Relationships between environmental variables and spatial and temporal distribution of jack mackerel (Trachurus japonicus) in the Beibu Gulf, South China Sea

Data collection

CPUE data for T. japonicus in Beibu Gulf (105.67–110.17°E, 17–21.75°N) was derived from fishery surveys in autumn (November) of 2013, and winter (February), spring (May), and summer (August) of 2014. Some 25 sites were sampled in each survey (Fig. 1) using a 441 kw vessel and bottom trawl of 38.5 m corkline length and 20 mm cod-end mesh. At each site, a trawl was towed for 2 h at a speed of 3 kn.

Shading denotes joint fishing zone (grids) between China and Vietnam. The triangle represents the study stations, and the number on the upper left represents the number of the station.

Environment and geographic data

Satellite remote sensed sea surface temperature (SST) and sea surface chlorophyll-a (Chl-a) data were derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). Sea surface salinity (SSS) data was downloaded from the Copernicus Marine Environment Management Service (CMES), and that for sea level anomalies (SLA) from Archiving, Validation, and Interpretation of Satellite Oceanographic (AVISO). Meridional (V) and zonal (U) current velocities were obtained from the Ocean General Circulation Model For the Earth Simulator (OFES). Depth data were derived from Google Earth elevation data (Table 1).

MATLAB software was used to extract SST, SSS, Chl-a, and SLA data from 2013 to 2014. Null were discarded and the data then averaged for each month. ArcGIS 10.3 (Esri, Redlands, CA, USA) was used to resample SST, SSS, and Chl-a data at different spatial resolutions and convert it into environmental data with a resolution of 0.25°. Remote sensed data are interpolated by ordinary Kriging using ArcGIS 10.3 software to plot a monthly images (Yu et al., 2018).

Catch and effort data

where, CPUE is density of T. japonicus by a trawl (unit: kilograms per square kilometer), C is the average catch per hour in a 0.25° × 0.25° (unit: kilograms per hour), a is the swept area per hour, and the swept width of the trawl is taken as 2/3 of the topline length (unit: Kilometers squared per hour), b is the catchability coefficient with a value of 0.5.

GAMs fitting procedures

GAMs is a nonparametric extension of the generalized linear model, which can deal directly with nonlinear relationships between response variables and multiple explanatory variables (Guisan, Edwards & Hastie, 2002). We use GAMs to analyze T. japonicus CPUE data and environmental factors. The

general expression of GAM is: (2)${\displaystyle \mathrm{Y}=\alpha +\sum _{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{f}}_{\mathrm{i}}\left({\mathrm{x}}_{\mathrm{j}}\right)+\varepsilon }$

where Y = the CPUE (kg/km²); x_j = x_j the explanatory variable (the spatiotemporal and environmental factors for each site); α = the intercept that fits for the function; ɛ = the residual error; and f_i(x_j) = a one-variable function of the independent variable (a spline smoothing function). We use the mgcv package in R 4.0.5 software to construct and test the model (Pedersen et al., 2018; Wood, 2006). To determine the expression form of the GAM, A stepwise method was used to select variables that have a significant influence on the model (Wood, 2004; Wood, 2011).

Model test

(1) The Akaike information criterion (AIC) was used to test the fit of the model after the gradual addition of factors; the smaller the value, the better the fit of the model (Burnhan & Anderson, 2002). Generalized cross validation (GCV) was used to evaluate predictive variables of the model; the smaller the value, the better the modeling ability (Shchetinnikov, 1992; Stone, 1985). Chi-square and F-tests were used to evaluate the nonlinear contribution of nonparametric effects and assess the significance of each factor (Stone, 1985). AIC is calculated as follows (Venables & Dichmont, 2004): (3)${\displaystyle \mathrm{AIC}=\theta +2\mathrm{df}\phi }$

where θ = the deviation; df = the effective degree of freedom; and φ = the variance. (2) The W’ normal test method were proposed by Shapiro et al. (Shapiro & Francia, 1972). The normal quantile plot was used to examine the distribution of GAM residuals, to indicate the distribution of model residuals and the histogram of residuals. A VIF (variable inflation factor) test suggested that factors without collinearity have VIF values <5 (Cornic & Rooker, 2018), except for Lat (VIF > 5) (Table 2).

