Standardized Catch Per Unit Effort 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.

Distribution Patterns of Grey Mullet in the Taiwan Strait in Relation to Oceanographic Conditions

The depletion of the grey mullet population in the Taiwan Strait has generated interest in conservation initiatives and to enhance the effectiveness of restoration initiatives, it is essential to comprehend the specific habitat preferences of the species in question, particularly in relation to environmental changes. In this study, habitat suitability modelling was used to investigate the distribution patterns of grey mullet. Sea surface height (SSH) was the most significant predictor of the grey mullet standardized catch per unit effort (S.CPUE). Furthermore, sea surface temperature (SST) was the second most important oceanographic parameter, followed by mixed-layer depth (MLD). The grey mullet S.CPUE peaked in regions with the summed SST, Chla, salinity, SSH, MLD and current values being 20 °C, 0.9 mg/m3, 34.5 psu, 0.45 m, 18–38 m, and 0.25 m2/s2, respectively. From late October, HSI levels in the northern Taiwan ocean increased, with a consistent upward trend. The distribution expanded in November and December, focusing on the northern and mid-ocean regions west of Taiwan. HSI levels peak in January and February, especially in Taiwan’s coastal region. High HSI distribution shifts away from coastlines and diminishes in April. The findings of this study will contribute to the understanding of this specific species and the approach used in this study may be applicable to other fisheries stocks also.

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.

Estimation of Preferred Habitats and Total Catch Amount of the Round Scad <i>Decapterus maruadsi</i> and Five Other Scad Species in the East China Sea and Sea of Japan

The preferred habitats of the round scad Decapterus maruadsi and five other scads (amberstripe scad D. muroadsi, mackerel scad D. macarellus, red scad D. akaadsi, roughear scad D. tabl, and shortfin scad D. macrosoma) in the East China Sea (ECS) and the Sea of Japan were examined based on catch records of Japanese large- and medium-type purse seine fisheries from 1993 to 2020. Delta-lognormal generalized additive models indicated that the habitats of the round scad and the other five scads were clearly segregated. A high round scad catch per unit effort (CPUE) was observed in the southwestern ECS on the continental shelf and coastal waters around Kyusyu, whereas for the other five scads, a high CPUE was observed around the shelf break and Kuroshio area. The standardized annual CPUEs of the scads decreased after 2010. Based on the proportion of round scad to all six scads calculated by spatial standardized CPUEs for each fishery grid, the total catch amount of round and the other five scads including coastal fisheries was approximately 2,071-13,382 tons and 3,496-24,617 tons, respectively. These estimates are important for scad stock assessment and developing effective management strategies.

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.

Ensemble Three-Dimensional Habitat Modeling of Indian Ocean Immature Albacore Tuna (Thunnus alalunga) Using Remote Sensing Data

This study evaluated the vertical distribution of immature albacore tuna (Thunnus alalunga) in the Indian Ocean as a function of various environmental parameters. Albacore tuna fishing data were gathered from the logbooks of large-sized Taiwanese longline vessels. Fishery and environmental data for the period from 1998 to 2016 were collected. In addition to the surface variable, the most influential vertical temperature, dissolved oxygen (OXY), chlorophyll, and salinity layers were found at various depths (i.e., 5, 26, and 53 m for SST; 200, 244, and 147 m for OXY; 508, 628, and 411 for SSCI; and 411, 508, and 773 m for SSS) among 20 vertical layers based on Akaike criterion information value of generalized linear model. Relative to the 20 vertical layers base models, these layers had the lowest Akaike information criteria. For the correlation between the standardized and predicted catch per unit effort (CPUE), the correlation values for the generalized linear model (GLM), generalized additive model (GAM), boosted regression tree (BRT), and random forest (RF) model were 0.798, 0.832, 0.841, and 0.856, respectively. The GAM-, BRT-, and RF-derived full models were selected, whereas the GLM-derived full model was excluded because its correlation value was the lowest among the four models. From March to September, a higher immature albacore standardized CPUE was mainly observed from 30°S to 40°S. A northward shift was observed after September, and the standardized CPUE was mainly concentrated at the south coast of Madagascar from November to January.

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.

Effects of decadal climate variability on spatiotemporal distribution of Indo-Pacific yellowfin tuna population

Spatial variations in tuna population and abundance are strongly linked to large-scale climate fluctuations, such as the Pacific decadal oscillation (PDO) and Atlantic multidecadal oscillation (AMO). However, the mechanisms underlying the association of climate indices with yellowfin tuna (YFT) abundance and habitat preference remain unclear. We analysed long-term longline fishery data for YFT and oceanic climate variability index data for 1971–2018. The standardized catch per unit effort (CPUE) of Indo-Pacific Ocean YFT was higher during negative AMO and positive PDO phases. In tropical Pacific Ocean, the trend of YFT habitat preference exhibited seesaw patterns because of the distinct environmental factors influenced by the PDO phase. The PDO changed the environmental parameters throughout the tropical Indian Ocean such that the habitat preference of YFT remained consistent throughout. However, the variations in habitat suitability did not correspond to the distribution or standardized CPUE of YFT throughout the Pacific Ocean during AMO events. Moreover, the changes in habitat suitability had a positive periodicity of 8–16 years with AMO in the Indian Ocean, but revealed opposite trends with the distribution or standardized CPUE of YFT. Our results provide sufficient information to distinguish the variations between PDO phase changing and YFT standardized CPUE/ habitat preference. Furthermore, the AMO phase shift period 60–100 years longer than that of the PDO (20–30 years), and models employing time series of fishery and environmental data must be extended the time period of our study to make the AMO match the fishery data more complete.

