Effects of Fresh Groundwater and Seawater Mixing Proportions and Salt-Freshwater Displacement on Coastal Aquifer Microbial Communities

Abstract

Seawater intrusion significantly affects the microbial communities within coastal aquifers. Investigating the spatial distribution of groundwater microbial communities in coastal regions is crucial for understanding seawater intrusion. The primary objective of this study is to develop a novel microbial index-based method for detecting seawater intrusion. Groundwater microbial samples were collected and sent to the laboratory in the west coastal area of Longkou City, Shandong Province. By characterizing the microbial community within the whole interval of seawater intrusion into fresh groundwater and discussing the effects of salt-freshwater displacement intensities on groundwater microbial communities, including diversity, structure, and function, using indoor domestication experiments, we reveal the response of microorganisms to the seawater intrusion process under in situ environmental conditions. The results show that the microbial community diversity is highest in environments with a seawater mixing proportion (P_(sm)) of 2.5% and lowest in those with a P_(sm) of 75%. When considering species abundance and evolutionary processes, the microbial community structure is similar at higher P_(sm) levels, while it is similar at lower P_(sm) levels based on the presence or absence of species. Tenericutes, Flavobacteriia, Rhodobacterales, Flavobacteriales, Rhodobacteraceae, Flavobacteriaceae, Cohaesibacteraceae, and Cohaesibacter are significantly positively correlated with the P_(sm). Strong salt-freshwater displacement enhanced the richness and evenness of the microbial community, whereas weak displacement showed the opposite trend. Strong displacement affects the functional profiles of the microbial community. This study effectively addressed the challenge of obtaining samples in coastal areas and also incorporated salt-freshwater displacement intensities, which can more comprehensively describe the microbial community characteristics within the groundwater of coastal aquifers.

1. Introduction

Seawater intrusion, a prevalent occurrence in coastal aquifers, poses a significant global concern. The primary hazards include the reduction in effective freshwater storage and the contamination of pumping wells [1,2,3,4], as the mixing of even 1% seawater by volume renders freshwater non-potable [5]. Accurately detecting the extent of seawater intrusion and implementing preventative measures are thus imperative [6].

Currently, there are various mainstream methods for seawater intrusion detection and monitoring, including chemical [7,8,9], geophysical [10,11], and isotope [12,13,14] analyses. Chloride (Cl⁻) is the most common indicator in chemical methods. In most countries, a Cl⁻ concentration of 250 mg/L is used to mark the interface between salt- and freshwater. However, because of the different background values of Cl⁻ in groundwater in different areas [15,16], the standards for distinguishing Cl⁻ are also different, and it is difficult to unify them. In geophysical prospecting, electrical resistivity tomography techniques are typically used to analyze and monitor the salt-freshwater transition interface [10,17,18]. Nevertheless, the results are inaccurate because of the great influence of groundwater salinity and geological conditions on the survey signal. Numerous isotopes have been applied to seawater intrusion. They can distinguish paleo-seawater intrusion and modern seawater intrusion, as well as identify different sources of saltwater and their spatial salinity distribution characteristics [19,20]. Many financial and material resources have been lost because of the need for the joint determination of multiple isotopes. There is a developing trend to adopt multiple indices and methods for comprehensive discrimination, and reliable results have been obtained by multi-linkage. Microbial indicators play key roles in this process [21,22,23]. Microbial communities are influenced by the physical and chemical properties of groundwater [24,25] and are closely related to the nutrient transport [26,27], geochemical element cycling [28,29,30], and pollutant degradation [31,32,33] in aquifers. Moreover, high-throughput sequencing technology for testing microorganisms is widely used, with a short testing time and low cost. It provides an advanced channel for a detailed and accurate understanding of bacterial diversity in groundwater.

In the early stages, some studies and analyses focusing on groundwater microbial communities within the salt-freshwater transition zone were conducted, including temporal and spatial comparisons, to identify microbial indicators that could indicate seawater intrusion [34,35]. However, there is little research on the characteristics of the groundwater microbial community in the whole interval of seawater intrusion, most of which only explores the area near the salt-freshwater transition interface. We do not know what is occurring near the coast, which makes the conclusions unrepresentative and incomprehensive. Analyses of the responses of the microbial community to specific salinity in the whole interval of seawater intrusion are thus needed.

