1. Introduction
Small-sized fish or forage fish play a pivotal role in the marine food chain, serving as essential links in the effective transfer of matter and energy from lower to higher trophic levels within marine ecosystems. This transfer process is crucial for maintaining the structure and function of marine ecosystems [1,2,3]. By examining the habitat distribution of these species, we can gain profound insights into the interactions among different organisms in the ocean ecosystem. Moreover, the dynamic changes in their population distribution are critical for devising effective area-based marine management strategies. Additionally, considering the variations induced by climate and other environmental factors, understanding species’ environmental preferences and spatiotemporal distribution patterns can help predict the potential impact of future changes on ecosystems [4].
That traditional economic fish resources in China’s coastal waters are in decline due to environmental pollution, overfishing, climate change, and interspecific interactions appears to be a consensus among ecologists, management professionals, and those working in the fishing industry [5]. In response, researchers have initiated systematic and protracted surveys of fishery resources. With a wealth of data gathered and analyzed, these experts have delved into discussions and examinations of the anthropogenic and environmental factors contributing to resource fluctuations and the remedial actions required, from an ecosystem, community, and population level. A majority of studies suggest that the structure of coastal fish communities continues to evolve in an unfavorable direction, characterized primarily by the miniaturization of fish species and shifts in the trophic structure of fish communities [6,7,8,9,10,11]. The status of key economic species remains grim. Conversely, small fish species, characterized by their early maturation, rapid growth, and high reproductive capacity, are increasingly becoming dominant in these ecosystems [12,13].
Located in the warm temperate zone, the south inshore waters of Zhejiang are under the perennial influence of coastal currents on the western nearshore side, while the eastern side is affected by the Taiwan Warm Current [14]. The varying strength of the Taiwan Warm Current significantly impacts this maritime region. Numerous studies have examined the fish community and population levels within this region [15]. Existing researches confirm a continuing decline in the resources of the area’s economic species; small-sized fish have emerged as the dominant species here [12,13]. However, the majority of attention has been on the resource status of principal economic species, their environmental preferences, and spatiotemporal distribution characteristics, with few researchers delving into studying the small-sized fish in these waters.
Thus, this research capitalized on trawl survey data collected from the south inshore waters of Zhejiang, China, integrating environmental variables, to devise distribution models for four pivotal small-sized fish species: Amblychaeturichthys hexanema (AMH), Apogon lineatus (also known as Jaydia lineata, APL), Benthosema pterotum (BEP), and Bregmaceros mcclellandi (BRM). These species were selected due to their prevalence as small baitfish and their pivotal roles within the intricate food web [12,13]. This study delved deeply into the environmental preferences of these species in the region, scrutinizing the spatiotemporal patterns of their resource distribution and interspecific variations, ultimately identifying crucial bait areas. Such invaluable insights will not only fortify scientific forecasting capabilities but also provide a solid foundation for the development of adaptive marine management strategies.
2. Materials and Methods
2.1. Data Collection
The data for this study were collected through bottom-trawling surveys conducted in four distinct seasons, spring (May), summer (August), autumn (November), and winter (January or February), spanning the years 2017 to 2020, along the south inshore of Zhejiang, China (Figure 1). The selection of sampling stations adhered to a predetermined fixed pattern. A total of 478 stations were investigated during the surveying periods. At each sampling location, trawling operations were executed at a consistent tow speed ranging between 2 and 4 knots for 30 min. The dimensions of the trawl mouth were standardized to a width of 40 m and a height of 7.5 m. Species were meticulously identified, and the catch was quantified. To ensure uniformity in data representation, the resource density of each species was standardized to g/3 nmi (grams per three nautical miles). All sampling procedures and biological measurements complied with the standards prescribed by the Specifications for Oceanographic Survey, ensuring reliability and consistency in the data collection process [16].
