Schaefer Model Research Articles

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

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 (Bend/k and B/BMSY) were estimated. The current stock status was defined by B/BMSY and fishing mortality (F/FMSY). The results indicate that seven stocks were overfished or below safe biological limits (B/BMSY < 0.5 or F/FMSY > 1), two stocks were in a recovery phase (0.5 < B/BMSY < 1, F/FMSY < 1), and three stocks were under sustainable fishing pressure with healthy biomass, capable of producing yields close to the MSY (B/BMSY > 1, F/FMSY < 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.

Analysis of the Status of Sustainable Utilization and Management of Flyfish (Decapterus russelli) Resources in Pasongsongan Waters, Sumenep Regency using the Walter-Hilborn Model

Flyfish (Decapterus russelli) is a small pelagic fish with a fairly high economic value. Flying fish are the fish with the highest production landed at the Pasongsongan Beach Fisheries Port, Sumenep Regency. In recent years, the production of flying fish in Pasongsongan has continued to decline, excessive fishing activities and an increase in the number of fleets are feared to interfere with the sustainability of flying fish resources. Therefore, sustainable management of flyfish resources needs to be carried out, the purpose of this study is to find out the sustainable potential of flyfish resources, to know the number of catches allowed, and to know the status of the utilization of flyfish resources in Pasongsongan waters. The methods used in this study are quantitative and qualitative methods. The analysis used is a surplus production approach using 3 models, namely the Schaefer model, the Fox model and the Walter-Hilborn model. The results of the analysis using the Walter Hilborn model are a surplus production model that is suitable for use with a maximum catch value (Ymsy) of 2,255 tons and a permissible catch (JTB) of 1,804 tons, the utilization rate of flying fish landed in PPP Pasongsongan is 87% with the utilization status in the Fully Exploited category, where in this condition illustrates that the stock of resources has been exploited by 75-100%. An increase in the number of fishing efforts is not recommended even though the number of catches can still increase because it will interfere with the sustainability of fish resources.

Stability and Bifurcation of a Gordon–Schaefer Model with Additive Allee Effect

The rarity of species increases its market price, consequently leading to the overexploitation of the species and even the extinction of the species. We study how the harvest intensity and the additive Allee effect impact on the Gordon–Schaefer model. In addition, by Sotomayor’s theorem and Poincaré–Andronov theorem, we prove the existence of Hopf bifurcation, saddle-node bifurcation and transcritical bifurcation, respectively. Finally, we illustrate our results by numerical simulations. We find that both the cost per unit of harvest and the additive Allee effect have a significant impact on human exploitation of the population. As the additive Allee effect reduces to the weak Allee effect, the lower harvest cost encourages humans to increase the exploitation of species. This threshold is a switch that controls the strong Allee effect. If it exceeds its threshold, then the motivation of humans to exploit the species increases.

Multiple Exploitation of a Renewable Resource — Optimal Solution of a Deterministic Singular Control Model

In this paper, we consider a singular control optimization problem which generalizes the Gordon–Schaefer model. A population of individuals with endogenous growth is harvested by two independent agents, and their joint profit is maximized. We show that adding a second control to the standard model greatly complicates the standard solution, which is a Most Rapid Approach. We prove that no solution where both controls are singular can exist. Using numerical experiments, we exhibit situations where the optimal steady state corresponds to one of the controls singular, and the other one is equal to one of its specific bounds. In some cases, the optimal path resembles the one-control solution, but with an additional threshold on the population beyond which both controls must be used. However, in other cases, the optimal control has more switching points and is not monotonous. We even exhibit cases where the optimal policy admits two steady states.

Mackerel Fish (Rastrelliger Kanagurta) Management Strategy at Karangantu Port

The aim of this research is to understand the management strategy for Mackerel Fish (Rastrelliger Kanagurta) at Karangantu Harbor. The research method used by researchers is a quantitative research method. The sample selection in this research used the primary data sampling technique, using a purposive sampling technique. Because the aim of this research was mackerel, sampling was carried out randomly on the fishing fleet that caught mackerel and landed at PPN Karangantu. Meanwhile, the researcher's data analysis used Catch Per Unit Effort (CPUE), Fox Model, Schaefer Model, Walter-Hilborn Model, Clarke Yoshimoto Pooley (CYP) Model, and Schnute Model, as well as analysis of management strategy preparation using qualitative descriptive analysis methods based on MSY values, JTB, and average annual production/catch at Karangantu Harbor. The results of the research show that the correlation coefficient (R2) at 20 days is 0.9537. The r value is close to 1 (one), indicating a close relationship between length growth and fish weight. The condition of the mackerel fishery in the waters of Serang Regency is experiencing a decline. For the management of mackerel, the size of the nets used to catch mackerel should be enlarged so that fewer fish are caught that are smaller than the size at which the gonads first mature.

