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Chub mackerel (Scomber japonicus) is a commercially important fish species which are widely distributed in the North Pacific. Based on the fishery data from China’s high-sea light-purse seine fishing from 2014 to 2020 and the marine environment factors, a mixed linear model considering the actual spatiotemporal stratification of the catch per unit effort (CPUE) was established to analyze the fixed and random effects of marine environmental factors on the CPUE of chub mackerel and to investigate the relationship between the abundance of chub mackerel resources in the Northwest Pacific and two marine environmental factors: sea surface temperature (SST) and chlorophyll-a concentration (CHL). The results showed that SST had a significant fixed effect on the CPUE. In contrast, the natural logarithm of chlorophyll (logCHL) had no fixed effect on the CPUE. Based on the monthly analysis, random fluctuations were observed in the impact of logCHL on the CPUE. LogCHL and CPUE show a positive correlation during spawning and wintering periods and a negative correlation during the feeding period. The study showed that when fishery sampling data exhibit spatiotemporal stratification, linear mixed models can effectively incorporate both the fixed and random effects of environmental factors on the CPUE of chub mackerel. Linear mixed models can play an important role in analyzing the fluctuations in resource abundance and the mechanisms governing the formation of fishing grounds for chub mackerel in the Northwest Pacific.

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.

The Northwest Indian Ocean is a key fishing ground for China’s pelagic fisheries, with the purpleback flying squid being a significant target. This study uses commercial fishing logs of the Indian Ocean between 2015 and 2021, alongside pelagic seawater temperature and its vertical temperature difference within the 0–200 m depth range, to construct generalized additive models (GAMs) and gradient boosting tree models (GBTs). These two models are evaluated using cross-validation to assess their ability to predict the distribution of purpleback flying squid. The findings show that factors like year, latitude, longitude, and month significantly influence the distribution of purpleback flying squid, while surface water temperature, 200 m water temperature, and the 150–200 m water layer temperature difference also play a role in the GBT model. Similar factors also take effects in the GAM. Comparing the two models, both GAM and GBT align with reality in predicting purpleback flying squid resource distribution, but the precision indices of GBT model outperform those of the GAM. The predicted distribution for 2021 by GBT also has a higher overlap with the actual fishing ground than that by GAM, indicating GBT’s superior forecasting ability for the purpleback flying squid fishing ground in the Northwest Indian Ocean.

Understanding the spatial patterns of neon flying squid is important for the monitoring and management of fishery resources. Mesoscale eddies and the Kuroshio Extension play important roles in the variation of the fishing ground of Ommastrephes bartramii. However, the way in which eddies and the Kuroshio influence the distribution of Ommastrephes bartramii requires further understanding. In this study, the spatial variation in the distribution of fishing activity and the change of fishing ground gravity of squid were analyzed using automatic identification system (AIS) data. There is a positive correlation between the fishing ground gravity in latitudinal direction and Kuroshio Extension indicators based on the high-frequency eddy kinetic energy (EKE), which describes the Kuroshio variations. Furthermore, the Kuroshio Extension indicators show a positive relationship with the number of the eddies generated in the fishing ground. The results suggest that the changes in the dynamics of SST anomalies could be influenced by eddy-shedding processes in the upstream KE and then alter the distribution of the fishing ground for Ommastrephes bartramii. The Kuroshio index (mean high-frequency eddy kinetic energy between 32° and 37° N, 142° and 149° E) can be used as a good indicator of Kuroshio extension variations to investigate the squid fishing ground in the Kuroshio–Oyashio transition area.

Pacific sardine (Sardinops sagax) is a commercially important species and supports important fisheries in the Northwest Pacific Ocean (NPO). Understanding the habitat distribution patterns of Pacific sardine is of great significance for fishing ground prediction and stock management. In this study, both single-algorithm and ensemble distribution models were established through the Biomod2 package for Pacific sardine by combining the species occurrence data, sea surface temperature (SST), sea surface height (SSH), sea surface salinity (SSS) and chlorophyll-a concentration (Chla) in the NPO during the main fishing season (June–November) from 2015 to 2020. The results indicated that the key environmental variables affecting the habitat distribution of Pacific sardine were the SSH and SST. The suitable habitat area for Pacific sardine showed significant monthly changes: the suitable habitat range in June was larger than that in July and August, while the suitable habitat range gradually increased from September to November. Furthermore, the monthly geometric centers of habitat suitability index (HSI) for Pacific sardine presented a counterclockwise pattern, gradually moving to the northeast from June, and then turning back to the southwest from August. Compared with single-algorithm models, the ensemble model had higher evaluation metric values and better spatial correspondence between habitat prediction and occurrence records data, which indicated that the ensemble model can provide more accurate prediction and is a promising tool for potential habitat forecasting and resource management.

Skipjack tuna (Katsuwonas pelamis) is the determinant catch species in the tuna fishery in the Western and Central Pacific Ocean (WCPO). Although models had showed tropical skipjack tuna habitat was away from the WCPO on the future warming scenarios, it is unclear the detail responses of skipjack tuna to the occurring sea surface temperature (SST) changes. To address this gap, we assessed the sensitivities and response rates of skipjack tuna to the SST during the global warming hiatus period using a distributed lag non-linear model. Strong and significant negative correlations were investigated between the SST and the mean catch-per-unit-efforts (CPUE) in the higher SST region, specifically in the region between 10°S and 10°N. Moreover, the mean CPUE significantly decreased after a 12-month lag when the SST was below 28.7°C. Our results recommended that more rigorous tuna fishery managements in the WCPO will be needed to carry out to adapt the future ocean warming. [ABSTRACT FROM AUTHOR]