Growth, Mortality and Stock Assessment of Squaliobarbus curriculus (Actinopterygii: Cypriniformes: Xenocyprididae) from Lanxi Section of Qiantang River, China

Population dynamics parameters and stock status of Squaliobarbus curriculus (Richardson, 1846) were analyzed from May to September 2021 in the Lanxi section of Qiantang River. FiSAT II software program was used. The growth coefficient K = 0.21 year–1, asymptomatic length L∞ = 39.48 cm, and age at theoretical zero-length t0 = –0.74 years were estimated. The von Bertalanffy growth function was calculated as Lt = 39.48[1 – e–0.21(t + 0.74)]. The growth curve for weight had an inflection at 5.86 years, corresponding to 29.61 cm in standard length and 372.29 g in weight. The natural mortality rate (M), the fishing mortality rate (F), and the total mortality rate (Z) were calculated as 0.51, 0.61, and 1.12 year–1, respectively. The exploitation ratio (E) was 0.54, which is greater than the value of 0.5 suggested by Gull (1971), indicating a probable state of overdevelopment. The annual average stock number and biomass of S. curriculus in the Lanxi section of Qiantang River were 31.86 × 106 individuals and 3656.82 t, respectively.

Application of a Bayesian method to data-poor stock assessment by using Indian Ocean albacore (Thunnus alalunga) stock assessment as an example

It is widely recognized that assessments of the status of data-poor fish stocks are challenging and that Bayesian analysis is one of the methods which can be used to improve the reliability of stock assessments in data-poor situations through borrowing strength from prior information deduced from species with good-quality data or other known information. Because there is considerable uncertainty remaining in the stock assessment of albacore tuna（Thunnus alalunga） in the Indian Ocean due to the limited and low-quality data, we investigate the advantages of a Bayesian method in data-poor stock assessment by using Indian Ocean albacore stock assessment as an example. Eight Bayesian biomass dynamics models with different prior assumptions and catch data series were developed to assess the stock. The results show（1） the rationality of choice of catch data series and assumption of parameters could be enhanced by analyzing the posterior distribution of the parameters;（2） the reliability of the stock assessment could be improved by using demographic methods to construct a prior for the intrinsic rate of increase（r）. Because we can make use of more information to improve the rationality of parameter estimation and the reliability of the stock assessment compared with traditional statistical methods by incorporating any available knowledge into the informative priors and analyzing the posterior distribution based on Bayesian framework in data-poor situations, we suggest that the Bayesian method should be an alternative method to be applied in data-poor species stock assessment, such as Indian Ocean albacore.

Fishery stock assessment of Kiddi shrimp (Parapenaeopsis stylifera) in the Northern Arabian Sea Coast of Pakistan by using surplus production models

Pakistani marine waters are under an open access regime. Due to poor management and policy implications, blind fishing is continued which may result in ecological as well as economic losses. Thus, it is of utmost importance to estimate fishery resources before harvesting. In this study, catch and effort data, 1996-2009, of Kiddi shrimp Parapenaeopsis stylifera fishery from Pakistani marine waters was analyzed by using specialized fishery software in order to know fishery stock status of this commercially important shrimp. Maximum, minimum and average capture production ofP. stylifera was observed as 15 912 metric tons （mr） （1997）, 9 438 mt （2009） and 11 667 mt/a. Two stock assessment tools viz. CEDA （catch and effort data analysis） and ASPIC （a stock production model incorporating covariates） were used to compute MSY （maximum sustainable yield） of this organism. In CEDA, three surplus production models, Fox, Schaefer and Pella-Tomlinson, along with three error assumptions, log, log normal and gamma, were used. For initial proportion （IP） 0.8, the Fox model computed MSY as 6 858 nat （CV=0.204, R^2=0.709） and 7 384 mt （CV=0.149, R^2=0.72） for log and log normal error assumption respectively. Here, gamma error produced minimization failure. Estimated MSY by using Schaefer and Pella-Tomlinson models remained the same for log, log normal and gamma error assumptions i.e. 7 083 mt, 8 209 mt and 7 242 mt correspondingly. The Schafer results showed highest goodness of fit R2 （0.712） values. ASPIC computed MSY, CV, R2, FMsv and BMsv parameters for the Fox model as 7 219 nat, 0.142, 0.872, 0.111 and 65 280, while for the Logistic model the computed values remained 7 720 mt, 0.148, 0.868, 0.107 and 72 110 correspondingly. Results obtained have shown that P. stylifera has been overexploited. Immediate steps are needed to conserve this fishery resource for the future and research on other species of commercial importance is urgently needed.

