The empirical habitat suitability index (HSI) has been widely used to examine the habitat characteristics of terrestrial animals, though rarely used in highly migratory fish such as tuna. This study used the geographic information system technique to establish empirical models of HSI for yellowfin tuna (YFT) in the Western and Central Pacific Ocean (WCPO). Daily catch data from the Taiwanese purse seine fishery during 2003–2007 were aggregated monthly into sequential degrees before match processing the conducted data to obtain monthly remote-sensing data for multi-environmental factors, including sea surface temperature (SST), chlorophyll-a (chl-a), sea surface height (SSH) and sea surface salinity (SSS). According to the frequency distribution of each factor on which YFT were caught, this study transformed the values of the four factors into a suitability index (SI) ranging from low to high (0–1). These SI values were consequently combined into different empirical HSI models, and the optimum models were selected using the general linear model. The optimum empirical HSI for YFT in the study area was converted for SI (SST, SSH, chl-a and SSS) using the arithmetic mean model, of which the correct prediction rate was 71.9%. An agreement was present between the average HSI and total YFT catch. Furthermore, the high HSI area corresponds with the displacement of catch per unit effort (CPUE).

Remote sensing (RS) technology, which can facilitate the sustainable management and development of fisheries, is easily accessible and exhibits high performance. It only requires the collection of sufficient information, establishment of databases and input of human and capital resources for analysis. However, many countries are unable to effectively ensure the sustainable development of marine fisheries due to technological limitations. The main challenge is the gap in the conditions for sustainable development between developed and developing countries. Therefore, this study applied the Web of Science database and geographic information systems to analyze the gaps in fisheries science in various countries over the past 10 years. Most studies have been conducted in the offshore marine areas of the northeastern United States of America. In addition, all research hotspots were located in the Northern Hemisphere, indicating a lack of relevant studies from the Southern Hemisphere. This study also found that research hotspots of satellite RS applications in fisheries were mainly conducted in (1) the northeastern sea area in the United States, (2) the high seas area of the North Atlantic Ocean, (3) the surrounding sea areas of France, Spain and Portugal, (4) the surrounding areas of the Indian Ocean and (5) the East China Sea, Yellow Sea and Bohai Bay sea areas to the north of Taiwan. A comparison of publications examining the three major oceans indicated that the Atlantic Ocean was the most extensively studied in terms of RS applications in fisheries, followed by the Indian Ocean, while the Pacific Ocean was less studied than the aforementioned two regions. In addition, all research hotspots were located in the Northern Hemisphere, indicating a lack of relevant studies from the Southern Hemisphere. The Atlantic Ocean and the Indian Ocean have been the subjects of many local in-depth studies; in the Pacific Ocean, the coastal areas have been abundantly investigated, while offshore local areas have only been sporadically addressed. Collaboration and partnership constitute an efficient approach for transferring skills and technology across countries. For the achievement of the sustainable development goals (SDGs) by 2030, research networks can be expanded to mitigate the research gaps and improve the sustainability of marine fisheries resources.

Remote sensing technology significantly contributes to fisheries management and marine ecosystem preservation. The development disparities among countries create gaps that hinder sustainable fisheries and ecosystem protection. Assessing progress and efforts across regions and countries is crucial for sustainable development. Effective measurement methods are used to identify shortcomings, guide academic development, and strengthen partnerships. Tracking and highlighting partnership achievements is challenging due to the difficulties in quantification. The objective of this study is to create indicators for evaluating the community of international academic cooperation. We analyzed and examined the trend in national efforts and international partnerships before and after the Sustainable Development Goals (SDGs) were released in 2015 using bibliometric methods and bibliographic information from the Web of Science (WoS). The results show that tracking the progress and evolution of international collaborations in fisheries remote sensing research can be facilitated by employing quantitative indicators that measure international cooperation among coauthors, institutions, and countries. Additionally, the number of partnerships in each country displays a significant relationship with the country’s level of national development. A comparison of indicators developed by actors with different orientations can be used as a strategic reference for developing partnerships among countries. Academic research in developing countries that rely heavily on fisheries plays a critical role in preserving life below water (SDG 14). The achievement of this SDG can be enhanced through global partnerships (SDG 17).

Taiwan is located at the intersection of tropical and subtropical islands in the western Pacific Ocean. This area is an important spawning and breeding ground for many economic and noneconomic species; however, little is known about the long-term dynamics of fish larvae and juveniles in these waters. In this study, we conducted an in-depth exploration of their spatial characteristics using 2007–2019 field survey samples. Our results demonstrated the seasonality and spatiality of the larvae and juveniles of different fish species. We also found that the continental shelf and offshore distance were key factors affecting fish larvae and juveniles. Changes in community structure were temporally correlated with the extreme rainfall of Typhoon Morakot (the worst typhoon ever recorded in Taiwan). These data can be used as a management reference for fisheries’ policymaking and provide key insights into nearby marine ecosystems and the early life history of fish.