Fine-Scale Structuring of Planktonic Vibrio spp. in the Chinese Marginal Seas

ABSTRACT

Vibrio is ubiquitous in marine environments with high metabolism flexibility and genome plasticity. Studies have investigated the ecological distribution of Vibrio spp. in several narrow zones, but a broad scale pattern of distribution and community assembly is still lacking. Here, we elucidated the distribution of Vibrio spp. in seawater along the Chinese marginal seas with a high spatial range. Comparison of Vibrio abundance between 3- and 0.2-μm-pore-size membranes showed distinction in preferential lifestyle. Vibrio spp. in the Yellow Sea (YS) was low in abundance and adopted a particle-associated lifestyle, whereas that in the East China Sea (ECS) and South China Sea (SCS) was more abundant and was likely in a temporary free-living state as a strategy to cope with nutrient limitation. Vibrio community compositions were also separated by sampling area, with different dominant groups in YS (Vibrio chagasii and Vibrio harveyi), ECS and SCS (Vibrio japonicus and V. chagasii). The community niche breadth was significantly wider in ECS and SCS than that of YS. Among species, V. chagasii and V. harveyi had the largest niche breadths likely reflecting strong competitive positions. Stochastic processes played important roles in shaping the geographical pattern of the vibrionic community. Environmental selection (e.g., temperature, salinity, and dissolved oxygen) had a much greater impact on the community in surface than in bottom water. The large proportions of unexplained variations (78.9%) imply complex mechanisms in their community assembly. Our study provides insights into the spatial distribution patterns and underlying assembly mechanisms of Vibrio at a broad spatial scale.

IMPORTANCE Vibrio spp. may exert large impacts on biogeochemical cycling in coastal habitats, and their ecological importance has drawn increasing attention. Here, we investigated the spatial distribution pattern and community assembly of Vibrio populations along the Chinese marginal seas, spanning a wide spatial scale. Our results showed that the abundances of the Vibrio population increased with decreasing latitude and their preferential lifestyle differed among adjacent coastal areas. The compositions of Vibrio spp. were also separated by geographical location, which was mainly attributable to stochastic processes. Overall, this work contributes to the understanding of the ecological distribution patterns and the community assembly mechanisms of marine vibrios at a high spatial range. The large proportion of unexplained variations indicates the existence of complex mechanisms in the assembly of vibrionic community which should be considered comprehensively in future.

INTRODUCTION

The genus Vibrio is a group of Gram-negative rods, belonging to the class Gammaproteobacteria with both respiratory and fermentative metabolisms. Vibrio spp. are widely distributed in estuarine and marine habitats worldwide, even, including deep-sea hydrothermal vents, and sediments and bottom seawater in trenches (1, 2). Species within the Vibrio genus are halophilic in nature, grow fast with a much shorter generation time (as short as ~10 min), and possess broad metabolic activity with the capability in producing multiple extracellular enzymes (3–5). More than 130 species of Vibrio have been validly described to date (https://lpsn.dsmz.de/genus/vibrio), and several well-known Vibrio spp. can cause disease to human and marine animals, such as Vibrio cholerae, Vibrio parahaemolyticus, and Vibrio vulnificus (6–10). However, more species are documented to be nonpathogenic and participate in the element transformation, e.g., Vibrio diazotrophicus and Vibrio nitrifigilis capable of nitrogen fixation (5, 11–13). Considering the wide distribution and remarkable capability in decomposing diverse carbon sources (e.g., chitin and alginate), Vibrio spp. may exert large impacts on biogeochemical cycling in coastal habitats (5, 14).

The ecological importance of Vibrio has drawn increasing attention in estuarine and coastal environments worldwide, including the North Carolina coast, the Gulf Coast of Mexico, the Sydney Harbor estuary, and the Northern Chinese marginal seas, etc. (3, 15–17). In estuarine and coastal waters, the average abundance of Vibrio population is 10⁴ to 10⁸ 16S rRNA gene copies L⁻¹ and generally occupies ~1% of the total bacterioplankton, whereas they can comprise up to 10% of the readily culturable bacteria (up to 10³ to 10⁶ CFU L⁻¹) (5, 18). Moreover, various Vibrio spp. may exist in different marine environments. Vibrio sp. OTU13800 and Vibrio mimicus are the dominant groups in the Sydney Harbor estuary (17), whereas V. cholerae, V. vulnificus, and V. parahaemolyticus appear at two Patagonia estuaries in Argentina (14). In the Northern Chinese marginal seas, warmer seawater could favor the growth of Vibrio, and their main growth strategy is free-living (3). The study about dynamics of the total and active Vibrio spp. throughout the Changjiang estuary finds Vibrio atlanticus and Vibrio owensii are the dominant and active species in winter and summer, respectively (4). Ecological distribution of vibrios has also been studied in many offshore bays, such as Dongshan Bay (Fujian, China), in which they reveal that Vibrio assemblages display a clear distance-decay pattern spanning four seasons across the whole year (10). However, most of the above-mentioned studies focus on only a narrow zone of the area (e.g., estuary area, harbor, or bay), neglecting the exploration of the shifts in the Vibrio community at a broad spatial scale. Additionally, microbial plankton are organized into clearly defined communities whose turnover is rapid and sharp, and thus the stability of planktonic Vibrio communities and populations in the ocean is largely unknown (19). Communities are ephemeral in the ocean water column as a possible consequence of water mass dynamics, hydrology, water quality, predation, or hyper mutation (13, 20, 21).

Numerous and various environmental factors increase the complexity and necessity of studying the ecological distribution of Vibrio in marginal seas (3). The major challenges in microbial ecology are revealing the assembly mechanisms that determine the community composition, and how microorganisms respond to environmental and spatial change, which mainly focus on deterministic and stochastic mechanisms (22, 23). Both environmental filtering and stochastic processes simultaneously shape microbial biogeography (22–24). Deterministic mechanism predicts that environmental factors will influence microbial community composition (25). Indeed, many studies have attempted to explain the affecting factors of Vibrio dynamics under various trophic conditions, including temperature, salinity, pH, dissolved oxygen, chlorophyll a, and nutrients (3, 4, 13, 17). From many reports above, temperature and salinity are considered the most common key factors that impact marine vibrios, whereas other parameters vary depending on the habitat (3, 4, 13). But in recent years, the role of stochastic processes has received more considerable discussion, which suggests that microbial community assembly is influenced by random events (e.g., birth, death, and dispersal) (26). Stochastic processes shape bacterial community assemblies in different environments such as soil, lake, and seawater (27–29). Similarly, Li et al. (26) reveal that stochastic processes govern the turnover of marine Vibrio communities in the Beibu Gulf, China. However, studies at a small scale can be one-sided, where the stochastic processes frequently play greater roles (30). Thus, to fully understand the importance of ecological processes, the sampling scale should be large enough.

