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bioRxiv · 10.1101/2022.03.01.482455

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

Kanamori, Y.·Yano, T.·Okamura, H.·Yagi, Y.
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Abstract

Climate change has disrupted natural phenological patterns, including migration. Despite extensive studies of phenological shifts in migration by climate change and driving factors of migration, a few issues remain unresolved. In particular, little is known about the complex effects of driving factors on migration with interactions and nonlinearity, and partitioning of the effects of factors into spatial, temporal, and spatio-temporal effects. The Pacific spiny dogfish Squalus suckleyi (hereafter "spiny dogfish") is a coastal elasmobranchii that migrates southward for parturition and northward for feeding in the western North Pacific. Here, to elucidate the migration patterns as well as the driving factors under climate change, we first examined long-term changes in the timing and geographic location of migration by applying a spatio-temporal model to ca. 5-decade time series data for the presence/absence of spiny dogfish in the western North Pacific. We then evaluated the spatial, temporal, and spatio-temporal effects of driving factors (sea surface temperature [SST], depth, productivity, and magnetic fields) on seasonal occurrence patterns using a machine learning model. We found that the migration area did not change over ca. 5 decades, whereas the migration timing advanced by a month after 2000. The spatial effects of magnetic fields and depth were consistently large and the spatial and spatio-temporal effects of SST increased in the migration season, even though temporal effect of SST was always weak. These results suggest that the migration area of spiny dogfish was stable over time because their spatial distribution was determined by geographic features, whereas the migration timing advanced by tracking a suitable SST location which increased steeply after 2000. Therefore, temperature as well as other factors influence migration simultaneously under climate change and underline the importance of paying attention biotic/abiotic factors including temperature and process-based understanding to predict future impacts of climate change on phenological shifts.

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Kanamori, Y., Yano, T., Okamura, H., Yagi, Y.. 2022-03-01. Spatio-temporal model and machine learning method reveal process of phenological shift under climate change of North Pacific spiny dogfish. https://doi.org/10.1101/2022.03.01.482455

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