The Effectiveness of Regulation of the Minister of Marine and Fisheries Number 2 of 2015 Concerning the Utilization of Demersal Fish Landed at PPN Brondong, Lamongan

The Nusantara Fishing Port of Brondong in Lamongan, is one of the largest fishing bases in East Java. Types of fish caught by fishermen are dominated by Nemipterus japonicus, Upeneus moluccensis, and Gerres oyena. The purpose of this study was to determine the utilization rate of demersal fish landed at PPN Brondong, Lamongan. This research was conducted using quantitative and descriptive statistical methods. Maximum sustainable yield analysis uses the production surplus model from Schaefer (1954). Based on the model of Schaefer (1954) demersal fish are stated in a fully exploited to over exploited condition. Analysis of the Schaefer model (1954) found the Fmsy value of 12,457 trips, the Cmsy value of 25,216 tons and the allowable catch of 20,173 tons. PERMEN KP Number 2 of 2015 in terms of response effectiveness according to respondents obtained a value of 52%. Based on research, the issuance of PERMEN KP Number 2 of 2015 was not effective on the level of utilization of demersal fish landed at Nusantara Fishing Port of Brondong, Lamongan.

Maximum sustainable yield as a reference point in the presence of fishing effort that follows an ideal free distribution

AbstractThe concept of maximum sustainable yield (MSY), and the underlying Schaefer model, remain relevant in fisheries management today in spite of past difficulties. However, this model is often applied without the consideration of the spatial structure or dynamics of fishing activities. We expand the Schaefer model to account for multiple fishing sites and fishing activities (effort) that follow an ideal free distribution (IFD, a form of Nash equilibrium) driven by exploitation. We explore this conceptual model using both analytical and numerical solutions. MSY across the fishery is unaffected by the spatial structure when fishing is allocated independently within sites. However, in other cases IFD effort dynamics depress MSY and modify the values of associated reference points. The game theoretic aspect of fleet dynamics should be considered in the use of MSY as a management reference point or during the application of the Schaefer model to data‐limited fisheries.

Assessment of Seabream Fisheries Stock of Oman Using the Monte Carlo Catch Maximum Sustainable Yield and the Bayesian Schaefer Model Methods

The establishment of managerial approaches for the sustainable use of fishery resources depends on a critical understanding of the stock status. The Monte Carlo catch maximum sustainable yield (CMSY) method and a Bayesian state–space implementation of the Schaefer model (BSM) are recent, but widely used, stock assessment methods for data-limited situations. Here, CMSY and BSM were used to evaluate the state and exploitation level of the seabream population. Collections of catch and effort data from 1988 to 2021, pertaining to time series, were obtained from the Fishery Statistics Book published by the Ministry of Agriculture, Fisheries and Water Resources of Oman. The CMSY and BSM results were similar, indicating that the seabream stock of Oman was overfished, as B/BMSY = 0.96 (<1) and F/FMSY = 1.25 (>1). The probability that the stock was being overfished and undergoing overfishing in 2021 was 53%, while the probability that the stock was healthy (high biomass and low fishing pressure) was only 16.2%, when the target should be higher than 75%. The conclusions are of a preliminary nature owing to the utilization of comparatively new methodologies employed to generate them, which commonly validate the condition and utilization of the populations under investigation. Our research suggests that the seabream population in Oman is overfished, and reducing fishing activity is necessary to restore its abundance.

New developments in the analysis of catch time series as the basis for fish stock assessments: The CMSY++ method

