Nominal Catch Per Unit Effort Research Articles

Enhancing Chub Mackerel Catch Per Unit Effort (CPUE) Standardization through High-Resolution Analysis of Korean Large Purse Seine Catch and Effort Using AIS Data

Accurate determination of fishing effort from Automatic Identification System (AIS) data improves catch per unit effort (CPUE) estimation and precise spatial management. By combining AIS data with catch information, a weighted distribution method is applied to allocate catches across various fishing trajectories, accounting for temporal dynamics. A Generalized Linear Model (GLM) and Generalized Additive Model (GAM) were used to examine the influence of spatial–temporal and environmental variables (year, month, Sea Surface Temperature (SST), Sea Surface Salinity (SSS), current velocity, depth, longitude, and latitude) and assess the quality of model fit for these effects on chub mackerel CPUE. Month, SST, and year exhibited the strongest relationship with CPUE in the GLM model, while the GAM model emphasizes the importance of month and year. CPUE peaked within specific temperature and salinity ranges and increased with longitude and specific latitudinal bands. Month emerged as the most influential variable, explaining 38% of the CPUE variance, emphasizing the impact of regulatory measures on fishery performance. The GAM model performed better, explaining 69.9% of the nominal CPUE variance. The time series of nominal and standardized indices indicated strong seasonal cycles, and the application of fine-scale fishing effort improved nominal and standardized CPUE estimates and model performance.

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.

Spatial estimation and yearly trends in abundance-index of Japanese jack mackerel (Trachurus japonicus) in the East China Sea and Sea of Japan

Catch per unit effort (CPUE) of commercial fisheries is recommended to be standardized because various factors can affect catch rates. Recently, the spatio-temporal analysis has been required to standardize fishery data, because catch is spatially distributed in proportion to fishery distribution and not in proportion to fish abundance. We developed the spatio-temporal models for the CPUE of large- and medium-sized purse seine fisheries for the different age groups of Japanese jack mackerel (Trachurus japonicus) to evaluate the potential spatial distribution and estimate the annual trends in abundance index. Japanese jack mackerel is widely distributed in the East Asian seas, including the East China Sea and Sea of Japan, and is an important fishery resource in Japan. Our models were delta-lognormal generalized additive mixed models that incorporated the temporal components of year and month, and size of fishing vessels as categorical variables, and the identity of the fishing vessels as a random variable. The spatial component represented by the latitude and longitude points of the fishing location was smoothed using an isotropic bivariate function. The water temperature at a depth of 50 m was incorporated to detect the effects of environmental factors. After all combinations of explanatory variables were evaluated, the model with the lowest Bayesian information criterion score was selected as the best model. All the best models among both binomial models and lognormal models contained all explanatory variables, although the best binomial model for age 2 exclude the vessel size and the best lognormal model for age 0 exclude the water temperature at a depth of 50 m. Our results indicate that the potential distribution areas for all ages of Japanese jack mackerel exist on the continental shelf in the southern part of the East China Sea, the western side of Kyushu Island, and along Honshu Island in the western part of the Sea of Japan. In contrast, the main distribution areas with higher density for age 0 were segregated into two: the southwestern East China Sea and the coastal area off Kyushu Island, although fish over age 1 were widely distributed. The standardized CPUE was higher than the nominal CPUE for age 0 from 2015 to 2020, and the standardized CPUE was also higher for age 1 in 2020. These results were derived from spatial models that compensated for the lack of spatial data due to the recent decrease in fishing operations in the Southeast China Sea. The yearly trends of standardized CPUE for each age could be used for the age-structured stock assessment model such as a tuned virtual population analysis. Improving the accuracy of the abundance index helps to further improve the reliability of stock assessments.

Standardized catch per unit effort and size compositions of Atlantic bonito, Sarda sarda (Bloch, 1793), harvested by artisanal fisheries in the Senegalese Exclusive Economic Zone (SEEZ)

