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Article

Numerical Study on the Transport and Settlement of Larval Hippocampus trimaculatus in the Northern South China Sea

by
Chi Zhang
1,2 and
Zengan Deng
1,*
1
School of Marine Science and Technology, Tianjin University, Tianjin 300072, China
2
State Key Laboratory of Target Vulnerability Assessment, Beijing 100036, China
*
Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2025, 13(5), 900; https://doi.org/10.3390/jmse13050900
Submission received: 25 March 2025 / Revised: 22 April 2025 / Accepted: 30 April 2025 / Published: 30 April 2025
(This article belongs to the Section Marine Biology)
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Abstract

:
The three-spot seahorse (Hippocampus trimaculatus) is an economically important marine species in the northern South China Sea (NSCS). However, due to overfishing and marine environmental changes, its wild populations have been gradually depleted. To investigate the transport and settlement mechanisms of H. trimaculatus larvae in the NSCS, a physical–biological coupled model was developed based on the ocean model CROCO and the biological model Ichthyop for the period 2016–2018. The results indicate that the transport and settlement processes of larvae are primarily regulated by the combined influence of the South China Sea Warm Current, coastal upwelling, and Kuroshio intrusion. The larvae predominantly undergo short distance (0–300 km) and mid-short distance (300–600 km) transport, exhibiting significant spatial aggregation along coastal waters, particularly in the Gulf of Tonkin, the Pearl River Estuary, Shantou, Xiamen, and the western coast of Taiwan. Furthermore, extreme weather events, such as typhoons, significantly enhance larval settlement success rates. Notably, Typhoon Hato in August 2017 increased settlement success by 12.2%. This study elucidates the transport and settlement mechanisms of H. trimaculatus larvae, providing a scientific foundation for the conservation and management of its populations in the NSCS.