The environmental data utilized in this study were obtained from the Copernicus Marine Environment Monitoring Service (CMEMS,
2.2. Species Distribution Model
A boosted regression tree (BRT) melds two algorithms from the realms of statistics and machine learning: a classification and regression tree (CART) and boosting. This fusion propels the construction of numerous simple decision tree models to elevate predictive prowess. An advantage of utilizing BRTs lies in their capacity to efficiently manage the correlation and collinearity effects among environmental variables, thereby eliminating the necessity for preliminary evaluations of predictor variables [20]. Renowned for its superior forecasting abilities, BRT has garnered substantial endorsement and application within the ecological research literature [21]. In numerous studies, BRT has outperformed regression-based models, such as generalized linear models (GLMs) and generalized additive models (GAMs), in analyzing complex species–habitat relationships [20,22,23]. In this study, we utilized the resource density of key small-sized fish species as response variables and environmental factors as predictor variables to develop a BRT model.
To achieve an optimal BRT model, it is essential to fine-tune the learning rate (lr), tree complexity (tc), and bag fraction. The learning rate, also known as the shrinkage parameter, plays a crucial role in preventing overfitting. A practical guideline for setting the learning rate is to ensure that it enables the BRT model to produce at least 1000 trees. Tree complexity denotes the number of nodes in each decision tree, whereas the bag fraction represents the proportion of data utilized for model construction at each step, typically ranging between 0.5 and 0.75 [20]. In this research, learning rates were set to 0.001, 0.005, and 0.01; tree complexities were configured to 2, 4, 6, 8, and 12; and the bag fraction was fixed at 0.75. A ten-fold cross-validation approach was employed to evaluate model robustness and to identify the parameter combination that results in the lowest average estimation bias while generating more than 1000 decision trees, thereby establishing the optimal model.
The potentially significant difference in the resource density of each species among seasons and areas was determined by a Wilcoxon test (α = 0.05).
All analyses were carried out in the R-4.3.1 software [24].
3. Results
3.1. Model Performance
The optimal parameters for each stock’s model are detailed in Table 1. The findings revealed that the ideal number of trees varied from 1050 to 2950, and the deviance explained by models ranged from 25.6% for BRM to 59.5% for APL. These results underscored that the models adeptly captured the association between catch rates and environmental variables for the majority of populations within the examined spatiotemporal context (Table 1).
The model results indicated that the impact of environmental factors on the resource distribution of different fish varies significantly. The relative contribution rate of environmental factors to the model’s explanatory power revealed that SSH was the most significant influencer of catch rates, with an average relative contribution rate of 29.1%, followed by Chl at 23.4%, SST at 17.4%, SSS at 16.5%, and DO at 13.7% (Figure 2).
3.2. Environmental Preference
The relationship between covariates and response variables is illustrated in Figure 3. The distribution of AMH was predominantly influenced by SSH and Chl, followed by SSS; SST, and DO. AMH tended to concentrate more in waters with elevated SSH (>0.15 m), higher concentrations of Chl (>1.2 mg·m−3), and comparatively lower SSS, while resource density diminished when SST exceeded 28 °C. In the case of APL, the primary environmental factors shaping its resource distribution were SSH, Chl, and SST, succeeded by DO and SSS. The environmental characteristics of regions with high APL density were as follows: SSH was elevated (>0.15 m), Chl concentration hovered around 0.5 mg·m−3 or 1.2 mg·m−3, SST fell within the range of 16–19 °C, while DO (<240 mmol·m−3) and SSS (<28) remained relatively low. As for BEP, Chl, SST, and SSH stood as the paramount environmental determinants of its resource distribution, with each factor making a nearly equal contribution to the model’s explanatory power, succeeded by DO and SSS. Areas rich in BEP resources were marked by Chl > 0.5 mg·m−3, SST spanning 19~25 °C, SSH fluctuating between 0 and 0.2 m, and relatively enhanced DO (>250 mmol·m−3) and SSS (>32). Regarding BRM, the principal environmental influencers of its resource distribution were SSS, SSH, and Chl, followed by DO and SST. Waters boasting increased BRM resource density were distinguished by elevated SSS (>32), SSH oscillating between 0.15 and 0.20 m, heightened Chl (>1.2 mg·m−3), and relatively reduced DO (<240 mmol·m−3) and SST (<25 °C) (Figure 3).
