# Larval dispersal dynamics of Maja squinado in the Northwestern Mediterranean: a biophysical modeling approach

C. Barrier, Thomas Beneteau, M.-C. Raffalli, N. Barrier, Christophe Lett, Vanina Pasqualini, Eric D.H. Durieux

► To cite this version:

C. Barrier, Thomas Beneteau, M.-C. Raffalli, N. Barrier, Christophe Lett, et al., Larval dispersal dynamics of Maja squinado in the Northwestern Mediterranean: a biophysical modeling approach. Estuarine, Coastal and Shelf Science, 2025, 316, pp.109183. 10.1016/j.ecss.2025.109183. hal-05084245

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# Larval dispersal dynamics of *Maja squinado* in the Northwestern Mediterranean: a biophysical modeling approach

C. Barrier<sup>a, *, †</sup>, T. Beneteau<sup>a</sup>, M.-C. Raffalli<sup>a, b †</sup>, N. Barrier<sup>c †</sup>, C. Lett<sup>c †</sup>, V. Pasqualini<sup>a, b</sup>, E.D.H. Durieux<sup>a, b, **</sup>

<figure><img src="image_3.png" /></figure>

## ARTICLE INFO

**Keywords:**

*Maja squinado*

Larval dispersal

Mediterranean sea

Biophysical models

Regional connectivity

## ABSTRACT

The Mediterranean spinous spider crab, *Maja squinado* (Herbst, 1788), is a prized crustacean exploited by Mediterranean coastal fisheries, contributing to local economies and culinary traditions. While stock declines have been reported in some regions, other areas continue to sustain fishing activities, albeit under pressure. Understanding the species' demography is therefore essential for effective future management. To investigate the larval dispersal dynamics of *M. squinado* in the Mediterranean, a biophysical model was developed, incorporating biological and ecological data such as larval duration and spawning habitats. The tool Ichthyop, designed to study ichthyoplankton dynamics, was employed to perform simulations spanning the period from 2010 to 2020. These simulations were analyzed in the context of global surface water warming trends, yielding maps that illustrated trajectory density, system connectivity, and variations in larval density and dispersal distance over time. The comparative analysis of various scenarios revealed the significant impact of environmental variations on larval connectivity. Specifically, the Tunisia-Sardinia-Corsica complex exhibited strong connectivity, while the Balearic Islands appeared isolated due to the species' short pelagic larval duration (PLD). The findings highlight the utility of biophysical models in hypothesizing population declines in isolated areas and underscore the necessity of employing diverse modeling approaches at multiple spatial and temporal resolutions. Kernel density estimation (KDE) maps were selected to analyze and visualize the observed simulation scenarios. The results emphasize the importance of considering these changes, particularly the synergies between environmental and biological parameters that influence larval dispersal and connectivity in this species. Such approaches could enhance future conservation and management strategies by accounting for the complex interactions driving population dynamics within the context of a changing Mediterranean ecosystem.

## 1. Introduction

The Mediterranean Sea is a biodiversity hotspot, representing 4–18% of global marine species (Bianchi and Morri, 2000). This reservoir has been under increasing pressure for several decades due to habitat degradation, marine and atmospheric pollution, invasive species, and climate change. Fishing is a major activity in the Mediterranean Sea, with catches reaching 1.19 million tons in total in 2020. The majority of fisheries are industrial, with an annual catch of 1 million tons. Yet, this activity represents only 17% of the total number of boats, with the remaining 83% engaged in artisanal fishing (FAO, 2020). The lack of

regulation of artisanal vessels, illicit fishing practices, the discard of unwanted catch, and the overfishing of fish stocks have collectively led to the overexploitation of marine resources and an irreversible decline in fish stocks (Carlson et al., 2016; Zaimen et al., 2021). Besides, the majority of specific fish stocks have not yet been assessed, making it difficult for political decision-makers to apply and establish regulations (Marengo et al., 2016). Among the marine species affected by these anthropogenic pressures, the Mediterranean spinous spider crab (*Maja squinado*) has gained particular attention due to its ecological and economic importance. Indeed, a sa locally important fishing resource, the Mediterranean spinous spider crab *Maja squinado* (Herbst, 1788) has

<footnote><sup>a</sup> Université de Corse, UMR CNRS 6134 Sciences pour l'Environnement, Corte, France</footnote>

<footnote><sup>b</sup> Université de Corse, UAR CNRS 3514 Stella Mare, Biguglia, France</footnote>

<footnote><sup>c</sup> MARBEC, Université de Montpellier, CNRS, Ifremer, IRD, Sète, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>** Corresponding author. Université de Corse, UMR CNRS 6134 Sciences pour l'Environnement, Corte, France.</footnote>

<footnote>E-mail addresses: BARRIER_C@univ-corse.fr (C. Barrier), durieux_e@univ-corse.fr (E.D.H. Durieux).</footnote>
experienced a significant decline in its population in recent years, likely
due to overexploitation (Abelló et al., 2014; Durán et al., 2013; Martín
et al., 2012; Rotllant et al., 2014).

In areas such as the Balearic Islands or the Columbretes Islands, the
species appears to be nearly extinct for approximately a decade,
prompting research and reintroduction initiatives (García, 2007; Torres
et al., 2013). Given this decrease in stocks, Maja squinado is interna-
tionally recognized as a species of ecological interest within the United
Nations Environment Programme's Action Plan for the Mediterranean
(UNEP/MAP). Additionally, it was included as a protected species in the
Bern Convention for the Conservation of European Wildlife and Natural
Habitats and was added to Appendix III of the Barcelona Convention in
2009. In France, the IUCN has not yet concluded on the species' status,
however regional decrees are already applied in order to limit and
regulate catches by recreational fishing. In Corsica, populations still
support small-scale fisheries (Bousquet et al., 2022); however, a notable
decline in CPUEs (Catches per Unit Effort) has been observed in recent
years (Marengo et al., 2023). To better understand the factors contrib-
uting to the decline of Maja squinado populations, it is essential to
examine its life history traits, reproductive biology, and dispersal
mechanisms, which play critical roles in the species' persistence and
connectivity.

Maja squinado, a member of the Majidae family, was initially
described as inhabiting both the Atlantic and the Mediterranean Sea. In
1998, Neumann proposed a distinction between Maja squinado and Maja
brachydactyla (Balss, 1922) based on morphological characteristics
identified by Balss in 1922, with the respective distribution areas
assigned to the Mediterranean and the Atlantic (Neumann, 1998). This
differentiation was further supported by a genetic study that analyzed
mitochondrial gene variations in Atlantic (Maja brachydactyla) and
Mediterranean (Maja squinado and Maja crispata) spider crab pop-
ulations (Sotelo et al., 2008). Given that a substantial portion of research
on Maja squinado has, in reality, focused on the Atlantic species Maja
brachydactyla, our knowledge of Maja squinado in the Mediterranean
remains relatively limited and varies depending on geographic region
and author. Gualtieri et al. (2013) for example, in their study along the
Corsican coast of the Mediterranean Sea using wireless acoustic sensors,
observed that spider crabs migrate to deeper waters in winter and return
to coastal areas during spring and summer. Their findings also indicate
that these crabs are stationary within their habitat at night but actively
move during daylight hours. Similarly, Rotllant et al. (2015) reported
that adult specimens, predominantly ovigerous females (those carrying
eggs), were captured along the Catalan coast (Spain) in April. Gravid
females ascend to shallower waters in summer to spawn, while males
typically remain in deeper habitats. These females possess a sperma-
theca, enabling them to spawn multiple times from winter matings. The
number of egg clutches and larvae varies according to authors,
geographical areas, and water temperatures. Each female produces
approximately three clutches of 30,000 to 200,000 larvae (Calado et al.,
2013; Durán et al., 2012). Gravid females incubate the eggs under their
abdomen before releasing the larvae at the zoea I stage, which then
progress through three stages: zoea I (ZI), zoea II (ZII), and megalopa.
Maja squinado eggs and larvae are carried by currents and drift in the
environment to complete their lifecycle. This process involves biological
and physical mechanisms that facilitate the movement of larvae from
breeding to recruitment sites (Begon et al., 2005).

Larval transport in species with multiphasic life cycles has histori-
cally been considered to be heavily influenced by current patterns (J.
Power, 1984). However, biotic factors such as migratory abilities driven
by diel or ontogenetic cycles, as well as swimming and orientation ca-
capities, can also play a significant role in these dispersion processes
(Adams et al., 2012; Gary et al., 2020). Broadly speaking, exchanges
across various spatial scales are deemed critical for understanding
connectivity and the dynamics of marine metapopulations (Pineda et al.,
2007). Crustacean larvae, particularly decapods, have not been
observed engaging in active swimming. Although vertical migration has

not been thoroughly investigated in *Maja squinado*, studies on other
crustacean species have demonstrated that larvae are capable of diel
vertical migrations, shifting between depths in response to day-night
cycles (Anger et al., 2015; Ospina-Alvarez et al., 2018).

