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Article

The Spatial Distribution Dynamics of Shark Bycatch by the Longline Fishery in the Western and Central Pacific Ocean

by
Shengyao Xia
1,
Jiaqi Wang
1,*,
Xiaodi Gao
2,*,
Yiwei Yang
1 and
Heyang Huang
1
1
Shanghai Ocean University, Shanghai 201306, China
2
East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China
*
Authors to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2025, 13(2), 315; https://doi.org/10.3390/jmse13020315
Submission received: 26 November 2024 / Revised: 5 January 2025 / Accepted: 6 February 2025 / Published: 8 February 2025
(This article belongs to the Section Marine Ecology)
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Abstract

:
Shark bycatch represents a substantial issue in the management of oceanic fisheries. Utilizing data on shark bycatch from the longline fishery, as released by the Western and Central Pacific Fisheries Commission, this study applied the boosted regression tree model to examine the impact of environmental factors on the bycatch per unit effort (BPUE) of key bycatch species, as well as to predict the spatial distribution dynamics of both BPUE and bycatch risk (BR). The findings emphasize that the oxygen concentration, sea surface temperature, and chlorophyll-a concentration are paramount to sharks’ BPUE. Furthermore, the study compared the variations in environmental preferences across diverse shark species, pinpointing key environmental attributes defining the ecological niches of distinct shark populations. The spatial predictions identified the hotspots of BPUE and BR for the bigeye thresher shark (Alopias superciliosus), longfin mako (Isurus paucus), silky shark (Carcharhinus falciformis), and oceanic whitetip shark (Carcharhinus longimanus) in tropical latitudes (10° S to 15° N), and for the blue shark (Prionace glauca) and shortfin mako (Isurus oxyrinchus) in temperate zones (south of 30° S or north of 30° N). The geometric center analysis indicated that all shark species exhibited large annual fluctuations in BPUE and BR, and most populations displayed significant shifting trends. Several grids (5° × 5°) were identified as high-risk areas due to their considerable contribution to bycatch. Furthermore, the geometric centers of BR were observed to shift eastward towards equatorial waters, compared to the geometric centers of BPUE. This underscores the necessity of considering factors beyond BPUE when identifying critical areas for the implementation of area-specific bycatch mitigation measures. The insights derived from this study can enhance and support the development and enforcement of targeted area-based fishery management initiatives.

1. Introduction

Since the 1970s, the alarming decline in dolphin populations due to bycatch or discard in the Eastern Pacific purse seine fishery has garnered significant attention. This spotlight on bycatch issues has since broadened to encompass various global fisheries [1]. Currently, bycatch has become an intrinsic challenge in fishery management and the conservation of marine biodiversity. Mitigating bycatch has ascended to a principal objective in fishery governance pursued by numerous international bodies, regions, and nations [2,3].
The annual global discard from marine fisheries amounted to 9.1 million tons, representing 10.8% of the total global catch production. The annual discard amount from global longline fishing was 360,000 tons [4], and the proportion of bycatch in the total catch could exceed 50% in some cases [5,6], with common species in bycatch including billfish, sharks, sea turtles, and marine mammals [7], many of which are considered top predators that play critical roles in ecosystem structure and function [8]. Elasmobranchs, which comprise sharks and rays, constitute a significant proportion of the bycatch in tuna longline fishery [6,9], and their numbers continue to dwindle. Studies have projected a staggering decline of up to 71% in oceanic shark and ray populations since 1970 [10]. Alarmingly, approximately 32% of elasmobranch species are presently classified as being at risk of extinction [11], highlighting the urgency of conservation efforts.
Efficiently minimizing the incidental capture of sharks within these fisheries is a crucial strategy for their conservation. Area-based management tools (ABMTs) as potential management tools to achieve species conservation and maintain fisheries sustainability have also been adopted by several RFMOs [12]. Comprehending habitat utilization characteristics is paramount to the development and successful execution of bycatch mitigation strategies within precise spatial–temporal ranges [13,14,15,16]. In the 1990s, several authors embarked on an analysis of the key factors that influenced the BPUE (bycatch per unit effort) of specific species, notably of the blue shark (Prionace glauca, BSH), in the expansive Eastern Pacific longline fishery. Their investigation encompassed both environmental and operational variables, utilizing the limited data available from observers. Drawing from these insights, they devised tailored bycatch mitigation strategies for various shark populations [17,18]. In recent years, the widespread implementation and standardization of extensive fishery monitoring systems, notably fishery observer programs, have significantly bolstered the accessibility of large-scale fishery data. This, in turn, has spurred advancements in the research of ecological traits, including environmental preferences and habitat distributions, of numerous highly migratory species, such as sharks, swordfish, and sea turtles [19,20,21,22,23,24,25,26]. However, thus far, research on the habitat preferences of Pacific sharks concerning salinity, temperature, and oxygen has primarily concentrated on localized regions, such as tropical waters [20,27,28,29,30,31] and temperate waters of the North Pacific [18,32]. The diverse nature of data sources has posed significant challenges in conducting comparative analyses, thereby impeding our comprehension of the ecological niches and disparities among these sharks across broader temporal and spatial dimensions.
The Western and Central Pacific Ocean (WCPO) holds paramount significance as a fishing ground for global tuna fisheries, contributing over 60% of the world’s tuna production. Prolonged and intense fishing activities have already inflicted, or are poised to inflict, detrimental effects on species within this ecosystem, including sharks and marine mammals [33]. As the global demand for offshore fishery resources, located beyond continental shelves, continues to escalate, the intensity of international competition is also increasing. The mounting fishing capacity of distant-water fleets across the globe poses a substantial threat to oceanic sharks. To mitigate this threat, the adoption of additional spatial–temporal management measures aimed at reducing the interaction between sharks and fishing activities has become an inevitable trend [12]. Therefore, this research endeavored to (1) delve into the environmental preference of the primary sharks bycaught in the longline fishery within the WCPO, (2) precisely locate the vital regions exhibiting heightened bycatch per unit effort (BPUE), and (3) uncover hotspots marked by substantial bycatch risk (BR), while also monitoring their yearly variations.

