Succession of phytoplankton community during intensive shrimp (Litopenaeus vannamei) cultivation and its effects on cultivation systems

Journal Pre-proof Succession of phytoplankton community during intensive shrimp (Litopenaeus vannamei) cultivation and its effects on cultivation systems

Wen Yang, Jinyong Zhu, Cheng Zheng, Betina Lukwambe, Regan Nicholaus, Kaihong Lu, Zhongming Zheng PII:

S0044-8486(19)31600-X

DOI:

https://doi.org/10.1016/j.aquaculture.2019.734733

Reference:

AQUA 734733

To appear in:

aquaculture

Received date:

29 June 2019

Revised date:

9 October 2019

Accepted date:

10 November 2019

Please cite this article as: W. Yang, J. Zhu, C. Zheng, et al., Succession of phytoplankton community during intensive shrimp (Litopenaeus vannamei) cultivation and its effects on cultivation systems, aquaculture (2018), https://doi.org/10.1016/ j.aquaculture.2019.734733

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© 2018 Published by Elsevier.

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Succession of phytoplankton community during intensive shrimp (Litopenaeus vannamei) cultivation and its effects on cultivation systems

Wen Yang a, Jinyong Zhu

a,#

, Cheng Zheng a, Betina Lukwambe a, Regan Nicholaus a, Kaihong Lu

a,b

,

Zhongming Zheng a,* School of Marine Science, Ningbo University, Ningbo, 315800, China

b

Ningbo Ocean and Fishery Bureau, Ningbo, 315010, China

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a

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Email: [email protected];

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*Corresponding author

Mailing address: Ningbo University Meishan Branch, No.169 Qixingnan Road, Beilun District, Ningbo

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Co-first author

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#

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City, Zhejiang Province, China.

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Abstract As the basic biological element in shrimp cultivation systems, phytoplankton performs irreplaceable ecological functions in aquaculture ecosystem, but knowledge of the its community succession is currently not sufficient. To fill this gap, we performed longitudinal dense sampling in six intensive shrimp (Litopenaeus vannamei) aquaculture ponds to track the time-series shift of

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phytoplankton community at the community and species levels and to discuss the effects of its

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succession on rearing environment and shrimp. We detected a distinct successional pattern in the

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phytoplankton community by principal coordinates analysis and time-lag regression analysis, and this

dissolved

oxygen,

salinity,

pH

and

bacterioplankton

(Actinobacteria,

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temperature,

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pattern could be divided into 3 clusters. Canonical correspondence analysis indicated that water

Gammaproteobacteria, Verrucomicrobiae, and Acidimicrobiia) were the extrinsic factors that were

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correlated with the phytoplankton community variation. The α-diversity and taxonomic composition of

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communities in different clusters were discrepant. By random forest regression, we identified

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biomarkers of phytoplankton succession which was corelated with shrimp cultivation time. Spearman’s rank correlation showed that at least one environmental variable was correlated with the biomarkers, which emphasized the interaction between phytoplankton and rearing environment. Moreover, network analysis revealed that correlations among individual phytoplankton and biotic and abiotic factors at different clusters were also discrepant. The decline in biomarkers indicated a bloom of pathogenic Gammaproteobacteria at the end of cultivation, which contribute to disease outbreaks. Overall, our findings imply that the succession of the phytoplankton community during shrimp cultivation follows a process in which phytoplankton succeed from initial establishment driven by abiotic and biotic factors, and this process might affect the function of the aquaculture ecosystem and the health of reared shrimp

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to some extent.

Keywords:

Phytoplankton

community;

Rearing

environment;

Biomarker;

Random

forest;

Opportunistic pathogen

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1 Introduction

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Phytoplankton are ubiquitous in various aquatic environments, and shrimp ponds are among the

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habitats in which phytoplankton can grow and survive well. As the primary producers of aquaculture

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ecosystems, phytoplankton play an irreplaceable role in energy flow and nutrient cycling (Behrenfeld

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& Boss, 2014; Falkowski et al., 2003). During shrimp cultivation, the residual feed and metabolites from cultured shrimp are decomposed into nutrients by bacteria (Kumar et al., 2016; Tan et al., 2016),

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and these nutrients are then used by phytoplankton, increasing their abundance (Burford et al., 2003).

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In return, the proliferation of phytoplankton could take away ammonia and nitrite suppressing the

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immune system of shrimp (Liao et al., 2012; Liu & Chen, 2004) from the cultivation system, which could serve as an excellent regulator and indicator of water quality (Case et al., 2008; Cremen et al., 2007). Additionally, the dissolved organic matter (DOM) exudated from healthy phytoplankton or lysed of senescent and dead ones also affect the assembly of bacteria communities (Landa et al., 2016; Logue et al., 2016; Lucas et al., 2010), which furnish a source of infectious agents and provide probiotics for reared shrimp (Zhu et al., 2016). As a consequence, phytoplankton have the capacity to manage rearing environment, and conversely, an accurate representation of the shrimp’ and rearing water’s current conditions can also be derived by observing phytoplankton in the ponds. Each phytoplankton species requires specific nutrients and conditions for healthy growth (Kilham,

