Acoustic Assessment of Fishery Resources in Jinwan Offshore Wind Farm Area

Abstract

After more than ten years of offshore wind farm (OWF) construction, the total installed capacity of China ranks first in the world. The effect of OWF on fish communities—to attract or banish—differs among fish species and wind farms. Studies on the effects of OWFs are limited in China and results from other regions may not be transferable due to different environmental and biological conditions. In October 2019, an acoustic survey was conducted in Jinwan OWF, outside the Pearl River Estuary, northern South China Sea, China, to assess the fish resources (biomass and abundance), community diversity, and distribution information of this area. According to the Index of Relative Importance (IRI), Harpadon nehereus and Brionobutis koilomatodon were the dominant fish species in the study area. The mean Shannon–Weiner diversity index was 1.74. The mean Margalef richness index and Pielou uniformity index were 2.51 and 0.84, respectively. The ABC curve indicated that the fish community was undisturbed. The mean acoustically-derived biomass and abundance densities were 195.40 ± 254.32 kg/km² and 6506.83 ± 11,098.96 individuals/km², respectively. The fishery resources had evident aggregate distribution patterns, and the southern part of the study area had more biomass than the northern part. Seven environmental factors were selected by canonical correspondence analysis (CCA) analysis to reveal the correlation between fish assemblages and environmental factors, including nitrate (NO₃⁻), ammonium (NH₄⁺), dissolved oxygen (DO), water depth, pH, Chlorophyll a (Chl a), and phosphate (PO₄⁺). However, the CCA only accounted for 45.49% of the total variation, indicating that other unexplained stresses affect the fish assemblage in Jinwan OWF. This is the first study to examine the fish distribution patterns and community structures of the Jinwan OWF area. In addition, it will help all sectors of society to more scientifically and objectively understand offshore wind farm projects. In future studies, control areas with more trawl samples can be set up to explore the long-term impact of OWF facilities on local fish communities.

1. Introduction

With the increasing pressure of energy demand and environmental protection, many countries have taken on the development of renewable energy as an essential measure to meet energy demand and supply. Wind energy is one such important development. With the large-scale utilization of onshore wind resources, offshore wind resources have gradually gained the favor of countries worldwide due to their stable supply, high wind speed, and no occupation of land resources [1].

China has a long coastline, and the exploitable offshore wind resource reserve is about 750 GW [2]. After more than ten years of offshore wind farm construction, the total installed capacity has ranks first in the world; the newly installed capacity was 16.88 GW in 2021, accounting for 80.02% of the world’s capacity in that year [3]. The Jinwan offshore wind farm (OWF), located in the Lingding Sea outside the Pearl River Estuary, northern South China Sea, China, was begun in August 2019 and finished in the middle of 2021. This area is an important fishing ground for coastal fishers, with more than 200 fish species documented. Brackish and freshwater meet in this area’s complex and variable marine environment. This area is also an important spawning ground for many fish species, and spawning occurs year-round; for example, Coilia mystus and Collichthys lucidus spawn in spring and summer, whereas Pampus argenteus spawns in autumn and winter [4]. Furthermore, since brackish and freshwater meet in this area, juvenile marine fish grow and swim back to the sea. Juvenile estuarine fish grow to maturity and spend most of their life here. Freshwater fish use this area as a nursery habitat. Moreover, this area is also an essential feeding ground for many marine mammals, such as the Chinese white dolphin (Sousa chinensis) and finless porpoise (Neophocaena phocaenoides) [5].

The effect of OWF on fish communities, to attract or banish, differs among fish species and wind farms. The underwater foundations could provide shelter and colonization substrates for many marine organisms, which may also attract foraging fish species [6]. On the other hand, the pile-driving, underwater noise, and changes in the concentration of resuspended sediment could drive local fish away from the OWF area [7].

There are few studies on the impact of OWF on fish communities in China [8,9,10], and the results from foreign research [6,7,11,12,13] may be unsuitable for this OWF given different ecological and environmental situations in China. Concerns about the impacts of wind farm projects indicate a need to obtain information on aquatic resources near offshore wind farms. Given the crucial ecological role of this area, the fishery resource information of this area should be confirmed through scientific investigation.

