# Genetic resources of macroalgae: Development of an efficient method using microsatellite markers in non-model organisms

Stéphane Mauger, Aurélien Baud, Gildas Le Corguillé, Gwenn Tanguy, Erwan Legeay, Emeline Creis, Myriam Valero, Philippe Potin, Christophe Destombe

► To cite this version:

Stéphane Mauger, Aurélien Baud, Gildas Le Corguillé, Gwenn Tanguy, Erwan Legeay, et al. Ge- netic resources of macroalgae: Development of an efficient method using microsatellite markers in non-model organisms. Algal Research - Biomass, Biofuels and Bioproducts, 2023, 75, pp.103251. 10.1016/j.algal.2023.103251. hal-04206217

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1 Genetic resources of macroalgae: development of an efficient

2 method using microsatellite markers in non-model organisms

3 Stéphane Mauger<sup>a*</sup>, Aurélien Baud<sup>a, d*</sup>, Gildas Le Corguillé<sup>b</sup>, Gwenn Tanguy<sup>c</sup>, Erwan Legeay<sup>c</sup>,

4 Emeline Creis<sup>a</sup>, Myriam Valero<sup>a</sup>, Philippe Potin<sup>d</sup> and Christophe Destombe<sup>a</sup>

5
6 <sup>a</sup>IRL EBEA 3614, Evolutionary Biology and Ecology of Algae, CNRS, Sorbonne Université, UC,

7 UACH, Station Biologique de Roscoff, CS 90074, Place Georges Teissier, 29688 Roscoff cedex

8 <sup>b</sup>ABiMS, FR2424, CNRS, Sorbonne Université, Station Biologique, Place Georges Teissier, 29688

9 Roscoff cedex

10 <sup>c</sup>Genomer, FR2424, CNRS, Sorbonne Université, Station Biologique, Place Georges Teissier, 29688

11 Roscoff cedex

12 <sup>d</sup>UMR 8227, Integrative Biology of Marine Models, CNRS, Sorbonne Université, Station

13 Biologique, Place Georges Teissier, 29688 Roscoff cedex

14 *Both authors contributed equally to the paper

15

16 ORCID

17 Stéphane Mauger: https://orcid.org/0000-0002-8779-1516

18 Aurélien Baud:

19 Gildas Le Corguillé: https://orcid.org/0000-0003-1742-9711

20 Gwenn Tanguy: https://orcid.org/0009-0003-0444-3810

21 Erwan Legeay:

22 Emeline Creis:

23 Myriam Valero: https://orcid.org/0000-0002-9000-1423

24 Philippe Potin: https://orcid.org/0000-0001-7358-6282

25 Christophe Destombe: https://orcid.org/0000-0001-5656-9659

26

27 **Corresponding author**: Stéphane Mauger stephane.mauger@sb-roscoff.fr and Christophe

28 christophe destombe@sb-roscoff.fr
29 **CRediT Authorship contribution statement**

S. Mauger: original concept, laboratory work, bioinformatics analysis, analysis of molecular data, led
manuscript drafting and editing; A. Baud: sampling, led manuscript drafting and editing; G. Le
Corguillé: bioinformatics analysis, editing manuscript; G. Tanguy: laboratory work and editing
manuscript; E. Legeay: laboratory work and editing manuscript; E. Creis: sampling and editing
manuscript; M. Valero: drafting and editing manuscript; P. Potin: funding and supervision, drafting
and editing manuscript; C. Destombe: funding and supervision, sampling and editing manuscript.

36

37 **ABSTRACT**

38 Red and brown seaweeds are species with high ecological and economic importance. Here we report
39 the feasibility of cost-effective molecular marker development in 6 species from different phyla.
40 Microsatellite markers of two brown seaweed species *Alaria esculenta* and *Pylaiella littoralis*, and of
41 four red seaweed species *Calliblepharis jubata*, *Gracilaria gracilis*, *Gracilaria dura* and *Palmaria*
42 *palmata* were identified and characterized in genomic sequences obtained using Double-Digest
43 Restriction site Associated DNA (ddRAD). A total of 64,623,186 reads were generated from two runs
44 of multiplexed Illumina Miseq sequencing, from which 30,636 reads containing microsatellite motifs
45 including 15,443 where primer pairs could be designed. Five hundred seventy-six primers pairs were
46 selected for amplification trials to determine levels of polymorphism. Of these 576 loci, 338 could be
47 amplified and 142 of the 338 loci were polymorphic. A total of 28 usable polymorphic markers were
48 developed in *A. esculenta*, 18 in *P. littoralis*, 11 in *C. jubata*, 14 in *G. gracilis*, 21 in *G. dura* and 13 in
49 *P. palmata*. The overall number of alleles per locus ranged from 2 to 22. These 105 new microsatellite
50 markers will be useful for further studies of population genetics, breeding programs and conservation
51 genetics of these species. Compared with traditional approaches, our study yielded thousands of
52 microsatellite loci for six different species in a short time and with affordable costs. This study, based
53 on the use of ddRADseq, provided preliminary data about the genetic structure and reproduction mode
54 of these six non-model species based on a small number of individuals from two geographically-
55 distant populations and on the genetic structure and reproduction mode of two non-model species, i.e.
detection of clonality for two red algae, *C. jubata* and *G. dura* and detection of highly genetically
divergent populations corresponding probably to different cryptic species under the name *P. littoralis*.

58
59 **Keywords:** genetic diversity, microsatellite markers, seaweed, Rhodophyta, Ochrophyta, ddRAD.

60
61 **1. Introduction**
62

63 Evaluation of genetic resources is crucial for the sustainable conservation of economically or
64 ecologically important species. It is also a key step in the domestication process of new species [1],
65 [2]. In the context of global change, most of the seaweed biomass in Europe comes from harvesting
66 wild populations (FAO, 2018), questioning the sustainability of the wild genetic resources [3].
67 Moreover, the development of seaweed aquaculture since the 1950s in China for the Japanese kelp, or
68 more recently in the 1980s in South East Asia and East Africa for tropical red seaweeds, has led to
69 long-distance translocations of a few genotypes. These aquaculture practices are raising concern about
70 their possible detrimental effects on local adaptation of wild populations that may be impacted by
71 human-mediated gene-flow when located in the vicinity of farms [4]. There is therefore, an urgent
72 need for genetic markers to conduct genetic studies on an increasing number of seaweed species that
73 may be negatively impacted by intensive exploitation of their natural populations, the future
74 development of aquaculture or the impact of global change. For example, recent studies reveal that
75 marine heat waves in recent years have played a role in the decline of several macroalgal species on
76 the coast of northern Spain [5], [6]; a possible influence of latitudinal patterns of genetic diversity on
77 the vulnerability of the kelp *Ecklonia radiata* to a marine heatwave on the South-Eastern Australian
78 coast [7]; multi-decadal decline in the cover of the giant kelp *Macrocystis pyrifera* at the southern
79 limit of its Australian range [8]. These studies highlight the need to step up coastal monitoring efforts
80 in order to analyse the impact of extreme temperature events on algal genetic biodiversity.

81 Even if genome-wide studies are more accessible as a result to next generation sequencing (NGS)
82 methods, there are still numerous non-model, but nonetheless valuable, algae species for which
83 genomic data are not available. This restricts the possibility to search for molecular markers in silico.
84 Double Digested Restriction site Associated DNA sequencing (ddRADseq) has already been used to
85 efficiently develop molecular markers such as Single Nucleotides Polymorphism (SNPs) [9] and, more
86 recently, Short Sequence Repeats (SSRs) or microsatellite markers for non-model species [10], [11].
87 For over twenty years, microsatellite markers have set the standard in the field of population genetic
88 markers, offering the possibility of population analysis over time and providing useful insights based
89 on the results of the many studies carried out with this type of molecular marker. These markers have
90 the characteristic of being transferable between related species of the same genus, opening the
91 possibility of studying the same locus in different species [12]. They also provide excellent results in
92 paternity analysis, where SNPs have not yielded accurate information [13]. Although SNPs have better
93 genome coverage than microsatellites, the bioinformatic skills required, the computational power and
94 the cost of reagents limit their application to large numbers of individuals. Moreover, development of
95 SNPs, also requires a greater number of sample replications because of the high error rate compared to
96 microsatellite markers. Recent studies have indicated that, when addressing genetic structure or
97 patterns of genetic diversity, a study with a dozen of microsatellite markers gave similar results to
98 those obtained with thousands of SNP markers [13], [14]. Microsatellite markers are therefore better
99 suited (cheaper) for large sample studies and require less bioinformatic skills and computer power than
100 SNPs [12], [15], making them more accessible. The ability of microsatellite markers to uncover
101 genetic variations at the same locus beyond time and species, along with their accessibility, makes
102 them a gold standard in population genetic studies.
103 Since 2010, data on whole genome sequencing have been accumulating for seaweeds facilitating the
104 search for microsatellites (i.e., for *Ectocarpus* species 7, referred to as *Ectocarpus siliculosus* [16]; for
105 *Saccharina japonica* [17]; for *Ecklonia radicosa* [18]). However, developing new microsatellite
106 markers for a non-model species can be challenging when there is a lack of genomic data for *in silico*
107 research [see for review: 19] because the traditional microsatellite marker isolation approach, such as
108 generation of an enriched library followed by cloning and Sanger sequencing or NGS pyrosequencing,
109 is a costly, labour-intensive and often inefficient process. Although the use of expressed sequence tag
110 (EST) databases has proved to be a valuable source for the development of SSR markers that are
potentially transferable between taxa [20], there are still some taxonomic groups where this type of sequence remains rare. Other Methods to develop microsatellite markers rapidly and cost-effectively are needed for population genetic studies of non-model species. An efficient method was developed during the last ten years for non-model animals and plants [10], [11], based on the generation of double-digest restriction site associated DNA (ddRADseq; [9]) from genomic DNA of a few individuals of the species of interest using Illumina sequencing technology. Microsatellite markers are mined *in silico* using tools that require limited bioinformatic skills and computer power compared to SNP development.

Marine macroalgae are a valuable resource with a wide range of applications, from ecosystem services, for example by acting as carbon sinks and fish nurseries, to food and feed production [21], [22]. As the demand for seaweed biomass increases, new tools such as species-specific genetic markers are needed for the sustainable exploitation of wild resources as well as for the implementation of cultivation of those valuable species [4]. Since ddRADseq allows DNA tagging for sample-pooled sequencing, the aim of this study was to extend the method of Mori et al. [11] to multiple species using a maximum of two sequencing runs. In this way, it was possible to increase yields and reduce the cost of microsatellite marker development for economically important non-model species of seaweeds. We focused our study on 6 seaweed species, commonly found along the European coast, which are known for their socio-economic, ecological and scientific values. *Palmaria palmata* and *Alaria esculenta* are used for human consumption, cosmetics and animal feed [23], [24]. *Callablepharis jubata* and *Pylaiella littoralis* are used for cosmetics [25]. *Gracilaria gracilis* and *Gracilaria dura* are known for their high agar content and serves as model species to test hypotheses related to the evolution of sex [26], [27], [28], [29]. Except for *A. esculenta* none of those six North Atlantic species are cultivated at large scales [30] but several cultivation trials have been conducted in the past twenty years for *P. palmata* [31], and more recently for *P. littoralis* and *C. jubata* [32]. The development of microsatellite markers, which was performed for individuals from two geographically-distinct populations, provided a set of new preliminary data on the genetic structure and mode of reproduction of the six non-model species: the two brown seaweeds (Phaeophyceae): *A. esculenta* and *P. littoralis* and the four red algae (Rhodophyta): *P. palmata*, *C. jubata*, *G. gracilis* and *G. dura*.
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## 2. Materials and methods

### 2.1. Sample collection and DNA extraction

A total of 275 individuals from six species of seaweed were collected from thirteen sampling locations (Table 1). The ploidy levels were known (either using phenological observations or flow-cytometry analyses) prior to genotyping for all study species except *G. dura* and *C. jubata*. Only later after genotyping, could ploidy levels could be assessed for all individuals. Upon collection, a piece of tissue was cut out from a spot that was free of algal and animal epiphytes and stored in silica gel. No specific treatments were carried out to remove bacteria from the tissue but we estimate the percentage of bacterial DNA after sequencing using the KAIJU software version 1.9.2 developed by Menzel et al., 2016 [33]. Total genomic DNA was extracted from 15 to 20 mg of dry tissue using the Nucleospin 96 plant kit (Macherey-Nagel, Germany). The extraction was performed according to the manufacturer's instructions except that samples were left in the PL1 lysis buffer at 65°C for 15 min rather than 30 min. The extracted DNA was eluted into 120 µl of the supplied elution buffer.

### 2.2. DNA library preparation, sequencing and assembly

Two individuals of each species of seaweed (one per locality, except Quitao for *G. dura*) were used to construct the genomic libraries according to the methods described by Peterson et al [9] with the following modifications (Table 1). Double-digest restriction site associated DNA was generated with *Pst*I and *Hha*I (New England Biolabs, USA) restriction enzymes in a total reaction volume of 50µl using 300 ng of high-quality genomic DNA by incubating for 3 hours at 37°C (Fig S1). Adapter ligation was performed following the instructions of Peterson et al [9] with a unique P1 adaptor for each sample (Table 1) and ligation reactions were inactivated using NucleoMag NGS clean-up beads (Macherey-Nagel, Germany) with a final elution into 40µl of water. Individual PCR amplifications
were carried out in 40µl reaction volumes containing 10 µl of Adapter-ligated DNA, 170 nM of
indexed primer mix at 10 µM, 200 µM of dNTPs (Thermo Fisher Scientific Inc., USA), 1X of Q5®
Reaction Buffer, 1X of Q5® High GC Enhancer, 0.8 Unit of Q5® High-fidelity DNA Polymerase
(New England Biolabs, USA). DNA amplifications were carried out in a Bio-Rad DNA Engine Peltier
Thermal Cycler using the following conditions: initial denaturation phase at 98 °C for 30 sec, followed
by 15 cycles of denaturation at 98 °C for 20 s, annealing at 60 °C for 30 sec, extension at 72 °C for 40
sec, and a final extension at 72 °C for 10 min. PCR products were purified with NucleoMag NGS
clean-up beads (Macherey-Nagel, Germany) with a final elution in 40µl of water. The pooling was
done before the size selection to generate two libraries with 6 samples each to obtain about 8 million
reads per sample (Illumina performance specifications for Miseq v3 kits is between 22-25 million
reads per run), Lib1 (A. esculenta, P. littoralis and C. jubata) and Lib2 (G. dura, P. palmata and G.
gracilis). The Sage Science Pippin-Prep (sagescience.com) method was used to carry out the size
selection to obtain the fragments of interest of between 350 and 550 bp (between 480 and 680 bp with
the full library construction fragments). Size selection and quality of both libraries were checked using
a Bioanalyzer and High Sensitivity DNA Chips (Agilent Technologies), and the final concentrations
were estimated through quantitative PCR (qPCR) using a KAPA Library Quantification Kit (Roche
Molecular Systems, Inc) and a LightCycler® 480 System (Roche Molecular Systems, Inc).
Sequencing was performed in two runs on a MiSeq sequencer (Illumina, San Diego, CA, USA) using
a Miseq Reagent Kit v3 600 cycles (2 x 300 paired-end reads) and libraries at 15pM mixed with 5% of
Phix control v3 (Illumina, San Diego, CA, USA).
To obtain clean, high-quality reads, we discarded low quality raw sequences with adapter
contamination or N > 10%. We used STACKS software version 2.52 [34] to demultiplex the pooled
samples based on a unique P1 adaptor (Table 1). Overlapping paired-end sequencing reads were
assembled using the FLASH v1.2.11 software [35]; the minimum required overlap length between two
reads to provide a confident overlap (m) was fifteen and the maximum overlap length expected in
approximately 90% of read pairs (M) was three hundred. To construct the reference sequences, the
dDocent pipeline version 2.8.7 [36] was run using default parameters and procedures as described in
the user manual, except that percent clustering similarity was set at 95% for the reference assembly.
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## 2.3. Microsatellite loci screening and primer design

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Microsatellite motifs were identified using the QDD pipeline program version 3.1.2 [37] and all reference sequences with sizes greater than 80 bp (PIPE1 in QDD) were used for detection. Both perfect and imperfect di-, tri-, tetra-, penta- and hexanucleotide motifs were targeted. Dinucleotide motifs with at least 4 repeats and longer motifs with at least 3 repeats were selected (PIPE1 in QDD).

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Reads with at least 95% identity were clustered as consensus sequences, while those that could not be grouped were classified as singletons using Blast+ [38] (PIPE2 in QDD). Primers were designed using the PRIMER v3.0 software [39]. Duplicate primers were removed and unique primers of the putative loci were retained. Primers were mainly selected according to the type and size of the microsatellite motif, including also the following criteria: primer length between 18 bp and 27 bp; 50 to 75% GC content; a distance greater than 20 bp between the primer motif and the repeated motif and amplicon sizes between 100 bp and 450 bp (PIPE3 in QDD). In order to test these primers cost-effectively for amplification, we reduced the number of selected microsatellite regions to 96 (the capacity of a microtiter plate) for each species by selecting the ones with the highest number of repeats for each motif category.

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## 2.4. Amplification trials and levels of polymorphism of microsatellite loci

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For each species, positive amplification of 96 primer pairs was tested using DNA from 8 random individuals. Polymorphic level and genetic diversity characterization of the primer pairs that amplified positively were tested using between 35 and 48 individuals per species (Table 1 - Fig 1).

