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Article

Fishery Resource Evaluation in Shantou Seas Based on Remote Sensing and Hydroacoustics

by
Xiaoqing Yin
1,2,
Dingtian Yang
1,3,* and
Ranran Du
1,2
1
State Key Laboratory of Tropical Marine Environment, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 511548, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China
*
Author to whom correspondence should be addressed.
Fishes 2022, 7(4), 163; https://doi.org/10.3390/fishes7040163
Submission received: 19 May 2022 / Revised: 29 June 2022 / Accepted: 29 June 2022 / Published: 4 July 2022
(This article belongs to the Section Sustainable Aquaculture)
Browse Figures
Review Reports Versions Notes

Abstract

:
The Shantou-Taiwan shoal fishing ground in southeastern China supports a significant population of pelagic fish, which play a key role in the marine ecosystem. An acoustic survey was carried out using a digital scientific echosounder in June 2019. In this paper, the spatial distribution of pelagic fish is analyzed based on acoustic data using geostatistical analysis tools. Meanwhile, the relationship between fish density from acoustic data and sea surface environment factors were evaluated by using generalized additive models (GAMs) based on the satellite-based oceanographic data of sea surface temperature, sea surface chlorophyll-a concentration, sea surface height and sea surface wind. The results showed the following: (1) Fish density and acoustic biomass have strong spatial correlation; the optimal model for acoustic biomass is exponential and the optimal model for fish density is gaussian; based on optimal model, spatial interpolation analysis of fish density and acoustic biomass was performed using the ordinary kriging method, and the higher values of density and acoustic biomass were located in the central and eastern parts of the study area. The total fish density and acoustic biomass is 2.56 × 1010 ind. and 1908.99 m2/m, respectively. (2) In vertical distribution, fish gradually move to the middle and lower layers of water during daytime, and gather in the middle and upper layers of water at night. (3) The variance explanation rate of GAM was 88.2% which indicates that the model has an excellent fitting degree, and the results of GAM showed that longitude, sea surface temperature (SST), sea surface wind (SSW), and sea surface height (SSH) had significant effects on fish density. Results of this study were meaningful for understanding the distribution of fishery resources, and as a guide for fish management in the Shantou offshore water.

Graphical Abstract

1. Introduction

The pelagic fish is a crucial fishery resource, which not only could provide a large amount of food [1] but also usually play an essential role in the marine ecosystem [2,3,4,5]. Due to aquatic exploitation activities, overfishing, and marine pollution, fishery resources are on the decline [6,7]. An accurate assessment of fishery resources is conducive to the effective management and sustainable development of fishery resources [8]. Compared with traditional methods for assessment of fish, such as net sampling [9,10,11,12,13,14,15], hydroacoustics has several advantages of being fast and efficient, having a wide survey area, continuous observation, causing no damage to the ecological environment of the survey area, and giving an accurate assessment of fish density and abundance [16,17]. Hydroacoustics has gradually become an important means of fishery evaluation [18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34].
The pelagic fish usually has the characteristics of short lifespans, fast growth rates and high aggregation [35,36], and its distribution of fish stock is dynamic and driven by various factors, including feeding, breeding, predator avoidance [37], and environmental variable [38]. Environment variables are generally accepted as the key factors [39]. The study of fish spatial and temporal distribution and its relationship with environmental factors is the foundation of resource change and fishery forecast. Especially with the wide application of geographic information systems (GISs) and remote sensing technology (RS), fisheries data and satellite remote sensing data have been widely applied to help under-stand the fundamental relationships between fisheries and the environment [40,41]. Method selection is important to study the relationship between fishery resources and the environment. The relationship between fisheries and environmental variables is complex, nonlinear [42,43], and non-additive [44]. The generalized additive model (GAM) [45] is a data-driven nonparametric model and does not need a prior model to fit consequences. The model can establish a nonlinear relationship between the response and multiple explanatory variables through non-parametric smoothing curves. Many experts and schoolars have conducted a lot of research on the relationship between pelagic fish and environ-mental factors based on this model [46,47,48,49,50,51,52,53].
The spatial distribution of biological populations is generally autocorrelated [54]. The classical statistical methods assume stationarity in space and time, and independence among the data and identical distribution of the parameters [55], while ignoring the influence of spatial location and distance on the distribution of samples, and making it difficult to present the spatial autocorrelation of samples. Geostatistics [56] is based on the theory of regionalized variables and consists of two parts, variance function and kriging interoplation, with the main purpose of modeling variability of variables in a given space and estimating variables based on this model [57]. Based on the classical statistical methods, geostatistics can integrate the spatial location of samples and the distance between samples to analyze the spatial distribution pattern and correlation of samples [58]. It is widely used in estimating fish stock abundance and biomass [59,60,61,62,63,64,65,66], fisheries survey and assessments [67], spatial heterogeneity [68,69] and population structure [70,71,72,73,74]. Hydroacoustic assessment of fish density combined with geostatistics has been recognized as the best method for modelling the spatial distribution of species of biomass or the joint spatial dependence between biomass and environmental parameters [62,75,76].
The Shantou-Taiwan shoal fishing ground is one of the most important fishing grounds in China, teeming with pelagic fish [77]. Many experts and scholars have con-ducted preliminary analysis into the relationship between fishery and environmental fac-tors. Shang et al. [78] found that the position of central fishing ground in the Taiwan Strait was affected by sea surface temperature (SST). Fang et al. [79] found that the aggregation of pelagic fish is linked to topography, water depth, wind direction, wind force, and water masses. Li [80] pointed out that the location of the central fishing ground of the Carangidae fish is consistent with the surface temperature front and chlorophyll-a concentration gradient. Few have quantitatively studied the relationship between pelagic fish and environmental factors in Shantou-Taiwan shoal fishing ground.
The objectives of this study are: (1) to evaluate spatial distribution and estimate total biomass of pelagic fish by geostatistical analysis; (2) to quantitatively analyze the relation-ship between various environmental factors and spatial distribution of the pelagic fish by GAM. This study can serve as a scientific basis for fishery resources management. Soon, offshore wind farms will be built in the study area. The results in the paper can also pro-vide primary data for the future study of the impact of wind farm construction on fishery resources.