In many OWF areas, fishery resources have been assessed using conventional sampling techniques such as scientific trawling or gill nets [6,8,9,13]. However, owing to fishing gear selectivity and coverage rate, the accuracy of these assessments may be relatively low, and the required distribution information might not be obtained. In recent years, underwater video and photography technology has been used for fish monitoring, but these methods are easily affected by factors in the external environment, such as underwater brightness or suspended solids [14]. On the other hand, acoustic assessment can obtain high-resolution, large-scale temporal and spatial observation data and get more accurate fish abundance data and distribution information, which has been recognized as a robust and reliable method for scientific studies and monitoring programs [15,16,17]. However, the identification of acoustic echograms from different marine biota has always been a complex problem for acoustic detection. Therefore, it is generally necessary to combine other methods to assist in the recognition of acoustic echograms [18]. This study used the acoustic assessment method combined with bottom trawl sampling to assess the local fish resources.

In October 2019, an acoustic survey of fishery resources was conducted in the Jinwan OWF area. In this study, we examined the fish resources (biomass and abundance), community diversity, and distribution patterns (for convenience, this study considered cephalopods as fish) using acoustic echograms and trawl catch data. Our main objectives were (1) to estimate fish resources and community structure, (2) to report the spatial distribution patterns of fish, and (3) to identify the fish assemblages and their environmental correlation factors.

2. Materials and Methods

2.1. Study Site

The fishing vessel used in the investigation was the “Guangdong Dongguan 00589”, with a length of 25.60 m and a total weight of 90 t. The acoustic survey period was 26–27 October 2019, and the survey was conducted in four parallel transects with environmental samples and with 4 otter trawl samples (Figure 1). The wind farm only completed the construction of one pile foundation in the northernmost direction in August (Figure 1), and no more construction activities were carried out during the survey period. The survey was undertaken only during daylight (9:00–17:00) to avoid the impact of diel vertical migration behaviour of many small pelagic fish species on its resource assessment and species composition [19].

2.2. Data Sets

The acoustic echogram was collected using a scientific split-beam echosounder system (Simrad EY60, Kongsberg, Norway) with an operating frequency of 70 kHz (

Table 1. Main settings of EY60 Transducer.

Parameter	Transducer Setting
transducer type	ES70-7C
beam frequency	70 kHz
pulse duration/μs	512
Ping interval/s	0.5
power/W	300
equivalent beam angle/dB	−21.00
transducer gain/dB	26.70
absorption coefficient/(dB/m)	0.017
Sa correction/dB	−0.39
Sound velocity/(m/s)	1540 m/s

). Because of this system’s lack of long-term stability, the scattering and absorption loss of echoes and the uncertain orientation of fish in the beam during the investigation would affect the reliability of acoustic data. The EY60 system was calibrated in open water (113°35′ E, 22°03′ N; 20 m water depth) on 25 October 2019, based on the standard guidelines [20]. In addition, to prevent vibrations and reduce noise, the transducer was fixed 1.0 m underwater outside the starboard side of the hull through the deflector. Real-time longitude and latitude information were collected synchronously with acoustic echograms using a Trimble differential GPS (Trimble, Sunnyvale, CA, USA). The average survey speed was 5.0 knots.

Acoustic echograms were analyzed using Echoview 6.0 software (Myriax Pty. Ltd., Hobart, Tasmania), referring to the recommendations outlined in the standard operating procedures for acoustic fishery surveys in the Great Lakes [19]. Considering the influence near-field effect of acoustic detection and blind zone, the initial water depth for echo integration was 4.0 m underwater, and the terminal water depth was 0.5 m above the seabed. The background noise in the echogram was reduced using the virtual variable module of the Ecoview system [21]. According to echogram inspection, other non-target signals (such as bubbles) were removed manually. Since there was no reference minimum volume backscattering strength (S_V) threshold for distinguishing fish and plankton echoes in this area, the threshold response module would be used to determine a confidence threshold for fish echo extraction [22]. According to the response result, −70 dB (decibels referenced to 1 m⁻¹ [dB re m⁻¹]) could be a threshold to remove the backscatter of plankton and minimize fish exclusion. The elementary distance sampling unit (EDSU) is the length of the transect along which the acoustic measurements are averaged to give one sample [23] and was set to 1 km in this study.

