Maximum Sustainable Yield Estimates of Ladypees,Sillago sihama(Forsskl),Fishery in Pakistan Using the ASPIC and CEDA Packages

Using surplus production model packages of ASPIC(a stock-production model incorporating covariates) and CEDA(Catch effort data analysis),we analyzed the catch and effort data of Sillago sihama fishery in Pakistan.ASPIC estimates the pa-rameters of MSY(maximum sustainable yield),Fmsy(fishing mortality),q(catchability coefficient),K(carrying capacity or unexploited biomass) and B1/K(maximum sustainable yield over initial biomass).The estimated non-bootstrapped value of MSY based on logistic was 598 t and that based on the Fox model was 415 t,which showed that the Fox model estimation was more conservative than that with the logistic model.The R2 with the logistic model(0.702) is larger than that with the Fox model(0.541),which indicates a better fit.The coefficient of variation(cv) of the estimated MSY was about 0.3,except for a larger value 88.87 and a smaller value of 0.173.In contrast to the ASPIC results,the R2 with the Fox model(0.651-0.692) was larger than that with the Schaefer model(0.435-0.567),indicating a better fit.The key parameters of CEDA are:MSY,K,q,and r(intrinsic growth),and the three error assumptions in using the models are normal,log normal and gamma.Parameter estimates from the Schaefer and Pella-Tomlinson models were similar.The MSY estimations from the above two models were 398 t,549 t and 398 t for normal,log-normal and gamma error distributions,re-spectively.The MSY estimates from the Fox model were 381 t,366 t and 366 t for the above three error assumptions,respectively.The Fox model estimates were smaller than those for the Schaefer and the Pella-Tomlinson models.In the light of the MSY estimations of 415 t from ASPIC for the Fox model and 381 t from CEDA for the Fox model,MSY for S.sihama is about 400 t.As the catch in 2003 was 401 t,we would suggest the fishery should be kept at the current level.Production models used here depend on the assumption that CPUE(catch per unit effort) data used in the study can reliably quantify temporal variability in population abundance,hence the mod-eling results would be wrong if such an assumption is not met.Because the reliability of this CPUE data in indexing fish population abundance is unknown,we should be cautious with the interpretation and use of the derived population and management parameters.     

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(EMSY) using Monte Carlo simulation analyses.A high coefficient of variation(CV) of the catch and effort values biased the estimates of MSY and EMSY.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 us...     

Application of the moving averaging technique in surplus production models

Surplus production models are the simplest analytical methods effective for fish stock assessment and fisheries management. In this paper, eight surplus production estimators (three estimation procedures) were tested on Schaefer and Fox type simulated data in three simulated fisheries (declining, well-managed, and restoring fisheries) at two white noise levels. Monte Carlo simulation was conducted to verify the utility of moving averaging (MA), which was an important technique for reducing the effect of noise in data in these models. The relative estimation error (REE) of maximum sustainable yield (MSY) was used as an indicator for the analysis, and one-way ANOVA was applied to test the significance of the REE calculated at four levels of MA. Simulation results suggested that increasing the value of MA could significantly improve the performance of the surplus production model (low REE) in all cases when the white noise level was low (coefficient of variation (CV)=0.02). However, when the white noise level increased (CV=0.25), adding the value of MA could still significantly enhance the performance of most models. Our results indicated that the best model performance occurred frequently when MA was equal to 3; however, some exceptions were observed when MA was higher.     

Application of CEDA and ASPIC computer packages to the hairtail (Trichiurus japonicus) fishery in the East China Sea

Surplus-production models are widely used in fish stock assessment and fisheries management due to their simplicity and lower data demands than age-structured models such as Virtual Population Analysis. The CEDA (catch-effort data analysis) and ASPIC (a surplus-production model incorporating covariates) computer packages are data-fitting or parameter estimation tools that have been developed to analyze catch-and-effort data using non-equilibrium surplus production models. We applied CEDA and ASPIC to the hairtail (Trichiurus japonicus) fishery in the East China Sea. Both packages produced robust results and yielded similar estimates. In CEDA, the Schaefer surplus production model with log-normal error assumption produced results close to those of ASPIC. CEDA is sensitive to the choice of initial proportion, while ASPIC is not. However, CEDA produced higher R 2 values than ASPIC.     