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Amplifications were carried out in 10 µL reaction volume with 2 µL of DNA template diluted 1:100 and following the method of Guzinski et al. [40]. The PCR products labeled, with the four different colors, were pooled (24 pools in total) and diluted 1:10 with water. Next, 2 µL of the diluted PCR product pool was added to 10 µL of loading buffer made up of 0.5 µL of the SM594 size standard [41]

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and 9.5 µL of Hi-Di formamide, denatured at 95°C for 3 min, and run in an ABI 3130 XL capillary
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sequencer (Applied Biosystems, USA). Genotypes were scored manually in Genemapper version 4.0
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(Applied Biosystems).

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2.5. Genetic analyses

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The results obtained for the populations of diploid individuals were analyzed with the following
software. Prior to the genetic analysis, we tested for null alleles using MICRO-CHECKER v2.2.3 [42].

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GENEPOP v4.7.5 [43] was used to test for linkage disequilibrium (global test employing Fisher's
method, the following Markov chain parameters were used: 100,000 for dememorization, 1000
batches and 50,000 iterations per batch). HIERFSTAT v0.5-10 R package [44] was used to obtain the
number of observed alleles per locus (Na), observed heterozygosity (Ho), expected heterozygosity
(He), the within-population deviation from Hardy-Weinberg equilibrium (FIS) and its significance per
locus and for all loci (tested with a permutation test; based on 10,000 permutations). The Polymorphic
information content (PIC) was calculated with CERVUS v3.0.7 [45].

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A modified analysis was used for the triploid individuals (G. dura): AUTOTET v1 software [46], a
program for the population analysis of allotetraploid microsatellite data, was used for the genetic
analysis and FIS index; its significance per locus and all loci (tested with a permutation test; based on
10,000 permutations) was determined using SPAGeDi v1.5d software [47]. The Polymorphic
information content (PIC) was calculated using "polysat" v.1.7 R package according to Clark and
Jasieniuk [48].

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The number of distinct Multilocus Genotypes (MLG), the genotypic richness (R) and the Genotype
accumulation curves (based on 10,000 permutations) were obtained with the "poppr" v2.9.3 R
package [49].

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Finally, to estimate the level of genetic differentiation between localities, pairwise FST [50] values
were computed in HIERFSTAT v0.5-10 R package [44]. The significance of the comparisons was

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estimated by performing 10,000 bootstraps over loci, with the comparisons judged significant if the
bootstrap-generated confidence intervals did not overlap zero.

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250 **3. Results**

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252 **3.1.** *ddRAD sequencing and assembly*

253

The sequencing procedure generated a total of 64.6 million reads (6.3 to 14.4 million per species) for
the six studied species [51] (Table S1). The percentage of bacterial DNA in the reads was less than 5%
with an average of 2.94% (Table S2). After editing/trimming, 377.7 Mb of high-quality sequences
(41.9 to 111.4 Mb per species) were available, which were assembled into 1,351,981 contigs (134,653
to 400,288 contigs per species). We obtained an average contig length of 294.0 bp (217.2 to 406.4 bp
per species), an average contig N50 size of 279.3 bp (173 to 407 bp per species) and an average GC
content of 51.2% (49.9% to 53.8% per species) (Table S1).

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262 **3.2.** *Microsatellite loci development and characterization*

263

Among the 1,351,981 assembled contigs, a total of 30,636 microsatellite motifs were found in the six
species (1,755 to 11,487 per species) using the QDD pipeline (Table S1). The microsatellite sequences
contained 14,288 (46.6%) di-, 14,529 (47.4%) tri-, 861 (2.8%) tetra-, 481 (1.6%) penta- and 477
(1.6%) hexanucleotide motifs (Table S3). After choosing regions with flanking sequences of sufficient
length and requiring a product size of between 100 and 450 bp, we obtained 15,443 primer pairs (752
to 6,592 per species – Table S1) corresponding to 7,327 (47.4%) di-, 6,997 (45.3%) tri-, 525 (3.4%)
tetra-, 316 (2.0%) penta- and 278 (1.8%) hexanucleotide motifs (Table S3). A total of 576 primer pairs
were tested for all species (96 primer pairs each, focusing on the ones with the highest number of
repeats), 338 were amplified including 104 (30.7%) di-, 77 (22.7%) tri-, 63 (18.6%) tetra-, 67 (19.8%)
penta- and 27 (7.9%) hexanucleotide motifs (Table S3). The 338 loci retained after the amplification
<figure><img src="image_2.png" /></figure>

tests (see Table S1 for details per species) were subsequently checked for level of polymorphism
within the sample of 8 individuals per species. A total of 142 out of these 338 putative loci including
31 for *A. esculenta*, 28 for *P. littoralis*, 15 for *C. jubata*, 20 for *G. gracilis*, 28 for *G. dura* and 20 for
*P. palmata* produced PCR products of the expected size, were polymorphic (Table S1) and
corresponding to 54 (38.0%) di-, 28 (19.7%) tri-, 27 (19.0%) tetra-, 27 (19.0%) penta- and 6 (4.2%)
hexanucleotide motifs (Table S2). Therefore, the rate of success for each of the five motif categories in
terms of acquisition of polymorphic microsatellite was 33.5% for di-, 19.1% for tri-, 23.5% for tetra-
26.0% for penta- and 12.2% for hexanucleotide repeats (Table S3).

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

<figure><img src="image_4.png" /></figure>

3.3. *Genetic diversity*

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

All the loci analyzed in this study (142 microsatellites markers for 6 species) were selected for their
amplification success and their polymorphism in each species. This allowed us to confirm the ploidy
levels for all individuals in the 4 species for which ploidy levels were known prior to genotyping (i.e.,
*A. esculenta*, *P. littoralis*, *P. palmata* and *G. gracilis*). For *G. dura* and *C. jubata*, the ploidy level was
determined after genotyping and triploid individuals were detected in *G. dura*. Data for the number of
alleles, null allele frequency, genetic diversity indices and departure from random mating (*F*<sub>IS</sub>) are
given in Table S4 for each species, locus and population. However, among the loci that correctly
amplified, several markers (3 in *A. esculenta*, 10 in *P. littoralis*, 4 in *C. jubata*, 6 in *G. gracilis*, 7 in *G.
dura* and 7 in *P. palmata*) were discarded because they appeared to be either monomorphic within
populations, or because they showed a significant frequency of null alleles, or an extreme departure
from random mating suggesting non-Mendelian inheritance (*F*<sub>IS</sub> values > 0.6 or *F*<sub>IS</sub> values < -0.6).
Overall, from our study, 28 markers appeared to be useful for *A. esculenta*, 18 for *P. littoralis*, 11 for
*C. jubata*, 14 for *G. gracilis*, 21 for *G. dura* and 13 for *P. palmata* (corresponds to the markers in bold
in the Table S4).

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

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<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>

The level of diversity was highly variable across species (Table S4). The mean number of alleles (Na)
computed over all loci and populations (i.e. global, Table S4) was higher in the two brown seaweeds

<figure><img src="image_21.png" /></figure>
A. esculenta (6.7) and P. littoralis (8.0) compared to C. jubata (3.8), G. gracilis (3.4), G. dura (6.2) and P. palmata (5.7). However, this trend was not retrieved for the other estimate of gene diversity (He). The highest He value was observed in the red alga G. dura (0.758) and explained by fixed heterozygosity compared to all other species: the two brown seaweeds A. esculenta (0.535) and P. littoralis (0.496) and the three remaining red algae C. jubata (0.344), G. gracilis (0.412) and P. palmata (0.489). Finally, polymorphic information content (PIC) was higher for P. littoralis (0.678) and G. dura (0.725) compared to A. esculenta (0.532), C. jubata (0.335), G. gracilis (0.421), and P. palmata (0.478).

Differences in the level of clonality were observed among species. No signal of clonality could be detected in *A. esculenta*, *P. littoralis*, *G. gracilis* and *P. palmata* as all multilocus genotypes (MLG) were distinct (*n* = MLG) and genotype richness (*R*) was maximum and equal to 1 (Table 2). In contrast, the number of multilocus genotypes (MLG) were not distinct in *G. dura* and *C. jubata* (*n* > MLG) and the mean values of genotypic richness (*R*) over populations were low: 0.378 and 0.848 respectively (Table 2). For these two partially clonal species, *F*<sub>IS</sub> values were significantly negative, revealing heterozygous excess, while for the four others sexually reproducing species, *F*<sub>IS</sub> values were not significantly different from 0, revealing random mating (Table 2). The genotype accumulation curves showed that between five and twelve loci were sufficient to differentiate all non-clonal individuals for each of the studied populations (Fig. 2).

Finally, the pattern of genetic differentiation among populations was highly significant (P < 0.001) for all studied species (Table 2). However, the magnitude of *F*<sub>ST</sub> values were very different among species ranging from 0.118 in *P. palmata* to 0.482 in *P. littoralis* (Table 2).

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## 4. Discussion

324

The development of NGS has improved our capacity to identify and develop microsatellite markers (for review see [52]). In particular, polymorphic loci in model and non-model, plant or animal, species can now be identified rapidly using ddRAD sequence data [10], [11], [53], [54]. The ddRADseq

327
328

method [9] was largely developed during the last 10 years for population genetics studies in many
organisms to search for SNPs and to decrease the cost of whole genome sequencing for genotyping. It
is still the object of continuous innovations [55] like those we have developed for multiple seaweed
species. However, even if several studies have developed ddRADseq methods for SNP discovery in
seaweeds in recent years (firstly in the brown algal model *Ectocarpus* [56] and mostly in the kelp or
large brown algal species *Undaria pinnatifida* [14]; *Saccharina latissima* [40]; *Ecklonia radiata* [57];
*Sargassum thunbergii* [58] and *Durvillaea antarctica* [59]), none of them was exploited to discover
microsatellites because markers were already available.

329

330

331

332

333

334

335

336

In this study, implementing the protocol developed by Mori et al. [11] with only two multiplexed
libraries for the 6 species, we successfully developed around 20 microsatellite markers per species in
different brown and red seaweeds of economic and ecological interest. This highly efficient method
allows rapid identification of microsatellite motifs distributed throughout the genome, ultimately
revealing polymorphic markers in the target species with an efficiency of about 25%. Interestingly, in
*G. gracilis*, more than 50% of the microsatellite loci already identified using classical library cloning
methods [60], [61] were found in this study confirming good coverage of the genome.

337

338

339

340

341

342

343

344

## 4.1. Characteristics of microsatellites between species

345

346

Our data yielded ten times more microsatellite motifs in brown than in red seaweeds but this
difference could not be clearly linked to their genome size (Table S1). However, for *Gracilaria* and
*Calliblepharis*, it may be important to consider their small genome size for future development of
microsatellite markers by, for example, adapting the coverage (e.g., sequencing 2 species per run
instead of 3 or more).

347

348

349

350

351

The occurrence and the frequency of different types of microsatellite motifs seem to be characteristic
of a species, which can be distant or closely related [12], [62], [63]. In our study, the distribution of
microsatellite marker repeats nucleotide types (di, tri, tetra, penta and hexanucleotide) was similar to
that obtained for the red alga *G. lichenoides* [64]. Dinucleotide and trinucleotides repeats were found

352

353

354
to be the most abundant motif, on average 46.6% and 47.4% respectively (Table S3), probably because short repeats are the starting point for subsequent extensions of Short Tandem Repeats (STRs) [65] which are the most variable type of DNA sequence in the genome [66]. The most common class of polymorphic marker contained dinucleotide motifs (35% - 40% of the polymorphic markers) for each species except for *A. esculenta* and *P. littoralis* for which polymorphic markers with tetranucleotide and pentanucleotide motifs were the most abundant, respectively (32.3% and 35.7% of the polymorphic markers respectively). In contrast to what was found for plants by Zhu *et al.* [63], hexanucleotide repeats in our study were the least abundant type of polymorphic markers. This could be due either to the phylogenetic distance between algae and land plants [63] or to an artefact related to the size of the fragments present in the ddRADseq libraries (partial genome vs complete genome) or to the types of restriction enzymes used for library construction.

The consequent yields and largely sufficient genome coverage imply that the method used in this study could be extended to more than 6 species. Future studies should be conducted to find the adequate balance between numbers of species and genome coverage, accounting for genome structure and length.

370

## 4.2. *New perspectives of using next-generation sequencing for population-level studies*

372

Several methods have been developed in many organisms to lower the cost and/or streamline next-generation sequencing for population-level studies [55]. Future developments for seaweeds will depend of many contingencies. With the need to develop knowledge on new species of seaweed to domesticate or to conserve in natural populations, only a few laboratories are developing approaches, depending on the means available for these studies. Large whole-genome sequencing efforts such as the Darwin tree of life in the UK, Atlasea in France and the ERGA (European Reference Genome Atlas) project at the EU level will offer new opportunities to enlarge the search for genetic markers for novel species. For the development of multiple PCR, synthesizing unique barcodes for each sample or even a subgroup of samples is expensive. Moreover, multi-, or single-level multiplexing and
382 demultiplexing of samples can be cumbersome, time consuming, and costly. Therefore, approaches for
383 targeted sequencing-based genetic screening such as the use of high multiplex PCR coupled to NGS
384 (Hi-Plex, [67]) can be developed for only a few species of seaweeds and mainly models of economic
385 importance [68].

386

387 **4.3. Limited population genetics data could provide useful information on genetic diversity**
388 *and reproductive systems*

389

390 The preliminary results of the population genetic analysis, even though they were carried out on a few
391 populations, provided interesting information about the reproductive systems and diversity of the
392 study species. Indeed, two of the four red algal species, *C. jubata* and *G. dura*, showed strong
393 signatures of asexual reproduction (heterozygous excess, low genotypic richness, repeated multilocus
394 genotypes). It is known that most Rhodophyta species are able to reproduce vegetatively either by
395 thallus fragmentation or propagule dispersal [69], [70], [71], however, the significance of asexual
396 reproduction in the field remains poorly known except for *Phymatolithon calcareum* (maerl beds)
397 [72], cultivated *G. chilensis* in farms [73] and the introduced species *G. vermiculophylla* [74]. Our
398 results for *C. jubata* suggest that this species partially undergoes natural fragmentation. Indeed, the
399 rhizoids could play a central role in vegetative propagation [71], by detaching from the parent thallus
400 to give a new individual. However, no field observations had been made until now. To our knowledge,
401 our results confirm this hypothesis for the first time. In contrast, in the genus *Gracilaria*, such
402 specialized organs have never been observed and vegetative multiplication occurs by spontaneous
403 fragmentation of the thallus. Cuttings are routinely used to reproduce macroalgae in aquaculture [75].
404 However, cuttings are not generally capable of giving rise to a new perennial individual [27]. In both
405 *G. chilensis* and *G. vermiculophylla* species for which there is strong evidence for asexual
406 reproduction [73], [74], it was shown that vegetative fragmentation could be facilitated by farming
407 practices (in *G. chilensis*) and explained rapid colonization during biological invasion (in *G.
408 vermiculophylla*). In the case of *G. dura*, as the individuals are attached to rocks, the fragmentation
hypothesis can be rejected. Instead, sexual reproduction could occur either by apomixis or apogamy, as observed in some triploid individuals of the red alga *Caloglossa leprieurii* [76]. Further investigation is therefore needed to better understand how and by what means this species reproduces asexually. The results obtained for *G. gracilis* and *P. littoralis*, confirmed that individuals reproduced sexually by outcrossing [77], [78]. Finally, our results suggested the occurrence of random mating systems in natural populations of *A. esculenta* and *P. palmata*.

The genetic diversity observed in this study was of the same order of magnitude as that observed previously for the same or closely related species. Overall, genetic diversity tended to be higher in brown than in red algae, probably due to differences in their effective population sizes. For example, the genetic diversity indices found in *A. esculenta* (*He* = 0.505 and *He* = 0.566) was close to that recorded for other kelp beds from the same region *Saccharina latissima* (*He* = 0.549), *Laminaria digitata* (*He* = 0.534) and *Laminaria hyperborea* (*He* = 0.591) [79], [80]. In the filamentous alga *P. littoralis*, genetic diversity (*He* = 0.556) was slightly reduced compared with that found in a previous study (*He* = 0.608) maybe because we used cross-amplification of microsatellite markers from *Ectocarpus siliculosus* [78]. In *G. gracilis*, we studied the same population using 14 newly developed markers (GN2, located at Cape Gris Nez) that was genotyped 20 years ago using seven microsatellite markers. Lower genetic diversity values were observed in our study (*He* = 0.514 in 2001, [77] vs *He* = 0.464 in 2021, this study) suggesting that temporal changes could have happened in this population. Finally, these markers were used to estimate differentiation between populations (*F*<sub>ST</sub>). Even though the scale of distance between populations varied greatly between species (from 50 to 2200 km), it was possible to detect significant genetic differentiation even between populations separated by distances of 50 km. Finally, in *Pylaiella* where the occurrence of two cryptic species along the French coast has been reported by Geoffroy et al. [78], the high values of genetic differentiation (*F*<sub>ST</sub> close to 0.5) observed between the two studied populations suggest that they belong to different cryptic species.

## 5. Conclusion
We were able to rapidly develop species-specific molecular markers for six phylogenetically distantly-related species of red and brown seaweeds with relatively low cost (in time and money). These markers are suitable for studying the diversity and genetic structure, and can also be used to detect and decipher clonality processes involved in the reproduction of these species. This work, which uses this rapid and inexpensive method, could facilitate evolutionary analyses in a wide variety of non-model taxa. The applications of these new species-specific genetic markers are numerous, such as breeding, selection and conservation programs, genetic resources studies, management of harvested stocks and monitoring of the origin of processed products. In addition, the use of ddRAD sequencing in the microsatellite development process still allows for the detection of SNPs with the generated data.

