Ecological Characteristics of Temperate Seagrass Beds in Qingdao Coastal Waters and Ecological Response Relationships with Benthic Macrofauna Communities and Environmental Factors

Abstract

Seagrass beds are among the most productive and ecologically valuable coastal ecosystems. However, temperate nearshore seagrass beds exposed to urban stressors remain understudied. From 2020 to 2024, this study investigated seagrass communities, environmental factors, and benthic macrofauna in Qingdao’s coastal bays (Qingdao Bay, Huiquan Bay and Tangdao Bay) using field sampling and remote sensing. Redundancy analysis (RDA), Spearman correlation, and PERMANOVA were applied to clarify the ecological response relationships among these components. Results revealed significant spatiotemporal variations: Qingdao Bay experienced severe degradation with an 88% decline in belowground biomass. Huiquan Bay showed shoot height increases but ecological instability, while Tangdao Bay maintained relatively stable conditions. Mollusks dominated Qingdao Bay (67.4%), whereas annelids were prevalent in Huiquan Bay (51.8%) and Tangdao Bay (69.6%). Tangdao Bay supported the most complex and stable benthic communities. Water depth acted as a stressor to seagrass growth, while the role of dissolved oxygen and salinity was complex, exhibiting context-dependent relationships with seagrass parameters. Dissolved inorganic nitrogen and reactive phosphate were shared positive drivers for both seagrasses and macrofauna. This study conclusively links specific environmental drivers to seagrass ecosystem dynamics, delivering essential insights for effective ecological management and restoration strategies.

1. Introduction

Seagrass beds are highly productive and ecologically valuable coastal ecosystems, delivering essential ecosystem services such as carbon sequestration, sediment stabilization, and nursery habitats for marine species [1,2,3,4]. Compared with adjacent unvegetated habitats, seagrass beds generally support a greater abundance and diversity of benthic macrofauna [5,6,7]. This elevated biodiversity is largely attributable to the enhanced food supply capacity of seagrass meadows. Tanaya et al. [8,9] demonstrated that seagrass canopies enhance detritus trapping and organic matter accumulation, thereby increasing food availability for benthic communities. Moreover, Kharlamenko et al. [10] found that invertebrate communities in seagrass habitats utilize organic carbon from multiple sources, including seagrasses, macroalgae, and benthic microalgae, with filter-feeding bivalves particularly reliant on resuspended, sediment-associated bacteria.

In temperate regions, seagrass meadows dominated by Zostera marina play disproportionately important roles in maintaining biodiversity and ecosystem functioning [11]. The Yellow Sea coast of China, including seagrass habitats in Qingdao, represents an important yet insufficiently studied temperate ecosystem. These ecosystems face increasing anthropogenic stressors such as eutrophication, coastal development, and climate change [12,13,14]. Such pressures threaten the balance between seagrass productivity, organic matter cycling, and benthic community structure [15]. The stability and biodiversity of seagrass beds are thus crucial for the functioning of coastal ecosystems [16,17,18], emphasizing the need for their investigation, protection, and restoration.

Research on seagrass ecosystems has made significant progress both globally and in China. Nonetheless, substantial gaps persist in studies of temperate nearshore seagrass beds subjected to multiple urban pressures. Existing research has largely concentrated on tropical seagrass beds or temperate beds under minimal anthropogenic stress [19]. In particular, limited comprehensive studies have addressed the multidimensional response relationships between environmental factors and biological communities, or the mechanisms driving seagrass bed degradation. Furthermore, existing studies in Qingdao’s coastal waters indicate that the dominant seagrass species, Zostera marina, responds to rising temperatures through northward distribution shifts and enhanced antioxidant enzyme activities [20]. Further investigation revealed that seagrass bed coverage in the area has declined by over 80%, with complete loss observed in some regions, a degradation pattern driven by multiple stressors including light limitation, nutrient enrichment and hydrodynamic forcing [21]. However, these prior efforts lack long-term monitoring data, constraining a systematic understanding of seagrass-benthic macrofauna community dynamics and impeding reliable scientific support for ecological restoration strategies. In response, this study undertook a systematic, three-year monitoring and multivariate analysis of seagrass ecosystems in Qingdao Bay, Huiquan Bay, and Tangdao Bay from 2020 to 2024, combining field surveys with satellite remote sensing. The objectives were to: (1) evaluate the impacts of multiple environmental factors on seagrass growth and identify key drivers; (2) elucidate the relationships between seagrass community structural parameters and benthic macrofauna communities, including α and β diversity; and (3) determine the main environmental drivers of benthic macrofauna dynamics within seagrass distribution zones. These findings clarify the status and mechanisms of seagrass bed degradation in temperate coastal regions of Qingdao and provide scientific guidance for ecological assessment, management, and restoration.