Spatiotemporal factors affecting fish harvest and their use in estimating the monthly yield of single otter trawls in Putuo district of Zhoushan, China

We used generalized additive models (GAM) to analyze the relationship between spatiotemporal factors and catch, and to estimate the monthly marine fishery yield of single otter trawls in Putuo district of Zhoushan, China. We used logbooks from five commercial fishing boats and data in government’s monthly statistical reports. We developed two GAM models: one included temporal variables (month and hauling time) and spatial variables (longitude and latitude), and another included just two variables, month and the number of fishing boats. Our results suggest that temporal factors explained more of the variability in catch than spatial factors. Furthermore, month explained the majority of variation in catch. Change in spatial distribution of fleet had a temporal component as the boats fished within a relatively small area within the same month, but the area varied among months. The number of boats fishing in each month also explained a large proportion of the variation in catch. Engine power had no effect on catch. The pseudo-coefficients (PCf) of the two GAMs were 0.13 and 0.29 respectively, indicating the both had good fits. The model yielded estimates that were very similar to those in the governmental reports between January to September, with relative estimate errors (REE) of <18%. However, the yields in October and November were significantly underestimated, with REEs of 36% and 27%, respectively.     

Modeling a habitat suitability index for the eastern fall cohort of Ommastrephes bartramii in the central North Pacific Ocean

The eastern fall cohort of the neon flying squid, Ommastrephes bartramii, has been commercially exploited by the Chinese squid jigging fleet in the central North Pacific Ocean since the late 1990s. To understand and identify their optimal habitat, we have developed a habitat suitability index (HSI) model using two potential important environmental variables - sea surface temperature (SST) and sea surface height anomaly (SSHA) - and fishery data from the main fishing ground (165°-180°E) during June and July of 1999-2003. A geometric mean model (GMM), minimum model (MM) and arithmetic weighted model (AWM) with different weights were compared and the best HSI model was selected using Akaike’s information criterion (AIC). The performance of the developed HSI model was evaluated using fishery data for 2004. This study suggests that the highest catch per unit effort (CPUE) and fishing effort are closely related to SST and SSHA. The best SST- and SSHA-based suitability index (SI) regression models were SISST-based = 0.7SIeffort-SST + 0.3 SICPUE-SST, and SISSHA-based = 0.5SIeffort-SSHA + 0.5SICPUE-SSHA, respectively, showing that fishing effort is more important than CPUE in the estimation of SI. The best HSI model was the AWM, defined as HSI=0.3SISST-based+ 0.7SISSHA-based, indicating that SSHA is more important than SST in estimating the HSI of squid. In 2004, monthly HSI values greater than 0.6 coincided with the distribution of productive fishing ground and high CPUE in June and July, suggesting that the models perform well. The proposed model provides an important tool in our efforts to develop forecasting capacity of squid spatial dynamics.     

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 of P.stylifera was observed as 15 912 metric tons(mt)(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 mt(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 R~2(0.712) values.ASPIC computed MSY,CV,R~2,F_(MSY)and B_(MSY) parameters for the Fox model as 7 219 mt,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.     

