summer (Figure S8; Table S4). In addition to 3 | RESULTS planktonic cyanobacteria, the relative abundance of other bacte- rial phyla in PBC and SBC showed various response patterns along 3.1 | Lacustrine physicochemical properties the lake TSIc in different seasons, such as positive linear, nega- tive linear, unimodal, and U-­shaped patterns (Table S4). Moreover, The measured physicochemical parameters in water (i.e. Chla, dis- we also observed that dominant bacterial phyla/classes (average solved oxygen, dissolved organic carbon, pH, TP, transparency, relative abundance ≥1%) within the winter PBC exhibited weaker turbidity, and water temperature) and sediments (i.e. LOI, NH3-­ correlations with lake trophic status in comparisons with other

N, NO3- N­ , N O 2-­N, and pH) presented significant seasonal differ- sample groups (Table S4). ences (two-­tailed Mann–­Whitney U test, p < 0.05; Table S3). PCA of environmental variables further indicated that both water and sediment samples were segregated by seasons (Figure 1a). The 3.3 | Relationship between bacterial community α-­ concentrations of TN and TP in water were significantly positively diversity and lake trophy across seasons correlated with the TN and TP in sediments (p < 0.01 in both cases; Figure S4). Within our sampled TSIc range, we observed a The results of mixed effect models indicated that relationships be- larger lake trophic gradient in summer (ΔTSIc = 26) than in winter tween lake TSIc and observed OTUs (richness) and Faith's PD of PBC (ΔTSIc = 15; Table S1). and SBC varied between seasons (Figure 2). In winter, SBC had a significant positive correlation of OTUs richness and Faith's PD with TSIc, whereas PBC showed no trend for both OTUs richness 3.2 | Bacterial community dissimilarities between and Faith's PD along the trophic gradient (Figure 2a,b). In summer, sample groups and taxonomic compositions along the PBC had a significant negative quadratic relationship between TSIc trophic gradient and both α-­diversity indices, while OTUs richness and Faith's PD of SBC decreased significantly with increased lake trophy (Figure 2c,d). Principal coordinate analysis based on Bray–­Curtis dissimilarity Similar results were also observed under the scenario where cyano- matrix indicated, in general with a few exceptions, that bacterial bacteria were separated from other bacteria (Figure S9a–­d). JIAO et al. | 7

FIGURE 1 Seasonal differences in environmental properties and bacterial community structure. (a) Principal component analysis (PCA) of environmental variables in water (upper panel) and sediments (lower panel). The environmental variables reduced to two principal components (i.e. PC1 and PC2) explained 53.0% and 59.7% of the total variation in water and sediments, respectively. Chla, chlorophyll a;

DO, dissolved oxygen; DOC, dissolved organic carbon; LOI, loss on ignition; NH3-­N, ammonia nitrogen; NO3-­N, nitrate nitrogen; NO2- N­ , nitrite nitrogen; TN, total nitrogen; TP, total phosphorus; WT, water temperature. WT was used as a proxy of surface sediment temperature. (b) Principal coordinate analysis (PCoA) of bacterial communities for various sample groups (i.e. winter water, winter sediment, summer water, and summer sediment) based on Bray–­Curtis dissimilarity. Samples in each group are colour-­coded based on their comprehensive trophic state index (TSIc); a darker colour indicates a greater lake TSIc. n denotes the number of samples

3.4 | Ecological processes regulating the both seasons, dispersal limitation made a larger contribution to assembly of bacterial communities the assembly of bacterial communities in sediments (16%–­24%) than in water (0%); in contrast, homogenising dispersal governing

Two metrics (i.e. βNTI and RCBray) based on null model analysis community assembly showed a greater proportion in water (9%–­ were used to estimate the relative contributions of various eco- 13%) than in sediments (0%–­1%; Figure 3). logical processes in different seasons. Our results suggested that For bacterial communities within habitats, we observed that heterogeneous selection was dominant in regulating the between-­ the phylogenetic βNTI of summer PBC was significantly cor- habitat community assembly in both seasons (Figure S10). Within related with differences in lake trophic levels (p < 0.05), whereas habitats, environmental selection, which explained more than there was no significant relationship in other sample groups 50% of community turnover in both seasons, was the primary (p > 0.05; Figure S11). To further understand the community as- contributor to the assembly of both PBC and SBC (Figure 3). In sembly by unravelling the environmental drivers of phylogenetic both seasons, homogeneous selection contributed a compara- community turnover, we performed partial Mantel tests. Our re- tively large fraction to the assembly of PBC, whereas SBC was sults indicated that among the measured physicochemical param- assembled mostly by heterogeneous selection (Figure 3). In addi- eters, water temperature and sediment pH were most strongly tion to deterministic processes, stochastic processes also played linked to the phylogenetic βNTI in total PBC and SBC, respec- an important role in influencing the assembly of PBC and SBC. In tively (Table 1). 8 | JIAO et al.

FIGURE 2 The relationships between lake trophic states and bacterial α-­ diversity in winter and summer. (a) winter water; (b) winter sediment; (c) summer water; (d) summer sediment. The α-­diversity was estimated by observed OTUs (richness) and Faith's phylogenetic diversity (Faith's PD). Linear and nonlinear mixed effect models were used with the α-­diversity metrics as dependent variables, lake trophic state index (TSIc) as a fixed independent variable and sampled site as a random effect. The Akaike information criterion (AIC), variation explained by fixed effect (r2), and its statistical significance (p-­value) are shown. Shaded areas indicate 95% confidence intervals. n indicates the number of samples. No pattern suggests that there is no trend between bacterial α-diversity­ and lake TSIc. Note the different scales of the x-­axis among panels

