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.

Black sea bream, Acanthopagrus schlegelii, is among the most commercially valuable species in the coastal fishery industry and marine ecosystems. Catch data comprising capture locations for the gillnet fisheries, remotely sensed environmental data (i.e., sea surface temperature, chlorophyll-a concentration, and current velocity), and topography (bathymetry) from 2015 to 2018 were used to construct a spatial habitat distribution of black sea bream. This species is concentrated in coastal waters (<3 nm) from December to April (spawning season). The maximum entropy (MaxEnt) method and corresponding habitat suitability index among seasons were used to clarify the species’ spatial distribution and identify the seasonal variations in habitat selection. The patterns corresponded closely to the changes in oceanographic conditions, and the species exhibited synchronous trends with the marine environment’s seasonal dynamics. Chlorophyll-a concentration and bathymetry substantially influenced (80.1–92.9%) black sea bream’s habitat selection. By applying the MaxEnt model, the optimal habitats were identified with four variables including depth and satellite-derived temperature, current velocity and chlorophyll-a concentration, which provides a foundation for the scientific assessment and management of black sea bream in coastal waters of the Eastern Taiwan Strait.

The environmental characteristics of the Taiwan Strait (TS) have been linked to variations in the abundance and distribution of greater amberjack (Seriola dumerili) populations. Greater amberjack is a commercially and ecologically valuable species in ecosystems, and its spatial distribution patterns are pivotal to fisheries management and conservation. However, the relationship between the catch rates of S. dumerili and the environmental changes and their impact on fish communities remains undetermined in the TS. The goal of this study was to determine the spatiotemporal distribution pattern of S. dumerili with environmental characteristics in the TS from south to north (20°N–29°N and 115°E–127°E), applying generalized additive models (GAMs) and spatiotemporal fisheries data from logbooks and voyage data recorders from Taiwanese fishing vessels (2014–2017) as well as satellite-derived remote sensing environmental data. We used the generalized linear model (GLM) and GAM to analyze the effect of environmental factors and catch rates. The predictive performance of the two statistical models was quantitatively assessed by using the root mean square difference. Results reveal that the GAM outperforms the GLM model in terms of the functional relationship of the GAM for generating a reliable predictive tool. The model selection process was based on the significance of model terms, increase in deviance explained, decrease in residual factor, and reduction in Akaike’s information criterion. We then developed a species distribution model based on the best GAMs. The deviance explained indicated that sea surface temperature, linked to high catch rates, was the key factor influencing S. dumerili distributions, whereas mixed layer depth was the least relevant factor. The model predicted a relatively high S. dumerili catch rate in the northwestern region of the TS in summer, with the area extending to the East China Sea. The target species is strongly influenced by biophysical environmental conditions, and potential fishing areas are located throughout the waters of the TS. The findings of this study showed how S. dumerili populations respond to environmental variables and predict species distributions. Data on the habitat preferences and distribution patterns of S. dumerili are essential for understanding the environmental conditions of the TS, which can inform future priorities for conservation planning and management.

Decision strategies in fisheries management are often directed by the geographic distribution and habitat preferences of target species. This study used remote sensing data to identify the optimal feeding habitat of albacore tuna in the Southern Atlantic Ocean (SAO) using an empirical habitat suitability model applying longline fisheries data during 2009–2015. An arithmetic mean model with sea surface temperature (SST) and sea surface chlorophyll-a concentration (SSC) was determined to be suitable for defining the albacore habitat in the SAO. The optimal ranges of SST and SSC for the habitat were approximately 16.5 °C–19.5 °C and 0.11–0.33 mg/m³, respectively. The study revealed a considerable positive trend between the suitable habitat area and standardized catch per unit effort (r = 0.97; p < 0.05); due to the west-to-east and northward development of the suitable habitat, albacore schools moved to the northeast of the SAO, thus increasing catch probability in April to August in that region. Overall, the frontal structure of SST and SSC plays an essential role in the formation of potential albacore habitats in the SAO. Our findings could contribute to the establishment of regional ecosystem-based fisheries management in the SAO.

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.