Catch Per Unit Effort Standardization Research Articles

Spatial-temporal neural networks for catch rate standardization and fish distribution modeling

Catch-per-unit-effort (CPUE) standardization is crucial for fishery stock assessment but often presents challenges due to spatial-temporal variations in species distribution and fishing effort. In this simulation study, we propose the use of customized artificial neural networks (ANNs) for modeling the spatial-temporal variations in CPUE standardization. This is achieved by encoding prior knowledge of the dependency structure between the variables into the architecture of the ANNs. We conducted numerical experiments on simulated data to compare our customized ANNs with Generalized Linear Models (GLMs), Generalized Additive Models (GAMs), and fully connected ANNs used in previous studies. Our simulated data cover three spatial-temporal dynamics scenarios with different degrees of species distribution shift over time: (1) steady fish distribution; (2) gradual directional shift over time; (3) sudden directional shift. In predicting the standardized CPUE in this simulation study, the customized ANNs demonstrated greater accuracy compared to the commonly used fully connected ANNs with an error reduction of over 70 %, more than 80 % compared to GLMs, and more than 40 % compared to GAMs, in terms of an error metric called the scaled mean absolute relative error. Our findings suggest that customized ANNs can serve as an alternative modeling tool alongside GLMs and GAMs in fisheries modeling.

Evaluation Performance of Three Standardization Models to Estimate Catch-per-Unit-Effort: A Case Study on Pacific Sardine (Sardinops sagax) in the Northwest Pacific Ocean

Catch-per-unit-effort (CPUE) standardization in fisheries is a critical foundation for conducting stock assessment and fishery conservation. The Pacific sardine (Sardinops sagax) is one of the economically important fish species in the Northwest Pacific Ocean (NPO). Hence, the importance of choosing an appropriate CPUE standardization model cannot be overstated when it comes to achieving a precise relative abundance index for the efficient management of Pacific sardine fishery. This study’s main aim was to assess and compare the efficacy of three models, specifically the General Linear Model (GLM), the Generalized Linear Mixed Model (GLMM), and the spatio-temporal GLMM (VAST), in the CPUE standardization for Pacific sardine fishery in the NPO, with the ultimate goal of identifying the most appropriate model. An influence analysis was applied to analyze the impact of individual variables on the disparity among standardized and nominal CPUE, and the main explanatory variables influencing standardized CPUE were identified. A coefficient–distribution–influence (CDI) plot was generated to analyze the impact of the different models on the annual standardized CPUE. Additionally, a simulation testing framework was developed to evaluate the estimated accuracy of the three models. The results indicated that the standardized CPUE and the nominal CPUE exhibited similar trends between 2014 and 2021 for the three models. Compared to the GLM and the GLMM, the VAST demonstrates larger conditional R2 and smaller conditional AIC, indicating a better performance in standardizing the CPUE for Pacific sardines due to its consideration of spatial and temporal variations. The interaction terms within the three models exert significant influences on the annual standardized CPUE, necessitating their inclusion in the model construction. CDI plots indicate that the spatio-temporal influence of the VAST model exhibits a smaller variation trend, suggesting that the VAST is more robust when standardizing the CPUE for Pacific sardines. Simulation testing additionally demonstrated that the VAST model displays smaller model root mean squared error (RMSE) and bias, establishing it as the superior performer for standardizing CPUE. Our results provide a theoretical basis for the scientific management of Pacific sardines in the NPO and can be extended to CPUE standardization for other small pelagic fish species worldwide.