## Acknowledgments

We would like to thank J. Mark Cock and the three anonymous reviewers for their comments on the manuscript. This work benefited from the support of the French Government through the National Research Agency with regards to an investment expenditure program IDEALG (ANR-10-BTBR-04), the EU Horizon 2020 project GENIALG (Grant Agreement No 727892), the European Maritime and Fisheries Fund (EMFF) through the POLISTR project and Région Bretagne (PhD grant ARED Domdulse from ED227 Sorbonne-Université/MNHN to AB and FUI 23 project OCEACTIF from the French Ministry of Research to CD and PP). We are grateful to the Roscoff Bioinformatic platform (ABiMS) for bioinformatics support, to the Genomer platform, Biogenouest genomics and EMBRC France partner core facility for its technical support. The authors are deeply indebted to the Service Mer & Observation (SMO) of Roscoff, Alexandre Geoffroy and Dan Potin for sampling.

## Ethical consent

This article does not contain any studies with human participants or animals performed by any of the authors.

## Consent for publication

All authors read and approved the manuscript for the publication in *Algal Research*.
464

465

**Declaration of competing interest**

466

The authors declare that they have no conflicts of interest concerning the present article.

467

468

**Availability of data**

469

All data generated during this study are openly available in a Dryad Digital Repository

470

https://doi.org/10.5061/dryad.63xsj3v6w

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<figure><img src="image_22.png" /><figcaption>**Fig. 1** Workflow and experimental set-up used for microsatellite markers development using ddRADseq</figcaption></figure>

<figure><img src="image_23.png" /><figcaption>**Fig. 2** Accumulation curves of the genotypes of the 6 species based on 10,000 permutations with "poppr" v2.9.3 R package. Photo credits: © Destombe C. for *G. gracilis* and *G. dura*; © Baud A. for *A. esculenta* and *P. palmata*; © Thomas W. for *P. littoralis*.</figcaption></figure>
<figure><img src="image_24.png" /></figure>

**Fig. S1** Agarose gel electrophoresis (1% w/v) of PstI/HhaI restriction enzyme-digested algal DNA. Lane Pl, *Pylaella Littoralis*. Line Cj, *Calliplepharis jubata*. Line Gg, *Gracilaria gracilis*. Line Gd, *Gracilaria dura*. Line Pp, *Palmaria palmata*. Line Ae, *Alaria esculenta*. Line M, SmartLadder 200bp-10kb (Eurogentec, Seraing, Belgium).
<table><caption>**Table 1** Geographical location of the populations used in the study, sample size (n), sexual phenotypes and barcodes used for ddRADseq.</caption><thead><tr><th>Species</th><th>Populations</th><th>n</th><th>Phase (ploidy level)</th><th>Latitude (N)</th><th>Longitude (W)</th><th>P1 adaptor Sequence 3'-5'</th></tr></thead><tbody><tr><td rowspan="2">Alaria esculenta</td><td>Roscoff, France</td><td>24</td><td>sporophyte (2n)</td><td>48.7316</td><td>-3.9907</td><td>CTGGTT</td></tr><tr><td>Plouguerneau, France</td><td>24</td><td>sporophyte (2n)</td><td>48.6365</td><td>-4.5674</td><td>AAGATA</td></tr><tr><td rowspan="2">Pylaiella littoralis</td><td>Wissant, France</td><td>24</td><td>sporophyte (2n)</td><td>50.9045</td><td>1.6326</td><td>ACTTCC</td></tr><tr><td>Ile de Aransa, Portugal</td><td>24</td><td>sporophyte (2n)</td><td>41.1571</td><td>-8.6892</td><td>TTACGG</td></tr><tr><td rowspan="2">Callablepharis jubata</td><td>Roscoff, France</td><td>24</td><td>tetrasporophyte (2n)</td><td>48.4344</td><td>-3.5920</td><td>AAGGAA</td></tr><tr><td>Le Havre, France</td><td>24</td><td>tetrasporophyte (2n)</td><td>49.7105</td><td>-1.8719</td><td>ATTCAT</td></tr><tr><td rowspan="2">Gracilaria gracilis</td><td>Gris-Nez, France</td><td>24</td><td>tetrasporophyte (2n)</td><td>50.8727</td><td>1.5924</td><td>CTGGTT</td></tr><tr><td>Porto, Portugal</td><td>24</td><td>tetrasporophyte (2n)</td><td>41.1532</td><td>-8.6804</td><td>AAGATA</td></tr><tr><td rowspan="3">Gracilaria dura</td><td>Locquirec, France</td><td>12</td><td>mixed phase (3n)</td><td>48.6954</td><td>-3.6528</td><td>AACGAA</td></tr><tr><td>Coroso, Spain</td><td>15</td><td>mixed phase (3n)</td><td>42.5691</td><td>-8.9778</td><td>ATTCAT</td></tr><tr><td>Quitao, Portugal</td><td>8</td><td>mixed phase (3n)</td><td>41.1945</td><td>-8.7090</td><td>Not used for sequencing</td></tr><tr><td rowspan="2">Palmaria palmata</td><td>Roscoff, France</td><td>24</td><td>tetasporophyte (2n)</td><td>48.7261</td><td>-3.9685</td><td>ACTTCC</td></tr><tr><td>Belle-Ile, France</td><td>24</td><td>tetasporophyte (2n)</td><td>47.3781</td><td>-3.2252</td><td>TTACGG</td></tr></tbody></table>
<table><caption>**Table 2** Mean genetic diversity among loci for the populations sampled for each the 6 species.</caption><thead><tr><th>Species</th><th>Populations</th><th>n</th><th>nLoc</th><th>Na</th><th>A</th><th>MLG</th><th>R</th><th>He</th><th>Fis</th><th>Loc dist</th><th>Fst</th></tr></thead><tbody><tr><td rowspan="2">Alaria esculenta</td><td>Roscoff, France</td><td>24</td><td>28</td><td>4.1</td><td>4.24 ± 0.15</td><td>24</td><td>1</td><td>0.505</td><td>-0.031</td><td rowspan="2">50</td><td rowspan="2">0.136*</td></tr><tr><td>Plouguerneau, France</td><td>24</td><td>28</td><td>5.8</td><td>3.32 ± 0.10</td><td>24</td><td>1</td><td>0.566</td><td>-0.006</td></tr><tr><td rowspan="2">Pylaiella littoralis</td><td>Wissant, France</td><td>24</td><td>18</td><td>5.3</td><td>4.09 ± 0.14</td><td>24</td><td>1</td><td>0.556</td><td>-0.083</td><td rowspan="2">2200</td><td rowspan="2">0.482*</td></tr><tr><td>Ile de Aransa, Portugal</td><td>24</td><td>18</td><td>3.7</td><td>2.97 ± 0.20</td><td>24</td><td>1</td><td>0.436</td><td>-0.008</td></tr><tr><td rowspan="2">Calliblepharis jubata</td><td>Roscoff, France</td><td>24</td><td>13</td><td>2.9</td><td>2.42 ± 0.17</td><td>20</td><td>0.826</td><td>0.346</td><td>-0.131$</td><td rowspan="2">510</td><td rowspan="2">0.132*</td></tr><tr><td>Le Havre, France</td><td>24</td><td>13</td><td>2.5</td><td>2.29 ± 0.12</td><td>21</td><td>0.870</td><td>0.394</td><td>-0.285$</td></tr><tr><td rowspan="2">Gracilaria gracilis</td><td>Gris-Nez, France</td><td>24</td><td>14</td><td>2.9</td><td>2.65 ± 0.10</td><td>24</td><td>1</td><td>0.464</td><td>-0.024</td><td rowspan="2">2150</td><td rowspan="2">0.262*</td></tr><tr><td>Porto, Portugal</td><td>24</td><td>14</td><td>2.8</td><td>2.28 ± 0.17</td><td>24</td><td>1</td><td>0.362</td><td>-0.075</td></tr><tr><td rowspan="3">Gracilaria dura</td><td>Locquirec, France (a)</td><td>12</td><td>28</td><td>3.8</td><td>3.43 ± 0.36</td><td>7</td><td>0.636</td><td>0.655</td><td>-0.186$</td><td>1470 (a-b)</td><td>0.263* (a-b)</td></tr><tr><td>Coroso, Spain (b)</td><td>15</td><td>28</td><td>3.2</td><td>2.86 ± 0.31</td><td>4</td><td>0.214</td><td>0.674</td><td>-0.390$</td><td>1600 (a-c)</td><td>0.299* (a-c)</td></tr><tr><td>Quitao, Portugal (c)</td><td>8</td><td>28</td><td>2.5</td><td>2.54</td><td>3</td><td>0.286</td><td>0.617</td><td>-0.350$</td><td>130 (b-c)</td><td>0.279* (b-c)</td></tr><tr><td rowspan="2">Palmaria palmata</td><td>Roscoff, France</td><td>24</td><td>13</td><td>5.1</td><td>3.51 ± 0.22</td><td>24</td><td>1</td><td>0.514</td><td>0.023</td><td rowspan="2">300</td><td rowspan="2">0.118*</td></tr><tr><td>Belle-Ile, France</td><td>24</td><td>13</td><td>3.9</td><td>3.12 ± 0.19</td><td>24</td><td>1</td><td>0.465</td><td>-0.014</td></tr></tbody></table>

*n* Number of genotyped individuals.

*nLOC* Number of loci retained as suitable polymorphic markers (i.e., polymorphic, no-significant *Nf*, no-significant *Fis* for no clonal species).

*Na* Means of number of alleles per locus.

*A* Standardized allelic richness for a common sample size of 8 individuals.

*MLG* Number of distinct Multilocus Genotypes.

*R* Genotypic richness (*G-1*) / (*N-1*) (Dorken and Eckert, 2001).

*He* Expected heterozygosity.

*Fis* Inbreeding coefficient as defined by Weir and Cockerham (1984) calculated overall polymorphic loci, *Fis* values significantly different from zero are shown with $.

*Loc Dist* distance between two localities in km.

*Fst* Pairwise estimates of genetic differentiation obtained with HIERFSTAT (Goudet 2014) *, P < 0.001.
<table><caption>**Table S1** Steps involved in the definition of microsatellite loci for each of the species studied.</caption><thead><tr><th></th><th>Alaria esculenta</th><th>Pylaiella littoralis</th><th>Calliblepharis jubata</th><th>Gracilaria gracilis</th><th>Gracilaria dura</th><th>Palmaria palmata</th><th>Total</th><th>Mean</th></tr></thead><tbody><tr><td>Genome size estimation (Mb)</td><td>381.5</td><td>322.5</td><td>156.0</td><td>72.5</td><td>72.5</td><td>782.0</td><td></td><td></td></tr><tr><td>Number of raw reads</td><td>13,059,425</td><td>6,283,637</td><td>7,161,739</td><td>12,544,477</td><td>14,367,115</td><td>11,206,793</td><td>64,623,186</td><td>10,770,531</td></tr><tr><td>Number of unique cleaned sequences</td><td>222,863</td><td>134,653</td><td>172,899</td><td>221,730</td><td>199,548</td><td>400,288</td><td>1,351,981</td><td>225,333</td></tr><tr><td>n bases (Mb)</td><td>52.2</td><td>54.7</td><td>64.0</td><td>53.5</td><td>41.9</td><td>111.4</td><td>377.7</td><td>62.9</td></tr><tr><td>Min length (bp)</td><td>80</td><td>80</td><td>80</td><td>80</td><td>80</td><td>80</td><td>-</td><td>80</td></tr><tr><td>Max length (bp)</td><td>572</td><td>549</td><td>577</td><td>552</td><td>577</td><td>578</td><td>-</td><td>567.5</td></tr><tr><td>Average length (bp)</td><td>242.2</td><td>406.4</td><td>371.0</td><td>246.0</td><td>217.2</td><td>281.4</td><td>-</td><td>294.0</td></tr><tr><td>N50 (bp)</td><td>202</td><td>407</td><td>383</td><td>204</td><td>173</td><td>307</td><td>-</td><td>279.3</td></tr><tr><td>(G+C)s</td><td>51.1%</td><td>53.8%</td><td>49.9%</td><td>50.1%</td><td>51.1%</td><td>51.4%</td><td>-</td><td>51.2%</td></tr><tr><td>Number of sequences with microsatellite</td><td>10,965</td><td>11,487</td><td>1,888</td><td>2,016</td><td>1,755</td><td>2,625</td><td>30,636</td><td>5,122.7</td></tr><tr><td>Number of sequences with primers</td><td>5,095</td><td>6,592</td><td>752</td><td>1,169</td><td>805</td><td>1,030</td><td>15,443</td><td>2,419.3</td></tr><tr><td>Number of primer pairs tested</td><td>96</td><td>96</td><td>96</td><td>96</td><td>96</td><td>96</td><td>576</td><td>96</td></tr><tr><td>Number of positives amplifications</td><td>61</td><td>54</td><td>67</td><td>52</td><td>36</td><td>68</td><td>338</td><td>56.3</td></tr><tr><td>Number of polymorphic markers</td><td>31</td><td>28</td><td>15</td><td>20</td><td>28</td><td>20</td><td>142</td><td>23.6</td></tr><tr><td>Number of polymorphic and usable markers</td><td>28</td><td>18</td><td>11</td><td>14</td><td>21</td><td>13</td><td>105</td><td>17.5</td></tr></tbody></table>
<table><caption>**Table S2** Percentage of bacterial DNA and algae DNA in ddRADseq reads.</caption><thead><tr><th>Species</th><th>Algae DNA</th><th>Bacterial DNA</th></tr></thead><tbody><tr><td>Alaria esculenta</td><td>98.18%</td><td>1.82%</td></tr><tr><td>Pylaiella littoralis</td><td>95.72%</td><td>4.28%</td></tr><tr><td>Calliplepharis jubata</td><td>98.87%</td><td>1.13%</td></tr><tr><td>Gracilaria gracilis</td><td>96.36%</td><td>3.64%</td></tr><tr><td>Gracilaria dura</td><td>97.16%</td><td>2.84%</td></tr><tr><td>Palmaria palmata</td><td>96.06%</td><td>3.94%</td></tr><tr><td><strong>Mean</strong></td><td><strong>97.06%</strong></td><td><strong>2.94%</strong></td></tr></tbody></table>

