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Received: 25 November 2024
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Published: 27 December 2024
Citation: Han, T.; Zhang, M.; Feng,
W.; Li, T.; Liu, X.; Wang, J. Effects of
Aeration Intensity on Water Quality,
Nutrient Cycling, and Microbial
Community Structure in the Biofloc
System of Pacific White Shrimp
Litopenaeus vannamei Culture. Water
2025,17, 41. https://doi.org/
10.3390/w17010041
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Article
Effects of Aeration Intensity on Water Quality, Nutrient Cycling,
and Microbial Community Structure in the Biofloc System of
Pacific White Shrimp Litopenaeus vannamei Culture
Tao Han, Mingmin Zhang, Wenping Feng * , Tianyou Li, Xueting Liu and Jiteng Wang
Fishery College, Zhejiang Ocean University, 1 Haida South Road Changzhi Island Lincheng New Area,
Zhoushan 316022, China
*Correspondence: wenpingfeng@outlook.com
Abstract: Biofloc technology (BFT) is an advanced aquaculture method that uses microbial
communities to enhance water quality and support aquatic species cultivation. Our re-
search aims to delve into the pivotal role of aeration intensity within BFT systems, revealing
its influence on microbial community structures, water quality, and nutrient cycling for L.
vannamei culture. Three aeration levels were set with intensities of V75 (75 L/min), V35
(35 L/min), and V10 (10 L/min). The results showed that the lowest aeration intensity
(V10) resulted in larger floc sizes and a reduction in the 2D-fractal dimensions, indicating a
decreased overall structural complexity of the bioflocs. In addition, water quality param-
eters, including total ammonia nitrogen and nitrite, remained low across all treatments,
highlighting the water-purifying capacity of biofloc. While protein and lipid contents in
biofloc did not differ significantly among treatments, docosahexaenoic acid (DHA) levels
were highest in the V75 treatment, suggesting that higher aeration promotes the accumu-
lation of essential fatty acids. RDA analysis revealed that microorganisms like Ruegeria
sp. and Sulfitobacter mediterraneus negatively correlated with ammonia and nitrite levels,
suggesting their key role in converting ammonia to nitrite and nitrate in marine nitrogen
cycles. The functional annotation of metagenomes across different aeration levels showed
the similarly active roles of microorganisms in nitrogen metabolism and protein synthesis.
In conclusion, while variations in aeration intensity affect floc size and the accumulation
of essential fatty acids in biofloc, they do not significantly impact overall water quality or
core microbial functions in L. vannamei aquaculture. Future research should focus on the
effects of aeration strategies on microbial community dynamics and the integration of these
data with performance metrics in L. vannamei. These insights can help optimize biofloc
cultivation and enhance environmental sustainability in the aquaculture industry.
Keywords: water quality; biofloc structure; essential fatty acids; microbial community
1. Introduction
The aquaculture industry has emerged as a vital sector in global food production,
contributing significantly to the supply of animal protein to meet the increasing demands of
the growing human population [
1
]. However, the rapid expansion of aquaculture practices
has not been without challenges. One of the most pressing issues is the environmental
impact, particularly in terms of water pollution [
2
]. Aquaculture waste, including leftover
feed and feces, may trigger algal blooms and lower dissolved oxygen levels. These effects
can damage aquatic ecosystems and reduce biodiversity [
3
]. Besides, this also poses risks to
the aquaculture species themselves, potentially leading to disease outbreaks and reduced
Water 2025,17, 41 https://doi.org/10.3390/w17010041
Water 2025,17, 41 2 of 19
productivity [
4
]. Therefore, it is crucial to balance the economic benefits of aquaculture
with the need to protect the environment and maintain the health of aquatic ecosystems.
The Pacific white shrimp Litopenaeus vannamei is currently one of the top three shrimp
species in global aquaculture production [
1
]. Advancements in aquaculture technology
have led to higher stocking densities. However, this can cause ammonia and nitrite levels
to surge beyond safe thresholds, weakening shrimp immunity and potentially causing
substantial economic losses [
5
]. To address these concerns, it is imperative to explore and
implement effective aquaculture models of L. vannamei.
Biofloc technology (BFT) is an advanced and sustainable aquaculture approach that
leverages microbial communities to improve water quality, recycle nutrients, and support
the growth and health of cultured aquatic species [
6
–
8
]. The nitrification process within
bioflocs occurs primarily in microbial biofilms, and its efficiency is strongly influenced
by aeration intensity. Aeration directly affects the oxygen availability needed for these
microbial processes, shaping microbial community structure and significantly enhancing
the water purification capacity of BFT systems [
9
]. Despite these insights, comprehensive
studies on the specific effects of aeration on microbial dynamics in BFT remain limited,
highlighting the need for further investigation.
Aeration is essential for supplying the dissolved oxygen required for the metabolic
processes of aerobic and facultative anaerobic microorganisms in bioflocs. This oxygen
supply promotes the growth and reproduction of these microorganisms [
10
]. An adequate
aeration system supports a diverse and active microbial community, which is responsi-
ble for the breakdown of organic matter in water and the formation of protein-rich and
lipid-rich bioflocs [
11
,
12
]. In addition, moderate aeration promotes the aggregation of
microorganisms into larger flocs, enhancing their settling properties and ease of removal
from the system [
13
]. In contrast, excessive aeration may lead to less stable flocs, poten-
tially diminishing the efficiency of biochemical transformation in water [
14
]. In traditional
aquatic culture systems, the aeration rate of 6–10 L/min is commonly employed [
15
,
16
].
However, in biofloc systems, a higher aeration rate is necessary due to the increased de-
mand for metabolic processes and the aggregation of microorganisms. This leads to higher
energy consumption compared to conventional systems [
17
]. Although the influence of
aeration intensity on the nitrification process within biofilms in L. vannamei culture has
been previously documented [
9
], the effects of aeration intensity on microbial communities,
which are linked to nutritional supplementation for aquatic species and water purification,
need further clarification. Our research aims to deepen the understanding of how aeration
levels influence floc formation and their potential positive effects on wastewater treatment.
This experiment will provide valuable insights for enhancing environmental sustainability
and promoting L. vannamei production.
2. Material and Methods
2.1. Experimental Design
Three aeration intensity groups (V75: flow rate 75 L/min; V35: flow rate 35 L/min; V10:
flow rate 10 L/min) with four replicates per treatment were set [
9
]. Twelve polyethylene
tanks with 40 L of seawater were used to culture biofloc. To simulate the culture environ-
ment of L. vannamei and maintain the growth of biofloc, one hundred and twenty juvenile
L. vannamei, sourced from a commercial hatchery in Taizhou, China, were distributed into
the twelve tanks. To achieve the adequate growth of biofloc, seawater temperature, salinity,
pH values, and dissolved oxygen (DO) were maintained at 28.11
±
1.91
◦
C, 25
±
1.13‰,
7.84
±
0.13, and 6.41
±
0.03 mg L
−1
, respectively. The biofloc culture was initiated by
daily adding glucose and formulate feeds of L. vannamei (3% of body weight) as the carbon
and nitrogen sources, respectively [
9
]. A carbon-to-nitrogen ratio of 20:1 was consistently
Water 2025,17, 41 3 of 19
upheld [18]. No water exchange was conducted throughout the experiment. Water lost to
evaporation in each tank was supplied by fresh water every week [
19
]. A central air pump
with air stones and control valves (CM12-ACO-016, Huapu mechanical and electrical equip-
ment Co., Ltd., Jinan, China), was utilized to control the aeration intensity of each treatment.
