| Citation: |
FEI Jiaojiao, LI Cheng, ZHANG Jian, TENG Yuxiu, WU Yuntao, SHI Jiangao. Effects of seamount characteristics in Central and Western Pacific Ocean on CPUEs of yellowfin tuna (Thunnus albacares) in longline and purse seine fisheries[J]. South China Fisheries Science, 2024, 20(2): 1-10. DOI: 10.12131/20230200
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Seamounts are one of the important habitat types on the seafloor and a hotspot for marine biodiversity. Yellowfin tuna (Thunnus albacares) is widely distributed in the Western and Central Pacific Ocean (WCPO) with high ecological and economic value. However, there are few studies on the mechanisms by which seamounts and their associated features affect the abundance and distribution of yellowfin tuna resources. In this study, we used longline and purse seine fishery data summarized by the Western and Central Pacific Fisheries Commission (WCPFC) from 2010–2021, in addition with seamount characteristics data, to analyze the impacts of two different types of seamounts on the abundance and distribution of yellowfin tuna resources using a generalized additive model (GAM). GAM was utilized to examine the connection between catch per unit effort (CPUE) and seamounts characteristics of yellowfin tuna in two different fishing methods. The results show that in the WCPO, the yellowfin tuna catches in the two fisheries mainly originated from seamount areas, and seamount characteristics had a highly significant effect on CPUEs of yellowfin tuna in both fisheries (P<0.001). In the longline fishery, higher CPUE occurred in seamount areas with less peak depth, roughness, base area, and seamount density as well as gentler slopes, whereas higher CPUE occurred in the purse seine fishery in seamount areas with less roughness, greater peak depth, greater base area, as well as steeper and denser slopes. In summary, we explore the mechanism of the influence of WCPO seamount characteristics on different populations of yellowfin tuna, which provides new ideas and references for further exploring the relationship between the distribution of yellowfin tuna populations and changes in resource abundance with the marine environment in the future.
Seamounts are topographic features that rise from the seafloor with an elevation of at least 1 000 m. There are approximately 40 000 seamounts worldwide, covering almost the entire seafloor [1]. As ecological "hotspots" in oceanic regions, the unique topographic features of seamounts maintain special hydrodynamic environments in surrounding waters, which facilitate the establishment of various marine biological communities [2]. Seamounts are one of the important habitats for marine organisms [1] and play a crucial role in marine ecosystems [3]. In recent years, research on the biodiversity of seamounts and of their adjacent waters has become a hotspot [4]. Numerous studies have found that seamount areas with shallower peak depths [5–7], gentler slopes [7–10], or greater elevations [11–13] can aggregate more marine species and lead to more complex biodiversity. The seafloor topography of the Western and Central Pacific Ocean (WCPO) is complex, with seamounts accounting for approximately half of the global seamount total [14]. The WCPO is the most typical seamount region among the world's oceans and is also the main production area for global tuna fisheries. Exploring the relationship between the seamount features of the WCPO and tuna fish stock populations is crucial for marine ecological protection and sustainable fisheries development.
Yellowfin tuna (Thunnus albacares) is a highly migratory oceanic predatory fish that mainly feeds on species with vertical migration habits in the upper layers of the ocean. As a high trophic level predator, it plays an important role in maintaining the stability of marine ecosystems and the balance of food webs [15]. Additionally, yellowfin tuna has high economic value and is one of the main target species for commercial tuna fisheries and recreational fisheries in the WCPO [16]. Yellowfin tuna swim fast, have strong activity capabilities, and migrate over long distances. However, they are highly required in a large amount of food and sensitive to the external environment, making their population distribution and resource abundance more susceptible to marine physical and chemical factors [17-22]. As the second largest target species of tuna fisheries in the WCPO, the conservation and sustainable development of yellowfin tuna resources have received high attention from the Western and Central Pacific Fisheries Commission (WCPFC) over the past decade [23]. Although research has found that seamounts and surrounding waters in the WCPO aggregate large numbers of yellowfin tuna, forming areas of relatively high resource abundance [24-26], few studies have focused on how seamounts and their characteristics affect yellowfin tuna resource abundance and population distribution mechanisms.