Estimation of the percentage of bacterial DNA contamination in ddRADseq reads using the KAIJU software version 1.9.2 (Menzel et al., 2016).
<table><caption>**Table S3** Steps involved in the selection of microsatellite markers, with the percentage of loci with specific motif types (Di-, Tri-, Tetra-, Penta- and Hexanucleotide) associated to each step.</caption><thead><tr><th>Reduction step</th><th>Di</th><th>Tri</th><th>Tetra</th><th>Penta</th><th>Hexa</th><th>Total</th></tr></thead><tbody><tr><td>QDD output</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Alaria esculenta</td><td>3,644 (33.5%)</td><td>6,406 (59.0%)</td><td>394 (3.6%)</td><td>256 (2.4%)</td><td>165 (1.5%)</td><td>10,865</td></tr><tr><td>Pylaiella littoralis</td><td>5,397 (47.0%)</td><td>5,436 (47.3%)</td><td>334 (2.9%)</td><td>162 (1.4%)</td><td>158 (1.4%)</td><td>11,487</td></tr><tr><td>Calliblepharis jubata</td><td>1,397 (74%)</td><td>425 (22.5%)</td><td>44 (2.3%)</td><td>7 (0.4%)</td><td>15 (0.8%)</td><td>1,888</td></tr><tr><td>Gracilaria gracilis</td><td>1,223 (60.7%)</td><td>699 (34.7%)</td><td>37 (1.8%)</td><td>22 (1.1%)</td><td>35 (1.7%)</td><td>2,016</td></tr><tr><td>Gracilaria dura</td><td>1,037 (59.1%)</td><td>662 (37.7%)</td><td>15 (0.9%)</td><td>12 (0.7%)</td><td>29 (1.7%)</td><td>1,755</td></tr><tr><td>Palmaria palmata</td><td>1,590 (60.6%)</td><td>901 (34.3%)</td><td>37 (1.4%)</td><td>22 (0.8%)</td><td>75 (2.9%)</td><td>2,625</td></tr><tr><td>Total</td><td>14,288 (46.6%)</td><td>14,529 (47.4%)</td><td>861 (2.8%)</td><td>481 (1.6%)</td><td>477 (1.6%)</td><td>30,636</td></tr><tr><td>Primer3 output</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Alaria esculenta</td><td>1,862 (36.5%)</td><td>2,771 (54.4%)</td><td>220 (4.3%)</td><td>152 (3.0%)</td><td>90 (1.8%)</td><td>5,095</td></tr><tr><td>Pylaiella littoralis</td><td>3,132 (47.5%)</td><td>2,983 (45.3%)</td><td>231 (3.5%)</td><td>124 (1.9%)</td><td>122 (1.9%)</td><td>6,592</td></tr><tr><td>Calliblepharis jubata</td><td>505 (67.2%)</td><td>218 (29.0%)</td><td>22 (2.9%)</td><td>6 (0.8%)</td><td>1 (0.1%)</td><td>752</td></tr><tr><td>Gracilaria gracilis</td><td>686 (58.7%)</td><td>428 (36.6%)</td><td>23 (2.0%)</td><td>16 (1.4%)</td><td>16 (1.4%)</td><td>1,169</td></tr><tr><td>Gracilaria dura</td><td>514 (63.9%)</td><td>264 (32.8%)</td><td>9 (1.1%)</td><td>8 (1.0%)</td><td>10 (1.2%)</td><td>805</td></tr><tr><td>Palmaria palmata</td><td>628 (61.0%)</td><td>333 (32.3%)</td><td>20 (1.9%)</td><td>10 (1.0%)</td><td>39 (3.8%)</td><td>1,030</td></tr><tr><td>Total</td><td>7,327 (47.4%)</td><td>6,997 (45.3%)</td><td>525 (3.4%)</td><td>316 (2.0%)</td><td>278 (1.8%)</td><td>15,443</td></tr><tr><td>Selection (highest number of repeats)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Alaria esculenta</td><td>24 (25.0%)</td><td>24 (25.0%)</td><td>24 (25.0%)</td><td>24 (25.0%)</td><td>0 (0%)</td><td>96</td></tr><tr><td>Pylaiella littoralis</td><td>24 (25.0%)</td><td>24 (25.0%)</td><td>24 (25.0%)</td><td>24 (25.0%)</td><td>0 (0%)</td><td>96</td></tr><tr><td>Calliblepharis jubata</td><td>24 (25.0%)</td><td>24 (25.0%)</td><td>24 (25.0%)</td><td>24 (25.0%)</td><td>0 (0%)</td><td>96</td></tr><tr><td>Gracilaria gracilis</td><td>28 (29.2%)</td><td>15 (15.6%)</td><td>22 (22.9%)</td><td>15 (15.6%)</td><td>16 (16.7%)</td><td>96</td></tr><tr><td>Gracilaria dura</td><td>36 (37.5%)</td><td>36 (37.5%)</td><td>6 (6.3%)</td><td>8 (8.3%)</td><td>10 (10.4%)</td><td>96</td></tr><tr><td>Palmaria palmata</td><td>25 (26.0%)</td><td>24 (25.0%)</td><td>15 (15.6%)</td><td>9 (9.4%)</td><td>23 (24.0%)</td><td>96</td></tr><tr><td>Total</td><td>161 (28.0%)</td><td>147 (25.5%)</td><td>115 (20.0%)</td><td>104 (18.1%)</td><td>49 (8.5%)</td><td>576</td></tr><tr><td>Positive amplifications</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Alaria esculenta</td><td>16 (26.2%)</td><td>12 (19.6%)</td><td>15 (24.5%)</td><td>18 (29.5%)</td><td>0 (0%)</td><td>61</td></tr><tr><td>Pylaiella littoralis</td><td>14 (25.9%)</td><td>10 (18.5%)</td><td>13 (24%)</td><td>17 (31.4%)</td><td>0 (0%)</td><td>54</td></tr><tr><td>Calliblepharis jubata</td><td>21 (31.3%)</td><td>12 (17.9%)</td><td>14 (20.8%)</td><td>20 (29.8%)</td><td>0 (0%)</td><td>67</td></tr><tr><td>Gracilaria gracilis</td><td>12 (23%)</td><td>12 (23%)</td><td>10 (19.2%)</td><td>5 (9.6%)</td><td>13 (25%)</td><td>52</td></tr><tr><td>Gracilaria dura</td><td>22 (61.1%)</td><td>11 (30.5%)</td><td>0 (0%)</td><td>1 (2.7%)</td><td>2 (5.5%)</td><td>36</td></tr><tr><td>Palmaria palmata</td><td>19 (27.9%)</td><td>20 (29.4%)</td><td>11 (16.1%)</td><td>6 (8.8%)</td><td>12 (17.6%)</td><td>68</td></tr><tr><td>Total</td><td>104 (30.7%)</td><td>77 (22.7%)</td><td>63 (18.6%)</td><td>67 (19.8%)</td><td>27 (7.9%)</td><td>338</td></tr><tr><td>Rate of success to obtain positive amplification</td><td>104/161 (64.5%)</td><td>77/147 (52.3%)</td><td>63/115 (54.7%)</td><td>67/104 (64.4%)</td><td>27/49 (55.1%)</td><td>338/576 (58.7%)</td></tr><tr><td>PCR product of the expected size and polymorphic</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Alaria esculenta</td><td>9 (29.0%)</td><td>3 (9.7%)</td><td>10 (32.3%)</td><td>9 (29.0%)</td><td>0 (0%)</td><td>31</td></tr><tr><td>Pylaiella littoralis</td><td>5 (17.9%)</td><td>7 (25.0%)</td><td>6 (21.4%)</td><td>10 (35.7%)</td><td>0 (0%)</td><td>28</td></tr><tr><td>Calliblepharis jubata</td><td>6 (40.0%)</td><td>0 (0%)</td><td>4 (26.7%)</td><td>5 (33.3%)</td><td>0 (0%)</td><td>15</td></tr><tr><td>Gracilaria gracilis</td><td>8 (40.0%)</td><td>6 (30.0%)</td><td>4 (20.0%)</td><td>0 (0%)</td><td>2 (10.0%)</td><td>20</td></tr><tr><td>Gracilaria dura</td><td>19 (67.9%)</td><td>9 (32.1%)</td><td>0 (0%)</td><td>0 (0%)</td><td>0 (0%)</td><td>28</td></tr><tr><td>Palmaria palmata</td><td>7 (35.0%)</td><td>3 (15.0%)</td><td>3 (15.0%)</td><td>3 (15.0%)</td><td>4 (20.0%)</td><td>20</td></tr><tr><td>Total</td><td>54 (38.0%)</td><td>28 (19.7%)</td><td>27 (19.0%)</td><td>27 (19.0%)</td><td>6 (4.2%)</td><td>142</td></tr><tr><td>Rate of success to obtain polymorphic marker</td><td>54/161 (33.5%)</td><td>28/147 (19.1%)</td><td>27/115 (23.5%)</td><td>27/104 (26.0%)</td><td>6/49 (12.2%)</td><td>142/576 (24.7%)</td></tr></tbody></table>
<caption>**Table S4** Technical information (primer sequence, dye) characteristics (repeat motif, observed size range) and polymorphism estimates (Nf, Na, Ho, He, Fis, PIC) for microsatellites selected for this study for each of the six species.</caption>

Alaria esculenta

<table><thead><tr><th rowspan="2">Locus</th><th rowspan="2">Primer sequences (5'-3')</th><th rowspan="2">Repeat motif</th><th rowspan="2">Size range (bp)</th><th rowspan="2">Dye</th><th colspan="6">Roscoff</th><th colspan="6">Plouguerneau</th><th colspan="6">Global</th></tr><tr><th>n</th><th>Nf</th><th>Na</th><th>Ho</th><th>He</th><th>Fis</th><th>PIC</th><th>n</th><th>Nf</th><th>Na</th><th>Ho</th><th>He</th><th>Fis</th><th>PIC</th><th>n</th><th>Nf</th><th>Na</th><th>Ho</th><th>He</th><th>PIC</th></tr></thead><tbody><tr><td>Aesc_01</td><td>F: GCATTTCGGTTCTGTTCGGT<br>R: GGTTTCGGTTTCGGTTATGTT</td><td>(GGTTC)14</td><td>230-370</td><td>NED</td><td>24</td><td>0.021</td><td>11</td><td>0.750</td><td>0.815</td><td>0.080</td><td>0.771</td><td>24</td><td>0.048</td><td>19</td><td>0.833</td><td>0.926</td><td>0.100</td><td>0.899</td><td>48</td><td>0.063</td><td>22</td><td>0.791</td><td>0.870</td><td>0.894</td></tr><tr><td>Aesc_02</td><td>F: GTCATCGGTTGGTTTGTGAG<br>R: GTGTGAGGGAATGTCAAGGAA</td><td>(GGTTT)18</td><td>227-381</td><td>FAM</td><td>24</td><td>0</td><td>8</td><td>0.916</td><td>0.850</td><td>-0.077</td><td>0.813</td><td>24</td><td>0</td><td>10</td><td>0.833</td><td>0.840</td><td>0.008</td><td>0.803</td><td>48</td><td>0.005</td><td>15</td><td>0.875</td><td>0.845</td><td>0.875</td></tr><tr><td>Aesc_03</td><td>F: AAAGCAACAACATCAACA<br>R: CCCGTCAACCTTATCCTT</td><td>(ACA)41</td><td>200-274</td><td>PET</td><td>24</td><td>0</td><td>8</td><td>0.833</td><td>0.779</td><td>-0.068</td><td>0.731</td><td>24</td><td>0.052</td><td>14</td><td>0.791</td><td>0.896</td><td>0.117</td><td>0.866</td><td>48</td><td>0.022</td><td>17</td><td>0.812</td><td>0.838</td><td>0.833</td></tr><tr><td>Aesc_04</td><td>F: GCTGCTATCCTGAACTAAA<br>R: GACACCACCAACCCATAAC</td><td>(GT)22</td><td>153-197</td><td>FAM</td><td>24</td><td>0</td><td>6</td><td>0.666</td><td>0.641</td><td>-0.039</td><td>0.592</td><td>24</td><td>0.007</td><td>12</td><td>0.791</td><td>0.825</td><td>0.040</td><td>0.787</td><td>48</td><td>0.053</td><td>13</td><td>0.729</td><td>0.733</td><td>0.782</td></tr><tr><td>Aesc_05</td><td>F: ACCGCACCAAATACGAGAAA<br>R: CAGCAGCAGGTAGTGAAGAATG</td><td>(TG)18</td><td>197-213</td><td>NED</td><td>24</td><td>0.014</td><td>6</td><td>0.750</td><td>0.785</td><td>0.045</td><td>0.731</td><td>24</td><td>0.008</td><td>6</td><td>0.750</td><td>0.769</td><td>0.024</td><td>0.717</td><td>48</td><td>0.031</td><td>6</td><td>0.750</td><td>0.777</td><td>0.769</td></tr><tr><td>Aesc_06</td><td>F: CGCTACTGCTGCCACTACTCT<br>R: AATACTGATTTGGAGGGATG</td><td>(CTA)48</td><td>318-399</td><td>FAM</td><td>24</td><td>0.003</td><td>6</td><td>0.708</td><td>0.731</td><td>0.031</td><td>0.670</td><td>24</td><td>0</td><td>8</td><td>0.791</td><td>0.703</td><td>-0.124</td><td>0.669</td><td>48</td><td>0.022</td><td>9</td><td>0.750</td><td>0.717</td><td>0.750</td></tr><tr><td>Aesc_07</td><td>F: TGCTGGATGTGGATTGTTAGA<br>R: AACTCCTTCCGTTACACTGCT</td><td>(AC)22</td><td>102-128</td><td>FAM</td><td>24</td><td>0</td><td>3</td><td>0.666</td><td>0.612</td><td>-0.088</td><td>0.516</td><td>24</td><td>0</td><td>9</td><td>0.916</td><td>0.859</td><td>-0.066</td><td>0.825</td><td>48</td><td>0</td><td>9</td><td>0.791</td><td>0.736</td><td>0.726</td></tr><tr><td>Aesc_08</td><td>F: CGAATCCCTTTGCCTGTG<br>R: GCGTCCGTCTTTATGGTTG</td><td>(CCAT)23</td><td>214-312</td><td>NED</td><td>24</td><td>0.065</td><td>4</td><td>0.583</td><td>0.682</td><td>0.144</td><td>0.607</td><td>24</td><td>0</td><td>4</td><td>0.666</td><td>0.680</td><td>0.020</td><td>0.608</td><td>48</td><td>0.062</td><td>5</td><td>0.625</td><td>0.681</td><td>0.684</td></tr><tr><td>Aesc_09</td><td>F: ATCAAGCAGCAGAGCAAGAAG<br>R: GCAACAGAAACCGAACAGAAAA</td><td>(GCCT)18</td><td>163-215</td><td>HEX</td><td>24</td><td>0</td><td>4</td><td>0.833</td><td>0.712</td><td>-0.169</td><td>0.652</td><td>24</td><td>0</td><td>4</td><td>0.666</td><td>0.606</td><td>-0.098</td><td>0.536</td><td>48</td><td>0</td><td>5</td><td>0.750</td><td>0.659</td><td>0.648</td></tr><tr><td>Aesc_10</td><td>F: TCAAGAATAGATGCCGAAAGAA<br>R: ATGAAATCCCAACACTCCC</td><td>(AG)31</td><td>225-237</td><td>FAM</td><td>24</td><td>0</td><td>4</td><td>0.750</td><td>0.683</td><td>-0.096</td><td>0.609</td><td>24</td><td>0.053</td><td>6</td><td>0.625</td><td>0.694</td><td>0.100</td><td>0.633</td><td>48</td><td>0</td><td>6</td><td>0.687</td><td>0.689</td><td>0.636</td></tr><tr><td>Aesc_11</td><td>F: CGTATCACCACGACTCTTTC<br>R: GCCTGTGTTTCGCCTGTTC</td><td>(GACA)18</td><td>269-309</td><td>HEX</td><td>24</td><td>0</td><td>3</td><td>0.708</td><td>0.597</td><td>-0.184</td><td>0.507</td><td>24</td><td>0</td><td>7</td><td>0.833</td><td>0.752</td><td>-0.107</td><td>0.704</td><td>48</td><td>0</td><td>7</td><td>0.770</td><td>0.675</td><td>0.633</td></tr><tr><td>Aesc_12</td><td>F: GCAGCCCGTAGCCGATGA<br>R: TAACCAAGCAGTCAATCCACC</td><td>(TGAT)41</td><td>237-365</td><td>FAM</td><td>24</td><td>0</td><td>3</td><td>0.333</td><td>0.296</td><td>-0.125</td><td>0.270</td><td>24</td><td>0.087</td><td>7</td><td>0.666</td><td>0.823</td><td>0.190</td><td>0.774</td><td>48</td><td>0.012</td><td>8</td><td>0.500</td><td>0.559</td><td>0.632</td></tr><tr><td>Aesc_13</td><td>F: GCATTTATTCCTTCATCTTCTG<br>R: GCTGTGCTGCCGAAACTAC</td><td>(ACACG)10</td><td>249-319</td><td>PET</td><td>24</td><td>0</td><td>5</td><td>0.750</td><td>0.699</td><td>-0.072</td><td>0.628</td><td>24</td><td>0.177*</td><td>7</td><td>0.333</td><td>0.523</td><td>0.363$</td><td>0.491</td><td>48</td><td>0.101*</td><td>9</td><td>0.541</td><td>0.611</td><td>0.627</td></tr><tr><td>Aesc_14</td><td>F: CGCAAGTGAGCAAGCAAA<br>R: ATCTGGAGGAGGGTTGGG</td><td>(AAAGC)13</td><td>135-225</td><td>HEX</td><td>24</td><td>0.190*</td><td>5</td><td>0.375</td><td>0.636</td><td>0.411</td><td>0.555</td><td>24</td><td>0.292*</td><td>5</td><td>0.250</td><td>0.713</td><td>0.649$</td><td>0.633</td><td>48</td><td>0.245*</td><td>6</td><td>0.312</td><td>0.675</td><td>0.603</td></tr><tr><td>Aesc_15</td><td>F: CCGTCAGGAACGAAGGTG<br>R: AACCGTAACCGAAACAGTAA</td><td>(AT)18</td><td>355-363</td><td>NED</td><td>24</td><td>0</td><td>4</td><td>0.666</td><td>0.644</td><td>-0.033</td><td>0.561</td><td>24</td><td>0</td><td>3</td><td>0.625</td><td>0.556</td><td>-0.123</td><td>0.479</td><td>48</td><td>0</td><td>4</td><td>0.645</td><td>0.600</td><td>0.572</td></tr><tr><td>Aesc_16</td><td>F: GCAGAGCAAGAACACACCCA<br>R: AATGAGTAATGAAACGGTATTT</td><td>(AC)17</td><td>296-306</td><td>HEX</td><td>24</td><td>0</td><td>4</td><td>0.500</td><td>0.423</td><td>-0.182</td><td>0.391</td><td>24</td><td>0.026</td><td>4</td><td>0.500</td><td>0.538</td><td>0.070</td><td>0.426</td><td>48</td><td>0.060</td><td>6</td><td>0.500</td><td>0.480</td><td>0.529</td></tr><tr><td>Aesc_17</td><td>F: GCACTTCCCATCTGACCT<br>R: ACTGCCTGGTATTTGTGG</td><td>(TCTGA)11</td><td>164-199</td><td>HEX</td><td>24</td><td>0</td><td>3</td><td>0.375</td><td>0.369</td><td>-0.014</td><td>0.310</td><td>24</td><td>0</td><td>4</td><td>0.750</td><td>0.640</td><td>-0.171</td><td>0.564</td><td>48</td><td>0</td><td>5</td><td>0.562</td><td>0.505</td><td>0.493</td></tr><tr><td>Aesc_18</td><td>F: AGGCATTGGCACGGACAC<br>R: AAACCATCTCAAATCAAATCC</td><td>(AC)17</td><td>263-273</td><td>HEX</td><td>24</td><td>0</td><td>2</td><td>0.458</td><td>0.467</td><td>0.019</td><td>0.353</td><td>24</td><td>0.151*</td><td>6</td><td>0.375</td><td>0.538</td><td>0.304</td><td>0.496</td><td>48</td><td>0.098*</td><td>6</td><td>0.416</td><td>0.503</td><td>0.489</td></tr><tr><td>Aesc_19</td><td>F: CGTCCGATGATGTTCCTG<br>R: TCAGTCCAGAGGTGCTTATTT</td><td>(GGTC)15</td><td>120-152</td><td>PET</td><td>24</td><td>0.073</td><td>4</td><td>0.541</td><td>0.615</td><td>0.120</td><td>0.560</td><td>24</td><td>0</td><td>3</td><td>0.416</td><td>0.354</td><td>-0.176</td><td>0.316</td><td>48</td><td>0.035</td><td>5</td><td>0.479</td><td>0.485</td><td>0.466</td></tr><tr><td>Aesc_20</td><td>F: TAGTAGTGGCAGTGTGCGGAG<br>R: CGAGTTGTTTCGTGTGAG</td><td>(TG)16</td><td>270-290</td><td>FAM</td><td>24</td><td>0</td><td>2</td><td>0.500</td><td>0.478</td><td>-0.045</td><td>0.359</td><td>24</td><td>0</td><td>3</td><td>0.333</td><td>0.296</td><td>-0.125</td><td>0.270</td><td>48</td><td>0.086</td><td>3</td><td>0.416</td><td>0.387</td><td>0.425</td></tr><tr><td>Aesc_21</td><td>F: GGTAGCACTTTGGTGGCTGA<br>R: GCTTTAGACCTCCTCTCCT</td><td>(GGAT)15</td><td>221-253</td><td>PET</td><td>24</td><td>0.087</td><td>3</td><td>0.458</td><td>0.570</td><td>0.196</td><td>0.457</td><td>24</td><td>0</td><td>3</td><td>0.375</td><td>0.376</td><td>0.004</td><td>0.325</td><td>48</td><td>0.095</td><td>4</td><td>0.416</td><td>0.473</td><td>0.424</td></tr><tr><td>Aesc_22</td><td>F: ATTGGCAGTTTCTCGGTC<br>R: GTTGGTTACGCTACGACGGTT</td><td>(CATAC)17</td><td>159-209</td><td>NED</td><td>24</td><td>0</td><td>4</td><td>0.291</td><td>0.268</td><td>-0.087</td><td>0.252</td><td>24</td><td>0</td><td>6</td><td>0.625</td><td>0.599</td><td>-0.042</td><td>0.537</td><td>48</td><td>0</td><td>6</td><td>0.458</td><td>0.433</td><td>0.422</td></tr></tbody></table>
<table><tbody><tr><td>Aesc_23</td><td>F: GATAACGAAACGAAACAGACC<br>R: CCTGGCTGGGTGTATGAAA</td><td>(AAACC)15</td><td>290-300</td><td>NED</td><td>24</td><td>0.036</td><td>3</td><td>0.375</td><td>0.376</td><td>0.004</td><td>0.325</td><td>24</td><td>0.105</td><td>2</td><td>0.250</td><td>0.338</td><td>0.262</td><td>0.275</td><td>48</td><td>0.186</td><td>3</td><td>0.312</td><td>0.357</td><td>0.405</td></tr><tr><td>Aesc_24</td><td>F: AAATGAAACGAACTGAACC<br>R: CTCGGGTAAGGAGGCGTG</td><td>(AAACG)10</td><td>220-245</td><td>PET</td><td>24</td><td>0</td><td>4</td><td>0.500</td><td>0.490</td><td>-0.018</td><td>0.436</td><td>24</td><td>0</td><td>5</td><td>0.333</td><td>0.302</td><td>-0.101</td><td>0.285</td><td>48</td><td>0</td><td>6</td><td>0.416</td><td>0.396</td><td>0.383</td></tr><tr><td>Aesc_25</td><td>F: AGGGAGGGAGAAGGAGAAAGA<br>R: GTTGTGATTGGTCGGTGGTT</td><td>(AAAC)25</td><td>193-225</td><td>NED</td><td>24</td><td>0</td><td>2</td><td>0.625</td><td>0.500</td><td>-0.250</td><td>0.371</td><td>24</td><td>0</td><td>2</td><td>0.416</td><td>0.422</td><td>0.012</td><td>0.328</td><td>48</td><td>0</td><td>2</td><td>0.520</td><td>0.461</td><td>0.356</td></tr><tr><td>Aesc_26</td><td>F: TCGGACACATCCCTTACCC<br>R: CCCTGTAGTTAGTTTCTGTTGT</td><td>(TTTG)20</td><td>190-217</td><td>HEX</td><td>24</td><td>0</td><td>3</td><td>0.250</td><td>0.230</td><td>-0.086</td><td>0.212</td><td>24</td><td>0.035</td><td>4</td><td>0.458</td><td>0.525</td><td>0.127</td><td>0.454</td><td>48</td><td>0.028</td><td>4</td><td>0.354</td><td>0.377</td><td>0.353</td></tr><tr><td>Aesc_27</td><td>F: CGATACGACAGGCTACTTTCA<br>R: AAGGACAAACATCACCGACC</td><td>(GATA)14</td><td>284-304</td><td>PET</td><td>24</td><td>0</td><td>2</td><td>0.166</td><td>0.155</td><td>-0.069</td><td>0.141</td><td>24</td><td>0</td><td>4</td><td>0.500</td><td>0.499</td><td>-0.001</td><td>0.429</td><td>48</td><td>0.009</td><td>5</td><td>0.333</td><td>0.327</td><td>0.332</td></tr><tr><td>Aesc_28</td><td>F: ATCCGACCCGTGAGGTTT<br>R: CGATGATTTATTCCAGGGCTT</td><td>(ACCCG)12</td><td>223-258</td><td>HEX</td><td>24</td><td>0.075</td><td>3</td><td>0.208</td><td>0.266</td><td>0.217</td><td>0.244</td><td>24</td><td>0</td><td>6</td><td>0.458</td><td>0.428</td><td>-0.069</td><td>0.400</td><td>48</td><td>0.010</td><td>6</td><td>0.333</td><td>0.347</td><td>0.326</td></tr><tr><td>Aesc_29</td><td>F: GATAGCAAACGAACCGTAGCA<br>R: TATGCCGAGGAAGGCTGA</td><td>(TGTC)20</td><td>223-271</td><td>NED</td><td>24</td><td>0</td><td>3</td><td>0.208</td><td>0.193</td><td>-0.074</td><td>0.178</td><td>24</td><td>0</td><td>3</td><td>0.416</td><td>0.413</td><td>-0.006</td><td>0.369</td><td>48</td><td>0</td><td>3</td><td>0.312</td><td>0.303</td><td>0.286</td></tr><tr><td>Aesc_30</td><td>F: AGGTTTGTCAGTTGTCGGTGA<br>R: GTCTGGGTGGAGCCTGTGTAG</td><td>(CT)18</td><td>375-391</td><td>NED</td><td>24</td><td>0</td><td>2</td><td>0.333</td><td>0.337</td><td>0.010</td><td>0.275</td><td>24</td><td>0</td><td>2</td><td>0.083</td><td>0.081</td><td>-0.022</td><td>0.077</td><td>48</td><td>0.022</td><td>2</td><td>0.208</td><td>0.209</td><td>0.195</td></tr><tr><td>Aesc_31</td><td>F: TCCAGAAATCCGCTTATCAA<br>R: AGGGAAATGGGCAAATCAA</td><td>(GAT)43</td><td>218-224</td><td>PET</td><td>24</td><td>0</td><td>2</td><td>0.041</td><td>0.041</td><td>0</td><td>0.040</td><td>24</td><td>0</td><td>2</td><td>0.083</td><td>0.081</td><td>-0.022</td><td>0.077</td><td>48</td><td>0</td><td>2</td><td>0.062</td><td>0.061</td><td>0.059</td></tr><tr><td colspan="2">Mean</td><td></td><td></td><td></td><td>24</td><td>0.013</td><td>4.1</td><td>0.519</td><td>0.505</td><td>-0.031</td><td>0.449</td><td>24</td><td>0.015</td><td>5.8</td><td>0.564</td><td>0.566</td><td>-0.006</td><td>0.515</td><td>48</td><td>0.029</td><td>6.7</td><td>0.541</td><td>0.535</td><td>0.532</td></tr></tbody></table>