Understanding the population dynamics of *Maja squinado* is crucial for developing effective management and conservation strategies. Although biophysical models are not often used for population dynamics, they are frequently employed to investigate pelagic larval dispersal and connectivity (Swearer et al., 2019). By integrating biophysical modeling, we can gain insights into the species’ larval stages, aiding in the prediction of dispersal patterns and connectivity, which are essential for informed management and conservation efforts. This approach offers numerous benefits for predicting and visualising the dynamics of population dispersal and for hypothesizing species vulnerabilities. For example, it can highlight isolated habitats or a lack of connectivity between populations, which are critical for effective conservation strategies (Jahnke and Jonsson, 2022; Lett et al., 2010). Individual-based models (IBMs) are designed to include detailed information about the life history traits of species for which researchers intend to model spatial and/or temporal dynamics (DeAngelis and Mooij, 2005). Each individual in the model is defined by a set of state variables and behaviours, which are determined according to available information on the species and the model’s desired level of precision. These include geographical location, physiological traits, and certain behavioural traits such as reproduction and habitat selection. In a biophysical model, the trajectories of particles representing larvae are influenced by ocean currents and other environmental factors. By incorporating larval behaviours and physiological processes, the model becomes more precise, allowing for refined predictions that align more closely with other scientific approaches studying the species of interest.

In many Brachyura species, larval dispersal and migration are shaped by dominant ocean currents, as well as seasonal changes, water temperature, and food availability (Anger, 1991; Bryars and Havenhand, 2006; Epifanio & Garvine, 2001b; Anger et al., 2015). In the Mediterranean Sea, particularly in its northwestern region, recent research (Margirier et al., 2020) has highlighted an annual increase in temperature of +0.06 °C and salinity of +0.012. Furthermore, a significant shift was observed in 2014, particularly in the properties of the offshore Ligurian Levantine Intermediate Water (LIW). This warming trend, which aligns with broader global patterns (Pastor et al., 2020; Yin et al., 2018), underscores the importance of investigating how environmental shifts may affect species such as *Maja squinado* and whether global change signals, such as rising temperatures, could play a role in the synergy of biotic and abiotic factors influencing larval dispersal and connectivity processes in species like the one studied in the present research. In light of these dynamics, a critical knowledge gap remains regarding the influence of environmental changes on larval dispersal and connectivity. Addressing this gap is crucial for predicting population persistence and informing conservation strategies.

Thus, the current observation is that despite having experienced a
substantial decline in certain regions, such as the Balearic Islands, Maja
squinado remains fished elsewhere—most notably around Corsica—
—although its IUCN status is currently non-evaluated. Because its
ecology and population dynamics are not fully understood, reliable
projections for the species’ future distribution remain challenging. To
address these challenges, this study adopts a biophysical modeling
approach to explore the larval dispersal and connectivity of Maja squi-
nado in the Northwestern Mediterranean. By doing so, we aim to provide
critical insights into how environmental and biological factors shape the
species’ spatial dynamics. Recognizing the significance of larval
dispersal for population persistence, this study harnesses a biophysical
modeling approach to explore the dispersal and connectivity of
M. squinado larvae across the Northwestern Mediterranean from 2010 to
2020. Specifically, we aim (i) to offer an initial prediction of larval
dispersal routes over an extended time frame, (ii) to evaluate connec-
tivity patterns among key habitats identified in the literature, and (iii) to
integrate scenarios that account for recent increases in sea surface temperature. By combining spatial and temporal analyses, we seek to shed light on how global warming signals may influence the connectivity of this vulnerable species and to provide a basis for more informed management strategies.

## 2. Material and methods

### 2.1. Mediterranean's surface circulation

The Mediterranean Sea is divided into two main basins, the Western and Eastern, separated by the Sicily-Tunisia Strait. The Western Basin extends from the Alboran Sea to the Tyrrhenian Sea, with the Atlantic Ocean connected to it via the Strait of Gibraltar. The Eastern Basin extends from the Adriatic to the Levantine Sea, including the Ionian and Libyan Seas. The Mediterranean Sea's intricate surface circulation, influenced by its segmented geography and complex seabed, predominantly exhibits anticyclonic movement. Fresh Atlantic waters enter the Mediterranean through the Strait of Gibraltar, increasing in density due to evaporation before exiting, typically taking 50-100 years to circulate. Surface currents are influenced by wind and topography, forming stable gyres and up to 1-km-wide eddies. However, some areas exhibit instabilities over time (Millot and Taupier-Letage, 2005; Pinardi et al., 2015) (Fig. 1A).

Surface currents in the northwestern Mediterranean are primarily influenced by thermohaline circulation, with cyclonic currents observed near the continental slopes. Notable currents include the Liguro-Provençal-Catalan Current, which flows in an east-to-west direction, and various gyres such as the Gulf of Lion Gyre and the Northern Tyrrhenian Gyre, which exhibit distinct circulatory patterns. Furthermore, the Tyrrhenian Sea's connectivity with the Corsican and Sardinian shores is characterised by the presence of various currents and gyres, which

collectively contribute to the formation of a dynamic marine environment (Pinardi et al., 2015) (Fig. 1B).

### 2.2. Hydrodynamical models

The simulations for this study were performed using two hydrodynamic models. The first one, MEDSEA_MULTIYEAR PHY_006_004 (MedMFC), was used to carry out simulations encompassing the entirety of particle release polygons representing larvae in the Western Mediterranean. This model, which presents a daily temporal resolution, integrates a hydrodynamic model contributed by the Nucleus for European Modelling of the Ocean (NEMO). It is further enhanced by a variational data assimilation approach known as OceanVAR, which processes vertical profiles of temperature and salinity, along with satellite-derived sea level anomaly data. The dataset includes a comprehensive reanalysis segment, as well as an interim segment that extends from the end of the reanalysis to one month prior to the current date. The model operates on a finely calibrated horizontal grid with a resolution of 1/24°, equivalent to approximately 4-5 km (Escudier et al., 2021). A second hydrodynamic model, MARS3DMed, was employed for focused analyses and to attempt observing phenomena at a finer scale. This model, derived from the MARS3D (Model for Application at Regional Scales 3D) code and developed by Ifremer, has a temporal resolution of 3 h, a horizontal resolution of about 1.2 km, and 60 vertical levels using a generalized sigma coordinate system on an Arakawa-C grid, extending from the seabed to the surface, and is adapted for fine graphical resolutions (Arakawa & Lamb, 1977; Lazure and Dumas, 2008).

The regional configuration of the MARS3D model been validated against observational data. Specifically, studies by Pairaud et al. (2011) and Gatti and Pairaud (2010). Similarly, the MedMFC model has undergone validations achieved through comparisons with in situ

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Map of the general circulation currents in the Mediterranean (A), covering all basins (adapted from Millot and Taupier-Letage, 2005). 1: Strait of Gibraltar, 2: Alboran Sea, 3: Sicilia-Tunisia Strait, 4: Algero-Provençal Basin, 5: Ligurian Sea, 6: Tyrrhenian Sea, 7: Adriatic Sea, 8: Ionian Sea, 9: Libyan Sea, 10: Aegean Sea, 11: Levantine Sea. **Focus on the Northwestern Mediterranean Sea (B)** adapted from the study by Pinardi et al. (2015). LPCC: Ligur-o-Provençal-Catalan Current, GLG: Gulf of Lion Gyre, NTG: Northern Tyrrhenian Gyre, ECC: Eastern Corsica Current, SSC: Southern Sardinia Current, MTC: Middle Tyrrhenian Current, and SETG: South-Eastern Tyrrhenian Gyre.</figcaption></figure>
observations, including Conductivity-Temperature-Depth (CTD) profiles, ARGO floats, and satellite-derived data such as altimetry and sea surface temperature (SST). The evaluation was further supported by the calculation of root mean square differences (RMSD), confirming the system's capability to accurately represent the physical state of the Mediterranean Sea (Escudier et al., 2021).

## 2.3. Model parametrisation in the Lagrangian transport tool

The larval dispersal model employed is Ichthyop v.3.3.16, an offline Java-based Lagrangian tool (Lett et al., 2008). This biophysical model has been widely applied in marine ecology (Rojas-Araos et al., 2024; Flores-Valiente et al., 2023) and physical oceanography (Al-Qattan et al., 2023; de Mello et al., 2023), allowing for the integration of both physical and biological factors in the study of ichthyoplankton dynamics. Ichthyop tracks the movement of water masses or particles based on outputs from ocean circulation models such as ROMS, MARS, NEMO, or SYMPHONIE (Imzilen et al., 2023). Particle trajectories are computed using a fourth-order Runge-Kutta method with a constant time step to ensure compliance with the Courant-Friedrichs-Lewy (CFL) condition. For MARS3DMed, a time step of 720 s was used, while a step of 2880 s was applied for MedMFC outputs. Particle positions were recorded at regular intervals (every 60th and 15th time step, respectively) to capture their trajectories every 12 h.

Horizontal diffusion parameters followed Ichthyop's default configuration (Peliz et al., 2007), with a dissipation rate of $10^{-9} m^2/s^3$. Vertical migration was based on diel cycles, reflecting the depth and timing of sunrise and sunset (Table 1). Notably, buoyancy was incorporated into the vertical velocity calculations, with particle density set to 0.9 g/cm$^3$. This value, supported by findings from Epifanio and Garvine (2001), ensures that particles remain in the upper layers of the water column, as observed in this crustacean larval studies. Furthermore, vertical migration during the day-night cycle was modeled as a critical aspect of the larval life cycle, with depths averaging 30 m during the day and 1 m at night (Anger et al., 2015; Ospina-Alvarez et al., 2018). While horizontal migration was not included, early larval stages (zoeae) were limited to vertical swimming, transitioning to walking and feeding appendages at later stages (megalopa and juvenile stages).

Particle release parameters were designed to simulate larval dispersal during the spawning period of Maja squinado in the Western Mediterranean, with documented spawning data from Corsica and Catalonia (Calado et al., 2013; Durán et al., 2012; Rotllant et al., 2014). Because precise measurements of release timing are lacking in the literature, particle release dates were evenly distributed across the identified spawning period, ensuring representative coverage. Each simulation year included eight release events (April 15–May 31), consistent with the literature (Table 1).

