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Article

Can the Ecological Quality of Several Bays in South Korea Be Accurately Assessed Using Multiple Benthic Biotic Indices?

by
Jian Liang
1,
Chae-Woo Ma
1,*,
Kwang-Bae Kim
2 and
Dae-Sun Son
3
1
Department of Life Science and Biotechnology, Soonchunhyang University, Asan 31538, Republic of Korea
2
Gyeonggi-do Maritime and Fisheries Resources Research Institute, Ansan 15651, Republic of Korea
3
Haerang Technology and Policy Research Institute, Suwon 16229, Republic of Korea
*
Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2024, 12(7), 1179; https://doi.org/10.3390/jmse12071179
Submission received: 16 June 2024 / Revised: 10 July 2024 / Accepted: 12 July 2024 / Published: 13 July 2024
(This article belongs to the Special Issue Coastal Environments: Recent Advances in Conservation and Sustainable Development)
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Abstract

:
Bays are extensively impacted by human activities, and an accurate assessment of ecological quality is crucial for the environmental management and protection of bays. Most studies indicate that evaluating the ecological quality of bays using a single index presents challenges. In our study, we used five benthic biotic indices and a composite index to assess the ecological quality of three bays in South Korea. Our results revealed disparities in the ecological quality status assessment using five benthic biotic indices. The composite index performed better than the other benthic biotic indices in a principal coordinate analysis. Significant differences were observed between the values classifying stations as having an acceptable or unacceptable final ecological quality in terms of species indices (except for the Pielou’s evenness index) and the abundance of opportunist species (p < 0.05). Consequently, we advocate for using a composite index to assess the ecological quality of the bays of the West Sea of South Korea. Furthermore, our study provides valuable information for marine ecological management and policy formulation in Korea and offers recommendations for using the composite index in future research.

1. Introduction

In recent decades, population growth has continued unabated, with urbanisation and industrialisation progressively expanding [1]. Consequently, human activities have extensively impacted coastal environments [2]. Protecting the marine environment has escalated into a pressing global concern [3,4]. An accurate assessment of ecological quality is essential for protecting the marine environment. This is crucial for advancing conservation efforts and formulating related policies [5]. Despite the establishment of indices to assess ecological quality based on various marine organisms, those based on macrobenthos indices (benthic biotic indices) have been widely used in evaluating the quality of marine ecosystems [6]. Furthermore, based on ecosystem modelling, ecosystem-based quality indices (EBQIs) have also been widely applied to various marine ecosystems [7].
Bays, as crucial components of coastal marine systems, influence much larger coastal areas than the bays themselves by providing habitats for many marine organisms and offering humans tourism attractions, ports, fisheries, and mariculture farms [8]. Consequently, bay ecosystems face immense human activity pressure, significantly changing their ecological environment [9]. Although benthic indices have been widely used to assess the ecological quality of bays, many research findings suggest that it is challenging to evaluate the ecological quality of bays using a single index [10]. Furthermore, the effectiveness of some benthic indices in semi-enclosed bays remains to be further confirmed [11]. In semi-enclosed bays, slower water currents facilitate organic matter accumulation in sediments. This may lead to the underestimation of ecological quality when using benthic biotic indices such as the AMBI and BENTIX [12].
Given that different indices often classify the ecological quality of the same study area in conflicting ways, the simultaneous use of multiple indices can lead to confusion regarding the state of ecological quality [13]. Blanchet et al. (2008) developed a composite index based on five biotic indices to assess ecological quality accurately [12]. This composite index addresses the limitations of single indices by integrating the assessment results of multiple indices, offering a more accurate evaluation of ecological quality. It has been successfully applied to the Yellow Sea ecosystem, accurately assessing the ecological quality of Laoshan Bay in China and Garolim Bay in South Korea [14,15].
Despite achieving remarkable economic success over the past half-century, South Korea’s growth has been accompanied by an increasingly profound impact on marine environments [16]. Among these, the ecosystem of the West Sea in South Korea has been subject to a broader range of impacts than the South Sea and the East Sea of Korea. Specifically, the Saemangeum reclamation project, the world’s largest sea reclamation endeavour, is situated in the West Sea of South Korea. This project has already inflicted irreversible impacts on the surrounding marine environment [17]. Although benthic indices have been widely used in South Korea and a Benthic Pollution Index (BPI) based on the Yellow Sea ecosystem has been developed, most studies have not discussed the applicability of benthic indices in their study areas [18]. Therefore, it is necessary to explore the applicability of benthic indices in the bays of South Korea.
In this study, we selected three representative bays of the West Sea in South Korea, namely Asan Bay, Cheonsu Bay, and Gyeonggi Bay. The three bays have been affected by human activities to varying degrees. We selected the AZTI’s Marine Biotic Index (AMBI) [19], BENTIX index [20], Benthic Opportunistic Polychaete Amphipoda (BOPA) index [21], and Multivariate AZTI’s Marine Biotic Index (M-AMBI) [22], which are widely used internationally [23], along with the Benthic Pollution index (BPI) [24], which is extensively employed in South Korea, for the assessment of ecological quality [25]. The primary objectives of this study are to (i) evaluate the applicability of five benthic biotic indices and a composite index in the bays of the West Sea of South Korea and (ii) provide valuable data for the assessment of the ecological quality of the bays of the West Sea of Korea.