A stock assessment for Illex argentinus in Southwest Atlantic using an environmentally dependent surplus production model

The southern Patagonian stock（SPS） of Argentinian shortfin squid, Illex argentinus, is an economically important squid fishery in the Southwest Atlantic. Environmental conditions in the region play an important role in regulating the population dynamics of the I. argentinus population. This study develops an environmentally dependent surplus production（EDSP） model to evaluate the stock abundance of I. argentines during the period of 2000 to 2010. The environmental factors（favorable spawning habitat areas with sea surface temperature of 16–18°C） were assumed to be closely associated with carrying capacity（K） in the EDSP model. Deviance Information Criterion（DIC） values suggest that the estimated EDSP model with environmental factors fits the data better than a Schaefer surplus model without environmental factors under uniform and normal scenarios.The EDSP model estimated a maximum sustainable yield（MSY） from 351 600 t to 685 100 t and a biomass from 1 322 400 t to1 803 000 t. The fishing mortality coefficient of I. argentinus from 2000 to 2010 was smaller than the values of F（0.1） and F（MSY）. Furthermore, the time series biomass plot of I. argentinus from 2000 to 2010 shows that the biomass of I.argentinus and this fishery were in a good state and not presently experiencing overfishing. This study suggests that the environmental conditions of the habitat should be considered within squid stock assessment and management.

Uncertainties in Stock Assessment and the Precautionary Management Suggestions for the Anchovy (Engraulis japonicus) Stock in the Yellow Sea

Anchovy(Engraulis japonicus) is an abundant fish species in the Yellow Sea,and its natural stock is decreasing rapidly in recent years. Based on the stock-recruitment(SR) data from 1987 to 2002 published in Zhao et al.(2003),the criterion BIC(Bayesian Information Criterion) is applied to selecting a suitable model from six normal and lognormal error structured SR statisti-cal models,the age-structured model is used to calculate the biological reference points(BRPs),and the precision of the SR parame-ters and BRPs are calculated using bootstrap method. The results indicate that the anchovy fishery resource in the Yellow Sea is in an over-fished state. The precaution management principle requires that the fishery should be closed immediately.

A risk table to address concerns external to stock assessments when developing fisheries harvest recommendations

This paper develops a risk table to facilitate incorporation of additional information into the fisheries stock assessment and management process.The risk table is designed to evaluate unanticipated ecosystem and environmental impacts on marine resources that may require a rapid management response.The risk table is a standardized framework to document concerns about the assessment model,population dynamics,and the ecosystem/environment that are not explicitly addressed within the stock assessment model.A scoring procedure is used to evaluate the severity of the concern.These concerns can then be evaluated in support for or against a reduction from the maximum Acceptable Biological Catch while providing reviewers and stakeholders transparent documentation of the concerns.The risk table was applied successfully to several stocks on a trial basis during the 2018 groundfish assessment cycle for the North Pacific Fishery Management Council,and will be used for all full groundfish assessments in 2019.Rapid changes in climate are likely for Alaska marine ecosystems in coming decades,and these changes are not entirely predicable.Therefore,we avocate that the risk table approach should be included in the suite of management tools used to address the effects of climate change on Alaska marine resources.

Impacts of distorted fishery statistical data on assessments of three surplus production models

We evaluated the effect of various error sources in fishery harvest/effort data on the maximum sustainable yield （MSY） and corresponding fishing effort （EMsv） using Monte Carlo simulation analyses. A high coefficient of variation （CV） of the catch and effort values biased the estimates of MSY and EMsv. Thus, the state of the fisheries resource and its exploitation was overestimated. We compared the effect using three surplus production models, Hilborn-Waters （H-W）, Schnute, and Prager models. The estimates generated using the H-W model were significantly affected by the CV. The Schnute model was least affected by errors in the underlying data. The CVof the catch data had a greater impact on the assessment than the CV of the fishing effort. Similarly, the changes in CV had a greater impact on the estimated maximum sustainable yield （MSY） than on the corresponding estimate of fishing effort （EMsY）. We discuss the likely effect of these biases on management efforts and provide suggestions for the improvement of fishery evaluations.