Center of gravity

The center of gravity (CoG) method was used to analyze spatial and temporal variability in CPUE (Chen et al., 2003). The CoG of T. japonicus CPUE is calculated as follows: (4)${\displaystyle \mathrm{X}=\sum _{\mathrm{i}=1}^{\mathrm{K}}\left({\mathrm{C}}_{\mathrm{i}}\times {\mathrm{X}}_{\mathrm{i}}\right)/\sum _{\mathrm{i}=1}^{\mathrm{K}}{\mathrm{C}}_{\mathrm{i}}}$ (5)${\displaystyle \mathrm{Y}=\sum _{\mathrm{i}=1}^{\mathrm{K}}\left({\mathrm{C}}_{\mathrm{i}}\times {\mathrm{Y}}_{\mathrm{i}}\right)/\sum _{\mathrm{i}=1}^{\mathrm{K}}{\mathrm{C}}_{\mathrm{i}}}$

where X and Y = the longitude and latitude of the CoG; C_i C_i = the yield of fishing area i; X_i and Y _i = the central longitude and latitude of fishing area i; and K = the total number of fishing areas.

Seasonal variation in T. japonicus CPUE

T. japonicus is widely distributed, and its density varied seasonally from autumn 2013 to winter 2014. Average resource density was highest in summer (184.8 kg/km²), then autumn (55.1 kg/km²), and lowest in winter (0.7 kg/km²). There was an abnormally high values in summer, of which the highest was 985.6 kg/km² (Fig. 2).

GAMs analysis

The model showed a skew normal distribution (Fig. 3), with a W′ statistic of 0.987 (<0.05), which reached the 95% reliability level of statistical significance. The residuals passed the normality test and adequately conformed to a normal distribution. Except for Lat (VIF > 5), we added all factors to the model. The importance of each variable was selected based on AIC and GCV values. Our GAM formula was (6)${\displaystyle log\left(Y+1\right)=s\left(Month\right)+s\left(SLA\right)+s\left(Depth\right)+s\left(SST\right)+s\left(SSS\right)+\varepsilon }$

where Y = T. japonicus density (because of 0 CPUE values, we log transformed our data after adding 1 (Y + 1) (Guan et al., 2009; Porch, 1995); s = the natural spline smoothing function; s(Month) = the effect of month; s(SLA) = the effect of sea level anomaly; s(Depth) = the effect of depth; s(SST) = sea surface temperature; s(SSS) = the effect of sea surface salinity; and ɛ = modeling error, which conforms to a Gaussian distribution. The cumulative explanatory bias of GAM on T. japonicus was 57%, with an R² of 0.51 (Table 3).

Results of GAM fitting indicate the best explained CPUE of T. japonicus included five explanatory variables (Tables 3, 4), of which SLA was the most important, explaining contributing 21.2%, followed by Month, Depth, SSS, and SST, contributing 18.7%, 10.7%, 5.1%, and 1.3%, respectively. Excepting SSS, an ANOVA F-test indicated that all model factors were significant (P < 0.05). However, when SSS was added to the model, AIC values decreased further, and the cumulative deviation interpretation increased, which indicates increased model fit. Therefore, SSS was kept in the model. A Chi-square test revealed the nonparametric smoothing effect of predictive variables, According to Chi-square tests, Month, SLA, Depth, and SST had the best nonparametric smoothing effects, while that of SSS was lower (Table 4).

The nonlinear relationship between environmental variables and CPUE is shown in Fig. 4. Generally, the narrower the confidence interval the higher a correlation. Relationships between CPUE and explanatory variables from GAMs revealed the highest CPUE of T. japonicus occurred in summer, with a significant increase from winter to summer. A positive effect of season on CPUE was also observed between spring and summer, but effects were negative from winter to spring (Fig. 4A). A positive effect of SLA on CPUE occurred below −0.05 m, but negative effects were noted above 0.05 m (Fig. 4B). A positive effect on CPUE was obvious between 35 and 75 m depth, while negative effects occurred below 40 m and above 75 m (Fig. 4C). The effect of SST on T. japonicus CPUE maintained a negative linear correlation between 16 and 32 °C; Below 28 °C was a positive effect, while above 28 °C was a negative effect (Fig. 4D). Salinity from 30.0 to 32.5 PSU had a positive effect on CPUE (Fig. 4E).