For MedMFC, 50,000 particles were released per event, amounting to 400,000 particles per simulation or 4,400,000 particles over the 11-year study, distributed evenly across 36 zones. In MARS3DMed, a proportional approach was adopted, with 31,250 particles released per event, yielding 250,000 particles per simulation or 2,750,000 over the study period, evenly allocated across 23 polygons in the Northwestern Mediterranean. These values adhere to recommendations from Simons et al. (2013), which establish guidelines on the particle numbers needed for robust modeling.

The dispersal period was capped at 20 days, with larval competency for settlement starting at 17 days, representing the onset of the juvenile stage (Durán et al., 2012; Rotllant et al., 2014). Particle retention was modeled such that particles located within recruitment zones after 17 days were immobilized, while those remaining outside recruitment zones after 20 days were classified as unsuccessful. Finally, particles interacted with physical boundaries through rebound behavior, overcoming these barriers with forces calibrated to ichthyoplankton dynamics.

<caption>**Table 1**</caption>

Biological parameters of the simulations applied to the dispersion of Maja squinado.

<table><thead><tr><th>Parameter</th><th>Value</th><th>Reference</th></tr></thead><tbody><tr><td>Pelagic larval duration (PLD)</td><td>20 days</td><td>Durán et al. (2012)</td></tr><tr><td>Release date (per year)</td><td>April 15 - Start of the laying period.<br>April 22 - One week after the start.<br>April 29 - Two weeks after the start.<br>May 6 - Three weeks after the start.<br>May 13 - Four weeks after the start.<br>May 20 - Five weeks after the start.<br>May 27 - Six weeks after the start.<br>May 31 - End of the laying period.</td><td></td></tr><tr><td>Particles number</td><td>MedMFC: 50000 particles were released on each release day (8 days in total), amounting to 400000 particles per simulation. This equates to 4400000 particles over the 11-year study period, distributed evenly across 36 zones. MARS3DMed: in order to maintain proportional relationships, 31250 particles were released each day of the release period (8 days in total), resulting in a total of 250000 particles per simulation, or 2750000 particles over the 11-year study period, distributed evenly across 23 polygons in the Northwestern Mediterranean.</td><td></td></tr><tr><td>Release depth</td><td>0-50m for MedMFC model</td><td>Gualtieri et al. (2013)</td></tr><tr><td>Release polygons</td><td>See Fig. 2 for geographical localization: 1: MA1, 2: SP2, 3: SP5, 4: SP6, 5: SP7, 6: SP8, 7: SP9, 8: FR2, 9: FR3, 10: FR-I, 11: CAPi, 12: NWCO The following codes correspond to the polygon numbers: 13: WCO, 14: SWCO, 15: SCO, 16: SECO, 17: NECO, 18: IT3, 19: IT4, 20: NSA, 21: NWSA, 22: SWSA, 23: SSA, 24: SESA, 25. The following abbreviations are used in this document: NESA, 26: IT7, 27: IT8, 28: IT9, 29: IT10, 30: SIC1, 31: SIC2, 32: USTi, 33: SIC3, 34: TUN, 35: AL3, 36: MA-AL.</td><td>Rotllant et al., (2014), Guerao and Rotllant (2010), Guerao et al., (2016), Seytre et al., (2013), Modena et al., (2001); Durán et al., (2012) and grey littérature.</td></tr><tr><td>Ideal habitat for recruitment polygons</td><td>See Fig. 3 for geographical localization: 1:MA1, 2:ALB, 3: SP1, 4:SP2, 5:SP3, 6:SP4, 7: SP5, 8:SP6, 9:COLUI, 10:SP7, 11:BA2, 12:BA1, 13:BA3, 14: BA4, 15:BA8, 16:BA5, 17: SP8, 18:BA7, 19:SP9, 20:BA6, 21:SP-FR, 22:FR1, 23:FR2, 24:FR3, 25:FR4, 26:FR-I, 27: IT1, 28:CAPi, 29:NWCO, 30: WCO, 31:SWCO, 32:SCO, 33: SECO, 34:NECO, 35:IT2, 36: IT3, 37:PIAI, 38:IT4, 39:IT5, 40:NSA, 41:NWSA, 42:SWSA, 43:SSA, 44:SESA, 45:NESA, 46:IT6, 47:IT7, 48:IT8, 49:</td><td>Rotllant et al., (2014), Guerao and Rotllant (2010), Guerao et al., (2016), Seytre et al., (2013), Modena et al., (2001); Durán et al., (2012) and grey littérature.</td></tr></tbody></table>

(continued on next page)
<table><caption>**Table 1 (continued)**</caption><thead><tr><th>Parameter</th><th>Value</th><th>Reference</th></tr></thead><tbody><tr><td>Minimum retention age</td><td>IT9, 50:IT10, 51:SIC1, 52: EOLI, 53:SIC2, 54:USTi, 55: SIC3, 56:TUN, 57:ALTUN, 58: GALI, 59:AL4, 60:AL3, 61: AL2, 62:AL1, 63:MA-AL.</td><td>Rotllant et al. (2014)</td></tr><tr><td>Buoyancy</td><td>0.9 g/cm<sup>3</sup></td><td>Epifanio and Garvine (2001a)</td></tr><tr><td>Vertical migration depth</td><td>day: 30m - night: 1m</td><td>Ospina-Alvarez et al. (2018)</td></tr><tr><td>Coastal particles behaviour</td><td>Bouncing</td><td></td></tr></tbody></table>

## 2.4. Delimitation of release and recruitment sites

The 36 zones used for the release of particles were designed based on the documented presence of *Maja squinado* in the literature (Rotllant et al., 2014; Guerao and Rotllant, 2010; Guerao et al., 2016; Seytre et al., 2013; Modena et al., 2001; Durán et al., 2012; Rotllant et al., 2015; Angeletti et al., 2014; Gualtieri et al., 2013; Durán et al., 2013; Calado et al., 2013; Sotelo et al., 2008; Vignoli et al., 2004; Mura and Corda, 2011; Pipitone and Arculeo, 2003, Fig. 2). Additionally, based on these bibliographic references, the 63 recruitment zones were parameterized by identifying habitats favorable for the settlement of larvae and juveniles, all within a realistic bathymetric range (<50 m; Fig. 3).

## 2.5. Environmental trends visualization and data analysis

The Ichthyop simulation outputs are files in NetCDF format, which were analyzed using R (version 4.0.4; R Core Team, 2021) and Python

(version 3.11) scripts. Density maps of particle trajectories were generated for the entire simulation period (2010–2020). Trajectory densities were calculated in several steps: first, the final positions of particles were extracted from the simulation outputs. The coordinates (longitude and latitude) of these particles were used to create spatial line objects, subsequently converted into spatial vector objects. These vectors were rasterized onto a background grid map, where each raster cell represented the cumulative number of particle trajectories passing through it. This process quantified the particle density spatially by summing the trajectories in each grid cell. To evaluate relative variability, a coefficient of variation (CV) was computed for the density maps. The CV facilitated comparisons of relative trajectory variability between years or periods, accounting for differences in absolute densities. Connectivity matrices between release zones and recruitment zones, as well as local retention leading to potential self-recruitment at each site. On connectivity matrices, a Gini coefficient (Hixon and Jones, 2005) was also displayed to assess disparities between years and sites. The Gini coefficient is a synthetic indicator used to measure the level of inequality for a given variable within a specific population. It ranges from 0 (perfect equality) to 1 (extreme inequality). The closer the index is to 1, the more unequal the contribution of years to the observed average connectivity percentages; conversely, the closer it is to 0, the more equal the contribution. This method enables the observation of relationships with varying stability over the study period through a color gradient displayed in the matrices.

The sea surface temperature data extracted from the Copernicus Marine Environment Monitoring Service (CMEMS) satellite product over the western Mediterranean Sea revealed a temperature increase trend of 0.034 ± 0.002 °C per year, with a 95% confidence interval (Appendix Figure A1). To analyze spatial trends and focus on the last decade, data from the MedMFC model were examined at all grid points in the Western Mediterranean (with a particular focus on the study

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Diagram of the 36 release polygons (yellow, panel A), shown along the coastlines. The polygons were positioned as close as possible to the 50 m bathymetric contour. The table to the right of the map provides a correspondence between zone codes and their indices, covering various zones and islands. The polygons were created in QGIS, adhering to the spatial resolution of the hydrodynamic model. A closer view of Zone 34 (code TUN for the Gulf of Tunis, panel B) illustrates in detail—rather than schematically—how the polygons are drawn to remain within the 0–50 m bathymetric range (panel B, red line), while staying inside the model’s data domain (blue color) without encroaching upon areas lacking data (white areas). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Diagram of the 63 recruitment polygons (green, panel A), shown along the coastlines. The polygons were positioned as close as possible to the 50 m bathymetric contour. The table to the right of the map provides a correspondence between zone codes and their indices, covering various zones and islands. A closer view of zone 57 (code TUN for the Gulf of Tunis, panel B) illustrates in detail how the polygons are drawn to remain within the 0–50 m bathymetric range (panel B, red line), while staying inside the model’s data domain (blue color) without encroaching upon areas lacking data (white areas). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

area), providing a more spatially detailed view of regional temperature variations. Linear regression was applied to compute a linear temperature trend for each grid point, with a color gradient ranging from blue (indicating a decrease in temperature) to red (indicating an increase). These trends were visualized on a map, and a comprehensive discussion of these findings is provided in the appendix section. To further understand the ecological implications of these temperature changes, we investigated the potential impact of environmental shifts on the dispersal of *M. squinado* by dividing the simulation outputs into two groups: 2010–2014 and 2015–2019. This grouping allowed for a comparison of periods with differing average temperatures while ensuring an equal number of simulations per scenario. A general visualization was performed using kernel density estimates (KDEs). The KDE calculation relies on a symmetric function centered on each data point, with the Gaussian kernel (normal distribution) being the most commonly used. The bandwidth parameter determines the degree of smoothing: smaller bandwidths result in more detailed estimates, whereas larger bandwidths produce smoother estimates. For particle position simulations modeling larval dispersal, KDEs enable the estimation of spatial density at different times, facilitating the visualization of areas with high particle concentrations and the detection of differences between the two scenarios. The kernels represent the spatial distributions of larval positions at the final step of the simulations, highlighting the areas where larval densities are highest.