Pylaiella littoralis

<table><thead><tr><th rowspan="2">Locus</th><th rowspan="2">Primer sequences (5'-3')</th><th rowspan="2">Repeat motif</th><th rowspan="2">Size range (bp)</th><th rowspan="2">Dye</th><th colspan="6">Wissant</th><th colspan="6">Ile de Aransa</th><th colspan="6">Global</th></tr><tr><th>n</th><th>Nf</th><th>Na</th><th>Ho</th><th>He</th><th>Fis</th><th>PIC</th><th>n</th><th>Nf</th><th>Na</th><th>Ho</th><th>He</th><th>Fis</th><th>PIC</th><th>n</th><th>Nf</th><th>Na</th><th>Ho</th><th>He</th><th>PIC</th></tr></thead><tbody><tr><td>Plit_01</td><td>F: CACCACTATCATTCAGTCAGGG<br>R: GAAGCAGCACTCCAGCGA</td><td>(ACA)30</td><td>183-450</td><td>PET</td><td>24</td><td>0.195*</td><td>17</td><td>0.541</td><td>0.913</td><td>0.407$</td><td>0.880</td><td>24</td><td>0.191*</td><td>17</td><td>0.541</td><td>0.933</td><td>0.420$</td><td>0.900</td><td>48</td><td>0.205*</td><td>28</td><td>0.541</td><td>0.923</td><td>0.929</td></tr><tr><td>Plit_02</td><td>F: ACGCACAGCAGCAATCAG<br>R: GCACTTAGAACACGCTACCC</td><td>(GTA)58</td><td>251-362</td><td>FAM</td><td>23</td><td>0.039</td><td>19</td><td>0.826</td><td>0.920</td><td>0.103$</td><td>0.891</td><td>24</td><td>0.166*</td><td>13</td><td>0.583</td><td>0.923</td><td>0.368$</td><td>0.889</td><td>47</td><td>0.119*</td><td>22</td><td>0.704</td><td>0.922</td><td>0.920</td></tr><tr><td>Plit_03</td><td>F: AAATGCGAACAAACCCTCACTT<br>R: CGACAGACACGCTGAAACC</td><td>(TC)29</td><td>218-272</td><td>PET</td><td>24</td><td>0.078*</td><td>13</td><td>0.750</td><td>0.916</td><td>0.181</td><td>0.885</td><td>24</td><td>0.163*</td><td>7</td><td>0.500</td><td>0.797</td><td>0.372$</td><td>0.743</td><td>48</td><td>0.148*</td><td>18</td><td>0.625</td><td>0.856</td><td>0.890</td></tr><tr><td>Plit_04</td><td>F: CGCTTCCTTGTTTGTGCTTAC<br>R: GTAGATGCTTGCTGACCCAGA</td><td>(CT)33</td><td>138-170</td><td>NED</td><td>24</td><td>0</td><td>9</td><td>0.875</td><td>0.820</td><td>-0.066</td><td>0.781</td><td>24</td><td>0.180*</td><td>5</td><td>0.458</td><td>0.750</td><td>0.388$</td><td>0.683</td><td>48</td><td>0.118*</td><td>11</td><td>0.666</td><td>0.785</td><td>0.855</td></tr><tr><td>Plit_05</td><td>F: AGTCATCCCTGCTCTGCC<br>R: ATGGGCTCGTCTCTGGTG</td><td>(ACAT)42</td><td>242-374</td><td>NED</td><td>24</td><td>0.051</td><td>20</td><td>0.833</td><td>0.946</td><td>0.119</td><td>0.920</td><td>24</td><td>0</td><td>2</td><td>0.458</td><td>0.467</td><td>0.019</td><td>0.353</td><td>48</td><td>0.125</td><td>22</td><td>0.645</td><td>0.707</td><td>0.834</td></tr><tr><td>Plit_06</td><td>F: CGACTCCCTGGTTCTCTTCA<br>R: CGTTTGTTCCACGCATTACA</td><td>(TTG)44</td><td>350-401</td><td>HEX</td><td>24</td><td>0</td><td>7</td><td>0.791</td><td>0.731</td><td>-0.081</td><td>0.676</td><td>24</td><td>0</td><td>5</td><td>0.791</td><td>0.676</td><td>-0.170</td><td>0.605</td><td>48</td><td>0.029</td><td>12</td><td>0.791</td><td>0.704</td><td>0.828</td></tr><tr><td>Plit_07</td><td>F: GCGAAAGACAACAACAACTAC<br>R: CGACACACGAGAAGGACAGA</td><td>(GTAT)36</td><td>194-301</td><td>HEX</td><td>24</td><td>0</td><td>6</td><td>0.833</td><td>0.762</td><td>-0.092</td><td>0.713</td><td>24</td><td>0.061</td><td>6</td><td>0.500</td><td>0.567</td><td>0.118</td><td>0.529</td><td>48</td><td>0.106</td><td>12</td><td>0.666</td><td>0.664</td><td>0.808</td></tr><tr><td>Plit_08</td><td>F: CTTTCTACCGCATCCACCAG<br>R: TTCTTCCTCAACTAAACGCCA</td><td>(AACAC)11</td><td>238-313</td><td>HEX</td><td>24</td><td>0</td><td>9</td><td>0.958</td><td>0.817</td><td>-0.172</td><td>0.781</td><td>24</td><td>0</td><td>3</td><td>0.541</td><td>0.530</td><td>-0.020</td><td>0.435</td><td>48</td><td>0.050</td><td>11</td><td>0.750</td><td>0.673</td><td>0.799</td></tr><tr><td>Plit_09</td><td>F: CACAATGGAAATCAGGTCA<br>R: TTCGTGTCGTTCTAAGCC</td><td>(ACA)39</td><td>250-301</td><td>NED</td><td>24</td><td>0.047</td><td>7</td><td>0.583</td><td>0.631</td><td>0.076</td><td>0.574</td><td>24</td><td>0.235*</td><td>8</td><td>0.416</td><td>0.809</td><td>0.485$</td><td>0.760</td><td>48</td><td>0.193*</td><td>10</td><td>0.500</td><td>0.720</td><td>0.792</td></tr><tr><td>Plit_10</td><td>F: TGTCTTACGCCTGTTTCGTTT<br>R: GAGTGTCCGTGGGAAGGTC</td><td>(TGT)37</td><td>240-309</td><td>NED</td><td>24</td><td>0.030</td><td>9</td><td>0.750</td><td>0.837</td><td>0.103</td><td>0.793</td><td>24</td><td>0.122*</td><td>7</td><td>0.583</td><td>0.798</td><td>0.269</td><td>0.746</td><td>48</td><td>0.081*</td><td>11</td><td>0.666</td><td>0.817</td><td>0.791</td></tr><tr><td>Plit_11</td><td>F: CCTGCTCGCTGAAGATTGT<br>R: TGTATGAAATGGAGTTGAGGAAA</td><td>(AATGC)15</td><td>162-212</td><td>FAM</td><td>24</td><td>0.068</td><td>8</td><td>0.708</td><td>0.836</td><td>0.152</td><td>0.794</td><td>24</td><td>0.070</td><td>8</td><td>0.666</td><td>0.772</td><td>0.137</td><td>0.726</td><td>48</td><td>0.082</td><td>10</td><td>0.687</td><td>0.804</td><td>0.787</td></tr><tr><td>Plit_12</td><td>F: GCATTATCTCCGCCGCAA<br>R: GCCAGGTTCTAAGTTTCACA</td><td>(AAGGC)10</td><td>153-213</td><td>HEX</td><td>24</td><td>0.077</td><td>3</td><td>0.500</td><td>0.615</td><td>0.188</td><td>0.533</td><td>24</td><td>0.002</td><td>7</td><td>0.541</td><td>0.559</td><td>0.032</td><td>0.518</td><td>48</td><td>0.168</td><td>8</td><td>0.520</td><td>0.587</td><td>0.748</td></tr><tr><td>Plit_13</td><td>F: GTAGACGGCACAGCACAA<br>R: TGTTCAGCAGCGTAGTCCC</td><td>(GGCAC)15</td><td>212-277</td><td>FAM</td><td>24</td><td>0</td><td>9</td><td>0.625</td><td>0.564</td><td>-0.107</td><td>0.529</td><td>24</td><td>0.195*</td><td>6</td><td>0.416</td><td>0.744</td><td>0.440$</td><td>0.691</td><td>48</td><td>0.166*</td><td>10</td><td>0.520</td><td>0.654</td><td>0.748</td></tr><tr><td>Plit_14</td><td>F: CTGCGAGAAACAGAACCGB<br>R: GAGAGGTGTGAATGGAGCGT</td><td>(AAAC)13</td><td>157-211</td><td>PET</td><td>24</td><td>0.051</td><td>6</td><td>0.375</td><td>0.399</td><td>0.061</td><td>0.372</td><td>24</td><td>0.121*</td><td>4</td><td>0.416</td><td>0.613</td><td>0.320$</td><td>0.529</td><td>48</td><td>0.233*</td><td>9</td><td>0.395</td><td>0.506</td><td>0.707</td></tr></tbody></table>
<table><tbody><tr><td>Plit_15</td><td>F: TTTGGAGTTGTTTCGTGTTTGA<br>R: GCGTTTCACTTGTATCCCACC</td><td>(TGAT)14 269-289 PET</td><td>24 0.020 3 0.416 0.465 0.105 0.410</td><td>24 0 3 0.583 0.527 -0.106 0.420</td><td>48 0.164 6 0.500 0.496 0.701</td></tr><tr><td>Plit_16</td><td>F: CACCGTATGCTTGAATGCTC<br>R: GAACTCAGATGGGAAACCCT</td><td>(CTT)36 251-308 NED</td><td>24 0.126 5 0.500 0.635 0.212 0.576</td><td>24 0.093 2 0.166 0.224 0.258 0.195</td><td>48 0.268 7 0.333 0.429 0.668</td></tr><tr><td>Plit_17</td><td>F: ACTACTGGAACCGCCATCGT<br>R: CTACTCGGAACACACAGCCC</td><td>(ATC)40 252-315 FAM</td><td>24 0 4 0.416 0.361 -0.152 0.332</td><td>24 0 2 0.500 0.496 -0.007 0.368</td><td>48 0.180 6 0.458 0.428 0.660</td></tr><tr><td>Plit_18</td><td>F: TCAGTCAGTCAGTCAATCAATCA<br>R: AGGTTTCGTGTCAAAGCAGAA</td><td>(CAAT)29 250-306 FAM</td><td>24 0 3 0.958 0.603 -0.588 0.514</td><td>24 0 2 0.208 0.190 -0.095 0.169</td><td>48 0.099 5 0.583 0.396 0.649</td></tr><tr><td>Plit_19</td><td>F: GCACCTGAGACAAGAGACGG<br>R: ACAGCAACATCAACAGCAACA</td><td>(TGTGA)21 260-295 FAM</td><td>24 0 3 0.458 0.385 -0.187 0.348</td><td>24 0 5 0.625 0.555 -0.125 0.513</td><td>48 0.117 6 0.541 0.470 0.638</td></tr><tr><td>Plit_20</td><td>F: CATAGGGTGTTCTTGGCATT<br>R: CGCACGACTTTCTGTCTTACC</td><td>(GCTCA)17 351-396 FAM</td><td>24 0 2 0.208 0.190 -0.095 0.169</td><td>24 0.014 5 0.666 0.680 0.020 0.620</td><td>48 0.193 6 0.437 0.435 0.638</td></tr><tr><td>Plit_21</td><td>F: AAGGAAGGACAGCACAATGAA<br>R: AGGTCGCACAACAATAACTGG</td><td>(CAAAA)10 285-360 PET</td><td>24 0 3 0.416 0.381 -0.092 0.336</td><td>24 0 2 0.791 0.503 -0.571 0.375</td><td>48 0.055 4 0.604 0.442 0.612</td></tr><tr><td>Plit_22</td><td>F: TTGATTGGTCTCTCCAGGTGT<br>R: ATAGGTGCGTGTGTAGCCGT</td><td>(TC)38 219-303 HEX</td><td>24 0.124 5 0.541 0.719 0.246 0.648</td><td>24 0.169* 4 0.291 0.509 0.427 0.462</td><td>48 0.183* 5 0.416 0.614 0.605</td></tr><tr><td>Plit_23</td><td>F: AGGAGCAGGGTGAGAGAAGAG<br>R: TAGGAAAGTGAGGCTGGACAA</td><td>(GA)34 286-336 NED</td><td>24 0 6 0.750 0.625 -0.200 0.583</td><td>24 0 4 0.208 0.197 -0.055 0.187</td><td>48 0.143 8 0.479 0.411 0.597</td></tr><tr><td>Plit_24</td><td>F: GTGTGTCTTGGTGCCTGTC<br>R: CGCATAGAGAGCGAGAGATT</td><td>(CT)29 196-228 PET</td><td>24 0 2 0.375 0.309 -0.210 0.258</td><td>24 0.179 4 0.208 0.331 0.371 0.298</td><td>48 0.273 6 0.291 0.320 0.593</td></tr><tr><td>Plit_25</td><td>F: CCTTCGCTTTGCCTCGTT<br>R: AAACTGCCATTCTTTCGGT</td><td>(TCTGC)13 274-324 NED</td><td>24 0.144* 5 0.500 0.725 0.310 0.661</td><td>24 0 2 0.458 0.402 -0.139 0.317</td><td>48 0.105* 6 0.479 0.563 0.584</td></tr><tr><td>Plit_26</td><td>F: GCAGGAATGGCAGAGAG<br>R: CGACAGAGAACTGAAAGGTGG</td><td>(CGGCA)11 173-213 HEX</td><td>24 0 4 0.458 0.445 -0.028 0.402</td><td>24 0 2 0.125 0.119 -0.045 0.110</td><td>48 0.268 6 0.291 0.282 0.574</td></tr><tr><td>Plit_27</td><td>F: CGTTGCGAGGTGATGATG<br>R: TCAAGGTGATTTGGGCTTT</td><td>(ACAG)13 224-248 PET</td><td>24 0 5 0.375 0.334 -0.122 0.313</td><td>24 0 2 0.166 0.155 -0.069 0.141</td><td>48 0.272 6 0.270 0.245 0.535</td></tr><tr><td>Plit_28</td><td>F: TACCACCAAGAAGAGTAGGG<br>R: CAGAAACAACACACAAATCGG</td><td>(GGTGT)15 264-289 HEX</td><td>24 0 3 0.708 0.565 -0.253 0.496</td><td>24 0.051 3 0.250 0.295 0.153 0.264</td><td>48 0.111 3 0.479 0.430 0.533</td></tr><tr><td>Mean</td><td></td><td></td><td>24 0.019 5.3 0.592 0.556 -0.083 0.509</td><td>24 0.026 3.7 0.444 0.436 -0.008 0.379</td><td>48 0.150 8.0 0.518 0.496 0.678</td></tr></tbody></table>