Evaluation of the influence of spatial treatments on catch-per-unit-effort standardization: A fishery application and simulation study of Pacific saury in the Northwestern Pacific Ocean

Fishery-dependent catch-per-unit-effort (CPUE) data often exhibit spatial heterogeneity over space and time, which means that the spatial treatment in statistical models used to standardize CPUE is critically important. We evaluated several spatial treatments to standardize CPUE data using Generalized Linear Mixed Models (GLMMs). Results include a real-world application and a simulation based on the Taiwanese stick-held dip net fishery for Pacific saury in the Northwestern Pacific Ocean. We compared the performance of three spatially stratified approaches in GLMMs, (i) Ad hoc; (ii) Binary (binary recursive area partitioning based on model selection criteria); and (iii) Spatial clustering (partitioning of grids into discrete strata based on the spatial proximity and average CPUE in each grid), to a spatio-temporal GLMM (VAST). An influence analysis was constructed to quantify discrepancies between unstandardized and standardized indices that assisted in identifying the annual influence of explanatory variables in GLMMs. We developed a simulation to corroborate the results from the case study and evaluated the four spatial treatments using data generated from two contrasted, random and preferential, sampling scenarios. Results from the real-world application indicated that VAST was statistically superior to the other approaches, based on conditional deviance explained, conditional Akaike Information Criterion, and five-fold cross-validations. The influence analysis indicated that the interaction of year and spatial effect or spatio-temporal variable had a major influence on the standardized CPUE. Both simulation scenarios showed that VAST performed the best, with the lowest model error (measured by root mean square error) and bias, for estimating relative abundance indices. Although the spatial clustering approach created a flexible shape for the area strata, the simulation results under preferential samplings showed that clustering with a stronger emphasis placed on average CPUE could lead to bias in estimated abundance indices. However, spatial clustering that balanced average CPUE with spatial proximity could be a reasonable alternative if it is not possible to apply a spatio-temporal approach. The importance of conducting influence analysis and the greater performance of a spatio-temporal approach are highlighted.

Kuroshio Intrusion and Its Impact on Swordtip Squid (Uroteuthis edulis) Abundance in the Southern East China Sea

Swordtip squid (Uroteuthis edulis) is a primary target species of the commercial fishery in the southern East China Sea (ECS), and they normally migrate to a quasi-permanent upwelling zone (called a cold dome) off northeastern Taiwan for spawning and growth during spring (March–May) and fall (October–December) in a year. We examined the connection of the variability in its standardized annual catch per unit effort (CPUE) during 2009–2017 in regard to the physical processes on the southern shelf of the ECS using temperature and wind observations from an isle north of Taiwan (Peng-jia-yu) as well as satellite sea surface temperature and absolute geostrophic velocity. The annual CPUE is positively correlated with the daily temperature anomaly at Peng-jia-yu in the cold dome in October of the previous year and April of the year. A warmer environment favors the recruitment and consequently the catch of the swordtip squid. During the spawning periods of the 9 years, the warm water carried by the Kuroshio frequently intruded atop the cold dome, which benefited the growth of the larvae and consequently helped maintain a certain value of the standardized annual CPUE. The anomalously low CPUE in 2012 and 2016 is attributed to the blocking of the Kuroshio intrusion due to cold and less salty China Coastal Water atop the cold dome in the spring spawning of 2012 and 2016. Based on the velocity strength in the cold dome and in a specified shelf region together with the daily temperature anomaly at Peng-jia-yu, an occupation intensity factor is used to evaluate the dominance of warm Kuroshio water and cold shelf water in the cold dome, which could help predict annual catches.

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.

Technological creep masks continued decline in a lobster (Homarus gammarus) fishery over a century

Fishery-dependent data are frequently used to inform management decisions. However, inferences about stock development based on commercial data such as Catch-Per-Unit-Effort (CPUE) can be severely biased due to a phenomenon known as technological creep, where fishing technology improves over time. Here we show how trap improvement over nine decades has driven technological creep in a European lobster (Homarus gammarus) fishery. We combined fishing data, experimental fishing with contemporary and older trap types, and information on depletion effects during fishing seasons. The resulting standardized CPUE time series indicates a 92% decline in lobster abundance between 1928 and 2019 compared to 70% if technological creep is not corrected for. Differences are most pronounced within the last 40 years when the most substantial shift in gear technology occurred: an uncorrected CPUE index suggests an 8% increase in lobster abundance during this period, while the corrected CPUE index declined by 57%. We conclude that technological creep has masked a continuous stock decline, particularly in recent decades and largely driven by the shift from one- to two-chambered traps, as well as the ability of newer trap designs to capture larger lobsters. Our study confirms the importance of adequate standardization, including technological development, when using fishery dependent CPUE for monitoring and management of data-limited fisheries.