A series of physical, chemical, and hydrological factors were selected or measured for all 16 sampling stations (Figure 1, S1–S16). Samplings were started when the vessel arrived at each station, and the acoustic data in the environmental sampling periods were not used for analysis. At each station, water depth was measured by the EY60 system, and sea surface temperature (SST), dissolved oxygen (DO), salinity, and pH were measured using a handheld YSI multiparameter instrument (professional plus) in situ. Suspended solids (SS) were measured by the weighting method (Chinese national standard for suspended substances) using 100 mL of river water filtered through nitrocellulose and cellulose-diacetate blend membrane (pore size 0.45 μm) [24]. Chemical oxygen demand (the amount of oxygen consumed during the chemical oxidation of oxidizable substances in the water body, COD) was determined by the potassium dichromate method; if the concentration of COD is less than 0.001 mg/L, it will not be detected (nd) (EPA 410.1).

Chlorophyll a (Chl a) was measured via the fluorescence method, where 500 mL of stream water samples were pre-filtered through a 200 μm mesh sieve to remove large zooplankton and debris. Filtered water was then passed through 0.7 μm GF/F filters (Whatman, England), with the filters then wrapped in aluminum foil and stored at −20 °C in darkness. Chl a on filters was extracted by 10 mL of 90% acetone at 0°C for 20 h in the dark and measured using a Trilogy Fluorometer (Turner Designs, Sunnyvale, CA, USA: Trilogy Module: CHL-A NA, Model #7200–046).

For nutrient analyses (nitrate [NO₃⁻], nitrite [NO₂⁻], ammonium [NH₄⁺) and phosphate [PO₄⁺]), the remaining water samples which had passed through 0.7 μm GF/F filters were analyzed by segmented flow automated colourimetry using the manufacturer’s standard procedures (San + + Automated Wet Chemistry Analyser, Skalar, Breda, The Netherlands).

The fishing vessel towed an otter trawl with a stretch mesh of 4.0 cm throughout the body, 1.0 cm stretch mesh cod-end, and a 2.2 m spread between doors. Four trawl samples were conducted during this survey (S2, S7, S10 and S15, Figure 1), covering all transects to obtain information on fish biology and species composition in the survey area to analyze the fish community structure and resource assessment. After the vessel arrived at the stations, it started trawling forward along the transects. The sample duration was limited to 30 min at approximately 3 knots. After each trawl sample, the vessel returned to the starting point to continue the environmental samples and acoustic detection. The echograms during the trawl samples were not used for data analysis. All catches were classified and counted on the deck. Since the catch number of all species was less than 50, the length (accurate to 1 mm) and weight (valid to 1 g) of each individual were measured.

2.3. Fish Density Estimation

The acoustically-derived density was estimated using the catch apportionment method [18]. The trawl catch composition was used as the primary basis for distributing the acoustic integral values S_a (m²/nmile², mean acoustic area backscattering coefficient of each EDSU). The number ρ_(i,a) (inds/km²) and biomass densities ρ_(i,b) (kg/km²) of the i-th species in each EDSU were calculated as follows:

$${\rho }_{i,a}{ߙ=c}_i\frac{s_a}{4\pi \overline{{\sigma }_{bs}}·1{.852}^2}$$

(1)

$${ \rho }_{i,b}{\hspace{0.17em}=\rho }_{i,a}\overline{w_i}{10}^{-3}$$

(2)

where c_i (%) is the number percentage of the i-th species in each trawl, $\overline{{\sigma }_{bs}}$ (m²) is the mean acoustic backscattering cross-section of the considered acoustically-detectable species in each transect, and $\overline{w_i}$ (g) is the mean body weight of the i-th species.

$$\overline{{\sigma }_{bs}}={\displaystyle \sum }_{i=1}^n{c}_i{10}^{\frac{{\mathrm{TS}}_i}{10}}$$

(3)

$${\mathit{TS}}_i{\hspace{0.17em}=20\log L}_i{+b}_{20,i}$$

(4)

where n is the number of considered fish species in each trawl, TS_i (dB re 1 m²) is the target strength of the i-th species, and L_i is the mean length of the i-th species. The slope of the TS-L relation was assumed to be 20 [25], and the b₂₀ of the considered fish species in

Table 2. Acoustically detectable species of OWF area and their b₂₀ values.

