# From population connectivity to the art of striping Russian dolls: the lessons from Pocillopora corals

Pauline Gélin, Cécile Fauvelot, Lionel Bigot, Joseph Baly, Hélène Magalon

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Pauline Gélin, Cécile Fauvelot, Lionel Bigot, Joseph Baly, Hélène Magalon. From population connectivity to the art of striping Russian dolls: the lessons from Pocillopora corals. Ecology and Evolution, 2018, 8 (2), pp.1411 - 1426. 10.1002/ece3.3747. hal-01907170

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# From population connectivity to the art of striping Russian dolls: the lessons from *Pocillopora* corals

Pauline Gélin<fnref n="1" />,<fnref n="2" /> | Cécile Fauvelot<fnref n="2" />,<fnref n="3" /> | Lionel Bigot<fnref n="1" />,<fnref n="2" /> | Joseph Baly<fnref n="3" />,<fnref n="2" /> |
Hélène Magalon<fnref n="1" />,<fnref n="2" /> ID

**Correspondence**

Hélène Magalon, UMR ENTROPIE, Faculté des Sciences et Technologies, Université de La Réunion, St Denis, La Réunion.
Email: helene.magalon@univ-reunion.fr

**Present address**

Cécile Fauvelot, Université Côte d'Azur, CNRS, Nice, France.

**Funding information**

LabEx CORAIL

**Abstract**

Here, we examined the genetic variability in the coral genus *Pocillopora*, in particular within the Primary Species Hypothesis PSH09, identified by Gélin, Postaire, Fauvelot and Magalon (2017) using species delimitation methods [also named *Pocillopora eydouxi/meandrina* complex sensu, Schmidt-Roach, Miller, Lundgren, & Andreakis (2014)] and which was found to split into three secondary species hypotheses (SSH09a, SSH09b, and SSH09c) according to assignment tests using multi-locus genotypes (13 microsatellites). From a large sampling (2,507 colonies) achieved in three marine provinces [Western Indian Ocean (WIO), Tropical Southwestern Pacific (TSP), and Southeast Polynesia (SEP)], genetic structuring analysis conducted with two clustering analyses (STRUCTURE and DAPC) using 13 microsatellites revealed that SSH09a was restricted to the WIO while SSH09b and SSH09c were almost exclusively in the TSP and SEP. More surprisingly, each SSH split into two to three genetically differentiated clusters, found in sympatry at the reef scale, leading to a pattern of nested hierarchical levels (PSH > SSH > cluster), each level hiding highly differentiated genetic groups. Thus, rather than structured populations within a single species, these three SSHs, and even the eight clusters, likely represent distinct genetic lineages engaged in a speciation process or real species. The issue is now to understand which hierarchical level (SSH, cluster, or even below) corresponds to the species one. Several hypotheses are discussed on the processes leading to this pattern of mixed clusters in sympatry, evoking formation of reproductive barriers, either by allopatric speciation or habitat selection.

**KEYWORDS**

Bayesian assignments, cluster, DAPC, microsatellites, *Pocillopora*, scleractinian

## 1 | INTRODUCTION

Studying population genetic connectivity is first a matter of knowing what we work on, that is, accurately delimiting the evolutionary units. Indeed, the populations among which we want to assess the exchanges of alleles must be composed of individuals that belong to a

unique and same species, in order to estimate genetic distances among comparable entities. That being said, it seems trivial, but the increasing discovery of highly divergent populations or divergent clusters among populations reveals the presence of possible cryptic species, somehow by serendipity, that the sole use of traditional taxonomic characters may have not highlighted. So population genetics data collected for

<footnote n="1">¹UMR ENTROPIE (Université de La Réunion, IRD, CNRS), Université de La Réunion, St Denis, La Réunion</footnote>

<footnote n="2">²Laboratoire d'excellence-CORAIL, Perpignan, France</footnote>

<footnote n="3">³UMR ENTROPIE (IRD, Université de La Réunion, CNRS), Centre IRD de Nouméa, Nouméa, New Caledonia</footnote>

<footnote>This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</footnote>

<footnote>© 2017 The Authors. *Ecology and Evolution* published by John Wiley & Sons Ltd.</footnote>
estimating connectivity, and more broadly phylogeographic analyses or barcoding, may in turn be used to refine taxonomic knowledge at the species rank, making them hardly ineluctable in an approach of integrative taxonomy. As an illustration, trying to understand the biogeographic shift in the kelp *Lessonia nigrescens*, Tellier, Meynard, Correa, Faugeron, and Valero (2009) identified two cryptic species using a combination of four genes among 1,000 individuals covering more than 2,500 km of coastline. The two divergent genetic lineages show a parapatric latitudinal distribution: one extends from southern Peru (17°S) to central Chile (30°S), and the other from central Chile (29°S) to Chiloe Island (42°S), both lineages spatially overlapping in a narrow area (29–30°S) in discrete patches where individuals belong to either the northern or southern species. Likewise, studying the ecological interactions between a coral host and its crustacean exosymbionts, Rouzé et al. (2017) used barcoding methods to identify the exosymbionts and found two cryptic species in the shrimp *Alpheus lottini*, revealing the key role of cryptic diversity in structuring communities of mutualists and the importance of taking into account this diversity in ecological studies to better perceive the complexity of ecological processes. Similarly, Souter, Henriksson, Olsson, and Grahn (2009) studied the connectivity pattern in the coral *Pocillopora damicornis* in East Africa and, after identifying two cryptic lineages using mitochondrial markers, chose to analyze them separately. Also, exploring the species diversity of the hydrozoans from the Aglaopheniidae family, Postaire, Magalon, Bourmaud, and Bruggemann (2016) revealed extensive lineage diversity and cryptic species in two common species, *Lytocarpia brevirostris* and *Macrorhynchia phoenicea*. Then, studying the connectivity of one of the cryptic species within the *L. brevirostris* complex using microsatellites, Postaire, Gélin, Bruggemann, and Magalon (2017) found a high genetic differentiation among populations, each island housing an independent evolutionary lineage, probably representing different species. *In fine*, two populations that have diverged enough can be considered as distinct units (e.g., species) on which, several studies, for example, genetic structuring and connectivity, environmental responses in face of perturbation, and conservation plan, will be set up. Nevertheless, the speciation process is slow and gradual. Thus, it is sometimes tricky to put a frontier between different units, notably when the speciation process is not achieved (De Queiroz, 2007), that is, in the gray zone starting from one species and ending to two new ones.

In marine systems, a large number of studies have shown that species with no or low dispersal (often linked with larval phase) tend to present significant genetic structure over small spatial scales while high dispersal abilities are not correlated with population subdivisions (Kelly & Palumbi, 2010). In general, high dispersal species present large population sizes, huge ranges, and rapid gene flow: characteristics that should slow species formation, confirmed by fossil data (Jablonski, 1986). Nevertheless, high dispersal potential does not always lead to high gene flow because of selection (Hilbish & Koehn, 1985), local genetic drift (Reeb & Avise, 1990), and complex homing behavior (Baker et al., 1990). Indeed, habitat preferences could lead to segregate individuals and promote divergence till sympatric speciation even with remaining gene flow (see for reviews, Pinho & Hey, 2010 or Bowen,

Rocha, Toonen, & Karl, 2013). Among marine organisms, scleractinians represent a good example of species with potentially high dispersal [e.g., larval lifetime of 30 days for *Heliopora coerulea* (Harii, Kayanne, Takigawa, Hayashibara, & Yamamoto, 2002), 100 days in *Pocillopora damicornis* (Richmond, 1987), and >200 days in some species from the genera *Acropora*, *Favia*, *Goniastrea*, *Monastrea* (Graham, Baird, & Connolly, 2008)], but their dispersal and settlement are constrained by several biotic and abiotic factors (hydrodynamics, light, temperature, gravity, surface texture, or presence of conspecifics; Rodriguez, Ojeda, & Inestrosa, 1993).