Construction of CPUE standardization model and its simulation testing for chub mackerel (Scomber japonicus) in the Northwest Pacific Ocean

Standardized catch per unit effort (CPUE) data not only yield precise and biologically meaningful abundance indices but also provide crucial insights into the spatio-temporal distribution of fisheries resources, critical for their sustainable utilization and management. In this study, we integrated chub mackerel fishery statistics from the Northwest Pacific Ocean (NPO) and marine remote sensing environmental data, and constructed CPUE standardization models for chub mackerel based on the generalized linear mixed model (GLMM) and spatio-temporal GLMM (Vector Autoregressive Spatio-Temporal, VAST) and evaluated their performance. GLMM is a statistical technique extending GLM with random effects for complex datasets with non-independent observations and correlations or hierarchies. The VAST model is a statistical model for multivariate time series data with observations at different spatial locations, displaying both temporal autocorrelation and spatial dependence. Additionally, influence analysis and simulation testing were employed to quantify the effects of explanatory variables on the differences between nominal CPUE and standardized CPUE, and to assess the estimation accuracy of different models in CPUE standardization, respectively. Finally, the distribution pattern of chub mackerel in the NPO was analyzed by estimating centers of gravity (COGs). The results indicated that: 1) The GLMM model containing all explanatory variables was considered the best model with the smallest conditional Akaike Information Criterion (CAIC), while the VAST model, with only the covariate SST, performed the best. 2) Interactions of year and spatial effects in GLMM and the spatio-temporal variable in VAST, exerted a notable influence on annual standardized CPUE and should be accounted for in the model. 3) Based on simulation testing results, the VAST model exhibited lower model error (root mean square error, RMSE) and model bias, outperforming the GLMM model in CPUE standardization. 4) From 2014 to 2018, chub mackerel exhibited high biomass density widely distributed throughout almost the entire NPO, which shifted to the central-north region from 2019 to 2021. There was no significant northing and easting shift in COGs for the population (P > 0.05). The findings of this research enhance our understanding of the variations in resource abundance indices and spatio-temporal distribution patterns of chub mackerel in the NPO.

Bringing in the experts: application of industry knowledge to advance catch rate standardization for northern shortfin squid (Illex illecebrosus)

Sources of fisheries information outside of fishery-independent surveys (e.g. fishery-dependent data) are especially valuable for species that support productive fisheries and lack reliable biological information, such as the northern shortfin squid (Illex illecebrosus). Fishery-dependent data streams are available for most species, however collaboration with industry members is critical to ensure that these fishery-dependent data are collected, applied, and interpreted correctly. Despite the need for collaboration and the frequency that fishery data are used in scientific research, there is limited literature on the structure of interactions and knowledge sharing that inform the analysis and application of fishery data. Between 2019 and 2022, a group of researchers collaborated with members of the northern shortfin squid fishing industry to bring together research data sets and knowledge from harvesters and processors to better describe the fishery dynamics, distribution, life history, and oceanographic drivers of the species. The collaboration focused on developing custom standardized fishery catch per unit effort (CPUE) indices to provide indicators of population trends that accounted for the impacts of technical and economic aspects of harvesting, processing and marketing on fishing effort, selectivity and landings of northern shortfin squid. We describe the methods used to inform and interpret the CPUE analyses, focusing on novel structure of interactions we had with industry members, and suggest best practices for integrating industry knowledge into CPUE standardization. The information shared and research products produced through this science-industry research collaboration advanced understanding of northern shortfin squid population and fishery dynamics, and contributed directly to the 2022 stock assessment and management process. Given the complex and stochastic nature of the northern shortfin squid population and fishery, we found it critical to maintain open communication and trust with processors and harvesters, who have unique insight into the factors that may be driving changes in catch, landings, and productivity of the valuable resource species.

