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Article

Management of Spartina alterniflora: Assessing the Efficacy of Plant Growth Regulators on Ecological and Microbial Dynamics

by
Chenyan Sha
1,†,
Zhixiong Wang
2,†,
Jiajie Cao
3,
Jing Chen
4,
Cheng Shen
1,
Jing Zhang
5,
Qiang Wang
6,* and
Min Wang
1,*
1
Shanghai Academy of Environmental Sciences, Shanghai 200235, China
2
State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical Process, East China University of Science and Technology, Shanghai 200237, China
3
College of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, China
4
Dongying Modern Agriculture Demonstration Zone Management Center, Dongying 257500, China
5
School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China
6
School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Sustainability 2024, 16(17), 7848; https://doi.org/10.3390/su16177848
Submission received: 30 May 2024 / Revised: 20 August 2024 / Accepted: 3 September 2024 / Published: 9 September 2024
(This article belongs to the Special Issue Wetlands: Conservation, Management, Restoration and Policy—2nd Edition)
Browse Figures
Versions Notes

Abstract

:
Spartina alterniflora is recognized as one of the most detrimental invasive species along China’s coastlines, highlighting the need for effective and environmentally safe management strategies to preserve intertidal zones. This study assessed the effectiveness of combining plant growth regulators (PRGs) with physical cutting to manage S. alterniflora, using 16S rRNA and 18S rRNA gene sequencing to evaluate the impacts on the plant and associated soil micro-organisms. The results showed that compared to the control (CK), the regeneration numbers for treatments with abscisic acid (ABA), gibberellin (GA), paclobutrazol (PP333), garcinol (GC), and glyphosate (GP) decreased by 29.75%, 23.25%, 15.75%, 94.50%, and 40.50%, respectively. Comparative analysis revealed no statistically significant variation in the inhibitory effects of ABA and GP on the germination of S. alterniflora (p > 0.05). Additionally, applying PRGs and herbicides increased the diversity indices of soil bacteria and fungi. Principal Coordinates Analysis (PCoA) showed that the impact of PRGs on the fungal community was less pronounced than that of herbicides. Significant differences were also noted in the abundance of microbial functional genes related to methanotrophy, hydrocarbon degradation, and denitrification compared to the control (p < 0.05). This study aimed to assess the potential of PRGs in controlling the invasion of S. alterniflora and to elucidate their impacts on soil microbial communities and functional gene expression.

