Assessments of 12 Commercial Species Stocks in a Subtropical Upwelling Ecosystem Using the CMSY and BSM Methods

Abstract

Twelve commercial species exploited in the eastern Guangdong and southern Fujian waters were assessed using the Catch-Maximum Sustainable Yield (CMSY) and Bayesian Schaefer Model (BSM) methods. The carrying capacity (k), intrinsic rate of population growth (r), maximum sustainable yield (MSY), and relative biomass (B_end/k and B/B_MSY) were estimated. The current stock status was defined by B/B_MSY and fishing mortality (F/F_MSY). The results indicate that seven stocks were overfished or below safe biological limits (B/B_MSY < 0.5 or F/F_MSY > 1), two stocks were in a recovery phase (0.5 < B/B_MSY < 1, F/F_MSY < 1), and three stocks were under sustainable fishing pressure with healthy biomass, capable of producing yields close to the MSY (B/B_MSY > 1, F/F_MSY < 1). The stock statuses are consistent with previous studies on the utilization of pelagic fisheries in the eastern Guangdong and southern Fujian waters and with those assessments in other waters. The results of the assessments suggest that these stocks could be expected to produce higher sustainable catches if permitted to rebuild; thus, more effective and proactive management is needed in this upwelling fishing ground.

1. Introduction

All the exploited fish stocks should require management [1], but limited data and expertise prevent this in many countries. Thus, to assess data-poor stocks, several methods have been developed [2,3,4,5,6,7,8,9]. Among those, the Catch Maximum Sustainable Yield (CMSY) method, initially proposed by Martell and Froese [10] and then updated by Froese et al. [11], is considered a promising approach [12,13,14].

Using a time series of catches with a few ancillary data, the CMSY method not only can estimate the intrinsic rate of population increase r (i.e., its resilience), carrying capacity (assumed to be equal to unexploited stock size) k, and Maximum Sustainable Yield (MSY), but also can provide reasonable predictions of relative biomass and exploitation rate [11]. The CMSY and related Bayesian Schaefer Model (BSM) methods have been successfully applied to nearly 400 European stocks [15] and 1320 exploited marine fish and invertebrate stocks throughout in the world [16]. However, only a few studies [17,18,19] have been carried out using to use these methods to assess Chinese fisheries.

The eastern Guangdong and southern Fujian waters, located at the southern end of the Taiwan Strait, are important upwelling ecosystems and fishing grounds of China. Pelagic fishes are more productive, with an annual catch of about 180 × 10³ t–200 × 10³ t in these fishing grounds [20]. In the pelagic fisheries of these fishing grounds, scads, mackerels, and round sardinella (Sardinella aurita) account for more than 60% of the total catch [21], and largehead hairtail (Trichiurus lepturus) and mitre squid (Uroteuthis chinensis) are also recognized as dominant species [22,23]. Demersal fishes and squids in the fishing grounds were reported to be overexploited [24,25,26], while the whole pelagic fishes (assessed in the fisheries as a whole) were not overexploited by the year 2000 [25,27,28]. In fact, the status of pelagic fishes has not been assessed since 2000, and few stock assessments using fishery-generated time series data have been conducted in this fishing ground.

Here, the newly developed CMSY method and the related BSM method [11] are applied to assess the stock status of 12 main species in the eastern Guangdong and southern Fujian waters. In addition, key fishery reference points, such as r, k, and the MSY, of these species are provided, which can enhance the assessment of the health condition of these stocks in this upwelling fishing ground and contribute to the sustainable development of fishery resources.

2. Materials and Methods

2.1. Data Sources

The sources of the catch time series data and CPUE (catch per unit of effort) of the 12 species are given in

Table 1. Summary of data available for the 12 stocks in the eastern Guangdong and southern Fujian waters, China.

