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Article

The Impacts of Marine Heatwaves on Economic Fisheries in Adjacent Sea Regions Around Japan Under Global Warming

by
Dan Liu
1,
Xinjun Chen
1,2,3,4,5,* and
Bilin Liu
1,2,3,4
1
College of Marine Living Resources Sciences and Management, Shanghai Ocean University, Shanghai 201306, China
2
National Engineering Research Center for Oceanic Fisheries, Shanghai 201306, China
3
Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai 201306, China
4
Key Laboratory of Oceanic Fisheries Exploration, Ministry of Agriculture and Rural Affairs, Shanghai 201306, China
5
Scientific Observing and Experimental Station of Oceanic Fishery Resources, Ministry of Agriculture and Rural Affairs, Shanghai 201306, China
*
Author to whom correspondence should be addressed.
Fishes 2025, 10(7), 299; https://doi.org/10.3390/fishes10070299
Submission received: 25 April 2025 / Revised: 11 June 2025 / Accepted: 19 June 2025 / Published: 20 June 2025
(This article belongs to the Section Environment and Climate Change)
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Abstract

Climate change has significantly affected marine fisheries. In recent years, marine heatwaves (MHWs) have intensified concurrently with increasing sea surface temperature (SST), particularly along the coast of Japan in the Northwest Pacific. Although the relationships between MHWs and large-scale climate patterns are well established, the long-term effects of MHWs on fisheries remain uncertain. Considering thermal adaptability, we analyzed the catches of warm- and cold-water species from commercially important fisheries in adjacent sea regions around Japan, correlating them with regional SSTs and MHW indices. Our results show that regional SSTs exhibited a persistent increasing trend, with major shifts occurring around 1988/89 and 1998/99. Pronounced interannual–decadal variabilities were observed in the leading principal components (PCs) of different species groups, with step changes concentrated in 1989~1992, 1999~2003, and 2009~2012. Notably, there was a significant negative response of cold groups to warming SSTs. Among warm-water species, only the Japanese sardine (Sardinops melanostictus) catch exhibited a strong correlation with climate change. Gradient forest analysis and threshold generalized additive models (TGAMs) further revealed the nonlinear, threshold-driven responses of the fish groups to environmental variability, which occurred after step changes in both the environmental factors and catches. Matching analysis between the annual change rates of catches and MHW indices confirmed the detrimental effects of strong MHWs on marine fisheries.
Keywords:
warming trend; warm anomaly; economic fishes; threshold; response pattern
Key Contribution: Regional sea surface temperatures (SSTs) increased with main step changes around 1988/89 and 1998/99, and marine heatwave (MHW) events occurred more frequently with increasing intensity. Cold-tolerant species are sensitive to SSTs and show obvious reactions to MHWs. Variations in warm pelagic species, especially species alternation between Japanese sardine and Japanese anchovy, are strongly related with climate change. Transitions in fish response to environmental change are prompted by the joint of regime shifts in climate change and fish populations.

1. Introduction

Long-term variabilities in marine ecosystems and fishery resources have already raised global concern. Climate change, especially variations in seawater temperature, has been recognized as one of the main challenges to sustainable fisheries [1,2,3,4]. Among multiple climate variables, rapid warming has been observed for large marine ecosystems in the world’s oceans, particularly for the North Pacific [5,6]. The warming trend has altered a series of marine ecological processes through direct and indirect impacts, including breaking down biogeographic boundaries and causing species redistribution [7,8,9], ultimately leading to the erosion of ecosystem resilience [10]. By 2010, climate change had reduced the maximum sustainable yield (MSY) of global marine fishery stocks by approximately 4.1% [11].
In addition to global warming under long-term climate change, extreme oceanic events triggered in short periods, such as deoxygenation events [12], periods of intense upwelling [13,14] and ocean temperatures that are unusually warm [15,16] and cold [17], can also strongly influence biological systems and human society. Extreme temperatures, in particular, cause shifts in species ranges and declines in important fishery species and even local extinctions [18,19,20]. These events are usually associated with large-scale climate patterns. For example, sustained periods of above-average temperatures are significantly correlated with the El Niño–Southern Oscillation (ENSO) [18,21]. Extreme ocean warming events have been termed marine heat waves (MHWs), which are defined as “prolonged discrete anomalously warm-water events” that have great environmental impacts [6,22]. The duration, intensity, rate of evolution, and spatial extent of MHWs are nonuniform around the world, while high-intensity MHWs occur mainly in areas with significant changes in sea surface temperature (SST) [6,23].
MHWs can lead to various changes in marine organisms from different habitats, such as northward migration, expansion or compression of geographical ranges, shifts in regional population structures, and low survival rates of some cold species. As a result, “winners” and “losers” have emerged in the marine ecosystem, which can be precisely reflected in the catch of marine fisheries. For example, during and after the MHWs between 2014 and 2016 in the Northeast Pacific, bluefin tuna (Thunnus orientalis) and rockfish (e.g., Sebastes polyspinus, Sebastes aleutianus) tended to increase, whereas salmon (Oncorhynchus spp.) and groundfish (e.g., Atheresthes stomias, Platichthys stellatus) tended to decrease rapidly, but the catch of sardine (e.g., Sardinops sagax) remained low during this period [24]. This finding further indicates that with diverse life history characteristics and climate adaptability, organism taxa, especially warm and cold species, usually show different variations and response patterns to external changes. This phenomenon can also occur between different species with similar habits [25,26]. Therefore, exploring the impact of climate change on marine fishery resources should consider the biological features of taxa and species to clarify their different variation patterns.
The Northwest Pacific, which contributes nearly a quarter of the world’s marine catch, is also facing severe climate issues. Under continuous and high fishing pressure from coastal countries, global warming has had significant influences on marine ecosystems and fisheries in the Northwest Pacific [9,27,28]. Recent studies have demonstrated significant correlations between variabilities in important marine species and fish assemblages around Japan with regional and large-scale climate changes, such as the Aleutian low pressure characterized by the PDO, the Siberian high pressure characterized by the Asian monsoon (MOI), and relatively high-frequency ENSO events [29,30]. In particular, climate regime shifts around 1977, 1989, and 1998 in the North Pacific, including the East Asian Marginal Seas, resulting in changes in the species composition and quality of plankton, which ultimately affected fish populations through marine food webs [31,32]. Meanwhile, from 1980 to 2021, there was a primary warming center over the Gulf of Alaska, a secondary center over the coast of Japan [33], and more intense marine heatwaves (MHWs) in the Kuroshio–Oyashio Transition Zone during summer [34]. Previous studies demonstrated that variations in marine fisheries in the Northeast Pacific during the short term were strongly connected with these warm anomalies [24]. While only a few articles have studied MHWs in the Northwest Pacific, which have mostly focused on the occurrence and mechanism of MHWs over a short time [35,36], research on their impacts on marine fishery resources and ecosystems over a longer time scale is lacking. Thus, it is important to investigate whether MHWs have similar influences on marine fisheries in the Northwest Pacific.
This study focuses on adjacent sea areas around Japan, especially the Kuroshio–Oyashio extension region, which is strongly affected by MHWs [33,34]. By characterizing temporal and spatial variations in marine heatwaves across the study area, our major objective is to assess and quantify the effects of regional SST and MHWs on distinct habitat groups and commercially important fisheries species. Our results demonstrate divergent response patterns of warm- and cold-water species to long-term SST trends and episodic MHW events. These insights offer a science-based framework for adaptive fisheries management under climate change.