The Chinese marginal seas span tropical, subtropical, and temperate zones and exhibit significant hydrographic diversities (3, 31). The ecosystems of the Chinese marginal seas are heavily affected by anthropogenic activities and receive large amounts of terrestrial pollutants, leading to high nutrient inputs (32). Moreover, the combined effects of complex water masses and ocean currents result in distinct distribution patterns of water columns (31, 33, 34). A reserve ecologic approach suggests species may be defined as gene flow unities or genomic unities which have specific ecological niches (35). The fine-scale resource partitioning may thus define conspecific populations which can evolve sympatrically (36). Certain Vibrio species have a more narrow distribution in the coastal bacterioplankton, whereas other groups (e.g., Vibrio harveyi and Vibrio splendidus) may be very common (37). Closely related Vibrio species may develop the so called public good for harvesting iron and/or resisting antibiotics in the water column (38). Thus, here is an excellent candidate for evaluating the spatial distribution of planktonic Vibrio spp. In this study, we investigated the marginal Vibrio spp. in the Yellow Sea (YS), East China Sea (ECS), and South China Sea (SCS) off the China coast. The abundance and community components of Vibrio spp. were studied using quantitative PCR (qPCR) and high-throughput sequencing, and the key drivers and assembly process of the Vibrio community were illustrated by multiple methods (e.g., neutral model). Our study helps to better understand the dynamics and preferential lifestyles of vibrios in the context of environmental heterogeneity.

RESULTS

Environmental conditions.

The environmental parameters measured during the study period are summarized in Table 1. Based on geographic location, all samples were divided into three clusters: YS, ECS, and SCS (Fig. 1). The seawater temperature, including surface and bottom samples, varied significantly of the sites among these three groups (P < 0.01; Table 1; see Fig. S1 and S2 in the supplemental material), with the mean value increased by 15.44°C from YS to SCS. The pH (8.13 ± 0.05) and NH₄⁺ (0.07 ± 0.60 μmol/L) concentrations in the YS group were significantly lower than those in the other two groups, whereas dissolved organic carbon (DOC) (1.60 ± 0.30 mg L⁻¹) in YS was highest. However, salinity (32.34 ± 0.54 to 33.40 ± 1.56 practical salinity units [PSU]) and the concentrations of dissolved oxygen (DO) (6.70 ± 0.48 to 6.89 ± 0.34 mg/L), NO₃⁻ (3.87 ± 6.21 to 9.27 ± 9.19 mol/L) and NO₂^– (0.10 ± 0.28 to 0.23 ± 0.13 mol/L) showed no significant differences among the three groups (P > 0.05). In addition, other physicochemical attributes also showed variations among different seas and layers (Fig. S1 and S2).

TABLE 1.

Spatial and environmental parameters throughout the Changjiang estuary

Physicochemical parametersa	Valueb
	Yellow Sea	East China Sea	South China Sea
Longitude (°E)	122.67 ± 1.58	122.49 ± 1.16	114.99 ± 2.84
Latitude (°N)	35.02 ± 0.81	28.29 ± 1.24	22.00 ± 1.58
Depth (m)	16.46 ± 22.82	44.71 ± 142.22	25.51 ± 34.13
Temp (°C)	8.55 ± 0.78	18.88 ± 2.97	23.99 ± 1.90
Salinity (PSU)	32.34 ± 0.54	32.21 ± 2.43	33.40 ± 1.56
Chl a (μg/L−1)	0.50 ± 0.54	1.51 ± 2.08	0.70 ± 0.58
DO (mg/L−1)	6.89 ± 0.34	6.86 ± 2.01	6.70 ± 0.48
pH	8.13 ± 0.05	8.43 ± 0.17	8.50 ± 0.08
NO3– (μmol/L−1)	6.18 ± 3.27	9.27 ± 9.19	3.87 ± 6.21
NO2– (μmol/L−1)	0.23 ± 0.13	0.23 ± 0.32	0.10 ± 0.28
NH4+ (μmol/L−1)	0.07 ± 0.60	0.10 ± 0.09	0.33 ± 0.43
PO43– (μmol/L−1)	0.35 ± 0.23	0.24 ± 0.44	0.01 ± 0.04
SiO32– (μmol/L−1)	9.20 ± 4.21	14.43 ± 16.70	3.66 ± 3.90
DOC (mg/L−1)	1.60 ± 0.30	1.10 ± 0.23	1.17 ± 0.37
DIC (mg/L−1)	ND	18.89 ± 3.65	16.46 ± 7.75
TN (mg/L−1)	ND	0.32 ± 0.11	0.16 ± 0.08

^aND, no data; Chl a, chlorophyll a; DO, dissolved oxygen; DOC, dissolved organic carbon; DIC, dissolved inorganic carbon; TN, total nitrogen.

^bStatistical differences after chi-square test.

FIG 1.

Map showing locations of study area and sampling sites. The map was created using Ocean Data View version 5.1.2 (R. Schlitzer, Ocean Data View, https://odv.awi.de, 2018). Details are shown in Table S2.

Abundances of total Vibrio spp. in different sea areas.

