The Role of Environmental Factors on the Fishery Catch of the Squid Uroteuthis Chinensis in the Pearl River Estuary, China

Journal of Marine Science and Engineering

Article The Role of Environmental Factors on the Fishery Catch of the Squid Uroteuthis chinensis in the Pearl River Estuary, China

Dongliang Wang 1,2,†, Lijun Yao 3,†, Jing Yu 1,* and Pimao Chen 1

1 South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Guangdong Provincial Key Laboratory of Fishery Ecology and Environment China, Scientiﬁc Observing and Experimental Station of South China Sea Fishery Resources & Environment, Ministry of Agriculture and Rural Affairs, Guangzhou 510300, China; [email protected] (D.W.); [email protected] (P.C.) 2 College of Marine Science, Shanghai Ocean University, Shanghai 201306, China 3 Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, China; [email protected] * Correspondence: [email protected] † Authors contributed equally to this work.

Abstract: The Pearl River Estuary (PRE) is one of the major fishing grounds for the squid Uroteuthis chinensis. Taking that into consideration, this study analyzes the environmental effects on the spatiotemporal variability of U. chinensis in the PRE, on the basis of the Generalized Additive Model (GAM) and Clustering Fishing Tactics (CFT), using satellite and in situ observations. Results show that 63.1% of the total variation in U. chinensis Catch Per Unit Effort (CPUE) in the PRE could be explained by looking into outside factors. The most important one was the interaction of sea surface temperature (SST) and month, with a contribution of 26.7%, followed by the interaction effect of depth and month, fishermen’s fishing tactics, sea surface salinity (SSS), chlorophyll a concentration (Chl a), and year, with contributions of 12.8%, 8.5%, 7.7%, 4.0%, and 3.1%, respectively. In summary, U. chinensis in the PRE was mainly distributed over areas with an SST of 22–29 ◦C, SSS of 32.5–34‰, Citation: Wang, D.; Yao, L.; Yu, J.; Chl a of 0–0.3 mg × m−3, and water depth of 40–140 m. The distribution of U. chinensis in the PRE Chen, P. The Role of Environmental was affected by the western Guangdong coastal current, distribution of marine primary productivity, Factors on the Fishery Catch of the and variation of habitat conditions. Lower stock of U. chinensis in the PRE was connected with La Squid Uroteuthis chinensis in the Pearl River Estuary, China. J. Mar. Sci. Eng. Niña in 2008. 2021, 9, 131. https://doi.org/ 10.3390/jmse9020131 Keywords: Uroteuthis chinensis; environmental factors; generalized additive model; remote sensing; Pearl River Estuary Academic Editors: Francesco Colloca and Francesco Tiralongo Received: 19 November 2020 Accepted: 22 January 2021 1. Introduction Published: 28 January 2021 Uroteuthis chinensis (Gray, 1849) (Cephalopoda: Loginidae), a species of squid, lives in warm continental shelf waters and is widely distributed in the South China Sea (SCS), Publisher’s Note: MDPI stays neutral East China Sea, and Japan [1]. It is a fast-growing and highly reproductive species with a with regard to jurisdictional claims in short life span (less than 7 months) and high yield (accounting for about 3/5 of the total published maps and institutional affil- production of the family Loliginidae) [2,3]. Because of these characteristics, the U. chinensis iations. is considered to be an ecological opportunist that can increase the population rapidly under a suitable environment [4]. For this reason, environmental factors play a critical role in the life cycle of U. chinensis. Studies showed that cephalopods are sensitive to water temperature [5–9], marine primary productivity [10], and food supply [11], with temperature being Copyright: © 2021 by the authors. the key factor affecting the population biomass and the species’ distribution [12,13]. Such Licensee MDPI, Basel, Switzerland. conditions could impact the population dynamics by acting on the spawning activity and This article is an open access article recruitment [7,8,14]. In the east of the Ionian Sea, the population structure and distribution distributed under the terms and of Illex coindetii depend on temperature, salinity, and circulation [15]. Any change in water conditions of the Creative Commons temperature, chlorophyll a concentration, and salinity would affect the catch of Ommas- Attribution (CC BY) license (https:// trephes bartramii in the northwestern Pacific to a great extent [16,17]. The available studies creativecommons.org/licenses/by/ 4.0/). on the U. chinensis in the SCS are all focused on biological characteristics [18–20], migration

J. Mar. Sci. Eng. 2021, 9, 131. https://doi.org/10.3390/jmse9020131 https://www.mdpi.com/journal/jmse J. Mar. Sci. Eng. 2021, 9 2 of 16