4 | DISCUSSION the two lacustrine habitats in both seasons. As such, our obtained results, coupled with previous findings (Han et al., 2019; Yang 4.1 | Compositional dissimilarity of bacterial et al., 2016; Zhang, Lei, et al., 2019) in other aquatic environments, communities differed between habitats and varied by suggest that there is a large compositional dissimilarity between the seasons PBC and SBC. Although mainly habitat filtering differentiated the assembly of bacterial communities, the compositional dissimilarities There was a close association between the water column and sur- between the PBC and SBC were comparatively greater in summer face sediments with regard to nutrients (especially nitrogen and than in winter. One possible explanation is that the relative impor- phosphorus), as also observed in riverine systems (Zhang, Lei, tance of niche segregation triggered by competitive exclusion of et al., 2019). Despite this potential nutrient exchange, there was bacterial taxa becomes stronger in summer (Ren et al., 2019). Within strong environmental filtering (i.e. the selection of certain taxa by habitats, SBC had significantly lower variation in community com- abiotic environmental conditions, Cadotte & Tucker, 2017) between position than PBC in both seasons, implying that SBC is more stable JIAO et al. | 9

lake trophic state in this study) in natural and in experimental set- tings (Langenheder et al., 2012; Ren et al., 2017). The conflicting results may be ascribed to the divergence in eutrophication condi- tions (Langenheder & Lindström, 2019). Alternatively, the discrepant patterns in bacterial β-­diversity along the trophic gradient yielded across studies may result from the differences in assembly mecha- nisms (Chase & Myers, 2011). Microbial community composition has been shown to vary along the environmental gradient in diverse systems (Thompson et al., 2017). According to the taxonomic analysis, we similarly found that the taxonomic community composition at the phylum level var- ied along the lake TSIc in both seasons. Furthermore, the relative abundance of dominant bacterial phyla showed highly divergent re- sponse patterns (e.g. linear, unimodal, and U-­shaped) along the lake TSIc, similar to the results of the various patterns observed in pre- vious studies along other measured environmental gradients, such as water depth (Wu et al., 2019) and elevation (Wang et al., 2012). Within a particular season, a bacterial phylum exhibited a variable response to lake TSIc depending on its habitat, which was congruent with previous observations (Zeng, Jiao, et al., 2019). This probably resulted from the differences in habitat conditions, and a bacterial phylum may therefore exhibit contrasting ecological traits (Newton et al., 2011). In addition, the relative abundance of dominant bacterial phyla in both PBC and SBC was more likely to produce nonlinear pat- terns (mainly unimodal) along the trophic gradient in summer than in winter. One possible reason for this is the comparatively larger range FIGURE 3 Relative importance of various ecological processes of TSIc in summer than in winter. Previous studies have suggested driving the assembly of bacterial community across different that productivity range can influence the detection of unimodal pat- sample groups. (a) winter water; (b) winter sediment; (c) summer terns, that is, the unimodal relationship is more likely to be detected water; (d) summer sediment. n denotes the number of lakes. Two within a larger range of productivity (Mittelbach et al., 2001; Pärtel null-­model-­based β-­diversity metrics (i.e. βNTI and RC ) were Bray et al., 2007). applied to infer community assembly processes. Note that we calculated βNTI and RCBray separately for each combination of samples collected from the same habitat type and season, and then calculated average β-­diversity values from the dissimilarities for 4.2 | Season influenced PDRs of bacterial samples belonging to each pairwise combination of lakes communities in water and sediments

Our findings were consistent with previous studies in aquatic systems, in community structure than PBC (Zeng, Jiao, et al., 2019). In addi- by indicating that PBC and SBC can exhibit PDRs comparable to those tion, the compositional dissimilarities of bacterial communities were of plants and animals (Horner-­Devine et al., 2003; Korhonen et al., 2011; greater between habitats than within habitats, in concordance with Wang et al., 2016). However, the PDR patterns of both PBC and SBC previous studies on various habitats (Thompson et al., 2017; Wang were season-­dependent. At colder temperatures (winter), we observed et al., 2013), indicating that bacteria are specialised on specific habi- no clear pattern of α-­diversity of PBC along the trophic gradient. This tats (Thompson et al., 2017; Wang et al., 2013). finding may be attributed to the following: first, the limited TSIc range in It has been reported that lake eutrophication resulting from winter may have prevented us from determining whether winter PBC di- nutrient enrichment may act as an ecological filter, which leads to versity could have a significant PDR pattern along a larger trophic gradi- biotic homogenisation (i.e. a decrease in β-­diversity) at local and ent (Mittelbach et al., 2001); second, the marked variation in community regional scales (Donohue et al., 2009; Zhang, Cheng, et al., 2019). compositions in the winter PBC may obscure differences in the distribu- Similarly, in the present study, we observed at the local (within-­lake) tion of α-­diversity between the different trophic levels (Ren et al., 2017); scale that the β-­diversity of summer PBC decreased along the lake last but not least, the high homogenising dispersal and other stochastic trophic gradient, despite no significant relationship in other sample processes regulating the winter bacterioplankton communities possibly groups. In contrast with our findings on summer PBC, other studies conceal the variation trend of α-­diversity along the winter trophic gradi- showed an increase in compositional dissimilarity of bacterioplank- ent. However, along the same winter trophic gradient, a linear increas- ton communities with increased productivity (as characterised by ing PDR pattern was found for SBC, congruent with the positive effect 10 | JIAO et al.

TABLE 1 A partial Mantel test run to quantify the Spearman's to both abiotic (e.g. resource supply) and biotic (e.g. competitive correlations between the Euclidean distance matrix of selected exclusion) drivers. The positive portion of the unimodal PDR curve environmental variables and bacterial community -­nearest taxon β appears linked to a greater resource availability provided by the in- index (βNTI) creasing primary productivity; the negative portion at high produc- βNTI of total PBC tivity may be due to cyanobacteria blooms (fast-­growing nutrient

Environmental variables r p-­Value opportunist species, Vallina et al., 2014). It has been reported that cyanobacteria blooms have significant negative effects on bacterial WT 0.215 0.005 α-­diversity (Su et al., 2017). In the lake sediments, the negative linear DOC 0.140 0.139 response of α-­diversity in the summer SBC to high TSIc may be due DO 0.125 0.088 to two factors: first, increased eutrophication leads to a deteriora- NO - N­ 0.092 0.216 2 tion of the water environment, affecting and reducing benthic bac-

NH3- N­ 0.064 0.285 terial diversity (Smith & Schindler, 2009); and second, as shown by TN −0.004 0.484 Fierer et al. (2007), bacterial phyla can be divided into oligotrophic Chla −0.170 0.933 and copiotrophic categories corresponding to the k-­ and r-­selected pH −0.203 0.989 categories used to depict the ecological attributes of plants and an- TP −0.221 0.972 imals. Competitive exclusion due to the copiotrophic bacterial taxa may foster a negative effect on α-­diversity (Geyer & Barrett, 2019). βNTI of total SBC