2. Materials and Methods

2.1. Study Area and Sampling Design

From April 2020 to June 2024, sampling of seagrasses, benthic macrofauna, seawater, and sediments was conducted within seagrass distribution ranges in three representative bays of Qingdao, Shandong Province, China (35°35′–37°09′ N, 119°30′–121°00′ E) (Figure 1). Qingdao Bay and Huiquan Bay are classified as open bays [22], whereas Tangdao Bay is semi-enclosed [23]. All study areas exhibit a regular semidiurnal tidal cycle [24]. Samplings occurred in Qingdao Bay and Huiquan Bay since 2020 (April 2020, June 2023, June 2024), while seagrass bed in Tangdao Bay were newly identified by our group in 2024, necessitating its inclusion to provide crucial spatial comparison data. According to seagrass bed distribution and habitat heterogeneity, three, one, and seven monitoring stations were established in Qingdao Bay, Huiquan Bay, and Tangdao Bay, respectively. At each station, three parallel sampling sites were designated. Study stations encompassed low- to high coverage zones of seagrass beds (coverage from <5% to >50%) [18]. The spatial distribution of the study area and sampling stations is shown in Figure 1.

2.2. Field Sampling and Laboratory Measurements

2.2.1. Seagrass Community Parameters

The macroscale distribution of seagrass meadows was characterized using satellite remote sensing interpretation and UAV aerial surveys, providing spatially explicit mapping prior to field validation. At each site, three quadrats (25 cm × 25 cm) were randomly collected within vegetated areas to investigate shoot density (SD), shoot height (SH), coverage degree (CD), aboveground biomass (AB), belowground biomass (BB), and total biomass (TB). All seagrass shoots within the quadrats were excavated intact to include both aboveground and belowground tissues. Samples were gently rinsed with filtered seawater to remove adherent sediments, placed in sterile bags, and transported under refrigerated conditions (4 °C) to the laboratory for further analysis.

Shoot density (SD; shoots m^–2): The number of seagrass shoots per species within each quadrat was counted to calculate species-specific density and total habitat density per station.

Shoot height (SH; cm): Following established seagrass monitoring protocols [25], six intact shoots were randomly selected per quadrat to ensure representative sampling. The vertical distance from the meristematic tissue to the apex of the longest leaf blade was measured after gently extending leaves to their maximum length without uprooting, while excluding the tallest 20% of leaves to minimize bias from extreme values. This approach follows the standardized methodology validated in our previous studies [18].

Coverage degree (CD; %): The percentage of seabed area covered by the vertical projection of seagrass canopies was determined. Quadrats devoid of seagrass were considered as 0% cover.

Biomass quantification (AB, BB, TB; g dry weight (DW) m^–2): Samples were oven-dried at 60 °C for 48 h until constant mass was obtained. AB and BB were separately weighed, with TB derived from their sum.

2.2.2. Environmental Factors

Environmental parameters were investigated following frameworks for submerged marine plant adaptability assessment proposed by Short et al. [26] and Tanner et al. [27]. Variables included water depth (WD), temperature (T), transparency (Tr), salinity (Sal), dissolved inorganic nitrogen (DIN), reactive phosphate (Rpho), dissolved oxygen (DO), suspended solids (SS), and petroleum hydrocarbons (PHCs) in seawater, as well as organic carbon (OC), sulfur (S), and median particle size (D50) in sediment. WD was measured using a calibrated echosounder (±0.01 m). A multi-parameter water quality meter (YSI Professional Pro, Yellow Springs, OH, USA) was used to measure T, Sal, and DO. Transparency was measured with an MQ-200 quantum radiometer (Apogee Instruments, Logan, UT, USA). DIN was analyzed using hypobromite oxidation, naphthylethylenediamine spectrophotometry, and cadmium column reduction. Rpho concentration was determined by the phosphomolybdenum blue spectrophotometric method. SS were measured by gravimetric analysis. PHCs were assessed using spectrophotometry following GB 17378.4-2007 [28]. OC was analyzed by potassium dichromate oxidation-reduction titration, and S was determined using iodometric titration (GB 17378.5-2007) [29]. D50 was measured using a Malvern Mastersizer 3000 laser particle size analyzer (Malvern, UK).

2.2.3. Benthic Macrofauna Community

A grab-type sediment sampler (0.05 m²) was used to collect three parallel samples at each station. Samples were washed and sieved through a 0.5 mm mesh. Retained organisms and sediment residues were transferred into 500 mL sampling bottles, fixed in 5% formaldehyde solution, and transported to the laboratory. Species identification and enumeration were performed under a stereomicroscope (Olympus SZX16, Tokyo, Japan). Biomass was determined by wet weight [30].