2. Materials and Methods

2.1. Fisheries Data

The bycatch data on sharks from longline fishery used in our analysis was sourced from the Western and Central Pacific Fisheries Commission (WCPFC) (https://www.wcpfc.int/node/29966, accessed on 8 April 2024). These data were gathered by observers from diverse fishing organizations, adhering to the Conservation and Management Measure for the Regional Observer Program (CMM 2018-05). Regrettably, our access was confined to information with a spatial resolution of 5 degrees and a temporal frequency measured in years, covering the period from 2013 to 2022. Additionally, information regarding fishing efforts from 2013 to 2021 for the Western and Central Pacific longline fishery was made publicly available by the WCPFC (https://www.wcpfc.int/wcpfc-public-domain-aggregated-catcheffort-data-download-page, accessed on 8 April 2024).
A total of 17 shark species or groups were recorded in the bycatch dataset. The WCPFC has established guidelines and lists to help protect vulnerable shark species due to their ecological importance and the pressures they face from fishing activities. Sharks featured on the list are designated as key sharks and are given heightened priority in areas such as data collection and scientific research [34]. Therefore, our research centers on key shark species that could be precisely identified to the species level. To mitigate issues related to zero inflation during the modeling process [35], we exclusively selected species with an occurrence frequency exceeding 30% for analysis (Table S1). Consequently, six species (comprising eight stocks) were chosen for further analysis, as detailed in Table 1. From a global viewpoint, the oceanic whitetip shark (Carcharhinus longimanus, OCS) has been evaluated for inclusion on the IUCN Red List as a critically endangered species. Meanwhile, the longfin mako (Isurus paucus, LMA) and shortfin mako (Isurus oxyrinchus, SMA) have been classified as endangered species, while the silky shark (Carcharhinus falciformis, FAL) and bigeye thresher shark (Alopias superciliosus, BTH) have been designated as vulnerable species. Notably, the BTH has also been identified as a near-threatened species.
Based on the most recent stock assessment conducted by the WCPFC, all shark species mentioned, with the exception of the OCS and FAL, are not currently experiencing overfishing or are not in a state of being overfished (https://www.wcpfc.int/current-stock-status-and-advice, accessed on 8 April 2024). This assessment provides valuable insights into the conservation status of these important oceanic species.

2.2. Environmental Data

The details of the environmental variables are shown in Table 2. The values of the dynamic oceanographic variables were derived from the Copernicus Marine Service (CMEMS) (https://marine.copernicus.eu/, accessed on 8 April 2024).
In order to align with the corresponding bycatch data, we meticulously processed the environmental data by calculating annual averages for each individual grid cell, with a dimension of 5° × 5°. This ensured that the environmental variables were accurately represented and aligned with the relevant bycatch information.