Environmental variables r p-­Value

pH 0.372 0.001 4.3 | Deterministic processes TP 0.348 0.001 dominated the seasonal assembly patterns of TN 0.191 0.028 planktonic and sediment bacterial communities along the trophic gradient NH3- N­ 0.118 0.074 WT 0.055 0.149 Our results support a broad consensus that both deterministic NO3- N­ −0.080 0.807 and stochastic processes account for the variation in community Note: Each variable was analysed individually (excluding the effects of composition (Chase & Myers, 2011; Nemergut et al., 2013; Zhou other environmental variables). Water temperature was used as a proxy et al., 2014). However, our phylogenetic null-­model analysis high- of sediment temperature. The significant r values are highlighted with bold format (p < 0.05). lighted that deterministic assembly processes played a more impor- Abbreviations: Chla, chlorophyll a; DO, dissolved oxygen; DOC, tant role than stochastic assembly processes in governing PBC and dissolved organic carbon; NH3-­N, ammonia nitrogen; NO2- N­ , SBC in both seasons. This result matches recent reports in black-­ nitrite nitrogen; NO -­N, nitrate nitrogen; PBC, planktonic bacterial 3 odour urban rivers (Cai et al., 2019) and freshwater lakes (Zeng, community; SBC, sediment bacterial community; TN, total nitrogen; TP, total phosphorus; WT, water temperature. Jiao, et al., 2019), although it contrasts with a larger-­scale study of lakes in western China (Yang et al., 2016). Such a discrepancy may be related to the different geographical scales being investigated of productivity on α-­diversity observed in previous studies (Korhonen (Langenheder & Lindström, 2019) or inconsistent approaches for et al., 2011; Soininen, 2012). This discrepancy between PBC and SBC inferring stochasticity (Zhou & Ning, 2017). In addition, it has been may be resulted from the difference in deterministic assembly processes suggested that productivity is a key environmental factor that in- along the trophic gradient (Zeng, Jiao, et al., 2019). fluences aquatic and terrestrial bacterial community assembly At higher temperatures (summer), PBC showed a downward (Langenheder & Lindström, 2019). We did not find a significant arched (parabolic) pattern of bacterial α-­diversity along the trophic relationship between lake trophic states and the assembly of SBC gradient. This unimodal response has been observed in previous in both seasons, implying that lake trophic status is of relatively studies on both macro-­ and microorganisms (Fraser et al., 2015; minor importance for the seasonal assembly of SBC. In contrast to Horner-­Devine et al., 2003; Smith, 2007). Nutrient enrichment (in- the findings observed in SBC, the influence of lake trophic states creased productivity) initially provides more resources to stimulate on the balance between deterministic and stochastic processes in microbial growth and thus increases the species diversity (Logue controlling the assembly of PBC varied seasonally. Lake trophic et al., 2012); however, beyond a certain threshold, decreased α-­ states were more significantly related to the assembly of PBC in diversity occurs at high nutrient concentrations (Wang et al., 2016). summer than in winter. This is probably due to the warmer tem- The decrease in α-­diversity at higher productivity environments has peratures in summer. It has been demonstrated that warming can been attributed to the increased effect of competitive exclusion significantly affect the assembly processes of bacterioplankton (Grime, 1979). Alternatively, high productivity decreases the hetero- communities in freshwater mesocosms (Ren et al., 2017). geneity of limiting resources, thereby leading to a decrease in diver- Our results suggested that lake trophy was not the most influ- sity (Tilman, 1982). Our unimodal PDR in summer is linked possibly ential factor driving the seasonal assembly patterns of PBC and JIAO et al. | 11

SBC. Partial Mantel tests indicated that water temperature and to the same TSIc in our data analyses. However, primary produc- sediment pH had the strongest correlations with seasonal assem- tivity is likely to be different between the water column and the bly patterns of PBC and SBC, respectively. Water temperature is surface sediments. Possibly due to the cyanobacteria capable of generally associated with many biotic and abiotic factors, such as migrating between the water and the sediments (Bouma-­Gregson nutrient availability, dissolved oxygen, metabolic rate, and biologi- et al., 2017), it has been suggested that Chla concentration exhib- cal (enzymatic) activity (Adams et al., 2010; Yan et al., 2017). Water ited seasonal changes in the water and sediments, that is, greater temperature may thus act as a complex surrogate for multiple asso- Chla concentration for water and sediments was found in sum- ciated biotic and abiotic factors, and thereby be closely correlated mer and winter, respectively (Spears et al., 2007). In this study, with seasonal variations in PBC assembly (Yan et al., 2017; Zeng, the calculation of the TSIc included the water Chla, whereas the Lin, et al., 2019). In the lake sediments, sediment pH overwhelmed sediment Chla is not integrated into the TSIc. Thus, the calculated nitrogen and phosphorus nutrients in regulating the seasonal pat- lake TSIc may have underestimated lake productivity, especially terns of SBC assembly. It is not a surprise that sediment pH dom- winter lake productivity. inantly affects the seasonal changes in SBC assembly because it Our results indicated that winter productivity exhibited a rel- has been suggested that sediment pH plays a crucial role in influ- atively weak association with the diversity of PBC. We speculate encing the diversity, community composition, and co-occurrence­ that this is also possibly related to the limitations of our field sam- networks of SBC in different seasons (Jiao et al., 2020; Zeng, Jiao, pling design. We sampled surface water (0.5 m depth) rather than et al., 2019; Zhang et al., 2017). Collectively, dissimilar environ- deeper water to measure Chla concentration and then calculate mental drivers of seasonal variations in bacterial community as- lake TSIc. It has been suggested that samples from a bit deeper lay- sembly observed between water and sediments may reflect the ers (e.g. from the subsurface) harbour high productivity, resulting divergent ecological niches occupied by bacterial taxa in different in a deep Chla maximum during winter (Queimaliños et al., 1999). habitats (Zeng, Jiao, et al., 2019; Zhao, Xu, et al., 2017). Here, we In addition, the gradient of our sampled sites does not fully repre- cannot ignore the importance of the unmeasured environmental sent a trophic gradient to its greatest possible extent due to the factors (e.g. solar irradiation) for the seasonal patterns of bacte- absence of oligotrophic and hypereutrophic lakes in our studied rial assemblages. Solar irradiation plays a pivotal role in controlling region (Qin et al., 2012). As such, the full potential response profile the fate of nutrients and influencing the growth of phytoplankton of bacterial diversity requires extending this gradient at both ex- and macrophytes, and therefore may be a strong seasonal driver tremes. Furthermore, many theoretical and empirical approaches of bacterial community assembly in aquatic ecosystems (Chróst & suggest that PDRs show scale-­dependence in both macrobiology Faust, 1999; Ruiz-­González et al., 2013). (Chase & Leibold, 2002; Whittaker & Heegaard, 2003) and micro- biology (Korhonen et al., 2011; Smith, 2007). Thus, whether our obtained results can be generalised to a larger scale remains to be 4.4 | Limitations of our field study determined.