3.3. Spatiotemporal Distribution
The spatial and temporal distributions of the resource density for the four small-sized fish species are presented in Figure 4. For AMH, the peak resource density was observed in autumn, followed by winter, summer, and spring. During autumn, the regions with relatively high resource density for AMH were located in the northern waters (zones a and b), in summer and winter mainly in zone a, and in spring in zone c. In the case of APL, the highest resource density was in autumn, followed by spring, winter, and summer. For autumn, the areas with relatively high resource density for APL were situated in zone a, in spring and winter in zone b, and in summer, the distribution was more uniform without significant high-density regions. Regarding BEP, the season with the highest resource density was autumn, followed by summer, spring, and winter. In autumn, the areas with relatively high resource density for BEP were found in zone b and a small portion of zone a, in summer in zone c, and in spring and winter in zone d. As for BRM, the resource density reaches its zenith in autumn, followed by winter, spring, and summer. In autumn, the regions with relatively high resource density for BRM were in zones b and d, with a minor portion in zone c, in winter and spring primarily in zone d, and in summer the distribution was notably uniform. In summary, the four small-sized fish examined in this study were predominantly distributed in the northern part of the south inshore of Zhejiang, China, exhibiting the highest resource density in autumn (Figure 4 and Figure 5).
4. Discussion
While numerous studies have underscored substantial changes in the fish community structure of the research area, characterized by the rising predominance of small-sized fish, our comprehension of these fish species’ habitat preferences remains profoundly limited. This study harnessed trawl survey data from the south inshore of Zhejiang, in conjunction with crucial environmental factors, to scrutinize the environmental preferences and spatiotemporal variation aspects of the key small-sized fish inhabiting these important foraging waters. The findings suggested that, despite variations in the environmental preferences among different species, SSH and Chl overwhelmingly influenced the resources and distribution of these fish species. The northern portion of the research area boasted more abundant resources compared to its southern counterpart, with resource densities peaking in autumn.
4.1. Dominant Environmental Variables
Primary productivity is a key factor influencing the resource density and individual size of fish larvae or small-sized fish that feed on plankton [25,26,27]. In the Northwest Pacific, changes in temperature and the intensity of upwelling can delay phytoplankton reproduction and subsequently affect zooplankton size [28]. These changes directly impact the recruitment of small pelagic fish, thereby influencing fish population sizes [29]. Since the mid-2000s, significant shifts in environmental conditions in the Mediterranean Lion Bay have affected the Chl, SST, upwelling, and frontal activity, which may be important contributors to the decline in resource density and size of small pelagic fish in that region [27]. In this study, SSH and Chl were identified as key environmental factors, revealing a high similarity in their utilization among the four small-sized fish species. Areas with high resource density exhibited relatively high levels of both SSH and Chl. SSH is generally associated with heat flux, wind, and eddy currents, which influence the transport of marine materials and indirectly indicate levels of primary productivity [25]. Conversely, Chl directly reflects phytoplankton biomass and the level of primary productivity in the ocean, subsequently affecting the food sources for zooplankton and herbivorous fish [25,26]. This highlights that the richness of primary productivity was also a critical determinant of small forage fish resource density in the south inshore waters of Zhejiang.
Salinity, temperature, and dissolved oxygen are well known to significantly influence fish physiology and serve as key drivers of fish migration [17]. In our analysis, the four small-sized fish species exhibited notable differences in their utilization of SST, SSS, and DO. For instance, AMH and APL preferred lower SSS environments, while the high resource densities of BEP and BRM were found in areas with higher SSS. With the exception of BRP, the resource densities of the other three species decreased to varying extents at elevated SST (>25 °C). These factors may be the primary reasons behind the observed differences in the temporal and spatial distribution of these small forage fish species.