This study used WCPFC aggregated longline and purse seine fishery data combined with seamount data from Yesson et al. [1] to analyze the relationship between catch per unit effort (CPUE) of yellowfin tuna in two different fishing methods and seamount characteristics. The study explored how seamount features such as peak depth, elevation, roughness, slope, base area, and density affect the resource abundance of different yellowfin tuna populations, aiming to provide a scientific basis for further conservation and sustainable development of yellowfin tuna resources in the WCPO.
Yellowfin tuna fishery data came from the WCPFC public domain's longline and purse seine fishery aggregated catch and effort data (https://www.wcpfc.int/public-domain/) from 2010 to 2021. The spatial scope of the data was 105°E–135°W and 60°N–85°S, with a resolution of 5°×5° and a monthly temporal resolution.
Seamount data came from the seamount list of the WCPO in the PANGAEA global seamount data catalog (https://doi.ppangae.de/10.1594/PANGAEA.757562/). The data covered seamount characteristics, including latitude, longitude, peak depth, elevation, and base area, with a spatial resolution of 0.0083°. Seamount slope and roughness data came from GEBCO's global bathymetry data (https://www.gebco.net/data_and_products/gridgri_bathymetry_data/).
ONI data were derived from the monthly average sea surface temperature anomaly (SSTa) of the corresponding months in the Niño3.4 region (120°W–170°W, 5°N–5°S) provided by the Climate Prediction Center of the National Oceanic and Atmospheric Administration (https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php).
CPUE is commonly used as an indicator to measure fishery resource abundance [27]. In this study, data standardization was performed based on catch and effort. The monthly average CPUE (XCPUE) within the latitude and longitude grid cells of 5°×5° was used as an indicator of yellowfin tuna resource abundance in the WCPO (longline fishery: fish·10-3 hooks; purse seine fishery: t·seine-1), i.e.:
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XCPUE,y,m,i=∑Cy,m,i∑Ey,m,i |
(1) |
where ∑Cy,m,i is the total monthly catch within the grid cell in the corresponding year; ∑Ey,m,i is the total monthly fishing effort within the grid cell in the corresponding year; y is the year; m is the month; i is the grid.
Before model construction, the variance inflation factor (VIF) was used for a multicollinearity test among explanatory variables. Generally, a VIF < 5 indicates no multicollinearity issues [28].
The generalized additive model (GAM) is a type of generalized linear model based on additive model concepts. In GAM, there is no strict restriction on functional form in the relationship between independent and dependent variables [28]. Non-parametric smoothing functions are used in GAM to fit relationships between variables, enabling the effective fitting of non-linear relationships between variables. Thus, GAM is widely applied in ecological fishery regression analysis and predictive modeling [29]. To explore the effect of seamounts and their characteristics on yellowfin tuna resource abundance, the CPUE of yellowfin tuna was taken as the response variable, with the main characteristic factors of seamounts, time, and spatial factors as explanatory variables. The oceanic El Niño index (ONI) was included as an explanatory variable to better reflect the relationship between seamount characteristics and the CPUE of yellowfin tuna because global climate change, especially the El Niño-southern oscillation (ENSO) phenomenon, leads to changes in marine environmental factors that affect yellowfin tuna population distribution and resource abundance [30–31]. A GAM model with a Gaussian distribution and natural logarithm as the link function was used to fit the relationship between the CPUE of yellowfin tuna and seamount characteristics. The general form of the GAM model is as follows:
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XCPUE=γ+∑nifi(xi)+ε |
(2) |
where XCPUE is the CPUE of yellowfin tuna; γ is the model intercept; ε is the random error term; xi represents explanatory variables; fi(xi) is an arbitrary univariate spline smoothing function for each explanatory variable xi.