Like almost all the morphospecies from the genus *Pocillopora*, *P. eydouxi* has been described widely distributed in the Pacific Ocean and Indian Ocean, and the Red Sea, but absent from the Atlantic Ocean. Some recent studies have revisited *Pocillopora* taxonomy in light of molecular data. Using species delimitation methods based on mitochondrial markers, Gélin, Postaire, Fauvelot and Magalon (2017) found that *P. eydouxi* forms a primary species hypothesis [PSH, sensu Pante et al. (2015)], named PSH09 therein [see Gélin, Postaire, et al. (2017) for the name correspondence with the other studies]. This PSH corresponded to the complex *P. eydouxi/meandrina* (sensu Schmidt-Roach, Miller, Lundgren, & Andreakis, 2014) and will be further named *P. eydouxi* or PSH09 to make the reading easier. Then, performing assignment tests on multilocus genotypes (257 colonies and 13 microsatellites), they further revealed the occurrence of three secondary species hypotheses [SSH, sensu Pante et al. (2015)]: SSH09a is restricted to the Western Indian Ocean, and SSH09b and SSH09c are found in sympatry but restricted to the Pacific Ocean. Generally, colonies belonging to PSH09 present common morphological characteristics: large colonies presenting robust erected or horizontal branches, rounded or flattened, with more or less pronounced verrucae that are uniform in shape and spacing. Nevertheless, the *corallum* macromorphology is not a diagnostic character in *Pocillopora* genus [e.g., Paz-Garcia et al. (2015) or Gélin, Postaire, et al. (2017)]. It is present on all reef slopes and less frequently in lagoons, from surface to 40 m (HM, pers. obs.). Its three-dimensional structure is an element of reef structuring, and its broad interbranch width provides habitats for a huge variety of species, making it a key species of coral reef ecosystems from the Indo-Pacific and Red Sea. On a biological point of view, *P. eydouxi* morphospecies has been described as a broadcast spawner (Hirose, Kinzie, & Hidaka, 2001). Moreover, they evidenced that zooxanthellae were maternally inherited in oocytes, suggesting that larvae are autotrophic and intuitively meaning that their dispersal abilities are not limited by intrinsic resources as lecithotrophic larvae could be. Apart from these studies, focusing mainly on biology, ecology, and taxonomy, no one has investigated yet population genetic diversity in this species complex.

In front of such incomplete knowledge in *P. eydouxi*, it seems urgent to collect and reinforce data for this too long ignored scleractinian species complex despite its undeniable role in reef architecture and maintenance over the Indian Ocean, the Pacific Ocean, and the Red Sea. Thus, this study aimed to explore the cryptic genetic diversity within PSH09, and more precisely, within each SSH (SSH09a, b, c) using a combination of population genetics data (13 microsatellites) along with different
kinds of clustering analyses, and so at different spatial scales (reef < island < ecoregion < province). The hierarchical sampling focused on three understudied provinces (from a genetic connectivity point of view): the Western Indian Ocean, the Tropical Southwestern Pacific, and Southeast Polynesia. This should allow refining species delimitation in this species complex, better estimating reef biodiversity and identifying the units on which connectivity should be assessed.

## 2 | MATERIAL AND METHODS

### 2.1 | Sampling

In the aim of exploring the *Pocillopora* genus diversity and in fine studying population connectivity, colonies of *Pocillopora* genus were sampled [tip of branches + photographs except for Tromelin Island (Scattered Islands) and Polynesia] independently of their *corallum* macromorphology, as it is not a diagnostic character in *Pocillopora* genus. So species identification was realized molecularly *a posteriori* of sampling and *a priori* of data analyses (see below), leading to a subset of 2,507 colonies corresponding

to PSH09 *sensu* Gélin, Postaire, et al. (2017). The sampling was achieved from April 2011 to October 2016, in three marine provinces extended over six ecoregions (Spalding et al., 2007): the Western Indian Ocean (WIO), the Tropical Southwestern Pacific (TSP), and the Southeast Polynesia (SEP). The sampling followed a hierarchical scheme with several islands within a province and several sites within an island (province > ecoregion > island > site; Figure 1, Table 1). It represented a total of 12 islands (included large islands: Madagascar and New Caledonia) and 65 sites. For a given site, colonies were usually sampled at the same depth (8–14 m), during one single dive, so that the range of sampling for each site did not exceed some hundreds of m² and the distance between two colonies within a site varied from few centimeters to few meters, depending on the density of *Pocillopora* colonies.

### 2.2 | DNA extraction, sequencing, and microsatellite genotyping

From the sampled colonies, DNA was extracted using DNeasy Blood & Tissue kit (Qiagen™). Genotyping was performed following the same