Serial Number	Species	b20 (dB)	In/Ex-Situ
X1	Plotosus lineatus	−72.5 [16]	ex
X2	Collichthys lucidus	−68 [16]	ex
X3	Siganus oramin	−74.1 [28]	in
X4	Loligo duvaucelii Orbigny	−78 [26]	in
X5	Polynemus sextarius	−80 [29]	ex
X6	Coilia mystus	−72.5 [16]	ex
X7	Harpadon nehereus	−78 [29]	ex
X8	Apogon ellioti	−72.5 [29]	ex
X9	Ophichthus evermanni	−76 [16]	ex
X10	Johnius belengerii	−68 [16]	ex
X11	Argyrosomus pawak	−68 [16]	ex
X12	Brionobutis koilomatodon	−76 [29]	ex
X13	Dysomma melanurum	−76 [16]	ex
X14	Apogon semilineatus	−72.5 [29]	ex
X15	Muraenesox cinereus	−76 [16]	ex
X16	Platycephalus indicus	−76 [16]	ex
X17	Trypauchen vagina	−76 [29]	ex
X18	Trichiurus lepturus	−66.1 [27]	ex
X19	Cynoglossus macrolepidotus	−72.5 [16]	ex
X20	Odontamblyopus rubicundus	−76 [29]	ex

were cited from relevant acoustic surveys [16,26,27,28,29].

When the densities of each EDSU were obtained, the mean $\overline{{\rho }_{i,a}}$ (inds/km²) and $\overline{{\rho }_{i,b}}$ (kg/km²) for the i-th species in the OWF area were calculated as:

$$\overline{{\rho }_{i,a}}=\frac{1}{\mathrm{M}}{\displaystyle \sum }_{k=1}^{\mathrm{M}}({\rho }_{\mathrm{i},a,1}+{\rho }_{\mathrm{i},a,2}+{\rho }_{\mathrm{i},a,3}+\cdots {\rho }_{\mathrm{i},a,\mathrm{M}})$$

(5)

$$\overline{{\rho }_{i,b}}=\frac{1}{\mathrm{M}}{\displaystyle \sum }_{k=1}^{\mathrm{M}}({\rho }_{i,b,1}+{\rho }_{i,b,2}+{\rho }_{i,b,3}+\cdots {\rho }_{i,b,\mathrm{M})}$$

(6)

where k = 1 to M indicates the number of EDSUs of the OWF area.

The precision of the acoustic survey detection was represented by the coefficient of variation (CV, %), and the formula is:

$$\mathrm{CV}=\mathrm{a}{\left(\raisebox{1ex}{$\mathrm{D}$}\!\left/ \!\raisebox{-1ex}{$\sqrt{\mathrm{A}}$}\right.\right)}^{\mathrm{b}}$$

(7)

where D is the total length of the transects, A is the size of the survey area, and a and b are formula coefficients [18].

To better analyze the distribution patterns of fishery resources, we divided the study area into two sub-areas, the northern part (S1–S8) and the southern part (S9–S16).

2.4. Fish Community Parameters

The Index of Relative Importance (IRI) was used to classify the dominant species in the fish groups [30]. The formula is as follows:

$$IRI=\left(B_i+A_i\right)\times F_i$$

(8)

where i represents individual fish species, $B_i$ is the catch biomass percentage (%) of a species, $A_i$ is the catch abundance percentage (%), and $F_i$ is the frequency of occurrence of the species in the trawl (%). Species with IRI ≥ 1000 were considered dominant species, those with 100 ≤ IRI < 1000 common species, 10 ≤ IRI < 100 general species, and those with IRI < 10 rare species [31].

The Shannon–Weiner diversity index (H′) represents the species diversity of the fish community.

$$H^{\prime }=-{\displaystyle \sum }_{i=1}^s{P}_i\times ln{P}_i$$

(9)

where H′ < 1: low diversity, 1< H′ < 3: moderate diversity, and H′ > 3: high diversity [32].

Margalef richness index (D):

$$D=\left(S-1\right)/lnN$$

(10)

Pielou uniformity index (J′):

$$J^{\prime }=H^{\prime }/lnS$$

(11)

where P_i is the catch number percentage of the i-th species in each net, N is the total catch number for each net, and S is the number of fish species in each net [33].

