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Article

Effects of Climate Change on the Distribution of Scomber japonicus and Konosirus punctatus in China’s Coastal and Adjacent Waters

by
Mingxia Xia
1,2,3,
Hui Jia
2,3,4,
Yibang Wang
2,3 and
Hui Zhang
2,3,5,*
1
School of Marine Science and Engineering, Qingdao Agricultural University, Qingdao 266109, China
2
Chinese Academy of Sciences Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
3
Yangtze Estuary Marine Ecosystem Research Station, Institute of Oceanology, Chinese Academy of Sciences, Nantong 226000, China
4
School of Marine Sciences, Ningbo University, Ningbo 315823, China
5
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Fishes 2024, 9(10), 395; https://doi.org/10.3390/fishes9100395
Submission received: 31 August 2024 / Revised: 29 September 2024 / Accepted: 29 September 2024 / Published: 30 September 2024
(This article belongs to the Special Issue Assessment and Management of Fishery Resources)

Abstract

:
Climate change has increasingly impacted the marine environment, with various marine environmental factors interacting to influence fish distribution. Assessing the impact of climate change on the future distribution of fish depends on understanding how biological responses interact with environmental conditions. Enhancing our understanding of the potential impacts of climate change is crucial for the sustainable development of marine fisheries. In this study, we analyzed the habitat suitability of Scomber japonicus and Konosirus punctatus in the coastal waters of China (17°–41° N, 107°–130° E) using marine environmental data, including, as follows: sea surface temperature (SST); sea surface salinity (SSS); pH; and biological occurrence data from 2000 to 2010. A maximum entropy (MaxEnt) model was employed to predict the future distribution of these two species based on the shared socioeconomic pathway (SSP) scenarios for 2040–2050 and 2090–2100. The results indicate that climate change will significantly affect the future habitat distribution of S. japonicus and K. punctatus, leading to a decrease in suitable habitat areas offshore China and a gradual shift northeastward in the center of these habitats. These findings are essential for understanding the impacts of climate change on the distribution of S. japonicus and K. punctatus, with significant implications for fisheries resource assessment and management.
Key Contribution: The AUC values of the optimized MaxEnt were all greater than 0.85, indicating that the model can accurately predict the future distribution of suitable habitats for Scomber japonicus and Konosirus punctatus under various climate change scenarios.