Standardizing fishery-dependent catch-rate information across gears and data collection programs for Alaska sablefish (Anoplopoma fimbria)

Abstract Indices of abundance used to inform stock assessment models are commonly derived from fishery-dependent data sources. However, fishery catch-per-unit-effort (CPUE) are often confounded by a myriad of factors for which corrections must be made using model-based standardization methods. The Alaska sablefish (Anoplopoma fimbria) fishery provides a fitting case study of such issues, wherein a regulatory change in 2017 disrupted historic fishery dynamics, promoting a rapid transition in use of pot gear over demersal hook-and-line gear in the Gulf of Alaska. To address this, we combined across both observer and logbook programs (data sources) and gear types to develop an intercalibrated abundance index. We first regressed observer records against vessel logbooks to understand potential biases that may arise from combining data sources during the CPUE standardization process. Here, we found that both data sources exhibited strong agreement in reported CPUEs when compared on a set-by-set basis. Therefore, we intercalibrated both CPUE data sources and developed an index of abundance that incorporated catch records from both demersal hook-and-line and pot gear fisheries for sablefish in Alaska, to account for the recent rapid change in gear use. This standardized index of abundance compared favourably with an index generated from a fishery-independent hook-and-line survey currently used in management, suggesting it is representative of sablefish population trends. Our findings not only represent a valuable contribution to the management of sablefish in Alaska, but also provide a widely applicable framework for standardizing fishery-dependent CPUE data to support the management of multi-gear fisheries.

CPUE Estimation and Standardization Based on VMS: A Case Study for Squid-Jigging Fishery in the Equatorial of Eastern Pacific Ocean

Different fisheries, even the same fishery, use different ways of quantifying fishing efforts such as the number of vessels, days, voyages, and hooks. In squid-jigging fisheries, fishing hours, fishing days, and the number of vessels are valid units for calculating the catch per unit effort (CPUE). A vessel monitoring system (VMS) provides vessel position data with high spatial and temporal resolution and offers the possibility to quantify the CPUE at a finer scale. Using the squid fishery in the equatorial waters of the eastern Pacific as a case study, the CPUE was evaluated and standardized based on VMS data. The drifting operating points of the squid fishing vessels were filtered by the speed threshold, solar radiation, and operating time setting methods, leading to the number of fishing hours per day, and the nominal CPUE was calculated by combining the catch data obtained from logbooks. Then, the generalized linear model (GLM) and generalized additive model (GAM) were applied to conduct CPUE standardization considering spatiotemporal factors and environmental variables including sea surface temperature (SST), sea surface salinity (SSS), sea surface height (SSH), and chlorophyll-a (Chl_a). The results showed that month, latitude, SST, SSH, and Chl-a all have a high significant effect on CPUE as demonstrated through the significance test conducted by GLM. The GAM including the significant factors was judged to be the best model according to the AIC guidelines. The latitude range for high CPUE in the fishery was 3°S~0°S, SST range 24~25 °C, SSH range 4~8 m, and Chl_a range 0.15~0.20 mg/m3. In addition, the nominal and standardized CPUEs were compared based on fishing hours and fishing days. The results indicated that the two types of CPUEs were highly related hence there was no significant difference.

CPUE retrieval from spaceborne lidar data: A case study in the Atlantic bigeye tuna fishing area and Antarctica fishing area

The measurement of Catch Per Unit Effort (CPUE) supports the assessment of status and trends by managers. This proportion of total catch to the harvesting effort estimates the abundance of fishery resources. Marine environmental data obtained by satellite remote sensing are essential in fishing efficiency estimation or CPUE standardization. Currently, remote sensing chlorophyll data used for fisheries resource assessment are mainly from passive ocean color remote sensing. However, high-resolution data are not available at night or in high-latitude areas such as polar regions due to insufficient solar light, clouds, and other factors. In this paper, a CPUE inversion method based on spaceborne lidar data is proposed, which is still feasible for polar regions and at nighttime. First, Atlantic bigeye tuna CPUE was modeled using Cloud aerosol lidar and infrared pathfinder satellite observations (CALIPSO) lidar-retrieved chlorophyll data in combination with sea surface temperature data. The Generalized Linear Model (GLM), Artificial Neural Network (ANN) and Support Vector Machine Methods (SVM) were used for modeling, and the three methods were compared and validated. The results showed that the correlation between predicted CPUE and nominal CPUE was higher for the ANN method, with an R2of 0.34, while the R2was 0.08 and 0.22 for GLM and SVM, respectively. Then, chlorophyll data in the polar regions were derived using CALIPSO diurnal data, and an ANN was used for Antarctic krill. The inversion result performed well, and it showed that the R2of the predicted CPUE to nominal CPUE was 0.92. Preliminary results suggest that (1) nighttime measurements can increase the understanding of the diurnal variability of the upper ocean; (2) CALIPSO measurements in polar regions fill the gap of passive measurements; and (3) comparison with field data shows that ANN-based lidar products perform well, and a neural network approach based on CALIPSO lidar data can be used to simulate CPUE inversions in polar regions.