Finally, a focused analysis was conducted on two specific zones—SP9, located at the France-Spain border, and SWCO, in southwest Corsica—to examine the distribution of distances traveled and directions followed from release to recruitment. These zones were selected because they exhibit both noteworthy similarities and distinct differences, underscoring their relevance for studying *M. squinado* population connectivity. SP9 demonstrates limited connectivity to the recruitment areas of the Balearic Islands, whereas SWCO is integrated into a broader larval connectivity network involving Tunisia, Sardinia, Corsica, and the Ligurian Sea. Notably, while *M. squinado* has been completely extirpated from areas such as the Balearic Islands, as documented in the literature, the species persists—though in decline—in Corsica and neighboring coastlines, where it is still exploited.

Distances traveled by the larvae were calculated using the Haversine formula, which accurately accounts for the Earth's curvature by computing great-circle distances between points. The resulting plots illustrate the distribution and density of traveled distances, enabling a comparison of particle densities from spawning areas across two climatic scenarios: pre- and post-warming periods in the Western Mediterranean. Additionally, wind rose diagrams were constructed to analyze the predominant orientations of particle dispersal upon their arrival at recruitment zones. Orientation angles were calculated by averaging the angles of individual trajectory segments, with weights proportional to the distances traveled within each segment. These complementary visualizations provide a detailed depiction of both the spatial and directional patterns of larval dispersal in response to environmental changes.

Finally, statistical analyses were conducted on the datasets derived from the simulation predictions for each year and scenario, with a specific focus on the distances traveled by particles that successfully reached a recruitment zone. These analyses began with a normality assessment using the Shapiro-Wilk test. When data deviated from normality, the non-parametric Kruskal-Wallis test was applied to identify significant differences in traveled distances within each zone, comparing the two periods in the context of environmental changes. When significant differences were detected, post hoc Dunn's tests were performed to pinpoint the specific years in which these differences occurred.

## 3. Results

### 3.1. Investigating temporal and spatial variability of dispersion pathways over the entire temporal period (2010–2020)

Maps of particle density's coefficient of variation (CV) computed over the entire study period and domain covered by the MedMFC model highlight the principal simulated routes and their temporal variability for larvae transported from spawning to recruitment zones (Fig. 4). We note a continuity in the simulated larval dispersal pattern of *Maja squinado* between the North and South around Corsica and Sardinia,
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Density map of particles that successfully reach a recruitment zone averaged for the period 2010 to 2020, based on simulations carried out with the MedMFC model in the Western Mediterranean. The interannual variability of these trajectories is expressed by a coefficient of variation (CV).</figcaption></figure>

with low CV values, indicating that this pattern is recurrent along the study period. In contrast, other areas, such as the Balearic Islands, have less connection with the coastlines, or at least less regularly along the study period. Lastly, the Alboran Sea emerges as a significant exchange hub for larvae along its various coastlines, yet it has limited connection with the rest of the Western Mediterranean.

The MARS3DMed model (Fig. 5) reveals that trajectories with low coefficients of variation primarily connect nearby coastal areas, at times forming interconnected sets over the course of the eleven considered years. This is exemplified by the routes appearing on both sides along Corsica and Sardinia. The broader geographic window used with MedMFC allows for the identification of routes in the southern Western Mediterranean, particularly in the Alboran Sea, which is minimally or not connected to the rest of the sub-basins. Furthermore, the Balearic Islands are geographically isolated, although a few trajectories (with very high coefficients of variation) exhibit weak connectivity to the Spanish coastlines over the studied period. The southern coastlines of Sardinia are connected with the Tunisian coastlines on a regular basis over time. This creates a group with probable larval exchanges between Tunisia, Sardinia, and then Corsica moving northward.

The Gulf of Lion is also a site of significant exchanges, with distinct routes emerging: one arriving from the east and another departing towards the west. Notwithstanding the high CV (panel C of Fig. 5) of these routes, they play a pivotal role in regional connectivity. In the easternmost part of the Western Mediterranean, there is also a coastal route along the Sicilian-Italian coastline with minimal connectivity to the rest of the basin, with the exception of a few trajectories (with high CV) connecting to the Tunisian coasts.

The connectivity matrix (Fig. 6) was organized in a manner consistent with the Mediterranean sub-basins. The maximum connectivity values of approximately 9% were obtained in the cluster Italy-Sicily-Tunisia, between the IT8 and IT9 (south Tyrrhenian Sea Coast). In the Alboran Sea, we observe regular connectivity relationships (as indicated by a low Gini index) and remarkably high local retention values for MA1.

<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Trajectory densities for the entire period from 2010 to 2020 generated using two hydrodynamic models with different geographical windows and foci. A: Simulations carried out with the MedMFC model in the western Mediterranean. B: Simulations carried out with the MARS3DMed model in the north-western Mediterranean, allowing finer resolution focuses in C (Gulf of Lion) and D (Sardinia-Corsica ensemble). The variability of the trajectories over time is expressed by the coefficients of variation (CVs).</figcaption></figure>
<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /><figcaption>**Fig. 6.** The connectivity matrix was developed based on simulations conducted using the MedMFC model in the Western Mediterranean region, covering the period from 2010 to 2020. The release zones are represented on the vertical axis, while the recruitment zones are shown on the horizontal axis. Color gradients indicate the Gini index values, and sub-basins representing clusters of zones are outlined. The accompanying map provides spatial context for the zones within the basin. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

(Strait of Gibraltar, Morocco) and SP2 (Andalusia coast, Spain). In the Balearic Sea, the most notable features are the high local retention within SP7, SP8 (Catalonia coasts, Spain), and SP9 (France-Spain border), all with low Gini indices, thus illustrating relatively consistent exchanges over time. The Balearic Islands (BA1 to BA9) are connected by small amounts of particles, with Gini indices higher than 0.5. The Corsica-Sardinia area and the coasts of the Ligurian Sea have connections close to the maximum of obtained connectivity values (e.g., IT3, the Tuscan Archipelago) with IT4 (north Tyrrhenian Sea coast), NWCO (northwest Corsica) with FR-I (Ligurian coast), WCO (west Corsica) with FR-I (Ligurian coast) with low Gini indices.

Local retention values in Corsica can reach notable percentages, as observed in the FR-I zone (France-I Italy border) with a mean connectivity ranging between 2% and 3%. However, the Gini index, ranging between 0.5 and 0.7, indicates a process that does not appear consistent over the entire temporal period according to simulation predictions. Similarly, a Gini index close to 0.5 is observed for the mean connectivity between the Corsican zones (e.g., NWCO, WCO, SCO) and the Sardinian zones (e.g., NWSA, SSA). Thus, in general, we observe relatively regular exchanges between Corsica, Sardinia, and the Ligurian coasts. Finally,

the last remarkable group is that of the Italian, Sicilian, and Tunisian coasts, where some of the highest connectivity levels are observed between the zones IT7 to IT10 (South Tyrrhenian Sea coasts), with strong local retention within each of these zones as well, and low Gini indices. The Tunisian zone (TUN, Gulf of Tunis) is connected to the Sardinian zones (e.g., SWSA, SSA) and the Sicilian zones (e.g., SIC1, SIC2, USTi) are connected to both the Aeolian Islands (EOLI) and the Italian zones south of the Tyrrhenian Sea (e.g., IT8, IT9).

## 3.2. Analysis of trends in light of global change signals in the western Mediterranean

### 3.2.1. Warming signals and comparison of larval recruitment kernels

The kernel density estimates (KDE) maps for the periods 2010–2014 and 2015–2019 were produced by dividing the simulation outputs into two distinct datasets. This approach aimed to identify discernible differences in larval kernel densities and their geographic distributions at a macroscopic scale at the end of their larval dispersal. The first map (Fig. 7A) represents the 2010–2014 scenario and illustrates the larval density across the geographic space during this period, calculated using
<figure><img src="image_11.png" /><figcaption>**Fig. 7.** Kernel density estimates (KDEs) of particle locations at the end of the PLD obtained using the MedMFC configuration for (A) 2010–2014 (B) 2015–2019 (C) shows the differences between the two time periods.</figcaption></figure>
the KDE method. Regions of high density represent areas where larvae concentrate at the end of their PLD. The second map (Fig. 7B) corresponds to the 2015–2019 scenario and similarly visualizes the spatial distribution of larvae for this period. Lastly, the third difference map (Fig. 7C) (2015–2019 vs. 2010–2014) spatially highlights variations. Specifically, red zones indicate an increase in density during 2015–2019 compared to 2010–2014, while blue zones show a decrease in density during the same comparison.

