H. Okamura

Forecasting fish recruitment using machine learning methods: A case study of arabesque greenling

This study uses machine learning to predict fish recruitment for Arabesque greenling, finding gradient boosting outperforms linear regression and random forest in accuracy.

Spatio-temporal model and machine learning method reveal process of phenological shift under climate change of North Pacific spiny dogfish

The paper uses a spatio-temporal model and machine learning to analyze climate-driven phenological shifts in spiny dogfish migration patterns over 5 decades.

Evaluating a harvest control rule to improve the sustainability of Japanese fisheries

This paper evaluates a new harvest control rule for Japanese fisheries, showing it improves sustainability and catch stability compared to traditional methods.

State-space Modeling Clarifies Productivity Regime Shifts of Japanese Flying Squid

The paper develops a state-space model to detect regime shifts in Japanese flying squid productivity, showing significant changes in maximum sustainable yields around 1991 and 2015.

Spatio-temporal model reduces species misidentification bias of spawning eggs in stock assessment of spotted mackerel in the western North Pacific

The paper uses a spatio-temporal model to reduce species misidentification bias in spotted mackerel stock assessment by incorporating chub mackerel egg density effects.

Estimating maximum sustainable yield of snow crab (Chionoecetes opilio) off Tohoku Japan via a state-space assessment model with time-varying natural mortality

The paper estimates the maximum sustainable yield of snow crab using a state-space model, finding that natural mortality and terminal molting probabilities have increased over time, affecting MSY estimates.

The methyl cycle is a conserved regulator of biological clocks.

This study shows the methyl cycle regulates circadian and somite segmentation clocks across species, highlighting its role as a conserved metabolic regulator of biological clocks.