2.3. Statistical Analysis

The boosted regression tree (BRT) combines two algorithms from the fields of statistics and machine learning: classification and regression trees (CARTs) and boosting. This integration facilitates the construction of numerous simple decision tree models, enhancing predictive performance. One advantage of using BRTs is their ability to efficiently manage correlation and collinearity among environmental variables, eliminating the need for preliminary evaluations of predictor variables [39]. Renowned for its superior forecasting abilities, BRT has garnered substantial endorsement and application within ecological research literature [40,41,42,43]. In various studies, BRT has outperformed regression-based models, such as generalized linear models (GLMs) and generalized additive models (GAMs), in analyzing complex species–habitat relationships [44,45]. In this study, we used the BRT to quantify the statistical relationship between the BPUE (ind./1000 hooks) and environmental variables.
Three parameters must be set for fitting the BRT model: (1) Tree complexity, which refers to the depth of a particular base decision tree or the number of leaf nodes. Higher complexity may make the model more flexible to adapt to complex data relationships but can also lead to overfitting. (2) The learning rate controls the contribution of each base model. A larger learning rate may make the model learn the details of the training data faster but also increase the risk of overfitting. (3) Bagging fraction was used to control the proportion of training data utilized by each base model, typically recommended to be between 0.5 and 0.75. We chose a consistent bagging fraction of 0.75, varied the tree complexity across four tiers (4, 6, 8, and 16), and employed two distinct learning rates (0.001 and 0.005), thereby constructing models with eight distinctive parameter configurations. The optimal parameter set was determined to be the one that yielded the minimal average estimation bias while also generating more than 1000 decision trees [39]. The response curves produced by the model serve to illustrate the relationship between BPUE and environmental factors, where the magnitude of each factor’s weight signifies its level of influence.
Using the optimal model, the BPUE of different sharks in each spatial grid was predicted annually. The potential bycatch for each shark species in each grid was derived by multiplying the BPUE by the fishing effort, and this potential bycatch was used as an indicator to quantify the BR. To remove the impact of varying magnitudes, the yearly bycatch was scaled as 0–1 by using the min–max normalization to characterize the local BR [46].
B R i , s , y = B y c a t c h i , s , y − B y c a t c h m i n , s , y B y c a t c h m a x , s , y − B y c a t c h m i n , s , y
where B R i , s , y represents the BR in gird i for species s in year y; B y c a t c h i , s , y is the bycatch of species s in ith grid corresponding to year y; B y c a t c h m i n , s , y and B y c a t c h m a x , s , y are the minimum and maximum bycatch of species s in year y, respectively.
In the process, we determined the yearly longitudinal and latitudinal geometric centers of BPUE and BR, termed LONG and LATG, respectively. This calculation aims to illustrate the shifts in the centroid occurring in the interplay between bycatch and tuna longline fishing operations. The annual centroid was determined as follows [47]:
L O N G s , y = ∑ ( L o n g t i t u d e i , y × I n d e x ( i , s , y ) ) ∑ I n d e x ( i , s , y )
L A T G s , y = ∑ ( L a t i t u d e i , y × I n d e x ( i ,   s ,   y ) ) ∑ I n d e x ( i ,   s ,   y )
where L O N G s , y and L A T G s , y represent the longitude and latitude of the geometric centroid of BPUE or BR of species S in year y, respectively. Longitude (i,y) and Latitude (i,y) are the longitude and latitude of the ith grid corresponding to year y. I n d e x ( i , s , y ) represents the BPUE or the BR of the species s in the ith grid in year y.
We also evaluated the disparity of the longitude and latitude centers between BPUE and BR for each species by using the following formulas:
D . L O N G s = ∑ ( L O N G B R , s , y − L O N G ( B P U E , s , y ) ) N
D . L A T G s = ∑ ( L A T G B R , s ,     y − L A T G ( B P U E ,   s ,   y ) ) N
where D . L O N G s and D . L A T G s represent the average disparity of the longitude and latitude centers between the BPUE and BR of species s, respectively. N is the number of years. L O N G B R , s , y and L O N G B P U E , s ,   y represent the longitude of geometric centroid of BR and BPUE for species s in year y.  L A T G B R , s , y and L A T G B P U E , s ,   y represent the latitude of the geometric centroid of the BR and BPUE for species s in year y.
To precisely pinpoint the hotspots of bycatch, we conducted a thorough analysis by calculating the annual average potential bycatch for each shark species within each grid. Subsequently, leveraging cumulative bycatch data, we systematically categorized these grids into four distinct tiers: high risk (encompassing the top quartile, or 25% of the grids), medium–high risk (comprising the subsequent 25% of grids, excluding those already classified as high risk), medium risk (another 25% of grids, excluding both the high and medium-high risk zones), and low risk (representing the remaining areas).
All the analyses were conducted using R 4.3.3 [48].

3. Results

3.1. Model Results

The optimal parameters for each BRT are summarized in Table S2. The analysis indicates that the optimal number of trees varied from 1300 to 2700, and the deviance explained by the models ranged from 36.2% for LMA to 91.6% for the south BSH. The individual contributions of the environmental variables to the model’s explanatory capacity revealed that O2 was the most important influencer on BPUE, with an average contribution of 26.29%. This was closely followed by SST at 23.25%, Chl at 18.28%, and SSH at 13.19%. In comparison, the SSS and MLT exhibited notably lower contributions, registering at 9.96% and 9.03%, respectively (Figure 1).