To quantify the air flow, separate rotometers (TRP-255-H-7 1 POL NPT-Tecnofluid
®
, Santa
Efigênia, Brazil) were connected to the aeration inlets of each test setup and adjusted to
deliver the specified flow rates as per the experimental protocol. All treatment groups
received continuous aeration throughout the two-month experimental period.
2.2. Growth and Water Quality Assessment
The body weight of the shrimps in each treatment was determined after the two-
month experiment, using an electronic scales with an accuracy of 0.01 g. In each week, pH
and temperature were determined using a multi-parameter meter (U-CHEN PH100B pH
meter, Shanghai Drawell Scientific Instrument Co., Ltd., Shanghai, China). Salinity was
measured with a refractometer (MASTER-S28
α
, ATAGO Co., Ltd., Tokyo, Japan). Then,
the water samples were collected and filtered, and the levels of dissolved oxygen (DO),
total ammonia nitrogen (TAN), and nitrate nitrogen (NO3-N) were assessed using a multi-
parameter spectrophotometer (LH-M900, Zhejiang Lohand Environment Technology Co.,
Ltd., Hangzhou, China) with corresponding seawater test kits (LH-DOA10, LH-ANA11,
LH-NO3A10, Zhejiang Lohand Environment Technology Co., Ltd., Hangzhou, China).
The floc volume (FV) was measured on-site using Imhoff cones, recording the volume of
bioflocs in a 1000 mL cylinder after 20 min of sedimentation [
20
]. Total suspended solids
(TSS) were determined by filtering 100 mL of water, drying the filter at 105
◦
C for 1 to 3 h,
and weighing it to calculate TSS [21].
2.3. Proximate Composition and Fatty Acid Analysis of Biofloc
To evaluate the role of bioflocs as a nutritional source for shrimp, the nutrient com-
positions of the bioflocs were analyzed. At the conclusion of the experiment, biofloc from
the remaining water in each tank was gathered. The biofloc composition was assessed
following the procedures described by AOAC (1995) [
22
]. Moisture content was measured
by freeze-drying the biofloc using a lyophilizer (LL1500, Thermo Scientific, Waltham, MA,
USA) at
−
110
◦
C. Crude protein content was determined using an automated Kjeldahl
analyzer (K355/K437, Buchi, Flawil, Switzerland), while ash content was measured with
a muffle furnace (KSW, Kewei, Beijing, China) at 550
◦
C. Lipid extraction was performed
using the chloroform/methanol mixture (2:1, v/v) according to the method established by
Folch et al. (1957) [
23
]. Fatty acid composition was then analyzed by gas chromatography
(GC7890B, Agilent Technologies, Santa Clara, CA, USA) following the procedure reported
by Liu et al. (2021) [24].
2.4. Biofloc Structure Analysis
To elucidate the effect of aeration intensity on biofloc structure, 1 L of cultured water
from each tank was collected biweekly and allowed to settle in Imhoff cones for one hour.
Then the sediments (i.e., biofloc) were removed, and their structures were examined under
a microscope (Olympus IX53, Olympus Corporation, Tokyo, Japan) with Image ProPlus
5.4 and Image J software (1.54j). To measure the size of bioflocs (area), images were first
converted to grayscale and then to binary using a fixed threshold. The sizes of the biofloc
were calculated by measuring the pixel-covered area divided by the total number of distinct
floc patches [
25
]. Two-dimensional (2D) fractal dimensions were used to quantify the
complexity and self-similarity of flocculent structures, offering insights into their irregular
and intricate patterns. The 2D fractal dimensions of biofloc were determined using the
box-counting method with ImageJ software, as described by Jarvis et al. (2005) [26].
Water 2025,17, 41 4 of 19
2.5. Microbiomics Analysis of Biofloc
To elucidate the mechanisms by which aeration intensity influences microbial-
mediated processes (water purification and nutrient supply for shrimp), metagenomic
analysis of biofloc was performed. After the experiment, 100 mL of cultured water from
each tank was evenly transferred into ten sterile centrifuge tubes. The samples were then
centrifuged at 4
◦
C and 4000 rpm for 20 min to pellet the microbial cells. To maximize the
collection of microorganisms in cultured water, the supernatant in each tube was subse-
quently filtered using a sterile 0.22
µ
m polycarbonate membrane (GTBP04700, Millipore,
Bedford, MA, USA). Both the sediment and the filtrate were then combined and stored at
−80 ◦C for subsequent analysis.
2.5.1. DNA Extraction, Library Construction, Illumina NovaSeq Sequencing, and
Metagenome Data Preprocessing
Microbial DNA was extracted from each sample using the CTAB method, as outlined
by Pang et al. (2021) [
27
] and Hultman et al. (2015) [
28
]. The DNA libraries were created
with the NEB Next
®
Ultra™ DNA Library Prep Kit (New England Biolabs, Ipswich, MA,
USA) in accordance with the manufacturer’s instructions. Qualified libraries were then
sequenced on an Illumina NovaSeq PE150 platform at Wekemo Tech Co., Ltd., in Shenzhen,
China, following standard preparation protocols. Blank controls were included during both
DNA extraction and library construction stages. Sample purity was confirmed through
agarose gel electrophoresis (AGE) to ensure the absence of contamination. Raw reads were
processed at Wekemo Tech Co. using the Knead Data tool [27].
2.5.2. Taxonomic Classification and KEGG Functional Annotations
To assess the taxonomic composition of the metagenomic data, the Wekemo bioincloud
platform was used, incorporating the metagenomics workflow [
29
]. Kraken2 and a custom
database from Wekemo Tech Co. were employed for the annotation and classification
of the clean sequences, while Bracken was utilized to estimate the actual relative species
abundance [
30
,
31
]. Seven taxonomic levels, from kingdom to species, were obtained from
each sample by applying the lowest common ancestor algorithm [
32
] and gene abundance
calculations. Additionally, unigene sequences were matched against the UniRef90 protein
database via DIAMOND (version 0.7.10.59) with default settings. Failed reads were re-
moved, and gene abundance tables were generated, enabling the construction of functional
abundance profiles for each sample based on the UniRef90 and KEGG databases.
2.6. Data Visualization and Statistical and Bioinformatics Analysis
The water quality, nutrient composition, and biofloc structure data were transformed
using a two-step algorithm to stabilize variance [
33
]. These datasets were then assessed
for normality (Shapiro-Wilk test) and homogeneity of variance (Levene’s test). A one-way
ANOVA, followed by Tukey’s multiple comparison test, was used to evaluate water quality
and nutrient composition data that followed a normal distribution. For biofloc structure
data that did not adhere to a normal distribution, the Kruskal-Wallis test, followed by
the Steel-Dwass multiple comparison test, was applied. Graphs were generated using
OriginPro 2022, and image analysis was conducted with ImageJ. SPSS 25.0 was used for
statistical analyses.