2. Materials and Methods

2.1. Study Area

The study area is located in the southern end of the Taiwan Strait and the junction of the South China Sea and East China Sea, covering a bathymetric range of between of 20 and 100 m (Figure 1). Shantou-Taiwan shoal fishing ground has superior geographical conditions and is located at the confluence of Kuroshio, coastal water, and mainland run-off. It is rich in fishery resources and has characteristics of high productivity, a short food chain, rapid nutrient and carbon cycling, and high efficiency of primary and secondary productivity conversion [81].

2.2. Hydroacoustic Data

The hydroacoustic survey was conducted in Shantou offshore water in the period 1–4 June 2019 (Figure 1). A BioSonic DT-X echosounder with a frequency of 200 kHz was used to collect the acoustic data. Acoustic data were collected and recorded with Visual Acquisition Software. Coordinate data were collected and recorded synchronously by the GPS receivers. The length of each transect is shown in Table 1. To avoid the influence of the engine noise of the investigation ship on the detector, the transducer was fixed on the left side of the investigation ship, far away from the engine of the investigation ship, and 1.5 m below the water surface. The direction of transducers was perpendicular to the wa-ter surface. The circular transducer had a beam opening angle of 6.8°. Pulse duration was 0.4 ms, and the target strength threshold was −130 dB. The echosounder was calibrated using a 36 mm Tungsten Carbide 200 kHz calibration sphere before the start of the survey [82].
Visual Analyzer software (version 4.3) was used for acoustic data processing, and the bottom tracking algorithm was used to identify the bottom automatically. The surface line was placed at 1.5 m below the transducer. The bottom line was increased by 1 m based on an automatic algorithm and manual correction to avoid the interference of bottom noise. The threshold was set to −60 dB to shield the integration of echo signals generated by other scatterers, such as plankton. A single station uses 1200 pulses as an analysis unit, and each station obtained six data. Each analysis datum includes volume backscattering strength (Sv, dB), backscattering cross-section(σ), Fish Per Unit Area (FPUA), Fish Per Cubic Meter (FPCM), the starting coordinates of the analysis unit, mean water depth, and the distribution of fish target strength. The echo recognition parameters were set as the minimum pulse coefficient of 0.75, the maximum pulse-back coefficient of 3, and the termination pulse width of –12 dB.
FPCM is an estimate of the density of fish in a given report or volume of water, and FPUA is an estimate of the number of fish in the water column. In an analysis unit,
N = H h
F P U A = ∑ i = 1 N F P C M i × h
where H is water depth, N is the number of water layers, and h is the water layer interval–in the paper, we choose 5 m.
In this study, acoustic biomass was expressed as the volume backscattering strength (Sv, dB). Sv can quantify the sum of fish backscattering cross-sections per volume, and is often used as a proxy for biomass [32,83,84]. The volume backscattering coefficient (sv, m2/m3), was gained from Sv (dB) through the equation: s v = 10 S v / 10 .