The identification of suitable underground wells for sample collection in coastal areas is a challenging task, as many wells do not exist or are difficult to find. This has significantly hampered the comprehensive understanding of microbial community characteristics in coastal groundwater. Under natural conditions, there is always a dynamic equilibrium between saltwater and freshwater in coastal aquifers. The rise and fall of global sea levels [36,37], the amount of regional rainfall, the development degree of groundwater resources [38,39,40], and the permeability change in aquifer medium [41] will all cause the salt-freshwater displacement to be strong and weak, the interface will swing, and the seawater content in the groundwater of a fixed position will change. The intensity of salt-freshwater displacement directly changes the environmental conditions and then affects the microbial community of groundwater. Within our knowledge, few studies have investigated the relationship between salt-freshwater displacement and groundwater microbial communities.

To address the above issues, a whole interval of seawater intrusion into a fresh groundwater system and a salt-freshwater displacement process were established indoors. The laboratory microbial domestication experiment was conducted at a constant temperature, and microbial analysis was performed on the experimental samples. Microbial communities were obtained by sequencing bacterial 16S rDNA. The primary goals of this study were to (1) quantitatively analyze the relationship between the groundwater microbial community and seawater content in the whole interval of seawater intrusion into fresh groundwater; (2) characterize the effect of salt-freshwater displacement on the groundwater microbial community because changes in the environment screen out more adaptable microbes; and (3) identify microbial indicators that can be used to detect seawater intrusion. We believe that the results of these experiments are very important for discussing the application of microbial indicators in the study of seawater intrusion.

2. Materials and Methods

2.1. Sampling

The sampling points were located west of Longkou City in Shandong Province. The climate of Longkou city typifies a warm–temperate sub-humid continental monsoon, characterized by a mean annual temperature of 11.7 °C. Over the past 20 years, the city has experienced an average annual rainfall of approximately 630.2 mm. Notably, approximately 72.7% of the annual precipitation occurs during the summer season. The annual evaporation is 540–580 mm. Saltwater samples were obtained from a monitoring well in the salt-freshwater transition zone for the extraction of microorganisms, and seawater near the west coast and fresh groundwater farther from the coast were used to obtain mixed water. The general indicators of the water samples collected are shown in Table S1.

Groundwater samples collected from the same location as the initial samples were used to extract microorganisms, and the microbial content of the initial samples was the same by default. The saltwater samples were vacuum-filtered through a 0.22 μm filter membrane (Millipore, Billerica, MA, USA) to collect microorganisms. Each filter membrane quantitatively filtered 2 L of the water sample (to ensure that the amount of water filtered by each filter membrane remained the same). Then, the microbial filter was placed in a 50 mL sterile centrifuge tube and stored in an ultra-low-temperature freezer at −80 °C. The seawater and fresh groundwater samples were also subjected to vacuum filtration through a 0.22 μm membrane (Millipore, Billerica, MA, USA) to filter out microorganisms. Filtered fresh groundwater and seawater samples were refrigerated at 4 °C and used to prepare the mixed water. The entire process was completed within a short period.

2.2. Experimental Material

The experiment utilized analytical-grade glucose (C₆H₁₂O₆) without the need for additional purification. Deionized water was used in the experiments. The instruments used in the experiment included an electronic balance (GL224-1SCN, Sartorius, Gottingen, Germany), constant-temperature incubator (303-5S, Xinnuo Instrument Group Co. Ltd., Shanghai, China), PTFE magnetic stirrer (B4, Koloke Biological & Medical Instrument Co.,Ltd., Shanghai, China), bench high-speed centrifuge (TG-18W, BIOBASE, Jinan, China), and speed-regulating multipurpose oscillator (HY-4A, Weier Experimental Supplies Co. Ltd., Suzhou, China).

2.3. Experimental Method

The experimental method was as follows:

(1) Seven groups (P1, P2, P3, P4, P5, P6, and P7) were established with different proportions of mixed water (seawater and fresh groundwater) as the domestication medium, and the corresponding glucose (C₆H₁₂O₆) was added to each group of mixed liquids. The specific mixing ratios and glucose contents are listed in

Table 1. Nutrient solution proportional distribution of seven groups in the full-scale seawater content experiment.

Sample	Freshwater (mL)	Seawater (mL)	C6H12O6 (mg)	P(sm)
P1	1000	0	750	0
P2	975	25	750	2.5%
P3	900	100	750	10%
P4	750	250	750	25%
P5	500	500	750	50%
P6	250	750	750	75%
P7	0	1000	750	100%

. Taking the proportion of seawater mixing (P_(sm)) as the characteristic value of mixed water, the calculation method is as follows (without considering the density of seawater and freshwater):

$$P_{\left(sm\right)}={\displaystyle \frac{\mathrm{Seawater} \left(\mathrm{m}\mathrm{L}\right)}{\mathrm{Seawater} \left(\mathrm{m}\mathrm{L}\right)+\mathrm{Fresh}\mathrm{Groundwater} \left(\mathrm{m}\mathrm{L}\right)}}$$

(2) The filter membrane containing microorganisms was treated to form a turbid liquid, which was placed in a 100 mL serum bottle with the corresponding proportion of the domestication medium.