In the present study, 39 sampling stations allowed us to obtain trophic spectra, ranging from oligo-­mesotrophic to light eutrophic in 5 | CONCLUSIONS winter, and mesotrophic to middle eutrophic in summer, which were quantified by calculating the TSIc values of each sampling station Our sampling of multiple freshwater lakes captured the nonlin- in 13 freshwater lakes (or lake zones). This is in accordance with an earities of ecological response to lake trophy and uncovered the earlier study that investigated the patterns in phytoplankton assem- underlying mechanisms structuring bacterial communities in dif- blages along a trophic spectrum provided by 19 sampling stations ferent seasons. Our study indicated that the linear and unimodal in four reservoirs (Caputo et al., 2008). Furthermore, it should be α-diversity­ patterns of lacustrine microbes detected along the explicitly stated that since a trophic classification of the lakes de- trophic gradient strongly resembled those observed for mac- pending on season may lead to discrepant results, this study did not roscopic taxa; however, the relationships between lake trophic directly compare the trophic classifications from various lakes ob- states and α-­diversity of both PBC and SBC varied between sea- tained from different seasons. sons. Moreover, the relative abundance of dominant bacterial Lake TSIc used as a proxy for primary productivity has some phyla in PBC and SBC exhibited highly variable response patterns limitations in this study. First, phytoplankton and macrophytes are (e.g. monotonic, unimodal, and U-­shaped) along the trophic gradi- the main primary producers in shallow freshwater lakes. However, ent in different seasons. PBC and SBC in neither season were as- the classical assessment of the lake trophic state used only the sembled randomly but were primarily controlled by environmental phytoplankton biomass (measured as phytoplankton Chla) and selection; however, the key environmental drivers of seasonal as- excluded the macrophyte biomass in the water column (Hu sembly patterns differed between PBC and SBC. Our results high- et al., 2014). Several lakes are covered with small amounts of mac- light the variable responses of the bacterial communities within rophytes in this study, but this kind of productivity is not picked the distinct lacustrine habitats (water vs. sediments) and between up by the TSIc. Second, both water and sediments are assigned seasons. This variability is related primarily to the differences in 12 | JIAO et al. environmental conditions and the changes in the relative contribu- Cai, Q. H., Liu, J. K., & King, L. (2002). A comprehensive model for assess- ing lake eutrophication. The Journal of Applied Ecology, 13, 1674–­1678. tions of ecological assembly processes. Cai, W., Li, Y. I., Shen, Y., Wang, C., Wang, P., Wang, L., … Zhang, W. (2019). Vertical distribution and assemblages of microbial communi- ACKNOWLEDGMENTS ties and their potential effects on sulfur metabolism in a black-­odor We are especially grateful to Yuqing Lin, Yu Chu, and Honghao urban river. Journal of Environmental Management, 235, 368–­376. Shi for their assistance in the sample collection and the measure- https://doi.org/10.1016/j.jenvm​an.2019.01.078 Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. ment of physicochemical parameters. This work was supported D., Costello, E. K., … Knight, R. (2010). QIIME allows analysis of high-­ by the National Key R&D Program of China (2016YFC0402710); throughput community sequencing data. Nature Methods, 7, 335–­ the National Natural Science Foundation of China (41671078 and 336. https://doi.org/10.1038/nmeth.f.303 41871096); the Natural Science Foundation of Jiangsu Province, Caporaso, J. G., Lauber, C. L., Walters, W. A., Berg-­Lyons, D., Huntley, J., Fierer, N., … Knight, R. (2012). Ultra-­high-­throughput microbial com- China (BK20181311); the Fundamental Research Funds for the munity analysis on the Illumina HiSeq and MiSeq platforms. The ISME Central Universities (B210202009 and B200203051); and the Journal, 6, 1621–­1624. https://doi.org/10.1038/ismej.2012.8 Postgraduate Research & Practice Innovation Program of Jiangsu Caputo, L., Naselli-­Flores, L., Ordonez, J., Ordonez, J., & Armengol, J. Province (KYCX20_0465). (2008). Phytoplankton distribution along trophic gradients within and among reservoirs in Catalonia (Spain). Freshwater Biology, 53, 2543–­2556. https://doi.org/10.1111/j.1365-­2427.2008.02082.x AUTHOR CONTRIBUTIONS Carlson, R. E. (1977). A trophic state index for lakes. Limnology and D.Z. and Z.Y. designed the experiments. C.J. carried out the field Oceanography, 22, 361–­369. https://doi.org/10.4319/lo.1977.22.2. sampling and performed the laboratory experiments. C.J. and R.H. 0361 Carlson, R. E., & Simpson, J. (1996). A coordinator’s guide to volunteer lake analysed the data and visualised the figures. C.J. and D.Z. wrote monitoring methods. Madison, WI: North American Lake Management the manuscript. C.J. and F.H. contributed to the discussion of the Society. manuscript. D.Z. revised the manuscript. D.Z. and Z.Y. contributed Chase, J. M., & Leibold, M. (2002). Spatial scale dictates the productivity-­ reagents and materials. biodiversity relationship. Nature, 416, 427–­430. https://doi.org/10. 