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1975). Along with cultivation activities progress, the nutrients and conditions of the rearing water change (Ma et al., 2013), which causes the composition of the phytoplankton community to also change dynamically (Burford & Glibert, 1997; Lemonnier et al., 2016). In other words, the process of cultivation is also a process in which the phytoplankton community continues to succeed from its establishment. The succession of phytoplankton community during shrimp cultivation has gained

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substantial research attention, with some promising results (Burford, 1997; Cremen et al., 2007;

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Lukwambe et al., 2015). However, our knowledge of phytoplankton community composition and

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dynamics in rearing ponds is still insufficient to explore the mechanisms underlying community

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succession. To better gain insight into the successional mechanism, it is necessary to study the

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community’s composition, biomass and variation pattern at the whole-community level (Xiong et al., 2014; Zhang et al., 2014) and to study the correlations between species at the individual species level

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(Lima-Mendez et al., 2015; Yang et al., 2018c). High-speed development of statistical techniques has

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provided a promising avenue for analyzing these three aspects, principally in bacterial ecology (Xiong

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et al., 2017; Yang et al., 2018a; Yang et al., 2018b; Zhang et al., 2018). However, research on these three aspects of the phytoplankton community is still insufficient (Dai et al., 2017; Yang et al., 2018c). In our previous study, we revealed the successional mechanism of the bacterioplankton community in ponds with intensive rearing of shrimp and its effects on rearing environment (Yang et al., 2018a), whereas these information on phytoplankton community, which is as important as that of bacterioplankton in aquaculture systems, is fragmentary. For this reason, here, we explored the successional pattern of phytoplankton in the same intensive shrimp (Litopenaeus vannamei) aquaculture systems and its potential interactions with rearing environment and shrimp. To this end, we performed longitudinal dense sampling during intensive shrimp cultivation and then integrated

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multivariate statistical analysis and network analysis (i) to study the successional process of the phytoplankton community at the community and species levels; (ii) to reveal the relationship between phytoplankton succession and rearing environmental variability; and (iii) to assess the potential effects of phytoplankton succession on rearing environment and shrimp performance. Our results have both basic and applied implications for maintaining healthy aquaculture environments and supplement the

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knowledge of microbial ecology in aquaculture ecosystems.

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2 Material and methods

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2.1 Experimental design and sample collection

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An 87-day sampling was conducted to investigate the physicochemical factors and phytoplankton and bacterioplankton communities in six intensive rearing ponds selected from a shrimp farm located

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in Zhanqi, Ningbo, eastern China (29°32′N, 121°31′E). These ponds were cement bottomed,

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approximately 2000 m2 in size and 1.5 m in depth and each housed within a greenhouse to maintain a

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relatively stable temperature. Bottom aeration was applied to maintain a suitable level of dissolved oxygen. Congeneric larval shrimp (L. vannamei) were introduced into the ponds on 8 April 2016. The growth of phytoplankton was stimulated by adding 2 g/m 3 commercial inorganic fertilizer urea (Tianchen Biotechnology Institute, Wuhan, China) 3 days prior to introducing. All these ponds were identically managed in terms of the 5% daily water exchange, stocking density (360,000 ind./pond), feeding type and schedule (feed ingredients were shown in Table A.1). To remove microorganisms and suspended particles, rearing seawater was disinfected with sodium hypochlorite and alum, and then was aerated in open reservoirs for 3 weeks before usage. No biological agent was introduced during the cultivation activities.

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A vibriosis outbreak on 4 July 2016 (87 days after introduction), which caused massive mortalities thereafter (Fig. A. 1). For this reason, cultivation was forcefully terminated on 10 July 2016. The diseased shrimp exhibited inactivity, lack of appetite, red hepatopancreas and white guts, and white fecal strings (Fig. A. 1), thus referred as white feces syndrome (Thitamadee et al., 2016). The growth status of shrimp over cultivation was shown in Table A.2. Samples were taken every 6 to 10 days from

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15 April 2016 (7 days after introduction) to 10 July 2016 (93 days after introduction). Considering the

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potential spatial variability within the ponds, we collected water samples from four representative

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points and mixed them to form a composite biological replicate sample (3 L) representing a given pond.

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The samples were stored in the dark at 4°C during transportation to the laboratory. In total, 78 water

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samples (6 ponds × 13 time points) were collected for further processing. 2.2 Phytoplankton analysis

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A 250-mL phytoplankton sample was obtained from the water sample and immediately fixed with

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Lugol’s solution. Phytoplankton taxa were counted in sedimentation chambers (Hydro-Bios

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Apparatebau GmbH, Kiel, Germany) with an inverted microscope (CK2, Olympus Corporation, Tokyo, Japan) according to Utermöhl (1958). Phytoplankton biomass was calculated by geometric approximations

by

using

a

computerized

counting

program

(OptiCount,

http://science.do-mix.de/software_opticount.php). 2.3 Environmental analysis Water temperature (WT), pH, salinity (SAL) and dissolved oxygen (DO) were recorded in situ with a YSI 6000 multiparameter probe (YSI Inc., Yellow Springs, USA) at a depth of 50 cm. The chlorophyll a (Chla) content was measured with a FluoroProbe fluorometer (FluoroProbe-III, bbe Moldaenke, Schwentinental, Germany). The levels of chemical oxygen demand (COD) and