2. Materials and Methods

2.1. Study Area

In accordance with its complex ocean current systems, five main regions are divided around Japan (Figure 1) [28], including the Kuroshio area (KA), Oyashio area (OA), Kuroshio–Oyashio Transition Zone (TZ), East China Sea (ECS), and Sea of Japan (JS). These regions are similar in range to marine ecoregions (MEs), which are the main contributors to marine fisheries and the fundamental carriers of ecosystem-based management [37].

2.2. Catch Data and SSTs

Annual catch data for seas around Japan from 1980 to 2022 were obtained from the ‘Fisheries resource assessment’ supplied by the Japan Fisheries Agency and Japan Fisheries Research and Education Agency (see Table S1 in Supplementary Materials). Due to different thermal tolerances, the response patterns to environment change usually vary among marine species [24,25,26]. To distinguish between different variations, 20 and 12 economically important species/taxa were classified into warm and cold groups, respectively. The pelagic and demersal habitat preferences of these species are detailed in Table 1. Based on observed fish migration patterns and the five biogeographic regions identified in our study area (Figure 1), we further categorized fishery catches into two major geographic assemblages: the Pacific group and the Tsushima group.
Several area-averaged SST datasets with different resolutions were used to represent regional environmental changes in the study area, i.e., the JS (127–141° E, 34–50° N), KA (131–142° E, 29–34° N), TZ (141–153° E, 34–40° N), and OA (141–153° E, 40–45° N) datasets. A monthly grid SST dataset for the period of 1980~2023 with a resolution of 1 degree (latitude × longitude) was obtained from the Met Office Hadley Centre observations datasets (https://www.metoffice.gov.uk/hadobs/hadisst/, accessed on 10 May 2024) to represent the long-term environmental variability of the four study regions. The seasonal average SSTs for summer (July–September) and winter (January–March) were calculated for different regions (similar to Ma et al. [38]). The NOAA daily high-resolution SST data (OISST V2) for the period of 1981~2023 with a resolution of 0.25 degrees (latitude × longitude) were provided by the National Oceanic and Atmospheric Administration (NOAA; https://psl.noaa.gov/data/gridded/index.html, accessed on 4 July 2024) to calculate regional MHW indices.

2.3. Marine Heatwaves

The quantitative definition of an MHW is a period of at least five days during which temperatures are above local threshold values [22]. Threshold values for each location for each day of the year are defined on the basis of the 90th percentile value. In this study, the R package “RmarineHeatWaves (version 0.17.0)” was applied to the daily SST dataset to isolate MHWs from long-term temperature variability [39]. The reference period here was set to 1983~2012, which was already used in most studies [6,23]. There are four indices used to describe annual MHWs, including imax (maximum intensity, °C), imean (mean intensity, °C), icum (cumulative intensity, °C × days), and duration. imax represents the maximum value of temperature above the climatological mean that is calculated over the reference period. imean and icum are the mean intensity and the sum of the daily intensities of MHWs within one year, respectively. The annual frequency and total days of MHW events were considered two additional indices.

2.4. Variation Trend Analysis

In this study, principal component analysis (PCA) was used to isolate the most important patterns of thermal variability in the study regions and variations in the catches of warm/cold groups from the Pacific and Tsushima areas. With the R package “psych” [40], PCA was applied to the annual catch of different fishery groups.
To analyze trends and step changes in fisheries and climatic data, sequential t test analyses of regime shifts (STARS) developed in the Visual Basic for Application (VBA) for Microsoft Excel for Windows [41] (http://www.beringclimate.noaa.gov/regimes/index.html, accessed on 4 June 2024) after a “prewhitening” procedure were applied to catch and SST datasets. The results of the STARS are determined by the cutoff length for the proposed regimes (L) and the Huber weight parameters (H), which define the range of departure from the observed mean beyond which observations are considered outliers. L was set to 20 and H to 1, and the significance level was set to 0.05 in this study.

2.5. Response Pattern Detection

Considering the presence of autocorrelation in the time series, an autocorrelation function (acf) was used to calculate the autocorrelation coefficient of catch and climatic data in the case of potentially significant autocorrelations in the time series. On this basis, the effective degree of freedom of the data was computed and adjusted. We then performed correlation analysis via “zoo” [42] to examine the quantitative relationships between all the environmental factors and the principal components of the warm and cold groups. Important factors were selected for subsequent analyses on the basis of the results of the significance test.
Gradient forest analysis (GFA), implemented with two R packages, “extendForest” and “gradientForest” [43], was used to further detect correlations between variations in fishery groups and environmental factors, including SSTs and MHW indices. Built on the basis of the random forest method, the gradient forest method inherits all its functionalities and extends to analyses that capture complex relationships between potentially correlated predictors and multiple response variables. This method allows one to quantify the degree of community change along the predictor gradient by integrating individual random forest analyses over different response variables [43,44]. We ran the gradient forest analysis (GFA) 1000 times to obtain the mean and standard deviation (sd) of goodness-of-fit (R2). The run with the highest R2 value was then used to derive all the statistics. By quantifying the changing patterns of response variables (i.e., PCA scores of warm and cold groups) along the gradients of selected variables and assessing predictor importance, gradient forest analysis can identify important thresholds in environmental gradients.
To further identify the response patterns of warm and cold groups to regional SSTs and MHWs, a threshold generalized additive model (TGAM; nonadditive and discontinuous model) was applied to the datasets. The TGAM is an extension of nonparametric regression techniques [45]. It can represent an abrupt change in the relationships between dependent and independent variables at a specific value. In this study, TGAMs provide nonstationary relationships between the response and explanatory variables in a specific year (i.e., a threshold year) at a specific value, showing different functions for two different periods [46,47,48]. The TGAM formulations are calculated as follows:
Y t = α 1 + s 1 X + ε t ,   i f   t < y α 2 + s 2 X + ε t ,   i f   t > y ,
where Y is the response variable (leading principal components of warm and cold groups), X is the explanatory variable (SSTs and MHW indices), s is the smooth function (with degrees of freedom k ≤ 4 to avoid overfitting), and α and ε are intercept and error terms, respectively. y is the threshold year that separates two periods with varying responses to drivers.