2.3. Remote Sensing Data

Satellite data were downloaded from the Pacific fisheries science center (https://oceanwatch.pifsc.noaa.gov/; accessed on 21 November 2021). These data include Sea Surface Temperature (SST), Chlorophyll-a Concentration (Chla), Zonal Sea Surface Wind (uwind), Meridional Sea Surface Wind (vwind), Sea Surface Height (SSH), and Sea Sur-face Temperature Anomaly (SSTA) (Table 2). All data were resampled into a 0.01° spatial resolution.

2.4. Variation Function and Kriging Interpolation

The variation function is defined as:
γ ( h ) = 1 2 N ( h ) ∑ i = 1 N ( h ) [ z ( x i ) − z ( x i + h ) ] 2
where z ( x i ) is variance at sampled station x i ; h is a displacement vector; and N(h) is the number of observation pairs the spatial locations that are separated by distance h.
The specific steps are as follows:
(1)
Verify the normality of fish density and acoustic biomass. If they do not meet, a log, square, or Box-Cox transformation was performed.
(2)
Onthe premise of isotropy, the semi-variogram function is modeled. These common models are spherical model, exponential model and Gaussian model. Each model can be described based on three parameters: (i) nugget variance, C 0 , the model Y-axis in-tercept; (ii) Sill, C 0 + C , the model asymptote; (iii) range, a, the distance over which spatial dependence is apparent. The regression coefficient ( R 2 ) and residual sums of squares (RSS) are indexs reflecting the precision of the fitting model.
(3)
After running all models, the model with the highest RSS values and smallest r 2   values was chosen to interpolate fish density and acoustic biomass. Kriging interpolation is performed based on the information provided by the variance function on the degree of spatial autocorrelation, so that optimal unbiased estimates can be obtained, while providing the error and accuracy of the estimates.
(4)
Cross-validation was used to evaluate the result of kriging.

2.5. GAM Model

The general expression of the GAM model is as follows:
g ( u i ) = β 0 + ∑ i = 1 n f i ( x i ) + ε ,
where g ( u i )   is the link function,     u i is the response variable, β 0 is the intercept, n is the number of explanatory variables, f ( x ) is the spline smoothing function, x i is the explana-tory variable, ε is the error term corresponding to normal distribution. In this study, GAM was constructed using the “mgcv” package, fish density per unit area (FPUA) was selected as the response variable, and marine environmental factors as the explanatory variable.
First, the Pearson correlation coefficient was used to judge the possible collinearity between environmental variables. If there was collinearity between the two variables, only one variable was chosen. The stepwise method was then used to optimize the model step by step (Table 3). In GAM, the deviance explained coefficient and Akaike’s Information Criterion (AIC) were changed by the addition of different variables. Model selection was based on the contribution of the predictor variable, the highest values of deviance ex-plained, and the lowest values of AIC [84]. A drop of 2 units in AIC means that the factor is essential [85].

3. Results

3.1. Fish Size Distribution

As a whole, the target strength (TS) distribution of fish was −58~−42 dB, and among them −58~−52 dB occupied about 86.95% (Figure 2). The empirical formula of fish target strength and fish length is as follows:
T S = 20 l o g 10 ( L ) + b 20
where, b 20 is the target intensity parameter for the assessed species. The pelagic fish being mainly dominated by Decapterus maruadsi, Scomber japonicus, and Sardinella aurita in the study area, and these three fish species account for more than 80% [86]. According to the relevant literature [29,30,87], b 20 is −72.5 dB. The total length distribution of fish was about 4−33 cm, of which 87% is less than 10 cm. The result indicates that fish in the study area were mainly small individuals (Figure 3). Mean values of fish length and TS between different sites were also analyzed. The maximum values of fish length and TS (15.42 cm, −49.71 dB) were at site 4, while the minimum values of fish length and TS (7.27 cm, −55.27 dB) were at site 2.