The ABC curve method analyzes the characteristics of the community under different disturbance conditions by calculating the distribution of species abundance and biomass using the W-statistic as a statistic of the ABC curve method [34]:

$$W={\displaystyle \sum }_{i=1}^S\frac{B_i-A_i}{50\left(S-1\right)}$$

(12)

where A_i and B_i represent the cumulative percentage of number and biomass of species i in the ABC curve, respectively, and S is the total number of fish species. When the biomass curve lies above the abundance curve, the community is generally undisturbed, and k-selected species (slow-growing, large, late maturing) would be dominant. With increasing disturbance, the biomass and abundance curves may cross or overlap, and the community is moderately disturbed. In heavily disturbed instances, the entire abundance curve lies above the biomass curve, and r-selected species (fast-growing, small, opportunistic) would be dominant. The W value represents the degree of disturbance and ranges from −1 to 1. A positive value suggests an undisturbed community, and a negative value indicates a disturbed community [34]. The ABC curve and the W index were calculated using Primer 6.0 software (Plymouth Marine Laboratory, Plymouth, UK).

2.5. Canonical Correspondence Analysis

A detrended correspondence analysis (DCA) was applied to the fish abundance matrix of environmental stations (station × abundance of each fish species of the EDSU where the station is located) to analyze the fish assemblages under the influence of environmental stresses [35]. Based on the value of the “lengths of gradient” item in the DCA, a unimodal model (canonical correspondence analysis, CCA) can be used if the value is greater than 4; a linear model (redundancy analysis, RDA) can be used if the value is less than 3; both models can be used if the value is between 3 and 4 [36]. According to the result, this study conducted a CCA model using R software (version 3.3.2 and ‘vegan’ package 2.4).

Fish abundance data were Hellinger-transformed [37], and environmental data were logarithmically transformed before CCA to eliminate the influence of extreme values on the ordination. Monte Carlo permutations (p < 0.05) were used to select the environmental factors that significantly affected fish distribution, and the permutation cycle number is 999. The environmental factors with high partial correlation coefficients (p < 0.05, |r| > 0.5) and variance inflation factors >20 were omitted from the final CCA [38].

3. Results

3.1. Catch Statistics

A total of 20 fish species were collected, belonging to 9 orders, 15 families, and 18 genera, all of which belonged to demersal or near-demersal fish (

Table 3. Catch data of the fish species in the Jinwan offshore wind farm area.

Species	Weight (g)	Length (mm)	IRI
	Mean ± SD	Mean ± SD
Plotosus lineatus	38.12 ± 8.72	104.06 ± 4.94	940.38
Collichthys lucidus	112	41	76.42
Siganus oramin	19.67 ± 5.55	110 ± 17.96	325.41
Loligo duvaucelii Orbigny	56	32	67.06
Polynemus sextarius	12.80 ± 2.40	80.40 ± 3.83	706.42
Coilia mystus	15.50 ± 4.39	139 ± 11.45	598.83
Harpadon nehereus	42.33 ± 29.41	165.11 ± 44.76	2801.08
Apogon ellioti	8.90 ± 2.34	86.30 ± 18.10	744.31
Ophichthus evermanni	340	322	387.36
Johnius belengerii	42	132	77.46
Argyrosomus pawak	35	122	70.18
Brionobutis koilomatodon	3.60 ± 0.66	41.50 ± 6.07	1297.42
Dysomma melanurum	287.50 ± 22.50	213.50 ± 13.50	665.53
Apogon semilineatus	2.50 ± 0.5	42 ± 1	145.53
Muraenesox cinereus	192	242	233.45
Platycephalus indicus	25	122	59.78
Trypauchen vagina	14	122	48.34
Trichiurus lepturus	13	72	47.30
Cynoglossus macrolepidotus	19	127	53.54
Odontamblyopus rubicundus	14.50 ± 2.50	129.50 ± 2.50	97.73

SD: standard deviation.

). According to the Index of Relative Importance (IRI), Harpadon nehereus and Brionobutis koilomatodon were the dominant fish species. The H′ index of the four trawl samples varied from 1.46 to 2.16, with an average value of 1.74. The D index ranged from 2.04 to 3.47, with an average value of 2.51. The J′ index varied from 0.66 to 0.98, with an average value of 0.84 (

Table 4. Fish community parameters of each trawl samples.

Trawl Samples	H′ Index	D Index	${\mathit{J}}^{\prime }$ Index
S2	1.46	2.40	0.66
S7	2.16	3.47	0.98
S10	1.79	2.12	0.92
S15	1.54	2.04	0.79

). According to the ABC curve, the fish community was undisturbed, and the W was 0.077 (Figure 2).

