The Impacts of Marine Heatwaves on the Spatiotemporal Distribution and Abundance of Japanese Chub Mackerel (Scomber japonicus) in the Northwest Pacific Ocean

Abstract

The Japanese chub mackerel (Scomber japonicus) is a small pelagic economically important fish species in the northwest Pacific Ocean, and its abundance and distribution are influenced by water temperature changes. In recent years, frequent marine heatwaves (MHWs), defined as prolonged anomalously warm sea surface temperature events, in this region have significantly impacted marine ecosystems and fishery resources. The effects of MHWs on Japanese chub mackerel remain poorly understood. This study analyzed the relationship between Japanese chub mackerel abundance and MHW characteristics in the northwest Pacific Ocean from 2014 to 2021. It includes comparative analyses on the spatiotemporal patterns of catch per unit effort (CPUE) and MHWs, an exploration of CPUE distribution under varying MHW intensities and durations, and an assessment of the relationship between MHW characteristics and CPUE using a Generalized Additive Model (GAM) approach. Additionally, CPUE variations before, during, and after MHWs in 2016, 2018, and 2021 across different regions are measured. Results reveal significant interannual variability in MHWs, with increasing trends in the frequency, intensity, and duration of MHWs. As the frequency, intensity, and duration of MHWs increased, the abundance of Japanese chub mackerel decreased, particularly in years with higher intensity and longer lasting MHWs. The study concludes that MHWs negatively impact Japanese chub mackerel, highlighting the urgent need for climate-adaptive fishing and management strategies.

1. Introduction

Japanese chub mackerel (Scomber japonicus) is widely distributed in the East China Sea, the Yellow Sea, the Sea of Japan, and the continental shelf waters of the northwest Pacific Ocean [1] (Figure 1). As a vital fishery resource in the northwest Pacific Ocean, its annual production reached 2.5 × 10⁶ tons according to the 2022 State of World Fisheries and Aquaculture report by the Food and Agriculture Organization of the United Nations [2]. The northwest Pacific Ocean, one of the most productive marine regions globally, hosts a complex current system and monsoon climate that shapes its unique marine ecosystem. Targeted by purse seine fisheries, Japanese chub mackerel in this region is commercially exploited by its surrounding nations such as China, Japan, and South Korea. Characterized by a short life cycle, rapid growth rates, high reproductive capacity, and seasonal migratory patterns [3,4], this species typically inhabits the upper and middle water layers above the thermocline during spring and summer, aggregating near the surface during spawning seasons [5], with a spatial distribution exhibiting pronounced environmental dependence. In addition to the documented characteristics, the behavior of Japanese chub mackerel often reflects subtle adaptive responses to their dynamic environment. Other studies [6], have noted that these fish can rapidly adjust their migratory routes and foraging patterns in response to short-term environmental fluctuations, highlighting their remarkable adaptability.

Research indicates that Japanese chub mackerel population dynamics demonstrate high sensitivity to marine environmental changes. The distribution and growth of juvenile mackerel are significantly influenced by water temperature and salinity [7], with temperature being the dominant factor influencing its abundance and spatial distribution patterns [8]. Rising sea temperatures have adversely affected its reproductive ecology, resulting in a marked decline in spawning biomass [9]. However, existing studies predominantly focus on climate change impacts on mackerel at interannual or decadal scales, lacking systematic assessments of extreme temperature events at weather timescales (e.g., marine heatwaves—MHWs) and their spatiotemporal compound effects. This knowledge gap hinders the development of climate-adaptive fisheries management strategies.