3.2. Spatial Distribution, Abundance and Biomass

Classical statistical results of fish density and acoustic biomass are shown in Table 4. A normality test was performed on the acoustic biomass and fish density, respectively, and it was found that these data did not satisfy the normal distribution. Then, logarithmic transformation was performed to meet the requirements. These transformed data were modelled using the variation function. The result showed that the optimal model for acoustic biomass is exponential and the optimal model for fish density is gaussian (Table 5). The C0 values of the optimal models of fish density and acoustic biomass were less than 0.25, indicating that the distribution of both biomass and density had strong spatial correlation.
Then, on the basis of spatial correlation analysis, ordinary kriging interpolation of fish density and biomass were performed based on the above optimal model (Figure 4). The interpolation results were evaluated using the cross-validation. As showed in Figure 5, the regression coefficients between the observed and predicted values of fish density and biomass were 0.70 and 0.55.
The results showed that the distribution of fish density and acoustic biomass were similar, with extreme values in the central and eastern parts of the study area. In the near-shore, both biomass and density values were low.
Finally, the total fish density and acoustic biomass of the study area were estimated based on the interpolation results. The total fish density was 2.56 × 1010 ind., while total fish acoustic biomass was 1908.99 m2/m.

3.3. Vertical Distribution of Fish Density and Acoustic Biomass

In vertical movement, FPCM was utilized to analyze the vertical distribution of fish. The vertical distribution of fish density and acoustic biomass is similar. During the day fish density and acoustic biomass increased with water depth, and fish density and acoustic biomass increased first and then reduced with the increasing of water depth at night (Figure 6a). As acoustic mappings show, fish schools are mainly distributed in the middle and lower water layers at 11:35 a.m. (Figure 6b), and in upper and middle water layers at 11:15 p.m. (Figure 6c).

3.4. Fish Distribution Affected by Environmental Factors—GAM Model

The optimal GAM model is expressed as:
log ( F P U A ) ~ s ( l o n ) + s ( s s t ) + s ( u w i n d ) + s ( s s h ) + s ( v w i n d ) + s ( c h l a ) ,
In the formula, l o n is longitude, s s t is sea surface temperature, u w i n d is zonal sea surface wind, s s h is sea surface high, v w i n d is meridional sea surface wind, c h l a is chlorophyll-a concentration.
The final selected model for GAM (variables, AIC, p-value, and deviance explained) is shown in Table 6. In GAM, the best-performing model has six variables (longitude, SST, zonal sea surface wind, SSH, chlorophyll-a concentration) that were significant (p < 0.05) with the lowest AIC (−291.111) and the highest deviance explained (88.2%).
Generalized additive model analysis indicated that four variables such as longitude, SST, uwind, SSH, and vwind significantly influenced fish density (p < 0.01). The im-portance of each variable was ranked based on the deviance explained and AIC (Table 5). The non-linear relationship between environmental variables and fish density is shown in Figure 7. GAM plots show the effect of each environmental variable on fish density. A narrow confidence interval represents high relevance. A negative effect of SST on fish density was observed. For SSH, a positive effect was observed between −0.07 and −0.03 m. A positive impact of vwind on fish density was observed between 1 and 2.5 m/s, while greater than 2.5 m/s adverse effects were noted, but with a wide confidence interval.