3.2. Environmental Factors

The SST in this area ranged from 26.60 to 27.30 °C, with an average value of 27.10 °C. Salinity ranged from 30.75 to 32.60 ppt, with an average value of 31.99 ppt. The water depth ranged from 10.49 to 22.14 m, with an average value of 15.96 m. The average pH was 8.63, with a maximum of 8.68 and a minimum of 8.47. The concentration of SS ranged from 1.33 to 17 mg/L, with an average value of 8.54 mg/L. The concentration of DO ranged from 6.38 to 6.83 mg/L, with an average value of 6.64 mg/L (

Table 5. Summary of data on environmental factors.

Factors	Max	Min	Mean ± SD
SST (°C)	27.30	26.60	27.05 ± 0.15
Salinity	32.60	30.75	31.99 ± 0.55
Depth (m)	22.14	10.49	15.96 ± 3.26
pH	8.68	8.47	8.64 ± 0.05
SS (mg/L)	17.00	1.33	8.54 ± 4.91
DO (mg/L)	6.83	6.38	6.64 ± 0.13
COD (mg/L)	0.348	nd	0.185 ± 0.107
NO2− (mg/L)	0.0635	0.0242	0.0455 ± 0.0108
NO3− (mg/L)	0.0360	0.0007	0.0121 ± 0.0115
NH4+ (mg/L)	0.0114	0.0011	0.0067 ± 0.006
PO4+ (mg/L)	0.0129	0.0059	0.0103 ± 0.0015
Chl a (μg/L)	2.92	0.96	1.71 ± 0.54

SD, standard deviation; SST, sea surface temperature; SS, suspended solids; DO, dissolved oxygen; COD, chemical oxygen demand.

).

The COD concentration ranged from nd (not detected in the S5 station) to 0.348 mg/L, with an average value of 0.185 mg/L. The concentration of NO₂⁻ ranged from 0.0242 to 0.0635 mg/L, with an average value of 0.0455 mg/L. The average value of NO₃⁻ was 0.0121 mg/L, ranging from 0.0007 to 0.0360 mg/L. The concentration of NH₄⁺ ranged from 0.0011 to 0.0114 mg/L, with an average value of 0.0067 mg/L. The concentration of PO₄⁺ ranged from 0.0059 to 0.0129 mg/L, with an average value of 0.0103 mg/L (Table 4). The concentration of Chl a ranged from 0.96 to 2.92 μg L⁻¹, with an average value of 1.71 μg L⁻¹.

3.3. Fish Species Densities

The mean acoustically-derived biomass and abundance densities were 195.40 ± 254.32 kg/km² and 6506.83 ± 11,098.96 individuals/km², respectively. Dysomma melanurum had the highest biomass density, followed by Muraenesox cinereus and Ophichthus evermanni. Trypauchen vagina had the lowest biomass density (Appendix A,

Table A1. The acoustically-derived biomass density of each fish species (mean ± SD).

Species	Biomass Density (kg/km2)
Plotosus lineatus	3.91 ± 9.99
Collichthys lucidus	0.46 ± 1.16
Siganus oramin	2.94 ± 8.13
Loligo duvaucelii Orbigny	0.36 ± 0.91
Polynemus sextarius	3.35 ± 5.12
Coilia mystus	2.57 ± 4.10
Harpadon nehereus	26.05 ± 36.88
Apogon ellioti	6.08 ± 17.14
Ophichthus evermanni	34.91 ± 111.43
Johnius belengerii	4.31 ± 13.76
Argyrosomus pawak	3.59 ± 11.47
Brionobutis koilomatodon	13.14 ± 25.69
Dysomma melanurum	41.05 ± 92.21
Apogon semilineatus	0.63 ± 1.21
Muraenesox cinereus	39.96 ± 113.68
Platycephalus indicus	5.20 ± 14.80
Trypauchen vagina	0.16 ± 0.40
Trichiurus lepturus	2.70 ± 7.69
Cynoglossus macrolepidotus	1.95 ± 6.23
Odontamblyopus rubicundus	2.07 ± 4.65

SD: standard deviation.

). Overall, the fishery resources of this study area had aggregate distribution patterns (Figure 3), and the southern part of the study area had more resources than the northern part (Appendix A,

Table A2. Mean values of fish biomass density and environmental factors of the southern part and northern part (mean ± SD).