1. Introduction

The three-spot seahorse (Hippocampus trimaculatus) belongs to the family Syngnathidae and is an economically important marine fish in China. It is widely distributed in the northern South China Sea (NSCS) and the East China Sea (ECS) [1]. As one of the most heavily traded seahorse species in the global market, its annual trade volume reached approximately 1.8 million individuals between 2004 and 2011 [2]. Additionally, marine pollution caused by increased industrial wastewater discharge [3], habitat degradation resulting from intensified coastal development [4], and the negative impacts of ocean warming on seahorse behavior and feeding rates have further threatened their survival [5]. Consequently, under the combined impacts of marine environmental changes and prolonged overfishing, wild populations of H. trimaculatus have declined significantly. In 2004, China designated all wild seahorse species as Class II nationally protected animals, and in 2012, the International Union for Conservation of Nature (IUCN) classified H. trimaculatus as a vulnerable species [6]. The transport process refers to the movement of fish larvae from their spawning or hatching areas to other regions, often over significant distances, primarily driven by ocean currents [7,8]. This process is crucial for the distribution and survival of fish species, as it determines where the larvae settle, grow, and ultimately contribute to the adult population. Previous studies have typically analyzed factors such as connectivity and transport distances during the transport duration to gain a better understanding of larval dynamics [9,10,11]. The settlement process refers to the transitional phase in the life cycle of marine fish, during which larvae cease their movement, recognize suitable habitats through physical or biological cues, and descend or attach to appropriate substrates to initiate their juvenile or adult life [12,13]. Therefore, a comprehensive understanding of the transport and settlement processes of H. trimaculatus is essential for effective conservation and population restoration.
In natural marine habitats, H. trimaculatus primarily inhabits seagrass beds, mangrove forests, coral reefs, and other associated ecosystems within shallow nearshore waters [14]. Due to their weak swimming ability, they typically use their prehensile tails to grasp coral branches or seagrass leaves, thereby limiting their mobility [15]. However, under strong hydrodynamic conditions, they can achieve longer distance dispersal by attaching to floating debris (e.g., seagrass leaves), which facilitates genetic exchange among populations [16]. Their small size and effective camouflage, along with the limited availability of observational data, make it challenging to accurately determine their spatial distribution. To address this issue, researchers have predominantly utilized Species Distribution Models (SDMs) and genomic resequencing to evaluate their habitat and examine the relationship between habitat selection and environmental factors [1,17,18]. Studies indicate that long-distance dispersal and genetic connectivity among populations primarily depend on meteorological events and oceanic currents [16]. These findings have enhanced our understanding of seahorse biogeography and habitat suitability, providing valuable insights into their potential distribution across different regions. However, the mechanisms of transport and settlement of H. trimaculatus in the NSCS require further investigation.
The NSCS (Figure 1) is characterized by a complex coastal topography, an extensive continental shelf, and diverse coral reef ecosystems, providing an ideal habitat for marine fish [19]. In addition, the region exhibits a complex oceanic structure, particularly in terms of current systems. Driven by the East Asian monsoon, the ocean circulation system in this region exhibits pronounced seasonal variations, which strongly influence fish transport and settlement processes [20,21]. In summer, the prevailing southwest monsoon drives an anticyclonic circulation pattern, with the Guangdong Coastal Current (GCC) flowing northeastward through the Taiwan Strait into the ECS. In winter, under the influence of the prevailing northeast monsoon, the circulation shifts to a cyclonic pattern, with the GCC flowing southwestward, ultimately re-entering the NSCS [22,23]. Additionally, a persistent year-round South China Sea Warm Current (SCSWC) exists between the continental slope and shelf, flowing northeastward along the continental shelf, with a portion of it continuing through the Taiwan Strait [24]. Meanwhile, this region is also influenced by the Kuroshio intrusion, with its branches penetrating northwestward through the Luzon Strait, substantially affecting the thermohaline structure and regional energy balance by modifying temperature, salinity, and eddy dynamics [25,26]. Moreover, local hydrodynamic processes in the NSCS are essential for nutrient cycling and biological productivity. For instance, summer coastal upwelling near Shantou brings nutrient-rich deep water to the surface, enhancing primary productivity in marine ecosystems [27,28]. Similarly, the Pearl River plume improves environmental and hydrodynamic conditions, fostering the growth and reproduction of planktonic organisms [29]. Overall, the complex circulation structures in the NSCS play a critical role in regulating fish distribution and transport processes.
Understanding fish larval dynamics is essential for predicting the population structure of adults. However, the scarcity of observational data on H. trimaculatus larvae limits our understanding of their early-stage transport and settlement processes. To overcome this limitation, the Individual-Based Model (IBM) approach has demonstrated effectiveness in assessing larval transport and settlement processes [30,31,32]. The IBM represents each individual as an independent entity, integrating physical factors and biological behaviors. By parameterizing these variables, IBM simulates individual movement to explore spatiotemporal population dynamics, thereby enhancing the ecological realism of simulations [33]. Therefore, this study integrates the ocean model CROCO (Coastal and Regional Ocean COmmunity Model) with the biological model Ichthyop (Ichthyoplankton Individual-Based Model) to develop a coupled physical–biological model. The coupled model aims to investigate the transport and settlement processes of H. trimaculatus larvae in the NSCS under varying hydrodynamic conditions, offering critical insights for habitat conservation and sustainable fisheries management.
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Figure 1. Schematic diagram of ocean currents in the NSCS, redrawn based on Shu et al. [34]. GCC: Guangdong Coastal Current; SCSWC: South China Sea Warm Current; PRP: Pearl River plume.
Figure 1. Schematic diagram of ocean currents in the NSCS, redrawn based on Shu et al. [34]. GCC: Guangdong Coastal Current; SCSWC: South China Sea Warm Current; PRP: Pearl River plume.
Jmse 13 00900 g001
The remainder of this paper is organized as follows. Section 2 describes the Materials and Methods, including the study time period and regional division, as well as the configuration of the CROCO and Ichthyop models. Section 3 presents the simulation results of the ocean model and the larval distribution derived from the biological model. Section 4 is the discussion, offering a comprehensive discussion of larval transport and settlement processes from multiple perspectives, and provides conservation and management recommendations based on the simulation results. Section 5 summarizes the conclusions and provides future research directions.

2. Materials and Methods

2.1. The Study Time Period and Regional Division

Based on the annual observational data of H. trimaculatus recorded in the NSCS, published by iSeahorse (Figure 2a), this study focuses on the period from 2016 to 2018 (January 1 to December 31 of each year), as these years exhibited the highest frequency of recorded observations. As a result, selecting these years enabled a more comprehensive and robust analysis of larval transport and settlement processes. Additionally, since August and September correspond to the peak spawning period of H. trimaculatus [35], newly hatched larvae during this period are highly susceptible to transport by ocean currents. As the seahorses grow, they become less influenced by currents and tend to remain within a limited range of movement. Therefore, this study selects August and September of 2016–2018 as the simulation period to ensure alignment with the species’ reproductive cycle and larval dispersal dynamics, thereby accurately capturing the larval transport and settlement processes.
The regional division is according to administrative boundaries (provinces and cities) and the distribution of larvae (Figure 2b) and their habitats, including seagrass beds and coral reefs [36]. Following the findings of Camins et al. [37], the 50 m isobath is incorporated as the outer boundary, representing the maximum observed activity depth of H. trimaculatus. Based on this framework, this study divides the coastal waters of the NSCS into eight regions (Figure 3). Among these, regions 1–5 and 8 are designated as released regions (spawning grounds) based on the high density of recorded observational data.