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biochemical oxygen demand (BOD) were analyzed following standard methods (AQSIQ, 2007), while the levels of ammonium (NH4+), nitrate (NO3−), nitrite (NO2−) and orthophosphate (PO43−) were measured with an automated spectrophotometer (Smart-Chem 200 Discrete Analyzer, Westco Scientific Instruments, Brookfield, USA). For bacterioplankton, we used paired-end 16S rRNA gene Illumina sequencing data (DDBJ accession number: DRA006634, http://www.ddbj.nig.ac.jp/) from our previous

Saprospirae,

Gammaproteobacteria,

Verrucomicrobiae,

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Planctomycetia,

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study (Yang et al., 2018a). Ten classes (Alphaproteobacteria, Actinobacteria, Flavobacteriia, Deltaproteobacteria,

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Acidimicrobiia and Cytophagia) were selected as representative based on their relative abundances.

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2.4 Data analysis

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All data analyses were performed using the ‘vegan’ and ‘stats’ packages in R v3.2.1 (Dixon, 2003), unless otherwise indicated. Before data analyses, all of the biotic data were transformed by Hellinger

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transformation and the abiotic data were normalized by Chord transformation using the function

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decostand to improve normality and homoscedasticity (Legendre & Gallagher, 2001).

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2.4.1 Community level analysis of phytoplankton succession Permutational multivariate analysis of variance (PERMANOVA) was conducted to quantitatively evaluate the contribution of sampling time and ponds to the variation in community composition by using the function adonis (Anderson, 2001). The overall structure of phytoplankton communities was ordinated using a principal coordinate analysis (PCoA) based on Bray-Curtis distance metrics by using the functions cmdscale. Cluster analysis and analysis of similarity (ANOSIM) were performed to categorize successional pattern of phytoplankton community by using the functions hclust and anosim, respectively. To quantify the temporal variation in phytoplankton community dynamics, linear regressions were performed on Bray-Curtis dissimilarity (dependent variables) versus the square root

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of the time lags (independent variables) through time-lag analysis (TLA) (Collins et al., 2000; Xiong et al., 2014). Mantel tests were performed to assess the relationship between the phytoplankton community and environmental variables (physicochemical factors and bacterioplankton classes) by using the function mantel (Mantel, 1967). To determine the factors shaping phytoplankton succession, a forward-selection

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procedure with Monte Carlo permutation tests was performed with the function ordistep to select a

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parsimonious set of factors that explained a significant (p < 0.05) amount of variation in the

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phytoplankton data in each sample (Borcard et al., 2011). To eliminate collinearity between the selected

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factors, we sequentially removed the explanatory variables with the highest variance inflation factor

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(VIF) by using the function VIF in the “fmsb” package until all VIFs were less than 20. Finally, a canonical correspondence analysis (CCA) was used to analyze the links between the selected factors

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and the variations in community structure by using the function cca.

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2.4.2 Species-level analysis of phytoplankton succession

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Alpha diversity indices (Shannon-Wiener index, Simpson index and Pilou's evenness) were calculated using the function diversity. One-way analysis of variance (ANOVA) was used to compare differences in α-diversity among clusters using the function aov (Churchill, 2004). To acquire the biomarker taxa of phytoplankton succession correlated with shrimp cultivation time, random forest regression was applied to regress the biomass of phytoplankton taxa against shrimp cultivation time using default parameters in the ‘randomForest’ package (Liaw & Wiener, 2002; Strobl et al., 2007). The model was constructed at the genus level in consideration of diagnose accuracy and ecological significance simultaneously. The top-ranking time-discriminatory populations were identified as biomarker genera using 10-fold cross-validation implemented with the function rfcv over

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100 iterations (Zhang et al., 2018). The correlations between biomarkers and environmental variables were tested by Spearman’s rank correlation using the function cor.test. To reveal correlations among individual phytoplankton and environmental variables, extended local similarity analysis (eLSA) was performed to determine the correlations of the phytoplankton species with each other and with bacterioplankton and physicochemical factors over time using a delay

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of up to 1 week (Ruan et al., 2006). The standardized biomass of 85 phytoplankton species and 10

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bacterioplankton classes and standardized data of 10 physicochemical factors were included in the

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analysis. The significance of the local similarity (LS) score was based on 1000 permutations.

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Correlations with an LS score > 0.20 and a p-value < 0.05 were considered significant (Shade et al.,

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2010). In total, 3 networks were constructed in this study to display network correlations in 3 clusters categorized by phytoplankton succession. The networks were visualized in Cytoscape v.3.2.1 (Shannon

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et al., 2003). Subnetworks were extracted from the entire network. The topological parameters of the

3 Results

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undirected.