Following an introduction to the nature of fisheries catches and their information content, a new development of CMSY, a data-limited stock assessment method for fishes and invertebrates, is presented. This new version, CMSY++, overcomes several of the deficiencies of CMSY, which itself improved upon the “Catch-MSY” method published by S. Martell and R. Froese in 2013. The catch-only application of CMSY++ uses a Bayesian implementation of a modified Schaefer model, which also allows the fitting of abundance indices should such information be available. In the absence of historical catch time series and abundance indices, CMSY++ depends strongly on the provision of appropriate and informative priors for plausible ranges of initial and final stock depletion. An Artificial Neural Network (ANN) now assists in selecting objective priors for relative stock size based on patterns in 400 catch time series used for training. Regarding the cross-validation of the ANN predictions, of the 400 real stocks used in the training of ANN, 94% of final relative biomass (B/k) Bayesian (BSM) estimates were within the approximate 95% confidence limits of the respective CMSY++ estimate. Also, the equilibrium catch-biomass relations of the modified Schaefer model are compared with those of alternative surplus-production and age-structured models, suggesting that the latter two can be strongly biased towards underestimating the biomass required to sustain catches at low abundance. Numerous independent applications demonstrate how CMSY++ can incorporate, in addition to the required catch time series, both abundance data and a wide variety of ancillary information. We stress, however, the caveats and pitfalls of naively using the built-in prior options, which should instead be evaluated case-by-case and ideally be replaced by independent prior knowledge.

New developments in the analysis of catch time series as the basis for fish stock assessments: The CMSY++ method

Following an introduction to the nature of fisheries catches and their information content, a new development of CMSY, a data-limited stock assessment method for fishes and invertebrates, is presented. This new version, CMSY++, overcomes several of the deficiencies of CMSY, which itself improved upon the “Catch-MSY” method published by S. Martell and R. Froese in 2013. The catch-only application of CMSY++ uses a Bayesian implementation of a modified Schaefer model, which also allows the fitting of abundance indices should such information be available. In the absence of historical catch time series and abundance indices, CMSY++ depends strongly on the provision of appropriate and informative priors for plausible ranges of initial and final stock depletion. An Artificial Neural Network (ANN) now assists in selecting objective priors for relative stock size based on patterns in 400 catch time series used for training. Regarding the cross-validation of the ANN predictions, of the 400 real stocks used in the training of ANN, 94% of final relative biomass (B/k) Bayesian (BSM) estimates were within the approximate 95% confidence limits of the respective CMSY++ estimate. Also, the equilibrium catch-biomass relations of the modified Schaefer model are compared with those of alternative surplus-production and age-structured models, suggesting that the latter two can be strongly biased towards underestimating the biomass required to sustain catches at low abundance. Numerous independent applications demonstrate how CMSY++ can incorporate, in addition to the required catch time series, both abundance data and a wide variety of ancillary information. We stress, however, the caveats and pitfalls of naively using the built-in prior options, which should instead be evaluated case-by-case and ideally be replaced by independent prior knowledge.

Sustainable stingray stock (Dasyatis sp.) in Malacca Strait, Serdang Bedagai Regency

Sustainable stingray stock catches and increasing the potential that these fish populations may become overfished this may have a negative impact on the size of fish stocks. In order to estimate stock models and prospective resources of stingrays in the seas of the Malacca Strait, the descriptive technique was utilized to collect secondary data. The necessary to conduct stock dynamics research that examines the exploitation rate of stingrays. Based on the Schaefer model, obtained the value of sustainable potential (MSY) for Stingray resources (Dasyatis sp.) at Tanjung Beringin fishing port of 37,24625 tons/year and the optimum effort value of 148,6119 trips/ years. The percentage of stingray resource utilization rate which landed in Tanjung Beringin TPI during the last 5 years has an average value of 0,925% and an effort level of 1,234%. TAC is 29,797 tons/year, shows that fishing can still be increased to get more optimal results. The value of the allowable catch must be below the MSY value which is 80%. The average allowable catch of stingray (Dasyatis sp.) is 80% of the MSY. This means that fishermen’s catches can still be optimized, but must not exceed MSY limit.

Model‐based offline reinforcement learning for sustainable fishery management

AbstractFisheries, as indispensable natural resources for human, need to be managed with both short‐term economical benefits and long‐term sustainability in consideration. This has remained a challenge, because the population and catch dynamics of the fisheries are complex and noisy, while the data available is often scarce and only provides partial information on the dynamics. To address these challenges, we formulate the population and catch dynamics as a Partially Observable Markov Decision Process (POMDP), and propose a model‐based offline reinforcement learning approach to learn an optimal management policy. Our approach allows learning fishery management policies from possibly incomplete fishery data generated by a stochastic fishery system. This involves first learning a POMDP fishery model using a novel least squares approach, and then computing the optimal policy for the learned POMDP. The learned fishery dynamics model is useful for explaining the resulting policy's performance. We perform systematic and comprehensive simulation study to quantify the effects of stochasticity in fishery dynamics, proliferation rates, missing values in fishery data, dynamics model misspecification, and variability of effort (e.g., the number of boat days). When the effort is sufficiently variable and the noise is moderate, our method can produce a competitive policy that achieves 85% of the optimal value, even for the hardest case of noisy incomplete data and a misspecified model. Interestingly, the learned policies seem to be robust in the presence of model learning errors. However, non‐identifiability kicks in if there is insufficient variability in the effort level and the fishery system is stochastic. This often results in poor policies, highlighting the need for sufficiently informative data. We also provide a theoretical analysis on model misspecification and discuss the tendency of a Schaefer model to overfit compared with a Beverton–Holt model.