Estimating time-based instantaneous total mortality rate based on the age-structured abundance index

The instantaneous total mortality rate(Z) of a fish population is one of the important parameters in fisheries stock assessment. The estimation of Z is crucial to fish population dynamics analysis,abundance and catch forecast,and fisheries management. A catch curve-based method for estimating time-based Z and its change trend from catch per unit effort(CPUE) data of multiple cohorts is developed. Unlike the traditional catch-curve method,the method developed here does not need the assumption of constant Z throughout the time,but the Z values in n continuous years are assumed constant,and then the Z values in different n continuous years are estimated using the age-based CPUE data within these years. The results of the simulation analyses show that the trends of the estimated time-based Z are consistent with the trends of the true Z,and the estimated rates of change from this approach are close to the true change rates(the relative differences between the change rates of the estimated Z and the true Z are smaller than 10%). Variations of both Z and recruitment can affect the estimates of Z value and the trend of Z. The most appropriate value of n can be different given the effects of different factors. Therefore,the appropriate value of n for different fisheries should be determined through a simulation analysis as we demonstrated in this study. Further analyses suggested that selectivity and age estimation are also two factors that can affect the estimated Z values if there is error in either of them,but the estimated change rates of Z are still close to the true change rates. We also applied this approach to the Atlantic cod(G adus morhua) fishery of eastern Newfoundland and Labrador from 1983 to 1997,and obtained reasonable estimates of time-based Z.     

Estimation of Natural Mortality Coefficient from Fish Abundance and Catch Data Using Virtual Population Analysis(VPA)

1 Introduction Natural mortality coefficient (M) is one of the key population parameters common to most analyses in fish stock assessment. Many mathematical models of fish stock dynamics include M directly or indirectly. However, until now M is the least known among the four factors affecting fish stock variation: recruitment, growth, natural death and fishing death (Quinn and Deriso, 1999). There is an extensive literature concerning the impact of errors in M on stock assessment (Mertz and…     

Standardizing CPUE of <Emphasis Type="Italic">Ommastrephes bartramii</Emphasis> for Chinese squid-jigging fishery in Northwest Pacific Ocean

Generalized linear models (GLM) and generalized additive models (GAM) were used to standardize catch per unit fishing effort (CPUE) of Ommastrephes bartramii for Chinese squid-jigging fishery in Northwest Pacific Ocean. Three groups of variables were considered in the standardization: spatial variables (longitude and latitude), temporal variables (year and month) and environmental variables, including sea surface temperature (SST), sea surface salinity (SSS) and sea level height (SLH). CPUE was treated as the dependent variable and its error distribution was assumed to be log-normal in each model. The model selections of GLM and GAM were based on the finite sample-corrected Akaike information criterion (AICC) and pseudo-coefficient (Pcf) combined P-value, respectively. Both GAM and GLM analysis showed that the month was the most important variable affecting CPUE and could explain 21.3% of variability in CPUE while other variables only explained 8.66%. The interaction of spatial and temporal variables weakly influenced the CPUE. Moreover, spatio-temporal factors may be more important in influencing the CPUE of this squid than environmental variables. The standardized and nominal CPUEs were similar and had the same trends in spatio-temporal distribution, but the standardized CPUE values tended to be smaller than the nominal CPUE. The CPUE tended to have much higher monthly variation than annual variations and their values increased with month. The CPUE became higher with increasing latitude-high CPUE usually occurred in 145°E–148°E and 149°E–162°E. The CPUE was higher when SST was 14–21°C and the SLH from −22 cm to −18 cm. In this study, GAM tended to be more suitable than GLM in analysis of CPUE.     

Evaluating the impact of spatio-temporal scale on CPUE standardization

This study focused on the quantitative evaluation of the impact of the spatio-temporal scale used in data collection and grouping on the standardization of CPUE(catch per unit effort).We used the Chinese squid-jigging fishery in the northwestern Pacific Ocean as an example to evaluate 24 scenarios at different spatio-temporal scales,with a combination of four levels of temporal scale(weekly,biweekly,monthly,and bimonthly)and six levels of spatial scale(longitude×latitude:0.5°×0.5°,0.5°×1°,0.5°×2°,1°×0.5°,1°×1°,and 1°×2°).We applied generalized additive models and generalized linear models to analyze the24 scenarios for CPUE standardization,and then the differences in the standardized CPUE among these scenarios were quantified.This study shows that combinations of different spatial and temporal scales could have different impacts on the standardization of CPUE.However,at a fine temporal scale(weekly)different spatial scales yielded similar results for standardized CPUE.The choice of spatio-temporal scale used in data collection and analysis may create added uncertainty in fisheries stock assessment and management.To identify a cost-effective spatio-temporal scale for data collection,we recommend a similar study be undertaken to facilitate the design of effective monitoring programs.     