Scientific Name (Common Name)	Catch	Abundance Data
Sardinella aurita (Round sardinella)	1971–2007	CPUE (1971–2007)
Decapterus maruadsi (Japanese scad)	1971–2007	CPUE (1992–2007)
Etrumeus sadina (Red-eye round herring)	1971–2007	CPUE (1983–2007)
Megalaspis cordyla (Torpedo scad)	1980–1991	CPUE (1980–1991)
Decapterus russelli (Indian scad)	1971–2007	CPUE (1997–2007)
Trachurus japonicus (Japanese jack mackerel)	1971–2007	CPUE (1993–2002)
Priacanthus macracanthus (Red bigeye)	1978–1989	CPUE (1982–1989)
Trichiurus lepturus (Largehead hairtail)	1971–1989	CPUE (1971–1980)
Rastrelliger kanagurta (Indian mackerel)	1971–2007	CPUE (2000–2007)
Auxis thazard (Frigate tuna)	1971–2007	CPUE (1987–2007)
Mene maculata (Moonfish)	1980–1998	CPUE (1994–1998)
Uroteuthis chinensis (Mitre squid)	1971–1989	CPUE (1971–1989)

. All the catch and relative abundance (CPUE) data were collected from the Guangdong and Fujian Fisheries Statistical Yearbook [29] and local fishing logs from eastern Guangdong and southern Fujian (Figure 1). The 12 species selected are important commercial species, including top predators, pelagic fishes, demersal fishes, and squid, all in the eastern Guangdong and southern Fujian waters. The catch data covering more than 20 years were collected from mixed fisheries in the areas (Figure 2). The longer time series data can represent the total catch of the species in previous studies and capture potential ecological changes and trends. The catch per unit effort (CPUE) was calculated from the catch and fishing effort data.

2.2. The CMSY and BSM Method

The CMSY method uses a Monte Carlo approach to trace multiple biomass trajectories of the biomass of the stock, given the time series of catches and a number of constraints [11]. When abundance data such as CPUE (used in this study) or biomass are available at the same time, a BSM [30] is applied to estimate the intrinsic population growth rate (r) and the carrying capacity (k) [11].

The BSM and CMSY methods based on the surplus production model [31,32] follow Equation (1):

$$B_{t+1}=B_t+r\left(1-B_t/k\right)B_t-C_t$$

(1)

where B_t is the biomass in the year t, B_t+1 is the biomass in year t + 1, r (resilience; year⁻¹) is the intrinsic rate of the growth of a population [11,33,34], k is the carrying capacity of the population, and C_t is the catch in the year t (year⁻¹).

If the stock biomass falls below 0.25 k (depleted severely), recruitment will often be reduced; this is expressed by Equation (1) being slightly modified [11]:

$$B_{t+1}=B_t+\left(4\mathrm{r}{B}_t/k\right)\left(1-B_t/k\right)B_t-C_t|B_t/k<0.25$$

(2)

where 4rB_t/k guarantee a recruitment decline below half of the biomass capable of maximum sustainable yield

2.3. Prior Parameter

The prior of k is computed from the following equations:

$$k_{low}=max\left(C\right)/r_{high};k_{high}=4max\left(C\right)/r_{low}$$

(3)

$$k_{low}=2max\left(C\right)/r_{high};k_{high}=12max\left(C\right)/r_{low}$$

(4)

where r_low and r_high are the lower and higher bounds of the range of r-values that the CMSY method explores, k_low and k_high are the lower and higher limits of k, and max(C) is the maximum catch in the available data. The program in R studio which implements the CMSY method provides a routine to explore wide ranges of k and r, and Equations (3) and (4) were applied to stocks with a low and high prior biomass at the end of the time series, respectively [11].

When running the BSM method (i.e., including relative abundance data), the calculated standard deviation of r is described between 0.001 and 0.02 irf [11]:

$$irf=3/\left(r_{high}-r_{low}\right)$$

(5)

where irf is a factor to estimate r, and r_high and r_low are shown in

Table 2. Ranges suggested by FishBase (www.fishbase.org (accessed on 24 August 2019 by Froese and Pauly)) for population growth rate (year⁻¹).

Resilience (r)	Suggested Prior	Range Assumed for the Stocks in Table 1
High	0.6–1.5	S. aurita, D. maruadsi, M. maculata
Medium	0.2–0.8	E. sadina, M. cordyla, D. russelli, T. japonicus, P. macracanthus, T. lepturus, R. kanagurta, A. thazard, U. chinensis
Low	0.05–0.5	---

below.