J. Mar. Sci. Eng. 2021, 9, 131 2 of 15 the catch of Ommastrephes bartramii in the northwestern Pacific to a great extent [16,17]. The available studies on the U. chinensis in the SCS are all focused on biological characteristics [18–20], migration characteristics [21], feeding behavior [22], and resource status characteristics[23]. Studies showed [21], feeding that U. behaviorchinensis did [22 ],not and migrate resource on a status large [scale,23]. Studies but moved showed in short that U.distances chinensis accordingdid not migrate to local on water a large temperatur scale, bute. moved It moved in shortnorthward distances from according the SCS toto localthe waterTaiwan temperature. bank and other It moved places, northward with the fromincrease the SCSof water to the temperature Taiwan bank every and spring, other places, and withmoved the southward increase of to water the SCS, temperature looking for every suitable spring, conditions and moved during southward winter [21,24]. to the These SCS, lookingstudies forhelped suitable to understand conditions more during about winter the [mi21,gration24]. These characters studies of helped the species, to understand but its morespatiotemporal about the distribution migration characters and quantitative of the relationship species, but with its spatiotemporal marine environment distribution in the andPearl quantitative River Estuary relationship (PRE) remains with marineunclear. environmentIt is still necessary in the to Pearl understand River Estuary the impact (PRE) remainsof environmental unclear. It variability is still necessary on U. chinensis to understand abundance the impact in the PRE. of environmental variability on U.Due chinensis to theabundance monsoon, inthe the PRE PRE. has two major currents: the Guangdong Coastal Cur- rent Due(GCC) to and the monsoon,the South theChina PRE Sea has Warm two majorCurrent currents: (SCSWC), the and Guangdong two coastal Coastal upwellings Current (GCC)(in the andeastern the SouthHainan China Island Sea and Warm the western Current Guangdong (SCSWC), andwaters) two [25–27]. coastal upwellingsIn addition, (ina thelarge eastern amount Hainan of fresh Island water and is thedischarged western from Guangdong the PRE waters) every year, [25– 27mixing]. In addition, with seawater a large amountto form ofthe fresh Pearl water River isplume discharged [28], which from radi theates PRE to every the coastal year, mixing waters with[29]. seawaterBecause of to formthese the dynamic Pearl Rivercharacteristics, plume [28 the], which PRE boasts radiates a high to the primary coastal productivity waters [29]. [22,30], Because making of these dynamicit one of the characteristics, major fishing the grounds PRE boasts for U. achinensis high primary in the world productivity [31]. Therefore, [22,30], the making status it oneof U. of chinensis the major stock fishing in the grounds PRE is of for greatU. chinensis researchin value. the world This study [31]. is Therefore, based on thesix years status ofof U.survey chinensis and satellitestock in remote the PRE sensing is of greatdata and research looks value.into the This relationship study is between based on the six yearsspatiotemporal of survey anddistribution satellite remoteof the U. sensing chinensis data and and some looks environmental into the relationship factors by between con- theducting spatiotemporal a quantitative distribution analysis. The of thepossibleU. chinensis mechanismand somedriving environmental the spatiotemporal factors dis- by conductingtribution of aU. quantitative chinensis in the analysis. PRE is Thealso possiblediscussed. mechanism Results of drivingthis research the spatiotemporal are helpful to distributionunderstand oftheU. migration chinensis characteristics,in the PRE is also to pr discussed.edict the Resultscenter of of the this fishing research ground, are helpful and toto understandprotect the key the migrationhabitat of the characteristics, species. to predict the center of the fishing ground, and to protect the key habitat of the species. 2. Materials and Methods 2. Materials and Methods 2.1.2.1. FisheryFishery Data TheThe researchresearch areaarea considered in this rese researcharch is located between between 19.15–22.15° 19.15–22.15◦ NN and and 111.12–115.37111.12–115.37°◦ E E (as (as shown shown in in Figure Figure1). 1). The TheU. U. chinensis chinensisdata data in thein the PRE PRE was was obtained obtained from thefrom monitoring the monitoring records records of light of falling-netlight falling-net fishing fishing vessels vessels from from August August 2005 2005 to May to May 2010, and2010, the and fishery the fishery data was data collected was collected at a spatial at a spatial resolution resolution of 0.25 of◦ 0.25°× 0.25 × 0.25°◦ and and summarized summa- inrized days. in Thedays. dataset The dataset is composed is composed by 359 by catch 359 datacatch in data total in andtotal 20 and zero 20 catch zero catch observations. obser- Thevations. statistics The statistics covered covered longitude longitude and latitude, and latitude, operating operating date, voyage, date, voyage, catch species catch spe- and catchescies and (Table catches1). Since(Table 1999, 1). Since China 1999, has suspendedChina has suspended fishing activities fishing in activities the SCS fromin the 1 JuneSCS tofrom 31 July. 1 June In 2009,to 31 theJuly. closed In 2009, fishing the seasonclosed wasfishing extended season from was Mayextended 16 to Augustfrom May 1. During 16 to theseAugust periods, 1. During ships these are periods, prohibited ships from are fishingprohibited in the from SCS fishing [32,33 in] andthe SCS this is[32,33] why and data fromthis is June why to data July from are missing June to July in this are study. missing in this study.

Figure 1. Research area and ﬁshing grid center. Figure 1. Research area and fishing grid center.

J. Mar. Sci. Eng. 2021, 9, 131 3 of 15

Table 1. Fishing date, voyage, number of nets, number of stations and number of catch species.

Year Month Voyage Number of Nets Number of Stations Number of Catch Species 8 A1, A2, A3, A4, A5 25 10 6 2005 9 A5, A6, A7, A8 14 6 3 8 B1, B2, B3, B4, B5 25 13 6 2006 9 B5, B6 8 4 4 1 B14, B15 11 4 4 2 B16 14 7 6 3 B18 3 3 3 2007 8 C1, C2, C3, C4 23 10 6 9 C4, C5, C6 21 8 6 12 C11 9 4 4 1 C12 11 3 5 2 C14 1 1 3 3 C14 1 1 2 2008 8 D1, D2, D4, D5 18 7 6 9 D5 7 3 6 11 D13 1 1 1 12 D13, D14, D15 17 9 6 1 D16, D17, D18 14 7 7 2 D18, D19, D20 20 4 8 3 D20, D21, D23 9 8 5 5 D23 4 4 4 2009 8 E1, E2 21 6 6 9 E4, E5, E6 14 8 6 11 E11, E12 16 7 6 12 E13, E14 16 8 6 1 E15 16 6 5 2010 2 E17, E18, E19 14 7 4 3 E20 6 2 5 Note: Number of catch species did not include unidentiﬁed species.