Relationship between oceanographic factors and T. japonicus CPUE

T. japonicus fishing grounds in the Beibu Gulf are concentrated mainly in waters of 25–30.5 °C SST and SSS of 31.2–33.5 PSU. Suitable SST varies from spring (28.1–29.1 °C, Fig. 5C), to summer (30–30.7 °C, Fig. 5D), to autumn (24.5–25.2 °C, Fig. 5A), and winter (20.6–21 °C, Fig. 5B). Most suitable SSS values for T. japonicus in spring are 32.5–32.7 PSU (Fig. 6C), summer (33.0–33.5 PSU, Fig. 6D), autumn (31.2–33.2 PSU, Fig. 6A), and winter (32.8–33.0 PSU, Fig. 6B).

Relationships between the spatial and temporal distribution of T. japonicus CPUE and depth revealed most T. japonicus to occur at <85 m, and for CPUE to first increase and then decrease with increased depth (10–75 m, Fig. 7), with a maximum at 67 m (989.6 kg/km²). From spring (May) to winter (Feb), suitable water depth ranges for T. japonicus first increased and then decreased: depths between 42 m and 50 m were suitable in spring (Fig. 7C), between 65 m and 75 m in summer (Fig. 7D), 40 m and 60 m in autumn (Fig. 7A), and 51 m and 60 m in winter (Fig. 7B).

Spatial and temporal variability in center of gravity of T. japonicus CPUE in the Beibu Gulf

The CoG shifted seasonally from the northeast to southwest (Fig. 8). In autumn the CoG was located off Lingao (19.89°N, 108.57°E), but shifted to off Dongfang (19.86°N, 108.53°E) in winter, before moving northwest in spring to off BechLongVi island (20.25°N, 108.35°E), and then to the sea area in the mouth of Beibu Gulf in (18.75°N, 107.65°E) August (Fig. 8).

The blue line indicates the longitude and the red line represents the latitude.

The predicted spatial distribution of T. japonicus CPUE

Our GAM model predictions fitted well with actual CPUE (R² = 0.66, p < 0.01; Fig. 9). The maximum R² occurred in summer (R² = 0.77, p < 0.01) and minimum R² in winter (R² = 0.05, p > 0.05; Fig. S1). The distribution of predicted seasonal T. japonicus CPUE correspond well with actual fishing CPUE (black dots in Fig. 10), and the size of symbol increases with the actual catch. Actual fishing locations largely coincide with predicted areas of high CPUE. T. japonicus mainly occurred in central sea areas of the Beibu Gulf from November (autumn) to May (spring), and in the mouth of Beibu Gulf in August (summer) (Fig. 10).

Spatial and temporal distribution of T. japonicus in the Beibu Gulf

Beibu Gulf is an important spawning and feeding ground and key habit for many fish species. The location of T. japonicus fishing grounds changes with month (Qiu, Lin & Wang, 2010; Venables & Dichmont, 2004). We report seasonal differences in fishing grounds in the Beibu Gulf throughout the year, with the highest resource density occurring in summer, and the lowest in winter–a result that consistent with previous research (Yan et al., 2019). Month greatly influences CPUE in the Beibu Gulf (explaining18.7%; Table 4). Seasonal variation in T. japonicus CPUE is mainly attributed to variation in biomass of this species in the Beibu Gulf, and differences in growth rate at different temperatures (Fan, Feng & Chen, 2018). Growth and migration of fish contribute greatly to seasonal variation in fish resources in the northwest Beibu Gulf (Fu et al., 2019).