2.2. Configuration of the Ocean Model CROCO

CROCO, which is built upon the Regional Ocean Modeling System (ROMS) and the Shallow Non-Hydrostatic (SNH) model, has been widely employed in hydrodynamic studies of the NSCS [38,39,40].
As shown in Figure 1, the study area encompasses the NSCS and part of the ECS, spanning 105–125° E and 15–28° N. The model configuration incorporates a closed boundary on the western side, while the eastern, southern, and northern boundaries remain open to facilitate oceanic exchange and apply boundary forcing. The horizontal resolution is set to 1/12°, and the vertical coordinate system utilizes a sigma-coordinate system with 32 vertical layers. To improve the simulation accuracy in shallow waters, the surface stretching coefficient is set to 5.0, and the bottom stretching coefficient is set to 1.2. The bathymetric data are derived from the ETOPO2 global terrain and seabed topography dataset, provided by the U.S. National Centers for Environmental Information (NCEI), which are available at https://sos.noaa.gov (accessed on 12 September 2024), with a spatial resolution of 2 arcminutes. The initial and boundary conditions for three-dimensional temperature, salinity, velocity fields, and sea surface height are obtained from the Nucleus for European Modelling of the Ocean (NEMO) reanalysis dataset, with daily-averaged outputs at a horizontal resolution of 1/12°. The atmospheric forcing data are sourced from the Fifth-Generation ECMWF Reanalysis (ERA5), available at https://cds.climate.copernicus.eu (accessed on 19 September 2024), with a temporal resolution of 6 h and a horizontal resolution of 0.25° × 0.25°. Tidal forcing is incorporated at the model boundaries based on 10 major tidal constituents (M2, S2, N2, K2, K1, O1, P1, Q1, M4, and MS4), with tidal data sourced from the TPXO9 global tidal model at a horizontal resolution of 1/30° × 1/30°, available at https://www.tpxo.net/global (accessed on 25 September 2024).
The CROCO model simulation is conducted for the period 2013–2018, with 2013–2015 designated as a spin-up period to ensure model stability and equilibrium. To drive the Ichthyop model, the 2016–2018 simulation results, including three-dimensional temperature, salinity, and current velocity, serve as forcing inputs, with a 6 h output time step.

2.3. Configuration of the Biological Model Ichthyop

Ichthyopis a Lagrangian particle-tracking Individual-Based Model (IBM) designed to simulate the dispersal, movement dynamics, and environmental interactions of fish larvae. More information is available at https://ichthyop.org (accessed on 2 December 2023).
H. trimaculatus has a peak spawning season from August to September and typically spawns between 06:30 and 08:30 AM, with each brood producing approximately 400 larvae [35]. Consequently, the model is initialized at 07:30 AM on August 1 and runs until 7:30 PM on September 30. Newly hatched small seahorses generally settle within 14–18 days, while larger species require 4–6 weeks. This information is available at https://projectseahorse.org (accessed on 5 January 2025). Since H. trimaculatus exhibits an intermediate size range, the transport duration is set to 30 days to reflect its expected settlement period. For H. trimaculatus, key habitats such as mangrove forests, seagrass beds, and coral reefs are predominantly distributed along isobaths of 5–25 m [36]. In this study, larvae that arrive at these isobath ranges within a 30-day period are considered to have successfully settled. Upon settlement, they cease movement and are no longer involved in subsequent simulation processes. To maintain numerical stability and accuracy, the computational time step is set to 600 s to satisfy the Courant–Friedrichs–Lewy (CFL) condition [41], and the output time step is set to 6 h.
In addition to the basic parameter settings mentioned above, the model incorporates three key advanced modules: transport, release, and biological behavior.
The transport module defines larval movement patterns, transport mechanisms, and turbulent diffusion processes, ensuring that larval trajectories realistically reflect both physical forcing and biological responses. The advection process is solved using the fourth-order Runge–Kutta method [42], ensuring high numerical accuracy and minimizing truncation errors, while turbulent dissipation follows the model’s built-in scheme. Empirical studies indicate that H. trimaculatus larvae exhibit Diel Vertical Migration (DVM), ascending to surface waters at night for feeding and descending to deeper layers during the day to reduce predation risk [43]. Accordingly, the model assigns a DVM range of 0–50 m, with larvae descending to deeper waters during the day (07:30 AM–7:30 PM) and ascending to shallower waters at night (7:30 PM–07:30 AM).
The release module defines larval release timing, quantity, and spawning locations. Spawning grounds 1–5 and 8 are designated as release regions, with release events scheduled at 07:30 AM on July 1 and August 1. A total of 4000 larvae are released per region, ensuring a uniform initial distribution.
The biological behavior module defines larval buoyancy, swimming ability, and environmental adaptability to accurately simulate their behavior in the marine environment. Given that newly hatched larvae exhibit positive buoyancy, a round shape, and dimensions of approximately 7 mm in length and 1 mg in weight [43], a new buoyancy formula (Equation (1)) is implemented, replacing Ichthyop’s default formulation originally designed for elliptical fish eggs.
V B = d 2 × g × ρ s − ρ f 18 μ
In the equation, V B represents the buoyancy velocity (cm/s), g is the gravitational acceleration (cm/s2), and d denotes the egg diameter (cm). ρ s and ρ f represent the densities of seawater and larvae (g/cm3), respectively, while μ denotes the dynamic viscosity of water (g/cm/s). In the model, d is set to 0.7 cm, ρ s is set to 0.12 g/cm3, and g is assigned a value of 978 cm/s2. The seawater density and dynamic viscosity are dynamically adjusted based on temperature and depth variations.
Therefore, the motion equations for H. trimaculatus larvae in the model are formulated as follows (Equations (2) and (3)):
d X p → d t x , y , z = V P → x , y , z
V P → x , y , z = V S → x , y , z + V T → x , y + V B → z + V D V M → z
where X p → and V P → represent the position and velocity of the larvae, respectively. V S → denotes the current velocity from the ocean model, while V T → represents the velocity induced by horizontal turbulent dissipation. V B → and V D V M → correspond to the vertical buoyancy velocity and DVM velocity, respectively.
In summary, the parameter settings and corresponding descriptions in Ichthyop are presented in Table 1.