1. Introduction

Biological invasions are increasingly acknowledged as major factors of environmental change and play a significant role in global ecological transformations [1,2]. These invasions significantly alter the biotic structure of ecosystems and result in substantial economic losses [3,4]. Invasive species disrupt biogeographic regions, modifying the abundance and distribution of native species through direct predation and competition for resources [5]. These changes can precipitate a heightened risk of local extinction for native flora, thereby undermining the genetic continuity within these populations. Additionally, these disruptions extend to the broader ecological framework, where the invasions can lead to a reconfiguration of phylogenetic diversity across communities and a destabilization of the intricate balance within food web dynamics [6].
Intertidal wetlands serve as critical transition zones between marine and terrestrial ecosystems and are especially vulnerable to invasions due to variable species composition and environmental fluctuations. These environmental conditions are conducive to the ingress and multiplication of invasive organisms, thereby increasing the propensity of these ecosystems for the intrusion of non-native species [2,7]. S. alterniflora, originally from North America’s Atlantic coast, has become a widespread invader globally [8]. This species grows vigorously and reproduces rapidly, often forming dense, monospecific stands that drastically disrupt local biodiversity and ecosystem functions [8,9]. In China’s coastal ecosystems, S. alterniflora poses significant ecological and economic challenges, fundamentally transforming the landscape and upsetting the ecological equilibrium [10].
S. alterniflora exhibits rapid growth, strong reproductive capabilities, and high resilience to diverse environmental stresses. It typically colonizes muddy tidal flats, which complicates its management and makes control efforts a globally recognized challenge [11]. Current strategies for managing S. alterniflora include physical, chemical, and ecological restoration methods, each with varying degrees of success [12]. Physical removal often fails if the root system remains, leading to regrowth. Chemical methods can be effective in the short term but may negatively impact the soil ecosystem over the long term. Moreover, while ecological restoration is environmentally sustainable, it usually takes a long time to achieve noticeable results [13].
PRGs present an alternative management strategy. These naturally occurring molecules critically regulate plant growth and reproduction [14]. For example, high levels of ABA have been proven to significantly slow plant growth, according to multiple studies [15,16]. Likewise, GA can inhibit growth under specific conditions [17], while compounds such as polybulobuzole can greatly suppress plant development [18]. These insights into plant physiology indicate that manipulating growth regulators could be a targeted, environmentally less disruptive method for controlling invasive species like S. alterniflora.
The microbial communities within the soil are indispensable for the proper functioning of ecosystems and exert pivotal influences on the biogeochemical transformations of essential elements such as carbon, nitrogen, and sulfur, among others [19]. The soil microbial community in wetland ecosystems is particularly sensitive to disturbances. This sensitivity allows these micro-organisms to respond dynamically to changes, making them valuable ecological indicators of environmental health and status [20]. Additionally, the priming effects driven by these microbes are significant in the context of climate change [21]. Their activities can increase atmospheric CO2 levels, linking them to broader environmental processes that influence global climate patterns. This highlights the importance of understanding microbial dynamics to assess ecological impacts and tackle environmental challenges.
This study adopts a hybrid management approach for S. alterniflora, integrating physical and chemical strategies [22]. We specifically assess the effectiveness of combining physical cutting with the application of PRGs and herbicides. Our research examines the following key aspects: (1) the effects of PRGs and herbicides on S. alterniflora, (2) the impact of these treatments on the soil microbial community associated with S. alterniflora, and (3) their potential influences on the biogeochemical cycles of carbon, nitrogen, and sulfur. The goal of this experimental approach is to deepen our understanding of how PRGs can be used to manage S. alterniflora and to explore their effects on essential biogeochemical processes within microbial communities. This information is vital for developing effective and sustainable management practices that prioritize ecological integrity and environmental health.

2. Materials and Methods

2.1. Study Area

The research was conducted on the Zhenhai tidal flats in the northern part of Ningbo city, Zhejiang Province, China (Figure 1). This area, strategically located at the southern end of the Yangtze River Delta, serves as a crucial gateway to a major seaport and marks a significant point where the Yangtze River meets the East China Sea. The flats lie within an ecologically sensitive zone, heavily influenced by substantial sediment loads from the Yangtze River, which are pivotal in shaping the dynamics and biodiversity of these ecosystems. Zhenhai features a typical subtropical monsoon climate with distinct seasonal variations and significant rainfall. The average annual temperature ranges from 16 °C to 18 °C, peaking at around 28 °C in July and dropping to 4 °C to 5 °C in January. The area receives an average annual precipitation of 1300 mm to 1600 mm, mainly from late spring to early summer, contributing to its rich biodiversity and complex ecosystem dynamics. The native vegetation, primarily Scirpus mariqueter and Phragmites australis, has dominated these tidal marshes for over two decades. However, recent observations indicate a rapid expansion of the invasive S. alterniflora across the region. This aggressive spread is facilitated by its high seed production and germination rates, increasingly displacing native species and altering the ecological landscape of the Zhejiang tidal flats.

2.2. Experimental Setup and Samples Collection

In August 2022, during the peak flowering phase of S. alterniflora, above-ground biomass was harvested from a grassland ecosystem located in the tidal flats. Later, in late September, within the experimental site, a 1 m × 3 m experimental plot was established for applying three PRGs—ABA, GA, and PP333—along with two herbicides—GC and GP. These chemicals were applied at concentrations listed in Supplementary Materials (Table S1), targeting both the roots and aerial parts of S. alterniflora.
The treatment regimen was repeated in November, with each plot undergoing three replicate applications to ensure uniform exposure. A control plot was also established, receiving only aqueous sprays under conditions identical to the treatment plots, to isolate the effects of the application process. Each plot was divided into three equal 1 m × 1 m sections. Soil samples were collected from each section using a five-point sampling technique for representativeness. Specifically, a soil auger extracted approximately 3 g of soil from each point, which was then placed into sterile vials and immediately preserved on dry ice for microbial community analysis at Shanghai Lingen Biology Laboratories. Additionally, 50 g of soil from each section was sealed in sterile bags for physicochemical property analysis. This comprehensive approach aimed to elucidate the effects of the applied growth regulators and herbicides on both the microbial and physicochemical aspects of the soil ecosystem within the treated plots.