Quantitative PCR was used to detect the pattern of the abundance of the vibrionic 16S rRNA gene, which showed significant differences among different sea areas (chi-square test, P < 0.01; Fig. 2A). Samples from sites located in the south tended to have higher copy numbers of the vibrionic 16S rRNA gene, and the mean value increased from 1.70 ± 0.84 log copies per mL⁻¹ to 2.78 ± 0.51 log copies per mL⁻¹ with decreasing latitude (Fig. S3). Meanwhile, we compared the Vibrio abundance on 3- and 0.2-μm-pore-size membranes to infer their preferential lifestyle. In ECS and SCS, Vibrio was more abundant in the free-living than the particle-associated fraction, whereas the particle-associated fraction was high in YS (Fig. 2). Similarly, in YS, comparison of the relative abundances of Vibrio (the proportion of Vibrio to total bacteria) in different size fractions revealed a higher relative abundance in the particle-associated group than in the free-living group (Fig. S4). Environmental correlation analysis showed that Vibrio abundance across all samples was positively correlated with temperature and pH (P < 0.01), and negatively correlated with longitude, latitude (r = −0.366; P < 0.01), depth, PO₄^3–, NO₂^–, SiO₃^2– and DOC across the entire data set (P < 0.01; Table 2). In contrast, the abundance of total bacteria had little relationship to latitude and showed higher values (7.50 log copies per mL⁻¹) in the ECS. Total bacteria were more abundant in the free-living fraction among sea areas and water depths except for the YS bottom water (Fig. 2); they were only negatively related to DOC (r = −0.261, P = 0.002; Table 2).

FIG 2.

Total Vibrio and bacteria abundance (log copies/mL) with different lifestyles among the YS, ECS and SCS. YS, blue boxplots; ECS, orange boxplots; SCS, purple boxplots. A. The abundance of free-living (F) and particle-attachment (P) Vibrio spp. in total samples. B. The abundance of free-living and particle-attachment bacteria in total samples. C and D: The abundance of Vibrio spp. and bacteria in surface seawater. E and F: The abundance of Vibrio spp. and bacteria in bottom seawater.

TABLE 2.

Vibrio and bacterial abundance in each marine area and the correlation analysis with environmental factors^a

Sea areas and environmental factors	Value for Vibrio spp.
	Total samples	Surface seawater			Bottom seawater
		Whole	Particle-associated fraction	Free-living fraction	Whole	Particle-associated fraction	Free-living fraction
YS	1.70 ± 0.84	1.84 ± 0.93	2.22 ± 1.00	1.50 ± 0.75	1.36 ± 0.43	1.46 ± 0.19	1.25 ± 0.61
ECS	2.16 ± 0.75	2.04 ± 0.73	1.99 ± 0.67	2.09 ± 0.79	2.28 ± 0.76	2.16 ± 0.62	2.40 ± 0.87
SCS	2.78 ± 0.51	2.83 ± 0.44	2.59 ± 0.37	3.07 ± 0.39	2.72 ± 0.57	2.45 ± 0.48	3.00 ± 0.54
Longitude (°E)	−0.372	−0.481	−0.381	−0.585
Latitude (°N)	−0.366	−0.379		−0.564	−0.316		−0.395
Depth (m)	−0.165	−0.543	−0.502	−0.587
Temp (°C)	0.431	0.446		0.664	0.376		0.551
Salinity (PSU)
Chl a (μg/L−1)
DO (mg/L−1)
pH	0.399	0.425		0.591	0.371		0.434
NH4+ (μmol/L−1)				0.454
PO43− (μmol/L−1)	−0.416	−0.566	−0.395	−0.728	−0.318		−0.479
NO3− (μmol/L−1)
NO2− (μmol/L−1)	−0.334	−0.287		−0.474	−0.413	−0.354	−0.454
SiO32− (μmol/L−1)	−0.236	−0.282		−0.437
DOC (mg/L−1)	−0.222				−0.271	−0.406
DIC (mg/L−1)						−0.54
TN (mg/L−1)	−0.242
Sea areas and environmental factors	Value for total bacteria
		Surface seawater			Bottom seawater
	Total samples	Whole	Particle-associated fraction	Free-living fraction	Whole	Particle-associated fraction	Free-living fraction
YS	5.88 ± 0.58	5.91 ± 0.64	5.67 ± 0.71	6.15 ± 0.47	5.78 ± 0.43	5.77 ± 0.52	5.80 ± 0.41
ECS	6.07 ± 0.57	5.89 ± 0.49	5.75 ± 0.43	6.04 ± 0.52	6.26 ± 0.58	6.18 ± 0.59	6.34 ± 0.57
SCS	6.00 ± 0.51	5.98 ± 0.57	5.63 ± 0.51	6.32 ± 0.41	6.03 ± 0.44	5.97 ± 0.51	6.10 ± 0.37
Longitude (°E)
Latitude (°N)
Depth (m)					−0.24	−0.478
Temp (°C)
Salinity (PSU)			−0.35			−0.374
Chl a (μg L−1)			0.347
DO (mg L−1)			0.339
pH
NH4+ (μmol/L−1)
PO43− (μmol/L−1)					0.239
NO3− (μmol/L−1)						0.467
NO2− (μmol/L−1)
SiO32− (μmol/L−1)					0.29	0.363
DOC (mg/L−1)					−0.308
DIC (mg/L−1)	−0.261
TN (mg/L−1)

^aOnly significant correlations (P < 0.05) were shown in table; boldface data, P < 0.01.

Richness and diversity estimators of Vibrio spp.

After Illumina sequencing, 2,512,623 overlapped reads were generated in total, ranging from 30,180 to 88,017 in all samples. After quality control, 1,347,265 reads were left (Table S1), and the read number in each sample was limited to 22,835 after rarefaction. The total sequences yielded 1,724 operational taxonomic units (OTUs) at a 97% sequence similarity level (Table S1). The Good’s coverage values ranged from 99.96 to 100.00% across entire samples, indicating that the retrieved sequences could represent most of the Vibrio community in the studied sites. The evenness and Shannon index (including both richness and evenness) were shown to be variable across sea areas (Fig. S5); they differed significantly between YS and SCS (Wilcoxon’s test, P < 0.05), and increased gradually from YS to SCS for total Vibrio spp. (Fig. S5B and C). There is a significant difference in the Shannon index between the bottom seawater of the ECS and SCS (Wilcoxon’s test, P < 0.05; Fig. S5I). In contrast, there was no significant difference in alpha diversity among these three sea areas in surface seawater (P > 0.05; Fig. S5D to F).

Spatial distribution and community classification of Vibrio species.