<figure><img src="image_2.png" /></figure>

**FIGURE 1** Sampling locations of *Pocillopora* colonies. Populations are numerically identified from the island code [GLO: Glorioso Islands, MAY: Mayotte, JDN: Juan de Nova Island; BAS: Bassas da India, EUR: Europa Island, MAD: Madagascar, REU: Reunion Island, ROD: Rodrigues Island, CHE: Chesterfield Islands, NCA: Grande Terre (New Caledonia), LOY: Loyalty Islands (New Caledonia), and MOR: Moorea (French Polynesia)]. Constructed using © OpenStreetMap contributors CC BY-SA (http://www.openstreetmap.org/copyright) for landmasses and UNEP-WCMC, WorldFish Centre, WRI, TNC (2010). Global distribution of coral reefs, compiled from multiple sources including the Millennium Coral Reef Mapping Project. Version 1.3. Includes contributions from IMaRS-USF and IRD (2005), IMaRS-USF (2005) and Spalding et al. (2001). Cambridge (UK): UNEP World Conservation Monitoring Centre (http://data.unep-wcmc.org/datasets/1) for coral reefs
<table><caption>**TABLE 1** Sampling of *Pocillopora eydouxi* (Primary Species Hypothesis SSH09 in Gélin, Postaire, et al. (2017) colonies. For each site, are indicated the island, the ecoregion, and the province. The total number of sampled colonies ($N_{TOT}$) and the number of sampled colonies per Secondary Species Hypothesis (SSH09a, SSH09b, and SSH09c) are indicated for each site.</caption><thead><tr><th>Province</th><th>Ecoregion</th><th>Island</th><th>Site</th><th>Site Name</th><th>Latitude</th><th>Longitude</th><th>N<sub>TOT</sub></th><th>SSH09a</th><th>SSH09b</th><th>SSH09c</th></tr></thead><tbody><tr><td rowspan="10">Western Indian Ocean</td><td rowspan="10">Western and northern Madagascar</td><td>Mayotte</td><td>MAY1</td><td>Grand Recif Nord-Est</td><td>-12.62755</td><td>45.17750</td><td>45</td><td>45</td><td rowspan="10">4</td></tr><tr><td rowspan="9">Glorioso Island</td><td>MAY2</td><td>Passe Bateau</td><td>-12.97175</td><td>44.97857</td><td>35</td><td>31</td></tr><tr><td>MAY3</td><td>Passe en S</td><td>-12.87910</td><td>45.27705</td><td>42</td><td>42</td></tr><tr><td>GLO1</td><td>Est</td><td>-11.5336</td><td>47.40126</td><td>31</td><td>31</td></tr><tr><td>GLO2</td><td>Nord</td><td>-11.53490</td><td>47.33545</td><td>29</td><td>29</td></tr><tr><td>GLO3</td><td>Sud-Ouest</td><td>-11.57074</td><td>47.27066</td><td>45</td><td>45</td></tr><tr><td>GLO4</td><td>Sud</td><td>-11.58543</td><td>47.28376</td><td>19</td><td>19</td></tr><tr><td>JDN1</td><td>Biodiv 2</td><td>-16.95337</td><td>42.76011</td><td>58</td><td>57</td><td>1</td></tr><tr><td>JDN2</td><td>Biodiv 6</td><td>-17.08177</td><td>42.72536</td><td>44</td><td>43</td><td>1</td></tr><tr><td>JDN3</td><td>Biodiv 7</td><td>-17.01493</td><td>42.65645</td><td>48</td><td>48</td></tr><tr><td>JDN4</td><td>Nord-Ouest</td><td>-17.01737</td><td>42.67027</td><td>31</td><td>31</td></tr><tr><td>JDN5</td><td>Nord</td><td>-17.02115</td><td>42.73402</td><td>29</td><td>29</td></tr><tr><td rowspan="5">Juan de Nova Island</td><td>BAS1</td><td>Nord</td><td>-21.43418</td><td>39.65295</td><td>28</td><td>28</td><td rowspan="5">1</td></tr><tr><td>EUR1</td><td>Station météo</td><td>-22.33505</td><td>40.33439</td><td>60</td><td>60</td></tr><tr><td>EUR2</td><td>Pointe Est</td><td>-22.35081</td><td>40.38706</td><td>39</td><td>39</td></tr><tr><td>EUR3</td><td>Ouest</td><td>-22.37300</td><td>40.32483</td><td>9</td><td>9</td></tr><tr><td>EUR4</td><td>Sud-Ouest</td><td>-22.38403</td><td>40.3375</td><td>11</td><td>10</td></tr><tr><td rowspan="4">Madagascar (Tuléar)</td><td>MAD1</td><td>Anakao Nosy Vé</td><td>-23.15016</td><td>43.56768</td><td>48</td><td>48</td><td rowspan="4">1</td></tr><tr><td>MAD2</td><td>Ifaty Sud Passe</td><td>-23.40106</td><td>43.63468</td><td>49</td><td>48</td></tr><tr><td>MAD3</td><td>Grand Récif de Tuléar</td><td>-23.65441</td><td>43.58764</td><td>68</td><td>68</td></tr><tr><td>MAD4</td><td>Ste Marie</td><td>-17.119681</td><td>49.819741</td><td>10</td><td>10</td></tr><tr><td rowspan="5">Madagascar (north)</td><td>MAD5</td><td>Cap Masoala</td><td>-16.01399</td><td>50.151709</td><td>30</td><td>30</td><td rowspan="5">1</td></tr><tr><td>MAD6</td><td>Baie de Diego</td><td>-12.22962</td><td>49.35554</td><td>41</td><td>39</td></tr><tr><td>MAD7</td><td>Nosy Hara</td><td>-12.24219</td><td>49.01742</td><td>51</td><td>47</td></tr><tr><td>MAD8</td><td>Nosy Hao</td><td>-12.09729</td><td>49.03414</td><td>28</td><td>28</td></tr><tr><td>MAD9</td><td>Radama</td><td>-14.11170</td><td>47.68078</td><td>67</td><td>62</td></tr></tbody><tfoot><tr><td colspan="11" style="text-align: right;">(Continues)</td></tr></tfoot></table>
<table><caption>TABLE 1 (Continued)</caption><thead><tr><th>Province</th><th>Ecoregion</th><th>Island</th><th>Site</th><th>Site Name</th><th>Latitude</th><th>Longitude</th><th>N<sub>Tot</sub></th><th>SSH09a</th><th>SSH09b</th><th>SSH09c</th></tr></thead><tbody><tr><td rowspan="10">Tropical South<br>western Pacific</td><td rowspan="10">Mascarene Islands</td><td rowspan="10">Reunion Island</td><td>REU1</td><td>Le Port</td><td>-20.94338</td><td>55.27963</td><td>50</td><td>44</td><td>3</td><td>3</td></tr><tr><td>REU2</td><td>Mahapany</td><td>-21.03401</td><td>55.21478</td><td>38</td><td>38</td><td></td><td></td></tr><tr><td>REU3</td><td>La Saline</td><td>-21.09812</td><td>55.22826</td><td>39</td><td>39</td><td></td><td></td></tr><tr><td>REU3b</td><td>Planch' Alizé</td><td>-21.09778</td><td>55.23239</td><td>15</td><td>15</td><td></td><td></td></tr><tr><td>REU4</td><td>Saint-Leu</td><td>-21.18029</td><td>55.28371</td><td>33</td><td>33</td><td></td><td></td></tr><tr><td>REU5</td><td>Sainte-Rose</td><td>-21.15695</td><td>55.8362</td><td>47</td><td>47</td><td></td><td></td></tr><tr><td>Rodrigues</td><td>Rivière Banane</td><td>-19.66726</td><td>63.46867</td><td>47</td><td>45</td><td></td><td>2</td></tr><tr><td>ROD1</td><td>Mourouk (SEMPA)</td><td>-19.75397</td><td>63.46803</td><td>54</td><td>53</td><td></td><td>1</td></tr><tr><td>ROD2</td><td>Boobies Island</td><td>-19.65073</td><td>63.40020</td><td>35</td><td>34</td><td></td><td>1</td></tr><tr><td>ROD3</td><td>Iles auc Cocos</td><td>-19.73523</td><td>63.28396</td><td>47</td><td>47</td><td></td><td></td></tr><tr><td rowspan="2">Cargados Carajos/<br>Tromelin Island</td><td rowspan="2">Tromelin Island</td><td>TRO1</td><td>Pointe Sud</td><td>-15.90072</td><td>54.53346</td><td>16</td><td>16</td><td></td><td></td></tr><tr><td>TRO2</td><td>Nord-Ouest</td><td>-15.88354</td><td>54.51773</td><td>16</td><td>16</td><td></td><td></td></tr><tr><td rowspan="10">New Caledonia</td><td rowspan="10">Chesterfield Islands</td><td rowspan="10">Bampton Reefs</td><td>CHE01</td><td>Ilot Reynard</td><td>-19.20365</td><td>158.93706</td><td>38</td><td>2</td><td>36</td><td></td></tr><tr><td>CHE02</td><td>Bampton Nord</td><td>-19.11688</td><td>158.60000</td><td>39</td><td></td><td></td><td>39</td></tr><tr><td>CHE03</td><td>Ilot Avon</td><td>-19.48349</td><td>158.25203</td><td>30</td><td></td><td></td><td>30</td></tr><tr><td>CHE04</td><td>Les Ilots du Mouillage</td><td>-19.80292</td><td>158.43595</td><td>38</td><td>6</td><td>32</td><td></td></tr><tr><td>CHE05</td><td>Ilot du Passage</td><td>-19.90287</td><td>158.35330</td><td>61</td><td>5</td><td>56</td><td></td></tr><tr><td>CHE06</td><td>Ilot de Sable</td><td>-19.65317</td><td>158.20108</td><td>50</td><td>2</td><td>48</td><td></td></tr><tr><td>CHE07</td><td>Récif Oiry</td><td>-21.38758</td><td>159.55353</td><td>48</td><td></td><td>48</td><td></td></tr><tr><td>CHE08</td><td>Récif de l'Anneau</td><td>-21.85115</td><td>159.43348</td><td>35</td><td>4</td><td>31</td><td></td></tr><tr><td>CHE09</td><td>Récif du Milieu</td><td>-21.45272</td><td>159.02081</td><td>56</td><td>7</td><td>49</td><td></td></tr><tr><td>CHE10</td><td>Bellona Nord-Ouest</td><td>-20.80306</td><td>158.45104</td><td>53</td><td>5</td><td>48</td><td></td></tr><tr><td rowspan="10">Grande Terre</td><td rowspan="10">Bellona Reefs</td><td>NCA01</td><td>Poum</td><td>-20.30244</td><td>163.88345</td><td>26</td><td>15</td><td>11</td><td></td></tr><tr><td>NCA02</td><td>Voh</td><td>-21.0339</td><td>164.61812</td><td>42</td><td>34</td><td>8</td><td></td></tr><tr><td>NCA03</td><td>Bourail</td><td>-21.70086</td><td>165.46918</td><td>31</td><td>10</td><td>21</td><td></td></tr><tr><td>NCA04</td><td>Nouméa</td><td>-22.33536</td><td>166.2219</td><td>25</td><td>11</td><td>14</td><td></td></tr><tr><td>NCA05</td><td>Port Bouquet</td><td>-21.59848</td><td>166.42591</td><td>54</td><td>4</td><td>50</td><td></td></tr><tr><td>NCA06</td><td>Kuakué</td><td>-21.7857</td><td>166.63385</td><td>56</td><td>13</td><td>43</td><td></td></tr><tr><td>NCA07</td><td>Goro</td><td>-22.33438</td><td>167.01815</td><td>48</td><td>19</td><td>29</td><td></td></tr><tr><td>NCA08</td><td>Pouébo</td><td>-20.16931</td><td>164.43608</td><td>61</td><td>15</td><td>46</td><td></td></tr><tr><td>NCA09</td><td>Hienghène</td><td>-20.55310</td><td>165.00238</td><td>38</td><td>13</td><td>25</td><td></td></tr><tr><td>NCA10</td><td>Poindimié</td><td>-20.8337</td><td>165.40346</td><td>37</td><td>4</td><td>33</td><td></td></tr></tbody></table>