2.3. DNA Extraction and Sequencing

A total of 36 soil samples were analyzed for DNA content using the E.Z.N.A.® Soil DNA Kit, strictly following the extraction protocol specified by the kit’s developer, Omega Bio-tek, located in Norcross, Georgia, United States. The quality and concentration of the purified DNA samples were determined with a Thermo Fisher Scientific NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA). Following this, the polymerase chain reaction (PCR) was utilized to selectively amplify the V3-V4 variable regions of the 16S rRNA genes from bacteria. The PCR cycling conditions were as follows: an initial denaturation at 95 °C for 30 s, annealing at 55 °C for 30 s, and extension at 72 °C for 45 s, with a total of 27 cycles [23]. The PCR mixture was prepared with 4 μL of 5× concentrated TransStart FastPfu Buffer (TransGen Biotech, Beijing, China), supplemented with 2 μL of dNTPs at a concentration of 2.5 mM. Primers 338F and 806R, each at 0.8 μL and a concentration of 5 μM, were included. The reaction was catalyzed by the addition of 0.4 μL TransStart FastPfu DNA Polymerase. Template DNA, amounting to 10 ng, was incorporated into the mixture. Nuclease-free water was added to bring the total volume to 20 μL.
Similarly, the amplification of fungal internal transcribed spacer (ITS) genes was performed within a 20 μL PCR mixture. This mixture included 4 μL of 5× FastPfu Buffer, 2 μL of 2.5 mM dNTPs, and 0.8 μL each of the ITS1F and ITS2R primers, both at a concentration of 5 μM, with the ITS1F primer featuring an integrated eight-base barcode for sample traceability. The enzymatic activity was provided by 0.4 μL of FastPfu Polymerase, and the reaction was initiated with 10 ng of template DNA. The PCR cycling protocol commenced with a denaturation phase at 95 °C for 2 min, succeeded by 25 cycles consisting of 95 °C for 30 s, 55 °C for 30 s, and 72 °C for 30 s, culminating in a final extension phase at 72 °C for 5 min [24,25].
Electrophoresis on agarose gel was utilized to verify the amplified DNA fragments, which were then sequenced using the Illumina MiSeq platform with the PE300 sequencing approach. Majorbio Bio-Pharm Technology Co. Ltd., a Shanghai-based company in China, offered the sequencing service. Subsequently, the data underwent bioinformatic analysis [24].

2.4. Bioinformatic Analyses

Sequence data preprocessing and quality control were performed using FLASH software, version 1.2.11. Bases with a quality score below 20 at the read tails were filtered out, and a 10-base pair (bp) sliding window was used to optimize data quality. Operational Taxonomic Units (OTUs) were clustered using Uparse v11 at 97% similarity. Singletons—sequences without replicates—were removed to improve data reliability. OTUs were taxonomically classified using the RDP Classifier, which employs a Bayesian algorithm to assign taxonomy at a 97% similarity threshold. This classification enabled detailed species composition analysis at various taxonomic levels for each sample. Alpha diversity indices such as observed OTU, Chao1 richness, Shannon, and Simpson indices were calculated using Mothur v1.30.2. Community similarity among samples was assessed through Principal Coordinate Analysis (PCoA) using the Weighted Unifrac dissimilarity metric, implemented in the Vegan v2.5-3 package [26]. Linear Discriminant Analysis (LDA) Effect Size (LEfSe) identified significantly abundant bacterial taxa across different groups, from phylum to genera (LDA score > 3.5, p < 0.05), available at LEfSe [27]. The impact of soil physicochemical factors on the bacterial community composition was assessed through Distance-based Redundancy Analysis (db-RDA), facilitated by the Vegan package in R. This method employed a forward selection process, coupled with Monte Carlo permutation tests to determine the statistical robustness, setting the number of permutations at 9999. The resultant axis scores and vector lengths provided insights into the relative importance of various soil properties in shaping the distribution of bacterial taxa among different communities. Functional assessments of the soil microbiome, encompassing both bacterial and fungal constituents, were executed with the aid of FAPROTAX and FUNGuild methodologies, respectively.