Produktivitas dan Pola Musim Tangkap Ikan Peperek (Leiognathus spp.) di Teluk Banten

Ponyfish (Leiognathus spp.) is one of the dominant types of fish landed at the Nusantara Fishing Port (PPN) Karangantu. Its high-intensity fishing activities are feared to cause future problems with the sustainability of ponyfish resources. This study aims to calculate fishing productivity, sustainable optimal fishing effort, maximum sustainable catch, and determine the pattern of fishing season in Banten Bay. The research was conducted from April to June 2021 at PPN Karangantu. The fishing gear analyzed were fixed lift nets, floating lift nets, and seine nets. Fishing productivity was calculated from catch per unit effort; the maximum sustainable catch was analyzed using the Schaefer, Fox, and Walter-Hilborn regression models. The seasonal pattern of the ponies was determined from the Fishing Season Index. The results showed that pony fishing productivity in PPN Karangantu decreased from 2016 to 2020. The optimum fishing effort value and the optimum catch through the Schaefer model were 2088 trips/year and 276.12 tons/year, respectively; the highest fishing season occurred in November (145.5%) and the lowest in May (70.2%). Based on the analysis of the Schaefer, Fox and Walter-Hilborn models, the exploitation of Ponyfish (Leiognathus spp.) in Banten Bay can be said to be over-exploited.
 
 Keywords: catching season, Banten bay, Leiognathus spp., productivity

How Well Do 'Catch-Only' Assessment Models Capture Catch Time Series Start Years and Default Life History Prior Values? A Preliminary Stock Assessment of the South Atlantic Ocean Blue Shark Using a Catch-Based Model.

Simple SummaryBlue shark species are at the top of the list of captured bycatch sharks in most tuna and tuna-like fisheries. As a consequence, their populations have been declining due to overfishing; thus, there is a need for the assessment of their stocks to better understand blue sharks’ stock status. Most bycatch species lack sufficient data for traditional stock assessment models to be implemented. Blue sharks in the South Atlantic have been assessed in the past using a state-space production model. Given the development of new assessment models and the use of up-to-date data, their stock status was evaluated using a new state-space production model (CMSY++). We used different catch time series, abundance indices and priors to measure the intrinsic growth rate r to evaluate their influence on the outputs of CMSY++. We identified from many scenarios that the blue shark stock in the South Atlantic may be witnessing overfishing and is being overfished.CMSY++, an improved version of the CMSY approach developed from Catch-MSY which uses a Bayesian implementation of a modified Schaefer model and can predict stock status and exploitation, was used in the present study. Evaluating relative performance is vital in situations when dealing with fisheries with different catch time series start years and biological prior information. To identify the influences of data inputs on CMSY++ outputs, this paper evaluated the use of a nominal reported catch and a reconstructed catch dataset of the South Atlantic blue shark alongside different priors of the blue shark’s productivity/resilience (r) coupled with different indices of abundance. Results from the present study showed that different catch time series start years did not have a significant influence on the estimation of the biomass and fishing reference points reported by CMSY++. However, uninformative priors of r affected the output results of the model. The developed model runs with varying and joint abundance indices showed conflicting results, as classification rates in the final year changed with respect to the type of index used. However, the model runs indicated that South Atlantic blue shark stock could be overfished (B2020/Bmsy = 0.623 to 1.15) and that overfishing could be occurring (F2020/Fmsy = 0.818 to 1.78). This result is consistent with the results from a previous assessment using a state-space surplus production model applied for the same stock in 2015. Though some potential could be observed when using CMSY++, the results from this model ought to be taken with caution. Additionally, the continuous development of prior information useful for this model would help strengthen its performance.