Moreover, the BSM allows the q to be estimated, given a prior derived from stocks with high recent prior biomass; in this case, q is defined as follows:

$$q_{low}=0.25r_{pgm}CPU{E}_{mean}/C_{mean};q_{high}=0.5r_{high}/CPU{E}_{mean}$$

(6)

where q_low and q_high are the lower and higher catchability coefficient q; r_pgm is the geometric mean of r; and CPUE_mean and C_mean are the mean value of CPUE and catch of the last 5 to 10 years, respectively.

Two priors, i.e., B_start/k and B_end/k, are also required for the CMSY method. B_start/k and B_end/k represent the relative biomass during the first year (Equation (1)) and the end year of the available catch time series of a stock, respectively.

The suggested ranges of B_start/k for the 12 species studied in this contribution are given in

Table 3. Suggested fractions of relative biomass (B_start/k) for the period before catch data became available *.

Depletion	Suggested Prior	Range Assumed for the Stocks in Table 1
Very low	0.9–1	D. russelli **, T. japonicus **, A. thazard **, M. maculata **,
Low	0.4–0.8	S. aurita, D. maraudsi **, E. sadina, M. cordyla, R. kanagurta, U. chinensis **
Medium	0.2–0.6	P. macracanthus **, T. lepturus
Strong	0.01–0.4	---
Very strong	0.01–0.2	---

* See the Supplementary Materials of [11]. ** Set NA for these species when running the program.

. For some of the species, we selected a low depletion (B_start/k = 0.4–0.8), because, from the 1970s, an increase in fishing efforts occurred, and the technological sophistication of those efforts, such as the light purse seine, also increased, generating an exploitation rate of around 0.4–0.5 [21,35,36]. For T. lepturus, the prior was set at a medium range (B_start/k = 0.2–0.6) [37]. For the other species, for which no targeted research had been performed, we set B_start/k as NA (not available).

Table 4. Suggested ranges of the fraction of relative biomass (B_end/k) for the period prior to catches being available *.

Depletion	Suggestion	Assumed Level of Final Depletion for the Stocks in Table 1
Low	0.4–0.8	E. sadina, M. cordyla
Medium	0.2–0.6	S. aurita, D. maruadsi **, R. kanagurta
Strong	0.01–0.4	D. russelli **, T. japonicus **, P. macracanthus **, T. lepturus, M. maculata **
Very strong	0.01–0.2	A. thazard **, U. chinensis **

* See the Supplementary Materials of [11]. ** Set NA for these species when running the program.

presents suggested ranges of B_end/k. Given previous assessments, we chose a ‘strong’ depletion, (i.e., B_end/k = 0.01–0.4) at the end of the time series, for T. lepturus [37]. For the others species, we had sources suggesting ‘medium’ depletions (B_end/k =0.2–0.6): S. aurita and R. kanagurta [36]. For species such as E. sadina [38] and M. cordyla [36], we chose a ‘low’ depletion (i.e., B_end/k = 0.4–0.8) at the end of the time series. For the other species, we set B_start/k as NA.

When additional abundance information is available, the BSM method can also be used [11], and the viable biomass trajectories that are generated by it can be compared with the results derived from CMSY, even when the abundance information covers a shorter time period (Table 1). Also, the q is estimated as the mean for the years with CPUE data (Equation (6)).

The B/B_MSY (relative biomass) in the final year, which expresses the status of a stock, can be estimated by both CMSY and BSM, with the latter being more accurate. In addition, Kobe plots based on B/B_SMY and F/F_MSY (relative fishing mortality coefficients and the ratio of fishing mortality to maximum sustainable fishing mortality) were used to assess the stock status for the most recent year for all the stocks simultaneously.

3. Results

Altogether, 12 species in the eastern Guangdong and southern Fujian waters were analyzed by the CMSY and BSM methods. The estimates of r-k pairs for S. aurita are shown in Figure 3 as an example, while the results of other species are listed in

Table 5. Estimates of intrinsic population growth rate (r), carrying capacity (k, in 10³ t), maximum sustainable yield (MSY, in 10³ t/year), relative biomass (B_end/k, B/B_MSY), fishing mortality rate (F/F_MSY), and status using the Bayesian Schaefer Model (BSM) method for 12 exploited nekton stocks in the eastern Guangdong and southern Fujian waters, China.