(3) The serum bottles were wrapped with foil for protection against light and then placed in a 25 °C incubator for incubation.

(4) The domestication medium was changed every two days to ensure adequate nutrition.

(5) Step (4) was repeated for 180 days of acclimation domestication.

(6) Two groups of samples, P4 and P5, were selected and divided into three parts, labelled P4.1, P4.2, P4.3, P5.1, P5.2, and P5.3.

(7) P4.1 was taken as the control group, and only the original solution was replaced.

(8) A weak displacement with a gradient of P_(sm) = 1% and strong displacement with a gradient of P_(sm) = 5% were applied to P4.2 and P4.3, respectively. The displacement conditions for each cycle of the experiment are listed in

Table 2. Displacement condition of one cycle of two groups in the salt-freshwater displacement experiment.

Time (Day)	P4-P(sm)			P5-P(sm)
	P4.1	P4.2	P4.3	P5.1	P5.2	P5.3
1	25%	25%	25%	50%	50%	50%
3	25%	26%	30%	50%	51%	55%
5	25%	27%	35%	50%	52%	60%
7	25%	28%	40%	50%	53%	65%
9	25%	27%	35%	50%	52%	60%
11	25%	26%	30%	50%	51%	55%
13	25%	25%	25%	50%	50%	50%
15	25%	24%	20%	50%	49%	45%
17	25%	23%	15%	50%	48%	40%
19	25%	22%	10%	50%	47%	35%
21	25%	23%	15%	50%	48%	40%
23	25%	24%	20%	50%	49%	45%

. The medium of different proportions was replaced every two days.

(9) The P5 group underwent the same procedure as the P4 group. The experiment was conducted in three cycles. The experimental procedure is illustrated in Figure 1.

2.4. Microbial Analysis

All samples were centrifuged in sterile tubes at 3000 rpm for 10 min to separate the supernatant and pellet, which were then used for microbiological analysis. Total DNA was extracted using a PowerWater DNA Isolation Kit (Mobio, Solana Beach, CA, USA) in triplicate [42]. The extracted DNA was then pooled and amplified by PCR using barcoded primers 341F (5′-CCTAYGGGRBGCASCAG-3′) and 806R (5′-GGACTACNNGGGTATCTAAT-3′) [43]. The data were preprocessed, and the initial data were divided into different units according to the barcode sequence. QIIME was used to analyze the sequencing data [44,45,46]. The obtained reads were compared to those in the Gold database to obtain the final valid data. We set a relative number of 0.1% as the taxa threshold.

2.5. Statistical Analysis

The alpha diversity indices of the microbial community were calculated using Mothur (version 1.30.1), including Shannon, Chao 1, observed species, and phylogenetic diversity (PD) [47,48,49]. The similarity patterns of microbial communities were analyzed by beta diversity at different points with beta diversity indices, including Weighted Unifrac, Unweighted Unifrac, and Bray–Curtis distances using the ‘vegan’ package in R (version 3.6.3). The Pearson correlation analysis was used to evaluate the relationship between the proportion of seawater mixing and species diversity at a p < 0.05 level using SPSS.19.0. The functional prediction of the groundwater microbial community was assessed using PICRUSt to obtain a function abundance table at each level [50].

3. Results

3.1. Different Proportions of Seawater Mixing

3.1.1. Microbial Community Diversity and Structure

The alpha diversity of the microbial communities was compared using indices such as Shannon, Chao1, observed species, and PD (

Table 3. Alpha diversity indices of seven groups in the full-scale seawater content experiment.

Sample	Diversity Indices
	Shannon	Chao1	Observed Species	PD
P1	4.38	279	167	11.9
P2	5.00	245	164	10.6
P3	4.05	226	139	9.6
P4	4.41	279	147	9.7
P5	4.00	275	148	10.4
P6	3.49	243	162	10.8
P7	4.96	297	189	10.5

). According to the Shannon index, P2 (original sample) is the maximum and P6 is the minimum, indicating that the microbial community diversity is the highest in the medium with a P_(sm) of 2.5% and the lowest in the medium with a P_(sm) of 75%. Chao1 and the observed species index show that P3 is the minimum, whereas P7 (pure seawater) is the maximum, indicating that the richness of these two groups of samples is the minimum and maximum, respectively.