Performance Comparison of Three Data-Poor Methods With Various Types of Data on Assessing Southern Atlantic Albacore Fishery

In the world, more than 80% of the fisheries by numbers and about half of the catches have not been formally analyzed and evaluated due to limited data. It has led to the fast growth of data-poor evaluation methods. There have been various studies carried out on the comparative performance of data-poor and data-moderate methods in evaluating fishery exploitation status. However, most studies to date have focused on coastal fish stocks with simple data sources. It is important to pay attention to high sea fisheries because they are exploited by multiple countries, fishing gears and data may be divrsified and inconsistent. Furthermore, a comparison of the performance of catch-based, length-based, and abundance-based methods to estimate fishery status is needed. This study is the first attempt to apply catch-based, length-based, and abundance-based data-poor methods to stock assessment for an oceanic tuna fishery and to compare the performance with a data-moderate model. Results showed that the three data-poor methods with various types of data did not produce an entirely consistent stock status of the southern Atlantic albacore (Thunnus alalunga) fishery in 2005, as the estimated B2005/BMSY ranged from 0.688 to 1.3 and F2005/FMSY ranged from 0.708 to 1.6. The Monte Carlo Catch maximum sustainable yield model (CMSY) produced a similar time series of B/BMSY and F/FMSY and stock status (recovering) to the Bayesian state-space Schaefer model (BSM). The abundance-based method (AMSY) gave the most conservative condition (overfished) of this fishery. Sensitivity analysis showed the results of the length-based Bayesian biomass estimation method (LBB) are sensitive to Linf settings, and the results with higher Linf were similar to those of other models. However, the LBB results with setting Linf at lower levels produced more optimistic conditions (healthy). Our results highlight that attention should be paid to the settings of model parameter priors and different trends implied in various types of data when using these data-poor methods.

Evaluation of the effects of stock enhancement on population dynamics using a state-space production model: A case study of Japanese flounder in the Seto Inland Sea

The effect of stock enhancement on increasing biomass is not well understood and is highly controversial because released fish might have a competitive impact on wild stocks. Quantitative assessments exploring the possible impacts of stock enhancement on population dynamics are rare, especially in data-limited fisheries. We developed a state-space production model incorporating stock enhancement to evaluate the impact of stock enhancement at a population scale. Since observation and process errors impair the accuracy of parameter estimation and biomass prediction, we investigated whether prior biomass reference information could improve analytical performance of the state-space production model. We compared the performance of maximum likelihood (ML) and maximum a posterior (MAP) estimation in a special case of a state-space Pella-Tomlinson model or in a state-space Schaefer model, for three simulated levels of process and observation errors. We found that the MAP parameter estimation with valid prior biomass reference data was generally superior to the ML parameter estimation. As a case study, we applied the extended state-space Pella-Tomlinson model to disentangle the impacts of stock enhancement for the Japanese flounder (Paralichthys olivaceus) in the Seto Inland Sea using long-term fishery data. The results showed a limited contribution of stock enhancement to Japanese flounder in the Seto Inland Sea, suggesting that most released flounder replaced the wild fish niche. Given the number of fish released in 2018, future projections for the next ten years under three different fishing pressures showed that stock enhancement was more effective at augmenting biomass when fishing pressure was higher, but that total biomass decreased simultaneously. Catch increase (decrease) in 30% resulted in a 9% decrease (8% increase) in the biomass and a 28% increase (15% decrease) in the net biomass augmentation from stocking. Therefore, a balance between stock enhancement and exploitation is important to obtain sustainable production and efficient stocking, especially facing global climate change which possibly influences the carrying capacity. Our results suggest that a state-space production model can be used to evaluate the impact of stock enhancement and implement better fishery management.

Harvest Control Rules of Multispecies Scads (Decapterus spp.) Fishery in Blitar Waters, East Java

Highlight ResearchThe feedback harvest control rule resulted in the lowest allowable biological catch estimation than the surplus production models. The feedback harvest control rule adapted the recent years of historical catch data.The surplus production models might result in over or under catch estimation, not considering the recent data.This multispecies fishery could not provide species specific data as surplus production models required; therefore, it might be better to use the feedback harvest control rule that has been validated for multispecies fisheries. AbstractScads fishery in Blitar waters is a multispecies fishery applying harvest control rules (HCRs) from conventional single-species approaches which technically, may not be applicable to employ in such mixed-species data composed from the multispecies fishery. The feedback HCR is an alternative validated harvest control rule that can be applied for a multispecies fishery. The aim of this study was to technically compare the application of the feedback HCR and conventional single species approaches in terms of estimating the future allowable biological catch (ABC). Therefore, in this study, ABC was estimated by applying three HCRs, the feedback HCR, and two conventional HCRs which was based on single-species approaches applying surplus production models, Schaefer Model and Fox model into catch and effort of scads fisheries data series of 2011 – 2020. The results showed that the ABC estimation of the feedback HCR was a half lower than that of both conventional models. It was biologically safer to apply the feedback HCR to set the annual total allowable catch (TAC) than the other two HCRs. The feedback HCR presented an initial step toward sustainably managing multispecies fisheries while dealing with data-limited conditions.