CPUE standardization for southern bluefin tuna (Thunnus maccoyii) in the Korean tuna longline fishery, accounting for spatiotemporal variation in targeting through data exploration and clustering.

Accounting for spatial and temporal variation in targeting is a concern in many catch per unit effort (CPUE) standardization exercises. In this study we standardized southern bluefin tuna (Thunnus maccoyii, SBT) CPUE from the Korean tuna longline fishery (1996–2018) using generalized linear models (GLMs) with operational set by set data. Data were first explored to investigate the operational characteristics of Korean tuna longline vessels fishing for SBT, such as the spatial and temporal distributions of effort, and changes in the nominal catch rates among major species and species composition. Then we estimated SBT CPUE by area used for the stock assessment in the CCSBT (Commission for the Conservation of Southern Bluefin Tuna) and identified two separate areas in which Korean tuna longline vessels have targeted SBT and albacore tuna (T. alalunga), with targeting patterns varying spatially, seasonally and longer term. We applied two approaches, data selection and cluster analysis of species composition, and compared their ability to address concerns about the changing patterns of targeting through time. Explanatory variables for the GLM analyses were year, month, vessel identifier, fishing location (5° cell), number of hooks, moon phase, and cluster. GLM results for each area suggested that location, year, targeting, and month effects were the principal factors affecting the nominal CPUE. The standardized CPUEs for both areas decreased until the mid-2000s and have shown an increasing trend since that time.

Catch and effort standardization for Taiwanese swordtip squid Uroteuthis edulis fisheries in the southern East China Sea

The swordtip squid Uroteuthis edulis is a neritic squid species that is widely distributed within the coastal waters of subtropical and tropical Indo-western Pacific regions. This squid is a commercially important species throughout its distribution range and plays a critical role in marine ecosystems. Stock assessments of the squid in the East China Sea (ECS) are scarce, although it is crucial information for the sustainability of the squid and relevant fisheries. The squid was harvested by various fisheries in the southern ECS, which could restrict methods for catch-effort standardization. In this study, catch-effort data of Taiwanese swordtip squid fisheries in the southern ECS between 2002 and 2013 were standardized by the relative catch per unit effort (CPUE) comparison method to examine annual abundance variations of the squid. The results revealed that the standardized CPUE of squid fisheries was lower than the nominal CPUE series. The standardized CPUE of the squid from Taiwanese torch-light vessels revealed slight variability between 2003 and 2009 and great inter-annual variations between 2010 and 2013. The effects of varying parameters on the relative power factor estimates of the vessels are examined and discussed. This study provides an alternative approach of CPUE standardization for swordtip squid fisheries in the southern ECS and a relative abundance index of the squid, which could support further stock assessment and management of fisheries in the region.

Performance of a finite mixture model in CPUE standardization for a longline fishery with target change

Standardized catch per unit effort (CPUE) is frequently used to represent annual fish-stock abundance trends. In multispecies fisheries, the catchability coefficient differs depending on the target strategy. Consideration of the target strategy in CPUE standardization is therefore crucial in improving the accuracy and precision of annual CPUE estimates. This study focuses on the potential of a finite mixture model (FMM), where target species and their annual trends are estimated simultaneously in a single model. The eight candidates of CPUE standardization methods—including the FMM—were used to evaluate the methodological performance as to whether they could identify the target strategy. A numerical simulation was undertaken using simplified data that mimic a Japanese longline fishery considering the target change over years, area effect, and multispecies abundance trends. Our simulation study indicated that the FMM outperformed other methods in the various scenarios, and therefore it is concluded that the FMM is an effective and robust methodology for clarifying the target strategy in the modeling of CPUE standardization.