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networks were calculated by using Network Analyzer in Cytoscape v.3.2.1, treating edges as

3.1 Dynamics of phytoplankton community structure during shrimp cultivation The PERMANOVA demonstrated that the variations in phytoplankton community composition were significantly explained by sampling time rather than by sampling ponds or the interaction of sampling time and ponds (Table 1). These results suggested that the phytoplankton community underwent a distinct pattern of succession over time during shrimp cultivation and that the successional processes of the six ponds we sampled were basically identical. Thus, we took the average of sampling

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ponds in subsequent PCoA and Cluster to highlight the successional process of community dynamics (Fig. 1A). The PCoA and Cluster revealed that the community succession could be clustered into three distinct phases: cluster I, which included samples from 7 day to 28 day; cluster II, which included samples from 34 day to 56 day; and cluster III, which included samples from 63 day to 93 day (Fig. 1A, Fig. A. 2). This pattern was further corroborated by the ANOSIM statistical test, revealing that the

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community structure significantly differed between clusters (R2 = 0.487, p < 0.01). In addition, a

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significant time-lag regression between phytoplankton community dissimilarity and cultivation time

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was detected with a positive slope (0.090) and a good fit to the predicted line (adjusted R2 = 0.150, p <

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consistently increased over time (Fig. 1B).

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0.01), which suggested that the magnitude of differences in phytoplankton community composition

[Table 1]

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[Fig. 1]

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3.2 Links between phytoplankton community structure and rearing environmental conditions

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To link phytoplankton community succession with environmental variables, Mantel tests and CCA of the phytoplankton communities were performed for the physicochemical factors and bacterioplankton detected in our study. The correlations between the phytoplankton community and physicochemical factors (adjusted R2 = 0.196, p < 0.01) and bacterioplankton (adjusted R2 = 0.274, p < 0.01) were identified as significant by the Mantel tests. Forward selection was used to select a parsimonious set of significant factors. As a result, WT, DO, pH, SAL, Actinobacteria, Gammaproteobacteria, Verrucomicrobiae, and Acidimicrobiia were selected based on p < 0.05 and VIF < 20. The CCA biplot revealed that the variation in community composition was closely correlated with these environmental variables (Fig. 2). Specifically, the communities in cluster III were distinct

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from those in cluster I, separated primarily by the first axis, which was positively correlated with Gamma and negatively correlated with DO, pH and SAL. In addition, communities in cluster II were separated from others by the axes of Acidi and Verruco. [Fig. 2] 3.3 Shifts in α-diversities and taxonomic composition during shrimp cultivation

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A total of 85 species (or genera) were identified across all samples, with larger contributions of

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Chlorophyta (49 taxa), Bacillariophyta (12 taxa) and Pyrrophyta (10 taxa) (Fig. A. 3A). The taxonomic

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composition of the phytoplankton community showed large differences at the phylum level across the

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entire cultivation period (p < 0.01). Moreover, an upward trend in phytoplankton biomass was detected

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during shrimp cultivation, with a maximum of 77 day (37.8 mg/L), which was almost 10-fold higher than the minimum of 7 day (4.0 mg/L) (Fig. A. 3B). However, their α-diversities increased in the initial

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cluster and then declined in cluster III (Fig. 3).

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[Fig. 3]

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To correlate taxonomic composition with cultivation activity, a model based on the random forest machine-learning algorithm was established. Ultimately, 6 genera were defined as biomarkers when considering the time-discriminatory importance of taxa and minimum cross-validation error of the model (Fig. 4A). The majority of biomarkers showed a high relative abundance at the corresponding shrimp cultivation time (Fig. A. 4). For example, Coscinodiscus started to accumulate in cluster II and remained at high levels until 87 day. Cyclotella, Fragilaria and Cymbella accumulated beginning in cluster I and dominated in cluster II but decreased at the end of cultivation. Spearman’s rank correlation coefficients were calculated between each biomarker and each environmental variable. Five genera were significantly (p < 0.05) correlated with at least one environmental variable (Fig. 4B),

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which suggested that the taxonomic composition of the phytoplankton community depended significantly on environmental variables. [Fig. 4] 3.4 Network correlations among individual phytoplankton and environmental variables To determine the dynamics of correlations among phytoplankton, physicochemical factors and

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bacterioplankton classes during shrimp cultivation, 3 networks based on eLSA were constructed for

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each of the 3 clusters (Fig. A. 5). Furthermore, their topological properties were calculated by Network

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Analyzer to compare the differences among these 3 networks (Table 2). Obvious differences were

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observed. The correlations in cluster I had the most complex network structure, with the largest number

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of edges and average number of neighbors and the shortest characteristic path length, while the network structure of cluster III was relatively simple, with the smallest average number of neighbors

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(Table 2).

blandus,

belonging

to

the

top-ranking

time-discriminatory

biomarker

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Coscinodiscus

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[Table 2]

Coscinodiscus, was detected in all 3 networks. Based on correlations with the neighbors, the topologies of C. blandus in the 3 networks were dramatically changed across the clusters categorized by phytoplankton succession (Fig. 5). For example, the correlations of C. blandus were all positive in clusters I and II, but in cluster III, these correlations mostly changed to negative. Furthermore, the correlations of C. blandus were limited to interspecific interactions during clusters I and II, but in cluster III, environmental variables began to be correlated with this species and play an important role. In summary, these results suggested that the network correlations among individual phytoplankton and environmental variables were dynamic during shrimp cultivation.