A comparison of Vibrio community composition among different sea areas was performed with principal-component analysis (PCA) at the OTU level. Spatial shifts in the Vibrio assemblages were observed (Fig. 3A), and the first two principal components accounted for 30.21% of the total variation. The YS samples were clearly separated from the ECS and SCS (analysis of similarity [ANOSIM], P < 0.01), whereas no significance was shown between ECS and SCS samples (Fig. 3), which may be caused by the effect of ocean currents and water masses. ECS24SW was located south of the ECS, but was positioned close to the YS cluster, which may be due to its proximity to the shore. Such a spatial heterogeneity was found in both the surface and bottom samples (ANOSIM, P < 0.05; Fig. 3B and C). Moreover, there was a significant distance-decay pattern for the Vibrio community in all samples (Fig. 5), with a Spearman correlation coefficient of 0.305 (P < 0.01) between the Bray-Curtis community dissimilarity and geographic distance. This pattern was more pronounced in the bottom samples (r = 0.403, P < 0.01) than the surface samples (r = 0.254, P < 0.01).

FIG 3.

PCA (A to C) and RDA (D to F) at the OTU level. (A and D) all samples; (B and E) surface seawater; (C and F) bottom seawater.

FIG 5.

The community compositions of Vibrio at species level. A. The community compositions of Vibrio across all samples. B. Their community compositions in surface seawater. C. Their compositions in bottom seawater.

To identify specific taxa that contributed to the observed spatial dynamics of Vibrio communities (Fig. 4), representative sequences of each OTU were compared against the EzBioCloud database to determine their taxonomic identities. Almost all sequences (98.27%) belonged to the Vibrionaceae family, and 74.37% were assigned to the Vibrio genus. Twenty-three abundant species (>94.80%) were found in total samples across all sites (Fig. 4). The most dominant species was V. chagasii occupying 25.40% of all sequences, followed by V. harveyi, V. japonicus OTU125 and OTU112 which jointly accounted for 35.60% of all sequences. Spatial heterogeneity in the abundant species was observed and most species (65.22%) were shown to be distinctly distributed across the three areas (P < 0.05), such as V. chagasii, V. japonicas, Vibrio marisflavi, Aliivibrio sifiae, and Photobacterium profundum (Fig. S6). For example, V. chagasii was less abundant in YS than in other sea areas (P < 0.01), while V. harveyi was slightly lower in ECS. The relative abundance of V. japonicus OTU125 and OTU112 were significantly higher in ECS and SCS than in YS (P < 0.01). In addition, we found a high relative abundance of Vibrio atypicus and A. sifiae in YS, Photobacterium phosphoreum in ECS, and Paraphotobacterium marinum in SCS (P < 0.01). The clustering analysis based on the most abundant species is similar to the OTU-level PCA, revealing three geographic groups corresponding to the different sampling areas (Fig. S6). In surface seawater, the components of Vibrio communities were more similar between ECS and SCS than YS-ECS and YS-SCS (one-way ANOVA; P < 0.05), whereas OTUs displayed different patterns in bottom seawater, i.e., clustering in three groups (Fig. S6).

FIG 4.

Distance-decay relationships (A to C) and variation partitioning analyses (D to F). Pairwise dissimilarities (the Bray-Curtis index) are plotted as a function of the distance among sites. The data are pairwise dissimilarities between the communities at 53 sites. The blue line represents the best linear regression result, and the gray areas represent confidence intervals greater than 95%. A, D: total samples; B, E: surface seawater; C, F: bottom seawater.

Relative importance of deterministic and stochastic processes in the Vibrio community assembly.

To explore the key environmental drivers shaping the Vibrio community, environmental correlations were analyzed by redundancy analysis (RDA) (Fig. 3). For total samples, the first and second axes explained 20.34% and 7.89% of the total variance, respectively (Fig. 3D). Monte Carlo permutation tests identified eight factors, i.e., latitude (F = 7.8, P = 0.001), temperature (F = 7.5, P = 0.001), longitude (F = 6.1, P = 0.002), pH (F = 5.6, P = 0.002), PO₄^3– (F = 5.0, P = 0.001), SiO₃^2– (F = 4.1, P = 0.005), salinity (F = 3.3, P = 0.003), and chlorophyll a (Chl a) (F = 2.4, P = 0.039), that significantly affected the Vibrio community structure. In surface samples, temperature, latitude, salinity, and DO showed significant effects on the community structure, whereas pH, latitude, temperature, longitude, and PO₄^3– showed significant effects on the community structure in bottom samples (Fig. 3). The Spearman’s correlations between the top 23 most abundant species and the environmental factors were calculated (Table 3). V. chagasii, the most abundant species, had significant correlations with longitude, latitude, temperature, PO₄^3–, SiO₃^2–, total nitrogen (TN), pH, and NO₂^– (P < 0.05). V. harveyi was only negatively related to PO₄^3– (P < 0.05). V. japonicus OTU125 and OTU112, the dominant groups in ECS and SCS, were positively related to temperature, Chl a, and pH, and negatively to PO₄^3– (P < 0.05). In addition, OTU125 was also positively correlated with DO (P < 0.05), whereas OTU112 was negatively correlated with latitude (P < 0.05). Detailed correlation coefficients between all taxa and environmental factors were summarized in Table 3.

TABLE 3.