2. Materials and Methods

2.1. Study Area

Asan Bay, Cheonsu Bay, and Gyeonggi Bay are in the West Sea of South Korea (Figure 1). The study areas were delineated within specific geographic coordinates: for Asan Bay, the boundaries were set from 36°59′ N to 37°01′ N latitude and from 126°41′ E to 126°46′ E longitude; for Cheonsu Bay, the latitude span was from 36°23′ N to 36°37′ N, and the longitude range was from 126°20′ E to 126°30′ E; and for Gyeonggi Bay, the area extended from 37°24.44′ N to 37°45.93′ N in latitude and from 126°9.6′ E to 126°37.44′ E in longitude.
Asan Bay, spanning 40 km from east to west and exhibiting an average tidal range of 6.1 m, has experienced significant marine environmental impacts due to industrialisation and urbanisation [26]. The average salinity of the surface seawater in Asan Bay is 32.2 g/kg, and the average water temperature is 13.3 °C. The sediment at the bottom primarily comprises silt and sand [27]. In Asan Bay, land reclamation for port development, dredging, and industrial activities are the primary human activities [28].
Cheonsu Bay, extending 40 km from north to south and characterised by an average tidal range of 3.4 m, has undergone extensive environmental transformations due to land reclamation efforts within the bay. These changes have notably included the attenuation of tidal currents and alterations in the composition of plankton and fish communities [29,30]. Additionally, land reclamation has led to a decline in the population of migrant fish in Cheonsu Bay [31]. The surface seawater salinity of Cheonsu Bay ranges from 27.2 to 35.5 g/kg, with an average water depth of 10 m [32]. The sediment at the bottom is primarily composed of silt and sand [33]. In Cheonsu Bay, the primary anthropogenic pressures are the discharge of large amounts of eutrophic freshwater from artificial lakes in the northern and western regions, along with the extensive presence of aquaculture farms within the bay [34].
Gyeonggi Bay exhibits an average tidal range of 6 m. The water quality in this bay is predominantly governed by freshwater from the Han River [35]. The surface seawater salinity of Gyeonggi Bay exceeds 33.4 g/kg, with an average water depth of 40 m [36]. The sediment at the bottom primarily comprises gravel and sand [37]. Anthropogenic industrial activities have resulted in persistent organic pollutants (POPs) in the surface sediments near Incheon Port in the southern part of Gyeonggi Bay [38]. Additionally, Gyeonggi Bay also has a substantial presence of aquaculture farms.

2.2. Sampling Collection and Processing

Samples were collected at each station using a Van Veen grab (0.1 m2), with three samples acquired—two for a macrobenthic analysis and one for a sediment analysis. The initial survey occurred in Gyeonggi Bay (G1–G13) during the summer of 2008. This was followed by a study in Asan Bay (A1–A10) in the summer of 2011. Most recently, in the summer of 2020, the investigation was carried out in Cheonsu Bay (C1–C22) (Figure 1). A Van Dorn water sampler was utilised to collect bottom seawater samples, and DO (dissolved oxygen), salinity, and pH measurements were conducted using a YSI-556 handheld multiparameter instrument (YSI Inc., Yellow Springs, OH, USA).
In the field, macrobenthos were collected using a 0.5 mm sieve and fixed in a 5% formalin solution. Approximately 200 g of sediment was collected using a plastic spoon and preserved at −20 °C before being transported to the laboratory for analysis.
At the Marine Ecological Laboratory at Soonchunhyang University, macrobenthic specimens were sorted and identified to the most precise taxonomic level achievable, with species-level identification whenever possible. The chemical oxygen demand (COD) content in sediments was quantified using the Dichromate Method, while the grain size of the sediments was determined by employing the sieving and pipette method [39]. The organic matter content (IL%) in the sediments was determined by heating them in a muffle furnace at 550 °C for 2 h [39].

2.3. Benthic Biotic Indices

The AMBI and the BENTIX index utilise the sensitivity of macrobenthos to organic enrichment for categorisation into five and three ecological groups, respectively [19,20]. The BOPA index is derived from the ratio of opportunistic polychaetes to amphipods [21]. The BPI focuses on the life histories of macrobenthos and categorises them into four functional groups [25]. M-AMBI values are produced by applying discriminant analysis and factor analysis techniques, which integrate AMBI values, species richness, and the Shannon–Wiener index [22]. The formulas and ecological quality status thresholds of the five benthic biotic indices are shown in Table 1.
We employed AMBI software version 6.0 to calculate the AMBI and M-AMBI values. Considering the influence of salinity on benthic communities, we established reference conditions for the M-AMBI across different salinity zones (Table S1) [40]. The method for setting reference conditions in the M-AMBI involves increasing the diversity and richness by 15% in each salinity zone from the highest observed values and using the lowest AMBI values [15].
The functional group allocation of the BPI referenced a study by Seo, 2016 [41]. Due to the possibility of the same macrobenthic species exhibiting different life histories across various geographical regions [42], EGIV and EGV within the AMBI were guided by the N4 categorisation in the BPI (Table S2). The allocation of the BENTIX referenced the AMBI ecological groups.

2.4. Data Analysis

2.4.1. Dominance Index

The dominance index (Y) calculates the dominant species within a given sampling area, with values equal to or exceeding 0.02. This index is derived through the equation Y = (ni/N) × fi, where N stands for the aggregate count of individuals encompassing all species, ni represents the count of individuals of the ith species, and fi signifies the frequency of the ith species at the sampling stations [43].