Historic,pre-stock enhancement,demographic conditions of exploited populations of dusky flathead,Platycephalus fuscus,in eastern Australia

Responsible stock enhancement initiatives require baseline data of wild population demographic conditions that can be used in testing management outcomes.This study provides the first fishery-independent assessment of length-and age-based biological characteristics of exploited populations of Platycephalus fuscus in eastern Australia prior to stock enhancement.Sampling was conducted over seven estuaries spanning seven degrees of latitude and the geographical range of proposed stock enhancements.Populations in all estuaries showed evidence of length and age truncation,especially those subject to commercial fisheries where young individuals of both sexes dominated populations.Maximum longevities were 12 and 11 years for females and males respectively,but few females>5 years and males>3 years were generally sampled.Females dominated populations,and on average,the mean lengths and ages of females were greater than males within each estuary and across all age classes.Sexually dimorphic variation in growth was evident across all estuaries,with females attaining greater maximum lengths than males.Estuary-specific differences in individual growth were not identified.On average,over 50%of females sampled in each estuary were>the minimum legal length(MLL),but the opposite was evident for males.In contrast,over 25%of males in each estuary were>the mean length at maturity(L50),whereas in all but one estuary<13%of the females were>the L50.Stocked male and female P.fuscus should recruit to the fishery in 2 and 3 years,and contribute to the spawning stock in 1.5 and 4.5 years,respectively.This study provides important historical baseline data that can contribute to testing stock enhancement outcomes on populations.

Assessing the pomfret stock for setting catch limits in the northern Bay of Bengal,Bangladesh

Pampus argenteus and Pampus chinensis form the high-value demersal Pomfret fishery of Bangladesh.But,due to a monotonic decline in catches over the last five years,it is essential to explore the current stock status concerning the removal rate to ensure the sustainability of this fishery.Therefore,given the reliability and minimal data requirements,this study employed an extended Bayesian State-Space Surplus Production Model,JABBA(Just Another Bayesian Biomass Assessment),to assess the stock rigorously.The results revealed that the stock biomass of the Pomfret fishery in the final year of the time series is significantly lower than BMSY,the biomass required to produce MSY.Consequently,this study recommends a yearly catch limit(TAC)of 10,000 metric tons to prevent further depletion of the stock biomass.Furthermore,to avoid growth overfishing by allowing all immature fishes to reproduce at least once before being caught,this study also calculated the optimum length(Lopt)for catch for both species at which biologically maximum yield and revenue can be obtained.The estimated Lopt is 25 cm for P.argenteus and 30 cm for P.chinensis,and not to capture fishes with lengths lower than these limits,this study further calculated the minimum mesh size limits for gill and set bag nets is 7.5 cm.Though the mesh size regulation was estimated using length-based reference points derived from an empirical equation,this regulation can be used as an associate reference point when TAC is applied to assure the sustainability of this fishery.

Evaluation of the fishery status for King Soldier Bream Argyrops spinifer in Pakistan using the software CEDA and ASPIC

Catch and effort data were analyzed to estimate the maximum sustainable yield （MSY） of King Soldier Bream, Argyrops spinifer （Forsskal, 1775, Family： Sparidae）, and to evaluate the present status of the fish stocks exploited in Pakistani waters. The catch and effort data for the 25-years period 1985-2009 were analyzed using two computer software packages, CEDA （catch and effort data analysis） and ASPIC （a surplus production model incorporating covariates）. The maximum catch of 3 458 t was observed in 1988 and the minimum catch of 1 324 t in 2005, while the average annual catch ofA. spinifer over the 25 years was 2 500 t. The surplus production models of Fox, Schaefer, and Pella Tomlinson under three error assumptions of normal, log-normal and gamma are in the CEDA package and the two surplus models of Fox and logistic are in the ASPIC package. In CEDA, the MSY was estimated by applying the initial proportion （IP） of 0.8, because the starting catch was approximately 80% of the maximum catch. Except for gamma, because gamma showed maximization failures, the estimated results of MSY using CEDA with the Fox surplus production model and two error assumptions, were 1 692.08 t （R^2=0.572） and 1 694.09 t （R^2=0.606）, respectively, and from the Schaefer and the Pella Tomlinson models with two error assumptions were 2 390.95 t （R^2=0.563）, and 2 380.06 t （R^2=0.605）, respectively. The MSY estimated by the Fox model was conservatively compared to the Schaefer and Pella Tomlinson models. The MSY values from Schaefer and Pella Tomlinson models were the same. The computed values of MSY using the ASPIC computer software program with the two surplus production models of Fox and logistic were 1 498 t （R^2=0.917）, and 2 488 t （R^2=0.897） respectively. The estimated values of MSY using CEDA were about 1 700-2 400 t and the values from ASPIC were 1 500-2 500 t. The estimates output by the CEDA and the ASPIC packages indicate that the stock is overfished, and needs some effective management to reduce the fishing effort of the species in Pakistani waters.

