Research on the prediction model of spatial distribution of Sthenoteuthis oualaniensis in the open sen Arabian Sea based on PCA-GAM

In order to scientifically predict the distribution of Sthenoteuthis oualaniensis and toutilize its resources,this study established the PCA-GAM prediction model of S. oualaniensis was established based on the production data of light seine in the open sea Arabian Sea from 2017 to 2019, combined with the data of salinity, temperature, 0, 50, 100, 150 and 200 m water layers, mixed layer thickness, sea level anomaly, chlorophyll a concentration, sea surface velocity, longitude and latitude. The correlation between environmental factors will cause multicollinearity, resulting in over-fitting of the model, and reducing the prediction ability of the model. The environmental data were transformed into a few unrelated principal components (PCs) which retained important information of these environmental factors based on the application of dimension reduction techniques such as principle component analysis (PCA). The average variance explanation rate of the top 8 PCs accounted for 87.34% (±0.86%). The top 8 PCs were taken as explanatory variables of the GAM model to construct the prediction model of the distribution of S. oualaniensis. The establishment of PCA-GAM prediction model was divided into two-stage GAM. The first stage GAM is to estimate the presence probability of S. oualaniensis. The second stage GAM is to estimate the log-transformed CPUE of S. oualaniensis. The overall log-transformed CPUE was the product of the results from the first and second stages of the GAM. The eight fold cross-validation results showed that the mean of the correlation coefficients between the predicted values and the practical CPUE (log-transformed) was 0.532 7, the mean of the slopes of the regression models was 0.708 7, and the mean of truncation values was 1.471 1. The degree of overlap between the predicted values and the practical CPUE (log-transformed) from January to April and September to December 2019 was very high in spatial distribution, which indicated that the PCA-GAM model was able to predict the spatial distribution of S. oualaniensis in the Arabian Sea.