3. Results

3.1. Simulation of the Ocean Model CROCO

3.1.1. Current Field

Accurate simulation of the current field is essential for modeling and analyzing key processes in the biological model, including larval movement, migration, and population dynamics. As shown in Figure 4, the simulated monthly mean current field, averaged over the upper 100 m, effectively reproduces the major circulation features and accurately captures the mesoscale eddy structures within the region.
To quantify the accuracy of the simulation, NEMO monthly reanalysis data are used as a reference to evaluate the Root Mean Square Error (RMSE) of the simulated current field. The NEMO reanalysis dataset has been extensively validated against observational records and is widely recognized for its reliability [44]. The RMSE calculation formula is given as follows (Equation (4)):
R M S E = 1 n ∑ i = 1 n y N − y S 2
where, y N represents the NEMO data, y S denotes the simulated data, and n is the total number of data points. The grid size in the study area is 240 × 156, resulting in a total of 37,440 data points.
Figure 5 presents the RMSE results, indicating that the maximum RMSE between the simulated and NEMO reanalysis current velocities is 0.138 m/s. This validates the accuracy of the simulation and ensures a robust current field background for the biological model.

3.1.2. Temperature Field

Variations in seawater temperature substantially affect physical properties such as water density, molecular viscosity, buoyancy, and turbulent diffusion [45], thereby influencing larval dispersal rates and movement trajectories. Thus, precise temperature field data are vital for biological modeling.
The simulated three-dimensional profiles of the monthly mean seawater temperature in the upper 100 m are presented in Figure 6 for a portion of the study area (116–119° E, 20–22° N), which includes the upwelling region near Shantou and the area influenced by the Kuroshio intrusion.
The simulated results indicate that in August (Figure 6a,c,e), the sea surface temperature (SST) mostly exceeds 30 °C and is significantly higher than in September (Figure 6b,d,f), demonstrating a clear seasonal climate influence. During summer (August), intense solar radiation raises SST, while in autumn (September), cold air masses induce cooling, illustrating a characteristic seasonal temperature transition [46,47]. Therefore, these simulation results effectively capture the seasonal variation in upper-layer seawater temperature, providing a reliable temperature field background for the biological model.

3.1.3. Salinity Field

To evaluate the model performance for salinity, we compared the simulation results with observations from Argo buoys, available at https://argo.ucsd.edu/ (accessed on 23 July 2024). The selected buoy locations are closest to the model grid points, and their observation times fall within each simulated month. As shown in Figure 7, the comparison reveals a high level of agreement between the simulated and observed salinity profiles throughout the 0–1000 m water column.
Since seahorses primarily inhabit the upper ocean layers, we conducted a further evaluation of salinity in the 0–100 m depth range. The results, summarized in Table 2, show that the average salinity in this layer remained between 33 and 35 psu throughout the simulation period. The RMSE between the simulated salinity and Argo buoy observations does not exceed 0.8 psu, and the correlation coefficients are all above 99.5%. These results indicate that the model exhibits strong performance in simulating salinity in the upper ocean.

3.2. Simulation of the Biological Model Ichthyop

Figure 8 shows the larval spatial distribution at the end of the simulation, based on output from the biological model Ichthyop. The distribution pattern closely resembles the structure of the current field across different time periods (Figure 4), indicating a strong relationship between larval distribution and ocean circulation. Under the continuous influence of the coastal current and the SCSWC, larvae exhibit a distinct aggregation pattern along the coastline throughout the simulation periods, with significantly higher concentrations observed in the coastal waters of the Gulf of Tonkin, the Pearl River Estuary, Shantou, Xiamen, and the western coast of Taiwan compared to other regions.
In addition to coastal aggregation, a notable accumulation of larvae is also observed around the Paracel Islands. In August (Figure 8a,c,e), most larvae in this region originate from the southern waters of Hainan. However, in September (Figure 8b,d,f), although Hainan remains a major source, a portion of the larvae are transported westward from the southern waters of Taiwan, driven by the influence of the Kuroshio intrusion.
Furthermore, in August (Figure 8a,c,e), larvae are primarily concentrated on the continental shelf, with some dispersing through the Luzon Strait into the Pacific Ocean. In September (Figure 8b,d,f), larvae are distributed both on and beyond the continental shelf, but no cross-strait dispersal through the Luzon Strait is observed. Additionally, the current velocity during August (Figure 4a,c,e) is higher than that in September (Figure 4b,d,f), which facilitates larval transport. As a result, the number of larvae crossing the Taiwan Strait into the ECS is greater in August compared to September. These results demonstrate significant differences in the spatial distribution of larvae between August and September.