4. Discussion

Shantou-Taiwan shoal fishing ground is the habitat, baiting, spawning, and winter-ing place for many kinds of fish, shrimp, crab, and shellfish (Figure 8). We found that fish are mainly small individuals. One of the principal reasons for this is the large-scale fishing before the fishing moratorium [88], and a second reason is that spring is the breeding sea-son for most fish. For example, the spawning period of Decapterus mariachi is from January to April [89], and Scomber japonicus spawn from March to May [90,91]. A large number of young fish appear in summer after spawning in spring. In vertical distribution, fish usually inhabit in middle and lower water layers during the day, and they swim up to the upper and middle water layer at night. This may be related to vertical migration of the pelagic fish [4]. Chen et al [36] also found that the demersal trawl catch of the Carangidae fish in the daytime is 1.64 times that at night in the continental shelf waters of the northern South China Sea.
The result of the GAM model shows that three environmental variables (SST, SSW, SSH) have significant effects on the distribution of fish in the study. Temperature is known as one of the critical factors, having direct or indirect effects on the distribution, migration, reproduction, and spawning of pelagic fish [47,96,97,98]. Environmental condition is com-plex and SST changes dramatically, due to the joint influence of Kuroshio with high tem-perature and salinity, Fujian-Zhejiang coastal current with lower temperature and salinity, eastern Guangdong coastal current with high temperature and low salinity, and upwelling. In the study area, warm-water species of fish account for 81% [99]. In June, large-scale temperature fronts exist east of the Taiwan Bank. Near the cold side, nutrients are rich and primary productivity is higher. This may account for SST haing a negative effect on fish density. Wang et al. [100] found that SST had the most significant influence on Japanese Scomber japonicu abundance in the East and Yellow Sea based on the GAM model. Niu et al. [48] found that SST is the most critical environmental factor affecting the CPUE of Trachurus murphyi. Yi et al. [101] also noted that SST was the most critical environmental factor affecting the formation of Scomber japonicu fisheries.
Sea surface wind and sea surface height are also important factors. Wind speed, di-rection, and duration could affect the location of the fishing ground and change in fishery resources [102]. Upwelling was closely related to the sea surface wind [103]. Due to their weak swimming ability, the distribution of juvenile fish is susceptible to wind [104,105]. Comerford et al. [106] found wind speed and direction were significant drivers of larval distribution. In this study, sea surface wind has a significant effect on fish density. The reasons may be as follows: (1) the southwest wind is prevailing, resulting in onshore cur-rent. Some pelagic fish may migrate onshore for breeding and feeding along with the cur-rent. (2) Southwest wind is also believed to be the major driving factors for offshore upwelling [107,108,109,110].
Sea surface height is usually related to dynamic ocean processes such as the front and vortex [111]. In front and vortex area, due to the vertical movement of seawater, the lower levels of water with rich nutrients are brought to the sea surface. This can promote the breeding of phytoplankton and zooplankton. Li et al. [47] found that Scomber japonicu fishing ground has a good matching relationship with SSH in the East China Sea. This study showed that sea level significantly affected the distribution of fish density, and there was a positive correlation between sea levels and fish density from −0.07 m to −0.03 m, and fish density reached the maximum at sea level −0.03 m. Currents with a negative sea level may has different degrees of turbulence and fronts due to divergence or shear action, resulting in rich nutrients in the area and attracting fish to engage.
The GAM result shows that chlorophyll-a has little effect on fish density. This result may be related to the time delay. Chlorophyll-a usually reflects phytoplankton’s biomass or primary productivity, and zooplankton feed on phytoplankton [95]. In this study, chlorophyll-a concentration is quasi-real-time data, so it does not reflect fish density.
In addition to natural factors, human activities, such as offshore wind farms (OWFs) construction, could also impact the fishery resources significantly. OWFs also affect the marine environment. Some studies have highlighted that OWF could disturb marine in-vertebrates, fish, and mammals via the generation of noise and electromagnetic fields [112,113,114,115,116,117,118]. Meanwhile, wind turbines can act as artificial reefs and no-take zones, which is conducive to the restoration of fishery resources and the improvement of biodiversity [119,120,121]. Furthermore, OWFs could also affect currents and wind fields [122].
Numerous relevant studies have shown that the abundance and distribution of fish varied seasonally [88,98,123]. However, we only analyze the relationship between environmental factors and fish density in June in the paper. In the future, we will need to collect fishery survey data and corresponding environmental data in other seasons or longer to facilitate the analysis of the distribution of fish resources, its relationship with environmental factors in different seasons, and the effects of wind farms on fishery re-sources.

5. Conclusions

In this study, we analyze the spatial distribution of the pelagic fish using geostatistical methods based on acoustic data. Then combining acoustic data with remote sensing data (sea surface temperature, sea surface wind, sea surface height, and chlorophyll-a concentration), we analyzed the relationship between fish density and marine environment variables in the Shantou offshore water. Fish are mainly small individuals in the study area in June. Fish density and acoustic biomass have strong spatial auto-correlation. The optimal variation function model for acoustic biomass and fish density is exponential and gaussian; based on the optimal model, ordinary kriging method was used for spatial interpolation analysis. The interpolation results show that the higher values of density and acoustic biomass were located in the central and eastern parts of the study area. The total fish density and acoustic biomass is 2.56 × 1010 ind. and 1908.99 m2/m, respectively. In vertical distribution, fish usually inhabit the middle and lower layers of the water during the day and swim up to the upper and middle water layer at night. The GAM model shows that SST, SSW, and SSH have significant effects on the distribution of fish density in June.