1038/416427a Chase, J. M., & Myers, J. A. (2011). Disentangling the importance of eco- DATA AVAILABILITY STATEMENT logical niches from stochastic processes across scales. Philosophical The raw sequencing data have been submitted to the NCBI (National Transactions of the Royal Society B: Biological Sciences, 366, 2351–­ Center for Biotechnology Information) Sequence Read Archive 2363. https://doi.org/10.1098/rstb.2011.0063 (SRA) database under the BioProject number PRJNA552955 (acces- Chróst, R. J., & Faust, M. A. (1999). Consequences of solar radiation on bacterial secondary production and growth rates in subtropi- sion numbers SRR9899571 to SRR9899720). cal coastal water (Atlantic Coral Reef off Belize, Central America). Aquatic Microbial Ecology, 20, 39–­48. https://doi.org/10.3354/ ORCID ame020039​ Dayong Zhao https://orcid.org/0000-0003-1149-4491 Cordovez, V., Dini-­Andreote, F., Carrión, V. J., & Raaijmakers, J. M. (2019). Ecology and evolution of plant microbiomes. Annual Review of Microbiology, 73, 69–88.­ https://doi.org/10.1146/annur​ev-­micro​ REFERENCES - 0­ 9 0 8 1 7​ - 0­ 6 2 5 2 4 Adams, H. E., Crump, B. C., & Kling, G. W. (2010). Temperature controls Cusens, J., Wright, S. D., McBride, P. D., & Gillman, L. N. (2012). What on aquatic bacterial production and community dynamics in arc- is the form of the productivity-­animal-­species-­richness relationship? tic lakes and streams. Environmental Microbiology, 12, 1319–­1333. A critical review and meta-­analysis. Ecology, 93, 2241–­2252. https:// https://doi.org/10.1111/j.1462-­2920.2010.02176.x doi.org/10.1890/11-­1861.1 Anderson, M. J. (2017). Permutational Multivariate Analysis of Variance Dai, Y., Yang, Y. Y., Wu, Z., Feng, Q. Y., Xie, S. G., & Liu, Y. (2016). (PERMANOVA). Wiley StatsRef: Statistics Reference Online, Spatiotemporal variation of planktonic and sediment bacterial as- Auckland, New Zealand. https://doi.org/10.1002/97811​18445​112. semblages in two plateau freshwater lakes at different trophic status. s t a t 0 7​ 8 4 1 Applied Microbiology and Biotechnology, 100, 4161–­4175. https://doi. Auer, B., & Arndt, H. (2001). Taxonomic composition and biomass of het- org/10.1007/s0025 3​ - 0­ 1 5 - 7­ 2 5 3 - 2­ erotrophic flagellates in relation to lake trophy and season. Freshwater Dini-­Andreote, F., Stegen, J. C., van Elsas, J. D., & Salles, J. F. (2015). Biology, 46, 959–­972. https://doi.org/10.1046/j.1365-­2427.2001. Disentangling mechanisms that mediate the balance between stochas- 00730.x tic and deterministic processes in microbial succession. Proceedings of Bartoń, K. (2019). Package MuMIn (Multi-­Model Inference). Version the National Academy of Sciences of the United States of America, 112, 1.43.17. E1326–­E1332. https://doi.org/10.1073/pnas.14142​61112 Bolger, A. M., Lohse, M., & Usadel, B. (2014). Trimmomatic: A flexible Donohue, I., Jackson, A. L., Pusch, M. T., & Irvine, K. (2009). Nutrient en- trimmer for Illumina sequence data. Bioinformatics, 30, 2114–­2120. richment homogenizes lake benthic assemblages at local and regional https://doi.org/10.1093/bioin​forma​tics/btu170 scales. Ecology, 90, 3470–­3477. https://doi.org/10.1890/09-­0415.1 Bouma-­Gregson, K., Power, M. E., & Bormans, M. (2017). Rise and fall Edgar, R. C. (2010). Search and clustering orders of magnitude faster than of toxic benthic freshwater cyanobacteria (Anabaena spp.) in the Eel BLAST. Bioinformatics, 26, 2460–­2461. https://doi.org/10.1093/ River: Buoyancy and dispersal. Harmful Algae, 66, 79–87.­ https://doi. bioin​forma​tics/btq461 org/10.1016/j.hal.2017.05.007 Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C., & Knight, R. (2011). Cadotte, M. W., & Tucker, C. M. (2017). Should environmental filtering be UCHIME improves sensitivity and speed of chimera detection. abandoned? Trends in Ecology & Evolution, 32, 429–­437. https://doi. Bioinformatics, 27, 2194–­2200. https://doi.org/10.1093/bioin​forma​ org/10.1016/j.tree.2017.03.004 tics/btr381 JIAO et al. | 13

