# Combining population genomics, larval dispersal modelling and graph theory to reveal connectivity patterns of the black-lipped pearl oyster in French Polynesia

Antoine Gradel, Laura Benestan, Térence Legrand, Jérémy Le Luyer, Serge Planes, Céline Reisser

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## To cite this version:

Antoine Gradel, Laura Benestan, Térence Legrand, Jérémy Le Luyer, Serge Planes, et al. Combining population genomics, larval dispersal modelling and graph theory to reveal connectivity patterns of the black-lipped pearl oyster in French Polynesia. Scientific Reports, 2025, 15 (1), pp.24611. 10.1038/s41598-025-10113-y. hal-05184689

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# Combining population genomics, larval dispersal modelling and graph theory to reveal connectivity patterns of the black-lipped pearl oyster in French Polynesia

Antoine Gradel<fnref n="1" />,⁵, Laura Benestan³,⁴, Térence Legrand², Jérémy Le Luyer³,⁴, Serge Planes⁵ & Céline M.O. Reisser<fnref n="1" />✉

Unravelling the mechanisms driving marine connectivity in open ocean environments remains a challenge, particularly for benthopelagic species, where larval dispersal further complicates analysis. Integrating currentology with population genomics provides a powerful framework for inferring population connectivity and guiding conservation strategies. In this study, we combined population genomics (26,080 SNPs on 872 individuals), multigeneration connectivity estimates from larval dispersal modelling, and graph theory to assess the connectivity patterns of the pearl oyster (Pinctada margaritifera) across French Polynesia. We identified unprecedented fine-scale population structure with genetically distinct populations among four of the five archipelagos (excluding the Austral Archipelago), revealing a unique genetic signature of these archipelago. Global population structure aligns with coalescent connectivity estimates (R2 = 0.6), but discrepancies between the two approaches highlighted the influence of both historical and contemporary processes on present-day population structure. The Last Glacial Maximum may have played a role in shaping the genetic structure of *P. margaritifera* in French Polynesia, while modern pearl farming practices appear to have contributed to genetic homogenization. These results provide crucial insights for delineating management units to ensure the sustainable exploitation and diversity conservation of pearl oysters in French Polynesia. They also show the value of combining approaches to study connectivity.

**Keywords** *Pinctada margaritifera*, Population genomics, French Polynesia, Coalescent connectivity

Understanding the movement of individuals or gametes across landscapes is fundamental to deciphering a species' population dynamics and resilience<fnref n="1" />⁻⁴. This task is however particularly challenging in marine coastal ecosystems, where many taxa disperse primarily through a larval stage, before settling and adopting a sessile or sedentary behavior. The dispersal of the planktonic phase is largely driven by ocean currents⁵, which can either promote connectivity over hundreds of kilometres⁶,⁷ or act as invisible barriers to dispersal⁸⁻¹⁰.

A cross-disciplinary approach that integrates population genetics/genomics with larval dispersal modeling offers a robust framework for interpreting connectivity patterns which were previously challenging to discern, at both broad and fine spatial scales¹¹⁻¹⁵. Numerous studies have highlighted the critical role of ocean currents in shaping the connectivity of organisms with a benthic-planktonic life cycle, including vertebrates¹² and invertebrates¹⁶,¹⁷. However, these studies often overlooked the temporal mismatch between genomic data and larval dispersal models. While larval dispersal modelling relies on mostly contemporary data to estimate potential demographic exchange over a single generation, population genomics captures the realized connectivity—reflecting settlement and reproduction—over multiple generations, thus providing insights on a longer timescale. Incorporating multigenerational connectivity estimates using graph theory (mathematical

<footnote n="1">¹MARBEC, Univ Montpellier, CNRS, IFREMER, Montpellier, IRD, France. ²Aix Marseille Univ, Université de Toulon, CNRS, IRD, MIO, 13228 Marseille, France. ³Ifremer, IRD, Institut Louis-Malardé, Univ Polynésie Française, EIO, F-98719 Taravao, Tahiti, Polynésie Française, France. ⁴Ifremer, Univ Brest, CNRS, IRD, UMR 6539, LEMAR, 29280 Plouzane, France. ⁵PSL Research University, EPHE-UPVD-CNRS, USR 3278 CRIOBE, Labex Corail, Université de Perpignan, 52 Avenue Paul Alduy, 66860 Perpignan Cedex, France. ✉email: celine.reisser@ifremer.fr</footnote>
structures used to model pairwise relationships between objects in a network) could help to bridge this temporal gap, improving concordance between the two approaches<sup>14,18–21</sup>. When this temporal perspective is further refined by considering implicit links among nodes (i.e. individuals) from a commun ancestor<sup>22</sup>, the alignment between such coalescent connectivity estimates and genetic differentiation observed through molecular markers improves significantly<sup>19</sup>. Such approach can provide critical insights into the eco-evolutionary processes shaping population connectivity at both large and fine spatial scales, offering valuable guidance for conservation and resource management<sup>23</sup>.

French Polynesia is composed of 118 islands spread over an area exceeding 4,000 km2, grouped into five archipelagos: Marquesas, Society, Tuamotu, Austral, and Gambier. Pearl farming plays a crucial role in the region's economy<sup>24</sup>, being the second largest economic resource for the country after tourism. The Tahitian black pearl industry relies on the exploitation of the black-lipped pearl oyster *Pinctada margaritifera* var. *cumingii*, a sessile broadcast spawner with a pelagic larval duration (PLD) of 21 to 30 days<sup>25,26</sup>. Natural populations of *P. margaritifera* are protected<sup>27</sup>, and pearl farming currently depends almost entirely on the collection of wild spat. Three main islands or atolls serve as spat collection sites: Mangareva in the Gambier Archipelago, Ahe and Manihi in the Tuamotu Archipelago. Juveniles from these collecting atolls are then transferred to “farming” atolls, which numbered 27 in 2022 (Supplementary Table 1). While such transfers were historically unregulated, they are now restricted to movements within the same archipelago, in order to limit the ecological and genetic risks related to transfers. Nowadays, spat production is declining in most islands, even in the collecting atolls<sup>24</sup>, and there is an increasing need to characterize the population structure of both natural and farmed populations of *P. margaritifera*, to implement sustainable management practices for the species.

Previous population genetic studies of *Pinctada margaritifera* have been limited to a handful of mitochondrial or nuclear markers (ranging from seven to 15) and a restricted number of localities across the five archipelagos (four to ten islands), mostly due to the high levels of habitat fragmentation in the region<sup>28–31</sup>. These studies revealed that the Marquesas Archipelago are genetically isolated from the rest of French Polynesia, whereas a homogeneous population structure was observed among the other archipelagos. The genetic distinctiveness of the Marquesas has also been noted in other marine species, inflicting its classification as a distinct province within the Eastern Indo-Pacific ecoregion<sup>32</sup>. In contrast, the absence of population structure among the Tuamotu, Society, Gambier and Austral Archipelagos in *Pinctada margaritifera* has been attributed to historical spat transfers for pearl farming<sup>29–31</sup>. However, the limited number of localities and genetic markers used in these studies may have influenced these findings, and while a recent phylogenetic study of *P. margaritifera* used thousands of genome-wide SNP markers from 580 individuals across 14 sampling locations, it only included a single site from French Polynesia (Arutua in the Tuamotu Archipelago)<sup>33</sup>. As a result, a comprehensive assessment of *P. margaritifera*'s population structure across the entirety of French Polynesia remains to be conducted.

A recent larval dispersal modeling of *Pinctada margaritifera* identified five distinct clusters corresponding to the five archipelagos, with additional sub-structuring within the Tuamotu Archipelago<sup>34</sup>. The model also showed complete isolation of the Austral Archipelago and a unidirectional connection from the Marquesas to the Tuamotu Archipelago. These single-generation connectivity matrices indicate that population structure at the archipelago level may currently exist, or may have existed in the past, and that a greater genetic differentiation of the Marquesas and Austral Archipelagos are expected.

In this study, we investigated the genetic and oceanographic connectivity of *Pinctada margaritifera* populations across French Polynesia by comparing, for the first time, genome-wide population structure data from 24 islands spanning the five archipelagos with multigeneration connectivity estimates obtained from larval dispersal modelling. Our analysis sought to answer three key questions: (i) Can we detect the presence of genetic clusters within and/or among archipelagos? (ii) Are the genetic patterns consistent with predictions from physical models? (iii) To what extent do demographic history and pearl farming practices shape the contemporary genetic structure of *P. margaritifera*?