1. Introduction

In recent years, climate change has become a focal point of global environmental concerns, with the oceans playing a crucial role in regulating climate change [1]. Climate change continues to impact the marine environment, affecting fish communities and the stability of marine ecosystems. It primarily influences marine environmental factors, which in turn affect the future distribution of fish habitats [2]. The most direct impact of climate change on the marine environment is the increase in SST [3]. Studies have shown that, since 1950, the average SST in China’s coastal waters has risen by 0.10–0.14 °C per decade, with a marked acceleration since the 1980s [4]. Research indicated that fish species typically migrate to higher latitudes and deeper waters under the influence of climate change [5]. For example, the habitat area of Scomber japonicus in the East China Sea and Yellow Sea decreased and gradually shifted northward as sea surface temperatures rose [6]. This shift was expected to result in a decline in species richness in southern Chinese waters and an increase in species richness in northern regions [7]. Projections under various future climate scenarios underscore the urgent need to implement climate change mitigation efforts to proactively address future risks.
Scomber japonicus and Konosirus punctatus are important economic fish species in China’s coastal waters. Both species are warm–temperate pelagic fish. Pelagic fish primarily inhabit the middle or upper layers of the water column [8]. In recent years, as bottom-dwelling and nearshore fishery resources have declined, pelagic fish have gained increasing importance in China’s marine fisheries due to their faster turnover rates and stronger recovery capabilities [9]. Pelagic fish, because of their preference for specific depths, are particularly vulnerable to changes in climate and marine environments. Even slight changes in environmental conditions can lead to significant shifts in their distribution [10]. Both S. japonicus and K. punctatus are migratory species, sharing similar migratory routes and nearshore spawning grounds. Their habitat distribution is closely linked to changes in the marine environment; their activity patterns are influenced by the complex and variable conditions of China’s coastal waters [11]. Studies have shown that climate change plays a crucial role in the distribution of the eggs of S. japonicus and K. punctatus. As important commercial fishes, there are abundant data on the distribution of S. japonicus and K. punctatus. The availability of these data provides a reliable basis for species distribution models (SDMs). Therefore, as climate change continues to impact fish distribution, it is essential to consider the potential changes in the distribution of S. japonicus and K. punctatus in China’s coastal waters to develop adaptive conservation and management strategies.
SDMs are powerful tools for assessing the relationships between the environment and organisms; they have been widely applied in ecology and species conservation [12]. SDMs have been extensively used to study the effects of climate change on the potential geographic distribution of species, as well as to investigate the interactions between species and their environment by predicting habitat changes under different climate change scenarios [13]. Commonly used SDMs include random forest (RF), generalized additive model (GAM), and MaxEnt, among others [14,15]. MaxEnt is a particularly important statistical method in ecological modeling. It is an ecological niche model based on machine learning and the principle of maximum entropy, using environmental variables and species occurrence records to simulate the potential geographic distribution of species [16]. Compared to other models, MaxEnt’s strength lies in its ability to perform simulations using presence-only data and to provide more accurate predictions and stability, even with small sample sizes. Furthermore, MaxEnt is especially suitable for species distribution modeling when the number of occurrence points varies and the correlations between environmental variables are unclear [17]. In recent years, MaxEnt has been widely used to predict the distribution of suitable habitats for terrestrial plants; its application in predicting future suitable habitats for fish species has also increased [18].
The Sixth Assessment Report of the IPCC (AR6) introduced a new set of emission scenarios known as shared socioeconomic pathways (SSPs), which were used to drive climate models [19]. These scenarios account for future changes in population, economic development, ecosystems, resources, and social factors, as well as potential climate change mitigation, adaptation, or response strategies [20]. The strategic planning for sustainable development outlines five different pathways for future economic and social systems, serving as a core basis for assessing climate change impacts and formulating climate policies. In this study, we selected three SSP scenarios—SSP126, SSP370, and SSP585—for analysis. SSP126 represents an optimistic scenario where the world gradually shifts towards a more sustainable path; SSP370 is a moderately pessimistic scenario where global competition hinders the effective implementation of sustainable solutions; and SSP585 is a pessimistic scenario in which developing countries follow the rapid economic growth trajectories of first-world nations, leading to a significant increase in greenhouse gas emissions [21]. By choosing these three scenarios, the study encompassed a wide range of possibilities, from optimistic to pessimistic, allowing for a comparison of the varying impacts of climate change on the habitats of S. japonicus and K. punctatus.
The focus of this study is to use MaxEnt (v.3.4.4) and ArcGIS (v.10.8) software to predict the future habitat distribution of S. japonicus and K. punctatus under different climate change scenarios. The current study aimed to: (i) assess the impact of climate change on the habitats of these species and understand how climate change affects their distribution: (ii) inform fisheries management to allow for sustainable development; and (iii) identify and protect critical future habitats to mitigate the negative impacts of climate change on S. japonicus and K. punctatus.

2. Materials and Methods

2.1. Study Area

As a coastal nation, China possesses abundant marine resources, serving as a critical habitat and prime fishing grounds for numerous marine species. China’s coastal waters primarily include the Bohai Sea, Yellow Sea, East China Sea, South China Sea, and parts of the Pacific Ocean east of Taiwan. These coastal regions are among the most sensitive to global warming; they are expected to experience some of the most significant temperature increases in the future, making them highly vulnerable to climate-related risks. Therefore, this study focuses on China’s coastal and adjacent waters, covering the geographic range of 17°–41° N and 107°–130° E (Figure 1).