3. Results

3.1. Physical Environment Under Climate Change

The summer and winter SSTs quickly increased in the study area. There was a significant zonal variation in the warming trends among the Kuroshio area (KA), Kuroshio–Oyashio Transition Zone (TZ), and Oyashio area (OA). Increasing summer SSTs showed a distinct latitudinal gradient, with the warming amplitude progressively intensifying from southern to northern regions. Conversely, winter SST increases displayed an inverse spatial pattern. Among all the regions, the Sea of Japan (JS) presented the highest increase in summer SST (Figure 2). Additionally, summer and winter SSTs in the Sea of Japan exhibit latitudinal differences, with a more pronounced increase in the southern region compared to the northern region (Figure S1). Nevertheless, based on the geographical division of marine ecoregions [28,37], this study analyzes the Sea of Japan as an integrated system.
The STARS analysis identified step changes in SSTs corresponding to regime shifts in the North Pacific around 1989 and 1998 [31,32,49]. Except for the East China Sea (ECS) and Oyashio area (OA), summer SSTs exhibited a pronounced shift in 1998/99 across all study regions (Figure 2). There were substantial differences in summer SSTs’ means of anomalies (RSmean) in different phases with values of 0.78 °C (ECS), 0.87 °C (Japan Sea, JS), 0.82 °C (OA), and 0.73 °C (TZ). Only the KA showed a more modest shift (<0.5 °C). A potential secondary step change in summer SSTs emerged in 2021/22, which occurred near the end of the time series and needs to be verified with additional data. Winter SSTs displayed decadal variations with the first step change concentrated in 1988~1990, and the second step changes occurring around 2019 (Figure 2). The differences between the winter SSTs’ RSmean values in the different phases in ECS, KA, and JS were 1.28 °C, 0.77 °C, and 0.60 °C, respectively, while TZ (0.45 °C) and OA (0.43 °C) exhibited more muted responses.
Marine heatwaves (MHWs) showed marked increases in both frequency and intensity across most of the study area in response to rising SSTs. Notably, the Oyashio area (OA) exhibited the highest maximum intensity (imax) of MHWs among all regions, while the Kuroshio area (KA) uniquely displayed a declining imax trend after 1998 and then remained below 2.0 °C (Figure 3). Correlation analysis demonstrated strong positive relationships between the MHW indices and regional SSTs, especially summer SSTs (Figures S4 and S6). Temporally, MHWs prior to 1990 were infrequent but characteristically intense. Since then, with MHW frequency escalating rapidly and peaking during 1998~2001, mean and maximum intensities (imean and imax) remained comparable to pre-1990 levels. In the Japan Sea (JS), the frequency and total days of MHWs showed sustained growth after 2000, with imax and icum rapidly rising after a brief cooling phase (2014–2015). Spatial heterogeneity was evident in Kuroshio-influenced zones, where MHW activity diminished post-2000 before intensifying again after 2010. There were two main active periods of MHWs. The first active period started from 1996/97 in all study areas, and the beginnings of the second active period in ECS, JS, KA, and OA occurred later than 2010.

3.2. Variations in Economic Fish in Adjacent Sea Regions Around Japan

Compared to demersal species, pelagic species exhibited significantly higher catches in Japanese inshore fisheries (Figure S2). Japanese sardine (Sardinops melanostictus) from the Pacific and Tsushima groups dominated early landings but experienced a dramatic collapse in the late 1980s with only a modest recovery observed after 2010 (Figure S2B,D). In contrast, catches of another warm species, Japanese anchovy (Engraulis japonica), showed the opposite variation trend. In the cold groups, Alaska pollock (Gadus chalcogrammus) and Pacific sandlance (Ammodytes personatus) from the Pacific area showed persistent declines, whereas Pacific cod (Gadus macrocephalus) from the Pacific and Tsushima groups slightly increased after 2005 (Figure S2C).
Principal component analysis (PCA) of the catches from eighteen warm-water species/taxa in the Tsushima group indicated three meaningful components (JW.PC1, JW.PC2, and JW.PC3), with JW.PC1 explaining 41.03% of the variance. Except for Japanese sardine and Japanese scad (Decapterus maruadsi), JW.PC1 was characterized by declining trends in most demersal species with two step changes around 1989/90 and 2005/06. JW.PC2 captured a different pattern in most pelagic warm-water species, which increased before the step change around 1989/90 and then showed a downward trend with step changes around 1999/2000 and 2016/17. JW.PC3 primarily reflected the catches of Japanese Spanish mackerel (Scomberomorus niphonius) and red-eye round herring (Etrumeus teres), which declined until the late 1990s with a step change around 1995/96, and then gradually increased with a step change around 2009/10 (Figure 4A and Figure S3A).
The PCA of the Tsushima cold group identified three significant components (JC.PC1, JC.PC2, and JC.PC3). JC.PC1, representing the catches of most demersal fishes, increased before 1997 with a step change around 1991/92 and then declined after the step change around 2009/10. JC.PC2 and JC.PC3 had similar variability explanations, with JC.PC2 showing continuous decreases in flatfish catches with step changes occurring around 1989/90 and 2003/04. JC.PC3 captured trends for Japanese sandfish (Arctoscopus japonicus) and flathead flounder (Hippoglosides dubius). It declined until the late 1990s, then increased with a step change around 2001/02, and decreased again after the second step change around 2016/17 (Figure 4B and Figure S3B).
In the Pacific warm group, the difference between the variability explanations of PW.PC1 and PW.PC2 was only 0.26%. PW.PC1 (pelagic fishes) quickly increased after the first step change at approximately 1990/91 and then declined with a step change around 2012/13. However, dominated by Japanese sardine and Japanese Spanish mackerel, PW.PC2 exhibited persistent declines with step changes around 1992/93 and 2010/11 (Figure 4C and Figure S3C).
In the Pacific cold group, PC.PC1 primarily reflected declines in demersal species with step changes around 1993/94, 2008/09, and 2016/17. Contributed by pelagic fishes (Pacific cod, broadbanded thornyhead Sebastolobus macrochir, and Pacific herring Clupea pallasii), PC.PC2 declined with a step change around 1990/91, followed by an upward trend with a step change around 2009/10 (Figure 4D).

3.3. Response of the Tsushima Group to Climate Variability

Correlation analyses revealed distinct environmental responses between the Tsushima warm and cold groups. The PC2 of the Tsushima cold group (JC.PC2) exhibited significant negative correlations with most of the environmental variables, whereas the PC1 of the Tsushima warm group (JW.PC1) only showed negative associations with winter SST in the Sea of Japan (JS.w) (Figure S3).
Based on the correlation analysis, the environmental factors that were significantly correlated with the catches of different groups were preliminarily selected for gradient forest analysis. The results revealed that the PC2 of the Tsushima cold group (JC.PC2) and the PC1 of the Tsushima warm group (JW.PC1) were more sensitive to climate change, with JC.PC2 exhibiting the highest R2 (goodness-of-fit R2) values (Figure 5A). The winter SST in the Sea of Japan (JS.w) emerged as the predominant environmental driver with the highest R2-weighted importance. The cumulative MHW intensity of JS (JS.int.cum) showed comparable influence, demonstrating the substantial impacts of marine heatwaves on the Tsushima warm and cold groups (Figure 5B).
Gradient forest analyses also showed the cumulative importance of warm and cold groups in response to all the predictor variables (Figure S5). Accordingly, TGAMs were applied to dependent variables with higher R2 values (i.e., JW.PC1 and JC.PC2) and the most important predictors (i.e., JS.w, JS.int.cum). The results indicated that JW.PC1 has a strong response to winter SST (JS.w) and a relatively weak response to MHWs, whereas JC.PC2 strongly responded to all the environmental predictors except for summer SST (JS.s) and the mean MHW intensity (JS.int.mean). The negative correlations between JW.PC1 and JC.PC2 with JS.w both converted into positive correlations of approximately 7.2 °C for the winter SST (JS.w) (Figure 6A,B). The influencing tendencies and thresholds of the MHW indices for these two dependent variables were similar to those of JS.w (Figure 6C,D). In addition, the threshold years of JW.PC1 occurred in the period of 2005~2008, whereas JC.PC2 occurred around 2002/03 (Figure 6).
To assess immediate MHW impacts, matching analyses were conducted between the annual change rates of all species/taxa from the warm and cold groups and MHW indices. The results demonstrated that when MHWs weakened or vanished after a strong MHW year, Japanese sardine and most cold species experienced fluctuations over the following 1~2 years (Figure 7A,B). Other warm-water species showed no consistent response pattern to MHWs, suggesting either greater thermal tolerance or compensatory ecological mechanisms.