## Methods

### Sampling and DNA extraction

A total of 884 wild individuals of *Pinctada margaritifera* were non-lethally sampled from 24 locations across the five archipelagos of French Polynesia (Fig. 1, Table 1). Additionally, a wild population from Indonesia was sampled as an outgroup to better assess the levels of genetic differentiation within French Polynesia and perform admixture analyses (Table 1). Individuals with varying shell lengths were selected to capture a broad age range and avoid potential cohort effects. On Nuku Hiva island (Marquesas Archipelago), lagoon habitats are absent, and sampling targeted the two known habitats of *P. margaritifera*: intertidal individuals were collected from separate tide pools to mitigate the family effect associated with spatially clustered individuals, while subtidal individuals were sampled from the external slopes using the same protocols as those for the lagoon individuals.

For each sample, a 3 mm³ piece of mantle tissue was extracted and immediately placed in a tube containing 95% ethanol (EtOH). The samples were stored 24 h at room temperature before being transferred at –20 °C storage until DNA extraction. DNA was extracted from 20 mg mantle tissue with a TECAN robot (Freedom Evo 100/4), using NucleoSpin® 96 Tissue Core DNA extraction kit (Machery-Nagel) following the manufacturer's recommendations, including an overnight digestion. Genomic DNA (gDNA) concentration was assessed using a Nanodrop, and was stored at –20 °C before shipping to Ramaciotti Centre for Genomics (University of New South Wales, Sydney), for genotyping on a custom ThermoFisher/Affymetrix 70 k Axiom Array specifically designed for *P. margaritifera* (chip development information in<sup>35</sup>).

### Filtering steps

From the Affymetrix chip genotyping, 43,312 SNPs were obtained for 884 individuals. We used Plink v.1.9<sup>36</sup> to remove individuals with more than 10% missing data, as well as SNPs with a genotype call rate below 80%. SNPs
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Sampling map for the genetic analysis in French Polynesia. Dotted circles delineate archipelagos. Red dot indicates island currently used in pearl farming and black dot islands without peal farming. Location code is explained in Table 1.</figcaption></figure>

were further filtered based on minor allele frequency (MAF > 0.05). We then controlled SNPs for departure from Hardy-Weinberg equilibrium with *gl.filter.hwe* function of DartR R v.2.9.7 package³⁷ and removed those having a p-value < 0.05 in 12 or more sampling localities. We calculated identity by state (IBS) among samples using the package DartR and the *gl.grm* function, in order to remove putative siblings: 4 pairs (3 at Takapoto, 1 at Katiu) were identified as sibling or parent-offspring (r = 0.25) and one pair from Motutunga (Tuamotu Archipelago) were identified as half-siblings (r = 0.125). These individuals were excluded from the analysis. After filtering, we retained a total of 872 individuals and 26,080 SNPs (see Supplementary Table 2. for filtering statistics).

Identifying non-neutral SNPs

To identify neutral and putatively selected SNPs (i.e. outliers) among our 24 sampled sites, we used three approaches: two F<sub>ST</sub>-based methods (BayeScan and OutFLANK) which require prior information on population structure, and one principal component analysis (PCAdapt) which has no prior on putative structuring. Bayesian 2.1<sup>38</sup> was run with 20 pilot runs of length 5,000, a burn-in of 100,000 steps, and a thinning interval of 10. OutFLANK v0.2<sup>39</sup> was run with left and right trimming fractions set at 0.05. PCAdapt v.4.3.3<sup>40,41</sup> was performed with two Principal Components (PCs) retained. Significance for all three analyses was determined using an FDR-adjusted q-value of 0.05.

To better characterize the presence of putative genetic structuring at a smaller scale, we reran the outlier detection using the same threshold but excluding individuals from the Marquesas Archipelago and the Indonesian samples (“reduced” dataset).

Two SNP panels (outlier and neutral) were generated for each dataset (global and reduced) and analyzed separately.
<table><caption>**Table 1.** Sampling characteristics of the study. ID: identification code of location in this study; Lat/Long: latitudinal and longitudinal coordinates; H<sub>O</sub>: observed heterozygosity; H<sub>E</sub>: expected heterozygosity; F<sub>IS</sub>: estimated F<sub>IS</sub>; a, b and c indicates the significant difference of H<sub>O</sub> among the archipelagos, from a Student t test.</caption><thead><tr><th rowspan="2">Island/Atoll</th><th rowspan="2">ID</th><th rowspan="2">N</th><th rowspan="2">Longitude</th><th rowspan="2">Latitude</th><th colspan="3">Neutral panel (24,007 SNPs)</th><th colspan="3">Outlier panel (2,073 SNPs)</th></tr><tr><th>H<sub>O</sub></th><th>H<sub>E</sub></th><th>F<sub>IS</sub></th><th>H<sub>O</sub></th><th>H<sub>E</sub></th><th>F<sub>IS</sub></th></tr></thead><tbody><tr><td colspan="10"><strong>Marquesas<sup>a</sup></strong></td></tr><tr><td>Nuku Hiva</td><td>NHV-S</td><td>17</td><td>-1,40,133</td><td>-8.866</td><td>0.33 ± 0.17</td><td>0.34 ± 0.15</td><td>0.05</td><td>0.14 ± 0.16</td><td>0.15 ± 0.16</td><td>0.1</td></tr><tr><td>Nuku Hiva</td><td>NHV-P</td><td>12</td><td>-1,40,133</td><td>-8.866</td><td>0.33 ± 0.19</td><td>0.34 ± 0.16</td><td>0.04</td><td>0.15 ± 0.17</td><td>0.15 ± 0.16</td><td>0.1</td></tr><tr><td>Ua Huka</td><td>UAH</td><td>30</td><td>-1,39,552</td><td>-8.909</td><td>0.33 ± 0.18</td><td>0.34 ± 0.16</td><td>0.05</td><td>0.14 ± 0.16</td><td>0.15 ± 0.16</td><td>0.1</td></tr><tr><td colspan="10"><strong>Society<sup>b</sup></strong></td></tr><tr><td>Scilly</td><td>SCI</td><td>90</td><td>-1,54,690</td><td>-16.52</td><td>0.37 ± 0.14</td><td>0.40 ± 0.13</td><td>0.08</td><td>0.37 ± 0.13</td><td>0.41 ± 0.12</td><td>0.1</td></tr><tr><td>Mopélia</td><td>MOP</td><td>36</td><td>-1,53,956</td><td>-16.813</td><td>0.37 ± 0.15</td><td>0.39 ± 0.13</td><td>0.08</td><td>0.37 ± 0.14</td><td>0.40 ± 0.13</td><td>0.1</td></tr><tr><td colspan="10"><strong>Tuamotus<sup>b</sup></strong></td></tr><tr><td>Ahe</td><td>AHE</td><td>50</td><td>-1,46,309</td><td>-14.486</td><td>0.37 ± 0.14</td><td>0.39 ± 0.13</td><td>0.08</td><td>0.38 ± 0.14</td><td>0.41 ± 0.12</td><td>0.1</td></tr><tr><td>Anaa</td><td>ANA</td><td>19</td><td>-1,45,494</td><td>-17.412</td><td>0.36 ± 0.17</td><td>0.39 ± 0.13</td><td>0.09</td><td>0.38 ± 0.17</td><td>0.40 ± 0.13</td><td>0.1</td></tr><tr><td>Aratika</td><td>ARA</td><td>33</td><td>-1,45,525</td><td>-15.532</td><td>0.36 ± 0.15</td><td>0.39 ± 0.13</td><td>0.09</td><td>0.38 ± 0.15</td><td>0.41 ± 0.12</td><td>0.1</td></tr><tr><td>Katiu</td><td>KAT</td><td>33</td><td>-1,44,357</td><td>-16.428</td><td>0.37 ± 0.15</td><td>0.39 ± 0.13</td><td>0.08</td><td>0.38 ± 0.15</td><td>0.41 ± 0.12</td><td>0.1</td></tr><tr><td>Kauehi</td><td>KAU</td><td>34</td><td>-1,45,151</td><td>-15.876</td><td>0.37 ± 0.15</td><td>0.39 ± 0.13</td><td>0.08</td><td>0.38 ± 0.15</td><td>0.41 ± 0.12</td><td>0.1</td></tr><tr><td>Manihi</td><td>MAN</td><td>35</td><td>-1,45,950</td><td>-14.402</td><td>0.37 ± 0.15</td><td>0.39 ± 0.13</td><td>0.08</td><td>0.38 ± 0.14</td><td>0.41 ± 0.12</td><td>0.1</td></tr><tr><td>Motutunga</td><td>MOT</td><td>33</td><td>-1,44,364</td><td>-17.104</td><td>0.37 ± 0.15</td><td>0.39 ± 0.13</td><td>0.08</td><td>0.38 ± 0.15</td><td>0.41 ± 0.12</td><td>0.1</td></tr><tr><td>Raraka</td><td>RAR</td><td>40</td><td>-1,44,889</td><td>-16.173</td><td>0.36 ± 0.15</td><td>0.39 ± 0.13</td><td>0.08</td><td>0.38 ± 0.15</td><td>0.41 ± 0.12</td><td>0.1</td></tr><tr><td>Tahanea</td><td>TAH</td><td>8</td><td>-1,44,758</td><td>-16.873</td><td>0.36 ± 0.21</td><td>0.37 ± 0.15</td><td>0.08</td><td>0.38 ± 0.21</td><td>0.38 ± 0.14</td><td>0.1</td></tr><tr><td>Takume</td><td>TAK</td><td>87</td><td>-1,42,198</td><td>-15.798</td><td>0.37 ± 0.14</td><td>0.40 ± 0.13</td><td>0.08</td><td>0.38 ± 0.14</td><td>0.41 ± 0.12</td><td>0.1</td></tr><tr><td>Taenga</td><td>TEA</td><td>34</td><td>-1,43,127</td><td>-16.352</td><td>0.37 ± 0.15</td><td>0.39 ± 0.13</td><td>0.08</td><td>0.38 ± 0.15</td><td>0.41 ± 0.13</td><td>0.1</td></tr><tr><td>Takapoto</td><td>TKP</td><td>34</td><td>-1,45,203</td><td>-14.628</td><td>0.37 ± 0.15</td><td>0.39 ± 0.13</td><td>0.08</td><td>0.38 ± 0.15</td><td>0.41 ± 0.12</td><td>0.1</td></tr><tr><td colspan="10"><strong>Gambier<sup>b</sup></strong></td></tr><tr><td>Mangarave</td><td>GMBW</td><td>48</td><td>-1,34,990</td><td>-23.1</td><td>0.37 ± 0.14</td><td>0.39 ± 0.13</td><td>0.08</td><td>0.38 ± 0.14</td><td>0.41 ± 0.12</td><td>0.1</td></tr><tr><td>Morane</td><td>MOR</td><td>11</td><td>-1,37,133</td><td>-23.158</td><td>0.36 ± 0.15</td><td>0.39 ± 0.13</td><td>0.08</td><td>0.36 ± 0.16</td><td>0.39 ± 0.13</td><td>0.1</td></tr><tr><td>Marutea Sud</td><td>MARS</td><td>30</td><td>-1,35,558</td><td>-21.519</td><td>0.37 ± 0.15</td><td>0.39 ± 0.13</td><td>0.08</td><td>0.38 ± 0.14</td><td>0.41 ± 0.12</td><td>0.1</td></tr><tr><td>Peiriki</td><td>PEI</td><td>40</td><td>-1,34,950</td><td>-23.08</td><td>0.36 ± 0.19</td><td>0.38 ± 0.14</td><td>0.08</td><td>0.37 ± 0.19</td><td>0.39 ± 0.14</td><td>0.1</td></tr><tr><td>Matai'utea</td><td>MAT</td><td>39</td><td>-1,34,930</td><td>-23.09</td><td>0.37 ± 0.15</td><td>0.39 ± 0.13</td><td>0.08</td><td>0.38 ± 0.14</td><td>0.41 ± 0.12</td><td>0.1</td></tr><tr><td>Rikitea</td><td>RIK</td><td>40</td><td>-1,34,970</td><td>-23.12</td><td>0.37 ± 0.15</td><td>0.39 ± 0.13</td><td>0.08</td><td>0.38 ± 0.14</td><td>0.41 ± 0.12</td><td>0.1</td></tr><tr><td>Austral<sup>b</sup></td><td>RVV</td><td>8</td><td>-1,47,670</td><td>-23,870</td><td>0.37 ± 0.21</td><td>0.37 ± 0.14</td><td>0.08</td><td>0.37 ± 0.20</td><td>0.39 ± 0.14</td><td>0.1</td></tr><tr><td>Indonesia<sup>c</sup></td><td>INDO</td><td>31</td><td>1,13,290</td><td>-7,350</td><td>0.34 ± 0.16</td><td>0.36 ± 0.14</td><td>0.09</td><td>0.28 ± 0.18</td><td>0.31 ± 0.17</td><td>0.1</td></tr></tbody></table>