According to the Akaike information criterion (AIC), explanatory variables were added successively to obtain the prediction model with the minimum AIC value based on the explanatory variable prediction function with the minimum AIC value. Generally, the smaller the AIC value, the closer R2 is to 1, and the higher the deviance explained rate, the better the model fits [28].
The model construction was performed in R (V4.2.0), with GAM construction achieved by the "mgcv" package [28] and VIF test performed by the car package. The significance level α was set to 0.01.
The distribution of yellowfin tuna resources in the WCPO from 2010–2021 is shown in Fig. 1. In the WCPO, the CPUE of Yellowfin tuna in longline fishery was mainly distributed between 30°S and 30°N (Fig. 1-a), while the CPUE of yellowfin tuna in purse seine fishery was mainly distributed between20°S to 10°N (Fig. 1-b), with higher CPUE in seamount areas. By calculating the proportion of catches from seamount areas in the overall catches in the WCPO from 2010–2021, it was found that approximately 8.3×105 t of yellowfin tuna was caught in longline fishery, with seamount area catches accounting for 98%. Besides, approximately 3.64×105 t of yellowfin tuna were caught in purse seine fishery, with seamount area catches accounting for 89%.
The multicollinearity test results for the initial variables are shown in Table 1, with all VIF values less than 5, indicating low multicollinearity among the initial variables. Therefore, year (Y), month (M), longitude (Lon), latitude (Lat), ONI, seamount density (DS, number·10-4 n mile2), peak depth (PD, m), elevation (E, m), base area (BA, km2), slope (SP), and roughness (R) were used as explanatory variables to construct the GAM model.
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The relationship between the CPUE of yellowfin tuna in the WCPO and various explanatory variables was fitted using the GAM model, and the AIC criterion was used for optimal model selection. The fitting results are shown in Table 2. The deviance explained rate of the optimal model for the longline fishery was 52.20%, with a determination coefficient R2 of 0.520. The deviance explained rate of the optimal model for purse seine fishery was 51.00%, with a determination coefficient R2 of 0.505. The optimal model expression was:
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ln(XCPUE+1)∼f(Y)+s(M)+s(Lon)+s(Lat)+s(ONI)+s(DS)+s(PD)+s(E)+s(BA)+s(SP)+s(R) |
(3) |
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where Y is treated as a factor; other variables are natural cubic spline smoother functions s.
The relationship between the CPUE of yellowfin tuna in the WCPO and various influencing factors is shown in Table 3. Under the condition of P < 0.01, variables that significantly affected the CPUE of yellowfin tuna in both longline and purse seine fisheries included temporal and spatial variables (month, longitude, latitude), climate variable (ONI), and seamount characteristic variables (peak depth, elevation, base area, slope, roughness, and seamount density).
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Fig. 2 demonstrates the seamount characteristics affecting the CPUE of yellowfin tuna in the WCPO in longline and purse seine fisheries. There were highly significant relationships between the CPUE of yellowfin tuna and seamount characteristics (Table 2, P < 0.001). In longline fishery, the CPUE of yellowfin tuna was decreased with increasing seamount peak depth (Fig. 2-a), elevation (Fig. 2-c), roughness (Fig. 2-e), slope (Fig. 2-g), base area (Fig. 2-i), and density (Fig. 2-k).
In purse seine fishery, the CPUE of yellowfin tuna was increased with increasing peak depth (Fig. 2-b), base area (Fig. 2-j), and density (Fig. 2-l). A decreasing and increasing trend was shown in the effect of seamount elevation (Fig. 2-d) and slope (Fig. 2-h) on the CPUE of yellowfin tuna, while a negative correlation was found between seamount roughness and the CPUE of yellowfin tuna (Fig. 2-f).