MHWs, defined as anomalously warm water events persisting for over five days with temperatures exceeding the 90th percentile of local seasonal thresholds [10], exhibit extensive spatial coverage and prolonged durations. Historical analyses by Oliver et al. reveal increasing trends in the frequency and duration of MHWs across multiple marine regions [11]. These events have been documented to cause severe ecological impacts [12,13], including coral bleaching [14,15], harmful algal blooms [16], increased mortality in higher trophic-level organisms such as seabirds and sea turtles [17,18,19], and reduced plankton biomass and chlorophyll-a concentrations [20,21,22]. Ecosystem models proposed by Cheung et al. suggest that temperature rise may indirectly affect fish population stability and persistence through modifications in food web structure and competition [23]. As a result of global warming, the escalating frequency, intensity, and duration of MHWs exert profound impacts across marine trophic levels [10,24,25]. As a thermally sensitive species, Japanese chub mackerel are inevitably affected by MHWs, resulting in spawning disruption and shifts in foraging patterns, forcing habitat shifts to more suitable waters. It is also worth noting that these environmental shifts not only influence the biology of the mackerel but also have broader implications for the entire marine ecosystem. Changes in water temperature can lead to alterations in predator–prey interactions and affect the availability of food resources. Such changes alter the survival conditions of the species and impact fishery resource abundance. Furthermore, Kamimura et al. indicate that extreme temperature fluctuations could indirectly reduce larval survival and growth by affecting phytoplankton and zooplankton biomass, thereby restructuring entire food webs [26]. Despite the critical importance of Japanese chub mackerel in the northwest Pacific Ocean fisheries, systematic investigations into the impacts of MHWs on its spatiotemporal distribution are lacking. Therefore, this study aims to analyze the relationship between MHW characteristics and the spatiotemporal distribution and abundance of Japanese chub mackerel in this region, providing essential insights for resource conservation and sustainable management during climate change.

2. Materials and Methods

2.1. Data Sources

The study area spans 135–170° E and 30–50° N (Figure 1) from June to November 2014 to 2021. Fisheries data for the Japanese chub mackerel were obtained from the Distant-Water Fisheries Data Center of Shanghai Ocean University, China. The dataset includes daily records of fishing operations, which were collected through fishing logbooks from Chinese enterprises, detailing the time, geographic coordinates, fishing effort (measured in fishing days), and catch (recorded in tons). The MHW dataset was derived from the National Oceanic and Atmospheric Administration (NOAA) Optimal Interpolated Sea Surface Temperature (OISST) product. OISST integrates high-resolution satellite observations with in situ measurements through interpolation, generating a spatially and temporally continuous temperature dataset. It features a spatial resolution of 0.25°×0.25° and spans from 1982 to the present. Due to its long-term coverage, high spatial resolution, and reliability, OISST has been widely used in MHW-related research [27,28] (data are available at https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html (accessed on 2 June 2024)). To align with these MHW data, the spatial resolution of the fisheries data was standardized to 0.25° × 0.25°. Two high-density fishing regions within the study area were selected for detailed analysis (Figure 1): Region a (150–155° E, 38–43° N) and Region b (144–149° E, 35–40° N), both of which provided sufficient data to evaluate CPUE variations before, during, and after MHWs.

2.2. Research Methods

The catch per unit effort (CPUE) was used as an indicator of mackerel abundance [29] and shifts in the centroid of fishing grounds were assessed through changes in the geographic center of catch distribution [30]. The CPUE and centroid coordinates were calculated as follows:

$$CPUE={\displaystyle \frac{{\sum }_i^n{C}_i}{n}}$$

(1)

$$Lon=\sum _{i=1}^n(C_i\times {Lon}_i)/\sum _{i=1}^n{C}_i$$

(2)

$$Lat=\sum _{i=1}^n(C_i\times {Lat}_i)/\sum _{i=1}^n{C}_i$$

(3)

where $C_i$ is the daily catch on the i-th day, $n$ is the number of fishing operations per day, and ${Lon}_i$ and ${Lat}_i$ are the longitude and latitude of the i-th fishing operation, respectively.

$$Ln\left(CPUE+1\right)~s\left(x\right)+ϵ$$

(4)

In this model, to avoid the influence of zero values in the CPUE data on the modeling process, the response variable is transformed as $Ln\left(CPUE+1\right)$. The function $s$ represents the natural spline fitting function applied to the environmental factors. The variable $x$ refers to the factors included in the model, which are intensity, duration, year, month, longitude, and latitude. $ϵ$ represents the error term.

MHWs were identified following Hobday et al. [10], who define MHWs as periods when daily sea surface temperature (SST) exceed a seasonally varying threshold (90th percentile of a 30-year (1982–2011) or longer baseline climatology) for at least five consecutive days. Events separated by ≤2 days were merged into a single MHW event. Three parameters—frequency, mean intensity, and duration—were used to characterize MHWs. The detection code for calculating these parameters is available at https://github.com/ZijieZhaoMMHW/m_mhw1.0 (accessed on 2 June 2024) [31].