<table><caption>**Table 2.** F<sub>ST</sub> values estimate with neutral panel (upper triangle) and outlier panel (lower triangle) between the different sampled archipelagos (A) and the corresponding 95% confidence interval (B). ns = non-significant</caption><thead><tr><th></th><th>Austral</th><th>Gambier</th><th>Society</th><th>Tuamotus</th><th>Marquesas</th><th>Indonesia</th></tr></thead><tbody><tr><td colspan="7"><strong>A.</strong></td></tr><tr><td>Indonesia</td><td>0.058</td><td>0.054</td><td>0.056</td><td>0.055</td><td>0.136</td><td>-</td></tr><tr><td>Marquesas</td><td>0.08</td><td>0.069</td><td>0.07</td><td>0.067</td><td>-</td><td>0.561</td></tr><tr><td>Tuamotus</td><td>0.000<sup>ns</sup></td><td>0.002</td><td>0.002</td><td>-</td><td>0.32</td><td>0.167</td></tr><tr><td>Society</td><td>0.002</td><td>0.003</td><td>-</td><td>0.016</td><td>0.364</td><td>0.167</td></tr><tr><td>Gambier</td><td>0.002<sup>ns</sup></td><td>-</td><td>0.015</td><td>0.009</td><td>0.345</td><td>0.159</td></tr><tr><td>Austral</td><td>-</td><td>0.006</td><td>0.012</td><td>0.001<sup>ns</sup></td><td>0.515</td><td>0.195</td></tr><tr><td colspan="7"><strong>B.</strong></td></tr><tr><td>Indonesia</td><td>(0.056; 0.060)</td><td>(0.053; 0.055)</td><td>(0.054; 0.057)</td><td>(0.054; 0.056)</td><td>(0.134;0.139)</td><td>-</td></tr><tr><td>Marquesas</td><td>(0.078; 0.082)</td><td>(0.068; 0.070)</td><td>(0.069; 0.072)</td><td>(0.066; 0.069)</td><td>-</td><td>(0.549;0.572)</td></tr><tr><td>Tuamotus</td><td>(0.000; 0.001)</td><td>(0.002; 0.002)</td><td>(0.002; 0.002)</td><td>-</td><td>(0.312; 0.329)</td><td>(0.158; 0.175)</td></tr><tr><td>Society</td><td>(0.001; 0.002)</td><td>(0.003; 0.003)</td><td>-</td><td>(0.014; 0.018)</td><td>(0.356;0.374)</td><td>(0.159; 0.177)</td></tr><tr><td>Gambier</td><td>(0.000; 0.002)</td><td>-</td><td>(0.013;0.017)</td><td>(0.008; 0.010)</td><td>(0.337;0.353)</td><td>(0.150;0.167)</td></tr><tr><td>Austral</td><td>-</td><td>(0.004;0.009)</td><td>(0.009; 0.015)</td><td>(0.000; 0.003)</td><td>(0.502; 0.529)</td><td>(0.183;0.207)</td></tr></tbody></table>
## Genetic diversity, differentiation and isolation by distance

Observed ($H_O$) and expected ($H_E$) heterozygosities, as well as the inbreeding coefficient ($F_{IS}$) were calculated for the global neutral panel with the R package dartR v.2.9.7 for each sampling location. A Student t-test was used to test for significant differences, with a significant level set at alpha equal to 0.05.

Weir and Cockerham $F_{ST}$ indices<sup>42</sup>, were calculated at both fine-scale (i.e. among islands) and broad-scale (i.e. among archipelagos) using the R package dartR v2.9.7, and significance was assessed through 999 bootstraps iterations.

We performed a hierarchical analysis of molecular variance (AMOVA) on the neutral global and Polynesian panels using the R package poppr v.2.9.3<sup>43</sup>, with 999 permutations to test the significance of the results.

Isolation by Distance (IBD)<sup>44</sup> was tested using a Mantel test approach in the R package vegan v2.6<sup>45</sup>, employing a transformed $F_{ST}/(1-F_{ST})$ matrix and a geographical distance matrix generated with *distGeo* function from the geosphere v.1.5 R package. This approach assumes that distances between islands can be approximated as straight lines, considering the fragmented nature and small size of the islands. Significance was assessed using a Mantel using 9,999 permutations.

## Multivariate population genetic structure and admixture

Principal component analysis (PCA) was performed using the dudi.pca function from the R package adegenet v.2.1.5<sup>46,47</sup>. Unsupervised clustering was conducted via the function *find.cluster* function. The optimal number of clusters (K) was selected using the Akaike Information Criterion (AIC) and Bayesian information criterion (BIC) to discriminate low-level structuring without giving higher penalties to more complex models. As recommended by Jombart et al. (2011), we kept 600 PC axes to capture 80% of the total genetic variation.

Levels of admixture among individuals were assessed using the ADMIXTURE software<sup>48</sup> with K values ranging from one to ten. The cross-validation method (with default parameters of fivefold CV) was used to determine the optimal K, based on the lowest CV error.