(Continues)
<table><caption>TABLE 1 (Continued)</caption><thead><tr><th>Province</th><th>Ecoregion</th><th>Island</th><th>Site</th><th>Site Name</th><th>Latitude</th><th>Longitude</th><th>N<sub>TOT</sub></th><th>SSH09a</th><th>SSH09b</th><th>SSH09c</th></tr></thead><tbody><tr><td></td><td></td><td>Loyalty Islands</td><td>LOY1</td><td>Maré</td><td>-21.40448</td><td>167.8007</td><td>10</td><td></td><td>3</td><td>7</td></tr><tr><td></td><td></td><td></td><td>LOY2</td><td>Maré</td><td>-21.48428</td><td>168.11743</td><td>13</td><td></td><td></td><td>13</td></tr><tr><td></td><td></td><td></td><td>LOY3</td><td>Lifou</td><td>-20.85568</td><td>167.29346</td><td>25</td><td></td><td>23</td><td>2</td></tr><tr><td></td><td></td><td></td><td>LOY4</td><td>Lifou</td><td>-20.77063</td><td>167.03459</td><td>55</td><td></td><td>34</td><td>21</td></tr><tr><td></td><td></td><td></td><td>LOY5</td><td>Ouvéa</td><td>-20.71891</td><td>166.39266</td><td>47</td><td></td><td>29</td><td>18</td></tr><tr><td></td><td></td><td></td><td>LOY6</td><td>Beautemps-Beaupré</td><td>-20.404</td><td>166.13475</td><td>49</td><td></td><td>43</td><td>6</td></tr><tr><td></td><td></td><td></td><td>MOR1</td><td>Haapiti</td><td>-17.57569</td><td>-149.87803</td><td>10</td><td></td><td>10</td><td></td></tr><tr><td>Southeast Polynesia</td><td>Society Islands</td><td>Moorea</td><td></td><td></td><td></td><td></td><td></td><td>2,507</td><td>1,403</td><td>323</td></tr><tr><td>Total</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>781</td></tr></tbody></table>

protocol than in Gélin, Postaire, et al. (2017) and so using the same 13 microsatellite loci. PCR products were genotyped using an ABI 3730 genetic analyser (Applied Biosystems), and allelic sizes were determined with GeneMapper v.4.0 (Applied Biosystems) using an internal size standard (Genescan LIZ-500, Applied Biosystems). Because colonies were sampled based on their *corallum* macromorphology, *P. eydouxi* lineage identity was verified *a priori* using assignment tests performed with STRUCTURE (Pritchard, Stephens, & Donnelly, 2000). For this purpose, we combined all sampled colonies for this study and the 943 colonies from Gélin, Postaire, et al. (2017) that were assigned to the different SSHs as in Gélin, Postaire, et al. (2017). From that, a total of 2,507 colonies grouped together in PSH09 and constituted the final dataset.

## 2.3 | MLG identification and assignment to the SSHs

To check for clonal propagation among the sampled colonies, identical multilocus genotypes (MLG) were identified using the R (R Development Core Team 2016) package *RClone* (Bailleul, Stoeckel, & Arnaud-Haond, 2016).

Then, keeping one representative per MLG, we assigned the colonies to the three SSHs (SSH09a, SSH09b and SSH09c) previously identified in Gélin, Postaire, et al. (2017). To do so, we performed assignment tests on the 2,507 genotyped colonies, including the 257 colonies from PSH09 used in Gélin, Postaire, et al. (2017) and using STRUCTURE (Pritchard et al., 2000) for K = 3. Five chains with $2 \times 10^6$ generation steps after a burn-in of $2 \times 10^5$ were run assuming admixture and correlated frequencies. The STRUCTURE outputs were summarized with CLUMPP v.1.0 (Jakobsson & Rosenberg, 2007) and drawn with DISTRUCT v.1.1 (Rosenberg, 2004).

The colonies showing an assignment probability for each of the five runs >0.75 to a given SSH were assigned to this SSH (practically identified by a unique color on Figures). Colonies that did not show an assignment >0.75 for a given SSH were considered admixed, that is, assigned to more than one SSH (due to hybridization or shared ancestry or bad assignment due to missing data). For each admixed colony, it was assigned to the SSHs presenting an assignment probability >0.1 (a probability < 0.1 was considered as noise). Moreover, we used NEWHYBRIDS v.1.1 (Anderson & Thompson, 2002), running $5 \times 10^5$ iterations after a burn-in period of $5 \times 10^4$, to detect whether the admixed colonies assigned to two SSHs found in STRUCTURE could be considered as hybrids. NEWHYBRIDS calculates the posterior probability that sampled colonies fall into each of a set of hybrid categories (Parent 1, Parent 2, F1, F2, backcross to Parent 1, backcross to Parent 2). Once identified, the admixed colonies were not considered in the subsequent analyses.

The genotypic linkage disequilibrium was assessed with F<sub>ST</sub> v.2.9.3 both on the whole dataset and for each SSH separately (Goudet, 2001), and pairwise differentiation among SSHs was estimated using the F<sub>ST</sub> (Weir & Cockerham, 1984) with ARLEQUIN v.3.5 (Schneider, Roessli, & Excoffier, 2000) and D<sub>est</sub> using the R package DEMEtics (Gerlach, Jueterbock, Kraemer, Deppermann, & Harmand, 2010). For each SSH, the allelic frequency distributions were plotted,
and the number of alleles (Na) and the number of private alleles (Np) were estimated using FSTAT v.2.9.3 (Goudet, 2001). Then, considering the whole dataset, the global FST (Weir & Cockerham, 1984) for each locus, was assessed with FSTAT v.2.9.3.

## 2.4 | Structuring analyses within each SSH

Further analyses were performed on each SSH separately. First, to determine the most likely number of genetically homogenous groups (K) within each SSH, a Bayesian analysis was performed using Structure v.2.3.4 (Pritchard et al., 2000) with the same conditions as described above. This analysis assumes that, within a set of samples, there are K genetic groups and colonies are assigned to each putative genetic groups under Hardy-Weinberg equilibrium (HWE) and minimized linkage disequilibrium (LD). To allow comparing the different outputs from the different types of analyses (see below), the colonies were assigned to clusters following the same rule than above (replacing SSH by cluster, and one color per cluster) and the same rule was applied to constitute the dataset (i.e., removing admixed colonies) for further analyses. As STRUCTURE demands strong assumptions on the genetic groups identified, we performed in parallel a discriminant analysis of principal components (DAPC; Jombart, Devillard, & Balloux, 2010) in order to test whether the structuring observed with Structure v.2.3.4 could be retrieved with DAPC, as this latter analysis does not make any assumption about HWE or LD (it transforms genotypes using PCA as a prior step to a discriminant analysis). DAPC was applied using the adegenet package (Jombart, 2008) for R (R Development Core Team 2016). Then to evaluate the congruency between STRUCTURE and DAPC for a given K, the number of colonies that were assigned differently between both methods was estimated.

In a hierarchical approach, these analyses were repeated on each cluster found in each SSH separately. Commonly, using STRUCTURE and DAPC, when the finest level of structuring is reached, adding a supplementary cluster leads to inconclusive assignments with colonies assigned to several clusters in the same proportions. Here, some colonies kept ongoing assigned to clusters when K increased above the most likely K, either using STRUCTURE or DAPC. Moreover, because the methods traditionally used to detect the most likely number of genetic groups within a dataset [Pr(X|K) (Pritchard et al., 2000), ΔK method (Evanno, Regnaut, & Goudet, 2005), the deviance information criterion (DIC) (Gao, Bryc, & Bustamante, 2011), the Bayesian information criterion (BIC) (Jombart et al., 2010), and the thermodynamics integration (TI) (Verity & Nichols, 2016)] might provide different outputs, and because they are purely mathematical, they might not reflect the biological truth. So to exclude mathematical artefacts, we chose to consider the highest number of genetic groups corresponding to the clustering and the individual assignments that were retrieved by all the four possible combinations [(STRUCTURE vs. DAPC) × (all colonies of a given SSH vs. each cluster within this SSH separately)].

Considering the clusters finally kept within each SSH, the pairwise differentiation among clusters within each SSH was estimated using the FST (Weir & Cockerham, 1984) with ARLEQUIN v.3.5

(Schneider et al., 2000) and D'est using the R package DEMETics (Gerlach et al., 2010). Then, for each cluster, the allelic frequencies spectrum for each locus were plotted, and considering each SSH, the global FST (Weir & Cockerham, 1984) for each locus was calculated using FSTAT v.2.9.3 (Goudet, 2001). Additionally, minimum spanning trees based on the shared alleles distance (DAS) were performed with EDENetwork (Kivelä, Arnaud-Haond, & Saramäki, 2015) and drawn with colonies colored according to the clusters found in the assignment analyses. Moreover, we used NEWHYBRIDS v.1.1 (Anderson & Thompson, 2002) between pairs of clusters within each SSH to detect whether the admixed colonies found in STRUCTURE could be considered as hybrids (same conditions as above). Finally, a hierarchical analysis of molecular variance (AMOVA) was performed using ARLEQUIN v.3.5 (Schneider et al., 2000) considering the whole dataset with SSH as group and clusters within SSH as populations.