Frequency was defined as the annual number of MHWs. The mean intensity was defined as the annual average SST anomaly during MHWs (°C), and the duration was defined as the annual average duration of MHW (in days). The SST data used to calculate the marine heatwave indicators is for the entire year. These were calculated as follows:

$$MeanInt=\sum _i^N\sum _j^{D_i}(T_{ij}-{\tilde{T}}_{iJ})/N$$

$$Duration=\sum _{i=1}^N\left(D_i\right)/N$$

where $D_i$ is the duration of the i-th MHW (in days), $N$ is the total number of MHW events, $T_{ij}$ is the SST on the j-th day of the i-th MHW event (°C), and ${\tilde{T}}_{iJ}$ is the corresponding climatological threshold for the j-th day (°C) [10].

To investigate the relationship between MHW characteristics (frequency, mean intensity, and duration) and the CPUE of the Japanese chub mackerel, a multi-dimensional analysis was conducted. First, the spatiotemporal heterogeneity of Japanese chub mackerel was identified by analyzing CPUE distribution patterns using spatiotemporal statistical methods. Second, the MHW characteristics were extracted at matching spatiotemporal scales to systematically characterize the evolution of MHWs; these parameters were matched in both space and time with CPUE data to facilitate a comparison of their variations. Third, the migration trajectories of the CPUE and MHW geographic centroids were compared to elucidate how the spatial distribution of Japanese chub mackerel responds to MHWs. CPUE interval distributions under varying MHW intensities and durations were analyzed to quantify resource dynamics coupled with MHWs parameters. Fourth, a Generalized Additive Model (GAM) was applied to assess the effects of MHW intensity on the CPUE of Japanese chub mackerel. The driving effects of MHWs were validated by comparing CPUE differences under MHW and non-MHW conditions. Lastly, three years (2016, 2018, 2021) were selected to analyze CPUE changes before, during, and after MHWs in different subregions, as there was a larger amount of matching data between MHWs and Japanese chub mackerel CPUE in these years, revealing both short-term and long-term and cumulative impacts of MHWs on Japanese chub mackerel.

Based on the life cycle of the Japanese chub mackerel and the seasonal patterns of the fishery, June to November is the main fishing season, during which the catch is relatively high. Therefore, this study focused on MHW characteristics and abundance/distribution relationships during these months from 2014 to 2021.

3. Results

3.1. The Spatiotemporal Distribution of the Japanese Chub Mackerel

Japanese chub mackerel CPUE was concentrated within the 145–160° E and 35–45° N region, with significant interannual variability in the spatial distribution (Figure 2a). In 2014, the distribution range was relatively broad, with numerous high CPUE values, indicating a higher abundance of Japanese chub mackerel that year. The CPUE distribution area was smallest in 2015 but expanded to its maximum in 2017. From 2018 to 2021, the distribution range remained smaller than in 2014, though it partially recovered in 2021. Monthly CPUE distributions were primarily observed between 145–155° E and 37–44° N, with broader spatial coverage in August and September, and comparatively narrower distribution ranges in October and November (Figure 2b).

Interannual and monthly time series analyses of CPUE (Figure 2c) revealed an overall decline from 2014 to 2021. The CPUE peaked at 20 t/d in 2014 and 2015 but dropped significantly after 2018, falling below 10 t/d, with the lowest values recorded in 2019 and 2021. Monthly CPUE variations showed a gradual increase, with lower values occurring in June and July, followed by an increase in August, with an expanded distribution. The CPUE continued to increase from September to November, reaching a pronounced peak in October. A two-way ANOVA was performed on the raw CPUE data for 2014–2021 (June to November), with year and month as fixed factors. The results showed significant differences in CPUE across years (p < 0.001), months (p < 0.001), and the interaction between year and month (p < 0.001), confirming that both the year and month significantly influence CPUE variations.