Atlantic bonito, Sarda sarda (Bloch, 1793), is a valuable small tuna species to coastal countries and local communities. This species is heavily exploited by artisanal fisheries in the Senegalese Exclusive Economic Zone (SEEZ). The common gears used in artisanal fisheries for catching Atlantic bonito are gillnets, purse seines, longlines, and sleeping nets. Although catches of this species have increased in recent years, little is known about the Atlantic bonito catch per unit effort (CPUE) and size selectivity in theregion. As this information is relevant for many stock assessments, available catch, effort and size data from Atlantic bonito harvested over 15 years (2004–2018) with different gears were used in the present study to calculate the nominal and standardized CPUE, size-frequency distribution and length at retentions (50 % and 95 % selectivity) of the species. To eliminate the effects of temporal, spatial, and environmental factors, the Atlantic bonito CPUE was standardized using generalized linear and additive models. The retention length at 50 % and 95 % for each year and gear was calculated from the cumulative length frequency distribution as a proxy for selectivity. Optimal model standardization CPUE results showed a significant trend for gillnets, ranging from 4.6 kg/trip in 2005–92.65 kg/trip in 2018, while sleeping nets showed lower CPUE values, ranging from 2.12 kg/trip in 2004 up to 0.83 kg/trip in 2018. This study showed that the year, month, and fishing Area variables were the main factors affecting the nominal CPUE for all scenarios evaluated in this study. Catch data, CPUE and selectivity results have shown that gillnets are the most selective and efficient among the four main gears used to catch Atlantic bonito in the SEEZ.

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.

Distribution patterns and relative abundance of shortfin mako shark caught by the Taiwanese large-scale longline fishery in the Indian Ocean

The shortfin mako shark, Isurus oxyrinchus, is one of the most common bycatch species of the Taiwanese tuna longline fishery (Taiwan was second-largest shortfin mako shark-catching nation) in the Indian Ocean. The present study estimated the distribution and relative abundance indices of the shortfin mako in the Indian Ocean using observer records and logbook data from the period 2005–2018. To deal with the high number of zero catch records (∼90%), the catch per unit effort (CPUE) was standardized using a Zero inflated negative binomial model (ZINB). Cluster analysis was used to identify the fishing operations associated with different fishing strategies and then incorporated as a factor related to target species into the ZINB. Considerable variation was observed in the size distribution by region and season in the Indian ocean. Larger mako sharks (age>15 and age>7 yr for female and male, respectively) tend to occur in equatorial and tropical regions, while smaller specimens appear at higher latitudes in temperate waters. Cluster analysis produced five fishing clusters based on the catch compositions of the main species. The target factor (cluster) was important in explaining the variance in ZINB models. Overall, the standardized CPUEs and nominal CPUEs analysis for the shortfin mako caught by the Taiwanese longline fishery indicates a stable and increasing trend in recent years.

Long‐term catch trends and risk assessment of the Critically Endangered white‐spotted wedgefish (Rhynchobatus djiddensis) from South Africa

Abstract The white‐spotted wedgefish (Rhynchobatus djiddensis) is a Critically Endangered shark‐like ray in the family Rhinidae. Throughout its Western Indian Ocean distribution, it is targeted for its valuable meat and fins and is reported to have undergone major population declines. However, there remains a need for species specific time‐series data to accurately assess localized population declines. This study used two independent long‐term (37 and 40 years) time‐series catch data from competitive shore angling and shark nets to investigate the size composition and catch per unit effort (CPUE) and conduct a risk assessment for the population on the east coast of South Africa. From 1977 to 2017 the competitive shore fishery captured 7,703 individual R. djiddensis, whilst shark nets in the same region captured 2,856 individuals from 1981 to 2017. Individuals captured in the nets had a sex ratio of 1.8:1 females to males, and were larger than those caught by the anglers. Although the mean annual sizes of net‐caught individuals were above the size of reported sexual maturity, there was little evidence to suggest that any individuals captured were reproductively active. Both the competitive shore fishery and shark net catches exhibited strong seasonal trends with the majority of R. djiddensis catches occurring from October to May peaking in austral summer. Standardized CPUE from the competitive shore fishery declined substantially between 1977 and 2017 and shark net catches exhibited a significant (p < 0.05) fourfold decline in annual nominal CPUE from 1981 to 2017. Ultimately, a risk assessment showed a 65.1% decline in abundance over a period of three generation lengths, which indicates that the sampled population of R. djiddensis in South Africa should be classified as Endangered according to the IUCN Red List using criterion A2b. The conservation implications of this are discussed.

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.