2.5. Statistical Analyses

Statistical evaluations of the effects of PGRs and herbicides on the regrowth of S. alterniflora, the soil’s characteristics, and the dynamics of microbial communities were carried out using SPSS software, version 26. The analysis was performed employing a one-way analysis of variance (ANOVA), complemented by subsequent post hoc tests for multiple comparisons. The threshold for statistical significance was determined as a p-value ≤ 0.05. To visually present the regrowth data of S. alterniflora, graphical representations were created using Origin 2022 software.

3. Results

3.1. Regeneration Dynamics of S. alterniflora Following Treatment

The analysis of regenerative capacity across treated and control groups of S. alterniflora revealed a differential response. The order of regenerative ability, measured as the number of regenerated plants per square meter, was observed as follows: CK > PP333 > GA > ABA > GP > GC) (Figure 2). Notably, the control group exhibited the highest regeneration number with an average of 133.33 ± 6.03 plants/m². Statistical analysis revealed a significant reduction in regeneration across the treatments compared to the control. A one-way analysis of variance confirmed significant differences in regeneration among all groups (p < 0.05). Further pairwise comparisons using a post hoc test highlighted significant differences in regeneration between the ABA, GA, and PP333 treatments (p < 0.05). However, comparative analysis revealed no statistically significant variation in the inhibitory effects of ABA and GP on the regrowth number of S. alterniflora (p > 0.05). Specifically, the regeneration rates decreased by 29.75% for ABA, 23.25% for GA, 15.75% for PP333, 94.50% for GC, and 40.50% for GP.

3.2. Impacts of Treatment on Microbial Diversity

Extensive sequencing efforts revealed significant insights into bacterial communities associated with S. alterniflora. From 18 samples, 72,688 OTUs and 549,396 effective bacterial sequences were obtained after stringent filtering. Similarly, for the fungal component, 3474 OTUs and 538,758 effective fungal sequences were identified. Analysis showed significant impacts of PRGs and herbicides on soil bacterial diversity (p < 0.05), although fungal diversity remained largely unaffected (p > 0.05). Notably, α-diversity in treated groups increased for both bacteria and fungi compared to controls, suggesting variable responses across microbial communities (Table 1).
Within the bacterial community, evenness and richness significantly differed post-treatment. The CK group displayed a Sobs index of 3198.00 ± 356.58, whereas the GP group showed a highest richness with a Sobs index of 4546.67 ± 100.53 (p < 0.05). The Chao index also reflected a substantial increase, with the GP group reaching 6023.13 ± 132.62 compared to 4257.02 ± 635.15 in the control. Furthermore, the Shannon and Simpson indices highlighted significant differences in diversity between groups. For fungal communities, the GA group showed a marked increase in species richness (Sobs index: 232.00 ± 14.00) and estimated richness (Chao index: 289.93 ± 27.61), substantially exceeding those of the CK group. Higher Shannon and lower Simpson indices in the GC group indicated a more even community composition.

