Genetic Structure of the Endemic Fiddler Crab Uca (Xeruca) formosensis on the West Coast of Taiwan
Abstract
Xeruca formosensis, an endemic and endangered fiddler crab confined to the intertidal zones of western Taiwan, persists in a fragmented coastal landscape with minimal adult dispersal ability. Despite these constraints, the species exhibits unexpectedly high genetic diversity and gene flow across its range. To uncover the mechanisms supporting this connectivity, we integrated genome-wide SNP data with hydrodynamic models simulating larval dispersal. Genetic analyses identified several loci under selection, along with a pattern of strong self-recruitment, high connectivity, and limited spatial genetic structure. Simulated larval trajectories closely followed the dominant south-to-north current system in the Taiwan Strait, suggesting ocean circulation plays a key role in shaping gene flow. Importantly, when typhoon events were included in the dispersal models, results showed that storm-driven flows significantly increased larval transport distances. These rare but ecologically important events created episodic opportunities for long-distance dispersal, allowing gene flow between otherwise isolated populations. Such typhoon-mediated dispersal appears to buffer the effects of habitat fragmentation and supports genetic cohesion at the regional scale. However, future changes in climate may alter typhoon frequency, intensity, and seasonal timing. A reduction in these dispersal opportunities could lead to increased genetic isolation and diminished connectivity among populations. Our findings highlight the ecological importance of extreme weather events in sustaining genetic exchange for coastal species with pelagic larvae. Incorporating stochastic physical processes like typhoon-induced dispersal into marine conservation strategies is critical for maintaining population connectivity and resilience. These insights are particularly relevant for the design of marine protected areas and for anticipating how climate-driven changes in oceanographic conditions may influence the long-term persistence of species restricted to dynamic coastal environments.
Methodology and Implementation Steps
Samples of Xeruca formosensis were collected from eight representative coastal wetland sites along the west coast of Taiwan, spanning the latitudinal range of the species from north to south as follows: Tamsui River, New Taipei City (TS: 25.172707, 121.406110), Xiangshan, Hsinchu City (XS: 24.786325, 120.909901), Gaomei, Taichung City (GM: 24.309685, 120.546957), Shengang, Changhua County (SG: 24.167326, 120.460932), Mailiao, Yunlin County (ML: 23.837211, 120.224266), Bazhang Estuary, Chiayi County (BZ: 23.326654, 120.115387), Qigu Zengwen Estuary, Tainan City (QG: 23.055399, 120.061944), and Gaoping Estuary, Kaohsiung City (GP: 22.478405, 120.415706). Sampling was conducted during low tide within three days before and after the spring tide in order to maximize accessibility to intertidal habitats and to standardize larval recruitment conditions among sites. At each locality, approximately 10 to 15 adult individuals were randomly captured by hand or small dip nets and temporarily stored in aerated containers. All specimens were transported to the laboratory on the same day to minimize stress, and a single walking leg from each individual was excised for DNA extraction before the crab was released back to its original capture location. Genomic DNA was isolated using a standard protocol and quantified for quality and concentration. We employed a double-digest RAD-sequencing (ddRAD) approach to obtain genome-wide SNPs. Genomic DNA was digested with selected restriction enzymes to generate sticky-ended fragments suitable for adaptor ligation. P1 and P2 adaptors incorporated individual or population-specific barcode sequences, Illumina PCR primer sites, and a protector sequence to avoid re-digestion. Enzyme choice and ligation conditions followed previously established protocols but were optimized for X. formosensis based on pilot tests. Sequencing was performed on an Illumina platform, and raw reads were demultiplexed by barcode using the process_radtags function in Stacks. After quality filtering, samples with excessive missing data or extreme divergence were excluded. High-quality loci were then assembled de novo and genotyped using ustacks, cstacks, gstacks, and sstacks modules within the Stacks pipeline. To examine larval dispersal, we used the Connectivity Modeling System (CMS), which is based on a Lagrangian particle-tracking framework and integrates a hydrodynamic model, an Individual-Based Model (IBM), and 11 sub-modules describing larval–ocean interactions. Oceanographic data were obtained from the Hybrid Coordinate Ocean Model (HYCOM) via the OPeNDAP data format, which provides a horizontal resolution of approximately 4 × 4 km for the domain spanning 116–122°E and 21–26°N. Coastal habitat grids delineating each sampling site were derived from the official coastal zone boundary dataset released by the National Land Administration of Taiwan on 8 April 2022. The IBM was parameterized according to larval traits, spawning location and timing, survival rates, vertical behavior, and pelagic larval duration documented for X. formosensis and closely related species. These parameters determined larval release schedules, vertical swimming responses, and settlement competency in the model, enabling realistic simulation of both passive and active dispersal processes
Innovation and Cross-Disciplinary Collaboration
This study presents an innovative cross-disciplinary framework that combines long-term oceanographic modeling with high-resolution molecular approaches to elucidate the mechanisms driving genetic connectivity in X. formosensis. We first used a 10-year hydrodynamic model of Taiwan Strait to simulate larval dispersal trajectories and settlement probabilities across fragmented coastal habitats. These dispersal simulations were then integrated with population genomic data from adult fiddler crabs to examine how larval transport processes influence observed gene flow and population structure at a fine spatial scale. Building on this baseline, we further assessed the effects of typhoon events by incorporating storm-driven hydrodynamic disturbances into the larval dispersal model. This integrated approach, linking physical oceanography, molecular ecology, and climate science, provides novel insights into how long-term ocean currents and episodic extreme weather events interact to shape genetic diversity and connectivity. Our findings also offer practical guidance for conserving coastal species under increasing climate variability.
Expected Results and Contributions
Our results showed thatX. formosensis is capable of maintaining genetic connectivity among geographically separated populations through the exchange of a limited number of migrants (Fig 2). However, this connectivity is inherently fragile and highly susceptible to disruption. Simulation outcomes indicate that while frequent typhoon events can severely damage or eliminate adult intertidal habitats, they also enhance larval dispersal by extending zoeal drift distances (Fig 3). This increased dispersal provides larvae with a greater opportunity to maintain gene flow among populations, suggesting that typhoons can simultaneously act as both ecological disturbances and mechanisms for connectivity (Fig 4). Under projected climate change scenarios, which anticipate a reduction in typhoon frequency alongside increasing storm intensity, the conditions that facilitate larval dispersal may become less frequent. When combined with ongoing habitat loss from coastal development, these environmental changes are likely to constrain gene exchange and increase the risk of population isolation. Once genetic connectivity is compromised, the endemic X. formosensis may face rapid demographic decline and an elevated risk of extinction. Despite these insights, the ecology and behavior of the larval stage remain poorly understood, highlighting the need to clarify dispersal mechanisms to better interpret adult genetic structure and forecast population resilience.
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