R software (version 3.8) and QIIME (version 2) were employed for visualizing and
analyzing microbial metagenomes. The Dunn test identified significant differences in
microbial abundance across groups (p< 0.05). Various indices—Chao1, Abundance-based
Coverage Estimator (ACE), Good’s Coverage, Shannon, and Simpson—were calculated to
reflect microbial community richness, evenness, and diversity. A Venn diagram illustrated
the core microbiome at the species level. Linear discriminant analysis effect size (LEfSe)
Water 2025,17, 41 5 of 19
identified differences in microbial community structure and function among treatment
groups, while redundancy analysis (RDA) assessed the relationship between species-level
microbiota and environmental variables. Statistical significance was determined via a
permutation test.
3. Results
3.1. Growth and Water Quality
The growth performance of the shrimp is presented in Supplementary Table S1. There
was no significant difference among different levels of aeration in relation to the final
weight and survival rate (p> 0.05).
Water quality parameters of TAN, nitrate, and nitrite under different aeration in-
tensities during experiment periods are shown in Figure 1. The TAN of each treatment
remained relatively constant during the experiment periods. The nitrate amounts of V35
and V10 groups increased in the second week, of which values were higher than that of V75
(p< 0.05). The FVs for all treatments rose throughout the experiment, with V75 showing
significantly higher values than V10 between the sixth and eighth weeks (p> 0.05).
Water 2025, 17, x FOR PEER REVIEW 5 of 20
OriginPro 2022, and image analysis was conducted with ImageJ. SPSS 25.0 was used for
statistical analyses.
R software (version 3.8) and QIIME (version 2) were employed for visualizing and
analyzing microbial metagenomes. The Dunn test identified significant differences in mi-
crobial abundance across groups (p < 0.05). Various indices—Chao1, Abundance-based
Coverage Estimator (ACE), Good’s Coverage, Shannon, and Simpson—were calculated to
reflect microbial community richness, evenness, and diversity. A Venn diagram illus-
trated the core microbiome at the species level. Linear discriminant analysis effect size
(LEfSe) identified differences in microbial community structure and function among treat-
ment groups, while redundancy analysis (RDA) assessed the relationship between spe-
cies-level microbiota and environmental variables. Statistical significance was determined
via a permutation test.
3. Results
3.1. Growth and Water Quality
The growth performance of the shrimp is presented in Supplementary Table S1.
There was no significant difference among different levels of aeration in relation to the
final weight and survival rate (p > 0.05).
Water quality parameters of TAN, nitrate, and nitrite under different aeration inten-
sities during experiment periods are shown in Figure 1. The TAN of each treatment re-
mained relatively constant during the experiment periods. The nitrate amounts of V35
and V10 groups increased in the second week, of which values were higher than that of
V75 (p < 0.05). The FVs for all treatments rose throughout the experiment, with V75 show-
ing significantly higher values than V10 between the sixth and eighth weeks (p > 0.05).
Figure 1. The effects of different aeration intensity on water quality parameters. Values are means ±
SD, n = 4.
3.2. Proximate Composition of Biofloc
The proximate composition and fay acid composition of biofloc for the three treat-
ments are presented in Tables 1 and 2, respectively. There was no significant difference in
crude protein, crude lipid, and moisture among different aeration intensity treatments (p
> 0.05) (Table 1). Ash content in the V75 group (69.87%) was higher than in the other two
groups (p < 0.05) (Table 1). The fay acids of biofloc significantly varied in terms of SFA
0.0
0.5
1.0
1.5
2.0
0123456789
TAN (mg/L)
11mg/L 8mg/L 5mg/L
0.0
0.2
0.4
0.6
0.8
1.0
0123456789
Nitrate (mg/L)
0.0
0.2
0.4
0.6
0.8
0123456789
Nitrite (mg/L)
0
9
18
27
36
0123456789
FV (mL/L)
V75 V35 V10
Weeks
Figure 1. The effects of different aeration intensity on water quality parameters. Values are
means ±SD, n = 4.
3.2. Proximate Composition of Biofloc
The proximate composition and fatty acid composition of biofloc for the three treat-
ments are presented in Tables 1and 2, respectively. There was no significant difference
in crude protein, crude lipid, and moisture among different aeration intensity treatments
(p> 0.05) (Table 1). Ash content in the V75 group (69.87%) was higher than in the other
two groups (p< 0.05) (Table 1). The fatty acids of biofloc significantly varied in terms of SFA
among treatments, with the lowest value observed in V75 (p< 0.05) (Table 2). Although
there was no significant difference in MUFA among treatments (p> 0.05), C22:1n-9 showed
the highest value in V10 (p< 0.05). Notably, the docosahexaenoic acid (DHA, C22:6n-3)
Water 2025,17, 41 6 of 19
content in V75 showed the highest values (1.08%) compared to those in the V35 and V10
groups (p< 0.05) (Table 2).
Table 1. The effects of varying aeration intensities on biofloc proximate composition.
Parameter Treatments
V75 V35 V10
Crude Protein 10.35 ±0.45 9.15 ±0.71 15.24 ±0.34
Crude Lipid 15.04 ±0.56 14.28 ±0.43 13.84 ±0.41
Moisture 95.64 ±2.32 95.73 ±0.45 95.23 ±0.69
Ash 69.87 ±1.21 b62.36 ±1.92 a60.74 ±1.05 a
Note: Values are means
±
SD (n = 4). Different letters indicate significance among different treatments (p< 0.05).
Table 2. The effects of different aeration intensities on the fatty acid composition (% total fatty acid)
of bioflocs.
Fatty Acids Treatments
V75 V35 V10
C14:0 3.52 4.21 4.81
C16:0 4.35 5.60 4.96
C18:0 13.27 15.09 13.26
C20:0 2.42 1.59 3.53
∑SFA 23.55 a26.5 b26.56 b
C16:1n-7 1.32 1.75 2.49
C18:1n-9 13.61 12.10 10.75
C22:1n-9 1.4 ab 1.02 a9.11 b
∑MUFA 16.32 14.87 22.34
C18:2n-6 12.70 13.56 13.36
C18:3n-3 7.31 7.77 6.82
C20:4n-6 1.17 0.83 1.65
C20:5n-3 2.04 0.79 1.54
C22:6n-3 1.08 b0.61 ab 0a
∑PUFA 24.30 23.57 23.38
∑n-3/∑n-6 0.78 0.66 0.58
Notes: Values are means
±
SD (n = 4). Different letters indicate significance (p< 0.05) among different treatments.