Proteobacteria are the most abundant in all samples. The relative abundance of Proteobacteria is highest and lowest in P2 and P7, accounting for 89.0% and 77.9% of all bacteria at the phylum level, respectively. In contrast, Bacteroidetes have the lowest relative abundance in P2 and the highest relative abundance in P7, accounting for 2.5% and 13.7%, respectively. Firmicutes (0.4–6.0%), Cyanobacteria (0.3–3.6%), and Actinobacteria (0.3–1.2%) are the most important phyla. Notably, Spirochaetes and Tenericutes are detected in P1, P4, P5, P6, and P7 but not in P2. Verrucomicrobia are only found in media with <10%, Chlorobi are only found in media with >50%, and Gemmatimonadetes are only detected in P1 (pure freshwater) (Figure 2A).

Figure 2B–D show the main microbial community structures at the class, order, and family levels of the seven groups of samples. Alphaproteobacteria (39.3–84.9%) are the dominant class of bacteria, Rhizobiales (15.0–46.5%) and Rhodobacterales (1.32–43.5%) are the dominant orders of bacteria, and there are more dominant bacteria at the family level. By comparing the composition of the microbial community, it has been observed that P3 exhibits the highest degree of similarity with P2 at the phylum and family levels. Simultaneously, P5 and P6 show similar microbial community compositions at the four levels (Figure 2).

To show the differences and trends of the seven groups of samples at the genus level more intuitively, the abundance values are presented in the form of a Bubble diagram, as shown in Figure 3. Pseudomonas, Acidovorax, Pseudoxanthomonas, Sphingomonas, Mycoplana, Exiguobacterium, Phenylobacterium, and Sphingobium are found to be in the full-scale range (P_(sm): 0–100%). Hydrogenophaga exist in media with <10%; Sporomusa are found only in the pure freshwater environment; and Sediminibacterium and Lysobacter are found in media with <25%. Maricaulis, Muricauda, and Winogradskyella are found in media with >25%. Diaphorobacter, Azorhizobium, and Asticcacaulis are present in environments with P_(sm) < 50%, whereas Vibrio, Nautella, and Balneola are found in environments with P_(sm) > 50%.

3.1.2. Relationship between Microbial Community and P_(sm)

The Pearson correlation analysis was performed on the microbial community abundance of five levels (phylum, class, order, family, genus) and P_(sm), and the results are shown in

Table 4. The Pearson correlation analysis of microbial communities of five levels and P_(sm) in the full-scale seawater content experiment (top 10 is selected for each level).

Level	Species	P(sm)	Level	Species	P(sm)
Phylum	Proteobacteria	−0.36	Order	Rhodospirillales	−0.45
Phylum	Bacteroidetes	0.49	Order	Flavobacteriales	0.95 **
Phylum	Firmicutes	−0.42	Order	Pseudomonadales	−0.47
Phylum	Cyanobacteria	−0.61	Order	Sphingobacteriales	−0.44
Phylum	Actinobacteria	0.78 *	Order	Myxococcales	−0.23
Phylum	TM7	−0.54	Family	Rhodobacteraceae	0.94 **
Phylum	Spirochaetes	0.68	Family	Methylocystaceae	−0.53
Phylum	Chloroflexi	−0.14	Family	Aurantimonadaceae	0.57
Phylum	[Thermi]	−0.59	Family	Hyphomonadaceae	0.58
Phylum	Tenericutes	0.93 **	Family	Comamonadaceae	−0.61
Class	Alphaproteobacteria	0.77 *	Family	Caulobacteraceae	−0.61
Class	Gammaproteobacteria	−0.78 *	Family	Xanthomonadaceae	−0.73
Class	Betaproteobacteria	−0.68	Family	Rhodospirillaceae	−0.45
Class	Flavobacteriia	0.95 **	Family	Flavobacteriaceae	0.95 **
Class	Deltaproteobacteria	−0.25	Family	Cohaesibacteraceae	0.92 **
Class	Sphingobacteriia	−0.44	Genus	Pleomorphomonas	−0.53
Class	Clostridia	−0.50	Genus	Martelella	0.57
Class	[Saprospirae]	−0.32	Genus	Hyphomonas	0.56
Class	ML635J-21	−0.61	Genus	Phenylobacterium	−0.36
Class	c_Actinobacteria	0.76 *	Genus	Cohaesibacter	0.92 **
Order	Rhizobiales	0.05	Genus	Hydrogenophaga	−0.62
Order	Rhodobacterales	0.93 **	Genus	Pseudomonas	−0.25
Order	Burkholderiales	−0.61	Genus	Vogesella	−0.88 **
Order	Caulobacterales	−0.61	Genus	Acidovorax	−0.54
Order	Xanthomonadales	−0.73	Genus	Clostridium	−0.60

Note: ** indicates significant differences at p < 0.01; * indicates significant differences at p < 0.05.