Correlation between percentage composition of taxa and environmental factors^a

Vibrio taxa	Lon	Lat	Depth	Tem	Sal	Chl a	DO	pH	NH4+	PO43-	NO3-	NO2-	SiO32-	DOC	DIC	TN
Vibrio chagasii OTU69	0.486	0.504		−0.555				−0.345		0.495		0.287	0.404			0.553
Vibrio harveyi OTU38										−0.325
Vibrio japonicus OTU125				0.321		0.362	0.293	0.583		−0.433
V. japonicus OTU112		−0.311		0.35		0.27		0.54		−0.351
Paraphotobacterium marinum OTU140	−0.444	−0.647		0.583	0.552	−0.442	−0.349	0.34			−0.397	−0.481	−0.509	−0.316	0.479	−0.723
Photobacterium leiognathi subsp. mandapamensis OTU59	−0.322	−0.389	0.327	0.346	0.376		−0.284	0.34						−0.556
Photobacterium phosphoreum OTU30			0.477				−0.307	−0.282		0.419			0.455
Vibrio sagamiensis OTU62	−0.539	−0.625		0.664	0.351			0.414		−0.581	−0.388	−0.471	−0.393
Vibrio sp. OTU26		0.366		−0.416				−0.463		0.462		0.389	0.353
Vibrio japonicus OTU110
Photobacterium angustum OTU50	−0.417	−0.518		0.429	0.294		−0.356							−0.333
Vibrio sp. OTU72	−0.451	−0.377		0.509				0.365		−0.503			−0.29
Aliivibrio sifiae OTU2	0.434	0.606	0.285	−0.699				−0.572	−0.459	0.753		0.414	0.461
Vibrio marisflavi OTU137	−0.421	−0.402		0.298
Vibrionaceae sp. OTU43			0.483		0.266		−0.316			0.293				−0.386
Photobacterium kishitanii OTU53			0.417		0.419	−0.321	−0.439							−0.568
Vibrio sp. OTU158								0.349	0.47
Vibrio atypicus OTU6				−0.293			−0.306	−0.303		0.381
Alteromonadaceae sp. OTU8							−0.33
Photobacterium profundum OTU11	0.443	0.552	0.312	−0.532		−0.36		−0.477	−0.432	0.54			0.293
Photobacterium damselae subsp. damselae OTU102	−0.373	−0.334	0.396		0.381		−0.365
P. kishitanii OTU46			0.297				−0.265						0.306	−0.582
Vibrio sp. OTU73	−0.403	−0.344		0.359		0.273				−0.456					0.372

^aOnly significant correlations (P < 0.05) are shown; boldface data, P < 0.01. Lon, longitude; Lat, latitude; Tem, temperature; Sal, salinity.

To determine how the deterministic (environmental factors) and stochastic processes contribute to community structure, variation partitioning analysis (VPA) and the neutral model were applied. The VPA analysis revealed that 21.1% of the variation in the entire community was explained by environmental and spatial variables (Fig. 5D). The pure effect of environmental factors (7.8%) was greater than that of spatial variables (2.2%; Fig. 5D). In surface seawater, the pure effect of environmental factors (21.7%) was significantly greater than that of spatial variables (4.0%; Fig. 5E); whereas the pure effect of spatial variables (4.3%) was slightly greater than that of environmental factors in bottom seawater (2.6%; Fig. 5F). However, VPA had an apparent limitation that there were high levels of unexplained variation (64.6 to 83.0%; Fig. 5). The relationship between the occurrence frequency of OTUs and their relative abundance was well described by the neutral community model, indicating that stochastic process played a crucial role in structuring the vibrionic communities (Fig. 6). Stochastic processes played a more important role in shaping the vibrionic communities in surface seawater (60.5%) than that in bottom seawater (58.6%; Fig. 6B and C). Further, all vibrionic plankton communities exhibited significantly wider niche breadths in the ECS and SCS than in YS (t test, P < 0.01; Fig. 6D). In surface samples, the average niche breadth showed no significance (t test, P > 0.05), whereas in bottom seawater, ECS, and SCS samples showed significantly wider niche breadths than that of YS (t test, P < 0.01). At the species level, V. chagasii and V. harveyi had the largest niche breadths, followed by V. japonicus, Vibrio sagamiensis and Photobacterium angustum (Fig. 7A), which may reflect stronger competitive positions. In contrast, V. atypicus and Aliivibrio sifiae showed relatively smaller niche breadths than other species (Fig. 7A). The niche overlaps also reflected the similarity and competition degree of resource utilization among species in the Chinese marginal seas (Table S3 and Fig. 7B). The five most abundant species (i.e., V. chagasii, V. harveyi, V. japonicus OTU125 and OTU112, and P. marinum) showed smaller niche overlap values than the rest (Table S3).

FIG 6.

Neutral modeling and niche breadth of vibrionic communities in the Chinese marginal seas. (A and D) all samples; (B and E) surface seawater; (C and F) bottom seawater. OTUs that occur more frequently than the value predicted by the model are shown in red, while those that occur less frequently than predicted are shown in blue. Gray points represent the frequency of occurrence within the 95% confidence interval range around the model prediction (black line). **, (P < 0.01) and ***, (P < 0.001) indicate significant difference using t test.

FIG 7.

Niche breadths and niche overlap values of the top 23 abundant species.

DISCUSSION

Vibrio spp. are indigenous and ubiquitous heterotrophic bacteria in marine environments that have been extensively studied (5, 26). Various reports aimed at understanding the abundances and community compositions of pelagic Vibrio spp. have focused mostly on several narrow zones worldwide, such as estuarine, harbor, or bay ecosystems (3, 4, 10, 26, 39). However, current information about exploration of the shifts in the planktonic Vibrio community at a broadly spatial scale is still limited. Here, as a complement to previous studies, our study assessed the spatial distribution of Vibrio communities as well as their lifestyles among YS, ECS, and SCS. To our knowledge, this is the first investigation regarding a geographic distribution pattern of vibrios in various marine environments of the Chinese marginal seas on such a large spatial range scale. Our results showed that Vibrio spp. exhibited substantial spatial heterogeneity and various lifestyles in seawater among different sea areas with a joint effect of spatial and environmental factors as well as assembly process.

Spatial heterogeneity of Vibrio spp. in seawater among YS, ECS, and SCS.

Previous studies have demonstrated that the community composition of Vibrio was clearly separated in different marine environments worldwide, and distinct Vibrio species might be the dominant group (5, 17, 39). For example, in Chinese marginal seas, V. atlanticus and V. owensii are the dominant and active species in winter and summer throughout the Changjiang estuary (4), whereas Vibrio fluvialis and Vibrio fortis occupy the largest proportion of the Vibrio community in the Maowei Sea (Beibu Gulf) and aquaculture areas in Dongshan Bay, respectively (10, 39). In this study, we found a significant distance-decay relationship of vibrionic communities in total samples across Chinese marginal seas. Several studies have reported microbial biogeography targeting the total bacterial population and abundant and rare subcommunities (29, 40). Rare OTUs were found to be more strongly influenced by distance-decay than abundant OTUs, suggesting that rare taxa are more diverse than abundant taxa at different locations (40). Due to the low relative abundance (<0.1%; Fig. 2), Vibrio can be seen as rare taxa (41). Our discovery of spatial heterogeneity was consistent with that observed in sedimentary Vibrio population (41) and rare subcommunities (29, 40). In addition, the distance-decay slope was steeper in the bottom seawater than in surface samples, and the sediments showed the steepest distance-decay slope (41), indicating that historical processes (such as geographical separation) have a greater influence in deeper horizon.