3.2. Environmental Characteristics in the High BPUE Areas

The BPUE of the BTH was primarily influenced by variations in SSH (29.4%), followed by significant contributions from SST (19.8%). The effects of Chl (15.5%), SSS (15.3%), and O2 (13.1%) on the BPUE of this species were comparable, while the effect of MLT (6.8%) was relatively minor. Areas experiencing high BPUE were typically associated with an annual mean SSH of 0 to 0.5 m, SST values within the range of 26 to 30 °C, relatively diminished Chl (below 0.2 mg/m3), lower SSS (34.5 or less), and increased levels of O2 (above 210) (Figure S1a).
The BPUE of the north BSH was predominantly affected by O2, with a 40.2% contribution, followed by Chl at 22.6%, and SST at 18.2%. The impacts of SSH at 8.7%, SSS at 5.3%, and MLT at 5.0% were relatively minor. Areas characterized by elevated BPUE typically exhibited higher annual mean concentrations of O2 and Chl, coupled with reduced SST, specifically below 26 °C (Figure S2a).
The BPUE of the south BSH in the South Pacific exhibited a relationship with environmental factors analogous to that observed in the North Pacific. O2 (30%) exerted the most significant influence, followed by SST (29.6%) and Chl (23.1%). The contributions from MLT (9.1%), SSH (4.8%), and SSS (3.3%) were notably smaller. Regions characterized by high BPUE were typically associated with annual mean O2 levels ranging between 225 and 245 mmol/m3, relatively low SST (below 18 °C), and elevated Chl (above 0.4 mg/m3) (Figure S3a).
The BPUE of the north SMA was significantly influenced by O2 (47.8%), followed by SST (15.6%), Chl (13.2%), and SSH (10.5%). The impacts of SSS (6.9%) and MLT (6.0%) were comparatively minor. High-BPUE areas were characterized by elevated annual mean O2 and Chl and reduced SST below 26 °C (Figure S4a).
In the South Pacific, the primary influence on the SMA’s BPUE was Chl (34.9%), with O2 (27.0%) and SST (23.2%) also playing significant roles. High-BPUE regions typically exhibited higher annual mean Chl (>0.3 mg/m3), O2 levels between 230 and 240 mmol/m3, and lower SST below 20 °C. The effects of MLT (6.5%), SSS (5.9%), and SSH (2.6%) on the BPUE were less pronounced (Figure S5a).
The BPUE of the FAL was dominantly affected by SST, which accounted for 47.6% of the influence, followed by SSS (13.0%), Chl (12.1%), and SSH (10.1%). Areas with increased BPUE were associated with higher SST above 26 °C, lower SSS below 34, relatively high Chl above 0.2 mg/m3, and reduced SSH. The contributions of MLT (9.0%) and O2 (8.2%) to the BPUE were relatively small (Figure S6a).
The environmental factors influencing the BPUE of LMA were fairly uniform in magnitude. High-BPUE areas were characterized by lower SSS below 34 and SSH below 0.4 m, deeper MLT exceeding 50 m, higher SST above 26 °C, elevated O2 above 210 mmol/m3, and increased Chl above 0.2 mg/m3 (Figure S7a).
The OCS’s BPUE was most influenced by O2 (27.3%), followed by SSH (21.1%), SST (14.5%), Chl (14.3%), MLT (12.3%), and SSS (10.4%). High BPUE regions exhibited O2 levels between 200 and 215, reduced SSH below 0.4, moderate SST around 25 °C in warmer waters, relatively high Chl above 0.2 mg/m3, deeper MLT exceeding 35 m, and higher SSS above 34.5 (Figure S8a).

3.3. Spatial Distribution of BPUE and BR

The predicted spatial distribution of the BPUE for the eight shark stocks showed that the BPUE hotspots could be segmented into three areas: south of 30° S, between 10° S and 15° N, and north of 30° N. Hotspots for the BPUE of the north BSH and SMA were predominantly located in the Northwest Pacific (30~50° N, 140~180° E), while the southern stocks’ BPUE mainly occurred near the waters surrounding New Zealand. The BPUE hotspots of the BTH and FAL were each centered in the waters south of Hawaii (5~15° N, 140°~180° W) and around to the east of Papua New Guinea (10° S~10° N, 130° E~160° W), respectively. The BPUE hotspots for the LMA and OCS were more dispersed, distributed between 10° S~15° N and 25° S~25° N, 170° E~140° W, respectively (Figure 2 and Figure S1b–S8b). The spatial distribution of BR was similar to the BPUE distribution pattern; however, it was notably more concentrated in specific regions and encompassed a relatively narrower extent as shown in Figure 3 and Figure S1c–S8c.
The diagram illustrated in Figure 4 displays the spatial configurations of the four BR levels corresponding to the eight shark stocks. For all species, grid cells designated as medium-to-high-risk levels exhibited a relatively concentrated distribution pattern. In contrast, cells marked with high BR demonstrated a patchier and dispersed spatial arrangement. Additionally, within tropical marine environments, pronounced inter-specific disparities were observed in the hotspots of BR, highlighting potential challenges in devising and implementing region-based conservation strategies tailored to mitigate bycatch impacts across diverse shark species effectively.