. Tenericutes, Flavobacteriia, Rhodobacterales, Flavobacteriales, Rhodobacteraceae, Flavobacteriaceae, Cohaesibacteraceae, and Cohaesibacter are significantly positively correlated with P_(sm) (p < 0.01), whereas Vogesella are significantly negatively correlated with P_(sm) (p < 0.01). Actinobacteria, Alphaproteobacteria, Gammaproteobacteria, and c_Actinobacteria are positively correlated with P_(sm) (p < 0.05).

3.1.3. Statistical Analysis

Principal coordinate analysis (PCoA) was performed to visually identify similarities and differences among the seven groups of samples [51,52]. As shown in Figure 4, P5, P6, and P7 cluster together in the Bray_Curtis and Weighted_Unifrac matrices, indicating that their microbial community structures are similar when considering species abundance and evolutionary progress. P1, P2, and P3 cluster together in the Unweighted_Unifrac matrices, indicating that their microbial community structures are similar when only the presence or absence of species is considered.

3.1.4. Functional Profiles of Microbial Community

Combined with the 16S rDNA sequencing data, PICRUSt is used to predict the microbial functions of the seven groups of samples and to explore the relationship between the functional profiles and P_(sm). There are seven and forty-one gene clusters at level 1 and level 2 of the functional gene classification of the microbial community, respectively (Figure S1). At level 1, the functional features are divided into seven categories: cellular processes, environmental information processing, genetic information processing, human diseases, metabolism, organismal systems, and unclassified. At level 2, membrane transport, amino acid metabolism, and carbohydrate metabolism dominate. The Pearson correlation analysis was performed for the 41 gene clusters at level 2 with P_(sm) in Table S2. Cancers and signal transduction are significantly correlated with P_(sm) (p < 0.01): the former is positively correlated, and the latter is negatively correlated. Amino acid metabolism, cardiovascular diseases, cell communication, the metabolism of cofactors and vitamins, the nervous system, and neurodegenerative diseases are positively correlated with P_(sm) (p < 0.05). Cell motility and cellular processes and signaling are negatively correlated with P_(sm) (p < 0.05).

3.2. Different Intensities of Salt-Freshwater Displacement

3.2.1. Microbial Community Diversity and Structure

The alpha diversity of the two groups of samples was analyzed, and four indices were calculated, as shown in

Table 5. Alpha diversity indices of two groups in the salt-freshwater displacement experiment.

Group	Sample	Diversity Indices
		Shannon	Chao1	Observed Species	PD
P4	P4.1	2.09	42	16	2.06
	P4.2	2.00	43	20	2.56
	P4.3	3.13	69	19	2.07
P5	P5.1	0.33	9	6	0.90
	P5.2	0.04	2	2	0.53
	P5.3	2.68	50	19	1.98

. The Shannon values and Chao1 values of the P4 experimental group show that P4.3 is obviously improved compared with P4.1, while P4.2 is almost unchanged or slightly decreased; the trend of the P5 experimental group is the same as that of P4. The PD values and observed species are correlated tightly with R² = 0.97 (p < 0.01), which indicates that the microbial communities are evenly distributed in the phylogenetic tree [21].

Figure 5 shows the community structures at the phylum, class, order, and family levels for the two groups of samples. Proteobacteria (88.9–99.8%) are the most abundant bacterial phylum in all samples. The relative abundance of Bacteroidetes (1.3%) in P4.3 is higher than that in P4.1 (0.5%) and P4.2 (0.2%), and the other phyla are low (Figure 5A). In all samples, Alphaproteobacteria (86.9–99.9%) are the most abundant class, followed by Deltaproteobacteria (2.5 ± 3.9%), Betaproteobacteria (0.6% ± 0.8%), and Gammaproteobacteria (0.6 ± 0.7%); they are all the proteobacterial subdivisions. The relative abundance of Alphaproteobacteria is P4.1 > P4.2 > P4.3, while that of Deltaproteobacteria is the opposite in the P4 experimental group. Betaproteobacteria and Gammaproteobacteria in P4.2 are significantly higher than in P4.1 and P4.3. In the P5 experimental group, Alphaproteobacteria in P5.2 are dominant, and there are more kinds of bacteria in P5.3 than in P5.1 and P5.2, including Bacteroidia and Sphingobacteriia (Figure 5B). At the order level, Rhodospirillales (43.6 ± 26.1%) are dominant, followed by Rhizobiales (23.1 ± 16.4%), Caulobacterales (16.0 ± 8.5%), Rhodobacterales (6.9 ± 5.0%), Myxococcales (4.9 ± 4.4%), Sphingomonadales (2.2 ± 2.2%), and Neisseriales (0.7 ± 0.8%) in the P4 experimental group. Among them, the relative abundance of Rhodospirillales and Neisseriales is as follows: P4.2 > P4.1 > P4.3; that of Rhizobiales and Rhodobacterales is as follows: P4.3 > P4.1 > P4.2; and that of Myxococcales and Sphingomonadales is as follows: P4.3 > P4.2 > P4.1. The results of the order level of P5.2 are consistent with those of the class level, Rhodobacterales are dominant, and there are many kinds of bacterial orders in P5.3 (Figure 5C).