A continuous time delay-difference type model (CTDDM) applied to stock assessment of the southern Atlantic albacore <Emphasis Type="Italic">Thunnus alalunga</Emphasis>

A continuous time delay-diff erence model (CTDDM) has been established that considers continuous time delays of biological processes. The southern Atlantic albacore (Thunnus alalunga) stock is the one of the commercially important tuna population in the marine world. The age structured production model (ASPM) and the surplus production model (SPM) have already been used to assess the albacore stock. However, the ASPM requires detailed biological information and the SPM lacks the biological realism. In this study, we focus on applying a CTDDM to the southern Atlantic albacore (T. alalunga) species, which provides an alternative method to assess this fishery. It is the first time that CTDDM has been provided for assessing the Atlantic albacore (T. alalunga) fishery. CTDDM obtained the 80% confidence interval of MSY (maximum sustainable yield) of (21 510 t, 23 118t). The catch in 2011 (24 100 t) is higher than the MSY values and the relative fishing mortality ratio (F ₂₀₁₁/F _MSY) is higher than 1.0. The results of CTDDM were analyzed to verify the proposed methodology and provide reference information for the sustainable management of the southern Atlantic albacore stock. The CTDDM treats the recruitment, the growth, and the mortality rates as all varying continuously over time and fills gaps between ASPM and SPM in this stock assessment.     

Standardization of CPUE for Chilean jack mackerel (Trachurus murphyi) from Chinese trawl fleets in the high seas of the Southeast Pacific Ocean

The generalized linear model (GLM) and generalized additive model (GAM) were applied to the standardization of catch per unit effort (CPUE) for Chilean jack mackerel from Chinese factory trawl fishing fleets in the Southeast Pacific Ocean from 2001 to 2010 by removing the operational, environmental, spatial and temporal impacts. A total of 9 factors were selected to build the GLM and GAM, i.e., Year, Month, Vessel, La Niña and El Niño events (ELE), Latitude, Longitude, Sea surface temperature (SST), SST anomaly (SSTA), Nino3.4 index and an interaction term between Longitude and Latitude. The first 5 factors were significant components in the GLM, which in combination explained 27.34% of the total variance in nominal CPUE. In the stepwise GAM, all factors explained 30.78% of the total variance, with Month, Year and Vessel as the main factors influencing CPUE. The higher CPUE occurred during the period April to July at a SST range of 12–15°C and a SSTA range of 0.2–1.0°C. The CPUE was significantly higher in normal years compared with that in La Niña and El Niño years. The abundance of Chilean jack mackerel declined during 2001 and 2010, with an increase in 2007. This work provided the relative abundance index of Chilean jack mackerel for stock assessment by standardizing catch and effort data of Chinese trawl fisheries and examined the influence of temporal, spatial, environmental and fisheries operational factors on Chilean jack mackerel CPUE.     