2.2. Occurrence Data of Fishes

The distribution data for this study were primarily sourced from databases and studies in the literature. The open-access databases included the Global Biodiversity Information Facility (GBIF), the Ocean Biogeographic Information System (OBIS), Fishbase, and Fishnet2. Searches were conducted using species names (S. japonicus and K. punctatus), study areas (China’s coastal and adjacent waters), and time periods (2000–2010), resulting in 224 records for S. japonicus and 114 records for K. punctatus. Additionally, distribution data were extracted from studies in the literature focusing on China’s offshore and neighboring waters using SPSS software [22].
By merging data from these various sources, latitude and longitude records were integrated into a single, unified dataset. During the data cleaning process, duplicate records from GBIF and OBIS were removed to ensure accuracy. ArcGIS was then used to further clean the data by excluding records on land or outside the defined study area. After filtering, the final number of distribution points for S. japonicus and K. punctatus was reduced to 125 and 75, respectively (Supplementary Table S1). These cleaned data points were then converted into CSV format for further analysis and preservation.

2.3. Environmental Variables

The climate variables used in this study were sourced from the Bio-ORACLE database, featuring a spatial resolution of 0.05 degrees. Bio-ORACLE is a comprehensive global marine environmental database that provides data layers for ecological modeling and species distribution analysis. It offers long-term data on key environmental variables [23,24]. Considering the availability of environmental data and the relevance of these variables to the distribution of pelagic fishes [25], 11 environmental variables were selected for analysis, including SST, SSS, pH, dissolved oxygen, chlorophyll concentration, primary productivity, current velocity, current direction, nitrate, silicate, and phosphate. These climate scenarios, covering both the current period (2000–2010) and future periods, 2040–2050 (2040s) and 2090–2100 (2100s), were obtained from the Bio-ORACLE database [23,24].
The 11 marine environmental datasets downloaded from the database must undergo a series of processing steps before they can be used for further analysis. First, the climate layers were processed using ArcMap software, where they were cropped (17°–41° N, 107°–130° E) and converted into ASC format. The geographic coordinate system for the maps was standardized to the GCS_WGS_1984 projection coordinate system. Second, to improve the reliability of the subsequent model data and the accuracy of the model results, we performed a correlation analysis on the current period marine environmental variables [26] (Figure 2, Supplementary Table S2). The covariance between each pair of environmental variables was examined. If the correlation coefficient |r| > 0.8 between two variables, we compared their contribution rates in the initial model and retained the variable with the higher contribution rate for model prediction [18].
After this rigorous screening process, the following eight environmental variables were identified for the subsequent MaxEnt analyses: SST; SSS; pH; dissolved oxygen; chlorophyll concentration; primary productivity; current velocity; and nitrate concentration. These variables were then used to construct MaxEnt to predict the distribution of S. japonicus and K. punctatus in China’s offshore waters. The model’s accuracy was thoroughly evaluated to ensure reliable simulation results.

2.4. Model Optimization

In the MaxEnt algorithm, the settings for the regularization multiplier (RM) and feature classes (FC) are crucial for balancing model fitting and complexity, as they determine the types of constraints applied in the model [26]. MaxEnt offers the following five types of feature classes: linear (L); quadratic (Q); hinge (H); product (P); and threshold (T). By default, the RM value is set to 1, and the selection of specific FCs is typically related to the number of species distribution points available. In this study, the RM was varied from 0.1 to 4, with an incremental step of 0.5; all five FCs were combined to explore the best model configuration.
The performance of each model was evaluated using the Akaike Information Criterion corrected for small sample sizes (AICc), which balances model fit and complexity. The AICc is a robust metric for assessing model performance, with a lower AICc value indicating a better model. A model with the minimum AICc value (AICc = 0) is considered the most optimal, providing the best balance between accurately fitting the data and avoiding overfitting by carefully adjusting the RM and FC settings [27].
The optimized maximum entropy model evaluates the performance of the model mainly through the ROC curve. By calculating the area under the ROC curve (AUC), the model’s predictive ability was quantified. AUC values range from 0.5 to 1, with higher values indicating better performance [10]. The model predictions were strong; the Jackknife procedure was used to assess the relative impact of the different variables on the future distribution of the studied fishes. Contribution rates reflect the importance of different variables, while permutation importance shows the model’s dependency on them. A higher permutation importance indicates a more critical role for that variable in the model [16].