2.2. Satellite Remote Sensing Dataremote Sensing Data Satellite remote sensing data includes sea surface temperature (SST), sea surface salinity (SSS), sea surface chlorophyll a concentration (Chl a), current, and net primary productivity (NPP). Among them, SST was obtained from the MODIS Aqua Level 3 data products (https://oceancolor.gsfc. nasa.gov/), with temporal resolution set at 1 d and spatial resolution set at 4 km. The SSS and current data could be collected with the help of Global Ocean Physical Reanalysis Product of the Copernicus Marine Environment Management Service (CMEMS, http://marine.copernicus.eu/), with the same temporal resolution and spatial resolution of 0.083◦ × 0.083◦. The NPP and Chl a were obtained from the CMEMS (http://marine.copernicus.eu/), with a spatial resolution of 0.25◦ × 0.25◦ and also under the same temporal conditions. The software R v.4.0.0 (R Development Core Team, 2020) was used to spatially fuse and match SST, SSS, Chl a, and fishery data, and ArcGIS 10.3 (Esri, Redlands, CA, USA) was used to plot the distribution of the flow field and NPP. Data fusion for the remote sensing data with different resolutions could be derived through the following algorithm [34]:

m ∑i=1 value(i)j Ave = (1) j m

where, Avej is the average value of each environmental factor in the research area after data fusion, with a resolution of 0.25◦ × 0.25◦; m is the number of pixels of each environmental factor in the area with a resolution of 0.25◦ × 0.25◦; the value(i) is the unit pixel value in J. Mar. Sci. Eng. 2021, 9, 131 4 of 15

the study area, and j represents the ﬁshing area, all with the same aforementioned spatial resolution of 0.25◦ × 0.25◦.

2.3. Catch Per Unit Effort The ﬁsh stock is expressed by catch per unit effort (CPUE), calculated with:

∑ Catch CPUE = (2) ∑ Fishing_days

where, ∑ Catch (units: tons [t]) is the sum of catches within a fishing grid of 0.25◦ × 0.25◦. ∑ Fishing_days is the sum of fishing days for the fishing vessel in the fishing grid, and the unit of CPUE is marked as t day−1. Day was chosen as the time step in grouping CPUE for each fishing grid.

2.4. Clustering Fishing Tactics Clustering Fishing Tactics (CFT) is a method widely used to identify fishing tactics from commercial data by capturing component records through clustering tech- niques [35–37]. A data matrix containing CPUE records of each species was constructed in the research area, and then the data was normalized to the relative proportion according to weight to eliminate the impact of catch rate. Next, the square root transformation of the standardized matrix was carried out so that the species with lower dominance could make similar contributions to the catch composition. After the initial steps, a principal component analysis (PCA) was applied to the multidimensional data matrix. It is worth noting that most of the changes in the data were explained by the first few axes of the PCA-converted data. Finally, all PCA axes were reserved for clustering analysis [36,38,39]. In this study, CFT identified the fishing tactics adopted by fishermen, so as to evaluate the impact of their behavior and other human factors in fishing activities on the stock of U. chinensis. R 4.0.0 (R Development Core Team, 2020) was used to conduct PCA and cluster analysis on fishery data, and the fishing tactic (FT) result taken as one of the explanation factors of GAM [38]. The fishing strategy was converted into a numerical variable for processing in order to calculate its impact on the change of CPUE.

2.5. GAM Analysis A Generalized Additive Model (GAM) is a nonparametric extension of the generalized linear model [40]. In fishery, GAM is widely used in quantitative analysis of the relationship between fishery resources and environmental factors, which is a non-linear regression [13,41,42]. The distribution of Tweedie was determined in 1984 [43], and it is suitable to describe nonnegative and biased random variables. The process includes several common important distributions (p = 0 normal distribution, p = 1 Poisson distribution, 1 < p < 2 Poisson gamma composite distribution, p = 2 gamma distribution, p = 3 inverse Gaussian distribution) [44]. If 1 < p < 2, it is considered that Tweedie distribution is suitable for analyzing CPUE [35,45]. This specific method is a special probability distribution part of the exponential distribution family, generally expressed by TwP(θ, ϕ) and completely determined by the variance function V(µ) = µP, where θ is a gauge parameter and ϕ is a decentralized parameter [43,44]. Since Tweedie distribution still belongs to the exponential distribution family, the statistical relationship model between the corresponding variables and the influencing factors can be established within the framework of the generalized linear model [46]. To avoid the zero-catch problem of CPUE, the GAM was applied together with the Tweedie distribution to analyze the environmental influence on the U. chinensis stock [45]. The GAM basic model constructed in this study is: ( CPUE ∼ TWp(θ, ϕ) (3) ln(µCPUE) = s(Month, SST) + s(Month, Depth) + s(Chl a) + s(SSS) + s(FT) + s(year) + ε

where, µCPUE is the mean of Tweedie distribution of CPUE; s is the function of smoothing splines; s(Month, SST) represents the interaction effect of month and SST; J. Mar. Sci. Eng. 2021, 9, 131 5 of 15

s(Month, Depth) is the interaction effect of month and water depth. Furthermore, s(Chl a) stands for the effect of Chl a; s(SSS) is the effect of SSS; s(FT) is the effect of fishing tactic; s(year) is the effect of year. Finally, ε represents the model error. The GAM was constructed and tested by the ‘mgcv’ package of software R v.4.0.0 [47]. A forward stepwise method was used to select variables that have significant influence on the model, so the specific expression of GAM could be determined.