4. Discussion

This section discusses the larval transport and settlement processes by analyzing connectivity, transport distance, and settlement success rate based on the simulation results. Additionally, it presents conservation and management recommendations from multiple perspectives.

4.1. Connectivity

Figure 9 illustrates the overall distribution of larvae in the study area. However, the specific distribution of larvae from each release region remains unclear. To better understand the transport processes of larvae from different release region, this study introduces the concept of larval connectivity between regions, which measures the degree of larval exchange and retention between different regions. The connectivity calculation formula is provided in Equation (5):
P i j = n i j N i × 100 %
where P i j represents the larval connectivity between regions, N i denotes the total number of larvae from the released region i , and n i j represents the number of larvae from region i that are retained in region j at the end of the simulation. The release regions i are defined as 1, 2, 3, 4, 5, and 8, while the destination regions j can be 1, 2, 3, …, 8.
As shown in Figure 8, larval connectivity exhibits significant variations across different regions. Notably, larvae released from region 1 (eastern Gulf of Tonkin) and region 8 (west of Taiwan) consistently show the highest local connectivity compared to other regions. This indicates that a substantial proportion of larvae originating from these regions tend to remain within or return to their source areas, suggesting that the larvae exhibit strong local retention and limited dispersal capacity in these regions. For region 1, this is primarily attributed to the semi-enclosed nature of the Gulf of Tonkin (Figure 1), which restricts water exchange with the open ocean [48], thereby promoting larval retention. Furthermore, the frequent occurrence of small-scale anticyclonic eddies within the gulf (Figure 4) further contributes to larval retention by trapping them within localized circulation systems, restricting their dispersal to nearby waters. This also explains the coastal aggregation observed in region 1 (Figure 4). For region 8, the Kuroshio branch induces a strong recirculation effect, forming retention zones that inhibit offshore larval dispersal. Meanwhile, the eastward SCSWC further inhibits this dispersal, thereby enhancing larval retention.
Similarly, larvae released from regions (2–5) also exhibit relatively higher levels of local connectivity than inter-regional connectivity, although not as pronounced as in regions 1 and 8. This can be attributed to several factors. First, a portion of the larvae are initially released near the coastline, facilitating their rapid settlement in suitable habitats. Second, the influence of the northeastward current gradually transports larvae toward the coastline, further strengthening their retention.
In addition, with few exceptions, the larval connectivity between release regions (2–5) and destination region 7 is generally higher in August than in September of the same year. This difference can be attributed to the stronger SCSWC and faster coastal current in August, which facilitate larval transport to region 7. In September, the weakening currents result in reduced connectivity between release and destination regions.

4.2. Transport Distance

The statistical analysis of larval transport distances during August and September is presented in Figure 8. The results indicate that most larvae are concentrated within short-distance (0–300 km) and mid-short distance (300–600 km) transport ranges, suggesting that the majority of larvae remain near their release regions. These findings explain the relatively high local connectivity of larvae compared to inter-regional connectivity.
Larvae released from region 1 consistently exhibit short-distance transport in both August and September, with over 80% remaining within this range. This phenomenon is closely associated with the semi-enclosed structure of the Gulf of Tonkin, as discussed previously.
Larvae released from regions 2, 3, and 4 exhibit a lower proportion of short-distance and mid-short distance transport in August (Figure 10a) than in September (Figure 10b). Specifically, short-distance transport increases by 3.7%, 4.4%, and 7.3%, while mid-short distance transport increases by 4.8%, 6.9%, and 1.9%, respectively. In contrast, the proportion of larvae undergoing mid-long (600–900 km) and long distance (900–1200 km) transport decreases, with reductions of 1.4%, 7%, and 5.1% for mid-long distances, and 1.7%, 3.0%, and 2.4% for long distances, respectively. This shift is primarily driven by monthly variations in current velocities, which affect the efficiency of larval transport.
Larvae released from region 5 exhibit a significantly higher proportion of mid-long distances transport in both months compared to those released from other regions, indicating that local hydrodynamic conditions promote extended dispersal.
Larvae released from region 8 show a greater proportion of short-distance transport in August (52.1%) compared to September (40.8%). In contrast, the proportions of larvae undergoing mid-short distance (35.6%) and mid-long distance (12.3%) transport are lower in August than in September (39.6% and 19.6%, respectively). This shift can be attributed to the intensification of the Kuroshio intrusion in September, which strengthens oceanic transport dynamics, facilitating the dispersal of larvae over longer distances.
Additionally, only a small fraction of larvae released from regions 2, 3, and 4 achieve long-distance and extreme long-distance (greater than 1200 km) transport. This is primarily because larvae released from other regions (5, 8) reach the eastern boundary of the model domain before the end of the simulation.