The distribution of the main families is shown in Figure 5D. The four families with a relative abundance greater than 1% are all from Alphaproteobacteria in the P4 experimental group, including Rhodospirillaceae, Caulobacteraceae, Methylocystaceae, and Rhodobacteraceae. Besides the common abundant families, several families of bacteria are only abundant in specific samples, such as Hyphomicrobiaceae (9.7%) of P4.1, Cohaesibacteraceae (4.7%) of P4.2, and Hyphomonadaceae (6.6%), Sphingomonadaceae (5.4%), and Rhizobiaceae (3.5%) of P4.3. There are seven families with a relative abundance of over 1% in P5.3, matching the P4 experimental group, all of which are affiliated with Alphaproteobacteria. The Rhodobacteraceae family predominates in P4.1 and P4.2, constituting 97.2% and 99.3% of the total families and categories, respectively. Figure 6 shows the distribution of microbial communities at the genus level, with both sample groups combined for analysis. Genera exhibiting a high relative abundance include Novispirillum, Pleomorphomonas, Phenylobacterium, and Martelella. Unclassified genera from the Rhodobacterales order are also abundant in both experiment groups.

3.2.2. Statistical Analysis

Three matrix calculation methods were used to analyze the differences between the microbial community groups of the samples and to explore the effects of different intensities of salt-freshwater displacement on the distribution of microbial community structures (Figure 7). In the Bray_Curtis and Weighted_Unifrac matrices, the P4.1 and P4.2 and P5.1 and P5.2 groups of microbial communities are clustered together, whereas the P4.3 and P5.3 groups are scattered. In the Unweighted_Unifrac matrix, the microbial communities of the P4.3 and P5.3 groups are clustered together, while the microbial communities of the P4.1, P4.2, P5.1, and P5.2 groups are scattered.

3.2.3. Functional Profiles of Microbial Community

The predicted microbial functions of the samples are presented in Figure 8. This result illustrates the differences in the functional genes of the microbial community among the strong displacement, weak displacement, and control groups. Based on the results, it can be concluded that weak displacement has a minimal impact on the functional profiles of the microbial community, with both P4 and P5 experimental groups showing the same results. In contrast, a significant influence is observed for strong displacement on the functional profiles. Specifically, strong displacement notably alters the distribution profiles of microbial functions in the P4 experimental group, resulting in significant differences among P4.3, P4.1, and P4.2. However, the changes in functional properties are not significant in the P5 experimental group.

4. Discussion

4.1. Characteristics of Groundwater Microbial Community along the Whole Interval of Seawater Intrusion into Fresh Groundwater

Seawater intrusion leads to the convergence of marine and freshwater microorganisms, generating a new ecological community. To identify seawater intrusion, it is crucial to analyze this new community and discuss its relationship with environmental factors. We analyzed the distribution of microbial communities at different seawater content locations (Figure 2 and Figure 3), which is the focus of our research.

The microbial diversity of P2 (original sample) exhibits the highest value among the other groups, indicating that environmental stability is an important condition for the growth of numerous microorganisms, while rapid environmental changes can be detrimental to individual microbial growth. The microbial community richness of P1 (pure freshwater) and P7 (pure seawater) are higher in the domestication experiment, suggesting that a single environment had little influence on microorganisms and is convenient for their growth [53]. Although salinity is acknowledged as a significant factor shaping microbial community richness and evenness [54,55], our experiment reveals that its influence on bacterial community diversity is insignificant.