## Calliblepharis jubata

<table><thead><tr><th rowspan="2">Locus</th><th rowspan="2">Primer sequences (5'-3')</th><th rowspan="2">Repeat<br>motif</th><th rowspan="2">Size<br>range<br>(bp)</th><th rowspan="2">Dye</th><th colspan="6">Roscoff</th><th colspan="6">Le Havre</th><th colspan="6">Global</th></tr><tr><th>n</th><th>Nf</th><th>Na</th><th>Ho</th><th>He</th><th>Fis</th><th>PIC</th><th>n</th><th>Nf</th><th>Na</th><th>Ho</th><th>He</th><th>Fis</th><th>PIC</th><th>n</th><th>Nf</th><th>Na</th><th>Ho</th><th>He</th><th>PIC</th></tr></thead><tbody><tr><td>Cjub_01</td><td>F: GAAGGAAACGGAGCAGCA<br>R: AATGTGGTGACGAGCGATTT</td><td>(CAAA)6 247-281</td><td>NED</td><td>24 0</td><td>4</td><td>0.583</td><td>0.515</td><td>-0.131</td><td>0.461</td><td>24 0.121</td><td>4</td><td>0.458</td><td>0.554</td><td>0.173</td><td>0.505</td><td>48 0.167</td><td>8</td><td>0.52</td><td>0.534</td><td>0.728</td></tr><tr><td>Cjub_02</td><td>F: GTATCCTGTCTGTGCCGCCT<br>R: CCATCAATCACCGTCCTACTC</td><td>(GA)20 247-261</td><td>HEX</td><td>24 0</td><td>4</td><td>0.625</td><td>0.535</td><td>-0.167</td><td>0.477</td><td>24 0</td><td>4</td><td>0.875</td><td>0.581</td><td>-0.504</td><td>0.485</td><td>48 0</td><td>6</td><td>0.75</td><td>0.558</td><td>0.519</td></tr><tr><td>Cjub_03</td><td>F: ATTATCTCCAGCATCGTTT<br>R: CGGTTTGGTCTGCGAAAGT</td><td>(AT)14 274-298</td><td>FAM</td><td>24 0.233* 5</td><td>0.291</td><td>0.573</td><td>0.491</td><td>0.513</td><td>24 0.133</td><td>3</td><td>0.208</td><td>0.32</td><td>0.35</td><td>0.274</td><td>48 0.271* 5</td><td>0.25</td><td>0.447</td><td>0.495</td><td></td></tr><tr><td>Cjub_04</td><td>F: CCACAGAAAGCCAGGTAAA<br>R: AACACCCGCTGCTACGAC</td><td>(GCACC)4 140-265</td><td>NED</td><td>24 0.018</td><td>3</td><td>0.416</td><td>0.458</td><td>0.090</td><td>0.397</td><td>24 0</td><td>2</td><td>0.75</td><td>0.472</td><td>-0.586</td><td>0.359</td><td>48 0</td><td>3</td><td>0.583</td><td>0.465</td><td>0.435</td></tr><tr><td>Cjub_05</td><td>F: AACTCTTCTCGCCTTCGTG<br>R: GATTAGATGGTCTGTGTAGGTGG</td><td>(CT)9 344-401</td><td>PET</td><td>24 0</td><td>4</td><td>0.833</td><td>0.562</td><td>-0.481</td><td>0.459</td><td>24 0</td><td>2</td><td>0.833</td><td>0.489</td><td>-0.703$</td><td>0.368</td><td>48 0</td><td>4</td><td>0.833</td><td>0.525</td><td>0.419</td></tr><tr><td>Cjub_06</td><td>F: ATAAGAAGCGACAGCAATCCA<br>R: AAACAGGTGCGGCGGAAG</td><td>(TGTT)5 205-261</td><td>HEX</td><td>24 0</td><td>2</td><td>0.958</td><td>0.500</td><td>-0.916$</td><td>0.375</td><td>24 0</td><td>2</td><td>0.916</td><td>0.498</td><td>-0.840$</td><td>0.373</td><td>48 0</td><td>2</td><td>0.937</td><td>0.499</td><td>0.374</td></tr><tr><td>Cjub_07</td><td>F: AGAGTGAAGAGAGCGACGA<br>R: CCTCACCAGTAAACCTCACCA</td><td>(GA)15 157-169</td><td>FAM</td><td>24 0</td><td>4</td><td>0.458</td><td>0.438</td><td>-0.045</td><td>0.388</td><td>24 0</td><td>2</td><td>0.583</td><td>0.418</td><td>-0.393</td><td>0.328</td><td>48 0</td><td>4</td><td>0.52</td><td>0.428</td><td>0.365</td></tr></tbody></table>
<table><tbody><tr><td>Cjub_08</td><td>F: GGCACCACTCCCATTGTATC<br>R: TTACCAACGCACGCTCCT</td><td>(GTGTA)4</td><td>206-211</td><td>PET</td><td>24 0</td><td>2</td><td>0.625</td><td>0.507</td><td>-0.232</td><td>0.375</td><td>24 0</td><td>2</td><td>0.458</td><td>0.358</td><td>-0.277</td><td>0.291</td><td>48 0</td><td>2</td><td>0.541</td><td>0.433</td><td>0.353</td></tr><tr><td>Cjub_09</td><td>F: AGCGGTGTGTGTGTGACC<br>R: ACCAGTGCTTCATTCAGTAA</td><td>(AC)14</td><td>133-137</td><td>FAM</td><td>24 0.029</td><td>3</td><td>0.291</td><td>0.329</td><td>0.115</td><td>0.298</td><td>24 0</td><td>3</td><td>0.416</td><td>0.414</td><td>-0.004</td><td>0.371</td><td>48 0</td><td>3</td><td>0.354</td><td>0.372</td><td>0.336</td></tr><tr><td>Cjub_10</td><td>F: CCCTGTGTCTAACTTTCATCT<br>R: CCTTCCTGCTACTGTGGTTCA</td><td>(TGTT)11</td><td>366-390</td><td>FAM</td><td>24 0</td><td>2</td><td>0.041</td><td>0.041</td><td>0</td><td>0.040</td><td>24 0.02</td><td>4</td><td>0.416</td><td>0.414</td><td>-0.004</td><td>0.371</td><td>48 0.064</td><td>5</td><td>0.229</td><td>0.228</td><td>0.233</td></tr><tr><td>Cjub_11</td><td>F: CCCGCTTTGTTGGGAATG<br>R: GACCTTGCTCTACACCCTCTTCT</td><td>(GA)9</td><td>161-169</td><td>PET</td><td>24 0</td><td>3</td><td>0.208</td><td>0.193</td><td>-0.074</td><td>0.178</td><td>24 0</td><td>2</td><td>0.375</td><td>0.309</td><td>-0.21</td><td>0.258</td><td>48 0</td><td>3</td><td>0.291</td><td>0.251</td><td>0.225</td></tr><tr><td>Cjub_12</td><td>F: AAGAAAGGAGGAACCCGAAA<br>R: ACTGGCTTGTGGAAACGG</td><td>(TGTTG)4</td><td>110-115</td><td>HEX</td><td>24 0.093</td><td>2</td><td>0.166</td><td>0.224</td><td>0.258</td><td>0.195</td><td>24 0.266*</td><td>2</td><td>0.041</td><td>0.259</td><td>0.839</td><td>0.218</td><td>48 0.190*</td><td>2</td><td>0.104</td><td>0.241</td><td>0.207</td></tr><tr><td>Cjub_13</td><td>F: ACAAGCAAGCAAACAAGCAAG<br>R: ATGTGAGGAAACGGAGGAGAG</td><td>(AAACA)5</td><td>115-130</td><td>NED</td><td>24 0.049</td><td>3</td><td>0.250</td><td>0.295</td><td>0.153</td><td>0.264</td><td>24 0</td><td>2</td><td>0.166</td><td>0.155</td><td>-0.069</td><td>0.141</td><td>48 0.023</td><td>3</td><td>0.208</td><td>0.225</td><td>0.206</td></tr><tr><td>Cjub_14</td><td>F: TGACAAAGATGGCAAACAACC<br>R: ACGGGAGCGGCGGAAGAAA</td><td>(GTTT)9</td><td>146-162</td><td>HEX</td><td>24 0</td><td>2</td><td>0.083</td><td>0.081</td><td>-0.022</td><td>0.077</td><td>24 0</td><td>2</td><td>0.416</td><td>0.335</td><td>-0.243</td><td>0.275</td><td>48 0</td><td>3</td><td>0.25</td><td>0.208</td><td>0.206</td></tr><tr><td>Cjub_15</td><td>F: CATCCCTTCCATCTATCTTTCT<br>R: GCTGAGTGGCGTAAGTTGTGA</td><td>(ACAAA)10</td><td>276-281</td><td>FAM</td><td>24 0</td><td>2</td><td>0.041</td><td>0.041</td><td>0</td><td>0.040</td><td>24 0</td><td>2</td><td>0.125</td><td>0.119</td><td>-0.045</td><td>0.110</td><td>48 0</td><td>2</td><td>0.083</td><td>0.08</td><td>0.077</td></tr><tr><td>Mean</td><td></td><td></td><td></td><td></td><td>24 0.009</td><td>2.9</td><td>0.329</td><td>0.312</td><td>-0.028</td><td>0.272</td><td>24 0.013</td><td>2.6</td><td>0.458</td><td>0.375</td><td>-0.197</td><td>0.318</td><td>48 0.023</td><td>3.8</td><td>0.393</td><td>0.344</td><td>0.335</td></tr></tbody></table>