2.4.2. Composite Index

The five ecological quality statuses were simplified into acceptable and unacceptable to facilitate assessing and managing the marine ecological environment [14,15]. The standards for categorising the five benthic biotic index values as acceptable or unacceptable when assessing ecological quality are shown in Table S3. In the evaluation of ecological quality, a benthic biotic index of 1 is assigned if the ecological quality is deemed acceptable, and a benthic biotic index of 0 is assigned if the ecological quality is deemed unacceptable. The final ecological quality is considered acceptable when the sum of the composite indices exceeds 3. The calculation method for the composite index and the threshold values for the final ecological quality are shown in Table 2.

2.4.3. Statistical Treatment

A principal component analysis (PCA) of environmental factors was conducted using PRIMER software version 7.0.23 to observe the distribution patterns of the three bays based on their environmental factors. Additionally, taxonomic richness, Pielou’s evenness index, Simpson indices, Shannon–Wiener index (log2), and species accumulation curves were calculated using PRIMER software version 7.0.23 (PRIMER-E, UK) (PRIMER-E, Auckland, New Zealand). A Spearman correlation analysis was conducted to examine the relationships between the benthic biotic indices, composite index, and environmental factors.
A principal coordinate analysis (PCoA) was conducted using Hiplot (ORG) (https://hiplot.org) (accessed on 2 May 2024) to assess the relationship between the benthic community structure and ecological quality grouping. A Kappa analysis was conducted to assess the agreement or disagreement among the seven biotic and composite indices. Previous research delineated consistency into eight distinct levels and stratified them according to kappa values [44] (Table S4). The independent samples t-test and Mann–Whitney U test were utilised to evaluate whether differences existed between stations classified as acceptable and unacceptable in the biotic indices. The Spearman correlation analysis, kappa analysis, independent samples t-test, and Mann–Whitney U test were conducted using SPSS 29 (IBM Corporation, Armonk, NY, USA).

3. Results

3.1. Environment Characteristics

The values of the environmental factors at each station are shown in Table S6. The standard deviation values for sand and silt were relatively high (Table S5). The coefficient of variation for sand was high (Table S6).
In the principal component analysis, PC1 (principal components 1) and PC2 (principal components 2) explained 67.5% of the environmental variation (Figure 2). The eigenvectors of the environmental factors with PC1 and PC2 are shown in Table S7. Most sampling stations in Cheonsu Bay are located on the right side of the principal component analysis figure (Figure 2), suggesting that Cheonsu Bay had a higher COD (chemical oxygen demand) and mean grain size than Asan Bay and Gyeonggi Bay.

3.2. Macrobenthos Characteristics and Dominant Species

In this study, 289 taxonomic groups of macrobenthos were identified. Most taxa belong to annelids for 119 taxa (41.2%), followed by molluscs for 83 taxa (28.7%), arthropods for 64 taxa (22.1%), echinoderms for 12 taxa (4.2%), and other animals for 11 taxa (3.8%) (Table S8). In Asan Bay, Cheonsu Bay, and Gyeonggi Bay, macrobenthos were identified for 68 taxa, 142 taxa, and 134 taxa, respectively (Table S8). In species accumulation curves, Asan Bay exhibited the lowest gamma species diversity (Figure 3). The number of species and abundance of species at each station in the three bays are shown in Table S8.
The average values of the taxonomic richness, Pielou’s evenness index, Simpson indices, and Shannon–Wiener index in the three bays were 21.44 ± 9.14, 0.72 ± 0.15, 0.78 ± 0.14, and 3.08 ± 0.80, respectively. The values of the taxonomic richness, Pielou’s evenness index, Simpson indices, and Shannon–Wiener index at each station are shown in Table S9. In Asan Bay, Cheonsu Bay, and Gyeonggi Bay, dominant species were confirmed for six species, four species, and two species, respectively (Table 3). In Asan Bay, Ampharete arctica showed the highest dominant value, at 0.17. In Cheonsu Bay, Theora lata showed the highest dominant value, at 0.13. In Gyeonggi Bay, Heteromastus filiformis showed the highest dominant value, at 0.25.

3.3. Benthic Biotic Indices

3.3.1. Ecological and Functional Groups

In the AMBI assessment, macrobenthic taxonomic groups were categorised as follows: 92 species were classified under EGI (31.8%), 98 species were classified under EGII (33.9%), 48 species were classified under EGIII (16.6%), 12 species were classified under EGIV (4.2%), 1 was classified species under EGV (0.3%), and 38 species were not assigned (13.2%) (Table S2).
In the BPI assessment, macrobenthic taxonomic groups were categorised as follows: 104 species were classified under N1 (36.0%), 107 species were classified under N2 (37.0%), 27 species were classified under N3 (9.3%), 13 were classified species under N4 (4.5%), and 38 species were not assigned (13.2%) (Table S2).
Compared with Asan Bay and Gyeonggi Bay, most of the sampling stations in Cheonsu Bay exhibited a substantial presence of opportunistic species (i.e., EGIV, EGV, and N4) (Figure 4).

3.3.2. AMBI

In Asan Bay, the AMBI values ranged from 0.47 to 3.33, and ecological quality was categorised as high or good, except for station 10, where the ecological quality was classified as moderate (Figure S1 and Table 4).
In Cheonsu Bay, the AMBI values ranged from 0.24 to 4.61, and the ecological quality of twenty stations was categorised as high or good. In comparison, the ecological quality of two stations was classified as moderate (Figure S2 and Table 5).
In Gyeonggi Bay, the AMBI values ranged from 0.25 to 2.80, and the ecological quality of thirteen stations was categorised as high or good (Figure S3 and Table 6).