4.3. Settlement Success Rate

Starting from the third day of the simulation, larvae located within the 5 m to 25 m isobaths are considered successfully settled, as mentioned in Section 2.3. The three-day delay is intended to exclude cases in which larvae settle immediately upon release. Accordingly, the formula for calculating the larval settlement success rate is presented in Equation (6):
P s = P 5 − 25 m P a l l × 100 %
where P s represents the larval settlement success rate, P a l l denotes the total number of released larvae (24,000), and P 5 − 25 m represents the number of larvae located within the 5–25 m isobath during the simulation.
Figure 11a,b illustrate the larval settlement success rates in August and September. The results indicate that settlement success rates are consistently higher in August than in September. In August (Figure 11a), the highest rate occurs in 2017 (31.6%), while the lowest happens in 2016 (23.1%). In September (Figure 11b), the highest rate is 19.8% in 2016, while the lowest is 14.6% in 2017. These variations are influenced not only by the previously discussed ocean dynamic processes, but also by the impact of typhoon activity.
The South China Sea is regularly affected by typhoons, which generate strong winds and oceanic disturbances. These events disrupt circulation patterns, enhance vertical mixing, and induce upwelling [49], consequently affecting larval transport and settlement. During August and September of 2016–2018, a total of five major typhoons affected the study region (Figure 12). These include Typhoon Meranti (20160913–20160915) and Typhoon Megi (20160926–20160929) in 2016, Typhoon Hato (20170821–20170824) and Typhoon Doksuri (20170911–20170915) in 2017, and Typhoon Mangkhut (20180914–20180917) in 2018. The best-track data for these typhoons are obtained from the Tropical Cyclone Data Center of the China Meteorological Administration [50].
The larval settlement success rates experience a marked increase within one week after typhoon events (Figure 11a,b). Among these, Typhoon Hato in 2017 has the most pronounced impact, elevating the settlement success rate from 19.4% to 31.6%, representing a 12.2% increase. In September 2016, Typhoons Meranti and Megi also contribute to a notable rise in settlement success rate, increasing from 10.1% to 16.4% and from 18.2% to 19.8%, respectively. Similarly, after Typhoon Mangkhut in September 2018, the settlement success rate increases from 9.8% to 14.1%, demonstrating a discernible increasing trend. In September 2017, Typhoon Doksuri has a weaker intensity and a more localized impact, resulting in the lowest settlement success rate of the year at 14.6%.
Furthermore, typhoon activity also influences the spatial distribution of larvae. In September 2016, under the impact of Typhoons Meranti and Megi, larvae exhibited the highest aggregation in region 6 (Figure 8b), accompanied by an increased connectivity between release regions and region 6 compared to other periods (Figure 9b). In August 2017, following Typhoon Hato, larval aggregation is more concentrated near region 4 (Figure 8c), corresponding to a notable rise in connectivity between release regions and region 4 (Figure 9c). Similarly, in September 2017, under the influence of Typhoon Doksuri, larvae predominantly accumulate in region 2 (Figure 8d), with a corresponding increase in connectivity between release regions and region 2 (Figure 9d). In 2018, following Typhoon Mangkhut, larval aggregation is pronounced in regions 3 and 4 (Figure 8f), with connectivity between release regions and these two regions also showing a significant increase (Figure 9f).
The above results indicate that typhoon events not only enhance larval settlement success but also broaden larval spatial distribution along typhoon pathways and strengthen inter-regional connectivity. These findings underscore the critical role of typhoons in influencing larval transport and settlement processes.

4.4. Conservation and Management Recommendations

A flagship species is a species chosen to represent an environmental cause, issue, or ecosystem, usually because of its charisma, cultural significance, or endangered status [51]. These species help raise awareness and funding for broader conservation efforts. Seahorses are considered as a flagship species for marine conservation due to their unique appearance, ecological sensitivity, and the public interest they generate [52]. Promoting their protection can help safeguard broader coastal ecosystems such as seagrass beds, mangrove forests, and coral reefs. In practice, one of the most widely used strategies for implementing such targeted protection is the establishment of Marine Protected Areas (MPAs) [53].
For example, in Guangdong Province, China, the Jieshi Bay Nature Reserve was established to protect species such as H. trimaculatus and H. mohnikei, while the Yingluo Bay Marine Ecological Reserve focuses on conserving seahorses and the Chinese white dolphin (Sousa chinensis). However, existing studies have shown that seahorse populations covered by current MPAs were mainly located in the lower-protection areas (i.e., multiple-use zones), flagging the effectiveness uncertainty of existing MPA networks. Therefore, based on the simulated results of larval spatial distribution (Figure 8), it is recommended to prioritize the proposal of establishing MPAs in the coastal waters of the Gulf of Tonkin, the Pearl River Estuary, Shantou, Xiamen, and the western coast of Taiwan, in order to improve the precision and effectiveness of seahorse conservation efforts.
Seahorse bycatch and the use of bottom-contact fishing gear are also contributing to the decline of seahorse populations [54]. Therefore, during the peak spawning season in August and September, seahorse bycatch should be strictly prohibited, particularly in fisheries employing bottom trawls and rake nets. Such seasonal restrictions would allow seahorses to fully grow and avoid disruption to their settlement process in suitable habitats.
Additionally, our study results indicate that typhoon activities have a positive effect on seahorse settlement. However, this finding does not account for the potential damage to their habitats caused by typhoon activities. Some studies have shown that typhoons can destroy marine ecosystems, such as coral reefs, leading to a decline in fish populations and overall ecosystem health [55]. Therefore, restoration and rehabilitation efforts in seahorse habitats following typhoon events are also important.