Gracilaria gracilis

<table><thead><tr><th rowspan="2">Locus</th><th rowspan="2">Primer sequences (5'-3')</th><th rowspan="2">Repeat<br>motif</th><th rowspan="2">Size<br>range<br>(bp)</th><th rowspan="2">Dye</th><th colspan="6">Gris-Nez</th><th colspan="6">Porto</th><th colspan="6">Global</th></tr><tr><th>n</th><th>Nf</th><th>Na</th><th>Ho</th><th>He</th><th>Fis</th><th>PIC</th><th>N</th><th>Nf</th><th>Na</th><th>Ho</th><th>He</th><th>Fis</th><th>PIC</th><th>N</th><th>Nf</th><th>Na</th><th>Ho</th><th>He</th><th>PIC</th></tr></thead><tbody><tr><td>Ggrac_01</td><td>F: TCATAATCGGGCGAGCCATC<br>R: CAGCTGAGCGAGCCGTTTC</td><td>(CCG)6</td><td>161-200</td><td>PET</td><td>24</td><td>0.307*</td><td>6</td><td>0.458</td><td>0.792</td><td>0.421$</td><td>0.731</td><td>24</td><td>0.247*</td><td>6</td><td>0.375</td><td>0.766</td><td>0.510$</td><td>0.704</td><td>48</td><td>0.237*</td><td>9</td><td>0.416</td><td>0.779</td><td>0.808</td></tr><tr><td>Ggrac_02</td><td>F: CGCCGTCCTGAAGAATCGAAGA<br>R: GCAGCATGCAACGAATTTAACC</td><td>(AC)14</td><td>122-150</td><td>FAM</td><td>24</td><td>0.084</td><td>7</td><td>0.583</td><td>0.695</td><td>0.161</td><td>0.641</td><td>24</td><td>0.129*</td><td>4</td><td>0.541</td><td>0.762</td><td>0.289</td><td>0.694</td><td>48</td><td>0.155*</td><td>9</td><td>0.562</td><td>0.729</td><td>0.807</td></tr><tr><td>Ggrac_03</td><td>F: TCGTAATTAGGCTTCTGCGCT<br>R: ATGCCGGTGTCTTTTGAGTCC</td><td>(AG)22</td><td>114-130</td><td>FAM</td><td>24</td><td>0</td><td>7</td><td>0.791</td><td>0.762</td><td>-0.038</td><td>0.712</td><td>24</td><td>0.209*</td><td>3</td><td>0.333</td><td>0.600</td><td>0.444</td><td>0.514</td><td>48</td><td>0.156*</td><td>9</td><td>0.562</td><td>0.681</td><td>0.784</td></tr><tr><td>Ggrac_04</td><td>F: CGCTCCATCGCTTCTTGTTC<br>R: GACGCGGAGGAGTATAAG</td><td>(AGC)5</td><td>245-269</td><td>FAM</td><td>24</td><td>0.167*</td><td>6</td><td>0.500</td><td>0.740</td><td>0.324</td><td>0.676</td><td>24</td><td>0.033</td><td>5</td><td>0.666</td><td>0.765</td><td>0.129</td><td>0.708</td><td>48</td><td>0.129*</td><td>8</td><td>0.583</td><td>0.752</td><td>0.778</td></tr><tr><td>Ggrac_05</td><td>F: GGATGACGAAATGAAGCCCG<br>R: TCCAGAATACCACAAGTTGCA</td><td>(AAG)5</td><td>212-230</td><td>NED</td><td>24</td><td>0.018</td><td>5</td><td>0.708</td><td>0.749</td><td>0.054</td><td>0.685</td><td>24</td><td>0.058</td><td>5</td><td>0.500</td><td>0.582</td><td>0.141</td><td>0.536</td><td>48</td><td>0.094</td><td>6</td><td>0.604</td><td>0.665</td><td>0.708</td></tr><tr><td>Ggrac_06</td><td>F: TTTCCACATCGCCACTTCGA<br>R: AGCGACAACATCTAGAGCGG</td><td>(AATGCG)7</td><td>227-245</td><td>FAM</td><td>24</td><td>0.186</td><td>4</td><td>0.333</td><td>0.491</td><td>0.322</td><td>0.431</td><td>24</td><td>0</td><td>3</td><td>0.750</td><td>0.501</td><td>-0.494</td><td>0.408</td><td>48</td><td>0.105</td><td>4</td><td>0.541</td><td>0.496</td><td>0.633</td></tr><tr><td>Ggrac_07</td><td>F: CTCTCCACTTCCGGCTTCTG<br>R: TCTCACCTCACTGCACAACG</td><td>(AG)9</td><td>132-142</td><td>HEX</td><td>24</td><td>0</td><td>4</td><td>0.791</td><td>0.757</td><td>-0.045</td><td>0.694</td><td>24</td><td>0</td><td>3</td><td>0.750</td><td>0.526</td><td>-0.425</td><td>0.405</td><td>48</td><td>0</td><td>5</td><td>0.770</td><td>0.641</td><td>0.631</td></tr><tr><td>Ggrac_08</td><td>F: AGTATGGCTCAACACACGCA<br>R: GTACAAACGCCAAGAGACACG</td><td>(AC)12</td><td>172-182</td><td>HEX</td><td>24</td><td>0.130*</td><td>4</td><td>0.500</td><td>0.690</td><td>0.275</td><td>0.621</td><td>24</td><td>0.193*</td><td>5</td><td>0.250</td><td>0.433</td><td>0.423</td><td>0.402</td><td>48</td><td>0.224*</td><td>5</td><td>0.375</td><td>0.562</td><td>0.626</td></tr><tr><td>Ggrac_09</td><td>F: CCTTCCTCATCGCCTTCGTC<br>R: CGCATCGCTCTATTCCACCA</td><td>(AAGACG)7</td><td>104-130</td><td>NED</td><td>24</td><td>0.148</td><td>3</td><td>0.375</td><td>0.516</td><td>0.273</td><td>0.396</td><td>24</td><td>0</td><td>2</td><td>0.416</td><td>0.335</td><td>-0.243</td><td>0.275</td><td>48</td><td>0.119</td><td>3</td><td>0.395</td><td>0.425</td><td>0.562</td></tr><tr><td>Ggrac_10</td><td>F: CGTCACTATCCACACGCTGA<br>R: CAACTGCCAAGAACACAACTCT</td><td>(GT)16</td><td>133-141</td><td>FAM</td><td>24</td><td>0</td><td>3</td><td>0.750</td><td>0.635</td><td>-0.181</td><td>0.549</td><td>24</td><td>0.043</td><td>3</td><td>0.375</td><td>0.440</td><td>0.148</td><td>0.388</td><td>48</td><td>0.032</td><td>4</td><td>0.562</td><td>0.537</td><td>0.557</td></tr><tr><td>Ggrac_11</td><td>F: CCGTGTCTACCGTTGGTACC<br>R: TGAGCTGGTGAACGAGCTTC</td><td>(AGCC)8</td><td>212-232</td><td>HEX</td><td>24</td><td>0</td><td>3</td><td>0.625</td><td>0.606</td><td>-0.031</td><td>0.523</td><td>24</td><td>0</td><td>4</td><td>0.541</td><td>0.493</td><td>-0.097</td><td>0.402</td><td>48</td><td>0.009</td><td>4</td><td>0.583</td><td>0.549</td><td>0.502</td></tr><tr><td>Ggrac_12</td><td>F: CGCACTCGCTGACGAAGG<br>R: GCGCTCAGAATGGTGGAGAT</td><td>(AGC)5</td><td>217-244</td><td>FAM</td><td>24</td><td>0.177</td><td>2</td><td>0.333</td><td>0.481</td><td>0.308</td><td>0.359</td><td>24</td><td>0</td><td>3</td><td>0.541</td><td>0.512</td><td>-0.056</td><td>0.396</td><td>48</td><td>0.051</td><td>3</td><td>0.437</td><td>0.497</td><td>0.378</td></tr><tr><td>Ggrac_13</td><td>F: TGCAGTGTTCACCTGTGAGT</td><td>(AC)6</td><td>189-191</td><td>HEX</td><td>24</td><td>0.029</td><td>2</td><td>0.500</td><td>0.496</td><td>-0.007</td><td>0.368</td><td>24</td><td>0.243*</td><td>2</td><td>0.250</td><td>0.501</td><td>0.501</td><td>0.368</td><td>48</td><td>0.118*</td><td>2</td><td>0.375</td><td>0.499</td><td>0.375</td></tr></tbody></table>
<table><tbody><tr><td>Ppalm_13</td><td>F: AAAGAAAGGCGACACCGACA<br>R: CCACCTCCCATAGCCCAATT</td><td>(ACTAGC)6</td><td>113-161</td><td>HEX</td><td>24</td><td>0.085</td><td>8</td><td>0.500</td><td>0.652</td><td>0.233</td><td>0.604</td><td>24</td><td>0.111</td><td>4</td><td>0.333</td><td>0.392</td><td>0.150</td><td>0.355</td><td>48</td><td>0.104</td><td>8</td><td>0.416</td><td>0.522</td><td>0.500</td></tr><tr><td>Ppalm_14</td><td>F: AAGAGCCGACCAAGAAGACG<br>R: AGGGATGTTCGCCGGCAG</td><td>(AGC)13</td><td>152-227</td><td>PET</td><td>24</td><td>0</td><td>2</td><td>0.625</td><td>0.500</td><td>-0.250</td><td>0.371</td><td>24</td><td>0.090</td><td>4</td><td>0.416</td><td>0.552</td><td>0.245</td><td>0.481</td><td>48</td><td>0.015</td><td>4</td><td>0.520</td><td>0.526</td><td>0.455</td></tr><tr><td>Ppalm_15</td><td>F: TAGCCCAGAAACCCACAAGC<br>R: CCGCGTGTGCTGAATATAGC</td><td>(AG)10</td><td>145-153</td><td>FAM</td><td>24</td><td>0.205*</td><td>5</td><td>0.291</td><td>0.505</td><td>0.422</td><td>0.432</td><td>24</td><td>0</td><td>2</td><td>0.208</td><td>0.190</td><td>-0.095</td><td>0.169</td><td>48</td><td>0.238*</td><td>5</td><td>0.250</td><td>0.347</td><td>0.424</td></tr><tr><td>Ppalm_16</td><td>F: TGCATATTGCCTAATAAAGCACC<br>R: GCTCGAAACAATGGAAATGTCC</td><td>(AT)6</td><td>99-125</td><td>FAM</td><td>24</td><td>0</td><td>4</td><td>0.375</td><td>0.388</td><td>0.035</td><td>0.350</td><td>24</td><td>0</td><td>4</td><td>0.291</td><td>0.266</td><td>-0.095</td><td>0.248</td><td>48</td><td>0</td><td>4</td><td>0.333</td><td>0.327</td><td>0.306</td></tr><tr><td>Ppalm_17</td><td>F: CGTCCCACTCATTTCCGGG<br>R: TCGTTCTCGCACAAGTCGTT</td><td>(AACAGG)6</td><td>91-103</td><td>FAM</td><td>24</td><td>0.072</td><td>3</td><td>0.208</td><td>0.228</td><td>0.087</td><td>0.206</td><td>24</td><td>0</td><td>3</td><td>0.375</td><td>0.385</td><td>0.028</td><td>0.344</td><td>48</td><td>0.028</td><td>3</td><td>0.291</td><td>0.307</td><td>0.280</td></tr><tr><td>Ppalm_18</td><td>F: CGTGAAGGCTAACCTAGAGAGC<br>R: GGCACCAAATCCTCGAAAGC</td><td>(AG)6</td><td>348-444</td><td>FAM</td><td>24</td><td>0</td><td>3</td><td>0.375</td><td>0.317</td><td>-0.182</td><td>0.274</td><td>24</td><td>0</td><td>2</td><td>0.375</td><td>0.309</td><td>-0.210</td><td>0.258</td><td>48</td><td>0</td><td>3</td><td>0.375</td><td>0.313</td><td>0.267</td></tr><tr><td>Ppalm_19</td><td>F: TGTCACCTTGATCGTACCGC<br>R: CGAAGGCCTCAGCTGTGTAT</td><td>(AAAT)6</td><td>138-146</td><td>NED</td><td>24</td><td>0.170</td><td>2</td><td>0.208</td><td>0.364</td><td>0.427</td><td>0.291</td><td>24</td><td>0</td><td>2</td><td>0.250</td><td>0.222</td><td>-0.122</td><td>0.195</td><td>48</td><td>0.091</td><td>2</td><td>0.229</td><td>0.293</td><td>0.249</td></tr><tr><td>Ppalm_20</td><td>F: ACCCGCTCGGCATTACAAAT<br>R: AGGAGTGCGAAAGGCTTTGA</td><td>(AT)6</td><td>142-152</td><td>FAM</td><td>24</td><td>0</td><td>4</td><td>0.125</td><td>0.122</td><td>-0.022</td><td>0.118</td><td>24</td><td>0</td><td>3</td><td>0.333</td><td>0.288</td><td>-0.153</td><td>0.254</td><td>48</td><td>0</td><td>4</td><td>0.229</td><td>0.205</td><td>0.197</td></tr><tr><td>Mean</td><td></td><td></td><td></td><td></td><td>24</td><td>0.047</td><td>5.1</td><td>0.503</td><td>0.514</td><td>0.023</td><td>0.458</td><td>24</td><td>0.039</td><td>3.9</td><td>0.467</td><td>0.465</td><td>-0.014</td><td>0.413</td><td>48</td><td>0.056</td><td>5.7</td><td>0.485</td><td>0.489</td><td>0.478</td></tr></tbody></table>