The CPUE of T. japonicus occurs mainly between 107.2–108.7°E and 18.3–20.2°N (Figs. 5, 6, 7). In spring, CPUE of T. japonicus is mainly distributed in 107.8–108.8°E and 20–21°N, possibly because the seabed is flat and the terrain inclines from °N orthwest to southeast (Figs. 5C, 6C, 7C). Many rivers along the coast transport nutrients into the sea (Song, Huang & Shi, 2004). Shallow depths and enhanced tidal currents in this area also enhance water column mixing, increasing surface nutrients, and promoting the phytoplankton growth conducive to feeding and aggregation of marine species (Wang et al., 2020b). The summer distribution of CPUE (mainly from 107.2–107.8°E) may be attributable to counter clockwise currents and enhanced northwest–southeast oriented ocean processes in the western part of the bay, influenced by southwest monsoon (Wang et al., 2011a) (Figs. 5D, 6D, 7D). SST and SSS in the bay also increased at this time, creating conditions suitable for growth of the young fish (Chen & Qiu, 2005; Yan et al., 2018). Alternatively, due to the summer rainfall increases has led to a significant expand in river runoff, bringing abundant land-sourced nutrients and bait for fish. Under the southwest monsoon, currents may have transported nutrients and plankton to the continental shelf and slope, and T. japonicus migrated to the southwest for feeding (Shi et al., 2019; Yan, 2019). In addition, juvenile T. japonicus mainly feed on planktonic crustaceans and copepods, while adult fish mainly feed on copepods, long tails and short tails (Qiu, 1980). In summer, the species of copepods generally increase from the northern community to the bay mouth. Therefore, the changes in food habits in spring and summer may also be the reason for the high CPUE in summer (Zeng et al., 2016). In autumn, T. japonicus occurred mainly between 108.3–108.7°E and 19.8–20.1°N and in the northern bay, possibly because temperatures in the Beibu Gulf fell, and fish within the bay migrated northeast to shallow seas to reproduce (Chen & Qiu, 2005) (Figs. 5A, 6A, 7A). In winter, a strong, stable, cold high pressure system increased the sea’s stability in northern monsoon conditions, and most fish migrated northeast or into the open sea to overwinter (Yan, 2019; Figs. 5B, 6B, 7B). The distribution and changes in T. japonicus CPUE appear to be significantly influenced by water masses and cyclones, and closely related to their lifespan.

Relationship between T. japonicus CPUE and environmental factors

Environmental variation may affect the spatial and temporal distribution of T. japonicus (Sassa et al., 2014; Sassa et al., 2008). We report SST, SSS, water depth, and SLA to significantly affect the distribution of T. japonicus in the Beibu Gulf. Temperature and salinity affect reproduction, survival, growth, and the distribution of marine species (Sassa & Konishi, 2010; Wang et al., 2011c).

Temperature is one of the major environmental factors affecting distribution of T. japonicus (Cui et al., 2012; Niu et al., 2010; Niu, Li & Xu, 2010). However, SST did not contribute significantly in this study (explaining 1.3%, Table 4), which may be due to the fact that T. japonicus are more sensitive to changes of water temperature gradients in the northern South China Sea (Fan, Feng & Chen, 2018). In addition, it may be related to the amount of data or covered by other factors in this study. We report suitable SST conditions for this species to differ seasonally, consistent with previous research (Li et al., 2016). The SST of T. japonicus fishing grounds in the Beibu Gulf ranges 17–31 °C, with high CPUE occurring in waters of SST 25.0–30.5 °C mainly from 18.27 to 20.13°N (Fig. 5). Suitable water temperature for T. japonicus in the Beibu Gulf exceeds that in the East China Sea (21–23 °C) (Sassa & Konishi, 2010) and southeast Pacific (15 °C isotherm) (Li et al., 2016), possibly because of lower water temperatures in the latter regions.