C14:0: myristic acid; C16:0: palmitic acid; C18:0: stearic acid; C20:0: arachidic acid;
∑
SFA: total saturated fatty
acids; C16:1n-7: palmitoleic acid; C18:1n-9: oleic acid; C22:1n-9: erucic acid;
∑
MUFA: total monounsaturated fatty
acids; C18:2n-6: linoleic acid; C18:3n-3:
α
-Linolenic acid; C20:4n-6: arachidonic acid; C20:5n-3: eicosapentaenoic
acid (EPA); C22:6n-3: docosahexaenoic acid (DHA);
∑
PUFA: total polyunsaturated fatty acids;
∑
n-3/
∑
n-6: the
ratio of total n-3 to total n-6 polyunsaturated fatty acids.
3.3. Biofloc Structure
The total area distribution and fractal dimension of biofloc in different aeration inten-
sity groups during the experiment are shown in Figure 2. The total area of flocs decreased
as the aeration intensity increased. The V10 groups showed the largest total area of bioflocs
in the fourth, sixth, and eighth weeks of the experiment (Figure 2A–C). In the V75 group,
the predominance of floc size shifted from 0.2–0.5 mm
2
in the fourth week to 0.6–0.8 mm
2
by the eighth week. Similar trends were observed in the other two groups, although the
changes were less pronounced (Figure 2A–C). The 2D fractal dimension measures the struc-
tural complexity of bioflocs. Specifically, a fractal dimension near 1 indicates linearity, while
one approaching 2 suggests a complex 2D pattern [
34
]. The 2D fractal dimensions of the
V10 and V35 groups significantly decreased in the sixth and eighth weeks of the experiment
(p< 0.05), while no significant changes were observed in the V75 group throughout the
experiment (p> 0.05) (Figure 2D).
Water 2025,17, 41 7 of 19
Water 2025, 17, x FOR PEER REVIEW 7 of 20
although the changes were less pronounced (Figure 2A–C). The 2D fractal dimension
measures the structural complexity of bioflocs. Specifically, a fractal dimension near 1 in-
dicates linearity, while one approaching 2 suggests a complex 2D paern [34]. The 2D
fractal dimensions of the V10 and V35 groups significantly decreased in the sixth and
eighth weeks of the experiment (p < 0.05), while no significant changes were observed in
the V75 group throughout the experiment (p > 0.05) (Figure 2D).
Figure 2. Total area distribution and fractal dimension analysis during experiment period. Note:
(A). The fourth week of the experiment; (B). The sixth week of the experiment; (C). The eighth week
of the experiment; (D). 2D fractional dimension of biofloc with different aeration intensities during
experiment periods. Different leers indicate significance (p < 0.05) among different treatments.
3.4. Microbial Composition and Abundance
The composition and abundance of the microbial community in different aeration
intensity groups are shown in Figure 3. In all three experimental groups, the Pseudomonad-
ota was predominant, followed by Bacteriodota, Planctomycetota, and Actinomycetota at the
phylum level (Figure 3A). The Alteromonadales and Microbacterium were dominant among
three treatments at class and genes levels, respectively (Figure 3B,C). At the species level,
Muricauda_sp. and Fuerstiella_marisgermanici demonstrated significantly high abundance
in the V75 group, while Ruegeria sp.-THAF33 and Gimesia chilikensis were dominant in the
V10 and V35 groups, respectively (Figure 3D,E).
The comparative analysis of microbial community composition among different ex-
perimental groups was conducted using the LEfSe to discern species significantly associ-
ated with variations in aeration intensity levels (Figure 4). In the V10 group, the genus
Gimesia and specifically the species Gimesia chilikensis exhibited a significant correlation
with aeration intensity levels (p < 0.05). In the V35 group, the species Microbacterium parao-
xydans stood out as significantly correlated with the aeration intensity (p < 0.05). In the
Figure 2. Total area distribution and fractal dimension analysis during experiment period. Note: (A).
The fourth week of the experiment; (B). The sixth week of the experiment; (C). The eighth week of
the experiment; (D). 2D fractional dimension of biofloc with different aeration intensities during
experiment periods. Different letters indicate significance (p< 0.05) among different treatments.
3.4. Microbial Composition and Abundance
The composition and abundance of the microbial community in different aeration
intensity groups are shown in Figure 3. In all three experimental groups, the Pseudomonadota
was predominant, followed by Bacteriodota,Planctomycetota, and Actinomycetota at the
phylum level (Figure 3A). The Alteromonadales and Microbacterium were dominant among
three treatments at class and genes levels, respectively (Figure 3B,C). At the species level,
Muricauda_sp. and Fuerstiella_marisgermanici demonstrated significantly high abundance in
the V75 group, while Ruegeria sp.-THAF33 and Gimesia chilikensis were dominant in the V10
and V35 groups, respectively (Figure 3D,E).
The comparative analysis of microbial community composition among different exper-
imental groups was conducted using the LEfSe to discern species significantly associated
with variations in aeration intensity levels (Figure 4). In the V10 group, the genus Gimesia
and specifically the species Gimesia chilikensis exhibited a significant correlation with aer-
ation intensity levels (p< 0.05). In the V35 group, the species Microbacterium paraoxydans
stood out as significantly correlated with the aeration intensity (p< 0.05). In the V75 group,
the order Alteromonadales was identified with a significant association (p< 0.05).
Water 2025,17, 41 8 of 19
3.5. Diversity Analysis
To evaluate the data adequacy and microbial diversity, a metagenomic
α
-diversity
rarefaction curve was performed (Supplementary Figure S1). The rarefaction curves for all
samples leveled off, suggesting that the sequencing depth was adequate to encompass most
of the microbial diversity in the samples. The samples of V75 demonstrated the highest
species richness, as evidenced by its consistently higher curve compared to samples of
V10 and V35 (Supplementary Figure S1). In addition, as shown in Table 3, the numbers of
operational taxonomic units (OTUs) obtained for the V75, V35, and V10 groups are 1022,
931, and 1093, respectively, with good coverage reaching 99%. A Venn diagram illustrates
the unique and shared OTUs among the most abundant organisms, showing that there
are 916 common OTUs between the three treatments (Figure 5). Notably, the number of
OTUs in the V10 group was the highest compared to the other groups. In addition, the
V10 group also has the highest community distribution abundance index (ACE) and Chao1
(1156 and 1180) but the lowest Simpson index (highest diversity) compared to those of the
V75 and V35 groups (Table 3). In addition, the results of microbial diversity showed a large
variation among different aeration intensity groups (Figure 6).
Water 2025, 17, x FOR PEER REVIEW 8 of 20
V75 group, the order Alteromonadales was identified with a significant association (p <
0.05).
Figure 3. The composition and abundance of the microbial community in different aeration intensity
groups. (A) Phylum level; (B) Class level; (C) Genus level; (D) Species level; (E) Heatmap of bacteria
taxa abundances at the species level.
Figure 3. The composition and abundance of the microbial community in different aeration intensity
groups. (A) Phylum level; (B) Class level; (C) Genus level; (D) Species level; (E) Heatmap of bacteria
taxa abundances at the species level.
Table 3. Diversity metrics for microbial communities of biofloc under different aeration intensities.