## 3 | RESULTS

### 3.1 | MLG identification and assignment to the SSHs

Among the 2,507 *Pocillopora* colonies assigned to PSH09 (Gélin, Postaire, et al. (2017)), each represented a unique MLG. First, assigning the colonies to the three SSHs, all the colonies from the TSP and the SEP (n = 1,075) were assigned to both SSH09b and SSH09c (none to SSH09a), while almost all the colonies from the WIO (n = 1,430) were assigned to SSH09a (n = 1,403), except 12 and 15 that were assigned to SSH09b and SSH09c, respectively (Table 1; Figure 2). Interestingly, looking at the site REU1 (n = 50) in Reunion Island, 44 colonies were assigned to SSH09a, three to SSH09b, and three to SSH09c. Noteworthy, all the colonies showed a probability of assignment to each SSH > 0.75, except six colonies (0.2%) that were found to be admixed between SSHs: one colony from New Caledonia (LOY6) was admixed between SSH09b and SSH09c, and five from North Madagascar were admixed: one (MAD8) between SSH09a and SSH09c, two colonies (MAD5 and MAD6) between SSH09a and SSH09c, and two (MAD6 and MAD7) between all the SSHs. Nevertheless, the multilocus genotypes of all these admixed colonies presented missing data (37% in average) and might just reflect a bad assignment. Additionally, NEWHYBRIDS detected only 18 hybrids over 2,507 colonies (0.7%), and all were F2 hybrids. Moreover, a high proportion of colonies assigned to one of the three SSHs identified with STRUCTURE (i.e., assignment probability > 0.75) were assigned as pure lineages with NEWHYBRIDS (i.e., 99.3, 98.1 and 98.8% for SSH09a, SSH09b, and SSH09c, respectively).

Inter-SSHs genetic differentiation varied from 0.158*** to 0.248*** for the FST estimates and from 0.376*** to 0.430*** for the D'est ones (Table 2). Then, considering the whole dataset irrespective to SSHs, 10 tests of genotypic disequilibrium were significant over 78, indicating 13% of genotypic disequilibrium among the 13 loci after Bonferroni correction, while, considering each SSH separately, none of the 78 tests revealed disequilibrium within both SSH09a and SSH09c and 10 of 78 within SSH09b.
<figure><img src="image_3.png" /><figcaption>**FIGURE 2** SSH09a clusters. (a) Results of the assignment tests for SSH09 at K = 3 and (b) the STRUCTURE plots for K = 2 and K = 3 are presented. (c) Results of the DAPC assignment for K = 3 and (d) the minimum spanning tree, both colored according to the three clusters identified by STRUCTURE</figcaption></figure>

<table><caption>**TABLE 2** Genetic differentiation among the three identified Secondary Species Hypotheses estimated with Weir and Cockerham's *F*<sub>ST</sub> (lower diagonal; Weir & Cockerham, 1984) and with Jost's *D*<sub>est</sub> (upper diagonal; Jost, 2008). In parentheses, is indicated the number of colonies. *** *P* < 0.001.</caption><thead><tr><th></th><th>SSH09a</th><th>SSH09b</th><th>SSH09c</th></tr></thead><tbody><tr><td>SSH09a (1,403)</td><td>-</td><td>0.402***</td><td>0.438***</td></tr><tr><td>SSH09b (323)</td><td>0.158***</td><td>-</td><td>0.376***</td></tr><tr><td>SSH09c (781)</td><td>0.248***</td><td>0.225***</td><td>-</td></tr></tbody></table>

The mean number of alleles (± standard error) per locus was high, varying from 13.00 ± 0.87 for SSH09b to 14.54 ± 1.27 for SSH09c (Appendix S1). For each SSH, between 11 and 14% of private alleles were identified, the mean number of private alleles per locus varying from 1.46 ± 0.33 for SSH09c to 1.85 ± 0.41 for SSH09b (Appendix S1). The allele frequency spectrum for each locus and for each SSH as well as the *F*<sub>ST</sub> per locus considering the whole dataset (Appendix S2) showed some high values from 0.009* to 0.769*** for Pd3-008 and a high variance (mean *F*<sub>ST</sub> ± *SE* = 0.188 ± 0.058). In addition, loci Pd11 and Pd13 showed a weaker amplification rate on colonies from SSH09a than from SSH09b and SSH09c (data not shown).

Overall, this indicates a high genetic differentiation among the three SSHs, indicating restricted gene flow between these SSHs. Thus, from now, we will consider these three SSHs as independent genetic lineages and will perform the subsequent analyses on each SSH separately (*N*<sub>SSH09a</sub> = 1,403; *N*<sub>SSH09b</sub> = 323; *N*<sub>SSH09c</sub> = 781; Table 1).

## 3.2 | Structuring analyses within each SSH

### 3.2.1 | SSH09a

Considering all the colonies assigned to SSH09a (exclusively located in the WIO; *n* = 1,403), DAPC showed nearly similar individual

assignments than STRUCTURE for both *K* = 2 and *K* = 3: for *K* = 2, we observed two clusters, SSH09a-1 and SSH09a-2, while for *K* = 3, SSH09a-2 was further divided into two clusters (SSH09a-2 and SSH09a-3, Figure 2). To explore in depth the partitioning, we re-analyzed alone the first cluster (SSH09a-1) found for *K* = 2: it did not separate anymore using STRUCTURE nor DAPC. Conversely, the second cluster (SSH09a-2) when re-analyzed alone did split in three distinct subclusters with STRUCTURE while with DAPC, two of these subclusters were overlapping. Then, reconsidering all the colonies and now *K* = 4, STRUCTURE did find four clusters, but they did not correspond to the ones found using DAPC, nor to the ones found when treating the two-first clusters independently (in this case, SSH09a-1 and SSH09a-2 both split into two).

Because our choice criterion was the congruency of the different methods [(STRUCTURE vs. DAPC) × (all colonies vs. substructuring)], we finally considered that all the colonies from SSH09a could be divided into three distinct clusters (SSH09a-1, SSH09a-2 and SSH09a-3; Figure 2). At *K* = 3 in STRUCTURE, each colony was assigned identically among the five runs. Then, 1,310 colonies (94%) were assigned to a unique cluster (*N*<sub>SSH09a-1</sub> = 600, *N*<sub>SSH09a-2</sub> = 237, and *N*<sub>SSH09a-3</sub> = 473), and 93 colonies (6%) were admixed in several clusters: 29 colonies (2%) between SSH09a-1 and SSH09a-2, 10 colonies (0.7%) between SSH09a-1 and SSH09a-3, 29 colonies (2%) between SSH09a-2 and SSH09a-3, and 25 colonies (1.7%) among the three clusters. Nevertheless, only 12 of these 93 colonies presented no missing data, suggesting that the other admixed colonies (21% of missing data in average) might be the result of bad assignment. Comparing both methods, only 4.7% of the colonies were assigned differently. The minimum spanning tree retrieved the three clusters (Figure 2d). Surprisingly, these latter were found nearly evenly distributed in all the sampling sites with no apparent geographical pattern, cluster SSH09a-1 being the most represented (46%; Figure 2). Pairwise *F*<sub>ST</sub> estimates among the three clusters were of the same order, between 0.125*** and 0.150*** (Table 3). Comparatively, *D*<sub>est</sub> values appeared lower, comprised between 0.085*** and 0.092*** (Table 3) but all significantly
<table><caption>**TABLE 3** Genetic differentiation between the three identified clusters within SSH09a estimated with Weir and Cockerham's $F_{ST}$ (lower diagonal; Weir & Cockerham, 1984) and with Jost's $D_{est}$ (upper diagonal; Jost, 2008). In parentheses, is indicated the number of colonies. ***P<0.001.</caption><thead><tr><th></th><th>SSH09a-1</th><th>SSH09a-2</th><th>SSH09a-3</th></tr></thead><tbody><tr><td>SSH09a-1 (651)</td><td>-</td><td>0.090***</td><td>0.092***</td></tr><tr><td>SSH09a-2 (256)</td><td>0.125***</td><td>-</td><td>0.085***</td></tr><tr><td>SSH09a-3 (496)</td><td>0.140***</td><td>0.150***</td><td>-</td></tr></tbody></table>

different from zero. Global $F_{ST}$ per locus ranged from 0.000<sup>ns</sup> to 0.772*** for PV7 (mean $F_{ST}$ ± SE = 0.061 ± 0.062; Appendix S3).