## Larval dispersal modelling to multigeneration connectivity estimates

Raapoto et al.<sup>34</sup>, modelled *Pinctada margaritifera* larval dispersal across French Polynesia using the software Ocean Parcels<sup>49</sup>. They released 100 particles daily from the 102 largest islands across the five archipelagos and employed current velocities and temperatures between 2010 and 2019 across the first 50 m of the water column to match the vertical distribution of *P. margaritifera* larvae. We used data from their scenario 7, which models a larval dispersal duration (PLD) of 35 days while setting the period of larval pre-competency (LPP) at 23 days. It results in 3,652 daily square connectivity matrices (102 × 102) for the period from 2010 to 2019. Each matrix reported the number of larvae exchanged between pairs of islands, indicating both the island where larvae were spawned and the island where larvae were recruited. The number of self-recruited larvae was indexed by the diagonal elements of the matrices. To convert these matrices into backward-in-time dispersal probabilities, we performed column normalization<sup>19</sup>. This approach emphasizes the immigration process, aligning with genetic theory. The connectivity matrices were then aggregated by element-wise averaged into a single 102 × 102 matrix, summarizing a decade of *Pinctada margaritifera* larvae dispersal and recruitment across the French Polynesian archipelagos.

We applied graph theory to the summarized connectivity matrix described above to compute multigeneration connectivity estimates between the 24 islands sampled for genetic analysis. Specifically, we calculated (i) the shortest path, which minimizes the sum of distances (number of vertices) along the links of the path between two nodes<sup>50</sup>, (ii) filial connectivity, which evaluates explicit (physically existing) links between the source and sink site, and<sup>22</sup> (iii) coalescent connectivity, which evaluates implicit links among sink sites with a common source site, and supposedly simulates genetic connectivity best<sup>19,22</sup>. We used Dijkstra algorithm from the R package igraph (version 2.0.1) among the distance transformed probabilities of connectivity, i.e. -log(probabilities), obtained from the summarized matrix to identify the shortest path between all pairs of island. We then multiplied the connection probabilities along the inferred shortest paths to estimate a multigeneration probability of connectivity. The number of stepping-stones required to connect each island pairs along the shortest path was also recorded, i.e. $A \rightarrow B$ is 0 step and $A \rightarrow X \rightarrow B$ is 1 step. Filial and coalescent multigeneration connectivity were computed for up to 500 generations. To match the symmetric feature of genetic differentiation data, we transformed the anisotropic multigeneration probability between sampled populations into a symmetric probability using the formula $P = PAB + PBA - PABPBA<sup>19</sup>$.

## Genetic and multigeneration connectivity estimates comparison

We compared the matrix of genetic differentiation for *Pinctada margaritifera* with the matrices derived from shortest path, filial, and coalescent estimates of connectivity. To account for the non-independence of pairwise comparisons, we used maximum-likelihood population-effects (MLPE) linear mixed models<sup>8,13,14,19</sup>. Before implementing the models, we log-transformed the connectivity probabilities of the multigeneration matrices into distances to align with the $F_{ST}$ metrics of the genetic matrix.

For each connectivity estimate, we built a model considering probability of connectivity as a fixed effect and the sampled island as a random effect (i.e. taking into account the position in the network, and placing weights on vertices). A null model was also tested, including only the sampled island as a random effect (I.e. only using the position of the islands in the network).

Hereafter, we refer to predicted values based on the MLPE linear mixed model as “predicted genetic differentiation”. We assessed the model’s performance by comparing predicted and observed genetic differentiation for each island using a linear model, for which AIC, goodness of fit (adjusted R2), and p-values were calculated.
## Centrality analysis

To identify key islands for management, i.e. that may play a large role in larval dispersal or contribute significantly to the integrity of the network, we computed centrality measures on the filial multigenerational network across the 102 islands islands by using the summarized connectivity matrix. We employed (i) harmonic centrality, which measures the average distance of a node to all other nodes in the network⁵¹ (ii) betweenness centrality, which quantifies how often each island serves as a stepping stone in the shortest paths between all pairs of islands in the network and (iii) eigenvector centrality, which calculates the direct (non-directional) connections of each island to the most connected islands in the network.

## Results

### Genetic diversity, differentiation and isolation-by distance

For the global dataset (all Polynesian samples and Indonesia), we identified 2,073 SNPs as outliers based on at least one approach, leaving 24,007 neutral SNPs to analyze. For the reduced dataset (removing the Indonesian and the three Marquesas populations), 762 SNPs were identified as outliers, with 307 of these overlapping with the global outlier panel, resulting in 25,318.

Observed heterozygosity ($H_o$) in the global neutral SNP panel was similar across the French Polynesian samples, with a mean of 0.37. However, the Marquesas Archipelago exhibited significantly lower heterozygosity compared to the other archipelagos (p-value < 0.001; Table 1). A similar trend was observed in the outlier SNP panel, but with a lower $H_o$ for the Marquesas Archipelago (Table 1). $F_{ST}$ estimates on the global neutral SNP panel revealed that the Marquesas Archipelago was highly differentiated from the rest of the French Polynesia (Table 2., Supplementary Table 3), with an average $F_{ST}$ value of 0.070 compared to the other Polynesian islands and up to 0.136 with Indonesia. Indonesia showed an average $F_{ST}$ values of 0.055 with the other four French Polynesian archipelagos. At the archipelago level, the lowest genetic differentiation was observed between Tuamotus and Austral Archipelagos ($F_{ST}$ = 0.000, CI low = 0.000, CI high = 0.001), while the highest was between Marquesas and Indonesia ($F_{ST}$ = 0.136, CI low = 0.134, CI high = 0.139). Apart from the Tuamotu/Austral pair, all $F_{ST}$ values between archipelagos were significant (Table 2.), while most within-archipelago island pairs had non-significant $F_{ST}$ or had a lower 95% confidence intervals close to zero (Supplementary Table 3). Within Gambier, Marutea Sud (MARS) exhibited significant $F_{ST}$ values (ranging from 0.004 and 0.007) with the Tuamotu, Austral and Society Archipelagos, as well as with the other Gambier islands. Within the Tuamotu Archipelago, Takapoto (TKP) had a significant $F_{ST}$ ranging from 0.001 to 0.002, with the Gambier Archipelago.

The Analysis of Molecular Variance (AMOVA) on the global neutral dataset revealed high intra-island variability ($F_{IS}$ = 6.88%, p-value < 0.001) along with significant variability among island within clusters, $F_{SC}$ = 0.15%, p-value < 0.001), and among clusters ($F_{CT}$ = 6.48%; p-value < 0.001; Table 3A). Similarly, the analysis on the neutral reduced dataset revealed a significant variability among islands within archipelagos ($F_{CT}$ = 0.07%; p-value < 0.001), and among archipelagos ($F_{CT}$ = 0.19%; p-value < 0.001; Table 3B). IBD was significant in both the global and the reduced datasets (Pearson $r^2$ = 0.345, p-value < 0.01 and Pearson $r^2$ = 0.506, p-value < 0.001 respectively). However, no IBD was detected within archipelagos (Tuamotus: Pearson $r^2$ = 0.114, p-value > 0.05; Gambier: Pearson $r^2$ = 0. 805, p-value > 0.05; Austral, Society and Marquesas were not tested because of the reduced number of sampling locations).

### Population structure

Principal Component Analysis (PCA) on the global dataset revealed three distinct genetic groups for both the neutral and outlier SNP panels, corresponding to geographic origin of the samples: (i) a Marquesas group, (ii) an Indonesian group, and (iii) a "southern" French Polynesian group comprising the Society, Tuamotu, Gambier, and Austral Archipelagos (Fig. 2A and 2B). The first and second principal components (PCs) explained 1.72% and 0.80% of the total variance respectively, for the neutral SNP panel, and 9.96% and 2.68% for the outlier SNP panel. The first PC separated the Marquesas Archipelago from the other samples, while the second PC

<table><caption>**Table 3.** Analysis of molecular variance (AMOVA) among (A) the three identified genetic clusters of the neutral global SNPs panel, and (B) the archipelagos within the neutral Polynesian SNPs panel.</caption><thead><tr><th colspan="3">Neutral Global panel (24,007 SNPs)</th></tr><tr><th>Source of Variation</th><th>Percentage of variance</th><th>Std. Variance</th></tr></thead><tbody><tr><td>Among clusters</td><td>6.48***</td><td>10.81</td></tr><tr><td>Among islands within clusters</td><td>0.15***</td><td>39.10</td></tr><tr><td>Among samples within islands</td><td>6.88***</td><td>10.72</td></tr><tr><td>Within Samples</td><td>86.49***</td><td>-13.60</td></tr></tbody></table><table><thead><tr><th colspan="3">Neutral Polynesian panel (25,318 SNPs)</th></tr><tr><th>Source of Variation</th><th>Percentage of variance</th><th>Std. Variance</th></tr></thead><tbody><tr><td>Among archipelagos</td><td>0.19***</td><td>7.77</td></tr><tr><td>Among islands within archipelagos</td><td>0.07***</td><td>14.26</td></tr><tr><td>Among samples within islands</td><td>7.51***</td><td>11.18</td></tr><tr><td>Within Samples</td><td>92.23***</td><td>-11.20</td></tr></tbody></table>
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Principal Component Analyses (PCA) of the global neutral (A) and outlier (B) SNP panels, and of the Polynesian neutral (C) and outlier (D) panels.</figcaption></figure>