Observed OTU ACE Chao1 Simpson Shannon Good’s Coverage
V75 1022 1105 1130 0.94 5.32 0.99
V35 931 981 995 0.94 5.34 0.99
V10 1093 1156 1180 0.92 5.29 0.99
Notes: OTU: operational taxonomic unit; ACE: abundance-based coverage estimator; Chao1: Chao1 richness
estimator; Simpson: Simpson diversity index; Shannon: Shannon-Wiener diversity index; Good’s Coverage:
Good’s coverage estimator.
Water 2025,17, 41 9 of 19
Water 2025, 17, x FOR PEER REVIEW 9 of 20
Figure 4. Linear discriminant analysis (LDA) effect size (LEfSe) showing significance differences in
relative abundance among different aeration intensity groups (DUNN test, p < 0.05, LDA cutoff >
3.0).
3.5. Diversity Analysis
To evaluate the data adequacy and microbial diversity, a metagenomic α-diversity
rarefaction curve was performed (Supplementary Figure S1). The rarefaction curves for
all samples leveled off, suggesting that the sequencing depth was adequate to encompass
most of the microbial diversity in the samples. The samples of V75 demonstrated the high-
est species richness, as evidenced by its consistently higher curve compared to samples of
V10 and V35 (Supplementary Figure S1). In addition, as shown in Table 3, the numbers of
operational taxonomic units (OTUs) obtained for the V75, V35, and V10 groups are 1022,
931, and 1093, respectively, with good coverage reaching 99%. A Venn diagram illustrates
the unique and shared OTUs among the most abundant organisms, showing that there
are 916 common OTUs between the three treatments (Figure 5). Notably, the number of
OTUs in the V10 group was the highest compared to the other groups. In addition, the
V10 group also has the highest community distribution abundance index (ACE) and
Chao1 (1156 and 1180) but the lowest Simpson index (highest diversity) compared to those
of the V75 and V35 groups (Table 3). In addition, the results of microbial diversity showed
a large variation among different aeration intensity groups (Figure 6).
Table 3. Diversity metrics for microbial communities of biofloc under different aeration intensities.
Observed OTU ACE Chao1 Simpson Shannon Good’s Coverage
V75 1022 1105 1130 0.94 5.32 0.99
V35 931 981 995 0.94 5.34 0.99
V10 1093 1156 1180 0.92 5.29 0.99
Figure 4. Linear discriminant analysis (LDA) effect size (LEfSe) showing significance differences in
relative abundance among different aeration intensity groups (DUNN test, p< 0.05, LDA cutoff > 3.0).
Water 2025, 17, x FOR PEER REVIEW 10 of 20
Notes: OTU: operational taxonomic unit; ACE: abundance-based coverage estimator; Chao1: Chao1
richness estimator; Simpson: Simpson diversity index; Shannon: Shannon-Wiener diversity index;
Good’s Coverage: Good’s coverage estimator.
Figure 5. Venn diagram showing the unique and shared operational taxonomic units (OTUs) con-
sidering the most abundant organisms.
Figure 6. The diversity analysis between aeration intensity groups: (A) Principal coordinate analysis
(PCoA) based on the unweighted Unifrac metric of microbiome among all samples. The percentage
of variation explained by indicated axis. (B) Non-metric multidimensional scaling (NMDS) plots
showing the difference of microbiome in different aeration intensity groups at the OTU level based
on Bray-Curtis distances. The 2D stress was 0.054.
3.6. Redundancy Analysis (RDA)
RDA was used to discern significant linkages between microbial community struc-
tures and water quality parameters/nutrient compositions of bioflocs under different aer-
ation intensities (Figure 7). In Figure 7A,B, the first two axes of the RDA axis account for
37.83% of the total variation, with the RDA ordination 1 accounting for 22.05% of the var-
iation. Aeration intensity significantly affected the water quality parameters (Figure 7A, p
= 0.006). However, no significant correlation between microbial community composition
Figure 5. Venn diagram showing the unique and shared operational taxonomic units (OTUs) consid-
ering the most abundant organisms.
Water 2025,17, 41 10 of 19
Water 2025, 17, x FOR PEER REVIEW 10 of 20
Notes: OTU: operational taxonomic unit; ACE: abundance-based coverage estimator; Chao1: Chao1
richness estimator; Simpson: Simpson diversity index; Shannon: Shannon-Wiener diversity index;
Good’s Coverage: Good’s coverage estimator.
Figure 5. Venn diagram showing the unique and shared operational taxonomic units (OTUs) con-
sidering the most abundant organisms.
Figure 6. The diversity analysis between aeration intensity groups: (A) Principal coordinate analysis
(PCoA) based on the unweighted Unifrac metric of microbiome among all samples. The percentage
of variation explained by indicated axis. (B) Non-metric multidimensional scaling (NMDS) plots
showing the difference of microbiome in different aeration intensity groups at the OTU level based
on Bray-Curtis distances. The 2D stress was 0.054.
3.6. Redundancy Analysis (RDA)
RDA was used to discern significant linkages between microbial community struc-
tures and water quality parameters/nutrient compositions of bioflocs under different aer-
ation intensities (Figure 7). In Figure 7A,B, the first two axes of the RDA axis account for
37.83% of the total variation, with the RDA ordination 1 accounting for 22.05% of the var-
iation. Aeration intensity significantly affected the water quality parameters (Figure 7A, p
= 0.006). However, no significant correlation between microbial community composition
Figure 6. The diversity analysis between aeration intensity groups: (A) Principal coordinate analysis
(PCoA) based on the unweighted Unifrac metric of microbiome among all samples. The percentage
of variation explained by indicated axis. (B) Non-metric multidimensional scaling (NMDS) plots
showing the difference of microbiome in different aeration intensity groups at the OTU level based
on Bray-Curtis distances. The 2D stress was 0.054.
3.6. Redundancy Analysis (RDA)
RDA was used to discern significant linkages between microbial community structures
and water quality parameters/nutrient compositions of bioflocs under different aeration
intensities (Figure 7). In Figure 7A,B, the first two axes of the RDA axis account for 37.83% of
the total variation, with the RDA ordination 1 accounting for 22.05% of the variation. Aeration
intensity significantly affected the water quality parameters (Figure 7A, p= 0.006). However,
no significant correlation between microbial community composition and water quality
parameters was detected (Table 4and Figure 7B). In addition, the aeration intensity signifi-
cantly affected the nutrient composition of the biofloc (p= 0.006, Figure 7C). Moreover, the
microbial community structures were positively correlated with LC-PUFA (p= 0.022) and
MUFA (p= 0.005) (Table 4). Ruegeria sp. and Salipiger profundus were positively correlated
with LC-PUFA, and MUFA showed positive correlations with Ruegeria sp. (Figure 7D).
Table 4. Results of redundancy analysis (RDA) showing the relationship between water parame-
ters/nutrient compositions and the first two RDA axes, including the coefficients of determination
(r2) and statistical significance (p-values).
