Comparison between two GAMs in quantifying the spatial distribution of Hexagrammos otakii in Haizhou Bay, China

Abstract

Species distribution models (SDMs) can be used to quantify the relationships between species distribution and environmental variables. The predictive skill of SDMs depends on whether appropriate explanatory variables and intrinsic processes are included in the model. In addition to abiotic environmental variables, biotic variables could also have significant impacts on the spatial distribution of marine organisms. Correlations between some explanatory variables will cause multicollinearity, which could result in overfitting of models and erroneous projections/forecasts of species distribution. Application of dimension reduction techniques such as principal component analysis (PCA) could be used to retain important information and avoid collinearity. We compared the performance of the generalized additive model (GAM) and the PCA-based GAM in predicting the spatial distribution of Hexagrammos otakii in Haizhou Bay, incorporating abiotic and biotic variables in these models. Results showed that the PCA-based GAM was able to reduce the multicollinearity introduced by explanatory variables and improve the performance of GAMs, according to a cross-validation test and predicted species distribution. Incorporating prey abundance in PCA-based GAM could improve the predictive skill of SDMs. The method proposed in this study could be extended to other marine organisms to enhance our understanding of the ecological mechanisms underlying the distribution of target species.