Catch per unit effort (CPUE) standardization of Argentine shortfin squid (Illex argentinus) in the Southwest Atlantic Ocean using a habitat-based model

ABSTRACT The Argentine shortfin squid (Illex argentinus) in the Southwest Atlantic Ocean is an economically important cephalopod. Catch per unit effort (CPUE) of species, a critical proxy for fish abundance, affected by many factors, including environmental variables. Given the importance and less considering of multiple environmental factors in the CPUE standardization for I. argentinus, this study provided an accurate standardized CPUE based on the habitat-based model incorporating the effect of environmental variables including sea surface temperature (SST), sea surface height (SSH), and chlorophyll a concentration (Chl-a). The results showed that the trends of yearly and monthly nominal CPUE were consistent with the standardized CPUE. As the highly concentrated fishing operations, the effective fishing effort was always lower than nominal fishing effort, subsequently, the standardized CPUE was found to be higher than nominal CPUE. Among the environmental factors, SSH and Chl-a are the primary environmental variables when standardizing I. argentinus CPUE revealed by habitat-based model. The precision of CPUE standardization could be improved by our methods. The findings will help to grasp the dynamics in the abundance and provide a basic information for the I. argentines stock assessment in the Southwest Atlantic Ocean.

CPUE standardization for the Pacific saury (Cololabis saira) fishery in the Northwest Pacific

In stock assessment and fisheries management, the standardization of catch per unit effort (CPUE) is critical to derive an effective index of the abundance of exploited fish stocks. In addition to developing various statistical models, a method including the relative fishing power (RFP) of individual vessels has been developed from an intuition concept and applied to bottom trawl fisheries using logbook-based data. This is a convenient and straightforward approach for standardizing the catch-effort data of a fishery. The Pacific saury is a pelagic fish species, and its stock in the Northwest Pacific has been targeted by international fleets since the early 1980s; annual production ranged from 328,000 to 630,000 tons during 2001- 2016. The Pacific saury stock has been one of the resources managed by the North Pacific Fisheries Commission (NPFC) since 2015. In this study, the RFP method in CPUE standardization was applied to the Taiwanese Pacific saury fishery in the Northwest Pacific, and the effects of varying the parameters of the model were analyzed. The results indicate that more than 98.8% of the fishing effort can be standardized according to the estimated RFP, with 20 being the minimum number of required comparisons in each of the 4-year periods during 2001-2016. The RFP values of the model generally correlate with the length and gross tonnage of fishing vessels in the saury fishery. The result of the model, applied to the Pacific saury fishery, is robust when the model considers the effects of varying the model parameters (the minimum number of comparisons and standard vessel). This study suggests that first, the RFP method is appropriate for the CPUE standardization of the Pacific saury fishery, and that second, the resulting CPUE can be an effective index of abundance that reflects the annual variability of the Pacific saury stock in the Northwest Pacific.