Parameters RDA1 RDA2 r2pValue
Water quality
pH 0.85 −0.52 0.45 0.16
FV −0.9 −0.32 0.14 0.57
TAN −0.96 −0.27 0.48 0.13
T 0.99 −0.04 0.46 0.16
SAL −0.97 0.25 0.42 0.19
DO −0.53 0.85 0.07 0.83
TSS −0.65 −0.76 0.01 0.99
Nitrate 0 0 0 1
Nitrite −0.73 0.68 0.09 0.72
FS 0.99 −0.13 0.19 0.52
Nutrient compositions
Water 2025,17, 41 11 of 19
Table 4. Cont.
Parameters RDA1 RDA2 r2pValue
Lipid 0.70 0.72 0.03 0.89
Protein 0.90 −0.43 0.12 0.67
LC-PUFA 0.95 −0.29 0.70 0.02
MUFA 0.59 0.81 0.83 0.01
SFA −0.78 0.63 0.30 0.33
Notes: FV: floc volume; TSS: total suspended solids; T: temperature; Sal: salinity; DO: dissolved oxygen; TAN:
ammonia nitrogen; FS: flocculation size; Lipid: crude lipid; Protein: crude protein; LC-PUFA: long-chain polyun-
saturated fatty acids; MUFA: monounsaturated fatty acids; SFA: saturated fatty acids.
Water 2025, 17, x FOR PEER REVIEW 12 of 20
Figure 7. Redundancy analysis (RDA) biplot showing the relationship between microbial commu-
nity composition and water quality parameters/nutrient compositions of biofloc under different aer-
ation intensities. Arrows represent water quality parameters (A,B) and nutrient compositions (C,D),
with their length indicating the strength of the association. The red circles in (C,D) represent micro-
bial abundance.
3.7. Functional Characteristics of the Microbiota
Analysis of the gene profile revealed nearly identical functions among three treat-
ment groups at each pathway level (Figure 8). A total of 3257 gene counts were annotated,
with the highest number of gene counts observed in the metabolism pathway (Figure 8A).
Although the V35 group had a relatively increased level of genetic information processing
compared with the other two groups at the first-level component of KEGG, no marked
difference was detected (Figure 8B). Meanwhile, the amino acid metabolism and carbohy-
drate metabolism were dominant in all experiment groups at the second level (Figure 8C).
Furthermore, the ribosome biogenesis, valine, leucine, isoleucine biosynthesis, citrate cy-
cle (TCA cycle), and C5-branched dibasic acid metabolism were the main functions de-
tected in all groups at the third-level component of KEGG (Figure 8D). In addition, at the
ortholog level, malate dehydrogenase (K00024) and glutamine synthetase (K01915) were
dominant in all three experimental groups. However, the expression of transketolase
(K00615) in group V10 was significantly higher than in the other two groups (p < 0.05).
Figure 7. Redundancy analysis (RDA) biplot showing the relationship between microbial community
composition and water quality parameters/nutrient compositions of biofloc under different aeration
intensities. Arrows represent water quality parameters (A,B) and nutrient compositions (C,D),
with their length indicating the strength of the association. The red circles in (C,D) represent
microbial abundance.
3.7. Functional Characteristics of the Microbiota
Analysis of the gene profile revealed nearly identical functions among three treatment
groups at each pathway level (Figure 8). A total of 3257 gene counts were annotated,
with the highest number of gene counts observed in the metabolism pathway (Figure 8A).
Water 2025,17, 41 12 of 19
Although the V35 group had a relatively increased level of genetic information processing
compared with the other two groups at the first-level component of KEGG, no marked
difference was detected (Figure 8B). Meanwhile, the amino acid metabolism and carbohy-
drate metabolism were dominant in all experiment groups at the second level (Figure 8C).
Furthermore, the ribosome biogenesis, valine, leucine, isoleucine biosynthesis, citrate cycle
(TCA cycle), and C5-branched dibasic acid metabolism were the main functions detected in
all groups at the third-level component of KEGG (Figure 8D). In addition, at the ortholog
level, malate dehydrogenase (K00024) and glutamine synthetase (K01915) were dominant
in all three experimental groups. However, the expression of transketolase (K00615) in
group V10 was significantly higher than in the other two groups (p< 0.05).
Water 2025, 17, x FOR PEER REVIEW 13 of 20
Figure 8. Comparing the functional characteristics of the microbiome among different aeration in-
tensity treatments. (A) The number of genes annotated in the Kyoto Encyclopedia of Genes from
the 40 samples. (B) The comparison of functional KEGG between the three groups at the first-level
component of KEGG, (C) the second-level component of KEGG, (D) the third-level component of
KEGG, and (E) ortholog level. The horizontal axis represents the relative abundance of annotated
genes. The histograms show the top 20 annotated genes predicted in the metabolic pathways.
The nitrogen cycling diagram highlights key microbial pathways and associated gene
expression across aeration intensity groups (Figure 9). Group V75 exhibited significant
involvement in nitrification and ANRA, while group V35 showed enhanced denitrifica-
tion activity, particularly in NO
2−
to NO and N
2
O to N
2
conversions (p < 0.05) (Figure 9A).
Groups V10 and V35 demonstrated notable DNRA activity, indicated by larger pathway
markers (p < 0.05) (Figure 9A). Group V75 showed elevated expression of nitrification
(nirK, nirA, nxrB) and ANRA (nasA, nirA) genes (p < 0.05) (Figure 9B). In contrast, group
V35 exhibited higher expression of denitrification genes (nirS, norB), consistent with its
role in nitrogen gas production (p < 0.05) (Figure 9B). Group V10 had increased expression
of DNRA-related genes (narG/narZ/nxrA and nirB) compared to the other groups (p <
0.05) (Figure 9B).
Figure 8. Comparing the functional characteristics of the microbiome among different aeration
intensity treatments. (A) The number of genes annotated in the Kyoto Encyclopedia of Genes from
the 40 samples. (B) The comparison of functional KEGG between the three groups at the first-level
component of KEGG, (C) the second-level component of KEGG, (D) the third-level component of
KEGG, and (E) ortholog level. The horizontal axis represents the relative abundance of annotated
genes. The histograms show the top 20 annotated genes predicted in the metabolic pathways.
The nitrogen cycling diagram highlights key microbial pathways and associated gene
expression across aeration intensity groups (Figure 9). Group V75 exhibited significant
involvement in nitrification and ANRA, while group V35 showed enhanced denitrification
activity, particularly in NO
2−
to NO and N
2
O to N
2
conversions (p< 0.05) (Figure 9A).
Groups V10 and V35 demonstrated notable DNRA activity, indicated by larger pathway
markers (p< 0.05) (Figure 9A). Group V75 showed elevated expression of nitrification (nirK,
nirA, nxrB) and ANRA (nasA, nirA) genes (p< 0.05) (Figure 9B). In contrast, group V35
exhibited higher expression of denitrification genes (nirS, norB), consistent with its role
in nitrogen gas production (p< 0.05) (Figure 9B). Group V10 had increased expression of
Water 2025,17, 41 13 of 19
DNRA-related genes (narG/narZ/nxrA and nirB) compared to the other groups (p< 0.05)
(Figure 9B).