4.2. Differences in Spatial and Temporal Distribution
4.2.1. Amblychaeturichthys hexanema (AMH)
AMH is widely distributed in the coastal waters of the northwest Pacific. In the broader East China Sea region, AMH exhibits relatively low dominance and is not considered a dominant small-sized fish species [30]. However, in the waters around Dachen, AMH has been observed to dominate across all seasons except summer, indicating its consistent presence in that area [31]. Additionally, it showed a relatively high dominance in the south inshore of Zhejiang, with a trend of increasing prevalence year by year [13]. This study also revealed that its resource density centroid was primarily located in nearshore waters. These findings further affirmed that AMH is a coastal or nearshore fish species, suggesting that the south inshore of Zhejiang may be one of its key habitats.
Beyond SSH and Chl, SSS and SST are significant factors influencing AMH distribution. Research has indicated that when salinity exceeded 30, there was a marked decline in the relative resource density of AMH in Haizhou Bay [19], which aligned with the results of this study. Excessively high salinity may disrupt AMH’s osmoregulation and metabolic functions, affecting the normal life processes of the fish and leading to a decrease in resource density. The optimal water temperature for AMH in Haizhou Bay ranged between 19 and 20 °C; when temperatures exceeded 19.8 °C, a significant decline in resource density occurred [32]. In contrast, the suitable SST range for AMH in the studied area was broader. The relatively stable SST conditions in the south inshore of Zhejiang may be a key factor contributing to the widespread distribution of this species in that region.
4.2.2. Apogon lineatus (or Jaydia lineata, APL)
Unlike AMH, APL is one of the main small fish species in the East China Sea, alongside BEP and Champsodon capensis [30], predominantly found in the southeastern waters of the East China Sea [33]. APL has a limited seasonal movement range, engaging in only short-distance migrations, with summer and autumn being their primary spawning season. It is a seasonally dominant species in the nearshore waters of eastern Zhoushan [34], central Zhejiang [35], and south Zhejiang [13]. This study found that during winter, APL was primarily distributed in the northeastern waters of the research area, with a trend shifting the resource centroid northward in spring. In summer, resource density in this region was at its lowest, likely because APL primarily spawns in the Yangtze River estuary to the north of the study area [19]. Following spawning, adults migrate toward the nutrient-rich nearshore waters of south Zhejiang in search of food.
In addition to the interspecific differences in environmental preferences highlighted by this study, significant variations in the environmental preferences were also observed among different waters and different life stages of APL. For instance, in Haizhou Bay during autumn, the optimal SSS for slim-flanked bream was between 31.4 and 32.0, with water temperatures ranging from 22.8 to 27.0 °C [36]. In contrast, APL in the studied area seemed to be better adapted to relatively cooler (16–19 °C) and lower-salinity (<28) waters. On the other hand, the eggs of APL in the East China Sea exhibited varying temperature preferences throughout the months; the SST in June was between 18.89 and 19.00 °C, while in July, they were primarily found in waters with an SST ranging from 23.89 to 29.60 °C [19]. This SST preference was higher than that observed for adult APL in this study. The salinity preferences for the eggs and adults of APL were found to be similar.
4.2.3. Benthosema pterotum (BEP)
Previous studies have found that the highest resource density of BEP occurs in autumn, primarily in the offshore waters of the northern East China Sea (30.5°–32.5° N, 124°–126.5° E), with some scattered distribution in the nearshore areas of south Zhejiang (27°–28° N, 122°–123° E) [37]. It tended to short-distance migration in response to seasonal changes; the center of distribution for the northern East China Sea population shifts short distances from southwest to northeast and from northeast to southwest between summer and autumn [37]. In this study, BEP also displayed the characteristic of having the highest resource density in autumn, with the center of this resource density located in the northern part of the study area, aligning with the observed short-distance movement of the northern population during the transition from summer to autumn. The suitable SST for the nearshore population of south Zhejiang was found to be between 24 and 25 °C [37], along with a salinity range of 31–33. During June and July, the optimal temperature and salinity for BEP larvae in the East China Sea were identified as 18–26 °C and 30–34, respectively [18], which correlates with the favorable SST and SSS characteristics for adult BEP identified in this research.