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[Fig. 5] 4 Discussion 4.1 Successional pattern of the phytoplankton community during shrimp cultivation The succession of the phytoplankton community during shrimp cultivation is typical rapid secondary succession. In our study, the phytoplankton community exhibited three distinct successional phases in terms of composition during shrimp cultivation (Fig. 1). The community’s α-diversities,

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β-diversities and interspecific interactions in different clusters were discrepant (Fig. 1, Fig. 3, Fig. A. 3).

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It is expected that the initial cultivation period was also the initial phase of phytoplankton community

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succession. Generally, pioneer species are opportunistic and can respond quickly to the appearance of

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new habitats (Mackenzie et al., 2001). Accordingly, the communities in the initial phase are composed of r-strategists, such as the fast-growing diatoms Fragilaria (Weithoff et al., 2001) and Cymbella

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(Jamet et al., 2005) in our study (Fig. 4B). In addition, during the initial phase of secondary succession,

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the community is characterized by abundant “empty” niches, thereby providing a resource-rich

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environment with less competitive pressure (Chaparro et al., 2013). This environment on the one hand made correlations among individual phytoplankton and environmental factors complex and positive (Table 2, Fig. A. 5) and on the other hand led to a rapid increase in the phytoplankton biomass (Fig. A. 3B). Additionally, this environment inevitably led to the dominance of stochasticity in community succession (Chase & Myers, 2011), which resulted in intense and disordered community variability (Fig. 1A). Subsequently, community complexity increases as succession progresses, often peaking in the mid-successional phase. A mid-successional community is characterized by high species diversity, which was in line with the phytoplankton communities in cluster II (Fig. 3). In most aquatic environments, after initial community establishment, the strength of competition increases as the

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depletion of resources caused by community growth and, hence, the dominance of negative correlations among phytoplankton are expected (Dini-Andreote et al., 2015; Jackson, 2003). However, in our study, the proportion of positive correlations in cluster II increased rather than decreased (Table 2, Fig. A. 5). One possible explanation for this result is that in contrast to most aquatic environments, rearing water contains nutrients that are gradually enriched during shrimp cultivation. This means that the accessible resources for phytoplankton were not only unrestricted but more abundant in cluster II, which led to the

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increase in positive correlations. Although the communities in cluster II had the highest α-diversity (Fig.

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3), their compositions were relatively similar, as their dominant species were mostly diatoms (Fig. 4B,

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Fig. A. 3B, Fig. A. 4). All these phenomena exhibited precursors of diatom bloom outbreak (Chalar,

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2009; Spatharis et al., 2007). Characteristics of the communities in cluster III with low α-diversity (Fig. 3) and dominance by Coscinodiscus (Fig. 4, Fig. A. 4) support this assertion. However, at the end of

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shrimp cultivation (cluster III), the proportion of negative correlations began to increase (Table 2, Fig.

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A. 5C), and the increase in antagonism in the ecological network indicated that the phytoplankton

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might have begun to compete with each other, even leading to death (Coyte et al., 2015). The phytoplankton fell drastically beginning on 77 days after introduction, which not only increased the pressure on the microflora to digest the DOM (Cotner & Biddanda, 2002) but also provided conditions for the bloom of opportunistic bacteria (such as the Gammaproteobacteria, as shown in Fig. 2) (Glasl et al., 2016; Skjermo et al., 1997). It is common for WT (Lv et al., 2014; Toseland et al., 2013), DO (Smith & Piedrahita, 1988; Yoshikawa et al., 2007), pH (Berge et al., 2010) and SAL (Gasiūnaitė et al., 2005; Pilkaitytë et al., 2004) to serve as extrinsic abiotic factors correlated with the succession of the phytoplankton community. However, none of the nutritional factors were found to significantly constrain the succession of the

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phytoplankton community structure, based on our CCA results (Fig. 2), which seem to be inconsistent with the notion that nutrient levels determine the composition of the phytoplankton community (Becker et al., 2010; Lv et al., 2014). In fact, the nutrients in the intensive aquaculture system were excessive throughout the culture process and would not become the limiting factor of community succession. Positive corrections between nutrients and phytoplankton throughout shrimp cultivation also

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substantiate this explanation (Fig. 4B, Fig. A. 5). Various field experiments and laboratory mesocosm

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studies have established that phytoplankton community structure is closely associated with

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bacterioplankton (Bruckner et al., 2008; Bunse et al., 2016; Teeling et al., 2016). On the one hand,

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phytoplankton could directly compete with bacterioplankton for limited living space and natural

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resources (Meseck et al., 2007; Ogbebo & Ochs, 2008) or could accrete with bacterioplankton by producing growth-stimulatory compounds (Gomez-Gil et al., 2002; Mandal et al., 2011). On the other

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hand, phytoplankton could release their primary production as DOM into the water by using up