2.5. Model Settings

The screened fish distribution points and cropped environmental layers were imported into MaxEnt software for modeling. For this process, 80% of the distribution points were randomly selected for model training, while the remaining 20% were used for validation. The maximum number of iterations was set to 1000; the Bootstrap method was employed with 10 repetitions to ensure model stability. To analyze the relationship between environmental factors and fish distribution, response curves and Jackknife tests were generated. The resulting model was then imported into ArcGIS software, where the potential distribution areas of the fish were categorized into four classes based on the percent fitness value, as follows: non-fit zone (0–0.1); low-fit zone (0.1–0.3); medium-fit zone (0.3–0.6); and high-fit zone (0.6–1). The accuracy of the model was assessed using the area under the curve (AUC) values of the receiver operating characteristic (ROC) curve, which provides a robust measure of model performance. ArcGIS was further utilized to refine the distribution of suitable habitats across different periods by narrowing down the data to a centroid of the distribution, which refers to the geometric center representing the average location of the species’ habitat distribution. The location of this distribution center was calculated for each time period, allowing for the analysis of changes over time. By measuring the shift in the center of distributional, the study was able to characterize changes in the spatial distribution pattern of the fish species in terms of latitude and longitude [28].

3. Results

3.1. Habitat Model Evaluation

Based on the AICc results (Table 1), the parameters for MaxEnt were optimized as follows: for S. japonicus, the RM was set to 2.5, and the FC was set to threshold (T), resulting in the lowest AICc value (AICc = 0). For K. punctatus, the RM was set to 3, with the FCs set to quadratic (Q), product (P), and threshold (T), also achieving the lowest AICc value (AICc = 0).
The accuracy of these optimized MaxEnt parameters was validated using AUC values derived from the ROC curves. The results showed that the average AUC for S. japonicus was 0.850, while for K. punctatus, it was 0.882. Both AUC values were above or equal to 0.85, indicating that the models provided reliable predictions of the potential fishing grounds for these two species. These findings confirmed that MaxEnt effectively captures the distribution of potential fishing grounds for S. japonicus and K. punctatus.

3.2. Importance Analysis of Environmental Variables

Different environmental variables contribute uniquely to the distribution of various fish species. In this study, the Jackknife method was employed to analyze the suitability of environmental factors for the distribution of S. japonicus and K. punctatus (Figure 3). The contribution rates, which typically refer to the relative importance of each environmental variable in model prediction, reflect their overall importance in model construction.
For S. japonicus, the environmental variables with the highest contributions were chlorophyll concentration (57.6%), SSS (13.9%), and SST (11.7%), with a cumulative contribution of 83.2%. In the case of K. punctatus, the key environmental variables were nitrate concentration (29.2%), SSS (23.7%), SST (21.2%), and dissolved oxygen (12.6%), with a cumulative contribution of 86.7%. These results highlight the significance of specific environmental factors in determining the future distribution of these species (Table 2).
Permutational importance was defined as the assessment of each variable’s importance to model predictions by repeatedly testing the model’s performance through random permutations of the values of each variable in the dataset. For S. japonicus, the permutation importance of SSS, chlorophyll concentration, and SST were 26.4%, 23.5%, and 19.8%, respectively. For K. punctatus, the permutation importance values for SST and primary productivity were significantly higher than those of other environmental variables, at 55.1% and 25.6%, respectively.