In contrast to the findings of Li et al. (2006) [37], this study discovered that BEP was widely distributed in the south inshore of Zhejiang. This discrepancy may result from differences in the fishing efficiency of the survey gear used for this species or may be attributed to environmental changes influencing BEP distribution. This suggests that, over the long term, there may not have been significant changes in the environmental adaptability of BEP. However, as nearshore environmental conditions have changed, its spatial and temporal distribution characteristics throughout the East China Sea may have undergone considerable alteration.
4.2.4. Bregmaceros mcclellandi (BRM)
BRM is primarily distributed in tropical and subtropical waters, mainly found in the southeastern regions of the East China Sea in China, where it is a seasonal dominant species during autumn and winter. It is rarely encountered in coastal waters [34,35], but it does appear as a seasonal dominant species in the offshore areas of central [38] and southern [13] Zhejiang, extending to the northern Taiwan Strait [39]. Consistent with previous studies, this research indicated that the resource density distribution centroids of the three species, excluding BRM, shifted between nearshore areas (zones a and d) and offshore waters (zones b and c) with changing seasons (Figure 4). In contrast, BRM is consistently found in more distant offshore waters.
4.3. Area-Based Marine Management Consideration
Area-based management tools (ABMTs) are regarded as pivotal in achieving marine conservation objectives. A crucial aspect of this strategy involves pinpointing key areas [40]. The south inshore of Zhejiang, China, holds immense significance as a breeding ground, offering a wealth of nourishment for fish and acting as a vital locale for the maturation and expansion of numerous species [15]. Safeguarding these areas ensures a consistent supply of fishery resources. Our study has enriched and clarified the spatial distribution and seasonal shifts of essential small-sized fish resources within this territory. The findings revealed that the northern segment of Zhejiang’s southern inshore harbored the densest concentration of bait resources, with seasonal fluctuations underscoring the need for temporally adaptable ABMTs aimed at preserving small-sized fish resources to bolster management efficacy.
Moreover, small-sized fish, in contrast to large commercial species, are more acutely influenced by environmental alterations [29], ultimately reverberating throughout the ecosystem via the food chain. Consequently, tracking the dynamic variations in small-sized fish resources within key areas can foster insight into the mechanisms driving fluctuations in commercial fish stocks and serve as a vital predictor of the impact of climatic and environmental changes on marine living resources [41]. It further constitutes a fundamental cornerstone for devising appropriate response strategies.
5. Conclusions
This study enhances our understanding of the environmental preferences and spatial and temporal distributions of small-sized fish in the south inshore of Zhejiang, China. Overall, primary productivity level was the core factor influencing the resource density and distribution of small forage fish species in this region. Although different species exhibited varying trends in resource density with increasing primary productivity, there was an overall upward trend, with the highest resource densities found in areas abundant in primary productivity. Additionally, differences in adaptability to SST, SSS, and DO may be the primary factors causing variations in the temporal and spatial distribution of these fish species. Notably, the coastal waters near Taizhou in autumn (the northern part of the study area) were the most abundant in small-sized fish resources. In the intricate interplay of numerous influencing factors, preserving the ecological service functions of nearshore ecosystems and fostering the sustainable exploitation of fishery resources have emerged as pivotal concerns in contemporary marine management. The invaluable insights garnered from this study, about the environmental preferences and spatiotemporal distribution patterns of crucial small fish species, offer crucial guidance for pinpointing key nearshore regions and facilitating informed conservation decisions pertaining to fishery resources.
