Kanamori, Y. | Search bioRxiv

bioRxiv2022

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

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.

ecology↗

bioRxiv2020

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

Species identification based on morphological characteristics includes species misidentification, leading to estimation bias of population size. The eggs of spotted mackerel Scomber australicus and chub mackerel S. japonicus in the western North Pacific has been identified based on egg diameter. Recent density of spotted mackerel was considerably high despite its low stock biomass. A possibility of this phenomenon is due to overestimation because the difference in egg diameter has become ambiguous between two species. However, we cannot test this possibility using DNA analysis because the eggs are fixed with formalin. Here, we estimated the index of egg density of spotted mackerel using a spatio-temporal model that incorporates the effect of egg density of chub mackerel on the catchability of spotted mackerel, using 15 years data of spawning eggs. We then examined how retrospective biases in estimated stock abundance were reduced when using the index from the model. The index estimated from the model decreased temporal fluctuation and showed smooth patterns. Especially, the recent index was considerably revised down rather than the nominal index. Additionally, the retrospective bias decreased ca. half compared with the nominal index. Therefore, incorporating species misidentification bias should be an essential process for improving stock assessment.

ecology↗