3.3.3. BENTIX

In Asan Bay, the BENTIX values ranged from 2.83 to 5.64, and the ecological quality of seven stations was categorised as high or good. In comparison, the ecological quality of three stations was classified as moderate (Figure S1 and Table 4).
In Cheonsu Bay, the BENTIX values ranged from 2.33 to 5.85, and the ecological quality of eleven stations was categorised as high or good. In comparison, the ecological quality of eleven stations was classified as moderate or poor (Figure S2 and Table 5).
In Gyeonggi Bay, the BENTIX values ranged from 2.33 to 5.46, and the ecological quality of nine stations was categorised as high or good. In comparison, the ecological quality of four stations was classified as moderate or poor (Figure S3 and Table 6).

3.3.4. BOPA

In Asan Bay, the BOPA values ranged from 0 to 0.02, and the ecological quality of all stations was categorised as high (Figure S1 and Table 4).
In Cheonsu Bay, the BOPA values ranged from 0 to 0.22, and the ecological quality of twenty-one stations was categorised as high or good. In comparison, the ecological quality of one station was classified as poor (Figure S2 and Table 5).
In Gyeonggi Bay, the BOPA values ranged from 0 to 0.28, and the ecological quality of all stations was categorised as high or good (Figure S3 and Table 6).

3.3.5. BPI

In Asan Bay, the BPI values ranged from 27.70 to 92.10, and the ecological quality of eight stations was categorised as high or good. In comparison, the ecological quality of two stations was classified as moderate or poor (Figure S1 and Table 4).
In Cheonsu Bay, the BPI values ranged from 19.00 to 80.00, and the ecological quality of thirteen stations was categorised as high or good. In comparison, the ecological quality of nine stations was classified as moderate, poor, or bad (Figure S2 and Table 5).
In Gyeonggi Bay, the BPI values ranged from 41.60 to 95.10, and the ecological quality of thirteen stations was categorised as high or good. (Figure S3 and Table 5).

3.3.6. M-AMBI

In Asan Bay, the M-AMBI values ranged from 0.43 to 0.76, and the ecological quality of seven stations was categorised as good. In comparison, the ecological quality of three stations was classified as moderate (Figure S1 and Table 4).
In Cheonsu Bay, the M-AMBI values ranged from 0.27 to 0.80, and the ecological quality of sixteen stations was categorised as high or good. In comparison, the ecological quality of six stations was classified as moderate or poor (Figure S2 and Table 5).
In Gyeonggi Bay, the M-AMBI values ranged from 0.48 to 0.85, and the ecological quality of nine stations was categorised as high or good. In comparison, the ecological quality of four stations was classified as moderate (Figure S3 and Table 6).

3.3.7. Ecological Quality Status as Acceptable or Unacceptable

The percentages of acceptable ecological quality in the benthic biotic indices vary across the three bays (60.0–97.8%). Overall, in the BOPA assessment, the highest acceptability rate for ecological quality reached 97.8% across the three bays. Conversely, in the BENTIX assessment, the lowest acceptability rate for ecological quality was 60.0% across the three bays (Figure 5).

3.4. Statistical Analysis

In the correlation analysis of the benthic biotic indices, the AMBI correlated with the BENTIX, BOPA, BPI, M-AMBI, and composite index. The BENTIX index showed a correlation with the BOPA, BPI, M-AMBI and composite index. The BOPA index showed a correlation with the BPI. The BPI exhibited a correlation with the M-AMBI and composite index. The M-AMBI correlated with the composite index (Figure 6).
The AMBI correlated with the COD, pH, IL, sand, silt, and mean grain in the correlation analysis of the environmental factors. The BENTIX index correlated with the COD, pH, IL, sand, silt, and mean grain. The BOPA index correlated with the COD, sal, pH, IL, sand, silt, and mean grain. The BPI exhibited correlations with the COD, DO, pH, IL, sand, silt, and mean grain. The M-AMBI exhibited correlations with the pH, sand, and silt. The composite index correlated with the DO, pH, IL, sand, silt, and mean grain (Figure 6). The correlation coefficient values are shown in Table S10.
In the kappa analysis, the highest kappa value between the BENTIX and composite indices was 0.906, indicating an “almost perfect” of agreement. In contrast, the lowest kappa value between the BENTIX and BOPA was 0.066, indicating a very low level of agreement (Table S11).

3.5. Final Ecological Quality and Composite Index

The composite index of the five benthic biotic indices used to determine the final ecological quality revealed that in Asan Bay, the final ecological quality was deemed acceptable at seven stations (70.0%); in Cheonsu Bay, the final ecological quality was deemed acceptable at thirteen stations (59.1%); in Gyeonggi Bay, the final ecological quality was deemed acceptable at nine stations (69.2%) (Figure 1 and Table S12).
According to the final ecological quality of the three bays assessed with the composite index, the average values of the taxonomic richness, species indices (Pielou’s evenness index, Simpson index, and Shannon–Wiener index), and the abundance of opportunistic species at the acceptable and unacceptable stations were calculated (Figure 7). Stations classified as acceptable consistently exhibited lower average values in a lower abundance of opportunistic species than those deemed unacceptable. Stations classified as acceptable consistently exhibited higher average values in the taxonomic richness, Pielou’s evenness index, Simpson indices, and Shannon–Wiener index than those considered unacceptable. The species indices (except for the Pielou’s evenness index) and the abundance of opportunistic species showed significant differences between the acceptable and unacceptable stations (p < 0.05) (Figure 7).
In the principal coordinate analysis, stations with an acceptable and an unacceptable ecological quality in Asan Bay, as assessed using the BENTIX, M-AMBI, and composite index, demonstrated clear separation. In Cheonsu Bay, stations classified as having acceptable and unacceptable ecological quality, based on the BPI and composite index, exhibited separation. In Gyeonggi Bay, stations classified as having acceptable and unacceptable ecological quality, based on the BENTIX, BPI, MAMBI, and composite index, exhibited separation (Figure 8).