Empirical habitat suitability model for immature albacore tuna in the North Pacific Ocean obtained using multisatellite remote sensing data

ABSTRACT This study developed an empirical habitat suitability model for identifying the optimal habitat of immature albacore tuna in the North Pacific Ocean (NPO) by using multisatellite remote sensing data and Taiwanese longline fisheries data between 2009 and 2016. An arithmetic mean model with five environmental variables is suggested to be appropriate for explaining the habitat variance of albacore in the NPO. A monthly albacore mean catch per unit effort (CPUE) of larger than 29.5 individuals/1,000 hooks occurred from November to March and was distributed over 25–40° N. The optimal range of environmental variables in the sea surface temperature, sea surface height, mixed layer depth, sea surface chlorophyll-a concentration of the previous month, and eddy kinetic energy for the habitat of albacore are suggested to be approximately 17–21°C, 0.24–0.84 m, 15–100 m, 0.07–0.29 mg m−3, and 0.0001–0.0031 m2 s−2, respectively. We discovered a significant positive relationship between the suitable habitat area and nominal CPUE of immature albacore in the NPO (Pearson’s correlation coefficient, r = 0.982). The mean position of the optimum habitat shifted southwardly from near 38° N in November to near 28–31° N during February to March. Overall, frontal structures play a major role in the stimulation of potential albacore habitats.

Historical analysis of blue marlin (Makaira nigricans Lacepède, 1802) catches by the pelagic longline fleet in the eastern Pacific Ocean

We analysed a historical (1959–2017) database of blue marlin catches reported by the industrial pelagic longline fleet operating in the eastern Pacific Ocean. The time series of catch-per-unit-effort (CPUE) for the time period 1959–2015 was modelled as a function of temporal and environmental variables by using generalised additive mixed models (GAMM). CPUE predictions were made on an independent dataset (2016–2017). Results suggested that a higher nominal CPUE occurred near the equator during boreal winter, spring and autumn, with a shift towards the north during the boreal summer. Two high CPUE events were observed, one in the early 1960s and one in the early 1990s. The final GAMM explained 61% of the total variance of the CPUE time series. Only a small percentage of total deviance was explained by the environmental variables, so we suggest that changes in fleet-targeting practices are the main cause of the large variability in the time series. The final GAMM can be used to predict blue marlin CPUE with a prediction accuracy of 3–8 fish per 1000 hooks. We observed an important decline in CPUE from the early 2000s; the factors responsible for this decline should be further investigated.

Impacts on CPUE from vessel fleet composition changes in an Australian lobster (Jasus edwardsii) fishery

ABSTRACTThe Australian Victorian Western Zone rock lobster fishery is assessed using standardised catch-per-unit-effort (CPUE). Nominal CPUE declined over 1978–2009, but this underrepresents the estimated decline in abundance, and since 2009 standardised CPUE rose notably less than nominal CPUE. This study identified vessel as a key factor that explains the discrepancy between nominal and standardised CPUE. The composition of the fleet changed since 2009, under the pressure of constraining total allowable catch quotas, with vessels exiting the fishery having substantially lower estimated catchability, which increased the average catchability of the remaining fleet. New diagnostic indices were constructed to quantify discrepancies between trends in nominal and standardised CPUE that assisted in identifying periods during which both catchability and fleet composition changed.

Examining spatiotemporal distribution and CPUE-environment relationships for the jumbo flying squid Dosidicus gigas offshore Peru based on spatial autoregressive model

The spatiotemporal distribution and relationship between nominal catch-per-unit-effort (CPUE) and environment for the jumbo flying squid ( Dosidicus gigas ) were examined in offshore Peruvian waters during 2009–2013. Three typical oceanographic factors affecting the squid habitat were investigated in this research, including sea surface temperature (SST), sea surface salinity (SSS) and sea surface height (SSH). We studied the CPUE-environment relationships for D. gigas using a spatially-lagged version of spatial autoregressive (SAR) model and a generalized additive model (GAM), with the latter for auxiliary and comparative purposes. The annual fishery centroids were distributed broadly in an area bounded by 79.5°–82.7°W and 11.9°–17.1°S, while the monthly fishery centroids were spatially close and lay in a smaller area bounded by 81.0°–81.2°W and 14.3°–15.4°S. Our results show that the preferred environmental ranges for D. gigas offshore Peru were 20.9°–21.9°C for SST, 35.16–35.32 for SSS and 27.2–31.5 cm for SSH in the areas bounded by 78°–80°W/82–84°W and 15°–18°S. Monthly spatial distributions during October to December were predicted using the calibrated GAM and SAR models and general similarities were found between the observed and predicted patterns for the nominal CPUE of D. gigas. The overall accuracies for the hotspots generated by the SAR model were much higher than those produced by the GAM model for all three months. Our results contribute to a better understanding of the spatiotemporal distributions of D. gigas offshore Peru, and offer a new SAR modeling method for advancing fishery science.