3.4. Response of the Pacific Group to Climate Variability

Correlation analysis revealed negative relationships between the PC2 of the Pacific warm group (PW.PC2) and the PC1 of the Pacific cold group (PC.PC1) with regional SSTs and MHW indices. The correlations between PW.PC2 and the environmental variables of the Kuroshio area (KA) were weak, whereas PC.PC1 was significantly related to the MHW indices of the Oyashio area (OA) (Figure S7).
Gradient forest analysis was performed on the selected environmental factors and the PC1/PC2 of the Pacific warm and cold groups. The results indicated that PC.PC1 was the most sensitive to environmental change with the highest R2 values, followed by PW.PC2 (Figure 8A). Among all the predictors, summer SSTs in the Kuroshio area (KA.s) and Kuroshio–Oyashio Transition Zone (TZ.s), MHW indices such as the cumulative intensity (icum) of the East China Sea (ECS) and Oyashio area (OA), and the maximum intensity (imax) of TZ have higher R2-weighted importance, indicating the strong impacts of these factors on the dynamics of Pacific groups (Figure 8B). In addition, the cumulative importance of Pacific groups in response to all predictors varied among the gradient. It further indicated the significant responses of PC.PC1 to the MHW indices of all regions, while PC.PC2 was sensitive only to the mean MHW intensity (imean) in ECS and KA (Figure S7). Compared with the cold group, the Pacific warm group was relatively weak in response to environmental variables. PW.PC1 and PW.PC2 were more sensitive to variations in regional SSTs. For example, PW.PC2 displayed a significant response to TZ.s, especially within the temperature range of 23.5~24.0 °C (Figure S7).
Based on the results of gradient forest analysis, TGAMs were applied to the dependent variables with higher R2 (i.e., PW.PC2 and PC.PC1) and important predictors (i.e., TZ.s, MHW indices including the imean of KA, icum of KA, OA, and ECS, and imax of TZ). It demonstrated that the response of PW.PC2 and PC.PC1 to TZ.s converted when the summer SST in TZ reached the range of 23.0~24.0 °C and the transition of PW.PC2 occurred much earlier (Figure 9A,F). In response to MHWs, both PW.PC2 and PC.PC1 were negatively correlated with the selected MHW indices, which then strengthened after the threshold years (Figure 9). The threshold year varied among the responses of the Pacific groups to the predictors. PW.PC2 responded to environmental variables with threshold years between 1998 and 2002 in TZ and OA and 2009~2010 in ECS and KA (Figure 9A–E), while the threshold years of PC.PC1 were concentrated in 1999/2000 in TZ and OA and 2011~2013 in ECS and KA (Figure 9F–J).
Similar to the results of the Tsushima groups, matching analyses between the annual change rates of all species/taxa and MHW indices indicated the significant response of cold-water species to MHWs. The catches of most Pacific cold-water species in this study decreased during strong MHW years in the Oyashio area (OA) and Kuroshio–Oyashio Transition Zone (TZ) and then quickly increased in the following years when MHWs weakened or vanished. There were minimal detectable responses to MHWs at the aggregate level observed among the change rates of Pacific warm-water species. In both warm and cold groups, the fluctuations of pelagic species were greater than that of demersal fishes (Figure 10). And variations in the change rates of Japanese sardine and Japanese anchovy displayed opposite trends.

4. Discussion

4.1. Variability in Warm Groups Under Continuous Warming SSTs

In Japan’s marine fisheries, warm-water species contributed higher catches than cold-water species (Figure S2). Pelagic species within warm groups, especially Japanese sardine, displayed obvious responses to regional sea surface temperatures (SSTs).
Following the step changes of winter SSTs in the study area around 1988/89, the Tsushima and Pacific groups experienced step changes between 1989 and 1992, whereas only small pelagic species from the Tsushima warm group (JW.PC2) reacted quickly to the step changes of summer SSTs around 1998/99. The step changes of the Pacific warm group from 2010 to 2012 were consistent with those of the summer SST in the Oyashio area (OA) around 2011/12 (Figure 2 and Figure 4A,C). In addition, the step changes of the PC2 and PC3 of the Tsushima warm group (JW.PC2, JW.PC3) occurred in 2009 and 2016, coinciding with the peaks of marine heatwaves (MHWs) in the Sea of Japan (Figure 3 and Figure 4A,C). These findings further support the strong influences of warming SSTs on the catch of small pelagic fishes around Japan [30,38,50].
In contrast to small pelagic fishes, high-trophic-level fishes, including Japanese Spanish mackerel and other demersal fish from the Tsushima and Pacific warm groups (JW.PC1, JW.PC3, and PW.PC2), exhibited step changes later than the regional SST shifts after the late 1980s (Figure 2 and Figure 4A,C). This aligns with catch trends in China’s marine fisheries [51]. Although the results of the gradient forest analysis suggested JW.PC1 and PW.PC2 were sensitive to variations in regional SSTs, their environmental response thresholds consistently lagged behind the 1998/99 step changes of SSTs. Additionally, after the second step change occurred in 2005/06, there were conversions of JW.PC1’s response to both the winter SST and the icum of MHWs in the Sea of Japan (Figure 4A). The lowest and highest value of threshold for JS.w (7.2~7.7) closely matched the RSmean values of its first and second stages (Figure 2, Figure 6A and Figure S5). Similarly, PW.PC2’s environmental thresholds appeared later than its 1992 step change (Figure 4C), with thresholds for summer SST in the Kuroshio–Oyashio Transition Zone (TZ.s) ranging between 23.2 and 23.8 °C. The lowest and highest values were also close to the RSmean values of the two stages of TZ.s (Figure 2). These patterns demonstrated that the transition in response patterns could be one of the outcomes of climate-driven regime shifts in marine fish populations [31,52], which potentially explained the delayed step changes in high-trophic-level species relative to climatic variables [51].
Moreover, for Japanese fisheries in the East China Sea, the Pacific side of Japan, and the Sea of Japan, the annual change rates of Japanese Spanish mackerel catches showed no significant response to MHWs (Figure S8). This suggests that while small pelagic fishes exhibit rapid responses to warming SSTs and climate regime shifts, the influence of MHWs—as short-term climate events—on decadal variations in marine fish populations, especially high-trophic-level organisms, appears limited. Notably, during 1992–1997 and 2015–2019, Japanese Spanish mackerel catches displayed contrasting trends between the East China Sea and the Sea of Japan (Figure S8), possibly indicating northward population shifts in response to elevated SSTs [53].