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available nutrients, which indirectly creates an environment that is exploited by various bacteria (Bell

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et al., 1974; Gilbert et al., 2012). In our study, the succession of the phytoplankton community in shrimp ponds was significantly correlated with the relative abundance of various bacterioplankton classes (Fig. 2). This result occurred because Pyrrophyta had a biomass advantage in cluster I (Fig. A. 3B), and Actinobacteria were positively correlated with them (Fig. 2), which has been widely confirmed in the process of dinoflagellate blooms (Bai et al., 2011; Bashenkhaeva et al., 2017). In addition, Bunse et al. (2016) observed that the bacterial phylum Verrucomicrobia was positively associated with the diatom:dinoflagellate ratio, coinciding with the trend of the phytoplankton community composition observed here (Fig. 2, Fig. A. 3). 4.2 Effect of phytoplankton succession on rearing environment and shrimp performance

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It is well known that the large blooms of some algae such as cyanobacteria (e.g., Phormidium tenue and Synechocystis diplococcus) and dinoflagellates (e.g., Alexandrium tamarense and Prorocentrum minimum) may induce shrimp mortality or growth diminution (Alonso-Rodriguez & Paez-Osuna, 2003; Tho et al., 2012). Previous studies have ascribed the emergence of these species to eutrophication in ponds (Brandenburg et al., 2017; Glibert et al., 2018; Lucas et al., 2010; Paerl &

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Otten, 2013). Indeed, a resource-rich environment could produce harmful species that are mostly

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opportunistic, but this is not the root cause. The root cause of this phenomenon is that eutrophication

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disrupts community stability and thereby provides “empty” niches for these fast-growing species (De

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Schryver et al., 2014; Yang et al., 2018c). Therefore, phytoplankton community stability should be of

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great interest during shrimp cultivation. In our study, the phytoplankton community was undergoing rapid changes (Fig. 1). The dissimilarity between phytoplankton communities in our rearing water was

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high, even at short time intervals (Fig. 1B). In other words, the phytoplankton community was in an

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unstable state throughout the entire cultivation period. In addition, in contrast to previous findings, the

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dominant species and biomarkers in our study generally belonged to Bacillariophyta (Fig. 4, Fig. A. 3, Fig. A. 4), which includes algae that are beneficial in shrimp rearing water (Alonso-Rodriguez & Paez-Osuna, 2003; Lukwambe et al., 2015). However, diseases were still breaking out at the end of our study period. This phenomenon was potentially caused by the sudden collapse of phytoplankton populations, which also implied week ecosystem stability and the loss of homeostatic mechanisms (Lemonnier et al., 2016). Stimulated by high nutrients, Coscinodiscus started to accumulate in cluster II and bloom in cluster III (Fig. 4B, Fig. A. 4). However, unlike open water systems, limited living space of rearing ponds could not only limit the carrying capacity of phytoplankton community (Burns, 1971), but also exacerbate environmental change. This series of factors might eventually cause a collapse of

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phytoplankton bloom. In view of the fact that some specific bacteria increase in abundance concurrently with the decline of phytoplankton due to their dependence on DOM from live or dead algal cells (Mayali & Azam, 2004), the collapse of Coscinodiscus indicated the rapid proliferation of opportunistic Gammaproteobacteria (Fig. 5). Similarly, the decrease in Cyclotella, Fragilaria and Cymbella in cluster III was also positively correlated with a bloom of Gammaproteobacteria.

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According to the result of our previous study (Fig. 2 in Yang et al. (2018a)), we could confirm that

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these opportunistic Gammaproteobacteria were mostly pathogenic bacteria affiliated to genus

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Photobacterium and order Vibrionales (Liu et al., 2016; Zheng et al., 2017; Zhu et al., 2016). Moreover,

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algal collapse might cause anoxia and substantial release of sulfides and even produce toxins (Cremen

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et al., 2007; Lukwambe et al., 2015). Such conditions could produce stress on the immune response of shrimp, which in turn made the reared shrimp more susceptible to pathogenic bacteria (Gao et al., 2017;

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Li et al., 2017; Parrilla-Taylor & Zenteno-Savin, 2011). Furthermore, the very high ecosystem

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productivity caused by abundant phytoplankton in cluster III might disrupt the stability of simple food

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chains, such as that in our aquaculture ecosystem (Leibold, 1999), thereby threatening the health of reared shrimp at the upper trophic level. Combined with these results, we could determine that the algal collapse and pathogens proliferation caused by the instability of phytoplankton community were the primary cause of shrimp-disease outbreak in the present study. Therefore, when regulating the phytoplankton community during shrimp cultivation, we suggest a management strategy manipulating the composition and biomass synchronously to sustain a stable and health rearing environment. 4.3 Comparison of succession between the phytoplankton community and the bacterioplankton community Based on a comparison of the successional process of the phytoplankton community in the present