<table><caption>**Table 4.** Summary of the predictive power of *P. margaritifera*'s genetic differentiation by the shortest path, filial, and coalescent estimates of multigeneration connectivity over French Polynesian networks. The maximum percentage of islands connected, as well as the generation at which the network reaches saturation, is indexed (see Supplementary Fig. 3). The number of pairs of sampled populations considered in the MLPE linear mixed models is indexed by n. The summary of the linear model between predicted and observed genetic differentiation is indexed (AIC, R2, ΔR2, and p-value). ΔR2 represents the difference between R2 obtained from the linear mixed model considering multigeneration connectivity as a fixed effect and the sampled population as a random effect, and R2 from the null linear mixed model considering only the sampled population as a random effect.</caption><thead><tr><th rowspan="2">Network</th><th rowspan="2">Multigeneration connectivity</th><th colspan="2">Network Saturation</th><th colspan="5">Multigeneration connectivity predictive power</th></tr><tr><th>Max % of islands connected</th><th>Saturation Generation</th><th>n</th><th>Optimal Generation</th><th>AIC</th><th>R<sup>2</sup></th><th>ΔR<sup>2</sup></th><th>p-value</th></tr></thead><tbody><tr><td rowspan="3">All archipelagos</td><td>Shortest path</td><td>83,03</td><td>2,78</td><td>153</td><td>-</td><td>-696,10</td><td>0,64</td><td>0,13</td><td>8,70E-42</td></tr><tr><td>Filial</td><td>83,03</td><td>7</td><td>153</td><td>15</td><td>-683,48</td><td>0,61</td><td>0,10</td><td>2,21E-27</td></tr><tr><td>Coalescent</td><td>90,68</td><td>4</td><td>153</td><td>500</td><td>-984,43</td><td>0,95</td><td>0,44</td><td>4,78E-162</td></tr><tr><td rowspan="3">Tuamotu-Gambier-Society</td><td>Shortest path</td><td>100</td><td>2,68</td><td>120</td><td>-</td><td>-1173,93</td><td>0,60</td><td>0,09</td><td>1,13E-22</td></tr><tr><td>Filial</td><td>100</td><td>7</td><td>120</td><td>7</td><td>-1167,45</td><td>0,58</td><td>0,07</td><td>1,40E-21</td></tr><tr><td>Coalescent</td><td>100</td><td>3</td><td>120</td><td>35</td><td>-1175,15</td><td>0,60</td><td>0,09</td><td>2,94E-23</td></tr></tbody></table>

distinguished the Indonesian population (INDO) from the rest (Fig. 2A). Unsupervised hierarchical clustering on the global dataset aligns with PCA results, identifying five genetic clusters corresponding to Indonesia, the Marquesas, Tuamotu, Gambier, and Society (Supplementary Table 4A). The Austral Archipelago individuals were not grouped into a distinct cluster but instead were distributed within the Tuamotu and Gambier clusters.

PCA on the reduced dataset revealed fine-scale population structure, separating most of the archipelagos. The Society, Tuamotus and Gambier Archipelagos formed genetically distinct groups, while the Austral Archipelago overlapped with both the Tuamotu and Gambier Archipelagos (Fig. 2C and 2D). This observation is also confirmed by unsupervised clustering performed on restricted neutral dataset according AIC (Supplementary Table 4B).

Admixture analyses on the global neutral dataset revealed three genetic clusters (CV = 0.64857), but the Cross Validation scores (CVs) were nearly identical across K=3, K=4 and K=5 (0.64,857, 0.64,885 and 0.64,927 respectively). At K=3, the analysis identified three distinct clusters: one corresponding to the Marquesas, another to Indonesia, and a third representing the “southern” Polynesian group (Fig. 3). When K=4 was tested, a slight sub-structure emerged within the southern Polynesian group, splitting it into two: the Society and Gambier
Archipelagos (individuals' admixture coefficients for cluster 4 > 0.5) and the Tuamotu and Austral Archipelagos (individuals' admixture coefficients of cluster 4 < 0.5). Notably, Marutea Sud (MARS) stood out from the rest of Gambier Archipelago, showing a particularly high proportion of cluster 4. No significant genetic structure was detected when using the reduced dataset (Supplementary Fig. 1).

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Genome-wide pattern of population structure using admixture program. Assignment probabilities of each individual with neutral SNP panel for K = 2 (CV = 0.65130), K = 3 (CV = 0.64857), K = 4 (CV = 0.64885) and K = 5 (CV = 0.64927). Individuals are grouped by sampling locations and archipelagos according to a geographical gradient from northeast to the southwest.</figcaption></figure>
## Evaluating genetic differentiation estimation of connectivity over multiple generations

Computing multigeneration connectivity estimates symmetrically connected all the islands of the Marquesas, Society, Tuamotu, and Gambier Archipelagos (Table 4). However, the Austral islands remained disconnected from the other archipelagos, forming a separate network from the outset, with a maximum of 90.68% of the islands successfully connected. Network saturation was reached after a maximum of seven generations (Table 4). The coalescent connectivity approach resulted in faster network saturation and connected a higher number of islands compared to filial connectivity and shortest path computations. This improved performance is largely due to the inclusion of implicit links representing connections among siblings from a common ancestor in contrast to the explicit links used in the shortest path computations that ignore sibling relationships, which are prevalent between the Marquesas and the Tuamotu, and within the Tuamotus themselves.

The optimal number of generations – defined as the number of generations for which the linear model between predicted and observed genetic differentiation yielded the minimal AIC value—varied depending on the connectivity estimates and network scenario (Table 4, Fig. 2). For the “All Archipelago” network (including the Marquesas), filial connectivity best predicted genetic differentiation at 15 generations, while coalescent connectivity reached its optimal at 500 generations (Table 4 and Supplementary Fig. 3). This substantial difference is likely driven by the genetic signature of the Marquesas Archipelago. When the Marquesas were excluded – “Tuamotu-Gambier-Society” network–, the optimal number of generations dropped to 7 for filial connectivity and 35 for coalescent connectivity.

Coalescent connectivity proved to be the most effective method for predicting genetic differentiation in French Polynesia, providing the lowest AIC values compared to filial connectivity. It also showed significant improvement over the null model, with a ΔR2 of 0.44 (Table 4). Additionally, coalescent connectivity improved overall network connectivity. The R2 of 0.95 (p-value < 0.05) between predicted and observed genetic differentiation can be attributed to the strong genetic differentiation observed in the Marquesas population, the low probability of connection between the Marquesas and the rest of French Polynesia, and the large number of generations considered. This led to intense, symmetric connections between, Society, Tuamotu, and Gambier Archipelagos, while isolating the Marquesas Archipelago. However, when the Marquesas were excluded, the goodness of fit between predicted and observed genetic differentiation decreased to R2 = 0.60. Despite this, coalescent connectivity still explains 9% of genetic variance in this scenario (i.e. the ΔR2 between this model and the null model which only considers the sampled population as a random effect, is 0.09).

A significant Kruskal–Wallis test (p-value < 0.001) and pairwise Wilcoxon Rank Sum tests (Supplementary Table 5) indicated that observed genetic differentiation of certain population pairs, such as Mangareva and Marutea Sud, deviated more from the linear fit than other population pairs (Fig. 4A and B). Specifically, the genetic differentiation for the Marutea Sud (MARS) population was stronger than predicted, while the genetic differentiation for the Mangareva population was weaker than expected. This findings suggest that factors other than larval dispersal may have influenced the genetic structure observed in these populations.