The KDE map for 2010–2014 (Fig. 7A) shows higher maximum density values compared to the 2015–2019 scenario (Fig. 7B). However, the areas with the highest concentrations remain largely consistent between the two periods, particularly in the Ligurian Sea, the southern Tyrrhenian Sea, the Balearic Sea, and, to a lesser extent, the area between Tunisia and southern Sardinia. The difference map (Fig. 7C)

highlights areas where larval densities increased during 2015–2019, such as the Spanish coasts near the France–Spain border (e.g., around zones SP8 and SP9), the northern Corsican coastline, and the coasts of the Ligurian Sea (e.g., NWCO, CAPI, IT1, FRI), as well as areas north of Sicily (e.g., SIC 1, 2, 3, USTi, EOLI, IT10, and IT9). Conversely, there are sectors where particle densities decreased over time, transitioning from the 2010–2014 to the 2015–2019 scenario, such as the Ligurian Sea’s French coasts (e.g., FR1, FR2, FR3), the Bonifacio Strait south of Corsica (including Corsican and Sardinian zones SCO and NSA), and the southern Tyrrhenian Sea.

### 3.2.2. Dispersal distances and orientations in a changing Mediterranean Sea

The following analyses and results present two contrasting areas

<figure><img src="image_12.png" /><figcaption>**Fig. 8.** Violin plots illustrating the distribution of distances traveled by larvae that successfully reached a recruitment area from the Catalan coast release zone (SP9; Fig. 2) across different time periods (2010–2014 in blue, with the average in grey, and 2015 to 2019, also with the average in grey). Wind roses depict the distribution of directions traveled by these larvae. Orientation is represented in cardinal directions: north (90°), south (270°), east (0°), and west (180°). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>
from the perspective of simulated connectivity and the status of *Maja squinado* in the Mediterranean. The Catalan coast or the France-Spain border (SP9; Fig. 2) is the area that connects the largest number of recruitment areas in the Balearic Islands, but with low connectivity percentages and high Gini indices (Fig. 6). Furthermore, the literature and supplementary approaches have demonstrated that *M. squinado* has almost disappeared from the Balearic Islands, rendering this area a primary focus of interest. This area contrasts with the Corsica-Sardinia zones were the spinous spider crab is still exploited. Consequently, it was decided to compare it in this analysis with the southwestern Corsica zone (SWCO; Fig. 2). The latter exhibits in the matrix a number of significant connections with other zones (Fig. 6). It is also included in what appears to be a cluster of larval exchange between Tunisia, Sardinia, Corsica, and the Ligurian Sea, in connection with other areas such as those in southern Corsica (SCO and SECO in Fig. 2).

The mean distance traveled by larvae from SP9 showed a slight

increase, rising from 127.06 km (2010–2014) to 135.60 km (2015–2019). Additionally, the standard deviation of the distance increased from 44.35 km to 58.82 km, reflecting greater variability in the distances traveled. The standard deviation of the direction of travel also rose, from 57.59° to 83.01°, indicating increased variability in dispersal directions, although the mean resultant direction remained southeast for both time periods. Notably, the distribution of directions was bimodal in 2015, 2016, 2018, and 2019, in contrast to being unimodal during all years from 2010 to 2014 (Fig. 8). However, statistical analysis using the Kruskal-Wallis test revealed no significant differences in the distances traveled between the two periods (Appendix Table A2). This result suggests that, while variability in larval dispersal increased over time, the overall mean distances remained statistically consistent between the two scenarios.

The mean distance traveled by larvae was found to be slightly shorter during the 2015–2019 period (174.07 km) compared to the 2010–2014

<figure><img src="image_13.png" /><figcaption>**Fig. 9.** Violin plots showing the distribution of distances traveled by larvae that have successfully reached a recruitment area from the Southwestern Corsican zone (SWCO; Fig. 2) for different time periods. Wind roses show the distribution of directions traveled by these larvae.</figcaption></figure>
period (185.60 km), based on data from SWCO. However, the variability in distances traveled was greater during the 2015–2019 period, with a standard deviation of 85.23 km compared to 77.93 km in the 2010–2014 period. The mean direction of movement also exhibited a notable shift, from 58.51° to 1.10°, with increased directional variability during the 2015–2019 period. The mean dispersal direction averaged over the 2010–2014 period displayed two peaks: one at approximately 135° in the northwest direction and another at 0° in the eastward direction. In contrast, during the 2015–2019 period, only the 0° eastward direction peak was observed (Fig. 9).

Statistical analysis (detailed in Appendix Table A2) confirmed that the differences in distances traveled between the two periods were sig- nificant, as indicated by the Kruskal-Wallis test. Post hoc Dunn tests further revealed that significant differences in distances were consis- tently observed in multiple year-to-year comparisons. Notably, distances in later years (2015–2019) consistently differed from those in earlier years (2010–2014). For instance, 2015 showed significant differences compared to 2010, and 2019 differed significantly from 2010 as well. Importantly, the significant differences observed in the Dunn tests pri- marily involved comparisons between years that were far apart (e.g., 2010 vs. 2015 or 2017 vs. 2010), whereas adjacent years (e.g., 2013 vs. 2014) often showed no significant differences. This trend aligns with the statistical results presented in Appendix Table A2. Comparisons across intermediate years also showed significant differences, such as between 2013 and 2015 and between 2017 and 2015.

## 4. Discussion

This study aimed to improve our understanding of the dispersal dynamics and connectivity of *Maja squinado* in the Western Mediterranean, a species with contrasting population abundances due to over- exploitation in some areas, while continuing to be fished along other coastlines. This work was conducted in the context of significant envi- ronmental changes in the Mediterranean, particularly the warming of surface waters. To achieve this, simulations were carried out using two hydrodynamic models, MedMFC and MARS3DMed, which provided complementary insights at different spatial resolutions.

The simulations generated by the MARS3DMed model are intended to reinforce and refine the findings observed in the MedMFC model simulations. This was made possible by meticulously delineating specific coastal exchanges, which could only be discerned with a higher level of detail. Consequently, the simulations are more closely aligned with field reality in the MARS3DMed model than in MedMFC, despite the broader geographic scope being essential to encompass pertinent release and recruitment sites. Indeed, research such as Swearer et al. (2019), em-phasizes that current hydrodynamic models often lack the resolution needed to capture fine-scale dynamics in coastal zones, which can compromise the accuracy of larval dispersal predictions. Improving spatial resolution would allow models to better represent the physical processes that are most relevant at these scales. The literature converges on the finding, as highlighted by Putman and He (2013), that high-resolution models more closely align with field-observed trajec- tories, thereby enhancing the validity of predictions. Along this line, a recent study on Mediterranean gorgonian species larval dispersal sim- ulations (Sciascia et al., 2022) in the Ligurian Sea identifies spatial resolution as the most influential factor in connectivity predictions, capturing both local-scale (larval processes) and regional-scale (popu- lation connectivity) dynamics. The authors underscore that finer reso- lutions yield better matches with empirical observations.

The results of simulations conducted in the Western Mediterranean have demonstrated, for the first time with a biophysical modelling approach at this spatiotemporal scale, that *Maja squinado*, with its relatively short PLD, exhibits limited dispersal capabilities. In general, the dispersion paths taken by the organism exhibit minimal variation over time, both in terms of distance and direction. These paths align with the predominant current patterns of the region and can be dispersed to

distances exceeding eight times the average observed distance. Medium- scale oceanographic features, such as fronts and eddies, have been identified as key mechanisms affecting dispersion in these specific areas (Alcaraz et al., 2007). Although vertical migration is commonly accepted in crustaceans of the Brachyurans group, its effects were not tested in this study and were parameterised as active in our simulations. However, in other species, this migration has been demonstrated in the literature to constrain the distance and speed of dispersion (Zakardjian et al., 1999). As with other Brachyurans, larval dispersion and migration may be defined here by the dominant current patterns of the location (Anger et al., 2015), as well as the presence or absence of suitable habitats for spawning and recruitment.

In the presented study, observations were made in the context of the warming of surface waters in the Mediterranean, with particular atten- tion paid to the acceleration observed between 2014 and 2015 (Margirier et al., 2020; Pastor et al., 2020). This warming is part of broader global and regional climatic trends that are increasingly recognized as key drivers of changes in oceanographic processes. Recent studies (Ser-Giacomi et al., 2020; de la Vara et al., 2022; Parras-Berrocal et al., 2024) further highlight that global and regional warming levels directly influence surface currents and their variability. These changes manifest through the intensification of turbulent activities, such as eddies, which are shaped by shifts in temperature as well as modifica- tions in the direction and intensity of gyres.

Such physical changes in the ocean system are likely to have cascading effects on the connectivity and dispersal dynamics of marine species, particularly those with planktonic larval stages like *Maja squi- nado*. The graphical observations from this study illustrate these dy- namics by showing differences between the two warming scenarios in the Mediterranean for the two subsampled areas, SP9 (France–Spain border zone) and SWCO (southwest Corsican coastal zone). The results suggest that warming-related changes in abiotic conditions could lead to shifts in the dispersal behaviours of *Maja squinado* larvae, with tradi- tionally favorable areas for larval retention potentially becoming less suitable, while other regions may experience increased larval concen- trations. To better understand these synergistic influences of global change on dispersal dynamics, future research should prioritize the collection of more extensive data, particularly in underrepresented areas like the Balearic Islands and their surrounding coastlines. These findings underscore the importance of continued investigations into the links between warming, hydrodynamic variability, and larval connectivity, as highlighted by the KDE results, which reinforce the hypothesis that accelerating sea surface temperature increases are reshaping dispersal trajectories and connectivity networks in the Mediterranean.