3.3. Diverse Treatment Impacts on Microbial Communities

At the phylum level, Proteobacteria emerged as the dominant phylum in all sampled groups, exhibiting relative abundances between 24.91% and 38.84% (Figure 3a). Following closely were Chloroflexi and Desulfobacterota, with ranges of 9.64% to 22.90% and 10.87% to 13.64%, respectively. Notably, the CK exhibited markedly higher relative abundances of Acidobacteriota and Actinobacteriota compared to the treatment groups. Conversely, Desulfobacterota and Nitrospirota showed markedly lower abundances in these groups. At the genus level, the CK group’s top 20 genera constituted about 20% of its microbial community, while this proportion exceeded 40% in other groups (Figure 3c). SBR1031 and Anaerolineaceae predominated across all groups. Furthermore, genera such as Desulfovibrionia, Desulfobulbaceae, and Desulfatitalea were found to be more prevalent in the experimental groups compared to the CK group, and all genera are classified within the phylum Desulfobacterota.
Within the soil fungal community, Ascomycota was the most prevalent phylum, accounting for 76.44% to 97.30% of the community, thus playing a pivotal role in soil fungal ecology (Figure 3b). At the genus level, Genera Fusarium and Sordariomycetes, both associated with the phylum Ascomycota, were particularly dominant. The relative abundance of Fusarium ranged from 38.05% to 69.39%, while Sordariomycetes varied from 20.68% to 50.01% (Figure 3d). These genera are integral to ecosystem processes in the soil.
The community structure differences among various groups were assessed qualitatively based on the characteristics observed in the Weighted UniFrac distance algorithm. Principal coordinate analysis (PCoA) of the bacterial community showed that the first two principal coordinates (PC1 and PC2) accounted for a substantial 76.15% of the total variance (Figure 4a). Inter-group differences in bacterial community structure were statistically significant (p < 0.01), with PC1 alone explaining 51.21% of the variation. This highlights the strong impact of environmental factors associated with PC1 on the bacterial communities. Spatial analysis revealed that experimental groups were more clustered along the PC1 axis, whereas the CK was distinctly separated, indicating significant environmental differences, particularly those represented by PC1. Further analysis linked soil organic carbon (SOC) with PC1, suggesting its significant influence on bacterial community dynamics (Table S2 and Figure S1). Additionally, the sample sites for treatments with ABA, GA, PP333, and GP aligned closely, indicating similar bacterial community compositions. In contrast, the CK group’s sites were markedly distant, suggesting that PRGs and herbicides significantly alter the soil microbial community.
The PCoA of the fungal community revealed that the first two principal coordinates (PC1 and PC2) together explained a significant 80.17% of the total variance (Figure 3b). This high explanatory power underscores the pronounced structuring of fungal communities among the samples. Notably, the samples treated with ABA, GA, and those in the CK clustered closely, suggesting similarities in their fungal community compositions. In contrast, samples treated with GP and GC showed considerable divergence from the CK samples, indicating distinct fungal community structures. These findings reveal that herbicide applications (GP and GC) significantly altered the soil fungal community structure, whereas the impact of PRGs (ABA and GA) was relatively minimal. This differential impact highlights the specific effects of chemical treatments on fungal diversity and community dynamics within the soil ecosystem.

3.4. Diverse Treatment Impacts on Microbial Functional Genes

In our study, we employed FAPROTAX functional prediction analysis to investigate the abundance of functional genes related to the carbon, nitrogen, and sulfur cycles. Notable findings include significant correlations among these genes (Figure 5a). Specifically, the methanotrophy demonstrated a positive correlation with the gene abundance engaged in the breakdown of hydrocarbons, the association to be highly statistically significant (p < 0.001). In contrast, a robust inverse association was observed between the abundance of methanotrophy and nitrogen-reactive genes (p < 0.05). However, no significant correlations were detected between genes involved in respiration-of-sulfur-compound and other functional gene groups. Moreover, our analysis indicated that the abundance of specific functional genes is closely linked to the microbial community composition. For instance, a significant correlation was identified between the gene abundance pertaining to methanotrophic and hydrocarbon-degrading capabilities and the relative abundance of microbial taxa, such as Chloroflexi and Desulfobacterota (p < 0.05, r > 0.5).
We standardized the assessment of functional genes across all groups to explore variations linked to the carbon, nitrogen, and sulfur cycles. As illustrated in Figure 5b, marked differences were observed between the CK group and the experimental groups. Specifically, the CK group showed significantly lower abundance of genes associated with methanotrophy and hydrocarbon degradation compared to the experimental groups (p < 0.05), indicating a reduced capacity for methane oxidation and hydrocarbon breakdown under control conditions. For the nitrogen cycle, the CK group exhibited higher expression levels of genes related to denitrification, nitrogen fixation, and nitrite respiration (p < 0.05), suggesting a more active nitrogen-transforming microbial community, except for nitrification, where no significant differences were observed. In terms of the sulfur cycle, genes associated with sulfate reduction were notably less abundant in the CK group compared to the experimental groups (p < 0.05). Among the treatments, the ABA-treated group displayed a significantly lower abundance of these genes compared to other experimental groups (p < 0.05).
Using FUNGuild for fungal functional prediction analysis, we observed significant variations in the abundance of functional genes across different treatment groups (Figure 5c). The CK displayed a significantly higher abundance of functional genes compared to the experimental groups (p < 0.05). This increase was particularly notable in several functional categories, including soil saprotrophs and plant pathogens. Additionally, the CK group showed elevated levels of genes linked to a composite group consisting of Endophytes, litter saprotrophs, soil saprotrophs, and undefined saprotrophs. The abundance of genes associated with animal pathogens, endophytes, lichen parasites, plant pathogens, and wood saprotrophs was also significantly greater in the CK group compared to other groups (p < 0.05).