Common Name	Scientific Names	r	K	MSY	Bend/k	B/BMSY	F/FMSY	Status
Round sardinella	Sardinella aurita	0.85	87.4	18.5	0.61	1.21	1.45	Overfishing
Japanese scad	Decapterus maruadsi	1.15	173	49.6	0.60	1.20	0.84	Healthy
Red-eye round herring	Etrumeus sadina	0.43	38.4	4.15	0.55	1.10	0.28	Healthy
Torpedo scad	Megalaspis cordyla	0.68	16	2.71	0.77	1.53	0.18	Healthy
Indian scad	Decapterus russelli	1.03	42.1	10.8	0.44	0.89	0.57	Recovering
Japanese jack mackerel	Trachurus japonicus	0.49	32.2	3.93	0.30	0.61	1.19	Overfished
Red bigeye	Priacanthus macracanthus	0.35	3.54	0.31	0.30	0.60	1.89	Overfished
Largehead hairtail	Trichiurus lepturus	0.87	0.66	0.14	0.35	0.69	0.73	Recovering
Indian mackerel	Rastrelliger kanagurta	0.59	10.5	1.55	0.23	0.47	3.67	Outside of safe biological limits
Frigate tuna	Auxis thazard	0.67	3.11	0.52	0.20	0.39	0.45	Outside of safe biological limits
Moonfish	Mene maculata	0.93	4.63	1.07	0.15	0.30	1.5	Outside of safe biological limits
Mitre squid	Uroteuthis chinensis	0.48	5.7	0.69	0.16	0.32	0.85	Outside of safe biological limits

. The estimated biomass trajectories from the CMSY (solid black lines) and BSM (dashed lines) analyses are shown in Figure 4. The overlap between the two crosses and the similar trends in biomass trajectories estimated by CMSY and BSM methods indicated that the results of the CMSY and BSM methods are similar, and thus more credible than if they were not.

The estimated r, k, MSY, and relative biomass are shown in Table 5. The model output combined B/B_MSY with F/F_MSY to assess the stock status of these species and showed that 7 of the 12 species were overfished or above safe biological limits (B/B_MSY < 0.5 or F/F_MSY > 1), with 1 stock having a healthy biomass that was starting to deplete due to overfishing (B/B_MSY > 1, F/F_MSY > 1), 4 species with biomass levels that are too low to exceed safe biological limits (B/B_MSY < 0.5), and 2 stocks with biomass levels too low to produce maximum sustainable yield and which were overfished (0.5 < B/B_MSY < 1, F/F_MSY > 1). Further, 2 stocks were under reduced fishing pressure and were recovering from low biomass levels (0.5 < B/B_MSY < 1, F/F_MSY < 1), and 3 stocks were under sustainable fishing pressure and had a healthy stock biomass capable of producing a high biomass close to MSY (B/B_MSY > 1, F/F_MSY < 1)(Figure 5).

4. Discussion

China has faced challenges related to the scarcity of historical data in its fisheries’ resource surveys, leading to limitations in assessment methods and species coverage [39,40]. CMSY, as a newly introduced stock assessment method, is relatively simple and can provide preliminary stock assessments using limited catch data and biological parameters in the absence of exhaustive data, which can be applied to many types of fish stocks and which help managers formulate appropriate fishing strategies [41,42,43]. With this in mind, it is possible to use the CMSY and BSM approaches to assess the status of stock resources in this study. However, despite the fact that the newly developed CMSY method can be used to infer biomass trajectories for some fish species with some success, there are still some limitations. On one hand, the applicability of this method may be influenced by the specific environmental conditions and the quality of catch data. On the other hand, the prior parameter settings in the CMSY model can also have a significant impact on the model’s effectiveness [44,45,46]. Therefore, when using these methods, it is essential to pay attention to the reliability and applicability of the data, as well as to set reasonable prior information to ensure the scientific validity and effectiveness of the results. Future stock assessments using the CMSY methods should strengthen the collection and monitoring of baseline data, especially reliable biomass and catch data over long periods of time, to improve the applicability and accuracy of the models [19]. Methods such as the length-based Bayesian biomass estimator (LBB) [47] use only length–frequency data, which can be used to predict the status of fisheries’ resources where data are available.