3.2. Spatiotemporal Patterns of MHW Characteristics

Figure 3 illustrates the interannual trends of MHWs in the northwest Pacific Ocean from 2014 to 2021, characterized by increasing frequency, fluctuating but rising intensity, and significantly prolonged duration. In 2014, the frequency reached approximately 10 events that year, primarily concentrated in the northeastern region. By 2015, MHW hotspots expanded toward the central region, with their spatial influence gradually widening. In 2016, high frequency zones shifted to the western central coastal waters of Japan, with a continuous increase in frequency and spatial coverage. By 2020, MHW-affected areas reached their maximum extent. Regarding intensity, average MHW temperatures ranged between 2–3 °C from 2014 to 2015, peaked at 3.5–4.5 °C in 2016, briefly declined in 2017, and gradually rebounded from 2018 onward. By 2021, MHW intensity approached that of 2016, but hotspots became more southerly concentrated along Japan’s coastline. The duration of MHWs generally persisted for 10–25 days in 2014, 2015, and 2017. This increased to 50 days in 2016 and exceeded 50 days in 2018, marking a significant prolongation. In 2019, localized MHWs persisted for up to 80 days, while durations in the central region surpassed 90 days from 2020 to 2021, indicating a persistent upward trend.

Figure 4 demonstrates distinct seasonal variability in the spatial distribution of MHW frequency, intensity, and duration in the northwest Pacific Ocean from 2014 to 2021. MHW frequency exhibited marked monthly variations, with relatively lower occurrences in June and November, primarily concentrated in the central-northern parts of the study area. In August and October, MHWs were more frequent, with hotspots clustered near Japan’s coastal waters, highlighting summer and autumn as peak periods for MHW activity. The average intensity of MHWs significantly strengthened between July and November, with particularly high intensity events observed in September, reflecting pronounced SST anomalies. MHW duration gradually lengthened over the months, particularly from August to November, where many regions experienced events lasting over 15 days, some of which reached 30 days. These prolonged MHWs predominantly occurred in the central and eastern regions, indicating sustained thermal stress within these zones. Furthermore, regional differences in MHW characteristics are evident. High MHW frequency and intensity were maintained in the central region throughout the study period, while the eastern region exhibited more pronounced seasonal variability, with significant increases in both frequency and duration during autumn.

3.3. The Relationship Between MHWs and Japanese Chub Mackerel CPUE

From 2014 to 2021, MHWs exhibited increasing trends in frequency, average intensity, and duration, while the Japanese chub mackerel CPUE showed a significant decline (Figure 5). A Mann–Kendall test for MHW frequency revealed a significant upward trend over the years (p < 0.001), and a linear regression slope analysis indicated a positive correlation between MHW frequency and average intensity (slope = 0.34, p < 0.001). The increase in MHW frequency correlated negatively with CPUE, particularly after 2016 when MHW frequency increased, coinciding with a continuous drop in CPUE. A two-sample t-test comparing CPUE during MHW and non-MHW periods confirmed that CPUE was significantly lower during MHWs (p < 0.001). Additionally, MHW frequency between July and September was substantially higher compared to the other months, particularly in August and September, intensifying ecological stress during these periods. A Pearson correlation test showed a strong negative correlation between MHW frequency and CPUE (r = −0.72, p < 0.001). Concurrently, MHW duration progressively lengthened over the months. Autumn MHWs exhibited stronger average intensity and longer durations compared to those occurring in summer. The CPUE displayed monthly variations, with higher volatility observed in summer, potentially linked to frequent MHW occurrences. In autumn, when MHW intensity was highest, CPUE declined significantly, further suggesting that MHWs exert substantial impacts on Japanese chub mackerel. A Mann–Kendall test on CPUE also revealed a significant seasonal decline (p < 0.01), which coincides with the extended MHW duration and intensity in autumn.