The abundant Vibrio populations were diverse along the coast of China and contained several species that were dominant and evenly distributed, such as V. chagasii OTU69, V. harveyi OTU38, V. japonicus OTU125 and OTU112 (Fig. 4). This dominance of common species among the different sea areas may have caused the relatively low variability in the population structure as evidenced by less than 50% explanatory power in the PCA analyses (Fig. 3). The most common species (i.e., V. chagasii OTU69 and V. harveyi OTU38) across all samples comprised the highest proportion in the Chinese marginal seas, indicating they can adapt to a wide range of environments. Indeed, V. chagasii and V. harveyi had the largest niche breadths (Fig. 3), which may reflect they have stronger competitive positions. The reason might be that V. chagasii and V. harveyi probably have capacity to utilize a broad range of substrates. It has been reported that many compounds (e.g., cellobiose, l-alanine, d-mannitol, d-psicose, and α-ketoglutaric acid) can be utilized by V. chagasii as sole carbon sources (42), whereas V. harveyi possesses the abilities to attach and form biofilms, such as quorum sensing, lipopolysaccharide, and various extracellular products (proteases and haemolysins) (43). Due to their genetically and metabolically diverse nature, Vibrio spp. may evolve several adaptive strategies to survive in various environments (5, 13). In this study, the most predominant Vibrio species (i.e., V. chagasii and V. harveyi) in seawater differed from other sea areas, e.g., Vibrio sp. 3459 and V. gigantis in sediments among YS, ECS, and SCS (41), and V. campbellii, V. caribbeanicus, and V. atlanticus in seawater of Bohai Sea and YS (3). Our study also found that the relative abundance of V. japonicus was significantly higher in ECS and SCS. V. japonicus can grow at 10 to 37°C (44), whereas the seawater temperature in YS (8.55 ± 0.78°C) was significantly lower than in ECS and SCS (Table 1). Additionally, specific local species occurred among YS, ECS, and SCS with the relative abundance even higher than those common species (Fig. S4). The reason might be that the separate environments in a particular sea selected for specific species that adapt and grow faster than common species (5, 41). Influenced by these site-specific species (e.g., V. atypicus and A. sifiae in YS; Fig. 4), total Vibrio community thus showed a significant distance-decay pattern (Fig. 5A). The ecological distribution and importance of Vibrio populations in various habitats worldwide merits further investigation in future.

The abundance of planktonic vibrios also showed obvious differences among the YS, ECS, and SCS (P < 0.01) with an increasing trend toward the decrement of latitude, in consistence with sedimentary Vibrio spp. (41). These may be contributed to by the various copy numbers of 16S rRNA genes among different Vibrio species, which possess from 6 to 14 operons (45). Special conditions of separate environments may affect the Vibrio abundance, and temperature and salinity are pivotal environmental determinants of total Vibrio numbers (13, 17, 46). In truth, in the present study, Vibrio abundance showed a positive correlation with temperature and negatively correlated with latitude (Table 2). However, there is no significant correlation between salinity and Vibrio abundance. The reason might be that few differences in salinity across sites, and other environmental factors also explain the variation to some extent (5, 15, 47), such as pH, DOC, and a series of inorganic nutrients in this study (P < 0.05; Table 2). Vibrio abundance showed no obvious differences between the surface and bottom seawater, and the reason might be that the sampling depths are too shallow, and the number of layers is too small. Indeed, Li et al. (48) collected water samples from 13 depths (0 to 230 m) in Sansha Yongle Blue Hole and found a dynamic change of Vibrio abundance from upper to bottom, and thus vertical variation in Vibrio sp. could be studied at high depth resolution in the future. In addition, the copies of 16S rRNA genes were ~3 orders of magnitude lower in seawater than values reported in sediments (10⁵ copies g⁻¹) from the same area (41). Seawater and sediment provide different living environments for vibrios, e.g., sediments may provide a variety of surfaces (biotic and abiotic) for bacterial attachment, and its higher organic matter concentration than that of the overlying water column (41, 49).

Ecological process and various abiotic factors affected the community assembly of Vibrio spp.

Certain environmental factors were often suggested to be predictors of Vibrio diversity, including hydrological gradients, spatial factors, inorganic and organic nutrients, pigments, and the abundance of host organisms (3–5, 10, 13, 17, 39, 41, 49). Among these factors, temperature and salinity exhibited the strongest correlation with Vibrio spp. (4, 10, 17, 18). In this study, the pure effect of environmental factors played greater roles in shaping planktonic Vibrio spp. across all samples (Fig. 5), and several deterministic factors (e.g., temperature, pH, salinity and Chl a) that affected the diversity of vibrios were found (Fig. 3). Comparing with the results of sedimentary Vibrio (41), the pure effect of environmental factors was greater than that of spatial variables in seawater (Fig. 5D). This may be explained by the combined effects of complex water masses, ocean currents, emission of industrial and domestic waste, as well as the differential discharge from the Changjiang River and Pearl River, resulting in significantly hydrographic diversities across the Chinese marginal seas (31, 50). Additionally, the major parameters for the biogeography of Vibrio spp. may also be different in separated layers due to the great difference in ranges of environmental factors (Fig. S1 and S2). Surface samples were mainly affected by environmental factors, and temperature, salinity, and DO showed significant effects (Fig. 3E). Indeed, the concentration of DO in surface seawater showed a larger range than that in bottom seawater across all the samples (Table S2). In consistent with the sedimentary Vibrio spp. (41), bottom samples were mainly affected by spatial factors. These interactions also include the latitude-dependent change in temperature.