4.2. Variations in Typical Warm Species

The variations in small pelagic fishes, particularly the dramatic rise and fall of the sardine catch, which dominate the fish assemblage around Japan, exert substantial influence on ecosystem structure and function [28,29,54]. As an important economic fish in Japanese fisheries, the catch of sardine declined sharply since the late 1980s (Figure S2), which could be driven by the combined pressure of intensive fishing and rising temperatures. Previous studies have established that high temperatures, especially warming trends in the Kuroshio–Oyashio Transition Zone (TZ), are not conducive to the growth of juvenile Japanese sardine, resulting in adverse impacts on recruitment in their populations [55,56]. And our results corroborate this relationship, demonstrating that the downward step changes in sardine catches aligned closely with winter SST shifts around 1988/89 (Figure 2 and Figure S9). Then, SSTs in the Oyashio area (OA) and TZ experienced a temporary cooling trend from 2002 to 2015, periodically falling below the RSmean of their first stage (Figure 2). This period coincided with an absence of MHW events in the TZ (Figure 3) and a gradual recovery in the catch of Pacific sardine after 2005 (Figure S9). These observations suggest that temperature conditions below established threshold ranges, such as the brief cooling stage of SSTs in TZ, may partially mitigate the long-term warming pressures on Japanese sardine populations. Furthermore, the implementation of the Resource Recovery Plan (2003) for sardine, anchovy, and chub mackerel in Northeastern Japan could have synergistically contributed to population recovery [30].
Interestingly, in both Pacific and Tsushima groups, the catch of Japanese sardine dropped quickly to an extremely low level following the first step change of winter SSTs during 1987~1989, whereas the catch of Japanese anchovy increased after a few years. And the decline in Japanese anchovy occurred before the increasing trend of Japanese sardine observed since 2004 (Figure S9). Furthermore, the annual change rates of these two species exhibited opposing trends in most cases (Figure 7 and Figure 10), consistent with a classic species alternation pattern [57]. Previous studies have demonstrated that the recruitment of both species was influenced by climate change [58], with their dramatic catch fluctuations in the late 1980s closely linked to marine regime shifts during that period [31,38,55]. However, the correlation and gradient forest analysis of JW.PC2 and PW.PC1 in this study indicate that compared to Japanese sardine, Japanese anchovy exhibited a lower sensitivity to environmental predictors. Meanwhile, the annual change rate of Japanese sardine highly matched the variations in regional MHWs. These differential responses to warming temperatures may stem from the species’ distinct thermal tolerances. One hypothesis explaining the stock fluctuations in anchovy and sardine suggests that their different optimal larval growth temperature (16.2 °C for the sardine and 22.0 °C for the anchovy) leads to the anchovy and sardine regimes observed in warm and cold periods, respectively [59].
Therefore, it is supposed that variations in the Japanese anchovy catch could be guided by thermal preference and temperature regime shifts. Based on the observed response pattern to environmental factors in this study, we can infer the fundamental dynamics of Japanese sardine and Japanese anchovy populations. When SSTs rise beyond the critical threshold, the catch of Japanese sardine declines sharply, while Japanese anchovy begins to occupy the ecological niche. This pattern persists until temperatures drop significantly below the mean of the current regime, creating favorable conditions for the growth of Japanese sardine. Consequently, sardine populations enter a resurgence phase, leading to a decline in the abundance of anchovy and establishing a cyclical species alternation pattern. Within this cycle, marine heatwaves (MHWs) exert a strong influence on the interannual catch variability of Japanese sardine. Notably, the sardine catch tends to rebound when MHWs weaken or disappear in the following 1 or 2 years but decline again during the next MHW event.

4.3. Response of Cold Groups to Environmental Changes

Compared to warm-water species in Japanese fisheries, cold-water species are more sensitive to long-term variabilities in seawater temperatures and exhibit pronounced responses to marine heatwaves (MHWs).
The step changes observed in the Tsushima and Pacific cold groups during 1989~1991 corresponded well with those of the regional winter SSTs around 1988/89. Similarly, the step change of summer SSTs in the Oyashio area (OA.s) in 2010 was close to those of the Pacific cold group (PC.PC1 and PC.PC2) and PC1 of the Tsushima cold group (JC.PC1) during 2008–2009 (Figure 2 and Figure 4B,D). Furthermore, the step changes of cold groups around 1993/94 and 2016/17 also coincided with the onset of the first and second active MHW periods, respectively (Figure 3 and Figure 4B,D). Notably, the shorter lag between the step changes of cold groups and environmental shifts suggested that the cold-water species respond more rapidly to temperature variations than warm-water species. This heightened sensitivity may stem from their narrower thermal tolerance ranges, as supported by previous studies on climate change impacts [60]. The significant negative correlations between cold groups and environmental predictors (Figures S4 and S6) further corroborate the detrimental effects of prolonged warming trends on cold-adapted marine species [61,62,63].
Gradient forest analysis further indicated the high environmental sensitivity of JC.PC2 (PC2 of the Tsushima cold group) and PC.PC1 (PC1 of the Pacific cold group). Integrating results from TGAMs, it is observed that the range of the threshold range of JS.w influencing JC.PC2 also aligned with the RSmean of the two distinct stages in JS.w (Figure 2, Figure 6C, and Figure S5). And after the first step change, the threshold years emerged for JC.PC2 in response to the winter SST and cumulative MHW intensity in the Sea of Japan (Figure 4B and Figure 6C,D). Analogous patterns were detected in PC1 of the Pacific cold group (Figure 4D and Figure 9F–I).
The response dynamics of cold groups to SSTs and MHWs mirrored those of warm groups. Collectively, the threshold years for both warm and cold groups consistently followed climatic and fishery regime shifts across different phases. However, Pacific groups exhibited transitions more closely synchronized with the step changes of SSTs around 1998/99 only in their responses to MHWs in the OA and TZ regions (Figure 9). This period coincided with a marked rise in MHW intensity and frequency in OA and TZ (Figure 3). In contrast, the MHW intensity of KA remained stable with a slight decline and even dissipated in the early 2010s, coinciding with threshold years for Pacific groups in their responses to KA’s MHWs (Figure 3 and Figure 9). These findings suggested that threshold years likely reflect significant environmental perturbations, including large-scale climate regime shifts and variations in MHWs. Consequently, our results support the hypothesis that marine fishes, following population restructuring due to climate shifts and fishing pressure [51,54], modify their response patterns to adapt to new environmental regimes [64].
Most cold-water species in the Pacific and Tsushima groups have declined rapidly in catches since the late 1990s, exhibiting negative responses to warming SSTs and frequent MHWs. These trends demonstrate the detrimental effects of warm anomalies on the catch of cold-water species [24]. However, catch rebounds were observed for some cold-water species during periods of diminished MHW activity. Notably, cold species with stable habitats (e.g., flathead flounder Hippoglossoides dubius) showed different changing patterns compared to more mobile species like Pacific cod (Gadus macrocephalus), whose catches began gradually increasing after 2000 (Figure S2B,D). This disparity may reflect that spatial expansions into newly favorable thermal habitats would potentially improve stock-recruitment relationships [65]. Such range shifts, including poleward migrations, could partially mitigate warming-induced population declines [24,66,67].