3.4. Annual Variations in Geometric Centers

The geometric centers of BPUE and BR for all stocks exhibited large annual fluctuations from 2013 to 2021, indicating potential shifts in their habitat distributions in response to annual changes in marine environments. The centers of both BPUE and BR of the north BSH, north SMA, and OCS consistently migrated eastward; those of the south BSH tended to move northeastward; while the centers of BR for the south SMA displayed a slight northward trend, similar to the patterns observed for both the BPUE and BR of the BTH. Others did not show significant spatial migration patterns (Figure 5).
Furthermore, there existed a discernible divergence between the centers for BR and BPUE across all populations, with the average disparity depicted in Figure 6. The geometric centers of BR for all the shark populations exhibited a trend of shifting eastward towards the equator, likely due to the greater concentration of fishing effort in tropical regions (Figure S9). This suggests that BPUE should not be the only metric used to assess regional risk or significance when developing area-based conservation measures for bycatch species.

4. Discussion

The highly migratory nature of oceanic sharks poses significant challenges and costs to data collection, making research on their habitat distribution perpetually challenging. As a result, the ongoing debate surrounding the mitigation of bycatch through ABMTs remains contentious. This study employed publicly available bycatch data from the WCPFC and utilized BRT to analyze the relationship between the BPUE of key sharks bycaught in the WCPO longline fishery and environmental factors on a broader spatiotemporal scale. The study identified regions with high BPUE and BR, along with their specific environmental characteristics. The findings reveal that on an annual scale, O2, SST, and Chl were the primary environmental factors influencing the BPUE of sharks bycaught in WCPO longline fishery. Both the BPUE and BR exhibited annual fluctuations, with most stocks showing distinct and consistent trends over time. Notably, the regions with the highest BPUE did not necessarily correlate with areas with the highest BR. These insights provide valuable information for the further exploration of area-based strategies for mitigating shark bycatch.

4.1. Environmental Effects on BPUE

Salinity, temperature, and dissolved oxygen are well known to significantly influence fish physiology and comprise key drivers of fish migration [49,50]. Our study not only delved into the environmental preferences of diverse shark species across a vast spatial expanse but also discerned distinct differences among them, emphasizing several crucial environmental attributes.
Although research on the role of oxygen in the spatial ecology of pelagic sharks remains limited, this abiotic factor plays a significant role in influencing shark abundance and distribution [50]. SMA and BSH exhibit high demands for dissolved oxygen. For instance, the actively swimming, regionally endothermic SMA demonstrated one of the highest oxygen requirements among all the evaluated elasmobranchs [51,52,53]. Similarly, elevated O2 demands have also been observed for BSH, likely associated with their frequent bursts of speed to optimize foraging opportunities [54,55,56]. In contrast, the great white shark (Carcharodon carcharias) and the pelagic thresher (Alopias pelagicus) exhibit a greater tolerance to low oxygen levels [57,58], whereas the BTH has adapted to hypoxic environments through specialized gills [59]. These findings are generally consistent with the trends observed in our study regarding the responses of various sharks’ BPUE to fluctuations in O2 levels.
Additionally, our study identified that O2 levels at 210, 230, and 260 mmol/m3 were critical thresholds. Specifically, the higher BPUEs for the BSH and SMA, which primarily inhabit temperate waters, were observed in areas with elevated O2 levels. In the Northern Hemisphere, a positive correlation existed between BPUE and O2 levels, with the BPUE remaining relatively stable above 260 mmol/m3. In contrast, the highest BPUE for these two species in the Southern Hemisphere occurred in waters with O2 ranging from 225 to 235 mmol/m3, where excessively high and low oxygen levels resulted in a reduced BPUE. Furthermore, we found that the four shark stocks (OCS, FAL, BTH, and LMA) primarily distributed in tropical waters exhibited greater tolerance to low-O2 conditions compared to the BSH and SMA. The OCS and FAL tended to achieve higher BPUE in low-O2 environments (<210 mmol/m3).
While oceanic sharks exhibit a wide range of temperature adaptability, significant inter-species differences are also evident. In the central Pacific region (0–30° N, 120–180° W), the BSH and SMA undergo 95% of their respective life stages in waters with temperatures ranging from 9.7 to 26.9 °C and 9.4 to 25.0 °C, respectively. In contrast, the BTH has been found to spend 95% of its time in waters with temperatures between 6.7 and 21.2 °C, while the FAL and OCS primarily inhabited waters with an SST between 25 and 27 °C [31]. In Southern California waters, the SMA was seldom found in an SST below 16 °C and was generally captured in the 17 to 22 °C range [60]. In the tropical waters of the Pacific and Atlantic, the FAL was more frequently located in areas with an SST around 24 to 30 °C [20,30,61], with larger individuals typically found in waters where the SST ranges from 25.6 to 31.4 °C [29]. Due to the limited research on the Pacific LMA, the impact of changing oceanic environments on this species remains uncertain.
These differences in SST adaptability also lead to significant variations in the BPUE among different species under similar environmental conditions. For instance, in the longline fishery of the Western and Central Pacific tropical waters, the BPUE of the BSH increased with rising temperatures, peaking in waters with an SST of 29.5 °C [62]. Conversely, in the North Pacific longline fishery, the BPUE of the BSH remained stable in waters with an SST between 16 and 26 °C but dropped significantly when the SST fell below 16 °C [32]. In the waters around the Marshall Islands, the highest BPUE for the BSH in longline fishery occurred at SSTs around 28 °C. Meanwhile, the BPUE for the BTH and OCS significantly decreased as the SST increased from 27.5 °C to 29.5 °C [27]. Our study further validates the SST preferences of these species and the inter-species differences among them. Furthermore, our research indicates that in tropical regions, areas with an elevated BPUE typically exhibited an SST of 26 °C or higher. In contrast, in temperate waters, a higher BPUE was generally associated with an SST of 17.5 °C or lower.
The Chl serves as a direct indicator of primary productivity levels in marine environments, which reflects the availability of prey species for large predators [50]. Notably, areas with a high BPUE of sharks and rays tend to exhibit elevated Chl [21,28,63,64,65]. Our research indicates that the areas with a high BPUE for various sharks typically occurred in tropical waters where the Chl exceeds 0.2 mg/m3, or in temperate waters where the Chl exceeds 0.4 mg/m3. The exception was that in areas with a high BPUE for the BTH, the Chl was relatively low, suggesting a preference for Chl-depleted waters.
SSH is frequently associated with heat flux, wind, and eddy currents, which impact the transport of marine matter and serve as indirect indicators of primary productivity [66]. This would explain why shark bycatch predominantly occurred in regions characterized by elevated SSH in our study. Furthermore, we found that in tropical regions, areas with high BPUE were typically associated with an SSH below 0.4 m. Conversely, in the temperate regions of the Northern Hemisphere, the BPUE exhibited a notable increasing trend with rising SSH, while no significant changes were observed in the Southern Hemisphere.
While certain shark species exhibit euryhaline, the majority are strictly stenohaline, with coastal species being more susceptible to salinity changes than their oceanic counterparts. The salinity of the open ocean typically ranges from 33 to 37 psu [50]. In this study, the BPUEs of the FAL, BTH, and LMA were significantly impacted by SSS. Notably, when the SSS exceeded 34 PSU, the BPUE of these species experienced a marked decline. In contrast, the OCS preferred higher-SAL environments, with the BPUE notably increasing when the SSS surpassed 34 PSU. Other stocks experienced a lower SSS impact, showing a low contribution (<10%) of SSS to the model’s explanatory capacity.