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study (Fig. 1) and the bacterioplankton community in our previous study (Fig. 1 in Yang et al. (2018a)), their direction and turnover rate (the slope of TLA) were unsurprisingly different. Theoretically, the variation in community dynamics is stronger in phytoplankton than in bacterioplankton (Liu et al., 2015) due to the traits associated with bacteria, such as widespread dispersal (Heino et al., 2015; Soininen, 2010) and rapid evolutionary adaptation (Hanson et al., 2012). However, the succession of

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the phytoplankton community seemed slower than that of the bacterioplankton community based on

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our results. Actually, the high dissimilarity between phytoplankton communities in our rearing water

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limited the range of the slope of time-lag regression. Therefore, we could still safely conclude that the

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temporal variation in the phytoplankton community was stronger than that in the bacterioplankton

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community. In most cases, this conclusion implies that the phytoplankton community exhibits a stronger response to environmental changes than does the bacterioplankton community (Liu et al.,

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2015). However, the Mantle test of our studies indicated that the correlation between physicochemical

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factors and the bacterioplankton community (adjusted R2 = 0.567, p = 0.001) was stronger than that

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between such factors and the phytoplankton community (adjusted R2 = 0. 196, p = 0.001) in the intensive shrimp aquaculture system. A potential reason for this contrasting result is that as mentioned above, the phytoplankton community might reach carrying capacity before cultivation terminated, which intensified the transformation of effects on community succession from extrinsic environmental constraints to intrinsic interspecific interaction forces (Fig. A. 5). Whereas, the bacterioplankton community had not exceeded their carrying capacity of the rearing water, since its α-diversity presented an upward trend (Table 2 in Yang et al. (2018a)). In addition, according to our study, the successional processes of the phytoplankton community in aquaculture aquatic environments are different from those in natural aquatic environments. Thus, given the uniqueness of rearing water, more studies are

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expected to be implemented to further explore the mechanisms underlying phytoplankton community succession. 5 Conclusion Our research was undertaken to determine the characteristic successional pattern of phytoplankton community in intensive shrimp pond and its effects on cultivation system. The results demonstrated

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that during shrimp cultivation, the phytoplankton community underwent a distinct pattern of succession,

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which could be clustered into 3 clusters. Overall, the phytoplankton community succeeded from a

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disordered and diverse state to a diatom bloom state along shrimp cultivation, and this process was

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correlated with both abiotic factors, such as WT, DO, pH and SAL, and biotic factors, such as the

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bacterial classes Actinobacteria, Gammaproteobacteria, Verrucomicrobiae, and Acidimicrobiia and interspecific interactions. Moreover, the network correlations of C. blandus, which was the biomarker

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of shrimp cultivation time, dramatically changed across clusters. At the end of shrimp cultivation, the

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decline in C. blandus possibly caused by the limited living space of the rearing ponds enabled a bloom

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of pathogenic Gammaproteobacteria, which provided conditions for the outbreak of the disease. Furthermore, by comparing the successional patterns of the phytoplankton community and bacterioplankton community in the same intensive shrimp aquaculture systems, we found that their underlying mechanisms were different. Our present and previous results showed that the associations between physicochemical factors, phytoplankton and bacterioplankton were inextricable. Given our observations, we recommend that in future aquaculture ecological studies, it would be better to conduct comprehensive research including these three components rather than to conduct three separate studies. Collectively, our studies contribute to an overall understanding of microbial ecology in aquaculture systems and emphasize the necessity of stability of rearing water microflora, which offers potential

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help to optimize and develop microbial management strategies.

Acknowledgments This work was supported by the Zhejiang Provincial Department of Education Scientific Research Project (Y201839309), the Zhejiang Provincial Natural Science Foundation of China (LY17C190004),

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the Zhejiang Public Welfare Technology Research Program (LGN18C190008) and the K.C. Wong

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Magna Fund at Ningbo University.

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Appendix A. Supplementary data

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The following are supplementary data related to this article. Supplementary Fig. A. 1-4, Table A.1 and

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2 show additional study details.

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Figures legends Fig. 1 Principal coordinate analysis (PCoA) ordination biplot (A) and time-lag regression analysis (B) based on Bray-Curtis dissimilarities in phytoplankton community composition. Different colors indicate samples in different clusters. The number represents cultivation days after introduction. The lines denote the least-square linear regressions across time lags and their 95% confidence intervals

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(gray-shaded areas). **P < 0.01.

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Fig. 2 Canonical correspondence analysis (CCA) of phytoplankton communities and the

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forward-selected environmental variables. WT: water temperature; DO: dissolved oxygen; SAL:

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salinity; Actino: relative abundance of the Actinobacteria class; Verruco: relative abundance of the Verrucomicrobiae class; Acidi: relative abundance of the Acidimicrobiia class; Gamma: relative

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abundance of the Gammaproteobacteria class. Different colors indicate samples in different clusters.

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Fig. 3 Comparison of phytoplankton community α-diversity indices over clusters. a,b: Different lowercase letters denote significant differences (p < 0.05) among clusters based on one-way ANOVA.

Fig. 4 Phytoplankton biomarkers of shrimp cultivation time identified by random forest regression (A) and the dynamics of their relative biomass against cultivation time (B). The insert of A represents the 10-fold cross-validation error as a function of the number of input genera used to regress against the shrimp cultivation time in order of variable importance. The correlations between biomarkers and environmental variables in B were tested by Spearman’s rank correlation. A red line indicates a positive correlation, while a blue line indicates a negative correlation.