2.3. Data Processing and Statistical Analysis

Multivariate analytical approaches were applied to investigate seagrass bed dynamics and associated environmental drivers. Prior to multivariate analysis, environmental variables were standardized using Z-scores to eliminate scale effects. Descriptive statistics were first used to compare interannual and spatial variation in seagrass parameters across years (2020–2024) and bays (Qingdao Bay, Huiquan Bay, Tangdao Bay). Data visualization was performed using bar charts and boxplots. For the benthic macrofauna community, α diversity was quantified using indices such as the Species richness, Berger–Parker index, Heip’s index of community evenness, Margalef richness index, Shannon diversity index, Simpson index, and Pielou evenness index [31,32,33], while β diversity was assessed using principal coordinate analysis (PCoA) and cluster analysis [33]. Relationships between environmental factors and biological communities were examined by redundancy analysis (RDA) and Spearman’s correlation analysis [34,35,36]. To address potential issues with multiple comparisons in the correlation analysis, the False Discovery Rate (FDR, Benjamini–Hochberg method) method was applied to correct the p-values, and subsequent visualizations were generated based on these corrected p-values. Furthermore, prior to performing the RDA, the environmental variables were checked for multicollinearity, which confirmed its presence. The community abundance matrix was subjected to Hellinger transformation for RDA, while for PCoA/cluster analysis/PERMANOVA, it was subjected to square root transformation followed by calculation based on Bray–Curtis distances. The choice of RDA over CCA (Canonical Correspondence Analysis) was informed by an initial Detrended Correspondence Analysis (DCA), where the gradient length of the first axis (Axis 1) was 2.4 standard deviations. As a gradient length less than 4 SD suggests a linear, rather than unimodal, species response model, RDA was deemed the more appropriate method. Ordination and statistical analyses were performed in R (vegan, stats) and PRIMER.v6 Inter-group differences were tested using the Kruskal–Wallis test. Data visualization and plotting were carried out with Origin 2023 and GraphPad Prism 9. Statistical and multivariate analyses were conducted using R software (v3.6.3; vegan and stats packages) and PRIMER software.

3. Results

3.1. Seagrass Community Status and Interannual Dynamic Changes

During the monitoring period from 2020 to 2024, significant interannual dynamics and spatial differentiation were observed in the seagrass communities of Qingdao Bay, Huiquan Bay, and Tangdao Bay (Figure 2). The seagrass bed in Qingdao Bay showed a clear trend of degradation, particularly evident in the sharp decline of belowground biomass (BB) (Figure 2A). BB decreased from 56.1 g DW/m² in 2020 to 6.7 g DW/m² in 2024, representing an 88% reduction (Kruskal–Wallis test, H = 15.6, p < 0.001). The root-to-shoot ratio (BB/AB) also dropped sharply from 1.06 to 0.04. Other indicators such as shoot height (SH), coverage degree (CD), shoot density (SD), and aboveground biomass (AB) maintained moderate to high values (e.g., SH was 45.2 cm in 2020, CD reached 90%, and AB was 105.0 g DW/m²). Their fluctuations were smaller compared with BB. The coordinated changes of SH, SD, CD, and AB reflected an integrated regulatory strategy of the seagrass community under environmental fluctuations. By 2024, the seagrass bed in Qingdao Bay displayed a fragmented distribution, with an average CD of only 41.7% and an average SD reduced to 219.6 shoots/m².

In contrast, seagrasses in Huiquan Bay exhibited an adaptive strategy centered on morphological plasticity (Figure 2B). From 2020 to 2024, SH increased from 36.8 cm to 83.1 cm, a 126% increase. AB also peaked at 215.9 g DW/m². However, SD declined by 66%, and BB remained low, decreasing to 6.0 g DW/m² in 2024. This led to a severely imbalanced root-to-shoot ratio (0.03). The results of the PCoA plots (Figure S1B) showed that the two axes together accounted for 91.93% of the variance, with significant interannual variation detected (R² = 0.52, p = 0.022; p < 0.05), suggesting that year was a strong explanatory factor for trait variation in Huiquan Bay.

In 2024, cross-bay comparisons highlighted the influence of habitat heterogeneity on seagrass community structure (Figure 2C). Qingdao Bay was characterized by the highest SD (219.6 shoots/m²) but low BB (6.7 g DW/m²), reflecting a degradation pattern of high density but limited resource allocation. Huiquan Bay displayed morphological adaptation with elevated SH (83.1 cm) and AB (215.9 g DW/m²). In contrast, Tangdao Bay exhibited the highest BB (14.6 g DW/m²) and root-to-shoot ratio (0.09), indicating a relatively stable community condition. Cluster analysis further revealed a spatial differentiation of seagrass communities in Qingdao Bay, Huiquan Bay, and Tangdao Bay, with the seagrass community in Huaquan Bay being more similar to that in Tangdao Bay (Figure 2D).

3.2. Benthic Macrofauna Community Structure and Diversity Characteristics in Seagrass Beds

3.2.1. Benthic Macrofauna Community Composition

From 2020 to 2024, significant differences in large benthic animal community structure were observed between Qingdao Bay and Huiquan Bay. In Qingdao Bay, 7 phyla, 71 families, and 104 species were identified. The average abundance was 1126 ± 284 ind/m², and biomass was 189 ± 47 g WW/m². Mollusca dominated (56.9%), followed by Annelida (24.8%) and Arthropoda (16.2%). Together, these accounted for 97.9% of total abundance, reflecting a high-biomass community dominated by filter-feeding bivalves. In Huiquan Bay, 7 phyla, 47 families, and 68 species were recorded. The average abundance was 987 ± 235 ind/m², and the biomass was 156 ± 38 g WW/m². The community was dominated by Annelida (75.0%), followed by Arthropoda (15.3%) and Mollusca (5.6%), together representing 95.9% of total abundance.