4.2. Shark Bycatch Hotspots

An analysis of observer data from longline and purse seine fisheries in the Western and Central Pacific from 1991 to 2011 revealed that regions with a high BPUE for the BSH and SMA were predominantly located in temperate areas. The BPUE for the OCS was concentrated between 10° S and 20° N, exhibiting an asymmetric distribution that spans from northwest to southeast. In contrast, the BPUE for the FAL were centered around the equator and displayed a more symmetric distribution [67]. In the Eastern Pacific, the OCS tended to inhabit offshore waters [28]. In the Central Pacific, longline fishery reported increased catches of the OCS with increasing distance from land [68]. These findings are generally consistent with those of this study.
In this study, the primary identification of high-BPUE zones for the BTH centered on the temperate waters of the Northern Hemisphere, specifically in the Central Pacific (10° N–30° N, 150° W–180° W), which aligned closely with the spatial distribution characteristics of BTH identified by Matsunaga and Yokawa, based on longline fishery data from Japan [69]. However, the high BPUE of thresher sharks, predominantly comprising the BTH and common threshers (Alopias vulpinus), tended to primarily occur in the temperate waters of the Northern Hemisphere in the Western Pacific (10° N–30° N, 160° E–180° E) [67]. This inconsistency may be primarily attributed to the inclusion of bycatch data from purse seine fisheries in the report. Moreover, our findings also reveal that akin to the FAL, regions exhibiting a high BPUE of LMA, which is seldom studied, were predominantly located in tropical Pacific waters (10° S–10° N). The key distinction lies in the concentration of high-BPUE zones for the FAL, which were predominantly found in the Western Pacific waters, whereas the high-BPUE zones for the LAM were more widely dispersed, appearing both in the central and western Pacific waters.