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Fig. 5 The dynamics of network correlations among Coscinodiscus blandus, belonging to the biomarker Coscinodiscus, other phytoplankton species and environmental variables in clusters I (A), II (B) and III (C) categorized by the succession of the phytoplankton community. Circles are phytoplankton species, diamonds are bacterioplankton, and squares are physicochemical factors. Colors

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of the nodes indicate the phytoplankton species affiliated with different phyla. A red edge indicates a

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positive association, whereas a blue edge indicates a negative association.

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Fig. A. 1 Photos of diseased shrimp. A shrimp disease (white feces syndrome) occurred at 87 days after

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introduction, which caused massive mortalities thereafter. Red arrows indicate disease signs and dead

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shrimp in ponds.

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species level.

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Fig. A. 2 Hierarchical clustering of samples based on the phytoplankton community profiles at the

Fig. A. 3 The variation of phyla composition and biomass of phytoplankton community along shrimp cultivation.

Fig. A. 4 The variation of the relative biomass of phytoplankton biomarkers along shrimp cultivation.

Fig. A. 5 Network interactions among phytoplankton species, bacterioplankton and physicochemical factors in clusters I (A), II (B) and III (C) categorized by the succession of the phytoplankton

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community. Circles are phytoplankton species, diamonds are bacterioplankton, and squares are physicochemical factors. Colors of the nodes indicate the species affiliated with different phyla (color code on the bottom). A red line indicates a positive correlation between two individual nodes, while a

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blue line indicates a negative correlation.

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Table 1 Quantitative effects of sampling time and ponds on variation in phytoplankton community structure and environmental variables (physicochemical factors and bacterioplankton classes) obtained using nonparametric permutational multivariate analysis of variance (PERMANOVA). Sampling ponds

Time : pond

p

R2

p

R2

p

Phytoplankton community structure

0.141

0.001

0.011

0.460

0.008

0.658

Physicochemical factors

0.713

0.001

0.003

0.376

0.001

0.996

Bacterioplankton classes

0.283

0.001

0.013

0.225

0.004

0.784

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R2

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Sampling time

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Note: The R2 values represent the proportion of the community variation explained by each of the

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variables and their interaction.

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Table 2 Topological parameters of extended local similarity analysis (eLSA) networks of correlations among phytoplankton, bacterioplankton and physicochemical factors in clusters categorized by the succession of phytoplankton community. Cluster I

Cluster II

Cluster III

Nodes (n)

56

27

38

Edges (n)

439

95

93

Clustering coefficient

0.702

0.619

Network centralization

0.496

Characteristic path length

1.909

Average number of neighbors

15.679

Network density Network heterogeneity

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0.455

e-

1.994

0.402 2.363

0.285

0.271

0.132

0.779

0.712

1.085

99.0/1.0

80.6/19.4

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4.895

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0.479

7.037

96.5/3.5

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Positive/negative correlation (%)

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Topological properties

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Table A.1 The percentages of shrimp feed ingredients over cultivation. To eliminate potential pathogens, feed is sterilized by irradiation during production. Postlarvae

Juveniles and Adults

Fish meal

55

38

Yeast powder

4

4

Soybean lecithin

6

5

Soybean powder

14

Peanut powder

0

Shrimp shell powder

7

Wheat gluten powder

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Pre-mixture

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Salt

Ca(H2PO4)2

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Marine algae powder

Vegetable oil

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Ingredients

18 6 10 0

7.1

10.5

0.4

0

0

1.5

0

3

4

4

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Table A.2 The growth status of shrimp over cultivation. Final

Initial

Final

Culture

Specific

length

length

weight

weight

days

growth rate

(cm)

(cm)

(cm)

(cm)

(d)

(%)

Shrimps

1.11 ±

8.17 ±

0.019 ±

7.625 ±

93

7.04 ±

(n=15)

0.07

0.29

0.005

0.407

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Initial

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Characteristics

0.28

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Succession of phytoplankton community during intensive shrimp (Litopenaeus vannamei) cultivation and its effects on cultivation systems Wen Yang a, Jinyong Zhu

a,#

, Cheng Zheng a, Betina Lukwambe a, Regan Nicholaus a, Kaihong Lu

a,b

,

Zhongming Zheng a,*

Significant directional temporal change in phytoplankton assemblages was occurred during

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

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Highlights:

Phytoplankton community succeed from a disordered and diverse state to a diatom bloom state

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

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shrimp cultivation.



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along shrimp cultivation.

Algal collapse and pathogens proliferation induced by the instability of phytoplankton community

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The interaction among physicochemical factors, phytoplankton and bacterioplankton were inextricable and indivisible.

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

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were the primary cause of shrimp-disease outbreak.

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Conflict of interest We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with our manuscript entitled ‘Succession of phytoplankton community during

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intensive shrimp (Litopenaeus vannamei) cultivation and its effects on cultivation systems’.

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5