4.4. Precautionary Fisheries Management Under Climate Change

Marine fisheries in the adjacent sea regions around Japan are strongly affected by fishing activities and climate change [28,29], making it challenging to disentangle the respective impacts of these two major drivers. Recent stock assessments revealed concerning trends, with approximately half of the 37 stocks being either overfished or subject to overfishing [68]. To mitigate fishing pressure and facilitate stock recovery, Japan implemented a series of regulatory measures. The Total Allowable Catch (TAC) System, implemented in 1997/1998 for seven important economic species (e.g., Japanese sardine, Alaska pollock), marked a significant policy shift. Subsequent management enhancements included the Japanese Resource Recovery Plan (2003), which systematically reduced fishing effort across multiple fisheries [30]. Further strengthening these efforts, the 2018 revisions to the Fisheries Act expanded the national government oversight of catch limits [69]. While these measures have contributed to localized fishery recoveries, their effectiveness continues to be undermined by accelerating ocean warming and the increasing frequency of extreme climatic events. The compounding effects of climate change now represent a persistent threat to the long-term sustainability of Japan’s marine fisheries.
It is evident that in addition to regulating fishing intensity, marine fisheries management must adapt to climate change, as it significantly influences the species composition and population distribution within marine ecosystems [70]. Effective fisheries management should be flexible for changing oceanic and ecosystem conditions, with tailored strategies for distinct biological groups or even species-specific characteristics. Recently, the Japanese government has progressively advanced the assessment of marine species based on the maximum sustainable yield (MSY), investigated the impact of non-fishing marine environmental changes on population fluctuations, and continued to develop fisheries structures grounded in science-based resource management, such as total allowable catch (TAC) systems aligned with these assessments. Especially, TAC regulations for key species (e.g., Pacific saury Cololabis saira, Pacific sardine) now incorporate annual adjustments based on climate-influenced stock assessments [71]. As part of an ecosystem-based flexible TAC framework, this approach integrates ecosystem indicators (e.g., SST anomalies) to dynamically adjust quotas. For example, the fishing quota for Pacific saury has been progressively reduced in recent years in response to the weakening of the Oyashio Current [72].
But there are still exceptions, such as the species alternation between sardine and anchovy. The abrupt decline in the catch of Japanese sardine was triggered by recruitment failures from 1988 to 1991, which closely linked to the North Pacific regime shift around 1988/89 [73,74]. Although the sardine catch has partially recovered in recent years, attributable to both the Resource Recovery Plan [30] and a transient cooling stage, Japanese anchovy (also included in the plan) has exhibited a sustained decline (Figure S9). This contrast highlights that while fisheries management interventions can facilitate localized stock recovery under global warming, broader oceanic conditions continue to be the predominant determinant of population trends. Consequently, the identification of long-term climatic trends constitutes a fundamental requirement for implementing sustainable and precautionary fisheries management approaches [75]. To develop effective adaptive strategies, researchers need to extend beyond retrospective and contemporary analyses by incorporating projections of future climate scenarios and marine environmental variability. Such comprehensive assessments enable more reliable predictions of ecosystem responses, which in turn facilitate improved accuracy in fisheries forecasting and science-informed adjustments to management policies.
Since the late 1980s, the northwestern North Pacific has undergone rapid warming. Spatiotemporal analyses of multiple commercially important marine species have demonstrated significant distributional shifts in response to changing oceanographic conditions during both warm (e.g., 1990s) and cold (e.g., 1970s–1980s) periods [24,65]. However, during the 2010s, all study regions exhibited lower temperatures than the RSmean of the current phase in most cases (Figure 2), coinciding with the weakening of MHWs in the same period (Figure 3). This pattern suggests a potential shift toward a cold regime (Figure 2), supported by shifts in species composition resembling historical cold-regime assemblages [28]. This transition was marked by the recovery of several cold-water species (e.g., Pacific herring) alongside the decline of catches in warm-water species such as Japanese anchovy and Japanese common squid (Todarodes pacificus) (Figure S2). Furthermore, while strong MHW events exerted negative effects on cold-water species, the brief decrease in SSTs during the 2010s created favorable conditions for the resurgence of some species, including chub mackerel (Scomber japonicus) and Japanese sardine in the Kuroshio–Oyashio Transition Zone (TZ) (Figure S2) [30]. Despite the recent increase in SSTs with step changes during 2019–2022 in the adjacent sea regions around Japan (Figure 2), the cold regime during the 2010s that was anomalous in light of the long-term warming trend appears to have had substantial and lasting impacts on the marine fish community structure and fishery catches [28]. These findings indicate that fisheries management strategies may require adaptation to address the potential impacts of the 2010s’ cooling stage.
For economically important fisheries in the adjacent sea regions around Japan, decadal variations in marine species are primarily driven by large-scale environmental regime shifts, whereas the impacts from MHWs tend to be short-term, typically affecting catch fluctuations within 1–2 years. In the marine ecosystem, cold-water species with limited thermal tolerance are particularly vulnerable to warm anomalies. During intense MHW years, elevated temperatures can disrupt prey availability, leading to reduced survival rates of Alaska pollock and Pacific cod, with subsequent catch declines over 1–2 years (Figure 7 and Figure 10) [24,76,77]. Consequently, sustainable fisheries management frameworks should also incorporate extreme climate events such as MHWs as critical indicators within early-warning systems to inform adaptive operational decisions across short-term (monthly to annual) timescales.

5. Conclusions

Two distinct step changes of warming sea surface temperatures (SSTs) were identified in the adjacent sea regions surrounding Japan around 1988/89 and 1998/99, with each region experiencing two separate periods of heightened marine heatwave (MHW) activity. Decadal variations in marine catches exhibited a strong association with long-term climate change, whereas MHWs typically influenced fisheries catches on shorter timescales (1–2 years). Among warm-water species, the Japanese sardine demonstrated a pronounced sensitivity to regional SSTs, displaying a typical species alternation with Japanese anchovy. In contrast, most cold-water species showed significant negative correlations with environmental predictors. Threshold analysis further revealed that transitions in fish population responses to environmental variability were likely triggered by concurrent regime shifts in both climate conditions and fish populations. These findings underscore the need for sustainable fisheries management strategies that integrate fishing pressure regulation with continuous environmental monitoring, which accounts for both long-term climate trends and acute disturbances like MHWs.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/fishes10070299/s1, Table S1: Data source of the original fishery data presented in the study (https://abchan.fra.go.jp/hyouka/datatable/, accessed on 4 July 2024); Figure S1: Variabilities in sea surface temperatures (SSTs) of different regions of JS (Sea of Japan); Figure S2: Catches of warm and cold groups in different regions around Japan; Figure S3: Species/taxa order of the contributions of variables in calculating the variability of leading principal components; Figure S4: Correlation analysis between the PCs of the Tsushima cold and warm groups and the environmental indices; Figure S5: Cumulative importance of the PCs of the Tsushima warm/cold groups in response to predictor variables; Figure S6: Correlation analysis between PCs of Pacific cold and warm groups and environmental indices; Figure S7: Cumulative importance of Pacific warm/cold group PCs in response to predictor variables; Figure S8: Annual change rates of Japanese catches of Japanese Spanish mackerel from the East China Sea, the Pacific side of Japan, and the Sea of Japan; Figure S9: Catches of Japanese anchovy and Japanese sardine from the Pacific warm group (A) and Tsushima warm group (B).