From 2014 to 2021, the longitudinal distribution of Japanese chub mackerel CPUE exhibited a gradual eastward shift, with the mean longitude progressing from approximately 151 to 153° E (Figure 6a). The monthly longitudinal distribution of CPUE shifted eastward from June to August, followed by a rapid westward movement thereafter (Figure 6b). This seasonal migration pattern highlights the species’ sensitivity to seasonal environmental changes, potentially influencing fishing efficiency. The latitudinal distribution of mackerel CPUE showed an interannual and seasonal (summer) northward shift, while higher CPUE was detected more southward in autumn. MHW frequency fluctuated notably in 2016 and 2019, aligning with concurrent increases in MHW intensity and duration. MHWs were predominantly concentrated near 154 °E, maintaining relative stability between June and November (Figure 6a,b). The interannual and monthly latitudinal distribution of MHWs remained relatively consistent, primarily clustered between 39 and 40° N (Figure 6c,d). Both the interannual and monthly distribution of the geographic centers (longitude and latitude) of mackerel CPUE significantly deviated from those of MHW frequency, average intensity, and duration. This spatial mismatch may reflect shifts in the marine habitat conditions critical for Japanese chub mackerel, suggesting dynamic adaptive responses of the population to MHW events.

The frequency distributions of CPUE across varying MHW intensity and duration ranges are shown in Figure 7. Analysis reveals that Japanese chub mackerel CPUE decreased with increasing MHW intensity. During periods of low MHW intensity (1.5–2.0 °C), the CPUE remained relatively high but gradually declined as intensity rose. During periods of high intensity (6.5–7.0 °C), the CPUE dropped significantly, indicating that severe MHWs impose substantial stress and adverse impacts on mackerel populations. CPUE variations across MHW duration intervals were more complex, with occasional extremes observed and an overall declining trend. Both MHW intensity and duration negatively affected Japanese chub mackerel CPUE. The GAM results (

Table 1. Statistical results of the GAM.

Model Predictors	Effective Degrees of Freedom	F-Value	p-Value	Accumulation of Deviance Explained	R2-adj
Intensity	6.49	15.74	<0.001	33.9%	0.336
Duration	6.824	18.18
Year	5.955	82.135
Month	6.56	47.323
Lon	6.604	16.648
Lat	3.212	1.076

, Figure 8) indicate that MHW intensity exerts a significant and non-linear effect on Japanese chub mackerel CPUE (edf = 6.49, F = 15.74), with the fitted smooth showing an overall declining tendency as intensity increases. Specifically, the partial effect of intensity drops rapidly from low intensity levels (≈1.5–2.3 °C), then remains relatively flat with a slight rebound at intermediate intensities (≈3–4.8 °C), before turning downward again and decreasing sharply at higher intensities (>5 °C), suggesting markedly reduced CPUE under stronger thermal anomalies. Overall model performance was moderate, explaining 33.9% of the deviance with an adjusted R² of 0.336, and other covariates (duration, year, month, longitude, and latitude) were included as additional smooth terms (Table 1), highlighting that both MHW characteristics and spatiotemporal factors jointly contribute to CPUE variability.

Figure 9 compares Japanese chub mackerel CPUE under conditions with and without MHWs. Results show that the CPUE (red line inside the box) was generally higher in the absence of MHWs, indicating that healthier ecological conditions support higher fishery yields. Conversely, the CPUE during periods with MHWs was lower, further corroborating the negative impact of MHWs on Japanese chub mackerel abundance. A Welch two-sample t-test was conducted to compare the means of CPUE during MHW and non-MHW periods. The results indicated that the CPUE during MHWs was significantly lower than during non-MHW periods (p < 0.001), confirming that MHWs have a significant negative effect on Japanese chub mackerel abundance. Additionally, a comparative analysis of CPUE before, during, and after MHWs in Regions a and b (Figure 1) for 2016, 2018, and 2021 (Figure 10,

Table 2. CPUE changes before, during and after MHWs in 2016, 2018 and 2021.

Year	Region	Status	Time Frame	CPUE/(t/d)
2016	a.	Pre-MHWs	30 July 2016–5 September 2016	14.61
		MHWs	5 September 2016–5 October 2016	8.84
		Post-MHWs	5 October 2016–29 October 2016	14.39
	b	Pre-MHWs	13 May 2016–29 May 2016	25.17
		MHWs	29 May 2016–10 June 2016	24.07
		Post-MHWs	10 June 2016–28 June 2016	24.95
2018	a	Pre-MHWs	7 September 2018–15 September 2018	16.04
		MHWs	15 September 2018–24 September 2018	14.08
		Post-MHWs	24 September 2018–5 October 2018	21.62
	b	Pre-MHWs	31 March 2018–2 May 2018	12.98
		MHWs	2 May 2018–9 June 2018	8.09
		Post-MHWs	9 June 2018–9 July 2018	8.57
2021	a.	Pre-MHWs	3 July 2021–22 July 2021	2.83
		MHWs	23 July 2021–11 August 2021	2.61
		Post-MHWs	11 August 2021–8 September 2021	5.27
	b	Pre-MHWs	30 March 2021–9 April 2021	1.96
		MHWs	9 April 2021–2 May 2021	1.73
		Post-MHWs	2 May 2021–16 May 2021	3.57