Comparative analysis of CPUE standardization of Chinese Pacific saury (Cololabis saira) fishery based on GLM and GAM

Pacific saury is an important high-seas fishery resource in the Northwest Pacific Ocean for the Chinese Mainland. Reliable and accurate catch per unit effort (CPUE) plays a significant rule in Pacific saury stock assessment. Many statistical models have been used in the previous CPUE standardization research. Here, we compare the performance of Generalized Linear Models (GLMs) and Generalized Additive Models (GAMs) using CPUE data collected from Chinese saury fishery in the Northwest Pacific Ocean from 2003 to 2017 (excluding data from Chinese Taipei), and evaluate the influence of spatial, temporal, environmental variables and vessel length on CPUE. Optimal GLM/GAM models were selected using the Bayesian information criterion (BIC). Explained deviance and 5-fold bootstrap cross-validation results were used to compare the performance of the two model types. Fitted GLMs accounted for 21.57% of the total model-explained deviance, while GAMs accounted for 38.95%. Predictive performance metrics and 5-fold cross-validation results showed that the best GAM performed better than the best GLM. Therefore, we recommend GAM as the preferred model for standardizing CPUE of Pacific saury in the Northwest Pacific Ocean.

Evaluation of the impacts of different treatments of spatio-temporal variation in catch-per-unit-effort standardization models

Many stock assessments heavily rely on indices of relative abundance derived from fisheries-dependent catch-per-unit-effort (CPUE) data. Therefore, it is critical to evaluate different CPUE standardization methods under varying scenarios of data generating processes. Here, we evaluated nine CPUE standardization methods offering contrasting treatments of spatio-temporal variation, ranging from the basic generalized linear model (GLM) method not integrating a year-area interaction term to a sophisticated method using the spatio-temporal modeling platform VAST. We compared the performance of these methods against simulated data constructed to mimic the processes generating fisheries-dependent information for Atlantic blue marlin (Makaira nigricans), a common bycatch population in pelagic longline fisheries. Data were generated using a longline data simulator for different population trajectories (increasing, decreasing, and static). These data were further subsampled to mimic an observer program where trips rather than sets form the sampling frame, with or without a bias towards trips with low catch rates, which might occur if the presence of an observer alters fishing behavior to avoid bycatch. The spatio-temporal modeling platform VAST achieved the best performance in simulation, namely generally had one of the lowest biases, one of the lowest mean absolute errors (MAEs), and 50% confidence interval coverage closest to 50%. Generalized additive models accounting for spatial autocorrelation at a broad spatial scale (one of the lowest MAEs and one of the lowest biases) and, to a lesser extent, non-spatial delta-lognormal GLMs including a year-area interaction as a random effect (one of the lowest MAEs and one of the best confidence interval coverages) also performed adequately. The VAST method provided the most comprehensive and consistent treatment of spatio-temporal variation, in contrast with methods that simply weight predictions by large spatial areas, where it is critical, but difficult, to get the a priori spatial stratification correct before weighting. Next, we applied the CPUE standardization methods to real data collected by the National Marine Fisheries Service Pelagic Observer Program. The indices of relative abundance predicted from real observer data were relatively similar across CPUE standardization methods for the period 1998–2017 and suggested that the blue marlin population of the Atlantic declined over the period 1998–2004 and was relatively stable afterwards. As spatio-temporal variation related to environmental changes or depletion becomes increasingly necessary to consider, greater use of spatio-temporal models for standardizing fisheries-dependent CPUE data will likely be warranted.

Target-based catch-per-unit-effort standardization in multispecies fisheries

Standardized catch per unit effort (CPUE) is a fundamental component of fishery stock assessment. In multispecies fisheries, catchability can differ depending on which species is being targeted, and so the yearly trend extracted from the standardized CPUE is likely to be biased. We have, therefore, developed a method for predicting the unobserved variable related to targeted species from among multispecies composition data using a mixture regression model for the transformed residuals. In contrast with traditional methods, the proposed method predicts the target variable in CPUE standardization without removing a subset of the data. Keeping the entire data set avoids information loss, and so CPUE standardization with the predicted target variable should yield an unbiased estimate of the yearly trend. Simple simulation tests demonstrate that our method outperforms traditional methods. We illustrate the use of our method by applying it to CPUE data on arabesque greenling (Pleurogrammus azonus) caught in multispecies trawl fisheries in Hokkaido, Japan.