Water 2025, 17, x FOR PEER REVIEW 14 of 20
Figure 9. Analysis of the pathways and genes involved in the nitrogen cycle and carbon cycle. Rel-
ative abundances of the pathways (A) and genes (B) in the nitrogen cycle. Note: The pie chart indi-
cates the relative abundance of each pathway in each metagenome sample. The size of pie charts
represents the total relative abundance of each pathway. ANRA: assimilatory nitrate reduction to
ammonium; DNRA: dissimilatory nitrate reduction to ammonium; Anammox: anaerobic ammo-
nium oxidation.
4. Discussion
The fluctuations in water quality parameters for each experimental group were
within the acceptable ranges for the aquaculture of L. vannamei [35]. Floc volume (FV)
indicates the total volume occupied by bioflocs in the water column, which is a key meas-
ure of the biofloc density and aggregation within the system. It helps in monitoring the
balance between the microbial biomass and the available nutrients, ensuring optimal con-
ditions for the growth and metabolic activities of the microorganisms [36]. In this study,
FV was positively correlated with the aeration intensity, showing the highest values in the
V75 group. Similar results are represented by Lara et al. (2017) [37], Lim et al. (2021) [38],
and Zhang et al. (2006) [39]. The high FV value typically indicates a robust microbial com-
munity that can effectively utilize and recycle nutrients within the system, contributing to
improved water quality and overall system stability [40]. In the BFT system, aerobic het-
erotrophic bacteria primarily consume organic maer and contribute to the aggregation
of particles, playing a crucial role in the formation and stabilization of bioflocs [12,41–43].
We found high aeration intensity (V75) supported the growth and activity of aerobic het-
erotrophic bacteria, such as Muricauda sp. and Fuerstiella marisgermanici, showing a trend
towards the formation of more flocs compared to the low aeration intensity group. This is
likely because the high aeration intensity increases oxygen availability, which accelerates
bacterial metabolism, supports efficient oxidative-reduction reactions, and enhances the
enzyme activity of aerobic bacteria, ultimately promoting their growth and reproduction
[44]. Additionally, high aeration facilitates the oxidation and decomposition of organic
maer (such as carbon sources), providing the energy and carbon necessary for the
growth of heterotrophic bacteria [45]. However, high levels of aeration intensity (V75 and
V35) may lead to the disintegration of flocs, which in turn results in a reduction in the floc
size. A similar finding was reported by Yu et al. (2011) [46], where a decrease in floc size
corresponded with an increase in aeration intensity.
The structure of bioflocs not only includes the size but also the complexity, which
determines the seling velocity of the flocs in water and the probability of being captured
by aquatic animals [47–49]. The 2D fractal dimension is a key indicator of the structural
complexity of bioflocs. Research has shown that aeration intensity influences the 2D frac-
tal dimension, which subsequently affects the ability of cultured species to capture
Figure 9. Analysis of the pathways and genes involved in the nitrogen cycle and carbon cycle. Relative
abundances of the pathways (A) and genes (B) in the nitrogen cycle. Note: The pie chart indicates the
relative abundance of each pathway in each metagenome sample. The size of pie charts represents
the total relative abundance of each pathway. ANRA: assimilatory nitrate reduction to ammonium;
DNRA: dissimilatory nitrate reduction to ammonium; Anammox: anaerobic ammonium oxidation.
4. Discussion
The fluctuations in water quality parameters for each experimental group were within
the acceptable ranges for the aquaculture of L. vannamei [
35
]. Floc volume (FV) indicates the
total volume occupied by bioflocs in the water column, which is a key measure of the biofloc
density and aggregation within the system. It helps in monitoring the balance between
the microbial biomass and the available nutrients, ensuring optimal conditions for the
growth and metabolic activities of the microorganisms [
36
]. In this study, FV was positively
correlated with the aeration intensity, showing the highest values in the V75 group. Similar
results are represented by Lara et al. (2017) [
37
], Lim et al. (2021) [
38
], and Zhang et al.
(2006) [
39
]. The high FV value typically indicates a robust microbial community that can
effectively utilize and recycle nutrients within the system, contributing to improved water
quality and overall system stability [
40
]. In the BFT system, aerobic heterotrophic bacteria
primarily consume organic matter and contribute to the aggregation of particles, playing a
crucial role in the formation and stabilization of bioflocs [
12
,
41
–
43
]. We found high aeration
intensity (V75) supported the growth and activity of aerobic heterotrophic bacteria, such as
Muricauda sp. and Fuerstiella marisgermanici, showing a trend towards the formation of more
flocs compared to the low aeration intensity group. This is likely because the high aeration
intensity increases oxygen availability, which accelerates bacterial metabolism, supports
efficient oxidative-reduction reactions, and enhances the enzyme activity of aerobic bacteria,
ultimately promoting their growth and reproduction [
44
]. Additionally, high aeration
facilitates the oxidation and decomposition of organic matter (such as carbon sources),
providing the energy and carbon necessary for the growth of heterotrophic bacteria [
45
].
However, high levels of aeration intensity (V75 and V35) may lead to the disintegration of
flocs, which in turn results in a reduction in the floc size. A similar finding was reported
by Yu et al. (2011) [
46
], where a decrease in floc size corresponded with an increase in
aeration intensity.
The structure of bioflocs not only includes the size but also the complexity, which
determines the settling velocity of the flocs in water and the probability of being captured
by aquatic animals [
47
–
49
]. The 2D fractal dimension is a key indicator of the structural
Water 2025,17, 41 14 of 19
complexity of bioflocs. Research has shown that aeration intensity influences the 2D
fractal dimension, which subsequently affects the ability of cultured species to capture
bioflocs [
47
,
48
]. In this study, the 2D fractal dimension was stable in the V75 group but
decreased in the V35 and V10 groups, indicating a reduction in the structural complexity of
bioflocs due to lower aeration intensity. This decrease may be due to less turbulent water
at lower aeration intensity [
50
]. The calmer environment can diminish the frequency of
collisions and interactions among floc particles, thereby reducing the likelihood of larger
particles fragmenting into smaller ones [
51
]. In addition, different aeration intensities
can significantly affect bubble size, distribution, and rise velocity. Lower aeration rates
produce fewer, larger bubbles, while higher rates create smaller, more numerous bubbles
due to increased flow speed. These variations can influence oxygen transfer efficiency,
biofloc formation, and the overall water environment in aquaculture systems [
52
–
54
]. In
the future, evaluation of the aeration effect on the bubble size, as well as its effect on the
biofloc formation, will be crucial to optimize floc structure, accommodating the needs of
various aquatic species and their distinct life stages.
The growth of heterotrophic bacteria in biofloc systems helps reduce harmful ammonia
and nitrite buildup in aquaculture wastewater, thus bolstering the integrity and health of
the aquatic ecosystem [
55
]. While the influence of aeration intensity on the nitrification
process within biofilms in L. vannamei culture has been previously reported [
9
], its effects
on microbial communities, key contributors to water purification, remain insufficiently
explored. In this study, nitrite, nitrate, and TAN remained relatively stable during the
experiment period among the three groups, except for the nitrate in the second week.