However, an apparent limitation of VPA was the high levels of unexplained variation, which are reasonably common in geometric morphometrics, but are generally not expected (51, 52). These could be the result of unmeasured biotic or abiotic factors, directly or indirectly influencing bacterial communities (53, 54). To verify this, further study of how the stochastic processes affect microbial community assembly based on neutral theory is needed (55). We applied the neutral model in this study and found it fit well to the community assembly, indicating that stochasticity played an unneglectable role in driving the assembly of marine Vibrio community among the YS, ECS, and SCS (Fig. 6). Several reports of planktonic systems (e.g., lakes, wetlands, rivers, and gulfs) indicated that stochastic-dominant results were common, and fluidity and connectivity could contribute to stochasticity (26, 27, 56). Movement and hydrologic mixing (water masses and ocean currents) of seawater in the Chinese marginal seas can bring marine Vibrio species into new sea areas, which would create specific niches and exert selection forces to affect the microbial community and further increase the influence of stochasticity (26, 40, 57). In general, the interactions and development of shared niches usually showed increases with the increased resource aggregation (40). In the present study, the niche breadth was narrower in the YS than in ECS and SCS (Fig. 6). In a more homogeneous environment, decreased niche differentiation among species can lead to increased neutrality in community assembly (40, 58). All the findings prompt us to reexamine the difficulty of predicting spatiotemporal variability of Vibrio spp., and the stochastic process should be considered in future studies to fill the gap in the knowledge regarding marine Vibrio community assembly.

The preferential lifestyles of Vibrio spp. may differ among the YS, ECS, and SCS.

Most planktonic environments contain free-living and particle-associated bacteria, and free-living bacteria usually predominate in total numbers (59, 60). Several studies have shown that bacteria attached to particles may be phylogenetically different from free-living bacteria, which indicates that there are selective forces present in particle microenvironments (61, 62). As opportunistic bacteria, the particle-associated lifestyle might be an important growth strategy for vibrios to gain sufficient nutrients and even become the dominant group (13, 45). In this study, we observed a potential transition of preferential lifestyles of Vibrio spp. from particle associated in the YS to free living in the ECS and SCS. During the spring in the YS, there is a high abundance of picoeukaryotes and thus the nutrient-rich environment rescues particle-associated Vibrio species from competitive exclusion (62, 63). However, vibrios in ECS and SCS with relatively oligotrophic nature as shown by the lower DOC concentration compared to YS (Table 1) may experience nutrient-deficient conditions (64, 65). In this context, a free-living lifestyle may help Vibrio spp. find appropriate trophic niches through chemotactic motility (66). The preference for a free-living lifestyle at a particular time or in a particular sea area has also been observed in other studies, such as the northern Chinese marginal seas, the Sansha Yongle Blue Hole, and the Baltic and Skagerrak Seas (3, 48, 67, 68). These studies jointly suggest that though particle-associated lifestyle is the major lifestyle of Vibrio spp., free-living may serve as a temporary state likely in response to adverse environments (e.g., nutrient limitation). Alternation of lifestyles may also result from predation pressure as association to particles is easier to be ingested by predators (71). Despite these, factors determining whether Vibrio remain free-living or particle-associated are still unknown. Both environmental and genetic determinants could play potential roles, such as temperature, pH, salinity, ion concentration, and starvation state (13, 69, 70). Additionally, the specific species may also exhibit different preferential lifestyles between the YS and another two areas. Indeed, V. japonicus had high relative abundance in ECS and SCS and was first isolated from seawater collected from the Setonaikai, Japan (44), whereas relatively more V. atypicus were found in YS, which was isolated from the digestive tract of Penaeus chinensis (72). Taken together, our results suggest that the Vibrio population exhibits these two alternative growth strategies, allowing them to compete with other bacteria when faced with environmental changes.

Conclusions.

The spatial distribution of total Vibrio in seawater along Chinese marginal seas was investigated by qPCR and high-throughput sequencing. Vibrio abundance increased with the decreasing latitude and their distinction in preferential lifestyle differed among sea areas. The Vibrio community in the YS adopted a particle-associated lifestyle, whereas that in the ECS and SCS was in a temporary free-living lifestyle. The compositions of Vibrio spp. were also separated by geographical location, with different dominant groups in YS (V. chagasii and V. harveyi), ECS, and SCS (V. japonicus and V. chagasii). The community niche breadth was significantly wider in ECS and SCS, and V. chagasii and V. harveyi had the largest niche breadths likely reflecting strong competitive positions. Stochastic processes play important roles in shaping the geographical pattern of the vibrionic community based on the neutral model. Environmental selection had a much greater impact on the community in surface than in bottom water. However, complex mechanisms in the assembly of the vibrionic community may exist due to the large proportions of unexplained variations. Our study provides insights into the spatial distribution patterns and the influence factors at a high spatial range, and in the future, the community assembly mechanisms of Vibrio should be considered by multiple methods, such as the null model and cooccurrence network, and testing the postulated niches of main species by experiments.

MATERIALS AND METHODS

Site description, sampling, and isolation of Vibrio strains.

A total of 53 sites were sampled to compare the Vibrio communities among the YS, ECS, and SCS. Surface (SW) and bottom seawater (BW) were collected using a Sealogger conductivity-temperature-depth (CTD) (SBE25, Electronic Inc., USA) rosette water sampler (73). Samples were collected aboard the R/V Dong Fang Hong 2 and an offshore vessel (R/V Haili) during two cruises in the spring of 2017 (from 27 March to 17 May; Fig. 1). Based on spatial locations, the samples were divided into three groups, i.e., 10 sites in the YS (YS01 to YS10), 26 sites in the ECS (ESC01 to ECS26), and 17 sites in the SCS (SCS01 to SCS17).

One L of water for DNA analyses was filtered in sequence through 3-μm (TSTP, 142 mm, Millipore) and 0.22-μm (GTTP, 142 mm, Millipore) polycarbonate membranes. All filters were stored in liquid nitrogen onboard and transferred to −80°C in the laboratory until DNA extraction. Water chemistries such as salinity, temperature, pH, and dissolved oxygen (DO) were monitored with the CTD. Samples for dissolved inorganic nutrients (NO₂^–, NO₃^–, NH₄⁺, SiO₃^2–, and PO₄^3–) and Chl a analyses were collected according to our previous report (4). Concentrations of dissolved organic carbon (DOC) were analyzed by the high-temperature catalytic oxidation method (74, 75), and dissolved inorganic carbon (DIC) were measured using a Shimadzu TOC-L analyzer equipped with an ASI-V autosampler (76). After acid fumigation to remove the carbonate fraction, the TN was determined by an elemental analyzer (vario MICRO cube EA, Elementar, Germany) interfaced with a continuous flow isotope ratio mass spectrometer (Isoprime IRMS, Elementar, Germany) (77, 78).

Nucleotide acid extraction and quantitative PCR (qPCR).

