Oceanographic indicators for the occurrence of anchovy eggs inferred from generalized additive models

Jin Yeong Kim,Young-Sang Suh,Jae Bong Lee

doi:10.1186/s41240-020-00161-y

Abstract

Three generalized additive models were applied to the distribution of anchovy eggs and oceanographic factors to determine the occurrence of anchovy spawning grounds in Korean waters and to identify the indicators of their occurrence using survey data from the spring and summer of 1985, 1995, and 2002. Binomial and Gaussian types of generalized additive models (GAM) and quantile generalized additive models (QGAM) revealed that egg density was influenced mostly by ocean temperature and salinity in spring, and the vertical structure of temperature, salinity, dissolved oxygen, and zooplankton biomass during summer in the upper quantiles of egg density. The GAM and QGAM model deviance explained 18.5–63.2% of the egg distribution in summer in the East and West Sea. For the principle component analysis-based GAMs, the variance explained by the final regression model was 27.3–67.0%, higher than the regular models and QGAMs for egg density in the East and West Sea. By analyzing the distribution of anchovy eggs off the Korean coast, our results revealed the optimal temperature and salinity conditions, in addition to high production and high vertical mixing, as the key indicators of the major spawning grounds of anchovies.

Highlights

IntroductionGeneralized additive models (GAMs) are non-parametric regression techniques that are not restricted by linear relationships, providing a flexible method for analysis when the relationship between variables is complex
For the principle component analysis-based Generalized additive models (GAMs), the variance explained by the final regression model was 27.3–67.0%, higher than the regular models and quantile generalized additive models (QGAMs) for egg density in the East and West Sea
Generalized additive models (GAMs) are non-parametric regression techniques that are not restricted by linear relationships, providing a flexible method for analysis when the relationship between variables is complex

Summary

Introduction

Generalized additive models (GAMs) are non-parametric regression techniques that are not restricted by linear relationships, providing a flexible method for analysis when the relationship between variables is complex. Principal component analysis-based (PCA-based) GAMs have been used in ecological research (Li et al 2017; Liu et al 2019; AlvarezFernandez et al 2012). These provide a tool for reducing the number of variables by grouping variables that influence mutual factors, depending on the relevance of the variables. Hydroacoustic and trawl surveys conducted during early spring have shown that anchovy wintering grounds are located in the coastal areas of the South Sea of Korea (Choi et al 2001). Anchovy eggs and larvae are widely distributed in Korean waters but are concentrated along the frontal areas between the coastal

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