4. Discussion

4.1. Environment Characteristics in the Three Bays

In the principal component analysis (Figure 2), Cheonsu Bay exhibited a higher organic matter content and mean grain size than Asan Bay and Gyeonggi Bay. This is attributed to Cheonsu Bay being a semi-enclosed bay with tidal currents ranging only from 2 to 4 m/s. Significantly, the bay’s northern region is subject to currents that can drop to speeds of 0.5 m per second. Additionally, the entry of nutrient-rich agricultural wastewater from artificial lakes in the bay’s northern and eastern sectors intensifies the build-up of organic material [45].

4.2. Benthic Biotic Indices in the Three Bays

Due to the complexity of marine environments and the varying human pressures across different regions, relying on a single index makes it challenging to assess ecological quality accurately [5,46]. For example, in a study by Liang et al., 2024 [47], despite using seven benthic biotic indices to assess the ecological quality of sandy beaches in South Korea, all benthic indices either overestimated or underestimated the ecological quality. Furthermore, a study by Ryu et al., 2016 [48] also demonstrated that relying on a single or a limited number of indices can overestimate or underestimate ecological quality along the Korean coast.
The AMBI, recognised as one of the most successful benthic indices [49], is extensively utilised not only along the coasts of Europe for assessing ecological quality but also in South Korea [50,51,52]. However, the accurate allocation of macrobenthos is crucial for assessing ecological quality [53,54]. For example, in AMBI software, Heteromastus filiformis was classified under EGIV. However, by referring to the BPI, we reclassified Heteromastus filiformis as EGIII (Table 3). This enhanced the performance of the AMBI in our study. In previous research on the intertidal zone of Cheonsu Bay, the AMBI did not respond to environmental factors [46]; in our study, the AMBI responded to most of the environmental factors (Figure 7). Additionally, a study by Gillett et al., 2015 indicated that the allocation of ecological groups within the framework of local biogeographical conditions results in the enhanced performance of the AMBI [54].
At station 10 in Asan Bay, while the four biotic indices assessed the ecological quality as unacceptable, only the BOPA index assessed the ecological quality as acceptable. This is attributed to the fact that, at station 10 in Asan Bay, amphipods accounted for 5.2% of the total individual count and opportunistic polychaetes did not appear. The BOPA index solely considers opportunistic polychaetes and amphipods, and the absence of any one taxon leads to inaccuracies in the BOPA values [55]. In conclusion, using the BOPA index cautiously or in conjunction with other indices is advisable.
Although the BPI was developed based on the Yellow Sea ecosystem, it is essential to note that the BPI is not without its imperfections. When N1 and N2 functional groups were present in large quantities, the BPI tended to overestimate the ecological quality, but in certain circumstances, it also underestimated the ecological quality [25,56]. Furthermore, the BPI lacks a unified functional group database, making the classification of functional groups challenging.
In the correlation analysis, most benthic biotic indices demonstrated relationships among themselves. However, in the kappa analysis, the kappa values between most benthic biotic indices were low. This indicates that using a single index to assess benthic ecological quality is not feasible, especially across expansive geographical range sizes. While most benthic biotic indices, except for the BOPA, correlated with the composite index, the BENTIX index exhibited an “almost perfect” level of agreement with the composite index in the kappa analysis. This suggests that, in specific scenarios, using the BENTIX index can achieve outcomes comparable to those of the composite index. However, it is crucial to recognise that the BENTIX index comes with its own set of limitations. Originating from research on the Eastern Mediterranean’s ecology, the BENTIX index is designed around an area where benthic communities are notably diverse and uniformly distributed, with no single species constituting more than 10.0% of the population by individual counts [57]. Consequently, the BENTIX index’s precision is particularly susceptible to the influence of species with high abundance levels [58,59]. Furthermore, by assigning equal calculation weights to opportunistic and sensitive taxonomic groups, the BENTIX index may underestimate ecological quality [11].
Despite the M-AMBI indices not responding to the organic matter content (IL%), MAMBI’s effectiveness in assessing ecological quality cannot be denied. Accurately setting reference conditions is crucial for calculating the M-AMBI [60]. In a study conducted by Dias et al., 2022 [61], the M-AMBI responses to different environmental factors were contingent upon varying reference conditions.