) reveals that the CPUE was typically lower during MHWs compared to the period preceding MHWs. After MHWs, the CPUE often rebounded, suggesting partial fish stock recovery. However, the absence of a complete return to pre-MHW levels in some regions and years indicates that while the immediate effects of MHWs may be reversible, the ecosystem may still be recovering from the disturbances caused by MHWs. The failure of CPUE to return to pre-MHW levels in the long term could signal a longer-lasting effect, particularly in terms of changes in fish population dynamics, habitat suitability, and resilience to future disturbances. Moreover, a paired t-test was conducted to compare the CPUE between Pre-MHWs and MHWs (t = 2.41, p = 0.0511), and between MHWs and post-MHWs (t = −2.76, p = 0.0398). The results indicate that the CPUE differences between MHWs and both Pre-MHWs and post-MHWs were significant.

4. Discussion

4.1. The Spatiotemporal Distribution of MHWs and Japanese Chub Mackerel in the Northwest Pacific Ocean

This study analyzed the spatiotemporal characteristics of Japanese chub mackerel CPUE and MHW characteristics (frequency, intensity, and duration) in the northwest Pacific Ocean during summer and autumn from 2014 to 2021, while exploring the impacts of MHWs on mackerel abundance and distribution.

Japanese chub mackerel CPUE was primarily distributed between 35–45° N and 145–160° E, with the smallest spatial range observed in 2015, and the largest in September. It is evident that such spatial variations highlight the influence of dynamic local oceanographic conditions on species distribution. Xue et al. using ecological niche modeling, identified the core distribution of the species in this region as 40–42° N and 147.5–152.5° E, with dynamic alignment between fishing grounds and habitat suitability, where SST was the dominant environmental driver [32]. Interannual CPUE exhibited a significant decline, particularly after 2018, while monthly variations highlighted pronounced seasonality. Zhuang et al. noted that Japanese chub mackerel aggregates during feeding migrations from August to October [33], with spatiotemporal coupling between these aggregations and biomass peaks, explaining higher autumn CPUE compared to summer.

MHWs in the northwest Pacific Ocean showed increasing trends in frequency, average intensity, and duration from 2014 to 2021. Global models by [27] identified the northwest Pacific Ocean as a hotspot for intensifying MHWs, a trend confirmed by this study. High intensity MHWs remained spatially stable near Japan’s coast, while prolonged durations dominated the eastern region, with MHW durations higher in autumn compared to summer. Amaya et al. attributed seasonal differences to deeper mixed-layer depths in autumn, delaying heat dissipation [34], aligning with our findings. Additionally, the interplay between regional currents and heatwave events might result in unexpected micro-scale distribution patterns that are not immediately apparent from broader analyses. The linear growth of MHWs reflects global warming-driven ocean temperature rises, with higher warming rates and variability at high latitudes amplifying MHW frequency, intensity, and duration [35]. These findings underscore the complexity and diversity of MHW spatiotemporal patterns in the northwest Pacific Ocean, revealing strong linkages between seasonal fluctuations and spatial distribution dynamics.

4.2. The Relationship Between MHWs and Japanese Chub Mackerel Dynamics

This study quantified the impacts of MHW on Japanese chub mackerel CPUE through spatial, temporal, and case study analyses. These results provide a vivid illustration of how even relatively short-term thermal anomalies can have lasting impacts on fish populations. For example, MHWs may cause these species to be unable to find sufficiently suitable habitats, thereby limiting their survival and reproduction. MHWs may also directly affect the food chain and food sources of Japanese chub mackerel, reducing their predation efficiency. Results demonstrated that increasing MHW intensity reduced CPUE, with intensified MHWs under global warming likely further constricting suitable habitats.