5. Conclusions and Future Directions

This study developed a coupled physical–biological model for H. trimaculatus larvae, based on the ocean model CROCO and the biological model Ichthyop. The coupled model was applied to investigate the transport and settlement processes of H. trimaculatus in the NSCS from 2016 to 2018. The results indicate that larval transport and settlement are primarily controlled by the combined effects of monsoon circulation, coastal upwelling, the SCSWC, and Kuroshio intrusion, exhibiting strong aggregation along China’s coastal waters and around the Paracel Islands.
During the transport process, larvae demonstrate high local connectivity and a significant proportion of short-distance transport, with the Gulf of Tonkin and the western coast of Taiwan showing the most pronounced effects, suggesting that populations in these areas experience strong self-recruitment and limited dispersal. Additionally, larvae exhibit higher inter-regional connectivity and further transport distances in August than in September, which may facilitate gene flow and population interaction across regions. During the settlement process, the settlement success rate of larvae in August is significantly higher than in September, showing a distinct monthly variation. Meanwhile, typhoon activity causes larvae to aggregate near the typhoon’s landfall, enhancing their chances of finding suitable habitats for settlement, thereby increasing their settlement success rate.
Future research will explore the influence of other marine environmental factors, such as temperature, pH, and salinity, on larval transport and settlement. Meanwhile, long-term simulations are also essential for assessing population variation in response to climate change and extreme weather events.

Author Contributions

Conceptualization, C.Z.; methodology, C.Z. and Z.D.; writing—original draft preparation, C.Z.; writing—review and editing, Z.D. 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 42176020; the National Key Research and Development Program of China, grant number 2022YFC3105002; the open Fund of the State Key Laboratory of Target Vulnerability Assessment, grant number, YSX2024KFYS001.