DNA extraction was performed according to the previous report (4). The extracted DNA was resuspended in 50 μL TE buffer (1 M Tris-HCl, 0.5 M EDTA, pH 8.0). The genomic DNA were stored at −80°C until use. The abundance of total Vibrio and bacteria on 3- and 0.2-μm-pore-size membranes were tracked to compare the lifestyle, using 16S rRNA gene-targeted quantitative PCR (qPCR) with SYBR-green detection. Although lifestyle partition based on filter membrane size has some limitations, this strategy has been widely used (62, 79). qPCR was performed using the StepOnePlus real-time PCR system (Applied Biosystems) and StepOne software version 2.2. V567F and V680R primers were used to quantify the Vibrio genus (80, 81), whereas B967F and B1046R were used for total bacteria (Table 4) (81, 82). The standards were prepared from 16S rRNA genes nucleic acid templates of Vibrio rotiferianus WXL191 (our laboratory). Standard curves, reaction mixture, and cycling conditions were as previously described by Wang et al. (4). All extracted DNA was diluted 5-fold to reduce pipetting errors, and all the above-described tests were performed in triplicate. All amplification efficiencies were between 95% and 105% with R² values >0.99.

TABLE 4.

16S rRNA oligonucleotide primers for qPCR amplification and sequencing

Primers	Sequences (5′–3′)	Information on target gene	Reference
V567F/V680R	GGCGTAAAGCGCATGCAGGT/GAAATTCTACCCCCCTCTACAG	General Vibrio spp. (113 bp)	Thompson et al., 2004; Vezzulli et al., 2012
B967F/B1046R	CAACGCGAAGAACCTTACC/CGACAGCCATGCANCACCT	Total bacteria (79 bp)	Vezzulli et al., 2012; Sogin et al., 2006
V169F/V680R	GGATAACCTATTGGAAACGATG/GAAATTCTACCCCCCTCTACAG	General Vibrio spp. (511 bp)	Siboni et al., 2016

Vibrio diversity analysis via high-throughput sequencing.

To determine the composition of the Vibrio community among different ecological niches, the Vibrio-specific 16S rRNA gene primers V169F and V680R were used (Table 4) (17). The PCR system and cycling conditions were as previously described by Wang et al. (4). Positive amplicons were pooled in equimolar and paired-end sequences (2 × 300) on the Illumina MiSeq platform (Illumina, San Diego, CA, USA) following the manufacturer’s guidelines. Raw data files were deposited in NCBI Sequence Read Archive (SRA) (accession number SRP350334) under bioproject number PRJNA786099.

Vibrio 16S rRNA gene sequences were analyzed using the Quantitative Insights into Microbial Ecology (QIIME) pipeline (version 1.9.1) (83). Following the methods described in earlier studies, clean data were obtained from raw reads (3, 4). OTUs were defined at a 97% sequence similarity level, and then chimera sequences were detected and removed with UCHIME (84), as recommended by QIIME tutorials. Taxonomy was assigned using the RDP Classifier version 2.2 against the SILVA version 138 16S rRNA gene reference database (http://www.arb-silva.de) with a minimum support threshold of 70% (85). Vibrio sequences were reannotated in the EzBioCloud database (https://www.ezbiocloud.net/) to obtain a more accurate classification. To remove the effect of sampling effort upon analysis, sequences were then rarefied to the lowest read number for all samples with a “single rarefaction” QIIME script (83, 86).

Statistical analysis.

Spatial differences in environmental parameters were tested with by chi-square test. To compare the total abundances of Vibrio spp. and bacteria, the data were log [x + 1] transformed, and the chi-square and Kruskal-Wallis tests were performed. Correlations between qPCR results and environmental parameters were assessed using Spearman’s rank correlation analysis package. All analyses were performed using STATISTICA version 22.0 (StatSoft, Tulsa, OK, USA). The distance-decay pattern of the Vibrio community was examined, and the significance of correlations was tested by Spearman’s rank correlation tests.

The alpha diversity indices were calculated using MOTHUR software packages (87), including Good’s coverage, Chao I, equitability (a Shannon index-based measure of evenness), and Shannon. The significant difference among seas were calculated by Wilcoxon’s test. For beta diversity, PCA was performed using Canoco version 5.0 software (Microcomputer Power) at the species level. The relationships between phylotypes and environmental factors were evaluated by RDA in Canoco version 5.0 with 9999 Monte Carlo permutation tests using square-root-transformed data. NO₃^–, NH₄⁺, DOC, DIC, and TN data were available to a small number of samples, where most of them were missing (Table S2). Thus, these variables were removed from the RDA analysis. The relative contributions of pure spatial variables (Geo) and pure environmental variables (Env), and the combined effects of both space and environment were estimated by VPA, with adjusted R² coefficients based on RDA. Spearman’s rank correlation coefficients were calculated to determine the relationship between representative species and abiotic factors.

Neutral community model and niche breadth.

To estimate the effects of stochastic processes on the Vibrio community assembly, the neutral community model was used (88). The neutral community model applies nonlinear least-squares to generate the best fit between occurrence frequency and relative abundance of the OTUs (89, 90). According to “Östman’s method,” R² value was calculated, which indicated the goodness of fit to the model (91). When R² is close to 1, the community assembly is more consistent with stochastic processes; whereas assembly is more affected by deterministic processes when R² is close to 0 (89). Model computations were performed with a custom R code reported previously (92).

To quantify habitat specialization, we calculated Levins’ niche breadth (B) index for Vibrio community using the formula:

$B_j\hspace{0.17em}=\hspace{0.17em}\frac{1}{{{\displaystyle \sum }}^{}\begin{array}{c}N\\ i=1\end{array}{P}_{\mathit{ij}}^2}$

where B_j is the niche breadth and P_ij represents the proportion of OTU j in each site i, and N indicates the total number of sites (91–93). The community level B value (Bcom) was calculated as the average of B values from all taxa occurring in one given community (93, 94). The occurrences of OTUs were generated by simulating 1,000 permutations (quasiswap permutation algorithms) performed using EcolUtils and spaa R packages (95, 96). A higher B value of OTUs indicates that the OTUs were present and more evenly distributed on a large scale, whereas OTUs occurred in fewer habitats and were unevenly distributed have lower B values (97).