4.3. The Final Ecological Quality and Composite Index in the Three Bays

In this study, most sampling stations with an unacceptable final ecological quality corresponded with higher human activities. For example, stations with unacceptable ecological quality in Cheonsu Bay and Gyeonggi Bay were mainly located near wastewater discharge outlets, aquaculture farms, and harbours. However, there were exceptions. In Asan Bay, stations 1, 2, and 3 showed an acceptable final ecological quality despite being near harbours. This is because using ports for distinct purposes results in varying levels of pressure on benthic communities [62].
At stations 1, 2, and 4 in Asan Bay, the echinoderm species Amphiodia craterodmeta accounted for 58.0%, 27.7%, and 62.6% of the total individual counts, respectively. In contrast, Amphiodia craterodmeta did not appear at the other stations. Some studies have indicated that trawl fishing and dredging activities can impact the population numbers of echinoderms [63,64]. Stations 1 and 2 are located near the coast, while station 4 is inside the bay. We believe that Asan Bay may be affected by trawl fishing or dredging activities.
The composite index exhibited superior performance compared to the other seven indices in this study. Specifically, the values of the human pressure index and species indices (except for the Pielou’s evenness index) displayed significant differences at the stations with an acceptable and unacceptable final ecological quality. Moreover, the composite index, which correlated with five biotic indices, responded to organic matter pressure. Many opportunistic species were present at stations with an unacceptable ecological quality. In the PCoA analysis, the composite index effectively differentiated between groups with acceptable and unacceptable ecological quality in Asan Bay and Cheonsu Bay.
Despite the cumulative ecological quality acceptance rate for the three bays being recorded at 64.4%, it has been observed that sampling stations exhibiting an unacceptable ecological quality are predominantly impacted by inputs from wastewater, aquaculture, and harbour activities. Because of these findings, we suggest that the South Korean government should pay attention to these sources of pollution in its endeavours to enhance environmental protection and craft relevant policies for the bays.

4.4. Future Research for the Composite Index

Since Blanchet et al. (2008) introduced the composite index method, 16 years have passed [12]. The composite index has been extensively utilised on a global scale. For example, Caglar and Albayrak (2012) employed the composite index to assess the ecological quality of the Marmara Sea [65]; Mulik et al. (2017) utilised the composite index to evaluate the ecological quality of the Ulhas estuary [66]. However, to the best of our knowledge, only a few studies have assessed the applicability of the composite index. For example, a study by Dong et al. (2023) shows that [10], in Laizhou Bay, the composite index is more robust than single indices in assessing ecological quality. Additionally, our study findings indicate that the composite index better assesses the ecological quality of the bays of the West Sea in Korea than single indices. Based on the assessments of multiple indices, the composite index not only effectively avoids the pitfalls of overestimating or underestimating ecological quality inherent to single indices but also reduces the contradictory classifications of ecological quality caused by using multiple single indices.
Although the performance of the composite index is superior to that of single indices, it still presents some challenges. First, the composite index does not specify the criteria for selecting individual indices. Second, there is no definitive standard regarding the number of single indices to be included. The issues above harm the promotion of the composite index and comparisons with other studies. Therefore, when using the composite index, we recommend employing indices validated for reliability in the study area or that have gained widespread recognition globally, such as the AMBI and M-AMBI. Regarding the number of indices, we recommend using five indices to calculate the composite index. Although more indices do not necessarily mean better results, a composite index based on five indices tends to be more robust.
The composite index was initially calculated using benthic biotic indices, but some studies have suggested incorporating benthic biotic and abiotic indices. For instance, in Dong et al.’s (2023) study [14], the composite index, which includes benthic biotic and heavy metal indices, demonstrated exemplary performance in Laoshan Bay. Therefore, we recommend that future studies on composite indices should not focus solely on indices based on macrobenthos. For example, composite indices could be calculated using indices based on microbenthos (e.g., Foram-AMBI) [67] or by using indices based on environmental DNA (e.g., gAMBI) [68].

5. Conclusions

Although the South Korean government proposed evaluation criteria for the Marine Ecological Quality Map (MEQM) in 2014, the initiative failed to gain endorsement from the scientific community. It did not pass due to legal issues. However, against this backdrop, an accurate assessment of ecological quality and the development of a reliable evaluation methodology are beneficial for formulating pertinent policies. In our study, we assessed the performance of seven indices in three bays in South Korea. We employed a composite index based on these indices to evaluate the ecological quality of the bays. Our findings endorse the superior efficacy of the composite index over individual indices. Consequently, we recommend using the composite index for accurately assessing the ecological quality of the bays along the west coast of South Korea in future research. Furthermore, future marine ecological quality assessments should emphasise ecosystem-based approaches. We believe that it is essential for future research to develop ecosystem-based quality indices (EBQIs) specifically for the coastal areas of South Korea.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/jmse12071179/s1, Table S1: Reference conditions for the M-AMBI across different salinity zones; Table S2: Categorisation of macrobenthos into ecological groups and functional groups for the AMBI and the BPI; Table S3: Standards for categorising benthic biotic index values as acceptable or unacceptable in assessing ecological quality; Table S4: Threshold for kappa value; Table S5: Values of environmental factors at each station in three bays; Table S6: Range and mean values of environmental factors; Table S7: Eigenvectors of environmental factors with PC1 and PC2; Table S8: Number of species and abundance of species at each station in three bays; Table S9: Values of taxonomic richness, Pielou’s evenness index, Simpson indices, and Shannon–Wiener index at each station; Table S10: Results of Spearman correlation analysis; Table S11: Results of kappa analysis; Table S12: Values of the composite index at each station; Figure S1: Values of benthic biotic indices at each station in Asan Bay; Figure S2: Values of benthic biotic indices at each station in Cheonsu Bay; Figure S3: Values of benthic biotic indices at each station in Gyeonggi Bay.

Author Contributions

Conceptualisation, J.L.; methodology, J.L.; software, J.L.; validation, J.L.; formal analysis, J.L.; investigation, K.-B.K. and D.-S.S.; resources, J.L.; data curation, J.L.; writing—original draft preparation, J.L.; writing—review and editing, J.L. and C.-W.M.; visualisation, C.-W.M.; supervision, C.-W.M.; project administration, C.-W.M.; funding acquisition, C.-W.M. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Soonchunhyang University Research Fund.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data will be made available on request.