灰色马尔柯夫预测模型及其在渔获量预测中的应用

为海洋、江河渔获量的短期和长期预测提供一种预测精度较好的预测方法，将灰色ＧＭ（１，１）预测和马尔柯夫概率矩阵预测两者结合起来，通过它们的优点互补，使灰色马尔柯夫预测模型对渔获量的预测结果更科学、更精确。并用灰色马尔柯夫预测模型，预测山东省黄姑鱼的年渔获量。结果是令人满意的。     

Assessment of South Pacific Albacore Stock （Thunnus alalunga ） by Improved Schaefer Model

Based on catch and effort data of tuna longline fishery operating in the South Pacific Ocean, the South Pacific al-bacore stock was assessed by an improved Schaefer model. The results revealed that the intrinsic growth rate was about 1.28374 and carrying capacities vareied in the range from 73734 to 266732 metric tons. The growth ability of this species is remarkable. Stock dynamics mainly depends on environmental conditions. The stock is still in good condition. However, the continuous decreasing of biomass in recent years should be noticed.     

Evaluation of three harvest control rules for Bigeye Tuna (Thunnus obesus) fisheries in the Indian Ocean

The stock of Bigeye tuna(Thunnus obesus) in the Indian Ocean supports an important international fishery and is considered to be fully exploited. The responsible management agency, the Indian Ocean Tuna Commission(IOTC), does not have an explicit management decision-making framework in place to prevent over-fishing. In this study, we evaluated three harvest control rules, i) constant fishing mortality(CF), from 0.2 to 0.6, ii) constant catch(CC), from 60000 to 140000 t, and iii) constant escapement(CE), from 0.3 to 0.7. The population dynamics simulated by the operating model was based on the most recent stock assessment using Stock Synthesis version Ⅲ(SS3). Three simulation scenarios(low, medium and high productivity) were designed to cover possible uncertainty in the stock assessment and biological parameters. Performances of three harvest control rules were compared on the basis of three management objectives(over 3, 10 and 25 years): i) the probability of maintaining spawning stock biomass above a level that can sustain maximum sustainable yield(MSY) on average, ii) the probability of achieving average catches between 0.8 MSY and 1.0 MSY, and iii) inter-annual variability in catches. The constant escapement strategy(CE=0.5), constant fishing mortality strategy(F=0.4) and constant catch(CC=80000) were the most rational among the respective management scenarios. It is concluded that the short-term annual catch is suggested at 80000 t, and the potential total allowable catch for a stable yield could be set at 120000 t once the stock had recovered successfully. All the strategies considered in this study to achieve a ‘tolerable' balance between resource conservation and utilization have been based around the management objectives of the IOTC.     

Growth and mortality of small yellow croaker (Larimichthys polyactis) inhabiting Haizhou bay of China

Length frequency data of small yellow croaker(Larimichthys polyactis) were acquired from the survey vessel in May,July,September and December,2011 in Haizhou Bay of China.In this study,921 fish individuals were analyzed for the estimation of growth and mortality parameters.Between length and weight,the power coefficient b was 2.7321,2.9703,3.0418 and 2.7252 for the 4 surveying months,respectively.The estimated von Bertalanffy growth function parameters were 230mm(L ∞) and 0.26yr-1(K) as were calculated with ELEFAN method equipped in FiSAT computer package.With length-converted catch curve analysis,the total mortality rate(Z) and its 95% confidence interval were 2.16(1.69-2.64) yr-1,0.59(0.15-1.04) yr-1,1.16(0.80-1.52) yr-1 and 0.96(0.70-1.23) yr-1 for the 4 surveying months,respectively,with the pooled data the value was 1.15(0.81-1.48) yr-1.The natural mortality rate(M) was 0.516 yr-1 as was calculated with Pauly’s equation(the annual average sea water temperature was 11℃).Therefore,fish mortality rate was 0.634 yr-1.The yield-per-recruit analysis indicated that when t c was 1,F max was 0.7 and F 0.1 was 0.55.Currently,the age at first capture is about 1 year and F current was 0.634.Therefore,F current was larger than F 0.1 and less than F max.This indicates that current fish mortality is at a dangerously high level.With Gulland method,the biological reference point for fishery(F opt) was estimated as 0.516 yr-1,lower than current fish mortality.Accordingly,reducing catch in the region was strongly recommended.     