Faith, D. P. (1992). Conservation evaluation and phylogenetic diver- bacterial metacommunity. The ISME Journal, 6, 1107–­1114. https:// sity. Biological Conservation, 61, 1–­10. https://doi.org/10.1016/ doi.org/10.1038/ismej.2011.177 0 0 0 6 - 3207(92)91201­ -​ 3­ Langenheder, S., & Lindström, E. S. (2019). Factors influenc- Fierer, N., Bradford, M. A., & Jackson, R. B. (2007). Toward an ecological ing aquatic and terrestrial bacterial community assembly. classification of soil bacteria. Ecology, 88, 1354–­1364. https://doi. Environmental Microbiology Reports, 11, 306–­315. https://doi. org/10.1890/05-­1839 org/10.1111/1758-­2229.12731 Fox, J., & Weisberg, S. (2018). An R companion to applied regression (3rd Li, Y., Gao, Y., Zhang, W., Wang, C., Wang, P., Niu, L., & Wu, H. (2019). ed.). Sage. Homogeneous selection dominates the microbial community as- Fraser, L. H., Pither, J., Jentsch, A., Sternberg, M., Zobel, M., Askarizadeh, sembly in the sediment of the three gorges reservoir. Science of D., … Zupo, T. (2015). Worldwide evidence of a unimodal relationship the Total Environment, 690, 50–­60. https://doi.org/10.1016/j.scito​ between productivity and plant species richness. Science, 349, 302–­ tenv.2019.07.014 305. https://doi.org/10.1126/scien​ce.aab3916 Liu, T., & Yang, H. (2020). Comparative analysis of the total and active Geyer, K. M., & Barrett, J. E. (2019). Unimodal productivity-­diversity bacterial communities in the surface sediment of Lake Taihu. FEMS relationships among bacterial communities in a simple polar soil Microbiology Ecology, 96, fiaa059. https://doi.org/10.1093/femse​c/ ecosystem. Environmental Microbiology, 21, 2523–­2532. https://doi. fiaa059 org/10.1111/1462-­2920.14639 Liu, T., Zhang, A. N., Wang, J., Liu, S., Jiang, X., Dang, C., … Ni, J. (2018). Gillman, L. N., & Wright, S. D. (2006). The influence of productivity on Integrated biogeography of planktonic and sedimentary bacterial the species richness of plants: A critical assessment. Ecology, 87, communities in the Yangtze River. Microbiome, 6, 16. https://doi. 1234–­1243. org/10.1186/s4016 8​ - 0­ 1 7 - 0­ 3 8 8 - x­ Grime, J. P. (1979). Plant strategies and vegetation processes. Wiley. Logue, J. B., Langenheder, S., Andersson, A. F., Bertilsson, S., Drakare, S., Han, M., Dsouza, M., Zhou, C., Li, H., Zhang, J., Chen, C., … Ning, K. (2019). Lanzen, A., & Lindstrom, E. S. (2012). Freshwater bacterioplankton Agricultural risk factors influence microbial ecology in Honghu richness in oligotrophic lakes depends on nutrient availability rather Lake. Genomics, Proteomics & Bioinformatics, 17, 76–­90. https://doi. than on species-­area relationships. The ISME Journal, 6, 1127–­1136. org/10.1016/j.gpb.2018.04.008 https://doi.org/10.1038/ismej.2011.184 Haukka, K., Kolmonen, E., Hyder, R., Hietala, J., Vakkilainen, K., Kairesalo, Magoč, T., & Salzberg, S. L. (2011). FLASH: Fast length adjustment of T., … Sivonen, K. (2006). Effect of nutrient loading on bacterioplank- short reads to improve genome assemblies. Bioinformatics, 27, 2957–­ ton community composition in lake mesocosms. Microbial Ecology, 51, 2963. https://doi.org/10.1093/bioin​forma​tics/btr507 1 3 7 – 146.­ https://doi.org/10.1007/s0024 8​ - 0­ 0 5 - 0­ 0 4 9 - 7­ Mittelbach, G. G., Steiner, C. F., Scheiner, S. M., Gross, K. L., Reynolds, Horner-­Devine, M. C., Leibold, M. A., Smith, V. H., & Bohannan, B. H. L., Waide, R. B., … Gough, L. (2001). What is the observed re- J. M. (2003). Bacterial diversity patterns along a gradient of pri- lationship between species richness and productivity? Ecology, 82, mary productivity. Ecology Letters, 6, 613–­622. https://doi. 2381–­2396. org/10.1046/j.1461-­0248.2003.00472.x Nemergut, D. R., Schmidt, S. K., Fukami, T., O'Neill, S. P., Bilinski, T. M., Hu, Z., Guo, L., Liu, T., Chuai, X., Chen, Q., Shi, F., … Yang, L. (2014). Stanish, L. F., … Ferrenberg, S. (2013). Patterns and processes of Uniformisation of phytoplankton chlorophyll a and macrophyte microbial community assembly. Microbiology and Molecular Biology biomass to characterise the potential trophic state of shallow Reviews, 77, 342–­356. https://doi.org/10.1128/MMBR.00051​-­12 lakes. Ecological Indicators, 37, 1–­9. https://doi.org/10.1016/j.ecoli​ Newton, R. J., Jones, S. E., Eiler, A., McMahon, K. D., & Bertilsson, S. nd.2013.10.007 (2011). A guide to the natural history of freshwater lake bacteria. Hurvich, C. M., & Tsai, C. L. (1989). Regression and time series model Microbiology and Molecular Biology Reviews, 75, 14–­49. https://doi. selection in small samples. Biometrika, 76, 297–­307. https://doi. org/10.1128/MMBR.00028​-­10 org/10.1093/biome​t/76.2.297 Ning, D., & Escalas, A. (2017). Ieggr: IEG and CCL General Functions R Jiao, C., Zhao, D., Huang, R., Cao, X., Zeng, J., Lin, Y., & Zhao, W. (2018). Package. Version 2.9. Abundant and rare bacterioplankton in freshwater lakes subjected to Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn, different levels of tourism disturbances. Water, 10, 1075. https://doi. D., … Wagner, H. (2018). Vegan: Community Ecology Package. org/10.3390/w1008​1075 Ordination methods, diversity analysis and other functions for com- Jiao, C., Zhao, D., Zeng, J., Guo, L., & Yu, Z. (2020). Disentangling the sea- munity and vegetation ecologists. Version 2.5-­3. sonal co-­occurrence patterns and ecological stochasticity of plank- Pärtel, M., Laanisto, L., & Zobel, M. (2007). Contrasting plant productivity-­ tonic and benthic bacterial communities within multiple lakes. Science diversity relationships across latitude: The role of evolutionary his- of the Total Environment, 740, 140010. https://doi.org/10.1016/j. tory. Ecology, 88, 1091–­1097. https://doi.org/10.1890/06-­0997 scito​tenv.2020.140010 Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., & R Core Team. (2019). Kembel, S. W., Cowan, P. D., Helmus, M. R., Cornwell, W. K., Morlon, H., nlme: Linear and Nonlinear Mixed Effects Models. R package version Ackerly, D. D., … Webb, C. O. (2010). Picante: R tools for integrating 3 . 1 - 141.­ https://CRAN.R- p­ r o j e ct.org/packa​ g​ e =nlme phylogenies and ecology. Bioinformatics, 26, 1463–­1464. https://doi. Price, M. N., Dehal, P. S., & Arkin, A. P. (2009). FastTree: Computing org/10.1093/bioin​forma​tics/btq166 large minimum-­evolution trees with profiles instead of a distance Korhonen, J. J., Wang, J. J., & Soininen, J. (2011). Productivity-­diversity matrix. Molecular Biology and Evolution, 26, 1641–­1650. https://doi. relationships in lake plankton communities. PLoS One, 6, e22041. org/10.1093/molbe​v/msp077 https://doi.org/10.1371/journ​al.pone.0022041 Prosser, J. I., Bohannan, B. J. M., Curtis, T. P., Ellis, R. J., Firestone, M. Kuehl, L. C., & Troelstrup, N. H. (2013). Relationships between net pri- K., Freckleton, R. P., … Young, J. P. W. (2007). The role of ecological mary production, water transparency, chlorophyll a, and total phos- theory in microbial ecology. Nature Reviews Microbiology, 5, 384–­392. phorus in Oak lake, Brookings County, South Dakota. Proceedings of https://doi.org/10.1038/nrmicro1643​ the South Dakota Academy of Science, 92, 67–­78. Qin, B. Q., Gao, G., Zhu, G. W., Zhang, Y. L., Song, Y. Z., Tang, X. M., … Ladau, J., & Eloe-­Fadrosh, E. A. (2019). Spatial, temporal, and phyloge- Deng, J. M. (2012). Lake eutrophication and its ecosystem response. netic scales of microbial ecology. Trends in Microbiology, 27, 662–­669. Chinese Science Bulletin, 58, 961–­970. https://doi.org/10.1007/s1143​ https://doi.org/10.1016/j.tim.2019.03.003 4 - 0­ 1 2 - 5­ 5 6 0 - x­ Langenheder, S., Berga, M., Östman, Ö., & Székely, A. J. (2012). Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., … Temporal variation of β-­diversity and assembly mechanisms in a Glöckner, F. O. (2013). The SILVA ribosomal RNA gene database 14 | JIAO et al.