By 2024, differences in community composition among the three bays became more pronounced (Figure 3). Qingdao Bay remained dominated by mollusks (67.4%), with the Manila clam Ruditapes philippinarum (Adams et Reeve, 1850) and the polychaete Scoloplos (Scoloplos) armiger (Müller, 1776) as dominant species. In Huiquan Bay, annelids were dominant (51.8%), with typical sediment-feeding taxa such as Paranthura japonica (Richardson, 1909) and Lumbrineris japonica (Marenzeller, 1879) contributing significantly to abundance. Tangdao Bay exhibited the most species-rich community, comprising 8 phyla, 54 families, and 75 species. Annelida dominated absolutely (69.6%), with Sternaspis sculata (Renier, 1807) and Lumbrineris cruzensis (Hartman, 1944) as key taxa.

3.2.2. α Diversity Characteristics

The α diversity indices of benthic macrofauna communities in the three bays exhibited distinct interannual variation patterns (Figure 4). In Qingdao Bay (Figure 4A), the Shannon diversity index (H′) decreased from 2.04 ± 0.07 in 2020 to 1.69 ± 0.20 in 2023, before rebounding to 1.98 ± 0.23 in 2024. However, the 2024 level did not return to that of 2020 (p > 0.05). The Margalef richness index (D) declined from 2.93 ± 0.30 in 2020 to 2.79 ± 0.34 in 2023, and then slightly increased to 3.31 ± 0.39 in 2024 (p > 0.05). Species richness in 2023 was 11.67 ± 1.57, and the Simpson index reached its lowest value of 0.69 ± 0.07. These results suggest that the community structure in 2023 was relatively simple. In 2024, species richness rebounded to 18.11 ± 2.51, though the Pielou evenness index was lowest (0.71 ± 0.06).

In Huiquan Bay (Figure 4B), the Shannon index decreased from 2.50 ± 0.17 in 2020 to 1.83 ± 0.60 in 2023, then recovered to 2.37 ± 0.27 in 2024, showing a fluctuating pattern (p > 0.05). Species richness declined from 20.67 ± 5.46 in 2020 to 13.00 ± 5.51 in 2023, then rose to 18.33 ± 2.33 in 2024. The Pielou evenness index in 2023 (0.859 ± 0.033) was slightly higher than in 2020 (0.862 ± 0.053) and 2024 (0.814 ± 0.057). This indicates that although the community was disturbed, evenness remained relatively stable.

A comparison of the three bays in 2024 (Figure 4C) showed that Qingdao Bay, Huiquan Bay, and Tangdao Bay had similar species richness. This suggests that the α diversity of benthic macrofauna in terms of richness is converging across the three bays, reflecting the shared influence of temperate seagrass bed habitats. However, the diversity indices and evenness varied. Tangdao Bay exhibited the highest Shannon index (2.61 ± 0.11), Margalef richness index (4.14 ± 0.37), Simpson index (0.90 ± 0.01, significantly higher than the other two bays, p < 0.05), and Pielou index (0.90 ± 0.02, extremely significantly higher, p < 0.01). This pattern was further supported by the Berger–Parker and Heip’s evenness indices, which also showed significant differences (p < 0.01 for both), confirming that Tangdao Bay had the lowest species dominance and the most even distribution of individuals among species. These findings indicate that the community structure in Tangdao Bay was more complex and stable.

3.2.3. β Diversity Characteristics

PERMANOVA analysis (Figure 5) showed that year significantly affected the β diversity of benthic macrofauna communities in Qingdao Bay (R² = 0.179, p = 0.002). The first two axes of the PCoA explained 29.68% of the variation. Partial overlap was observed between the 2020 and 2023 sample points, indicating some continuity in community composition. The 2024 points were more clustered and spatially closer to the 2020 samples in the ordination plot (Figure 5A and Table S1).

In Huiquan Bay, the year effect was stronger (R² = 0.376, p = 0.018). The first two axes of the PCoA explained 50.8% of the variation (Axis 1: 29.43%, Axis 2: 21.37%). The 2023 points clearly deviated from the 2020 baseline cluster (Figure 5B and Table S1). By 2024, the sample points migrated toward the 2020 reference cluster. However, some sites had not fully returned, indicating persistent spatial heterogeneity.

The β diversity differences among the three bays in 2024 were highly significant (R² = 0.155, p = 0.002). The first two axes of the PCoA explained 29.02% of the variation. Sample points in Tangdao Bay were relatively clustered, whereas those in Qingdao Bay and Huiquan Bay showed considerable overlap (Figure 5C).