### 3.2.2 | SSH09b

Concerning SSH09b (n = 323; nearly exclusively found in the South Pacific), for K = 2, colonies from the WIO, Chesterfield Islands, and French Polynesia were assigned in the first cluster (SSH09b-1; Figure 3b), while colonies from New Caledonia in both clusters (SSH09b-1 and SSH09b-2) with no apparent geographical pattern. Moreover, colonies of each cluster were not evenly spread across sites. For K = 3 and K = 4, individual assignments were not congruent between STRUCTURE and DAPC (data not shown), even when analyzing both clusters separately.

Thus, we considered that SSH09b split into two clusters, [SSH09b-1 (n = 256) and SSH09b-2 (n = 67); Figure 3]. For K = 2 in STRUCTURE, each colony was assigned identically among the five runs. Then, 307 colonies (95%) were assigned to a unique cluster ($N_{SSH09b-1}$ = 244 and $N_{SSH09b-2}$ = 63) and 16 colonies (5%) were admixed between SSH09b-1 and SSH09b-2. Nevertheless, only three colonies presented no missing data, suggesting that the 13 other admixed colonies (23% of missing data in average) might be the result of bad assignment. Only 2.9% of the colonies were assigned differently between both methods. The minimum spanning tree retrieved the

two clusters (Figure 3d), which were highly differentiated from each other ($F_{ST}$ = 0.131*** and $D_{est}$ = 0.259***). Global $F_{ST}$ per locus ranged from 0.001<sup>ns</sup> to 0.283*** for Poc40 (mean $F_{ST}$ ± SE = 0.104 ± 0.029; Appendix S4).

### 3.2.3 | SSH09c

Concerning SSH09c (n = 781), for K = 2, the structuring pattern nearly corresponded to a geographical pattern. Indeed, on one hand, colonies from Chesterfield Islands together with those of LOY4, LOY5 (Loyalty Islands) and the 15 colonies from the WIO were grouped in a first cluster (SSH09c-1; Figure 4b), and on the other hand, all the other colonies from New Caledonia (Grande Terre and Loyalty Islands) composed the second cluster (SSH09c-2; Figure 4b). For K = 3, SSH09c-1 further split into two clusters, the colonies from Loyalty Islands all appearing differentiated in a third cluster (SSH09c-3) while colonies from Chesterfield Islands segregated in both SSH09c-1 and SSH09c-3. Using DAPC, the same partitioning was observed either for K = 2 or K = 3 (Figure 4b).

Thus, following our choice criterion, three clusters were identified. At K = 3 in STRUCTURE, each colony was assigned identically among the five runs. Then, 756 colonies (97%) were assigned to a unique cluster ($N_{SSH09c-1}$ = 273, $N_{SSH09c-2}$ = 302, and $N_{SSH09c-3}$ = 181), and 25 colonies (3%) were admixed in several clusters: two colonies between SSH09c-1 and SSH09c-2, 11 colonies between SSH09c-1 and SSH09c-3, nine colonies between SSH09c-2 and SSH09c-3, and three colonies among the three clusters. Nevertheless, only four colonies presented no missing data, suggesting that the other admixed colonies (19% of missing data in average) might be the result of bad assignment. Only 1.9% of the colonies were assigned differently among the two methods. The three clusters were retrieved in the minimum spanning tree (Figure 4d), and genetic differentiation among them was high, either considering $F_{ST}$ (from 0.16*** to 0.34***) or $D_{est}$ (from 0.09*** to 0.31***) estimates (Table 4). Global $F_{ST}$ per locus ranged from 0.001<sup>ns</sup> to 0.737*** for Pd4 (mean $F_{ST}$ ± SE = 0.097 ± 0.063; Appendix S5).

<figure><img src="image_4.png" /><figcaption>**FIGURE 3** SSH09b clusters. (a) Results of the assignment tests for SSH09 at K = 3 and (b) the STRUCTURE plots for K = 2 are presented. (c) Results of the DAPC assignment for K = 2 and (d) the minimum spanning tree, both colored according to both clusters identified by STRUCTURE</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**FIGURE 4** SSH09c clusters. (a) Results of the assignment tests for SSH09 at K = 3 and (b) the STRUCTURE plots for K = 2 and K = 3 are presented. (c) Results of the DAPC assignment for K = 3 and (d) the minimum spanning tree, both colored according to the three clusters identified by STRUCTURE</figcaption></figure>

<table><caption>**TABLE 4** Genetic differentiation between the three identified clusters within SSH09c estimated with Weir and Cockerham's *F*<sub>ST</sub> (lower diagonal; Weir & Cockerham, 1984) and with Jost's *D*<sub>est</sub> (upper diagonal; Jost, 2008). In parentheses, is indicated the number of colonies. *** *P* < 0.001</caption><thead><tr><th></th><th>SSH09c-1</th><th>SSH09c-2</th><th>SSH09c-3</th></tr></thead><tbody><tr><td>SSH09c-1 (278)</td><td>-</td><td>0.310***</td><td>0.087***</td></tr><tr><td>SSH09c-2 (308)</td><td>0.339***</td><td>-</td><td>0.219***</td></tr><tr><td>SSH09c-3 (195)</td><td>0.163***</td><td>0.221***</td><td>-</td></tr></tbody></table>

Additionally, within each SSH, only F2 hybrids were detected using NEWHYBRIDS between pairs of clusters. Nevertheless, colonies assigned to a unique cluster with STRUCTURE (assignment probability > 0.75) were mainly assigned in pure lineages with NEWHYBRIDS (i.e., 97, 89.4 and 98.7% in average among the clusters of SSH09a, SSH09b, and SSH09c, respectively).

Finally, considering this hierarchical clustering of the whole dataset (three SSHs splitting in several clusters), the AMOVA revealed that nearly 15% of the overall genetic variation was explained by the partition into three SSHs and 12% by the partition into clusters within SSH (Appendix S6).

# 4 | DISCUSSION

## 4.1 | Russian dolls

The present study focuses on the genetic variability among colonies from the Primary Species Hypothesis SSH09 found in Gélin, Postaire, et al. (2017). SSH09 corresponds to two ORF and four Dloop haplotypes and was attributed *P. eydouxi* name. In this previous analysis, based on microsatellite loci, Gélin, Postaire, et al. (2017) identified three genetically distinct clusters within SSH09, two of which were found in sympatry, leading to the recognition of three Secondary

Species Hypotheses within SSH09 (SSH09a, b, and c). Here, using microsatellite data and assignment tests on a much larger sampling, we further revealed that each of them was found in sympatry with one or the other, with the three being found on a single reef in the WIO (Reunion Island, REU1). Furthermore, according to our choice criterion to determine the number of genetic groups K, we even found that each of these SSHs was partitioned into two to three additional clusters, which were also all found in sympatry at least at the island scale within the distribution range of their respective SSH, with very rare hybrids (only F2 and none F1). Noteworthy, these clusters were as much genetically differentiated from each other as SSHs were, or as SSHs belonging to different PSHs were [see Appendix S13 in Gélin, Postaire, et al. (2017)]. To summarize, when dissecting the genetic variability of SSH09, we revealed several nested hierarchical levels (PSH > SSH > cluster), each level hiding highly differentiated genetic groups as in Russian dolls, not in agreement with geography for most of them.

This pattern of interspersed genetic clusters among populations, revealed using STRUCTURE assignments and microsatellite data, has already been reported in the literature. In the coral *Seriatopora hystrix* in Japan, Nakajima et al. (2017) found three interspersed genetic clusters in sympatry among the sampled sites corresponding to three distinct genetic lineages identified by mitochondrial DNA. Aside from a mix of mitochondrial lineages, the existence of ecological gradients has been proposed to explain a mix of clusters at the site level. As an illustration, Van Oppen, Bongaerts, Underwood, Peplow, and Cooper (2011) found that colonies of *S. hystrix* in North Australia were assigned in genetic clusters that corresponded to depth. Moreover, they evidenced vertical migration between shallow and deep habitats in each site explaining the mix of clusters found for some particular depths. Serrano et al. (2016) also revealed a pattern of depth structuring for *Porites astreoides* from Florida. Zayasu et al. (2016), studying *Acropora tenuis* in southwestern Japan, hypothesized the existence of two source populations (two genetic clusters) in both farthest sites of the sampling
area and a mix of both clusters in the middle of the sampling area that should be linked to migration from extreme sites to the intermediate ones. A similar pattern was observed in *P. damicornis* β (Gélin, Fauvelot, Mehn, Bureau, Rouzé & Magalon, 2017) in Reunion Island with both clusters mixed in one intermediate site, evoking a geographic barrier or an asynchronous spawning of the most remote populations. In the Caribbean coral *Orbicella faveolata*, Porto-Hannes et al. (2015) found two genetic clusters in sympatry at the reef scale, mainly on two sites from Belize. They suggested that those sites would have been more recently colonized than other sites because the sampled colonies were smaller than the colonies from the other sites, reflecting a reduction in larval production due to mortality that could favor local recruitment. Jia et al. (2016) found six genetic clusters interspersed among seven populations of the sandy shrub *Salix psammophila*, but they did not propose any hypothesis to explain this pattern. Finally, with the growing body of population genetics studies, these patterns of mixed clusters among populations are increasingly revealed without understanding clearly the underlying processes.