Conflicts of Interest

Dae-Sun Son was employed by the Haerang Technology and Policy Research Institute. Kwang-Bae Kim was employed by the Gyeonggi-do Maritime and Fisheries Resources Research Institute. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Figure 1. Map showing sampling stations and final ecological quality of three bays in South Korea. Note: black stations, acceptable final ecological quality; red stations, unacceptable final ecological quality.
Figure 1. Map showing sampling stations and final ecological quality of three bays in South Korea. Note: black stations, acceptable final ecological quality; red stations, unacceptable final ecological quality.
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Figure 2. Principal component analysis (PCA) of the environmental factors of the three bays.
Figure 2. Principal component analysis (PCA) of the environmental factors of the three bays.
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Figure 3. Cumulative species richness curves in the three bays.
Figure 3. Cumulative species richness curves in the three bays.
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Figure 4. Percentages of AMBI ecological and BPI functional groups at each station in the three bays. Note: EGI, disturbance-sensitive species; EGII, disturbance-indifferent species; EGIII, disturbance-tolerant species; EGIV, second-order opportunistic species; EGV, first-order opportunistic species; N1, filter feeders or large carnivores; N2, surface deposit feeders or small carnivores; N3, subterranean deposit feeders; N4, opportunistic species.
Figure 4. Percentages of AMBI ecological and BPI functional groups at each station in the three bays. Note: EGI, disturbance-sensitive species; EGII, disturbance-indifferent species; EGIII, disturbance-tolerant species; EGIV, second-order opportunistic species; EGV, first-order opportunistic species; N1, filter feeders or large carnivores; N2, surface deposit feeders or small carnivores; N3, subterranean deposit feeders; N4, opportunistic species.
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Figure 5. Percentages of ecological quality status as acceptable or unacceptable for benthic indices in the three bays.
Figure 5. Percentages of ecological quality status as acceptable or unacceptable for benthic indices in the three bays.
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Figure 6. Results of Spearman correlation analysis. Note: DO, dissolved oxygen; COD, chemical oxygen demand; IL, ignition loss; sal, salinity.
Figure 6. Results of Spearman correlation analysis. Note: DO, dissolved oxygen; COD, chemical oxygen demand; IL, ignition loss; sal, salinity.
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Figure 7. Box plots of the taxonomic richness, species indices, and abundance of opportunistic species at stations with an acceptable or unacceptable final ecological quality. Note: the independent samples t-test was used for the Pielou’s evenness index and Shannon–Wiener index. The Mann–Whitney U test was used for taxonomic richness, Simpson index, Shannon–Wiener index, and abundance of opportunistic species. *: p < 0.05.
Figure 7. Box plots of the taxonomic richness, species indices, and abundance of opportunistic species at stations with an acceptable or unacceptable final ecological quality. Note: the independent samples t-test was used for the Pielou’s evenness index and Shannon–Wiener index. The Mann–Whitney U test was used for taxonomic richness, Simpson index, Shannon–Wiener index, and abundance of opportunistic species. *: p < 0.05.
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Figure 8. Principal coordinate analysis (PCoA) based on the abundance of species in different indices. Note: aa, ecological quality deemed acceptable in Asan Bay; au, ecological quality deemed unacceptable in Asan Bay; ca, ecological quality deemed acceptable in Cheonsu Bay; cu, ecological quality deemed unacceptable in Cheonsu Bay; ga, ecological quality deemed acceptable in Gyeonggi Bay; gu, ecological quality deemed unacceptable in Gyeonggi Bay.
Figure 8. Principal coordinate analysis (PCoA) based on the abundance of species in different indices. Note: aa, ecological quality deemed acceptable in Asan Bay; au, ecological quality deemed unacceptable in Asan Bay; ca, ecological quality deemed acceptable in Cheonsu Bay; cu, ecological quality deemed unacceptable in Cheonsu Bay; ga, ecological quality deemed acceptable in Gyeonggi Bay; gu, ecological quality deemed unacceptable in Gyeonggi Bay.
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Table 1. The algorithms and ecological quality status (EcoQs) thresholds of benthic biotic indices.
Table 1. The algorithms and ecological quality status (EcoQs) thresholds of benthic biotic indices.
IndicesAlgorithmIndex ValuesEcoQsReferences
AMBI = [ ( 0 × %   E G I ) + ( 1.5 × %   E G I I ) + ( 3 × %   E G I I I ) + ( 4.5 × %   E G I V ) ( 6 × %   E G V ) ] / 100 0.0–1.2High[19]
1.2–3.3Good
3.3–5.0Moderate
5.0–6.0Poor
>6.0Bad
BENTIX = [ 6 × %   G 1   + 2 ( %   G 2 + %   G 3 ) ] / 100 6–4.5High[20]
4.5–3.5Good
3.5–2.5Moderate
2.5–2.0Poor
0.0Bad
BOPA = l o g ⁡ [ ( f P ) / ( f A + 1 ) + 1 ) ] 0–0.02452High[21]
0.02453–0.13002Good
0.13003–0.19884Moderate
0.19885–0.25512Poor
0.25513–0.30103Bad
BPI = [ 1 − ( a × N 1 + b × N 2 + c × N 3 + d × N 4 ) / ( N 1 + N 2 + N 3 + N 4 ) / d ] × 100 60–100High[25]
40–60Good
30–40Moderate
20–30Poor
0–20Bad
M-AMBI = K + ( a × A M B I ) + b × H ′ + ( c × S ) >0.77High[22]
0.53–0.77Good
0.38–0.53Moderate
0.20–0.38Poor
≤0.2Bad
Notes: For AMBI, EGI = disturbance-sensitive species; EGII = disturbance-indifferent species; EGIII = disturbance-tolerant species; EGIV = second-order opportunistic species; EGV = first-order opportunistic species. For BENTIX, GI = EGI + EGII; GII = EGIII + EGIV; GIII = EGV. For BOPA, fP = the frequency of opportunistic polychaetes; fA = the frequency of amphipods. For BPI, N1 = filter feeders or large carnivores; N2 = surface deposit feeders or small carnivores; N3 = subterranean deposit feeders; N4 = opportunistic species. For H′, pi = the ratio of the number of individuals of the ith species to the total number of individuals. For M-AMBI, H′= Shannon–Wiener index; S = the number of species.
Table 2. The calculation method for the composite index and the threshold values for the final ecological quality.
Table 2. The calculation method for the composite index and the threshold values for the final ecological quality.
Composite IndexAssessmentFinal Ecological Quality
0All indices have assessed the ecological quality of the station as unacceptable.Unacceptable
1Four indices have assessed the ecological quality of the station as unacceptable.Unacceptable
2Three indices have assessed the ecological quality of the station as unacceptable.Unacceptable
3Two indices have assessed the ecological quality of the station as unacceptable.Unacceptable
4One index has assessed the ecological quality of the station as unacceptable.Acceptable
5Five indices have assessed the ecological quality of the station as acceptable.Acceptable
Table 3. Dominant species and dominant values in the three bays.
Table 3. Dominant species and dominant values in the three bays.
Sampling AreaTaxaSpecieDominant ValueAMBI Ecological GroupBPI Functional Group
Asan BayPolychaetaAmpharete arctica0.17EGIN2
OphiuroideaAmphiodia craterodmeta0.15EGIN1
MalacostracaCorophium sp.0.10EGIIIN2
PolychaetaHeteromastus filiformis0.04EGIIIN3
MalacostracaAmpelisca bocki0.02EGIN1
BivalviaRaetella pulchella0.02EGIVN4
Cheonsu BayBivalviaTheora lata0.13EGIVN4
PolychaetaHeteromastus koreanus0.12EGIIIN3
MalacostracaMonocorophium sp.0.03EGIIIN2
PolychaetaSigambra hanaokai0.03EGIVN4
Gyeonggi BayPolychaetaHeteromastus filiformis0.25EGIIIN3
PolychaetaSternaspis scutata0.04EGIIIN3
Table 4. Percentages of EcoQs for the five benthic biotic indices in Asan Bay.
Table 4. Percentages of EcoQs for the five benthic biotic indices in Asan Bay.
Benthic IndicesHighGoodModeratePoorBad
AMBI40%50%10%0%0%
BENTIX50%20%30%0%0%
BOPA100%0%0%0%0%
BPI70%10%10%10%0%
MAMBI0%70%30%0%0%
Table 5. Percentages of EcoQs for the five benthic biotic indices in Cheonsu Bay.
Table 5. Percentages of EcoQs for the five benthic biotic indices in Cheonsu Bay.
Benthic IndicesHighGoodModeratePoorBad
AMBI5%86%9%0%0%
BENTIX23%27%46%4%0%
BOPA55%41%0%5%0%
BPI32%27%18%18%5%
MAMBI5%68%23%4%0%
Table 6. Percentages of EcoQs for the five benthic biotic indices in Gyeonggi Bay.
Table 6. Percentages of EcoQs for the five benthic biotic indices in Gyeonggi Bay.
Benthic IndicesHighGoodModeratePoorBad
AMBI23%77%0%0%0%
BENTIX38%31%23%8%0%
BOPA92%8%0%0%0%
BPI62%24%14%0%0%
MAMBI15%62%23%0%0%
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MDPI and ACS Style