3.3. Response Relationships Between Seagrass Communities and Environmental Factors

The heatmap revealed multi-scale regulatory mechanisms of environmental factors on the seagrass bed parameters (Figure 6). In Qingdao Bay, Sal was highly positively correlated with all seagrass parameters (p < 0.01 or p < 0.05). Tr was highly positively correlated with SH, AB, CD, and SD (p < 0.05). The cumulative variance explained by the first two RDA axes reached 87.0% (RDA1: 65.6%, RDA2: 21.4%) (Figure 7A). DO, DIN, PHC, and Sal extended negatively along the RDA1 axis, consistent with the distribution of BB. Most other factors (e.g., WD, OC, S) showed inverse distributions relative to seagrass growth parameters, except BB.

Huiquan Bay exhibited a unique nutrient-driven adaptive strategy (Figure 6). DO was highly positively correlated with all seagrass parameters (p < 0.05). The cumulative variance explained by the two RDA axes reached 75.1% (RDA1: 47.9%, RDA2: 27.2%) (Figure 7B). DIN, S, T, and Rpho extended positively along the RDA1 axis, aligning with SH, CD, and AB, indicating that these factors promote aboveground growth. Conversely, WD and PHC showed an inverse distribution with SH, CD, and AB, suggesting that these factors may inhibit certain seagrass growth parameters by altering habitat structure.

The integrated 2024 analysis of the three bays highlights both common stresses and differentiated responses (Figure 6). WD, DO, Tr and Sal showed significant or highly significant inhibitory effects on various seagrass parameters (p < 0.001–0.05). Meanwhile, DIN and Rpho acted as shared promoting factors, showing significant or highly significant positive correlations with several seagrass parameters (p < 0.05–0.001). For 2024, the cumulative variance explained by the first two RDA axes for the three bays reached 70.2% (RDA1: 40.2%, RDA2: 30.0%) (Figure 7C). Parameters such as Tr, WD, DO, and Sal extended positively along the RDA1 axis, aligning with the Huiquan Bay samples. This indicates that these variables facilitated seagrass growth in Huiquan Bay. Additionally, DIN, D50, T, OC, and S extended in the same direction as BB, SH, AB, CD, and SD along the RDA1 axis, reflecting their promotive effects.

3.4. Coupling Effects of Benthic Animal Communities with Seagrass and Environmental Factors

This study provides an integrative analysis of seagrass bed ecosystems across the three bays. Correlation analysis and RDA ordination (Figure 8 and Figure 9) revealed the response mechanisms of major benthic macrofaunal phyla to both environmental variables and seagrass community characteristics. In Qingdao Bay (Figure 8A), positive correlations were observed between Arthropoda and Annelida with both SS and DIN (p > 0.05), while significant negative associations were found with T (p > 0.05). Echinodermata showed positive correlations with all seagrass parameters (p > 0.05). Cnidaria exhibited positive correlations with WD, D50, OC and Rpho (p > 0.05). RDA ordination (Figure 9A) substantiated these relationships, with 98.0% of the variance explained by RDA1 and RDA2. Annelida were closely associated with DIN and PHC, while Mollusca clustered in regions with high SD, BB, and CD, indicating a preference for dense, well-covered seagrass beds. Negative correlations were noted between DO, Sal, WD, and most benthic taxa, suggesting that these conditions in Qingdao Bay may approach the tolerance limits of certain groups.

In Huiquan Bay (Figure 8B), positive correlations were found between Arthropoda and Annelida with Tr, Sal, OC, S, and D50 (p > 0.05), while T, Rpho, and SS showed negative associations (p > 0.05). Most benthic taxa were positively correlated with AB, and exhibited positive correlations with D50 (p > 0.05). RDA ordination (Figure 9B) showed that RDA1 and RDA2 accounted for 100% of the variance. Annelida were closely associated with SS, OC, and COD, while Arthropoda aligned with Tr, PHC, and WD. Arthropoda, Mollusca, and Echinodermata clustered in high-value zones of AB and SH.

In the integrated 2024 analysis (Figure 8C), Echinodermata were significantly positively correlated with SD, CD, and BB (p < 0.05) but negatively correlated with DO (p < 0.05). Nemertea were highly negatively correlated with DO (p < 0.05). Mollusca exhibited positive correlations with SD, showing significant positive associations (p < 0.05). RDA ordination (Figure 9C) showed that RDA1 and RDA2 explained 99.4% of the variance. Mollusca and Arthropoda were significantly correlated with COD and Sal, closely aligned with SD and CD. Annelida were strongly associated with OC and seagrass parameters AB, SH, and CD, highlighting the influence of organic matter and robust seagrass growth on their distribution. Most environmental factors correlated positively with BB and SH but showed inverse relationships with benthic taxa.