3. Results 3.1. Analysis of Fishing Tactics On the basis of the cluster analysis, the catch matrix was identified in five groups, assigned to five different fishing tactics. Among them, FT1 and FT3 mainly included offshore pelagic economic fishes. FT1 was represented by Decapterus (77.08%) and FT3 represented by U. chinensis (62.02%). FT2 mainly included middle and demersal fishes, represented by Trichiurus haumela (75.57%). Furthermore, FT4 mainly considers unidentified species (68.44%), and had a high catch of U. chinensis (30.98%), without a bycatch of Decapterus and Scombridae. Last, the majority of FT5 included oceanodromous fishes, represented by Scombridae (49.04%), mainly composed of Katsuwonus pelamis (Table2).

Table 2. Major catch species and proportion under ﬁve ﬁshing tactics (FTs).

Species FT1 FT2 FT3 FT4 FT5 U. chinensis 9.00 13.37 62.02 * 30.98 13.77 Scombridae 1.88 1.40 2.52 0.00 49.04 * Decapterus 77.08 * 8.43 18.22 0.00 8.08 Trichiurus haumela 2.39 75.57 * 9.74 0.58 10.18 Rastrelliger kanagurta 5.86 0.00 0.63 0.00 2.17 Formio niger 0.32 0.38 0.78 0.00 11.04 Sparidae 1.69 0.00 1.32 0.00 1.08 Navodon 1.38 0.06 1.83 0.00 3.48 unidentiﬁed species 0.39 0.79 2.93 68.44 * 1.17 * indicates species with the highest weight proportion in each net.

3.2. GAM Analysis The cumulative bias explanatory rate of spatiotemporal and environmental factors on U. chinensis CPUE was obtained by using the GAM to fit and predict the effect of adding variables to the model (Table2). The explanatory variables selected for the model included the interaction effect of month and sea surface temperature (Month, SST), the interaction effect of month and water depth (Month, Depth), chlorophyll a concentration (Chl a), sea surface salinity (SSS), fishing tactic (FT), and year (Year). The cumulative bias explanatory rate of these factors for U. chinensis CPUE was 63.1%, and the correlation coefficient R2 was 0.51 (Table3). In GAMs, the contribution of selected factors to CPUE represents the influencing degree of each factor on U. chinensis CPUE (Table4). Among them, the interaction effect of month and SST was the most influencing factor, with a contribution of 26.7%; followed by the interaction effects of month and depth, FT, SSS, Chl a, and year, with contribution of 12.8%, 8.5%, 7.7%, 4.0%, and 3.1%, respectively. The Chi-square and F test showed that the selected explanatory variables were significantly correlated with CPUE (p < 0.05; Table4). The interaction effect of month and SST based on GAM showed that from December to February in the waters with SST of 21–22 ◦C and from July to August in the waters with SST of 28–30 ◦C, month and SST had a positive effect on U. chinensis CPUE, which showed an upward trend in this range (Figure2a). The result of the interaction effect of month and depth indicates that the positive effect of month and water depth on U. chinensis CPUE was the highest in the area of 40–60 m from January to June, when U. chinensis CPUE increased (Figure2b). In other months (from July to December), no obvious tendency towards depth was shown. The species was evenly distributed in the water depth of 40–130 m. J. Mar. Sci. Eng. 2021, 9 7 of 16

In GAMs, the contribution of selected factors to CPUE represents the influencing degree of each factor on U. chinensis CPUE (Table 4). Among them, the interaction effect of month and SST was the most influencing factor, with a contribution of 26.7%; followed by the interaction effects of month and depth, FT, SSS, Chl a, and year, with contribution of 12.8%, 8.5%, 7.7%, 4.0%, and 3.1%, respectively. The Chi-square and F test showed that the selected explanatory variables were significantly correlated with CPUE (p < 0.05; Table 4).

Table 4. Contribution and significance test in GAMs. J. Mar. Sci. Eng. 2021, 9, 131 6 of 15 Contribution Variables d.f. Pr(F) Pr(chi) (%)

Table 3. Deviance analysis for thes(Month, general additive SST) models (GAMs) ﬁtted 26.7 to U. chinensis 27.9Catch Per Unit< 0.001 Effort (CPUE). < 0.001

s(Month, Depth) 12.8 18.7 Deviance < 0.001 Residual < 0.001 Inﬂuencing Factors (p = 1.7) AIC GCV Adjusted R2 Explained (%) Deviance CPUE~NULL s(FT) 5122.37 8.563.02 0 2.5 < 0.00.001 22,498.85 < 0.001 CPUE~s(Month, SST) 4926.65 53.46 0.14 26.7 16,500.28 CPUE~s(Month, SST) + s(Chls(SSS) a) 4896.35 7.752.05 0.17 7.5 < 30.7 0.001 15,581.08 < 0.001 CPUE~s(Month, SST) + s(Chl a) + s(SSS) 4831.30 48.19 0.23 38.4 13,864.01 CPUE~s(Month, SST) + s(Chl a) + s(SSS) +s(Chl s(Month, a) Depth) 4715.83 4.041.56 0.37 6.3 51.60.015 10,885.54 0.013 CPUE~s(Month, SST) + s(Chl a) + s(SSS) + s(Month, Depth) + s(FT) 4616.75 34.62 0.48 60.1 8969.72 CPUE~s(Month, SST) + s(Chl a)+s(SSS)+s(Month,s(Year) Depth) + s(FT) + s(Year) 4587.61 3.133.02 0.51 5.0 < 63.1 0.001 8295.15< 0.001

TableThe 4. Contribution interaction and effect signiﬁcance of month test and in GAMs.SST based on GAM showed that from December to February in the waters with SST of 21–22 °C and from July to August in the waters with SST ofVariables 28–30 °C, month Contribution and SST had (%) a positive d.f.effect on U. chinensis Pr(F) CPUE, which Pr(chi) showed an upwards(Month, trend SST) in this range 26.7 (Figure 2a). The result 27.9 of the interaction < 0.001 effect of < month 0.001 and depths(Month, indicates Depth) that the positive 12.8 effect of month 18.7 and water depth < 0.001 on U. chinensis < 0.001 CPUE s(FT) 8.5 2.5 < 0.001 < 0.001 was the highest in the area of 40–60 m from January to June, when U. chinensis CPUE s(SSS) 7.7 7.5 < 0.001 < 0.001 increaseds(Chl (Figure a) 2b). In other 4.0 months (from July 6.3 to December), 0.015 no obvious tendency 0.013 to- wardss(Year) depth was shown. The 3.1 species was evenly 5.0 distributed in < 0.001the water depth < 0.001 of 40–130 m.