In addition to these physical drivers, another crucial link between global change and larval dispersion dynamics in *Maja squinado* lies in its biological responses to temperature. Temperature is likely a significant factor in conditioning spawning schedules, thereby affecting both the quality and quantity of eggs produced. For instance, studies have shown that ovarian mutations can be triggered by temperature changes in the freshwater shrimp *Penaeus merguiensis* (Hoang et al., 2002), while a study on brachyuran crabs found that rising temperatures tend to advance spawning periods (Azra et al., 2019). Similarly, temperature fluctuations have been shown to correlate with the spatial and temporal variation in the breeding cycles of the crab *Portunus armatus* (Johnson and Yeoh, 2021). However, no studies to date have specifically focused on *Maja squinado* in the Western Mediterranean to investigate its ther- mal tolerances and spawning schedule in the context of global change and warming sea surface temperatures. Future research into these bio- logical responses would not only refine the calibration of dispersal models but also enhance our understanding of population dynamics in a rapidly changing Mediterranean ecosystem.

These findings highlight the complex interplay of environmental and biological factors, underscoring the need to view larval dispersal dy- namics through the lens of a synergy rather than as a simple cause-and- effect relationship between global change and dispersal or connectivity
patterns. While current patterns remain the primary driver of larval dispersion for species with non-swimming larvae like *Maja squinado*, global warming-induced shifts in current regimes have cascading effects on zooplankton species and their dispersion. Numerous factors—including seasonal variations in currents, water temperature, and food availability—have been identified as critical to the survival and dispersal of crustacean larvae (*Anger*, 1991; *Bryars and Havenhand*, 2006; *Epifanio & Garvine*, 2001a). This study's findings suggest that even in the absence of warming scenarios, interannual variability in connectivity between sites is a key factor, likely exacerbated by global warming's effects on surface temperatures and current dynamics. A recommendation from the present study would be to incorporate these environmental and biological parameters into biophysical modeling in greater detail to enhance our understanding of observed dispersion patterns and to better elucidate the role of global change in driving interannual variability (*Corrochano-Fraile et al.*, 2022; *Sargač et al.*, 2022).

These predictions, when considered alongside the population dynamics of an exploited species with an IUCN status of non-evaluated, offer valuable insights that could contribute to the development of future management measures. Nonetheless, the predictions of the current model remain preliminary. While the model provides critical information about broad dispersal pathways, their temporal variability, and the connectivity relationships between essential habitats for *Maja squinado*, as well as hypotheses regarding the synergy between biotic and abiotic factors associated with global change, it requires further refinement to generate robust results suitable for management applications. Indeed, as models are adapted to nearshore environments, downscaling will be essential to better capture small-scale physical processes. However, this necessitates extensive field validation and calibration to ensure their accuracy and reliability (*Taebi et al.*, 2012). The need for enhanced parameterization of both physical and biological components is emphasized as a crucial step for producing predictions that can effectively inform management strategies (*Swearer et al.*, 2019). It is also highlighted that coarse-resolution models may fail to accurately represent coastal currents and local dynamics. This limitation represents a significant source of uncertainty in predictions used to guide the design of marine protected areas (*Sciascia et al.*, 2022). Furthermore, the sensitivity of dispersal estimates to the spatial and temporal resolution of ocean circulation models remains a critical issue. *Putman and He* (2013) demonstrated that averaging model outputs over coarser resolutions can obscure key oceanographic processes, potentially leading to dispersal trajectories that could diverge from observed drifter paths.

The findings of this study enable the prediction of large-scale and long-term trends in *Maja squinado*, a species whose ecology and population dynamics remain poorly understood. Along the coasts where areas favorable to the spawning of the spinous spider crab have been parameterized, connectivity over multiple sites can occur between relatively distant locations through various intermediaries. It is likely that these coastal zones exhibit small-scale circulation dynamics that trap larvae near the release site, influenced by interactions with eddies and seafloor topography. Sites with high local retention can maintain significant local recruitment, supporting a population over time and reducing the risk of larval loss or recruitment failure due to phenotype-environment mismatches (*King et al.*, 2023). Based on the selected release sites, larval exchanges in the Mediterranean appear to be confined within distinct basins. For instance, there is a scarcity of larval exchanges between the Algero-Provençal basin and the Tyrrhenian Sea via the Ligurian Sea. Similarly, the Alboran Sea and the Balearic Islands appear isolated from the rest of the coastlines. With the exception of the Northwestern Mediterranean, where connectivity between Corsica, Italy, and the French coastlines is consistent and regular over the study period, it is challenging to estimate links between more distant sites due to the relatively short larval duration of the spider crab. There is almost no observable connection between Catalonia and the Balearic Islands,

suggesting low connectivity with these islands. This isolation can be attributed to the hydrodynamically complex nature of the Balearic Sea, resulting from interactions between different water masses and the island's topography, which limits species exchange between these areas and other regions (*López-Jurado et al.*, 1995; *Pinot et al.*, 2002). This research contributes to our understanding of the exploitation and preservation challenges of *M. squinado* in the Mediterranean by offering initial observations on connectivity and population dynamics, highlighting the constraints on larval exchanges within the Mediterranean's distinct basins.

The reliability and significance of predictions derived from biophysical models are increasingly apparent when compared to alternative scientific methodologies. Consequently, population genetic studies on *Maja squinado* could offer invaluable insights to refine and enhance the parameterization of future simulations. For instance, such studies could improve our understanding of the species' dispersal patterns by identifying specific sub-populations around islands and across broader spatial scales. However, this integration is not straightforward. As studies have demonstrated, the genetic structures observed in populations often reflect cumulative gene flow over multiple generations, a dynamic that single-generation dispersal models are unable to capture (*Jahnke et al.*, 2018). Multi-generational dispersal models, by incorporating cumulative implicit links (e.g., siblings sharing a common ancestor) and stepping-stone dynamics, offer a more robust approach to understanding connectivity across larger spatial and temporal scales (*Ser-Giacomi et al.*, 2021). Moreover, such models have the potential to challenge traditional assumptions of strict isolation imposed by physical barriers such as gyres or strong currents, instead revealing indirect connectivity through intermediate stepping-stones (*Pascual et al.*, 2017).

Nevertheless, these genetic insights complement, rather than replace, biophysical approaches. For example, while this study focuses on larval dispersal, it is critical to acknowledge the multitude of factors influencing larval recruitment, including predation, competition, and ontogenetic traits (*Anger et al.*, 2015). Larvae may serve as a resource for some species while simultaneously being preyed upon by others, further complicating recruitment success (*Anger et al.*, 2015; *Torres et al.*, 2013). The present model does not incorporate factors affecting larval mortality, which are often driven by interspecific relationships. Future simulations would benefit from additional studies aimed at refining these parameters. For instance, it would be particularly valuable to quantify the quality and quantity of spawning events and to identify the biotic and abiotic factors influencing the survival of larval cohorts across different years. Such advancements would significantly improve the predictive power and ecological relevance of biophysical models, making them better suited for applications in conservation and fisheries management.

The findings of this study highlight that the ability to identify source and sink sites within the dispersal system is critical. From a management perspective, understanding the dispersal characteristics of these sites is essential for assessing their significance within a localized system. For instance, sites that release more larvae than they retain may be particularly vulnerable to population collapse due to overfishing, as this can reduce the number of larvae available to replenish other areas (*Cowen et al.*, 2002; *Steneck*, 2006). This issue is especially relevant for the Balearic Islands, which experience limited larval recruitment from Spain and potentially the Maghreb, while also facing significant fishing pressure (*Garcia*, 2007). The predominance of short-distance larval dispersal, compared to other species with higher dispersal ranges that raise concerns for invasions, prompts questions about the role of islands in broader species dispersal dynamics.

Areas characterized by high connectivity, such as the Corsican and Sardinian coastlines, along with Sicily, Tunisia, and southern Sardinia, offer promising opportunities for studies on the benefits of establishing a network of marine protected areas (MPAs). Such a network would safeguard larval exchange and sustain pools of breeding individuals, thereby reducing the risk of population collapses in regions that are
overly isolated and cut off from larval exchanges. For example, the depleted stocks and subsequent cessation of fishing activities observed in the Balearic Islands serve as a cautionary example of what can happen in the absence of robust larval connectivity.

These findings could be further corroborated by genetic and phylo-
genetic studies, which would provide insights into genetic differentia-
tion and relatedness between populations, ultimately revealing the
existence of distinct sub-populations. Such genetic studies have already
been conducted on the sister species *Maja brachydactyla* in the Atlantic,
where the importance of the Strait of Gibraltar in defining species
boundaries has been demonstrated (Abelló et al., 2014). Identifying
genetic barriers and patterns of gene flow would offer valuable in-
dicators for assessing larval dispersal and differentiation between pop-
ulations (Gilg and Hilbish, 2003). This information is vital for the design
of effective MPAs and the identification of nursery zones crucial to the
conservation and management of *Maja squinado*.