4. Discussion

4.1. Seagrass Community Dynamics and Environmental Adaptation Mechanisms

This study revealed significant interannual and spatial variation in the seagrass communities of Qingdao Bay, Huiquan Bay, and Tangdao Bay from 2020 to 2024. These findings align with the general response patterns of temperate seagrass beds to environmental heterogeneity and multiple stressors [37]. In Qingdao Bay, an open bay, seagrass beds exhibited a clear degradation trend. Between 2020 and 2024, BB decreased by 88%, and the root-to-shoot ratio declined sharply from 1.06 to 0.04, suggesting the severe inhibition of root development. This pattern suggests that the seagrass community sustained aboveground functionality through structural compensation mechanisms, while the underground portion exhibited greater sensitivity to environmental stressors. This might also be directly linked to increased WD, which causes light attenuation (reduced Tr), and to increased sediment particle size (D50). RDA analysis revealed highly significant negative correlations between WD and seagrass CD and SD (p < 0.01). D50 was significantly negatively correlated with all seagrass parameters (p < 0.05), confirming the importance of fine-grained sediment for root anchorage and nutrient retention [38]. In addition, Qingdao Bay is affected by sewage discharge and shipping activities, which increase the SS concentrations. Elevated SS likely reduces light availability and further accelerates seagrass decline, consistent with the “light limitation–seagrass decline” hypothesis proposed by Short et al. [26].

In contrast, seagrass in Huiquan Bay responded to eutrophic conditions through morphological plasticity. SH increased by 126%, and AB reached 215.9 g DW/m². This “taller-shoot–higher-aboveground-biomass” strategy may represent an adaptive response of temperate seagrass to nutrient pulses. However, the severely imbalanced root-to-shoot ratio (0.03) suggests the potential compromise of belowground structures and long-term stability. This observation aligns with Maxwell et al. [15], who noted that nutrient enrichment may induce allocation imbalances in seagrass.

Tangdao Bay displayed relatively healthy ecological conditions. Its higher BB (14.6 g DW/m²) and root-to-shoot ratio (0.09) showed significant positive correlations with DIN (p < 0.01). This indicates that the weak hydrodynamics of semi-enclosed bays promote organic matter accumulation and root development while mitigating the toxic effects of S on roots [10]. The differences in resource allocation strategies identified here, driven by hydrodynamics and sedimentary environments, are consistent with the findings of Henderson et al. [39] in Moreton Bay. These results emphasize the importance of seagrass patch size and hydrological connectivity for ecosystem functioning.

4.2. Ecological Drivers of Macro-Benthic Community Differentiation

Marked differences in benthic community structure were observed among the three bays, reflecting cascading effects of seagrass habitat degradation and hydrological changes. In Qingdao Bay, Mollusca dominated (67.4%), with the Manila clam Ruditapes philippinarum (Adams et Reeve, 1850) as the leading species. This pattern of high biomass but low diversity may be associated with habitat simplification following seagrass decline. The Shannon index decreased significantly, indicating a simplified community structure. The β-diversity analysis revealed substantial interannual fluctuations, further confirming the strong impact of environmental disturbance on community structure. The “apparent recovery” observed in 2024 may represent an ecological trap under continued seagrass decline, as the sharp drop in the root-to-shoot ratio from 1.06 to 0.04 implies loss of nursery function, causing community reassembly to lag behind habitat degradation [37].

In Huiquan Bay, Annelida dominated (51.8%), and community evenness was relatively high. Although the Shannon index fluctuated interannually, it remained generally stable, suggesting that the benthic community has a buffering capacity against environmental variability. Tangdao Bay displayed the most balanced distribution of benthic taxa. Its Shannon index, Simpson index, and evenness were all significantly higher than those of the other two bays, reflecting complex habitat structure and stronger community stability. This supports the stable coexistence of multi-trophic benthic communities [40].

PERMANOVA analysis revealed significant spatiotemporal variations in β-diversity, highlighting the dual influence of interannual dynamics and habitat heterogeneity in structuring benthic communities. The significant interannual changes point to the role of environmental fluctuations in driving community succession, while the pronounced differences among bays underscore the importance of habitat features such as bay morphology and sediment composition. Notably, the spatial convergence of the 2024 community toward the 2020 baseline in Qingdao and Huiquan Bays coincides with the observed stabilization of seagrass bed conditions, suggesting a potential recovery trajectory for the associated benthic macrofauna. This interpretation is reinforced by the considerable overlap in benthic communities between Qingdao and Huiquan Bays—likely attributable to their geographical proximity and shared environmental conditions, including similar tidal regimes, sediment characteristics, and exposure to urban stressors. This suggests that the overall habitat template shaped by hydrodynamic and sedimentary conditions may filter for functionally analogous benthic assemblages, despite differences in seagrass vegetation structure between the two bays. In contrast, the distinct community structure in the semi-enclosed Tangdao Bay further emphasizes the role of habitat heterogeneity in driving β-diversity patterns. These results align with Lawrence [41], who demonstrated through structural equation modeling (SEM) in Langebaan Lagoon that seagrass density and leaf width directly enhance benthic animal abundance, while environmental factors such as temperature and turbidity exert indirect effects. Similarly, Duffy [42] emphasized the role of biodiversity in maintaining seagrass ecosystem functions, including productivity, stability, and nutrient cycling. High-diversity communities tend to show greater resistance and resilience to environmental disturbances. Consistent with these findings, the present study also detected strong correlations between seagrass community characteristics and benthic animal diversity, reinforcing the importance of seagrass habitat structure in sustaining benthic biodiversity and stability.