Using Data-Limited Methods to Assess the Status of Bartail Flathead Platycephalus indicus Stocks in the Bohai and Yellow Seas

We applied Catch and Maximum Sustainable Yield (CMSY), Bayesian Schaefer model (BSM), and Abundance Maximum Sustainable Yield (AMSY) methods to estimate the status of Platycephalus indicus stocks in the Bohai and Yellow Seas, assessed model performance, and determined the impact of priors derived from expert knowledge on the performance of each model. Results showed that P. indicus stock in the Bohai Sea had collapsed, while that in the Yellow Sea stock was recovering. With the input of an expert prior derived from the length-based Bayesian biomass (LBB) estimation method, the CIs of each model narrowed, but the changes in biomass trajectory were not significant, and the estimates of B/BMSY were differed compared with model results using default rules. These three models can be used to evaluate data-limited stocks to reflect stock dynamics when reliable inputs are available. However, the ranges of priors, which are preferably obtained from other stock assessment tools, should be carefully established.

Harvest control rules of pelagic fisheries in the Bali Strait, Indonesia

Abstract. Harlyan LI, Badriyah L, Rahman MA, Sutjipto DO, Sari WK. 2021. Harvest control rules of pelagic fisheries in the Bali Strait, Indonesia. Biodiversitas 23: 947-953. Multispecies pelagic fisheries have contributed as the main fishery production for the Bali Strait fisheries. To maintain the sustainability of the fish stock, sets of harvest control rules (HCRs) have been introduced in terms of fishery management. Conventional single-species surplus production models have been used extensively to determine the future annual allowable biological catch (ABC). However, they may not technically apply to multispecies fishery management. The feedback harvest control rule HCR has been validated and applied to maintain the catch variability of the multispecies fishery. This study aims to technically compare the use of the surplus production model and the feedback HCR in the ABC estimation. The catch and effort data series of the three dominant species from 2010-2019, collected from Pengambengan and Muncar fishing port, were analyzed by two HCRs, the Schaefer surplus production model and the feedback HCR to obtain the ABC. As a result, the Schaefer model showed a higher estimation of ABC than the feedback HCR because the feedback HCR may reflect the recent historical catch consistent with precautionary principles. The feedback HCR showed an initial step toward sustainably managing multispecies fisheries while dealing with unavailability in species-specific data conditions.

Fishery Dynamics, Status, and Rebuilding Based on Catch-Only Data in Coastal Waters of China

China has become the largest contributor to marine fisheries in the world with its fishing fleets explosively increasing their fishing effort and resulting catch, but its fishery composition and sustainability have deteriorated. Limited information on fishery exploitation status encumbers effective resource management. In this study, a data-poor Monte Carlo method, the Catch-Maximum Sustainable Yield (CMSY) method, was used to estimate the historical exploited dynamics and current stock status of ten Chinese economic marine fish stocks, includingTrichiurus lepturus,Larimichthys crocea,Larimichthys polyactis,Thamnaconus modestus,Scomberomorus niphonius,Ilisha elongate,Decapterus maruadsi,Scomber japonicus,Engraulis japonicus, andClupea pallasii, which accounted for about 50% of total fish catches in the coastal waters of China and covered five functional groups (i.e., large, medium benthopelagic, large, medium, and small pelagic). SpeciesL. croceaandL. polyactishad been subjected to overfishing since the 1950s. The others showed a decreasing trend in biomass along with the explosively increasing fishing efforts since the 1990s. Benthopelagic fish experienced overfishing pressure about a decade earlier than pelagic species. All the fish stocks investigated in this study were depleted (current biomass lower than the biomass capable of producing maximum sustainable yields, i.e.,B<Bmsy) in 2019, and most species were still facing high-fishing pressure (current fishing mortality higher than the mortality capable of producing maximum sustainable yields, i.e.,F>Fmsy). Also, a Schaefer model was used to assess stocks rebuilding status until 2030 under four exploitation scenarios, i.e., fishing mortality equals 0.5, 0.6, 0.8, or 0.95 timesFmsy. Most species stocks will likely recover to theBmsy, which indicates that reduction of fishing pressure is probably the most effective way for fishery recovery.