Spatio-Temporal Variability of the Habitat Suitability Index for the Todarodes pacificus (Japanese Common Squid) around South Korea

Abstract

The climate-induced changes in marine fishery resources in South Korea have been a big concern over the last decades. The climate regime shift has led to not only a change in the dominant fishery resources, but also a decline in fishery landings in several species. The habitat suitability index (HSI) has been widely used to detect and forecast fishing ground formation. In this study, the catch data of the Todarodes pacificus (Japanese Common Squid) and satellite-derived environmental parameters were used to estimate the HSI for the T. pacificus around South Korea. More than 80% of the total catch was found in regions with a sea surface temperature (SST) of 14.91–27.26 ℃, sea surface height anomaly (SSHA) of 0.05–0.20 m, chlorophyll-a of 0.32–1.35 mg m-3, and primary production of 480.41–850.18 mg C m-2 d-1. Based on these results, the HSI model for T. pacificus was derived. A strong positive relationship (R2 = 0.9260) was found between the HSI and the fishery landings. The climatological monthly mean HSI from 2002 to 2016 showed several hotspots, coinciding with the spawning and feeding grounds of T. pacificus. This outcome implies that our estimated HSI can yield a reliable prediction of the fishing ground for T. pacificus around South Korea. Furthermore, the approach with the simple HSI model used in this study can be applied elsewhere, and will help us to understand the spatial and temporal distribution of fishery resources.

Climate-induced habitat suitability intensifies fishing-induced life-history variation of a migratory long-lived fish

“…In our HSI model, we used standardized abundance, HSI, as response variable, and three environmental variables with strongest correlation and the best data availability, depth, SST and SSS, as predictor. Firstly, we constructed both fitting-based (Lee et al 2019, Hua et al 2020, Yu et al 2020) and regression-based (Chang et al 2019, Jin et al 2020) suitability index (SI) models to describe the relationship between each environmental variable and L. crocea abundance (Supporting information). Then, we combined two types of SI models into HSI models, respectively.…”

“…For each type of SI model, we used two empirical HSI models: the arithmetic mean model and the geometric mean model (Supporting information Fig. S1), under different environmental variable combinations (Lee et al 2019).…”

Multisatellite-Based Feeding Habitat Suitability Modeling of Albacore Tuna in the Southern Atlantic Ocean

“…The calculated SI values for each environmental variables based on Equation 3were used as observed values to fit SI models with the midpoints of each environmental variables class interval. Finally, the relationship between SI and selected environmental variables were calculated using the following formula described by Chen et al [50] and Lee et al [51,52]:…”

“…The model that yielded the lowest AIC value was selected as the best model and then used for model testing and validation. Model performance was evaluated based on summed monthly standardized CPUE from 2009 to 2015 tested according to intervals of analyzed HSI values (see Lee et al [51,52]). Then, the goodness of fit of the linear correlation between CPUE and HSI values was also evaluated based on the minimum AIC value, with adjusted R 2 to justify the HSI model for the prediction of potential habitats.…”

First Concurrent Measurement of Primary Production in the Yellow Sea, the South Sea of Korea, and the East/Japan Sea, 2018

“…The primary production of phytoplankton is widely used as an important indicator to predict annual fishery yield in various oceanic regions [3][4][5], because it is one of key factors in determining amount of food source for upper-trophic-level consumers [6,7]. Lee et al [8,9] also reported that an algorithm for estimation of the habitat suitability index for the mackerels and squids around the Korean peninsula was largely improved by including a primary production term. The physiological conditions and community structures of phytoplankton are closely related to physical and chemical factors (e.g., light regime, nutrients, and temperature) [10][11][12], which induce greatly different phytoplankton productions in various marine ecosystems [3,13,14].…”