Items	Southern Part	Northern Part
Biomass density (kg/km2)	247.65 ± 305.70	142.40 ± 174.25
Water depth (m)	18.55 ± 2.15	13.38 ± 1.79
pH	8.65 ± 0.04	8.63 ± 0.06
DO (mg/L)	6.76 ± 0.07	6.52 ± 0.05
NO3− (mg/L)	0.0181 ± 0.0116	0.0062 ± 0.0077
NH4+ (mg/L)	0.0074 ± 0.0081	0.0060 ± 0.0025
Chl a (μg/L)	1.87 ± 0.66	1.55 ± 0.31
PO4+ (mg/L)	0.0096 ± 0.0015	0.0111 ± 0.0010

SD: standard deviation.

).

3.4. Fish Assemblages with Environmental Factors

Seven environmental factors were selected to reveal the correlation between fish assemblages and environmental factors, including nitrate (NO₃⁻), ammonium (NH₄⁺), DO, water depth, pH, Chl a, and phosphate (PO₄⁺). The first two axes of Figure 4 accounted for 45.59% of the total variance (axis 1: 26.96%; axis 2: 18.63%). The first axis was primarily defined by water depth, pH, and DO in a positive direction and PO₄⁺ in a negative direction. The second axis was primarily defined by DO, NO₃⁻, NH₄⁺, and Chl a in a positive direction (Figure 4).

4. Discussion

4.1. Fish Community

In this study, the biomass curve was higher than the abundance curve, revealing that the fish community was undisturbed. However, the starting point of the biomass curve was close to the abundance curve, and the W value was near negative, indicating that the ability of the fish community to resist external disturbance was weak [39]. In autumn, due to the continuous impact of overfishing activities since the end of the closed fishing season, large individual fish have been caught at a high intensity; a high proportion of small individuals or juveniles of large individuals characterizes the fish community in this area, such as Brionobutis koilomatodon (3.60 ± 0.66 g), Plotosus lineatus (38.12 ± 8.72 g), and Polynemus sextarius (12.80 ± 2.40).

The H′ index is an essential indicator of community stability. When a community is under external interference, the number of fish species and individuals decreases, eventually decreasing the H′ index [40]. According to Equation 9, the fish community of the Jinwan OWF area was of moderate diversity [32] and might be easily affected by external stress [40]. Overall, this area is still suitable for fish growth and breeding, but it is necessary to strengthen the protection of water quality and the environment in the future.

4.2. Acoustic Estimate Method Errors

The method of distributing the acoustic values Sa to different fish species is an essential factor affecting the evaluation accuracy. There are two main methods for Sa distributing: (1) through the in-situ target strength detection method and (2) through the catch proportional number or weight of each species used in this study. The former method will be a good choice if the catch number of one kind of fish species accounts for the vast majority (generally more than 90%) [26]. The second method can be used in areas with more similar fish compositions. However, the fixed trawl sampling strategy used in this study had large randomness, and there would be an error in the catch proportional information, leading to incorrect Sa distribution results. In future studies, combining fixed and random trawl sampling strategies can improve the accuracy of the catch proportional information and the acoustic estimate results.

The target strength parameter b₂₀ was also an essential factor affecting estimation accuracy. The parameters in Table 2 were cited from relevant acoustic surveys in the South China Sea, which may not be appropriate for this study. For example, Chen et al. [28] measured the b₂₀ of Siganus oramin at 120 kHz, whereas the present study’s acoustic frequency was 70 kHz. Citing the b₂₀ value at 120 kHz would underestimate the backscattering cross-sectional area σ, resulting in an overestimation of the density results. There have been few studies on fish target strength in the South China Sea, which has presented substantial difficulties for acoustic estimation work. At present, researchers mostly use in situ detection [41] and cage [42], and model methods [43] to measure the fish target strength. Considering the situation of the Jinwan OWF area, the cage and model methods could be suitable for future studies.

4.3. CCA Result

Environmental factors shape different fish assemblages by influencing their habitat choice and ecological tolerance [44,45]. CCA analysis revealed seven environmental factors that affected fish assemblages in the Jinwan OWF area (Figure 4). The southern part of the study area had more biomass than the northern part, and this trend was consistent with the correlated environmental factors (Appendix A, Table A2). Water depth, pH, DO, NO₃⁻, NH₄⁺, and Chl a positively affected fish assemblages, and the mean values of these factors were higher in the southern part; PO₄⁺ negatively affected fish assemblages, and its mean value was higher in the northern part (Appendix A, Table A2).