Liang, J.; Ma, C.-W.; Kim, K.-B.; Son, D.-S. Can the Ecological Quality of Several Bays in South Korea Be Accurately Assessed Using Multiple Benthic Biotic Indices? J. Mar. Sci. Eng. 2024, 12, 1179. https://doi.org/10.3390/jmse12071179

AMA Style

Liang J, Ma C-W, Kim K-B, Son D-S. Can the Ecological Quality of Several Bays in South Korea Be Accurately Assessed Using Multiple Benthic Biotic Indices? Journal of Marine Science and Engineering. 2024; 12(7):1179. https://doi.org/10.3390/jmse12071179

Chicago/Turabian Style

Liang, Jian, Chae-Woo Ma, Kwang-Bae Kim, and Dae-Sun Son. 2024. "Can the Ecological Quality of Several Bays in South Korea Be Accurately Assessed Using Multiple Benthic Biotic Indices?" Journal of Marine Science and Engineering 12, no. 7: 1179. https://doi.org/10.3390/jmse12071179

APA Style

Liang, J., Ma, C.-W., Kim, K.-B., & Son, D.-S. (2024). Can the Ecological Quality of Several Bays in South Korea Be Accurately Assessed Using Multiple Benthic Biotic Indices? Journal of Marine Science and Engineering, 12(7), 1179. https://doi.org/10.3390/jmse12071179

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