Spatial mismatches between the geographic centers of the CPUE and MHWs aligned with the thermal displacement hypothesis, where species shift poleward or into cooler waters to avoid thermal stress [36,37]. In this study (Figure 6), it was found that the centroid of CPUE primarily shifts along the longitudinal direction (eastward), with the species migrating to more suitable habitats to escape MHWs. MHWs may degrade habitat conditions (e.g., elevated SST and reduced chlorophyll-a) and disrupt food webs. Declining chlorophyll-a reduces prey availability for pelagic fish, while warming lowers dissolved oxygen concentration, impairing the survival and reproductive success of species [38,39]. Prolonged MHWs exacerbate these impacts by extending the exposure of suboptimal conditions to marine organisms. It is apparent that the behavioral responses of mackerel to thermal stress may vary regionally, indicating a complex balance between biological resilience and environmental pressure. These patterns underscore the critical role of MHWs characteristics in driving spatiotemporal fluctuations in fishery productivity.

SST is a key driver of Japanese chub mackerel habitat dynamics in summer and autumn [40]. Studies on the seasonal migration patterns of mackerel reveal northeastward summer and southwestward autumn distribution shifts [41], consistent with the CPUE centroid trends reported in this study. Previous studies identified SST and chlorophyll-a as critical factors in habitat formation [42,43], supporting our hypothesis that recent eastward/northward habitat shifts reflect MHW-driven degradation of western habitats (e.g., SST rise and chlorophyll-a decline). Juvenile Japanese chub mackerel exhibit optimal growth in water temperatures near 25 °C; MHWs exceeding this threshold may suppress growth or survival of the species [44,45]. GAM results confirmed nonlinear declines in CPUE with changing MHW intensity, while [46] linked chlorophyll-a reductions to CPUE declines via trophic cascades. Such observations underscore the intricate relationship between temperature variations and the adaptive strategies employed by mackerel.

MHWs profoundly impact the spatiotemporal distribution and abundance of Japanese chub mackerel in the northwest Pacific Ocean, particularly under rising MHW frequencies. Despite rapid growth and reproduction, the species remains vulnerable to thermal anomalies. Prolonged MHWs elevate metabolic demands [47], reducing energy reserves and recruitment. By altering plankton communities and habitat conditions, MHWs directly and indirectly impact growth and reproductive capabilities of marine species, threatening biodiversity and ecosystem services [48]. However, beyond these long-term impacts, we must also consider the immediate effects of MHWs on Japanese chub mackerel. In response to thermal stress from MHWs, mackerel may migrate to other areas to escape unsuitable temperatures. We hypothesize that mackerel may migrate vertically into deeper waters to seek more favorable temperature layers, which could make these fish temporarily inaccessible to the fishing fleet. Additionally, apart from vertical migration, mackerel may also engage in horizontal spatial migration, moving to cooler regions, which could further impact fishing efficiency. Behavioral changes are another crucial factor; mackerel might alter their feeding patterns or migration routes, leading to changes in fish density and distribution, thus affecting fisheries. It is also noteworthy that MHWs may lead to changes in mortality rates, particularly under extreme temperature conditions, where thermal stress could result in the death of some fish, affecting resource abundance. In summary, the combination of rising MHWs and shifting environmental conditions emphasizes the necessity for continuous, fine-scale monitoring of mackerel populations to better understand and predict future ecological dynamics. These impacts extend beyond short-term CPUE fluctuations, reshaping fishery dynamics and fish resilience.

5. Conclusions

While this study highlights MHW-driven declines in Japanese chub mackerel, the long-term impacts and confounding factors (e.g., overfishing and ecosystem shifts) require further investigation. The key limitations of this study include a narrow environmental focus, excluding other potential drivers such as salinity and currents, data constraints, with limited fishery data (7800 MHW-CPUE matched records), and model complexity where the GAM accuracy may have been affected by the data limitations. Future research should expand the datasets, integrate additional environmental variables, and employ multi-model frameworks to comprehensively assess MHW impacts on Japanese chub mackerel. Addressing these gaps will enhance adaptive management strategies for sustainable fisheries under climate change.