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We developed a new statistical spatiotemporal model for chlorophyll-a (chl-a) distribution over the Sea of Japan, derived from the satellite-based Sea-viewing Wide Field-of-view Sensor (SeaWiFS). Because preliminary analysis showed that the SeaWiFS data exhibit anisotropy in space and autocorrelation in time, we propose a new spatiotemporal model for chl-a distribution and its predictor. Numerical prediction experiments applying the SeaWiFS data showed that the predictor could forecast chl-a distributions in summer and early fall well, although further changes in the model structure will be necessary to predict aspects of the spring and late fall blooms. 相似文献
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Tsukasa Hokimoto Hidetada Kiyofuji 《Stochastic Environmental Research and Risk Assessment (SERRA)》2014,28(5):1099-1124
The influence of the sea environment on fish behaviors is an important ecological and environmental factor. In this article, we analyze the relationship between phytoplankton distribution in the sea and the behavioral process of a diadromous fish, albacore (Thunnus alalunga), by development of statistical models. It is well known that animals change their behavioral patterns, such as traveling and foraging, according to the conditions of their encountered habitat. To account for these characteristics in fish behavior, we develop a regime switching model with a hidden latent process, whose output is influenced by phytoplankton concentration. Geolocation prediction experiments suggested that the model can improve the prediction accuracies compared with the general regime switching model with no covariates, or traditional linear nonstationary time series models. Furthermore, robust geolocation predictions are yielded by the model even when the fish switches its migratory behavior. 相似文献
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