Automatic Detection of Nephrops Norvegicus Burrows from Underwater Imagery Using Deep Learning

The Norway lobster,Nephrops norvegicus,is one of the main commercial crustacean fisheries in Europe.The abundance of Nephrops norvegicus stocks is assessed based on identifying and counting the burrows where they live from underwater videos collected by camera systems mounted on sledges.The Spanish Oceanographic Institute(IEO)andMarine Institute Ireland(MIIreland)conducts annual underwater television surveys(UWTV)to estimate the total abundance of Nephrops within the specified area,with a coefficient of variation(CV)or relative standard error of less than 20%.Currently,the identification and counting of the Nephrops burrows are carried out manually by the marine experts.This is quite a time-consuming job.As a solution,we propose an automated system based on deep neural networks that automatically detects and counts the Nephrops burrows in video footage with high precision.The proposed system introduces a deep-learning-based automated way to identify and classify the Nephrops burrows.This research work uses the current state-of-the-art Faster RCNN models Inceptionv2 and MobileNetv2 for object detection and classification.We conduct experiments on two data sets,namely,the Smalls Nephrops survey(FU 22)and Cadiz Nephrops survey(FU 30),collected by Marine Institute Ireland and Spanish Oceanographic Institute,respectively.From the results,we observe that the Inception model achieved a higher precision and recall rate than theMobileNetmodel.The best mean Average Precision(mAP)recorded by the Inception model is 81.61%compared to MobileNet,which achieves the best mAP of 75.12%.

Using data-limited approaches to assess data-rich Indian Ocean bigeye tuna:Data quantity evaluation and critical information for management implications

The majority of fishery stocks in the world are data limited,which limits formal stock assessments.Identifying the impacts of input data on stock assessment is critical for improving stock assessment and developing precautionary management strategies.We compare catch advice obtained from applications of various datalimited methods(DLMs)with forecasted catch advice from existing data-rich stock assessment models for the Indian Ocean bigeye tuna(Thunnus obesus).Our goal was to evaluate the consistency of catch advice derived from data-rich methods and data-limited approaches when only a subset of data is available.The Stock Synthesis(SS)results were treated as benchmarks for comparison because they reflect the most comprehensive and best possible scientific information of the stock.This study indicated that although the DLMs examined appeared robust for the Indian Ocean bigeye tuna,the implied catch advice differed between data-limited approaches and the current assessment,due to different data inputs and model assumptions.Most DLMs tended to provide more optimistic catch advice compared with the SS,which was mostly influenced by historical catches,current abundance and depletion estimates,and natural mortality,but was less sensitive to life-history parameters(particularly those related to growth).This study highlights the utility of DLMs and their implications on catch advice for the management of tuna stocks.

Estimating blue marlin(Makaira nigricans)sustainable yield in the Indian Ocean using a data-poor approach

Blue marlin(Makaira nigricans)is a common bycatch species in the global tuna longline fishery.In this study,we applied a common data-poor approach,i.e.,depletion-corrected average catch(DCAC)to assess stock status of the Indian Ocean blue marlin.Sustainable yield(Ysust),one reference point in this case,was estimated,and its uncertainty was integrated by using Monte Carlo simulation.The results revealed the estimate of Ysust by DCAC was lower than MSY of 11,926 t by BSP-SS and is also lower than the provisional reference point of 11,704 t by the management proposal.DCAC is reliable for blue marlin in driving precautionary management quantity based on the CPUE of Japan(1980–2015).This study also implies that DCAC could be applied to other billfish stocks and uncertainty be estimated for sustainable yield.However,data-poor methods could be adjusted with precautionary approaches.

Fisheries management of round sardinella Sardinella aurita along North Sinai coast

Objective:To estimate the biological and population parameters required for proposing a preparation to sustain and manage this valuable fish resource.Methods:Aging was done by scales reading,and growth was estimated by using the back-calculation method.The values of growth parameters L_(∞),k and t_(o)were calculated by von Bertalanffy model.Results:The results of growth parameters L_(∞),k and t_(o)were 28.36 cm,0.184 per year and-0.8437 per year,respectively.Mortality coefficient,survival and exploitation rates estimated to perceive yield per recruit and biomass per recruit.Conclusions:Biological reference points and virtual population analysis were prepared to plan appropriate managements forSardinella aurita fisheries.