Figure 2. a Figure 2. InteractionInteraction effects effects based based on on general general additive additive model model (GAM): (GAM): (a) (interaction) interaction effect effect of month of month and andsea surface sea surface tem- temperatureperature (SST); (SST); (b) (interactionb) interaction effect effect of month of month and and depth. depth. The inﬂuence of such factors as FT, SSS, Chl a, and year on the U. chinensis CPUE The influence of such factors as FT, SSS, Chl a, and year on the U. chinensis CPUE was was analyzed on the basis of GAMs (Figure3). Results showed that in FT1–FT3, the analyzed on the basis of GAMs (Figure 3). Results showed that in FT1–FT3, the U. chinensis U. chinensis CPUE increased with the increase of FT; in FT3-FT5, the U. chinensis CPUE CPUE increased with the increase of FT; in FT3-FT5, the U. chinensis CPUE decreased with decreased with the increase of FT. FT3 had a positive effect on U. chinensis CPUE (Figure3a ). the increase of FT. FT3 had a positive effect on U. chinensis CPUE (Figure 3a). When the When the SSS was in 30.0–33.0‰, the U. chinensis CPUE increased together with the, SSS SSS was in 30.0–33.0‰, the U. chinensis CPUE increased together with the, SSS but when but when the SSS was in 33–34‰, it got diminished with the increase of SSS. It was the SSS was in 33–34‰, it got diminished with the increase of SSS. It was also observed also observed that SSS in 33.0‰ had a positive effect on U. chinensis CPUE (Figure3b ). When Chl a was 0–0.5 mg × m−3, the U. chinensis CPUE decreased as the levels of Chl a enhanced. Differently, when Chl a was 0.5–0.8 × mg m−3, the U. chinensis CPUE increased in accordance with Chl a. Another possibility that was observed involved Chl a in the range of 0.8–2 mg × m−3, and the U. chinensis CPUE decreasing with it. Furthermore, Chl a of 0–0.2 mg × m−3 had a positive effect on U. chinensis CPUE (as shown in Figure3c). From 2005 to 2008, the U. chinensis CPUE decreased as the years passed. Oppositely, from 2008 to 2010, the U. chinensis CPUE increased with the year. Being more speciﬁc, the year of 2008 had a negative effect on U. chinensis CPUE (Figure3d). J. Mar. Sci. Eng. 2021, 9 8 of 16

that SSS in 33.0‰ had a positive effect on U. chinensis CPUE (Figure 3b). When Chl a was 0–0.5 mg × m−3, the U. chinensis CPUE decreased as the levels of Chl a enhanced. Differ- ently, when Chl a was 0.5–0.8 × mg m−3, the U. chinensis CPUE increased in accordance with Chl a. Another possibility that was observed involved Chl a in the range of 0.8–2 mg × m−3, and the U. chinensis CPUE decreasing with it. Furthermore, Chl a of 0–0.2 mg × m−3 had a positive effect on U. chinensis CPUE (as shown in Figure 3c). From 2005 to 2008, the U. chinensis CPUE decreased as the years passed. Oppositely, from 2008 to 2010, the U. J. Mar. Sci. Eng. 2021, 9, 131 chinensis CPUE increased with the year. Being more specific, the year of7 of 2008 15 had a negative effect on U. chinensis CPUE (Figure 3d).