Conceptualization, M.X., X.G. and W.L.; methodology, M.X.; formal analysis, M.X. and J.W.; data curation, W.L.; writing—original draft preparation, M.X.; writing—review and editing, X.G., W.L. and J.W.; funding acquisition, W.L. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
The data presented in this study are available on reasonable request from the corresponding author.
We would like to thank the teachers and students from the laboratory of Shanghai Ocean University for their work and help in sample collection and data analysis and valuable comments on the revision of the paper.
The authors declare no conflicts of interest.
Footnotes
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Enlarge this image.Figure 1. The trawl survey area for fishery resources in the south inshore of Zhejiang, China. The fill color within the right plot signifies the bathymetry, measured in meters (m), and the dashed line within it represents the bathymetric contour.
Enlarge this image.Figure 2. The relative contribution rate of environmental factors to the model’s explanatory power. The y-axis represents species, with AMH referring to Amblychaeturichthys hexanema, APL to Apogon lineatus (or Jaydia lineata), BEP to Benthosema pterotum, and BRM standing for Bregmaceros mcclellandi. SST, SSS, SSH, Chl, and DO are abbreviations for specific environmental factors, which represent sea surface temperature, sea surface salinity, sea surface height, chlorophyll-a concentration, and oxygen concentration, respectively.
Enlarge this image.Figure 3. Environmental factor effects on the relative resource density of the four small-sized fish in the south inshore of Zhejiang, China. The solid black lines depict the index’s response curve in relation to environmental changes, whereas the dashed red lines serve as a smoothed rendition of the former.
Enlarge this image.Figure 4. The seasonal distribution of the four small-sized fish in the south inshore of Zhejiang, China. The hues depicted in the plot signify the average relative resource density spanning from 2017 to 2020, quantified in units of grams per three nautical miles (g/3 nmi). The dashed lines in the figure represent the regional boundary. The north–south boundary is delineated by the 28° north latitude line, while the east–west boundary is defined by the line connecting the points (26.5° N, 121.25° E) and (29.5° N, 122.75° E). In the figure, a, b, c, and d, respectively, represent the northwest, northeast, southwest, and southeast waters of the study area.
![View Image - Figure 5. Seasonal (A) and regional (B) differences in resource density of the four small-sized fish in the south inshore of Zhejiang, China (ns: p [greater than] 0.05, **: p <= 0.01, ***: p <= 0.001, ****: p <= 0.0001).](https://media.proquest.com/media/hms/THUM/10/xKQMa?_a=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%3D%3D&_s=H32F%2FSH6SCuLLzXFr6uJ58laHaE%3D "View Image - Figure 5. Seasonal (A) and regional (B) differences in resource density of the four small-sized fish in the south inshore of Zhejiang, China (ns: p [greater than] 0.05, **: p <= 0.01, ***: p <= 0.001, ****: p <= 0.0001).")
Enlarge this image.Figure 5. Seasonal (A) and regional (B) differences in resource density of the four small-sized fish in the south inshore of Zhejiang, China (ns: p [greater than] 0.05, **: p <= 0.01, ***: p <= 0.001, ****: p <= 0.0001).
Optimal parameter tuning for BRT models and the variance explained by these models.
| Species | Code | Number of Trees | Tree Complexity (tc) | Learning Rate (lr) | Bagging Fraction | Explained Variance% |
|---|---|---|---|---|---|---|
| Amblychaeturichthys hexanema | AMH | 1250 | 12 | 0.001 | 0.75 | 40.0 |
| Apogon lineatus (or Jaydia lineata) | APL | 2550 | 12 | 0.001 | 0.75 | 59.5 |
| Benthosema pterotum | BEP | 2150 | 12 | 0.001 | 0.75 | 52.3 |
| Bregmaceros mcclellandi | BRM | 1500 | 6 | 0.001 | 0.75 | 25.7 |
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