4. Discussion

In this study, we applied six different treatments to S. alterniflora to assess their impact on the plant and soil micro-organisms. The results demonstrated that the treatments administered to the experimental groups were markedly superior in efficacy to those of the CK, with a synergistic approach combining physical mowing and chemical application proving to be particularly efficacious [22]. Previous researchers have shown that herbicides like Haloxyfop-R-methyl and GP can control S. alterniflora with efficiencies between 84% to 99% and 12% to 100%, respectively [22,28]. However, these studies also point to potential environmental risks associated with herbicides [22,28]. In contrast, PRGs, known for their lower toxicity and minimal environmental impact, are considered safer as they work within normal plant growth processes and require lower concentrations [29,30]. In our experiment, while herbicidal treatments were generally more effective, the efficacy of the ABA treatment was nearly comparable to that of the GC group. Given their lower environmental risks, the use of PRGs on S. alterniflora shows promising potential and merits further investigation.
Soil microbial diversity is a critical indicator for assessing community characteristics and stability. While research indicates that PRGs and herbicides may initially decrease soil microbial diversity [31,32], microbial communities typically exhibit adaptability and resilience, leading to an eventual increase in diversity indices as they adjust to these chemicals [33,34]. Our study found that both the evenness and richness of micro-organisms in the experimental group were higher than in the CK. This suggests that the microbial community may use the added chemicals as carbon sources for growth. In comparison to the CK group, a pronounced elevation in the proportion of the A4b and MBNT15 genera was observed within the GC. This suggests that these genera may represent potential degraders of GC. Additionally, the modification of soil properties by these chemicals can impact microbial communities. Notably, there were significant differences in SOC levels among the groups (Table S2). Research by Song Wang et al. [35] on GP use in Chinese fir plantations showed that although GP reduced total organic carbon (TOC), dissolved organic carbon (DOC), and NH4+-N contents, the microbial diversity index increased, highlighting the strong influence of SOC on micro-organisms, as depicted in Figure S1. In our experimental group, SOC content was lower than in the control group, likely because the soil SOC was utilized by micro-organisms as an energy and nutrient source, thus reducing soil SOC levels. Furthermore, previous findings have demonstrated that fungi exhibit a greater sensitivity to these alterations compared to bacteria [33,36], a pattern that is consistent with our overall observations.
The addition of PRGs and herbicides led to significant differences among microbial groups (Figure S2), which is due to the fact that PRGs and herbicides have a promoting or inhibiting effect on the growth of certain microbial communities [37,38,39]. When the soil is stressed by herbicides, microbial communities capable of degrading these chemicals participate in the degradation process, thereby inducing the microbial community to evolve towards one with specific degradation functions [40,41]. The impact of PRGs on microbial communities is similar to that of herbicides under this mechanism [42]. According to the LEfSe analysis (Figure S3), it can be observed that at an LDA value >3.5, there are still a considerable number of species that exhibit significant differences across the groups in this experiment, which may be due to the inducing effect of PRGs and herbicides on the presence of microbes. Additionally, it is noteworthy that PRGs and herbicides affect the relative abundance of plant pathogens such as Fusarium [43] and Phaeosphaeria [44] (Figure 3d). Although previous reports have shown varying results regarding the impact of GP on the abundance of Fusarium in the soil [43,45], this could be related to the growth environment of the microbial communities. In this experiment, the impact of different types of PRGs and herbicides on the relative abundance of plant pathogens varied, which may be related to the metabolic pathways of the microbes.
Changes in soil microbial community structures significantly affect their functional capabilities. This study found that applying PRGs and herbicides altered the abundance of microbial functional genes. Our findings indicate an upregulation of methanotrophy-associated functional genes, which may signify a potential reduction in methane emissions. This potential reduction is likely due to the increased consumption of methane by methanotrophic micro-organisms, a process that could contribute to the attenuation of atmospheric methane levels [46,47]. Additionally, the abundance of genes involved in the nitrogen cycle—including nitrification, denitrification, and nitrogen fixation—was impacted. Nitrogen plays a crucial role in the growth and reproduction of soil micro-organisms [48], with nitrogen cycling genes being vital for ecosystem health through their role in biogeochemical processes [49,50].
In the oxygen-limited conditions of the intertidal zone, micro-organisms rely on the energy derived from degrading sulfate and other sulfur compounds, a critical part of their survival and metabolic functions [51]. Sulfate-reducing bacteria contribute to the biogeochemical cycles of carbon, sulfur, and nitrogen, thus influencing global elemental cycles [52]. Notably, we found significant positive correlations between Desulfobacterota and methanotrophy functional genes (p < 0.01, r > 0.5), suggesting that members of the phylum Desulfobacterota may enhance methane oxidation through catalytic reactions [53]. Therefore, the use of PRGs and herbicides is implicated in changing the abundance of soil microbial functional genes, which subsequently affects the carbon, nitrogen, and sulfur cycling processes in these ecosystems.