Proteobacteria are the most abundant and largest species at the phylum level in all samples, with a low relative abundance in P1 (pure freshwater) and P7 (pure seawater). Notably, Bacteroides is highly represented in P7, which is consistent with the previous research results of field sampling data [21,56] and indirectly indicates the reliability of the experiment. P5 and P6 exhibit similar microbial community structures (Figure 2), indirectly indicating that the locations at P_(sm) of 50% and 75% are very close. Despite differences in seawater content, the groundwater microorganisms are less affected due to their similar geographical location. Through the analysis of the microbial community at the genus level, we identified microorganisms adapted to different environments (Figure 3). Hydrogenophaga is exclusively enriched in groundwater with low salinity yet absent in the areas near the coastal zone. Some studies have demonstrated that Hydrogenophaga is more dependent on an oxidizing groundwater environment [48] and is generally enriched in freshwater environment [34,35], indicating that a low content of Hydrogenophaga corresponds to high degrees of seawater intrusion.

The result of PCoA shows that when the groundwater salinity is within a relatively low range (P_(sm) < 10%), only the microbial species remain unaffected. In contrast, when salinity levels are comparatively high (P_(sm) > 50%), the impact on microbial richness is not significant, which is consistent with previous research findings [57,58]. To analyze the interface between saltwater and freshwater through microbial indicators, it is crucial to give due consideration to changes in microbial abundance. This is because the interface typically occurs at low salinity levels.

4.2. Relationship between Microbial Communities and P_(sm)

Representative bacteria were screened out by the correlation between microbial community abundance, functional profiles, and P_(sm), which can be used to detect seawater intrusion (Table 4 and Table S2). It was found that there was a significant correlation between a variety of bacteria and P_(sm). At the phylum level, a positive correlation between the relative abundance of Actinobacteria and salinity was identified. However, Actinobacteria naturally inhabit both marine and fresh groundwater environments [59]. To delve into the role of Actinobacteria in the context of seawater intrusion, we believe it is essential to focus more on the distribution and dynamics of marine Actinobacteria within groundwater ecosystems. In this study, we have not yet distinguished their initial sources—marine or fresh groundwater environments. At the class level, an increase in Alphaproteobacteria and Flavobacteriia is found with increasing P_(sm), which is consistent with the results of previous studies [60,61]. Gammaproteobacteria were also identified as a class which increases with salinity [61,62], which is contrary to our conclusion. This is probably due to the narrow range of salinity they considered, and our upper limit of salinity is close to seawater. Some studies found that Betaproteobacteria decreased with salinity in some regions [51,63]. Although there is no obvious correlation, the trend is the same in this experiment. At the family level, Rhodobacteraceae are often identified in estuaries and open oceans [64,65]. Flavobacteriaceae also exist in marine environments [66]. Unno et al. explored Flavobacteriaceae and Rhodobacteraceae as microbial indicators of seawater intrusion when describing the effects of seawater intrusion on groundwater microbial community [67]. We determined a significant positive correlation between two families of bacteria and P_(sm) through indoor domestication experiments, thereby proving that they can be used to detect seawater intrusion. Cohaesibacteraceae are formed by strains from coastal marine environments [68] and contain only a single genus, Cohaesibacter [69]. From the results of the relationship between Cohaesibacteraceae, Cohaesibacter, and P_(sm), it can be inferred that they invaded the groundwater environment from the seawater environment and survived. However, at present, there is relatively little discussion about them, and whether they can be used as new indicators of seawater intrusion needs to be further explored.

Cancers, cardiovascular diseases, and neurodegenerative diseases are positively correlated with P_(sm), indicating that groundwater with a higher salt content is undrinkable [70] and even causes diseases in the human body. There is a relationship between the metabolic functional genes involved in amino acid metabolism and the metabolism of cofactors and vitamins and P_(sm), implying the potential effect of salinity on the metabolic functions of microbial communities [71]. Functional genes responsible for the signal transduction mechanism, which is vital for microbes in dealing with the changes in environmental conditions [72], are negatively correlated with P_(sm), and this indicates the response of microbial communities to salinity. Since salinity could cause signaling molecules to inhibit biofilm formation [73,74,75,76], it may have prevented the formation of microbial community functions in signal transduction. The above results show that the functional genes of bacteria can be used as an auxiliary index to detect seawater intrusion.

4.3. Effects of Salt-Freshwater Displacement on Groundwater Microbial Community

Salt-freshwater displacement affects the diversity of the microbial community. Strong displacement increases the richness and evenness, whereas weak displacement decreases them, indicating that drastic environmental changes have a significant impact on microbial diversity [77,78]. Rhodobacterales are the most abundant order of bacteria, and strong displacement decreases their relative abundance, whereas weak displacement increases their relative abundance. It is reported that Rhodobacterales are a widespread, abundant, and metabolically versatile order of bacteria found in oceans worldwide [79,80]. They are mainly associated with aerobic anoxygenic photosynthetic activity and are actively involved in organic carbon degradation [81]. The role of Rhodobacterales in promoting genetic exchange in natural environments makes them even more compelling microorganisms [79], and Rhodobacterales show strong seasonal variations in abundance [82]. However, there are many environmental factors that change with the seasons, and the root cause may be the intensity of salt-freshwater displacement, as mentioned above. Alphaproteobacteria and Rhodospirillaceae also show the same trend.