In comparison to flat seafloors, productivity is higher in seamounts and adjacent waters [32]. Their unique topographic features and oceanic patterns are the main factors contributing to the high variability in food web structures and functions in these waters [33]. Influenced by the Earth's rotation, currents are split when they are obstructed by seamounts and converge on the other side, forming an isolated anticyclonic gyre and isopycnal dome around the seamount, known as a Taylor column [34]. The Taylor column can bring nutrient-rich deep water up to the seamount peak area [3]. These nutrients can promote the growth of plankton and form the foundation of the food web, contributing to the dispersal and aggregation of consumers and top predators around seamounts [25]. As one of the top predators in the ocean, the polyphagous yellowfin tuna mainly feeds on highly aggregated small fish, cephalopods, and crustaceans[18]. Seamount characteristics affect yellowfin tuna resource abundance, distribution, and migration, often serving as navigation landmarks, foraging stopovers, and spawning grounds during their migration [26]. In this study, a GAM model is used for an in-depth analysis of the relationship between the CPUE of yellowfin tuna and seamount characteristics, revealing a series of important correlations. In the WCPO region, seamount peak depth, elevation, slope, roughness, base area, and density all had highly significant effects on the CPUE of yellowfin tuna in both longline and purse seine fisheries.
The seamount ecosystem formation highly depends on nutrient concentrations and primary productivity in seamount areas. Taylor columns can lift deep, nutrient-rich waters to the seamount peak [34]. Peak depth is a key factor affecting Taylor column formation [35], and shallow seamounts are typically higher in productivity [6]. Morato et al. [25–26] found that pelagic fishes, such as tuna, often aggregate around shallow seamounts for food. This study also found that in longline fishery, a higher CPUE of yellowfin tuna occurred in areas with shallower seamount peak depths, confirming the above viewpoint—the abundant food sources at the tops of shallow seamounts satisfy the feeding needs of yellowfin tuna. However, in purse seine fishery, the CPUE of yellowfin tuna was increased with increasing peak depth, possibly due to differences in group behaviours as follows. For one thing, the yellowfin tuna caught in longline fishery are mainly scattered adult individuals, while the ones caught in purse seine fishery are mainly younger [36]. As opportunistic predators [17], yellowfin tuna may abandon the abundant food resources at seamount peaks to avoid predator threats. For another, purse seine operations cover a wider range and may catch yellowfin tuna schools that are chasing prey in deeper waters [19]. Seamount elevation was also a factor affecting the CPUE of yellowfin tuna. In the longline fishery, the CPUE of yellowfin tuna decreased with increasing seamount elevation. The reason for this was probably that excessively high seamounts are located above cold water masses [37], unfavorable for biological growth. Consequently, food resources for tuna were limited. In purse seine fishery, there was a decreasing then increasing trend in the effect of seamount elevation on the CPUE of yellowfin tuna, with CPUE mainly concentrated in areas with moderate-elevation seamounts. This also reflected the habitat preference of young yellowfin tuna schools, as these seamounts may provide more complex habitats for yellowfin tuna to escape predator threats [3]. The effect of seamount elevation on yellowfin tuna requires further investigation.
The density and base area of seamounts affect biodiversity in seamount areas, with greater biomass in seamounts with larger base areas and higher densities [9, 32]. The results of this study showed that in longline fishery, the CPUE of yellowfin tuna decreased with increasing density and base area of seamounts, which might be caused by the competition among individual yellowfin tuna and behavioral adjustments. On the one hand, seamount areas with larger base areas and density result in more habitats and food sources [38]. When yellowfin tuna is attracted and aggregated in a limited area, different individuals must compete for relatively limited food resources, and cannibalism may even occur, resulting in lower CPUE in these areas. On the other hand, yellowfin tuna are highly migratory fish that can adjust their migration paths and predation behavior based on food resource availability [20]. When they perceive increased predator density around seamounts and difficulty in food acquisition, some individuals may choose to leave in search of more favorable predation grounds. In the purse seine fishery, the base area and density of seamounts were positively correlated with the CPUE of yellowfin tuna. As mentioned above, yellowfin tuna caught in purse seine fishery are mostly juvenile schools with poorer swimming ability [39], more vulnerable to predator threats. Therefore, seamount areas with higher density and larger base areas may provide more survival opportunities for juvenile yellowfin tuna schools.