3.3. Future Distribution of Suitable Areas under Different Climate Scenarios

The study results indicated that climate change predominantly negatively affects the potential future distribution areas of S. japonicus and K. punctatus. The changes in suitable habitats for these species were found to be consistent across various time frames and climate scenarios. As climate change progresses, the impacts on the suitable areas for both S. japonicus and K. punctatus are expected to intensify, leading to a corresponding reduction in the size of these areas. The distribution of potential suitable habitat areas for S. japonicus and K. punctatus, as predicted by MaxEnt, are shown in Figure 4.
In the current period, the total area of high and medium suitability for S. japonicus is 1.27 × 106 km2, while for K. punctatus, it is 0.94 × 106 km2 (Table 3). Currently, the proportion of these high and medium suitable areas has decreased to 39.89% for S. japonicus and 28.79% for K. punctatus. Compared to the present climate, the distribution ranges of high- suitability, moderate-suitability, and low-suitability areas for these species are expected to change significantly under future climate scenarios.
The contraction of these suitable areas is projected to be more pronounced by 2100 than by 2050, with the most significant changes observed under the SSP585 scenario. This scenario suggested that both species are particularly sensitive to temperature increase. For both S. japonicus and K. punctatus, the areas of high-suitability and moderate-suitability habitats are expected to decrease, while the areas of low-suitability habitats are anticipated to increase. The smallest extent of suitable habitat is projected under the SSP585 scenario by 2100, highlighting the potential for significant habitat loss if high-emission pathways continue. The study predicts that changes in suitable habitats under future climate scenarios will show a trend of continuous reduction. As climate change intensifies, particularly under high-emission scenarios (such as SSP585), the contraction of suitable habitats is expected to accelerate. Additionally, the increase in the area of low-suitability habitats may reflect heightened survival pressures on these species, indicating a clear trend of ongoing decreases in high- and medium-suitability areas alongside an expansion of low-suitability areas.
Specifically, by 2100, under the SSP585 scenario, the total area of high- and medium-suitability for S. japonicus is expected to decrease by approximately 0.20 × 106 km2, reducing the proportion of the combined high- and medium-suitability area to 32.65%. Meanwhile, K. punctatus was projected to lose around 0.32 × 106 km2 of high- and medium-suitability habitat, with the proportion of these areas declining significantly to 18.63%.

3.4. Changes in Future Potential Suitable Habitat

The statistical analysis revealed that the habitat areas for S. japonicus and K. punctatus remained relatively suitable overall, with a noticeable contraction along the southern periphery. The contraction area for K. punctatus was found to be larger than that for S. japonicus. Both species exhibited a decline in potential habitat over time, with more pronounced changes under the SSP585 climate scenario compared to the SSP126 and SSP370 scenarios (Figure 5).
For S. japonicus, the SSP370 scenario in 2100 showed the most significant contraction of suitable habitat, with an area reduction of approximately 8.18 × 104 km2. Under the SSP370 and SSP585 scenarios in 2100, K. punctatus experienced the greatest contraction in its suitable habitat, with decreases of approximately 2.71 × 105 km2 and 2.93 × 105 km2, respectively. These findings suggested that both S. japonicus and K. punctatus could become potential losers under climate change, with their habitats projected to decrease in size across all three climate scenarios.

3.5. Core Distributional Shifts in Suitable Habitats under Different Climatic Scenarios

In this study, the spatial analysis capabilities of ArcGIS, combined with the simulation results from MaxEnt, were used to investigate the migration characteristics of the habitat centroids for S. japonicus and K. punctatus under various climate change scenarios (Figure 6). The results indicated that the contraction zones for these species are located at the southern edge of their distribution areas, while the expansion zones are found at the northern edge. The centroids of distribution for both species are situated in the East China Sea. Across the SSP126, SSP370, and SSP585 scenarios, there is a general trend of northeastward migration in the habitats of both species.
Additionally, the centroids of the fishing grounds for S. japonicus shift more northeastward than that of K. punctatus. The migration distance of the centroid for S. japonicus is greater from the present to 2050 compared to the period from 2050 to 2100 across different climate change scenarios. Conversely, K. punctatus exhibits a greater migration distance of its centroid from the present to 2050 than from 2050 to 2100. The study shows that S. japonicus and K. punctatus migrate as far as 123.13° E, 31.77° N, and 123.36° E, 32.5° N. Overall, the results indicated that the shift in the distributional centroid for K. punctatus is more pronounced than that for S. japonicus.

4. Discussion

4.1. MaxEnt Model for Predicting Fish Habitats

MaxEnt is based on the principle of maximum entropy and utilizes “presence-only” species distribution and environmental data to predict the potential distribution range of species [29]. The AUC test results demonstrated that MaxEnt provides reliable predictions of the potential fishing grounds for S. japonicus and K. punctatus. A study found that MaxEnt has stronger predictive capabilities compared to other models, particularly in cases where fish sample sizes are small or where there are complex nonlinear relationships [30]. MaxEnt was shown to be accurate in predicting the future spatial distribution patterns of suitable habitats for fish. In contrast, models like neural networks and GAMs were more susceptible to time and space-related factors, which can introduce uncontrollable errors into the results [31,32]. Additionally, habitat suitability index (HSI) models tend to overpredict suitable habitats and heavily rely on high-quality data; any lack of data or the presence of errors can lead to inaccuracies in suitability indices [33]. MaxEnt, however, requires fewer samples and is highly tolerant of data with limited or biased occurrences, needing only the geographic coordinates where the fish species were observed [34]. This reduces the high costs associated with obtaining fishery data, making MaxEnt a valuable auxiliary tool for fishery resource surveys, conservation, and management.