Figure 7 shows the effects of the intensity of salt-freshwater displacement on the distribution of microbial communities. The microbial communities of the control and weak displacement groups are similar in structure when species abundance and evolution process are considered and only the presence or absence of species is considered, and strong displacement makes the microbial community structure similar regardless of the original environment. Salt-freshwater displacement also affects the functional characteristics of microbial communities. The lower the P_(sm) in the original environment, the greater the impact of strong displacement on microbial function. In other words, the more strongly the environmental microbes are perturbed, the more significant the changes in their functional properties [83].

We do not divide the microbial communities into rare and dominant taxa [21], which may cause some structural bias because the intensity of displacement changes is subtle in itself, and exploring the response of rare microorganisms in this process may be more instructive for understanding the biogeochemical processes of coastal groundwater.

4.4. The Shortcomings of the Application of Microbial Indicators in Seawater Intrusion

Using microbial indicators to detect seawater intrusion, while valuable, also has several inherent shortcomings. This is primarily reflected in the following aspects: Firstly, the microbial diversity in areas affected by seawater intrusion is exceedingly complex, and current analytical technologies may not be fully capable of capturing the entirety of microbial communities, particularly those that are rare or difficult to cultivate [15,84]. Secondly, there is interference from environmental factors. Environmental factors in areas affected by seawater intrusion, such as salinity, temperature, and pH, may influence the distribution and activity of microorganisms. These factors could potentially conflict with the stability and reliability of microbial indicators [21,34,35]. Thirdly, there are limitations in our understanding of microbial ecological functions. While current research has made strides in studying the structure of microbial communities, our knowledge of their ecological functions and the mechanisms by which they operate remains relatively constrained [85,86]. This gap in understanding can limit the comprehensive interpretation of microbial activity and its implications in the context of seawater intrusion. Lastly, there is the issue of the generalizability of microbial indicators. While certain indicators may be effective in specific areas where seawater intrusion is occurring, they may not be universally applicable to all types of coastal environments. Various coastal areas have unique factors that influence the intrusion of seawater, including the speed, mode, and pathways [6]. All of these factors contribute to differences in microbial communities.

This variability underscores the need for a more complete approach that considers the unique characteristics of each coastal ecosystem when applying microbial indicators as a tool for environmental assessment and monitoring.

5. Conclusions

This study provides the first insights into the groundwater microbial community in the whole interval of seawater intrusion into fresh groundwater and evaluates the effect of salt-freshwater displacement on the microbial community through indoor domestication experiments. The results showed that the diversity of the microbial community was the highest and lowest in environments with a P_(sm) of 2.5% and 75%, respectively. According to the PCoA, when considering the species abundance and evolution process, the microbial community structure was similar at P_(sm) values of 50%, 75%, and 100%. When considering the presence or absence of species, the microbial community structure was similar at P_(sm) values of 0%, 2.5%, and 10%. According to the Pearson correlation analysis, Tenericutes, Flavobacteriia, Rhodobacterales, Flavobacteriales, Rhodobacteraceae, Flavobacteriaceae, Cohaesibacteraceae, and Cohaesibacter were significantly positively correlated with P_(sm). Although the relative abundance and function of many bacteria are associated with salinity, they cannot all serve as indicators of seawater intrusion due to their susceptibility to other factors. A comprehensive consideration of species abundance, evolution process, and the presence or absence of species is essential. Flavobacteriaceae and Rhodobacteraceae could serve as microbial indicators for assessing seawater intrusion, while further exploration is needed for Tenericutes and Cohaesibacteraceae. Environmental stability is a crucial prerequisite for the growth of many microorganisms, and rapid environmental changes can hinder the growth of individual microorganisms. The salt-freshwater displacement alters the diversity, distribution, and function of the microbial community. Strong displacements enhance the richness and evenness of the microbial community, whereas weak displacements exhibit the opposite trend. Strong displacement impacted the functional characteristics of the microbial community, with a lower salinity environment exhibiting a greater impact. These findings extend our understanding of microbial community dynamics during seawater intrusion and pave the way for novel detection methods of seawater intrusion. Subsequent studies are expected to broaden the experimental horizons, concentrating on the refinement of fresh groundwater and seawater mixing proportions and the enrichment of key environmental factors. These methodological enhancements are poised to yield a more precise depiction of the ambient conditions prevalent in the designated study area.