The effect of hook spacing on longline catch rates: Implications for catch rate standardization

Catch per unit effort (CPUE) is a widely used index of population abundance for informing stock assessments for the purpose of estimating population status and setting fishing policies. However, for CPUE to be an unbiased index, influences that are not related to population abundance (e.g., spatial variation in effort and temporal changes in gear efficiency) must be accounted for in analyses known as CPUE standardization. In longline fisheries, one important factor that can affect CPUE is the spacing between hooks (‘spacing effect’), which influences effective effort and has largely been ignored in previous analyses. Here, we use the Pacific halibut (Hippoglossus stenolepis) long-line fishery as a case study to explore the spacing effect. Both commercial and experimental (fishery-independent) data with hook spacing, and a survey-based CPUE series, are available for this fishery. It thus provides a unique opportunity to explore the effect of hook spacing and its effect on CPUE trends. We quantify this effect using non-parametric and parametric relationships inside a spatially-explicit (geospatial) CPUE standardization model for commercial data, and non-linear mixed-effects model for experimental data. We found a clear non-linear spacing effect (i.e., hooks were less effective the closer they were), but accounting for space had a larger effect on CPUE trends than accounting for hook spacing. For this stock, it is likely the effect of hook spacing on CPUE was minimal due to little variation in average hook spacing over time. Regardless, historical and future trends in hook spacing can have important effects on longline CPUE standardization, highlighting the value of collecting this information. Accounting for hook spacing effects in other fisheries may improve estimates of trends in relative abundance and lead to better management.

Think outside the grids: An objective approach to define spatial strata for catch and effort analysis

A method is developed to objectively stratify a study area for standardizing catch per unit effort (CPUE) data for use in a generalized linear modeling framework. The method involves two steps: (1) subdividing the study area into small grids and applying a boosted regression tree method based on the full model to reconstruct a rough population pattern among grids and (2) applying a k-medoids algorithm to partition the grids into discrete strata based on the trend and magnitude of expected CPUE in each grid (time series clustering) or the spatial proximity and average CPUE in each grid (spatial clustering). The main advantage of this method compared to other area stratification approaches is that the shape of the resulting strata best matches the population structure as inferred from CPUE. This approach reduced bias in indices of abundance compared to ad-hoc methods. The variant of the area stratification method based on time series clustering performed best. However, this approach often performed as poorly as ad-hoc methods when there was a unidirectional shift in fishing locations over time because strata did not necessarily capture the shifts in fishing grounds over time. However, when the strata were selected to capture the shift in fishing grounds, both arbitrary and cluster-based method produced an index of abundance that was close to unbiased. Finally, the study showed that goodness of fit criteria (AICc or BIC) can be used to select the optimal number of strata for CPUE standardization, but this does not guarantee improved accuracy of the derived index of abundance compared to a pre-specified number of strata. The importance of conducting sensitivity analysis and the use of an ensemble approach is highlighted.

Reducing Bias and Filling in Spatial Gaps in Fishery‐Dependent Catch‐per‐Unit‐Effort Data by Geostatistical Prediction, II. Application to a Scallop Fishery

AbstractFishery‐dependent catch per unit effort (CPUE) comprises critical input for many stock assessments. Construction of CPUE indices usually employs some method of data standardization. However, conventional methods based on linear models do not effectively deal with the fact that samples are collected with a selection bias or with the problem of filling spatial gaps. Geostatistical interpolation methods can ameliorate some of the biases caused by both of these problems while remaining complementary to traditional linear model‐based CPUE standardization. In this paper we present geostatistical estimates of sea scallop Placopecten magellanicus CPUE from tows recorded by onboard observers during an opening of Georges Bank Closed Area II in 1999. By selecting tows for which there was little prior effort (on the basis of accumulated effort measured by vessel monitoring systems), we obtained tows that reflected initial abundance as closely as possible. These tows were used to obtain a variogram which was used in geostatistical prediction of sea scallop CPUE. The kriged mean was substantially lower than the arithmetic sample mean, indicating that a geostatistical approach reduced the influence of repeated sampling in locations of extremely high CPUE and increased the weight of isolated observations in areas of low CPUE. The results produced a map that was qualitatively similar to that obtained from a preseason fishery‐independent survey. Overall differences between the two approaches were driven by the extension of predictions into areas at the edges of spatial autocorrelation where kriging predictions approached the grand mean of the data set.Received July 26, 2013; accepted June 2, 2014