## 4.2 | Is this the real life? Is this just fantasy?

Here, we used microsatellite genetic data coupled with different methods to refine lineage boundaries within the *P. eydouxi* complex (PSH09; Gélin, Postaire, et al. (2017)). Thus, despite the fact that microsatellites are not commonly used in species delimitation but rather in population genetics, their high polymorphism can help in refining boundaries between closely related species (e.g., Dawson et al., 2010). Although *STRUCTURE* and DAPC differ in their a priori hypotheses, we observed very few differences (3.6% over the whole dataset) between the outputs of these two methods, highlighting their congruency and comforting us that the observed clusters were independent of the method. The main problem when using clustering methods is to determine the true number of genetically differentiated clusters, *K*, present in the dataset, subject of debate over the last decade. Nevertheless, all the estimators of *K* traditionally used can all give different values for a given dataset, and not a single estimator can provide a true value of *K*, as suggested by Jombart et al. (2010). Recently, Verity and Nichols (2016) suggested that *K* should be viewed as a flexible parameter that describes just one point on a continuously varying scale of population structure. Therefore, comparing the outputs of the different analyses and keeping the highest value of *K* that gave congruent results among analyses seemed to be a good compromise to estimate the best number of genetic groups.

Overall in the whole sampling (2,507 colonies), we did not find at least two colonies sharing the same MLG, suggesting that the analyzed colonies do not exhibit clonal propagation, in accordance with their massive morphology and their stout branches, limiting fragmentation. So the genotypic linkage observed in the whole dataset was not due to the presence of repeated MLGs (clones), but to some particular alleles that were preferentially associated within SSH, as well as in SSH09b considering the non-random association of alleles within each cluster. Thus, (1) the number of private alleles found in each SSH and in each cluster within SSH along with (2) the variation of allele frequencies

among SSHs and also among clusters within SSH (high values of global $F_{ST}$ per locus) both played a role in the genetic differentiation observed among SSHs and clusters.

Finally, rather than structured populations within a single species, these three SSHs, and even the eight clusters, likely represent distinct genetic lineages, though incompletely sorted for the ORF gene, engaged in a speciation process (half-way in the "gray zone" between two populations highly differentiated and two sister species) or real species following the unified concept of De Queiroz (2007). In this way, Johnston et al. (2017) highlighted a genetic distinction between *P. eydouxi* and *P. meandrina* (which are not diagnosable using the ORF mitochondrial marker), suggesting that both might be recognizable using nuclear DNA. Thus, the SSHs (or even clusters) we observed might be the reflection of the presence of these two genetic lineages among our samples distinguished by the set of microsatellites used. The issue is now to understand in our case which hierarchical level (SSH, cluster or even below) corresponds to the species one. An extended integrative approach (microstructure, microenvironment ecology, symbionts, and genomics) is needed to fully conclude where to put the boundaries between species, whether these three SSHs actually correspond to three distinct species or whether each SSH would represent a complex of species, each cluster being a single species (or a complex of, more or less cryptic) with its own distribution area.

## 4.3 | Origin of divergence

Even if we cannot fully conclude regarding the status of each of these SSHs and clusters, we can discuss the possible processes that lead to the appearance of such genetically divergent lineages, even if the use of microsatellites does not allow estimating the time of divergence among lineages.

Starting with the first hierarchical level, the three SSHs may re-present ancestral lineages that were isolated in the past and evolved separately for a period of time long enough for reproductive barriers to emerge. The fact that SSH09a is restricted to WIO while SSH09b and SSH09c are almost exclusively found in the South Pacific Ocean strongly suggests the role of the Indo-Pacific Barrier, a widely recognized partition (based on faunal distributions) that separates the Pacific and Indian Ocean provinces (Briggs, 1974), in generating the genetic divergences among SSHs. With the onset of Pleistocene glaciation cycles about 3 million years ago (Mya), global sea levels have fluctuated with maximum amplitudes of up to 140 m (Lambeck, Esat, & Potter, 2002). The sea level reached 120 m below present-day level twice over the last two glacial periods, with the last one, occurring ca. 17-18,000 years ago, largely exposing Sunda and Sahul Shelves and restricting water exchanges between Indian Ocean and Pacific Ocean (Bard et al., 1996; Voris, 2000). As a consequence, the opportunity for genetic exchange for marine organisms between the two oceans strongly decreased and populations evolved independently on both sides of this semi-permeable barrier, providing an occasion for population differentiation and incipient speciation to occur. Meanwhile, these sea-level regressions profoundly affected the distribution of shallow-water reef habitats (Lambeck & Chappell, 2001), generating
population size reductions in reef-associated organisms, which may have caused local extinction of some species (Fauvelot, Bernardi, & Planes, 2003). Although the magnitude and timing of relative sea-level stands vary across the Indo-Pacific region (Woodroffe & Horton, 2005), during low sea-level periods, viable populations survived in coral reef refuges that were isolated from each other (Pellissier et al., 2014). Once sea levels rose again (to reach present-day level), surviving populations, by then forming differentiated lineages, re-expanded to reach their current geographic distributions (Fauvelot et al., 2003).

Then, several nonexclusive hypotheses may explain the nested partitioning within each SSH (i.e., the presence of differentiated clusters in sympatry at the island scale):

1. In all our provinces, genetically divergent clusters within SSH might be the result of a systematic sampling of sink populations without source populations, assuming that (a) these latter are assigned in one unique cluster each, (b) colonies belonging to one cluster do not reproduce with colonies from other clusters, implying some reproductive barriers among clusters (otherwise, we should observe more genetically homogeneous populations or hybrids), and (c) the source populations provide in individuals each sink population equally.

2. Alternatively, genetically divergent clusters within SSH may represent past clonal lineages. Reproductively isolated clones (pre- or post-zygotic barriers impeding reproduction with closely related individuals) would have spread and diversified resulting in the different clusters observed. Nevertheless, as we did not detect clonal reproduction in our sampling, it seems unlikely, although clonal propagation has been evidenced in the TEP (Baums et al., 2014; Pinzón, Reyes-Bonilla, Baums, & LaJeunesse, 2012) for ORF type 1 colonies [sensu Pinzón et al. (2012)]. If so, clonal reproduction may represent an ancestral (or re-acquired) character in the TEP (east margin of *Pocillopora* distribution area), which would have been lost in other localities. However, the colonies studied (Baums et al., 2014; Pinzón et al., 2012) were described as *P. damicornis*-like colonies, a morph that presents thin branches susceptible to break, favoring fragmentation.

3. Populations experiencing a reduction in gene flow (such as in allopatric speciation processes) could exhibit heterogeneity in genetic differentiation along the genome (Tine et al., 2014). This heterogeneity could lead to consider two populations as two distinct species when looking at the highly differentiated zones of the genome. To date, we ignore where the microsatellite loci we used are located [except PV7 which is in the Internal Transcribed Spacer 1 (HM pers. com. and Magalon, Samadi, Richard, Adjeroud, & Veuille, 2004)] and whether they could be located in highly differentiated zones of the genome. Additionally, the presence of $F_{ST}$ outliers in divergence hitchhiking regions can largely influence the presence of sub-structuring (reviewed in Via, 2012) and be a sign of adaptive divergence (Kulmuni & Westram, 2017). In our case, we highlighted some loci showing high values of $F_{ST}$ for the different levels: PSH09 is split into three SSHs mainly because of Pd3-008 ($F_{ST} = 0.769^{***}$), SSH09a into three clusters mainly because of PV7 ($F_{ST} = 0.772^{***}$) and SSH09c into three mainly because of Pd4

($F_{ST} = 0.734^{***}$). Nevertheless, each of these loci does not show any diagnostic allele, and taken separately from the others, it does not allow to retrieve the partitioning observed that likely results from the evolutionary history of all loci.