Author Contributions

All authors contributed to this study. R.D. conducted acoustic investigation; X.Y. processed and analyzed data and wrote the original draft manuscript; D.Y. reviewed and edited the draft manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research is funded by the National Key R&D Program of China under the Grant Nos. 2018YFD0900901, also by the Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) (Project No. GML2019ZD0602), and the Natural Science Foundation of China (No. 41776180).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The datasets for this study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Map of study area and acoustic survey transects. The black box represents the study area, and rea lines represent the acoustic route.
Figure 1. Map of study area and acoustic survey transects. The black box represents the study area, and rea lines represent the acoustic route.
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Figure 2. Frequency profile of target strength in study area.
Figure 2. Frequency profile of target strength in study area.
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Figure 3. The mean TS values in decibel and the mean fish size derived using Love’s equation (1971) at survey sites. Box plots show median values (solid horizontal line), and the lower and upper ends of the box are the 25 and 75% quartiles, respectively. The whiskers indicate 1.5 times the interquartile range, and points beyond this range are shown by empty circles.
Figure 3. The mean TS values in decibel and the mean fish size derived using Love’s equation (1971) at survey sites. Box plots show median values (solid horizontal line), and the lower and upper ends of the box are the 25 and 75% quartiles, respectively. The whiskers indicate 1.5 times the interquartile range, and points beyond this range are shown by empty circles.
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Figure 4. Spatial interpolation of fish density and acoustic biomass.
Figure 4. Spatial interpolation of fish density and acoustic biomass.
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Figure 5. Correlation between predicted and measured values. (a) fish density; (b) acoustic biomass.
Figure 5. Correlation between predicted and measured values. (a) fish density; (b) acoustic biomass.
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Figure 6. (a) The vertical distribution of fish density and acoustic biomass in study area. The red, bule, green and yellow lines represent fish density (night), acoustic biomass (night), fish density (day) and acoustic biomass (day) respectively; (b) acoustic mapping of fish school at 11:35 a.m.; (c) acoustic mapping of fish school at 11:15 p.m.
Figure 6. (a) The vertical distribution of fish density and acoustic biomass in study area. The red, bule, green and yellow lines represent fish density (night), acoustic biomass (night), fish density (day) and acoustic biomass (day) respectively; (b) acoustic mapping of fish school at 11:35 a.m.; (c) acoustic mapping of fish school at 11:15 p.m.
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Figure 7. Partial effect plots of the variables retained in the final model. The vertical coordinate represents the spline function s(x). The numbers in parentheses are the estimated degree of freedom. Dashed line indicates 95% confidence band and ticks on the horizontal coordinate denote the data points. l o n is longitude, s s t is sea surface temperature, u w i n d is zonal sea surface wind, s s h is sea surface high, v w i n d is meridional sea surface wind, c h l a is Chlorophyll-a concentration.
Figure 7. Partial effect plots of the variables retained in the final model. The vertical coordinate represents the spline function s(x). The numbers in parentheses are the estimated degree of freedom. Dashed line indicates 95% confidence band and ticks on the horizontal coordinate denote the data points. l o n is longitude, s s t is sea surface temperature, u w i n d is zonal sea surface wind, s s h is sea surface high, v w i n d is meridional sea surface wind, c h l a is Chlorophyll-a concentration.