## Centrality analysis

Centrality analysis highlighted the significant role of the Tuamotu Archipelago within the broader Polynesian network. Several Tuamotu islands emerged as key players in facilitating gene flow across the network: (i) islands that either received or transmit gene flow (e.g., Katiu (KAT) and Kauehi (KAU), which rank in the top 10% of harmonic centrality among sampled islands, Fig. 5A) (ii) through them (i.e., Taenga (TEA) show top 10% of betweenness centrality of sampled islands, Fig. 5B) or (iii) among them (i.e., Takume and Tahanea show top 10% of eigenvector centrality, Supp. Table 6). On the other hand, Mangareva (GMBW), located at the edge of the Gambier Archipelago, stands out as the most isolated sampled population from the Society-Tuamotu-Gambier complex, regardless of the centrality index considered (Fig. 5). Manihi (MAN) is also isolated in term of gene flow, as 90% of other sampled islands are characterized by a higher harmonic and betweenness centrality (Fig. 5A, B). Approximately 60% of the farmed islands (either currently or historically exploited) display high eigenvector centralities (> 0.5), primarily within the Tuamotu Archipelago. Specifically, Hikueru, Makemo, Kauehi (KAU) and Katiu (KAT) were the most central atolls in the network for pearl farming (Supplementary Table 6). Furthermore, Ravahere-Marokau and Hereheretue islands play a pivotal role in the Polynesian network. These islands have the highest betweenness and medium to low eigenvector centrality values, acting as critical hubs that connect the north-west and south-east regions (Fig. 5D).

## Discussion

Despite the socio-economic importance of the pearl oyster, significant gaps remain in our understanding of its dispersal across the seascape and the potential role of certain atolls in maintaining population connectivity. To fill these gaps, we combined three complementary approaches: genetic connectivity, larval dispersal modelling and graph theory. This powerful framework enabled us to (i) uncover the presence of genetically divergent clusters corresponding to the different Polynesian archipelagos, (ii) delineate how larval dispersal may shape genetic connectivity and (iii) identify key islands contributing to the connectivity of the species across French Polynesia. These findings provide valuable guidance for the sustainable management of both natural and farmed populations of *Pinctada margaritifera*.

## Regional genetic structure matches coalescent connectivity

Although the Marquesas Archipelago was already known to be genetically differentiated from the rest of the French Polynesian oysters populations²⁹⁻³¹, previous genetic studies failed to find significant population structure among the Society, Tuamotu, and Gambier Archipelagos. Our study is the first to uncover this regional population structure, leveraging extensive sampling across all five archipelagos and high-resolution genomic data. Across all analyses, from multivariate statistics to FST indices, we consistently detected a regional population structure,
<figure><img src="image_7.png" /></figure>

**Fig. 4.** Genetic and multigeneration connectivity estimates comparison. Predicted genetic differentiation obtained from the coalescent multigeneration connectivity is compared against the observed genetic differentiation, depicted in panel A for the network considering only the Tuamotu, Gambier, and Society archipelagos. Dotted black lines indicate the linear regression trend and blue shading represents the corresponding 95% confidence interval. In panel A, violet dots highlight pairwise genetic differentiation involving Marutea Sud populations, while orange dots highlight pairwise genetic differentiation involving Mangareva populations. Blue/green dots represent pairwise genetic differentiation between Tuamotu/Gambier archipelago and the remaining Polynesian populations, respectively. Panel B displays the distribution of the deviation of observed genetic differentiation from the linear fit per sampled exploited population.

primarily at the archipelago level. The coalescent connectivity estimates aligned with the genetic connectivity estimates, although some discrepancies remained. The overall lack of genetic structuring within archipelagos in French Polynesia, especially in the Tuamotu where the larval dispersal model indicated a substructure that was not identified through neutral $F_{ST}$ estimates, may be attributed to (i) a genetic homogenization resulting from intra-archipelago transfers for pearl farming since the 1970s, and/or (ii) naturally high connectivity levels driven by larval dispersal among islands within an archipelago, achieved within a relative short number of generations (three generations), as suggested by the coalescent connectivity analysis. Our results support current management strategies, which permit intra-archipelago while prohibiting inter-archipelago transfers.

Larval dispersal model suggests that the Austral Archipelago is isolated from the rest of French Polynesia³⁴. However, we found a lack of genetic differentiation between the Austral Archipelago and three other archipelagos (the Society, Tuamotu, and Gambier), suggesting ongoing or very recent exchanges between these populations. The small sample size on this archipelago, only 7 individuals from 1 island Raivavae (RVV) could lead to incorrect inferences and downward biased estimates of the number of subpopulations, since Admixture's model will tend to artificially group smaller populations with larger ones, even when genetic differences exist⁵². Expanding genetic sampling to other Austral Islands will be crucial to attest the presence of this genetic homogeneity, and if confirmed, to discuss hypotheses, including anthropogenic transfer from other archipelagos during previous pearl farming assay or other artisanal purposes. Indeed even a limited number of introduced individuals could have genetically homogenized the local population within a few generations⁵³.

## The gambier archipelago: Particularities of the marutea sud and mangareva islands

In our analysis, two islands in the Gambier Archipelago showed a significant mismatch between the coalescent and genetic connectivity. The first, Marutea Sud (MARS), exhibited higher observed genetic differentiation than predicted by coalescent connectivity. This discrepancy is likely due to the island's completely enclosed lagoon, which lacks a natural pass, potentially restricting or preventing larval exchange with surrounding waters. Nowadays, Marutea Sud (MARS) ranks among the fourth most exploited atolls in terms of lagoon surface area dedicated to pearl farming⁵⁴. The wild population of the atoll remains healthy, boasting one of the largest effective population sizes, sufficient to supply the spat required for local pearl farming operations in the Gambier Archipelago⁵⁵. While no biological transfers into Marutea Sud have been reported, substantial transfers from Marutea Sud (MARS) to other islands in the Gambier archipelago have been documented (DRMM, personal communications). The second island, Mangareva, was expected to be more genetically divergent than was actually observed. A possible explanation for this discrepancy is the historical introduction of exogeneous
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Centrality of French Polynesia. Panel A depicts harmonic centrality, panel B betweenness centrality and panel C eigenvector centrality over the network considering the Tuamotu, Gambier, and Society archipelagos. Panel D shows harmonic (x-axis) and betweenness (y-axis) centrality position and coloured by archipelago purple for Society, green for Gambier and brown for Tuamotu. Sampled islands are indicating with a white dot. Values were transformed with min-max scaling.</figcaption></figure>

oysters into Mangareva (GMBW), particularly from the Tuamotus, during the early stages of pearl farming. These introductions may have increased genetic similarity between Mangareva (GMBW) and other populations. Additionally, these discrepancies may have been reinforced by the use of a single connectivity matrix from 2010 to 2019 in our multigeneration estimates, which may not have fully accounted for day-to-day variability in atoll connections when estimating coalescent connectivity⁵⁶.

## The marquesas archipelago: A complex evolutionary history and distinct habitat specificity

The pronounced genetic divergence of the Marquesas Archipelago from the rest of French Polynesia has been documented in previous studies on *Pinctada margaritifera* populations²⁹⁻³¹. However, our study demonstrates this level of divergence is even greater than that between Indonesia and the rest of French Polynesia. This striking pattern likely results from a combination of demographic and environmental processes that have shaped the unique genetic signature of the Marquesas population.

The greater genetic similarity between Indonesia and the Tuamotu, Gambier, Society and Austral Archipelgos could be attributed to a recent recolonization of lagoon habitats from western coral reef refugia⁵⁷ following the Last Glacial Maximum (LGM). In contrast, the Marquesas likely acted as a refugia, remaining isolated and receiving no migrants. The persistence of refugia in the central Pacific, particularly on islands with a system of reef flats that open to the sea, has been hypothesized for the Austral (particularly Rapa Island) and the Marquesas Archipelagos⁵⁸⁻⁶². The Marquesas populations could have conserved the ancestral genetic signature of French Polynesia (before the Pleistocene glaciations), while the recolonized lagoons would have a genetic signature more similar to that of the western refugia.

A demographic event such as a founder effect, followed by vicariance and genetic drift could also lead to high genetic differentiation between two populations. Our study reveals that the Marquesas have lower relative genetic diversity than the rest of French Polynesia. Additionally, the larval dispersal model shows that the Marquesas are unidirectionally connected to the Tuamotus, with potential gene flow directed from the Marquesas to the Tuamotus. This supports the idea that the Marquesas populations may have been initially founded by a small
number of individuals, while subsequent isolation from other populations in French Polynesia likely contributed
to their differentiation overtime.

The hypotheses presented here to explain the high differentiation of the Marquesas populations both imply
a distinct demographic history of the Marquesas Archipelago from the rest of French Polynesia. Demographic
reconstruction using whole-genome data⁶³ will help to test which combination of parameters (e.g. population
size variation, time of divergence, migration restrictions) most likely contributed to the current level of
differentiation of the Marquesas Archipelago.