Author Contributions

D.L.: Writing—original draft, Methodology, Investigation, Conceptualization. X.C.: Writing—review and editing, Supervision. B.L.: Writing—review and editing, Supervision. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Key Research and Development Program (NKRDP) of China (grant number: 2023YFD2401302) and the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning (grant number: GZ2022011).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original fishery data presented in this study are openly available in ‘Fisheries resource assessment’ supplied by the Japan Fisheries Agency and Japan Fisheries Research and Education Agency at https://abchan.fra.go.jp/hyouka/datatable/.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
MHWMarine Heat Wave
GFAGradient Forest Analysis
TGAMThreshold Generalized Additive Model
SSTSea Surface Temperature
KAKuroshio Area
TZKuroshio–Oyashio Transition Zone
OAOyashio Area
JSSea of Japan
ECSEast China Sea
JW.PCPrincipal Components of the Tsushima Warm group
JC.PCPrincipal Components of the Tsushima Cold group
PW.PCPrincipal Components of the Pacific Warm group
PC.PCPrincipal Components of the Pacific Cold group

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Figure 1. Schematic illustration of the study area. The rectangles with black solid lines indicate regions with average sea surface temperature (SST) and calculated marine heatwave (MHW) indices: East China Sea (ECS), Sea of Japan (JS), Kuroshio area (KA), the Transition Zone (TZ), and Oyashio area (OA). Red and blue arrows represent warm and cold currents: Kuroshio current (a), Tsushima warm current (b), Oyashio current (c), and Liman cold current (d).
Figure 1. Schematic illustration of the study area. The rectangles with black solid lines indicate regions with average sea surface temperature (SST) and calculated marine heatwave (MHW) indices: East China Sea (ECS), Sea of Japan (JS), Kuroshio area (KA), the Transition Zone (TZ), and Oyashio area (OA). Red and blue arrows represent warm and cold currents: Kuroshio current (a), Tsushima warm current (b), Oyashio current (c), and Liman cold current (d).
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Figure 2. Variabilities in the sea surface temperatures (SSTs) of different regions around Japan. The black lines represent the regional SSTs of East China Sea (ECS), Sea of Japan (JS), Kuroshio area (KA), the Transition Zone (TZ), and Oyashio area (OA), respectively. The blue lines represent the means of anomalies (RSmean) in different phases that were calculated via STARS. Summer SSTs are represented by “s”, and winter SSTs are represented by “w”.
Figure 2. Variabilities in the sea surface temperatures (SSTs) of different regions around Japan. The black lines represent the regional SSTs of East China Sea (ECS), Sea of Japan (JS), Kuroshio area (KA), the Transition Zone (TZ), and Oyashio area (OA), respectively. The blue lines represent the means of anomalies (RSmean) in different phases that were calculated via STARS. Summer SSTs are represented by “s”, and winter SSTs are represented by “w”.
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Figure 3. Marine heatwaves in different regions around Japan including East China Sea (ECS), Sea of Japan (JS), Kuroshio area (KA), the Transition Zone (TZ), and Oyashio area (OA). In the right column, the shaded areas represent the icum (cumulative intensity, °C × days) of MHWs, the orange lines represent the imax (maximum intensity, °C) of MHWs, and the blue lines represent the imean (mean intensity, °C) of MHWs.
Figure 3. Marine heatwaves in different regions around Japan including East China Sea (ECS), Sea of Japan (JS), Kuroshio area (KA), the Transition Zone (TZ), and Oyashio area (OA). In the right column, the shaded areas represent the icum (cumulative intensity, °C × days) of MHWs, the orange lines represent the imax (maximum intensity, °C) of MHWs, and the blue lines represent the imean (mean intensity, °C) of MHWs.
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Figure 4. Principal component analysis (PCA) of warm and cold groups from different regions revealed regime shifts in the leading principal components (PC1, PC2, and PC3). (A) PCA of the Tsushima warm group (JW); (B) PCA of the Tsushima cold group (JC); (C) PCA of the Pacific warm group (PW); (D) PCA of the Pacific cold group (PC). The regime shift index (RSI) was calculated by STARS.
Figure 4. Principal component analysis (PCA) of warm and cold groups from different regions revealed regime shifts in the leading principal components (PC1, PC2, and PC3). (A) PCA of the Tsushima warm group (JW); (B) PCA of the Tsushima cold group (JC); (C) PCA of the Pacific warm group (PW); (D) PCA of the Pacific cold group (PC). The regime shift index (RSI) was calculated by STARS.
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Figure 5. (A) Goodness-of-fit R2 of the gradient forest analysis for the leading principal components (PC1, PC2, and PC3) of the Tsushima warm and cold groups (represented by JW and JC); (B) the mean importance of each variable weighted by species R2, including the icum, imax, and imean of MHWs (represented by JS.int.cum, JS.int.max, and JS.int.mean, respectively), and summer and winter SSTs (JS.s and JS.w) in the Sea of Japan.
Figure 5. (A) Goodness-of-fit R2 of the gradient forest analysis for the leading principal components (PC1, PC2, and PC3) of the Tsushima warm and cold groups (represented by JW and JC); (B) the mean importance of each variable weighted by species R2, including the icum, imax, and imean of MHWs (represented by JS.int.cum, JS.int.max, and JS.int.mean, respectively), and summer and winter SSTs (JS.s and JS.w) in the Sea of Japan.
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Figure 6. Results of the TGAMs between the PC1 of the Tsushima warm group (JW.PC1) with (A) winter SST (JS.w) and (B) the icum of MHWs (JS.int.cum) in the Sea of Japan. The right column showed TGAM results between the PC2 of the Tsushima cold group (JW.PC2) with JS.w (C) and JS.int.cum (D). Each response pattern was separated into two phases, which are represented by red and blue lines.
Figure 6. Results of the TGAMs between the PC1 of the Tsushima warm group (JW.PC1) with (A) winter SST (JS.w) and (B) the icum of MHWs (JS.int.cum) in the Sea of Japan. The right column showed TGAM results between the PC2 of the Tsushima cold group (JW.PC2) with JS.w (C) and JS.int.cum (D). Each response pattern was separated into two phases, which are represented by red and blue lines.
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Figure 7. Annual change rates of catches of economic fishes from the Tsushima warm (A) and cold groups (C). The black lines represent the maximum intensity of MHWs (int_max) in the Sea of Japan (B).
Figure 7. Annual change rates of catches of economic fishes from the Tsushima warm (A) and cold groups (C). The black lines represent the maximum intensity of MHWs (int_max) in the Sea of Japan (B).