Figure 3. GAM analysis of influencing factors on the U. chinensis Catch Per Unit Effort (CPUE): (a) Figurefishing tactic 3. GAM (FT); (analysisb) sea surface of influencing salinity (SSS); factors (c) chlorophyll on the aU. concentration chinensis Catch (Chl a); Per and Unit (d) year Effort (CPUE): (a) fishing(Year). Shadow tactic areas,(FT); 95%(b) sea confidence surface intervals. salinity Rug (SSS); plots ( onc) chlorophyll the x-axis, the dataa concentration density dotted (Chl line a); and (d) yearindicates (Year). a reference Shadow line areas, for zero 95% values. confidence intervals. Rug plots on the x-axis, the data density dotted line indicates a reference line for zero values. 3.3. Seasonal Variation of U. chinensis 3.3. SeasonalIn summer Variation (from August of Uroteuthis to September ChinensisU. in that zone), chinensis along the outside of the SCSWC, the SST was higher and Chl a was lower. Furthermore, SSS in the area were distributed in a ladderIn shapesummer along (from the PRE August to the SCSto September (Figure4e,g,i). in The that fishing zone), grounds along of theU. outside chinensis of the SCSWC, ◦ thein the SST PRE was were higher mainly and distributed Chl a inwas the lower. sea area Furthe at a longitudermore, of 111–114SSS in theE and area latitude were distributed in ◦ × −1 aof ladder 19.5–20.5 shapeN. High along CPUEs the (1001–2000PRE to the kg SCSday (Figure) were 4e,g,i). majorly The spread fishing in the grounds sea area of U. chinensis with a velocity of about 0.3 m × s−1, NPP of 3–5 mg × m−3·day−1, SST of 28.8–29.0 ◦C, inSSS the of 33.5–33.6‰,PRE were mainly and Chl adistributed of 0–0.1 mg ×inm the−3 (Figuresea area4a,c,e,g,i). at a longitude of 111–114° E and latitude of 19.5–20.5°In winter (from N. High December CPUEs to February), (1001–2000 SSS kg gradually × day− decreased1) were majorly from east spread to west in the sea area withalong thea velocity GCC, together of about with 0.3 Chl m a that× s− also1, NPP got slowlyof 3–5 reduced mg × m along−3·day the−1 coast, SST of of Guang- 28.8–29.0 °C, SSS of dong to the open sea. Major U. chinensis concentrated in the area outside the GCC with 33.5–33.6‰, and Chl a of 0–0.1 mg × m−3 (Figures 4a,c,e,g,i). higher SST and lower Chl a (Figure4f,h,j). The fishing grounds of U. chinensis in the PRE wereIn winter mainly (from distributed December in the sea to area February), at a longitude SSS gradually of 111–115.5 decreased◦E and latitude from east to west alongof 19–22 the◦N. GCC, High together CPUEs (501–1000 with Chl kg a× thatday −also1) were got allocatedslowly reduced in the sea along area with the acoast of Guang- − − − dongcurrent to velocity the open of 0.2 msea.× sMajor1, NPP U. of 17–19chinensis mg × concentratedm 3·day 1, SST in of the 21.0–23.0 area °Coutside, SSS of the GCC with × −3 higher34.0–34.1‰, SST and and Chl lower a of 0.4–0.6 Chl a mg (Figurem 4f,h,j).(Figure 4Theb,d,f,h,j). fishing grounds of U. chinensis in the PRE were mainly distributed in the sea area at a longitude of 111–115.5 °E and latitude of 19- 22 °N. High CPUEs (501–1000 kg × day-1) were allocated in the sea area with a current velocity of 0.2 m × s-1, NPP of 17–19 mg × m-3·day-1, SST of 21.0–23.0 ℃, SSS of 34.0–34.1‰, and Chl a of 0.4–0.6 mg × m-3 (Figures 4b,d,f,h,j).

J.J. Mar.Mar. Sci.Sci. Eng.Eng.2021 2021,,9 9, 131 98 of 1516

FigureFigure 4.4. SpatiotemporalSpatiotemporal distribution distribution of of the theU. U. chinensis chinensisCPUE, CPUE, current current and and net net primary primary productivity productivity (NPP): (NPP): (a) ( flowa) flow field field in summer;in summer; (b) ( flowb) flow field field in winter;in winter; (c) ( NPPc) NPP in in summer; summer; (d ()d NPP) NPP in in winter; winter; (e ()e SST) SST in in summer; summer; ( f()f) SST SST in in winter; winter; ( g(g)) SSS SSS inin summer;summer; ((hh)) SSSSSS inin winter;winter; ((ii)) chlorophyllchlorophyll aa concentrationconcentration (Chl(Chl a)a) inin summer;summer; ((jj)) ChlChl aa inin winterwinter (the(the blackblack boxbox representsrepresents thethe highhigh CPUECPUE area).area).

4.4. Discussion 4.1.4.1. Relationship betweenbetween thethe U.U. chinensischinensis andand MarineMarine EnvironmentEnvironment TheThe factfact thatthat waterwater temperaturetemperature affectedaffected eacheach stagestage ofof thethe lifelife historyhistory ofof squidsquid fromfrom embryos,embryos, juveniles to to adults adults [7,14,48], [7,14,48], was was on onee of ofthe the important important factors factors indicating indicating its spa- its spatiotemporaltiotemporal distribution distribution [12,41]. [12,41 It]. Itwas was verified veriﬁed that that temperature temperature variation variation could could signifi- signiﬁ- cantly change the growth and development of the reproductive systems of squid [49,50]. cantly change the growth and development of the reproductive systems of squid [49,50].

J. Mar. Sci. Eng. 2021, 9, 131 9 of 15

Warmer temperatures may lead to the rapid development of embryos and juveniles, shorten their hatching period [48], and thus affect the recruitment of the squid population [7]. In addition, the water temperature affected fish stock in some specific months [50,51]. There- fore, the interaction effect of month and temperature was considered for GAM fitting. Results showed that the interaction effect of SST and month had the greatest impact on the U. chinensis CPUE (with a contribution of 26.7%; Table4). SST played a positive role on the U. chinensis CPUE in summer (from August to September; Figure3a). This happened partly because warm water speeded up the embryonic development and the growth process of juvenile squid [14]. In addition, due to the short life span of the squid, its abundance directly depended on the recruitment capacity of the current year [52]. The CPUE increased if the number of individuals hatched in spring and summer—under the appropriate water temperature from August to September. In the East China Sea, a higher SST in summer, es- pecially at 29 ◦C, could improve the growth rate and reproductive capacity of U. edulis [50]. In winter (from December to February), SST showed a positive effect on the U. chinensis CPUE (Figure2a). This might be related to the thermophilic-feeding migration of the species, hatched in the autumn, considered to be the spawning season. In the fishing grounds in the south of the Taiwan Strait, the U. chinensis migrated to warmer areas for feeding every winter [53]. According to its age analysis caught by fishing vessels [2,54], generally, U. chinensis will become the target size of the fishing vessel and be captured when they grow to be 2–5 months old. Therefore, from December to February, SST 21–23 ◦C can be one of the indicators for the distribution of U. chinensis in the PRE. It was also observed that the levels of salinity affected the physiological activities of marine organisms by changing their osmotic pressure [55]. The GAM analysis showed that SSS contributes 7.7% to the U. chinensis stock (Table4). The species was mainly distributed in the waters with a SSS of 32.5–34‰ (Figure3b). The narrow suitable salinity range for U. chinensis is mainly a result of the poor osmolality regulation mechanism of most cephalopods, since it is a stenohaline species and tends to prefer medium or high salinity environment [31]. Moreover, the salt levels also affected the survival rate of squid embryos, consequently affecting the squid stock as well [56]. When the salinity was lower than 32‰ or higher than 38‰, the survival rate of squid embryos was greatly reduced. It is also noteworthy that large fluctuations in salinity may cause the eggs to die [57]. When it comes to the chlorophyll a concentration, it reflected the phytoplankton in stock in the sea area. As the major feeding source of zooplankton and some marine organisms, phytoplankton is an important part of marine primary productivity and reflects the primary productivity level of the sea area [42,58]. The GAM analysis indicated that the contribution of Chl a to the U. chinensis stock was 4.0% (Table4). The trophic level of the U. chinensis was 2.7–3.6 [18], as it mainly fed on fish and cephalopods [59]. In the PRE, U. chinensis tended to live in waters with low Chl a (Figure3c), and the U. chinensis CPUE decreased, while the Chl a did the opposite and kept increasing. This might be related to the hysteresis in the response of the species to chlorophyll a [12]. In addition, the U. chinensis vision was affected by the transparency of sea water. In the southeast waters of Brazil, the abundance of U. chinensis was negatively correlated with the chlorophyll a concentration, as the sea area with a low concentration of the pigment had a high transparency, causing U. chinensis to be easier to be attracted by baits. The consequence of all of this was an increase in the catch [8].