4.3. Bycatch Mitigation Measures Consideration

Over the past few decades, RFMOs have made significant progress in developing various management measures, including fin controls, catch limits, operational and gear controls, fishing closures, trade controls, and mitigation methods such as modifying fishing gear and practices and using deterrents [7,70,71]. Nevertheless, the majority of these policies continue to emphasize alleviating mortality after capture through remedial measures and mandating or promoting research and data gathering, rather than preventing or diminishing the inadvertent capture of sharks [70]. The WCPFC has invested a significant amount of effort into shark research, including data collection, observer programs, ecological risk assessment, stock assessment, and exploration of ecosystem-based fishery management. However, similar to other RFMOs, management measures aimed at reducing shark bycatch primarily involve gear modification, setting total allowable catches, prohibiting retention, and promoting safe release. There is a limited focus on ABMTs to prevent interactions between fishing activities and sharks [70]. The regions with elevated BPUE levels identified in this study underscore the significance of critical shark habitats, as outlined in Lawson’s 2011 report [67]. Nevertheless, it was noteworthy that these high-BPUE zones did not necessarily align with regions of high BR. This observation underscores the fact that while safeguarding key shark habitats is paramount, solely targeting these areas might not yield substantial results in mitigating shark bycatch in longline fishery in the long term. Conversely, high-BR areas signify a delicate equilibrium between BPUE and the intensity of fishing activities, suggesting that implementing measures to mitigate bycatch in these zones could potentially be more productive.
Oceanic sharks are known for their high temporal variability, with aggregation patterns shifting in response to changes in ecosystem dynamics [72]. This study revealed a certain level of inter-annual variability in both the high-BPUE and high-BR areas. These findings underscore the dynamic nature of bycatch species distribution and indicate that the interaction between sharks and longline fishery is also subject to change. Traditional ABMTs often center around static protected zones, which may prove inadequate in ensuring the long-term sustainability of fisheries, especially considering species migration patterns. While it may be crucial to impose fishing restrictions or bans across broader areas to meet conservation objectives effectively [73], it is imperative to refrain from imposing unnecessary constraints within regions or periods of reduced interaction between bycatch species and fisheries. Dynamic Ocean Management (DOM) represents a versatile approach that adapts the spatial–temporal configuration of measures in response to ongoing shifts in oceanic conditions, drawing on real-time biological, oceanographic, and additional data sources. This strategy holds promise in striking a balance between exploiting marine resources and safeguarding conservation interests, offering a valuable tool for navigating the intricacies of managing ocean ecosystems amidst continual environmental fluctuations [73,74,75]. Widely adopted in various fisheries, DOM has proven effective in addressing bycatch concerns for species like southern bluefin tuna, sea turtles, and whales [76,77,78,79]. In light of this, the implementation of DOM could potentially provide a more efficient means of addressing shark bycatch issues in tuna longline fisheries. Unfortunately, the restricted spatial and temporal resolution of the data employed in our research impeded a comprehensive evaluation of these indices on a more granular level. Consequently, the precise identification of high-risk zones and their dynamic attributes is paramount for crafting effective future management strategies.

5. Conclusions

Despite the limited spatial and temporal resolution of the data, this study yielded invaluable insights into the environmental preferences and spatial distribution dynamics of the primary bycatch sharks in WCPO longline fishery. This research illuminates both the similarities and disparities in the environmental preference of these key sharks, highlighting several crucial environmental thresholds where notable variations in BPUE were observed. The BPUE and BR hotspots exhibited substantial variability across species and from year to year. Additionally, upon comparison with the geometric centers of BPUE, it was discovered that the geometric centers of BR shifted eastward and drew closer to the equator. These revelations serve as a solid foundation for the formulation and execution of effective shark conservation and management strategies.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/jmse13020315/s1, Figure S1: Environmental factors’ effects on the BPUE of the Bigeye thresher shark (a), the predicted spatial distribution of BPUE from 2013 to 2022 (b), and the predicted spatial distribution of bycatch risks from 2013 to 2021 (c); Figure S2: Environmental factors’ effects on the BPUE of the north blue shark (a), the predicted spatial distribution of BPUE from 2013 to 2022 (b), and the predicted spatial distribution of bycatch risks from 2013 to 2021 (c); Figure S3: Environmental factors’ effects on the BPUE of the south blue shark (a), the predicted spatial distribution of BPUE from 2013 to 2022 (b), and the predicted spatial distribution of bycatch risks from 2013 to 2021 (c); Figure S4: Environmental factors’ effects on the BPUE of the north shortfin mako (a), the predicted spatial distribution of BPUE from 2013 to 2022 (b), and the predicted spatial distribution of bycatch risks from 2013 to 2021 (c); Figure S5: Environmental factors’ effects on the BPUE of the south shortfin mako (a), the predicted spatial distribution of BPUE from 2013 to 2022 (b), and the predicted spatial distribution of bycatch risks from 2013 to 2021 (c). Figure S6: Environmental factors’ effects on the BPUE of the silky shark (a), the predicted spatial distribution of BPUE from 2013 to 2022 (b), and the predicted spatial distribution of bycatch risks from 2013 to 2021 (c). Figure S7: Environmental factors’ effects on the BPUE of the longfin mako (a), and the predicted spatial distribution of BPUE from 2013 to 2022 (b); Figure S8: Environmental factors’ effects on the BPUE of the oceanic whitetip shark (a), the predicted spatial distribution of BPUE from 2013 to 2022 (b), and the predicted spatial distribution of bycatch risks from 2013 to 2021 (c); Figure S9: Fishing effort (number of 1000 hooks) distribution of tuna longline fisheries in the Western and Central Pacific Ocean from 2013 to 2021; Table S1: The observed present frequencies and captures numbers of sharks in the tuna longline fishery of the Western and Central Pacific Ocean from 2013 to 2022; Table S2: Parameter configuration for BRT models and proportion of variance explained by the best model.