Dissolved oxygen is vital to the respiratory metabolism of fish and plays a crucial role in shaping the fish community structures in many estuarine systems [46]. Salinity also plays a significant role in partitioning fish assemblages [47]. However, salinity was excluded from the present study based on a forward selection criterion of p < 0.05, which may be due to the small size of the study area or the influence of marine hydrological processes [46]. In addition, the water depth varies significantly in this area. Therefore, it acts as an impact factor, similar to the Yangtze River estuary [44] and the Dapeng Bay marine ecosystem [36]. NO₃⁻, NH₄⁺, and PO₄⁺ are also important factors affecting the distribution patterns of fish species. These inorganic salts could provide nutrients for fish growth, such as calcium and phosphorus. The relatively high concentration of these inorganic salts in the water may benefit fish assemblages [31].

As the CCA only accounted for 45.49% of the total variation, indicating that other unexplained stresses affect the fish assemblage in Jinwan OWF. One possibility is the interaction between fish variables (both inter- and intra-species); another possibility is human-induced impacts, such as fishing activities, human pollution, and OWF, which need more research to confirm.

The offshore wind farm could affect the local fishery resources in many ways. First, the underwater sound of OWF could hurt fish species with a well-developed hearing capacity. Wahlberg and Westerberg [48] concluded that underwater sound might decrease the effective range for sound communication of fish. Gill et al. [49] indicated in their review that underwater sound can mask communication signals, lead to behavioural reactions, hearing loss (temporary or permanent), injury, or even killed by high received sound levels, and the distance of behavioural responses potentially up to several km.

However, turbine foundations can provide shelter and colonization substrates for many marine organisms, which may attract fish species. Andersson and Ohman [11] studied fish communities on and around wind-turbine foundations in the Baltic Sea seven years after construction. Their results showed that the foundations sustained more fish than surrounding waters, as the turbine provided a unique habitat opportunity. The study of van Deurs et al. [12] researched the short- (1 year after construction) and long-term (7 year after construction) OWF effects on three sandeel species by before-after-control-impact analysis. A positive short-term impact on the densities of both juveniles and adults and a long-term negative effect on juveniles of dominated species in all years. Compared to the baseline survey, the OWF represents neither a direct benefit nor a definite threat to sandeels 7 years after construction. Bergström et al. [6] compared the OWF effects on demersal fish communities before and after the operation. The Shannon index, species richness, and total fish biomass were higher during the operational phase than during the baseline. No significant difference in fish diversity and abundance was detected when comparing the wind farm area with the reference areas. However, in small spatial scales, the fish densities were larger nearby turbine foundations in the first years of operation. Bergström et al. [6] did not address the reasons for this study’s findings regarding overall increase in fish diversity and abundance for both the wind farm and reference areas. The study of van Hal et al. [7] studied the OWF effects on fish communities on a small spatial scale in a Dutch OWF, which had been in operation for five years. Different fish species were observed near the hard substrate turbines and on the sandy bottom in the middle of the farm. High fish densities near the turbines were observed sometimes, and the turbines may only be used temporarily for fish shelter or feeding. Their study did not set up reference areas to compare the effects of OWF on fish communities on a large spatial scale.

The effect of OWF on fish communities, to attract or banish, is still uncertain. The construction activities of Jinwan OWF was just begun; our study can be used as a baseline and provide a new attempt to study the impact of OWF on local fish communities by setting up control areas with more trawl samples in the future.

5. Conclusions

This study examined the fish distribution patterns and community structures of the Jinwan OWF area. It is the first relevant research in this region and a supplement to the topic of the OWF effect on local fish communities. In addition, it will help all sectors of society to more scientifically and objectively understand offshore wind farm projects. The fishery resources had evident aggregate distribution patterns, and the southern part of the study area had more resources than the northern part. The ABC curve also indicated that the fish community was undisturbed, as the biomass curve was higher than the abundance curve. CCA analysis revealed that DO, water depth, pH, Chl a, and three nutrient inorganic salts were closely linked with the fish assemblages. However, the CCA analysis only accounted for 45.49% of the total variation, indicating that other unexplained stresses affect the fish assemblage in the Jinwan OWF. The acoustic assessment could provide information on fishery resources’ precise quantity and distribution patterns, and the CCA analysis improved our understanding of the relations between fish assemblages and environmental factors. However, some limitations still exist in this one small time point study, and control study areas with more trawl samples are required in future repeat surveys to understand the temporal effects of the wind farm on local fish communities.