Fishery biology of jumbo flying squid Dosidicus gigas off Costa Rica Dome

The jumbo flying squid(Dosidicus gigas) population was surveyed with the help of Chinese squid jigging vessels off the Costa Rica Dome(4°–11°N, 90°–100°W) in 2009 and 2010. The daily catch of D. gigas in the two survey cruises ranged from 0 to 5.5 t and was mostly obtained from the areas bounded by 6°–9°N and 91°–94°W and by 6°30′–7°30′N and 96°–97°W. The sea surface temperature in the areas yielding the most catch ranged from 27.5 to 29℃. The sex ratio of the total catch was 3.75:1(female: male). The mantle length of the squid ranged from 211 to 355 mm(male) and from 204 to 429 mm(female) with an average of 297.9 and 306.7 mm, respectively. In the relationship of the mantle length(mm) and body weight(g) of the squid, there was no significant difference between sexes. The female and male were at a similar maturity, and most individuals are maturing or have matured with a few females being spent. The size(mantle length) and age at the first sexual maturity were 297 mm and 195 d in females, and less than 211 mm and 130 d in males, respectively. Most of the sampled stomachs(70.6%) had no food remains. The major preys of the squids were fish, cephalopods and crustaceans, with the most abundant Myctophum orientale and D. gigas. The preys in more than 65% of the non-empty sampled stomachs evidenced the cannibalism of D. gigas. The results improved current understanding of the fishery biology of D. gigas off the Costa Rica Dome, which may facilitate the assessment and management of relative fishery resources.     

基于地理神经网络加权回归的中国PM2.5浓度空间分布估算方法

中国空气污染问题日益严重,为获得连续的PM_2.5浓度空间分布,现有研究建立了多种基于统计回归的PM_2.5估算模型。然而,由于PM_2.5回归关系显著的空间非平稳性和复杂的非线性特征,如何实现高精度、高合理性的PM_2.5浓度空间大面估计仍然面临挑战,尤其在地形变化复杂、覆盖范围广阔的中国地区更为突出。本文引入了一种将普通线性回归（OLR）和神经网络结合的地理神经网络加权回归（GNNWR）模型,通过集成遥感数据、气象数据和地理信息数据建立了基于GNNWR的PM_2.5浓度空间估算方法。文章以中国2017年PM_2.5年平均浓度估算为例,开展了该模型与OLR、地理加权回归（GWR）的比较实验。实验结果表明,基于GNNWR的PM_2.浓度估算性能指标均明显优于OLR和GWR,且预测精度显著高于GWR。此外,GNNWR获得的PM_2.5浓度空间分布也更为合理,较为细致地刻画了中国地区PM_2.5浓度的局部空间变化和细节层次。     

中国沿海海洋站GNSS坐标时间序列噪声模型的建立与分析

选取中国沿海海洋站中与验潮室并址的22个GNSS基准站近9 a的观测资料,利用最大似然估计法分析各站时间序列的噪声特性,建立最优噪声模型;然后顾及有色噪声,利用最优噪声模型估计测站速度,并与纯白噪声模型和GLOBK获取的速度及误差进行对比分析。结果表明：1）沿海海洋站的GNSS时间序列均含有有色噪声,各分量的噪声特性不完全一致,E方向和U分量均以白噪声+闪烁噪声为主,N分量以白噪声+闪烁噪声和白噪声+一阶马尔科夫噪声+随机漫步噪声为主。2）全国沿海3个海区N、E分量的白噪声和闪烁噪声基本呈现越往南噪声越大的规律,南海海区U分量的白噪声和闪烁噪声最大。3）顾及有色噪声的速度中误差是仅考虑白噪声和GLOBK估计的速度中误差估计值的5～10倍,这种差异比内陆观测站的要大。4）在对海洋站GNSS时间序列进行速度分析时,为获取正确的速度值,应该先准确判断噪声的类型,再顾及有色噪声的影响估计测站速度。