5. Conclusion

This study represents the first larval dispersal model of *Maja squinado*
on a Western Mediterranean scale. Our analyses, combining graphical
observations, have provided insights into the dispersal patterns and
potential impacts of climate change on the larval stages of this
commercially exploited species. The relatively short pelagic larval
duration (PLD) of *Maja squinado* likely explains the observed relation-
ship between the disappearance of the species in heavily fished areas
and poorly connected zones. For example, the Balearic Islands, which
are geographically isolated, do not consistently connect with other
coastal areas due to the limited pelagic larval duration of the spinous
spider crab. This isolation could be a primary factor in the species'
decline in these regions. However, this hypothesis requires further
investigation, including genetic analyses, to better understand the level
of population isolation. The predictions from our simulations allow us to
observe interannual changes, especially in the context of post-warming
acceleration shifts. It is of the utmost importance to incorporate these

climatic considerations into future models, as global changes not only
affect the dynamics of ocean currents, which are crucial for larval
dispersal, but also directly impact the physiology of the modeled or-
ganisms. Our results emphasize the necessity to integrate biophysical
modelling approaches with other methodologies, particularly genetic
studies. For instance, it is of the utmost importance to determine
whether the populations within the Sardinia-Corsica cluster are homo-
geneous or exhibit significant genetic differentiation. Such combined
approaches will provide a more comprehensive understanding of the
population dynamics and connectivity of *Maja squinado*. In conclusion,
while the current study provides valuable insights, it also highlights the
necessity for multidisciplinary research to address the complex in-
teractions between environmental changes, species physiology, and
population connectivity.

CRediT authorship contribution statement

**C. Barrier:** Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation, Conceptualization, Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **T. Beneteau:** Writing – review & editing, Methodology, Investigation, Data curation, Conceptualization. **M.-C. Raffalli:** Writing – review & editing. **N. Barrier:** Writing – review & editing, Software, Methodology. **C. Lett:** Writing – review & editing, Software, Methodology. **V. Pasqualini:** Writing – review & editing, Supervision, Methodology. **E.D.H. Durieux:** Writing – review & editing, Supervision, Methodology.

Declaration of competing interest

The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.

Appendices.

A1: The sea surface temperature data extracted from the Copernicus Marine Environment Monitoring Service (CMEMS) satellite product over the western Mediterranean Sea demonstrated a temperature increase trend of 0.034 ± 0.002 °C per year, with a 95% confidence interval. To analyze trends spatially and focus on the last decade, data from the Med MFC physical multiyear product (Mediterranean Sea Physics Reanalysis) were examined at all points in the Western Mediterranean (focusing on our study window). A linear temperature trend was calculated for each grid point based on time series data through linear regression. A color gradient from blue (indicating a decrease in temperature) to red (indicating an increase in temperature) was used to map these trends. The detailed analysis is presented in figure A1 below.

<figure><img src="image_14.png" /><figcaption>Fig. A1. Sea Surface Temperature Trends in the Western Mediterranean from 2010 to 2020. This map illustrates the decadal trend in sea surface temperatures, with the color scale ranging from -0.050 °C (blue) to +0.150 °C (red), highlighting areas of temperature decrease and increase relative to the period average.</figcaption></figure>

A2: Table of statistical test results for larval dispersal distances: this table presents the results of three statistical analyses applied to the distances traveled by larvae successfully reaching potential recruitment areas, originating from selected zones: SP9 (France-Spain border) and SWCO (southern coast of Corsica). The tests include the Shapiro-Wilk test for normality, the Kruskal-Wallis test for differences between distributions, and Dunn's post hoc test (with Bonferroni correction applied). The significance threshold was set at α = 0.05. Results detail whether the normality
assumption was met, significant differences were found between scenarios, and pairwise comparisons yielded statistically significant results.

<table><thead><tr><th>Zone</th><th>Period</th><th>Test</th><th>P-value</th><th>Result</th></tr></thead><tbody><tr><td>SP9</td><td>2010–2014</td><td>Shapiro-Wilk</td><td>&lt;1e-42</td><td>Non normal</td></tr><tr><td>SP9</td><td>2015–2019</td><td>Shapiro-Wilk</td><td>&lt;1e-43</td><td>Non normal</td></tr><tr><td>SP9</td><td>2010–2014 vs 2015–2019</td><td>Kruskal-Wallis</td><td>0.374</td><td>Non significant</td></tr><tr><td>SWCO</td><td>2010–2014</td><td>Shapiro-Wilk</td><td>&lt;1e-49</td><td>Non normal</td></tr><tr><td>SWCO</td><td>2015–2019</td><td>Shapiro-Wilk</td><td>&lt;1e-51</td><td>Non normal</td></tr><tr><td>SWCO</td><td>2010–2014 vs 2015–2019</td><td>Kruskal-Wallis</td><td>&lt;1e-42</td><td>Significant</td></tr><tr><td>SWCO</td><td>2010 vs 2010</td><td>Dunn</td><td>1.0</td><td>Non significant</td></tr><tr><td>SWCO</td><td>2011 vs 2010</td><td>Dunn</td><td>&lt;1e-47</td><td>Significant</td></tr><tr><td>SWCO</td><td>2012 vs 2010</td><td>Dunn</td><td>&lt;1e-39</td><td>Significant</td></tr><tr><td>SWCO</td><td>2013 vs 2010</td><td>Dunn</td><td>&lt;1e-37</td><td>Significant</td></tr><tr><td>SWCO</td><td>2014 vs 2010</td><td>Dunn</td><td>0.0</td><td>Significant</td></tr><tr><td>SWCO</td><td>2015 vs 2010</td><td>Dunn</td><td>&lt;1e-143</td><td>Significant</td></tr><tr><td>SWCO</td><td>2016 vs 2010</td><td>Dunn</td><td>0.052</td><td>Non significant</td></tr><tr><td>SWCO</td><td>2017 vs 2010</td><td>Dunn</td><td>&lt;1e-114</td><td>Significant</td></tr><tr><td>SWCO</td><td>2018 vs 2010</td><td>Dunn</td><td>&lt;1e-28</td><td>Significant</td></tr><tr><td>SWCO</td><td>2019 vs 2010</td><td>Dunn</td><td>&lt;1e-12</td><td>Significant</td></tr><tr><td>SWCO</td><td>2010 vs 2011</td><td>Dunn</td><td>&lt;1e-47</td><td>Significant</td></tr><tr><td>SWCO</td><td>2011 vs 2011</td><td>Dunn</td><td>1.0</td><td>Non significant</td></tr><tr><td>SWCO</td><td>2012 vs 2011</td><td>Dunn</td><td>0.275</td><td>Non significant</td></tr><tr><td>SWCO</td><td>2013 vs 2011</td><td>Dunn</td><td>0.031</td><td>Significant</td></tr><tr><td>SWCO</td><td>2014 vs 2011</td><td>Dunn</td><td>&lt;1e-100</td><td>Significant</td></tr><tr><td>SWCO</td><td>2015 vs 2011</td><td>Dunn</td><td>&lt;1e-32</td><td>Significant</td></tr><tr><td>SWCO</td><td>2016 vs 2011</td><td>Dunn</td><td>&lt;1e-16</td><td>Significant</td></tr><tr><td>SWCO</td><td>2017 vs 2011</td><td>Dunn</td><td>&lt;1e-287</td><td>Significant</td></tr><tr><td>SWCO</td><td>2018 vs 2011</td><td>Dunn</td><td>1.0</td><td>Non significant</td></tr><tr><td>SWCO</td><td>2019 vs 2011</td><td>Dunn</td><td>&lt;1e-106</td><td>Significant</td></tr><tr><td>SWCO</td><td>2010 vs 2012</td><td>Dunn</td><td>&lt;1e-39</td><td>Significant</td></tr><tr><td>SWCO</td><td>2011 vs 2012</td><td>Dunn</td><td>0.275</td><td>Non significant</td></tr><tr><td>SWCO</td><td>2012 vs 2012</td><td>Dunn</td><td>1.0</td><td>Non significant</td></tr><tr><td>SWCO</td><td>2013 vs 2012</td><td>Dunn</td><td>1.0</td><td>Non significant</td></tr><tr><td>SWCO</td><td>2014 vs 2012</td><td>Dunn</td><td>&lt;1e-72</td><td>Significant</td></tr><tr><td>SWCO</td><td>2015 vs 2012</td><td>Dunn</td><td>&lt;1e-10</td><td>Significant</td></tr><tr><td>SWCO</td><td>2016 vs 2012</td><td>Dunn</td><td>&lt;1e-19</td><td>Significant</td></tr><tr><td>SWCO</td><td>2017 vs 2012</td><td>Dunn</td><td>&lt;1e-185</td><td>Significant</td></tr><tr><td>SWCO</td><td>2018 vs 2012</td><td>Dunn</td><td>1.0</td><td>Non significant</td></tr><tr><td>SWCO</td><td>2019 vs 2012</td><td>Dunn</td><td>&lt;1e-77</td><td>Significant</td></tr><tr><td>SWCO</td><td>2010 vs 2013</td><td>Dunn</td><td>&lt;1e-37</td><td>Significant</td></tr><tr><td>SWCO</td><td>2011 vs 2013</td><td>Dunn</td><td>0.031</td><td>Significant</td></tr><tr><td>SWCO</td><td>2012 vs 2013</td><td>Dunn</td><td>1.0</td><td>Non significant</td></tr><tr><td>SWCO</td><td>2013 vs 2013</td><td>Dunn</td><td>1.0</td><td>Non significant</td></tr><tr><td>SWCO</td><td>2014 vs 2013</td><td>Dunn</td><td>&lt;1e-65</td><td>Significant</td></tr><tr><td>SWCO</td><td>2015 vs 2013</td><td>Dunn</td><td>0.0</td><td>Significant</td></tr><tr><td>SWCO</td><td>2016 vs 2013</td><td>Dunn</td><td>&lt;1e-19</td><td>Significant</td></tr><tr><td>SWCO</td><td>2017 vs 2013</td><td>Dunn</td><td>&lt;1e-162</td><td>Significant</td></tr><tr><td>SWCO</td><td>2018 vs 2013</td><td>Dunn</td><td>1.0</td><td>Non significant</td></tr><tr><td>SWCO</td><td>2019 vs 2013</td><td>Dunn</td><td>&lt;1e-70</td><td>Significant</td></tr><tr><td>SWCO</td><td>2010 vs 2014</td><td>Dunn</td><td>0.0</td><td>Significant</td></tr><tr><td>SWCO</td><td>2011 vs 2014</td><td>Dunn</td><td>&lt;1e-100</td><td>Significant</td></tr><tr><td>SWCO</td><td>2012 vs 2014</td><td>Dunn</td><td>&lt;1e-72</td><td>Significant</td></tr><tr><td>SWCO</td><td>2013 vs 2014</td><td>Dunn</td><td>&lt;1e-65</td><td>Significant</td></tr><tr><td>SWCO</td><td>2014 vs 2014</td><td>Dunn</td><td>1.0</td><td>Non significant</td></tr><tr><td>SWCO</td><td>2015 vs 2014</td><td>Dunn</td><td>&lt;1e-218</td><td>Significant</td></tr><tr><td>SWCO</td><td>2016 vs 2014</td><td>Dunn</td><td>&lt;1e-15</td><td>Significant</td></tr><tr><td>SWCO</td><td>2017 vs 2014</td><td>Dunn</td><td>&lt;1e-61</td><td>Significant</td></tr><tr><td>SWCO</td><td>2018 vs 2014</td><td>Dunn</td><td>&lt;1e-52</td><td>Significant</td></tr><tr><td>SWCO</td><td>2019 vs 2014</td><td>Dunn</td><td>1.0</td><td>Non significant</td></tr><tr><td>SWCO</td><td>2010 vs 2015</td><td>Dunn</td><td>&lt;1e-143</td><td>Significant</td></tr><tr><td>SWCO</td><td>2011 vs 2015</td><td>Dunn</td><td>&lt;1e-32</td><td>Significant</td></tr><tr><td>SWCO</td><td>2012 vs 2015</td><td>Dunn</td><td>&lt;1e-10</td><td>Significant</td></tr><tr><td>SWCO</td><td>2013 vs 2015</td><td>Dunn</td><td>0.0</td><td>Significant</td></tr><tr><td>SWCO</td><td>2014 vs 2015</td><td>Dunn</td><td>&lt;1e-218</td><td>Significant</td></tr><tr><td>SWCO</td><td>2015 vs 2015</td><td>Dunn</td><td>1.0</td><td>Non significant</td></tr><tr><td>SWCO</td><td>2016 vs 2015</td><td>Dunn</td><td>&lt;1e-78</td><td>Significant</td></tr><tr><td>SWCO</td><td>2017 vs 2015</td><td>Dunn</td><td>&lt;1e-300</td><td>Significant</td></tr><tr><td>SWCO</td><td>2018 vs 2015</td><td>Dunn</td><td>0.0</td><td>Significant</td></tr><tr><td>SWCO</td><td>2019 vs 2015</td><td>Dunn</td><td>&lt;1e-224</td><td>Significant</td></tr><tr><td>SWCO</td><td>2010 vs 2016</td><td>Dunn</td><td>0.052</td><td>Non significant</td></tr><tr><td>SWCO</td><td>2011 vs 2016</td><td>Dunn</td><td>&lt;1e-16</td><td>Significant</td></tr><tr><td>SWCO</td><td>2012 vs 2016</td><td>Dunn</td><td>&lt;1e-19</td><td>Significant</td></tr><tr><td>SWCO</td><td>2013 vs 2016</td><td>Dunn</td><td>&lt;1e-19</td><td>Significant</td></tr><tr><td>SWCO</td><td>2014 vs 2016</td><td>Dunn</td><td>&lt;1e-15</td><td>Significant</td></tr><tr><td>SWCO</td><td>2015 vs 2016</td><td>Dunn</td><td>&lt;1e-78</td><td>Significant</td></tr><tr><td>SWCO</td><td>2016 vs 2016</td><td>Dunn</td><td>1.0</td><td>Non significant</td></tr></tbody></table>