4.2. Key Environmental Variables Influencing Fish Habitats

This study identified SST, SSS, and chlorophyll concentration as the most important environmental variables for predicting suitable habitats. In the northwest Atlantic, SST and chlorophyll concentration have been shown to influence the habitat distribution of pelagic fish species such as Clupea harengus [35]. Similarly, research on the effects of climate change indicates that SST, chlorophyll, and upwelling are key drivers of habitat shifts for Rachycentron canadum [36]. Additionally, chlorophyll-a concentration and SST were major factors influencing the distribution of K. punctatus [37]. These findings suggest that different environmental factors interact to shape fish habitat distribution and that the driving mechanisms may vary depending on the species and region studied. However, SST appeared to be the most significant and universal driver of fish distribution [38,39]. Under the influence of climate change, with SST gradually rising, species like S. japonicus and K. punctatus, which are sensitive to temperature changes, are likely to migrate to higher latitudes where temperatures are more favorable for their survival and reproduction [40]. These species also have limited tolerance for changes in SSS and chlorophyll concentration; when these variables exceed certain thresholds, the fish may relocate to more suitable areas for growth and development [41]. Understanding the impact of environmental factors on fish distribution is crucial for developing management strategies to mitigate the effects of climate change.

4.3. Climate Induced Shifts in Fish Distribution Patterns

As climate change progresses, particularly with the rise in SST, fish species may respond by shifting their distribution northward, experiencing a reduction in their range, or expanding into new areas [42,43]. These different responses are often driven by varying sensitivities of fish to different environmental factors. Studies have shown that in the East China Sea and Yellow Sea, the habitat area of S. japonicus has gradually decreased with rising SST. Pelagic fish species with lower adaptability were more likely to be negatively affected by climate change, with S. japonicus and K. punctatus predicted to be potential losers under climate change [6,44,45]. Research has indicated that anchovy species migrate to higher latitudes, potentially reducing their range in lower latitudes while expanding in higher latitudes [46,47]. However, overall, under increasing climate change scenarios, the total distribution area of anchovy is expected to gradually decrease [48]. This shift is likely to result in a decline in species richness in southern Chinese waters, while increasing species richness in northern regions [49].

4.4. Fish Migration in the Context of Climate Change

This study found that under different climate scenarios, the spatial distribution patterns and the centroid shifts of pelagic fish species generally exhibited a poleward migration trend, moving from lower to higher latitudes [50]. Additionally, the rate and extent of migration were significantly greater under high-emission scenarios compared to low-emission scenarios [51]. The results of the centroid shift also indicated that the suitable habitats for S. japonicus and K. punctatus in the southern waters of Taiwan are expanding. This expansion was likely associated with oceanic dynamic processes such as the Kuroshio Current. The interaction between the Oyashio and Kuroshio currents forms a unique oceanic front [52], altering key marine environmental factors, such as SST, chlorophyll concentration, and SSS, thereby modifying the hydrographic structure and providing more favorable conditions for marine life [53]. These changes have facilitated the distribution of S. japonicus and K. punctatus. Studies have shown that variations in SST near the Kuroshio region significantly impact the stock of Sardinops melanostictus [54]. Under the influence of climate change, S. japonicus was expected to migrate towards the Kuroshio region [55], highlighting the critical role of the Kuroshio Current in the distribution of S. japonicus fishing grounds. Furthermore, upwelling processes bring nutrient-rich, cold, deep water to the surface, providing substantial nutrients and organic matter, which in turn promote the proliferation of prey species, creating favorable conditions for the expansion of S. japonicus and K. punctatus habitats [56]. These findings offer scientific insights for the future management of fishery and contribute to the development of more informed conservation and management strategies to address the impacts of climate change on marine life.