Habitat Suitability Modeling for the Feeding Ground of Immature Albacore in the Southern Indian Ocean Using Satellite-Derived Sea Surface Temperature and Chlorophyll Data

In the current study, remotely sensed sea surface ocean temperature (SST) and sea surface chlorophyll (SSC), an indicator of tuna abundance, were used to determine the optimal feeding habitat zone of the southern Indian Ocean (SIO) albacore using a habitat suitability model applied to the 2000–2016 Taiwanese longline fishery data. The analysis showed a stronger correlation between the 2-month lag SSC and standardized catch per unit effort (CPUE) than 0-, 1-, 3-, and 4-month lag SSC. SST also exhibited a stronger correlation with standardized CPUE. Therefore, SST and SSC_2 were selected as final variables for model construction. An arithmetic mean model with SST and SSC_2 was deemed suitable to predict the albacore feeding habitat zone in the SIO. The preferred ranges of SSC_2 and SST for the feeding habitat of immature albacore were 0.07–0.09 mg m−3 and 16.5–18.5 °C, respectively, and mainly centralized at 17.5 °C SST and 0.08 mg m−3 SSC_2. The selected habitat suitability index model displayed a high correlation (R2 = 0.8276) with standardized CPUE. Overall, temperature and ocean chlorophyll were found to be essential for albacore habitat formation in the SIO, consistent with previous studies. The results of this study can contribute to ecosystem-based fisheries management in the SIO by providing insights into the habitat preference of immature albacore tuna in the SIO.

Observed links between coastal ocean processes and Indian Oil Sardine (Sardinella longiceps) fishery along the southwest coast of India

The annual fishery of the Indian Oil Sardine (IOS), Sardinella longiceps, seems to be influenced by the ambient conditions prevailing in the habitats during their early life-history. The present study is aimed at understanding the putative behaviour of IOS along the southwest coast of India (SWCI) to monsoonal upwelling which triggers surface productivity. Mixed layer temperature is observed to be an indicator of the habitat choice and reproductive performance of IOS. A Multi-Criteria Analysis (MCA) model was envisioned to explain the interannual variability of the IOS fishery. In this model, the coactions of variables such as standardized catch per unit effort (SCPUE) of IOS in the first quarter (January to March), southwest monsoonal upwelling index (SWM-UI), and pre-monsoonal mixed layer temperature (PM-MLT) over three decades from 1985 to 2016 were looked upon as indicators to explain the interannual fluctuations in the fishery. Eight different criteria for eight ranks were assigned using reference values for variables. The model ranks the years based on the respective conditions defined based on the suitability criteria for IOS. The model showed a strong relationship of the variables on the annual IOS fishery and revealed that the higher first quarter SCPUE (>3.678) and SWM-UI (>758.7 kg/m/s) influences the interannual variability of the fishery significantly. The primary causative factor for the collapse of this fishery during 1992-99 was the frequent occurrence or co-occurrence of extreme climatic events such as El Niño, La Niña and Indian Ocean Dipole (IOD). Further, a significant increase (p<0.05) in IOS fishery was observed during 2000-07 due to favourable upwelling conditions along the SWCI. The uncertainties prevailing in a complex tropical ecosystem and associated challenges in resolving a multi-craft-gear fishery were addressed using statistical tools to infer logical explanation on the decline and revival of IOS.

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.

Comparación de índices de abundancia de polaca (Micromesistius australis) a partir de dos fuentes de información mediante la aplicación de Modelos Lineales Mixtos.

En este trabajo se realizó la estandarización de la captura por unidad de esfuerzo (CPUE) de polaca (Micromesistius australis) accesible a la flota surimera que operó en el océano Atlántico Sudoccidental durante el período 1993-2018. Se aplicaron Modelos Lineales Mixtos (MLM), a partir de la información de la estadística pesquera (MLMEST) y la colectada por los observadores a bordo (MLMOBS) de dichos buques con el objetivo de comparar entre sí los modelos realizados a partir de ambas fuentes de información y el modelo utilizado hasta la actualidad para la estimación de índices de abundancia de polaca. La aplicación de los MLM mejoró el ajuste con respecto al modelo utilizado hasta la actualidad, sin variar abruptamente la tendencia estimada, lo cual permitiría predecir con mayor exactitud los valores medios de CPUE estandarizados a lo largo de los años. El MLMOBS mostró un mejor ajuste y una mayor variabilidad explicada que el MLMEST. A fin de obtener estimaciones más certeras y, de esta forma poder calibrar adecuadamente los modelos de evaluación de la especie, se recomienda utilizar ambas fuentes de información, teniendo en cuenta el mayor o menor grado de representatividad de los datos.&#x0D;