5. Conclusions

PRGs significantly impact the on management of S. alterniflora, with ABA demonstrating notable effectiveness, achieving up to 29.75% treatment success. These regulators not only alter the diversity and composition of soil microbial communities but also influence their functional gene expression. This affects critical biogeochemical processes involving carbon, nitrogen, and sulfur, offering an environmentally considerate management strategy for S. alterniflora. This study fills a significant research gap and improves understanding of the complex interactions between S. alterniflora, PRGs, and soil microbial dynamics. Future research should examine the long-term ecological impacts of these regulators, including their environmental persistence and potential accumulation. Expanding studies to encompass a broader range of environmental conditions will also enhance our understanding of how these treatments affect various ecosystems. Such insights are essential for developing guidelines for the use of PRGs across diverse environmental settings and ensuring sustainable practices in managing invasive species.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/su16177848/s1, Table S1: Table of experimental concentrations of three plant growth regulators and two herbicides; Table S2: Soil physicochemical properties under different treatments; Figure S1: RDA Analysis of Microbial Genus Distribution in Relation to Environmental Factors; Figure S2: Kruskal–Wallis Rank Sum Test Reveals Significant Differences in Microbial Genus Composition; Figure S3: LDA Score Plot Distinguishing Microbial Taxa by Environmental Group Using LefSe Analysis.