4.5. Mitigating Climate Change Impacts on Fish Habitats

The study found that under climate change scenarios, the suitable distribution areas for S. japonicus and K. punctatus are expected to gradually decrease, indicating that climate change negatively impacts the suitable habitats for these two fish species. To mitigate the effects of climate change on fish, several measures can be implemented in the future. These include reducing greenhouse gas emissions to address global climate change at its source, establishing a comprehensive fishery and environmental monitoring system to anticipate and respond to the impacts of climate change, and implementing sustainable fishery management practices such as setting catch limits and extending fishing bans. Additionally, the creation of marine protected areas (MPAs) can effectively prevent the decline in marine biodiversity [57]. By predicting future changes in the marine environment, shifts in the center of mass, and the future distribution of fish species, this study emphasized the importance of identifying and protecting high-suitability areas in advance. Doing so can mitigate the impact of climate change on fish populations and ensure the ecological integrity of critical habitats. Furthermore, these preventive conservation measures will provide a foundation for developing scientifically sound fishery management strategies and for the establishment of MPAs.

4.6. Future Perspectives in Fish Habitat Modeling

This study successfully applied species distribution models to simulate the future distribution of S. japonicus and K. punctatus in China under climate change scenarios. However, due to the limitations of the study area and data availability, there are certain constraints. This research only considered marine environmental factors and did not account for anthropogenic influences, such as environmental pollution and fishing pressure, which could also impact the future distribution of these fish species [58]. Environmental pollution can threaten spawning grounds and habitats, leading to a decline in nearshore fish resources and contributing to changes in the spatial patterns of fish habitats. Future research could improve the modeling approach by incorporating these anthropogenic factors into the distribution model predictions [59]. By doing so, the accuracy of the predictions regarding future habitat distribution could be significantly enhanced.

5. Conclusions

In summary, this study identified SST, SSS, and nitrate concentration as key environmental factors driving the distribution of these species. The potential ranges of both species are projected to shrink significantly under future climate change scenarios, with a northeastward shift in their distribution centers. Notably, the distribution center of K. punctatus will shift slightly northwestward under the SSP126 scenario in 2100.The impacts of climate warming on their geographic distribution are expected to manifest primarily as a contraction of their potential ranges and a shift to higher latitudes. The observed patterns of shrinkage and shifting for S. japonicus and K. punctatus align with these predictions. This study provides valuable scientific insights for habitat conservation and fisheries ecosystem management, which will support the sustainable development of fisheries for these species.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/fishes9100395/s1, Table S1: species distribution data, Table S2: correlation analysis.