project: Improved data processing and web-­based tools. Nucleic Acids microbial communities. The ISME Journal, 6, 1653–­1664. https://doi. Research, 41, D590–­D596. https://doi.org/10.1093/nar/gks1219 org/10.1038/ismej.2012.22 Queimaliños, C. P., Modenutti, B. E., & Balseiro, E. G. (1999). Symbiotic Su, X., Steinman, A. D., Tang, X. M., Xue, Q. J., Zhao, Y. Y., & Xie, L. Q. association of the ciliate Ophrydium naumanni with Chlorella causing (2017). Response of bacterial communities to cyanobacterial harm- a deep chlorophyll a maximum in an oligotrophic South Andes lake. ful algal blooms in Lake Taihu, China. Harmful Algae, 68, 168–­177. Journal of Plankton Research, 21, 167–­178. https://doi.org/10.1093/ https://doi.org/10.1016/j.hal.2017.08.007 plank​t/21.1.167 Thompson, L. R., Sanders, J. G., McDonald, D., Amir, A., Ladau, J., Locey, R Core Team. (2019). R: A language and environment for statistical comput- K. J., … Knight, R. (2017). A communal catalogue reveals Earth’s ing. Vienna, Austria: R Foundation for Statistical Computing. multiscale microbial diversity. Nature, 551, 457–­463. https://doi. Ren, L., He, D., Chen, Z., Jeppesen, E., Lauridsen, T. L., Søndergaard, M., org/10.1038/natur​e24621 … Wu, Q. L. (2017). Warming and nutrient enrichment in combination Tilman, D. (1982). Resource competition and community structure. increase stochasticity and beta diversity of bacterioplankton assem- Princeton University Press. blages across freshwater mesocosms. The ISME Journal, 11, 613–­625. Vallina, S. M., Follows, M. J., Dutkiewicz, S., Montoya, J. M., Cermeno, https://doi.org/10.1038/ismej.2016.159 P., & Loreau, M. (2014). Global relationship between phytoplankton Ren, L., Song, X., He, D., Wang, J., Tan, M., Xia, X., … Wu, Q. L. (2019). diversity and productivity in the ocean. Nature Communications, 5, Bacterioplankton metacommunity processes across thermal gradi- 4299. https://doi.org/10.1038/ncomm​s5299 ents: Weaker species sorting but stronger niche segregation in sum- Van Der Gucht, K., Sabbe, K., De Meester, L., Vloemans, N., Zwart, G., mer than in winter in a subtropical bay. Applied and Environmental Gillis, M., & Vyverman, W. (2001). Contrasting bacterioplankton Microbiology, 85, e02088-­18. https://doi.org/10.1128/AEM.02088​ community composition and seasonal dynamics in two neighbouring - 1­ 8 hypertrophic freshwater lakes. Environmental Microbiology, 3, 680–­ Rognes, T., Flouri, T., Nichols, B., Quince, C., & Mahé, F. (2016). VSEARCH: 690. https://doi.org/10.1046/j.1462-­2920.2001.00242.x A versatile open source tool for metagenomics. PeerJ, 4, e2584. Wang, J., Pan, F., Soininen, J., Heino, J., & Shen, J. (2016). Nutrient enrich- https://doi.org/10.7717/peerj.2584 ment modifies temperature-­biodiversity relationships in large-­scale Ruiz-­González, C., Simó, R., Sommaruga, R., & Gasol, J. M. (2013). Away field experiments. Nature Communications, 7, 13960. https://doi. from darkness: A review on the effects of solar radiation on hetero- org/10.1038/ncomm​s13960 trophic bacterioplankton activity. Frontiers in Microbiology, 4, 131. Wang, J., Shen, J. I., Wu, Y., Tu, C., Soininen, J., Stegen, J. C., … Zhang, E. https://doi.org/10.3389/fmicb.2013.00131 (2013). Phylogenetic beta diversity in bacterial assemblages across Schindler, D. W. (1978). Factors regulating phytoplankton produc- ecosystems: Deterministic versus stochastic processes. The ISME tion and standing crop in the world's freshwaters. Limnology and Journal, 7, 1310–­1321. https://doi.org/10.1038/ismej.2013.30 Oceanography, 23, 478–­486. https://doi.org/10.4319/lo.1978.23.3. Wang, J., Soininen, J., He, J., & Shen, J. (2012). Phylogenetic clustering 0478 increases with elevation for microbes. Environmental Microbiology Shade, A., Read, J. S., Youngblut, N. D., Fierer, N., Knight, R., Kratz, T. K., Reports, 4, 217–­226. https://doi.org/10.1111/j.1758-­2229.2011. … McMahon, K. D. (2012). Lake microbial communities are resilient 00324.x after a whole-­ecosystem disturbance. The ISME Journal, 6, 2153–­ Wang, K., Yan, H., Peng, X., Hu, H., Zhang, H., Hou, D., … Zhang, D. (2020). 2167. https://doi.org/10.1038/ismej.2012.56 Community assembly of bacteria and archaea in coastal waters gov- Smith, V. H. (2007). Microbial diversity–­productivity relationships in erned by contrasting mechanisms: A seasonal perspective. Molecular aquatic ecosystems. FEMS Microbiology Ecology, 62, 181–­186. https:// Ecology, 29, 3762–­3776. https://doi.org/10.1111/mec.15600 doi.org/10.1111/j.1574-­6941.2007.00381.x Weiss, S., Xu, Z. Z., Peddada, S., Amir, A., Bittinger, K., Gonzalez, A., … Smith, V. H., & Schindler, D. W. (2009). Eutrophication science: Where do Knight, R. (2017). Normalization and microbial differential abun- we go from here? Trends in Ecology & Evolution, 24, 201–­207. https:// dance strategies depend upon data characteristics. Microbiome, 5, doi.org/10.1016/j.tree.2008.11.009 27. https://doi.org/10.1186/s4016 8​ - 0­ 1 7 - 0­ 2 3 7 - y­ Soininen, J. (2012). Macroecology of unicellular organisms–­patterns and Whittaker, R. J., & Heegaard, E. (2003). What is the observed relationship processes. Environmental Microbiology Reports, 4, 10–­22. https://doi. between species richness and productivity? Comment. Ecology, 84, org/10.1111/j.1758-­2229.2011.00308.x 3384–­3390. https://doi.org/10.1890/02-­3128 Soininen, J., & Teittinen, A. (2019). Fifteen important questions in the Willig, M. R. (2011). Biodiversity and productivity. Science, 333, 1709–­ spatial ecology of diatoms. Freshwater Biology, 64, 2071–­2083. 1710. https://doi.org/10.1126/scien​ce.1212453 https://doi.org/10.1111/fwb.13384 Wu, K., Zhao, W., Wang, Q., Yang, X., Zhu, L., Shen, J. I., … Wang, J. Spears, B. M., Carvalho, L., Perkins, R., Kirika, A., & Paterson, D. M. (2019). The relative abundance of benthic bacterial Phyla along a (2007). Sediment phosphorus cycling in a large shallow lake: Spatio-­ water-depth­ gradient in a plateau Lake: Physical, chemical, and biotic temporal variation in phosphorus pools and release. Hydrobiologia, drivers. Frontiers in Microbiology, 10, 1521. https://doi.org/10.3389/ 584, 37–­48. https://doi.org/10.1007/s1075​0-007-­ 0610-­ ­0 fmicb.2019.01521 Spiess, A., & Neumeyer, N. (2010). An evaluation of R2 as an inadequate Wu, W., Lu, H. P., Sastri, A., Yeh, Y. C., Gong, G. C., Chou, W. C., & measure for nonlinear models in pharmacological and biochemical Hsieh, C. H. (2018). Contrasting the relative importance of species research: A Monte Carlo approach. BMC Pharmacology, 10, 6. https:// sorting and dispersal limitation in shaping marine bacterial versus doi.org/10.1186/1471- 2­ 2 1 0 - 1­ 0 - 6­ protist communities. The ISME Journal, 12, 485–­494. https://doi. Stegen, J. C., Lin, X., Fredrickson, J. K., Chen, X., Kennedy, D. W., Murray, org/10.1038/ismej.2017.183 C. J., … Konopka, A. (2013). Quantifying community assembly pro- Xu, X., Wang, N., Lipson, D., Sinsabaugh, R., Schimel, J., He, L., … cesses and identifying features that impose them. The ISME Journal, Tedersoo, L. (2020). Microbial macroecology: In search of mecha- 7, 2069–­2079. https://doi.org/10.1038/ismej.2013.93 nisms governing microbial biogeographic patterns. Global Ecology and Stegen, J. C., Lin, X. J., Fredrickson, J. K., & Konopka, A. E. (2015). Biogeography, 29, 1870–­1886. https://doi.org/10.1111/geb.13162 Estimating and mapping ecological processes influencing microbial Yan, Q., Stegen, J. C., Yu, Y., Deng, Y. E., Li, X., Wu, S., … Zhou, J. (2017). community assembly. Frontiers in Microbiology, 6, 370. https://doi. Nearly a decade-­long repeatable seasonal diversity patterns of bac- org/10.3389/fmicb.2015.00370. terioplankton communities in the eutrophic Lake Donghu (Wuhan, Stegen, J. C., Lin, X. J., Konopka, A. E., & Fredrickson, J. K. (2012). China). Molecular Ecology, 26, 3839–­3850. https://doi.org/10.1111/ Stochastic and deterministic assembly processes in subsurface mec.14151 JIAO et al. | 15