SSS also affects T. japonicus CPUE (explaining 5.1%, Table 4). Salinity fronts are generally associated with monsoon winds, ocean currents, topography, and boundaries between water masses (Chen, 2009), and across them temperature, salinity and other oceanographic parameters can change rapidly (Chen, Wang & Guo, 2006). These fronts are characterized by mixing, string, enhanced productivity, and ecotones (Park, Chung & Kim, 2004; Park & Chu, 2006). We report fishing locations to concentrate near salinity fronts, particularly in summer and autumn (Figs. 6D, 6A), possibly because microalgae and zooplankton accumulate in these areas, and food is abundant (Bertrand et al., 2002). Water temperature and salinity may also shift with climate change (Bell et al., 2013), which ultimately will affect the spatial and temporal distributions of T. japonicus.

Depth affects T. japonicus CPUE (explaining 10.7%, Table 4). Depth directly affects temporal and spatial changes in hydrological elements, especially temperature, salinity, and current velocity. Moreover, as a generalization, increased depth is associated with decreased variation in each of these parameters (Chen, 2004). We report the highest CPUE of T. japonicus to occur mainly between 50 and 75 m near the mouth of Beibu Gulf, possibly because deeper waters provide a more favorable habitat for this species (Yan et al., 2019).

When SLA is added into the GAM model, the model AIC value decreases and its explaining increases (explaining 21.2%, Table 4), which indicates that this variable is an important one to monitor with respect to T. japonicus fishing ground locations. SLA is the characterization of seawater dynamics (Xu et al., 2019a). Generally, surface flow fields corresponding to positive or negative SLA values represent anticyclonic or cyclonic distributions, respectively (Ansorge et al., 2015; Dong et al., 2014). We report high T. japonicus CPUE along the edges of anticyclonic and cyclonic eddies, and low CPUE within anticyclonic eddies (Figs. S2A, S2D). This may be due to higher chlorophyll concentrations at the edge of anticyclonic eddies than inside the eddies (Gaube et al., 2015). In addition, surface waters within anticyclonic eddies are irradiated and subject to downward flow caused by eddy pumping, which is nutrient-poor and has low chlorophyll concentration (Yang et al., 2020). The T. japonicus CPUE peaked at SLA around −0.2 m and 0.2 m, with a maximum value at −0.2 m (Fig. 4B), possibly because high fishing grounds often occur in areas of sea between high and low SLA values (Guan et al., 2009), which may be related to fronts formed by cold and warm eddies (Yang et al., 2019). It is also possible that in waters near the intersection of sea surface height anomalies >0 m and <0 m that the mixing and stirring of warm and cold water masses cause nutrients and chlorophyll from deeper waters to reach the surface, which provides abundant food for T. japonicus (Guo et al., 2020). Negative sea surface height values also indicate the convergence or shearing of currents, enriched nutrients and increased Chl-a (Xu et al., 2019b), and an abundance of food for fish. We speculate that currents (e.g., eddies, upwelling) at SLA values from −0.2 m to 0.2 m provide favorable conditions for T. japonicus but negatively affect T. japonicus CPUE at SLA >0.2 m. We report no positive effect of Chl-a concentration on CPUE of T. japonicus, which may be related to the feeding habits (Niu et al., 2010), growth (Niu, Li & Xu, 2010), and developmental stages of T. japonicus in the Beibu Gulf, which is consistent with previous research (Arcos, Cubillos & Núez, 2001).

Fishery management and future improvements

Ecosystem-based fishery management (EFM) is considered essential for sustainable fishery management (Dolan, Patrick & Link, 2015; Pikitch et al., 2004). Implementation of this approach requires knowledge of the impacts of fisheries on the environment (Kellner et al., 2011; Link & Browman, 2014). The Beibu Gulf as a key habitat for nearly 500 fish species (Wang et al., 2011b). we should establish a systematic marine management and suitable habitat mechanism of fishing grounds as soon as possible. Seeking interdisciplinary cooperation from causes as well as impacts of Catch rates trend downward to provide a powerful support for the conservation of fishery resources in the Beibu Gulf.

Our GAM is based on data for four months only considers the general environmental factors. But fishing grounds for T. japonicus are closely related to biological, physical and chemical factors (Xu & Zhang, 2007), further biotic and abiotic predictor variables, such as dissolved oxygen and El Niño & La Niña events, should be taken into consideration in future studies.