Author Contributions

Conceptualization, M.X., H.J., Y.W. and H.Z.; methodology, M.X., and H.Z.; software, M.X.; validation, H.Z.; formal analysis, M.X. and H.J.; investigation, M.X.; resources, H.Z.; data curation, H.Z.; writing—original draft preparation, M.X.; writing—review and editing, M.X. and H.Z.; visualization, M.X.; supervision, H.Z.; project administration, H.Z.; funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This work is supported by National Key Research and Development Project of China, No. 2022YFE0112800; and the Taishan Scholars Program.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data underlying this article will be shared on reasonable request to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Map of the study area and sampling sites of S. japonicus and K. punctatus in the coastal and surrounding waters of China.
Figure 1. Map of the study area and sampling sites of S. japonicus and K. punctatus in the coastal and surrounding waters of China.
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Figure 2. Correlation analysis of environmental variables. The darker the color, the stronger the correlation; blue indicates a positive correlation, while red indicates a negative correlation.
Figure 2. Correlation analysis of environmental variables. The darker the color, the stronger the correlation; blue indicates a positive correlation, while red indicates a negative correlation.
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Figure 3. Jackknife test for the importance of environmental variables in a scenario model of the distribution of S. japonicus and K. punctatus. Colors of the bars represent the contribution when the model is executed only with the variable, without the variable, or with all variables.
Figure 3. Jackknife test for the importance of environmental variables in a scenario model of the distribution of S. japonicus and K. punctatus. Colors of the bars represent the contribution when the model is executed only with the variable, without the variable, or with all variables.
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Figure 4. Distribution of suitable areas for S. japonicus and K. punctatus under different climatic scenarios: (a) S. japonicus; and (b) K. punctatus.
Figure 4. Distribution of suitable areas for S. japonicus and K. punctatus under different climatic scenarios: (a) S. japonicus; and (b) K. punctatus.
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Figure 5. Changes in suitable areas of S. japonicus and K. punctatus under different climatic scenarios: (a) S. japonicus; and (b) K. punctatus.
Figure 5. Changes in suitable areas of S. japonicus and K. punctatus under different climatic scenarios: (a) S. japonicus; and (b) K. punctatus.
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Figure 6. Trajectories of the centroid of distribution in the suitable zone for S. japonicus and K. punctatus under different climatic scenarios.
Figure 6. Trajectories of the centroid of distribution in the suitable zone for S. japonicus and K. punctatus under different climatic scenarios.
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Table 1. Model performance evaluation of S. japonicus and K. punctatus under climate change scenarios.
Table 1. Model performance evaluation of S. japonicus and K. punctatus under climate change scenarios.
SpeciesFCRMAUC
Scomber japonicusT2.50.850
Konosirus punctatusQPT30.882
Table 2. Contribution rates and permutation importance of environmental variables in MaxEnt for S. japonicus and K. punctatus.
Table 2. Contribution rates and permutation importance of environmental variables in MaxEnt for S. japonicus and K. punctatus.
Environmental
Variable
Scomber japonicusKonosirus punctatus
Percent
Contribution
(%)
Permutation
Importance
(%)
Percent
Contribution
(%)
Permutation Importance
(%)
SST11.719.821.255.1
SSS13.926.423.75.2
pH5.913.73.40.5
Dissolved oxygen3.95.812.60.8
Primary productivity1.21.65.725.6
Chlorophyll57.623.53.80.1
Current velocity4.38.30.41.3
nitrate1.50.929.211.5
Table 3. Areas of potential distribution under different climate scenarios of S. japonicus and K. punctatus.
Table 3. Areas of potential distribution under different climate scenarios of S. japonicus and K. punctatus.
SpeciesTimeClimate
Scenario
Low Suitability
(×106 km2)
Medium Suitability
(×106 km2)
High Suitability
(×106 km2)
Scomber japonicuscurrent 0.2951.0550.216
2050 sSSP1260.2151.0160.255
SSP3700.1741.0590.258
SSP5850.2231.0220.234
2100 sSSP1260.1941.0520.242
SSP3700.2801.0260.164
SSP5850.4150.9210.146
Konosirus punctatuscurrent 0.4490.7540.187
2050 sSSP1260.7030.6650.211
SSP3700.6950.7030.211
SSP5850.6700.7040.188
2100 sSSP1260.7080.6800.192
SSP3700.6180.6240.095
SSP5850.7030.5600.049
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Xia, M.; Jia, H.; Wang, Y.; Zhang, H. Effects of Climate Change on the Distribution of Scomber japonicus and Konosirus punctatus in China’s Coastal and Adjacent Waters. Fishes 2024, 9, 395. https://doi.org/10.3390/fishes9100395

AMA Style

Xia M, Jia H, Wang Y, Zhang H. Effects of Climate Change on the Distribution of Scomber japonicus and Konosirus punctatus in China’s Coastal and Adjacent Waters. Fishes. 2024; 9(10):395. https://doi.org/10.3390/fishes9100395

Chicago/Turabian Style

Xia, Mingxia, Hui Jia, Yibang Wang, and Hui Zhang. 2024. "Effects of Climate Change on the Distribution of Scomber japonicus and Konosirus punctatus in China’s Coastal and Adjacent Waters" Fishes 9, no. 10: 395. https://doi.org/10.3390/fishes9100395

APA Style

Xia, M., Jia, H., Wang, Y., & Zhang, H. (2024). Effects of Climate Change on the Distribution of Scomber japonicus and Konosirus punctatus in China’s Coastal and Adjacent Waters. Fishes, 9(10), 395. https://doi.org/10.3390/fishes9100395

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