4.2. Relationship between U. chinensis and CFT The GAM analysis also showed that the contribution of fishing tactics to the U. chinensis CPUE was 8.5% (Table4). Considering that the northern SCS is a typical sea area with multi-species fishes, and with a variety of resource species and complex composition [60,61], fishermen might target species on the basis of economic benefits and the condition of the sea they are acting on. The major species caught in the PRE were U. chinensis (FT3), Euthynnus alletteratus (FT5), Decapterus (FT1), and hairtail (FT2). The examination of these five clustering fishing tactics showed that four clustering fishing tactics (FT1, J. Mar. Sci. Eng. 2021, 9, 131 10 of 15

FT2, FT3, FT5) were aimed at a single species (Table2). This might be due to the spatial segregation of habitats of major commercial species in the PRE. The spatial segregation of habitat usually led to a negative correlation between the CPUE of the bycatch species and target species [62]. Studies on habitat utilization and feeding behavior of four different species of squid (U. duvaucelii, U. edulis, U. chinensis and Loliolus uyii) in the northern SCS showed that although the feeding strategy was similar, their habitats did not overlap. This spatial segregation could reduce their competition for resources and buffer their trophic interactions, thus increasing the possibility of the species coexisting in the region [22].

4.3. Spatiotemporal Variation of U. chinensis Water depth also affected the spawning activity of squid by changing the level of dissolved oxygen in water [63]. Here, it is important to consider that there is an appropriate season when squid migrate to shallow waters for spawning [31,41,64]. The GAM analysis showed that the interaction effect of month and depth contributes 12.8% to the U. chinensis CPUE (Table4) and that the species was widely distributed in the water depth of 40–140 m. From January to March, in waters that are 40–60 m deep, water depth played a positive role on the U. chinensis CPUE (Figure2b). This phenomenon might be related to the mass spawning behavior of the U. chinensis. In the coastal waters of Guangdong Province, the period between February to May was the peak spawning period of the U. chinensis as they migrate to the shallow sea to spawn [31]. This seasonal spawning activity led to a regional fishing season [53]. U. chinensis can spawn in any season, but the peak happens when there is a suitable and appropriate environment for the process to occur [2,65]. In the PRE, the SST during January and March ranged from 20–24 ◦C, meeting the spawning requirements of the species [31]. This also resulted in an increase of its CPUE in waters from 40–60 m depth and within the period from January to March (Figure2a,b). From August to November, the water in the PRE became warmer, boosting the metabolism of the U. chinensis, and driving their demand for food, which led to increased feeding activity. Moreover, warmer temperatures provided a more suitable condition for the early lives of U. chinensis [14,48], partly leading to a more even distribution of the animals along the study area (Figure2b). The GAM analysis also provided that the contribution of interannual variation to the fluctuation of U. chinensis CPUE was 3.1%. The CPUE decreased significantly from 2007 to 2008 (Figure3d). It was also observed that large-scale climate variability, such as the North Atlantic Oscillation, affects the abundance of a myriad squid species (e.g., Illex illecebrosus, Loligo pealeii)[66]. For instance, the yield of Loligo Opalesens decreased after the El Nino-Southern Oscillation event occurred in the Southern California Bay [67]. La Niña also affected the recruitment of squid by changing the environment of spawning grounds, see also [16]. In Antarctica, squid got more abundant in the sea area, due to a change in the water temperature caused by La Niña [68]. In the offshore area of the sea area, this resulted in less squid abundance [69]. From August 2007 to April 2008, a La Niña event [70] was formed, which led to the decrease of SST in the SCS. Furthermore, the center of the Symlectoteuthis oualaniensis fishing ground in the Xisha-Zhongsha waters shifted to the south by about 2◦ N[13]. Furthermore, La Niña also made rain happen more often and also affected the wind speed in the region [70], as well as the wind speed that strengthened the wave field in the area in terms of cycle and frequency. This new characteristic of the wave field disturbed the near shore spawning grounds, deviating mature fish swarm from these areas, imposing a negative impact on the fish stock [71]. Therefore, it can be said that the decrease of U. chinensis stock in 2008 may be connected with La Niña.