At steep seamounts lie exposed rocks, on whose surface grow giant algae and corals, creating complex seamount habitat environments [40]. In contrast, gentle seamounts are covered with large amounts of sediment, providing a nutrient-rich substrate, a source of life for marine organisms [3]. Roughness is an important factor in terrestrial hydrological simulation and prediction, and is also a crucial physical property for measuring seafloor sediments [41]. This study found that in both longline and purse seine fisheries, seamount roughness was negatively correlated with the CPUE of yellowfin tuna. As mentioned above, in rougher seamount areas with large food abundance, competition among yellowfin tuna individuals and the threats they face will also increase, which may be one reason for this phenomenon. The effect of seamount slope on the CPUE of yellowfin tuna differed between the two fisheries. In the longline fishery, the CPUE of yellowfin tuna decreased with increasing seamount slope. In areas with smaller seamount slopes, there are stable substrates, suitable flow conditions, and higher biodiversity, providing rich food sources for yellowfin tuna [42]. In purse seine fishery, the CPUE of yellowfin tuna was generally increased with increasing seamount slope, possibly suggesting more complex habitat environmental conditions in steeper seamounts. As fishing difficulty increases, these areas provide more opportunities for juvenile yellowfin tuna to escape [43].
In the WCPO, yellowfin tuna is widely distributed between 30°N and 20°S [17]. They typically stay between the surface layer and the mixed layer, or reside at the top of the thermocline (18–31℃) [44]. According to some studies, to chase prey or escape predators, they can rapidly dive to depths of 1 600 m or engage in repetitive diving within the 200–400 m layer [45], Therefore, it is difficult to establish static protected areas for yellowfin tuna in the ocean [46]. Furthermore, from a fishery enforcement and management perspective, it may be impractical to establish yellowfin tuna protected areas across the entire ocean [47]. However, the protection for migratory species like yellowfin tuna can be achieved by establishing targeted local marine protected areas to protect key areas where yellowfin tuna is found, such as spawning grounds and foraging aggregation areas [48]. Therefore, for marine protected area design, it is crucial to identify these key areas. The results of this study can provide some basis for yellowfin tuna fishery management. In longline fishery, fishing activities in seamount areas with shallower peak depths or gentler slopes should be restricted, as these seamounts may be foraging grounds for yellowfin tuna [32]. In purse seine fishery, attention should be paid to fishing activities in seamount areas with larger base areas and higher densities or steeper slopes, as these areas are shelters and even spawning grounds [25] for yellowfin tuna [43]. For sustainable use of yellowfin tuna resources, fishing activities could be adjusted based on different seamount characteristics, or seamount characteristics could be used for categorization to help determine priority orders for yellowfin tuna fishing ground conservation [5], and to formulate relevant fishery management policies [49-51].
Large pelagic fish, especially tuna, tend to aggregate in upwelling areas [52], and near marine continental slopes, islands, or seamounts [53]. Seamounts play a crucial role in maintaining marine biodiversity and ecological balance [54], providing complex habitats for marine organisms [8, 25, 55]. Yellowfin tuna is a part of the seamount ecosystem, and its resource abundance and distribution are influenced by seamount characteristics [3]. Therefore, protecting and managing seamount ecosystems is significant for maintaining sustainable catches of yellowfin tuna [33]. Seamount areas not only provide abundant food for tuna but are also important spawning grounds [56–58]. The effect of seamounts and their characteristics on tuna populations reflected based on fishery CPUE is more likely to represent the response of schooling and foraging behavior to seamounts. However, further exploration is still required in the mechanisms of seamounts' effect on spawning grounds.
This study revealed the relationship between WCPO seamount characteristics and the CPUE of yellowfin tuna, with some insights into the complex interactions between yellowfin tuna and seamount ecosystems. The results provide theoretical references for the sustainable development and utilization of yellowfin tuna resources and the protection of seamount ecosystems. To further understand the relationship between migratory oceanic species populations and seamounts, future research should consider factors including oceanic fronts, eddies, migration routes, and spawning ground studies when constructing models to develop more comprehensive models to explain changes in migratory species populations.
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