4. Hybridization could also explain the unexpected clustering pattern, each cluster being the result of different mixes between two sufficiently different genetic entities, either not sampled or ancestral. Indeed, hybridization is not uncommon in corals (e.g., Flot et al., 2011; Frade et al., 2010; Isomura, Iwao, & Fukami, 2013; Márquez, Van Oppen, Willis, Reyes, & Miller, 2002; Thomas et al., 2014; Vollmer & Palumbi, 2002). As an example, *Acropora prolifera* has been evidenced to be a hybrid (and not a hybrid species, see Willis, Van Oppen, Miller, Vollmer, & Ayre, 2006) between *A. cervicornis* and *A. palmata* (Van Oppen, Willis, Vugt, & Miller, 2000). Likewise, Combosch and Vollmer (2015) revealed hybridization between *Pocillopora* type 1 and type 3 using RADseq. Moreover, each time hybridization has been demonstrated in *Pocillopora* corals (Combosch, Guzman, Schuhmacher, & Vollmer, 2008; Combosch & Vollmer, 2015; Pinzón & LaJeunesse, 2011), it seemed dominated by type 1 maternal lineages since all hybrid colonies exhibited mitochondrial type 1 haplotypes (Combosch & Vollmer, 2015). Frequent events of hybridization could comfort Veron's suggestion about metaspecies (or syngameon) existence in corals (Veron, 1995). Whichever the causes, PSH09 (and even the whole *Pocillopora* genus) may represent a metaspecies with some hybrids between entities (such as the colonies that were assigned to two different SSHs or clusters herein). Nevertheless, a recent study resolving the phylogenetic relationships among seven species of *Pocillopora* using RAD-seq did not provide any proof for hybridization among *P. eydouxi* and other *Pocillopora* species which were all found to be reciprocally monophyletic, although possible introgressive hybridization may have occurred between the most recently derived sister species *P. damicornis* and *P. acuta* (Johnston et al., 2017).

5. The observed clusters within SSH could be the result of habitat selection by individuals that could have specialized in different habitat types. Habitat selection has already been highlighted as a speciation factor. Indeed, for European anchovy populations, the habitat type (marine vs. coastal) accounts for most of the genetic structuring (Le Moan, Gagnaire, & Bonhomme, 2016): the genomic approach strongly supported a model of ecotypic divergence shaped by recent differential gene flow after a period of complete isolation (Le Moan et al., 2016). For corals, microhabitat selection could be the result of variability in light, current exposure, salinity, food availability, and/or temperature. Summarizing different habitat particularities, depth could be a factor of population structuring according to habitat type (e.g., *S. hystrix* in Australia; Van Oppen et al., 2011). However, in our case, colonies within each sampling site were systematically collected during a single dive, usually at the same depth (8-14 m) along a linear transect, and the clusters were not segregated in space within a site. Moreover, in Madagascar, while the colonies from two sites were found at different depths (MAD7, a reef flat between 1 and 2 m and MAD9, a pinnacle between 13 and 26 m), no genetic differentiation was found related
to depth. Likewise, previous studies failed to detect a link between
depth and genetic groups in the *Seriatopora* genus (Flot et al., 2008;
Nakajima et al., 2017). All in all, while we cannot fully reject this
hypothesis, depth does not appear an explanatory factor of genetic
differentiation in our case, but microhabitat might be, considering
fine-scale variations of both abiotic and biotic factors that could be
indiscernible to the human eye. Particularly, coral larvae are at-
tracted to the substratum by sensory receptors (e.g., Tran &
Hadfield, 2012) and are notably sensitive to biofilms produced by
crustose coralline algae (e.g., Morse, Hooker, Morse, & Jensen,
1988). Therefore, the affinity for different kinds of coralline algae
may play a role in micro-habitat selection, as revealed in the pea
aphid for which chemosensory gene families are determinant in
host plant specialization (Smadja et al., 2012).

6. Finally, host-symbiont associations could play a role in the structur-
ing pattern observed in this study. The evolution of coral hosts and
their endosymbionts remains unclear. As an illustration, studying 69
genera (20 families) of octocorals and their endosymbionts, Van
Oppen, Mieog, Sánchez, and Fabricius (2005) revealed that the sym-
biotic associations (at the level of phylogenetic clades) are not easily
explained by taxonomic affiliation of the hosts. Nevertheless, host-
symbiont association could be, on the contrary, more specific at the
genus level. Indeed, Pinzón and LaJeunesse (2011) found some ex-
clusive associations between subclades of *Symbiodinium* Clade C and
ORF type of *Pocillopora* [e.g., C1b-c and D were exclusively found in
association with *Pocillopora* type 1 (PSH09 herein)]. The host-symbi-
ont association should be further studied considering the clusters
within the three SSHs to investigate the existence of specific asso-
ciations at the different levels of genetic structuring in this host.

To date, we cannot favor one or another hypothesis and more in-
vestigations are needed to fully conclude regarding the origin of the ge-
netically divergent clusters observed within PSH09. Several hypotheses
imply an ancestral reduction in gene flow that created reproductive bar-
riers or genome incompatibilities among the different clusters for each
SSH, which contemporary gene flows have not homogenized yet.

5 | CONCLUSION AND PERSPECTIVES

Examining the population structure of the *Pocillopora eydouxi* species
hypothesis (PSH09; Gélin, Postaire, et al. 2017) revealed a nested
partitioning of the different SSHs, obliging to think about the unit on
which connectivity should be assessed. Whatever the causes, facing
to this over-partitioning of our dataset, the matter is not how to esti-
mate connectivity but on what. As each SSH is a mix of several geneti-
cally differentiated clusters found in sympatry, we prefer considering
the eight different clusters as our reference unit to assess genetic dif-
ferentiation among populations.

Several hypotheses have been proposed to explain the observed
pattern, but more investigations are needed to understand the
structuring pattern of this species complex. Knowing more about
*Pocillopora* genome seems now to be a fundamental key to improve

the understanding of its history of divergence and would offer clues to
favor one or another exposed hypothesis.

ACKNOWLEDGMENTS

Sampling of corals in New Caledonia (HM) was carried out dur-
ing COBELO (https://doi.org/10.17600/14003700), BIBELOT
(https://doi.org/10.17600/13100100), and CHEST (https://doi.
org/10.17600/15004500) oceanographic campaigns on board of RV
Alis (IRD), and in the northeast and northwest of Madagascar during
MAD (https://doi.org/10.17600/16004700) oceanographic campaign
on board of RV Antea (IRD). Sampling in Reunion Island (HM, PG)
was supported by program CONPOCINPA (LabEx CORAIL fund);
in the South of Madagascar (HM) in collaboration with the Institut
Halieutique des Sciences Marines (Tulear) and in Rodrigues Island (HM)
with the collaboration of the Rodrigues Regional Assembly and the
South-East Marine Protected Area supported by project Biodiversity
(POCT FEDER fund); in Europa, Juan de Nova, and Glorioso Islands
(HM) by program BIORECIE (financial supports from INEE, INSU, IRD,
AAMP, FRB, TAAF, and the foundation Veolia Environnement); in
Tromelin Island (HM) by program ORCIE (INEE); and in Mayotte (HM)
by program SIREME (FED). HM thanks all the buddies who helped in
photographs during diving (J. Butscher, S. Andréfouët, M. Pinault, A.
Renaud). We acknowledge the Plateforme Gentyane of the Institut
National de la Recherche Agronomique (INRA, Clermont-Ferrand,
France) for guidance and technical support. PG was financially sup-
ported by a PhD contract from the LabEx CORAIL.

CONFLICT OF INTEREST

None declared.

DATA ACCESSIBILITY

Microsatellite genotypes: Zenodo https://doi.org/10.5281/zenodo.
1042513

AUTHOR CONTRIBUTION

HM, C.F. and P.G. designed research. H.M., C.F. and L.B. performed
sampling. J.B. provided laboratory assistance. P.G. conducted mo-
lecular work and performed analyses. P.G., H.M. and C.F. wrote the
manuscript.

ORCID

Hélène Magalon ID http://orcid.org/0000-0002-7061-955X

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**How to cite this article:** Gélin P, Fauvelot C, Bigot L, Baly J, Magalon H. From population connectivity to the art of striping Russian dolls: The lessons from *Pocillopora* corals. *Ecol Evol.* 2018;8:1411–1426. https://doi.org/10.1002/ece3.3747