4.4. Analysis of Seasonal Variation of U. chinensis Current and wind fields are also considered important factors in the life span of cephalopods [72] that affect the variation of water temperature and salinity [73,74], and may further change the composition, structure, and thermophilic characteristics of fish communities [75]. The fronts and vortices formed by the flow field served as key spawning J. Mar. Sci. Eng. 2021, 9, 131 11 of 15

habitats for pelagic fishes [76]. Fishing grounds with abundant resources can be formed around the large-scale current, such as the Illex argentinus in the southwest Atlantic Ocean, the Ommastrephes bartramii in the North Pacific Ocean, and the Todarodes pacificus in the waters around Japan, all of them distributed in the western boundary current [1,77]. The current velocity was an important factor for the success of fish migration and feeding [78]. Studies showed that in summer, driven by the southwest monsoon [79], SCSWC was stronger in the PRE. In the southern boundary of the current where the velocity was about 0.3 m × s−1, a number of areas with high U. chinensis CPUE were formed (Figure4a). In winter, under the control of the northeast monsoon [27], the southwesterly Guangdong Coast Current (GCC) started to form. At the southern boundary of the current where the velocity is 0.2 m × s−1, several areas with high U. chinensis CPUE were identified. In the PRE, the current velocity in different seasons can be considered one of the indicators to predict the distribution of U. chinensis fishing grounds. As one of most important indicators of the nutritional potential of the basic links in the marine food chain, primary productivity plays an important role in marine ecology [80]. Its size determines the potential yield of marine fishery [81] and can be used to indicate the spatiotemporal variation of fish stock [10]. In the spawning season, high NPP suggests high plankton biomass, an important feature for the growth and recruitment of juvenile fish. In the catching season, the NPP acts like the comprehensive index of biological concentration of squid bait, and the interaction between them can directly affect the annual stock level of squid [82]. A significant correlation between the interannual stock variation of Ommastraphes bartrami and the average NPP was observed in spawning and catching months [82]. Hence, high primary productivity areas caused by strong upwelling were generally considered as potential spawning grounds [83]. However, the relationship between primary productivity and squid was also affected by the cascade effect. The increase of primary productivity did not act on squid directly, and high levels of primary productivity can eventually lead to the increase of both prey and predator. On the western coast of California, the increase of primary productivity anomaly led to the decline of squid stock after three months [84]. In the PRE, the suitable NPP of U. chinensis differed depending on the month. In summer, the high resource was mainly distributed in the narrow zone with a NPP of about 3–5 mg × m−3day−1, while in winter it was mainly distributed in the water area with a NPP of 16–19 mg m−3day−1 (Figure4c,d). In summer, the PRE was majorly affected by the SCSWC and the Pearl River plume (PPP) (Figure4) and the environmental characteristics on both sides of the SCSWC were significantly different. During this period, the PRE was in the wet season and a great amount of fresh water from land discharged to the PRE [85]. Therefore, the distribution of SSS was mainly affected by the PPP. The U. chinensis was mainly distributed in the waters outside the SCSWC, located in the area where the diluted water from PRE and the high salinity water from SCS were mixed. In winter, the area was mainly influenced by GCC [85]. In this case, the species could be found in the area where high salinity and low salinity seawater mixed and its CPUE was lower than in summer (Figure4). Therefore, the relationship between the U. chinensis and the environment was similar in winter and summer, and it was adjusted by the changes of currents and marine environments. In the SCS, the high Sthenoteuthis oualaniensis CPUE was concentrated in the area with a high SST. With the seasonal warming, the same species shifted to lower latitudes [13], similar to the migration characteristics of the U. chinensis presented throughout this study. In addition, the U. chinensis CPUE in summer was higher than in winter, an observation that is consistent with the peak fishing season of squid in the Beibu Gulf [86]. However, in the Gulf of Cadiz, according to the bottom trawl survey, the abundance of Loligo vulgaris was the highest in autumn [52]. This difference may be related to the different fishing methods, latitude, and spawning periods, which need to be further explored in related future research. J. Mar. Sci. Eng. 2021, 9, 131 12 of 15

5. Conclusions This paper studied the effect of marine environmental factors on the spatiotemporal distribution of U. chinensis in the PRE on the basis of the long-term satellite remote sensing and survey data. It was observed that the interaction effect of SST and month was the most important environmental factor affecting the U. chinensis stock (accounting for 26.7% of the U. chinensis CPUE), followed by the interaction effect of depth and month (accounting for 12.8% of the U. chinensis CPUE). In the PRE, U. chinensis was mainly distributed in the sea area with an SST of 22–29 ◦C, SSS of 32.5–34‰, Chl a of 0–0.3 mg × m−3, and water depth of 40–140 m. It is important to clarify that, in this study, only data related to SST, SSS, Chl a, depth, NPP, and current available from satellite remote sensing were analyzed. Morphological characteristics such as body length, age, and parameters such as dissolved oxygen and transparency were not considered and represent possibilities for follow-up studies to improve the accuracy of the model, thus providing a scientiﬁc basis for protecting the habitat of the U. chinensis.

Author Contributions: L.Y. and D.W. designed the study. J.Y. and L.Y. collected the fishery data. D.W. analyzed the data. L.Y. and P.C. helped with data collection and analysis. J.Y. and D.W. wrote the article. All authors have read and agreed to the published version of the manuscript. Funding: This study was supported by the following funds: (1) National Key R&D Program of China (2018YFD0900901), (2) Natural Science Foundation of Guangdong Province, China (2018A030313120), (3) Central Public-Interest Scientific Institution Basal Research Fund, CAFS, China (2018HY-ZD0104). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy policy. Conflicts of Interest: The authors declare no conflict of interest.

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