Based on the results of RDA analysis between microflora and water quality parameters,
the Ruegeria sp. and Celeribacter indicus, which are members of Roseobacteraceae, were
negatively correlated with TAN and nitrite levels. These bacteria play a crucial role in
the nitrogen cycle, mediating ammonia and nitrite oxidation to produce nitrate [
2
,
56
,
57
].
They also contribute to denitrification, reducing nitrate to nitrogen gas, which is vital for
maintaining nitrogen balance in marine environments [
58
–
60
]. Accordingly, analysis of
nitrogen cycle pathways and associated genes revealed that Group V75 was primarily
involved in nitrification, with upregulated expression of genes nirK, nirA, and nxrB, as
well as assimilatory nitrate reduction to ammonium, marked by increased expression
of nasA and nirA. In contrast, group V35 showed heightened activity in denitrification
(nirS, norB), particularly in the conversion of NO
2−
to NO and N
2
O to N
2
. These results
indicate that high aeration effectively reduces nitrate and nitrite concentrations, maintaining
levels within the optimal range for sustainable shrimp aquaculture [
61
]. Our results
underscore the importance of Roseobacteraceae in maintaining the ecological health of
marine systems. To be noted, it is important to highlight the significance of microbial toxicity
tests in understanding nitrification, denitrification, and the anammox process [
62
,
63
].
These tests can provide valuable insights into how toxic compounds or environmental
stressors affect nitrogen metabolism in biofloc systems. Including such assessments in
future studies will help optimize microbial efficiency and improve nitrogen management
in aquaculture systems.
Bioflocs serve as an additional nutritional source for L. vannamei, providing essential
nutrients, such as fatty acids, that support growth, immune function, and overall health [
64
].
In this study, the contents of crude protein, lipid, and polyunsaturated fatty acids (PUFAs)
in the biofloc did not differ significantly across the three treatments. However, the DHA
content varied significantly, with the highest value observed in the V75 treatment. DHA is a
component of integral cell membranes, contributing to membrane fluidity and function [
65
].
Microbial cells may adjust their membrane composition to maintain homeostasis under
varying oxygen conditions. In the high aeration intensity (V75) group, the increase in DHA
Water 2025,17, 41 15 of 19
levels can be attributed to its complex enzymatic process involving key enzymes such as
elongase and desaturase. This process implicates oxidative reactions, where oxygen acts as
the terminal electron acceptor in the electron transport chain [
66
]. Manoj, (2015) [
67
] high-
lighted the crucial role of oxygen in the generation of adenosine triphosphate (ATP), which
is an essential molecule that powers the enzymatic reactions required for DHA synthesis.
Since DHA is a long-chain fatty acid with twenty-two carbon atoms and six unsaturated
bonds, its synthesis process requires more ATP compared to other fatty acids. Therefore,
enhanced aeration provides the necessary oxygen for the DHA synthesis process, support-
ing the synthesis of DHA and ultimately leading to the observed increase in DHA levels.
Interestingly, aeration intensity did not significantly affect the levels of other fatty acids
in the bioflocs, and the underlying mechanisms behind this phenomenon remain unclear.
The possible explanation is that the biosynthetic pathways of these fatty acids may be less
sensitive to changes in oxygen availability [
68
]. This specific response could be attributed
to the microecological complexity of the biofloc system and the functional partitioning of
microbial metabolism. To be noted, some physiological parameters of the bioflocs in the
system were not determined in this study. These parameters may include the respiration
activity, nitrification activity, and dehydrogenase activity of microorganisms
[62,63]
. Since
these parameters reflect the microbial metabolic and enzymatic activities that drive nutrient
cycling and degradation processes of microorganisms [
62
,
63
], they would be very useful
for future investigations. Such data could provide deeper insights into the efficiency of
nutrient transformation, microbial community dynamics, and overall system sustainability.
The functional annotation of genes within microbiota across different groups revealed
nearly identical metabolic activities, highlighting the similarity of microbial communities
in response to different aeration intensities. Specifically, the detection of ribosome-related
functions indicates that a significant portion of the microbiota is engaged in protein syn-
thesis, which is fundamental for bacterial growth and replication [
69
,
70
]. Additionally, the
biosynthesis of valine, leucine, and isoleucine, known as the branched-chain amino acids
(BCAAs), was dominant in the total detected functions. BCAAs are integral to numerous
metabolic pathways and are frequently recognized as indicators of microbial activity [
71
].
Furthermore, the citrate cycle (TCA cycle) is a central metabolic pathway involved in
the oxidation of acetyl-CoA derived from carbohydrates, fats, and proteins to produce
ATP and other metabolic intermediates [
72
]. The results indicated continuous aerobic
respiration and energy production within the microbiota of BFT. Therefore, regulating
biofloc technology enables the maintenance of its efficacy in purifying water while concur-
rently supplying necessary nutrients to aquaculture species, thus yielding ecological and
economic advantages.
5. Conclusions
Aeration intensity influences biofloc characteristics, with lower intensity producing
larger, simpler flocs and higher intensity enhancing DHA accumulation. Regardless of
aeration levels, biofloc technology effectively reduces ammonia and nitrite concentrations.
Microbial functions related to nitrogen metabolism and protein synthesis remain consistent
across all aeration conditions, ensuring stable biofloc performance. Our study highlights
the importance of optimizing aeration strategies for L. vannamei farming and effective
aquaculture waste management. Future research should explore the impact of aeration on
microbial communities and integrate these insights with performance metrics. In addition,
biofloc technology is currently widely used in the aquaculture of L. vannamei and tilapia.
This study’s findings offer valuable insights for farming these species, though further
evaluation is needed to assess its applicability to other aquaculture species.
Water 2025,17, 41 16 of 19
Supplementary Materials: The following supporting information can be downloaded at: https://
www.mdpi.com/article/10.3390/w17010041/s1, Figure S1: The metagenomic
α
-diversity rarefaction
curves (mean) under different aeration intensity treatments; Table S1: The growth performance of the
Litopennaeus vannamei over the two-month study period.
Author Contributions: Conceptualization, W.F.; data curation, M.Z. and X.L.; formal analysis,
M.Z. and T.L.; funding acquisition, W.F. and J.W.; investigation, M.Z. and X.L.; methodology, T.H.;
supervision, W.F.; writing—original draft, Han Tao; writing—review &and editing, T.H., W.F. and
J.W. All authors have read and agreed to the published version of the manuscript.
Funding: Funding for this study was provided by several key initiatives, including the Major
Scientific and Technological Research Projects of Zhoushan with the grant number 2023C13017, the
Zhejiang Province “Three Agricultural Six-Party” Science and Technology Collaboration Program
under the identifier 2024SNJF059, the Key Research and Development Program of Zhejiang Province
with the project number 2021C04016, and the Science and Technology Bureau of Zhoushan, which
supported the project with the code 2022C41021.
Data Availability Statement: Data will be made available on request.
Conflicts of Interest: The authors declare that they have no known competing financial interest or
personal relationships that could have appeared to influence the work reported in this paper.
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