Application of a mixed modelling approach to standardize catch-per-unit-effort data for an abalone dive fishery in Western Victoria, Australia

Despite the prevalence of catch per unit effort (CPUE) as a key metric in fisheries assessments it can be fraught with inherent problems that often cause its use as an index of abundance to become contentious. This is particularly the case with abalone, a sedentary shellfish targeted by commercial dive fishers around the globe. It is common practice to standardize CPUE to at least partly address issues about how well it reflects the actual abundance of a stock. Differences between standardized and unstandardized trends may lead to controversy between scientists and stakeholders when standardized trends provide a less optimistic picture of stock status. It is within this context that we applied Linear Mixed Model (LMM) and Generalized Linear Mixed Model (GLMM) methods to standardize CPUE for the Western Zone blacklip abalone fishery in Victoria, Australia. This fishery was chosen for our evaluation because it included substantial population losses from a disease shock during the middle of the time series. The effects of diver, reef location, month and their interactions with year were included as random effects in these models and the results compared with nominal geometric means. The two standardization methods provided similar standardized CPUE trends and clearly demonstrated that a large proportion of the variance could be attributed to diver and spatial effects. The GLMM seemed to explain more variability in the data and produced better precision for standardized CPUEs than LMM. The temporal trend in variability attributed to divers and spatial scales reveals the impact of disease as well as any homo/heterogeneity effect. The CPUE trends responded to the impact of disease against a backdrop of declining stock, however when compared with the inter-annual pattern in nominal CPUE, the standardized trends showed that the decline immediately following the onset of disease was less precipitous. In contrast to what appeared to be an increase in the nominal series during the more recent post-disease period, there was only a slight non-significant increase observable in the standardized trends.

Vulnerability to longline fisheries of three hammerhead shark Sphyrna species in the south-western and equatorial Atlantic Ocean.

Catch and effort data from 29 418 longline sets from Brazilian tuna longline vessels operating in the south-western and equatorial Atlantic Ocean between 2004 and 2011 were analysed to investigate the distribution, catch rate and size of three species of hammerhead sharks (Sphyrna lewini, Sphyrna mokarran and Sphyrna zygaena). During that period, 6172 hammerhead sharks were caught. Among the elasmobranchs, the highest percentage of hammerhead sharks were caught in 2007, when they accounted for 3·90% of the group, while the lowest value of 0·40% was recorded in 2010. In general, the spatial distribution of the mean catch per unit effort (CPUE) by years and quarters showed a trend of higher catches near the equatorial region and in southern Brazil. The nominal mean CPUE was 0·12 Sphyrna spp. 1000(-1) hooks, with the highest value being recorded in 2007 (0·30 Sphyrna spp. 1000(-1) hooks). The standardized yearly CPUE estimated by a generalized linear model assuming a zero inflated negative binomial (ZINB) distribution were not much different from nominal values. Of the 205 sexed specimens, 117 were females and 88 were males, resulting in a sex ratio with a predominance of females (1·30:1·00), although not statistically significant. The total length of females ranged from 1200 to 2800 mm and of males from 1100 to 3100 mm. Juvenile hammerhead sharks represented 82 and 54% of the sexed female and male specimens, respectively.

Proof of concept for a novel procedure to standardize multispecies catch and effort data

The effect of variability in targeting needs to be removed from catch-per-unit-effort (CPUE) data to estimate reliable abundance indices for multispecies fisheries. We test a Generalized Additive Model (GAM) that includes principal component scores (PCs) derived from the species composition in the catch, called the ‘Direct Principal Component’ (DPC) procedure, for its ability to remove the effect of variable targeting. Biomass trends are simulated for two multispecies, multi-habitat fishery scenarios: (i) four species distributed differentially across two habitats and (ii) ten species distributed differentially across four habitats. Tweedie distributed CPUE records are generated from the biomass trends for a fishery with constant targeting (control scenarios) and time-varying targeting (test scenarios). The DPC procedure is simulation-tested for its ability to estimate the underlying biomass trends for all species relative to the non-standardized CPUE index. The DPC procedure proved to be more accurate compared to nominal CPUE trends in the test scenarios. Even in the control scenarios, the DPC procedure offers greater accuracy for the estimated year effect by removing substantial variation from the data, with a small penalty on the accuracy of the underlying abundance trend. However, caution is advised if the DPC-derived index diverges noticeably from alternative models despite no indications for shifts in targeting. A selection procedure based on eigenvalues of the PCs is suitable to identifying the best-performing number of PCs to include in the GAM. The DPC procedure should be applicable for a variety of multispecies fisheries.

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 Nina and El Nino 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 Nina and El Nino 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.