Data Availability Statement

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

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 2. Temporal occurrences (a) and spatial distribution (b) of H. trimaculatus in the NSCS during the period from 1 January 2016 to 31 December 2018, based on data published by iSeahorse, which are available at https://www.gbif.org/ (accessed on 15 January 2025). In panel (b), yellow dots represent the observation locations of H. trimaculatus.
Figure 2. Temporal occurrences (a) and spatial distribution (b) of H. trimaculatus in the NSCS during the period from 1 January 2016 to 31 December 2018, based on data published by iSeahorse, which are available at https://www.gbif.org/ (accessed on 15 January 2025). In panel (b), yellow dots represent the observation locations of H. trimaculatus.
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Figure 3. Map of regional division, coast water of 1: east of the Gulf of Tonkin; 2: east of Hainan; 3: from Zhanjiang to Yangjiang; 4: near the Pearl River Estuary; 5: from Shanwei to Shantou; 6: from Shantou to Fuzhou; 7: north of Fuzhou; and 8: west of Taiwan, based on the classification proposed by Zheng et al. [36].
Figure 3. Map of regional division, coast water of 1: east of the Gulf of Tonkin; 2: east of Hainan; 3: from Zhanjiang to Yangjiang; 4: near the Pearl River Estuary; 5: from Shanwei to Shantou; 6: from Shantou to Fuzhou; 7: north of Fuzhou; and 8: west of Taiwan, based on the classification proposed by Zheng et al. [36].
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Figure 4. Simulated monthly mean current fields averaged over the upper 100 m. Panels (a,c,e) represent the results for August from 2016 to 2018, while panels (b,d,f) show the corresponding results for September from 2016 to 2018.
Figure 4. Simulated monthly mean current fields averaged over the upper 100 m. Panels (a,c,e) represent the results for August from 2016 to 2018, while panels (b,d,f) show the corresponding results for September from 2016 to 2018.
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Figure 5. The RMSE between the simulated data and the NEMO reanalysis data for different months.
Figure 5. The RMSE between the simulated data and the NEMO reanalysis data for different months.
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Figure 6. Simulated monthly mean temperature fields. Panels (a,c,e) represent the results for August from 2016 to 2018, while panels (b,d,f) show the corresponding results for September from 2016 to 2018.
Figure 6. Simulated monthly mean temperature fields. Panels (a,c,e) represent the results for August from 2016 to 2018, while panels (b,d,f) show the corresponding results for September from 2016 to 2018.
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Figure 7. The vertical salinity profiles between the CROCO simulations and Argo buoy observations. Panels (a–c) represent the results for August from 2016 to 2018, while panels (d–f) show the corresponding results for September from 2016 to 2018.
Figure 7. The vertical salinity profiles between the CROCO simulations and Argo buoy observations. Panels (a–c) represent the results for August from 2016 to 2018, while panels (d–f) show the corresponding results for September from 2016 to 2018.
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Figure 8. Larval spatial distribution. Panels (a,c,e) represent the results for August from 2016 to 2018, while panels (b,d,f) show the corresponding results for September from 2016 to 2018.
Figure 8. Larval spatial distribution. Panels (a,c,e) represent the results for August from 2016 to 2018, while panels (b,d,f) show the corresponding results for September from 2016 to 2018.
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Figure 9. Larval connectivity between release and destination regions. Panels (a,c,e) represent the results for August from 2016 to 2018, while panels (b,d,f) show the corresponding results for September from 2016 to 2018.
Figure 9. Larval connectivity between release and destination regions. Panels (a,c,e) represent the results for August from 2016 to 2018, while panels (b,d,f) show the corresponding results for September from 2016 to 2018.
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Figure 10. Larval transport distance in different months. Panel (a) represents the average results for August across three years, while panel (b) shows the result for September.
Figure 10. Larval transport distance in different months. Panel (a) represents the average results for August across three years, while panel (b) shows the result for September.
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Figure 11. Larval settlement success rates and corresponding typhoon events. Panels (a,b) show larval settlement success rates in August and September, respectively.
Figure 11. Larval settlement success rates and corresponding typhoon events. Panels (a,b) show larval settlement success rates in August and September, respectively.
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Figure 12. The best-track typhoon path map.
Figure 12. The best-track typhoon path map.
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Table 1. Ichthyop model parameter settings and corresponding descriptions.
Table 1. Ichthyop model parameter settings and corresponding descriptions.
VariableValue and Descriptions
Start and end timeAugust 1 and September 30 (2016–2018)
Transport duration30 days, based on the body size
Computational time step600 s, to satisfy the CFL condition
Recording frequency6 h
Numerical scheme of advection processRunge–Kutta 4, reducing errors
Scheme of turbulent dissipation1 × 10−9 m2/s3
Release depthUniform release at depths of 0–50 m
DVM behavior07:30–19:30 deeper; 19:30–07:30 shallower
Release regionsRegions 1–5 and 8, as shown in Figure 2
Release timeAugust 1 and September 1, 7:30 AM
Release number4000/release region
Buoyancy V B , as shown in Equation (1)
Table 2. Evaluation metrics for salinity (psu) in the 0–100 m layer: comparison between CROCO simulations and Argo buoy observations, including mean value (MEAN), standard deviation (STD), RMSE, and correlation coefficient (CC).
Table 2. Evaluation metrics for salinity (psu) in the 0–100 m layer: comparison between CROCO simulations and Argo buoy observations, including mean value (MEAN), standard deviation (STD), RMSE, and correlation coefficient (CC).
DateMeanSTDRMSECCLocation
ArgoCROCOArgoCROCOLatitude
(° N)		Longitude
(° E)
2016081334.734.60.10.20.399.8%21.272112.113
2016091134.134.00.20.20.499.9%16.834117.274
2017080933.833.90.40.30.899.6%19.064119.518
2017091834.434.30.30.30.599.8%19.201123.839
2018081034.334.10.30.20.399.7%17.827122.738
2018090533.533.10.40.50.999.6%16.932119.568
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Zhang, C.; Deng, Z. Numerical Study on the Transport and Settlement of Larval Hippocampus trimaculatus in the Northern South China Sea. J. Mar. Sci. Eng. 2025, 13, 900. https://doi.org/10.3390/jmse13050900

AMA Style

Zhang C, Deng Z. Numerical Study on the Transport and Settlement of Larval Hippocampus trimaculatus in the Northern South China Sea. Journal of Marine Science and Engineering. 2025; 13(5):900. https://doi.org/10.3390/jmse13050900

Chicago/Turabian Style

Zhang, Chi, and Zengan Deng. 2025. "Numerical Study on the Transport and Settlement of Larval Hippocampus trimaculatus in the Northern South China Sea" Journal of Marine Science and Engineering 13, no. 5: 900. https://doi.org/10.3390/jmse13050900

APA Style

Zhang, C., & Deng, Z. (2025). Numerical Study on the Transport and Settlement of Larval Hippocampus trimaculatus in the Northern South China Sea. Journal of Marine Science and Engineering, 13(5), 900. https://doi.org/10.3390/jmse13050900

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