Development of integrated habitat suitability index models of skipjack tuna, Pacific saury and neon flying squid in the western North Pacific

An accurate estimate of a potential habitat of fish species enables us not only to understand the response of species to environmental changes but also to utilize it for an efficient use of fish resources. Habitat suitability index (HSI) model is widely used as tool in ecological impact assessments, ecological restoration studies, and practical exploration of potential fishing zone. It describes the relationships between fish abundance and ocean environmental variables and then estimates the level of habitat suitability as an HSI score representing “poor” to “good” habitat qualities. In this study, we developed an integrated HSI models for skipjack tuna (Katsuwonus pelamis), Pacific saury (Cololabis saira) and neon flying squid (Ommastrephes bartramii) in the western North Pacific for estimating potential fishing grounds of these species and their spatial relationships by applying MAXENT and other machine learning algorithms to Japanese fishery datasets including surveys, QRY reports, AIS/VMS data, and satellite OLS lightning data. As ocean environments, we used daily SST and chl-a data derived from HIMAWARI satellite that has 0.03*0.03 degree of latitude and longitude, and an ocean reanalysis dataset FORA (4-dimensional variational Ocean Re-Analysis)-WNP30 produced by JAMSTEC and MRI-JMA. The horizontal resolution of FORA is 0.1*0.1 degree with 54 vertical levels, which can provide realistic fields of 3-dimensional ocean circulation and environmental structures up to 1500m depth. We investigated the relationships between HSI distribution of three species to clarify an impact of ecological interactions between these species compared to ocean environmental changes occurring in this area. Furthermore, the performances of the HSI models were also investigated by taking interactive effects of three species into account.