Gracilaria dura

<table><thead><tr><th rowspan="2">Locus</th><th rowspan="2">Primer sequences (5'-3')</th><th rowspan="2">Repeat<br>motif</th><th rowspan="2">Size<br>range (bp)</th><th rowspan="2">Dye</th><th colspan="5">Locquirec</th><th colspan="5">Coroso</th><th colspan="5">Quitao</th><th colspan="5">Global</th></tr><tr><th>n</th><th>Na</th><th>Ho</th><th>He</th><th>Fis</th><th>PIC</th><th>n</th><th>Na</th><th>Ho</th><th>He</th><th>Fis</th><th>PIC</th><th>n</th><th>Na</th><th>Ho</th><th>He</th><th>Fis</th><th>PIC</th><th>n</th><th>Na</th><th>Ho</th><th>He</th><th>PIC</th></tr></thead><tbody><tr><td>Gdura_01</td><td>F: GCAGCATGCAACGAATTTAACC<br>R: CGCCGTCTGAAGAATCGAAGA</td><td>(AC)17</td><td>103-135</td><td>FAM</td><td>12</td><td>8</td><td>0.736</td><td>0.840</td><td>0.124</td><td>0.683</td><td>15</td><td>5</td><td>0.859</td><td>0.780</td><td>-0.101</td><td>0.700</td><td>7</td><td>5</td><td>1.000</td><td>0.752</td><td>-0.329</td><td>0.690</td><td>34</td><td>11</td><td>0.844</td><td>0.890</td><td>0.839</td></tr><tr><td>Gdura_02</td><td>F: TACGCGGTCGTGGAATATC<br>R: AGCGTACGGGCAAAGTACAA</td><td>(AAG)14</td><td>212-278</td><td>NED</td><td>12</td><td>4</td><td>0.667</td><td>0.676</td><td>0.014</td><td>0.619</td><td>15</td><td>4</td><td>0.722</td><td>0.575</td><td>-0.257</td><td>0.462</td><td>8</td><td>3</td><td>0.833</td><td>0.598</td><td>-0.393</td><td>0.606</td><td>35</td><td>7</td><td>0.729</td><td>0.836</td><td>0.793</td></tr><tr><td>Gdura_03</td><td>F: GTCGGAGCGTGAGGACGG<br>R: CGCATCGCTCTATTCCACCA</td><td>(ACG)7</td><td>166-184</td><td>PET</td><td>11</td><td>4</td><td>0.700</td><td>0.738</td><td>0.052</td><td>0.626</td><td>15</td><td>4</td><td>0.964</td><td>0.747</td><td>-0.291</td><td>0.578</td><td>8</td><td>3</td><td>0.952</td><td>0.662</td><td>-0.439</td><td>0.638</td><td>34</td><td>5</td><td>0.876</td><td>0.789</td><td>0.785</td></tr><tr><td>Gdura_04</td><td>F: AGGTTCAATGCCTCCTGACC<br>R: GTACGCTTCGTTGCCGATTC</td><td>(AAG)11</td><td>183-210</td><td>NED</td><td>12</td><td>4</td><td>0.764</td><td>0.748</td><td>-0.021</td><td>0.733</td><td>15</td><td>6</td><td>0.956</td><td>0.808</td><td>-0.183</td><td>0.633</td><td>8</td><td>3</td><td>0.792</td><td>0.556</td><td>-0.423</td><td>0.687</td><td>35</td><td>6</td><td>0.852</td><td>0.766</td><td>0.782</td></tr><tr><td>Gdura_05</td><td>F: CATCGCACCCAGATGGTGAA<br>R: GCGTTATGGTCCACTGCTCT</td><td>(AG)15</td><td>225-331</td><td>PET</td><td>12</td><td>6</td><td>0.667</td><td>0.618</td><td>-0.079</td><td>0.661</td><td>15</td><td>6</td><td>1.000</td><td>0.791</td><td>-0.264</td><td>0.816</td><td>8</td><td>8</td><td>0.979</td><td>0.829</td><td>-0.181</td><td>0.609</td><td>35</td><td>9</td><td>0.880</td><td>0.814</td><td>0.782</td></tr><tr><td>Gdura_06</td><td>F: TTGTGCGCTGTGAGTAGCAA<br>R: ACGCTCGTGAACTCCGAAAT</td><td>(AC)10</td><td>335-369</td><td>NED</td><td>12</td><td>5</td><td>0.681</td><td>0.676</td><td>-0.006</td><td>0.611</td><td>15</td><td>4</td><td>0.974</td><td>0.740</td><td>-0.317</td><td>0.499</td><td>8</td><td>3</td><td>0.833</td><td>0.592</td><td>-0.408</td><td>0.613</td><td>35</td><td>6</td><td>0.833</td><td>0.807</td><td>0.777</td></tr><tr><td>Gdura_07</td><td>F: GCAGAACTGTACGTCGCTCT<br>R: TGCCCTCCTCTACCGTATGG</td><td>(AG)12</td><td>277-309</td><td>FAM</td><td>12</td><td>6</td><td>0.778</td><td>0.719</td><td>-0.082</td><td>0.627</td><td>15</td><td>4</td><td>0.956</td><td>0.732</td><td>-0.306</td><td>0.560</td><td>8</td><td>3</td><td>0.896</td><td>0.660</td><td>-0.358</td><td>0.646</td><td>35</td><td>8</td><td>0.881</td><td>0.826</td><td>0.778</td></tr><tr><td>Gdura_08</td><td>F: TGTTTGATCCTGATCCTACCGT<br>R: CGTACGCGTTTCATAAATGACA</td><td>(AG)12</td><td>278-312</td><td>NED</td><td>12</td><td>6</td><td>0.639</td><td>0.726</td><td>0.120</td><td>0.627</td><td>15</td><td>4</td><td>0.910</td><td>0.664</td><td>-0.371</td><td>0.693</td><td>8</td><td>5</td><td>0.979</td><td>0.751</td><td>-0.303</td><td>0.624</td><td>35</td><td>7</td><td>0.828</td><td>0.817</td><td>0.773</td></tr><tr><td>Gdura_09</td><td>F: ACGGCTCGTCATGTTTCTCA<br>R: AGCTACGTTGGTACAGCTCG</td><td>(AG)15</td><td>102-120</td><td>NED</td><td>12</td><td>5</td><td>0.879</td><td>0.731</td><td>-0.203</td><td>0.668</td><td>15</td><td>6</td><td>0.988</td><td>0.786</td><td>-0.257</td><td>0.409</td><td>8</td><td>2</td><td>0.833</td><td>0.444</td><td>-0.875$</td><td>0.679</td><td>35</td><td>7</td><td>0.914</td><td>0.819</td><td>0.765</td></tr><tr><td>Gdura_10</td><td>F: TAACCGCACTACTTTCGCCA<br>R: AATGTTCTTCGCGTGTTGGG</td><td>(AGC)7</td><td>114-132</td><td>PET</td><td>12</td><td>4</td><td>0.903</td><td>0.692</td><td>-0.304</td><td>0.720</td><td>15</td><td>4</td><td>0.881</td><td>0.726</td><td>-0.214</td><td>0.555</td><td>8</td><td>3</td><td>0.875</td><td>0.639</td><td>-0.369</td><td>0.690</td><td>35</td><td>5</td><td>0.887</td><td>0.765</td><td>0.745</td></tr><tr><td>Gdura_11</td><td>F: GTGCGTTGGTTTCGCGTGATT<br>R: TTACCCGTCGTTTCTGCCTC</td><td>(AGC)5</td><td>219-234</td><td>PET</td><td>12</td><td>4</td><td>0.861</td><td>0.673</td><td>-0.280</td><td>0.737</td><td>15</td><td>5</td><td>0.889</td><td>0.774</td><td>-0.148</td><td>0.560</td><td>8</td><td>3</td><td>0.896</td><td>0.642</td><td>-0.395</td><td>0.518</td><td>35</td><td>5</td><td>0.881</td><td>0.768</td><td>0.746</td></tr><tr><td>Gdura_12</td><td>F: CGCTTCCGCTTACTGACCAT</td><td>(ACT)7</td><td>115-151</td><td>PET</td><td>12</td><td>8</td><td>0.792</td><td>0.842</td><td>0.060</td><td>0.614</td><td>15</td><td>4</td><td>0.845</td><td>0.650</td><td>-0.300</td><td>0.565</td><td>8</td><td>3</td><td>0.937</td><td>0.654</td><td>-0.434</td><td>0.729</td><td>35</td><td>9</td><td>0.848</td><td>0.824</td><td>0.755</td></tr></tbody></table>
<table>
  <tbody>
    <tr>
      <td>Gdura_13</td>
      <td>F: GCCTATGGGAGCATCGGAAT<br/>R: GGTGATGTAGGATGGTAATGCC</td>
      <td>(AG)10</td>
      <td>154-170</td>
      <td>NED</td>
      <td>12</td>
      <td>3</td>
      <td>0.722</td>
      <td>0.605</td>
      <td>-0.194</td>
      <td>0.671</td>
      <td>15</td>
      <td>4</td>
      <td>0.978</td>
      <td>0.736</td>
      <td>-0.328</td>
      <td>0.556</td>
      <td>8</td>
      <td>3</td>
      <td>0.929</td>
      <td>0.650</td>
      <td>-0.428</td>
      <td>0.601</td>
      <td>35</td>
      <td>6</td>
      <td>0.877</td>
      <td>0.773</td>
      <td>0.739</td>
    </tr>
    <tr>
      <td>Gdura_14</td>
      <td>F: ATGTTTGAACGTTCTGGCTCAC<br/>R: ACGTAGTGGATCTAGAATGCTAGA</td>
      <td>(AC)15</td>
      <td>177-211</td>
      <td>FAM</td>
      <td>11</td>
      <td>5</td>
      <td>0.545</td>
      <td>0.552</td>
      <td>0.011</td>
      <td>0.615</td>
      <td>15</td>
      <td>7</td>
      <td>0.856</td>
      <td>0.700</td>
      <td>-0.222</td>
      <td>0.725</td>
      <td>8</td>
      <td>6</td>
      <td>1.000</td>
      <td>0.779</td>
      <td>-0.283</td>
      <td>0.498</td>
      <td>34</td>
      <td>8</td>
      <td>0.789</td>
      <td>0.781</td>
      <td>0.730</td>
    </tr>
    <tr>
      <td>Gdura_15</td>
      <td>F: GGTGACTCTTAACTCCCGTGG<br/>R: CGCTTCTTTAACCAGAAACGCA</td>
      <td>(AG)26</td>
      <td>147-181</td>
      <td>FAM</td>
      <td>12</td>
      <td>6</td>
      <td>0.833</td>
      <td>0.763</td>
      <td>-0.092</td>
      <td>0.424</td>
      <td>15</td>
      <td>2</td>
      <td>0.500</td>
      <td>0.388</td>
      <td>-0.289</td>
      <td>0.511</td>
      <td>8</td>
      <td>3</td>
      <td>0.548</td>
      <td>0.559</td>
      <td>0.020</td>
      <td>0.695</td>
      <td>35</td>
      <td>8</td>
      <td>0.627</td>
      <td>0.823</td>
      <td>0.715</td>
    </tr>
    <tr>
      <td>Gdura_16</td>
      <td>F: GTTGCCGCAGAAGAGACTCA<br/>R: TAGAATTGGACAAGCGGCGG</td>
      <td>(AGC)11</td>
      <td>224-242</td>
      <td>NED</td>
      <td>12</td>
      <td>3</td>
      <td>0.611</td>
      <td>0.514</td>
      <td>-0.188</td>
      <td>0.592</td>
      <td>14</td>
      <td>2</td>
      <td>0.810</td>
      <td>0.472</td>
      <td>-0.716</td>
      <td>0.648</td>
      <td>8</td>
      <td>3</td>
      <td>0.750</td>
      <td>0.604</td>
      <td>-0.243</td>
      <td>0.572</td>
      <td>34</td>
      <td>5</td>
      <td>0.725</td>
      <td>0.733</td>
      <td>0.700</td>
    </tr>
    <tr>
      <td>Gdura_17</td>
      <td>F: ACCGAACCAGCAACATCCTC<br/>R: GAAAGTGGGCGCAAGGATTG</td>
      <td>(AG)8</td>
      <td>264-298</td>
      <td>HEX</td>
      <td>12</td>
      <td>3</td>
      <td>0.611</td>
      <td>0.654</td>
      <td>0.065</td>
      <td>0.609</td>
      <td>15</td>
      <td>3</td>
      <td>0.989</td>
      <td>0.666</td>
      <td>-0.486$</td>
      <td>0.409</td>
      <td>8</td>
      <td>2</td>
      <td>0.833</td>
      <td>0.444</td>
      <td>-0.875$</td>
      <td>0.564</td>
      <td>35</td>
      <td>4</td>
      <td>0.824</td>
      <td>0.722</td>
      <td>0.701</td>
    </tr>
    <tr>
      <td>Gdura_18</td>
      <td>F: AGCAACAAGGTCGTCCTTCA<br/>R: GCCACCTACATGACAACGTG</td>
      <td>(AG)10</td>
      <td>318-346</td>
      <td>FAM</td>
      <td>12</td>
      <td>4</td>
      <td>0.569</td>
      <td>0.486</td>
      <td>-0.171</td>
      <td>0.663</td>
      <td>15</td>
      <td>4</td>
      <td>0.789</td>
      <td>0.650</td>
      <td>-0.214</td>
      <td>0.626</td>
      <td>7</td>
      <td>4</td>
      <td>1.000</td>
      <td>0.694</td>
      <td>-0.440</td>
      <td>0.487</td>
      <td>34</td>
      <td>6</td>
      <td>0.755</td>
      <td>0.623</td>
      <td>0.682</td>
    </tr>
    <tr>
      <td>Gdura_19</td>
      <td>F: AGCGAGAGAACGTTTCTTCGA<br/>R: CGTCCCATATTCTCACTCCAGGT</td>
      <td>(AG)14</td>
      <td>139-143</td>
      <td>FAM</td>
      <td>11</td>
      <td>3</td>
      <td>0.652</td>
      <td>0.491</td>
      <td>-0.326</td>
      <td>0.652</td>
      <td>15</td>
      <td>3</td>
      <td>0.811</td>
      <td>0.615</td>
      <td>-0.320</td>
      <td>0.449</td>
      <td>7</td>
      <td>2</td>
      <td>0.500</td>
      <td>0.386</td>
      <td>-0.297</td>
      <td>0.596</td>
      <td>33</td>
      <td>3</td>
      <td>0.692</td>
      <td>0.604</td>
      <td>0.658</td>
    </tr>
    <tr>
      <td>Gdura_20</td>
      <td>F: CCCGTGTTTAGCCTTCGG<br/>R: ACAGGAGGAAATCATTGACGAA</td>
      <td>(AT)6</td>
      <td>247-251</td>
      <td>HEX</td>
      <td>12</td>
      <td>3</td>
      <td>0.708</td>
      <td>0.571</td>
      <td>-0.240</td>
      <td>0.587</td>
      <td>15</td>
      <td>3</td>
      <td>0.900</td>
      <td>0.636</td>
      <td>-0.415</td>
      <td>0.499</td>
      <td>8</td>
      <td>3</td>
      <td>0.833</td>
      <td>0.592</td>
      <td>-0.408</td>
      <td>0.654</td>
      <td>35</td>
      <td>3</td>
      <td>0.819</td>
      <td>0.622</td>
      <td>0.656</td>
    </tr>
    <tr>
      <td>Gdura_21</td>
      <td>F: GCGTACCCTGGTACATACTCC<br/>R: GGTAACTATCTGGAAACGGTTGTC</td>
      <td>(AG)18</td>
      <td>181-207</td>
      <td>FAM</td>
      <td>12</td>
      <td>6</td>
      <td>0.750</td>
      <td>0.815</td>
      <td>0.080</td>
      <td>0.513</td>
      <td>15</td>
      <td>3</td>
      <td>0.500</td>
      <td>0.654</td>
      <td>0.235</td>
      <td>0.330</td>
      <td>8</td>
      <td>1</td>
      <td>0.556</td>
      <td>0.308</td>
      <td>-0.801$</td>
      <td>0.694</td>
      <td>35</td>
      <td>8</td>
      <td>0.608</td>
      <td>0.810</td>
      <td>0.662</td>
    </tr>
    <tr>
      <td>Gdura_22</td>
      <td>F: GTGCTCAACAATGGTGGACG<br/>R: GGGTCCCGGTCTATCCAGAT</td>
      <td>(AG)8</td>
      <td>206-224</td>
      <td>NED</td>
      <td>12</td>
      <td>4</td>
      <td>0.861</td>
      <td>0.632</td>
      <td>-0.363</td>
      <td>0.737</td>
      <td>15</td>
      <td>5</td>
      <td>0.978</td>
      <td>0.785</td>
      <td>-0.246</td>
      <td>0.499</td>
      <td>8</td>
      <td>3</td>
      <td>0.833</td>
      <td>0.607</td>
      <td>-0.374</td>
      <td>0.513</td>
      <td>35</td>
      <td>5</td>
      <td>0.905</td>
      <td>0.738</td>
      <td>0.658</td>
    </tr>
    <tr>
      <td>Gdura_23</td>
      <td>F: CGCCATCAAGAGTGAAAGTAGC<br/>R: AGACGGCCACAGTGAAACC</td>
      <td>(AC)8</td>
      <td>106-114</td>
      <td>HEX</td>
      <td>12</td>
      <td>3</td>
      <td>0.653</td>
      <td>0.626</td>
      <td>-0.043</td>
      <td>0.511</td>
      <td>15</td>
      <td>3</td>
      <td>0.789</td>
      <td>0.541</td>
      <td>-0.458</td>
      <td>0.320</td>
      <td>8</td>
      <td>1</td>
      <td>0.542</td>
      <td>0.252</td>
      <td>-0.146</td>
      <td>0.618</td>
      <td>35</td>
      <td>4</td>
      <td>0.686</td>
      <td>0.638</td>
      <td>0.585</td>
    </tr>
    <tr>
      <td>Gdura_24</td>
      <td>F: GCCTGATCTCGCCGAAGATA<br/>R: GCAGAAGCGTTTCGTTTCCG</td>
      <td>(AG)13</td>
      <td>174-226</td>
      <td>FAM</td>
      <td>12</td>
      <td>4</td>
      <td>0.708</td>
      <td>0.567</td>
      <td>-0.250$</td>
      <td>0.644</td>
      <td>15</td>
      <td>4</td>
      <td>0.967</td>
      <td>0.715</td>
      <td>-0.351$</td>
      <td>0.348</td>
      <td>8</td>
      <td>2</td>
      <td>0.583</td>
      <td>0.153</td>
      <td>-0.818$</td>
      <td>0.438</td>
      <td>35</td>
      <td>5</td>
      <td>0.790</td>
      <td>0.654</td>
      <td>0.576</td>
    </tr>
    <tr>
      <td>Gdura_25</td>
      <td>F: TAGGCCACCCACTTCGTTTG<br/>R: AGTGTGGTTCGATTTCGTTTCC</td>
      <td>(AG)7</td>
      <td>185-201</td>
      <td>HEX</td>
      <td>11</td>
      <td>3</td>
      <td>0.652</td>
      <td>0.527</td>
      <td>-0.237</td>
      <td>0.556</td>
      <td>15</td>
      <td>3</td>
      <td>0.922</td>
      <td>0.618</td>
      <td>-0.492$</td>
      <td>0.421</td>
      <td>8</td>
      <td>2</td>
      <td>0.792</td>
      <td>0.413</td>
      <td>-0.916$</td>
      <td>0.395</td>
      <td>34</td>
      <td>4</td>
      <td>0.804</td>
      <td>0.689</td>
      <td>0.559</td>
    </tr>
    <tr>
      <td>Gdura_26</td>
      <td>F: GCCTTGTACCTCGCTATCCG<br/>R: GCTACTCTTGCACAGTACGC</td>
      <td>(AAC)5</td>
      <td>252-258</td>
      <td>PET</td>
      <td>12</td>
      <td>3</td>
      <td>0.833</td>
      <td>0.654</td>
      <td>-0.275</td>
      <td>0.431</td>
      <td>15</td>
      <td>2</td>
      <td>0.833</td>
      <td>0.470</td>
      <td>-0.773$</td>
      <td>0.409</td>
      <td>8</td>
      <td>2</td>
      <td>0.833</td>
      <td>0.486</td>
      <td>-0.714$</td>
      <td>0.447</td>
      <td>35</td>
      <td>3</td>
      <td>0.833</td>
      <td>0.660</td>
      <td>0.553</td>
    </tr>
    <tr>
      <td>Gdura_27</td>
      <td>F: CCATGTCTTAGGATGCCCGA<br/>R: CCGCAGTTTGATGTTCGGTG</td>
      <td>(AG)5</td>
      <td>201-220</td>
      <td>HEX</td>
      <td>12</td>
      <td>3</td>
      <td>0.583</td>
      <td>0.509</td>
      <td>-0.146</td>
      <td>0.506</td>
      <td>15</td>
      <td>3</td>
      <td>0.844</td>
      <td>0.559</td>
      <td>-0.512</td>
      <td>0.421</td>
      <td>8</td>
      <td>2</td>
      <td>0.792</td>
      <td>0.413</td>
      <td>-0.916$</td>
      <td>0.352</td>
      <td>35</td>
      <td>4</td>
      <td>0.743</td>
      <td>0.675</td>
      <td>0.537</td>
    </tr>
    <tr>
      <td>Gdura_28</td>
      <td>F: CTAGGCGGAGTTGCATGCTT<br/>R: GTCCCGCTGCGACAAATTG</td>
      <td>(AAC)6</td>
      <td>114-126</td>
      <td>PET</td>
      <td>12</td>
      <td>4</td>
      <td>0.694</td>
      <td>0.572</td>
      <td>-0.214</td>
      <td>0.627</td>
      <td>15</td>
      <td>3</td>
      <td>0.782</td>
      <td>0.654</td>
      <td>-0.196</td>
      <td>0.409</td>
      <td>8</td>
      <td>2</td>
      <td>0.708</td>
      <td>0.497</td>
      <td>-0.425</td>
      <td>0.432</td>
      <td>35</td>
      <td>5</td>
      <td>0.732</td>
      <td>0.684</td>
      <td>0.543</td>
    </tr>
    <tr>
      <td>Mean</td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td>11.9</td>
      <td>4.6</td>
      <td>0.716</td>
      <td>0.655</td>
      <td>-0.106</td>
      <td>0.635</td>
      <td>15.0</td>
      <td>4.1</td>
      <td>0.864</td>
      <td>0.674</td>
      <td>-0.293</td>
      <td>0.565</td>
      <td>7.9</td>
      <td>3.4</td>
      <td>0.839</td>
      <td>0.617</td>
      <td>-0.336</td>
      <td>0.606</td>
      <td>34.6</td>
      <td>6.2</td>
      <td>0.807</td>
      <td>0.758</td>
      <td>0.725</td>
    </tr>
  </tbody>
</table>

n Number of genotyped individuals.

Nf Null allele frequency obtained with MICROCHECKER (Van Oosterhout et al. 2004), locus showing significant frequencies of null alleles are shown with *.

Na number of alleles per locus, He expected heterozygosity (Nei 1978), Ho observed heterozygosity obtained with HIERFSTAT (Goudet 2014) excepted G.dura obtained with AUTOTET (Thrall et al 2000).

Fis estimate of deviation from random mating obtained with HIERFSTAT (Goudet 2014) excepted G.dura obtained with SPAGeDi (Hardy, O. J. & X. Vekemans 2002). FIS values significantly different from zero are shown with $.

PIC polymorphic information content obtained with CERVUS software (Marshall et al. 1998) excepted G.dura obtained with POLYSAT R package (Clark et al. 2011).

Locus retained as suitable polymorphic markers for future studies are shown in bold (i.e., polymorphic, no-significant Nf, no-significant FIS).

In italic the mean values over loci were calculated over locus retained as suitable polymorphic markers.