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Figure 8. (A) Goodness-of-fit R2 of the gradient forest analysis for the leading principal components (PC1 and PC2) of Pacific warm and cold groups (represented by PW and PC); (B) the mean importance of each variable weighted by species R2, including the icum, imax, and imean of MHWs (represented by int.cum, int.max, and int.mean, respectively) and summer and winter SSTs (abbreviated as s and w) in the East China Sea (ECS), Kuroshio area (KA), Oyashio area (OA), and Kuroshio–Oyashio Transition Zone (TZ).
Figure 8. (A) Goodness-of-fit R2 of the gradient forest analysis for the leading principal components (PC1 and PC2) of Pacific warm and cold groups (represented by PW and PC); (B) the mean importance of each variable weighted by species R2, including the icum, imax, and imean of MHWs (represented by int.cum, int.max, and int.mean, respectively) and summer and winter SSTs (abbreviated as s and w) in the East China Sea (ECS), Kuroshio area (KA), Oyashio area (OA), and Kuroshio–Oyashio Transition Zone (TZ).
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Figure 9. Results of TGAMs between the PC2 of the Pacific warm group (PW.PC2) and the PC1 of the Pacific cold group (PC.PC1) and important factors. Each response pattern was separated into two phases, which are represented by red and blue lines. The left column showed TGAM results between PW.PC2 with (A) summer SST of Kuroshio–Oyashio Transition Zone (TZ.s), (B) the mean intensity of MHWs in Kuroshio area (KA.int.mean), (C) the cumulative intensity of MHWs in Oyashio area (OA.int.cum), (D) maximum intensity of MHWs in TZ (TZ.int.max), and (E) the cumulative intensity of MHWs in the East China Sea (ECS.int.cum). The right column showed TGAM results between PC.PC1 with (F) TZ.s, (G) KA.int.cum, (H) OA.int.cum, (I) TZ.int.max, and (J) ECS.int.cum.
Figure 9. Results of TGAMs between the PC2 of the Pacific warm group (PW.PC2) and the PC1 of the Pacific cold group (PC.PC1) and important factors. Each response pattern was separated into two phases, which are represented by red and blue lines. The left column showed TGAM results between PW.PC2 with (A) summer SST of Kuroshio–Oyashio Transition Zone (TZ.s), (B) the mean intensity of MHWs in Kuroshio area (KA.int.mean), (C) the cumulative intensity of MHWs in Oyashio area (OA.int.cum), (D) maximum intensity of MHWs in TZ (TZ.int.max), and (E) the cumulative intensity of MHWs in the East China Sea (ECS.int.cum). The right column showed TGAM results between PC.PC1 with (F) TZ.s, (G) KA.int.cum, (H) OA.int.cum, (I) TZ.int.max, and (J) ECS.int.cum.
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Figure 10. Annual change rates of catches of economic fishes from the Pacific warm (A) and cold groups (C). The orange line, black line, and blue line represent the cumulative intensity of MHWs (int_cum) in the East China Sea (ECS), Oyashio area (OA), and Kuroshio area (KA), respectively (B).
Figure 10. Annual change rates of catches of economic fishes from the Pacific warm (A) and cold groups (C). The orange line, black line, and blue line represent the cumulative intensity of MHWs (int_cum) in the East China Sea (ECS), Oyashio area (OA), and Kuroshio area (KA), respectively (B).
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Table 1. List of species/taxa included in the analyses with their ecological habits (based on FishBase; https://www.fishbase.se/search.php, accessed on 4 June 2024).
Table 1. List of species/taxa included in the analyses with their ecological habits (based on FishBase; https://www.fishbase.se/search.php, accessed on 4 June 2024).
Common Name of Species/TaxaScientific NameThermal AdaptabilityHaibitat
warm groupJapanese sardineSardinops melanostictusSubtropicalpelagic; migratory
Japanese jack mackerelTrachurus japonicusTropicalpelagic; migratory
Chub mackerelScomber japonicusSubtropicalpelagic; migratory
Blue mackerelScomber australasicusSubtropicalpelagic; migratory
Red-eye round herringEtrumeus teresSubtropicalpelagic; migratory
Japanese anchovyEngraulis japonicsWarm-temperatepelagic; migratory
Japanese scadDecapterus maruadsiTropicalpelagic; reef-associated
Japanese Spanish mackerelScomberomorus niphoniusWarm-temperatepelagic; migratory
Japanese common squidTodarodes pacificusWarm-temperatepelagic; migratory
Squide.g., Heterololigo bleekeriWarm-temperatepelagic; migratory
Uroteuthis edulis
Amberjacke.g., Seriola lalandiSubtropicalbenthopelagic
Seriola quinqueradiata demersal; migratory
Deep-sea smeltGlossanodon semifasciatusWarm-temperatebenthopelagic
Yellow goosefishLophius litulonWarm-temperatebathydemersal
Splendid alfonsinoBeryx splendensSubtropicalbenthopelagic
Red seabreamPagrus majorSubtropicaldemersal; migratory
Yellowback seabreamDentex tumifronsSubtropicaldemersal
Largehead hairtailTrichiurus japonicusSubtropicalbenthopelagic
Lizardfishe.g., Saurida elongataWarm-temperatedemersal
Daggertooth pike congerMuraenesox cinereusSubtropicaldemersal; migratory
Pomfrete.g., Pampus argenteusSubtropicalbenthopelagic; migratory
Psenopsis anomalaTropicalbenthopelagic
cold groupPacific herringClupea pallasiiCold-temperatepelagic; non-migratory
Pacific sandlanceAmmodytes personatusCold-temperatedemersal; migratory
Alaska pollockGadus chalcogrammusPolarbenthopelagic; non-migratory
Pacific codGadus macrocephalusBorealdemersal; migratory
Atka mackerelPleurogrammus azonusCold-temperatedemersal; migratory
Broadbanded thornyheadSebastolobus macrochirCold-temperatebathydemersal
Japanese sandfishArctoscopus japonicusBorealbathydemersal
Bastard halibutParalichthys olivaceusSubtropicaldemersal; migratory
Shotted halibutEopsetta grigorjewiSubtropicaldemersal
SôhachiHippoglossoides pinetorumCold-temperatedemersal
Flathead flounderHippoglossoides dubiusCold-temperatedemersal; migratory
Yellow striped flounderPleuronectes herzensteiniCold-temperatedemersal
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Liu, D.; Chen, X.; Liu, B. The Impacts of Marine Heatwaves on Economic Fisheries in Adjacent Sea Regions Around Japan Under Global Warming. Fishes 2025, 10, 299. https://doi.org/10.3390/fishes10070299

AMA Style

Liu D, Chen X, Liu B. The Impacts of Marine Heatwaves on Economic Fisheries in Adjacent Sea Regions Around Japan Under Global Warming. Fishes. 2025; 10(7):299. https://doi.org/10.3390/fishes10070299

Chicago/Turabian Style

Liu, Dan, Xinjun Chen, and Bilin Liu. 2025. "The Impacts of Marine Heatwaves on Economic Fisheries in Adjacent Sea Regions Around Japan Under Global Warming" Fishes 10, no. 7: 299. https://doi.org/10.3390/fishes10070299

APA Style

Liu, D., Chen, X., & Liu, B. (2025). The Impacts of Marine Heatwaves on Economic Fisheries in Adjacent Sea Regions Around Japan Under Global Warming. Fishes, 10(7), 299. https://doi.org/10.3390/fishes10070299

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