4.3. Multiple Coupling Relationships Among Seagrass, Benthic Animals, and Environmental Factors

This study revealed a tripartite coupling relationship among environmental factors, seagrass, and benthic animals in Qingdao waters. Environmental variables directly regulated seagrass growth and indirectly influenced benthic community structure. The relationship between DO and seagrass parameters revealed a noteworthy context-dependent pattern. While separate analyses of Qingdao Bay and Huiquan Bay indicated positive correlations between DO and various seagrass factors, the integrated 2024 analysis across all three bays showed a significant negative correlation. This apparent contradiction is likely attributable to the significant temperature gradient observed among the bays during the survey. Tangdao Bay, which exhibited the most favorable seagrass growth (e.g., highest belowground biomass and community stability), experienced substantially higher water temperatures. Since oxygen solubility decreases with increasing temperature, this could have led to lower DO concentrations in Tangdao Bay. In contrast, the comparatively cooler waters of Qingdao Bay and Huiquan Bay maintained higher DO levels but were associated with poorer seagrass status, largely due to other stressors. Thus, the negative correlation emerging from the cross-bay comparison likely reflects the overriding influence of temperature on DO solubility and does not imply a direct inhibitory effect of DO itself. This interpretation is supported by established ecological knowledge that adequate dissolved oxygen is typically beneficial for seagrass respiration and sediment biogeochemistry, as evidenced in previous studies [36,43,44,45]. In contrast to the complex role of DO, DIN and Rpho served as consistent promoting factors, consistent with the nutrient-limited status of temperate seagrasses [3]. The promotive effects of DIN and Rpho on seagrass growth observed in our study are consistent with the nutrient-limited status of many temperate seagrass systems [36,46,47]. It is important to note that this positive relationship likely exists within an optimal concentration range, beyond which eutrophication stress would predominate. The fact that our study sites still exhibit positive correlations suggests that nutrient loading may not yet have exceeded critical thresholds for seagrass survival in these bays [48]. Moderate supplementation of nitrogen and phosphorus enhanced seagrass biomass, thereby providing additional food and habitat for benthic fauna.

Region-specific patterns were also observed. In Qingdao Bay, Sal promoted seagrass BB (p < 0.05 or p < 0.01). The strong positive correlation between Sal and BB in Qingdao Bay may reflect its role as a proxy for beneficial oceanographic conditions. Within this open bay, higher salinity likely indicates stronger water exchange with the open ocean. This enhanced flushing could promote seagrass root development by mitigating multiple stressors, such as reducing the accumulation of sediment toxins and replenishing dissolved inorganic carbon, rather than representing a direct physiological effect of salt ions [49,50]. Meanwhile, in Tangdao Bay, Tr positively regulated SD (p < 0.01 or p < 0.001). These differential responses reflect the phenotypic plasticity of Zostera marina populations to distinct environmental drivers across the bays. The distinct response patterns across the three bays underscore the operation of both habitat filtering and regional adaptation processes in shaping seagrass ecological strategies.

The structural and functional variations in seagrass communities subsequently influenced benthic fauna distributions through habitat facilitation. Positive correlations between seagrass and benthic animals further highlighted habitat facilitation. Mollusca, for example, were positively correlated with seagrass SD and CD, underscoring the habitat-building role of seagrass beds [51]. Seagrasses trap organic debris and attenuate hydrodynamic disturbance, creating stable conditions that support benthic organisms [52].

These association patterns reveal taxon-specific response mechanisms. In Qingdao Bay, Echinodermata depended on seagrass structural complexity, while Cnidaria preferred coarse-grained, nutrient-rich environments, reflecting differential adaptation strategies among taxa. In Huiquan Bay, improved water clarity enhanced benthic feeding and habitat utilization, whereas elevated temperature and eutrophication exerted negative effects. The differentiated distribution of taxa along seagrass biomass and sediment grain size gradients confirms the crucial role of habitat filtering in community assembly.

The comprehensive analysis indicates that seagrass communities effectively mitigate the negative impacts of nutrient enrichment and low oxygen stress on most benthic groups by providing complex microhabitats and diverse resources. Functional groups displayed distinct responses: Arthropoda were more influenced by nutrient conditions, while Mollusca distributions were closely linked to seagrass structural characteristics. These taxon-specific distribution patterns reveal that seagrass beds shape the distribution patterns of benthic fauna through multiple mechanisms including food resource provision, habitat complexity enhancement, and tolerance threshold regulation. Our findings align with previous studies that emphasize the ecosystem engineering role of seagrasses. Staveley et al. [53] demonstrated that seagrass landscape complexity strongly influences fish and benthic assemblages. Kindeberg et al. [54] emphasized the synergistic role of seagrass density and leaf length in enhancing benthic biodiversity and metabolism. Similarly, Muller et al. [55], through partial structural equation modeling (pSEM), showed that environmental factors (e.g., temperature, tidal range, flow velocity) directly influence benthic α and β diversity while indirectly affecting community structure by modifying Zostera marina traits such as leaf width and BB. Their findings stress that at the regional scale, environmental factors dominate community variation, whereas at the local scale, plant traits and environmental conditions interact to shape benthic biodiversity and abundance. Franco Rodil et al. [56] demonstrated that enhanced macrofaunal biodiversity not only boosts secondary production but also facilitates organic matter turnover and carbon metabolism, thereby positively influencing carbon cycling in seagrass ecosystems. More recently, Millot et al. [57] employed structural equation modeling to quantify both direct and indirect pathways through which environmental factors influence seagrass and their associated fauna. These insights align closely with the triadic coupling relationships revealed in this study and reaffirm the role of seagrass as an ecosystem engineer bridging environmental drivers and biological communities.