Author Contributions

Conceptualization, S.X., J.W., and X.G.; formal analysis, S.X. and J.W.; data curation, S.X., Y.Y., and H.H.; writing—original draft preparation, S.X.; writing—review and editing, J.W., X.G., Y.Y., and H.H.; funding acquisition, J.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China, grant number 32403024.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on reasonable request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The relative contribution (%) to the model’s explanatory capacity of the six environmental variables.
Figure 1. The relative contribution (%) to the model’s explanatory capacity of the six environmental variables.
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Figure 2. The predicted spatial distribution of the annual average BPUE (ind./1000 hooks). Note: the “N” and “S” in the plot represent the North and South Pacific stocks, respectively. Below is the same.
Figure 2. The predicted spatial distribution of the annual average BPUE (ind./1000 hooks). Note: the “N” and “S” in the plot represent the North and South Pacific stocks, respectively. Below is the same.
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Figure 3. The spatial distribution of the annual average BR.
Figure 3. The spatial distribution of the annual average BR.
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Figure 4. The spatial distribution of the four BR levels corresponding to the eight shark stocks.
Figure 4. The spatial distribution of the four BR levels corresponding to the eight shark stocks.
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Figure 5. Annual variations in the geometric centers of BPUE and BR for the eight shark stocks from 2013 to 2021. The directions indicated by the solid and dashed arrows represent the trends in changes for BPUE and BR, respectively.
Figure 5. Annual variations in the geometric centers of BPUE and BR for the eight shark stocks from 2013 to 2021. The directions indicated by the solid and dashed arrows represent the trends in changes for BPUE and BR, respectively.
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Figure 6. The average disparity of the geometric centers between BPUE and BR.
Figure 6. The average disparity of the geometric centers between BPUE and BR.
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Table 1. The shark species analyzed in our study and their stock status [36,37,38,39].
Table 1. The shark species analyzed in our study and their stock status [36,37,38,39].
SpeciesScientific NamesSpecies CodeStock Status from WCPFCVulnerabilityIUCN Status
OverfishingOverfished Latest Year
North blue sharkPrionace glaucaBSHNN2022≥mediumNear Threatened (2018)
South blue sharkNN2022
North shortfin makoIsurus oxyrinchusSMANN2019highEndangered (2018)
South shortfin makoNN2022
Silky sharkCarcharhinus falciformisFALYN2019≥mediumVulnerable (2017)
Bigeye thresher sharkAlopias superciliosusBTH---highVulnerable (2018)
Oceanic whitetip sharkCarcharhinus longimanusOCSYY2019≥mediumCritically Endangered (2018)
Longfin makoIsurus paucusLMA---highEndangered (2018)
Note: “≥” means the vulnerability of the stock was assessed as high or medium in several different types of research. “-” means the stock status was not available currently.
Table 2. Summary of the environmental variables used in the species distribution models.
Table 2. Summary of the environmental variables used in the species distribution models.
Variable AcronymVariable NameUnitsSpatial ResolutionTemporal Resolution
SSTSea surface temperature°C0.25°Monthly
SSSSea surface salinityPSU0.25°Monthly
SSHSea surface heightm0.25°Monthly
MLTMixed layer thicknessm0.25°Monthly
ChlChlorophyll-a concentrationmg/m30.25°Monthly
O2Oxygen concentrationmmol/m30.25°Monthly
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Xia, S.; Wang, J.; Gao, X.; Yang, Y.; Huang, H. The Spatial Distribution Dynamics of Shark Bycatch by the Longline Fishery in the Western and Central Pacific Ocean. J. Mar. Sci. Eng. 2025, 13, 315. https://doi.org/10.3390/jmse13020315

AMA Style

Xia S, Wang J, Gao X, Yang Y, Huang H. The Spatial Distribution Dynamics of Shark Bycatch by the Longline Fishery in the Western and Central Pacific Ocean. Journal of Marine Science and Engineering. 2025; 13(2):315. https://doi.org/10.3390/jmse13020315

Chicago/Turabian Style

Xia, Shengyao, Jiaqi Wang, Xiaodi Gao, Yiwei Yang, and Heyang Huang. 2025. "The Spatial Distribution Dynamics of Shark Bycatch by the Longline Fishery in the Western and Central Pacific Ocean" Journal of Marine Science and Engineering 13, no. 2: 315. https://doi.org/10.3390/jmse13020315

APA Style

Xia, S., Wang, J., Gao, X., Yang, Y., & Huang, H. (2025). The Spatial Distribution Dynamics of Shark Bycatch by the Longline Fishery in the Western and Central Pacific Ocean. Journal of Marine Science and Engineering, 13(2), 315. https://doi.org/10.3390/jmse13020315

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