The continuous historical catch data used in this study can to some extent reflect the changes in the total biomass of the stock [40], and the use of valid relative abundance data (i.e., CPUE) to support the assessment and parameter estimation, taking into account the correction of the predictive bias of the catch data, which improves the accuracy and robustness of the stock assessment [18]. In this study, the BSM method was used in conjunction with the CMSY method to provide a more detailed and scientific assessment of the stock. Demersal fishes and squids in the fishing grounds were assessed as a whole to be overexploited [24,25,26]; however, a few individual exploited species were assessed due to a lack of basic information for general stock assessment. Therefore, 12 species, which are the main components in catches of local fisheries, were assessed by the CMSY and BSM methods under the data-poor circumstances. Moreover, the assessment results of the 12 stocks using CMSY and BSM methods are similar to those obtained from other methods in the eastern Guangdong and southern Fujian waters [24,25,26], which suggests that the CMSY and BSM methods are applicable in Chinese marine fishery assessments. Thus, it is suggested to use these methods in other Chinese areas where the fishery data are limited or poor in order to assess the stocks effectively and use the resource sustainably.

Three small pelagic fishes (D. maruadsi, E. sadina, and M. cordyla) were assessed as having healthy statuses. These r-selected species are capable of resisting strong fishing pressure. Their climate-driven environmental variability coupled with the upwelling effect may provide favorable environmental conditions for these species in the fishing grounds to maintain a healthy status. In addition, the mesh sizes of the gear used to catch it increased from 25 to 27 mm in 1980s to 29 to 35 mm in the 2000s [38], which enabled more of the equally phototactic juveniles to escape capture and thus to contribute to stock recovery.

Indian scad (D. russelli), an important commercial species accounting for 9.0% of the purse seine fishery in the southern Taiwan Straits [48], was assessed as recovering. Unlike Japanese scad (D. maruadsi), Indian scad has a lower fecundity that may have affected its stock recovery under the strong fishing pressure prevailing since 1987 [38].

However, more than half (7 of 12) of the selected species in these fishing grounds are overexploited. The ever-increasing fishing pressure, which grew from 28.55 × 10⁴ kw in 1990 to 53.19 × 10⁴ kw in 2000 [49], may be attributed to these stocks being overexploited. Meanwhile, catches in these fishing grounds remain at high levels, with the average annual catches at 47.27 × 10⁴ t from 1995 to 2000 [49]. Among these species, Japanese jack mackerel [17], red bigeye [50,51], largehead hairtail [19], frigate tuna [52], moonfish [17], and mitre squid [23] were reported to be overexploited in these fishing grounds and their adjacent waters.

The results of these assessments indicate that fisheries’ management in the eastern Guangdong and southern Fujian waters, at least in part for these seven overfished species, are still insufficient. Therefore, the overall size of fishing efforts in these fishing grounds should be strictly controlled.

5. Conclusions

The CMSY and BSM methods were applied here to estimate 12 exploited stocks’ statuses in the eastern Guangdong and southern Fujian waters. However, more than half (7 of 12) of the selected species in this fishing ground are overexploited, which is consistent with other studies in this area.

At present, only a few fish stocks that are reported to be moderately to severely overfished have been found to be well managed and in good condition, both nationally and internationally [6,15,53,54], highlighting the need to improve management practices globally so that the simultaneous integration of the results of stock assessment simulations with the orientation of national fishery policies can better guide the behavior of fishermen and promote the sustainable use of fishery resources [55]. The summer fishing moratorium in the East and South China Sea was extended by one month in 2017; as such, this serves as a short-term conservation measure. However, its effect is limited [56], and more focused fishery management interventions will be required to maintain the fisheries of the eastern Guangdong and southern Fujian waters.