The tuamotu archipelago: central position for oyster' conservation and exploitation

In species management, betweenness centrality is a key metric as it identifies locations that act as vital links between different areas, functioning as dispersal corridors⁶⁴. In our study, Ravahere-Marokau and Hereheretue islands exhibited high betweenness centrality, suggesting that they may play a pivotal role in connecting different islands groups in French Polynesia. Historically, these islands supported large natural pearl oyster stocks. Marokau, in particular, was a major site for natural pearl harvesting in the Tuamotu Archipelago from the nineteenth century until 1965⁶⁵, after which exploitation declined significantly⁶⁶. Napuka also has a high betweenness centrality, as it is one of the islands predicted to receive larvae from the Marquesas Archipelago³⁴. Interestingly, the Napuka pearl oyster population used to be large and was exploited for the button and natural pearl industries⁶⁷. Given their key role in connectivity and historical importance, these three islands—Marokau, Hereheretue and Napuka—represent islands to be conserved as a priority, by protecting the viability of the habitat and size of these populations in order to maintain genetic connectivity within and between archipelagos. While potential connectivity estimates provides valuable insights into potential management policies, decisions should also be informed by in situ observations to validate our findings, and a comprehensive assessment of the contemporary population density of *Pinctada margaritifera* on these islands will be essential for its sustainable management.

Most of the islands currently exploited for pearl farming fall in the upper half of harmonic and eigenvector
centrality, but in the lower half of betweenness centrality. This suggests that, although they are well-connected
within the network, they contribute relatively little to its overall cohesion. High harmonic centrality may indicate
good population resilience, as these populations can be effectively replenished, restored, or maintained by
multiple sources⁶⁸. However, this high connectivity also poses a risk: it facilitates the rapid spread of pathogens,
making these populations particularly vulnerable to disease outbreaks⁶⁹. Islands with a high degree of centrality,
such as Katiu and Kauehi, should therefore be monitored.

Finally, the two collecting atolls in the Tuamotu Archipelago, Ahe and Manihi (MAN), have lower centrality values, which could make them less susceptible to pathogens spread, but their recovery from a stock collapse could be slow and challenging, unlike other Tuamotu atolls. Careful monitoring of the demographic parameters, such as recruitment rates, genetic diversity and density of these populations, is therefore crucial to ensure their long-term sustainability.

Conclusions

In this study, we demonstrated the value of integrating population genomics with multigeneration connectivity
estimates derived from larval dispersal modelling to provide a deep understanding of both historical and
contemporary processes shaping natural population connectivity. Our findings support the management of
pearl oyster populations at the scale of individual archipelagos, as four of the five archipelagos exhibit distinct
genetic signatures.

We have identified key islands functioning as critical connectivity corridors both within and among
archipelagos (Napuka, Hereheretue, and Marokau) that could be essential for the cohesion of the Polynesian
network. An estimate of current densities, as well as putative restoration projects could be envisioned within
a sustainable management framework. We have also shown that most of the exploited Tuamotu islands have
a high harmonic centrality, making them well connected but also more prone to health risks, while the two
Tuamotu collecting atolls have a lower harmonic centrality, and are less likely to recover demographically from
an possible stock collapse. Specific management should be applied to all these islands, with targeted monitoring
of population health and density, to ensure the sustainability of the resource.

Data availability

Genetic datasets used in this manuscript are available on request to Céline MO. REISSER at celine.reisser@ifremer. The scripts for the genomic analysis are available at: https://github.com/Gradel-A/Pinctadapt_connectivity. The scripts for the coalescent and filial physical connectivity matrices calculations and analysis are available at: https://github.com/TerenceLegrand/connectivity_polynesia_oyster.

Received: 16 April 2025; Accepted: 1 July 2025

Published online: 09 July 2025

References

1. Cayuela, H. et al. Demographic and genetic approaches to study dispersal in wild animal populations: A methodological review. *Mol. Ecol.* **27**, 3976–4010 (2018).

2. Clobert, J., Danchin, E., Dhondt, A. A. & Nichols, J. D. *Dispersal* (Oxford University Press, 2001).

3. Cowen, R., Gawarkiewicz, G., Pineda, J., Thorrold, S. & Werner, F. Population connectivity in marine systems: An overview. *Oceanography* **20**, 14–21 (2007).

4. Williams, P. D. & Hastings, A. Stochastic dispersal and population persistence in marine organisms. *Am. Nat.* **182**, 271–282 (2013).
5. Pineda, J., Hare, J. & Sponaugle, S. Larval transport and dispersal in the coastal ocean and consequences for population connectivity. *Oceanography* **20**, 22–39 (2007).

6. Grummer, J. A. et al. Aquatic landscape genomics and environmental effects on genetic variation. *Trend. Ecol. Evol.* **34**, 641–654 (2019).

7. Werner, F. E., Cowen, R. K. & Paris, C. B. Coupled biological and physical models: Present capabilities and necessary developments for future studies of population connectivity. *Oceanography* **20**, 54–69 (2007).

8. Selkoe, K. A. et al. A decade of seascape genetics: Contributions to basic and applied marine connectivity. *Mar. Ecol. Prog. Ser.* **554**, 1–19 (2016).

9. Riginos, C., Crandall, E. D., Liggins, L., Bongaerts, P. & Treml, E. A. Navigating the currents of seascape genomics: How spatial analyses can augment population genomic studies. *Curr. Zool.* **62**, 581–601 (2016).

10. Pascual, M., Rives, B., Schunter, C. & Macpherson, E. Impact of life history traits on gene flow: A multispecies systematic review across oceanographic barriers in the Mediterranean Sea. *PLoS ONE* **12**, e0176419 (2017).

11. Reisser, C. M. O. et al. Population connectivity and genetic assessment of exploited and natural populations of pearl oysters within a french polynesian atoll lagoon. *Genes* **11**, 426 (2020).

12. Benestan, L. et al. Restricted dispersal in a sea of gene flow. *Proc. R. Soc. B Biol. Sci.* **288**, 20210458 (2021).

13. Jahnke, M. & Jonsson, P. R. Biophysical models of dispersal contribute to seascape genetic analyses. *Philos. Trans. R. Soc. B Biol. Sci.* **377**, 20210024 (2022).

14. Legrand, T. et al. Unravelling the role of oceanographic connectivity in the distribution of genetic diversity of marine forests at the global scale. *Glob. Ecol. Biogeogr.* **33**, e13857 (2024).

15. Foster, N. L. et al. Connectivity of caribbean coral populations: Complementary insights from empirical and modelled gene flow. *Mol. Ecol.* **21**, 1143–1157 (2012).

16. Xuereb, A. et al. Asymmetric oceanographic processes mediate connectivity and population genetic structure, as revealed by RADseq, in a highly dispersive marine invertebrate (Parastichopus californicus). *Mol. Ecol.* **27**, 2347–2364 (2018).

17. Benestan, L. et al. Seascape genomics provides evidence for thermal adaptation and current-mediated population structure in American lobster (Homarus americanus). *Mol. Ecol.* **25**, 5073–5092 (2016).

18. Boulanger, E., Dalongeville, A., Andrello, M., Mouillot, D. & Manel, S. Spatial graphs highlight how multi-generational dispersal shapes landscape genetic patterns. *Ecography* **43**, 1167–1179 (2020).

19. Legrand, T. et al. Spatial coalescent connectivity through multi-generation dispersal modelling predicts gene flow across marine phyla. *Nat. Commun.* **13**, 5861 (2022).

20. Jahnke, M. et al. Seascape genetics and biophysical connectivity modelling support conservation of the seagrass Zostera marina in the Skagerrak-Kattegat region of the eastern North Sea. *Evol. Appl.* **11**, 645–661 (2018).

21. Schraidt, C. E. et al. Dispersive currents explain patterns of population connectivity in an ecologically and economically important fish. *Evol. Appl.* **16**, 1284–1301 (2023).

22. Ser-Giacomi, E., Legrand, T., Hernández-Carrasco, I. & Rossi, V. Explicit and implicit network connectivity: Analytical formulation and application to transport processes. *Phys. Rev. E* **103**, 042309 (2021).

23. Vogt-Vincent, N. S., Burt, A. J., van der Ven, R. M. & Johnson, H. L. Coral reef potential connectivity in the southwest Indian Ocean. *Coral Reef* **43**, 1037–1051 (2024).

24. Siu, D. 2019, une année difficile pour la perle. (2021).

25. Sangare, N. et al. Impact of environmental variability on Pinctada margaritifera life-history traits: A full life cycle deb modeling approach. *Ecol. Model.* **423**, 109006 (2020).

26. Doroudi, M. S. & Southgate, P. C. Embryonic and larval development of pinctada margaritifera (Linnaeus, 1758). *Molluscan Res.* **23**, 101–107 (2003).

27. Fritch, E. et al. *Loi Du Pays N° 2017–16 Du 18/07/2017 Réglementant Les Activités Professionnelles Liées à La Production et La Commercialisation Des Produits Perliers et Nacriers En Polynésie Française.* (2017).

28. Anderson, S. Area and Endemism. *Q. Rev. Biol.* **69**, 451–471 (1994).

29. Lemer, S. & Planes, S. Effects of habitat fragmentation on the genetic structure and connectivity of the black-lipped pearl oyster Pinctada margaritifera populations in French Polynesia. *Mar. Biol.* **161**, (2014).