Billfish CPUE standardization in the Hawaii longline fishery: Model selection and multimodel inference

This paper presents catch per unit effort (CPUE) standardizations and model selection procedures for four billfish species (Family Istiophoridae) caught primarily as bycatch in the Hawaii-based pelagic longline fishery during 1995–2011: Blue marlin Makaira nigricans; Striped marlin Kajikia audax; Shortbill spearfish Tetrapturus angustirostris; and Sailfish Istiophorus platypterus. The first three species were analyzed on a fishery-wide basis. For sailfish, the fishery data came exclusively from tuna-targeted longline sets in the deep-set sector of the Hawaii-based fishery. We used fishery observer data from the NOAA Fisheries Pacific Islands Regional Observer Program to fit the CPUE standardization models. In this context, our objective was to investigate the quality of model fit for five types of generalized linear models (GLMs: Poisson; negative binomial; zero-inflated Poisson; zero-inflated negative binomial; delta-Gamma). Each of these models represented a different hypothesis about the capture process for a bycatch species for which the catch data primarily consisted of zero catch observations. The five GLMs were fitted by forward entry variable selection, and the best fitting GLM for each species was selected on the basis of Akaike Information Criterion values and calculated Akaike weights. The best-fitting model selected for each species was a zero-inflated negative binomial GLM (ZINB). The ZINB model was comprised of a negative binomial counts model for expected zero catch sets and a positive catch per set distribution along with a binomial inflation model to account for excess zeros. For each species, the important explanatory variables for standardizing CPUE were fishing year, fishing (i.e., calendar) quarter, and fishing region. The best-fitting models indicated that standardized CPUE for striped and blue marlins decreased significantly during the study period. Because the ZINB model was selected as the best fitting model for all species, we suggest that longline CPUE for incidentally caught billfishes is best represented as a process characterized by zero inflation and overdispersion in the positive catches and expected zero catches. We therefore recommend that ZINB models be considered as an a priori model for CPUE standardizations of billfishes and other bycatch species in longline fisheries.

Model selection between traditional and popular methods for standardizing catch rates of target species: A case study of Japanese Spanish mackerel in the gillnet fishery

Improving existing catch per unit effort (CPUE) models for construction of a fishery abundance index is important to fish stock assessment and management. CPUE standardization research is a rapidly developing field, and many statistical models have been used, including generalized linear models (GLMs), generalized additive models (GAMs), regression trees (RTs) and artificial neural networks (ANNs). However, the popular and influential methods, random forests (RFs) and support vector machines (SVMs) have not been used in this field. We evaluate the performance of six candidate methods (GLMs, GAMs, RTs, RFs, ANNs and SVMs) using gillnet data for Japanese Spanish mackerel (Scomberomorus niphonius) collected by a fishery-dependent survey (National Basic Research Program of China, NBRPC) in the south of the Yellow Sea from 2006 to 2012. Predictive performance metrics and Regression Error Characteristic (REC) curves computed by 10-fold cross-validation results showed that the SVM provided the best performance among the six candidate models and slightly improved the prediction accuracies compared to RF. However, the traditional methods GLM and GAM were inferior to the other four nonlinear statistical models (RTs, ANNs, RFs and SVMs). In general, RFs and SVMs should be considered as potential statistical methods for CPUE standardization. Model performance was affected by several factors, including data structure and model construction. Therefore, further research should focus these factors to improve model functionality.