Author Contributions

C.S. (Chenyan Sha), Z.W., J.C. (Jiejie Cao), J.C. (Jing Chen), C.S. (Cheng Shen), J.Z., Q.W. and M.W. conceived and designed the experiments. C.S. (Chenyan Sha), Z.W., C.S. (Cheng Shen) and J.Z. performed the experiments. C.S. (Cheng Shen), Z.W. and J.C. (Jiejie Cao) analyzed the data. C.S. (Chenyan Sha), Z.W., J.C. (Jiejie Cao), J.C. (Jing Chen) and C.S. (Cheng Shen) wrote the manuscript, and other authors provided editorial advice. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Natural Science Foundation of China (NO. 32071832).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data will be available on request.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. The experimental site of this study. Note: This map provides a comprehensive geographical reference for the experimental area, with the experimental sites marked by Sustainability 16 07848 i001 to ensure precise location identification for the study.
Figure 1. The experimental site of this study. Note: This map provides a comprehensive geographical reference for the experimental area, with the experimental sites marked by Sustainability 16 07848 i001 to ensure precise location identification for the study.
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Figure 2. Germination number of S. alterniflora treated with mowing and different PRGs/herbicides in the next year. Note: Different lowercase letters indicate significant differences in the number of regenerated S. alterniflora among different treatments (p < 0.05).
Figure 2. Germination number of S. alterniflora treated with mowing and different PRGs/herbicides in the next year. Note: Different lowercase letters indicate significant differences in the number of regenerated S. alterniflora among different treatments (p < 0.05).
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Figure 3. Post-treatment comparative analysis of the relative abundance of dominant microbial taxa at the phylum and genus levels within the microbial communities. (a) Comparative abundance of bacterial phyla among the microbial communities; (b) comparative abundance of fungal phyla among the microbial communities; (c) comparative abundance of bacterial genera among the microbial communities; (d) comparative abundance of fungal genera among the microbial communities.
Figure 3. Post-treatment comparative analysis of the relative abundance of dominant microbial taxa at the phylum and genus levels within the microbial communities. (a) Comparative abundance of bacterial phyla among the microbial communities; (b) comparative abundance of fungal phyla among the microbial communities; (c) comparative abundance of bacterial genera among the microbial communities; (d) comparative abundance of fungal genera among the microbial communities.
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Figure 4. Weighted UniFrac distances were employed to perform Principal Coordinates Analysis (PCoA), illustrating the variations in the soil microbiome’s bacterial. (a) Bacterial community; (b) fungal community.
Figure 4. Weighted UniFrac distances were employed to perform Principal Coordinates Analysis (PCoA), illustrating the variations in the soil microbiome’s bacterial. (a) Bacterial community; (b) fungal community.
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Figure 5. Functional gene prediction map (a) Mantel test diagram between functional genes and species; (b) bubble map of bacteria functional gene abundance; (c) bubble map of fungal functional gene abundance.
Figure 5. Functional gene prediction map (a) Mantel test diagram between functional genes and species; (b) bubble map of bacteria functional gene abundance; (c) bubble map of fungal functional gene abundance.
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Table 1. Major alpha diversity indices of the bacterial and fungal communities in the soil under the treatment of PRGs/herbicides.
Table 1. Major alpha diversity indices of the bacterial and fungal communities in the soil under the treatment of PRGs/herbicides.
GroupSobsChaoShannonSimpson
BacteriaCK 3198.00 ± 356.58 a 4257.02 ± 635.15 a 6.76 ± 0.11 a 0.0036 ± 0.0012 a
ABA 4005.00 ± 268.3 bc 5222.95 ± 396.95 ab 7.10 ± 0.12 b 0.0024 ± 0.0004 a
GA 4428.33 ± 419.12 bc 5939.44 ± 633.85 b 7.20 ± 0.20 b 0.0024 ± 0.0006 a
PP333 4136.33 ± 181.95 bc 5418.03 ± 333.38 b 7.12 ± 0.02 b 0.0025 ± 0.0001 a
GC 3915.00 ± 442.2 b 5147.30 ± 772.64 ab 7.04 ± 0.2 b 0.0027 ± 0.0006 a
GP 4546.67 ± 100.53 c 6023.13 ± 132.62 b 7.27 ± 0.01 b 0.0022 ± 0.0002 a
FungiCK 141.67 ± 70.19 a 166.13 ± 67.52 a 1.12 ± 0.25 a 0.55 ± 0.12 a
ABA 198.33 ± 37.58 ab 268.58 ± 69.32 b 1.67 ± 0.14 b 0.44 ± 0.09 ab
GA 232.00 ± 14.00 b 289.94 ± 27.61 b 1.41 ± 0.14 ab 0.45 ± 0.01 ab
PP333 176.33 ± 39.21 ab 234.01 ± 47.05 ab 1.66 ± 0.38 b 0.37 ± 0.14 bc
GC 223.67 ± 23.01 b 284.27 ± 31.54 b 2.21 ± 0.16 c 0.26 ± 0.05 c
GP 186.00 ± 35.76 ab 251.27 ± 35.23 ab 1.72 ± 0.36 b 0.33 ± 0.07 bc
Data represent three repeated mean ± standard deviation. Different superscript lowercase letters indicate significant difference in data among different vegetation types of the same index (p < 0.05).
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Sha, C.; Wang, Z.; Cao, J.; Chen, J.; Shen, C.; Zhang, J.; Wang, Q.; Wang, M. Management of Spartina alterniflora: Assessing the Efficacy of Plant Growth Regulators on Ecological and Microbial Dynamics. Sustainability 2024, 16, 7848. https://doi.org/10.3390/su16177848

AMA Style

Sha C, Wang Z, Cao J, Chen J, Shen C, Zhang J, Wang Q, Wang M. Management of Spartina alterniflora: Assessing the Efficacy of Plant Growth Regulators on Ecological and Microbial Dynamics. Sustainability. 2024; 16(17):7848. https://doi.org/10.3390/su16177848

Chicago/Turabian Style

Sha, Chenyan, Zhixiong Wang, Jiajie Cao, Jing Chen, Cheng Shen, Jing Zhang, Qiang Wang, and Min Wang. 2024. "Management of Spartina alterniflora: Assessing the Efficacy of Plant Growth Regulators on Ecological and Microbial Dynamics" Sustainability 16, no. 17: 7848. https://doi.org/10.3390/su16177848

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