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Figure 8. Distribution of spawning grounds and migration routes of pelagic fish in Shantou–Taiwan shoal fishing ground [92,93]. Forward slash area represents spawning grounds of the Pelagic Fish. Red arrow lines and green arrow lines respectively represent reproductive-prey migration and temperature-prey migration of Loligo formosana. The pelagic fishes usually overwinter and reproduce in deeper sea areas in winter. In spring and summer, they migrate from south to north or northeast, and in late autumn they migrate from north to south due to decreasing temperature [86,94,95].
Figure 8. Distribution of spawning grounds and migration routes of pelagic fish in Shantou–Taiwan shoal fishing ground [92,93]. Forward slash area represents spawning grounds of the Pelagic Fish. Red arrow lines and green arrow lines respectively represent reproductive-prey migration and temperature-prey migration of Loligo formosana. The pelagic fishes usually overwinter and reproduce in deeper sea areas in winter. In spring and summer, they migrate from south to north or northeast, and in late autumn they migrate from north to south due to decreasing temperature [86,94,95].
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Table
Table 1. The length of each station (nautical mile(nm)).
Table 1. The length of each station (nautical mile(nm)).
StationLength (nm)StationLength (nm)StationLength (nm)
12.3992.59172.67
22.12101.80182.18
32.46112.42192.71
42.18122.54202.45
52.54132.20212.52
62.44142.34222.37
72.27152.56232.93
82.17163.04243.59
Table
Table 2. The units, mean, range and description of environmental variables in GAM model.
Table 2. The units, mean, range and description of environmental variables in GAM model.
VariablesUnitsMeanRangeDescription
SST°C27.5126.65–27.9Sea surface temperature
Chlorophyll-amg/m30.2290.1093–1.123Chlorophyll concentration
uwindm/s2.230.59–3.82Zonal sea surface wind
vwindm/s1.60.52–2.18Meridional sea surface wind
SSTA°C0.990.44–1.66Sea surface temperature anomaly
SSHm−0.0456−0.087–0.02Sea surface high
Table
Table 3. Statistical characteristics of the model.
Table 3. Statistical characteristics of the model.
ModelR2AIC
Log(FPUA)~s(lon)0.31433.081
Log(FPUA)~s(lon)+s(sst)0.576−72.554
Log(FPUA)~s(lon)+s(sst)+s(uwind)0.701−147.451
Log(FPUA)~s(lon)+s(sst)+s(uwind)+s(ssh)0.775−204.835
Log(FPUA)~s(lon)+s(sst)+s(uwind)+s(ssh)+s(vwind)0.84−279.005
Log(FPUA)~s(lon)+s(sst)+s(uwind)+s(ssh)+s(vwind)+s(chla)0.851−291.111
Table
Table 4. Classical statistical of acoustic biomass and fish density.
Table 4. Classical statistical of acoustic biomass and fish density.
VariancesMaxMinMeanKurtosisSkewnessStandard DeviationCoefficient of Variation
Acoustic biomass2.14 × 10−6 m2/m38.6 × 10−8 m2/m33.43 × 10−7 m2/m310.492.953.170.92
Fish density7.98 ind./m20.20 ind./m22.70 ind./m22.3441.2811.430.53
Table
Table 5. Goodness-of fit measures for geostatistical model.
Table 5. Goodness-of fit measures for geostatistical model.
ModelFish DensityAcoustic Biomass
ExponentialSphericalGaussianExponentialSphericalGaussian
Nugget (C0)0.01330.00740.01720.1710.08020.0968
Sill (C0 + C)0.09960.09280.09340.5790.37040.3686
Range (A)/m37,20019,50017,493.71168,90019,60014,849.23
RSS0.0089910.0087270.0086350.1580.1860.185
R20.4130.4300.4360.3850.2790.280
Proportion
(C/(C0 + C)		0.8660.8160.8160.7050.7830.737
Table
Table 6. Result of GAM model.
Table 6. Result of GAM model.
VaiablesEdfFp-ValueR2Deviance Explained/%Cumulation of Deviance Explained/%AIC
Lon6.3666.6634.08 × 10−60.31433.833.833.081
sst8.4387.5161.96 × 10−90.57626.760.5−72.554
uwind8.98019.0162 × 10−160.70112.773.2−147.451
ssh8.98611.7291.91 × 10−100.7757.680.8−204.835
vwind8.1948.035.58 × 10−100.846.186.9−279.005
chla7.4601.9920.04110.8511.388.2−291.111
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Yin, X.; Yang, D.; Du, R. Fishery Resource Evaluation in Shantou Seas Based on Remote Sensing and Hydroacoustics. Fishes 2022, 7, 163. https://doi.org/10.3390/fishes7040163

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Yin X, Yang D, Du R. Fishery Resource Evaluation in Shantou Seas Based on Remote Sensing and Hydroacoustics. Fishes. 2022; 7(4):163. https://doi.org/10.3390/fishes7040163

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Yin, Xiaoqing, Dingtian Yang, and Ranran Du. 2022. "Fishery Resource Evaluation in Shantou Seas Based on Remote Sensing and Hydroacoustics" Fishes 7, no. 4: 163. https://doi.org/10.3390/fishes7040163

APA Style

Yin, X., Yang, D., & Du, R. (2022). Fishery Resource Evaluation in Shantou Seas Based on Remote Sensing and Hydroacoustics. Fishes, 7(4), 163. https://doi.org/10.3390/fishes7040163

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