30. Reisser, C. et al. Strong genetic isolation of the black-lipped pearl oyster (Pinctada margaritifera) in the Marquesas archipelago (French Polynesia). *Sci. Rep.* **9**, 11420 (2019).

31. Arnaud-Haond, S. et al. Genetic structure at different spatial scales in the pearl oyster (Pinctada margaritifera cumingii) in French Polynesian lagoons: Beware of sampling strategy and genetic patchiness. *Mar. Biol.* **155**, 147–157 (2008).

32. Spalding, M. D. et al. Marine Ecoregions of the World: A Bioregionalization of Coastal and Shelf Areas. *Bioscience* **57**, 573–583 (2007).

33. Lal, M. M., Southgate, P. C., Jerry, D. R. & Zenger, K. R. Genome-wide comparisons reveal evidence for a species complex in the black-lip pearl oyster Pinctada margaritifera (Bivalvia: Pteriidae). *Sci. Rep.* **8**, 191 (2018).

34. Raapoto, H., Monaco, C. J., Van Wynsberge, S., Le Gendre, R. & Le Luyer, J. Assessing regional connectivity patterns of bivalvia in fragmented archipelagos: Insights from biophysical modeling in French Polynesia. *Ecol. Model.* **489**, 110626 (2024).

35. Benestan, L. *et al*. Using a comprehensive donor and host dataset to decipher the heritability and genetic architecture of pearl traits in the black-lipped oyster (*Pinctada margaritifera*). *Aquaculture* **742837** https://doi.org/10.1016/j.aquaculture.2025.742837 (2025).

36. Chang, C. C. *et al*. Second-generation PLINK: rising to the challenge of larger and richer datasets. *GigaScience* **4**, s13742-015-0047-8 (2015).

37. Mijangos, J. L., Gruber, B., Berry, O., Pacioni, C. & Georges, A. dartR v2: An accessible genetic analysis platform for conservation, ecology and agriculture. *Method. Ecol. Evol.* **13**, 2150–2158 (2022).

38. Foll, M. & Gaggiotti, O. A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: A Bayesian perspective. *Genetics* **180**, 977–993 (2008).

39. Whitlock, M. C. & Lotterhos, K. E. Reliable detection of Loci responsible for local adaptation: Inference of a null model through trimming the distribution of F(ST). *Am. Nat.* **186**(Suppl 1), S24-36 (2015).

40. Luu, K., Bazin, E. & Blum, M. G. B. pcadapt: An R package to perform genome scans for selection based on principal component analysis. *Mol. Ecol. Resour.* **17**, 67–77 (2017).

41. Privé, F., Luu, K., Vilhjálmsson, B. J. & Blum, M. G. B. Performing highly efficient genome scans for local adaptation with R package pcadapt version 4. *Mol. Biol. Evol.* **37**, 2153–2154 (2020).

42. Weir, B. S. & Cockerham, C. C. Estimating F-statistics for the analysis of population structure. *Evolution* **38**, 1358–1370 (1984).

43. Kamvar, Z. N., Tabima, J. F. & Grünwald, N. J. Poppr: An R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. *PeerJ* **2**, e281 (2014).

44. Wright, S. Isolation by distance. *Genetics* **28**, 114–138 (1943).

45. Dixon, P. VEGAN, a package of R functions for community ecology. *J. Veg. Sci.* **14**, 927–930 (2003).

46. Jombart, T. adegenet: An R package for the multivariate analysis of genetic markers. *Bioinformatic* **24**, 1403–1405 (2008).

47. Jombart, T. & Ahmed, I. Adegenet 1.3-1: New tools for the analysis of genome-wide SNP data. *Bioinformatic* **27**, 3070–3071 (2011).

48. Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. *Genome. Res.* **19**, 1655–1664 (2009).

49. Delandmeter, P. & van Sebille, E. The Parcels v2.0 Lagrangian framework: New field interpolation schemes. *Geosci. Model Dev.* **12**, 3571–3584 (2019).
50. Dijkstra, E. W. A note on two problems in connexion with graphs. *Numer. Math.* **1**, 269–271 (1959).

51. Marchiori, M. & Latora, V. Harmony in the small-world. *Phys. Stat. Mech. Its Appl.* **285**, 539–546 (2000).

52. Puechmaille, S. J. The program structure does not reliably recover the correct population structure when sampling is uneven: Subsampling and new estimators alleviate the problem. *Mol. Ecol. Resour.* **16**, 608–627 (2016).

53. Settepani, V., Bechsgaard, J. & Bilde, T. Low genetic diversity and strong but shallow population differentiation suggests genetic homogenization by metapopulation dynamics in a social spider. *J. Evol. Biol.* **27**, 2850–2855 (2014).

54. Siu, D. *Perliculture - Lannée 2017 Renoue Avec La Croissance*. https://www.ispf.pf/publication/1202 (2020).

55. Andréfouët, S., Thomas, Y., Dumas, F. & Lo, C. Revisiting wild stocks of black lip oyster *Pinctada margaritifera* in the Tuamotu Archipelago: The case of Ahe and Takaroa atolls and implications for the cultured pearl industry. *Estuar. Coast. Shelf Sci.* **182**, 243–253 (2016).

56. Vogt-Vincent, N. S., Mitarai, S. & Johnson, H. L. High-frequency variability dominates potential connectivity between remote coral reefs. *Limnol. Oceanogr.* **68**, 2733–2748 (2023).

57. Pellissier, L. et al. Quaternary coral reef refugia preserved fish diversity. *Science* **344**, 1016–1019 (2014).

58. Paulay, G. Effects of late Cenozoic sea-level fluctuations on the bivalve faunas of tropical oceanic islands. *Paleobiology* **16**, 415–434 (1990).

59. Riquet, F. et al. Insights into the genetic makeup of French Polynesian peripheral populations of the small giant clam *Tridacna maxima*. *Aquat. Conserv. Mar. Freshw. Ecosyst.* **33**, 1514–1534 (2023).

60. Gaither, M. R., Toonen, R. J., Robertson, D. R., Planes, S. & Bowen, B. W. Genetic evaluation of marine biogeographical barriers: Perspectives from two widespread Indo-Pacific snappers (Lutjanus kasmira and Lutjanus fulvus). *J. Biogeogr.* **37**, 133–147 (2010).

61. Crandall, E. D., Taffel, J. R. & Barber, P. H. High gene flow due to pelagic larval dispersal among South Pacific archipelagos in two amphidromous gastropods (Neritomorpha: Neritidae). *Heredity* **104**, 563–572 (2010).

62. Planes, S. & Fauvelot, C. Isolation by distance and vicariance drive genetic structure of a coral reef fish in the Pacific Ocean. *Evolution* **56**, 378–399 (2002).

63. Gutenkunst, R. N., Hernandez, R. D., Williamson, S. H. & Bustamante, C. D. Inferring the joint demographic history of multiple populations from multidimensional SNP frequency data. *PLOS Genet.* **5**, e1000695 (2009).

64. Rozenfeld, A. F. et al. Network analysis identifies weak and strong links in a metapopulation system. *Proc. Natl. Acad. Sci.* **105**, 18824–18829 (2008).

65. Blanchet, G. Place et rôle des activités nacrières dans l'économie et la société locales. (2000).

66. Rapaport, M. Pearl farming in the Tuamotus: Atoll development and its consequences. *Pac. Stud.* **18**, (1995).

67. Intès, A. La Nacre en Polynésie française: Evolution des stocks naturels et de leur exploitation. *Océanographie* **16**, (1982).

68. Bhatia, U., Dubey, S., Gouhier, T. C. & Ganguly, A. R. Network-based restoration strategies maximize ecosystem recovery. *Commun. Biol.* **6**, 1–10 (2023).

69. Wei, X., Zhao, J., Liu, S. & Wang, Y. Identifying influential spreaders in complex networks for disease spread and control. *Sci. Rep.* **12**, 5550 (2022).

## Acknowledgments

We thank Chin Long Ky for providing samples for a few sampling sites used in this study. We thank Erwan Vigouroux and Ific Sculier for their help during the sampling. We thank Julie Fievet for the DNA extraction. We thank Yann Dorant for providing the VCF dataset used in this study.

## Author contributions

AG performed genetic diversity and population structure analyses. TL performed the coalescent connectivity analyses and their comparison with the genetic connectivity matrices. SP, JL and CMOR planned, organized, sampled, and obtained funding for this study. All authors contributed to the writing and the correction of the manuscript.

## Funding

Funding for this study was obtained through the project PINCTADAPT (Ifremer).

## Declarations

### Competing interests

The authors declare no competing interests.

### Additional information

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1038/s41598-025-10113-y.

**Correspondence** and requests for materials should be addressed to C.M.R.

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