(continued on next page)
(continued)

<table><thead><tr><th>Zone</th><th>Period</th><th>Test</th><th>P-value</th><th>Result</th></tr></thead><tbody><tr><td>SWCO</td><td>2017 vs 2016</td><td>Dunn</td><td>&lt;1e-99</td><td>Significant</td></tr><tr><td>SWCO</td><td>2018 vs 2016</td><td>Dunn</td><td>&lt;1e-13</td><td>Significant</td></tr><tr><td>SWCO</td><td>2019 vs 2016</td><td>Dunn</td><td>&lt;1e-18</td><td>Significant</td></tr><tr><td>SWCO</td><td>2010 vs 2017</td><td>Dunn</td><td>&lt;1e-114</td><td>Significant</td></tr><tr><td>SWCO</td><td>2011 vs 2017</td><td>Dunn</td><td>&lt;1e-287</td><td>Significant</td></tr><tr><td>SWCO</td><td>2012 vs 2017</td><td>Dunn</td><td>&lt;1e-185</td><td>Significant</td></tr><tr><td>SWCO</td><td>2013 vs 2017</td><td>Dunn</td><td>&lt;1e-162</td><td>Significant</td></tr><tr><td>SWCO</td><td>2014 vs 2017</td><td>Dunn</td><td>&lt;1e-61</td><td>Significant</td></tr><tr><td>SWCO</td><td>2015 vs 2017</td><td>Dunn</td><td>&lt;1e-300</td><td>Significant</td></tr><tr><td>SWCO</td><td>2016 vs 2017</td><td>Dunn</td><td>&lt;1e-99</td><td>Significant</td></tr><tr><td>SWCO</td><td>2017 vs 2017</td><td>Dunn</td><td>1.0</td><td>Non significant</td></tr><tr><td>SWCO</td><td>2018 vs 2017</td><td>Dunn</td><td>&lt;1e-139</td><td>Significant</td></tr><tr><td>SWCO</td><td>2019 vs 2017</td><td>Dunn</td><td>&lt;1e-50</td><td>Significant</td></tr><tr><td>SWCO</td><td>2010 vs 2018</td><td>Dunn</td><td>&lt;1e-28</td><td>Significant</td></tr><tr><td>SWCO</td><td>2011 vs 2018</td><td>Dunn</td><td>1.0</td><td>Non significant</td></tr><tr><td>SWCO</td><td>2012 vs 2018</td><td>Dunn</td><td>1.0</td><td>Non significant</td></tr><tr><td>SWCO</td><td>2013 vs 2018</td><td>Dunn</td><td>1.0</td><td>Non significant</td></tr><tr><td>SWCO</td><td>2014 vs 2018</td><td>Dunn</td><td>&lt;1e-52</td><td>Significant</td></tr><tr><td>SWCO</td><td>2015 vs 2018</td><td>Dunn</td><td>0.0</td><td>Significant</td></tr><tr><td>SWCO</td><td>2016 vs 2018</td><td>Dunn</td><td>&lt;1e-13</td><td>Significant</td></tr><tr><td>SWCO</td><td>2017 vs 2018</td><td>Dunn</td><td>&lt;1e-139</td><td>Significant</td></tr><tr><td>SWCO</td><td>2018 vs 2018</td><td>Dunn</td><td>1.0</td><td>Non significant</td></tr><tr><td>SWCO</td><td>2019 vs 2018</td><td>Dunn</td><td>&lt;1e-56</td><td>Significant</td></tr><tr><td>SWCO</td><td>2010 vs 2019</td><td>Dunn</td><td>&lt;1e-12</td><td>Significant</td></tr><tr><td>SWCO</td><td>2011 vs 2019</td><td>Dunn</td><td>&lt;1e-106</td><td>Significant</td></tr><tr><td>SWCO</td><td>2012 vs 2019</td><td>Dunn</td><td>&lt;1e-77</td><td>Significant</td></tr><tr><td>SWCO</td><td>2013 vs 2019</td><td>Dunn</td><td>&lt;1e-70</td><td>Significant</td></tr><tr><td>SWCO</td><td>2014 vs 2019</td><td>Dunn</td><td>1.0</td><td>Non significant</td></tr><tr><td>SWCO</td><td>2015 vs 2019</td><td>Dunn</td><td>&lt;1e-224</td><td>Significant</td></tr><tr><td>SWCO</td><td>2016 vs 2019</td><td>Dunn</td><td>&lt;1e-18</td><td>Significant</td></tr><tr><td>SWCO</td><td>2017 vs 2019</td><td>Dunn</td><td>&lt;1e-50</td><td>Significant</td></tr><tr><td>SWCO</td><td>2018 vs 2019</td><td>Dunn</td><td>&lt;1e-56</td><td>Significant</td></tr><tr><td>SWCO</td><td>2019 vs 2019</td><td>Dunn</td><td>1.0</td><td>Non significant</td></tr></tbody></table>

## Data availability

Data will be made available on request.

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Further reading

Arakawa, A., 1977. Computational design of the basic dynamical processes of the UCLA general circulation model. Methods Comput. Phys: Adv. Res. Appl. 17, 337. General circulation models of the atmosphere.

Balss, H., Crustacea, V.I.I., 1922. Decapoda Brachyura (Oxyrhyncha und Brachyrhyncha) und geographische Übersicht über Crustacea Decapoda. Beitr. Kennt. Meeresfauna Westafr. 3, 71–110.

Steneck, R.S., 2006. Staying connected in a turbulent world. Science 311 (5760), 480–481.