4.4. Global Implications for Temperate Seagrass Ecosystems

The ecological patterns observed in Qingdao’s seagrass beds align closely with established global trends in temperate seagrass ecology. The dramatic reduction (88% decline) in belowground biomass in Qingdao Bay substantiates the findings by Vonk et al. [58] that degradation of subsurface structures serves as a critical indicator of seagrass ecosystem resilience. The persistent degradation trajectory in this bay further reflects the pattern documented by Di Carlo & Kenworthy [59], wherein belowground biomass recovery substantially lags behind aboveground regeneration following disturbance. Concurrently, the trade-off observed in Huiquan Bay—increased aboveground biomass at the expense of belowground development—echoes observations by Hesselbarth & Allgeier [60] and Lapointe et al. [61] across multiple temperate systems, confirming this as a characteristic seagrass response to nutrient enrichment.

The positive relationship between seagrass structural complexity and benthic diversity demonstrated across our three bays reinforces globally documented patterns. The gradient of community responses we observed supports earlier findings by Di Carlo & Kenworthy [59] and Gaubert-Boussarie et al. [62] that structural simplification drives benthic community homogenization. Furthermore, the water depth effect observed in our study, manifested through light limitation mechanisms, demonstrates universal applicability, with Zieman & Zieman [63] and Bulmer et al. [64] providing mechanistic support for the inverse relationship between the depth and seagrass coverage we documented. Collectively, these findings underscore that despite regional particularities, temperate seagrass ecosystems respond to environmental pressures through conserved ecological mechanisms, thereby providing a scientifically robust foundation for developing process-based conservation strategies.

4.5. Limitations and Future Directions

This research provided three-year monitoring data from 2020 to 2024. However, Tangdao Bay was only sampled in 2024, which may not fully capture the interannual dynamics. Additionally, direct impacts of human activities (e.g., aquaculture, tourism) were not quantified. Future work should integrate socio-economic datasets to enable coupled ecological-social analyses. This study also emphasized community-level responses, with limited exploration of interspecies interactions (e.g., predation, symbiosis) between seagrasses and benthic fauna. Boström et al. [65] highlighted that seagrass density and aboveground biomass strongly influence predation pressure and benthic animal distributions. High-density and high-biomass patches often provide effective refuges by altering flow, shading, and predator behavior. This mechanism supports the “taller-shoot–higher-aboveground-biomass” restoration strategy identified in this research.

Future research could employ molecular ecology techniques (e.g., stable isotope analysis) to investigate nutrient pathways and food–web interactions [66]. Quantifying predator–prey dynamics at varying levels of structural complexity would improve our understanding of ecological regulation in temperate seagrass beds. Franco Rodil et al. [67] emphasized the importance of cross-habitat comparative studies for seagrass conservation. Expanding the research scale to include diverse bay types and broader environmental gradients would strengthen the generalizability and management relevance of these findings.

5. Conclusions

This study, conducted from 2020 to 2024, demonstrates pronounced spatial and temporal dynamics in temperate seagrass beds, driven by a tightly coupled relationship among environmental factors, seagrass status, and benthic community structure. The three bays represented a degradation gradient: Qingdao Bay experienced severe degradation (88% BB decrease), Huiquan Bay exhibited morphological plasticity but instability, while Tangdao Bay maintained stable communities with high BB.

Correspondingly, the shifts in benthic community composition and diversity underscored their strong dependence on seagrass condition. In Qingdao Bay, mollusks dominated, but diversity declined and the community structure simplified. In Huiquan Bay and Tangdao Bay, annelids were dominant, with Tangdao Bay supporting more stable, species-rich communities. The β-diversity analysis highlighted habitat heterogeneity as a key driver of spatial differentiation. Environmental analysis identified DIN as key promoting factors, while WD functioned as primary stressors. The role of dissolved oxygen and salinity was complex, exhibiting context-dependent relationships with seagrass parameters.

This work confirms that seagrass beds mitigate key environmental stressors—particularly nutrient enrichment, hydrodynamic disturbance, and bottom-water hypoxia—by providing structural habitat, stabilizing sediment environments, and modifying local hydrographic conditions. The established “environment–vegetation–benthic animal” multidimensional analytical framework provides scientific support for the conservation and restoration of temperate seagrass beds. Future management strategies should account for bay-specific environmental features and adopt differentiated restoration approaches.