Yang, J., Jiang, H. C., Wu, G., Liu, W., & Zhang, G. J. (2016). Distinct microbial community between different nutrient loading lake zones factors shape aquatic and sedimentary microbial community struc- in Taihu Lake. Applied Microbiology and Biotechnology, 101, 1–­11. tures in the lakes of western China. Frontiers in Microbiology, 7, 1782. https://doi.org/10.1007/s0025 3​ - 0­ 1 7 - 8­ 3 2 7 - 0­ https://doi.org/10.3389/fmicb.2016.01782 Zhao, D., Xu, H., Zeng, J., Cao, X., Huang, R., Shen, F., & Yu, Z. (2017). Zeglin, L. H. (2015). Stream microbial diversity in response to environ- Community composition and assembly processes of the free-­living mental changes: Review and synthesis of existing research. Frontiers and particle-­attached bacteria in Taihu Lake. FEMS Microbiology in Microbiology, 6, 454. https://doi.org/10.3389/fmicb.2015.00454 Ecology, 93, fix062. https://doi.org/10.1093/femse​c/fix062 Zeng, J., Jiao, C., Zhao, D., Xu, H., Huang, R., Cao, X., … Wu, Q. L. (2019). Zhou, J., Deng, Y. E., Zhang, P., Xue, K., Liang, Y., Van Nostrand, J. D., … Patterns and assembly processes of planktonic and sedimentary bacterial Arkin, A. P. (2014). Stochasticity, succession, and environmental per- community differ along a trophic gradient in freshwater lakes. Ecological turbations in a fluidic ecosystem. Proceedings of the National Academy Indicators, 106, 105491. https://doi.org/10.1016/j.ecoli​nd.2019.105491 of Sciences of the United States of America, 111, E836–­E845. https:// Zeng, J., Lin, Y., Zhao, D., Huang, R., Xu, H., & Jiao, C. (2019). Seasonality doi.org/10.1073/pnas.13240​44111 overwhelms aquacultural activity in determining the composition and Zhou, J. Z., & Ning, D. L. (2017). Stochastic community assembly: Does assembly of the bacterial community in Lake Taihu, China. Science of it matter in microbial ecology? Microbiology and Molecular Biology the Total Environment, 683, 427–­435. https://doi.org/10.1016/j.scito​ Reviews, 81, e00002-­17. https://doi.org/10.1128/MMBR.00002​-­17 tenv.2019.05.256 Zwirglmaier, K., Keiz, K., Engel, M., Geist, J., & Raeder, U. (2015). Zeng, J., Zhao, D., Li, H., Huang, R., Wang, J., & Wu, Q. L. (2016). A mono- Seasonal and spatial patterns of microbial diversity along a trophic tonically declining elevational pattern of bacterial diversity in fresh- gradient in the interconnected lakes of the Osterseen Lake District, water lake sediments. Environmental Microbiology, 18, 5175–­5186. Bavaria. Frontiers in Microbiology, 6, 1168. https://doi.org/10.3389/ https://doi.org/10.1111/1462-­2920.13526 fmicb.2015.01168 Zhang, N., Xiao, X., Pei, M., Liu, X., & Liang, Y. (2017). Discordant tem- poral turnovers of sediment bacterial and eukaryotic communities in response to dredging: Nonresilience and functional changes. SUPPORTING INFORMATION Applied and Environmental Microbiology, 83, e02526-­16. https://doi. org/10.1128/AEM.02526 -​ 1­ 6 Additional supporting information may be found online in the Zhang, W., Lei, M., Li, Y. I., Wang, P., Wang, C., Gao, Y. U., … Zhang, H. Supporting Information section. (2019). Determination of vertical and horizontal assemblage driv- ers of bacterial community in a heavily polluted urban river. Water Research, 161, 98–­107. https://doi.org/10.1016/j.watres.2019.05.107 How to cite this article: Jiao C, Zhao D, Huang R, He F, Yu Z. Zhang, Y., Cheng, L., Li, K., Zhang, L., Cai, Y., Wang, X., & Heino, J. Habitats and seasons differentiate the assembly of bacterial (2019). Nutrient enrichment homogenizes taxonomic and func- tional diversity of benthic macroinvertebrate assemblages in shal- communities along a trophic gradient of freshwater lakes. low lakes. Limnology and Oceanography, 64, 1047–­1058. https://doi. Freshw Biol. 2021;00:1–­15. https://doi.org/10.1111/ org/10.1002/lno.11096 fwb.13735 Zhao, D., Cao, X., Huang, R., Zeng, J., Shen, F., Xu, H., … Yu, Z. (2017). The heterogeneity of composition and assembly processes of the